0s autopkgtest [10:35:27]: starting date and time: 2025-03-15 10:35:27+0000
  0s autopkgtest [10:35:27]: git checkout: 325255d2 Merge branch 'pin-any-arch' into 'ubuntu/production'
  0s autopkgtest [10:35:27]: host juju-7f2275-prod-proposed-migration-environment-20; command line: /home/ubuntu/autopkgtest/runner/autopkgtest --output-dir /tmp/autopkgtest-work.5w8_2ddf/out --timeout-copy=6000 --setup-commands /home/ubuntu/autopkgtest-cloud/worker-config-production/setup-canonical.sh --apt-pocket=proposed=src:glibc --apt-upgrade octave-statistics --timeout-short=300 --timeout-copy=20000 --timeout-build=20000 --env=ADT_TEST_TRIGGERS=glibc/2.41-1ubuntu2 -- ssh -s /home/ubuntu/autopkgtest/ssh-setup/nova -- --flavor builder-cpu2-ram4-disk20 --security-groups autopkgtest-juju-7f2275-prod-proposed-migration-environment-20@bos03-16.secgroup --name adt-plucky-amd64-octave-statistics-20250315-103527-juju-7f2275-prod-proposed-migration-environment-20-90633440-8d87-4427-a6b8-514d5af20acd --image adt/ubuntu-plucky-amd64-server --keyname testbed-juju-7f2275-prod-proposed-migration-environment-20 --net-id=net_prod-proposed-migration-amd64 -e TERM=linux -e ''"'"'http_proxy=http://squid.internal:3128'"'"'' -e ''"'"'https_proxy=http://squid.internal:3128'"'"'' -e ''"'"'no_proxy=127.0.0.1,127.0.1.1,login.ubuntu.com,localhost,localdomain,novalocal,internal,archive.ubuntu.com,ports.ubuntu.com,security.ubuntu.com,ddebs.ubuntu.com,changelogs.ubuntu.com,keyserver.ubuntu.com,launchpadlibrarian.net,launchpadcontent.net,launchpad.net,10.24.0.0/24,keystone.ps5.canonical.com,objectstorage.prodstack5.canonical.com,radosgw.ps5.canonical.com'"'"'' --mirror=http://ftpmaster.internal/ubuntu/
 50s autopkgtest [10:36:17]: testbed dpkg architecture: amd64
 50s autopkgtest [10:36:17]: testbed apt version: 2.9.31ubuntu1
 51s autopkgtest [10:36:18]: @@@@@@@@@@@@@@@@@@@@ test bed setup
 51s autopkgtest [10:36:18]: testbed release detected to be: None
 51s autopkgtest [10:36:18]: updating testbed package index (apt update)
 52s Get:1 http://ftpmaster.internal/ubuntu plucky-proposed InRelease [126 kB]
 52s Hit:2 http://ftpmaster.internal/ubuntu plucky InRelease
 52s Hit:3 http://ftpmaster.internal/ubuntu plucky-updates InRelease
 52s Hit:4 http://ftpmaster.internal/ubuntu plucky-security InRelease
 52s Get:5 http://ftpmaster.internal/ubuntu plucky-proposed/multiverse Sources [15.8 kB]
 52s Get:6 http://ftpmaster.internal/ubuntu plucky-proposed/universe Sources [410 kB]
 53s Get:7 http://ftpmaster.internal/ubuntu plucky-proposed/main Sources [46.2 kB]
 53s Get:8 http://ftpmaster.internal/ubuntu plucky-proposed/main amd64 Packages [90.3 kB]
 53s Get:9 http://ftpmaster.internal/ubuntu plucky-proposed/main i386 Packages [64.9 kB]
 53s Get:10 http://ftpmaster.internal/ubuntu plucky-proposed/main amd64 c-n-f Metadata [1916 B]
 53s Get:11 http://ftpmaster.internal/ubuntu plucky-proposed/restricted amd64 c-n-f Metadata [116 B]
 53s Get:12 http://ftpmaster.internal/ubuntu plucky-proposed/universe i386 Packages [181 kB]
 53s Get:13 http://ftpmaster.internal/ubuntu plucky-proposed/universe amd64 Packages [364 kB]
 53s Get:14 http://ftpmaster.internal/ubuntu plucky-proposed/universe amd64 c-n-f Metadata [17.0 kB]
 53s Get:15 http://ftpmaster.internal/ubuntu plucky-proposed/multiverse amd64 Packages [17.0 kB]
 53s Get:16 http://ftpmaster.internal/ubuntu plucky-proposed/multiverse i386 Packages [8452 B]
 53s Get:17 http://ftpmaster.internal/ubuntu plucky-proposed/multiverse amd64 c-n-f Metadata [912 B]
 53s Fetched 1345 kB in 1s (1345 kB/s)
 54s Reading package lists...
 54s Reading package lists...
 55s Building dependency tree...
 55s Reading state information...
 55s Calculating upgrade...
 55s Calculating upgrade...
 55s The following package was automatically installed and is no longer required:
 55s   libnl-genl-3-200
 55s Use 'sudo apt autoremove' to remove it.
 55s The following NEW packages will be installed:
 55s   bpftool libdebuginfod-common libdebuginfod1t64 linux-headers-6.14.0-10
 55s   linux-headers-6.14.0-10-generic linux-image-6.14.0-10-generic
 55s   linux-modules-6.14.0-10-generic linux-modules-extra-6.14.0-10-generic
 55s   linux-perf linux-tools-6.14.0-10 linux-tools-6.14.0-10-generic pnp.ids
 55s The following packages will be upgraded:
 55s   apparmor apt apt-utils binutils binutils-common binutils-x86-64-linux-gnu
 55s   cloud-init cloud-init-base curl dosfstools exfatprogs fwupd gcc-15-base
 55s   gir1.2-girepository-2.0 gir1.2-glib-2.0 htop hwdata initramfs-tools
 55s   initramfs-tools-bin initramfs-tools-core libapparmor1 libapt-pkg7.0
 55s   libassuan9 libatomic1 libaudit-common libaudit1 libbinutils libbrotli1
 55s   libc-bin libc-dev-bin libc6 libc6-dev libcap-ng0 libctf-nobfd0 libctf0
 55s   libcurl3t64-gnutls libcurl4t64 libestr0 libftdi1-2 libfwupd3 libgcc-s1
 55s   libgirepository-1.0-1 libglib2.0-0t64 libglib2.0-data libgpgme11t64
 55s   libgprofng0 libjemalloc2 liblz4-1 liblzma5 libmm-glib0 libncurses6
 55s   libncursesw6 libnewt0.52 libnl-3-200 libnl-genl-3-200 libnl-route-3-200
 55s   libnss-systemd libpam-systemd libparted2t64 libpci3 libpython3-stdlib
 55s   libpython3.13 libpython3.13-minimal libpython3.13-stdlib libseccomp2
 55s   libselinux1 libsemanage-common libsemanage2 libsframe1 libsqlite3-0
 55s   libstdc++6 libsystemd-shared libsystemd0 libtinfo6 libudev1 libxml2
 55s   linux-firmware linux-generic linux-headers-generic linux-headers-virtual
 55s   linux-image-generic linux-image-virtual linux-libc-dev linux-tools-common
 55s   linux-virtual locales media-types ncurses-base ncurses-bin ncurses-term
 55s   parted pci.ids pciutils python-apt-common python3 python3-apt python3-bcrypt
 55s   python3-cffi-backend python3-dbus python3-gi python3-jinja2 python3-lazr.uri
 55s   python3-markupsafe python3-minimal python3-newt python3-rpds-py
 55s   python3-systemd python3-yaml python3.13 python3.13-gdbm python3.13-minimal
 55s   rsync rsyslog strace systemd systemd-cryptsetup systemd-resolved
 55s   systemd-sysv systemd-timesyncd ubuntu-kernel-accessories ubuntu-minimal
 55s   ubuntu-standard udev whiptail xz-utils
 55s 125 upgraded, 12 newly installed, 0 to remove and 0 not upgraded.
 55s Need to get 829 MB of archives.
 55s After this operation, 325 MB of additional disk space will be used.
 55s Get:1 http://ftpmaster.internal/ubuntu plucky/main amd64 ncurses-bin amd64 6.5+20250216-2 [194 kB]
 56s Get:2 http://ftpmaster.internal/ubuntu plucky/main amd64 libc-dev-bin amd64 2.41-1ubuntu1 [24.7 kB]
 56s Get:3 http://ftpmaster.internal/ubuntu plucky/main amd64 libc6-dev amd64 2.41-1ubuntu1 [2182 kB]
 56s Get:4 http://ftpmaster.internal/ubuntu plucky/main amd64 locales all 2.41-1ubuntu1 [4246 kB]
 57s Get:5 http://ftpmaster.internal/ubuntu plucky/main amd64 libc6 amd64 2.41-1ubuntu1 [3327 kB]
 58s Get:6 http://ftpmaster.internal/ubuntu plucky/main amd64 libc-bin amd64 2.41-1ubuntu1 [701 kB]
 58s Get:7 http://ftpmaster.internal/ubuntu plucky/main amd64 linux-libc-dev amd64 6.14.0-10.10 [1723 kB]
 58s Get:8 http://ftpmaster.internal/ubuntu plucky/main amd64 libatomic1 amd64 15-20250222-0ubuntu1 [10.4 kB]
 58s Get:9 http://ftpmaster.internal/ubuntu plucky/main amd64 gcc-15-base amd64 15-20250222-0ubuntu1 [53.4 kB]
 58s Get:10 http://ftpmaster.internal/ubuntu plucky/main amd64 libgcc-s1 amd64 15-20250222-0ubuntu1 [77.8 kB]
 58s Get:11 http://ftpmaster.internal/ubuntu plucky/main amd64 libstdc++6 amd64 15-20250222-0ubuntu1 [798 kB]
 58s Get:12 http://ftpmaster.internal/ubuntu plucky/main amd64 ncurses-base all 6.5+20250216-2 [25.9 kB]
 58s Get:13 http://ftpmaster.internal/ubuntu plucky/main amd64 ncurses-term all 6.5+20250216-2 [276 kB]
 58s Get:14 http://ftpmaster.internal/ubuntu plucky/main amd64 liblz4-1 amd64 1.10.0-4 [66.4 kB]
 58s Get:15 http://ftpmaster.internal/ubuntu plucky/main amd64 liblzma5 amd64 5.6.4-1 [157 kB]
 58s Get:16 http://ftpmaster.internal/ubuntu plucky/main amd64 libsystemd0 amd64 257.3-1ubuntu3 [595 kB]
 58s Get:17 http://ftpmaster.internal/ubuntu plucky/main amd64 libnss-systemd amd64 257.3-1ubuntu3 [199 kB]
 58s Get:18 http://ftpmaster.internal/ubuntu plucky/main amd64 systemd-sysv amd64 257.3-1ubuntu3 [11.9 kB]
 58s Get:19 http://ftpmaster.internal/ubuntu plucky/main amd64 systemd-resolved amd64 257.3-1ubuntu3 [345 kB]
 58s Get:20 http://ftpmaster.internal/ubuntu plucky/main amd64 libpam-systemd amd64 257.3-1ubuntu3 [302 kB]
 58s Get:21 http://ftpmaster.internal/ubuntu plucky/main amd64 libsystemd-shared amd64 257.3-1ubuntu3 [2371 kB]
 59s Get:22 http://ftpmaster.internal/ubuntu plucky/main amd64 systemd amd64 257.3-1ubuntu3 [3052 kB]
 59s Get:23 http://ftpmaster.internal/ubuntu plucky/main amd64 systemd-timesyncd amd64 257.3-1ubuntu3 [42.1 kB]
 59s Get:24 http://ftpmaster.internal/ubuntu plucky/main amd64 systemd-cryptsetup amd64 257.3-1ubuntu3 [124 kB]
 59s Get:25 http://ftpmaster.internal/ubuntu plucky/main amd64 udev amd64 257.3-1ubuntu3 [1404 kB]
 59s Get:26 http://ftpmaster.internal/ubuntu plucky/main amd64 libudev1 amd64 257.3-1ubuntu3 [215 kB]
 59s Get:27 http://ftpmaster.internal/ubuntu plucky/main amd64 libaudit-common all 1:4.0.2-2ubuntu2 [6628 B]
 59s Get:28 http://ftpmaster.internal/ubuntu plucky/main amd64 libcap-ng0 amd64 0.8.5-4build1 [15.6 kB]
 59s Get:29 http://ftpmaster.internal/ubuntu plucky/main amd64 libaudit1 amd64 1:4.0.2-2ubuntu2 [54.0 kB]
 59s Get:30 http://ftpmaster.internal/ubuntu plucky/main amd64 libseccomp2 amd64 2.5.5-1ubuntu6 [53.5 kB]
 59s Get:31 http://ftpmaster.internal/ubuntu plucky/main amd64 libselinux1 amd64 3.7-3ubuntu3 [87.3 kB]
 59s Get:32 http://ftpmaster.internal/ubuntu plucky/main amd64 libapparmor1 amd64 4.1.0~beta5-0ubuntu8 [55.0 kB]
 59s Get:33 http://ftpmaster.internal/ubuntu plucky/main amd64 libapt-pkg7.0 amd64 2.9.33 [1138 kB]
 59s Get:34 http://ftpmaster.internal/ubuntu plucky/main amd64 apt amd64 2.9.33 [1439 kB]
 59s Get:35 http://ftpmaster.internal/ubuntu plucky/main amd64 apt-utils amd64 2.9.33 [222 kB]
 59s Get:36 http://ftpmaster.internal/ubuntu plucky/main amd64 python3-minimal amd64 3.13.2-2 [27.7 kB]
 59s Get:37 http://ftpmaster.internal/ubuntu plucky/main amd64 python3 amd64 3.13.2-2 [24.0 kB]
 59s Get:38 http://ftpmaster.internal/ubuntu plucky/main amd64 libpython3.13 amd64 3.13.2-2 [2341 kB]
 60s Get:39 http://ftpmaster.internal/ubuntu plucky/main amd64 media-types all 13.0.0 [29.9 kB]
 60s Get:40 http://ftpmaster.internal/ubuntu plucky/main amd64 libncurses6 amd64 6.5+20250216-2 [126 kB]
 60s Get:41 http://ftpmaster.internal/ubuntu plucky/main amd64 libncursesw6 amd64 6.5+20250216-2 [165 kB]
 60s Get:42 http://ftpmaster.internal/ubuntu plucky/main amd64 libtinfo6 amd64 6.5+20250216-2 [119 kB]
 60s Get:43 http://ftpmaster.internal/ubuntu plucky/main amd64 libsqlite3-0 amd64 3.46.1-2 [715 kB]
 60s Get:44 http://ftpmaster.internal/ubuntu plucky/main amd64 python3.13 amd64 3.13.2-2 [735 kB]
 60s Get:45 http://ftpmaster.internal/ubuntu plucky/main amd64 python3.13-minimal amd64 3.13.2-2 [2365 kB]
 60s Get:46 http://ftpmaster.internal/ubuntu plucky/main amd64 libpython3.13-minimal amd64 3.13.2-2 [883 kB]
 60s Get:47 http://ftpmaster.internal/ubuntu plucky/main amd64 libpython3.13-stdlib amd64 3.13.2-2 [2066 kB]
 60s Get:48 http://ftpmaster.internal/ubuntu plucky/main amd64 libpython3-stdlib amd64 3.13.2-2 [10.4 kB]
 60s Get:49 http://ftpmaster.internal/ubuntu plucky/main amd64 rsync amd64 3.4.1+ds1-3 [482 kB]
 60s Get:50 http://ftpmaster.internal/ubuntu plucky/main amd64 libdebuginfod-common all 0.192-4 [15.4 kB]
 60s Get:51 http://ftpmaster.internal/ubuntu plucky/main amd64 libsemanage-common all 3.7-2.1build1 [7268 B]
 60s Get:52 http://ftpmaster.internal/ubuntu plucky/main amd64 libsemanage2 amd64 3.7-2.1build1 [106 kB]
 60s Get:53 http://ftpmaster.internal/ubuntu plucky/main amd64 libassuan9 amd64 3.0.2-2 [43.1 kB]
 60s Get:54 http://ftpmaster.internal/ubuntu plucky/main amd64 gir1.2-girepository-2.0 amd64 1.83.4-1 [25.3 kB]
 60s Get:55 http://ftpmaster.internal/ubuntu plucky/main amd64 gir1.2-glib-2.0 amd64 2.84.0-1 [184 kB]
 60s Get:56 http://ftpmaster.internal/ubuntu plucky/main amd64 libglib2.0-0t64 amd64 2.84.0-1 [1669 kB]
 61s Get:57 http://ftpmaster.internal/ubuntu plucky/main amd64 libgirepository-1.0-1 amd64 1.83.4-1 [89.5 kB]
 61s Get:58 http://ftpmaster.internal/ubuntu plucky/main amd64 libestr0 amd64 0.1.11-2 [8340 B]
 61s Get:59 http://ftpmaster.internal/ubuntu plucky/main amd64 libglib2.0-data all 2.84.0-1 [53.0 kB]
 61s Get:60 http://ftpmaster.internal/ubuntu plucky/main amd64 python3-newt amd64 0.52.24-4ubuntu2 [21.1 kB]
 61s Get:61 http://ftpmaster.internal/ubuntu plucky/main amd64 libnewt0.52 amd64 0.52.24-4ubuntu2 [55.7 kB]
 61s Get:62 http://ftpmaster.internal/ubuntu plucky/main amd64 libxml2 amd64 2.12.7+dfsg+really2.9.14-0.2ubuntu5 [772 kB]
 61s Get:63 http://ftpmaster.internal/ubuntu plucky/main amd64 python-apt-common all 2.9.9build1 [21.3 kB]
 61s Get:64 http://ftpmaster.internal/ubuntu plucky/main amd64 python3-apt amd64 2.9.9build1 [172 kB]
 61s Get:65 http://ftpmaster.internal/ubuntu plucky/main amd64 python3-cffi-backend amd64 1.17.1-2build2 [96.6 kB]
 61s Get:66 http://ftpmaster.internal/ubuntu plucky/main amd64 python3-dbus amd64 1.3.2-5build5 [102 kB]
 61s Get:67 http://ftpmaster.internal/ubuntu plucky/main amd64 python3-gi amd64 3.50.0-4build1 [252 kB]
 61s Get:68 http://ftpmaster.internal/ubuntu plucky/main amd64 python3-yaml amd64 6.0.2-1build2 [144 kB]
 61s Get:69 http://ftpmaster.internal/ubuntu plucky/main amd64 rsyslog amd64 8.2412.0-2ubuntu2 [555 kB]
 61s Get:70 http://ftpmaster.internal/ubuntu plucky/main amd64 whiptail amd64 0.52.24-4ubuntu2 [19.1 kB]
 61s Get:71 http://ftpmaster.internal/ubuntu plucky/main amd64 ubuntu-minimal amd64 1.549 [11.5 kB]
 61s Get:72 http://ftpmaster.internal/ubuntu plucky/main amd64 apparmor amd64 4.1.0~beta5-0ubuntu8 [701 kB]
 61s Get:73 http://ftpmaster.internal/ubuntu plucky/main amd64 dosfstools amd64 4.2-1.2 [95.0 kB]
 61s Get:74 http://ftpmaster.internal/ubuntu plucky/main amd64 libnl-genl-3-200 amd64 3.7.0-1 [12.2 kB]
 61s Get:75 http://ftpmaster.internal/ubuntu plucky/main amd64 libnl-route-3-200 amd64 3.7.0-1 [191 kB]
 61s Get:76 http://ftpmaster.internal/ubuntu plucky/main amd64 libnl-3-200 amd64 3.7.0-1 [64.9 kB]
 61s Get:77 http://ftpmaster.internal/ubuntu plucky/main amd64 parted amd64 3.6-5 [53.9 kB]
 61s Get:78 http://ftpmaster.internal/ubuntu plucky/main amd64 libparted2t64 amd64 3.6-5 [158 kB]
 61s Get:79 http://ftpmaster.internal/ubuntu plucky/main amd64 pci.ids all 0.0~2025.03.09-1 [285 kB]
 61s Get:80 http://ftpmaster.internal/ubuntu plucky/main amd64 pciutils amd64 1:3.13.0-2 [110 kB]
 61s Get:81 http://ftpmaster.internal/ubuntu plucky/main amd64 libpci3 amd64 1:3.13.0-2 [39.8 kB]
 61s Get:82 http://ftpmaster.internal/ubuntu plucky/main amd64 strace amd64 6.13+ds-1ubuntu1 [622 kB]
 61s Get:83 http://ftpmaster.internal/ubuntu plucky/main amd64 xz-utils amd64 5.6.4-1 [278 kB]
 61s Get:84 http://ftpmaster.internal/ubuntu plucky/main amd64 ubuntu-standard amd64 1.549 [11.5 kB]
 61s Get:85 http://ftpmaster.internal/ubuntu plucky/main amd64 libgprofng0 amd64 2.44-3ubuntu1 [886 kB]
 61s Get:86 http://ftpmaster.internal/ubuntu plucky/main amd64 libctf0 amd64 2.44-3ubuntu1 [96.5 kB]
 61s Get:87 http://ftpmaster.internal/ubuntu plucky/main amd64 libctf-nobfd0 amd64 2.44-3ubuntu1 [98.9 kB]
 61s Get:88 http://ftpmaster.internal/ubuntu plucky/main amd64 binutils-x86-64-linux-gnu amd64 2.44-3ubuntu1 [1108 kB]
 61s Get:89 http://ftpmaster.internal/ubuntu plucky/main amd64 libbinutils amd64 2.44-3ubuntu1 [585 kB]
 61s Get:90 http://ftpmaster.internal/ubuntu plucky/main amd64 binutils amd64 2.44-3ubuntu1 [208 kB]
 61s Get:91 http://ftpmaster.internal/ubuntu plucky/main amd64 binutils-common amd64 2.44-3ubuntu1 [215 kB]
 61s Get:92 http://ftpmaster.internal/ubuntu plucky/main amd64 libsframe1 amd64 2.44-3ubuntu1 [14.8 kB]
 61s Get:93 http://ftpmaster.internal/ubuntu plucky/main amd64 hwdata all 0.393-3 [1562 B]
 61s Get:94 http://ftpmaster.internal/ubuntu plucky/main amd64 pnp.ids all 0.393-3 [29.5 kB]
 61s Get:95 http://ftpmaster.internal/ubuntu plucky/main amd64 linux-tools-common all 6.14.0-10.10 [295 kB]
 61s Get:96 http://ftpmaster.internal/ubuntu plucky/main amd64 bpftool amd64 7.6.0+6.14.0-10.10 [1147 kB]
 62s Get:97 http://ftpmaster.internal/ubuntu plucky/main amd64 python3-markupsafe amd64 2.1.5-1build4 [13.4 kB]
 62s Get:98 http://ftpmaster.internal/ubuntu plucky/main amd64 python3-jinja2 all 3.1.5-2ubuntu1 [109 kB]
 62s Get:99 http://ftpmaster.internal/ubuntu plucky/main amd64 cloud-init-base all 25.1-0ubuntu3 [616 kB]
 62s Get:100 http://ftpmaster.internal/ubuntu plucky/main amd64 libbrotli1 amd64 1.1.0-2build4 [365 kB]
 62s Get:101 http://ftpmaster.internal/ubuntu plucky/main amd64 curl amd64 8.12.1-3ubuntu1 [258 kB]
 62s Get:102 http://ftpmaster.internal/ubuntu plucky/main amd64 libcurl4t64 amd64 8.12.1-3ubuntu1 [437 kB]
 62s Get:103 http://ftpmaster.internal/ubuntu plucky/main amd64 exfatprogs amd64 1.2.8-1 [76.3 kB]
 62s Get:104 http://ftpmaster.internal/ubuntu plucky/main amd64 libcurl3t64-gnutls amd64 8.12.1-3ubuntu1 [432 kB]
 63s Get:105 http://ftpmaster.internal/ubuntu plucky/main amd64 fwupd amd64 2.0.6-4 [5408 kB]
 64s Get:106 http://ftpmaster.internal/ubuntu plucky/main amd64 libfwupd3 amd64 2.0.6-4 [136 kB]
 64s Get:107 http://ftpmaster.internal/ubuntu plucky/main amd64 libmm-glib0 amd64 1.23.4-0ubuntu3 [251 kB]
 64s Get:108 http://ftpmaster.internal/ubuntu plucky/main amd64 htop amd64 3.4.0-2 [195 kB]
 64s Get:109 http://ftpmaster.internal/ubuntu plucky/main amd64 linux-firmware amd64 20250310.git9e1370d3-0ubuntu1 [571 MB]
158s Get:110 http://ftpmaster.internal/ubuntu plucky/main amd64 initramfs-tools all 0.146ubuntu1 [7920 B]
158s Get:111 http://ftpmaster.internal/ubuntu plucky/main amd64 initramfs-tools-core all 0.146ubuntu1 [51.9 kB]
158s Get:112 http://ftpmaster.internal/ubuntu plucky/main amd64 initramfs-tools-bin amd64 0.146ubuntu1 [26.2 kB]
158s Get:113 http://ftpmaster.internal/ubuntu plucky/main amd64 libdebuginfod1t64 amd64 0.192-4 [21.0 kB]
158s Get:114 http://ftpmaster.internal/ubuntu plucky/main amd64 libftdi1-2 amd64 1.5-8build1 [30.2 kB]
158s Get:115 http://ftpmaster.internal/ubuntu plucky/main amd64 libgpgme11t64 amd64 1.24.2-1ubuntu2 [155 kB]
158s Get:116 http://ftpmaster.internal/ubuntu plucky/main amd64 libjemalloc2 amd64 5.3.0-3 [277 kB]
158s Get:117 http://ftpmaster.internal/ubuntu plucky/main amd64 linux-modules-6.14.0-10-generic amd64 6.14.0-10.10 [41.2 MB]
164s Get:118 http://ftpmaster.internal/ubuntu plucky/main amd64 linux-image-6.14.0-10-generic amd64 6.14.0-10.10 [15.3 MB]
166s Get:119 http://ftpmaster.internal/ubuntu plucky/main amd64 linux-modules-extra-6.14.0-10-generic amd64 6.14.0-10.10 [120 MB]
185s Get:120 http://ftpmaster.internal/ubuntu plucky/main amd64 linux-generic amd64 6.14.0-10.10 [1730 B]
185s Get:121 http://ftpmaster.internal/ubuntu plucky/main amd64 linux-image-generic amd64 6.14.0-10.10 [11.1 kB]
185s Get:122 http://ftpmaster.internal/ubuntu plucky/main amd64 linux-virtual amd64 6.14.0-10.10 [1722 B]
185s Get:123 http://ftpmaster.internal/ubuntu plucky/main amd64 linux-image-virtual amd64 6.14.0-10.10 [11.1 kB]
185s Get:124 http://ftpmaster.internal/ubuntu plucky/main amd64 linux-headers-virtual amd64 6.14.0-10.10 [1642 B]
185s Get:125 http://ftpmaster.internal/ubuntu plucky/main amd64 linux-headers-6.14.0-10 all 6.14.0-10.10 [14.2 MB]
187s Get:126 http://ftpmaster.internal/ubuntu plucky/main amd64 linux-headers-6.14.0-10-generic amd64 6.14.0-10.10 [3915 kB]
187s Get:127 http://ftpmaster.internal/ubuntu plucky/main amd64 linux-headers-generic amd64 6.14.0-10.10 [11.0 kB]
187s Get:128 http://ftpmaster.internal/ubuntu plucky/main amd64 linux-perf amd64 6.14.0-10.10 [4122 kB]
188s Get:129 http://ftpmaster.internal/ubuntu plucky/main amd64 linux-tools-6.14.0-10 amd64 6.14.0-10.10 [1394 kB]
188s Get:130 http://ftpmaster.internal/ubuntu plucky/main amd64 linux-tools-6.14.0-10-generic amd64 6.14.0-10.10 [830 B]
188s Get:131 http://ftpmaster.internal/ubuntu plucky/main amd64 python3-lazr.uri all 1.0.6-6 [13.7 kB]
188s Get:132 http://ftpmaster.internal/ubuntu plucky/main amd64 python3-rpds-py amd64 0.21.0-2ubuntu2 [278 kB]
188s Get:133 http://ftpmaster.internal/ubuntu plucky/main amd64 python3-systemd amd64 235-1build6 [43.9 kB]
188s Get:134 http://ftpmaster.internal/ubuntu plucky/main amd64 python3.13-gdbm amd64 3.13.2-2 [31.9 kB]
188s Get:135 http://ftpmaster.internal/ubuntu plucky/main amd64 ubuntu-kernel-accessories amd64 1.549 [11.2 kB]
188s Get:136 http://ftpmaster.internal/ubuntu plucky/main amd64 cloud-init all 25.1-0ubuntu3 [2100 B]
188s Get:137 http://ftpmaster.internal/ubuntu plucky/main amd64 python3-bcrypt amd64 4.2.0-2.1build1 [221 kB]
189s Preconfiguring packages ...
189s Fetched 829 MB in 2min 13s (6223 kB/s)
189s (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 109140 files and directories currently installed.)
189s Preparing to unpack .../ncurses-bin_6.5+20250216-2_amd64.deb ...
189s Unpacking ncurses-bin (6.5+20250216-2) over (6.5+20250216-1) ...
189s Setting up ncurses-bin (6.5+20250216-2) ...
189s (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 109140 files and directories currently installed.)
189s Preparing to unpack .../libc-dev-bin_2.41-1ubuntu1_amd64.deb ...
189s Unpacking libc-dev-bin (2.41-1ubuntu1) over (2.40-4ubuntu1) ...
189s Preparing to unpack .../libc6-dev_2.41-1ubuntu1_amd64.deb ...
189s Unpacking libc6-dev:amd64 (2.41-1ubuntu1) over (2.40-4ubuntu1) ...
189s Preparing to unpack .../locales_2.41-1ubuntu1_all.deb ...
189s Unpacking locales (2.41-1ubuntu1) over (2.40-4ubuntu1) ...
190s Preparing to unpack .../libc6_2.41-1ubuntu1_amd64.deb ...
190s Checking for services that may need to be restarted...
190s Checking init scripts...
190s Checking for services that may need to be restarted...
190s Checking init scripts...
190s Stopping some services possibly affected by the upgrade (will be restarted later):
190s   cron: stopping...done.
190s 
190s Unpacking libc6:amd64 (2.41-1ubuntu1) over (2.40-4ubuntu1) ...
190s Setting up libc6:amd64 (2.41-1ubuntu1) ...
190s Checking for services that may need to be restarted...
190s Checking init scripts...
190s Restarting services possibly affected by the upgrade:
190s   cron: restarting...done.
190s 
190s Services restarted successfully.
190s (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 109141 files and directories currently installed.)
190s Preparing to unpack .../libc-bin_2.41-1ubuntu1_amd64.deb ...
190s Unpacking libc-bin (2.41-1ubuntu1) over (2.40-4ubuntu1) ...
190s Setting up libc-bin (2.41-1ubuntu1) ...
191s (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 109141 files and directories currently installed.)
191s Preparing to unpack .../linux-libc-dev_6.14.0-10.10_amd64.deb ...
191s Unpacking linux-libc-dev:amd64 (6.14.0-10.10) over (6.12.0-16.16) ...
191s Preparing to unpack .../libatomic1_15-20250222-0ubuntu1_amd64.deb ...
191s Unpacking libatomic1:amd64 (15-20250222-0ubuntu1) over (15-20250213-1ubuntu1) ...
191s Preparing to unpack .../gcc-15-base_15-20250222-0ubuntu1_amd64.deb ...
191s Unpacking gcc-15-base:amd64 (15-20250222-0ubuntu1) over (15-20250213-1ubuntu1) ...
191s Setting up gcc-15-base:amd64 (15-20250222-0ubuntu1) ...
191s (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 109146 files and directories currently installed.)
191s Preparing to unpack .../libgcc-s1_15-20250222-0ubuntu1_amd64.deb ...
191s Unpacking libgcc-s1:amd64 (15-20250222-0ubuntu1) over (15-20250213-1ubuntu1) ...
191s Setting up libgcc-s1:amd64 (15-20250222-0ubuntu1) ...
191s (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 109146 files and directories currently installed.)
191s Preparing to unpack .../libstdc++6_15-20250222-0ubuntu1_amd64.deb ...
191s Unpacking libstdc++6:amd64 (15-20250222-0ubuntu1) over (15-20250213-1ubuntu1) ...
191s Setting up libstdc++6:amd64 (15-20250222-0ubuntu1) ...
191s (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 109146 files and directories currently installed.)
191s Preparing to unpack .../ncurses-base_6.5+20250216-2_all.deb ...
191s Unpacking ncurses-base (6.5+20250216-2) over (6.5+20250216-1) ...
191s Setting up ncurses-base (6.5+20250216-2) ...
191s (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 109146 files and directories currently installed.)
191s Preparing to unpack .../ncurses-term_6.5+20250216-2_all.deb ...
191s Unpacking ncurses-term (6.5+20250216-2) over (6.5+20250216-1) ...
192s Preparing to unpack .../liblz4-1_1.10.0-4_amd64.deb ...
192s Unpacking liblz4-1:amd64 (1.10.0-4) over (1.10.0-3) ...
192s Setting up liblz4-1:amd64 (1.10.0-4) ...
192s (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 109146 files and directories currently installed.)
192s Preparing to unpack .../liblzma5_5.6.4-1_amd64.deb ...
192s Unpacking liblzma5:amd64 (5.6.4-1) over (5.6.3-1) ...
192s Setting up liblzma5:amd64 (5.6.4-1) ...
192s (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 109146 files and directories currently installed.)
192s Preparing to unpack .../libsystemd0_257.3-1ubuntu3_amd64.deb ...
192s Unpacking libsystemd0:amd64 (257.3-1ubuntu3) over (257.2-3ubuntu1) ...
192s Setting up libsystemd0:amd64 (257.3-1ubuntu3) ...
192s (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 109146 files and directories currently installed.)
192s Preparing to unpack .../libnss-systemd_257.3-1ubuntu3_amd64.deb ...
192s Unpacking libnss-systemd:amd64 (257.3-1ubuntu3) over (257.2-3ubuntu1) ...
192s Preparing to unpack .../systemd-sysv_257.3-1ubuntu3_amd64.deb ...
192s Unpacking systemd-sysv (257.3-1ubuntu3) over (257.2-3ubuntu1) ...
192s Preparing to unpack .../systemd-resolved_257.3-1ubuntu3_amd64.deb ...
192s Unpacking systemd-resolved (257.3-1ubuntu3) over (257.2-3ubuntu1) ...
192s Preparing to unpack .../libpam-systemd_257.3-1ubuntu3_amd64.deb ...
192s Unpacking libpam-systemd:amd64 (257.3-1ubuntu3) over (257.2-3ubuntu1) ...
192s Preparing to unpack .../libsystemd-shared_257.3-1ubuntu3_amd64.deb ...
192s Unpacking libsystemd-shared:amd64 (257.3-1ubuntu3) over (257.2-3ubuntu1) ...
192s Setting up libsystemd-shared:amd64 (257.3-1ubuntu3) ...
192s (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 109146 files and directories currently installed.)
192s Preparing to unpack .../systemd_257.3-1ubuntu3_amd64.deb ...
192s Unpacking systemd (257.3-1ubuntu3) over (257.2-3ubuntu1) ...
193s Preparing to unpack .../systemd-timesyncd_257.3-1ubuntu3_amd64.deb ...
193s Unpacking systemd-timesyncd (257.3-1ubuntu3) over (257.2-3ubuntu1) ...
193s Preparing to unpack .../systemd-cryptsetup_257.3-1ubuntu3_amd64.deb ...
193s Unpacking systemd-cryptsetup (257.3-1ubuntu3) over (257.2-3ubuntu1) ...
193s Preparing to unpack .../udev_257.3-1ubuntu3_amd64.deb ...
193s Unpacking udev (257.3-1ubuntu3) over (257.2-3ubuntu1) ...
193s Preparing to unpack .../libudev1_257.3-1ubuntu3_amd64.deb ...
193s Unpacking libudev1:amd64 (257.3-1ubuntu3) over (257.2-3ubuntu1) ...
193s Setting up libudev1:amd64 (257.3-1ubuntu3) ...
193s (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 109146 files and directories currently installed.)
193s Preparing to unpack .../libaudit-common_1%3a4.0.2-2ubuntu2_all.deb ...
193s Unpacking libaudit-common (1:4.0.2-2ubuntu2) over (1:4.0.2-2ubuntu1) ...
193s Setting up libaudit-common (1:4.0.2-2ubuntu2) ...
193s (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 109146 files and directories currently installed.)
193s Preparing to unpack .../libcap-ng0_0.8.5-4build1_amd64.deb ...
193s Unpacking libcap-ng0:amd64 (0.8.5-4build1) over (0.8.5-4) ...
193s Setting up libcap-ng0:amd64 (0.8.5-4build1) ...
193s (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 109146 files and directories currently installed.)
193s Preparing to unpack .../libaudit1_1%3a4.0.2-2ubuntu2_amd64.deb ...
193s Unpacking libaudit1:amd64 (1:4.0.2-2ubuntu2) over (1:4.0.2-2ubuntu1) ...
193s Setting up libaudit1:amd64 (1:4.0.2-2ubuntu2) ...
193s (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 109146 files and directories currently installed.)
193s Preparing to unpack .../libseccomp2_2.5.5-1ubuntu6_amd64.deb ...
193s Unpacking libseccomp2:amd64 (2.5.5-1ubuntu6) over (2.5.5-1ubuntu5) ...
193s Setting up libseccomp2:amd64 (2.5.5-1ubuntu6) ...
193s (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 109146 files and directories currently installed.)
193s Preparing to unpack .../libselinux1_3.7-3ubuntu3_amd64.deb ...
193s Unpacking libselinux1:amd64 (3.7-3ubuntu3) over (3.7-3ubuntu2) ...
193s Setting up libselinux1:amd64 (3.7-3ubuntu3) ...
194s (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 109146 files and directories currently installed.)
194s Preparing to unpack .../libapparmor1_4.1.0~beta5-0ubuntu8_amd64.deb ...
194s Unpacking libapparmor1:amd64 (4.1.0~beta5-0ubuntu8) over (4.1.0~beta5-0ubuntu5) ...
194s Preparing to unpack .../libapt-pkg7.0_2.9.33_amd64.deb ...
194s Unpacking libapt-pkg7.0:amd64 (2.9.33) over (2.9.31ubuntu1) ...
194s Setting up libapt-pkg7.0:amd64 (2.9.33) ...
194s (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 109146 files and directories currently installed.)
194s Preparing to unpack .../archives/apt_2.9.33_amd64.deb ...
194s Unpacking apt (2.9.33) over (2.9.31ubuntu1) ...
194s Setting up apt (2.9.33) ...
194s (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 109146 files and directories currently installed.)
194s Preparing to unpack .../apt-utils_2.9.33_amd64.deb ...
194s Unpacking apt-utils (2.9.33) over (2.9.31ubuntu1) ...
194s Preparing to unpack .../python3-minimal_3.13.2-2_amd64.deb ...
194s Unpacking python3-minimal (3.13.2-2) over (3.13.2-1) ...
195s Setting up python3-minimal (3.13.2-2) ...
195s (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 109146 files and directories currently installed.)
195s Preparing to unpack .../0-python3_3.13.2-2_amd64.deb ...
195s Unpacking python3 (3.13.2-2) over (3.13.2-1) ...
195s Preparing to unpack .../1-libpython3.13_3.13.2-2_amd64.deb ...
195s Unpacking libpython3.13:amd64 (3.13.2-2) over (3.13.2-1) ...
195s Preparing to unpack .../2-media-types_13.0.0_all.deb ...
195s Unpacking media-types (13.0.0) over (12.0.0) ...
195s Preparing to unpack .../3-libncurses6_6.5+20250216-2_amd64.deb ...
195s Unpacking libncurses6:amd64 (6.5+20250216-2) over (6.5+20250216-1) ...
195s Preparing to unpack .../4-libncursesw6_6.5+20250216-2_amd64.deb ...
195s Unpacking libncursesw6:amd64 (6.5+20250216-2) over (6.5+20250216-1) ...
195s Preparing to unpack .../5-libtinfo6_6.5+20250216-2_amd64.deb ...
195s Unpacking libtinfo6:amd64 (6.5+20250216-2) over (6.5+20250216-1) ...
195s Setting up libtinfo6:amd64 (6.5+20250216-2) ...
195s (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 109146 files and directories currently installed.)
195s Preparing to unpack .../0-libsqlite3-0_3.46.1-2_amd64.deb ...
195s Unpacking libsqlite3-0:amd64 (3.46.1-2) over (3.46.1-1) ...
195s Preparing to unpack .../1-python3.13_3.13.2-2_amd64.deb ...
195s Unpacking python3.13 (3.13.2-2) over (3.13.2-1) ...
195s Preparing to unpack .../2-python3.13-minimal_3.13.2-2_amd64.deb ...
195s Unpacking python3.13-minimal (3.13.2-2) over (3.13.2-1) ...
195s Preparing to unpack .../3-libpython3.13-minimal_3.13.2-2_amd64.deb ...
195s Unpacking libpython3.13-minimal:amd64 (3.13.2-2) over (3.13.2-1) ...
195s Preparing to unpack .../4-libpython3.13-stdlib_3.13.2-2_amd64.deb ...
195s Unpacking libpython3.13-stdlib:amd64 (3.13.2-2) over (3.13.2-1) ...
195s Preparing to unpack .../5-libpython3-stdlib_3.13.2-2_amd64.deb ...
195s Unpacking libpython3-stdlib:amd64 (3.13.2-2) over (3.13.2-1) ...
196s Preparing to unpack .../6-rsync_3.4.1+ds1-3_amd64.deb ...
196s Unpacking rsync (3.4.1+ds1-3) over (3.4.1-0syncable1) ...
196s Selecting previously unselected package libdebuginfod-common.
196s Preparing to unpack .../7-libdebuginfod-common_0.192-4_all.deb ...
196s Unpacking libdebuginfod-common (0.192-4) ...
196s Preparing to unpack .../8-libsemanage-common_3.7-2.1build1_all.deb ...
196s Unpacking libsemanage-common (3.7-2.1build1) over (3.7-2.1) ...
196s Setting up libsemanage-common (3.7-2.1build1) ...
196s (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 109155 files and directories currently installed.)
196s Preparing to unpack .../libsemanage2_3.7-2.1build1_amd64.deb ...
196s Unpacking libsemanage2:amd64 (3.7-2.1build1) over (3.7-2.1) ...
196s Setting up libsemanage2:amd64 (3.7-2.1build1) ...
196s (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 109155 files and directories currently installed.)
196s Preparing to unpack .../libassuan9_3.0.2-2_amd64.deb ...
196s Unpacking libassuan9:amd64 (3.0.2-2) over (3.0.1-2) ...
196s Setting up libassuan9:amd64 (3.0.2-2) ...
196s (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 109155 files and directories currently installed.)
196s Preparing to unpack .../00-gir1.2-girepository-2.0_1.83.4-1_amd64.deb ...
196s Unpacking gir1.2-girepository-2.0:amd64 (1.83.4-1) over (1.82.0-4) ...
196s Preparing to unpack .../01-gir1.2-glib-2.0_2.84.0-1_amd64.deb ...
196s Unpacking gir1.2-glib-2.0:amd64 (2.84.0-1) over (2.83.5-1) ...
196s Preparing to unpack .../02-libglib2.0-0t64_2.84.0-1_amd64.deb ...
196s Unpacking libglib2.0-0t64:amd64 (2.84.0-1) over (2.83.5-1) ...
196s Preparing to unpack .../03-libgirepository-1.0-1_1.83.4-1_amd64.deb ...
196s Unpacking libgirepository-1.0-1:amd64 (1.83.4-1) over (1.82.0-4) ...
196s Preparing to unpack .../04-libestr0_0.1.11-2_amd64.deb ...
196s Unpacking libestr0:amd64 (0.1.11-2) over (0.1.11-1build1) ...
196s Preparing to unpack .../05-libglib2.0-data_2.84.0-1_all.deb ...
196s Unpacking libglib2.0-data (2.84.0-1) over (2.83.5-1) ...
196s Preparing to unpack .../06-python3-newt_0.52.24-4ubuntu2_amd64.deb ...
196s Unpacking python3-newt:amd64 (0.52.24-4ubuntu2) over (0.52.24-4ubuntu1) ...
196s Preparing to unpack .../07-libnewt0.52_0.52.24-4ubuntu2_amd64.deb ...
196s Unpacking libnewt0.52:amd64 (0.52.24-4ubuntu2) over (0.52.24-4ubuntu1) ...
196s Preparing to unpack .../08-libxml2_2.12.7+dfsg+really2.9.14-0.2ubuntu5_amd64.deb ...
196s Unpacking libxml2:amd64 (2.12.7+dfsg+really2.9.14-0.2ubuntu5) over (2.12.7+dfsg+really2.9.14-0.2ubuntu4) ...
197s Preparing to unpack .../09-python-apt-common_2.9.9build1_all.deb ...
197s Unpacking python-apt-common (2.9.9build1) over (2.9.9) ...
197s Preparing to unpack .../10-python3-apt_2.9.9build1_amd64.deb ...
197s Unpacking python3-apt (2.9.9build1) over (2.9.9) ...
197s Preparing to unpack .../11-python3-cffi-backend_1.17.1-2build2_amd64.deb ...
197s Unpacking python3-cffi-backend:amd64 (1.17.1-2build2) over (1.17.1-2build1) ...
197s Preparing to unpack .../12-python3-dbus_1.3.2-5build5_amd64.deb ...
197s Unpacking python3-dbus (1.3.2-5build5) over (1.3.2-5build4) ...
197s Preparing to unpack .../13-python3-gi_3.50.0-4build1_amd64.deb ...
197s Unpacking python3-gi (3.50.0-4build1) over (3.50.0-4) ...
197s Preparing to unpack .../14-python3-yaml_6.0.2-1build2_amd64.deb ...
197s Unpacking python3-yaml (6.0.2-1build2) over (6.0.2-1build1) ...
197s Preparing to unpack .../15-rsyslog_8.2412.0-2ubuntu2_amd64.deb ...
197s Unpacking rsyslog (8.2412.0-2ubuntu2) over (8.2412.0-2ubuntu1) ...
197s Preparing to unpack .../16-whiptail_0.52.24-4ubuntu2_amd64.deb ...
197s Unpacking whiptail (0.52.24-4ubuntu2) over (0.52.24-4ubuntu1) ...
197s Preparing to unpack .../17-ubuntu-minimal_1.549_amd64.deb ...
197s Unpacking ubuntu-minimal (1.549) over (1.548) ...
197s Preparing to unpack .../18-apparmor_4.1.0~beta5-0ubuntu8_amd64.deb ...
198s Unpacking apparmor (4.1.0~beta5-0ubuntu8) over (4.1.0~beta5-0ubuntu5) ...
198s Preparing to unpack .../19-dosfstools_4.2-1.2_amd64.deb ...
198s Unpacking dosfstools (4.2-1.2) over (4.2-1.1build1) ...
198s Preparing to unpack .../20-libnl-genl-3-200_3.7.0-1_amd64.deb ...
198s Unpacking libnl-genl-3-200:amd64 (3.7.0-1) over (3.7.0-0.3build2) ...
198s Preparing to unpack .../21-libnl-route-3-200_3.7.0-1_amd64.deb ...
198s Unpacking libnl-route-3-200:amd64 (3.7.0-1) over (3.7.0-0.3build2) ...
198s Preparing to unpack .../22-libnl-3-200_3.7.0-1_amd64.deb ...
198s Unpacking libnl-3-200:amd64 (3.7.0-1) over (3.7.0-0.3build2) ...
198s Preparing to unpack .../23-parted_3.6-5_amd64.deb ...
198s Unpacking parted (3.6-5) over (3.6-4build1) ...
198s Preparing to unpack .../24-libparted2t64_3.6-5_amd64.deb ...
198s Adding 'diversion of /lib/x86_64-linux-gnu/libparted.so.2 to /lib/x86_64-linux-gnu/libparted.so.2.usr-is-merged by libparted2t64'
198s Adding 'diversion of /lib/x86_64-linux-gnu/libparted.so.2.0.5 to /lib/x86_64-linux-gnu/libparted.so.2.0.5.usr-is-merged by libparted2t64'
198s Unpacking libparted2t64:amd64 (3.6-5) over (3.6-4build1) ...
198s Preparing to unpack .../25-pci.ids_0.0~2025.03.09-1_all.deb ...
198s Unpacking pci.ids (0.0~2025.03.09-1) over (0.0~2025.02.12-1) ...
198s Preparing to unpack .../26-pciutils_1%3a3.13.0-2_amd64.deb ...
198s Unpacking pciutils (1:3.13.0-2) over (1:3.13.0-1) ...
198s Preparing to unpack .../27-libpci3_1%3a3.13.0-2_amd64.deb ...
198s Unpacking libpci3:amd64 (1:3.13.0-2) over (1:3.13.0-1) ...
198s Preparing to unpack .../28-strace_6.13+ds-1ubuntu1_amd64.deb ...
198s Unpacking strace (6.13+ds-1ubuntu1) over (6.11-0ubuntu1) ...
198s Preparing to unpack .../29-xz-utils_5.6.4-1_amd64.deb ...
198s Unpacking xz-utils (5.6.4-1) over (5.6.3-1) ...
198s Preparing to unpack .../30-ubuntu-standard_1.549_amd64.deb ...
198s Unpacking ubuntu-standard (1.549) over (1.548) ...
198s Preparing to unpack .../31-libgprofng0_2.44-3ubuntu1_amd64.deb ...
198s Unpacking libgprofng0:amd64 (2.44-3ubuntu1) over (2.44-2ubuntu1) ...
199s Preparing to unpack .../32-libctf0_2.44-3ubuntu1_amd64.deb ...
199s Unpacking libctf0:amd64 (2.44-3ubuntu1) over (2.44-2ubuntu1) ...
199s Preparing to unpack .../33-libctf-nobfd0_2.44-3ubuntu1_amd64.deb ...
199s Unpacking libctf-nobfd0:amd64 (2.44-3ubuntu1) over (2.44-2ubuntu1) ...
199s Preparing to unpack .../34-binutils-x86-64-linux-gnu_2.44-3ubuntu1_amd64.deb ...
199s Unpacking binutils-x86-64-linux-gnu (2.44-3ubuntu1) over (2.44-2ubuntu1) ...
199s Preparing to unpack .../35-libbinutils_2.44-3ubuntu1_amd64.deb ...
199s Unpacking libbinutils:amd64 (2.44-3ubuntu1) over (2.44-2ubuntu1) ...
199s Preparing to unpack .../36-binutils_2.44-3ubuntu1_amd64.deb ...
199s Unpacking binutils (2.44-3ubuntu1) over (2.44-2ubuntu1) ...
199s Preparing to unpack .../37-binutils-common_2.44-3ubuntu1_amd64.deb ...
199s Unpacking binutils-common:amd64 (2.44-3ubuntu1) over (2.44-2ubuntu1) ...
199s Preparing to unpack .../38-libsframe1_2.44-3ubuntu1_amd64.deb ...
199s Unpacking libsframe1:amd64 (2.44-3ubuntu1) over (2.44-2ubuntu1) ...
199s Preparing to unpack .../39-hwdata_0.393-3_all.deb ...
199s Unpacking hwdata (0.393-3) over (0.392-1) ...
199s Selecting previously unselected package pnp.ids.
199s Preparing to unpack .../40-pnp.ids_0.393-3_all.deb ...
199s Unpacking pnp.ids (0.393-3) ...
199s Preparing to unpack .../41-linux-tools-common_6.14.0-10.10_all.deb ...
199s Unpacking linux-tools-common (6.14.0-10.10) over (6.12.0-16.16) ...
199s Selecting previously unselected package bpftool.
199s Preparing to unpack .../42-bpftool_7.6.0+6.14.0-10.10_amd64.deb ...
199s Unpacking bpftool (7.6.0+6.14.0-10.10) ...
199s Preparing to unpack .../43-python3-markupsafe_2.1.5-1build4_amd64.deb ...
199s Unpacking python3-markupsafe (2.1.5-1build4) over (2.1.5-1build3) ...
199s Preparing to unpack .../44-python3-jinja2_3.1.5-2ubuntu1_all.deb ...
199s Unpacking python3-jinja2 (3.1.5-2ubuntu1) over (3.1.5-2) ...
199s Preparing to unpack .../45-cloud-init-base_25.1-0ubuntu3_all.deb ...
199s Unpacking cloud-init-base (25.1-0ubuntu3) over (25.1-0ubuntu2) ...
200s Preparing to unpack .../46-libbrotli1_1.1.0-2build4_amd64.deb ...
200s Unpacking libbrotli1:amd64 (1.1.0-2build4) over (1.1.0-2build3) ...
200s Preparing to unpack .../47-curl_8.12.1-3ubuntu1_amd64.deb ...
200s Unpacking curl (8.12.1-3ubuntu1) over (8.12.1-2ubuntu1) ...
200s Preparing to unpack .../48-libcurl4t64_8.12.1-3ubuntu1_amd64.deb ...
200s Unpacking libcurl4t64:amd64 (8.12.1-3ubuntu1) over (8.12.1-2ubuntu1) ...
200s Preparing to unpack .../49-exfatprogs_1.2.8-1_amd64.deb ...
200s Unpacking exfatprogs (1.2.8-1) over (1.2.7-3) ...
200s Preparing to unpack .../50-libcurl3t64-gnutls_8.12.1-3ubuntu1_amd64.deb ...
200s Unpacking libcurl3t64-gnutls:amd64 (8.12.1-3ubuntu1) over (8.12.1-2ubuntu1) ...
200s Preparing to unpack .../51-fwupd_2.0.6-4_amd64.deb ...
200s Unpacking fwupd (2.0.6-4) over (2.0.6-3) ...
200s Preparing to unpack .../52-libfwupd3_2.0.6-4_amd64.deb ...
200s Unpacking libfwupd3:amd64 (2.0.6-4) over (2.0.6-3) ...
200s Preparing to unpack .../53-libmm-glib0_1.23.4-0ubuntu3_amd64.deb ...
200s Unpacking libmm-glib0:amd64 (1.23.4-0ubuntu3) over (1.23.4-0ubuntu2) ...
200s Preparing to unpack .../54-htop_3.4.0-2_amd64.deb ...
200s Unpacking htop (3.4.0-2) over (3.3.0-5) ...
200s Preparing to unpack .../55-linux-firmware_20250310.git9e1370d3-0ubuntu1_amd64.deb ...
200s Unpacking linux-firmware (20250310.git9e1370d3-0ubuntu1) over (20250204.git0fd450ee-0ubuntu1) ...
203s Preparing to unpack .../56-initramfs-tools_0.146ubuntu1_all.deb ...
203s Unpacking initramfs-tools (0.146ubuntu1) over (0.145ubuntu3) ...
203s Preparing to unpack .../57-initramfs-tools-core_0.146ubuntu1_all.deb ...
203s Unpacking initramfs-tools-core (0.146ubuntu1) over (0.145ubuntu3) ...
203s Preparing to unpack .../58-initramfs-tools-bin_0.146ubuntu1_amd64.deb ...
203s Unpacking initramfs-tools-bin (0.146ubuntu1) over (0.145ubuntu3) ...
203s Selecting previously unselected package libdebuginfod1t64:amd64.
203s Preparing to unpack .../59-libdebuginfod1t64_0.192-4_amd64.deb ...
203s Unpacking libdebuginfod1t64:amd64 (0.192-4) ...
203s Preparing to unpack .../60-libftdi1-2_1.5-8build1_amd64.deb ...
203s Unpacking libftdi1-2:amd64 (1.5-8build1) over (1.5-8) ...
203s Preparing to unpack .../61-libgpgme11t64_1.24.2-1ubuntu2_amd64.deb ...
203s Unpacking libgpgme11t64:amd64 (1.24.2-1ubuntu2) over (1.24.2-1ubuntu1) ...
203s Preparing to unpack .../62-libjemalloc2_5.3.0-3_amd64.deb ...
203s Unpacking libjemalloc2:amd64 (5.3.0-3) over (5.3.0-2build1) ...
203s Selecting previously unselected package linux-modules-6.14.0-10-generic.
203s Preparing to unpack .../63-linux-modules-6.14.0-10-generic_6.14.0-10.10_amd64.deb ...
203s Unpacking linux-modules-6.14.0-10-generic (6.14.0-10.10) ...
204s Selecting previously unselected package linux-image-6.14.0-10-generic.
204s Preparing to unpack .../64-linux-image-6.14.0-10-generic_6.14.0-10.10_amd64.deb ...
204s Unpacking linux-image-6.14.0-10-generic (6.14.0-10.10) ...
204s Selecting previously unselected package linux-modules-extra-6.14.0-10-generic.
204s Preparing to unpack .../65-linux-modules-extra-6.14.0-10-generic_6.14.0-10.10_amd64.deb ...
204s Unpacking linux-modules-extra-6.14.0-10-generic (6.14.0-10.10) ...
205s Preparing to unpack .../66-linux-generic_6.14.0-10.10_amd64.deb ...
205s Unpacking linux-generic (6.14.0-10.10) over (6.12.0-16.16+2) ...
205s Preparing to unpack .../67-linux-image-generic_6.14.0-10.10_amd64.deb ...
205s Unpacking linux-image-generic (6.14.0-10.10) over (6.12.0-16.16+2) ...
205s Preparing to unpack .../68-linux-virtual_6.14.0-10.10_amd64.deb ...
205s Unpacking linux-virtual (6.14.0-10.10) over (6.12.0-16.16+2) ...
205s Preparing to unpack .../69-linux-image-virtual_6.14.0-10.10_amd64.deb ...
205s Unpacking linux-image-virtual (6.14.0-10.10) over (6.12.0-16.16+2) ...
205s Preparing to unpack .../70-linux-headers-virtual_6.14.0-10.10_amd64.deb ...
205s Unpacking linux-headers-virtual (6.14.0-10.10) over (6.12.0-16.16+2) ...
205s Selecting previously unselected package linux-headers-6.14.0-10.
205s Preparing to unpack .../71-linux-headers-6.14.0-10_6.14.0-10.10_all.deb ...
205s Unpacking linux-headers-6.14.0-10 (6.14.0-10.10) ...
207s Selecting previously unselected package linux-headers-6.14.0-10-generic.
207s Preparing to unpack .../72-linux-headers-6.14.0-10-generic_6.14.0-10.10_amd64.deb ...
207s Unpacking linux-headers-6.14.0-10-generic (6.14.0-10.10) ...
208s Preparing to unpack .../73-linux-headers-generic_6.14.0-10.10_amd64.deb ...
208s Unpacking linux-headers-generic (6.14.0-10.10) over (6.12.0-16.16+2) ...
208s Selecting previously unselected package linux-perf.
208s Preparing to unpack .../74-linux-perf_6.14.0-10.10_amd64.deb ...
208s Unpacking linux-perf (6.14.0-10.10) ...
209s Selecting previously unselected package linux-tools-6.14.0-10.
209s Preparing to unpack .../75-linux-tools-6.14.0-10_6.14.0-10.10_amd64.deb ...
209s Unpacking linux-tools-6.14.0-10 (6.14.0-10.10) ...
209s Selecting previously unselected package linux-tools-6.14.0-10-generic.
209s Preparing to unpack .../76-linux-tools-6.14.0-10-generic_6.14.0-10.10_amd64.deb ...
209s Unpacking linux-tools-6.14.0-10-generic (6.14.0-10.10) ...
209s Preparing to unpack .../77-python3-lazr.uri_1.0.6-6_all.deb ...
209s Unpacking python3-lazr.uri (1.0.6-6) over (1.0.6-5) ...
209s Preparing to unpack .../78-python3-rpds-py_0.21.0-2ubuntu2_amd64.deb ...
209s Unpacking python3-rpds-py (0.21.0-2ubuntu2) over (0.21.0-2ubuntu1) ...
209s Preparing to unpack .../79-python3-systemd_235-1build6_amd64.deb ...
209s Unpacking python3-systemd (235-1build6) over (235-1build5) ...
209s Preparing to unpack .../80-python3.13-gdbm_3.13.2-2_amd64.deb ...
209s Unpacking python3.13-gdbm (3.13.2-2) over (3.13.2-1) ...
209s Preparing to unpack .../81-ubuntu-kernel-accessories_1.549_amd64.deb ...
209s Unpacking ubuntu-kernel-accessories (1.549) over (1.548) ...
209s Preparing to unpack .../82-cloud-init_25.1-0ubuntu3_all.deb ...
209s Unpacking cloud-init (25.1-0ubuntu3) over (25.1-0ubuntu2) ...
209s Preparing to unpack .../83-python3-bcrypt_4.2.0-2.1build1_amd64.deb ...
209s Unpacking python3-bcrypt (4.2.0-2.1build1) over (4.2.0-2.1) ...
209s Setting up linux-headers-6.14.0-10 (6.14.0-10.10) ...
209s Setting up media-types (13.0.0) ...
209s Installing new version of config file /etc/mime.types ...
209s Setting up linux-headers-6.14.0-10-generic (6.14.0-10.10) ...
209s Setting up ubuntu-kernel-accessories (1.549) ...
209s Setting up libapparmor1:amd64 (4.1.0~beta5-0ubuntu8) ...
209s Setting up pci.ids (0.0~2025.03.09-1) ...
209s Setting up libnewt0.52:amd64 (0.52.24-4ubuntu2) ...
209s Setting up apt-utils (2.9.33) ...
209s Setting up libdebuginfod-common (0.192-4) ...
209s Setting up exfatprogs (1.2.8-1) ...
209s Setting up linux-firmware (20250310.git9e1370d3-0ubuntu1) ...
209s Setting up bpftool (7.6.0+6.14.0-10.10) ...
209s Setting up libestr0:amd64 (0.1.11-2) ...
209s Setting up libbrotli1:amd64 (1.1.0-2build4) ...
209s Setting up libsqlite3-0:amd64 (3.46.1-2) ...
209s Setting up dosfstools (4.2-1.2) ...
209s Setting up rsyslog (8.2412.0-2ubuntu2) ...
209s info: The user `syslog' is already a member of `adm'.
210s Setting up binutils-common:amd64 (2.44-3ubuntu1) ...
210s Setting up libcurl3t64-gnutls:amd64 (8.12.1-3ubuntu1) ...
210s Setting up linux-libc-dev:amd64 (6.14.0-10.10) ...
210s Setting up libctf-nobfd0:amd64 (2.44-3ubuntu1) ...
210s Setting up systemd (257.3-1ubuntu3) ...
210s /usr/lib/tmpfiles.d/legacy.conf:14: Duplicate line for path "/run/lock", ignoring.

210s Created symlink '/run/systemd/system/tmp.mount' → '/dev/null'.

211s /usr/lib/tmpfiles.d/legacy.conf:14: Duplicate line for path "/run/lock", ignoring.

211s Setting up libparted2t64:amd64 (3.6-5) ...
211s Removing 'diversion of /lib/x86_64-linux-gnu/libparted.so.2 to /lib/x86_64-linux-gnu/libparted.so.2.usr-is-merged by libparted2t64'
211s Removing 'diversion of /lib/x86_64-linux-gnu/libparted.so.2.0.5 to /lib/x86_64-linux-gnu/libparted.so.2.0.5.usr-is-merged by libparted2t64'
211s Setting up linux-headers-generic (6.14.0-10.10) ...
211s Setting up libjemalloc2:amd64 (5.3.0-3) ...
211s Setting up locales (2.41-1ubuntu1) ...
211s Installing new version of config file /etc/locale.alias ...
212s Generating locales (this might take a while)...
213s   en_US.UTF-8... done
213s Generation complete.
213s Setting up libsframe1:amd64 (2.44-3ubuntu1) ...
213s Setting up libpython3.13-minimal:amd64 (3.13.2-2) ...
213s Setting up apparmor (4.1.0~beta5-0ubuntu8) ...
213s Installing new version of config file /etc/apparmor.d/fusermount3 ...
213s Installing new version of config file /etc/apparmor.d/lsusb ...
213s Installing new version of config file /etc/apparmor.d/openvpn ...
214s Reloading AppArmor profiles 
215s Setting up libftdi1-2:amd64 (1.5-8build1) ...
215s Setting up libglib2.0-data (2.84.0-1) ...
215s Setting up systemd-cryptsetup (257.3-1ubuntu3) ...
215s Setting up libncurses6:amd64 (6.5+20250216-2) ...
215s Setting up strace (6.13+ds-1ubuntu1) ...
215s Setting up xz-utils (5.6.4-1) ...
216s Setting up systemd-timesyncd (257.3-1ubuntu3) ...
216s systemd-time-wait-sync.service is a disabled or a static unit not running, not starting it.
216s Setting up libatomic1:amd64 (15-20250222-0ubuntu1) ...
216s Setting up udev (257.3-1ubuntu3) ...
217s Setting up linux-modules-6.14.0-10-generic (6.14.0-10.10) ...
218s Setting up libncursesw6:amd64 (6.5+20250216-2) ...
218s Setting up libpci3:amd64 (1:3.13.0-2) ...
218s Setting up whiptail (0.52.24-4ubuntu2) ...
218s Setting up python-apt-common (2.9.9build1) ...
218s Setting up pnp.ids (0.393-3) ...
218s Setting up libnl-3-200:amd64 (3.7.0-1) ...
219s Setting up python3.13-minimal (3.13.2-2) ...
219s Setting up libgpgme11t64:amd64 (1.24.2-1ubuntu2) ...
219s Setting up libbinutils:amd64 (2.44-3ubuntu1) ...
219s Setting up libc-dev-bin (2.41-1ubuntu1) ...
219s Setting up libpython3.13-stdlib:amd64 (3.13.2-2) ...
219s Setting up libxml2:amd64 (2.12.7+dfsg+really2.9.14-0.2ubuntu5) ...
219s Setting up rsync (3.4.1+ds1-3) ...
220s rsync.service is a disabled or a static unit not running, not starting it.
220s Setting up python3.13-gdbm (3.13.2-2) ...
220s Setting up libpython3-stdlib:amd64 (3.13.2-2) ...
220s Setting up systemd-resolved (257.3-1ubuntu3) ...
220s Setting up initramfs-tools-bin (0.146ubuntu1) ...
220s Setting up ncurses-term (6.5+20250216-2) ...
220s Setting up libctf0:amd64 (2.44-3ubuntu1) ...
220s Setting up libpython3.13:amd64 (3.13.2-2) ...
220s Setting up libdebuginfod1t64:amd64 (0.192-4) ...
220s Setting up systemd-sysv (257.3-1ubuntu3) ...
220s Setting up linux-headers-virtual (6.14.0-10.10) ...
220s Setting up libcurl4t64:amd64 (8.12.1-3ubuntu1) ...
220s Setting up python3.13 (3.13.2-2) ...
221s Setting up htop (3.4.0-2) ...
221s Setting up linux-image-6.14.0-10-generic (6.14.0-10.10) ...
223s I: /boot/vmlinuz.old is now a symlink to vmlinuz-6.12.0-16-generic
223s I: /boot/initrd.img.old is now a symlink to initrd.img-6.12.0-16-generic
223s I: /boot/vmlinuz is now a symlink to vmlinuz-6.14.0-10-generic
223s I: /boot/initrd.img is now a symlink to initrd.img-6.14.0-10-generic
223s Setting up parted (3.6-5) ...
223s Setting up libnss-systemd:amd64 (257.3-1ubuntu3) ...
223s Setting up python3 (3.13.2-2) ...
223s Setting up python3-newt:amd64 (0.52.24-4ubuntu2) ...
223s Setting up python3-markupsafe (2.1.5-1build4) ...
224s Setting up linux-modules-extra-6.14.0-10-generic (6.14.0-10.10) ...
225s Setting up libnl-route-3-200:amd64 (3.7.0-1) ...
225s Setting up hwdata (0.393-3) ...
225s Setting up python3-jinja2 (3.1.5-2ubuntu1) ...
226s Setting up libglib2.0-0t64:amd64 (2.84.0-1) ...
226s No schema files found: doing nothing.
226s Setting up libgprofng0:amd64 (2.44-3ubuntu1) ...
226s Setting up linux-perf (6.14.0-10.10) ...
226s Setting up gir1.2-glib-2.0:amd64 (2.84.0-1) ...
226s Setting up pciutils (1:3.13.0-2) ...
226s Setting up python3-rpds-py (0.21.0-2ubuntu2) ...
226s Setting up libmm-glib0:amd64 (1.23.4-0ubuntu3) ...
226s Setting up libnl-genl-3-200:amd64 (3.7.0-1) ...
226s Setting up libpam-systemd:amd64 (257.3-1ubuntu3) ...
226s Setting up libc6-dev:amd64 (2.41-1ubuntu1) ...
226s Setting up libgirepository-1.0-1:amd64 (1.83.4-1) ...
226s Setting up curl (8.12.1-3ubuntu1) ...
226s Setting up linux-image-virtual (6.14.0-10.10) ...
226s Setting up initramfs-tools-core (0.146ubuntu1) ...
226s Setting up linux-tools-common (6.14.0-10.10) ...
226s Setting up python3-systemd (235-1build6) ...
226s Setting up python3-cffi-backend:amd64 (1.17.1-2build2) ...
226s Setting up binutils-x86-64-linux-gnu (2.44-3ubuntu1) ...
226s Setting up linux-image-generic (6.14.0-10.10) ...
226s Setting up python3-dbus (1.3.2-5build5) ...
226s Setting up linux-tools-6.14.0-10 (6.14.0-10.10) ...
226s Setting up initramfs-tools (0.146ubuntu1) ...
226s Installing new version of config file /etc/kernel/postinst.d/initramfs-tools ...
226s Installing new version of config file /etc/kernel/postrm.d/initramfs-tools ...
226s update-initramfs: deferring update (trigger activated)
226s Setting up linux-generic (6.14.0-10.10) ...
226s Setting up ubuntu-minimal (1.549) ...
226s Setting up python3-apt (2.9.9build1) ...
226s Setting up python3-bcrypt (4.2.0-2.1build1) ...
226s Setting up python3-yaml (6.0.2-1build2) ...
226s Setting up libfwupd3:amd64 (2.0.6-4) ...
226s Setting up python3-lazr.uri (1.0.6-6) ...
226s Setting up binutils (2.44-3ubuntu1) ...
226s Setting up ubuntu-standard (1.549) ...
226s Setting up cloud-init-base (25.1-0ubuntu3) ...
228s Setting up linux-virtual (6.14.0-10.10) ...
228s Setting up gir1.2-girepository-2.0:amd64 (1.83.4-1) ...
228s Setting up python3-gi (3.50.0-4build1) ...
228s Setting up linux-tools-6.14.0-10-generic (6.14.0-10.10) ...
228s Setting up fwupd (2.0.6-4) ...
228s fwupd-refresh.service is a disabled or a static unit not running, not starting it.
228s fwupd.service is a disabled or a static unit not running, not starting it.
228s Setting up cloud-init (25.1-0ubuntu3) ...
228s Processing triggers for man-db (2.13.0-1) ...
230s Processing triggers for dbus (1.16.2-1ubuntu1) ...
230s Processing triggers for shared-mime-info (2.4-5) ...
230s Warning: program compiled against libxml 212 using older 209
230s Processing triggers for libc-bin (2.41-1ubuntu1) ...
230s Processing triggers for linux-image-6.14.0-10-generic (6.14.0-10.10) ...
230s /etc/kernel/postinst.d/initramfs-tools:
230s update-initramfs: Generating /boot/initrd.img-6.14.0-10-generic
230s W: No lz4 in /usr/bin:/sbin:/bin, using gzip
240s /etc/kernel/postinst.d/zz-update-grub:
240s Sourcing file `/etc/default/grub'
240s Sourcing file `/etc/default/grub.d/50-cloudimg-settings.cfg'
240s Sourcing file `/etc/default/grub.d/90-autopkgtest.cfg'
240s Generating grub configuration file ...
240s Found linux image: /boot/vmlinuz-6.14.0-10-generic
240s Found initrd image: /boot/initrd.img-6.14.0-10-generic
240s Found linux image: /boot/vmlinuz-6.12.0-16-generic
240s Found initrd image: /boot/initrd.img-6.12.0-16-generic
240s Found linux image: /boot/vmlinuz-6.11.0-8-generic
240s Found initrd image: /boot/initrd.img-6.11.0-8-generic
240s Warning: os-prober will not be executed to detect other bootable partitions.
240s Systems on them will not be added to the GRUB boot configuration.
240s Check GRUB_DISABLE_OS_PROBER documentation entry.
240s Adding boot menu entry for UEFI Firmware Settings ...
240s done
240s Processing triggers for initramfs-tools (0.146ubuntu1) ...
240s update-initramfs: Generating /boot/initrd.img-6.14.0-10-generic
240s W: No lz4 in /usr/bin:/sbin:/bin, using gzip
250s Reading package lists...
250s Building dependency tree...
250s Reading state information...
250s Solving dependencies...
250s The following packages will be REMOVED:
250s   libnl-genl-3-200* libnsl2* libpython3.12-minimal* libpython3.12-stdlib*
250s   libpython3.12t64* linux-headers-6.11.0-8* linux-headers-6.11.0-8-generic*
250s   linux-headers-6.12.0-16* linux-headers-6.12.0-16-generic*
250s   linux-image-6.11.0-8-generic* linux-image-6.12.0-16-generic*
250s   linux-modules-6.11.0-8-generic* linux-modules-6.12.0-16-generic*
250s   linux-modules-extra-6.12.0-16-generic* linux-tools-6.11.0-8*
250s   linux-tools-6.11.0-8-generic* linux-tools-6.12.0-16*
250s   linux-tools-6.12.0-16-generic*
250s 0 upgraded, 0 newly installed, 18 to remove and 5 not upgraded.
250s After this operation, 545 MB disk space will be freed.
250s (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 148643 files and directories currently installed.)
250s Removing libnl-genl-3-200:amd64 (3.7.0-1) ...
250s Removing linux-tools-6.11.0-8-generic (6.11.0-8.8) ...
250s Removing linux-tools-6.11.0-8 (6.11.0-8.8) ...
250s Removing libpython3.12t64:amd64 (3.12.9-1) ...
250s Removing libpython3.12-stdlib:amd64 (3.12.9-1) ...
251s Removing libnsl2:amd64 (1.3.0-3build3) ...
251s Removing libpython3.12-minimal:amd64 (3.12.9-1) ...
251s Removing linux-headers-6.11.0-8-generic (6.11.0-8.8) ...
251s Removing linux-headers-6.11.0-8 (6.11.0-8.8) ...
253s Removing linux-headers-6.12.0-16-generic (6.12.0-16.16) ...
253s Removing linux-headers-6.12.0-16 (6.12.0-16.16) ...
255s Removing linux-image-6.11.0-8-generic (6.11.0-8.8) ...
255s /etc/kernel/postrm.d/initramfs-tools:
255s update-initramfs: Deleting /boot/initrd.img-6.11.0-8-generic
256s /etc/kernel/postrm.d/zz-update-grub:
256s Sourcing file `/etc/default/grub'
256s Sourcing file `/etc/default/grub.d/50-cloudimg-settings.cfg'
256s Sourcing file `/etc/default/grub.d/90-autopkgtest.cfg'
256s Generating grub configuration file ...
256s Found linux image: /boot/vmlinuz-6.14.0-10-generic
256s Found initrd image: /boot/initrd.img-6.14.0-10-generic
256s Found linux image: /boot/vmlinuz-6.12.0-16-generic
256s Found initrd image: /boot/initrd.img-6.12.0-16-generic
256s Warning: os-prober will not be executed to detect other bootable partitions.
256s Systems on them will not be added to the GRUB boot configuration.
256s Check GRUB_DISABLE_OS_PROBER documentation entry.
256s Adding boot menu entry for UEFI Firmware Settings ...
256s done
256s Removing linux-image-6.12.0-16-generic (6.12.0-16.16) ...
256s W: Removing the running kernel
256s I: /boot/vmlinuz.old is now a symlink to vmlinuz-6.14.0-10-generic
256s I: /boot/initrd.img.old is now a symlink to initrd.img-6.14.0-10-generic
256s /etc/kernel/postrm.d/initramfs-tools:
256s update-initramfs: Deleting /boot/initrd.img-6.12.0-16-generic
256s /etc/kernel/postrm.d/zz-update-grub:
256s Sourcing file `/etc/default/grub'
256s Sourcing file `/etc/default/grub.d/50-cloudimg-settings.cfg'
256s Sourcing file `/etc/default/grub.d/90-autopkgtest.cfg'
256s Generating grub configuration file ...
256s Found linux image: /boot/vmlinuz-6.14.0-10-generic
256s Found initrd image: /boot/initrd.img-6.14.0-10-generic
257s Warning: os-prober will not be executed to detect other bootable partitions.
257s Systems on them will not be added to the GRUB boot configuration.
257s Check GRUB_DISABLE_OS_PROBER documentation entry.
257s Adding boot menu entry for UEFI Firmware Settings ...
257s done
257s Removing linux-modules-6.11.0-8-generic (6.11.0-8.8) ...
257s Removing linux-modules-extra-6.12.0-16-generic (6.12.0-16.16) ...
258s Removing linux-modules-6.12.0-16-generic (6.12.0-16.16) ...
258s Removing linux-tools-6.12.0-16-generic (6.12.0-16.16) ...
258s Removing linux-tools-6.12.0-16 (6.12.0-16.16) ...
258s Processing triggers for libc-bin (2.41-1ubuntu1) ...
258s (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 76972 files and directories currently installed.)
258s Purging configuration files for linux-image-6.11.0-8-generic (6.11.0-8.8) ...
258s Purging configuration files for libpython3.12-minimal:amd64 (3.12.9-1) ...
258s Purging configuration files for linux-modules-extra-6.12.0-16-generic (6.12.0-16.16) ...
259s Purging configuration files for linux-modules-6.12.0-16-generic (6.12.0-16.16) ...
259s dpkg: warning: while removing linux-modules-6.12.0-16-generic, directory '/lib/modules/6.12.0-16-generic' not empty so not removed
259s Purging configuration files for linux-modules-6.11.0-8-generic (6.11.0-8.8) ...
259s Purging configuration files for linux-image-6.12.0-16-generic (6.12.0-16.16) ...
259s rmdir: failed to remove '/lib/modules/6.12.0-16-generic': Directory not empty
259s autopkgtest [10:39:46]: upgrading testbed (apt dist-upgrade and autopurge)
259s Reading package lists...
259s Building dependency tree...
259s Reading state information...
260s Calculating upgrade...Starting pkgProblemResolver with broken count: 0
260s Starting 2 pkgProblemResolver with broken count: 0
260s Done
260s Entering ResolveByKeep
260s 
260s Calculating upgrade...
260s The following packages will be upgraded:
260s   libc-bin libc-dev-bin libc6 libc6-dev locales
261s 5 upgraded, 0 newly installed, 0 to remove and 0 not upgraded.
261s Need to get 10.5 MB of archives.
261s After this operation, 1024 B of additional disk space will be used.
261s Get:1 http://ftpmaster.internal/ubuntu plucky-proposed/main amd64 libc6-dev amd64 2.41-1ubuntu2 [2183 kB]
261s Get:2 http://ftpmaster.internal/ubuntu plucky-proposed/main amd64 libc-dev-bin amd64 2.41-1ubuntu2 [24.7 kB]
261s Get:3 http://ftpmaster.internal/ubuntu plucky-proposed/main amd64 libc6 amd64 2.41-1ubuntu2 [3327 kB]
262s Get:4 http://ftpmaster.internal/ubuntu plucky-proposed/main amd64 libc-bin amd64 2.41-1ubuntu2 [700 kB]
262s Get:5 http://ftpmaster.internal/ubuntu plucky-proposed/main amd64 locales all 2.41-1ubuntu2 [4246 kB]
263s Preconfiguring packages ...
263s Fetched 10.5 MB in 2s (4212 kB/s)
263s (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 76968 files and directories currently installed.)
263s Preparing to unpack .../libc6-dev_2.41-1ubuntu2_amd64.deb ...
263s Unpacking libc6-dev:amd64 (2.41-1ubuntu2) over (2.41-1ubuntu1) ...
263s Preparing to unpack .../libc-dev-bin_2.41-1ubuntu2_amd64.deb ...
263s Unpacking libc-dev-bin (2.41-1ubuntu2) over (2.41-1ubuntu1) ...
263s Preparing to unpack .../libc6_2.41-1ubuntu2_amd64.deb ...
264s Unpacking libc6:amd64 (2.41-1ubuntu2) over (2.41-1ubuntu1) ...
264s Setting up libc6:amd64 (2.41-1ubuntu2) ...
264s (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 76968 files and directories currently installed.)
264s Preparing to unpack .../libc-bin_2.41-1ubuntu2_amd64.deb ...
264s Unpacking libc-bin (2.41-1ubuntu2) over (2.41-1ubuntu1) ...
264s Setting up libc-bin (2.41-1ubuntu2) ...
264s (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 76968 files and directories currently installed.)
264s Preparing to unpack .../locales_2.41-1ubuntu2_all.deb ...
264s Unpacking locales (2.41-1ubuntu2) over (2.41-1ubuntu1) ...
264s Setting up locales (2.41-1ubuntu2) ...
265s Generating locales (this might take a while)...
266s   en_US.UTF-8... done
266s Generation complete.
266s Setting up libc-dev-bin (2.41-1ubuntu2) ...
266s Setting up libc6-dev:amd64 (2.41-1ubuntu2) ...
266s Processing triggers for man-db (2.13.0-1) ...
267s Processing triggers for systemd (257.3-1ubuntu3) ...
267s Reading package lists...
268s Building dependency tree...
268s Reading state information...
268s Starting pkgProblemResolver with broken count: 0
268s Starting 2 pkgProblemResolver with broken count: 0
268s Done
268s Solving dependencies...
268s 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded.
268s autopkgtest [10:39:55]: rebooting testbed after setup commands that affected boot
292s autopkgtest [10:40:19]: testbed running kernel: Linux 6.14.0-10-generic #10-Ubuntu SMP PREEMPT_DYNAMIC Wed Mar 12 16:07:00 UTC 2025
295s autopkgtest [10:40:22]: @@@@@@@@@@@@@@@@@@@@ apt-source octave-statistics
298s Get:1 http://ftpmaster.internal/ubuntu plucky/universe octave-statistics 1.7.3-2 (dsc) [2285 B]
298s Get:2 http://ftpmaster.internal/ubuntu plucky/universe octave-statistics 1.7.3-2 (tar) [1302 kB]
298s Get:3 http://ftpmaster.internal/ubuntu plucky/universe octave-statistics 1.7.3-2 (diff) [9744 B]
298s gpgv: Signature made Thu Feb 20 15:34:23 2025 UTC
298s gpgv:                using RSA key 53951D95272E0C5B82BE8C4A2CECE9350ECEBE4A
298s gpgv: Can't check signature: No public key
298s dpkg-source: warning: cannot verify inline signature for ./octave-statistics_1.7.3-2.dsc: no acceptable signature found
298s autopkgtest [10:40:25]: testing package octave-statistics version 1.7.3-2
299s autopkgtest [10:40:26]: build not needed
300s autopkgtest [10:40:27]: test command1: preparing testbed
300s Reading package lists...
300s Building dependency tree...
300s Reading state information...
300s Starting pkgProblemResolver with broken count: 0
300s Starting 2 pkgProblemResolver with broken count: 0
300s Done
301s The following NEW packages will be installed:
301s   aglfn appstream autoconf automake autopoint autotools-dev cme comerr-dev cpp
301s   cpp-14 cpp-14-x86-64-linux-gnu cpp-x86-64-linux-gnu debhelper debugedit
301s   dh-autoreconf dh-octave dh-octave-autopkgtest dh-strip-nondeterminism
301s   diffstat dwz fontconfig fontconfig-config fonts-dejavu-core
301s   fonts-dejavu-mono fonts-freefont-otf g++ g++-14 g++-14-x86-64-linux-gnu
301s   g++-x86-64-linux-gnu gcc gcc-14 gcc-14-x86-64-linux-gnu gcc-x86-64-linux-gnu
301s   gettext gfortran gfortran-14 gfortran-14-x86-64-linux-gnu
301s   gfortran-x86-64-linux-gnu gnuplot-data gnuplot-nox hdf5-helpers
301s   intltool-debian krb5-multidev libaec-dev libaec0 libalgorithm-c3-perl
301s   libaliased-perl libamd3 libaom3 libapp-cmd-perl libappstream5
301s   libapt-pkg-perl libarchive-zip-perl libarpack2t64 libarray-intspan-perl
301s   libasan8 libasound2-data libasound2t64 libavahi-client3 libavahi-common-data
301s   libavahi-common3 libb-hooks-endofscope-perl libb-hooks-op-check-perl libb2-1
301s   libberkeleydb-perl libblas-dev libblas3 libboolean-perl libbrotli-dev
301s   libcairo2 libcamd3 libcapture-tiny-perl libcarp-assert-more-perl libcc1-0
301s   libccolamd3 libcgi-pm-perl libcholmod5 libclass-c3-perl
301s   libclass-data-inheritable-perl libclass-inspector-perl libclass-load-perl
301s   libclass-method-modifiers-perl libclass-xsaccessor-perl libclone-choose-perl
301s   libclone-perl libcolamd3 libconfig-model-backend-yaml-perl
301s   libconfig-model-dpkg-perl libconfig-model-perl libconfig-tiny-perl
301s   libconst-fast-perl libconvert-binhex-perl libcpanel-json-xs-perl libcups2t64
301s   libcurl4-openssl-dev libcxsparse4 libdata-dpath-perl
301s   libdata-messagepack-perl libdata-optlist-perl libdata-section-perl
301s   libdata-validate-domain-perl libdata-validate-ip-perl
301s   libdata-validate-uri-perl libdatrie1 libde265-0 libdebhelper-perl
301s   libdeflate0 libdevel-callchecker-perl libdevel-size-perl
301s   libdevel-stacktrace-perl libdouble-conversion3 libdrm-intel1 libduktape207
301s   libdynaloader-functions-perl libegl-mesa0 libegl1 libemail-address-xs-perl
301s   libencode-locale-perl liberror-perl libevent-2.1-7t64
301s   libexception-class-perl libexporter-lite-perl libexporter-tiny-perl
301s   libfftw3-bin libfftw3-dev libfftw3-double3 libfftw3-long3 libfftw3-quad3
301s   libfftw3-single3 libfile-basedir-perl libfile-find-rule-perl
301s   libfile-homedir-perl libfile-listing-perl libfile-sharedir-perl
301s   libfile-stripnondeterminism-perl libfile-which-perl libflac12t64
301s   libfltk-gl1.3t64 libfltk1.3t64 libfont-ttf-perl libfontconfig1 libfontenc1
301s   libgbm1 libgcc-14-dev libgd3 libgetopt-long-descriptive-perl
301s   libgfortran-14-dev libgfortran5 libgl-dev libgl1 libgl1-mesa-dri libgl2ps1.4
301s   libglpk40 libglu1-mesa libglvnd0 libglx-dev libglx-mesa0 libglx0 libgmp-dev
301s   libgmpxx4ldbl libgnutls-dane0t64 libgnutls-openssl27t64 libgnutls28-dev
301s   libgomp1 libgraphicsmagick++-q16-12t64 libgraphicsmagick-q16-3t64
301s   libgraphite2-3 libgssrpc4t64 libharfbuzz0b libhash-merge-perl libhdf5-310
301s   libhdf5-cpp-310 libhdf5-dev libhdf5-fortran-310 libhdf5-hl-310
301s   libhdf5-hl-cpp-310 libhdf5-hl-fortran-310 libheif-plugin-aomdec
301s   libheif-plugin-libde265 libheif1 libhtml-form-perl
301s   libhtml-html5-entities-perl libhtml-parser-perl libhtml-tagset-perl
301s   libhtml-tokeparser-simple-perl libhtml-tree-perl libhttp-cookies-perl
301s   libhttp-date-perl libhttp-message-perl libhttp-negotiate-perl libhwasan0
301s   libhwy1t64 libice6 libidn2-dev libimagequant0 libimport-into-perl
301s   libindirect-perl libinput-bin libinput10 libio-html-perl
301s   libio-interactive-perl libio-socket-ssl-perl libio-string-perl
301s   libio-stringy-perl libio-tiecombine-perl libipc-run3-perl
301s   libipc-system-simple-perl libisl23 libiterator-perl libiterator-util-perl
301s   libitm1 libjack-jackd2-0 libjbig0 libjpeg-dev libjpeg-turbo8
301s   libjpeg-turbo8-dev libjpeg8 libjpeg8-dev libjson-maybexs-perl libjson-perl
301s   libjxl0.11 libkadm5clnt-mit12 libkadm5srv-mit12 libkdb5-10t64 libkrb5-dev
301s   liblapack-dev liblapack3 liblcms2-2 libldap-dev liblerc4
301s   liblist-compare-perl liblist-moreutils-perl liblist-moreutils-xs-perl
301s   liblist-someutils-perl liblist-utilsby-perl liblog-any-adapter-screen-perl
301s   liblog-any-perl liblog-log4perl-perl liblsan0 libltdl7 liblua5.4-0
301s   liblwp-mediatypes-perl liblwp-protocol-https-perl libmailtools-perl
301s   libmarkdown2 libmd4c0 libmime-tools-perl libmldbm-perl
301s   libmodule-implementation-perl libmodule-pluggable-perl
301s   libmodule-runtime-perl libmoo-perl libmoox-aliases-perl libmouse-perl
301s   libmousex-nativetraits-perl libmousex-strictconstructor-perl libmp3lame0
301s   libmpc3 libmpg123-0t64 libmro-compat-perl libmtdev1t64
301s   libnamespace-clean-perl libncurses-dev libnet-domain-tld-perl
301s   libnet-http-perl libnet-ipv6addr-perl libnet-netmask-perl
301s   libnet-smtp-ssl-perl libnet-ssleay-perl libnetaddr-ip-perl libnghttp2-dev
301s   libnumber-compare-perl libobject-pad-perl libogg0 libopengl0 libopus0
301s   libp11-kit-dev libpackage-stash-perl libpango-1.0-0 libpangocairo-1.0-0
301s   libpangoft2-1.0-0 libparams-classify-perl libparams-util-perl
301s   libparams-validate-perl libparse-debcontrol-perl libparse-recdescent-perl
301s   libpath-iterator-rule-perl libpath-tiny-perl libpciaccess0 libpcre2-16-0
301s   libperlio-gzip-perl libperlio-utf8-strict-perl libpixman-1-0 libpkgconf3
301s   libpod-constants-perl libpod-parser-perl libpod-pom-perl libportaudio2
301s   libproc-processtable-perl libproxy1v5 libpsl-dev libqhull-r8.0 libqrupdate1
301s   libqscintilla2-qt6-15 libqscintilla2-qt6-l10n libqt6core5compat6
301s   libqt6core6t64 libqt6dbus6 libqt6gui6 libqt6help6 libqt6network6
301s   libqt6opengl6 libqt6openglwidgets6 libqt6printsupport6 libqt6sql6
301s   libqt6widgets6 libqt6xml6 libquadmath0 libraqm0 libreadline-dev
301s   libregexp-common-perl libregexp-pattern-license-perl libregexp-pattern-perl
301s   libregexp-wildcards-perl librole-tiny-perl librtmp-dev libsamplerate0
301s   libsereal-decoder-perl libsereal-encoder-perl libset-intspan-perl
301s   libsharpyuv0 libsm6 libsndfile1 libsoftware-copyright-perl
301s   libsoftware-license-perl libsoftware-licensemoreutils-perl
301s   libsort-versions-perl libspqr4 libssh2-1-dev libssl-dev libstdc++-14-dev
301s   libstemmer0d libstrictures-perl libstring-copyright-perl
301s   libstring-escape-perl libstring-license-perl libstring-rewriteprefix-perl
301s   libsub-exporter-perl libsub-exporter-progressive-perl libsub-identify-perl
301s   libsub-install-perl libsub-name-perl libsub-quote-perl libsub-uplevel-perl
301s   libsuitesparseconfig7 libsyntax-keyword-try-perl libsz2 libtasn1-6-dev
301s   libterm-readkey-perl libtest-exception-perl libtext-autoformat-perl
301s   libtext-glob-perl libtext-levenshtein-damerau-perl
301s   libtext-levenshteinxs-perl libtext-markdown-discount-perl
301s   libtext-reform-perl libtext-template-perl libtext-unidecode-perl
301s   libtext-xslate-perl libthai-data libthai0 libtiff6 libtime-duration-perl
301s   libtime-moment-perl libtimedate-perl libtoml-tiny-perl libtool
301s   libtry-tiny-perl libts0t64 libtsan2 libubsan1 libumfpack6 libunbound8
301s   libunicode-utf8-perl liburi-perl libvariable-magic-perl libvorbis0a
301s   libvorbisenc2 libvulkan1 libwacom-common libwacom9 libwayland-client0
301s   libwayland-server0 libwebp7 libwebpmux3 libwmflite-0.2-7
301s   libwww-mechanize-perl libwww-perl libwww-robotrules-perl libx11-dev
301s   libx11-xcb1 libxau-dev libxaw7 libxcb-cursor0 libxcb-dri3-0 libxcb-glx0
301s   libxcb-icccm4 libxcb-image0 libxcb-keysyms1 libxcb-present0 libxcb-randr0
301s   libxcb-render-util0 libxcb-render0 libxcb-shape0 libxcb-shm0 libxcb-sync1
301s   libxcb-util1 libxcb-xfixes0 libxcb-xinput0 libxcb-xkb1 libxcb1-dev
301s   libxcursor1 libxdmcp-dev libxfixes3 libxfont2 libxft2 libxinerama1
301s   libxkbcommon-x11-0 libxkbfile1 libxml-libxml-perl
301s   libxml-namespacesupport-perl libxml-sax-base-perl libxml-sax-perl libxmu6
301s   libxpm4 libxrandr2 libxrender1 libxs-parse-keyword-perl
301s   libxs-parse-sublike-perl libxshmfence1 libxt6t64 libxxf86vm1
301s   libyaml-libyaml-perl libyaml-pp-perl libyaml-tiny-perl libzstd-dev
301s   licensecheck lintian lzip lzop m4 mesa-libgallium nettle-dev octave
301s   octave-common octave-dev octave-io octave-statistics
301s   octave-statistics-common patchutils perl-openssl-defaults pkgconf
301s   pkgconf-bin po-debconf t1utils tex-common texinfo texinfo-lib unzip
301s   x11-common x11-xkb-utils x11proto-dev xorg-sgml-doctools xserver-common
301s   xtrans-dev xvfb zlib1g-dev
301s 0 upgraded, 477 newly installed, 0 to remove and 0 not upgraded.
301s Need to get 207 MB of archives.
301s After this operation, 731 MB of additional disk space will be used.
301s Get:1 http://ftpmaster.internal/ubuntu plucky/main amd64 libstemmer0d amd64 2.2.0-4build1 [161 kB]
302s Get:2 http://ftpmaster.internal/ubuntu plucky/main amd64 libappstream5 amd64 1.0.4-1 [247 kB]
302s Get:3 http://ftpmaster.internal/ubuntu plucky/main amd64 appstream amd64 1.0.4-1 [74.8 kB]
302s Get:4 http://ftpmaster.internal/ubuntu plucky/main amd64 m4 amd64 1.4.19-7 [263 kB]
302s Get:5 http://ftpmaster.internal/ubuntu plucky/main amd64 autoconf all 2.72-3ubuntu1 [383 kB]
302s Get:6 http://ftpmaster.internal/ubuntu plucky/main amd64 autotools-dev all 20220109.1 [44.9 kB]
302s Get:7 http://ftpmaster.internal/ubuntu plucky/main amd64 automake all 1:1.17-3ubuntu1 [572 kB]
302s Get:8 http://ftpmaster.internal/ubuntu plucky/main amd64 autopoint all 0.23.1-1 [619 kB]
303s Get:9 http://ftpmaster.internal/ubuntu plucky/main amd64 libcapture-tiny-perl all 0.50-1 [20.7 kB]
303s Get:10 http://ftpmaster.internal/ubuntu plucky/main amd64 libparams-util-perl amd64 1.102-3build1 [21.4 kB]
303s Get:11 http://ftpmaster.internal/ubuntu plucky/main amd64 libsub-install-perl all 0.929-1 [9764 B]
303s Get:12 http://ftpmaster.internal/ubuntu plucky/main amd64 libdata-optlist-perl all 0.114-1 [9708 B]
303s Get:13 http://ftpmaster.internal/ubuntu plucky/main amd64 libb-hooks-op-check-perl amd64 0.22-3build2 [9624 B]
303s Get:14 http://ftpmaster.internal/ubuntu plucky/main amd64 libdynaloader-functions-perl all 0.004-1 [11.4 kB]
303s Get:15 http://ftpmaster.internal/ubuntu plucky/main amd64 libdevel-callchecker-perl amd64 0.009-1build1 [14.2 kB]
303s Get:16 http://ftpmaster.internal/ubuntu plucky/main amd64 libparams-classify-perl amd64 0.015-2build6 [20.1 kB]
303s Get:17 http://ftpmaster.internal/ubuntu plucky/main amd64 libmodule-runtime-perl all 0.016-2 [16.4 kB]
303s Get:18 http://ftpmaster.internal/ubuntu plucky/main amd64 libtry-tiny-perl all 0.32-1 [21.2 kB]
303s Get:19 http://ftpmaster.internal/ubuntu plucky/main amd64 libmodule-implementation-perl all 0.09-2 [12.0 kB]
303s Get:20 http://ftpmaster.internal/ubuntu plucky/main amd64 libpackage-stash-perl all 0.40-1 [19.5 kB]
303s Get:21 http://ftpmaster.internal/ubuntu plucky/universe amd64 libclass-load-perl all 0.25-2 [12.7 kB]
303s Get:22 http://ftpmaster.internal/ubuntu plucky/main amd64 libio-stringy-perl all 2.113-2 [45.3 kB]
303s Get:23 http://ftpmaster.internal/ubuntu plucky/universe amd64 libparams-validate-perl amd64 1.31-2build4 [53.7 kB]
303s Get:24 http://ftpmaster.internal/ubuntu plucky/main amd64 libsub-exporter-perl all 0.990-1 [49.0 kB]
303s Get:25 http://ftpmaster.internal/ubuntu plucky/universe amd64 libgetopt-long-descriptive-perl all 0.116-2 [25.0 kB]
303s Get:26 http://ftpmaster.internal/ubuntu plucky/universe amd64 libio-tiecombine-perl all 1.005-3 [9464 B]
303s Get:27 http://ftpmaster.internal/ubuntu plucky/universe amd64 libmodule-pluggable-perl all 5.2-5 [19.5 kB]
303s Get:28 http://ftpmaster.internal/ubuntu plucky/universe amd64 libstring-rewriteprefix-perl all 0.009-1 [6310 B]
303s Get:29 http://ftpmaster.internal/ubuntu plucky/universe amd64 libapp-cmd-perl all 0.337-2 [58.3 kB]
303s Get:30 http://ftpmaster.internal/ubuntu plucky/universe amd64 libboolean-perl all 0.46-3 [8430 B]
303s Get:31 http://ftpmaster.internal/ubuntu plucky/universe amd64 libsub-uplevel-perl all 0.2800-3 [11.6 kB]
303s Get:32 http://ftpmaster.internal/ubuntu plucky/universe amd64 libtest-exception-perl all 0.43-3 [13.4 kB]
303s Get:33 http://ftpmaster.internal/ubuntu plucky/universe amd64 libcarp-assert-more-perl all 2.8.0-1 [19.2 kB]
303s Get:34 http://ftpmaster.internal/ubuntu plucky/main amd64 libfile-which-perl all 1.27-2 [12.5 kB]
303s Get:35 http://ftpmaster.internal/ubuntu plucky/main amd64 libfile-homedir-perl all 1.006-2 [37.0 kB]
303s Get:36 http://ftpmaster.internal/ubuntu plucky/universe amd64 libclone-choose-perl all 0.010-2 [7738 B]
303s Get:37 http://ftpmaster.internal/ubuntu plucky/universe amd64 libhash-merge-perl all 0.302-1 [13.0 kB]
303s Get:38 http://ftpmaster.internal/ubuntu plucky/main amd64 libjson-perl all 4.10000-1 [81.9 kB]
303s Get:39 http://ftpmaster.internal/ubuntu plucky/main amd64 libexporter-tiny-perl all 1.006002-1 [36.8 kB]
303s Get:40 http://ftpmaster.internal/ubuntu plucky/universe amd64 liblist-moreutils-xs-perl amd64 0.430-4build1 [42.9 kB]
303s Get:41 http://ftpmaster.internal/ubuntu plucky/universe amd64 liblist-moreutils-perl all 0.430-2 [38.2 kB]
303s Get:42 http://ftpmaster.internal/ubuntu plucky/universe amd64 liblog-log4perl-perl all 1.57-1 [345 kB]
303s Get:43 http://ftpmaster.internal/ubuntu plucky/main amd64 libmouse-perl amd64 2.5.11-1build1 [134 kB]
304s Get:44 http://ftpmaster.internal/ubuntu plucky/universe amd64 libmousex-nativetraits-perl all 1.09-3 [53.2 kB]
304s Get:45 http://ftpmaster.internal/ubuntu plucky/universe amd64 libmousex-strictconstructor-perl all 0.02-3 [4582 B]
304s Get:46 http://ftpmaster.internal/ubuntu plucky/universe amd64 libparse-recdescent-perl all 1.967015+dfsg-4 [139 kB]
304s Get:47 http://ftpmaster.internal/ubuntu plucky/main amd64 libpath-tiny-perl all 0.146-1 [47.5 kB]
304s Get:48 http://ftpmaster.internal/ubuntu plucky/universe amd64 libpod-pom-perl all 2.01-4 [61.3 kB]
304s Get:49 http://ftpmaster.internal/ubuntu plucky/main amd64 libregexp-common-perl all 2024080801-1 [162 kB]
304s Get:50 http://ftpmaster.internal/ubuntu plucky/main amd64 libyaml-tiny-perl all 1.76-1 [24.2 kB]
304s Get:51 http://ftpmaster.internal/ubuntu plucky/universe amd64 libconfig-model-perl all 2.155-1 [356 kB]
304s Get:52 http://ftpmaster.internal/ubuntu plucky/universe amd64 libyaml-pp-perl all 0.39.0-1 [107 kB]
304s Get:53 http://ftpmaster.internal/ubuntu plucky/universe amd64 cme all 1.041-1 [65.4 kB]
304s Get:54 http://ftpmaster.internal/ubuntu plucky/main amd64 libisl23 amd64 0.27-1 [685 kB]
304s Get:55 http://ftpmaster.internal/ubuntu plucky/main amd64 libmpc3 amd64 1.3.1-1build2 [55.3 kB]
304s Get:56 http://ftpmaster.internal/ubuntu plucky/main amd64 cpp-14-x86-64-linux-gnu amd64 14.2.0-17ubuntu3 [11.9 MB]
310s Get:57 http://ftpmaster.internal/ubuntu plucky/main amd64 cpp-14 amd64 14.2.0-17ubuntu3 [1030 B]
310s Get:58 http://ftpmaster.internal/ubuntu plucky/main amd64 cpp-x86-64-linux-gnu amd64 4:14.2.0-1ubuntu1 [5586 B]
310s Get:59 http://ftpmaster.internal/ubuntu plucky/main amd64 cpp amd64 4:14.2.0-1ubuntu1 [22.4 kB]
310s Get:60 http://ftpmaster.internal/ubuntu plucky/main amd64 libdebhelper-perl all 13.24.1ubuntu2 [95.4 kB]
310s Get:61 http://ftpmaster.internal/ubuntu plucky/main amd64 libcc1-0 amd64 15-20250222-0ubuntu1 [47.0 kB]
310s Get:62 http://ftpmaster.internal/ubuntu plucky/main amd64 libgomp1 amd64 15-20250222-0ubuntu1 [148 kB]
311s Get:63 http://ftpmaster.internal/ubuntu plucky/main amd64 libitm1 amd64 15-20250222-0ubuntu1 [28.6 kB]
311s Get:64 http://ftpmaster.internal/ubuntu plucky/main amd64 libasan8 amd64 15-20250222-0ubuntu1 [3055 kB]
312s Get:65 http://ftpmaster.internal/ubuntu plucky/main amd64 liblsan0 amd64 15-20250222-0ubuntu1 [1355 kB]
313s Get:66 http://ftpmaster.internal/ubuntu plucky/main amd64 libtsan2 amd64 15-20250222-0ubuntu1 [2756 kB]
314s Get:67 http://ftpmaster.internal/ubuntu plucky/main amd64 libubsan1 amd64 15-20250222-0ubuntu1 [1205 kB]
315s Get:68 http://ftpmaster.internal/ubuntu plucky/main amd64 libhwasan0 amd64 15-20250222-0ubuntu1 [1677 kB]
315s Get:69 http://ftpmaster.internal/ubuntu plucky/main amd64 libquadmath0 amd64 15-20250222-0ubuntu1 [152 kB]
316s Get:70 http://ftpmaster.internal/ubuntu plucky/main amd64 libgcc-14-dev amd64 14.2.0-17ubuntu3 [2816 kB]
317s Get:71 http://ftpmaster.internal/ubuntu plucky/main amd64 gcc-14-x86-64-linux-gnu amd64 14.2.0-17ubuntu3 [23.3 MB]
327s Get:72 http://ftpmaster.internal/ubuntu plucky/main amd64 gcc-14 amd64 14.2.0-17ubuntu3 [536 kB]
328s Get:73 http://ftpmaster.internal/ubuntu plucky/main amd64 gcc-x86-64-linux-gnu amd64 4:14.2.0-1ubuntu1 [1208 B]
328s Get:74 http://ftpmaster.internal/ubuntu plucky/main amd64 gcc amd64 4:14.2.0-1ubuntu1 [5004 B]
328s Get:75 http://ftpmaster.internal/ubuntu plucky/main amd64 libtool all 2.5.4-4 [168 kB]
328s Get:76 http://ftpmaster.internal/ubuntu plucky/main amd64 dh-autoreconf all 20 [16.1 kB]
328s Get:77 http://ftpmaster.internal/ubuntu plucky/main amd64 libarchive-zip-perl all 1.68-1 [90.2 kB]
328s Get:78 http://ftpmaster.internal/ubuntu plucky/main amd64 libfile-stripnondeterminism-perl all 1.14.1-2 [20.3 kB]
328s Get:79 http://ftpmaster.internal/ubuntu plucky/main amd64 dh-strip-nondeterminism all 1.14.1-2 [5064 B]
328s Get:80 http://ftpmaster.internal/ubuntu plucky/main amd64 debugedit amd64 1:5.1-2 [47.0 kB]
328s Get:81 http://ftpmaster.internal/ubuntu plucky/main amd64 dwz amd64 0.15-1build6 [115 kB]
328s Get:82 http://ftpmaster.internal/ubuntu plucky/main amd64 gettext amd64 0.23.1-1 [1086 kB]
328s Get:83 http://ftpmaster.internal/ubuntu plucky/main amd64 intltool-debian all 0.35.0+20060710.6 [23.2 kB]
328s Get:84 http://ftpmaster.internal/ubuntu plucky/main amd64 po-debconf all 1.0.21+nmu1 [233 kB]
329s Get:85 http://ftpmaster.internal/ubuntu plucky/main amd64 debhelper all 13.24.1ubuntu2 [895 kB]
329s Get:86 http://ftpmaster.internal/ubuntu plucky/universe amd64 aglfn all 1.7+git20191031.4036a9c-2 [30.6 kB]
329s Get:87 http://ftpmaster.internal/ubuntu plucky/universe amd64 gnuplot-data all 6.0.2+dfsg1-1 [75.4 kB]
329s Get:88 http://ftpmaster.internal/ubuntu plucky/main amd64 fonts-dejavu-mono all 2.37-8 [502 kB]
329s Get:89 http://ftpmaster.internal/ubuntu plucky/main amd64 fonts-dejavu-core all 2.37-8 [835 kB]
330s Get:90 http://ftpmaster.internal/ubuntu plucky/universe amd64 fonts-freefont-otf all 20211204+svn4273-2 [4596 kB]
332s Get:91 http://ftpmaster.internal/ubuntu plucky/main amd64 fontconfig-config amd64 2.15.0-2ubuntu1 [37.4 kB]
332s Get:92 http://ftpmaster.internal/ubuntu plucky/main amd64 libfontconfig1 amd64 2.15.0-2ubuntu1 [164 kB]
332s Get:93 http://ftpmaster.internal/ubuntu plucky/main amd64 libpixman-1-0 amd64 0.44.0-3 [427 kB]
332s Get:94 http://ftpmaster.internal/ubuntu plucky/main amd64 libxcb-render0 amd64 1.17.0-2 [16.2 kB]
332s Get:95 http://ftpmaster.internal/ubuntu plucky/main amd64 libxcb-shm0 amd64 1.17.0-2 [5758 B]
332s Get:96 http://ftpmaster.internal/ubuntu plucky/main amd64 libxrender1 amd64 1:0.9.10-1.1build1 [19.0 kB]
332s Get:97 http://ftpmaster.internal/ubuntu plucky/main amd64 libcairo2 amd64 1.18.2-2 [569 kB]
332s Get:98 http://ftpmaster.internal/ubuntu plucky/main amd64 libsharpyuv0 amd64 1.5.0-0.1 [25.9 kB]
332s Get:99 http://ftpmaster.internal/ubuntu plucky/main amd64 libaom3 amd64 3.12.0-1 [2742 kB]
333s Get:100 http://ftpmaster.internal/ubuntu plucky/main amd64 libheif-plugin-aomdec amd64 1.19.7-1 [11.6 kB]
333s Get:101 http://ftpmaster.internal/ubuntu plucky/main amd64 libde265-0 amd64 1.0.15-1build5 [285 kB]
334s Get:102 http://ftpmaster.internal/ubuntu plucky/main amd64 libheif-plugin-libde265 amd64 1.19.7-1 [9060 B]
334s Get:103 http://ftpmaster.internal/ubuntu plucky/main amd64 libheif1 amd64 1.19.7-1 [428 kB]
336s Get:104 http://ftpmaster.internal/ubuntu plucky/main amd64 libimagequant0 amd64 2.18.0-1build1 [36.3 kB]
336s Get:105 http://ftpmaster.internal/ubuntu plucky/main amd64 libjpeg-turbo8 amd64 2.1.5-3ubuntu2 [179 kB]
336s Get:106 http://ftpmaster.internal/ubuntu plucky/main amd64 libjpeg8 amd64 8c-2ubuntu11 [2148 B]
336s Get:107 http://ftpmaster.internal/ubuntu plucky/main amd64 libgraphite2-3 amd64 1.3.14-2ubuntu1 [73.1 kB]
336s Get:108 http://ftpmaster.internal/ubuntu plucky/main amd64 libharfbuzz0b amd64 10.2.0-1 [543 kB]
337s Get:109 http://ftpmaster.internal/ubuntu plucky/main amd64 libraqm0 amd64 0.10.2-1 [17.2 kB]
337s Get:110 http://ftpmaster.internal/ubuntu plucky/main amd64 libdeflate0 amd64 1.23-1 [64.1 kB]
337s Get:111 http://ftpmaster.internal/ubuntu plucky/main amd64 libjbig0 amd64 2.1-6.1ubuntu2 [29.7 kB]
337s Get:112 http://ftpmaster.internal/ubuntu plucky/main amd64 liblerc4 amd64 4.0.0+ds-5ubuntu1 [271 kB]
337s Get:113 http://ftpmaster.internal/ubuntu plucky/main amd64 libwebp7 amd64 1.5.0-0.1 [378 kB]
337s Get:114 http://ftpmaster.internal/ubuntu plucky/main amd64 libtiff6 amd64 4.5.1+git230720-4ubuntu4 [200 kB]
338s Get:115 http://ftpmaster.internal/ubuntu plucky/main amd64 libxpm4 amd64 1:3.5.17-1build2 [36.5 kB]
338s Get:116 http://ftpmaster.internal/ubuntu plucky/main amd64 libgd3 amd64 2.3.3-12ubuntu3 [127 kB]
338s Get:117 http://ftpmaster.internal/ubuntu plucky/main amd64 liblua5.4-0 amd64 5.4.7-1 [196 kB]
338s Get:118 http://ftpmaster.internal/ubuntu plucky/main amd64 fontconfig amd64 2.15.0-2ubuntu1 [180 kB]
338s Get:119 http://ftpmaster.internal/ubuntu plucky/main amd64 libthai-data all 0.1.29-2build1 [158 kB]
338s Get:120 http://ftpmaster.internal/ubuntu plucky/main amd64 libdatrie1 amd64 0.2.13-3build1 [19.0 kB]
338s Get:121 http://ftpmaster.internal/ubuntu plucky/main amd64 libthai0 amd64 0.1.29-2build1 [18.9 kB]
338s Get:122 http://ftpmaster.internal/ubuntu plucky/main amd64 libpango-1.0-0 amd64 1.56.1-1 [255 kB]
338s Get:123 http://ftpmaster.internal/ubuntu plucky/main amd64 libpangoft2-1.0-0 amd64 1.56.1-1 [52.6 kB]
338s Get:124 http://ftpmaster.internal/ubuntu plucky/main amd64 libpangocairo-1.0-0 amd64 1.56.1-1 [29.1 kB]
338s Get:125 http://ftpmaster.internal/ubuntu plucky/main amd64 libwebpmux3 amd64 1.5.0-0.1 [27.6 kB]
338s Get:126 http://ftpmaster.internal/ubuntu plucky/universe amd64 gnuplot-nox amd64 6.0.2+dfsg1-1 [1170 kB]
339s Get:127 http://ftpmaster.internal/ubuntu plucky/universe amd64 dh-octave-autopkgtest all 1.8.0 [10.1 kB]
339s Get:128 http://ftpmaster.internal/ubuntu plucky/main amd64 libapt-pkg-perl amd64 0.1.41build1 [71.0 kB]
339s Get:129 http://ftpmaster.internal/ubuntu plucky/main amd64 libarray-intspan-perl all 2.004-2 [25.0 kB]
339s Get:130 http://ftpmaster.internal/ubuntu plucky/main amd64 libyaml-libyaml-perl amd64 0.903.0+ds-1 [31.0 kB]
339s Get:131 http://ftpmaster.internal/ubuntu plucky/universe amd64 libconfig-model-backend-yaml-perl all 2.134-2 [10.5 kB]
339s Get:132 http://ftpmaster.internal/ubuntu plucky/universe amd64 libexporter-lite-perl all 0.09-2 [9748 B]
339s Get:133 http://ftpmaster.internal/ubuntu plucky/main amd64 libencode-locale-perl all 1.05-3 [11.6 kB]
339s Get:134 http://ftpmaster.internal/ubuntu plucky/main amd64 libtimedate-perl all 2.3300-2 [34.0 kB]
339s Get:135 http://ftpmaster.internal/ubuntu plucky/main amd64 libhttp-date-perl all 6.06-1 [10.2 kB]
339s Get:136 http://ftpmaster.internal/ubuntu plucky/main amd64 libfile-listing-perl all 6.16-1 [11.3 kB]
339s Get:137 http://ftpmaster.internal/ubuntu plucky/main amd64 libhtml-tagset-perl all 3.24-1 [14.1 kB]
339s Get:138 http://ftpmaster.internal/ubuntu plucky/main amd64 liburi-perl all 5.30-1 [94.4 kB]
339s Get:139 http://ftpmaster.internal/ubuntu plucky/main amd64 libhtml-parser-perl amd64 3.83-1build1 [86.2 kB]
339s Get:140 http://ftpmaster.internal/ubuntu plucky/main amd64 libhtml-tree-perl all 5.07-3 [200 kB]
339s Get:141 http://ftpmaster.internal/ubuntu plucky/main amd64 libclone-perl amd64 0.47-1 [10.7 kB]
339s Get:142 http://ftpmaster.internal/ubuntu plucky/main amd64 libio-html-perl all 1.004-3 [15.9 kB]
339s Get:143 http://ftpmaster.internal/ubuntu plucky/main amd64 liblwp-mediatypes-perl all 6.04-2 [20.1 kB]
340s Get:144 http://ftpmaster.internal/ubuntu plucky/main amd64 libhttp-message-perl all 7.00-2ubuntu1 [75.9 kB]
340s Get:145 http://ftpmaster.internal/ubuntu plucky/main amd64 libhttp-cookies-perl all 6.11-1 [18.2 kB]
340s Get:146 http://ftpmaster.internal/ubuntu plucky/main amd64 libhttp-negotiate-perl all 6.01-2 [12.4 kB]
340s Get:147 http://ftpmaster.internal/ubuntu plucky/main amd64 perl-openssl-defaults amd64 7build3 [6626 B]
340s Get:148 http://ftpmaster.internal/ubuntu plucky/main amd64 libnet-ssleay-perl amd64 1.94-3 [318 kB]
340s Get:149 http://ftpmaster.internal/ubuntu plucky/main amd64 libio-socket-ssl-perl all 2.089-1 [200 kB]
340s Get:150 http://ftpmaster.internal/ubuntu plucky/main amd64 libnet-http-perl all 6.23-1 [22.3 kB]
340s Get:151 http://ftpmaster.internal/ubuntu plucky/main amd64 liblwp-protocol-https-perl all 6.14-1 [9040 B]
340s Get:152 http://ftpmaster.internal/ubuntu plucky/main amd64 libwww-robotrules-perl all 6.02-1 [12.6 kB]
340s Get:153 http://ftpmaster.internal/ubuntu plucky/main amd64 libwww-perl all 6.78-1 [139 kB]
340s Get:154 http://ftpmaster.internal/ubuntu plucky/main amd64 liberror-perl all 0.17030-1 [23.5 kB]
340s Get:155 http://ftpmaster.internal/ubuntu plucky/universe amd64 libparse-debcontrol-perl all 2.005-6 [20.4 kB]
340s Get:156 http://ftpmaster.internal/ubuntu plucky/universe amd64 libsoftware-copyright-perl all 0.014-1 [14.5 kB]
340s Get:157 http://ftpmaster.internal/ubuntu plucky/universe amd64 libalgorithm-c3-perl all 0.11-2 [10.2 kB]
340s Get:158 http://ftpmaster.internal/ubuntu plucky/universe amd64 libclass-c3-perl all 0.35-2 [18.4 kB]
340s Get:159 http://ftpmaster.internal/ubuntu plucky/universe amd64 libmro-compat-perl all 0.15-2 [10.1 kB]
340s Get:160 http://ftpmaster.internal/ubuntu plucky/universe amd64 libdata-section-perl all 0.200008-1 [11.6 kB]
340s Get:161 http://ftpmaster.internal/ubuntu plucky/universe amd64 libtext-template-perl all 1.61-1 [48.5 kB]
340s Get:162 http://ftpmaster.internal/ubuntu plucky/universe amd64 libsoftware-license-perl all 0.104006-1 [117 kB]
340s Get:163 http://ftpmaster.internal/ubuntu plucky/universe amd64 libsoftware-licensemoreutils-perl all 1.009-1 [21.5 kB]
340s Get:164 http://ftpmaster.internal/ubuntu plucky/main amd64 libsort-versions-perl all 1.62-3 [7378 B]
340s Get:165 http://ftpmaster.internal/ubuntu plucky/universe amd64 libtext-reform-perl all 1.20-5 [35.4 kB]
340s Get:166 http://ftpmaster.internal/ubuntu plucky/universe amd64 libtext-autoformat-perl all 1.750000-2 [29.8 kB]
340s Get:167 http://ftpmaster.internal/ubuntu plucky/universe amd64 libtext-levenshtein-damerau-perl all 0.41-3 [10.8 kB]
340s Get:168 http://ftpmaster.internal/ubuntu plucky/universe amd64 libtoml-tiny-perl all 0.19-1 [21.6 kB]
340s Get:169 http://ftpmaster.internal/ubuntu plucky/main amd64 libclass-inspector-perl all 1.36-3 [15.4 kB]
340s Get:170 http://ftpmaster.internal/ubuntu plucky/main amd64 libfile-sharedir-perl all 1.118-3 [14.0 kB]
340s Get:171 http://ftpmaster.internal/ubuntu plucky/main amd64 libindirect-perl amd64 0.39-2build5 [22.2 kB]
340s Get:172 http://ftpmaster.internal/ubuntu plucky/main amd64 libxs-parse-keyword-perl amd64 0.48-2 [63.0 kB]
340s Get:173 http://ftpmaster.internal/ubuntu plucky/main amd64 libxs-parse-sublike-perl amd64 0.37-1 [45.6 kB]
340s Get:174 http://ftpmaster.internal/ubuntu plucky/main amd64 libobject-pad-perl amd64 0.820-1 [133 kB]
340s Get:175 http://ftpmaster.internal/ubuntu plucky/main amd64 libsyntax-keyword-try-perl amd64 0.30-1 [24.4 kB]
340s Get:176 http://ftpmaster.internal/ubuntu plucky/main amd64 libio-interactive-perl all 1.026-1 [10.8 kB]
340s Get:177 http://ftpmaster.internal/ubuntu plucky/main amd64 liblog-any-perl all 1.717-1 [73.2 kB]
340s Get:178 http://ftpmaster.internal/ubuntu plucky/main amd64 liblog-any-adapter-screen-perl all 0.141-1 [12.9 kB]
340s Get:179 http://ftpmaster.internal/ubuntu plucky/main amd64 libsub-exporter-progressive-perl all 0.001013-3 [6718 B]
340s Get:180 http://ftpmaster.internal/ubuntu plucky/main amd64 libvariable-magic-perl amd64 0.64-1build1 [36.3 kB]
340s Get:181 http://ftpmaster.internal/ubuntu plucky/main amd64 libb-hooks-endofscope-perl all 0.28-1 [15.8 kB]
340s Get:182 http://ftpmaster.internal/ubuntu plucky/main amd64 libsub-identify-perl amd64 0.14-3build4 [9904 B]
340s Get:183 http://ftpmaster.internal/ubuntu plucky/main amd64 libsub-name-perl amd64 0.28-1 [10.7 kB]
340s Get:184 http://ftpmaster.internal/ubuntu plucky/main amd64 libnamespace-clean-perl all 0.27-2 [14.0 kB]
340s Get:185 http://ftpmaster.internal/ubuntu plucky/main amd64 libnumber-compare-perl all 0.03-3 [5974 B]
340s Get:186 http://ftpmaster.internal/ubuntu plucky/main amd64 libtext-glob-perl all 0.11-3 [6780 B]
340s Get:187 http://ftpmaster.internal/ubuntu plucky/main amd64 libpath-iterator-rule-perl all 1.015-2 [39.9 kB]
340s Get:188 http://ftpmaster.internal/ubuntu plucky/main amd64 libpod-parser-perl all 1.67-1 [80.6 kB]
340s Get:189 http://ftpmaster.internal/ubuntu plucky/main amd64 libpod-constants-perl all 0.19-2 [16.3 kB]
340s Get:190 http://ftpmaster.internal/ubuntu plucky/main amd64 libset-intspan-perl all 1.19-3 [24.8 kB]
340s Get:191 http://ftpmaster.internal/ubuntu plucky/main amd64 libstring-copyright-perl all 0.003014-1 [20.5 kB]
341s Get:192 http://ftpmaster.internal/ubuntu plucky/main amd64 libstring-escape-perl all 2010.002-3 [16.1 kB]
341s Get:193 http://ftpmaster.internal/ubuntu plucky/main amd64 libregexp-pattern-license-perl all 3.11.2-1 [85.3 kB]
341s Get:194 http://ftpmaster.internal/ubuntu plucky/main amd64 libregexp-pattern-perl all 0.2.14-2 [17.6 kB]
341s Get:195 http://ftpmaster.internal/ubuntu plucky/main amd64 libstring-license-perl all 0.0.11-1ubuntu1 [34.3 kB]
341s Get:196 http://ftpmaster.internal/ubuntu plucky/main amd64 licensecheck all 3.3.9-1ubuntu1 [37.7 kB]
341s Get:197 http://ftpmaster.internal/ubuntu plucky/main amd64 diffstat amd64 1.67-1 [33.1 kB]
341s Get:198 http://ftpmaster.internal/ubuntu plucky/main amd64 libberkeleydb-perl amd64 0.66-1 [119 kB]
341s Get:199 http://ftpmaster.internal/ubuntu plucky/main amd64 libclass-xsaccessor-perl amd64 1.19-4build6 [33.8 kB]
341s Get:200 http://ftpmaster.internal/ubuntu plucky/main amd64 libconfig-tiny-perl all 2.30-1 [14.7 kB]
341s Get:201 http://ftpmaster.internal/ubuntu plucky/main amd64 libconst-fast-perl all 0.014-2 [8034 B]
341s Get:202 http://ftpmaster.internal/ubuntu plucky/main amd64 libcpanel-json-xs-perl amd64 4.39-1 [116 kB]
341s Get:203 http://ftpmaster.internal/ubuntu plucky/main amd64 libaliased-perl all 0.34-3 [12.8 kB]
341s Get:204 http://ftpmaster.internal/ubuntu plucky/main amd64 libclass-data-inheritable-perl all 0.10-1 [8038 B]
341s Get:205 http://ftpmaster.internal/ubuntu plucky/main amd64 libdevel-stacktrace-perl all 2.0500-1 [22.1 kB]
341s Get:206 http://ftpmaster.internal/ubuntu plucky/main amd64 libexception-class-perl all 1.45-1 [28.6 kB]
341s Get:207 http://ftpmaster.internal/ubuntu plucky/main amd64 libiterator-perl all 0.03+ds1-2 [18.8 kB]
341s Get:208 http://ftpmaster.internal/ubuntu plucky/main amd64 libiterator-util-perl all 0.02+ds1-2 [14.1 kB]
341s Get:209 http://ftpmaster.internal/ubuntu plucky/main amd64 libdata-dpath-perl all 0.60-1 [37.3 kB]
341s Get:210 http://ftpmaster.internal/ubuntu plucky/main amd64 libnet-domain-tld-perl all 1.75-4 [29.0 kB]
341s Get:211 http://ftpmaster.internal/ubuntu plucky/main amd64 libdata-validate-domain-perl all 0.15-1 [10.4 kB]
341s Get:212 http://ftpmaster.internal/ubuntu plucky/main amd64 libnet-ipv6addr-perl all 1.02-1 [21.0 kB]
341s Get:213 http://ftpmaster.internal/ubuntu plucky/main amd64 libnet-netmask-perl all 2.0002-2 [24.8 kB]
341s Get:214 http://ftpmaster.internal/ubuntu plucky/main amd64 libnetaddr-ip-perl amd64 4.079+dfsg-2build5 [80.0 kB]
341s Get:215 http://ftpmaster.internal/ubuntu plucky/main amd64 libdata-validate-ip-perl all 0.31-1 [17.2 kB]
341s Get:216 http://ftpmaster.internal/ubuntu plucky/main amd64 libdata-validate-uri-perl all 0.07-3 [10.8 kB]
341s Get:217 http://ftpmaster.internal/ubuntu plucky/main amd64 libdevel-size-perl amd64 0.84-1build1 [19.6 kB]
341s Get:218 http://ftpmaster.internal/ubuntu plucky/main amd64 libemail-address-xs-perl amd64 1.05-1build5 [29.5 kB]
341s Get:219 http://ftpmaster.internal/ubuntu plucky/main amd64 libipc-system-simple-perl all 1.30-2 [22.3 kB]
341s Get:220 http://ftpmaster.internal/ubuntu plucky/main amd64 libfile-basedir-perl all 0.09-2 [14.4 kB]
341s Get:221 http://ftpmaster.internal/ubuntu plucky/main amd64 libfile-find-rule-perl all 0.34-3 [24.4 kB]
341s Get:222 http://ftpmaster.internal/ubuntu plucky/main amd64 libio-string-perl all 1.08-4 [11.1 kB]
341s Get:223 http://ftpmaster.internal/ubuntu plucky/main amd64 libfont-ttf-perl all 1.06-2 [323 kB]
342s Get:224 http://ftpmaster.internal/ubuntu plucky/main amd64 libhtml-html5-entities-perl all 0.004-3 [21.6 kB]
342s Get:225 http://ftpmaster.internal/ubuntu plucky/main amd64 libhtml-tokeparser-simple-perl all 3.16-4 [38.0 kB]
342s Get:226 http://ftpmaster.internal/ubuntu plucky/main amd64 libipc-run3-perl all 0.049-1 [28.8 kB]
342s Get:227 http://ftpmaster.internal/ubuntu plucky/main amd64 libjson-maybexs-perl all 1.004008-1 [11.1 kB]
342s Get:228 http://ftpmaster.internal/ubuntu plucky/main amd64 liblist-compare-perl all 0.55-2 [62.9 kB]
342s Get:229 http://ftpmaster.internal/ubuntu plucky/main amd64 liblist-someutils-perl all 0.59-1 [30.4 kB]
342s Get:230 http://ftpmaster.internal/ubuntu plucky/main amd64 liblist-utilsby-perl all 0.12-2 [14.9 kB]
342s Get:231 http://ftpmaster.internal/ubuntu plucky/main amd64 libmldbm-perl all 2.05-4 [16.0 kB]
342s Get:232 http://ftpmaster.internal/ubuntu plucky/main amd64 libclass-method-modifiers-perl all 2.15-1 [16.1 kB]
342s Get:233 http://ftpmaster.internal/ubuntu plucky/main amd64 libimport-into-perl all 1.002005-2 [10.7 kB]
342s Get:234 http://ftpmaster.internal/ubuntu plucky/main amd64 librole-tiny-perl all 2.002004-1 [16.3 kB]
342s Get:235 http://ftpmaster.internal/ubuntu plucky/main amd64 libsub-quote-perl all 2.006008-1ubuntu1 [20.7 kB]
342s Get:236 http://ftpmaster.internal/ubuntu plucky/main amd64 libmoo-perl all 2.005005-1 [47.4 kB]
342s Get:237 http://ftpmaster.internal/ubuntu plucky/main amd64 libstrictures-perl all 2.000006-1 [16.3 kB]
342s Get:238 http://ftpmaster.internal/ubuntu plucky/main amd64 libmoox-aliases-perl all 0.001006-2 [6796 B]
342s Get:239 http://ftpmaster.internal/ubuntu plucky/main amd64 libperlio-gzip-perl amd64 0.20-1build5 [14.8 kB]
342s Get:240 http://ftpmaster.internal/ubuntu plucky/main amd64 libperlio-utf8-strict-perl amd64 0.010-1build4 [11.1 kB]
342s Get:241 http://ftpmaster.internal/ubuntu plucky/main amd64 libproc-processtable-perl amd64 0.636-1build4 [35.9 kB]
342s Get:242 http://ftpmaster.internal/ubuntu plucky/main amd64 libregexp-wildcards-perl all 1.05-3 [12.9 kB]
342s Get:243 http://ftpmaster.internal/ubuntu plucky/main amd64 libsereal-decoder-perl amd64 5.004+ds-1build4 [101 kB]
342s Get:244 http://ftpmaster.internal/ubuntu plucky/main amd64 libsereal-encoder-perl amd64 5.004+ds-1build4 [105 kB]
342s Get:245 http://ftpmaster.internal/ubuntu plucky/main amd64 libterm-readkey-perl amd64 2.38-2build5 [23.2 kB]
342s Get:246 http://ftpmaster.internal/ubuntu plucky/main amd64 libtext-levenshteinxs-perl amd64 0.03-5build5 [8116 B]
342s Get:247 http://ftpmaster.internal/ubuntu plucky/main amd64 libmarkdown2 amd64 2.2.7-2.1 [42.3 kB]
342s Get:248 http://ftpmaster.internal/ubuntu plucky/main amd64 libtext-markdown-discount-perl amd64 0.18-1 [12.4 kB]
342s Get:249 http://ftpmaster.internal/ubuntu plucky/main amd64 libdata-messagepack-perl amd64 1.02-1build5 [31.6 kB]
342s Get:250 http://ftpmaster.internal/ubuntu plucky/main amd64 libtext-xslate-perl amd64 3.5.9-2build1 [161 kB]
342s Get:251 http://ftpmaster.internal/ubuntu plucky/main amd64 libtime-duration-perl all 1.21-2 [12.3 kB]
342s Get:252 http://ftpmaster.internal/ubuntu plucky/main amd64 libtime-moment-perl amd64 0.44-2build5 [71.6 kB]
342s Get:253 http://ftpmaster.internal/ubuntu plucky/main amd64 libunicode-utf8-perl amd64 0.62-2build4 [18.3 kB]
342s Get:254 http://ftpmaster.internal/ubuntu plucky/main amd64 libcgi-pm-perl all 4.67-1 [185 kB]
342s Get:255 http://ftpmaster.internal/ubuntu plucky/main amd64 libhtml-form-perl all 6.12-1 [31.1 kB]
342s Get:256 http://ftpmaster.internal/ubuntu plucky/main amd64 libwww-mechanize-perl all 2.19-1ubuntu1 [93.3 kB]
342s Get:257 http://ftpmaster.internal/ubuntu plucky/main amd64 libxml-namespacesupport-perl all 1.12-2 [13.5 kB]
342s Get:258 http://ftpmaster.internal/ubuntu plucky/main amd64 libxml-sax-base-perl all 1.09-3 [18.9 kB]
342s Get:259 http://ftpmaster.internal/ubuntu plucky/main amd64 libxml-sax-perl all 1.02+dfsg-4 [52.4 kB]
342s Get:260 http://ftpmaster.internal/ubuntu plucky/main amd64 libxml-libxml-perl amd64 2.0207+dfsg+really+2.0134-5build1 [305 kB]
343s Get:261 http://ftpmaster.internal/ubuntu plucky/main amd64 lzip amd64 1.25-2 [87.1 kB]
343s Get:262 http://ftpmaster.internal/ubuntu plucky/main amd64 lzop amd64 1.04-2build3 [82.2 kB]
343s Get:263 http://ftpmaster.internal/ubuntu plucky/main amd64 patchutils amd64 0.4.2-1build3 [77.0 kB]
343s Get:264 http://ftpmaster.internal/ubuntu plucky/main amd64 t1utils amd64 1.41-4build3 [61.3 kB]
343s Get:265 http://ftpmaster.internal/ubuntu plucky/main amd64 unzip amd64 6.0-28ubuntu6 [181 kB]
343s Get:266 http://ftpmaster.internal/ubuntu plucky/main amd64 lintian all 2.121.1+nmu1ubuntu2 [1075 kB]
344s Get:267 http://ftpmaster.internal/ubuntu plucky/universe amd64 libconfig-model-dpkg-perl all 3.010 [176 kB]
344s Get:268 http://ftpmaster.internal/ubuntu plucky/main amd64 libconvert-binhex-perl all 1.125-3 [27.1 kB]
344s Get:269 http://ftpmaster.internal/ubuntu plucky/main amd64 libnet-smtp-ssl-perl all 1.04-2 [6218 B]
344s Get:270 http://ftpmaster.internal/ubuntu plucky/main amd64 libmailtools-perl all 2.22-1 [77.1 kB]
344s Get:271 http://ftpmaster.internal/ubuntu plucky/main amd64 libmime-tools-perl all 5.515-1 [187 kB]
344s Get:272 http://ftpmaster.internal/ubuntu plucky/main amd64 libsuitesparseconfig7 amd64 1:7.8.3+dfsg-3 [13.5 kB]
344s Get:273 http://ftpmaster.internal/ubuntu plucky/universe amd64 libamd3 amd64 1:7.8.3+dfsg-3 [31.0 kB]
344s Get:274 http://ftpmaster.internal/ubuntu plucky/main amd64 libblas3 amd64 3.12.1-2 [359 kB]
344s Get:275 http://ftpmaster.internal/ubuntu plucky/main amd64 libgfortran5 amd64 15-20250222-0ubuntu1 [919 kB]
345s Get:276 http://ftpmaster.internal/ubuntu plucky/main amd64 liblapack3 amd64 3.12.1-2 [3179 kB]
347s Get:277 http://ftpmaster.internal/ubuntu plucky/universe amd64 libarpack2t64 amd64 3.9.1-4 [120 kB]
347s Get:278 http://ftpmaster.internal/ubuntu plucky/universe amd64 libccolamd3 amd64 1:7.8.3+dfsg-3 [27.5 kB]
347s Get:279 http://ftpmaster.internal/ubuntu plucky/universe amd64 libcamd3 amd64 1:7.8.3+dfsg-3 [27.6 kB]
347s Get:280 http://ftpmaster.internal/ubuntu plucky/main amd64 libcolamd3 amd64 1:7.8.3+dfsg-3 [19.5 kB]
347s Get:281 http://ftpmaster.internal/ubuntu plucky/universe amd64 libcholmod5 amd64 1:7.8.3+dfsg-3 [876 kB]
348s Get:282 http://ftpmaster.internal/ubuntu plucky/universe amd64 libcxsparse4 amd64 1:7.8.3+dfsg-3 [83.2 kB]
348s Get:283 http://ftpmaster.internal/ubuntu plucky/main amd64 libfftw3-double3 amd64 3.3.10-2fakesync1build1 [839 kB]
349s Get:284 http://ftpmaster.internal/ubuntu plucky/main amd64 libfftw3-single3 amd64 3.3.10-2fakesync1build1 [871 kB]
349s Get:285 http://ftpmaster.internal/ubuntu plucky/main amd64 libxfixes3 amd64 1:6.0.0-2build1 [10.8 kB]
349s Get:286 http://ftpmaster.internal/ubuntu plucky/main amd64 libxcursor1 amd64 1:1.2.3-1 [23.1 kB]
349s Get:287 http://ftpmaster.internal/ubuntu plucky/main amd64 libxft2 amd64 2.3.6-1build1 [45.3 kB]
349s Get:288 http://ftpmaster.internal/ubuntu plucky/main amd64 libxinerama1 amd64 2:1.1.4-3build1 [6396 B]
349s Get:289 http://ftpmaster.internal/ubuntu plucky/universe amd64 libfltk1.3t64 amd64 1.3.8-6.1build2 [606 kB]
349s Get:290 http://ftpmaster.internal/ubuntu plucky/main amd64 libglvnd0 amd64 1.7.0-1build1 [69.6 kB]
349s Get:291 http://ftpmaster.internal/ubuntu plucky/main amd64 libx11-xcb1 amd64 2:1.8.10-2 [7944 B]
349s Get:292 http://ftpmaster.internal/ubuntu plucky/main amd64 libxcb-dri3-0 amd64 1.17.0-2 [7508 B]
349s Get:293 http://ftpmaster.internal/ubuntu plucky/main amd64 libxcb-glx0 amd64 1.17.0-2 [24.8 kB]
350s Get:294 http://ftpmaster.internal/ubuntu plucky/main amd64 libxcb-present0 amd64 1.17.0-2 [6064 B]
350s Get:295 http://ftpmaster.internal/ubuntu plucky/main amd64 libxcb-xfixes0 amd64 1.17.0-2 [10.2 kB]
350s Get:296 http://ftpmaster.internal/ubuntu plucky/main amd64 libxxf86vm1 amd64 1:1.1.4-1build4 [9282 B]
350s Get:297 http://ftpmaster.internal/ubuntu plucky/main amd64 libpciaccess0 amd64 0.17-3build1 [18.6 kB]
350s Get:298 http://ftpmaster.internal/ubuntu plucky/main amd64 libdrm-intel1 amd64 2.4.123-1 [68.8 kB]
350s Get:299 http://ftpmaster.internal/ubuntu plucky/main amd64 libxcb-randr0 amd64 1.17.0-2 [17.9 kB]
350s Get:300 http://ftpmaster.internal/ubuntu plucky/main amd64 libxcb-sync1 amd64 1.17.0-2 [9312 B]
350s Get:301 http://ftpmaster.internal/ubuntu plucky/main amd64 libxshmfence1 amd64 1.3-1build5 [4764 B]
350s Get:302 http://ftpmaster.internal/ubuntu plucky/main amd64 mesa-libgallium amd64 25.0.1-2ubuntu1 [12.1 MB]
362s Get:303 http://ftpmaster.internal/ubuntu plucky/main amd64 libwayland-server0 amd64 1.23.1-3 [37.2 kB]
362s Get:304 http://ftpmaster.internal/ubuntu plucky/main amd64 libgbm1 amd64 25.0.1-2ubuntu1 [33.4 kB]
363s Get:305 http://ftpmaster.internal/ubuntu plucky/main amd64 libvulkan1 amd64 1.4.304.0-1 [147 kB]
363s Get:306 http://ftpmaster.internal/ubuntu plucky/main amd64 libgl1-mesa-dri amd64 25.0.1-2ubuntu1 [35.7 kB]
363s Get:307 http://ftpmaster.internal/ubuntu plucky/main amd64 libglx-mesa0 amd64 25.0.1-2ubuntu1 [146 kB]
363s Get:308 http://ftpmaster.internal/ubuntu plucky/main amd64 libglx0 amd64 1.7.0-1build1 [38.6 kB]
363s Get:309 http://ftpmaster.internal/ubuntu plucky/main amd64 libgl1 amd64 1.7.0-1build1 [102 kB]
363s Get:310 http://ftpmaster.internal/ubuntu plucky/universe amd64 libfltk-gl1.3t64 amd64 1.3.8-6.1build2 [43.0 kB]
363s Get:311 http://ftpmaster.internal/ubuntu plucky/universe amd64 libgl2ps1.4 amd64 1.4.2+dfsg1-2build1 [41.9 kB]
363s Get:312 http://ftpmaster.internal/ubuntu plucky/main amd64 libltdl7 amd64 2.5.4-4 [46.5 kB]
363s Get:313 http://ftpmaster.internal/ubuntu plucky/universe amd64 libglpk40 amd64 5.0-1build2 [369 kB]
363s Get:314 http://ftpmaster.internal/ubuntu plucky/main amd64 libopengl0 amd64 1.7.0-1build1 [32.8 kB]
363s Get:315 http://ftpmaster.internal/ubuntu plucky/main amd64 libglu1-mesa amd64 9.0.2-1.1build1 [152 kB]
363s Get:316 http://ftpmaster.internal/ubuntu plucky/universe amd64 libhwy1t64 amd64 1.2.0-3ubuntu3 [734 kB]
364s Get:317 http://ftpmaster.internal/ubuntu plucky/main amd64 liblcms2-2 amd64 2.16-2 [212 kB]
364s Get:318 http://ftpmaster.internal/ubuntu plucky/universe amd64 libjxl0.11 amd64 0.11.1-1 [1363 kB]
365s Get:319 http://ftpmaster.internal/ubuntu plucky/main amd64 libwmflite-0.2-7 amd64 0.2.13-1.1build3 [68.6 kB]
365s Get:320 http://ftpmaster.internal/ubuntu plucky/universe amd64 libgraphicsmagick-q16-3t64 amd64 1.4+really1.3.45+hg17689-1 [1584 kB]
366s Get:321 http://ftpmaster.internal/ubuntu plucky/universe amd64 libgraphicsmagick++-q16-12t64 amd64 1.4+really1.3.45+hg17689-1 [116 kB]
366s Get:322 http://ftpmaster.internal/ubuntu plucky/universe amd64 libaec0 amd64 1.1.3-1 [22.7 kB]
366s Get:323 http://ftpmaster.internal/ubuntu plucky/universe amd64 libsz2 amd64 1.1.3-1 [5456 B]
366s Get:324 http://ftpmaster.internal/ubuntu plucky/universe amd64 libhdf5-310 amd64 1.14.5+repack-3 [1519 kB]
366s Get:325 http://ftpmaster.internal/ubuntu plucky/main amd64 libasound2-data all 1.2.13-1build1 [21.1 kB]
366s Get:326 http://ftpmaster.internal/ubuntu plucky/main amd64 libasound2t64 amd64 1.2.13-1build1 [437 kB]
367s Get:327 http://ftpmaster.internal/ubuntu plucky/main amd64 libopus0 amd64 1.5.2-2 [2913 kB]
368s Get:328 http://ftpmaster.internal/ubuntu plucky/main amd64 libsamplerate0 amd64 0.2.2-4build1 [1344 kB]
369s Get:329 http://ftpmaster.internal/ubuntu plucky/main amd64 libjack-jackd2-0 amd64 1.9.22~dfsg-4 [312 kB]
369s Get:330 http://ftpmaster.internal/ubuntu plucky/universe amd64 libportaudio2 amd64 19.6.0-1.2build3 [67.9 kB]
369s Get:331 http://ftpmaster.internal/ubuntu plucky/universe amd64 libqhull-r8.0 amd64 2020.2-6build1 [193 kB]
369s Get:332 http://ftpmaster.internal/ubuntu plucky/universe amd64 libqrupdate1 amd64 1.1.5-1 [50.7 kB]
369s Get:333 http://ftpmaster.internal/ubuntu plucky/universe amd64 libqscintilla2-qt6-l10n all 2.14.1+dfsg-1build4 [56.4 kB]
369s Get:334 http://ftpmaster.internal/ubuntu plucky/universe amd64 libb2-1 amd64 0.98.1-1.1build1 [45.0 kB]
369s Get:335 http://ftpmaster.internal/ubuntu plucky/universe amd64 libdouble-conversion3 amd64 3.3.1-1 [41.7 kB]
369s Get:336 http://ftpmaster.internal/ubuntu plucky/main amd64 libpcre2-16-0 amd64 10.45-1 [268 kB]
369s Get:337 http://ftpmaster.internal/ubuntu plucky/universe amd64 libqt6core6t64 amd64 6.8.2+dfsg-5 [2136 kB]
370s Get:338 http://ftpmaster.internal/ubuntu plucky/main amd64 libwayland-client0 amd64 1.23.1-3 [29.7 kB]
370s Get:339 http://ftpmaster.internal/ubuntu plucky/main amd64 libegl-mesa0 amd64 25.0.1-2ubuntu1 [136 kB]
370s Get:340 http://ftpmaster.internal/ubuntu plucky/main amd64 libegl1 amd64 1.7.0-1build1 [28.7 kB]
370s Get:341 http://ftpmaster.internal/ubuntu plucky/main amd64 x11-common all 1:7.7+23ubuntu3 [21.7 kB]
370s Get:342 http://ftpmaster.internal/ubuntu plucky/main amd64 libice6 amd64 2:1.1.1-1 [44.1 kB]
370s Get:343 http://ftpmaster.internal/ubuntu plucky/main amd64 libmtdev1t64 amd64 1.1.7-1 [16.3 kB]
370s Get:344 http://ftpmaster.internal/ubuntu plucky/main amd64 libwacom-common all 2.14.0-1 [103 kB]
370s Get:345 http://ftpmaster.internal/ubuntu plucky/main amd64 libwacom9 amd64 2.14.0-1 [27.3 kB]
370s Get:346 http://ftpmaster.internal/ubuntu plucky/main amd64 libinput-bin amd64 1.27.1-1 [23.4 kB]
370s Get:347 http://ftpmaster.internal/ubuntu plucky/main amd64 libinput10 amd64 1.27.1-1 [155 kB]
370s Get:348 http://ftpmaster.internal/ubuntu plucky/universe amd64 libmd4c0 amd64 0.5.2-2 [50.1 kB]
370s Get:349 http://ftpmaster.internal/ubuntu plucky/universe amd64 libqt6dbus6 amd64 6.8.2+dfsg-5 [290 kB]
370s Get:350 http://ftpmaster.internal/ubuntu plucky/main amd64 libsm6 amd64 2:1.2.4-1 [17.4 kB]
370s Get:351 http://ftpmaster.internal/ubuntu plucky/universe amd64 libts0t64 amd64 1.22-1.1build1 [63.8 kB]
370s Get:352 http://ftpmaster.internal/ubuntu plucky/main amd64 libxcb-util1 amd64 0.4.1-1 [11.2 kB]
370s Get:353 http://ftpmaster.internal/ubuntu plucky/main amd64 libxcb-image0 amd64 0.4.0-2build1 [10.8 kB]
370s Get:354 http://ftpmaster.internal/ubuntu plucky/main amd64 libxcb-render-util0 amd64 0.3.10-1 [10.8 kB]
370s Get:355 http://ftpmaster.internal/ubuntu plucky/universe amd64 libxcb-cursor0 amd64 0.1.5-1 [10.8 kB]
370s Get:356 http://ftpmaster.internal/ubuntu plucky/main amd64 libxcb-icccm4 amd64 0.4.2-1 [11.1 kB]
370s Get:357 http://ftpmaster.internal/ubuntu plucky/main amd64 libxcb-keysyms1 amd64 0.4.1-1 [8738 B]
370s Get:358 http://ftpmaster.internal/ubuntu plucky/main amd64 libxcb-shape0 amd64 1.17.0-2 [6092 B]
370s Get:359 http://ftpmaster.internal/ubuntu plucky/main amd64 libxcb-xinput0 amd64 1.17.0-2 [33.2 kB]
370s Get:360 http://ftpmaster.internal/ubuntu plucky/main amd64 libxcb-xkb1 amd64 1.17.0-2 [32.3 kB]
370s Get:361 http://ftpmaster.internal/ubuntu plucky/main amd64 libxkbcommon-x11-0 amd64 1.7.0-2 [15.2 kB]
370s Get:362 http://ftpmaster.internal/ubuntu plucky/universe amd64 libqt6gui6 amd64 6.8.2+dfsg-5 [3835 kB]
372s Get:363 http://ftpmaster.internal/ubuntu plucky/main amd64 libavahi-common-data amd64 0.8-16ubuntu1 [30.9 kB]
372s Get:364 http://ftpmaster.internal/ubuntu plucky/main amd64 libavahi-common3 amd64 0.8-16ubuntu1 [23.6 kB]
372s Get:365 http://ftpmaster.internal/ubuntu plucky/main amd64 libavahi-client3 amd64 0.8-16ubuntu1 [27.5 kB]
372s Get:366 http://ftpmaster.internal/ubuntu plucky/main amd64 libcups2t64 amd64 2.4.11-0ubuntu2 [292 kB]
372s Get:367 http://ftpmaster.internal/ubuntu plucky/universe amd64 libqt6widgets6 amd64 6.8.2+dfsg-5 [2999 kB]
373s Get:368 http://ftpmaster.internal/ubuntu plucky/universe amd64 libqt6printsupport6 amd64 6.8.2+dfsg-5 [233 kB]
374s Get:369 http://ftpmaster.internal/ubuntu plucky/universe amd64 libqscintilla2-qt6-15 amd64 2.14.1+dfsg-1build4 [1160 kB]
374s Get:370 http://ftpmaster.internal/ubuntu plucky/universe amd64 libqt6core5compat6 amd64 6.8.2-3 [162 kB]
374s Get:371 http://ftpmaster.internal/ubuntu plucky/universe amd64 libqt6sql6 amd64 6.8.2+dfsg-5 [151 kB]
374s Get:372 http://ftpmaster.internal/ubuntu plucky/universe amd64 libqt6help6 amd64 6.8.2-3 [207 kB]
374s Get:373 http://ftpmaster.internal/ubuntu plucky/main amd64 libduktape207 amd64 2.7.0+tests-0ubuntu3 [143 kB]
374s Get:374 http://ftpmaster.internal/ubuntu plucky/main amd64 libproxy1v5 amd64 0.5.9-1 [27.9 kB]
374s Get:375 http://ftpmaster.internal/ubuntu plucky/universe amd64 libqt6network6 amd64 6.8.2+dfsg-5 [899 kB]
375s Get:376 http://ftpmaster.internal/ubuntu plucky/universe amd64 libqt6opengl6 amd64 6.8.2+dfsg-5 [465 kB]
375s Get:377 http://ftpmaster.internal/ubuntu plucky/universe amd64 libqt6openglwidgets6 amd64 6.8.2+dfsg-5 [43.6 kB]
375s Get:378 http://ftpmaster.internal/ubuntu plucky/universe amd64 libqt6xml6 amd64 6.8.2+dfsg-5 [84.8 kB]
375s Get:379 http://ftpmaster.internal/ubuntu plucky/main amd64 libogg0 amd64 1.3.5-3build1 [22.7 kB]
375s Get:380 http://ftpmaster.internal/ubuntu plucky/main amd64 libflac12t64 amd64 1.4.3+ds-4 [268 kB]
375s Get:381 http://ftpmaster.internal/ubuntu plucky/main amd64 libmp3lame0 amd64 3.100-6build1 [142 kB]
375s Get:382 http://ftpmaster.internal/ubuntu plucky/main amd64 libmpg123-0t64 amd64 1.32.10-1 [191 kB]
375s Get:383 http://ftpmaster.internal/ubuntu plucky/main amd64 libvorbis0a amd64 1.3.7-2 [98.4 kB]
375s Get:384 http://ftpmaster.internal/ubuntu plucky/main amd64 libvorbisenc2 amd64 1.3.7-2 [80.8 kB]
375s Get:385 http://ftpmaster.internal/ubuntu plucky/main amd64 libsndfile1 amd64 1.2.2-2 [275 kB]
375s Get:386 http://ftpmaster.internal/ubuntu plucky/universe amd64 libspqr4 amd64 1:7.8.3+dfsg-3 [169 kB]
376s Get:387 http://ftpmaster.internal/ubuntu plucky/universe amd64 libumfpack6 amd64 1:7.8.3+dfsg-3 [337 kB]
376s Get:388 http://ftpmaster.internal/ubuntu plucky/universe amd64 libtext-unidecode-perl all 1.30-3 [105 kB]
376s Get:389 http://ftpmaster.internal/ubuntu plucky/universe amd64 texinfo-lib amd64 7.1.1-1 [136 kB]
376s Get:390 http://ftpmaster.internal/ubuntu plucky/universe amd64 tex-common all 6.19 [29.8 kB]
376s Get:391 http://ftpmaster.internal/ubuntu plucky/universe amd64 texinfo all 7.1.1-1 [1272 kB]
376s Get:392 http://ftpmaster.internal/ubuntu plucky/universe amd64 octave-common all 9.4.0-1 [6087 kB]
379s Get:393 http://ftpmaster.internal/ubuntu plucky/universe amd64 octave amd64 9.4.0-1 [10.8 MB]
383s Get:394 http://ftpmaster.internal/ubuntu plucky/main amd64 libncurses-dev amd64 6.5+20250216-2 [424 kB]
384s Get:395 http://ftpmaster.internal/ubuntu plucky/main amd64 libreadline-dev amd64 8.2-6 [184 kB]
384s Get:396 http://ftpmaster.internal/ubuntu plucky/universe amd64 libhdf5-fortran-310 amd64 1.14.5+repack-3 [108 kB]
384s Get:397 http://ftpmaster.internal/ubuntu plucky/universe amd64 libhdf5-hl-310 amd64 1.14.5+repack-3 [62.6 kB]
384s Get:398 http://ftpmaster.internal/ubuntu plucky/universe amd64 libhdf5-hl-fortran-310 amd64 1.14.5+repack-3 [32.4 kB]
384s Get:399 http://ftpmaster.internal/ubuntu plucky/universe amd64 libhdf5-cpp-310 amd64 1.14.5+repack-3 [129 kB]
384s Get:400 http://ftpmaster.internal/ubuntu plucky/universe amd64 libhdf5-hl-cpp-310 amd64 1.14.5+repack-3 [11.7 kB]
384s Get:401 http://ftpmaster.internal/ubuntu plucky/main amd64 zlib1g-dev amd64 1:1.3.dfsg+really1.3.1-1ubuntu1 [895 kB]
384s Get:402 http://ftpmaster.internal/ubuntu plucky/main amd64 libjpeg-turbo8-dev amd64 2.1.5-3ubuntu2 [324 kB]
384s Get:403 http://ftpmaster.internal/ubuntu plucky/main amd64 libjpeg8-dev amd64 8c-2ubuntu11 [1484 B]
384s Get:404 http://ftpmaster.internal/ubuntu plucky/main amd64 libjpeg-dev amd64 8c-2ubuntu11 [1482 B]
385s Get:405 http://ftpmaster.internal/ubuntu plucky/universe amd64 libaec-dev amd64 1.1.3-1 [19.8 kB]
385s Get:406 http://ftpmaster.internal/ubuntu plucky/main amd64 libbrotli-dev amd64 1.1.0-2build4 [381 kB]
385s Get:407 http://ftpmaster.internal/ubuntu plucky/main amd64 libidn2-dev amd64 2.3.7-2build2 [120 kB]
386s Get:408 http://ftpmaster.internal/ubuntu plucky/main amd64 comerr-dev amd64 2.1-1.47.2-1ubuntu1 [44.9 kB]
386s Get:409 http://ftpmaster.internal/ubuntu plucky/main amd64 libgssrpc4t64 amd64 1.21.3-4ubuntu2 [58.1 kB]
386s Get:410 http://ftpmaster.internal/ubuntu plucky/main amd64 libkadm5clnt-mit12 amd64 1.21.3-4ubuntu2 [41.3 kB]
386s Get:411 http://ftpmaster.internal/ubuntu plucky/main amd64 libkdb5-10t64 amd64 1.21.3-4ubuntu2 [42.0 kB]
386s Get:412 http://ftpmaster.internal/ubuntu plucky/main amd64 libkadm5srv-mit12 amd64 1.21.3-4ubuntu2 [55.5 kB]
386s Get:413 http://ftpmaster.internal/ubuntu plucky/main amd64 krb5-multidev amd64 1.21.3-4ubuntu2 [125 kB]
386s Get:414 http://ftpmaster.internal/ubuntu plucky/main amd64 libkrb5-dev amd64 1.21.3-4ubuntu2 [11.9 kB]
386s Get:415 http://ftpmaster.internal/ubuntu plucky/main amd64 libldap-dev amd64 2.6.9+dfsg-1~exp2ubuntu1 [335 kB]
386s Get:416 http://ftpmaster.internal/ubuntu plucky/main amd64 libpkgconf3 amd64 1.8.1-4 [32.3 kB]
386s Get:417 http://ftpmaster.internal/ubuntu plucky/main amd64 pkgconf-bin amd64 1.8.1-4 [21.6 kB]
386s Get:418 http://ftpmaster.internal/ubuntu plucky/main amd64 pkgconf amd64 1.8.1-4 [16.8 kB]
386s Get:419 http://ftpmaster.internal/ubuntu plucky/main amd64 libnghttp2-dev amd64 1.64.0-1 [123 kB]
386s Get:420 http://ftpmaster.internal/ubuntu plucky/main amd64 libpsl-dev amd64 0.21.2-1.1build1 [76.4 kB]
387s Get:421 http://ftpmaster.internal/ubuntu plucky/main amd64 libgmpxx4ldbl amd64 2:6.3.0+dfsg-3ubuntu1 [10.0 kB]
387s Get:422 http://ftpmaster.internal/ubuntu plucky/main amd64 libgmp-dev amd64 2:6.3.0+dfsg-3ubuntu1 [341 kB]
387s Get:423 http://ftpmaster.internal/ubuntu plucky/main amd64 libevent-2.1-7t64 amd64 2.1.12-stable-10 [144 kB]
387s Get:424 http://ftpmaster.internal/ubuntu plucky/main amd64 libunbound8 amd64 1.22.0-1ubuntu1 [519 kB]
388s Get:425 http://ftpmaster.internal/ubuntu plucky/main amd64 libgnutls-dane0t64 amd64 3.8.9-2ubuntu2 [24.4 kB]
388s Get:426 http://ftpmaster.internal/ubuntu plucky/main amd64 libgnutls-openssl27t64 amd64 3.8.9-2ubuntu2 [24.6 kB]
388s Get:427 http://ftpmaster.internal/ubuntu plucky/main amd64 libp11-kit-dev amd64 0.25.5-2ubuntu3 [23.5 kB]
388s Get:428 http://ftpmaster.internal/ubuntu plucky/main amd64 libtasn1-6-dev amd64 4.20.0-2 [98.1 kB]
388s Get:429 http://ftpmaster.internal/ubuntu plucky/main amd64 nettle-dev amd64 3.10.1-1 [1187 kB]
389s Get:430 http://ftpmaster.internal/ubuntu plucky/main amd64 libgnutls28-dev amd64 3.8.9-2ubuntu2 [1118 kB]
390s Get:431 http://ftpmaster.internal/ubuntu plucky/main amd64 librtmp-dev amd64 2.4+20151223.gitfa8646d.1-2build7 [66.7 kB]
390s Get:432 http://ftpmaster.internal/ubuntu plucky/main amd64 libssl-dev amd64 3.4.1-1ubuntu1 [2873 kB]
393s Get:433 http://ftpmaster.internal/ubuntu plucky/main amd64 libssh2-1-dev amd64 1.11.1-1 [292 kB]
394s Get:434 http://ftpmaster.internal/ubuntu plucky/main amd64 libzstd-dev amd64 1.5.6+dfsg-2 [444 kB]
394s Get:435 http://ftpmaster.internal/ubuntu plucky/main amd64 libcurl4-openssl-dev amd64 8.12.1-3ubuntu1 [522 kB]
395s Get:436 http://ftpmaster.internal/ubuntu plucky/universe amd64 hdf5-helpers amd64 1.14.5+repack-3 [17.0 kB]
395s Get:437 http://ftpmaster.internal/ubuntu plucky/universe amd64 libhdf5-dev amd64 1.14.5+repack-3 [3505 kB]
399s Get:438 http://ftpmaster.internal/ubuntu plucky/main amd64 xorg-sgml-doctools all 1:1.11-1.1 [10.9 kB]
399s Get:439 http://ftpmaster.internal/ubuntu plucky/main amd64 x11proto-dev all 2024.1-1 [606 kB]
399s Get:440 http://ftpmaster.internal/ubuntu plucky/main amd64 libxau-dev amd64 1:1.0.11-1 [9798 B]
399s Get:441 http://ftpmaster.internal/ubuntu plucky/main amd64 libxdmcp-dev amd64 1:1.1.5-1 [26.7 kB]
399s Get:442 http://ftpmaster.internal/ubuntu plucky/main amd64 xtrans-dev all 1.4.0-1 [68.9 kB]
400s Get:443 http://ftpmaster.internal/ubuntu plucky/main amd64 libxcb1-dev amd64 1.17.0-2 [86.4 kB]
400s Get:444 http://ftpmaster.internal/ubuntu plucky/main amd64 libx11-dev amd64 2:1.8.10-2 [777 kB]
400s Get:445 http://ftpmaster.internal/ubuntu plucky/main amd64 libglx-dev amd64 1.7.0-1build1 [14.2 kB]
400s Get:446 http://ftpmaster.internal/ubuntu plucky/main amd64 libgl-dev amd64 1.7.0-1build1 [102 kB]
401s Get:447 http://ftpmaster.internal/ubuntu plucky/main amd64 libblas-dev amd64 3.12.1-2 [338 kB]
401s Get:448 http://ftpmaster.internal/ubuntu plucky/main amd64 liblapack-dev amd64 3.12.1-2 [6489 kB]
408s Get:449 http://ftpmaster.internal/ubuntu plucky/main amd64 libfftw3-long3 amd64 3.3.10-2fakesync1build1 [378 kB]
409s Get:450 http://ftpmaster.internal/ubuntu plucky/main amd64 libfftw3-quad3 amd64 3.3.10-2fakesync1build1 [669 kB]
409s Get:451 http://ftpmaster.internal/ubuntu plucky/main amd64 libfftw3-bin amd64 3.3.10-2fakesync1build1 [39.2 kB]
409s Get:452 http://ftpmaster.internal/ubuntu plucky/main amd64 libfftw3-dev amd64 3.3.10-2fakesync1build1 [2349 kB]
412s Get:453 http://ftpmaster.internal/ubuntu plucky/main amd64 libgfortran-14-dev amd64 14.2.0-17ubuntu3 [966 kB]
413s Get:454 http://ftpmaster.internal/ubuntu plucky/main amd64 gfortran-14-x86-64-linux-gnu amd64 14.2.0-17ubuntu3 [12.6 MB]
426s Get:455 http://ftpmaster.internal/ubuntu plucky/main amd64 gfortran-14 amd64 14.2.0-17ubuntu3 [13.6 kB]
426s Get:456 http://ftpmaster.internal/ubuntu plucky/main amd64 gfortran-x86-64-linux-gnu amd64 4:14.2.0-1ubuntu1 [1022 B]
426s Get:457 http://ftpmaster.internal/ubuntu plucky/main amd64 gfortran amd64 4:14.2.0-1ubuntu1 [1174 B]
426s Get:458 http://ftpmaster.internal/ubuntu plucky/main amd64 libstdc++-14-dev amd64 14.2.0-17ubuntu3 [2486 kB]
429s Get:459 http://ftpmaster.internal/ubuntu plucky/main amd64 g++-14-x86-64-linux-gnu amd64 14.2.0-17ubuntu3 [13.4 MB]
443s Get:460 http://ftpmaster.internal/ubuntu plucky/main amd64 g++-14 amd64 14.2.0-17ubuntu3 [21.8 kB]
443s Get:461 http://ftpmaster.internal/ubuntu plucky/main amd64 g++-x86-64-linux-gnu amd64 4:14.2.0-1ubuntu1 [968 B]
443s Get:462 http://ftpmaster.internal/ubuntu plucky/main amd64 g++ amd64 4:14.2.0-1ubuntu1 [1100 B]
443s Get:463 http://ftpmaster.internal/ubuntu plucky/universe amd64 octave-dev amd64 9.4.0-1 [463 kB]
444s Get:464 http://ftpmaster.internal/ubuntu plucky/universe amd64 dh-octave all 1.8.0 [19.7 kB]
444s Get:465 http://ftpmaster.internal/ubuntu plucky/main amd64 libfontenc1 amd64 1:1.1.8-1build1 [14.0 kB]
444s Get:466 http://ftpmaster.internal/ubuntu plucky/main amd64 libxt6t64 amd64 1:1.2.1-1.2build1 [171 kB]
444s Get:467 http://ftpmaster.internal/ubuntu plucky/main amd64 libxmu6 amd64 2:1.1.3-3build2 [47.6 kB]
444s Get:468 http://ftpmaster.internal/ubuntu plucky/main amd64 libxaw7 amd64 2:1.0.16-1 [207 kB]
445s Get:469 http://ftpmaster.internal/ubuntu plucky/main amd64 libxfont2 amd64 1:2.0.6-1build1 [93.0 kB]
445s Get:470 http://ftpmaster.internal/ubuntu plucky/main amd64 libxkbfile1 amd64 1:1.1.0-1build4 [70.0 kB]
445s Get:471 http://ftpmaster.internal/ubuntu plucky/main amd64 libxrandr2 amd64 2:1.5.4-1 [19.6 kB]
445s Get:472 http://ftpmaster.internal/ubuntu plucky/universe amd64 octave-io amd64 2.6.4-3build2 [223 kB]
445s Get:473 http://ftpmaster.internal/ubuntu plucky/universe amd64 octave-statistics-common all 1.7.3-2 [932 kB]
446s Get:474 http://ftpmaster.internal/ubuntu plucky/universe amd64 octave-statistics amd64 1.7.3-2 [206 kB]
446s Get:475 http://ftpmaster.internal/ubuntu plucky/main amd64 x11-xkb-utils amd64 7.7+9 [169 kB]
447s Get:476 http://ftpmaster.internal/ubuntu plucky/main amd64 xserver-common all 2:21.1.16-1ubuntu1 [34.4 kB]
447s Get:477 http://ftpmaster.internal/ubuntu plucky/universe amd64 xvfb amd64 2:21.1.16-1ubuntu1 [975 kB]
449s Fetched 207 MB in 2min 27s (1406 kB/s)
449s Selecting previously unselected package libstemmer0d:amd64.
449s (Reading database ... 
(Reading database ... 5%
(Reading database ... 10%
(Reading database ... 15%
(Reading database ... 20%
(Reading database ... 25%
(Reading database ... 30%
(Reading database ... 35%
(Reading database ... 40%
(Reading database ... 45%
(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
(Reading database ... 65%
(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 76968 files and directories currently installed.)
449s Preparing to unpack .../000-libstemmer0d_2.2.0-4build1_amd64.deb ...
449s Unpacking libstemmer0d:amd64 (2.2.0-4build1) ...
449s Selecting previously unselected package libappstream5:amd64.
449s Preparing to unpack .../001-libappstream5_1.0.4-1_amd64.deb ...
449s Unpacking libappstream5:amd64 (1.0.4-1) ...
449s Selecting previously unselected package appstream.
449s Preparing to unpack .../002-appstream_1.0.4-1_amd64.deb ...
449s Unpacking appstream (1.0.4-1) ...
449s Selecting previously unselected package m4.
449s Preparing to unpack .../003-m4_1.4.19-7_amd64.deb ...
449s Unpacking m4 (1.4.19-7) ...
449s Selecting previously unselected package autoconf.
449s Preparing to unpack .../004-autoconf_2.72-3ubuntu1_all.deb ...
449s Unpacking autoconf (2.72-3ubuntu1) ...
449s Selecting previously unselected package autotools-dev.
449s Preparing to unpack .../005-autotools-dev_20220109.1_all.deb ...
449s Unpacking autotools-dev (20220109.1) ...
449s Selecting previously unselected package automake.
449s Preparing to unpack .../006-automake_1%3a1.17-3ubuntu1_all.deb ...
449s Unpacking automake (1:1.17-3ubuntu1) ...
449s Selecting previously unselected package autopoint.
449s Preparing to unpack .../007-autopoint_0.23.1-1_all.deb ...
449s Unpacking autopoint (0.23.1-1) ...
449s Selecting previously unselected package libcapture-tiny-perl.
449s Preparing to unpack .../008-libcapture-tiny-perl_0.50-1_all.deb ...
449s Unpacking libcapture-tiny-perl (0.50-1) ...
449s Selecting previously unselected package libparams-util-perl.
449s Preparing to unpack .../009-libparams-util-perl_1.102-3build1_amd64.deb ...
449s Unpacking libparams-util-perl (1.102-3build1) ...
449s Selecting previously unselected package libsub-install-perl.
449s Preparing to unpack .../010-libsub-install-perl_0.929-1_all.deb ...
449s Unpacking libsub-install-perl (0.929-1) ...
449s Selecting previously unselected package libdata-optlist-perl.
449s Preparing to unpack .../011-libdata-optlist-perl_0.114-1_all.deb ...
449s Unpacking libdata-optlist-perl (0.114-1) ...
449s Selecting previously unselected package libb-hooks-op-check-perl:amd64.
449s Preparing to unpack .../012-libb-hooks-op-check-perl_0.22-3build2_amd64.deb ...
449s Unpacking libb-hooks-op-check-perl:amd64 (0.22-3build2) ...
449s Selecting previously unselected package libdynaloader-functions-perl.
449s Preparing to unpack .../013-libdynaloader-functions-perl_0.004-1_all.deb ...
449s Unpacking libdynaloader-functions-perl (0.004-1) ...
449s Selecting previously unselected package libdevel-callchecker-perl:amd64.
449s Preparing to unpack .../014-libdevel-callchecker-perl_0.009-1build1_amd64.deb ...
449s Unpacking libdevel-callchecker-perl:amd64 (0.009-1build1) ...
449s Selecting previously unselected package libparams-classify-perl:amd64.
449s Preparing to unpack .../015-libparams-classify-perl_0.015-2build6_amd64.deb ...
449s Unpacking libparams-classify-perl:amd64 (0.015-2build6) ...
449s Selecting previously unselected package libmodule-runtime-perl.
449s Preparing to unpack .../016-libmodule-runtime-perl_0.016-2_all.deb ...
449s Unpacking libmodule-runtime-perl (0.016-2) ...
449s Selecting previously unselected package libtry-tiny-perl.
449s Preparing to unpack .../017-libtry-tiny-perl_0.32-1_all.deb ...
449s Unpacking libtry-tiny-perl (0.32-1) ...
449s Selecting previously unselected package libmodule-implementation-perl.
449s Preparing to unpack .../018-libmodule-implementation-perl_0.09-2_all.deb ...
449s Unpacking libmodule-implementation-perl (0.09-2) ...
449s Selecting previously unselected package libpackage-stash-perl.
449s Preparing to unpack .../019-libpackage-stash-perl_0.40-1_all.deb ...
449s Unpacking libpackage-stash-perl (0.40-1) ...
449s Selecting previously unselected package libclass-load-perl.
449s Preparing to unpack .../020-libclass-load-perl_0.25-2_all.deb ...
449s Unpacking libclass-load-perl (0.25-2) ...
449s Selecting previously unselected package libio-stringy-perl.
449s Preparing to unpack .../021-libio-stringy-perl_2.113-2_all.deb ...
449s Unpacking libio-stringy-perl (2.113-2) ...
449s Selecting previously unselected package libparams-validate-perl:amd64.
449s Preparing to unpack .../022-libparams-validate-perl_1.31-2build4_amd64.deb ...
449s Unpacking libparams-validate-perl:amd64 (1.31-2build4) ...
449s Selecting previously unselected package libsub-exporter-perl.
449s Preparing to unpack .../023-libsub-exporter-perl_0.990-1_all.deb ...
449s Unpacking libsub-exporter-perl (0.990-1) ...
449s Selecting previously unselected package libgetopt-long-descriptive-perl.
449s Preparing to unpack .../024-libgetopt-long-descriptive-perl_0.116-2_all.deb ...
449s Unpacking libgetopt-long-descriptive-perl (0.116-2) ...
450s Selecting previously unselected package libio-tiecombine-perl.
450s Preparing to unpack .../025-libio-tiecombine-perl_1.005-3_all.deb ...
450s Unpacking libio-tiecombine-perl (1.005-3) ...
450s Selecting previously unselected package libmodule-pluggable-perl.
450s Preparing to unpack .../026-libmodule-pluggable-perl_5.2-5_all.deb ...
450s Unpacking libmodule-pluggable-perl (5.2-5) ...
450s Selecting previously unselected package libstring-rewriteprefix-perl.
450s Preparing to unpack .../027-libstring-rewriteprefix-perl_0.009-1_all.deb ...
450s Unpacking libstring-rewriteprefix-perl (0.009-1) ...
450s Selecting previously unselected package libapp-cmd-perl.
450s Preparing to unpack .../028-libapp-cmd-perl_0.337-2_all.deb ...
450s Unpacking libapp-cmd-perl (0.337-2) ...
450s Selecting previously unselected package libboolean-perl.
450s Preparing to unpack .../029-libboolean-perl_0.46-3_all.deb ...
450s Unpacking libboolean-perl (0.46-3) ...
450s Selecting previously unselected package libsub-uplevel-perl.
450s Preparing to unpack .../030-libsub-uplevel-perl_0.2800-3_all.deb ...
450s Unpacking libsub-uplevel-perl (0.2800-3) ...
450s Selecting previously unselected package libtest-exception-perl.
450s Preparing to unpack .../031-libtest-exception-perl_0.43-3_all.deb ...
450s Unpacking libtest-exception-perl (0.43-3) ...
450s Selecting previously unselected package libcarp-assert-more-perl.
450s Preparing to unpack .../032-libcarp-assert-more-perl_2.8.0-1_all.deb ...
450s Unpacking libcarp-assert-more-perl (2.8.0-1) ...
450s Selecting previously unselected package libfile-which-perl.
450s Preparing to unpack .../033-libfile-which-perl_1.27-2_all.deb ...
450s Unpacking libfile-which-perl (1.27-2) ...
450s Selecting previously unselected package libfile-homedir-perl.
450s Preparing to unpack .../034-libfile-homedir-perl_1.006-2_all.deb ...
450s Unpacking libfile-homedir-perl (1.006-2) ...
450s Selecting previously unselected package libclone-choose-perl.
450s Preparing to unpack .../035-libclone-choose-perl_0.010-2_all.deb ...
450s Unpacking libclone-choose-perl (0.010-2) ...
450s Selecting previously unselected package libhash-merge-perl.
450s Preparing to unpack .../036-libhash-merge-perl_0.302-1_all.deb ...
450s Unpacking libhash-merge-perl (0.302-1) ...
450s Selecting previously unselected package libjson-perl.
450s Preparing to unpack .../037-libjson-perl_4.10000-1_all.deb ...
450s Unpacking libjson-perl (4.10000-1) ...
450s Selecting previously unselected package libexporter-tiny-perl.
450s Preparing to unpack .../038-libexporter-tiny-perl_1.006002-1_all.deb ...
450s Unpacking libexporter-tiny-perl (1.006002-1) ...
450s Selecting previously unselected package liblist-moreutils-xs-perl.
450s Preparing to unpack .../039-liblist-moreutils-xs-perl_0.430-4build1_amd64.deb ...
450s Unpacking liblist-moreutils-xs-perl (0.430-4build1) ...
450s Selecting previously unselected package liblist-moreutils-perl.
450s Preparing to unpack .../040-liblist-moreutils-perl_0.430-2_all.deb ...
450s Unpacking liblist-moreutils-perl (0.430-2) ...
450s Selecting previously unselected package liblog-log4perl-perl.
450s Preparing to unpack .../041-liblog-log4perl-perl_1.57-1_all.deb ...
450s Unpacking liblog-log4perl-perl (1.57-1) ...
450s Selecting previously unselected package libmouse-perl:amd64.
450s Preparing to unpack .../042-libmouse-perl_2.5.11-1build1_amd64.deb ...
450s Unpacking libmouse-perl:amd64 (2.5.11-1build1) ...
450s Selecting previously unselected package libmousex-nativetraits-perl.
450s Preparing to unpack .../043-libmousex-nativetraits-perl_1.09-3_all.deb ...
450s Unpacking libmousex-nativetraits-perl (1.09-3) ...
450s Selecting previously unselected package libmousex-strictconstructor-perl.
450s Preparing to unpack .../044-libmousex-strictconstructor-perl_0.02-3_all.deb ...
450s Unpacking libmousex-strictconstructor-perl (0.02-3) ...
450s Selecting previously unselected package libparse-recdescent-perl.
450s Preparing to unpack .../045-libparse-recdescent-perl_1.967015+dfsg-4_all.deb ...
450s Unpacking libparse-recdescent-perl (1.967015+dfsg-4) ...
450s Selecting previously unselected package libpath-tiny-perl.
450s Preparing to unpack .../046-libpath-tiny-perl_0.146-1_all.deb ...
450s Unpacking libpath-tiny-perl (0.146-1) ...
450s Selecting previously unselected package libpod-pom-perl.
450s Preparing to unpack .../047-libpod-pom-perl_2.01-4_all.deb ...
450s Unpacking libpod-pom-perl (2.01-4) ...
450s Selecting previously unselected package libregexp-common-perl.
450s Preparing to unpack .../048-libregexp-common-perl_2024080801-1_all.deb ...
450s Unpacking libregexp-common-perl (2024080801-1) ...
450s Selecting previously unselected package libyaml-tiny-perl.
450s Preparing to unpack .../049-libyaml-tiny-perl_1.76-1_all.deb ...
450s Unpacking libyaml-tiny-perl (1.76-1) ...
450s Selecting previously unselected package libconfig-model-perl.
450s Preparing to unpack .../050-libconfig-model-perl_2.155-1_all.deb ...
450s Unpacking libconfig-model-perl (2.155-1) ...
450s Selecting previously unselected package libyaml-pp-perl.
450s Preparing to unpack .../051-libyaml-pp-perl_0.39.0-1_all.deb ...
450s Unpacking libyaml-pp-perl (0.39.0-1) ...
450s Selecting previously unselected package cme.
450s Preparing to unpack .../052-cme_1.041-1_all.deb ...
450s Unpacking cme (1.041-1) ...
450s Selecting previously unselected package libisl23:amd64.
450s Preparing to unpack .../053-libisl23_0.27-1_amd64.deb ...
450s Unpacking libisl23:amd64 (0.27-1) ...
450s Selecting previously unselected package libmpc3:amd64.
450s Preparing to unpack .../054-libmpc3_1.3.1-1build2_amd64.deb ...
450s Unpacking libmpc3:amd64 (1.3.1-1build2) ...
450s Selecting previously unselected package cpp-14-x86-64-linux-gnu.
450s Preparing to unpack .../055-cpp-14-x86-64-linux-gnu_14.2.0-17ubuntu3_amd64.deb ...
450s Unpacking cpp-14-x86-64-linux-gnu (14.2.0-17ubuntu3) ...
451s Selecting previously unselected package cpp-14.
451s Preparing to unpack .../056-cpp-14_14.2.0-17ubuntu3_amd64.deb ...
451s Unpacking cpp-14 (14.2.0-17ubuntu3) ...
451s Selecting previously unselected package cpp-x86-64-linux-gnu.
451s Preparing to unpack .../057-cpp-x86-64-linux-gnu_4%3a14.2.0-1ubuntu1_amd64.deb ...
451s Unpacking cpp-x86-64-linux-gnu (4:14.2.0-1ubuntu1) ...
451s Selecting previously unselected package cpp.
451s Preparing to unpack .../058-cpp_4%3a14.2.0-1ubuntu1_amd64.deb ...
451s Unpacking cpp (4:14.2.0-1ubuntu1) ...
451s Selecting previously unselected package libdebhelper-perl.
451s Preparing to unpack .../059-libdebhelper-perl_13.24.1ubuntu2_all.deb ...
451s Unpacking libdebhelper-perl (13.24.1ubuntu2) ...
451s Selecting previously unselected package libcc1-0:amd64.
451s Preparing to unpack .../060-libcc1-0_15-20250222-0ubuntu1_amd64.deb ...
451s Unpacking libcc1-0:amd64 (15-20250222-0ubuntu1) ...
451s Selecting previously unselected package libgomp1:amd64.
451s Preparing to unpack .../061-libgomp1_15-20250222-0ubuntu1_amd64.deb ...
451s Unpacking libgomp1:amd64 (15-20250222-0ubuntu1) ...
451s Selecting previously unselected package libitm1:amd64.
451s Preparing to unpack .../062-libitm1_15-20250222-0ubuntu1_amd64.deb ...
451s Unpacking libitm1:amd64 (15-20250222-0ubuntu1) ...
451s Selecting previously unselected package libasan8:amd64.
451s Preparing to unpack .../063-libasan8_15-20250222-0ubuntu1_amd64.deb ...
451s Unpacking libasan8:amd64 (15-20250222-0ubuntu1) ...
451s Selecting previously unselected package liblsan0:amd64.
451s Preparing to unpack .../064-liblsan0_15-20250222-0ubuntu1_amd64.deb ...
451s Unpacking liblsan0:amd64 (15-20250222-0ubuntu1) ...
451s Selecting previously unselected package libtsan2:amd64.
451s Preparing to unpack .../065-libtsan2_15-20250222-0ubuntu1_amd64.deb ...
451s Unpacking libtsan2:amd64 (15-20250222-0ubuntu1) ...
451s Selecting previously unselected package libubsan1:amd64.
451s Preparing to unpack .../066-libubsan1_15-20250222-0ubuntu1_amd64.deb ...
451s Unpacking libubsan1:amd64 (15-20250222-0ubuntu1) ...
451s Selecting previously unselected package libhwasan0:amd64.
451s Preparing to unpack .../067-libhwasan0_15-20250222-0ubuntu1_amd64.deb ...
451s Unpacking libhwasan0:amd64 (15-20250222-0ubuntu1) ...
451s Selecting previously unselected package libquadmath0:amd64.
451s Preparing to unpack .../068-libquadmath0_15-20250222-0ubuntu1_amd64.deb ...
451s Unpacking libquadmath0:amd64 (15-20250222-0ubuntu1) ...
451s Selecting previously unselected package libgcc-14-dev:amd64.
451s Preparing to unpack .../069-libgcc-14-dev_14.2.0-17ubuntu3_amd64.deb ...
451s Unpacking libgcc-14-dev:amd64 (14.2.0-17ubuntu3) ...
451s Selecting previously unselected package gcc-14-x86-64-linux-gnu.
451s Preparing to unpack .../070-gcc-14-x86-64-linux-gnu_14.2.0-17ubuntu3_amd64.deb ...
451s Unpacking gcc-14-x86-64-linux-gnu (14.2.0-17ubuntu3) ...
451s Selecting previously unselected package gcc-14.
451s Preparing to unpack .../071-gcc-14_14.2.0-17ubuntu3_amd64.deb ...
451s Unpacking gcc-14 (14.2.0-17ubuntu3) ...
451s Selecting previously unselected package gcc-x86-64-linux-gnu.
451s Preparing to unpack .../072-gcc-x86-64-linux-gnu_4%3a14.2.0-1ubuntu1_amd64.deb ...
451s Unpacking gcc-x86-64-linux-gnu (4:14.2.0-1ubuntu1) ...
452s Selecting previously unselected package gcc.
452s Preparing to unpack .../073-gcc_4%3a14.2.0-1ubuntu1_amd64.deb ...
452s Unpacking gcc (4:14.2.0-1ubuntu1) ...
452s Selecting previously unselected package libtool.
452s Preparing to unpack .../074-libtool_2.5.4-4_all.deb ...
452s Unpacking libtool (2.5.4-4) ...
452s Selecting previously unselected package dh-autoreconf.
452s Preparing to unpack .../075-dh-autoreconf_20_all.deb ...
452s Unpacking dh-autoreconf (20) ...
452s Selecting previously unselected package libarchive-zip-perl.
452s Preparing to unpack .../076-libarchive-zip-perl_1.68-1_all.deb ...
452s Unpacking libarchive-zip-perl (1.68-1) ...
452s Selecting previously unselected package libfile-stripnondeterminism-perl.
452s Preparing to unpack .../077-libfile-stripnondeterminism-perl_1.14.1-2_all.deb ...
452s Unpacking libfile-stripnondeterminism-perl (1.14.1-2) ...
452s Selecting previously unselected package dh-strip-nondeterminism.
452s Preparing to unpack .../078-dh-strip-nondeterminism_1.14.1-2_all.deb ...
452s Unpacking dh-strip-nondeterminism (1.14.1-2) ...
452s Selecting previously unselected package debugedit.
452s Preparing to unpack .../079-debugedit_1%3a5.1-2_amd64.deb ...
452s Unpacking debugedit (1:5.1-2) ...
452s Selecting previously unselected package dwz.
452s Preparing to unpack .../080-dwz_0.15-1build6_amd64.deb ...
452s Unpacking dwz (0.15-1build6) ...
452s Selecting previously unselected package gettext.
452s Preparing to unpack .../081-gettext_0.23.1-1_amd64.deb ...
452s Unpacking gettext (0.23.1-1) ...
452s Selecting previously unselected package intltool-debian.
452s Preparing to unpack .../082-intltool-debian_0.35.0+20060710.6_all.deb ...
452s Unpacking intltool-debian (0.35.0+20060710.6) ...
452s Selecting previously unselected package po-debconf.
452s Preparing to unpack .../083-po-debconf_1.0.21+nmu1_all.deb ...
452s Unpacking po-debconf (1.0.21+nmu1) ...
452s Selecting previously unselected package debhelper.
452s Preparing to unpack .../084-debhelper_13.24.1ubuntu2_all.deb ...
452s Unpacking debhelper (13.24.1ubuntu2) ...
452s Selecting previously unselected package aglfn.
452s Preparing to unpack .../085-aglfn_1.7+git20191031.4036a9c-2_all.deb ...
452s Unpacking aglfn (1.7+git20191031.4036a9c-2) ...
452s Selecting previously unselected package gnuplot-data.
452s Preparing to unpack .../086-gnuplot-data_6.0.2+dfsg1-1_all.deb ...
452s Unpacking gnuplot-data (6.0.2+dfsg1-1) ...
452s Selecting previously unselected package fonts-dejavu-mono.
452s Preparing to unpack .../087-fonts-dejavu-mono_2.37-8_all.deb ...
452s Unpacking fonts-dejavu-mono (2.37-8) ...
452s Selecting previously unselected package fonts-dejavu-core.
452s Preparing to unpack .../088-fonts-dejavu-core_2.37-8_all.deb ...
452s Unpacking fonts-dejavu-core (2.37-8) ...
452s Selecting previously unselected package fonts-freefont-otf.
452s Preparing to unpack .../089-fonts-freefont-otf_20211204+svn4273-2_all.deb ...
452s Unpacking fonts-freefont-otf (20211204+svn4273-2) ...
452s Selecting previously unselected package fontconfig-config.
452s Preparing to unpack .../090-fontconfig-config_2.15.0-2ubuntu1_amd64.deb ...
452s Unpacking fontconfig-config (2.15.0-2ubuntu1) ...
452s Selecting previously unselected package libfontconfig1:amd64.
452s Preparing to unpack .../091-libfontconfig1_2.15.0-2ubuntu1_amd64.deb ...
452s Unpacking libfontconfig1:amd64 (2.15.0-2ubuntu1) ...
452s Selecting previously unselected package libpixman-1-0:amd64.
452s Preparing to unpack .../092-libpixman-1-0_0.44.0-3_amd64.deb ...
452s Unpacking libpixman-1-0:amd64 (0.44.0-3) ...
452s Selecting previously unselected package libxcb-render0:amd64.
452s Preparing to unpack .../093-libxcb-render0_1.17.0-2_amd64.deb ...
452s Unpacking libxcb-render0:amd64 (1.17.0-2) ...
452s Selecting previously unselected package libxcb-shm0:amd64.
452s Preparing to unpack .../094-libxcb-shm0_1.17.0-2_amd64.deb ...
452s Unpacking libxcb-shm0:amd64 (1.17.0-2) ...
452s Selecting previously unselected package libxrender1:amd64.
452s Preparing to unpack .../095-libxrender1_1%3a0.9.10-1.1build1_amd64.deb ...
452s Unpacking libxrender1:amd64 (1:0.9.10-1.1build1) ...
452s Selecting previously unselected package libcairo2:amd64.
452s Preparing to unpack .../096-libcairo2_1.18.2-2_amd64.deb ...
452s Unpacking libcairo2:amd64 (1.18.2-2) ...
452s Selecting previously unselected package libsharpyuv0:amd64.
452s Preparing to unpack .../097-libsharpyuv0_1.5.0-0.1_amd64.deb ...
452s Unpacking libsharpyuv0:amd64 (1.5.0-0.1) ...
453s Selecting previously unselected package libaom3:amd64.
453s Preparing to unpack .../098-libaom3_3.12.0-1_amd64.deb ...
453s Unpacking libaom3:amd64 (3.12.0-1) ...
453s Selecting previously unselected package libheif-plugin-aomdec:amd64.
453s Preparing to unpack .../099-libheif-plugin-aomdec_1.19.7-1_amd64.deb ...
453s Unpacking libheif-plugin-aomdec:amd64 (1.19.7-1) ...
453s Selecting previously unselected package libde265-0:amd64.
453s Preparing to unpack .../100-libde265-0_1.0.15-1build5_amd64.deb ...
453s Unpacking libde265-0:amd64 (1.0.15-1build5) ...
453s Selecting previously unselected package libheif-plugin-libde265:amd64.
453s Preparing to unpack .../101-libheif-plugin-libde265_1.19.7-1_amd64.deb ...
453s Unpacking libheif-plugin-libde265:amd64 (1.19.7-1) ...
453s Selecting previously unselected package libheif1:amd64.
453s Preparing to unpack .../102-libheif1_1.19.7-1_amd64.deb ...
453s Unpacking libheif1:amd64 (1.19.7-1) ...
453s Selecting previously unselected package libimagequant0:amd64.
453s Preparing to unpack .../103-libimagequant0_2.18.0-1build1_amd64.deb ...
453s Unpacking libimagequant0:amd64 (2.18.0-1build1) ...
453s Selecting previously unselected package libjpeg-turbo8:amd64.
453s Preparing to unpack .../104-libjpeg-turbo8_2.1.5-3ubuntu2_amd64.deb ...
453s Unpacking libjpeg-turbo8:amd64 (2.1.5-3ubuntu2) ...
453s Selecting previously unselected package libjpeg8:amd64.
453s Preparing to unpack .../105-libjpeg8_8c-2ubuntu11_amd64.deb ...
453s Unpacking libjpeg8:amd64 (8c-2ubuntu11) ...
453s Selecting previously unselected package libgraphite2-3:amd64.
453s Preparing to unpack .../106-libgraphite2-3_1.3.14-2ubuntu1_amd64.deb ...
453s Unpacking libgraphite2-3:amd64 (1.3.14-2ubuntu1) ...
453s Selecting previously unselected package libharfbuzz0b:amd64.
453s Preparing to unpack .../107-libharfbuzz0b_10.2.0-1_amd64.deb ...
453s Unpacking libharfbuzz0b:amd64 (10.2.0-1) ...
453s Selecting previously unselected package libraqm0:amd64.
453s Preparing to unpack .../108-libraqm0_0.10.2-1_amd64.deb ...
453s Unpacking libraqm0:amd64 (0.10.2-1) ...
453s Selecting previously unselected package libdeflate0:amd64.
453s Preparing to unpack .../109-libdeflate0_1.23-1_amd64.deb ...
453s Unpacking libdeflate0:amd64 (1.23-1) ...
453s Selecting previously unselected package libjbig0:amd64.
453s Preparing to unpack .../110-libjbig0_2.1-6.1ubuntu2_amd64.deb ...
453s Unpacking libjbig0:amd64 (2.1-6.1ubuntu2) ...
453s Selecting previously unselected package liblerc4:amd64.
453s Preparing to unpack .../111-liblerc4_4.0.0+ds-5ubuntu1_amd64.deb ...
453s Unpacking liblerc4:amd64 (4.0.0+ds-5ubuntu1) ...
453s Selecting previously unselected package libwebp7:amd64.
453s Preparing to unpack .../112-libwebp7_1.5.0-0.1_amd64.deb ...
453s Unpacking libwebp7:amd64 (1.5.0-0.1) ...
453s Selecting previously unselected package libtiff6:amd64.
453s Preparing to unpack .../113-libtiff6_4.5.1+git230720-4ubuntu4_amd64.deb ...
453s Unpacking libtiff6:amd64 (4.5.1+git230720-4ubuntu4) ...
453s Selecting previously unselected package libxpm4:amd64.
453s Preparing to unpack .../114-libxpm4_1%3a3.5.17-1build2_amd64.deb ...
453s Unpacking libxpm4:amd64 (1:3.5.17-1build2) ...
453s Selecting previously unselected package libgd3:amd64.
453s Preparing to unpack .../115-libgd3_2.3.3-12ubuntu3_amd64.deb ...
453s Unpacking libgd3:amd64 (2.3.3-12ubuntu3) ...
453s Selecting previously unselected package liblua5.4-0:amd64.
453s Preparing to unpack .../116-liblua5.4-0_5.4.7-1_amd64.deb ...
453s Unpacking liblua5.4-0:amd64 (5.4.7-1) ...
453s Selecting previously unselected package fontconfig.
453s Preparing to unpack .../117-fontconfig_2.15.0-2ubuntu1_amd64.deb ...
453s Unpacking fontconfig (2.15.0-2ubuntu1) ...
453s Selecting previously unselected package libthai-data.
453s Preparing to unpack .../118-libthai-data_0.1.29-2build1_all.deb ...
453s Unpacking libthai-data (0.1.29-2build1) ...
453s Selecting previously unselected package libdatrie1:amd64.
453s Preparing to unpack .../119-libdatrie1_0.2.13-3build1_amd64.deb ...
453s Unpacking libdatrie1:amd64 (0.2.13-3build1) ...
453s Selecting previously unselected package libthai0:amd64.
453s Preparing to unpack .../120-libthai0_0.1.29-2build1_amd64.deb ...
453s Unpacking libthai0:amd64 (0.1.29-2build1) ...
453s Selecting previously unselected package libpango-1.0-0:amd64.
453s Preparing to unpack .../121-libpango-1.0-0_1.56.1-1_amd64.deb ...
453s Unpacking libpango-1.0-0:amd64 (1.56.1-1) ...
453s Selecting previously unselected package libpangoft2-1.0-0:amd64.
453s Preparing to unpack .../122-libpangoft2-1.0-0_1.56.1-1_amd64.deb ...
453s Unpacking libpangoft2-1.0-0:amd64 (1.56.1-1) ...
453s Selecting previously unselected package libpangocairo-1.0-0:amd64.
453s Preparing to unpack .../123-libpangocairo-1.0-0_1.56.1-1_amd64.deb ...
453s Unpacking libpangocairo-1.0-0:amd64 (1.56.1-1) ...
453s Selecting previously unselected package libwebpmux3:amd64.
453s Preparing to unpack .../124-libwebpmux3_1.5.0-0.1_amd64.deb ...
453s Unpacking libwebpmux3:amd64 (1.5.0-0.1) ...
453s Selecting previously unselected package gnuplot-nox.
453s Preparing to unpack .../125-gnuplot-nox_6.0.2+dfsg1-1_amd64.deb ...
453s Unpacking gnuplot-nox (6.0.2+dfsg1-1) ...
454s Selecting previously unselected package dh-octave-autopkgtest.
454s Preparing to unpack .../126-dh-octave-autopkgtest_1.8.0_all.deb ...
454s Unpacking dh-octave-autopkgtest (1.8.0) ...
454s Selecting previously unselected package libapt-pkg-perl.
454s Preparing to unpack .../127-libapt-pkg-perl_0.1.41build1_amd64.deb ...
454s Unpacking libapt-pkg-perl (0.1.41build1) ...
454s Selecting previously unselected package libarray-intspan-perl.
454s Preparing to unpack .../128-libarray-intspan-perl_2.004-2_all.deb ...
454s Unpacking libarray-intspan-perl (2.004-2) ...
454s Selecting previously unselected package libyaml-libyaml-perl.
454s Preparing to unpack .../129-libyaml-libyaml-perl_0.903.0+ds-1_amd64.deb ...
454s Unpacking libyaml-libyaml-perl (0.903.0+ds-1) ...
454s Selecting previously unselected package libconfig-model-backend-yaml-perl.
454s Preparing to unpack .../130-libconfig-model-backend-yaml-perl_2.134-2_all.deb ...
454s Unpacking libconfig-model-backend-yaml-perl (2.134-2) ...
454s Selecting previously unselected package libexporter-lite-perl.
454s Preparing to unpack .../131-libexporter-lite-perl_0.09-2_all.deb ...
454s Unpacking libexporter-lite-perl (0.09-2) ...
454s Selecting previously unselected package libencode-locale-perl.
454s Preparing to unpack .../132-libencode-locale-perl_1.05-3_all.deb ...
454s Unpacking libencode-locale-perl (1.05-3) ...
454s Selecting previously unselected package libtimedate-perl.
454s Preparing to unpack .../133-libtimedate-perl_2.3300-2_all.deb ...
454s Unpacking libtimedate-perl (2.3300-2) ...
454s Selecting previously unselected package libhttp-date-perl.
454s Preparing to unpack .../134-libhttp-date-perl_6.06-1_all.deb ...
454s Unpacking libhttp-date-perl (6.06-1) ...
454s Selecting previously unselected package libfile-listing-perl.
454s Preparing to unpack .../135-libfile-listing-perl_6.16-1_all.deb ...
454s Unpacking libfile-listing-perl (6.16-1) ...
454s Selecting previously unselected package libhtml-tagset-perl.
454s Preparing to unpack .../136-libhtml-tagset-perl_3.24-1_all.deb ...
454s Unpacking libhtml-tagset-perl (3.24-1) ...
454s Selecting previously unselected package liburi-perl.
454s Preparing to unpack .../137-liburi-perl_5.30-1_all.deb ...
454s Unpacking liburi-perl (5.30-1) ...
454s Selecting previously unselected package libhtml-parser-perl:amd64.
454s Preparing to unpack .../138-libhtml-parser-perl_3.83-1build1_amd64.deb ...
454s Unpacking libhtml-parser-perl:amd64 (3.83-1build1) ...
454s Selecting previously unselected package libhtml-tree-perl.
454s Preparing to unpack .../139-libhtml-tree-perl_5.07-3_all.deb ...
454s Unpacking libhtml-tree-perl (5.07-3) ...
454s Selecting previously unselected package libclone-perl:amd64.
454s Preparing to unpack .../140-libclone-perl_0.47-1_amd64.deb ...
454s Unpacking libclone-perl:amd64 (0.47-1) ...
454s Selecting previously unselected package libio-html-perl.
454s Preparing to unpack .../141-libio-html-perl_1.004-3_all.deb ...
454s Unpacking libio-html-perl (1.004-3) ...
454s Selecting previously unselected package liblwp-mediatypes-perl.
454s Preparing to unpack .../142-liblwp-mediatypes-perl_6.04-2_all.deb ...
454s Unpacking liblwp-mediatypes-perl (6.04-2) ...
454s Selecting previously unselected package libhttp-message-perl.
454s Preparing to unpack .../143-libhttp-message-perl_7.00-2ubuntu1_all.deb ...
454s Unpacking libhttp-message-perl (7.00-2ubuntu1) ...
454s Selecting previously unselected package libhttp-cookies-perl.
454s Preparing to unpack .../144-libhttp-cookies-perl_6.11-1_all.deb ...
454s Unpacking libhttp-cookies-perl (6.11-1) ...
454s Selecting previously unselected package libhttp-negotiate-perl.
454s Preparing to unpack .../145-libhttp-negotiate-perl_6.01-2_all.deb ...
454s Unpacking libhttp-negotiate-perl (6.01-2) ...
454s Selecting previously unselected package perl-openssl-defaults:amd64.
454s Preparing to unpack .../146-perl-openssl-defaults_7build3_amd64.deb ...
454s Unpacking perl-openssl-defaults:amd64 (7build3) ...
454s Selecting previously unselected package libnet-ssleay-perl:amd64.
454s Preparing to unpack .../147-libnet-ssleay-perl_1.94-3_amd64.deb ...
454s Unpacking libnet-ssleay-perl:amd64 (1.94-3) ...
454s Selecting previously unselected package libio-socket-ssl-perl.
454s Preparing to unpack .../148-libio-socket-ssl-perl_2.089-1_all.deb ...
454s Unpacking libio-socket-ssl-perl (2.089-1) ...
454s Selecting previously unselected package libnet-http-perl.
454s Preparing to unpack .../149-libnet-http-perl_6.23-1_all.deb ...
454s Unpacking libnet-http-perl (6.23-1) ...
454s Selecting previously unselected package liblwp-protocol-https-perl.
454s Preparing to unpack .../150-liblwp-protocol-https-perl_6.14-1_all.deb ...
454s Unpacking liblwp-protocol-https-perl (6.14-1) ...
454s Selecting previously unselected package libwww-robotrules-perl.
454s Preparing to unpack .../151-libwww-robotrules-perl_6.02-1_all.deb ...
454s Unpacking libwww-robotrules-perl (6.02-1) ...
454s Selecting previously unselected package libwww-perl.
454s Preparing to unpack .../152-libwww-perl_6.78-1_all.deb ...
454s Unpacking libwww-perl (6.78-1) ...
454s Selecting previously unselected package liberror-perl.
454s Preparing to unpack .../153-liberror-perl_0.17030-1_all.deb ...
454s Unpacking liberror-perl (0.17030-1) ...
454s Selecting previously unselected package libparse-debcontrol-perl.
454s Preparing to unpack .../154-libparse-debcontrol-perl_2.005-6_all.deb ...
454s Unpacking libparse-debcontrol-perl (2.005-6) ...
454s Selecting previously unselected package libsoftware-copyright-perl.
454s Preparing to unpack .../155-libsoftware-copyright-perl_0.014-1_all.deb ...
454s Unpacking libsoftware-copyright-perl (0.014-1) ...
454s Selecting previously unselected package libalgorithm-c3-perl.
454s Preparing to unpack .../156-libalgorithm-c3-perl_0.11-2_all.deb ...
454s Unpacking libalgorithm-c3-perl (0.11-2) ...
454s Selecting previously unselected package libclass-c3-perl.
454s Preparing to unpack .../157-libclass-c3-perl_0.35-2_all.deb ...
454s Unpacking libclass-c3-perl (0.35-2) ...
454s Selecting previously unselected package libmro-compat-perl.
454s Preparing to unpack .../158-libmro-compat-perl_0.15-2_all.deb ...
454s Unpacking libmro-compat-perl (0.15-2) ...
454s Selecting previously unselected package libdata-section-perl.
454s Preparing to unpack .../159-libdata-section-perl_0.200008-1_all.deb ...
454s Unpacking libdata-section-perl (0.200008-1) ...
454s Selecting previously unselected package libtext-template-perl.
454s Preparing to unpack .../160-libtext-template-perl_1.61-1_all.deb ...
454s Unpacking libtext-template-perl (1.61-1) ...
454s Selecting previously unselected package libsoftware-license-perl.
454s Preparing to unpack .../161-libsoftware-license-perl_0.104006-1_all.deb ...
454s Unpacking libsoftware-license-perl (0.104006-1) ...
455s Selecting previously unselected package libsoftware-licensemoreutils-perl.
455s Preparing to unpack .../162-libsoftware-licensemoreutils-perl_1.009-1_all.deb ...
455s Unpacking libsoftware-licensemoreutils-perl (1.009-1) ...
455s Selecting previously unselected package libsort-versions-perl.
455s Preparing to unpack .../163-libsort-versions-perl_1.62-3_all.deb ...
455s Unpacking libsort-versions-perl (1.62-3) ...
455s Selecting previously unselected package libtext-reform-perl.
455s Preparing to unpack .../164-libtext-reform-perl_1.20-5_all.deb ...
455s Unpacking libtext-reform-perl (1.20-5) ...
455s Selecting previously unselected package libtext-autoformat-perl.
455s Preparing to unpack .../165-libtext-autoformat-perl_1.750000-2_all.deb ...
455s Unpacking libtext-autoformat-perl (1.750000-2) ...
455s Selecting previously unselected package libtext-levenshtein-damerau-perl.
455s Preparing to unpack .../166-libtext-levenshtein-damerau-perl_0.41-3_all.deb ...
455s Unpacking libtext-levenshtein-damerau-perl (0.41-3) ...
455s Selecting previously unselected package libtoml-tiny-perl.
455s Preparing to unpack .../167-libtoml-tiny-perl_0.19-1_all.deb ...
455s Unpacking libtoml-tiny-perl (0.19-1) ...
455s Selecting previously unselected package libclass-inspector-perl.
455s Preparing to unpack .../168-libclass-inspector-perl_1.36-3_all.deb ...
455s Unpacking libclass-inspector-perl (1.36-3) ...
455s Selecting previously unselected package libfile-sharedir-perl.
455s Preparing to unpack .../169-libfile-sharedir-perl_1.118-3_all.deb ...
455s Unpacking libfile-sharedir-perl (1.118-3) ...
455s Selecting previously unselected package libindirect-perl.
455s Preparing to unpack .../170-libindirect-perl_0.39-2build5_amd64.deb ...
455s Unpacking libindirect-perl (0.39-2build5) ...
455s Selecting previously unselected package libxs-parse-keyword-perl.
455s Preparing to unpack .../171-libxs-parse-keyword-perl_0.48-2_amd64.deb ...
455s Unpacking libxs-parse-keyword-perl (0.48-2) ...
455s Selecting previously unselected package libxs-parse-sublike-perl:amd64.
455s Preparing to unpack .../172-libxs-parse-sublike-perl_0.37-1_amd64.deb ...
455s Unpacking libxs-parse-sublike-perl:amd64 (0.37-1) ...
455s Selecting previously unselected package libobject-pad-perl.
455s Preparing to unpack .../173-libobject-pad-perl_0.820-1_amd64.deb ...
455s Unpacking libobject-pad-perl (0.820-1) ...
455s Selecting previously unselected package libsyntax-keyword-try-perl.
455s Preparing to unpack .../174-libsyntax-keyword-try-perl_0.30-1_amd64.deb ...
455s Unpacking libsyntax-keyword-try-perl (0.30-1) ...
455s Selecting previously unselected package libio-interactive-perl.
455s Preparing to unpack .../175-libio-interactive-perl_1.026-1_all.deb ...
455s Unpacking libio-interactive-perl (1.026-1) ...
455s Selecting previously unselected package liblog-any-perl.
455s Preparing to unpack .../176-liblog-any-perl_1.717-1_all.deb ...
455s Unpacking liblog-any-perl (1.717-1) ...
455s Selecting previously unselected package liblog-any-adapter-screen-perl.
455s Preparing to unpack .../177-liblog-any-adapter-screen-perl_0.141-1_all.deb ...
455s Unpacking liblog-any-adapter-screen-perl (0.141-1) ...
455s Selecting previously unselected package libsub-exporter-progressive-perl.
455s Preparing to unpack .../178-libsub-exporter-progressive-perl_0.001013-3_all.deb ...
455s Unpacking libsub-exporter-progressive-perl (0.001013-3) ...
455s Selecting previously unselected package libvariable-magic-perl.
455s Preparing to unpack .../179-libvariable-magic-perl_0.64-1build1_amd64.deb ...
455s Unpacking libvariable-magic-perl (0.64-1build1) ...
455s Selecting previously unselected package libb-hooks-endofscope-perl.
455s Preparing to unpack .../180-libb-hooks-endofscope-perl_0.28-1_all.deb ...
455s Unpacking libb-hooks-endofscope-perl (0.28-1) ...
455s Selecting previously unselected package libsub-identify-perl.
455s Preparing to unpack .../181-libsub-identify-perl_0.14-3build4_amd64.deb ...
455s Unpacking libsub-identify-perl (0.14-3build4) ...
455s Selecting previously unselected package libsub-name-perl:amd64.
455s Preparing to unpack .../182-libsub-name-perl_0.28-1_amd64.deb ...
455s Unpacking libsub-name-perl:amd64 (0.28-1) ...
455s Selecting previously unselected package libnamespace-clean-perl.
455s Preparing to unpack .../183-libnamespace-clean-perl_0.27-2_all.deb ...
455s Unpacking libnamespace-clean-perl (0.27-2) ...
455s Selecting previously unselected package libnumber-compare-perl.
455s Preparing to unpack .../184-libnumber-compare-perl_0.03-3_all.deb ...
455s Unpacking libnumber-compare-perl (0.03-3) ...
455s Selecting previously unselected package libtext-glob-perl.
455s Preparing to unpack .../185-libtext-glob-perl_0.11-3_all.deb ...
455s Unpacking libtext-glob-perl (0.11-3) ...
455s Selecting previously unselected package libpath-iterator-rule-perl.
455s Preparing to unpack .../186-libpath-iterator-rule-perl_1.015-2_all.deb ...
455s Unpacking libpath-iterator-rule-perl (1.015-2) ...
455s Selecting previously unselected package libpod-parser-perl.
455s Preparing to unpack .../187-libpod-parser-perl_1.67-1_all.deb ...
455s Adding 'diversion of /usr/bin/podselect to /usr/bin/podselect.bundled by libpod-parser-perl'
455s Adding 'diversion of /usr/share/man/man1/podselect.1.gz to /usr/share/man/man1/podselect.bundled.1.gz by libpod-parser-perl'
455s Unpacking libpod-parser-perl (1.67-1) ...
455s Selecting previously unselected package libpod-constants-perl.
455s Preparing to unpack .../188-libpod-constants-perl_0.19-2_all.deb ...
455s Unpacking libpod-constants-perl (0.19-2) ...
455s Selecting previously unselected package libset-intspan-perl.
455s Preparing to unpack .../189-libset-intspan-perl_1.19-3_all.deb ...
455s Unpacking libset-intspan-perl (1.19-3) ...
455s Selecting previously unselected package libstring-copyright-perl.
455s Preparing to unpack .../190-libstring-copyright-perl_0.003014-1_all.deb ...
455s Unpacking libstring-copyright-perl (0.003014-1) ...
455s Selecting previously unselected package libstring-escape-perl.
455s Preparing to unpack .../191-libstring-escape-perl_2010.002-3_all.deb ...
455s Unpacking libstring-escape-perl (2010.002-3) ...
455s Selecting previously unselected package libregexp-pattern-license-perl.
455s Preparing to unpack .../192-libregexp-pattern-license-perl_3.11.2-1_all.deb ...
455s Unpacking libregexp-pattern-license-perl (3.11.2-1) ...
455s Selecting previously unselected package libregexp-pattern-perl.
455s Preparing to unpack .../193-libregexp-pattern-perl_0.2.14-2_all.deb ...
455s Unpacking libregexp-pattern-perl (0.2.14-2) ...
455s Selecting previously unselected package libstring-license-perl.
455s Preparing to unpack .../194-libstring-license-perl_0.0.11-1ubuntu1_all.deb ...
455s Unpacking libstring-license-perl (0.0.11-1ubuntu1) ...
455s Selecting previously unselected package licensecheck.
455s Preparing to unpack .../195-licensecheck_3.3.9-1ubuntu1_all.deb ...
455s Unpacking licensecheck (3.3.9-1ubuntu1) ...
455s Selecting previously unselected package diffstat.
455s Preparing to unpack .../196-diffstat_1.67-1_amd64.deb ...
455s Unpacking diffstat (1.67-1) ...
455s Selecting previously unselected package libberkeleydb-perl:amd64.
456s Preparing to unpack .../197-libberkeleydb-perl_0.66-1_amd64.deb ...
456s Unpacking libberkeleydb-perl:amd64 (0.66-1) ...
456s Selecting previously unselected package libclass-xsaccessor-perl.
456s Preparing to unpack .../198-libclass-xsaccessor-perl_1.19-4build6_amd64.deb ...
456s Unpacking libclass-xsaccessor-perl (1.19-4build6) ...
456s Selecting previously unselected package libconfig-tiny-perl.
456s Preparing to unpack .../199-libconfig-tiny-perl_2.30-1_all.deb ...
456s Unpacking libconfig-tiny-perl (2.30-1) ...
456s Selecting previously unselected package libconst-fast-perl.
456s Preparing to unpack .../200-libconst-fast-perl_0.014-2_all.deb ...
456s Unpacking libconst-fast-perl (0.014-2) ...
456s Selecting previously unselected package libcpanel-json-xs-perl:amd64.
456s Preparing to unpack .../201-libcpanel-json-xs-perl_4.39-1_amd64.deb ...
456s Unpacking libcpanel-json-xs-perl:amd64 (4.39-1) ...
456s Selecting previously unselected package libaliased-perl.
456s Preparing to unpack .../202-libaliased-perl_0.34-3_all.deb ...
456s Unpacking libaliased-perl (0.34-3) ...
456s Selecting previously unselected package libclass-data-inheritable-perl.
456s Preparing to unpack .../203-libclass-data-inheritable-perl_0.10-1_all.deb ...
456s Unpacking libclass-data-inheritable-perl (0.10-1) ...
456s Selecting previously unselected package libdevel-stacktrace-perl.
456s Preparing to unpack .../204-libdevel-stacktrace-perl_2.0500-1_all.deb ...
456s Unpacking libdevel-stacktrace-perl (2.0500-1) ...
456s Selecting previously unselected package libexception-class-perl.
456s Preparing to unpack .../205-libexception-class-perl_1.45-1_all.deb ...
456s Unpacking libexception-class-perl (1.45-1) ...
456s Selecting previously unselected package libiterator-perl.
456s Preparing to unpack .../206-libiterator-perl_0.03+ds1-2_all.deb ...
456s Unpacking libiterator-perl (0.03+ds1-2) ...
456s Selecting previously unselected package libiterator-util-perl.
456s Preparing to unpack .../207-libiterator-util-perl_0.02+ds1-2_all.deb ...
456s Unpacking libiterator-util-perl (0.02+ds1-2) ...
456s Selecting previously unselected package libdata-dpath-perl.
456s Preparing to unpack .../208-libdata-dpath-perl_0.60-1_all.deb ...
456s Unpacking libdata-dpath-perl (0.60-1) ...
456s Selecting previously unselected package libnet-domain-tld-perl.
456s Preparing to unpack .../209-libnet-domain-tld-perl_1.75-4_all.deb ...
456s Unpacking libnet-domain-tld-perl (1.75-4) ...
456s Selecting previously unselected package libdata-validate-domain-perl.
456s Preparing to unpack .../210-libdata-validate-domain-perl_0.15-1_all.deb ...
456s Unpacking libdata-validate-domain-perl (0.15-1) ...
456s Selecting previously unselected package libnet-ipv6addr-perl.
456s Preparing to unpack .../211-libnet-ipv6addr-perl_1.02-1_all.deb ...
456s Unpacking libnet-ipv6addr-perl (1.02-1) ...
456s Selecting previously unselected package libnet-netmask-perl.
456s Preparing to unpack .../212-libnet-netmask-perl_2.0002-2_all.deb ...
456s Unpacking libnet-netmask-perl (2.0002-2) ...
456s Selecting previously unselected package libnetaddr-ip-perl.
456s Preparing to unpack .../213-libnetaddr-ip-perl_4.079+dfsg-2build5_amd64.deb ...
456s Unpacking libnetaddr-ip-perl (4.079+dfsg-2build5) ...
456s Selecting previously unselected package libdata-validate-ip-perl.
456s Preparing to unpack .../214-libdata-validate-ip-perl_0.31-1_all.deb ...
456s Unpacking libdata-validate-ip-perl (0.31-1) ...
456s Selecting previously unselected package libdata-validate-uri-perl.
456s Preparing to unpack .../215-libdata-validate-uri-perl_0.07-3_all.deb ...
456s Unpacking libdata-validate-uri-perl (0.07-3) ...
456s Selecting previously unselected package libdevel-size-perl.
456s Preparing to unpack .../216-libdevel-size-perl_0.84-1build1_amd64.deb ...
456s Unpacking libdevel-size-perl (0.84-1build1) ...
456s Selecting previously unselected package libemail-address-xs-perl.
456s Preparing to unpack .../217-libemail-address-xs-perl_1.05-1build5_amd64.deb ...
456s Unpacking libemail-address-xs-perl (1.05-1build5) ...
456s Selecting previously unselected package libipc-system-simple-perl.
456s Preparing to unpack .../218-libipc-system-simple-perl_1.30-2_all.deb ...
456s Unpacking libipc-system-simple-perl (1.30-2) ...
456s Selecting previously unselected package libfile-basedir-perl.
456s Preparing to unpack .../219-libfile-basedir-perl_0.09-2_all.deb ...
456s Unpacking libfile-basedir-perl (0.09-2) ...
456s Selecting previously unselected package libfile-find-rule-perl.
456s Preparing to unpack .../220-libfile-find-rule-perl_0.34-3_all.deb ...
456s Unpacking libfile-find-rule-perl (0.34-3) ...
456s Selecting previously unselected package libio-string-perl.
456s Preparing to unpack .../221-libio-string-perl_1.08-4_all.deb ...
456s Unpacking libio-string-perl (1.08-4) ...
456s Selecting previously unselected package libfont-ttf-perl.
456s Preparing to unpack .../222-libfont-ttf-perl_1.06-2_all.deb ...
456s Unpacking libfont-ttf-perl (1.06-2) ...
456s Selecting previously unselected package libhtml-html5-entities-perl.
456s Preparing to unpack .../223-libhtml-html5-entities-perl_0.004-3_all.deb ...
456s Unpacking libhtml-html5-entities-perl (0.004-3) ...
456s Selecting previously unselected package libhtml-tokeparser-simple-perl.
456s Preparing to unpack .../224-libhtml-tokeparser-simple-perl_3.16-4_all.deb ...
456s Unpacking libhtml-tokeparser-simple-perl (3.16-4) ...
456s Selecting previously unselected package libipc-run3-perl.
456s Preparing to unpack .../225-libipc-run3-perl_0.049-1_all.deb ...
456s Unpacking libipc-run3-perl (0.049-1) ...
456s Selecting previously unselected package libjson-maybexs-perl.
456s Preparing to unpack .../226-libjson-maybexs-perl_1.004008-1_all.deb ...
456s Unpacking libjson-maybexs-perl (1.004008-1) ...
456s Selecting previously unselected package liblist-compare-perl.
456s Preparing to unpack .../227-liblist-compare-perl_0.55-2_all.deb ...
456s Unpacking liblist-compare-perl (0.55-2) ...
456s Selecting previously unselected package liblist-someutils-perl.
456s Preparing to unpack .../228-liblist-someutils-perl_0.59-1_all.deb ...
456s Unpacking liblist-someutils-perl (0.59-1) ...
456s Selecting previously unselected package liblist-utilsby-perl.
456s Preparing to unpack .../229-liblist-utilsby-perl_0.12-2_all.deb ...
456s Unpacking liblist-utilsby-perl (0.12-2) ...
456s Selecting previously unselected package libmldbm-perl.
456s Preparing to unpack .../230-libmldbm-perl_2.05-4_all.deb ...
456s Unpacking libmldbm-perl (2.05-4) ...
457s Selecting previously unselected package libclass-method-modifiers-perl.
457s Preparing to unpack .../231-libclass-method-modifiers-perl_2.15-1_all.deb ...
457s Unpacking libclass-method-modifiers-perl (2.15-1) ...
457s Selecting previously unselected package libimport-into-perl.
457s Preparing to unpack .../232-libimport-into-perl_1.002005-2_all.deb ...
457s Unpacking libimport-into-perl (1.002005-2) ...
457s Selecting previously unselected package librole-tiny-perl.
457s Preparing to unpack .../233-librole-tiny-perl_2.002004-1_all.deb ...
457s Unpacking librole-tiny-perl (2.002004-1) ...
457s Selecting previously unselected package libsub-quote-perl.
457s Preparing to unpack .../234-libsub-quote-perl_2.006008-1ubuntu1_all.deb ...
457s Unpacking libsub-quote-perl (2.006008-1ubuntu1) ...
457s Selecting previously unselected package libmoo-perl.
457s Preparing to unpack .../235-libmoo-perl_2.005005-1_all.deb ...
457s Unpacking libmoo-perl (2.005005-1) ...
457s Selecting previously unselected package libstrictures-perl.
457s Preparing to unpack .../236-libstrictures-perl_2.000006-1_all.deb ...
457s Unpacking libstrictures-perl (2.000006-1) ...
457s Selecting previously unselected package libmoox-aliases-perl.
457s Preparing to unpack .../237-libmoox-aliases-perl_0.001006-2_all.deb ...
457s Unpacking libmoox-aliases-perl (0.001006-2) ...
457s Selecting previously unselected package libperlio-gzip-perl.
457s Preparing to unpack .../238-libperlio-gzip-perl_0.20-1build5_amd64.deb ...
457s Unpacking libperlio-gzip-perl (0.20-1build5) ...
457s Selecting previously unselected package libperlio-utf8-strict-perl.
457s Preparing to unpack .../239-libperlio-utf8-strict-perl_0.010-1build4_amd64.deb ...
457s Unpacking libperlio-utf8-strict-perl (0.010-1build4) ...
457s Selecting previously unselected package libproc-processtable-perl:amd64.
457s Preparing to unpack .../240-libproc-processtable-perl_0.636-1build4_amd64.deb ...
457s Unpacking libproc-processtable-perl:amd64 (0.636-1build4) ...
457s Selecting previously unselected package libregexp-wildcards-perl.
457s Preparing to unpack .../241-libregexp-wildcards-perl_1.05-3_all.deb ...
457s Unpacking libregexp-wildcards-perl (1.05-3) ...
457s Selecting previously unselected package libsereal-decoder-perl.
457s Preparing to unpack .../242-libsereal-decoder-perl_5.004+ds-1build4_amd64.deb ...
457s Unpacking libsereal-decoder-perl (5.004+ds-1build4) ...
457s Selecting previously unselected package libsereal-encoder-perl.
457s Preparing to unpack .../243-libsereal-encoder-perl_5.004+ds-1build4_amd64.deb ...
457s Unpacking libsereal-encoder-perl (5.004+ds-1build4) ...
457s Selecting previously unselected package libterm-readkey-perl.
457s Preparing to unpack .../244-libterm-readkey-perl_2.38-2build5_amd64.deb ...
457s Unpacking libterm-readkey-perl (2.38-2build5) ...
457s Selecting previously unselected package libtext-levenshteinxs-perl.
457s Preparing to unpack .../245-libtext-levenshteinxs-perl_0.03-5build5_amd64.deb ...
457s Unpacking libtext-levenshteinxs-perl (0.03-5build5) ...
457s Selecting previously unselected package libmarkdown2:amd64.
457s Preparing to unpack .../246-libmarkdown2_2.2.7-2.1_amd64.deb ...
457s Unpacking libmarkdown2:amd64 (2.2.7-2.1) ...
457s Selecting previously unselected package libtext-markdown-discount-perl.
457s Preparing to unpack .../247-libtext-markdown-discount-perl_0.18-1_amd64.deb ...
457s Unpacking libtext-markdown-discount-perl (0.18-1) ...
457s Selecting previously unselected package libdata-messagepack-perl.
457s Preparing to unpack .../248-libdata-messagepack-perl_1.02-1build5_amd64.deb ...
457s Unpacking libdata-messagepack-perl (1.02-1build5) ...
457s Selecting previously unselected package libtext-xslate-perl:amd64.
457s Preparing to unpack .../249-libtext-xslate-perl_3.5.9-2build1_amd64.deb ...
457s Unpacking libtext-xslate-perl:amd64 (3.5.9-2build1) ...
457s Selecting previously unselected package libtime-duration-perl.
457s Preparing to unpack .../250-libtime-duration-perl_1.21-2_all.deb ...
457s Unpacking libtime-duration-perl (1.21-2) ...
457s Selecting previously unselected package libtime-moment-perl.
457s Preparing to unpack .../251-libtime-moment-perl_0.44-2build5_amd64.deb ...
457s Unpacking libtime-moment-perl (0.44-2build5) ...
457s Selecting previously unselected package libunicode-utf8-perl.
457s Preparing to unpack .../252-libunicode-utf8-perl_0.62-2build4_amd64.deb ...
457s Unpacking libunicode-utf8-perl (0.62-2build4) ...
457s Selecting previously unselected package libcgi-pm-perl.
457s Preparing to unpack .../253-libcgi-pm-perl_4.67-1_all.deb ...
457s Unpacking libcgi-pm-perl (4.67-1) ...
457s Selecting previously unselected package libhtml-form-perl.
457s Preparing to unpack .../254-libhtml-form-perl_6.12-1_all.deb ...
457s Unpacking libhtml-form-perl (6.12-1) ...
457s Selecting previously unselected package libwww-mechanize-perl.
457s Preparing to unpack .../255-libwww-mechanize-perl_2.19-1ubuntu1_all.deb ...
457s Unpacking libwww-mechanize-perl (2.19-1ubuntu1) ...
457s Selecting previously unselected package libxml-namespacesupport-perl.
457s Preparing to unpack .../256-libxml-namespacesupport-perl_1.12-2_all.deb ...
457s Unpacking libxml-namespacesupport-perl (1.12-2) ...
457s Selecting previously unselected package libxml-sax-base-perl.
457s Preparing to unpack .../257-libxml-sax-base-perl_1.09-3_all.deb ...
457s Unpacking libxml-sax-base-perl (1.09-3) ...
457s Selecting previously unselected package libxml-sax-perl.
457s Preparing to unpack .../258-libxml-sax-perl_1.02+dfsg-4_all.deb ...
457s Unpacking libxml-sax-perl (1.02+dfsg-4) ...
457s Selecting previously unselected package libxml-libxml-perl.
457s Preparing to unpack .../259-libxml-libxml-perl_2.0207+dfsg+really+2.0134-5build1_amd64.deb ...
457s Unpacking libxml-libxml-perl (2.0207+dfsg+really+2.0134-5build1) ...
457s Selecting previously unselected package lzip.
457s Preparing to unpack .../260-lzip_1.25-2_amd64.deb ...
457s Unpacking lzip (1.25-2) ...
457s Selecting previously unselected package lzop.
457s Preparing to unpack .../261-lzop_1.04-2build3_amd64.deb ...
457s Unpacking lzop (1.04-2build3) ...
457s Selecting previously unselected package patchutils.
457s Preparing to unpack .../262-patchutils_0.4.2-1build3_amd64.deb ...
457s Unpacking patchutils (0.4.2-1build3) ...
458s Selecting previously unselected package t1utils.
458s Preparing to unpack .../263-t1utils_1.41-4build3_amd64.deb ...
458s Unpacking t1utils (1.41-4build3) ...
458s Selecting previously unselected package unzip.
458s Preparing to unpack .../264-unzip_6.0-28ubuntu6_amd64.deb ...
458s Unpacking unzip (6.0-28ubuntu6) ...
458s Selecting previously unselected package lintian.
458s Preparing to unpack .../265-lintian_2.121.1+nmu1ubuntu2_all.deb ...
458s Unpacking lintian (2.121.1+nmu1ubuntu2) ...
458s Selecting previously unselected package libconfig-model-dpkg-perl.
458s Preparing to unpack .../266-libconfig-model-dpkg-perl_3.010_all.deb ...
458s Unpacking libconfig-model-dpkg-perl (3.010) ...
458s Selecting previously unselected package libconvert-binhex-perl.
458s Preparing to unpack .../267-libconvert-binhex-perl_1.125-3_all.deb ...
458s Unpacking libconvert-binhex-perl (1.125-3) ...
458s Selecting previously unselected package libnet-smtp-ssl-perl.
458s Preparing to unpack .../268-libnet-smtp-ssl-perl_1.04-2_all.deb ...
458s Unpacking libnet-smtp-ssl-perl (1.04-2) ...
458s Selecting previously unselected package libmailtools-perl.
458s Preparing to unpack .../269-libmailtools-perl_2.22-1_all.deb ...
458s Unpacking libmailtools-perl (2.22-1) ...
458s Selecting previously unselected package libmime-tools-perl.
458s Preparing to unpack .../270-libmime-tools-perl_5.515-1_all.deb ...
458s Unpacking libmime-tools-perl (5.515-1) ...
458s Selecting previously unselected package libsuitesparseconfig7:amd64.
458s Preparing to unpack .../271-libsuitesparseconfig7_1%3a7.8.3+dfsg-3_amd64.deb ...
458s Unpacking libsuitesparseconfig7:amd64 (1:7.8.3+dfsg-3) ...
458s Selecting previously unselected package libamd3:amd64.
458s Preparing to unpack .../272-libamd3_1%3a7.8.3+dfsg-3_amd64.deb ...
458s Unpacking libamd3:amd64 (1:7.8.3+dfsg-3) ...
458s Selecting previously unselected package libblas3:amd64.
458s Preparing to unpack .../273-libblas3_3.12.1-2_amd64.deb ...
458s Unpacking libblas3:amd64 (3.12.1-2) ...
458s Selecting previously unselected package libgfortran5:amd64.
458s Preparing to unpack .../274-libgfortran5_15-20250222-0ubuntu1_amd64.deb ...
458s Unpacking libgfortran5:amd64 (15-20250222-0ubuntu1) ...
458s Selecting previously unselected package liblapack3:amd64.
458s Preparing to unpack .../275-liblapack3_3.12.1-2_amd64.deb ...
458s Unpacking liblapack3:amd64 (3.12.1-2) ...
458s Selecting previously unselected package libarpack2t64:amd64.
458s Preparing to unpack .../276-libarpack2t64_3.9.1-4_amd64.deb ...
458s Unpacking libarpack2t64:amd64 (3.9.1-4) ...
458s Selecting previously unselected package libccolamd3:amd64.
458s Preparing to unpack .../277-libccolamd3_1%3a7.8.3+dfsg-3_amd64.deb ...
458s Unpacking libccolamd3:amd64 (1:7.8.3+dfsg-3) ...
458s Selecting previously unselected package libcamd3:amd64.
458s Preparing to unpack .../278-libcamd3_1%3a7.8.3+dfsg-3_amd64.deb ...
458s Unpacking libcamd3:amd64 (1:7.8.3+dfsg-3) ...
458s Selecting previously unselected package libcolamd3:amd64.
458s Preparing to unpack .../279-libcolamd3_1%3a7.8.3+dfsg-3_amd64.deb ...
458s Unpacking libcolamd3:amd64 (1:7.8.3+dfsg-3) ...
458s Selecting previously unselected package libcholmod5:amd64.
458s Preparing to unpack .../280-libcholmod5_1%3a7.8.3+dfsg-3_amd64.deb ...
458s Unpacking libcholmod5:amd64 (1:7.8.3+dfsg-3) ...
458s Selecting previously unselected package libcxsparse4:amd64.
458s Preparing to unpack .../281-libcxsparse4_1%3a7.8.3+dfsg-3_amd64.deb ...
458s Unpacking libcxsparse4:amd64 (1:7.8.3+dfsg-3) ...
458s Selecting previously unselected package libfftw3-double3:amd64.
458s Preparing to unpack .../282-libfftw3-double3_3.3.10-2fakesync1build1_amd64.deb ...
458s Unpacking libfftw3-double3:amd64 (3.3.10-2fakesync1build1) ...
458s Selecting previously unselected package libfftw3-single3:amd64.
459s Preparing to unpack .../283-libfftw3-single3_3.3.10-2fakesync1build1_amd64.deb ...
459s Unpacking libfftw3-single3:amd64 (3.3.10-2fakesync1build1) ...
459s Selecting previously unselected package libxfixes3:amd64.
459s Preparing to unpack .../284-libxfixes3_1%3a6.0.0-2build1_amd64.deb ...
459s Unpacking libxfixes3:amd64 (1:6.0.0-2build1) ...
459s Selecting previously unselected package libxcursor1:amd64.
459s Preparing to unpack .../285-libxcursor1_1%3a1.2.3-1_amd64.deb ...
459s Unpacking libxcursor1:amd64 (1:1.2.3-1) ...
459s Selecting previously unselected package libxft2:amd64.
459s Preparing to unpack .../286-libxft2_2.3.6-1build1_amd64.deb ...
459s Unpacking libxft2:amd64 (2.3.6-1build1) ...
459s Selecting previously unselected package libxinerama1:amd64.
459s Preparing to unpack .../287-libxinerama1_2%3a1.1.4-3build1_amd64.deb ...
459s Unpacking libxinerama1:amd64 (2:1.1.4-3build1) ...
459s Selecting previously unselected package libfltk1.3t64:amd64.
459s Preparing to unpack .../288-libfltk1.3t64_1.3.8-6.1build2_amd64.deb ...
459s Unpacking libfltk1.3t64:amd64 (1.3.8-6.1build2) ...
459s Selecting previously unselected package libglvnd0:amd64.
459s Preparing to unpack .../289-libglvnd0_1.7.0-1build1_amd64.deb ...
459s Unpacking libglvnd0:amd64 (1.7.0-1build1) ...
459s Selecting previously unselected package libx11-xcb1:amd64.
459s Preparing to unpack .../290-libx11-xcb1_2%3a1.8.10-2_amd64.deb ...
459s Unpacking libx11-xcb1:amd64 (2:1.8.10-2) ...
459s Selecting previously unselected package libxcb-dri3-0:amd64.
459s Preparing to unpack .../291-libxcb-dri3-0_1.17.0-2_amd64.deb ...
459s Unpacking libxcb-dri3-0:amd64 (1.17.0-2) ...
459s Selecting previously unselected package libxcb-glx0:amd64.
459s Preparing to unpack .../292-libxcb-glx0_1.17.0-2_amd64.deb ...
459s Unpacking libxcb-glx0:amd64 (1.17.0-2) ...
459s Selecting previously unselected package libxcb-present0:amd64.
459s Preparing to unpack .../293-libxcb-present0_1.17.0-2_amd64.deb ...
459s Unpacking libxcb-present0:amd64 (1.17.0-2) ...
459s Selecting previously unselected package libxcb-xfixes0:amd64.
459s Preparing to unpack .../294-libxcb-xfixes0_1.17.0-2_amd64.deb ...
459s Unpacking libxcb-xfixes0:amd64 (1.17.0-2) ...
459s Selecting previously unselected package libxxf86vm1:amd64.
459s Preparing to unpack .../295-libxxf86vm1_1%3a1.1.4-1build4_amd64.deb ...
459s Unpacking libxxf86vm1:amd64 (1:1.1.4-1build4) ...
459s Selecting previously unselected package libpciaccess0:amd64.
459s Preparing to unpack .../296-libpciaccess0_0.17-3build1_amd64.deb ...
459s Unpacking libpciaccess0:amd64 (0.17-3build1) ...
459s Selecting previously unselected package libdrm-intel1:amd64.
459s Preparing to unpack .../297-libdrm-intel1_2.4.123-1_amd64.deb ...
459s Unpacking libdrm-intel1:amd64 (2.4.123-1) ...
459s Selecting previously unselected package libxcb-randr0:amd64.
459s Preparing to unpack .../298-libxcb-randr0_1.17.0-2_amd64.deb ...
459s Unpacking libxcb-randr0:amd64 (1.17.0-2) ...
459s Selecting previously unselected package libxcb-sync1:amd64.
459s Preparing to unpack .../299-libxcb-sync1_1.17.0-2_amd64.deb ...
459s Unpacking libxcb-sync1:amd64 (1.17.0-2) ...
459s Selecting previously unselected package libxshmfence1:amd64.
459s Preparing to unpack .../300-libxshmfence1_1.3-1build5_amd64.deb ...
459s Unpacking libxshmfence1:amd64 (1.3-1build5) ...
459s Selecting previously unselected package mesa-libgallium:amd64.
459s Preparing to unpack .../301-mesa-libgallium_25.0.1-2ubuntu1_amd64.deb ...
459s Unpacking mesa-libgallium:amd64 (25.0.1-2ubuntu1) ...
459s Selecting previously unselected package libwayland-server0:amd64.
459s Preparing to unpack .../302-libwayland-server0_1.23.1-3_amd64.deb ...
459s Unpacking libwayland-server0:amd64 (1.23.1-3) ...
459s Selecting previously unselected package libgbm1:amd64.
459s Preparing to unpack .../303-libgbm1_25.0.1-2ubuntu1_amd64.deb ...
459s Unpacking libgbm1:amd64 (25.0.1-2ubuntu1) ...
459s Selecting previously unselected package libvulkan1:amd64.
459s Preparing to unpack .../304-libvulkan1_1.4.304.0-1_amd64.deb ...
459s Unpacking libvulkan1:amd64 (1.4.304.0-1) ...
459s Selecting previously unselected package libgl1-mesa-dri:amd64.
459s Preparing to unpack .../305-libgl1-mesa-dri_25.0.1-2ubuntu1_amd64.deb ...
459s Unpacking libgl1-mesa-dri:amd64 (25.0.1-2ubuntu1) ...
459s Selecting previously unselected package libglx-mesa0:amd64.
459s Preparing to unpack .../306-libglx-mesa0_25.0.1-2ubuntu1_amd64.deb ...
459s Unpacking libglx-mesa0:amd64 (25.0.1-2ubuntu1) ...
459s Selecting previously unselected package libglx0:amd64.
459s Preparing to unpack .../307-libglx0_1.7.0-1build1_amd64.deb ...
459s Unpacking libglx0:amd64 (1.7.0-1build1) ...
459s Selecting previously unselected package libgl1:amd64.
459s Preparing to unpack .../308-libgl1_1.7.0-1build1_amd64.deb ...
459s Unpacking libgl1:amd64 (1.7.0-1build1) ...
459s Selecting previously unselected package libfltk-gl1.3t64:amd64.
459s Preparing to unpack .../309-libfltk-gl1.3t64_1.3.8-6.1build2_amd64.deb ...
459s Unpacking libfltk-gl1.3t64:amd64 (1.3.8-6.1build2) ...
460s Selecting previously unselected package libgl2ps1.4.
460s Preparing to unpack .../310-libgl2ps1.4_1.4.2+dfsg1-2build1_amd64.deb ...
460s Unpacking libgl2ps1.4 (1.4.2+dfsg1-2build1) ...
460s Selecting previously unselected package libltdl7:amd64.
460s Preparing to unpack .../311-libltdl7_2.5.4-4_amd64.deb ...
460s Unpacking libltdl7:amd64 (2.5.4-4) ...
460s Selecting previously unselected package libglpk40:amd64.
460s Preparing to unpack .../312-libglpk40_5.0-1build2_amd64.deb ...
460s Unpacking libglpk40:amd64 (5.0-1build2) ...
460s Selecting previously unselected package libopengl0:amd64.
460s Preparing to unpack .../313-libopengl0_1.7.0-1build1_amd64.deb ...
460s Unpacking libopengl0:amd64 (1.7.0-1build1) ...
460s Selecting previously unselected package libglu1-mesa:amd64.
460s Preparing to unpack .../314-libglu1-mesa_9.0.2-1.1build1_amd64.deb ...
460s Unpacking libglu1-mesa:amd64 (9.0.2-1.1build1) ...
460s Selecting previously unselected package libhwy1t64:amd64.
460s Preparing to unpack .../315-libhwy1t64_1.2.0-3ubuntu3_amd64.deb ...
460s Unpacking libhwy1t64:amd64 (1.2.0-3ubuntu3) ...
460s Selecting previously unselected package liblcms2-2:amd64.
460s Preparing to unpack .../316-liblcms2-2_2.16-2_amd64.deb ...
460s Unpacking liblcms2-2:amd64 (2.16-2) ...
460s Selecting previously unselected package libjxl0.11:amd64.
460s Preparing to unpack .../317-libjxl0.11_0.11.1-1_amd64.deb ...
460s Unpacking libjxl0.11:amd64 (0.11.1-1) ...
460s Selecting previously unselected package libwmflite-0.2-7:amd64.
460s Preparing to unpack .../318-libwmflite-0.2-7_0.2.13-1.1build3_amd64.deb ...
460s Unpacking libwmflite-0.2-7:amd64 (0.2.13-1.1build3) ...
460s Selecting previously unselected package libgraphicsmagick-q16-3t64.
460s Preparing to unpack .../319-libgraphicsmagick-q16-3t64_1.4+really1.3.45+hg17689-1_amd64.deb ...
460s Unpacking libgraphicsmagick-q16-3t64 (1.4+really1.3.45+hg17689-1) ...
460s Selecting previously unselected package libgraphicsmagick++-q16-12t64.
460s Preparing to unpack .../320-libgraphicsmagick++-q16-12t64_1.4+really1.3.45+hg17689-1_amd64.deb ...
460s Unpacking libgraphicsmagick++-q16-12t64 (1.4+really1.3.45+hg17689-1) ...
460s Selecting previously unselected package libaec0:amd64.
460s Preparing to unpack .../321-libaec0_1.1.3-1_amd64.deb ...
460s Unpacking libaec0:amd64 (1.1.3-1) ...
460s Selecting previously unselected package libsz2:amd64.
460s Preparing to unpack .../322-libsz2_1.1.3-1_amd64.deb ...
460s Unpacking libsz2:amd64 (1.1.3-1) ...
460s Selecting previously unselected package libhdf5-310:amd64.
460s Preparing to unpack .../323-libhdf5-310_1.14.5+repack-3_amd64.deb ...
460s Unpacking libhdf5-310:amd64 (1.14.5+repack-3) ...
460s Selecting previously unselected package libasound2-data.
460s Preparing to unpack .../324-libasound2-data_1.2.13-1build1_all.deb ...
460s Unpacking libasound2-data (1.2.13-1build1) ...
460s Selecting previously unselected package libasound2t64:amd64.
460s Preparing to unpack .../325-libasound2t64_1.2.13-1build1_amd64.deb ...
460s Unpacking libasound2t64:amd64 (1.2.13-1build1) ...
460s Selecting previously unselected package libopus0:amd64.
460s Preparing to unpack .../326-libopus0_1.5.2-2_amd64.deb ...
460s Unpacking libopus0:amd64 (1.5.2-2) ...
460s Selecting previously unselected package libsamplerate0:amd64.
460s Preparing to unpack .../327-libsamplerate0_0.2.2-4build1_amd64.deb ...
460s Unpacking libsamplerate0:amd64 (0.2.2-4build1) ...
460s Selecting previously unselected package libjack-jackd2-0:amd64.
460s Preparing to unpack .../328-libjack-jackd2-0_1.9.22~dfsg-4_amd64.deb ...
460s Unpacking libjack-jackd2-0:amd64 (1.9.22~dfsg-4) ...
460s Selecting previously unselected package libportaudio2:amd64.
460s Preparing to unpack .../329-libportaudio2_19.6.0-1.2build3_amd64.deb ...
460s Unpacking libportaudio2:amd64 (19.6.0-1.2build3) ...
460s Selecting previously unselected package libqhull-r8.0:amd64.
460s Preparing to unpack .../330-libqhull-r8.0_2020.2-6build1_amd64.deb ...
460s Unpacking libqhull-r8.0:amd64 (2020.2-6build1) ...
460s Selecting previously unselected package libqrupdate1:amd64.
460s Preparing to unpack .../331-libqrupdate1_1.1.5-1_amd64.deb ...
460s Unpacking libqrupdate1:amd64 (1.1.5-1) ...
460s Selecting previously unselected package libqscintilla2-qt6-l10n.
460s Preparing to unpack .../332-libqscintilla2-qt6-l10n_2.14.1+dfsg-1build4_all.deb ...
460s Unpacking libqscintilla2-qt6-l10n (2.14.1+dfsg-1build4) ...
460s Selecting previously unselected package libb2-1:amd64.
460s Preparing to unpack .../333-libb2-1_0.98.1-1.1build1_amd64.deb ...
460s Unpacking libb2-1:amd64 (0.98.1-1.1build1) ...
460s Selecting previously unselected package libdouble-conversion3:amd64.
460s Preparing to unpack .../334-libdouble-conversion3_3.3.1-1_amd64.deb ...
460s Unpacking libdouble-conversion3:amd64 (3.3.1-1) ...
460s Selecting previously unselected package libpcre2-16-0:amd64.
460s Preparing to unpack .../335-libpcre2-16-0_10.45-1_amd64.deb ...
460s Unpacking libpcre2-16-0:amd64 (10.45-1) ...
460s Selecting previously unselected package libqt6core6t64:amd64.
460s Preparing to unpack .../336-libqt6core6t64_6.8.2+dfsg-5_amd64.deb ...
460s Unpacking libqt6core6t64:amd64 (6.8.2+dfsg-5) ...
460s Selecting previously unselected package libwayland-client0:amd64.
461s Preparing to unpack .../337-libwayland-client0_1.23.1-3_amd64.deb ...
461s Unpacking libwayland-client0:amd64 (1.23.1-3) ...
461s Selecting previously unselected package libegl-mesa0:amd64.
461s Preparing to unpack .../338-libegl-mesa0_25.0.1-2ubuntu1_amd64.deb ...
461s Unpacking libegl-mesa0:amd64 (25.0.1-2ubuntu1) ...
461s Selecting previously unselected package libegl1:amd64.
461s Preparing to unpack .../339-libegl1_1.7.0-1build1_amd64.deb ...
461s Unpacking libegl1:amd64 (1.7.0-1build1) ...
461s Selecting previously unselected package x11-common.
461s Preparing to unpack .../340-x11-common_1%3a7.7+23ubuntu3_all.deb ...
461s Unpacking x11-common (1:7.7+23ubuntu3) ...
461s Selecting previously unselected package libice6:amd64.
461s Preparing to unpack .../341-libice6_2%3a1.1.1-1_amd64.deb ...
461s Unpacking libice6:amd64 (2:1.1.1-1) ...
461s Selecting previously unselected package libmtdev1t64:amd64.
461s Preparing to unpack .../342-libmtdev1t64_1.1.7-1_amd64.deb ...
461s Unpacking libmtdev1t64:amd64 (1.1.7-1) ...
461s Selecting previously unselected package libwacom-common.
461s Preparing to unpack .../343-libwacom-common_2.14.0-1_all.deb ...
461s Unpacking libwacom-common (2.14.0-1) ...
461s Selecting previously unselected package libwacom9:amd64.
461s Preparing to unpack .../344-libwacom9_2.14.0-1_amd64.deb ...
461s Unpacking libwacom9:amd64 (2.14.0-1) ...
461s Selecting previously unselected package libinput-bin.
461s Preparing to unpack .../345-libinput-bin_1.27.1-1_amd64.deb ...
461s Unpacking libinput-bin (1.27.1-1) ...
461s Selecting previously unselected package libinput10:amd64.
461s Preparing to unpack .../346-libinput10_1.27.1-1_amd64.deb ...
461s Unpacking libinput10:amd64 (1.27.1-1) ...
461s Selecting previously unselected package libmd4c0:amd64.
461s Preparing to unpack .../347-libmd4c0_0.5.2-2_amd64.deb ...
461s Unpacking libmd4c0:amd64 (0.5.2-2) ...
461s Selecting previously unselected package libqt6dbus6:amd64.
461s Preparing to unpack .../348-libqt6dbus6_6.8.2+dfsg-5_amd64.deb ...
461s Unpacking libqt6dbus6:amd64 (6.8.2+dfsg-5) ...
461s Selecting previously unselected package libsm6:amd64.
461s Preparing to unpack .../349-libsm6_2%3a1.2.4-1_amd64.deb ...
461s Unpacking libsm6:amd64 (2:1.2.4-1) ...
461s Selecting previously unselected package libts0t64:amd64.
461s Preparing to unpack .../350-libts0t64_1.22-1.1build1_amd64.deb ...
461s Unpacking libts0t64:amd64 (1.22-1.1build1) ...
461s Selecting previously unselected package libxcb-util1:amd64.
461s Preparing to unpack .../351-libxcb-util1_0.4.1-1_amd64.deb ...
461s Unpacking libxcb-util1:amd64 (0.4.1-1) ...
461s Selecting previously unselected package libxcb-image0:amd64.
461s Preparing to unpack .../352-libxcb-image0_0.4.0-2build1_amd64.deb ...
461s Unpacking libxcb-image0:amd64 (0.4.0-2build1) ...
461s Selecting previously unselected package libxcb-render-util0:amd64.
461s Preparing to unpack .../353-libxcb-render-util0_0.3.10-1_amd64.deb ...
461s Unpacking libxcb-render-util0:amd64 (0.3.10-1) ...
461s Selecting previously unselected package libxcb-cursor0:amd64.
461s Preparing to unpack .../354-libxcb-cursor0_0.1.5-1_amd64.deb ...
461s Unpacking libxcb-cursor0:amd64 (0.1.5-1) ...
461s Selecting previously unselected package libxcb-icccm4:amd64.
461s Preparing to unpack .../355-libxcb-icccm4_0.4.2-1_amd64.deb ...
461s Unpacking libxcb-icccm4:amd64 (0.4.2-1) ...
461s Selecting previously unselected package libxcb-keysyms1:amd64.
461s Preparing to unpack .../356-libxcb-keysyms1_0.4.1-1_amd64.deb ...
461s Unpacking libxcb-keysyms1:amd64 (0.4.1-1) ...
461s Selecting previously unselected package libxcb-shape0:amd64.
461s Preparing to unpack .../357-libxcb-shape0_1.17.0-2_amd64.deb ...
461s Unpacking libxcb-shape0:amd64 (1.17.0-2) ...
461s Selecting previously unselected package libxcb-xinput0:amd64.
461s Preparing to unpack .../358-libxcb-xinput0_1.17.0-2_amd64.deb ...
461s Unpacking libxcb-xinput0:amd64 (1.17.0-2) ...
461s Selecting previously unselected package libxcb-xkb1:amd64.
461s Preparing to unpack .../359-libxcb-xkb1_1.17.0-2_amd64.deb ...
461s Unpacking libxcb-xkb1:amd64 (1.17.0-2) ...
461s Selecting previously unselected package libxkbcommon-x11-0:amd64.
461s Preparing to unpack .../360-libxkbcommon-x11-0_1.7.0-2_amd64.deb ...
461s Unpacking libxkbcommon-x11-0:amd64 (1.7.0-2) ...
461s Selecting previously unselected package libqt6gui6:amd64.
461s Preparing to unpack .../361-libqt6gui6_6.8.2+dfsg-5_amd64.deb ...
461s Unpacking libqt6gui6:amd64 (6.8.2+dfsg-5) ...
462s Selecting previously unselected package libavahi-common-data:amd64.
462s Preparing to unpack .../362-libavahi-common-data_0.8-16ubuntu1_amd64.deb ...
462s Unpacking libavahi-common-data:amd64 (0.8-16ubuntu1) ...
462s Selecting previously unselected package libavahi-common3:amd64.
462s Preparing to unpack .../363-libavahi-common3_0.8-16ubuntu1_amd64.deb ...
462s Unpacking libavahi-common3:amd64 (0.8-16ubuntu1) ...
462s Selecting previously unselected package libavahi-client3:amd64.
462s Preparing to unpack .../364-libavahi-client3_0.8-16ubuntu1_amd64.deb ...
462s Unpacking libavahi-client3:amd64 (0.8-16ubuntu1) ...
462s Selecting previously unselected package libcups2t64:amd64.
462s Preparing to unpack .../365-libcups2t64_2.4.11-0ubuntu2_amd64.deb ...
462s Unpacking libcups2t64:amd64 (2.4.11-0ubuntu2) ...
462s Selecting previously unselected package libqt6widgets6:amd64.
462s Preparing to unpack .../366-libqt6widgets6_6.8.2+dfsg-5_amd64.deb ...
462s Unpacking libqt6widgets6:amd64 (6.8.2+dfsg-5) ...
462s Selecting previously unselected package libqt6printsupport6:amd64.
462s Preparing to unpack .../367-libqt6printsupport6_6.8.2+dfsg-5_amd64.deb ...
462s Unpacking libqt6printsupport6:amd64 (6.8.2+dfsg-5) ...
462s Selecting previously unselected package libqscintilla2-qt6-15:amd64.
462s Preparing to unpack .../368-libqscintilla2-qt6-15_2.14.1+dfsg-1build4_amd64.deb ...
462s Unpacking libqscintilla2-qt6-15:amd64 (2.14.1+dfsg-1build4) ...
462s Selecting previously unselected package libqt6core5compat6:amd64.
462s Preparing to unpack .../369-libqt6core5compat6_6.8.2-3_amd64.deb ...
462s Unpacking libqt6core5compat6:amd64 (6.8.2-3) ...
462s Selecting previously unselected package libqt6sql6:amd64.
462s Preparing to unpack .../370-libqt6sql6_6.8.2+dfsg-5_amd64.deb ...
462s Unpacking libqt6sql6:amd64 (6.8.2+dfsg-5) ...
462s Selecting previously unselected package libqt6help6:amd64.
462s Preparing to unpack .../371-libqt6help6_6.8.2-3_amd64.deb ...
462s Unpacking libqt6help6:amd64 (6.8.2-3) ...
462s Selecting previously unselected package libduktape207:amd64.
462s Preparing to unpack .../372-libduktape207_2.7.0+tests-0ubuntu3_amd64.deb ...
462s Unpacking libduktape207:amd64 (2.7.0+tests-0ubuntu3) ...
462s Selecting previously unselected package libproxy1v5:amd64.
462s Preparing to unpack .../373-libproxy1v5_0.5.9-1_amd64.deb ...
462s Unpacking libproxy1v5:amd64 (0.5.9-1) ...
462s Selecting previously unselected package libqt6network6:amd64.
462s Preparing to unpack .../374-libqt6network6_6.8.2+dfsg-5_amd64.deb ...
462s Unpacking libqt6network6:amd64 (6.8.2+dfsg-5) ...
462s Selecting previously unselected package libqt6opengl6:amd64.
462s Preparing to unpack .../375-libqt6opengl6_6.8.2+dfsg-5_amd64.deb ...
462s Unpacking libqt6opengl6:amd64 (6.8.2+dfsg-5) ...
462s Selecting previously unselected package libqt6openglwidgets6:amd64.
462s Preparing to unpack .../376-libqt6openglwidgets6_6.8.2+dfsg-5_amd64.deb ...
462s Unpacking libqt6openglwidgets6:amd64 (6.8.2+dfsg-5) ...
462s Selecting previously unselected package libqt6xml6:amd64.
462s Preparing to unpack .../377-libqt6xml6_6.8.2+dfsg-5_amd64.deb ...
462s Unpacking libqt6xml6:amd64 (6.8.2+dfsg-5) ...
462s Selecting previously unselected package libogg0:amd64.
462s Preparing to unpack .../378-libogg0_1.3.5-3build1_amd64.deb ...
462s Unpacking libogg0:amd64 (1.3.5-3build1) ...
462s Selecting previously unselected package libflac12t64:amd64.
462s Preparing to unpack .../379-libflac12t64_1.4.3+ds-4_amd64.deb ...
462s Unpacking libflac12t64:amd64 (1.4.3+ds-4) ...
462s Selecting previously unselected package libmp3lame0:amd64.
462s Preparing to unpack .../380-libmp3lame0_3.100-6build1_amd64.deb ...
462s Unpacking libmp3lame0:amd64 (3.100-6build1) ...
462s Selecting previously unselected package libmpg123-0t64:amd64.
462s Preparing to unpack .../381-libmpg123-0t64_1.32.10-1_amd64.deb ...
462s Unpacking libmpg123-0t64:amd64 (1.32.10-1) ...
462s Selecting previously unselected package libvorbis0a:amd64.
462s Preparing to unpack .../382-libvorbis0a_1.3.7-2_amd64.deb ...
462s Unpacking libvorbis0a:amd64 (1.3.7-2) ...
462s Selecting previously unselected package libvorbisenc2:amd64.
462s Preparing to unpack .../383-libvorbisenc2_1.3.7-2_amd64.deb ...
462s Unpacking libvorbisenc2:amd64 (1.3.7-2) ...
462s Selecting previously unselected package libsndfile1:amd64.
462s Preparing to unpack .../384-libsndfile1_1.2.2-2_amd64.deb ...
462s Unpacking libsndfile1:amd64 (1.2.2-2) ...
462s Selecting previously unselected package libspqr4:amd64.
462s Preparing to unpack .../385-libspqr4_1%3a7.8.3+dfsg-3_amd64.deb ...
462s Unpacking libspqr4:amd64 (1:7.8.3+dfsg-3) ...
462s Selecting previously unselected package libumfpack6:amd64.
462s Preparing to unpack .../386-libumfpack6_1%3a7.8.3+dfsg-3_amd64.deb ...
462s Unpacking libumfpack6:amd64 (1:7.8.3+dfsg-3) ...
462s Selecting previously unselected package libtext-unidecode-perl.
462s Preparing to unpack .../387-libtext-unidecode-perl_1.30-3_all.deb ...
462s Unpacking libtext-unidecode-perl (1.30-3) ...
462s Selecting previously unselected package texinfo-lib.
463s Preparing to unpack .../388-texinfo-lib_7.1.1-1_amd64.deb ...
463s Unpacking texinfo-lib (7.1.1-1) ...
463s Selecting previously unselected package tex-common.
463s Preparing to unpack .../389-tex-common_6.19_all.deb ...
463s Unpacking tex-common (6.19) ...
463s Selecting previously unselected package texinfo.
463s Preparing to unpack .../390-texinfo_7.1.1-1_all.deb ...
463s Unpacking texinfo (7.1.1-1) ...
463s Selecting previously unselected package octave-common.
463s Preparing to unpack .../391-octave-common_9.4.0-1_all.deb ...
463s Unpacking octave-common (9.4.0-1) ...
463s Selecting previously unselected package octave.
463s Preparing to unpack .../392-octave_9.4.0-1_amd64.deb ...
463s Unpacking octave (9.4.0-1) ...
463s Selecting previously unselected package libncurses-dev:amd64.
463s Preparing to unpack .../393-libncurses-dev_6.5+20250216-2_amd64.deb ...
463s Unpacking libncurses-dev:amd64 (6.5+20250216-2) ...
463s Selecting previously unselected package libreadline-dev:amd64.
463s Preparing to unpack .../394-libreadline-dev_8.2-6_amd64.deb ...
463s Unpacking libreadline-dev:amd64 (8.2-6) ...
463s Selecting previously unselected package libhdf5-fortran-310:amd64.
463s Preparing to unpack .../395-libhdf5-fortran-310_1.14.5+repack-3_amd64.deb ...
463s Unpacking libhdf5-fortran-310:amd64 (1.14.5+repack-3) ...
463s Selecting previously unselected package libhdf5-hl-310:amd64.
463s Preparing to unpack .../396-libhdf5-hl-310_1.14.5+repack-3_amd64.deb ...
463s Unpacking libhdf5-hl-310:amd64 (1.14.5+repack-3) ...
463s Selecting previously unselected package libhdf5-hl-fortran-310:amd64.
463s Preparing to unpack .../397-libhdf5-hl-fortran-310_1.14.5+repack-3_amd64.deb ...
463s Unpacking libhdf5-hl-fortran-310:amd64 (1.14.5+repack-3) ...
463s Selecting previously unselected package libhdf5-cpp-310:amd64.
463s Preparing to unpack .../398-libhdf5-cpp-310_1.14.5+repack-3_amd64.deb ...
463s Unpacking libhdf5-cpp-310:amd64 (1.14.5+repack-3) ...
463s Selecting previously unselected package libhdf5-hl-cpp-310:amd64.
463s Preparing to unpack .../399-libhdf5-hl-cpp-310_1.14.5+repack-3_amd64.deb ...
463s Unpacking libhdf5-hl-cpp-310:amd64 (1.14.5+repack-3) ...
463s Selecting previously unselected package zlib1g-dev:amd64.
463s Preparing to unpack .../400-zlib1g-dev_1%3a1.3.dfsg+really1.3.1-1ubuntu1_amd64.deb ...
463s Unpacking zlib1g-dev:amd64 (1:1.3.dfsg+really1.3.1-1ubuntu1) ...
463s Selecting previously unselected package libjpeg-turbo8-dev:amd64.
463s Preparing to unpack .../401-libjpeg-turbo8-dev_2.1.5-3ubuntu2_amd64.deb ...
463s Unpacking libjpeg-turbo8-dev:amd64 (2.1.5-3ubuntu2) ...
463s Selecting previously unselected package libjpeg8-dev:amd64.
463s Preparing to unpack .../402-libjpeg8-dev_8c-2ubuntu11_amd64.deb ...
463s Unpacking libjpeg8-dev:amd64 (8c-2ubuntu11) ...
463s Selecting previously unselected package libjpeg-dev:amd64.
463s Preparing to unpack .../403-libjpeg-dev_8c-2ubuntu11_amd64.deb ...
463s Unpacking libjpeg-dev:amd64 (8c-2ubuntu11) ...
463s Selecting previously unselected package libaec-dev:amd64.
463s Preparing to unpack .../404-libaec-dev_1.1.3-1_amd64.deb ...
463s Unpacking libaec-dev:amd64 (1.1.3-1) ...
463s Selecting previously unselected package libbrotli-dev:amd64.
463s Preparing to unpack .../405-libbrotli-dev_1.1.0-2build4_amd64.deb ...
463s Unpacking libbrotli-dev:amd64 (1.1.0-2build4) ...
464s Selecting previously unselected package libidn2-dev:amd64.
464s Preparing to unpack .../406-libidn2-dev_2.3.7-2build2_amd64.deb ...
464s Unpacking libidn2-dev:amd64 (2.3.7-2build2) ...
464s Selecting previously unselected package comerr-dev:amd64.
464s Preparing to unpack .../407-comerr-dev_2.1-1.47.2-1ubuntu1_amd64.deb ...
464s Unpacking comerr-dev:amd64 (2.1-1.47.2-1ubuntu1) ...
464s Selecting previously unselected package libgssrpc4t64:amd64.
464s Preparing to unpack .../408-libgssrpc4t64_1.21.3-4ubuntu2_amd64.deb ...
464s Unpacking libgssrpc4t64:amd64 (1.21.3-4ubuntu2) ...
464s Selecting previously unselected package libkadm5clnt-mit12:amd64.
464s Preparing to unpack .../409-libkadm5clnt-mit12_1.21.3-4ubuntu2_amd64.deb ...
464s Unpacking libkadm5clnt-mit12:amd64 (1.21.3-4ubuntu2) ...
464s Selecting previously unselected package libkdb5-10t64:amd64.
464s Preparing to unpack .../410-libkdb5-10t64_1.21.3-4ubuntu2_amd64.deb ...
464s Unpacking libkdb5-10t64:amd64 (1.21.3-4ubuntu2) ...
464s Selecting previously unselected package libkadm5srv-mit12:amd64.
464s Preparing to unpack .../411-libkadm5srv-mit12_1.21.3-4ubuntu2_amd64.deb ...
464s Unpacking libkadm5srv-mit12:amd64 (1.21.3-4ubuntu2) ...
464s Selecting previously unselected package krb5-multidev:amd64.
464s Preparing to unpack .../412-krb5-multidev_1.21.3-4ubuntu2_amd64.deb ...
464s Unpacking krb5-multidev:amd64 (1.21.3-4ubuntu2) ...
464s Selecting previously unselected package libkrb5-dev:amd64.
464s Preparing to unpack .../413-libkrb5-dev_1.21.3-4ubuntu2_amd64.deb ...
464s Unpacking libkrb5-dev:amd64 (1.21.3-4ubuntu2) ...
464s Selecting previously unselected package libldap-dev:amd64.
464s Preparing to unpack .../414-libldap-dev_2.6.9+dfsg-1~exp2ubuntu1_amd64.deb ...
464s Unpacking libldap-dev:amd64 (2.6.9+dfsg-1~exp2ubuntu1) ...
464s Selecting previously unselected package libpkgconf3:amd64.
464s Preparing to unpack .../415-libpkgconf3_1.8.1-4_amd64.deb ...
464s Unpacking libpkgconf3:amd64 (1.8.1-4) ...
464s Selecting previously unselected package pkgconf-bin.
464s Preparing to unpack .../416-pkgconf-bin_1.8.1-4_amd64.deb ...
464s Unpacking pkgconf-bin (1.8.1-4) ...
464s Selecting previously unselected package pkgconf:amd64.
464s Preparing to unpack .../417-pkgconf_1.8.1-4_amd64.deb ...
464s Unpacking pkgconf:amd64 (1.8.1-4) ...
464s Selecting previously unselected package libnghttp2-dev:amd64.
464s Preparing to unpack .../418-libnghttp2-dev_1.64.0-1_amd64.deb ...
464s Unpacking libnghttp2-dev:amd64 (1.64.0-1) ...
464s Selecting previously unselected package libpsl-dev:amd64.
464s Preparing to unpack .../419-libpsl-dev_0.21.2-1.1build1_amd64.deb ...
464s Unpacking libpsl-dev:amd64 (0.21.2-1.1build1) ...
464s Selecting previously unselected package libgmpxx4ldbl:amd64.
464s Preparing to unpack .../420-libgmpxx4ldbl_2%3a6.3.0+dfsg-3ubuntu1_amd64.deb ...
464s Unpacking libgmpxx4ldbl:amd64 (2:6.3.0+dfsg-3ubuntu1) ...
464s Selecting previously unselected package libgmp-dev:amd64.
464s Preparing to unpack .../421-libgmp-dev_2%3a6.3.0+dfsg-3ubuntu1_amd64.deb ...
464s Unpacking libgmp-dev:amd64 (2:6.3.0+dfsg-3ubuntu1) ...
464s Selecting previously unselected package libevent-2.1-7t64:amd64.
464s Preparing to unpack .../422-libevent-2.1-7t64_2.1.12-stable-10_amd64.deb ...
464s Unpacking libevent-2.1-7t64:amd64 (2.1.12-stable-10) ...
464s Selecting previously unselected package libunbound8:amd64.
464s Preparing to unpack .../423-libunbound8_1.22.0-1ubuntu1_amd64.deb ...
464s Unpacking libunbound8:amd64 (1.22.0-1ubuntu1) ...
464s Selecting previously unselected package libgnutls-dane0t64:amd64.
464s Preparing to unpack .../424-libgnutls-dane0t64_3.8.9-2ubuntu2_amd64.deb ...
464s Unpacking libgnutls-dane0t64:amd64 (3.8.9-2ubuntu2) ...
464s Selecting previously unselected package libgnutls-openssl27t64:amd64.
464s Preparing to unpack .../425-libgnutls-openssl27t64_3.8.9-2ubuntu2_amd64.deb ...
464s Unpacking libgnutls-openssl27t64:amd64 (3.8.9-2ubuntu2) ...
464s Selecting previously unselected package libp11-kit-dev:amd64.
464s Preparing to unpack .../426-libp11-kit-dev_0.25.5-2ubuntu3_amd64.deb ...
464s Unpacking libp11-kit-dev:amd64 (0.25.5-2ubuntu3) ...
464s Selecting previously unselected package libtasn1-6-dev:amd64.
464s Preparing to unpack .../427-libtasn1-6-dev_4.20.0-2_amd64.deb ...
464s Unpacking libtasn1-6-dev:amd64 (4.20.0-2) ...
464s Selecting previously unselected package nettle-dev:amd64.
464s Preparing to unpack .../428-nettle-dev_3.10.1-1_amd64.deb ...
464s Unpacking nettle-dev:amd64 (3.10.1-1) ...
464s Selecting previously unselected package libgnutls28-dev:amd64.
464s Preparing to unpack .../429-libgnutls28-dev_3.8.9-2ubuntu2_amd64.deb ...
464s Unpacking libgnutls28-dev:amd64 (3.8.9-2ubuntu2) ...
464s Selecting previously unselected package librtmp-dev:amd64.
464s Preparing to unpack .../430-librtmp-dev_2.4+20151223.gitfa8646d.1-2build7_amd64.deb ...
464s Unpacking librtmp-dev:amd64 (2.4+20151223.gitfa8646d.1-2build7) ...
464s Selecting previously unselected package libssl-dev:amd64.
464s Preparing to unpack .../431-libssl-dev_3.4.1-1ubuntu1_amd64.deb ...
464s Unpacking libssl-dev:amd64 (3.4.1-1ubuntu1) ...
464s Selecting previously unselected package libssh2-1-dev:amd64.
465s Preparing to unpack .../432-libssh2-1-dev_1.11.1-1_amd64.deb ...
465s Unpacking libssh2-1-dev:amd64 (1.11.1-1) ...
465s Selecting previously unselected package libzstd-dev:amd64.
465s Preparing to unpack .../433-libzstd-dev_1.5.6+dfsg-2_amd64.deb ...
465s Unpacking libzstd-dev:amd64 (1.5.6+dfsg-2) ...
465s Selecting previously unselected package libcurl4-openssl-dev:amd64.
465s Preparing to unpack .../434-libcurl4-openssl-dev_8.12.1-3ubuntu1_amd64.deb ...
465s Unpacking libcurl4-openssl-dev:amd64 (8.12.1-3ubuntu1) ...
465s Selecting previously unselected package hdf5-helpers.
465s Preparing to unpack .../435-hdf5-helpers_1.14.5+repack-3_amd64.deb ...
465s Unpacking hdf5-helpers (1.14.5+repack-3) ...
465s Selecting previously unselected package libhdf5-dev.
465s Preparing to unpack .../436-libhdf5-dev_1.14.5+repack-3_amd64.deb ...
465s Unpacking libhdf5-dev (1.14.5+repack-3) ...
465s Selecting previously unselected package xorg-sgml-doctools.
465s Preparing to unpack .../437-xorg-sgml-doctools_1%3a1.11-1.1_all.deb ...
465s Unpacking xorg-sgml-doctools (1:1.11-1.1) ...
465s Selecting previously unselected package x11proto-dev.
465s Preparing to unpack .../438-x11proto-dev_2024.1-1_all.deb ...
465s Unpacking x11proto-dev (2024.1-1) ...
465s Selecting previously unselected package libxau-dev:amd64.
465s Preparing to unpack .../439-libxau-dev_1%3a1.0.11-1_amd64.deb ...
465s Unpacking libxau-dev:amd64 (1:1.0.11-1) ...
465s Selecting previously unselected package libxdmcp-dev:amd64.
465s Preparing to unpack .../440-libxdmcp-dev_1%3a1.1.5-1_amd64.deb ...
465s Unpacking libxdmcp-dev:amd64 (1:1.1.5-1) ...
465s Selecting previously unselected package xtrans-dev.
465s Preparing to unpack .../441-xtrans-dev_1.4.0-1_all.deb ...
465s Unpacking xtrans-dev (1.4.0-1) ...
465s Selecting previously unselected package libxcb1-dev:amd64.
465s Preparing to unpack .../442-libxcb1-dev_1.17.0-2_amd64.deb ...
465s Unpacking libxcb1-dev:amd64 (1.17.0-2) ...
465s Selecting previously unselected package libx11-dev:amd64.
465s Preparing to unpack .../443-libx11-dev_2%3a1.8.10-2_amd64.deb ...
465s Unpacking libx11-dev:amd64 (2:1.8.10-2) ...
465s Selecting previously unselected package libglx-dev:amd64.
465s Preparing to unpack .../444-libglx-dev_1.7.0-1build1_amd64.deb ...
465s Unpacking libglx-dev:amd64 (1.7.0-1build1) ...
465s Selecting previously unselected package libgl-dev:amd64.
465s Preparing to unpack .../445-libgl-dev_1.7.0-1build1_amd64.deb ...
465s Unpacking libgl-dev:amd64 (1.7.0-1build1) ...
465s Selecting previously unselected package libblas-dev:amd64.
465s Preparing to unpack .../446-libblas-dev_3.12.1-2_amd64.deb ...
465s Unpacking libblas-dev:amd64 (3.12.1-2) ...
465s Selecting previously unselected package liblapack-dev:amd64.
465s Preparing to unpack .../447-liblapack-dev_3.12.1-2_amd64.deb ...
465s Unpacking liblapack-dev:amd64 (3.12.1-2) ...
465s Selecting previously unselected package libfftw3-long3:amd64.
465s Preparing to unpack .../448-libfftw3-long3_3.3.10-2fakesync1build1_amd64.deb ...
465s Unpacking libfftw3-long3:amd64 (3.3.10-2fakesync1build1) ...
465s Selecting previously unselected package libfftw3-quad3:amd64.
465s Preparing to unpack .../449-libfftw3-quad3_3.3.10-2fakesync1build1_amd64.deb ...
465s Unpacking libfftw3-quad3:amd64 (3.3.10-2fakesync1build1) ...
465s Selecting previously unselected package libfftw3-bin.
465s Preparing to unpack .../450-libfftw3-bin_3.3.10-2fakesync1build1_amd64.deb ...
465s Unpacking libfftw3-bin (3.3.10-2fakesync1build1) ...
465s Selecting previously unselected package libfftw3-dev:amd64.
465s Preparing to unpack .../451-libfftw3-dev_3.3.10-2fakesync1build1_amd64.deb ...
465s Unpacking libfftw3-dev:amd64 (3.3.10-2fakesync1build1) ...
465s Selecting previously unselected package libgfortran-14-dev:amd64.
465s Preparing to unpack .../452-libgfortran-14-dev_14.2.0-17ubuntu3_amd64.deb ...
465s Unpacking libgfortran-14-dev:amd64 (14.2.0-17ubuntu3) ...
466s Selecting previously unselected package gfortran-14-x86-64-linux-gnu.
466s Preparing to unpack .../453-gfortran-14-x86-64-linux-gnu_14.2.0-17ubuntu3_amd64.deb ...
466s Unpacking gfortran-14-x86-64-linux-gnu (14.2.0-17ubuntu3) ...
466s Selecting previously unselected package gfortran-14.
466s Preparing to unpack .../454-gfortran-14_14.2.0-17ubuntu3_amd64.deb ...
466s Unpacking gfortran-14 (14.2.0-17ubuntu3) ...
466s Selecting previously unselected package gfortran-x86-64-linux-gnu.
466s Preparing to unpack .../455-gfortran-x86-64-linux-gnu_4%3a14.2.0-1ubuntu1_amd64.deb ...
466s Unpacking gfortran-x86-64-linux-gnu (4:14.2.0-1ubuntu1) ...
466s Selecting previously unselected package gfortran.
466s Preparing to unpack .../456-gfortran_4%3a14.2.0-1ubuntu1_amd64.deb ...
466s Unpacking gfortran (4:14.2.0-1ubuntu1) ...
466s Selecting previously unselected package libstdc++-14-dev:amd64.
466s Preparing to unpack .../457-libstdc++-14-dev_14.2.0-17ubuntu3_amd64.deb ...
466s Unpacking libstdc++-14-dev:amd64 (14.2.0-17ubuntu3) ...
466s Selecting previously unselected package g++-14-x86-64-linux-gnu.
466s Preparing to unpack .../458-g++-14-x86-64-linux-gnu_14.2.0-17ubuntu3_amd64.deb ...
466s Unpacking g++-14-x86-64-linux-gnu (14.2.0-17ubuntu3) ...
466s Selecting previously unselected package g++-14.
466s Preparing to unpack .../459-g++-14_14.2.0-17ubuntu3_amd64.deb ...
466s Unpacking g++-14 (14.2.0-17ubuntu3) ...
466s Selecting previously unselected package g++-x86-64-linux-gnu.
466s Preparing to unpack .../460-g++-x86-64-linux-gnu_4%3a14.2.0-1ubuntu1_amd64.deb ...
466s Unpacking g++-x86-64-linux-gnu (4:14.2.0-1ubuntu1) ...
466s Selecting previously unselected package g++.
466s Preparing to unpack .../461-g++_4%3a14.2.0-1ubuntu1_amd64.deb ...
466s Unpacking g++ (4:14.2.0-1ubuntu1) ...
466s Selecting previously unselected package octave-dev.
466s Preparing to unpack .../462-octave-dev_9.4.0-1_amd64.deb ...
466s Unpacking octave-dev (9.4.0-1) ...
466s Selecting previously unselected package dh-octave.
466s Preparing to unpack .../463-dh-octave_1.8.0_all.deb ...
466s Unpacking dh-octave (1.8.0) ...
466s Selecting previously unselected package libfontenc1:amd64.
466s Preparing to unpack .../464-libfontenc1_1%3a1.1.8-1build1_amd64.deb ...
466s Unpacking libfontenc1:amd64 (1:1.1.8-1build1) ...
466s Selecting previously unselected package libxt6t64:amd64.
466s Preparing to unpack .../465-libxt6t64_1%3a1.2.1-1.2build1_amd64.deb ...
466s Unpacking libxt6t64:amd64 (1:1.2.1-1.2build1) ...
466s Selecting previously unselected package libxmu6:amd64.
466s Preparing to unpack .../466-libxmu6_2%3a1.1.3-3build2_amd64.deb ...
466s Unpacking libxmu6:amd64 (2:1.1.3-3build2) ...
466s Selecting previously unselected package libxaw7:amd64.
467s Preparing to unpack .../467-libxaw7_2%3a1.0.16-1_amd64.deb ...
467s Unpacking libxaw7:amd64 (2:1.0.16-1) ...
467s Selecting previously unselected package libxfont2:amd64.
467s Preparing to unpack .../468-libxfont2_1%3a2.0.6-1build1_amd64.deb ...
467s Unpacking libxfont2:amd64 (1:2.0.6-1build1) ...
467s Selecting previously unselected package libxkbfile1:amd64.
467s Preparing to unpack .../469-libxkbfile1_1%3a1.1.0-1build4_amd64.deb ...
467s Unpacking libxkbfile1:amd64 (1:1.1.0-1build4) ...
467s Selecting previously unselected package libxrandr2:amd64.
467s Preparing to unpack .../470-libxrandr2_2%3a1.5.4-1_amd64.deb ...
467s Unpacking libxrandr2:amd64 (2:1.5.4-1) ...
467s Selecting previously unselected package octave-io.
467s Preparing to unpack .../471-octave-io_2.6.4-3build2_amd64.deb ...
467s Unpacking octave-io (2.6.4-3build2) ...
467s Selecting previously unselected package octave-statistics-common.
467s Preparing to unpack .../472-octave-statistics-common_1.7.3-2_all.deb ...
467s Unpacking octave-statistics-common (1.7.3-2) ...
467s Selecting previously unselected package octave-statistics.
467s Preparing to unpack .../473-octave-statistics_1.7.3-2_amd64.deb ...
467s Unpacking octave-statistics (1.7.3-2) ...
467s Selecting previously unselected package x11-xkb-utils.
467s Preparing to unpack .../474-x11-xkb-utils_7.7+9_amd64.deb ...
467s Unpacking x11-xkb-utils (7.7+9) ...
467s Selecting previously unselected package xserver-common.
467s Preparing to unpack .../475-xserver-common_2%3a21.1.16-1ubuntu1_all.deb ...
467s Unpacking xserver-common (2:21.1.16-1ubuntu1) ...
467s Selecting previously unselected package xvfb.
467s Preparing to unpack .../476-xvfb_2%3a21.1.16-1ubuntu1_amd64.deb ...
467s Unpacking xvfb (2:21.1.16-1ubuntu1) ...
467s Setting up libapt-pkg-perl (0.1.41build1) ...
467s Setting up libhwy1t64:amd64 (1.2.0-3ubuntu3) ...
467s Setting up libmodule-pluggable-perl (5.2-5) ...
467s Setting up libgraphite2-3:amd64 (1.3.14-2ubuntu1) ...
467s Setting up libstring-escape-perl (2010.002-3) ...
467s Setting up libgnutls-openssl27t64:amd64 (3.8.9-2ubuntu2) ...
467s Setting up libxcb-dri3-0:amd64 (1.17.0-2) ...
467s Setting up liblcms2-2:amd64 (2.16-2) ...
467s Setting up libberkeleydb-perl:amd64 (0.66-1) ...
467s Setting up libpixman-1-0:amd64 (0.44.0-3) ...
467s Setting up libsharpyuv0:amd64 (1.5.0-0.1) ...
467s Setting up libwayland-server0:amd64 (1.23.1-3) ...
467s Setting up libaom3:amd64 (3.12.0-1) ...
467s Setting up libx11-xcb1:amd64 (2:1.8.10-2) ...
467s Setting up libpciaccess0:amd64 (0.17-3build1) ...
467s Setting up libfile-which-perl (1.27-2) ...
467s Setting up libdouble-conversion3:amd64 (3.3.1-1) ...
467s Setting up libncurses-dev:amd64 (6.5+20250216-2) ...
467s Setting up libunicode-utf8-perl (0.62-2build4) ...
467s Setting up libset-intspan-perl (1.19-3) ...
467s Setting up libxcb-xfixes0:amd64 (1.17.0-2) ...
467s Setting up libogg0:amd64 (1.3.5-3build1) ...
467s Setting up libmouse-perl:amd64 (2.5.11-1build1) ...
467s Setting up libzstd-dev:amd64 (1.5.6+dfsg-2) ...
467s Setting up liblerc4:amd64 (4.0.0+ds-5ubuntu1) ...
467s Setting up libpod-pom-perl (2.01-4) ...
467s Setting up libxpm4:amd64 (1:3.5.17-1build2) ...
467s Setting up hdf5-helpers (1.14.5+repack-3) ...
467s Setting up libwmflite-0.2-7:amd64 (0.2.13-1.1build3) ...
467s Setting up libregexp-pattern-perl (0.2.14-2) ...
467s Setting up libdata-messagepack-perl (1.02-1build5) ...
467s Setting up libclass-inspector-perl (1.36-3) ...
467s Setting up libxcb-xinput0:amd64 (1.17.0-2) ...
467s Setting up libxrender1:amd64 (1:0.9.10-1.1build1) ...
467s Setting up libdynaloader-functions-perl (0.004-1) ...
467s Setting up libdatrie1:amd64 (0.2.13-3build1) ...
467s Setting up libtext-glob-perl (0.11-3) ...
467s Setting up libclass-method-modifiers-perl (2.15-1) ...
467s Setting up liblist-compare-perl (0.55-2) ...
467s Setting up libxcb-render0:amd64 (1.17.0-2) ...
467s Setting up libclone-perl:amd64 (0.47-1) ...
467s Setting up libarchive-zip-perl (1.68-1) ...
467s Setting up libsub-identify-perl (0.14-3build4) ...
467s Setting up libcpanel-json-xs-perl:amd64 (4.39-1) ...
467s Setting up libglvnd0:amd64 (1.7.0-1build1) ...
467s Setting up libio-stringy-perl (2.113-2) ...
467s Setting up libhtml-tagset-perl (3.24-1) ...
467s Setting up libts0t64:amd64 (1.22-1.1build1) ...
467s Setting up liblog-any-perl (1.717-1) ...
467s Setting up libyaml-pp-perl (0.39.0-1) ...
467s Setting up libxcb-glx0:amd64 (1.17.0-2) ...
467s Setting up libdevel-size-perl (0.84-1build1) ...
467s Setting up unzip (6.0-28ubuntu6) ...
467s Setting up libdebhelper-perl (13.24.1ubuntu2) ...
467s Setting up libregexp-pattern-license-perl (3.11.2-1) ...
467s Setting up libconvert-binhex-perl (1.125-3) ...
467s Setting up libdrm-intel1:amd64 (2.4.123-1) ...
467s Setting up liblwp-mediatypes-perl (6.04-2) ...
467s Setting up libyaml-libyaml-perl (0.903.0+ds-1) ...
467s Setting up fonts-freefont-otf (20211204+svn4273-2) ...
467s Setting up libio-interactive-perl (1.026-1) ...
467s Setting up libxcb-keysyms1:amd64 (0.4.1-1) ...
467s Setting up libxcb-shape0:amd64 (1.17.0-2) ...
467s Setting up x11-common (1:7.7+23ubuntu3) ...
467s Setting up libtry-tiny-perl (0.32-1) ...
467s Setting up libdeflate0:amd64 (1.23-1) ...
467s Setting up perl-openssl-defaults:amd64 (7build3) ...
467s Setting up libmldbm-perl (2.05-4) ...
467s Setting up libxml-namespacesupport-perl (1.12-2) ...
467s Setting up m4 (1.4.19-7) ...
467s Setting up libevent-2.1-7t64:amd64 (2.1.12-stable-10) ...
467s Setting up libclone-choose-perl (0.010-2) ...
467s Setting up libqhull-r8.0:amd64 (2020.2-6build1) ...
467s Setting up libxcb-render-util0:amd64 (0.3.10-1) ...
467s Setting up libtime-moment-perl (0.44-2build5) ...
467s Setting up libencode-locale-perl (1.05-3) ...
467s Setting up libxcb-shm0:amd64 (1.17.0-2) ...
467s Setting up libxcb-icccm4:amd64 (0.4.2-1) ...
467s Setting up texinfo-lib (7.1.1-1) ...
467s Setting up libreadline-dev:amd64 (8.2-6) ...
467s Setting up libmpg123-0t64:amd64 (1.32.10-1) ...
467s Setting up libgomp1:amd64 (15-20250222-0ubuntu1) ...
467s Setting up libconfig-tiny-perl (2.30-1) ...
467s Setting up libsereal-encoder-perl (5.004+ds-1build4) ...
467s Setting up liblist-utilsby-perl (0.12-2) ...
467s Setting up libyaml-tiny-perl (1.76-1) ...
467s Setting up libjbig0:amd64 (2.1-6.1ubuntu2) ...
467s Setting up octave-common (9.4.0-1) ...
467s Setting up libregexp-common-perl (2024080801-1) ...
467s Setting up libpcre2-16-0:amd64 (10.45-1) ...
467s Setting up libaec0:amd64 (1.1.3-1) ...
467s Setting up libnet-netmask-perl (2.0002-2) ...
467s Setting up libopengl0:amd64 (1.7.0-1build1) ...
467s Setting up libsub-install-perl (0.929-1) ...
467s Setting up libxcb-util1:amd64 (0.4.1-1) ...
467s Setting up libpsl-dev:amd64 (0.21.2-1.1build1) ...
467s Setting up libxxf86vm1:amd64 (1:1.1.4-1build4) ...
467s Setting up libindirect-perl (0.39-2build5) ...
467s Setting up libxcb-xkb1:amd64 (1.17.0-2) ...
467s Setting up libxcb-image0:amd64 (0.4.0-2build1) ...
467s Setting up libnumber-compare-perl (0.03-3) ...
467s Setting up libxcb-present0:amd64 (1.17.0-2) ...
467s Setting up liberror-perl (0.17030-1) ...
467s Setting up libasound2-data (1.2.13-1build1) ...
467s Setting up patchutils (0.4.2-1build3) ...
467s Setting up tex-common (6.19) ...
467s update-language: texlive-base not installed and configured, doing nothing!
467s Setting up libjson-maybexs-perl (1.004008-1) ...
467s Setting up libxml-sax-base-perl (1.09-3) ...
467s Setting up libio-string-perl (1.08-4) ...
467s Setting up libboolean-perl (0.46-3) ...
467s Setting up libnetaddr-ip-perl (4.079+dfsg-2build5) ...
467s Setting up xtrans-dev (1.4.0-1) ...
467s Setting up libfontenc1:amd64 (1:1.1.8-1build1) ...
467s Setting up autotools-dev (20220109.1) ...
467s Setting up libblas3:amd64 (3.12.1-2) ...
468s update-alternatives: using /usr/lib/x86_64-linux-gnu/blas/libblas.so.3 to provide /usr/lib/x86_64-linux-gnu/libblas.so.3 (libblas.so.3-x86_64-linux-gnu) in auto mode
468s Setting up libclass-data-inheritable-perl (0.10-1) ...
468s Setting up libunbound8:amd64 (1.22.0-1ubuntu1) ...
468s Setting up libpkgconf3:amd64 (1.8.1-4) ...
468s Setting up libgmpxx4ldbl:amd64 (2:6.3.0+dfsg-3ubuntu1) ...
468s Setting up libalgorithm-c3-perl (0.11-2) ...
468s Setting up libasound2t64:amd64 (1.2.13-1build1) ...
468s Setting up liblog-log4perl-perl (1.57-1) ...
468s Setting up libtext-reform-perl (1.20-5) ...
468s Setting up libgnutls-dane0t64:amd64 (3.8.9-2ubuntu2) ...
468s Setting up libfile-find-rule-perl (0.34-3) ...
468s Setting up libxfixes3:amd64 (1:6.0.0-2build1) ...
468s Setting up libxcb-sync1:amd64 (1.17.0-2) ...
468s Setting up libipc-system-simple-perl (1.30-2) ...
468s Setting up libio-tiecombine-perl (1.005-3) ...
468s Setting up libnet-domain-tld-perl (1.75-4) ...
468s Setting up libgssrpc4t64:amd64 (1.21.3-4ubuntu2) ...
468s Setting up libperlio-utf8-strict-perl (0.010-1build4) ...
468s Setting up libldap-dev:amd64 (2.6.9+dfsg-1~exp2ubuntu1) ...
468s Setting up aglfn (1.7+git20191031.4036a9c-2) ...
468s Setting up libxcb-cursor0:amd64 (0.1.5-1) ...
468s Setting up lzip (1.25-2) ...
468s update-alternatives: using /usr/bin/lzip.lzip to provide /usr/bin/lzip (lzip) in auto mode
468s update-alternatives: using /usr/bin/lzip.lzip to provide /usr/bin/lzip-compressor (lzip-compressor) in auto mode
468s update-alternatives: using /usr/bin/lzip.lzip to provide /usr/bin/lzip-decompressor (lzip-decompressor) in auto mode
468s Setting up libavahi-common-data:amd64 (0.8-16ubuntu1) ...
468s Setting up libopus0:amd64 (1.5.2-2) ...
468s Setting up t1utils (1.41-4build3) ...
468s Setting up libquadmath0:amd64 (15-20250222-0ubuntu1) ...
468s Setting up libxinerama1:amd64 (2:1.1.4-3build1) ...
468s Setting up diffstat (1.67-1) ...
468s Setting up libimagequant0:amd64 (2.18.0-1build1) ...
468s Setting up comerr-dev:amd64 (2.1-1.47.2-1ubuntu1) ...
468s Setting up libxkbcommon-x11-0:amd64 (1.7.0-2) ...
468s Setting up fonts-dejavu-mono (2.37-8) ...
468s Setting up libssl-dev:amd64 (3.4.1-1ubuntu1) ...
468s Setting up libmpc3:amd64 (1.3.1-1build2) ...
468s Setting up libvorbis0a:amd64 (1.3.7-2) ...
468s Setting up libvariable-magic-perl (0.64-1build1) ...
468s Setting up libio-html-perl (1.004-3) ...
468s Setting up libxrandr2:amd64 (2:1.5.4-1) ...
468s Setting up libtext-template-perl (1.61-1) ...
468s Setting up libpod-parser-perl (1.67-1) ...
468s Setting up autopoint (0.23.1-1) ...
468s Setting up libb-hooks-op-check-perl:amd64 (0.22-3build2) ...
468s Setting up fonts-dejavu-core (2.37-8) ...
468s Setting up liblist-moreutils-xs-perl (0.430-4build1) ...
468s Setting up pkgconf-bin (1.8.1-4) ...
468s Setting up libjpeg-turbo8:amd64 (2.1.5-3ubuntu2) ...
468s Setting up libqscintilla2-qt6-l10n (2.14.1+dfsg-1build4) ...
468s Setting up libltdl7:amd64 (2.5.4-4) ...
468s Setting up libidn2-dev:amd64 (2.3.7-2build2) ...
468s Setting up libfftw3-double3:amd64 (3.3.10-2fakesync1build1) ...
468s Setting up libparams-util-perl (1.102-3build1) ...
468s Setting up libgfortran5:amd64 (15-20250222-0ubuntu1) ...
468s Setting up libvulkan1:amd64 (1.4.304.0-1) ...
468s Setting up libtime-duration-perl (1.21-2) ...
468s Setting up autoconf (2.72-3ubuntu1) ...
468s Setting up libtext-xslate-perl:amd64 (3.5.9-2build1) ...
468s Setting up libsub-exporter-progressive-perl (0.001013-3) ...
468s Setting up libwebp7:amd64 (1.5.0-0.1) ...
468s Setting up libarray-intspan-perl (2.004-2) ...
468s Setting up libcapture-tiny-perl (0.50-1) ...
468s Setting up libtimedate-perl (2.3300-2) ...
468s Setting up libexporter-lite-perl (0.09-2) ...
468s Setting up libubsan1:amd64 (15-20250222-0ubuntu1) ...
468s Setting up libsub-name-perl:amd64 (0.28-1) ...
468s Setting up zlib1g-dev:amd64 (1:1.3.dfsg+really1.3.1-1ubuntu1) ...
468s Setting up dwz (0.15-1build6) ...
468s Setting up libdata-validate-domain-perl (0.15-1) ...
468s Setting up libproc-processtable-perl:amd64 (0.636-1build4) ...
468s Setting up libparse-recdescent-perl (1.967015+dfsg-4) ...
468s Setting up libmtdev1t64:amd64 (1.1.7-1) ...
468s Setting up libduktape207:amd64 (2.7.0+tests-0ubuntu3) ...
468s Setting up libxshmfence1:amd64 (1.3-1build5) ...
468s Setting up libhwasan0:amd64 (15-20250222-0ubuntu1) ...
468s Setting up libxcb-randr0:amd64 (1.17.0-2) ...
468s Setting up libpath-tiny-perl (0.146-1) ...
468s Setting up libasan8:amd64 (15-20250222-0ubuntu1) ...
468s Setting up lzop (1.04-2build3) ...
468s Setting up libjson-perl (4.10000-1) ...
468s Setting up liblog-any-adapter-screen-perl (0.141-1) ...
468s Setting up librole-tiny-perl (2.002004-1) ...
468s Setting up debugedit (1:5.1-2) ...
468s Setting up libipc-run3-perl (0.049-1) ...
468s Setting up libmd4c0:amd64 (0.5.2-2) ...
468s Setting up libregexp-wildcards-perl (1.05-3) ...
468s Setting up libmousex-strictconstructor-perl (0.02-3) ...
468s Setting up libfile-sharedir-perl (1.118-3) ...
468s Setting up libsub-uplevel-perl (0.2800-3) ...
468s Setting up libsuitesparseconfig7:amd64 (1:7.8.3+dfsg-3) ...
468s Setting up liblua5.4-0:amd64 (5.4.7-1) ...
468s Setting up libaliased-perl (0.34-3) ...
468s Setting up libharfbuzz0b:amd64 (10.2.0-1) ...
468s Setting up libthai-data (0.1.29-2build1) ...
468s Setting up xorg-sgml-doctools (1:1.11-1.1) ...
468s Setting up libstrictures-perl (2.000006-1) ...
468s Setting up libsub-quote-perl (2.006008-1ubuntu1) ...
468s Setting up libdevel-stacktrace-perl (2.0500-1) ...
468s Setting up libclass-xsaccessor-perl (1.19-4build6) ...
468s Setting up libtext-autoformat-perl (1.750000-2) ...
468s Setting up libglu1-mesa:amd64 (9.0.2-1.1build1) ...
468s Setting up libflac12t64:amd64 (1.4.3+ds-4) ...
468s Setting up libtoml-tiny-perl (0.19-1) ...
468s Setting up libstemmer0d:amd64 (2.2.0-4build1) ...
468s Setting up libxkbfile1:amd64 (1:1.1.0-1build4) ...
468s Setting up libsort-versions-perl (1.62-3) ...
468s Setting up libtsan2:amd64 (15-20250222-0ubuntu1) ...
468s Setting up libexporter-tiny-perl (1.006002-1) ...
468s Setting up libterm-readkey-perl (2.38-2build5) ...
468s Setting up libisl23:amd64 (0.27-1) ...
468s Setting up libtext-unidecode-perl (1.30-3) ...
468s Setting up libde265-0:amd64 (1.0.15-1build5) ...
468s Setting up libfont-ttf-perl (1.06-2) ...
468s Setting up libfile-homedir-perl (1.006-2) ...
468s Setting up libsamplerate0:amd64 (0.2.2-4build1) ...
468s Setting up libtasn1-6-dev:amd64 (4.20.0-2) ...
468s Setting up libwebpmux3:amd64 (1.5.0-0.1) ...
468s Setting up libtext-levenshteinxs-perl (0.03-5build5) ...
468s Setting up libperlio-gzip-perl (0.20-1build5) ...
468s Setting up libjxl0.11:amd64 (0.11.1-1) ...
468s Setting up libxfont2:amd64 (1:2.0.6-1build1) ...
468s Setting up libhtml-html5-entities-perl (0.004-3) ...
468s Setting up libtext-levenshtein-damerau-perl (0.41-3) ...
468s Setting up libsereal-decoder-perl (5.004+ds-1build4) ...
468s Setting up libmarkdown2:amd64 (2.2.7-2.1) ...
468s Setting up libcc1-0:amd64 (15-20250222-0ubuntu1) ...
468s Setting up liburi-perl (5.30-1) ...
468s Setting up libnet-ipv6addr-perl (1.02-1) ...
468s Setting up libbrotli-dev:amd64 (1.1.0-2build4) ...
468s Setting up liblsan0:amd64 (15-20250222-0ubuntu1) ...
468s Setting up libp11-kit-dev:amd64 (0.25.5-2ubuntu3) ...
468s Setting up libmp3lame0:amd64 (3.100-6build1) ...
468s Setting up libblas-dev:amd64 (3.12.1-2) ...
468s update-alternatives: using /usr/lib/x86_64-linux-gnu/blas/libblas.so to provide /usr/lib/x86_64-linux-gnu/libblas.so (libblas.so-x86_64-linux-gnu) in auto mode
468s Setting up libsz2:amd64 (1.1.3-1) ...
468s Setting up libitm1:amd64 (15-20250222-0ubuntu1) ...
468s Setting up libvorbisenc2:amd64 (1.3.7-2) ...
468s Setting up libkadm5clnt-mit12:amd64 (1.21.3-4ubuntu2) ...
468s Setting up libdata-validate-ip-perl (0.31-1) ...
468s Setting up libwacom-common (2.14.0-1) ...
468s Setting up libmousex-nativetraits-perl (1.09-3) ...
468s Setting up libemail-address-xs-perl (1.05-1build5) ...
468s Setting up libwayland-client0:amd64 (1.23.1-3) ...
468s Setting up libnet-ssleay-perl:amd64 (1.94-3) ...
468s Setting up libjpeg8:amd64 (8c-2ubuntu11) ...
468s Setting up automake (1:1.17-3ubuntu1) ...
468s update-alternatives: using /usr/bin/automake-1.17 to provide /usr/bin/automake (automake) in auto mode
468s Setting up libb2-1:amd64 (0.98.1-1.1build1) ...
468s Setting up x11proto-dev (2024.1-1) ...
468s Setting up libfile-stripnondeterminism-perl (1.14.1-2) ...
468s Setting up gnuplot-data (6.0.2+dfsg1-1) ...
468s Setting up libice6:amd64 (2:1.1.1-1) ...
468s Setting up libqt6core6t64:amd64 (6.8.2+dfsg-5) ...
468s Setting up libhttp-date-perl (6.06-1) ...
468s Setting up mesa-libgallium:amd64 (25.0.1-2ubuntu1) ...
468s Setting up libjpeg-turbo8-dev:amd64 (2.1.5-3ubuntu2) ...
468s Setting up liblapack3:amd64 (3.12.1-2) ...
468s update-alternatives: using /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3 to provide /usr/lib/x86_64-linux-gnu/liblapack.so.3 (liblapack.so.3-x86_64-linux-gnu) in auto mode
468s Setting up libproxy1v5:amd64 (0.5.9-1) ...
468s Setting up libfile-basedir-perl (0.09-2) ...
468s Setting up gettext (0.23.1-1) ...
468s Setting up libarpack2t64:amd64 (3.9.1-4) ...
468s Setting up libfftw3-single3:amd64 (3.3.10-2fakesync1build1) ...
468s Setting up libgmp-dev:amd64 (2:6.3.0+dfsg-3ubuntu1) ...
468s Setting up libamd3:amd64 (1:7.8.3+dfsg-3) ...
468s Setting up libfile-listing-perl (6.16-1) ...
468s Setting up libxau-dev:amd64 (1:1.0.11-1) ...
468s Setting up nettle-dev:amd64 (3.10.1-1) ...
468s Setting up libkdb5-10t64:amd64 (1.21.3-4ubuntu2) ...
468s Setting up libgbm1:amd64 (25.0.1-2ubuntu1) ...
468s Setting up libcolamd3:amd64 (1:7.8.3+dfsg-3) ...
468s Setting up libwacom9:amd64 (2.14.0-1) ...
468s Setting up fontconfig-config (2.15.0-2ubuntu1) ...
468s Setting up liblist-moreutils-perl (0.430-2) ...
468s Setting up libxcursor1:amd64 (1:1.2.3-1) ...
468s Setting up libpod-constants-perl (0.19-2) ...
468s Setting up libgl1-mesa-dri:amd64 (25.0.1-2ubuntu1) ...
468s Setting up libhash-merge-perl (0.302-1) ...
468s Setting up libsoftware-copyright-perl (0.014-1) ...
468s Setting up libaec-dev:amd64 (1.1.3-1) ...
468s Setting up libavahi-common3:amd64 (0.8-16ubuntu1) ...
468s Setting up libcxsparse4:amd64 (1:7.8.3+dfsg-3) ...
468s Setting up libfftw3-long3:amd64 (3.3.10-2fakesync1build1) ...
468s Setting up libnet-http-perl (6.23-1) ...
468s Setting up libpath-iterator-rule-perl (1.015-2) ...
468s Setting up libtext-markdown-discount-perl (0.18-1) ...
468s Setting up libappstream5:amd64 (1.0.4-1) ...
468s Setting up libexception-class-perl (1.45-1) ...
468s Setting up libclass-c3-perl (0.35-2) ...
468s Setting up libqrupdate1:amd64 (1.1.5-1) ...
468s Setting up libdevel-callchecker-perl:amd64 (0.009-1build1) ...
468s Setting up libxml-sax-perl (1.02+dfsg-4) ...
468s update-perl-sax-parsers: Registering Perl SAX parser XML::SAX::PurePerl with priority 10...
468s update-perl-sax-parsers: Updating overall Perl SAX parser modules info file...
468s Creating config file /etc/perl/XML/SAX/ParserDetails.ini with new version
468s Setting up libcamd3:amd64 (1:7.8.3+dfsg-3) ...
468s Setting up pkgconf:amd64 (1.8.1-4) ...
468s Setting up libinput-bin (1.27.1-1) ...
468s Setting up libxs-parse-sublike-perl:amd64 (0.37-1) ...
468s Setting up intltool-debian (0.35.0+20060710.6) ...
468s Setting up libthai0:amd64 (0.1.29-2build1) ...
468s Setting up libxdmcp-dev:amd64 (1:1.1.5-1) ...
468s Setting up libegl-mesa0:amd64 (25.0.1-2ubuntu1) ...
468s Setting up libdata-validate-uri-perl (0.07-3) ...
468s Setting up libxs-parse-keyword-perl (0.48-2) ...
468s Setting up libtest-exception-perl (0.43-3) ...
468s Setting up appstream (1.0.4-1) ...
468s ✔ Metadata cache was updated successfully.
468s Setting up libqt6xml6:amd64 (6.8.2+dfsg-5) ...
468s Setting up libglpk40:amd64 (5.0-1build2) ...
468s Setting up libqt6sql6:amd64 (6.8.2+dfsg-5) ...
468s Setting up libstring-copyright-perl (0.003014-1) ...
468s Setting up libraqm0:amd64 (0.10.2-1) ...
468s Setting up libfftw3-quad3:amd64 (3.3.10-2fakesync1build1) ...
468s Setting up liblapack-dev:amd64 (3.12.1-2) ...
468s update-alternatives: using /usr/lib/x86_64-linux-gnu/lapack/liblapack.so to provide /usr/lib/x86_64-linux-gnu/liblapack.so (liblapack.so-x86_64-linux-gnu) in auto mode
468s Setting up libdata-optlist-perl (0.114-1) ...
468s Setting up cpp-14-x86-64-linux-gnu (14.2.0-17ubuntu3) ...
468s Setting up libssh2-1-dev:amd64 (1.11.1-1) ...
468s Setting up cpp-14 (14.2.0-17ubuntu3) ...
468s Setting up libccolamd3:amd64 (1:7.8.3+dfsg-3) ...
468s Setting up libxml-libxml-perl (2.0207+dfsg+really+2.0134-5build1) ...
468s update-perl-sax-parsers: Registering Perl SAX parser XML::LibXML::SAX::Parser with priority 50...
468s update-perl-sax-parsers: Registering Perl SAX parser XML::LibXML::SAX with priority 50...
468s update-perl-sax-parsers: Updating overall Perl SAX parser modules info file...
469s Replacing config file /etc/perl/XML/SAX/ParserDetails.ini with new version
469s Setting up dh-strip-nondeterminism (1.14.1-2) ...
469s Setting up libwww-robotrules-perl (6.02-1) ...
469s Setting up libsyntax-keyword-try-perl (0.30-1) ...
469s Setting up libjack-jackd2-0:amd64 (1.9.22~dfsg-4) ...
469s Setting up libhdf5-310:amd64 (1.14.5+repack-3) ...
469s Setting up libtiff6:amd64 (4.5.1+git230720-4ubuntu4) ...
469s Setting up libhtml-parser-perl:amd64 (3.83-1build1) ...
469s Setting up libkadm5srv-mit12:amd64 (1.21.3-4ubuntu2) ...
469s Setting up libegl1:amd64 (1.7.0-1build1) ...
469s Setting up libqt6core5compat6:amd64 (6.8.2-3) ...
469s Setting up libfontconfig1:amd64 (2.15.0-2ubuntu1) ...
469s Setting up libsndfile1:amd64 (1.2.2-2) ...
469s Setting up libmro-compat-perl (0.15-2) ...
469s Setting up libgcc-14-dev:amd64 (14.2.0-17ubuntu3) ...
469s Setting up libjpeg8-dev:amd64 (8c-2ubuntu11) ...
469s Setting up libhdf5-fortran-310:amd64 (1.14.5+repack-3) ...
469s Setting up libstdc++-14-dev:amd64 (14.2.0-17ubuntu3) ...
469s Setting up libsm6:amd64 (2:1.2.4-1) ...
469s Setting up libavahi-client3:amd64 (0.8-16ubuntu1) ...
469s Setting up libio-socket-ssl-perl (2.089-1) ...
469s Setting up libsub-exporter-perl (0.990-1) ...
469s Setting up libqt6dbus6:amd64 (6.8.2+dfsg-5) ...
469s Setting up libhttp-message-perl (7.00-2ubuntu1) ...
469s Setting up libhtml-form-perl (6.12-1) ...
469s Setting up krb5-multidev:amd64 (1.21.3-4ubuntu2) ...
469s Setting up libhdf5-cpp-310:amd64 (1.14.5+repack-3) ...
469s Setting up cpp-x86-64-linux-gnu (4:14.2.0-1ubuntu1) ...
469s Setting up libgfortran-14-dev:amd64 (14.2.0-17ubuntu3) ...
469s Setting up libiterator-perl (0.03+ds1-2) ...
469s Setting up libgnutls28-dev:amd64 (3.8.9-2ubuntu2) ...
469s Setting up libinput10:amd64 (1.27.1-1) ...
469s Setting up libnghttp2-dev:amd64 (1.64.0-1) ...
469s Setting up libhdf5-hl-310:amd64 (1.14.5+repack-3) ...
469s Setting up libportaudio2:amd64 (19.6.0-1.2build3) ...
469s Setting up libhttp-negotiate-perl (6.01-2) ...
469s Setting up fontconfig (2.15.0-2ubuntu1) ...
471s Regenerating fonts cache... done.
471s Setting up libcarp-assert-more-perl (2.8.0-1) ...
471s Setting up libcholmod5:amd64 (1:7.8.3+dfsg-3) ...
471s Setting up libxft2:amd64 (2.3.6-1build1) ...
471s Setting up libglx-mesa0:amd64 (25.0.1-2ubuntu1) ...
471s Setting up libxcb1-dev:amd64 (1.17.0-2) ...
471s Setting up libiterator-util-perl (0.02+ds1-2) ...
471s Setting up libglx0:amd64 (1.7.0-1build1) ...
471s Setting up libhttp-cookies-perl (6.11-1) ...
471s Setting up libspqr4:amd64 (1:7.8.3+dfsg-3) ...
471s Setting up libfftw3-bin (3.3.10-2fakesync1build1) ...
471s Setting up po-debconf (1.0.21+nmu1) ...
471s Setting up libhtml-tree-perl (5.07-3) ...
471s Setting up libparams-classify-perl:amd64 (0.015-2build6) ...
471s Setting up libpango-1.0-0:amd64 (1.56.1-1) ...
471s Setting up libcgi-pm-perl (4.67-1) ...
471s Setting up libjpeg-dev:amd64 (8c-2ubuntu11) ...
471s Setting up libx11-dev:amd64 (2:1.8.10-2) ...
471s Setting up libcairo2:amd64 (1.18.2-2) ...
471s Setting up libobject-pad-perl (0.820-1) ...
471s Setting up libkrb5-dev:amd64 (1.21.3-4ubuntu2) ...
471s Setting up libgl1:amd64 (1.7.0-1build1) ...
471s Setting up libqt6gui6:amd64 (6.8.2+dfsg-5) ...
471s Setting up libnet-smtp-ssl-perl (1.04-2) ...
471s Setting up libmodule-runtime-perl (0.016-2) ...
471s Setting up libmailtools-perl (2.22-1) ...
471s Setting up libconfig-model-perl (2.155-1) ...
471s Setting up libxt6t64:amd64 (1:1.2.1-1.2build1) ...
471s Setting up librtmp-dev:amd64 (2.4+20151223.gitfa8646d.1-2build7) ...
471s Setting up texinfo (7.1.1-1) ...
471s Setting up libumfpack6:amd64 (1:7.8.3+dfsg-3) ...
471s Setting up libconst-fast-perl (0.014-2) ...
471s Setting up libqt6network6:amd64 (6.8.2+dfsg-5) ...
471s Setting up cpp (4:14.2.0-1ubuntu1) ...
471s Setting up libdata-section-perl (0.200008-1) ...
471s Setting up libglx-dev:amd64 (1.7.0-1build1) ...
471s Setting up gcc-14-x86-64-linux-gnu (14.2.0-17ubuntu3) ...
471s Setting up libpangoft2-1.0-0:amd64 (1.56.1-1) ...
471s Setting up libdata-dpath-perl (0.60-1) ...
471s Setting up libfltk1.3t64:amd64 (1.3.8-6.1build2) ...
471s Setting up libfftw3-dev:amd64 (3.3.10-2fakesync1build1) ...
471s Setting up libcups2t64:amd64 (2.4.11-0ubuntu2) ...
471s Setting up libgl-dev:amd64 (1.7.0-1build1) ...
471s Setting up libstring-rewriteprefix-perl (0.009-1) ...
471s Setting up libpangocairo-1.0-0:amd64 (1.56.1-1) ...
471s Setting up libhdf5-hl-cpp-310:amd64 (1.14.5+repack-3) ...
471s Setting up libconfig-model-backend-yaml-perl (2.134-2) ...
471s Setting up libhdf5-hl-fortran-310:amd64 (1.14.5+repack-3) ...
471s Setting up libxmu6:amd64 (2:1.1.3-3build2) ...
471s Setting up gcc-x86-64-linux-gnu (4:14.2.0-1ubuntu1) ...
471s Setting up libmodule-implementation-perl (0.09-2) ...
471s Setting up libpackage-stash-perl (0.40-1) ...
471s Setting up libimport-into-perl (1.002005-2) ...
471s Setting up libmoo-perl (2.005005-1) ...
471s Setting up libqt6opengl6:amd64 (6.8.2+dfsg-5) ...
471s Setting up liblist-someutils-perl (0.59-1) ...
471s Setting up libxaw7:amd64 (2:1.0.16-1) ...
471s Setting up libmime-tools-perl (5.515-1) ...
471s Setting up libsoftware-license-perl (0.104006-1) ...
471s Setting up libclass-load-perl (0.25-2) ...
471s Setting up libgl2ps1.4 (1.4.2+dfsg1-2build1) ...
471s Setting up gcc-14 (14.2.0-17ubuntu3) ...
471s Setting up gfortran-14-x86-64-linux-gnu (14.2.0-17ubuntu3) ...
471s Setting up libqt6widgets6:amd64 (6.8.2+dfsg-5) ...
471s Setting up libfltk-gl1.3t64:amd64 (1.3.8-6.1build2) ...
471s Setting up libcurl4-openssl-dev:amd64 (8.12.1-3ubuntu1) ...
471s Setting up libhdf5-dev (1.14.5+repack-3) ...
471s update-alternatives: using /usr/lib/x86_64-linux-gnu/pkgconfig/hdf5-serial.pc to provide /usr/lib/x86_64-linux-gnu/pkgconfig/hdf5.pc (hdf5.pc) in auto mode
471s Setting up g++-14-x86-64-linux-gnu (14.2.0-17ubuntu3) ...
471s Setting up g++-x86-64-linux-gnu (4:14.2.0-1ubuntu1) ...
471s Setting up g++-14 (14.2.0-17ubuntu3) ...
471s Setting up libmoox-aliases-perl (0.001006-2) ...
471s Setting up gfortran-14 (14.2.0-17ubuntu3) ...
471s Setting up libparams-validate-perl:amd64 (1.31-2build4) ...
471s Setting up libqt6openglwidgets6:amd64 (6.8.2+dfsg-5) ...
471s Setting up libb-hooks-endofscope-perl (0.28-1) ...
471s Setting up libqt6printsupport6:amd64 (6.8.2+dfsg-5) ...
471s Setting up libtool (2.5.4-4) ...
471s Setting up libsoftware-licensemoreutils-perl (1.009-1) ...
471s Setting up x11-xkb-utils (7.7+9) ...
471s Setting up libqt6help6:amd64 (6.8.2-3) ...
471s Setting up libqscintilla2-qt6-15:amd64 (2.14.1+dfsg-1build4) ...
471s Setting up gfortran-x86-64-linux-gnu (4:14.2.0-1ubuntu1) ...
471s Setting up gcc (4:14.2.0-1ubuntu1) ...
471s Setting up dh-autoreconf (20) ...
471s Setting up libnamespace-clean-perl (0.27-2) ...
471s Setting up libstring-license-perl (0.0.11-1ubuntu1) ...
471s Setting up libgetopt-long-descriptive-perl (0.116-2) ...
471s Setting up g++ (4:14.2.0-1ubuntu1) ...
471s update-alternatives: using /usr/bin/g++ to provide /usr/bin/c++ (c++) in auto mode
471s Setting up xserver-common (2:21.1.16-1ubuntu1) ...
471s Setting up licensecheck (3.3.9-1ubuntu1) ...
471s Setting up libapp-cmd-perl (0.337-2) ...
471s Setting up xvfb (2:21.1.16-1ubuntu1) ...
471s Setting up debhelper (13.24.1ubuntu2) ...
471s Setting up gfortran (4:14.2.0-1ubuntu1) ...
471s update-alternatives: using /usr/bin/gfortran to provide /usr/bin/f95 (f95) in auto mode
471s update-alternatives: warning: skip creation of /usr/share/man/man1/f95.1.gz because associated file /usr/share/man/man1/gfortran.1.gz (of link group f95) doesn't exist
471s update-alternatives: using /usr/bin/gfortran to provide /usr/bin/f77 (f77) in auto mode
471s update-alternatives: warning: skip creation of /usr/share/man/man1/f77.1.gz because associated file /usr/share/man/man1/gfortran.1.gz (of link group f77) doesn't exist
471s Setting up cme (1.041-1) ...
471s Setting up libheif-plugin-aomdec:amd64 (1.19.7-1) ...
471s Setting up libwww-perl (6.78-1) ...
471s Setting up libheif1:amd64 (1.19.7-1) ...
471s Setting up libparse-debcontrol-perl (2.005-6) ...
471s Setting up libhtml-tokeparser-simple-perl (3.16-4) ...
471s Setting up libwww-mechanize-perl (2.19-1ubuntu1) ...
471s Setting up libgd3:amd64 (2.3.3-12ubuntu3) ...
471s Setting up gnuplot-nox (6.0.2+dfsg1-1) ...
471s update-alternatives: using /usr/bin/gnuplot-nox to provide /usr/bin/gnuplot (gnuplot) in auto mode
471s Setting up liblwp-protocol-https-perl (6.14-1) ...
471s Setting up libheif-plugin-libde265:amd64 (1.19.7-1) ...
471s Setting up libgraphicsmagick-q16-3t64 (1.4+really1.3.45+hg17689-1) ...
471s Setting up lintian (2.121.1+nmu1ubuntu2) ...
471s Setting up libgraphicsmagick++-q16-12t64 (1.4+really1.3.45+hg17689-1) ...
471s Setting up libconfig-model-dpkg-perl (3.010) ...
471s Setting up dh-octave-autopkgtest (1.8.0) ...
471s Setting up octave (9.4.0-1) ...
471s Setting up octave-dev (9.4.0-1) ...
471s Setting up octave-io (2.6.4-3build2) ...
471s Setting up octave-statistics-common (1.7.3-2) ...
471s Setting up octave-statistics (1.7.3-2) ...
471s Setting up dh-octave (1.8.0) ...
471s Processing triggers for libc-bin (2.41-1ubuntu2) ...
471s Processing triggers for man-db (2.13.0-1) ...
473s Processing triggers for udev (257.3-1ubuntu3) ...
473s Processing triggers for install-info (7.1.1-1) ...
475s autopkgtest [10:43:22]: test command1: DH_OCTAVE_TEST_ENV="xvfb-run -a" /usr/bin/dh_octave_check --use-installed-package
475s autopkgtest [10:43:22]: test command1: [-----------------------
475s Checking package...
476s Run the unit tests...
476s Checking m files ...
476s [inst/mahal.m]
476s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/mahal.m
476s ***** error mahal ()
476s ***** error mahal (1, 2, 3)
476s ***** error mahal ("A", "B")
476s ***** error <must be numeric> mahal ([1, 2], ["A", "B"])
476s ***** error mahal (ones (2, 2, 2))
477s ***** error <must be 2-D matrices> mahal (ones (2, 2), ones (2, 2, 2))
477s ***** error <same number of columns> mahal (ones (2, 2), ones (2, 3))
477s ***** test
477s  X = [1 0; 0 1; 1 1; 0 0];
477s  assert (mahal (X, X), [1.5; 1.5; 1.5; 1.5], 10*eps)
477s  assert (mahal (X, X+1), [7.5; 7.5; 1.5; 13.5], 10*eps)
477s ***** assert (mahal ([true; true], [false; true]), [0.5; 0.5], eps)
477s 9 tests, 9 passed, 0 known failure, 0 skipped
477s [inst/fitcknn.m]
477s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/fitcknn.m
477s ***** demo
477s  ## Train a k-nearest neighbor classifier for k = 10
477s  ## and plot the decision boundaries.
477s 
477s  load fisheriris
477s  idx = ! strcmp (species, "setosa");
477s  X = meas(idx,3:4);
477s  Y = cast (strcmpi (species(idx), "virginica"), "double");
477s  obj = fitcknn (X, Y, "Standardize", 1, "NumNeighbors", 10, "NSMethod", "exhaustive")
477s  x1 = [min(X(:,1)):0.03:max(X(:,1))];
477s  x2 = [min(X(:,2)):0.02:max(X(:,2))];
477s  [x1G, x2G] = meshgrid (x1, x2);
477s  XGrid = [x1G(:), x2G(:)];
477s  pred = predict (obj, XGrid);
477s  gidx = logical (str2num (cell2mat (pred)));
477s 
477s  figure
477s  scatter (XGrid(gidx,1), XGrid(gidx,2), "markerfacecolor", "magenta");
477s  hold on
477s  scatter (XGrid(!gidx,1), XGrid(!gidx,2), "markerfacecolor", "red");
477s  plot (X(Y == 0, 1), X(Y == 0, 2), "ko", X(Y == 1, 1), X(Y == 1, 2), "kx");
477s  xlabel ("Petal length (cm)");
477s  ylabel ("Petal width (cm)");
477s  title ("5-Nearest Neighbor Classifier Decision Boundary");
477s  legend ({"Versicolor Region", "Virginica Region", ...
477s          "Sampled Versicolor", "Sampled Virginica"}, ...
477s          "location", "northwest")
477s  axis tight
477s  hold off
477s ***** test
477s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
477s  y = ["a"; "a"; "b"; "b"];
477s  a = fitcknn (x, y);
477s  assert (class (a), "ClassificationKNN");
477s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
477s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
477s  assert ({a.BucketSize}, {50})
477s ***** test
477s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
477s  y = ["a"; "a"; "b"; "b"];
477s  a = fitcknn (x, y, "NSMethod", "exhaustive");
477s  assert (class (a), "ClassificationKNN");
477s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
477s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
477s  assert ({a.BucketSize}, {50})
477s ***** test
477s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
477s  y = ["a"; "a"; "b"; "b"];
477s  k = 10;
477s  a = fitcknn (x, y, "NumNeighbors" ,k);
477s  assert (class (a), "ClassificationKNN");
477s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
477s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
477s  assert ({a.BucketSize}, {50})
477s ***** test
477s  x = ones (4, 11);
477s  y = ["a"; "a"; "b"; "b"];
477s  k = 10;
477s  a = fitcknn (x, y, "NumNeighbors" ,k);
477s  assert (class (a), "ClassificationKNN");
477s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
477s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
477s  assert ({a.BucketSize}, {50})
477s ***** test
477s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
477s  y = ["a"; "a"; "b"; "b"];
477s  k = 10;
477s  a = fitcknn (x, y, "NumNeighbors" ,k, "NSMethod", "exhaustive");
477s  assert (class (a), "ClassificationKNN");
477s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
477s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
477s  assert ({a.BucketSize}, {50})
477s ***** test
477s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
477s  y = ["a"; "a"; "b"; "b"];
477s  k = 10;
477s  a = fitcknn (x, y, "NumNeighbors" ,k, "Distance", "hamming");
477s  assert (class (a), "ClassificationKNN");
477s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
477s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "hamming"})
477s  assert ({a.BucketSize}, {50})
477s ***** test
477s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
477s  y = ["a"; "a"; "b"; "b"];
477s  weights = ones (4,1);
477s  a = fitcknn (x, y, "Standardize", 1);
477s  assert (class (a), "ClassificationKNN");
477s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
477s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
477s  assert ({a.Standardize}, {true})
477s  assert ({a.Sigma}, {std(x, [], 1)})
477s  assert ({a.Mu}, {[3.75, 4.25, 4.75]})
477s ***** test
477s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
477s  y = ["a"; "a"; "b"; "b"];
477s  weights = ones (4,1);
477s  a = fitcknn (x, y, "Standardize", false);
477s  assert (class (a), "ClassificationKNN");
477s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
477s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
477s  assert ({a.Standardize}, {false})
477s  assert ({a.Sigma}, {[]})
477s  assert ({a.Mu}, {[]})
477s ***** test
477s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
477s  y = ["a"; "a"; "b"; "b"];
477s  s = ones (1, 3);
477s  a = fitcknn (x, y, "Scale" , s, "Distance", "seuclidean");
477s  assert (class (a), "ClassificationKNN");
477s  assert ({a.DistParameter}, {s})
477s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "seuclidean"})
477s  assert ({a.BucketSize}, {50})
477s ***** test
477s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
477s  y = ["a"; "a"; "b"; "b"];
477s  a = fitcknn (x, y, "Exponent" , 5, "Distance", "minkowski");
477s  assert (class (a), "ClassificationKNN");
477s  assert (a.DistParameter, 5)
477s  assert ({a.NSMethod, a.Distance}, {"kdtree", "minkowski"})
477s ***** test
477s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
477s  y = ["a"; "a"; "b"; "b"];
477s  a = fitcknn (x, y, "Exponent" , 5, "Distance", "minkowski", ...
477s                     "NSMethod", "exhaustive");
477s  assert (class (a), "ClassificationKNN");
477s  assert (a.DistParameter, 5)
477s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "minkowski"})
477s ***** test
477s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
477s  y = ["a"; "a"; "b"; "b"];
477s  a = fitcknn (x, y, "BucketSize" , 20, "distance", "mahalanobis");
477s  assert (class (a), "ClassificationKNN");
477s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "mahalanobis"})
477s  assert ({a.BucketSize}, {20})
477s ***** test
477s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
477s  y = ["a"; "a"; "b"; "b"];
477s  a = fitcknn (x, y, "IncludeTies", true);
477s  assert (class (a), "ClassificationKNN");
477s  assert (a.IncludeTies, true);
477s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
477s ***** test
477s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
477s  y = ["a"; "a"; "b"; "b"];
477s  a = fitcknn (x, y);
477s  assert (class (a), "ClassificationKNN");
477s  assert (a.IncludeTies, false);
477s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
477s ***** test
477s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
477s  y = ["a"; "a"; "b"; "b"];
477s  a = fitcknn (x, y);
477s  assert (class (a), "ClassificationKNN")
477s  assert (a.Prior, [0.5; 0.5])
477s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
477s  assert ({a.BucketSize}, {50})
477s ***** test
477s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
477s  y = ["a"; "a"; "b"; "b"];
477s  prior = [0.5; 0.5];
477s  a = fitcknn (x, y, "Prior", "empirical");
477s  assert (class (a), "ClassificationKNN")
477s  assert (a.Prior, prior)
477s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
477s  assert ({a.BucketSize}, {50})
477s ***** test
477s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
477s  y = ["a"; "a"; "a"; "b"];
477s  prior = [0.75; 0.25];
477s  a = fitcknn (x, y, "Prior", "empirical");
477s  assert (class (a), "ClassificationKNN")
477s  assert (a.Prior, prior)
477s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
477s  assert ({a.BucketSize}, {50})
477s ***** test
477s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
477s  y = ["a"; "a"; "a"; "b"];
477s  prior = [0.5; 0.5];
477s  a = fitcknn (x, y, "Prior", "uniform");
477s  assert (class (a), "ClassificationKNN")
477s  assert (a.Prior, prior)
477s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
477s  assert ({a.BucketSize}, {50})
477s ***** test
477s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
477s  y = ["a"; "a"; "b"; "b"];
477s  cost = eye (2);
477s  a = fitcknn (x, y, "Cost", cost);
477s  assert (class (a), "ClassificationKNN")
477s  assert (a.Cost, [1, 0; 0, 1])
477s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
477s  assert ({a.BucketSize}, {50})
477s ***** test
477s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
477s  y = ["a"; "a"; "b"; "b"];
477s  cost = eye (2);
477s  a = fitcknn (x, y, "Cost", cost, "Distance", "hamming" );
477s  assert (class (a), "ClassificationKNN")
477s  assert (a.Cost, [1, 0; 0, 1])
477s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "hamming"})
477s  assert ({a.BucketSize}, {50})
477s ***** test
477s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
477s  y = ["a"; "a"; "b"; "b"];
477s  a = fitcknn (x, y, "NSMethod", "exhaustive", "CrossVal", "on");
477s  assert (class (a), "ClassificationPartitionedModel");
477s  assert ({a.X, a.Y, a.Trained{1}.NumNeighbors}, {x, y, 1})
477s  assert (a.ModelParameters.NSMethod, "exhaustive")
477s  assert (a.ModelParameters.Distance, "euclidean")
477s  assert ({a.Trained{1}.BucketSize}, {50})
477s ***** error<fitcknn: too few arguments.> fitcknn ()
477s ***** error<fitcknn: too few arguments.> fitcknn (ones (4,1))
477s ***** error<fitcknn: Name-Value arguments must be in pairs.>
477s  fitcknn (ones (4,2), ones (4, 1), "K")
477s ***** error<fitcknn: number of rows in X and Y must be equal.>
477s  fitcknn (ones (4,2), ones (3, 1))
477s ***** error<fitcknn: number of rows in X and Y must be equal.>
477s  fitcknn (ones (4,2), ones (3, 1), "K", 2)
477s ***** error <fitcknn: 'CrossVal' must be either 'off' or 'on'.>
477s  fitcknn (ones (4,2), ones (4, 1), "CrossVal", 2)
477s ***** error <fitcknn: 'CrossVal' must be either 'off' or 'on'.>
477s  fitcknn (ones (4,2), ones (4, 1), "CrossVal", 'a')
477s ***** error <fitcknn: You can use only one cross-validation name-value pair argument> ...
477s  fitcknn (ones (4,2), ones (4, 1), "KFold", 10, "Holdout", 0.3)
477s 29 tests, 29 passed, 0 known failure, 0 skipped
477s [inst/chi2gof.m]
477s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/chi2gof.m
477s ***** demo
477s  x = normrnd (50, 5, 100, 1);
477s  [h, p, stats] = chi2gof (x)
477s  [h, p, stats] = chi2gof (x, "cdf", @(x)normcdf (x, mean(x), std(x)))
477s  [h, p, stats] = chi2gof (x, "cdf", {@normcdf, mean(x), std(x)})
477s ***** demo
477s  x = rand (100,1 );
477s  n = length (x);
477s  binedges = linspace (0, 1, 11);
477s  expectedCounts = n * diff (binedges);
477s  [h, p, stats] = chi2gof (x, "binedges", binedges, "expected", expectedCounts)
477s ***** demo
477s  bins = 0:5;
477s  obsCounts = [6 16 10 12 4 2];
477s  n = sum(obsCounts);
477s  lambdaHat = sum(bins.*obsCounts) / n;
477s  expCounts = n * poisspdf(bins,lambdaHat);
477s  [h, p, stats] = chi2gof (bins, "binctrs", bins, "frequency", obsCounts, ...
477s                           "expected", expCounts, "nparams",1)
477s ***** error chi2gof ()
477s ***** error chi2gof ([2,3;3,4])
477s ***** error chi2gof ([1,2,3,4], "nbins", 3, "ctrs", [2,3,4])
477s ***** error chi2gof ([1,2,3,4], "frequency", [2,3,2])
477s ***** error chi2gof ([1,2,3,4], "frequency", [2,3,2,-2])
477s ***** error chi2gof ([1,2,3,4], "frequency", [2,3,2,2], "nparams", i)
477s ***** error chi2gof ([1,2,3,4], "frequency", [2,3,2,2], "alpha", 1.3)
477s ***** error chi2gof ([1,2,3,4], "expected", [-3,2,2])
477s ***** error chi2gof ([1,2,3,4], "expected", [3,2,2], "nbins", 5)
477s ***** error chi2gof ([1,2,3,4], "cdf", @normcdff)
477s ***** test
477s  x = [1 2 1 3 2 4 3 2 4 3 2 2];
477s  [h, p, stats] = chi2gof (x);
477s  assert (h, 0);
477s  assert (p, NaN);
477s  assert (stats.chi2stat, 0.1205375022748029, 1e-14);
477s  assert (stats.df, 0);
477s  assert (stats.edges, [1, 2.5, 4], 1e-14);
477s  assert (stats.O, [7, 5], 1e-14);
477s  assert (stats.E, [6.399995519909668, 5.600004480090332], 1e-14);
477s 11 tests, 11 passed, 0 known failure, 0 skipped
477s [inst/vartest.m]
477s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/vartest.m
477s ***** error<vartest: too few input arguments.> vartest ();
477s ***** error<vartest: invalid value for variance.> vartest ([1, 2, 3, 4], -0.5);
477s ***** error<vartest: invalid value for alpha.> ...
477s  vartest ([1, 2, 3, 4], 1, "alpha", 0);
477s ***** error<vartest: invalid value for alpha.> ...
477s  vartest ([1, 2, 3, 4], 1, "alpha", 1.2);
477s ***** error<vartest: invalid value for alpha.> ...
477s  vartest ([1, 2, 3, 4], 1, "alpha", "val");
477s ***** error<vartest: invalid value for tail.>  ...
477s  vartest ([1, 2, 3, 4], 1, "tail", "val");
477s ***** error<vartest: invalid value for tail.>  ...
477s  vartest ([1, 2, 3, 4], 1, "alpha", 0.01, "tail", "val");
477s ***** error<vartest: invalid value for operating dimension.> ...
477s  vartest ([1, 2, 3, 4], 1, "dim", 3);
477s ***** error<vartest: invalid value for operating dimension.> ...
477s  vartest ([1, 2, 3, 4], 1, "alpha", 0.01, "tail", "both", "dim", 3);
477s ***** error<vartest: invalid name for optional arguments.> ...
477s  vartest ([1, 2, 3, 4], 1, "alpha", 0.01, "tail", "both", "badoption", 3);
477s ***** error<vartest: optional arguments must be in name/value pairs.> ...
477s  vartest ([1, 2, 3, 4], 1, "alpha", 0.01, "tail");
477s ***** test
477s  load carsmall
477s  [h, pval, ci] = vartest (MPG, 7^2);
477s  assert (h, 1);
477s  assert (pval, 0.04335086742174443, 1e-14);
477s  assert (ci, [49.397; 88.039], 1e-3);
477s 12 tests, 12 passed, 0 known failure, 0 skipped
477s [inst/vartest2.m]
477s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/vartest2.m
477s ***** error<vartest2: too few input arguments.> vartest2 ();
477s ***** error<vartest2: too few input arguments.> vartest2 (ones (20,1));
477s ***** error<vartest2: X and Y must be vectors or matrices or N-D arrays.> ...
477s  vartest2 (rand (20,1), 5);
477s ***** error<vartest2: invalid value for alpha.> ...
477s  vartest2 (rand (20,1), rand (25,1)*2, "alpha", 0);
477s ***** error<vartest2: invalid value for alpha.> ...
477s  vartest2 (rand (20,1), rand (25,1)*2, "alpha", 1.2);
477s ***** error<vartest2: invalid value for alpha.> ...
477s  vartest2 (rand (20,1), rand (25,1)*2, "alpha", "some");
477s ***** error<vartest2: invalid value for alpha.> ...
477s  vartest2 (rand (20,1), rand (25,1)*2, "alpha", [0.05, 0.001]);
477s ***** error<vartest2: invalid value for tail.> ...
477s  vartest2 (rand (20,1), rand (25,1)*2, "tail", [0.05, 0.001]);
477s ***** error<vartest2: invalid value for tail.> ...
477s  vartest2 (rand (20,1), rand (25,1)*2, "tail", "some");
477s ***** error<vartest2: invalid value for operating dimension.> ...
477s  vartest2 (rand (20,1), rand (25,1)*2, "dim", 3);
477s ***** error<vartest2: invalid value for operating dimension.> ...
477s  vartest2 (rand (20,1), rand (25,1)*2, "alpha", 0.001, "dim", 3);
477s ***** error<vartest2: invalid name for optional arguments.> ...
477s  vartest2 (rand (20,1), rand (25,1)*2, "some", 3);
477s ***** error<vartest2: optional arguments must be in name/value pairs.> ...
477s  vartest2 (rand (20,1), rand (25,1)*2, "some");
477s ***** test
477s  load carsmall
477s  [h, pval, ci, stat] = vartest2 (MPG(Model_Year==82), MPG(Model_Year==76));
477s  assert (h, 0);
477s  assert (pval, 0.6288022362718455, 1e-13);
477s  assert (ci, [0.4139; 1.7193], 1e-4);
477s  assert (stat.fstat, 0.8384, 1e-4);
477s  assert (stat.df1, 30);
477s  assert (stat.df2, 33);
477s 14 tests, 14 passed, 0 known failure, 0 skipped
477s [inst/ztest.m]
477s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/ztest.m
477s ***** error<ztest: too few input arguments.> ztest ();
477s ***** error<ztest: invalid value for standard deviation.> ...
477s  ztest ([1, 2, 3, 4], 2, -0.5);
477s ***** error<ztest: invalid VALUE for alpha.> ...
477s  ztest ([1, 2, 3, 4], 1, 2, "alpha", 0);
477s ***** error<ztest: invalid VALUE for alpha.> ...
477s  ztest ([1, 2, 3, 4], 1, 2, "alpha", 1.2);
477s ***** error<ztest: invalid VALUE for alpha.> ...
477s  ztest ([1, 2, 3, 4], 1, 2, "alpha", "val");
477s ***** error<ztest: invalid VALUE for tail.>  ...
477s  ztest ([1, 2, 3, 4], 1, 2, "tail", "val");
477s ***** error<ztest: invalid VALUE for tail.>  ...
477s  ztest ([1, 2, 3, 4], 1, 2, "alpha", 0.01, "tail", "val");
477s ***** error<ztest: invalid VALUE for operating dimension.> ...
477s  ztest ([1, 2, 3, 4], 1, 2, "dim", 3);
477s ***** error<ztest: invalid VALUE for operating dimension.> ...
477s  ztest ([1, 2, 3, 4], 1, 2, "alpha", 0.01, "tail", "both", "dim", 3);
477s ***** error<ztest: invalid NAME for optional arguments.> ...
477s  ztest ([1, 2, 3, 4], 1, 2, "alpha", 0.01, "tail", "both", "badoption", 3);
477s ***** test
477s  load carsmall
477s  [h, pval, ci] = ztest (MPG, mean (MPG, "omitnan"), std (MPG, "omitnan"));
477s  assert (h, 0);
477s  assert (pval, 1, 1e-14);
477s  assert (ci, [22.094; 25.343], 1e-3);
477s ***** test
477s  load carsmall
477s  [h, pval, ci] = ztest (MPG, 26, 8);
477s  assert (h, 1);
477s  assert (pval, 0.00568359158544743, 1e-14);
477s  assert (ci, [22.101; 25.335], 1e-3);
477s ***** test
477s  load carsmall
477s  [h, pval, ci] = ztest (MPG, 26, 4);
477s  assert (h, 1);
477s  assert (pval, 3.184168011941316e-08, 1e-14);
477s  assert (ci, [22.909; 24.527], 1e-3);
477s 13 tests, 13 passed, 0 known failure, 0 skipped
477s [inst/bar3.m]
477s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/bar3.m
477s ***** demo
477s  ## Ploting 5 bars in the same series.
477s 
477s  z = [50; 40; 30; 20; 10];
477s  bar3 (z);
477s ***** demo
477s  ## Ploting 5 bars in different groups.
477s 
477s  z = [50, 40, 30, 20, 10];
477s  bar3 (z);
477s ***** demo
477s  ## A 3D bar graph with each series corresponding to a column in z.
477s 
477s  z = [1, 4, 7; 2, 5, 8; 3, 6, 9; 4, 7, 10];
477s  bar3 (z);
477s ***** demo
477s  ## Specify y-axis locations as tick names. y must be a column vector!
477s 
477s  y = [1950, 1960, 1970, 1980, 1990]';
477s  z = [16, 8, 4, 2, 1]';
477s  bar3 (y, z);
477s ***** demo
477s  ## Plot 3 series as a grouped plot without any space between the grouped bars
477s 
477s  z = [70 50 33 10; 75 55 35 15; 80 60 40 20];
477s  bar3 (z, 1, 'grouped');
477s ***** demo
477s  ## Plot a stacked style 3D bar graph
477s 
477s  z = [19, 30, 21, 30; 40, 16, 32, 12];
477s  b = bar3 (z, 0.5, 'stacked');
477s ***** error <bar3: Z must be numeric.> bar3 ("A")
477s ***** error <bar3: Z must be numeric.> bar3 ({2,3,4,5})
477s ***** error <bar3: inconsistent size in Y and Z input arguments.> ...
477s  bar3 ([1,2,3]', ones (2))
477s ***** error <bar3: WIDTH must be a scalar in the range> ...
477s  bar3 ([1:5], 1.2)
477s ***** error <bar3: WIDTH must be a scalar in the range> ...
477s  bar3 ([1:5]', ones (5), 1.2)
477s ***** error <bar3: numeric COLOR must be a 1x3 vector of an Nx3 matrix> ...
477s  bar3 ([1:5]', ones (5), [0.8, 0.7])
477s ***** error <bar3: missing value for optional argument 'width'.> ...
477s  bar3 (ones (5), 'width')
477s ***** error <bar3: invalid value for optional argument 'width'.> ...
477s  bar3 (ones (5), 'width', 1.2)
477s ***** error <bar3: invalid value for optional argument 'width'.> ...
477s  bar3 (ones (5), 'width', [0.8, 0.8, 0.8])
477s ***** error <bar3: missing value for optional argument 'color'.> ...
477s  bar3 (ones (5), 'color')
477s ***** error <bar3: numeric COLOR must be a 1x3 vector of an Nx3 matrix> ...
477s  bar3 (ones (5), 'color', [0.8, 0.8])
477s ***** error <bar3: invalid value for optional argument 'color'.> ...
477s  bar3 (ones (5), 'color', "brown")
477s ***** error <bar3: invalid value for optional argument 'color'.> ...
477s  bar3 (ones (5), 'color', {"r", "k", "c", "m", "brown"})
477s ***** error <bar3: missing value for optional argument 'xlabel'.> ...
477s  bar3 (ones (5), 'xlabel')
477s ***** error <bar3: invalid value for optional argument 'xlabel'.> ...
477s  bar3 (ones (5), 'xlabel', 4)
477s ***** error <bar3: missing value for optional argument 'ylabel'.> ...
477s  bar3 (ones (5), 'ylabel')
477s ***** error <bar3: invalid value for optional argument 'ylabel'.> ...
477s  bar3 (ones (5), 'ylabel', 4)
477s ***** error <bar3: invalid optional argument.> bar3 (ones (5), 'this', 4)
477s ***** error <bar3: the elements in 'xlabel' must equal the columns in Z.> ...
477s  bar3 (ones (5), 'xlabel', {"A", "B", "C"})
477s ***** error <bar3: the elements in 'ylabel' must equal the rows in Z.> ...
477s  bar3 (ones (5), 'ylabel', {"A", "B", "C"})
477s 20 tests, 20 passed, 0 known failure, 0 skipped
477s [inst/binotest.m]
477s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/binotest.m
477s ***** demo
477s  % flip a coin 1000 times, showing 475 heads
477s  % Hypothesis: coin is fair, i.e. p=1/2
477s  [h,p_val,ci] = binotest(475,1000,0.5)
477s  % Result: h = 0 : null hypothesis not rejected, coin could be fair
477s  %         P value 0.12, i.e. hypothesis not rejected for alpha up to 12%
477s  %         0.444 <= p <= 0.506 with 95% confidence
477s ***** demo
477s  % flip a coin 100 times, showing 65 heads
477s  % Hypothesis: coin shows less than 50% heads, i.e. p<=1/2
477s  [h,p_val,ci] = binotest(65,100,0.5,'tail','left','alpha',0.01)
477s  % Result: h = 1 : null hypothesis is rejected, i.e. coin shows more heads than tails
477s  %         P value 0.0018, i.e. hypothesis not rejected for alpha up to 0.18%
477s  %         0 <= p <= 0.76 with 99% confidence
477s ***** test #example from https://en.wikipedia.org/wiki/Binomial_test
477s  [h,p_val,ci] = binotest (51,235,1/6);
477s  assert (p_val, 0.0437, 0.00005)
477s  [h,p_val,ci] = binotest (51,235,1/6,'tail','left');
477s  assert (p_val, 0.027, 0.0005)
477s 1 test, 1 passed, 0 known failure, 0 skipped
477s [inst/crossval.m]
477s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/crossval.m
477s ***** test
477s  load fisheriris
477s  y = meas(:, 1);
477s  X = [ones(size(y)) meas(:, 2:4)];
477s  f = @(X1, y1, X2, y2) meansq (y2 - X2*regress(y1, X1));
477s  results0 = crossval (f, X, y);
477s  results1 = crossval (f, X, y, 'KFold', 10);
477s  folds = 5;
477s  results2 = crossval (f, X, y, 'KFold', folds);
477s  results3 = crossval (f, X, y, 'Partition', cvpartition (numel (y), 'KFold', folds));
477s  results4 = crossval (f, X, y, 'LeaveOut', 1);
477s  mcreps = 2; n_holdout = 20;
477s  results5 = crossval (f, X, y, 'HoldOut', n_holdout, 'mcreps', mcreps);
477s 
477s  ## ensure equal representation of iris species in the training set -- tends
477s  ## to slightly reduce cross-validation mean square error
477s  results6 = crossval (f, X, y, 'KFold', 5, 'stratify', grp2idx(species));
477s 
477s  assert (results0, results1, 2e-15);
477s  assert (results2, results3, 5e-17);
477s  assert (size(results4), [1 numel(y)]);
477s  assert (mean(results4), 0.1018, 1e-4);
477s  assert (size(results5), [mcreps 1]);
477s warning: strmatch is obsolete; use strncmp or strcmp instead
477s 1 test, 1 passed, 0 known failure, 0 skipped
477s [inst/procrustes.m]
477s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/procrustes.m
477s ***** demo
477s  ## Create some random points in two dimensions
477s  n = 10;
477s  randn ("seed", 1);
477s  X = normrnd (0, 1, [n, 2]);
477s 
477s  ## Those same points, rotated, scaled, translated, plus some noise
477s  S = [0.5, -sqrt(3)/2; sqrt(3)/2, 0.5]; # rotate 60 degrees
477s  Y = normrnd (0.5*X*S + 2, 0.05, n, 2);
477s 
477s  ## Conform Y to X, plot original X and Y, and transformed Y
477s  [d, Z] = procrustes (X, Y);
477s  plot (X(:,1), X(:,2), "rx", Y(:,1), Y(:,2), "b.", Z(:,1), Z(:,2), "bx");
477s ***** demo
477s  ## Find Procrustes distance and plot superimposed shape
477s 
477s  X = [40 88; 51 88; 35 78; 36 75; 39 72; 44 71; 48 71; 52 74; 55 77];
477s  Y = [36 43; 48 42; 31 26; 33 28; 37 30; 40 31; 45 30; 48 28; 51 24];
477s  plot (X(:,1),X(:,2),"x");
477s  hold on
477s  plot (Y(:,1),Y(:,2),"o");
477s  xlim ([0 100]);
477s  ylim ([0 100]);
477s  legend ("Target shape (X)", "Source shape (Y)");
477s  [d, Z] = procrustes (X, Y)
477s  plot (Z(:,1), Z(:,2), "s");
477s  legend ("Target shape (X)", "Source shape (Y)", "Transformed shape (Z)");
477s  hold off
477s ***** demo
477s  ## Apply Procrustes transformation to larger set of points
477s 
477s  ## Create matrices with landmark points for two triangles
477s  X = [5, 0; 5, 5; 8, 5];   # target
477s  Y = [0, 0; 1, 0; 1, 1];   # source
477s 
477s  ## Create a matrix with more points on the source triangle
477s  Y_mp = [linspace(Y(1,1),Y(2,1),10)', linspace(Y(1,2),Y(2,2),10)'; ...
477s          linspace(Y(2,1),Y(3,1),10)', linspace(Y(2,2),Y(3,2),10)'; ...
477s          linspace(Y(3,1),Y(1,1),10)', linspace(Y(3,2),Y(1,2),10)'];
477s 
477s  ## Plot both shapes, including the larger set of points for the source shape
477s  plot ([X(:,1); X(1,1)], [X(:,2); X(1,2)], "bx-");
477s  hold on
477s  plot ([Y(:,1); Y(1,1)], [Y(:,2); Y(1,2)], "ro-", "MarkerFaceColor", "r");
477s  plot (Y_mp(:,1), Y_mp(:,2), "ro");
477s  xlim ([-1 10]);
477s  ylim ([-1 6]);
477s  legend ("Target shape (X)", "Source shape (Y)", ...
477s          "More points on Y", "Location", "northwest");
477s  hold off
477s 
477s  ## Obtain the Procrustes transformation
477s  [d, Z, transform] = procrustes (X, Y)
477s 
477s  ## Use the Procrustes transformation to superimpose the more points (Y_mp)
477s  ## on the source shape onto the target shape, and then visualize the results.
477s  Z_mp = transform.b * Y_mp * transform.T + transform.c(1,:);
477s  figure
477s  plot ([X(:,1); X(1,1)], [X(:,2); X(1,2)], "bx-");
477s  hold on
477s  plot ([Y(:,1); Y(1,1)], [Y(:,2); Y(1,2)], "ro-", "MarkerFaceColor", "r");
477s  plot (Y_mp(:,1), Y_mp(:,2), "ro");
477s  xlim ([-1 10]);
477s  ylim ([-1 6]);
477s  plot ([Z(:,1); Z(1,1)],[Z(:,2); Z(1,2)],"ks-","MarkerFaceColor","k");
477s  plot (Z_mp(:,1),Z_mp(:,2),"ks");
477s  legend ("Target shape (X)", "Source shape (Y)", ...
477s          "More points on Y", "Transformed source shape (Z)", ...
477s          "Transformed additional points", "Location", "northwest");
477s  hold off
477s ***** demo
477s  ## Compare shapes without reflection
477s 
477s  T = [33, 93; 33, 87; 33, 80; 31, 72; 32, 65; 32, 58; 30, 72; ...
477s       28, 72; 25, 69; 22, 64; 23, 59; 26, 57; 30, 57];
477s  S = [48, 83; 48, 77; 48, 70; 48, 65; 49, 59; 49, 56; 50, 66; ...
477s       52, 66; 56, 65; 58, 61; 57, 57; 54, 56; 51, 55];
477s  plot (T(:,1), T(:,2), "x-");
477s  hold on
477s  plot (S(:,1), S(:,2), "o-");
477s  legend ("Target shape (d)", "Source shape (b)");
477s  hold off
477s  d_false = procrustes (T, S, "reflection", false);
477s  printf ("Procrustes distance without reflection: %f\n", d_false);
477s  d_true = procrustes (T, S, "reflection", true);
477s  printf ("Procrustes distance with reflection: %f\n", d_true);
477s  d_best = procrustes (T, S, "reflection", "best");
477s  printf ("Procrustes distance with best fit: %f\n", d_true);
477s ***** error procrustes ();
477s ***** error procrustes (1, 2, 3, 4, 5, 6);
477s ***** error<procrustes: X and Y must be 2-dimensional matrices.> ...
477s  procrustes (ones (2, 2, 2), ones (2, 2, 2));
477s ***** error<procrustes: values in X and Y must be real.> ...
477s  procrustes ([1, 2; -3, 4; 2, 3], [1, 2; -3, 4; 2, 3+i]);
477s ***** error<procrustes: values in X and Y must be real.> ...
477s  procrustes ([1, 2; -3, 4; 2, 3], [1, 2; -3, 4; 2, NaN]);
477s ***** error<procrustes: values in X and Y must be real.> ...
477s  procrustes ([1, 2; -3, 4; 2, 3], [1, 2; -3, 4; 2, Inf]);
477s ***** error<procrustes: X and Y must have equal number of rows.> ...
477s  procrustes (ones (10 ,3), ones (11, 3));
477s ***** error<procrustes: X must have at least as many columns as Y.> ...
477s  procrustes (ones (10 ,3), ones (10, 4));
477s ***** error<procrustes: optional arguments must be in Name-Value pairs.> ...
477s  procrustes (ones (10 ,3), ones (10, 3), "reflection");
477s ***** error<procrustes: optional arguments must be in Name-Value pairs.> ...
477s  procrustes (ones (10 ,3), ones (10, 3), true);
477s ***** error<procrustes: invalid value for scaling.> ...
477s  procrustes (ones (10 ,3), ones (10, 3), "scaling", 0);
477s ***** error<procrustes: invalid value for scaling.> ...
477s  procrustes (ones (10 ,3), ones (10, 3), "scaling", [true true]);
477s ***** error<procrustes: invalid value for reflection.> ...
477s  procrustes (ones (10 ,3), ones (10, 3), "reflection", 1);
477s ***** error<procrustes: invalid value for reflection.> ...
477s  procrustes (ones (10 ,3), ones (10, 3), "reflection", "some");
477s ***** error<procrustes: invalid name for optional arguments.> ...
477s  procrustes (ones (10 ,3), ones (10, 3), "param1", "some");
477s 15 tests, 15 passed, 0 known failure, 0 skipped
477s [inst/barttest.m]
477s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/barttest.m
477s ***** error<barttest: invalid number of input arguments.> barttest ()
477s ***** error<barttest: NaN values in input are not allowed.> barttest ([2,NaN;3,4])
477s ***** error<barttest: wrong value for alpha.> barttest (ones (30, 4), "alpha")
477s ***** error<barttest: wrong value for alpha.> barttest (ones (30, 4), 0)
477s ***** error<barttest: wrong value for alpha.> barttest (ones (30, 4), 1.2)
477s ***** error<barttest: wrong value for alpha.> barttest (ones (30, 4), [0.2, 0.05])
477s ***** error<barttest: not enough data in X.> barttest (ones (30, 1))
477s ***** error<barttest: not enough data in X.> barttest (ones (30, 1), 0.05)
477s ***** test
477s  x = [2, 3, 4, 5, 6, 7, 8, 9; 1, 2, 3, 4, 5, 6, 7, 8]';
477s  [ndim, pval, chisq] = barttest (x);
477s  assert (ndim, 2);
477s  assert (pval, 0);
477s  ## assert (chisq, 512.0558, 1e-4); Result differs between octave 6 and 7 ?
477s ***** test
477s  x = [0.53767,  0.62702,   -0.10224,   -0.25485,   1.4193,   1.5237  ; ...
477s       1.8339,   1.6452,    -0.24145,   -0.23444,   0.29158,  0.1634  ; ...
477s      -2.2588,  -2.1351,     0.31286,    0.39396,   0.19781,  0.20995 ; ...
477s       0.86217,  1.0835,     0.31286,    0.46499,   1.5877,   1.495   ; ...
477s       0.31877,  0.38454,   -0.86488,   -0.63839,  -0.80447, -0.7536  ; ...
477s      -1.3077,  -1.1487,    -0.030051,  -0.017629,  0.69662,  0.60497 ; ...
477s      -0.43359, -0.32672,   -0.16488,   -0.37364,   0.83509,  0.89586 ; ...
477s       0.34262,  0.29639,    0.62771,    0.51672,  -0.24372, -0.13698 ; ...
477s       3.5784,   3.5841,     1.0933,     0.93258,   0.21567,  0.455   ; ...
477s       2.7694,   2.6307,     1.1093,     1.4298,   -1.1658,  -1.1816  ; ...
477s      -1.3499,  -1.2111,    -0.86365,   -0.94186,  -1.148,   -1.4381  ; ...
477s       3.0349,   2.8428,     0.077359,   0.18211,   0.10487, -0.014613; ...
477s       0.7254,   0.56737,   -1.2141,    -1.2291,    0.72225,  0.90612 ; ...
477s      -0.063055,-0.17662,   -1.1135,    -0.97701,   2.5855,   2.4084  ; ...
477s       0.71474,  0.29225,   -0.0068493, -0.11468,  -0.66689, -0.52466 ; ...
477s      -0.20497, -7.8874e-06, 1.5326,     1.3195,    0.18733,  0.20296 ; ...
477s      -0.12414, -0.077029,  -0.76967,   -0.96262,  -0.082494, 0.121   ; ...
477s       1.4897,   1.3683,     0.37138,    0.43653,  -1.933,   -2.1903  ; ...
477s       1.409,    1.5882,    -0.22558,   -0.24835,  -0.43897, -0.46247 ; ...
477s       1.4172,   1.1616,     1.1174,     1.0785,   -1.7947,  -1.9471  ];
477s  [ndim, pval, chisq] = barttest (x);
477s  assert (ndim, 3);
477s  assert (pval, [0; 0; 0; 0.52063; 0.34314], 1e-5);
477s  chisq_out = [251.6802; 210.2670; 153.1773; 4.2026; 2.1392];
477s  assert (chisq, chisq_out, 1e-4);
477s 10 tests, 10 passed, 0 known failure, 0 skipped
477s [inst/signrank.m]
477s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/signrank.m
477s ***** test
477s  load gradespaired.mat
477s  [p, h, stats] = signrank (gradespaired(:,1), ...
477s                            gradespaired(:,2), 'tail', 'left');
477s  assert (p, 0.0047, 1e-4);
477s  assert (h, true);
477s  assert (stats.zval, -2.5982, 1e-4);
477s  assert (stats.signedrank, 2017.5);
477s ***** test
477s  load ('gradespaired.mat');
477s  [p, h, stats] = signrank (gradespaired(:,1), gradespaired(:,2), ...
477s                            'tail', 'left', 'method', 'exact');
477s  assert (p, 0.0045, 1e-4);
477s  assert (h, true);
477s  assert (stats.zval, NaN);
477s  assert (stats.signedrank, 2017.5);
477s ***** test
477s  load mileage
477s  [p, h, stats] = signrank (mileage(:,2), 33);
477s  assert (p, 0.0312, 1e-4);
477s  assert (h, true);
477s  assert (stats.zval, NaN);
477s  assert (stats.signedrank, 21);
477s ***** test
477s  load mileage
477s  [p, h, stats] = signrank (mileage(:,2), 33, 'tail', 'right');
477s  assert (p, 0.0156, 1e-4);
477s  assert (h, true);
477s  assert (stats.zval, NaN);
477s  assert (stats.signedrank, 21);
477s ***** test
477s  load mileage
477s  [p, h, stats] = signrank (mileage(:,2), 33, 'tail', 'right', ...
477s                            'alpha', 0.01, 'method', 'approximate');
477s  assert (p, 0.0180, 1e-4);
477s  assert (h, false);
477s  assert (stats.zval, 2.0966, 1e-4);
477s  assert (stats.signedrank, 21);
477s ***** error <signrank: X must be a vector.> signrank (ones (2))
477s ***** error <signrank: Y must be either a scalar of a vector.> ...
477s  signrank ([1, 2, 3, 4], ones (2))
477s ***** error <signrank: X and Y vectors have different lengths.> ...
477s  signrank ([1, 2, 3, 4], [1, 2, 3])
477s ***** error <signrank: optional arguments must be in pairs.> ...
477s  signrank ([1, 2, 3, 4], [], 'tail')
477s ***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
477s  signrank ([1, 2, 3, 4], [], 'alpha', 1.2)
477s ***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
477s  signrank ([1, 2, 3, 4], [], 'alpha', 0)
477s ***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
477s  signrank ([1, 2, 3, 4], [], 'alpha', -0.05)
477s ***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
477s  signrank ([1, 2, 3, 4], [], 'alpha', "a")
477s ***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
477s  signrank ([1, 2, 3, 4], [], 'alpha', [0.01, 0.05])
477s ***** error <signrank: 'tail' argument must be a character vector.> ...
477s  signrank ([1, 2, 3, 4], [], 'tail', 0.01)
477s ***** error <signrank: 'tail' argument must be a character vector.> ...
477s  signrank ([1, 2, 3, 4], [], 'tail', {"both"})
477s ***** error <signrank: 'tail' value must be either 'both', right' or 'left'.> ...
477s  signrank ([1, 2, 3, 4], [], 'tail', "some")
477s ***** error <signrank: 'tail' value must be either 'both', right' or 'left'.> ...
477s  signrank ([1, 2, 3, 4], [], 'method', 'exact', 'tail', "some")
477s ***** error <signrank: 'method' argument must be a character vector.> ...
477s  signrank ([1, 2, 3, 4], [], 'method', 0.01)
477s ***** error <signrank: 'method' argument must be a character vector.> ...
477s  signrank ([1, 2, 3, 4], [], 'method', {"exact"})
477s ***** error <signrank: 'method' value must be either 'exact' or 'approximate'.> ...
477s  signrank ([1, 2, 3, 4], [], 'method', "some")
477s ***** error <signrank: 'method' value must be either 'exact' or 'approximate'.> ...
477s  signrank ([1, 2, 3, 4], [], 'tail', "both", 'method', "some")
477s 22 tests, 22 passed, 0 known failure, 0 skipped
477s [inst/histfit.m]
477s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/histfit.m
477s ***** demo
477s  histfit (randn (100, 1))
477s ***** demo
477s  histfit (poissrnd (2, 1000, 1), 10, "Poisson")
477s ***** demo
477s  histfit (betarnd (3, 10, 1000, 1), 10, "beta")
477s ***** test
477s  hf = figure ("visible", "off");
477s  unwind_protect
477s    x = [2, 4, 3, 2, 4, 3, 2, 5, 6, 4, 7, 5, 9, 8, 10, 4, 11];
477s    histfit (x);
477s  unwind_protect_cleanup
477s    close (hf);
477s  end_unwind_protect
477s warning: using the gnuplot graphics toolkit is discouraged
477s 
477s The gnuplot graphics toolkit is not actively maintained and has a number
477s of limitations that are unlikely to be fixed.  Communication with gnuplot
477s uses a one-directional pipe and limited information is passed back to the
477s Octave interpreter so most changes made interactively in the plot window
477s will not be reflected in the graphics properties managed by Octave.  For
477s example, if the plot window is closed with a mouse click, Octave will not
477s be notified and will not update its internal list of open figure windows.
477s The qt toolkit is recommended instead.
477s ***** test
477s  hf = figure ("visible", "off");
477s  unwind_protect
477s    x = [2, 4, 3, 2, NaN, 3, 2, 5, 6, 4, 7, 5, 9, 8, 10, 4, 11];
477s    histfit (x);
477s  unwind_protect_cleanup
477s    close (hf);
477s  end_unwind_protect
478s ***** test
478s  hf = figure ("visible", "off");
478s  unwind_protect
478s    x = [2, 4, 3, 2, NaN, 3, 2, 5, 6, 4, 7, 5, 9, 8, 10, 4, 11];
478s    histfit (x, 3);
478s  unwind_protect_cleanup
478s    close (hf);
478s  end_unwind_protect
478s ***** test
478s  hf = figure ("visible", "off");
478s  unwind_protect
478s    histfit (randn (100, 1));
478s  unwind_protect_cleanup
478s    close (hf);
478s  end_unwind_protect
478s ***** test
478s  hf = figure ("visible", "off");
478s  unwind_protect
478s    histfit (poissrnd (2, 1000, 1), 10, "Poisson");
478s  unwind_protect_cleanup
478s    close (hf);
478s  end_unwind_protect
478s ***** test
478s  hf = figure ("visible", "off");
478s  unwind_protect
478s    histfit (betarnd (3, 10, 1000, 1), 10, "beta");
478s  unwind_protect_cleanup
478s    close (hf);
478s  end_unwind_protect
478s ***** test
478s  hf = figure ("visible", "off");
478s  unwind_protect
478s    ax = gca ();
478s    histfit (ax, randn (100, 1));
478s  unwind_protect_cleanup
478s    close (hf);
478s  end_unwind_protect
478s ***** test
478s  hf = figure ("visible", "off");
478s  unwind_protect
478s    ax = gca ();
478s    histfit (ax, poissrnd (2, 1000, 1), 10, "Poisson");
478s  unwind_protect_cleanup
478s    close (hf);
478s  end_unwind_protect
478s ***** test
478s  hf = figure ("visible", "off");
478s  unwind_protect
478s    ax = gca ();
478s    histfit (ax, betarnd (3, 10, 1000, 1), 10, "beta");
478s  unwind_protect_cleanup
478s    close (hf);
478s  end_unwind_protect
478s ***** test
478s  hf = figure ("visible", "off");
478s  unwind_protect
478s    ax = axes ("parent", hf);
478s    fail ("histfit (ax)", "histfit: too few input arguments.");
478s  unwind_protect_cleanup
478s    close (hf);
478s  end_unwind_protect
478s ***** error<histfit: X must be a numeric vector of real numbers.> ...
478s  histfit ('wer')
478s ***** error<histfit: no data in X.> histfit ([NaN, NaN, NaN]);
478s ***** error<histfit: NBINS must be a real scalar integer value.> ...
478s  histfit (randn (100, 1), 5.6)
478s ***** error<histfit: DISTNAME must be a character vector.> ...
478s  histfit (randn (100, 1), 8, 5)
478s ***** error<histfit: DISTNAME must be a character vector.> ...
478s  histfit (randn (100, 1), 8, {'normal'})
478s ***** error<histfit: 'Kernel' distribution is not supported yet.> ...
478s  histfit (randn (100, 1), 8, 'Kernel')
478s ***** error<histfit: unrecognized distribution name.> ...
478s  histfit (randn (100, 1), 8, 'ASDASDASD')
478s 17 tests, 17 passed, 0 known failure, 0 skipped
478s [inst/sampsizepwr.m]
478s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/sampsizepwr.m
478s ***** demo
478s  ## Compute the mean closest to 100 that can be determined to be
478s  ## significantly different from 100 using a t-test with a sample size
478s  ## of 60 and a power of 0.8.
478s  mu1 = sampsizepwr ("t", [100, 10], [], 0.8, 60);
478s  disp (mu1);
478s ***** demo
478s  ## Compute the sample sizes required to distinguish mu0 = 100 from
478s  ## mu1 = 110 by a two-sample t-test with a ratio of the larger and the
478s  ## smaller sample sizes of 1.5 and a power of 0.6.
478s  [N1,N2] = sampsizepwr ("t2", [100, 10], 110, 0.6, [], "ratio", 1.5)
478s ***** demo
478s  ## Compute the sample size N required to distinguish p=.26 from p=.2
478s  ## with a binomial test.  The result is approximate, so make a plot to
478s  ## see if any smaller N values also have the required power of 0.6.
478s  Napprox = sampsizepwr ("p", 0.2, 0.26, 0.6);
478s  nn = 1:250;
478s  pwr = sampsizepwr ("p", 0.2, 0.26, [], nn);
478s  Nexact = min (nn(pwr >= 0.6));
478s  plot(nn,pwr,'b-', [Napprox Nexact],pwr([Napprox Nexact]),'ro');
478s  grid on
478s ***** demo
478s  ## The company must test 52 bottles to detect the difference between a mean
478s  ## volume of 100 mL and 102 mL with a power of 0.80.  Generate a power curve
478s  ## to visualize how the sample size affects the power of the test.
478s 
478s  nout = sampsizepwr('t',[100 5],102,0.80);
478s  nn = 1:100;
478s  pwrout = sampsizepwr('t',[100 5],102,[],nn);
478s 
478s  figure;
478s  plot (nn, pwrout, "b-", nout, 0.8, "ro")
478s  title ("Power versus Sample Size")
478s  xlabel ("Sample Size")
478s  ylabel ("Power")
478s ***** error<sampsizepwr: test type must be a non-empty string.> ...
478s  out = sampsizepwr ([], [100, 10], [], 0.8, 60);
478s ***** error<sampsizepwr: test type must be a non-empty string.> ...
478s  out = sampsizepwr (3, [100, 10], [], 0.8, 60);
478s ***** error<sampsizepwr: test type must be a non-empty string.> ...
478s  out = sampsizepwr ({"t", "t2"}, [100, 10], [], 0.8, 60);
478s ***** error<sampsizepwr: invalid test type.> ...
478s  out = sampsizepwr ("reg", [100, 10], [], 0.8, 60);
478s ***** error<sampsizepwr: parameters must be numeric.> ...
478s  out = sampsizepwr ("t", ["a", "e"], [], 0.8, 60);
478s ***** error<sampsizepwr: invalid size of parameters for this test type.> ...
478s  out = sampsizepwr ("z", 100, [], 0.8, 60);
478s ***** error<sampsizepwr: invalid size of parameters for this test type.> ...
478s  out = sampsizepwr ("t", 100, [], 0.8, 60);
478s ***** error<sampsizepwr: invalid size of parameters for this test type.> ...
478s  out = sampsizepwr ("t2", 60, [], 0.8, 60);
478s ***** error<sampsizepwr: invalid size of parameters for this test type.> ...
478s  out = sampsizepwr ("var", [100, 10], [], 0.8, 60);
478s ***** error<sampsizepwr: invalid size of parameters for this test type.> ...
478s  out = sampsizepwr ("p", [100, 10], [], 0.8, 60);
478s ***** error<sampsizepwr: invalid size of parameters for this test type.> ...
478s  out = sampsizepwr ("r", [100, 10], [], 0.8, 60);
478s ***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
478s  [out, N1] = sampsizepwr ("z", [100, 10], [], 0.8, 60);
478s ***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
478s  [out, N1] = sampsizepwr ("t", [100, 10], [], 0.8, 60);
478s ***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
478s  [out, N1] = sampsizepwr ("var", 2, [], 0.8, 60);
478s ***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
478s  [out, N1] = sampsizepwr ("p", 0.1, [], 0.8, 60);
478s ***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
478s  [out, N1] = sampsizepwr ("r", 0.5, [], 0.8, 60);
478s ***** error<sampsizepwr: negative or zero variance.> ...
478s  out = sampsizepwr ("z", [100, 0], [], 0.8, 60);
478s ***** error<sampsizepwr: negative or zero variance.> ...
478s  out = sampsizepwr ("z", [100, -5], [], 0.8, 60);
478s ***** error<sampsizepwr: negative or zero variance.> ...
478s  out = sampsizepwr ("t", [100, 0], [], 0.8, 60);
478s ***** error<sampsizepwr: negative or zero variance.> ...
478s  out = sampsizepwr ("t", [100, -5], [], 0.8, 60);
478s ***** error<sampsizepwr: negative or zero variance.> ...
478s  [out, N1] = sampsizepwr ("t2", [100, 0], [], 0.8, 60);
478s ***** error<sampsizepwr: negative or zero variance.> ...
478s  [out, N1] = sampsizepwr ("t2", [100, -5], [], 0.8, 60);
478s ***** error<sampsizepwr: negative or zero variance.> ...
478s  out = sampsizepwr ("var", 0, [], 0.8, 60);
478s ***** error<sampsizepwr: negative or zero variance.> ...
478s  out = sampsizepwr ("var", -5, [], 0.8, 60);
478s ***** error<sampsizepwr: out of range probability.> ...
478s  out = sampsizepwr ("p", 0, [], 0.8, 60);
478s ***** error<sampsizepwr: out of range probability.> ...
478s  out = sampsizepwr ("p", 1.2, [], 0.8, 60);
478s ***** error<sampsizepwr: out of range regression coefficient.> ...
478s  out = sampsizepwr ("r", -1.5, [], 0.8, 60);
478s ***** error<sampsizepwr: out of range regression coefficient.> ...
478s  out = sampsizepwr ("r", -1, [], 0.8, 60);
478s ***** error<sampsizepwr: out of range regression coefficient.> ...
478s  out = sampsizepwr ("r", 1.2, [], 0.8, 60);
478s ***** error<sampsizepwr: regression coefficient must not be 0.> ...
478s  out = sampsizepwr ("r", 0, [], 0.8, 60);
478s ***** error<sampsizepwr: invalid value for 'alpha' parameter.> ...
478s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "alpha", -0.2);
478s ***** error<sampsizepwr: invalid value for 'alpha' parameter.> ...
478s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "alpha", 0);
478s ***** error<sampsizepwr: invalid value for 'alpha' parameter.> ...
478s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "alpha", 1.5);
478s ***** error<sampsizepwr: invalid value for 'alpha' parameter.> ...
478s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "alpha", "zero");
478s ***** error<sampsizepwr: 'tail' parameter must be a non-empty string.> ...
478s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "tail", 1.5);
478s ***** error<sampsizepwr: 'tail' parameter must be a non-empty string.> ...
478s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "tail", {"both", "left"});
478s ***** error<sampsizepwr: invalid value for 'tail' parameter.> ...
478s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "tail", "other");
478s ***** error<sampsizepwr: invalid value for 'ratio' parameter.> ...
478s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "ratio", "some");
478s ***** error<sampsizepwr: invalid value for 'ratio' parameter.> ...
478s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "ratio", 0.5);
478s ***** error<sampsizepwr: invalid value for 'ratio' parameter.> ...
478s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "ratio", [2, 1.3, 0.3]);
478s ***** error<sampsizepwr: only one of either p1, power, or n must be missing.> ...
478s  out = sampsizepwr ("z", [100, 5], [], [], 60);
478s ***** error<sampsizepwr: only one of either p1, power, or n must be missing.> ...
478s  out = sampsizepwr ("z", [100, 5], 110, [], []);
478s ***** error<sampsizepwr: only one of either p1, power, or n must be missing.> ...
478s  out = sampsizepwr ("z", [100, 5], [], 0.8, []);
478s ***** error<sampsizepwr: only one of either p1, power, or n must be missing.> ...
478s  out = sampsizepwr ("z", [100, 5], 110, 0.8, 60);
478s ***** error<sampsizepwr: alternative hypothesis parameter must be numeric.> ...
478s  out = sampsizepwr ("z", [100, 5], "mu", [], 60);
478s ***** error<sampsizepwr: alternative hypothesis parameter out of range.> ...
478s  out = sampsizepwr ("var", 5, -1, [], 60);
478s ***** error<sampsizepwr: alternative hypothesis parameter out of range.> ...
478s  out = sampsizepwr ("p", 0.8, 1.2, [], 60, "tail", "right");
478s ***** error<sampsizepwr: alternative hypothesis parameter out of range.> ...
478s  out = sampsizepwr ("r", 0.8, 1.2, [], 60);
478s ***** error<sampsizepwr: alternative hypothesis parameter out of range.> ...
478s  out = sampsizepwr ("r", 0.8, -1.2, [], 60);
478s ***** error<sampsizepwr: invalid value for POWER.> ...
478s  out = sampsizepwr ("z", [100, 5], 110, 1.2);
478s ***** error<sampsizepwr: invalid value for POWER.> ...
478s  out = sampsizepwr ("z", [100, 5], 110, 0);
478s ***** error<sampsizepwr: Cannot compute N or P1 unless POWER> ...
478s  out = sampsizepwr ("z", [100, 5], 110, 0.05, [], "alpha", 0.1);
478s ***** error<sampsizepwr: input arguments size mismatch.> ...
478s  out = sampsizepwr ("z", [100, 5], [], [0.8, 0.7], [60, 80, 100]);
478s ***** error<sampsizepwr: Same value for null and alternative hypothesis.> ...
478s  out = sampsizepwr ("t", [100, 5], 100, 0.8, []);
478s ***** error<sampsizepwr: Invalid P1 for testing left tail.> ...
478s  out = sampsizepwr ("t", [100, 5], 110, 0.8, [], "tail", "left");
478s ***** error<sampsizepwr: Invalid P1 for testing right tail.> ...
478s  out = sampsizepwr ("t", [100, 5], 90, 0.8, [], "tail", "right");
478s ***** warning<sampsizepwr: approximate N.> ...
478s  Napprox = sampsizepwr ("p", 0.2, 0.26, 0.6);
478s ***** warning<sampsizepwr: approximate N.> ...
478s  Napprox = sampsizepwr ("p", 0.30, 0.36, 0.8);
478s ***** test
478s  mu1 = sampsizepwr ("t", [100, 10], [], 0.8, 60);
478s  assert (mu1, 103.67704316, 1e-8);
479s ***** test
479s  [N1,N2] = sampsizepwr ("t2", [100, 10], 110, 0.6, [], "ratio", 1.5);
479s  assert (N1, 9);
479s  assert (N2, 14);
479s ***** test
479s  nn = 1:250;
479s  pwr = sampsizepwr ("p", 0.2, 0.26, [], nn);
479s  pwr_out = [0, 0.0676, 0.0176, 0.0566, 0.0181, 0.0431, 0.0802, 0.0322];
479s  assert (pwr([1:8]), pwr_out, 1e-4 * ones (1,8));
479s  pwr_out = [0.59275, 0.6073, 0.62166, 0.6358, 0.6497, 0.6087, 0.6229, 0.6369];
479s  assert (pwr([243:end]), pwr_out, 1e-4 * ones (1,8));
479s ***** test
479s  nout = sampsizepwr ("t", [100, 5], 102, 0.80);
479s  assert (nout, 52);
479s ***** test
479s  power = sampsizepwr ("t", [20, 5], 25, [], 5, "Tail", "right");
479s  assert (power, 0.5797373588621888, 1e-14);
479s ***** test
479s  nout = sampsizepwr ("t", [20, 5], 25, 0.99, [], "Tail", "right");
479s  assert (nout, 18);
479s ***** test
479s  p1out = sampsizepwr ("t", [20, 5], [], 0.95, 10, "Tail", "right");
479s  assert (p1out, 25.65317979360237, 1e-14);
479s ***** test
479s  pwr = sampsizepwr ("t2", [1.4, 0.2], 1.7, [], 5, "Ratio", 2);
479s  assert (pwr, 0.716504004686586, 1e-14);
479s ***** test
479s  n = sampsizepwr ("t2", [1.4, 0.2], 1.7, 0.9, []);
479s  assert (n, 11);
480s ***** test
480s  [n1, n2] = sampsizepwr ("t2", [1.4, 0.2], 1.7, 0.9, [], "Ratio", 2);
480s  assert ([n1, n2], [8, 16]);
480s 68 tests, 68 passed, 0 known failure, 0 skipped
480s [inst/fitcdiscr.m]
480s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/fitcdiscr.m
480s ***** demo
480s  ## Train a linear discriminant classifier for Gamma = 0.5
480s  ## and plot the decision boundaries.
480s 
480s  load fisheriris
480s  idx = ! strcmp (species, "setosa");
480s  X = meas(idx,3:4);
480s  Y = cast (strcmpi (species(idx), "virginica"), "double");
480s  obj = fitcdiscr (X, Y, "Gamma", 0.5)
480s  x1 = [min(X(:,1)):0.03:max(X(:,1))];
480s  x2 = [min(X(:,2)):0.02:max(X(:,2))];
480s  [x1G, x2G] = meshgrid (x1, x2);
480s  XGrid = [x1G(:), x2G(:)];
480s  pred = predict (obj, XGrid);
480s  gidx = logical (str2num (cell2mat (pred)));
480s 
480s  figure
480s  scatter (XGrid(gidx,1), XGrid(gidx,2), "markerfacecolor", "magenta");
480s  hold on
480s  scatter (XGrid(!gidx,1), XGrid(!gidx,2), "markerfacecolor", "red");
480s  plot (X(Y == 0, 1), X(Y == 0, 2), "ko", X(Y == 1, 1), X(Y == 1, 2), "kx");
480s  xlabel ("Petal length (cm)");
480s  ylabel ("Petal width (cm)");
480s  title ("Linear Discriminant Analysis Decision Boundary");
480s  legend ({"Versicolor Region", "Virginica Region", ...
480s          "Sampled Versicolor", "Sampled Virginica"}, ...
480s          "location", "northwest")
480s  axis tight
480s  hold off
480s ***** test
480s  load fisheriris
480s  Mdl = fitcdiscr (meas, species, "Gamma", 0.5);
480s  [label, score, cost] = predict (Mdl, [2, 2, 2, 2]);
480s  assert (label, {'versicolor'})
480s  assert (score, [0, 0.9999, 0.0001], 1e-4)
480s  assert (cost, [1, 0.0001, 0.9999], 1e-4)
480s  [label, score, cost] = predict (Mdl, [2.5, 2.5, 2.5, 2.5]);
480s  assert (label, {'versicolor'})
480s  assert (score, [0, 0.6368, 0.3632], 1e-4)
480s  assert (cost, [1, 0.3632, 0.6368], 1e-4)
480s  assert (class (Mdl), "ClassificationDiscriminant");
480s  assert ({Mdl.X, Mdl.Y, Mdl.NumObservations}, {meas, species, 150})
480s  assert ({Mdl.DiscrimType, Mdl.ResponseName}, {"linear", "Y"})
480s  assert ({Mdl.Gamma, Mdl.MinGamma}, {0.5, 0})
480s  assert (Mdl.ClassNames, unique (species))
480s  sigma = [0.265008, 0.046361, 0.083757, 0.019201; ...
480s           0.046361, 0.115388, 0.027622, 0.016355; ...
480s           0.083757, 0.027622, 0.185188, 0.021333; ...
480s           0.019201, 0.016355, 0.021333, 0.041882];
480s  assert (Mdl.Sigma, sigma, 1e-6)
480s  mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
480s        5.9360, 2.7700, 4.2600, 1.3260; ...
480s        6.5880, 2.9740, 5.5520, 2.0260];
480s  assert (Mdl.Mu, mu, 1e-14)
480s  assert (Mdl.LogDetSigma, -8.6884, 1e-4)
480s ***** error<fitcdiscr: too few arguments.> fitcdiscr ()
480s ***** error<fitcdiscr: too few arguments.> fitcdiscr (ones (4,1))
480s ***** error<fitcdiscr: name-value arguments must be in pairs.>
480s  fitcdiscr (ones (4,2), ones (4, 1), "K")
480s ***** error<fitcdiscr: number of rows in X and Y must be equal.>
480s  fitcdiscr (ones (4,2), ones (3, 1))
480s ***** error<fitcdiscr: number of rows in X and Y must be equal.>
480s  fitcdiscr (ones (4,2), ones (3, 1), "K", 2)
480s 6 tests, 6 passed, 0 known failure, 0 skipped
480s [inst/dist_obj/RicianDistribution.m]
480s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_obj/RicianDistribution.m
480s ***** shared pd, t
480s  pd = RicianDistribution;
480s  t = truncate (pd, 2, 4);
480s ***** assert (cdf (pd, [0:5]), [0, 0.2671, 0.7310, 0.9563, 0.9971, 0.9999], 1e-4);
480s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.8466, 1, 1], 1e-4);
480s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.5120, 0.7310, 0.9563, 0.9971, NaN], 1e-4);
480s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.8466, 1, NaN], 1e-4);
480s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.8501, 1.2736, 1.6863, 2.2011, Inf], 1e-4);
480s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1517, 2.3296, 2.5545, 2.8868, 4], 1e-4);
480s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1.2736, 1.6863, 2.2011, Inf, NaN], 1e-4);
480s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.3296, 2.5545, 2.8868, 4, NaN], 1e-4);
480s ***** assert (iqr (pd), 1.0890, 1e-4);
480s ***** assert (iqr (t), 0.5928, 1e-4);
480s ***** assert (mean (pd), 1.5486, 1e-4);
480s ***** assert (mean (t), 2.5380, 1e-4);
480s ***** assert (median (pd), 1.4755, 1e-4);
480s ***** assert (median (t), 2.4341, 1e-4);
480s ***** assert (pdf (pd, [0:5]), [0, 0.4658, 0.3742, 0.0987, 0.0092, 0.0003], 1e-4);
480s ***** assert (pdf (t, [0:5]), [0, 0, 1.4063, 0.3707, 0.0346, 0], 1e-4);
480s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.4864, NaN], 1e-4);
480s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
480s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
480s ***** assert (any (random (t, 1000, 1) < 2), false);
480s ***** assert (any (random (t, 1000, 1) > 4), false);
480s ***** assert (std (pd), 0.7758, 1e-4);
480s ***** assert (std (t), 0.4294, 1e-4);
480s ***** assert (var (pd), 0.6019, 1e-4);
480s ***** assert (var (t), 0.1844, 1e-4);
480s ***** error <RicianDistribution: NU must be a non-negative real scalar.> ...
480s  RicianDistribution(-eps, 1)
480s ***** error <RicianDistribution: NU must be a non-negative real scalar.> ...
480s  RicianDistribution(-1, 1)
480s ***** error <RicianDistribution: NU must be a non-negative real scalar.> ...
480s  RicianDistribution(Inf, 1)
480s ***** error <RicianDistribution: NU must be a non-negative real scalar.> ...
480s  RicianDistribution(i, 1)
480s ***** error <RicianDistribution: NU must be a non-negative real scalar.> ...
480s  RicianDistribution("a", 1)
480s ***** error <RicianDistribution: NU must be a non-negative real scalar.> ...
480s  RicianDistribution([1, 2], 1)
480s ***** error <RicianDistribution: NU must be a non-negative real scalar.> ...
480s  RicianDistribution(NaN, 1)
480s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
480s  RicianDistribution(1, 0)
480s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
480s  RicianDistribution(1, -1)
480s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
480s  RicianDistribution(1, Inf)
480s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
480s  RicianDistribution(1, i)
480s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
480s  RicianDistribution(1, "a")
480s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
480s  RicianDistribution(1, [1, 2])
480s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
480s  RicianDistribution(1, NaN)
480s ***** error <cdf: invalid argument for upper tail.> ...
480s  cdf (RicianDistribution, 2, "uper")
480s ***** error <cdf: invalid argument for upper tail.> ...
480s  cdf (RicianDistribution, 2, 3)
480s ***** shared x
480s  x = gevrnd (1, 1, 1, [1, 100]);
480s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
480s  paramci (RicianDistribution.fit (x), "alpha")
480s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
480s  paramci (RicianDistribution.fit (x), "alpha", 0)
480s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
480s  paramci (RicianDistribution.fit (x), "alpha", 1)
480s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
480s  paramci (RicianDistribution.fit (x), "alpha", [0.5 2])
480s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
480s  paramci (RicianDistribution.fit (x), "alpha", "")
481s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
481s  paramci (RicianDistribution.fit (x), "alpha", {0.05})
481s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
481s  paramci (RicianDistribution.fit (x), "parameter", "s", "alpha", {0.05})
481s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
481s  paramci (RicianDistribution.fit (x), "parameter", {"s", "sigma", "param"})
481s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
481s  paramci (RicianDistribution.fit (x), "alpha", 0.01, ...
481s           "parameter", {"s", "sigma", "param"})
481s ***** error <paramci: unknown distribution parameter.> ...
481s  paramci (RicianDistribution.fit (x), "parameter", "param")
481s ***** error <paramci: unknown distribution parameter.> ...
481s  paramci (RicianDistribution.fit (x), "alpha", 0.01, "parameter", "param")
481s ***** error <paramci: invalid NAME for optional argument.> ...
481s  paramci (RicianDistribution.fit (x), "NAME", "value")
481s ***** error <paramci: invalid NAME for optional argument.> ...
481s  paramci (RicianDistribution.fit (x), "alpha", 0.01, "NAME", "value")
481s ***** error <paramci: invalid NAME for optional argument.> ...
481s  paramci (RicianDistribution.fit (x), "alpha", 0.01, "parameter", "s", ...
481s           "NAME", "value")
481s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
481s  plot (RicianDistribution, "Parent")
481s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
481s  plot (RicianDistribution, "PlotType", 12)
481s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
481s  plot (RicianDistribution, "PlotType", {"pdf", "cdf"})
481s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
481s  plot (RicianDistribution, "PlotType", "pdfcdf")
481s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
481s  plot (RicianDistribution, "Discrete", "pdfcdf")
481s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
481s  plot (RicianDistribution, "Discrete", [1, 0])
481s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
481s  plot (RicianDistribution, "Discrete", {true})
481s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
481s  plot (RicianDistribution, "Parent", 12)
481s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
481s  plot (RicianDistribution, "Parent", "hax")
481s ***** error <plot: invalid NAME for optional argument.> ...
481s  plot (RicianDistribution, "invalidNAME", "pdf")
481s ***** error <plot: no fitted DATA to plot a probability plot.> ...
481s  plot (RicianDistribution, "PlotType", "probability")
481s ***** error <proflik: no fitted data available.> ...
481s  proflik (RicianDistribution, 2)
481s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
481s  proflik (RicianDistribution.fit (x), 3)
481s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
481s  proflik (RicianDistribution.fit (x), [1, 2])
481s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
481s  proflik (RicianDistribution.fit (x), {1})
481s ***** error <proflik: SETPARAM must be a numeric vector.> ...
481s  proflik (RicianDistribution.fit (x), 1, ones (2))
481s ***** error <proflik: missing VALUE for 'Display' argument.> ...
481s  proflik (RicianDistribution.fit (x), 1, "Display")
481s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
481s  proflik (RicianDistribution.fit (x), 1, "Display", 1)
481s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
481s  proflik (RicianDistribution.fit (x), 1, "Display", {1})
481s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
481s  proflik (RicianDistribution.fit (x), 1, "Display", {"on"})
481s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
481s  proflik (RicianDistribution.fit (x), 1, "Display", ["on"; "on"])
481s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
481s  proflik (RicianDistribution.fit (x), 1, "Display", "onnn")
481s ***** error <proflik: invalid NAME for optional arguments.> ...
481s  proflik (RicianDistribution.fit (x), 1, "NAME", "on")
481s ***** error <proflik: invalid optional argument.> ...
481s  proflik (RicianDistribution.fit (x), 1, {"NAME"}, "on")
481s ***** error <proflik: invalid optional argument.> ...
481s  proflik (RicianDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
481s ***** error <truncate: missing input argument.> ...
481s  truncate (RicianDistribution)
481s ***** error <truncate: missing input argument.> ...
481s  truncate (RicianDistribution, 2)
481s ***** error <truncate: invalid lower upper limits.> ...
481s  truncate (RicianDistribution, 4, 2)
481s ***** shared pd
481s  pd = RicianDistribution(1, 1);
481s  pd(2) = RicianDistribution(1, 3);
481s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
481s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
481s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
481s ***** error <mean: requires a scalar probability distribution.> mean (pd)
481s ***** error <median: requires a scalar probability distribution.> median (pd)
481s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
481s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
481s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
481s ***** error <plot: requires a scalar probability distribution.> plot (pd)
481s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
481s ***** error <random: requires a scalar probability distribution.> random (pd)
481s ***** error <std: requires a scalar probability distribution.> std (pd)
481s ***** error <truncate: requires a scalar probability distribution.> ...
481s  truncate (pd, 2, 4)
481s ***** error <var: requires a scalar probability distribution.> var (pd)
481s 97 tests, 97 passed, 0 known failure, 0 skipped
481s [inst/dist_obj/GammaDistribution.m]
481s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_obj/GammaDistribution.m
481s ***** shared pd, t
481s  pd = GammaDistribution (1, 1);
481s  t = truncate (pd, 2, 4);
481s ***** assert (cdf (pd, [0:5]), [0, 0.6321, 0.8647, 0.9502, 0.9817, 0.9933], 1e-4);
481s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7311, 1, 1], 1e-4);
481s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.7769, 0.8647, 0.9502, 0.9817], 1e-4);
481s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7311, 1], 1e-4);
481s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.2231, 0.5108, 0.9163, 1.6094, Inf], 1e-4);
481s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1899, 2.4244, 2.7315, 3.1768, 4], 1e-4);
481s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5108, 0.9163, 1.6094, Inf, NaN], 1e-4);
481s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4244, 2.7315, 3.1768, 4, NaN], 1e-4);
481s ***** assert (iqr (pd), 1.0986, 1e-4);
481s ***** assert (iqr (t), 0.8020, 1e-4);
481s ***** assert (mean (pd), 1);
481s ***** assert (mean (t), 2.6870, 1e-4);
481s ***** assert (median (pd), 0.6931, 1e-4);
481s ***** assert (median (t), 2.5662, 1e-4);
481s ***** assert (pdf (pd, [0:5]), [1, 0.3679, 0.1353, 0.0498, 0.0183, 0.0067], 1e-4);
481s ***** assert (pdf (t, [0:5]), [0, 0, 1.1565, 0.4255, 0.1565, 0], 1e-4);
481s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3679, 0.1353, 0.0498, 0.0183, NaN], 1e-4);
481s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.1565, 0.4255, 0.1565, NaN], 1e-4);
481s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
481s ***** assert (any (random (t, 1000, 1) < 2), false);
481s ***** assert (any (random (t, 1000, 1) > 4), false);
481s ***** assert (std (pd), 1);
481s ***** assert (std (t), 0.5253, 1e-4);
481s ***** assert (var (pd), 1);
481s ***** assert (var (t), 0.2759, 1e-4);
481s ***** error <GammaDistribution: A must be a positive real scalar.> ...
481s  GammaDistribution(0, 1)
481s ***** error <GammaDistribution: A must be a positive real scalar.> ...
481s  GammaDistribution(Inf, 1)
481s ***** error <GammaDistribution: A must be a positive real scalar.> ...
481s  GammaDistribution(i, 1)
481s ***** error <GammaDistribution: A must be a positive real scalar.> ...
481s  GammaDistribution("a", 1)
481s ***** error <GammaDistribution: A must be a positive real scalar.> ...
481s  GammaDistribution([1, 2], 1)
481s ***** error <GammaDistribution: A must be a positive real scalar.> ...
481s  GammaDistribution(NaN, 1)
481s ***** error <GammaDistribution: B must be a positive real scalar.> ...
481s  GammaDistribution(1, 0)
481s ***** error <GammaDistribution: B must be a positive real scalar.> ...
481s  GammaDistribution(1, -1)
481s ***** error <GammaDistribution: B must be a positive real scalar.> ...
481s  GammaDistribution(1, Inf)
481s ***** error <GammaDistribution: B must be a positive real scalar.> ...
481s  GammaDistribution(1, i)
481s ***** error <GammaDistribution: B must be a positive real scalar.> ...
481s  GammaDistribution(1, "a")
481s ***** error <GammaDistribution: B must be a positive real scalar.> ...
481s  GammaDistribution(1, [1, 2])
481s ***** error <GammaDistribution: B must be a positive real scalar.> ...
481s  GammaDistribution(1, NaN)
481s ***** error <cdf: invalid argument for upper tail.> ...
481s  cdf (GammaDistribution, 2, "uper")
481s ***** error <cdf: invalid argument for upper tail.> ...
481s  cdf (GammaDistribution, 2, 3)
481s ***** shared x
481s  x = gamrnd (1, 1, [100, 1]);
481s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
481s  paramci (GammaDistribution.fit (x), "alpha")
481s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
481s  paramci (GammaDistribution.fit (x), "alpha", 0)
481s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
481s  paramci (GammaDistribution.fit (x), "alpha", 1)
481s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
481s  paramci (GammaDistribution.fit (x), "alpha", [0.5 2])
481s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
481s  paramci (GammaDistribution.fit (x), "alpha", "")
482s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
482s  paramci (GammaDistribution.fit (x), "alpha", {0.05})
482s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
482s  paramci (GammaDistribution.fit (x), "parameter", "a", "alpha", {0.05})
482s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
482s  paramci (GammaDistribution.fit (x), "parameter", {"a", "b", "param"})
482s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
482s  paramci (GammaDistribution.fit (x), "alpha", 0.01, ...
482s           "parameter", {"a", "b", "param"})
482s ***** error <paramci: unknown distribution parameter.> ...
482s  paramci (GammaDistribution.fit (x), "parameter", "param")
482s ***** error <paramci: unknown distribution parameter.> ...
482s  paramci (GammaDistribution.fit (x), "alpha", 0.01, "parameter", "param")
482s ***** error <paramci: invalid NAME for optional argument.> ...
482s  paramci (GammaDistribution.fit (x), "NAME", "value")
482s ***** error <paramci: invalid NAME for optional argument.> ...
482s  paramci (GammaDistribution.fit (x), "alpha", 0.01, "NAME", "value")
482s ***** error <paramci: invalid NAME for optional argument.> ...
482s  paramci (GammaDistribution.fit (x), "alpha", 0.01, "parameter", "a", ...
482s           "NAME", "value")
482s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
482s  plot (GammaDistribution, "Parent")
482s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
482s  plot (GammaDistribution, "PlotType", 12)
482s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
482s  plot (GammaDistribution, "PlotType", {"pdf", "cdf"})
482s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
482s  plot (GammaDistribution, "PlotType", "pdfcdf")
482s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
482s  plot (GammaDistribution, "Discrete", "pdfcdf")
482s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
482s  plot (GammaDistribution, "Discrete", [1, 0])
482s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
482s  plot (GammaDistribution, "Discrete", {true})
482s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
482s  plot (GammaDistribution, "Parent", 12)
482s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
482s  plot (GammaDistribution, "Parent", "hax")
482s ***** error <plot: invalid NAME for optional argument.> ...
482s  plot (GammaDistribution, "invalidNAME", "pdf")
482s ***** error <plot: no fitted DATA to plot a probability plot.> ...
482s  plot (GammaDistribution, "PlotType", "probability")
482s ***** error <proflik: no fitted data available.> ...
482s  proflik (GammaDistribution, 2)
482s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
482s  proflik (GammaDistribution.fit (x), 3)
482s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
482s  proflik (GammaDistribution.fit (x), [1, 2])
482s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
482s  proflik (GammaDistribution.fit (x), {1})
482s ***** error <proflik: SETPARAM must be a numeric vector.> ...
482s  proflik (GammaDistribution.fit (x), 1, ones (2))
482s ***** error <proflik: missing VALUE for 'Display' argument.> ...
482s  proflik (GammaDistribution.fit (x), 1, "Display")
482s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
482s  proflik (GammaDistribution.fit (x), 1, "Display", 1)
482s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
482s  proflik (GammaDistribution.fit (x), 1, "Display", {1})
482s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
482s  proflik (GammaDistribution.fit (x), 1, "Display", {"on"})
482s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
482s  proflik (GammaDistribution.fit (x), 1, "Display", ["on"; "on"])
482s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
482s  proflik (GammaDistribution.fit (x), 1, "Display", "onnn")
482s ***** error <proflik: invalid NAME for optional arguments.> ...
482s  proflik (GammaDistribution.fit (x), 1, "NAME", "on")
482s ***** error <proflik: invalid optional argument.> ...
482s  proflik (GammaDistribution.fit (x), 1, {"NAME"}, "on")
482s ***** error <proflik: invalid optional argument.> ...
482s  proflik (GammaDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
482s ***** error <truncate: missing input argument.> ...
482s  truncate (GammaDistribution)
482s ***** error <truncate: missing input argument.> ...
482s  truncate (GammaDistribution, 2)
482s ***** error <truncate: invalid lower upper limits.> ...
482s  truncate (GammaDistribution, 4, 2)
482s ***** shared pd
482s  pd = GammaDistribution(1, 1);
482s  pd(2) = GammaDistribution(1, 3);
482s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
482s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
482s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
482s ***** error <mean: requires a scalar probability distribution.> mean (pd)
482s ***** error <median: requires a scalar probability distribution.> median (pd)
482s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
482s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
482s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
482s ***** error <plot: requires a scalar probability distribution.> plot (pd)
482s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
482s ***** error <random: requires a scalar probability distribution.> random (pd)
482s ***** error <std: requires a scalar probability distribution.> std (pd)
482s ***** error <truncate: requires a scalar probability distribution.> ...
482s  truncate (pd, 2, 4)
482s ***** error <var: requires a scalar probability distribution.> var (pd)
482s 96 tests, 96 passed, 0 known failure, 0 skipped
482s [inst/dist_obj/RayleighDistribution.m]
482s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_obj/RayleighDistribution.m
482s ***** shared pd, t
482s  pd = RayleighDistribution;
482s  t = truncate (pd, 2, 4);
482s ***** assert (cdf (pd, [0:5]), [0, 0.3935, 0.8647, 0.9889, 0.9997, 1], 1e-4);
482s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.9202, 1, 1], 1e-4);
482s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.6753, 0.8647, 0.9889, 0.9997, NaN], 1e-4);
482s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.9202, 1, NaN], 1e-4);
482s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.6680, 1.0108, 1.3537, 1.7941, Inf], 1e-4);
482s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1083, 2.2402, 2.4135, 2.6831, 4], 1e-4);
482s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1.0108, 1.3537, 1.7941, Inf, NaN], 1e-4);
482s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.2402, 2.4135, 2.6831, 4, NaN], 1e-4);
482s ***** assert (iqr (pd), 0.9066, 1e-4);
482s ***** assert (iqr (t), 0.4609, 1e-4);
482s ***** assert (mean (pd), 1.2533, 1e-4);
482s ***** assert (mean (t), 2.4169, 1e-4);
482s ***** assert (median (pd), 1.1774, 1e-4);
482s ***** assert (median (t), 2.3198, 1e-4);
482s ***** assert (pdf (pd, [0:5]), [0, 0.6065, 0.2707, 0.0333, 0.0013, 0], 1e-4);
482s ***** assert (pdf (t, [0:5]), [0, 0, 2.0050, 0.2469, 0.0099, 0], 1e-4);
482s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.4870, NaN], 1e-4);
482s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
482s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
482s ***** assert (any (random (t, 1000, 1) < 2), false);
482s ***** assert (any (random (t, 1000, 1) > 4), false);
482s ***** assert (std (pd), 0.6551, 1e-4);
482s ***** assert (std (t), 0.3591, 1e-4);
482s ***** assert (var (pd), 0.4292, 1e-4);
482s ***** assert (var (t), 0.1290, 1e-4);
482s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
482s  RayleighDistribution(0)
482s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
482s  RayleighDistribution(-1)
482s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
482s  RayleighDistribution(Inf)
482s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
482s  RayleighDistribution(i)
482s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
482s  RayleighDistribution("a")
482s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
482s  RayleighDistribution([1, 2])
482s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
482s  RayleighDistribution(NaN)
482s ***** error <cdf: invalid argument for upper tail.> ...
482s  cdf (RayleighDistribution, 2, "uper")
482s ***** error <cdf: invalid argument for upper tail.> ...
482s  cdf (RayleighDistribution, 2, 3)
482s ***** shared x
482s  x = raylrnd (1, [1, 100]);
482s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
482s  paramci (RayleighDistribution.fit (x), "alpha")
482s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
482s  paramci (RayleighDistribution.fit (x), "alpha", 0)
482s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
482s  paramci (RayleighDistribution.fit (x), "alpha", 1)
482s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
482s  paramci (RayleighDistribution.fit (x), "alpha", [0.5 2])
482s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
482s  paramci (RayleighDistribution.fit (x), "alpha", "")
482s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
482s  paramci (RayleighDistribution.fit (x), "alpha", {0.05})
482s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
482s  paramci (RayleighDistribution.fit (x), "parameter", "sigma", "alpha", {0.05})
482s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
482s  paramci (RayleighDistribution.fit (x), "parameter", {"sigma", "param"})
482s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
482s  paramci (RayleighDistribution.fit (x), "alpha", 0.01, ...
482s           "parameter", {"sigma", "param"})
482s ***** error <paramci: unknown distribution parameter.> ...
482s  paramci (RayleighDistribution.fit (x), "parameter", "param")
482s ***** error <paramci: unknown distribution parameter.> ...
482s  paramci (RayleighDistribution.fit (x), "alpha", 0.01, "parameter", "param")
482s ***** error <paramci: invalid NAME for optional argument.> ...
482s  paramci (RayleighDistribution.fit (x), "NAME", "value")
482s ***** error <paramci: invalid NAME for optional argument.> ...
482s  paramci (RayleighDistribution.fit (x), "alpha", 0.01, "NAME", "value")
482s ***** error <paramci: invalid NAME for optional argument.> ...
482s  paramci (RayleighDistribution.fit (x), "alpha", 0.01, ...
482s           "parameter", "sigma", "NAME", "value")
482s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
482s  plot (RayleighDistribution, "Parent")
482s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
482s  plot (RayleighDistribution, "PlotType", 12)
482s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
482s  plot (RayleighDistribution, "PlotType", {"pdf", "cdf"})
482s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
482s  plot (RayleighDistribution, "PlotType", "pdfcdf")
482s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
482s  plot (RayleighDistribution, "Discrete", "pdfcdf")
482s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
482s  plot (RayleighDistribution, "Discrete", [1, 0])
482s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
482s  plot (RayleighDistribution, "Discrete", {true})
482s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
482s  plot (RayleighDistribution, "Parent", 12)
482s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
482s  plot (RayleighDistribution, "Parent", "hax")
482s ***** error <plot: invalid NAME for optional argument.> ...
482s  plot (RayleighDistribution, "invalidNAME", "pdf")
482s ***** error <plot: no fitted DATA to plot a probability plot.> ...
482s  plot (RayleighDistribution, "PlotType", "probability")
482s ***** error <proflik: no fitted data available.> ...
482s  proflik (RayleighDistribution, 2)
482s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
482s  proflik (RayleighDistribution.fit (x), 3)
482s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
482s  proflik (RayleighDistribution.fit (x), [1, 2])
482s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
482s  proflik (RayleighDistribution.fit (x), {1})
482s ***** error <proflik: SETPARAM must be a numeric vector.> ...
482s  proflik (RayleighDistribution.fit (x), 1, ones (2))
482s ***** error <proflik: missing VALUE for 'Display' argument.> ...
482s  proflik (RayleighDistribution.fit (x), 1, "Display")
482s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
482s  proflik (RayleighDistribution.fit (x), 1, "Display", 1)
482s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
482s  proflik (RayleighDistribution.fit (x), 1, "Display", {1})
482s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
482s  proflik (RayleighDistribution.fit (x), 1, "Display", {"on"})
482s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
482s  proflik (RayleighDistribution.fit (x), 1, "Display", ["on"; "on"])
482s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
482s  proflik (RayleighDistribution.fit (x), 1, "Display", "onnn")
482s ***** error <proflik: invalid NAME for optional arguments.> ...
482s  proflik (RayleighDistribution.fit (x), 1, "NAME", "on")
482s ***** error <proflik: invalid optional argument.> ...
482s  proflik (RayleighDistribution.fit (x), 1, {"NAME"}, "on")
482s ***** error <proflik: invalid optional argument.> ...
482s  proflik (RayleighDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
482s ***** error <truncate: missing input argument.> ...
482s  truncate (RayleighDistribution)
482s ***** error <truncate: missing input argument.> ...
482s  truncate (RayleighDistribution, 2)
482s ***** error <truncate: invalid lower upper limits.> ...
482s  truncate (RayleighDistribution, 4, 2)
482s ***** shared pd
482s  pd = RayleighDistribution(1);
482s  pd(2) = RayleighDistribution(3);
482s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
482s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
482s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
482s ***** error <mean: requires a scalar probability distribution.> mean (pd)
482s ***** error <median: requires a scalar probability distribution.> median (pd)
482s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
482s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
482s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
482s ***** error <plot: requires a scalar probability distribution.> plot (pd)
482s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
482s ***** error <random: requires a scalar probability distribution.> random (pd)
482s ***** error <std: requires a scalar probability distribution.> std (pd)
482s ***** error <truncate: requires a scalar probability distribution.> ...
482s  truncate (pd, 2, 4)
482s ***** error <var: requires a scalar probability distribution.> var (pd)
482s 90 tests, 90 passed, 0 known failure, 0 skipped
482s [inst/dist_obj/GeneralizedExtremeValueDistribution.m]
482s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_obj/GeneralizedExtremeValueDistribution.m
482s ***** shared pd, t
482s  pd = GeneralizedExtremeValueDistribution;
482s  t = truncate (pd, 2, 4);
482s ***** assert (cdf (pd, [0:5]), [0.3679, 0.6922, 0.8734, 0.9514, 0.9819, 0.9933], 1e-4);
482s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7195, 1, 1], 1e-4);
482s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8, 0.8734, 0.9514, 0.9819], 1e-4);
482s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7195, 1], 1e-4);
482s ***** assert (icdf (pd, [0:0.2:1]), [-Inf, -0.4759, 0.0874, 0.6717, 1.4999, Inf], 1e-4);
482s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1999, 2.4433, 2.7568, 3.2028, 4], 1e-4);
482s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.0874, 0.6717, 1.4999, Inf, NaN], 1e-4);
482s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4433, 2.7568, 3.2028, 4, NaN], 1e-4);
482s ***** assert (iqr (pd), 1.5725, 1e-4);
482s ***** assert (iqr (t), 0.8164, 1e-4);
482s ***** assert (mean (pd), 0.5772, 1e-4);
482s ***** assert (mean (t), 2.7043, 1e-4);
482s ***** assert (median (pd), 0.3665, 1e-4);
482s ***** assert (median (t), 2.5887, 1e-4);
482s ***** assert (pdf (pd, [0:5]), [0.3679, 0.2546, 0.1182, 0.0474, 0.0180, 0.0067], 1e-4);
482s ***** assert (pdf (t, [0:5]), [0, 0, 1.0902, 0.4369, 0.1659, 0], 1e-4);
482s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0.1794, 0.2546, 0.1182, 0.0474, 0.0180, NaN], 1e-4);
482s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.0902, 0.4369, 0.1659, NaN], 1e-4);
482s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
482s ***** assert (any (random (t, 1000, 1) < 2), false);
482s ***** assert (any (random (t, 1000, 1) > 4), false);
482s ***** assert (std (pd), 1.2825, 1e-4);
482s ***** assert (std (t), 0.5289, 1e-4);
482s ***** assert (var (pd), 1.6449, 1e-4);
482s ***** assert (var (t), 0.2798, 1e-4);
482s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
482s  GeneralizedExtremeValueDistribution(Inf, 1, 1)
482s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
482s  GeneralizedExtremeValueDistribution(i, 1, 1)
482s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
482s  GeneralizedExtremeValueDistribution("a", 1, 1)
482s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
482s  GeneralizedExtremeValueDistribution([1, 2], 1, 1)
482s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
482s  GeneralizedExtremeValueDistribution(NaN, 1, 1)
482s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
482s  GeneralizedExtremeValueDistribution(1, 0, 1)
482s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
482s  GeneralizedExtremeValueDistribution(1, -1, 1)
482s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
482s  GeneralizedExtremeValueDistribution(1, Inf, 1)
482s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
482s  GeneralizedExtremeValueDistribution(1, i, 1)
482s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
482s  GeneralizedExtremeValueDistribution(1, "a", 1)
482s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
482s  GeneralizedExtremeValueDistribution(1, [1, 2], 1)
482s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
482s  GeneralizedExtremeValueDistribution(1, NaN, 1)
482s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
482s  GeneralizedExtremeValueDistribution(1, 1, Inf)
482s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
482s  GeneralizedExtremeValueDistribution(1, 1, i)
482s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
482s  GeneralizedExtremeValueDistribution(1, 1, "a")
482s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
482s  GeneralizedExtremeValueDistribution(1, 1, [1, 2])
482s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
482s  GeneralizedExtremeValueDistribution(1, 1, NaN)
482s ***** error <cdf: invalid argument for upper tail.> ...
482s  cdf (GeneralizedExtremeValueDistribution, 2, "uper")
482s ***** error <cdf: invalid argument for upper tail.> ...
482s  cdf (GeneralizedExtremeValueDistribution, 2, 3)
482s ***** shared x
482s  x = gevrnd (1, 1, 1, [1, 100]);
482s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
482s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha")
482s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
482s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0)
482s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
482s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 1)
482s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
482s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", [0.5 2])
483s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
483s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", "")
483s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
483s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", {0.05})
483s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
483s  paramci (GeneralizedExtremeValueDistribution.fit (x), ...
483s           "parameter", "sigma", "alpha", {0.05})
483s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
483s  paramci (GeneralizedExtremeValueDistribution.fit (x), ...
483s           "parameter", {"k", "sigma", "mu", "param"})
483s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
483s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0.01, ...
483s           "parameter", {"k", "sigma", "mu", "param"})
483s ***** error <paramci: unknown distribution parameter.> ...
483s  paramci (GeneralizedExtremeValueDistribution.fit (x), "parameter", "param")
483s ***** error <paramci: unknown distribution parameter.> ...
483s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0.01, ...
483s           "parameter", "param")
483s ***** error <paramci: invalid NAME for optional argument.> ...
483s  paramci (GeneralizedExtremeValueDistribution.fit (x), "NAME", "value")
483s ***** error <paramci: invalid NAME for optional argument.> ...
483s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0.01, ...
483s           "NAME", "value")
483s ***** error <paramci: invalid NAME for optional argument.> ...
483s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0.01, ...
483s           "parameter", "sigma", "NAME", "value")
483s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
483s  plot (GeneralizedExtremeValueDistribution, "Parent")
483s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
483s  plot (GeneralizedExtremeValueDistribution, "PlotType", 12)
483s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
483s  plot (GeneralizedExtremeValueDistribution, "PlotType", {"pdf", "cdf"})
483s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
483s  plot (GeneralizedExtremeValueDistribution, "PlotType", "pdfcdf")
483s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
483s  plot (GeneralizedExtremeValueDistribution, "Discrete", "pdfcdf")
483s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
483s  plot (GeneralizedExtremeValueDistribution, "Discrete", [1, 0])
483s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
483s  plot (GeneralizedExtremeValueDistribution, "Discrete", {true})
483s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
483s  plot (GeneralizedExtremeValueDistribution, "Parent", 12)
483s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
483s  plot (GeneralizedExtremeValueDistribution, "Parent", "hax")
483s ***** error <plot: invalid NAME for optional argument.> ...
483s  plot (GeneralizedExtremeValueDistribution, "invalidNAME", "pdf")
483s ***** error <plot: no fitted DATA to plot a probability plot.> ...
483s  plot (GeneralizedExtremeValueDistribution, "PlotType", "probability")
483s ***** error <proflik: no fitted data available.> ...
483s  proflik (GeneralizedExtremeValueDistribution, 2)
483s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
483s  proflik (GeneralizedExtremeValueDistribution.fit (x), 4)
483s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
483s  proflik (GeneralizedExtremeValueDistribution.fit (x), [1, 2])
483s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
483s  proflik (GeneralizedExtremeValueDistribution.fit (x), {1})
483s ***** error <proflik: SETPARAM must be a numeric vector.> ...
483s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, ones (2))
484s ***** error <proflik: missing VALUE for 'Display' argument.> ...
484s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display")
484s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
484s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display", 1)
484s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
484s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display", {1})
484s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
484s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display", {"on"})
484s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
484s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, ...
484s           "Display", ["on"; "on"])
484s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
484s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display", "onnn")
484s ***** error <proflik: invalid NAME for optional arguments.> ...
484s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "NAME", "on")
484s ***** error <proflik: invalid optional argument.> ...
484s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, {"NAME"}, "on")
484s ***** error <proflik: invalid optional argument.> ...
484s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, {[1 2 3 4]}, ...
484s           "Display", "on")
484s ***** error <truncate: missing input argument.> ...
484s  truncate (GeneralizedExtremeValueDistribution)
484s ***** error <truncate: missing input argument.> ...
484s  truncate (GeneralizedExtremeValueDistribution, 2)
484s ***** error <truncate: invalid lower upper limits.> ...
484s  truncate (GeneralizedExtremeValueDistribution, 4, 2)
484s ***** shared pd
484s  pd = GeneralizedExtremeValueDistribution(1, 1, 1);
484s  pd(2) = GeneralizedExtremeValueDistribution(1, 3, 1);
484s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
484s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
484s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
484s ***** error <mean: requires a scalar probability distribution.> mean (pd)
484s ***** error <median: requires a scalar probability distribution.> median (pd)
484s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
484s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
484s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
484s ***** error <plot: requires a scalar probability distribution.> plot (pd)
484s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
484s ***** error <random: requires a scalar probability distribution.> random (pd)
484s ***** error <std: requires a scalar probability distribution.> std (pd)
484s ***** error <truncate: requires a scalar probability distribution.> ...
484s  truncate (pd, 2, 4)
484s ***** error <var: requires a scalar probability distribution.> var (pd)
484s 100 tests, 100 passed, 0 known failure, 0 skipped
484s [inst/dist_obj/WeibullDistribution.m]
484s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_obj/WeibullDistribution.m
484s ***** shared pd, t
484s  pd = WeibullDistribution;
484s  t = truncate (pd, 2, 4);
484s ***** assert (cdf (pd, [0:5]), [0, 0.6321, 0.8647, 0.9502, 0.9817, 0.9933], 1e-4);
484s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7311, 1, 1], 1e-4);
484s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.7769, 0.8647, 0.9502, 0.9817, NaN], 1e-4);
484s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.7311, 1, NaN], 1e-4);
484s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.2231, 0.5108, 0.9163, 1.6094, Inf], 1e-4);
484s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1899, 2.4244, 2.7315, 3.1768, 4], 1e-4);
484s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5108, 0.9163, 1.6094, Inf, NaN], 1e-4);
484s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4244, 2.7315, 3.1768, 4, NaN], 1e-4);
484s ***** assert (iqr (pd), 1.0986, 1e-4);
484s ***** assert (iqr (t), 0.8020, 1e-4);
484s ***** assert (mean (pd), 1, 1e-14);
484s ***** assert (mean (t), 2.6870, 1e-4);
484s ***** assert (median (pd), 0.6931, 1e-4);
484s ***** assert (median (t), 2.5662, 1e-4);
484s ***** assert (pdf (pd, [0:5]), [1, 0.3679, 0.1353, 0.0498, 0.0183, 0.0067], 1e-4);
484s ***** assert (pdf (t, [0:5]), [0, 0, 1.1565, 0.4255, 0.1565, 0], 1e-4);
484s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.2231, NaN], 1e-4);
484s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
484s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
484s ***** assert (any (random (t, 1000, 1) < 2), false);
484s ***** assert (any (random (t, 1000, 1) > 4), false);
484s ***** assert (std (pd), 1, 1e-14);
484s ***** assert (std (t), 0.5253, 1e-4);
484s ***** assert (var (pd), 1, 1e-14);
484s ***** assert (var (t), 0.2759, 1e-4);
484s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
484s  WeibullDistribution(0, 1)
484s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
484s  WeibullDistribution(-1, 1)
484s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
484s  WeibullDistribution(Inf, 1)
484s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
484s  WeibullDistribution(i, 1)
484s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
484s  WeibullDistribution("a", 1)
484s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
484s  WeibullDistribution([1, 2], 1)
484s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
484s  WeibullDistribution(NaN, 1)
484s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
484s  WeibullDistribution(1, 0)
484s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
484s  WeibullDistribution(1, -1)
484s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
484s  WeibullDistribution(1, Inf)
484s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
484s  WeibullDistribution(1, i)
484s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
484s  WeibullDistribution(1, "a")
484s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
484s  WeibullDistribution(1, [1, 2])
484s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
484s  WeibullDistribution(1, NaN)
484s ***** error <cdf: invalid argument for upper tail.> ...
484s  cdf (WeibullDistribution, 2, "uper")
484s ***** error <cdf: invalid argument for upper tail.> ...
484s  cdf (WeibullDistribution, 2, 3)
484s ***** shared x
484s  x = wblrnd (1, 1, [1, 100]);
484s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
484s  paramci (WeibullDistribution.fit (x), "alpha")
484s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
484s  paramci (WeibullDistribution.fit (x), "alpha", 0)
484s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
484s  paramci (WeibullDistribution.fit (x), "alpha", 1)
484s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
484s  paramci (WeibullDistribution.fit (x), "alpha", [0.5 2])
484s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
484s  paramci (WeibullDistribution.fit (x), "alpha", "")
484s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
484s  paramci (WeibullDistribution.fit (x), "alpha", {0.05})
484s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
484s  paramci (WeibullDistribution.fit (x), "parameter", "k", "alpha", {0.05})
484s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
484s  paramci (WeibullDistribution.fit (x), "parameter", {"lambda", "k", "param"})
484s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
484s  paramci (WeibullDistribution.fit (x), "alpha", 0.01, ...
484s           "parameter", {"lambda", "k", "param"})
484s ***** error <paramci: unknown distribution parameter.> ...
484s  paramci (WeibullDistribution.fit (x), "parameter", "param")
484s ***** error <paramci: unknown distribution parameter.> ...
484s  paramci (WeibullDistribution.fit (x), "alpha", 0.01, "parameter", "param")
484s ***** error <paramci: invalid NAME for optional argument.> ...
484s  paramci (WeibullDistribution.fit (x), "NAME", "value")
484s ***** error <paramci: invalid NAME for optional argument.> ...
484s  paramci (WeibullDistribution.fit (x), "alpha", 0.01, "NAME", "value")
484s ***** error <paramci: invalid NAME for optional argument.> ...
484s  paramci (WeibullDistribution.fit (x), "alpha", 0.01, "parameter", "k", ...
484s           "NAME", "value")
484s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
484s  plot (WeibullDistribution, "Parent")
484s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
484s  plot (WeibullDistribution, "PlotType", 12)
484s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
484s  plot (WeibullDistribution, "PlotType", {"pdf", "cdf"})
484s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
484s  plot (WeibullDistribution, "PlotType", "pdfcdf")
484s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
484s  plot (WeibullDistribution, "Discrete", "pdfcdf")
484s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
484s  plot (WeibullDistribution, "Discrete", [1, 0])
484s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
484s  plot (WeibullDistribution, "Discrete", {true})
484s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
484s  plot (WeibullDistribution, "Parent", 12)
484s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
484s  plot (WeibullDistribution, "Parent", "hax")
484s ***** error <plot: invalid NAME for optional argument.> ...
484s  plot (WeibullDistribution, "invalidNAME", "pdf")
484s ***** error <plot: no fitted DATA to plot a probability plot.> ...
484s  plot (WeibullDistribution, "PlotType", "probability")
484s ***** error <proflik: no fitted data available.> ...
484s  proflik (WeibullDistribution, 2)
484s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
484s  proflik (WeibullDistribution.fit (x), 3)
484s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
484s  proflik (WeibullDistribution.fit (x), [1, 2])
484s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
484s  proflik (WeibullDistribution.fit (x), {1})
484s ***** error <proflik: SETPARAM must be a numeric vector.> ...
484s  proflik (WeibullDistribution.fit (x), 1, ones (2))
484s ***** error <proflik: missing VALUE for 'Display' argument.> ...
484s  proflik (WeibullDistribution.fit (x), 1, "Display")
484s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
484s  proflik (WeibullDistribution.fit (x), 1, "Display", 1)
484s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
484s  proflik (WeibullDistribution.fit (x), 1, "Display", {1})
484s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
484s  proflik (WeibullDistribution.fit (x), 1, "Display", {"on"})
484s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
484s  proflik (WeibullDistribution.fit (x), 1, "Display", ["on"; "on"])
484s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
484s  proflik (WeibullDistribution.fit (x), 1, "Display", "onnn")
484s ***** error <proflik: invalid NAME for optional arguments.> ...
484s  proflik (WeibullDistribution.fit (x), 1, "NAME", "on")
484s ***** error <proflik: invalid optional argument.> ...
484s  proflik (WeibullDistribution.fit (x), 1, {"NAME"}, "on")
484s ***** error <proflik: invalid optional argument.> ...
484s  proflik (WeibullDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
484s ***** error <truncate: missing input argument.> ...
484s  truncate (WeibullDistribution)
484s ***** error <truncate: missing input argument.> ...
484s  truncate (WeibullDistribution, 2)
484s ***** error <truncate: invalid lower upper limits.> ...
484s  truncate (WeibullDistribution, 4, 2)
484s ***** shared pd
484s  pd = WeibullDistribution(1, 1);
484s  pd(2) = WeibullDistribution(1, 3);
484s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
484s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
484s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
484s ***** error <mean: requires a scalar probability distribution.> mean (pd)
484s ***** error <median: requires a scalar probability distribution.> median (pd)
484s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
484s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
484s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
484s ***** error <plot: requires a scalar probability distribution.> plot (pd)
484s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
484s ***** error <random: requires a scalar probability distribution.> random (pd)
484s ***** error <std: requires a scalar probability distribution.> std (pd)
484s ***** error <truncate: requires a scalar probability distribution.> ...
484s  truncate (pd, 2, 4)
484s ***** error <var: requires a scalar probability distribution.> var (pd)
484s 97 tests, 97 passed, 0 known failure, 0 skipped
484s [inst/dist_obj/BinomialDistribution.m]
484s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_obj/BinomialDistribution.m
484s ***** shared pd, t, t_inf
484s  pd = BinomialDistribution (5, 0.5);
484s  t = truncate (pd, 2, 4);
484s  t_inf = truncate (pd, 2, Inf);
484s ***** assert (cdf (pd, [0:5]), [0.0312, 0.1875, 0.5, 0.8125, 0.9688, 1], 1e-4);
484s ***** assert (cdf (t, [0:5]), [0, 0, 0.4, 0.8, 1, 1], 1e-4);
484s ***** assert (cdf (t_inf, [0:5]), [0, 0, 0.3846, 0.7692, 0.9615, 1], 1e-4);
484s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.1875, 0.5, 0.8125, 0.9688, NaN], 1e-4);
484s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0.4, 0.8, 1, NaN], 1e-4);
484s ***** assert (icdf (pd, [0:0.2:1]), [0, 2, 2, 3, 3, 5], 1e-4);
484s ***** assert (icdf (t, [0:0.2:1]), [2, 2, 2, 3, 3, 4], 1e-4);
484s ***** assert (icdf (t_inf, [0:0.2:1]), [2, 2, 3, 3, 4, 5], 1e-4);
484s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 2, 3, 3, 5, NaN], 1e-4);
484s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2, 3, 3, 4, NaN], 1e-4);
484s ***** assert (iqr (pd), 1);
484s ***** assert (iqr (t), 1);
484s ***** assert (mean (pd), 2.5, 1e-10);
484s ***** assert (mean (t), 2.8, 1e-10);
484s ***** assert (mean (t_inf), 2.8846, 1e-4);
484s ***** assert (median (pd), 2.5);
484s ***** assert (median (t), 3);
484s ***** assert (pdf (pd, [0:5]), [0.0312, 0.1562, 0.3125, 0.3125, 0.1562, 0.0312], 1e-4);
484s ***** assert (pdf (t, [0:5]), [0, 0, 0.4, 0.4, 0.2, 0], 1e-4);
484s ***** assert (pdf (t_inf, [0:5]), [0, 0, 0.3846, 0.3846, 0.1923, 0.0385], 1e-4);
484s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
484s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
484s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
484s ***** assert (any (random (t, 1000, 1) < 2), false);
484s ***** assert (any (random (t, 1000, 1) > 4), false);
484s ***** assert (std (pd), 1.1180, 1e-4);
484s ***** assert (std (t), 0.7483, 1e-4);
484s ***** assert (std (t_inf), 0.8470, 1e-4);
484s ***** assert (var (pd), 1.2500, 1e-4);
484s ***** assert (var (t), 0.5600, 1e-4);
484s ***** assert (var (t_inf), 0.7175, 1e-4);
484s ***** error <BinomialDistribution: N must be a positive integer scalar.> ...
484s  BinomialDistribution(Inf, 0.5)
484s ***** error <BinomialDistribution: N must be a positive integer scalar.> ...
484s  BinomialDistribution(i, 0.5)
484s ***** error <BinomialDistribution: N must be a positive integer scalar.> ...
484s  BinomialDistribution("a", 0.5)
484s ***** error <BinomialDistribution: N must be a positive integer scalar.> ...
484s  BinomialDistribution([1, 2], 0.5)
484s ***** error <BinomialDistribution: N must be a positive integer scalar.> ...
484s  BinomialDistribution(NaN, 0.5)
484s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
484s  BinomialDistribution(1, 1.01)
484s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
484s  BinomialDistribution(1, -0.01)
484s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
484s  BinomialDistribution(1, Inf)
484s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
484s  BinomialDistribution(1, i)
484s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
484s  BinomialDistribution(1, "a")
484s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
484s  BinomialDistribution(1, [1, 2])
484s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
484s  BinomialDistribution(1, NaN)
484s ***** error <cdf: invalid argument for upper tail.> ...
484s  cdf (BinomialDistribution, 2, "uper")
484s ***** error <cdf: invalid argument for upper tail.> ...
484s  cdf (BinomialDistribution, 2, 3)
484s ***** shared x
484s  rand ("seed", 2);
484s  x = binornd (5, 0.5, [1, 100]);
484s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
484s  paramci (BinomialDistribution.fit (x, 6), "alpha")
484s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
484s  paramci (BinomialDistribution.fit (x, 6), "alpha", 0)
484s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
484s  paramci (BinomialDistribution.fit (x, 6), "alpha", 1)
484s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
484s  paramci (BinomialDistribution.fit (x, 6), "alpha", [0.5 2])
485s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
485s  paramci (BinomialDistribution.fit (x, 6), "alpha", "")
485s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
485s  paramci (BinomialDistribution.fit (x, 6), "alpha", {0.05})
485s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
485s  paramci (BinomialDistribution.fit (x, 6), "parameter", "p", ...
485s           "alpha", {0.05})
485s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
485s  paramci (BinomialDistribution.fit (x, 6), ...
485s           "parameter", {"N", "p", "param"})
485s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
485s  paramci (BinomialDistribution.fit (x, 6), "alpha", 0.01, ...
485s           "parameter", {"N", "p", "param"})
485s ***** error <paramci: unknown distribution parameter.> ...
485s  paramci (BinomialDistribution.fit (x, 6), "parameter", "param")
485s ***** error <paramci: unknown distribution parameter.> ...
485s  paramci (BinomialDistribution.fit (x, 6), "parameter", "N")
485s ***** error <paramci: unknown distribution parameter.> ...
485s  paramci (BinomialDistribution.fit (x, 6), "alpha", 0.01, ...
485s           "parameter", "param")
485s ***** error <paramci: invalid NAME for optional argument.> ...
485s  paramci (BinomialDistribution.fit (x, 6), "NAME", "value")
485s ***** error <paramci: invalid NAME for optional argument.> ...
485s  paramci (BinomialDistribution.fit (x, 6), "alpha", 0.01, ...
485s           "NAME", "value")
485s ***** error <paramci: invalid NAME for optional argument.> ...
485s  paramci (BinomialDistribution.fit (x, 6), "alpha", 0.01, ...
485s           "parameter", "p", "NAME", "value")
485s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
485s  plot (BinomialDistribution, "Parent")
485s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
485s  plot (BinomialDistribution, "PlotType", 12)
485s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
485s  plot (BinomialDistribution, "PlotType", {"pdf", "cdf"})
485s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
485s  plot (BinomialDistribution, "PlotType", "pdfcdf")
485s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
485s  plot (BinomialDistribution, "Discrete", "pdfcdf")
485s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
485s  plot (BinomialDistribution, "Discrete", [1, 0])
485s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
485s  plot (BinomialDistribution, "Discrete", {true})
485s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
485s  plot (BinomialDistribution, "Parent", 12)
485s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
485s  plot (BinomialDistribution, "Parent", "hax")
485s ***** error <plot: invalid NAME for optional argument.> ...
485s  plot (BinomialDistribution, "invalidNAME", "pdf")
485s ***** error <plot: no fitted DATA to plot a probability plot.> ...
485s  plot (BinomialDistribution, "PlotType", "probability")
485s ***** error <proflik: no fitted data available.> ...
485s  proflik (BinomialDistribution, 2)
485s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
485s  proflik (BinomialDistribution.fit (x, 6), 3)
485s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
485s  proflik (BinomialDistribution.fit (x, 6), [1, 2])
485s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
485s  proflik (BinomialDistribution.fit (x, 6), {1})
485s ***** error <proflik: SETPARAM must be a numeric vector.> ...
485s  proflik (BinomialDistribution.fit (x, 6), 2, ones (2))
485s ***** error <proflik: missing VALUE for 'Display' argument.> ...
485s  proflik (BinomialDistribution.fit (x, 6), 2, "Display")
485s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
485s  proflik (BinomialDistribution.fit (x, 6), 2, "Display", 1)
485s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
485s  proflik (BinomialDistribution.fit (x, 6), 2, "Display", {1})
485s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
485s  proflik (BinomialDistribution.fit (x, 6), 2, "Display", {"on"})
485s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
485s  proflik (BinomialDistribution.fit (x, 6), 2, "Display", ["on"; "on"])
485s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
485s  proflik (BinomialDistribution.fit (x, 6), 2, "Display", "onnn")
485s ***** error <proflik: invalid NAME for optional arguments.> ...
485s  proflik (BinomialDistribution.fit (x, 6), 2, "NAME", "on")
485s ***** error <proflik: invalid optional argument.> ...
485s  proflik (BinomialDistribution.fit (x, 6), 2, {"NAME"}, "on")
485s ***** error <proflik: invalid optional argument.> ...
485s  proflik (BinomialDistribution.fit (x, 6), 2, {[1 2 3]}, "Display", "on")
485s ***** error <truncate: missing input argument.> ...
485s  truncate (BinomialDistribution)
485s ***** error <truncate: missing input argument.> ...
485s  truncate (BinomialDistribution, 2)
485s ***** error <truncate: invalid lower upper limits.> ...
485s  truncate (BinomialDistribution, 4, 2)
485s ***** shared pd
485s  pd = BinomialDistribution(1, 0.5);
485s  pd(2) = BinomialDistribution(1, 0.6);
485s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
485s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
485s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
485s ***** error <mean: requires a scalar probability distribution.> mean (pd)
485s ***** error <median: requires a scalar probability distribution.> median (pd)
485s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
485s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
485s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
485s ***** error <plot: requires a scalar probability distribution.> plot (pd)
485s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
485s ***** error <random: requires a scalar probability distribution.> random (pd)
485s ***** error <std: requires a scalar probability distribution.> std (pd)
485s ***** error <truncate: requires a scalar probability distribution.> ...
485s  truncate (pd, 2, 4)
485s ***** error <var: requires a scalar probability distribution.> var (pd)
485s 102 tests, 102 passed, 0 known failure, 0 skipped
485s [inst/dist_obj/GeneralizedParetoDistribution.m]
485s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_obj/GeneralizedParetoDistribution.m
485s ***** shared pd, t
485s  pd = GeneralizedParetoDistribution (1, 1, 1);
485s  t = truncate (pd, 2, 4);
485s ***** assert (cdf (pd, [0:5]), [0, 0, 0.5, 0.6667, 0.75, 0.8], 1e-4);
485s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.6667, 1, 1], 1e-4);
485s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.3333, 0.5, 0.6667, 0.75], 1e-4);
485s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.6667, 1], 1e-4);
485s ***** assert (icdf (pd, [0:0.2:1]), [1, 1.25, 1.6667, 2.5, 5, Inf], 1e-4);
485s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2222, 2.5, 2.8571, 3.3333, 4], 1e-4);
485s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1.6667, 2.5, 5, Inf, NaN], 1e-4);
485s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5, 2.8571, 3.3333, 4, NaN], 1e-4);
485s ***** assert (iqr (pd), 2.6667, 1e-4);
485s ***** assert (iqr (t), 0.9143, 1e-4);
485s ***** assert (mean (pd), Inf);
485s ***** assert (mean (t), 2.7726, 1e-4);
485s ***** assert (median (pd), 2);
485s ***** assert (median (t), 2.6667, 1e-4);
485s ***** assert (pdf (pd, [0:5]), [0, 1, 0.25, 0.1111, 0.0625, 0.04], 1e-4);
485s ***** assert (pdf (t, [0:5]), [0, 0, 1, 0.4444, 0.25, 0], 1e-4);
485s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 1, 0.25, 0.1111, 0.0625, NaN], 1e-4);
485s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1, 0.4444, 0.25, NaN], 1e-4);
485s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
485s ***** assert (any (random (t, 1000, 1) < 2), false);
485s ***** assert (any (random (t, 1000, 1) > 4), false);
485s ***** assert (std (pd), Inf);
485s ***** assert (std (t), 0.5592, 1e-4);
485s ***** assert (var (pd), Inf);
485s ***** assert (var (t), 0.3128, 1e-4);
485s ***** error <GeneralizedParetoDistribution: MU must be a real scalar.> ...
485s  GeneralizedParetoDistribution(Inf, 1, 1)
485s ***** error <GeneralizedParetoDistribution: MU must be a real scalar.> ...
485s  GeneralizedParetoDistribution(i, 1, 1)
485s ***** error <GeneralizedParetoDistribution: MU must be a real scalar.> ...
485s  GeneralizedParetoDistribution("a", 1, 1)
485s ***** error <GeneralizedParetoDistribution: MU must be a real scalar.> ...
485s  GeneralizedParetoDistribution([1, 2], 1, 1)
485s ***** error <GeneralizedParetoDistribution: MU must be a real scalar.> ...
485s  GeneralizedParetoDistribution(NaN, 1, 1)
485s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
485s  GeneralizedParetoDistribution(1, 0, 1)
485s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
485s  GeneralizedParetoDistribution(1, -1, 1)
485s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
485s  GeneralizedParetoDistribution(1, Inf, 1)
485s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
485s  GeneralizedParetoDistribution(1, i, 1)
485s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
485s  GeneralizedParetoDistribution(1, "a", 1)
485s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
485s  GeneralizedParetoDistribution(1, [1, 2], 1)
485s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
485s  GeneralizedParetoDistribution(1, NaN, 1)
485s ***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
485s  GeneralizedParetoDistribution(1, 1, Inf)
485s ***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
485s  GeneralizedParetoDistribution(1, 1, i)
485s ***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
485s  GeneralizedParetoDistribution(1, 1, "a")
485s ***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
485s  GeneralizedParetoDistribution(1, 1, [1, 2])
485s ***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
485s  GeneralizedParetoDistribution(1, 1, NaN)
485s ***** error <cdf: invalid argument for upper tail.> ...
485s  cdf (GeneralizedParetoDistribution, 2, "uper")
485s ***** error <cdf: invalid argument for upper tail.> ...
485s  cdf (GeneralizedParetoDistribution, 2, 3)
485s ***** shared x
485s  x = gprnd (1, 1, 1, [1, 100]);
485s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
485s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha")
485s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
485s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0)
485s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
485s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 1)
485s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
485s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", [0.5 2])
485s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
485s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", "")
485s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
485s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", {0.05})
485s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
485s  paramci (GeneralizedParetoDistribution.fit (x, 1), ...
485s           "parameter", "sigma", "alpha", {0.05})
485s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
485s  paramci (GeneralizedParetoDistribution.fit (x, 1), ...
485s           "parameter", {"k", "sigma", "param"})
485s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
485s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0.01, ...
485s           "parameter", {"k", "sigma", "param"})
485s ***** error <paramci: unknown distribution parameter.> ...
485s  paramci (GeneralizedParetoDistribution.fit (x, 1), "parameter", "param")
485s ***** error <paramci: unknown distribution parameter.> ...
485s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0.01, ...
485s           "parameter", "param")
485s ***** error <paramci: invalid NAME for optional argument.> ...
485s  paramci (GeneralizedParetoDistribution.fit (x, 1), "NAME", "value")
485s ***** error <paramci: invalid NAME for optional argument.> ...
485s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0.01, ...
485s           "NAME", "value")
485s ***** error <paramci: invalid NAME for optional argument.> ...
485s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0.01, ...
485s           "parameter", "sigma", "NAME", "value")
485s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
485s  plot (GeneralizedParetoDistribution, "Parent")
485s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
485s  plot (GeneralizedParetoDistribution, "PlotType", 12)
485s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
485s  plot (GeneralizedParetoDistribution, "PlotType", {"pdf", "cdf"})
485s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
485s  plot (GeneralizedParetoDistribution, "PlotType", "pdfcdf")
485s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
485s  plot (GeneralizedParetoDistribution, "Discrete", "pdfcdf")
485s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
485s  plot (GeneralizedParetoDistribution, "Discrete", [1, 0])
485s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
485s  plot (GeneralizedParetoDistribution, "Discrete", {true})
485s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
485s  plot (GeneralizedParetoDistribution, "Parent", 12)
485s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
485s  plot (GeneralizedParetoDistribution, "Parent", "hax")
485s ***** error <plot: invalid NAME for optional argument.> ...
485s  plot (GeneralizedParetoDistribution, "invalidNAME", "pdf")
485s ***** error <plot: no fitted DATA to plot a probability plot.> ...
485s  plot (GeneralizedParetoDistribution, "PlotType", "probability")
485s ***** error <proflik: no fitted data available.> ...
485s  proflik (GeneralizedParetoDistribution, 2)
485s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
485s  proflik (GeneralizedParetoDistribution.fit (x, 1), 3)
485s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
485s  proflik (GeneralizedParetoDistribution.fit (x, 1), [1, 2])
485s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
485s  proflik (GeneralizedParetoDistribution.fit (x, 1), {1})
485s ***** error <proflik: SETPARAM must be a numeric vector.> ...
485s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, ones (2))
485s ***** error <proflik: missing VALUE for 'Display' argument.> ...
485s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display")
485s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
485s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display", 1)
485s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
485s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display", {1})
485s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
485s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display", {"on"})
485s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
485s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, ...
485s           "Display", ["on"; "on"])
485s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
485s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display", "onnn")
485s ***** error <proflik: invalid NAME for optional arguments.> ...
485s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "NAME", "on")
485s ***** error <proflik: invalid optional argument.> ...
485s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, {"NAME"}, "on")
485s ***** error <proflik: invalid optional argument.> ...
485s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, {[1 2 3 4]}, ...
485s           "Display", "on")
485s ***** error <truncate: missing input argument.> ...
485s  truncate (GeneralizedParetoDistribution)
485s ***** error <truncate: missing input argument.> ...
485s  truncate (GeneralizedParetoDistribution, 2)
485s ***** error <truncate: invalid lower upper limits.> ...
485s  truncate (GeneralizedParetoDistribution, 4, 2)
485s ***** shared pd
485s  pd = GeneralizedParetoDistribution(1, 1, 1);
485s  pd(2) = GeneralizedParetoDistribution(1, 3, 1);
485s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
485s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
485s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
485s ***** error <mean: requires a scalar probability distribution.> mean (pd)
485s ***** error <median: requires a scalar probability distribution.> median (pd)
485s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
485s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
485s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
485s ***** error <plot: requires a scalar probability distribution.> plot (pd)
485s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
485s ***** error <random: requires a scalar probability distribution.> random (pd)
485s ***** error <std: requires a scalar probability distribution.> std (pd)
485s ***** error <truncate: requires a scalar probability distribution.> ...
485s  truncate (pd, 2, 4)
485s ***** error <var: requires a scalar probability distribution.> var (pd)
485s 100 tests, 100 passed, 0 known failure, 0 skipped
485s [inst/dist_obj/BirnbaumSaundersDistribution.m]
485s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_obj/BirnbaumSaundersDistribution.m
485s ***** demo
485s  ## Generate a data set of 5000 random samples from a Birnbaum-Saunders
485s  ## distribution with parameters β = 1 and γ = 0.5.  Fit a Birnbaum-Saunders
485s  ## distribution to this data and plot a PDF of the fitted distribution
485s  ## superimposed on a histogram of the data
485s 
485s  pd = makedist ("BirnbaumSaunders", "beta", 1, "gamma", 0.5)
485s  randg ("seed", 21);
485s  data = random (pd, 5000, 1);
485s  pd = fitdist (data, "BirnbaumSaunders")
485s  plot (pd)
485s  msg = "Fitted Birnbaum-Saunders distribution with a = %0.2f and b = %0.2f";
485s  title (sprintf (msg, pd.beta, pd.gamma))
485s ***** demo
485s  ## Plot the PDF of a Birnbaum-Saunders distribution, with parameters beta = 1
485s  ## and gamma = 0.5, truncated at [0, 2] intervals.  Generate 10000 random
485s  ## samples from this truncated distribution and superimpose a histogram with
485s  ## 100 bins scaled accordingly
485s 
485s  pd = makedist ("BirnbaumSaunders", "beta", 1, "gamma", 0.5)
485s  t = truncate (pd, 0, 2)
485s  randg ("seed", 21);
485s  data = random (t, 10000, 1);
485s  plot (t)
485s  title ("Birnbaum-Saunders distribution (a = 2, b = 4) truncated at [0.1, 0.8]")
485s  hold on
485s  hist (data, 100, 50)
485s  hold off
485s ***** demo
485s  ## Generate a data set of 100 random samples from a Birnbaum-Saunders
485s  ## distribution with parameters β = 1 and γ = 0.5.  Fit a Birnbaum-Saunders
485s  ## distribution to this data and plot its CDF superimposed over an empirical
485s  ## CDF of the data
485s 
485s  pd = makedist ("BirnbaumSaunders", "beta", 1, "gamma", 0.5)
485s  randg ("seed", 21);
485s  data = random (pd, 100, 1);
485s  pd = fitdist (data, "BirnbaumSaunders")
485s  plot (pd, "plottype", "cdf")
485s  title (sprintf ("Fitted Beta distribution with a = %0.2f and b = %0.2f", ...
485s                  pd.beta, pd.gamma))
485s  legend ({"empirical CDF", "fitted CDF"}, "location", "east")
485s ***** shared pd, t
485s  pd = BirnbaumSaundersDistribution;
485s  t = truncate (pd, 2, 4);
485s ***** assert (cdf (pd, [0:5]), [0, 0.5, 0.7602, 0.8759, 0.9332, 0.9632], 1e-4);
485s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.6687, 1, 1], 1e-4);
485s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.6585, 0.7602, 0.8759, 0.9332, NaN], 1e-4);
485s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.6687, 1, NaN], 1e-4);
485s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.4411, 0.7767, 1.2875, 2.2673, Inf], 1e-4);
485s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2293, 2.5073, 2.8567, 3.3210, 4], 1e-4);
485s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.7767, 1.2875, 2.2673, Inf, NaN], 1e-4);
485s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5073, 2.8567, 3.3210, 4, NaN], 1e-4);
485s ***** assert (iqr (pd), 1.4236, 1e-4);
485s ***** assert (iqr (t), 0.8968, 1e-4);
485s ***** assert (mean (pd), 1.5, eps);
485s ***** assert (mean (t), 2.7723, 1e-4);
485s ***** assert (median (pd), 1, 1e-4);
485s ***** assert (median (t), 2.6711, 1e-4);
485s ***** assert (pdf (pd, [0:5]), [0, 0.3989, 0.1648, 0.0788, 0.0405, 0.0216], 1e-4);
485s ***** assert (pdf (t, [0:5]), [0, 0, 0.9528, 0.4559, 0.2340, 0], 1e-4);
485s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.2497, NaN], 1e-4);
485s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
485s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
485s ***** assert (any (random (t, 1000, 1) < 2), false);
485s ***** assert (any (random (t, 1000, 1) > 4), false);
485s ***** assert (std (pd), 1.5, eps);
485s ***** assert (std (t), 0.5528, 1e-4);
485s ***** assert (var (pd), 2.25, eps);
485s ***** assert (var (t), 0.3056, 1e-4);
485s ***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
485s  BirnbaumSaundersDistribution(0, 1)
485s ***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
485s  BirnbaumSaundersDistribution(Inf, 1)
485s ***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
485s  BirnbaumSaundersDistribution(i, 1)
485s ***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
485s  BirnbaumSaundersDistribution("beta", 1)
485s ***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
485s  BirnbaumSaundersDistribution([1, 2], 1)
485s ***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
485s  BirnbaumSaundersDistribution(NaN, 1)
485s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
485s  BirnbaumSaundersDistribution(1, 0)
485s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
485s  BirnbaumSaundersDistribution(1, -1)
485s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
485s  BirnbaumSaundersDistribution(1, Inf)
485s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
485s  BirnbaumSaundersDistribution(1, i)
485s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
485s  BirnbaumSaundersDistribution(1, "beta")
485s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
485s  BirnbaumSaundersDistribution(1, [1, 2])
485s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
485s  BirnbaumSaundersDistribution(1, NaN)
485s ***** error <cdf: invalid argument for upper tail.> ...
485s  cdf (BirnbaumSaundersDistribution, 2, "uper")
485s ***** error <cdf: invalid argument for upper tail.> ...
485s  cdf (BirnbaumSaundersDistribution, 2, 3)
485s ***** shared x
485s  rand ("seed", 5);
485s  x = bisarnd (1, 1, [100, 1]);
485s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
485s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha")
485s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
485s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0)
485s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
485s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 1)
485s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
485s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", [0.5 2])
485s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
485s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", "")
485s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
485s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", {0.05})
485s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
485s  paramci (BirnbaumSaundersDistribution.fit (x), "parameter", ...
485s           "beta", "alpha", {0.05})
485s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
485s  paramci (BirnbaumSaundersDistribution.fit (x), ...
485s           "parameter", {"beta", "gamma", "param"})
485s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
485s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0.01, ...
485s           "parameter", {"beta", "gamma", "param"})
485s ***** error <paramci: unknown distribution parameter.> ...
485s  paramci (BirnbaumSaundersDistribution.fit (x), "parameter", "param")
486s ***** error <paramci: unknown distribution parameter.> ...
486s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0.01, ...
486s           "parameter", "param")
486s ***** error <paramci: invalid NAME for optional argument.> ...
486s  paramci (BirnbaumSaundersDistribution.fit (x), "NAME", "value")
486s ***** error <paramci: invalid NAME for optional argument.> ...
486s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0.01, ...
486s           "NAME", "value")
486s ***** error <paramci: invalid NAME for optional argument.> ...
486s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0.01, ...
486s           "parameter", "beta", "NAME", "value")
486s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
486s  plot (BirnbaumSaundersDistribution, "Parent")
486s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
486s  plot (BirnbaumSaundersDistribution, "PlotType", 12)
486s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
486s  plot (BirnbaumSaundersDistribution, "PlotType", {"pdf", "cdf"})
486s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
486s  plot (BirnbaumSaundersDistribution, "PlotType", "pdfcdf")
486s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
486s  plot (BirnbaumSaundersDistribution, "Discrete", "pdfcdf")
486s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
486s  plot (BirnbaumSaundersDistribution, "Discrete", [1, 0])
486s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
486s  plot (BirnbaumSaundersDistribution, "Discrete", {true})
486s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
486s  plot (BirnbaumSaundersDistribution, "Parent", 12)
486s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
486s  plot (BirnbaumSaundersDistribution, "Parent", "hax")
486s ***** error <plot: invalid NAME for optional argument.> ...
486s  plot (BirnbaumSaundersDistribution, "invalidNAME", "pdf")
486s ***** error <plot: no fitted DATA to plot a probability plot.> ...
486s  plot (BirnbaumSaundersDistribution, "PlotType", "probability")
486s ***** error <proflik: no fitted data available.> ...
486s  proflik (BirnbaumSaundersDistribution, 2)
486s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
486s  proflik (BirnbaumSaundersDistribution.fit (x), 3)
486s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
486s  proflik (BirnbaumSaundersDistribution.fit (x), [1, 2])
486s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
486s  proflik (BirnbaumSaundersDistribution.fit (x), {1})
486s ***** error <proflik: SETPARAM must be a numeric vector.> ...
486s  proflik (BirnbaumSaundersDistribution.fit (x), 1, ones (2))
486s ***** error <proflik: missing VALUE for 'Display' argument.> ...
486s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display")
486s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
486s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", 1)
486s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
486s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", {1})
486s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
486s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", {"on"})
486s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
486s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", ["on"; "on"])
486s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
486s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", "onnn")
486s ***** error <proflik: invalid NAME for optional arguments.> ...
486s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "NAME", "on")
486s ***** error <proflik: invalid optional argument.> ...
486s  proflik (BirnbaumSaundersDistribution.fit (x), 1, {"NAME"}, "on")
486s ***** error <proflik: invalid optional argument.> ...
486s  proflik (BirnbaumSaundersDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
486s ***** error <truncate: missing input argument.> ...
486s  truncate (BirnbaumSaundersDistribution)
486s ***** error <truncate: missing input argument.> ...
486s  truncate (BirnbaumSaundersDistribution, 2)
486s ***** error <truncate: invalid lower upper limits.> ...
486s  truncate (BirnbaumSaundersDistribution, 4, 2)
486s ***** shared pd
486s  pd = BirnbaumSaundersDistribution(1, 1);
486s  pd(2) = BirnbaumSaundersDistribution(1, 3);
486s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
486s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
486s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
486s ***** error <mean: requires a scalar probability distribution.> mean (pd)
486s ***** error <median: requires a scalar probability distribution.> median (pd)
486s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
486s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
486s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
486s ***** error <plot: requires a scalar probability distribution.> plot (pd)
486s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
486s ***** error <random: requires a scalar probability distribution.> random (pd)
486s ***** error <std: requires a scalar probability distribution.> std (pd)
486s ***** error <truncate: requires a scalar probability distribution.> ...
486s  truncate (pd, 2, 4)
486s ***** error <var: requires a scalar probability distribution.> var (pd)
486s 96 tests, 96 passed, 0 known failure, 0 skipped
486s [inst/dist_obj/MultinomialDistribution.m]
486s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_obj/MultinomialDistribution.m
486s ***** shared pd, t
486s  pd = MultinomialDistribution ([0.1, 0.2, 0.3, 0.2, 0.1, 0.1]);
486s  t = truncate (pd, 2, 4);
486s ***** assert (cdf (pd, [2, 3, 4]), [0.3, 0.6, 0.8], eps);
486s ***** assert (cdf (t, [2, 3, 4]), [0.2857, 0.7143, 1], 1e-4);
486s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.1, 0.3, 0.6, 0.8], eps);
486s ***** assert (cdf (pd, [1.5, 2-eps, 3, 4]), [0.1, 0.1, 0.6, 0.8], eps);
486s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0.2857, 0.7143, 1], 1e-4);
486s ***** assert (cdf (t, [1.5, 2-eps, 3, 4]), [0, 0, 0.7143, 1], 1e-4);
486s ***** assert (cdf (pd, [1, 2.5, 4, 6]), [0.1, 0.3, 0.8, 1], eps);
486s ***** assert (icdf (pd, [0, 0.2857, 0.7143, 1]), [1, 2, 4, 6]);
486s ***** assert (icdf (t, [0, 0.2857, 0.7143, 1]), [2, 2, 4, 4]);
486s ***** assert (icdf (t, [0, 0.35, 0.7143, 1]), [2, 3, 4, 4]);
486s ***** assert (icdf (t, [0, 0.35, 0.7143, 1, NaN]), [2, 3, 4, 4, NaN]);
486s ***** assert (icdf (t, [-0.5, 0, 0.35, 0.7143, 1, NaN]), [NaN, 2, 3, 4, 4, NaN]);
486s ***** assert (icdf (pd, [-0.5, 0, 0.35, 0.7143, 1, NaN]), [NaN, 1, 3, 4, 6, NaN]);
486s ***** assert (iqr (pd), 2);
486s ***** assert (iqr (t), 2);
486s ***** assert (mean (pd), 3.3, 1e-14);
486s ***** assert (mean (t), 3, eps);
486s ***** assert (median (pd), 3);
486s ***** assert (median (t), 3);
486s ***** assert (pdf (pd, [-5, 1, 2.5, 4, 6, NaN, 9]), [0, 0.1, 0, 0.2, 0.1, NaN, 0]);
486s ***** assert (pdf (pd, [-5, 1, 2, 3, 4, 6, NaN, 9]), ...
486s  [0, 0.1, 0.2, 0.3, 0.2, 0.1, NaN, 0]);
486s ***** assert (pdf (t, [-5, 1, 2, 3, 4, 6, NaN, 0]), ...
486s  [0, 0, 0.2857, 0.4286, 0.2857, 0, NaN, 0], 1e-4);
486s ***** assert (pdf (t, [-5, 1, 2, 4, 6, NaN, 0]), ...
486s  [0, 0, 0.2857, 0.2857, 0, NaN, 0], 1e-4);
486s ***** assert (unique (random (pd, 1000, 5)), [1, 2, 3, 4, 5, 6]');
486s ***** assert (unique (random (t, 1000, 5)), [2, 3, 4]');
486s ***** assert (std (pd), 1.4177, 1e-4);
486s ***** assert (std (t), 0.7559, 1e-4);
486s ***** assert (var (pd), 2.0100, 1e-4);
486s ***** assert (var (t), 0.5714, 1e-4);
486s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
486s  MultinomialDistribution(0)
486s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
486s  MultinomialDistribution(-1)
486s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
486s  MultinomialDistribution(Inf)
486s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
486s  MultinomialDistribution(i)
486s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
486s  MultinomialDistribution("a")
486s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
486s  MultinomialDistribution([1, 2])
486s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
486s  MultinomialDistribution(NaN)
486s ***** error <cdf: invalid argument for upper tail.> ...
486s  cdf (MultinomialDistribution, 2, "uper")
486s ***** error <cdf: invalid argument for upper tail.> ...
486s  cdf (MultinomialDistribution, 2, 3)
486s ***** error <cdf: X must be real.> ...
486s  cdf (MultinomialDistribution, i)
486s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
486s  plot (MultinomialDistribution, "Parent")
486s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
486s  plot (MultinomialDistribution, "PlotType", 12)
486s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
486s  plot (MultinomialDistribution, "PlotType", {"pdf", "cdf"})
486s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
486s  plot (MultinomialDistribution, "PlotType", "pdfcdf")
486s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
486s  plot (MultinomialDistribution, "Discrete", "pdfcdf")
486s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
486s  plot (MultinomialDistribution, "Discrete", [1, 0])
486s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
486s  plot (MultinomialDistribution, "Discrete", {true})
486s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
486s  plot (MultinomialDistribution, "Parent", 12)
486s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
486s  plot (MultinomialDistribution, "Parent", "hax")
486s ***** error <plot: invalid NAME for optional argument.> ...
486s  plot (MultinomialDistribution, "invalidNAME", "pdf")
486s ***** error <plot: 'probability' PlotType is not supported for 'MultinomialDistribution'.> ...
486s  plot (MultinomialDistribution, "PlotType", "probability")
486s ***** error <truncate: is_nan input argument.> ...
486s  truncate (MultinomialDistribution)
486s ***** error <truncate: is_nan input argument.> ...
486s  truncate (MultinomialDistribution, 2)
486s ***** error <truncate: invalid lower upper limits.> ...
486s  truncate (MultinomialDistribution, 4, 2)
486s ***** shared pd
486s  pd = MultinomialDistribution([0.1, 0.2, 0.3, 0.4]);
486s  pd(2) = MultinomialDistribution([0.1, 0.2, 0.3, 0.4]);
486s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
486s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
486s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
486s ***** error <mean: requires a scalar probability distribution.> mean (pd)
486s ***** error <median: requires a scalar probability distribution.> median (pd)
486s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
486s ***** error <plot: requires a scalar probability distribution.> plot (pd)
486s ***** error <random: requires a scalar probability distribution.> random (pd)
486s ***** error <std: requires a scalar probability distribution.> std (pd)
486s ***** error <truncate: requires a scalar probability distribution.> ...
486s  truncate (pd, 2, 4)
486s ***** error <var: requires a scalar probability distribution.> var (pd)
486s 64 tests, 64 passed, 0 known failure, 0 skipped
486s [inst/dist_obj/NakagamiDistribution.m]
486s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_obj/NakagamiDistribution.m
486s ***** shared pd, t
486s  pd = NakagamiDistribution;
486s  t = truncate (pd, 2, 4);
486s ***** assert (cdf (pd, [0:5]), [0, 0.6321, 0.9817, 0.9999, 1, 1], 1e-4);
486s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.9933, 1, 1], 1e-4);
486s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8946, 0.9817, 0.9999, 1], 1e-4);
486s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.9933, 1], 1e-4);
486s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.4724, 0.7147, 0.9572, 1.2686, Inf], 1e-4);
486s ***** assert (icdf (t, [0:0.2:1]), [2, 2.0550, 2.1239, 2.2173, 2.3684, 4], 1e-4);
486s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.7147, 0.9572, 1.2686, Inf, NaN], 1e-4);
486s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.1239, 2.2173, 2.3684, 4, NaN], 1e-4);
486s ***** assert (iqr (pd), 0.6411, 1e-4);
486s ***** assert (iqr (t), 0.2502, 1e-4);
486s ***** assert (mean (pd), 0.8862, 1e-4);
486s ***** assert (mean (t), 2.2263, 1e-4);
486s ***** assert (median (pd), 0.8326, 1e-4);
486s ***** assert (median (t), 2.1664, 1e-4);
486s ***** assert (pdf (pd, [0:5]), [0, 0.7358, 0.0733, 0.0007, 0, 0], 1e-4);
486s ***** assert (pdf (t, [0:5]), [0, 0, 4, 0.0404, 0, 0], 1e-4);
486s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.7358, 0.0733, 0.0007, 0, NaN], 1e-4);
486s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 4, 0.0404, 0, NaN], 1e-4);
486s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
486s ***** assert (any (random (t, 1000, 1) < 2), false);
486s ***** assert (any (random (t, 1000, 1) > 4), false);
486s ***** assert (std (pd), 0.4633, 1e-4);
486s ***** assert (std (t), 0.2083, 1e-4);
486s ***** assert (var (pd), 0.2146, 1e-4);
486s ***** assert (var (t), 0.0434, 1e-4);
486s ***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
486s  NakagamiDistribution(Inf, 1)
486s ***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
486s  NakagamiDistribution(i, 1)
486s ***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
486s  NakagamiDistribution("a", 1)
486s ***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
486s  NakagamiDistribution([1, 2], 1)
486s ***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
486s  NakagamiDistribution(NaN, 1)
486s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
486s  NakagamiDistribution(1, 0)
486s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
486s  NakagamiDistribution(1, -1)
486s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
486s  NakagamiDistribution(1, Inf)
486s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
486s  NakagamiDistribution(1, i)
486s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
486s  NakagamiDistribution(1, "a")
486s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
486s  NakagamiDistribution(1, [1, 2])
486s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
486s  NakagamiDistribution(1, NaN)
486s ***** error <cdf: invalid argument for upper tail.> ...
486s  cdf (NakagamiDistribution, 2, "uper")
486s ***** error <cdf: invalid argument for upper tail.> ...
486s  cdf (NakagamiDistribution, 2, 3)
486s ***** shared x
486s  x = nakarnd (1, 0.5, [1, 100]);
486s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
486s  paramci (NakagamiDistribution.fit (x), "alpha")
486s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
486s  paramci (NakagamiDistribution.fit (x), "alpha", 0)
486s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
486s  paramci (NakagamiDistribution.fit (x), "alpha", 1)
486s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
486s  paramci (NakagamiDistribution.fit (x), "alpha", [0.5 2])
486s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
486s  paramci (NakagamiDistribution.fit (x), "alpha", "")
486s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
486s  paramci (NakagamiDistribution.fit (x), "alpha", {0.05})
486s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
486s  paramci (NakagamiDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
486s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
486s  paramci (NakagamiDistribution.fit (x), "parameter", {"mu", "omega", "param"})
486s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
486s  paramci (NakagamiDistribution.fit (x), "alpha", 0.01, ...
486s           "parameter", {"mu", "omega", "param"})
486s ***** error <paramci: unknown distribution parameter.> ...
486s  paramci (NakagamiDistribution.fit (x), "parameter", "param")
486s ***** error <paramci: unknown distribution parameter.> ...
486s  paramci (NakagamiDistribution.fit (x), "alpha", 0.01, "parameter", "param")
486s ***** error <paramci: invalid NAME for optional argument.> ...
486s  paramci (NakagamiDistribution.fit (x), "NAME", "value")
486s ***** error <paramci: invalid NAME for optional argument.> ...
486s  paramci (NakagamiDistribution.fit (x), "alpha", 0.01, "NAME", "value")
486s ***** error <paramci: invalid NAME for optional argument.> ...
486s  paramci (NakagamiDistribution.fit (x), "alpha", 0.01, "parameter", "mu", ...
486s           "NAME", "value")
486s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
486s  plot (NakagamiDistribution, "Parent")
486s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
486s  plot (NakagamiDistribution, "PlotType", 12)
486s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
486s  plot (NakagamiDistribution, "PlotType", {"pdf", "cdf"})
486s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
486s  plot (NakagamiDistribution, "PlotType", "pdfcdf")
486s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
486s  plot (NakagamiDistribution, "Discrete", "pdfcdf")
486s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
486s  plot (NakagamiDistribution, "Discrete", [1, 0])
486s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
486s  plot (NakagamiDistribution, "Discrete", {true})
486s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
486s  plot (NakagamiDistribution, "Parent", 12)
486s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
486s  plot (NakagamiDistribution, "Parent", "hax")
486s ***** error <plot: invalid NAME for optional argument.> ...
486s  plot (NakagamiDistribution, "invalidNAME", "pdf")
486s ***** error <plot: no fitted DATA to plot a probability plot.> ...
486s  plot (NakagamiDistribution, "PlotType", "probability")
486s ***** error <proflik: no fitted data available.> ...
486s  proflik (NakagamiDistribution, 2)
486s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
486s  proflik (NakagamiDistribution.fit (x), 3)
486s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
486s  proflik (NakagamiDistribution.fit (x), [1, 2])
486s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
486s  proflik (NakagamiDistribution.fit (x), {1})
486s ***** error <proflik: SETPARAM must be a numeric vector.> ...
486s  proflik (NakagamiDistribution.fit (x), 1, ones (2))
486s ***** error <proflik: missing VALUE for 'Display' argument.> ...
486s  proflik (NakagamiDistribution.fit (x), 1, "Display")
486s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
486s  proflik (NakagamiDistribution.fit (x), 1, "Display", 1)
486s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
486s  proflik (NakagamiDistribution.fit (x), 1, "Display", {1})
486s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
486s  proflik (NakagamiDistribution.fit (x), 1, "Display", {"on"})
486s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
486s  proflik (NakagamiDistribution.fit (x), 1, "Display", ["on"; "on"])
486s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
486s  proflik (NakagamiDistribution.fit (x), 1, "Display", "onnn")
486s ***** error <proflik: invalid NAME for optional arguments.> ...
486s  proflik (NakagamiDistribution.fit (x), 1, "NAME", "on")
486s ***** error <proflik: invalid optional argument.> ...
486s  proflik (NakagamiDistribution.fit (x), 1, {"NAME"}, "on")
486s ***** error <proflik: invalid optional argument.> ...
486s  proflik (NakagamiDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
487s ***** error <truncate: missing input argument.> ...
487s  truncate (NakagamiDistribution)
487s ***** error <truncate: missing input argument.> ...
487s  truncate (NakagamiDistribution, 2)
487s ***** error <truncate: invalid lower upper limits.> ...
487s  truncate (NakagamiDistribution, 4, 2)
487s ***** shared pd
487s  pd = NakagamiDistribution(1, 0.5);
487s  pd(2) = NakagamiDistribution(1, 0.6);
487s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
487s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
487s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
487s ***** error <mean: requires a scalar probability distribution.> mean (pd)
487s ***** error <median: requires a scalar probability distribution.> median (pd)
487s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
487s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
487s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
487s ***** error <plot: requires a scalar probability distribution.> plot (pd)
487s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
487s ***** error <random: requires a scalar probability distribution.> random (pd)
487s ***** error <std: requires a scalar probability distribution.> std (pd)
487s ***** error <truncate: requires a scalar probability distribution.> ...
487s  truncate (pd, 2, 4)
487s ***** error <var: requires a scalar probability distribution.> var (pd)
487s 95 tests, 95 passed, 0 known failure, 0 skipped
487s [inst/dist_obj/LogisticDistribution.m]
487s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_obj/LogisticDistribution.m
487s ***** shared pd, t
487s  pd = LogisticDistribution (0, 1);
487s  t = truncate (pd, 2, 4);
487s ***** assert (cdf (pd, [0:5]), [0.5, 0.7311, 0.8808, 0.9526, 0.9820, 0.9933], 1e-4);
487s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7091, 1, 1], 1e-4);
487s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8176, 0.8808, 0.9526, 0.9820], 1e-4);
487s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7091, 1], 1e-4);
487s ***** assert (icdf (pd, [0:0.2:1]), [-Inf, -1.3863, -0.4055, 0.4055, 1.3863, Inf], 1e-4);
487s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2088, 2.4599, 2.7789, 3.2252, 4], 1e-4);
487s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, -0.4055, 0.4055, 1.3863, Inf, NaN], 1e-4);
487s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4599, 2.7789, 3.2252, 4, NaN], 1e-4);
487s ***** assert (iqr (pd), 2.1972, 1e-4);
487s ***** assert (iqr (t), 0.8286, 1e-4);
487s ***** assert (mean (pd), 0, 1e-4);
487s ***** assert (mean (t), 2.7193, 1e-4);
487s ***** assert (median (pd), 0);
487s ***** assert (median (t), 2.6085, 1e-4);
487s ***** assert (pdf (pd, [0:5]), [0.25, 0.1966, 0.1050, 0.0452, 0.0177, 0.0066], 1e-4);
487s ***** assert (pdf (t, [0:5]), [0, 0, 1.0373, 0.4463, 0.1745, 0], 1e-4);
487s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0.1966, 0.1966, 0.1050, 0.0452, 0.0177, NaN], 1e-4);
487s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.0373, 0.4463, 0.1745, NaN], 1e-4);
487s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
487s ***** assert (any (random (t, 1000, 1) < 2), false);
487s ***** assert (any (random (t, 1000, 1) > 4), false);
487s ***** assert (std (pd), 1.8138, 1e-4);
487s ***** assert (std (t), 0.5320, 1e-4);
487s ***** assert (var (pd), 3.2899, 1e-4);
487s ***** assert (var (t), 0.2830, 1e-4);
487s ***** error <LogisticDistribution: MU must be a nonnegative real scalar.> ...
487s  LogisticDistribution(Inf, 1)
487s ***** error <LogisticDistribution: MU must be a nonnegative real scalar.> ...
487s  LogisticDistribution(i, 1)
487s ***** error <LogisticDistribution: MU must be a nonnegative real scalar.> ...
487s  LogisticDistribution("a", 1)
487s ***** error <LogisticDistribution: MU must be a nonnegative real scalar.> ...
487s  LogisticDistribution([1, 2], 1)
487s ***** error <LogisticDistribution: MU must be a nonnegative real scalar.> ...
487s  LogisticDistribution(NaN, 1)
487s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
487s  LogisticDistribution(1, 0)
487s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
487s  LogisticDistribution(1, -1)
487s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
487s  LogisticDistribution(1, Inf)
487s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
487s  LogisticDistribution(1, i)
487s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
487s  LogisticDistribution(1, "a")
487s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
487s  LogisticDistribution(1, [1, 2])
487s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
487s  LogisticDistribution(1, NaN)
487s ***** error <cdf: invalid argument for upper tail.> ...
487s  cdf (LogisticDistribution, 2, "uper")
487s ***** error <cdf: invalid argument for upper tail.> ...
487s  cdf (LogisticDistribution, 2, 3)
487s ***** shared x
487s  x = logirnd (1, 1, [1, 100]);
487s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
487s  paramci (LogisticDistribution.fit (x), "alpha")
487s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
487s  paramci (LogisticDistribution.fit (x), "alpha", 0)
487s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
487s  paramci (LogisticDistribution.fit (x), "alpha", 1)
487s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
487s  paramci (LogisticDistribution.fit (x), "alpha", [0.5 2])
487s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
487s  paramci (LogisticDistribution.fit (x), "alpha", "")
487s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
487s  paramci (LogisticDistribution.fit (x), "alpha", {0.05})
487s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
487s  paramci (LogisticDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
487s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
487s  paramci (LogisticDistribution.fit (x), "parameter", {"mu", "sigma", "param"})
487s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
487s  paramci (LogisticDistribution.fit (x), "alpha", 0.01, ...
487s           "parameter", {"mu", "sigma", "param"})
487s ***** error <paramci: unknown distribution parameter.> ...
487s  paramci (LogisticDistribution.fit (x), "parameter", "param")
487s ***** error <paramci: unknown distribution parameter.> ...
487s  paramci (LogisticDistribution.fit (x), "alpha", 0.01, "parameter", "param")
487s ***** error <paramci: invalid NAME for optional argument.> ...
487s  paramci (LogisticDistribution.fit (x), "NAME", "value")
487s ***** error <paramci: invalid NAME for optional argument.> ...
487s  paramci (LogisticDistribution.fit (x), "alpha", 0.01, "NAME", "value")
487s ***** error <paramci: invalid NAME for optional argument.> ...
487s  paramci (LogisticDistribution.fit (x), "alpha", 0.01, "parameter", "mu", ...
487s           "NAME", "value")
487s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
487s  plot (LogisticDistribution, "Parent")
487s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
487s  plot (LogisticDistribution, "PlotType", 12)
487s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
487s  plot (LogisticDistribution, "PlotType", {"pdf", "cdf"})
487s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
487s  plot (LogisticDistribution, "PlotType", "pdfcdf")
487s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
487s  plot (LogisticDistribution, "Discrete", "pdfcdf")
487s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
487s  plot (LogisticDistribution, "Discrete", [1, 0])
487s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
487s  plot (LogisticDistribution, "Discrete", {true})
487s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
487s  plot (LogisticDistribution, "Parent", 12)
487s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
487s  plot (LogisticDistribution, "Parent", "hax")
487s ***** error <plot: invalid NAME for optional argument.> ...
487s  plot (LogisticDistribution, "invalidNAME", "pdf")
487s ***** error <plot: no fitted DATA to plot a probability plot.> ...
487s  plot (LogisticDistribution, "PlotType", "probability")
487s ***** error <proflik: no fitted data available.> ...
487s  proflik (LogisticDistribution, 2)
487s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
487s  proflik (LogisticDistribution.fit (x), 3)
487s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
487s  proflik (LogisticDistribution.fit (x), [1, 2])
487s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
487s  proflik (LogisticDistribution.fit (x), {1})
487s ***** error <proflik: SETPARAM must be a numeric vector.> ...
487s  proflik (LogisticDistribution.fit (x), 1, ones (2))
487s ***** error <proflik: missing VALUE for 'Display' argument.> ...
487s  proflik (LogisticDistribution.fit (x), 1, "Display")
487s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
487s  proflik (LogisticDistribution.fit (x), 1, "Display", 1)
487s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
487s  proflik (LogisticDistribution.fit (x), 1, "Display", {1})
487s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
487s  proflik (LogisticDistribution.fit (x), 1, "Display", {"on"})
487s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
487s  proflik (LogisticDistribution.fit (x), 1, "Display", ["on"; "on"])
487s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
487s  proflik (LogisticDistribution.fit (x), 1, "Display", "onnn")
487s ***** error <proflik: invalid NAME for optional arguments.> ...
487s  proflik (LogisticDistribution.fit (x), 1, "NAME", "on")
487s ***** error <proflik: invalid optional argument.> ...
487s  proflik (LogisticDistribution.fit (x), 1, {"NAME"}, "on")
487s ***** error <proflik: invalid optional argument.> ...
487s  proflik (LogisticDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
487s ***** error <truncate: missing input argument.> ...
487s  truncate (LogisticDistribution)
487s ***** error <truncate: missing input argument.> ...
487s  truncate (LogisticDistribution, 2)
487s ***** error <truncate: invalid lower upper limits.> ...
487s  truncate (LogisticDistribution, 4, 2)
487s ***** shared pd
487s  pd = LogisticDistribution(1, 1);
487s  pd(2) = LogisticDistribution(1, 3);
487s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
487s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
487s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
487s ***** error <mean: requires a scalar probability distribution.> mean (pd)
487s ***** error <median: requires a scalar probability distribution.> median (pd)
487s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
487s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
487s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
487s ***** error <plot: requires a scalar probability distribution.> plot (pd)
487s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
487s ***** error <random: requires a scalar probability distribution.> random (pd)
487s ***** error <std: requires a scalar probability distribution.> std (pd)
487s ***** error <truncate: requires a scalar probability distribution.> ...
487s  truncate (pd, 2, 4)
487s ***** error <var: requires a scalar probability distribution.> var (pd)
487s 95 tests, 95 passed, 0 known failure, 0 skipped
487s [inst/dist_obj/UniformDistribution.m]
487s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_obj/UniformDistribution.m
487s ***** shared pd, t
487s  pd = UniformDistribution (0, 5);
487s  t = truncate (pd, 2, 4);
487s ***** assert (cdf (pd, [0:5]), [0, 0.2, 0.4, 0.6, 0.8, 1], 1e-4);
487s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.5, 1, 1], 1e-4);
487s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.3, 0.4, 0.6, 0.8, NaN], 1e-4);
487s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.5, 1, NaN], 1e-4);
487s ***** assert (icdf (pd, [0:0.2:1]), [0, 1, 2, 3, 4, 5], 1e-4);
487s ***** assert (icdf (t, [0:0.2:1]), [2, 2.4, 2.8, 3.2, 3.6, 4], 1e-4);
487s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 2, 3, 4, 5, NaN], 1e-4);
487s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.8, 3.2, 3.6, 4, NaN], 1e-4);
487s ***** assert (iqr (pd), 2.5, 1e-14);
487s ***** assert (iqr (t), 1, 1e-14);
487s ***** assert (mean (pd), 2.5, 1e-14);
487s ***** assert (mean (t), 3, 1e-14);
487s ***** assert (median (pd), 2.5, 1e-14);
487s ***** assert (median (t), 3, 1e-14);
487s ***** assert (pdf (pd, [0:5]), [0.2, 0.2, 0.2, 0.2, 0.2, 0.2], 1e-4);
487s ***** assert (pdf (t, [0:5]), [0, 0, 0.5, 0.5, 0.5, 0], 1e-4);
487s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.2, NaN], 1e-4);
487s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
487s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
487s ***** assert (any (random (t, 1000, 1) < 2), false);
487s ***** assert (any (random (t, 1000, 1) > 4), false);
487s ***** assert (std (pd), 1.4434, 1e-4);
487s ***** assert (std (t), 0.5774, 1e-4);
487s ***** assert (var (pd), 2.0833, 1e-4);
487s ***** assert (var (t), 0.3333, 1e-4);
487s ***** error <UniformDistribution: LOWER must be a real scalar.> ...
487s  UniformDistribution (i, 1)
487s ***** error <UniformDistribution: LOWER must be a real scalar.> ...
487s  UniformDistribution (Inf, 1)
487s ***** error <UniformDistribution: LOWER must be a real scalar.> ...
487s  UniformDistribution ([1, 2], 1)
487s ***** error <UniformDistribution: LOWER must be a real scalar.> ...
487s  UniformDistribution ("a", 1)
487s ***** error <UniformDistribution: LOWER must be a real scalar.> ...
487s  UniformDistribution (NaN, 1)
487s ***** error <UniformDistribution: UPPER must be a real scalar.> ...
487s  UniformDistribution (1, i)
487s ***** error <UniformDistribution: UPPER must be a real scalar.> ...
487s  UniformDistribution (1, Inf)
487s ***** error <UniformDistribution: UPPER must be a real scalar.> ...
487s  UniformDistribution (1, [1, 2])
487s ***** error <UniformDistribution: UPPER must be a real scalar.> ...
487s  UniformDistribution (1, "a")
487s ***** error <UniformDistribution: UPPER must be a real scalar.> ...
487s  UniformDistribution (1, NaN)
487s ***** error <UniformDistribution: LOWER must be less than UPPER.> ...
487s  UniformDistribution (2, 1)
487s ***** error <cdf: invalid argument for upper tail.> ...
487s  cdf (UniformDistribution, 2, "uper")
487s ***** error <cdf: invalid argument for upper tail.> ...
487s  cdf (UniformDistribution, 2, 3)
487s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
487s  plot (UniformDistribution, "Parent")
487s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
487s  plot (UniformDistribution, "PlotType", 12)
487s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
487s  plot (UniformDistribution, "PlotType", {"pdf", "cdf"})
487s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
487s  plot (UniformDistribution, "PlotType", "pdfcdf")
487s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
487s  plot (UniformDistribution, "Discrete", "pdfcdf")
487s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
487s  plot (UniformDistribution, "Discrete", [1, 0])
487s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
487s  plot (UniformDistribution, "Discrete", {true})
487s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
487s  plot (UniformDistribution, "Parent", 12)
487s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
487s  plot (UniformDistribution, "Parent", "hax")
487s ***** error <plot: invalid NAME for optional argument.> ...
487s  plot (UniformDistribution, "invalidNAME", "pdf")
487s ***** error <plot: 'probability' PlotType is not supported for 'UniformDistribution'.> ...
487s  plot (UniformDistribution, "PlotType", "probability")
487s ***** error <truncate: missing input argument.> ...
487s  truncate (UniformDistribution)
487s ***** error <truncate: missing input argument.> ...
487s  truncate (UniformDistribution, 2)
487s ***** error <truncate: invalid lower upper limits.> ...
487s  truncate (UniformDistribution, 4, 2)
487s ***** shared pd
487s  pd = UniformDistribution (0, 1);
487s  pd(2) = UniformDistribution (0, 2);
487s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
487s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
487s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
487s ***** error <mean: requires a scalar probability distribution.> mean (pd)
487s ***** error <median: requires a scalar probability distribution.> median (pd)
487s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
487s ***** error <plot: requires a scalar probability distribution.> plot (pd)
487s ***** error <random: requires a scalar probability distribution.> random (pd)
487s ***** error <std: requires a scalar probability distribution.> std (pd)
487s ***** error <truncate: requires a scalar probability distribution.> ...
487s  truncate (pd, 2, 4)
487s ***** error <var: requires a scalar probability distribution.> var (pd)
487s 63 tests, 63 passed, 0 known failure, 0 skipped
487s [inst/dist_obj/BurrDistribution.m]
487s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_obj/BurrDistribution.m
487s ***** demo
487s  ## Generate a data set of 5000 random samples from a Burr type XII
487s  ## distribution with parameters alpha = 1, c = 2, and k = 1.  Fit a Burr type
487s  ## XII distribution to this data and plot a PDF of the fitted distribution
487s  ## superimposed on a histogram of the data
487s 
487s  pd = makedist ("Burr", "alpha", 1, "c", 2, "k", 1)
487s  rand ("seed", 21);
487s  data = random (pd, 5000, 1);
487s  pd = fitdist (data, "Burr")
487s  plot (pd)
487s  msg = strcat (["Fitted Burr type XII distribution with"], ...
487s                 [" alpha = %0.2f, c =  %0.2f, and k = %0.2f"]);
487s  title (sprintf (msg, pd.alpha, pd.c, pd.k))
487s ***** demo
487s  ## Plot the PDF of a Burr type XII distribution, with parameters alpha = 1,
487s  ## c = 2, and k = 1, truncated at [0, 2] intervals. Generate 10000 random
487s  ## samples from this truncated distribution and superimpose a histogram with
487s  ## 100 bins scaled accordingly
487s 
487s  pd = makedist ("Burr", "alpha", 1, "c", 2, "k", 1)
487s  t = truncate (pd, 0.5, 2.5)
487s  rand ("seed", 21);
487s  data = random (t, 10000, 1);
487s  plot (t)
487s  title ("Burr type XII distribution (alpha = 1, c = 2, k = 1) truncated at [0.5, 2.5]")
487s  hold on
487s  hist (data, 100, 50)
487s  hold off
487s ***** demo
487s  ## Generate a data set of 100 random samples from a Burr type XII
487s  ## distribution with parameters alpha = 1, c = 2, and k = 1.  Fit a Burr type
487s  ## XII  distribution to this data and plot its CDF superimposed over an
487s  ## empirical CDF of the data
487s 
487s  pd = makedist ("Burr", "alpha", 1, "c", 2, "k", 1)
487s  rand ("seed", 21);
487s  data = random (pd, 100, 1);
487s  pd = fitdist (data, "Burr")
487s  plot (pd, "plottype", "cdf")
487s  msg = strcat (["Fitted Burr type XII distribution with"], ...
487s                 [" alpha = %0.2f, c =  %0.2f, and k = %0.2f"]);
487s  title (sprintf (msg, pd.alpha, pd.c, pd.k))
487s  legend ({"empirical CDF", "fitted CDF"}, "location", "east")
487s ***** shared pd, t
487s  pd = BurrDistribution;
487s  t = truncate (pd, 2, 4);
487s ***** assert (cdf (pd, [0:5]), [0, 0.5, 0.6667, 0.75, 0.8, 0.8333], 1e-4);
487s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.625, 1, 1], 1e-4);
487s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.6, 0.6667, 0.75, 0.8], 1e-4);
487s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.625, 1], 1e-4);
487s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.25, 0.6667, 1.5, 4, Inf], 1e-4);
487s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2609, 2.5714, 2.9474, 3.4118, 4], 1e-4);
487s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.6667, 1.5, 4, Inf, NaN], 1e-4);
487s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5714, 2.9474, 3.4118, 4, NaN], 1e-4);
487s ***** assert (iqr (pd), 2.6667, 1e-4);
487s ***** assert (iqr (t), 0.9524, 1e-4);
487s ***** assert (mean (pd), Inf);
487s ***** assert (mean (t), 2.8312, 1e-4);
488s ***** assert (median (pd), 1, 1e-4);
488s ***** assert (median (t), 2.75, 1e-4);
488s ***** assert (pdf (pd, [0:5]), [1, 0.25, 0.1111, 0.0625, 0.04, 0.0278], 1e-4);
488s ***** assert (pdf (t, [0:5]), [0, 0, 0.8333, 0.4687, 0.3, 0], 1e-4);
488s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.25, 0.1111, 0.0625, 0.04, NaN], 1e-4);
488s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.8333, 0.4687, 0.3, NaN], 1e-4);
488s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
488s ***** assert (any (random (t, 1000, 1) < 2), false);
488s ***** assert (any (random (t, 1000, 1) > 4), false);
488s ***** assert (std (pd), Inf);
488s ***** assert (std (t), 0.5674, 1e-4);
488s ***** assert (var (pd), Inf);
488s ***** assert (var (t), 0.3220, 1e-4);
488s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
488s  BurrDistribution(0, 1, 1)
488s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
488s  BurrDistribution(-1, 1, 1)
488s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
488s  BurrDistribution(Inf, 1, 1)
488s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
488s  BurrDistribution(i, 1, 1)
488s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
488s  BurrDistribution("a", 1, 1)
488s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
488s  BurrDistribution([1, 2], 1, 1)
488s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
488s  BurrDistribution(NaN, 1, 1)
488s ***** error <BurrDistribution: C must be a positive real scalar.> ...
488s  BurrDistribution(1, 0, 1)
488s ***** error <BurrDistribution: C must be a positive real scalar.> ...
488s  BurrDistribution(1, -1, 1)
488s ***** error <BurrDistribution: C must be a positive real scalar.> ...
488s  BurrDistribution(1, Inf, 1)
488s ***** error <BurrDistribution: C must be a positive real scalar.> ...
488s  BurrDistribution(1, i, 1)
488s ***** error <BurrDistribution: C must be a positive real scalar.> ...
488s  BurrDistribution(1, "a", 1)
488s ***** error <BurrDistribution: C must be a positive real scalar.> ...
488s  BurrDistribution(1, [1, 2], 1)
488s ***** error <BurrDistribution: C must be a positive real scalar.> ...
488s  BurrDistribution(1, NaN, 1)
488s ***** error <BurrDistribution: K must be a positive real scalar.> ...
488s  BurrDistribution(1, 1, 0)
488s ***** error <BurrDistribution: K must be a positive real scalar.> ...
488s  BurrDistribution(1, 1, -1)
488s ***** error <BurrDistribution: K must be a positive real scalar.> ...
488s  BurrDistribution(1, 1, Inf)
488s ***** error <BurrDistribution: K must be a positive real scalar.> ...
488s  BurrDistribution(1, 1, i)
488s ***** error <BurrDistribution: K must be a positive real scalar.> ...
488s  BurrDistribution(1, 1, "a")
488s ***** error <BurrDistribution: K must be a positive real scalar.> ...
488s  BurrDistribution(1, 1, [1, 2])
488s ***** error <BurrDistribution: K must be a positive real scalar.> ...
488s  BurrDistribution(1, 1, NaN)
488s ***** error <cdf: invalid argument for upper tail.> ...
488s  cdf (BurrDistribution, 2, "uper")
488s ***** error <cdf: invalid argument for upper tail.> ...
488s  cdf (BurrDistribution, 2, 3)
488s ***** shared x
488s  rand ("seed", 4);
488s  x = burrrnd (1, 1, 1, [1, 100]);
488s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
488s  paramci (BurrDistribution.fit (x), "alpha")
488s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
488s  paramci (BurrDistribution.fit (x), "alpha", 0)
488s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
488s  paramci (BurrDistribution.fit (x), "alpha", 1)
488s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
488s  paramci (BurrDistribution.fit (x), "alpha", [0.5 2])
488s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
488s  paramci (BurrDistribution.fit (x), "alpha", "")
488s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
488s  paramci (BurrDistribution.fit (x), "alpha", {0.05})
488s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
488s  paramci (BurrDistribution.fit (x), "parameter", "c", "alpha", {0.05})
488s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
488s  paramci (BurrDistribution.fit (x), "parameter", {"alpha", "c", "k", "param"})
488s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
488s  paramci (BurrDistribution.fit (x), "alpha", 0.01, ...
488s           "parameter", {"alpha", "c", "k", "param"})
488s ***** error <paramci: unknown distribution parameter.> ...
488s  paramci (BurrDistribution.fit (x), "parameter", "param")
488s ***** error <paramci: unknown distribution parameter.> ...
488s  paramci (BurrDistribution.fit (x), "alpha", 0.01, "parameter", "param")
488s ***** error <paramci: invalid NAME for optional argument.> ...
488s  paramci (BurrDistribution.fit (x), "NAME", "value")
488s ***** error <paramci: invalid NAME for optional argument.> ...
488s  paramci (BurrDistribution.fit (x), "alpha", 0.01, "NAME", "value")
488s ***** error <paramci: invalid NAME for optional argument.> ...
488s  paramci (BurrDistribution.fit (x), "alpha", 0.01, "parameter", "c", ...
488s           "NAME", "value")
488s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
488s  plot (BurrDistribution, "Parent")
488s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
488s  plot (BurrDistribution, "PlotType", 12)
488s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
488s  plot (BurrDistribution, "PlotType", {"pdf", "cdf"})
488s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
488s  plot (BurrDistribution, "PlotType", "pdfcdf")
488s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
488s  plot (BurrDistribution, "Discrete", "pdfcdf")
488s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
488s  plot (BurrDistribution, "Discrete", [1, 0])
488s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
488s  plot (BurrDistribution, "Discrete", {true})
488s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
488s  plot (BurrDistribution, "Parent", 12)
488s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
488s  plot (BurrDistribution, "Parent", "hax")
488s ***** error <plot: invalid NAME for optional argument.> ...
488s  plot (BurrDistribution, "invalidNAME", "pdf")
488s ***** error <plot: no fitted DATA to plot a probability plot.> ...
488s  plot (BurrDistribution, "PlotType", "probability")
488s ***** error <proflik: no fitted data available.> ...
488s  proflik (BurrDistribution, 2)
488s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
488s  proflik (BurrDistribution.fit (x), 4)
488s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
488s  proflik (BurrDistribution.fit (x), [1, 2])
488s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
488s  proflik (BurrDistribution.fit (x), {1})
488s ***** error <proflik: SETPARAM must be a numeric vector.> ...
488s  proflik (BurrDistribution.fit (x), 1, ones (2))
488s ***** error <proflik: missing VALUE for 'Display' argument.> ...
488s  proflik (BurrDistribution.fit (x), 1, "Display")
488s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
488s  proflik (BurrDistribution.fit (x), 1, "Display", 1)
489s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
489s  proflik (BurrDistribution.fit (x), 1, "Display", {1})
489s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
489s  proflik (BurrDistribution.fit (x), 1, "Display", {"on"})
489s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
489s  proflik (BurrDistribution.fit (x), 1, "Display", ["on"; "on"])
489s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
489s  proflik (BurrDistribution.fit (x), 1, "Display", "onnn")
489s ***** error <proflik: invalid NAME for optional arguments.> ...
489s  proflik (BurrDistribution.fit (x), 1, "NAME", "on")
489s ***** error <proflik: invalid optional argument.> ...
489s  proflik (BurrDistribution.fit (x), 1, {"NAME"}, "on")
489s ***** error <proflik: invalid optional argument.> ...
489s  proflik (BurrDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
489s ***** error <truncate: missing input argument.> ...
489s  truncate (BurrDistribution)
489s ***** error <truncate: missing input argument.> ...
489s  truncate (BurrDistribution, 2)
489s ***** error <truncate: invalid lower upper limits.> ...
489s  truncate (BurrDistribution, 4, 2)
489s ***** shared pd
489s  pd = BurrDistribution(1, 1, 1);
489s  pd(2) = BurrDistribution(1, 3, 1);
489s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
489s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
489s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
489s ***** error <mean: requires a scalar probability distribution.> mean (pd)
489s ***** error <median: requires a scalar probability distribution.> median (pd)
489s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
489s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
489s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
489s ***** error <plot: requires a scalar probability distribution.> plot (pd)
489s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
489s ***** error <random: requires a scalar probability distribution.> random (pd)
489s ***** error <std: requires a scalar probability distribution.> std (pd)
489s ***** error <truncate: requires a scalar probability distribution.> ...
489s  truncate (pd, 2, 4)
489s ***** error <var: requires a scalar probability distribution.> var (pd)
489s 104 tests, 104 passed, 0 known failure, 0 skipped
489s [inst/dist_obj/TriangularDistribution.m]
489s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_obj/TriangularDistribution.m
489s ***** shared pd, t
489s  pd = TriangularDistribution (0, 3, 5);
489s  t = truncate (pd, 2, 4);
489s ***** assert (cdf (pd, [0:5]), [0, 0.0667, 0.2667, 0.6000, 0.9000, 1], 1e-4);
489s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.5263, 1, 1], 1e-4);
489s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.1500, 0.2667, 0.6, 0.9, NaN], 1e-4);
489s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.5263, 1, NaN], 1e-4);
489s ***** assert (icdf (pd, [0:0.2:1]), [0, 1.7321, 2.4495, 3, 3.5858, 5], 1e-4);
489s ***** assert (icdf (t, [0:0.2:1]), [2, 2.4290, 2.7928, 3.1203, 3.4945, 4], 1e-4);
489s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4495, 3, 3.5858, 5, NaN], 1e-4);
489s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.7928, 3.1203, 3.4945, 4, NaN], 1e-4);
489s ***** assert (iqr (pd), 1.4824, 1e-4);
489s ***** assert (iqr (t), 0.8678, 1e-4);
489s ***** assert (mean (pd), 2.6667, 1e-4);
489s ***** assert (mean (t), 2.9649, 1e-4);
489s ***** assert (median (pd), 2.7386, 1e-4);
489s ***** assert (median (t), 2.9580, 1e-4);
489s ***** assert (pdf (pd, [0:5]), [0, 0.1333, 0.2667, 0.4, 0.2, 0], 1e-4);
489s ***** assert (pdf (t, [0:5]), [0, 0, 0.4211, 0.6316, 0.3158, 0], 1e-4);
489s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.2, NaN], 1e-4);
489s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
489s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
489s ***** assert (any (random (t, 1000, 1) < 2), false);
489s ***** assert (any (random (t, 1000, 1) > 4), false);
489s ***** assert (std (pd), 1.0274, 1e-4);
489s ***** assert (std (t), 0.5369, 1e-4);
489s ***** assert (var (pd), 1.0556, 1e-4);
489s ***** assert (var (t), 0.2882, 1e-4);
489s ***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
489s  TriangularDistribution (i, 1, 2)
489s ***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
489s  TriangularDistribution (Inf, 1, 2)
489s ***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
489s  TriangularDistribution ([1, 2], 1, 2)
489s ***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
489s  TriangularDistribution ("a", 1, 2)
489s ***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
489s  TriangularDistribution (NaN, 1, 2)
489s ***** error <TriangularDistribution: mode B must be a real scalar.> ...
489s  TriangularDistribution (1, i, 2)
489s ***** error <TriangularDistribution: mode B must be a real scalar.> ...
489s  TriangularDistribution (1, Inf, 2)
489s ***** error <TriangularDistribution: mode B must be a real scalar.> ...
489s  TriangularDistribution (1, [1, 2], 2)
489s ***** error <TriangularDistribution: mode B must be a real scalar.> ...
489s  TriangularDistribution (1, "a", 2)
489s ***** error <TriangularDistribution: mode B must be a real scalar.> ...
489s  TriangularDistribution (1, NaN, 2)
489s ***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
489s  TriangularDistribution (1, 2, i)
489s ***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
489s  TriangularDistribution (1, 2, Inf)
489s ***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
489s  TriangularDistribution (1, 2, [1, 2])
489s ***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
489s  TriangularDistribution (1, 2, "a")
489s ***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
489s  TriangularDistribution (1, 2, NaN)
489s ***** error <TriangularDistribution: lower limit A must be less than upper limit C.> ...
489s  TriangularDistribution (1, 1, 1)
489s ***** error <TriangularDistribution: mode B must be within lower limit A and upper limit C.> ...
489s  TriangularDistribution (1, 0.5, 2)
489s ***** error <cdf: invalid argument for upper tail.> ...
489s  cdf (TriangularDistribution, 2, "uper")
489s ***** error <cdf: invalid argument for upper tail.> ...
489s  cdf (TriangularDistribution, 2, 3)
489s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
489s  plot (TriangularDistribution, "Parent")
489s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
489s  plot (TriangularDistribution, "PlotType", 12)
489s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
489s  plot (TriangularDistribution, "PlotType", {"pdf", "cdf"})
489s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
489s  plot (TriangularDistribution, "PlotType", "pdfcdf")
489s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
489s  plot (TriangularDistribution, "Discrete", "pdfcdf")
489s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
489s  plot (TriangularDistribution, "Discrete", [1, 0])
489s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
489s  plot (TriangularDistribution, "Discrete", {true})
489s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
489s  plot (TriangularDistribution, "Parent", 12)
489s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
489s  plot (TriangularDistribution, "Parent", "hax")
489s ***** error <plot: invalid NAME for optional argument.> ...
489s  plot (TriangularDistribution, "invalidNAME", "pdf")
489s ***** error <'probability' PlotType is not supported for 'TriangularDistribution'.> ...
489s  plot (TriangularDistribution, "PlotType", "probability")
489s ***** error <truncate: missing input argument.> ...
489s  truncate (TriangularDistribution)
489s ***** error <truncate: missing input argument.> ...
489s  truncate (TriangularDistribution, 2)
489s ***** error <truncate: invalid lower upper limits.> ...
489s  truncate (TriangularDistribution, 4, 2)
489s ***** shared pd
489s  pd = TriangularDistribution (0, 1, 2);
489s  pd(2) = TriangularDistribution (0, 1, 2);
489s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
489s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
489s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
489s ***** error <mean: requires a scalar probability distribution.> mean (pd)
489s ***** error <median: requires a scalar probability distribution.> median (pd)
489s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
489s ***** error <plot: requires a scalar probability distribution.> plot (pd)
489s ***** error <random: requires a scalar probability distribution.> random (pd)
489s ***** error <std: requires a scalar probability distribution.> std (pd)
489s ***** error <truncate: requires a scalar probability distribution.> ...
489s  truncate (pd, 2, 4)
489s ***** error <var: requires a scalar probability distribution.> var (pd)
489s 69 tests, 69 passed, 0 known failure, 0 skipped
489s [inst/dist_obj/PiecewiseLinearDistribution.m]
489s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_obj/PiecewiseLinearDistribution.m
489s ***** shared pd, t
489s  load patients
489s  [f, x] = ecdf (Weight);
489s  f = f(1:5:end);
489s  x = x(1:5:end);
489s  pd = PiecewiseLinearDistribution (x, f);
489s  t = truncate (pd, 130, 180);
489s ***** assert (cdf (pd, [120, 130, 140, 150, 200]), [0.0767, 0.25, 0.4629, 0.5190, 0.9908], 1e-4);
489s ***** assert (cdf (t, [120, 130, 140, 150, 200]), [0, 0, 0.4274, 0.5403, 1], 1e-4);
489s ***** assert (cdf (pd, [100, 250, NaN]), [0, 1, NaN], 1e-4);
489s ***** assert (cdf (t, [115, 290, NaN]), [0, 1, NaN], 1e-4);
489s ***** assert (icdf (pd, [0:0.2:1]), [111, 127.5, 136.62, 169.67, 182.17, 202], 1e-2);
489s ***** assert (icdf (t, [0:0.2:1]), [130, 134.15, 139.26, 162.5, 173.99, 180], 1e-2);
489s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NA, 136.62, 169.67, 182.17, 202, NA], 1e-2);
489s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NA, 139.26, 162.5, 173.99, 180, NA], 1e-2);
489s ***** assert (iqr (pd), 50.0833, 1e-4);
489s ***** assert (iqr (t), 36.8077, 1e-4);
489s ***** assert (mean (pd), 153.61, 1e-10);
489s ***** assert (mean (t), 152.311, 1e-3);
491s ***** assert (median (pd), 142, 1e-10);
491s ***** assert (median (t), 141.9462, 1e-4);
491s ***** assert (pdf (pd, [120, 130, 140, 150, 200]), [0.0133, 0.0240, 0.0186, 0.0024, 0.0046], 1e-4);
491s ***** assert (pdf (t, [120, 130, 140, 150, 200]), [0, 0.0482, 0.0373, 0.0048, 0], 1e-4);
491s ***** assert (pdf (pd, [100, 250, NaN]), [0, 0, NaN], 1e-4);
491s ***** assert (pdf (t, [100, 250, NaN]), [0, 0, NaN], 1e-4);
491s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
492s ***** assert (any (random (t, 1000, 1) < 130), false);
492s ***** assert (any (random (t, 1000, 1) > 180), false);
492s ***** assert (std (pd), 26.5196, 1e-4);
492s ***** assert (std (t), 18.2941, 1e-4);
496s ***** assert (var (pd), 703.2879, 1e-4);
496s ***** assert (var (t), 334.6757, 1e-4);
502s ***** error <PiecewiseLinearDistribution: X must be a real vector.> ...
502s  PiecewiseLinearDistribution ([0, i], [0, 1])
502s ***** error <PiecewiseLinearDistribution: X must be a real vector.> ...
502s  PiecewiseLinearDistribution ([0, Inf], [0, 1])
502s ***** error <PiecewiseLinearDistribution: X must be a real vector.> ...
502s  PiecewiseLinearDistribution (["a", "c"], [0, 1])
502s ***** error <PiecewiseLinearDistribution: X must be a real vector.> ...
502s  PiecewiseLinearDistribution ([NaN, 1], [0, 1])
502s ***** error <PiecewiseLinearDistribution: Fx must be a real vector.> ...
502s  PiecewiseLinearDistribution ([0, 1], [0, i])
502s ***** error <PiecewiseLinearDistribution: Fx must be a real vector.> ...
502s  PiecewiseLinearDistribution ([0, 1], [0, Inf])
502s ***** error <PiecewiseLinearDistribution: Fx must be a real vector.> ...
502s  PiecewiseLinearDistribution ([0, 1], ["a", "c"])
502s ***** error <PiecewiseLinearDistribution: Fx must be a real vector.> ...
502s  PiecewiseLinearDistribution ([0, 1], [NaN, 1])
502s ***** error <PiecewiseLinearDistribution: X and FX must be vectors of equal size.> ...
502s  PiecewiseLinearDistribution ([0, 1], [0, 0.5, 1])
502s ***** error <PiecewiseLinearDistribution: X and FX must be at least two-elements long.> ...
502s  PiecewiseLinearDistribution ([0], [1])
502s ***** error <PiecewiseLinearDistribution: FX must be bounded in the range> ...
502s  PiecewiseLinearDistribution ([0, 0.5, 1], [0, 1, 1.5])
502s ***** error <cdf: invalid argument for upper tail.> ...
502s  cdf (PiecewiseLinearDistribution, 2, "uper")
502s ***** error <cdf: invalid argument for upper tail.> ...
502s  cdf (PiecewiseLinearDistribution, 2, 3)
502s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
502s  plot (PiecewiseLinearDistribution, "Parent")
502s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
502s  plot (PiecewiseLinearDistribution, "PlotType", 12)
502s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
502s  plot (PiecewiseLinearDistribution, "PlotType", {"pdf", "cdf"})
502s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
502s  plot (PiecewiseLinearDistribution, "PlotType", "pdfcdf")
502s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
502s  plot (PiecewiseLinearDistribution, "Discrete", "pdfcdf")
502s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
502s  plot (PiecewiseLinearDistribution, "Discrete", [1, 0])
502s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
502s  plot (PiecewiseLinearDistribution, "Discrete", {true})
502s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
502s  plot (PiecewiseLinearDistribution, "Parent", 12)
502s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
502s  plot (PiecewiseLinearDistribution, "Parent", "hax")
502s ***** error <plot: invalid NAME for optional argument.> ...
502s  plot (PiecewiseLinearDistribution, "invalidNAME", "pdf")
502s ***** error <plot: 'probability' PlotType is not supported for 'PiecewiseLinearDistribution'.> ...
502s  plot (PiecewiseLinearDistribution, "PlotType", "probability")
502s ***** error <truncate: missing input argument.> ...
502s  truncate (PiecewiseLinearDistribution)
502s ***** error <truncate: missing input argument.> ...
502s  truncate (PiecewiseLinearDistribution, 2)
502s ***** error <truncate: invalid lower upper limits.> ...
502s  truncate (PiecewiseLinearDistribution, 4, 2)
502s ***** shared pd
502s  pd = PiecewiseLinearDistribution ();
502s  pd(2) = PiecewiseLinearDistribution ();
502s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
502s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
502s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
502s ***** error <mean: requires a scalar probability distribution.> mean (pd)
502s ***** error <median: requires a scalar probability distribution.> median (pd)
502s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
502s ***** error <plot: requires a scalar probability distribution.> plot (pd)
502s ***** error <random: requires a scalar probability distribution.> random (pd)
502s ***** error <std: requires a scalar probability distribution.> std (pd)
502s ***** error <truncate: requires a scalar probability distribution.> ...
502s  truncate (pd, 2, 4)
502s ***** error <var: requires a scalar probability distribution.> var (pd)
502s 63 tests, 63 passed, 0 known failure, 0 skipped
502s [inst/dist_obj/NegativeBinomialDistribution.m]
502s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_obj/NegativeBinomialDistribution.m
502s ***** shared pd, t, t_inf
502s  pd = NegativeBinomialDistribution (5, 0.5);
502s  t = truncate (pd, 2, 4);
502s  t_inf = truncate (pd, 2, Inf);
502s ***** assert (cdf (pd, [0:5]), [0.0312, 0.1094, 0.2266, 0.3633, 0.5, 0.6230], 1e-4);
502s ***** assert (cdf (t, [0:5]), [0, 0, 0.3, 0.65, 1, 1], 1e-4);
502s ***** assert (cdf (t_inf, [0:5]), [0, 0, 0.1316, 0.2851, 0.4386, 0.5768], 1e-4);
502s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.1094, 0.2266, 0.3633, 0.5000], 1e-4);
502s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0.3, 0.65, 1], 1e-4);
502s ***** assert (icdf (pd, [0:0.2:1]), [0, 2, 4, 5, 7, Inf], 1e-4);
502s ***** assert (icdf (t, [0:0.2:1]), [2, 2, 3, 3, 4, 4], 1e-4);
502s ***** assert (icdf (t_inf, [0:0.2:1]), [2, 3, 4, 6, 8, Inf], 1e-4);
502s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 4, 5, 7, Inf, NaN], 1e-4);
502s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 3, 3, 4, 4, NaN], 1e-4);
502s ***** assert (iqr (pd), 4);
502s ***** assert (iqr (t), 2);
502s ***** assert (mean (pd), 5);
502s ***** assert (mean (t), 3.0500, 1e-4);
502s ***** assert (mean (t_inf), 5.5263, 1e-4);
502s ***** assert (median (pd), 4);
502s ***** assert (median (t), 3);
502s ***** assert (pdf (pd, [0:5]), [0.0312, 0.0781, 0.1172, 0.1367, 0.1367, 0.1230], 1e-4);
502s ***** assert (pdf (t, [0:5]), [0, 0, 0.3, 0.35, 0.35, 0], 1e-4);
502s ***** assert (pdf (t_inf, [0:5]), [0, 0, 0.1316, 0.1535, 0.1535, 0.1382], 1e-4);
502s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.0781, 0.1172, 0.1367, 0.1367, NaN], 1e-4);
502s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.3, 0.35, 0.35, NaN], 1e-4);
502s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
502s ***** assert (any (random (t, 1000, 1) < 2), false);
502s ***** assert (any (random (t, 1000, 1) > 4), false);
502s ***** assert (std (pd), 3.1623, 1e-4);
502s ***** assert (std (t), 0.8047, 1e-4);
502s ***** assert (std (t_inf), 2.9445, 1e-4);
502s ***** assert (var (pd), 10);
502s ***** assert (var (t), 0.6475, 1e-4);
502s ***** assert (var (t_inf), 8.6704, 1e-4);
502s ***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
502s  NegativeBinomialDistribution(Inf, 1)
502s ***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
502s  NegativeBinomialDistribution(i, 1)
502s ***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
502s  NegativeBinomialDistribution("a", 1)
502s ***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
502s  NegativeBinomialDistribution([1, 2], 1)
502s ***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
502s  NegativeBinomialDistribution(NaN, 1)
502s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
502s  NegativeBinomialDistribution(1, 0)
502s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
502s  NegativeBinomialDistribution(1, -1)
502s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
502s  NegativeBinomialDistribution(1, Inf)
502s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
502s  NegativeBinomialDistribution(1, i)
502s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
502s  NegativeBinomialDistribution(1, "a")
502s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
502s  NegativeBinomialDistribution(1, [1, 2])
502s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
502s  NegativeBinomialDistribution(1, NaN)
502s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
502s  NegativeBinomialDistribution(1, 1.2)
502s ***** error <cdf: invalid argument for upper tail.> ...
502s  cdf (NegativeBinomialDistribution, 2, "uper")
502s ***** error <cdf: invalid argument for upper tail.> ...
502s  cdf (NegativeBinomialDistribution, 2, 3)
502s ***** shared x
502s  x = nbinrnd (1, 0.5, [1, 100]);
502s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
502s  paramci (NegativeBinomialDistribution.fit (x), "alpha")
502s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
502s  paramci (NegativeBinomialDistribution.fit (x), "alpha", 0)
502s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
502s  paramci (NegativeBinomialDistribution.fit (x), "alpha", 1)
502s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
502s  paramci (NegativeBinomialDistribution.fit (x), "alpha", [0.5 2])
502s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
502s  paramci (NegativeBinomialDistribution.fit (x), "alpha", "")
502s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
502s  paramci (NegativeBinomialDistribution.fit (x), "alpha", {0.05})
502s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
502s  paramci (NegativeBinomialDistribution.fit (x), "parameter", "R", ...
502s           "alpha", {0.05})
502s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
502s  paramci (NegativeBinomialDistribution.fit (x), ...
502s           "parameter", {"R", "P", "param"})
502s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
502s  paramci (NegativeBinomialDistribution.fit (x), "alpha", 0.01, ...
502s           "parameter", {"R", "P", "param"})
502s ***** error <paramci: unknown distribution parameter.> ...
502s  paramci (NegativeBinomialDistribution.fit (x), "parameter", "param")
502s ***** error <paramci: unknown distribution parameter.> ...
502s  paramci (NegativeBinomialDistribution.fit (x), "alpha", 0.01, ...
502s           "parameter", "param")
502s ***** error <paramci: invalid NAME for optional argument.> ...
502s  paramci (NegativeBinomialDistribution.fit (x), "NAME", "value")
502s ***** error <paramci: invalid NAME for optional argument.> ...
502s  paramci (NegativeBinomialDistribution.fit (x), "alpha", 0.01, ...
502s           "NAME", "value")
502s ***** error <paramci: invalid NAME for optional argument.> ...
502s  paramci (NegativeBinomialDistribution.fit (x), "alpha", 0.01, ...
502s           "parameter", "R", "NAME", "value")
502s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
502s  plot (NegativeBinomialDistribution, "Parent")
502s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
502s  plot (NegativeBinomialDistribution, "PlotType", 12)
502s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
502s  plot (NegativeBinomialDistribution, "PlotType", {"pdf", "cdf"})
502s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
502s  plot (NegativeBinomialDistribution, "PlotType", "pdfcdf")
502s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
502s  plot (NegativeBinomialDistribution, "Discrete", "pdfcdf")
502s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
502s  plot (NegativeBinomialDistribution, "Discrete", [1, 0])
502s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
502s  plot (NegativeBinomialDistribution, "Discrete", {true})
503s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
503s  plot (NegativeBinomialDistribution, "Parent", 12)
503s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
503s  plot (NegativeBinomialDistribution, "Parent", "hax")
503s ***** error <plot: invalid NAME for optional argument.> ...
503s  plot (NegativeBinomialDistribution, "invalidNAME", "pdf")
503s ***** error <plot: no fitted DATA to plot a probability plot.> ...
503s  plot (NegativeBinomialDistribution, "PlotType", "probability")
503s ***** error <proflik: no fitted data available.> ...
503s  proflik (NegativeBinomialDistribution, 2)
503s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
503s  proflik (NegativeBinomialDistribution.fit (x), 3)
503s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
503s  proflik (NegativeBinomialDistribution.fit (x), [1, 2])
503s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
503s  proflik (NegativeBinomialDistribution.fit (x), {1})
503s ***** error <proflik: SETPARAM must be a numeric vector.> ...
503s  proflik (NegativeBinomialDistribution.fit (x), 1, ones (2))
503s ***** error <proflik: missing VALUE for 'Display' argument.> ...
503s  proflik (NegativeBinomialDistribution.fit (x), 1, "Display")
503s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
503s  proflik (NegativeBinomialDistribution.fit (x), 1, "Display", 1)
503s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
503s  proflik (NegativeBinomialDistribution.fit (x), 1, "Display", {1})
503s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
503s  proflik (NegativeBinomialDistribution.fit (x), 1, "Display", {"on"})
503s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
503s  proflik (NegativeBinomialDistribution.fit (x), 1, "Display", ["on"; "on"])
503s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
503s  proflik (NegativeBinomialDistribution.fit (x), 1, "Display", "onnn")
503s ***** error <proflik: invalid NAME for optional arguments.> ...
503s  proflik (NegativeBinomialDistribution.fit (x), 1, "NAME", "on")
503s ***** error <proflik: invalid optional argument.> ...
503s  proflik (NegativeBinomialDistribution.fit (x), 1, {"NAME"}, "on")
503s ***** error <proflik: invalid optional argument.> ...
503s  proflik (NegativeBinomialDistribution.fit (x), 1, {[1 2 3]}, "Display", "on")
503s ***** error <truncate: missing input argument.> ...
503s  truncate (NegativeBinomialDistribution)
503s ***** error <truncate: missing input argument.> ...
503s  truncate (NegativeBinomialDistribution, 2)
503s ***** error <truncate: invalid lower upper limits.> ...
503s  truncate (NegativeBinomialDistribution, 4, 2)
503s ***** shared pd
503s  pd = NegativeBinomialDistribution(1, 0.5);
503s  pd(2) = NegativeBinomialDistribution(1, 0.6);
503s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
503s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
503s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
503s ***** error <mean: requires a scalar probability distribution.> mean (pd)
503s ***** error <median: requires a scalar probability distribution.> median (pd)
503s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
503s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
503s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
503s ***** error <plot: requires a scalar probability distribution.> plot (pd)
503s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
503s ***** error <random: requires a scalar probability distribution.> random (pd)
503s ***** error <std: requires a scalar probability distribution.> std (pd)
503s ***** error <truncate: requires a scalar probability distribution.> ...
503s  truncate (pd, 2, 4)
503s ***** error <var: requires a scalar probability distribution.> var (pd)
503s 102 tests, 102 passed, 0 known failure, 0 skipped
503s [inst/dist_obj/LognormalDistribution.m]
503s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_obj/LognormalDistribution.m
503s ***** shared pd, t
503s  pd = LognormalDistribution;
503s  t = truncate (pd, 2, 4);
503s ***** assert (cdf (pd, [0:5]), [0, 0.5, 0.7559, 0.8640, 0.9172, 0.9462], 1e-4);
503s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.6705, 1, 1], 1e-4);
503s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.6574, 0.7559, 0.8640, 0.9172], 1e-4);
503s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.6705, 1], 1e-4);
503s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.4310, 0.7762, 1.2883, 2.3201, Inf], 1e-4);
503s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2256, 2.5015, 2.8517, 3.3199, 4], 1e-4);
503s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.7762, 1.2883, 2.3201, Inf, NaN], 1e-4);
503s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5015, 2.8517, 3.3199, 4, NaN], 1e-4);
503s ***** assert (iqr (pd), 1.4536, 1e-4);
503s ***** assert (iqr (t), 0.8989, 1e-4);
503s ***** assert (mean (pd), 1.6487, 1e-4);
503s ***** assert (mean (t), 2.7692, 1e-4);
503s ***** assert (median (pd), 1, 1e-4);
503s ***** assert (median (t), 2.6653, 1e-4);
503s ***** assert (pdf (pd, [0:5]), [0, 0.3989, 0.1569, 0.0727, 0.0382, 0.0219], 1e-4);
503s ***** assert (pdf (t, [0:5]), [0, 0, 0.9727, 0.4509, 0.2366, 0], 1e-4);
503s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3989, 0.1569, 0.0727, 0.0382, NaN], 1e-4);
503s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.9727, 0.4509, 0.2366, NaN], 1e-4);
503s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
503s ***** assert (any (random (t, 1000, 1) < 2), false);
503s ***** assert (any (random (t, 1000, 1) > 4), false);
503s ***** assert (std (pd), 2.1612, 1e-4);
503s ***** assert (std (t), 0.5540, 1e-4);
503s ***** assert (var (pd), 4.6708, 1e-4);
503s ***** assert (var (t), 0.3069, 1e-4);
503s ***** error <LognormalDistribution: MU must be a real scalar.> ...
503s  LognormalDistribution(Inf, 1)
503s ***** error <LognormalDistribution: MU must be a real scalar.> ...
503s  LognormalDistribution(i, 1)
503s ***** error <LognormalDistribution: MU must be a real scalar.> ...
503s  LognormalDistribution("a", 1)
503s ***** error <LognormalDistribution: MU must be a real scalar.> ...
503s  LognormalDistribution([1, 2], 1)
503s ***** error <LognormalDistribution: MU must be a real scalar.> ...
503s  LognormalDistribution(NaN, 1)
503s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
503s  LognormalDistribution(1, 0)
503s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
503s  LognormalDistribution(1, -1)
503s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
503s  LognormalDistribution(1, Inf)
503s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
503s  LognormalDistribution(1, i)
503s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
503s  LognormalDistribution(1, "a")
503s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
503s  LognormalDistribution(1, [1, 2])
503s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
503s  LognormalDistribution(1, NaN)
503s ***** error <cdf: invalid argument for upper tail.> ...
503s  cdf (LognormalDistribution, 2, "uper")
503s ***** error <cdf: invalid argument for upper tail.> ...
503s  cdf (LognormalDistribution, 2, 3)
503s ***** shared x
503s  randn ("seed", 1);
503s  x = lognrnd (1, 1, [1, 100]);
503s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
503s  paramci (LognormalDistribution.fit (x), "alpha")
503s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
503s  paramci (LognormalDistribution.fit (x), "alpha", 0)
503s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
503s  paramci (LognormalDistribution.fit (x), "alpha", 1)
503s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
503s  paramci (LognormalDistribution.fit (x), "alpha", [0.5 2])
503s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
503s  paramci (LognormalDistribution.fit (x), "alpha", "")
503s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
503s  paramci (LognormalDistribution.fit (x), "alpha", {0.05})
503s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
503s  paramci (LognormalDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
503s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
503s  paramci (LognormalDistribution.fit (x), "parameter", {"mu", "sigma", "parm"})
503s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
503s  paramci (LognormalDistribution.fit (x), "alpha", 0.01, ...
503s           "parameter", {"mu", "sigma", "param"})
503s ***** error <paramci: unknown distribution parameter.> ...
503s  paramci (LognormalDistribution.fit (x), "parameter", "param")
503s ***** error <paramci: unknown distribution parameter.> ...
503s  paramci (LognormalDistribution.fit (x), "alpha", 0.01, "parameter", "param")
503s ***** error <paramci: invalid NAME for optional argument.> ...
503s  paramci (LognormalDistribution.fit (x), "NAME", "value")
503s ***** error <paramci: invalid NAME for optional argument.> ...
503s  paramci (LognormalDistribution.fit (x), "alpha", 0.01, "NAME", "value")
503s ***** error <paramci: invalid NAME for optional argument.> ...
503s  paramci (LognormalDistribution.fit (x), "alpha", 0.01, "parameter", "mu", ...
503s           "NAME", "value")
503s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
503s  plot (LognormalDistribution, "Parent")
503s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
503s  plot (LognormalDistribution, "PlotType", 12)
503s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
503s  plot (LognormalDistribution, "PlotType", {"pdf", "cdf"})
503s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
503s  plot (LognormalDistribution, "PlotType", "pdfcdf")
503s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
503s  plot (LognormalDistribution, "Discrete", "pdfcdf")
503s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
503s  plot (LognormalDistribution, "Discrete", [1, 0])
503s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
503s  plot (LognormalDistribution, "Discrete", {true})
503s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
503s  plot (LognormalDistribution, "Parent", 12)
503s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
503s  plot (LognormalDistribution, "Parent", "hax")
503s ***** error <plot: invalid NAME for optional argument.> ...
503s  plot (LognormalDistribution, "invalidNAME", "pdf")
503s ***** error <plot: no fitted DATA to plot a probability plot.> ...
503s  plot (LognormalDistribution, "PlotType", "probability")
503s ***** error <proflik: no fitted data available.> ...
503s  proflik (LognormalDistribution, 2)
503s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
503s  proflik (LognormalDistribution.fit (x), 3)
503s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
503s  proflik (LognormalDistribution.fit (x), [1, 2])
503s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
503s  proflik (LognormalDistribution.fit (x), {1})
503s ***** error <proflik: SETPARAM must be a numeric vector.> ...
503s  proflik (LognormalDistribution.fit (x), 1, ones (2))
504s ***** error <proflik: missing VALUE for 'Display' argument.> ...
504s  proflik (LognormalDistribution.fit (x), 1, "Display")
504s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
504s  proflik (LognormalDistribution.fit (x), 1, "Display", 1)
504s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
504s  proflik (LognormalDistribution.fit (x), 1, "Display", {1})
504s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
504s  proflik (LognormalDistribution.fit (x), 1, "Display", {"on"})
504s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
504s  proflik (LognormalDistribution.fit (x), 1, "Display", ["on"; "on"])
504s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
504s  proflik (LognormalDistribution.fit (x), 1, "Display", "onnn")
504s ***** error <proflik: invalid NAME for optional arguments.> ...
504s  proflik (LognormalDistribution.fit (x), 1, "NAME", "on")
504s ***** error <proflik: invalid optional argument.> ...
504s  proflik (LognormalDistribution.fit (x), 1, {"NAME"}, "on")
504s ***** error <proflik: invalid optional argument.> ...
504s  proflik (LognormalDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
504s ***** error <truncate: missing input argument.> ...
504s  truncate (LognormalDistribution)
504s ***** error <truncate: missing input argument.> ...
504s  truncate (LognormalDistribution, 2)
504s ***** error <truncate: invalid lower upper limits.> ...
504s  truncate (LognormalDistribution, 4, 2)
504s ***** shared pd
504s  pd = LognormalDistribution(1, 1);
504s  pd(2) = LognormalDistribution(1, 3);
504s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
504s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
504s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
504s ***** error <mean: requires a scalar probability distribution.> mean (pd)
504s ***** error <median: requires a scalar probability distribution.> median (pd)
504s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
504s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
504s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
504s ***** error <plot: requires a scalar probability distribution.> plot (pd)
504s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
504s ***** error <random: requires a scalar probability distribution.> random (pd)
504s ***** error <std: requires a scalar probability distribution.> std (pd)
504s ***** error <truncate: requires a scalar probability distribution.> ...
504s  truncate (pd, 2, 4)
504s ***** error <var: requires a scalar probability distribution.> var (pd)
504s 95 tests, 95 passed, 0 known failure, 0 skipped
504s [inst/dist_obj/NormalDistribution.m]
504s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_obj/NormalDistribution.m
504s ***** shared pd, t
504s  pd = NormalDistribution;
504s  t = truncate (pd, -2, 2);
504s ***** assert (cdf (pd, [0:5]), [0.5, 0.8413, 0.9772, 0.9987, 1, 1], 1e-4);
504s ***** assert (cdf (t, [0:5]), [0.5, 0.8576, 1, 1, 1, 1], 1e-4);
504s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.9332, 0.9772, 0.9987, 1], 1e-4);
504s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0.9538, 1, 1, 1], 1e-4);
504s ***** assert (icdf (pd, [0:0.2:1]), [-Inf, -0.8416, -0.2533, 0.2533, 0.8416, Inf], 1e-4);
504s ***** assert (icdf (t, [0:0.2:1]), [-2, -0.7938, -0.2416, 0.2416, 0.7938, 2], 1e-4);
504s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, -0.2533, 0.2533, 0.8416, Inf, NaN], 1e-4);
504s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, -0.2416, 0.2416, 0.7938, 2, NaN], 1e-4);
504s ***** assert (iqr (pd), 1.3490, 1e-4);
504s ***** assert (iqr (t), 1.2782, 1e-4);
504s ***** assert (mean (pd), 0);
504s ***** assert (mean (t), 0, 3e-16);
504s ***** assert (median (pd), 0);
504s ***** assert (median (t), 0, 3e-16);
504s ***** assert (pdf (pd, [0:5]), [0.3989, 0.2420, 0.0540, 0.0044, 0.0001, 0], 1e-4);
504s ***** assert (pdf (t, [0:5]), [0.4180, 0.2535, 0.0566, 0, 0, 0], 1e-4);
504s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0.2420, 0.2420, 0.0540, 0.0044, 0.0001, NaN], 1e-4);
504s ***** assert (pdf (t, [-1, 1:4, NaN]), [0.2535, 0.2535, 0.0566, 0, 0, NaN], 1e-4);
504s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
504s ***** assert (any (random (t, 1000, 1) < -2), false);
504s ***** assert (any (random (t, 1000, 1) > 2), false);
504s ***** assert (std (pd), 1);
504s ***** assert (std (t), 0.8796, 1e-4);
504s ***** assert (var (pd), 1);
504s ***** assert (var (t), 0.7737, 1e-4);
504s ***** error <NormalDistribution: MU must be a real scalar.> ...
504s  NormalDistribution(Inf, 1)
504s ***** error <NormalDistribution: MU must be a real scalar.> ...
504s  NormalDistribution(i, 1)
504s ***** error <NormalDistribution: MU must be a real scalar.> ...
504s  NormalDistribution("a", 1)
504s ***** error <NormalDistribution: MU must be a real scalar.> ...
504s  NormalDistribution([1, 2], 1)
504s ***** error <NormalDistribution: MU must be a real scalar.> ...
504s  NormalDistribution(NaN, 1)
504s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
504s  NormalDistribution(1, 0)
504s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
504s  NormalDistribution(1, -1)
504s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
504s  NormalDistribution(1, Inf)
504s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
504s  NormalDistribution(1, i)
504s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
504s  NormalDistribution(1, "a")
504s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
504s  NormalDistribution(1, [1, 2])
504s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
504s  NormalDistribution(1, NaN)
504s ***** error <cdf: invalid argument for upper tail.> ...
504s  cdf (NormalDistribution, 2, "uper")
504s ***** error <cdf: invalid argument for upper tail.> ...
504s  cdf (NormalDistribution, 2, 3)
504s ***** shared x
504s  x = normrnd (1, 1, [1, 100]);
504s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
504s  paramci (NormalDistribution.fit (x), "alpha")
504s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
504s  paramci (NormalDistribution.fit (x), "alpha", 0)
504s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
504s  paramci (NormalDistribution.fit (x), "alpha", 1)
504s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
504s  paramci (NormalDistribution.fit (x), "alpha", [0.5 2])
504s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
504s  paramci (NormalDistribution.fit (x), "alpha", "")
504s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
504s  paramci (NormalDistribution.fit (x), "alpha", {0.05})
504s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
504s  paramci (NormalDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
504s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
504s  paramci (NormalDistribution.fit (x), "parameter", {"mu", "sigma", "param"})
504s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
504s  paramci (NormalDistribution.fit (x), "alpha", 0.01, ...
504s           "parameter", {"mu", "sigma", "param"})
504s ***** error <paramci: unknown distribution parameter.> ...
504s  paramci (NormalDistribution.fit (x), "parameter", "param")
504s ***** error <paramci: unknown distribution parameter.> ...
504s  paramci (NormalDistribution.fit (x), "alpha", 0.01, "parameter", "param")
504s ***** error <paramci: invalid NAME for optional argument.> ...
504s  paramci (NormalDistribution.fit (x), "NAME", "value")
505s ***** error <paramci: invalid NAME for optional argument.> ...
505s  paramci (NormalDistribution.fit (x), "alpha", 0.01, "NAME", "value")
505s ***** error <paramci: invalid NAME for optional argument.> ...
505s  paramci (NormalDistribution.fit (x), "alpha", 0.01, "parameter", "mu", ...
505s           "NAME", "value")
505s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
505s  plot (NormalDistribution, "Parent")
505s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
505s  plot (NormalDistribution, "PlotType", 12)
505s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
505s  plot (NormalDistribution, "PlotType", {"pdf", "cdf"})
505s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
505s  plot (NormalDistribution, "PlotType", "pdfcdf")
505s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
505s  plot (NormalDistribution, "Discrete", "pdfcdf")
505s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
505s  plot (NormalDistribution, "Discrete", [1, 0])
505s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
505s  plot (NormalDistribution, "Discrete", {true})
505s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
505s  plot (NormalDistribution, "Parent", 12)
505s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
505s  plot (NormalDistribution, "Parent", "hax")
505s ***** error <plot: invalid NAME for optional argument.> ...
505s  plot (NormalDistribution, "invalidNAME", "pdf")
505s ***** error <plot: no fitted DATA to plot a probability plot.> ...
505s  plot (NormalDistribution, "PlotType", "probability")
505s ***** error <proflik: no fitted data available.> ...
505s  proflik (NormalDistribution, 2)
505s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
505s  proflik (NormalDistribution.fit (x), 3)
505s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
505s  proflik (NormalDistribution.fit (x), [1, 2])
505s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
505s  proflik (NormalDistribution.fit (x), {1})
505s ***** error <proflik: SETPARAM must be a numeric vector.> ...
505s  proflik (NormalDistribution.fit (x), 1, ones (2))
505s ***** error <proflik: missing VALUE for 'Display' argument.> ...
505s  proflik (NormalDistribution.fit (x), 1, "Display")
505s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
505s  proflik (NormalDistribution.fit (x), 1, "Display", 1)
505s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
505s  proflik (NormalDistribution.fit (x), 1, "Display", {1})
505s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
505s  proflik (NormalDistribution.fit (x), 1, "Display", {"on"})
505s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
505s  proflik (NormalDistribution.fit (x), 1, "Display", ["on"; "on"])
505s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
505s  proflik (NormalDistribution.fit (x), 1, "Display", "onnn")
505s ***** error <proflik: invalid NAME for optional arguments.> ...
505s  proflik (NormalDistribution.fit (x), 1, "NAME", "on")
505s ***** error <proflik: invalid optional argument.> ...
505s  proflik (NormalDistribution.fit (x), 1, {"NAME"}, "on")
505s ***** error <proflik: invalid optional argument.> ...
505s  proflik (NormalDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
505s ***** error <truncate: missing input argument.> ...
505s  truncate (NormalDistribution)
505s ***** error <truncate: missing input argument.> ...
505s  truncate (NormalDistribution, 2)
505s ***** error <truncate: invalid lower upper limits.> ...
505s  truncate (NormalDistribution, 4, 2)
505s ***** shared pd
505s  pd = NormalDistribution(1, 1);
505s  pd(2) = NormalDistribution(1, 3);
505s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
505s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
505s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
505s ***** error <mean: requires a scalar probability distribution.> mean (pd)
505s ***** error <median: requires a scalar probability distribution.> median (pd)
505s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
505s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
505s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
505s ***** error <plot: requires a scalar probability distribution.> plot (pd)
505s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
505s ***** error <random: requires a scalar probability distribution.> random (pd)
505s ***** error <std: requires a scalar probability distribution.> std (pd)
505s ***** error <truncate: requires a scalar probability distribution.> ...
505s  truncate (pd, 2, 4)
505s ***** error <var: requires a scalar probability distribution.> var (pd)
505s 95 tests, 95 passed, 0 known failure, 0 skipped
505s [inst/dist_obj/LoglogisticDistribution.m]
505s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_obj/LoglogisticDistribution.m
505s ***** shared pd, t
505s  pd = LoglogisticDistribution;
505s  t = truncate (pd, 2, 4);
505s ***** assert (cdf (pd, [0:5]), [0, 0.5, 0.6667, 0.75, 0.8, 0.8333], 1e-4);
505s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.625, 1, 1], 1e-4);
505s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.6, 0.6667, 0.75, 0.8], 1e-4);
505s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.625, 1], 1e-4);
505s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.25, 0.6667, 1.5, 4, Inf], 1e-4);
505s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2609, 2.5714, 2.9474, 3.4118, 4], 1e-4);
505s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.6667, 1.5, 4, Inf, NaN], 1e-4);
505s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5714, 2.9474, 3.4118, 4, NaN], 1e-4);
505s ***** assert (iqr (pd), 2.6667, 1e-4);
505s ***** assert (iqr (t), 0.9524, 1e-4);
505s ***** assert (mean (pd), Inf);
505s ***** assert (mean (t), 2.8312, 1e-4);
505s ***** assert (median (pd), 1, 1e-4);
505s ***** assert (median (t), 2.75, 1e-4);
505s ***** assert (pdf (pd, [0:5]), [0, 0.25, 0.1111, 0.0625, 0.04, 0.0278], 1e-4);
505s ***** assert (pdf (t, [0:5]), [0, 0, 0.8333, 0.4687, 0.3, 0], 1e-4);
505s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.25, 0.1111, 0.0625, 0.04, NaN], 1e-4);
505s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.8333, 0.4687, 0.3, NaN], 1e-4);
505s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
505s ***** assert (any (random (t, 1000, 1) < 2), false);
505s ***** assert (any (random (t, 1000, 1) > 4), false);
505s ***** assert (std (pd), Inf);
505s ***** assert (std (t), 0.5674, 1e-4);
505s ***** assert (var (pd), Inf);
505s ***** assert (var (t), 0.3220, 1e-4);
505s ***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
505s  LoglogisticDistribution(Inf, 1)
505s ***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
505s  LoglogisticDistribution(i, 1)
505s ***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
505s  LoglogisticDistribution("a", 1)
505s ***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
505s  LoglogisticDistribution([1, 2], 1)
505s ***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
505s  LoglogisticDistribution(NaN, 1)
505s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
505s  LoglogisticDistribution(1, 0)
505s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
505s  LoglogisticDistribution(1, -1)
505s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
505s  LoglogisticDistribution(1, Inf)
505s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
505s  LoglogisticDistribution(1, i)
505s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
505s  LoglogisticDistribution(1, "a")
505s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
505s  LoglogisticDistribution(1, [1, 2])
505s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
505s  LoglogisticDistribution(1, NaN)
505s ***** error <cdf: invalid argument for upper tail.> ...
505s  cdf (LoglogisticDistribution, 2, "uper")
505s ***** error <cdf: invalid argument for upper tail.> ...
505s  cdf (LoglogisticDistribution, 2, 3)
505s ***** shared x
505s  x = loglrnd (1, 1, [1, 100]);
505s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
505s  paramci (LoglogisticDistribution.fit (x), "alpha")
505s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
505s  paramci (LoglogisticDistribution.fit (x), "alpha", 0)
505s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
505s  paramci (LoglogisticDistribution.fit (x), "alpha", 1)
505s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
505s  paramci (LoglogisticDistribution.fit (x), "alpha", [0.5 2])
505s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
505s  paramci (LoglogisticDistribution.fit (x), "alpha", "")
505s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
505s  paramci (LoglogisticDistribution.fit (x), "alpha", {0.05})
505s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
505s  paramci (LoglogisticDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
505s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
505s  paramci (LoglogisticDistribution.fit (x), "parameter", {"mu", "sigma", "pa"})
505s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
505s  paramci (LoglogisticDistribution.fit (x), "alpha", 0.01, ...
505s           "parameter", {"mu", "sigma", "param"})
505s ***** error <paramci: unknown distribution parameter.> ...
505s  paramci (LoglogisticDistribution.fit (x), "parameter", "param")
505s ***** error <paramci: unknown distribution parameter.> ...
505s  paramci (LoglogisticDistribution.fit (x), "alpha", 0.01, "parameter", "parm")
505s ***** error <paramci: invalid NAME for optional argument.> ...
505s  paramci (LoglogisticDistribution.fit (x), "NAME", "value")
506s ***** error <paramci: invalid NAME for optional argument.> ...
506s  paramci (LoglogisticDistribution.fit (x), "alpha", 0.01, "NAME", "value")
506s ***** error <paramci: invalid NAME for optional argument.> ...
506s  paramci (LoglogisticDistribution.fit (x), "alpha", 0.01, ...
506s           "parameter", "mu", "NAME", "value")
506s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
506s  plot (LoglogisticDistribution, "Parent")
506s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
506s  plot (LoglogisticDistribution, "PlotType", 12)
506s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
506s  plot (LoglogisticDistribution, "PlotType", {"pdf", "cdf"})
506s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
506s  plot (LoglogisticDistribution, "PlotType", "pdfcdf")
506s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
506s  plot (LoglogisticDistribution, "Discrete", "pdfcdf")
506s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
506s  plot (LoglogisticDistribution, "Discrete", [1, 0])
506s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
506s  plot (LoglogisticDistribution, "Discrete", {true})
506s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
506s  plot (LoglogisticDistribution, "Parent", 12)
506s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
506s  plot (LoglogisticDistribution, "Parent", "hax")
506s ***** error <plot: invalid NAME for optional argument.> ...
506s  plot (LoglogisticDistribution, "invalidNAME", "pdf")
506s ***** error <plot: no fitted DATA to plot a probability plot.> ...
506s  plot (LoglogisticDistribution, "PlotType", "probability")
506s ***** error <proflik: no fitted data available.> ...
506s  proflik (LoglogisticDistribution, 2)
506s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
506s  proflik (LoglogisticDistribution.fit (x), 3)
506s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
506s  proflik (LoglogisticDistribution.fit (x), [1, 2])
506s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
506s  proflik (LoglogisticDistribution.fit (x), {1})
506s ***** error <proflik: SETPARAM must be a numeric vector.> ...
506s  proflik (LoglogisticDistribution.fit (x), 1, ones (2))
506s ***** error <proflik: missing VALUE for 'Display' argument.> ...
506s  proflik (LoglogisticDistribution.fit (x), 1, "Display")
506s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
506s  proflik (LoglogisticDistribution.fit (x), 1, "Display", 1)
506s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
506s  proflik (LoglogisticDistribution.fit (x), 1, "Display", {1})
506s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
506s  proflik (LoglogisticDistribution.fit (x), 1, "Display", {"on"})
506s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
506s  proflik (LoglogisticDistribution.fit (x), 1, "Display", ["on"; "on"])
506s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
506s  proflik (LoglogisticDistribution.fit (x), 1, "Display", "onnn")
506s ***** error <proflik: invalid NAME for optional arguments.> ...
506s  proflik (LoglogisticDistribution.fit (x), 1, "NAME", "on")
506s ***** error <proflik: invalid optional argument.> ...
506s  proflik (LoglogisticDistribution.fit (x), 1, {"NAME"}, "on")
506s ***** error <proflik: invalid optional argument.> ...
506s  proflik (LoglogisticDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
506s ***** error <truncate: missing input argument.> ...
506s  truncate (LoglogisticDistribution)
506s ***** error <truncate: missing input argument.> ...
506s  truncate (LoglogisticDistribution, 2)
506s ***** error <truncate: invalid lower upper limits.> ...
506s  truncate (LoglogisticDistribution, 4, 2)
506s ***** shared pd
506s  pd = LoglogisticDistribution(1, 1);
506s  pd(2) = LoglogisticDistribution(1, 3);
506s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
506s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
506s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
506s ***** error <mean: requires a scalar probability distribution.> mean (pd)
506s ***** error <median: requires a scalar probability distribution.> median (pd)
506s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
506s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
506s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
506s ***** error <plot: requires a scalar probability distribution.> plot (pd)
506s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
506s ***** error <random: requires a scalar probability distribution.> random (pd)
506s ***** error <std: requires a scalar probability distribution.> std (pd)
506s ***** error <truncate: requires a scalar probability distribution.> ...
506s  truncate (pd, 2, 4)
506s ***** error <var: requires a scalar probability distribution.> var (pd)
506s 95 tests, 95 passed, 0 known failure, 0 skipped
506s [inst/dist_obj/tLocationScaleDistribution.m]
506s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_obj/tLocationScaleDistribution.m
506s ***** shared pd, t
506s  pd = tLocationScaleDistribution;
506s  t = truncate (pd, 2, 4);
506s ***** assert (cdf (pd, [0:5]), [0.5, 0.8184, 0.9490, 0.9850, 0.9948, 0.9979], 1e-4);
506s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7841, 1, 1], 1e-4);
506s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.9030, 0.9490, 0.9850, 0.9948, NaN], 1e-4);
506s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.7841, 1, NaN], 1e-4);
506s ***** assert (icdf (pd, [0:0.2:1]), [-Inf, -0.9195, -0.2672, 0.2672, 0.9195, Inf], 1e-4);
506s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1559, 2.3533, 2.6223, 3.0432, 4], 1e-4);
506s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, -0.2672, 0.2672, 0.9195, Inf, NaN], 1e-4);
506s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.3533, 2.6223, 3.0432, 4, NaN], 1e-4);
506s ***** assert (iqr (pd), 1.4534, 1e-4);
506s ***** assert (iqr (t), 0.7139, 1e-4);
506s ***** assert (mean (pd), 0, eps);
506s ***** assert (mean (t), 2.6099, 1e-4);
506s ***** assert (median (pd), 0, eps);
506s ***** assert (median (t), 2.4758, 1e-4);
506s ***** assert (pdf (pd, [0:5]), [0.3796, 0.2197, 0.0651, 0.0173, 0.0051, 0.0018], 1e-4);
506s ***** assert (pdf (t, [0:5]), [0, 0, 1.4209, 0.3775, 0.1119, 0], 1e-4);
506s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0.2197, 0.1245, NaN], 1e-4);
506s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
506s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
506s ***** assert (any (random (t, 1000, 1) < 2), false);
506s ***** assert (any (random (t, 1000, 1) > 4), false);
506s ***** assert (std (pd), 1.2910, 1e-4);
506s ***** assert (std (t), 0.4989, 1e-4);
506s ***** assert (var (pd), 1.6667, 1e-4);
506s ***** assert (var (t), 0.2489, 1e-4);
506s ***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
506s  tLocationScaleDistribution(i, 1, 1)
506s ***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
506s  tLocationScaleDistribution(Inf, 1, 1)
506s ***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
506s  tLocationScaleDistribution([1, 2], 1, 1)
506s ***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
506s  tLocationScaleDistribution("a", 1, 1)
506s ***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
506s  tLocationScaleDistribution(NaN, 1, 1)
506s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
506s  tLocationScaleDistribution(0, 0, 1)
506s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
506s  tLocationScaleDistribution(0, -1, 1)
506s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
506s  tLocationScaleDistribution(0, Inf, 1)
506s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
506s  tLocationScaleDistribution(0, i, 1)
506s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
506s  tLocationScaleDistribution(0, "a", 1)
506s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
506s  tLocationScaleDistribution(0, [1, 2], 1)
506s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
506s  tLocationScaleDistribution(0, NaN, 1)
506s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
506s  tLocationScaleDistribution(0, 1, 0)
506s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
506s  tLocationScaleDistribution(0, 1, -1)
506s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
506s  tLocationScaleDistribution(0, 1, Inf)
506s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
506s  tLocationScaleDistribution(0, 1, i)
506s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
506s  tLocationScaleDistribution(0, 1, "a")
506s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
506s  tLocationScaleDistribution(0, 1, [1, 2])
506s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
506s  tLocationScaleDistribution(0, 1, NaN)
506s ***** error <cdf: invalid argument for upper tail.> ...
506s  cdf (tLocationScaleDistribution, 2, "uper")
506s ***** error <cdf: invalid argument for upper tail.> ...
506s  cdf (tLocationScaleDistribution, 2, 3)
506s ***** shared x
506s  x = tlsrnd (0, 1, 1, [1, 100]);
506s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
506s  paramci (tLocationScaleDistribution.fit (x), "alpha")
506s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
506s  paramci (tLocationScaleDistribution.fit (x), "alpha", 0)
506s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
506s  paramci (tLocationScaleDistribution.fit (x), "alpha", 1)
506s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
506s  paramci (tLocationScaleDistribution.fit (x), "alpha", [0.5 2])
506s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
506s  paramci (tLocationScaleDistribution.fit (x), "alpha", "")
506s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
506s  paramci (tLocationScaleDistribution.fit (x), "alpha", {0.05})
506s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
506s  paramci (tLocationScaleDistribution.fit (x), "parameter", "mu", ...
506s           "alpha", {0.05})
506s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
506s  paramci (tLocationScaleDistribution.fit (x), ...
506s           "parameter", {"mu", "sigma", "nu", "param"})
506s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
506s  paramci (tLocationScaleDistribution.fit (x), "alpha", 0.01, ...
506s           "parameter", {"mu", "sigma", "nu", "param"})
506s ***** error <paramci: unknown distribution parameter.> ...
506s  paramci (tLocationScaleDistribution.fit (x), "parameter", "param")
507s ***** error <paramci: unknown distribution parameter.> ...
507s  paramci (tLocationScaleDistribution.fit (x), "alpha", 0.01, ...
507s           "parameter", "param")
507s ***** error <paramci: invalid NAME for optional argument.> ...
507s  paramci (tLocationScaleDistribution.fit (x), "NAME", "value")
507s ***** error <paramci: invalid NAME for optional argument.> ...
507s  paramci (tLocationScaleDistribution.fit (x), "alpha", 0.01, "NAME", "value")
507s ***** error <paramci: invalid NAME for optional argument.> ...
507s  paramci (tLocationScaleDistribution.fit (x), "alpha", 0.01, ...
507s           "parameter", "mu", "NAME", "value")
507s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
507s  plot (tLocationScaleDistribution, "Parent")
507s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
507s  plot (tLocationScaleDistribution, "PlotType", 12)
507s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
507s  plot (tLocationScaleDistribution, "PlotType", {"pdf", "cdf"})
507s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
507s  plot (tLocationScaleDistribution, "PlotType", "pdfcdf")
507s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
507s  plot (tLocationScaleDistribution, "Discrete", "pdfcdf")
507s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
507s  plot (tLocationScaleDistribution, "Discrete", [1, 0])
507s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
507s  plot (tLocationScaleDistribution, "Discrete", {true})
507s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
507s  plot (tLocationScaleDistribution, "Parent", 12)
507s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
507s  plot (tLocationScaleDistribution, "Parent", "hax")
507s ***** error <plot: invalid NAME for optional argument.> ...
507s  plot (tLocationScaleDistribution, "invalidNAME", "pdf")
507s ***** error <plot: no fitted DATA to plot a probability plot.> ...
507s  plot (tLocationScaleDistribution, "PlotType", "probability")
507s ***** error <proflik: no fitted data available.> ...
507s  proflik (tLocationScaleDistribution, 2)
507s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
507s  proflik (tLocationScaleDistribution.fit (x), 4)
507s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
507s  proflik (tLocationScaleDistribution.fit (x), [1, 2])
507s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
507s  proflik (tLocationScaleDistribution.fit (x), {1})
507s ***** error <proflik: SETPARAM must be a numeric vector.> ...
507s  proflik (tLocationScaleDistribution.fit (x), 1, ones (2))
507s ***** error <proflik: missing VALUE for 'Display' argument.> ...
507s  proflik (tLocationScaleDistribution.fit (x), 1, "Display")
507s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
507s  proflik (tLocationScaleDistribution.fit (x), 1, "Display", 1)
507s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
507s  proflik (tLocationScaleDistribution.fit (x), 1, "Display", {1})
507s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
507s  proflik (tLocationScaleDistribution.fit (x), 1, "Display", {"on"})
507s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
507s  proflik (tLocationScaleDistribution.fit (x), 1, "Display", ["on"; "on"])
507s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
507s  proflik (tLocationScaleDistribution.fit (x), 1, "Display", "onnn")
507s ***** error <proflik: invalid NAME for optional arguments.> ...
507s  proflik (tLocationScaleDistribution.fit (x), 1, "NAME", "on")
507s ***** error <proflik: invalid optional argument.> ...
507s  proflik (tLocationScaleDistribution.fit (x), 1, {"NAME"}, "on")
507s ***** error <proflik: invalid optional argument.> ...
507s  proflik (tLocationScaleDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
507s ***** error <truncate: missing input argument.> ...
507s  truncate (tLocationScaleDistribution)
507s ***** error <truncate: missing input argument.> ...
507s  truncate (tLocationScaleDistribution, 2)
507s ***** error <truncate: invalid lower upper limits.> ...
507s  truncate (tLocationScaleDistribution, 4, 2)
507s ***** shared pd
507s  pd = tLocationScaleDistribution (0, 1, 1);
507s  pd(2) = tLocationScaleDistribution (0, 1, 3);
507s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
507s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
507s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
507s ***** error <mean: requires a scalar probability distribution.> mean (pd)
507s ***** error <median: requires a scalar probability distribution.> median (pd)
507s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
507s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
507s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
507s ***** error <plot: requires a scalar probability distribution.> plot (pd)
507s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
507s ***** error <random: requires a scalar probability distribution.> random (pd)
507s ***** error <std: requires a scalar probability distribution.> std (pd)
507s ***** error <truncate: requires a scalar probability distribution.> ...
507s  truncate (pd, 2, 4)
507s ***** error <var: requires a scalar probability distribution.> var (pd)
507s 102 tests, 102 passed, 0 known failure, 0 skipped
507s [inst/dist_obj/LoguniformDistribution.m]
507s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_obj/LoguniformDistribution.m
507s ***** shared pd, t
507s  pd = LoguniformDistribution (1, 4);
507s  t = truncate (pd, 2, 4);
507s ***** assert (cdf (pd, [0, 1, 2, 3, 4, 5]), [0, 0, 0.5, 0.7925, 1, 1], 1e-4);
507s ***** assert (cdf (t, [0, 1, 2, 3, 4, 5]), [0, 0, 0, 0.5850, 1, 1], 1e-4);
507s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.2925, 0.5, 0.7925, 1], 1e-4);
507s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.5850, 1], 1e-4);
507s ***** assert (icdf (pd, [0:0.2:1]), [1, 1.3195, 1.7411, 2.2974, 3.0314, 4], 1e-4);
507s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2974, 2.6390, 3.0314, 3.4822, 4], 1e-4);
507s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1.7411, 2.2974, 3.0314, 4, NaN], 1e-4);
507s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.6390, 3.0314, 3.4822, 4, NaN], 1e-4);
507s ***** assert (iqr (pd), 1.4142, 1e-4);
507s ***** assert (iqr (t), 0.9852, 1e-4);
507s ***** assert (mean (pd), 2.1640, 1e-4);
507s ***** assert (mean (t), 2.8854, 1e-4);
507s ***** assert (median (pd), 2);
507s ***** assert (median (t), 2.8284, 1e-4);
507s ***** assert (pdf (pd, [0, 1, 2, 3, 4, 5]), [0, 0.7213, 0.3607, 0.2404, 0.1803, 0], 1e-4);
507s ***** assert (pdf (t, [0, 1, 2, 3, 4, 5]), [0, 0, 0.7213, 0.4809, 0.3607, 0], 1e-4);
507s ***** assert (pdf (pd, [-1, 1, 2, 3, 4, NaN]), [0, 0.7213, 0.3607, 0.2404, 0.1803, NaN], 1e-4);
507s ***** assert (pdf (t, [-1, 1, 2, 3, 4, NaN]), [0, 0, 0.7213, 0.4809, 0.3607, NaN], 1e-4);
507s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
507s ***** assert (any (random (pd, 1000, 1) < 1), false);
507s ***** assert (any (random (pd, 1000, 1) > 4), false);
507s ***** assert (any (random (t, 1000, 1) < 2), false);
507s ***** assert (any (random (t, 1000, 1) > 4), false);
507s ***** assert (std (pd), 0.8527, 1e-4);
507s ***** assert (std (t), 0.5751, 1e-4);
507s ***** assert (var (pd), 0.7270, 1e-4);
507s ***** assert (var (t), 0.3307, 1e-4);
507s ***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
507s  LoguniformDistribution (i, 1)
507s ***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
507s  LoguniformDistribution (Inf, 1)
507s ***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
507s  LoguniformDistribution ([1, 2], 1)
507s ***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
507s  LoguniformDistribution ("a", 1)
507s ***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
507s  LoguniformDistribution (NaN, 1)
507s ***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
507s  LoguniformDistribution (1, i)
507s ***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
507s  LoguniformDistribution (1, Inf)
507s ***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
507s  LoguniformDistribution (1, [1, 2])
507s ***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
507s  LoguniformDistribution (1, "a")
507s ***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
507s  LoguniformDistribution (1, NaN)
507s ***** error <LoguniformDistribution: LOWER must be less than UPPER.> ...
507s  LoguniformDistribution (2, 1)
507s ***** error <cdf: invalid argument for upper tail.> ...
507s  cdf (LoguniformDistribution, 2, "uper")
507s ***** error <cdf: invalid argument for upper tail.> ...
507s  cdf (LoguniformDistribution, 2, 3)
507s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
507s  plot (LoguniformDistribution, "Parent")
507s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
507s  plot (LoguniformDistribution, "PlotType", 12)
507s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
507s  plot (LoguniformDistribution, "PlotType", {"pdf", "cdf"})
507s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
507s  plot (LoguniformDistribution, "PlotType", "pdfcdf")
507s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
507s  plot (LoguniformDistribution, "Discrete", "pdfcdf")
507s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
507s  plot (LoguniformDistribution, "Discrete", [1, 0])
507s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
507s  plot (LoguniformDistribution, "Discrete", {true})
507s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
507s  plot (LoguniformDistribution, "Parent", 12)
507s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
507s  plot (LoguniformDistribution, "Parent", "hax")
507s ***** error <plot: invalid NAME for optional argument.> ...
507s  plot (LoguniformDistribution, "invalidNAME", "pdf")
507s ***** error <plot: 'probability' PlotType is not supported for 'LoguniformDistribution'.> ...
507s  plot (LoguniformDistribution, "PlotType", "probability")
507s ***** error <truncate: missing input argument.> ...
507s  truncate (LoguniformDistribution)
507s ***** error <truncate: missing input argument.> ...
507s  truncate (LoguniformDistribution, 2)
507s ***** error <truncate: invalid lower upper limits.> ...
507s  truncate (LoguniformDistribution, 4, 2)
507s ***** shared pd
507s  pd = LoguniformDistribution(1, 4);
507s  pd(2) = LoguniformDistribution(2, 5);
507s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
507s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
507s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
507s ***** error <mean: requires a scalar probability distribution.> mean (pd)
507s ***** error <median: requires a scalar probability distribution.> median (pd)
507s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
507s ***** error <plot: requires a scalar probability distribution.> plot (pd)
507s ***** error <random: requires a scalar probability distribution.> random (pd)
507s ***** error <std: requires a scalar probability distribution.> std (pd)
507s ***** error <truncate: requires a scalar probability distribution.> ...
507s  truncate (pd, 2, 4)
507s ***** error <var: requires a scalar probability distribution.> var (pd)
507s 65 tests, 65 passed, 0 known failure, 0 skipped
507s [inst/dist_obj/HalfNormalDistribution.m]
508s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_obj/HalfNormalDistribution.m
508s ***** shared pd, t
508s  pd = HalfNormalDistribution (0, 1);
508s  t = truncate (pd, 2, 4);
508s ***** assert (cdf (pd, [0:5]), [0, 0.6827, 0.9545, 0.9973, 0.9999, 1], 1e-4);
508s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.9420, 1, 1], 1e-4);
508s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8664, 0.9545, 0.9973, 0.9999], 1e-4);
508s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.9420, 1], 1e-4);
508s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.2533, 0.5244, 0.8416, 1.2816, Inf], 1e-4);
508s ***** assert (icdf (t, [0:0.2:1]), [2, 2.0923, 2.2068, 2.3607, 2.6064, 4], 1e-4);
508s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5244, 0.8416, 1.2816, Inf, NaN], 1e-4);
508s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.2068, 2.3607, 2.6064, 4, NaN], 1e-4);
508s ***** assert (iqr (pd), 0.8317, 1e-4);
508s ***** assert (iqr (t), 0.4111, 1e-4);
508s ***** assert (mean (pd), 0.7979, 1e-4);
508s ***** assert (mean (t), 2.3706, 1e-4);
508s ***** assert (median (pd), 0.6745, 1e-4);
508s ***** assert (median (t), 2.2771, 1e-4);
508s ***** assert (pdf (pd, [0:5]), [0.7979, 0.4839, 0.1080, 0.0089, 0.0003, 0], 1e-4);
508s ***** assert (pdf (t, [0:5]), [0, 0, 2.3765, 0.1951, 0.0059, 0], 1e-4);
508s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.4839, 0.1080, 0.0089, 0.0003, NaN], 1e-4);
508s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 2.3765, 0.1951, 0.0059, NaN], 1e-4);
508s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
508s ***** assert (any (random (t, 1000, 1) < 2), false);
508s ***** assert (any (random (t, 1000, 1) > 4), false);
508s ***** assert (std (pd), 0.6028, 1e-4);
508s ***** assert (std (t), 0.3310, 1e-4);
508s ***** assert (var (pd), 0.3634, 1e-4);
508s ***** assert (var (t), 0.1096, 1e-4);
508s ***** error <HalfNormalDistribution: MU must be a real scalar.> ...
508s  HalfNormalDistribution(Inf, 1)
508s ***** error <HalfNormalDistribution: MU must be a real scalar.> ...
508s  HalfNormalDistribution(i, 1)
508s ***** error <HalfNormalDistribution: MU must be a real scalar.> ...
508s  HalfNormalDistribution("a", 1)
508s ***** error <HalfNormalDistribution: MU must be a real scalar.> ...
508s  HalfNormalDistribution([1, 2], 1)
508s ***** error <HalfNormalDistribution: MU must be a real scalar.> ...
508s  HalfNormalDistribution(NaN, 1)
508s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
508s  HalfNormalDistribution(1, 0)
508s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
508s  HalfNormalDistribution(1, -1)
508s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
508s  HalfNormalDistribution(1, Inf)
508s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
508s  HalfNormalDistribution(1, i)
508s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
508s  HalfNormalDistribution(1, "a")
508s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
508s  HalfNormalDistribution(1, [1, 2])
508s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
508s  HalfNormalDistribution(1, NaN)
508s ***** error <cdf: invalid argument for upper tail.> ...
508s  cdf (HalfNormalDistribution, 2, "uper")
508s ***** error <cdf: invalid argument for upper tail.> ...
508s  cdf (HalfNormalDistribution, 2, 3)
508s ***** shared x
508s  x = hnrnd (1, 1, [1, 100]);
508s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
508s  paramci (HalfNormalDistribution.fit (x, 1), "alpha")
508s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
508s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0)
508s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
508s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", 1)
508s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
508s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", [0.5 2])
508s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
508s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", "")
508s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
508s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", {0.05})
508s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
508s  paramci (HalfNormalDistribution.fit (x, 1), "parameter", "sigma", ...
508s           "alpha", {0.05})
508s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
508s  paramci (HalfNormalDistribution.fit (x, 1), ...
508s           "parameter", {"mu", "sigma", "param"})
508s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
508s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0.01, ...
508s           "parameter", {"mu", "sigma", "param"})
508s ***** error <paramci: unknown distribution parameter.> ...
508s  paramci (HalfNormalDistribution.fit (x, 1), "parameter", "param")
508s ***** error <paramci: unknown distribution parameter.> ...
508s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0.01, ...
508s           "parameter", "param")
508s ***** error <paramci: invalid NAME for optional argument.> ...
508s  paramci (HalfNormalDistribution.fit (x, 1),"NAME", "value")
508s ***** error <paramci: invalid NAME for optional argument.> ...
508s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0.01, ...
508s           "NAME", "value")
508s ***** error <paramci: invalid NAME for optional argument.> ...
508s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0.01, ...
508s           "parameter", "sigma", "NAME", "value")
508s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
508s  plot (HalfNormalDistribution, "Parent")
508s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
508s  plot (HalfNormalDistribution, "PlotType", 12)
508s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
508s  plot (HalfNormalDistribution, "PlotType", {"pdf", "cdf"})
508s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
508s  plot (HalfNormalDistribution, "PlotType", "pdfcdf")
508s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
508s  plot (HalfNormalDistribution, "Discrete", "pdfcdf")
508s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
508s  plot (HalfNormalDistribution, "Discrete", [1, 0])
508s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
508s  plot (HalfNormalDistribution, "Discrete", {true})
508s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
508s  plot (HalfNormalDistribution, "Parent", 12)
508s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
508s  plot (HalfNormalDistribution, "Parent", "hax")
508s ***** error <plot: invalid NAME for optional argument.> ...
508s  plot (HalfNormalDistribution, "invalidNAME", "pdf")
508s ***** error <plot: no fitted DATA to plot a probability plot.> ...
508s  plot (HalfNormalDistribution, "PlotType", "probability")
508s ***** error <proflik: no fitted data available.> ...
508s  proflik (HalfNormalDistribution, 2)
508s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
508s  proflik (HalfNormalDistribution.fit (x, 1), 3)
508s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
508s  proflik (HalfNormalDistribution.fit (x, 1), [1, 2])
508s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
508s  proflik (HalfNormalDistribution.fit (x, 1), {1})
508s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
508s  proflik (HalfNormalDistribution.fit (x, 1), 1)
508s ***** error <proflik: SETPARAM must be a numeric vector.> ...
508s  proflik (HalfNormalDistribution.fit (x, 1), 2, ones (2))
508s ***** error <proflik: missing VALUE for 'Display' argument.> ...
508s  proflik (HalfNormalDistribution.fit (x, 1), 2, "Display")
508s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
508s  proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", 1)
508s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
508s  proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", {1})
508s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
508s  proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", {"on"})
508s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
508s  proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", ["on"; "on"])
508s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
508s  proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", "onnn")
508s ***** error <proflik: invalid NAME for optional arguments.> ...
508s  proflik (HalfNormalDistribution.fit (x, 1), 2, "NAME", "on")
508s ***** error <proflik: invalid optional argument.> ...
508s  proflik (HalfNormalDistribution.fit (x, 1), 2, {"NAME"}, "on")
508s ***** error <proflik: invalid optional argument.> ...
508s  proflik (HalfNormalDistribution.fit (x, 1), 2, {[1 2 3 4]}, ...
508s           "Display", "on")
508s ***** error <truncate: missing input argument.> ...
508s  truncate (HalfNormalDistribution)
508s ***** error <truncate: missing input argument.> ...
508s  truncate (HalfNormalDistribution, 2)
508s ***** error <truncate: invalid lower upper limits.> ...
508s  truncate (HalfNormalDistribution, 4, 2)
508s ***** shared pd
508s  pd = HalfNormalDistribution(1, 1);
508s  pd(2) = HalfNormalDistribution(1, 3);
508s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
508s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
508s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
508s ***** error <mean: requires a scalar probability distribution.> mean (pd)
508s ***** error <median: requires a scalar probability distribution.> median (pd)
508s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
508s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
508s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
508s ***** error <plot: requires a scalar probability distribution.> plot (pd)
508s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
508s ***** error <random: requires a scalar probability distribution.> random (pd)
508s ***** error <std: requires a scalar probability distribution.> std (pd)
508s ***** error <truncate: requires a scalar probability distribution.> ...
508s  truncate (pd, 2, 4)
508s ***** error <var: requires a scalar probability distribution.> var (pd)
508s 96 tests, 96 passed, 0 known failure, 0 skipped
508s [inst/dist_obj/ExponentialDistribution.m]
508s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_obj/ExponentialDistribution.m
508s ***** shared pd, t
508s  pd = ExponentialDistribution (1);
508s  t = truncate (pd, 2, 4);
508s ***** assert (cdf (pd, [0:5]), [0, 0.6321, 0.8647, 0.9502, 0.9817, 0.9933], 1e-4);
508s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7311, 1, 1], 1e-4);
508s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.7769, 0.8647, 0.9502, 0.9817], 1e-4);
509s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7311, 1], 1e-4);
509s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.2231, 0.5108, 0.9163, 1.6094, Inf], 1e-4);
509s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1899, 2.4244, 2.7315, 3.1768, 4], 1e-4);
509s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5108, 0.9163, 1.6094, Inf, NaN], 1e-4);
509s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4244, 2.7315, 3.1768, 4, NaN], 1e-4);
509s ***** assert (iqr (pd), 1.0986, 1e-4);
509s ***** assert (iqr (t), 0.8020, 1e-4);
509s ***** assert (mean (pd), 1);
509s ***** assert (mean (t), 2.6870, 1e-4);
509s ***** assert (median (pd), 0.6931, 1e-4);
509s ***** assert (median (t), 2.5662, 1e-4);
509s ***** assert (pdf (pd, [0:5]), [1, 0.3679, 0.1353, 0.0498, 0.0183, 0.0067], 1e-4);
509s ***** assert (pdf (t, [0:5]), [0, 0, 1.1565, 0.4255, 0.1565, 0], 1e-4);
509s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3679, 0.1353, 0.0498, 0.0183, NaN], 1e-4);
509s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.1565, 0.4255, 0.1565, NaN], 1e-4);
509s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
509s ***** assert (any (random (t, 1000, 1) < 2), false);
509s ***** assert (any (random (t, 1000, 1) > 4), false);
509s ***** assert (std (pd), 1);
509s ***** assert (std (t), 0.5253, 1e-4);
509s ***** assert (var (pd), 1);
509s ***** assert (var (t), 0.2759, 1e-4);
509s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
509s  ExponentialDistribution(0)
509s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
509s  ExponentialDistribution(-1)
509s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
509s  ExponentialDistribution(Inf)
509s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
509s  ExponentialDistribution(i)
509s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
509s  ExponentialDistribution("a")
509s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
509s  ExponentialDistribution([1, 2])
509s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
509s  ExponentialDistribution(NaN)
509s ***** error <cdf: invalid argument for upper tail.> ...
509s  cdf (ExponentialDistribution, 2, "uper")
509s ***** error <cdf: invalid argument for upper tail.> ...
509s  cdf (ExponentialDistribution, 2, 3)
509s ***** shared x
509s  x = exprnd (1, [100, 1]);
509s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
509s  paramci (ExponentialDistribution.fit (x), "alpha")
509s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
509s  paramci (ExponentialDistribution.fit (x), "alpha", 0)
509s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
509s  paramci (ExponentialDistribution.fit (x), "alpha", 1)
509s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
509s  paramci (ExponentialDistribution.fit (x), "alpha", [0.5 2])
509s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
509s  paramci (ExponentialDistribution.fit (x), "alpha", "")
509s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
509s  paramci (ExponentialDistribution.fit (x), "alpha", {0.05})
509s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
509s  paramci (ExponentialDistribution.fit (x), "parameter", "mu", ...
509s           "alpha", {0.05})
509s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
509s  paramci (ExponentialDistribution.fit (x), "parameter", {"mu", "param"})
509s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
509s  paramci (ExponentialDistribution.fit (x), "alpha", 0.01, ...
509s           "parameter", {"mu", "param"})
509s ***** error <paramci: unknown distribution parameter.> ...
509s  paramci (ExponentialDistribution.fit (x), "parameter", "param")
509s ***** error <paramci: unknown distribution parameter.> ...
509s  paramci (ExponentialDistribution.fit (x), "alpha", 0.01, "parameter", "parm")
509s ***** error <paramci: invalid NAME for optional argument.> ...
509s  paramci (ExponentialDistribution.fit (x), "NAME", "value")
509s ***** error <paramci: invalid NAME for optional argument.> ...
509s  paramci (ExponentialDistribution.fit (x), "alpha", 0.01, "NAME", "value")
509s ***** error <paramci: invalid NAME for optional argument.> ...
509s  paramci (ExponentialDistribution.fit (x), "alpha", 0.01, ...
509s           "parameter", "mu", "NAME", "value")
509s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
509s  plot (ExponentialDistribution, "Parent")
509s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
509s  plot (ExponentialDistribution, "PlotType", 12)
509s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
509s  plot (ExponentialDistribution, "PlotType", {"pdf", "cdf"})
509s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
509s  plot (ExponentialDistribution, "PlotType", "pdfcdf")
509s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
509s  plot (ExponentialDistribution, "Discrete", "pdfcdf")
509s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
509s  plot (ExponentialDistribution, "Discrete", [1, 0])
509s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
509s  plot (ExponentialDistribution, "Discrete", {true})
509s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
509s  plot (ExponentialDistribution, "Parent", 12)
509s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
509s  plot (ExponentialDistribution, "Parent", "hax")
509s ***** error <plot: invalid NAME for optional argument.> ...
509s  plot (ExponentialDistribution, "invalidNAME", "pdf")
509s ***** error <plot: no fitted DATA to plot a probability plot.> ...
509s  plot (ExponentialDistribution, "PlotType", "probability")
509s ***** error <proflik: no fitted data available.> ...
509s  proflik (ExponentialDistribution, 2)
509s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
509s  proflik (ExponentialDistribution.fit (x), 3)
509s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
509s  proflik (ExponentialDistribution.fit (x), [1, 2])
509s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
509s  proflik (ExponentialDistribution.fit (x), {1})
509s ***** error <proflik: SETPARAM must be a numeric vector.> ...
509s  proflik (ExponentialDistribution.fit (x), 1, ones (2))
509s ***** error <proflik: missing VALUE for 'Display' argument.> ...
509s  proflik (ExponentialDistribution.fit (x), 1, "Display")
509s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
509s  proflik (ExponentialDistribution.fit (x), 1, "Display", 1)
509s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
509s  proflik (ExponentialDistribution.fit (x), 1, "Display", {1})
509s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
509s  proflik (ExponentialDistribution.fit (x), 1, "Display", {"on"})
509s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
509s  proflik (ExponentialDistribution.fit (x), 1, "Display", ["on"; "on"])
509s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
509s  proflik (ExponentialDistribution.fit (x), 1, "Display", "onnn")
509s ***** error <proflik: invalid NAME for optional arguments.> ...
509s  proflik (ExponentialDistribution.fit (x), 1, "NAME", "on")
509s ***** error <proflik: invalid optional argument.> ...
509s  proflik (ExponentialDistribution.fit (x), 1, {"NAME"}, "on")
509s ***** error <proflik: invalid optional argument.> ...
509s  proflik (ExponentialDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
509s ***** error <truncate: missing input argument.> ...
509s  truncate (ExponentialDistribution)
509s ***** error <truncate: missing input argument.> ...
509s  truncate (ExponentialDistribution, 2)
509s ***** error <truncate: invalid lower upper limits.> ...
509s  truncate (ExponentialDistribution, 4, 2)
509s ***** shared pd
509s  pd = ExponentialDistribution(1);
509s  pd(2) = ExponentialDistribution(3);
509s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
509s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
509s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
509s ***** error <mean: requires a scalar probability distribution.> mean (pd)
509s ***** error <median: requires a scalar probability distribution.> median (pd)
509s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
509s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
509s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
509s ***** error <plot: requires a scalar probability distribution.> plot (pd)
509s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
509s ***** error <random: requires a scalar probability distribution.> random (pd)
509s ***** error <std: requires a scalar probability distribution.> std (pd)
509s ***** error <truncate: requires a scalar probability distribution.> ...
509s  truncate (pd, 2, 4)
509s ***** error <var: requires a scalar probability distribution.> var (pd)
509s 90 tests, 90 passed, 0 known failure, 0 skipped
509s [inst/dist_obj/PoissonDistribution.m]
509s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_obj/PoissonDistribution.m
509s ***** shared pd, t, t_inf
509s  pd = PoissonDistribution;
509s  t = truncate (pd, 2, 4);
509s  t_inf = truncate (pd, 2, Inf);
509s ***** assert (cdf (pd, [0:5]), [0.3679, 0.7358, 0.9197, 0.9810, 0.9963, 0.9994], 1e-4);
509s ***** assert (cdf (t, [0:5]), [0, 0, 0.7059, 0.9412, 1, 1], 1e-4);
509s ***** assert (cdf (t_inf, [0:5]), [0, 0, 0.6961, 0.9281, 0.9861, 0.9978], 1e-4);
509s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.7358, 0.9197, 0.9810, 0.9963], 1e-4);
509s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0.7059, 0.9412, 1], 1e-4);
509s ***** assert (icdf (pd, [0:0.2:1]), [0, 0, 1, 1, 2, Inf], 1e-4);
509s ***** assert (icdf (t, [0:0.2:1]), [2, 2, 2, 2, 3, 4], 1e-4);
509s ***** assert (icdf (t_inf, [0:0.2:1]), [2, 2, 2, 2, 3, Inf], 1e-4);
509s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1, 1, 2, Inf, NaN], 1e-4);
509s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2, 2, 3, 4, NaN], 1e-4);
509s ***** assert (iqr (pd), 2);
509s ***** assert (iqr (t), 1);
509s ***** assert (mean (pd), 1);
509s ***** assert (mean (t), 2.3529, 1e-4);
509s ***** assert (mean (t_inf), 2.3922, 1e-4);
509s ***** assert (median (pd), 1);
509s ***** assert (median (t), 2);
509s ***** assert (median (t_inf), 2);
509s ***** assert (pdf (pd, [0:5]), [0.3679, 0.3679, 0.1839, 0.0613, 0.0153, 0.0031], 1e-4);
509s ***** assert (pdf (t, [0:5]), [0, 0, 0.7059, 0.2353, 0.0588, 0], 1e-4);
509s ***** assert (pdf (t_inf, [0:5]), [0, 0, 0.6961, 0.2320, 0.0580, 0.0116], 1e-4);
509s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3679, 0.1839, 0.0613, 0.0153, NaN], 1e-4);
509s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.7059, 0.2353, 0.0588, NaN], 1e-4);
509s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
509s ***** assert (any (random (t, 1000, 1) < 2), false);
509s ***** assert (any (random (t, 1000, 1) > 4), false);
509s ***** assert (std (pd), 1);
509s ***** assert (std (t), 0.5882, 1e-4);
509s ***** assert (std (t_inf), 0.6738, 1e-4);
509s ***** assert (var (pd), 1);
509s ***** assert (var (t), 0.3460, 1e-4);
509s ***** assert (var (t_inf), 0.4540, 1e-4);
509s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
509s  PoissonDistribution(0)
509s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
509s  PoissonDistribution(-1)
509s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
509s  PoissonDistribution(Inf)
509s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
509s  PoissonDistribution(i)
509s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
509s  PoissonDistribution("a")
509s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
509s  PoissonDistribution([1, 2])
509s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
509s  PoissonDistribution(NaN)
509s ***** error <cdf: invalid argument for upper tail.> ...
509s  cdf (PoissonDistribution, 2, "uper")
509s ***** error <cdf: invalid argument for upper tail.> ...
509s  cdf (PoissonDistribution, 2, 3)
509s ***** shared x
509s  x = poissrnd (1, [1, 100]);
509s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
509s  paramci (PoissonDistribution.fit (x), "alpha")
509s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
509s  paramci (PoissonDistribution.fit (x), "alpha", 0)
509s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
509s  paramci (PoissonDistribution.fit (x), "alpha", 1)
509s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
509s  paramci (PoissonDistribution.fit (x), "alpha", [0.5 2])
509s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
509s  paramci (PoissonDistribution.fit (x), "alpha", "")
509s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
509s  paramci (PoissonDistribution.fit (x), "alpha", {0.05})
509s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
509s  paramci (PoissonDistribution.fit (x), "parameter", "lambda", "alpha", {0.05})
509s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
509s  paramci (PoissonDistribution.fit (x), "parameter", {"lambda", "param"})
509s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
509s  paramci (PoissonDistribution.fit (x), "alpha", 0.01, ...
509s           "parameter", {"lambda", "param"})
509s ***** error <paramci: unknown distribution parameter.> ...
509s  paramci (PoissonDistribution.fit (x), "parameter", "param")
509s ***** error <paramci: unknown distribution parameter.> ...
509s  paramci (PoissonDistribution.fit (x), "alpha", 0.01, "parameter", "param")
509s ***** error <paramci: invalid NAME for optional argument.> ...
509s  paramci (PoissonDistribution.fit (x), "NAME", "value")
509s ***** error <paramci: invalid NAME for optional argument.> ...
509s  paramci (PoissonDistribution.fit (x), "alpha", 0.01, "NAME", "value")
509s ***** error <paramci: invalid NAME for optional argument.> ...
509s  paramci (PoissonDistribution.fit (x), "alpha", 0.01, ...
509s           "parameter", "lambda", "NAME", "value")
509s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
509s  plot (PoissonDistribution, "Parent")
509s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
509s  plot (PoissonDistribution, "PlotType", 12)
509s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
509s  plot (PoissonDistribution, "PlotType", {"pdf", "cdf"})
509s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
509s  plot (PoissonDistribution, "PlotType", "pdfcdf")
509s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
509s  plot (PoissonDistribution, "Discrete", "pdfcdf")
509s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
509s  plot (PoissonDistribution, "Discrete", [1, 0])
509s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
509s  plot (PoissonDistribution, "Discrete", {true})
509s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
509s  plot (PoissonDistribution, "Parent", 12)
509s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
509s  plot (PoissonDistribution, "Parent", "hax")
509s ***** error <plot: invalid NAME for optional argument.> ...
509s  plot (PoissonDistribution, "invalidNAME", "pdf")
509s ***** error <plot: no fitted DATA to plot a probability plot.> ...
509s  plot (PoissonDistribution, "PlotType", "probability")
509s ***** error <proflik: no fitted data available.> ...
509s  proflik (PoissonDistribution, 2)
509s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
509s  proflik (PoissonDistribution.fit (x), 3)
509s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
509s  proflik (PoissonDistribution.fit (x), [1, 2])
509s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
509s  proflik (PoissonDistribution.fit (x), {1})
509s ***** error <proflik: SETPARAM must be a numeric vector.> ...
509s  proflik (PoissonDistribution.fit (x), 1, ones (2))
509s ***** error <proflik: missing VALUE for 'Display' argument.> ...
509s  proflik (PoissonDistribution.fit (x), 1, "Display")
509s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
509s  proflik (PoissonDistribution.fit (x), 1, "Display", 1)
509s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
509s  proflik (PoissonDistribution.fit (x), 1, "Display", {1})
509s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
509s  proflik (PoissonDistribution.fit (x), 1, "Display", {"on"})
509s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
509s  proflik (PoissonDistribution.fit (x), 1, "Display", ["on"; "on"])
509s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
509s  proflik (PoissonDistribution.fit (x), 1, "Display", "onnn")
509s ***** error <proflik: invalid NAME for optional arguments.> ...
509s  proflik (PoissonDistribution.fit (x), 1, "NAME", "on")
509s ***** error <proflik: invalid optional argument.> ...
509s  proflik (PoissonDistribution.fit (x), 1, {"NAME"}, "on")
509s ***** error <proflik: invalid optional argument.> ...
509s  proflik (PoissonDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
509s ***** error <truncate: missing input argument.> ...
509s  truncate (PoissonDistribution)
509s ***** error <truncate: missing input argument.> ...
509s  truncate (PoissonDistribution, 2)
509s ***** error <truncate: invalid lower upper limits.> ...
509s  truncate (PoissonDistribution, 4, 2)
509s ***** shared pd
509s  pd = PoissonDistribution(1);
509s  pd(2) = PoissonDistribution(3);
509s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
509s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
509s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
509s ***** error <mean: requires a scalar probability distribution.> mean (pd)
509s ***** error <median: requires a scalar probability distribution.> median (pd)
509s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
509s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
509s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
509s ***** error <plot: requires a scalar probability distribution.> plot (pd)
509s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
509s ***** error <random: requires a scalar probability distribution.> random (pd)
509s ***** error <std: requires a scalar probability distribution.> std (pd)
509s ***** error <truncate: requires a scalar probability distribution.> ...
509s  truncate (pd, 2, 4)
509s ***** error <var: requires a scalar probability distribution.> var (pd)
509s 97 tests, 97 passed, 0 known failure, 0 skipped
509s [inst/dist_obj/InverseGaussianDistribution.m]
509s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_obj/InverseGaussianDistribution.m
509s ***** shared pd, t
509s  pd = InverseGaussianDistribution (1, 1);
509s  t = truncate (pd, 2, 4);
509s ***** assert (cdf (pd, [0:5]), [0, 0.6681, 0.8855, 0.9532, 0.9791, 0.9901], 1e-4);
509s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7234, 1, 1], 1e-4);
509s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8108, 0.8855, 0.9532, 0.9791], 1e-4);
509s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7234, 1], 1e-4);
509s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.3320, 0.5411, 0.8483, 1.4479, Inf], 1e-4);
509s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1889, 2.4264, 2.7417, 3.1993, 4], 1e-4);
509s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5411, 0.8483, 1.4479, Inf, NaN], 1e-4);
509s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4264, 2.7417, 3.1993, 4, NaN], 1e-4);
509s ***** assert (iqr (pd), 0.8643, 1e-4);
509s ***** assert (iqr (t), 0.8222, 1e-4);
509s ***** assert (mean (pd), 1);
509s ***** assert (mean (t), 2.6953, 1e-4);
509s ***** assert (median (pd), 0.6758, 1e-4);
509s ***** assert (median (t), 2.5716, 1e-4);
509s ***** assert (pdf (pd, [0:5]), [0, 0.3989, 0.1098, 0.0394, 0.0162, 0.0072], 1e-4);
509s ***** assert (pdf (t, [0:5]), [0, 0, 1.1736, 0.4211, 0.1730, 0], 1e-4);
509s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3989, 0.1098, 0.0394, 0.0162, NaN], 1e-4);
509s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.1736, 0.4211, 0.1730, NaN], 1e-4);
509s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
509s ***** assert (any (random (t, 1000, 1) < 2), false);
509s ***** assert (any (random (t, 1000, 1) > 4), false);
509s ***** assert (std (pd), 1);
509s ***** assert (std (t), 0.5332, 1e-4);
509s ***** assert (var (pd), 1);
509s ***** assert (var (t), 0.2843, 1e-4);
509s ***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
509s  InverseGaussianDistribution(0, 1)
509s ***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
509s  InverseGaussianDistribution(Inf, 1)
509s ***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
509s  InverseGaussianDistribution(i, 1)
509s ***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
509s  InverseGaussianDistribution("a", 1)
509s ***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
509s  InverseGaussianDistribution([1, 2], 1)
509s ***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
509s  InverseGaussianDistribution(NaN, 1)
509s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
509s  InverseGaussianDistribution(1, 0)
509s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
509s  InverseGaussianDistribution(1, -1)
509s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
509s  InverseGaussianDistribution(1, Inf)
509s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
509s  InverseGaussianDistribution(1, i)
509s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
509s  InverseGaussianDistribution(1, "a")
509s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
509s  InverseGaussianDistribution(1, [1, 2])
509s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
509s  InverseGaussianDistribution(1, NaN)
509s ***** error <cdf: invalid argument for upper tail.> ...
509s  cdf (InverseGaussianDistribution, 2, "uper")
509s ***** error <cdf: invalid argument for upper tail.> ...
509s  cdf (InverseGaussianDistribution, 2, 3)
509s ***** shared x
509s  x = invgrnd (1, 1, [1, 100]);
509s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
509s  paramci (InverseGaussianDistribution.fit (x), "alpha")
509s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
509s  paramci (InverseGaussianDistribution.fit (x), "alpha", 0)
509s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
509s  paramci (InverseGaussianDistribution.fit (x), "alpha", 1)
509s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
509s  paramci (InverseGaussianDistribution.fit (x), "alpha", [0.5 2])
509s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
509s  paramci (InverseGaussianDistribution.fit (x), "alpha", "")
509s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
509s  paramci (InverseGaussianDistribution.fit (x), "alpha", {0.05})
509s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
509s  paramci (InverseGaussianDistribution.fit (x), "parameter", "mu", ...
509s           "alpha", {0.05})
509s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
509s  paramci (InverseGaussianDistribution.fit (x), ...
509s           "parameter", {"mu", "lambda", "param"})
509s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
509s  paramci (InverseGaussianDistribution.fit (x), "alpha", 0.01, ...
509s           "parameter", {"mu", "lambda", "param"})
509s ***** error <paramci: unknown distribution parameter.> ...
509s  paramci (InverseGaussianDistribution.fit (x), "parameter", "param")
509s ***** error <paramci: unknown distribution parameter.> ...
509s  paramci (InverseGaussianDistribution.fit (x), "alpha", 0.01, ...
509s           "parameter", "param")
509s ***** error <paramci: invalid NAME for optional argument.> ...
509s  paramci (InverseGaussianDistribution.fit (x), "NAME", "value")
509s ***** error <paramci: invalid NAME for optional argument.> ...
509s  paramci (InverseGaussianDistribution.fit (x), "alpha", 0.01, "NAME", "value")
509s ***** error <paramci: invalid NAME for optional argument.> ...
509s  paramci (InverseGaussianDistribution.fit (x), "alpha", 0.01, ...
509s           "parameter", "mu", "NAME", "value")
509s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
509s  plot (InverseGaussianDistribution, "Parent")
509s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
509s  plot (InverseGaussianDistribution, "PlotType", 12)
509s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
509s  plot (InverseGaussianDistribution, "PlotType", {"pdf", "cdf"})
509s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
509s  plot (InverseGaussianDistribution, "PlotType", "pdfcdf")
509s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
509s  plot (InverseGaussianDistribution, "Discrete", "pdfcdf")
509s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
509s  plot (InverseGaussianDistribution, "Discrete", [1, 0])
509s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
509s  plot (InverseGaussianDistribution, "Discrete", {true})
509s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
509s  plot (InverseGaussianDistribution, "Parent", 12)
509s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
509s  plot (InverseGaussianDistribution, "Parent", "hax")
509s ***** error <plot: invalid NAME for optional argument.> ...
509s  plot (InverseGaussianDistribution, "invalidNAME", "pdf")
509s ***** error <plot: no fitted DATA to plot a probability plot.> ...
509s  plot (InverseGaussianDistribution, "PlotType", "probability")
509s ***** error <proflik: no fitted data available.> ...
509s  proflik (InverseGaussianDistribution, 2)
509s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
509s  proflik (InverseGaussianDistribution.fit (x), 3)
509s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
509s  proflik (InverseGaussianDistribution.fit (x), [1, 2])
509s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
509s  proflik (InverseGaussianDistribution.fit (x), {1})
509s ***** error <proflik: SETPARAM must be a numeric vector.> ...
509s  proflik (InverseGaussianDistribution.fit (x), 1, ones (2))
509s ***** error <proflik: missing VALUE for 'Display' argument.> ...
509s  proflik (InverseGaussianDistribution.fit (x), 1, "Display")
509s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
509s  proflik (InverseGaussianDistribution.fit (x), 1, "Display", 1)
509s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
509s  proflik (InverseGaussianDistribution.fit (x), 1, "Display", {1})
509s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
509s  proflik (InverseGaussianDistribution.fit (x), 1, "Display", {"on"})
509s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
509s  proflik (InverseGaussianDistribution.fit (x), 1, "Display", ["on"; "on"])
509s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
509s  proflik (InverseGaussianDistribution.fit (x), 1, "Display", "onnn")
509s ***** error <proflik: invalid NAME for optional arguments.> ...
509s  proflik (InverseGaussianDistribution.fit (x), 1, "NAME", "on")
509s ***** error <proflik: invalid optional argument.> ...
509s  proflik (InverseGaussianDistribution.fit (x), 1, {"NAME"}, "on")
509s ***** error <proflik: invalid optional argument.> ...
509s  proflik (InverseGaussianDistribution.fit (x), 1, {[1 2 3]}, "Display", "on")
509s ***** error <truncate: missing input argument.> ...
509s  truncate (InverseGaussianDistribution)
509s ***** error <truncate: missing input argument.> ...
509s  truncate (InverseGaussianDistribution, 2)
509s ***** error <truncate: invalid lower upper limits.> ...
509s  truncate (InverseGaussianDistribution, 4, 2)
509s ***** shared pd
509s  pd = InverseGaussianDistribution(1, 1);
509s  pd(2) = InverseGaussianDistribution(1, 3);
509s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
509s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
509s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
509s ***** error <mean: requires a scalar probability distribution.> mean (pd)
509s ***** error <median: requires a scalar probability distribution.> median (pd)
509s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
509s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
509s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
509s ***** error <plot: requires a scalar probability distribution.> plot (pd)
509s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
509s ***** error <random: requires a scalar probability distribution.> random (pd)
509s ***** error <std: requires a scalar probability distribution.> std (pd)
509s ***** error <truncate: requires a scalar probability distribution.> ...
509s  truncate (pd, 2, 4)
509s ***** error <var: requires a scalar probability distribution.> var (pd)
509s 96 tests, 96 passed, 0 known failure, 0 skipped
509s [inst/dist_obj/ExtremeValueDistribution.m]
509s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_obj/ExtremeValueDistribution.m
509s ***** shared pd, t
509s  pd = ExtremeValueDistribution (0, 1);
509s  t = truncate (pd, 2, 4);
509s ***** assert (cdf (pd, [0:5]), [0.6321, 0.9340, 0.9994, 1, 1, 1], 1e-4);
509s ***** assert (cdf (t, [0:5]), [0, 0, 0, 1, 1, 1], 1e-4);
509s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.9887, 0.9994, 1, 1], 1e-4);
509s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 1, 1], 1e-4);
510s ***** assert (icdf (pd, [0:0.2:1]), [-Inf, -1.4999, -0.6717, -0.0874, 0.4759, Inf], 1e-4);
510s ***** assert (icdf (t, [0:0.2:1]), [2, 2.0298, 2.0668, 2.1169, 2.1971, 4], 1e-4);
510s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, -0.6717, -0.0874, 0.4759, Inf, NaN], 1e-4);
510s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.0668, 2.1169, 2.1971, 4, NaN], 1e-4);
510s ***** assert (iqr (pd), 1.5725, 1e-4);
510s ***** assert (iqr (t), 0.1338, 1e-4);
510s ***** assert (mean (pd), -0.5772, 1e-4);
510s ***** assert (mean (t), 2.1206, 1e-4);
510s ***** assert (median (pd), -0.3665, 1e-4);
510s ***** assert (median (t), 2.0897, 1e-4);
510s ***** assert (pdf (pd, [0:5]), [0.3679, 0.1794, 0.0046, 0, 0, 0], 1e-4);
510s ***** assert (pdf (t, [0:5]), [0, 0, 7.3891, 0.0001, 0, 0], 1e-4);
510s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0.2546, 0.1794, 0.0046, 0, 0, NaN], 1e-4);
510s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 7.3891, 0.0001, 0, NaN], 1e-4);
510s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
510s ***** assert (any (random (t, 1000, 1) < 2), false);
510s ***** assert (any (random (t, 1000, 1) > 4), false);
510s ***** assert (std (pd), 1.2825, 1e-4);
510s ***** assert (std (t), 0.1091, 1e-4);
510s ***** assert (var (pd), 1.6449, 1e-4);
510s ***** assert (var (t), 0.0119, 1e-4);
510s ***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
510s  ExtremeValueDistribution(Inf, 1)
510s ***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
510s  ExtremeValueDistribution(i, 1)
510s ***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
510s  ExtremeValueDistribution("a", 1)
510s ***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
510s  ExtremeValueDistribution([1, 2], 1)
510s ***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
510s  ExtremeValueDistribution(NaN, 1)
510s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
510s  ExtremeValueDistribution(1, 0)
510s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
510s  ExtremeValueDistribution(1, -1)
510s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
510s  ExtremeValueDistribution(1, Inf)
510s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
510s  ExtremeValueDistribution(1, i)
510s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
510s  ExtremeValueDistribution(1, "a")
510s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
510s  ExtremeValueDistribution(1, [1, 2])
510s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
510s  ExtremeValueDistribution(1, NaN)
510s ***** error <cdf: invalid argument for upper tail.> ...
510s  cdf (ExtremeValueDistribution, 2, "uper")
510s ***** error <cdf: invalid argument for upper tail.> ...
510s  cdf (ExtremeValueDistribution, 2, 3)
510s ***** shared x
510s  rand ("seed", 1);
510s  x = evrnd (1, 1, [1000, 1]);
510s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
510s  paramci (ExtremeValueDistribution.fit (x), "alpha")
510s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
510s  paramci (ExtremeValueDistribution.fit (x), "alpha", 0)
510s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
510s  paramci (ExtremeValueDistribution.fit (x), "alpha", 1)
510s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
510s  paramci (ExtremeValueDistribution.fit (x), "alpha", [0.5 2])
510s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
510s  paramci (ExtremeValueDistribution.fit (x), "alpha", "")
510s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
510s  paramci (ExtremeValueDistribution.fit (x), "alpha", {0.05})
510s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
510s  paramci (ExtremeValueDistribution.fit (x), ...
510s           "parameter", "mu", "alpha", {0.05})
510s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
510s  paramci (ExtremeValueDistribution.fit (x), ...
510s           "parameter", {"mu", "sigma", "param"})
510s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
510s  paramci (ExtremeValueDistribution.fit (x), "alpha", 0.01, ...
510s           "parameter", {"mu", "sigma", "param"})
510s ***** error <paramci: unknown distribution parameter.> ...
510s  paramci (ExtremeValueDistribution.fit (x), "parameter", "param")
510s ***** error <paramci: unknown distribution parameter.> ...
510s  paramci (ExtremeValueDistribution.fit (x), "alpha", 0.01, ...
510s           "parameter", "param")
510s ***** error <paramci: invalid NAME for optional argument.> ...
510s  paramci (ExtremeValueDistribution.fit (x), "NAME", "value")
510s ***** error <paramci: invalid NAME for optional argument.> ...
510s  paramci (ExtremeValueDistribution.fit (x), "alpha", 0.01, "NAME", "value")
510s ***** error <paramci: invalid NAME for optional argument.> ...
510s  paramci (ExtremeValueDistribution.fit (x), "alpha", 0.01, ...
510s           "parameter", "mu", "NAME", "value")
510s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
510s  plot (ExtremeValueDistribution, "Parent")
510s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
510s  plot (ExtremeValueDistribution, "PlotType", 12)
510s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
510s  plot (ExtremeValueDistribution, "PlotType", {"pdf", "cdf"})
510s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
510s  plot (ExtremeValueDistribution, "PlotType", "pdfcdf")
510s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
510s  plot (ExtremeValueDistribution, "Discrete", "pdfcdf")
510s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
510s  plot (ExtremeValueDistribution, "Discrete", [1, 0])
510s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
510s  plot (ExtremeValueDistribution, "Discrete", {true})
510s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
510s  plot (ExtremeValueDistribution, "Parent", 12)
510s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
510s  plot (ExtremeValueDistribution, "Parent", "hax")
510s ***** error <plot: invalid NAME for optional argument.> ...
510s  plot (ExtremeValueDistribution, "invalidNAME", "pdf")
510s ***** error <plot: no fitted DATA to plot a probability plot.> ...
510s  plot (ExtremeValueDistribution, "PlotType", "probability")
510s ***** error <proflik: no fitted data available.> ...
510s  proflik (ExtremeValueDistribution, 2)
510s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
510s  proflik (ExtremeValueDistribution.fit (x), 3)
510s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
510s  proflik (ExtremeValueDistribution.fit (x), [1, 2])
510s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
510s  proflik (ExtremeValueDistribution.fit (x), {1})
510s ***** error <proflik: SETPARAM must be a numeric vector.> ...
510s  proflik (ExtremeValueDistribution.fit (x), 1, ones (2))
510s ***** error <proflik: missing VALUE for 'Display' argument.> ...
510s  proflik (ExtremeValueDistribution.fit (x), 1, "Display")
510s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
510s  proflik (ExtremeValueDistribution.fit (x), 1, "Display", 1)
510s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
510s  proflik (ExtremeValueDistribution.fit (x), 1, "Display", {1})
510s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
510s  proflik (ExtremeValueDistribution.fit (x), 1, "Display", {"on"})
510s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
510s  proflik (ExtremeValueDistribution.fit (x), 1, "Display", ["on"; "on"])
510s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
510s  proflik (ExtremeValueDistribution.fit (x), 1, "Display", "onnn")
510s ***** error <proflik: invalid NAME for optional arguments.> ...
510s  proflik (ExtremeValueDistribution.fit (x), 1, "NAME", "on")
510s ***** error <proflik: invalid optional argument.> ...
510s  proflik (ExtremeValueDistribution.fit (x), 1, {"NAME"}, "on")
510s ***** error <proflik: invalid optional argument.> ...
510s  proflik (ExtremeValueDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
510s ***** error <truncate: missing input argument.> ...
510s  truncate (ExtremeValueDistribution)
510s ***** error <truncate: missing input argument.> ...
510s  truncate (ExtremeValueDistribution, 2)
510s ***** error <truncate: invalid lower upper limits.> ...
510s  truncate (ExtremeValueDistribution, 4, 2)
510s ***** shared pd
510s  pd = ExtremeValueDistribution(1, 1);
510s  pd(2) = ExtremeValueDistribution(1, 3);
510s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
510s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
510s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
510s ***** error <mean: requires a scalar probability distribution.> mean (pd)
510s ***** error <median: requires a scalar probability distribution.> median (pd)
510s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
510s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
510s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
510s ***** error <plot: requires a scalar probability distribution.> plot (pd)
510s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
510s ***** error <random: requires a scalar probability distribution.> random (pd)
510s ***** error <std: requires a scalar probability distribution.> std (pd)
510s ***** error <truncate: requires a scalar probability distribution.> ...
510s  truncate (pd, 2, 4)
510s ***** error <var: requires a scalar probability distribution.> var (pd)
510s 95 tests, 95 passed, 0 known failure, 0 skipped
510s [inst/dist_obj/BetaDistribution.m]
510s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_obj/BetaDistribution.m
510s ***** demo
510s  ## Generate a data set of 5000 random samples from a Beta distribution with
510s  ## parameters a = 2 and b = 4.  Fit a Beta distribution to this data and plot
510s  ## a PDF of the fitted distribution superimposed on a histogram of the data
510s 
510s  pd = makedist ("Beta", "a", 2, "b", 4)
510s  randg ("seed", 21);
510s  data = random (pd, 5000, 1);
510s  pd = fitdist (data, "Beta")
510s  plot (pd)
510s  title (sprintf ("Fitted Beta distribution with a = %0.2f and b = %0.2f", ...
510s                  pd.a, pd.b))
510s ***** demo
510s  ## Plot the PDF of a Beta distribution, with parameters a = 2 and b = 4,
510s  ## truncated at [0.1, 0.8] intervals.  Generate 10000 random samples from
510s  ## this truncated distribution and superimpose a histogram with 100 bins
510s  ## scaled accordingly
510s 
510s  pd = makedist ("Beta", "a", 2, "b", 4)
510s  t = truncate (pd, 0.1, 0.8)
510s  randg ("seed", 21);
510s  data = random (t, 10000, 1);
510s  plot (t)
510s  title ("Beta distribution (a = 2, b = 4) truncated at [0.1, 0.8]")
510s  hold on
510s  hist (data, 100, 140)
510s  hold off
510s ***** demo
510s  ## Generate a data set of 100 random samples from a Beta distribution with
510s  ## parameters a = 2 and b = 4.  Fit a Beta distribution to this data and plot
510s  ## its CDF superimposed over an empirical CDF of the data
510s 
510s  pd = makedist ("Beta", "a", 2, "b", 4)
510s  randg ("seed", 21);
510s  data = random (pd, 100, 1);
510s  pd = fitdist (data, "Beta")
510s  plot (pd, "plottype", "cdf")
510s  title (sprintf ("Fitted Beta distribution with a = %0.2f and b = %0.2f", ...
510s                  pd.a, pd.b))
510s  legend ({"empirical CDF", "fitted CDF"}, "location", "east")
510s ***** demo
510s  ## Generate a data set of 200 random samples from a Beta distribution with
510s  ## parameters a = 2 and b = 4.  Display a probability plot for the Beta
510s  ## distribution fit to the data.
510s 
510s  pd = makedist ("Beta", "a", 2, "b", 4)
510s  randg ("seed", 21);
510s  data = random (pd, 200, 1);
510s  pd = fitdist (data, "Beta")
510s  plot (pd, "plottype", "probability")
510s  title (sprintf ("Probability plot of a fitted Beta distribution with a = %0.2f and b = %0.2f", ...
510s                  pd.a, pd.b))
510s  legend ({"empirical CDF", "fitted CDF"}, "location", "southeast")
510s ***** shared pd, t
510s  pd = BetaDistribution;
510s  t = truncate (pd, 0.2, 0.8);
510s ***** assert (cdf (pd, [0:0.2:1]), [0, 0.2, 0.4, 0.6, 0.8, 1], 1e-4);
510s ***** assert (cdf (t, [0:0.2:1]), [0, 0, 0.3333, 0.6667, 1, 1], 1e-4);
510s ***** assert (cdf (pd, [-1, 1, NaN]), [0, 1, NaN], 1e-4);
510s ***** assert (cdf (t, [-1, 1, NaN]), [0, 1, NaN], 1e-4);
510s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.2, 0.4, 0.6, 0.8, 1], 1e-4);
510s ***** assert (icdf (t, [0:0.2:1]), [0.2, 0.32, 0.44, 0.56, 0.68, 0.8], 1e-4);
510s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.4, 0.6, 0.8, 1, NaN], 1e-4);
510s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 0.44, 0.56, 0.68, 0.8, NaN], 1e-4);
510s ***** assert (iqr (pd), 0.5, 1e-4);
510s ***** assert (iqr (t), 0.3, 1e-4);
510s ***** assert (mean (pd), 0.5);
510s ***** assert (mean (t), 0.5, 1e-6);
510s ***** assert (median (pd), 0.5);
510s ***** assert (median (t), 0.5, 1e-6);
510s ***** assert (pdf (pd, [0:0.2:1]), [1, 1, 1, 1, 1, 1], 1e-4);
510s ***** assert (pdf (t, [0:0.2:1]), [0, 1.6667, 1.6667, 1.6667, 1.6667, 0], 1e-4);
510s ***** assert (pdf (pd, [-1, 1, NaN]), [0, 1, NaN], 1e-4);
510s ***** assert (pdf (t, [-1, 1, NaN]), [0, 0, NaN], 1e-4);
510s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
510s ***** assert (any (random (t, 1000, 1) < 0.2), false);
510s ***** assert (any (random (t, 1000, 1) > 0.8), false);
510s ***** assert (std (pd), 0.2887, 1e-4);
510s ***** assert (std (t), 0.1732, 1e-4);
510s ***** assert (var (pd), 0.0833, 1e-4);
510s ***** assert (var (t), 0.0300, 1e-4);
510s ***** error <BetaDistribution: A must be a positive real scalar.> ...
510s  BetaDistribution(0, 1)
510s ***** error <BetaDistribution: A must be a positive real scalar.> ...
510s  BetaDistribution(Inf, 1)
510s ***** error <BetaDistribution: A must be a positive real scalar.> ...
510s  BetaDistribution(i, 1)
510s ***** error <BetaDistribution: A must be a positive real scalar.> ...
510s  BetaDistribution("a", 1)
510s ***** error <BetaDistribution: A must be a positive real scalar.> ...
510s  BetaDistribution([1, 2], 1)
511s ***** error <BetaDistribution: A must be a positive real scalar.> ...
511s  BetaDistribution(NaN, 1)
511s ***** error <BetaDistribution: B must be a positive real scalar.> ...
511s  BetaDistribution(1, 0)
511s ***** error <BetaDistribution: B must be a positive real scalar.> ...
511s  BetaDistribution(1, -1)
511s ***** error <BetaDistribution: B must be a positive real scalar.> ...
511s  BetaDistribution(1, Inf)
511s ***** error <BetaDistribution: B must be a positive real scalar.> ...
511s  BetaDistribution(1, i)
511s ***** error <BetaDistribution: B must be a positive real scalar.> ...
511s  BetaDistribution(1, "a")
511s ***** error <BetaDistribution: B must be a positive real scalar.> ...
511s  BetaDistribution(1, [1, 2])
511s ***** error <BetaDistribution: B must be a positive real scalar.> ...
511s  BetaDistribution(1, NaN)
511s ***** error <cdf: invalid argument for upper tail.> ...
511s  cdf (BetaDistribution, 2, "uper")
511s ***** error <cdf: invalid argument for upper tail.> ...
511s  cdf (BetaDistribution, 2, 3)
511s ***** shared x
511s  randg ("seed", 1);
511s  x = betarnd (1, 1, [100, 1]);
511s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
511s  paramci (BetaDistribution.fit (x), "alpha")
511s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
511s  paramci (BetaDistribution.fit (x), "alpha", 0)
511s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
511s  paramci (BetaDistribution.fit (x), "alpha", 1)
511s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
511s  paramci (BetaDistribution.fit (x), "alpha", [0.5 2])
511s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
511s  paramci (BetaDistribution.fit (x), "alpha", "")
511s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
511s  paramci (BetaDistribution.fit (x), "alpha", {0.05})
511s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
511s  paramci (BetaDistribution.fit (x), "parameter", "a", "alpha", {0.05})
511s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
511s  paramci (BetaDistribution.fit (x), "parameter", {"a", "b", "param"})
511s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
511s  paramci (BetaDistribution.fit (x), "alpha", 0.01, ...
511s           "parameter", {"a", "b", "param"})
511s ***** error <paramci: unknown distribution parameter.> ...
511s  paramci (BetaDistribution.fit (x), "parameter", "param")
511s ***** error <paramci: unknown distribution parameter.> ...
511s  paramci (BetaDistribution.fit (x), "alpha", 0.01, "parameter", "param")
511s ***** error <paramci: invalid NAME for optional argument.> ...
511s  paramci (BetaDistribution.fit (x), "NAME", "value")
511s ***** error <paramci: invalid NAME for optional argument.> ...
511s  paramci (BetaDistribution.fit (x), "alpha", 0.01, "NAME", "value")
511s ***** error <paramci: invalid NAME for optional argument.> ...
511s  paramci (BetaDistribution.fit (x), "alpha", 0.01, "parameter", "a", ...
511s           "NAME", "value")
511s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
511s  plot (BetaDistribution, "Parent")
511s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
511s  plot (BetaDistribution, "PlotType", 12)
511s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
511s  plot (BetaDistribution, "PlotType", {"pdf", "cdf"})
511s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
511s  plot (BetaDistribution, "PlotType", "pdfcdf")
511s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
511s  plot (BetaDistribution, "Discrete", "pdfcdf")
511s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
511s  plot (BetaDistribution, "Discrete", [1, 0])
511s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
511s  plot (BetaDistribution, "Discrete", {true})
511s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
511s  plot (BetaDistribution, "Parent", 12)
511s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
511s  plot (BetaDistribution, "Parent", "hax")
511s ***** error <plot: invalid NAME for optional argument.> ...
511s  plot (BetaDistribution, "invalidNAME", "pdf")
511s ***** error <plot: no fitted DATA to plot a probability plot.> ...
511s  plot (BetaDistribution, "PlotType", "probability")
511s ***** error <proflik: no fitted data available.> ...
511s  proflik (BetaDistribution, 2)
511s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
511s  proflik (BetaDistribution.fit (x), 3)
511s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
511s  proflik (BetaDistribution.fit (x), [1, 2])
511s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
511s  proflik (BetaDistribution.fit (x), {1})
511s ***** error <proflik: SETPARAM must be a numeric vector.> ...
511s  proflik (BetaDistribution.fit (x), 1, ones (2))
511s ***** error <proflik: missing VALUE for 'Display' argument.> ...
511s  proflik (BetaDistribution.fit (x), 1, "Display")
511s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
511s  proflik (BetaDistribution.fit (x), 1, "Display", 1)
511s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
511s  proflik (BetaDistribution.fit (x), 1, "Display", {1})
511s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
511s  proflik (BetaDistribution.fit (x), 1, "Display", {"on"})
511s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
511s  proflik (BetaDistribution.fit (x), 1, "Display", ["on"; "on"])
511s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
511s  proflik (BetaDistribution.fit (x), 1, "Display", "onnn")
511s ***** error <proflik: invalid NAME for optional arguments.> ...
511s  proflik (BetaDistribution.fit (x), 1, "NAME", "on")
511s ***** error <proflik: invalid optional argument.> ...
511s  proflik (BetaDistribution.fit (x), 1, {"NAME"}, "on")
511s ***** error <proflik: invalid optional argument.> ...
511s  proflik (BetaDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
511s ***** error <truncate: missing input argument.> ...
511s  truncate (BetaDistribution)
511s ***** error <truncate: missing input argument.> ...
511s  truncate (BetaDistribution, 2)
511s ***** error <truncate: invalid lower upper limits.> ...
511s  truncate (BetaDistribution, 4, 2)
511s ***** shared pd
511s  pd = BetaDistribution(1, 1);
511s  pd(2) = BetaDistribution(1, 3);
511s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
511s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
511s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
511s ***** error <mean: requires a scalar probability distribution.> mean (pd)
511s ***** error <median: requires a scalar probability distribution.> median (pd)
511s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
511s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
511s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
511s ***** error <plot: requires a scalar probability distribution.> plot (pd)
511s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
511s ***** error <random: requires a scalar probability distribution.> random (pd)
511s ***** error <std: requires a scalar probability distribution.> std (pd)
511s ***** error <truncate: requires a scalar probability distribution.> ...
511s  truncate (pd, 2, 4)
511s ***** error <var: requires a scalar probability distribution.> var (pd)
511s 96 tests, 96 passed, 0 known failure, 0 skipped
511s [inst/einstein.m]
511s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/einstein.m
511s ***** demo
511s  einstein (0.4, 0.6)
511s ***** demo
511s  einstein (0.2, 0.5)
511s ***** demo
511s  einstein (0.6, 0.1)
511s ***** test
511s  hf = figure ("visible", "off");
511s  unwind_protect
511s    tiles = einstein (0.4, 0.6);
511s    assert (isstruct (tiles), true);
511s  unwind_protect_cleanup
511s    close (hf);
511s  end_unwind_protect
511s ***** error<Invalid call to einstein.  Correct usage is> einstein
511s ***** error<Invalid call to einstein.  Correct usage is> einstein (0.5)
511s ***** error<einstein: A and B must be within the open interval> einstein (0, 0.9)
511s ***** error<einstein: A and B must be within the open interval> einstein (0.4, 1)
511s ***** error<einstein: A and B must be within the open interval> einstein (-0.4, 1)
511s 6 tests, 6 passed, 0 known failure, 0 skipped
511s [inst/geomean.m]
511s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/geomean.m
511s ***** test
511s  x = [0:10];
511s  y = [x;x+5;x+10];
511s  assert (geomean (x), 0);
511s  m = [0 9.462942809849169 14.65658770861967];
511s  assert (geomean (y, 2), m', 4e-14);
511s  assert (geomean (y, "all"), 0);
511s  y(2,4) = NaN;
511s  m(2) = 9.623207231679554;
511s  assert (geomean (y, 2), [0 NaN m(3)]', 4e-14);
511s  assert (geomean (y', "omitnan"), m, 4e-14);
511s  z = y + 20;
511s  assert (geomean (z, "all"), NaN);
511s  assert (geomean (z, "all", "includenan"), NaN);
511s  assert (geomean (z, "all", "omitnan"), 29.59298474535024, 4e-14);
511s  m = [24.79790781765634 NaN 34.85638839503932];
511s  assert (geomean (z'), m, 4e-14);
511s  assert (geomean (z', "includenan"), m, 4e-14);
511s  m(2) = 30.02181156156319;
511s  assert (geomean (z', "omitnan"), m, 4e-14);
511s  assert (geomean (z, 2, "omitnan"), m', 4e-14);
511s ***** test
511s  x = repmat ([1:20;6:25], [5 2 6 3]);
511s  assert (size (geomean (x, [3 2])), [10 1 1 3]);
511s  assert (size (geomean (x, [1 2])), [1 1 6 3]);
511s  assert (size (geomean (x, [1 2 4])), [1 1 6]);
511s  assert (size (geomean (x, [1 4 3])), [1 40]);
511s  assert (size (geomean (x, [1 2 3 4])), [1 1]);
511s ***** test
511s  x = repmat ([1:20;6:25], [5 2 6 3]);
511s  m = repmat ([8.304361203739333;14.3078118884256], [5 1 1 3]);
511s  assert (geomean (x, [3 2]), m, 4e-13);
511s  x(2,5,6,3) = NaN;
511s  m(2,3) = NaN;
511s  assert (geomean (x, [3 2]), m, 4e-13);
511s  m(2,3) = 14.3292729579901;
511s  assert (geomean (x, [3 2], "omitnan"), m, 4e-13);
511s ***** error <geomean: X must contain real nonnegative values.> geomean ("char")
511s ***** error <geomean: X must contain real nonnegative values.> geomean ([1 -1 3])
511s ***** error <geomean: DIM must be a positive integer scalar or vector.> ...
511s  geomean (repmat ([1:20;6:25], [5 2 6 3 5]), -1)
511s ***** error <geomean: DIM must be a positive integer scalar or vector.> ...
511s  geomean (repmat ([1:20;6:25], [5 2 6 3 5]), 0)
511s ***** error <geomean: VECDIM must contain non-repeating positive integers.> ...
511s  geomean (repmat ([1:20;6:25], [5 2 6 3 5]), [1 1])
511s 8 tests, 8 passed, 0 known failure, 0 skipped
511s [inst/regress_gp.m]
511s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/regress_gp.m
511s ***** demo
511s  ## Linear fitting of 1D Data
511s  rand ("seed", 125);
511s  X = 2 * rand (5, 1) - 1;
511s  randn ("seed", 25);
511s  Y = 2 * X - 1 + 0.3 * randn (5, 1);
511s 
511s  ## Points for interpolation/extrapolation
511s  Xfit = linspace (-2, 2, 10)';
511s 
511s  ## Fit regression model
511s  [Yfit, Yint, m] = regress_gp (X, Y, Xfit);
511s 
511s  ## Plot fitted data
511s  plot (X, Y, "xk", Xfit, Yfit, "r-", Xfit, Yint, "b-");
511s  title ("Gaussian process regression with linear kernel");
511s ***** demo
511s  ## Linear fitting of 2D Data
511s  rand ("seed", 135);
511s  X = 2 * rand (4, 2) - 1;
511s  randn ("seed", 35);
511s  Y = 2 * X(:,1) - 3 * X(:,2) - 1 + 1 * randn (4, 1);
511s 
511s  ## Mesh for interpolation/extrapolation
511s  [x1, x2] = meshgrid (linspace (-1, 1, 10));
511s  Xfit = [x1(:), x2(:)];
511s 
511s  ## Fit regression model
511s  [Ypred, Yint, Ysd] = regress_gp (X, Y, Xfit);
511s  Ypred = reshape (Ypred, 10, 10);
511s  YintU = reshape (Yint(:,1), 10, 10);
511s  YintL = reshape (Yint(:,2), 10, 10);
511s 
511s  ## Plot fitted data
511s  plot3 (X(:,1), X(:,2), Y, ".k", "markersize", 16);
511s  hold on;
511s  h = mesh (x1, x2, Ypred, zeros (10, 10));
511s  set (h, "facecolor", "none", "edgecolor", "yellow");
511s  h = mesh (x1, x2, YintU, ones (10, 10));
511s  set (h, "facecolor", "none", "edgecolor", "cyan");
511s  h = mesh (x1, x2, YintL, ones (10, 10));
511s  set (h, "facecolor", "none", "edgecolor", "cyan");
511s  hold off
511s  axis tight
511s  view (75, 25)
511s  title ("Gaussian process regression with linear kernel");
511s ***** demo
511s  ## Projection over basis function with linear kernel
511s  pp = [2, 2, 0.3, 1];
511s  n = 10;
511s  rand ("seed", 145);
511s  X = 2 * rand (n, 1) - 1;
511s  randn ("seed", 45);
511s  Y = polyval (pp, X) + 0.3 * randn (n, 1);
511s 
511s  ## Powers
511s  px = [sqrt(abs(X)), X, X.^2, X.^3];
511s 
511s  ## Points for interpolation/extrapolation
511s  Xfit = linspace (-1, 1, 100)';
511s  pxi = [sqrt(abs(Xfit)), Xfit, Xfit.^2, Xfit.^3];
511s 
511s  ## Define a prior covariance assuming that the sqrt component is not present
511s  Sp = 100 * eye (size (px, 2) + 1);
511s  Sp(2,2) = 1; # We don't believe the sqrt(abs(X)) is present
511s 
511s  ## Fit regression model
511s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, Sp);
511s 
511s  ## Plot fitted data
511s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
511s                          Xfit, polyval (pp, Xfit), "g-;True;");
511s  axis tight
511s  axis manual
511s  hold on
511s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
511s  hold off
511s  title ("Linear kernel over basis function with prior covariance");
511s ***** demo
511s  ## Projection over basis function with linear kernel
511s  pp = [2, 2, 0.3, 1];
511s  n = 10;
511s  rand ("seed", 145);
511s  X = 2 * rand (n, 1) - 1;
511s  randn ("seed", 45);
511s  Y = polyval (pp, X) + 0.3 * randn (n, 1);
511s 
511s  ## Powers
511s  px = [sqrt(abs(X)), X, X.^2, X.^3];
511s 
511s  ## Points for interpolation/extrapolation
511s  Xfit = linspace (-1, 1, 100)';
511s  pxi = [sqrt(abs(Xfit)), Xfit, Xfit.^2, Xfit.^3];
511s 
511s  ## Fit regression model without any assumption on prior covariance
511s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi);
511s 
511s  ## Plot fitted data
511s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
511s                          Xfit, polyval (pp, Xfit), "g-;True;");
511s  axis tight
511s  axis manual
511s  hold on
511s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
511s  hold off
511s  title ("Linear kernel over basis function without prior covariance");
511s ***** demo
511s  ## Projection over basis function with rbf kernel
511s  pp = [2, 2, 0.3, 1];
511s  n = 10;
511s  rand ("seed", 145);
511s  X = 2 * rand (n, 1) - 1;
511s  randn ("seed", 45);
511s  Y = polyval (pp, X) + 0.3 * randn (n, 1);
511s 
511s  ## Powers
511s  px = [sqrt(abs(X)), X, X.^2, X.^3];
511s 
511s  ## Points for interpolation/extrapolation
511s  Xfit = linspace (-1, 1, 100)';
511s  pxi = [sqrt(abs(Xfit)), Xfit, Xfit.^2, Xfit.^3];
511s 
511s  ## Fit regression model with RBF kernel (standard parameters)
511s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf");
511s 
511s  ## Plot fitted data
511s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
511s                          Xfit, polyval (pp, Xfit), "g-;True;");
511s  axis tight
511s  axis manual
511s  hold on
511s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
511s  hold off
511s  title ("RBF kernel over basis function with standard parameters");
511s  text (-0.5, 4, "theta = 5\n g = 0.01");
511s ***** demo
511s  ## Projection over basis function with rbf kernel
511s  pp = [2, 2, 0.3, 1];
511s  n = 10;
511s  rand ("seed", 145);
511s  X = 2 * rand (n, 1) - 1;
511s  randn ("seed", 45);
511s  Y = polyval (pp, X) + 0.3 * randn (n, 1);
511s 
511s  ## Powers
511s  px = [sqrt(abs(X)), X, X.^2, X.^3];
511s 
511s  ## Points for interpolation/extrapolation
511s  Xfit = linspace (-1, 1, 100)';
511s  pxi = [sqrt(abs(Xfit)), Xfit, Xfit.^2, Xfit.^3];
511s 
511s  ## Fit regression model with RBF kernel with different parameters
511s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf", 10, 0.01);
511s 
511s  ## Plot fitted data
511s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
511s                          Xfit, polyval (pp, Xfit), "g-;True;");
511s  axis tight
511s  axis manual
511s  hold on
511s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
511s  hold off
511s  title ("GP regression with RBF kernel and non default parameters");
511s  text (-0.5, 4, "theta = 10\n g = 0.01");
511s 
511s  ## Fit regression model with RBF kernel with different parameters
511s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf", 50, 0.01);
511s 
511s  ## Plot fitted data
511s  figure
511s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
511s                          Xfit, polyval (pp, Xfit), "g-;True;");
511s  axis tight
511s  axis manual
511s  hold on
511s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
511s  hold off
511s  title ("GP regression with RBF kernel and non default parameters");
511s  text (-0.5, 4, "theta = 50\n g = 0.01");
511s 
511s  ## Fit regression model with RBF kernel with different parameters
511s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf", 50, 0.001);
511s 
511s  ## Plot fitted data
511s  figure
511s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
511s                          Xfit, polyval (pp, Xfit), "g-;True;");
511s  axis tight
511s  axis manual
511s  hold on
511s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
511s  hold off
511s  title ("GP regression with RBF kernel and non default parameters");
511s  text (-0.5, 4, "theta = 50\n g = 0.001");
511s 
511s  ## Fit regression model with RBF kernel with different parameters
511s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf", 50, 0.05);
511s 
511s  ## Plot fitted data
511s  figure
511s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
511s                          Xfit, polyval (pp, Xfit), "g-;True;");
511s  axis tight
511s  axis manual
511s  hold on
511s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
511s  hold off
511s  title ("GP regression with RBF kernel and non default parameters");
511s  text (-0.5, 4, "theta = 50\n g = 0.05");
511s ***** demo
511s  ## RBF fitting on noiseless 1D Data
511s  x = [0:2*pi/7:2*pi]';
511s  y = 5 * sin (x);
511s 
511s  ## Predictive grid of 500 equally spaced locations
511s  xi = [-0.5:(2*pi+1)/499:2*pi+0.5]';
511s 
511s  ## Fit regression model with RBF kernel
511s  [Yfit, Yint, Ysd] = regress_gp (x, y, xi, "rbf");
511s 
511s  ## Plot fitted data
511s  r = mvnrnd (Yfit, diag (Ysd)', 50);
511s  plot (xi, r', "c-");
511s  hold on
511s  plot (xi, Yfit, "r-;Estimation;", xi, Yint, "b-;Confidence interval;");
511s  plot (x, y, ".k;Predictor points;", "markersize", 20)
511s  plot (xi, 5 * sin (xi), "-y;True Function;");
511s  xlim ([-0.5,2*pi+0.5]);
511s  ylim ([-10,10]);
511s  hold off
511s  title ("GP regression with RBF kernel on noiseless 1D data");
511s  text (0, -7, "theta = 5\n g = 0.01");
511s ***** demo
511s  ## RBF fitting on noisy 1D Data
511s  x = [0:2*pi/7:2*pi]';
511s  x = [x; x];
511s  y = 5 * sin (x) + randn (size (x));
511s 
511s  ## Predictive grid of 500 equally spaced locations
511s  xi = [-0.5:(2*pi+1)/499:2*pi+0.5]';
511s 
511s  ## Fit regression model with RBF kernel
511s  [Yfit, Yint, Ysd] = regress_gp (x, y, xi, "rbf");
511s 
511s  ## Plot fitted data
511s  r = mvnrnd (Yfit, diag (Ysd)', 50);
511s  plot (xi, r', "c-");
511s  hold on
511s  plot (xi, Yfit, "r-;Estimation;", xi, Yint, "b-;Confidence interval;");
511s  plot (x, y, ".k;Predictor points;", "markersize", 20)
511s  plot (xi, 5 * sin (xi), "-y;True Function;");
511s  xlim ([-0.5,2*pi+0.5]);
511s  ylim ([-10,10]);
511s  hold off
511s  title ("GP regression with RBF kernel on noisy 1D data");
511s  text (0, -7, "theta = 5\n g = 0.01");
511s ***** error<Invalid call to regress_gp.> regress_gp (ones (20, 2))
511s ***** error<Invalid call to regress_gp.> regress_gp (ones (20, 2), ones (20, 1))
512s ***** error<regress_gp: X must be a 2-D matrix.> ...
512s  regress_gp (ones (20, 2, 3), ones (20, 1), ones (20, 2))
512s ***** error<regress_gp: Y must be a column vector.> ...
512s  regress_gp (ones (20, 2), ones (20, 2), ones (20, 2))
512s ***** error<regress_gp: rows in X must equal the length of Y.> ...
512s  regress_gp (ones (20, 2), ones (15, 1), ones (20, 2))
512s ***** error<regress_gp: X and XI must have the same number of columns.> ...
512s  regress_gp (ones (20, 2), ones (20, 1), ones (20, 3))
512s ***** error<regress_gp: invalid 4th argument.> ...
512s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), {[3]})
512s ***** error<regress_gp: invalid 4th argument.> ...
512s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "kernel")
512s ***** error<regress_gp: theta must be a scalar when using RBF kernel.> ...
512s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", ones (4))
512s ***** error<regress_gp: wrong size for prior covariance matrix Sp.> ...
512s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "linear", 1)
512s ***** error<regress_gp: invalid 5th argument.> ...
512s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", "value")
512s ***** error<regress_gp: invalid 5th argument.> ...
512s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", {5})
512s ***** error<regress_gp: invalid 5th argument.> ...
512s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), ones (3), 5)
512s ***** error<regress_gp: wrong size for prior covariance matrix Sp.> ...
512s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "linear", 5)
512s ***** error<regress_gp: invalid 6th argument.> ...
512s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", 5, {5})
512s ***** error<regress_gp: invalid 6th argument.> ...
512s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", 5, ones (2))
512s ***** error<regress_gp: invalid 6th argument.> ...
512s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), 5, 0.01, [1, 1])
512s ***** error<regress_gp: invalid 6th argument.> ...
512s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), 5, 0.01, "f")
512s ***** error<regress_gp: invalid 6th argument.> ...
512s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), 5, 0.01, "f")
512s ***** error<regress_gp: invalid 7th argument.> ...
512s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", 5, 0.01, "f")
512s ***** error<regress_gp: invalid 7th argument.> ...
512s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", 5, 0.01, [1, 1])
512s ***** error<regress_gp: wrong size for prior covariance matrix Sp.> ...
512s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "linear", 1)
512s 22 tests, 22 passed, 0 known failure, 0 skipped
512s [inst/ecdf.m]
512s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/ecdf.m
512s ***** demo
512s  y = exprnd (10, 50, 1);    ## random failure times are exponential(10)
512s  d = exprnd (20, 50, 1);    ## drop-out times are exponential(20)
512s  t = min (y, d);            ## we observe the minimum of these times
512s  censored = (y > d);        ## we also observe whether the subject failed
512s 
512s  ## Calculate and plot the empirical cdf and confidence bounds
512s  [f, x, flo, fup] = ecdf (t, "censoring", censored);
512s  stairs (x, f);
512s  hold on;
512s  stairs (x, flo, "r:"); stairs (x, fup, "r:");
512s 
512s  ## Superimpose a plot of the known true cdf
512s  xx = 0:.1:max (t); yy = 1 - exp (-xx / 10); plot (xx, yy, "g-");
512s  hold off;
512s ***** demo
512s  R = wblrnd (100, 2, 100, 1);
512s  ecdf (R, "Function", "survivor", "Alpha", 0.01, "Bounds", "on");
512s  hold on
512s  x = 1:1:250;
512s  wblsurv = 1 - cdf ("weibull", x, 100, 2);
512s  plot (x, wblsurv, "g-", "LineWidth", 2)
512s  legend ("Empirical survivor function", "Lower confidence bound", ...
512s          "Upper confidence bound", "Weibull survivor function", ...
512s          "Location", "northeast");
512s  hold off
512s ***** error ecdf ();
512s ***** error ecdf (randi (15,2));
512s ***** error ecdf ([3,2,4,3+2i,5]);
512s ***** error kstest ([2,3,4,5,6],"tail");
512s ***** error kstest ([2,3,4,5,6],"tail", "whatever");
512s ***** error kstest ([2,3,4,5,6],"function", "");
512s ***** error kstest ([2,3,4,5,6],"badoption", 0.51);
512s ***** error kstest ([2,3,4,5,6],"tail", 0);
512s ***** error kstest ([2,3,4,5,6],"alpha", 0);
512s ***** error kstest ([2,3,4,5,6],"alpha", NaN);
512s ***** error kstest ([NaN,NaN,NaN,NaN,NaN],"tail", "unequal");
512s ***** error kstest ([2,3,4,5,6],"alpha", 0.05, "CDF", [2,3,4;1,3,4;1,2,1]);
512s ***** test
512s  hf = figure ("visible", "off");
512s  unwind_protect
512s    x = [2, 3, 4, 3, 5, 4, 6, 5, 8, 3, 7, 8, 9, 0];
512s    [F, x, Flo, Fup] = ecdf (x);
512s    F_out = [0; 0.0714; 0.1429; 0.3571; 0.5; 0.6429; 0.7143; 0.7857; 0.9286; 1];
512s    assert (F, F_out, ones (10,1) * 1e-4);
512s    x_out = [0 0 2 3 4 5 6 7 8 9]';
512s    assert (x, x_out);
512s    Flo_out = [NaN, 0, 0, 0.1061, 0.2381, 0.3919, 0.4776, 0.5708, 0.7937, NaN]';
512s    assert (Flo, Flo_out, ones (10,1) * 1e-4);
512s    Fup_out = [NaN, 0.2063, 0.3262, 0.6081, 0.7619, 0.8939, 0.9509, 1, 1, NaN]';
512s    assert (Fup, Fup_out, ones (10,1) * 1e-4);
512s  unwind_protect_cleanup
512s    close (hf);
512s  end_unwind_protect
512s ***** test
512s  hf = figure ("visible", "off");
512s  unwind_protect
512s    x = [2, 3, 4, 3, 5, 4, 6, 5, 8, 3, 7, 8, 9, 0];
512s    ecdf (x);
512s  unwind_protect_cleanup
512s    close (hf);
512s  end_unwind_protect
512s 14 tests, 14 passed, 0 known failure, 0 skipped
512s [inst/trimmean.m]
512s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/trimmean.m
512s ***** test
512s  x = reshape (1:40, [5, 4, 2]);
512s  x([3, 37]) = -100;
512s  assert (trimmean (x, 10, "all"), 19.4722, 1e-4);
512s ***** test
512s  x = reshape (1:40, [5, 4, 2]);
512s  x([3, 37]) = -100;
512s  out = trimmean (x, 10, [1, 2]);
512s  assert (out(1,1,1), 10.3889, 1e-4);
512s  assert (out(1,1,2), 29.6111, 1e-4);
512s ***** test
512s  x = reshape (1:40, [5, 4, 2]);
512s  x([3, 37]) = -100;
512s  x([4, 38]) = NaN;
512s  assert (trimmean (x, 10, "all"), 19.3824, 1e-4);
512s ***** test
512s  x = reshape (1:40, [5, 4, 2]);
512s  x([3, 37]) = -100;
512s  out = trimmean (x, 10, 1);
512s  assert (out(:,:,1), [-17.6, 8, 13, 18]);
512s  assert (out(:,:,2), [23, 28, 33, 10.6]);
512s ***** test
512s  x = reshape (1:40, [5, 4, 2]);
512s  x([3, 37]) = -100;
512s  x([4, 38]) = NaN;
512s  out = trimmean (x, 10, 1);
512s  assert (out(:,:,1), [-23, 8, 13, 18]);
512s  assert (out(:,:,2), [23, 28, 33, 3.75]);
512s ***** test
512s  x = reshape (1:40, [5, 4, 2]);
512s  x([3, 37]) = -100;
512s  out = trimmean (x, 10, 2);
512s  assert (out(:,:,1), [8.5; 9.5; -15.25; 11.5; 12.5]);
512s  assert (out(:,:,2), [28.5; -4.75; 30.5; 31.5; 32.5]);
512s ***** test
512s  x = reshape (1:40, [5, 4, 2]);
512s  x([3, 37]) = -100;
512s  x([4, 38]) = NaN;
512s  out = trimmean (x, 10, 2);
512s  assert (out(:,:,1), [8.5; 9.5; -15.25; 14; 12.5]);
512s  assert (out(:,:,2), [28.5; -4.75; 28; 31.5; 32.5]);
512s ***** test
512s  x = reshape (1:40, [5, 4, 2]);
512s  x([3, 37]) = -100;
512s  out = trimmean (x, 10, [1, 2, 3]);
512s  assert (out, trimmean (x, 10, "all"));
512s ***** test
512s  x = reshape (1:40, [5, 4, 2]);
512s  x([3, 37]) = -100;
512s  x([4, 38]) = NaN;
512s  out = trimmean (x, 10, [1, 2]);
512s  assert (out(1,1,1), 10.7647, 1e-4);
512s  assert (out(1,1,2), 29.1176, 1e-4);
512s ***** test
512s  x = reshape (1:40, [5, 4, 2]);
512s  x([3, 37]) = -100;
512s  x([4, 38]) = NaN;
512s  out = trimmean (x, 10, [1, 3]);
512s  assert (out, [2.5556, 18, 23, 11.6667], 1e-4);
512s ***** test
512s  x = reshape (1:40, [5, 4, 2]);
512s  x([3, 37]) = -100;
512s  x([4, 38]) = NaN;
512s  out = trimmean (x, 10, [2, 3]);
512s  assert (out, [18.5; 2.3750; 3.2857; 24; 22.5], 1e-4);
512s ***** test
512s  x = reshape (1:40, [5, 4, 2]);
512s  x([3, 37]) = -100;
512s  x([4, 38]) = NaN;
512s  out = trimmean (x, 10, [1, 2, 3]);
512s  assert (out, trimmean (x, 10, "all"));
512s ***** test
512s  x = reshape (1:40, [5, 4, 2]);
512s  x([3, 37]) = -100;
512s  x([4, 38]) = NaN;
512s  out = trimmean (x, 10, [2, 3, 5]);
512s  assert (out, [18.5; 2.3750; 3.2857; 24; 22.5], 1e-4);
512s ***** assert (trimmean (reshape (1:40, [5, 4, 2]), 10, 4), reshape(1:40, [5, 4, 2]))
512s ***** assert (trimmean ([], 10), NaN)
512s ***** assert (trimmean ([1;2;3;4;5], 10, 2), [1;2;3;4;5])
512s ***** error<Invalid call to trimmean.  Correct usage is:> trimmean (1)
512s ***** error<Invalid call to trimmean.  Correct usage is:> trimmean (1,2,3,4,5)
512s ***** error<trimmean: invalid percent.> trimmean ([1 2 3 4], -10)
512s ***** error<trimmean: invalid percent.> trimmean ([1 2 3 4], 100)
512s ***** error<trimmean: invalid FLAG argument.> trimmean ([1 2 3 4], 10, "flag")
512s ***** error<trimmean: invalid FLAG argument.> trimmean ([1 2 3 4], 10, "flag", 1)
512s ***** error<trimmean: DIM must be a positive integer scalar or vector.> ...
512s  trimmean ([1 2 3 4], 10, -1)
512s ***** error<trimmean: DIM must be a positive integer scalar or vector.> ...
512s  trimmean ([1 2 3 4], 10, "floor", -1)
512s ***** error<trimmean: DIM must be a positive integer scalar or vector.> ...
512s  trimmean (reshape (1:40, [5, 4, 2]), 10, [-1, 2])
512s ***** error<trimmean: VECDIM must contain non-repeating positive integers.> ...
512s  trimmean (reshape (1:40, [5, 4, 2]), 10, [1, 2, 2])
512s 26 tests, 26 passed, 0 known failure, 0 skipped
512s [inst/gmdistribution.m]
512s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/gmdistribution.m
512s ***** test
512s  mu = eye(2);
512s  Sigma = eye(2);
512s  GM = gmdistribution (mu, Sigma);
512s  density = GM.pdf ([0 0; 1 1]);
512s  assert (density(1) - density(2), 0, 1e-6);
512s 
512s  [idx, nlogl, P, logpdf,M] = cluster (GM, eye(2));
512s  assert (idx, [1; 2]);
512s  [idx2,nlogl2,P2,logpdf2] = GM.cluster (eye(2));
512s  assert (nlogl - nlogl2, 0, 1e-6);
512s  [idx3,nlogl3,P3] = cluster (GM, eye(2));
512s  assert (P - P3, zeros (2), 1e-6);
512s  [idx4,nlogl4] = cluster (GM, eye(2));
512s  assert (size (nlogl4), [1 1]);
512s  idx5 = cluster (GM, eye(2));
512s  assert (idx - idx5, zeros (2,1));
512s 
512s  D = GM.mahal ([1;0]);
512s  assert (D - M(1,:), zeros (1,2), 1e-6);
512s 
512s  P = GM.posterior ([0 1]);
512s  assert (P - P2(2,:), zeros (1,2), 1e-6);
512s 
512s  R = GM.random(20);
512s  assert (size(R), [20, 2]);
512s 
512s  R = GM.random();
512s  assert (size(R), [1, 2]);
512s 1 test, 1 passed, 0 known failure, 0 skipped
512s [inst/hmmestimate.m]
512s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/hmmestimate.m
512s ***** test
512s  sequence = [1, 2, 1, 1, 1, 2, 2, 1, 2, 3, 3, ...
512s              3, 3, 2, 3, 1, 1, 1, 1, 3, 3, 2, 3, 1, 3];
512s  states =   [1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, ...
512s              1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1];
512s  [transprobest, outprobest] = hmmestimate (sequence, states);
512s  expectedtransprob = [0.88889, 0.11111; 0.28571, 0.71429];
512s  expectedoutprob = [0.16667, 0.33333, 0.50000; 1.00000, 0.00000, 0.00000];
512s  assert (transprobest, expectedtransprob, 0.001);
512s  assert (outprobest, expectedoutprob, 0.001);
512s ***** test
512s  sequence = {"A", "B", "A", "A", "A", "B", "B", "A", "B", "C", "C", "C", ...
512s              "C", "B", "C", "A", "A", "A", "A", "C", "C", "B", "C", "A", "C"};
512s  states = {"One", "One", "Two", "Two", "Two", "One", "One", "One", "One", ...
512s            "One", "One", "One", "One", "One", "One", "Two", "Two", "Two", ...
512s            "Two", "One", "One", "One", "One", "One", "One"};
512s  symbols = {"A", "B", "C"};
512s  statenames = {"One", "Two"};
512s  [transprobest, outprobest] = hmmestimate (sequence, states, "symbols", ...
512s                                            symbols, "statenames", statenames);
512s  expectedtransprob = [0.88889, 0.11111; 0.28571, 0.71429];
512s  expectedoutprob = [0.16667, 0.33333, 0.50000; 1.00000, 0.00000, 0.00000];
512s  assert (transprobest, expectedtransprob, 0.001);
512s  assert (outprobest, expectedoutprob, 0.001);
512s ***** test
512s  sequence = [1, 2, 1, 1, 1, 2, 2, 1, 2, 3, 3, 3, ...
512s              3, 2, 3, 1, 1, 1, 1, 3, 3, 2, 3, 1, 3];
512s  states =   [1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, ...
512s              1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1];
512s  pseudotransitions = [8, 2; 4, 6];
512s  pseudoemissions = [2, 4, 4; 7, 2, 1];
512s  [transprobest, outprobest] = hmmestimate (sequence, states, ...
512s   "pseudotransitions", pseudotransitions, "pseudoemissions", pseudoemissions);
512s  expectedtransprob = [0.85714, 0.14286; 0.35294, 0.64706];
512s  expectedoutprob = [0.178571, 0.357143, 0.464286; ...
512s                     0.823529, 0.117647, 0.058824];
512s  assert (transprobest, expectedtransprob, 0.001);
512s  assert (outprobest, expectedoutprob, 0.001);
512s 3 tests, 3 passed, 0 known failure, 0 skipped
512s [inst/kstest2.m]
512s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/kstest2.m
512s ***** error kstest2 ([1,2,3,4,5,5])
512s ***** error kstest2 (ones(2,4), [1,2,3,4,5,5])
512s ***** error kstest2 ([2,3,5,7,3+3i], [1,2,3,4,5,5])
512s ***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"tail")
512s ***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"tail", "whatever")
512s ***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"badoption", 0.51)
512s ***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"tail", 0)
512s ***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"alpha", 0)
512s ***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"alpha", NaN)
512s ***** error kstest2 ([NaN,NaN,NaN,NaN,NaN],[3;5;7;8;7;6;5],"tail", "unequal")
512s ***** test
512s  load examgrades
512s  [h, p] = kstest2 (grades(:,1), grades(:,2));
512s  assert (h, false);
512s  assert (p, 0.1222791870137312, 1e-14);
512s ***** test
512s  load examgrades
512s  [h, p] = kstest2 (grades(:,1), grades(:,2), "tail", "larger");
512s  assert (h, false);
512s  assert (p, 0.1844421391011258, 1e-14);
512s ***** test
512s  load examgrades
512s  [h, p] = kstest2 (grades(:,1), grades(:,2), "tail", "smaller");
512s  assert (h, false);
512s  assert (p, 0.06115357930171663, 1e-14);
512s ***** test
512s  load examgrades
512s  [h, p] = kstest2 (grades(:,1), grades(:,2), "tail", "smaller", "alpha", 0.1);
512s  assert (h, true);
512s  assert (p, 0.06115357930171663, 1e-14);
512s 14 tests, 14 passed, 0 known failure, 0 skipped
512s [inst/shadow9/var.m]
512s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/shadow9/var.m
512s ***** assert (var (13), 0)
512s ***** assert (var (single (13)), single (0))
512s ***** assert (var ([1,2,3]), 1)
512s ***** assert (var ([1,2,3], 1), 2/3, eps)
512s ***** assert (var ([1,2,3], [], 1), [0,0,0])
512s ***** assert (var ([1,2,3], [], 3), [0,0,0])
512s ***** assert (var (5, 99), 0)
512s ***** assert (var (5, 99, 1), 0)
512s ***** assert (var (5, 99, 2), 0)
512s ***** assert (var ([5 3], [99 99], 2), 1)
512s ***** assert (var ([1:7], [1:7]), 3)
512s ***** assert (var ([eye(3)], [1:3]), [5/36, 2/9, 1/4], eps)
512s ***** assert (var (ones (2,2,2), [1:2], 3), [(zeros (2,2))])
512s ***** assert (var ([1 2; 3 4], 0, 'all'), var ([1:4]))
512s ***** assert (var (reshape ([1:8], 2, 2, 2), 0, [1 3]), [17/3 17/3], eps)
512s ***** assert (var ([1 2 3;1 2 3], [], [1 2]), 0.8, eps)
512s ***** test
512s  x = [-10:10];
512s  y = [x;x+5;x-5];
512s  assert (var (x), 38.5);
512s  assert (var (y, [], 2), [38.5; 38.5; 38.5]);
512s  assert (var (y, 0, 2), [38.5; 38.5; 38.5]);
512s  assert (var (y, 1, 2), ones (3,1) * 36.66666666666666, 1e-14);
512s  assert (var (y, "all"), 54.19354838709678, 1e-14);
512s  y(2,4) = NaN;
512s  assert (var (y, "all"), NaN);
512s  assert (var (y, "all", "includenan"), NaN);
512s  assert (var (y, "all", "omitnan"), 55.01533580116342, 1e-14);
512s  assert (var (y, 0, 2, "includenan"), [38.5; NaN; 38.5]);
512s  assert (var (y, [], 2), [38.5; NaN; 38.5]);
512s  assert (var (y, [], 2, "omitnan"), [38.5; 37.81842105263158; 38.5], 1e-14);
512s ***** assert (var ([1 NaN 3], [1 2 3], "omitnan"), 0.75, eps)
512s ***** assert (var ([1 2 3], [1 NaN 3], "omitnan"), 0.75, eps)
512s ***** assert (var (magic(3), [1 NaN 3], "omitnan"), [3 12 3], eps)
512s ***** assert (var ([1 NaN 3], [1 2 3], "omitnan", "all"), 0.75, eps)
512s ***** assert (var ([1 NaN 3], [1 2 3], "all", "omitnan"), 0.75, eps)
512s ***** assert (var ([1 2 3], [1 NaN 3], "omitnan", "all"), 0.75, eps)
512s ***** assert (var ([1 NaN 3], [1 2 3], 2, "omitnan"), 0.75, eps)
512s ***** assert (var ([1 2 3], [1 NaN 3], 2, "omitnan"), 0.75, eps)
512s ***** assert (var (magic(3), [1 NaN 3], 1, "omitnan"), [3 12 3], eps)
512s ***** assert (var (magic(3), [1 NaN 3], 2, "omitnan"), [0.75;3;0.75], eps)
512s ***** assert (var ([4 4; 4 6; 6 6], [1 3], 2, 'omitnan'), [0;0.75;0], eps)
512s ***** assert (var ([4 NaN; 4 6; 6 6], [1 2 3], 1, 'omitnan'), [1 0])
512s ***** assert (var ([4 NaN; 4 6; 6 6], [1 3], 2, 'omitnan'), [0;0.75;0], eps)
512s ***** assert (var (3*reshape(1:18, [3 3 2]), [1 2 3], 1, 'omitnan'), ones(1,3,2)*5)
512s ***** assert (var (reshape(1:18, [3 3 2]), [1 2 3], 2, 'omitnan'), 5*ones(3,1,2))
512s ***** assert (var (3*reshape(1:18, [3 3 2]), ones (3,3,2), [1 2], 'omitnan'), ...
512s          60 * ones(1,1,2))
512s ***** assert (var (3*reshape(1:18, [3 3 2]), ones (3,3,2), [1 4], 'omitnan'), ...
512s          6 * ones(1,3,2))
512s ***** assert (var (6*reshape(1:18, [3 3 2]), ones (3,3,2), [1:3], 'omitnan'), 969)
512s ***** test
512s  x = reshape(1:18, [3 3 2]);
512s  x([2, 14]) = NaN;
512s  w = ones (3,3,2);
512s  assert (var (16*x, w, [1:3], 'omitnan'), 6519);
512s ***** test
512s  x = reshape(1:18, [3 3 2]);
512s  w = ones (3,3,2);
512s  w([2, 14]) = NaN;
512s  assert (var (16*x, w, [1:3], 'omitnan'), 6519);
512s ***** assert (var ([1 2 3], "aLl"), 1);
512s ***** assert (var ([1 2 3], "OmitNan"), 1);
512s ***** assert (var ([1 2 3], "IncludeNan"), 1);
512s ***** test
512s  x = repmat ([1:20;6:25], [5, 2, 6, 3]);
512s  assert (size (var (x, 0, [3 2])), [10, 1, 1, 3]);
512s  assert (size (var (x, 1, [1 2])), [1, 1, 6, 3]);
512s  assert (size (var (x, [], [1 2 4])), [1, 1, 6]);
512s  assert (size (var (x, 0, [1 4 3])), [1, 40]);
512s  assert (size (var (x, [], [1 2 3 4])), [1, 1]);
512s ***** assert (var (3*magic(3)), [63 144 63])
512s ***** assert (var (3*magic(3), 'omitnan'), [63 144 63])
512s ***** assert (var (3*magic(3), 1), [42 96 42])
512s ***** assert (var (3*magic(3), 1, 'omitnan'), [42 96 42])
512s ***** assert (var (3*magic(3), ones(1,3), 1), [42 96 42])
512s ***** assert (var (3*magic(3), ones(1,3), 1, 'omitnan'), [42 96 42])
512s ***** assert (var (2*magic(3), [1 1 NaN], 1, 'omitnan'), [25 16 1])
512s ***** assert (var (3*magic(3), ones(3,3)), [42 96 42])
512s ***** assert (var (3*magic(3), ones(3,3), 'omitnan'), [42 96 42])
512s ***** assert (var (3*magic(3), [1 1 1; 1 1 1; 1 NaN 1], 'omitnan'), [42 36 42])
512s ***** assert (var (3*magic(3), ones(3,3), 1), [42 96 42])
512s ***** assert (var (3*magic(3), ones(3,3), 1, 'omitnan'), [42 96 42])
512s ***** assert (var (3*magic(3), [1 1 1; 1 1 1; 1 NaN 1], 1, 'omitnan'), [42 36 42])
512s ***** assert (var (3*magic(3), ones(3,3), [1 4]), [42 96 42])
512s ***** assert (var (3*magic(3), ones(3,3), [1 4], 'omitnan'), [42 96 42])
512s ***** assert (var (3*magic(3), [1 1 1; 1 1 1; 1 NaN 1],[1 4],'omitnan'), [42 36 42])
512s ***** test
512s  x = repmat ([1:20;6:25], [5, 2, 6, 3]);
512s  v = repmat (33.38912133891213, [10, 1, 1, 3]);
512s  assert (var (x, 0, [3, 2]), v, 1e-14);
512s  v = repmat (33.250, [10, 1, 1, 3]);
512s  assert (var (x, 1, [3, 2]), v, 1e-14);
512s  x(2,5,6,3) = NaN;
512s  v(2,1,1,3) = NaN;
512s  assert (var (x, 1, [3, 2]), v, 4e-14);
512s  v = repmat (33.38912133891213, [10 1 1 3]);
512s  v(2,1,1,3) = NaN;
512s  assert (var (x, [], [3, 2]), v, 4e-14);
512s  v(2,1,1,3) = 33.40177912169048;
512s  assert (var (x, [], [3, 2], "omitnan"), v, 4e-14);
512s ***** assert (var (ones (2,2,2), [1:2], 3), [(zeros (2, 2))])
512s ***** assert (var (magic (3), [1:9], "all"), 6.666666666666667, 1e-14)
512s ***** assert (var (ones (2,2), [], 3), zeros (2,2))
512s ***** assert (var (ones (2,2,2), [], 99), zeros (2,2,2))
512s ***** assert (var (magic (3), [], 3), zeros (3,3))
512s ***** assert (var (magic (3), [], 1), [7, 16, 7])
512s ***** assert (var (magic (3), [], [1 3]), [7, 16, 7])
512s ***** assert (var (magic (3), [], [1 99]), [7, 16, 7])
512s ***** assert (var ([]), NaN)
512s ***** assert (class (var (single ([]))), "single")
512s ***** assert (var ([],[],1), NaN(1,0))
512s ***** assert (var ([],[],2), NaN(0,1))
512s ***** assert (var ([],[],3), [])
512s ***** assert (class (var (single ([]), [], 1)), "single")
512s ***** assert (var (ones (1,0)), NaN)
512s ***** assert (var (ones (1,0), [], 1), NaN(1,0))
512s ***** assert (var (ones (1,0), [], 2), NaN)
512s ***** assert (var (ones (1,0), [], 3), NaN(1,0))
512s ***** assert (class (var (ones (1, 0, "single"), [], 1)), "single")
512s ***** assert (var (ones (0,1)), NaN)
512s ***** assert (var (ones (0,1), [], 1), NaN)
512s ***** assert (var (ones (0,1), [], 2), NaN(0,1))
512s ***** assert (var (ones (0,1), [], 3), NaN(0,1))
512s ***** assert (var (ones (1,3,0,2)), NaN(1,1,0,2))
512s ***** assert (var (ones (1,3,0,2), [], 1), NaN(1,3,0,2))
512s ***** assert (var (ones (1,3,0,2), [], 2), NaN(1,1,0,2))
512s ***** assert (var (ones (1,3,0,2), [], 3), NaN(1,3,1,2))
512s ***** assert (var (ones (1,3,0,2), [], 4), NaN(1,3,0))
512s ***** test
512s  [~, m] = var ([]);
512s  assert (m, NaN);
512s ***** test <*62395>
512s  [~, m] = var (13);
512s  assert (m, 13);
512s  [~, m] = var (single(13));
512s  assert (m, single(13));
512s  [~, m] = var ([1, 2, 3; 3 2 1], []);
512s  assert (m, [2 2 2]);
512s  [~, m] = var ([1, 2, 3; 3 2 1], [], 1);
512s  assert (m, [2 2 2]);
512s  [~, m] = var ([1, 2, 3; 3 2 1], [], 2);
512s  assert (m, [2 2]');
512s  [~, m] = var ([1, 2, 3; 3 2 1], [], 3);
512s  assert (m, [1 2 3; 3 2 1]);
512s ***** test <*62395>
512s  [~, m] = var (5,99);
512s  assert (m, 5);
512s  [~, m] = var ([1:7], [1:7]);
512s  assert (m, 5);
512s  [~, m] = var ([eye(3)], [1:3]);
512s  assert (m, [1/6, 1/3, 0.5], eps);
512s  [~, m] = var (ones (2,2,2), [1:2], 3);
512s  assert (m, ones (2,2));
512s  [~, m] = var ([1 2; 3 4], 0, 'all');
512s  assert (m, 2.5, eps);
512s  [~, m] = var (reshape ([1:8], 2, 2, 2), 0, [1 3]);
512s  assert (m, [3.5, 5.5], eps);
512s ***** test
512s  [v, m] = var (4 * eye (2), [1, 3]);
512s  assert (v, [3, 3]);
512s  assert (m, [1, 3]);
512s ***** test <*62395>
512s  [~, m] = var ([]);
512s  assert (m, NaN);
512s ***** test <*62395>
512s  x = repmat ([1:20;6:25], [5, 2, 6, 3]);
512s  [~, m] = var (x, 0, [3 2]);
512s  assert (m, mean (x, [3 2]));
512s  [~, m] = var (x, 0, [1 2]);
512s  assert (m, mean (x, [1 2]));
512s  [~, m] = var (x, 0, [1 3 4]);
512s  assert (m, mean (x, [1 3 4]));
512s ***** test
512s  x = repmat ([1:20;6:25], [5, 2, 6, 3]);
512s  x(2,5,6,3) = NaN;
512s  [~, m] = var (x, 0, [3 2], "omitnan");
512s  assert (m, mean (x, [3 2], "omitnan"));
512s ***** test <*63203>
512s  [v, m] = var (Inf);
512s  assert (v, NaN);
512s  assert (m, Inf);
512s ***** test <*63203>
512s  [v, m] = var (NaN);
512s  assert (v, NaN);
512s  assert (m, NaN);
512s ***** test <*63203>
512s  [v, m] = var ([1, Inf, 3]);
512s  assert (v, NaN);
512s  assert (m, Inf);
512s ***** test <*63203>
512s  [v, m] = var ([1, Inf, 3]');
512s  assert (v, NaN);
512s  assert (m, Inf);
512s ***** test <*63203>
512s  [v, m] = var ([1, NaN, 3]);
512s  assert (v, NaN);
512s  assert (m, NaN);
512s ***** test <*63203>
512s  [v, m] = var ([1, NaN, 3]');
512s  assert (v, NaN);
512s  assert (m, NaN);
512s ***** test <*63203>
512s  [v, m] = var ([1, Inf, 3], [], 1);
512s  assert (v, [0, NaN, 0]);
512s  assert (m, [1, Inf, 3]);
512s ***** test <*63203>
512s  [v, m] = var ([1, Inf, 3], [], 2);
512s  assert (v, NaN);
512s  assert (m, Inf);
512s ***** test <*63203>
512s  [v, m] = var ([1, Inf, 3], [], 3);
512s  assert (v, [0, NaN, 0]);
512s  assert (m, [1, Inf, 3]);
512s ***** test <*63203>
512s  [v, m] = var ([1, NaN, 3], [], 1);
512s  assert (v, [0, NaN, 0]);
512s  assert (m, [1, NaN, 3]);
512s ***** test <*63203>
512s  [v, m] = var ([1, NaN, 3], [], 2);
512s  assert (v, NaN);
512s  assert (m, NaN);
512s ***** test <*63203>
512s  [v, m] = var ([1, NaN, 3], [], 3);
512s  assert (v, [0, NaN, 0]);
512s  assert (m, [1, NaN, 3]);
512s ***** test <*63203>
512s  [v, m] = var ([1, 2, 3; 3, Inf, 5]);
512s  assert (v, [2, NaN, 2]);
512s  assert (m, [2, Inf, 4]);
512s ***** test <*63203>
512s  [v, m] = var ([1, Inf, 3; 3, Inf, 5]);
512s  assert (v, [2, NaN, 2]);
512s  assert (m, [2, Inf, 4]);
512s ***** test <*63203>
512s  [v, m] = var ([1, 2, 3; 3, NaN, 5]);
512s  assert (v, [2, NaN, 2]);
512s  assert (m, [2, NaN, 4]);
512s ***** test <*63203>
512s  [v, m] = var ([1, NaN, 3; 3, NaN, 5]);
512s  assert (v, [2, NaN, 2]);
512s  assert (m, [2, NaN, 4]);
512s ***** test <*63203>
512s  [v, m] = var ([Inf, 2, NaN]);
512s  assert (v, NaN);
512s  assert (m, NaN);
512s ***** test <*63203>
512s  [v, m] = var ([Inf, 2, NaN]');
512s  assert (v, NaN);
512s  assert (m, NaN);
512s ***** test <*63203>
512s  [v, m] = var ([NaN, 2, Inf]);
512s  assert (v, NaN);
512s  assert (m, NaN);
512s ***** test <*63203>
512s  [v, m] = var ([NaN, 2, Inf]');
512s  assert (v, NaN);
512s  assert (m, NaN);
512s ***** test <*63203>
512s  [v, m] = var ([Inf, 2, NaN], [], 1);
512s  assert (v, [NaN, 0, NaN]);
512s  assert (m, [Inf, 2, NaN]);
512s ***** test <*63203>
512s  [v, m] = var ([Inf, 2, NaN], [], 2);
512s  assert (v, NaN);
512s  assert (m, NaN);
512s ***** test <*63203>
512s  [v, m] = var ([NaN, 2, Inf], [], 1);
512s  assert (v, [NaN, 0, NaN]);
512s  assert (m, [NaN, 2, Inf]);
512s ***** test <*63203>
512s  [v, m] = var ([NaN, 2, Inf], [], 2);
512s  assert (v, NaN);
512s  assert (m, NaN);
512s ***** test <*63203>
512s  [v, m] = var ([1, 3, NaN; 3, 5, Inf]);
512s  assert (v, [2, 2, NaN]);
512s  assert (m, [2, 4, NaN]);
512s ***** test <*63203>
512s  [v, m] = var ([1, 3, Inf; 3, 5, NaN]);
512s  assert (v, [2, 2, NaN]);
512s  assert (m, [2, 4, NaN]);
512s ***** test <*63291>
512s  [v, m] = var (2 * eye (2));
512s  assert (v, [2, 2]);
512s  assert (m, [1, 1]);
512s ***** test <*63291>
512s  [v, m] = var (4 * eye (2), [1, 3]);
512s  assert (v, [3, 3]);
512s  assert (m, [1, 3]);
512s ***** test <*63291>
512s  [v, m] = var (sparse (2 * eye (2)));
512s  assert (full (v), [2, 2]);
512s  assert (full (m), [1, 1]);
512s ***** test <*63291>
512s  [v, m] = var (sparse (4 * eye (2)), [1, 3]);
512s  assert (full (v), [3, 3]);
512s  assert (full (m), [1, 3]);
512s ***** test<*63291>
512s  [v, m] = var (sparse (eye (2)));
512s  assert (issparse (v));
512s  assert (issparse (m));
512s ***** test<*63291>
512s  [v, m] = var (sparse (eye (2)), [1, 3]);
512s  assert (issparse (v));
512s  assert (issparse (m));
512s ***** error <Invalid call> var ()
513s ***** error <Invalid call> var (1, 2, "omitnan", 3)
513s ***** error <Invalid call> var (1, 2, 3, 4)
513s ***** error <Invalid call> var (1, 2, 3, 4, 5)
513s ***** error <Invalid call> var (1, "foo")
513s ***** error <Invalid call> var (1, [], "foo")
514s ***** error <normalization scalar must be either 0 or 1> var ([1 2 3], 2)
514s ***** error <normalization scalar must be either 0 or 1> var ([1 2], 2, "all")
514s ***** error <normalization scalar must be either 0 or 1> var ([1 2],0.5, "all")
514s ***** error <weights must not contain any negative values> var (1, -1)
514s ***** error <weights must not contain any negative values> var (1, [1 -1])
514s ***** error <weights must not contain any negative values> ...
514s  var ([1 2 3], [1 -1 0])
514s ***** error <X must be a numeric vector or matrix> var ({1:5})
514s ***** error <X must be a numeric vector or matrix> var ("char")
514s ***** error <X must be a numeric vector or matrix> var (['A'; 'B'])
514s ***** error <DIM must be a positive integer> var (1, [], ones (2,2))
514s ***** error <DIM must be a positive integer> var (1, 0, 1.5)
514s ***** error <DIM must be a positive integer> var (1, [], 0)
514s ***** error <DIM must be a positive integer> var (1, [], 1.5)
514s ***** error <DIM must be a positive integer> var ([1 2 3], [], [-1 1])
514s ***** error <VECDIM must contain non-repeating positive integers> ...
514s  var (repmat ([1:20;6:25], [5 2 6 3]), 0, [1 2 2 2])
514s ***** error <weight matrix or array does not match X in size> ...
514s  var ([1 2], eye (2))
514s ***** error <weight matrix or array does not match X in size> ...
514s  var ([1 2 3 4], [1 2; 3 4])
514s ***** error <weight matrix or array does not match X in size> ...
514s  var ([1 2 3 4], [1 2; 3 4], 1)
514s ***** error <weight matrix or array does not match X in size> ...
514s  var ([1 2 3 4], [1 2; 3 4], [2 3])
514s ***** error <weight matrix or array does not match X in size> ...
514s  var (ones (2, 2), [1 2], [1 2])
514s ***** error <weight matrix or array does not match X in size> ...
514s  var ([1 2 3 4; 5 6 7 8], [1 2 1 2 1; 1 2 1 2 1], 1)
514s ***** error <weight matrix or array does not match X in size> ...
514s  var (repmat ([1:20;6:25], [5 2 6 3]), repmat ([1:20;6:25], [5 2 3]), [2 3])
514s ***** error <weight vector length does not match> var ([1 2 3; 2 3 4], [1 3 4])
514s ***** error <weight vector length does not match> var ([1 2], [1 2 3])
514s ***** error <weight vector length does not match> var (1, [1 2])
514s ***** error <weight vector length does not match> var ([1 2 3; 2 3 4], [1 3 4], 1)
514s ***** error <weight vector length does not match> var ([1 2 3; 2 3 4], [1 3], 2)
514s ***** error <weight vector length does not match> var ([1 2], [1 2], 1)
514s ***** error <'all' flag cannot be used with DIM or VECDIM options> ...
514s  var (1, [], 1, "all")
514s ***** error <weight vector element count does not match X> ...
514s  var ([1 2 3; 2 3 4], [1 3], "all")
514s ***** error <weight matrix or array does not match X in size> ...
514s  var (repmat ([1:20;6:25], [5 2 6 3]), repmat ([1:20;6:25], [5 2 3]), "all")
514s 162 tests, 162 passed, 0 known failure, 0 skipped
514s [inst/shadow9/mean.m]
514s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/shadow9/mean.m
514s ***** test
514s  x = -10:10;
514s  y = x';
514s  z = [y, y+10];
514s  assert (mean (x), 0);
514s  assert (mean (y), 0);
514s  assert (mean (z), [0, 10]);
514s ***** assert (mean (magic (3), 1), [5, 5, 5])
514s ***** assert (mean (magic (3), 2), [5; 5; 5])
514s ***** assert (mean (logical ([1 0 1 1])), 0.75)
514s ***** assert (mean (single ([1 0 1 1])), single (0.75))
514s ***** assert (mean ([1 2], 3), [1 2])
514s ***** test
514s  in = [1 2 3];
514s  out = 2;
514s  assert (mean (in, "default"), mean (in));
514s  assert (mean (in, "default"), out);
514s  assert (mean (in, "double"), out);
514s  assert (mean (in, "native"), out);
514s ***** test
514s  in = single ([1 2 3]);
514s  out = 2;
514s  assert (mean (in, "default"), mean (in));
514s  assert (mean (in, "default"), single (out));
514s  assert (mean (in, "double"), out);
514s  assert (mean (in, "native"), single (out));
514s ***** test
514s  in = logical ([1 0 1]);
514s  out = 2/3;
514s  assert (mean (in, "default"), mean (in), eps);
514s  assert (mean (in, "default"), out, eps);
514s  assert (mean (in, "double"), out, eps);
514s  assert (mean (in, "native"), out, eps);
514s ***** test
514s  in = char ("ab");
514s  out = 97.5;
514s  assert (mean (in, "default"), mean (in), eps);
514s  assert (mean (in, "default"), out, eps);
514s  assert (mean (in, "double"), out, eps);
514s ***** test
514s  in = uint8 ([1 2 3]);
514s  out = 2;
514s  assert (mean (in, "default"), mean (in));
514s  assert (mean (in, "default"), out);
514s  assert (mean (in, "double"), out);
514s  assert (mean (in, "native"), uint8 (out));
514s ***** test
514s  in = uint8 ([0 1 2 3]);
514s  out = 1.5;
514s  out_u8 = 2;
514s  assert (mean (in, "default"), mean (in), eps);
514s  assert (mean (in, "default"), out, eps);
514s  assert (mean (in, "double"), out, eps);
514s  assert (mean (in, "native"), uint8 (out_u8));
514s  assert (class (mean (in, "native")), "uint8");
514s ***** test # internal sum exceeding intmax
514s  in = uint8 ([3 141 141 255]);
514s  out = 135;
514s  assert (mean (in, "default"), mean (in));
514s  assert (mean (in, "default"), out);
514s  assert (mean (in, "double"), out);
514s  assert (mean (in, "native"), uint8 (out));
514s  assert (class (mean (in, "native")), "uint8");
514s ***** test # fractional answer with internal sum exceeding intmax
514s  in = uint8 ([1 141 141 255]);
514s  out = 134.5;
514s  out_u8 = 135;
514s  assert (mean (in, "default"), mean (in));
514s  assert (mean (in, "default"), out);
514s  assert (mean (in, "double"), out);
514s  assert (mean (in, "native"), uint8 (out_u8));
514s  assert (class (mean (in, "native")), "uint8");
514s ***** test <54567> # large int64 sum exceeding intmax and double precision limit
514s  in_same = uint64 ([intmax("uint64") intmax("uint64")-2]);
514s  out_same = intmax ("uint64")-1;
514s  in_opp = int64 ([intmin("int64"), intmax("int64")-1]);
514s  out_opp = -1;
514s  in_neg = int64 ([intmin("int64") intmin("int64")+2]);
514s  out_neg = intmin ("int64")+1;
514s 
514s  ## both positive
514s  assert (mean (in_same, "default"), mean (in_same));
514s  assert (mean (in_same, "default"), double (out_same));
514s  assert (mean (in_same, "double"), double (out_same));
514s  assert (mean (in_same, "native"), uint64 (out_same));
514s  assert (class (mean (in_same, "native")), "uint64");
514s 
514s  ## opposite signs
514s  assert (mean (in_opp, "default"), mean (in_opp));
514s  assert (mean (in_opp, "default"), double (out_opp));
514s  assert (mean (in_opp, "double"), double (out_opp));
514s  assert (mean (in_opp, "native"), int64 (out_opp));
514s  assert (class (mean (in_opp, "native")), "int64");
514s 
514s  ## both negative
514s  assert (mean (in_neg, "default"), mean (in_neg));
514s  assert (mean (in_neg, "default"), double(out_neg));
514s  assert (mean (in_neg, "double"), double(out_neg));
514s  assert (mean (in_neg, "native"), int64(out_neg));
514s  assert (class (mean (in_neg, "native")), "int64");
514s ***** test <54567>
514s  in = [(intmin('int64')+5), (intmax('int64'))-5];
514s  assert (mean (in, "native"), int64(-1));
514s  assert (class (mean (in, "native")), "int64");
514s  assert (mean (double(in)), double(0) );
514s  assert (mean (in), double(-0.5) );
514s  assert (mean (in, "default"), double(-0.5) );
514s  assert (mean (in, "double"), double(-0.5) );
514s  assert (mean (in, "all", "native"), int64(-1));
514s  assert (mean (in, 2, "native"), int64(-1));
514s  assert (mean (in, [1 2], "native"), int64(-1));
514s  assert (mean (in, [2 3], "native"), int64(-1));
514s  assert (mean ([intmin("int64"), in, intmax("int64")]), double(-0.5))
514s  assert (mean ([in; int64([1 3])], 2, "native"), int64([-1; 2]));
514s ***** test
514s  x = [-10:10];
514s  y = [x;x+5;x-5];
514s  assert (mean (x), 0);
514s  assert (mean (y, 2), [0, 5, -5]');
514s  assert (mean (y, "all"), 0);
514s  y(2,4) = NaN;
514s  assert (mean (y', "omitnan"), [0 5.35 -5]);
514s  z = y + 20;
514s  assert (mean (z, "all"), NaN);
514s  assert (mean (z, "all", "includenan"), NaN);
514s  assert (mean (z, "all", "omitnan"), 20.03225806451613, 4e-14);
514s  m = [20 NaN 15];
514s  assert (mean (z'), m);
514s  assert (mean (z', "includenan"), m);
514s  m = [20 25.35 15];
514s  assert (mean (z', "omitnan"), m);
514s  assert (mean (z, 2, "omitnan"), m');
514s  assert (mean (z, 2, "native", "omitnan"), m');
514s  assert (mean (z, 2, "omitnan", "native"), m');
514s ***** test
514s  assert (mean (true, "all"), 1);
514s  assert (mean (false), 0);
514s  assert (mean ([true false true]), 2/3, 4e-14);
514s  assert (mean ([true false true], 1), [1 0 1]);
514s  assert (mean ([true false NaN], 1), [1 0 NaN]);
514s  assert (mean ([true false NaN], 2), NaN);
514s  assert (mean ([true false NaN], 2, "omitnan"), 0.5);
514s  assert (mean ([true false NaN], 2, "omitnan", "native"), 0.5);
514s ***** assert (mean ("abc"), double (98))
514s ***** assert (mean ("ab"), double (97.5), eps)
514s ***** assert (mean ("abc", "double"), double (98))
514s ***** assert (mean ("abc", "default"), double (98))
514s ***** test
514s  x = magic (4);
514s  x([2, 9:12]) = NaN;
514s  assert (mean (x), [NaN 8.5, NaN, 8.5], eps);
514s  assert (mean (x,1), [NaN 8.5, NaN, 8.5], eps);
514s  assert (mean (x,2), NaN(4,1), eps);
514s  assert (mean (x,3), x, eps);
514s  assert (mean (x, 'omitnan'), [29/3, 8.5, NaN, 8.5], eps);
514s  assert (mean (x, 1, 'omitnan'), [29/3, 8.5, NaN, 8.5], eps);
514s  assert (mean (x, 2, 'omitnan'), [31/3; 9.5; 28/3; 19/3], eps);
514s  assert (mean (x, 3, 'omitnan'), x, eps);
514s ***** assert (mean ([]), NaN(1,1))
514s ***** assert (mean (single([])), NaN(1,1,"single"))
514s ***** assert (mean ([], 1), NaN(1,0))
514s ***** assert (mean ([], 2), NaN(0,1))
514s ***** assert (mean ([], 3), NaN(0,0))
514s ***** assert (mean (ones(1,0)), NaN(1,1))
514s ***** assert (mean (ones(1,0), 1), NaN(1,0))
514s ***** assert (mean (ones(1,0), 2), NaN(1,1))
514s ***** assert (mean (ones(1,0), 3), NaN(1,0))
514s ***** assert (mean (ones(0,1)), NaN(1,1))
514s ***** assert (mean (ones(0,1), 1), NaN(1,1))
514s ***** assert (mean (ones(0,1), 2), NaN(0,1))
514s ***** assert (mean (ones(0,1), 3), NaN(0,1))
514s ***** assert (mean (ones(0,1,0)), NaN(1,1,0))
514s ***** assert (mean (ones(0,1,0), 1), NaN(1,1,0))
514s ***** assert (mean (ones(0,1,0), 2), NaN(0,1,0))
514s ***** assert (mean (ones(0,1,0), 3), NaN(0,1,1))
514s ***** assert (mean (ones(0,0,1,0)), NaN(1,0,1,0))
514s ***** assert (mean (ones(0,0,1,0), 1), NaN(1,0,1,0))
514s ***** assert (mean (ones(0,0,1,0), 2), NaN(0,1,1,0))
514s ***** assert (mean (ones(0,0,1,0), 3), NaN(0,0,1,0))
514s ***** test
514s  x = repmat ([1:20;6:25], [5 2 6 3]);
514s  assert (size (mean (x, [3 2])), [10 1 1 3]);
514s  assert (size (mean (x, [1 2])), [1 1 6 3]);
514s  assert (size (mean (x, [1 2 4])), [1 1 6]);
514s  assert (size (mean (x, [1 4 3])), [1 40]);
514s  assert (size (mean (x, [1 2 3 4])), [1 1]);
514s ***** assert (mean (ones (2,2), 3), ones (2,2))
514s ***** assert (mean (ones (2,2,2), 99), ones (2,2,2))
514s ***** assert (mean (magic (3), 3), magic (3))
514s ***** assert (mean (magic (3), [1 3]), [5, 5, 5])
514s ***** assert (mean (magic (3), [1 99]), [5, 5, 5])
514s ***** test
514s  x = repmat ([1:20;6:25], [5 2 6 3]);
514s  m = repmat ([10.5;15.5], [5 1 1 3]);
514s  assert (mean (x, [3 2]), m, 4e-14);
514s  x(2,5,6,3) = NaN;
514s  m(2,1,1,3) = NaN;
514s  assert (mean (x, [3 2]), m, 4e-14);
514s  m(2,1,1,3) = 15.52301255230125;
514s  assert (mean (x, [3 2], "omitnan"), m, 4e-14);
514s ***** assert (mean ([1 2 3], "aLL"), 2)
514s ***** assert (mean ([1 2 3], "OmitNan"), 2)
514s ***** assert (mean ([1 2 3], "DOUBle"), 2)
514s ***** assert <*63848> (mean (ones (80e6, 1, "single")), 1, eps)
514s ***** assert <*63848> (mean (ones (80e6, 1, "single"), "all"), 1, eps)
515s ***** assert <*63848> (mean (ones (80e6, 1, "single"), 1), 1, eps)
515s ***** assert <*63848> (mean (ones (80e6, 1, "single"), [1 2]), 1, eps)
515s ***** assert <*63848> (mean (ones (80e6, 1, "single"), [1 3]), 1, eps)
516s ***** assert <63848> (mean ([flintmax("double"), ones(1, 2^8-1, "double")]), ...
516s                                35184372088833-1/(2^8), eps(35184372088833))
516s !!!!! known bug: https://octave.org/testfailure/?63848
516s ASSERT errors for:  assert (mean ([flintmax("double"), ones(1, 2 ^ 8 - 1, "double")]),35184372088833 - 1 / (2 ^ 8),eps (35184372088833))
516s 
516s   Location  |  Observed  |  Expected  |  Reason
516s      ()      35184372088832 35184372088833   Abs err 1 exceeds tol 0.0078125 by 1
516s ***** error <Invalid call to mean.  Correct usage is> mean ()
516s ***** error <Invalid call to mean.  Correct usage is> mean (1, 2, 3)
516s ***** error <Invalid call to mean.  Correct usage is> mean (1, 2, 3, 4)
516s ***** error <Invalid call to mean.  Correct usage is> mean (1, "all", 3)
517s ***** error <Invalid call to mean.  Correct usage is> mean (1, "b")
517s ***** error <Invalid call to mean.  Correct usage is> mean (1, 1, "foo")
517s ***** error <OUTTYPE 'native' cannot be used with char> mean ("abc", "native")
517s ***** error <X must be either a numeric, boolean, or character> mean ({1:5})
517s ***** error <DIM must be a positive integer> mean (1, ones (2,2))
517s ***** error <DIM must be a positive integer> mean (1, 1.5)
517s ***** error <DIM must be a positive integer> mean (1, 0)
517s ***** error <DIM must be a positive integer> mean (1, [])
517s ***** error <DIM must be a positive integer> mean (1, -1)
517s ***** error <DIM must be a positive integer> mean (1, -1.5)
517s ***** error <DIM must be a positive integer> mean (1, NaN)
517s ***** error <DIM must be a positive integer> mean (1, Inf)
517s ***** error <DIM must be a positive integer> mean (repmat ([1:20;6:25], [5 2]), -1)
517s ***** error <DIM must be a positive integer> mean (repmat ([1:5;5:9], [5 2]), [1 -1])
517s ***** error <DIM must be a positive integer> mean (1, ones(1,0))
517s ***** error <VECDIM must contain non-repeating> mean (1, [2 2])
517s 80 tests, 79 passed, 0 known failure, 1 skipped
517s [inst/shadow9/median.m]
517s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/shadow9/median.m
517s ***** assert (median (1), 1)
517s ***** assert (median ([1,2,3]), 2)
517s ***** assert (median ([1,2,3]'), 2)
517s ***** assert (median (cat(3,3,1,2)), 2)
517s ***** assert (median ([3,1,2]), 2)
517s ***** assert (median ([2,4,6,8]), 5)
517s ***** assert (median ([8,2,6,4]), 5)
517s ***** assert (median (single ([1,2,3])), single (2))
517s ***** assert (median ([1,2], 3), [1,2])
517s ***** test
517s  x = [1, 2, 3, 4, 5, 6];
517s  x2 = x';
517s  y = [1, 2, 3, 4, 5, 6, 7];
517s  y2 = y';
517s 
517s  assert (median (x) == median (x2) && median (x) == 3.5);
517s  assert (median (y) == median (y2) && median (y) == 4);
517s  assert (median ([x2, 2 * x2]), [3.5, 7]);
517s  assert (median ([y2, 3 * y2]), [4, 12]);
517s ***** test
517s  in = [1 2 3];
517s  out = 2;
517s  assert (median (in, "default"), median (in));
517s  assert (median (in, "default"), out);
517s ***** test
517s  in = single ([1 2 3]);
517s  out = 2;
517s  assert (median (in, "default"), single (median (in)));
517s  assert (median (in, "default"), single (out));
517s  assert (median (in, "double"), double (out));
517s  assert (median (in, "native"), single (out));
517s ***** test
517s  in = uint8 ([1 2 3]);
517s  out = 2;
517s  assert (median (in, "default"), double (median (in)));
517s  assert (median (in, "default"), double (out));
517s  assert (median (in, "double"), out);
517s  assert (median (in, "native"), uint8 (out));
517s ***** test
517s  in = logical ([1 0 1]);
517s  out = 1;
517s  assert (median (in, "default"), double (median (in)));
517s  assert (median (in, "default"), double (out));
517s  assert (median (in, "double"), double (out));
517s  assert (median (in, "native"), double (out));
517s ***** test
517s  x = repmat ([2 2.1 2.2 2 NaN; 3 1 2 NaN 5; 1 1.1 1.4 5 3], [1, 1, 4]);
517s  y = repmat ([2 1.1 2 NaN NaN], [1, 1, 4]);
517s  assert (median (x), y);
517s  assert (median (x, 1), y);
517s  y = repmat ([2 1.1 2 3.5 4], [1, 1, 4]);
517s  assert (median (x, "omitnan"), y);
517s  assert (median (x, 1, "omitnan"), y);
517s  y = repmat ([2.05; 2.5; 1.4], [1, 1, 4]);
517s  assert (median (x, 2, "omitnan"), y);
517s  y = repmat ([NaN; NaN; 1.4], [1, 1, 4]);
517s  assert (median (x, 2), y);
517s  assert (median (x, "all"), NaN);
517s  assert (median (x, "all", "omitnan"), 2);
517s ***** assert (median (cat (3, 3, 1, NaN, 2), "omitnan"), 2)
517s ***** assert (median (cat (3, 3, 1, NaN, 2), 3, "omitnan"), 2)
517s ***** test
517s  assert (median (true, "all"), logical (1));
517s  assert (median (false), logical (0));
517s  assert (median ([true false true]), true);
517s  assert (median ([true false true], 2), true);
517s  assert (median ([true false true], 1), logical ([1 0 1]));
517s  assert (median ([true false NaN], 1), [1 0 NaN]);
517s  assert (median ([true false NaN], 2), NaN);
517s  assert (median ([true false NaN], 2, "omitnan"), 0.5);
517s  assert (median ([true false NaN], 2, "omitnan", "native"), double(0.5));
517s ***** test
517s  x = repmat ([1:20;6:25], [5 2 6 3]);
517s  assert (size (median (x, [3 2])), [10 1 1 3]);
517s  assert (size (median (x, [1 2])), [1 1 6 3]);
517s  assert (size (median (x, [1 2 4])), [1 1 6]);
517s  assert (size (median (x, [1 4 3])), [1 40]);
517s  assert (size (median (x, [1 2 3 4])), [1 1]);
517s ***** assert (median (ones (2,2), 3), ones (2,2))
517s ***** assert (median (ones (2,2,2), 99), ones (2,2,2))
517s ***** assert (median (magic (3), 3), magic (3))
517s ***** assert (median (magic (3), [1 3]), [4, 5, 6])
517s ***** assert (median (magic (3), [1 99]), [4, 5, 6])
517s ***** test
517s  x = repmat ([2 2.1 2.2 2 NaN; 3 1 2 NaN 5; 1 1.1 1.4 5 3], [1, 1, 4]);
517s  assert (median (x, [3 2]), [NaN NaN 1.4]');
517s  assert (median (x, [3 2], "omitnan"), [2.05 2.5 1.4]');
517s  assert (median (x, [1 3]), [2 1.1 2 NaN NaN]);
517s  assert (median (x, [1 3], "omitnan"), [2 1.1 2 3.5 4]);
517s ***** assert (median (NaN), NaN)
517s ***** assert (median (NaN, "omitnan"), NaN)
517s ***** assert (median (NaN (2)), [NaN NaN])
517s ***** assert (median (NaN (2), "omitnan"), [NaN NaN])
517s ***** assert (median ([1 NaN 3]), NaN)
517s ***** assert (median ([1 NaN 3], 1), [1 NaN 3])
517s ***** assert (median ([1 NaN 3], 2), NaN)
517s ***** assert (median ([1 NaN 3]'), NaN)
517s ***** assert (median ([1 NaN 3]', 1), NaN)
517s ***** assert (median ([1 NaN 3]', 2), [1; NaN; 3])
517s ***** assert (median ([1 NaN 3], "omitnan"), 2)
517s ***** assert (median ([1 NaN 3]', "omitnan"), 2)
517s ***** assert (median ([1 NaN 3], 1, "omitnan"), [1 NaN 3])
517s ***** assert (median ([1 NaN 3], 2, "omitnan"), 2)
517s ***** assert (median ([1 NaN 3]', 1, "omitnan"), 2)
517s ***** assert (median ([1 NaN 3]', 2, "omitnan"), [1; NaN; 3])
517s ***** assert (median ([1 2 NaN 3]), NaN)
517s ***** assert (median ([1 2 NaN 3], "omitnan"), 2)
517s ***** assert (median ([1,2,NaN;4,5,6;NaN,8,9]), [NaN, 5, NaN])
517s ***** assert <*64011> (median ([1,2,NaN;4,5,6;NaN,8,9], "omitnan"), [2.5, 5, 7.5], eps)
517s ***** assert (median ([1 2 ; NaN 4]), [NaN 3])
517s ***** assert (median ([1 2 ; NaN 4], "omitnan"), [1 3])
517s ***** assert (median ([1 2 ; NaN 4], 1, "omitnan"), [1 3])
517s ***** assert (median ([1 2 ; NaN 4], 2, "omitnan"), [1.5; 4], eps)
517s ***** assert (median ([1 2 ; NaN 4], 3, "omitnan"), [1 2 ; NaN 4])
517s ***** assert (median ([NaN 2 ; NaN 4]), [NaN 3])
517s ***** assert (median ([NaN 2 ; NaN 4], "omitnan"), [NaN 3])
517s ***** assert (median (ones (1, 0, 3)), NaN (1, 1, 3))
517s ***** assert <*65405> (median ([NaN NaN], 1, "omitnan"), [NaN NaN])
517s ***** assert <*65405> (median ([NaN NaN], 2, "omitnan"), NaN)
517s ***** assert <*65405> (median ([NaN NaN]', 1, "omitnan"), NaN)
517s ***** assert <*65405> (median ([NaN NaN]', 2, "omitnan"), [NaN; NaN])
517s ***** assert <*65405> (median ([NaN NaN], "omitnan"), NaN)
517s ***** assert <*65405> (median ([NaN NaN]', "omitnan"), NaN)
517s ***** assert <*65405> (median (NaN(1,9), 1, "omitnan"), NaN(1,9))
517s ***** assert <*65405> (median (NaN(1,9), 2, "omitnan"), NaN)
517s ***** assert <*65405> (median (NaN(1,9), 3, "omitnan"), NaN(1,9))
517s ***** assert <*65405> (median (NaN(9,1), 1, "omitnan"), NaN)
517s ***** assert <*65405> (median (NaN(9,1), 2, "omitnan"), NaN(9,1))
517s ***** assert <*65405> (median (NaN(9,1), 3, "omitnan"), NaN(9,1))
517s ***** assert <*65405> (median (NaN(9,2), 1, "omitnan"), NaN(1,2))
517s ***** assert <*65405> (median (NaN(9,2), 2, "omitnan"), NaN(9,1))
517s ***** assert <*65405> (median (NaN(9,2), "omitnan"), NaN(1,2))
517s ***** assert (median (NaN("single")), NaN("single"))
517s ***** assert (median (NaN("single"), "omitnan"), NaN("single"))
517s ***** assert (median (NaN("single"), "double"), NaN("double"))
517s ***** assert (median (single([1 2 ; NaN 4])), single([NaN 3]))
517s ***** assert (median (single([1 2 ; NaN 4]), "double"), double([NaN 3]))
517s ***** assert (median (single([1 2 ; NaN 4]), "omitnan"), single([1 3]))
517s ***** assert (median (single([1 2 ; NaN 4]), "omitnan", "double"), double([1 3]))
517s ***** assert (median (single([NaN 2 ; NaN 4]), "double"), double([NaN 3]))
517s ***** assert (median (single([NaN 2 ; NaN 4]), "omitnan"), single([NaN 3]))
517s ***** assert (median (single([NaN 2 ; NaN 4]), "omitnan", "double"), double([NaN 3]))
517s ***** test <*64011>
517s  x = [magic(3), magic(3)];
517s  x([3, 7, 11, 12, 16, 17]) = NaN;
517s  ynan = [NaN, 5, NaN, NaN, 5, NaN];
517s  yomitnan = [5.5, 5, 4.5, 8, 5, 2];
517s  assert (median (x), ynan);
517s  assert (median (x, "omitnan"), yomitnan, eps);
517s  assert (median (cat (3, x, x)), cat (3, ynan, ynan));
517s  assert (median (cat (3, x, x), "omitnan"), cat (3, yomitnan, yomitnan), eps);
517s ***** assert (median (Inf), Inf)
517s ***** assert (median (-Inf), -Inf)
517s ***** assert (median ([-Inf Inf]), NaN)
517s ***** assert (median ([3 Inf]), Inf)
517s ***** assert (median ([3 4 Inf]), 4)
517s ***** assert (median ([Inf 3 4]), 4)
517s ***** assert (median ([Inf 3 Inf]), Inf)
517s ***** assert (median ([1, 2, Inf]), 2)
517s ***** assert (median ([1, 2, Inf, Inf]), Inf)
517s ***** assert (median ([1, -Inf, Inf, Inf]), Inf)
517s ***** assert (median ([-Inf, -Inf, Inf, Inf]), NaN)
517s ***** assert (median([-Inf, Inf, Inf, Inf]), Inf)
517s ***** assert (median([-Inf, -Inf, -Inf, Inf]), -Inf)
517s ***** assert (median([-Inf, -Inf, -Inf, 2]), -Inf)
517s ***** assert (median([-Inf, -Inf, 1, 2]), -Inf)
517s ***** assert (median ([]), NaN)
517s ***** assert (median (ones(1,0)), NaN)
517s ***** assert (median (ones(0,1)), NaN)
517s ***** assert (median ([], 1), NaN(1,0))
517s ***** assert (median ([], 2), NaN(0,1))
517s ***** assert (median ([], 3), NaN(0,0))
517s ***** assert (median (ones(1,0), 1), NaN(1,0))
517s ***** assert (median (ones(1,0), 2), NaN(1,1))
517s ***** assert (median (ones(1,0), 3), NaN(1,0))
517s ***** assert (median (ones(0,1), 1), NaN(1,1))
517s ***** assert (median (ones(0,1), 2), NaN(0,1))
517s ***** assert (median (ones(0,1), 3), NaN(0,1))
517s ***** assert (median (ones(0,1,0,1), 1), NaN(1,1,0))
517s ***** assert (median (ones(0,1,0,1), 2), NaN(0,1,0))
517s ***** assert (median (ones(0,1,0,1), 3), NaN(0,1,1))
517s ***** assert (median (ones(0,1,0,1), 4), NaN(0,1,0))
517s ***** assert (median([1 3 3i 2 1i]), 2)
517s ***** assert (median([1 2 4i; 3 2i 4]), [2, 1+1i, 2+2i])
517s ***** shared a, b, x, y
517s  old_state = rand ("state");
517s  restore_state = onCleanup (@() rand ("state", old_state));
517s  rand ("state", 2);
517s  a = rand (2,3,4,5);
517s  b = rand (3,4,6,5);
517s  x = sort (a, 4);
517s  y = sort (b, 3);
517s ***** assert <*35679> (median (a, 4), x(:, :, :, 3))
517s ***** assert <*35679> (median (b, 3), (y(:, :, 3, :) + y(:, :, 4, :))/2)
517s ***** shared   ## Clear shared to prevent variable echo for any later test failures
517s ***** test
517s  x = ones(15,1,4);
517s  x([13,15],1,:) = NaN;
517s  assert (median (x, 1, "omitnan"), ones (1,1,4))
517s ***** assert (median ([true, false]), true)
517s ***** assert (median (logical ([])), false)
517s ***** assert (median (uint8 ([1, 3])), uint8 (2))
517s ***** assert (median (uint8 ([])), uint8 (NaN))
517s ***** assert (median (uint8 ([NaN 10])), uint8 (5))
517s ***** assert (median (int8 ([1, 3, 4])), int8 (3))
517s ***** assert (median (int8 ([])), int8 (NaN))
517s ***** assert (median (single ([1, 3, 4])), single (3))
517s ***** assert (median (single ([1, 3, NaN])), single (NaN))
517s ***** assert <54567> (median (uint8 ([253, 255])), uint8 (254))
517s ***** assert <54567> (median (uint8 ([253, 254])), uint8 (254))
517s ***** assert <54567> (median (int8 ([127, 126, 125, 124; 1 3 5 9])), ...
517s                   int8 ([64 65 65 67]))
517s ***** assert <54567> (median (int8 ([127, 126, 125, 124; 1 3 5 9]), 2), ...
517s                   int8 ([126; 4]))
517s ***** assert <54567> (median (int64 ([intmax("int64"), intmax("int64")-2])), ...
517s                   intmax ("int64") - 1)
517s ***** assert <54567> (median ( ...
517s                  int64 ([intmax("int64"), intmax("int64")-2; 1 2]), 2), ...
517s                  int64([intmax("int64") - 1; 2]))
517s ***** assert <54567> (median (uint64 ([intmax("uint64"), intmax("uint64")-2])), ...
517s                   intmax ("uint64") - 1)
517s ***** assert <54567> (median ( ...
517s                  uint64 ([intmax("uint64"), intmax("uint64")-2; 1 2]), 2), ...
517s                  uint64([intmax("uint64") - 1; 2]))
517s ***** assert <54567> (median (...
517s  [intmin('int8') intmin('int8')+5 intmax('int8')-5 intmax('int8')]), ...
517s  int8(-1))
517s ***** assert <54567> (median ([int8([1 2 3 4]); ...
517s  intmin('int8') intmin('int8')+5 intmax('int8')-5 intmax('int8')], 2), ...
517s  int8([3;-1]))
517s ***** assert <54567> (median (...
517s  [intmin('int64') intmin('int64')+5 intmax('int64')-5 intmax('int64')]), ...
517s  int64(-1))
517s ***** assert <54567> (median ([int64([1 2 3 4]); ...
517s  intmin('int64') intmin('int64')+5 intmax('int64')-5 intmax('int64')], 2), ...
517s  int64([3;-1]))
517s ***** assert <54567> (median ([intmax("uint64"), intmax("uint64")-2]), ...
517s   intmax("uint64")-1)
517s ***** assert <54567> (median ([intmax("uint64"), intmax("uint64")-2], "default"), ...
517s   double(intmax("uint64")-1))
517s ***** assert <54567> (median ([intmax("uint64"), intmax("uint64")-2], "double"), ...
517s   double(intmax("uint64")-1))
517s ***** assert <54567> (median ([intmax("uint64"), intmax("uint64")-2], "native"), ...
517s   intmax("uint64")-1)
517s ***** assert (median ([1 2 3], "aLL"), 2)
517s ***** assert (median ([1 2 3], "OmitNan"), 2)
517s ***** assert (median ([1 2 3], "DOUBle"), 2)
517s ***** error <Invalid call> median ()
517s ***** error <Invalid call> median (1, 2, 3)
517s ***** error <Invalid call> median (1, 2, 3, 4)
517s ***** error <Invalid call> median (1, "all", 3)
518s ***** error <Invalid call> median (1, "b")
518s ***** error <Invalid call> median (1, 1, "foo")
518s ***** error <'all' cannot be used with> median (1, 3, "all")
518s ***** error <'all' cannot be used with> median (1, [2 3], "all")
518s ***** error <X must be either numeric or logical> median ({1:5})
518s ***** error <X must be either numeric or logical> median ("char")
518s ***** error <only one OUTTYPE can be specified> median(1, "double", "native")
518s ***** error <DIM must be a positive integer> median (1, ones (2,2))
518s ***** error <DIM must be a positive integer> median (1, 1.5)
518s ***** error <DIM must be a positive integer> median (1, 0)
518s ***** error <DIM must be a positive integer> median ([1 2 3], [-1 1])
518s ***** error <VECDIM must contain non-repeating> median(1, [1 2 2])
518s 159 tests, 159 passed, 0 known failure, 0 skipped
518s [inst/shadow9/std.m]
518s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/shadow9/std.m
518s ***** assert (std (13), 0)
518s ***** assert (std (single (13)), single (0))
518s ***** assert (std ([1,2,3]), 1)
518s ***** assert (std ([1,2,3], 1), sqrt (2/3), eps)
518s ***** assert (std ([1,2,3], [], 1), [0,0,0])
518s ***** assert (std ([1,2,3], [], 3), [0,0,0])
518s ***** assert (std (5, 99), 0)
518s ***** assert (std (5, 99, 1), 0)
518s ***** assert (std (5, 99, 2), 0)
518s ***** assert (std ([5 3], [99 99], 2), 1)
518s ***** assert (std ([1:7], [1:7]), sqrt (3))
518s ***** assert (std ([eye(3)], [1:3]), sqrt ([5/36, 2/9, 1/4]), eps)
518s ***** assert (std (ones (2,2,2), [1:2], 3), [(zeros (2,2))])
518s ***** assert (std ([1 2; 3 4], 0, 'all'), std ([1:4]))
518s ***** assert (std (reshape ([1:8], 2, 2, 2), 0, [1 3]), sqrt ([17/3 17/3]), eps)
518s ***** assert (std ([1 2 3;1 2 3], [], [1 2]), sqrt (0.8), eps)
518s ***** test
518s  x = [-10:10];
518s  y = [x;x+5;x-5];
518s  assert (std (x), sqrt (38.5), 1e-14);
518s  assert (std (y, [], 2), sqrt ([38.5; 38.5; 38.5]), 1e-14);
518s  assert (std (y, 0, 2), sqrt ([38.5; 38.5; 38.5]), 1e-14);
518s  assert (std (y, 1, 2), ones (3,1) * sqrt (36.66666666666666), 1e-14);
518s  assert (std (y, "all"), sqrt (54.19354838709678), 1e-14);
518s  y(2,4) = NaN;
518s  assert (std (y, "all"), NaN);
518s  assert (std (y, "all", "includenan"), NaN);
518s  assert (std (y, "all", "omitnan"), sqrt (55.01533580116342), 1e-14);
518s  assert (std (y, 0, 2, "includenan"), sqrt ([38.5; NaN; 38.5]), 1e-14);
518s  assert (std (y, [], 2), sqrt ([38.5; NaN; 38.5]), 1e-14);
518s  assert (std (y, [], 2, "omitnan"), ...
518s          sqrt ([38.5; 37.81842105263158; 38.5]), 1e-14);
518s ***** assert (std ([4 NaN 6], [1 2 1], "omitnan"), 1, eps)
518s ***** assert (std ([4 5 6], [1 NaN 1], "omitnan"), 1, eps)
518s ***** assert (std (magic(3), [1 NaN 3], "omitnan"), sqrt(3)*[1 2 1], eps)
518s ***** assert (std ([4 NaN 6], [1 2 1], "omitnan", "all"), 1, eps)
518s ***** assert (std ([4 NaN 6], [1 2 1], "all", "omitnan"), 1, eps)
518s ***** assert (std ([4 5 6], [1 NaN 1], "omitnan", "all"), 1, eps)
518s ***** assert (std ([4 NaN 6], [1 2 1], 2, "omitnan"), 1, eps)
518s ***** assert (std ([4 5 6], [1 NaN 1], 2, "omitnan"), 1, eps)
518s ***** assert (std (magic(3), [1 NaN 3], 1, "omitnan"), sqrt(3)*[1 2 1], eps)
518s ***** assert (std (magic(3), [1 NaN 3], 2, "omitnan"), sqrt(3)*[0.5;1;0.5], eps)
518s ***** assert (std (4*[4 5; 6 7; 8 9], [1 3], 2, 'omitnan'), sqrt(3)*[1;1;1], eps)
518s ***** assert (std ([4 NaN; 6 7; 8 9], [1 1 3], 1, 'omitnan'), [1.6 sqrt(3)/2], eps)
518s ***** assert (std (4*[4 NaN; 6 7; 8 9], [1 3], 2, 'omitnan'), sqrt(3)*[0;1;1], eps)
518s ***** assert (std (3*reshape(1:18, [3 3 2]), [1 2 3], 1, 'omitnan'), ...
518s           sqrt(5)*ones(1,3,2), eps)
518s ***** assert (std (reshape(1:18, [3 3 2]), [1 2 3], 2, 'omitnan'), ...
518s           sqrt(5)*ones(3,1,2), eps)
518s ***** assert (std (3*reshape(1:18, [3 3 2]), ones (3,3,2), [1 2], 'omitnan'), ...
518s           sqrt(60)*ones(1,1,2),eps)
518s ***** assert (std (3*reshape(1:18, [3 3 2]), ones (3,3,2), [1 4], 'omitnan'), ...
518s           sqrt(6)*ones(1,3,2),eps)
518s ***** assert (std (6*reshape(1:18, [3 3 2]), ones (3,3,2), [1:3], 'omitnan'), ...
518s           sqrt(969),eps)
518s ***** test
518s  x = reshape(1:18, [3 3 2]);
518s  x([2, 14]) = NaN;
518s  w = ones (3,3,2);
518s  assert (std (16*x, w, [1:3], 'omitnan'), sqrt(6519), eps);
518s ***** test
518s  x = reshape(1:18, [3 3 2]);
518s  w = ones (3,3,2);
518s  w([2, 14]) = NaN;
518s  assert (std (16*x, w, [1:3], 'omitnan'), sqrt(6519), eps);
518s ***** assert (std ([1 2 3], "aLl"), 1);
518s ***** assert (std ([1 2 3], "OmitNan"), 1);
518s ***** assert (std ([1 2 3], "IncludeNan"), 1);
518s ***** test
518s  x = repmat ([1:20;6:25], [5, 2, 6, 3]);
518s  assert (size (std (x, 0, [3 2])), [10, 1, 1, 3]);
518s  assert (size (std (x, 1, [1 2])), [1, 1, 6, 3]);
518s  assert (size (std (x, [], [1 2 4])), [1, 1, 6]);
518s  assert (size (std (x, 0, [1 4 3])), [1, 40]);
518s  assert (size (std (x, [], [1 2 3 4])), [1, 1]);
518s ***** assert (std (3*magic(3)), sqrt([63 144 63]), eps)
518s ***** assert (std (3*magic(3), 'omitnan'), sqrt([63 144 63]), eps)
518s ***** assert (std (3*magic(3), 1), sqrt([42 96 42]), eps)
518s ***** assert (std (3*magic(3), 1, 'omitnan'), sqrt([42 96 42]), eps)
518s ***** assert (std (3*magic(3), ones(1,3), 1), sqrt([42 96 42]), eps)
518s ***** assert (std (3*magic(3), ones(1,3), 1, 'omitnan'), sqrt([42 96 42]), eps)
518s ***** assert (std (2*magic(3), [1 1 NaN], 1, 'omitnan'), [5 4 1], eps)
518s ***** assert (std (3*magic(3), ones(3,3)), sqrt([42 96 42]), eps)
518s ***** assert (std (3*magic(3), ones(3,3), 'omitnan'), sqrt([42 96 42]), eps)
518s ***** assert (std (3*magic(3), [1 1 1; 1 1 1; 1 NaN 1], 'omitnan'), ...
518s          sqrt([42 36 42]), eps)
518s ***** assert (std (3*magic(3), ones(3,3), 1), sqrt([42 96 42]), eps)
518s ***** assert (std (3*magic(3), ones(3,3), 1, 'omitnan'), sqrt([42 96 42]), eps)
518s ***** assert (std (3*magic(3), [1 1 1; 1 1 1; 1 NaN 1], 1, 'omitnan'), ...
518s          sqrt([42 36 42]), eps)
518s ***** assert (std (3*magic(3), ones(3,3), [1 4]), sqrt([42 96 42]), eps)
518s ***** assert (std (3*magic(3), ones(3,3), [1 4], 'omitnan'), sqrt([42 96 42]), eps)
518s ***** assert (std (3*magic(3), [1 1 1; 1 1 1; 1 NaN 1],[1 4],'omitnan'), ...
518s          sqrt([42 36 42]), eps)
518s ***** test
518s  x = repmat ([1:20;6:25], [5, 2, 6, 3]);
518s  v = repmat (sqrt (33.38912133891213), [10, 1, 1, 3]);
518s  assert (std (x, 0, [3, 2]), v, 1e-14);
518s  v = repmat (sqrt (33.250), [10, 1, 1, 3]);
518s  assert (std (x, 1, [3, 2]), v, 1e-14);
518s  x(2,5,6,3) = NaN;
518s  v(2,1,1,3) = NaN;
518s  assert (std (x, 1, [3, 2]), v, 1e-14);
518s  v = repmat (sqrt (33.38912133891213), [10 1 1 3]);
518s  v(2,1,1,3) = NaN;
518s  assert (std (x, [], [3, 2]), v, 1e-14);
518s  v(2,1,1,3) = sqrt (33.40177912169048);
518s  assert (std (x, [], [3, 2], "omitnan"), v, 1e-14);
518s ***** assert (std (ones (2,2,2), [1:2], 3), [(zeros (2, 2))])
518s ***** assert (std (magic (3), [1:9], "all"), 2.581988897471611, 1e-14)
518s ***** assert (std (ones (2,2), [], 3), zeros (2,2))
518s ***** assert (std (ones (2,2,2), [], 99), zeros (2,2,2))
518s ***** assert (std (magic (3), [], 3), zeros (3,3))
518s ***** assert (std (magic (3), [], 1), sqrt ([7, 16, 7]))
518s ***** assert (std (magic (3), [], [1 3]), sqrt ([7, 16, 7]))
518s ***** assert (std (magic (3), [], [1 99]), sqrt ([7, 16, 7]))
518s ***** assert (std ([]), NaN)
518s ***** assert (class (var (single ([]))), "single")
518s ***** assert (std ([],[],1), NaN(1,0))
518s ***** assert (std ([],[],2), NaN(0,1))
518s ***** assert (std ([],[],3), [])
518s ***** assert (class (var (single ([]), [], 1)), "single")
518s ***** assert (std (ones (1,0)), NaN)
518s ***** assert (std (ones (1,0), [], 1), NaN(1,0))
518s ***** assert (std (ones (1,0), [], 2), NaN)
518s ***** assert (std (ones (1,0), [], 3), NaN(1,0))
518s ***** assert (class (var (ones (1, 0, "single"), [], 1)), "single")
518s ***** assert (std (ones (0,1)), NaN)
518s ***** assert (std (ones (0,1), [], 1), NaN)
518s ***** assert (std (ones (0,1), [], 2), NaN(0,1))
518s ***** assert (std (ones (0,1), [], 3), NaN(0,1))
518s ***** assert (std (ones (1,3,0,2)), NaN(1,1,0,2))
518s ***** assert (std (ones (1,3,0,2), [], 1), NaN(1,3,0,2))
518s ***** assert (std (ones (1,3,0,2), [], 2), NaN(1,1,0,2))
518s ***** assert (std (ones (1,3,0,2), [], 3), NaN(1,3,1,2))
518s ***** assert (std (ones (1,3,0,2), [], 4), NaN(1,3,0))
518s ***** test
518s  [~, m] = std ([]);
518s  assert (m, NaN);
518s ***** test <*62395>
518s  [~, m] = std (13);
518s  assert (m, 13);
518s  [~, m] = std (single(13));
518s  assert (m, single(13));
518s  [~, m] = std ([1, 2, 3; 3 2 1], []);
518s  assert (m, [2 2 2]);
518s  [~, m] = std ([1, 2, 3; 3 2 1], [], 1);
518s  assert (m, [2 2 2]);
518s  [~, m] = std ([1, 2, 3; 3 2 1], [], 2);
518s  assert (m, [2 2]');
518s  [~, m] = std ([1, 2, 3; 3 2 1], [], 3);
518s  assert (m, [1 2 3; 3 2 1]);
518s ***** test <*62395>
518s  [~, m] = std (5,99);
518s  assert (m, 5);
518s  [~, m] = std ([1:7], [1:7]);
518s  assert (m, 5);
518s  [~, m] = std ([eye(3)], [1:3]);
518s  assert (m, [1/6, 1/3, 0.5], eps);
518s  [~, m] = std (ones (2,2,2), [1:2], 3);
518s  assert (m, ones (2,2));
518s  [~, m] = std ([1 2; 3 4], 0, 'all');
518s  assert (m, 2.5, eps);
518s  [~, m] = std (reshape ([1:8], 2, 2, 2), 0, [1 3]);
518s  assert (m, [3.5, 5.5], eps);
518s ***** test
518s  [v, m] = std (4 * eye (2), [1, 3]);
518s  assert (v, sqrt ([3, 3]), 1e-14);
518s  assert (m, [1, 3]);
518s ***** test <*62395>
518s  [~, m] = std ([]);
518s  assert (m, NaN);
518s ***** test
518s  x = repmat ([1:20;6:25], [5, 2, 6, 3]);
518s  [~, m] = std (x, 0, [3 2]);
518s  assert (m, mean (x, [3 2]));
518s  [~, m] = std (x, 0, [1 2]);
518s  assert (m, mean (x, [1 2]));
518s  [~, m] = std (x, 0, [1 3 4]);
518s  assert (m, mean (x, [1 3 4]));
518s ***** test
518s  x = repmat ([1:20;6:25], [5, 2, 6, 3]);
518s  x(2,5,6,3) = NaN;
518s  [~, m] = std (x, 0, [3 2], "omitnan");
518s  assert (m, mean (x, [3 2], "omitnan"));
518s ***** test <*63203>
518s  [v, m] = std (Inf);
518s  assert (v, NaN);
518s  assert (m, Inf);
518s ***** test <*63203>
518s  [v, m] = std (NaN);
518s  assert (v, NaN);
518s  assert (m, NaN);
518s ***** test <*63203>
518s  [v, m] = std ([1, Inf, 3]);
518s  assert (v, NaN);
518s  assert (m, Inf);
518s ***** test <*63203>
518s  [v, m] = std ([1, Inf, 3]');
518s  assert (v, NaN);
518s  assert (m, Inf);
518s ***** test <*63203>
518s  [v, m] = std ([1, NaN, 3]);
518s  assert (v, NaN);
518s  assert (m, NaN);
518s ***** test <*63203>
518s  [v, m] = std ([1, NaN, 3]');
518s  assert (v, NaN);
518s  assert (m, NaN);
518s ***** test <*63203>
518s  [v, m] = std ([1, Inf, 3], [], 1);
518s  assert (v, [0, NaN, 0]);
518s  assert (m, [1, Inf, 3]);
518s ***** test <*63203>
518s  [v, m] = std ([1, Inf, 3], [], 2);
518s  assert (v, NaN);
518s  assert (m, Inf);
518s ***** test <*63203>
518s  [v, m] = std ([1, Inf, 3], [], 3);
518s  assert (v, [0, NaN, 0]);
518s  assert (m, [1, Inf, 3]);
518s ***** test <*63203>
518s  [v, m] = std ([1, NaN, 3], [], 1);
518s  assert (v, [0, NaN, 0]);
518s  assert (m, [1, NaN, 3]);
518s ***** test <*63203>
518s  [v, m] = std ([1, NaN, 3], [], 2);
518s  assert (v, NaN);
518s  assert (m, NaN);
518s ***** test <*63203>
518s  [v, m] = std ([1, NaN, 3], [], 3);
518s  assert (v, [0, NaN, 0]);
518s  assert (m, [1, NaN, 3]);
518s ***** test <*63203>
518s  [v, m] = std ([1, 2, 3; 3, Inf, 5]);
518s  assert (v, sqrt ([2, NaN, 2]));
518s  assert (m, [2, Inf, 4]);
518s ***** test <*63203>
518s  [v, m] = std ([1, Inf, 3; 3, Inf, 5]);
518s  assert (v, sqrt ([2, NaN, 2]));
518s  assert (m, [2, Inf, 4]);
518s ***** test <*63203>
518s  [v, m] = std ([1, 2, 3; 3, NaN, 5]);
518s  assert (v, sqrt ([2, NaN, 2]));
518s  assert (m, [2, NaN, 4]);
518s ***** test <*63203>
518s  [v, m] = std ([1, NaN, 3; 3, NaN, 5]);
518s  assert (v, sqrt ([2, NaN, 2]));
518s  assert (m, [2, NaN, 4]);
518s ***** test <*63203>
518s  [v, m] = std ([Inf, 2, NaN]);
518s  assert (v, NaN);
518s  assert (m, NaN);
518s ***** test <*63203>
518s  [v, m] = std ([Inf, 2, NaN]');
518s  assert (v, NaN);
518s  assert (m, NaN);
518s ***** test <*63203>
518s  [v, m] = std ([NaN, 2, Inf]);
518s  assert (v, NaN);
518s  assert (m, NaN);
518s ***** test <*63203>
518s  [v, m] = std ([NaN, 2, Inf]');
518s  assert (v, NaN);
518s  assert (m, NaN);
518s ***** test <*63203>
518s  [v, m] = std ([Inf, 2, NaN], [], 1);
518s  assert (v, [NaN, 0, NaN]);
518s  assert (m, [Inf, 2, NaN]);
518s ***** test <*63203>
518s  [v, m] = std ([Inf, 2, NaN], [], 2);
518s  assert (v, NaN);
518s  assert (m, NaN);
518s ***** test <*63203>
518s  [v, m] = std ([NaN, 2, Inf], [], 1);
518s  assert (v, [NaN, 0, NaN]);
518s  assert (m, [NaN, 2, Inf]);
518s ***** test <*63203>
518s  [v, m] = std ([NaN, 2, Inf], [], 2);
518s  assert (v, NaN);
518s  assert (m, NaN);
518s ***** test <*63203>
518s  [v, m] = std ([1, 3, NaN; 3, 5, Inf]);
518s  assert (v, sqrt ([2, 2, NaN]));
518s  assert (m, [2, 4, NaN]);
518s ***** test <*63203>
518s  [v, m] = std ([1, 3, Inf; 3, 5, NaN]);
518s  assert (v, sqrt ([2, 2, NaN]));
518s  assert (m, [2, 4, NaN]);
518s ***** test <*63291>
518s  [v, m] = std (2 * eye (2));
518s  assert (v, sqrt ([2, 2]));
518s  assert (m, [1, 1]);
518s ***** test <*63291>
518s  [v, m] = std (4 * eye (2), [1, 3]);
518s  assert (v, sqrt ([3, 3]));
518s  assert (m, [1, 3]);
518s ***** test <*63291>
518s  [v, m] = std (sparse (2 * eye (2)));
518s  assert (full (v), sqrt ([2, 2]));
518s  assert (full (m), [1, 1]);
518s ***** test <*63291>
518s  [v, m] = std (sparse (4 * eye (2)), [1, 3]);
518s  assert (full (v), sqrt ([3, 3]));
518s  assert (full (m), [1, 3]);
518s ***** test <*63291>
518s  [v, m] = std (sparse (eye (2)));
518s  assert (issparse (v));
518s  assert (issparse (m));
518s ***** test <*63291>
518s  [v, m] = std (sparse (eye (2)), [1, 3]);
518s  assert (issparse (v));
518s  assert (issparse (m));
518s ***** error <Invalid call> std ()
518s ***** error <Invalid call> std (1, 2, "omitnan", 3)
518s ***** error <Invalid call> std (1, 2, 3, 4)
519s ***** error <Invalid call> std (1, 2, 3, 4, 5)
519s ***** error <Invalid call> std (1, "foo")
519s ***** error <Invalid call> std (1, [], "foo")
519s ***** error <normalization scalar must be either 0 or 1> std ([1 2 3], 2)
519s ***** error <normalization scalar must be either 0 or 1> std ([1 2], 2, "all")
519s ***** error <normalization scalar must be either 0 or 1> std ([1 2],0.5, "all")
519s ***** error <weights must not contain any negative values> std (1, -1)
519s ***** error <weights must not contain any negative values> std (1, [1 -1])
519s ***** error <weights must not contain any negative values> ...
519s  std ([1 2 3], [1 -1 0])
519s ***** error <X must be a numeric vector or matrix> std ({1:5})
519s ***** error <X must be a numeric vector or matrix> std ("char")
519s ***** error <X must be a numeric vector or matrix> std (['A'; 'B'])
519s ***** error <DIM must be a positive integer> std (1, [], ones (2,2))
519s ***** error <DIM must be a positive integer> std (1, 0, 1.5)
519s ***** error <DIM must be a positive integer> std (1, [], 0)
519s ***** error <DIM must be a positive integer> std (1, [], 1.5)
519s ***** error <DIM must be a positive integer> std ([1 2 3], [], [-1 1])
519s ***** error <VECDIM must contain non-repeating positive integers> ...
519s  std (repmat ([1:20;6:25], [5 2 6 3]), 0, [1 2 2 2])
519s ***** error <weight matrix or array does not match X in size> ...
519s  std ([1 2], eye (2))
519s ***** error <weight matrix or array does not match X in size> ...
519s  std ([1 2 3 4], [1 2; 3 4])
519s ***** error <weight matrix or array does not match X in size> ...
519s  std ([1 2 3 4], [1 2; 3 4], 1)
519s ***** error <weight matrix or array does not match X in size> ...
519s  std ([1 2 3 4], [1 2; 3 4], [2 3])
519s ***** error <weight matrix or array does not match X in size> ...
519s  std (ones (2, 2), [1 2], [1 2])
519s ***** error <weight matrix or array does not match X in size> ...
519s  std ([1 2 3 4; 5 6 7 8], [1 2 1 2 1; 1 2 1 2 1], 1)
519s ***** error <weight matrix or array does not match X in size> ...
519s  std (repmat ([1:20;6:25], [5 2 6 3]), repmat ([1:20;6:25], [5 2 3]), [2 3])
519s ***** error <weight vector length does not match> std ([1 2 3; 2 3 4], [1 3 4])
519s ***** error <weight vector length does not match> std ([1 2], [1 2 3])
519s ***** error <weight vector length does not match> std (1, [1 2])
519s ***** error <weight vector length does not match> std ([1 2 3; 2 3 4], [1 3 4], 1)
519s ***** error <weight vector length does not match> std ([1 2 3; 2 3 4], [1 3], 2)
519s ***** error <weight vector length does not match> std ([1 2], [1 2], 1)
519s ***** error <'all' flag cannot be used with DIM or VECDIM options> ...
519s  std (1, [], 1, "all")
519s ***** error <weight vector element count does not match X> ...
519s  std ([1 2 3; 2 3 4], [1 3], "all")
519s ***** error <weight matrix or array does not match X in size> ...
519s  std (repmat ([1:20;6:25], [5 2 6 3]), repmat ([1:20;6:25], [5 2 3]), "all")
519s 162 tests, 162 passed, 0 known failure, 0 skipped
519s [inst/hotelling_t2test.m]
519s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/hotelling_t2test.m
519s ***** error<Invalid call to hotelling_t2test.  Correct usage> hotelling_t2test ();
519s ***** error<hotelling_t2test: X must be a vector or a 2D matrix.> ...
519s  hotelling_t2test (1);
519s ***** error<hotelling_t2test: X must be a vector or a 2D matrix.> ...
519s  hotelling_t2test (ones(2,2,2));
519s ***** error<hotelling_t2test: invalid value for alpha.> ...
519s  hotelling_t2test (ones(20,2), [0, 0], "alpha", 1);
519s ***** error<hotelling_t2test: invalid value for alpha.> ...
519s  hotelling_t2test (ones(20,2), [0, 0], "alpha", -0.2);
519s ***** error<hotelling_t2test: invalid value for alpha.> ...
519s  hotelling_t2test (ones(20,2), [0, 0], "alpha", "a");
519s ***** error<hotelling_t2test: invalid value for alpha.> ...
519s  hotelling_t2test (ones(20,2), [0, 0], "alpha", [0.01, 0.05]);
519s ***** error<hotelling_t2test: invalid Name argument.> ...
519s  hotelling_t2test (ones(20,2), [0, 0], "name", 0.01);
519s ***** error<hotelling_t2test: if X is a vector, M must be a scalar.> ...
519s  hotelling_t2test (ones(20,1), [0, 0]);
519s ***** error<hotelling_t2test: X must have more rows than columns.> ...
519s  hotelling_t2test (ones(4,5), [0, 0, 0, 0, 0]);
519s ***** error<hotelling_t2test: if X is a matrix, M must be a vector of length> ...
519s  hotelling_t2test (ones(20,5), [0, 0, 0, 0]);
519s ***** test
519s  randn ("seed", 1);
519s  x = randn (50000, 5);
519s  [h, pval, stats] = hotelling_t2test (x);
519s  assert (h, 0);
519s  assert (stats.df1, 5);
519s  assert (stats.df2, 49995);
519s ***** test
519s  randn ("seed", 1);
519s  x = randn (50000, 5);
519s  [h, pval, stats] = hotelling_t2test (x, ones (1, 5) * 10);
519s  assert (h, 1);
519s  assert (stats.df1, 5);
519s  assert (stats.df2, 49995);
519s 13 tests, 13 passed, 0 known failure, 0 skipped
519s [inst/Classification/ClassificationPartitionedModel.m]
519s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/Classification/ClassificationPartitionedModel.m
519s ***** demo
519s 
519s  load fisheriris
519s  x = meas;
519s  y = species;
519s 
519s  ## Create a KNN classifier model
519s  obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);
519s 
519s  ## Create a partition for 5-fold cross-validation
519s  partition = cvpartition (y, "KFold", 5);
519s 
519s  ## Create the ClassificationPartitionedModel object
519s  cvModel = crossval (obj, 'cvPartition', partition)
519s ***** demo
519s 
519s  load fisheriris
519s  x = meas;
519s  y = species;
519s 
519s  ## Create a KNN classifier model
519s  obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);
519s 
519s  ## Create the ClassificationPartitionedModel object
519s  cvModel = crossval (obj);
519s 
519s  ## Predict the class labels for the observations not used for training
519s  [label, score, cost] = kfoldPredict (cvModel);
519s  fprintf ("Cross-validated accuracy = %1.2f%% (%d/%d)\n", ...
519s           sum (strcmp (label, y)) / numel (y) *100, ...
519s           sum (strcmp (label, y)), numel (y))
519s ***** test
519s  load fisheriris
519s  a = fitcdiscr (meas, species, "gamma", 0.3);
519s  cvModel = crossval (a, "KFold", 5);
519s  assert (class (cvModel), "ClassificationPartitionedModel");
519s  assert (cvModel.NumObservations, 150);
519s  assert (numel (cvModel.Trained), 5);
519s  assert (class (cvModel.Trained{1}), "CompactClassificationDiscriminant");
519s  assert (cvModel.CrossValidatedModel, "ClassificationDiscriminant");
519s  assert (cvModel.KFold, 5);
519s ***** test
519s  load fisheriris
519s  a = fitcdiscr (meas, species, "gamma", 0.5, "fillcoeffs", "off");
519s  cvModel = crossval (a, "HoldOut", 0.3);
519s  assert (class (cvModel), "ClassificationPartitionedModel");
519s  assert ({cvModel.X, cvModel.Y}, {meas, species});
519s  assert (cvModel.NumObservations, 150);
519s  assert (numel (cvModel.Trained), 1);
519s  assert (class (cvModel.Trained{1}), "CompactClassificationDiscriminant");
519s  assert (cvModel.CrossValidatedModel, "ClassificationDiscriminant");
519s ***** test
519s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
519s  y = ["a"; "a"; "b"; "b"];
519s  a = fitcgam (x, y, "Interactions", "all");
519s  cvModel = crossval (a, "KFold", 5);
519s  assert (class (cvModel), "ClassificationPartitionedModel");
519s  assert (cvModel.NumObservations, 4);
519s  assert (numel (cvModel.Trained), 5);
519s  assert (class (cvModel.Trained{1}), "CompactClassificationGAM");
519s  assert (cvModel.CrossValidatedModel, "ClassificationGAM");
519s  assert (cvModel.KFold, 5);
537s ***** test
537s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
537s  y = ["a"; "a"; "b"; "b"];
537s  a = fitcgam (x, y);
537s  cvModel = crossval (a, "LeaveOut", "on");
537s  assert (class (cvModel), "ClassificationPartitionedModel");
537s  assert ({cvModel.X, cvModel.Y}, {x, y});
537s  assert (cvModel.NumObservations, 4);
537s  assert (numel (cvModel.Trained), 4);
537s  assert (class (cvModel.Trained{1}), "CompactClassificationGAM");
537s  assert (cvModel.CrossValidatedModel, "ClassificationGAM");
542s ***** test
542s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
542s  y = ["a"; "a"; "b"; "b"];
542s  a = fitcknn (x, y);
542s  partition = cvpartition (y, "KFold", 5);
542s  cvModel = ClassificationPartitionedModel (a, partition);
542s  assert (class (cvModel), "ClassificationPartitionedModel");
542s  assert (class (cvModel.Trained{1}), "ClassificationKNN");
542s  assert (cvModel.NumObservations, 4);
542s  assert (cvModel.ModelParameters.NumNeighbors, 1);
542s  assert (cvModel.ModelParameters.NSMethod, "kdtree");
542s  assert (cvModel.ModelParameters.Distance, "euclidean");
542s  assert (! cvModel.ModelParameters.Standardize);
542s ***** test
542s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
542s  y = ["a"; "a"; "b"; "b"];
542s  a = fitcknn (x, y, "NSMethod", "exhaustive");
542s  partition = cvpartition (y, "HoldOut", 0.2);
542s  cvModel = ClassificationPartitionedModel (a, partition);
542s  assert (class (cvModel), "ClassificationPartitionedModel");
542s  assert (class (cvModel.Trained{1}), "ClassificationKNN");
542s  assert ({cvModel.X, cvModel.Y}, {x, y});
542s  assert (cvModel.NumObservations, 4);
542s  assert (cvModel.ModelParameters.NumNeighbors, 1);
542s  assert (cvModel.ModelParameters.NSMethod, "exhaustive");
542s  assert (cvModel.ModelParameters.Distance, "euclidean");
542s  assert (! cvModel.ModelParameters.Standardize);
542s ***** test
542s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
542s  y = ["a"; "a"; "b"; "b"];
542s  k = 3;
542s  a = fitcknn (x, y, "NumNeighbors" ,k);
542s  partition = cvpartition (y, "LeaveOut");
542s  cvModel = ClassificationPartitionedModel (a, partition);
542s  assert (class (cvModel), "ClassificationPartitionedModel");
542s  assert (class (cvModel.Trained{1}), "ClassificationKNN");
542s  assert ({cvModel.X, cvModel.Y}, {x, y});
542s  assert (cvModel.NumObservations, 4);
542s  assert (cvModel.ModelParameters.NumNeighbors, k);
542s  assert (cvModel.ModelParameters.NSMethod, "kdtree");
542s  assert (cvModel.ModelParameters.Distance, "euclidean");
542s  assert (! cvModel.ModelParameters.Standardize);
542s ***** test
542s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
542s  y = {"a"; "a"; "b"; "b"};
542s  a = fitcnet (x, y, "IterationLimit", 50);
542s  cvModel = crossval (a, "KFold", 5);
542s  assert (class (cvModel), "ClassificationPartitionedModel");
542s  assert (cvModel.NumObservations, 4);
542s  assert (numel (cvModel.Trained), 5);
542s  assert (class (cvModel.Trained{1}), "CompactClassificationNeuralNetwork");
542s  assert (cvModel.CrossValidatedModel, "ClassificationNeuralNetwork");
542s  assert (cvModel.KFold, 5);
542s ***** test
542s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
542s  y = {"a"; "a"; "b"; "b"};
542s  a = fitcnet (x, y, "LayerSizes", [5, 3]);
542s  cvModel = crossval (a, "LeaveOut", "on");
542s  assert (class (cvModel), "ClassificationPartitionedModel");
542s  assert ({cvModel.X, cvModel.Y}, {x, y});
542s  assert (cvModel.NumObservations, 4);
542s  assert (numel (cvModel.Trained), 4);
542s  assert (class (cvModel.Trained{1}), "CompactClassificationNeuralNetwork");
542s  assert (cvModel.CrossValidatedModel, "ClassificationNeuralNetwork");
542s ***** test
542s  load fisheriris
542s  inds = ! strcmp (species, 'setosa');
542s  x = meas(inds, 3:4);
542s  y = grp2idx (species(inds));
542s  SVMModel = fitcsvm (x,y);
542s  CVMdl = crossval (SVMModel, "KFold", 5);
542s  assert (class (CVMdl), "ClassificationPartitionedModel")
542s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
542s  assert (CVMdl.KFold == 5)
542s  assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
542s  assert (CVMdl.CrossValidatedModel, "ClassificationSVM");
542s ***** test
542s  load fisheriris
542s  inds = ! strcmp (species, 'setosa');
542s  x = meas(inds, 3:4);
542s  y = grp2idx (species(inds));
542s  obj = fitcsvm (x, y);
542s  CVMdl = crossval (obj, "HoldOut", 0.2);
542s  assert (class (CVMdl), "ClassificationPartitionedModel")
542s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
542s  assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
542s  assert (CVMdl.CrossValidatedModel, "ClassificationSVM");
542s ***** test
542s  load fisheriris
542s  inds = ! strcmp (species, 'setosa');
542s  x = meas(inds, 3:4);
542s  y = grp2idx (species(inds));
542s  obj = fitcsvm (x, y);
542s  CVMdl = crossval (obj, "LeaveOut", 'on');
542s  assert (class (CVMdl), "ClassificationPartitionedModel")
542s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
542s  assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
542s  assert (CVMdl.CrossValidatedModel, "ClassificationSVM");
545s ***** error<ClassificationPartitionedModel: too few input arguments.> ...
545s  ClassificationPartitionedModel ()
545s ***** error<ClassificationPartitionedModel: too few input arguments.> ...
545s  ClassificationPartitionedModel (ClassificationKNN (ones (4,2), ones (4,1)))
545s ***** error<ClassificationPartitionedModel: unsupported model type.> ...
545s  ClassificationPartitionedModel (RegressionGAM (ones (40,2), ...
545s  randi ([1, 2], 40, 1)), cvpartition (randi ([1, 2], 40, 1), 'Holdout', 0.3))
545s ***** error<ClassificationPartitionedModel: invalid 'cvpartition' object.> ...
545s  ClassificationPartitionedModel (ClassificationKNN (ones (4,2), ...
545s  ones (4,1)), 'Holdout')
545s ***** test
545s  load fisheriris
545s  a = fitcdiscr (meas, species, "gamma", 0.5, "fillcoeffs", "off");
545s  cvModel = crossval (a, "Kfold", 4);
545s  [label, score, cost] = kfoldPredict (cvModel);
545s  assert (class(cvModel), "ClassificationPartitionedModel");
545s  assert ({cvModel.X, cvModel.Y}, {meas, species});
545s  assert (cvModel.NumObservations, 150);
545s ***** # assert (label, {"b"; "b"; "a"; "a"});
545s ***** # assert (score, [4.5380e-01, 5.4620e-01; 2.4404e-01, 7.5596e-01; ...
545s ***** #         9.9392e-01, 6.0844e-03; 9.9820e-01, 1.8000e-03], 1e-4);
545s ***** # assert (cost, [5.4620e-01, 4.5380e-01; 7.5596e-01, 2.4404e-01; ...
545s ***** #         6.0844e-03, 9.9392e-01; 1.8000e-03, 9.9820e-01], 1e-4);
545s ***** test
545s  x = ones(4, 11);
545s  y = {"a"; "a"; "b"; "b"};
545s  k = 3;
545s  a = fitcknn (x, y, "NumNeighbors", k);
545s  partition = cvpartition (y, "LeaveOut");
545s  cvModel = ClassificationPartitionedModel (a, partition);
545s  [label, score, cost] = kfoldPredict (cvModel);
545s  assert (class(cvModel), "ClassificationPartitionedModel");
545s  assert ({cvModel.X, cvModel.Y}, {x, y});
545s  assert (cvModel.NumObservations, 4);
545s  assert (cvModel.ModelParameters.NumNeighbors, k);
545s  assert (cvModel.ModelParameters.NSMethod, "exhaustive");
545s  assert (cvModel.ModelParameters.Distance, "euclidean");
545s  assert (! cvModel.ModelParameters.Standardize);
545s  assert (label, {"b"; "b"; "a"; "a"});
545s  assert (score, [0.3333, 0.6667; 0.3333, 0.6667; 0.6667, 0.3333; ...
545s           0.6667, 0.3333], 1e-4);
545s  assert (cost, [0.6667, 0.3333; 0.6667, 0.3333; 0.3333, 0.6667; ...
545s           0.3333, 0.6667], 1e-4);
545s ***** error<ClassificationPartitionedModel.kfoldPredict: 'Cost' output is not supported for ClassificationSVM cross validated models.> ...
545s  [label, score, cost] = kfoldPredict (crossval (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1))))
545s ***** error<ClassificationPartitionedModel.kfoldPredict: 'Cost' output is not supported for ClassificationNeuralNetwork cross validated models.> ...
545s  [label, score, cost] = kfoldPredict (crossval (ClassificationNeuralNetwork (ones (40,2), randi ([1, 2], 40, 1))))
545s 20 tests, 20 passed, 0 known failure, 0 skipped
545s [inst/Classification/CompactClassificationSVM.m]
545s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/Classification/CompactClassificationSVM.m
545s ***** demo
545s  ## Create a support vectors machine classifier and its compact version
545s  # and compare their size
545s 
545s  load fisheriris
545s  X = meas;
545s  Y = species;
545s 
545s  Mdl = fitcsvm (X, Y, 'ClassNames', unique (species))
545s  CMdl = crossval (Mdl)
545s ***** error<CompactClassificationSVM: invalid classification object.> ...
545s  CompactClassificationSVM (1)
545s ***** shared x, y, CMdl
545s  load fisheriris
545s  inds = ! strcmp (species, 'setosa');
545s  x = meas(inds, 3:4);
545s  y = grp2idx (species(inds));
545s ***** test
545s  xc = [min(x); mean(x); max(x)];
545s  Mdl = fitcsvm (x, y, 'KernelFunction', 'rbf', 'Tolerance', 1e-7);
545s  CMdl = compact (Mdl);
545s  assert (isempty (CMdl.Alpha), true)
545s  assert (sum (CMdl.IsSupportVector), numel (CMdl.Beta))
545s  [label, score] = predict (CMdl, xc);
545s  assert (label, [1; 2; 2]);
545s  assert (score(:,1), [0.99285; -0.080296; -0.93694], 1e-5);
545s  assert (score(:,1), -score(:,2), eps)
545s ***** test
545s  Mdl = fitcsvm (x, y);
545s  CMdl = compact (Mdl);
545s  assert (isempty (CMdl.Beta), true)
545s  assert (sum (CMdl.IsSupportVector), numel (CMdl.Alpha))
545s  assert (numel (CMdl.Alpha), 24)
545s  assert (CMdl.Bias, -14.415, 1e-3)
545s  xc = [min(x); mean(x); max(x)];
545s  label = predict (CMdl, xc);
545s  assert (label, [1; 2; 2]);
545s ***** error<CompactClassificationSVM.predict: too few input arguments.> ...
545s  predict (CMdl)
545s ***** error<CompactClassificationSVM.predict: XC is empty.> ...
545s  predict (CMdl, [])
545s ***** error<CompactClassificationSVM.predict: XC must have the same number of predictors as the trained SVM model.> ...
545s  predict (CMdl, 1)
545s ***** test
545s  CMdl.ScoreTransform = "a";
545s ***** error<CompactClassificationSVM.predict: 'ScoreTransform' must be a 'function_handle' object.> ...
545s  [labels, scores] = predict (CMdl, x);
545s ***** test
545s  rand ("seed", 1);
545s  C = cvpartition (y, 'HoldOut', 0.15);
545s  Mdl = fitcsvm (x(training (C),:), y(training (C)), ...
545s                 'KernelFunction', 'rbf', 'Tolerance', 1e-7);
545s  CMdl = compact (Mdl);
545s  testInds = test (C);
545s  expected_margin = [2.0000;  0.8579;  1.6690;  3.4141;  3.4552; ...
545s                     2.6605;  3.5251; -4.0000; -6.3411; -6.4511; ...
545s                    -3.0532; -7.5054; -1.6700; -5.6227; -7.3640];
545s  computed_margin = margin (CMdl, x(testInds,:), y(testInds,:));
545s  assert (computed_margin, expected_margin, 1e-4);
545s ***** error<CompactClassificationSVM.margin: too few input arguments.> ...
545s  margin (CMdl)
545s ***** error<CompactClassificationSVM.margin: too few input arguments.> ...
545s  margin (CMdl, zeros (2))
545s ***** error<CompactClassificationSVM.margin: X is empty.> ...
545s  margin (CMdl, [], 1)
545s ***** error<CompactClassificationSVM.margin: X must have the same number of predictors as the trained SVM model.> ...
545s  margin (CMdl, 1, 1)
545s ***** error<CompactClassificationSVM.margin: Y is empty.> ...
545s  margin (CMdl, [1, 2], [])
545s ***** error<CompactClassificationSVM.margin: Y must have the same number of rows as X.> ...
545s  margin (CMdl, [1, 2], [1; 2])
545s ***** test
545s  rand ("seed", 1);
545s  C = cvpartition (y, 'HoldOut', 0.15);
545s  Mdl = fitcsvm (x(training (C),:), y(training (C)), ...
545s                 'KernelFunction', 'rbf', 'Tolerance', 1e-7);
545s  CMdl = compact (Mdl);
545s  testInds = test (C);
545s  L1 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'binodeviance');
545s  L2 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'classiferror');
545s  L3 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'exponential');
545s  L4 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'hinge');
545s  L5 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'logit');
545s  L6 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'quadratic');
545s  assert (L1, 2.8711, 1e-4);
545s  assert (L2, 0.5333, 1e-4);
545s  assert (L3, 10.9685, 1e-4);
545s  assert (L4, 1.9827, 1e-4);
545s  assert (L5, 1.5849, 1e-4);
545s  assert (L6, 7.6739, 1e-4);
545s ***** error<CompactClassificationSVM.loss: too few input arguments.> ...
545s  loss (CMdl)
545s ***** error<CompactClassificationSVM.loss: too few input arguments.> ...
545s  loss (CMdl, zeros (2))
545s ***** error<CompactClassificationSVM.loss: Name-Value arguments must be in pairs.> ...
545s  loss (CMdl, [1, 2], 1, "LossFun")
545s ***** error<CompactClassificationSVM.loss: X is empty.> ...
545s  loss (CMdl, [], zeros (2))
545s ***** error<CompactClassificationSVM.loss: X must have the same number of predictors as the trained SVM model.> ...
545s  loss (CMdl, 1, zeros (2))
545s ***** error<CompactClassificationSVM.loss: Y is empty.> ...
545s  loss (CMdl, [1, 2], [])
545s ***** error<CompactClassificationSVM.loss: Y must have the same number of rows as X.> ...
545s  loss (CMdl, [1, 2], [1; 2])
545s ***** error<CompactClassificationSVM.loss: 'LossFun' must be a character vector.> ...
545s  loss (CMdl, [1, 2], 1, "LossFun", 1)
545s ***** error<CompactClassificationSVM.loss: unsupported Loss function.> ...
545s  loss (CMdl, [1, 2], 1, "LossFun", "some")
545s ***** error<CompactClassificationSVM.loss: 'Weights' must be a numeric vector.> ...
545s  loss (CMdl, [1, 2], 1, "Weights", ['a', 'b'])
545s ***** error<CompactClassificationSVM.loss: 'Weights' must be a numeric vector.> ...
545s  loss (CMdl, [1, 2], 1, "Weights", 'a')
545s ***** error<CompactClassificationSVM.loss: size of 'Weights' must be equal to the number of rows in X.> ...
545s  loss (CMdl, [1, 2], 1, "Weights", [1, 2])
545s ***** error<CompactClassificationSVM.loss: invalid parameter name in optional pair arguments.> ...
545s  loss (CMdl, [1, 2], 1, "some", "some")
545s 29 tests, 29 passed, 0 known failure, 0 skipped
545s [inst/Classification/ConfusionMatrixChart.m]
545s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/Classification/ConfusionMatrixChart.m
545s ***** demo
545s  ## Create a simple ConfusionMatrixChart Object
545s 
545s  cm = ConfusionMatrixChart (gca, [1 2; 1 2], {"A","B"}, {"XLabel","LABEL A"})
545s  NormalizedValues = cm.NormalizedValues
545s  ClassLabels = cm.ClassLabels
545s ***** test
545s  hf = figure ("visible", "off");
545s  unwind_protect
545s    cm = ConfusionMatrixChart (gca, [1 2; 1 2], {"A","B"}, {"XLabel","LABEL A"});
545s    assert (isa (cm, "ConfusionMatrixChart"), true);
545s  unwind_protect_cleanup
545s    close (hf);
545s  end_unwind_protect
545s 1 test, 1 passed, 0 known failure, 0 skipped
545s [inst/Classification/ClassificationKNN.m]
545s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/Classification/ClassificationKNN.m
545s ***** demo
545s  ## Create a k-nearest neighbor classifier for Fisher's iris data with k = 5.
545s  ## Evaluate some model predictions on new data.
545s 
545s  load fisheriris
545s  x = meas;
545s  y = species;
545s  xc = [min(x); mean(x); max(x)];
545s  obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);
545s  [label, score, cost] = predict (obj, xc)
545s ***** demo
545s  load fisheriris
545s  x = meas;
545s  y = species;
545s  obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);
545s 
545s  ## Create a cross-validated model
545s  CVMdl = crossval (obj)
545s ***** demo
545s  load fisheriris
545s  x = meas;
545s  y = species;
545s  covMatrix = cov (x);
545s 
545s  ## Fit the k-NN model using the 'mahalanobis' distance
545s  ## and the custom covariance matrix
545s  obj = fitcknn(x, y, 'NumNeighbors', 5, 'Distance','mahalanobis', ...
545s  'Cov', covMatrix);
545s 
545s  ## Create a partition model using cvpartition
545s  Partition = cvpartition (size (x, 1), 'kfold', 12);
545s 
545s  ## Create cross-validated model using 'cvPartition' name-value argument
545s  CVMdl = crossval (obj, 'cvPartition', Partition)
545s 
545s  ## Access the trained model from first fold of cross-validation
545s  CVMdl.Trained{1}
545s ***** demo
545s  X = [1, 2; 3, 4; 5, 6];
545s  Y = {'A'; 'B'; 'A'};
545s  model = fitcknn (X, Y);
545s  customLossFun = @(C, S, W, Cost) sum (W .* sum (abs (C - S), 2));
545s  ## Calculate loss using custom loss function
545s  L = loss (model, X, Y, 'LossFun', customLossFun)
545s ***** demo
545s  X = [1, 2; 3, 4; 5, 6];
545s  Y = {'A'; 'B'; 'A'};
545s  model = fitcknn (X, Y);
545s  ## Calculate loss using 'mincost' loss function
545s  L = loss (model, X, Y, 'LossFun', 'mincost')
545s ***** demo
545s  X = [1, 2; 3, 4; 5, 6];
545s  Y = ['1'; '2'; '3'];
545s  model = fitcknn (X, Y);
545s  X_test = [3, 3; 5, 7];
545s  Y_test = ['1'; '2'];
545s  ## Specify custom Weights
545s  W = [1; 2];
545s  L = loss (model, X_test, Y_test, 'LossFun', 'logit', 'Weights', W);
545s ***** demo
545s  load fisheriris
545s  mdl = fitcknn (meas, species);
545s  X = mean (meas);
545s  Y = {'versicolor'};
545s  m = margin (mdl, X, Y)
545s ***** demo
545s  X = [1, 2; 4, 5; 7, 8; 3, 2];
545s  Y = [2; 1; 3; 2];
545s  ## Train the model
545s  mdl = fitcknn (X, Y);
545s  ## Specify Vars and Labels
545s  Vars = 1;
545s  Labels = 2;
545s  ## Calculate partialDependence
545s  [pd, x, y] = partialDependence (mdl, Vars, Labels);
545s ***** demo
545s  X = [1, 2; 4, 5; 7, 8; 3, 2];
545s  Y = [2; 1; 3; 2];
545s  ## Train the model
545s  mdl = fitcknn (X, Y);
545s  ## Specify Vars and Labels
545s  Vars = 1;
545s  Labels = 1;
545s  queryPoints = [linspace(0, 1, 3)', linspace(0, 1, 3)'];
545s  ## Calculate partialDependence using queryPoints
545s  [pd, x, y] = partialDependence (mdl, Vars, Labels, 'QueryPoints', ...
545s  queryPoints)
545s ***** test
545s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
545s  y = ["a"; "a"; "b"; "b"];
545s  a = ClassificationKNN (x, y);
545s  assert (class (a), "ClassificationKNN");
545s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
545s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
545s  assert ({a.BucketSize}, {50})
545s ***** test
545s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
545s  y = ["a"; "a"; "b"; "b"];
545s  a = ClassificationKNN (x, y, "NSMethod", "exhaustive");
545s  assert (class (a), "ClassificationKNN");
545s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
545s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
545s  assert ({a.BucketSize}, {50})
545s ***** test
545s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
545s  y = ["a"; "a"; "b"; "b"];
545s  k = 10;
545s  a = ClassificationKNN (x, y, "NumNeighbors" ,k);
545s  assert (class (a), "ClassificationKNN");
545s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
545s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
545s  assert ({a.BucketSize}, {50})
545s ***** test
545s  x = ones (4, 11);
545s  y = ["a"; "a"; "b"; "b"];
545s  k = 10;
545s  a = ClassificationKNN (x, y, "NumNeighbors" ,k);
545s  assert (class (a), "ClassificationKNN");
545s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
545s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
545s  assert ({a.BucketSize}, {50})
545s ***** test
545s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
545s  y = ["a"; "a"; "b"; "b"];
545s  k = 10;
545s  a = ClassificationKNN (x, y, "NumNeighbors" ,k, "NSMethod", "exhaustive");
545s  assert (class (a), "ClassificationKNN");
545s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
545s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
545s  assert ({a.BucketSize}, {50})
545s ***** test
545s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
545s  y = ["a"; "a"; "b"; "b"];
545s  k = 10;
545s  a = ClassificationKNN (x, y, "NumNeighbors" ,k, "Distance", "hamming");
545s  assert (class (a), "ClassificationKNN");
545s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
545s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "hamming"})
545s  assert ({a.BucketSize}, {50})
545s ***** test
545s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
545s  y = ["a"; "a"; "b"; "b"];
545s  weights = ones (4,1);
545s  a = ClassificationKNN (x, y, "Standardize", 1);
545s  assert (class (a), "ClassificationKNN");
545s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
545s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
545s  assert ({a.Standardize}, {true})
545s  assert ({a.Sigma}, {std(x, [], 1)})
545s  assert ({a.Mu}, {[3.75, 4.25, 4.75]})
545s ***** test
545s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
545s  y = ["a"; "a"; "b"; "b"];
545s  weights = ones (4,1);
545s  a = ClassificationKNN (x, y, "Standardize", false);
545s  assert (class (a), "ClassificationKNN");
545s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
545s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
545s  assert ({a.Standardize}, {false})
545s  assert ({a.Sigma}, {[]})
545s  assert ({a.Mu}, {[]})
545s ***** test
545s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
545s  y = ["a"; "a"; "b"; "b"];
545s  s = ones (1, 3);
545s  a = ClassificationKNN (x, y, "Scale" , s, "Distance", "seuclidean");
545s  assert (class (a), "ClassificationKNN");
545s  assert ({a.DistParameter}, {s})
545s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "seuclidean"})
545s  assert ({a.BucketSize}, {50})
545s ***** test
545s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
545s  y = ["a"; "a"; "b"; "b"];
545s  a = ClassificationKNN (x, y, "Exponent" , 5, "Distance", "minkowski");
545s  assert (class (a), "ClassificationKNN");
545s  assert (a.DistParameter, 5)
545s  assert ({a.NSMethod, a.Distance}, {"kdtree", "minkowski"})
545s ***** test
545s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
545s  y = ["a"; "a"; "b"; "b"];
545s  a = ClassificationKNN (x, y, "Exponent" , 5, "Distance", "minkowski", ...
545s                         "NSMethod", "exhaustive");
545s  assert (class (a), "ClassificationKNN");
545s  assert (a.DistParameter, 5)
545s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "minkowski"})
545s ***** test
545s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
545s  y = ["a"; "a"; "b"; "b"];
545s  a = ClassificationKNN (x, y, "BucketSize" , 20, "distance", "mahalanobis");
545s  assert (class (a), "ClassificationKNN");
545s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "mahalanobis"})
545s  assert ({a.BucketSize}, {20})
545s ***** test
545s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
545s  y = ["a"; "a"; "b"; "b"];
545s  a = ClassificationKNN (x, y, "IncludeTies", true);
545s  assert (class (a), "ClassificationKNN");
545s  assert (a.IncludeTies, true);
545s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
545s ***** test
545s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
545s  y = ["a"; "a"; "b"; "b"];
545s  a = ClassificationKNN (x, y);
545s  assert (class (a), "ClassificationKNN");
545s  assert (a.IncludeTies, false);
545s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
545s ***** test
545s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
545s  y = ["a"; "a"; "b"; "b"];
545s  a = ClassificationKNN (x, y);
545s  assert (class (a), "ClassificationKNN")
545s  assert (a.Prior, [0.5; 0.5])
545s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
545s  assert ({a.BucketSize}, {50})
545s ***** test
545s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
545s  y = ["a"; "a"; "b"; "b"];
545s  prior = [0.5; 0.5];
545s  a = ClassificationKNN (x, y, "Prior", "empirical");
545s  assert (class (a), "ClassificationKNN")
545s  assert (a.Prior, prior)
545s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
545s  assert ({a.BucketSize}, {50})
546s ***** test
546s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
546s  y = ["a"; "a"; "a"; "b"];
546s  prior = [0.75; 0.25];
546s  a = ClassificationKNN (x, y, "Prior", "empirical");
546s  assert (class (a), "ClassificationKNN")
546s  assert (a.Prior, prior)
546s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
546s  assert ({a.BucketSize}, {50})
546s ***** test
546s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
546s  y = ["a"; "a"; "a"; "b"];
546s  prior = [0.5; 0.5];
546s  a = ClassificationKNN (x, y, "Prior", "uniform");
546s  assert (class (a), "ClassificationKNN")
546s  assert (a.Prior, prior)
546s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
546s  assert ({a.BucketSize}, {50})
546s ***** test
546s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
546s  y = ["a"; "a"; "b"; "b"];
546s  cost = eye (2);
546s  a = ClassificationKNN (x, y, "Cost", cost);
546s  assert (class (a), "ClassificationKNN")
546s  assert (a.Cost, [1, 0; 0, 1])
546s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
546s  assert ({a.BucketSize}, {50})
546s ***** test
546s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
546s  y = ["a"; "a"; "b"; "b"];
546s  cost = eye (2);
546s  a = ClassificationKNN (x, y, "Cost", cost, "Distance", "hamming" );
546s  assert (class (a), "ClassificationKNN")
546s  assert (a.Cost, [1, 0; 0, 1])
546s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "hamming"})
546s  assert ({a.BucketSize}, {50})
546s ***** test
546s  x = [1, 2; 3, 4; 5,6; 5, 8];
546s  y = {'9'; '9'; '6'; '7'};
546s  a = ClassificationKNN (x, y);
546s  assert (a.Prior, [0.5; 0.25; 0.25])
546s ***** test
546s  load fisheriris
546s  x = meas;
546s  y = species;
546s  ClassNames = {'setosa', 'versicolor', 'virginica'};
546s  a = ClassificationKNN (x, y, 'ClassNames', ClassNames);
546s  assert (a.ClassNames, ClassNames')
546s ***** error<ClassificationKNN: too few input arguments.> ClassificationKNN ()
546s ***** error<ClassificationKNN: too few input arguments.> ...
546s  ClassificationKNN (ones(4, 1))
546s ***** error<ClassificationKNN: number of rows in X and Y must be equal.> ...
546s  ClassificationKNN (ones (4,2), ones (1,4))
546s ***** error<ClassificationKNN: 'Standardize' must be either true or false.> ...
546s  ClassificationKNN (ones (5,3), ones (5,1), "standardize", "a")
546s ***** error<ClassificationKNN: 'Standardize' cannot simultaneously be specified with> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "scale", [1 1], "standardize", true)
546s ***** error<ClassificationKNN: 'PredictorNames' must be supplied as a cellstring array.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "PredictorNames", ["A"])
546s ***** error<ClassificationKNN: 'PredictorNames' must be supplied as a cellstring array.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "PredictorNames", "A")
546s ***** error<ClassificationKNN: 'PredictorNames' must have the same number of columns as X.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "PredictorNames", {"A", "B", "C"})
546s ***** error<ClassificationKNN: 'ResponseName' must be a character vector.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "ResponseName", {"Y"})
546s ***** error<ClassificationKNN: 'ResponseName' must be a character vector.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "ResponseName", 1)
546s ***** error<ClassificationKNN: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
546s  ClassificationKNN (ones(10,2), ones (10,1), "ClassNames", @(x)x)
546s ***** error<ClassificationKNN: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
546s  ClassificationKNN (ones(10,2), ones (10,1), "ClassNames", ['a'])
546s ***** error<ClassificationKNN: not all 'ClassNames' are present in Y.> ...
546s  ClassificationKNN (ones(10,2), ones (10,1), "ClassNames", [1, 2])
546s ***** error<ClassificationKNN: not all 'ClassNames' are present in Y.> ...
546s  ClassificationKNN (ones(5,2), {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
546s ***** error<ClassificationKNN: not all 'ClassNames' are present in Y.> ...
546s  ClassificationKNN (ones(10,2), logical (ones (10,1)), "ClassNames", [true, false])
546s ***** error<ClassificationKNN: 'BreakTies' must be a character vector.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "BreakTies", 1)
546s ***** error<ClassificationKNN: 'BreakTies' must be a character vector.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "BreakTies", {"1"})
546s ***** error<ClassificationKNN: invalid value for 'BreakTies'.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "BreakTies", "some")
546s ***** error<ClassificationKNN: 'Prior' must be either a numeric vector or a character vector.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "Prior", {"1", "2"})
546s ***** error<ClassificationKNN: 'Cost' must be a numeric square matrix.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "Cost", [1, 2])
546s ***** error<ClassificationKNN: 'Cost' must be a numeric square matrix.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "Cost", "string")
546s ***** error<ClassificationKNN: 'Cost' must be a numeric square matrix.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "Cost", {eye(2)})
546s ***** error<ClassificationKNN: 'NumNeighbors' must be a positive integer.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "NumNeighbors", 0)
546s ***** error<ClassificationKNN: 'NumNeighbors' must be a positive integer.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "NumNeighbors", 15.2)
546s ***** error<ClassificationKNN: 'NumNeighbors' must be a positive integer.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "NumNeighbors", "asd")
546s ***** error<ClassificationKNN: unsupported distance metric.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", "somemetric")
546s ***** error<ClassificationKNN: invalid function handle for distance metric.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", ...
546s                     @(v,m)sqrt(repmat(v,rows(m),1)-m,2))
546s ***** error<ClassificationKNN: custom distance function produces wrong output size.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", ...
546s                     @(v,m)sqrt(sum(sumsq(repmat(v,rows(m),1)-m,2))))
546s ***** error<ClassificationKNN: invalid distance metric.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", [1 2 3])
546s ***** error<ClassificationKNN: invalid distance metric.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", {"mahalanobis"})
546s ***** error<ClassificationKNN: invalid distance metric.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", logical (5))
546s ***** error<ClassificationKNN: function handle for distance weight must return the> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "DistanceWeight", @(x)sum(x))
546s ***** error<ClassificationKNN: invalid distance weight.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "DistanceWeight", "text")
546s ***** error<ClassificationKNN: invalid distance weight.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "DistanceWeight", [1 2 3])
546s ***** error<ClassificationKNN: 'Scale' must be a numeric vector.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "Scale", "scale")
546s ***** error<ClassificationKNN: 'Scale' must be a numeric vector.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "Scale", {[1 2 3]})
546s ***** error<ClassificationKNN: 'Scale' cannot simultaneously be specified with> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "standardize", true, "scale", [1 1])
546s ***** error<ClassificationKNN: 'Cov' must be a symmetric positive definite matrix.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "Cov", ones (2), "Distance", "mahalanobis")
546s ***** error<ClassificationKNN: 'Cov' cannot simultaneously be specified with> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "scale", [1 1], "Cov", ones (2))
546s ***** error<ClassificationKNN: 'Exponent' must be a positive integer.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "Exponent", 12.5)
546s ***** error<ClassificationKNN: 'Exponent' must be a positive integer.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "Exponent", -3)
546s ***** error<ClassificationKNN: 'Exponent' must be a positive integer.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "Exponent", "three")
546s ***** error<ClassificationKNN: 'Exponent' must be a positive integer.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "Exponent", {3})
546s ***** error<ClassificationKNN: 'NSMethod' must be a character vector.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "NSMethod", {"kdtree"})
546s ***** error<ClassificationKNN: 'NSMethod' must be a character vector.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "NSMethod", 3)
546s ***** error<ClassificationKNN: 'NSMethod' must be either 'kdtree' or 'exhaustive'.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "NSMethod", "some")
546s ***** error<ClassificationKNN: 'IncludeTies' must be either true or false.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "IncludeTies", "some")
546s ***** error<ClassificationKNN: 'BucketSize' must be a positive integer.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "BucketSize", 42.5)
546s ***** error<ClassificationKNN: 'BucketSize' must be a positive integer.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "BucketSize", -50)
546s ***** error<ClassificationKNN: 'BucketSize' must be a positive integer.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "BucketSize", "some")
546s ***** error<ClassificationKNN: 'BucketSize' must be a positive integer.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "BucketSize", {50})
546s ***** error<ClassificationKNN: invalid parameter name in optional pair arguments.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "some", "some")
546s ***** error<ClassificationKNN: invalid values in X.> ...
546s  ClassificationKNN ([1;2;3;'a';4], ones (5,1))
546s ***** error<ClassificationKNN: invalid values in X.> ...
546s  ClassificationKNN ([1;2;3;Inf;4], ones (5,1))
546s ***** error<ClassificationKNN: the elements in 'Prior' do not correspond to selected classes in Y.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "Prior", [1 2])
546s ***** error<ClassificationKNN: the number of rows and columns in 'Cost' must correspond to selected classes in Y.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "Cost", [1 2; 1 3])
546s ***** error<ClassificationKNN: 'Scale' is only valid when distance metric is seuclidean.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "Scale", [1 1])
546s ***** error<ClassificationKNN: 'Scale' vector must have equal length to the number of columns in X.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "Scale", [1 1 1], "Distance", "seuclidean")
546s ***** error<ClassificationKNN: 'Scale' vector must contain nonnegative scalar values.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "Scale", [1 -1], "Distance", "seuclidean")
546s ***** error<ClassificationKNN: 'Cov' is only valid when distance metric is 'mahalanobis'.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "Cov", eye (2))
546s ***** error<ClassificationKNN: 'Cov' matrix must have equal columns as X.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "Cov", eye (3), "Distance", "mahalanobis")
546s ***** error<ClassificationKNN: 'Exponent' is only valid when distance metric is 'minkowski'.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "Exponent", 3)
546s ***** error<ClassificationKNN: 'kdtree' method is only valid for 'euclidean', 'cityblock', 'manhattan', 'minkowski', and 'chebychev' distance metrics.> ...
546s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", "hamming", "NSMethod", "kdtree")
546s ***** shared x, y
546s  load fisheriris
546s  x = meas;
546s  y = species;
546s ***** test
546s  xc = [min(x); mean(x); max(x)];
546s  obj = fitcknn (x, y, "NumNeighbors", 5);
546s  [l, s, c] = predict (obj, xc);
546s  assert (l, {"setosa"; "versicolor"; "virginica"})
546s  assert (s, [1, 0, 0; 0, 1, 0; 0, 0, 1])
546s  assert (c, [0, 1, 1; 1, 0, 1; 1, 1, 0])
546s ***** test
546s  xc = [min(x); mean(x); max(x)];
546s  obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);
546s  [l, s, c] = predict (obj, xc);
546s  assert (l, {"versicolor"; "versicolor"; "virginica"})
546s  assert (s, [0.4, 0.6, 0; 0, 1, 0; 0, 0, 1])
546s  assert (c, [0.6, 0.4, 1; 1, 0, 1; 1, 1, 0])
546s ***** test
546s  xc = [min(x); mean(x); max(x)];
546s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "mahalanobis");
546s  [l, s, c] = predict (obj, xc);
546s  assert (s, [0.3, 0.7, 0; 0, 0.9, 0.1; 0.2, 0.2, 0.6], 1e-4)
546s  assert (c, [0.7, 0.3, 1; 1, 0.1, 0.9; 0.8, 0.8, 0.4], 1e-4)
546s ***** test
546s  xc = [min(x); mean(x); max(x)];
546s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "cosine");
546s  [l, s, c] = predict (obj, xc);
546s  assert (l, {"setosa"; "versicolor"; "virginica"})
546s  assert (s, [1, 0, 0; 0, 1, 0; 0, 0.3, 0.7], 1e-4)
546s  assert (c, [0, 1, 1; 1, 0, 1; 1, 0.7, 0.3], 1e-4)
546s ***** test
546s  xc = [5.2, 4.1, 1.5, 0.1; 5.1, 3.8, 1.9, 0.4; ...
546s          5.1, 3.8, 1.5, 0.3; 4.9, 3.6, 1.4, 0.1];
546s  obj = fitcknn (x, y, "NumNeighbors", 5);
546s  [l, s, c] = predict (obj, xc);
546s  assert (l, {"setosa"; "setosa"; "setosa"; "setosa"})
546s  assert (s, [1, 0, 0; 1, 0, 0; 1, 0, 0; 1, 0, 0])
546s  assert (c, [0, 1, 1; 0, 1, 1; 0, 1, 1; 0, 1, 1])
546s ***** test
546s  xc = [5, 3, 5, 1.45];
546s  obj = fitcknn (x, y, "NumNeighbors", 5);
546s  [l, s, c] = predict (obj, xc);
546s  assert (l, {"versicolor"})
546s  assert (s, [0, 0.6, 0.4], 1e-4)
546s  assert (c, [1, 0.4, 0.6], 1e-4)
546s ***** test
546s  xc = [5, 3, 5, 1.45];
546s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "minkowski", "Exponent", 5);
546s  [l, s, c] = predict (obj, xc);
546s  assert (l, {"versicolor"})
546s  assert (s, [0, 0.5, 0.5], 1e-4)
546s  assert (c, [1, 0.5, 0.5], 1e-4)
546s ***** test
546s  xc = [5, 3, 5, 1.45];
546s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "jaccard");
546s  [l, s, c] = predict (obj, xc);
546s  assert (l, {"setosa"})
546s  assert (s, [0.9, 0.1, 0], 1e-4)
546s  assert (c, [0.1, 0.9, 1], 1e-4)
546s ***** test
546s  xc = [5, 3, 5, 1.45];
546s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "mahalanobis");
546s  [l, s, c] = predict (obj, xc);
546s  assert (l, {"versicolor"})
546s  assert (s, [0.1000, 0.5000, 0.4000], 1e-4)
546s  assert (c, [0.9000, 0.5000, 0.6000], 1e-4)
546s ***** test
546s  xc = [5, 3, 5, 1.45];
546s  obj = fitcknn (x, y, "NumNeighbors", 5, "distance", "jaccard");
546s  [l, s, c] = predict (obj, xc);
546s  assert (l, {"setosa"})
546s  assert (s, [0.8, 0.2, 0], 1e-4)
546s  assert (c, [0.2, 0.8, 1], 1e-4)
546s ***** test
546s  xc = [5, 3, 5, 1.45];
546s  obj = fitcknn (x, y, "NumNeighbors", 5, "distance", "seuclidean");
546s  [l, s, c] = predict (obj, xc);
546s  assert (l, {"versicolor"})
546s  assert (s, [0, 1, 0], 1e-4)
546s  assert (c, [1, 0, 1], 1e-4)
546s ***** test
546s  xc = [5, 3, 5, 1.45];
546s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "chebychev");
546s  [l, s, c] = predict (obj, xc);
546s  assert (l, {"versicolor"})
546s  assert (s, [0, 0.7, 0.3], 1e-4)
546s  assert (c, [1, 0.3, 0.7], 1e-4)
546s ***** test
546s  xc = [5, 3, 5, 1.45];
546s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "cityblock");
546s  [l, s, c] = predict (obj, xc);
546s  assert (l, {"versicolor"})
546s  assert (s, [0, 0.6, 0.4], 1e-4)
546s  assert (c, [1, 0.4, 0.6], 1e-4)
546s ***** test
546s  xc = [5, 3, 5, 1.45];
546s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "cosine");
546s  [l, s, c] = predict (obj, xc);
546s  assert (l, {"virginica"})
546s  assert (s, [0, 0.1, 0.9], 1e-4)
546s  assert (c, [1, 0.9, 0.1], 1e-4)
546s ***** test
546s  xc = [5, 3, 5, 1.45];
546s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "correlation");
546s  [l, s, c] = predict (obj, xc);
546s  assert (l, {"virginica"})
546s  assert (s, [0, 0.1, 0.9], 1e-4)
546s  assert (c, [1, 0.9, 0.1], 1e-4)
546s ***** test
546s  xc = [5, 3, 5, 1.45];
546s  obj = fitcknn (x, y, "NumNeighbors", 30, "distance", "spearman");
546s  [l, s, c] = predict (obj, xc);
546s  assert (l, {"versicolor"})
546s  assert (s, [0, 1, 0], 1e-4)
546s  assert (c, [1, 0, 1], 1e-4)
546s ***** test
546s  xc = [5, 3, 5, 1.45];
546s  obj = fitcknn (x, y, "NumNeighbors", 30, "distance", "hamming");
546s  [l, s, c] = predict (obj, xc);
546s  assert (l, {"setosa"})
546s  assert (s, [0.4333, 0.3333, 0.2333], 1e-4)
546s  assert (c, [0.5667, 0.6667, 0.7667], 1e-4)
546s ***** test
546s  xc = [5, 3, 5, 1.45];
546s  obj = fitcknn (x, y, "NumNeighbors", 5, "distance", "hamming");
546s  [l, s, c] = predict (obj, xc);
546s  assert (l, {"setosa"})
546s  assert (s, [0.8, 0.2, 0], 1e-4)
546s  assert (c, [0.2, 0.8, 1], 1e-4)
546s ***** test
546s  xc = [min(x); mean(x); max(x)];
546s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "correlation");
546s  [l, s, c] = predict (obj, xc);
546s  assert (l, {"setosa"; "versicolor"; "virginica"})
546s  assert (s, [1, 0, 0; 0, 1, 0; 0, 0.4, 0.6], 1e-4)
546s  assert (c, [0, 1, 1; 1, 0, 1; 1, 0.6, 0.4], 1e-4)
546s ***** test
546s  xc = [min(x); mean(x); max(x)];
546s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "hamming");
546s  [l, s, c] = predict (obj, xc);
546s  assert (l, {"setosa";"setosa";"setosa"})
546s  assert (s, [0.9, 0.1, 0; 1, 0, 0; 0.5, 0, 0.5], 1e-4)
546s  assert (c, [0.1, 0.9, 1; 0, 1, 1; 0.5, 1, 0.5], 1e-4)
546s ***** error<ClassificationKNN.predict: too few input arguments.> ...
546s  predict (ClassificationKNN (ones (4,2), ones (4,1)))
546s ***** error<ClassificationKNN.predict: XC is empty.> ...
546s  predict (ClassificationKNN (ones (4,2), ones (4,1)), [])
546s ***** error<ClassificationKNN.predict: XC must have the same number of predictors as the trained model.> ...
546s  predict (ClassificationKNN (ones (4,2), ones (4,1)), 1)
546s ***** test
546s  load fisheriris
546s  model = fitcknn (meas, species, 'NumNeighbors', 5);
546s  X = mean (meas);
546s  Y = {'versicolor'};
546s  L = loss (model, X, Y);
546s  assert (L, 0)
546s ***** test
546s  X = [1, 2; 3, 4; 5, 6];
546s  Y = {'A'; 'B'; 'A'};
546s  model = fitcknn (X, Y);
546s  X_test = [1, 6; 3, 3];
546s  Y_test = {'A'; 'B'};
546s  L = loss (model, X_test, Y_test);
546s  assert (abs (L - 0.6667) > 1e-5)
546s ***** test
546s  X = [1, 2; 3, 4; 5, 6];
546s  Y = {'A'; 'B'; 'A'};
546s  model = fitcknn (X, Y);
546s  X_with_nan = [1, 2; NaN, 4];
546s  Y_test = {'A'; 'B'};
546s  L = loss (model, X_with_nan, Y_test);
546s  assert (abs (L - 0.3333) < 1e-4)
546s ***** test
546s  X = [1, 2; 3, 4; 5, 6];
546s  Y = {'A'; 'B'; 'A'};
546s  model = fitcknn (X, Y);
546s  X_with_nan = [1, 2; NaN, 4];
546s  Y_test = {'A'; 'B'};
546s  L = loss (model, X_with_nan, Y_test, 'LossFun', 'logit');
546s  assert (isnan (L))
546s ***** test
546s  X = [1, 2; 3, 4; 5, 6];
546s  Y = {'A'; 'B'; 'A'};
546s  model = fitcknn (X, Y);
546s  customLossFun = @(C, S, W, Cost) sum (W .* sum (abs (C - S), 2));
546s  L = loss (model, X, Y, 'LossFun', customLossFun);
546s  assert (L, 0)
546s ***** test
546s  X = [1, 2; 3, 4; 5, 6];
546s  Y = [1; 2; 1];
546s  model = fitcknn (X, Y);
546s  L = loss (model, X, Y, 'LossFun', 'classiferror');
546s  assert (L, 0)
546s ***** test
546s  X = [1, 2; 3, 4; 5, 6];
546s  Y = [true; false; true];
546s  model = fitcknn (X, Y);
546s  L = loss (model, X, Y, 'LossFun', 'binodeviance');
546s  assert (abs (L - 0.1269) < 1e-4)
546s ***** test
546s  X = [1, 2; 3, 4; 5, 6];
546s  Y = ['1'; '2'; '1'];
546s  model = fitcknn (X, Y);
546s  L = loss (model, X, Y, 'LossFun', 'classiferror');
546s  assert (L, 0)
546s ***** test
546s  X = [1, 2; 3, 4; 5, 6];
546s  Y = ['1'; '2'; '3'];
546s  model = fitcknn (X, Y);
546s  X_test = [3, 3];
546s  Y_test = ['1'];
546s  L = loss (model, X_test, Y_test, 'LossFun', 'quadratic');
546s  assert (L, 1)
546s ***** test
546s  X = [1, 2; 3, 4; 5, 6];
546s  Y = ['1'; '2'; '3'];
546s  model = fitcknn (X, Y);
546s  X_test = [3, 3; 5, 7];
546s  Y_test = ['1'; '2'];
546s  L = loss (model, X_test, Y_test, 'LossFun', 'classifcost');
546s  assert (L, 1)
546s ***** test
546s  X = [1, 2; 3, 4; 5, 6];
546s  Y = ['1'; '2'; '3'];
546s  model = fitcknn (X, Y);
546s  X_test = [3, 3; 5, 7];
546s  Y_test = ['1'; '2'];
546s  L = loss (model, X_test, Y_test, 'LossFun', 'hinge');
546s  assert (L, 1)
546s ***** test
546s  X = [1, 2; 3, 4; 5, 6];
546s  Y = ['1'; '2'; '3'];
546s  model = fitcknn (X, Y);
546s  X_test = [3, 3; 5, 7];
546s  Y_test = ['1'; '2'];
546s  W = [1; 2];
546s  L = loss (model, X_test, Y_test, 'LossFun', 'logit', 'Weights', W);
546s  assert (abs (L - 0.6931) < 1e-4)
546s ***** error<ClassificationKNN.loss: too few input arguments.> ...
546s  loss (ClassificationKNN (ones (4,2), ones (4,1)))
546s ***** error<ClassificationKNN.loss: too few input arguments.> ...
546s  loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2))
546s ***** error<ClassificationKNN.loss: X is empty.> ...
546s  loss (ClassificationKNN (ones (40,2), randi ([1, 2], 40, 1)), [], zeros (2))
546s ***** error<ClassificationKNN.loss: X must have the same number of predictors as the trained model.> ...
546s  loss (ClassificationKNN (ones (40,2), randi ([1, 2], 40, 1)), 1, zeros (2))
546s ***** error<ClassificationKNN.loss: name-value arguments must be in pairs.> ...
546s  loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ...
546s         ones (4,1), 'LossFun')
546s ***** error<ClassificationKNN.loss: Y must have the same number of rows as X.> ...
546s  loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ones (3,1))
546s ***** error<ClassificationKNN.loss: invalid loss function.> ...
546s  loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ...
546s         ones (4,1), 'LossFun', 'a')
546s ***** error<ClassificationKNN.loss: invalid Weights.> ...
546s  loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ...
546s         ones (4,1), 'Weights', 'w')
546s ***** test
546s  load fisheriris
546s  mdl = fitcknn (meas, species, 'NumNeighbors', 5);
546s  X = mean (meas);
546s  Y = {'versicolor'};
546s  m = margin (mdl, X, Y);
546s  assert (m, 1)
546s ***** test
546s  X = [1, 2; 3, 4; 5, 6];
546s  Y = [1; 2; 3];
546s  mdl = fitcknn (X, Y);
546s  m = margin (mdl, X, Y);
546s  assert (m, [1; 1; 1])
546s ***** test
546s  X = [7, 8; 9, 10];
546s  Y = ['1'; '2'];
546s  mdl = fitcknn (X, Y);
546s  m = margin (mdl, X, Y);
546s  assert (m, [1; 1])
546s ***** test
546s  X = [11, 12];
546s  Y = {'1'};
546s  mdl = fitcknn (X, Y);
546s  m = margin (mdl, X, Y);
546s  assert (isnan (m))
546s ***** test
546s  X = [1, 2; 3, 4; 5, 6];
546s  Y = [1; 2; 3];
546s  mdl = fitcknn (X, Y);
546s  X1 = [15, 16];
546s  Y1 = [1];
546s  m = margin (mdl, X1, Y1);
546s  assert (m, -1)
546s ***** error<ClassificationKNN.margin: too few input arguments.> ...
546s  margin (ClassificationKNN (ones (4,2), ones (4,1)))
546s ***** error<ClassificationKNN.margin: too few input arguments.> ...
546s  margin (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2))
546s ***** error<ClassificationKNN.margin: X is empty.> ...
546s  margin (ClassificationKNN (ones (40,2), randi ([1, 2], 40, 1)), [], zeros (2))
546s ***** error<ClassificationKNN.margin: X must have the same number of predictors as the trained model.> ...
546s  margin (ClassificationKNN (ones (40,2), randi ([1, 2], 40, 1)), 1, zeros (2))
546s ***** error<ClassificationKNN.margin: Y must have the same number of rows as X.> ...
546s  margin (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ones (3,1))
546s ***** shared X, Y, mdl
546s  X = [1, 2; 4, 5; 7, 8; 3, 2];
546s  Y = [2; 1; 3; 2];
546s  mdl = fitcknn (X, Y);
546s ***** test
546s  Vars = 1;
546s  Labels = 2;
546s  [pd, x, y] = partialDependence (mdl, Vars, Labels);
546s  pdm = [0.7500, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
546s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
546s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
546s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
546s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
546s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
546s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
546s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
546s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
546s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
546s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
546s  0.5000, 0.5000];
546s  assert (pd, pdm)
546s ***** test
546s  Vars = 1;
546s  Labels = 2;
546s  [pd, x, y] = partialDependence (mdl, Vars, Labels, ...
546s  'NumObservationsToSample', 5);
546s  pdm = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
546s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
546s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
546s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
546s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0];
546s  assert (abs (pdm - pd) < 1)
547s ***** test
547s  Vars = 1;
547s  Labels = 2;
547s  [pd, x, y] = partialDependence (mdl, Vars, Labels, 'UseParallel', true);
547s  pdm = [0.7500, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
547s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
547s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
547s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
547s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
547s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
547s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
547s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
547s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
547s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
547s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
547s  0.5000, 0.5000];
547s  assert (pd, pdm)
547s ***** test
547s  Vars = [1, 2];
547s  Labels = 1;
547s  queryPoints = {linspace(0, 1, 3)', linspace(0, 1, 3)'};
547s  [pd, x, y] = partialDependence (mdl, Vars, Labels, 'QueryPoints', ...
547s                             queryPoints, 'UseParallel', true);
547s  pdm = [0, 0, 0; 0, 0, 0; 0, 0, 0];
547s  assert (pd, pdm)
547s ***** test
547s  Vars = 1;
547s  Labels = [1; 2];
547s  [pd, x, y] = partialDependence (mdl, Vars, Labels);
547s  pdm = [0.2500, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
547s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
547s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
547s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
547s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
547s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.2500, 0.2500, 0.2500, ...
547s  0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
547s  0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
547s  0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
547s  0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
547s  0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
547s  0.2500, 0.2500; 0.7500, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
547s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
547s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
547s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
547s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
547s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
547s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
547s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
547s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
547s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
547s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
547s  0.5000, 0.5000, 0.5000];
547s  assert (pd, pdm)
547s ***** test
547s  Vars = [1, 2];
547s  Labels = [1; 2];
547s  queryPoints = {linspace(0, 1, 3)', linspace(0, 1, 3)'};
547s  [pd, x, y] = partialDependence (mdl, Vars, Labels, 'QueryPoints', queryPoints);
547s  pdm(:,:,1) = [0, 0, 0; 1, 1, 1];
547s  pdm(:,:,2) = [0, 0, 0; 1, 1, 1];
547s  pdm(:,:,3) = [0, 0, 0; 1, 1, 1];
547s  assert (pd, pdm)
547s ***** test
547s  X1 = [1; 2; 4; 5; 7; 8; 3; 2];
547s  X2 = ['2'; '3'; '1'; '3'; '1'; '3'; '2'; '2'];
547s  X = [X1, double(X2)];
547s  Y = [1; 2; 3; 3; 2; 1; 2; 1];
547s  mdl = fitcknn (X, Y, 'ClassNames', {'1', '2', '3'});
547s  Vars = 1;
547s  Labels = 1;
547s  [pd, x, y] = partialDependence (mdl, Vars, Labels);
547s  pdm = [1.0000, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, ...
547s  0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, ...
547s  0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
547s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
547s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3750, ...
547s  0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, ...
547s  0.3750, 0.3750, 0.3750, 0.3750, 0.7500, 0.7500, 0.7500, 0.7500, 0.7500, ...
547s  0.7500, 0.7500, 0.7500];
547s  assert (pd, pdm)
547s ***** test
547s  X1 = [1; 2; 4; 5; 7; 8; 3; 2];
547s  X2 = ['2'; '3'; '1'; '3'; '1'; '3'; '2'; '2'];
547s  X = [X1, double(X2)];
547s  Y = [1; 2; 3; 3; 2; 1; 2; 1];
547s  predictorNames = {'Feature1', 'Feature2'};
547s  mdl = fitcknn (X, Y, 'PredictorNames', predictorNames);
547s  Vars = 'Feature1';
547s  Labels = 1;
547s  [pd, x, y] = partialDependence (mdl, Vars, Labels);
547s  pdm = [1.0000, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, ...
547s  0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, ...
547s  0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
547s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
547s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3750, ...
547s  0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, ...
547s  0.3750, 0.3750, 0.3750, 0.3750, 0.7500, 0.7500, 0.7500, 0.7500, 0.7500, ...
547s  0.7500, 0.7500, 0.7500];
547s  assert (pd, pdm)
548s ***** test
548s  X1 = [1; 2; 4; 5; 7; 8; 3; 2];
548s  X2 = ['2'; '3'; '1'; '3'; '1'; '3'; '2'; '2'];
548s  X = [X1, double(X2)];
548s  Y = [1; 2; 3; 3; 2; 1; 2; 1];
548s  predictorNames = {'Feature1', 'Feature2'};
548s  mdl = fitcknn (X, Y, 'PredictorNames', predictorNames);
548s  new_X1 = [10; 5; 6; 8; 9; 20; 35; 6];
548s  new_X2 = ['2'; '2'; '1'; '2'; '1'; '3'; '3'; '2'];
548s  new_X = [new_X1, double(new_X2)];
548s  Vars = 'Feature1';
548s  Labels = 1;
548s  [pd, x, y] = partialDependence (mdl, Vars, Labels, new_X);
548s  pdm = [0, 0, 0, 0, 0, 0.2500, 0.2500, 0.2500, 0.2500, 0.7500, 0.7500, ...
548s  0.7500, 0.7500, 0.7500, 0.7500, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, ...
548s  1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, ...
548s  1.0000, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
548s  1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
548s  1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1];
548s  assert (pd, pdm)
548s ***** error<ClassificationKNN.partialDependence: too few input arguments.> ...
548s  partialDependence (ClassificationKNN (ones (4,2), ones (4,1)))
548s ***** error<ClassificationKNN.partialDependence: too few input arguments.> ...
548s  partialDependence (ClassificationKNN (ones (4,2), ones (4,1)), 1)
548s ***** error<ClassificationKNN.partialDependence: name-value arguments must be in pairs.> ...
548s  partialDependence (ClassificationKNN (ones (4,2), ones (4,1)), 1, ...
548s           ones (4,1), 'NumObservationsToSample')
548s ***** error<ClassificationKNN.partialDependence: name-value arguments must be in pairs.> ...
548s  partialDependence (ClassificationKNN (ones (4,2), ones (4,1)), 1, ...
548s           ones (4,1), 2)
548s ***** shared x, y, obj
548s  load fisheriris
548s  x = meas;
548s  y = species;
548s  covMatrix = cov (x);
548s  obj = fitcknn (x, y, 'NumNeighbors', 5, 'Distance', ...
548s       'mahalanobis', 'Cov', covMatrix);
548s ***** test
548s  CVMdl = crossval (obj);
548s  assert (class (CVMdl), "ClassificationPartitionedModel")
548s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
548s  assert (CVMdl.KFold == 10)
548s  assert (CVMdl.ModelParameters.NumNeighbors == 5)
548s  assert (strcmp (CVMdl.ModelParameters.Distance, "mahalanobis"))
548s  assert (class (CVMdl.Trained{1}), "ClassificationKNN")
548s  assert (!CVMdl.ModelParameters.Standardize)
548s ***** test
548s  CVMdl = crossval (obj, "KFold", 5);
548s  assert (class (CVMdl), "ClassificationPartitionedModel")
548s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
548s  assert (CVMdl.KFold == 5)
548s  assert (CVMdl.ModelParameters.NumNeighbors == 5)
548s  assert (strcmp (CVMdl.ModelParameters.Distance, "mahalanobis"))
548s  assert (class (CVMdl.Trained{1}), "ClassificationKNN")
548s  assert (CVMdl.ModelParameters.Standardize == obj.Standardize)
548s ***** test
548s  obj = fitcknn (x, y, "NumNeighbors", 5, "Distance", "cityblock");
548s  CVMdl = crossval (obj, "HoldOut", 0.2);
548s  assert (class (CVMdl), "ClassificationPartitionedModel")
548s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
548s  assert (CVMdl.ModelParameters.NumNeighbors == 5)
548s  assert (strcmp (CVMdl.ModelParameters.Distance, "cityblock"))
548s  assert (class (CVMdl.Trained{1}), "ClassificationKNN")
548s  assert (CVMdl.ModelParameters.Standardize == obj.Standardize)
548s ***** test
548s  obj = fitcknn (x, y, "NumNeighbors", 10, "Distance", "cityblock");
548s  CVMdl = crossval (obj, "LeaveOut", 'on');
548s  assert (class (CVMdl), "ClassificationPartitionedModel")
548s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
548s  assert (CVMdl.ModelParameters.NumNeighbors == 10)
548s  assert (strcmp (CVMdl.ModelParameters.Distance, "cityblock"))
548s  assert (class (CVMdl.Trained{1}), "ClassificationKNN")
548s  assert (CVMdl.ModelParameters.Standardize == obj.Standardize)
548s ***** test
548s  obj = fitcknn (x, y, "NumNeighbors", 10, "Distance", "cityblock");
548s  partition = cvpartition (y, 'KFold', 3);
548s  CVMdl = crossval (obj, 'cvPartition', partition);
548s  assert (class (CVMdl), "ClassificationPartitionedModel")
548s  assert (CVMdl.KFold == 3)
548s  assert (CVMdl.ModelParameters.NumNeighbors == 10)
548s  assert (strcmp (CVMdl.ModelParameters.Distance, "cityblock"))
548s  assert (class (CVMdl.Trained{1}), "ClassificationKNN")
548s  assert (CVMdl.ModelParameters.Standardize == obj.Standardize)
548s ***** error<ClassificationKNN.crossval: Name-Value arguments must be in pairs.> ...
548s  crossval (ClassificationKNN (ones (4,2), ones (4,1)), "kfold")
548s ***** error<ClassificationKNN.crossval: specify only one of the optional Name-Value paired arguments.>...
548s  crossval (ClassificationKNN (ones (4,2), ones (4,1)), "kfold", 12, "holdout", 0.2)
548s ***** error<ClassificationKNN.crossval: 'KFold' must be an integer value greater than 1.> ...
548s  crossval (ClassificationKNN (ones (4,2), ones (4,1)), "kfold", 'a')
548s ***** error<ClassificationKNN.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
548s  crossval (ClassificationKNN (ones (4,2), ones (4,1)), "holdout", 2)
548s ***** error<ClassificationKNN.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
548s  crossval (ClassificationKNN (ones (4,2), ones (4,1)), "leaveout", 1)
548s ***** error<ClassificationKNN.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
548s  crossval (ClassificationKNN (ones (4,2), ones (4,1)), "cvpartition", 1)
548s 162 tests, 162 passed, 0 known failure, 0 skipped
548s [inst/Classification/ClassificationDiscriminant.m]
548s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/Classification/ClassificationDiscriminant.m
548s ***** demo
548s  ## Create discriminant classifier
548s  ## Evaluate some model predictions on new data.
548s 
548s  load fisheriris
548s  x = meas;
548s  y = species;
548s  xc = [min(x); mean(x); max(x)];
548s  obj = fitcdiscr (x, y);
548s  [label, score, cost] = predict (obj, xc);
548s ***** demo
548s  load fisheriris
548s  model = fitcdiscr (meas, species);
548s  X = mean (meas);
548s  Y = {'versicolor'};
548s  ## Compute loss for discriminant model
548s  L = loss (model, X, Y)
548s ***** demo
548s  load fisheriris
548s  mdl = fitcdiscr (meas, species);
548s  X = mean (meas);
548s  Y = {'versicolor'};
548s  ## Margin for discriminant model
548s  m = margin (mdl, X, Y)
548s ***** demo
548s  load fisheriris
548s  x = meas;
548s  y = species;
548s  obj = fitcdiscr (x, y, "gamma", 0.4);
548s  ## Cross-validation for discriminant model
548s  CVMdl = crossval (obj)
548s ***** test
548s  load fisheriris
548s  x = meas;
548s  y = species;
548s  PredictorNames = {'Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width'};
548s  Mdl = ClassificationDiscriminant (x, y, "PredictorNames", PredictorNames);
548s  sigma = [0.265008, 0.092721, 0.167514, 0.038401; ...
548s           0.092721, 0.115388, 0.055244, 0.032710; ...
548s           0.167514, 0.055244, 0.185188, 0.042665; ...
548s           0.038401, 0.032710, 0.042665, 0.041882];
548s  mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
548s        5.9360, 2.7700, 4.2600, 1.3260; ...
548s        6.5880, 2.9740, 5.5520, 2.0260];
548s  xCentered = [ 9.4000e-02,  7.2000e-02, -6.2000e-02, -4.6000e-02; ...
548s               -1.0600e-01, -4.2800e-01, -6.2000e-02, -4.6000e-02; ...
548s               -3.0600e-01, -2.2800e-01, -1.6200e-01, -4.6000e-02];
548s  assert (class (Mdl), "ClassificationDiscriminant");
548s  assert ({Mdl.X, Mdl.Y, Mdl.NumObservations}, {x, y, 150})
548s  assert ({Mdl.DiscrimType, Mdl.ResponseName}, {"linear", "Y"})
548s  assert ({Mdl.Gamma, Mdl.MinGamma}, {0, 0}, 1e-15)
548s  assert (Mdl.ClassNames, unique (species))
548s  assert (Mdl.Sigma, sigma, 1e-6)
548s  assert (Mdl.Mu, mu, 1e-14)
548s  assert (Mdl.XCentered([1:3],:), xCentered, 1e-14)
548s  assert (Mdl.LogDetSigma, -9.9585, 1e-4)
548s  assert (Mdl.PredictorNames, PredictorNames)
549s ***** test
549s  load fisheriris
549s  x = meas;
549s  y = species;
549s  Mdl = ClassificationDiscriminant (x, y, "Gamma", 0.5);
549s  sigma = [0.265008, 0.046361, 0.083757, 0.019201; ...
549s           0.046361, 0.115388, 0.027622, 0.016355; ...
549s           0.083757, 0.027622, 0.185188, 0.021333; ...
549s           0.019201, 0.016355, 0.021333, 0.041882];
549s  mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
549s        5.9360, 2.7700, 4.2600, 1.3260; ...
549s        6.5880, 2.9740, 5.5520, 2.0260];
549s  xCentered = [ 9.4000e-02,  7.2000e-02, -6.2000e-02, -4.6000e-02; ...
549s               -1.0600e-01, -4.2800e-01, -6.2000e-02, -4.6000e-02; ...
549s               -3.0600e-01, -2.2800e-01, -1.6200e-01, -4.6000e-02];
549s  assert (class (Mdl), "ClassificationDiscriminant");
549s  assert ({Mdl.X, Mdl.Y, Mdl.NumObservations}, {x, y, 150})
549s  assert ({Mdl.DiscrimType, Mdl.ResponseName}, {"linear", "Y"})
549s  assert ({Mdl.Gamma, Mdl.MinGamma}, {0.5, 0})
549s  assert (Mdl.ClassNames, unique (species))
549s  assert (Mdl.Sigma, sigma, 1e-6)
549s  assert (Mdl.Mu, mu, 1e-14)
549s  assert (Mdl.XCentered([1:3],:), xCentered, 1e-14)
549s  assert (Mdl.LogDetSigma, -8.6884, 1e-4)
549s ***** shared X, Y, MODEL
549s  X = rand (10,2);
549s  Y = [ones(5,1);2*ones(5,1)];
549s  MODEL = ClassificationDiscriminant (X, Y);
549s ***** error<ClassificationDiscriminant: too few input arguments.> ClassificationDiscriminant ()
549s ***** error<ClassificationDiscriminant: too few input arguments.> ...
549s  ClassificationDiscriminant (ones(4, 1))
549s ***** error<ClassificationDiscriminant: number of rows in X and Y must be equal.> ...
549s  ClassificationDiscriminant (ones (4,2), ones (1,4))
549s ***** error<ClassificationDiscriminant: 'PredictorNames' must be supplied as a cellstring array.> ...
549s  ClassificationDiscriminant (X, Y, "PredictorNames", ["A"])
549s ***** error<ClassificationDiscriminant: 'PredictorNames' must be supplied as a cellstring array.> ...
549s  ClassificationDiscriminant (X, Y, "PredictorNames", "A")
549s ***** error<ClassificationDiscriminant: 'PredictorNames' must equal the number of columns in X.> ...
549s  ClassificationDiscriminant (X, Y, "PredictorNames", {"A", "B", "C"})
549s ***** error<ClassificationDiscriminant: 'ResponseName' must be a character vector.> ...
549s  ClassificationDiscriminant (X, Y, "ResponseName", {"Y"})
549s ***** error<ClassificationDiscriminant: 'ResponseName' must be a character vector.> ...
549s  ClassificationDiscriminant (X, Y, "ResponseName", 1)
549s ***** error<ClassificationDiscriminant: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
549s  ClassificationDiscriminant (X, Y, "ClassNames", @(x)x)
549s ***** error<ClassificationDiscriminant: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
549s  ClassificationDiscriminant (X, Y, "ClassNames", ['a'])
549s ***** error<ClassificationDiscriminant: not all 'ClassNames' are present in Y.> ...
549s  ClassificationDiscriminant (X, ones (10,1), "ClassNames", [1, 2])
549s ***** error<ClassificationDiscriminant: not all 'ClassNames' are present in Y.> ...
549s  ClassificationDiscriminant ([1;2;3;4;5], {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
549s ***** error<ClassificationDiscriminant: not all 'ClassNames' are present in Y.> ...
549s  ClassificationDiscriminant (X, logical (ones (10,1)), "ClassNames", [true, false])
549s ***** error<ClassificationDiscriminant: 'Prior' must be either a numeric or a character vector.> ...
549s  ClassificationDiscriminant (X, Y, "Prior", {"1", "2"})
549s ***** error<ClassificationDiscriminant: the elements in 'Prior' do not correspond to the selected classes in Y.> ...
549s  ClassificationDiscriminant (X, ones (10,1), "Prior", [1 2])
549s ***** error<ClassificationDiscriminant: 'Cost' must be a numeric square matrix.> ...
549s  ClassificationDiscriminant (X, Y, "Cost", [1, 2])
549s ***** error<ClassificationDiscriminant: 'Cost' must be a numeric square matrix.> ...
549s  ClassificationDiscriminant (X, Y, "Cost", "string")
549s ***** error<ClassificationDiscriminant: 'Cost' must be a numeric square matrix.> ...
549s  ClassificationDiscriminant (X, Y, "Cost", {eye(2)})
549s ***** error<ClassificationDiscriminant: the number of rows and columns in 'Cost' must correspond to selected classes in Y.> ...
549s  ClassificationDiscriminant (X, Y, "Cost", ones (3))
549s ***** error<ClassificationDiscriminant: Predictor 'x1' has zero within-class variance.> ...
549s  ClassificationDiscriminant (ones (5,2), [1; 1; 2; 2; 2])
549s ***** error<ClassificationDiscriminant: Predictor 'A' has zero within-class variance.> ...
549s  ClassificationDiscriminant (ones (5,2), [1; 1; 2; 2; 2], "PredictorNames", {"A", "B"})
549s ***** error<ClassificationDiscriminant: Predictor 'x2' has zero within-class variance.> ...
549s  ClassificationDiscriminant ([1,2;2,2;3,2;4,2;5,2], ones (5, 1))
549s ***** error<ClassificationDiscriminant: Predictor 'B' has zero within-class variance.> ...
549s  ClassificationDiscriminant ([1,2;2,2;3,2;4,2;5,2], ones (5, 1), "PredictorNames", {"A", "B"})
549s ***** test
549s  load fisheriris
549s  x = meas;
549s  y = species;
549s  Mdl = fitcdiscr (meas, species, "Gamma", 0.5);
549s  [label, score, cost] = predict (Mdl, [2, 2, 2, 2]);
549s  assert (label, {'versicolor'})
549s  assert (score, [0, 0.9999, 0.0001], 1e-4)
549s  assert (cost, [1, 0.0001, 0.9999], 1e-4)
549s  [label, score, cost] = predict (Mdl, [2.5, 2.5, 2.5, 2.5]);
549s  assert (label, {'versicolor'})
549s  assert (score, [0, 0.6368, 0.3632], 1e-4)
549s  assert (cost, [1, 0.3632, 0.6368], 1e-4)
549s ***** test
549s  load fisheriris
549s  x = meas;
549s  y = species;
549s  xc = [min(x); mean(x); max(x)];
549s  Mdl = fitcdiscr (x, y);
549s  [label, score, cost] = predict (Mdl, xc);
549s  l = {'setosa'; 'versicolor'; 'virginica'};
549s  s = [1, 0, 0; 0, 1, 0; 0, 0, 1];
549s  c = [0, 1, 1; 1, 0, 1; 1, 1, 0];
549s  assert (label, l)
549s  assert (score, s, 1e-4)
549s  assert (cost, c, 1e-4)
549s ***** error<ClassificationDiscriminant.predict: too few input arguments.> ...
549s  predict (MODEL)
549s ***** error<ClassificationDiscriminant.predict: XC is empty.> ...
549s  predict (MODEL, [])
549s ***** error<ClassificationDiscriminant.predict: XC must have the same number of predictors as the trained model.> ...
549s  predict (MODEL, 1)
549s ***** test
549s  load fisheriris
549s  model = fitcdiscr (meas, species);
549s  x = mean (meas);
549s  y = {'versicolor'};
549s  L = loss (model, x, y);
549s  assert (L, 0)
549s ***** test
549s  x = [1, 2; 3, 4; 5, 6];
549s  y = {'A'; 'B'; 'A'};
549s  model = fitcdiscr (x, y, "Gamma", 0.4);
549s  x_test = [1, 6; 3, 3];
549s  y_test = {'A'; 'B'};
549s  L = loss (model, x_test, y_test);
549s  assert (L, 0.3333, 1e-4)
549s ***** test
549s  x = [1, 2; 3, 4; 5, 6; 7, 8];
549s  y = ['1'; '2'; '3'; '1'];
549s  model = fitcdiscr (x, y, "gamma" , 0.5);
549s  x_test = [3, 3];
549s  y_test = ['1'];
549s  L = loss (model, x_test, y_test, 'LossFun', 'quadratic');
549s  assert (L, 0.2423, 1e-4)
549s ***** test
549s  x = [1, 2; 3, 4; 5, 6; 7, 8];
549s  y = ['1'; '2'; '3'; '1'];
549s  model = fitcdiscr (x, y, "gamma" , 0.5);
549s  x_test = [3, 3; 5, 7];
549s  y_test = ['1'; '2'];
549s  L = loss (model, x_test, y_test, 'LossFun', 'classifcost');
549s  assert (L, 0.3333, 1e-4)
549s ***** test
549s  x = [1, 2; 3, 4; 5, 6; 7, 8];
549s  y = ['1'; '2'; '3'; '1'];
549s  model = fitcdiscr (x, y, "gamma" , 0.5);
549s  x_test = [3, 3; 5, 7];
549s  y_test = ['1'; '2'];
549s  L = loss (model, x_test, y_test, 'LossFun', 'hinge');
549s  assert (L, 0.5886, 1e-4)
549s ***** test
549s  x = [1, 2; 3, 4; 5, 6; 7, 8];
549s  y = ['1'; '2'; '3'; '1'];
549s  model = fitcdiscr (x, y, "gamma" , 0.5);
549s  x_test = [3, 3; 5, 7];
549s  y_test = ['1'; '2'];
549s  W = [1; 2];
549s  L = loss (model, x_test, y_test, 'LossFun', 'logit', 'Weights', W);
549s  assert (L, 0.5107, 1e-4)
549s ***** test
549s  x = [1, 2; 3, 4; 5, 6];
549s  y = {'A'; 'B'; 'A'};
549s  model = fitcdiscr (x, y, "gamma" , 0.5);
549s  x_with_nan = [1, 2; NaN, 4];
549s  y_test = {'A'; 'B'};
549s  L = loss (model, x_with_nan, y_test);
549s  assert (L, 0.3333, 1e-4)
549s ***** test
549s  x = [1, 2; 3, 4; 5, 6];
549s  y = {'A'; 'B'; 'A'};
549s  model = fitcdiscr (x, y);
549s  x_with_nan = [1, 2; NaN, 4];
549s  y_test = {'A'; 'B'};
549s  L = loss (model, x_with_nan, y_test, 'LossFun', 'logit');
549s  assert (isnan (L))
549s ***** test
549s  x = [1, 2; 3, 4; 5, 6];
549s  y = {'A'; 'B'; 'A'};
549s  model = fitcdiscr (x, y);
549s  customLossFun = @(C, S, W, Cost) sum (W .* sum (abs (C - S), 2));
549s  L = loss (model, x, y, 'LossFun', customLossFun);
549s  assert (L, 0.8889, 1e-4)
549s ***** test
549s  x = [1, 2; 3, 4; 5, 6];
549s  y = [1; 2; 1];
549s  model = fitcdiscr (x, y);
549s  L = loss (model, x, y, 'LossFun', 'classiferror');
549s  assert (L, 0.3333, 1e-4)
549s ***** error<ClassificationDiscriminant.loss: too few input arguments.> ...
549s  loss (MODEL)
549s ***** error<ClassificationDiscriminant.loss: too few input arguments.> ...
549s  loss (MODEL, ones (4,2))
549s ***** error<ClassificationDiscriminant.loss: X is empty.> ...
549s  loss (MODEL, [], zeros (2))
549s ***** error<ClassificationDiscriminant.loss: X must have the same number of predictors as the trained model.> ...
549s  loss (MODEL, 1, zeros (2))
549s ***** error<ClassificationDiscriminant.loss: name-value arguments must be in pairs.> ...
549s  loss (MODEL, ones (4,2), ones (4,1), 'LossFun')
549s ***** error<ClassificationDiscriminant.loss: Y must have the same number of rows as X.> ...
549s  loss (MODEL, ones (4,2), ones (3,1))
549s ***** error<ClassificationDiscriminant.loss: invalid loss function.> ...
549s  loss (MODEL, ones (4,2), ones (4,1), 'LossFun', 'a')
549s ***** error<ClassificationDiscriminant.loss: invalid 'Weights'.> ...
549s  loss (MODEL, ones (4,2), ones (4,1), 'Weights', 'w')
549s  load fisheriris
549s  mdl = fitcdiscr (meas, species);
549s  X = mean (meas);
549s  Y = {'versicolor'};
549s  m = margin (mdl, X, Y);
549s  assert (m, 1, 1e-6)
549s ***** test
549s  X = [1, 2; 3, 4; 5, 6];
549s  Y = [1; 2; 1];
549s  mdl = fitcdiscr (X, Y, "gamma", 0.5);
549s  m = margin (mdl, X, Y);
549s  assert (m, [0.3333; -0.3333; 0.3333], 1e-4)
549s ***** error<ClassificationDiscriminant.margin: too few input arguments.> ...
549s  margin (MODEL)
549s ***** error<ClassificationDiscriminant.margin: too few input arguments.> ...
549s  margin (MODEL, ones (4,2))
549s ***** error<ClassificationDiscriminant.margin: X is empty.> ...
549s  margin (MODEL, [], zeros (2))
549s ***** error<ClassificationDiscriminant.margin: X must have the same number of predictors as the trained model.> ...
549s  margin (MODEL, 1, zeros (2))
549s ***** error<ClassificationDiscriminant.margin: Y must have the same number of rows as X.> ...
549s  margin (MODEL, ones (4,2), ones (3,1))
549s ***** shared x, y, obj
549s  load fisheriris
549s  x = meas;
549s  y = species;
549s  obj = fitcdiscr (x, y, "gamma", 0.4);
549s ***** test
549s  CVMdl = crossval (obj);
549s  assert (class (CVMdl), "ClassificationPartitionedModel")
549s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
549s  assert (CVMdl.KFold == 10)
549s  assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
549s  assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
549s ***** test
549s  CVMdl = crossval (obj, "KFold", 3);
549s  assert (class (CVMdl), "ClassificationPartitionedModel")
549s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
549s  assert (CVMdl.KFold == 3)
549s  assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
549s  assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
549s ***** test
549s  CVMdl = crossval (obj, "HoldOut", 0.2);
549s  assert (class (CVMdl), "ClassificationPartitionedModel")
549s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
549s  assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
549s  assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
549s ***** test
549s  CVMdl = crossval (obj, "LeaveOut", 'on');
549s  assert (class (CVMdl), "ClassificationPartitionedModel")
549s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
549s  assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
549s  assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
550s ***** test
550s  partition = cvpartition (y, 'KFold', 3);
550s  CVMdl = crossval (obj, 'cvPartition', partition);
550s  assert (class (CVMdl), "ClassificationPartitionedModel")
550s  assert (CVMdl.KFold == 3)
550s  assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
550s  assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
550s ***** error<ClassificationDiscriminant.crossval: Name-Value arguments must be in pairs.> ...
550s  crossval (obj, "kfold")
550s ***** error<ClassificationDiscriminant.crossval: specify only one of the optional Name-Value paired arguments.>...
550s  crossval (obj, "kfold", 12, "holdout", 0.2)
550s ***** error<ClassificationDiscriminant.crossval: 'KFold' must be an integer value greater than 1.> ...
550s  crossval (obj, "kfold", 'a')
550s ***** error<ClassificationDiscriminant.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
550s  crossval (obj, "holdout", 2)
550s ***** error<ClassificationDiscriminant.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
550s  crossval (obj, "leaveout", 1)
550s ***** error<ClassificationDiscriminant.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
550s  crossval (obj, "cvpartition", 1)
550s 65 tests, 65 passed, 0 known failure, 0 skipped
550s [inst/Classification/ClassificationSVM.m]
550s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/Classification/ClassificationSVM.m
550s ***** demo
550s  ## Create a Support Vector Machine classifier and determine margin for test
550s  ## data.
550s  load fisheriris
550s  rng(1);  ## For reproducibility
550s 
550s  ## Select indices of the non-setosa species
550s  inds = !strcmp(species, 'setosa');
550s 
550s   ## Select features and labels for non-setosa species
550s  X = meas(inds, 3:4);
550s  Y = grp2idx(species(inds));
550s 
550s  ##  Convert labels to +1 and -1
550s  unique_classes = unique(Y);
550s  Y(Y == unique_classes(1)) = -1;
550s  Y(Y == unique_classes(2)) = 1;
550s 
550s  ## Partition data for training and testing
550s  cv = cvpartition(Y, 'HoldOut', 0.15);
550s  X_train = X(training(cv), :);
550s  Y_train = Y(training(cv));
550s  X_test = X(test(cv), :);
550s  Y_test = Y(test(cv));
550s 
550s  ## Train the SVM model
550s  CVSVMModel = fitcsvm(X_train, Y_train);
550s 
550s  ## Calculate margins
550s  m = margin(CVSVMModel, X_test, Y_test);
550s  disp(m);
550s ***** demo
550s  ## Create a Support Vector Machine classifier and determine loss for test
550s  ## data.
550s  load fisheriris
550s  rng(1);  ## For reproducibility
550s 
550s   ## Select indices of the non-setosa species
550s  inds = !strcmp(species, 'setosa');
550s 
550s   ## Select features and labels for non-setosa species
550s  X = meas(inds, 3:4);
550s  Y = grp2idx(species(inds));
550s 
550s  ##  Convert labels to +1 and -1
550s  unique_classes = unique(Y);
550s  Y(Y == unique_classes(1)) = -1;
550s  Y(Y == unique_classes(2)) = 1;
550s 
550s  ## Randomly partition the data into training and testing sets
550s  cv = cvpartition(Y, 'HoldOut', 0.3); # 30% data for testing, 60% for training
550s 
550s  X_train = X(training(cv), :);
550s  Y_train = Y(training(cv));
550s 
550s  X_test = X(test(cv), :);
550s  Y_test = Y(test(cv));
550s 
550s  ## Train the SVM model
550s  SVMModel = fitcsvm(X_train, Y_train);
550s 
550s  ## Calculate loss
550s 
550s  L = loss(SVMModel,X_test,Y_test,'LossFun','binodeviance')
550s  L = loss(SVMModel,X_test,Y_test,'LossFun','classiferror')
550s  L = loss(SVMModel,X_test,Y_test,'LossFun','exponential')
550s  L = loss(SVMModel,X_test,Y_test,'LossFun','hinge')
550s  L = loss(SVMModel,X_test,Y_test,'LossFun','logit')
550s  L = loss(SVMModel,X_test,Y_test,'LossFun','quadratic')
550s ***** test
550s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1; 4, 5, 6; 7, 8, 9; ...
550s  3, 2, 1; 4, 5, 6; 7, 8, 9; 3, 2, 1; 4, 5, 6; 7, 8, 9; 3, 2, 1];
550s  y = [1; 2; 3; 4; 2; 3; 4; 2; 3; 4; 2; 3; 4];
550s  a = ClassificationSVM (x, y, "ClassNames", [1, 2]);
550s  assert (class (a), "ClassificationSVM");
550s  assert (a.RowsUsed, [1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0]');
550s  assert ({a.X, a.Y}, {x, y})
550s  assert (a.NumObservations, 5)
550s  assert ({a.ResponseName, a.PredictorNames}, {"Y", {"x1", "x2", "x3"}})
550s  assert ({a.ClassNames, a.ModelParameters.SVMtype}, {[1; 2], "c_svc"})
550s ***** test
550s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
550s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
550s  a = ClassificationSVM (x, y);
550s  assert (class (a), "ClassificationSVM");
550s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "linear"})
550s  assert (a.ModelParameters.BoxConstraint, 1)
550s  assert (a.ClassNames, [1; -1])
550s  assert (a.ModelParameters.KernelOffset, 0)
550s ***** test
550s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
550s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
550s  a = ClassificationSVM (x, y, "KernelFunction", "rbf", "BoxConstraint", 2, ...
550s  "KernelOffset", 2);
550s  assert (class (a), "ClassificationSVM");
550s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "rbf"})
550s  assert (a.ModelParameters.BoxConstraint, 2)
550s  assert (a.ModelParameters.KernelOffset, 2)
550s ***** test
550s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
550s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
550s  a = ClassificationSVM (x, y, "KernelFunction", "polynomial", ...
550s  "PolynomialOrder", 3);
550s  assert (class (a), "ClassificationSVM");
550s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "polynomial"})
550s  assert (a.ModelParameters.PolynomialOrder, 3)
550s ***** error<ClassificationSVM: too few input arguments.> ClassificationSVM ()
550s ***** error<ClassificationSVM: too few input arguments.> ...
550s  ClassificationSVM (ones(10,2))
550s ***** error<ClassificationSVM: number of rows in X and Y must be equal.> ...
550s  ClassificationSVM (ones(10,2), ones (5,1))
550s ***** error<ClassificationSVM: 'Standardize' must be either true or false.> ...
550s  ClassificationSVM (ones(10,2), ones (10,1), "Standardize", 'a')
550s ***** error<ClassificationSVM: 'PredictorNames' must be supplied as a cellstring array.> ...
550s  ClassificationSVM (ones(10,2), ones (10,1), "PredictorNames", ['x1';'x2'])
550s ***** error<ClassificationSVM: 'PredictorNames' must have the same number of columns as X.> ...
550s  ClassificationSVM (ones(10,2), ones (10,1), "PredictorNames", {'x1','x2','x3'})
550s ***** error<ClassificationSVM: 'ResponseName' must be a character vector.> ...
550s  ClassificationSVM (ones(10,2), ones (10,1), "ResponseName", {'Y'})
550s ***** error<ClassificationSVM: 'ResponseName' must be a character vector.> ...
550s  ClassificationSVM (ones(10,2), ones (10,1), "ResponseName", 21)
550s ***** error<ClassificationSVM: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
550s  ClassificationSVM (ones(10,2), ones (10,1), "ClassNames", @(x)x)
550s ***** error<ClassificationSVM: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
550s  ClassificationSVM (ones(10,2), ones (10,1), "ClassNames", ['a'])
550s ***** error<ClassificationSVM: not all 'ClassNames' are present in Y.> ...
550s  ClassificationSVM (ones(10,2), ones (10,1), "ClassNames", [1, 2])
550s ***** error<ClassificationSVM: not all 'ClassNames' are present in Y.> ...
550s  ClassificationSVM (ones(5,2), {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
550s ***** error<ClassificationSVM: not all 'ClassNames' are present in Y.> ...
550s  ClassificationSVM (ones(10,2), logical (ones (10,1)), "ClassNames", [true, false])
550s ***** error<ClassificationSVM: 'Prior' must be either a numeric vector or a character vector.> ...
550s  ClassificationSVM (ones(10,2), ones(10,1), "Prior", {"asd"})
550s ***** error<ClassificationSVM: 'Prior' must be either a numeric vector or a character vector.> ...
550s  ClassificationSVM (ones(10,2), ones(10,1), "Prior", ones (2))
550s ***** error<ClassificationSVM: 'Cost' must be a numeric square matrix.> ...
550s  ClassificationSVM (ones(10,2), ones(10,1), "Cost", [1:4])
550s ***** error<ClassificationSVM: 'Cost' must be a numeric square matrix.> ...
550s  ClassificationSVM (ones(10,2), ones(10,1), "Cost", {0,1;1,0})
550s ***** error<ClassificationSVM: 'Cost' must be a numeric square matrix.> ...
550s  ClassificationSVM (ones(10,2), ones(10,1), "Cost", 'a')
550s ***** error<ClassificationSVM: 'SVMtype' must be 'c_svc', 'nu_svc', or 'one_class_svm'.> ...
550s  ClassificationSVM (ones(10,2), ones(10,1), "svmtype", 123)
550s ***** error<ClassificationSVM: 'SVMtype' must be 'c_svc', 'nu_svc', or 'one_class_svm'.> ...
550s  ClassificationSVM (ones(10,2), ones(10,1), "svmtype", 'some_type')
550s ***** error<ClassificationSVM: 'OutlierFraction' must be a positive scalar in the range 0 =< OutlierFraction < 1.> ...
550s  ClassificationSVM (ones(10,2), ones(10,1), "OutlierFraction", -1)
550s ***** error<ClassificationSVM: 'KernelFunction' must be a character vector.> ...
550s  ClassificationSVM (ones(10,2), ones(10,1), "KernelFunction", 123)
550s ***** error<ClassificationSVM: unsupported Kernel function.> ...
550s  ClassificationSVM (ones(10,2), ones(10,1), "KernelFunction", "fcn")
550s ***** error<ClassificationSVM: 'PolynomialOrder' must be a positive integer.> ...
550s  ClassificationSVM (ones(10,2), ones(10,1), "PolynomialOrder", -1)
550s ***** error<ClassificationSVM: 'PolynomialOrder' must be a positive integer.> ...
550s  ClassificationSVM (ones(10,2), ones(10,1), "PolynomialOrder", 0.5)
550s ***** error<ClassificationSVM: 'PolynomialOrder' must be a positive integer.> ...
550s  ClassificationSVM (ones(10,2), ones(10,1), "PolynomialOrder", [1,2])
550s ***** error<ClassificationSVM: 'KernelScale' must be a positive scalar.> ...
550s  ClassificationSVM (ones(10,2), ones (10,1), "KernelScale", -1)
550s ***** error<ClassificationSVM: 'KernelScale' must be a positive scalar.> ...
550s  ClassificationSVM (ones(10,2), ones (10,1), "KernelScale", 0)
550s ***** error<ClassificationSVM: 'KernelScale' must be a positive scalar.> ...
550s  ClassificationSVM (ones(10,2), ones (10,1), "KernelScale", [1, 2])
550s ***** error<ClassificationSVM: 'KernelScale' must be a positive scalar.> ...
550s  ClassificationSVM (ones(10,2), ones (10,1), "KernelScale", "invalid")
550s ***** error<ClassificationSVM: 'KernelOffset' must be a non-negative scalar.> ...
550s  ClassificationSVM (ones(10,2), ones(10,1), "KernelOffset", -1)
550s ***** error<ClassificationSVM: 'KernelOffset' must be a non-negative scalar.> ...
550s  ClassificationSVM (ones(10,2), ones(10,1), "KernelOffset", [1,2])
550s ***** error<ClassificationSVM: 'BoxConstraint' must be a positive scalar.> ...
550s  ClassificationSVM (ones(10,2), ones (10,1), "BoxConstraint", -1)
550s ***** error<ClassificationSVM: 'BoxConstraint' must be a positive scalar.> ...
550s  ClassificationSVM (ones(10,2), ones (10,1), "BoxConstraint", 0)
550s ***** error<ClassificationSVM: 'BoxConstraint' must be a positive scalar.> ...
550s  ClassificationSVM (ones(10,2), ones (10,1), "BoxConstraint", [1, 2])
550s ***** error<ClassificationSVM: 'BoxConstraint' must be a positive scalar.> ...
550s  ClassificationSVM (ones(10,2), ones (10,1), "BoxConstraint", "invalid")
550s ***** error<ClassificationSVM: 'Nu' must be a positive scalar in the range 0 < Nu <= 1.> ...
550s  ClassificationSVM (ones(10,2), ones(10,1), "nu", -0.5)
550s ***** error<ClassificationSVM: 'Nu' must be a positive scalar in the range 0 < Nu <= 1.> ...
550s  ClassificationSVM (ones(10,2), ones(10,1), "nu", 0)
550s ***** error<ClassificationSVM: 'Nu' must be a positive scalar in the range 0 < Nu <= 1.> ...
550s  ClassificationSVM (ones(10,2), ones(10,1), "nu", 1.5)
550s ***** error<ClassificationSVM: 'CacheSize' must be a positive scalar.> ...
550s  ClassificationSVM (ones(10,2), ones(10,1), "CacheSize", -1)
550s ***** error<ClassificationSVM: 'CacheSize' must be a positive scalar.> ...
550s  ClassificationSVM (ones(10,2), ones(10,1), "CacheSize", [1,2])
550s ***** error<ClassificationSVM: 'Tolerance' must be a positive scalar.> ...
550s  ClassificationSVM (ones(10,2), ones(10,1), "Tolerance", -0.1)
550s ***** error<ClassificationSVM: 'Tolerance' must be a positive scalar.> ...
550s  ClassificationSVM (ones(10,2), ones(10,1), "Tolerance", [0.1,0.2])
550s ***** error<ClassificationSVM: 'Shrinking' must be either 0 or 1.> ...
550s  ClassificationSVM (ones(10,2), ones(10,1), "shrinking", 2)
550s ***** error<ClassificationSVM: 'Shrinking' must be either 0 or 1.> ...
550s  ClassificationSVM (ones(10,2), ones(10,1), "shrinking", -1)
550s ***** error<ClassificationSVM: 'Shrinking' must be either 0 or 1.> ...
550s  ClassificationSVM (ones(10,2), ones(10,1), "shrinking", [1 0])
550s ***** error<ClassificationSVM: invalid parameter name in optional pair arguments.> ...
550s  ClassificationSVM (ones(10,2), ones(10,1), "invalid_name", 'c_svc')
550s ***** error<ClassificationSVM: cannot train a binary problem with only one class available.> ...
550s  ClassificationSVM (ones(10,2), ones(10,1), "SVMtype", 'c_svc')
550s ***** error<ClassificationSVM: can only be used for one-class or two-class learning.> ...
550s  ClassificationSVM (ones(10,2), [1;1;1;1;2;2;2;2;3;3])
550s ***** error<ClassificationSVM: invalid values in X.> ...
550s  ClassificationSVM ([ones(9,2);2,Inf], ones(10,1))
550s ***** error<ClassificationSVM: the elements in 'Prior' do not correspond to selected classes in Y.> ...
550s  ClassificationSVM (ones (5,2), ones (5,1), "Prior", [0,1])
550s ***** error<ClassificationSVM: the elements in 'Prior' do not correspond to selected classes in Y.> ...
550s  ClassificationSVM (ones (5,2), [1;1;2;2;3], "ClassNames", [1,2], "Prior", [0,0.4,0.6])
550s ***** error<ClassificationSVM: the number of rows and columns in 'Cost' must correspond to the selected classes in Y.> ...
550s  ClassificationSVM (ones (5,2), [1;1;2;2;3], "ClassNames", [1,2], "Cost", ones (3))
550s ***** shared x, y, x_train, x_test, y_train, y_test, objST
550s  load fisheriris
550s  inds = ! strcmp (species, 'setosa');
550s  x = meas(inds, 3:4);
550s  y = grp2idx (species(inds));
550s ***** test
550s  xc = [min(x); mean(x); max(x)];
550s  obj = fitcsvm (x, y, 'KernelFunction', 'rbf', 'Tolerance', 1e-7);
550s  assert (isempty (obj.Alpha), true)
550s  assert (sum (obj.IsSupportVector), numel (obj.Beta))
550s  [label, score] = predict (obj, xc);
550s  assert (label, [1; 2; 2]);
550s  assert (score(:,1), [0.99285; -0.080296; -0.93694], 2e-5);
550s  assert (score(:,1), -score(:,2), eps)
550s  obj = fitPosterior (obj);
550s  [label, probs] = predict (obj, xc);
550s  assert (probs(:,2), [0.97555; 0.428164; 0.030385], 2e-5);
550s  assert (probs(:,1) + probs(:,2), [1; 1; 1], 0.05)
550s ***** test
550s  obj = fitcsvm (x, y);
550s  assert (isempty (obj.Beta), true)
550s  assert (sum (obj.IsSupportVector), numel (obj.Alpha))
550s  assert (numel (obj.Alpha), 24)
550s  assert (obj.Bias, -14.415, 1e-3)
550s  xc = [min(x); mean(x); max(x)];
550s  label = predict (obj, xc);
550s  assert (label, [1; 2; 2]);
550s ***** error<ClassificationSVM.predict: too few input arguments.> ...
550s  predict (ClassificationSVM (ones (40,2), ones (40,1)))
550s ***** error<ClassificationSVM.predict: XC is empty.> ...
550s  predict (ClassificationSVM (ones (40,2), ones (40,1)), [])
550s ***** error<ClassificationSVM.predict: XC must have the same number of predictors as the trained model.> ...
550s  predict (ClassificationSVM (ones (40,2), ones (40,1)), 1)
550s ***** test
550s  objST = fitcsvm (x, y);
550s  objST.ScoreTransform = "a";
550s ***** error<ClassificationSVM.predict: 'ScoreTransform' must be a 'function_handle' object.> ...
550s  [labels, scores] = predict (objST, x);
550s ***** error<ClassificationSVM.resubPredict: 'ScoreTransform' must be a 'function_handle' object.> ...
550s  [labels, scores] = resubPredict (objST);
550s ***** test
550s  rand ("seed", 1);
550s  CVSVMModel = fitcsvm (x, y, 'KernelFunction', 'rbf', 'HoldOut', 0.15, ...
550s                        'Tolerance', 1e-7);
550s  obj = CVSVMModel.Trained{1};
550s  testInds = test (CVSVMModel.Partition);
550s  expected_margin = [2.0000;  0.8579;  1.6690;  3.4141;  3.4552; ...
550s                     2.6605;  3.5251; -4.0000; -6.3411; -6.4511; ...
550s                    -3.0532; -7.5054; -1.6700; -5.6227; -7.3640];
550s  computed_margin = margin (obj, x(testInds,:), y(testInds,:));
550s  assert (computed_margin, expected_margin, 1e-4);
550s ***** error<ClassificationSVM.margin: too few input arguments.> ...
550s  margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)))
550s ***** error<ClassificationSVM.margin: too few input arguments.> ...
550s  margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2))
550s ***** error<ClassificationSVM.margin: X is empty.> ...
550s  margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), [], zeros (2))
550s ***** error<ClassificationSVM.margin: X must have the same number of predictors as the trained model.> ...
550s  margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), 1, zeros (2))
550s ***** error<ClassificationSVM.margin: Y is empty.> ...
550s  margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), [])
550s ***** error<ClassificationSVM.margin: Y must have the same number of rows as X.> ...
550s  margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), 1)
550s ***** test
550s  rand ("seed", 1);
550s  CVSVMModel = fitcsvm (x, y, 'KernelFunction', 'rbf', 'HoldOut', 0.15);
550s  obj = CVSVMModel.Trained{1};
550s  testInds = test (CVSVMModel.Partition);
550s  L1 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'binodeviance');
550s  L2 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'classiferror');
550s  L3 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'exponential');
550s  L4 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'hinge');
550s  L5 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'logit');
550s  L6 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'quadratic');
550s  assert (L1, 2.8711, 1e-4);
550s  assert (L2, 0.5333, 1e-4);
550s  assert (L3, 10.9685, 1e-4);
550s  assert (L4, 1.9827, 1e-4);
550s  assert (L5, 1.5849, 1e-4);
550s  assert (L6, 7.6739, 1e-4);
550s ***** error<ClassificationSVM.loss: too few input arguments.> ...
550s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)))
550s ***** error<ClassificationSVM.loss: too few input arguments.> ...
550s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2))
550s ***** error<ClassificationSVM.loss: Name-Value arguments must be in pairs.> ...
550s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
550s  ones(2,1), "LossFun")
550s ***** error<ClassificationSVM.loss: X is empty.> ...
550s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), [], zeros (2))
550s ***** error<ClassificationSVM.loss: X must have the same number of predictors as the trained model.> ...
550s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), 1, zeros (2))
550s ***** error<ClassificationSVM.loss: Y is empty.> ...
550s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), [])
550s ***** error<ClassificationSVM.loss: Y must have the same number of rows as X.> ...
550s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), 1)
550s ***** error<ClassificationSVM.loss: 'LossFun' must be a character vector.> ...
550s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
550s  ones (2,1), "LossFun", 1)
550s ***** error<ClassificationSVM.loss: unsupported Loss function.> ...
550s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
550s  ones (2,1), "LossFun", "some")
550s ***** error<ClassificationSVM.loss: 'Weights' must be a numeric vector.> ...
550s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
550s  ones (2,1), "Weights", ['a','b'])
550s ***** error<ClassificationSVM.loss: 'Weights' must be a numeric vector.> ...
550s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
550s  ones (2,1), "Weights", 'a')
550s ***** error<ClassificationSVM.loss: size of 'Weights' must be equal to the number> ...
550s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
550s  ones (2,1), "Weights", [1,2,3])
550s ***** error<ClassificationSVM.loss: size of 'Weights' must be equal to the number> ...
550s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
550s  ones (2,1), "Weights", 3)
550s ***** error<ClassificationSVM.loss: invalid parameter name in optional pair arg> ...
550s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
550s  ones (2,1), "some", "some")
550s ***** error<ClassificationSVM.resubLoss: Name-Value arguments must be in pairs.> ...
550s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "LossFun")
550s ***** error<ClassificationSVM.resubLoss: 'LossFun' must be a character vector.> ...
550s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "LossFun", 1)
550s ***** error<ClassificationSVM.resubLoss: unsupported Loss function.> ...
550s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "LossFun", "some")
550s ***** error<ClassificationSVM.resubLoss: 'Weights' must be a numeric vector.> ...
550s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "Weights", ['a','b'])
550s ***** error<ClassificationSVM.resubLoss: 'Weights' must be a numeric vector.> ...
550s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "Weights", 'a')
550s ***** error<ClassificationSVM.resubLoss: size of 'Weights' must be equal to the n> ...
550s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "Weights", [1,2,3])
550s ***** error<ClassificationSVM.resubLoss: size of 'Weights' must be equal to the n> ...
550s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "Weights", 3)
550s ***** error<ClassificationSVM.resubLoss: invalid parameter name in optional pai> ...
550s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "some", "some")
550s ***** test
550s  SVMModel = fitcsvm (x, y);
550s  CVMdl = crossval (SVMModel, "KFold", 5);
550s  assert (class (CVMdl), "ClassificationPartitionedModel")
550s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
550s  assert (CVMdl.KFold == 5)
550s  assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
550s  assert (CVMdl.CrossValidatedModel, "ClassificationSVM")
550s ***** test
550s  obj = fitcsvm (x, y);
550s  CVMdl = crossval (obj, "HoldOut", 0.2);
550s  assert (class (CVMdl), "ClassificationPartitionedModel")
550s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
550s  assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
550s  assert (CVMdl.CrossValidatedModel, "ClassificationSVM")
550s ***** test
550s  obj = fitcsvm (x, y);
550s  CVMdl = crossval (obj, "LeaveOut", 'on');
550s  assert (class (CVMdl), "ClassificationPartitionedModel")
550s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
550s  assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
550s  assert (CVMdl.CrossValidatedModel, "ClassificationSVM")
551s ***** error<ClassificationSVM.crossval: Name-Value arguments must be in pairs.> ...
551s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold")
551s ***** error<ClassificationSVM.crossval: specify only one of the optional Name-Value paired arguments.> ...
551s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), ...
551s  "KFold", 5, "leaveout", 'on')
551s ***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
551s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", 'a')
551s ***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
551s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", 1)
551s ***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
551s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", -1)
551s ***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
551s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", 11.5)
551s ***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
551s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", [1,2])
551s ***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
551s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", 'a')
551s ***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
551s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", 11.5)
551s ***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
551s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", -1)
551s ***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
551s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", 0)
551s ***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
551s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", 1)
551s ***** error<ClassificationSVM.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
551s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Leaveout", 1)
551s ***** error<ClassificationSVM.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
551s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "CVPartition", 1)
551s ***** error<ClassificationSVM.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
551s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "CVPartition", 'a')
551s ***** error<ClassificationSVM.crossval: invalid parameter name in optional paired arguments> ...
551s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "some", "some")
551s 114 tests, 114 passed, 0 known failure, 0 skipped
551s [inst/Classification/ClassificationGAM.m]
551s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/Classification/ClassificationGAM.m
551s ***** demo
551s  ## Train a GAM classifier for binary classification
551s  ## using specific data and plot the decision boundaries.
551s 
551s  ## Define specific data
551s  X = [1, 2; 2, 3; 3, 3; 4, 5; 5, 5; ...
551s      6, 7; 7, 8; 8, 8; 9, 9; 10, 10];
551s  Y = [0; 0; 0; 0; 0; ...
551s      1; 1; 1; 1; 1];
551s 
551s  ## Train the GAM model
551s  obj = fitcgam (X, Y, "Interactions", "all")
551s 
551s  ## Create a grid of values for prediction
551s  x1 = [min(X(:,1)):0.1:max(X(:,1))];
551s  x2 = [min(X(:,2)):0.1:max(X(:,2))];
551s  [x1G, x2G] = meshgrid (x1, x2);
551s  XGrid = [x1G(:), x2G(:)];
551s  [labels, score] = predict (obj, XGrid);
551s ***** test
551s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
551s  y = [0; 0; 1; 1];
551s  PredictorNames = {'Feature1', 'Feature2', 'Feature3'};
551s  a = ClassificationGAM (x, y, "PredictorNames", PredictorNames);
551s  assert (class (a), "ClassificationGAM");
551s  assert ({a.X, a.Y, a.NumObservations}, {x, y, 4})
551s  assert ({a.NumPredictors, a.ResponseName}, {3, "Y"})
551s  assert (a.ClassNames, {'0'; '1'})
551s  assert (a.PredictorNames, PredictorNames)
551s  assert (a.BaseModel.Intercept, 0)
551s ***** test
551s  load fisheriris
551s  inds = strcmp (species,'versicolor') | strcmp (species,'virginica');
551s  X = meas(inds, :);
551s  Y = species(inds, :)';
551s  Y = strcmp (Y, 'virginica')';
551s  a = ClassificationGAM (X, Y, 'Formula', 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3');
551s  assert (class (a), "ClassificationGAM");
551s  assert ({a.X, a.Y, a.NumObservations}, {X, Y, 100})
551s  assert ({a.NumPredictors, a.ResponseName}, {4, "Y"})
551s  assert (a.ClassNames, {'0'; '1'})
551s  assert (a.Formula, 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3')
551s  assert (a.PredictorNames, {'x1', 'x2', 'x3', 'x4'})
551s  assert (a.ModelwInt.Intercept, 0)
554s ***** test
554s  X = [2, 3, 5; 4, 6, 8; 1, 2, 3; 7, 8, 9; 5, 4, 3];
554s  Y = [0; 1; 0; 1; 1];
554s  a = ClassificationGAM (X, Y, 'Knots', [4, 4, 4], 'Order', [3, 3, 3]);
554s  assert (class (a), "ClassificationGAM");
554s  assert ({a.X, a.Y, a.NumObservations}, {X, Y, 5})
554s  assert ({a.NumPredictors, a.ResponseName}, {3, "Y"})
554s  assert (a.ClassNames, {'0'; '1'})
554s  assert (a.PredictorNames, {'x1', 'x2', 'x3'})
554s  assert (a.Knots, [4, 4, 4])
554s  assert (a.Order, [3, 3, 3])
554s  assert (a.DoF, [7, 7, 7])
554s  assert (a.BaseModel.Intercept, 0.4055, 1e-1)
556s ***** error<ClassificationGAM: too few input arguments.> ClassificationGAM ()
556s ***** error<ClassificationGAM: too few input arguments.> ...
556s  ClassificationGAM (ones(4, 1))
556s ***** error<ClassificationGAM: number of rows in X and Y must be equal.> ...
556s  ClassificationGAM (ones (4,2), ones (1,4))
556s ***** error<ClassificationGAM: 'PredictorNames' must be supplied as a cellstring array.> ...
556s  ClassificationGAM (ones (5,2), ones (5,1), "PredictorNames", ["A"])
556s ***** error<ClassificationGAM: 'PredictorNames' must be supplied as a cellstring array.> ...
556s  ClassificationGAM (ones (5,2), ones (5,1), "PredictorNames", "A")
556s ***** error<ClassificationGAM: 'PredictorNames' must equal the number of columns in X.> ...
556s  ClassificationGAM (ones (5,2), ones (5,1), "PredictorNames", {"A", "B", "C"})
556s ***** error<ClassificationGAM: 'ResponseName' must be a character vector.> ...
556s  ClassificationGAM (ones (5,2), ones (5,1), "ResponseName", {"Y"})
556s ***** error<ClassificationGAM: 'ResponseName' must be a character vector.> ...
556s  ClassificationGAM (ones (5,2), ones (5,1), "ResponseName", 1)
556s ***** error<ClassificationGAM: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
556s  ClassificationGAM (ones(10,2), ones (10,1), "ClassNames", @(x)x)
556s ***** error<ClassificationGAM: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
556s  ClassificationGAM (ones(10,2), ones (10,1), "ClassNames", ['a'])
556s ***** error<ClassificationGAM: not all 'ClassNames' are present in Y.> ...
556s  ClassificationGAM (ones(10,2), ones (10,1), "ClassNames", [1, 2])
556s ***** error<ClassificationGAM: not all 'ClassNames' are present in Y.> ...
556s  ClassificationGAM (ones(5,2), {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
556s ***** error<ClassificationGAM: not all 'ClassNames' are present in Y.> ...
556s  ClassificationGAM (ones(10,2), logical (ones (10,1)), "ClassNames", [true, false])
556s ***** error<ClassificationGAM: 'Cost' must be a numeric square matrix.> ...
556s  ClassificationGAM (ones (5,2), ones (5,1), "Cost", [1, 2])
556s ***** error<ClassificationGAM: 'Cost' must be a numeric square matrix.> ...
556s  ClassificationGAM (ones (5,2), ones (5,1), "Cost", "string")
556s ***** error<ClassificationGAM: 'Cost' must be a numeric square matrix.> ...
556s  ClassificationGAM (ones (5,2), ones (5,1), "Cost", {eye(2)})
556s ***** test
556s  x = [1, 2; 3, 4; 5, 6; 7, 8; 9, 10];
556s  y = [1; 0; 1; 0; 1];
556s  a = ClassificationGAM (x, y, "interactions", "all");
556s  l = {'0'; '0'; '0'; '0'; '0'};
556s  s = [0.3760, 0.6240; 0.4259, 0.5741; 0.3760, 0.6240; ...
556s       0.4259, 0.5741; 0.3760, 0.6240];
556s  [labels, scores] = predict (a, x);
556s  assert (class (a), "ClassificationGAM");
556s  assert ({a.X, a.Y, a.NumObservations}, {x, y, 5})
556s  assert ({a.NumPredictors, a.ResponseName}, {2, "Y"})
556s  assert (a.ClassNames, {'1'; '0'})
556s  assert (a.PredictorNames, {'x1', 'x2'})
556s  assert (a.ModelwInt.Intercept, 0.4055, 1e-1)
556s  assert (labels, l)
556s  assert (scores, s, 1e-1)
556s ***** test
556s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
556s  y = [0; 0; 1; 1];
556s  interactions = [false, true, false; true, false, true; false, true, false];
556s  a = fitcgam (x, y, "learningrate", 0.2, "interactions", interactions);
556s  [label, score] = predict (a, x, "includeinteractions", true);
556s  l = {'0'; '0'; '1'; '1'};
556s  s = [0.5106, 0.4894; 0.5135, 0.4865; 0.4864, 0.5136; 0.4847, 0.5153];
556s  assert (class (a), "ClassificationGAM");
556s  assert ({a.X, a.Y, a.NumObservations}, {x, y, 4})
556s  assert ({a.NumPredictors, a.ResponseName}, {3, "Y"})
556s  assert (a.ClassNames, {'0'; '1'})
556s  assert (a.PredictorNames, {'x1', 'x2', 'x3'})
556s  assert (a.ModelwInt.Intercept, 0)
556s  assert (label, l)
556s  assert (score, s, 1e-1)
559s ***** error<ClassificationGAM.predict: too few input arguments.> ...
559s  predict (ClassificationGAM (ones (4,2), ones (4,1)))
559s ***** error<ClassificationGAM.predict: XC is empty.> ...
559s  predict (ClassificationGAM (ones (4,2), ones (4,1)), [])
559s ***** error<ClassificationGAM.predict: XC must have the same number of predictors as the trained model.> ...
559s  predict (ClassificationGAM (ones (4,2), ones (4,1)), 1)
560s ***** shared x, y, obj
560s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
560s  y = [0; 0; 1; 1];
560s  obj = fitcgam (x, y);
561s ***** test
561s  CVMdl = crossval (obj);
561s  assert (class (CVMdl), "ClassificationPartitionedModel")
561s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
561s  assert (CVMdl.KFold == 10)
561s  assert (class (CVMdl.Trained{1}), "CompactClassificationGAM")
561s  assert (CVMdl.CrossValidatedModel, "ClassificationGAM")
569s ***** test
569s  CVMdl = crossval (obj, "KFold", 5);
569s  assert (class (CVMdl), "ClassificationPartitionedModel")
569s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
569s  assert (CVMdl.KFold == 5)
569s  assert (class (CVMdl.Trained{1}), "CompactClassificationGAM")
569s  assert (CVMdl.CrossValidatedModel, "ClassificationGAM")
573s ***** test
573s  CVMdl = crossval (obj, "HoldOut", 0.2);
573s  assert (class (CVMdl), "ClassificationPartitionedModel")
573s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
573s  assert (class (CVMdl.Trained{1}), "CompactClassificationGAM")
573s  assert (CVMdl.CrossValidatedModel, "ClassificationGAM")
574s ***** test
574s  partition = cvpartition (y, 'KFold', 3);
574s  CVMdl = crossval (obj, 'cvPartition', partition);
574s  assert (class (CVMdl), "ClassificationPartitionedModel")
574s  assert (CVMdl.KFold == 3)
574s  assert (class (CVMdl.Trained{1}), "CompactClassificationGAM")
574s  assert (CVMdl.CrossValidatedModel, "ClassificationGAM")
577s ***** error<ClassificationGAM.crossval: Name-Value arguments must be in pairs.> ...
577s  crossval (obj, "kfold")
577s ***** error<ClassificationGAM.crossval: specify only one of the optional Name-Value paired arguments.>...
577s  crossval (obj, "kfold", 12, "holdout", 0.2)
577s ***** error<ClassificationGAM.crossval: 'KFold' must be an integer value greater than 1.> ...
577s  crossval (obj, "kfold", 'a')
577s ***** error<ClassificationGAM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
577s  crossval (obj, "holdout", 2)
577s ***** error<ClassificationGAM.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
577s  crossval (obj, "leaveout", 1)
577s ***** error<ClassificationGAM.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
577s  crossval (obj, "cvpartition", 1)
577s 34 tests, 34 passed, 0 known failure, 0 skipped
577s [inst/Classification/CompactClassificationNeuralNetwork.m]
577s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/Classification/CompactClassificationNeuralNetwork.m
577s ***** demo
577s  ## Create a neural network classifier and its compact version
577s  # and compare their size
577s 
577s  load fisheriris
577s  X = meas;
577s  Y = species;
577s 
577s  Mdl = fitcnet (X, Y, 'ClassNames', unique (species))
577s  CMdl = crossval (Mdl)
577s ***** error<CompactClassificationDiscriminant: invalid classification object.> ...
577s  CompactClassificationDiscriminant (1)
577s ***** shared x, y, CMdl
577s  load fisheriris
577s  x = meas;
577s  y = grp2idx (species);
577s  Mdl = fitcnet (x, y, "IterationLimit", 100);
577s  CMdl = compact (Mdl);
577s ***** error<CompactClassificationNeuralNetwork.predict: too few input arguments.> ...
577s  predict (CMdl)
577s ***** error<CompactClassificationNeuralNetwork.predict: XC is empty.> ...
577s  predict (CMdl, [])
577s ***** error<CompactClassificationNeuralNetwork.predict: XC must have the same number of predictors as the trained neural network.> ...
577s  predict (CMdl, 1)
577s ***** test
577s  CMdl.ScoreTransform = "a";
577s ***** error<CompactClassificationNeuralNetwork.predict: 'ScoreTransform' must be a 'function_handle' object.> ...
577s  [labels, scores] = predict (CMdl, x);
577s 6 tests, 6 passed, 0 known failure, 0 skipped
577s [inst/Classification/CompactClassificationGAM.m]
577s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/Classification/CompactClassificationGAM.m
577s ***** demo
577s  ## Create a generalized additive model classifier and its compact version
577s  # and compare their size
577s 
577s  load fisheriris
577s  X = meas;
577s  Y = species;
577s 
577s  Mdl = fitcdiscr (X, Y, 'ClassNames', unique (species))
577s  CMdl = crossval (Mdl)
577s ***** test
577s  Mdl = CompactClassificationGAM ();
577s  assert (class (Mdl), "CompactClassificationGAM")
577s ***** test
577s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
577s  y = [0; 0; 1; 1];
577s  PredictorNames = {'Feature1', 'Feature2', 'Feature3'};
577s  Mdl = fitcgam (x, y, "PredictorNames", PredictorNames);
577s  CMdl = compact (Mdl);
577s  assert (class (CMdl), "CompactClassificationGAM");
577s  assert ({CMdl.NumPredictors, CMdl.ResponseName}, {3, "Y"})
577s  assert (CMdl.ClassNames, {'0'; '1'})
577s  assert (CMdl.PredictorNames, PredictorNames)
577s  assert (CMdl.BaseModel.Intercept, 0)
578s ***** test
578s  load fisheriris
578s  inds = strcmp (species,'versicolor') | strcmp (species,'virginica');
578s  X = meas(inds, :);
578s  Y = species(inds, :)';
578s  Y = strcmp (Y, 'virginica')';
578s  Mdl = fitcgam (X, Y, 'Formula', 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3');
578s  CMdl = compact (Mdl);
578s  assert (class (CMdl), "CompactClassificationGAM");
578s  assert ({CMdl.NumPredictors, CMdl.ResponseName}, {4, "Y"})
578s  assert (CMdl.ClassNames, {'0'; '1'})
578s  assert (CMdl.Formula, 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3')
578s  assert (CMdl.PredictorNames, {'x1', 'x2', 'x3', 'x4'})
578s  assert (CMdl.ModelwInt.Intercept, 0)
580s ***** test
580s  X = [2, 3, 5; 4, 6, 8; 1, 2, 3; 7, 8, 9; 5, 4, 3];
580s  Y = [0; 1; 0; 1; 1];
580s  Mdl = fitcgam (X, Y, 'Knots', [4, 4, 4], 'Order', [3, 3, 3]);
580s  CMdl = compact (Mdl);
580s  assert (class (CMdl), "CompactClassificationGAM");
580s  assert ({CMdl.NumPredictors, CMdl.ResponseName}, {3, "Y"})
580s  assert (CMdl.ClassNames, {'0'; '1'})
580s  assert (CMdl.PredictorNames, {'x1', 'x2', 'x3'})
580s  assert (CMdl.Knots, [4, 4, 4])
580s  assert (CMdl.Order, [3, 3, 3])
580s  assert (CMdl.DoF, [7, 7, 7])
580s  assert (CMdl.BaseModel.Intercept, 0.4055, 1e-1)
581s ***** error<CompactClassificationGAM: invalid classification object.> ...
581s  CompactClassificationGAM (1)
581s ***** test
581s  x = [1, 2; 3, 4; 5, 6; 7, 8; 9, 10];
581s  y = [1; 0; 1; 0; 1];
581s  Mdl = fitcgam (x, y, "interactions", "all");
581s  CMdl = compact (Mdl);
581s  l = {'0'; '0'; '0'; '0'; '0'};
581s  s = [0.3760, 0.6240; 0.4259, 0.5741; 0.3760, 0.6240; ...
581s       0.4259, 0.5741; 0.3760, 0.6240];
581s  [labels, scores] = predict (CMdl, x);
581s  assert (class (CMdl), "CompactClassificationGAM");
581s  assert ({CMdl.NumPredictors, CMdl.ResponseName}, {2, "Y"})
581s  assert (CMdl.ClassNames, {'1'; '0'})
581s  assert (CMdl.PredictorNames, {'x1', 'x2'})
581s  assert (CMdl.ModelwInt.Intercept, 0.4055, 1e-1)
581s  assert (labels, l)
581s  assert (scores, s, 1e-1)
583s ***** test
583s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
583s  y = [0; 0; 1; 1];
583s  interactions = [false, true, false; true, false, true; false, true, false];
583s  Mdl = fitcgam (x, y, "learningrate", 0.2, "interactions", interactions);
583s  CMdl = compact (Mdl);
583s  [label, score] = predict (CMdl, x, "includeinteractions", true);
583s  l = {'0'; '0'; '1'; '1'};
583s  s = [0.5106, 0.4894; 0.5135, 0.4865; 0.4864, 0.5136; 0.4847, 0.5153];
583s  assert (class (CMdl), "CompactClassificationGAM");
583s  assert ({CMdl.NumPredictors, CMdl.ResponseName}, {3, "Y"})
583s  assert (CMdl.ClassNames, {'0'; '1'})
583s  assert (CMdl.PredictorNames, {'x1', 'x2', 'x3'})
583s  assert (CMdl.ModelwInt.Intercept, 0)
583s  assert (label, l)
583s  assert (score, s, 1e-1)
586s ***** shared CMdl
586s  Mdl = fitcgam (ones (4,2), ones (4,1));
586s  CMdl = compact (Mdl);
586s ***** error<CompactClassificationGAM.predict: too few input arguments.> ...
586s  predict (CMdl)
586s ***** error<CompactClassificationGAM.predict: XC is empty.> ...
586s  predict (CMdl, [])
586s ***** error<CompactClassificationGAM.predict: XC must have the same number of features as the trained model.> ...
586s  predict (CMdl, 1)
586s 10 tests, 10 passed, 0 known failure, 0 skipped
586s [inst/Classification/CompactClassificationDiscriminant.m]
586s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/Classification/CompactClassificationDiscriminant.m
586s ***** demo
586s  ## Create a discriminant analysis classifier and its compact version
586s  # and compare their size
586s 
586s  load fisheriris
586s  X = meas;
586s  Y = species;
586s 
586s  Mdl = fitcdiscr (X, Y, 'ClassNames', unique (species))
586s  CMdl = crossval (Mdl)
586s ***** test
586s  load fisheriris
586s  x = meas;
586s  y = species;
586s  PredictorNames = {'Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width'};
586s  Mdl = fitcdiscr (x, y, "PredictorNames", PredictorNames);
586s  CMdl = compact (Mdl);
586s  sigma = [0.265008, 0.092721, 0.167514, 0.038401; ...
586s           0.092721, 0.115388, 0.055244, 0.032710; ...
586s           0.167514, 0.055244, 0.185188, 0.042665; ...
586s           0.038401, 0.032710, 0.042665, 0.041882];
586s  mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
586s        5.9360, 2.7700, 4.2600, 1.3260; ...
586s        6.5880, 2.9740, 5.5520, 2.0260];
586s  xCentered = [ 9.4000e-02,  7.2000e-02, -6.2000e-02, -4.6000e-02; ...
586s               -1.0600e-01, -4.2800e-01, -6.2000e-02, -4.6000e-02; ...
586s               -3.0600e-01, -2.2800e-01, -1.6200e-01, -4.6000e-02];
586s  assert (class (CMdl), "CompactClassificationDiscriminant");
586s  assert ({CMdl.DiscrimType, CMdl.ResponseName}, {"linear", "Y"})
586s  assert ({CMdl.Gamma, CMdl.MinGamma}, {0, 0}, 1e-15)
586s  assert (CMdl.ClassNames, unique (species))
586s  assert (CMdl.Sigma, sigma, 1e-6)
586s  assert (CMdl.Mu, mu, 1e-14)
586s  assert (CMdl.XCentered([1:3],:), xCentered, 1e-14)
586s  assert (CMdl.LogDetSigma, -9.9585, 1e-4)
586s  assert (CMdl.PredictorNames, PredictorNames)
586s ***** test
586s  load fisheriris
586s  x = meas;
586s  y = species;
586s  Mdl = fitcdiscr (x, y, "Gamma", 0.5);
586s  CMdl = compact (Mdl);
586s  sigma = [0.265008, 0.046361, 0.083757, 0.019201; ...
586s           0.046361, 0.115388, 0.027622, 0.016355; ...
586s           0.083757, 0.027622, 0.185188, 0.021333; ...
586s           0.019201, 0.016355, 0.021333, 0.041882];
586s  mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
586s        5.9360, 2.7700, 4.2600, 1.3260; ...
586s        6.5880, 2.9740, 5.5520, 2.0260];
586s  xCentered = [ 9.4000e-02,  7.2000e-02, -6.2000e-02, -4.6000e-02; ...
586s               -1.0600e-01, -4.2800e-01, -6.2000e-02, -4.6000e-02; ...
586s               -3.0600e-01, -2.2800e-01, -1.6200e-01, -4.6000e-02];
586s  assert (class (CMdl), "CompactClassificationDiscriminant");
586s  assert ({CMdl.DiscrimType, CMdl.ResponseName}, {"linear", "Y"})
586s  assert ({CMdl.Gamma, CMdl.MinGamma}, {0.5, 0})
586s  assert (CMdl.ClassNames, unique (species))
586s  assert (CMdl.Sigma, sigma, 1e-6)
586s  assert (CMdl.Mu, mu, 1e-14)
586s  assert (CMdl.XCentered([1:3],:), xCentered, 1e-14)
586s  assert (CMdl.LogDetSigma, -8.6884, 1e-4)
586s ***** error<CompactClassificationDiscriminant: invalid classification object.> ...
586s  CompactClassificationDiscriminant (1)
586s ***** test
586s  load fisheriris
586s  x = meas;
586s  y = species;
586s  Mdl = fitcdiscr (meas, species, "Gamma", 0.5);
586s  CMdl = compact (Mdl);
586s  [label, score, cost] = predict (CMdl, [2, 2, 2, 2]);
586s  assert (label, {'versicolor'})
586s  assert (score, [0, 0.9999, 0.0001], 1e-4)
586s  assert (cost, [1, 0.0001, 0.9999], 1e-4)
586s  [label, score, cost] = predict (CMdl, [2.5, 2.5, 2.5, 2.5]);
586s  assert (label, {'versicolor'})
586s  assert (score, [0, 0.6368, 0.3632], 1e-4)
586s  assert (cost, [1, 0.3632, 0.6368], 1e-4)
586s ***** test
586s  load fisheriris
586s  x = meas;
586s  y = species;
586s  xc = [min(x); mean(x); max(x)];
586s  Mdl = fitcdiscr (x, y);
586s  CMdl = compact (Mdl);
586s  [label, score, cost] = predict (CMdl, xc);
586s  l = {'setosa'; 'versicolor'; 'virginica'};
586s  s = [1, 0, 0; 0, 1, 0; 0, 0, 1];
586s  c = [0, 1, 1; 1, 0, 1; 1, 1, 0];
586s  assert (label, l)
586s  assert (score, s, 1e-4)
586s  assert (cost, c, 1e-4)
586s ***** shared MODEL
586s  X = rand (10,2);
586s  Y = [ones(5,1);2*ones(5,1)];
586s  MODEL = compact (ClassificationDiscriminant (X, Y));
586s ***** error<CompactClassificationDiscriminant.predict: too few input arguments.> ...
586s  predict (MODEL)
586s ***** error<CompactClassificationDiscriminant.predict: XC is empty.> ...
586s  predict (MODEL, [])
586s ***** error<CompactClassificationDiscriminant.predict: XC must have the same number of features as the trained model.> ...
586s  predict (MODEL, 1)
586s ***** test
586s  load fisheriris
586s  model = fitcdiscr (meas, species);
586s  x = mean (meas);
586s  y = {'versicolor'};
586s  L = loss (model, x, y);
586s  assert (L, 0)
586s ***** test
586s  x = [1, 2; 3, 4; 5, 6];
586s  y = {'A'; 'B'; 'A'};
586s  model = fitcdiscr (x, y, "Gamma", 0.4);
586s  x_test = [1, 6; 3, 3];
586s  y_test = {'A'; 'B'};
586s  L = loss (model, x_test, y_test);
586s  assert (L, 0.3333, 1e-4)
586s ***** test
586s  x = [1, 2; 3, 4; 5, 6; 7, 8];
586s  y = ['1'; '2'; '3'; '1'];
586s  model = fitcdiscr (x, y, "gamma" , 0.5);
586s  x_test = [3, 3];
586s  y_test = ['1'];
586s  L = loss (model, x_test, y_test, 'LossFun', 'quadratic');
586s  assert (L, 0.2423, 1e-4)
586s ***** test
586s  x = [1, 2; 3, 4; 5, 6; 7, 8];
586s  y = ['1'; '2'; '3'; '1'];
586s  model = fitcdiscr (x, y, "gamma" , 0.5);
586s  x_test = [3, 3; 5, 7];
586s  y_test = ['1'; '2'];
586s  L = loss (model, x_test, y_test, 'LossFun', 'classifcost');
586s  assert (L, 0.3333, 1e-4)
586s ***** test
586s  x = [1, 2; 3, 4; 5, 6; 7, 8];
586s  y = ['1'; '2'; '3'; '1'];
586s  model = fitcdiscr (x, y, "gamma" , 0.5);
586s  x_test = [3, 3; 5, 7];
586s  y_test = ['1'; '2'];
586s  L = loss (model, x_test, y_test, 'LossFun', 'hinge');
586s  assert (L, 0.5886, 1e-4)
586s ***** test
586s  x = [1, 2; 3, 4; 5, 6; 7, 8];
586s  y = ['1'; '2'; '3'; '1'];
586s  model = fitcdiscr (x, y, "gamma" , 0.5);
586s  x_test = [3, 3; 5, 7];
586s  y_test = ['1'; '2'];
586s  W = [1; 2];
586s  L = loss (model, x_test, y_test, 'LossFun', 'logit', 'Weights', W);
586s  assert (L, 0.5107, 1e-4)
586s ***** test
586s  x = [1, 2; 3, 4; 5, 6];
586s  y = {'A'; 'B'; 'A'};
586s  model = fitcdiscr (x, y, "gamma" , 0.5);
586s  x_with_nan = [1, 2; NaN, 4];
586s  y_test = {'A'; 'B'};
586s  L = loss (model, x_with_nan, y_test);
586s  assert (L, 0.3333, 1e-4)
586s ***** test
586s  x = [1, 2; 3, 4; 5, 6];
586s  y = {'A'; 'B'; 'A'};
586s  model = fitcdiscr (x, y);
586s  x_with_nan = [1, 2; NaN, 4];
586s  y_test = {'A'; 'B'};
586s  L = loss (model, x_with_nan, y_test, 'LossFun', 'logit');
586s  assert (isnan (L))
586s ***** test
586s  x = [1, 2; 3, 4; 5, 6];
586s  y = {'A'; 'B'; 'A'};
586s  model = fitcdiscr (x, y);
586s  customLossFun = @(C, S, W, Cost) sum (W .* sum (abs (C - S), 2));
586s  L = loss (model, x, y, 'LossFun', customLossFun);
586s  assert (L, 0.8889, 1e-4)
586s ***** test
586s  x = [1, 2; 3, 4; 5, 6];
586s  y = [1; 2; 1];
586s  model = fitcdiscr (x, y);
586s  L = loss (model, x, y, 'LossFun', 'classiferror');
586s  assert (L, 0.3333, 1e-4)
586s ***** error<CompactClassificationDiscriminant.loss: too few input arguments.> ...
586s  loss (MODEL)
586s ***** error<CompactClassificationDiscriminant.loss: too few input arguments.> ...
586s  loss (MODEL, ones (4,2))
586s ***** error<CompactClassificationDiscriminant.loss: name-value arguments must be in pairs.> ...
586s  loss (MODEL, ones (4,2), ones (4,1), 'LossFun')
586s ***** error<CompactClassificationDiscriminant.loss: Y must have the same number of rows as X.> ...
586s  loss (MODEL, ones (4,2), ones (3,1))
586s ***** error<CompactClassificationDiscriminant.loss: invalid loss function.> ...
586s  loss (MODEL, ones (4,2), ones (4,1), 'LossFun', 'a')
586s ***** error<CompactClassificationDiscriminant.loss: invalid 'Weights'.> ...
586s  loss (MODEL, ones (4,2), ones (4,1), 'Weights', 'w')
586s  load fisheriris
586s  mdl = fitcdiscr (meas, species);
586s  X = mean (meas);
586s  Y = {'versicolor'};
586s  m = margin (mdl, X, Y);
586s  assert (m, 1, 1e-6)
586s ***** test
586s  X = [1, 2; 3, 4; 5, 6];
586s  Y = [1; 2; 1];
586s  mdl = fitcdiscr (X, Y, "gamma", 0.5);
586s  m = margin (mdl, X, Y);
586s  assert (m, [0.3333; -0.3333; 0.3333], 1e-4)
586s ***** error<CompactClassificationDiscriminant.margin: too few input arguments.> ...
586s  margin (MODEL)
586s ***** error<CompactClassificationDiscriminant.margin: too few input arguments.> ...
586s  margin (MODEL, ones (4,2))
586s ***** error<CompactClassificationDiscriminant.margin: Y must have the same number of rows as X.> ...
586s  margin (MODEL, ones (4,2), ones (3,1))
586s 28 tests, 28 passed, 0 known failure, 0 skipped
586s [inst/Classification/ClassificationNeuralNetwork.m]
586s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/Classification/ClassificationNeuralNetwork.m
586s ***** error<ClassificationNeuralNetwork: too few input arguments.> ...
586s  ClassificationNeuralNetwork ()
586s ***** error<ClassificationNeuralNetwork: too few input arguments.> ...
586s  ClassificationNeuralNetwork (ones(10,2))
586s ***** error<ClassificationNeuralNetwork: number of rows in X and Y must be equal.> ...
586s  ClassificationNeuralNetwork (ones(10,2), ones (5,1))
586s ***** error<ClassificationNeuralNetwork: 'Standardize' must be either true or false.> ...
586s  ClassificationNeuralNetwork (ones (5,3), ones (5,1), "standardize", "a")
586s ***** error<ClassificationNeuralNetwork: 'PredictorNames' must be supplied as a cellstring array.> ...
586s  ClassificationNeuralNetwork (ones (5,2), ones (5,1), "PredictorNames", ["A"])
586s ***** error<ClassificationNeuralNetwork: 'PredictorNames' must be supplied as a cellstring array.> ...
586s  ClassificationNeuralNetwork (ones (5,2), ones (5,1), "PredictorNames", "A")
586s ***** error<ClassificationNeuralNetwork: 'PredictorNames' must have the same number of columns as X.> ...
586s  ClassificationNeuralNetwork (ones (5,2), ones (5,1), "PredictorNames", {"A", "B", "C"})
586s ***** error<ClassificationNeuralNetwork: 'ResponseName' must be a character vector.> ...
586s  ClassificationNeuralNetwork (ones (5,2), ones (5,1), "ResponseName", {"Y"})
586s ***** error<ClassificationNeuralNetwork: 'ResponseName' must be a character vector.> ...
586s  ClassificationNeuralNetwork (ones (5,2), ones (5,1), "ResponseName", 1)
586s ***** error<ClassificationNeuralNetwork: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
586s  ClassificationNeuralNetwork (ones(10,2), ones (10,1), "ClassNames", @(x)x)
586s ***** error<ClassificationNeuralNetwork: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
586s  ClassificationNeuralNetwork (ones(10,2), ones (10,1), "ClassNames", ['a'])
586s ***** error<ClassificationNeuralNetwork: not all 'ClassNames' are present in Y.> ...
586s  ClassificationNeuralNetwork (ones(10,2), ones (10,1), "ClassNames", [1, 2])
586s ***** error<ClassificationNeuralNetwork: not all 'ClassNames' are present in Y.> ...
586s  ClassificationNeuralNetwork (ones(5,2), {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
586s ***** error<ClassificationNeuralNetwork: not all 'ClassNames' are present in Y.> ...
586s  ClassificationNeuralNetwork (ones(10,2), logical (ones (10,1)), "ClassNames", [true, false])
586s ***** error<ClassificationNeuralNetwork: 'LayerSizes' must be a positive integer vector.> ...
586s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", -1)
586s ***** error<ClassificationNeuralNetwork: 'LayerSizes' must be a positive integer vector.> ...
586s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", 0.5)
586s ***** error<ClassificationNeuralNetwork: 'LayerSizes' must be a positive integer vector.> ...
586s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", [1,-2])
586s ***** error<ClassificationNeuralNetwork: 'LayerSizes' must be a positive integer vector.> ...
586s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", [10,20,30.5])
586s ***** error<ClassificationNeuralNetwork: 'LearningRate' must be a positive scalar.> ...
586s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LearningRate", -0.1)
586s ***** error<ClassificationNeuralNetwork: 'LearningRate' must be a positive scalar.> ...
586s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LearningRate", [0.1, 0.01])
586s ***** error<ClassificationNeuralNetwork: 'LearningRate' must be a positive scalar.> ...
586s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LearningRate", "a")
586s ***** error<ClassificationNeuralNetwork: 'Activations' must be a character vector or a cellstring vector.> ...
586s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "Activations", 123)
586s ***** error<ClassificationNeuralNetwork: unsupported 'Activation' function.> ...
586s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "Activations", "unsupported_type")
586s ***** error<ClassificationNeuralNetwork: unsupported 'Activation' functions.> ...
586s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", [10, 5], ...
586s  "Activations", {"sigmoid", "unsupported_type"})
586s ***** error<ClassificationNeuralNetwork: 'Activations' vector does not match the number of layers.> ...
586s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "Activations", {"sigmoid", "relu", "softmax"})
586s ***** error<ClassificationNeuralNetwork: 'OutputLayerActivation' must be a character vector.> ...
586s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "OutputLayerActivation", 123)
586s ***** error<ClassificationNeuralNetwork: unsupported 'OutputLayerActivation' function.> ...
586s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "OutputLayerActivation", "unsupported_type")
586s ***** error<ClassificationNeuralNetwork: 'IterationLimit' must be a positive integer.> ...
586s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "IterationLimit", -1)
586s ***** error<ClassificationNeuralNetwork: 'IterationLimit' must be a positive integer.> ...
586s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "IterationLimit", 0.5)
586s ***** error<ClassificationNeuralNetwork: 'IterationLimit' must be a positive integer.> ...
586s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "IterationLimit", [1,2])
586s ***** error<ClassificationNeuralNetwork: 'ScoreTransform' must be a character vector or a function handle.> ...
586s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "ScoreTransform", [1,2])
586s ***** error<ClassificationNeuralNetwork: unrecognized 'ScoreTransform' function.> ...
586s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "ScoreTransform", "unsupported_type")
586s ***** error<ClassificationNeuralNetwork: invalid parameter name in optional pair arguments.> ...
586s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "some", "some")
586s ***** error<ClassificationNeuralNetwork: invalid values in X.> ...
586s  ClassificationNeuralNetwork ([1;2;3;'a';4], ones (5,1))
586s ***** error<ClassificationNeuralNetwork: invalid values in X.> ...
586s  ClassificationNeuralNetwork ([1;2;3;Inf;4], ones (5,1))
586s ***** shared x, y, objST, Mdl
586s  load fisheriris
586s  x = meas;
586s  y = grp2idx (species);
586s  Mdl = fitcnet (x, y, "IterationLimit", 100);
586s ***** error<ClassificationNeuralNetwork.predict: too few input arguments.> ...
586s  predict (Mdl)
586s ***** error<ClassificationNeuralNetwork.predict: XC is empty.> ...
586s  predict (Mdl, [])
586s ***** error<ClassificationNeuralNetwork.predict: XC must have the same number of predictors as the trained model.> ...
586s  predict (Mdl, 1)
586s ***** test
586s  objST = fitcnet (x, y, "IterationLimit", 100);
586s  objST.ScoreTransform = "a";
587s ***** error<ClassificationNeuralNetwork.predict: 'ScoreTransform' must be a 'function_handle' object.> ...
587s  [labels, scores] = predict (objST, x);
587s ***** error<ClassificationNeuralNetwork.resubPredict: 'ScoreTransform' must be a 'function_handle' object.> ...
587s  [labels, scores] = resubPredict (objST);
587s ***** test
587s  CVMdl = crossval (Mdl, "KFold", 5);
587s  assert (class (CVMdl), "ClassificationPartitionedModel")
587s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
587s  assert (CVMdl.KFold == 5)
587s  assert (class (CVMdl.Trained{1}), "CompactClassificationNeuralNetwork")
587s  assert (CVMdl.CrossValidatedModel, "ClassificationNeuralNetwork")
587s ***** test
587s  CVMdl = crossval (Mdl, "HoldOut", 0.2);
587s  assert (class (CVMdl), "ClassificationPartitionedModel")
587s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
587s  assert (class (CVMdl.Trained{1}), "CompactClassificationNeuralNetwork")
587s  assert (CVMdl.CrossValidatedModel, "ClassificationNeuralNetwork")
587s ***** error<ClassificationNeuralNetwork.crossval: Name-Value arguments must be in pairs.> ...
587s  crossval (Mdl, "KFold")
587s ***** error<ClassificationNeuralNetwork.crossval: specify only one of the optional Name-Value paired arguments.> ...
587s  crossval (Mdl, "KFold", 5, "leaveout", 'on')
587s ***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
587s  crossval (Mdl, "KFold", 'a')
587s ***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
587s  crossval (Mdl, "KFold", 1)
587s ***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
587s  crossval (Mdl, "KFold", -1)
587s ***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
587s  crossval (Mdl, "KFold", 11.5)
587s ***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
587s  crossval (Mdl, "KFold", [1,2])
587s ***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
587s  crossval (Mdl, "Holdout", 'a')
587s ***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
587s  crossval (Mdl, "Holdout", 11.5)
587s ***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
587s  crossval (Mdl, "Holdout", -1)
587s ***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
587s  crossval (Mdl, "Holdout", 0)
587s ***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
587s  crossval (Mdl, "Holdout", 1)
587s ***** error<ClassificationNeuralNetwork.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
587s  crossval (Mdl, "Leaveout", 1)
587s ***** error<ClassificationNeuralNetwork.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
587s  crossval (Mdl, "CVPartition", 1)
587s ***** error<ClassificationNeuralNetwork.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
587s  crossval (Mdl, "CVPartition", 'a')
587s ***** error<ClassificationNeuralNetwork.crossval: invalid parameter name in optional paired arguments> ...
587s  crossval (Mdl, "some", "some")
587s 59 tests, 59 passed, 0 known failure, 0 skipped
587s [inst/grpstats.m]
587s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/grpstats.m
587s ***** demo
587s  load carsmall;
587s  [m,p,g] = grpstats (Weight, Model_Year, {"mean", "predci", "gname"})
587s  n = length(m);
587s  errorbar((1:n)',m,p(:,2)-m);
587s  set (gca, "xtick", 1:n, "xticklabel", g);
587s  title ("95% prediction intervals for mean weight by year");
587s ***** demo
587s  load carsmall;
587s  [m,p,g] = grpstats ([Acceleration,Weight/1000],Cylinders, ...
587s                      {"mean", "meanci", "gname"}, 0.05)
587s  [c,r] = size (m);
587s  errorbar((1:c)'.*ones(c,r),m,p(:,[(1:r)])-m);
587s  set (gca, "xtick", 1:c, "xticklabel", g);
587s  title ("95% prediction intervals for mean weight by year");
587s ***** test
587s  load carsmall
587s  means = grpstats (Acceleration, Origin);
587s  assert (means, [14.4377; 18.0500; 15.8867; 16.3778; 16.6000; 15.5000], 0.001);
587s ***** test
587s  load carsmall
587s  [grpMin,grpMax,grp] = grpstats (Acceleration, Origin, {"min","max","gname"});
587s  assert (grpMin, [8.0; 15.3; 13.9; 12.2; 15.7; 15.5]);
587s  assert (grpMax, [22.2; 21.9; 18.2; 24.6; 17.5; 15.5]);
587s ***** test
587s  load carsmall
587s  [grpMin,grpMax,grp] = grpstats (Acceleration, Origin, {"min","max","gname"});
587s  assert (grp', {"USA", "France", "Japan", "Germany", "Sweden", "Italy"});
587s ***** test
587s  load carsmall
587s  [m,p,g] = grpstats ([Acceleration,Weight/1000], Cylinders, ...
587s                      {"mean", "meanci", "gname"}, 0.05);
587s  assert (p(:,1), [11.17621760075134, 16.13845847655224, 16.16222663683362]', ...
587s                  [1e-14, 2e-14, 1e-14]');
587s 4 tests, 4 passed, 0 known failure, 0 skipped
587s [inst/bartlett_test.m]
587s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/bartlett_test.m
587s ***** error<bartlett_test: invalid number of input arguments.> bartlett_test ()
587s ***** error<bartlett_test: invalid number of input arguments.> ...
587s  bartlett_test (1, 2, 3, 4);
587s ***** error<bartlett_test: wrong value for alpha.> bartlett_test (randn (50, 2), 0);
587s ***** error<bartlett_test: GROUP and X mismatch.> ...
587s  bartlett_test (randn (50, 2), [1, 2, 3]);
587s ***** error<bartlett_test: GROUP and X mismatch.> ...
587s  bartlett_test (randn (50, 1), ones (55, 1));
587s ***** error<bartlett_test: invalid second input argument.> ...
587s  bartlett_test (randn (50, 1), ones (50, 2));
587s ***** error<bartlett_test: wrong value for alpha.> ...
587s  bartlett_test (randn (50, 2), [], 1.2);
587s ***** error<bartlett_test: wrong value for alpha.> ...
587s  bartlett_test (randn (50, 2), [], "alpha");
587s ***** error<bartlett_test: wrong value for alpha.> ...
587s  bartlett_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], 1.2);
587s ***** error<bartlett_test: wrong value for alpha.> ...
587s  bartlett_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], "err");
587s ***** warning<bartlett_test: GROUP> ...
587s  bartlett_test (randn (50, 1), [ones(24, 1); 2*ones(25, 1); 3]);
587s ***** test
587s  load examgrades
587s  [h, pval, chisq, df] = bartlett_test (grades);
587s  assert (h, 1);
587s  assert (pval, 7.908647337018238e-08, 1e-14);
587s  assert (chisq, 38.73324, 1e-5);
587s  assert (df, 4);
587s ***** test
587s  load examgrades
587s  [h, pval, chisq, df] = bartlett_test (grades(:,[2:4]));
587s  assert (h, 1);
587s  assert (pval, 0.01172, 1e-5);
587s  assert (chisq, 8.89274, 1e-5);
587s  assert (df, 2);
587s ***** test
587s  load examgrades
587s  [h, pval, chisq, df] = bartlett_test (grades(:,[1,4]));
587s  assert (h, 0);
587s  assert (pval, 0.88118, 1e-5);
587s  assert (chisq, 0.02234, 1e-5);
587s  assert (df, 1);
587s ***** test
587s  load examgrades
587s  grades = [grades; nan(10, 5)];
587s  [h, pval, chisq, df] = bartlett_test (grades(:,[1,4]));
587s  assert (h, 0);
587s  assert (pval, 0.88118, 1e-5);
587s  assert (chisq, 0.02234, 1e-5);
587s  assert (df, 1);
587s ***** test
587s  load examgrades
587s  [h, pval, chisq, df] = bartlett_test (grades(:,[2,5]), 0.01);
587s  assert (h, 0);
587s  assert (pval, 0.01791, 1e-5);
587s  assert (chisq, 5.60486, 1e-5);
587s  assert (df, 1);
587s 16 tests, 16 passed, 0 known failure, 0 skipped
587s [inst/ppplot.m]
587s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/ppplot.m
587s ***** test
587s  hf = figure ("visible", "off");
587s  unwind_protect
587s    ppplot ([2 3 3 4 4 5 6 5 6 7 8 9 8 7 8 9 0 8 7 6 5 4 6 13 8 15 9 9]);
587s  unwind_protect_cleanup
587s    close (hf);
587s  end_unwind_protect
587s ***** error ppplot ()
587s ***** error <ppplot: X must be a numeric vector> ppplot (ones (2,2))
587s ***** error <ppplot: X must be a numeric vector> ppplot (1, 2)
587s ***** error <ppplot: DIST must be a strin> ppplot ([1 2 3 4], 2)
587s 5 tests, 5 passed, 0 known failure, 0 skipped
587s [inst/adtest.m]
587s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/adtest.m
587s ***** error<Invalid call to adtest.  Correct usage is:> adtest ();
587s ***** error<adtest: X must be a vector of real numbers.> adtest (ones (20,2));
587s ***** error<adtest: X must be a vector of real numbers.> adtest ([1+i,0-3i]);
587s ***** error<adtest: invalid distribution family in char string.> ...
587s  adtest (ones (20,1), "Distribution", "normal");
587s ***** error<adtest: invalid distribution family in cell array.> ...
587s  adtest (rand (20,1), "Distribution", {"normal", 5, 3});
587s ***** error<adtest: invalid distribution parameters in cell array.> ...
587s  adtest (rand (20,1), "Distribution", {"norm", 5});
587s ***** error<adtest: invalid distribution parameters in cell array.> ...
587s  adtest (rand (20,1), "Distribution", {"exp", 5, 4});
587s ***** error<adtest: invalid distribution parameters in cell array.> ...
587s  adtest (rand (20,1), "Distribution", {"ev", 5});
587s ***** error<adtest: invalid distribution parameters in cell array.> ...
587s  adtest (rand (20,1), "Distribution", {"logn", 5, 3, 2});
587s ***** error<adtest: invalid distribution parameters in cell array.> ...
587s  adtest (rand (20,1), "Distribution", {"Weibull", 5});
587s ***** error<adtest: invalid distribution option.> ...
587s  adtest (rand (20,1), "Distribution", 35);
587s ***** error<adtest: invalid Name argument.> ...
587s  adtest (rand (20,1), "Name", "norm");
587s ***** error<adtest: invalid Name argument.> ...
587s  adtest (rand (20,1), "Name", {"norm", 75, 10});
587s ***** error<adtest: asymptotic option is not valid for the composite> ...
587s  adtest (rand (20,1), "Distribution", "norm", "Asymptotic", true);
587s ***** error<adtest: asymptotic option is not valid for the composite> ...
587s  adtest (rand (20,1), "MCTol", 0.001, "Asymptotic", true);
587s ***** error<adtest: asymptotic option is not valid for the Monte Carlo> ...
587s  adtest (rand (20,1), "Distribution", {"norm", 5, 3}, "MCTol", 0.001, ...
587s          "Asymptotic", true);
587s ***** error<adtest: out of range invalid alpha - lower limit: 0.0005 upper> ...
587s  [h, pval, ADstat, CV] = adtest (ones (20,1), "Distribution", {"norm",5,3},...
587s                                  "Alpha", 0.000000001);
587s ***** error<adtest: out of range invalid alpha - lower limit: 0.0005 upper> ...
587s  [h, pval, ADstat, CV] = adtest (ones (20,1), "Distribution", {"norm",5,3},...
587s                                  "Alpha", 0.999999999);
587s ***** error<adtest: not enough data for composite testing.> ...
587s  adtest (10);
587s ***** warning<adtest: out of range min p-value:> ...
587s  randn ("seed", 34);
587s  adtest (ones (20,1), "Alpha", 0.000001);
587s ***** warning<adtest: alpha not within the lookup table.> ...
587s  randn ("seed", 34);
587s  adtest (normrnd(0,1,100,1), "Alpha", 0.99999);
587s ***** warning<adtest: alpha not within the lookup table.> ...
587s  randn ("seed", 34);
587s  adtest (normrnd(0,1,100,1), "Alpha", 0.00001);
587s ***** test
587s  load examgrades
587s  x = grades(:,1);
587s  [h, pval, adstat, cv] = adtest (x);
587s  assert (h, false);
587s  assert (pval, 0.1854, 1e-4);
587s  assert (adstat, 0.5194, 1e-4);
587s  assert (cv, 0.7470, 1e-4);
587s ***** test
587s  load examgrades
587s  x = grades(:,1);
587s  [h, pval, adstat, cv] = adtest (x, "Distribution", "ev");
587s  assert (h, false);
587s  assert (pval, 0.071363, 1e-6);
587s ***** test
587s  load examgrades
587s  x = grades(:,1);
587s  [h, pval, adstat, cv] = adtest (x, "Distribution", {"norm", 75, 10});
587s  assert (h, false);
587s  assert (pval, 0.4687, 1e-4);
587s 25 tests, 25 passed, 0 known failure, 0 skipped
587s [inst/monotone_smooth.m]
587s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/monotone_smooth.m
587s ***** error<Invalid call to monotone_smooth.  Correct usage is> ...
587s  monotone_smooth (1)
588s ***** error<monotone_smooth: X must be a numeric vector.> ...
588s  monotone_smooth ("char", 1)
588s ***** error<monotone_smooth: X must be a numeric vector.> ...
588s  monotone_smooth ({1,2,3}, 1)
588s ***** error<monotone_smooth: X must be a numeric vector.> ...
588s  monotone_smooth (ones(20,3), 1)
588s ***** error<monotone_smooth: Y must be a numeric vector.> ...
588s  monotone_smooth (1, "char")
588s ***** error<monotone_smooth: Y must be a numeric vector.> ...
588s  monotone_smooth (1, {1,2,3})
588s ***** error<monotone_smooth: Y must be a numeric vector.> ...
588s  monotone_smooth (1, ones(20,3))
588s ***** error<monotone_smooth: H>  monotone_smooth (ones (10,1), ones(10,1), [1, 2])
588s ***** error<monotone_smooth: H>  monotone_smooth (ones (10,1), ones(10,1), {2})
588s ***** error<monotone_smooth: H>  monotone_smooth (ones (10,1), ones(10,1), "char")
588s 10 tests, 10 passed, 0 known failure, 0 skipped
588s [inst/manova1.m]
588s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/manova1.m
588s ***** demo
588s  load carbig
588s  [d,p] = manova1([MPG, Acceleration, Weight, Displacement], Origin)
588s ***** test
588s  load carbig
588s  [d,p] = manova1([MPG, Acceleration, Weight, Displacement], Origin);
588s  assert (d, 3);
588s  assert (p, [0, 3.140583347827075e-07, 0.007510999577743149, ...
588s              0.1934100745898493]', [1e-12, 1e-12, 1e-12, 1e-12]');
588s ***** test
588s  load carbig
588s  [d,p] = manova1([MPG, Acceleration, Weight], Origin);
588s  assert (d, 2);
588s  assert (p, [0, 0.00516082975137544, 0.1206528056514453]', ...
588s             [1e-12, 1e-12, 1e-12]');
588s 2 tests, 2 passed, 0 known failure, 0 skipped
588s [inst/knnsearch.m]
588s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/knnsearch.m
588s ***** demo
588s  ## find 10 nearest neighbour of a point using different distance metrics
588s  ## and compare the results by plotting
588s  load fisheriris
588s  X = meas(:,3:4);
588s  Y = species;
588s  point = [5, 1.45];
588s 
588s  ## calculate 10 nearest-neighbours by minkowski distance
588s  [id, d] = knnsearch (X, point, "K", 10);
588s 
588s  ## calculate 10 nearest-neighbours by minkowski distance
588s  [idm, dm] = knnsearch (X, point, "K", 10, "distance", "minkowski", "p", 5);
588s 
588s  ## calculate 10 nearest-neighbours by chebychev distance
588s  [idc, dc] = knnsearch (X, point, "K", 10, "distance", "chebychev");
588s 
588s  ## plotting the results
588s  gscatter (X(:,1), X(:,2), species, [.75 .75 0; 0 .75 .75; .75 0 .75], ".", 20);
588s  title ("Fisher's Iris Data - Nearest Neighbors with different types of distance metrics");
588s  xlabel("Petal length (cm)");
588s  ylabel("Petal width (cm)");
588s 
588s  line (point(1), point(2), "marker", "X", "color", "k", ...
588s        "linewidth", 2, "displayname", "query point")
588s  line (X(id,1), X(id,2), "color", [0.5 0.5 0.5], "marker", "o", ...
588s        "linestyle", "none", "markersize", 10, "displayname", "eulcidean")
588s  line (X(idm,1), X(idm,2), "color", [0.5 0.5 0.5], "marker", "d", ...
588s        "linestyle", "none", "markersize", 10, "displayname", "Minkowski")
588s  line (X(idc,1), X(idc,2), "color", [0.5 0.5 0.5], "marker", "p", ...
588s        "linestyle", "none", "markersize", 10, "displayname", "chebychev")
588s  xlim ([4.5 5.5]);
588s  ylim ([1 2]);
588s  axis square;
588s ***** demo
588s  ## knnsearch on iris dataset using kdtree method
588s  load fisheriris
588s  X = meas(:,3:4);
588s  gscatter (X(:,1), X(:,2), species, [.75 .75 0; 0 .75 .75; .75 0 .75], ".", 20);
588s  title ("Fisher's iris dataset : Nearest Neighbors with kdtree search");
588s 
588s  ## new point to be predicted
588s  point = [5 1.45];
588s 
588s  line (point(1), point(2), "marker", "X", "color", "k", ...
588s        "linewidth", 2, "displayname", "query point")
588s 
588s  ## knnsearch using kdtree method
588s  [idx, d] = knnsearch (X, point, "K", 10, "NSMethod", "kdtree");
588s 
588s  ## plotting predicted neighbours
588s  line (X(idx,1), X(idx,2), "color", [0.5 0.5 0.5], "marker", "o", ...
588s        "linestyle", "none", "markersize", 10, ...
588s        "displayname", "nearest neighbour")
588s  xlim ([4 6])
588s  ylim ([1 3])
588s  axis square
588s  ## details of predicted labels
588s  tabulate (species(idx))
588s 
588s  ctr = point - d(end);
588s  diameter = 2 * d(end);
588s  ##  Draw a circle around the 10 nearest neighbors.
588s  h = rectangle ("position", [ctr, diameter, diameter], "curvature", [1 1]);
588s 
588s  ## here only 8 neighbours are plotted instead of 10 since the dataset
588s  ## contains duplicate values
588s ***** shared X, Y
588s  X = [1, 2, 3, 4; 2, 3, 4, 5; 3, 4, 5, 6];
588s  Y = [1, 2, 2, 3; 2, 3, 3, 4];
588s ***** test
588s  [idx, D] = knnsearch (X, Y, "Distance", "euclidean");
588s  assert (idx, [1; 1]);
588s  assert (D, ones (2, 1) * sqrt (2));
588s ***** test
588s  eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
588s  [idx, D] = knnsearch (X, Y, "Distance", eucldist);
588s  assert (idx, [1; 1]);
588s  assert (D, ones (2, 1) * sqrt (2));
588s ***** test
588s  [idx, D] = knnsearch (X, Y, "Distance", "euclidean", "includeties", true);
588s  assert (iscell (idx), true);
588s  assert (iscell (D), true)
588s  assert (idx {1}, [1]);
588s  assert (idx {2}, [1, 2]);
588s  assert (D{1}, ones (1, 1) * sqrt (2));
588s  assert (D{2}, ones (1, 2) * sqrt (2));
588s ***** test
588s  [idx, D] = knnsearch (X, Y, "Distance", "euclidean", "k", 2);
588s  assert (idx, [1, 2; 1, 2]);
588s  assert (D, [sqrt(2), 3.162277660168380; sqrt(2), sqrt(2)], 1e-14);
588s ***** test
588s  [idx, D] = knnsearch (X, Y, "Distance", "seuclidean");
588s  assert (idx, [1; 1]);
588s  assert (D, ones (2, 1) * sqrt (2));
588s ***** test
588s  [idx, D] = knnsearch (X, Y, "Distance", "seuclidean", "k", 2);
588s  assert (idx, [1, 2; 1, 2]);
588s  assert (D, [sqrt(2), 3.162277660168380; sqrt(2), sqrt(2)], 1e-14);
588s ***** test
588s  xx = [1, 2; 1, 3; 2, 4; 3, 6];
588s  yy = [2, 4; 2, 6];
588s  [idx, D] = knnsearch (xx, yy, "Distance", "mahalanobis");
588s  assert (idx, [3; 2]);
588s  assert (D, [0; 3.162277660168377], 1e-14);
588s ***** test
588s  [idx, D] = knnsearch (X, Y, "Distance", "minkowski");
588s  assert (idx, [1; 1]);
588s  assert (D, ones (2, 1) * sqrt (2));
588s ***** test
588s  [idx, D] = knnsearch (X, Y, "Distance", "minkowski", "p", 3);
588s  assert (idx, [1; 1]);
588s  assert (D, ones (2, 1) * 1.259921049894873, 1e-14);
588s ***** test
588s  [idx, D] = knnsearch (X, Y, "Distance", "cityblock");
588s  assert (idx, [1; 1]);
588s  assert (D, [2; 2]);
588s ***** test
588s  [idx, D] = knnsearch (X, Y, "Distance", "chebychev");
588s  assert (idx, [1; 1]);
588s  assert (D, [1; 1]);
588s ***** test
588s  [idx, D] = knnsearch (X, Y, "Distance", "cosine");
588s  assert (idx, [2; 3]);
588s  assert (D, [0.005674536395645; 0.002911214328620], 1e-14);
588s ***** test
588s  [idx, D] = knnsearch (X, Y, "Distance", "correlation");
588s  assert (idx, [1; 1]);
588s  assert (D, ones (2, 1) * 0.051316701949486, 1e-14);
588s ***** test
588s  [idx, D] = knnsearch (X, Y, "Distance", "spearman");
588s  assert (idx, [1; 1]);
588s  assert (D, ones (2, 1) * 0.051316701949486, 1e-14);
588s ***** test
588s  [idx, D] = knnsearch (X, Y, "Distance", "hamming");
588s  assert (idx, [1; 1]);
588s  assert (D, [0.5; 0.5]);
588s ***** test
588s  [idx, D] = knnsearch (X, Y, "Distance", "jaccard");
588s  assert (idx, [1; 1]);
588s  assert (D, [0.5; 0.5]);
588s ***** test
588s  [idx, D] = knnsearch (X, Y, "Distance", "jaccard", "k", 2);
588s  assert (idx, [1, 2; 1, 2]);
588s  assert (D, [0.5, 1; 0.5, 0.5]);
588s ***** test
588s  a = [1, 5; 1, 2; 2, 2; 1.5, 1.5; 5, 1; 2 -1.34; 1, -3; 4, -4; -3, 1; 8, 9];
588s  b = [1, 1];
588s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree", "includeties", true);
588s  assert (iscell (idx), true);
588s  assert (iscell (D), true)
588s  assert (cell2mat (idx), [4, 2, 3, 6, 1, 5, 7, 9]);
588s  assert (cell2mat (D), [0.7071, 1.0000, 1.4142, 2.5447, 4.0000, 4.0000, 4.0000, 4.0000],1e-4);
588s ***** test
588s  a = [1, 5; 1, 2; 2, 2; 1.5, 1.5; 5, 1; 2 -1.34; 1, -3; 4, -4; -3, 1; 8, 9];
588s  b = [1, 1];
588s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "exhaustive", "includeties", true);
588s  assert (iscell (idx), true);
588s  assert (iscell (D), true)
588s  assert (cell2mat (idx), [4, 2, 3, 6, 1, 5, 7, 9]);
588s  assert (cell2mat (D), [0.7071, 1.0000, 1.4142, 2.5447, 4.0000, 4.0000, 4.0000, 4.0000],1e-4);
588s ***** test
588s  a = [1, 5; 1, 2; 2, 2; 1.5, 1.5; 5, 1; 2 -1.34; 1, -3; 4, -4; -3, 1; 8, 9];
588s  b = [1, 1];
588s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree", "includeties", false);
588s  assert (iscell (idx), false);
588s  assert (iscell (D), false)
588s  assert (idx, [4, 2, 3, 6, 1]);
588s  assert (D, [0.7071, 1.0000, 1.4142, 2.5447, 4.0000],1e-4);
588s ***** test
588s  a = [1, 5; 1, 2; 2, 2; 1.5, 1.5; 5, 1; 2 -1.34; 1, -3; 4, -4; -3, 1; 8, 9];
588s  b = [1, 1];
588s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "exhaustive", "includeties", false);
588s  assert (iscell (idx), false);
588s  assert (iscell (D), false)
588s  assert (idx, [4, 2, 3, 6, 1]);
588s  assert (D, [0.7071, 1.0000, 1.4142, 2.5447, 4.0000],1e-4);
588s ***** test
588s  load fisheriris
588s  a = meas;
588s  b = min(meas);
588s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree");
588s  assert (idx, [42, 9, 14, 39, 13]);
588s  assert (D, [0.5099, 0.9950, 1.0050, 1.0536, 1.1874],1e-4);
588s ***** test
588s  load fisheriris
588s  a = meas;
588s  b = mean(meas);
588s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree");
588s  assert (idx, [65, 83, 89, 72, 100]);
588s  assert (D, [0.3451, 0.3869, 0.4354, 0.4481, 0.4625],1e-4);
588s ***** test
588s  load fisheriris
588s  a = meas;
588s  b = max(meas);
588s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree");
588s  assert (idx, [118, 132, 110, 106, 136]);
588s  assert (D, [0.7280, 0.9274, 1.3304, 1.5166, 1.6371],1e-4);
588s 
588s ***** test
588s  load fisheriris
588s  a = meas;
588s  b = max(meas);
588s  [idx, D] = knnsearch (a, b, "K", 5, "includeties", true);
588s  assert ( iscell (idx), true);
588s  assert ( iscell (D), true);
588s  assert (cell2mat (idx), [118, 132, 110, 106, 136]);
588s  assert (cell2mat (D), [0.7280, 0.9274, 1.3304, 1.5166, 1.6371],1e-4);
588s ***** error<knnsearch: too few input arguments.> knnsearch (1)
588s ***** error<knnsearch: number of columns in X and Y must match.> ...
588s  knnsearch (ones (4, 5), ones (4))
588s ***** error<knnsearch: invalid NAME in optional pairs of arguments.> ...
588s  knnsearch (ones (4, 2), ones (3, 2), "Distance", "euclidean", "some", "some")
588s ***** error<knnsearch: only a single distance parameter can be defined.> ...
588s  knnsearch (ones (4, 5), ones (1, 5), "scale", ones (1, 5), "P", 3)
588s ***** error<knnsearch: invalid value of K.> ...
588s  knnsearch (ones (4, 5), ones (1, 5), "K", 0)
588s ***** error<knnsearch: invalid value of Minkowski Exponent.> ...
588s  knnsearch (ones (4, 5), ones (1, 5), "P",-2)
588s ***** error<knnsearch: invalid value in Scale or the size of Scale.> ...
588s  knnsearch (ones (4, 5), ones (1, 5), "scale", ones(4,5), "distance", "euclidean")
588s ***** error<knnsearch: invalid value in Cov, Cov can only be given for mahalanobis distance.> ...
588s  knnsearch (ones (4, 5), ones (1, 5), "cov", ["some" "some"])
588s ***** error<knnsearch: invalid value in Cov, Cov can only be given for mahalanobis distance.> ...
588s  knnsearch (ones (4, 5), ones (1, 5), "cov", ones(4,5), "distance", "euclidean")
588s ***** error<knnsearch: invalid value of bucketsize.> ...
588s  knnsearch (ones (4, 5), ones (1, 5), "bucketsize", -1)
588s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
588s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "cosine")
588s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
588s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "mahalanobis")
588s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
588s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "correlation")
588s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
588s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "seuclidean")
588s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
588s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "spearman")
588s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
588s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "hamming")
588s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
588s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "jaccard")
588s 42 tests, 42 passed, 0 known failure, 0 skipped
588s [inst/linkage.m]
588s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/linkage.m
588s ***** shared x, t
588s  x = reshape (mod (magic (6),5), [], 3);
588s  t = 1e-6;
588s ***** assert (cond (linkage (pdist (x))),                   34.119045, t);
588s ***** assert (cond (linkage (pdist (x), "complete")),       21.793345, t);
588s ***** assert (cond (linkage (pdist (x), "average")),        27.045012, t);
588s ***** assert (cond (linkage (pdist (x), "weighted")),       27.412889, t);
588s  lastwarn(); # Clear last warning before the test
588s ***** warning <cluster distances> linkage (pdist (x), "centroid");
588s ***** test
588s  warning off Octave:clustering
588s  assert (cond (linkage (pdist (x), "centroid")),      27.457477, t);
588s  warning on Octave:clustering
588s ***** warning <cluster distances> linkage (pdist (x), "median");
588s ***** test
588s  warning off Octave:clustering
588s  assert (cond (linkage (pdist (x), "median")),        27.683325, t);
588s  warning on Octave:clustering
588s ***** assert (cond (linkage (pdist (x), "ward")),           17.195198, t);
588s ***** assert (cond (linkage (x, "ward", "euclidean")),      17.195198, t);
588s ***** assert (cond (linkage (x, "ward", {"euclidean"})),    17.195198, t);
588s ***** assert (cond (linkage (x, "ward", {"minkowski", 2})), 17.195198, t);
588s 12 tests, 12 passed, 0 known failure, 0 skipped
588s [inst/ttest.m]
588s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/ttest.m
588s ***** test
588s  x = 8:0.1:12;
588s  [h, pval, ci] = ttest (x, 10);
588s  assert (h, 0)
588s  assert (pval, 1, 10*eps)
588s  assert (ci, [9.6219 10.3781], 1E-5)
588s  [h, pval, ci0] = ttest (x, 0);
588s  assert (h, 1)
588s  assert (pval, 0)
588s  assert (ci0, ci, 2e-15)
588s  [h, pval, ci] = ttest (x, 10, "tail", "right", "dim", 2, "alpha", 0.05);
588s  assert (h, 0)
588s  assert (pval, 0.5, 10*eps)
588s  assert (ci, [9.68498 Inf], 1E-5)
588s ***** error ttest ([8:0.1:12], 10, "tail", "invalid");
588s ***** error ttest ([8:0.1:12], 10, "tail", 25);
588s 3 tests, 3 passed, 0 known failure, 0 skipped
588s [inst/@cvpartition/set.m]
588s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/@cvpartition/set.m
588s ***** shared C
588s  C = cvpartition (ones (10, 1), "KFold", 5);
588s ***** test
588s  Cnew = set (C, "inds", [1 2 2 2 3 4 3 4 5 5]');
588s  assert (get (Cnew, "inds"), [1 2 2 2 3 4 3 4 5 5]');
588s ***** error<set: expecting field/value pairs.> set (C)
588s ***** error<set: expecting field/value pairs.> set (C, "NumObservations")
588s ***** error<set: invalid field some.> set (C, "some", 15)
588s ***** error<set: expecting the field to be a string.> set (C, 15, 15)
588s 5 tests, 5 passed, 0 known failure, 0 skipped
588s [inst/@cvpartition/display.m]
588s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/@cvpartition/display.m
588s ***** test
588s  C = cvpartition (ones (10, 1), "KFold", 5);
588s  s = evalc ("display (C)");
588s  sout = "K-fold cross validation partition";
588s  assert (strcmpi (s(1:length (sout)), sout), true);
588s ***** test
588s  C = cvpartition (ones (10, 1), "HoldOut", 5);
588s  s = evalc ("display (C)");
588s  sout = "HoldOut cross validation partition";
588s  assert (strcmpi (s(1:length (sout)), sout), true);
588s ***** test
588s  C = cvpartition (ones (10, 1), "LeaveOut", 5);
588s  s = evalc ("display (C)");
588s  sout = "Leave-One-Out cross validation partition";
588s  assert (strcmpi (s(1:length (sout)), sout), true);
589s ***** test
589s  C = cvpartition (ones (10, 1), "resubstitution", 5);
589s  s = evalc ("display (C)");
589s  sout = "Resubstitution cross validation partition";
589s  assert (strcmpi (s(1:length (sout)), sout), true);
589s ***** test
589s  C = cvpartition (ones (10, 1), "Given", 5);
589s  s = evalc ("display (C)");
589s  sout = "Given cross validation partition";
589s  assert (strcmpi (s(1:length (sout)), sout), true);
589s ***** error<Invalid call to display.  Correct usage is> display ()
589s 6 tests, 6 passed, 0 known failure, 0 skipped
589s [inst/@cvpartition/get.m]
589s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/@cvpartition/get.m
589s ***** shared C
589s  C = cvpartition (ones (10, 1), "KFold", 5);
589s ***** assert (get (C, "NumObservations"), 10);
589s ***** assert (get (C, "NumTestSets"), 5);
589s ***** assert (get (C, "TrainSize"), ones(5,1) * 8);
589s ***** assert (get (C, "TestSize"), ones (5,1) * 2);
589s ***** assert (get (C, "inds"), [1 1 2 2 3 3 4 4 5 5]');
589s ***** assert (get (C, "Type"), "kfold");
589s ***** error<get: invalid property some.> get (C, "some")
589s ***** error<get: expecting the property to be a string.> get (C, 25)
589s ***** error<get: expecting the property to be a string.> get (C, {25})
589s 9 tests, 9 passed, 0 known failure, 0 skipped
589s [inst/@cvpartition/repartition.m]
589s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/@cvpartition/repartition.m
589s ***** test
589s  C = cvpartition (ones (10, 1), "KFold", 5);
589s  Cnew = repartition (C);
589s  assert (isa (Cnew, "cvpartition"), true);
589s ***** test
589s  C = cvpartition (ones (100, 1), "HoldOut", 5);
589s  Cnew = repartition (C);
589s  indC = get (C, "inds");
589s  indCnew = get (Cnew, "inds");
589s  assert (isequal (indC, indCnew), false);
589s 2 tests, 2 passed, 0 known failure, 0 skipped
589s [inst/@cvpartition/test.m]
589s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/@cvpartition/test.m
589s ***** shared C
589s  C = cvpartition (ones (10, 1), "KFold", 5);
589s ***** assert (test (C, 1), logical ([1 1 0 0 0 0 0 0 0 0]'))
589s ***** assert (test (C, 2), logical ([0 0 1 1 0 0 0 0 0 0]'))
589s ***** assert (test (C, 3), logical ([0 0 0 0 1 1 0 0 0 0]'))
589s ***** assert (test (C, 4), logical ([0 0 0 0 0 0 1 1 0 0]'))
589s ***** assert (test (C, 5), logical ([0 0 0 0 0 0 0 0 1 1]'))
589s ***** test
589s  C = set (C, "inds", [1 2 2 2 3 4 3 4 5 5]');
589s ***** assert (test (C), logical ([1 0 0 0 0 0 0 0 0 0]'))
589s ***** assert (test (C, 2), logical ([0 1 1 1 0 0 0 0 0 0]'))
589s ***** assert (test (C, 3), logical ([0 0 0 0 1 0 1 0 0 0]'))
589s 9 tests, 9 passed, 0 known failure, 0 skipped
589s [inst/@cvpartition/training.m]
589s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/@cvpartition/training.m
589s ***** shared C
589s  C = cvpartition (ones (10, 1), "KFold", 5);
589s ***** assert (training (C, 1), logical ([0 0 1 1 1 1 1 1 1 1]'))
589s ***** assert (training (C, 2), logical ([1 1 0 0 1 1 1 1 1 1]'))
589s ***** assert (training (C, 3), logical ([1 1 1 1 0 0 1 1 1 1]'))
589s ***** assert (training (C, 4), logical ([1 1 1 1 1 1 0 0 1 1]'))
589s ***** assert (training (C, 5), logical ([1 1 1 1 1 1 1 1 0 0]'))
589s ***** test
589s  C = set (C, "inds", [1 2 2 2 3 4 3 4 5 5]');
589s ***** assert (training (C), logical ([0 1 1 1 1 1 1 1 1 1]'))
589s ***** assert (training (C, 2), logical ([1 0 0 0 1 1 1 1 1 1]'))
589s ***** assert (training (C, 3), logical ([1 1 1 1 0 1 0 1 1 1]'))
589s 9 tests, 9 passed, 0 known failure, 0 skipped
589s [inst/@cvpartition/cvpartition.m]
589s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/@cvpartition/cvpartition.m
589s ***** demo
589s  ## Partition with Fisher iris dataset (n = 150)
589s  ## Stratified by species
589s  load fisheriris
589s  y = species;
589s  ## 10-fold cross-validation partition
589s  c = cvpartition (species, 'KFold', 10)
589s  ## leave-10-out partition
589s  c1 = cvpartition (species, 'HoldOut', 10)
589s  idx1 = test (c, 2);
589s  idx2 = training (c, 2);
589s  ## another leave-10-out partition
589s  c2 = repartition (c1)
589s ***** test
589s  C = cvpartition (ones (10, 1));
589s  assert (isa (C, "cvpartition"), true);
589s ***** test
589s  C = cvpartition (ones (10, 1), "KFold", 5);
589s  assert (get (C, "NumObservations"), 10);
589s  assert (get (C, "NumTestSets"), 5);
589s  assert (get (C, "TrainSize"), ones(5,1) * 8);
589s  assert (get (C, "TestSize"), ones (5,1) * 2);
589s  assert (get (C, "inds"), [1 1 2 2 3 3 4 4 5 5]');
589s  assert (get (C, "Type"), "kfold");
589s ***** test
589s  C = cvpartition (ones (10, 1), "KFold", 2);
589s  assert (get (C, "NumObservations"), 10);
589s  assert (get (C, "NumTestSets"), 2);
589s  assert (get (C, "TrainSize"), [5; 5]);
589s  assert (get (C, "TestSize"), [5; 5]);
589s  assert (get (C, "inds"), [1 1 1 1 1 2 2 2 2 2]');
589s  assert (get (C, "Type"), "kfold");
589s ***** test
589s  C = cvpartition (ones (10, 1), "HoldOut", 5);
589s  assert (get (C, "NumObservations"), 10);
589s  assert (get (C, "NumTestSets"), 1);
589s  assert (get (C, "TrainSize"), 5);
589s  assert (get (C, "TestSize"), 5);
589s  assert (class (get (C, "inds")), "logical");
589s  assert (length (get (C, "inds")), 10);
589s  assert (get (C, "Type"), "holdout");
589s ***** test
589s  C = cvpartition ([1 2 3 4 5 6 7 8 9 10], "LeaveOut", 5);
589s  assert (get (C, "NumObservations"), 10);
589s  assert (get (C, "NumTestSets"), 10);
589s  assert (get (C, "TrainSize"), ones (10, 1));
589s  assert (get (C, "TestSize"), ones (10, 1) * 9);
589s  assert (get (C, "inds"), []);
589s  assert (get (C, "Type"), "leaveout");
589s ***** test
589s  C = cvpartition ([1 2 3 4 5 6 7 8 9 10], "resubstitution", 5);
589s  assert (get (C, "NumObservations"), 10);
589s  assert (get (C, "NumTestSets"), 1);
589s  assert (get (C, "TrainSize"), 10);
589s  assert (get (C, "TestSize"), 10);
589s  assert (get (C, "inds"), []);
589s  assert (get (C, "Type"), "resubstitution");
589s ***** test
589s  C = cvpartition ([1 2 3 4 5 6 7 8 9 10], "Given", 2);
589s  assert (get (C, "NumObservations"), 10);
589s  assert (get (C, "NumTestSets"), 10);
589s  assert (get (C, "TrainSize"), ones (10, 1) * 9);
589s  assert (get (C, "TestSize"), ones (10, 1));
589s  assert (get (C, "inds"), [1:10]');
589s  assert (get (C, "Type"), "given");
589s ***** warning<cvpartition: unrecognized type, using KFold.> ...
589s  C = cvpartition ([1 2 3 4 5 6 7 8 9 10], "some", 2);
589s 8 tests, 8 passed, 0 known failure, 0 skipped
589s [inst/fitcsvm.m]
589s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/fitcsvm.m
589s ***** demo
589s  ## Use a subset of Fisher's iris data set
589s 
589s  load fisheriris
589s  inds = ! strcmp (species, 'setosa');
589s  X = meas(inds, [3,4]);
589s  Y = species(inds);
589s 
589s  ## Train a linear SVM classifier
589s  SVMModel = fitcsvm (X, Y)
589s 
589s  ## Plot a scatter diagram of the data and circle the support vectors.
589s  sv = SVMModel.SupportVectors;
589s  figure
589s  gscatter (X(:,1), X(:,2), Y)
589s  hold on
589s  plot (sv(:,1), sv(:,2), 'ko', 'MarkerSize', 10)
589s  legend ('versicolor', 'virginica', 'Support Vector')
589s  hold off
589s ***** test
589s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
589s  y = {"a"; "a"; "b"; "b"};
589s  a = fitcsvm (x, y);
589s  assert (class (a), "ClassificationSVM");
589s  assert ({a.X, a.Y}, {x, y})
589s  assert (a.NumObservations, 4)
589s  assert ({a.ResponseName, a.PredictorNames}, {"Y", {"x1", "x2", "x3"}})
589s  assert (a.ModelParameters.SVMtype, "c_svc")
589s  assert (a.ClassNames, {"a"; "b"})
589s ***** test
589s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
589s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
589s  a = fitcsvm (x, y);
589s  assert (class (a), "ClassificationSVM");
589s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "linear"})
589s  assert (a.ModelParameters.BoxConstraint, 1)
589s  assert (a.ModelParameters.KernelOffset, 0)
589s  assert (a.ClassNames, [1; -1])
589s ***** test
589s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
589s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
589s  a = fitcsvm (x, y, "KernelFunction", "rbf", "BoxConstraint", 2, ...
589s  "KernelOffset", 2);
589s  assert (class (a), "ClassificationSVM");
589s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "rbf"})
589s  assert (a.ModelParameters.BoxConstraint, 2)
589s  assert (a.ModelParameters.KernelOffset, 2)
589s  assert (isempty (a.Alpha), true)
589s  assert (isempty (a.Beta), false)
589s ***** test
589s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
589s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
589s  a = fitcsvm (x, y, "KernelFunction", "polynomial", "PolynomialOrder", 3);
589s  assert (class (a), "ClassificationSVM");
589s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "polynomial"})
589s  assert (a.ModelParameters.PolynomialOrder, 3)
589s  assert (isempty (a.Alpha), true)
589s  assert (isempty (a.Beta), false)
589s ***** test
589s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
589s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
589s  a = fitcsvm (x, y, "KernelFunction", "linear", "PolynomialOrder", 3);
589s  assert (class (a), "ClassificationSVM");
589s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "linear"})
589s  assert (a.ModelParameters.PolynomialOrder, 3)
589s  assert (isempty (a.Alpha), false)
589s  assert (isempty (a.Beta), true)
589s ***** test
589s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
589s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
589s  a = fitcsvm (x, y, "KernelFunction", "linear", "CrossVal", 'on');
589s  assert (class (a), "ClassificationPartitionedModel");
589s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "linear"})
589s  assert (a.ModelParameters.PolynomialOrder, 3)
589s  assert (isempty (a.Trained{1}.Alpha), false)
589s  assert (isempty (a.Trained{1}.Beta), true)
589s ***** error<fitcsvm: too few arguments.> fitcsvm ()
589s ***** error<fitcsvm: too few arguments.> fitcsvm (ones (4,1))
589s ***** error<fitcsvm: Name-Value arguments must be in pairs.>
589s  fitcsvm (ones (4,2), ones (4, 1), 'KFold')
589s ***** error<fitcsvm: number of rows in X and Y must be equal.>
589s  fitcsvm (ones (4,2), ones (3, 1))
589s ***** error<fitcsvm: number of rows in X and Y must be equal.>
589s  fitcsvm (ones (4,2), ones (3, 1), 'KFold', 2)
589s ***** error <fitcsvm: 'CrossVal' must be either 'off' or 'on'.>
589s  fitcsvm (ones (4,2), ones (4, 1), "CrossVal", 2)
589s ***** error <fitcsvm: 'CrossVal' must be either 'off' or 'on'.>
589s  fitcsvm (ones (4,2), ones (4, 1), "CrossVal", 'a')
589s ***** error <fitcsvm: You can use only one cross-validation name-value pair argument> ...
589s  fitcsvm (ones (4,2), ones (4, 1), "KFold", 10, "Holdout", 0.3)
589s 14 tests, 14 passed, 0 known failure, 0 skipped
589s [inst/grp2idx.m]
589s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/grp2idx.m
589s ***** test
589s  in = [true false false true];
589s  out = {[1; 2; 2; 1] {"1"; "0"} [true; false]};
589s  assert (nthargout (1:3, @grp2idx, in), out)
589s  assert (nthargout (1:3, @grp2idx, in), nthargout (1:3, @grp2idx, in'))
589s ***** test
589s  assert (nthargout (1:3, @grp2idx, [false, true]),
589s          {[1; 2] {"0"; "1"} [false; true]});
589s  assert (nthargout (1:3, @grp2idx, [true, false]),
589s          {[1; 2] {"1"; "0"} [true; false]});
589s ***** assert (nthargout (1:3, @grp2idx, ["oct"; "sci"; "oct"; "oct"; "sci"]),
589s         {[1; 2; 1; 1; 2] {"oct"; "sci"} ["oct"; "sci"]});
589s ***** assert (nthargout (1:3, @grp2idx, {"oct"; "sci"; "oct"; "oct"; "sci"}),
589s         {[1; 2; 1; 1; 2] {"oct"; "sci"} {"oct"; "sci"}});
589s ***** assert (nthargout (1:3, @grp2idx, [ 1 -3 -2 -3 -3  2  1 -1  3 -3]),
589s         {[1; 2; 3; 2; 2; 4; 1; 5; 6; 2], {"1"; "-3"; "-2"; "2"; "-1"; "3"}, ...
589s          [1; -3; -2; 2; -1; 3]});
589s ***** assert (nthargout (1:3, @grp2idx, [2 2 3 NaN 2 3]),
589s         {[1; 1; 2; NaN; 1; 2] {"2"; "3"} [2; 3]})
589s ***** assert (nthargout (1:3, @grp2idx, {"et" "sa" "sa" "" "et"}),
589s         {[1; 2; 2; NaN; 1] {"et"; "sa"} {"et"; "sa"}})
589s ***** test assert (nthargout (1:3, @grp2idx, ["sci"; "oct"; "sci"; "oct"; "oct"]),
589s         {[1; 2; 1; 2; 2] {"sci"; "oct"} ["sci"; "oct"]});
589s ***** test assert (nthargout (1:3, @grp2idx, {"sci"; "oct"; "sci"; "oct"; "oct"}),
589s         {[1; 2; 1; 2; 2] {"sci"; "oct"} {"sci"; "oct"}});
589s ***** test assert (nthargout (1:3, @grp2idx, {"sa" "et" "et" "" "sa"}),
589s         {[1; 2; 2; NaN; 1] {"sa"; "et"} {"sa"; "et"}})
589s 10 tests, 10 passed, 0 known failure, 0 skipped
589s [inst/pdist.m]
589s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/pdist.m
589s ***** shared xy, t, eucl, x
589s  xy = [0 1; 0 2; 7 6; 5 6];
589s  t = 1e-3;
589s  eucl = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
589s  x = [1 2 3; 4 5 6; 7 8 9; 3 2 1];
589s ***** assert (pdist (xy),                 [1.000 8.602 7.071 8.062 6.403 2.000], t);
589s ***** assert (pdist (xy, eucl),           [1.000 8.602 7.071 8.062 6.403 2.000], t);
589s ***** assert (pdist (xy, "euclidean"),    [1.000 8.602 7.071 8.062 6.403 2.000], t);
589s ***** assert (pdist (xy, "seuclidean"),   [0.380 2.735 2.363 2.486 2.070 0.561], t);
589s ***** assert (pdist (xy, "mahalanobis"),  [1.384 1.967 2.446 2.384 1.535 2.045], t);
589s ***** assert (pdist (xy, "cityblock"),    [1.000 12.00 10.00 11.00 9.000 2.000], t);
589s ***** assert (pdist (xy, "minkowski"),    [1.000 8.602 7.071 8.062 6.403 2.000], t);
589s ***** assert (pdist (xy, "minkowski", 3), [1.000 7.763 6.299 7.410 5.738 2.000], t);
589s ***** assert (pdist (xy, "cosine"),       [0.000 0.349 0.231 0.349 0.231 0.013], t);
589s ***** assert (pdist (xy, "correlation"),  [0.000 2.000 0.000 2.000 0.000 2.000], t);
589s ***** assert (pdist (xy, "spearman"),     [0.000 2.000 0.000 2.000 0.000 2.000], t);
589s ***** assert (pdist (xy, "hamming"),      [0.500 1.000 1.000 1.000 1.000 0.500], t);
589s ***** assert (pdist (xy, "jaccard"),      [1.000 1.000 1.000 1.000 1.000 0.500], t);
589s ***** assert (pdist (xy, "chebychev"),    [1.000 7.000 5.000 7.000 5.000 2.000], t);
589s ***** assert (pdist (x), [5.1962, 10.3923, 2.8284, 5.1962, 5.9161, 10.7703], 1e-4);
589s ***** assert (pdist (x, "euclidean"), ...
589s         [5.1962, 10.3923, 2.8284, 5.1962, 5.9161, 10.7703], 1e-4);
589s ***** assert (pdist (x, eucl), ...
589s         [5.1962, 10.3923, 2.8284, 5.1962, 5.9161, 10.7703], 1e-4);
589s ***** assert (pdist (x, "squaredeuclidean"), [27, 108, 8, 27, 35, 116]);
589s ***** assert (pdist (x, "seuclidean"), ...
589s         [1.8071, 3.6142, 0.9831, 1.8071, 1.8143, 3.4854], 1e-4);
589s ***** warning<pdist: matrix is close to singular> ...
589s  pdist (x, "mahalanobis");
589s ***** assert (pdist (x, "cityblock"), [9, 18, 4, 9, 9, 18]);
589s ***** assert (pdist (x, "minkowski"), ...
589s         [5.1962, 10.3923, 2.8284, 5.1962, 5.9161, 10.7703], 1e-4);
589s ***** assert (pdist (x, "minkowski", 3), ...
589s         [4.3267, 8.6535, 2.5198, 4.3267, 5.3485, 9.2521], 1e-4);
589s ***** assert (pdist (x, "cosine"), ...
589s         [0.0254, 0.0406, 0.2857, 0.0018, 0.1472, 0.1173], 1e-4);
589s ***** assert (pdist (x, "correlation"), [0, 0, 2, 0, 2, 2], 1e-14);
589s ***** assert (pdist (x, "spearman"), [0, 0, 2, 0, 2, 2], 1e-14);
589s ***** assert (pdist (x, "hamming"), [1, 1, 2/3, 1, 1, 1]);
589s ***** assert (pdist (x, "jaccard"), [1, 1, 2/3, 1, 1, 1]);
589s ***** assert (pdist (x, "chebychev"), [3, 6, 2, 3, 5, 8]);
589s 29 tests, 29 passed, 0 known failure, 0 skipped
589s [inst/canoncorr.m]
589s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/canoncorr.m
589s ***** shared X,Y,A,B,r,U,V,k
589s  k = 10;
589s  X = [1:k; sin(1:k); cos(1:k)]'; Y = [tan(1:k); tanh((1:k)/k)]';
589s  [A,B,r,U,V,stats] = canoncorr (X,Y);
589s ***** assert (A, [-0.329229   0.072908; 0.074870   1.389318; -0.069302  -0.024109], 1E-6);
589s ***** assert (B, [-0.017086  -0.398402; -4.475049  -0.824538], 1E-6);
589s ***** assert (r, [0.99590   0.26754], 1E-5);
589s ***** assert (U, center(X) * A, 10*eps);
589s ***** assert (V, center(Y) * B, 10*eps);
589s ***** assert (cov(U), eye(size(U, 2)), 10*eps);
589s ***** assert (cov(V), eye(size(V, 2)), 10*eps);
589s  rand ("state", 1); [A,B,r] = canoncorr (rand(5, 10),rand(5, 20));
589s ***** assert (r, ones(1, 5), 10*eps);
589s 8 tests, 8 passed, 0 known failure, 0 skipped
589s [inst/friedman.m]
589s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/friedman.m
589s ***** demo
589s  load popcorn;
589s  friedman (popcorn, 3);
589s ***** demo
589s  load popcorn;
589s  [p, atab] = friedman (popcorn, 3, "off");
589s  disp (p);
589s ***** test
589s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
589s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
589s  [p, atab] = friedman (popcorn, 3, "off");
589s  assert (p, 0.001028853354594794, 1e-14);
589s  assert (atab{2,2}, 99.75, 1e-14);
589s  assert (atab{2,3}, 2, 0);
589s  assert (atab{2,4}, 49.875, 1e-14);
589s  assert (atab{2,5}, 13.75862068965517, 1e-14);
589s  assert (atab{2,6}, 0.001028853354594794, 1e-14);
589s  assert (atab{3,2}, 0.08333333333333215, 1e-14);
589s  assert (atab{3,4}, 0.04166666666666607, 1e-14);
589s  assert (atab{4,3}, 12, 0);
589s ***** test
589s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
589s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
589s  [p, atab, stats] = friedman (popcorn, 3, "off");
589s  assert (atab{5,2}, 116, 0);
589s  assert (atab{5,3}, 17, 0);
589s  assert (stats.source, "friedman");
589s  assert (stats.n, 2);
589s  assert (stats.meanranks, [8, 4.75, 2.25], 0);
589s  assert (stats.sigma, 2.692582403567252, 1e-14);
589s 2 tests, 2 passed, 0 known failure, 0 skipped
589s [inst/pcacov.m]
589s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/pcacov.m
589s ***** demo
589s  x = [ 7    26     6    60;
589s        1    29    15    52;
589s       11    56     8    20;
589s       11    31     8    47;
589s        7    52     6    33;
589s       11    55     9    22;
589s        3    71    17     6;
589s        1    31    22    44;
589s        2    54    18    22;
589s       21    47     4    26;
589s        1    40    23    34;
589s       11    66     9    12;
589s       10    68     8    12
589s      ];
589s  Kxx = cov (x);
589s  [coeff, latent, explained] = pcacov (Kxx)
589s ***** test
589s  load hald
589s  Kxx = cov (ingredients);
589s  [coeff,latent,explained] = pcacov(Kxx);
589s  c_out = [-0.0678, -0.6460,  0.5673, 0.5062; ...
589s           -0.6785, -0.0200, -0.5440, 0.4933; ...
589s            0.0290,  0.7553,  0.4036, 0.5156; ...
589s            0.7309, -0.1085, -0.4684, 0.4844];
589s  l_out = [517.7969; 67.4964; 12.4054; 0.2372];
589s  e_out = [ 86.5974; 11.2882;  2.0747; 0.0397];
589s  assert (coeff, c_out, 1e-4);
589s  assert (latent, l_out, 1e-4);
589s  assert (explained, e_out, 1e-4);
589s ***** error<pcacov: K must be a square matrix.> pcacov (ones (2,3))
589s ***** error<pcacov: K must be a square matrix.> pcacov (ones (3,3,3))
589s 3 tests, 3 passed, 0 known failure, 0 skipped
589s [inst/pdist2.m]
589s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/pdist2.m
589s ***** shared x, y, xx
589s  x = [1, 1, 1; 2, 2, 2; 3, 3, 3];
589s  y = [0, 0, 0; 1, 2, 3; 0, 2, 4; 4, 7, 1];
589s  xx = [1 2 3; 4 5 6; 7 8 9; 3 2 1];
589s ***** test
589s  d = sqrt([3, 5, 11, 45; 12, 2, 8, 30; 27, 5, 11, 21]);
589s  assert (pdist2 (x, y), d);
589s ***** test
589s  d = [5.1962, 2.2361, 3.3166, 6.7082; ...
589s       3.4641, 2.2361, 3.3166, 5.4772];
589s  i = [3, 1, 1, 1; 2, 3, 3, 2];
589s  [D, I] = pdist2 (x, y, "euclidean", "largest", 2);
589s  assert ({D, I}, {d, i}, 1e-4);
589s ***** test
589s  d = [1.7321, 1.4142, 2.8284, 4.5826; ...
589s       3.4641, 2.2361, 3.3166, 5.4772];
589s  i = [1, 2, 2, 3;2, 1, 1, 2];
589s  [D, I] = pdist2 (x, y, "euclidean", "smallest", 2);
589s  assert ({D, I}, {d, i}, 1e-4);
589s ***** test
589s  yy = [1 2 3;5 6 7;9 5 1];
589s  d = [0, 6.1644, 5.3852; 1.4142, 6.9282, 8.7750; ...
589s       3.7417, 7.0711, 9.9499; 6.1644, 10.4881, 10.3441];
589s  i = [2, 4, 4; 3, 2, 2; 1, 3, 3; 4, 1, 1];
589s  [D, I] = pdist2 (y, yy, "euclidean", "smallest", 4);
589s  assert ({D, I}, {d, i}, 1e-4);
589s ***** test
589s  yy = [1 2 3;5 6 7;9 5 1];
589s  d = [0, 38, 29; 2, 48, 77; 14, 50, 99; 38, 110, 107];
589s  i = [2, 4, 4; 3, 2, 2; 1, 3, 3; 4, 1, 1];
589s  [D, I] = pdist2 (y, yy, "squaredeuclidean", "smallest", 4);
589s  assert ({D, I}, {d, i}, 1e-4);
589s ***** test
589s  yy = [1 2 3;5 6 7;9 5 1];
589s  d = [0, 3.3256, 2.7249; 0.7610, 3.3453, 4.4799; ...
589s       1.8514, 3.3869, 5.0703; 2.5525, 5.0709, 5.1297];
589s  i = [2, 2, 4; 3, 4, 2; 1, 3, 1; 4, 1, 3];
589s  [D, I] = pdist2 (y, yy, "seuclidean", "smallest", 4);
589s  assert ({D, I}, {d, i}, 1e-4);
589s ***** test
589s  d = [2.1213, 4.2426, 6.3640; 1.2247, 2.4495, 4.4159; ...
589s       3.2404, 4.8990, 6.8191; 2.7386, 4.2426, 6.1237];
589s  assert (pdist2 (y, x, "mahalanobis"), d, 1e-4);
589s ***** test
589s  xx = [1, 3, 4; 3, 5, 4; 8, 7, 6];
589s  d = [1.3053, 1.8257, 15.0499; 1.3053, 3.3665, 16.5680];
589s  i = [2, 2, 2; 3, 4, 4];
589s  [D, I] = pdist2 (y, xx, "mahalanobis", "smallest", 2);
589s  assert ({D, I}, {d, i}, 1e-4);
589s ***** test
589s  d = [2.5240, 4.1633, 17.3638; 2.0905, 3.9158, 17.0147];
589s  i = [1, 1, 3; 4, 3, 1];
589s  [D, I] = pdist2 (y, xx, "mahalanobis", "largest", 2);
589s  assert ({D, I}, {d, i}, 1e-4);
589s ***** test
589s  d = [3, 3, 5, 9; 6, 2, 4, 8; 9, 3, 5, 7];
589s  assert (pdist2 (x, y, "cityblock"), d);
589s ***** test
589s  d = [1, 2, 3, 6; 2, 1, 2, 5; 3, 2, 3, 4];
589s  assert (pdist2 (x, y, "chebychev"), d);
589s ***** test
589s  d = repmat ([NaN, 0.0742, 0.2254, 0.1472], [3, 1]);
589s  assert (pdist2 (x, y, "cosine"), d, 1e-4);
589s ***** test
589s  yy = [1 2 3;5 6 7;9 5 1];
589s  d = [0, 0, 0.5; 0, 0, 2; 1.5, 1.5, 2; NaN, NaN, NaN];
589s  i = [2, 2, 4; 3, 3, 2; 4, 4, 3; 1, 1, 1];
589s  [D, I] = pdist2 (y, yy, "correlation", "smallest", 4);
589s  assert ({D, I}, {d, i}, eps);
589s  [D, I] = pdist2 (y, yy, "spearman", "smallest", 4);
589s  assert ({D, I}, {d, i}, eps);
589s ***** test
589s  d = [1, 2/3, 1, 1; 1, 2/3, 1, 1; 1, 2/3, 2/3, 2/3];
589s  i = [1, 1, 1, 2; 2, 2, 3, 3; 3, 3, 2, 1];
589s  [D, I] = pdist2 (x, y, "hamming", "largest", 4);
589s  assert ({D, I}, {d, i}, eps);
589s  [D, I] = pdist2 (x, y, "jaccard", "largest", 4);
589s  assert ({D, I}, {d, i}, eps);
589s ***** test
589s  xx = [1, 2, 3, 4; 2, 3, 4, 5; 3, 4, 5, 6];
589s  yy = [1, 2, 2, 3; 2, 3, 3, 4];
589s  [D, I] = pdist2 (x, y, "euclidean", "Smallest", 4);
589s  eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
589s  [d, i] = pdist2 (x, y, eucldist, "Smallest", 4);
589s  assert ({D, I}, {d, i});
589s ***** warning<pdist2: matrix is close to singular> ...
589s  pdist2 (xx, xx, "mahalanobis");
589s ***** error<pdist2: too few input arguments.> pdist2 (1)
589s ***** error<pdist2: X and Y must have equal number of columns.> ...
589s  pdist2 (ones (4, 5), ones (4))
589s ***** error<pdist2: X and Y must be 2 dimensional matrices.> ...
589s  pdist2 (ones (4, 2, 3), ones (3, 2))
589s ***** error<pdist2: missing value in optional name/value paired arguments.> ...
589s  pdist2 (ones (3), ones (3), "euclidean", "Largest")
589s ***** error<pdist2: missing value in optional name/value paired arguments.> ...
589s  pdist2 (ones (3), ones (3), "minkowski", 3, "Largest")
589s ***** error<pdist2: invalid NAME in optional pairs of arguments.> ...
589s  pdist2 (ones (3), ones (3), "minkowski", 3, "large", 4)
589s ***** error<pdist2: you can only use either Smallest or Largest.> ...
589s  pdist2 (ones (3), ones (3), "minkowski", 3, "Largest", 4, "smallest", 5)
589s ***** error<pdist2: Smallest or Largest must be specified to compute second output.> ...
589s  [d, i] = pdist2(ones (3), ones (3), "minkowski", 3)
589s ***** error<pdist2: DistParameter for standardized euclidean must be a vector of> ...
589s  pdist2 (ones (3), ones (3), "seuclidean", 3)
589s ***** error<pdist2: DistParameter for standardized euclidean must be a nonnegative> ...
589s  pdist2 (ones (3), ones (3), "seuclidean", [1, -1, 3])
589s ***** error<pdist2: DistParameter for mahalanobis distance must be a covariance> ...
589s  pdist2 (ones (3), eye (3), "mahalanobis", eye(2))
589s ***** error<pdist2: covariance matrix for mahalanobis distance must be symmetric> ...
589s  pdist2 (ones (3), eye (3), "mahalanobis", ones(3))
589s ***** error<pdist2: DistParameter for minkowski distance must be a positive scalar.> ...
589s  pdist2 (ones (3), eye (3), "minkowski", 0)
589s ***** error<pdist2: DistParameter for minkowski distance must be a positive scalar.> ...
589s  pdist2 (ones (3), eye (3), "minkowski", -5)
589s ***** error<pdist2: DistParameter for minkowski distance must be a positive scalar.> ...
589s  pdist2 (ones (3), eye (3), "minkowski", [1, 2])
589s ***** error<pdist2: invalid function handle for distance metric.> ...
589s  pdist2 (ones (3), ones (3), @(v,m) sqrt(repmat(v,rows(m),1)-m,2))
589s ***** error<pdist2: custom distance function produces wrong output size.> ...
589s  pdist2 (ones (3), ones (3), @(v,m) sqrt(sum(sumsq(repmat(v,rows(m),1)-m,2))))
589s 33 tests, 33 passed, 0 known failure, 0 skipped
589s [inst/anovan.m]
589s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/anovan.m
589s ***** demo
589s 
589s  # Two-sample unpaired test on independent samples (equivalent to Student's
589s  # t-test). Note that the absolute value of t-statistic can be obtained by
589s  # taking the square root of the reported F statistic. In this example,
589s  # t = sqrt (1.44) = 1.20.
589s 
589s  score = [54 23 45 54 45 43 34 65 77 46 65]';
589s  gender = {"male" "male" "male" "male" "male" "female" "female" "female" ...
589s            "female" "female" "female"}';
589s 
589s  [P, ATAB, STATS] = anovan (score, gender, "display", "on", "varnames", "gender");
589s ***** demo
589s 
589s  # Two-sample paired test on dependent or matched samples equivalent to a
589s  # paired t-test. As for the first example, the t-statistic can be obtained by
589s  # taking the square root of the reported F statistic. Note that the interaction
589s  # between treatment x subject was dropped from the full model by assigning
589s  # subject as a random factor (').
589s 
589s  score = [4.5 5.6; 3.7 6.4; 5.3 6.4; 5.4 6.0; 3.9 5.7]';
589s  treatment = {"before" "after"; "before" "after"; "before" "after";
589s               "before" "after"; "before" "after"}';
589s  subject = {"GS" "GS"; "JM" "JM"; "HM" "HM"; "JW" "JW"; "PS" "PS"}';
589s 
589s  [P, ATAB, STATS] = anovan (score(:), {treatment(:), subject(:)}, ...
589s                             "model", "full", "random", 2, "sstype", 2, ...
589s                             "varnames", {"treatment", "subject"}, ...
589s                             "display", "on");
589s ***** demo
589s 
589s  # One-way ANOVA on the data from a study on the strength of structural beams,
589s  # in Hogg and Ledolter (1987) Engineering Statistics. New York: MacMillan
589s 
589s  strength = [82 86 79 83 84 85 86 87 74 82 ...
589s             78 75 76 77 79 79 77 78 82 79]';
589s  alloy = {"st","st","st","st","st","st","st","st", ...
589s           "al1","al1","al1","al1","al1","al1", ...
589s           "al2","al2","al2","al2","al2","al2"}';
589s 
589s  [P, ATAB, STATS] = anovan (strength, alloy, "display", "on", ...
589s                             "varnames", "alloy");
589s ***** demo
589s 
589s  # One-way repeated measures ANOVA on the data from a study on the number of
589s  # words recalled by 10 subjects for three time condtions, in Loftus & Masson
589s  # (1994) Psychon Bull Rev. 1(4):476-490, Table 2. Note that the interaction
589s  # between seconds x subject was dropped from the full model by assigning
589s  # subject as a random factor (').
589s 
589s  words = [10 13 13; 6 8 8; 11 14 14; 22 23 25; 16 18 20; ...
589s           15 17 17; 1 1 4; 12 15 17;  9 12 12;  8 9 12];
589s  seconds = [1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5; ...
589s             1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5;];
589s  subject = [ 1  1  1;  2  2  2;  3  3  3;  4  4  4;  5  5  5; ...
589s              6  6  6;  7  7  7;  8  8  8;  9  9  9; 10 10 10];
589s 
589s  [P, ATAB, STATS] = anovan (words(:), {seconds(:), subject(:)}, ...
589s                             "model", "full", "random", 2, "sstype", 2, ...
589s                             "display", "on", "varnames", {"seconds", "subject"});
589s ***** demo
589s 
589s  # Balanced two-way ANOVA with interaction on the data from a study of popcorn
589s  # brands and popper types, in Hogg and Ledolter (1987) Engineering Statistics.
589s  # New York: MacMillan
589s 
589s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
589s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
589s  brands = {"Gourmet", "National", "Generic"; ...
589s            "Gourmet", "National", "Generic"; ...
589s            "Gourmet", "National", "Generic"; ...
589s            "Gourmet", "National", "Generic"; ...
589s            "Gourmet", "National", "Generic"; ...
589s            "Gourmet", "National", "Generic"};
589s  popper = {"oil", "oil", "oil"; "oil", "oil", "oil"; "oil", "oil", "oil"; ...
589s            "air", "air", "air"; "air", "air", "air"; "air", "air", "air"};
589s 
589s  [P, ATAB, STATS] = anovan (popcorn(:), {brands(:), popper(:)}, ...
589s                             "display", "on", "model", "full", ...
589s                             "varnames", {"brands", "popper"});
589s ***** demo
589s 
589s  # Unbalanced two-way ANOVA (2x2) on the data from a study on the effects of
589s  # gender and having a college degree on salaries of company employees,
589s  # in Maxwell, Delaney and Kelly (2018): Chapter 7, Table 15
589s 
589s  salary = [24 26 25 24 27 24 27 23 15 17 20 16, ...
589s            25 29 27 19 18 21 20 21 22 19]';
589s  gender = {"f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f"...
589s            "m" "m" "m" "m" "m" "m" "m" "m" "m" "m"}';
589s  degree = [1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0]';
589s 
589s  [P, ATAB, STATS] = anovan (salary, {gender, degree}, "model", "full", ...
589s                             "sstype", 3, "display", "on", "varnames", ...
589s                             {"gender", "degree"});
589s ***** demo
589s 
589s  # Unbalanced two-way ANOVA (3x2) on the data from a study of the effect of
589s  # adding sugar and/or milk on the tendency of coffee to make people babble,
589s  # in from Navarro (2019): 16.10
589s 
589s  sugar = {"real" "fake" "fake" "real" "real" "real" "none" "none" "none" ...
589s           "fake" "fake" "fake" "real" "real" "real" "none" "none" "fake"}';
589s  milk = {"yes" "no" "no" "yes" "yes" "no" "yes" "yes" "yes" ...
589s          "no" "no" "yes" "no" "no" "no" "no" "no" "yes"}';
589s  babble = [4.6 4.4 3.9 5.6 5.1 5.5 3.9 3.5 3.7...
589s            5.6 4.7 5.9 6.0 5.4 6.6 5.8 5.3 5.7]';
589s 
589s  [P, ATAB, STATS] = anovan (babble, {sugar, milk}, "model", "full",  ...
589s                             "sstype", 3, "display", "on", ...
589s                             "varnames", {"sugar", "milk"});
589s ***** demo
589s 
589s  # Unbalanced three-way ANOVA (3x2x2) on the data from a study of the effects
589s  # of three different drugs, biofeedback and diet on patient blood pressure,
589s  # adapted* from Maxwell, Delaney and Kelly (2018): Chapter 8, Table 12
589s  # * Missing values introduced to make the sample sizes unequal to test the
589s  #   calculation of different types of sums-of-squares
589s 
589s  drug = {"X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" ...
589s          "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X";
589s          "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" ...
589s          "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y";
589s          "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" ...
589s          "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z"};
589s  feedback = [1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
589s              1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
589s              1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0];
589s  diet = [0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1;
589s          0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1;
589s          0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1];
589s  BP = [170 175 165 180 160 158 161 173 157 152 181 190 ...
589s        173 194 197 190 176 198 164 190 169 164 176 175;
589s        186 194 201 215 219 209 164 166 159 182 187 174 ...
589s        189 194 217 206 199 195 171 173 196 199 180 NaN;
589s        180 187 199 170 204 194 162 184 183 156 180 173 ...
589s        202 228 190 206 224 204 205 199 170 160 NaN NaN];
589s 
589s  [P, ATAB, STATS] = anovan (BP(:), {drug(:), feedback(:), diet(:)}, ...
589s                                     "model", "full", "sstype", 3, ...
589s                                     "display", "on", ...
589s                                     "varnames", {"drug", "feedback", "diet"});
589s ***** demo
589s 
589s  # Balanced three-way ANOVA (2x2x2) with one of the factors being a blocking
589s  # factor. The data is from a randomized block design study on the effects
589s  # of antioxidant treatment on glutathione-S-transferase (GST) levels in
589s  # different mouse strains, from Festing (2014), ILAR Journal, 55(3):427-476.
589s  # Note that all interactions involving block were dropped from the full model
589s  # by assigning block as a random factor (').
589s 
589s  measurement = [444 614 423 625 408  856 447 719 ...
589s                 764 831 586 782 609 1002 606 766]';
589s  strain= {"NIH","NIH","BALB/C","BALB/C","A/J","A/J","129/Ola","129/Ola", ...
589s           "NIH","NIH","BALB/C","BALB/C","A/J","A/J","129/Ola","129/Ola"}';
589s  treatment={"C" "T" "C" "T" "C" "T" "C" "T" "C" "T" "C" "T" "C" "T" "C" "T"}';
589s  block = [1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2]';
589s 
589s  [P, ATAB, STATS] = anovan (measurement/10, {strain, treatment, block}, ...
589s                             "sstype", 2, "model", "full", "random", 3, ...
589s                             "display", "on", ...
589s                             "varnames", {"strain", "treatment", "block"});
589s ***** demo
589s 
589s  # One-way ANCOVA on data from a study of the additive effects of species
589s  # and temperature on chirpy pulses of crickets, from Stitch, The Worst Stats
589s  # Text eveR
589s 
589s  pulse = [67.9 65.1 77.3 78.7 79.4 80.4 85.8 86.6 87.5 89.1 ...
589s           98.6 100.8 99.3 101.7 44.3 47.2 47.6 49.6 50.3 51.8 ...
589s           60 58.5 58.9 60.7 69.8 70.9 76.2 76.1 77 77.7 84.7]';
589s  temp = [20.8 20.8 24 24 24 24 26.2 26.2 26.2 26.2 28.4 ...
589s          29 30.4 30.4 17.2 18.3 18.3 18.3 18.9 18.9 20.4 ...
589s          21 21 22.1 23.5 24.2 25.9 26.5 26.5 26.5 28.6]';
589s  species = {"ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" ...
589s             "ex" "ex" "ex" "niv" "niv" "niv" "niv" "niv" "niv" "niv" ...
589s             "niv" "niv" "niv" "niv" "niv" "niv" "niv" "niv" "niv" "niv"};
589s 
589s  [P, ATAB, STATS] = anovan (pulse, {species, temp}, "model", "linear", ...
589s                            "continuous", 2, "sstype", "h", "display", "on", ...
589s                            "varnames", {"species", "temp"});
589s ***** demo
589s 
589s  # Factorial ANCOVA on data from a study of the effects of treatment and
589s  # exercise on stress reduction score after adjusting for age. Data from R
589s  # datarium package).
589s 
589s  score = [95.6 82.2 97.2 96.4 81.4 83.6 89.4 83.8 83.3 85.7 ...
589s           97.2 78.2 78.9 91.8 86.9 84.1 88.6 89.8 87.3 85.4 ...
589s           81.8 65.8 68.1 70.0 69.9 75.1 72.3 70.9 71.5 72.5 ...
589s           84.9 96.1 94.6 82.5 90.7 87.0 86.8 93.3 87.6 92.4 ...
589s           100. 80.5 92.9 84.0 88.4 91.1 85.7 91.3 92.3 87.9 ...
589s           91.7 88.6 75.8 75.7 75.3 82.4 80.1 86.0 81.8 82.5]';
589s  treatment = {"yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
589s               "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
589s               "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
589s               "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
589s               "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
589s               "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"}';
589s  exercise = {"lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
589s              "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
589s              "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  ...
589s              "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
589s              "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
589s              "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"}';
589s  age = [59 65 70 66 61 65 57 61 58 55 62 61 60 59 55 57 60 63 62 57 ...
589s         58 56 57 59 59 60 55 53 55 58 68 62 61 54 59 63 60 67 60 67 ...
589s         75 54 57 62 65 60 58 61 65 57 56 58 58 58 52 53 60 62 61 61]';
589s 
589s  [P, ATAB, STATS] = anovan (score, {treatment, exercise, age}, ...
589s                             "model", [1 0 0; 0 1 0; 0 0 1; 1 1 0], ...
589s                             "continuous", 3, "sstype", "h", "display", "on", ...
589s                             "varnames", {"treatment", "exercise", "age"});
589s ***** demo
589s 
589s  # Unbalanced one-way ANOVA with custom, orthogonal contrasts. The statistics
589s  # relating to the contrasts are shown in the table of model parameters, and
589s  # can be retrieved from the STATS.coeffs output.
589s 
589s  dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
589s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
589s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
589s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
589s         25.694 ]';
589s  g = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 ...
589s       4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
589s  C = [ 0.4001601  0.3333333  0.5  0.0
589s        0.4001601  0.3333333 -0.5  0.0
589s        0.4001601 -0.6666667  0.0  0.0
589s       -0.6002401  0.0000000  0.0  0.5
589s       -0.6002401  0.0000000  0.0 -0.5];
589s 
589s  [P,ATAB, STATS] = anovan (dv, g, "contrasts", C, "varnames", "score", ...
589s                           "alpha", 0.05, "display", "on");
589s ***** demo
589s 
589s  # One-way ANOVA with the linear model fit by weighted least squares to
589s  # account for heteroskedasticity. In this example, the variance appears
589s  # proportional to the outcome, so weights have been estimated by initially
589s  # fitting the model without weights and regressing the absolute residuals on
589s  # the fitted values. Although this data could have been analysed by Welch's
589s  # ANOVA test, the approach here can generalize to ANOVA models with more than
589s  # one factor.
589s 
589s  g = [1, 1, 1, 1, 1, 1, 1, 1, ...
589s       2, 2, 2, 2, 2, 2, 2, 2, ...
589s       3, 3, 3, 3, 3, 3, 3, 3]';
589s  y = [13, 16, 16,  7, 11,  5,  1,  9, ...
589s       10, 25, 66, 43, 47, 56,  6, 39, ...
589s       11, 39, 26, 35, 25, 14, 24, 17]';
589s 
589s  [P,ATAB,STATS] = anovan(y, g, "display", "off");
589s  fitted = STATS.X * STATS.coeffs(:,1); # fitted values
589s  b = polyfit (fitted, abs (STATS.resid), 1);
589s  v = polyval (b, fitted);  # Variance as a function of the fitted values
589s  figure("Name", "Regression of the absolute residuals on the fitted values");
589s  plot (fitted, abs (STATS.resid),'ob');hold on; plot(fitted,v,'-r'); hold off;
589s  xlabel("Fitted values"); ylabel("Absolute residuals");
589s 
589s  [P,ATAB,STATS] = anovan (y, g, "weights", v.^-1);
589s ***** test
589s  score = [54 23 45 54 45 43 34 65 77 46 65]';
589s  gender = {'male' 'male' 'male' 'male' 'male' 'female' 'female' 'female' ...
589s            'female' 'female' 'female'}';
589s 
589s  [P, T, STATS] = anovan (score,gender,'display','off');
589s  assert (P(1), 0.2612876773271042, 1e-09);              # compared to p calculated by MATLAB anovan
589s  assert (sqrt(T{2,6}), abs(1.198608733288208), 1e-09);  # compared to abs(t) calculated from sqrt(F) by MATLAB anovan
589s  assert (P(1), 0.2612876773271047, 1e-09);              # compared to p calculated by MATLAB ttest2
589s  assert (sqrt(T{2,6}), abs(-1.198608733288208), 1e-09); # compared to abs(t) calculated by MATLAB ttest2
589s ***** test
589s  score = [4.5 5.6; 3.7 6.4; 5.3 6.4; 5.4 6.0; 3.9 5.7]';
589s  treatment = {'before' 'after'; 'before' 'after'; 'before' 'after';
589s               'before' 'after'; 'before' 'after'}';
589s  subject = {'GS' 'GS'; 'JM' 'JM'; 'HM' 'HM'; 'JW' 'JW'; 'PS' 'PS'}';
589s 
589s  [P, ATAB, STATS] = anovan (score(:),{treatment(:),subject(:)},'display','off','sstype',2);
589s  assert (P(1), 0.016004356735364, 1e-09);              # compared to p calculated by MATLAB anovan
589s  assert (sqrt(ATAB{2,6}), abs(4.00941576558195), 1e-09);  # compared to abs(t) calculated from sqrt(F) by MATLAB anovan
589s  assert (P(1), 0.016004356735364, 1e-09);              # compared to p calculated by MATLAB ttest2
589s  assert (sqrt(ATAB{2,6}), abs(-4.00941576558195), 1e-09); # compared to abs(t) calculated by MATLAB ttest2
589s ***** test
589s  strength = [82 86 79 83 84 85 86 87 74 82 ...
589s             78 75 76 77 79 79 77 78 82 79]';
589s  alloy = {'st','st','st','st','st','st','st','st', ...
589s           'al1','al1','al1','al1','al1','al1', ...
589s           'al2','al2','al2','al2','al2','al2'}';
589s 
589s  [P, ATAB, STATS] = anovan (strength,{alloy},'display','off');
589s  assert (P(1), 0.000152643638830491, 1e-09);
589s  assert (ATAB{2,6}, 15.4, 1e-09);
589s ***** test
589s  words = [10 13 13; 6 8 8; 11 14 14; 22 23 25; 16 18 20; ...
589s           15 17 17; 1 1 4; 12 15 17;  9 12 12;  8 9 12];
589s  subject = [ 1  1  1;  2  2  2;  3  3  3;  4  4  4;  5  5  5; ...
589s              6  6  6;  7  7  7;  8  8  8;  9  9  9; 10 10 10];
589s  seconds = [1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5; ...
589s             1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5;];
589s 
589s  [P, ATAB, STATS] = anovan (words(:),{seconds(:),subject(:)},'model','full','random',2,'sstype',2,'display','off');
589s  assert (P(1), 1.51865926758752e-07, 1e-09);
589s  assert (ATAB{2,2}, 52.2666666666667, 1e-09);
589s  assert (ATAB{3,2}, 942.533333333333, 1e-09);
589s  assert (ATAB{4,2}, 11.0666666666667, 1e-09);
589s ***** test
589s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
589s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
589s  brands = {'Gourmet', 'National', 'Generic'; ...
589s            'Gourmet', 'National', 'Generic'; ...
589s            'Gourmet', 'National', 'Generic'; ...
589s            'Gourmet', 'National', 'Generic'; ...
589s            'Gourmet', 'National', 'Generic'; ...
589s            'Gourmet', 'National', 'Generic'};
589s  popper = {'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; ...
589s            'air', 'air', 'air'; 'air', 'air', 'air'; 'air', 'air', 'air'};
589s 
589s  [P, ATAB, STATS] = anovan (popcorn(:),{brands(:),popper(:)},'display','off','model','full');
589s  assert (P(1), 7.67895738278171e-07, 1e-09);
589s  assert (P(2), 0.000100373896304998, 1e-09);
589s  assert (P(3), 0.746215396636649, 1e-09);
589s  assert (ATAB{2,6}, 56.7, 1e-09);
589s  assert (ATAB{3,6}, 32.4, 1e-09);
589s  assert (ATAB{4,6}, 0.29999999999997, 1e-09);
589s ***** test
589s  salary = [24 26 25 24 27 24 27 23 15 17 20 16, ...
589s            25 29 27 19 18 21 20 21 22 19]';
589s  gender = {'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f'...
589s            'm' 'm' 'm' 'm' 'm' 'm' 'm' 'm' 'm' 'm'}';
589s  degree = [1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0]';
589s 
589s  [P, ATAB, STATS] = anovan (salary,{gender,degree},'model','full','sstype',1,'display','off');
589s  assert (P(1), 0.747462549227232, 1e-09);
589s  assert (P(2), 1.03809316857694e-08, 1e-09);
589s  assert (P(3), 0.523689833702691, 1e-09);
589s  assert (ATAB{2,2}, 0.296969696969699, 1e-09);
589s  assert (ATAB{3,2}, 272.391841491841, 1e-09);
589s  assert (ATAB{4,2}, 1.17482517482512, 1e-09);
589s  assert (ATAB{5,2}, 50.0000000000001, 1e-09);
589s  [P, ATAB, STATS] = anovan (salary,{degree,gender},'model','full','sstype',1,'display','off');
589s  assert (P(1), 2.53445097305047e-08, 1e-09);
589s  assert (P(2), 0.00388133678528749, 1e-09);
589s  assert (P(3), 0.523689833702671, 1e-09);
589s  assert (ATAB{2,2}, 242.227272727273, 1e-09);
589s  assert (ATAB{3,2}, 30.4615384615384, 1e-09);
589s  assert (ATAB{4,2}, 1.17482517482523, 1e-09);
589s  assert (ATAB{5,2}, 50.0000000000001, 1e-09);
589s  [P, ATAB, STATS] = anovan (salary,{gender,degree},'model','full','sstype',2,'display','off');
589s  assert (P(1), 0.00388133678528743, 1e-09);
589s  assert (P(2), 1.03809316857694e-08, 1e-09);
589s  assert (P(3), 0.523689833702691, 1e-09);
589s  assert (ATAB{2,2}, 30.4615384615385, 1e-09);
589s  assert (ATAB{3,2}, 272.391841491841, 1e-09);
589s  assert (ATAB{4,2}, 1.17482517482512, 1e-09);
589s  assert (ATAB{5,2}, 50.0000000000001, 1e-09);
589s  [P, ATAB, STATS] = anovan (salary,{gender,degree},'model','full','sstype',3,'display','off');
589s  assert (P(1), 0.00442898146583742, 1e-09);
589s  assert (P(2), 1.30634252053587e-08, 1e-09);
589s  assert (P(3), 0.523689833702691, 1e-09);
589s  assert (ATAB{2,2}, 29.3706293706294, 1e-09);
589s  assert (ATAB{3,2}, 264.335664335664, 1e-09);
589s  assert (ATAB{4,2}, 1.17482517482512, 1e-09);
589s  assert (ATAB{5,2}, 50.0000000000001, 1e-09);
590s ***** test
590s  sugar = {'real' 'fake' 'fake' 'real' 'real' 'real' 'none' 'none' 'none' ...
590s           'fake' 'fake' 'fake' 'real' 'real' 'real' 'none' 'none' 'fake'}';
590s  milk = {'yes' 'no' 'no' 'yes' 'yes' 'no' 'yes' 'yes' 'yes' ...
590s          'no' 'no' 'yes' 'no' 'no' 'no' 'no' 'no' 'yes'}';
590s  babble = [4.6 4.4 3.9 5.6 5.1 5.5 3.9 3.5 3.7...
590s            5.6 4.7 5.9 6.0 5.4 6.6 5.8 5.3 5.7]';
590s 
590s  [P, ATAB, STATS] = anovan (babble,{sugar,milk},'model','full','sstype',1,'display','off');
590s  assert (P(1), 0.0108632139833963, 1e-09);
590s  assert (P(2), 0.0810606976703546, 1e-09);
590s  assert (P(3), 0.00175433329935627, 1e-09);
590s  assert (ATAB{2,2}, 3.55752380952381, 1e-09);
590s  assert (ATAB{3,2}, 0.956108477471702, 1e-09);
590s  assert (ATAB{4,2}, 5.94386771300448, 1e-09);
590s  assert (ATAB{5,2}, 3.1625, 1e-09);
590s  [P, ATAB, STATS] = anovan (babble,{milk,sugar},'model','full','sstype',1,'display','off');
590s  assert (P(1), 0.0373333189297505, 1e-09);
590s  assert (P(2), 0.017075098787169, 1e-09);
590s  assert (P(3), 0.00175433329935627, 1e-09);
590s  assert (ATAB{2,2}, 1.444, 1e-09);
590s  assert (ATAB{3,2}, 3.06963228699552, 1e-09);
590s  assert (ATAB{4,2}, 5.94386771300448, 1e-09);
590s  assert (ATAB{5,2}, 3.1625, 1e-09);
590s  [P, ATAB, STATS] = anovan (babble,{sugar,milk},'model','full','sstype',2,'display','off');
590s  assert (P(1), 0.017075098787169, 1e-09);
590s  assert (P(2), 0.0810606976703546, 1e-09);
590s  assert (P(3), 0.00175433329935627, 1e-09);
590s  assert (ATAB{2,2}, 3.06963228699552, 1e-09);
590s  assert (ATAB{3,2}, 0.956108477471702, 1e-09);
590s  assert (ATAB{4,2}, 5.94386771300448, 1e-09);
590s  assert (ATAB{5,2}, 3.1625,  1e-09);
590s  [P, ATAB, STATS] = anovan (babble,{sugar,milk},'model','full','sstype',3,'display','off');
590s  assert (P(1), 0.0454263063473954, 1e-09);
590s  assert (P(2), 0.0746719907091438, 1e-09);
590s  assert (P(3), 0.00175433329935627, 1e-09);
590s  assert (ATAB{2,2}, 2.13184977578476, 1e-09);
590s  assert (ATAB{3,2}, 1.00413461538462, 1e-09);
590s  assert (ATAB{4,2}, 5.94386771300448, 1e-09);
590s  assert (ATAB{5,2}, 3.1625, 1e-09);
590s ***** test
590s  drug = {'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' ...
590s          'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X';
590s          'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' ...
590s          'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y';
590s          'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' ...
590s          'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z'};
590s  feedback = [1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
590s              1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
590s              1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0];
590s  diet = [0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1;
590s          0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1;
590s          0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1];
590s  BP = [170 175 165 180 160 158 161 173 157 152 181 190 ...
590s        173 194 197 190 176 198 164 190 169 164 176 175;
590s        186 194 201 215 219 209 164 166 159 182 187 174 ...
590s        189 194 217 206 199 195 171 173 196 199 180 NaN;
590s        180 187 199 170 204 194 162 184 183 156 180 173 ...
590s        202 228 190 206 224 204 205 199 170 160 NaN NaN];
590s 
590s  [P, ATAB, STATS] = anovan (BP(:),{drug(:),feedback(:),diet(:)},'model','full','sstype', 1,'display','off');
590s  assert (P(1), 7.02561843825325e-05, 1e-09);
590s  assert (P(2), 0.000425806013389362, 1e-09);
590s  assert (P(3), 6.16780773446401e-07, 1e-09);
590s  assert (P(4), 0.261347622678438, 1e-09);
590s  assert (P(5), 0.0542278432357043, 1e-09);
590s  assert (P(6), 0.590353225626655, 1e-09);
590s  assert (P(7), 0.0861628249564267, 1e-09);
590s  assert (ATAB{2,2}, 3614.70355731226, 1e-09);
590s  assert (ATAB{3,2}, 2227.46639771024, 1e-09);
590s  assert (ATAB{4,2}, 5008.25614451819, 1e-09);
590s  assert (ATAB{5,2}, 437.066007908781, 1e-09);
590s  assert (ATAB{6,2}, 976.180770397332, 1e-09);
590s  assert (ATAB{7,2}, 46.616653365254, 1e-09);
590s  assert (ATAB{8,2}, 814.345251396648, 1e-09);
590s  assert (ATAB{9,2}, 9065.8,  1e-09);
590s  [P, ATAB, STATS] = anovan (BP(:),{drug(:),feedback(:),diet(:)},'model','full','sstype',2,'display','off');
590s  assert (P(1), 9.4879638470754e-05, 1e-09);
590s  assert (P(2), 0.00124177666315809, 1e-09);
590s  assert (P(3), 6.86162012732911e-07, 1e-09);
590s  assert (P(4), 0.260856132341256, 1e-09);
590s  assert (P(5), 0.0523758623892078, 1e-09);
590s  assert (P(6), 0.590353225626655, 1e-09);
590s  assert (P(7), 0.0861628249564267, 1e-09);
590s  assert (ATAB{2,2}, 3481.72176560122, 1e-09);
590s  assert (ATAB{3,2}, 1837.08812970469, 1e-09);
590s  assert (ATAB{4,2}, 4957.20277938622, 1e-09);
590s  assert (ATAB{5,2}, 437.693674777847, 1e-09);
590s  assert (ATAB{6,2}, 988.431929811402, 1e-09);
590s  assert (ATAB{7,2}, 46.616653365254, 1e-09);
590s  assert (ATAB{8,2}, 814.345251396648, 1e-09);
590s  assert (ATAB{9,2}, 9065.8,  1e-09);
590s  [P, ATAB, STATS] = anovan (BP(:),{drug(:),feedback(:),diet(:)},'model','full','sstype', 3,'display','off');
590s  assert (P(1), 0.000106518678028207, 1e-09);
590s  assert (P(2), 0.00125371366571508, 1e-09);
590s  assert (P(3), 5.30813260778464e-07, 1e-09);
590s  assert (P(4), 0.308353667232981, 1e-09);
590s  assert (P(5), 0.0562901327343161, 1e-09);
590s  assert (P(6), 0.599091042141092, 1e-09);
590s  assert (P(7), 0.0861628249564267, 1e-09);
590s  assert (ATAB{2,2}, 3430.88156424581, 1e-09);
590s  assert (ATAB{3,2}, 1833.68031496063, 1e-09);
590s  assert (ATAB{4,2}, 5080.48346456693, 1e-09);
590s  assert (ATAB{5,2}, 382.07709497207, 1e-09);
590s  assert (ATAB{6,2}, 963.037988826813, 1e-09);
590s  assert (ATAB{7,2}, 44.4519685039322, 1e-09);
590s  assert (ATAB{8,2}, 814.345251396648, 1e-09);
590s  assert (ATAB{9,2}, 9065.8, 1e-09);
590s ***** test
590s  measurement = [444 614 423 625 408  856 447 719 ...
590s                 764 831 586 782 609 1002 606 766]';
590s  strain= {'NIH','NIH','BALB/C','BALB/C','A/J','A/J','129/Ola','129/Ola', ...
590s           'NIH','NIH','BALB/C','BALB/C','A/J','A/J','129/Ola','129/Ola'}';
590s  treatment={'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T'}';
590s  block = [1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2]';
590s 
590s  [P, ATAB, STATS] = anovan (measurement/10,{strain,treatment,block},'model','full','random',3,'display','off');
590s  assert (P(1), 0.0914352969909372, 1e-09);
590s  assert (P(2), 5.04077373924908e-05, 1e-09);
590s  assert (P(4), 0.0283196918836667, 1e-09);
590s  assert (ATAB{2,2}, 286.132500000002, 1e-09);
590s  assert (ATAB{3,2}, 2275.29, 1e-09);
590s  assert (ATAB{4,2}, 1242.5625, 1e-09);
590s  assert (ATAB{5,2}, 495.905000000001, 1e-09);
590s  assert (ATAB{6,2}, 207.007499999999, 1e-09);
590s ***** test
590s  pulse = [67.9 65.1 77.3 78.7 79.4 80.4 85.8 86.6 87.5 89.1 ...
590s           98.6 100.8 99.3 101.7 44.3 47.2 47.6 49.6 50.3 51.8 ...
590s           60 58.5 58.9 60.7 69.8 70.9 76.2 76.1 77 77.7 84.7]';
590s  temp = [20.8 20.8 24 24 24 24 26.2 26.2 26.2 26.2 28.4 ...
590s          29 30.4 30.4 17.2 18.3 18.3 18.3 18.9 18.9 20.4 ...
590s          21 21 22.1 23.5 24.2 25.9 26.5 26.5 26.5 28.6]';
590s  species = {'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' ...
590s             'ex' 'ex' 'ex' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' ...
590s             'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv'};
590s 
590s  [P, ATAB, STATS] = anovan (pulse,{species,temp},'model','linear','continuous',2,'sstype','h','display','off');
590s  assert (P(1), 6.27153318786007e-14, 1e-09);
590s  assert (P(2), 2.48773241196644e-25, 1e-09);
590s  assert (ATAB{2,2}, 598.003953318404, 1e-09);
590s  assert (ATAB{3,2}, 4376.08256843712, 1e-09);
590s  assert (ATAB{4,2}, 89.3498685376726, 1e-09);
590s  assert (ATAB{2,6}, 187.399388123951, 1e-09);
590s  assert (ATAB{3,6}, 1371.35413763454, 1e-09);
590s ***** test
590s  score = [95.6 82.2 97.2 96.4 81.4 83.6 89.4 83.8 83.3 85.7 ...
590s           97.2 78.2 78.9 91.8 86.9 84.1 88.6 89.8 87.3 85.4 ...
590s           81.8 65.8 68.1 70.0 69.9 75.1 72.3 70.9 71.5 72.5 ...
590s           84.9 96.1 94.6 82.5 90.7 87.0 86.8 93.3 87.6 92.4 ...
590s           100. 80.5 92.9 84.0 88.4 91.1 85.7 91.3 92.3 87.9 ...
590s           91.7 88.6 75.8 75.7 75.3 82.4 80.1 86.0 81.8 82.5]';
590s  treatment = {'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' ...
590s               'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' ...
590s               'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' ...
590s               'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  ...
590s               'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  ...
590s               'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'}';
590s  exercise = {'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  ...
590s              'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' ...
590s              'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  ...
590s              'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  ...
590s              'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' ...
590s              'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'}';
590s  age = [59 65 70 66 61 65 57 61 58 55 62 61 60 59 55 57 60 63 62 57 ...
590s         58 56 57 59 59 60 55 53 55 58 68 62 61 54 59 63 60 67 60 67 ...
590s         75 54 57 62 65 60 58 61 65 57 56 58 58 58 52 53 60 62 61 61]';
590s 
590s  [P, ATAB, STATS] = anovan (score,{treatment,exercise,age},'model','full','continuous',3,'sstype','h','display','off');
590s  assert (P(5), 0.9245630968248468, 1e-09);
590s  assert (P(6), 0.791115159521822, 1e-09);
590s  assert (P(7), 0.9296668751457956, 1e-09);
590s  [P, ATAB, STATS] = anovan (score,{treatment,exercise,age},'model',[1 0 0; 0 1 0; 0 0 1; 1 1 0],'continuous',3,'sstype','h','display','off');
590s  assert (P(1), 0.00158132928938933, 1e-09);
590s  assert (P(2), 2.12537505039986e-07, 1e-09);
590s  assert (P(3), 0.00390292555160047, 1e-09);
590s  assert (P(4), 0.0164086580775543, 1e-09);
590s  assert (ATAB{2,6}, 11.0956027650549, 1e-09);
590s  assert (ATAB{3,6}, 20.8195665467178, 1e-09);
590s  assert (ATAB{4,6}, 9.10966630720186, 1e-09);
590s  assert (ATAB{5,6}, 4.4457923698584, 1e-09);
590s ***** test
590s  dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
590s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
590s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
590s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
590s         25.694 ]';
590s  g = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
590s  C = [ 0.4001601  0.3333333  0.5  0.0
590s        0.4001601  0.3333333 -0.5  0.0
590s        0.4001601 -0.6666667  0.0  0.0
590s       -0.6002401  0.0000000  0.0  0.5
590s       -0.6002401  0.0000000  0.0 -0.5];
590s 
590s  [P,ATAB,STATS] = anovan (dv,g,'contrasts',{C},'display','off');
590s  assert (STATS.coeffs(1,1), 19.4001, 1e-04);
590s  assert (STATS.coeffs(2,1), -9.3297, 1e-04);
590s  assert (STATS.coeffs(3,1), -5.0000, 1e-04);
590s  assert (STATS.coeffs(4,1), -8.0000, 1e-04);
590s  assert (STATS.coeffs(5,1), -8.0000, 1e-04);
590s  assert (STATS.coeffs(1,2), 0.4831, 1e-04);
590s  assert (STATS.coeffs(2,2), 0.9694, 1e-04);
590s  assert (STATS.coeffs(3,2), 1.3073, 1e-04);
590s  assert (STATS.coeffs(4,2), 1.6411, 1e-04);
590s  assert (STATS.coeffs(5,2), 1.4507, 1e-04);
590s  assert (STATS.coeffs(1,5), 40.161, 1e-03);
590s  assert (STATS.coeffs(2,5), -9.624, 1e-03);
590s  assert (STATS.coeffs(3,5), -3.825, 1e-03);
590s  assert (STATS.coeffs(4,5), -4.875, 1e-03);
590s  assert (STATS.coeffs(5,5), -5.515, 1e-03);
590s  assert (STATS.coeffs(2,6), 5.74e-11, 1e-12);
590s  assert (STATS.coeffs(3,6), 0.000572, 1e-06);
590s  assert (STATS.coeffs(4,6), 2.86e-05, 1e-07);
590s  assert (STATS.coeffs(5,6), 4.44e-06, 1e-08);
590s 12 tests, 12 passed, 0 known failure, 0 skipped
590s [inst/dcov.m]
590s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dcov.m
590s ***** demo
590s  base=@(x) (x- min(x))./(max(x)-min(x));
590s  N = 5e2;
590s  x = randn (N,1); x = base (x);
590s  z = randn (N,1); z = base (z);
590s  # Linear relations
590s  cy = [1 0.55 0.3 0 -0.3 -0.55 -1];
590s  ly = x .* cy;
590s  ly(:,[1:3 5:end]) = base (ly(:,[1:3 5:end]));
590s  # Correlated Gaussian
590s  cz = 1 - abs (cy);
590s  gy = base ( ly + cz.*z);
590s  # Shapes
590s  sx      = repmat (x,1,7);
590s  sy      = zeros (size (ly));
590s  v       = 2 * rand (size(x,1),2) - 1;
590s  sx(:,1) = v(:,1); sy(:,1) = cos(2*pi*sx(:,1)) + 0.5*v(:,2).*exp(-sx(:,1).^2/0.5);
590s  R       =@(d) [cosd(d) sind(d); -sind(d) cosd(d)];
590s  tmp     = R(35) * v.';
590s  sx(:,2) = tmp(1,:); sy(:,2) = tmp(2,:);
590s  tmp     = R(45) * v.';
590s  sx(:,3) = tmp(1,:); sy(:,3) = tmp(2,:);
590s  sx(:,4) = v(:,1); sy(:,4) = sx(:,4).^2 + 0.5*v(:,2);
590s  sx(:,5) = v(:,1); sy(:,5) = 3*sign(v(:,2)).*(sx(:,5)).^2  + v(:,2);
590s  sx(:,6) = cos (2*pi*v(:,1)) + 0.5*(x-0.5);
590s  sy(:,6) = sin (2*pi*v(:,1)) + 0.5*(z-0.5);
590s  sx(:,7) = x + sign(v(:,1)); sy(:,7) = z + sign(v(:,2));
590s  sy      = base (sy);
590s  sx      = base (sx);
590s  # scaled shape
590s  sc  = 1/3;
590s  ssy = (sy-0.5) * sc + 0.5;
590s  n = size (ly,2);
590s  ym = 1.2;
590s  xm = 0.5;
590s  fmt={'horizontalalignment','center'};
590s  ff = "% .2f";
590s  figure (1)
590s  for i=1:n
590s    subplot(4,n,i);
590s    plot (x, gy(:,i), '.b');
590s    axis tight
590s    axis off
590s    text (xm,ym,sprintf (ff, dcov (x,gy(:,i))),fmt{:})
590s 
590s    subplot(4,n,i+n);
590s    plot (x, ly(:,i), '.b');
590s    axis tight
590s    axis off
590s    text (xm,ym,sprintf (ff, dcov (x,ly(:,i))),fmt{:})
590s 
590s    subplot(4,n,i+2*n);
590s    plot (sx(:,i), sy(:,i), '.b');
590s    axis tight
590s    axis off
590s    text (xm,ym,sprintf (ff, dcov (sx(:,i),sy(:,i))),fmt{:})
590s    v = axis ();
590s 
590s    subplot(4,n,i+3*n);
590s    plot (sx(:,i), ssy(:,i), '.b');
590s    axis (v)
590s    axis off
590s    text (xm,ym,sprintf (ff, dcov (sx(:,i),ssy(:,i))),fmt{:})
590s  endfor
590s ***** error dcov (randn (30, 5), randn (25,5))
590s 1 test, 1 passed, 0 known failure, 0 skipped
590s [inst/kruskalwallis.m]
590s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/kruskalwallis.m
590s ***** demo
590s  x = meshgrid (1:6);
590s  x = x + normrnd (0, 1, 6, 6);
590s  kruskalwallis (x, [], 'off');
590s ***** demo
590s  x = meshgrid (1:6);
590s  x = x + normrnd (0, 1, 6, 6);
590s  [p, atab] = kruskalwallis(x);
590s ***** demo
590s  x = ones (30, 4) .* [-2, 0, 1, 5];
590s  x = x + normrnd (0, 2, 30, 4);
590s  group = {"A", "B", "C", "D"};
590s  kruskalwallis (x, group);
590s ***** test
590s  data = [1.006, 0.996, 0.998, 1.000, 0.992, 0.993, 1.002, 0.999, 0.994, 1.000, ...
590s          0.998, 1.006, 1.000, 1.002, 0.997, 0.998, 0.996, 1.000, 1.006, 0.988, ...
590s          0.991, 0.987, 0.997, 0.999, 0.995, 0.994, 1.000, 0.999, 0.996, 0.996, ...
590s          1.005, 1.002, 0.994, 1.000, 0.995, 0.994, 0.998, 0.996, 1.002, 0.996, ...
590s          0.998, 0.998, 0.982, 0.990, 1.002, 0.984, 0.996, 0.993, 0.980, 0.996, ...
590s          1.009, 1.013, 1.009, 0.997, 0.988, 1.002, 0.995, 0.998, 0.981, 0.996, ...
590s          0.990, 1.004, 0.996, 1.001, 0.998, 1.000, 1.018, 1.010, 0.996, 1.002, ...
590s          0.998, 1.000, 1.006, 1.000, 1.002, 0.996, 0.998, 0.996, 1.002, 1.006, ...
590s          1.002, 0.998, 0.996, 0.995, 0.996, 1.004, 1.004, 0.998, 0.999, 0.991, ...
590s          0.991, 0.995, 0.984, 0.994, 0.997, 0.997, 0.991, 0.998, 1.004, 0.997];
590s  group = [1:10] .* ones (10,10);
590s  group = group(:);
590s  [p, tbl] = kruskalwallis (data, group, "off");
590s  assert (p, 0.048229, 1e-6);
590s  assert (tbl{2,5}, 17.03124, 1e-5);
590s  assert (tbl{2,3}, 9, 0);
590s  assert (tbl{4,2}, 82655.5, 1e-16);
590s  data = reshape (data, 10, 10);
590s  [p, tbl, stats] = kruskalwallis (data, [], "off");
590s  assert (p, 0.048229, 1e-6);
590s  assert (tbl{2,5}, 17.03124, 1e-5);
590s  assert (tbl{2,3}, 9, 0);
590s  assert (tbl{4,2}, 82655.5, 1e-16);
590s  means = [51.85, 60.45, 37.6, 51.1, 29.5, 54.25, 64.55, 66.7, 53.65, 35.35];
590s  N = 10 * ones (1, 10);
590s  assert (stats.meanranks, means, 1e-6);
590s  assert (length (stats.gnames), 10, 0);
590s  assert (stats.n, N, 0);
590s 1 test, 1 passed, 0 known failure, 0 skipped
590s [inst/manovacluster.m]
590s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/manovacluster.m
590s ***** demo
590s  load carbig
590s  X = [MPG Acceleration Weight Displacement];
590s  [d, p, stats] = manova1 (X, Origin);
590s  manovacluster (stats)
590s ***** test
590s  hf = figure ("visible", "off");
590s  unwind_protect
590s    load carbig
590s    X = [MPG Acceleration Weight Displacement];
590s    [d, p, stats] = manova1 (X, Origin);
590s    manovacluster (stats);
590s  unwind_protect_cleanup
590s    close (hf);
590s  end_unwind_protect
590s ***** error manovacluster (stats, "some");
590s 2 tests, 2 passed, 0 known failure, 0 skipped
590s [inst/bar3h.m]
590s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/bar3h.m
590s ***** demo
590s  ## Ploting 5 bars in the same series.
590s 
590s  y = [50; 40; 30; 20; 10];
590s  bar3h (y);
590s ***** demo
590s  ## Ploting 5 bars in different groups.
590s 
590s  y = [50, 40, 30, 20, 10];
590s  bar3h (y);
590s ***** demo
590s  ## A 3D bar graph with each series corresponding to a column in y.
590s 
590s  y = [1, 4, 7; 2, 5, 8; 3, 6, 9; 4, 7, 10];
590s  bar3h (y);
590s ***** demo
590s  ## Specify z-axis locations as tick names. z must be a column vector!
590s 
590s  z = [1950, 1960, 1970, 1980, 1990]';
590s  y = [16, 8, 4, 2, 1]';
590s  bar3h (z, y);
590s ***** demo
590s  ## Plot 3 series as a grouped plot without any space between the grouped bars
590s 
590s  y = [70 50 33 10; 75 55 35 15; 80 60 40 20];
590s  bar3h (y, 1, 'grouped');
590s ***** demo
590s  ## Plot a stacked style 3D bar graph
590s 
590s  y = [19, 30, 21, 30; 40, 16, 32, 12];
590s  b = bar3h (y, 0.5, 'stacked');
590s ***** error <bar3h: Z must be numeric.> bar3h ("A")
590s ***** error <bar3h: Z must be numeric.> bar3h ({2,3,4,5})
590s ***** error <bar3h: inconsistent size in Y and Z input arguments.> ...
590s  bar3h ([1,2,3]', ones (2))
590s ***** error <bar3h: WIDTH must be a scalar in the range> ...
590s  bar3h ([1:5], 1.2)
590s ***** error <bar3h: WIDTH must be a scalar in the range> ...
590s  bar3h ([1:5]', ones (5), 1.2)
590s ***** error <bar3h: numeric COLOR must be a 1x3 vector of an Nx3 matrix> ...
590s  bar3h ([1:5]', ones (5), [0.8, 0.7])
590s ***** error <bar3h: missing value for optional argument 'width'.> ...
590s  bar3h (ones (5), 'width')
590s ***** error <bar3h: invalid value for optional argument 'width'.> ...
590s  bar3h (ones (5), 'width', 1.2)
590s ***** error <bar3h: invalid value for optional argument 'width'.> ...
590s  bar3h (ones (5), 'width', [0.8, 0.8, 0.8])
590s ***** error <bar3h: missing value for optional argument 'color'.> ...
590s  bar3h (ones (5), 'color')
590s ***** error <bar3h: numeric COLOR must be a 1x3 vector of an Nx3 matrix> ...
590s  bar3h (ones (5), 'color', [0.8, 0.8])
590s ***** error <bar3h: invalid value for optional argument 'color'.> ...
590s  bar3h (ones (5), 'color', "brown")
590s ***** error <bar3h: invalid value for optional argument 'color'.> ...
590s  bar3h (ones (5), 'color', {"r", "k", "c", "m", "brown"})
590s ***** error <bar3h: missing value for optional argument 'xlabel'.> ...
590s  bar3h (ones (5), 'xlabel')
590s ***** error <bar3h: invalid value for optional argument 'xlabel'.> ...
590s  bar3h (ones (5), 'xlabel', 4)
590s ***** error <bar3h: missing value for optional argument 'zlabel'.> ...
590s  bar3h (ones (5), 'zlabel')
590s ***** error <bar3h: invalid value for optional argument 'zlabel'.> ...
590s  bar3h (ones (5), 'zlabel', 4)
590s ***** error <bar3h: invalid optional argument.> bar3h (ones (5), 'this', 4)
590s ***** error <bar3h: the elements in 'xlabel' must equal the columns in Z.> ...
590s  bar3h (ones (5), 'xlabel', {"A", "B", "C"})
590s ***** error <bar3h: the elements in 'zlabel' must equal the rows in Z.> ...
590s  bar3h (ones (5), 'zlabel', {"A", "B", "C"})
590s 20 tests, 20 passed, 0 known failure, 0 skipped
590s [inst/qrandn.m]
590s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/qrandn.m
590s ***** demo
590s  z = qrandn (-5, 5e6);
590s  [c x] = hist (z,linspace(-1.5,1.5,200),1);
590s  figure(1)
590s  plot(x,c,"r."); axis tight; axis([-1.5,1.5]);
590s 
590s  z = qrandn (-0.14286, 5e6);
590s  [c x] = hist (z,linspace(-2,2,200),1);
590s  figure(2)
590s  plot(x,c,"r."); axis tight; axis([-2,2]);
590s 
590s  z = qrandn (2.75, 5e6);
590s  [c x] = hist (z,linspace(-1e3,1e3,1e3),1);
590s  figure(3)
590s  semilogy(x,c,"r."); axis tight; axis([-100,100]);
590s 
590s  # ---------
590s  # Figures from the reference paper.
590s ***** error<qrandn: the parameter q must be a scalar.> qrandn ([1 2], 1)
590s ***** error<qrandn: the parameter q must be lower than 3.> qrandn (4, 1)
590s ***** error<qrandn: the parameter q must be lower than 3.> qrandn (3, 1)
590s ***** error qrandn (2.5, 1, 2, 3)
590s ***** error qrandn (2.5)
590s ***** test
590s  q = 1.5;
590s  s = [2, 3];
590s  z = qrandn (q, s);
590s  assert (isnumeric (z) && isequal (size (z), s));
590s 6 tests, 6 passed, 0 known failure, 0 skipped
590s [inst/ismissing.m]
590s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/ismissing.m
590s ***** assert (ismissing ([1,NaN,3]), [false,true,false])
590s ***** assert (ismissing ('abcd f'), [false,false,false,false,true,false])
590s ***** assert (ismissing ({'xxx','','xyz'}), [false,true,false])
590s ***** assert (ismissing ({'x','','y'}), [false,true,false])
590s ***** assert (ismissing ({'x','','y';'z','a',''}), logical([0,1,0;0,0,1]))
590s ***** assert (ismissing ([1,2;NaN,2]), [false,false;true,false])
590s ***** assert (ismissing ([1,2;NaN,2], 2), [false,true;false,true])
590s ***** assert (ismissing ([1,2;NaN,2], [1 2]), [true,true;false,true])
590s ***** assert (ismissing ([1,2;NaN,2], NaN), [false,false;true,false])
590s ***** assert (ismissing (cat(3,magic(2),magic(2))), logical (zeros (2,2,2)))
590s ***** assert (ismissing (cat(3,magic(2),[1 2;3 NaN])), logical (cat(3,[0,0;0,0],[0,0;0,1])))
590s ***** assert (ismissing ([1 2; 3 4], [5 1; 2 0]), logical([1 1; 0 0]))
590s ***** assert (ismissing (cat(3,'f oo','ba r')), logical(cat(3,[0 1 0 0],[0 0 1 0])))
590s ***** assert (ismissing (cat(3,{'foo'},{''},{'bar'})), logical(cat(3,0,1,0)))
590s ***** assert (ismissing (double (NaN)), true)
590s ***** assert (ismissing (single (NaN)), true)
590s ***** assert (ismissing (' '), true)
590s ***** assert (ismissing ({''}), true)
590s ***** assert (ismissing ({' '}), false)
590s ***** assert (ismissing (double (eye(3)), single (1)), logical(eye(3)))
590s ***** assert (ismissing (double (eye(3)), true), logical(eye(3)))
590s ***** assert (ismissing (double (eye(3)), int32 (1)), logical(eye(3)))
590s ***** assert (ismissing (single (eye(3)), true), logical(eye(3)))
590s ***** assert (ismissing (single (eye(3)), double (1)), logical(eye(3)))
590s ***** assert (ismissing (single(eye(3)), int32 (1)), logical(eye(3)))
590s ***** assert (ismissing ({'123', '', 123}), [false false false])
590s ***** assert (ismissing (logical ([1 0 1])), [false false false])
590s ***** assert (ismissing (int32 ([1 2 3])), [false false false])
590s ***** assert (ismissing (uint32 ([1 2 3])), [false false false])
590s ***** assert (ismissing ({1, 2, 3}), [false false false])
590s ***** assert (ismissing ([struct struct struct]), [false false false])
590s ***** assert (ismissing (logical (eye(3)), true), logical(eye(3)))
590s ***** assert (ismissing (logical (eye(3)), double (1)), logical(eye(3)))
590s ***** assert (ismissing (logical (eye(3)), single (1)), logical(eye(3)))
590s ***** assert (ismissing (logical (eye(3)), int32 (1)), logical(eye(3)))
590s ***** assert (ismissing (int32 (eye(3)), int32 (1)), logical(eye(3)))
590s ***** assert (ismissing (int32 (eye(3)), true), logical(eye(3)))
590s ***** assert (ismissing (int32 (eye(3)), double (1)), logical(eye(3)))
590s ***** assert (ismissing (int32 (eye(3)), single (1)), logical(eye(3)))
590s ***** assert (ismissing ([]), logical([]))
590s ***** assert (ismissing (''), logical([]))
590s ***** assert (ismissing (ones (0,1)), logical(ones(0,1)))
590s ***** assert (ismissing (ones (1,0)), logical(ones(1,0)))
591s ***** assert (ismissing (ones (1,2,0)), logical(ones(1,2,0)))
591s ***** error ismissing ()
591s ***** error <'indicator' and 'A' must have the same> ismissing ([1 2; 3 4], "abc")
591s ***** error <'indicator' and 'A' must have the same> ismissing ({"", "", ""}, 1)
591s ***** error <'indicator' and 'A' must have the same> ismissing (1, struct)
591s ***** error <indicators not supported for data type> ismissing (struct, 1)
591s 49 tests, 49 passed, 0 known failure, 0 skipped
591s [inst/cophenet.m]
591s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/cophenet.m
591s ***** demo
591s  randn ("seed", 5)  # for reproducibility
591s  X = randn (10,2);
591s  y = pdist (X);
591s  Z = linkage (y, "average");
591s  cophenet (Z, y)
591s ***** error<cophenet: function called with too few input arguments.> cophenet ()
591s ***** error<cophenet: function called with too few input arguments.> cophenet (1)
591s ***** error<cophenet: Z must be a matrix as generated by the linkage function.> ...
591s  cophenet (ones (2,2), 1)
591s ***** error<cophenet: Y must be a vector of euclidean distances.> ...
591s  cophenet ([1 2 1], "a")
591s ***** error<cophenet: Y must be a vector of euclidean distances.> ...
591s  cophenet ([1 2 1], [1 2])
591s 5 tests, 5 passed, 0 known failure, 0 skipped
591s [inst/jackknife.m]
591s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/jackknife.m
591s ***** demo
591s  for k = 1:1000
591s    rand ("seed", k);  # for reproducibility
591s    x = rand (10, 1);
591s    s(k) = std (x);
591s    jackstat = jackknife (@std, x);
591s    j(k) = 10 * std (x) - 9 * mean (jackstat);
591s  endfor
591s  figure();
591s  hist ([s', j'], 0:sqrt(1/12)/10:2*sqrt(1/12))
591s ***** demo
591s  for k = 1:1000
591s    randn ("seed", k); # for reproducibility
591s    x = randn (1, 50);
591s    rand ("seed", k);  # for reproducibility
591s    y = rand (1, 50);
591s    jackstat = jackknife (@(x) std(x{1})/std(x{2}), y, x);
591s    j(k) = 50 * std (y) / std (x) - 49 * mean (jackstat);
591s    v(k) = sumsq ((50 * std (y) / std (x) - 49 * jackstat) - j(k)) / (50 * 49);
591s  endfor
591s  t = (j - sqrt (1 / 12)) ./ sqrt (v);
591s  figure();
591s  plot (sort (tcdf (t, 49)), ...
591s        "-;Almost linear mapping indicates good fit with t-distribution.;")
591s ***** test
591s  ##Example from Quenouille, Table 1
591s  d=[0.18 4.00 1.04 0.85 2.14 1.01 3.01 2.33 1.57 2.19];
591s  jackstat = jackknife ( @(x) 1/mean(x), d );
591s  assert ( 10 / mean(d) - 9 * mean(jackstat), 0.5240, 1e-5 );
591s 1 test, 1 passed, 0 known failure, 0 skipped
591s [inst/regression_ttest.m]
591s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/regression_ttest.m
591s ***** error<Invalid call to regression_ttest.  Correct usage> regression_ttest ();
591s ***** error<Invalid call to regression_ttest.  Correct usage> regression_ttest (1);
591s ***** error<regression_ttest: Y must contain finite real numbers.> ...
591s  regression_ttest ([1 2 NaN]', [2 3 4]');
591s ***** error<regression_ttest: Y must contain finite real numbers.> ...
591s  regression_ttest ([1 2 Inf]', [2 3 4]');
591s ***** error<regression_ttest: Y must contain finite real numbers.> ...
591s  regression_ttest ([1 2 3+i]', [2 3 4]');
591s ***** error<regression_ttest: X must contain finite real numbers.> ...
591s  regression_ttest ([1 2 3]', [2 3 NaN]');
591s ***** error<regression_ttest: X must contain finite real numbers.> ...
591s  regression_ttest ([1 2 3]', [2 3 Inf]');
591s ***** error<regression_ttest: X must contain finite real numbers.> ...
591s  regression_ttest ([1 2 3]', [3 4 3+i]');
591s ***** error<regression_ttest: Y and X must be vectors of equal length.> ...
591s  regression_ttest ([1 2 3]', [3 4 4 5]');
591s ***** error<regression_ttest: invalid value for alpha.> ...
591s  regression_ttest ([1 2 3]', [2 3 4]', "alpha", 0);
591s ***** error<regression_ttest: invalid value for alpha.> ...
591s  regression_ttest ([1 2 3]', [2 3 4]', "alpha", 1.2);
591s ***** error<regression_ttest: invalid value for alpha.> ...
591s  regression_ttest ([1 2 3]', [2 3 4]', "alpha", [.02 .1]);
591s ***** error<regression_ttest: invalid value for alpha.> ...
591s  regression_ttest ([1 2 3]', [2 3 4]', "alpha", "a");
591s ***** error<regression_ttest: invalid Name argument.> ...
591s  regression_ttest ([1 2 3]', [2 3 4]', "some", 0.05);
591s ***** error<regression_ttest: invalid value for tail.>  ...
591s  regression_ttest ([1 2 3]', [2 3 4]', "tail", "val");
591s ***** error<regression_ttest: invalid value for tail.>  ...
591s  regression_ttest ([1 2 3]', [2 3 4]', "alpha", 0.01, "tail", "val");
591s 16 tests, 16 passed, 0 known failure, 0 skipped
591s [inst/isoutlier.m]
591s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/isoutlier.m
591s ***** demo
591s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
591s  TF = isoutlier (A, "mean")
591s ***** demo
591s  ## Use a moving detection method to detect local outliers in a sine wave
591s 
591s  x = -2*pi:0.1:2*pi;
591s  A = sin(x);
591s  A(47) = 0;
591s  time = datenum (2023,1,1,0,0,0) + (1/24)*[0:length(x)-1] - 730485;
591s  TF = isoutlier (A, "movmedian", 5*(1/24), "SamplePoints", time);
591s  plot (time, A)
591s  hold on
591s  plot (time(TF), A(TF), "x")
591s  datetick ('x', 20, 'keepticks')
591s  legend ("Original Data", "Outlier Data")
591s ***** demo
591s  ## Locate an outlier in a vector of data and visualize the outlier
591s 
591s  x = 1:10;
591s  A = [60 59 49 49 58 100 61 57 48 58];
591s  [TF, L, U, C] = isoutlier (A);
591s  plot (x, A);
591s  hold on
591s  plot (x(TF), A(TF), "x");
591s  xlim ([1,10]);
591s  line ([1,10], [L, L], "Linestyle", ":");
591s  text (1.1, L-2, "Lower Threshold");
591s  line ([1,10], [U, U], "Linestyle", ":");
591s  text (1.1, U-2, "Upper Threshold");
591s  line ([1,10], [C, C], "Linestyle", ":");
591s  text (1.1, C-3, "Center Value");
591s  legend ("Original Data", "Outlier Data");
591s ***** test
591s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
591s  assert (isoutlier (A, "mean"), logical([zeros(1,8) 1 zeros(1,6)]))
591s  assert (isoutlier (A, "median"), ...
591s  logical([zeros(1,3) 1 zeros(1,4) 1 zeros(1,6)]))
591s ***** test
591s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
591s  [TF, L, U, C] = isoutlier (A, "mean");
591s  assert (L, -109.2459044922864, 1e-12)
591s  assert (U, 264.9792378256198, 1e-12)
591s  assert (C, 77.8666666666666, 1e-12)
591s ***** test
591s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
591s  [TF, L, U, C] = isoutlier (A, "median");
591s  assert (L, 50.104386688966386, 1e-12)
591s  assert (U, 67.895613311033610, 1e-12)
591s  assert (C, 59)
591s ***** test
591s  A = magic(5) + diag(200*ones(1,5));
591s  T = logical (eye (5));
591s  assert (isoutlier (A, 2), T)
591s ***** test
591s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
591s  [TF, L, U, C] = isoutlier (A, "movmedian", 5);
591s  l = [54.5522, 52.8283, 54.5522, 54.5522, 54.5522, 53.5522, 53.5522, ...
591s       53.5522, 47.6566, 56.5522, 57.5522, 56.5522, 51.1044, 52.3283, 53.5522];
591s  u = [63.4478, 66.1717, 63.4478, 63.4478, 63.4478, 62.4478, 62.4478, ...
591s       62.4478, 74.3434, 65.4478, 66.4478, 65.4478, 68.8956, 65.6717, 62.4478];
591s  c = [59, 59.5, 59, 59, 59, 58, 58, 58, 61, 61, 62, 61, 60, 59, 58];
591s  assert (L, l, 1e-4)
591s  assert (U, u, 1e-4)
591s  assert (C, c)
591s ***** test
591s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
591s  [TF, L, U, C] = isoutlier (A, "movmedian", 5, "SamplePoints", [1:15]);
591s  l = [54.5522, 52.8283, 54.5522, 54.5522, 54.5522, 53.5522, 53.5522, ...
591s       53.5522, 47.6566, 56.5522, 57.5522, 56.5522, 51.1044, 52.3283, 53.5522];
591s  u = [63.4478, 66.1717, 63.4478, 63.4478, 63.4478, 62.4478, 62.4478, ...
591s       62.4478, 74.3434, 65.4478, 66.4478, 65.4478, 68.8956, 65.6717, 62.4478];
591s  c = [59, 59.5, 59, 59, 59, 58, 58, 58, 61, 61, 62, 61, 60, 59, 58];
591s  assert (L, l, 1e-4)
591s  assert (U, u, 1e-4)
591s  assert (C, c)
591s ***** test
591s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
591s  [TF, L, U, C] = isoutlier (A, "movmean", 5);
591s  l = [54.0841,  6.8872, 11.5608, 12.1518, 11.0210, 10.0112, -218.2840, ...
591s       -217.2375, -215.1239, -213.4890, -211.3264, 55.5800, 52.9589, ...
591s       52.5979, 51.0627];
591s  u = [63.2492, 131.1128, 122.4392, 122.2482, 122.5790, 122.7888, 431.0840, ...
591s       430.8375, 430.3239, 429.8890, 429.3264, 65.6200, 66.6411, 65.9021, ...
591s       66.9373];
591s  c = [58.6667, 69, 67, 67.2, 66.8, 66.4, 106.4, 106.8, 107.6, 108.2, 109, ...
591s       60.6, 59.8, 59.25, 59];
591s  assert (L, l, 1e-4)
591s  assert (U, u, 1e-4)
591s  assert (C, c, 1e-4)
591s ***** test
591s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
591s  [TF, L, U, C] = isoutlier (A, "movmean", 5, "SamplePoints", [1:15]);
591s  l = [54.0841,  6.8872, 11.5608, 12.1518, 11.0210, 10.0112, -218.2840, ...
591s       -217.2375, -215.1239, -213.4890, -211.3264, 55.5800, 52.9589, ...
591s       52.5979, 51.0627];
591s  u = [63.2492, 131.1128, 122.4392, 122.2482, 122.5790, 122.7888, 431.0840, ...
591s       430.8375, 430.3239, 429.8890, 429.3264, 65.6200, 66.6411, 65.9021, ...
591s       66.9373];
591s  c = [58.6667, 69, 67, 67.2, 66.8, 66.4, 106.4, 106.8, 107.6, 108.2, 109, ...
591s       60.6, 59.8, 59.25, 59];
591s  assert (L, l, 1e-4)
591s  assert (U, u, 1e-4)
591s  assert (C, c, 1e-4)
591s ***** test
591s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
591s  [TF, L, U, C] = isoutlier (A, "gesd");
591s  assert (TF, logical ([0 0 0 1 0 0 0 0 1 0 0 0 0 0 0]))
591s  assert (L, 34.235977035439944, 1e-12)
591s  assert (U, 89.764022964560060, 1e-12)
591s  assert (C, 62)
591s ***** test
591s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
591s  [TF, L, U, C] = isoutlier (A, "gesd", "ThresholdFactor", 0.01);
591s  assert (TF, logical ([0 0 0 1 0 0 0 0 1 0 0 0 0 0 0]))
591s  assert (L, 31.489256770616173, 1e-12)
591s  assert (U, 92.510743229383820, 1e-12)
591s  assert (C, 62)
591s ***** test
591s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
591s  [TF, L, U, C] = isoutlier (A, "gesd", "ThresholdFactor", 5e-10);
591s  assert (TF, logical ([0 0 0 0 0 0 0 0 1 0 0 0 0 0 0]))
591s  assert (L, 23.976664158788935, 1e-12)
591s  assert (U, 100.02333584121110, 1e-12)
591s  assert (C, 62)
591s ***** test
591s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
591s  [TF, L, U, C] = isoutlier (A, "grubbs");
591s  assert (TF, logical ([0 0 0 1 0 0 0 0 1 0 0 0 0 0 0]))
591s  assert (L, 54.642809574646606, 1e-12)
591s  assert (U, 63.511036579199555, 1e-12)
591s  assert (C, 59.076923076923080, 1e-12)
591s ***** test
591s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
591s  [TF, L, U, C] = isoutlier (A, "grubbs", "ThresholdFactor", 0.01);
591s  assert (TF, logical ([0 0 0 1 0 0 0 0 1 0 0 0 0 0 0]))
591s  assert (L, 54.216083184201850, 1e-12)
591s  assert (U, 63.937762969644310, 1e-12)
591s  assert (C, 59.076923076923080, 1e-12)
591s ***** test
591s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
591s  [TF, L, U, C] = isoutlier (A,  "percentiles", [10 90]);
591s  assert (TF, logical ([0 0 0 0 0 0 0 0 1 0 0 0 0 0 0]))
591s  assert (L, 57)
591s  assert (U, 100)
591s  assert (C, 78.5)
591s ***** test
591s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
591s  [TF, L, U, C] = isoutlier (A,  "percentiles", [20 80]);
591s  assert (TF, logical ([1 0 0 1 0 0 1 0 1 0 0 0 0 0 1]))
591s  assert (L, 57.5)
591s  assert (U, 62)
591s  assert (C, 59.75)
591s ***** shared A
591s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
591s ***** error<isoutlier: WINDOW must be a positive scalar> ...
591s  isoutlier (A, "movmedian", 0);
591s ***** error<isoutlier: WINDOW must be a positive scalar> ...
591s  isoutlier (A, "movmedian", []);
591s ***** error<isoutlier: WINDOW must be a positive scalar> ...
591s  isoutlier (A, "movmedian", [2 3 4]);
591s ***** error<isoutlier: WINDOW must be a positive integer> ...
591s  isoutlier (A, "movmedian", 1.4);
591s ***** error<isoutlier: WINDOW must be a positive scalar> ...
591s  isoutlier (A, "movmedian", [0 1]);
591s ***** error<isoutlier: WINDOW must be a positive scalar> ...
591s  isoutlier (A, "movmedian", [2 -1]);
591s ***** error<isoutlier: WINDOW must be a positive scalar> ...
591s  isoutlier (A, "movmedian", {2 3});
591s ***** error<isoutlier: WINDOW must be a positive scalar> ...
591s  isoutlier (A, "movmedian", "char");
591s 
591s ***** error<isoutlier: WINDOW must be a positive scalar> ...
591s  isoutlier (A, "movmean", 0);
591s ***** error<isoutlier: WINDOW must be a positive scalar> ...
591s  isoutlier (A, "movmean", []);
591s ***** error<isoutlier: WINDOW must be a positive scalar> ...
591s  isoutlier (A, "movmean", [2 3 4]);
591s ***** error<isoutlier: WINDOW must be a positive integer> ...
591s  isoutlier (A, "movmean", 1.4);
591s ***** error<isoutlier: WINDOW must be a positive scalar> ...
591s  isoutlier (A, "movmean", [0 1]);
591s ***** error<isoutlier: WINDOW must be a positive scalar> ...
591s  isoutlier (A, "movmean", [2 -1]);
591s ***** error<isoutlier: WINDOW must be a positive scalar> ...
591s  isoutlier (A, "movmean", {2 3});
591s ***** error<isoutlier: WINDOW must be a positive scalar> ...
591s  isoutlier (A, "movmean", "char");
591s 
591s ***** error<isoutlier: THRESHOLD must be a two-element> ...
591s  isoutlier (A, "percentiles", [-1 90]);
591s ***** error<isoutlier: THRESHOLD must be a two-element> ...
591s  isoutlier (A, "percentiles", [10 -90]);
591s ***** error<isoutlier: THRESHOLD must be a two-element> ...
591s  isoutlier (A, "percentiles", [90]);
591s ***** error<isoutlier: THRESHOLD must be a two-element> ...
591s  isoutlier (A, "percentiles", [90 20]);
591s ***** error<isoutlier: THRESHOLD must be a two-element> ...
591s  isoutlier (A, "percentiles", [90 20]);
591s ***** error<isoutlier: THRESHOLD must be a two-element> ...
591s  isoutlier (A, "percentiles", [10 20 90]);
591s ***** error<isoutlier: THRESHOLD must be a two-element> ...
591s  isoutlier (A, "percentiles", {10 90});
591s ***** error<isoutlier: THRESHOLD must be a two-element> ...
591s  isoutlier (A, "percentiles", "char");
591s 
591s ***** error<isoutlier: sample points must be a vector.> ...
591s  isoutlier (A, "movmean", 5, "SamplePoints", ones(3,15));
591s ***** error<isoutlier: sample points must be a vector.> ...
591s  isoutlier (A, "movmean", 5, "SamplePoints", 15);
591s ***** error<isoutlier: sample points must be unique.> ...
591s  isoutlier (A, "movmean", 5, "SamplePoints", [1,1:14]);
591s ***** error<isoutlier: sample points must be sorted.> ...
591s  isoutlier (A, "movmean", 5, "SamplePoints", [2,1,3:15]);
591s ***** error<isoutlier: sample points must have the same size> ...
591s  isoutlier (A, "movmean", 5, "SamplePoints", [1:14]);
591s 
591s ***** error<isoutlier: threshold factor must be a nonnegative scalar.> ...
591s  isoutlier (A, "movmean", 5, "ThresholdFactor", [1:14]);
591s ***** error<isoutlier: threshold factor must be a nonnegative scalar.> ...
591s  isoutlier (A, "movmean", 5, "ThresholdFactor", -1);
591s ***** error<isoutlier: threshold factor must must be in> ...
591s  isoutlier (A, "gesd", "ThresholdFactor", 3);
591s ***** error<isoutlier: threshold factor must must be in> ...
591s  isoutlier (A, "grubbs", "ThresholdFactor", 3);
591s 
591s ***** error<isoutlier: maximum outlier count must be a positive integer scalar.> ...
591s  isoutlier (A, "movmean", 5, "MaxNumOutliers", [1:14]);
591s ***** error<isoutlier: maximum outlier count must be a positive integer scalar.> ...
591s  isoutlier (A, "movmean", 5, "MaxNumOutliers", -1);
591s ***** error<isoutlier: maximum outlier count must be a positive integer scalar.> ...
591s  isoutlier (A, "movmean", 5, "MaxNumOutliers", 0);
591s ***** error<isoutlier: maximum outlier count must be a positive integer scalar.> ...
591s  isoutlier (A, "movmean", 5, "MaxNumOutliers", 1.5);
591s 
591s ***** error<isoutlier: invalid input argument.> ...
591s  isoutlier (A, {"movmean"}, 5, "SamplePoints", [1:15]);
591s ***** error<isoutlier: invalid input argument.> isoutlier (A, {1});
591s ***** error<isoutlier: invalid input argument.> isoutlier (A, true);
591s ***** error<isoutlier: invalid input argument.> isoutlier (A, false);
591s ***** error<isoutlier: DIM must be a positive integer scalar.> isoutlier (A, 0);
591s ***** error<isoutlier: DIM must be a positive integer scalar.> isoutlier (A, [1 2]);
591s ***** error<isoutlier: DIM must be a positive integer scalar.> isoutlier (A, -2);
591s 59 tests, 59 passed, 0 known failure, 0 skipped
591s [inst/dendrogram.m]
591s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dendrogram.m
591s ***** demo
591s  ## simple dendrogram
591s  y = [4, 5; 2, 6; 3, 7; 8, 9; 1, 10];
591s  y(:,3) = 1:5;
591s  dendrogram (y);
591s  title ("simple dendrogram");
591s ***** demo
591s  ## another simple dendrogram
591s  v = 2 * rand (30, 1) - 1;
591s  d = abs (bsxfun (@minus, v(:, 1), v(:, 1)'));
591s  y = linkage (squareform (d, "tovector"));
591s  dendrogram (y);
591s  title ("another simple dendrogram");
591s ***** demo
591s  ## collapsed tree, find all the leaves of node 5
591s  X = randn (60, 2);
591s  D = pdist (X);
591s  y = linkage (D, "average");
591s  subplot (2, 1, 1);
591s  title ("original tree");
591s  dendrogram (y, 0);
591s  subplot (2, 1, 2);
591s  title ("collapsed tree");
591s  [~, t] = dendrogram (y, 20);
591s  find(t == 5)
591s ***** demo
591s  ## optimal leaf order
591s  X = randn (30, 2);
591s  D = pdist (X);
591s  y = linkage (D, "average");
591s  order = optimalleaforder (y, D);
591s  subplot (2, 1, 1);
591s  title ("original leaf order");
591s  dendrogram (y);
591s  subplot (2, 1, 2);
591s  title ("optimal leaf order");
591s  dendrogram (y, "Reorder", order);
591s ***** demo
591s  ## horizontal orientation and labels
591s  X = randn (8, 2);
591s  D = pdist (X);
591s  L = ["Snow White"; "Doc"; "Grumpy"; "Happy"; "Sleepy"; "Bashful"; ...
591s       "Sneezy"; "Dopey"];
591s  y = linkage (D, "average");
591s  dendrogram (y, "Orientation", "left", "Labels", L);
591s  title ("horizontal orientation and labels");
591s ***** shared visibility_setting
591s  visibility_setting = get (0, "DefaultFigureVisible");
591s ***** test
591s  hf = figure ("visible", "off");
591s  unwind_protect
591s    y = [4, 5; 2, 6; 3, 7; 8, 9; 1, 10];
591s    y(:,3) = 1:5;
591s    dendrogram (y);
591s  unwind_protect_cleanup
591s    close (hf);
591s  end_unwind_protect
591s ***** test
591s  hf = figure ("visible", "off");
591s  unwind_protect
591s    y = [4, 5; 2, 6; 3, 7; 8, 9; 1, 10];
591s    y(:,3) = 1:5;
591s    dendrogram (y);
591s  unwind_protect_cleanup
591s    close (hf);
591s  end_unwind_protect
591s ***** test
591s  hf = figure ("visible", "off");
591s  unwind_protect
591s    v = 2 * rand (30, 1) - 1;
591s    d = abs (bsxfun (@minus, v(:, 1), v(:, 1)'));
591s    y = linkage (squareform (d, "tovector"));
591s    dendrogram (y);
591s  unwind_protect_cleanup
591s    close (hf);
591s  end_unwind_protect
592s ***** test
592s  hf = figure ("visible", "off");
592s  unwind_protect
592s    X = randn (30, 2);
592s    D = pdist (X);
592s    y = linkage (D, "average");
592s    order = optimalleaforder (y, D);
592s    subplot (2, 1, 1);
592s    title ("original leaf order");
592s    dendrogram (y);
592s    subplot (2, 1, 2);
592s    title ("optimal leaf order");
592s    dendrogram (y, "Reorder", order);
592s  unwind_protect_cleanup
592s    close (hf);
592s  end_unwind_protect
593s ***** error dendrogram ();
593s ***** error <tree must be .*> dendrogram (ones (2, 2), 1);
593s ***** error <unknown property .*> dendrogram ([1 2 1], 1, "xxx", "xxx");
593s ***** error <reorder.*> dendrogram ([1 2 1], "Reorder", "xxx");
593s ***** error <reorder.*> dendrogram ([1 2 1], "Reorder", [1 2 3 4]);
593s  fail ('dendrogram ([1 2 1], "Orientation", "north")', "invalid orientation .*")
593s 9 tests, 9 passed, 0 known failure, 0 skipped
593s [inst/boxplot.m]
593s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/boxplot.m
593s ***** demo
593s  axis ([0, 3]);
593s  randn ("seed", 1);    # for reproducibility
593s  girls = randn (10, 1) * 5 + 140;
593s  randn ("seed", 2);    # for reproducibility
593s  boys = randn (13, 1) * 8 + 135;
593s  boxplot ({girls, boys});
593s  set (gca (), "xtick", [1 2], "xticklabel", {"girls", "boys"})
593s  title ("Grade 3 heights");
593s ***** demo
593s  randn ("seed", 7);    # for reproducibility
593s  A = randn (10, 1) * 5 + 140;
593s  randn ("seed", 8);    # for reproducibility
593s  B = randn (25, 1) * 8 + 135;
593s  randn ("seed", 9);    # for reproducibility
593s  C = randn (20, 1) * 6 + 165;
593s  data = [A; B; C];
593s  groups = [(ones (10, 1)); (ones (25, 1) * 2); (ones (20, 1) * 3)];
593s  labels = {"Team A", "Team B", "Team C"};
593s  pos = [2, 1, 3];
593s  boxplot (data, groups, "Notch", "on", "Labels", labels, "Positions", pos, ...
593s           "OutlierTags", "on", "BoxStyle", "filled");
593s  title ("Example of Group splitting with paired vectors");
593s ***** demo
593s  randn ("seed", 1);    # for reproducibility
593s  data = randn (100, 9);
593s  boxplot (data, "notch", "on", "boxstyle", "filled", ...
593s           "colors", "ygcwkmb", "whisker", 1.2);
593s  title ("Example of different colors specified with characters");
593s ***** demo
593s  randn ("seed", 5);    # for reproducibility
593s  data = randn (100, 13);
593s  colors = [0.7 0.7 0.7; ...
593s            0.0 0.4 0.9; ...
593s            0.7 0.4 0.3; ...
593s            0.7 0.1 0.7; ...
593s            0.8 0.7 0.4; ...
593s            0.1 0.8 0.5; ...
593s            0.9 0.9 0.2];
593s  boxplot (data, "notch", "on", "boxstyle", "filled", ...
593s           "colors", colors, "whisker", 1.3, "boxwidth", "proportional");
593s  title ("Example of different colors specified as RGB values");
593s ***** error <numerical array or cell array containing> boxplot ("a")
593s ***** error <data cells must contain> boxplot ({[1 2 3], "a"})
593s ***** error <grouping vector may only be passed> boxplot ([1 2 3], 1, {2, 3})
593s ***** error <grouping vector must be numerical> boxplot ([1 2 3], {"a", "b"})
593s ***** error <'Notch' input argument accepts> boxplot ([1:10], "notch", "any")
593s ***** error <illegal Notch value> boxplot ([1:10], "notch", i)
593s ***** error <illegal Notch value> boxplot ([1:10], "notch", {})
593s ***** error <must be character> boxplot (1, "symbol", 1)
593s ***** error <'Orientation' input argument accepts only> boxplot (1, "orientation", "diagonal")
593s ***** error <illegal Orientation value> boxplot (1, "orientation", {})
593s ***** error <'Whisker' input argument accepts only> boxplot (1, "whisker", "a")
593s ***** error <'Whisker' input argument accepts only> boxplot (1, "whisker", [1 3])
593s ***** error <'OutlierTags' input argument accepts only> boxplot (3, "OutlierTags", "maybe")
593s ***** error <illegal OutlierTags value> boxplot (3, "OutlierTags", {})
593s ***** error <'Sample_IDs' input argument accepts only> boxplot (1, "sample_IDs", 1)
593s ***** error <'BoxWidth' input argument accepts only> boxplot (1, "boxwidth", 2)
593s ***** error <'BoxWidth' input argument accepts only> boxplot (1, "boxwidth", "anything")
593s ***** error <'Widths' input argument accepts only> boxplot (5, "widths", "a")
593s ***** error <'Widths' input argument accepts only> boxplot (5, "widths", [1:4])
593s ***** error <'Widths' input argument accepts only> boxplot (5, "widths", [])
593s ***** error <'CapWidths' input argument accepts only> boxplot (5, "capwidths", "a")
593s ***** error <'CapWidths' input argument accepts only> boxplot (5, "capwidths", [1:4])
593s ***** error <'CapWidths' input argument accepts only> boxplot (5, "capwidths", [])
593s ***** error <'BoxStyle' input argument accepts only> boxplot (1, "Boxstyle", 1)
593s ***** error <'BoxStyle' input argument accepts only> boxplot (1, "Boxstyle", "garbage")
593s ***** error <'Positions' input argument accepts only> boxplot (1, "positions", "aa")
593s ***** error <'Labels' input argument accepts only> boxplot (3, "labels", [1 5])
593s ***** error <'Colors' input argument accepts only> boxplot (1, "colors", {})
593s ***** error <'Colors' input argument accepts only> boxplot (2, "colors", [1 2 3 4])
593s ***** error <Sample_IDs must match the data> boxplot (randn (10, 3), 'Sample_IDs', {"a", "b"})
593s ***** error <with the formalism> boxplot (rand (3, 3), [1 2])
593s ***** test
593s  hf = figure ("visible", "off");
593s  unwind_protect
593s    [a, b] = boxplot (rand (10, 3));
593s    assert (size (a), [7, 3]);
593s    assert (numel (b.box), 3);
593s    assert (numel (b.whisker), 12);
593s    assert (numel (b.median), 3);
593s  unwind_protect_cleanup
593s    close (hf);
593s  end_unwind_protect
593s ***** test
593s  hf = figure ("visible", "off");
593s  unwind_protect
593s    [~, b] = boxplot (rand (10, 3), "BoxStyle", "filled", "colors", "ybc");
593s    assert (numel (b.box_fill), 3);
593s  unwind_protect_cleanup
593s    close (hf);
593s  end_unwind_protect
593s ***** test
593s  hf = figure ("visible", "off");
593s  unwind_protect
593s    hold on
593s    [a, b] = boxplot (rand (10, 3));
593s    assert (ishold, true);
593s  unwind_protect_cleanup
593s    close (hf);
593s  end_unwind_protect
593s 34 tests, 34 passed, 0 known failure, 0 skipped
593s [inst/runstest.m]
593s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/runstest.m
593s ***** test
593s  ## NIST beam deflection data
593s  ## http://www.itl.nist.gov/div898/handbook/eda/section4/eda425.htm
593s  data = [-213, -564, -35, -15, 141, 115, -420, -360, 203, -338, -431, ...
593s           194, -220, -513, 154, -125, -559, 92, -21, -579, -52, 99, -543, ...
593s          -175, 162, -457, -346, 204, -300, -474, 164, -107, -572, -8, 83, ...
593s          -541, -224, 180, -420, -374, 201, -236, -531, 83, 27, -564, -112, ...
593s           131, -507, -254, 199, -311, -495, 143, -46, -579, -90, 136, ...
593s          -472, -338, 202, -287, -477, 169, -124, -568, 17, 48, -568, -135, ...
593s           162, -430, -422, 172, -74, -577, -13, 92, -534, -243, 194, -355, ...
593s          -465, 156, -81, -578, -64, 139, -449, -384, 193, -198, -538, 110, ...
593s           -44, -577, -6, 66, -552, -164, 161, -460, -344, 205, -281, -504, ...
593s           134, -28, -576, -118, 156, -437, -381, 200, -220, -540, 83, 11, ...
593s          -568, -160, 172, -414, -408, 188, -125, -572, -32, 139, -492, ...
593s          -321, 205, -262, -504, 142, -83, -574, 0, 48, -571, -106, 137, ...
593s          -501, -266, 190, -391, -406, 194, -186, -553, 83, -13, -577, -49, ...
593s           103, -515, -280, 201, 300, -506, 131, -45, -578, -80, 138, -462, ...
593s          -361, 201, -211, -554, 32, 74, -533, -235, 187, -372, -442, 182, ...
593s          -147, -566, 25, 68, -535, -244, 194, -351, -463, 174, -125, -570, ...
593s            15, 72, -550, -190, 172, -424, -385, 198, -218, -536, 96];
593s  [h, p, stats] = runstest (data, median (data));
593s  expected_h = 1;
593s  expected_p = 0.008562;
593s  expected_z = 2.6229;
593s  assert (h, expected_h);
593s  assert (p, expected_p, 1E-6);
593s  assert (stats.z, expected_z, 1E-4);
593s ***** shared x
593s  x = [45, -60, 1.225, 55.4, -9 27];
593s ***** test
593s  [h, p, stats] = runstest (x);
593s  assert (h, 0);
593s  assert (p, 0.6, 1e-14);
593s  assert (stats.nruns, 5);
593s  assert (stats.n1, 3);
593s  assert (stats.n0, 3);
593s  assert (stats.z, 0.456435464587638, 1e-14);
593s ***** test
593s  [h, p, stats] = runstest (x, [], "method", "approximate");
593s  assert (h, 0);
593s  assert (p, 0.6481, 1e-4);
593s  assert (stats.z, 0.456435464587638, 1e-14);
593s ***** test
593s  [h, p, stats] = runstest (x, [], "tail", "left");
593s  assert (h, 0);
593s  assert (p, 0.9, 1e-14);
593s  assert (stats.z, 1.369306393762915, 1e-14);
593s ***** error<runstest: X must be a vector a scalar values.> runstest (ones (2,20))
593s ***** error<runstest: X must be a vector a scalar values.> runstest (["asdasda"])
593s ***** error<runstest: V must be either a scalar number or> ...
593s  runstest ([2 3 4 3 2 3 4], "updown")
593s ***** error<runstest: invalid value for alpha.> ...
593s  runstest ([2 3 4 3 2 3 4], [], "alpha", 0)
593s ***** error<runstest: invalid value for alpha.> ...
593s  runstest ([2 3 4 3 2 3 4], [], "alpha", [0.02 0.2])
593s ***** error<runstest: invalid value for alpha.> ...
593s  runstest ([2 3 4 3 2 3 4], [], "alpha", 1.2)
593s ***** error<runstest: invalid value for alpha.> ...
593s  runstest ([2 3 4 3 2 3 4], [], "alpha", -0.05)
593s ***** error<runstest: invalid value for method.> ...
593s  runstest ([2 3 4 3 2 3 4], [], "method", "some")
593s ***** error<runstest: invalid value for tail.> ...
593s  runstest ([2 3 4 3 2 3 4], [], "tail", "some")
593s ***** error<runstest: invalid optional argument.> ...
593s  runstest ([2 3 4 3 2 3 4], [], "option", "some")
593s 14 tests, 14 passed, 0 known failure, 0 skipped
593s [inst/dist_stat/normstat.m]
593s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/normstat.m
593s ***** error<normstat: function called with too few input arguments.> normstat ()
593s ***** error<normstat: function called with too few input arguments.> normstat (1)
593s ***** error<normstat: MU and SIGMA must be numeric.> normstat ({}, 2)
593s ***** error<normstat: MU and SIGMA must be numeric.> normstat (1, "")
593s ***** error<normstat: MU and SIGMA must not be complex.> normstat (i, 2)
593s ***** error<normstat: MU and SIGMA must not be complex.> normstat (1, i)
593s ***** error<normstat: MU and SIGMA must be of common size or scalars.> ...
593s  normstat (ones (3), ones (2))
593s ***** error<normstat: MU and SIGMA must be of common size or scalars.> ...
593s  normstat (ones (2), ones (3))
593s ***** test
593s  mu = 1:6;
593s  sigma = 0.2:0.2:1.2;
593s  [m, v] = normstat (mu, sigma);
593s  expected_v = [0.0400, 0.1600, 0.3600, 0.6400, 1.0000, 1.4400];
593s  assert (m, mu);
593s  assert (v, expected_v, 0.001);
593s ***** test
593s  sigma = 0.2:0.2:1.2;
593s  [m, v] = normstat (0, sigma);
593s  expected_mn = [0, 0, 0, 0, 0, 0];
593s  expected_v = [0.0400, 0.1600, 0.3600, 0.6400, 1.0000, 1.4400];
593s  assert (m, expected_mn, 0.001);
593s  assert (v, expected_v, 0.001);
593s 10 tests, 10 passed, 0 known failure, 0 skipped
593s [inst/dist_stat/expstat.m]
593s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/expstat.m
593s ***** error<expstat: function called with too few input arguments.> expstat ()
593s ***** error<expstat: MU must be numeric.> expstat ({})
593s ***** error<expstat: MU must be numeric.> expstat ("")
593s ***** error<expstat: MU must not be complex.> expstat (i)
593s ***** test
593s  mu = 1:6;
593s  [m, v] = expstat (mu);
593s  assert (m, [1, 2, 3, 4, 5, 6], 0.001);
593s  assert (v, [1, 4, 9, 16, 25, 36], 0.001);
593s 5 tests, 5 passed, 0 known failure, 0 skipped
593s [inst/dist_stat/ncx2stat.m]
593s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/ncx2stat.m
593s ***** error<ncx2stat: function called with too few input arguments.> ncx2stat ()
593s ***** error<ncx2stat: function called with too few input arguments.> ncx2stat (1)
593s ***** error<ncx2stat: DF and LAMBDA must be numeric.> ncx2stat ({}, 2)
593s ***** error<ncx2stat: DF and LAMBDA must be numeric.> ncx2stat (1, "")
593s ***** error<ncx2stat: DF and LAMBDA must not be complex.> ncx2stat (i, 2)
593s ***** error<ncx2stat: DF and LAMBDA must not be complex.> ncx2stat (1, i)
593s ***** error<ncx2stat: DF and LAMBDA must be of common size or scalars.> ...
593s  ncx2stat (ones (3), ones (2))
593s ***** error<ncx2stat: DF and LAMBDA must be of common size or scalars.> ...
593s  ncx2stat (ones (2), ones (3))
593s ***** shared df, d1
593s  df = [2, 0, -1, 1, 4];
593s  d1 = [1, NaN, 3, -1, 2];
593s ***** assert (ncx2stat (df, d1), [3, NaN, NaN, NaN, 6]);
593s ***** assert (ncx2stat ([df(1:2), df(4:5)], 1), [3, NaN, 2, 5]);
593s ***** assert (ncx2stat ([df(1:2), df(4:5)], 3), [5, NaN, 4, 7]);
593s ***** assert (ncx2stat ([df(1:2), df(4:5)], 2), [4, NaN, 3, 6]);
593s ***** assert (ncx2stat (2, [d1(1), d1(3:5)]), [3, 5, NaN, 4]);
593s ***** assert (ncx2stat (0, [d1(1), d1(3:5)]), [NaN, NaN, NaN, NaN]);
593s ***** assert (ncx2stat (1, [d1(1), d1(3:5)]), [2, 4, NaN, 3]);
593s ***** assert (ncx2stat (4, [d1(1), d1(3:5)]), [5, 7, NaN, 6]);
593s 16 tests, 16 passed, 0 known failure, 0 skipped
593s [inst/dist_stat/binostat.m]
593s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/binostat.m
593s ***** error<binostat: function called with too few input arguments.> binostat ()
593s ***** error<binostat: function called with too few input arguments.> binostat (1)
593s ***** error<binostat: N and PS must be numeric.> binostat ({}, 2)
593s ***** error<binostat: N and PS must be numeric.> binostat (1, "")
593s ***** error<binostat: N and PS must not be complex.> binostat (i, 2)
593s ***** error<binostat: N and PS must not be complex.> binostat (1, i)
593s ***** error<binostat: N and PS must be of common size or scalars.> ...
593s  binostat (ones (3), ones (2))
593s ***** error<binostat: N and PS must be of common size or scalars.> ...
593s  binostat (ones (2), ones (3))
593s ***** test
593s  n = 1:6;
593s  ps = 0:0.2:1;
593s  [m, v] = binostat (n, ps);
593s  expected_m = [0.00, 0.40, 1.20, 2.40, 4.00, 6.00];
593s  expected_v = [0.00, 0.32, 0.72, 0.96, 0.80, 0.00];
593s  assert (m, expected_m, 0.001);
593s  assert (v, expected_v, 0.001);
593s ***** test
593s  n = 1:6;
593s  [m, v] = binostat (n, 0.5);
593s  expected_m = [0.50, 1.00, 1.50, 2.00, 2.50, 3.00];
593s  expected_v = [0.25, 0.50, 0.75, 1.00, 1.25, 1.50];
593s  assert (m, expected_m, 0.001);
593s  assert (v, expected_v, 0.001);
593s ***** test
593s  n = [-Inf -3 5 0.5 3 NaN 100, Inf];
593s  [m, v] = binostat (n, 0.5);
593s  assert (isnan (m), [true true false true false true false false])
593s  assert (isnan (v), [true true false true false true false false])
593s  assert (m(end), Inf);
593s  assert (v(end), Inf);
594s ***** assert (nthargout (1:2, @binostat, 5, []), {[], []})
594s ***** assert (nthargout (1:2, @binostat, [], 5), {[], []})
594s ***** assert (size (binostat (randi (100, 10, 5, 4), rand (10, 5, 4))), [10 5 4])
594s ***** assert (size (binostat (randi (100, 10, 5, 4), 7)), [10 5 4])
594s 15 tests, 15 passed, 0 known failure, 0 skipped
594s [inst/dist_stat/burrstat.m]
594s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/burrstat.m
594s ***** error<burrstat: function called with too few input arguments.> burrstat ()
594s ***** error<burrstat: function called with too few input arguments.> burrstat (1)
594s ***** error<burrstat: function called with too few input arguments.> burrstat (1, 2)
594s ***** error<burrstat: LAMBDA, C, and K must be numeric.> burrstat ({}, 2, 3)
594s ***** error<burrstat: LAMBDA, C, and K must be numeric.> burrstat (1, "", 3)
594s ***** error<burrstat: LAMBDA, C, and K must be numeric.> burrstat (1, 2, "")
594s ***** error<burrstat: LAMBDA, C, and K must not be complex.> burrstat (i, 2, 3)
594s ***** error<burrstat: LAMBDA, C, and K must not be complex.> burrstat (1, i, 3)
594s ***** error<burrstat: LAMBDA, C, and K must not be complex.> burrstat (1, 2, i)
594s ***** error<burrstat: LAMBDA, C, and K must be of common size or scalars.> ...
594s  burrstat (ones (3), ones (2), 3)
594s ***** error<burrstat: LAMBDA, C, and K must be of common size or scalars.> ...
594s  burrstat (ones (2), 2, ones (3))
594s ***** error<burrstat: LAMBDA, C, and K must be of common size or scalars.> ...
594s  burrstat (1, ones (2), ones (3))
594s ***** test
594s  [m, v] = burrstat (1, 2, 5);
594s  assert (m, 0.4295, 1e-4);
594s  assert (v, 0.0655, 1e-4);
594s ***** test
594s  [m, v] = burrstat (1, 1, 1);
594s  assert (m, Inf);
594s  assert (v, Inf);
594s ***** test
594s  [m, v] = burrstat (2, 4, 1);
594s  assert (m, 2.2214, 1e-4);
594s  assert (v, 1.3484, 1e-4);
594s 15 tests, 15 passed, 0 known failure, 0 skipped
594s [inst/dist_stat/wblstat.m]
594s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/wblstat.m
594s ***** error<wblstat: function called with too few input arguments.> wblstat ()
594s ***** error<wblstat: function called with too few input arguments.> wblstat (1)
594s ***** error<wblstat: LAMBDA and K must be numeric.> wblstat ({}, 2)
594s ***** error<wblstat: LAMBDA and K must be numeric.> wblstat (1, "")
594s ***** error<wblstat: LAMBDA and K must not be complex.> wblstat (i, 2)
594s ***** error<wblstat: LAMBDA and K must not be complex.> wblstat (1, i)
594s ***** error<wblstat: LAMBDA and K must be of common size or scalars.> ...
594s  wblstat (ones (3), ones (2))
594s ***** error<wblstat: LAMBDA and K must be of common size or scalars.> ...
594s  wblstat (ones (2), ones (3))
594s ***** test
594s  lambda = 3:8;
594s  k = 1:6;
594s  [m, v] = wblstat (lambda, k);
594s  expected_m = [3.0000, 3.5449, 4.4649, 5.4384, 6.4272, 7.4218];
594s  expected_v = [9.0000, 3.4336, 2.6333, 2.3278, 2.1673, 2.0682];
594s  assert (m, expected_m, 0.001);
594s  assert (v, expected_v, 0.001);
594s ***** test
594s  k = 1:6;
594s  [m, v] = wblstat (6, k);
594s  expected_m = [ 6.0000, 5.3174, 5.3579, 5.4384, 5.5090, 5.5663];
594s  expected_v = [36.0000, 7.7257, 3.7920, 2.3278, 1.5923, 1.1634];
594s  assert (m, expected_m, 0.001);
594s  assert (v, expected_v, 0.001);
594s 10 tests, 10 passed, 0 known failure, 0 skipped
594s [inst/dist_stat/fstat.m]
594s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/fstat.m
594s ***** error<fstat: function called with too few input arguments.> fstat ()
594s ***** error<fstat: function called with too few input arguments.> fstat (1)
594s ***** error<fstat: DF1 and DF2 must be numeric.> fstat ({}, 2)
594s ***** error<fstat: DF1 and DF2 must be numeric.> fstat (1, "")
594s ***** error<fstat: DF1 and DF2 must not be complex.> fstat (i, 2)
594s ***** error<fstat: DF1 and DF2 must not be complex.> fstat (1, i)
594s ***** error<fstat: DF1 and DF2 must be of common size or scalars.> ...
594s  fstat (ones (3), ones (2))
594s ***** error<fstat: DF1 and DF2 must be of common size or scalars.> ...
594s  fstat (ones (2), ones (3))
594s ***** test
594s  df1 = 1:6;
594s  df2 = 5:10;
594s  [m, v] = fstat (df1, df2);
594s  expected_mn = [1.6667, 1.5000, 1.4000, 1.3333, 1.2857, 1.2500];
594s  expected_v = [22.2222, 6.7500, 3.4844, 2.2222, 1.5869, 1.2153];
594s  assert (m, expected_mn, 0.001);
594s  assert (v, expected_v, 0.001);
594s ***** test
594s  df1 = 1:6;
594s  [m, v] = fstat (df1, 5);
594s  expected_mn = [1.6667, 1.6667, 1.6667, 1.6667, 1.6667, 1.6667];
594s  expected_v = [22.2222, 13.8889, 11.1111, 9.7222, 8.8889, 8.3333];
594s  assert (m, expected_mn, 0.001);
594s  assert (v, expected_v, 0.001);
594s 10 tests, 10 passed, 0 known failure, 0 skipped
594s [inst/dist_stat/nakastat.m]
594s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/nakastat.m
594s ***** error<nakastat: function called with too few input arguments.> nakastat ()
594s ***** error<nakastat: function called with too few input arguments.> nakastat (1)
594s ***** error<nakastat: MU and OMEGA must be numeric.> nakastat ({}, 2)
594s ***** error<nakastat: MU and OMEGA must be numeric.> nakastat (1, "")
594s ***** error<nakastat: MU and OMEGA must not be complex.> nakastat (i, 2)
594s ***** error<nakastat: MU and OMEGA must not be complex.> nakastat (1, i)
594s ***** error<nakastat: MU and OMEGA must be of common size or scalars.> ...
594s  nakastat (ones (3), ones (2))
594s ***** error<nakastat: MU and OMEGA must be of common size or scalars.> ...
594s  nakastat (ones (2), ones (3))
594s ***** test
594s  [m, v] = nakastat (1, 1);
594s  assert (m, 0.8862269254, 1e-10);
594s  assert (v, 0.2146018366, 1e-10);
594s ***** test
594s  [m, v] = nakastat (1, 2);
594s  assert (m, 1.25331413731, 1e-10);
594s  assert (v, 0.42920367321, 1e-10);
594s ***** test
594s  [m, v] = nakastat (2, 1);
594s  assert (m, 0.93998560299, 1e-10);
594s  assert (v, 0.11642706618, 1e-10);
594s 11 tests, 11 passed, 0 known failure, 0 skipped
594s [inst/dist_stat/ricestat.m]
594s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/ricestat.m
594s ***** error<ricestat: function called with too few input arguments.> ricestat ()
594s ***** error<ricestat: function called with too few input arguments.> ricestat (1)
594s ***** error<ricestat: S and SIGMA must be numeric.> ricestat ({}, 2)
594s ***** error<ricestat: S and SIGMA must be numeric.> ricestat (1, "")
594s ***** error<ricestat: S and SIGMA must not be complex.> ricestat (i, 2)
594s ***** error<ricestat: S and SIGMA must not be complex.> ricestat (1, i)
594s ***** error<ricestat: S and SIGMA must be of common size or scalars.> ...
594s  ricestat (ones (3), ones (2))
594s ***** error<ricestat: S and SIGMA must be of common size or scalars.> ...
594s  ricestat (ones (2), ones (3))
594s ***** shared s, sigma
594s  s = [2, 0, -1, 1, 4];
594s  sigma = [1, NaN, 3, -1, 2];
594s ***** assert (ricestat (s, sigma), [2.2724, NaN, NaN, NaN, 4.5448], 1e-4);
594s ***** assert (ricestat ([s(1:2), s(4:5)], 1), [2.2724, 1.2533, 1.5486, 4.1272], 1e-4);
594s ***** assert (ricestat ([s(1:2), s(4:5)], 3), [4.1665, 3.7599, 3.8637, 5.2695], 1e-4);
594s ***** assert (ricestat ([s(1:2), s(4:5)], 2), [3.0971, 2.5066, 2.6609, 4.5448], 1e-4);
594s ***** assert (ricestat (2, [sigma(1), sigma(3:5)]), [2.2724, 4.1665, NaN, 3.0971], 1e-4);
594s ***** assert (ricestat (0, [sigma(1), sigma(3:5)]), [1.2533, 3.7599, NaN, 2.5066], 1e-4);
594s ***** assert (ricestat (1, [sigma(1), sigma(3:5)]), [1.5486, 3.8637, NaN, 2.6609], 1e-4);
594s ***** assert (ricestat (4, [sigma(1), sigma(3:5)]), [4.1272, 5.2695, NaN, 4.5448], 1e-4);
594s 16 tests, 16 passed, 0 known failure, 0 skipped
594s [inst/dist_stat/unifstat.m]
594s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/unifstat.m
594s ***** error<unifstat: function called with too few input arguments.> unifstat ()
594s ***** error<unifstat: function called with too few input arguments.> unifstat (1)
594s ***** error<unifstat: A and B must be numeric.> unifstat ({}, 2)
594s ***** error<unifstat: A and B must be numeric.> unifstat (1, "")
594s ***** error<unifstat: A and B must not be complex.> unifstat (i, 2)
594s ***** error<unifstat: A and B must not be complex.> unifstat (1, i)
594s ***** error<unifstat: A and B must be of common size or scalars.> ...
594s  unifstat (ones (3), ones (2))
594s ***** error<unifstat: A and B must be of common size or scalars.> ...
594s  unifstat (ones (2), ones (3))
594s ***** test
594s  a = 1:6;
594s  b = 2:2:12;
594s  [m, v] = unifstat (a, b);
594s  expected_m = [1.5000, 3.0000, 4.5000, 6.0000, 7.5000, 9.0000];
594s  expected_v = [0.0833, 0.3333, 0.7500, 1.3333, 2.0833, 3.0000];
594s  assert (m, expected_m, 0.001);
594s  assert (v, expected_v, 0.001);
594s ***** test
594s  a = 1:6;
594s  [m, v] = unifstat (a, 10);
594s  expected_m = [5.5000, 6.0000, 6.5000, 7.0000, 7.5000, 8.0000];
594s  expected_v = [6.7500, 5.3333, 4.0833, 3.0000, 2.0833, 1.3333];
594s  assert (m, expected_m, 0.001);
594s  assert (v, expected_v, 0.001);
594s 10 tests, 10 passed, 0 known failure, 0 skipped
594s [inst/dist_stat/tlsstat.m]
594s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/tlsstat.m
594s ***** error<tlsstat: function called with too few input arguments.> tlsstat ()
594s ***** error<tlsstat: function called with too few input arguments.> tlsstat (1)
594s ***** error<tlsstat: function called with too few input arguments.> tlsstat (1, 2)
594s ***** error<tlsstat: MU, SIGMA, and NU must be numeric.> tlsstat ({}, 2, 3)
594s ***** error<tlsstat: MU, SIGMA, and NU must be numeric.> tlsstat (1, "", 3)
594s ***** error<tlsstat: MU, SIGMA, and NU must be numeric.> tlsstat (1, 2, ["d"])
594s ***** error<tlsstat: MU, SIGMA, and NU must not be complex.> tlsstat (i, 2, 3)
594s ***** error<tlsstat: MU, SIGMA, and NU must not be complex.> tlsstat (1, i, 3)
594s ***** error<tlsstat: MU, SIGMA, and NU must not be complex.> tlsstat (1, 2, i)
594s ***** error<tlsstat: MU, SIGMA, and NU must be of common size or scalars.> ...
594s  tlsstat (ones (3), ones (2), 1)
594s ***** error<tlsstat: MU, SIGMA, and NU must be of common size or scalars.> ...
594s  tlsstat (ones (2), 1, ones (3))
594s ***** error<tlsstat: MU, SIGMA, and NU must be of common size or scalars.> ...
594s  tlsstat (1, ones (2), ones (3))
594s ***** test
594s  [m, v] = tlsstat (0, 1, 0);
594s  assert (m, NaN);
594s  assert (v, NaN);
594s ***** test
594s  [m, v] = tlsstat (0, 1, 1);
594s  assert (m, NaN);
594s  assert (v, NaN);
594s ***** test
594s  [m, v] = tlsstat (2, 1, 1);
594s  assert (m, NaN);
594s  assert (v, NaN);
594s ***** test
594s  [m, v] = tlsstat (-2, 1, 1);
594s  assert (m, NaN);
594s  assert (v, NaN);
594s ***** test
594s  [m, v] = tlsstat (0, 1, 2);
594s  assert (m, 0);
594s  assert (v, NaN);
594s ***** test
594s  [m, v] = tlsstat (2, 1, 2);
594s  assert (m, 2);
594s  assert (v, NaN);
594s ***** test
594s  [m, v] = tlsstat (-2, 1, 2);
594s  assert (m, -2);
594s  assert (v, NaN);
594s ***** test
594s  [m, v] = tlsstat (0, 2, 2);
594s  assert (m, 0);
594s  assert (v, NaN);
594s ***** test
594s  [m, v] = tlsstat (2, 2, 2);
594s  assert (m, 2);
594s  assert (v, NaN);
594s ***** test
594s  [m, v] = tlsstat (-2, 2, 2);
594s  assert (m, -2);
594s  assert (v, NaN);
594s ***** test
594s  [m, v] = tlsstat (0, 1, 3);
594s  assert (m, 0);
594s  assert (v, 3);
594s ***** test
594s  [m, v] = tlsstat (0, 2, 3);
594s  assert (m, 0);
594s  assert (v, 6);
594s ***** test
594s  [m, v] = tlsstat (2, 1, 3);
594s  assert (m, 2);
594s  assert (v, 3);
594s ***** test
594s  [m, v] = tlsstat (2, 2, 3);
594s  assert (m, 2);
594s  assert (v, 6);
594s ***** test
594s  [m, v] = tlsstat (-2, 1, 3);
594s  assert (m, -2);
594s  assert (v, 3);
594s ***** test
594s  [m, v] = tlsstat (-2, 2, 3);
594s  assert (m, -2);
594s  assert (v, 6);
594s 28 tests, 28 passed, 0 known failure, 0 skipped
594s [inst/dist_stat/geostat.m]
594s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/geostat.m
594s ***** error<geostat: function called with too few input arguments.> geostat ()
594s ***** error<geostat: PS must be numeric.> geostat ({})
594s ***** error<geostat: PS must be numeric.> geostat ("")
594s ***** error<geostat: PS must not be complex.> geostat (i)
594s ***** test
594s  ps = 1 ./ (1:6);
594s  [m, v] = geostat (ps);
594s  assert (m, [0, 1, 2, 3, 4, 5], 0.001);
594s  assert (v, [0, 2, 6, 12, 20, 30], 0.001);
594s 5 tests, 5 passed, 0 known failure, 0 skipped
594s [inst/dist_stat/bisastat.m]
594s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/bisastat.m
594s ***** error<bisastat: function called with too few input arguments.> bisastat ()
594s ***** error<bisastat: function called with too few input arguments.> bisastat (1)
594s ***** error<bisastat: BETA and GAMMA must be numeric.> bisastat ({}, 2)
594s ***** error<bisastat: BETA and GAMMA must be numeric.> bisastat (1, "")
594s ***** error<bisastat: BETA and GAMMA must not be complex.> bisastat (i, 2)
594s ***** error<bisastat: BETA and GAMMA must not be complex.> bisastat (1, i)
594s ***** error<bisastat: BETA and GAMMA must be of common size or scalars.> ...
594s  bisastat (ones (3), ones (2))
594s ***** error<bisastat: BETA and GAMMA must be of common size or scalars.> ...
594s  bisastat (ones (2), ones (3))
594s ***** test
594s  beta = 1:6;
594s  gamma = 1:0.2:2;
594s  [m, v] = bisastat (beta, gamma);
594s  expected_m = [1.50, 3.44, 5.94,  9.12,  13.10, 18];
594s  expected_v = [2.25, 16.128, 60.858, 172.032, 409.050, 864];
594s  assert (m, expected_m, 1e-2);
594s  assert (v, expected_v, 1e-3);
594s ***** test
594s  beta = 1:6;
594s  [m, v] = bisastat (beta, 1.5);
594s  expected_m = [2.125, 4.25, 6.375, 8.5, 10.625, 12.75];
594s  expected_v = [8.5781, 34.3125, 77.2031, 137.2500, 214.4531, 308.8125];
594s  assert (m, expected_m, 1e-3);
594s  assert (v, expected_v, 1e-4);
594s 10 tests, 10 passed, 0 known failure, 0 skipped
594s [inst/dist_stat/hnstat.m]
594s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/hnstat.m
594s ***** error<hnstat: function called with too few input arguments.> hnstat ()
594s ***** error<hnstat: function called with too few input arguments.> hnstat (1)
594s ***** error<hnstat: MU and SIGMA must be numeric.> hnstat ({}, 2)
594s ***** error<hnstat: MU and SIGMA must be numeric.> hnstat (1, "")
594s ***** error<hnstat: MU and SIGMA must not be complex.> hnstat (i, 2)
594s ***** error<hnstat: MU and SIGMA must not be complex.> hnstat (1, i)
594s ***** error<hnstat: MU and SIGMA must be of common size or scalars.> ...
594s  hnstat (ones (3), ones (2))
594s ***** error<hnstat: MU and SIGMA must be of common size or scalars.> ...
594s  hnstat (ones (2), ones (3))
594s ***** test
594s  [m, v] = hnstat (0, 1);
594s  assert (m, 0.7979, 1e-4);
594s  assert (v, 0.3634, 1e-4);
594s ***** test
594s  [m, v] = hnstat (2, 1);
594s  assert (m, 2.7979, 1e-4);
594s  assert (v, 0.3634, 1e-4);
594s ***** test
594s  [m, v] = hnstat (2, 2);
594s  assert (m, 3.5958, 1e-4);
594s  assert (v, 1.4535, 1e-4);
594s ***** test
594s  [m, v] = hnstat (2, 2.5);
594s  assert (m, 3.9947, 1e-4);
594s  assert (v, 2.2711, 1e-4);
594s ***** test
594s  [m, v] = hnstat (1.5, 0.5);
594s  assert (m, 1.8989, 1e-4);
594s  assert (v, 0.0908, 1e-4);
594s ***** test
594s  [m, v] = hnstat (-1.5, 0.5);
594s  assert (m, -1.1011, 1e-4);
594s  assert (v, 0.0908, 1e-4);
594s 14 tests, 14 passed, 0 known failure, 0 skipped
594s [inst/dist_stat/plstat.m]
594s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/plstat.m
594s ***** shared x, Fx
594s  x = [0, 1, 3, 4, 7, 10];
594s  Fx = [0, 0.2, 0.5, 0.6, 0.7, 1];
594s ***** assert (plstat (x, Fx), 4.15)
594s ***** test
594s  [m, v] = plstat (x, Fx);
594s  assert (v, 10.3775, 1e-14)
594s ***** error<plstat: function called with too few input arguments.> plstat ()
594s ***** error<plstat: function called with too few input arguments.> plstat (1)
594s ***** error<plstat: X and FX must be vectors of equal size.> ...
594s  plstat ([0, 1, 2], [0, 1])
594s ***** error<plstat: X and FX must be at least two-elements long.> ...
594s  plstat ([0], [1])
594s ***** error<plstat: FX must be bounded in the range> ...
594s  plstat ([0, 1, 2], [0, 1, 1.5])
594s ***** error<plstat: FX must be bounded in the range> ...
594s  plstat ([0, 1, 2], [0, i, 1])
594s ***** error<plstat: X and FX must not be complex.> ...
594s  plstat ([0, i, 2], [0, 0.5, 1])
594s ***** error<plstat: X and FX must not be complex.> ...
594s  plstat ([0, i, 2], [0, 0.5i, 1])
594s 10 tests, 10 passed, 0 known failure, 0 skipped
594s [inst/dist_stat/gpstat.m]
594s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/gpstat.m
594s ***** error<gpstat: function called with too few input arguments.> gpstat ()
594s ***** error<gpstat: function called with too few input arguments.> gpstat (1)
594s ***** error<gpstat: function called with too few input arguments.> gpstat (1, 2)
594s ***** error<gpstat: K, SIGMA, and MU must be numeric.> gpstat ({}, 2, 3)
594s ***** error<gpstat: K, SIGMA, and MU must be numeric.> gpstat (1, "", 3)
594s ***** error<gpstat: K, SIGMA, and MU must be numeric.> gpstat (1, 2, "")
594s ***** error<gpstat: K, SIGMA, and MU must not be complex.> gpstat (i, 2, 3)
594s ***** error<gpstat: K, SIGMA, and MU must not be complex.> gpstat (1, i, 3)
594s ***** error<gpstat: K, SIGMA, and MU must not be complex.> gpstat (1, 2, i)
594s ***** error<gpstat: K, SIGMA, and MU must be of common size or scalars.> ...
594s  gpstat (ones (3), ones (2), 3)
594s ***** error<gpstat: K, SIGMA, and MU must be of common size or scalars.> ...
594s  gpstat (ones (2), 2, ones (3))
594s ***** error<gpstat: K, SIGMA, and MU must be of common size or scalars.> ...
594s  gpstat (1, ones (2), ones (3))
594s ***** shared x, y
594s  x = [-Inf, -1, 0, 1/2, 1, Inf];
594s  y = [0, 0.5, 1, 2, Inf, Inf];
594s ***** assert (gpstat (x, ones (1,6), zeros (1,6)), y, eps)
594s ***** assert (gpstat (single (x), 1, 0), single (y), eps("single"))
594s ***** assert (gpstat (x, single (1), 0), single (y), eps("single"))
594s ***** assert (gpstat (x, 1, single (0)), single (y), eps("single"))
594s ***** assert (gpstat (single ([x, NaN]), 1, 0), single ([y, NaN]), eps("single"))
594s ***** assert (gpstat ([x, NaN], single (1), 0), single ([y, NaN]), eps("single"))
594s ***** assert (gpstat ([x, NaN], 1, single (0)), single ([y, NaN]), eps("single"))
594s 19 tests, 19 passed, 0 known failure, 0 skipped
594s [inst/dist_stat/gevstat.m]
594s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/gevstat.m
594s ***** error<gevstat: function called with too few input arguments.> gevstat ()
594s ***** error<gevstat: function called with too few input arguments.> gevstat (1)
594s ***** error<gevstat: function called with too few input arguments.> gevstat (1, 2)
594s ***** error<gevstat: K, SIGMA, and MU must be numeric.> gevstat ({}, 2, 3)
594s ***** error<gevstat: K, SIGMA, and MU must be numeric.> gevstat (1, "", 3)
594s ***** error<gevstat: K, SIGMA, and MU must be numeric.> gevstat (1, 2, "")
594s ***** error<gevstat: K, SIGMA, and MU must not be complex.> gevstat (i, 2, 3)
594s ***** error<gevstat: K, SIGMA, and MU must not be complex.> gevstat (1, i, 3)
594s ***** error<gevstat: K, SIGMA, and MU must not be complex.> gevstat (1, 2, i)
594s ***** error<gevstat: K, SIGMA, and MU must be of common size or scalars.> ...
594s  gevstat (ones (3), ones (2), 3)
594s ***** error<gevstat: K, SIGMA, and MU must be of common size or scalars.> ...
594s  gevstat (ones (2), 2, ones (3))
594s ***** error<gevstat: K, SIGMA, and MU must be of common size or scalars.> ...
594s  gevstat (1, ones (2), ones (3))
594s ***** test
594s  k = [-1, -0.5, 0, 0.2, 0.4, 0.5, 1];
594s  sigma = 2;
594s  mu = 1;
594s  [m, v] = gevstat (k, sigma, mu);
594s  expected_m = [1, 1.4551, 2.1544, 2.6423, 3.4460, 4.0898, Inf];
594s  expected_v = [4, 3.4336, 6.5797, 13.3761, 59.3288, Inf, Inf];
594s  assert (m, expected_m, -0.001);
594s  assert (v, expected_v, -0.001);
594s 13 tests, 13 passed, 0 known failure, 0 skipped
594s [inst/dist_stat/logistat.m]
594s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/logistat.m
594s ***** error<logistat: function called with too few input arguments.> logistat ()
594s ***** error<logistat: function called with too few input arguments.> logistat (1)
594s ***** error<logistat: MU and SIGMA must be numeric.> logistat ({}, 2)
594s ***** error<logistat: MU and SIGMA must be numeric.> logistat (1, "")
594s ***** error<logistat: MU and SIGMA must not be complex.> logistat (i, 2)
594s ***** error<logistat: MU and SIGMA must not be complex.> logistat (1, i)
594s ***** error<logistat: MU and SIGMA must be of common size or scalars.> ...
594s  logistat (ones (3), ones (2))
594s ***** error<logistat: MU and SIGMA must be of common size or scalars.> ...
594s  logistat (ones (2), ones (3))
594s ***** test
594s  [m, v] = logistat (0, 1);
594s  assert (m, 0);
594s  assert (v, 3.2899, 0.001);
594s ***** test
594s  [m, v] = logistat (0, 0.8);
594s  assert (m, 0);
594s  assert (v, 2.1055, 0.001);
594s ***** test
594s  [m, v] = logistat (1, 0.6);
594s  assert (m, 1);
594s  assert (v, 1.1844, 0.001);
594s ***** test
594s  [m, v] = logistat (0, 0.4);
594s  assert (m, 0);
594s  assert (v, 0.5264, 0.001);
594s ***** test
594s  [m, v] = logistat (-1, 0.2);
594s  assert (m, -1);
594s  assert (v, 0.1316, 0.001);
594s 13 tests, 13 passed, 0 known failure, 0 skipped
594s [inst/dist_stat/tristat.m]
594s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/tristat.m
594s ***** error<tristat: function called with too few input arguments.> tristat ()
594s ***** error<tristat: function called with too few input arguments.> tristat (1)
594s ***** error<tristat: function called with too few input arguments.> tristat (1, 2)
594s ***** error<tristat: A, B, and C must be numeric.> tristat ("i", 2, 1)
594s ***** error<tristat: A, B, and C must be numeric.> tristat (0, "d", 1)
594s ***** error<tristat: A, B, and C must be numeric.> tristat (0, 3, {})
594s ***** error<tristat: A, B, and C must be real.> tristat (i, 2, 1)
594s ***** error<tristat: A, B, and C must be real.> tristat (0, i, 1)
594s ***** error<tristat: A, B, and C must be real.> tristat (0, 3, i)
594s ***** test
594s  a = 1:5;
594s  b = 3:7;
594s  c = 5:9;
594s  [m, v] = tristat (a, b, c);
594s  expected_m = [3, 4, 5, 6, 7];
594s  assert (m, expected_m);
594s  assert (v, ones (1, 5) * (2/3));
594s 10 tests, 10 passed, 0 known failure, 0 skipped
594s [inst/dist_stat/lognstat.m]
594s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/lognstat.m
594s ***** error<lognstat: function called with too few input arguments.> lognstat ()
594s ***** error<lognstat: function called with too few input arguments.> lognstat (1)
594s ***** error<lognstat: MU and SIGMA must be numeric.> lognstat ({}, 2)
594s ***** error<lognstat: MU and SIGMA must be numeric.> lognstat (1, "")
594s ***** error<lognstat: MU and SIGMA must not be complex.> lognstat (i, 2)
594s ***** error<lognstat: MU and SIGMA must not be complex.> lognstat (1, i)
594s ***** error<lognstat: MU and SIGMA must be of common size or scalars.> ...
594s  lognstat (ones (3), ones (2))
594s ***** error<lognstat: MU and SIGMA must be of common size or scalars.> ...
594s  lognstat (ones (2), ones (3))
594s ***** test
594s  mu = 0:0.2:1;
594s  sigma = 0.2:0.2:1.2;
594s  [m, v] = lognstat (mu, sigma);
594s  expected_m = [1.0202, 1.3231, 1.7860, 2.5093,  3.6693,   5.5845];
594s  expected_v = [0.0425, 0.3038, 1.3823, 5.6447, 23.1345, 100.4437];
594s  assert (m, expected_m, 0.001);
594s  assert (v, expected_v, 0.001);
594s ***** test
594s  sigma = 0.2:0.2:1.2;
594s  [m, v] = lognstat (0, sigma);
594s  expected_m = [1.0202, 1.0833, 1.1972, 1.3771, 1.6487,  2.0544];
594s  expected_v = [0.0425, 0.2036, 0.6211, 1.7002, 4.6708, 13.5936];
594s  assert (m, expected_m, 0.001);
594s  assert (v, expected_v, 0.001);
594s 10 tests, 10 passed, 0 known failure, 0 skipped
594s [inst/dist_stat/nbinstat.m]
594s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/nbinstat.m
594s ***** error<nbinstat: function called with too few input arguments.> nbinstat ()
594s ***** error<nbinstat: function called with too few input arguments.> nbinstat (1)
594s ***** error<nbinstat: R and PS must be numeric.> nbinstat ({}, 2)
594s ***** error<nbinstat: R and PS must be numeric.> nbinstat (1, "")
594s ***** error<nbinstat: R and PS must not be complex.> nbinstat (i, 2)
594s ***** error<nbinstat: R and PS must not be complex.> nbinstat (1, i)
594s ***** error<nbinstat: R and PS must be of common size or scalars.> ...
594s  nbinstat (ones (3), ones (2))
594s ***** error<nbinstat: R and PS must be of common size or scalars.> ...
594s  nbinstat (ones (2), ones (3))
594s ***** test
594s  r = 1:4;
594s  ps = 0.2:0.2:0.8;
594s  [m, v] = nbinstat (r, ps);
594s  expected_m = [ 4.0000, 3.0000, 2.0000, 1.0000];
594s  expected_v = [20.0000, 7.5000, 3.3333, 1.2500];
594s  assert (m, expected_m, 0.001);
594s  assert (v, expected_v, 0.001);
594s ***** test
594s  r = 1:4;
594s  [m, v] = nbinstat (r, 0.5);
594s  expected_m = [1, 2, 3, 4];
594s  expected_v = [2, 4, 6, 8];
594s  assert (m, expected_m, 0.001);
594s  assert (v, expected_v, 0.001);
594s 10 tests, 10 passed, 0 known failure, 0 skipped
594s [inst/dist_stat/unidstat.m]
594s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/unidstat.m
594s ***** error<unidstat: function called with too few input arguments.> unidstat ()
594s ***** error<unidstat: N must be numeric.> unidstat ({})
594s ***** error<unidstat: N must be numeric.> unidstat ("")
594s ***** error<unidstat: N must not be complex.> unidstat (i)
594s ***** test
594s  N = 1:6;
594s  [m, v] = unidstat (N);
594s  expected_m = [1.0000, 1.5000, 2.0000, 2.5000, 3.0000, 3.5000];
594s  expected_v = [0.0000, 0.2500, 0.6667, 1.2500, 2.0000, 2.9167];
594s  assert (m, expected_m, 0.001);
594s  assert (v, expected_v, 0.001);
594s 5 tests, 5 passed, 0 known failure, 0 skipped
594s [inst/dist_stat/ncfstat.m]
594s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/ncfstat.m
594s ***** error<ncfstat: function called with too few input arguments.> ncfstat ()
594s ***** error<ncfstat: function called with too few input arguments.> ncfstat (1)
594s ***** error<ncfstat: function called with too few input arguments.> ncfstat (1, 2)
594s ***** error<ncfstat: DF1, DF2, and LAMBDA must be numeric.> ncfstat ({}, 2, 3)
594s ***** error<ncfstat: DF1, DF2, and LAMBDA must be numeric.> ncfstat (1, "", 3)
594s ***** error<ncfstat: DF1, DF2, and LAMBDA must be numeric.> ncfstat (1, 2, "")
594s ***** error<ncfstat: DF1, DF2, and LAMBDA must not be complex.> ncfstat (i, 2, 3)
594s ***** error<ncfstat: DF1, DF2, and LAMBDA must not be complex.> ncfstat (1, i, 3)
594s ***** error<ncfstat: DF1, DF2, and LAMBDA must not be complex.> ncfstat (1, 2, i)
594s ***** error<ncfstat: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
594s  ncfstat (ones (3), ones (2), 3)
594s ***** error<ncfstat: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
594s  ncfstat (ones (2), 2, ones (3))
594s ***** error<ncfstat: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
594s  ncfstat (1, ones (2), ones (3))
594s ***** shared df1, df2, lambda
594s  df1 = [2, 0, -1, 1, 4, 5];
594s  df2 = [2, 4, -1, 5, 6, 7];
594s  lambda = [1, NaN, 3, 0, 2, -1];
594s ***** assert (ncfstat (df1, df2, lambda), [NaN, NaN, NaN, 1.6667, 2.25, 1.12], 1e-4);
594s ***** assert (ncfstat (df1(4:6), df2(4:6), 1), [3.3333, 1.8750, 1.6800], 1e-4);
594s ***** assert (ncfstat (df1(4:6), df2(4:6), 2), [5.0000, 2.2500, 1.9600], 1e-4);
594s ***** assert (ncfstat (df1(4:6), df2(4:6), 3), [6.6667, 2.6250, 2.2400], 1e-4);
594s ***** assert (ncfstat (2, [df2(1), df2(4:6)], 5), [NaN,5.8333,5.2500,4.9000], 1e-4);
594s ***** assert (ncfstat (0, [df2(1), df2(4:6)], 5), [NaN, Inf, Inf, Inf]);
594s ***** assert (ncfstat (1, [df2(1), df2(4:6)], 5), [NaN, 10, 9, 8.4], 1e-14);
594s ***** assert (ncfstat (4, [df2(1), df2(4:6)], 5), [NaN, 3.75, 3.375, 3.15], 1e-14);
594s 20 tests, 20 passed, 0 known failure, 0 skipped
594s [inst/dist_stat/chi2stat.m]
594s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/chi2stat.m
594s ***** error<chi2stat: function called with too few input arguments.> chi2stat ()
594s ***** error<chi2stat: DF must be numeric.> chi2stat ({})
594s ***** error<chi2stat: DF must be numeric.> chi2stat ("")
594s ***** error<chi2stat: DF must not be complex.> chi2stat (i)
594s ***** test
594s  df = 1:6;
594s  [m, v] = chi2stat (df);
594s  assert (m, df);
594s  assert (v, [2, 4, 6, 8, 10, 12], 0.001);
594s 5 tests, 5 passed, 0 known failure, 0 skipped
594s [inst/dist_stat/poisstat.m]
594s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/poisstat.m
594s ***** error<poisstat: function called with too few input arguments.> poisstat ()
594s ***** error<poisstat: SIGMA must be numeric.> poisstat ({})
594s ***** error<poisstat: SIGMA must be numeric.> poisstat ("")
594s ***** error<poisstat: SIGMA must not be complex.> poisstat (i)
594s ***** test
594s  lambda = 1 ./ (1:6);
594s  [m, v] = poisstat (lambda);
594s  assert (m, lambda);
594s  assert (v, lambda);
594s 5 tests, 5 passed, 0 known failure, 0 skipped
594s [inst/dist_stat/evstat.m]
594s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/evstat.m
594s ***** error<evstat: function called with too few input arguments.> evstat ()
594s ***** error<evstat: function called with too few input arguments.> evstat (1)
594s ***** error<evstat: MU and SIGMA must be numeric.> evstat ({}, 2)
594s ***** error<evstat: MU and SIGMA must be numeric.> evstat (1, "")
594s ***** error<evstat: MU and SIGMA must not be complex.> evstat (i, 2)
594s ***** error<evstat: MU and SIGMA must not be complex.> evstat (1, i)
594s ***** error<evstat: MU and SIGMA must be of common size or scalars.> ...
594s  evstat (ones (3), ones (2))
594s ***** error<evstat: MU and SIGMA must be of common size or scalars.> ...
594s  evstat (ones (2), ones (3))
594s ***** shared x, y0, y1
594s  x = [-5, 0, 1, 2, 3];
594s  y0 = [NaN, NaN, 0.4228, 0.8456, 1.2684];
594s  y1 = [-5.5772, -3.4633, -3.0405, -2.6177, -2.1949];
594s ***** assert (evstat (x, x), y0, 1e-4)
594s ***** assert (evstat (x, x+6), y1, 1e-4)
594s ***** assert (evstat (x, x-6), NaN (1,5))
594s 11 tests, 11 passed, 0 known failure, 0 skipped
594s [inst/dist_stat/nctstat.m]
594s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/nctstat.m
594s ***** error<nctstat: function called with too few input arguments.> nctstat ()
594s ***** error<nctstat: function called with too few input arguments.> nctstat (1)
594s ***** error<nctstat: DF and MU must be numeric.> nctstat ({}, 2)
594s ***** error<nctstat: DF and MU must be numeric.> nctstat (1, "")
594s ***** error<nctstat: DF and MU must not be complex.> nctstat (i, 2)
594s ***** error<nctstat: DF and MU must not be complex.> nctstat (1, i)
594s ***** error<nctstat: DF and MU must be of common size or scalars.> ...
594s  nctstat (ones (3), ones (2))
594s ***** error<nctstat: DF and MU must be of common size or scalars.> ...
594s  nctstat (ones (2), ones (3))
594s ***** shared df, mu
594s  df = [2, 0, -1, 1, 4];
594s  mu = [1, NaN, 3, -1, 2];
594s ***** assert (nctstat (df, mu), [1.7725, NaN, NaN, NaN, 2.5066], 1e-4);
594s ***** assert (nctstat ([df(1:2), df(4:5)], 1), [1.7725, NaN, NaN, 1.2533], 1e-4);
594s ***** assert (nctstat ([df(1:2), df(4:5)], 3), [5.3174, NaN, NaN, 3.7599], 1e-4);
594s ***** assert (nctstat ([df(1:2), df(4:5)], 2), [3.5449, NaN, NaN, 2.5066], 1e-4);
594s ***** assert (nctstat (2, [mu(1), mu(3:5)]), [1.7725,5.3174,-1.7725,3.5449], 1e-4);
594s ***** assert (nctstat (0, [mu(1), mu(3:5)]), [NaN, NaN, NaN, NaN]);
594s ***** assert (nctstat (1, [mu(1), mu(3:5)]), [NaN, NaN, NaN, NaN]);
594s ***** assert (nctstat (4, [mu(1), mu(3:5)]), [1.2533,3.7599,-1.2533,2.5066], 1e-4);
594s 16 tests, 16 passed, 0 known failure, 0 skipped
594s [inst/dist_stat/raylstat.m]
594s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/raylstat.m
594s ***** error<raylstat: function called with too few input arguments.> raylstat ()
594s ***** error<raylstat: SIGMA must be numeric.> raylstat ({})
594s ***** error<raylstat: SIGMA must be numeric.> raylstat ("")
594s ***** error<raylstat: SIGMA must not be complex.> raylstat (i)
594s ***** test
594s  sigma = 1:6;
594s  [m, v] = raylstat (sigma);
594s  expected_m = [1.2533, 2.5066, 3.7599, 5.0133, 6.2666, 7.5199];
594s  expected_v = [0.4292, 1.7168, 3.8628, 6.8673, 10.7301, 15.4513];
594s  assert (m, expected_m, 0.001);
594s  assert (v, expected_v, 0.001);
594s 5 tests, 5 passed, 0 known failure, 0 skipped
594s [inst/dist_stat/tstat.m]
594s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/tstat.m
594s ***** error<tstat: function called with too few input arguments.> tstat ()
594s ***** error<tstat: DF must be numeric.> tstat ({})
594s ***** error<tstat: DF must be numeric.> tstat ("")
594s ***** error<tstat: DF must not be complex.> tstat (i)
594s ***** test
594s  df = 3:8;
594s  [m, v] = tstat (df);
594s  expected_m = [0, 0, 0, 0, 0, 0];
594s  expected_v = [3.0000, 2.0000, 1.6667, 1.5000, 1.4000, 1.3333];
594s  assert (m, expected_m);
594s  assert (v, expected_v, 0.001);
594s 5 tests, 5 passed, 0 known failure, 0 skipped
594s [inst/dist_stat/hygestat.m]
594s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/hygestat.m
594s ***** error<hygestat: function called with too few input arguments.> hygestat ()
594s ***** error<hygestat: function called with too few input arguments.> hygestat (1)
594s ***** error<hygestat: function called with too few input arguments.> hygestat (1, 2)
594s ***** error<hygestat: M, K, and N must be numeric.> hygestat ({}, 2, 3)
594s ***** error<hygestat: M, K, and N must be numeric.> hygestat (1, "", 3)
594s ***** error<hygestat: M, K, and N must be numeric.> hygestat (1, 2, "")
594s ***** error<hygestat: M, K, and N must not be complex.> hygestat (i, 2, 3)
594s ***** error<hygestat: M, K, and N must not be complex.> hygestat (1, i, 3)
594s ***** error<hygestat: M, K, and N must not be complex.> hygestat (1, 2, i)
594s ***** error<hygestat: M, K, and N must be of common size or scalars.> ...
594s  hygestat (ones (3), ones (2), 3)
594s ***** error<hygestat: M, K, and N must be of common size or scalars.> ...
594s  hygestat (ones (2), 2, ones (3))
594s ***** error<hygestat: M, K, and N must be of common size or scalars.> ...
594s  hygestat (1, ones (2), ones (3))
594s ***** test
594s  m = 4:9;
594s  k = 0:5;
594s  n = 1:6;
594s  [mn, v] = hygestat (m, k, n);
594s  expected_mn = [0.0000, 0.4000, 1.0000, 1.7143, 2.5000, 3.3333];
594s  expected_v = [0.0000, 0.2400, 0.4000, 0.4898, 0.5357, 0.5556];
594s  assert (mn, expected_mn, 0.001);
594s  assert (v, expected_v, 0.001);
594s ***** test
594s  m = 4:9;
594s  k = 0:5;
594s  [mn, v] = hygestat (m, k, 2);
594s  expected_mn = [0.0000, 0.4000, 0.6667, 0.8571, 1.0000, 1.1111];
594s  expected_v = [0.0000, 0.2400, 0.3556, 0.4082, 0.4286, 0.4321];
594s  assert (mn, expected_mn, 0.001);
594s  assert (v, expected_v, 0.001);
594s 14 tests, 14 passed, 0 known failure, 0 skipped
594s [inst/dist_stat/betastat.m]
594s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/betastat.m
594s ***** error<betastat: function called with too few input arguments.> betastat ()
594s ***** error<betastat: function called with too few input arguments.> betastat (1)
594s ***** error<betastat: A and B must be numeric.> betastat ({}, 2)
594s ***** error<betastat: A and B must be numeric.> betastat (1, "")
594s ***** error<betastat: A and B must not be complex.> betastat (i, 2)
594s ***** error<betastat: A and B must not be complex.> betastat (1, i)
594s ***** error<betastat: A and B must be of common size or scalars.> ...
594s  betastat (ones (3), ones (2))
594s ***** error<betastat: A and B must be of common size or scalars.> ...
594s  betastat (ones (2), ones (3))
594s ***** test
594s  a = -2:6;
594s  b = 0.4:0.2:2;
594s  [m, v] = betastat (a, b);
594s  expected_m = [NaN NaN NaN 1/2 2/3.2 3/4.4 4/5.6 5/6.8 6/8];
594s  expected_v = [NaN NaN NaN 0.0833, 0.0558, 0.0402, 0.0309, 0.0250, 0.0208];
594s  assert (m, expected_m, eps*100);
594s  assert (v, expected_v, 0.001);
594s ***** test
594s  a = -2:1:6;
594s  [m, v] = betastat (a, 1.5);
594s  expected_m = [NaN NaN NaN 1/2.5 2/3.5 3/4.5 4/5.5 5/6.5 6/7.5];
594s  expected_v = [NaN NaN NaN 0.0686, 0.0544, 0.0404, 0.0305, 0.0237, 0.0188];
594s  assert (m, expected_m);
594s  assert (v, expected_v, 0.001);
594s ***** test
594s  a = [14  Inf   10  NaN  10];
594s  b = [12    9  NaN  Inf  12];
594s  [m, v] = betastat (a, b);
594s  expected_m = [14/26 NaN NaN NaN 10/22];
594s  expected_v = [168/18252 NaN NaN NaN 120/11132];
594s  assert (m, expected_m);
594s  assert (v, expected_v);
594s ***** assert (nthargout (1:2, @betastat, 5, []), {[], []})
594s ***** assert (nthargout (1:2, @betastat, [], 5), {[], []})
594s ***** assert (size (betastat (rand (10, 5, 4), rand (10, 5, 4))), [10 5 4])
594s ***** assert (size (betastat (rand (10, 5, 4), 7)), [10 5 4])
594s 15 tests, 15 passed, 0 known failure, 0 skipped
594s [inst/dist_stat/invgstat.m]
594s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/invgstat.m
594s ***** error<invgstat: function called with too few input arguments.> invgstat ()
594s ***** error<invgstat: function called with too few input arguments.> invgstat (1)
594s ***** error<invgstat: MU and LAMBDA must be numeric.> invgstat ({}, 2)
594s ***** error<invgstat: MU and LAMBDA must be numeric.> invgstat (1, "")
594s ***** error<invgstat: MU and LAMBDA must not be complex.> invgstat (i, 2)
594s ***** error<invgstat: MU and LAMBDA must not be complex.> invgstat (1, i)
594s ***** error<invgstat: MU and LAMBDA must be of common size or scalars.> ...
594s  invgstat (ones (3), ones (2))
594s ***** error<invgstat: MU and LAMBDA must be of common size or scalars.> ...
594s  invgstat (ones (2), ones (3))
594s ***** test
594s  [m, v] = invgstat (1, 1);
594s  assert (m, 1);
594s  assert (v, 1);
594s ***** test
594s  [m, v] = invgstat (2, 1);
594s  assert (m, 2);
594s  assert (v, 8);
594s ***** test
594s  [m, v] = invgstat (2, 2);
594s  assert (m, 2);
594s  assert (v, 4);
594s ***** test
594s  [m, v] = invgstat (2, 2.5);
594s  assert (m, 2);
594s  assert (v, 3.2);
594s ***** test
594s  [m, v] = invgstat (1.5, 0.5);
594s  assert (m, 1.5);
594s  assert (v, 6.75);
594s 13 tests, 13 passed, 0 known failure, 0 skipped
594s [inst/dist_stat/gamstat.m]
594s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/gamstat.m
594s ***** error<gamstat: function called with too few input arguments.> gamstat ()
594s ***** error<gamstat: function called with too few input arguments.> gamstat (1)
594s ***** error<gamstat: A and B must be numeric.> gamstat ({}, 2)
594s ***** error<gamstat: A and B must be numeric.> gamstat (1, "")
594s ***** error<gamstat: A and B must not be complex.> gamstat (i, 2)
594s ***** error<gamstat: A and B must not be complex.> gamstat (1, i)
594s ***** error<gamstat: A and B must be of common size or scalars.> ...
594s  gamstat (ones (3), ones (2))
594s ***** error<gamstat: A and B must be of common size or scalars.> ...
594s  gamstat (ones (2), ones (3))
594s ***** test
594s  a = 1:6;
594s  b = 1:0.2:2;
594s  [m, v] = gamstat (a, b);
594s  expected_m = [1.00, 2.40, 4.20,  6.40,  9.00, 12.00];
594s  expected_v = [1.00, 2.88, 5.88, 10.24, 16.20, 24.00];
594s  assert (m, expected_m, 0.001);
594s  assert (v, expected_v, 0.001);
594s ***** test
594s  a = 1:6;
594s  [m, v] = gamstat (a, 1.5);
594s  expected_m = [1.50, 3.00, 4.50, 6.00,  7.50,  9.00];
594s  expected_v = [2.25, 4.50, 6.75, 9.00, 11.25, 13.50];
594s  assert (m, expected_m, 0.001);
594s  assert (v, expected_v, 0.001);
594s 10 tests, 10 passed, 0 known failure, 0 skipped
594s [inst/dist_stat/loglstat.m]
594s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_stat/loglstat.m
594s ***** error<loglstat: function called with too few input arguments.> loglstat ()
594s ***** error<loglstat: function called with too few input arguments.> loglstat (1)
594s ***** error<loglstat: MU and SIGMA must be numeric.> loglstat ({}, 2)
594s ***** error<loglstat: MU and SIGMA must be numeric.> loglstat (1, "")
594s ***** error<loglstat: MU and SIGMA must not be complex.> loglstat (i, 2)
594s ***** error<loglstat: MU and SIGMA must not be complex.> loglstat (1, i)
594s ***** error<loglstat: MU and SIGMA must be of common size or scalars.> ...
594s  loglstat (ones (3), ones (2))
594s ***** error<loglstat: MU and SIGMA must be of common size or scalars.> ...
594s  loglstat (ones (2), ones (3))
594s ***** test
594s  [m, v] = loglstat (0, 1);
594s  assert (m, Inf, 0.001);
594s  assert (v, Inf, 0.001);
594s ***** test
594s  [m, v] = loglstat (0, 0.8);
594s  assert (m, 4.2758, 0.001);
594s  assert (v, Inf, 0.001);
594s ***** test
594s  [m, v] = loglstat (0, 0.6);
594s  assert (m, 1.9820, 0.001);
594s  assert (v, Inf, 0.001);
594s ***** test
594s  [m, v] = loglstat (0, 0.4);
594s  assert (m, 1.3213, 0.001);
594s  assert (v, 2.5300, 0.001);
594s ***** test
594s  [m, v] = loglstat (0, 0.2);
594s  assert (m, 1.0690, 0.001);
594s  assert (v, 0.1786, 0.001);
594s 13 tests, 13 passed, 0 known failure, 0 skipped
594s [inst/pcares.m]
594s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/pcares.m
594s ***** demo
594s  x = [ 7    26     6    60;
594s        1    29    15    52;
594s       11    56     8    20;
594s       11    31     8    47;
594s        7    52     6    33;
594s       11    55     9    22;
594s        3    71    17     6;
594s        1    31    22    44;
594s        2    54    18    22;
594s       21    47     4    26;
594s        1    40    23    34;
594s       11    66     9    12;
594s       10    68     8    12];
594s 
594s  ## As we increase the number of principal components, the norm
594s  ## of the residuals matrix will decrease
594s  r1 = pcares (x,1);
594s  n1 = norm (r1)
594s  r2 = pcares (x,2);
594s  n2 = norm (r2)
594s  r3 = pcares (x,3);
594s  n3 = norm (r3)
594s  r4 = pcares (x,4);
594s  n4 = norm (r4)
594s ***** test
594s  load hald
594s  r1 = pcares (ingredients,1);
594s  r2 = pcares (ingredients,2);
594s  r3 = pcares (ingredients,3);
594s  assert (r1(1,:), [2.0350,  2.8304, -6.8378, 3.0879], 1e-4);
594s  assert (r2(1,:), [-2.4037, 2.6930, -1.6482, 2.3425], 1e-4);
594s  assert (r3(1,:), [ 0.2008, 0.1957,  0.2045, 0.1921], 1e-4);
594s ***** error<pcares: too few input arguments.> pcares (ones (20, 3))
594s ***** error<pcares: NDIM must be less than or equal to the column of X.> ...
594s  pcares (ones (30, 2), 3)
594s 3 tests, 3 passed, 0 known failure, 0 skipped
594s [inst/mnrfit.m]
594s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/mnrfit.m
594s ***** error<mnrfit: too few input arguments.> mnrfit (ones (50,1))
594s ***** error<mnrfit: Predictors must be numeric.> ...
594s  mnrfit ({1 ;2 ;3 ;4 ;5}, ones (5,1))
594s ***** error<mnrfit: Predictors must be a vector or a 2D matrix.> ...
594s  mnrfit (ones (50, 4, 2), ones (50, 1))
594s ***** error<mnrfit: Response must be a vector or a 2D matrix.> ...
594s  mnrfit (ones (50, 4), ones (50, 1, 3))
594s ***** error<mnrfit: Y must have the same number of rows as X.> ...
594s  mnrfit (ones (50, 4), ones (45,1))
594s ***** error<mnrfit: Response labels must be a character array, a cell vector> ...
594s  mnrfit (ones (5, 4), {1 ;2 ;3 ;4 ;5})
594s ***** error<mnrfit: optional arguments must be in pairs.> ...
594s  mnrfit (ones (5, 4), ones (5, 1), "model")
594s ***** error<mnrfit: Y must be a column vector when given as cellstr.> ...
594s  mnrfit (ones (5, 4), {"q","q";"w","w";"q","q";"w","w";"q","q"})
594s ***** error<mnrfit: Y must contain only 1 and 0 when given as a 2D matrix.> ...
594s  mnrfit (ones (5, 4), [1, 2; 1, 2; 1, 2; 1, 2; 1, 2])
594s ***** error<mnrfit: Y must contain positive integer category numbers.> ...
594s  mnrfit (ones (5, 4), [1; -1; 1; 2; 1])
594s ***** error<mnrfit: fitting more than 2 nominal responses not supported.> ...
594s  mnrfit (ones (5, 4), [1; 2; 3; 2; 1], "model", "nominal")
594s ***** error<mnrfit: fitting hierarchical responses not supported.> ...
594s  mnrfit (ones (5, 4), [1; 2; 3; 2; 1], "model", "hierarchical")
594s ***** error<mnrfit: model type not recognised.> ...
594s  mnrfit (ones (5, 4), [1; 2; 3; 2; 1], "model", "whatever")
594s 13 tests, 13 passed, 0 known failure, 0 skipped
594s [inst/optimalleaforder.m]
594s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/optimalleaforder.m
594s ***** demo
594s  randn ("seed", 5)  # for reproducibility
594s  X = randn (10, 2);
594s  D = pdist (X);
594s  tree = linkage(D, 'average');
594s  optimalleaforder (tree, D, 'Transformation', 'linear')
594s ***** error optimalleaforder ()
594s ***** error optimalleaforder (1)
595s ***** error <tree must be .*> optimalleaforder (ones (2, 2), 1)
595s ***** error <character inputs expected> optimalleaforder ([1 2 3], [1 2; 3 4], "criteria", 5)
595s ***** error <D must be .*> optimalleaforder ([1 2 1], [1 2 3])
595s ***** error <unknown property .*> optimalleaforder ([1 2 1], 1, "xxx", "xxx")
595s ***** error optimalleaforder ([1 2 1], 1, "Transformation", "xxx")
595s 7 tests, 7 passed, 0 known failure, 0 skipped
595s [inst/hotelling_t2test2.m]
595s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/hotelling_t2test2.m
595s ***** error<Invalid call to hotelling_t2test2.  Correct usage> hotelling_t2test2 ();
595s ***** error<Invalid call to hotelling_t2test2.  Correct usage> ...
595s  hotelling_t2test2 ([2, 3, 4, 5, 6]);
595s ***** error<hotelling_t2test2: X must be a vector or a 2D matrix.> ...
595s  hotelling_t2test2 (1, [2, 3, 4, 5, 6]);
595s ***** error<hotelling_t2test2: X must be a vector or a 2D matrix.> ...
595s  hotelling_t2test2 (ones (2,2,2), [2, 3, 4, 5, 6]);
595s ***** error<hotelling_t2test2: Y must be a vector or a 2D matrix.> ...
595s  hotelling_t2test2 ([2, 3, 4, 5, 6], 2);
595s ***** error<hotelling_t2test2: Y must be a vector or a 2D matrix.> ...
595s  hotelling_t2test2 ([2, 3, 4, 5, 6], ones (2,2,2));
595s ***** error<hotelling_t2test2: invalid value for alpha.> ...
595s  hotelling_t2test2 (ones (20,2), ones (20,2), "alpha", 1);
595s ***** error<hotelling_t2test2: invalid value for alpha.> ...
595s  hotelling_t2test2 (ones (20,2), ones (20,2), "alpha", -0.2);
595s ***** error<hotelling_t2test2: invalid value for alpha.> ...
595s  hotelling_t2test2 (ones (20,2), ones (20,2), "alpha", "a");
595s ***** error<hotelling_t2test2: invalid value for alpha.> ...
595s  hotelling_t2test2 (ones (20,2), ones (20,2), "alpha", [0.01, 0.05]);
595s ***** error<hotelling_t2test2: invalid Name argument.> ...
595s  hotelling_t2test2 (ones (20,2), ones (20,2), "name", 0.01);
595s ***** error<hotelling_t2test2: if X is a vector, Y must also be a vector.> ...
595s  hotelling_t2test2 (ones (20,1), ones (20,2));
595s ***** error<hotelling_t2test2: X and Y must have the same number of columns.> ...
595s  hotelling_t2test2 (ones (20,2), ones (25,3));
595s ***** test
595s  randn ("seed", 1);
595s  x1 = randn (60000, 5);
595s  randn ("seed", 5);
595s  x2 = randn (30000, 5);
595s  [h, pval, stats] = hotelling_t2test2 (x1, x2);
595s  assert (h, 0);
595s  assert (stats.df1, 5);
595s  assert (stats.df2, 89994);
595s 14 tests, 14 passed, 0 known failure, 0 skipped
595s [inst/fitgmdist.m]
595s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/fitgmdist.m
595s ***** demo
595s  ## Generate a two-cluster problem
595s  C1 = randn (100, 2) + 2;
595s  C2 = randn (100, 2) - 2;
595s  data = [C1; C2];
595s 
595s  ## Perform clustering
595s  GMModel = fitgmdist (data, 2);
595s 
595s  ## Plot the result
595s  figure
595s  [heights, bins] = hist3([C1; C2]);
595s  [xx, yy] = meshgrid(bins{1}, bins{2});
595s  bbins = [xx(:), yy(:)];
595s  contour (reshape (GMModel.pdf (bbins), size (heights)));
595s ***** demo
595s  Angle_Theta = [ 30 + 10 * randn(1, 10),  60 + 10 * randn(1, 10) ]';
595s  nbOrientations = 2;
595s  initial_orientations = [38.0; 18.0];
595s  initial_weights = ones (1, nbOrientations) / nbOrientations;
595s  initial_Sigma = 10 * ones (1, 1, nbOrientations);
595s  start = struct ("mu", initial_orientations, "Sigma", initial_Sigma, ...
595s                  "ComponentProportion", initial_weights);
595s  GMModel_Theta = fitgmdist (Angle_Theta, nbOrientations, "Start", start , ...
595s                             "RegularizationValue", 0.0001)
595s ***** test
595s  load fisheriris
595s  classes = unique (species);
595s  [~, score] = pca (meas, "NumComponents", 2);
595s  options.MaxIter = 1000;
595s  options.TolFun = 1e-6;
595s  options.Display = "off";
595s  GMModel = fitgmdist (score, 2, "Options", options);
595s  assert (isa (GMModel, "gmdistribution"), true);
595s  assert (GMModel.mu, [1.3212, -0.0954; -2.6424, 0.1909], 1e-4);
595s 1 test, 1 passed, 0 known failure, 0 skipped
595s [inst/fitcnet.m]
595s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/fitcnet.m
595s ***** demo
595s  ## Train a Neural Network on the Fisher's Iris data set and display
595s  ## a confusion chart with the classification results.
595s 
595s  load fisheriris
595s  Mdl = fitcnet (meas, species);
595s  pred_species = resubPredict (Mdl);
595s  confusionchart (species, pred_species);
595s ***** test
595s  load fisheriris
595s  x = meas;
595s  y = grp2idx (species);
595s  Mdl = fitcnet (x, y, "IterationLimit", 50);
595s  assert (class (Mdl), "ClassificationNeuralNetwork");
595s  assert (numel (Mdl.ModelParameters.LayerWeights), 2);
595s  assert (size (Mdl.ModelParameters.LayerWeights{1}), [10, 5]);
595s  assert (size (Mdl.ModelParameters.LayerWeights{2}), [3, 11]);
595s ***** error<fitcnet: too few arguments.> fitcnet ()
595s ***** error<fitcnet: too few arguments.> fitcnet (ones (4,1))
595s ***** error<fitcnet: Name-Value arguments must be in pairs.>
595s  fitcnet (ones (4,2), ones (4, 1), 'LayerSizes')
595s ***** error<fitcnet: number of rows in X and Y must be equal.>
595s  fitcnet (ones (4,2), ones (3, 1))
595s ***** error<fitcnet: number of rows in X and Y must be equal.>
595s  fitcnet (ones (4,2), ones (3, 1), 'LayerSizes', 2)
595s 6 tests, 6 passed, 0 known failure, 0 skipped
595s [inst/normplot.m]
595s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/normplot.m
595s ***** demo
595s  h = normplot([1:20]);
595s ***** demo
595s  h = normplot([1:20;5:2:44]');
595s ***** demo
595s  ax = newplot();
595s  h = normplot(ax, [1:20]);
595s  ax = gca;
595s  h = normplot(ax, [-10:10]);
595s  set (ax, "xlim", [-11, 21]);
595s ***** error normplot ();
595s ***** error normplot (23);
595s ***** error normplot (23, [1:20]);
595s ***** error normplot (ones(3,4,5));
595s ***** test
595s  hf = figure ("visible", "off");
595s  unwind_protect
595s    ax = newplot (hf);
595s    h = normplot (ax, [1:20]);
595s    ax = gca;
595s    h = normplot(ax, [-10:10]);
595s    set (ax, "xlim", [-11, 21]);
595s  unwind_protect_cleanup
595s    close (hf);
595s  end_unwind_protect
595s ***** test
595s  hf = figure ("visible", "off");
595s  unwind_protect
595s    h = normplot([1:20;5:2:44]');
595s  unwind_protect_cleanup
595s    close (hf);
595s  end_unwind_protect
595s 6 tests, 6 passed, 0 known failure, 0 skipped
595s [inst/Clustering/CalinskiHarabaszEvaluation.m]
595s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/Clustering/CalinskiHarabaszEvaluation.m
595s ***** test
595s  load fisheriris
595s  eva = evalclusters (meas, "kmeans", "calinskiharabasz", "KList", [1:6]);
595s  assert (class (eva), "CalinskiHarabaszEvaluation");
595s 1 test, 1 passed, 0 known failure, 0 skipped
595s [inst/Clustering/ClusterCriterion.m]
595s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/Clustering/ClusterCriterion.m
595s ***** error <ClusterCriterion: 'x' must be a numeric matrix> ...
595s  ClusterCriterion ("1", "kmeans", [1:6])
595s ***** error <ClusterCriterion: unknown clustering algorithm 'k'> ...
595s  ClusterCriterion ([1, 2, 1, 3, 2, 4, 3], "k", [1:6])
595s ***** error <ClusterCriterion: invalid matrix of clustering solutions> ...
595s  ClusterCriterion ([1, 2, 1; 3, 2, 4], 1, [1:6])
595s ***** error <ClusterCriterion: invalid argument> ...
595s  ClusterCriterion ([1, 2, 1; 3, 2, 4], ones (2, 2, 2), [1:6])
595s 4 tests, 4 passed, 0 known failure, 0 skipped
595s [inst/Clustering/SilhouetteEvaluation.m]
595s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/Clustering/SilhouetteEvaluation.m
595s ***** test
595s  load fisheriris
595s  eva = evalclusters (meas, "kmeans", "silhouette", "KList", [1:6]);
595s  assert (class (eva), "SilhouetteEvaluation");
596s 1 test, 1 passed, 0 known failure, 0 skipped
596s [inst/Clustering/DaviesBouldinEvaluation.m]
596s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/Clustering/DaviesBouldinEvaluation.m
596s ***** test
596s  load fisheriris
596s  eva = evalclusters (meas, "kmeans", "DaviesBouldin", "KList", [1:6]);
596s  assert (class (eva), "DaviesBouldinEvaluation");
596s 1 test, 1 passed, 0 known failure, 0 skipped
596s [inst/Clustering/GapEvaluation.m]
596s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/Clustering/GapEvaluation.m
596s ***** test
596s  load fisheriris
596s  eva = evalclusters (meas([1:50],:), "kmeans", "gap", "KList", [1:3], ...
596s                      "referencedistribution", "uniform");
596s  assert (class (eva), "GapEvaluation");
598s 1 test, 1 passed, 0 known failure, 0 skipped
598s [inst/fullfact.m]
598s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/fullfact.m
598s ***** demo
598s  ## Full factorial design with 3 binary variables
598s  fullfact (3)
598s ***** demo
598s  ## Full factorial design with 3 ordinal variables
598s  fullfact ([2, 3, 4])
598s ***** error fullfact ();
598s ***** error fullfact (2, 5);
598s ***** error fullfact (2.5);
598s ***** error fullfact (0);
598s ***** error fullfact (-3);
598s ***** error fullfact (3+2i);
598s ***** error fullfact (Inf);
598s ***** error fullfact (NaN);
598s ***** error fullfact ([1, 2, -3]);
598s ***** error fullfact ([0, 1, 2]);
598s ***** error fullfact ([1, 2, NaN]);
598s ***** error fullfact ([1, 2, Inf]);
598s ***** test
598s  A = fullfact (2);
598s  assert (A, [0, 0; 0, 1; 1, 0; 1, 1]);
598s ***** test
598s  A = fullfact ([1, 2]);
598s  assert (A, [1, 1; 1, 2]);
598s ***** test
598s  A = fullfact ([1, 2, 4]);
598s  A_out = [1, 1, 1; 1, 1, 2; 1, 1, 3; 1, 1, 4; ...
598s           1, 2, 1; 1, 2, 2; 1, 2, 3; 1, 2, 4];
598s  assert (A, A_out);
598s 15 tests, 15 passed, 0 known failure, 0 skipped
598s [inst/pca.m]
598s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/pca.m
598s ***** shared COEFF,SCORE,latent,tsquare,m,x,R,V,lambda,i,S,F
598s ***** test
598s  x=[7 4 3
598s     4 1 8
598s     6 3 5
598s     8 6 1
598s     8 5 7
598s     7 2 9
598s     5 3 3
598s     9 5 8
598s     7 4 5
598s     8 2 2];
598s  R = corrcoef (x);
598s  [V, lambda] = eig (R);
598s  [~, i] = sort(diag(lambda), "descend"); #arrange largest PC first
598s  S = V(:, i) * diag(sqrt(diag(lambda)(i)));
598s ***** assert(diag(S(:, 1:2)*S(:, 1:2)'), [0.8662; 0.8420; 0.9876], 1E-4); #contribution of first 2 PCs to each original variable
598s  B = V(:, i) * diag( 1./ sqrt(diag(lambda)(i)));
598s  F = zscore(x)*B;
598s  [COEFF,SCORE,latent,tsquare] = pca(zscore(x, 1));
598s ***** assert(tsquare,sumsq(F, 2),1E4*eps);
598s ***** test
598s  x=[1,2,3;2,1,3]';
598s  [COEFF,SCORE,latent,tsquare] = pca(x, "Economy", false);
598s  m=[sqrt(2),sqrt(2);sqrt(2),-sqrt(2);-2*sqrt(2),0]/2;
598s  m(:,1) = m(:,1)*sign(COEFF(1,1));
598s  m(:,2) = m(:,2)*sign(COEFF(1,2));
598s ***** assert(COEFF,m(1:2,:),10*eps);
598s ***** assert(SCORE,-m,10*eps);
598s ***** assert(latent,[1.5;.5],10*eps);
598s ***** assert(tsquare,[4;4;4]/3,10*eps);
598s  [COEFF,SCORE,latent,tsquare] = pca(x, "Economy", false, "weights", [1 2 1], "variableweights", "variance");
598s ***** assert(COEFF, [0.632455532033676 -0.632455532033676; 0.741619848709566 0.741619848709566], 10*eps);
598s ***** assert(SCORE, [-0.622019449426284 0.959119380657905; -0.505649896847432 -0.505649896847431; 1.633319243121148 0.052180413036957], 10*eps);
598s ***** assert(latent, [1.783001790889027; 0.716998209110974], 10*eps);
598s ***** xtest assert(tsquare, [1.5; 0.5; 1.5], 10*eps);  #currently, [4; 2; 4]/3 is actually returned; see comments above
598s !!!!! known failure
598s ASSERT errors for:  assert (tsquare,[1.5; 0.5; 1.5],10 * eps)
598s 
598s   Location  |  Observed  |  Expected  |  Reason
598s     (1)         1.3333        1.5        Abs err 0.16667 exceeds tol 2.2204e-15 by 0.2
598s     (2)        0.66667        0.5        Abs err 0.16667 exceeds tol 2.2204e-15 by 0.2
598s     (3)         1.3333        1.5        Abs err 0.16667 exceeds tol 2.2204e-15 by 0.2
598s ***** test
598s  x=x';
598s  [COEFF,SCORE,latent,tsquare] = pca(x, "Economy", false);
598s  m=[sqrt(2),sqrt(2),0;-sqrt(2),sqrt(2),0;0,0,2]/2;
598s  m(:,1) = m(:,1)*sign(COEFF(1,1));
598s  m(:,2) = m(:,2)*sign(COEFF(1,2));
598s  m(:,3) = m(:,3)*sign(COEFF(3,3));
598s ***** assert(COEFF,m,10*eps);
598s ***** assert(SCORE(:,1),-m(1:2,1),10*eps);
598s ***** assert(SCORE(:,2:3),zeros(2),10*eps);
598s ***** assert(latent,[1;0;0],10*eps);
598s ***** assert(tsquare,[0.5;0.5],10*eps)
598s ***** test
598s  [COEFF,SCORE,latent,tsquare] = pca(x);
598s ***** assert(COEFF,m(:, 1),10*eps);
598s ***** assert(SCORE,-m(1:2,1),10*eps);
598s ***** assert(latent,[1],10*eps);
598s ***** assert(tsquare,[0.5;0.5],10*eps)
598s ***** error <invalid algorithm> pca([1 2; 3 4], "Algorithm", "xxx")
598s ***** error <'centered' requires a boolean value> pca([1 2; 3 4], "Centered", "xxx")
598s ***** error <must be a positive integer> pca([1 2; 3 4], "NumComponents", -4)
598s ***** error <invalid value for rows> pca([1 2; 3 4], "Rows", 1)
598s ***** error <weights must be> pca([1 2; 3 4], "Weights", [1 2 3])
598s ***** error <weights must be> pca([1 2; 3 4], "Weights", [-1 2])
598s ***** error <variable weights must be> pca([1 2; 3 4], "VariableWeights", [-1 2])
598s ***** error <variable weights must be> pca([1 2; 3 4], "VariableWeights", "xxx")
598s ***** error <unknown property> pca([1 2; 3 4], "XXX", 1)
598s 32 tests, 31 passed, 1 known failure, 0 skipped
598s [inst/nanmin.m]
598s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/nanmin.m
598s ***** demo
598s  ## Find the column minimum values and their indices
598s  ## for matrix data with missing values.
598s 
598s  x = magic (3);
598s  x([1, 6:9]) = NaN
598s  [y, ind] = nanmin (x)
598s ***** demo
598s  ## Find the minimum of all the values in an array, ignoring missing values.
598s  ## Create a 2-by-5-by-3 array x with some missing values.
598s 
598s  x = reshape (1:30, [2, 5, 3]);
598s  x([10:12, 25]) = NaN
598s 
598s  ## Find the minimum of the elements of x.
598s 
598s  y = nanmin (x, [], 'all')
598s ***** assert (nanmin ([2, 4, NaN, 7]), 2)
598s ***** assert (nanmin ([2, 4, NaN, -Inf]), -Inf)
598s ***** assert (nanmin ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN]), [1, 5, 3])
598s ***** assert (nanmin ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN]'), [1, 5, 7])
598s ***** assert (nanmin (single ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN])), single ([1, 5, 3]))
598s ***** shared x, y
598s  x(:,:,1) = [1.77, -0.005, NaN, -2.95; NaN, 0.34, NaN, 0.19];
598s  x(:,:,2) = [1.77, -0.005, NaN, -2.95; NaN, 0.34, NaN, 0.19] + 5;
598s  y = x;
598s  y(2,3,1) = 0.51;
598s ***** assert (nanmin (x, [], [1, 2])(:), [-2.95; 2.05])
598s ***** assert (nanmin (x, [], [1, 3])(:), [1.77; -0.005; NaN; -2.95])
598s ***** assert (nanmin (x, [], [2, 3])(:), [-2.95; 0.19])
598s ***** assert (nanmin (x, [], [1, 2, 3]), -2.95)
598s ***** assert (nanmin (x, [], 'all'), -2.95)
598s ***** assert (nanmin (y, [], [1, 3])(:), [1.77; -0.005; 0.51; -2.95])
598s ***** assert (nanmin (x(1,:,1), x(2,:,1)), [1.77, -0.005, NaN, -2.95])
598s ***** assert (nanmin (x(1,:,2), x(2,:,2)), [6.77, 4.995, NaN, 2.05])
598s ***** assert (nanmin (y(1,:,1), y(2,:,1)), [1.77, -0.005, 0.51, -2.95])
598s ***** assert (nanmin (y(1,:,2), y(2,:,2)), [6.77, 4.995, NaN, 2.05])
598s ***** test
598s  xx = repmat ([1:20;6:25], [5 2 6 3]);
598s  assert (size (nanmin (xx, [], [3, 2])), [10, 1, 1, 3]);
598s  assert (size (nanmin (xx, [], [1, 2])), [1, 1, 6, 3]);
598s  assert (size (nanmin (xx, [], [1, 2, 4])), [1, 1, 6]);
598s  assert (size (nanmin (xx, [], [1, 4, 3])), [1, 40]);
598s  assert (size (nanmin (xx, [], [1, 2, 3, 4])), [1, 1]);
598s ***** assert (nanmin (ones (2), [], 3), ones (2, 2))
598s ***** assert (nanmin (ones (2, 2, 2), [], 99), ones (2, 2, 2))
598s ***** assert (nanmin (magic (3), [], 3), magic (3))
598s ***** assert (nanmin (magic (3), [], [1, 3]), [3, 1, 2])
598s ***** assert (nanmin (magic (3), [], [1, 99]), [3, 1, 2])
598s ***** assert (nanmin (ones (2), 3), ones (2,2))
598s ***** error <nanmin: VECDIM must contain non-repeating positive integers.> ...
598s  nanmin (y, [], [1, 1, 2])
598s ***** error <nanmin: a second output is not supported with this syntax.> ...
598s  [v, idx] = nanmin(x, y, [1 2])
598s 24 tests, 24 passed, 0 known failure, 0 skipped
598s [inst/glmfit.m]
598s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/glmfit.m
598s ***** demo
598s  x = [210, 230, 250, 270, 290, 310, 330, 350, 370, 390, 410, 430]';
598s  n = [48, 42, 31, 34, 31, 21, 23, 23, 21, 16, 17, 21]';
598s  y = [1, 2, 0, 3, 8, 8, 14, 17, 19, 15, 17, 21]';
598s  b = glmfit (x, [y n], "binomial", "Link", "probit");
598s  yfit = glmval (b, x, "probit", "Size", n);
598s  plot (x, y./n, 'o', x, yfit ./ n, '-')
598s ***** demo
598s  load fisheriris
598s  X = meas (51:end, :);
598s  y = strcmp ("versicolor", species(51:end));
598s  b = glmfit (X, y, "binomial", "link", "logit")
598s ***** test
598s  load fisheriris;
598s  X = meas(51:end,:);
598s  y = strcmp ("versicolor", species(51:end));
598s  b = glmfit (X, y, "binomial", "link", "logit");
598s  assert (b, [42.6379; 2.4652; 6.6809; -9.4294; -18.2861], 1e-4);
598s ***** test
598s  X = [1.2, 2.3, 3.4, 4.5, 5.6, 6.7, 7.8, 8.9, 9.0, 10.1]';
598s  y = [0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4]';
598s  [Bnew, dev] = glmfit (X, y, "gamma", "link", "log");
598s  b_matlab = [-0.7631; 0.1113];
598s  dev_matlab = 0.0111;
598s  assert (Bnew, b_matlab, 0.001);
598s  assert (dev, dev_matlab, 0.001);
598s ***** test
598s  X = [1.2, 2.3, 3.4, 4.5, 5.6, 6.7, 7.8, 8.9, 9.0, 10.1]';
598s  y = [0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4]';
598s  p_input = 1;
598s  [Bnew, dev] = glmfit (X, y, "inverse gaussian", "link", p_input);
598s  b_matlab = [0.3813; 0.0950];
598s  dev_matlab = 0.0051;
598s  assert (Bnew, b_matlab, 0.001);
598s  assert (dev, dev_matlab, 0.001);
598s ***** error <glmfit: too few input arguments.> glmfit ()
598s ***** error <glmfit: too few input arguments.> glmfit (1)
598s ***** error <glmfit: too few input arguments.> glmfit (1, 2)
598s ***** error <glmfit: Name-Value arguments must be in pairs.> ...
598s  glmfit (rand (6, 1), rand (6, 1), 'poisson', 'link')
598s ***** error <glmfit: X must be a numeric matrix.> ...
598s  glmfit ('abc', rand (6, 1), 'poisson')
598s ***** error <glmfit: X must be a numeric matrix.> ...
598s  glmfit ([], rand (6, 1), 'poisson')
598s ***** error <glmfit: Y must be either a numeric matrix or a logical vector.> ...
598s  glmfit (rand (5, 2), 'abc', 'poisson')
598s ***** error <glmfit: Y must be either a numeric matrix or a logical vector.> ...
598s  glmfit (rand (5, 2), [], 'poisson')
598s ***** error <glmfit: X and Y must have the same number of observations.> ...
598s  glmfit (rand (5, 2), rand (6, 1), 'poisson')
598s ***** error <glmfit: DISTRIBUTION must be a character vector.> ...
598s  glmfit (rand (6, 2), rand (6, 1), 3)
598s ***** error <glmfit: DISTRIBUTION must be a character vector.> ...
598s  glmfit (rand (6, 2), rand (6, 1), {'poisson'})
598s ***** error <glmfit: for a 'binomial' distribution, Y must be an n-by-1 or n-by-2 matrix.> ...
598s  glmfit (rand (5, 2), rand (5, 3), 'binomial')
598s ***** error <glmfit: n-by-2 matrix Y for 'binomial' distribution must be numeric.> ...
598s  glmfit (rand (2, 2), [true, true; false, false], 'binomial')
598s ***** error <glmfit: for distributions other than 'binomial', Y must be an n-by-1 column vector> ...
598s  glmfit (rand (5, 2), rand (5, 2), 'normal')
598s ***** error <glmfit: unsupported distribution.> ...
598s  glmfit (rand (5, 2), rand (5, 1), 'chebychev')
598s ***** error <glmfit: 'B0' must be a numeric vector of the same size as Y.> ...
598s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'B0', [1; 2; 3; 4])
598s ***** error <glmfit: 'Constant' should be either 'on' or 'off'.> ...
598s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'constant', 1)
598s ***** error <glmfit: 'Constant' should be either 'on' or 'off'.> ...
598s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'constant', 'o')
598s ***** error <glmfit: 'Constant' should be either 'on' or 'off'.> ...
598s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'constant', true)
598s ***** error <glmfit: 'EstDisp' should be either 'on' or 'off'.> ...
598s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'estdisp', 1)
598s ***** error <glmfit: 'EstDisp' should be either 'on' or 'off'.> ...
598s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'estdisp', 'o')
598s ***** error <glmfit: 'EstDisp' should be either 'on' or 'off'.> ...
598s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'estdisp', true)
598s ***** error <glmfit: structure with custom link functions must be a scalar.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", {1, 2}))
598s ***** error <glmfit: structure with custom link functions requires the fields 'Link', 'Derivative', and 'Inverse'.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", "norminv"))
598s ***** error <glmfit: bad 'Link' function in custom link function structure.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", "some", "Derivative", @(x)x, "Inverse", "normcdf"))
598s ***** error <glmfit: bad 'Link' function in custom link function structure.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", 1, "Derivative", @(x)x, "Inverse", "normcdf"))
598s ***** error <glmfit: custom 'Link' function must return an output of the same size as input.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x) [x, x], "Derivative", @(x)x, "Inverse", "normcdf"))
598s ***** error <glmfit: invalid custom 'Link' function.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", "what", "Derivative", @(x)x, "Inverse", "normcdf"))
598s ***** error <glmfit: bad 'Derivative' function in custom link function structure.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "some", "Inverse", "normcdf"))
598s ***** error <glmfit: bad 'Derivative' function in custom link function structure.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", 1, "Inverse", "normcdf"))
598s ***** error <glmfit: custom 'Derivative' function must return an output of the same size as input.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", @(x) [x, x], "Inverse", "normcdf"))
598s ***** error <glmfit: invalid custom 'Derivative' function.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "what", "Inverse", "normcdf"))
598s ***** error <glmfit: bad 'Inverse' function in custom link function structure.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", "some"))
598s ***** error <glmfit: bad 'Inverse' function in custom link function structure.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", 1))
598s ***** error <glmfit: custom 'Inverse' function must return an output of the same size as input.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", @(x) [x, x]))
598s ***** error <glmfit: invalid custom 'Inverse' function.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", "what"))
598s ***** error <glmfit: cell array with custom link functions must have three elements.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {'log'})
598s ***** error <glmfit: cell array with custom link functions must have three elements.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {'log', 'hijy'})
598s ***** error <glmfit: cell array with custom link functions must have three elements.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {1, 2, 3, 4})
598s ***** error <glmfit: bad 'Link' function in custom link function cell array.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {"log", "dfv", "dfgvd"})
598s ***** error <glmfit: custom 'Link' function must return an output of the same size as input.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) [x, x], "dfv", "dfgvd"})
598s ***** error <glmfit: invalid custom 'Link' function.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) what (x), "dfv", "dfgvd"})
598s ***** error <glmfit: bad 'Derivative' function in custom link function cell array.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, "dfv", "dfgvd"})
598s ***** error <glmfit: custom 'Derivative' function must return an output of the same size as input.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, @(x) [x, x], "dfgvd"})
598s ***** error <glmfit: invalid custom 'Derivative' function.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, @(x) what (x), "dfgvd"})
598s ***** error <glmfit: bad 'Inverse' function in custom link function cell array.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, @(x) x, "dfgvd"})
598s ***** error <glmfit: custom 'Inverse' function must return an output of the same size as input.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, @(x) x, @(x) [x, x]})
598s ***** error <glmfit: invalid custom 'Inverse' function.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, @(x) x, @(x) what (x)})
598s ***** error <glmfit: numeric input for custom link function must be a finite real scalar value.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', NaN)
598s ***** error <glmfit: numeric input for custom link function must be a finite real scalar value.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', [1, 2])
598s ***** error <glmfit: numeric input for custom link function must be a finite real scalar value.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', [1i])
598s ***** error <glmfit: canonical link function name must be a character vector.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', ["log"; "log1"])
598s ***** error <glmfit: canonical link function 'somelinkfunction' is not supported.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', 'somelinkfunction')
598s ***** error <glmfit: invalid value for custom link function.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', true)
598s ***** error <glmfit: 'Options' must be a structure containing the fields 'MaxIter', and 'TolX'.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', true)
598s ***** error <glmfit: 'Options' must be a structure containing the fields 'MaxIter', and 'TolX'.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 100))
598s ***** error <glmfit: 'MaxIter' in 'Options' structure must be a positive integer.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 4.5, "TolX", 1e-6))
598s ***** error <glmfit: 'MaxIter' in 'Options' structure must be a positive integer.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 0, "TolX", 1e-6))
598s ***** error <glmfit: 'MaxIter' in 'Options' structure must be a positive integer.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", -100, "TolX", 1e-6))
598s ***** error <glmfit: 'MaxIter' in 'Options' structure must be a positive integer.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", [50 ,50], "TolX", 1e-6))
598s ***** error <glmfit: 'TolX' in 'Options' structure must be a positive scalar.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 100, "TolX", 0))
598s ***** error <glmfit: 'TolX' in 'Options' structure must be a positive scalar.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 100, "TolX", -1e-6))
598s ***** error <glmfit: 'TolX' in 'Options' structure must be a positive scalar.> ...
598s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 100, "TolX", [1e-6, 1e-6]))
598s ***** error <glmfit: 'Offset' must be a numeric vector of the same size as Y.> ...
598s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'offset', [1; 2; 3; 4])
598s ***** error <glmfit: 'Offset' must be a numeric vector of the same size as Y.> ...
598s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'offset', 'asdfg')
598s ***** error <glmfit: 'Weights' must be a numeric vector of the same size as Y.> ...
598s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'weights', [1; 2; 3; 4])
598s ***** error <glmfit: 'Weights' must be a numeric vector of the same size as Y.> ...
598s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'weights', 'asdfg')
598s 70 tests, 70 passed, 0 known failure, 0 skipped
598s [inst/ttest2.m]
598s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/ttest2.m
598s ***** test
598s  a = 1:5;
598s  b = 6:10;
598s  b(5) = NaN;
598s  [h,p,ci,stats] = ttest2 (a,b);
598s  assert (h, 1);
598s  assert (p, 0.002535996080258229, 1e-14);
598s  assert (ci, [-6.822014919225481, -2.17798508077452], 1e-14);
598s  assert (stats.tstat, -4.582575694955839, 1e-14);
598s  assert (stats.df, 7);
598s  assert (stats.sd, 1.4638501094228, 1e-13);
598s ***** error ttest2 ([8:0.1:12], [8:0.1:12], "tail", "invalid");
598s ***** error ttest2 ([8:0.1:12], [8:0.1:12], "tail", 25);
598s 3 tests, 3 passed, 0 known failure, 0 skipped
598s [inst/fitrgam.m]
598s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/fitrgam.m
598s ***** demo
598s  # Train a RegressionGAM Model for synthetic values
598s 
598s  f1 = @(x) cos (3 *x);
598s  f2 = @(x) x .^ 3;
598s 
598s  # generate x1 and x2 for f1 and f2
598s  x1 = 2 * rand (50, 1) - 1;
598s  x2 = 2 * rand (50, 1) - 1;
598s 
598s  # calculate y
598s  y = f1(x1) + f2(x2);
598s 
598s  # add noise
598s  y = y + y .* 0.2 .* rand (50,1);
598s  X = [x1, x2];
598s 
598s  # create an object
598s  a = fitrgam (X, y, "tol", 1e-3)
598s ***** test
598s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
598s  y = [1; 2; 3; 4];
598s  a = fitrgam (x, y);
598s  assert ({a.X, a.Y}, {x, y})
598s  assert ({a.BaseModel.Intercept}, {2.5000})
598s  assert ({a.Knots, a.Order, a.DoF}, {[5, 5, 5], [3, 3, 3], [8, 8, 8]})
598s  assert ({a.NumObservations, a.NumPredictors}, {4, 3})
598s  assert ({a.ResponseName, a.PredictorNames}, {"Y", {"x1", "x2", "x3"}})
598s  assert ({a.Formula}, {[]})
598s ***** test
598s  x = [1, 2, 3, 4; 4, 5, 6, 7; 7, 8, 9, 1; 3, 2, 1, 2];
598s  y = [1; 2; 3; 4];
598s  pnames = {"A", "B", "C", "D"};
598s  formula = "Y ~ A + B + C + D + A:C";
598s  intMat = logical ([1,0,0,0;0,1,0,0;0,0,1,0;0,0,0,1;1,0,1,0]);
598s  a = fitrgam (x, y, "predictors", pnames, "formula", formula);
598s  assert ({a.IntMatrix}, {intMat})
598s  assert ({a.ResponseName, a.PredictorNames}, {"Y", pnames})
598s  assert ({a.Formula}, {formula})
598s ***** error<fitrgam: too few arguments.> fitrgam ()
598s ***** error<fitrgam: too few arguments.> fitrgam (ones(10,2))
598s ***** error<fitrgam: Name-Value arguments must be in pairs.>
598s  fitrgam (ones (4,2), ones (4, 1), "K")
598s ***** error<fitrgam: number of rows in X and Y must be equal.>
598s  fitrgam (ones (4,2), ones (3, 1))
598s ***** error<fitrgam: number of rows in X and Y must be equal.>
598s  fitrgam (ones (4,2), ones (3, 1), "K", 2)
598s 7 tests, 7 passed, 0 known failure, 0 skipped
598s [inst/combnk.m]
598s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/combnk.m
598s ***** demo
598s  c = combnk (1:5, 2);
598s  disp ("All pairs of integers between 1 and 5:");
598s  disp (c);
598s ***** test
598s  c = combnk (1:3, 2);
598s  assert (c, [1, 2; 1, 3; 2, 3]);
598s ***** test
598s  c = combnk (1:3, 6);
598s  assert (isempty (c));
598s ***** test
598s  c = combnk ({1, 2, 3}, 2);
598s  assert (c, {1, 2; 1, 3; 2, 3});
598s ***** test
598s  c = combnk ("hello", 2);
598s  assert (c, ["lo"; "lo"; "ll"; "eo"; "el"; "el"; "ho"; "hl"; "hl"; "he"]);
598s 4 tests, 4 passed, 0 known failure, 0 skipped
598s [inst/cluster.m]
598s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/cluster.m
598s ***** error cluster ()
598s ***** error <Z must be .* generated by the linkage .*> cluster ([1 1], "Cutoff", 1)
598s ***** error <unknown option .*> cluster ([1 2 1], "Bogus", 1)
598s ***** error <C must be a positive scalar .*> cluster ([1 2 1], "Cutoff", -1)
598s ***** error <unknown property .*> cluster ([1 2 1], "Cutoff", 1, "Bogus", 1)
598s ***** test
598s 6 tests, 6 passed, 0 known failure, 0 skipped
598s [inst/logistic_regression.m]
598s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/logistic_regression.m
598s ***** test
598s  # Output compared to following MATLAB commands
598s  # [B, DEV, STATS] = mnrfit(X,Y+1,'model','ordinal');
598s  # P = mnrval(B,X)
598s  X = [1.489381332449196, 1.1534152241851305; ...
598s       1.8110085304863965, 0.9449666896938425; ...
598s       -0.04453299665130296, 0.34278203449678646; ...
598s       -0.36616019468850347, 1.130254275908322; ...
598s        0.15339143291005095, -0.7921044310668951; ...
598s       -1.6031878794469698, -1.8343471035233376; ...
598s       -0.14349521143198166, -0.6762996896828459; ...
598s       -0.4403818557740143, -0.7921044310668951; ...
598s       -0.7372685001160434, -0.027793137932169563; ...
598s       -0.11875465773681024, 0.5512305689880763];
598s  Y = [1,1,1,1,1,0,0,0,0,0]';
598s  [INTERCEPT, SLOPE, DEV, DL, D2L, P] = logistic_regression (Y, X, false);
598s ***** test
598s  # Output compared to following MATLAB commands
598s  # [B, DEV, STATS] = mnrfit(X,Y+1,'model','ordinal');
598s  load carbig
598s  X = [Acceleration Displacement Horsepower Weight];
598s  miles = [1,1,1,1,1,1,1,1,1,1,NaN,NaN,NaN,NaN,NaN,1,1,NaN,1,1,2,2,1,2,2,2, ...
598s           2,2,2,2,2,1,1,1,1,2,2,2,2,NaN,2,1,1,2,1,1,1,1,1,1,1,1,1,2,2,1,2, ...
598s           2,3,3,3,3,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,2,2,2,2,2, ...
598s           2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,1,1,1,1,1,2,2,2,1,2,2, ...
598s           2,1,1,3,2,2,2,1,2,2,1,2,2,2,1,3,2,3,2,1,1,1,1,1,1,1,1,3,2,2,3,3, ...
598s           2,2,2,2,2,3,2,1,1,1,1,1,1,1,1,1,1,1,2,2,1,3,2,2,2,2,2,2,1,3,2,2, ...
598s           2,2,2,3,2,2,2,2,2,1,1,1,1,2,2,2,2,3,2,3,3,2,1,1,1,3,3,2,2,2,1,2, ...
598s           2,1,1,1,1,1,3,3,3,2,3,1,1,1,1,1,2,2,1,1,1,1,1,3,2,2,2,3,3,3,3,2, ...
598s           2,2,4,3,3,4,3,2,2,2,2,2,2,2,2,2,2,2,1,1,2,1,1,1,3,2,2,3,2,2,2,2, ...
598s           2,1,2,1,3,3,2,2,2,2,2,1,1,1,1,1,1,2,1,3,3,3,2,2,2,2,2,3,3,3,3,2, ...
598s           2,2,3,4,3,3,3,2,2,2,2,3,3,3,3,3,4,2,4,4,4,3,3,4,4,3,3,3,2,3,2,3, ...
598s           2,2,2,2,3,4,4,3,3,3,3,3,3,3,3,3,3,3,3,3,3,2,NaN,3,2,2,2,2,2,1,2, ...
598s           2,3,3,3,2,2,2,3,3,3,3,3,3,3,3,3,3,3,2,3,2,2,3,3,2,2,4,3,2,3]';
598s  [INTERCEPT, SLOPE, DEV, DL, D2L, P] = logistic_regression (miles, X, false);
598s  assert (DEV, 433.197174495549, 1e-05);
598s  assert (INTERCEPT(1), -16.6895155618903, 1e-05);
598s  assert (INTERCEPT(2), -11.7207818178493, 1e-05);
598s  assert (INTERCEPT(3), -8.0605768506075, 1e-05);
598s  assert (SLOPE(1), 0.104762463756714, 1e-05);
598s  assert (SLOPE(2), 0.0103357623191891, 1e-05);
598s  assert (SLOPE(3), 0.0645199313242276, 1e-05);
598s  assert (SLOPE(4), 0.00166377028388103, 1e-05);
598s 2 tests, 2 passed, 0 known failure, 0 skipped
598s [inst/cdfplot.m]
598s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/cdfplot.m
598s ***** demo
598s  x = randn(100,1);
598s  cdfplot (x);
598s ***** test
598s  hf = figure ("visible", "off");
598s  unwind_protect
598s    x = [2, 4, 3, 2, 4, 3, 2, 5, 6, 4];
598s    [hCDF, stats] = cdfplot (x);
598s    assert (stats.min, 2);
598s    assert (stats.max, 6);
598s    assert (stats.median, 3.5);
598s    assert (stats.std, 1.35400640077266, 1e-14);
598s  unwind_protect_cleanup
598s    close (hf);
598s  end_unwind_protect
598s ***** test
598s  hf = figure ("visible", "off");
598s  unwind_protect
598s    x = randn(100,1);
598s    cdfplot (x);
598s  unwind_protect_cleanup
598s    close (hf);
598s  end_unwind_protect
598s ***** error cdfplot ();
598s ***** error cdfplot ([x',x']);
598s ***** error cdfplot ([NaN, NaN, NaN, NaN]);
598s 5 tests, 5 passed, 0 known failure, 0 skipped
598s [inst/standardizeMissing.m]
598s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/standardizeMissing.m
598s ***** assert (standardizeMissing (1, 1), NaN)
598s ***** assert (standardizeMissing (1, 0), 1)
598s ***** assert (standardizeMissing (eye(2), 1), [NaN 0;0 NaN])
598s ***** assert (standardizeMissing ([1:3;4:6], [2 3; 4 5]), [1, NaN, NaN; NaN, NaN, 6])
598s ***** assert (standardizeMissing (cat (3,1,2,3,4), 3), cat (3,1,2,NaN,4))
598s ***** assert (standardizeMissing ('foo', 'a'), 'foo')
598s ***** assert (standardizeMissing ('foo', 'f'), ' oo')
598s ***** assert (standardizeMissing ('foo', 'o'), 'f  ')
598s ***** assert (standardizeMissing ('foo', 'oo'), 'f  ')
598s ***** assert (standardizeMissing ({'foo'}, 'f'), {'foo'})
598s ***** assert (standardizeMissing ({'foo'}, {'f'}), {'foo'})
598s ***** assert (standardizeMissing ({'foo'}, 'test'), {'foo'})
598s ***** assert (standardizeMissing ({'foo'}, {'test'}), {'foo'})
598s ***** assert (standardizeMissing ({'foo'}, 'foo'), {''})
598s ***** assert (standardizeMissing ({'foo'}, {'foo'}), {''})
598s ***** assert (standardizeMissing (['foo';'bar'], 'oar'), ['f  ';'b  '])
598s ***** assert (standardizeMissing (['foo';'bar'], ['o';'a';'r']), ['f  ';'b  '])
598s ***** assert (standardizeMissing (['foo';'bar'], ['o ';'ar']), ['f  ';'b  '])
598s ***** assert (standardizeMissing ({'foo','bar'}, 'foo'), {'','bar'})
598s ***** assert (standardizeMissing ({'foo','bar'}, 'f'), {'foo','bar'})
598s ***** assert (standardizeMissing ({'foo','bar'}, {'foo', 'a'}), {'','bar'})
598s ***** assert (standardizeMissing ({'foo'}, {'f', 'oo'}), {'foo'})
598s ***** assert (standardizeMissing ({'foo','bar'}, {'foo'}), {'','bar'})
598s ***** assert (standardizeMissing ({'foo','bar'}, {'foo', 'a'}), {'','bar'})
598s ***** assert (standardizeMissing (double (1), single (1)), double (NaN))
598s ***** assert (standardizeMissing (single (1), single (1)), single (NaN))
598s ***** assert (standardizeMissing (single (1), double (1)), single (NaN))
598s ***** assert (standardizeMissing (single (1), true), single (NaN))
598s ***** assert (standardizeMissing (double (1), int32(1)), double (NaN))
598s ***** assert (standardizeMissing (true, true), true)
598s ***** assert (standardizeMissing (true, 1), true)
598s ***** assert (standardizeMissing (int32 (1), int32 (1)), int32 (1))
598s ***** assert (standardizeMissing (int32 (1), 1), int32 (1))
598s ***** assert (standardizeMissing (uint32 (1), uint32 (1)), uint32 (1))
598s ***** assert (standardizeMissing (uint32 (1), 1), uint32 (1))
598s ***** error standardizeMissing ();
598s ***** error standardizeMissing (1);
599s ***** error standardizeMissing (1,2,3);
599s ***** error <only cells of strings> standardizeMissing ({'abc', 1}, 1);
599s ***** error <unsupported data type> standardizeMissing (struct ('a','b'), 1);
599s ***** error <'indicator' and 'A' must have > standardizeMissing ([1 2 3], {1});
599s ***** error <'indicator' and 'A' must have > standardizeMissing ([1 2 3], 'a');
599s ***** error <'indicator' and 'A' must have > standardizeMissing ([1 2 3], struct ('a', 1));
599s ***** error <'indicator' and 'A' must have > standardizeMissing ('foo', 1);
599s ***** error <'indicator' and 'A' must have > standardizeMissing ('foo', {1});
599s ***** error <'indicator' and 'A' must have > standardizeMissing ('foo', {'f'});
599s ***** error <'indicator' and 'A' must have > standardizeMissing ('foo', struct ('a', 1));
599s ***** error <'indicator' and 'A' must have > standardizeMissing ({'foo'}, 1);
599s ***** error <'indicator' and 'A' must have > standardizeMissing ({'foo'}, 1);
599s 49 tests, 49 passed, 0 known failure, 0 skipped
599s [inst/confusionmat.m]
599s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/confusionmat.m
599s ***** test
599s  Yt = [8 5 6 8 5 3 1 6 4 2 5 3 1 4]';
599s  Yp = [8 5 6 8 5 2 3 4 4 5 5 7 2 6]';
599s  C  = [0 1 1 0 0 0 0 0; 0 0 0 0 1 0 0 0; 0 1 0 0 0 0 1 0; 0 0 0 1 0 1 0 0; ...
599s        0 0 0 0 3 0 0 0; 0 0 0 1 0 1 0 0; 0 0 0 0 0 0 0 0; 0 0 0 0 0 0 0 2];
599s  assert (confusionmat (Yt, Yp), C)
599s 1 test, 1 passed, 0 known failure, 0 skipped
599s [inst/squareform.m]
599s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/squareform.m
599s ***** shared v, m
599s  v = 1:6;
599s  m = [0 1 2 3;1 0 4 5;2 4 0 6;3 5 6 0];
599s ***** assert (squareform (v), m)
599s ***** assert (squareform (squareform (v)), v)
599s ***** assert (squareform (m), v)
599s ***** assert (squareform (v'), m)
599s ***** assert (squareform (1), [0 1;1 0])
599s ***** assert (squareform (1, "tomatrix"), [0 1; 1 0])
599s ***** assert (squareform (0, "tovector"), zeros (1, 0))
599s ***** warning <not a symmetric matrix> squareform ([0 1 2; 3 0 4; 5 6 0]);
599s ***** test
599s  for c = {@single, @double, @uint8, @uint32, @uint64}
599s    f = c{1};
599s    assert (squareform (f (v)), f (m))
599s    assert (squareform (f (m)), f (v))
599s  endfor
599s 9 tests, 9 passed, 0 known failure, 0 skipped
599s [inst/fillmissing.m]
599s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/fillmissing.m
599s ***** assert (fillmissing ([1, 2, 3], "constant", 99), [1, 2, 3])
599s ***** assert (fillmissing ([1, 2, NaN], "constant", 99), [1, 2, 99])
599s ***** assert (fillmissing ([NaN, 2, NaN], "constant", 99), [99, 2, 99])
599s ***** assert (fillmissing ([1, 2, 3]', "constant", 99), [1, 2, 3]')
599s ***** assert (fillmissing ([1, 2, NaN]', "constant", 99), [1, 2, 99]')
599s ***** assert (fillmissing ([1, 2, 3; 4, 5, 6], "constant", 99), [1, 2, 3; 4, 5, 6])
599s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "constant", 99), [1, 2, 99; 4, 99, 6])
599s ***** assert (fillmissing ([NaN, 2, NaN; 4, NaN, 6], "constant", [97, 98, 99]), [97, 2, 99; 4, 98, 6])
599s ***** test
599s  x = cat (3, [1, 2, NaN; 4, NaN, 6], [NaN, 2, 3; 4, 5, NaN]);
599s  y = cat (3, [1, 2, 99; 4, 99, 6], [99, 2, 3; 4, 5, 99]);
599s  assert (fillmissing (x, "constant", 99), y);
599s  y = cat (3, [1, 2, 96; 4, 95, 6], [97, 2, 3; 4, 5, 99]);
599s  assert (fillmissing (x, "constant", [94:99]), y);
599s  assert (fillmissing (x, "constant", [94:99]'), y);
599s  assert (fillmissing (x, "constant", permute ([94:99], [1 3 2])), y);
599s  assert (fillmissing (x, "constant", [94, 96, 98; 95, 97, 99]), y);
599s  assert (fillmissing (x, "constant", [94:99], 1), y);
599s  y = cat (3, [1, 2, 96; 4, 97, 6], [98, 2, 3; 4, 5, 99]);
599s  assert (fillmissing (x, "constant", [96:99], 2), y);
599s  y = cat (3, [1, 2, 98; 4, 97, 6], [94, 2, 3; 4, 5, 99]);
599s  assert (fillmissing (x, "constant", [94:99], 3), y);
599s  y = cat (3, [1, 2, 92; 4, 91, 6], [94, 2, 3; 4, 5, 99]);
599s  assert (fillmissing (x, "constant", [88:99], 99), y);
599s ***** test
599s  x = reshape ([1:24], 4, 3, 2);
599s  x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
599s  y = x;
599s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [94, 95, 95, 96, 96, 97, 97, 98, 99, 99];
599s  assert (fillmissing (x, "constant", [94:99], 1), y);
599s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [92, 93, 94, 92, 95, 97, 99, 98, 97, 98];
599s  assert (fillmissing (x, "constant", [92:99], 2), y);
599s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [88, 93, 94, 96, 99, 89, 91, 94, 97, 98];
599s  assert (fillmissing (x, "constant", [88:99], 3), y);
599s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [76, 81, 82, 84, 87, 89, 91, 94, 97, 98];
599s  assert (fillmissing (x, "constant", [76:99], 99), y);
599s ***** assert (fillmissing ([1, 2, 3], "constant", 99, "endvalues", 88), [1, 2, 3])
599s ***** assert (fillmissing ([1, NaN, 3], "constant", 99, "endvalues", 88), [1, 99, 3])
599s ***** assert (fillmissing ([1, 2, NaN], "constant", 99, "endvalues", 88), [1, 2, 88])
599s ***** assert (fillmissing ([NaN, 2, 3], "constant", 99, "endvalues", 88), [88, 2, 3])
599s ***** assert (fillmissing ([NaN, NaN, 3], "constant", 99, "endvalues", 88), [88, 88, 3])
599s ***** assert (fillmissing ([1, NaN, NaN], "constant", 99, "endvalues", 88), [1, 88, 88])
599s ***** assert (fillmissing ([NaN, 2, NaN], "constant", 99, "endvalues", 88), [88, 2, 88])
599s ***** assert (fillmissing ([NaN, 2, NaN]', "constant", 99, "endvalues", 88), [88, 2, 88]')
599s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", 99, "endvalues", 88), [1, 99, 3, 99, 5])
599s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "constant", 99, "endvalues", 88), [1, 99, 99, 99, 5])
599s ***** assert (fillmissing ([NaN, NaN, NaN, NaN, 5], "constant", 99, "endvalues", 88), [88, 88, 88, 88, 5])
599s ***** assert (fillmissing ([1, NaN, 3, 4, NaN], "constant", 99, "endvalues", 88), [1, 99, 3, 4, 88])
599s ***** assert (fillmissing ([1, NaN, 3, 4, NaN], "constant", 99, 1, "endvalues", 88), [1, 88, 3, 4, 88])
599s ***** assert (fillmissing ([1, NaN, 3, 4, NaN], "constant", 99, 1, "endvalues", "extrap"), [1, 99, 3, 4, 99])
599s ***** test
599s  x = reshape ([1:24], 3, 4, 2);
599s  y = x;
599s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
599s  y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 88;
599s  y([8]) = 99;
599s  assert (fillmissing (x, "constant", 99, "endvalues", 88), y);
599s  assert (fillmissing (x, "constant", 99, 1, "endvalues", 88), y);
599s  y = x;
599s  y([1, 2, 5, 8, 10, 13, 16, 19, 22]) = 88;
599s  y([6, 18, 20, 21]) = 99;
599s  assert (fillmissing (x, "constant", 99, 2, "endvalues", 88), y);
599s  y(y == 99) = 88;
599s  assert (fillmissing (x, "constant", 99, 3, "endvalues", 88), y);
599s  assert (fillmissing (x, "constant", 99, 4, "endvalues", 88), y);
599s  assert (fillmissing (x, "constant", 99, 99, "endvalues", 88), y);
599s  y([8]) = 94;
599s  assert (fillmissing (x, "constant", [92:99], 1, "endvalues", 88), y);
599s  y([6, 8, 18, 20, 21]) = [96, 88, 99, 98, 99];
599s  assert (fillmissing (x, "constant", [94:99], 2, "endvalues", 88), y);
599s  y = x;
599s  y(isnan (y)) = 88;
599s  assert (fillmissing (x, "constant", [88:99], 3, "endvalues", 88), y);
599s  y = x;
599s  y(isnan (y)) = [82, 82, 83, 83, 94, 85, 86, 87, 87, 88, 88, 88, 89];
599s  assert (fillmissing (x, "constant", [92:99], 1, "endvalues", [82:89]), y);
599s  y = x;
599s  y(isnan (y)) = [84, 85, 85, 96, 85, 84, 87, 87, 99, 87, 98, 99, 87];
599s  assert (fillmissing (x, "constant", [94:99], 2, "endvalues", [84:89]), y);
599s  y = x;
599s  y(isnan (y)) = [68, 69, 72, 73, 75, 77, 68, 71, 73, 74, 75, 76, 77];
599s  assert (fillmissing (x, "constant", [88:99], 3, "endvalues", [68:79]), y);
599s  assert (fillmissing (x, "constant", [88:93; 94:99]', 3, "endvalues", [68:73; 74:79]'), y)
599s ***** test
599s  x = reshape ([1:24],4,3,2);
599s  x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
599s  y = x;
599s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [94, 95, 95, 96, 96, 97, 97, 98, 99, 99];
599s  assert (fillmissing (x, "constant", [94:99], 1), y);
599s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [92, 93, 94, 92, 95, 97, 99, 98, 97, 98];
599s  assert (fillmissing (x, "constant", [92:99], 2), y);
599s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [88, 93, 94, 96, 99, 89, 91, 94, 97, 98];
599s  assert (fillmissing (x, "constant", [88:99], 3), y);
599s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [76, 81, 82, 84, 87, 89, 91, 94, 97, 98];
599s  assert (fillmissing (x, "constant", [76:99], 99), y);
599s ***** assert (fillmissing ([1, 2, 3], "previous"), [1, 2, 3])
599s ***** assert (fillmissing ([1, 2, 3], "next"), [1, 2, 3])
599s ***** assert (fillmissing ([1, 2, 3]', "previous"), [1, 2, 3]')
599s ***** assert (fillmissing ([1, 2, 3]', "next"), [1, 2, 3]')
599s ***** assert (fillmissing ([1, 2, NaN], "previous"), [1, 2, 2])
599s ***** assert (fillmissing ([1, 2, NaN], "next"), [1, 2, NaN])
599s ***** assert (fillmissing ([NaN, 2, NaN], "previous"), [NaN, 2, 2])
599s ***** assert (fillmissing ([NaN, 2, NaN], "next"), [2, 2, NaN])
599s ***** assert (fillmissing ([1, NaN, 3], "previous"), [1, 1, 3])
599s ***** assert (fillmissing ([1, NaN, 3], "next"), [1, 3, 3])
599s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "previous", 1), [1, 2, NaN; 4, 2, 6])
599s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "previous", 2), [1, 2, 2; 4, 4, 6])
599s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "previous", 3), [1, 2, NaN; 4, NaN, 6])
599s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "next", 1), [1, 2, 6; 4, NaN, 6])
599s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "next", 2), [1, 2, NaN; 4, 6, 6])
599s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "next", 3), [1, 2, NaN; 4, NaN, 6])
599s ***** test
599s  x = reshape ([1:24], 4, 3, 2);
599s  x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
599s  y = x;
599s  y([1, 6, 7, 9, 14, 19, 22, 23]) = [2, 8, 8, 10, 15, 20, 24, 24];
599s  assert (fillmissing (x, "next", 1), y);
599s  y = x;
599s  y([1, 6, 7, 14, 16]) = [5, 10, 11, 18, 20];
599s  assert (fillmissing (x, "next", 2), y);
599s  y = x;
599s  y([1, 6, 9, 12]) = [13, 18, 21, 24];
599s  assert (fillmissing (x, "next", 3), y);
599s  assert (fillmissing (x, "next", 99), x);
599s  y = x;
599s  y([6, 7, 12, 14, 16, 19, 22, 23]) = [5, 5, 11, 13, 15, 18, 21, 21];
599s  assert (fillmissing (x, "previous", 1), y);
599s  y = x;
599s  y([6, 7, 9, 12, 19, 22, 23]) = [2, 3, 5, 8, 15, 18, 15];
599s  assert (fillmissing (x, "previous", 2), y);
599s  y = x;
599s  y([14, 16, 22, 23]) = [2, 4, 10, 11];
599s  assert (fillmissing (x, "previous", 3), y);
599s  assert (fillmissing (x, "previous", 99), x);
599s ***** assert (fillmissing ([1, 2, 3], "constant", 0, "endvalues", "previous"), [1, 2, 3])
599s ***** assert (fillmissing ([1, 2, 3], "constant", 0, "endvalues", "next"), [1, 2, 3])
599s ***** assert (fillmissing ([1, NaN, 3], "constant", 0, "endvalues", "previous"), [1, 0, 3])
599s ***** assert (fillmissing ([1, NaN, 3], "constant", 0, "endvalues", "next"), [1, 0, 3])
599s ***** assert (fillmissing ([1, 2, NaN], "constant", 0, "endvalues", "previous"), [1, 2, 2])
599s ***** assert (fillmissing ([1, 2, NaN], "constant", 0, "endvalues", "next"), [1, 2, NaN])
599s ***** assert (fillmissing ([1, NaN, NaN], "constant", 0, "endvalues", "previous"), [1, 1, 1])
599s ***** assert (fillmissing ([1, NaN, NaN], "constant", 0, "endvalues", "next"), [1, NaN, NaN])
599s ***** assert (fillmissing ([NaN, 2, 3], "constant", 0, "endvalues", "previous"), [NaN, 2, 3])
599s ***** assert (fillmissing ([NaN, 2, 3], "constant", 0, "endvalues", "next"), [2, 2, 3])
599s ***** assert (fillmissing ([NaN, NaN, 3], "constant", 0, "endvalues", "previous"), [NaN, NaN, 3])
599s ***** assert (fillmissing ([NaN, NaN, 3], "constant", 0, "endvalues", "next"), [3, 3, 3])
599s ***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "endvalues", "previous"), [NaN, NaN, NaN])
599s ***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "endvalues", "next"), [NaN, NaN, NaN])
599s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, "endvalues", "previous"), [NaN, 2, 0, 4, 4])
599s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, "endvalues", "next"), [2, 2, 0, 4, NaN])
599s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 1, "endvalues", "previous"), [NaN, 2, NaN, 4, NaN])
599s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 1, "endvalues", "next"), [NaN, 2, NaN, 4, NaN])
599s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 2, "endvalues", "previous"), [NaN, 2, 0, 4, 4])
599s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 2, "endvalues", "next"), [2, 2, 0, 4, NaN])
599s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 3, "endvalues", "previous"), [NaN, 2, NaN, 4, NaN])
599s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 3, "endvalues", "next"), [NaN, 2, NaN, 4, NaN])
599s ***** test
599s  x = reshape ([1:24], 3, 4, 2);
599s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
599s  y = x;
599s  y([5, 6, 8, 18]) = [4, 4, 0, 17];
599s  assert (fillmissing (x, "constant", 0, "endvalues", "previous"), y);
599s  assert (fillmissing (x, "constant", 0, 1, "endvalues", "previous"), y);
599s  y = x;
599s  y([6, 10, 18, 20, 21]) = [0, 7, 0, 0, 0];
599s  assert (fillmissing (x, "constant", 0, 2, "endvalues", "previous"), y);
599s  y = x;
599s  y([16, 19, 21]) = [4, 7, 9];
599s  assert (fillmissing (x, "constant", 0, 3, "endvalues", "previous"), y);
599s  assert (fillmissing (x, "constant", 0, 4, "endvalues", "previous"), x);
599s  assert (fillmissing (x, "constant", 0, 99, "endvalues", "previous"), x);
599s  y = x;
599s  y([1, 2, 8, 10, 13, 16, 22]) = [3, 3, 0, 11, 14, 17, 23];
599s  assert (fillmissing (x, "constant", 0, "endvalues", "next"), y);
599s  assert (fillmissing (x, "constant", 0, 1, "endvalues", "next"), y);
599s  y = x;
599s  y([1, 2, 5, 6, 8, 18, 20, 21]) = [4, 11, 11, 0, 11, 0, 0, 0];
599s  assert (fillmissing (x, "constant", 0, 2, "endvalues", "next"), y);
599s  y = x;
599s  y([2, 5]) = [14, 17];
599s  assert (fillmissing (x, "constant", 0, 3, "endvalues", "next"), y);
599s  assert (fillmissing (x, "constant", 0, 4, "endvalues", "next"), x);
599s  assert (fillmissing (x, "constant", 0, 99, "endvalues", "next"), x);
599s ***** assert (fillmissing ([1, 2, 3], "nearest"), [1, 2, 3])
599s ***** assert (fillmissing ([1, 2, 3]', "nearest"), [1, 2, 3]')
599s ***** assert (fillmissing ([1, 2, NaN], "nearest"), [1, 2, 2])
599s ***** assert (fillmissing ([NaN, 2, NaN], "nearest"), [2, 2, 2])
599s ***** assert (fillmissing ([1, NaN, 3], "nearest"), [1, 3, 3])
599s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "nearest", 1), [1, 2, 6; 4, 2, 6])
599s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "nearest", 2), [1, 2, 2; 4, 6, 6])
599s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "nearest", 3), [1, 2, NaN; 4, NaN, 6])
599s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "nearest"), [1, 3, 3, 5, 5])
599s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "nearest", "samplepoints", [0, 1, 2, 3, 4]), [1, 3, 3, 5, 5])
599s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "nearest", "samplepoints", [0.5, 1, 2, 3, 5]), [1, 1, 3, 3, 5])
599s ***** test
599s  x = reshape ([1:24], 4, 3, 2);
599s  x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
599s  y = x;
599s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [2, 5, 8, 10, 11, 15, 15, 20, 21, 24];
599s  assert (fillmissing (x, "nearest", 1), y);
599s  y = x;
599s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [5, 10, 11, 5, 8, 18, 20, 15, 18, 15];
599s  assert (fillmissing (x, "nearest", 2), y);
599s  y = x;
599s  y([1, 6, 9, 12, 14, 16, 22, 23]) = [13, 18, 21, 24, 2, 4, 10, 11];
599s  assert (fillmissing (x, "nearest", 3), y);
599s  assert (fillmissing (x, "nearest", 99), x);
599s ***** assert (fillmissing ([1, 2, 3], "constant", 0, "endvalues", "nearest"), [1, 2, 3])
599s ***** assert (fillmissing ([1, NaN, 3], "constant", 0, "endvalues", "nearest"), [1 0 3])
599s ***** assert (fillmissing ([1, 2, NaN], "constant", 0, "endvalues", "nearest"), [1, 2, 2])
599s ***** assert (fillmissing ([1, NaN, NaN], "constant", 0, "endvalues", "nearest"), [1, 1, 1])
599s ***** assert (fillmissing ([NaN, 2, 3], "constant", 0, "endvalues", "nearest"), [2, 2, 3])
599s ***** assert (fillmissing ([NaN, NaN, 3], "constant", 0, "endvalues", "nearest"), [3, 3, 3])
599s ***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "endvalues", "nearest"), [NaN, NaN, NaN])
599s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, "endvalues", "nearest"), [2, 2, 0, 4, 4])
599s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 1, "endvalues", "nearest"), [NaN, 2, NaN, 4, NaN])
599s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 2, "endvalues", "nearest"), [2, 2, 0, 4, 4])
599s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 3, "endvalues", "nearest"), [NaN, 2, NaN, 4, NaN])
599s ***** test
599s  x = reshape ([1:24], 3, 4, 2);
599s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
599s  y = x;
599s  y([1, 2, 5, 6, 8, 10, 13, 16, 18, 22]) = [3, 3, 4, 4, 0, 11, 14, 17, 17, 23];
599s  assert (fillmissing (x, "constant", 0, "endvalues", "nearest"), y);
599s  assert (fillmissing (x, "constant", 0, 1, "endvalues", "nearest"), y);
599s  y = x;
599s  y([1, 2, 5, 6, 8, 10, 18, 20, 21]) = [4, 11, 11, 0, 11, 7, 0, 0, 0];
599s  assert (fillmissing (x, "constant", 0, 2, "endvalues", "nearest"), y);
599s  y = x;
599s  y([2, 5, 16, 19, 21]) = [14, 17, 4, 7, 9];
599s  assert (fillmissing (x, "constant", 0, 3, "endvalues", "nearest"), y);
599s  assert (fillmissing (x, "constant", 0, 99, "endvalues", "nearest"), x);
599s ***** assert (fillmissing ([1, 2, 3], "linear"), [1, 2, 3])
599s ***** assert (fillmissing ([1, 2, 3]', "linear"), [1, 2, 3]')
599s ***** assert (fillmissing ([1, 2, NaN], "linear"), [1, 2, 3])
599s ***** assert (fillmissing ([NaN, 2, NaN], "linear"), [NaN, 2, NaN])
599s ***** assert (fillmissing ([1, NaN, 3], "linear"), [1, 2, 3])
599s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "linear", 1), [1, 2, NaN; 4, NaN, 6])
599s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "linear", 2), [1, 2, 3; 4, 5, 6])
599s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "linear", 3), [1, 2, NaN; 4, NaN, 6])
599s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "linear"), [1, 2, 3, 4, 5])
599s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "linear", "samplepoints", [0, 1, 2, 3, 4]), [1, 2, 3, 4, 5])
599s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "linear", "samplepoints", [0, 1.5, 2, 5, 14]), [1, 2.5, 3, 3.5, 5], eps)
599s ***** test
599s  x = reshape ([1:24], 4, 3, 2);
599s  x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
599s  assert (fillmissing (x, "linear", 1), reshape ([1:24], 4, 3, 2));
599s  y = reshape ([1:24], 4, 3, 2);
599s  y([1, 9, 14, 19, 22, 23]) = NaN;
599s  assert (fillmissing (x, "linear", 2), y);
599s  y = reshape ([1:24], 4, 3, 2);
599s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
599s  assert (fillmissing (x, "linear", 3), y);
599s  assert (fillmissing (x, "linear", 99), x);
599s ***** assert (fillmissing ([1, 2, 3], "linear", "endvalues", 0), [1, 2, 3])
599s ***** assert (fillmissing ([1, NaN, 3], "linear", "endvalues", 0), [1, 2, 3])
599s ***** assert (fillmissing ([1, 2, NaN], "linear", "endvalues", 0), [1, 2, 0])
599s ***** assert (fillmissing ([1, NaN, NaN], "linear", "endvalues", 0), [1, 0, 0])
599s ***** assert (fillmissing ([NaN, 2, 3], "linear", "endvalues", 0), [0, 2, 3])
599s ***** assert (fillmissing ([NaN, NaN, 3], "linear", "endvalues", 0), [0, 0, 3])
599s ***** assert (fillmissing ([NaN, NaN, NaN], "linear", "endvalues", 0), [0, 0, 0])
599s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "linear", "endvalues", 0), [0, 2, 3, 4, 0])
599s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "linear", 1, "endvalues", 0), [0, 2, 0, 4, 0])
599s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "linear", 2, "endvalues", 0), [0, 2, 3, 4, 0])
599s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "linear", 3, "endvalues", 0), [0, 2, 0, 4, 0])
599s ***** test
599s  x = reshape ([1:24], 3, 4, 2);
599s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
599s  y = x;
599s  y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 0;
599s  y(8) = 8;
599s  assert (fillmissing (x, "linear", "endvalues", 0), y);
599s  assert (fillmissing (x, "linear", 1, "endvalues", 0), y);
599s  y = x;
599s  y([1, 2, 5, 8, 10, 13, 16, 19, 22]) = 0;
599s  y([6, 18, 20, 21]) = [6, 18, 20, 21];
599s  assert (fillmissing (x, "linear", 2, "endvalues", 0), y);
599s  y = x;
599s  y(isnan(y)) = 0;
599s  assert (fillmissing (x, "linear", 3, "endvalues", 0), y);
599s  assert (fillmissing (x, "linear", 99, "endvalues", 0), y);
599s ***** assert (fillmissing ([1, 2, 3], "constant", 99, "endvalues", "linear"), [1, 2, 3])
599s ***** assert (fillmissing ([1, NaN, 3], "constant", 99, "endvalues", "linear"), [1, 99, 3])
599s ***** assert (fillmissing ([1, NaN, 3, NaN], "constant", 99, "endvalues", "linear"), [1, 99, 3, 4])
599s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "linear"), [1, 2, 99, 4, 5])
599s ***** assert (fillmissing ([NaN, 2, NaN, NaN], "constant", 99, "endvalues", "linear"), [NaN, 2, NaN, NaN])
599s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "linear", "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 99, 4, 5])
599s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "linear", "samplepoints", [0, 2, 3, 4, 10]), [0, 2, 99, 4, 10])
599s ***** test
599s  x = reshape ([1:24], 3, 4, 2);
599s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
599s  y = x;
599s  y([1, 6, 10, 18, 20, 21]) = [2.5, 5, 8.5, 17.25, 21, 21.75];
599s  assert (fillmissing (x, "linear", 2, "samplepoints", [2 4 8 10]), y, eps);
599s  y([1, 6, 10, 18, 20, 21]) = [2.5, 4.5, 8.5, 17.25, 21.5, 21.75];
599s  assert (fillmissing (x, "spline", 2, "samplepoints", [2, 4, 8, 10]), y, eps);
599s  y([1, 6, 10, 18, 20, 21]) = [2.5, 4.559386973180077, 8.5, 17.25, 21.440613026819925, 21.75];
599s  assert (fillmissing (x, "pchip", 2, "samplepoints", [2, 4, 8, 10]), y, 10*eps);
599s ***** test <60965>
599s  x = reshape ([1:24], 3, 4, 2);
599s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
599s  y = x;
599s  y([1, 6, 10, 18, 20, 21]) = [2.5, 4.609523809523809, 8.5, 17.25, 21.390476190476186, 21.75];
599s  assert (fillmissing (x, "makima", 2, "samplepoints", [2, 4, 8, 10]), y, 10*eps);
599s !!!!! known bug: https://octave.org/testfailure/?60965
599s interp1: invalid METHOD 'makima'
599s ***** assert (fillmissing ([1, 2, 3], "constant", 99, "endvalues", "spline"), [1, 2, 3])
599s ***** assert (fillmissing ([1, NaN, 3], "constant", 99, "endvalues", "spline"), [1, 99, 3])
599s ***** assert (fillmissing ([1, NaN, 3, NaN], "constant", 99, "endvalues", "spline"), [1, 99, 3, 4])
599s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "spline"), [1, 2, 99, 4, 5])
599s ***** assert (fillmissing ([NaN, 2, NaN, NaN], "constant", 99, "endvalues", "spline"), [NaN, 2, NaN, NaN])
599s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "spline", "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 99, 4, 5])
599s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "spline", "samplepoints", [0, 2, 3, 4, 10]), [0, 2, 99, 4, 10])
599s ***** assert (fillmissing ([1, 2, 3], "movmean", 1), [1, 2, 3])
599s ***** assert (fillmissing ([1, 2, NaN], "movmean", 1), [1, 2, NaN])
599s ***** assert (fillmissing ([1, 2, 3], "movmean", 2), [1, 2, 3])
599s ***** assert (fillmissing ([1, 2, 3], "movmean", [1, 0]), [1, 2, 3])
599s ***** assert (fillmissing ([1, 2, 3]', "movmean", 2), [1, 2, 3]')
599s ***** assert (fillmissing ([1, 2, NaN], "movmean", 2), [1, 2, 2])
599s ***** assert (fillmissing ([1, 2, NaN], "movmean", [1, 0]), [1, 2, 2])
599s ***** assert (fillmissing ([1, 2, NaN], "movmean", [1, 0]'), [1, 2, 2])
599s ***** assert (fillmissing ([NaN, 2, NaN], "movmean", 2), [NaN, 2, 2])
599s ***** assert (fillmissing ([NaN, 2, NaN], "movmean", [1, 0]), [NaN, 2, 2])
599s ***** assert (fillmissing ([NaN, 2, NaN], "movmean", [0, 1]), [2, 2, NaN])
599s ***** assert (fillmissing ([NaN, 2, NaN], "movmean", [0, 1.1]), [2, 2, NaN])
599s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmean", [3, 0]), [1, 1, 3, 2, 5])
599s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmean", 3, 1), [1, 2, 6; 4, 2, 6])
599s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmean", 3, 2), [1, 2, 2; 4, 5, 6])
599s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmean", 3, 3), [1, 2, NaN; 4, NaN, 6])
599s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmean", 99), [1, 3, 3, 3, 5])
599s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmean", 99, 1), [1, NaN, 3, NaN, 5])
599s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "movmean", 99, 1), [1, 3, 3, 3, 5]')
599s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmean", 99, 2), [1, 3, 3, 3, 5])
599s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "movmean", 99, 2), [1, NaN, 3, NaN, 5]')
599s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 3, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
599s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1, 1], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
599s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1.5, 1.5], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
599s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 4, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 1, 5, 5])
599s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [2, 2], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 3, 5, 5])
599s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 4.0001, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 3, 5, 5])
599s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 3, "samplepoints", [1.5, 2, 3, 4, 5]), [1, 1, 1, 5, 5])
599s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 3, "samplepoints", [1 2, 3, 4, 4.5]), [1, 1, NaN, 5, 5])
599s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 3, "samplepoints", [1.5, 2, 3, 4, 4.5]), [1, 1, 1, 5, 5])
599s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1.5, 1.5], "samplepoints", [1.5, 2, 3, 4, 5]), [1, 1, 1, 5, 5])
599s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1.5, 1.5], "samplepoints", [1, 2, 3, 4, 4.5]), [1, 1, 5, 5, 5])
599s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1.5, 1.5], "samplepoints", [1.5, 2 3, 4, 4.5]), [1, 1, 3, 5, 5])
599s ***** test
599s  x = reshape ([1:24], 3, 4, 2);
599s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
599s  y = x;
599s  y([2, 5, 8, 10, 13, 16, 18, 22]) = [3, 4, 8, 11, 14, 17, 17, 23];
599s  assert (fillmissing (x, "movmean", 3), y);
599s  assert (fillmissing (x, "movmean", [1, 1]), y);
599s  assert (fillmissing (x, "movmean", 3, "endvalues", "extrap"), y);
599s  assert (fillmissing (x, "movmean", 3, "samplepoints", [1, 2, 3]), y);
599s  y = x;
599s  y([1, 6, 8, 10, 18, 20, 21]) = [4, 6, 11, 7, 15, 20, 24];
599s  assert (fillmissing (x, "movmean", 3, 2), y);
599s  assert (fillmissing (x, "movmean", [1, 1], 2), y);
599s  assert (fillmissing (x, "movmean", 3, 2, "endvalues", "extrap"), y);
599s  assert (fillmissing (x, "movmean", 3, 2, "samplepoints", [1, 2, 3, 4]), y);
599s  y([1, 18]) = NaN;
599s  y(6) = 9;
599s  assert (fillmissing (x, "movmean", 3, 2, "samplepoints", [0, 2, 3, 4]), y);
599s  y = x;
599s  y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 99;
599s  y(8) = 8;
599s  assert (fillmissing (x, "movmean", 3, "endvalues", 99), y);
599s  y = x;
599s  y([1, 2, 5, 8, 10, 13, 16, 19, 22]) = 99;
599s  y([6, 18, 20, 21]) = [6, 15, 20, 24];
599s  assert (fillmissing (x, "movmean", 3, 2, "endvalues", 99), y);
599s ***** assert (fillmissing ([1, 2, 3], "movmedian", 1), [1, 2, 3])
599s ***** assert (fillmissing ([1, 2, NaN], "movmedian", 1), [1, 2, NaN])
599s ***** assert (fillmissing ([1, 2, 3], "movmedian", 2), [1, 2, 3])
599s ***** assert (fillmissing ([1, 2, 3], "movmedian", [1, 0]), [1, 2, 3])
599s ***** assert (fillmissing ([1, 2, 3]', "movmedian", 2), [1, 2, 3]')
599s ***** assert (fillmissing ([1, 2, NaN], "movmedian", 2), [1, 2, 2])
599s ***** assert (fillmissing ([1, 2, NaN], "movmedian", [1, 0]), [1, 2, 2])
599s ***** assert (fillmissing ([1, 2, NaN], "movmedian", [1, 0]'), [1, 2, 2])
599s ***** assert (fillmissing ([NaN, 2, NaN], "movmedian", 2), [NaN, 2, 2])
599s ***** assert (fillmissing ([NaN, 2, NaN], "movmedian", [1, 0]), [NaN, 2, 2])
599s ***** assert (fillmissing ([NaN, 2, NaN], "movmedian", [0, 1]), [2, 2, NaN])
599s ***** assert (fillmissing ([NaN, 2, NaN], "movmedian", [0, 1.1]), [2, 2, NaN])
599s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmedian", [3, 0]), [1, 1, 3, 2, 5])
599s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmedian", 3, 1), [1, 2, 6; 4, 2, 6])
599s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmedian", 3, 2), [1, 2, 2; 4, 5, 6])
599s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmedian", 3, 3), [1, 2, NaN; 4, NaN, 6])
599s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmedian", 99), [1, 3, 3, 3, 5])
599s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmedian", 99, 1), [1, NaN, 3, NaN, 5])
599s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "movmedian", 99, 1), [1, 3, 3, 3, 5]')
599s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmedian", 99, 2), [1, 3, 3, 3, 5])
599s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "movmedian", 99, 2), [1, NaN, 3, NaN, 5]')
599s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 3, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
599s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1, 1], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
599s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1.5, 1.5], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
599s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 4, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 1, 5, 5])
599s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [2, 2], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 3, 5, 5])
599s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 4.0001, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 3, 5, 5])
599s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 3, "samplepoints", [1.5 2 3 4 5]), [1, 1, 1, 5, 5])
599s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 3, "samplepoints", [1 2 3 4 4.5]), [1, 1, NaN, 5, 5])
599s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 3, "samplepoints", [1.5 2 3 4 4.5]), [1, 1, 1, 5, 5])
599s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1.5, 1.5], "samplepoints", [1.5 2 3 4 5]), [1, 1, 1, 5, 5])
599s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1.5, 1.5], "samplepoints", [1 2 3 4 4.5]), [1, 1, 5, 5, 5])
599s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1.5, 1.5], "samplepoints", [1.5 2 3 4 4.5]), [1, 1, 3, 5, 5])
599s ***** test
599s  x = reshape ([1:24], 3, 4, 2);
599s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
599s  y = x;
599s  y([2, 5, 8, 10, 13, 16, 18, 22]) = [3, 4, 8, 11, 14, 17, 17, 23];
599s  assert (fillmissing (x, "movmedian", 3), y);
599s  assert (fillmissing (x, "movmedian", [1, 1]), y);
599s  assert (fillmissing (x, "movmedian", 3, "endvalues", "extrap"), y);
599s  assert (fillmissing (x, "movmedian", 3, "samplepoints", [1, 2, 3]), y);
599s  y = x;
599s  y([1, 6, 8, 10, 18, 20, 21]) = [4, 6, 11, 7, 15, 20, 24];
599s  assert (fillmissing (x, "movmedian", 3, 2), y);
599s  assert (fillmissing (x, "movmedian", [1, 1], 2), y);
599s  assert (fillmissing (x, "movmedian", 3, 2, "endvalues", "extrap"), y);
599s  assert (fillmissing (x, "movmedian", 3, 2, "samplepoints", [1, 2, 3, 4]), y);
599s  y([1,18]) = NaN;
599s  y(6) = 9;
599s  assert (fillmissing (x, "movmedian", 3, 2, "samplepoints", [0, 2, 3, 4]), y);
599s  y = x;
599s  y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 99;
599s  y(8) = 8;
599s  assert (fillmissing (x, "movmedian", 3, "endvalues", 99), y);
599s  y = x;
599s  y([1, 2, 5, 8, 10, 13, 16, 19, 22]) = 99;
599s  y([6, 18, 20, 21]) = [6, 15, 20, 24];
599s  assert (fillmissing (x, "movmedian", 3, 2, "endvalues", 99), y);
599s ***** assert (fillmissing ([1, 2, 3], @(x,y,z) x+y+z, 2), [1, 2, 3])
599s ***** assert (fillmissing ([1, 2, NaN], @(x,y,z) x+y+z, 1), [1, 2, NaN])
599s ***** assert (fillmissing ([1, 2, 3], @(x,y,z) x+y+z, 2), [1, 2, 3])
599s ***** assert (fillmissing ([1, 2, 3], @(x,y,z) x+y+z, [1, 0]), [1, 2, 3])
599s ***** assert (fillmissing ([1, 2, 3]', @(x,y,z) x+y+z, 2), [1, 2, 3]')
599s ***** assert (fillmissing ([1, 2, NaN], @(x,y,z) x+y+z, 2), [1, 2, 7])
599s ***** assert (fillmissing ([1, 2, NaN], @(x,y,z) x+y+z, [1, 0]), [1, 2, 7])
599s ***** assert (fillmissing ([1, 2, NaN], @(x,y,z) x+y+z, [1, 0]'), [1, 2, 7])
599s ***** assert (fillmissing ([NaN, 2, NaN], @(x,y,z) x+y+z, 2), [5, 2, 7])
599s ***** assert (fillmissing ([NaN, 2, NaN], @(x,y,z) x+y+z, [1, 0]), [NaN, 2, 7])
599s ***** assert (fillmissing ([NaN, 2, NaN], @(x,y,z) x+y+z, [0, 1]), [5, 2, NaN])
599s ***** assert (fillmissing ([NaN, 2, NaN], @(x,y,z) x+y+z, [0, 1.1]), [5, 2, NaN])
599s ***** assert (fillmissing ([1, 2, NaN, NaN, 3, 4], @(x,y,z) x+y+z, 2), [1, 2, 7, 12, 3, 4])
599s ***** assert (fillmissing ([1, 2, NaN, NaN, 3, 4], @(x,y,z) x+y+z, 0.5), [1, 2, NaN, NaN, 3, 4])
599s ***** function A = testfcn (x, y, z)
599s   if (isempty (y))
599s     A = z;
599s   elseif (numel (y) == 1)
599s     A = repelem (x(1), numel(z));
599s   else
599s     A = interp1 (y, x, z, "linear", "extrap");
599s   endif
599s ***** endfunction
599s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, [3, 0]), [1, 1, 3, NaN, 5])
599s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], @testfcn, 3, 1), [1, 2, 6; 4, 2, 6])
599s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], @testfcn, 3, 2), [1, 2, 2; 4, 5, 6])
599s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], @testfcn, 3, 3), [1, 2, NaN; 4, NaN, 6])
599s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, 99), [1, 2, 3, 4, 5])
599s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, 99, 1), [1, NaN, 3, NaN, 5]) ##known not-compatible. matlab bug ML2022a: [1, 1, 3, 1, 5]
599s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', @testfcn, 99, 1), [1, 2, 3, 4, 5]')
599s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, 99, 2), [1, 2, 3, 4, 5])
599s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', @testfcn, 99, 2), [1, NaN, 3, NaN, 5]') ##known not-compatible. matlab bug ML2022a: [1, 1, 3, 1, 5]'
599s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, 99, 3), [1, NaN, 3, NaN, 5])
599s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', @testfcn, 99, 3), [1, NaN, 3, NaN, 5]')
599s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, 3, "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
599s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, [1, 1], "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
599s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, [1.5, 1.5], "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
599s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, 4, "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
599s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, [2, 2], "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
599s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, 3, "samplepoints", [1, 2, 2.5, 3, 3.5]), [1, 2.6, 3.4, 4.2, 5], 10*eps)
599s ***** assert (fillmissing ([NaN, NaN, 3, NaN, 5], @testfcn, 99, 1), [NaN, NaN, 3, NaN, 5]) ##known not-compatible. matlab bug ML2022a: [1, 1, 3, 1, 5]
599s ***** test
599s ***** function A = testfcn (x, y, z)
599s   if (isempty (y))
599s     A = z;
599s   elseif (numel (y) == 1)
599s     A = repelem (x(1), numel(z));
599s   else
599s     A = interp1 (y, x, z, "linear", "extrap");
599s   endif
599s ***** endfunction
599s  x = reshape ([1:24], 3, 4, 2);
599s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
599s  y = x;
599s  y([1, 2, 5, 6, 8, 10, 13, 16, 18, 22]) = [3, 3, 4, 4, 8, 11, 14, 17, 17, 23];
599s  assert (fillmissing (x, @testfcn, 3), y);
599s  assert (fillmissing (x, @testfcn, [1, 1]), y);
599s  assert (fillmissing (x, @testfcn, 3, "endvalues", "extrap"), y);
599s  assert (fillmissing (x, @testfcn, 3, "samplepoints", [1, 2, 3]), y);
599s  y= x;
599s  y(isnan (x)) = 99;
599s  y(8) = 8;
599s  assert (fillmissing (x, @testfcn, 3, "endvalues", 99), y)
599s  y = x;
599s  y([1, 2, 5, 6, 8, 10, 18, 20, 21]) = [4, 11, 11, 6, 11, 7, 18, 20, 21];
599s  assert (fillmissing (x, @testfcn, 3, 2), y);
599s  assert (fillmissing (x, @testfcn, [1, 1], 2), y);
599s  assert (fillmissing (x, @testfcn, 3, 2, "endvalues", "extrap"), y);
599s  assert (fillmissing (x, @testfcn, 3, 2, "samplepoints", [1, 2, 3, 4]), y);
599s  y(1) = NaN;
599s  y([6, 18, 21]) = [9, 24, 24];
599s  assert (fillmissing (x, @testfcn, 3, 2, "samplepoints", [0, 2, 3, 4]), y);
599s  y = x;
599s  y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 99;
599s  y(8) = 8;
599s  assert (fillmissing (x, @testfcn, 3, "endvalues", 99), y);
599s  y([6, 18, 20, 21]) = [6, 18, 20, 21];
599s  y(8) = 99;
599s  assert (fillmissing (x, @testfcn, 3, 2, "endvalues", 99), y);
599s  y([6, 18, 20, 21]) = 99;
599s  assert (fillmissing (x, @testfcn, 3, 3, "endvalues", 99), y);
599s ***** assert (fillmissing ([1, 2, 3], "constant", 0, "maxgap", 1), [1, 2, 3])
599s ***** assert (fillmissing ([1, 2, 3], "constant", 0, "maxgap", 99), [1, 2, 3])
599s ***** assert (fillmissing ([1, NaN, 3], "constant", 0, "maxgap", 1), [1, NaN, 3])
599s ***** assert (fillmissing ([1, NaN, 3], "constant", 0, "maxgap", 1.999), [1, NaN, 3])
599s ***** assert (fillmissing ([1, NaN, 3], "constant", 0, "maxgap", 2), [1, 0, 3])
599s ***** assert (fillmissing ([1, NaN, NaN, 4], "constant", 0, "maxgap", 2), [1, NaN, NaN, 4])
599s ***** assert (fillmissing ([1, NaN, NaN, 4], "constant", 0, "maxgap", 3), [1, 0, 0, 4])
599s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", 0, "maxgap", 2), [1, 0, 3, 0, 5])
599s ***** assert (fillmissing ([NaN, 2, NaN], "constant", 0, "maxgap", 0.999), [NaN, 2, NaN])
599s ***** assert (fillmissing ([NaN, 2, NaN], "constant", 0, "maxgap", 1), [0, 2, 0])
599s ***** assert (fillmissing ([NaN, 2, NaN, NaN], "constant", 0, "maxgap", 1), [0, 2, NaN, NaN])
599s ***** assert (fillmissing ([NaN, 2, NaN, NaN], "constant", 0, "maxgap", 2), [0, 2, 0, 0])
599s ***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "maxgap", 1), [NaN, NaN, NaN])
599s ***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "maxgap", 3), [NaN, NaN, NaN])
599s ***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "maxgap", 999), [NaN, NaN, NaN])
599s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", 0, "maxgap", 2, "samplepoints", [0, 1, 2, 3, 5]), [1, 0, 3, NaN, 5])
599s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "constant", 0, "maxgap", 2, "samplepoints", [0, 1, 2, 3, 5]), [1, 0, 3, NaN, 5]')
599s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", 0, "maxgap", 2, "samplepoints", [0, 2, 3, 4, 5]), [1, NaN, 3, 0, 5])
599s ***** assert (fillmissing ([1, NaN, 3, NaN, 5; 1, NaN, 3, NaN, 5], "constant", 0, 2, "maxgap", 2, "samplepoints", [0, 2, 3, 4, 5]), [1, NaN, 3, 0, 5; 1, NaN, 3, 0, 5])
599s ***** test
599s  x = cat (3, [1, 2, NaN; 4, NaN, NaN], [NaN, 2, 3; 4, 5, NaN]);
599s  assert (fillmissing (x, "constant", 0, "maxgap", 0.1), x);
599s  y = x;
599s  y([4, 7, 12]) = 0;
599s  assert (fillmissing (x, "constant", 0, "maxgap", 1), y);
599s  assert (fillmissing (x, "constant", 0, 1, "maxgap", 1), y);
599s  y = x;
599s  y([5, 7, 12]) = 0;
599s  assert (fillmissing (x, "constant", 0, 2, "maxgap", 1), y);
599s  y = x;
599s  y([4, 5, 7]) = 0;
599s  assert (fillmissing (x, "constant", 0, 3, "maxgap", 1), y);
599s ***** test
599s  x = cat (3, [1, 2, NaN; 4, NaN, NaN], [NaN, 2, 3; 4, 5, NaN]);
599s  [~, idx] = fillmissing (x, "constant", 0, "maxgap", 1);
599s  assert (idx, logical (cat (3, [0, 0, 0; 0, 1, 0], [1, 0, 0; 0, 0, 1])));
599s  [~, idx] = fillmissing (x, "constant", 0, 1, "maxgap", 1);
599s  assert (idx, logical (cat (3, [0, 0, 0; 0, 1, 0], [1, 0, 0; 0, 0, 1])));
599s  [~, idx] = fillmissing (x, "constant", 0, 2, "maxgap", 1);
599s  assert (idx, logical (cat (3, [0, 0, 1; 0, 0, 0], [1, 0, 0; 0, 0, 1])));
599s  [~, idx] = fillmissing (x, "constant", 0, 3, "maxgap", 1);
599s  assert (idx, logical (cat (3, [0, 0, 1; 0, 1, 0], [1, 0, 0; 0, 0, 0])));
599s ***** test
599s  x = [NaN, 2, 3];
599s  [~, idx] = fillmissing (x, "previous");
599s  assert (idx, logical ([0, 0, 0]));
599s  [~, idx] = fillmissing (x, "movmean", 1);
599s  assert (idx, logical ([0, 0, 0]));
599s  x = [1:3; 4:6; 7:9];
599s  x([2, 4, 7, 9]) = NaN;
599s  [~, idx] = fillmissing (x, "linear");
599s  assert (idx, logical ([0, 1, 0; 1, 0, 0; 0, 0, 0]));
599s  [~, idx] = fillmissing (x, "movmean", 2);
599s  assert (idx, logical ([0, 0, 0; 1, 0, 0; 0, 0, 1]));
599s  [A, idx] = fillmissing ([1, 2, 3, NaN, NaN], "movmean",2);
599s  assert (A, [1, 2, 3, 3, NaN]);
599s  assert (idx, logical ([0, 0, 0, 1, 0]));
599s  [A, idx] = fillmissing ([1, 2, 3, NaN, NaN], "movmean",3);
599s  assert (A, [1, 2, 3, 3, NaN]);
599s  assert (idx, logical ([0, 0, 0, 1, 0]));
599s  [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmedian", 2);
599s  assert (A, [1, 2, 2, NaN, NaN]);
599s  assert (idx, logical ([0, 0, 1, 0, 0]));
599s  [A, idx] = fillmissing ([1, 2, 3, NaN, NaN], "movmedian", 3);
599s  assert (A, [1, 2, 3, 3, NaN]);
599s  assert (idx, logical ([0, 0, 0, 1, 0]));
599s  [A, idx] = fillmissing ([1, NaN, 1, NaN, 1],  @(x,y,z) z, 3);
599s  assert (A, [1, 2, 1, 4, 1]);
599s  assert (idx, logical ([0, 1, 0, 1, 0]));
599s  [A, idx] = fillmissing ([1, NaN, 1, NaN, 1],  @(x,y,z) NaN (size (z)), 3);
599s  assert (A, [1, NaN, 1, NaN, 1]);
599s  assert (idx, logical ([0, 0, 0, 0, 0]));
599s ***** assert (fillmissing ([1, 2, 3], "constant", 99, "missinglocations", logical ([0, 0, 0])), [1, 2, 3])
599s ***** assert (fillmissing ([1, 2, 3], "constant", 99, "missinglocations", logical ([1, 1, 1])), [99, 99, 99])
599s ***** assert (fillmissing ([1, NaN, 2, 3, NaN], "constant", 99, "missinglocations", logical ([1, 0, 1, 0, 1])), [99, NaN, 99, 3, 99])
599s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", NaN, "missinglocations", logical ([0, 1, 1, 1, 0])), [1, NaN, NaN, NaN, 5])
599s ***** assert (fillmissing (["foo "; " bar"], "constant", "X", "missinglocations", logical ([0, 0, 0, 0; 0, 0, 0, 0])), ["foo "; " bar"])
599s ***** assert (fillmissing (["foo "; " bar"], "constant", "X", "missinglocations", logical ([1, 0, 1, 0; 0, 1, 1, 0])), ["XoX "; " XXr"])
599s ***** assert (fillmissing ({"foo", "", "bar"}, "constant", "X", "missinglocations", logical ([0, 0, 0])), {"foo", "", "bar"})
599s ***** assert (fillmissing ({"foo", "", "bar"}, "constant", "X", "missinglocations", logical ([1, 1, 0])), {"X", "X", "bar"})
599s ***** test
599s  [~, idx] = fillmissing ([1, NaN, 3, NaN, 5], "constant", NaN);
599s  assert (idx, logical ([0, 0, 0, 0, 0]));
599s  [~, idx] = fillmissing ([1 NaN 3 NaN 5], "constant", NaN, "missinglocations", logical ([0, 1, 1, 1, 0]));
599s  assert (idx, logical ([0, 1, 1, 1, 0]));
599s  [A, idx] = fillmissing ([1, 2, NaN, 1, NaN], "movmean", 3.1, "missinglocations", logical ([0, 0, 1, 1, 0]));
599s  assert (A, [1, 2, 2, NaN, NaN]);
599s  assert (idx, logical ([0, 0, 1, 0, 0]));
599s  [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmean", 2, "missinglocations", logical ([0, 0, 1, 1, 0]));
599s  assert (A, [1, 2, 2, NaN, NaN]);
599s  assert (idx, logical ([0, 0, 1, 0, 0]));
599s  [A, idx] = fillmissing ([1, 2, NaN, 1, NaN], "movmean", 3, "missinglocations", logical ([0, 0, 1, 1, 0]));
599s  assert (A, [1, 2, 2, NaN, NaN]);
599s  assert (idx, logical ([0, 0, 1, 0, 0]));
599s  [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmean", 3, "missinglocations", logical ([0, 0, 1, 1, 0]));
599s  assert (A, [1, 2, 2, NaN, NaN]);
599s  assert (idx, logical ([0, 0, 1, 0, 0]));
599s  [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmedian", 2, "missinglocations", logical ([0, 0, 1, 1, 0]));
599s  assert (A, [1, 2, 2, NaN, NaN]);
599s  assert (idx, logical ([0, 0, 1, 0, 0]));
599s  [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmedian", 3, "missinglocations", logical ([0, 0, 1, 1, 0]));
599s  assert (A, [1, 2, 2, NaN, NaN]);
599s  assert (idx, logical ([0, 0, 1, 0, 0]));
599s  [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmedian", 3.1, "missinglocations", logical ([0, 0, 1, 1, 0]));
599s  assert (A, [1, 2, 2, NaN, NaN]);
599s  assert (idx, logical ([0, 0, 1, 0, 0]));
599s  [A, idx] = fillmissing ([1, NaN, 1, NaN, 1],  @(x,y,z) ones (size (z)), 3, "missinglocations", logical ([0, 1, 0, 1, 1]));
599s  assert (A, [1, 1, 1, 1, 1]);
599s  assert (idx, logical ([0, 1, 0, 1, 1]));
599s  [A, idx] = fillmissing ([1, NaN, 1, NaN, 1],  @(x,y,z) NaN (size (z)), 3, "missinglocations", logical ([0, 1, 0, 1, 1]));
599s  assert (A, [1, NaN, 1, NaN, NaN]);
599s  assert (idx, logical ([0, 0, 0, 0, 0]));
599s ***** test
599s  [A, idx] = fillmissing ([1, 2, 5], "movmedian", 3, "missinglocations", logical ([0, 1, 0]));
599s  assert (A, [1, 3, 5]);
599s  assert (idx, logical ([0, 1, 0]));
599s ***** assert (fillmissing (" foo bar ", "constant", "X"), "XfooXbarX")
599s ***** assert (fillmissing ([" foo"; "bar "], "constant", "X"), ["Xfoo"; "barX"])
599s ***** assert (fillmissing ([" foo"; "bar "], "next"), ["bfoo"; "bar "])
599s ***** assert (fillmissing ([" foo"; "bar "], "next", 1), ["bfoo"; "bar "])
599s ***** assert (fillmissing ([" foo"; "bar "], "previous"), [" foo"; "baro"])
599s ***** assert (fillmissing ([" foo"; "bar "], "previous", 1), [" foo"; "baro"])
599s ***** assert (fillmissing ([" foo"; "bar "], "nearest"), ["bfoo"; "baro"])
599s ***** assert (fillmissing ([" foo"; "bar "], "nearest", 1), ["bfoo"; "baro"])
599s ***** assert (fillmissing ([" foo"; "bar "], "next", 2), ["ffoo"; "bar "])
599s ***** assert (fillmissing ([" foo"; "bar "], "previous", 2), [" foo"; "barr"])
599s ***** assert (fillmissing ([" foo"; "bar "], "nearest", 2), ["ffoo"; "barr"])
599s ***** assert (fillmissing ([" foo"; "bar "], "next", 3), [" foo"; "bar "])
599s ***** assert (fillmissing ([" foo"; "bar "], "previous", 3), [" foo"; "bar "])
599s ***** assert (fillmissing ([" foo"; "bar "], "nearest", 3), [" foo"; "bar "])
599s ***** assert (fillmissing ({"foo", "bar"}, "constant", "a"), {"foo", "bar"})
599s ***** assert (fillmissing ({"foo", "bar"}, "constant", {"a"}), {"foo", "bar"})
599s ***** assert (fillmissing ({"foo", "", "bar"}, "constant", "a"), {"foo", "a", "bar"})
599s ***** assert (fillmissing ({"foo", "", "bar"}, "constant", {"a"}), {"foo", "a", "bar"})
599s ***** assert (fillmissing ({"foo", "", "bar"}, "previous"), {"foo", "foo", "bar"})
599s ***** assert (fillmissing ({"foo", "", "bar"}, "next"), {"foo", "bar", "bar"})
599s ***** assert (fillmissing ({"foo", "", "bar"}, "nearest"), {"foo", "bar", "bar"})
599s ***** assert (fillmissing ({"foo", "", "bar"}, "previous", 2), {"foo", "foo", "bar"})
599s ***** assert (fillmissing ({"foo", "", "bar"}, "next", 2), {"foo", "bar", "bar"})
599s ***** assert (fillmissing ({"foo", "", "bar"}, "nearest", 2), {"foo", "bar", "bar"})
599s ***** assert (fillmissing ({"foo", "", "bar"}, "previous", 1), {"foo", "", "bar"})
599s ***** assert (fillmissing ({"foo", "", "bar"}, "previous", 1), {"foo", "", "bar"})
599s ***** assert (fillmissing ({"foo", "", "bar"}, "next", 1), {"foo", "", "bar"})
599s ***** assert (fillmissing ({"foo", "", "bar"}, "nearest", 1), {"foo", "", "bar"})
599s ***** assert (fillmissing ("abc ", @(x,y,z) x+y+z, 2), "abcj")
599s ***** assert (fillmissing ({"foo", "", "bar"}, @(x,y,z) x(1), 3), {"foo", "foo", "bar"})
600s ***** test
600s  [A, idx] = fillmissing (" a b c", "constant", " ");
600s  assert (A, " a b c");
600s  assert (idx, logical ([0, 0, 0, 0, 0, 0]));
600s  [A, idx] = fillmissing ({"foo", "", "bar", ""}, "constant", "");
600s  assert (A, {"foo", "", "bar", ""});
600s  assert (idx, logical ([0, 0, 0, 0]));
600s  [A, idx] = fillmissing ({"foo", "", "bar", ""}, "constant", {""});
600s  assert (A, {"foo", "", "bar", ""});
600s  assert (idx, logical ([0, 0, 0, 0]));
600s  [A,idx] = fillmissing (" f o o ", @(x,y,z) repelem ("a", numel (z)), 3);
600s  assert (A, "afaoaoa");
600s  assert (idx, logical ([1, 0, 1, 0, 1, 0, 1]));
600s  [A,idx] = fillmissing (" f o o ", @(x,y,z) repelem (" ", numel (z)), 3);
600s  assert (A, " f o o ");
600s  assert (idx, logical ([0, 0, 0, 0, 0, 0, 0]));
600s  [A,idx] = fillmissing ({"", "foo", ""}, @(x,y,z) repelem ({"a"}, numel (z)), 3);
600s  assert (A, {"a", "foo", "a"});
600s  assert (idx, logical ([1, 0, 1]));
600s  [A,idx] = fillmissing ({"", "foo", ""}, @(x,y,z) repelem ({""}, numel (z)), 3);
600s  assert (A, {"", "foo", ""});
600s  assert (idx, logical ([0, 0, 0]));
600s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "constant", true), logical ([1, 0, 1, 0, 1]))
600s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "constant", false, "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([0, 0, 0, 0, 0]))
600s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "previous",  "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([1, 0, 0, 0, 0]))
600s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "next",  "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([0, 0, 0, 0, 1]))
600s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "nearest", "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([0, 0, 0, 0, 0]))
600s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]),  @(x,y,z) false(size(z)), 3), logical ([1, 0, 1, 0, 1]))
600s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]),  @(x,y,z) false(size(z)), 3, "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([0, 0, 0, 0, 0]))
600s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]),  @(x,y,z) false(size(z)), [2, 0], "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([1, 0, 0, 0, 0]))
600s ***** test
600s  x = logical ([1, 0, 1, 0, 1]);
600s  [~, idx] = fillmissing (x, "constant", true);
600s  assert (idx, logical ([0, 0, 0, 0, 0]));
600s  [~, idx] = fillmissing (x, "constant", false, "missinglocations", logical ([1, 0, 1, 0, 1]));
600s  assert (idx, logical ([1, 0, 1, 0, 1]));
600s  [~, idx] = fillmissing (x, "constant", true, "missinglocations", logical ([1, 0, 1, 0, 1]));
600s  assert (idx, logical ([1, 0, 1, 0, 1]));
600s  [~, idx] = fillmissing (x, "previous", "missinglocations", logical ([1, 0, 1, 0, 1]));
600s  assert (idx, logical ([0, 0, 1, 0, 1]));
600s  [~, idx] = fillmissing (x, "next",  "missinglocations", logical ([1, 0, 1, 0, 1]));
600s  assert (idx, logical ([1, 0, 1, 0, 0]));
600s  [~, idx] = fillmissing (x, "nearest", "missinglocations", logical ([1, 0, 1, 0, 1]));
600s  assert (idx, logical ([1, 0, 1, 0, 1]));
600s  [~, idx] = fillmissing (x, @(x,y,z) false(size(z)), 3);
600s  assert (idx, logical ([0, 0, 0, 0, 0]))
600s  [~, idx] = fillmissing (x, @(x,y,z) false(size(z)), 3, "missinglocations", logical ([1, 0, 1, 0, 1]));
600s  assert (idx, logical ([1, 0, 1, 0, 1]))
600s  [~, idx] = fillmissing (x, @(x,y,z) false(size(z)), [2 0], "missinglocations", logical ([1, 0, 1, 0, 1]));
600s  assert (idx, logical ([0, 0, 1, 0, 1]))
600s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "constant", 0), int32 ([1, 2, 3, 4, 5]))
600s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "constant", 0, "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([0, 2, 0, 4, 0]))
600s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "previous", "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([1, 2, 2, 4, 4]))
600s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "next", "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([2, 2, 4, 4, 5]))
600s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "nearest", "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([2, 2, 4, 4, 4]))
600s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), @(x,y,z) z+10, 3), int32 ([1, 2, 3, 4, 5]))
600s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), @(x,y,z) z+10, 3, "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([11, 2, 13, 4, 15]))
600s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), @(x,y,z) z+10, [2, 0], "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([1, 2, 13, 4, 15]))
600s ***** test
600s  x = int32 ([1, 2, 3, 4, 5]);
600s  [~, idx] = fillmissing (x, "constant", 0);
600s  assert (idx, logical ([0, 0, 0, 0, 0]));
600s  [~, idx] = fillmissing (x, "constant", 0, "missinglocations", logical ([1, 0, 1, 0, 1]));
600s  assert (idx, logical ([1, 0, 1, 0, 1]));
600s  [~, idx] = fillmissing (x, "constant", 3, "missinglocations", logical ([0, 0, 1, 0, 0]));
600s  assert (idx, logical ([0, 0, 1, 0, 0]));
600s  [~, idx] = fillmissing (x, "previous", "missinglocations", logical ([1, 0, 1, 0, 1]));
600s  assert (idx, logical ([0, 0, 1, 0, 1]));
600s  [~, idx] = fillmissing (x, "next", "missinglocations", logical ([1, 0, 1, 0, 1]));
600s  assert (idx, logical ([1, 0, 1, 0, 0]));
600s  [~, idx] = fillmissing (x, "nearest", "missinglocations", logical ([1, 0, 1, 0, 1]));
600s  assert (idx, logical ([1, 0, 1, 0, 1]));
600s  [~, idx] = fillmissing (x, @(x,y,z) z+10, 3);
600s  assert (idx, logical ([0, 0, 0, 0, 0]));
600s  [~, idx] = fillmissing (x, @(x,y,z) z+10, 3, "missinglocations", logical ([1, 0, 1, 0, 1]));
600s  assert (idx, logical ([1, 0, 1, 0, 1]));
600s  [~, idx] = fillmissing (x, @(x,y,z) z+10, [2 0], "missinglocations", logical ([1, 0, 1, 0, 1]));
600s  assert (idx, logical ([0, 0, 1, 0, 1]));
600s ***** test
600s  [A, idx] = fillmissing ([struct, struct], "constant", 1);
600s  assert (A, [struct, struct])
600s  assert (idx, [false, false])
600s ***** error <Invalid call> fillmissing ()
600s ***** error <Invalid call> fillmissing (1)
600s ***** error <Invalid call> fillmissing (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
600s ***** error <second input must be a> fillmissing (1, 2)
600s ***** error <unknown fill method 'foo'> fillmissing (1, "foo")
600s ***** error <fill function must accept at least> fillmissing (1, @(x) x, 1)
600s ***** error <fill function must accept at least> fillmissing (1, @(x,y) x+y, 1)
600s ***** error <interpolation methods only valid for numeric> fillmissing ("a b c", "linear")
600s ***** error <interpolation methods only valid for numeric> fillmissing ({"a", "b"}, "linear")
600s ***** error <'movmean' and 'movmedian' methods only valid for numeric> fillmissing ("a b c", "movmean", 2)
600s ***** error <'movmean' and 'movmedian' methods only valid for numeric> fillmissing ({"a", "b"}, "movmean", 2)
600s ***** error <'constant' method must be followed by> fillmissing (1, "constant")
600s ***** error <a numeric fill value cannot be emtpy> fillmissing (1, "constant", [])
600s ***** error <fill value must be the same data type> fillmissing (1, "constant", "a")
600s ***** error <fill value must be the same data type> fillmissing ("a", "constant", 1)
600s ***** error <fill value must be the same data type> fillmissing ("a", "constant", {"foo"})
600s ***** error <fill value must be the same data type> fillmissing ({"foo"}, "constant", 1)
600s ***** error <moving window method must be followed by> fillmissing (1, "movmean")
600s ***** error <moving window method must be followed by> fillmissing (1, "movmedian")
600s ***** error <DIM must be a positive scalar> fillmissing (1, "constant", 1, 0)
600s ***** error <DIM must be a positive scalar> fillmissing (1, "constant", 1, -1)
600s ***** error <DIM must be a positive scalar> fillmissing (1, "constant", 1, [1, 2])
600s ***** error <properties must be given as> fillmissing (1, "constant", 1, "samplepoints")
600s ***** error <properties must be given as> fillmissing (1, "constant", 1, "foo")
600s ***** error <properties must be given as> fillmissing (1, "constant", 1, 1, "foo")
600s ***** error <invalid parameter name specified> fillmissing (1, "constant", 1, 2, {1}, 4)
600s ***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", [1, 2])
600s ***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", [3, 1, 2])
600s ***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", [1, 1, 2])
600s ***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", "abc")
600s ***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", logical ([1, 1, 1]))
600s ***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 1, "samplepoints", [1, 2, 3])
600s ***** error <EndValues method 'constant' only valid> fillmissing ("foo", "next", "endvalues", 1)
600s ***** error <invalid EndValues method 'foo'> fillmissing (1, "constant", 1, 1, "endvalues", "foo")
600s ***** error <EndValues must be a scalar or a 1 element array> fillmissing ([1, 2, 3], "constant", 1, 2, "endvalues", [1, 2, 3])
600s ***** error <EndValues must be a scalar or a 3 element array> fillmissing ([1, 2, 3], "constant", 1, 1, "endvalues", [1, 2])
600s ***** error <EndValues must be a scalar or a 12 element array> fillmissing (randi(5,4,3,2), "constant", 1, 3, "endvalues", [1, 2])
600s ***** error <EndValues must be numeric or a> fillmissing (1, "constant", 1, 1, "endvalues", {1})
600s ***** error <invalid parameter name 'foo'> fillmissing (1, "constant", 1, 2, "foo", 4)
600s ***** error <MissingLocations option is not compatible with> fillmissing (struct, "constant", 1, "missinglocations", false)
600s ***** error <MissingLocations and MaxGap options> fillmissing (1, "constant", 1, 2, "maxgap", 1, "missinglocations", false)
600s ***** error <MissingLocations and MaxGap options> fillmissing (1, "constant", 1, 2, "missinglocations", false, "maxgap", 1)
600s ***** error <the 'replacevalues' option has not> fillmissing (1, "constant", 1, "replacevalues", true)
600s ***** error <the 'datavariables' option has not> fillmissing (1, "constant", 1, "datavariables", "Varname")
600s ***** error <MissingLocations must be a> fillmissing (1, "constant", 1, 2, "missinglocations", 1)
600s ***** error <MissingLocations must be a> fillmissing (1, "constant", 1, 2, "missinglocations", "a")
600s ***** error <MissingLocations must be a> fillmissing (1, "constant", 1, 2, "missinglocations", [true, false])
600s ***** error <MissingLocations cannot be used with method> fillmissing (true, "linear", "missinglocations", true)
600s ***** error <MissingLocations cannot be used with method> fillmissing (int8 (1), "linear", "missinglocations", true)
600s ***** error <MissingLocations cannot be used with EndValues method> fillmissing (true, "next", "missinglocations", true, "EndValues", "linear")
600s ***** error <MissingLocations cannot be used with EndValues method> fillmissing (true, "next", "EndValues", "linear", "missinglocations", true)
600s ***** error <MissingLocations cannot be used with EndValues method> fillmissing (int8 (1), "next", "missinglocations", true, "EndValues", "linear")
600s ***** error <MissingLocations cannot be used with EndValues method> fillmissing (int8 (1), "next", "EndValues", "linear", "missinglocations", true)
600s ***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", true)
600s ***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", "a")
600s ***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", [1, 2])
600s ***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", 0)
600s ***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", -1)
600s ***** error <fill value 'V' must be a scalar or a 1> fillmissing ([1, 2, 3], "constant", [1, 2, 3])
600s ***** error <fill value 'V' must be a scalar or a 1> fillmissing ([1, 2, 3]', "constant", [1, 2, 3])
600s ***** error <fill value 'V' must be a scalar or a 1> fillmissing ([1, 2, 3]', "constant", [1, 2, 3], 1)
600s ***** error <fill value 'V' must be a scalar or a 1> fillmissing ([1, 2, 3], "constant", [1, 2, 3], 2)
600s ***** error <fill value 'V' must be a scalar or a 6> fillmissing (randi (5, 4, 3, 2), "constant", [1, 2], 1)
600s ***** error <fill value 'V' must be a scalar or a 8> fillmissing (randi (5, 4, 3, 2), "constant", [1, 2], 2)
600s ***** error <fill value 'V' must be a scalar or a 12> fillmissing (randi (5, 4, 3, 2), "constant", [1, 2], 3)
600s ***** error <fill function handle must be followed by> fillmissing (1, @(x,y,z) x+y+z)
600s ***** error <fill function return values must be the same size> fillmissing ([1, NaN, 2], @(x,y,z) [1, 2], 2)
600s 380 tests, 379 passed, 0 known failure, 1 skipped
600s [inst/hmmviterbi.m]
600s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/hmmviterbi.m
600s ***** test
600s  sequence = [1, 2, 1, 1, 1, 2, 2, 1, 2, 3, 3, 3, ...
600s              3, 2, 3, 1, 1, 1, 1, 3, 3, 2, 3, 1, 3];
600s  transprob = [0.8, 0.2; 0.4, 0.6];
600s  outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
600s  vpath = hmmviterbi (sequence, transprob, outprob);
600s  expected = [1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, ...
600s              1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1];
600s  assert (vpath, expected);
600s ***** test
600s  sequence = {"A", "B", "A", "A", "A", "B", "B", "A", "B", "C", "C", "C", ...
600s              "C", "B", "C", "A", "A", "A", "A", "C", "C", "B", "C", "A", "C"};
600s  transprob = [0.8, 0.2; 0.4, 0.6];
600s  outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
600s  symbols = {"A", "B", "C"};
600s  statenames = {"One", "Two"};
600s  vpath = hmmviterbi (sequence, transprob, outprob, "symbols", symbols, ...
600s                                                    "statenames", statenames);
600s  expected = {"One", "One", "Two", "Two", "Two", "One", "One", "One", ...
600s              "One", "One", "One", "One", "One", "One", "One", "Two", ...
600s              "Two", "Two", "Two", "One", "One", "One", "One", "One", "One"};
600s  assert (vpath, expected);
600s 2 tests, 2 passed, 0 known failure, 0 skipped
600s [inst/confusionchart.m]
600s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/confusionchart.m
600s ***** demo
600s  ## Setting the chart properties
600s  Yt = [8 5 6 8 5 3 1 6 4 2 5 3 1 4]';
600s  Yp = [8 5 6 8 5 2 3 4 4 5 5 7 2 6]';
600s  confusionchart (Yt, Yp, "Title", ...
600s    "Demonstration with summaries","Normalization",...
600s    "absolute","ColumnSummary", "column-normalized","RowSummary",...
600s    "row-normalized")
600s ***** demo
600s  ## Cellstr as inputs
600s  Yt = {"Positive", "Positive", "Positive", "Negative", "Negative"};
600s  Yp = {"Positive", "Positive", "Negative", "Negative", "Negative"};
600s  m = confusionmat (Yt, Yp);
600s  confusionchart (m, {"Positive", "Negative"});
600s ***** demo
600s  ## Editing the object properties
600s  Yt = {"Positive", "Positive", "Positive", "Negative", "Negative"};
600s  Yp = {"Positive", "Positive", "Negative", "Negative", "Negative"};
600s  cm = confusionchart (Yt, Yp);
600s  cm.Title = "This is an example with a green diagonal";
600s  cm.DiagonalColor = [0.4660, 0.6740, 0.1880];
600s ***** demo
600s  ## Confusion chart in a uipanel
600s  h = uipanel ();
600s  Yt = {"Positive", "Positive", "Positive", "Negative", "Negative"};
600s  Yp = {"Positive", "Positive", "Negative", "Negative", "Negative"};
600s  cm = confusionchart (h, Yt, Yp);
600s ***** demo
600s  ## Sorting classes
600s  Yt = [8 5 6 8 5 3 1 6 4 2 5 3 1 4]';
600s  Yp = [8 5 6 8 5 2 3 4 4 5 5 7 2 6]';
600s  cm = confusionchart (Yt, Yp, "Title", ...
600s    "Classes are sorted in ascending order");
600s  cm = confusionchart (Yt, Yp, "Title", ...
600s    "Classes are sorted according to clusters");
600s  sortClasses (cm, "cluster");
600s ***** shared visibility_setting
600s  visibility_setting = get (0, "DefaultFigureVisible");
600s ***** test
600s  set (0, "DefaultFigureVisible", "off");
600s  fail ("confusionchart ()", "Invalid call");
600s  set (0, "DefaultFigureVisible", visibility_setting);
600s ***** test
600s  set (0, "DefaultFigureVisible", "off");
600s  fail ("confusionchart ([1 1; 2 2; 3 3])", "invalid argument");
600s  set (0, "DefaultFigureVisible", visibility_setting);
600s ***** test
600s  set (0, "DefaultFigureVisible", "off");
600s  fail ("confusionchart ([1 2], [0 1], 'xxx', 1)", "invalid property");
600s  set (0, "DefaultFigureVisible", visibility_setting);
600s ***** test
600s  set (0, "DefaultFigureVisible", "off");
600s  fail ("confusionchart ([1 2], [0 1], 'XLabel', 1)", "XLabel .* string");
600s  set (0, "DefaultFigureVisible", visibility_setting);
600s ***** test
600s  set (0, "DefaultFigureVisible", "off");
600s  fail ("confusionchart ([1 2], [0 1], 'YLabel', [1 0])", ...
600s        ".* YLabel .* string");
600s  set (0, "DefaultFigureVisible", visibility_setting);
600s ***** test
600s  set (0, "DefaultFigureVisible", "off");
600s  fail ("confusionchart ([1 2], [0 1], 'Title', .5)", ".* Title .* string");
600s  set (0, "DefaultFigureVisible", visibility_setting);
600s ***** test
600s  set (0, "DefaultFigureVisible", "off");
600s  fail ("confusionchart ([1 2], [0 1], 'FontName', [])", ...
600s        ".* FontName .* string");
600s  set (0, "DefaultFigureVisible", visibility_setting);
601s ***** test
601s  set (0, "DefaultFigureVisible", "off");
601s  fail ("confusionchart ([1 2], [0 1], 'FontSize', 'b')", ...
601s        ".* FontSize .* numeric");
601s  set (0, "DefaultFigureVisible", visibility_setting);
601s ***** test
601s  set (0, "DefaultFigureVisible", "off");
601s  fail ("confusionchart ([1 2], [0 1], 'DiagonalColor', 'h')", ...
601s        ".* DiagonalColor .* color");
601s  set (0, "DefaultFigureVisible", visibility_setting);
601s ***** test
601s  set (0, "DefaultFigureVisible", "off");
601s  fail ("confusionchart ([1 2], [0 1], 'OffDiagonalColor', [])", ...
601s        ".* OffDiagonalColor .* color");
601s  set (0, "DefaultFigureVisible", visibility_setting);
601s ***** test
601s  set (0, "DefaultFigureVisible", "off");
601s  fail ("confusionchart ([1 2], [0 1], 'Normalization', '')", ...
601s        ".* invalid .* Normalization");
601s  set (0, "DefaultFigureVisible", visibility_setting);
601s ***** test
601s  set (0, "DefaultFigureVisible", "off");
601s  fail ("confusionchart ([1 2], [0 1], 'ColumnSummary', [])", ...
601s        ".* invalid .* ColumnSummary");
601s  set (0, "DefaultFigureVisible", visibility_setting);
601s ***** test
601s  set (0, "DefaultFigureVisible", "off");
601s  fail ("confusionchart ([1 2], [0 1], 'RowSummary', 1)", ...
601s        ".* invalid .* RowSummary");
601s  set (0, "DefaultFigureVisible", visibility_setting);
601s ***** test
601s  set (0, "DefaultFigureVisible", "off");
601s  fail ("confusionchart ([1 2], [0 1], 'GridVisible', .1)", ...
601s        ".* invalid .* GridVisible");
601s  set (0, "DefaultFigureVisible", visibility_setting);
601s ***** test
601s  set (0, "DefaultFigureVisible", "off");
601s  fail ("confusionchart ([1 2], [0 1], 'HandleVisibility', .1)", ...
601s        ".* invalid .* HandleVisibility");
601s  set (0, "DefaultFigureVisible", visibility_setting);
601s ***** test
601s  set (0, "DefaultFigureVisible", "off");
601s  fail ("confusionchart ([1 2], [0 1], 'OuterPosition', .1)", ...
601s        ".* invalid .* OuterPosition");
601s  set (0, "DefaultFigureVisible", visibility_setting);
601s ***** test
601s  set (0, "DefaultFigureVisible", "off");
601s  fail ("confusionchart ([1 2], [0 1], 'Position', .1)", ...
601s        ".* invalid .* Position");
601s  set (0, "DefaultFigureVisible", visibility_setting);
601s ***** test
601s  set (0, "DefaultFigureVisible", "off");
601s  fail ("confusionchart ([1 2], [0 1], 'Units', .1)", ".* invalid .* Units");
601s  set (0, "DefaultFigureVisible", visibility_setting);
601s 18 tests, 18 passed, 0 known failure, 0 skipped
601s [inst/tabulate.m]
601s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/tabulate.m
601s ***** demo
601s  ## Generate a frequency table for a vector of data in a cell array
601s  load patients
601s 
601s  ## Display the first seven entries of the Gender variable
601s  gender = Gender(1:7)
601s 
601s  ## Compute the equency table that shows the number and
601s  ## percentage of Male and Female patients
601s  tabulate (Gender)
601s ***** demo
601s  ## Create a frequency table for a vector of positive integers
601s  load patients
601s 
601s  ## Display the first seven entries of the Gender variable
601s  height = Height(1:7)
601s 
601s  ## Create a frequency table that shows, in its second and third columns,
601s  ## the number and percentage of patients with a particular height.
601s  table = tabulate (Height);
601s 
601s  ## Display the first and last seven entries of the frequency table
601s  first = table(1:7,:)
601s 
601s  last = table(end-6:end,:)
601s ***** demo
601s  ## Create a frequency table from a character array
601s  load carsmall
601s 
601s  ## Tabulate the data in the Origin variable, which shows the
601s  ## country of origin of each car in the data set
601s  tabulate (Origin)
601s ***** demo
601s  ## Create a frequency table from a numeric vector with NaN values
601s  load carsmall
601s 
601s  ## The carsmall dataset contains measurements of 100 cars
601s  total_cars = length (MPG)
601s  ## For six cars, the MPG value is missing
601s  missingMPG = length (MPG(isnan (MPG)))
601s 
601s  ## Create a frequency table using MPG
601s  tabulate (MPG)
601s  table = tabulate (MPG);
601s 
601s  ## Only 94 cars were used
601s  valid_cars = sum (table(:,2))
601s ***** test
601s  load patients
601s  table = tabulate (Gender);
601s  assert (table{1,1}, "Male");
601s  assert (table{2,1}, "Female");
601s  assert (table{1,2}, 47);
601s  assert (table{2,2}, 53);
601s ***** test
601s  load patients
601s  table = tabulate (Height);
601s  assert (table(end-4,:), [68, 15, 15]);
601s  assert (table(end-3,:), [69, 8, 8]);
601s  assert (table(end-2,:), [70, 11, 11]);
601s  assert (table(end-1,:), [71, 10, 10]);
601s  assert (table(end,:), [72, 4, 4]);
601s ***** error<tabulate: X must be either a numeric vector> tabulate (ones (3))
601s ***** error<tabulate: X must be either a numeric vector> tabulate ({1, 2, 3, 4})
601s ***** error<tabulate: X must be either a numeric vector> ...
601s  tabulate ({"a", "b"; "a", "c"})
601s 5 tests, 5 passed, 0 known failure, 0 skipped
601s [inst/crosstab.m]
601s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/crosstab.m
601s ***** error crosstab ()
601s ***** error crosstab (1)
601s ***** error <crosstab: x1, x2 ... xn must be vectors.> crosstab (ones (2), [1 1])
601s ***** error <crosstab: x1, x2 ... xn must be vectors.> crosstab ([1 1], ones (2))
601s ***** error <x1, x2 .* must be vectors of the same length> crosstab ([1], [1 2])
601s ***** error <x1, x2 .* must be vectors of the same length> crosstab ([1 2], [1])
601s ***** test
601s  load carbig
601s  [table, chisq, p, labels] = crosstab (cyl4, when, org);
601s  assert (table(2,3,1), 38);
601s  assert (labels{3,3}, "Japan");
601s ***** test
601s  load carbig
601s  [table, chisq, p, labels] = crosstab (cyl4, when, org);
601s  assert (table(2,3,2), 17);
601s  assert (labels{1,3}, "USA");
601s 8 tests, 8 passed, 0 known failure, 0 skipped
601s [inst/wblplot.m]
601s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/wblplot.m
601s ***** demo
601s  x = [16 34 53 75 93 120];
601s  wblplot (x);
601s ***** demo
601s  x = [2 3 5 7 11 13 17 19 23 29 31 37 41 43 47 53 59 61 67]';
601s  c = [0 1 0 1 0 1 1 1 0 0 1 0 1 0 1 1 0 1 1]';
601s  [h, p] = wblplot (x, c);
601s  p
601s ***** demo
601s  x = [16, 34, 53, 75, 93, 120, 150, 191, 240 ,339];
601s  [h, p] = wblplot (x, [], [], 0.05);
601s  p
601s  ## Benchmark Reliasoft eta = 146.2545 beta 1.1973 rho = 0.9999
601s ***** demo
601s  x = [46 64 83 105 123 150 150];
601s  c = [0 0 0 0 0 0 1];
601s  f = [1 1 1 1 1 1 4];
601s  wblplot (x, c, f, 0.05);
601s ***** demo
601s  x = [46 64 83 105 123 150 150];
601s  c = [0 0 0 0 0 0 1];
601s  f = [1 1 1 1 1 1 4];
601s  ## Subtract 30.92 from x to simulate a 3 parameter wbl with gamma = 30.92
601s  wblplot (x - 30.92, c, f, 0.05);
601s ***** test
601s  hf = figure ("visible", "off");
601s  unwind_protect
601s    x = [16, 34, 53, 75, 93, 120, 150, 191, 240 ,339];
601s    [h, p] = wblplot (x, [], [], 0.05);
601s    assert (numel (h), 4)
601s    assert (p(1), 146.2545, 1E-4)
601s    assert (p(2), 1.1973, 1E-4)
601s    assert (p(3), 0.9999, 5E-5)
601s  unwind_protect_cleanup
601s    close (hf);
601s  end_unwind_protect
602s 1 test, 1 passed, 0 known failure, 0 skipped
602s [inst/gscatter.m]
602s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/gscatter.m
602s ***** demo
602s  load fisheriris;
602s  X = meas(:,3:4);
602s  cidcs = kmeans (X, 3, "Replicates", 5);
602s  gscatter (X(:,1), X(:,2), cidcs, [.75 .75 0; 0 .75 .75; .75 0 .75], "os^");
602s  title ("Fisher's iris data");
602s ***** shared visibility_setting
602s  visibility_setting = get (0, "DefaultFigureVisible");
602s ***** test
602s  hf = figure ("visible", "off");
602s  unwind_protect
602s    load fisheriris;
602s    X = meas(:,3:4);
602s    cidcs = kmeans (X, 3, "Replicates", 5);
602s    gscatter (X(:,1), X(:,2), cidcs, [.75 .75 0; 0 .75 .75; .75 0 .75], "os^");
602s    title ("Fisher's iris data");
602s  unwind_protect_cleanup
602s    close (hf);
602s  end_unwind_protect
602s warning: legend: 'best' not yet implemented for location specifier, using 'northeast' instead
602s ***** error gscatter ();
602s ***** error gscatter ([1]);
602s ***** error gscatter ([1], [2]);
602s ***** error <x must be a numeric vector> gscatter ('abc', [1 2 3], [1]);
602s ***** error <x and y must have the same size> gscatter ([1 2 3], [1 2], [1]);
602s ***** error <y must be a numeric vector> gscatter ([1 2 3], 'abc', [1]);
602s ***** error <g must have the same size as x and y> gscatter ([1 2], [1 2], [1]);
602s ***** error <invalid dolegend> gscatter ([1 2], [1 2], [1 2], 'rb', 'so', 12, 'xxx');
602s 9 tests, 9 passed, 0 known failure, 0 skipped
602s [inst/rmmissing.m]
602s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/rmmissing.m
602s ***** assert (rmmissing ([1,NaN,3]), [1,3])
602s ***** assert (rmmissing ('abcd f'), 'abcdf')
602s ***** assert (rmmissing ({'xxx','','xyz'}), {'xxx','xyz'})
602s ***** assert (rmmissing ({'xxx','';'xyz','yyy'}), {'xyz','yyy'})
602s ***** assert (rmmissing ({'xxx','';'xyz','yyy'}, 2), {'xxx';'xyz'})
602s ***** assert (rmmissing ([1,2;NaN,2]), [1,2])
602s ***** assert (rmmissing ([1,2;NaN,2], 2), [2,2]')
602s ***** assert (rmmissing ([1,2;NaN,4;NaN,NaN],"MinNumMissing", 2), [1,2;NaN,4])
602s ***** test
602s  x = [1:6];
602s  x([2,4]) = NaN;
602s  [~, idx] = rmmissing (x);
602s  assert (idx, logical ([0, 1, 0, 1, 0, 0]));
602s  assert (class(idx), 'logical');
602s  x = reshape (x, [2, 3]);
602s  [~, idx] = rmmissing (x);
602s  assert (idx, logical ([0; 1]));
602s  assert (class(idx), 'logical');
602s  [~, idx] = rmmissing (x, 2);
602s  assert (idx, logical ([1, 1, 0]));
602s  assert (class(idx), 'logical');
602s  [~, idx] = rmmissing (x, 1, "MinNumMissing", 2);
602s  assert (idx, logical ([0; 1]));
602s  assert (class(idx), 'logical');
602s  [~, idx] = rmmissing (x, 2, "MinNumMissing", 2);
602s  assert (idx, logical ([0, 0, 0]));
602s  assert (class(idx), 'logical');
602s ***** assert (rmmissing (single ([1 2 NaN; 3 4 5])), single ([3 4 5]))
602s ***** assert (rmmissing (logical (ones (3))), logical (ones (3)))
602s ***** assert (rmmissing (int32 (ones (3))), int32 (ones (3)))
602s ***** assert (rmmissing (uint32 (ones (3))), uint32 (ones (3)))
602s ***** assert (rmmissing ({1, 2, 3}), {1, 2, 3})
602s ***** assert (rmmissing ([struct, struct, struct]), [struct, struct, struct])
602s ***** assert (rmmissing ([]), [])
602s ***** assert (rmmissing (ones (1,0)), ones (1,0))
602s ***** assert (rmmissing (ones (1,0), 1), ones (1,0))
602s ***** assert (rmmissing (ones (1,0), 2), ones (1,0))
602s ***** assert (rmmissing (ones (0,1)), ones (0,1))
602s ***** assert (rmmissing (ones (0,1), 1), ones (0,1))
602s ***** assert (rmmissing (ones (0,1), 2), ones (0,1))
602s ***** error <input dimension> rmmissing (ones (0,1,2))
602s ***** error rmmissing ()
602s ***** error <input dimension> rmmissing (ones(2,2,2))
602s ***** error <must be either 1 or 2> rmmissing ([1 2; 3 4], 5)
602s ***** error <unknown parameter name> rmmissing ([1 2; 3 4], "XXX", 1)
602s ***** error <'MinNumMissing'> rmmissing ([1 2; 3 4], 2, "MinNumMissing", -2)
602s ***** error <'MinNumMissing'> rmmissing ([1 2; 3 4], "MinNumMissing", 3.8)
602s ***** error <'MinNumMissing'> rmmissing ([1 2; 3 4], "MinNumMissing", [1 2 3])
602s ***** error <'MinNumMissing'> rmmissing ([1 2; 3 4], "MinNumMissing", 'xxx')
602s 31 tests, 31 passed, 0 known failure, 0 skipped
602s [inst/ranksum.m]
602s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/ranksum.m
602s ***** test
602s  mileage = [33.3, 34.5, 37.4; 33.4, 34.8, 36.8; ...
602s             32.9, 33.8, 37.6; 32.6, 33.4, 36.6; ...
602s             32.5, 33.7, 37.0; 33.0, 33.9, 36.7];
602s  [p,h,stats] = ranksum(mileage(:,1),mileage(:,2));
602s  assert (p, 0.004329004329004329, 1e-14);
602s  assert (h, true);
602s  assert (stats.ranksum, 21.5);
602s ***** test
602s  year1 = [51 52 62 62 52 52 51 53 59 63 59 56 63 74 68 86 82 70 69 75 73 ...
602s           49 47 50 60 59 60 62 61 71]';
602s  year2 = [54 53 64 66 57 53 54 54 62 66 59 59 67 76 75 86 82 67 74 80 75 ...
602s           54 50 53 62 62 62 72 60 67]';
602s  [p,h,stats] = ranksum(year1, year2, "alpha", 0.01, "tail", "left");
602s  assert (p, 0.1270832752950605, 1e-14);
602s  assert (h, false);
602s  assert (stats.ranksum, 837.5);
602s  assert (stats.zval, -1.140287483634606, 1e-14);
602s  [p,h,stats] = ranksum(year1, year2, "alpha", 0.01, "tail", "left", ...
602s                        "method", "exact");
602s  assert (p, 0.127343916432862, 1e-14);
602s  assert (h, false);
602s  assert (stats.ranksum, 837.5);
608s 2 tests, 2 passed, 0 known failure, 0 skipped
608s [inst/clusterdata.m]
608s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/clusterdata.m
608s ***** demo
608s  randn ("seed", 1)  # for reproducibility
608s  r1 = randn (10, 2) * 0.25 + 1;
608s  randn ("seed", 5)  # for reproducibility
608s  r2 = randn (20, 2) * 0.5 - 1;
608s  X = [r1; r2];
608s 
608s  wnl = warning ("off", "Octave:linkage_savemem", "local");
608s  T = clusterdata (X, "linkage", "ward", "MaxClust", 2);
608s  scatter (X(:,1), X(:,2), 36, T, "filled");
608s ***** error<clusterdata: function called with too few input arguments.> ...
608s  clusterdata ()
608s ***** error<clusterdata: function called with too few input arguments.> ...
608s  clusterdata (1)
608s ***** error <unknown property .*> clusterdata ([1 1], "Bogus", 1)
608s ***** error <specify .* 'MaxClust' or 'Cutoff' .*> clusterdata ([1 1], "Depth", 1)
608s 4 tests, 4 passed, 0 known failure, 0 skipped
608s [inst/loadmodel.m]
608s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/loadmodel.m
608s ***** error<loadmodel: too few arguments.> loadmodel ()
608s ***** error<loadmodel: 'fisheriris.mat' does not contain a Classification or Regression object.> ...
608s  loadmodel ("fisheriris.mat")
608s ***** error<loadmodel: 'ClassificationModel' is not supported.> ...
608s  loadmodel ("fail_loadmodel.mdl")
608s ***** error<ClassificationKNN.load_model: invalid model in 'fail_load_model.mdl'.> ...
608s  loadmodel ("fail_load_model.mdl")
608s 4 tests, 4 passed, 0 known failure, 0 skipped
608s [inst/Regression/RegressionGAM.m]
608s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/Regression/RegressionGAM.m
608s ***** demo
608s  ## Train a RegressionGAM Model for synthetic values
608s  f1 = @(x) cos (3 * x);
608s  f2 = @(x) x .^ 3;
608s  x1 = 2 * rand (50, 1) - 1;
608s  x2 = 2 * rand (50, 1) - 1;
608s  y = f1(x1) + f2(x2);
608s  y = y + y .* 0.2 .* rand (50,1);
608s  X = [x1, x2];
608s  a = fitrgam (X, y, "tol", 1e-3)
608s ***** demo
608s  ## Declare two different functions
608s  f1 = @(x) cos (3 * x);
608s  f2 = @(x) x .^ 3;
608s 
608s  ## Generate 80 samples for f1 and f2
608s  x = [-4*pi:0.1*pi:4*pi-0.1*pi]';
608s  X1 = f1 (x);
608s  X2 = f2 (x);
608s 
608s  ## Create a synthetic response by adding noise
608s  rand ("seed", 3);
608s  Ytrue = X1 + X2;
608s  Y = Ytrue + Ytrue .* 0.2 .* rand (80,1);
608s 
608s  ## Assemble predictor data
608s  X = [X1, X2];
608s 
608s  ## Train the GAM and test on the same data
608s  a = fitrgam (X, Y, "order", [5, 5]);
608s  [ypred, ySDsd, yInt] = predict (a, X);
608s 
608s  ## Plot the results
608s  figure
608s  [sortedY, indY] = sort (Ytrue);
608s  plot (sortedY, "r-");
608s  xlim ([0, 80]);
608s  hold on
608s  plot (ypred(indY), "g+")
608s  plot (yInt(indY,1), "k:")
608s  plot (yInt(indY,2), "k:")
608s  xlabel ("Predictor samples");
608s  ylabel ("Response");
608s  title ("actual vs predicted values for function f1(x) = cos (3x) ");
608s  legend ({"Theoretical Response", "Predicted Response", "Prediction Intervals"});
608s 
608s  ## Use 30% Holdout partitioning for training and testing data
608s  C = cvpartition (80, "HoldOut", 0.3);
608s  [ypred, ySDsd, yInt] = predict (a, X(test(C),:));
608s 
608s  ## Plot the results
608s  figure
608s  [sortedY, indY] = sort (Ytrue(test(C)));
608s  plot (sortedY, 'r-');
608s  xlim ([0, sum(test(C))]);
608s  hold on
608s  plot (ypred(indY), "g+")
608s  plot (yInt(indY,1),'k:')
608s  plot (yInt(indY,2),'k:')
608s  xlabel ("Predictor samples");
608s  ylabel ("Response");
608s  title ("actual vs predicted values for function f1(x) = cos (3x) ");
608s  legend ({"Theoretical Response", "Predicted Response", "Prediction Intervals"});
608s ***** test
608s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
608s  y = [1; 2; 3; 4];
608s  a = RegressionGAM (x, y);
608s  assert ({a.X, a.Y}, {x, y})
608s  assert ({a.BaseModel.Intercept}, {2.5000})
608s  assert ({a.Knots, a.Order, a.DoF}, {[5, 5, 5], [3, 3, 3], [8, 8, 8]})
608s  assert ({a.NumObservations, a.NumPredictors}, {4, 3})
608s  assert ({a.ResponseName, a.PredictorNames}, {"Y", {"x1", "x2", "x3"}})
608s  assert ({a.Formula}, {[]})
608s ***** test
608s  x = [1, 2, 3, 4; 4, 5, 6, 7; 7, 8, 9, 1; 3, 2, 1, 2];
608s  y = [1; 2; 3; 4];
608s  pnames = {"A", "B", "C", "D"};
608s  formula = "Y ~ A + B + C + D + A:C";
608s  intMat = logical ([1,0,0,0;0,1,0,0;0,0,1,0;0,0,0,1;1,0,1,0]);
608s  a = RegressionGAM (x, y, "predictors", pnames, "formula", formula);
608s  assert ({a.IntMatrix}, {intMat})
608s  assert ({a.ResponseName, a.PredictorNames}, {"Y", pnames})
608s  assert ({a.Formula}, {formula})
608s ***** error<RegressionGAM: too few input arguments.> RegressionGAM ()
608s ***** error<RegressionGAM: too few input arguments.> RegressionGAM (ones(10,2))
608s ***** error<RegressionGAM: number of rows in X and Y must be equal.> ...
608s  RegressionGAM (ones(10,2), ones (5,1))
608s ***** error<RegressionGAM: invalid values in X.> ...
608s  RegressionGAM ([1;2;3;"a";4], ones (5,1))
608s ***** error<RegressionGAM: invalid parameter name in optional pair arguments.> ...
608s  RegressionGAM (ones(10,2), ones (10,1), "some", "some")
608s ***** error<RegressionGAM: Formula must be a string.>
608s  RegressionGAM (ones(10,2), ones (10,1), "formula", {"y~x1+x2"})
608s ***** error<RegressionGAM: Formula must be a string.>
608s  RegressionGAM (ones(10,2), ones (10,1), "formula", [0, 1, 0])
608s ***** error<RegressionGAM: invalid syntax in Formula.> ...
608s  RegressionGAM (ones(10,2), ones (10,1), "formula", "something")
608s ***** error<RegressionGAM: no predictor terms in Formula.> ...
608s  RegressionGAM (ones(10,2), ones (10,1), "formula", "something~")
608s ***** error<RegressionGAM: no predictor terms in Formula.> ...
608s  RegressionGAM (ones(10,2), ones (10,1), "formula", "something~")
608s ***** error<RegressionGAM: some predictors have not been identified> ...
608s  RegressionGAM (ones(10,2), ones (10,1), "formula", "something~x1:")
608s ***** error<RegressionGAM: invalid Interactions parameter.> ...
608s  RegressionGAM (ones(10,2), ones (10,1), "interactions", "some")
608s ***** error<RegressionGAM: invalid Interactions parameter.> ...
608s  RegressionGAM (ones(10,2), ones (10,1), "interactions", -1)
608s ***** error<RegressionGAM: invalid Interactions parameter.> ...
608s  RegressionGAM (ones(10,2), ones (10,1), "interactions", [1 2 3 4])
608s ***** error<RegressionGAM: number of interaction terms requested is larger than> ...
608s  RegressionGAM (ones(10,2), ones (10,1), "interactions", 3)
608s ***** error<RegressionGAM: Formula has been already defined.> ...
608s  RegressionGAM (ones(10,2), ones (10,1), "formula", "y ~ x1 + x2", "interactions", 1)
608s ***** error<RegressionGAM: Interactions have been already defined.> ...
608s  RegressionGAM (ones(10,2), ones (10,1), "interactions", 1, "formula", "y ~ x1 + x2")
608s ***** error<RegressionGAM: invalid value for Knots.> ...
608s  RegressionGAM (ones(10,2), ones (10,1), "knots", "a")
608s ***** error<RegressionGAM: DoF and Order have been set already.> ...
608s  RegressionGAM (ones(10,2), ones (10,1), "order", 3, "dof", 2, "knots", 5)
608s ***** error<RegressionGAM: invalid value for DoF.> ...
608s  RegressionGAM (ones(10,2), ones (10,1), "dof", 'a')
608s ***** error<RegressionGAM: Knots and Order have been set already.> ...
608s  RegressionGAM (ones(10,2), ones (10,1), "knots", 5, "order", 3, "dof", 2)
608s ***** error<RegressionGAM: invalid value for Order.> ...
608s  RegressionGAM (ones(10,2), ones (10,1), "order", 'a')
608s ***** error<RegressionGAM: DoF and Knots have been set already.> ...
608s  RegressionGAM (ones(10,2), ones (10,1), "knots", 5, "dof", 2, "order", 2)
608s ***** error<RegressionGAM: Tolerance must be a Positive scalar.> ...
608s  RegressionGAM (ones(10,2), ones (10,1), "tol", -1)
608s ***** error<RegressionGAM: ResponseName must be a char string.> ...
608s  RegressionGAM (ones(10,2), ones (10,1), "responsename", -1)
608s ***** error<RegressionGAM: PredictorNames must be a cellstring array.> ...
608s  RegressionGAM (ones(10,2), ones (10,1), "predictors", -1)
608s ***** error<RegressionGAM: PredictorNames must be a cellstring array.> ...
608s  RegressionGAM (ones(10,2), ones (10,1), "predictors", ['a','b','c'])
608s ***** error<RegressionGAM: PredictorNames must have same number of columns as X.> ...
608s  RegressionGAM (ones(10,2), ones (10,1), "predictors", {'a','b','c'})
608s ***** error<RegressionGAM.predict: too few arguments.> ...
608s  predict (RegressionGAM (ones(10,1), ones(10,1)))
608s ***** error<RegressionGAM.predict: Xfit is empty.> ...
608s  predict (RegressionGAM (ones(10,1), ones(10,1)), [])
608s ***** error<RegressionGAM.predict: Xfit must have the same number of features> ...
608s  predict (RegressionGAM(ones(10,2), ones(10,1)), 2)
608s ***** error<RegressionGAM.predict: invalid NAME in optional pairs of arguments.> ...
608s  predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "some", "some")
608s ***** error<RegressionGAM.predict: includeinteractions must be a logical value.> ...
608s  predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "includeinteractions", "some")
608s ***** error<RegressionGAM.predict: includeinteractions must be a logical value.> ...
608s  predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "includeinteractions", 5)
608s ***** error<RegressionGAM.predict: alpha must be a scalar value between 0 and 1.> ...
608s  predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "alpha", 5)
608s ***** error<RegressionGAM.predict: alpha must be a scalar value between 0 and 1.> ...
608s  predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "alpha", -1)
608s ***** error<RegressionGAM.predict: alpha must be a scalar value between 0 and 1.> ...
608s  predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "alpha", 'a')
608s 39 tests, 39 passed, 0 known failure, 0 skipped
608s [inst/kmeans.m]
608s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/kmeans.m
608s ***** demo
608s  ## Generate a two-cluster problem
608s  randn ("seed", 31)  # for reproducibility
608s  C1 = randn (100, 2) + 1;
608s  randn ("seed", 32)  # for reproducibility
608s  C2 = randn (100, 2) - 1;
608s  data = [C1; C2];
608s 
608s  ## Perform clustering
608s  rand ("seed", 1)  # for reproducibility
608s  [idx, centers] = kmeans (data, 2);
608s 
608s  ## Plot the result
608s  figure;
608s  plot (data (idx==1, 1), data (idx==1, 2), "ro");
608s  hold on;
608s  plot (data (idx==2, 1), data (idx==2, 2), "bs");
608s  plot (centers (:, 1), centers (:, 2), "kv", "markersize", 10);
608s  hold off;
608s ***** demo
608s  ## Cluster data using k-means clustering, then plot the cluster regions
608s  ## Load Fisher's iris data set and use the petal lengths and widths as
608s  ## predictors
608s 
608s  load fisheriris
608s  X = meas(:,3:4);
608s 
608s  figure;
608s  plot (X(:,1), X(:,2), "k*", "MarkerSize", 5);
608s  title ("Fisher's Iris Data");
608s  xlabel ("Petal Lengths (cm)");
608s  ylabel ("Petal Widths (cm)");
608s 
608s  ## Cluster the data. Specify k = 3 clusters
608s  rand ("seed", 1)  # for reproducibility
608s  [idx, C] = kmeans (X, 3);
608s  x1 = min (X(:,1)):0.01:max (X(:,1));
608s  x2 = min (X(:,2)):0.01:max (X(:,2));
608s  [x1G, x2G] = meshgrid (x1, x2);
608s  XGrid = [x1G(:), x2G(:)];
608s 
608s  idx2Region = kmeans (XGrid, 3, "MaxIter", 1, "Start", C);
608s  figure;
608s  gscatter (XGrid(:,1), XGrid(:,2), idx2Region, ...
608s            [0, 0.75, 0.75; 0.75, 0, 0.75; 0.75, 0.75, 0], "..");
608s  hold on;
608s  plot (X(:,1), X(:,2), "k*", "MarkerSize", 5);
608s  title ("Fisher's Iris Data");
608s  xlabel ("Petal Lengths (cm)");
608s  ylabel ("Petal Widths (cm)");
608s  legend ("Region 1", "Region 2", "Region 3", "Data", "Location", "SouthEast");
608s  hold off
608s ***** demo
608s  ## Partition Data into Two Clusters
608s 
608s  randn ("seed", 1)  # for reproducibility
608s  r1 = randn (100, 2) * 0.75 + ones (100, 2);
608s  randn ("seed", 2)  # for reproducibility
608s  r2 = randn (100, 2) * 0.5 - ones (100, 2);
608s  X = [r1; r2];
608s 
608s  figure;
608s  plot (X(:,1), X(:,2), ".");
608s  title ("Randomly Generated Data");
608s  rand ("seed", 1)  # for reproducibility
608s  [idx, C] = kmeans (X, 2, "Distance", "cityblock", ...
608s                           "Replicates", 5, "Display", "final");
608s  figure;
608s  plot (X(idx==1,1), X(idx==1,2), "r.", "MarkerSize", 12);
608s  hold on
608s  plot(X(idx==2,1), X(idx==2,2), "b.", "MarkerSize", 12);
608s  plot (C(:,1), C(:,2), "kx", "MarkerSize", 15, "LineWidth", 3);
608s  legend ("Cluster 1", "Cluster 2", "Centroids", "Location", "NorthWest");
608s  title ("Cluster Assignments and Centroids");
608s  hold off
608s ***** demo
608s  ## Assign New Data to Existing Clusters
608s 
608s  ## Generate a training data set using three distributions
608s  randn ("seed", 5)  # for reproducibility
608s  r1 = randn (100, 2) * 0.75 + ones (100, 2);
608s  randn ("seed", 7)  # for reproducibility
608s  r2 = randn (100, 2) * 0.5 - ones (100, 2);
608s  randn ("seed", 9)  # for reproducibility
608s  r3 = randn (100, 2) * 0.75;
608s  X = [r1; r2; r3];
608s 
608s  ## Partition the training data into three clusters by using kmeans
608s 
608s  rand ("seed", 1)  # for reproducibility
608s  [idx, C] = kmeans (X, 3);
608s 
608s  ## Plot the clusters and the cluster centroids
608s 
608s  figure
608s  gscatter (X(:,1), X(:,2), idx, "bgm", "***");
608s  hold on
608s  plot (C(:,1), C(:,2), "kx");
608s  legend ("Cluster 1", "Cluster 2", "Cluster 3", "Cluster Centroid")
608s 
608s  ## Generate a test data set
608s  randn ("seed", 25)  # for reproducibility
608s  r1 = randn (100, 2) * 0.75 + ones (100, 2);
608s  randn ("seed", 27)  # for reproducibility
608s  r2 = randn (100, 2) * 0.5 - ones (100, 2);
608s  randn ("seed", 29)  # for reproducibility
608s  r3 = randn (100, 2) * 0.75;
608s  Xtest = [r1; r2; r3];
608s 
608s  ## Classify the test data set using the existing clusters
608s  ## Find the nearest centroid from each test data point by using pdist2
608s 
608s  D = pdist2 (C, Xtest, "euclidean");
608s  [group, ~] = find (D == min (D));
608s 
608s  ## Plot the test data and label the test data using idx_test with gscatter
608s 
608s  gscatter (Xtest(:,1), Xtest(:,2), group, "bgm", "ooo");
608s  legend ("Cluster 1", "Cluster 2", "Cluster 3", "Cluster Centroid", ...
608s          "Data classified to Cluster 1", "Data classified to Cluster 2", ...
608s          "Data classified to Cluster 3", "Location", "NorthWest");
608s  title ("Assign New Data to Existing Clusters");
608s ***** test
608s  samples = 4;
608s  dims = 3;
608s  k = 2;
608s  [cls, c, d, z] = kmeans (rand (samples,dims), k, "start", rand (k,dims, 5),
608s                           "emptyAction", "singleton");
608s  assert (size (cls), [samples, 1]);
608s  assert (size (c), [k, dims]);
608s  assert (size (d), [k, 1]);
608s  assert (size (z), [samples, k]);
608s ***** test
608s  samples = 4;
608s  dims = 3;
608s  k = 2;
608s  [cls, c, d, z] = kmeans (rand (samples,dims), [], "start", rand (k,dims, 5),
608s                           "emptyAction", "singleton");
608s  assert (size (cls), [samples, 1]);
608s  assert (size (c), [k, dims]);
608s  assert (size (d), [k, 1]);
608s  assert (size (z), [samples, k]);
608s ***** test
608s  [cls, c] = kmeans ([1 0; 2 0], 2, "start", [8,0;0,8], "emptyaction", "drop");
608s  assert (cls, [1; 1]);
608s  assert (c, [1.5, 0; NA, NA]);
608s ***** test
608s  kmeans (rand (4,3), 2, "start", rand (2,3, 5), "replicates", 5,
608s          "emptyAction", "singleton");
608s ***** test
608s  kmeans (rand (3,4), 2, "start", "sample", "emptyAction", "singleton");
608s ***** test
608s  kmeans (rand (3,4), 2, "start", "plus", "emptyAction", "singleton");
608s ***** test
608s  kmeans (rand (3,4), 2, "start", "cluster", "emptyAction", "singleton");
608s ***** test
608s  kmeans (rand (3,4), 2, "start", "uniform", "emptyAction", "singleton");
608s ***** test
608s  kmeans (rand (4,3), 2, "distance", "sqeuclidean", "emptyAction", "singleton");
608s ***** test
608s  kmeans (rand (4,3), 2, "distance", "cityblock", "emptyAction", "singleton");
608s ***** test
608s  kmeans (rand (4,3), 2, "distance", "cosine", "emptyAction", "singleton");
608s ***** test
608s  kmeans (rand (4,3), 2, "distance", "correlation", "emptyAction", "singleton");
608s ***** test
608s  kmeans (rand (4,3), 2, "distance", "hamming", "emptyAction", "singleton");
608s ***** test
608s  kmeans ([1 0; 1.1 0], 2, "start", eye(2), "emptyaction", "singleton");
608s ***** error kmeans (rand (3,2), 4);
608s ***** error kmeans ([1 0; 1.1 0], 2, "start", eye(2), "emptyaction", "panic");
608s ***** error kmeans (rand (4,3), 2, "start", rand (2,3, 5), "replicates", 1);
608s ***** error kmeans (rand (4,3), 2, "start", rand (2,2));
608s ***** error kmeans (rand (4,3), 2, "distance", "manhattan");
608s ***** error kmeans (rand (3,4), 2, "start", "normal");
608s ***** error kmeans (rand (4,3), 2, "replicates", i);
608s ***** error kmeans (rand (4,3), 2, "replicates", -1);
608s ***** error kmeans (rand (4,3), 2, "replicates", []);
608s ***** error kmeans (rand (4,3), 2, "replicates", [1 2]);
608s ***** error kmeans (rand (4,3), 2, "replicates", "one");
608s ***** error kmeans (rand (4,3), 2, "MAXITER", i);
608s ***** error kmeans (rand (4,3), 2, "MaxIter", -1);
608s ***** error kmeans (rand (4,3), 2, "maxiter", []);
608s ***** error kmeans (rand (4,3), 2, "maxiter", [1 2]);
608s ***** error kmeans (rand (4,3), 2, "maxiter", "one");
608s ***** error <empty cluster created> kmeans ([1 0; 1.1 0], 2, "start", eye(2), "emptyaction", "error");
608s 31 tests, 31 passed, 0 known failure, 0 skipped
608s [inst/slicesample.m]
608s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/slicesample.m
608s ***** demo
608s  ## Define function to sample
608s  d = 2;
608s  mu = [-1; 2];
608s  rand ("seed", 5)  # for reproducibility
608s  Sigma = rand (d);
608s  Sigma = (Sigma + Sigma');
608s  Sigma += eye (d)*abs (eigs (Sigma, 1, "sa")) * 1.1;
608s  pdf = @(x)(2*pi)^(-d/2)*det(Sigma)^-.5*exp(-.5*sum((x.'-mu).*(Sigma\(x.'-mu)),1));
608s 
608s  ## Inputs
608s  start = ones (1,2);
608s  nsamples = 500;
608s  K = 500;
608s  m = 10;
608s  rande ("seed", 4);  rand ("seed", 5)  # for reproducibility
608s  [smpl, accept] = slicesample (start, nsamples, "pdf", pdf, "burnin", K, "thin", m, "width", [20, 30]);
608s  figure;
608s  hold on;
608s  plot (smpl(:,1), smpl(:,2), 'x');
608s  [x, y] = meshgrid (linspace (-6,4), linspace(-3,7));
608s  z = reshape (pdf ([x(:), y(:)]), size(x));
608s  mesh (x, y, z, "facecolor", "None");
608s 
608s  ## Using sample points to find the volume of half a sphere with radius of .5
608s  f = @(x) ((.25-(x(:,1)+1).^2-(x(:,2)-2).^2).^.5.*(((x(:,1)+1).^2+(x(:,2)-2).^2)<.25)).';
608s  int = mean (f (smpl) ./ pdf (smpl));
608s  errest = std (f (smpl) ./ pdf (smpl)) / nsamples^.5;
608s  trueerr = abs (2/3*pi*.25^(3/2)-int);
608s  fprintf ("Monte Carlo integral estimate int f(x) dx = %f\n", int);
608s  fprintf ("Monte Carlo integral error estimate %f\n", errest);
608s  fprintf ("The actual error %f\n", trueerr);
608s  mesh (x,y,reshape (f([x(:), y(:)]), size(x)), "facecolor", "None");
608s ***** demo
608s  ## Integrate truncated normal distribution to find normilization constant
608s  pdf = @(x) exp (-.5*x.^2)/(pi^.5*2^.5);
608s  nsamples = 1e3;
608s  rande ("seed", 4);  rand ("seed", 5)  # for reproducibility
608s  [smpl, accept] = slicesample (1, nsamples, "pdf", pdf, "thin", 4);
608s  f = @(x) exp (-.5 * x .^ 2) .* (x >= -2 & x <= 2);
608s  x = linspace (-3, 3, 1000);
608s  area (x, f(x));
608s  xlabel ("x");
608s  ylabel ("f(x)");
608s  int = mean (f (smpl) ./ pdf (smpl));
608s  errest = std (f (smpl) ./ pdf (smpl)) / nsamples ^ 0.5;
608s  trueerr = abs (erf (2 ^ 0.5) * 2 ^ 0.5 * pi ^ 0.5 - int);
608s  fprintf("Monte Carlo integral estimate int f(x) dx = %f\n", int);
608s  fprintf("Monte Carlo integral error estimate %f\n", errest);
608s  fprintf("The actual error %f\n", trueerr);
608s ***** test
608s  start = 0.5;
608s  nsamples = 1e3;
608s  pdf = @(x) exp (-.5*(x-1).^2)/(2*pi)^.5;
608s  [smpl, accept] = slicesample (start, nsamples, "pdf", pdf, "thin", 2, "burnin", 0, "width", 5);
608s  assert (mean (smpl, 1), 1, .15);
608s  assert (var (smpl, 1), 1, .25);
609s ***** error slicesample ();
609s ***** error slicesample (1);
609s ***** error slicesample (1, 1);
609s 4 tests, 4 passed, 0 known failure, 0 skipped
609s [inst/nanmax.m]
609s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/nanmax.m
609s ***** demo
609s  ## Find the column maximum values and their indices
609s  ## for matrix data with missing values.
609s 
609s  x = magic (3);
609s  x([1, 6:9]) = NaN
609s  [y, ind] = nanmax (x)
609s ***** demo
609s  ## Find the maximum of all the values in an array, ignoring missing values.
609s  ## Create a 2-by-5-by-3 array x with some missing values.
609s 
609s  x = reshape (1:30, [2, 5, 3]);
609s  x([10:12, 25]) = NaN
609s 
609s  ## Find the maximum of the elements of x.
609s 
609s  y = nanmax (x, [], 'all')
609s ***** assert (nanmax ([2, 4, NaN, 7]), 7)
609s ***** assert (nanmax ([2, 4, NaN, Inf]), Inf)
609s ***** assert (nanmax ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN]), [7, 8, 6])
609s ***** assert (nanmax ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN]'), [3, 6, 8])
609s ***** assert (nanmax (single ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN])), single ([7, 8, 6]))
609s ***** shared x, y
609s  x(:,:,1) = [1.77, -0.005, NaN, -2.95; NaN, 0.34, NaN, 0.19];
609s  x(:,:,2) = [1.77, -0.005, NaN, -2.95; NaN, 0.34, NaN, 0.19] + 5;
609s  y = x;
609s  y(2,3,1) = 0.51;
609s ***** assert (nanmax (x, [], [1, 2])(:), [1.77;6.77])
609s ***** assert (nanmax (x, [], [1, 3])(:), [6.77;5.34;NaN;5.19])
609s ***** assert (nanmax (x, [], [2, 3])(:), [6.77;5.34])
609s ***** assert (nanmax (x, [], [1, 2, 3]), 6.77)
609s ***** assert (nanmax (x, [], 'all'), 6.77)
609s ***** assert (nanmax (y, [], [1, 3])(:), [6.77;5.34;0.51;5.19])
609s ***** assert (nanmax (x(1,:,1), x(2,:,1)), [1.77, 0.34, NaN, 0.19])
609s ***** assert (nanmax (x(1,:,2), x(2,:,2)), [6.77, 5.34, NaN, 5.19])
609s ***** assert (nanmax (y(1,:,1), y(2,:,1)), [1.77, 0.34, 0.51, 0.19])
609s ***** assert (nanmax (y(1,:,2), y(2,:,2)), [6.77, 5.34, NaN, 5.19])
609s ***** test
609s  xx = repmat ([1:20;6:25], [5 2 6 3]);
609s  assert (size (nanmax (xx, [], [3, 2])), [10, 1, 1, 3]);
609s  assert (size (nanmax (xx, [], [1, 2])), [1, 1, 6, 3]);
609s  assert (size (nanmax (xx, [], [1, 2, 4])), [1, 1, 6]);
609s  assert (size (nanmax (xx, [], [1, 4, 3])), [1, 40]);
609s  assert (size (nanmax (xx, [], [1, 2, 3, 4])), [1, 1]);
609s ***** assert (nanmax (ones (2), [], 3), ones (2, 2))
609s ***** assert (nanmax (ones (2, 2, 2), [], 99), ones (2, 2, 2))
609s ***** assert (nanmax (magic (3), [], 3), magic (3))
609s ***** assert (nanmax (magic (3), [], [1, 3]), [8, 9, 7])
609s ***** assert (nanmax (magic (3), [], [1, 99]), [8, 9, 7])
609s ***** assert (nanmax (ones (2), 3), 3 * ones (2,2))
609s ***** error <nanmax: VECDIM must contain non-repeating positive integers.> ...
609s  nanmax (y, [], [1, 1, 2])
609s ***** error <nanmax: a second output is not supported with this syntax.> ...
609s  [v, idx] = nanmax(x, y, [1 2])
609s 24 tests, 24 passed, 0 known failure, 0 skipped
609s [inst/silhouette.m]
609s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/silhouette.m
609s ***** demo
609s  load fisheriris;
609s  X = meas(:,3:4);
609s  cidcs = kmeans (X, 3, "Replicates", 5);
609s  silhouette (X, cidcs);
609s  y_labels(cidcs([1 51 101])) = unique (species);
609s  set (gca, "yticklabel", y_labels);
609s  title ("Fisher's iris data");
609s ***** error silhouette ();
609s ***** error silhouette ([1 2; 1 1]);
609s ***** error <X .* doesn't match .* clust> silhouette ([1 2; 1 1], [1 2 3]');
609s ***** error <invalid metric> silhouette ([1 2; 1 1], [1 2]', "xxx");
609s 4 tests, 4 passed, 0 known failure, 0 skipped
609s [inst/cdfcalc.m]
609s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/cdfcalc.m
609s ***** test
609s  x = [2, 4, 3, 2, 4, 3, 2, 5, 6, 4];
609s  [yCDF, xCDF, n, emsg, eid] = cdfcalc (x);
609s  assert (yCDF, [0, 0.3, 0.5, 0.8, 0.9, 1]');
609s  assert (xCDF, [2, 3, 4, 5, 6]');
609s  assert (n, 10);
609s ***** shared x
609s  x = [2, 4, 3, 2, 4, 3, 2, 5, 6, 4];
609s ***** error yCDF = cdfcalc (x);
609s ***** error [yCDF, xCDF] = cdfcalc ();
609s ***** error [yCDF, xCDF] = cdfcalc (x, x);
609s ***** warning [yCDF, xCDF] = cdfcalc (ones(10,2));
609s 5 tests, 5 passed, 0 known failure, 0 skipped
609s [inst/levene_test.m]
609s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/levene_test.m
609s ***** error<levene_test: invalid number of input arguments.> levene_test ()
609s ***** error<levene_test: invalid number of input arguments.> ...
609s  levene_test (1, 2, 3, 4, 5);
609s ***** error<levene_test: wrong value for alpha.> levene_test (randn (50, 2), 0);
609s ***** error<levene_test: GROUP and X mismatch.> ...
609s  levene_test (randn (50, 2), [1, 2, 3]);
609s ***** error<levene_test: GROUP and X mismatch.> ...
609s  levene_test (randn (50, 1), ones (55, 1));
609s ***** error<levene_test: invalid second input argument.> ...
609s  levene_test (randn (50, 1), ones (50, 2));
609s ***** error<levene_test: wrong value for alpha.> ...
609s  levene_test (randn (50, 2), [], 1.2);
609s ***** error<levene_test: GROUP and X mismatch.> ...
609s  levene_test (randn (50, 2), "some_string");
609s ***** error<levene_test: invalid third input argument.> ...
609s  levene_test (randn (50, 2), [], "alpha");
609s ***** error<levene_test: wrong value for alpha.> ...
609s  levene_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], 1.2);
609s ***** error<levene_test: invalid third input argument.> ...
609s  levene_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], "err");
609s ***** error<levene_test: invalid option for TESTTYPE as 4th argument.> ...
609s  levene_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], 0.05, "type");
609s ***** warning<levene_test: GROUP> ...
609s  levene_test (randn (50, 1), [ones(24, 1); 2*ones(25, 1); 3]);
609s ***** test
609s  load examgrades
609s  [h, pval, W, df] = levene_test (grades);
609s  assert (h, 1);
609s  assert (pval, 9.523239714592791e-07, 1e-14);
609s  assert (W, 8.59529, 1e-5);
609s  assert (df, [4, 595]);
609s ***** test
609s  load examgrades
609s  [h, pval, W, df] = levene_test (grades, [], "quadratic");
609s  assert (h, 1);
609s  assert (pval, 9.523239714592791e-07, 1e-14);
609s  assert (W, 8.59529, 1e-5);
609s  assert (df, [4, 595]);
609s ***** test
609s  load examgrades
609s  [h, pval, W, df] = levene_test (grades, [], "median");
609s  assert (h, 1);
609s  assert (pval, 1.312093241723211e-06, 1e-14);
609s  assert (W, 8.415969, 1e-6);
609s  assert (df, [4, 595]);
609s ***** test
609s  load examgrades
609s  [h, pval, W, df] = levene_test (grades(:,[1:3]));
609s  assert (h, 1);
609s  assert (pval, 0.004349390980463497, 1e-14);
609s  assert (W, 5.52139, 1e-5);
609s  assert (df, [2, 357]);
609s ***** test
609s  load examgrades
609s  [h, pval, W, df] = levene_test (grades(:,[1:3]), "median");
609s  assert (h, 1);
609s  assert (pval, 0.004355216763951453, 1e-14);
609s  assert (W, 5.52001, 1e-5);
609s  assert (df, [2, 357]);
609s ***** test
609s  load examgrades
609s  [h, pval, W, df] = levene_test (grades(:,[3,4]), "quadratic");
609s  assert (h, 0);
609s  assert (pval, 0.1807494957440653, 2e-14);
609s  assert (W, 1.80200, 1e-5);
609s  assert (df, [1, 238]);
609s ***** test
609s  load examgrades
609s  [h, pval, W, df] = levene_test (grades(:,[3,4]), "median");
609s  assert (h, 0);
609s  assert (pval, 0.1978225622063785, 2e-14);
609s  assert (W, 1.66768, 1e-5);
609s  assert (df, [1, 238]);
609s 20 tests, 20 passed, 0 known failure, 0 skipped
609s [inst/cl_multinom.m]
609s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/cl_multinom.m
609s ***** demo
609s  CL = cl_multinom ([27; 43; 19; 11], 10000, 0.05)
609s ***** error<Invalid call to cl_multinom.  Correct usage> cl_multinom ();
609s ***** error cl_multinom (1, 2, 3, 4, 5);
609s ***** error<cl_multinom: argument method must be a string.> ...
609s  cl_multinom (1, 2, 3, 4);
609s ***** error<cl_multinom: unknown calculation type.> ...
609s  cl_multinom (1, 2, 3, "some string");
609s 4 tests, 4 passed, 0 known failure, 0 skipped
609s [inst/qqplot.m]
609s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/qqplot.m
609s ***** test
609s  hf = figure ("visible", "off");
609s  unwind_protect
609s    qqplot ([2 3 3 4 4 5 6 5 6 7 8 9 8 7 8 9 0 8 7 6 5 4 6 13 8 15 9 9]);
609s  unwind_protect_cleanup
609s    close (hf);
609s  end_unwind_protect
609s ***** error qqplot ()
610s ***** error <qqplot: X must be a numeric vector> qqplot ({1})
610s ***** error <qqplot: X must be a numeric vector> qqplot (ones (2,2))
610s ***** error <qqplot: X must be a numeric vecto> qqplot (1, "foobar")
610s ***** error <qqplot: no inverse CDF found> qqplot ([1 2 3], "foobar")
610s 6 tests, 6 passed, 0 known failure, 0 skipped
610s [inst/dist_fit/logifit.m]
610s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/logifit.m
610s ***** demo
610s  ## Sample 3 populations from different logistic distibutions
610s  rand ("seed", 5)  # for reproducibility
610s  r1 = logirnd (2, 1, 2000, 1);
610s  rand ("seed", 2)   # for reproducibility
610s  r2 = logirnd (5, 2, 2000, 1);
610s  rand ("seed", 7)   # for reproducibility
610s  r3 = logirnd (9, 4, 2000, 1);
610s  r = [r1, r2, r3];
610s 
610s  ## Plot them normalized and fix their colors
610s  hist (r, [-6:20], 1);
610s  h = findobj (gca, "Type", "patch");
610s  set (h(1), "facecolor", "c");
610s  set (h(2), "facecolor", "g");
610s  set (h(3), "facecolor", "r");
610s  ylim ([0, 0.3]);
610s  xlim ([-5, 20]);
610s  hold on
610s 
610s  ## Estimate their MU and LAMBDA parameters
610s  mu_sA = logifit (r(:,1));
610s  mu_sB = logifit (r(:,2));
610s  mu_sC = logifit (r(:,3));
610s 
610s  ## Plot their estimated PDFs
610s  x = [-5:0.5:20];
610s  y = logipdf (x, mu_sA(1), mu_sA(2));
610s  plot (x, y, "-pr");
610s  y = logipdf (x, mu_sB(1), mu_sB(2));
610s  plot (x, y, "-sg");
610s  y = logipdf (x, mu_sC(1), mu_sC(2));
610s  plot (x, y, "-^c");
610s  hold off
610s  legend ({"Normalized HIST of sample 1 with μ=1 and s=0.5", ...
610s           "Normalized HIST of sample 2 with μ=2 and s=0.3", ...
610s           "Normalized HIST of sample 3 with μ=4 and s=0.5", ...
610s           sprintf("PDF for sample 1 with estimated μ=%0.2f and s=%0.2f", ...
610s                   mu_sA(1), mu_sA(2)), ...
610s           sprintf("PDF for sample 2 with estimated μ=%0.2f and s=%0.2f", ...
610s                   mu_sB(1), mu_sB(2)), ...
610s           sprintf("PDF for sample 3 with estimated μ=%0.2f and s=%0.2f", ...
610s                   mu_sC(1), mu_sC(2))})
610s  title ("Three population samples from different logistic distibutions")
610s  hold off
610s ***** test
610s  paramhat = logifit ([1:50]);
610s  paramhat_out = [25.5, 8.7724];
610s  assert (paramhat, paramhat_out, 1e-4);
610s ***** test
610s  paramhat = logifit ([1:5]);
610s  paramhat_out = [3, 0.8645];
610s  assert (paramhat, paramhat_out, 1e-4);
610s ***** test
610s  paramhat = logifit ([1:6], [], [], [1 1 1 1 1 0]);
610s  paramhat_out = [3, 0.8645];
610s  assert (paramhat, paramhat_out, 1e-4);
610s ***** test
610s  paramhat = logifit ([1:5], [], [], [1 1 1 1 2]);
610s  paramhat_out = logifit ([1:5, 5]);
610s  assert (paramhat, paramhat_out, 1e-4);
610s ***** error<logifit: X must be a vector.> logifit (ones (2,5));
610s ***** error<logifit: wrong value for ALPHA.> logifit ([1, 2, 3, 4, 5], 1.2);
610s ***** error<logifit: wrong value for ALPHA.> logifit ([1, 2, 3, 4, 5], 0);
610s ***** error<logifit: wrong value for ALPHA.> logifit ([1, 2, 3, 4, 5], "alpha");
610s ***** error<logifit: X and CENSOR vectors mismatch.> ...
610s  logifit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
610s ***** error<logifit: X and CENSOR vectors mismatch.> ...
610s  logifit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
610s ***** error<logifit: X and FREQ vectors mismatch.> ...
610s  logifit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
610s ***** error<logifit: X and FREQ vectors mismatch.> ...
610s  logifit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
610s ***** error<logifit: 'options' 5th argument must be a structure> ...
610s  logifit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
610s 13 tests, 13 passed, 0 known failure, 0 skipped
610s [inst/dist_fit/lognfit.m]
610s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/lognfit.m
610s ***** demo
610s  ## Sample 3 populations from 3 different log-normal distibutions
610s  randn ("seed", 1);    # for reproducibility
610s  r1 = lognrnd (0, 0.25, 1000, 1);
610s  randn ("seed", 2);    # for reproducibility
610s  r2 = lognrnd (0, 0.5, 1000, 1);
610s  randn ("seed", 3);    # for reproducibility
610s  r3 = lognrnd (0, 1, 1000, 1);
610s  r = [r1, r2, r3];
610s 
610s  ## Plot them normalized and fix their colors
610s  hist (r, 30, 2);
610s  h = findobj (gca, "Type", "patch");
610s  set (h(1), "facecolor", "c");
610s  set (h(2), "facecolor", "g");
610s  set (h(3), "facecolor", "r");
610s  hold on
610s 
610s  ## Estimate their mu and sigma parameters
610s  mu_sigmaA = lognfit (r(:,1));
610s  mu_sigmaB = lognfit (r(:,2));
610s  mu_sigmaC = lognfit (r(:,3));
610s 
610s  ## Plot their estimated PDFs
610s  x = [0:0.1:6];
610s  y = lognpdf (x, mu_sigmaA(1), mu_sigmaA(2));
610s  plot (x, y, "-pr");
610s  y = lognpdf (x, mu_sigmaB(1), mu_sigmaB(2));
610s  plot (x, y, "-sg");
610s  y = lognpdf (x, mu_sigmaC(1), mu_sigmaC(2));
610s  plot (x, y, "-^c");
610s  ylim ([0, 2])
610s  xlim ([0, 6])
610s  hold off
610s  legend ({"Normalized HIST of sample 1 with mu=0, σ=0.25", ...
610s           "Normalized HIST of sample 2 with mu=0, σ=0.5", ...
610s           "Normalized HIST of sample 3 with mu=0, σ=1", ...
610s           sprintf("PDF for sample 1 with estimated mu=%0.2f and σ=%0.2f", ...
610s                   mu_sigmaA(1), mu_sigmaA(2)), ...
610s           sprintf("PDF for sample 2 with estimated mu=%0.2f and σ=%0.2f", ...
610s                   mu_sigmaB(1), mu_sigmaB(2)), ...
610s           sprintf("PDF for sample 3 with estimated mu=%0.2f and σ=%0.2f", ...
610s                   mu_sigmaC(1), mu_sigmaC(2))}, "location", "northeast")
610s  title ("Three population samples from different log-normal distibutions")
610s  hold off
610s ***** test
610s  randn ("seed", 1);
610s  x = lognrnd (3, 5, [1000, 1]);
610s  [paramhat, paramci] = lognfit (x, 0.01);
610s  assert (paramci(1,1) < 3);
610s  assert (paramci(1,2) > 3);
610s  assert (paramci(2,1) < 5);
610s  assert (paramci(2,2) > 5);
610s ***** error<lognfit: X must be a numeric vector of positive values.> ...
610s  lognfit (ones (20,3))
610s ***** error<lognfit: X must be a numeric vector of positive values.> ...
610s  lognfit ({1, 2, 3, 4, 5})
610s ***** error<lognfit: X must be a numeric vector of positive values.> ...
610s  lognfit ([-1, 2, 3, 4, 5])
610s ***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), 0)
610s ***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), -0.3)
610s ***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), 1.2)
610s ***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), [0.05,  0.1])
610s ***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), 0.02+i)
610s ***** error<lognfit: X and CENSOR vectors mismatch.> ...
610s  lognfit (ones (20,1), [], zeros(15,1))
610s ***** error<lognfit: X and FREQ vectors mismatch.> ...
610s  lognfit (ones (20,1), [], zeros(20,1), ones(25,1))
610s ***** error<lognfit: > lognfit (ones (20,1), [], zeros(20,1), ones(20,1), "options")
610s 12 tests, 12 passed, 0 known failure, 0 skipped
610s [inst/dist_fit/invgfit.m]
610s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/invgfit.m
610s ***** demo
610s  ## Sample 3 populations from different inverse Gaussian distibutions
610s  rand ("seed", 5); randn ("seed", 5);   # for reproducibility
610s  r1 = invgrnd (1, 0.2, 2000, 1);
610s  rand ("seed", 2); randn ("seed", 2);   # for reproducibility
610s  r2 = invgrnd (1, 3, 2000, 1);
610s  rand ("seed", 7); randn ("seed", 7);   # for reproducibility
610s  r3 = invgrnd (3, 1, 2000, 1);
610s  r = [r1, r2, r3];
610s 
610s  ## Plot them normalized and fix their colors
610s  hist (r, [0.1:0.1:3.2], 9);
610s  h = findobj (gca, "Type", "patch");
610s  set (h(1), "facecolor", "c");
610s  set (h(2), "facecolor", "g");
610s  set (h(3), "facecolor", "r");
610s  ylim ([0, 3]);
610s  xlim ([0, 3]);
610s  hold on
610s 
610s  ## Estimate their MU and LAMBDA parameters
610s  mu_lambdaA = invgfit (r(:,1));
610s  mu_lambdaB = invgfit (r(:,2));
610s  mu_lambdaC = invgfit (r(:,3));
610s 
610s  ## Plot their estimated PDFs
610s  x = [0:0.1:3];
610s  y = invgpdf (x, mu_lambdaA(1), mu_lambdaA(2));
610s  plot (x, y, "-pr");
610s  y = invgpdf (x, mu_lambdaB(1), mu_lambdaB(2));
610s  plot (x, y, "-sg");
610s  y = invgpdf (x, mu_lambdaC(1), mu_lambdaC(2));
610s  plot (x, y, "-^c");
610s  hold off
610s  legend ({"Normalized HIST of sample 1 with μ=1 and λ=0.5", ...
610s           "Normalized HIST of sample 2 with μ=2 and λ=0.3", ...
610s           "Normalized HIST of sample 3 with μ=4 and λ=0.5", ...
610s           sprintf("PDF for sample 1 with estimated μ=%0.2f and λ=%0.2f", ...
610s                   mu_lambdaA(1), mu_lambdaA(2)), ...
610s           sprintf("PDF for sample 2 with estimated μ=%0.2f and λ=%0.2f", ...
610s                   mu_lambdaB(1), mu_lambdaB(2)), ...
610s           sprintf("PDF for sample 3 with estimated μ=%0.2f and λ=%0.2f", ...
610s                   mu_lambdaC(1), mu_lambdaC(2))})
610s  title ("Three population samples from different inverse Gaussian distibutions")
610s  hold off
610s ***** test
610s  paramhat = invgfit ([1:50]);
610s  paramhat_out = [25.5, 19.6973];
610s  assert (paramhat, paramhat_out, 1e-4);
610s ***** test
610s  paramhat = invgfit ([1:5]);
610s  paramhat_out = [3, 8.1081];
610s  assert (paramhat, paramhat_out, 1e-4);
610s ***** error<invgfit: X must be a vector.> invgfit (ones (2,5));
610s ***** error<invgfit: X must contain only positive values.> invgfit ([-1 2 3 4]);
610s ***** error<invgfit: wrong value for ALPHA.> invgfit ([1, 2, 3, 4, 5], 1.2);
610s ***** error<invgfit: wrong value for ALPHA.> invgfit ([1, 2, 3, 4, 5], 0);
610s ***** error<invgfit: wrong value for ALPHA.> invgfit ([1, 2, 3, 4, 5], "alpha");
610s ***** error<invgfit: X and CENSOR vectors mismatch.> ...
610s  invgfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
610s ***** error<invgfit: X and CENSOR vectors mismatch.> ...
610s  invgfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
610s ***** error<invgfit: X and FREQ vectors mismatch.> ...
610s  invgfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
610s ***** error<invgfit: X and FREQ vectors mismatch.> ...
610s  invgfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
610s ***** error<invgfit: 'options' 5th argument must be a structure> ...
610s  invgfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
610s 12 tests, 12 passed, 0 known failure, 0 skipped
610s [inst/dist_fit/poissfit.m]
610s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/poissfit.m
610s ***** demo
610s  ## Sample 3 populations from 3 different Poisson distibutions
610s  randp ("seed", 2);    # for reproducibility
610s  r1 = poissrnd (1, 1000, 1);
610s  randp ("seed", 2);    # for reproducibility
610s  r2 = poissrnd (4, 1000, 1);
610s  randp ("seed", 3);    # for reproducibility
610s  r3 = poissrnd (10, 1000, 1);
610s  r = [r1, r2, r3];
610s 
610s  ## Plot them normalized and fix their colors
610s  hist (r, [0:20], 1);
610s  h = findobj (gca, "Type", "patch");
610s  set (h(1), "facecolor", "c");
610s  set (h(2), "facecolor", "g");
610s  set (h(3), "facecolor", "r");
610s  hold on
610s 
610s  ## Estimate their lambda parameter
610s  lambdahat = poissfit (r);
610s 
610s  ## Plot their estimated PDFs
610s  x = [0:20];
610s  y = poisspdf (x, lambdahat(1));
610s  plot (x, y, "-pr");
610s  y = poisspdf (x, lambdahat(2));
610s  plot (x, y, "-sg");
610s  y = poisspdf (x, lambdahat(3));
610s  plot (x, y, "-^c");
610s  xlim ([0, 20])
610s  ylim ([0, 0.4])
610s  legend ({"Normalized HIST of sample 1 with λ=1", ...
610s           "Normalized HIST of sample 2 with λ=4", ...
610s           "Normalized HIST of sample 3 with λ=10", ...
610s           sprintf("PDF for sample 1 with estimated λ=%0.2f", ...
610s                   lambdahat(1)), ...
610s           sprintf("PDF for sample 2 with estimated λ=%0.2f", ...
610s                   lambdahat(2)), ...
610s           sprintf("PDF for sample 3 with estimated λ=%0.2f", ...
610s                   lambdahat(3))})
610s  title ("Three population samples from different Poisson distibutions")
610s  hold off
610s ***** test
610s  x = [1 3 2 4 5 4 3 4];
610s  [lhat, lci] = poissfit (x);
610s  assert (lhat, 3.25)
610s  assert (lci, [2.123007901949543; 4.762003010390628], 1e-14)
610s ***** test
610s  x = [1 3 2 4 5 4 3 4];
610s  [lhat, lci] = poissfit (x, 0.01);
610s  assert (lhat, 3.25)
610s  assert (lci, [1.842572740234582; 5.281369033298528], 1e-14)
610s ***** test
610s  x = [1 2 3 4 5];
610s  f = [1 1 2 3 1];
610s  [lhat, lci] = poissfit (x, [], f);
610s  assert (lhat, 3.25)
610s  assert (lci, [2.123007901949543; 4.762003010390628], 1e-14)
610s ***** test
610s  x = [1 2 3 4 5];
610s  f = [1 1 2 3 1];
610s  [lhat, lci] = poissfit (x, 0.01, f);
610s  assert (lhat, 3.25)
610s  assert (lci, [1.842572740234582; 5.281369033298528], 1e-14)
610s ***** error<poissfit: X cannot have negative values.> poissfit ([1 2 -1 3])
610s ***** error<poissfit: wrong value for ALPHA.> poissfit ([1 2 3], 0)
610s ***** error<poissfit: wrong value for ALPHA.> poissfit ([1 2 3], 1.2)
610s ***** error<poissfit: wrong value for ALPHA.> poissfit ([1 2 3], [0.02 0.05])
610s ***** error<poissfit: X and FREQ vectors mismatch.>
610s  poissfit ([1 2 3], [], [1 5])
610s ***** error<poissfit: FREQ must not contain negative values.>
610s  poissfit ([1 2 3], [], [1 5 -1])
610s 10 tests, 10 passed, 0 known failure, 0 skipped
610s [inst/dist_fit/wbllike.m]
610s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/wbllike.m
610s ***** test
610s  x = 1:50;
610s  [nlogL, acov] = wbllike ([2.3, 1.2], x);
610s  avar_out = [0.0250, 0.0062; 0.0062, 0.0017];
610s  assert (nlogL, 945.9589180651594, 1e-12);
610s  assert (acov, avar_out, 1e-4);
610s ***** test
610s  x = 1:50;
610s  [nlogL, acov] = wbllike ([2.3, 1.2], x * 0.5);
610s  avar_out = [-0.3238, -0.1112; -0.1112, -0.0376];
610s  assert (nlogL, 424.9879809704742, 6e-14);
610s  assert (acov, avar_out, 1e-4);
610s ***** test
610s  x = 1:50;
610s  [nlogL, acov] = wbllike ([21, 15], x);
610s  avar_out = [-0.00001236, -0.00001166; -0.00001166, -0.00001009];
610s  assert (nlogL, 1635190.328991511, 1e-8);
610s  assert (acov, avar_out, 1e-8);
610s ***** error<wbllike: too few input arguments.> wbllike ([12, 15]);
610s ***** error<wbllike: wrong parameters length.> wbllike ([12, 15, 3], [1:50]);
610s ***** error<wbllike: X must be a vector.> wbllike ([12, 3], ones (10, 2));
610s ***** error<wbllike: X and CENSOR> wbllike ([12, 15], [1:50], [1, 2, 3]);
610s ***** error<wbllike: X and FREQ> wbllike ([12, 15], [1:50], [], [1, 2, 3]);
610s ***** error<wbllike: FREQ cannot have negative values.> ...
610s  wbllike ([12, 15], [1:5], [], [1, 2, 3, -1, 0]);
610s 9 tests, 9 passed, 0 known failure, 0 skipped
610s [inst/dist_fit/gevlike.m]
610s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/gevlike.m
610s ***** test
610s  x = 1;
610s  k = 0.2;
610s  sigma = 0.3;
610s  mu = 0.5;
610s  [L, C] = gevlike ([k sigma mu], x);
610s  expected_L = 0.75942;
610s  expected_C = [-0.12547 1.77884 1.06731; 1.77884 16.40761 8.48877; 1.06731 8.48877 0.27979];
610s  assert (L, expected_L, 0.001);
610s  assert (C, inv (expected_C), 0.001);
610s ***** test
610s  x = 1;
610s  k = 0;
610s  sigma = 0.3;
610s  mu = 0.5;
610s  [L, C] = gevlike ([k sigma mu], x);
610s  expected_L = 0.65157;
610s  expected_C = [0.090036 3.41229 2.047337; 3.412229 24.760027 12.510190; 2.047337 12.510190 2.098618];
610s  assert (L, expected_L, 0.001);
610s  assert (C, inv (expected_C), 0.001);
610s ***** test
610s  x = -5:-1;
610s  k = -0.2;
610s  sigma = 0.3;
610s  mu = 0.5;
610s  [L, C] = gevlike ([k sigma mu], x);
610s  expected_L = 3786.4;
610s  expected_C = [1.6802e-07, 4.6110e-06, 8.7297e-05; ...
610s                4.6110e-06, 7.5693e-06, 1.2034e-05; ...
610s                8.7297e-05, 1.2034e-05, -0.0019125];
610s  assert (L, expected_L, -0.001);
610s  assert (C, expected_C, -0.001);
610s ***** test
610s  x = -5:0;
610s  k = -0.2;
610s  sigma = 0.3;
610s  mu = 0.5;
610s  [L, C] = gevlike ([k sigma mu], x, [1, 1, 1, 1, 1, 0]);
610s  expected_L = 3786.4;
610s  expected_C = [1.6802e-07, 4.6110e-06, 8.7297e-05; ...
610s                4.6110e-06, 7.5693e-06, 1.2034e-05; ...
610s                8.7297e-05, 1.2034e-05, -0.0019125];
610s  assert (L, expected_L, -0.001);
610s  assert (C, expected_C, -0.001);
610s ***** error<gevlike: function called with too few input arguments.> gevlike (3.25)
610s ***** error<gevlike: X must be a vector.> gevlike ([1, 2, 3], ones (2))
610s ***** error<gevlike: PARAMS must be a three-element vector.> ...
610s  gevlike ([1, 2], [1, 3, 5, 7])
610s ***** error<gevlike: PARAMS must be a three-element vector.> ...
610s  gevlike ([1, 2, 3, 4], [1, 3, 5, 7])
610s ***** error<gevlike: X and FREQ vectors mismatch.> ...
610s  gevlike ([5, 0.2, 1], ones (10, 1), ones (8,1))
610s ***** error<gevlike: FREQ must not contain negative values.> ...
610s  gevlike ([5, 0.2, 1], ones (1, 8), [1 1 1 1 1 1 1 -1])
610s ***** error<gevlike: FREQ must contain integer values.> ...
610s  gevlike ([5, 0.2, 1], ones (1, 8), [1 1 1 1 1 1 1 1.5])
610s 11 tests, 11 passed, 0 known failure, 0 skipped
610s [inst/dist_fit/tlslike.m]
610s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/tlslike.m
610s ***** test
610s  x = [-1.2352, -0.2741, 0.1726, 7.4356, 1.0392, 16.4165];
610s  [nlogL, acov] = tlslike ([0.035893, 0.862711, 0.649261], x);
610s  acov_out = [0.2525, 0.0670, 0.0288; ...
610s              0.0670, 0.5724, 0.1786; ...
610s              0.0288, 0.1786, 0.1789];
610s  assert (nlogL, 17.9979636579, 1e-10);
610s  assert (acov, acov_out, 1e-4);
610s ***** error<tlslike: too few input arguments.> tlslike ([12, 15, 1]);
610s ***** error<tlslike: wrong parameters length.> tlslike ([12, 15], [1:50]);
610s ***** error<tlslike: X must be a vector.> tlslike ([12, 3, 1], ones (10, 2));
610s ***** error<tlslike: X and CENSOR> tlslike ([12, 15, 1], [1:50], [1, 2, 3]);
610s ***** error<tlslike: X and FREQ> tlslike ([12, 15, 1], [1:50], [], [1, 2, 3]);
610s ***** error<tlslike: FREQ cannot> tlslike ([12, 15, 1], [1:3], [], [1, 2, -3]);
610s 7 tests, 7 passed, 0 known failure, 0 skipped
610s [inst/dist_fit/burrlike.m]
610s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/burrlike.m
610s ***** error<burrlike: function called with too few input arguments.> burrlike (3.25)
610s ***** error<burrlike: X must be a vector.> burrlike ([1, 2, 3], ones (2))
610s ***** error<burrlike: X cannot have negative values.> burrlike ([1, 2, 3], [-1, 3])
610s ***** error<burrlike: PARAMS must be a three-element vector.> ...
610s  burrlike ([1, 2], [1, 3, 5, 7])
610s ***** error<burrlike: PARAMS must be a three-element vector.> ...
610s  burrlike ([1, 2, 3, 4], [1, 3, 5, 7])
610s ***** error<burrlike: X and CENSOR vector mismatch.> ...
610s  burrlike ([1, 2, 3], [1:5], [0, 0, 0])
610s ***** error<burrlike: X and FREQ vector mismatch.> ...
610s  burrlike ([1, 2, 3], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
610s ***** error<burrlike: X and FREQ vector mismatch.> ...
610s  burrlike ([1, 2, 3], [1:5], [], [1, 1, 1])
610s 8 tests, 8 passed, 0 known failure, 0 skipped
610s [inst/dist_fit/burrfit.m]
610s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/burrfit.m
610s ***** demo
610s  ## Sample 3 populations from different Burr type XII distibutions
610s  rand ("seed", 4);    # for reproducibility
610s  r1 = burrrnd (3.5, 2, 2.5, 10000, 1);
610s  rand ("seed", 2);    # for reproducibility
610s  r2 = burrrnd (1, 3, 1, 10000, 1);
610s  rand ("seed", 9);    # for reproducibility
610s  r3 = burrrnd (0.5, 2, 3, 10000, 1);
610s  r = [r1, r2, r3];
610s 
610s  ## Plot them normalized and fix their colors
610s  hist (r, [0.1:0.2:20], [18, 5, 3]);
610s  h = findobj (gca, "Type", "patch");
610s  set (h(1), "facecolor", "c");
610s  set (h(2), "facecolor", "g");
610s  set (h(3), "facecolor", "r");
610s  ylim ([0, 3]);
610s  xlim ([0, 5]);
610s  hold on
610s 
610s  ## Estimate their α and β parameters
610s  lambda_c_kA = burrfit (r(:,1));
610s  lambda_c_kB = burrfit (r(:,2));
610s  lambda_c_kC = burrfit (r(:,3));
610s 
610s  ## Plot their estimated PDFs
610s  x = [0.01:0.15:15];
610s  y = burrpdf (x, lambda_c_kA(1), lambda_c_kA(2), lambda_c_kA(3));
610s  plot (x, y, "-pr");
610s  y = burrpdf (x, lambda_c_kB(1), lambda_c_kB(2), lambda_c_kB(3));
610s  plot (x, y, "-sg");
610s  y = burrpdf (x, lambda_c_kC(1), lambda_c_kC(2), lambda_c_kC(3));
610s  plot (x, y, "-^c");
610s  hold off
610s  legend ({"Normalized HIST of sample 1 with λ=3.5, c=2, and k=2.5", ...
610s           "Normalized HIST of sample 2 with λ=1, c=3, and k=1", ...
610s           "Normalized HIST of sample 3 with λ=0.5, c=2, and k=3", ...
610s   sprintf("PDF for sample 1 with estimated λ=%0.2f, c=%0.2f, and k=%0.2f", ...
610s           lambda_c_kA(1), lambda_c_kA(2), lambda_c_kA(3)), ...
610s   sprintf("PDF for sample 2 with estimated λ=%0.2f, c=%0.2f, and k=%0.2f", ...
610s           lambda_c_kB(1), lambda_c_kB(2), lambda_c_kB(3)), ...
610s   sprintf("PDF for sample 3 with estimated λ=%0.2f, c=%0.2f, and k=%0.2f", ...
610s           lambda_c_kC(1), lambda_c_kC(2), lambda_c_kC(3))})
610s  title ("Three population samples from different Burr type XII distibutions")
610s  hold off
610s ***** test
610s  l = 1; c = 2; k = 3;
610s  r = burrrnd (l, c, k, 100000, 1);
610s  lambda_c_kA = burrfit (r);
610s  assert (lambda_c_kA(1), l, 0.2);
610s  assert (lambda_c_kA(2), c, 0.2);
610s  assert (lambda_c_kA(3), k, 0.3);
611s ***** test
611s  l = 0.5; c = 1; k = 3;
611s  r = burrrnd (l, c, k, 100000, 1);
611s  lambda_c_kA = burrfit (r);
611s  assert (lambda_c_kA(1), l, 0.2);
611s  assert (lambda_c_kA(2), c, 0.2);
611s  assert (lambda_c_kA(3), k, 0.3);
612s ***** test
612s  l = 1; c = 3; k = 1;
612s  r = burrrnd (l, c, k, 100000, 1);
612s  lambda_c_kA = burrfit (r);
612s  assert (lambda_c_kA(1), l, 0.2);
612s  assert (lambda_c_kA(2), c, 0.2);
612s  assert (lambda_c_kA(3), k, 0.3);
613s ***** test
613s  l = 3; c = 2; k = 1;
613s  r = burrrnd (l, c, k, 100000, 1);
613s  lambda_c_kA = burrfit (r);
613s  assert (lambda_c_kA(1), l, 0.2);
613s  assert (lambda_c_kA(2), c, 0.2);
613s  assert (lambda_c_kA(3), k, 0.3);
614s ***** test
614s  l = 4; c = 2; k = 4;
614s  r = burrrnd (l, c, k, 100000, 1);
614s  lambda_c_kA = burrfit (r);
614s  assert (lambda_c_kA(1), l, 0.2);
614s  assert (lambda_c_kA(2), c, 0.2);
614s  assert (lambda_c_kA(3), k, 0.3);
615s ***** error<burrfit: X must be a vector.> burrfit (ones (2,5));
615s ***** error<burrfit: X must contain only positive values.> burrfit ([-1 2 3 4]);
615s ***** error<burrfit: wrong value for ALPHA.> burrfit ([1, 2, 3, 4, 5], 1.2);
615s ***** error<burrfit: wrong value for ALPHA.> burrfit ([1, 2, 3, 4, 5], 0);
615s ***** error<burrfit: wrong value for ALPHA.> burrfit ([1, 2, 3, 4, 5], "alpha");
615s ***** error<burrfit: X and CENSOR vectors mismatch.> ...
615s  burrfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
615s ***** error<burrfit: X and CENSOR vectors mismatch.> ...
615s  burrfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
615s ***** error<burrfit: X and FREQ vectors mismatch.>
615s  burrfit ([1, 2, 3, 4, 5], 0.05, [], [1, 1, 5])
615s ***** error<burrfit: FREQ must not contain negative values.>
615s  burrfit ([1, 2, 3, 4, 5], 0.05, [], [1, 5, 1, 1, -1])
615s ***** error<burrfit: 'options' 5th argument must be a structure> ...
615s  burrfit ([1:10], 0.05, [], [], 5)
615s 15 tests, 15 passed, 0 known failure, 0 skipped
615s [inst/dist_fit/invglike.m]
615s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/invglike.m
615s ***** test
615s  nlogL = invglike ([25.5, 19.6973], [1:50]);
615s  assert (nlogL, 219.1516, 1e-4);
615s ***** test
615s  nlogL = invglike ([3, 8.1081], [1:5]);
615s  assert (nlogL, 9.0438, 1e-4);
615s ***** error<invglike: function called with too few input arguments.> invglike (3.25)
615s ***** error<invglike: X must be a vector.> invglike ([5, 0.2], ones (2))
615s ***** error<invglike: X must have positive values.> invglike ([5, 0.2], [-1, 3])
615s ***** error<invglike: PARAMS must be a two-element vector.> ...
615s  invglike ([1, 0.2, 3], [1, 3, 5, 7])
615s ***** error<invglike: X and CENSOR vector mismatch.> ...
615s  invglike ([1.5, 0.2], [1:5], [0, 0, 0])
615s ***** error<invglike: X and FREQ vector mismatch.> ...
615s  invglike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
615s ***** error<invglike: X and FREQ vector mismatch.> ...
615s  invglike ([1.5, 0.2], [1:5], [], [1, 1, 1])
615s 9 tests, 9 passed, 0 known failure, 0 skipped
615s [inst/dist_fit/loglfit.m]
615s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/loglfit.m
615s ***** demo
615s  ## Sample 3 populations from different log-logistic distibutions
615s  rand ("seed", 5)  # for reproducibility
615s  r1 = loglrnd (0, 1, 2000, 1);
615s  rand ("seed", 2)   # for reproducibility
615s  r2 = loglrnd (0, 0.5, 2000, 1);
615s  rand ("seed", 7)   # for reproducibility
615s  r3 = loglrnd (0, 0.125, 2000, 1);
615s  r = [r1, r2, r3];
615s 
615s  ## Plot them normalized and fix their colors
615s  hist (r, [0.05:0.1:2.5], 10);
615s  h = findobj (gca, "Type", "patch");
615s  set (h(1), "facecolor", "c");
615s  set (h(2), "facecolor", "g");
615s  set (h(3), "facecolor", "r");
615s  ylim ([0, 3.5]);
615s  xlim ([0, 2.0]);
615s  hold on
615s 
615s  ## Estimate their MU and LAMBDA parameters
615s  a_bA = loglfit (r(:,1));
615s  a_bB = loglfit (r(:,2));
615s  a_bC = loglfit (r(:,3));
615s 
615s  ## Plot their estimated PDFs
615s  x = [0.01:0.1:2.01];
615s  y = loglpdf (x, a_bA(1), a_bA(2));
615s  plot (x, y, "-pr");
615s  y = loglpdf (x, a_bB(1), a_bB(2));
615s  plot (x, y, "-sg");
615s  y = loglpdf (x, a_bC(1), a_bC(2));
615s  plot (x, y, "-^c");
615s  legend ({"Normalized HIST of sample 1 with α=1 and β=1", ...
615s           "Normalized HIST of sample 2 with α=1 and β=2", ...
615s           "Normalized HIST of sample 3 with α=1 and β=8", ...
615s           sprintf("PDF for sample 1 with estimated α=%0.2f and β=%0.2f", ...
615s                   a_bA(1), a_bA(2)), ...
615s           sprintf("PDF for sample 2 with estimated α=%0.2f and β=%0.2f", ...
615s                   a_bB(1), a_bB(2)), ...
615s           sprintf("PDF for sample 3 with estimated α=%0.2f and β=%0.2f", ...
615s                   a_bC(1), a_bC(2))})
615s  title ("Three population samples from different log-logistic distibutions")
615s  hold off
615s ***** test
615s  [paramhat, paramci] = loglfit ([1:50]);
615s  paramhat_out = [3.09717, 0.468525];
615s  paramci_out = [2.87261, 0.370616; 3.32174, 0.5923];
615s  assert (paramhat, paramhat_out, 1e-5);
615s  assert (paramci, paramci_out, 1e-5);
615s ***** test
615s  paramhat = loglfit ([1:5]);
615s  paramhat_out = [1.01124, 0.336449];
615s  assert (paramhat, paramhat_out, 1e-5);
615s ***** test
615s  paramhat = loglfit ([1:6], [], [], [1 1 1 1 1 0]);
615s  paramhat_out = [1.01124, 0.336449];
615s  assert (paramhat, paramhat_out, 1e-4);
615s ***** test
615s  paramhat = loglfit ([1:5], [], [], [1 1 1 1 2]);
615s  paramhat_out = loglfit ([1:5, 5]);
615s  assert (paramhat, paramhat_out, 1e-4);
615s ***** error<loglfit: X must be a vector.> loglfit (ones (2,5));
615s ***** error<loglfit: wrong value for ALPHA.> loglfit ([1, 2, 3, 4, 5], 1.2);
615s ***** error<loglfit: wrong value for ALPHA.> loglfit ([1, 2, 3, 4, 5], 0);
615s ***** error<loglfit: wrong value for ALPHA.> loglfit ([1, 2, 3, 4, 5], "alpha");
615s ***** error<loglfit: X and CENSOR vectors mismatch.> ...
615s  loglfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
615s ***** error<loglfit: X and CENSOR vectors mismatch.> ...
615s  loglfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
615s ***** error<loglfit: X and FREQ vectors mismatch.> ...
615s  loglfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
615s ***** error<loglfit: X and FREQ vectors mismatch.> ...
615s  loglfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
615s ***** error<loglfit: 'options' 5th argument must be a structure> ...
615s  loglfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
615s 13 tests, 13 passed, 0 known failure, 0 skipped
615s [inst/dist_fit/rayllike.m]
615s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/rayllike.m
615s ***** test
615s  x = [1 3 2 4 5 4 3 4];
615s  [nlogL, acov] = rayllike (3.25, x);
615s  assert (nlogL, 14.7442, 1e-4)
615s ***** test
615s  x = [1 2 3 4 5];
615s  f = [1 1 2 3 1];
615s  [nlogL, acov] = rayllike (3.25, x, [], f);
615s  assert (nlogL, 14.7442, 1e-4)
615s ***** test
615s  x = [1 2 3 4 5 6];
615s  f = [1 1 2 3 1 0];
615s  [nlogL, acov] = rayllike (3.25, x, [], f);
615s  assert (nlogL, 14.7442, 1e-4)
615s ***** test
615s  x = [1 2 3 4 5 6];
615s  c = [0 0 0 0 0 1];
615s  f = [1 1 2 3 1 0];
615s  [nlogL, acov] = rayllike (3.25, x, c, f);
615s  assert (nlogL, 14.7442, 1e-4)
615s ***** error<rayllike: function called with too few input arguments.> rayllike (1)
615s ***** error<rayllike: SIGMA must be a positive scalar.> rayllike ([1 2 3], [1 2])
615s ***** error<rayllike: X must be a vector of non-negative values.> ...
615s  rayllike (3.25, ones (10, 2))
615s ***** error<rayllike: X must be a vector of non-negative values.> ...
615s  rayllike (3.25, [1 2 3 -4 5])
615s ***** error<rayllike: X and CENSOR vectors mismatch.> ...
615s  rayllike (3.25, [1, 2, 3, 4, 5], [1 1 0]);
615s ***** error<rayllike: X and CENSOR vectors mismatch.> ...
615s  rayllike (3.25, [1, 2, 3, 4, 5], [1 1 0 1 1]');
615s ***** error<rayllike: X and FREQ vectors mismatch.> ...
615s  rayllike (3.25, [1, 2, 3, 4, 5], zeros (1,5), [1 1 0]);
615s ***** error<rayllike: X and FREQ vectors mismatch.> ...
615s  rayllike (3.25, [1, 2, 3, 4, 5], [], [1 1 0 1 1]');
615s ***** error<rayllike: FREQ must not contain negative values.> ...
615s  rayllike (3.25, ones (1, 8), [], [1 1 1 1 1 1 1 -1])
615s 13 tests, 13 passed, 0 known failure, 0 skipped
615s [inst/dist_fit/betafit.m]
615s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/betafit.m
615s ***** demo
615s  ## Sample 2 populations from different Beta distibutions
615s  randg ("seed", 1);   # for reproducibility
615s  r1 = betarnd (2, 5, 500, 1);
615s  randg ("seed", 2);   # for reproducibility
615s  r2 = betarnd (2, 2, 500, 1);
615s  r = [r1, r2];
615s 
615s  ## Plot them normalized and fix their colors
615s  hist (r, 12, 15);
615s  h = findobj (gca, "Type", "patch");
615s  set (h(1), "facecolor", "c");
615s  set (h(2), "facecolor", "g");
615s  hold on
615s 
615s  ## Estimate their shape parameters
615s  a_b_A = betafit (r(:,1));
615s  a_b_B = betafit (r(:,2));
615s 
615s  ## Plot their estimated PDFs
615s  x = [min(r(:)):0.01:max(r(:))];
615s  y = betapdf (x, a_b_A(1), a_b_A(2));
615s  plot (x, y, "-pr");
615s  y = betapdf (x, a_b_B(1), a_b_B(2));
615s  plot (x, y, "-sg");
615s  ylim ([0, 4])
615s  legend ({"Normalized HIST of sample 1 with α=2 and β=5", ...
615s           "Normalized HIST of sample 2 with α=2 and β=2", ...
615s           sprintf("PDF for sample 1 with estimated α=%0.2f and β=%0.2f", ...
615s                   a_b_A(1), a_b_A(2)), ...
615s           sprintf("PDF for sample 2 with estimated α=%0.2f and β=%0.2f", ...
615s                   a_b_B(1), a_b_B(2))})
615s  title ("Two population samples from different Beta distibutions")
615s  hold off
615s ***** test
615s  x = 0.01:0.02:0.99;
615s  [paramhat, paramci] = betafit (x);
615s  paramhat_out = [1.0199, 1.0199];
615s  paramci_out = [0.6947, 0.6947; 1.4974, 1.4974];
615s  assert (paramhat, paramhat_out, 1e-4);
615s  assert (paramci, paramci_out, 1e-4);
615s ***** test
615s  x = 0.01:0.02:0.99;
615s  [paramhat, paramci] = betafit (x, 0.01);
615s  paramci_out = [0.6157, 0.6157; 1.6895, 1.6895];
615s  assert (paramci, paramci_out, 1e-4);
615s ***** test
615s  x = 0.00:0.02:1;
615s  [paramhat, paramci] = betafit (x);
615s  paramhat_out = [0.0875, 0.1913];
615s  paramci_out = [0.0822, 0.1490; 0.0931, 0.2455];
615s  assert (paramhat, paramhat_out, 1e-4);
615s  assert (paramci, paramci_out, 1e-4);
615s ***** error<betafit: X must be a vector of real values.> betafit ([0.2, 0.5+i]);
615s ***** error<betafit: X must be a vector of real values.> betafit (ones (2,2) * 0.5);
615s ***** error<betafit: X must be in the range> betafit ([0.5, 1.2]);
615s ***** error<betafit: X must contain distinct values.> betafit ([0.1, 0.1]);
615s ***** error<betafit: wrong value for ALPHA.> betafit ([0.01:0.1:0.99], 1.2);
615s ***** error<betafit: X and FREQ vectors mismatch.> ...
615s  betafit ([0.01:0.01:0.05], 0.05, [1, 2, 3, 2]);
615s ***** error<betafit: FREQ must not contain negative values.> ...
615s  betafit ([0.01:0.01:0.05], 0.05, [1, 2, 3, 2, -1]);
615s ***** error<betafit: FREQ must contain integer values.> ...
615s  betafit ([0.01:0.01:0.05], 0.05, [1, 2, 3, 2, 1.5]);
615s ***** error<betafit: 'options' argument must be a structure> ...
615s  betafit ([0.01:0.01:0.05], 0.05, struct ("option", 234));
615s ***** error<betafit: 'options' argument must be a structure> ...
615s  betafit ([0.01:0.01:0.05], 0.05, ones (1,5), struct ("option", 234));
615s 13 tests, 13 passed, 0 known failure, 0 skipped
615s [inst/dist_fit/gamfit.m]
615s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/gamfit.m
615s ***** demo
615s  ## Sample 3 populations from different Gamma distibutions
615s  randg ("seed", 5);    # for reproducibility
615s  r1 = gamrnd (1, 2, 2000, 1);
615s  randg ("seed", 2);    # for reproducibility
615s  r2 = gamrnd (2, 2, 2000, 1);
615s  randg ("seed", 7);    # for reproducibility
615s  r3 = gamrnd (7.5, 1, 2000, 1);
615s  r = [r1, r2, r3];
615s 
615s  ## Plot them normalized and fix their colors
615s  hist (r, 75, 4);
615s  h = findobj (gca, "Type", "patch");
615s  set (h(1), "facecolor", "c");
615s  set (h(2), "facecolor", "g");
615s  set (h(3), "facecolor", "r");
615s  ylim ([0, 0.62]);
615s  xlim ([0, 12]);
615s  hold on
615s 
615s  ## Estimate their α and β parameters
615s  a_bA = gamfit (r(:,1));
615s  a_bB = gamfit (r(:,2));
615s  a_bC = gamfit (r(:,3));
615s 
615s  ## Plot their estimated PDFs
615s  x = [0.01,0.1:0.2:18];
615s  y = gampdf (x, a_bA(1), a_bA(2));
615s  plot (x, y, "-pr");
615s  y = gampdf (x, a_bB(1), a_bB(2));
615s  plot (x, y, "-sg");
615s  y = gampdf (x, a_bC(1), a_bC(2));
615s  plot (x, y, "-^c");
615s  hold off
615s  legend ({"Normalized HIST of sample 1 with α=1 and β=2", ...
615s           "Normalized HIST of sample 2 with α=2 and β=2", ...
615s           "Normalized HIST of sample 3 with α=7.5 and β=1", ...
615s           sprintf("PDF for sample 1 with estimated α=%0.2f and β=%0.2f", ...
615s                   a_bA(1), a_bA(2)), ...
615s           sprintf("PDF for sample 2 with estimated α=%0.2f and β=%0.2f", ...
615s                   a_bB(1), a_bB(2)), ...
615s           sprintf("PDF for sample 3 with estimated α=%0.2f and β=%0.2f", ...
615s                   a_bC(1), a_bC(2))})
615s  title ("Three population samples from different Gamma distibutions")
615s  hold off
615s ***** shared x
615s  x = [1.2 1.6 1.7 1.8 1.9 2.0 2.2 2.6 3.0 3.5 4.0 4.8 5.6 6.6 7.6];
615s ***** test
615s  [paramhat, paramci] = gamfit (x);
615s  assert (paramhat, [3.4248, 0.9752], 1e-4);
615s  assert (paramci, [1.7287, 0.4670; 6.7852, 2.0366], 1e-4);
615s ***** test
615s  [paramhat, paramci] = gamfit (x, 0.01);
615s  assert (paramhat, [3.4248, 0.9752], 1e-4);
615s  assert (paramci, [1.3945, 0.3705; 8.4113, 2.5668], 1e-4);
615s ***** test
615s  freq = [1 1 1 1 2 1 1 1 1 2 1 1 1 1 2];
615s  [paramhat, paramci] = gamfit (x, [], [], freq);
615s  assert (paramhat, [3.3025, 1.0615], 1e-4);
615s  assert (paramci, [1.7710, 0.5415; 6.1584, 2.0806], 1e-4);
615s ***** test
615s  [paramhat, paramci] = gamfit (x, [], [], [1:15]);
615s  assert (paramhat, [4.4484, 0.9689], 1e-4);
615s  assert (paramci, [3.4848, 0.7482; 5.6785, 1.2546], 1e-4);
615s ***** test
615s  [paramhat, paramci] = gamfit (x, 0.01, [], [1:15]);
615s  assert (paramhat, [4.4484, 0.9689], 1e-4);
615s  assert (paramci, [3.2275, 0.6899; 6.1312, 1.3608], 1e-4);
615s ***** test
615s  cens = [0 0 0 0 1 0 0 0 0 0 0 0 0 0 0];
615s  [paramhat, paramci] = gamfit (x, [], cens, [1:15]);
615s  assert (paramhat, [4.7537, 0.9308], 1e-4);
615s  assert (paramci, [3.7123, 0.7162; 6.0872, 1.2097], 1e-4);
615s ***** test
615s  cens = [0 0 0 0 1 0 0 0 0 0 0 0 0 0 0];
615s  freq = [1 1 1 1 2 1 1 1 1 2 1 1 1 1 2];
615s  [paramhat, paramci] = gamfit (x, [], cens, freq);
615s  assert (paramhat, [3.4736, 1.0847], 1e-4);
615s  assert (paramci, [1.8286, 0.5359; 6.5982, 2.1956], 1e-4);
615s ***** test
615s  [paramhat, paramci] = gamfit ([1 1 1 1 1 1]);
615s  assert (paramhat, [Inf, 0]);
615s  assert (paramci, [Inf, 0; Inf, 0]);
615s ***** test
615s  [paramhat, paramci] = gamfit ([1 1 1 1 1 1], [], [1 1 1 1 1 1]);
615s  assert (paramhat, [NaN, NaN]);
615s  assert (paramci, [NaN, NaN; NaN, NaN]);
615s ***** test
615s  [paramhat, paramci] = gamfit ([1 1 1 1 1 1], [], [], [1 1 1 1 1 1]);
615s  assert (paramhat, [Inf, 0]);
615s  assert (paramci, [Inf, 0; Inf, 0]);
615s ***** assert (class (gamfit (single (x))), "single")
615s ***** error<gamfit: X must be a vector.> gamfit (ones (2))
615s ***** error<gamfit: wrong value for ALPHA.> gamfit (x, 1)
615s ***** error<gamfit: wrong value for ALPHA.> gamfit (x, -1)
615s ***** error<gamfit: wrong value for ALPHA.> gamfit (x, {0.05})
615s ***** error<gamfit: wrong value for ALPHA.> gamfit (x, "a")
615s ***** error<gamfit: wrong value for ALPHA.> gamfit (x, i)
615s ***** error<gamfit: wrong value for ALPHA.> gamfit (x, [0.01 0.02])
615s ***** error<gamfit: X and FREQ vectors mismatch.>
615s  gamfit ([1 2 3], 0.05, [], [1 5])
615s ***** error<gamfit: FREQ must not contain negative values.>
615s  gamfit ([1 2 3], 0.05, [], [1 5 -1])
615s ***** error<gamfit: 'options' 5th argument must be a structure> ...
615s  gamfit ([1:10], 0.05, [], [], 5)
615s ***** error<gamfit: X cannot contain negative values.> gamfit ([1 2 3 -4])
615s ***** error<gamfit: X must contain positive values when censored.> ...
615s  gamfit ([1 2 0], [], [1 0 0])
615s 23 tests, 23 passed, 0 known failure, 0 skipped
615s [inst/dist_fit/binolike.m]
615s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/binolike.m
615s ***** assert (binolike ([3, 0.333], [0:3]), 6.8302, 1e-4)
615s ***** assert (binolike ([3, 0.333], 0), 1.2149, 1e-4)
615s ***** assert (binolike ([3, 0.333], 1), 0.8109, 1e-4)
615s ***** assert (binolike ([3, 0.333], 2), 1.5056, 1e-4)
615s ***** assert (binolike ([3, 0.333], 3), 3.2988, 1e-4)
615s ***** test
615s  [nlogL, acov] = binolike ([3, 0.333], 3);
615s  assert (acov(4), 0.0740, 1e-4)
616s ***** error<binolike: function called with too few input arguments.> binolike (3.25)
616s ***** error<binolike: X must be a vector.> binolike ([5, 0.2], ones (2))
616s ***** error<binolike: PARAMS must be a two-element vector.> ...
616s  binolike ([1, 0.2, 3], [1, 3, 5, 7])
616s ***** error<binolike: number of trials,> binolike ([1.5, 0.2], 1)
616s ***** error<binolike: number of trials,> binolike ([-1, 0.2], 1)
616s ***** error<binolike: number of trials,> binolike ([Inf, 0.2], 1)
616s ***** error<binolike: probability of success,> binolike ([5, 1.2], [3, 5])
616s ***** error<binolike: probability of success,> binolike ([5, -0.2], [3, 5])
616s ***** error<binolike: X and FREQ vectors mismatch.> ...
616s  binolike ([5, 0.5], ones (10, 1), ones (8,1))
616s ***** error<binolike: FREQ must not contain negative values.> ...
616s  binolike ([5, 0.5], ones (1, 8), [1 1 1 1 1 1 1 -1])
616s ***** error<binolike: X cannot have negative values.> binolike ([5, 0.2], [-1, 3])
616s ***** error<binolike: number of successes,> binolike ([5, 0.2], [3, 5, 7])
616s 18 tests, 18 passed, 0 known failure, 0 skipped
616s [inst/dist_fit/gplike.m]
616s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/gplike.m
616s ***** test
616s  k = 0.8937; sigma = 1.3230; theta = 1;
616s  x = [2.2196, 11.9301, 4.3673, 1.0949, 6.5626, ...
616s       1.2109, 1.8576, 1.0039, 12.7917, 2.2590];
616s  [nlogL, acov] = gplike ([k, sigma, theta], x);
616s  assert (nlogL, 21.736, 1e-3);
616s  assert (acov, [0.7249, -0.7351, 0; -0.7351, 1.3040, 0; 0, 0, 0], 1e-4);
616s ***** assert (gplike ([2, 3, 0], 4), 3.047536764863501, 1e-14)
616s ***** assert (gplike ([2, 3, 4], 8), 3.047536764863501, 1e-14)
616s ***** assert (gplike ([1, 2, 0], 4), 2.890371757896165, 1e-14)
616s ***** assert (gplike ([1, 2, 4], 8), 2.890371757896165, 1e-14)
616s ***** assert (gplike ([2, 3, 0], [1:10]), 32.57864322725392, 1e-14)
616s ***** assert (gplike ([2, 3, 2], [1:10] + 2), 32.57864322725392, 1e-14)
616s ***** assert (gplike ([2, 3, 0], [1:10], ones (1,10)), 32.57864322725392, 1e-14)
616s ***** assert (gplike ([1, 2, 0], [1:10]), 31.65666282460443, 1e-14)
616s ***** assert (gplike ([1, 2, 3], [1:10] + 3), 31.65666282460443, 1e-14)
616s ***** assert (gplike ([1, 2, 0], [1:10], ones (1,10)), 31.65666282460443, 1e-14)
616s ***** assert (gplike ([1, NaN, 0], [1:10]), NaN)
616s ***** error<gplike: function called with too few input arguments.> gplike ()
616s ***** error<gplike: function called with too few input arguments.> gplike (1)
616s ***** error<gplike: X must be a vector.> gplike ([1, 2, 0], [])
616s ***** error<gplike: X must be a vector.> gplike ([1, 2, 0], ones (2))
616s ***** error<gplike: PARAMS must be a three-element vector.> gplike (2, [1:10])
616s ***** error<gplike: PARAMS must be a three-element vector.> gplike ([2, 3], [1:10])
616s ***** error<gplike: X and FREQ vectors mismatch.> ...
616s  gplike ([1, 2, 0], ones (10, 1), ones (8,1))
616s ***** error<gplike: FREQ must not contain negative values.> ...
616s  gplike ([1, 2, 0], ones (1, 8), [1 1 1 1 1 1 1 -1])
616s 20 tests, 20 passed, 0 known failure, 0 skipped
616s [inst/dist_fit/unifit.m]
616s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/unifit.m
616s ***** demo
616s  ## Sample 2 populations from different continuous uniform distibutions
616s  rand ("seed", 5);    # for reproducibility
616s  r1 = unifrnd (2, 5, 2000, 1);
616s  rand ("seed", 6);    # for reproducibility
616s  r2 = unifrnd (3, 9, 2000, 1);
616s  r = [r1, r2];
616s 
616s  ## Plot them normalized and fix their colors
616s  hist (r, 0:0.5:10, 2);
616s  h = findobj (gca, "Type", "patch");
616s  set (h(1), "facecolor", "c");
616s  set (h(2), "facecolor", "g");
616s  hold on
616s 
616s  ## Estimate their probability of success
616s  a_bA = unifit (r(:,1));
616s  a_bB = unifit (r(:,2));
616s 
616s  ## Plot their estimated PDFs
616s  x = [0:10];
616s  y = unifpdf (x, a_bA(1), a_bA(2));
616s  plot (x, y, "-pg");
616s  y = unifpdf (x, a_bB(1), a_bB(2));
616s  plot (x, y, "-sc");
616s  xlim ([1, 10])
616s  ylim ([0, 0.5])
616s  legend ({"Normalized HIST of sample 1 with a=2 and b=5", ...
616s           "Normalized HIST of sample 2 with a=3 and b=9", ...
616s           sprintf("PDF for sample 1 with estimated a=%0.2f and b=%0.2f", ...
616s                   a_bA(1), a_bA(2)), ...
616s           sprintf("PDF for sample 2 with estimated a=%0.2f and b=%0.2f", ...
616s                   a_bB(1), a_bB(2))})
616s  title ("Two population samples from different continuous uniform distibutions")
616s  hold off
616s ***** test
616s  x = 0:5;
616s  [paramhat, paramci] = unifit (x);
616s  assert (paramhat, [0, 5]);
616s  assert (paramci, [-3.2377, 8.2377; 0, 5], 1e-4);
616s ***** test
616s  x = 0:5;
616s  [paramhat, paramci] = unifit (x, [], [1 1 1 1 1 1]);
616s  assert (paramhat, [0, 5]);
616s  assert (paramci, [-3.2377, 8.2377; 0, 5], 1e-4);
616s ***** assert (unifit ([1 1 2 3]), unifit ([1 2 3], [] ,[2 1 1]))
616s ***** error<unifit: function called with too few input arguments.> unifit ()
616s ***** error<unifit: X cannot have negative values.> unifit (-1, [1 2 3 3])
616s ***** error<unifit: wrong value for ALPHA.> unifit (1, 0)
616s ***** error<unifit: wrong value for ALPHA.> unifit (1, 1.2)
616s ***** error<unifit: wrong value for ALPHA.> unifit (1, [0.02 0.05])
616s ***** error<unifit: X and FREQ vector mismatch.> ...
616s  unifit ([1.5, 0.2], [], [0, 0, 0, 0, 0])
616s ***** error<unifit: FREQ cannot have negative values.> ...
616s  unifit ([1.5, 0.2], [], [1, -1])
616s ***** error<unifit: X and FREQ vector mismatch.> ...
616s  unifit ([1.5, 0.2], [], [1, 1, 1])
616s 11 tests, 11 passed, 0 known failure, 0 skipped
616s [inst/dist_fit/raylfit.m]
616s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/raylfit.m
616s ***** demo
616s  ## Sample 3 populations from 3 different Rayleigh distibutions
616s  rand ("seed", 2);    # for reproducibility
616s  r1 = raylrnd (1, 1000, 1);
616s  rand ("seed", 2);    # for reproducibility
616s  r2 = raylrnd (2, 1000, 1);
616s  rand ("seed", 3);    # for reproducibility
616s  r3 = raylrnd (4, 1000, 1);
616s  r = [r1, r2, r3];
616s 
616s  ## Plot them normalized and fix their colors
616s  hist (r, [0.5:0.5:10.5], 2);
616s  h = findobj (gca, "Type", "patch");
616s  set (h(1), "facecolor", "c");
616s  set (h(2), "facecolor", "g");
616s  set (h(3), "facecolor", "r");
616s  hold on
616s 
616s  ## Estimate their lambda parameter
616s  sigmaA = raylfit (r(:,1));
616s  sigmaB = raylfit (r(:,2));
616s  sigmaC = raylfit (r(:,3));
616s 
616s  ## Plot their estimated PDFs
616s  x = [0:0.1:10];
616s  y = raylpdf (x, sigmaA);
616s  plot (x, y, "-pr");
616s  y = raylpdf (x, sigmaB);
616s  plot (x, y, "-sg");
616s  y = raylpdf (x, sigmaC);
616s  plot (x, y, "-^c");
616s  xlim ([0, 10])
616s  ylim ([0, 0.7])
616s  legend ({"Normalized HIST of sample 1 with σ=1", ...
616s           "Normalized HIST of sample 2 with σ=2", ...
616s           "Normalized HIST of sample 3 with σ=4", ...
616s           sprintf("PDF for sample 1 with estimated σ=%0.2f", ...
616s                   sigmaA), ...
616s           sprintf("PDF for sample 2 with estimated σ=%0.2f", ...
616s                   sigmaB), ...
616s           sprintf("PDF for sample 3 with estimated σ=%0.2f", ...
616s                   sigmaC)})
616s  title ("Three population samples from different Rayleigh distibutions")
616s  hold off
616s ***** test
616s  x = [1 3 2 4 5 4 3 4];
616s  [shat, sci] = raylfit (x);
616s  assert (shat, 2.4495, 1e-4)
616s  assert (sci, [1.8243; 3.7279], 1e-4)
616s ***** test
616s  x = [1 3 2 4 5 4 3 4];
616s  [shat, sci] = raylfit (x, 0.01);
616s  assert (shat, 2.4495, 1e-4)
616s  assert (sci, [1.6738; 4.3208], 1e-4)
616s ***** test
616s  x = [1 2 3 4 5];
616s  f = [1 1 2 3 1];
616s  [shat, sci] = raylfit (x, [], [], f);
616s  assert (shat, 2.4495, 1e-4)
616s  assert (sci, [1.8243; 3.7279], 1e-4)
616s ***** test
616s  x = [1 2 3 4 5];
616s  f = [1 1 2 3 1];
616s  [shat, sci] = raylfit (x, 0.01, [], f);
616s  assert (shat, 2.4495, 1e-4)
616s  assert (sci, [1.6738; 4.3208], 1e-4)
616s ***** test
616s  x = [1 2 3 4 5 6];
616s  c = [0 0 0 0 0 1];
616s  f = [1 1 2 3 1 1];
616s  [shat, sci] = raylfit (x, 0.01, c, f);
616s  assert (shat, 2.4495, 1e-4)
616s  assert (sci, [1.6738; 4.3208], 1e-4)
616s ***** error<raylfit: X must be a vector.> raylfit (ones (2,5));
616s ***** error<raylfit: X cannot have negative values.> raylfit ([1 2 -1 3])
616s ***** error<raylfit: wrong value for ALPHA.> raylfit ([1 2 3], 0)
616s ***** error<raylfit: wrong value for ALPHA.> raylfit ([1 2 3], 1.2)
616s ***** error<raylfit: wrong value for ALPHA.> raylfit ([1 2 3], [0.02 0.05])
616s ***** error<raylfit: X and CENSOR vectors mismatch.> ...
616s  raylfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
616s ***** error<raylfit: X and CENSOR vectors mismatch.> ...
616s  raylfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
616s ***** error<raylfit: X and FREQ vectors mismatch.> ...
616s  raylfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
616s ***** error<raylfit: X and FREQ vectors mismatch.> ...
616s  raylfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
616s ***** error<raylfit: X and FREQ vectors mismatch.>
616s  raylfit ([1 2 3], [], [], [1 5])
616s ***** error<raylfit: FREQ must not contain negative values.>
616s  raylfit ([1 2 3], [], [], [1 5 -1])
616s 16 tests, 16 passed, 0 known failure, 0 skipped
616s [inst/dist_fit/unidfit.m]
616s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/unidfit.m
616s ***** demo
616s  ## Sample 2 populations from different discrete uniform distibutions
616s  rand ("seed", 1);    # for reproducibility
616s  r1 = unidrnd (5, 1000, 1);
616s  rand ("seed", 2);    # for reproducibility
616s  r2 = unidrnd (9, 1000, 1);
616s  r = [r1, r2];
616s 
616s  ## Plot them normalized and fix their colors
616s  hist (r, 0:0.5:20.5, 1);
616s  h = findobj (gca, "Type", "patch");
616s  set (h(1), "facecolor", "c");
616s  set (h(2), "facecolor", "g");
616s  hold on
616s 
616s  ## Estimate their probability of success
616s  NhatA = unidfit (r(:,1));
616s  NhatB = unidfit (r(:,2));
616s 
616s  ## Plot their estimated PDFs
616s  x = [0:10];
616s  y = unidpdf (x, NhatA);
616s  plot (x, y, "-pg");
616s  y = unidpdf (x, NhatB);
616s  plot (x, y, "-sc");
616s  xlim ([0, 10])
616s  ylim ([0, 0.4])
616s  legend ({"Normalized HIST of sample 1 with N=5", ...
616s           "Normalized HIST of sample 2 with N=9", ...
616s           sprintf("PDF for sample 1 with estimated N=%0.2f", NhatA), ...
616s           sprintf("PDF for sample 2 with estimated N=%0.2f", NhatB)})
616s  title ("Two population samples from different discrete uniform distibutions")
616s  hold off
616s ***** test
616s  x = 0:5;
616s  [Nhat, Nci] = unidfit (x);
616s  assert (Nhat, 5);
616s  assert (Nci, [5; 9]);
616s ***** test
616s  x = 0:5;
616s  [Nhat, Nci] = unidfit (x, [], [1 1 1 1 1 1]);
616s  assert (Nhat, 5);
616s  assert (Nci, [5; 9]);
616s ***** assert (unidfit ([1 1 2 3]), unidfit ([1 2 3], [] ,[2 1 1]))
616s ***** error<unidfit: function called with too few input arguments.> unidfit ()
616s ***** error<unidfit: X cannot have negative values.> unidfit (-1, [1 2 3 3])
616s ***** error<unidfit: wrong value for ALPHA.> unidfit (1, 0)
616s ***** error<unidfit: wrong value for ALPHA.> unidfit (1, 1.2)
616s ***** error<unidfit: wrong value for ALPHA.> unidfit (1, [0.02 0.05])
616s ***** error<unidfit: X and FREQ vector mismatch.> ...
616s  unidfit ([1.5, 0.2], [], [0, 0, 0, 0, 0])
616s ***** error<unidfit: X and FREQ vector mismatch.> ...
616s  unidfit ([1.5, 0.2], [], [1, 1, 1])
616s ***** error<unidfit: FREQ cannot have negative values.> ...
616s  unidfit ([1.5, 0.2], [], [1, -1])
616s 11 tests, 11 passed, 0 known failure, 0 skipped
616s [inst/dist_fit/lognlike.m]
616s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/lognlike.m
616s ***** test
616s  x = 1:50;
616s  [nlogL, avar] = lognlike ([0, 0.25], x);
616s  avar_out = [-5.4749e-03, 2.8308e-04; 2.8308e-04, -1.1916e-05];
616s  assert (nlogL, 3962.330333301793, 1e-10);
616s  assert (avar, avar_out, 1e-7);
616s ***** test
616s  x = 1:50;
616s  [nlogL, avar] = lognlike ([0, 0.25], x * 0.5);
616s  avar_out = [-7.6229e-03, 4.8722e-04; 4.8722e-04, -2.6754e-05];
616s  assert (nlogL, 2473.183051225747, 1e-10);
616s  assert (avar, avar_out, 1e-7);
616s ***** test
616s  x = 1:50;
616s  [nlogL, avar] = lognlike ([0, 0.5], x);
616s  avar_out = [-2.1152e-02, 2.2017e-03; 2.2017e-03, -1.8535e-04];
616s  assert (nlogL, 1119.072424020455, 1e-12);
616s  assert (avar, avar_out, 1e-6);
616s ***** test
616s  x = 1:50;
616s  censor = ones (1, 50);
616s  censor([2, 4, 6, 8, 12, 14]) = 0;
616s  [nlogL, avar] = lognlike ([0, 0.5], x, censor);
616s  avar_out = [-1.9823e-02, 2.0370e-03; 2.0370e-03, -1.6618e-04];
616s  assert (nlogL, 1091.746371145497, 1e-12);
616s  assert (avar, avar_out, 1e-6);
616s ***** test
616s  x = 1:50;
616s  censor = ones (1, 50);
616s  censor([2, 4, 6, 8, 12, 14]) = 0;
616s  [nlogL, avar] = lognlike ([0, 1], x, censor);
616s  avar_out = [-6.8634e-02, 1.3968e-02; 1.3968e-02, -2.1664e-03];
616s  assert (nlogL, 349.3969104144271, 1e-12);
616s  assert (avar, avar_out, 1e-6);
616s ***** error<lognlike: function called with too few input arguments.> ...
616s  lognlike ([12, 15]);
616s ***** error<lognlike: X must be a vector.> lognlike ([12, 15], ones (2));
616s ***** error<lognlike: PARAMS must be a two-element vector.> ...
616s  lognlike ([12, 15, 3], [1:50]);
616s ***** error<lognlike: X and CENSOR vectors mismatch.> ...
616s  lognlike ([12, 15], [1:50], [1, 2, 3]);
616s ***** error<lognlike: X and FREQ vectors mismatch.> ...
616s  lognlike ([12, 15], [1:50], [], [1, 2, 3]);
616s 10 tests, 10 passed, 0 known failure, 0 skipped
616s [inst/dist_fit/nbinfit.m]
616s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/nbinfit.m
616s ***** demo
616s  ## Sample 2 populations from different negative binomial distibutions
616s  randp ("seed", 5); randg ("seed", 5);    # for reproducibility
616s  r1 = nbinrnd (2, 0.15, 5000, 1);
616s  randp ("seed", 8); randg ("seed", 8);    # for reproducibility
616s  r2 = nbinrnd (5, 0.2, 5000, 1);
616s  r = [r1, r2];
616s 
616s  ## Plot them normalized and fix their colors
616s  hist (r, [0:51], 1);
616s  h = findobj (gca, "Type", "patch");
616s  set (h(1), "facecolor", "c");
616s  set (h(2), "facecolor", "g");
616s  hold on
616s 
616s  ## Estimate their probability of success
616s  r_psA = nbinfit (r(:,1));
616s  r_psB = nbinfit (r(:,2));
616s 
616s  ## Plot their estimated PDFs
616s  x = [0:40];
616s  y = nbinpdf (x, r_psA(1), r_psA(2));
616s  plot (x, y, "-pg");
616s  x = [min(r(:,2)):max(r(:,2))];
616s  y = nbinpdf (x, r_psB(1), r_psB(2));
616s  plot (x, y, "-sc");
616s  ylim ([0, 0.1])
616s  xlim ([0, 50])
616s  legend ({"Normalized HIST of sample 1 with r=2 and ps=0.15", ...
616s           "Normalized HIST of sample 2 with r=5 and ps=0.2", ...
616s           sprintf("PDF for sample 1 with estimated r=%0.2f and ps=%0.2f", ...
616s                   r_psA(1), r_psA(2)), ...
616s           sprintf("PDF for sample 2 with estimated r=%0.2f and ps=%0.2f", ...
616s                   r_psB(1), r_psB(2))})
616s  title ("Two population samples from negative different binomial distibutions")
616s  hold off
616s ***** test
616s  [paramhat, paramci] = nbinfit ([1:50]);
616s  assert (paramhat, [2.420857, 0.086704], 1e-6);
616s  assert (paramci(:,1), [1.382702; 3.459012], 1e-6);
616s  assert (paramci(:,2), [0.049676; 0.123732], 1e-6);
616s ***** test
616s  [paramhat, paramci] = nbinfit ([1:20]);
616s  assert (paramhat, [3.588233, 0.254697], 1e-6);
616s  assert (paramci(:,1), [0.451693; 6.724774], 1e-6);
616s  assert (paramci(:,2), [0.081143; 0.428251], 1e-6);
616s ***** test
616s  [paramhat, paramci] = nbinfit ([1:10]);
616s  assert (paramhat, [8.8067, 0.6156], 1e-4);
616s  assert (paramci(:,1), [0; 30.7068], 1e-4);
616s  assert (paramci(:,2), [0.0217; 1], 1e-4);
616s ***** test
616s  [paramhat, paramci] = nbinfit ([1:10], 0.05, ones (1, 10));
616s  assert (paramhat, [8.8067, 0.6156], 1e-4);
616s  assert (paramci(:,1), [0; 30.7068], 1e-4);
616s  assert (paramci(:,2), [0.0217; 1], 1e-4);
616s ***** test
616s  [paramhat, paramci] = nbinfit ([1:11], 0.05, [ones(1, 10), 0]);
616s  assert (paramhat, [8.8067, 0.6156], 1e-4);
616s  assert (paramci(:,1), [0; 30.7068], 1e-4);
616s  assert (paramci(:,2), [0.0217; 1], 1e-4);
616s ***** error<nbinfit: X cannot have negative values.> nbinfit ([-1 2 3 3])
616s ***** error<nbinfit: X must be a vector.> nbinfit (ones (2))
616s ***** error<nbinfit: X must be a non-negative integer.> nbinfit ([1 2 1.2 3])
616s ***** error<nbinfit: wrong value for ALPHA.> nbinfit ([1 2 3], 0)
616s ***** error<nbinfit: wrong value for ALPHA.> nbinfit ([1 2 3], 1.2)
616s ***** error<nbinfit: wrong value for ALPHA.> nbinfit ([1 2 3], [0.02 0.05])
616s ***** error<nbinfit: X and FREQ vectors mismatch.> ...
616s  nbinfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2]);
616s ***** error<nbinfit: FREQ must not contain negative values.> ...
616s  nbinfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2, -1]);
616s ***** error<nbinfit: FREQ must contain integer values.> ...
616s  nbinfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2, 1.5]);
616s ***** error<nbinfit: 'options' argument must be a structure> ...
616s  nbinfit ([1, 2, 3, 4, 5], 0.05, struct ("option", 234));
616s ***** error<nbinfit: 'options' argument must be a structure> ...
616s  nbinfit ([1, 2, 3, 4, 5], 0.05, ones (1,5), struct ("option", 234));
616s 16 tests, 16 passed, 0 known failure, 0 skipped
616s [inst/dist_fit/geofit.m]
616s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/geofit.m
616s ***** demo
616s  ## Sample 2 populations from different geometric distibutions
616s  rande ("seed", 1);    # for reproducibility
616s  r1 = geornd (0.15, 1000, 1);
616s  rande ("seed", 2);    # for reproducibility
616s  r2 = geornd (0.5, 1000, 1);
616s  r = [r1, r2];
616s 
616s  ## Plot them normalized and fix their colors
616s  hist (r, 0:0.5:20.5, 1);
616s  h = findobj (gca, "Type", "patch");
616s  set (h(1), "facecolor", "c");
616s  set (h(2), "facecolor", "g");
616s  hold on
616s 
616s  ## Estimate their probability of success
616s  pshatA = geofit (r(:,1));
616s  pshatB = geofit (r(:,2));
616s 
616s  ## Plot their estimated PDFs
616s  x = [0:15];
616s  y = geopdf (x, pshatA);
616s  plot (x, y, "-pg");
616s  y = geopdf (x, pshatB);
616s  plot (x, y, "-sc");
616s  xlim ([0, 15])
616s  ylim ([0, 0.6])
616s  legend ({"Normalized HIST of sample 1 with ps=0.15", ...
616s           "Normalized HIST of sample 2 with ps=0.50", ...
616s           sprintf("PDF for sample 1 with estimated ps=%0.2f", ...
616s                   mean (pshatA)), ...
616s           sprintf("PDF for sample 2 with estimated ps=%0.2f", ...
616s                   mean (pshatB))})
616s  title ("Two population samples from different geometric distibutions")
616s  hold off
616s ***** test
616s  x = 0:5;
616s  [pshat, psci] = geofit (x);
616s  assert (pshat, 0.2857, 1e-4);
616s  assert (psci, [0.092499; 0.478929], 1e-5);
616s ***** test
616s  x = 0:5;
616s  [pshat, psci] = geofit (x, [], [1 1 1 1 1 1]);
616s  assert (pshat, 0.2857, 1e-4);
616s  assert (psci, [0.092499; 0.478929], 1e-5);
616s ***** assert (geofit ([1 1 2 3]), geofit ([1 2 3], [] ,[2 1 1]))
616s ***** error<geofit: function called with too few input arguments.> geofit ()
616s ***** error<geofit: X cannot have negative values.> geofit (-1, [1 2 3 3])
616s ***** error<geofit: wrong value for ALPHA.> geofit (1, 0)
616s ***** error<geofit: wrong value for ALPHA.> geofit (1, 1.2)
616s ***** error<geofit: wrong value for ALPHA.> geofit (1, [0.02 0.05])
616s ***** error<geofit: X and FREQ vector mismatch.> ...
616s  geofit ([1.5, 0.2], [], [0, 0, 0, 0, 0])
616s ***** error<geofit: X and FREQ vector mismatch.> ...
616s  geofit ([1.5, 0.2], [], [1, 1, 1])
616s 10 tests, 10 passed, 0 known failure, 0 skipped
616s [inst/dist_fit/evfit.m]
616s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/evfit.m
616s ***** demo
616s  ## Sample 3 populations from different extreme value distibutions
616s  rand ("seed", 1);    # for reproducibility
616s  r1 = evrnd (2, 5, 400, 1);
616s  rand ("seed", 12);    # for reproducibility
616s  r2 = evrnd (-5, 3, 400, 1);
616s  rand ("seed", 13);    # for reproducibility
616s  r3 = evrnd (14, 8, 400, 1);
616s  r = [r1, r2, r3];
616s 
616s  ## Plot them normalized and fix their colors
616s  hist (r, 25, 0.4);
616s  h = findobj (gca, "Type", "patch");
616s  set (h(1), "facecolor", "c");
616s  set (h(2), "facecolor", "g");
616s  set (h(3), "facecolor", "r");
616s  ylim ([0, 0.28])
616s  xlim ([-30, 30]);
616s  hold on
616s 
616s  ## Estimate their MU and SIGMA parameters
616s  mu_sigmaA = evfit (r(:,1));
616s  mu_sigmaB = evfit (r(:,2));
616s  mu_sigmaC = evfit (r(:,3));
616s 
616s  ## Plot their estimated PDFs
616s  x = [min(r(:)):max(r(:))];
616s  y = evpdf (x, mu_sigmaA(1), mu_sigmaA(2));
616s  plot (x, y, "-pr");
616s  y = evpdf (x, mu_sigmaB(1), mu_sigmaB(2));
616s  plot (x, y, "-sg");
616s  y = evpdf (x, mu_sigmaC(1), mu_sigmaC(2));
616s  plot (x, y, "-^c");
616s  legend ({"Normalized HIST of sample 1 with μ=2 and σ=5", ...
616s           "Normalized HIST of sample 2 with μ=-5 and σ=3", ...
616s           "Normalized HIST of sample 3 with μ=14 and σ=8", ...
616s           sprintf("PDF for sample 1 with estimated μ=%0.2f and σ=%0.2f", ...
616s                   mu_sigmaA(1), mu_sigmaA(2)), ...
616s           sprintf("PDF for sample 2 with estimated μ=%0.2f and σ=%0.2f", ...
616s                   mu_sigmaB(1), mu_sigmaB(2)), ...
616s           sprintf("PDF for sample 3 with estimated μ=%0.2f and σ=%0.2f", ...
616s                   mu_sigmaC(1), mu_sigmaC(2))})
616s  title ("Three population samples from different extreme value distibutions")
616s  hold off
616s ***** test
616s  x = 1:50;
616s  [paramhat, paramci] = evfit (x);
616s  paramhat_out = [32.6811, 13.0509];
616s  paramci_out = [28.8504, 10.5294; 36.5118, 16.1763];
616s  assert (paramhat, paramhat_out, 1e-4);
616s  assert (paramci, paramci_out, 1e-4);
616s ***** test
616s  x = 1:50;
616s  [paramhat, paramci] = evfit (x, 0.01);
616s  paramci_out = [27.6468, 9.8426; 37.7155, 17.3051];
616s  assert (paramci, paramci_out, 1e-4);
616s ***** error<evfit: X must be a double-precision vector.> evfit (ones (2,5));
616s ***** error<evfit: X must be a double-precision vector.> evfit (single (ones (1,5)));
616s ***** error<evfit: X must NOT contain missing values> evfit ([1, 2, 3, 4, NaN]);
616s ***** error<evfit: wrong value for ALPHA.> evfit ([1, 2, 3, 4, 5], 1.2);
616s ***** error<evfit: X and FREQ vectors mismatch.>
616s  evfit ([1 2 3], 0.05, [], [1 5])
616s ***** error<evfit: FREQ must not contain negative values.>
616s  evfit ([1 2 3], 0.05, [], [1 5 -1])
616s ***** error<evfit: 'options' 5th argument must be a structure> ...
616s  evfit ([1:10], 0.05, [], [], 5)
616s 9 tests, 9 passed, 0 known failure, 0 skipped
616s [inst/dist_fit/gumbellike.m]
616s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/gumbellike.m
616s ***** test
616s  x = 1:50;
616s  [nlogL, avar] = gumbellike ([2.3, 1.2], x);
616s  avar_out = [-1.2778e-13, 3.1859e-15; 3.1859e-15, -7.9430e-17];
616s  assert (nlogL, 3.242264755689906e+17, 1e-14);
616s  assert (avar, avar_out, 1e-3);
616s ***** test
616s  x = 1:50;
616s  [nlogL, avar] = gumbellike ([2.3, 1.2], x * 0.5);
616s  avar_out = [-7.6094e-05, 3.9819e-06; 3.9819e-06, -2.0836e-07];
616s  assert (nlogL, 481898704.0472211, 1e-6);
616s  assert (avar, avar_out, 1e-3);
616s ***** test
616s  x = 1:50;
616s  [nlogL, avar] = gumbellike ([21, 15], x);
616s  avar_out = [11.73913876598908, -5.9546128523121216; ...
616s              -5.954612852312121, 3.708060045170236];
616s  assert (nlogL, 223.7612479380652, 1e-13);
616s  assert (avar, avar_out, 1e-14);
616s ***** error<gumbellike: too few input arguments.> gumbellike ([12, 15]);
616s ***** error<gumbellike: wrong parameters length.> gumbellike ([12, 15, 3], [1:50]);
616s ***** error<gumbellike: X must be a vector.> gumbellike ([12, 3], ones (10, 2));
616s ***** error<gumbellike: X and CENSOR> gumbellike ([12, 15], [1:50], [1, 2, 3]);
616s ***** error<gumbellike: X and FREQ> gumbellike ([12, 15], [1:50], [], [1, 2, 3]);
616s 8 tests, 8 passed, 0 known failure, 0 skipped
616s [inst/dist_fit/expfit.m]
616s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/expfit.m
616s ***** demo
616s  ## Sample 3 populations from 3 different exponential distibutions
616s  rande ("seed", 1);   # for reproducibility
616s  r1 = exprnd (2, 4000, 1);
616s  rande ("seed", 2);   # for reproducibility
616s  r2 = exprnd (5, 4000, 1);
616s  rande ("seed", 3);   # for reproducibility
616s  r3 = exprnd (12, 4000, 1);
616s  r = [r1, r2, r3];
616s 
616s  ## Plot them normalized and fix their colors
616s  hist (r, 48, 0.52);
616s  h = findobj (gca, "Type", "patch");
616s  set (h(1), "facecolor", "c");
616s  set (h(2), "facecolor", "g");
616s  set (h(3), "facecolor", "r");
616s  hold on
616s 
616s  ## Estimate their mu parameter
616s  muhat = expfit (r);
616s 
616s  ## Plot their estimated PDFs
616s  x = [0:max(r(:))];
616s  y = exppdf (x, muhat(1));
616s  plot (x, y, "-pr");
616s  y = exppdf (x, muhat(2));
616s  plot (x, y, "-sg");
616s  y = exppdf (x, muhat(3));
616s  plot (x, y, "-^c");
616s  ylim ([0, 0.6])
616s  xlim ([0, 40])
616s  legend ({"Normalized HIST of sample 1 with μ=2", ...
616s           "Normalized HIST of sample 2 with μ=5", ...
616s           "Normalized HIST of sample 3 with μ=12", ...
616s           sprintf("PDF for sample 1 with estimated μ=%0.2f", muhat(1)), ...
616s           sprintf("PDF for sample 2 with estimated μ=%0.2f", muhat(2)), ...
616s           sprintf("PDF for sample 3 with estimated μ=%0.2f", muhat(3))})
616s  title ("Three population samples from different exponential distibutions")
616s  hold off
616s ***** assert (expfit (1), 1)
616s ***** assert (expfit (1:3), 2)
616s ***** assert (expfit ([1:3]'), 2)
616s ***** assert (expfit (1:3, []), 2)
616s ***** assert (expfit (1:3, [], [], []), 2)
616s ***** assert (expfit (magic (3)), [5 5 5])
616s ***** assert (expfit (cat (3, magic (3), 2*magic (3))), cat (3,[5 5 5], [10 10 10]))
616s ***** assert (expfit (1:3, 0.1, [0 0 0], [1 1 1]), 2)
616s ***** assert (expfit ([1:3]', 0.1, [0 0 0]', [1 1 1]'), 2)
616s ***** assert (expfit (1:3, 0.1, [0 0 0]', [1 1 1]'), 2)
616s ***** assert (expfit (1:3, 0.1, [1 0 0], [1 1 1]), 3)
616s ***** assert (expfit (1:3, 0.1, [0 0 0], [4 1 1]), 1.5)
616s ***** assert (expfit (1:3, 0.1, [1 0 0], [4 1 1]), 4.5)
616s ***** assert (expfit (1:3, 0.1, [1 0 1], [4 1 1]), 9)
616s ***** assert (expfit (1:3, 0.1, [], [-1 1 1]), 4)
616s ***** assert (expfit (1:3, 0.1, [], [0.5 1 1]), 2.2)
616s ***** assert (expfit (1:3, 0.1, [1 1 1]), NaN)
616s ***** assert (expfit (1:3, 0.1, [], [0 0 0]), NaN)
616s ***** assert (expfit (reshape (1:9, [3 3])), [2 5 8])
616s ***** assert (expfit (reshape (1:9, [3 3]), [], eye(3)), [3 7.5 12])
616s ***** assert (expfit (reshape (1:9, [3 3]), [], 2*eye(3)), [3 7.5 12])
616s ***** assert (expfit (reshape (1:9, [3 3]), [], [], [2 2 2; 1 1 1; 1 1 1]), ...
616s  [1.75 4.75 7.75])
616s ***** assert (expfit (reshape (1:9, [3 3]), [], [], [2 2 2; 1 1 1; 1 1 1]), ...
616s  [1.75 4.75 7.75])
616s ***** assert (expfit (reshape (1:9, [3 3]), [], eye(3), [2 2 2; 1 1 1; 1 1 1]), ...
616s  [3.5 19/3 31/3])
616s ***** assert ([~,muci] = expfit (1:3, 0), [0; Inf])
616s ***** assert ([~,muci] = expfit (1:3, 2), [Inf; 0])
616s ***** assert ([~,muci] = expfit (1:3, 0.1, [1 1 1]), [NaN; NaN])
616s ***** assert ([~,muci] = expfit (1:3, 0.1, [], [0 0 0]), [NaN; NaN])
616s ***** assert ([~,muci] = expfit (1:3, -1), [NaN; NaN])
616s ***** assert ([~,muci] = expfit (1:3, 5), [NaN; NaN])
616s ***** assert ([~,muci] = expfit (1:3), [0.830485728373393; 9.698190330474096], ...
616s              1000*eps)
616s ***** assert ([~,muci] = expfit (1:3, 0.1), ...
616s                           [0.953017262058213; 7.337731146400207], 1000*eps)
616s ***** assert ([~,muci] = expfit ([1:3;2:4]), ...
616s              [0.538440777613095, 0.897401296021825, 1.256361814430554; ...
616s              12.385982973214016, 20.643304955356694, 28.900626937499371], ...
616s              1000*eps)
616s ***** assert ([~,muci] = expfit ([1:3;2:4], [], [1 1 1; 0 0 0]), ...
616s              100*[0.008132550920455, 0.013554251534091, 0.018975952147727; ...
616s              1.184936706156216, 1.974894510260360, 2.764852314364504], ...
616s              1000*eps)
616s ***** assert ([~,muci] = expfit ([1:3;2:4], [], [], [3 3 3; 1 1 1]), ...
616s              [0.570302756652583, 1.026544961974649, 1.482787167296715; ...
616s              4.587722594914109, 8.257900670845396, 11.928078746776684], ...
616s              1000*eps)
616s ***** assert ([~,muci] = expfit ([1:3;2:4], [], [0 0 0; 1 1 1], [3 3 3; 1 1 1]), ...
616s              [0.692071440311161, 1.245728592560089, 1.799385744809018; ...
616s              8.081825275395081, 14.547285495711145, 21.012745716027212], ...
616s              1000*eps)
616s ***** test
616s  x = reshape (1:8, [4 2]);
616s  x(4) = NaN;
616s  [muhat,muci] = expfit (x);
616s  assert ({muhat, muci}, {[NaN, 6.5], ...
616s          [NaN, 2.965574334593430;NaN, 23.856157493553368]}, 1000*eps);
616s ***** test
616s  x = magic (3);
616s  censor = [0 1 0; 0 1 0; 0 1 0];
616s  freq = [1 1 0; 1 1 0; 1 1 0];
616s  [muhat,muci] = expfit (x, [], censor, freq);
616s  assert ({muhat, muci}, {[5 NaN NaN], ...
616s                  [[2.076214320933482; 24.245475826185242],NaN(2)]}, 1000*eps);
616s ***** error expfit ()
616s ***** error expfit (1,2,3,4,5)
616s ***** error [a b censor] = expfit (1)
616s ***** error <ALPHA must be a scalar quantity> expfit (1, [1 2])
616s ***** error <X cannot be negative> expfit ([-1 2 3 4 5])
616s ***** error <CENSOR must be a numeric or logical array> expfit ([1:5], [], "test")
616s ***** error <FREQ must be a numeric or logical array> expfit ([1:5], [], [], "test")
616s ***** error <X and CENSOR vectors mismatch.> expfit ([1:5], [], [0 0 0 0])
616s ***** error <X and FREQ vectors mismatch.> expfit ([1:5], [], [], [1 1 1 1])
616s 47 tests, 47 passed, 0 known failure, 0 skipped
616s [inst/dist_fit/hnlike.m]
616s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/hnlike.m
616s ***** test
616s  x = 1:20;
616s  paramhat = hnfit (x, 0);
616s  [nlogL, acov] = hnlike (paramhat, x);
616s  assert (nlogL, 64.179177404891300, 1e-14);
616s ***** test
616s  x = 1:20;
616s  paramhat = hnfit (x, 0);
616s  [nlogL, acov] = hnlike (paramhat, x, ones (1, 20));
616s  assert (nlogL, 64.179177404891300, 1e-14);
616s ***** error<hnlike: function called with too few input arguments.> ...
616s  hnlike ([12, 15]);
616s ***** error<hnlike: wrong parameters length.> hnlike ([12, 15, 3], [1:50]);
616s ***** error<hnlike: wrong parameters length.> hnlike ([3], [1:50]);
616s ***** error<hnlike: X must be a vector of real values.> ...
616s  hnlike ([0, 3], ones (2));
616s ***** error<hnlike: X must be a vector of real values.> ...
616s  hnlike ([0, 3], [1, 2, 3, 4, 5+i]);
616s ***** error<hnlike: X and FREQ vectors mismatch.> ...
616s  hnlike ([1, 2], ones (10, 1), ones (8,1))
616s ***** error<hnlike: FREQ must not contain negative values.> ...
616s  hnlike ([1, 2], ones (1, 8), [1 1 1 1 1 1 1 -1])
616s 9 tests, 9 passed, 0 known failure, 0 skipped
616s [inst/dist_fit/wblfit.m]
616s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/wblfit.m
616s ***** demo
616s  ## Sample 3 populations from 3 different Weibull distibutions
616s  rande ("seed", 1);    # for reproducibility
616s  r1 = wblrnd(2, 4, 2000, 1);
616s  rande ("seed", 2);    # for reproducibility
616s  r2 = wblrnd(5, 2, 2000, 1);
616s  rande ("seed", 5);    # for reproducibility
616s  r3 = wblrnd(1, 5, 2000, 1);
616s  r = [r1, r2, r3];
616s 
616s  ## Plot them normalized and fix their colors
616s  hist (r, 30, [2.5 2.1 3.2]);
616s  h = findobj (gca, "Type", "patch");
616s  set (h(1), "facecolor", "c");
616s  set (h(2), "facecolor", "g");
616s  set (h(3), "facecolor", "r");
616s  ylim ([0, 2]);
616s  xlim ([0, 10]);
616s  hold on
616s 
616s  ## Estimate their lambda parameter
616s  lambda_kA = wblfit (r(:,1));
616s  lambda_kB = wblfit (r(:,2));
616s  lambda_kC = wblfit (r(:,3));
616s 
616s  ## Plot their estimated PDFs
616s  x = [0:0.1:15];
616s  y = wblpdf (x, lambda_kA(1), lambda_kA(2));
616s  plot (x, y, "-pr");
616s  y = wblpdf (x, lambda_kB(1), lambda_kB(2));
616s  plot (x, y, "-sg");
616s  y = wblpdf (x, lambda_kC(1), lambda_kC(2));
616s  plot (x, y, "-^c");
616s  hold off
616s  legend ({"Normalized HIST of sample 1 with λ=2 and k=4", ...
616s           "Normalized HIST of sample 2 with λ=5 and k=2", ...
616s           "Normalized HIST of sample 3 with λ=1 and k=5", ...
616s           sprintf("PDF for sample 1 with estimated λ=%0.2f and k=%0.2f", ...
616s                   lambda_kA(1), lambda_kA(2)), ...
616s           sprintf("PDF for sample 2 with estimated λ=%0.2f and k=%0.2f", ...
616s                   lambda_kB(1), lambda_kB(2)), ...
616s           sprintf("PDF for sample 3 with estimated λ=%0.2f and k=%0.2f", ...
616s                   lambda_kC(1), lambda_kC(2))})
616s  title ("Three population samples from different Weibull distibutions")
616s  hold off
616s ***** test
616s  x = 1:50;
616s  [paramhat, paramci] = wblfit (x);
616s  paramhat_out = [28.3636, 1.7130];
616s  paramci_out = [23.9531, 1.3551; 33.5861, 2.1655];
616s  assert (paramhat, paramhat_out, 1e-4);
616s  assert (paramci, paramci_out, 1e-4);
616s ***** test
616s  x = 1:50;
616s  [paramhat, paramci] = wblfit (x, 0.01);
616s  paramci_out = [22.7143, 1.2589; 35.4179, 2.3310];
616s  assert (paramci, paramci_out, 1e-4);
616s ***** error<wblfit: X must be a vector.> wblfit (ones (2,5));
616s ***** error<wblfit: X must contain only positive values.> wblfit ([-1 2 3 4]);
616s ***** error<wblfit: wrong value for ALPHA.> wblfit ([1, 2, 3, 4, 5], 1.2);
616s ***** error<wblfit: wrong value for ALPHA.> wblfit ([1, 2, 3, 4, 5], 0);
616s ***** error<wblfit: wrong value for ALPHA.> wblfit ([1, 2, 3, 4, 5], "alpha");
616s ***** error<wblfit: X and CENSOR vectors mismatch.> ...
616s  wblfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
616s ***** error<wblfit: X and CENSOR vectors mismatch.> ...
616s  wblfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
616s ***** error<wblfit: X and FREQ vectors mismatch.> ...
616s  wblfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
616s ***** error<wblfit: FREQ cannot have negative values.> ...
616s  wblfit ([1, 2, 3, 4, 5], [], [], [1 1 0 -1 1]);
616s ***** error<wblfit: X and FREQ vectors mismatch.> ...
616s  wblfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
616s ***** error<wblfit: 'options' 5th argument must be a structure> ...
616s  wblfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
616s 13 tests, 13 passed, 0 known failure, 0 skipped
616s [inst/dist_fit/binofit.m]
616s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/binofit.m
616s ***** demo
616s  ## Sample 2 populations from different binomial distibutions
616s  rand ("seed", 1);    # for reproducibility
616s  r1 = binornd (50, 0.15, 1000, 1);
616s  rand ("seed", 2);    # for reproducibility
616s  r2 = binornd (100, 0.5, 1000, 1);
616s  r = [r1, r2];
616s 
616s  ## Plot them normalized and fix their colors
616s  hist (r, 23, 0.35);
616s  h = findobj (gca, "Type", "patch");
616s  set (h(1), "facecolor", "c");
616s  set (h(2), "facecolor", "g");
616s  hold on
616s 
616s  ## Estimate their probability of success
616s  pshatA = binofit (r(:,1), 50);
616s  pshatB = binofit (r(:,2), 100);
616s 
616s  ## Plot their estimated PDFs
616s  x = [min(r(:,1)):max(r(:,1))];
616s  y = binopdf (x, 50, mean (pshatA));
616s  plot (x, y, "-pg");
616s  x = [min(r(:,2)):max(r(:,2))];
616s  y = binopdf (x, 100, mean (pshatB));
616s  plot (x, y, "-sc");
616s  ylim ([0, 0.2])
616s  legend ({"Normalized HIST of sample 1 with ps=0.15", ...
616s           "Normalized HIST of sample 2 with ps=0.50", ...
616s           sprintf("PDF for sample 1 with estimated ps=%0.2f", ...
616s                   mean (pshatA)), ...
616s           sprintf("PDF for sample 2 with estimated ps=%0.2f", ...
616s                   mean (pshatB))})
616s  title ("Two population samples from different binomial distibutions")
616s  hold off
616s ***** test
616s  x = 0:3;
616s  [pshat, psci] = binofit (x, 3);
616s  assert (pshat, [0, 0.3333, 0.6667, 1], 1e-4);
616s  assert (psci(1,:), [0, 0.7076], 1e-4);
616s  assert (psci(2,:), [0.0084, 0.9057], 1e-4);
616s  assert (psci(3,:), [0.0943, 0.9916], 1e-4);
616s  assert (psci(4,:), [0.2924, 1.0000], 1e-4);
616s ***** error<binofit: function called with too few input arguments.> ...
616s  binofit ([1 2 3 4])
616s ***** error<binofit: X cannot have negative values.> ...
616s  binofit ([-1, 4, 3, 2], [1, 2, 3, 3])
616s ***** error<binofit: X must be a vector.> binofit (ones(2), [1, 2, 3, 3])
616s ***** error<binofit: N must be a non-negative integer.> ...
616s  binofit ([1, 4, 3, 2], [1, 2, -1, 3])
616s ***** error<binofit: N must be a scalar or the same size as X.> ...
616s  binofit ([1, 4, 3, 2], [5, 5, 5])
616s ***** error<binofit: N must be at least as large as X.> ...
616s  binofit ([1, 4, 3, 2], [5, 3, 5, 5])
616s ***** error<binofit: wrong value for ALPHA.> binofit ([1, 2, 1], 3, 1.2);
616s ***** error<binofit: wrong value for ALPHA.> binofit ([1, 2, 1], 3, 0);
616s ***** error<binofit: wrong value for ALPHA.> binofit ([1, 2, 1], 3, "alpha");
616s 10 tests, 10 passed, 0 known failure, 0 skipped
616s [inst/dist_fit/nakafit.m]
616s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/nakafit.m
616s ***** demo
616s  ## Sample 3 populations from different Nakagami distibutions
616s  randg ("seed", 5)  # for reproducibility
616s  r1 = nakarnd (0.5, 1, 2000, 1);
616s  randg ("seed", 2)   # for reproducibility
616s  r2 = nakarnd (5, 1, 2000, 1);
616s  randg ("seed", 7)   # for reproducibility
616s  r3 = nakarnd (2, 2, 2000, 1);
616s  r = [r1, r2, r3];
616s 
616s  ## Plot them normalized and fix their colors
616s  hist (r, [0.05:0.1:3.5], 10);
616s  h = findobj (gca, "Type", "patch");
616s  set (h(1), "facecolor", "c");
616s  set (h(2), "facecolor", "g");
616s  set (h(3), "facecolor", "r");
616s  ylim ([0, 2.5]);
616s  xlim ([0, 3.0]);
616s  hold on
616s 
616s  ## Estimate their MU and LAMBDA parameters
616s  mu_omegaA = nakafit (r(:,1));
616s  mu_omegaB = nakafit (r(:,2));
616s  mu_omegaC = nakafit (r(:,3));
616s 
616s  ## Plot their estimated PDFs
616s  x = [0.01:0.1:3.01];
616s  y = nakapdf (x, mu_omegaA(1), mu_omegaA(2));
616s  plot (x, y, "-pr");
616s  y = nakapdf (x, mu_omegaB(1), mu_omegaB(2));
616s  plot (x, y, "-sg");
616s  y = nakapdf (x, mu_omegaC(1), mu_omegaC(2));
616s  plot (x, y, "-^c");
616s  legend ({"Normalized HIST of sample 1 with μ=0.5 and ω=1", ...
616s           "Normalized HIST of sample 2 with μ=5 and ω=1", ...
616s           "Normalized HIST of sample 3 with μ=2 and ω=2", ...
616s           sprintf("PDF for sample 1 with estimated μ=%0.2f and ω=%0.2f", ...
616s                   mu_omegaA(1), mu_omegaA(2)), ...
616s           sprintf("PDF for sample 2 with estimated μ=%0.2f and ω=%0.2f", ...
616s                   mu_omegaB(1), mu_omegaB(2)), ...
616s           sprintf("PDF for sample 3 with estimated μ=%0.2f and ω=%0.2f", ...
616s                   mu_omegaC(1), mu_omegaC(2))})
616s  title ("Three population samples from different Nakagami distibutions")
616s  hold off
616s ***** test
616s  paramhat = nakafit ([1:50]);
616s  paramhat_out = [0.7355, 858.5];
616s  assert (paramhat, paramhat_out, 1e-4);
616s ***** test
616s  paramhat = nakafit ([1:5]);
616s  paramhat_out = [1.1740, 11];
616s  assert (paramhat, paramhat_out, 1e-4);
616s ***** test
616s  paramhat = nakafit ([1:6], [], [], [1 1 1 1 1 0]);
616s  paramhat_out = [1.1740, 11];
616s  assert (paramhat, paramhat_out, 1e-4);
616s ***** test
616s  paramhat = nakafit ([1:5], [], [], [1 1 1 1 2]);
616s  paramhat_out = nakafit ([1:5, 5]);
616s  assert (paramhat, paramhat_out, 1e-4);
616s ***** error<nakafit: X must be a vector.> nakafit (ones (2,5));
616s ***** error<nakafit: wrong value for ALPHA.> nakafit ([1, 2, 3, 4, 5], 1.2);
616s ***** error<nakafit: wrong value for ALPHA.> nakafit ([1, 2, 3, 4, 5], 0);
616s ***** error<nakafit: wrong value for ALPHA.> nakafit ([1, 2, 3, 4, 5], "alpha");
616s ***** error<nakafit: X and CENSOR vectors mismatch.> ...
616s  nakafit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
616s ***** error<nakafit: X and CENSOR vectors mismatch.> ...
616s  nakafit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
616s ***** error<nakafit: X and FREQ vectors mismatch.> ...
616s  nakafit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
616s ***** error<nakafit: X and FREQ vectors mismatch.> ...
616s  nakafit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
616s ***** error<nakafit: FREQ must not contain negative values.> ...
616s  nakafit ([1, 2, 3, 4, 5], [], [], [1 1 -1 1 1]);
616s ***** error<nakafit: FREQ must contain integer values.> ...
616s  nakafit ([1, 2, 3, 4, 5], [], [], [1 1 1.5 1 1]);
616s ***** error<nakafit: 'options' 5th argument must be a structure> ...
616s  nakafit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
616s 15 tests, 15 passed, 0 known failure, 0 skipped
616s [inst/dist_fit/bisafit.m]
616s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/bisafit.m
616s ***** demo
616s  ## Sample 3 populations from different Birnbaum-Saunders distibutions
616s  rand ("seed", 5);    # for reproducibility
616s  r1 = bisarnd (1, 0.5, 2000, 1);
616s  rand ("seed", 2);    # for reproducibility
616s  r2 = bisarnd (2, 0.3, 2000, 1);
616s  rand ("seed", 7);    # for reproducibility
616s  r3 = bisarnd (4, 0.5, 2000, 1);
616s  r = [r1, r2, r3];
616s 
616s  ## Plot them normalized and fix their colors
616s  hist (r, 80, 4.2);
616s  h = findobj (gca, "Type", "patch");
616s  set (h(1), "facecolor", "c");
616s  set (h(2), "facecolor", "g");
616s  set (h(3), "facecolor", "r");
616s  ylim ([0, 1.1]);
616s  xlim ([0, 8]);
616s  hold on
616s 
616s  ## Estimate their α and β parameters
616s  beta_gammaA = bisafit (r(:,1));
616s  beta_gammaB = bisafit (r(:,2));
616s  beta_gammaC = bisafit (r(:,3));
616s 
616s  ## Plot their estimated PDFs
616s  x = [0:0.1:8];
616s  y = bisapdf (x, beta_gammaA(1), beta_gammaA(2));
616s  plot (x, y, "-pr");
616s  y = bisapdf (x, beta_gammaB(1), beta_gammaB(2));
616s  plot (x, y, "-sg");
616s  y = bisapdf (x, beta_gammaC(1), beta_gammaC(2));
616s  plot (x, y, "-^c");
616s  hold off
616s  legend ({"Normalized HIST of sample 1 with β=1 and γ=0.5", ...
616s           "Normalized HIST of sample 2 with β=2 and γ=0.3", ...
616s           "Normalized HIST of sample 3 with β=4 and γ=0.5", ...
616s           sprintf("PDF for sample 1 with estimated β=%0.2f and γ=%0.2f", ...
616s                   beta_gammaA(1), beta_gammaA(2)), ...
616s           sprintf("PDF for sample 2 with estimated β=%0.2f and γ=%0.2f", ...
616s                   beta_gammaB(1), beta_gammaB(2)), ...
616s           sprintf("PDF for sample 3 with estimated β=%0.2f and γ=%0.2f", ...
616s                   beta_gammaC(1), beta_gammaC(2))})
616s  title ("Three population samples from different Birnbaum-Saunders distibutions")
616s  hold off
616s ***** test
616s  paramhat = bisafit ([1:50]);
616s  paramhat_out = [16.2649, 1.0156];
616s  assert (paramhat, paramhat_out, 1e-4);
616s ***** test
616s  paramhat = bisafit ([1:5]);
616s  paramhat_out = [2.5585, 0.5839];
616s  assert (paramhat, paramhat_out, 1e-4);
616s ***** error<bisafit: X must be a vector.> bisafit (ones (2,5));
616s ***** error<bisafit: X must contain only positive values.> bisafit ([-1 2 3 4]);
616s ***** error<bisafit: wrong value for ALPHA.> bisafit ([1, 2, 3, 4, 5], 1.2);
616s ***** error<bisafit: wrong value for ALPHA.> bisafit ([1, 2, 3, 4, 5], 0);
616s ***** error<bisafit: wrong value for ALPHA.> bisafit ([1, 2, 3, 4, 5], "alpha");
616s ***** error<bisafit: X and CENSOR vectors mismatch.> ...
616s  bisafit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
616s ***** error<bisafit: X and CENSOR vectors mismatch.> ...
616s  bisafit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
616s ***** error<bisafit: X and FREQ vectors mismatch.> ...
616s  bisafit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
616s ***** error<bisafit: X and FREQ vectors mismatch.> ...
616s  bisafit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
616s ***** error<bisafit: 'options' 5th argument must be a structure> ...
616s  bisafit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
616s 12 tests, 12 passed, 0 known failure, 0 skipped
616s [inst/dist_fit/logilike.m]
616s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/logilike.m
616s ***** test
616s  nlogL = logilike ([25.5, 8.7725], [1:50]);
616s  assert (nlogL, 206.6769, 1e-4);
616s ***** test
616s  nlogL = logilike ([3, 0.8645], [1:5]);
616s  assert (nlogL, 9.0699, 1e-4);
616s ***** error<logilike: function called with too few input arguments.> logilike (3.25)
616s ***** error<logilike: X must be a vector.> logilike ([5, 0.2], ones (2))
616s ***** error<logilike: PARAMS must be a two-element vector.> ...
616s  logilike ([1, 0.2, 3], [1, 3, 5, 7])
616s ***** error<logilike: X and CENSOR vector mismatch.> ...
616s  logilike ([1.5, 0.2], [1:5], [0, 0, 0])
616s ***** error<logilike: X and FREQ vector mismatch.> ...
616s  logilike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
616s ***** error<logilike: X and FREQ vector mismatch.> ...
616s  logilike ([1.5, 0.2], [1:5], [], [1, 1, 1])
616s 8 tests, 8 passed, 0 known failure, 0 skipped
616s [inst/dist_fit/gevfit.m]
616s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/gevfit.m
616s ***** demo
616s  ## Sample 2 populations from 2 different exponential distibutions
616s  rand ("seed", 1);   # for reproducibility
616s  r1 = gevrnd (-0.5, 1, 2, 5000, 1);
616s  rand ("seed", 2);   # for reproducibility
616s  r2 = gevrnd (0, 1, -4, 5000, 1);
616s  r = [r1, r2];
616s 
616s  ## Plot them normalized and fix their colors
616s  hist (r, 50, 5);
616s  h = findobj (gca, "Type", "patch");
616s  set (h(1), "facecolor", "c");
616s  set (h(2), "facecolor", "g");
616s  hold on
616s 
616s  ## Estimate their k, sigma, and mu parameters
616s  k_sigma_muA = gevfit (r(:,1));
616s  k_sigma_muB = gevfit (r(:,2));
616s 
616s  ## Plot their estimated PDFs
616s  x = [-10:0.5:20];
616s  y = gevpdf (x, k_sigma_muA(1), k_sigma_muA(2), k_sigma_muA(3));
616s  plot (x, y, "-pr");
616s  y = gevpdf (x, k_sigma_muB(1), k_sigma_muB(2), k_sigma_muB(3));
616s  plot (x, y, "-sg");
616s  ylim ([0, 0.7])
616s  xlim ([-7, 5])
616s  legend ({"Normalized HIST of sample 1 with k=-0.5, σ=1, μ=2", ...
616s           "Normalized HIST of sample 2 with k=0, σ=1, μ=-4",
616s      sprintf("PDF for sample 1 with estimated k=%0.2f, σ=%0.2f, μ=%0.2f", ...
616s                  k_sigma_muA(1), k_sigma_muA(2), k_sigma_muA(3)), ...
616s      sprintf("PDF for sample 3 with estimated k=%0.2f, σ=%0.2f, μ=%0.2f", ...
616s                  k_sigma_muB(1), k_sigma_muB(2), k_sigma_muB(3))})
616s  title ("Two population samples from different exponential distibutions")
616s  hold off
616s ***** test
616s  x = 1:50;
616s  [pfit, pci] = gevfit (x);
616s  pfit_out = [-0.4407, 15.1923, 21.5309];
616s  pci_out = [-0.7532, 11.5878, 16.5686; -0.1282, 19.9183, 26.4926];
616s  assert (pfit, pfit_out, 1e-3);
616s  assert (pci, pci_out, 1e-3);
616s ***** test
616s  x = 1:2:50;
616s  [pfit, pci] = gevfit (x);
616s  pfit_out = [-0.4434, 15.2024, 21.0532];
616s  pci_out = [-0.8904, 10.3439, 14.0168; 0.0035, 22.3429, 28.0896];
616s  assert (pfit, pfit_out, 1e-3);
616s  assert (pci, pci_out, 1e-3);
616s ***** error<gevfit: X must be a vector.> gevfit (ones (2,5));
616s ***** error<gevfit: wrong value for ALPHA.> gevfit ([1, 2, 3, 4, 5], 1.2);
616s ***** error<gevfit: wrong value for ALPHA.> gevfit ([1, 2, 3, 4, 5], 0);
616s ***** error<gevfit: wrong value for ALPHA.> gevfit ([1, 2, 3, 4, 5], "alpha");
616s ***** error<gevfit: X and FREQ vectors mismatch.> ...
616s  gevfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2]);
616s ***** error<gevfit: FREQ must not contain negative values.> ...
616s  gevfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2, -1]);
616s ***** error<gevfit: FREQ must contain integer values.> ...
616s  gevfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2, 1.5]);
616s ***** error<gevfit: 'options' argument must be a structure> ...
616s  gevfit ([1, 2, 3, 4, 5], 0.05, struct ("option", 234));
616s ***** error<gevfit: 'options' argument must be a structure> ...
616s  gevfit ([1, 2, 3, 4, 5], 0.05, ones (1,5), struct ("option", 234));
616s 11 tests, 11 passed, 0 known failure, 0 skipped
616s [inst/dist_fit/ricefit.m]
616s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/ricefit.m
616s ***** demo
616s  ## Sample 3 populations from different Gamma distibutions
616s  randg ("seed", 5);    # for reproducibility
616s  randp ("seed", 6);
616s  r1 = ricernd (1, 2, 3000, 1);
616s  randg ("seed", 2);    # for reproducibility
616s  randp ("seed", 8);
616s  r2 = ricernd (2, 4, 3000, 1);
616s  randg ("seed", 7);    # for reproducibility
616s  randp ("seed", 9);
616s  r3 = ricernd (7.5, 1, 3000, 1);
616s  r = [r1, r2, r3];
616s 
616s  ## Plot them normalized and fix their colors
616s  hist (r, 75, 4);
616s  h = findobj (gca, "Type", "patch");
616s  set (h(1), "facecolor", "c");
616s  set (h(2), "facecolor", "g");
616s  set (h(3), "facecolor", "r");
616s  ylim ([0, 0.7]);
616s  xlim ([0, 12]);
616s  hold on
616s 
616s  ## Estimate their α and β parameters
616s  s_sigmaA = ricefit (r(:,1));
616s  s_sigmaB = ricefit (r(:,2));
616s  s_sigmaC = ricefit (r(:,3));
616s 
616s  ## Plot their estimated PDFs
616s  x = [0.01,0.1:0.2:18];
616s  y = ricepdf (x, s_sigmaA(1), s_sigmaA(2));
616s  plot (x, y, "-pr");
616s  y = ricepdf (x, s_sigmaB(1), s_sigmaB(2));
616s  plot (x, y, "-sg");
616s  y = ricepdf (x, s_sigmaC(1), s_sigmaC(2));
616s  plot (x, y, "-^c");
616s  hold off
616s  legend ({"Normalized HIST of sample 1 with s=1 and σ=2", ...
616s           "Normalized HIST of sample 2 with s=2 and σ=4", ...
616s           "Normalized HIST of sample 3 with s=7.5 and σ=1", ...
616s           sprintf("PDF for sample 1 with estimated s=%0.2f and σ=%0.2f", ...
616s                   s_sigmaA(1), s_sigmaA(2)), ...
616s           sprintf("PDF for sample 2 with estimated s=%0.2f and σ=%0.2f", ...
616s                   s_sigmaB(1), s_sigmaB(2)), ...
616s           sprintf("PDF for sample 3 with estimated s=%0.2f and σ=%0.2f", ...
616s                   s_sigmaC(1), s_sigmaC(2))})
616s  title ("Three population samples from different Rician distibutions")
616s  hold off
616s ***** test
616s  [paramhat, paramci] = ricefit ([1:50]);
616s  assert (paramhat, [15.3057, 17.6668], 1e-4);
616s  assert (paramci, [9.5468, 11.7802; 24.5383, 26.4952], 1e-4);
616s ***** test
616s  [paramhat, paramci] = ricefit ([1:50], 0.01);
616s  assert (paramhat, [15.3057, 17.6668], 1e-4);
616s  assert (paramci, [8.2309, 10.3717; 28.4615, 30.0934], 1e-4);
616s ***** test
616s  [paramhat, paramci] = ricefit ([1:5]);
616s  assert (paramhat, [2.3123, 1.6812], 1e-4);
616s  assert (paramci, [1.0819, 0.6376; 4.9424, 4.4331], 1e-4);
616s ***** test
616s  [paramhat, paramci] = ricefit ([1:5], 0.01);
616s  assert (paramhat, [2.3123, 1.6812], 1e-4);
616s  assert (paramci, [0.8521, 0.4702; 6.2747, 6.0120], 1e-4);
616s ***** test
616s  freq = [1 1 1 1 5];
616s  [paramhat, paramci] = ricefit ([1:5], [], [], freq);
616s  assert (paramhat, [3.5181, 1.5565], 1e-4);
616s  assert (paramci, [2.5893, 0.9049; 4.7801, 2.6772], 1e-4);
617s ***** test
617s  censor = [1 0 0 0 0];
617s  [paramhat, paramci] = ricefit ([1:5], [], censor);
617s  assert (paramhat, [3.2978, 1.1527], 1e-4);
617s  assert (paramci, [2.3192, 0.5476; 4.6895, 2.4261], 1e-4);
617s ***** assert (class (ricefit (single ([1:50]))), "single")
617s ***** error<ricefit: X must be a vector.> ricefit (ones (2))
617s ***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], 1)
617s ***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], -1)
617s ***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], {0.05})
617s ***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], "k")
617s ***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], i)
617s ***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], [0.01 0.02])
617s ***** error<ricefit: X and CENSOR vectors mismatch.> ricefit ([1:50], [], [1 1])
617s ***** error<ricefit: X and FREQ vectors mismatch.> ricefit ([1:50], [], [], [1 1])
617s ***** error<ricefit: FREQ cannot have negative values.> ...
617s  ricefit ([1:5], [], [], [1, 1, 2, 1, -1])
617s ***** error<ricefit: X must contain positive values.> ricefit ([1 2 3 -4])
617s ***** error<ricefit: X must contain positive values.> ricefit ([1 2 0], [], [1 0 0])
617s 19 tests, 19 passed, 0 known failure, 0 skipped
617s [inst/dist_fit/normfit.m]
617s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/normfit.m
617s ***** demo
617s  ## Sample 3 populations from 3 different normal distibutions
617s  randn ("seed", 1);    # for reproducibility
617s  r1 = normrnd (2, 5, 5000, 1);
617s  randn ("seed", 2);    # for reproducibility
617s  r2 = normrnd (5, 2, 5000, 1);
617s  randn ("seed", 3);    # for reproducibility
617s  r3 = normrnd (9, 4, 5000, 1);
617s  r = [r1, r2, r3];
617s 
617s  ## Plot them normalized and fix their colors
617s  hist (r, 15, 0.4);
617s  h = findobj (gca, "Type", "patch");
617s  set (h(1), "facecolor", "c");
617s  set (h(2), "facecolor", "g");
617s  set (h(3), "facecolor", "r");
617s  hold on
617s 
617s  ## Estimate their mu and sigma parameters
617s  [muhat, sigmahat] = normfit (r);
617s 
617s  ## Plot their estimated PDFs
617s  x = [min(r(:)):max(r(:))];
617s  y = normpdf (x, muhat(1), sigmahat(1));
617s  plot (x, y, "-pr");
617s  y = normpdf (x, muhat(2), sigmahat(2));
617s  plot (x, y, "-sg");
617s  y = normpdf (x, muhat(3), sigmahat(3));
617s  plot (x, y, "-^c");
617s  ylim ([0, 0.5])
617s  xlim ([-20, 20])
617s  hold off
617s  legend ({"Normalized HIST of sample 1 with mu=2, σ=5", ...
617s           "Normalized HIST of sample 2 with mu=5, σ=2", ...
617s           "Normalized HIST of sample 3 with mu=9, σ=4", ...
617s           sprintf("PDF for sample 1 with estimated mu=%0.2f and σ=%0.2f", ...
617s                   muhat(1), sigmahat(1)), ...
617s           sprintf("PDF for sample 2 with estimated mu=%0.2f and σ=%0.2f", ...
617s                   muhat(2), sigmahat(2)), ...
617s           sprintf("PDF for sample 3 with estimated mu=%0.2f and σ=%0.2f", ...
617s                   muhat(3), sigmahat(3))}, "location", "northwest")
617s  title ("Three population samples from different normal distibutions")
617s  hold off
617s ***** test
617s  load lightbulb
617s  idx = find (lightbulb(:,2) == 0);
617s  censoring = lightbulb(idx,3) == 1;
617s  [muHat, sigmaHat] = normfit (lightbulb(idx,1), [], censoring);
617s  assert (muHat, 9496.59586737857, 1e-11);
617s  assert (sigmaHat, 3064.021012796456, 2e-12);
617s ***** test
617s  randn ("seed", 234);
617s  x = normrnd (3, 5, [1000, 1]);
617s  [muHat, sigmaHat, muCI, sigmaCI] = normfit (x, 0.01);
617s  assert (muCI(1) < 3);
617s  assert (muCI(2) > 3);
617s  assert (sigmaCI(1) < 5);
617s  assert (sigmaCI(2) > 5);
617s ***** error<normfit: X must not be a multi-dimensional array.> ...
617s  normfit (ones (3,3,3))
617s ***** error<normfit: matrix data acceptable only under 2-arg syntax.> ...
617s  normfit (ones (20,3), [], zeros (20,1))
617s ***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), 0)
617s ***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), -0.3)
617s ***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), 1.2)
617s ***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), [0.05 0.1])
617s ***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), 0.02+i)
617s ***** error<normfit: X and CENSOR vectors mismatch.> ...
617s  normfit (ones (20,1), [], zeros(15,1))
617s ***** error<normfit: X and FREQ vectors mismatch.> ...
617s  normfit (ones (20,1), [], zeros(20,1), ones(25,1))
617s ***** error<normfit: FREQ must not contain negative values.> ...
617s  normfit (ones (5,1), [], zeros(5,1), [1, 2, 1, 2, -1]')
617s ***** error<normfit: > normfit (ones (20,1), [], zeros(20,1), ones(20,1), "options")
617s 13 tests, 13 passed, 0 known failure, 0 skipped
617s [inst/dist_fit/poisslike.m]
617s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/poisslike.m
617s ***** test
617s  x = [1 3 2 4 5 4 3 4];
617s  [nlogL, avar] = poisslike (3.25, x);
617s  assert (nlogL, 13.9533, 1e-4)
617s ***** test
617s  x = [1 2 3 4 5];
617s  f = [1 1 2 3 1];
617s  [nlogL, avar] = poisslike (3.25, x, f);
617s  assert (nlogL, 13.9533, 1e-4)
617s ***** error<poisslike: function called with too few input arguments.> poisslike (1)
617s ***** error<poisslike: LAMBDA must be a positive scalar.> poisslike ([1 2 3], [1 2])
617s ***** error<poisslike: X must be a vector of non-negative values.> ...
617s  poisslike (3.25, ones (10, 2))
617s ***** error<poisslike: X must be a vector of non-negative values.> ...
617s  poisslike (3.25, [1 2 3 -4 5])
617s ***** error<poisslike: X and FREQ vectors mismatch.> ...
617s  poisslike (3.25, ones (10, 1), ones (8,1))
617s ***** error<poisslike: FREQ must not contain negative values.> ...
617s  poisslike (3.25, ones (1, 8), [1 1 1 1 1 1 1 -1])
617s 8 tests, 8 passed, 0 known failure, 0 skipped
617s [inst/dist_fit/gpfit.m]
617s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/gpfit.m
617s ***** demo
617s  ## Sample 2 populations from different generalized Pareto distibutions
617s  ## Assume location parameter θ is known
617s  theta = 0;
617s  rand ("seed", 5);    # for reproducibility
617s  r1 = gprnd (1, 2, theta, 20000, 1);
617s  rand ("seed", 2);    # for reproducibility
617s  r2 = gprnd (3, 1, theta, 20000, 1);
617s  r = [r1, r2];
617s 
617s  ## Plot them normalized and fix their colors
617s  hist (r, [0.1:0.2:100], 5);
617s  h = findobj (gca, "Type", "patch");
617s  set (h(1), "facecolor", "r");
617s  set (h(2), "facecolor", "c");
617s  ylim ([0, 1]);
617s  xlim ([0, 5]);
617s  hold on
617s 
617s  ## Estimate their α and β parameters
617s  k_sigmaA = gpfit (r(:,1), theta);
617s  k_sigmaB = gpfit (r(:,2), theta);
617s 
617s  ## Plot their estimated PDFs
617s  x = [0.01, 0.1:0.2:18];
617s  y = gppdf (x, k_sigmaA(1), k_sigmaA(2), theta);
617s  plot (x, y, "-pc");
617s  y = gppdf (x, k_sigmaB(1), k_sigmaB(2), theta);
617s  plot (x, y, "-sr");
617s  hold off
617s  legend ({"Normalized HIST of sample 1 with k=1 and σ=2", ...
617s           "Normalized HIST of sample 2 with k=2 and σ=2", ...
617s           sprintf("PDF for sample 1 with estimated k=%0.2f and σ=%0.2f", ...
617s                   k_sigmaA(1), k_sigmaA(2)), ...
617s           sprintf("PDF for sample 3 with estimated k=%0.2f and σ=%0.2f", ...
617s                   k_sigmaB(1), k_sigmaB(2))})
617s  title ("Three population samples from different generalized Pareto distibutions")
617s  text (2, 0.7, "Known location parameter θ = 0")
617s  hold off
617s ***** test
617s  k = 0.8937; sigma = 1.3230; theta = 1;
617s  x = [2.2196, 11.9301, 4.3673, 1.0949, 6.5626, ...
617s       1.2109, 1.8576, 1.0039, 12.7917, 2.2590];
617s  [hat, ci] = gpfit (x, theta);
617s  assert (hat, [k, sigma, theta], 1e-4);
617s  assert (ci, [-0.7750, 0.2437, 1; 2.5624, 7.1820, 1], 1e-4);
617s ***** error<gpfit: function called with too few input arguments.> gpfit ()
617s ***** error<gpfit: function called with too few input arguments.> gpfit (1)
617s ***** error<gpfit: X must be a vector of real values.> gpfit ([0.2, 0.5+i], 0);
617s ***** error<gpfit: X must be a vector of real values.> gpfit (ones (2,2) * 0.5, 0);
617s ***** error<gpfit: THETA must be a real scalar value.> ...
617s  gpfit ([0.5, 1.2], [0, 1]);
617s ***** error<gpfit: THETA must be a real scalar value.> ...
617s  gpfit ([0.5, 1.2], 5+i);
617s ***** error<gpfit: X cannot contain values less than THETA.> ...
617s  gpfit ([1:5], 2);
617s ***** error<gpfit: wrong value for ALPHA.> gpfit ([0.01:0.1:0.99], 0, 1.2);
617s ***** error<gpfit: wrong value for ALPHA.> gpfit ([0.01:0.1:0.99], 0, i);
617s ***** error<gpfit: wrong value for ALPHA.> gpfit ([0.01:0.1:0.99], 0, -1);
617s ***** error<gpfit: wrong value for ALPHA.> gpfit ([0.01:0.1:0.99], 0, [0.05, 0.01]);
617s ***** error<gpfit: X and FREQ vectors mismatch.>
617s  gpfit ([1 2 3], 0, [], [1 5])
617s ***** error<gpfit: FREQ must not contain negative values.>
617s  gpfit ([1 2 3], 0, [], [1 5 -1])
617s ***** error<gpfit: 'options' 5th argument must be a structure> ...
617s  gpfit ([1:10], 1, 0.05, [], 5)
617s 15 tests, 15 passed, 0 known failure, 0 skipped
617s [inst/dist_fit/tlsfit.m]
617s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/tlsfit.m
617s ***** demo
617s  ## Sample 3 populations from 3 different location-scale T distibutions
617s  randn ("seed", 1);    # for reproducibility
617s  randg ("seed", 2);    # for reproducibility
617s  r1 = tlsrnd (-4, 3, 1, 2000, 1);
617s  randn ("seed", 3);    # for reproducibility
617s  randg ("seed", 4);    # for reproducibility
617s  r2 = tlsrnd (0, 3, 1, 2000, 1);
617s  randn ("seed", 5);    # for reproducibility
617s  randg ("seed", 6);    # for reproducibility
617s  r3 = tlsrnd (5, 5, 4, 2000, 1);
617s  r = [r1, r2, r3];
617s 
617s  ## Plot them normalized and fix their colors
617s  hist (r, [-21:21], [1, 1, 1]);
617s  h = findobj (gca, "Type", "patch");
617s  set (h(1), "facecolor", "c");
617s  set (h(2), "facecolor", "g");
617s  set (h(3), "facecolor", "r");
617s  ylim ([0, 0.25]);
617s  xlim ([-20, 20]);
617s  hold on
617s 
617s  ## Estimate their lambda parameter
617s  mu_sigma_nuA = tlsfit (r(:,1));
617s  mu_sigma_nuB = tlsfit (r(:,2));
617s  mu_sigma_nuC = tlsfit (r(:,3));
617s 
617s  ## Plot their estimated PDFs
617s  x = [-20:0.1:20];
617s  y = tlspdf (x, mu_sigma_nuA(1), mu_sigma_nuA(2), mu_sigma_nuA(3));
617s  plot (x, y, "-pr");
617s  y = tlspdf (x, mu_sigma_nuB(1), mu_sigma_nuB(2), mu_sigma_nuB(3));
617s  plot (x, y, "-sg");
617s  y = tlspdf (x, mu_sigma_nuC(1), mu_sigma_nuC(2), mu_sigma_nuC(3));
617s  plot (x, y, "-^c");
617s  hold off
617s  legend ({"Normalized HIST of sample 1 with μ=0, σ=2 and nu=1", ...
617s           "Normalized HIST of sample 2 with μ=5, σ=2 and nu=1", ...
617s           "Normalized HIST of sample 3 with μ=3, σ=4 and nu=3", ...
617s           sprintf("PDF for sample 1 with estimated μ=%0.2f, σ=%0.2f, and ν=%0.2f", ...
617s                   mu_sigma_nuA(1), mu_sigma_nuA(2), mu_sigma_nuA(3)), ...
617s           sprintf("PDF for sample 2 with estimated μ=%0.2f, σ=%0.2f, and ν=%0.2f", ...
617s                   mu_sigma_nuB(1), mu_sigma_nuB(2), mu_sigma_nuB(3)), ...
617s           sprintf("PDF for sample 3 with estimated μ=%0.2f, σ=%0.2f, and ν=%0.2f", ...
617s                   mu_sigma_nuC(1), mu_sigma_nuC(2), mu_sigma_nuC(3))})
617s  title ("Three population samples from different location-scale T distibutions")
617s  hold off
617s ***** test
617s  x = [-1.2352, -0.2741, 0.1726, 7.4356, 1.0392, 16.4165];
617s  [paramhat, paramci] = tlsfit (x);
617s  paramhat_out = [0.035893, 0.862711, 0.649261];
617s  paramci_out = [-0.949034, 0.154655, 0.181080; 1.02082, 4.812444, 2.327914];
617s  assert (paramhat, paramhat_out, 1e-6);
617s  assert (paramci, paramci_out, 1e-5);
617s ***** test
617s  x = [-1.2352, -0.2741, 0.1726, 7.4356, 1.0392, 16.4165];
617s  [paramhat, paramci] = tlsfit (x, 0.01);
617s  paramci_out = [-1.2585, 0.0901, 0.1212; 1.3303, 8.2591, 3.4771];
617s  assert (paramci, paramci_out, 1e-4);
617s ***** error<tlsfit: X must be a vector.> tlsfit (ones (2,5));
617s ***** error<tlsfit: wrong value for ALPHA.> tlsfit ([1, 2, 3, 4, 5], 1.2);
617s ***** error<tlsfit: wrong value for ALPHA.> tlsfit ([1, 2, 3, 4, 5], 0);
617s ***** error<tlsfit: wrong value for ALPHA.> tlsfit ([1, 2, 3, 4, 5], "alpha");
617s ***** error<tlsfit: X and CENSOR vectors mismatch.> ...
617s  tlsfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
617s ***** error<tlsfit: X and CENSOR vectors mismatch.> ...
617s  tlsfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
617s ***** error<tlsfit: X and FREQ vectors mismatch.> ...
617s  tlsfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
617s ***** error<tlsfit: X and FREQ vectors mismatch.> ...
617s  tlsfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
617s ***** error<tlsfit: FREQ cannot have negative values.> ...
617s  tlsfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 -1]);
617s ***** error<tlsfit: 'options' 5th argument must be a structure> ...
617s  tlsfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
617s 12 tests, 12 passed, 0 known failure, 0 skipped
617s [inst/dist_fit/logllike.m]
617s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/logllike.m
617s ***** test
617s  [nlogL, acov] = logllike ([3.09717, 0.468525], [1:50]);
617s  assert (nlogL, 211.2965, 1e-4);
617s  assert (acov, [0.0131, -0.0007; -0.0007, 0.0031], 1e-4);
617s ***** test
617s  [nlogL, acov] = logllike ([1.01124, 0.336449], [1:5]);
617s  assert (nlogL, 9.2206, 1e-4);
617s  assert (acov, [0.0712, -0.0032; -0.0032, 0.0153], 1e-4);
617s ***** error<logllike: function called with too few input arguments.> logllike (3.25)
617s ***** error<logllike: X must be a vector.> logllike ([5, 0.2], ones (2))
617s ***** error<logllike: PARAMS must be a two-element vector.> ...
617s  logllike ([1, 0.2, 3], [1, 3, 5, 7])
617s ***** error<logllike: X and CENSOR vector mismatch.> ...
617s  logllike ([1.5, 0.2], [1:5], [0, 0, 0])
617s ***** error<logllike: X and FREQ vector mismatch.> ...
617s  logllike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
617s ***** error<logllike: X and FREQ vector mismatch.> ...
617s  logllike ([1.5, 0.2], [1:5], [], [1, 1, 1])
617s 8 tests, 8 passed, 0 known failure, 0 skipped
617s [inst/dist_fit/hnfit.m]
617s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/hnfit.m
617s ***** demo
617s  ## Sample 2 populations from different half-normal distibutions
617s  rand ("seed", 1);   # for reproducibility
617s  r1 = hnrnd (0, 5, 5000, 1);
617s  rand ("seed", 2);   # for reproducibility
617s  r2 = hnrnd (0, 2, 5000, 1);
617s  r = [r1, r2];
617s 
617s  ## Plot them normalized and fix their colors
617s  hist (r, [0.5:20], 1);
617s  h = findobj (gca, "Type", "patch");
617s  set (h(1), "facecolor", "c");
617s  set (h(2), "facecolor", "g");
617s  hold on
617s 
617s  ## Estimate their shape parameters
617s  mu_sigmaA = hnfit (r(:,1), 0);
617s  mu_sigmaB = hnfit (r(:,2), 0);
617s 
617s  ## Plot their estimated PDFs
617s  x = [0:0.2:10];
617s  y = hnpdf (x, mu_sigmaA(1), mu_sigmaA(2));
617s  plot (x, y, "-pr");
617s  y = hnpdf (x, mu_sigmaB(1), mu_sigmaB(2));
617s  plot (x, y, "-sg");
617s  xlim ([0, 10])
617s  ylim ([0, 0.5])
617s  legend ({"Normalized HIST of sample 1 with μ=0 and σ=5", ...
617s           "Normalized HIST of sample 2 with μ=0 and σ=2", ...
617s           sprintf("PDF for sample 1 with estimated μ=%0.2f and σ=%0.2f", ...
617s                   mu_sigmaA(1), mu_sigmaA(2)), ...
617s           sprintf("PDF for sample 2 with estimated μ=%0.2f and σ=%0.2f", ...
617s                   mu_sigmaB(1), mu_sigmaB(2))})
617s  title ("Two population samples from different half-normal distibutions")
617s  hold off
617s ***** test
617s  x = 1:20;
617s  [paramhat, paramci] = hnfit (x, 0);
617s  assert (paramhat, [0, 11.9791], 1e-4);
617s  assert (paramci, [0, 9.1648; 0, 17.2987], 1e-4);
617s ***** test
617s  x = 1:20;
617s  [paramhat, paramci] = hnfit (x, 0, 0.01);
617s  assert (paramci, [0, 8.4709; 0, 19.6487], 1e-4);
617s ***** error<hnfit: function called with too few input arguments.> hnfit ()
617s ***** error<hnfit: function called with too few input arguments.> hnfit (1)
617s ***** error<hnfit: X must be a vector of real values.> hnfit ([0.2, 0.5+i], 0);
617s ***** error<hnfit: X must be a vector of real values.> hnfit (ones (2,2) * 0.5, 0);
617s ***** error<hnfit: MU must be a real scalar value.> ...
617s  hnfit ([0.5, 1.2], [0, 1]);
617s ***** error<hnfit: MU must be a real scalar value.> ...
617s  hnfit ([0.5, 1.2], 5+i);
617s ***** error<hnfit: X cannot contain values less than MU.> ...
617s  hnfit ([1:5], 2);
617s ***** error<hnfit: wrong value for ALPHA.> hnfit ([0.01:0.1:0.99], 0, 1.2);
617s ***** error<hnfit: wrong value for ALPHA.> hnfit ([0.01:0.1:0.99], 0, i);
617s ***** error<hnfit: wrong value for ALPHA.> hnfit ([0.01:0.1:0.99], 0, -1);
617s ***** error<hnfit: wrong value for ALPHA.> hnfit ([0.01:0.1:0.99], 0, [0.05, 0.01]);
617s ***** error<hnfit: X and FREQ vectors mismatch.>
617s  hnfit ([1 2 3], 0, [], [1 5])
617s ***** error<hnfit: FREQ must not contain negative values.>
617s  hnfit ([1 2 3], 0, [], [1 5 -1])
617s 15 tests, 15 passed, 0 known failure, 0 skipped
617s [inst/dist_fit/ricelike.m]
617s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/ricelike.m
617s ***** test
617s  nlogL = ricelike ([15.3057344, 17.6668458], [1:50]);
617s  assert (nlogL, 204.5230311010569, 1e-12);
617s ***** test
617s  nlogL = ricelike ([2.312346885, 1.681228265], [1:5]);
617s  assert (nlogL, 8.65562164930058, 1e-12);
617s ***** error<ricelike: function called with too few input arguments.> ricelike (3.25)
617s ***** error<ricelike: X must be a vector.> ricelike ([5, 0.2], ones (2))
617s ***** error<ricelike: PARAMS must be a two-element vector.> ...
617s  ricelike ([1, 0.2, 3], [1, 3, 5, 7])
617s ***** error<ricelike: X and CENSOR vector mismatch.> ...
617s  ricelike ([1.5, 0.2], [1:5], [0, 0, 0])
617s ***** error<ricelike: X and FREQ vector mismatch.> ...
617s  ricelike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
617s ***** error<ricelike: X and FREQ vector mismatch.> ...
617s  ricelike ([1.5, 0.2], [1:5], [], [1, 1, 1])
617s ***** error<ricelike: FREQ must not contain negative values.> ...
617s  ricelike ([1.5, 0.2], [1:5], [], [1, 1, 1, 0, -1])
617s 9 tests, 9 passed, 0 known failure, 0 skipped
617s [inst/dist_fit/gevfit_lmom.m]
617s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/gevfit_lmom.m
617s ***** xtest <31070>
617s  data = 1:50;
617s  [pfit, pci] = gevfit_lmom (data);
617s  expected_p = [-0.28 15.01 20.22]';
617s  assert (pfit, expected_p, 0.1);
617s 1 test, 1 passed, 0 known failure, 0 skipped
617s [inst/dist_fit/explike.m]
617s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/explike.m
617s ***** test
617s  x = 12;
617s  beta = 5;
617s  [L, V] = explike (beta, x);
617s  expected_L = 4.0094;
617s  expected_V = 6.5789;
617s  assert (L, expected_L, 0.001);
617s  assert (V, expected_V, 0.001);
617s ***** test
617s  x = 1:5;
617s  beta = 2;
617s  [L, V] = explike (beta, x);
617s  expected_L = 10.9657;
617s  expected_V = 0.4;
617s  assert (L, expected_L, 0.001);
617s  assert (V, expected_V, 0.001);
617s ***** error<explike: function called with too few input arguments.> explike ()
617s ***** error<explike: function called with too few input arguments.> explike (2)
617s ***** error<explike: MU must be a scalar.> explike ([12, 3], [1:50])
617s ***** error<explike: X must be a vector.> explike (3, ones (10, 2))
617s ***** error<explike: X and CENSOR vectors mismatch.> ...
617s  explike (3, [1:50], [1, 2, 3])
617s ***** error<explike: X and FREQ vectors mismatch.> ...
617s  explike (3, [1:50], [], [1, 2, 3])
617s 8 tests, 8 passed, 0 known failure, 0 skipped
617s [inst/dist_fit/evlike.m]
617s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/evlike.m
617s ***** test
617s  x = 1:50;
617s  [nlogL, acov] = evlike ([2.3, 1.2], x);
617s  avar_out = [-1.2778e-13, 3.1859e-15; 3.1859e-15, -7.9430e-17];
617s  assert (nlogL, 3.242264755689906e+17, 1e-14);
617s  assert (acov, avar_out, 1e-3);
617s ***** test
617s  x = 1:50;
617s  [nlogL, acov] = evlike ([2.3, 1.2], x * 0.5);
617s  avar_out = [-7.6094e-05, 3.9819e-06; 3.9819e-06, -2.0836e-07];
617s  assert (nlogL, 481898704.0472211, 1e-6);
617s  assert (acov, avar_out, 1e-3);
617s ***** test
617s  x = 1:50;
617s  [nlogL, acov] = evlike ([21, 15], x);
617s  avar_out = [11.73913876598908, -5.9546128523121216; ...
617s              -5.954612852312121, 3.708060045170236];
617s  assert (nlogL, 223.7612479380652, 1e-13);
617s  assert (acov, avar_out, 1e-14);
617s ***** error<evlike: function called with too few input arguments.> evlike ([12, 15])
617s ***** error<evlike: wrong parameters length.> evlike ([12, 15, 3], [1:50])
617s ***** error<evlike: X must be a vector.> evlike ([12, 3], ones (10, 2))
617s ***** error<evlike: X and CENSOR vectors mismatch.> ...
617s  evlike ([12, 15], [1:50], [1, 2, 3])
617s ***** error<evlike: X and FREQ vectors mismatch.> ...
617s  evlike ([12, 15], [1:50], [], [1, 2, 3])
617s 8 tests, 8 passed, 0 known failure, 0 skipped
617s [inst/dist_fit/gamlike.m]
617s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/gamlike.m
617s ***** test
617s  [nlogL, acov] = gamlike([2, 3], [2, 3, 4, 5, 6, 7, 8, 9]);
617s  assert (nlogL, 19.4426, 1e-4);
617s  assert (acov, [2.7819, -5.0073; -5.0073, 9.6882], 1e-4);
617s ***** test
617s  [nlogL, acov] = gamlike([2, 3], [5:45]);
617s  assert (nlogL, 305.8070, 1e-4);
617s  assert (acov, [0.0423, -0.0087; -0.0087, 0.0167], 1e-4);
617s ***** test
617s  [nlogL, acov] = gamlike([2, 13], [5:45]);
617s  assert (nlogL, 163.2261, 1e-4);
617s  assert (acov, [0.2362, -1.6631; -1.6631, 13.9440], 1e-4);
617s ***** error<gamlike: function called with too few input arguments.> ...
617s  gamlike ([12, 15])
617s ***** error<gamlike: wrong parameters length.> gamlike ([12, 15, 3], [1:50])
617s ***** error<gamlike: X must be a vector.> gamlike ([12, 3], ones (10, 2))
617s ***** error<gamlike: X and CENSOR vectors mismatch.> ...
617s  gamlike ([12, 15], [1:50], [1, 2, 3])
617s ***** error<gamlike: X and FREQ vectors mismatch.> ...
617s  gamlike ([12, 15], [1:50], [], [1, 2, 3])
617s 8 tests, 8 passed, 0 known failure, 0 skipped
617s [inst/dist_fit/normlike.m]
617s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/normlike.m
617s ***** error<normlike: too few input arguments.> normlike ([12, 15]);
617s ***** error<normlike: X must be a vector.> normlike ([12, 15], ones (2));
617s ***** error<normlike: PARAMS must be a two-element vector.> ...
617s  normlike ([12, 15, 3], [1:50]);
617s ***** error<normlike: X and CENSOR vectors mismatch.> ...
617s  normlike ([12, 15], [1:50], [1, 2, 3]);
617s ***** error<normlike: X and FREQ vectors mismatch.> ...
617s  normlike ([12, 15], [1:50], [], [1, 2, 3]);
617s ***** error<normlike: FREQ must not contain negative values.> ...
617s  normlike ([12, 15], [1:5], [], [1, 2, 3, 2, -1]);
617s ***** test
617s  x = 1:50;
617s  [nlogL, avar] = normlike ([2.3, 1.2], x);
617s  avar_out = [7.5767e-01, -1.8850e-02; -1.8850e-02, 4.8750e-04];
617s  assert (nlogL, 13014.95883783327, 1e-10);
617s  assert (avar, avar_out, 1e-4);
617s ***** test
617s  x = 1:50;
617s  [nlogL, avar] = normlike ([2.3, 1.2], x * 0.5);
617s  avar_out = [3.0501e-01, -1.5859e-02; -1.5859e-02, 9.1057e-04];
617s  assert (nlogL, 2854.802587833265, 1e-10);
617s  assert (avar, avar_out, 1e-4);
617s ***** test
617s  x = 1:50;
617s  [nlogL, avar] = normlike ([21, 15], x);
617s  avar_out = [5.460474308300396, -1.600790513833993; ...
617s              -1.600790513833993, 2.667984189723321];
617s  assert (nlogL, 206.738325604233, 1e-12);
617s  assert (avar, avar_out, 1e-14);
617s ***** test
617s  x = 1:50;
617s  censor = ones (1, 50);
617s  censor([2, 4, 6, 8, 12, 14]) = 0;
617s  [nlogL, avar] = normlike ([2.3, 1.2], x, censor);
617s  avar_out = [3.0501e-01, -1.5859e-02; -1.5859e-02, 9.1057e-04];
617s  assert (nlogL, Inf);
617s  assert (avar, [NaN, NaN; NaN, NaN]);
617s ***** test
617s  x = 1:50;
617s  censor = ones (1, 50);
617s  censor([2, 4, 6, 8, 12, 14]) = 0;
617s  [nlogL, avar] = normlike ([21, 15], x, censor);
617s  avar_out = [24.4824488866131, -10.6649544179636; ...
617s              -10.6649544179636, 6.22827849965737];
617s  assert (nlogL, 86.9254371829733, 1e-12);
617s  assert (avar, avar_out, 8e-14);
617s 11 tests, 11 passed, 0 known failure, 0 skipped
617s [inst/dist_fit/nbinlike.m]
617s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/nbinlike.m
617s ***** assert (nbinlike ([2.42086, 0.0867043], [1:50]), 205.5942, 1e-4)
617s ***** assert (nbinlike ([3.58823, 0.254697], [1:20]), 63.6435, 1e-4)
617s ***** assert (nbinlike ([8.80671, 0.615565], [1:10]), 24.7410, 1e-4)
617s ***** assert (nbinlike ([22.1756, 0.831306], [1:8]), 17.9528, 1e-4)
617s ***** assert (nbinlike ([22.1756, 0.831306], [1:9], [ones(1,8), 0]), 17.9528, 1e-4)
617s ***** error<nbinlike: function called with too few input arguments.> nbinlike (3.25)
617s ***** error<nbinlike: X must be a vector.> nbinlike ([5, 0.2], ones (2))
617s ***** error<nbinlike: X cannot have negative values.> nbinlike ([5, 0.2], [-1, 3])
617s ***** error<nbinlike: PARAMS must be a two-element vector.> ...
617s  nbinlike ([1, 0.2, 3], [1, 3, 5, 7])
617s ***** error<nbinlike: number of successes,> nbinlike ([-5, 0.2], [1:15])
617s ***** error<nbinlike: number of successes,> nbinlike ([0, 0.2], [1:15])
617s ***** error<nbinlike: probability of success,> nbinlike ([5, 1.2], [3, 5])
617s ***** error<nbinlike: probability of success,> nbinlike ([5, -0.2], [3, 5])
617s ***** error<nbinlike: X and FREQ vectors mismatch.> ...
617s  nbinlike ([5, 0.2], ones (10, 1), ones (8,1))
617s ***** error<nbinlike: FREQ must not contain negative values.> ...
617s  nbinlike ([5, 0.2], ones (1, 8), [1 1 1 1 1 1 1 -1])
617s ***** error<nbinlike: FREQ must contain integer values.> ...
617s  nbinlike ([5, 0.2], ones (1, 8), [1 1 1 1 1 1 1 1.5])
617s 16 tests, 16 passed, 0 known failure, 0 skipped
617s [inst/dist_fit/gumbelfit.m]
617s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/gumbelfit.m
617s ***** demo
617s  ## Sample 3 populations from different Gumbel distibutions
617s  rand ("seed", 1);    # for reproducibility
617s  r1 = gumbelrnd (2, 5, 400, 1);
617s  rand ("seed", 11);    # for reproducibility
617s  r2 = gumbelrnd (-5, 3, 400, 1);
617s  rand ("seed", 16);    # for reproducibility
617s  r3 = gumbelrnd (14, 8, 400, 1);
617s  r = [r1, r2, r3];
617s 
617s  ## Plot them normalized and fix their colors
617s  hist (r, 25, 0.32);
617s  h = findobj (gca, "Type", "patch");
617s  set (h(1), "facecolor", "c");
617s  set (h(2), "facecolor", "g");
617s  set (h(3), "facecolor", "r");
617s  ylim ([0, 0.28])
617s  xlim ([-11, 50]);
617s  hold on
617s 
617s  ## Estimate their MU and BETA parameters
617s  mu_betaA = gumbelfit (r(:,1));
617s  mu_betaB = gumbelfit (r(:,2));
617s  mu_betaC = gumbelfit (r(:,3));
617s 
617s  ## Plot their estimated PDFs
617s  x = [min(r(:)):max(r(:))];
617s  y = gumbelpdf (x, mu_betaA(1), mu_betaA(2));
617s  plot (x, y, "-pr");
617s  y = gumbelpdf (x, mu_betaB(1), mu_betaB(2));
617s  plot (x, y, "-sg");
617s  y = gumbelpdf (x, mu_betaC(1), mu_betaC(2));
617s  plot (x, y, "-^c");
617s  legend ({"Normalized HIST of sample 1 with μ=2 and β=5", ...
617s           "Normalized HIST of sample 2 with μ=-5 and β=3", ...
617s           "Normalized HIST of sample 3 with μ=14 and β=8", ...
617s           sprintf("PDF for sample 1 with estimated μ=%0.2f and β=%0.2f", ...
617s                   mu_betaA(1), mu_betaA(2)), ...
617s           sprintf("PDF for sample 2 with estimated μ=%0.2f and β=%0.2f", ...
617s                   mu_betaB(1), mu_betaB(2)), ...
617s           sprintf("PDF for sample 3 with estimated μ=%0.2f and β=%0.2f", ...
617s                   mu_betaC(1), mu_betaC(2))})
617s  title ("Three population samples from different Gumbel distibutions")
617s  hold off
617s ***** test
617s  x = 1:50;
617s  [paramhat, paramci] = gumbelfit (x);
617s  paramhat_out = [18.3188, 13.0509];
617s  paramci_out = [14.4882, 10.5294; 22.1495, 16.1763];
617s  assert (paramhat, paramhat_out, 1e-4);
617s  assert (paramci, paramci_out, 1e-4);
617s ***** test
617s  x = 1:50;
617s  [paramhat, paramci] = gumbelfit (x, 0.01);
617s  paramci_out = [13.2845, 9.8426; 23.3532, 17.3051];
617s  assert (paramci, paramci_out, 1e-4);
617s ***** error<gumbelfit: X must be a double-precision vector.> gumbelfit (ones (2,5));
617s ***** error<gumbelfit: X must be a double-precision vector.> ...
617s  gumbelfit (single (ones (1,5)));
617s ***** error<gumbelfit: X must NOT contain missing values> ...
617s  gumbelfit ([1, 2, 3, 4, NaN]);
617s ***** error<gumbelfit: wrong value for ALPHA.> gumbelfit ([1, 2, 3, 4, 5], 1.2);
617s ***** error<gumbelfit: X and CENSOR vectors mismatch.> ...
617s  gumbelfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
617s ***** error<gumbelfit: X and FREQ vectors mismatch.> ...
617s  gumbelfit ([1, 2, 3, 4, 5], 0.05, [], [1 1 0]);
617s ***** error<gamfit: FREQ must not contain negative values.>
617s  gamfit ([1, 2, 3], 0.05, [], [1 5 -1])
617s ***** error<gumbelfit: 'options' 5th argument> ...
617s  gumbelfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
617s 10 tests, 10 passed, 0 known failure, 0 skipped
617s [inst/dist_fit/nakalike.m]
617s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/nakalike.m
617s ***** test
617s  nlogL = nakalike ([0.735504, 858.5], [1:50]);
617s  assert (nlogL, 202.8689, 1e-4);
617s ***** test
617s  nlogL = nakalike ([1.17404, 11], [1:5]);
617s  assert (nlogL, 8.6976, 1e-4);
617s ***** test
617s  nlogL = nakalike ([1.17404, 11], [1:5], [], [1, 1, 1, 1, 1]);
617s  assert (nlogL, 8.6976, 1e-4);
617s ***** test
617s  nlogL = nakalike ([1.17404, 11], [1:6], [], [1, 1, 1, 1, 1, 0]);
617s  assert (nlogL, 8.6976, 1e-4);
617s ***** error<nakalike: function called with too few input arguments.> nakalike (3.25)
617s ***** error<nakalike: X must be a vector.> nakalike ([5, 0.2], ones (2))
617s ***** error<nakalike: PARAMS must be a two-element vector.> ...
617s  nakalike ([1, 0.2, 3], [1, 3, 5, 7])
617s ***** error<nakalike: X and CENSOR vector mismatch.> ...
617s  nakalike ([1.5, 0.2], [1:5], [0, 0, 0])
617s ***** error<nakalike: X and FREQ vector mismatch.> ...
617s  nakalike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
617s ***** error<nakalike: X and FREQ vector mismatch.> ...
617s  nakalike ([1.5, 0.2], [1:5], [], [1, 1, 1])
617s ***** error<nakalike: FREQ must not contain negative values.> ...
617s  nakalike ([1.5, 0.2], [1:5], [], [1, 1, 1, 1, -1])
617s 11 tests, 11 passed, 0 known failure, 0 skipped
617s [inst/dist_fit/betalike.m]
617s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/betalike.m
617s ***** test
617s  x = 0.01:0.02:0.99;
617s  [nlogL, avar] = betalike ([2.3, 1.2], x);
617s  avar_out = [0.03691678, 0.02803056; 0.02803056, 0.03965629];
617s  assert (nlogL, 17.873477715879040, 3e-14);
617s  assert (avar, avar_out, 1e-7);
617s ***** test
617s  x = 0.01:0.02:0.99;
617s  [nlogL, avar] = betalike ([1, 4], x);
617s  avar_out = [0.02793282, 0.02717274; 0.02717274, 0.03993361];
617s  assert (nlogL, 79.648061114839550, 1e-13);
617s  assert (avar, avar_out, 1e-7);
617s ***** test
617s  x = 0.00:0.02:1;
617s  [nlogL, avar] = betalike ([1, 4], x);
617s  avar_out = [0.00000801564765, 0.00000131397245; ...
617s              0.00000131397245, 0.00070827639442];
617s  assert (nlogL, 573.2008434477486, 1e-10);
617s  assert (avar, avar_out, 1e-14);
617s ***** error<betalike: function called with too few input arguments.> ...
617s  betalike ([12, 15]);
617s ***** error<betalike: wrong parameters length.> betalike ([12, 15, 3], [1:50]);
617s ***** error<betalike: X and FREQ vectors mismatch.> ...
617s  betalike ([12, 15], ones (10, 1), ones (8,1))
617s ***** error<betalike: FREQ must not contain negative values.> ...
617s  betalike ([12, 15], ones (1, 8), [1 1 1 1 1 1 1 -1])
617s ***** error<betalike: FREQ must contain integer values.> ...
617s  betalike ([12, 15], ones (1, 8), [1 1 1 1 1 1 1 1.5])
617s 8 tests, 8 passed, 0 known failure, 0 skipped
617s [inst/dist_fit/bisalike.m]
617s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fit/bisalike.m
617s ***** test
617s  nlogL = bisalike ([16.2649, 1.0156], [1:50]);
617s  assert (nlogL, 215.5905, 1e-4);
617s ***** test
617s  nlogL = bisalike ([2.5585, 0.5839], [1:5]);
617s  assert (nlogL, 8.9950, 1e-4);
617s ***** error<bisalike: function called with too few input arguments.> bisalike (3.25)
617s ***** error<bisalike: X must be a vector.> bisalike ([5, 0.2], ones (2))
617s ***** error<bisalike: X cannot have negative values.> bisalike ([5, 0.2], [-1, 3])
617s ***** error<bisalike: PARAMS must be a two-element vector.> ...
617s  bisalike ([1, 0.2, 3], [1, 3, 5, 7])
617s ***** error<bisalike: X and CENSOR vector mismatch.> ...
617s  bisalike ([1.5, 0.2], [1:5], [0, 0, 0])
617s ***** error<bisalike: X and FREQ vector mismatch.> ...
617s  bisalike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
617s ***** error<bisalike: X and FREQ vector mismatch.> ...
617s  bisalike ([1.5, 0.2], [1:5], [], [1, 1, 1])
617s 9 tests, 9 passed, 0 known failure, 0 skipped
617s [inst/stepwisefit.m]
617s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/stepwisefit.m
617s ***** test
617s  % Sample data from Draper and Smith (n = 13, k = 4)
617s  X = [7 1 11 11 7 11 3 1 2 21 1 11 10; ...
617s      26 29 56 31 52 55 71 31 54 47 40 66 68; ...
617s      6 15 8 8 6 9 17 22 18 4 23 9 8; ...
617s      60 52 20 47 33 22 6 44 22 26 34 12 12]';
617s  y = [78.5 74.3 104.3 87.6 95.9 109.2 102.7 72.5 93.1 115.9 83.8 113.3 109.4]';
617s  [X_use, b, bint, r, rint, stats] = stepwisefit(y, X);
617s  assert(X_use, [4 1])
617s  assert(b, regress(y, [ones(size(y)) X(:, X_use)], 0.05))
617s  [X_use, b, bint, r, rint, stats] = stepwisefit(y, X, 0.05, 0.1, "corr");
617s  assert(X_use, [4 1])
617s  assert(b, regress(y, [ones(size(y)) X(:, X_use)], 0.05))
617s  [X_use, b, bint, r, rint, stats] = stepwisefit(y, X, [], [], "p");
617s  assert(X_use, [4 1])
617s  assert(b, regress(y, [ones(size(y)) X(:, X_use)], 0.05))
617s 1 test, 1 passed, 0 known failure, 0 skipped
617s [inst/tiedrank.m]
617s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/tiedrank.m
617s ***** test
617s  [r,tieadj] = tiedrank ([10, 20, 30, 40, 20]);
617s  assert (r, [1, 2.5, 4, 5, 2.5]);
617s  assert (tieadj, 3);
617s ***** test
617s  [r,tieadj] = tiedrank ([10; 20; 30; 40; 20]);
617s  assert (r, [1; 2.5; 4; 5; 2.5]);
617s  assert (tieadj, 3);
617s ***** test
617s  [r,tieadj] = tiedrank ([10, 20, 30, 40, 20], 1);
617s  assert (r, [1, 2.5, 4, 5, 2.5]);
617s  assert (tieadj, [1; 0; 18]);
617s ***** test
617s  [r,tieadj] = tiedrank ([10, 20, 30, 40, 20], 0, 1);
617s  assert (r, [1, 2.5, 2, 1, 2.5]);
617s  assert (tieadj, 3);
617s ***** test
617s  [r,tieadj] = tiedrank ([10, 20, 30, 40, 20], 1, 1);
617s  assert (r, [1, 2.5, 2, 1, 2.5]);
617s  assert (tieadj, [1; 0; 18]);
617s ***** error <tiedrank: X must be a vector.> tiedrank (ones (2))
617s ***** error <tiedrank: TIEFLAG must be a numeric or boolean scalar.> ...
617s  tiedrank ([1, 2, 3, 4, 5], [1, 1])
617s ***** error <tiedrank: TIEFLAG must be a numeric or boolean scalar.> ...
617s  tiedrank ([1, 2, 3, 4, 5], "A")
617s ***** error <tiedrank: TIEFLAG must be a numeric or boolean scalar.> ...
617s  tiedrank ([1, 2, 3, 4, 5], [true, true])
617s ***** error <tiedrank: BIDIR must be a numeric or boolean scalar.> ...
617s  tiedrank ([1, 2, 3, 4, 5], 0, [1, 1])
617s ***** error <tiedrank: BIDIR must be a numeric or boolean scalar.> ...
617s  tiedrank ([1, 2, 3, 4, 5], 0, "A")
617s ***** error <tiedrank: BIDIR must be a numeric or boolean scalar.> ...
617s  tiedrank ([1, 2, 3, 4, 5], 0, [true, true])
617s 12 tests, 12 passed, 0 known failure, 0 skipped
617s [inst/regression_ftest.m]
617s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/regression_ftest.m
617s ***** error<Invalid call to regression_ftest.  Correct usage> regression_ftest ();
618s ***** error<Invalid call to regression_ftest.  Correct usage> ...
618s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]');
618s ***** error<regression_ftest: Y must contain finite real numbers.> ...
618s  regression_ftest ([1 2 NaN]', [2 3 4; 3 4 5]', [1 0.5]);
618s ***** error<regression_ftest: Y must contain finite real numbers.> ...
618s  regression_ftest ([1 2 Inf]', [2 3 4; 3 4 5]', [1 0.5]);
618s ***** error<regression_ftest: Y must contain finite real numbers.> ...
618s  regression_ftest ([1 2 3+i]', [2 3 4; 3 4 5]', [1 0.5]);
618s ***** error<regression_ftest: X must contain finite real numbers.> ...
618s  regression_ftest ([1 2 3]', [2 3 NaN; 3 4 5]', [1 0.5]);
618s ***** error<regression_ftest: X must contain finite real numbers.> ...
618s  regression_ftest ([1 2 3]', [2 3 Inf; 3 4 5]', [1 0.5]);
618s ***** error<regression_ftest: X must contain finite real numbers.> ...
618s  regression_ftest ([1 2 3]', [2 3 4; 3 4 3+i]', [1 0.5]);
618s ***** error<regression_ftest: invalid value for alpha.> ...
618s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "alpha", 0);
618s ***** error<regression_ftest: invalid value for alpha.> ...
618s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "alpha", 1.2);
618s ***** error<regression_ftest: invalid value for alpha.> ...
618s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "alpha", [.02 .1]);
618s ***** error<regression_ftest: invalid value for alpha.> ...
618s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "alpha", "a");
618s ***** error<regression_ftest: invalid Name argument.> ...
618s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "some", 0.05);
618s ***** error<regression_ftest: Y must be a vector of length> ...
618s  regression_ftest ([1 2 3]', [2 3; 3 4]', [1 0.5]);
618s ***** error<regression_ftest: Y must be a vector of length> ...
618s  regression_ftest ([1 2; 3 4]', [2 3; 3 4]', [1 0.5]);
618s ***** error<regression_ftest: reduced model, RM, must be a numeric vector or> ...
618s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], ones (2));
618s ***** error<regression_ftest: reduced model, RM, must be a numeric vector or> ...
618s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], "alpha");
618s ***** error<regression_ftest: reduced model, RM, must have smaller length than> ...
618s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [1 2]);
618s 18 tests, 18 passed, 0 known failure, 0 skipped
618s [inst/datasample.m]
618s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/datasample.m
618s ***** error datasample();
618s ***** error datasample(1);
618s ***** error <data must be a vector or matrix> datasample({1, 2, 3}, 1);
618s ***** error <k must be a positive integer scalar> datasample([1 2], -1);
618s ***** error <k must be a positive integer scalar> datasample([1 2], 1.5);
618s ***** error <k must be a positive integer scalar> datasample([1 2], [1 1]);
618s ***** error <k must be a positive integer scalar> datasample([1 2], 'g', [1 1]);
618s ***** error <DIM must be a positive integer scalar> datasample([1 2], 1, -1);
618s ***** error <DIM must be a positive integer scalar> datasample([1 2], 1, 1.5);
618s ***** error <DIM must be a positive integer scalar> datasample([1 2], 1, [1 1]);
618s ***** error <Replace> datasample([1 2], 1, 1, "Replace", -2);
618s ***** error <weights must be defined> datasample([1 2], 1, 1, "Weights", "abc");
618s ***** error <weights must be defined> datasample([1 2], 1, 1, "Weights", [1 -2 3]);
618s ***** error <weights must be defined> datasample([1 2], 1, 1, "Weights", ones (2));
618s ***** error <weights must be equal> datasample([1 2], 1, 1, "Weights", [1 2 3]);
618s ***** test
618s  dat = randn (10, 4);
618s  assert (size (datasample (dat, 3, 1)), [3 4]);
618s ***** test
618s  dat = randn (10, 4);
618s  assert (size (datasample (dat, 3, 2)), [10 3]);
618s 17 tests, 17 passed, 0 known failure, 0 skipped
618s [inst/fitcgam.m]
618s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/fitcgam.m
618s ***** demo
618s  ## Train a GAM classifier for binary classification
618s  ## using specific data and plot the decision boundaries.
618s 
618s  ## Define specific data
618s  X = [1, 2; 2, 3; 3, 3; 4, 5; 5, 5; ...
618s      6, 7; 7, 8; 8, 8; 9, 9; 10, 10];
618s  Y = [0; 0; 0; 0; 0; ...
618s      1; 1; 1; 1; 1];
618s 
618s  ## Train the GAM model
618s  obj = fitcgam (X, Y, "Interactions", "all");
618s 
618s  ## Create a grid of values for prediction
618s  x1 = [min(X(:,1)):0.1:max(X(:,1))];
618s  x2 = [min(X(:,2)):0.1:max(X(:,2))];
618s  [x1G, x2G] = meshgrid (x1, x2);
618s  XGrid = [x1G(:), x2G(:)];
618s  pred = predict (obj, XGrid);
618s 
618s  ## Plot decision boundaries and data points
618s  predNumeric = str2double (pred);
618s  gidx = predNumeric > 0.5;
618s 
618s  figure
618s  scatter(XGrid(gidx,1), XGrid(gidx,2), "markerfacecolor", "magenta");
618s  hold on
618s  scatter(XGrid(!gidx,1), XGrid(!gidx,2), "markerfacecolor", "red");
618s  plot(X(Y == 0, 1), X(Y == 0, 2), "ko", X(Y == 1, 1), X(Y == 1, 2), "kx");
618s  xlabel("Feature 1");
618s  ylabel("Feature 2");
618s  title("Generalized Additive Model (GAM) Decision Boundary");
618s  legend({"Class 1 Region", "Class 0 Region", ...
618s        "Class 1 Samples", "Class 0 Samples"}, ...
618s        "location", "northwest")
618s  axis tight
618s  hold off
618s ***** test
618s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
618s  y = [0; 0; 1; 1];
618s  PredictorNames = {'Feature1', 'Feature2', 'Feature3'};
618s  a = fitcgam (x, y, "PredictorNames", PredictorNames);
618s  assert (class (a), "ClassificationGAM");
618s  assert ({a.X, a.Y, a.NumObservations}, {x, y, 4})
618s  assert ({a.NumPredictors, a.ResponseName}, {3, "Y"})
618s  assert (a.ClassNames, {'0'; '1'})
618s  assert (a.PredictorNames, PredictorNames)
618s  assert (a.BaseModel.Intercept, 0)
619s ***** test
619s  x = [1, 2; 3, 4; 5, 6; 7, 8; 9, 10];
619s  y = [1; 0; 1; 0; 1];
619s  a = fitcgam (x, y, "interactions", "all");
619s  assert (class (a), "ClassificationGAM");
619s  assert ({a.X, a.Y, a.NumObservations}, {x, y, 5})
619s  assert ({a.NumPredictors, a.ResponseName}, {2, "Y"})
619s  assert (a.ClassNames, {'1'; '0'})
619s  assert (a.PredictorNames, {'x1', 'x2'})
619s  assert (a.ModelwInt.Intercept, 0.4055, 1e-1)
620s ***** test
620s  load fisheriris
620s  inds = strcmp (species,'versicolor') | strcmp (species,'virginica');
620s  X = meas(inds, :);
620s  Y = species(inds, :)';
620s  Y = strcmp (Y, 'virginica')';
620s  a = fitcgam (X, Y, 'Formula', 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3');
620s  assert (class (a), "ClassificationGAM");
620s  assert ({a.X, a.Y, a.NumObservations}, {X, Y, 100})
620s  assert ({a.NumPredictors, a.ResponseName}, {4, "Y"})
620s  assert (a.ClassNames, {'0'; '1'})
620s  assert (a.Formula, 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3')
620s  assert (a.PredictorNames, {'x1', 'x2', 'x3', 'x4'})
620s  assert (a.ModelwInt.Intercept, 0)
623s ***** error<fitcgam: too few arguments.> fitcgam ()
623s ***** error<fitcgam: too few arguments.> fitcgam (ones (4,1))
623s ***** error<fitcgam: name-value arguments must be in pairs.>
623s  fitcgam (ones (4,2), ones (4, 1), "K")
623s ***** error<fitcgam: number of rows in X and Y must be equal.>
623s  fitcgam (ones (4,2), ones (3, 1))
623s ***** error<fitcgam: number of rows in X and Y must be equal.>
623s  fitcgam (ones (4,2), ones (3, 1), "K", 2)
623s 8 tests, 8 passed, 0 known failure, 0 skipped
623s [inst/hmmgenerate.m]
623s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/hmmgenerate.m
623s ***** test
623s  len = 25;
623s  transprob = [0.8, 0.2; 0.4, 0.6];
623s  outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
623s  [sequence, states] = hmmgenerate (len, transprob, outprob);
623s  assert (length (sequence), len);
623s  assert (length (states), len);
623s  assert (min (sequence) >= 1);
623s  assert (max (sequence) <= columns (outprob));
623s  assert (min (states) >= 1);
623s  assert (max (states) <= rows (transprob));
623s ***** test
623s  len = 25;
623s  transprob = [0.8, 0.2; 0.4, 0.6];
623s  outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
623s  symbols = {"A", "B", "C"};
623s  statenames = {"One", "Two"};
623s  [sequence, states] = hmmgenerate (len, transprob, outprob, ...
623s                       "symbols", symbols, "statenames", statenames);
623s  assert (length (sequence), len);
623s  assert (length (states), len);
623s  assert (strcmp (sequence, "A") + strcmp (sequence, "B") + ...
623s                                   strcmp (sequence, "C") == ones (1, len));
623s  assert (strcmp (states, "One") + strcmp (states, "Two") == ones (1, len));
623s 2 tests, 2 passed, 0 known failure, 0 skipped
623s [inst/randsample.m]
623s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/randsample.m
623s ***** test
623s  n = 20;
623s  k = 5;
623s  x = randsample(n, k);
623s  assert (size(x), [1 k]);
623s  x = randsample(n, k, true);
623s  assert (size(x), [1 k]);
623s  x = randsample(n, k, false);
623s  assert (size(x), [1 k]);
623s  x = randsample(n, k, true, ones(n, 1));
623s  assert (size(x), [1 k]);
623s  x = randsample(1:n, k);
623s  assert (size(x), [1 k]);
623s  x = randsample(1:n, k, true);
623s  assert (size(x), [1 k]);
623s  x = randsample(1:n, k, false);
623s  assert (size(x), [1 k]);
623s  x = randsample(1:n, k, true, ones(n, 1));
623s  assert (size(x), [1 k]);
623s  x = randsample((1:n)', k);
623s  assert (size(x), [k 1]);
623s  x = randsample((1:n)', k, true);
623s  assert (size(x), [k 1]);
623s  x = randsample((1:n)', k, false);
623s  assert (size(x), [k 1]);
623s  x = randsample((1:n)', k, true, ones(n, 1));
623s  assert (size(x), [k 1]);
623s  n = 10;
623s  k = 100;
623s  x = randsample(n, k, true, 1:n);
623s  assert (size(x), [1 k]);
623s  x = randsample((1:n)', k, true);
623s  assert (size(x), [k 1]);
623s  x = randsample(k, k, false, 1:k);
623s  assert (size(x), [1 k]);
623s 1 test, 1 passed, 0 known failure, 0 skipped
623s [inst/mhsample.m]
623s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/mhsample.m
623s ***** demo
623s  ## Define function to sample
623s  d = 2;
623s  mu = [-1; 2];
623s  rand ("seed", 5)  # for reproducibility
623s  Sigma = rand (d);
623s  Sigma = (Sigma + Sigma');
623s  Sigma += eye (d) * abs (eigs (Sigma, 1, "sa")) * 1.1;
623s  pdf = @(x)(2*pi)^(-d/2)*det(Sigma)^-.5*exp(-.5*sum((x.'-mu).*(Sigma\(x.'-mu)),1));
623s  ## Inputs
623s  start = ones (1, 2);
623s  nsamples = 500;
623s  sym = true;
623s  K = 500;
623s  m = 10;
623s  rand ("seed", 8)  # for reproducibility
623s  proprnd = @(x) (rand (size (x)) - .5) * 3 + x;
623s  [smpl, accept] = mhsample (start, nsamples, "pdf", pdf, "proprnd", proprnd, ...
623s                             "symmetric", sym, "burnin", K, "thin", m);
623s  figure;
623s  hold on;
623s  plot (smpl(:, 1), smpl(:, 2), 'x');
623s  [x, y] = meshgrid (linspace (-6, 4), linspace(-3, 7));
623s  z = reshape (pdf ([x(:), y(:)]), size(x));
623s  mesh (x, y, z, "facecolor", "None");
623s  ## Using sample points to find the volume of half a sphere with radius of .5
623s  f = @(x) ((.25-(x(:,1)+1).^2-(x(:,2)-2).^2).^.5.*(((x(:,1)+1).^2+(x(:,2)-2).^2)<.25)).';
623s  int = mean (f (smpl) ./ pdf (smpl));
623s  errest = std (f (smpl) ./ pdf (smpl)) / nsamples ^ .5;
623s  trueerr = abs (2 / 3 * pi * .25 ^ (3 / 2) - int);
623s  printf ("Monte Carlo integral estimate int f(x) dx = %f\n", int);
623s  printf ("Monte Carlo integral error estimate %f\n", errest);
623s  printf ("The actual error %f\n", trueerr);
623s  mesh (x, y, reshape (f([x(:), y(:)]), size(x)), "facecolor", "None");
623s ***** demo
623s  ## Integrate truncated normal distribution to find normilization constant
623s  pdf = @(x) exp (-.5*x.^2)/(pi^.5*2^.5);
623s  nsamples = 1e3;
623s  rand ("seed", 5)  # for reproducibility
623s  proprnd = @(x) (rand (size (x)) - .5) * 3 + x;
623s  [smpl, accept] = mhsample (1, nsamples, "pdf", pdf, "proprnd", proprnd, ...
623s                             "symmetric", true, "thin", 4);
623s  f = @(x) exp(-.5 * x .^ 2) .* (x >= -2 & x <= 2);
623s  x = linspace (-3, 3, 1000);
623s  area(x, f(x));
623s  xlabel ('x');
623s  ylabel ('f(x)');
623s  int = mean (f (smpl) ./ pdf (smpl));
623s  errest = std (f (smpl) ./ pdf (smpl)) / nsamples^ .5;
623s  trueerr = abs (erf (2 ^ .5) * 2 ^ .5 * pi ^ .5 - int);
623s  printf ("Monte Carlo integral estimate int f(x) dx = %f\n", int);
623s  printf ("Monte Carlo integral error estimate %f\n", errest);
623s  printf ("The actual error %f\n", trueerr);
623s ***** test
623s  nchain = 1e4;
623s  start = rand (nchain, 1);
623s  nsamples = 1e3;
623s  pdf = @(x) exp (-.5*(x-1).^2)/(2*pi)^.5;
623s  proppdf = @(x, y) 1/3;
623s  proprnd = @(x) 3 * (rand (size (x)) - .5) + x;
623s  [smpl, accept] = mhsample (start, nsamples, "pdf", pdf, "proppdf", proppdf, ...
623s                             "proprnd", proprnd, "thin", 2, "nchain", nchain, ...
623s                             "burnin", 0);
623s  assert (mean (mean (smpl, 1), 3), 1, .01);
623s  assert (mean (var (smpl, 1), 3), 1, .01)
626s ***** error mhsample ();
626s ***** error mhsample (1);
626s ***** error mhsample (1, 1);
626s ***** error mhsample (1, 1, "pdf", @(x)x);
626s ***** error mhsample (1, 1, "pdf", @(x)x, "proprnd", @(x)x+rand(size(x)));
626s 6 tests, 6 passed, 0 known failure, 0 skipped
626s [inst/ztest2.m]
626s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/ztest2.m
626s ***** error ztest2 ();
626s ***** error ztest2 (1);
626s ***** error ztest2 (1, 2);
626s ***** error ztest2 (1, 2, 3);
626s ***** error<ztest2: optional arguments must be in NAME-VALUE pairs.> ...
626s  ztest2 (1, 2, 3, 4, "alpha")
626s ***** error<ztest2: invalid VALUE for alpha.> ...
626s  ztest2 (1, 2, 3, 4, "alpha", 0);
626s ***** error<ztest2: invalid VALUE for alpha.> ...
626s  ztest2 (1, 2, 3, 4, "alpha", 1.2);
626s ***** error<ztest2: invalid VALUE for alpha.> ...
626s  ztest2 (1, 2, 3, 4, "alpha", "val");
626s ***** error<ztest2: invalid VALUE for tail.>  ...
626s  ztest2 (1, 2, 3, 4, "tail", "val");
626s ***** error<ztest2: invalid VALUE for tail.>  ...
626s  ztest2 (1, 2, 3, 4, "alpha", 0.01, "tail", "val");
626s ***** error<ztest2: invalid NAME for optional arguments.> ...
626s  ztest2 (1, 2, 3, 4, "alpha", 0.01, "tail", "both", "badoption", 3);
626s 11 tests, 11 passed, 0 known failure, 0 skipped
626s [inst/probit.m]
627s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/probit.m
627s ***** assert (probit ([-1, 0, 0.5, 1, 2]), [NaN, -Inf, 0, Inf, NaN])
627s ***** assert (probit ([0.2, 0.99]), norminv ([0.2, 0.99]))
627s ***** error probit ()
627s ***** error probit (1, 2)
627s 4 tests, 4 passed, 0 known failure, 0 skipped
627s [inst/fishertest.m]
627s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/fishertest.m
627s ***** demo
627s  ## A Fisher's exact test example
627s 
627s  x = [3, 1; 1, 3]
627s  [h, p, stats] = fishertest(x)
627s ***** assert (fishertest ([3, 4; 5, 7]), false);
627s ***** assert (isa (fishertest ([3, 4; 5, 7]), "logical"), true);
627s ***** test
627s  [h, pval, stats] = fishertest ([3, 4; 5, 7]);
627s  assert (pval, 1, 1e-14);
627s  assert (stats.OddsRatio, 1.05);
627s  CI = [0.159222057151289, 6.92429189601808];
627s  assert (stats.ConfidenceInterval, CI, 1e-14)
627s ***** test
627s  [h, pval, stats] = fishertest ([3, 4; 5, 0]);
627s  assert (pval, 0.08080808080808080, 1e-14);
627s  assert (stats.OddsRatio, 0);
627s  assert (stats.ConfidenceInterval, [-Inf, Inf])
627s ***** error fishertest ();
627s ***** error fishertest (1, 2, 3, 4, 5, 6);
627s ***** error<fishertest: X must be a 2-dimensional matrix.> ...
627s  fishertest (ones (2, 2, 2));
627s ***** error<fishertest: X must contain only non-negative real integers.> ...
627s  fishertest ([1, 2; -3, 4]);
627s ***** error<fishertest: X must contain only non-negative real integers.> ...
627s  fishertest ([1, 2; 3, 4+i]);
627s ***** error<fishertest: X must contain only non-negative real integers.> ...
627s  fishertest ([1, 2; 3, 4.2]);
627s ***** error<fishertest: X must contain only non-negative real integers.> ...
627s  fishertest ([NaN, 2; 3, 4]);
627s ***** error<fishertest: X must contain only non-negative real integers.> ...
627s  fishertest ([1, Inf; 3, 4]);
627s ***** error<fishertest: cannot handle large entries> ...
627s  fishertest (ones (2) * 1e8);
627s ***** error<fishertest: invalid value for alpha.> ...
627s  fishertest ([1, 2; 3, 4], "alpha", 0);
627s ***** error<fishertest: invalid value for alpha.> ...
627s  fishertest ([1, 2; 3, 4], "alpha", 1.2);
627s ***** error<fishertest: invalid value for alpha.> ...
627s  fishertest ([1, 2; 3, 4], "alpha", "val");
627s ***** error<fishertest: invalid value for tail.>  ...
627s  fishertest ([1, 2; 3, 4], "tail", "val");
627s ***** error<fishertest: invalid value for tail.>  ...
627s  fishertest ([1, 2; 3, 4], "alpha", 0.01, "tail", "val");
627s ***** error<fishertest: invalid name for optional arguments.> ...
627s  fishertest ([1, 2; 3, 4], "alpha", 0.01, "badoption", 3);
627s 19 tests, 19 passed, 0 known failure, 0 skipped
627s [inst/inconsistent.m]
627s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/inconsistent.m
627s ***** error inconsistent ()
627s ***** error inconsistent ([1 2 1], 2, 3)
627s ***** error <Z must be .* generated by the linkage .*> inconsistent (ones (2, 2))
627s ***** error <d must be a positive integer scalar> inconsistent ([1 2 1], -1)
627s ***** error <d must be a positive integer scalar> inconsistent ([1 2 1], 1.3)
627s ***** error <d must be a positive integer scalar> inconsistent ([1 2 1], [1 1])
627s ***** error <Z must be .* generated by the linkage .*> inconsistent (ones (2, 3))
627s ***** test
627s  load fisheriris;
627s  Z = linkage(meas, 'average', 'chebychev');
627s  assert (cond (inconsistent (Z)), 39.9, 1e-3);
627s 8 tests, 8 passed, 0 known failure, 0 skipped
627s [inst/ff2n.m]
627s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/ff2n.m
627s ***** error ff2n ();
627s ***** error ff2n (2, 5);
627s ***** error ff2n (2.5);
627s ***** error ff2n (0);
627s ***** error ff2n (-3);
627s ***** error ff2n (3+2i);
627s ***** error ff2n (Inf);
627s ***** error ff2n (NaN);
627s ***** test
627s  A = ff2n (3);
627s  assert (A, fullfact (3));
627s ***** test
627s  A = ff2n (8);
627s  assert (A, fullfact (8));
627s 10 tests, 10 passed, 0 known failure, 0 skipped
627s [inst/plsregress.m]
627s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/plsregress.m
627s ***** demo
627s  ## Perform Partial Least-Squares Regression
627s 
627s  ## Load the spectra data set and use the near infrared (NIR) spectral
627s  ## intensities (NIR) as the predictor and the corresponding octave
627s  ## ratings (octave) as the response.
627s  load spectra
627s 
627s  ## Perform PLS regression with 10 components
627s  [xload, yload, xscore, yscore, coef, ptcVar] = plsregress (NIR, octane, 10);
627s 
627s  ## Plot the percentage of explained variance in the response variable
627s  ## (PCTVAR) as a function of the number of components.
627s  plot (1:10, cumsum (100 * ptcVar(2,:)), "-ro");
627s  xlim ([1, 10]);
627s  xlabel ("Number of PLS components");
627s  ylabel ("Percentage of Explained Variance in octane");
627s  title ("Explained Variance per PLS components");
627s 
627s  ## Compute the fitted response and display the residuals.
627s  octane_fitted = [ones(size(NIR,1),1), NIR] * coef;
627s  residuals = octane - octane_fitted;
627s  figure
627s  stem (residuals, "color", "r", "markersize", 4, "markeredgecolor", "r")
627s  xlabel ("Observations");
627s  ylabel ("Residuals");
627s  title ("Residuals in octane's fitted responce");
627s ***** demo
627s  ## Calculate Variable Importance in Projection (VIP) for PLS Regression
627s 
627s  ## Load the spectra data set and use the near infrared (NIR) spectral
627s  ## intensities (NIR) as the predictor and the corresponding octave
627s  ## ratings (octave) as the response.  Variables with a VIP score greater than
627s  ## 1 are considered important for the projection of the PLS regression model.
627s  load spectra
627s 
627s  ## Perform PLS regression with 10 components
627s  [xload, yload, xscore, yscore, coef, pctVar, mse, stats] = ...
627s                                                  plsregress (NIR, octane, 10);
627s 
627s  ## Calculate the normalized PLS weights
627s  W0 = stats.W ./ sqrt(sum(stats.W.^2,1));
627s 
627s  ## Calculate the VIP scores for 10 components
627s  nobs = size (xload, 1);
627s  SS = sum (xscore .^ 2, 1) .* sum (yload .^ 2, 1);
627s  VIPscore = sqrt (nobs * sum (SS .* (W0 .^ 2), 2) ./ sum (SS, 2));
627s 
627s  ## Find variables with a VIP score greater than or equal to 1
627s  VIPidx = find (VIPscore >= 1);
627s 
627s  ## Plot the VIP scores
627s  scatter (1:length (VIPscore), VIPscore, "xb");
627s  hold on
627s  scatter (VIPidx, VIPscore (VIPidx), "xr");
627s  plot ([1, length(VIPscore)], [1, 1], "--k");
627s  hold off
627s  axis ("tight");
627s  xlabel ("Predictor Variables");
627s  ylabel ("VIP scores");
627s  title ("VIP scores for each predictror variable with 10 components");
627s ***** test
627s  load spectra
627s  [xload, yload, xscore, yscore, coef, pctVar] = plsregress (NIR, octane, 10);
627s  xload1_out = [-0.0170, 0.0039, 0.0095,  0.0258, 0.0025, ...
627s                -0.0075, 0.0000, 0.0018, -0.0027, 0.0020];
627s  yload_out = [6.6384, 9.3106, 2.0505, 0.6471, 0.9625, ...
627s               0.5905, 0.4244, 0.2437, 0.3516, 0.2548];
627s  xscore1_out = [-0.0401, -0.1764, -0.0340, 0.1669,  0.1041, ...
627s                 -0.2067,  0.0457,  0.1565, 0.0706, -0.1471];
627s  yscore1_out = [-12.4635, -15.0003,  0.0638,  0.0652, -0.0070, ...
627s                  -0.0634,   0.0062, -0.0012, -0.0151, -0.0173];
627s  assert (xload(1,:), xload1_out, 1e-4);
627s  assert (yload, yload_out, 1e-4);
627s  assert (xscore(1,:), xscore1_out, 1e-4);
627s  assert (yscore(1,:), yscore1_out, 1e-4);
627s ***** test
627s  load spectra
627s  [xload, yload, xscore, yscore, coef, pctVar] = plsregress (NIR, octane, 5);
627s  xload1_out = [-0.0170, 0.0039, 0.0095, 0.0258, 0.0025];
627s  yload_out = [6.6384, 9.3106, 2.0505, 0.6471, 0.9625];
627s  xscore1_out = [-0.0401, -0.1764, -0.0340, 0.1669, 0.1041];
627s  yscore1_out = [-12.4635, -15.0003, 0.0638, 0.0652, -0.0070];
627s  assert (xload(1,:), xload1_out, 1e-4);
627s  assert (yload, yload_out, 1e-4);
627s  assert (xscore(1,:), xscore1_out, 1e-4);
627s  assert (yscore(1,:), yscore1_out, 1e-4);
627s ***** error<plsregress: function called with too few input arguments.>
627s  plsregress (1)
627s ***** error<plsregress: X and Y must be real matrices.> plsregress (1, "asd")
627s ***** error<plsregress: X and Y must be real matrices.> plsregress (1, {1,2,3})
627s ***** error<plsregress: X and Y must be real matrices.> plsregress ("asd", 1)
627s ***** error<plsregress: X and Y must be real matrices.> plsregress ({1,2,3}, 1)
627s ***** error<plsregress: X and Y observations mismatch.> ...
627s  plsregress (ones (20,3), ones (15,1))
627s ***** error<plsregress: invalid value for NCOMP.> ...
627s  plsregress (ones (20,3), ones (20,1), 0)
627s ***** error<plsregress: invalid value for NCOMP.> ...
627s  plsregress (ones (20,3), ones (20,1), -5)
627s ***** error<plsregress: invalid value for NCOMP.> ...
627s  plsregress (ones (20,3), ones (20,1), 3.2)
627s ***** error<plsregress: invalid value for NCOMP.> ...
627s  plsregress (ones (20,3), ones (20,1), [2, 3])
627s ***** error<plsregress: NCOMP exceeds maximum components for X.> ...
627s  plsregress (ones (20,3), ones (20,1), 4)
627s ***** error<plsregress: invalid VALUE for 'cv' optional argument.> ...
627s  plsregress (ones (20,3), ones (20,1), 3, "cv", 4.5)
627s ***** error<plsregress: invalid VALUE for 'cv' optional argument.> ...
627s  plsregress (ones (20,3), ones (20,1), 3, "cv", -1)
627s ***** error<plsregress: invalid VALUE for 'cv' optional argument.> ...
627s  plsregress (ones (20,3), ones (20,1), 3, "cv", "somestring")
627s ***** error<plsregress: invalid VALUE for 'mcreps' optional argument.> ...
627s  plsregress (ones (20,3), ones (20,1), 3, "cv", 3, "mcreps", 2.2)
627s ***** error<plsregress: invalid VALUE for 'mcreps' optional argument.> ...
627s  plsregress (ones (20,3), ones (20,1), 3, "cv", 3, "mcreps", -2)
627s ***** error<plsregress: invalid VALUE for 'mcreps' optional argument.> ...
627s  plsregress (ones (20,3), ones (20,1), 3, "cv", 3, "mcreps", [1, 2])
627s ***** error<plsregress: invalid NAME argument.> ...
627s  plsregress (ones (20,3), ones (20,1), 3, "Name", 3, "mcreps", 1)
627s ***** error<plsregress: invalid NAME argument.> ...
627s  plsregress (ones (20,3), ones (20,1), 3, "cv", 3, "Name", 1)
627s ***** error<plsregress: 'mcreps' must be 1 when 'resubstitution' is specified> ...
627s  plsregress (ones (20,3), ones (20,1), 3, "mcreps", 2)
627s ***** error<plsregress: 'mcreps' must be 1 when 'resubstitution' is specified> ...
627s  plsregress (ones (20,3), ones (20,1), 3, "cv", "resubstitution", "mcreps", 2)
627s ***** error<Invalid call to plsregress.  Correct usage is:> plsregress (1, 2)
627s 24 tests, 24 passed, 0 known failure, 0 skipped
627s [inst/vartestn.m]
627s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/vartestn.m
627s ***** demo
627s  ## Test the null hypothesis that the variances are equal across the five
627s  ## columns of data in the students’ exam grades matrix, grades.
627s 
627s  load examgrades
627s  vartestn (grades)
627s ***** demo
627s  ## Test the null hypothesis that the variances in miles per gallon (MPG) are
627s  ## equal across different model years.
627s 
627s  load carsmall
627s  vartestn (MPG, Model_Year)
627s ***** demo
627s  ## Use Levene’s test to test the null hypothesis that the variances in miles
627s  ## per gallon (MPG) are equal across different model years.
627s 
627s  load carsmall
627s  p = vartestn (MPG, Model_Year, "TestType", "LeveneAbsolute")
627s ***** demo
627s  ## Test the null hypothesis that the variances are equal across the five
627s  ## columns of data in the students’ exam grades matrix, grades, using the
627s  ## Brown-Forsythe test.  Suppress the display of the summary table of
627s  ## statistics and the box plot.
627s 
627s  load examgrades
627s  [p, stats] = vartestn (grades, "TestType", "BrownForsythe", "Display", "off")
627s ***** error<vartestn: too few input arguments.> vartestn ();
627s ***** error<vartestn: X must be a vector or a matrix.> vartestn (1);
627s ***** error<vartestn: if X is a vector then a group vector is required.> ...
627s  vartestn ([1, 2, 3, 4, 5, 6, 7]);
627s ***** error<vartestn: if X is a vector then a group vector is required.> ...
627s  vartestn ([1, 2, 3, 4, 5, 6, 7], []);
627s ***** error<vartestn: if X is a vector then a group vector is required.> ...
627s  vartestn ([1, 2, 3, 4, 5, 6, 7], "TestType", "LeveneAbsolute");
627s ***** error<vartestn: if X is a vector then a group vector is required.> ...
627s  vartestn ([1, 2, 3, 4, 5, 6, 7], [], "TestType", "LeveneAbsolute");
627s ***** error<vartestn: invalid value for display.> ...
627s  vartestn ([1, 2, 3, 4, 5, 6, 7], [1, 1, 1, 2, 2, 2, 2], "Display", "some");
627s ***** error<vartestn: invalid value for display.> ...
627s  vartestn (ones (50,3), "Display", "some");
627s ***** error<vartestn: invalid value for testtype.> ...
627s  vartestn (ones (50,3), "Display", "off", "testtype", "some");
627s ***** error<vartestn: optional arguments must be in name/value pairs.> ...
627s  vartestn (ones (50,3), [], "som");
627s ***** error<vartestn: invalid name for optional arguments.> ...
627s  vartestn (ones (50,3), [], "some", "some");
627s ***** error<vartestn: columns in X and GROUP length do not match.> ...
627s  vartestn (ones (50,3), [1, 2], "Display", "off");
627s ***** test
627s  load examgrades
627s  [p, stat] = vartestn (grades, "Display", "off");
627s  assert (p, 7.908647337018238e-08, 1e-14);
627s  assert (stat.chisqstat, 38.7332, 1e-4);
627s  assert (stat.df, 4);
627s ***** test
627s  load examgrades
627s  [p, stat] = vartestn (grades, "Display", "off", "TestType", "LeveneAbsolute");
627s  assert (p, 9.523239714592791e-07, 1e-14);
627s  assert (stat.fstat, 8.5953, 1e-4);
627s  assert (stat.df, [4, 595]);
627s ***** test
627s  load examgrades
627s  [p, stat] = vartestn (grades, "Display", "off", "TestType", "LeveneQuadratic");
627s  assert (p, 7.219514351897161e-07, 1e-14);
627s  assert (stat.fstat, 8.7503, 1e-4);
627s  assert (stat.df, [4, 595]);
627s ***** test
627s  load examgrades
627s  [p, stat] = vartestn (grades, "Display", "off", "TestType", "BrownForsythe");
627s  assert (p, 1.312093241723211e-06, 1e-14);
627s  assert (stat.fstat, 8.4160, 1e-4);
627s  assert (stat.df, [4, 595]);
627s ***** test
627s  load examgrades
627s  [p, stat] = vartestn (grades, "Display", "off", "TestType", "OBrien");
627s  assert (p, 8.235660885480556e-07, 1e-14);
627s  assert (stat.fstat, 8.6766, 1e-4);
627s  assert (stat.df, [4, 595]);
627s 17 tests, 17 passed, 0 known failure, 0 skipped
627s [inst/princomp.m]
627s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/princomp.m
627s ***** shared COEFF,SCORE,latent,tsquare,m,x,R,V,lambda,i,S,F
627s ***** test
627s  x=[7 4 3
627s     4 1 8
627s     6 3 5
627s     8 6 1
627s     8 5 7
627s     7 2 9
627s     5 3 3
627s     9 5 8
627s     7 4 5
627s     8 2 2];
627s  R = corrcoef (x);
627s  [V, lambda] = eig (R);
627s  [~, i] = sort(diag(lambda), "descend"); #arrange largest PC first
627s  S = V(:, i) * diag(sqrt(diag(lambda)(i)));
627s  ## contribution of first 2 PCs to each original variable
627s ***** assert(diag(S(:, 1:2)*S(:, 1:2)'), [0.8662; 0.8420; 0.9876], 1E-4);
627s  B = V(:, i) * diag( 1./ sqrt(diag(lambda)(i)));
627s  F = zscore(x)*B;
627s  [COEFF,SCORE,latent,tsquare] = princomp(zscore(x, 1));
627s ***** assert(tsquare,sumsq(F, 2),1E4*eps);
627s ***** test
627s  x=[1,2,3;2,1,3]';
627s  [COEFF,SCORE,latent,tsquare] = princomp(x);
627s  m=[sqrt(2),sqrt(2);sqrt(2),-sqrt(2);-2*sqrt(2),0]/2;
627s  m(:,1) = m(:,1)*sign(COEFF(1,1));
627s  m(:,2) = m(:,2)*sign(COEFF(1,2));
627s ***** assert(COEFF,m(1:2,:),10*eps);
627s ***** assert(SCORE,-m,10*eps);
627s ***** assert(latent,[1.5;.5],10*eps);
627s ***** assert(tsquare,[4;4;4]/3,10*eps);
627s ***** test
627s  x=x';
627s  [COEFF,SCORE,latent,tsquare] = princomp(x);
627s  m=[sqrt(2),sqrt(2),0;-sqrt(2),sqrt(2),0;0,0,2]/2;
627s  m(:,1) = m(:,1)*sign(COEFF(1,1));
627s  m(:,2) = m(:,2)*sign(COEFF(1,2));
627s  m(:,3) = m(:,3)*sign(COEFF(3,3));
627s ***** assert(COEFF,m,10*eps);
627s ***** assert(SCORE(:,1),-m(1:2,1),10*eps);
627s ***** assert(SCORE(:,2:3),zeros(2),10*eps);
627s ***** assert(latent,[1;0;0],10*eps);
627s ***** assert(tsquare,[0.5;0.5],10*eps)
627s ***** test
627s  [COEFF,SCORE,latent,tsquare] = princomp(x, "econ");
627s ***** assert(COEFF,m(:, 1),10*eps);
627s ***** assert(SCORE,-m(1:2,1),10*eps);
627s ***** assert(latent,[1],10*eps);
627s ***** assert(tsquare,[0.5;0.5],10*eps)
627s 19 tests, 19 passed, 0 known failure, 0 skipped
627s [inst/ridge.m]
627s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/ridge.m
627s ***** demo
627s  ## Perform ridge regression for a range of ridge parameters and observe
627s  ## how the coefficient estimates change based on the acetylene dataset.
627s 
627s  load acetylene
627s 
627s  X = [x1, x2, x3];
627s 
627s  x1x2 = x1 .* x2;
627s  x1x3 = x1 .* x3;
627s  x2x3 = x2 .* x3;
627s 
627s  D = [x1, x2, x3, x1x2, x1x3, x2x3];
627s 
627s  k = 0:1e-5:5e-3;
627s 
627s  b = ridge (y, D, k);
627s 
627s  figure
627s  plot (k, b, "LineWidth", 2)
627s  ylim ([-100, 100])
627s  grid on
627s  xlabel ("Ridge Parameter")
627s  ylabel ("Standardized Coefficient")
627s  title ("Ridge Trace")
627s  legend ("x1", "x2", "x3", "x1x2", "x1x3", "x2x3")
627s 
627s ***** demo
627s 
627s  load carbig
627s  X = [Acceleration Weight Displacement Horsepower];
627s  y = MPG;
627s 
627s  n = length(y);
627s 
627s  rand("seed",1); % For reproducibility
627s 
627s  c = cvpartition(n,'HoldOut',0.3);
627s  idxTrain = training(c,1);
627s  idxTest = ~idxTrain;
627s 
627s  idxTrain = training(c,1);
627s  idxTest = ~idxTrain;
627s 
627s  k = 5;
627s  b = ridge(y(idxTrain),X(idxTrain,:),k,0);
627s 
627s  % Predict MPG values for the test data using the model.
627s  yhat = b(1) + X(idxTest,:)*b(2:end);
627s  scatter(y(idxTest),yhat)
627s 
627s  hold on
627s  plot(y(idxTest),y(idxTest),"r")
627s  xlabel('Actual MPG')
627s  ylabel('Predicted MPG')
627s  hold off
627s 
627s ***** test
627s  b = ridge ([1 2 3 4]', [1 2 3 4; 2 3 4 5]', 1);
627s  assert (b, [0.5533; 0.5533], 1e-4);
627s ***** test
627s  b = ridge ([1 2 3 4]', [1 2 3 4; 2 3 4 5]', 2);
627s  assert (b, [0.4841; 0.4841], 1e-4);
627s ***** test
627s  load acetylene
627s  x = [x1, x2, x3];
627s  b = ridge (y, x, 0);
627s  assert (b,[10.2273;1.97128;-0.601818],1e-4);
627s ***** test
627s  load acetylene
627s  x = [x1, x2, x3];
627s  b = ridge (y, x, 0.0005);
627s  assert (b,[10.2233;1.9712;-0.6056],1e-4);
627s ***** test
627s  load acetylene
627s  x = [x1, x2, x3];
627s  b = ridge (y, x, 0.001);
627s  assert (b,[10.2194;1.9711;-0.6094],1e-4);
627s ***** test
627s  load acetylene
627s  x = [x1, x2, x3];
627s  b = ridge (y, x, 0.002);
627s  assert (b,[10.2116;1.9709;-0.6169],1e-4);
627s ***** test
627s  load acetylene
627s  x = [x1, x2, x3];
627s  b = ridge (y, x, 0.005);
627s  assert (b,[10.1882;1.9704;-0.6393],1e-4);
627s ***** test
627s  load acetylene
627s  x = [x1, x2, x3];
627s  b = ridge (y, x, 0.01);
627s  assert (b,[10.1497;1.9695;-0.6761],1e-4);
627s ***** error<ridge: function called with too few input arguments.> ridge (1)
627s ***** error<ridge: function called with too few input arguments.> ridge (1, 2)
627s ***** error<ridge: Y must be a numeric column vector.> ridge (ones (3), ones (3), 2)
627s ***** error<ridge: Y must be a numeric column vector.> ridge ([1, 2], ones (2), 2)
627s ***** error<ridge: Y must be a numeric column vector.> ridge ([], ones (3), 2)
627s ***** error<ridge: X must be a numeric matrix.> ridge (ones (5,1), [], 2)
627s ***** error<ridge: Y and X must contain the same number of rows.> ...
627s  ridge ([1; 2; 3; 4; 5], ones (3), 3)
627s ***** error<ridge: wrong value for SCALED argument.> ...
627s  ridge ([1; 2; 3], ones (3), 3, 2)
627s ***** error<ridge: wrong value for SCALED argument.> ...
627s  ridge ([1; 2; 3], ones (3), 3, "some")
627s 17 tests, 17 passed, 0 known failure, 0 skipped
627s [inst/cmdscale.m]
627s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/cmdscale.m
627s ***** shared m, n, X, D
627s  m = randi(100) + 1; n = randi(100) + 1; X = rand(m, n); D = pdist(X);
627s ***** assert(norm(pdist(cmdscale(D))), norm(D), sqrt(eps))
627s ***** assert(norm(pdist(cmdscale(squareform(D)))), norm(D), sqrt(eps))
627s 2 tests, 2 passed, 0 known failure, 0 skipped
627s [inst/cholcov.m]
627s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/cholcov.m
627s ***** demo
627s  C1 = [2, 1, 1, 2; 1, 2, 1, 2; 1, 1, 2, 2; 2, 2, 2, 3]
627s  T = cholcov (C1)
627s  C2 = T'*T
627s ***** test
627s  C1 = [2, 1, 1, 2; 1, 2, 1, 2; 1, 1, 2, 2; 2, 2, 2, 3];
627s  T = cholcov (C1);
627s  assert (C1, T'*T, 1e-15 * ones (size (C1)));
627s 1 test, 1 passed, 0 known failure, 0 skipped
627s [inst/anova1.m]
627s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/anova1.m
627s ***** demo
627s  x = meshgrid (1:6);
627s  randn ("seed", 15);    # for reproducibility
627s  x = x + normrnd (0, 1, 6, 6);
627s  anova1 (x, [], 'off');
627s ***** demo
627s  x = meshgrid (1:6);
627s  randn ("seed", 15);    # for reproducibility
627s  x = x + normrnd (0, 1, 6, 6);
627s  [p, atab] = anova1(x);
627s ***** demo
627s  x = ones (50, 4) .* [-2, 0, 1, 5];
627s  randn ("seed", 13);    # for reproducibility
627s  x = x + normrnd (0, 2, 50, 4);
627s  groups = {"A", "B", "C", "D"};
627s  anova1 (x, groups);
627s ***** demo
627s  y = [54 87 45; 23 98 39; 45 64 51; 54 77 49; 45 89 50; 47 NaN 55];
627s  g = [1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ];
627s  anova1 (y(:), g(:), "on", "unequal");
627s ***** test
627s  data = [1.006, 0.996, 0.998, 1.000, 0.992, 0.993, 1.002, 0.999, 0.994, 1.000, ...
627s          0.998, 1.006, 1.000, 1.002, 0.997, 0.998, 0.996, 1.000, 1.006, 0.988, ...
627s          0.991, 0.987, 0.997, 0.999, 0.995, 0.994, 1.000, 0.999, 0.996, 0.996, ...
627s          1.005, 1.002, 0.994, 1.000, 0.995, 0.994, 0.998, 0.996, 1.002, 0.996, ...
627s          0.998, 0.998, 0.982, 0.990, 1.002, 0.984, 0.996, 0.993, 0.980, 0.996, ...
627s          1.009, 1.013, 1.009, 0.997, 0.988, 1.002, 0.995, 0.998, 0.981, 0.996, ...
627s          0.990, 1.004, 0.996, 1.001, 0.998, 1.000, 1.018, 1.010, 0.996, 1.002, ...
627s          0.998, 1.000, 1.006, 1.000, 1.002, 0.996, 0.998, 0.996, 1.002, 1.006, ...
627s          1.002, 0.998, 0.996, 0.995, 0.996, 1.004, 1.004, 0.998, 0.999, 0.991, ...
627s          0.991, 0.995, 0.984, 0.994, 0.997, 0.997, 0.991, 0.998, 1.004, 0.997];
627s  group = [1:10] .* ones (10,10);
627s  group = group(:);
627s  [p, tbl] = anova1 (data, group, "off");
627s  assert (p, 0.022661, 1e-6);
627s  assert (tbl{2,5}, 2.2969, 1e-4);
627s  assert (tbl{2,3}, 9, 0);
627s  assert (tbl{4,2}, 0.003903, 1e-6);
627s  data = reshape (data, 10, 10);
627s  [p, tbl, stats] = anova1 (data, [], "off");
627s  assert (p, 0.022661, 1e-6);
627s  assert (tbl{2,5}, 2.2969, 1e-4);
627s  assert (tbl{2,3}, 9, 0);
627s  assert (tbl{4,2}, 0.003903, 1e-6);
627s  means = [0.998, 0.9991, 0.9954, 0.9982, 0.9919, 0.9988, 1.0015, 1.0004, 0.9983, 0.9948];
627s  N = 10 * ones (1, 10);
627s  assert (stats.means, means, 1e-6);
627s  assert (length (stats.gnames), 10, 0);
627s  assert (stats.n, N, 0);
627s ***** test
627s  y = [54 87 45; 23 98 39; 45 64 51; 54 77 49; 45 89 50; 47 NaN 55];
627s  g = [1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ];
627s  [p, tbl] = anova1 (y(:), g(:), "off", "equal");
627s  assert (p, 0.00004163, 1e-6);
627s  assert (tbl{2,5}, 22.573418, 1e-6);
627s  assert (tbl{2,3}, 2, 0);
627s  assert (tbl{3,3}, 14, 0);
627s  [p, tbl] = anova1 (y(:), g(:), "off", "unequal");
627s  assert (p, 0.00208877, 1e-8);
627s  assert (tbl{2,5}, 15.523192, 1e-6);
627s  assert (tbl{2,3}, 2, 0);
627s  assert (tbl{2,4}, 7.5786897, 1e-6);
627s 2 tests, 2 passed, 0 known failure, 0 skipped
627s [inst/sigma_pts.m]
627s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/sigma_pts.m
627s ***** demo
627s  K      = [1 0.5; 0.5 1]; # covaraince matrix
627s  # calculate and build associated ellipse
627s  [R,S,~] = svd (K);
627s  theta   = atan2 (R(2,1), R(1,1));
627s  v       = sqrt (diag (S));
627s  v       = v .* [cos(theta) sin(theta); -sin(theta) cos(theta)];
627s  t       = linspace (0, 2*pi, 100).';
627s  xe      = v(1,1) * cos (t) + v(2,1) * sin (t);
627s  ye      = v(1,2) * cos (t) + v(2,2) * sin (t);
627s 
627s  figure(1); clf; hold on
627s  # Plot ellipse and axes
627s  line ([0 0; v(:,1).'],[0 0; v(:,2).'])
627s  plot (xe,ye,'-r');
627s 
627s  col = 'rgb';
627s  l     = [-1.8 -1 1.5];
627s  for li = 1:3
627s   p     = sigma_pts (2, [], K, l(li));
627s   tmp   = plot (p(2:end,1), p(2:end,2), ['x' col(li)], ...
627s                p(1,1), p(1,2), ['o' col(li)]);
627s   h(li) = tmp(1);
627s  endfor
627s  hold off
627s  axis image
627s  legend (h, arrayfun (@(x) sprintf ("l:%.2g", x), l, "unif", 0));
627s ***** test
627s  p = sigma_pts (5);
627s  assert (mean (p), zeros(1,5), sqrt(eps));
627s  assert (cov (p), eye(5), sqrt(eps));
627s ***** test
627s  m = randn(1, 5);
627s  p = sigma_pts (5, m);
627s  assert (mean (p), m, sqrt(eps));
627s  assert (cov (p), eye(5), sqrt(eps));
627s ***** test
627s  x = linspace (0,1,5);
627s  K = exp (- (x.' - x).^2/ 0.5);
627s  p = sigma_pts (5, [], K);
627s  assert (mean (p), zeros(1,5), sqrt(eps));
627s  assert (cov (p), K, sqrt(eps));
627s ***** error sigma_pts(2,1);
627s ***** error sigma_pts(2,[],1);
627s ***** error sigma_pts(2,1,1);
627s ***** error sigma_pts(2,[0.5 0.5],[-1 0; 0 0]);
627s 7 tests, 7 passed, 0 known failure, 0 skipped
627s [inst/violin.m]
627s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/violin.m
627s ***** demo
627s  clf
627s  x = zeros (9e2, 10);
627s  for i=1:10
627s    x(:,i) = (0.1 * randn (3e2, 3) * (randn (3,1) + 1) + 2 * randn (1,3))(:);
627s  endfor
627s  h = violin (x, "color", "c");
627s  axis tight
627s  set (h.violin, "linewidth", 2);
627s  set (gca, "xgrid", "on");
627s  xlabel ("Variables")
627s  ylabel ("Values")
627s ***** demo
627s  clf
627s  data = {randn(100,1)*5+140, randn(130,1)*8+135};
627s  subplot (1,2,1)
627s  title ("Grade 3 heights - vertical");
627s  set (gca, "xtick", 1:2, "xticklabel", {"girls"; "boys"});
627s  violin (data, "Nbins", 10);
627s  axis tight
627s 
627s  subplot(1,2,2)
627s  title ("Grade 3 heights - horizontal");
627s  set (gca, "ytick", 1:2, "yticklabel", {"girls"; "boys"});
627s  violin (data, "horizontal", "Nbins", 10);
627s  axis tight
627s ***** demo
627s  clf
627s  data = exprnd (0.1, 500,4);
627s  violin (data, "nbins", {5,10,50,100});
627s  axis ([0 5 0 max(data(:))])
627s ***** demo
627s  clf
627s  data = exprnd (0.1, 500,4);
627s  violin (data, "color", jet(4));
627s  axis ([0 5 0 max(data(:))])
627s ***** demo
627s  clf
627s  data = repmat(exprnd (0.1, 500,1), 1, 4);
627s  violin (data, "width", linspace (0.1,0.5,4));
627s  axis ([0 5 0 max(data(:))])
627s ***** demo
627s  clf
627s  data = repmat(exprnd (0.1, 500,1), 1, 4);
627s  violin (data, "nbins", [5,10,50,100], "smoothfactor", [4 4 8 10]);
627s  axis ([0 5 0 max(data(:))])
627s ***** test
627s  hf = figure ("visible", "off");
627s  unwind_protect
627s    data = exprnd (0.1, 500,4);
627s    violin (data, "color", jet(4));
627s    axis ([0 5 0 max(data(:))])
627s  unwind_protect_cleanup
627s    close (hf);
627s  end_unwind_protect
628s ***** test
628s  hf = figure ("visible", "off");
628s  unwind_protect
628s    data = {randn(100,1)*5+140, randn(130,1)*8+135};
628s    subplot (1,2,1)
628s    title ("Grade 3 heights - vertical");
628s    set (gca, "xtick", 1:2, "xticklabel", {"girls"; "boys"});
628s    violin (data, "Nbins", 10);
628s    axis tight
628s  unwind_protect_cleanup
628s    close (hf);
628s  end_unwind_protect
628s ***** test
628s  hf = figure ("visible", "off");
628s  unwind_protect
628s    data = {randn(100,1)*5+140, randn(130,1)*8+135};
628s    subplot (1,2,1)
628s    title ("Grade 3 heights - vertical");
628s    set (gca, "xtick", 1:2, "xticklabel", {"girls"; "boys"});
628s    violin (data, "Nbins", 10);
628s    axis tight
628s    subplot(1,2,2)
628s    title ("Grade 3 heights - horizontal");
628s    set (gca, "ytick", 1:2, "yticklabel", {"girls"; "boys"});
628s    violin (data, "horizontal", "Nbins", 10);
628s    axis tight
628s  unwind_protect_cleanup
628s    close (hf);
628s  end_unwind_protect
628s ***** test
628s  hf = figure ("visible", "off");
628s  unwind_protect
628s    data = repmat(exprnd (0.1, 500,1), 1, 4);
628s    violin (data, "nbins", [5,10,50,100], "smoothfactor", [4 4 8 10]);
628s    axis ([0 5 0 max(data(:))])
628s  unwind_protect_cleanup
628s    close (hf);
628s  end_unwind_protect
628s ***** test
628s  hf = figure ("visible", "off");
628s  unwind_protect
628s    data = repmat(exprnd (0.1, 500,1), 1, 4);
628s    violin (data, "width", linspace (0.1,0.5,4));
628s    axis ([0 5 0 max(data(:))])
628s  unwind_protect_cleanup
628s    close (hf);
628s  end_unwind_protect
629s 5 tests, 5 passed, 0 known failure, 0 skipped
629s [inst/chi2test.m]
629s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/chi2test.m
629s ***** error chi2test ();
629s ***** error chi2test ([1, 2, 3, 4, 5]);
629s ***** error chi2test ([1, 2; 2, 1+3i]);
629s ***** error chi2test ([NaN, 6; 34, 12]);
629s ***** error<chi2test: optional arguments are not supported for 2-way> ...
629s  p = chi2test (ones (3, 3), "mutual", []);
629s ***** error<chi2test: invalid model name for testing a 3-way table.> ...
629s  p = chi2test (ones (3, 3, 3), "testtype", 2);
629s ***** error<chi2test: optional arguments must be in pairs.> ...
629s  p = chi2test (ones (3, 3, 3), "mutual");
629s ***** error<chi2test: value must be numeric in optional argument> ...
629s  p = chi2test (ones (3, 3, 3), "joint", ["a"]);
629s ***** error<chi2test: value must be empty or scalar in optional argument> ...
629s  p = chi2test (ones (3, 3, 3), "joint", [2, 3]);
629s ***** error<chi2test: optional arguments are not supported for k> ...
629s  p = chi2test (ones (3, 3, 3, 4), "mutual", [])
629s ***** warning<chi2test: Expected values less than 5.> p = chi2test (ones (2));
629s ***** warning<chi2test: Expected values less than 5.> p = chi2test (ones (3, 2));
629s ***** warning<chi2test: Expected values less than 1.> p = chi2test (0.4 * ones (3));
629s ***** test
629s  x = [11, 3, 8; 2, 9, 14; 12, 13, 28];
629s  p = chi2test (x);
629s  assert (p, 0.017787, 1e-6);
629s ***** test
629s  x = [11, 3, 8; 2, 9, 14; 12, 13, 28];
629s  [p, chisq] = chi2test (x);
629s  assert (chisq, 11.9421, 1e-4);
629s ***** test
629s  x = [11, 3, 8; 2, 9, 14; 12, 13, 28];
629s  [p, chisq, df] = chi2test (x);
629s  assert (df, 4);
629s ***** test
629s ***** shared x
629s  x(:,:,1) = [59, 32; 9,16];
629s  x(:,:,2) = [55, 24;12,33];
629s  x(:,:,3) = [107,80;17,56];%!
629s ***** assert (chi2test (x), 2.282063427117009e-11, 1e-14);
629s ***** assert (chi2test (x, "mutual", []), 2.282063427117009e-11, 1e-14);
629s ***** assert (chi2test (x, "joint", 1), 1.164834895206468e-11, 1e-14);
629s ***** assert (chi2test (x, "joint", 2), 7.771350230001417e-11, 1e-14);
629s ***** assert (chi2test (x, "joint", 3), 0.07151361728026107, 1e-14);
629s ***** assert (chi2test (x, "marginal", 1), 0, 1e-14);
629s ***** assert (chi2test (x, "marginal", 2), 6.347555814301131e-11, 1e-14);
629s ***** assert (chi2test (x, "marginal", 3), 0, 1e-14);
629s ***** assert (chi2test (x, "conditional", 1), 0.2303114201312508, 1e-14);
629s ***** assert (chi2test (x, "conditional", 2), 0.0958810684407079, 1e-14);
629s ***** assert (chi2test (x, "conditional", 3), 2.648037344954446e-11, 1e-14);
629s ***** assert (chi2test (x, "homogeneous", []), 0.4485579470993741, 1e-14);
629s ***** test
629s  [pval, chisq, df, E] = chi2test (x);
629s  assert (chisq, 64.0982, 1e-4);
629s  assert (df, 7);
629s  assert (E(:,:,1), [42.903, 39.921; 17.185, 15.991], ones (2, 2) * 1e-3);
629s ***** test
629s  [pval, chisq, df, E] = chi2test (x, "joint", 2);
629s  assert (chisq, 56.0943, 1e-4);
629s  assert (df, 5);
629s  assert (E(:,:,2), [40.922, 23.310; 38.078, 21.690], ones (2, 2) * 1e-3);
629s ***** test
629s  [pval, chisq, df, E] = chi2test (x, "marginal", 3);
629s  assert (chisq, 146.6058, 1e-4);
629s  assert (df, 9);
629s  assert (E(:,1,1), [61.642; 57.358], ones (2, 1) * 1e-3);
629s ***** test
629s  [pval, chisq, df, E] = chi2test (x, "conditional", 3);
629s  assert (chisq, 52.2509, 1e-4);
629s  assert (df, 3);
629s  assert (E(:,:,1), [53.345, 37.655; 14.655, 10.345], ones (2, 2) * 1e-3);
629s ***** test
629s  [pval, chisq, df, E] = chi2test (x, "homogeneous", []);
629s  assert (chisq, 1.6034, 1e-4);
629s  assert (df, 2);
629s  assert (E(:,:,1), [60.827, 31.382; 7.173, 16.618], ones (2, 2) * 1e-3);
629s 34 tests, 34 passed, 0 known failure, 0 skipped
629s [inst/correlation_test.m]
629s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/correlation_test.m
629s ***** error<Invalid call to correlation_test.  Correct usage> correlation_test ();
629s ***** error<Invalid call to correlation_test.  Correct usage> correlation_test (1);
629s ***** error<correlation_test: X must contain finite real numbers.> ...
629s  correlation_test ([1 2 NaN]', [2 3 4]');
629s ***** error<correlation_test: X must contain finite real numbers.> ...
629s  correlation_test ([1 2 Inf]', [2 3 4]');
629s ***** error<correlation_test: X must contain finite real numbers.> ...
629s  correlation_test ([1 2 3+i]', [2 3 4]');
629s ***** error<correlation_test: Y must contain finite real numbers.> ...
629s  correlation_test ([1 2 3]', [2 3 NaN]');
629s ***** error<correlation_test: Y must contain finite real numbers.> ...
629s  correlation_test ([1 2 3]', [2 3 Inf]');
629s ***** error<correlation_test: Y must contain finite real numbers.> ...
629s  correlation_test ([1 2 3]', [3 4 3+i]');
629s ***** error<correlation_test: X and Y must be vectors of equal length.> ...
629s  correlation_test ([1 2 3]', [3 4 4 5]');
629s ***** error<correlation_test: invalid value for alpha.> ...
629s  correlation_test ([1 2 3]', [2 3 4]', "alpha", 0);
629s ***** error<correlation_test: invalid value for alpha.> ...
629s  correlation_test ([1 2 3]', [2 3 4]', "alpha", 1.2);
629s ***** error<correlation_test: invalid value for alpha.> ...
629s  correlation_test ([1 2 3]', [2 3 4]', "alpha", [.02 .1]);
629s ***** error<correlation_test: invalid value for alpha.> ...
629s  correlation_test ([1 2 3]', [2 3 4]', "alpha", "a");
629s ***** error<correlation_test: invalid Name argument.> ...
629s  correlation_test ([1 2 3]', [2 3 4]', "some", 0.05);
629s ***** error<correlation_test: invalid value for tail.>  ...
629s  correlation_test ([1 2 3]', [2 3 4]', "tail", "val");
629s ***** error<correlation_test: invalid value for tail.>  ...
629s  correlation_test ([1 2 3]', [2 3 4]', "alpha", 0.01, "tail", "val");
629s ***** error<correlation_test: invalid value for method.>  ...
629s  correlation_test ([1 2 3]', [2 3 4]', "method", 0.01);
629s ***** error<correlation_test: invalid value for method.>  ...
629s  correlation_test ([1 2 3]', [2 3 4]', "method", "some");
629s ***** test
629s  x = [6 7 7 9 10 12 13 14 15 17];
629s  y = [19 22 27 25 30 28 30 29 25 32];
629s  [h, pval, stats] = correlation_test (x, y);
629s  assert (stats.corrcoef, corr (x', y'), 1e-14);
629s  assert (pval, 0.0223, 1e-4);
629s ***** test
629s  x = [6 7 7 9 10 12 13 14 15 17]';
629s  y = [19 22 27 25 30 28 30 29 25 32]';
629s  [h, pval, stats] = correlation_test (x, y);
629s  assert (stats.corrcoef, corr (x, y), 1e-14);
629s  assert (pval, 0.0223, 1e-4);
629s 20 tests, 20 passed, 0 known failure, 0 skipped
629s [inst/normalise_distribution.m]
629s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/normalise_distribution.m
629s ***** test
629s  v = normalise_distribution ([1 2 3], [], 1);
629s  assert (v, [0 0 0])
629s ***** test
629s  v = normalise_distribution ([1 2 3], [], 2);
629s  assert (v, norminv ([1 3 5] / 6), 3 * eps)
629s ***** test
629s  v = normalise_distribution ([1 2 3]', [], 2);
629s  assert (v, [0 0 0]')
629s ***** test
629s  v = normalise_distribution ([1 2 3]', [], 1);
629s  assert (v, norminv ([1 3 5]' / 6), 3 * eps)
629s ***** test
629s  v = normalise_distribution ([1 1 2 2 3 3], [], 2);
629s  assert (v, norminv ([3 3 7 7 11 11] / 12), 3 * eps)
629s ***** test
629s  v = normalise_distribution ([1 1 2 2 3 3]', [], 1);
629s  assert (v, norminv ([3 3 7 7 11 11]' / 12), 3 * eps)
629s ***** test
629s  A = randn ( 10 );
629s  N = normalise_distribution (A, @normcdf);
629s  assert (A, N, 10000 * eps)
629s ***** test
629s  A = exprnd (1, 100);
629s  N = normalise_distribution (A, @(x)(expcdf (x, 1)));
629s  assert (mean (vec (N)), 0, 0.1)
629s  assert (std (vec (N)), 1, 0.1)
629s ***** test
629s  A = rand (1000,1);
629s  N = normalise_distribution (A, {@(x)(unifcdf (x, 0, 1))});
629s  assert (mean (vec (N)), 0, 0.2)
629s  assert (std (vec (N)), 1, 0.1)
629s ***** test
629s  A = [rand(1000,1), randn(1000, 1)];
629s  N = normalise_distribution (A, {@(x)(unifcdf (x, 0, 1)), @normcdf});
629s  assert (mean (N), [0, 0], 0.2)
629s  assert (std (N), [1, 1], 0.1)
629s ***** test
629s  A = [rand(1000,1), randn(1000, 1), exprnd(1, 1000, 1)]';
629s  N = normalise_distribution  (A, {@(x)(unifcdf (x, 0, 1)); @normcdf; @(x)(expcdf (x, 1))}, 2);
629s  assert (mean (N, 2), [0, 0, 0]', 0.2);
629s  assert (std (N, [], 2), [1, 1, 1]', 0.1);
629s ***** xtest
629s  A = exprnd (1, 1000, 9); A (300:500, 4:6) = 17;
629s  N = normalise_distribution (A);
629s  assert (mean (N), [0 0 0 0.38 0.38 0.38 0 0 0], 0.1);
629s  assert (var (N), [1 1 1 2.59 2.59 2.59 1 1 1], 0.1);
629s ***** test
629s ***** error normalise_distribution (zeros (3, 4), ...
629s  {@(x)(unifcdf (x, 0, 1)); @normcdf; @(x)(expcdf (x,1))});
629s 14 tests, 14 passed, 0 known failure, 0 skipped
629s [inst/kstest.m]
629s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/kstest.m
629s ***** demo
629s  ## Use the stock return data set to test the null hypothesis that the data
629s  ## come from a standard normal distribution against the alternative
629s  ## hypothesis that the population CDF of the data is larger that the
629s  ## standard normal CDF.
629s 
629s  load stockreturns;
629s  x = stocks(:,2);
629s  [h, p, k, c] = kstest (x, "Tail", "larger")
629s 
629s  ## Compute the empirical CDF and plot against the standard normal CDF
629s  [f, x_values] = ecdf (x);
629s  h1 = plot (x_values, f);
629s  hold on;
629s  h2 = plot (x_values, normcdf (x_values), 'r--');
629s  set (h1, "LineWidth", 2);
629s  set (h2, "LineWidth", 2);
629s  legend ([h1, h2], "Empirical CDF", "Standard Normal CDF", ...
629s          "Location", "southeast");
629s  title ("Empirical CDF of stock return data against standard normal CDF")
629s ***** error<kstest: too few inputs.> kstest ()
629s ***** error<kstest: X must be a vector of real numbers.> kstest (ones (2, 4))
629s ***** error<kstest: X must be a vector of real numbers.> kstest ([2, 3, 5, 3+3i])
629s ***** error<kstest: unknown option 'opt'.> kstest ([2, 3, 4, 5, 6], "opt", 0.51)
629s ***** error<kstest: optional parameters must be in name/value pairs.> ...
629s  kstest ([2, 3, 4, 5, 6], "tail")
629s ***** error<kstest: alpha must be a numeric scalar in the range> ...
629s  kstest ([2,3,4,5,6],"alpha", [0.05, 0.05])
629s ***** error<kstest: alpha must be a numeric scalar in the range> ...
629s  kstest ([2, 3, 4, 5, 6], "alpha", NaN)
629s ***** error<kstest: tail argument must be a string.> ...
629s  kstest ([2, 3, 4, 5, 6], "tail", 0)
629s ***** error<kstest: tail value must be either 'both', right' or 'left'.> ...
629s  kstest ([2,3,4,5,6], "tail", "whatever")
629s ***** error<kstest: invalid function handle.> ...
629s  kstest ([1, 2, 3, 4, 5], "CDF", @(x) repmat (x, 2, 3))
629s ***** error<kstest: 'somedist' is not a supported distribution.> ...
629s  kstest ([1, 2, 3, 4, 5], "CDF", "somedist")
629s ***** error<kstest: 'CDF' must be a probability distribution object.> ...
629s  kstest ([1, 2, 3, 4, 5], "CDF", cvpartition (5))
629s ***** error<kstest: numerical CDF should have only 2 columns.> ...
629s  kstest ([2, 3, 4, 5, 6], "alpha", 0.05, "CDF", [2, 3, 4; 1, 3, 4; 1, 2, 1])
629s ***** error<kstest: numerical CDF should have at least one row.> ...
629s  kstest ([2, 3, 4, 5, 6], "alpha", 0.05, "CDF", nan (5, 2))
629s ***** error<kstest: non-incrementing numerical CDF.> ...
629s  kstest ([2, 3, 4, 5, 6], "CDF", [2, 3; 1, 4; 3, 2])
629s ***** error<kstest: wrong duplicates in numerical CDF.> ...
629s  kstest ([2, 3, 4, 5, 6], "CDF", [2, 3; 2, 4; 3, 5])
629s ***** error<kstest: invalid value parsed as CDF optinonal argument.> ...
629s  kstest ([2, 3, 4, 5, 6], "CDF", {1, 2, 3, 4, 5})
629s ***** test
629s  load examgrades
629s  [h, p] = kstest (grades(:,1));
629s  assert (h, true);
629s  assert (p, 7.58603305206105e-107, 1e-14);
629s ***** test
629s  load examgrades
629s  [h, p] = kstest (grades(:,1), "CDF", @(x) normcdf(x, 75, 10));
629s  assert (h, false);
629s  assert (p, 0.5612, 1e-4);
629s ***** test
629s  load examgrades
629s  x = grades(:,1);
629s  test_cdf = makedist ("tlocationscale", "mu", 75, "sigma", 10, "nu", 1);
629s  [h, p] = kstest (x, "alpha", 0.01, "CDF", test_cdf);
629s  assert (h, true);
629s  assert (p, 0.0021, 1e-4);
630s ***** test
630s  load stockreturns
630s  x = stocks(:,3);
630s  [h,p,k,c] = kstest (x, "Tail", "larger");
630s  assert (h, true);
630s  assert (p, 5.085438806199252e-05, 1e-14);
630s  assert (k, 0.2197, 1e-4);
630s  assert (c, 0.1207, 1e-4);
630s 21 tests, 21 passed, 0 known failure, 0 skipped
630s [inst/x2fx.m]
630s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/x2fx.m
630s ***** test
630s  X = [1, 10; 2, 20; 3, 10; 4, 20; 5, 15; 6, 15];
630s  D = x2fx(X,'quadratic');
630s  assert (D(1,:) , [1, 1, 10, 10, 1, 100]);
630s  assert (D(2,:) , [1, 2, 20, 40, 4, 400]);
630s ***** test
630s  X = [1, 10; 2, 20; 3, 10; 4, 20; 5, 15; 6, 15];
630s  model = [0, 0; 1, 0; 0, 1; 1, 1; 2, 0];
630s  D = x2fx(X,model);
630s  assert (D(1,:) , [1, 1, 10, 10, 1]);
630s  assert (D(2,:) , [1, 2, 20, 40, 4]);
630s  assert (D(4,:) , [1, 4, 20, 80, 16]);
630s ***** error x2fx ([1, 10; 2, 20; 3, 10], [0; 1]);
630s ***** error x2fx ([1, 10, 15; 2, 20, 40; 3, 10, 25], [0, 0; 1, 0; 0, 1; 1, 1; 2, 0]);
630s ***** error x2fx ([1, 10; 2, 20; 3, 10], "whatever");
630s 5 tests, 5 passed, 0 known failure, 0 skipped
630s [inst/harmmean.m]
630s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/harmmean.m
630s ***** test
630s  x = [0:10];
630s  y = [x;x+5;x+10];
630s  assert (harmmean (x), 0);
630s  m = [0 8.907635160795225 14.30854471766802];
630s  assert (harmmean (y, 2), m', 4e-14);
630s  assert (harmmean (y, "all"), 0);
630s  y(2,4) = NaN;
630s  m(2) = 9.009855936313949;
630s  assert (harmmean (y, 2), [0 NaN m(3)]', 4e-14);
630s  assert (harmmean (y', "omitnan"), m, 4e-14);
630s  z = y + 20;
630s  assert (harmmean (z, "all"), NaN);
630s  assert (harmmean (z, "all", "includenan"), NaN);
630s  assert (harmmean (z, "all", "omitnan"), 29.1108719858295, 4e-14);
630s  m = [24.59488458841874 NaN 34.71244385944397];
630s  assert (harmmean (z'), m, 4e-14);
630s  assert (harmmean (z', "includenan"), m, 4e-14);
630s  m(2) = 29.84104075528277;
630s  assert (harmmean (z', "omitnan"), m, 4e-14);
630s  assert (harmmean (z, 2, "omitnan"), m', 4e-14);
630s ***** test
630s  x = repmat ([1:20;6:25], [5 2 6 3]);
630s  assert (size (harmmean (x, [3 2])), [10 1 1 3]);
630s  assert (size (harmmean (x, [1 2])), [1 1 6 3]);
630s  assert (size (harmmean (x, [1 2 4])), [1 1 6]);
630s  assert (size (harmmean (x, [1 4 3])), [1 40]);
630s  assert (size (harmmean (x, [1 2 3 4])), [1 1]);
630s ***** test
630s  x = repmat ([1:20;6:25], [5 2 6 3]);
630s  m = repmat ([5.559045930488016;13.04950789021461], [5 1 1 3]);
630s  assert (harmmean (x, [3 2]), m, 4e-14);
630s  x(2,5,6,3) = NaN;
630s  m(2,3) = NaN;
630s  assert (harmmean (x, [3 2]), m, 4e-14);
630s  m(2,3) = 13.06617961315406;
630s  assert (harmmean (x, [3 2], "omitnan"), m, 4e-14);
630s ***** error <harmmean: X must contain real nonnegative values.> harmmean ("char")
630s ***** error <harmmean: X must contain real nonnegative values.> harmmean ([1 -1 3])
630s ***** error <harmmean: DIM must be a positive integer scalar or vector.> ...
630s  harmmean (repmat ([1:20;6:25], [5 2 6 3 5]), -1)
630s ***** error <harmmean: DIM must be a positive integer scalar or vector.> ...
630s  harmmean (repmat ([1:20;6:25], [5 2 6 3 5]), 0)
630s ***** error <harmmean: VECDIM must contain non-repeating positive integers.> ...
630s  harmmean (repmat ([1:20;6:25], [5 2 6 3 5]), [1 1])
630s 8 tests, 8 passed, 0 known failure, 0 skipped
630s [inst/multcompare.m]
630s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/multcompare.m
630s ***** demo
630s 
630s  ## Demonstration using balanced one-way ANOVA from anova1
630s 
630s  x = ones (50, 4) .* [-2, 0, 1, 5];
630s  randn ("seed", 1);    # for reproducibility
630s  x = x + normrnd (0, 2, 50, 4);
630s  groups = {"A", "B", "C", "D"};
630s  [p, tbl, stats] = anova1 (x, groups, "off");
630s  multcompare (stats);
630s ***** demo
630s 
630s  ## Demonstration using unbalanced one-way ANOVA example from anovan
630s 
630s  dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
630s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
630s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
630s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
630s         25.694 ]';
630s  g = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 ...
630s       4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
630s 
630s  [P,ATAB, STATS] = anovan (dv, g, "varnames", "score", "display", "off");
630s 
630s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "holm", ...
630s                                   "ControlGroup", 1, "display", "on")
630s 
630s ***** demo
630s 
630s  ## Demonstration using factorial ANCOVA example from anovan
630s 
630s  score = [95.6 82.2 97.2 96.4 81.4 83.6 89.4 83.8 83.3 85.7 ...
630s  97.2 78.2 78.9 91.8 86.9 84.1 88.6 89.8 87.3 85.4 ...
630s  81.8 65.8 68.1 70.0 69.9 75.1 72.3 70.9 71.5 72.5 ...
630s  84.9 96.1 94.6 82.5 90.7 87.0 86.8 93.3 87.6 92.4 ...
630s  100. 80.5 92.9 84.0 88.4 91.1 85.7 91.3 92.3 87.9 ...
630s  91.7 88.6 75.8 75.7 75.3 82.4 80.1 86.0 81.8 82.5]';
630s  treatment = {"yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
630s               "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
630s               "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
630s               "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
630s               "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
630s               "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"}';
630s  exercise = {"lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
630s              "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
630s              "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  ...
630s              "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
630s              "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
630s              "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"}';
630s  age = [59 65 70 66 61 65 57 61 58 55 62 61 60 59 55 57 60 63 62 57 ...
630s  58 56 57 59 59 60 55 53 55 58 68 62 61 54 59 63 60 67 60 67 ...
630s  75 54 57 62 65 60 58 61 65 57 56 58 58 58 52 53 60 62 61 61]';
630s 
630s  [P, ATAB, STATS] = anovan (score, {treatment, exercise, age}, "model", ...
630s                             [1 0 0; 0 1 0; 0 0 1; 1 1 0], "continuous", 3, ...
630s                             "sstype", "h", "display", "off", "contrasts", ...
630s                             {"simple","poly",""});
630s 
630s  [C, M, H, GNAMES] = multcompare (STATS, "dim", [1 2], "ctype", "holm", ...
630s                                   "display", "on")
630s 
630s ***** demo
630s 
630s  ## Demonstration using one-way ANOVA from anovan, with fit by weighted least
630s  ## squares to account for heteroskedasticity.
630s 
630s  g = [1, 1, 1, 1, 1, 1, 1, 1, ...
630s       2, 2, 2, 2, 2, 2, 2, 2, ...
630s       3, 3, 3, 3, 3, 3, 3, 3]';
630s 
630s  y = [13, 16, 16,  7, 11,  5,  1,  9, ...
630s       10, 25, 66, 43, 47, 56,  6, 39, ...
630s       11, 39, 26, 35, 25, 14, 24, 17]';
630s 
630s  [P,ATAB,STATS] = anovan(y, g, "display", "off");
630s  fitted = STATS.X * STATS.coeffs(:,1); # fitted values
630s  b = polyfit (fitted, abs (STATS.resid), 1);
630s  v = polyval (b, fitted);  # Variance as a function of the fitted values
630s  [P,ATAB,STATS] = anovan (y, g, "weights", v.^-1, "display", "off");
630s  [C, M] =  multcompare (STATS, "display", "on", "ctype", "mvt")
630s ***** demo
630s 
630s  ## Demonstration of p-value adjustments to control the false discovery rate
630s  ## Data from Westfall (1997) JASA. 92(437):299-306
630s 
630s  p = [.005708; .023544; .024193; .044895; ...
630s        .048805; .221227; .395867; .693051; .775755];
630s 
630s  padj = multcompare(p,'ctype','fdr')
630s ***** test
630s 
630s  ## Tests using unbalanced one-way ANOVA example from anovan and anova1
630s 
630s  ## Test for anovan - compare pairwise comparisons with matlab for CTYPE "lsd"
630s 
630s  dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
630s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
630s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
630s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
630s         25.694 ]';
630s  g = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 ...
630s       4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
630s 
630s  [P, ATAB, STATS] = anovan (dv, g, "varnames", "score", "display", "off");
630s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "lsd", ...
630s                                   "display", "off");
630s  assert (C(1,6), 2.85812420217898e-05, 1e-09);
630s  assert (C(2,6), 5.22936741204085e-07, 1e-09);
630s  assert (C(3,6), 2.12794763209146e-08, 1e-09);
630s  assert (C(4,6), 7.82091664406946e-15, 1e-09);
630s  assert (C(5,6), 0.546591417210693, 1e-09);
630s  assert (C(6,6), 0.0845897945254446, 1e-09);
630s  assert (C(7,6), 9.47436557975328e-08, 1e-09);
630s  assert (C(8,6), 0.188873478781067, 1e-09);
630s  assert (C(9,6), 4.08974010364197e-08, 1e-09);
630s  assert (C(10,6), 4.44427348175241e-06, 1e-09);
630s  assert (M(1,1), 10, 1e-09);
630s  assert (M(2,1), 18, 1e-09);
630s  assert (M(3,1), 19, 1e-09);
630s  assert (M(4,1), 21.0001428571429, 1e-09);
630s  assert (M(5,1), 29.0001111111111, 1e-09);
630s  assert (M(1,2), 1.0177537954095, 1e-09);
630s  assert (M(2,2), 1.28736803631001, 1e-09);
630s  assert (M(3,2), 1.0177537954095, 1e-09);
630s  assert (M(4,2), 1.0880245732889, 1e-09);
630s  assert (M(5,2), 0.959547480416536, 1e-09);
630s 
630s  ## Compare "fdr" adjusted p-values to those obtained using p.adjust in R
630s 
630s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "fdr", ...
630s                                   "display", "off");
630s  assert (C(1,6), 4.08303457454140e-05, 1e-09);
630s  assert (C(2,6), 1.04587348240817e-06, 1e-09);
630s  assert (C(3,6), 1.06397381604573e-07, 1e-09);
630s  assert (C(4,6), 7.82091664406946e-14, 1e-09);
630s  assert (C(5,6), 5.46591417210693e-01, 1e-09);
630s  assert (C(6,6), 1.05737243156806e-01, 1e-09);
630s  assert (C(7,6), 2.36859139493832e-07, 1e-09);
630s  assert (C(8,6), 2.09859420867852e-01, 1e-09);
630s  assert (C(9,6), 1.36324670121399e-07, 1e-09);
630s  assert (C(10,6), 7.40712246958735e-06, 1e-09);
630s 
630s  ## Compare "hochberg" adjusted p-values to those obtained using p.adjust in R
630s 
630s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "hochberg", ...
630s                                   "display", "off");
630s  assert (C(1,6), 1.14324968087159e-04, 1e-09);
630s  assert (C(2,6), 3.13762044722451e-06, 1e-09);
630s  assert (C(3,6), 1.91515286888231e-07, 1e-09);
630s  assert (C(4,6), 7.82091664406946e-14, 1e-09);
630s  assert (C(5,6), 5.46591417210693e-01, 1e-09);
630s  assert (C(6,6), 2.53769383576334e-01, 1e-09);
630s  assert (C(7,6), 6.63205590582730e-07, 1e-09);
630s  assert (C(8,6), 3.77746957562134e-01, 1e-09);
630s  assert (C(9,6), 3.27179208291358e-07, 1e-09);
630s  assert (C(10,6), 2.22213674087620e-05, 1e-09);
630s 
630s  ## Compare "holm" adjusted p-values to those obtained using p.adjust in R
630s 
630s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "holm", ...
630s                                   "display", "off");
630s  assert (C(1,6), 1.14324968087159e-04, 1e-09);
630s  assert (C(2,6), 3.13762044722451e-06, 1e-09);
630s  assert (C(3,6), 1.91515286888231e-07, 1e-09);
630s  assert (C(4,6), 7.82091664406946e-14, 1e-09);
630s  assert (C(5,6), 5.46591417210693e-01, 1e-09);
630s  assert (C(6,6), 2.53769383576334e-01, 1e-09);
630s  assert (C(7,6), 6.63205590582730e-07, 1e-09);
630s  assert (C(8,6), 3.77746957562134e-01, 1e-09);
630s  assert (C(9,6), 3.27179208291358e-07, 1e-09);
630s  assert (C(10,6), 2.22213674087620e-05, 1e-09);
630s 
630s  ## Compare "scheffe" adjusted p-values to those obtained using 'scheffe' in Matlab
630s 
630s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "scheffe", ...
630s                                   "display", "off");
630s  assert (C(1,6), 0.00108105386141085, 1e-09);
630s  assert (C(2,6), 2.7779386789517e-05, 1e-09);
630s  assert (C(3,6), 1.3599854038198e-06, 1e-09);
630s  assert (C(4,6), 7.58830197867751e-13, 1e-09);
630s  assert (C(5,6), 0.984039948220281, 1e-09);
630s  assert (C(6,6), 0.539077018557706, 1e-09);
630s  assert (C(7,6), 5.59475764460574e-06, 1e-09);
630s  assert (C(8,6), 0.771173490574105, 1e-09);
630s  assert (C(9,6), 2.52838425729905e-06, 1e-09);
630s  assert (C(10,6), 0.000200719143889168, 1e-09);
630s 
630s  ## Compare "bonferroni" adjusted p-values to those obtained using p.adjust in R
630s 
630s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "bonferroni", ...
630s                                   "display", "off");
630s  assert (C(1,6), 2.85812420217898e-04, 1e-09);
630s  assert (C(2,6), 5.22936741204085e-06, 1e-09);
630s  assert (C(3,6), 2.12794763209146e-07, 1e-09);
630s  assert (C(4,6), 7.82091664406946e-14, 1e-09);
630s  assert (C(5,6), 1.00000000000000e+00, 1e-09);
630s  assert (C(6,6), 8.45897945254446e-01, 1e-09);
630s  assert (C(7,6), 9.47436557975328e-07, 1e-09);
630s  assert (C(8,6), 1.00000000000000e+00, 1e-09);
630s  assert (C(9,6), 4.08974010364197e-07, 1e-09);
630s  assert (C(10,6), 4.44427348175241e-05, 1e-09);
630s 
630s  ## Test for anova1 ("equal")- comparison of results from Matlab
630s 
630s  [P, ATAB, STATS] = anova1 (dv, g, "off", "equal");
630s  [C, M, H, GNAMES] = multcompare (STATS, "ctype", "lsd", "display", "off");
630s  assert (C(1,6), 2.85812420217898e-05, 1e-09);
630s  assert (C(2,6), 5.22936741204085e-07, 1e-09);
630s  assert (C(3,6), 2.12794763209146e-08, 1e-09);
630s  assert (C(4,6), 7.82091664406946e-15, 1e-09);
630s  assert (C(5,6), 0.546591417210693, 1e-09);
630s  assert (C(6,6), 0.0845897945254446, 1e-09);
630s  assert (C(7,6), 9.47436557975328e-08, 1e-09);
630s  assert (C(8,6), 0.188873478781067, 1e-09);
630s  assert (C(9,6), 4.08974010364197e-08, 1e-09);
630s  assert (C(10,6), 4.44427348175241e-06, 1e-09);
630s  assert (M(1,1), 10, 1e-09);
630s  assert (M(2,1), 18, 1e-09);
630s  assert (M(3,1), 19, 1e-09);
630s  assert (M(4,1), 21.0001428571429, 1e-09);
630s  assert (M(5,1), 29.0001111111111, 1e-09);
630s  assert (M(1,2), 1.0177537954095, 1e-09);
630s  assert (M(2,2), 1.28736803631001, 1e-09);
630s  assert (M(3,2), 1.0177537954095, 1e-09);
630s  assert (M(4,2), 1.0880245732889, 1e-09);
630s  assert (M(5,2), 0.959547480416536, 1e-09);
630s 
630s  ## Test for anova1 ("unequal") - comparison with results from GraphPad Prism 8
630s  [P, ATAB, STATS] = anova1 (dv, g, "off", "unequal");
630s  [C, M, H, GNAMES] = multcompare (STATS, "ctype", "lsd", "display", "off");
630s  assert (C(1,6), 0.001247025266382, 1e-09);
630s  assert (C(2,6), 0.000018037115146, 1e-09);
630s  assert (C(3,6), 0.000002974595187, 1e-09);
630s  assert (C(4,6), 0.000000000786046, 1e-09);
630s  assert (C(5,6), 0.5693192886650109, 1e-09);
630s  assert (C(6,6), 0.110501699029776, 1e-09);
630s  assert (C(7,6), 0.000131226488700, 1e-09);
630s  assert (C(8,6), 0.1912101409715992, 1e-09);
630s  assert (C(9,6), 0.000005385256394, 1e-09);
630s  assert (C(10,6), 0.000074089106171, 1e-09);
630s ***** test
630s 
630s  ## Test for anova2 ("interaction") - comparison with results from Matlab for column effect
630s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
630s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
630s  [P, ATAB, STATS] = anova2 (popcorn, 3, "off");
630s  [C, M, H, GNAMES] = multcompare (STATS, "estimate", "column",...
630s                                   "ctype", "lsd", "display", "off");
630s  assert (C(1,6), 1.49311100811177e-05, 1e-09);
630s  assert (C(2,6), 2.20506904243535e-07, 1e-09);
630s  assert (C(3,6), 0.00449897860490058, 1e-09);
630s  assert (M(1,1), 6.25, 1e-09);
630s  assert (M(2,1), 4.75, 1e-09);
630s  assert (M(3,1), 4, 1e-09);
630s  assert (M(1,2), 0.152145154862547, 1e-09);
630s  assert (M(2,2), 0.152145154862547, 1e-09);
630s  assert (M(3,2), 0.152145154862547, 1e-09);
630s ***** test
630s 
630s  ## Test for anova2 ("linear") - comparison with results from GraphPad Prism 8
630s  words = [10 13 13; 6 8 8; 11 14 14; 22 23 25; 16 18 20; ...
630s           15 17 17; 1 1 4; 12 15 17;  9 12 12;  8 9 12];
630s  [P, ATAB, STATS] = anova2 (words, 1, "off", "linear");
630s  [C, M, H, GNAMES] = multcompare (STATS, "estimate", "column",...
630s                                   "ctype", "lsd", "display", "off");
630s  assert (C(1,6), 0.000020799832702, 1e-09);
630s  assert (C(2,6), 0.000000035812410, 1e-09);
630s  assert (C(3,6), 0.003038942449215, 1e-09);
630s ***** test
630s 
630s  ## Test for anova2 ("nested") - comparison with results from GraphPad Prism 8
630s  data = [4.5924 7.3809 21.322; -0.5488 9.2085 25.0426; ...
630s          6.1605 13.1147 22.66; 2.3374 15.2654 24.1283; ...
630s          5.1873 12.4188 16.5927; 3.3579 14.3951 10.2129; ...
630s          6.3092 8.5986 9.8934; 3.2831 3.4945 10.0203];
630s  [P, ATAB, STATS] = anova2 (data, 4, "off", "nested");
630s  [C, M, H, GNAMES] = multcompare (STATS, "estimate", "column",...
630s                                   "ctype", "lsd", "display", "off");
630s  assert (C(1,6), 0.261031111511073, 1e-09);
630s  assert (C(2,6), 0.065879755907745, 1e-09);
630s  assert (C(3,6), 0.241874613529270, 1e-09);
630s ***** shared visibility_setting
630s  visibility_setting = get (0, "DefaultFigureVisible");
630s ***** test
630s  set (0, "DefaultFigureVisible", "off");
630s 
630s  ## Test for kruskalwallis - comparison with results from MATLAB
630s  data = [3,2,4; 5,4,4; 4,2,4; 4,2,4; 4,1,5; ...
630s          4,2,3; 4,3,5; 4,2,4; 5,2,4; 5,3,3];
630s  group = [1:3] .* ones (10,3);
630s  [P, ATAB, STATS] = kruskalwallis (data(:), group(:), "off");
630s  C = multcompare (STATS, "ctype", "lsd", "display", "off");
630s  assert (C(1,6), 0.000163089828959986, 1e-09);
630s  assert (C(2,6), 0.630298044801257, 1e-09);
630s  assert (C(3,6), 0.00100567660695682, 1e-09);
630s  C = multcompare (STATS, "ctype", "bonferroni", "display", "off");
630s  assert (C(1,6), 0.000489269486879958, 1e-09);
630s  assert (C(2,6), 1, 1e-09);
630s  assert (C(3,6), 0.00301702982087047, 1e-09);
630s  C = multcompare(STATS, "ctype", "scheffe", "display", "off");
630s  assert (C(1,6), 0.000819054880289573, 1e-09);
630s  assert (C(2,6), 0.890628039849261, 1e-09);
630s  assert (C(3,6), 0.00447816059021654, 1e-09);
630s  set (0, "DefaultFigureVisible", visibility_setting);
630s ***** test
630s  set (0, "DefaultFigureVisible", "off");
630s  ## Test for friedman - comparison with results from MATLAB
630s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
630s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
630s  [P, ATAB, STATS] = friedman (popcorn, 3, "off");
630s  C = multcompare(STATS, "ctype", "lsd", "display", "off");
630s  assert (C(1,6), 0.227424558028569, 1e-09);
630s  assert (C(2,6), 0.0327204848315735, 1e-09);
630s  assert (C(3,6), 0.353160353315988, 1e-09);
630s  C = multcompare(STATS, "ctype", "bonferroni", "display", "off");
630s  assert (C(1,6), 0.682273674085708, 1e-09);
630s  assert (C(2,6), 0.0981614544947206, 1e-09);
630s  assert (C(3,6), 1, 1e-09);
630s  C = multcompare(STATS, "ctype", "scheffe", "display", "off");
630s  assert (C(1,6), 0.482657360384373, 1e-09);
630s  assert (C(2,6), 0.102266573027672, 1e-09);
630s  assert (C(3,6), 0.649836502233148, 1e-09);
630s  set (0, "DefaultFigureVisible", visibility_setting);
630s ***** test
630s  set (0, "DefaultFigureVisible", "off");
630s  ## Test for fitlm - same comparisons as for first anovan example
630s  y =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
630s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
630s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
630s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
630s         25.694 ]';
630s  X = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
630s  [TAB,STATS] = fitlm (X,y,"linear","categorical",1,"display","off",...
630s                       "contrasts","simple");
630s  [C, M] = multcompare(STATS, "ctype", "lsd", "display", "off");
630s  assert (C(1,6), 2.85812420217898e-05, 1e-09);
630s  assert (C(2,6), 5.22936741204085e-07, 1e-09);
630s  assert (C(3,6), 2.12794763209146e-08, 1e-09);
630s  assert (C(4,6), 7.82091664406946e-15, 1e-09);
630s  assert (C(5,6), 0.546591417210693, 1e-09);
630s  assert (C(6,6), 0.0845897945254446, 1e-09);
630s  assert (C(7,6), 9.47436557975328e-08, 1e-09);
630s  assert (C(8,6), 0.188873478781067, 1e-09);
630s  assert (C(9,6), 4.08974010364197e-08, 1e-09);
630s  assert (C(10,6), 4.44427348175241e-06, 1e-09);
630s  assert (M(1,1), 10, 1e-09);
630s  assert (M(2,1), 18, 1e-09);
630s  assert (M(3,1), 19, 1e-09);
630s  assert (M(4,1), 21.0001428571429, 1e-09);
630s  assert (M(5,1), 29.0001111111111, 1e-09);
630s  assert (M(1,2), 1.0177537954095, 1e-09);
630s  assert (M(2,2), 1.28736803631001, 1e-09);
630s  assert (M(3,2), 1.0177537954095, 1e-09);
630s  assert (M(4,2), 1.0880245732889, 1e-09);
630s  assert (M(5,2), 0.959547480416536, 1e-09);
630s  set (0, "DefaultFigureVisible", visibility_setting);
630s ***** test
630s  ## Test p-value adjustments compared to R stats package function p.adjust
630s  ## Data from Westfall (1997) JASA. 92(437):299-306
630s  p = [.005708; .023544; .024193; .044895; ...
630s        .048805; .221227; .395867; .693051; .775755];
630s  padj = multcompare (p);
630s  assert (padj(1), 0.051372, 1e-06);
630s  assert (padj(2), 0.188352, 1e-06);
630s  assert (padj(3), 0.188352, 1e-06);
630s  assert (padj(4), 0.269370, 1e-06);
630s  assert (padj(5), 0.269370, 1e-06);
630s  assert (padj(6), 0.884908, 1e-06);
630s  assert (padj(7), 1.000000, 1e-06);
630s  assert (padj(8), 1.000000, 1e-06);
630s  assert (padj(9), 1.000000, 1e-06);
630s  padj = multcompare(p,'ctype','holm');
630s  assert (padj(1), 0.051372, 1e-06);
630s  assert (padj(2), 0.188352, 1e-06);
630s  assert (padj(3), 0.188352, 1e-06);
630s  assert (padj(4), 0.269370, 1e-06);
630s  assert (padj(5), 0.269370, 1e-06);
630s  assert (padj(6), 0.884908, 1e-06);
630s  assert (padj(7), 1.000000, 1e-06);
630s  assert (padj(8), 1.000000, 1e-06);
630s  assert (padj(9), 1.000000, 1e-06);
630s  padj = multcompare(p,'ctype','hochberg');
630s  assert (padj(1), 0.051372, 1e-06);
630s  assert (padj(2), 0.169351, 1e-06);
630s  assert (padj(3), 0.169351, 1e-06);
630s  assert (padj(4), 0.244025, 1e-06);
630s  assert (padj(5), 0.244025, 1e-06);
630s  assert (padj(6), 0.775755, 1e-06);
630s  assert (padj(7), 0.775755, 1e-06);
630s  assert (padj(8), 0.775755, 1e-06);
630s  assert (padj(9), 0.775755, 1e-06);
630s  padj = multcompare(p,'ctype','fdr');
630s  assert (padj(1), 0.0513720, 1e-07);
630s  assert (padj(2), 0.0725790, 1e-07);
630s  assert (padj(3), 0.0725790, 1e-07);
630s  assert (padj(4), 0.0878490, 1e-07);
630s  assert (padj(5), 0.0878490, 1e-07);
630s  assert (padj(6), 0.3318405, 1e-07);
630s  assert (padj(7), 0.5089719, 1e-07);
630s  assert (padj(8), 0.7757550, 1e-07);
630s  assert (padj(9), 0.7757550, 1e-07);
630s 8 tests, 8 passed, 0 known failure, 0 skipped
630s [inst/nansum.m]
630s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/nansum.m
630s ***** assert (nansum ([2 4 NaN 7]), 13)
630s ***** assert (nansum ([2 4 NaN Inf]), Inf)
630s ***** assert (nansum ([1 NaN 3; NaN 5 6; 7 8 NaN]), [8 13 9])
630s ***** assert (nansum ([1 NaN 3; NaN 5 6; 7 8 NaN], 2), [4; 11; 15])
630s ***** assert (nansum (single ([1 NaN 3; NaN 5 6; 7 8 NaN])), single ([8 13 9]))
630s ***** assert (nansum (single ([1 NaN 3; NaN 5 6; 7 8 NaN]), "double"), [8 13 9])
630s ***** assert (nansum (uint8 ([2 4 1 7])), 14)
630s ***** assert (nansum (uint8 ([2 4 1 7]), "native"), uint8 (14))
630s ***** assert (nansum (uint8 ([2 4 1 7])), 14)
630s 9 tests, 9 passed, 0 known failure, 0 skipped
630s [inst/evalclusters.m]
630s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/evalclusters.m
630s ***** demo
630s  load fisheriris;
630s  eva = evalclusters (meas, "kmeans", "calinskiharabasz", "KList", [1:6])
630s  plot (eva)
630s ***** error evalclusters ()
630s ***** error evalclusters ([1 1;0 1])
630s ***** error evalclusters ([1 1;0 1], "kmeans")
630s ***** error <'x' must be a numeric*> evalclusters ("abc", "kmeans", "gap")
630s ***** error <unknown clustering*> evalclusters ([1 1;0 1], "xxx", "gap")
630s ***** error <invalid matrix*> evalclusters ([1 1;0 1], [1 2], "gap")
630s ***** error <invalid argument*> evalclusters ([1 1;0 1], 1.2, "gap")
630s ***** error <invalid criterion*> evalclusters ([1 1;0 1], [1; 2], 123)
630s ***** error <unknown criterion*> evalclusters ([1 1;0 1], [1; 2], "xxx")
630s ***** error <'KList' can be empty*> evalclusters ([1 1;0 1], "kmeans", "gap")
630s ***** error <invalid parameter*> evalclusters ([1 1;0 1], [1; 2], "gap", 1)
630s ***** error <invalid property*> evalclusters ([1 1;0 1], [1; 2], "gap", 1, 1)
630s ***** error <unknown property*> evalclusters ([1 1;0 1], [1; 2], "gap", "xxx", 1)
630s ***** error <'KList'*> evalclusters ([1 1;0 1], [1; 2], "gap", "KList", [-1 0])
630s ***** error <'KList'*> evalclusters ([1 1;0 1], [1; 2], "gap", "KList", [1 .5])
630s ***** error <'KList'*> evalclusters ([1 1;0 1], [1; 2], "gap", "KList", [1 1; 1 1])
630s ***** error <unknown distance*> evalclusters ([1 1;0 1], [1; 2], "gap", ...
630s                                         "distance", "a")
630s ***** error <distance metric*> evalclusters ([1 1;0 1], [1; 2], "daviesbouldin", ...
630s                                        "distance", "a")
630s ***** error <cluster prior*> evalclusters ([1 1;0 1], [1; 2], "gap", ...
630s                                      "clusterpriors", "equal")
630s ***** error <invalid cluster prior*> evalclusters ([1 1;0 1], [1; 2], ...
630s                                          "silhouette", "clusterpriors", "xxx")
630s ***** error <'clust' must be a clustering*> evalclusters ([1 1;0 1], [1; 2], "gap")
630s ***** test
630s  load fisheriris;
630s  eva = evalclusters (meas, "kmeans", "calinskiharabasz", "KList", [1:6]);
630s  assert (isa (eva, "CalinskiHarabaszEvaluation"));
630s  assert (eva.NumObservations, 150);
630s  assert (eva.OptimalK, 3);
630s  assert (eva.InspectedK, [1 2 3 4 5 6]);
630s 22 tests, 22 passed, 0 known failure, 0 skipped
630s [inst/regress.m]
630s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/regress.m
630s ***** test
630s  % Longley data from the NIST Statistical Reference Dataset
630s  Z = [  60323    83.0   234289   2356     1590    107608  1947
630s         61122    88.5   259426   2325     1456    108632  1948
630s         60171    88.2   258054   3682     1616    109773  1949
630s         61187    89.5   284599   3351     1650    110929  1950
630s         63221    96.2   328975   2099     3099    112075  1951
630s         63639    98.1   346999   1932     3594    113270  1952
630s         64989    99.0   365385   1870     3547    115094  1953
630s         63761   100.0   363112   3578     3350    116219  1954
630s         66019   101.2   397469   2904     3048    117388  1955
630s         67857   104.6   419180   2822     2857    118734  1956
630s         68169   108.4   442769   2936     2798    120445  1957
630s         66513   110.8   444546   4681     2637    121950  1958
630s         68655   112.6   482704   3813     2552    123366  1959
630s         69564   114.2   502601   3931     2514    125368  1960
630s         69331   115.7   518173   4806     2572    127852  1961
630s         70551   116.9   554894   4007     2827    130081  1962 ];
630s  % Results certified by NIST using 500 digit arithmetic
630s  % b and standard error in b
630s  V = [  -3482258.63459582         890420.383607373
630s          15.0618722713733         84.9149257747669
630s         -0.358191792925910E-01    0.334910077722432E-01
630s         -2.02022980381683         0.488399681651699
630s         -1.03322686717359         0.214274163161675
630s         -0.511041056535807E-01    0.226073200069370
630s          1829.15146461355         455.478499142212 ];
630s  Rsq = 0.995479004577296;
630s  F = 330.285339234588;
630s  y = Z(:,1); X = [ones(rows(Z),1), Z(:,2:end)];
630s  alpha = 0.05;
630s  [b, bint, r, rint, stats] = regress (y, X, alpha);
630s  assert(b,V(:,1),4e-6);
630s  assert(stats(1),Rsq,1e-12);
630s  assert(stats(2),F,3e-8);
630s  assert(((bint(:,1)-bint(:,2))/2)/tinv(alpha/2,9),V(:,2),-1.e-5);
630s warning: matrix singular to machine precision, rcond = 3.50566e-20
630s warning: called from
630s     regress at line 131 column 7
630s     __test__ at line 33 column 28
630s     test at line 682 column 11
630s     /tmp/tmp.gzRsVyCRjZ at line 2118 column 31
630s 
630s 1 test, 1 passed, 0 known failure, 0 skipped
630s [inst/hist3.m]
630s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/hist3.m
630s ***** demo
630s  X = [
630s     1    1
630s     1    1
630s     1   10
630s     1   10
630s     5    5
630s     5    5
630s     5    5
630s     5    5
630s     5    5
630s     7    3
630s     7    3
630s     7    3
630s    10   10
630s    10   10];
630s  hist3 (X)
630s ***** test
630s  N_exp = [ 0  0  0  5 20
630s            0  0 10 15  0
630s            0 15 10  0  0
630s           20  5  0  0  0];
630s 
630s  n = 100;
630s  x = [1:n]';
630s  y = [n:-1:1]';
630s  D = [x y];
630s  N = hist3 (D, [4 5]);
630s  assert (N, N_exp);
630s ***** test
630s  N_exp = [0  0  0  0  1
630s           0  0  0  0  1
630s           0  0  0  0  1
630s           1  1  1  1 93];
630s 
630s  n = 100;
630s  x = [1:n]';
630s  y = [n:-1:1]';
630s  D = [x y];
630s  C{1} = [1 1.7 3 4];
630s  C{2} = [1:5];
630s  N = hist3 (D, C);
630s  assert (N, N_exp);
630s ***** test
630s  D = [1 1; 3 1; 3 3; 3 1];
630s  [c, nn] = hist3 (D, {0:4, 0:4});
630s  exp_c = zeros (5);
630s  exp_c([7 9 19]) = [1 2 1];
630s  assert (c, exp_c);
630s  assert (nn, {0:4, 0:4});
630s ***** test
630s  for i = 10
630s    assert (size (hist3 (rand (9, 2), "Edges", {[0:.2:1]; [0:.2:1]})), [6 6])
630s  endfor
630s ***** test
630s  edge_1 = linspace (0, 10, 10);
630s  edge_2 = linspace (0, 50, 10);
630s  [c, nn] = hist3 ([1:10; 1:5:50]', "Edges", {edge_1, edge_2});
630s  exp_c = zeros (10, 10);
630s  exp_c([1 12 13 24 35 46 57 68 79 90]) = 1;
630s  assert (c, exp_c);
630s 
630s  assert (nn{1}, edge_1 + edge_1(2)/2, eps*10^4)
630s  assert (nn{2}, edge_2 + edge_2(2)/2, eps*10^4)
631s ***** shared X
631s  X = [
631s   5  2
631s   5  3
631s   1  4
631s   5  3
631s   4  4
631s   1  2
631s   2  3
631s   3  3
631s   5  4
631s   5  3];
631s ***** test
631s  N = zeros (10);
631s  N([1 10 53 56 60 91 98 100]) = [1 1 1 1 3 1 1 1];
631s  C = {(1.2:0.4:4.8), (2.1:0.2:3.9)};
631s  assert (nthargout ([1 2], @hist3, X), {N C}, eps*10^3)
631s ***** test
631s  N = zeros (5, 7);
631s  N([1 5 17 18 20 31 34 35]) = [1 1 1 1 3 1 1 1];
631s  C = {(1.4:0.8:4.6), ((2+(1/7)):(2/7):(4-(1/7)))};
631s  assert (nthargout ([1 2], @hist3, X, [5 7]), {N C}, eps*10^3)
631s  assert (nthargout ([1 2], @hist3, X, "Nbins", [5 7]), {N C}, eps*10^3)
631s ***** test
631s  N = [0 1 0; 0 1 0; 0 0 1; 0 0 0];
631s  C = {(2:5), (2.5:1:4.5)};
631s  assert (nthargout ([1 2], @hist3, X, "Edges", {(1.5:4.5), (2:4)}), {N C})
631s ***** test
631s  N = [0 0 1 0 1 0; 0 0 0 1 0 0; 0 0 1 4 2 0];
631s  C = {(1.2:3.2), (0:5)};
631s  assert (nthargout ([1 2], @hist3, X, "Ctrs", C), {N C})
631s  assert (nthargout ([1 2], @hist3, X, C), {N C})
631s ***** test
631s  [~, C] = hist3 (rand (10, 2), "Edges", {[0 .05 .15 .35 .55 .95],
631s                                          [-1 .05 .07 .2 .3 .5 .89 1.2]});
631s  C_exp = {[ 0.025  0.1   0.25   0.45  0.75  1.15], ...
631s           [-0.475  0.06  0.135  0.25  0.4   0.695  1.045  1.355]};
631s  assert (C, C_exp, eps*10^2)
631s ***** test
631s  Xv = repmat ([1:10]', [1 2]);
631s 
631s  ## Test Centers
631s  assert (hist3 (Xv, "Ctrs", {1:10, 1:10}), eye (10))
631s 
631s  N_exp = eye (6);
631s  N_exp([1 end]) = 3;
631s  assert (hist3 (Xv, "Ctrs", {3:8, 3:8}), N_exp)
631s 
631s  N_exp = zeros (8, 6);
631s  N_exp([1 2 11 20 29 38 47 48]) = [2 1 1 1 1 1 1 2];
631s  assert (hist3 (Xv, "Ctrs", {2:9, 3:8}), N_exp)
631s 
631s  ## Test Edges
631s  assert (hist3 (Xv, "Edges", {1:10, 1:10}), eye (10))
631s  assert (hist3 (Xv, "Edges", {3:8, 3:8}), eye (6))
631s  assert (hist3 (Xv, "Edges", {2:9, 3:8}), [zeros(1, 6); eye(6); zeros(1, 6)])
631s 
631s  N_exp = zeros (14);
631s  N_exp(3:12, 3:12) = eye (10);
631s  assert (hist3 (Xv, "Edges", {-1:12, -1:12}), N_exp)
631s 
631s  ## Test for Nbins
631s  assert (hist3 (Xv), eye (10))
631s  assert (hist3 (Xv, [10 10]), eye (10))
631s  assert (hist3 (Xv, "nbins", [10 10]), eye (10))
631s  assert (hist3 (Xv, [5 5]), eye (5) * 2)
631s 
631s  N_exp = zeros (7, 5);
631s  N_exp([1 9 10 18 26 27 35]) = [2 1 1 2 1 1 2];
631s  assert (hist3 (Xv, [7 5]), N_exp)
631s ***** test # bug #51059
631s  D = [1 1; NaN 2; 3 1; 3 3; 1 NaN; 3 1];
631s  [c, nn] = hist3 (D, {0:4, 0:4});
631s  exp_c = zeros (5);
631s  exp_c([7 9 19]) = [1 2 1];
631s  assert (c, exp_c)
631s  assert (nn, {0:4, 0:4})
631s ***** test
631s  [c, nn] = hist3 ([1 8]);
631s  exp_c = zeros (10, 10);
631s  exp_c(6, 6) = 1;
631s  exp_nn = {-4:5, 3:12};
631s  assert (c, exp_c)
631s  assert (nn, exp_nn, eps)
631s 
631s  [c, nn] = hist3 ([1 8], [10 11]);
631s  exp_c = zeros (10, 11);
631s  exp_c(6, 6) = 1;
631s  exp_nn = {-4:5, 3:13};
631s  assert (c, exp_c)
631s  assert (nn, exp_nn, eps)
631s ***** test
631s  [c, nn] = hist3 ([1 NaN; 2 3; 6 9; 8 NaN]);
631s  exp_c = zeros (10, 10);
631s  exp_c(2, 1) = 1;
631s  exp_c(8, 10) = 1;
631s  exp_nn = {linspace(1.35, 7.65, 10) linspace(3.3, 8.7, 10)};
631s  assert (c, exp_c)
631s  assert (nn, exp_nn, eps*100)
631s ***** test
631s  [c, nn] = hist3 ([1 NaN; 2 NaN; 6 NaN; 8 NaN]);
631s  exp_c = zeros (10, 10);
631s  exp_nn = {linspace(1.35, 7.65, 10) NaN(1, 10)};
631s  assert (c, exp_c)
631s  assert (nn, exp_nn, eps*100)
631s ***** test
631s  [c, nn] = hist3 ([1 NaN; NaN 3; NaN 9; 8 NaN]);
631s  exp_c = zeros (10, 10);
631s  exp_nn = {linspace(1.35, 7.65, 10) linspace(3.3, 8.7, 10)};
631s  assert (c, exp_c)
631s  assert (nn, exp_nn, eps*100)
631s 16 tests, 16 passed, 0 known failure, 0 skipped
631s [inst/dist_wrap/fitdist.m]
631s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_wrap/fitdist.m
631s ***** test
631s  x = betarnd (1, 1, 100, 1);
631s  pd = fitdist (x, "Beta");
631s  [phat, pci] = betafit (x);
631s  assert ([pd.a, pd.b], phat);
631s  assert (paramci (pd), pci);
631s ***** test
631s  x1 = betarnd (1, 1, 100, 1);
631s  x2 = betarnd (5, 2, 100, 1);
631s  pd = fitdist ([x1; x2], "Beta", "By", [ones(100,1); 2*ones(100,1)]);
631s  [phat, pci] = betafit (x1);
631s  assert ([pd(1).a, pd(1).b], phat);
631s  assert (paramci (pd(1)), pci);
631s  [phat, pci] = betafit (x2);
631s  assert ([pd(2).a, pd(2).b], phat);
631s  assert (paramci (pd(2)), pci);
631s ***** test
631s  N = 1;
631s  x = binornd (N, 0.5, 100, 1);
631s  pd = fitdist (x, "binomial");
631s  [phat, pci] = binofit (sum (x), numel (x));
631s  assert ([pd.N, pd.p], [N, phat]);
631s  assert (paramci (pd), pci);
631s ***** test
631s  N = 3;
631s  x = binornd (N, 0.4, 100, 1);
631s  pd = fitdist (x, "binomial", "ntrials", N);
631s  [phat, pci] = binofit (sum (x), numel (x) * N);
631s  assert ([pd.N, pd.p], [N, phat]);
631s  assert (paramci (pd), pci);
631s ***** test
631s  N = 1;
631s  x1 = binornd (N, 0.5, 100, 1);
631s  x2 = binornd (N, 0.7, 100, 1);
631s  pd = fitdist ([x1; x2], "binomial", "By", [ones(100,1); 2*ones(100,1)]);
631s  [phat, pci] = binofit (sum (x1), numel (x1));
631s  assert ([pd(1).N, pd(1).p], [N, phat]);
631s  assert (paramci (pd(1)), pci);
631s  [phat, pci] = binofit (sum (x2), numel (x2));
631s  assert ([pd(2).N, pd(2).p], [N, phat]);
631s  assert (paramci (pd(2)), pci);
631s ***** test
631s  N = 5;
631s  x1 = binornd (N, 0.5, 100, 1);
631s  x2 = binornd (N, 0.8, 100, 1);
631s  pd = fitdist ([x1; x2], "binomial", "ntrials", N, ...
631s                "By", [ones(100,1); 2*ones(100,1)]);
631s  [phat, pci] = binofit (sum (x1), numel (x1) * N);
631s  assert ([pd(1).N, pd(1).p], [N, phat]);
631s  assert (paramci (pd(1)), pci);
631s  [phat, pci] = binofit (sum (x2), numel (x2) * N);
631s  assert ([pd(2).N, pd(2).p], [N, phat]);
631s  assert (paramci (pd(2)), pci);
631s ***** test
631s  x = bisarnd (1, 1, 100, 1);
631s  pd = fitdist (x, "BirnbaumSaunders");
631s  [phat, pci] = bisafit (x);
631s  assert ([pd.beta, pd.gamma], phat);
631s  assert (paramci (pd), pci);
631s ***** test
631s  x1 = bisarnd (1, 1, 100, 1);
631s  x2 = bisarnd (5, 2, 100, 1);
631s  pd = fitdist ([x1; x2], "bisa", "By", [ones(100,1); 2*ones(100,1)]);
631s  [phat, pci] = bisafit (x1);
631s  assert ([pd(1).beta, pd(1).gamma], phat);
631s  assert (paramci (pd(1)), pci);
631s  [phat, pci] = bisafit (x2);
631s  assert ([pd(2).beta, pd(2).gamma], phat);
631s  assert (paramci (pd(2)), pci);
631s ***** test
631s  x = burrrnd (1, 2, 1, 100, 1);
631s  pd = fitdist (x, "Burr");
631s  [phat, pci] = burrfit (x);
631s  assert ([pd.alpha, pd.c, pd.k], phat);
631s  assert (paramci (pd), pci);
631s ***** xtest
631s  x1 = burrrnd (1, 2, 1, 100, 1);
631s  x2 = burrrnd (1, 0.5, 2, 100, 1);
631s  pd = fitdist ([x1; x2], "burr", "By", [ones(100,1); 2*ones(100,1)]);
631s  [phat, pci] = burrfit (x1);
631s  assert ([pd(1).alpha, pd(1).c, pd(1).k], phat);
631s  assert (paramci (pd(1)), pci);
631s  [phat, pci] = burrfit (x2);
631s  assert ([pd(2).alpha, pd(2).c, pd(2).k], phat);
631s  assert (paramci (pd(2)), pci);
632s ***** test
632s  x = exprnd (1, 100, 1);
632s  pd = fitdist (x, "exponential");
632s  [muhat, muci] = expfit (x);
632s  assert ([pd.mu], muhat);
632s  assert (paramci (pd), muci);
632s ***** test
632s  x1 = exprnd (1, 100, 1);
632s  x2 = exprnd (5, 100, 1);
632s  pd = fitdist ([x1; x2], "exponential", "By", [ones(100,1); 2*ones(100,1)]);
632s  [muhat, muci] = expfit (x1);
632s  assert ([pd(1).mu], muhat);
632s  assert (paramci (pd(1)), muci);
632s  [muhat, muci] = expfit (x2);
632s  assert ([pd(2).mu], muhat);
632s  assert (paramci (pd(2)), muci);
632s ***** test
632s  x = evrnd (1, 1, 100, 1);
632s  pd = fitdist (x, "ev");
632s  [phat, pci] = evfit (x);
632s  assert ([pd.mu, pd.sigma], phat);
632s  assert (paramci (pd), pci);
632s ***** test
632s  x1 = evrnd (1, 1, 100, 1);
632s  x2 = evrnd (5, 2, 100, 1);
632s  pd = fitdist ([x1; x2], "extremevalue", "By", [ones(100,1); 2*ones(100,1)]);
632s  [phat, pci] = evfit (x1);
632s  assert ([pd(1).mu, pd(1).sigma], phat);
632s  assert (paramci (pd(1)), pci);
632s  [phat, pci] = evfit (x2);
632s  assert ([pd(2).mu, pd(2).sigma], phat);
632s  assert (paramci (pd(2)), pci);
632s ***** test
632s  x = gamrnd (1, 1, 100, 1);
632s  pd = fitdist (x, "Gamma");
632s  [phat, pci] = gamfit (x);
632s  assert ([pd.a, pd.b], phat);
632s  assert (paramci (pd), pci);
632s ***** test
632s  x1 = gamrnd (1, 1, 100, 1);
632s  x2 = gamrnd (5, 2, 100, 1);
632s  pd = fitdist ([x1; x2], "Gamma", "By", [ones(100,1); 2*ones(100,1)]);
632s  [phat, pci] = gamfit (x1);
632s  assert ([pd(1).a, pd(1).b], phat);
632s  assert (paramci (pd(1)), pci);
632s  [phat, pci] = gamfit (x2);
632s  assert ([pd(2).a, pd(2).b], phat);
632s  assert (paramci (pd(2)), pci);
632s ***** test
632s  rand ("seed", 4);   # for reproducibility
632s  x = gevrnd (-0.5, 1, 2, 1000, 1);
632s  pd = fitdist (x, "generalizedextremevalue");
632s  [phat, pci] = gevfit (x);
632s  assert ([pd.k, pd.sigma, pd.mu], phat);
632s  assert (paramci (pd), pci);
632s ***** test
632s  rand ("seed", 5);   # for reproducibility
632s  x1 = gevrnd (-0.5, 1, 2, 1000, 1);
632s  rand ("seed", 9);   # for reproducibility
632s  x2 = gevrnd (0, 1, -4, 1000, 1);
632s  pd = fitdist ([x1; x2], "gev", "By", [ones(1000,1); 2*ones(1000,1)]);
632s  [phat, pci] = gevfit (x1);
632s  assert ([pd(1).k, pd(1).sigma, pd(1).mu], phat);
632s  assert (paramci (pd(1)), pci);
632s  [phat, pci] = gevfit (x2);
632s  assert ([pd(2).k, pd(2).sigma, pd(2).mu], phat);
632s  assert (paramci (pd(2)), pci);
632s ***** test
632s  x = gprnd (1, 1, 1, 100, 1);
632s  pd = fitdist (x, "GeneralizedPareto");
632s  [phat, pci] = gpfit (x, 1);
632s  assert ([pd.k, pd.sigma, pd.theta], phat);
632s  assert (paramci (pd), pci);
632s ***** test
632s  x = gprnd (1, 1, 2, 100, 1);
632s  pd = fitdist (x, "GeneralizedPareto", "theta", 2);
632s  [phat, pci] = gpfit (x, 2);
632s  assert ([pd.k, pd.sigma, pd.theta], phat);
632s  assert (paramci (pd), pci);
632s ***** test
632s  x1 = gprnd (1, 1, 1, 100, 1);
632s  x2 = gprnd (0, 2, 1, 100, 1);
632s  pd = fitdist ([x1; x2], "gp", "By", [ones(100,1); 2*ones(100,1)]);
632s  [phat, pci] = gpfit (x1, 1);
632s  assert ([pd(1).k, pd(1).sigma, pd(1).theta], phat);
632s  assert (paramci (pd(1)), pci);
632s  [phat, pci] = gpfit (x2, 1);
632s  assert ([pd(2).k, pd(2).sigma, pd(2).theta], phat);
632s  assert (paramci (pd(2)), pci);
633s ***** test
633s  x1 = gprnd (3, 2, 2, 100, 1);
633s  x2 = gprnd (2, 3, 2, 100, 1);
633s  pd = fitdist ([x1; x2], "GeneralizedPareto", "theta", 2, ...
633s                "By", [ones(100,1); 2*ones(100,1)]);
633s  [phat, pci] = gpfit (x1, 2);
633s  assert ([pd(1).k, pd(1).sigma, pd(1).theta], phat);
633s  assert (paramci (pd(1)), pci);
633s  [phat, pci] = gpfit (x2, 2);
633s  assert ([pd(2).k, pd(2).sigma, pd(2).theta], phat);
633s  assert (paramci (pd(2)), pci);
633s ***** test
633s  x = hnrnd (0, 1, 100, 1);
633s  pd = fitdist (x, "HalfNormal");
633s  [phat, pci] = hnfit (x, 0);
633s  assert ([pd.mu, pd.sigma], phat);
633s  assert (paramci (pd), pci);
633s ***** test
633s  x = hnrnd (1, 1, 100, 1);
633s  pd = fitdist (x, "HalfNormal", "mu", 1);
633s  [phat, pci] = hnfit (x, 1);
633s  assert ([pd.mu, pd.sigma], phat);
633s  assert (paramci (pd), pci);
633s ***** test
633s  x1 = hnrnd (0, 1, 100, 1);
633s  x2 = hnrnd (0, 2, 100, 1);
633s  pd = fitdist ([x1; x2], "HalfNormal", "By", [ones(100,1); 2*ones(100,1)]);
633s  [phat, pci] = hnfit (x1, 0);
633s  assert ([pd(1).mu, pd(1).sigma], phat);
633s  assert (paramci (pd(1)), pci);
633s  [phat, pci] = hnfit (x2, 0);
633s  assert ([pd(2).mu, pd(2).sigma], phat);
633s  assert (paramci (pd(2)), pci);
633s ***** test
633s  x1 = hnrnd (2, 1, 100, 1);
633s  x2 = hnrnd (2, 2, 100, 1);
633s  pd = fitdist ([x1; x2], "HalfNormal", "mu", 2, ...
633s                "By", [ones(100,1); 2*ones(100,1)]);
633s  [phat, pci] = hnfit (x1, 2);
633s  assert ([pd(1).mu, pd(1).sigma], phat);
633s  assert (paramci (pd(1)), pci);
633s  [phat, pci] = hnfit (x2, 2);
633s  assert ([pd(2).mu, pd(2).sigma], phat);
633s  assert (paramci (pd(2)), pci);
633s ***** test
633s  x = invgrnd (1, 1, 100, 1);
633s  pd = fitdist (x, "InverseGaussian");
633s  [phat, pci] = invgfit (x);
633s  assert ([pd.mu, pd.lambda], phat);
633s  assert (paramci (pd), pci);
633s ***** test
633s  x1 = invgrnd (1, 1, 100, 1);
633s  x2 = invgrnd (5, 2, 100, 1);
633s  pd = fitdist ([x1; x2], "InverseGaussian", "By", [ones(100,1); 2*ones(100,1)]);
633s  [phat, pci] = invgfit (x1);
633s  assert ([pd(1).mu, pd(1).lambda], phat);
633s  assert (paramci (pd(1)), pci);
633s  [phat, pci] = invgfit (x2);
633s  assert ([pd(2).mu, pd(2).lambda], phat);
633s  assert (paramci (pd(2)), pci);
633s ***** test
633s  x = logirnd (1, 1, 100, 1);
633s  pd = fitdist (x, "logistic");
633s  [phat, pci] = logifit (x);
633s  assert ([pd.mu, pd.sigma], phat);
633s  assert (paramci (pd), pci);
633s ***** test
633s  x1 = logirnd (1, 1, 100, 1);
633s  x2 = logirnd (5, 2, 100, 1);
633s  pd = fitdist ([x1; x2], "logistic", "By", [ones(100,1); 2*ones(100,1)]);
633s  [phat, pci] = logifit (x1);
633s  assert ([pd(1).mu, pd(1).sigma], phat);
633s  assert (paramci (pd(1)), pci);
633s  [phat, pci] = logifit (x2);
633s  assert ([pd(2).mu, pd(2).sigma], phat);
633s  assert (paramci (pd(2)), pci);
633s ***** test
633s  x = loglrnd (1, 1, 100, 1);
633s  pd = fitdist (x, "loglogistic");
633s  [phat, pci] = loglfit (x);
633s  assert ([pd.mu, pd.sigma], phat);
633s  assert (paramci (pd), pci);
633s ***** test
633s  x1 = loglrnd (1, 1, 100, 1);
633s  x2 = loglrnd (5, 2, 100, 1);
633s  pd = fitdist ([x1; x2], "loglogistic", "By", [ones(100,1); 2*ones(100,1)]);
633s  [phat, pci] = loglfit (x1);
633s  assert ([pd(1).mu, pd(1).sigma], phat);
633s  assert (paramci (pd(1)), pci);
633s  [phat, pci] = loglfit (x2);
633s  assert ([pd(2).mu, pd(2).sigma], phat);
633s  assert (paramci (pd(2)), pci);
634s ***** test
634s  x = lognrnd (1, 1, 100, 1);
634s  pd = fitdist (x, "lognormal");
634s  [phat, pci] = lognfit (x);
634s  assert ([pd.mu, pd.sigma], phat);
634s  assert (paramci (pd), pci);
634s ***** test
634s  x1 = lognrnd (1, 1, 100, 1);
634s  x2 = lognrnd (5, 2, 100, 1);
634s  pd = fitdist ([x1; x2], "lognormal", "By", [ones(100,1); 2*ones(100,1)]);
634s  [phat, pci] = lognfit (x1);
634s  assert ([pd(1).mu, pd(1).sigma], phat);
634s  assert (paramci (pd(1)), pci);
634s  [phat, pci] = lognfit (x2);
634s  assert ([pd(2).mu, pd(2).sigma], phat);
634s  assert (paramci (pd(2)), pci);
634s ***** test
634s  x = nakarnd (2, 0.5, 100, 1);
634s  pd = fitdist (x, "Nakagami");
634s  [phat, pci] = nakafit (x);
634s  assert ([pd.mu, pd.omega], phat);
634s  assert (paramci (pd), pci);
634s ***** test
634s  x1 = nakarnd (2, 0.5, 100, 1);
634s  x2 = nakarnd (5, 0.8, 100, 1);
634s  pd = fitdist ([x1; x2], "Nakagami", "By", [ones(100,1); 2*ones(100,1)]);
634s  [phat, pci] = nakafit (x1);
634s  assert ([pd(1).mu, pd(1).omega], phat);
634s  assert (paramci (pd(1)), pci);
634s  [phat, pci] = nakafit (x2);
634s  assert ([pd(2).mu, pd(2).omega], phat);
634s  assert (paramci (pd(2)), pci);
634s ***** test
634s  randp ("seed", 123);
634s  randg ("seed", 321);
634s  x = nbinrnd (2, 0.5, 100, 1);
634s  pd = fitdist (x, "negativebinomial");
634s  [phat, pci] = nbinfit (x);
634s  assert ([pd.R, pd.P], phat);
634s  assert (paramci (pd), pci);
634s ***** test
634s  randp ("seed", 345);
634s  randg ("seed", 543);
634s  x1 = nbinrnd (2, 0.5, 100, 1);
634s  randp ("seed", 432);
634s  randg ("seed", 234);
634s  x2 = nbinrnd (5, 0.8, 100, 1);
634s  pd = fitdist ([x1; x2], "nbin", "By", [ones(100,1); 2*ones(100,1)]);
634s  [phat, pci] = nbinfit (x1);
634s  assert ([pd(1).R, pd(1).P], phat);
634s  assert (paramci (pd(1)), pci);
634s  [phat, pci] = nbinfit (x2);
634s  assert ([pd(2).R, pd(2).P], phat);
634s  assert (paramci (pd(2)), pci);
634s ***** test
634s  x = normrnd (1, 1, 100, 1);
634s  pd = fitdist (x, "normal");
634s  [muhat, sigmahat, muci, sigmaci] = normfit (x);
634s  assert ([pd.mu, pd.sigma], [muhat, sigmahat]);
634s  assert (paramci (pd), [muci, sigmaci]);
634s ***** test
634s  x1 = normrnd (1, 1, 100, 1);
634s  x2 = normrnd (5, 2, 100, 1);
634s  pd = fitdist ([x1; x2], "normal", "By", [ones(100,1); 2*ones(100,1)]);
634s  [muhat, sigmahat, muci, sigmaci] = normfit (x1);
634s  assert ([pd(1).mu, pd(1).sigma], [muhat, sigmahat]);
634s  assert (paramci (pd(1)), [muci, sigmaci]);
634s  [muhat, sigmahat, muci, sigmaci] = normfit (x2);
634s  assert ([pd(2).mu, pd(2).sigma], [muhat, sigmahat]);
634s  assert (paramci (pd(2)), [muci, sigmaci]);
634s ***** test
634s  x = poissrnd (1, 100, 1);
634s  pd = fitdist (x, "poisson");
634s  [phat, pci] = poissfit (x);
634s  assert (pd.lambda, phat);
634s  assert (paramci (pd), pci);
635s ***** test
635s  x1 = poissrnd (1, 100, 1);
635s  x2 = poissrnd (5, 100, 1);
635s  pd = fitdist ([x1; x2], "poisson", "By", [ones(100,1); 2*ones(100,1)]);
635s  [phat, pci] = poissfit (x1);
635s  assert (pd(1).lambda, phat);
635s  assert (paramci (pd(1)), pci);
635s  [phat, pci] = poissfit (x2);
635s  assert (pd(2).lambda, phat);
635s  assert (paramci (pd(2)), pci);
635s ***** test
635s  x = raylrnd (1, 100, 1);
635s  pd = fitdist (x, "rayleigh");
635s  [phat, pci] = raylfit (x);
635s  assert (pd.sigma, phat);
635s  assert (paramci (pd), pci);
635s ***** test
635s  x1 = raylrnd (1, 100, 1);
635s  x2 = raylrnd (5, 100, 1);
635s  pd = fitdist ([x1; x2], "rayleigh", "By", [ones(100,1); 2*ones(100,1)]);
635s  [phat, pci] = raylfit (x1);
635s  assert ( pd(1).sigma, phat);
635s  assert (paramci (pd(1)), pci);
635s  [phat, pci] = raylfit (x2);
635s  assert (pd(2).sigma, phat);
635s  assert (paramci (pd(2)), pci);
635s ***** test
635s  x = ricernd (1, 1, 100, 1);
635s  pd = fitdist (x, "rician");
635s  [phat, pci] = ricefit (x);
635s  assert ([pd.s, pd.sigma], phat);
635s  assert (paramci (pd), pci);
635s ***** test
635s  x1 = ricernd (1, 1, 100, 1);
635s  x2 = ricernd (5, 2, 100, 1);
635s  pd = fitdist ([x1; x2], "rician", "By", [ones(100,1); 2*ones(100,1)]);
635s  [phat, pci] = ricefit (x1);
635s  assert ([pd(1).s, pd(1).sigma], phat);
635s  assert (paramci (pd(1)), pci);
635s  [phat, pci] = ricefit (x2);
635s  assert ([pd(2).s, pd(2).sigma], phat);
635s  assert (paramci (pd(2)), pci);
635s ***** warning <fitdist: 'Stable' distribution not supported yet.> ...
635s  fitdist ([1 2 3 4 5], "Stable");
635s ***** test
635s  x = tlsrnd (0, 1, 1, 100, 1);
635s  pd = fitdist (x, "tlocationscale");
635s  [phat, pci] = tlsfit (x);
635s  assert ([pd.mu, pd.sigma, pd.nu], phat);
635s  assert (paramci (pd), pci);
635s ***** test
635s  x1 = tlsrnd (0, 1, 1, 100, 1);
635s  x2 = tlsrnd (5, 2, 1, 100, 1);
635s  pd = fitdist ([x1; x2], "tlocationscale", "By", [ones(100,1); 2*ones(100,1)]);
635s  [phat, pci] = tlsfit (x1);
635s  assert ([pd(1).mu, pd(1).sigma, pd(1).nu], phat);
635s  assert (paramci (pd(1)), pci);
635s  [phat, pci] = tlsfit (x2);
635s  assert ([pd(2).mu, pd(2).sigma, pd(2).nu], phat);
635s  assert (paramci (pd(2)), pci);
635s ***** test
635s  x = [1 2 3 4 5];
635s  pd = fitdist (x, "weibull");
635s  [phat, pci] = wblfit (x);
635s  assert ([pd.lambda, pd.k], phat);
635s  assert (paramci (pd), pci);
635s ***** test
635s  x = [1 2 3 4 5 6 7 8 9 10];
635s  pd = fitdist (x, "weibull", "By", [1 1 1 1 1 2 2 2 2 2]);
635s  [phat, pci] = wblfit (x(1:5));
635s  assert ([pd(1).lambda, pd(1).k], phat);
635s  assert (paramci (pd(1)), pci);
635s  [phat, pci] = wblfit (x(6:10));
635s  assert ([pd(2).lambda, pd(2).k], phat);
635s  assert (paramci (pd(2)), pci);
635s ***** error <fitdist: DISTNAME is required.> fitdist (1)
635s ***** error <fitdist: DISTNAME must be a character vector.> fitdist (1, ["as";"sd"])
635s ***** error <fitdist: unrecognized distribution name.> fitdist (1, "some")
635s ***** error <fitdist: X must be a numeric vector of real values.> ...
635s  fitdist (ones (2), "normal")
635s ***** error <fitdist: X must be a numeric vector of real values.> ...
635s  fitdist ([i, 2, 3], "normal")
635s ***** error <fitdist: X must be a numeric vector of real values.> ...
635s  fitdist (["a", "s", "d"], "normal")
635s ***** error <fitdist: optional arguments must be in NAME-VALUE pairs.> ...
635s  fitdist ([1, 2, 3], "normal", "By")
635s ***** error <fitdist: GROUPVAR argument must have the same size as the input data in X.> ...
635s  fitdist ([1, 2, 3], "normal", "By", [1, 2])
635s ***** error <fitdist: 'censoring' argument must have the same size as the input data in X.> ...
635s  fitdist ([1, 2, 3], "normal", "Censoring", [1, 2])
635s ***** error <fitdist: 'frequency' argument must have the same size as the input data in X.> ...
635s  fitdist ([1, 2, 3], "normal", "frequency", [1, 2])
635s ***** error <fitdist: 'frequency' argument must contain non-negative integer values.> ...
635s  fitdist ([1, 2, 3], "negativebinomial", "frequency", [1, -2, 3])
635s ***** error <fitdist: invalid value for 'alpha' argument.> ...
635s  fitdist ([1, 2, 3], "normal", "alpha", [1, 2])
635s ***** error <fitdist: invalid value for 'alpha' argument.> ...
635s  fitdist ([1, 2, 3], "normal", "alpha", i)
635s ***** error <fitdist: invalid value for 'alpha' argument.> ...
635s  fitdist ([1, 2, 3], "normal", "alpha", -0.5)
635s ***** error <fitdist: invalid value for 'alpha' argument.> ...
635s  fitdist ([1, 2, 3], "normal", "alpha", 1.5)
635s ***** error <fitdist: 'ntrials' argument must be a positive integer scalar value.> ...
635s  fitdist ([1, 2, 3], "normal", "ntrials", [1, 2])
635s ***** error <fitdist: 'ntrials' argument must be a positive integer scalar value.> ...
635s  fitdist ([1, 2, 3], "normal", "ntrials", 0)
635s ***** error <fitdist: 'options' argument must be a structure compatible for 'fminsearch'.> ...
635s  fitdist ([1, 2, 3], "normal", "options", 0)
635s ***** error <fitdist: 'options' argument must be a structure compatible for 'fminsearch'.> ...
635s  fitdist ([1, 2, 3], "normal", "options", struct ("options", 1))
635s ***** warning fitdist ([1, 2, 3], "kernel", "kernel", "normal");
635s ***** warning fitdist ([1, 2, 3], "kernel", "support", "positive");
635s ***** warning fitdist ([1, 2, 3], "kernel", "width", 1);
635s ***** error <fitdist: unknown parameter name.> ...
635s  fitdist ([1, 2, 3], "normal", "param", struct ("options", 1))
635s ***** error <fitdist: must define GROUPVAR for more than one output arguments.> ...
635s  [pdca, gn, gl] = fitdist ([1, 2, 3], "normal");
635s ***** error <fitdist: invalid THETA value for generalized Pareto distribution.> ...
635s  fitdist ([1, 2, 3], "generalizedpareto", "theta", 2);
635s ***** error <fitdist: invalid MU value for half-normal distribution.> ...
635s  fitdist ([1, 2, 3], "halfnormal", "mu", 2);
635s 77 tests, 77 passed, 0 known failure, 0 skipped
635s [inst/dist_wrap/pdf.m]
635s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_wrap/pdf.m
635s ***** shared x
635s  x = [1:5];
635s ***** assert (pdf ("Beta", x, 5, 2), betapdf (x, 5, 2))
635s ***** assert (pdf ("beta", x, 5, 2), betapdf (x, 5, 2))
635s ***** assert (pdf ("Binomial", x, 5, 2), binopdf (x, 5, 2))
635s ***** assert (pdf ("bino", x, 5, 2), binopdf (x, 5, 2))
635s ***** assert (pdf ("Birnbaum-Saunders", x, 5, 2), bisapdf (x, 5, 2))
635s ***** assert (pdf ("bisa", x, 5, 2), bisapdf (x, 5, 2))
635s ***** assert (pdf ("Burr", x, 5, 2, 2), burrpdf (x, 5, 2, 2))
635s ***** assert (pdf ("burr", x, 5, 2, 2), burrpdf (x, 5, 2, 2))
635s ***** assert (pdf ("Cauchy", x, 5, 2), cauchypdf (x, 5, 2))
635s ***** assert (pdf ("cauchy", x, 5, 2), cauchypdf (x, 5, 2))
635s ***** assert (pdf ("Chi-squared", x, 5), chi2pdf (x, 5))
635s ***** assert (pdf ("chi2", x, 5), chi2pdf (x, 5))
635s ***** assert (pdf ("Extreme Value", x, 5, 2), evpdf (x, 5, 2))
635s ***** assert (pdf ("ev", x, 5, 2), evpdf (x, 5, 2))
635s ***** assert (pdf ("Exponential", x, 5), exppdf (x, 5))
635s ***** assert (pdf ("exp", x, 5), exppdf (x, 5))
635s ***** assert (pdf ("F-Distribution", x, 5, 2), fpdf (x, 5, 2))
635s ***** assert (pdf ("f", x, 5, 2), fpdf (x, 5, 2))
635s ***** assert (pdf ("Gamma", x, 5, 2), gampdf (x, 5, 2))
635s ***** assert (pdf ("gam", x, 5, 2), gampdf (x, 5, 2))
635s ***** assert (pdf ("Geometric", x, 5), geopdf (x, 5))
635s ***** assert (pdf ("geo", x, 5), geopdf (x, 5))
635s ***** assert (pdf ("Generalized Extreme Value", x, 5, 2, 2), gevpdf (x, 5, 2, 2))
635s ***** assert (pdf ("gev", x, 5, 2, 2), gevpdf (x, 5, 2, 2))
635s ***** assert (pdf ("Generalized Pareto", x, 5, 2, 2), gppdf (x, 5, 2, 2))
636s ***** assert (pdf ("gp", x, 5, 2, 2), gppdf (x, 5, 2, 2))
636s ***** assert (pdf ("Gumbel", x, 5, 2), gumbelpdf (x, 5, 2))
636s ***** assert (pdf ("gumbel", x, 5, 2), gumbelpdf (x, 5, 2))
636s ***** assert (pdf ("Half-normal", x, 5, 2), hnpdf (x, 5, 2))
636s ***** assert (pdf ("hn", x, 5, 2), hnpdf (x, 5, 2))
636s ***** assert (pdf ("Hypergeometric", x, 5, 2, 2), hygepdf (x, 5, 2, 2))
636s ***** assert (pdf ("hyge", x, 5, 2, 2), hygepdf (x, 5, 2, 2))
636s ***** assert (pdf ("Inverse Gaussian", x, 5, 2), invgpdf (x, 5, 2))
636s ***** assert (pdf ("invg", x, 5, 2), invgpdf (x, 5, 2))
636s ***** assert (pdf ("Laplace", x, 5, 2), laplacepdf (x, 5, 2))
636s ***** assert (pdf ("laplace", x, 5, 2), laplacepdf (x, 5, 2))
636s ***** assert (pdf ("Logistic", x, 5, 2), logipdf (x, 5, 2))
636s ***** assert (pdf ("logi", x, 5, 2), logipdf (x, 5, 2))
636s ***** assert (pdf ("Log-Logistic", x, 5, 2), loglpdf (x, 5, 2))
636s ***** assert (pdf ("logl", x, 5, 2), loglpdf (x, 5, 2))
636s ***** assert (pdf ("Lognormal", x, 5, 2), lognpdf (x, 5, 2))
636s ***** assert (pdf ("logn", x, 5, 2), lognpdf (x, 5, 2))
636s ***** assert (pdf ("Nakagami", x, 5, 2), nakapdf (x, 5, 2))
636s ***** assert (pdf ("naka", x, 5, 2), nakapdf (x, 5, 2))
636s ***** assert (pdf ("Negative Binomial", x, 5, 2), nbinpdf (x, 5, 2))
636s ***** assert (pdf ("nbin", x, 5, 2), nbinpdf (x, 5, 2))
636s ***** assert (pdf ("Noncentral F-Distribution", x, 5, 2, 2), ncfpdf (x, 5, 2, 2))
636s ***** assert (pdf ("ncf", x, 5, 2, 2), ncfpdf (x, 5, 2, 2))
636s ***** assert (pdf ("Noncentral Student T", x, 5, 2), nctpdf (x, 5, 2))
636s ***** assert (pdf ("nct", x, 5, 2), nctpdf (x, 5, 2))
636s ***** assert (pdf ("Noncentral Chi-Squared", x, 5, 2), ncx2pdf (x, 5, 2))
636s ***** assert (pdf ("ncx2", x, 5, 2), ncx2pdf (x, 5, 2))
636s ***** assert (pdf ("Normal", x, 5, 2), normpdf (x, 5, 2))
636s ***** assert (pdf ("norm", x, 5, 2), normpdf (x, 5, 2))
636s ***** assert (pdf ("Poisson", x, 5), poisspdf (x, 5))
636s ***** assert (pdf ("poiss", x, 5), poisspdf (x, 5))
636s ***** assert (pdf ("Rayleigh", x, 5), raylpdf (x, 5))
636s ***** assert (pdf ("rayl", x, 5), raylpdf (x, 5))
636s ***** assert (pdf ("Rician", x, 5, 1), ricepdf (x, 5, 1))
636s ***** assert (pdf ("rice", x, 5, 1), ricepdf (x, 5, 1))
636s ***** assert (pdf ("Student T", x, 5), tpdf (x, 5))
636s ***** assert (pdf ("t", x, 5), tpdf (x, 5))
636s ***** assert (pdf ("location-scale T", x, 5, 1, 2), tlspdf (x, 5, 1, 2))
636s ***** assert (pdf ("tls", x, 5, 1, 2), tlspdf (x, 5, 1, 2))
636s ***** assert (pdf ("Triangular", x, 5, 2, 2), tripdf (x, 5, 2, 2))
636s ***** assert (pdf ("tri", x, 5, 2, 2), tripdf (x, 5, 2, 2))
636s ***** assert (pdf ("Discrete Uniform", x, 5), unidpdf (x, 5))
636s ***** assert (pdf ("unid", x, 5), unidpdf (x, 5))
636s ***** assert (pdf ("Uniform", x, 5, 2), unifpdf (x, 5, 2))
636s ***** assert (pdf ("unif", x, 5, 2), unifpdf (x, 5, 2))
636s ***** assert (pdf ("Von Mises", x, 5, 2), vmpdf (x, 5, 2))
636s ***** assert (pdf ("vm", x, 5, 2), vmpdf (x, 5, 2))
636s ***** assert (pdf ("Weibull", x, 5, 2), wblpdf (x, 5, 2))
636s ***** assert (pdf ("wbl", x, 5, 2), wblpdf (x, 5, 2))
636s ***** error<pdf: distribution NAME must a char string.> pdf (1)
636s ***** error<pdf: distribution NAME must a char string.> pdf ({"beta"})
636s ***** error<pdf: X must be numeric.> pdf ("beta", {[1 2 3 4 5]})
636s ***** error<pdf: X must be numeric.> pdf ("beta", "text")
636s ***** error<pdf: values in X must be real.> pdf ("beta", 1+i)
636s ***** error<pdf: distribution parameters must be numeric.> ...
636s  pdf ("Beta", x, "a", 2)
636s ***** error<pdf: distribution parameters must be numeric.> ...
636s  pdf ("Beta", x, 5, "")
636s ***** error<pdf: distribution parameters must be numeric.> ...
636s  pdf ("Beta", x, 5, {2})
636s ***** error<pdf: chi2 distribution requires 1 parameter.> pdf ("chi2", x)
636s ***** error<pdf: Beta distribution requires 2 parameters.> pdf ("Beta", x, 5)
636s ***** error<pdf: Burr distribution requires 3 parameters.> pdf ("Burr", x, 5)
636s ***** error<pdf: Burr distribution requires 3 parameters.> pdf ("Burr", x, 5, 2)
636s 86 tests, 86 passed, 0 known failure, 0 skipped
636s [inst/dist_wrap/mle.m]
636s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_wrap/mle.m
636s ***** error <mle: X must be a numeric vector of real values.> mle (ones (2))
636s ***** error <mle: X must be a numeric vector of real values.> mle ("text")
636s ***** error <mle: X must be a numeric vector of real values.> mle ([1, 2, 3, i, 5])
636s ***** error <mle: optional arguments must be in NAME-VALUE pairs.> ...
636s  mle ([1:50], "distribution")
636s ***** error <mle: 'censoring' argument must have the same size as the input data in X.> ...
636s  mle ([1:50], "censoring", logical ([1,0,1,0]))
636s ***** error <mle: 'frequency' argument must have the same size as the input data in X.> ...
636s  mle ([1:50], "frequency", [1,0,1,0])
636s ***** error <mle: 'frequency' argument must contain non-negative integer values.> ...
636s  mle ([1 0 1 0], "frequency", [-1 1 0 0])
636s ***** error <mle: 'frequency' argument must contain non-negative integer values.> ...
636s  mle ([1 0 1 0], "distribution", "nbin", "frequency", [-1 1 0 0])
636s ***** error <mle: invalid value for 'alpha' argument.> mle ([1:50], "alpha", [0.05, 0.01])
636s ***** error <mle: invalid value for 'alpha' argument.> mle ([1:50], "alpha", 1)
636s ***** error <mle: invalid value for 'alpha' argument.> mle ([1:50], "alpha", -1)
636s ***** error <mle: invalid value for 'alpha' argument.> mle ([1:50], "alpha", i)
636s ***** error <mle: 'ntrials' argument must be a positive integer scalar value.> ...
636s  mle ([1:50], "ntrials", -1)
636s ***** error <mle: 'ntrials' argument must be a positive integer scalar value.> ...
636s  mle ([1:50], "ntrials", [20, 50])
636s ***** error <mle: 'ntrials' argument must be a positive integer scalar value.> ...
636s  mle ([1:50], "ntrials", [20.3])
636s ***** error <mle: 'ntrials' argument must be a positive integer scalar value.> ...
636s  mle ([1:50], "ntrials", 3i)
636s ***** error <mle: 'options' argument must be a structure compatible for 'fminsearch'.> ...
636s  mle ([1:50], "options", 4)
636s ***** error <mle: 'options' argument must be a structure compatible for 'fminsearch'.> ...
636s  mle ([1:50], "options", struct ("x", 3))
636s ***** error <mle: unknown parameter name.> mle ([1:50], "NAME", "value")
636s ***** error <mle: censoring is not supported for the Bernoulli distribution.> ...
636s  mle ([1 0 1 0], "distribution", "bernoulli", "censoring", [1 1 0 0])
636s ***** error <mle: invalid data for the Bernoulli distribution.> ...
636s  mle ([1 2 1 0], "distribution", "bernoulli")
636s ***** error <mle: censoring is not supported for the Beta distribution.> ...
636s  mle ([1 0 1 0], "distribution", "beta", "censoring", [1 1 0 0])
636s ***** error <mle: censoring is not supported for the Binomial distribution.> ...
636s  mle ([1 0 1 0], "distribution", "bino", "censoring", [1 1 0 0])
636s ***** error <mle: 'Ntrials' parameter is required for the Binomial distribution.> ...
636s  mle ([1 0 1 0], "distribution", "bino")
636s ***** error <mle: censoring is not supported for the Geometric distribution.> ...
636s  mle ([1 0 1 0], "distribution", "geo", "censoring", [1 1 0 0])
636s ***** error <mle: censoring is not supported for the Generalized Extreme Value distribution.> ...
636s  mle ([1 0 1 0], "distribution", "gev", "censoring", [1 1 0 0])
636s ***** error <mle: censoring is not supported for the Generalized Pareto distribution.> ...
636s  mle ([1 0 1 0], "distribution", "gp", "censoring", [1 1 0 0])
636s ***** error <mle: invalid 'theta' location parameter for the Generalized Pareto distribution.> ...
636s  mle ([1 0 -1 0], "distribution", "gp")
636s ***** error <mle: censoring is not supported for the Half Normal distribution.> ...
636s  mle ([1 0 1 0], "distribution", "hn", "censoring", [1 1 0 0])
636s ***** error <mle: invalid 'mu' location parameter for the Half Normal distribution.> ...
636s  mle ([1 0 -1 0], "distribution", "hn")
636s ***** error <mle: censoring is not supported for the Negative Binomial distribution.> ...
636s  mle ([1 0 1 0], "distribution", "nbin", "censoring", [1 1 0 0])
636s ***** error <mle: censoring is not supported for the Poisson distribution.> ...
636s  mle ([1 0 1 0], "distribution", "poisson", "censoring", [1 1 0 0])
636s ***** error <mle: censoring is not supported for the Discrete Uniform distribution.> ...
636s  mle ([1 0 1 0], "distribution", "unid", "censoring", [1 1 0 0])
636s ***** error <mle: censoring is not supported for the Continuous Uniform distribution.> ...
636s  mle ([1 0 1 0], "distribution", "unif", "censoring", [1 1 0 0])
636s ***** error <mle: unrecognized distribution name.> mle ([1:50], "distribution", "value")
636s ***** error <mle: censoring is not supported for the Continuous Uniform distribution.> ...
636s  mle ([1 0 1 0], "distribution", "unif", "censoring", [1 1 0 0])
636s 36 tests, 36 passed, 0 known failure, 0 skipped
636s [inst/dist_wrap/makedist.m]
636s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_wrap/makedist.m
636s ***** test
636s  pd = makedist ("beta");
636s  assert (class (pd), "BetaDistribution");
636s  assert (pd.a, 1);
636s  assert (pd.b, 1);
636s ***** test
636s  pd = makedist ("beta", "a", 5);
636s  assert (pd.a, 5);
636s  assert (pd.b, 1);
636s ***** test
636s  pd = makedist ("beta", "b", 5);
636s  assert (pd.a, 1);
636s  assert (pd.b, 5);
636s ***** test
636s  pd = makedist ("beta", "a", 3, "b", 5);
636s  assert (pd.a, 3);
636s  assert (pd.b, 5);
636s ***** test
636s  pd = makedist ("binomial");
636s  assert (class (pd), "BinomialDistribution");
636s  assert (pd.N, 1);
636s  assert (pd.p, 0.5);
636s ***** test
636s  pd = makedist ("binomial", "N", 5);
636s  assert (pd.N, 5);
636s  assert (pd.p, 0.5);
636s ***** test
636s  pd = makedist ("binomial", "p", 0.2);
636s  assert (pd.N, 1);
636s  assert (pd.p, 0.2);
636s ***** test
636s  pd = makedist ("binomial", "N", 3, "p", 0.3);
636s  assert (pd.N, 3);
636s  assert (pd.p, 0.3);
636s ***** test
636s  pd = makedist ("birnbaumsaunders");
636s  assert (class (pd), "BirnbaumSaundersDistribution");
636s  assert (pd.beta, 1);
636s  assert (pd.gamma, 1);
636s ***** test
636s  pd = makedist ("birnbaumsaunders", "beta", 5);
636s  assert (pd.beta, 5);
636s  assert (pd.gamma, 1);
636s ***** test
636s  pd = makedist ("birnbaumsaunders", "gamma", 5);
636s  assert (pd.beta, 1);
636s  assert (pd.gamma, 5);
636s ***** test
636s  pd = makedist ("birnbaumsaunders", "beta", 3, "gamma", 5);
636s  assert (pd.beta, 3);
636s  assert (pd.gamma, 5);
636s ***** test
636s  pd = makedist ("burr");
636s  assert (class (pd), "BurrDistribution");
636s  assert (pd.alpha, 1);
636s  assert (pd.c, 1);
636s  assert (pd.k, 1);
636s ***** test
636s  pd = makedist ("burr", "k", 5);
636s  assert (pd.alpha, 1);
636s  assert (pd.c, 1);
636s  assert (pd.k, 5);
636s ***** test
636s  pd = makedist ("burr", "c", 5);
636s  assert (pd.alpha, 1);
636s  assert (pd.c, 5);
636s  assert (pd.k, 1);
636s ***** test
636s  pd = makedist ("burr", "alpha", 3, "c", 5);
636s  assert (pd.alpha, 3);
636s  assert (pd.c, 5);
636s  assert (pd.k, 1);
636s ***** test
636s  pd = makedist ("burr", "k", 3, "c", 5);
636s  assert (pd.alpha, 1);
636s  assert (pd.c, 5);
636s  assert (pd.k, 3);
636s ***** test
636s  pd = makedist ("exponential");
636s  assert (class (pd), "ExponentialDistribution");
636s  assert (pd.mu, 1);
636s ***** test
636s  pd = makedist ("exponential", "mu", 5);
636s  assert (pd.mu, 5);
636s ***** test
636s  pd = makedist ("extremevalue");
636s  assert (class (pd), "ExtremeValueDistribution");
636s  assert (pd.mu, 0);
636s  assert (pd.sigma, 1);
636s ***** test
636s  pd = makedist ("extremevalue", "mu", 5);
636s  assert (class (pd), "ExtremeValueDistribution");
636s  assert (pd.mu, 5);
636s  assert (pd.sigma, 1);
636s ***** test
636s  pd = makedist ("ev", "sigma", 5);
636s  assert (class (pd), "ExtremeValueDistribution");
636s  assert (pd.mu, 0);
636s  assert (pd.sigma, 5);
636s ***** test
636s  pd = makedist ("ev", "mu", -3, "sigma", 5);
636s  assert (class (pd), "ExtremeValueDistribution");
636s  assert (pd.mu, -3);
636s  assert (pd.sigma, 5);
636s ***** test
636s  pd = makedist ("gamma");
636s  assert (class (pd), "GammaDistribution");
636s  assert (pd.a, 1);
636s  assert (pd.b, 1);
636s ***** test
636s  pd = makedist ("gamma", "a", 5);
636s  assert (pd.a, 5);
636s  assert (pd.b, 1);
636s ***** test
636s  pd = makedist ("gamma", "b", 5);
636s  assert (pd.a, 1);
636s  assert (pd.b, 5);
636s ***** test
636s  pd = makedist ("gamma", "a", 3, "b", 5);
636s  assert (pd.a, 3);
636s  assert (pd.b, 5);
636s ***** test
636s  pd = makedist ("GeneralizedExtremeValue");
636s  assert (class (pd), "GeneralizedExtremeValueDistribution");
636s  assert (pd.k, 0);
636s  assert (pd.sigma, 1);
636s  assert (pd.mu, 0);
636s ***** test
636s  pd = makedist ("GeneralizedExtremeValue", "k", 5);
636s  assert (pd.k, 5);
636s  assert (pd.sigma, 1);
636s  assert (pd.mu, 0);
636s ***** test
636s  pd = makedist ("GeneralizedExtremeValue", "sigma", 5);
636s  assert (pd.k, 0);
636s  assert (pd.sigma, 5);
636s  assert (pd.mu, 0);
636s ***** test
636s  pd = makedist ("GeneralizedExtremeValue", "k", 3, "sigma", 5);
636s  assert (pd.k, 3);
636s  assert (pd.sigma, 5);
636s  assert (pd.mu, 0);
636s ***** test
636s  pd = makedist ("GeneralizedExtremeValue", "mu", 3, "sigma", 5);
636s  assert (pd.k, 0);
636s  assert (pd.sigma, 5);
636s  assert (pd.mu, 3);
636s ***** test
636s  pd = makedist ("GeneralizedPareto");
636s  assert (class (pd), "GeneralizedParetoDistribution");
636s  assert (pd.k, 1);
636s  assert (pd.sigma, 1);
636s  assert (pd.theta, 1);
636s ***** test
636s  pd = makedist ("GeneralizedPareto", "k", 5);
636s  assert (pd.k, 5);
636s  assert (pd.sigma, 1);
636s  assert (pd.theta, 1);
636s ***** test
636s  pd = makedist ("GeneralizedPareto", "sigma", 5);
636s  assert (pd.k, 1);
636s  assert (pd.sigma, 5);
636s  assert (pd.theta, 1);
636s ***** test
636s  pd = makedist ("GeneralizedPareto", "k", 3, "sigma", 5);
636s  assert (pd.k, 3);
636s  assert (pd.sigma, 5);
636s  assert (pd.theta, 1);
636s ***** test
636s  pd = makedist ("GeneralizedPareto", "theta", 3, "sigma", 5);
636s  assert (pd.k, 1);
636s  assert (pd.sigma, 5);
636s  assert (pd.theta, 3);
636s ***** test
636s  pd = makedist ("HalfNormal");
636s  assert (class (pd), "HalfNormalDistribution");
636s  assert (pd.mu, 0);
636s  assert (pd.sigma, 1);
636s ***** test
636s  pd = makedist ("HalfNormal", "mu", 5);
636s  assert (pd.mu, 5);
636s  assert (pd.sigma, 1);
636s ***** test
636s  pd = makedist ("HalfNormal", "sigma", 5);
636s  assert (pd.mu, 0);
636s  assert (pd.sigma, 5);
636s ***** test
636s  pd = makedist ("HalfNormal", "mu", 3, "sigma", 5);
636s  assert (pd.mu, 3);
636s  assert (pd.sigma, 5);
636s ***** test
636s  pd = makedist ("InverseGaussian");
636s  assert (class (pd), "InverseGaussianDistribution");
636s  assert (pd.mu, 1);
636s  assert (pd.lambda, 1);
636s ***** test
636s  pd = makedist ("InverseGaussian", "mu", 5);
636s  assert (pd.mu, 5);
636s  assert (pd.lambda, 1);
636s ***** test
636s  pd = makedist ("InverseGaussian", "lambda", 5);
636s  assert (pd.mu, 1);
636s  assert (pd.lambda, 5);
636s ***** test
636s  pd = makedist ("InverseGaussian", "mu", 3, "lambda", 5);
636s  assert (pd.mu, 3);
636s  assert (pd.lambda, 5);
636s ***** test
636s  pd = makedist ("logistic");
636s  assert (class (pd), "LogisticDistribution");
636s  assert (pd.mu, 0);
636s  assert (pd.sigma, 1);
636s ***** test
636s  pd = makedist ("logistic", "mu", 5);
636s  assert (pd.mu, 5);
636s  assert (pd.sigma, 1);
636s ***** test
636s  pd = makedist ("logistic", "sigma", 5);
636s  assert (pd.mu, 0);
636s  assert (pd.sigma, 5);
636s ***** test
636s  pd = makedist ("logistic", "mu", 3, "sigma", 5);
636s  assert (pd.mu, 3);
636s  assert (pd.sigma, 5);
636s ***** test
636s  pd = makedist ("loglogistic");
636s  assert (class (pd), "LoglogisticDistribution");
636s  assert (pd.mu, 0);
636s  assert (pd.sigma, 1);
636s ***** test
636s  pd = makedist ("loglogistic", "mu", 5);
636s  assert (pd.mu, 5);
636s  assert (pd.sigma, 1);
636s ***** test
636s  pd = makedist ("loglogistic", "sigma", 5);
636s  assert (pd.mu, 0);
636s  assert (pd.sigma, 5);
636s ***** test
636s  pd = makedist ("loglogistic", "mu", 3, "sigma", 5);
636s  assert (pd.mu, 3);
636s  assert (pd.sigma, 5);
636s ***** test
636s  pd = makedist ("Lognormal");
636s  assert (class (pd), "LognormalDistribution");
636s  assert (pd.mu, 0);
636s  assert (pd.sigma, 1);
636s ***** test
636s  pd = makedist ("Lognormal", "mu", 5);
636s  assert (pd.mu, 5);
636s  assert (pd.sigma, 1);
636s ***** test
636s  pd = makedist ("Lognormal", "sigma", 5);
636s  assert (pd.mu, 0);
636s  assert (pd.sigma, 5);
636s ***** test
636s  pd = makedist ("Lognormal", "mu", -3, "sigma", 5);
636s  assert (pd.mu, -3);
636s  assert (pd.sigma, 5);
636s ***** test
636s  pd = makedist ("Loguniform");
636s  assert (class (pd), "LoguniformDistribution");
636s  assert (pd.Lower, 1);
636s  assert (pd.Upper, 4);
636s ***** test
636s  pd = makedist ("Loguniform", "Lower", 2);
636s  assert (pd.Lower, 2);
636s  assert (pd.Upper, 4);
636s ***** test
636s  pd = makedist ("Loguniform", "Lower", 1, "Upper", 3);
636s  assert (pd.Lower, 1);
636s  assert (pd.Upper, 3);
636s ***** test
636s  pd = makedist ("Multinomial");
636s  assert (class (pd), "MultinomialDistribution");
636s  assert (pd.Probabilities, [0.5, 0.5]);
636s ***** test
636s  pd = makedist ("Multinomial", "Probabilities", [0.2, 0.3, 0.1, 0.4]);
636s  assert (class (pd), "MultinomialDistribution");
636s  assert (pd.Probabilities, [0.2, 0.3, 0.1, 0.4]);
636s ***** test
636s  pd = makedist ("Nakagami");
636s  assert (class (pd), "NakagamiDistribution");
636s  assert (pd.mu, 1);
636s  assert (pd.omega, 1);
636s ***** test
636s  pd = makedist ("Nakagami", "mu", 5);
636s  assert (class (pd), "NakagamiDistribution");
636s  assert (pd.mu, 5);
636s  assert (pd.omega, 1);
636s ***** test
636s  pd = makedist ("Nakagami", "omega", 0.3);
636s  assert (class (pd), "NakagamiDistribution");
636s  assert (pd.mu, 1);
636s  assert (pd.omega, 0.3);
636s ***** test
636s  pd = makedist ("NegativeBinomial");
636s  assert (class (pd), "NegativeBinomialDistribution");
636s  assert (pd.R, 1);
636s  assert (pd.P, 0.5);
636s ***** test
636s  pd = makedist ("NegativeBinomial", "R", 5);
636s  assert (class (pd), "NegativeBinomialDistribution");
636s  assert (pd.R, 5);
636s  assert (pd.P, 0.5);
636s ***** test
636s  pd = makedist ("NegativeBinomial", "p", 0.3);
636s  assert (class (pd), "NegativeBinomialDistribution");
636s  assert (pd.R, 1);
636s  assert (pd.P, 0.3);
636s ***** test
636s  pd = makedist ("Normal");
636s  assert (class (pd), "NormalDistribution");
636s  assert (pd.mu, 0);
636s  assert (pd.sigma, 1);
636s ***** test
636s  pd = makedist ("Normal", "mu", 5);
636s  assert (class (pd), "NormalDistribution");
636s  assert (pd.mu, 5);
636s  assert (pd.sigma, 1);
636s ***** test
636s  pd = makedist ("Normal", "sigma", 5);
636s  assert (class (pd), "NormalDistribution");
636s  assert (pd.mu, 0);
636s  assert (pd.sigma, 5);
636s ***** test
636s  pd = makedist ("Normal", "mu", -3, "sigma", 5);
636s  assert (class (pd), "NormalDistribution");
636s  assert (pd.mu, -3);
636s  assert (pd.sigma, 5);
636s ***** test
636s  pd = makedist ("PiecewiseLinear");
636s  assert (class (pd), "PiecewiseLinearDistribution");
636s  assert (pd.x, [0; 1]);
636s  assert (pd.Fx, [0; 1]);
636s ***** test
636s  pd = makedist ("PiecewiseLinear", "x", [0, 1, 2], "Fx", [0, 0.5, 1]);
636s  assert (pd.x, [0; 1; 2]);
636s  assert (pd.Fx, [0; 0.5; 1]);
636s ***** test
636s  pd = makedist ("Poisson");
636s  assert (class (pd), "PoissonDistribution");
636s  assert (pd.lambda, 1);
636s ***** test
636s  pd = makedist ("Poisson", "lambda", 5);
636s  assert (pd.lambda, 5);
636s ***** test
636s  pd = makedist ("Rayleigh");
636s  assert (class (pd), "RayleighDistribution");
636s  assert (pd.sigma, 1);
636s ***** test
636s  pd = makedist ("Rayleigh", "sigma", 5);
636s  assert (pd.sigma, 5);
636s ***** test
636s  pd = makedist ("Rician");
636s  assert (class (pd), "RicianDistribution");
636s  assert (pd.s, 1);
636s  assert (pd.sigma, 1);
636s ***** test
636s  pd = makedist ("Rician", "s", 3);
636s  assert (pd.s, 3);
636s  assert (pd.sigma, 1);
636s ***** test
636s  pd = makedist ("Rician", "sigma", 3);
636s  assert (pd.s, 1);
636s  assert (pd.sigma, 3);
636s ***** test
636s  pd = makedist ("Rician", "s", 2, "sigma", 3);
636s  assert (pd.s, 2);
636s  assert (pd.sigma, 3);
636s ***** warning
636s  pd = makedist ("stable");
636s  assert (class (pd), "double");
636s  assert (isempty (pd), true);
636s ***** test
636s  pd = makedist ("tlocationscale");
636s  assert (class (pd), "tLocationScaleDistribution");
636s  assert (pd.mu, 0);
636s  assert (pd.sigma, 1);
636s  assert (pd.nu, 5);
636s ***** test
636s  pd = makedist ("tlocationscale", "mu", 5);
636s  assert (pd.mu, 5);
636s  assert (pd.sigma, 1);
636s  assert (pd.nu, 5);
636s ***** test
636s  pd = makedist ("tlocationscale", "sigma", 2);
636s  assert (pd.mu, 0);
636s  assert (pd.sigma, 2);
636s  assert (pd.nu, 5);
636s ***** test
636s  pd = makedist ("tlocationscale", "mu", 5, "sigma", 2);
636s  assert (pd.mu, 5);
636s  assert (pd.sigma, 2);
636s  assert (pd.nu, 5);
636s ***** test
636s  pd = makedist ("tlocationscale", "nu", 1, "sigma", 2);
636s  assert (pd.mu, 0);
636s  assert (pd.sigma, 2);
636s  assert (pd.nu, 1);
636s ***** test
636s  pd = makedist ("tlocationscale", "mu", -2, "sigma", 3, "nu", 1);
636s  assert (pd.mu, -2);
636s  assert (pd.sigma, 3);
636s  assert (pd.nu, 1);
636s ***** test
636s  pd = makedist ("Triangular");
636s  assert (class (pd), "TriangularDistribution");
636s  assert (pd.A, 0);
636s  assert (pd.B, 0.5);
636s  assert (pd.C, 1);
636s ***** test
636s  pd = makedist ("Triangular", "A", -2);
636s  assert (pd.A, -2);
636s  assert (pd.B, 0.5);
636s  assert (pd.C, 1);
636s ***** test
636s  pd = makedist ("Triangular", "A", 0.5, "B", 0.9);
636s  assert (pd.A, 0.5);
636s  assert (pd.B, 0.9);
636s  assert (pd.C, 1);
636s ***** test
636s  pd = makedist ("Triangular", "A", 1, "B", 2, "C", 5);
636s  assert (pd.A, 1);
636s  assert (pd.B, 2);
636s  assert (pd.C, 5);
636s ***** test
636s  pd = makedist ("Uniform");
636s  assert (class (pd), "UniformDistribution");
636s  assert (pd.Lower, 0);
636s  assert (pd.Upper, 1);
636s ***** test
636s  pd = makedist ("Uniform", "Lower", -2);
636s  assert (pd.Lower, -2);
636s  assert (pd.Upper, 1);
636s ***** test
636s  pd = makedist ("Uniform", "Lower", 1, "Upper", 3);
636s  assert (pd.Lower, 1);
636s  assert (pd.Upper, 3);
636s ***** test
636s  pd = makedist ("Weibull");
636s  assert (class (pd), "WeibullDistribution");
636s  assert (pd.lambda, 1);
636s  assert (pd.k, 1);
636s ***** test
636s  pd = makedist ("Weibull", "lambda", 3);
636s  assert (pd.lambda, 3);
636s  assert (pd.k, 1);
636s ***** test
636s  pd = makedist ("Weibull", "lambda", 3, "k", 2);
636s  assert (pd.lambda, 3);
636s  assert (pd.k, 2);
636s ***** error <makedist: DISTNAME must be a character vector.> makedist (1)
636s ***** error <makedist: DISTNAME must be a character vector.> makedist (["as";"sd"])
636s ***** error <makedist: unrecognized distribution name.> makedist ("some")
636s ***** error <makedist: optional arguments must be in NAME-VALUE pairs.> ...
636s  makedist ("Beta", "a")
636s ***** error <makedist: unknown parameter for 'Beta' distribution.> ...
636s  makedist ("Beta", "a", 1, "Q", 23)
636s ***** error <makedist: unknown parameter for 'Binomial' distribution.> ...
636s  makedist ("Binomial", "N", 1, "Q", 23)
636s ***** error <makedist: unknown parameter for 'BirnbaumSaunders' distribution.> ...
636s  makedist ("BirnbaumSaunders", "N", 1)
636s ***** error <makedist: unknown parameter for 'Burr' distribution.> ...
636s  makedist ("Burr", "lambda", 1, "sdfs", 34)
636s ***** error <makedist: unknown parameter for 'ExtremeValue' distribution.> ...
636s  makedist ("extremevalue", "mu", 1, "sdfs", 34)
636s ***** error <makedist: unknown parameter for 'Exponential' distribution.> ...
636s  makedist ("exponential", "mu", 1, "sdfs", 34)
636s ***** error <makedist: unknown parameter for 'Gamma' distribution.> ...
636s  makedist ("Gamma", "k", 1, "sdfs", 34)
636s ***** error <makedist: unknown parameter for 'GeneralizedExtremeValue' distribution.> ...
636s  makedist ("GeneralizedExtremeValue", "k", 1, "sdfs", 34)
636s ***** error <makedist: unknown parameter for 'GeneralizedPareto' distribution.> ...
636s  makedist ("GeneralizedPareto", "k", 1, "sdfs", 34)
636s ***** error <makedist: unknown parameter for 'HalfNormal' distribution.> ...
636s  makedist ("HalfNormal", "k", 1, "sdfs", 34)
636s ***** error <makedist: unknown parameter for 'InverseGaussian' distribution.> ...
636s  makedist ("InverseGaussian", "k", 1, "sdfs", 34)
636s ***** error <makedist: unknown parameter for 'Logistic' distribution.> ...
636s  makedist ("Logistic", "k", 1, "sdfs", 34)
636s ***** error <makedist: unknown parameter for 'Loglogistic' distribution.> ...
636s  makedist ("Loglogistic", "k", 1, "sdfs", 34)
636s ***** error <makedist: unknown parameter for 'Lognormal' distribution.> ...
636s  makedist ("Lognormal", "k", 1, "sdfs", 34)
636s ***** error <makedist: unknown parameter for 'Loguniform' distribution.> ...
636s  makedist ("Loguniform", "k", 1, "sdfs", 34)
636s ***** error <makedist: unknown parameter for 'Multinomial' distribution.> ...
636s  makedist ("Multinomial", "k", 1, "sdfs", 34)
636s ***** error <makedist: unknown parameter for 'Nakagami' distribution.> ...
636s  makedist ("Nakagami", "mu", 1, "sdfs", 34)
636s ***** error <makedist: unknown parameter for 'NegativeBinomial' distribution.> ...
636s  makedist ("NegativeBinomial", "mu", 1, "sdfs", 34)
636s ***** error <makedist: unknown parameter for 'Normal' distribution.> ...
636s  makedist ("Normal", "mu", 1, "sdfs", 34)
636s ***** error <makedist: unknown parameter for 'PiecewiseLinear' distribution.> ...
636s  makedist ("PiecewiseLinear", "mu", 1, "sdfs", 34)
636s ***** error <makedist: unknown parameter for 'Poisson' distribution.> ...
636s  makedist ("Poisson", "mu", 1, "sdfs", 34)
636s ***** error <makedist: unknown parameter for 'Rayleigh' distribution.> ...
636s  makedist ("Rayleigh", "mu", 1, "sdfs", 34)
636s ***** error <makedist: unknown parameter for 'Rician' distribution.> ...
636s  makedist ("Rician", "mu", 1, "sdfs", 34)
636s ***** error <makedist: unknown parameter for 'Stable' distribution.> ...
636s  makedist ("Stable", "mu", 1, "sdfs", 34)
636s ***** error <makedist: unknown parameter for 'tLocationScale' distribution.> ...
636s  makedist ("tLocationScale", "mu", 1, "sdfs", 34)
636s ***** error <makedist: unknown parameter for 'Triangular' distribution.> ...
636s  makedist ("Triangular", "mu", 1, "sdfs", 34)
636s ***** error <makedist: unknown parameter for 'Uniform' distribution.> ...
636s  makedist ("Uniform", "mu", 1, "sdfs", 34)
636s ***** error <makedist: unknown parameter for 'Weibull' distribution.> ...
636s  makedist ("Weibull", "mu", 1, "sdfs", 34)
636s 131 tests, 131 passed, 0 known failure, 0 skipped
636s [inst/dist_wrap/icdf.m]
636s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_wrap/icdf.m
636s ***** shared p
636s  p = [0.05:0.05:0.5];
636s ***** assert (icdf ("Beta", p, 5, 2), betainv (p, 5, 2))
636s ***** assert (icdf ("beta", p, 5, 2), betainv (p, 5, 2))
636s ***** assert (icdf ("Binomial", p, 5, 2), binoinv (p, 5, 2))
636s ***** assert (icdf ("bino", p, 5, 2), binoinv (p, 5, 2))
636s ***** assert (icdf ("Birnbaum-Saunders", p, 5, 2), bisainv (p, 5, 2))
636s ***** assert (icdf ("bisa", p, 5, 2), bisainv (p, 5, 2))
636s ***** assert (icdf ("Burr", p, 5, 2, 2), burrinv (p, 5, 2, 2))
636s ***** assert (icdf ("burr", p, 5, 2, 2), burrinv (p, 5, 2, 2))
636s ***** assert (icdf ("Cauchy", p, 5, 2), cauchyinv (p, 5, 2))
636s ***** assert (icdf ("cauchy", p, 5, 2), cauchyinv (p, 5, 2))
636s ***** assert (icdf ("Chi-squared", p, 5), chi2inv (p, 5))
636s ***** assert (icdf ("chi2", p, 5), chi2inv (p, 5))
636s ***** assert (icdf ("Extreme Value", p, 5, 2), evinv (p, 5, 2))
636s ***** assert (icdf ("ev", p, 5, 2), evinv (p, 5, 2))
636s ***** assert (icdf ("Exponential", p, 5), expinv (p, 5))
636s ***** assert (icdf ("exp", p, 5), expinv (p, 5))
636s ***** assert (icdf ("F-Distribution", p, 5, 2), finv (p, 5, 2))
636s ***** assert (icdf ("f", p, 5, 2), finv (p, 5, 2))
636s ***** assert (icdf ("Gamma", p, 5, 2), gaminv (p, 5, 2))
636s ***** assert (icdf ("gam", p, 5, 2), gaminv (p, 5, 2))
636s ***** assert (icdf ("Geometric", p, 5), geoinv (p, 5))
636s ***** assert (icdf ("geo", p, 5), geoinv (p, 5))
636s ***** assert (icdf ("Generalized Extreme Value", p, 5, 2, 2), gevinv (p, 5, 2, 2))
636s ***** assert (icdf ("gev", p, 5, 2, 2), gevinv (p, 5, 2, 2))
636s ***** assert (icdf ("Generalized Pareto", p, 5, 2, 2), gpinv (p, 5, 2, 2))
636s ***** assert (icdf ("gp", p, 5, 2, 2), gpinv (p, 5, 2, 2))
636s ***** assert (icdf ("Gumbel", p, 5, 2), gumbelinv (p, 5, 2))
636s ***** assert (icdf ("gumbel", p, 5, 2), gumbelinv (p, 5, 2))
636s ***** assert (icdf ("Half-normal", p, 5, 2), hninv (p, 5, 2))
636s ***** assert (icdf ("hn", p, 5, 2), hninv (p, 5, 2))
636s ***** assert (icdf ("Hypergeometric", p, 5, 2, 2), hygeinv (p, 5, 2, 2))
636s ***** assert (icdf ("hyge", p, 5, 2, 2), hygeinv (p, 5, 2, 2))
636s ***** assert (icdf ("Inverse Gaussian", p, 5, 2), invginv (p, 5, 2))
636s ***** assert (icdf ("invg", p, 5, 2), invginv (p, 5, 2))
636s ***** assert (icdf ("Laplace", p, 5, 2), laplaceinv (p, 5, 2))
636s ***** assert (icdf ("laplace", p, 5, 2), laplaceinv (p, 5, 2))
636s ***** assert (icdf ("Logistic", p, 5, 2), logiinv (p, 5, 2))
636s ***** assert (icdf ("logi", p, 5, 2), logiinv (p, 5, 2))
636s ***** assert (icdf ("Log-Logistic", p, 5, 2), loglinv (p, 5, 2))
636s ***** assert (icdf ("logl", p, 5, 2), loglinv (p, 5, 2))
636s ***** assert (icdf ("Lognormal", p, 5, 2), logninv (p, 5, 2))
636s ***** assert (icdf ("logn", p, 5, 2), logninv (p, 5, 2))
636s ***** assert (icdf ("Nakagami", p, 5, 2), nakainv (p, 5, 2))
636s ***** assert (icdf ("naka", p, 5, 2), nakainv (p, 5, 2))
636s ***** assert (icdf ("Negative Binomial", p, 5, 2), nbininv (p, 5, 2))
636s ***** assert (icdf ("nbin", p, 5, 2), nbininv (p, 5, 2))
636s ***** assert (icdf ("Noncentral F-Distribution", p, 5, 2, 2), ncfinv (p, 5, 2, 2))
637s ***** assert (icdf ("ncf", p, 5, 2, 2), ncfinv (p, 5, 2, 2))
637s ***** assert (icdf ("Noncentral Student T", p, 5, 2), nctinv (p, 5, 2))
637s ***** assert (icdf ("nct", p, 5, 2), nctinv (p, 5, 2))
638s ***** assert (icdf ("Noncentral Chi-Squared", p, 5, 2), ncx2inv (p, 5, 2))
638s ***** assert (icdf ("ncx2", p, 5, 2), ncx2inv (p, 5, 2))
638s ***** assert (icdf ("Normal", p, 5, 2), norminv (p, 5, 2))
638s ***** assert (icdf ("norm", p, 5, 2), norminv (p, 5, 2))
638s ***** assert (icdf ("Poisson", p, 5), poissinv (p, 5))
638s ***** assert (icdf ("poiss", p, 5), poissinv (p, 5))
638s ***** assert (icdf ("Rayleigh", p, 5), raylinv (p, 5))
638s ***** assert (icdf ("rayl", p, 5), raylinv (p, 5))
638s ***** assert (icdf ("Rician", p, 5, 1), riceinv (p, 5, 1))
638s ***** assert (icdf ("rice", p, 5, 1), riceinv (p, 5, 1))
638s ***** assert (icdf ("Student T", p, 5), tinv (p, 5))
638s ***** assert (icdf ("t", p, 5), tinv (p, 5))
638s ***** assert (icdf ("location-scale T", p, 5, 1, 2), tlsinv (p, 5, 1, 2))
638s ***** assert (icdf ("tls", p, 5, 1, 2), tlsinv (p, 5, 1, 2))
638s ***** assert (icdf ("Triangular", p, 5, 2, 2), triinv (p, 5, 2, 2))
638s ***** assert (icdf ("tri", p, 5, 2, 2), triinv (p, 5, 2, 2))
638s ***** assert (icdf ("Discrete Uniform", p, 5), unidinv (p, 5))
638s ***** assert (icdf ("unid", p, 5), unidinv (p, 5))
638s ***** assert (icdf ("Uniform", p, 5, 2), unifinv (p, 5, 2))
638s ***** assert (icdf ("unif", p, 5, 2), unifinv (p, 5, 2))
638s ***** assert (icdf ("Von Mises", p, 5, 2), vminv (p, 5, 2))
643s ***** assert (icdf ("vm", p, 5, 2), vminv (p, 5, 2))
647s ***** assert (icdf ("Weibull", p, 5, 2), wblinv (p, 5, 2))
647s ***** assert (icdf ("wbl", p, 5, 2), wblinv (p, 5, 2))
647s ***** error<icdf: distribution NAME must a char string.> icdf (1)
647s ***** error<icdf: distribution NAME must a char string.> icdf ({"beta"})
647s ***** error<icdf: P must be numeric.> icdf ("beta", {[1 2 3 4 5]})
647s ***** error<icdf: P must be numeric.> icdf ("beta", "text")
647s ***** error<icdf: values in P must be real.> icdf ("beta", 1+i)
647s ***** error<icdf: distribution parameters must be numeric.> ...
647s  icdf ("Beta", p, "a", 2)
647s ***** error<icdf: distribution parameters must be numeric.> ...
647s  icdf ("Beta", p, 5, "")
647s ***** error<icdf: distribution parameters must be numeric.> ...
647s  icdf ("Beta", p, 5, {2})
647s ***** error<icdf: chi2 distribution requires 1 parameter.> icdf ("chi2", p)
647s ***** error<icdf: Beta distribution requires 2 parameters.> icdf ("Beta", p, 5)
647s ***** error<icdf: Burr distribution requires 3 parameters.> icdf ("Burr", p, 5)
647s ***** error<icdf: Burr distribution requires 3 parameters.> icdf ("Burr", p, 5, 2)
647s 86 tests, 86 passed, 0 known failure, 0 skipped
647s [inst/dist_wrap/random.m]
647s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_wrap/random.m
647s ***** assert (size (random ("Beta", 5, 2, 2, 10)), size (betarnd (5, 2, 2, 10)))
647s ***** assert (size (random ("beta", 5, 2, 2, 10)), size (betarnd (5, 2, 2, 10)))
647s ***** assert (size (random ("Binomial", 5, 2, [10, 20])), size (binornd (5, 2, 10, 20)))
647s ***** assert (size (random ("bino", 5, 2, [10, 20])), size (binornd (5, 2, 10, 20)))
647s ***** assert (size (random ("Birnbaum-Saunders", 5, 2, [10, 20])), size (bisarnd (5, 2, 10, 20)))
647s ***** assert (size (random ("bisa", 5, 2, [10, 20])), size (bisarnd (5, 2, 10, 20)))
647s ***** assert (size (random ("Burr", 5, 2, 2, [10, 20])), size (burrrnd (5, 2, 2, 10, 20)))
647s ***** assert (size (random ("burr", 5, 2, 2, [10, 20])), size (burrrnd (5, 2, 2, 10, 20)))
647s ***** assert (size (random ("Cauchy", 5, 2, [10, 20])), size (cauchyrnd (5, 2, 10, 20)))
647s ***** assert (size (random ("cauchy", 5, 2, [10, 20])), size (cauchyrnd (5, 2, 10, 20)))
647s ***** assert (size (random ("Chi-squared", 5, [10, 20])), size (chi2rnd (5, 10, 20)))
647s ***** assert (size (random ("chi2", 5, [10, 20])), size (chi2rnd (5, 10, 20)))
647s ***** assert (size (random ("Extreme Value", 5, 2, [10, 20])), size (evrnd (5, 2, 10, 20)))
647s ***** assert (size (random ("ev", 5, 2, [10, 20])), size (evrnd (5, 2, 10, 20)))
647s ***** assert (size (random ("Exponential", 5, [10, 20])), size (exprnd (5, 10, 20)))
647s ***** assert (size (random ("exp", 5, [10, 20])), size (exprnd (5, 10, 20)))
647s ***** assert (size (random ("F-Distribution", 5, 2, [10, 20])), size (frnd (5, 2, 10, 20)))
647s ***** assert (size (random ("f", 5, 2, [10, 20])), size (frnd (5, 2, 10, 20)))
647s ***** assert (size (random ("Gamma", 5, 2, [10, 20])), size (gamrnd (5, 2, 10, 20)))
647s ***** assert (size (random ("gam", 5, 2, [10, 20])), size (gamrnd (5, 2, 10, 20)))
647s ***** assert (size (random ("Geometric", 5, [10, 20])), size (geornd (5, 10, 20)))
647s ***** assert (size (random ("geo", 5, [10, 20])), size (geornd (5, 10, 20)))
647s ***** assert (size (random ("Generalized Extreme Value", 5, 2, 2, [10, 20])), size (gevrnd (5, 2, 2, 10, 20)))
647s ***** assert (size (random ("gev", 5, 2, 2, [10, 20])), size (gevrnd (5, 2, 2, 10, 20)))
647s ***** assert (size (random ("Generalized Pareto", 5, 2, 2, [10, 20])), size (gprnd (5, 2, 2, 10, 20)))
647s ***** assert (size (random ("gp", 5, 2, 2, [10, 20])), size (gprnd (5, 2, 2, 10, 20)))
647s ***** assert (size (random ("Gumbel", 5, 2, [10, 20])), size (gumbelrnd (5, 2, 10, 20)))
647s ***** assert (size (random ("gumbel", 5, 2, [10, 20])), size (gumbelrnd (5, 2, 10, 20)))
647s ***** assert (size (random ("Half-normal", 5, 2, [10, 20])), size (hnrnd (5, 2, 10, 20)))
647s ***** assert (size (random ("hn", 5, 2, [10, 20])), size (hnrnd (5, 2, 10, 20)))
647s ***** assert (size (random ("Hypergeometric", 5, 2, 2, [10, 20])), size (hygernd (5, 2, 2, 10, 20)))
647s ***** assert (size (random ("hyge", 5, 2, 2, [10, 20])), size (hygernd (5, 2, 2, 10, 20)))
647s ***** assert (size (random ("Inverse Gaussian", 5, 2, [10, 20])), size (invgrnd (5, 2, 10, 20)))
647s ***** assert (size (random ("invg", 5, 2, [10, 20])), size (invgrnd (5, 2, 10, 20)))
647s ***** assert (size (random ("Laplace", 5, 2, [10, 20])), size (laplacernd (5, 2, 10, 20)))
647s ***** assert (size (random ("laplace", 5, 2, [10, 20])), size (laplacernd (5, 2, 10, 20)))
647s ***** assert (size (random ("Logistic", 5, 2, [10, 20])), size (logirnd (5, 2, 10, 20)))
647s ***** assert (size (random ("logi", 5, 2, [10, 20])), size (logirnd (5, 2, 10, 20)))
647s ***** assert (size (random ("Log-Logistic", 5, 2, [10, 20])), size (loglrnd (5, 2, 10, 20)))
647s ***** assert (size (random ("logl", 5, 2, [10, 20])), size (loglrnd (5, 2, 10, 20)))
647s ***** assert (size (random ("Lognormal", 5, 2, [10, 20])), size (lognrnd (5, 2, 10, 20)))
647s ***** assert (size (random ("logn", 5, 2, [10, 20])), size (lognrnd (5, 2, 10, 20)))
647s ***** assert (size (random ("Nakagami", 5, 2, [10, 20])), size (nakarnd (5, 2, 10, 20)))
647s ***** assert (size (random ("naka", 5, 2, [10, 20])), size (nakarnd (5, 2, 10, 20)))
647s ***** assert (size (random ("Negative Binomial", 5, 2, [10, 20])), size (nbinrnd (5, 2, 10, 20)))
647s ***** assert (size (random ("nbin", 5, 2, [10, 20])), size (nbinrnd (5, 2, 10, 20)))
647s ***** assert (size (random ("Noncentral F-Distribution", 5, 2, 2, [10, 20])), size (ncfrnd (5, 2, 2, 10, 20)))
647s ***** assert (size (random ("ncf", 5, 2, 2, [10, 20])), size (ncfrnd (5, 2, 2, 10, 20)))
648s ***** assert (size (random ("Noncentral Student T", 5, 2, [10, 20])), size (nctrnd (5, 2, 10, 20)))
648s ***** assert (size (random ("nct", 5, 2, [10, 20])), size (nctrnd (5, 2, 10, 20)))
648s ***** assert (size (random ("Noncentral Chi-Squared", 5, 2, [10, 20])), size (ncx2rnd (5, 2, 10, 20)))
648s ***** assert (size (random ("ncx2", 5, 2, [10, 20])), size (ncx2rnd (5, 2, 10, 20)))
648s ***** assert (size (random ("Normal", 5, 2, [10, 20])), size (normrnd (5, 2, 10, 20)))
648s ***** assert (size (random ("norm", 5, 2, [10, 20])), size (normrnd (5, 2, 10, 20)))
648s ***** assert (size (random ("Poisson", 5, [10, 20])), size (poissrnd (5, 10, 20)))
648s ***** assert (size (random ("poiss", 5, [10, 20])), size (poissrnd (5, 10, 20)))
648s ***** assert (size (random ("Rayleigh", 5, [10, 20])), size (raylrnd (5, 10, 20)))
648s ***** assert (size (random ("rayl", 5, [10, 20])), size (raylrnd (5, 10, 20)))
648s ***** assert (size (random ("Rician", 5, 1, [10, 20])), size (ricernd (5, 1, 10, 20)))
648s ***** assert (size (random ("rice", 5, 1, [10, 20])), size (ricernd (5, 1, 10, 20)))
648s ***** assert (size (random ("Student T", 5, [10, 20])), size (trnd (5, 10, 20)))
648s ***** assert (size (random ("t", 5, [10, 20])), size (trnd (5, 10, 20)))
648s ***** assert (size (random ("location-scale T", 5, 1, 2, [10, 20])), size (tlsrnd (5, 1, 2, 10, 20)))
648s ***** assert (size (random ("tls", 5, 1, 2, [10, 20])), size (tlsrnd (5, 1, 2, 10, 20)))
648s ***** assert (size (random ("Triangular", 5, 2, 2, [10, 20])), size (trirnd (5, 2, 2, 10, 20)))
648s ***** assert (size (random ("tri", 5, 2, 2, [10, 20])), size (trirnd (5, 2, 2, 10, 20)))
648s ***** assert (size (random ("Discrete Uniform", 5, [10, 20])), size (unidrnd (5, 10, 20)))
648s ***** assert (size (random ("unid", 5, [10, 20])), size (unidrnd (5, 10, 20)))
648s ***** assert (size (random ("Uniform", 5, 2, [10, 20])), size (unifrnd (5, 2, 10, 20)))
648s ***** assert (size (random ("unif", 5, 2, [10, 20])), size (unifrnd (5, 2, 10, 20)))
648s ***** assert (size (random ("Von Mises", 5, 2, [10, 20])), size (vmrnd (5, 2, 10, 20)))
648s ***** assert (size (random ("vm", 5, 2, [10, 20])), size (vmrnd (5, 2, 10, 20)))
648s ***** assert (size (random ("Weibull", 5, 2, [10, 20])), size (wblrnd (5, 2, 10, 20)))
648s ***** assert (size (random ("wbl", 5, 2, [10, 20])), size (wblrnd (5, 2, 10, 20)))
648s ***** error<random: distribution NAME must a char string.> random (1)
648s ***** error<random: distribution NAME must a char string.> random ({"beta"})
648s ***** error<random: distribution parameters must be numeric.> ...
648s  random ("Beta", "a", 2)
648s ***** error<random: distribution parameters must be numeric.> ...
648s  random ("Beta", 5, "")
648s ***** error<random: distribution parameters must be numeric.> ...
648s  random ("Beta", 5, {2})
648s ***** error<random: distribution parameters must be numeric.> ...
648s  random ("Beta", "a", 2, 2, 10)
648s ***** error<random: distribution parameters must be numeric.> ...
648s  random ("Beta", 5, "", 2, 10)
648s ***** error<random: distribution parameters must be numeric.> ...
648s  random ("Beta", 5, {2}, 2, 10)
648s ***** error<random: distribution parameters must be numeric.> ...
648s  random ("Beta", 5, "", 2, 10)
648s ***** error<random: chi2 distribution requires 1 parameter.> random ("chi2")
648s ***** error<random: Beta distribution requires 2 parameters.> random ("Beta", 5)
648s ***** error<random: Burr distribution requires 3 parameters.> random ("Burr", 5)
648s ***** error<random: Burr distribution requires 3 parameters.> random ("Burr", 5, 2)
648s 87 tests, 87 passed, 0 known failure, 0 skipped
648s [inst/dist_wrap/cdf.m]
648s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_wrap/cdf.m
648s ***** shared x
648s  x = [1:5];
648s ***** assert (cdf ("Beta", x, 5, 2), betacdf (x, 5, 2))
648s ***** assert (cdf ("beta", x, 5, 2, "upper"), betacdf (x, 5, 2, "upper"))
648s ***** assert (cdf ("Binomial", x, 5, 2), binocdf (x, 5, 2))
648s ***** assert (cdf ("bino", x, 5, 2, "upper"), binocdf (x, 5, 2, "upper"))
648s ***** assert (cdf ("Birnbaum-Saunders", x, 5, 2), bisacdf (x, 5, 2))
648s ***** assert (cdf ("bisa", x, 5, 2, "upper"), bisacdf (x, 5, 2, "upper"))
648s ***** assert (cdf ("Burr", x, 5, 2, 2), burrcdf (x, 5, 2, 2))
648s ***** assert (cdf ("burr", x, 5, 2, 2, "upper"), burrcdf (x, 5, 2, 2, "upper"))
648s ***** assert (cdf ("Cauchy", x, 5, 2), cauchycdf (x, 5, 2))
648s ***** assert (cdf ("cauchy", x, 5, 2, "upper"), cauchycdf (x, 5, 2, "upper"))
648s ***** assert (cdf ("Chi-squared", x, 5), chi2cdf (x, 5))
648s ***** assert (cdf ("chi2", x, 5, "upper"), chi2cdf (x, 5, "upper"))
648s ***** assert (cdf ("Extreme Value", x, 5, 2), evcdf (x, 5, 2))
648s ***** assert (cdf ("ev", x, 5, 2, "upper"), evcdf (x, 5, 2, "upper"))
648s ***** assert (cdf ("Exponential", x, 5), expcdf (x, 5))
648s ***** assert (cdf ("exp", x, 5, "upper"), expcdf (x, 5, "upper"))
648s ***** assert (cdf ("F-Distribution", x, 5, 2), fcdf (x, 5, 2))
648s ***** assert (cdf ("f", x, 5, 2, "upper"), fcdf (x, 5, 2, "upper"))
648s ***** assert (cdf ("Gamma", x, 5, 2), gamcdf (x, 5, 2))
648s ***** assert (cdf ("gam", x, 5, 2, "upper"), gamcdf (x, 5, 2, "upper"))
648s ***** assert (cdf ("Geometric", x, 5), geocdf (x, 5))
648s ***** assert (cdf ("geo", x, 5, "upper"), geocdf (x, 5, "upper"))
648s ***** assert (cdf ("Generalized Extreme Value", x, 5, 2, 2), gevcdf (x, 5, 2, 2))
648s ***** assert (cdf ("gev", x, 5, 2, 2, "upper"), gevcdf (x, 5, 2, 2, "upper"))
648s ***** assert (cdf ("Generalized Pareto", x, 5, 2, 2), gpcdf (x, 5, 2, 2))
648s ***** assert (cdf ("gp", x, 5, 2, 2, "upper"), gpcdf (x, 5, 2, 2, "upper"))
648s ***** assert (cdf ("Gumbel", x, 5, 2), gumbelcdf (x, 5, 2))
648s ***** assert (cdf ("gumbel", x, 5, 2, "upper"), gumbelcdf (x, 5, 2, "upper"))
648s ***** assert (cdf ("Half-normal", x, 5, 2), hncdf (x, 5, 2))
648s ***** assert (cdf ("hn", x, 5, 2, "upper"), hncdf (x, 5, 2, "upper"))
648s ***** assert (cdf ("Hypergeometric", x, 5, 2, 2), hygecdf (x, 5, 2, 2))
648s ***** assert (cdf ("hyge", x, 5, 2, 2, "upper"), hygecdf (x, 5, 2, 2, "upper"))
648s ***** assert (cdf ("Inverse Gaussian", x, 5, 2), invgcdf (x, 5, 2))
648s ***** assert (cdf ("invg", x, 5, 2, "upper"), invgcdf (x, 5, 2, "upper"))
648s ***** assert (cdf ("Laplace", x, 5, 2), laplacecdf (x, 5, 2))
648s ***** assert (cdf ("laplace", x, 5, 2, "upper"), laplacecdf (x, 5, 2, "upper"))
648s ***** assert (cdf ("Logistic", x, 5, 2), logicdf (x, 5, 2))
648s ***** assert (cdf ("logi", x, 5, 2, "upper"), logicdf (x, 5, 2, "upper"))
648s ***** assert (cdf ("Log-Logistic", x, 5, 2), loglcdf (x, 5, 2))
648s ***** assert (cdf ("logl", x, 5, 2, "upper"), loglcdf (x, 5, 2, "upper"))
648s ***** assert (cdf ("Lognormal", x, 5, 2), logncdf (x, 5, 2))
648s ***** assert (cdf ("logn", x, 5, 2, "upper"), logncdf (x, 5, 2, "upper"))
648s ***** assert (cdf ("Nakagami", x, 5, 2), nakacdf (x, 5, 2))
648s ***** assert (cdf ("naka", x, 5, 2, "upper"), nakacdf (x, 5, 2, "upper"))
648s ***** assert (cdf ("Negative Binomial", x, 5, 2), nbincdf (x, 5, 2))
648s ***** assert (cdf ("nbin", x, 5, 2, "upper"), nbincdf (x, 5, 2, "upper"))
648s ***** assert (cdf ("Noncentral F-Distribution", x, 5, 2, 2), ncfcdf (x, 5, 2, 2))
648s ***** assert (cdf ("ncf", x, 5, 2, 2, "upper"), ncfcdf (x, 5, 2, 2, "upper"))
648s ***** assert (cdf ("Noncentral Student T", x, 5, 2), nctcdf (x, 5, 2))
648s ***** assert (cdf ("nct", x, 5, 2, "upper"), nctcdf (x, 5, 2, "upper"))
648s ***** assert (cdf ("Noncentral Chi-Squared", x, 5, 2), ncx2cdf (x, 5, 2))
648s ***** assert (cdf ("ncx2", x, 5, 2, "upper"), ncx2cdf (x, 5, 2, "upper"))
648s ***** assert (cdf ("Normal", x, 5, 2), normcdf (x, 5, 2))
648s ***** assert (cdf ("norm", x, 5, 2, "upper"), normcdf (x, 5, 2, "upper"))
648s ***** assert (cdf ("Poisson", x, 5), poisscdf (x, 5))
648s ***** assert (cdf ("poiss", x, 5, "upper"), poisscdf (x, 5, "upper"))
648s ***** assert (cdf ("Rayleigh", x, 5), raylcdf (x, 5))
648s ***** assert (cdf ("rayl", x, 5, "upper"), raylcdf (x, 5, "upper"))
648s ***** assert (cdf ("Rician", x, 5, 1), ricecdf (x, 5, 1))
648s ***** assert (cdf ("rice", x, 5, 1, "upper"), ricecdf (x, 5, 1, "upper"))
648s ***** assert (cdf ("Student T", x, 5), tcdf (x, 5))
648s ***** assert (cdf ("t", x, 5, "upper"), tcdf (x, 5, "upper"))
648s ***** assert (cdf ("location-scale T", x, 5, 1, 2), tlscdf (x, 5, 1, 2))
648s ***** assert (cdf ("tls", x, 5, 1, 2, "upper"), tlscdf (x, 5, 1, 2, "upper"))
648s ***** assert (cdf ("Triangular", x, 5, 2, 2), tricdf (x, 5, 2, 2))
648s ***** assert (cdf ("tri", x, 5, 2, 2, "upper"), tricdf (x, 5, 2, 2, "upper"))
648s ***** assert (cdf ("Discrete Uniform", x, 5), unidcdf (x, 5))
648s ***** assert (cdf ("unid", x, 5, "upper"), unidcdf (x, 5, "upper"))
648s ***** assert (cdf ("Uniform", x, 5, 2), unifcdf (x, 5, 2))
648s ***** assert (cdf ("unif", x, 5, 2, "upper"), unifcdf (x, 5, 2, "upper"))
648s ***** assert (cdf ("Von Mises", x, 5, 2), vmcdf (x, 5, 2))
648s ***** assert (cdf ("vm", x, 5, 2, "upper"), vmcdf (x, 5, 2, "upper"))
648s ***** assert (cdf ("Weibull", x, 5, 2), wblcdf (x, 5, 2))
648s ***** assert (cdf ("wbl", x, 5, 2, "upper"), wblcdf (x, 5, 2, "upper"))
648s ***** error<cdf: distribution NAME must a char string.> cdf (1)
648s ***** error<cdf: distribution NAME must a char string.> cdf ({"beta"})
648s ***** error<cdf: X must be numeric.> cdf ("beta", {[1 2 3 4 5]})
648s ***** error<cdf: X must be numeric.> cdf ("beta", "text")
648s ***** error<cdf: values in X must be real.> cdf ("beta", 1+i)
648s ***** error<cdf: distribution parameters must be numeric.> ...
648s  cdf ("Beta", x, "a", 2)
648s ***** error<cdf: distribution parameters must be numeric.> ...
648s  cdf ("Beta", x, 5, "")
648s ***** error<cdf: distribution parameters must be numeric.> ...
648s  cdf ("Beta", x, 5, {2})
648s ***** error<cdf: chi2 distribution requires 1 parameter.> cdf ("chi2", x)
648s ***** error<cdf: Beta distribution requires 2 parameters.> cdf ("Beta", x, 5)
648s ***** error<cdf: Burr distribution requires 3 parameters.> cdf ("Burr", x, 5)
648s ***** error<cdf: Burr distribution requires 3 parameters.> cdf ("Burr", x, 5, 2)
648s 86 tests, 86 passed, 0 known failure, 0 skipped
648s [inst/glmval.m]
648s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/glmval.m
648s ***** demo
648s  x = [210, 230, 250, 270, 290, 310, 330, 350, 370, 390, 410, 430]';
648s  n = [48, 42, 31, 34, 31, 21, 23, 23, 21, 16, 17, 21]';
648s  y = [1, 2, 0, 3, 8, 8, 14, 17, 19, 15, 17, 21]';
648s  b = glmfit (x, [y n], "binomial", "Link", "probit");
648s  yfit = glmval (b, x, "probit", "Size", n);
648s  plot (x, y./n, 'o', x, yfit ./ n, '-')
648s ***** error <glmval: too few input arguments.> glmval ()
648s ***** error <glmval: too few input arguments.> glmval (1)
648s ***** error <glmval: too few input arguments.> glmval (1, 2)
648s ***** error <glmval: B must be a numeric vector of coefficient estimates.> ...
648s  glmval ("asd", [1; 1; 1], 'probit')
648s ***** error <glmval: B must be a numeric vector of coefficient estimates.> ...
648s  glmval ([], [1; 1; 1], 'probit')
648s ***** error <glmval: X must be a numeric matrix.> ...
648s  glmval ([0.1; 0.3; 0.4], [], 'probit')
648s ***** error <glmval: X must be a numeric matrix.> ...
648s  glmval ([0.1; 0.3; 0.4], "asd", 'probit')
648s ***** error <glmval: structure with custom link functions must be a scalar.> ...
648s  glmval (rand (3,1), rand (5,2), struct ("Link", {1, 2}))
648s ***** error <glmval: structure with custom link functions requires the fields 'Link', 'Derivative', and 'Inverse'.> ...
648s  glmval (rand (3,1), rand (5,2), struct ("Link", "norminv"))
648s ***** error <glmval: bad 'Link' function in custom link function structure.> ...
648s  glmval (rand (3,1), rand (5,2), struct ("Link", "some", "Derivative", @(x)x, "Inverse", "normcdf"))
648s ***** error <glmval: bad 'Link' function in custom link function structure.> ...
648s  glmval (rand (3,1), rand (5,2), struct ("Link", 1, "Derivative", @(x)x, "Inverse", "normcdf"))
648s ***** error <glmval: custom 'Link' function must return an output of the same size as input.> ...
648s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x) [x, x], "Derivative", @(x)x, "Inverse", "normcdf"))
648s ***** error <glmval: invalid custom 'Link' function.> ...
648s  glmval (rand (3,1), rand (5,2), struct ("Link", "what", "Derivative", @(x)x, "Inverse", "normcdf"))
648s ***** error <glmval: bad 'Derivative' function in custom link function structure.> ...
648s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "some", "Inverse", "normcdf"))
648s ***** error <glmval: bad 'Derivative' function in custom link function structure.> ...
648s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", 1, "Inverse", "normcdf"))
648s ***** error <glmval: custom 'Derivative' function must return an output of the same size as input.> ...
648s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", @(x) [x, x], "Inverse", "normcdf"))
648s ***** error <glmval: invalid custom 'Derivative' function.> ...
648s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "what", "Inverse", "normcdf"))
648s ***** error <glmval: bad 'Inverse' function in custom link function structure.> ...
648s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", "some"))
648s ***** error <glmval: bad 'Inverse' function in custom link function structure.> ...
648s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", 1))
648s ***** error <glmval: custom 'Inverse' function must return an output of the same size as input.> ...
648s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", @(x) [x, x]))
648s ***** error <glmval: invalid custom 'Inverse' function.> ...
648s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", "what"))
648s ***** error <glmval: cell array with custom link functions must have three elements.> ...
648s  glmval (rand (3,1), rand (5,2), {'log'})
648s ***** error <glmval: cell array with custom link functions must have three elements.> ...
648s  glmval (rand (3,1), rand (5,2), {'log', 'hijy'})
648s ***** error <glmval: cell array with custom link functions must have three elements.> ...
648s  glmval (rand (3,1), rand (5,2), {1, 2, 3, 4})
648s ***** error <glmval: bad 'Link' function in custom link function cell array.> ...
648s  glmval (rand (3,1), rand (5,2), {"log", "dfv", "dfgvd"})
648s ***** error <glmval: custom 'Link' function must return an output of the same size as input.> ...
648s  glmval (rand (3,1), rand (5,2), {@(x) [x, x], "dfv", "dfgvd"})
648s ***** error <glmval: invalid custom 'Link' function.> ...
648s  glmval (rand (3,1), rand (5,2), {@(x) what (x), "dfv", "dfgvd"})
648s ***** error <glmval: bad 'Derivative' function in custom link function cell array.> ...
648s  glmval (rand (3,1), rand (5,2), {@(x) x, "dfv", "dfgvd"})
648s ***** error <glmval: custom 'Derivative' function must return an output of the same size as input.> ...
648s  glmval (rand (3,1), rand (5,2), {@(x) x, @(x) [x, x], "dfgvd"})
648s ***** error <glmval: invalid custom 'Derivative' function.> ...
648s  glmval (rand (3,1), rand (5,2), {@(x) x, @(x) what (x), "dfgvd"})
648s ***** error <glmval: bad 'Inverse' function in custom link function cell array.> ...
648s  glmval (rand (3,1), rand (5,2), {@(x) x, @(x) x, "dfgvd"})
648s ***** error <glmval: custom 'Inverse' function must return an output of the same size as input.> ...
648s  glmval (rand (3,1), rand (5,2), {@(x) x, @(x) x, @(x) [x, x]})
648s ***** error <glmval: invalid custom 'Inverse' function.> ...
648s  glmval (rand (3,1), rand (5,2), {@(x) x, @(x) x, @(x) what (x)})
648s ***** error <glmval: numeric input for custom link function must be a finite real scalar value.> ...
648s  glmval (rand (3,1), rand (5,2), NaN)
648s ***** error <glmval: numeric input for custom link function must be a finite real scalar value.> ...
648s  glmval (rand (3,1), rand (5,2), [1, 2])
648s ***** error <glmval: numeric input for custom link function must be a finite real scalar value.> ...
648s  glmval (rand (3,1), rand (5,2), [1i])
648s ***** error <glmval: canonical link function name must be a character vector.> ...
648s  glmval (rand (3,1), rand (5,2), ["log"; "log1"])
648s ***** error <glmval: canonical link function 'somelinkfunction' is not supported.> ...
648s  glmval (rand (3,1), rand (5,2), 'somelinkfunction')
648s ***** error <glmval: invalid value for custom link function.> ...
648s  glmval (rand (3,1), rand (5,2), true)
648s ***** error <glmval: invalid 'stats' structure.> ...
648s  glmval (rand (3,1), rand (5,2), 'probit', struct ("s", 1))
648s ***** error <glmval: Name-Value arguments must be in pairs.> ...
648s  glmval (rand (3,1), rand (5,2), 'probit', 'confidence')
648s ***** error <glmval: 'Confidence' must be a scalar between 0 and 1.> ...
648s  glmval (rand (3,1), rand (5,2), 'probit', 'confidence', 0)
648s ***** error <glmval: 'Confidence' must be a scalar between 0 and 1.> ...
648s  glmval (rand (3,1), rand (5,2), 'probit', 'confidence', 1.2)
648s ***** error <glmval: 'Confidence' must be a scalar between 0 and 1.> ...
648s  glmval (rand (3,1), rand (5,2), 'probit', 'confidence', [0.9, 0.95])
648s ***** error <glmval: 'Constant' should be either 'on' or 'off'.> ...
648s  glmval (rand (3, 1), rand (5, 2), 'probit', 'constant', 1)
648s ***** error <glmval: 'Constant' should be either 'on' or 'off'.> ...
648s  glmval (rand (3, 1), rand (5, 2), 'probit', 'constant', 'o')
648s ***** error <glmval: 'Constant' should be either 'on' or 'off'.> ...
648s  glmval (rand (3, 1), rand (5, 2), 'probit', 'constant', true)
648s ***** error <glmval: 'Offset' must be a numeric vector of the same length as the rows in X.> ...
648s  glmval (rand (3, 1), rand (5, 2), 'probit', 'offset', [1; 2; 3; 4])
648s ***** error <glmval: 'Offset' must be a numeric vector of the same length as the rows in X.> ...
648s  glmval (rand (3, 1), rand (5, 2), 'probit', 'offset', 'asdfg')
648s ***** error <glmval: 'simultaneous' must be a boolean scalar.> ...
648s  glmval (rand (3, 1), rand (5, 2), 'probit', 'simultaneous', 'asdfg')
648s ***** error <glmval: 'simultaneous' must be a boolean scalar.> ...
648s  glmval (rand (3, 1), rand (5, 2), 'probit', 'simultaneous', [true, false])
648s ***** error <glmval: 'size' must be a scalar or a vector with one value for each row of X.> ...
648s  glmval (rand (3, 1), rand (5, 2), 'probit', 'size', "asd")
648s ***** error <glmval: 'size' must be a scalar or a vector with one value for each row of X.> ...
648s  glmval (rand (3, 1), rand (5, 2), 'probit', 'size', [2, 3, 4])
648s ***** error <glmval: 'size' must be a scalar or a vector with one value for each row of X.> ...
648s  glmval (rand (3, 1), rand (5, 2), 'probit', 'size', [2; 3; 4])
648s ***** error <glmval: 'size' must be a scalar or a vector with one value for each row of X.> ...
648s  glmval (rand (3, 1), rand (5, 2), 'probit', 'size', ones (3))
648s ***** error <glmval: unknown parameter name.> ...
648s  glmval (rand (3, 1), rand (5, 2), 'probit', 'someparam', 4)
648s ***** error <glmval: cannot compute confidence intervals without STATS structure.> ...
648s  [y,lo,hi] = glmval (rand (3, 1), rand (5, 2), 'probit')
648s 57 tests, 57 passed, 0 known failure, 0 skipped
648s [inst/mcnemar_test.m]
648s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/mcnemar_test.m
648s ***** test
648s  [h, pval, chisq] = mcnemar_test ([101,121;59,33]);
648s  assert (h, 1);
648s  assert (pval, 3.8151e-06, 1e-10);
648s  assert (chisq, 21.356, 1e-3);
648s ***** test
648s  [h, pval, chisq] = mcnemar_test ([59,6;16,80]);
648s  assert (h, 1);
648s  assert (pval, 0.034690, 1e-6);
648s  assert (isempty (chisq), true);
648s ***** test
648s  [h, pval, chisq] = mcnemar_test ([59,6;16,80], 0.01);
648s  assert (h, 0);
648s  assert (pval, 0.034690, 1e-6);
648s  assert (isempty (chisq), true);
648s ***** test
648s  [h, pval, chisq] = mcnemar_test ([59,6;16,80], "mid-p");
648s  assert (h, 1);
648s  assert (pval, 0.034690, 1e-6);
648s  assert (isempty (chisq), true);
648s ***** test
648s  [h, pval, chisq] = mcnemar_test ([59,6;16,80], "asymptotic");
648s  assert (h, 1);
648s  assert (pval, 0.033006, 1e-6);
648s  assert (chisq, 4.5455, 1e-4);
648s ***** test
648s  [h, pval, chisq] = mcnemar_test ([59,6;16,80], "exact");
648s  assert (h, 0);
648s  assert (pval, 0.052479, 1e-6);
648s  assert (isempty (chisq), true);
648s ***** test
648s  [h, pval, chisq] = mcnemar_test ([59,6;16,80], "corrected");
648s  assert (h, 0);
648s  assert (pval, 0.055009, 1e-6);
648s  assert (chisq, 3.6818, 1e-4);
648s ***** test
648s  [h, pval, chisq] = mcnemar_test ([59,6;16,80], 0.1, "corrected");
648s  assert (h, 1);
648s  assert (pval, 0.055009, 1e-6);
648s  assert (chisq, 3.6818, 1e-4);
648s ***** error<mcnemar_test: too many input arguments.> mcnemar_test (59, 6, 16, 80)
648s ***** error<mcnemar_test: X must be a 2x2 matrix.> mcnemar_test (ones (3, 3))
648s ***** error<mcnemar_test: all entries of X must be non-negative integers.> ...
648s  mcnemar_test ([59,6;16,-80])
648s ***** error<mcnemar_test: all entries of X must be non-negative integers.> ...
648s  mcnemar_test ([59,6;16,4.5])
648s ***** error<mcnemar_test: invalid 2nd input argument.> ...
648s  mcnemar_test ([59,6;16,80], {""})
648s ***** error<mcnemar_test: invalid value for ALPHA.> ...
648s  mcnemar_test ([59,6;16,80], -0.2)
648s ***** error<mcnemar_test: invalid value for ALPHA.> ...
648s  mcnemar_test ([59,6;16,80], [0.05, 0.1])
648s ***** error<mcnemar_test: invalid value for ALPHA.> ...
648s  mcnemar_test ([59,6;16,80], 1)
648s ***** error<mcnemar_test: invalid value for TESTTYPE.> ...
648s  mcnemar_test ([59,6;16,80], "")
648s 17 tests, 17 passed, 0 known failure, 0 skipped
648s [inst/signtest.m]
648s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/signtest.m
648s ***** test
648s  [pval, h, stats] = signtest ([-ones(1, 1000) 1], 0, "tail", "left");
648s  assert (pval, 1.091701889420221e-218, 1e-14);
648s  assert (h, 1);
648s  assert (stats.zval, -31.5437631079266, 1e-14);
648s ***** test
648s  [pval, h, stats] = signtest ([-2 -1 0 2 1 3 1], 0);
648s  assert (pval, 0.6875000000000006, 1e-14);
648s  assert (h, 0);
648s  assert (stats.zval, NaN);
648s  assert (stats.sign, 4);
648s ***** test
648s  [pval, h, stats] = signtest ([-2 -1 0 2 1 3 1], 0, "method", "approximate");
648s  assert (pval, 0.6830913983096086, 1e-14);
648s  assert (h, 0);
648s  assert (stats.zval, 0.4082482904638631, 1e-14);
648s  assert (stats.sign, 4);
648s ***** error <signtest: X must be a vector.> signtest (ones (2))
648s ***** error <signtest: Y must be either a scalar of a vector.> ...
648s  signtest ([1, 2, 3, 4], ones (2))
648s ***** error <signtest: X and Y vectors have different lengths.> ...
648s  signtest ([1, 2, 3, 4], [1, 2, 3])
648s ***** error <signtest: optional arguments must be in pairs.> ...
648s  signtest ([1, 2, 3, 4], [], 'tail')
648s ***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
648s  signtest ([1, 2, 3, 4], [], 'alpha', 1.2)
648s ***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
648s  signtest ([1, 2, 3, 4], [], 'alpha', 0)
648s ***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
648s  signtest ([1, 2, 3, 4], [], 'alpha', -0.05)
648s ***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
648s  signtest ([1, 2, 3, 4], [], 'alpha', "a")
648s ***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
648s  signtest ([1, 2, 3, 4], [], 'alpha', [0.01, 0.05])
648s ***** error <signtest: 'tail' argument must be a character vector.> ...
648s  signtest ([1, 2, 3, 4], [], 'tail', 0.01)
648s ***** error <signtest: 'tail' argument must be a character vector.> ...
648s  signtest ([1, 2, 3, 4], [], 'tail', {"both"})
648s ***** error <signtest: 'tail' value must be either 'both', right' or 'left'.> ...
648s  signtest ([1, 2, 3, 4], [], 'tail', "some")
648s ***** error <signtest: 'tail' value must be either 'both', right' or 'left'.> ...
648s  signtest ([1, 2, 3, 4], [], 'method', 'exact', 'tail', "some")
648s ***** error <signtest: 'method' argument must be a character vector.> ...
648s  signtest ([1, 2, 3, 4], [], 'method', 0.01)
648s ***** error <signtest: 'method' argument must be a character vector.> ...
648s  signtest ([1, 2, 3, 4], [], 'method', {"exact"})
648s ***** error <signtest: 'method' value must be either 'exact' or 'approximate'.> ...
648s  signtest ([1, 2, 3, 4], [], 'method', "some")
648s ***** error <signtest: 'method' value must be either 'exact' or 'approximate'.> ...
648s  signtest ([1, 2, 3, 4], [], 'tail', "both", 'method', "some")
648s 20 tests, 20 passed, 0 known failure, 0 skipped
648s [inst/fitlm.m]
648s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/fitlm.m
648s ***** demo
648s  y =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
648s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
648s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
648s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
648s         25.694 ]';
648s  X = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
648s 
648s  [TAB,STATS] = fitlm (X,y,"linear","CategoricalVars",1,"display","on");
648s ***** demo
648s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
648s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
648s  brands = {'Gourmet', 'National', 'Generic'; ...
648s            'Gourmet', 'National', 'Generic'; ...
648s            'Gourmet', 'National', 'Generic'; ...
648s            'Gourmet', 'National', 'Generic'; ...
648s            'Gourmet', 'National', 'Generic'; ...
648s            'Gourmet', 'National', 'Generic'};
648s  popper = {'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; ...
648s            'air', 'air', 'air'; 'air', 'air', 'air'; 'air', 'air', 'air'};
648s 
648s  [TAB, STATS] = fitlm ({brands(:),popper(:)},popcorn(:),"interactions",...
648s                           "CategoricalVars",[1,2],"display","on");
648s ***** test
648s  y =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
648s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
648s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
648s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
648s         25.694 ]';
648s  X = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
648s  [TAB,STATS] = fitlm (X,y,"continuous",[],"display","off");
648s  [TAB,STATS] = fitlm (X,y,"CategoricalVars",1,"display","off");
648s  [TAB,STATS] = fitlm (X,y,"constant","categorical",1,"display","off");
648s  [TAB,STATS] = fitlm (X,y,"linear","categorical",1,"display","off");
648s  [TAB,STATS] = fitlm (X,y,[0,0;1,0],"categorical",1,"display","off");
648s  assert (TAB{2,2}, 10, 1e-04);
648s  assert (TAB{3,2}, 7.99999999999999, 1e-09);
648s  assert (TAB{4,2}, 8.99999999999999, 1e-09);
648s  assert (TAB{5,2}, 11.0001428571429, 1e-09);
648s  assert (TAB{6,2}, 19.0001111111111, 1e-09);
648s  assert (TAB{2,3}, 1.01775379540949, 1e-09);
648s  assert (TAB{3,3}, 1.64107868458008, 1e-09);
648s  assert (TAB{4,3}, 1.43932122062479, 1e-09);
648s  assert (TAB{5,3}, 1.48983900477565, 1e-09);
648s  assert (TAB{6,3}, 1.3987687997822, 1e-09);
648s  assert (TAB{2,6}, 9.82555903510687, 1e-09);
648s  assert (TAB{3,6}, 4.87484242844031, 1e-09);
648s  assert (TAB{4,6}, 6.25294748040552, 1e-09);
648s  assert (TAB{5,6}, 7.38344399756088, 1e-09);
648s  assert (TAB{6,6}, 13.5834536158296, 1e-09);
648s  assert (TAB{3,7}, 2.85812420217862e-05, 1e-12);
648s  assert (TAB{4,7}, 5.22936741204002e-07, 1e-06);
648s  assert (TAB{5,7}, 2.12794763209106e-08, 1e-07);
648s  assert (TAB{6,7}, 7.82091664406755e-15, 1e-08);
649s ***** test
649s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
649s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
649s  brands = bsxfun (@times, ones(6,1), [1,2,3]);
649s  popper = bsxfun (@times, [1;1;1;2;2;2], ones(1,3));
649s 
649s  [TAB, STATS] = fitlm ({brands(:),popper(:)},popcorn(:),"interactions",...
649s                           "categoricalvars",[1,2],"display","off");
649s  assert (TAB{2,2}, 5.66666666666667, 1e-09);
649s  assert (TAB{3,2}, -1.33333333333333, 1e-09);
649s  assert (TAB{4,2}, -2.16666666666667, 1e-09);
649s  assert (TAB{5,2}, 1.16666666666667, 1e-09);
649s  assert (TAB{6,2}, -0.333333333333334, 1e-09);
649s  assert (TAB{7,2}, -0.166666666666667, 1e-09);
649s  assert (TAB{2,3}, 0.215165741455965, 1e-09);
649s  assert (TAB{3,3}, 0.304290309725089, 1e-09);
649s  assert (TAB{4,3}, 0.304290309725089, 1e-09);
649s  assert (TAB{5,3}, 0.304290309725089, 1e-09);
649s  assert (TAB{6,3}, 0.43033148291193, 1e-09);
649s  assert (TAB{7,3}, 0.43033148291193, 1e-09);
649s  assert (TAB{2,6}, 26.3362867542108, 1e-09);
649s  assert (TAB{3,6}, -4.38178046004138, 1e-09);
649s  assert (TAB{4,6}, -7.12039324756724, 1e-09);
649s  assert (TAB{5,6}, 3.83405790253621, 1e-09);
649s  assert (TAB{6,6}, -0.774596669241495, 1e-09);
649s  assert (TAB{7,6}, -0.387298334620748, 1e-09);
649s  assert (TAB{2,7}, 5.49841502258254e-12, 1e-09);
649s  assert (TAB{3,7}, 0.000893505495903642, 1e-09);
649s  assert (TAB{4,7}, 1.21291454302428e-05, 1e-09);
649s  assert (TAB{5,7}, 0.00237798044119407, 1e-09);
649s  assert (TAB{6,7}, 0.453570536021938, 1e-09);
649s  assert (TAB{7,7}, 0.705316781644046, 1e-09);
649s  ## Test with string ids for categorical variables
649s  brands = {'Gourmet', 'National', 'Generic'; ...
649s            'Gourmet', 'National', 'Generic'; ...
649s            'Gourmet', 'National', 'Generic'; ...
649s            'Gourmet', 'National', 'Generic'; ...
649s            'Gourmet', 'National', 'Generic'; ...
649s            'Gourmet', 'National', 'Generic'};
649s  popper = {'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; ...
649s            'air', 'air', 'air'; 'air', 'air', 'air'; 'air', 'air', 'air'};
649s  [TAB, STATS] = fitlm ({brands(:),popper(:)},popcorn(:),"interactions",...
649s                           "categoricalvars",[1,2],"display","off");
649s ***** test
649s  load carsmall
649s  X = [Weight,Horsepower,Acceleration];
649s  [TAB, STATS] = fitlm (X, MPG,"constant","display","off");
649s  [TAB, STATS] = fitlm (X, MPG,"linear","display","off");
649s  assert (TAB{2,2}, 47.9767628118615, 1e-09);
649s  assert (TAB{3,2}, -0.00654155878851796, 1e-09);
649s  assert (TAB{4,2}, -0.0429433065881864, 1e-09);
649s  assert (TAB{5,2}, -0.0115826516894871, 1e-09);
649s  assert (TAB{2,3}, 3.87851641748551, 1e-09);
649s  assert (TAB{3,3}, 0.00112741016370336, 1e-09);
649s  assert (TAB{4,3}, 0.0243130608813806, 1e-09);
649s  assert (TAB{5,3}, 0.193325043113178, 1e-09);
649s  assert (TAB{2,6}, 12.369874881944, 1e-09);
649s  assert (TAB{3,6}, -5.80228828790225, 1e-09);
649s  assert (TAB{4,6}, -1.76626492228599, 1e-09);
649s  assert (TAB{5,6}, -0.0599128364487485, 1e-09);
649s  assert (TAB{2,7}, 4.89570341688996e-21, 1e-09);
649s  assert (TAB{3,7}, 9.87424814144e-08, 1e-09);
649s  assert (TAB{4,7}, 0.0807803098213114, 1e-09);
649s  assert (TAB{5,7}, 0.952359384151778, 1e-09);
649s 3 tests, 3 passed, 0 known failure, 0 skipped
649s [inst/logit.m]
649s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/logit.m
649s ***** test
649s  p = [0.01:0.01:0.99];
649s  assert (logit (p), log (p ./ (1-p)), 25*eps);
649s ***** assert (logit ([-1, 0, 0.5, 1, 2]), [NaN, -Inf, 0, +Inf, NaN])
649s ***** error logit ()
649s ***** error logit (1, 2)
649s 4 tests, 4 passed, 0 known failure, 0 skipped
649s [inst/rangesearch.m]
649s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/rangesearch.m
649s ***** demo
649s  ## Generate 1000 random 2D points from each of five distinct multivariate
649s  ## normal distributions that form five separate classes
649s  N = 1000;
649s  d = 10;
649s  randn ("seed", 5);
649s  X1 = mvnrnd (d * [0, 0], eye (2), 1000);
649s  randn ("seed", 6);
649s  X2 = mvnrnd (d * [1, 1], eye (2), 1000);
649s  randn ("seed", 7);
649s  X3 = mvnrnd (d * [-1, -1], eye (2), 1000);
649s  randn ("seed", 8);
649s  X4 = mvnrnd (d * [1, -1], eye (2), 1000);
649s  randn ("seed", 8);
649s  X5 = mvnrnd (d * [-1, 1], eye (2), 1000);
649s  X = [X1; X2; X3; X4; X5];
649s 
649s  ## For each point in X, find the points in X that are within a radius d
649s  ## away from the points in X.
649s  Idx = rangesearch (X, X, d, "NSMethod", "exhaustive");
649s 
649s  ## Select the first point in X (corresponding to the first class) and find
649s  ## its nearest neighbors within the radius d.  Display these points in
649s  ## one color and the remaining points in a different color.
649s  x = X(1,:);
649s  nearestPoints = X (Idx{1},:);
649s  nonNearestIdx = true (size (X, 1), 1);
649s  nonNearestIdx(Idx{1}) = false;
649s 
649s  scatter (X(nonNearestIdx,1), X(nonNearestIdx,2))
649s  hold on
649s  scatter (nearestPoints(:,1),nearestPoints(:,2))
649s  scatter (x(1), x(2), "black", "filled")
649s  hold off
649s 
649s  ## Select the last point in X (corresponding to the fifth class) and find
649s  ## its nearest neighbors within the radius d.  Display these points in
649s  ## one color and the remaining points in a different color.
649s  x = X(end,:);
649s  nearestPoints = X (Idx{1},:);
649s  nonNearestIdx = true (size (X, 1), 1);
649s  nonNearestIdx(Idx{1}) = false;
649s 
649s  figure
649s  scatter (X(nonNearestIdx,1), X(nonNearestIdx,2))
649s  hold on
649s  scatter (nearestPoints(:,1),nearestPoints(:,2))
649s  scatter (x(1), x(2), "black", "filled")
649s  hold off
649s ***** shared x, y, X, Y
649s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
649s  y = [2, 3, 4; 1, 4, 3];
649s  X = [1, 2, 3, 4; 2, 3, 4, 5; 3, 4, 5, 6];
649s  Y = [1, 2, 2, 3; 2, 3, 3, 4];
649s ***** test
649s  [idx, D] = rangesearch (x, y, 4);
649s  assert (idx, {[1, 4, 2]; [1, 4]});
649s  assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
649s ***** test
649s  [idx, D] = rangesearch (x, y, 4, "NSMethod", "exhaustive");
649s  assert (idx, {[1, 4, 2]; [1, 4]});
649s  assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
649s ***** test
649s  [idx, D] = rangesearch (x, y, 4, "NSMethod", "kdtree");
649s  assert (idx, {[1, 4, 2]; [1, 4]});
649s  assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
649s ***** test
649s  [idx, D] = rangesearch (x, y, 4, "SortIndices", true);
649s  assert (idx, {[1, 4, 2]; [1, 4]});
649s  assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
649s ***** test
649s  [idx, D] = rangesearch (x, y, 4, "SortIndices", false);
649s  assert (idx, {[1, 2, 4]; [1, 4]});
649s  assert (D, {[1.7321, 3.4641, 3.3166]; [2, 3.4641]}, 1e-4);
649s ***** test
649s  [idx, D] = rangesearch (x, y, 4, "NSMethod", "exhaustive", ...
649s                          "SortIndices", false);
649s  assert (idx, {[1, 2, 4]; [1, 4]});
649s  assert (D, {[1.7321, 3.4641, 3.3166]; [2, 3.4641]}, 1e-4);
649s ***** test
649s  eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
649s  [idx, D] = rangesearch (x, y, 4, "Distance", eucldist);
649s  assert (idx, {[1, 4, 2]; [1, 4]});
649s  assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
649s ***** test
649s  eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
649s  [idx, D] = rangesearch (x, y, 4, "Distance", eucldist, ...
649s                          "NSMethod", "exhaustive");
649s  assert (idx, {[1, 4, 2]; [1, 4]});
649s  assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
649s ***** test
649s  [idx, D] = rangesearch (x, y, 1.5, "Distance", "seuclidean", ...
649s                          "NSMethod", "exhaustive");
649s  assert (idx, {[1, 4, 2]; [1, 4]});
649s  assert (D, {[0.6024, 1.0079, 1.2047]; [0.6963, 1.2047]}, 1e-4);
649s ***** test
649s  [idx, D] = rangesearch (x, y, 1.5, "Distance", "seuclidean", ...
649s                          "NSMethod", "exhaustive", "SortIndices", false);
649s  assert (idx, {[1, 2, 4]; [1, 4]});
649s  assert (D, {[0.6024, 1.2047, 1.0079]; [0.6963, 1.2047]}, 1e-4);
649s ***** test
649s  [idx, D] = rangesearch (X, Y, 4);
649s  assert (idx, {[1, 2]; [1, 2, 3]});
649s  assert (D, {[1.4142, 3.1623]; [1.4142, 1.4142, 3.1623]}, 1e-4);
649s ***** test
649s  [idx, D] = rangesearch (X, Y, 2);
649s  assert (idx, {[1]; [1, 2]});
649s  assert (D, {[1.4142]; [1.4142, 1.4142]}, 1e-4);
649s ***** test
649s  eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
649s  [idx, D] = rangesearch (X, Y, 4, "Distance", eucldist);
649s  assert (idx, {[1, 2]; [1, 2, 3]});
649s  assert (D, {[1.4142, 3.1623]; [1.4142, 1.4142, 3.1623]}, 1e-4);
649s ***** test
649s  [idx, D] = rangesearch (X, Y, 4, "SortIndices", false);
649s  assert (idx, {[1, 2]; [1, 2, 3]});
649s  assert (D, {[1.4142, 3.1623]; [1.4142, 1.4142, 3.1623]}, 1e-4);
649s ***** test
649s  [idx, D] = rangesearch (X, Y, 4, "Distance", "seuclidean", ...
649s                          "NSMethod", "exhaustive");
649s  assert (idx, {[1, 2]; [1, 2, 3]});
649s  assert (D, {[1.4142, 3.1623]; [1.4142, 1.4142, 3.1623]}, 1e-4);
649s ***** error<rangesearch: too few input arguments.> rangesearch (1)
649s ***** error<rangesearch: number of columns in X and Y must match.> ...
649s  rangesearch (ones (4, 5), ones (4))
649s ***** error<rangesearch: invalid NAME in optional pairs of arguments.> ...
649s  rangesearch (ones (4, 2), ones (3, 2), 1, "Distance", "euclidean", "some", "some")
649s ***** error<rangesearch: only a single distance parameter can be defined.> ...
649s  rangesearch (ones (4, 5), ones (1, 5), 1, "scale", ones (1, 5), "P", 3)
649s ***** error<rangesearch: invalid value of Minkowski Exponent.> ...
649s  rangesearch (ones (4, 5), ones (1, 5), 1, "P",-2)
649s ***** error<rangesearch: invalid value in Scale or the size of Scale.> ...
649s  rangesearch (ones (4, 5), ones (1, 5), 1, "scale", ones(4,5), "distance", "euclidean")
649s ***** error<rangesearch: invalid value in Cov, Cov can only be given for mahalanobis distance.> ...
649s  rangesearch (ones (4, 5), ones (1, 5), 1, "cov", ["some" "some"])
649s ***** error<rangesearch: invalid value in Cov, Cov can only be given for mahalanobis distance.> ...
649s  rangesearch (ones (4, 5), ones (1, 5), 1, "cov", ones(4,5), "distance", "euclidean")
649s ***** error<rangesearch: invalid value of bucketsize.> ...
649s  rangesearch (ones (4, 5), ones (1, 5), 1, "bucketsize", -1)
649s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
649s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "cosine")
649s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
649s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "mahalanobis")
649s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
649s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "correlation")
649s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
649s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "seuclidean")
649s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
649s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "spearman")
649s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
649s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "hamming")
649s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
649s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "jaccard")
649s 31 tests, 31 passed, 0 known failure, 0 skipped
649s [inst/dist_fun/jsucdf.m]
649s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/jsucdf.m
649s ***** error jsucdf ()
649s ***** error jsucdf (1, 2, 3, 4)
649s ***** error<jsucdf: X, ALPHA1, and ALPHA2 must be of common size or scalars.> ...
649s  jsucdf (1, ones (2), ones (3))
649s 3 tests, 3 passed, 0 known failure, 0 skipped
649s [inst/dist_fun/cauchypdf.m]
649s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/cauchypdf.m
649s ***** demo
649s  ## Plot various PDFs from the Cauchy distribution
649s  x = -5:0.01:5;
649s  y1 = cauchypdf (x, 0, 0.5);
649s  y2 = cauchypdf (x, 0, 1);
649s  y3 = cauchypdf (x, 0, 2);
649s  y4 = cauchypdf (x, -2, 1);
649s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
649s  grid on
649s  xlim ([-5, 5])
649s  ylim ([0, 0.7])
649s  legend ({"x0 = 0, γ = 0.5", "x0 = 0, γ = 1", ...
649s           "x0 = 0, γ = 2", "x0 = -2, γ = 1"}, "location", "northeast")
649s  title ("Cauchy PDF")
649s  xlabel ("values in x")
649s  ylabel ("density")
649s ***** shared x, y
649s  x = [-1 0 0.5 1 2];
649s  y = 1/pi * ( 2 ./ ((x-1).^2 + 2^2) );
649s ***** assert (cauchypdf (x, ones (1,5), 2*ones (1,5)), y)
649s ***** assert (cauchypdf (x, 1, 2*ones (1,5)), y)
649s ***** assert (cauchypdf (x, ones (1,5), 2), y)
649s ***** assert (cauchypdf (x, [-Inf 1 NaN 1 Inf], 2), [NaN y(2) NaN y(4) NaN])
649s ***** assert (cauchypdf (x, 1, 2*[0 1 NaN 1 Inf]), [NaN y(2) NaN y(4) NaN])
649s ***** assert (cauchypdf ([x, NaN], 1, 2), [y, NaN])
649s ***** assert (cauchypdf (single ([x, NaN]), 1, 2), single ([y, NaN]), eps ("single"))
649s ***** assert (cauchypdf ([x, NaN], single (1), 2), single ([y, NaN]), eps ("single"))
649s ***** assert (cauchypdf ([x, NaN], 1, single (2)), single ([y, NaN]), eps ("single"))
649s ***** test
649s  x = rand (10, 1);
649s  assert (cauchypdf (x, 0, 1), tpdf (x, 1), eps);
649s ***** error<cauchypdf: function called with too few input arguments.> cauchypdf ()
649s ***** error<cauchypdf: function called with too few input arguments.> cauchypdf (1)
649s ***** error<cauchypdf: function called with too few input arguments.> ...
649s  cauchypdf (1, 2)
649s ***** error<cauchypdf: function called with too many inputs> cauchypdf (1, 2, 3, 4)
649s ***** error<cauchypdf: X, X0, and GAMMA must be of common size or scalars.> ...
649s  cauchypdf (ones (3), ones (2), ones(2))
649s ***** error<cauchypdf: X, X0, and GAMMA must be of common size or scalars.> ...
649s  cauchypdf (ones (2), ones (3), ones(2))
649s ***** error<cauchypdf: X, X0, and GAMMA must be of common size or scalars.> ...
649s  cauchypdf (ones (2), ones (2), ones(3))
649s ***** error<cauchypdf: X, X0, and GAMMA must not be complex.> cauchypdf (i, 4, 3)
649s ***** error<cauchypdf: X, X0, and GAMMA must not be complex.> cauchypdf (1, i, 3)
649s ***** error<cauchypdf: X, X0, and GAMMA must not be complex.> cauchypdf (1, 4, i)
649s 20 tests, 20 passed, 0 known failure, 0 skipped
649s [inst/dist_fun/fcdf.m]
649s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/fcdf.m
649s ***** demo
649s  ## Plot various CDFs from the F distribution
649s  x = 0.01:0.01:4;
649s  p1 = fcdf (x, 1, 2);
649s  p2 = fcdf (x, 2, 1);
649s  p3 = fcdf (x, 5, 2);
649s  p4 = fcdf (x, 10, 1);
649s  p5 = fcdf (x, 100, 100);
649s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
649s  grid on
649s  legend ({"df1 = 1, df2 = 2", "df1 = 2, df2 = 1", ...
649s           "df1 = 5, df2 = 2", "df1 = 10, df2 = 1", ...
649s           "df1 = 100, df2 = 100"}, "location", "southeast")
649s  title ("F CDF")
649s  xlabel ("values in x")
649s  ylabel ("probability")
649s ***** shared x, y
649s  x = [-1, 0, 0.5, 1, 2, Inf];
649s  y = [0, 0, 1/3, 1/2, 2/3, 1];
649s ***** assert (fcdf (x, 2*ones (1,6), 2*ones (1,6)), y, eps)
649s ***** assert (fcdf (x, 2, 2*ones (1,6)), y, eps)
649s ***** assert (fcdf (x, 2*ones (1,6), 2), y, eps)
649s ***** assert (fcdf (x, [0 NaN Inf 2 2 2], 2), [NaN NaN 0.1353352832366127 y(4:6)], eps)
649s ***** assert (fcdf (x, 2, [0 NaN Inf 2 2 2]), [NaN NaN 0.3934693402873666 y(4:6)], eps)
649s ***** assert (fcdf ([x(1:2) NaN x(4:6)], 2, 2), [y(1:2) NaN y(4:6)], eps)
649s ***** assert (fcdf ([x, NaN], 2, 2), [y, NaN], eps)
649s ***** assert (fcdf (single ([x, NaN]), 2, 2), single ([y, NaN]), eps ("single"))
649s ***** assert (fcdf ([x, NaN], single (2), 2), single ([y, NaN]), eps ("single"))
649s ***** assert (fcdf ([x, NaN], 2, single (2)), single ([y, NaN]), eps ("single"))
649s ***** error<fcdf: function called with too few input arguments.> fcdf ()
649s ***** error<fcdf: function called with too few input arguments.> fcdf (1)
649s ***** error<fcdf: function called with too few input arguments.> fcdf (1, 2)
649s ***** error<fcdf: invalid argument for upper tail.> fcdf (1, 2, 3, 4)
649s ***** error<fcdf: invalid argument for upper tail.> fcdf (1, 2, 3, "tail")
649s ***** error<fcdf: X, DF1, and DF2 must be of common size or scalars.> ...
649s  fcdf (ones (3), ones (2), ones (2))
649s ***** error<fcdf: X, DF1, and DF2 must be of common size or scalars.> ...
649s  fcdf (ones (2), ones (3), ones (2))
649s ***** error<fcdf: X, DF1, and DF2 must be of common size or scalars.> ...
649s  fcdf (ones (2), ones (2), ones (3))
649s ***** error<fcdf: X, DF1, and DF2 must not be complex.> fcdf (i, 2, 2)
649s ***** error<fcdf: X, DF1, and DF2 must not be complex.> fcdf (2, i, 2)
649s ***** error<fcdf: X, DF1, and DF2 must not be complex.> fcdf (2, 2, i)
649s 21 tests, 21 passed, 0 known failure, 0 skipped
649s [inst/dist_fun/laplacernd.m]
649s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/laplacernd.m
649s ***** assert (size (laplacernd (1, 1)), [1 1])
649s ***** assert (size (laplacernd (1, ones (2,1))), [2, 1])
649s ***** assert (size (laplacernd (1, ones (2,2))), [2, 2])
649s ***** assert (size (laplacernd (ones (2,1), 1)), [2, 1])
649s ***** assert (size (laplacernd (ones (2,2), 1)), [2, 2])
649s ***** assert (size (laplacernd (1, 1, 3)), [3, 3])
649s ***** assert (size (laplacernd (1, 1, [4, 1])), [4, 1])
649s ***** assert (size (laplacernd (1, 1, 4, 1)), [4, 1])
649s ***** assert (size (laplacernd (1, 1, 4, 1, 5)), [4, 1, 5])
649s ***** assert (size (laplacernd (1, 1, 0, 1)), [0, 1])
649s ***** assert (size (laplacernd (1, 1, 1, 0)), [1, 0])
649s ***** assert (size (laplacernd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
649s ***** assert (class (laplacernd (1, 1)), "double")
649s ***** assert (class (laplacernd (1, single (1))), "single")
649s ***** assert (class (laplacernd (1, single ([1, 1]))), "single")
649s ***** assert (class (laplacernd (single (1), 1)), "single")
649s ***** assert (class (laplacernd (single ([1, 1]), 1)), "single")
649s ***** error<laplacernd: function called with too few input arguments.> laplacernd ()
649s ***** error<laplacernd: function called with too few input arguments.> laplacernd (1)
649s ***** error<laplacernd: MU and BETA must be of common size or scalars.> ...
649s  laplacernd (ones (3), ones (2))
649s ***** error<laplacernd: MU and BETA must be of common size or scalars.> ...
649s  laplacernd (ones (2), ones (3))
649s ***** error<laplacernd: MU and BETA must not be complex.> laplacernd (i, 2, 3)
649s ***** error<laplacernd: MU and BETA must not be complex.> laplacernd (1, i, 3)
649s ***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
649s  laplacernd (1, 2, -1)
649s ***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
649s  laplacernd (1, 2, 1.2)
649s ***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
649s  laplacernd (1, 2, ones (2))
649s ***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
649s  laplacernd (1, 2, [2 -1 2])
649s ***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
649s  laplacernd (1, 2, [2 0 2.5])
649s ***** error<laplacernd: dimensions must be non-negative integers.> ...
649s  laplacernd (1, 2, 2, -1, 5)
649s ***** error<laplacernd: dimensions must be non-negative integers.> ...
649s  laplacernd (1, 2, 2, 1.5, 5)
649s ***** error<laplacernd: MU and BETA must be scalars or of size SZ.> ...
649s  laplacernd (2, ones (2), 3)
649s ***** error<laplacernd: MU and BETA must be scalars or of size SZ.> ...
649s  laplacernd (2, ones (2), [3, 2])
649s ***** error<laplacernd: MU and BETA must be scalars or of size SZ.> ...
649s  laplacernd (2, ones (2), 3, 2)
649s 33 tests, 33 passed, 0 known failure, 0 skipped
649s [inst/dist_fun/hnrnd.m]
649s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/hnrnd.m
649s ***** assert (size (hnrnd (1, 1, 1)), [1, 1])
649s ***** assert (size (hnrnd (1, 1, 2)), [2, 2])
649s ***** assert (size (hnrnd (1, 1, [2, 1])), [2, 1])
649s ***** assert (size (hnrnd (1, zeros (2, 2))), [2, 2])
649s ***** assert (size (hnrnd (1, ones (2, 1))), [2, 1])
649s ***** assert (size (hnrnd (1, ones (2, 2))), [2, 2])
649s ***** assert (size (hnrnd (ones (2, 1), 1)), [2, 1])
649s ***** assert (size (hnrnd (ones (2, 2), 1)), [2, 2])
649s ***** assert (size (hnrnd (1, 1, 3)), [3, 3])
649s ***** assert (size (hnrnd (1, 1, [4 1])), [4, 1])
649s ***** assert (size (hnrnd (1, 1, 4, 1)), [4, 1])
649s ***** test
649s  r =  hnrnd (1, [1, 0, -1]);
649s  assert (r([2:3]), [NaN, NaN])
649s ***** assert (class (hnrnd (1, 0)), "double")
649s ***** assert (class (hnrnd (1, single (0))), "single")
649s ***** assert (class (hnrnd (1, single ([0 0]))), "single")
649s ***** assert (class (hnrnd (1, single (1))), "single")
649s ***** assert (class (hnrnd (1, single ([1 1]))), "single")
649s ***** assert (class (hnrnd (single (1), 1)), "single")
649s ***** assert (class (hnrnd (single ([1 1]), 1)), "single")
649s ***** error<hnrnd: function called with too few input arguments.> hnrnd ()
649s ***** error<hnrnd: function called with too few input arguments.> hnrnd (1)
649s ***** error<hnrnd: MU and SIGMA must be of common size or scalars.> ...
649s  hnrnd (ones (3), ones (2))
649s ***** error<hnrnd: MU and SIGMA must be of common size or scalars.> ...
649s  hnrnd (ones (2), ones (3))
649s ***** error<hnrnd: MU and SIGMA must not be complex.> hnrnd (i, 2, 3)
649s ***** error<hnrnd: MU and SIGMA must not be complex.> hnrnd (1, i, 3)
649s ***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
649s  hnrnd (1, 2, -1)
649s ***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
649s  hnrnd (1, 2, 1.2)
649s ***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
649s  hnrnd (1, 2, ones (2))
649s ***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
649s  hnrnd (1, 2, [2 -1 2])
649s ***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
649s  hnrnd (1, 2, [2 0 2.5])
649s ***** error<hnrnd: dimensions must be non-negative integers.> ...
649s  hnrnd (1, 2, 2, -1, 5)
649s ***** error<hnrnd: dimensions must be non-negative integers.> ...
649s  hnrnd (1, 2, 2, 1.5, 5)
649s ***** error<hnrnd: MU and SIGMA must be scalars or of size SZ.> ...
649s  hnrnd (2, ones (2), 3)
649s ***** error<hnrnd: MU and SIGMA must be scalars or of size SZ.> ...
649s  hnrnd (2, ones (2), [3, 2])
649s ***** error<hnrnd: MU and SIGMA must be scalars or of size SZ.> ...
649s  hnrnd (2, ones (2), 3, 2)
649s 35 tests, 35 passed, 0 known failure, 0 skipped
649s [inst/dist_fun/ricecdf.m]
649s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/ricecdf.m
649s ***** demo
649s  ## Plot various CDFs from the Rician distribution
649s  x = 0:0.01:10;
649s  p1 = ricecdf (x, 0, 1);
649s  p2 = ricecdf (x, 0.5, 1);
649s  p3 = ricecdf (x, 1, 1);
649s  p4 = ricecdf (x, 2, 1);
649s  p5 = ricecdf (x, 4, 1);
649s  plot (x, p1, "-b", x, p2, "g", x, p3, "-r", x, p4, "-m", x, p5, "-k")
649s  grid on
649s  ylim ([0, 1])
649s  xlim ([0, 8])
649s  legend ({"s = 0, σ = 1", "s = 0.5, σ = 1", "s = 1, σ = 1", ...
649s           "s = 2, σ = 1", "s = 4, σ = 1"}, "location", "southeast")
649s  title ("Rician CDF")
649s  xlabel ("values in x")
649s  ylabel ("probability")
649s ***** demo
649s  ## Plot various CDFs from the Rician distribution
649s  x = 0:0.01:10;
649s  p1 = ricecdf (x, 0, 0.5);
649s  p2 = ricecdf (x, 0, 2);
649s  p3 = ricecdf (x, 0, 3);
649s  p4 = ricecdf (x, 2, 2);
649s  p5 = ricecdf (x, 4, 2);
649s  plot (x, p1, "-b", x, p2, "g", x, p3, "-r", x, p4, "-m", x, p5, "-k")
649s  grid on
649s  ylim ([0, 1])
649s  xlim ([0, 8])
649s  legend ({"ν = 0, σ = 0.5", "ν = 0, σ = 2", "ν = 0, σ = 3", ...
649s           "ν = 2, σ = 2", "ν = 4, σ = 2"}, "location", "southeast")
649s  title ("Rician CDF")
649s  xlabel ("values in x")
649s  ylabel ("probability")
649s ***** test
649s  x = 0:0.5:2.5;
649s  s = 1:6;
649s  p = ricecdf (x, s, 1);
649s  expected_p = [0.0000, 0.0179, 0.0108, 0.0034, 0.0008, 0.0001];
649s  assert (p, expected_p, 0.001);
649s ***** test
649s  x = 0:0.5:2.5;
649s  sigma = 1:6;
649s  p = ricecdf (x, 1, sigma);
649s  expected_p = [0.0000, 0.0272, 0.0512, 0.0659, 0.0754, 0.0820];
649s  assert (p, expected_p, 0.001);
649s ***** test
649s  x = 0:0.5:2.5;
649s  p = ricecdf (x, 0, 1);
649s  expected_p = [0.0000, 0.1175, 0.3935, 0.6753, 0.8647, 0.9561];
649s  assert (p, expected_p, 0.001);
649s ***** test
649s  x = 0:0.5:2.5;
649s  p = ricecdf (x, 1, 1);
649s  expected_p = [0.0000, 0.0735, 0.2671, 0.5120, 0.7310, 0.8791];
649s  assert (p, expected_p, 0.001);
649s ***** shared x, p
649s  x = [-1, 0, 1, 2, Inf];
649s  p = [0, 0, 0.26712019620318, 0.73098793996409, 1];
649s ***** assert (ricecdf (x, 1, 1), p, 1e-14)
649s ***** assert (ricecdf (x, 1, 1, "upper"), 1 - p, 1e-14)
649s ***** error<ricecdf: function called with too few input arguments.> ricecdf ()
649s ***** error<ricecdf: function called with too few input arguments.> ricecdf (1)
649s ***** error<ricecdf: function called with too few input arguments.> ricecdf (1, 2)
649s ***** error<ricecdf: invalid argument for upper tail.> ricecdf (1, 2, 3, "uper")
649s ***** error<ricecdf: invalid argument for upper tail.> ricecdf (1, 2, 3, 4)
649s ***** error<ricecdf: X, S, and SIGMA must be of common size or scalars.> ...
649s  ricecdf (ones (3), ones (2), ones (2))
649s ***** error<ricecdf: X, S, and SIGMA must be of common size or scalars.> ...
649s  ricecdf (ones (2), ones (3), ones (2))
649s ***** error<ricecdf: X, S, and SIGMA must be of common size or scalars.> ...
649s  ricecdf (ones (2), ones (2), ones (3))
649s ***** error<ricecdf: X, S, and SIGMA must not be complex.> ricecdf (i, 2, 3)
649s ***** error<ricecdf: X, S, and SIGMA must not be complex.> ricecdf (2, i, 3)
649s ***** error<ricecdf: X, S, and SIGMA must not be complex.> ricecdf (2, 2, i)
649s 17 tests, 17 passed, 0 known failure, 0 skipped
649s [inst/dist_fun/evrnd.m]
649s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/evrnd.m
649s ***** assert (size (evrnd (1, 1)), [1 1])
649s ***** assert (size (evrnd (1, ones (2,1))), [2, 1])
649s ***** assert (size (evrnd (1, ones (2,2))), [2, 2])
649s ***** assert (size (evrnd (ones (2,1), 1)), [2, 1])
649s ***** assert (size (evrnd (ones (2,2), 1)), [2, 2])
649s ***** assert (size (evrnd (1, 1, 3)), [3, 3])
649s ***** assert (size (evrnd (1, 1, [4, 1])), [4, 1])
649s ***** assert (size (evrnd (1, 1, 4, 1)), [4, 1])
649s ***** assert (size (evrnd (1, 1, 4, 1, 5)), [4, 1, 5])
649s ***** assert (size (evrnd (1, 1, 0, 1)), [0, 1])
649s ***** assert (size (evrnd (1, 1, 1, 0)), [1, 0])
649s ***** assert (size (evrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
649s ***** assert (class (evrnd (1, 1)), "double")
649s ***** assert (class (evrnd (1, single (1))), "single")
649s ***** assert (class (evrnd (1, single ([1, 1]))), "single")
649s ***** assert (class (evrnd (single (1), 1)), "single")
649s ***** assert (class (evrnd (single ([1, 1]), 1)), "single")
649s ***** error<evrnd: function called with too few input arguments.> evrnd ()
649s ***** error<evrnd: function called with too few input arguments.> evrnd (1)
649s ***** error<evrnd: MU and SIGMA must be of common size or scalars.> ...
649s  evrnd (ones (3), ones (2))
649s ***** error<evrnd: MU and SIGMA must be of common size or scalars.> ...
649s  evrnd (ones (2), ones (3))
649s ***** error<evrnd: MU and SIGMA must not be complex.> evrnd (i, 2, 3)
649s ***** error<evrnd: MU and SIGMA must not be complex.> evrnd (1, i, 3)
649s ***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
649s  evrnd (1, 2, -1)
649s ***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
649s  evrnd (1, 2, 1.2)
649s ***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
649s  evrnd (1, 2, ones (2))
649s ***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
649s  evrnd (1, 2, [2 -1 2])
649s ***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
649s  evrnd (1, 2, [2 0 2.5])
649s ***** error<evrnd: dimensions must be non-negative integers.> ...
649s  evrnd (1, 2, 2, -1, 5)
649s ***** error<evrnd: dimensions must be non-negative integers.> ...
649s  evrnd (1, 2, 2, 1.5, 5)
649s ***** error<evrnd: MU and SIGMA must be scalars or of size SZ.> ...
649s  evrnd (2, ones (2), 3)
649s ***** error<evrnd: MU and SIGMA must be scalars or of size SZ.> ...
649s  evrnd (2, ones (2), [3, 2])
649s ***** error<evrnd: MU and SIGMA must be scalars or of size SZ.> ...
649s  evrnd (2, ones (2), 3, 2)
649s 33 tests, 33 passed, 0 known failure, 0 skipped
649s [inst/dist_fun/logncdf.m]
649s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/logncdf.m
649s ***** demo
649s  ## Plot various CDFs from the log-normal distribution
649s  x = 0:0.01:3;
649s  p1 = logncdf (x, 0, 1);
649s  p2 = logncdf (x, 0, 0.5);
649s  p3 = logncdf (x, 0, 0.25);
649s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r")
649s  grid on
649s  legend ({"μ = 0, σ = 1", "μ = 0, σ = 0.5", "μ = 0, σ = 0.25"}, ...
649s          "location", "southeast")
649s  title ("Log-normal CDF")
649s  xlabel ("values in x")
649s  ylabel ("probability")
649s ***** shared x, y
649s  x = [-1, 0, 1, e, Inf];
649s  y = [0, 0, 0.5, 1/2+1/2*erf(1/2), 1];
649s ***** assert (logncdf (x, zeros (1,5), sqrt(2)*ones (1,5)), y, eps)
649s ***** assert (logncdf (x, zeros (1,5), sqrt(2)*ones (1,5), []), y, eps)
649s ***** assert (logncdf (x, 0, sqrt(2)*ones (1,5)), y, eps)
649s ***** assert (logncdf (x, zeros (1,5), sqrt(2)), y, eps)
649s ***** assert (logncdf (x, [0 1 NaN 0 1], sqrt(2)), [0 0 NaN y(4:5)], eps)
649s ***** assert (logncdf (x, 0, sqrt(2)*[0 NaN Inf 1 1]), [NaN NaN y(3:5)], eps)
649s ***** assert (logncdf ([x(1:3) NaN x(5)], 0, sqrt(2)), [y(1:3) NaN y(5)], eps)
649s ***** assert (logncdf ([x, NaN], 0, sqrt(2)), [y, NaN], eps)
649s ***** assert (logncdf (single ([x, NaN]), 0, sqrt(2)), single ([y, NaN]), eps ("single"))
649s ***** assert (logncdf ([x, NaN], single (0), sqrt(2)), single ([y, NaN]), eps ("single"))
649s ***** assert (logncdf ([x, NaN], 0, single (sqrt(2))), single ([y, NaN]), eps ("single"))
649s ***** error<logncdf: invalid number of input arguments.> logncdf ()
649s ***** error<logncdf: invalid number of input arguments.> logncdf (1,2,3,4,5,6,7)
649s ***** error<logncdf: invalid argument for upper tail.> logncdf (1, 2, 3, 4, "uper")
649s ***** error<logncdf: X, MU, and SIGMA must be of common size or scalars.> ...
649s  logncdf (ones (3), ones (2), ones (2))
649s ***** error<logncdf: invalid size of covariance matrix.> logncdf (2, 3, 4, [1, 2])
649s ***** error<logncdf: covariance matrix is required for confidence bounds.> ...
649s  [p, plo, pup] = logncdf (1, 2, 3)
649s ***** error<logncdf: invalid value for alpha.> [p, plo, pup] = ...
649s  logncdf (1, 2, 3, [1, 0; 0, 1], 0)
649s ***** error<logncdf: invalid value for alpha.> [p, plo, pup] = ...
649s  logncdf (1, 2, 3, [1, 0; 0, 1], 1.22)
649s ***** error<logncdf: invalid value for alpha.> [p, plo, pup] = ...
649s  logncdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
649s ***** error<logncdf: X, MU, and SIGMA must not be complex.> logncdf (i, 2, 2)
649s ***** error<logncdf: X, MU, and SIGMA must not be complex.> logncdf (2, i, 2)
649s ***** error<logncdf: X, MU, and SIGMA must not be complex.> logncdf (2, 2, i)
649s ***** error<logncdf: bad covariance matrix.> ...
649s  [p, plo, pup] =logncdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
649s 24 tests, 24 passed, 0 known failure, 0 skipped
649s [inst/dist_fun/iwishpdf.m]
649s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/iwishpdf.m
649s ***** assert(iwishpdf(4, 3, 3.1), 0.04226595, 1E-7);
649s ***** assert(iwishpdf([2 -0.3;-0.3 4], [1 0.3;0.3 1], 4), 1.60166e-05, 1E-10);
649s ***** assert(iwishpdf([6 2 5; 2 10 -5; 5 -5 25], ...
649s  [9 5 5; 5 10 -8; 5 -8 22], 5.1), 4.946831e-12, 1E-17);
649s ***** error iwishpdf ()
649s ***** error iwishpdf (1, 2)
650s ***** error iwishpdf (1, 2, 0)
650s 6 tests, 6 passed, 0 known failure, 0 skipped
650s [inst/dist_fun/invgcdf.m]
650s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/invgcdf.m
650s ***** demo
650s  ## Plot various CDFs from the inverse Gaussian distribution
650s  x = 0:0.001:3;
650s  p1 = invgcdf (x, 1, 0.2);
650s  p2 = invgcdf (x, 1, 1);
650s  p3 = invgcdf (x, 1, 3);
650s  p4 = invgcdf (x, 3, 0.2);
650s  p5 = invgcdf (x, 3, 1);
650s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-y")
650s  grid on
650s  xlim ([0, 3])
650s  legend ({"μ = 1, σ = 0.2", "μ = 1, σ = 1", "μ = 1, σ = 3", ...
650s           "μ = 3, σ = 0.2", "μ = 3, σ = 1"}, "location", "southeast")
650s  title ("Inverse Gaussian CDF")
650s  xlabel ("values in x")
650s  ylabel ("probability")
650s ***** shared x, p1, p1u, y2, y2u, y3, y3u
650s  x = [-Inf, -1, 0, 1/2, 1, Inf];
650s  p1 = [0, 0, 0, 0.3650, 0.6681, 1];
650s  p1u = [1, 1, 1, 0.6350, 0.3319, 0];
650s ***** assert (invgcdf (x, ones (1,6), ones (1,6)), p1, 1e-4)
650s ***** assert (invgcdf (x, 1, 1), p1, 1e-4)
650s ***** assert (invgcdf (x, 1, ones (1,6)), p1, 1e-4)
650s ***** assert (invgcdf (x, ones (1,6), 1), p1, 1e-4)
650s ***** assert (invgcdf (x, 1, [1, 1, 1, NaN, 1, 1]), [p1(1:3), NaN, p1(5:6)], 1e-4)
650s ***** assert (invgcdf (x, [1, 1, 1, NaN, 1, 1], 1), [p1(1:3), NaN, p1(5:6)], 1e-4)
650s ***** assert (invgcdf ([x(1:3), NaN, x(5:6)], 1, 1), [p1(1:3), NaN, p1(5:6)], 1e-4)
650s ***** assert (invgcdf (x, ones (1,6), ones (1,6), "upper"), p1u, 1e-4)
650s ***** assert (invgcdf (x, 1, 1, "upper"), p1u, 1e-4)
650s ***** assert (invgcdf (x, 1, ones (1,6), "upper"), p1u, 1e-4)
650s ***** assert (invgcdf (x, ones (1,6), 1, "upper"), p1u, 1e-4)
650s ***** assert (class (invgcdf (single ([x, NaN]), 1, 1)), "single")
650s ***** assert (class (invgcdf ([x, NaN], 1, single (1))), "single")
650s ***** assert (class (invgcdf ([x, NaN], single (1), 1)), "single")
650s ***** error<invgcdf: function called with too few input arguments.> invgcdf ()
650s ***** error<invgcdf: function called with too few input arguments.> invgcdf (1)
650s ***** error<invgcdf: function called with too few input arguments.> invgcdf (1, 2)
650s ***** error<invgcdf: invalid argument for upper tail.> invgcdf (1, 2, 3, "tail")
650s ***** error<invgcdf: invalid argument for upper tail.> invgcdf (1, 2, 3, 5)
650s ***** error<invgcdf: X, MU, and LAMBDA must be of common size or scalars.> ...
650s  invgcdf (ones (3), ones (2), ones(2))
650s ***** error<invgcdf: X, MU, and LAMBDA must be of common size or scalars.> ...
650s  invgcdf (ones (2), ones (3), ones(2))
650s ***** error<invgcdf: X, MU, and LAMBDA must be of common size or scalars.> ...
650s  invgcdf (ones (2), ones (2), ones(3))
650s ***** error<invgcdf: X, MU, and LAMBDA must not be complex.> invgcdf (i, 2, 3)
650s ***** error<invgcdf: X, MU, and LAMBDA must not be complex.> invgcdf (1, i, 3)
650s ***** error<invgcdf: X, MU, and LAMBDA must not be complex.> invgcdf (1, 2, i)
650s 25 tests, 25 passed, 0 known failure, 0 skipped
650s [inst/dist_fun/betarnd.m]
650s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/betarnd.m
650s ***** assert (size (betarnd (2, 1/2)), [1 1])
650s ***** assert (size (betarnd (2 * ones (2, 1), 1/2)), [2, 1])
650s ***** assert (size (betarnd (2 * ones (2, 2), 1/2)), [2, 2])
650s ***** assert (size (betarnd (2, 1/2 * ones (2, 1))), [2, 1])
650s ***** assert (size (betarnd (1, 1/2 * ones (2, 2))), [2, 2])
650s ***** assert (size (betarnd (ones (2, 1), 1)), [2, 1])
650s ***** assert (size (betarnd (ones (2, 2), 1)), [2, 2])
650s ***** assert (size (betarnd (2, 1/2, 3)), [3, 3])
650s ***** assert (size (betarnd (1, 1, [4, 1])), [4, 1])
650s ***** assert (size (betarnd (1, 1, 4, 1)), [4, 1])
650s ***** assert (size (betarnd (1, 1, 4, 1, 5)), [4, 1, 5])
650s ***** assert (size (betarnd (1, 1, 0, 1)), [0, 1])
650s ***** assert (size (betarnd (1, 1, 1, 0)), [1, 0])
650s ***** assert (size (betarnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
650s ***** assert (class (betarnd (1, 1)), "double")
650s ***** assert (class (betarnd (1, single (0))), "single")
650s ***** assert (class (betarnd (1, single ([0, 0]))), "single")
650s ***** assert (class (betarnd (1, single (1), 2)), "single")
650s ***** assert (class (betarnd (1, single ([1, 1]), 1, 2)), "single")
650s ***** assert (class (betarnd (single (1), 1, 2)), "single")
650s ***** assert (class (betarnd (single ([1, 1]), 1, 1, 2)), "single")
650s ***** error<betarnd: function called with too few input arguments.> betarnd ()
650s ***** error<betarnd: function called with too few input arguments.> betarnd (1)
650s ***** error<betarnd: A and B must be of common size or scalars.> ...
650s  betarnd (ones (3), ones (2))
650s ***** error<betarnd: A and B must be of common size or scalars.> ...
650s  betarnd (ones (2), ones (3))
650s ***** error<betarnd: A and B must not be complex.> betarnd (i, 2)
650s ***** error<betarnd: A and B must not be complex.> betarnd (1, i)
650s ***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
650s  betarnd (1, 1/2, -1)
650s ***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
650s  betarnd (1, 1/2, 1.2)
650s ***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
650s  betarnd (1, 1/2, ones (2))
650s ***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
650s  betarnd (1, 1/2, [2 -1 2])
650s ***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
650s  betarnd (1, 1/2, [2 0 2.5])
650s ***** error<betarnd: dimensions must be non-negative integers.> ...
650s  betarnd (1, 1/2, 2, -1, 5)
650s ***** error<betarnd: dimensions must be non-negative integers.> ...
650s  betarnd (1, 1/2, 2, 1.5, 5)
650s ***** error<betarnd: A and B must be scalars or of size SZ.> ...
650s  betarnd (2, 1/2 * ones (2), 3)
650s ***** error<betarnd: A and B must be scalars or of size SZ.> ...
650s  betarnd (2, 1/2 * ones (2), [3, 2])
650s ***** error<betarnd: A and B must be scalars or of size SZ.> ...
650s  betarnd (2, 1/2 * ones (2), 3, 2)
650s ***** error<betarnd: A and B must be scalars or of size SZ.> ...
650s  betarnd (2 * ones (2), 1/2, 3)
650s ***** error<betarnd: A and B must be scalars or of size SZ.> ...
650s  betarnd (2 * ones (2), 1/2, [3, 2])
650s ***** error<betarnd: A and B must be scalars or of size SZ.> ...
650s  betarnd (2 * ones (2), 1/2, 3, 2)
650s 40 tests, 40 passed, 0 known failure, 0 skipped
650s [inst/dist_fun/loglrnd.m]
650s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/loglrnd.m
650s ***** assert (size (loglrnd (1, 1)), [1 1])
650s ***** assert (size (loglrnd (1, ones (2,1))), [2, 1])
650s ***** assert (size (loglrnd (1, ones (2,2))), [2, 2])
650s ***** assert (size (loglrnd (ones (2,1), 1)), [2, 1])
650s ***** assert (size (loglrnd (ones (2,2), 1)), [2, 2])
650s ***** assert (size (loglrnd (1, 1, 3)), [3, 3])
650s ***** assert (size (loglrnd (1, 1, [4, 1])), [4, 1])
650s ***** assert (size (loglrnd (1, 1, 4, 1)), [4, 1])
650s ***** assert (size (loglrnd (1, 1, 4, 1, 5)), [4, 1, 5])
650s ***** assert (size (loglrnd (1, 1, 0, 1)), [0, 1])
650s ***** assert (size (loglrnd (1, 1, 1, 0)), [1, 0])
650s ***** assert (size (loglrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
650s ***** assert (class (loglrnd (1, 1)), "double")
650s ***** assert (class (loglrnd (1, single (1))), "single")
650s ***** assert (class (loglrnd (1, single ([1, 1]))), "single")
650s ***** assert (class (loglrnd (single (1), 1)), "single")
650s ***** assert (class (loglrnd (single ([1, 1]), 1)), "single")
650s ***** error<loglrnd: function called with too few input arguments.> loglrnd ()
650s ***** error<loglrnd: function called with too few input arguments.> loglrnd (1)
650s ***** error<loglrnd: MU and SIGMA must be of common size or scalars.> ...
650s  loglrnd (ones (3), ones (2))
650s ***** error<loglrnd: MU and SIGMA must be of common size or scalars.> ...
650s  loglrnd (ones (2), ones (3))
650s ***** error<loglrnd: MU and SIGMA must not be complex.> loglrnd (i, 2, 3)
650s ***** error<loglrnd: MU and SIGMA must not be complex.> loglrnd (1, i, 3)
650s ***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
650s  loglrnd (1, 2, -1)
650s ***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
650s  loglrnd (1, 2, 1.2)
650s ***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
650s  loglrnd (1, 2, ones (2))
650s ***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
650s  loglrnd (1, 2, [2 -1 2])
650s ***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
650s  loglrnd (1, 2, [2 0 2.5])
650s ***** error<loglrnd: dimensions must be non-negative integers.> ...
650s  loglrnd (1, 2, 2, -1, 5)
650s ***** error<loglrnd: dimensions must be non-negative integers.> ...
650s  loglrnd (1, 2, 2, 1.5, 5)
650s ***** error<loglrnd: MU and SIGMA must be scalars or of size SZ.> ...
650s  loglrnd (2, ones (2), 3)
650s ***** error<loglrnd: MU and SIGMA must be scalars or of size SZ.> ...
650s  loglrnd (2, ones (2), [3, 2])
650s ***** error<loglrnd: MU and SIGMA must be scalars or of size SZ.> ...
650s  loglrnd (2, ones (2), 3, 2)
650s 33 tests, 33 passed, 0 known failure, 0 skipped
650s [inst/dist_fun/gevpdf.m]
650s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/gevpdf.m
650s ***** demo
650s  ## Plot various PDFs from the generalized extreme value distribution
650s  x = -1:0.001:10;
650s  y1 = gevpdf (x, 1, 1, 1);
650s  y2 = gevpdf (x, 0.5, 1, 1);
650s  y3 = gevpdf (x, 1, 1, 5);
650s  y4 = gevpdf (x, 1, 2, 5);
650s  y5 = gevpdf (x, 1, 5, 5);
650s  y6 = gevpdf (x, 1, 0.5, 5);
650s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", ...
650s        x, y4, "-c", x, y5, "-m", x, y6, "-k")
650s  grid on
650s  xlim ([-1, 10])
650s  ylim ([0, 1.1])
650s  legend ({"k = 1, σ = 1, μ = 1", "k = 0.5, σ = 1, μ = 1", ...
650s           "k = 1, σ = 1, μ = 5", "k = 1, σ = 2, μ = 5", ...
650s           "k = 1, σ = 5, μ = 5", "k = 1, σ = 0.5, μ = 5"}, ...
650s          "location", "northeast")
650s  title ("Generalized extreme value PDF")
650s  xlabel ("values in x")
650s  ylabel ("density")
650s ***** test
650s  x = 0:0.5:2.5;
650s  sigma = 1:6;
650s  k = 1;
650s  mu = 0;
650s  y = gevpdf (x, k, sigma, mu);
650s  expected_y = [0.367879   0.143785   0.088569   0.063898   0.049953   0.040997];
650s  assert (y, expected_y, 0.001);
650s ***** test
650s  x = -0.5:0.5:2.5;
650s  sigma = 0.5;
650s  k = 1;
650s  mu = 0;
650s  y = gevpdf (x, k, sigma, mu);
650s  expected_y = [0 0.735759   0.303265   0.159229   0.097350   0.065498   0.047027];
650s  assert (y, expected_y, 0.001);
650s ***** test # check for continuity for k near 0
650s  x = 1;
650s  sigma = 0.5;
650s  k = -0.03:0.01:0.03;
650s  mu = 0;
650s  y = gevpdf (x, k, sigma, mu);
650s  expected_y = [0.23820   0.23764   0.23704   0.23641   0.23576   0.23508   0.23438];
650s  assert (y, expected_y, 0.001);
650s ***** error<gevpdf: function called with too few input arguments.> gevpdf ()
650s ***** error<gevpdf: function called with too few input arguments.> gevpdf (1)
650s ***** error<gevpdf: function called with too few input arguments.> gevpdf (1, 2)
650s ***** error<gevpdf: function called with too few input arguments.> gevpdf (1, 2, 3)
650s ***** error<gevpdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
650s  gevpdf (ones (3), ones (2), ones(2), ones(2))
650s ***** error<gevpdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
650s  gevpdf (ones (2), ones (3), ones(2), ones(2))
650s ***** error<gevpdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
650s  gevpdf (ones (2), ones (2), ones(3), ones(2))
650s ***** error<gevpdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
650s  gevpdf (ones (2), ones (2), ones(2), ones(3))
650s ***** error<gevpdf: X, K, SIGMA, and MU must not be complex.> gevpdf (i, 2, 3, 4)
650s ***** error<gevpdf: X, K, SIGMA, and MU must not be complex.> gevpdf (1, i, 3, 4)
650s ***** error<gevpdf: X, K, SIGMA, and MU must not be complex.> gevpdf (1, 2, i, 4)
650s ***** error<gevpdf: X, K, SIGMA, and MU must not be complex.> gevpdf (1, 2, 3, i)
650s 15 tests, 15 passed, 0 known failure, 0 skipped
650s [inst/dist_fun/invgpdf.m]
650s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/invgpdf.m
650s ***** demo
650s  ## Plot various PDFs from the inverse Gaussian distribution
650s  x = 0:0.001:3;
650s  y1 = invgpdf (x, 1, 0.2);
650s  y2 = invgpdf (x, 1, 1);
650s  y3 = invgpdf (x, 1, 3);
650s  y4 = invgpdf (x, 3, 0.2);
650s  y5 = invgpdf (x, 3, 1);
650s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-y")
650s  grid on
650s  xlim ([0, 3])
650s  ylim ([0, 3])
650s  legend ({"μ = 1, σ = 0.2", "μ = 1, σ = 1", "μ = 1, σ = 3", ...
650s           "μ = 3, σ = 0.2", "μ = 3, σ = 1"}, "location", "northeast")
650s  title ("Inverse Gaussian PDF")
650s  xlabel ("values in x")
650s  ylabel ("density")
650s ***** shared x, y
650s  x = [-Inf, -1, 0, 1/2, 1, Inf];
650s  y = [0, 0, 0, 0.8788, 0.3989, 0];
650s ***** assert (invgpdf ([x, NaN], 1, 1), [y, NaN], 1e-4)
650s ***** assert (invgpdf (x, 1, [-2, -1, 0, 1, 1, 1]), [nan(1,3), y([4:6])], 1e-4)
650s ***** assert (class (hncdf (single ([x, NaN]), 1, 1)), "single")
650s ***** assert (class (hncdf ([x, NaN], 1, single (1))), "single")
650s ***** assert (class (hncdf ([x, NaN], single (1), 1)), "single")
650s ***** error<invgpdf: function called with too few input arguments.> invgpdf ()
650s ***** error<invgpdf: function called with too few input arguments.> invgpdf (1)
650s ***** error<invgpdf: function called with too few input arguments.> invgpdf (1, 2)
650s ***** error<invgpdf: X, MU, and LAMBDA must be of common size or scalars.> ...
650s  invgpdf (1, ones (2), ones (3))
650s ***** error<invgpdf: X, MU, and LAMBDA must be of common size or scalars.> ...
650s  invgpdf (ones (2), 1, ones (3))
650s ***** error<invgpdf: X, MU, and LAMBDA must be of common size or scalars.> ...
650s  invgpdf (ones (2), ones (3), 1)
650s ***** error<invgpdf: X, MU, and LAMBDA must not be complex.> invgpdf (i, 2, 3)
650s ***** error<invgpdf: X, MU, and LAMBDA must not be complex.> invgpdf (1, i, 3)
650s ***** error<invgpdf: X, MU, and LAMBDA must not be complex.> invgpdf (1, 2, i)
650s 14 tests, 14 passed, 0 known failure, 0 skipped
650s [inst/dist_fun/nakarnd.m]
650s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/nakarnd.m
650s ***** assert (size (nakarnd (1, 1)), [1 1])
650s ***** assert (size (nakarnd (1, ones (2,1))), [2, 1])
650s ***** assert (size (nakarnd (1, ones (2,2))), [2, 2])
650s ***** assert (size (nakarnd (ones (2,1), 1)), [2, 1])
650s ***** assert (size (nakarnd (ones (2,2), 1)), [2, 2])
650s ***** assert (size (nakarnd (1, 1, 3)), [3, 3])
650s ***** assert (size (nakarnd (1, 1, [4, 1])), [4, 1])
650s ***** assert (size (nakarnd (1, 1, 4, 1)), [4, 1])
650s ***** assert (size (nakarnd (1, 1, 4, 1, 5)), [4, 1, 5])
650s ***** assert (size (nakarnd (1, 1, 0, 1)), [0, 1])
650s ***** assert (size (nakarnd (1, 1, 1, 0)), [1, 0])
650s ***** assert (size (nakarnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
650s ***** assert (class (nakarnd (1, 1)), "double")
650s ***** assert (class (nakarnd (1, single (1))), "single")
650s ***** assert (class (nakarnd (1, single ([1, 1]))), "single")
650s ***** assert (class (nakarnd (single (1), 1)), "single")
650s ***** assert (class (nakarnd (single ([1, 1]), 1)), "single")
650s ***** error<nakarnd: function called with too few input arguments.> nakarnd ()
650s ***** error<nakarnd: function called with too few input arguments.> nakarnd (1)
650s ***** error<nakarnd: MU and OMEGA must be of common size or scalars.> ...
650s  nakarnd (ones (3), ones (2))
650s ***** error<nakarnd: MU and OMEGA must be of common size or scalars.> ...
650s  nakarnd (ones (2), ones (3))
650s ***** error<nakarnd: MU and OMEGA must not be complex.> nakarnd (i, 2, 3)
650s ***** error<nakarnd: MU and OMEGA must not be complex.> nakarnd (1, i, 3)
650s ***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
650s  nakarnd (1, 2, -1)
650s ***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
650s  nakarnd (1, 2, 1.2)
650s ***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
650s  nakarnd (1, 2, ones (2))
650s ***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
650s  nakarnd (1, 2, [2 -1 2])
650s ***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
650s  nakarnd (1, 2, [2 0 2.5])
650s ***** error<nakarnd: dimensions must be non-negative integers.> ...
650s  nakarnd (1, 2, 2, -1, 5)
650s ***** error<nakarnd: dimensions must be non-negative integers.> ...
650s  nakarnd (1, 2, 2, 1.5, 5)
650s ***** error<nakarnd: MU and OMEGA must be scalars or of size SZ.> ...
650s  nakarnd (2, ones (2), 3)
650s ***** error<nakarnd: MU and OMEGA must be scalars or of size SZ.> ...
650s  nakarnd (2, ones (2), [3, 2])
650s ***** error<nakarnd: MU and OMEGA must be scalars or of size SZ.> ...
650s  nakarnd (2, ones (2), 3, 2)
650s 33 tests, 33 passed, 0 known failure, 0 skipped
650s [inst/dist_fun/wblrnd.m]
650s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/wblrnd.m
650s ***** assert (size (wblrnd (1, 1)), [1 1])
650s ***** assert (size (wblrnd (1, ones (2,1))), [2, 1])
650s ***** assert (size (wblrnd (1, ones (2,2))), [2, 2])
650s ***** assert (size (wblrnd (ones (2,1), 1)), [2, 1])
650s ***** assert (size (wblrnd (ones (2,2), 1)), [2, 2])
650s ***** assert (size (wblrnd (1, 1, 3)), [3, 3])
650s ***** assert (size (wblrnd (1, 1, [4, 1])), [4, 1])
650s ***** assert (size (wblrnd (1, 1, 4, 1)), [4, 1])
650s ***** assert (size (wblrnd (1, 1, 4, 1, 5)), [4, 1, 5])
650s ***** assert (size (wblrnd (1, 1, 0, 1)), [0, 1])
650s ***** assert (size (wblrnd (1, 1, 1, 0)), [1, 0])
650s ***** assert (size (wblrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
650s ***** assert (class (wblrnd (1, 1)), "double")
650s ***** assert (class (wblrnd (1, single (1))), "single")
650s ***** assert (class (wblrnd (1, single ([1, 1]))), "single")
650s ***** assert (class (wblrnd (single (1), 1)), "single")
650s ***** assert (class (wblrnd (single ([1, 1]), 1)), "single")
650s ***** error<wblrnd: function called with too few input arguments.> wblrnd ()
650s ***** error<wblrnd: function called with too few input arguments.> wblrnd (1)
650s ***** error<wblrnd: LAMBDA and K must be of common size or scalars.> ...
650s  wblrnd (ones (3), ones (2))
650s ***** error<wblrnd: LAMBDA and K must be of common size or scalars.> ...
650s  wblrnd (ones (2), ones (3))
650s ***** error<wblrnd: LAMBDA and K must not be complex.> wblrnd (i, 2, 3)
650s ***** error<wblrnd: LAMBDA and K must not be complex.> wblrnd (1, i, 3)
650s ***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
650s  wblrnd (1, 2, -1)
650s ***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
650s  wblrnd (1, 2, 1.2)
650s ***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
650s  wblrnd (1, 2, ones (2))
650s ***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
650s  wblrnd (1, 2, [2 -1 2])
650s ***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
650s  wblrnd (1, 2, [2 0 2.5])
650s ***** error<wblrnd: dimensions must be non-negative integers.> ...
650s  wblrnd (1, 2, 2, -1, 5)
650s ***** error<wblrnd: dimensions must be non-negative integers.> ...
650s  wblrnd (1, 2, 2, 1.5, 5)
650s ***** error<wblrnd: LAMBDA and K must be scalar or of size SZ.> ...
650s  wblrnd (2, ones (2), 3)
650s ***** error<wblrnd: LAMBDA and K must be scalar or of size SZ.> ...
650s  wblrnd (2, ones (2), [3, 2])
650s ***** error<wblrnd: LAMBDA and K must be scalar or of size SZ.> ...
650s  wblrnd (2, ones (2), 3, 2)
650s 33 tests, 33 passed, 0 known failure, 0 skipped
650s [inst/dist_fun/poisscdf.m]
650s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/poisscdf.m
650s ***** demo
650s  ## Plot various CDFs from the Poisson distribution
650s  x = 0:20;
650s  p1 = poisscdf (x, 1);
650s  p2 = poisscdf (x, 4);
650s  p3 = poisscdf (x, 10);
650s  plot (x, p1, "*b", x, p2, "*g", x, p3, "*r")
650s  grid on
650s  ylim ([0, 1])
650s  legend ({"λ = 1", "λ = 4", "λ = 10"}, "location", "southeast")
650s  title ("Poisson CDF")
650s  xlabel ("values in x (number of occurences)")
650s  ylabel ("probability")
650s ***** shared x, y
650s  x = [-1 0 1 2 Inf];
650s  y = [0, gammainc(1, (x(2:4) +1), "upper"), 1];
650s ***** assert (poisscdf (x, ones (1,5)), y)
650s ***** assert (poisscdf (x, 1), y)
650s ***** assert (poisscdf (x, [1 0 NaN 1 1]), [y(1) 1 NaN y(4:5)])
650s ***** assert (poisscdf ([x(1:2) NaN Inf x(5)], 1), [y(1:2) NaN 1 y(5)])
650s ***** assert (poisscdf ([x, NaN], 1), [y, NaN])
650s ***** assert (poisscdf (single ([x, NaN]), 1), single ([y, NaN]), eps ("single"))
650s ***** assert (poisscdf ([x, NaN], single (1)), single ([y, NaN]), eps ("single"))
650s ***** error<poisscdf: function called with too few input arguments.> poisscdf ()
650s ***** error<poisscdf: function called with too few input arguments.> poisscdf (1)
650s ***** error<poisscdf: invalid argument for upper tail.> poisscdf (1, 2, 3)
650s ***** error<poisscdf: invalid argument for upper tail.> poisscdf (1, 2, "tail")
650s ***** error<poisscdf: X and LAMBDA must be of common size or scalars.> ...
650s  poisscdf (ones (3), ones (2))
650s ***** error<poisscdf: X and LAMBDA must be of common size or scalars.> ...
650s  poisscdf (ones (2), ones (3))
650s ***** error<poisscdf: X and LAMBDA must not be complex.> poisscdf (i, 2)
650s ***** error<poisscdf: X and LAMBDA must not be complex.> poisscdf (2, i)
650s 15 tests, 15 passed, 0 known failure, 0 skipped
650s [inst/dist_fun/jsupdf.m]
650s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/jsupdf.m
650s ***** error jsupdf ()
650s ***** error jsupdf (1, 2, 3, 4)
650s ***** error<jsupdf: X, ALPHA1, and ALPHA2 must be of common size or scalars.> ...
650s  jsupdf (1, ones (2), ones (3))
650s 3 tests, 3 passed, 0 known failure, 0 skipped
650s [inst/dist_fun/laplaceinv.m]
650s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/laplaceinv.m
650s ***** demo
650s  ## Plot various iCDFs from the Laplace distribution
650s  p = 0.001:0.001:0.999;
650s  x1 = cauchyinv (p, 0, 1);
650s  x2 = cauchyinv (p, 0, 2);
650s  x3 = cauchyinv (p, 0, 4);
650s  x4 = cauchyinv (p, -5, 4);
650s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
650s  grid on
650s  ylim ([-10, 10])
650s  legend ({"μ = 0, β = 1", "μ = 0, β = 2", ...
650s           "μ = 0, β = 4", "μ = -5, β = 4"}, "location", "northwest")
650s  title ("Laplace iCDF")
650s  xlabel ("probability")
650s  ylabel ("values in x")
650s ***** shared p, x
650s  p = [-1 0 0.5 1 2];
650s  x = [NaN, -Inf, 0, Inf, NaN];
650s ***** assert (laplaceinv (p, 0, 1), x)
650s ***** assert (laplaceinv (p, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), Inf, NaN])
650s ***** assert (laplaceinv ([p, NaN], 0, 1), [x, NaN])
650s ***** assert (laplaceinv (single ([p, NaN]), 0, 1), single ([x, NaN]))
650s ***** assert (laplaceinv ([p, NaN], single (0), 1), single ([x, NaN]))
650s ***** assert (laplaceinv ([p, NaN], 0, single (1)), single ([x, NaN]))
650s ***** error<laplaceinv: function called with too few input arguments.> laplaceinv ()
650s ***** error<laplaceinv: function called with too few input arguments.> laplaceinv (1)
650s ***** error<laplaceinv: function called with too few input arguments.> ...
650s  laplaceinv (1, 2)
650s ***** error<laplaceinv: function called with too many inputs> laplaceinv (1, 2, 3, 4)
650s ***** error<laplaceinv: P, MU, and BETA must be of common size or scalars.> ...
650s  laplaceinv (1, ones (2), ones (3))
650s ***** error<laplaceinv: P, MU, and BETA must be of common size or scalars.> ...
650s  laplaceinv (ones (2), 1, ones (3))
650s ***** error<laplaceinv: P, MU, and BETA must be of common size or scalars.> ...
650s  laplaceinv (ones (2), ones (3), 1)
650s ***** error<laplaceinv: P, MU, and BETA must not be complex.> laplaceinv (i, 2, 3)
650s ***** error<laplaceinv: P, MU, and BETA must not be complex.> laplaceinv (1, i, 3)
650s ***** error<laplaceinv: P, MU, and BETA must not be complex.> laplaceinv (1, 2, i)
650s 16 tests, 16 passed, 0 known failure, 0 skipped
650s [inst/dist_fun/nbincdf.m]
650s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/nbincdf.m
650s ***** demo
650s  ## Plot various CDFs from the negative binomial distribution
650s  x = 0:50;
650s  p1 = nbincdf (x, 2, 0.15);
650s  p2 = nbincdf (x, 5, 0.2);
650s  p3 = nbincdf (x, 4, 0.4);
650s  p4 = nbincdf (x, 10, 0.3);
650s  plot (x, p1, "*r", x, p2, "*g", x, p3, "*k", x, p4, "*m")
650s  grid on
650s  xlim ([0, 40])
650s  legend ({"r = 2, ps = 0.15", "r = 5, ps = 0.2", "r = 4, p = 0.4", ...
650s           "r = 10, ps = 0.3"}, "location", "southeast")
650s  title ("Negative binomial CDF")
650s  xlabel ("values in x (number of failures)")
650s  ylabel ("probability")
650s ***** shared x, y
650s  x = [-1 0 1 2 Inf];
650s  y = [0 1/2 3/4 7/8 1];
650s ***** assert (nbincdf (x, ones (1,5), 0.5*ones (1,5)), y)
650s ***** assert (nbincdf (x, 1, 0.5*ones (1,5)), y)
650s ***** assert (nbincdf (x, ones (1,5), 0.5), y)
650s ***** assert (nbincdf (x, ones (1,5), 0.5, "upper"), 1 - y, eps)
650s ***** assert (nbincdf ([x(1:3) 0 x(5)], [0 1 NaN 1.5 Inf], 0.5), ...
650s  [NaN 1/2 NaN nbinpdf(0,1.5,0.5) NaN], eps)
650s ***** assert (nbincdf (x, 1, 0.5*[-1 NaN 4 1 1]), [NaN NaN NaN y(4:5)])
650s ***** assert (nbincdf ([x(1:2) NaN x(4:5)], 1, 0.5), [y(1:2) NaN y(4:5)])
650s ***** assert (nbincdf ([x, NaN], 1, 0.5), [y, NaN])
650s ***** assert (nbincdf (single ([x, NaN]), 1, 0.5), single ([y, NaN]))
650s ***** assert (nbincdf ([x, NaN], single (1), 0.5), single ([y, NaN]))
650s ***** assert (nbincdf ([x, NaN], 1, single (0.5)), single ([y, NaN]))
650s ***** error<nbincdf: function called with too few input arguments.> nbincdf ()
650s ***** error<nbincdf: function called with too few input arguments.> nbincdf (1)
650s ***** error<nbincdf: function called with too few input arguments.> nbincdf (1, 2)
650s ***** error<nbincdf: invalid argument for upper tail.> nbincdf (1, 2, 3, 4)
650s ***** error<nbincdf: invalid argument for upper tail.> nbincdf (1, 2, 3, "some")
650s ***** error<nbincdf: X, R, and PS must be of common size or scalars.> ...
650s  nbincdf (ones (3), ones (2), ones (2))
650s ***** error<nbincdf: X, R, and PS must be of common size or scalars.> ...
650s  nbincdf (ones (2), ones (3), ones (2))
650s ***** error<nbincdf: X, R, and PS must be of common size or scalars.> ...
650s  nbincdf (ones (2), ones (2), ones (3))
650s ***** error<nbincdf: X, R, and PS must not be complex.> nbincdf (i, 2, 2)
650s ***** error<nbincdf: X, R, and PS must not be complex.> nbincdf (2, i, 2)
650s ***** error<nbincdf: X, R, and PS must not be complex.> nbincdf (2, 2, i)
650s 22 tests, 22 passed, 0 known failure, 0 skipped
650s [inst/dist_fun/ncfrnd.m]
650s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/ncfrnd.m
650s ***** assert (size (ncfrnd (1, 1, 1)), [1 1])
650s ***** assert (size (ncfrnd (1, ones (2,1), 1)), [2, 1])
650s ***** assert (size (ncfrnd (1, ones (2,2), 1)), [2, 2])
650s ***** assert (size (ncfrnd (ones (2,1), 1, 1)), [2, 1])
650s ***** assert (size (ncfrnd (ones (2,2), 1, 1)), [2, 2])
650s ***** assert (size (ncfrnd (1, 1, 1, 3)), [3, 3])
650s ***** assert (size (ncfrnd (1, 1, 1, [4, 1])), [4, 1])
650s ***** assert (size (ncfrnd (1, 1, 1, 4, 1)), [4, 1])
650s ***** assert (size (ncfrnd (1, 1, 1, 4, 1, 5)), [4, 1, 5])
650s ***** assert (size (ncfrnd (1, 1, 1, 0, 1)), [0, 1])
650s ***** assert (size (ncfrnd (1, 1, 1, 1, 0)), [1, 0])
650s ***** assert (size (ncfrnd (1, 1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
650s ***** assert (class (ncfrnd (1, 1, 1)), "double")
650s ***** assert (class (ncfrnd (1, single (1), 1)), "single")
650s ***** assert (class (ncfrnd (1, 1, single (1))), "single")
650s ***** assert (class (ncfrnd (1, single ([1, 1]), 1)), "single")
650s ***** assert (class (ncfrnd (1, 1, single ([1, 1]))), "single")
650s ***** assert (class (ncfrnd (single (1), 1, 1)), "single")
650s ***** assert (class (ncfrnd (single ([1, 1]), 1, 1)), "single")
650s ***** error<ncfrnd: function called with too few input arguments.> ncfrnd ()
650s ***** error<ncfrnd: function called with too few input arguments.> ncfrnd (1)
650s ***** error<ncfrnd: function called with too few input arguments.> ncfrnd (1, 2)
650s ***** error<ncfrnd: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
650s  ncfrnd (ones (3), ones (2), ones (2))
650s ***** error<ncfrnd: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
650s  ncfrnd (ones (2), ones (3), ones (2))
650s ***** error<ncfrnd: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
650s  ncfrnd (ones (2), ones (2), ones (3))
650s ***** error<ncfrnd: DF1, DF2, and LAMBDA must not be complex.> ncfrnd (i, 2, 3)
650s ***** error<ncfrnd: DF1, DF2, and LAMBDA must not be complex.> ncfrnd (1, i, 3)
650s ***** error<ncfrnd: DF1, DF2, and LAMBDA must not be complex.> ncfrnd (1, 2, i)
650s ***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
650s  ncfrnd (1, 2, 3, -1)
650s ***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
650s  ncfrnd (1, 2, 3, 1.2)
650s ***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
650s  ncfrnd (1, 2, 3, ones (2))
650s ***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
650s  ncfrnd (1, 2, 3, [2 -1 2])
650s ***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
650s  ncfrnd (1, 2, 3, [2 0 2.5])
650s ***** error<ncfrnd: dimensions must be non-negative integers.> ...
650s  ncfrnd (1, 2, 3, 2, -1, 5)
650s ***** error<ncfrnd: dimensions must be non-negative integers.> ...
650s  ncfrnd (1, 2, 3, 2, 1.5, 5)
650s ***** error<ncfrnd: DF1, DF2, and LAMBDA must be scalars or of size SZ.> ...
650s  ncfrnd (2, ones (2), 2, 3)
650s ***** error<ncfrnd: DF1, DF2, and LAMBDA must be scalars or of size SZ.> ...
650s  ncfrnd (2, ones (2), 2, [3, 2])
650s ***** error<ncfrnd: DF1, DF2, and LAMBDA must be scalars or of size SZ.> ...
650s  ncfrnd (2, ones (2), 2, 3, 2)
650s 38 tests, 38 passed, 0 known failure, 0 skipped
650s [inst/dist_fun/bvncdf.m]
650s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/bvncdf.m
650s ***** demo
650s  mu = [1, -1];
650s  sigma = [0.9, 0.4; 0.4, 0.3];
650s  [X1, X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
650s  x = [X1(:), X2(:)];
650s  p = bvncdf (x, mu, sigma);
650s  Z = reshape (p, 25, 25);
650s  surf (X1, X2, Z);
650s  title ("Bivariate Normal Distribution");
650s  ylabel "X1"
650s  xlabel "X2"
650s ***** test
650s  mu = [1, -1];
650s  sigma = [0.9, 0.4; 0.4, 0.3];
650s  [X1,X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
650s  x = [X1(:), X2(:)];
650s  p = bvncdf (x, mu, sigma);
650s  p_out = [0.00011878988774500, 0.00034404112322371, ...
650s           0.00087682502191813, 0.00195221905058185, ...
650s           0.00378235566873474, 0.00638175749734415, ...
650s           0.00943764224329656, 0.01239164888125426, ...
650s           0.01472750274376648, 0.01623228313374828]';
650s  assert (p([1:10]), p_out, 1e-16);
650s ***** test
650s  mu = [1, -1];
650s  sigma = [0.9, 0.4; 0.4, 0.3];
650s  [X1,X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
650s  x = [X1(:), X2(:)];
650s  p = bvncdf (x, mu, sigma);
650s  p_out = [0.8180695783608276, 0.8854485749482751, ...
650s           0.9308108777385832, 0.9579855743025508, ...
650s           0.9722897881414742, 0.9788150170059926, ...
650s           0.9813597788804785, 0.9821977956568989, ...
650s           0.9824283794464095, 0.9824809345614861]';
650s  assert (p([616:625]), p_out, 3e-16);
650s ***** error bvncdf (randn (25,3), [], [1, 1; 1, 1]);
650s ***** error bvncdf (randn (25,2), [], [1, 1; 1, 1]);
650s ***** error bvncdf (randn (25,2), [], ones (3, 2));
650s 5 tests, 5 passed, 0 known failure, 0 skipped
650s [inst/dist_fun/gpinv.m]
650s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/gpinv.m
650s ***** demo
650s  ## Plot various iCDFs from the generalized Pareto distribution
650s  p = 0.001:0.001:0.999;
650s  x1 = gpinv (p, 1, 1, 0);
650s  x2 = gpinv (p, 5, 1, 0);
650s  x3 = gpinv (p, 20, 1, 0);
650s  x4 = gpinv (p, 1, 2, 0);
650s  x5 = gpinv (p, 5, 2, 0);
650s  x6 = gpinv (p, 20, 2, 0);
650s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", ...
650s        p, x4, "-c", p, x5, "-m", p, x6, "-k")
650s  grid on
650s  ylim ([0, 5])
650s  legend ({"k = 1, σ = 1, θ = 0", "k = 5, σ = 1, θ = 0", ...
650s           "k = 20, σ = 1, θ = 0", "k = 1, σ = 2, θ = 0", ...
650s           "k = 5, σ = 2, θ = 0", "k = 20, σ = 2, θ = 0"}, ...
650s          "location", "southeast")
650s  title ("Generalized Pareto iCDF")
650s  xlabel ("probability")
650s  ylabel ("values in x")
650s ***** shared p, y1, y2, y3
650s  p = [-1, 0, 1/2, 1, 2];
650s  y1 = [NaN, 0, 0.6931471805599453, Inf, NaN];
650s  y2 = [NaN, 0, 1, Inf, NaN];
650s  y3 = [NaN, 0, 1/2, 1, NaN];
650s ***** assert (gpinv (p, zeros (1,5), ones (1,5), zeros (1,5)), y1)
650s ***** assert (gpinv (p, 0, 1, zeros (1,5)), y1)
650s ***** assert (gpinv (p, 0, ones (1,5), 0), y1)
650s ***** assert (gpinv (p, zeros (1,5), 1, 0), y1)
650s ***** assert (gpinv (p, 0, 1, 0), y1)
650s ***** assert (gpinv (p, 0, 1, [0, 0, NaN, 0, 0]), [y1(1:2), NaN, y1(4:5)])
650s ***** assert (gpinv (p, 0, [1, 1, NaN, 1, 1], 0), [y1(1:2), NaN, y1(4:5)])
650s ***** assert (gpinv (p, [0, 0, NaN, 0, 0], 1, 0), [y1(1:2), NaN, y1(4:5)])
650s ***** assert (gpinv ([p(1:2), NaN, p(4:5)], 0, 1, 0), [y1(1:2), NaN, y1(4:5)])
650s ***** assert (gpinv (p, ones (1,5), ones (1,5), zeros (1,5)), y2)
650s ***** assert (gpinv (p, 1, 1, zeros (1,5)), y2)
650s ***** assert (gpinv (p, 1, ones (1,5), 0), y2)
650s ***** assert (gpinv (p, ones (1,5), 1, 0), y2)
650s ***** assert (gpinv (p, 1, 1, 0), y2)
650s ***** assert (gpinv (p, 1, 1, [0, 0, NaN, 0, 0]), [y2(1:2), NaN, y2(4:5)])
650s ***** assert (gpinv (p, 1, [1, 1, NaN, 1, 1], 0), [y2(1:2), NaN, y2(4:5)])
650s ***** assert (gpinv (p, [1, 1, NaN, 1, 1], 1, 0), [y2(1:2), NaN, y2(4:5)])
650s ***** assert (gpinv ([p(1:2), NaN, p(4:5)], 1, 1, 0), [y2(1:2), NaN, y2(4:5)])
650s ***** assert (gpinv (p, -ones (1,5), ones (1,5), zeros (1,5)), y3)
650s ***** assert (gpinv (p, -1, 1, zeros (1,5)), y3)
650s ***** assert (gpinv (p, -1, ones (1,5), 0), y3)
650s ***** assert (gpinv (p, -ones (1,5), 1, 0), y3)
650s ***** assert (gpinv (p, -1, 1, 0), y3)
650s ***** assert (gpinv (p, -1, 1, [0, 0, NaN, 0, 0]), [y3(1:2), NaN, y3(4:5)])
650s ***** assert (gpinv (p, -1, [1, 1, NaN, 1, 1], 0), [y3(1:2), NaN, y3(4:5)])
650s ***** assert (gpinv (p, -[1, 1, NaN, 1, 1], 1, 0), [y3(1:2), NaN, y3(4:5)])
650s ***** assert (gpinv ([p(1:2), NaN, p(4:5)], -1, 1, 0), [y3(1:2), NaN, y3(4:5)])
650s ***** assert (gpinv (single ([p, NaN]), 0, 1, 0), single ([y1, NaN]))
650s ***** assert (gpinv ([p, NaN], 0, 1, single (0)), single ([y1, NaN]))
650s ***** assert (gpinv ([p, NaN], 0, single (1), 0), single ([y1, NaN]))
650s ***** assert (gpinv ([p, NaN], single (0), 1, 0), single ([y1, NaN]))
650s ***** assert (gpinv (single ([p, NaN]), 1, 1, 0), single ([y2, NaN]))
650s ***** assert (gpinv ([p, NaN], 1, 1, single (0)), single ([y2, NaN]))
650s ***** assert (gpinv ([p, NaN], 1, single (1), 0), single ([y2, NaN]))
650s ***** assert (gpinv ([p, NaN], single (1), 1, 0), single ([y2, NaN]))
650s ***** assert (gpinv (single ([p, NaN]), -1, 1, 0), single ([y3, NaN]))
650s ***** assert (gpinv ([p, NaN], -1, 1, single (0)), single ([y3, NaN]))
650s ***** assert (gpinv ([p, NaN], -1, single (1), 0), single ([y3, NaN]))
650s ***** assert (gpinv ([p, NaN], single (-1), 1, 0), single ([y3, NaN]))
650s ***** error<gpinv: function called with too few input arguments.> gpinv ()
650s ***** error<gpinv: function called with too few input arguments.> gpinv (1)
650s ***** error<gpinv: function called with too few input arguments.> gpinv (1, 2)
650s ***** error<gpinv: function called with too few input arguments.> gpinv (1, 2, 3)
650s ***** error<gpinv: P, K, SIGMA, and THETA must be of common size or scalars.> ...
650s  gpinv (ones (3), ones (2), ones(2), ones(2))
650s ***** error<gpinv: P, K, SIGMA, and THETA must be of common size or scalars.> ...
650s  gpinv (ones (2), ones (3), ones(2), ones(2))
650s ***** error<gpinv: P, K, SIGMA, and THETA must be of common size or scalars.> ...
650s  gpinv (ones (2), ones (2), ones(3), ones(2))
650s ***** error<gpinv: P, K, SIGMA, and THETA must be of common size or scalars.> ...
650s  gpinv (ones (2), ones (2), ones(2), ones(3))
650s ***** error<gpinv: P, K, SIGMA, and THETA must not be complex.> gpinv (i, 2, 3, 4)
650s ***** error<gpinv: P, K, SIGMA, and THETA must not be complex.> gpinv (1, i, 3, 4)
650s ***** error<gpinv: P, K, SIGMA, and THETA must not be complex.> gpinv (1, 2, i, 4)
650s ***** error<gpinv: P, K, SIGMA, and THETA must not be complex.> gpinv (1, 2, 3, i)
650s 51 tests, 51 passed, 0 known failure, 0 skipped
650s [inst/dist_fun/logirnd.m]
650s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/logirnd.m
650s ***** assert (size (logirnd (1, 1)), [1 1])
650s ***** assert (size (logirnd (1, ones (2,1))), [2, 1])
650s ***** assert (size (logirnd (1, ones (2,2))), [2, 2])
650s ***** assert (size (logirnd (ones (2,1), 1)), [2, 1])
650s ***** assert (size (logirnd (ones (2,2), 1)), [2, 2])
650s ***** assert (size (logirnd (1, 1, 3)), [3, 3])
650s ***** assert (size (logirnd (1, 1, [4, 1])), [4, 1])
650s ***** assert (size (logirnd (1, 1, 4, 1)), [4, 1])
650s ***** assert (size (logirnd (1, 1, 4, 1, 5)), [4, 1, 5])
650s ***** assert (size (logirnd (1, 1, 0, 1)), [0, 1])
650s ***** assert (size (logirnd (1, 1, 1, 0)), [1, 0])
650s ***** assert (size (logirnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
650s ***** assert (class (logirnd (1, 1)), "double")
650s ***** assert (class (logirnd (1, single (1))), "single")
650s ***** assert (class (logirnd (1, single ([1, 1]))), "single")
650s ***** assert (class (logirnd (single (1), 1)), "single")
650s ***** assert (class (logirnd (single ([1, 1]), 1)), "single")
650s ***** error<logirnd: function called with too few input arguments.> logirnd ()
650s ***** error<logirnd: function called with too few input arguments.> logirnd (1)
650s ***** error<logirnd: MU and SIGMA must be of common size or scalars.> ...
650s  logirnd (ones (3), ones (2))
650s ***** error<logirnd: MU and SIGMA must be of common size or scalars.> ...
650s  logirnd (ones (2), ones (3))
650s ***** error<logirnd: MU and SIGMA must not be complex.> logirnd (i, 2, 3)
650s ***** error<logirnd: MU and SIGMA must not be complex.> logirnd (1, i, 3)
650s ***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
650s  logirnd (1, 2, -1)
650s ***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
650s  logirnd (1, 2, 1.2)
650s ***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
650s  logirnd (1, 2, ones (2))
650s ***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
650s  logirnd (1, 2, [2 -1 2])
650s ***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
650s  logirnd (1, 2, [2 0 2.5])
650s ***** error<logirnd: dimensions must be non-negative integers.> ...
650s  logirnd (1, 2, 2, -1, 5)
650s ***** error<logirnd: dimensions must be non-negative integers.> ...
650s  logirnd (1, 2, 2, 1.5, 5)
650s ***** error<logirnd: MU and SIGMA must be scalars or of size SZ.> ...
650s  logirnd (2, ones (2), 3)
650s ***** error<logirnd: MU and SIGMA must be scalars or of size SZ.> ...
650s  logirnd (2, ones (2), [3, 2])
650s ***** error<logirnd: MU and SIGMA must be scalars or of size SZ.> ...
650s  logirnd (2, ones (2), 3, 2)
650s 33 tests, 33 passed, 0 known failure, 0 skipped
650s [inst/dist_fun/tlsinv.m]
650s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/tlsinv.m
650s ***** demo
650s  ## Plot various iCDFs from the location-scale Student's T distribution
650s  p = 0.001:0.001:0.999;
650s  x1 = tlsinv (p, 0, 1, 1);
650s  x2 = tlsinv (p, 0, 2, 2);
650s  x3 = tlsinv (p, 3, 2, 5);
650s  x4 = tlsinv (p, -1, 3, Inf);
650s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-m")
650s  grid on
650s  xlim ([0, 1])
650s  ylim ([-8, 8])
650s  legend ({"mu = 0, sigma = 1, nu = 1", "mu = 0, sigma = 2, nu = 2", ...
650s           "mu = 3, sigma = 2, nu = 5", 'mu = -1, sigma = 3, nu = \infty'}, ...
650s          "location", "southeast")
650s  title ("Location-scale Student's T iCDF")
650s  xlabel ("probability")
650s  ylabel ("values in x")
650s ***** shared p
650s  p = [-1 0 0.5 1 2];
650s ***** assert (tlsinv (p, 0, 1, ones (1,5)), [NaN -Inf 0 Inf NaN])
650s ***** assert (tlsinv (p, 0, 1, 1), [NaN -Inf 0 Inf NaN], eps)
650s ***** assert (tlsinv (p, 0, 1, [1 0 NaN 1 1]), [NaN NaN NaN Inf NaN], eps)
650s ***** assert (tlsinv ([p(1:2) NaN p(4:5)], 0, 1, 1), [NaN -Inf NaN Inf NaN])
650s ***** assert (class (tlsinv ([p, NaN], 0, 1, 1)), "double")
650s ***** assert (class (tlsinv (single ([p, NaN]), 0, 1, 1)), "single")
650s ***** assert (class (tlsinv ([p, NaN], single (0), 1, 1)), "single")
650s ***** assert (class (tlsinv ([p, NaN], 0, single (1), 1)), "single")
650s ***** assert (class (tlsinv ([p, NaN], 0, 1, single (1))), "single")
650s ***** error<tlsinv: function called with too few input arguments.> tlsinv ()
650s ***** error<tlsinv: function called with too few input arguments.> tlsinv (1)
650s ***** error<tlsinv: function called with too few input arguments.> tlsinv (1, 2)
650s ***** error<tlsinv: function called with too few input arguments.> tlsinv (1, 2, 3)
650s ***** error<tlsinv: P, MU, SIGMA, and NU must be of common size or scalars.> ...
650s  tlsinv (ones (3), ones (2), 1, 1)
650s ***** error<tlsinv: P, MU, SIGMA, and NU must be of common size or scalars.> ...
650s  tlsinv (ones (2), 1, ones (3), 1)
650s ***** error<tlsinv: P, MU, SIGMA, and NU must be of common size or scalars.> ...
650s  tlsinv (ones (2), 1, 1, ones (3))
650s ***** error<tlsinv: P, MU, SIGMA, and NU must not be complex.> tlsinv (i, 2, 3, 4)
650s ***** error<tlsinv: P, MU, SIGMA, and NU must not be complex.> tlsinv (2, i, 3, 4)
650s ***** error<tlsinv: P, MU, SIGMA, and NU must not be complex.> tlsinv (2, 2, i, 4)
650s ***** error<tlsinv: P, MU, SIGMA, and NU must not be complex.> tlsinv (2, 2, 3, i)
650s 20 tests, 20 passed, 0 known failure, 0 skipped
650s [inst/dist_fun/gprnd.m]
650s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/gprnd.m
650s ***** assert (size (gprnd (0, 1, 0)), [1, 1])
650s ***** assert (size (gprnd (0, 1, zeros (2,1))), [2, 1])
650s ***** assert (size (gprnd (0, 1, zeros (2,2))), [2, 2])
650s ***** assert (size (gprnd (0, ones (2,1), 0)), [2, 1])
650s ***** assert (size (gprnd (0, ones (2,2), 0)), [2, 2])
650s ***** assert (size (gprnd (zeros (2,1), 1, 0)), [2, 1])
650s ***** assert (size (gprnd (zeros (2,2), 1, 0)), [2, 2])
650s ***** assert (size (gprnd (0, 1, 0, 3)), [3, 3])
650s ***** assert (size (gprnd (0, 1, 0, [4 1])), [4, 1])
650s ***** assert (size (gprnd (0, 1, 0, 4, 1)), [4, 1])
650s ***** assert (size (gprnd (1,1,0)), [1, 1])
650s ***** assert (size (gprnd (1, 1, zeros (2,1))), [2, 1])
650s ***** assert (size (gprnd (1, 1, zeros (2,2))), [2, 2])
650s ***** assert (size (gprnd (1, ones (2,1), 0)), [2, 1])
650s ***** assert (size (gprnd (1, ones (2,2), 0)), [2, 2])
650s ***** assert (size (gprnd (ones (2,1), 1, 0)), [2, 1])
650s ***** assert (size (gprnd (ones (2,2), 1, 0)), [2, 2])
650s ***** assert (size (gprnd (1, 1, 0, 3)), [3, 3])
650s ***** assert (size (gprnd (1, 1, 0, [4 1])), [4, 1])
650s ***** assert (size (gprnd (1, 1, 0, 4, 1)), [4, 1])
650s ***** assert (size (gprnd (-1, 1, 0)), [1, 1])
650s ***** assert (size (gprnd (-1, 1, zeros (2,1))), [2, 1])
650s ***** assert (size (gprnd (1, -1, zeros (2,2))), [2, 2])
650s ***** assert (size (gprnd (-1, ones (2,1), 0)), [2, 1])
650s ***** assert (size (gprnd (-1, ones (2,2), 0)), [2, 2])
650s ***** assert (size (gprnd (-ones (2,1), 1, 0)), [2, 1])
650s ***** assert (size (gprnd (-ones (2,2), 1, 0)), [2, 2])
650s ***** assert (size (gprnd (-1, 1, 0, 3)), [3, 3])
650s ***** assert (size (gprnd (-1, 1, 0, [4, 1])), [4, 1])
650s ***** assert (size (gprnd (-1, 1, 0, 4, 1)), [4, 1])
650s ***** assert (class (gprnd (0, 1, 0)), "double")
650s ***** assert (class (gprnd (0, 1, single (0))), "single")
650s ***** assert (class (gprnd (0, 1, single ([0, 0]))), "single")
650s ***** assert (class (gprnd (0, single (1),0)), "single")
650s ***** assert (class (gprnd (0, single ([1, 1]),0)), "single")
650s ***** assert (class (gprnd (single (0), 1, 0)), "single")
650s ***** assert (class (gprnd (single ([0, 0]), 1, 0)), "single")
650s ***** error<gprnd: function called with too few input arguments.> gprnd ()
650s ***** error<gprnd: function called with too few input arguments.> gprnd (1)
650s ***** error<gprnd: function called with too few input arguments.> gprnd (1, 2)
650s ***** error<gprnd: K, SIGMA, and THETA must be of common size or scalars.> ...
650s  gprnd (ones (3), ones (2), ones (2))
650s ***** error<gprnd: K, SIGMA, and THETA must be of common size or scalars.> ...
650s  gprnd (ones (2), ones (3), ones (2))
650s ***** error<gprnd: K, SIGMA, and THETA must be of common size or scalars.> ...
650s  gprnd (ones (2), ones (2), ones (3))
650s ***** error<gprnd: K, SIGMA, and THETA must not be complex.> gprnd (i, 2, 3)
650s ***** error<gprnd: K, SIGMA, and THETA must not be complex.> gprnd (1, i, 3)
650s ***** error<gprnd: K, SIGMA, and THETA must not be complex.> gprnd (1, 2, i)
650s ***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
650s  gprnd (1, 2, 3, -1)
650s ***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
650s  gprnd (1, 2, 3, 1.2)
650s ***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
650s  gprnd (1, 2, 3, ones (2))
650s ***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
650s  gprnd (1, 2, 3, [2 -1 2])
650s ***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
650s  gprnd (1, 2, 3, [2 0 2.5])
650s ***** error<gprnd: dimensions must be non-negative integers.> ...
650s  gprnd (1, 2, 3, 2, -1, 5)
650s ***** error<gprnd: dimensions must be non-negative integers.> ...
650s  gprnd (1, 2, 3, 2, 1.5, 5)
650s ***** error<gprnd: K, SIGMA, and THETA must be scalars or of size SZ.> ...
650s  gprnd (2, ones (2), 2, 3)
650s ***** error<gprnd: K, SIGMA, and THETA must be scalars or of size SZ.> ...
650s  gprnd (2, ones (2), 2, [3, 2])
650s ***** error<gprnd: K, SIGMA, and THETA must be scalars or of size SZ.> ...
650s  gprnd (2, ones (2), 2, 3, 2)
650s 56 tests, 56 passed, 0 known failure, 0 skipped
650s [inst/dist_fun/wblpdf.m]
651s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/wblpdf.m
651s ***** demo
651s  ## Plot various PDFs from the Weibul distribution
651s  x = 0:0.001:2.5;
651s  y1 = wblpdf (x, 1, 0.5);
651s  y2 = wblpdf (x, 1, 1);
651s  y3 = wblpdf (x, 1, 1.5);
651s  y4 = wblpdf (x, 1, 5);
651s  plot (x, y1, "-b", x, y2, "-r", x, y3, "-m", x, y4, "-g")
651s  grid on
651s  ylim ([0, 2.5])
651s  legend ({"λ = 5, k = 0.5", "λ = 9, k = 1", ...
651s           "λ = 6, k = 1.5", "λ = 2, k = 5"}, "location", "northeast")
651s  title ("Weibul PDF")
651s  xlabel ("values in x")
651s  ylabel ("density")
651s ***** shared x,y
651s  x = [-1 0 0.5 1 Inf];
651s  y = [0, exp(-x(2:4)), NaN];
651s ***** assert (wblpdf (x, ones (1,5), ones (1,5)), y)
651s ***** assert (wblpdf (x, 1, ones (1,5)), y)
651s ***** assert (wblpdf (x, ones (1,5), 1), y)
651s ***** assert (wblpdf (x, [0 NaN Inf 1 1], 1), [NaN NaN NaN y(4:5)])
651s ***** assert (wblpdf (x, 1, [0 NaN Inf 1 1]), [NaN NaN NaN y(4:5)])
651s ***** assert (wblpdf ([x, NaN], 1, 1), [y, NaN])
651s ***** assert (wblpdf (single ([x, NaN]), 1, 1), single ([y, NaN]))
651s ***** assert (wblpdf ([x, NaN], single (1), 1), single ([y, NaN]))
651s ***** assert (wblpdf ([x, NaN], 1, single (1)), single ([y, NaN]))
651s ***** error wblpdf ()
651s ***** error wblpdf (1,2,3,4)
651s ***** error wblpdf (ones (3), ones (2), ones (2))
651s ***** error wblpdf (ones (2), ones (3), ones (2))
651s ***** error wblpdf (ones (2), ones (2), ones (3))
651s ***** error wblpdf (i, 2, 2)
651s ***** error wblpdf (2, i, 2)
651s ***** error wblpdf (2, 2, i)
651s 17 tests, 17 passed, 0 known failure, 0 skipped
651s [inst/dist_fun/hygernd.m]
651s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/hygernd.m
651s ***** assert (size (hygernd (4,2,2)), [1, 1])
651s ***** assert (size (hygernd (4*ones (2,1), 2,2)), [2, 1])
651s ***** assert (size (hygernd (4*ones (2,2), 2,2)), [2, 2])
651s ***** assert (size (hygernd (4, 2*ones (2,1), 2)), [2, 1])
651s ***** assert (size (hygernd (4, 2*ones (2,2), 2)), [2, 2])
651s ***** assert (size (hygernd (4, 2, 2*ones (2,1))), [2, 1])
651s ***** assert (size (hygernd (4, 2, 2*ones (2,2))), [2, 2])
651s ***** assert (size (hygernd (4, 2, 2, 3)), [3, 3])
651s ***** assert (size (hygernd (4, 2, 2, [4 1])), [4, 1])
651s ***** assert (size (hygernd (4, 2, 2, 4, 1)), [4, 1])
651s ***** assert (class (hygernd (4,2,2)), "double")
651s ***** assert (class (hygernd (single (4),2,2)), "single")
651s ***** assert (class (hygernd (single ([4 4]),2,2)), "single")
651s ***** assert (class (hygernd (4,single (2),2)), "single")
651s ***** assert (class (hygernd (4,single ([2 2]),2)), "single")
651s ***** assert (class (hygernd (4,2,single (2))), "single")
651s ***** assert (class (hygernd (4,2,single ([2 2]))), "single")
651s ***** error<hygernd: function called with too few input arguments.> hygernd ()
651s ***** error<hygernd: function called with too few input arguments.> hygernd (1)
651s ***** error<hygernd: function called with too few input arguments.> hygernd (1, 2)
651s ***** error<hygernd: T, M, and N must be of common size or scalars.> ...
651s  hygernd (ones (3), ones (2), ones (2))
651s ***** error<hygernd: T, M, and N must be of common size or scalars.> ...
651s  hygernd (ones (2), ones (3), ones (2))
651s ***** error<hygernd: T, M, and N must be of common size or scalars.> ...
651s  hygernd (ones (2), ones (2), ones (3))
651s ***** error<hygernd: T, M, and N must not be complex.> hygernd (i, 2, 3)
651s ***** error<hygernd: T, M, and N must not be complex.> hygernd (1, i, 3)
651s ***** error<hygernd: T, M, and N must not be complex.> hygernd (1, 2, i)
651s ***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
651s  hygernd (1, 2, 3, -1)
651s ***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
651s  hygernd (1, 2, 3, 1.2)
651s ***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
651s  hygernd (1, 2, 3, ones (2))
651s ***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
651s  hygernd (1, 2, 3, [2 -1 2])
651s ***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
651s  hygernd (1, 2, 3, [2 0 2.5])
651s ***** error<hygernd: dimensions must be non-negative integers.> ...
651s  hygernd (1, 2, 3, 2, -1, 5)
651s ***** error<hygernd: dimensions must be non-negative integers.> ...
651s  hygernd (1, 2, 3, 2, 1.5, 5)
651s ***** error<hygernd: T, M, and N must be scalars or of size SZ.> ...
651s  hygernd (2, ones (2), 2, 3)
651s ***** error<hygernd: T, M, and N must be scalars or of size SZ.> ...
651s  hygernd (2, ones (2), 2, [3, 2])
651s ***** error<hygernd: T, M, and N must be scalars or of size SZ.> ...
651s  hygernd (2, ones (2), 2, 3, 2)
651s 36 tests, 36 passed, 0 known failure, 0 skipped
651s [inst/dist_fun/ncx2inv.m]
651s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/ncx2inv.m
651s ***** demo
651s  ## Plot various iCDFs from the noncentral chi-squared distribution
651s  p = 0.001:0.001:0.999;
651s  x1 = ncx2inv (p, 2, 1);
651s  x2 = ncx2inv (p, 2, 2);
651s  x3 = ncx2inv (p, 2, 3);
651s  x4 = ncx2inv (p, 4, 1);
651s  x5 = ncx2inv (p, 4, 2);
651s  x6 = ncx2inv (p, 4, 3);
651s  plot (p, x1, "-r", p, x2, "-g", p, x3, "-k", ...
651s        p, x4, "-m", p, x5, "-c", p, x6, "-y")
651s  grid on
651s  ylim ([0, 10])
651s  legend ({"df = 2, λ = 1", "df = 2, λ = 2", ...
651s           "df = 2, λ = 3", "df = 4, λ = 1", ...
651s           "df = 4, λ = 2", "df = 4, λ = 3"}, "location", "northwest")
651s  title ("Noncentral chi-squared iCDF")
651s  xlabel ("probability")
651s  ylabel ("values in x")
651s ***** demo
651s  ## Compare the noncentral chi-squared CDF with LAMBDA = 2 to the
651s  ## chi-squared CDF with the same number of degrees of freedom (4).
651s 
651s  p = 0.001:0.001:0.999;
651s  x1 = ncx2inv (p, 4, 2);
651s  x2 = chi2inv (p, 4);
651s  plot (p, x1, "-", p, x2, "-");
651s  grid on
651s  ylim ([0, 10])
651s  legend ({"Noncentral χ^2(4,2)", "χ^2(4)"}, "location", "northwest")
651s  title ("Noncentral chi-squared vs chi-squared quantile functions")
651s  xlabel ("probability")
651s  ylabel ("values in x")
651s ***** test
651s  x = [0,0.3443,0.7226,1.1440,1.6220,2.1770,2.8436,3.6854,4.8447,6.7701,Inf];
651s  assert (ncx2inv ([0:0.1:1], 2, 1), x, 1e-4);
651s ***** test
651s  x = [0,0.8295,1.6001,2.3708,3.1785,4.0598,5.0644,6.2765,7.8763,10.4199,Inf];
651s  assert (ncx2inv ([0:0.1:1], 2, 3), x, 1e-4);
651s ***** test
651s  x = [0,0.5417,1.3483,2.1796,3.0516,4.0003,5.0777,6.3726,8.0748,10.7686,Inf];
651s  assert (ncx2inv ([0:0.1:1], 1, 4), x, 1e-4);
651s ***** test
651s  x = [0.1808, 0.6456, 1.1842, 1.7650, 2.3760, 3.0105];
651s  assert (ncx2inv (0.05, [1, 2, 3, 4, 5, 6], 4), x, 1e-4);
651s ***** test
651s  x = [0.4887, 0.6699, 0.9012, 1.1842, 1.5164, 1.8927];
651s  assert (ncx2inv (0.05, 3, [1, 2, 3, 4, 5, 6]), x, 1e-4);
651s ***** test
651s  x = [1.3941, 1.6824, 2.0103, 2.3760, NaN, 3.2087];
651s  assert (ncx2inv (0.05, 5, [1, 2, 3, 4, -1, 6]), x, 1e-4);
651s ***** test
651s  assert (ncx2inv (0.996, 5, 8), 35.51298862765576, 3e-13);
651s ***** error<ncx2inv: function called with too few input arguments.> ncx2inv ()
651s ***** error<ncx2inv: function called with too few input arguments.> ncx2inv (1)
651s ***** error<ncx2inv: function called with too few input arguments.> ncx2inv (1, 2)
651s ***** error<ncx2inv: P, DF, and LAMBDA must be of common size or scalars.> ...
651s  ncx2inv (ones (3), ones (2), ones (2))
651s ***** error<ncx2inv: P, DF, and LAMBDA must be of common size or scalars.> ...
651s  ncx2inv (ones (2), ones (3), ones (2))
651s ***** error<ncx2inv: P, DF, and LAMBDA must be of common size or scalars.> ...
651s  ncx2inv (ones (2), ones (2), ones (3))
651s ***** error<ncx2inv: P, DF, and LAMBDA must not be complex.> ncx2inv (i, 2, 2)
651s ***** error<ncx2inv: P, DF, and LAMBDA must not be complex.> ncx2inv (2, i, 2)
651s ***** error<ncx2inv: P, DF, and LAMBDA must not be complex.> ncx2inv (2, 2, i)
651s 16 tests, 16 passed, 0 known failure, 0 skipped
651s [inst/dist_fun/tlspdf.m]
651s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/tlspdf.m
651s ***** demo
651s  ## Plot various PDFs from the Student's T distribution
651s  x = -8:0.01:8;
651s  y1 = tlspdf (x, 0, 1, 1);
651s  y2 = tlspdf (x, 0, 2, 2);
651s  y3 = tlspdf (x, 3, 2, 5);
651s  y4 = tlspdf (x, -1, 3, Inf);
651s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-m")
651s  grid on
651s  xlim ([-8, 8])
651s  ylim ([0, 0.41])
651s  legend ({"mu = 0, sigma = 1, nu = 1", "mu = 0, sigma = 2, nu = 2", ...
651s           "mu = 3, sigma = 2, nu = 5", 'mu = -1, sigma = 3, nu = \infty'}, ...
651s          "location", "northwest")
651s  title ("Location-scale Student's T PDF")
651s  xlabel ("values in x")
651s  ylabel ("density")
651s ***** test
651s  x = rand (10,1);
651s  y = 1./(pi * (1 + x.^2));
651s  assert (tlspdf (x, 0, 1, 1), y, 5*eps);
651s  assert (tlspdf (x+5, 5, 1, 1), y, 5*eps);
651s  assert (tlspdf (x.*2, 0, 2, 1), y./2, 5*eps);
651s ***** shared x, y
651s  x = [-Inf 0 0.5 1 Inf];
651s  y = 1./(pi * (1 + x.^2));
651s ***** assert (tlspdf (x, 0, 1, ones (1,5)), y, eps)
651s ***** assert (tlspdf (x, 0, 1, 1), y, eps)
651s ***** assert (tlspdf (x, 0, 1, [0 NaN 1 1 1]), [NaN NaN y(3:5)], eps)
651s ***** assert (tlspdf (x, 0, 1, Inf), normpdf (x))
651s ***** assert (class (tlspdf ([x, NaN], 1, 1, 1)), "double")
651s ***** assert (class (tlspdf (single ([x, NaN]), 1, 1, 1)), "single")
651s ***** assert (class (tlspdf ([x, NaN], single (1), 1, 1)), "single")
651s ***** assert (class (tlspdf ([x, NaN], 1, single (1), 1)), "single")
651s ***** assert (class (tlspdf ([x, NaN], 1, 1, single (1))), "single")
651s ***** error<tlspdf: function called with too few input arguments.> tlspdf ()
651s ***** error<tlspdf: function called with too few input arguments.> tlspdf (1)
651s ***** error<tlspdf: function called with too few input arguments.> tlspdf (1, 2)
651s ***** error<tlspdf: function called with too few input arguments.> tlspdf (1, 2, 3)
651s ***** error<tlspdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
651s  tlspdf (ones (3), ones (2), 1, 1)
651s ***** error<tlspdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
651s  tlspdf (ones (2), 1, ones (3), 1)
651s ***** error<tlspdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
651s  tlspdf (ones (2), 1, 1, ones (3))
651s ***** error<tlspdf: X, MU, SIGMA, and NU must not be complex.> tlspdf (i, 2, 1, 1)
651s ***** error<tlspdf: X, MU, SIGMA, and NU must not be complex.> tlspdf (2, i, 1, 1)
651s ***** error<tlspdf: X, MU, SIGMA, and NU must not be complex.> tlspdf (2, 1, i, 1)
651s ***** error<tlspdf: X, MU, SIGMA, and NU must not be complex.> tlspdf (2, 1, 1, i)
651s 21 tests, 21 passed, 0 known failure, 0 skipped
651s [inst/dist_fun/mnpdf.m]
651s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/mnpdf.m
651s ***** test
651s  x = [1, 4, 2];
651s  pk = [0.2, 0.5, 0.3];
651s  y = mnpdf (x, pk);
651s  assert (y, 0.11812, 0.001);
651s ***** test
651s  x = [1, 4, 2; 1, 0, 9];
651s  pk = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8];
651s  y = mnpdf (x, pk);
651s  assert (y, [0.11812; 0.13422], 0.001);
651s 2 tests, 2 passed, 0 known failure, 0 skipped
651s [inst/dist_fun/wishrnd.m]
651s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/wishrnd.m
651s ***** assert(size (wishrnd (1,2)), [1, 1]);
651s ***** assert(size (wishrnd (1,2,[])), [1, 1]);
651s ***** assert(size (wishrnd (1,2,1)), [1, 1]);
651s ***** assert(size (wishrnd ([],2,1)), [1, 1]);
651s ***** assert(size (wishrnd ([3 1; 1 3], 2.00001, [], 1)), [2, 2]);
651s ***** assert(size (wishrnd (eye(2), 2, [], 3)), [2, 2, 3]);
651s ***** error wishrnd ()
651s ***** error wishrnd (1)
652s ***** error wishrnd ([1; 1], 2)
652s 9 tests, 9 passed, 0 known failure, 0 skipped
652s [inst/dist_fun/hninv.m]
652s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/hninv.m
652s ***** demo
652s  ## Plot various iCDFs from the half-normal distribution
652s  p = 0.001:0.001:0.999;
652s  x1 = hninv (p, 0, 1);
652s  x2 = hninv (p, 0, 2);
652s  x3 = hninv (p, 0, 3);
652s  x4 = hninv (p, 0, 5);
652s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
652s  grid on
652s  ylim ([0, 10])
652s  legend ({"μ = 0, σ = 1", "μ = 0, σ = 2", ...
652s           "μ = 0, σ = 3", "μ = 0, σ = 5"}, "location", "northwest")
652s  title ("Half-normal iCDF")
652s  xlabel ("probability")
652s  ylabel ("x")
652s ***** shared p, x
652s  p = [0, 0.3829, 0.6827, 1];
652s  x = [0, 1/2, 1, Inf];
652s ***** assert (hninv (p, 0, 1), x, 1e-4);
652s ***** assert (hninv (p, 5, 1), x + 5, 1e-4);
652s ***** assert (hninv (p, 0, ones (1,4)), x, 1e-4);
652s ***** assert (hninv (p, 0, [-1, 0, 1, 1]), [NaN, NaN, x(3:4)], 1e-4)
652s ***** assert (class (hninv (single ([p, NaN]), 0, 1)), "single")
652s ***** assert (class (hninv ([p, NaN], single (0), 1)), "single")
652s ***** assert (class (hninv ([p, NaN], 0, single (1))), "single")
652s ***** error<hninv: function called with too few input arguments.> hninv (1)
652s ***** error<hninv: function called with too few input arguments.> hninv (1, 2)
652s ***** error<hninv: P, MU, and SIGMA must be of common size or scalars.> ...
652s  hninv (1, ones (2), ones (3))
652s ***** error<hninv: P, MU, and SIGMA must be of common size or scalars.> ...
652s  hninv (ones (2), 1, ones (3))
652s ***** error<hninv: P, MU, and SIGMA must be of common size or scalars.> ...
652s  hninv (ones (2), ones (3), 1)
652s ***** error<hninv: P, MU, and SIGMA must not be complex.> hninv (i, 2, 3)
652s ***** error<hninv: P, MU, and SIGMA must not be complex.> hninv (1, i, 3)
652s ***** error<hninv: P, MU, and SIGMA must not be complex.> hninv (1, 2, i)
652s 15 tests, 15 passed, 0 known failure, 0 skipped
652s [inst/dist_fun/tinv.m]
652s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/tinv.m
652s ***** demo
652s  ## Plot various iCDFs from the Student's T distribution
652s  p = 0.001:0.001:0.999;
652s  x1 = tinv (p, 1);
652s  x2 = tinv (p, 2);
652s  x3 = tinv (p, 5);
652s  x4 = tinv (p, Inf);
652s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-m")
652s  grid on
652s  xlim ([0, 1])
652s  ylim ([-5, 5])
652s  legend ({"df = 1", "df = 2", ...
652s           "df = 5", 'df = \infty'}, "location", "northwest")
652s  title ("Student's T iCDF")
652s  xlabel ("probability")
652s  ylabel ("values in x")
652s ***** shared p
652s  p = [-1 0 0.5 1 2];
652s ***** assert (tinv (p, ones (1,5)), [NaN -Inf 0 Inf NaN])
652s ***** assert (tinv (p, 1), [NaN -Inf 0 Inf NaN], eps)
652s ***** assert (tinv (p, [1 0 NaN 1 1]), [NaN NaN NaN Inf NaN], eps)
652s ***** assert (tinv ([p(1:2) NaN p(4:5)], 1), [NaN -Inf NaN Inf NaN])
652s ***** assert (tinv ([p, NaN], 1), [NaN -Inf 0 Inf NaN NaN], eps)
652s ***** assert (tinv (single ([p, NaN]), 1), single ([NaN -Inf 0 Inf NaN NaN]), eps ("single"))
652s ***** assert (tinv ([p, NaN], single (1)), single ([NaN -Inf 0 Inf NaN NaN]), eps ("single"))
652s ***** error<tinv: function called with too few input arguments.> tinv ()
652s ***** error<tinv: function called with too few input arguments.> tinv (1)
652s ***** error<tinv: P and DF must be of common size or scalars.> ...
652s  tinv (ones (3), ones (2))
652s ***** error<tinv: P and DF must be of common size or scalars.> ...
652s  tinv (ones (2), ones (3))
652s ***** error<tinv: P and DF must not be complex.> tinv (i, 2)
652s ***** error<tinv: P and DF must not be complex.> tinv (2, i)
652s 13 tests, 13 passed, 0 known failure, 0 skipped
652s [inst/dist_fun/tripdf.m]
652s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/tripdf.m
652s ***** demo
652s  ## Plot various CDFs from the triangular distribution
652s  x = 0.001:0.001:10;
652s  y1 = tripdf (x, 3, 4, 6);
652s  y2 = tripdf (x, 1, 2, 5);
652s  y3 = tripdf (x, 2, 3, 9);
652s  y4 = tripdf (x, 2, 5, 9);
652s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
652s  grid on
652s  xlim ([0, 10])
652s  legend ({"a = 3, b = 4, c = 6", "a = 1, b = 2, c = 5", ...
652s           "a = 2, b = 3, c = 9", "a = 2, b = 5, c = 9"}, ...
652s          "location", "northeast")
652s  title ("Triangular CDF")
652s  xlabel ("values in x")
652s  ylabel ("probability")
652s ***** shared x, y, deps
652s  x = [-1, 0, 0.1, 0.5, 0.9, 1, 2] + 1;
652s  y = [0, 0, 0.4, 2, 0.4, 0, 0];
652s  deps = 2*eps;
652s ***** assert (tripdf (x, ones (1,7), 1.5*ones (1,7), 2*ones (1,7)), y, deps)
652s ***** assert (tripdf (x, 1*ones (1,7), 1.5, 2), y, deps)
652s ***** assert (tripdf (x, 1, 1.5, 2*ones (1,7)), y, deps)
652s ***** assert (tripdf (x, 1, 1.5*ones (1,7), 2), y, deps)
652s ***** assert (tripdf (x, 1, 1.5, 2), y, deps)
652s ***** assert (tripdf (x, [1, 1, NaN, 1, 1, 1, 1], 1.5, 2), [y(1:2), NaN, y(4:7)], deps)
652s ***** assert (tripdf (x, 1, 1.5, 2*[1, 1, NaN, 1, 1, 1, 1]), [y(1:2), NaN, y(4:7)], deps)
652s ***** assert (tripdf (x, 1, 1.5*[1, 1, NaN, 1, 1, 1, 1], 2), [y(1:2), NaN, y(4:7)], deps)
652s ***** assert (tripdf ([x, NaN], 1, 1.5, 2), [y, NaN], deps)
652s ***** assert (tripdf (single ([x, NaN]), 1, 1.5, 2), single ([y, NaN]), eps("single"))
652s ***** assert (tripdf ([x, NaN], single (1), 1.5, 2), single ([y, NaN]), eps("single"))
652s ***** assert (tripdf ([x, NaN], 1, 1.5, single (2)), single ([y, NaN]), eps("single"))
652s ***** assert (tripdf ([x, NaN], 1, single (1.5), 2), single ([y, NaN]), eps("single"))
652s ***** error<tripdf: function called with too few input arguments.> tripdf ()
652s ***** error<tripdf: function called with too few input arguments.> tripdf (1)
652s ***** error<tripdf: function called with too few input arguments.> tripdf (1, 2)
652s ***** error<tripdf: function called with too few input arguments.> tripdf (1, 2, 3)
652s ***** error<tripdf: function called with too many inputs> ...
652s  tripdf (1, 2, 3, 4, 5)
652s ***** error<tripdf: X, A, B, and C must be of common size or scalars.> ...
652s  tripdf (ones (3), ones (2), ones(2), ones(2))
652s ***** error<tripdf: X, A, B, and C must be of common size or scalars.> ...
652s  tripdf (ones (2), ones (3), ones(2), ones(2))
652s ***** error<tripdf: X, A, B, and C must be of common size or scalars.> ...
652s  tripdf (ones (2), ones (2), ones(3), ones(2))
652s ***** error<tripdf: X, A, B, and C must be of common size or scalars.> ...
652s  tripdf (ones (2), ones (2), ones(2), ones(3))
652s ***** error<tripdf: X, A, B, and C must not be complex.> tripdf (i, 2, 3, 4)
652s ***** error<tripdf: X, A, B, and C must not be complex.> tripdf (1, i, 3, 4)
652s ***** error<tripdf: X, A, B, and C must not be complex.> tripdf (1, 2, i, 4)
652s ***** error<tripdf: X, A, B, and C must not be complex.> tripdf (1, 2, 3, i)
652s 26 tests, 26 passed, 0 known failure, 0 skipped
652s [inst/dist_fun/poissinv.m]
652s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/poissinv.m
652s ***** demo
652s  ## Plot various iCDFs from the Poisson distribution
652s  p = 0.001:0.001:0.999;
652s  x1 = poissinv (p, 13);
652s  x2 = poissinv (p, 4);
652s  x3 = poissinv (p, 10);
652s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
652s  grid on
652s  ylim ([0, 20])
652s  legend ({"λ = 1", "λ = 4", "λ = 10"}, "location", "northwest")
652s  title ("Poisson iCDF")
652s  xlabel ("probability")
652s  ylabel ("values in x (number of occurences)")
652s ***** shared p
652s  p = [-1 0 0.5 1 2];
652s ***** assert (poissinv (p, ones (1,5)), [NaN 0 1 Inf NaN])
652s ***** assert (poissinv (p, 1), [NaN 0 1 Inf NaN])
652s ***** assert (poissinv (p, [1 0 NaN 1 1]), [NaN NaN NaN Inf NaN])
652s ***** assert (poissinv ([p(1:2) NaN p(4:5)], 1), [NaN 0 NaN Inf NaN])
652s ***** assert (poissinv ([p, NaN], 1), [NaN 0 1 Inf NaN NaN])
652s ***** assert (poissinv (single ([p, NaN]), 1), single ([NaN 0 1 Inf NaN NaN]))
652s ***** assert (poissinv ([p, NaN], single (1)), single ([NaN 0 1 Inf NaN NaN]))
652s ***** error<poissinv: function called with too few input arguments.> poissinv ()
652s ***** error<poissinv: function called with too few input arguments.> poissinv (1)
652s ***** error<poissinv: P and LAMBDA must be of common size or scalars.> ...
652s  poissinv (ones (3), ones (2))
652s ***** error<poissinv: P and LAMBDA must be of common size or scalars.> ...
652s  poissinv (ones (2), ones (3))
652s ***** error<poissinv: P and LAMBDA must not be complex.> poissinv (i, 2)
652s ***** error<poissinv: P and LAMBDA must not be complex.> poissinv (2, i)
652s 13 tests, 13 passed, 0 known failure, 0 skipped
652s [inst/dist_fun/wblcdf.m]
652s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/wblcdf.m
652s ***** demo
652s  ## Plot various CDFs from the Weibull distribution
652s  x = 0:0.001:2.5;
652s  p1 = wblcdf (x, 1, 0.5);
652s  p2 = wblcdf (x, 1, 1);
652s  p3 = wblcdf (x, 1, 1.5);
652s  p4 = wblcdf (x, 1, 5);
652s  plot (x, p1, "-b", x, p2, "-r", x, p3, "-m", x, p4, "-g")
652s  grid on
652s  legend ({"λ = 1, k = 0.5", "λ = 1, k = 1", ...
652s           "λ = 1, k = 1.5", "λ = 1, k = 5"}, "location", "southeast")
652s  title ("Weibull CDF")
652s  xlabel ("values in x")
652s  ylabel ("probability")
652s ***** shared x, y
652s  x = [-1 0 0.5 1 Inf];
652s  y = [0, 1-exp(-x(2:4)), 1];
652s ***** assert (wblcdf (x, ones (1,5), ones (1,5)), y, 1e-16)
652s ***** assert (wblcdf (x, ones (1,5), ones (1,5), "upper"), 1 - y)
652s ***** assert (wblcdf (x, "upper"), 1 - y)
652s ***** assert (wblcdf (x, 1, ones (1,5)), y, 1e-16)
652s ***** assert (wblcdf (x, ones (1,5), 1), y, 1e-16)
652s ***** assert (wblcdf (x, [0 1 NaN Inf 1], 1), [NaN 0 NaN 0 1])
652s ***** assert (wblcdf (x, [0 1 NaN Inf 1], 1, "upper"), 1 - [NaN 0 NaN 0 1])
652s ***** assert (wblcdf (x, 1, [0 1 NaN Inf 1]), [NaN 0 NaN y(4:5)])
652s ***** assert (wblcdf (x, 1, [0 1 NaN Inf 1], "upper"), 1 - [NaN 0 NaN y(4:5)])
652s ***** assert (wblcdf ([x(1:2) NaN x(4:5)], 1, 1), [y(1:2) NaN y(4:5)])
652s ***** assert (wblcdf ([x(1:2) NaN x(4:5)], 1, 1, "upper"), 1 - [y(1:2) NaN y(4:5)])
652s ***** assert (wblcdf ([x, NaN], 1, 1), [y, NaN], 1e-16)
652s ***** assert (wblcdf (single ([x, NaN]), 1, 1), single ([y, NaN]))
652s ***** assert (wblcdf ([x, NaN], single (1), 1), single ([y, NaN]))
652s ***** assert (wblcdf ([x, NaN], 1, single (1)), single ([y, NaN]))
652s ***** error<wblcdf: invalid number of input arguments.> wblcdf ()
652s ***** error<wblcdf: invalid number of input arguments.> wblcdf (1,2,3,4,5,6,7)
652s ***** error<wblcdf: invalid argument for upper tail.> wblcdf (1, 2, 3, 4, "uper")
652s ***** error<wblcdf: X, LAMBDA, and K must be of common size or scalars.> ...
652s  wblcdf (ones (3), ones (2), ones (2))
652s ***** error<wblcdf: invalid size of covariance matrix.> wblcdf (2, 3, 4, [1, 2])
652s ***** error<wblcdf: covariance matrix is required for confidence bounds.> ...
652s  [p, plo, pup] = wblcdf (1, 2, 3)
652s ***** error<wblcdf: invalid value for alpha.> [p, plo, pup] = ...
652s  wblcdf (1, 2, 3, [1, 0; 0, 1], 0)
652s ***** error<wblcdf: invalid value for alpha.> [p, plo, pup] = ...
652s  wblcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
652s ***** error<wblcdf: invalid value for alpha.> [p, plo, pup] = ...
652s  wblcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
652s ***** error<wblcdf: X, LAMBDA, and K must not be complex.> wblcdf (i, 2, 2)
652s ***** error<wblcdf: X, LAMBDA, and K must not be complex.> wblcdf (2, i, 2)
652s ***** error<wblcdf: X, LAMBDA, and K must not be complex.> wblcdf (2, 2, i)
652s ***** error<wblcdf: bad covariance matrix.> ...
652s  [p, plo, pup] =wblcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
652s 28 tests, 28 passed, 0 known failure, 0 skipped
652s [inst/dist_fun/mnrnd.m]
652s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/mnrnd.m
652s ***** test
652s  n = 10;
652s  pk = [0.2, 0.5, 0.3];
652s  r = mnrnd (n, pk);
652s  assert (size (r), size (pk));
652s  assert (all (r >= 0));
652s  assert (all (round (r) == r));
652s  assert (sum (r) == n);
652s ***** test
652s  n = 10 * ones (3, 1);
652s  pk = [0.2, 0.5, 0.3];
652s  r = mnrnd (n, pk);
652s  assert (size (r), [length(n), length(pk)]);
652s  assert (all (r >= 0));
652s  assert (all (round (r) == r));
652s  assert (all (sum (r, 2) == n));
652s ***** test
652s  n = (1:2)';
652s  pk = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8];
652s  r = mnrnd (n, pk);
652s  assert (size (r), size (pk));
652s  assert (all (r >= 0));
652s  assert (all (round (r) == r));
652s  assert (all (sum (r, 2) == n));
652s 3 tests, 3 passed, 0 known failure, 0 skipped
652s [inst/dist_fun/gumbelcdf.m]
652s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/gumbelcdf.m
652s ***** demo
652s  ## Plot various CDFs from the Gumbel distribution
652s  x = -5:0.01:20;
652s  p1 = gumbelcdf (x, 0.5, 2);
652s  p2 = gumbelcdf (x, 1.0, 2);
652s  p3 = gumbelcdf (x, 1.5, 3);
652s  p4 = gumbelcdf (x, 3.0, 4);
652s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
652s  grid on
652s  legend ({"μ = 0.5, β = 2", "μ = 1.0, β = 2", ...
652s           "μ = 1.5, β = 3", "μ = 3.0, β = 4"}, "location", "southeast")
652s  title ("Gumbel CDF")
652s  xlabel ("values in x")
652s  ylabel ("probability")
652s ***** shared x, y
652s  x = [-Inf, 1, 2, Inf];
652s  y = [0, 0.3679, 0.6922, 1];
652s ***** assert (gumbelcdf (x, ones (1,4), ones (1,4)), y, 1e-4)
652s ***** assert (gumbelcdf (x, 1, ones (1,4)), y, 1e-4)
652s ***** assert (gumbelcdf (x, ones (1,4), 1), y, 1e-4)
652s ***** assert (gumbelcdf (x, [0, -Inf, NaN, Inf], 1), [0, 1, NaN, NaN], 1e-4)
652s ***** assert (gumbelcdf (x, 1, [Inf, NaN, -1, 0]), [NaN, NaN, NaN, NaN], 1e-4)
652s ***** assert (gumbelcdf ([x(1:2), NaN, x(4)], 1, 1), [y(1:2), NaN, y(4)], 1e-4)
652s ***** assert (gumbelcdf (x, "upper"), [1, 0.3078, 0.1266, 0], 1e-4)
652s ***** assert (gumbelcdf ([x, NaN], 1, 1), [y, NaN], 1e-4)
652s ***** assert (gumbelcdf (single ([x, NaN]), 1, 1), single ([y, NaN]), 1e-4)
652s ***** assert (gumbelcdf ([x, NaN], single (1), 1), single ([y, NaN]), 1e-4)
652s ***** assert (gumbelcdf ([x, NaN], 1, single (1)), single ([y, NaN]), 1e-4)
652s ***** error<gumbelcdf: invalid number of input arguments.> gumbelcdf ()
652s ***** error<gumbelcdf: invalid number of input arguments.> gumbelcdf (1,2,3,4,5,6,7)
652s ***** error<gumbelcdf: invalid argument for upper tail.> gumbelcdf (1, 2, 3, 4, "uper")
652s ***** error<gumbelcdf: X, MU, and BETA must be of common size or scalars.> ...
652s  gumbelcdf (ones (3), ones (2), ones (2))
652s ***** error<gumbelcdf: invalid size of covariance matrix.> gumbelcdf (2, 3, 4, [1, 2])
652s ***** error<gumbelcdf: covariance matrix is required for confidence bounds.> ...
652s  [p, plo, pup] = gumbelcdf (1, 2, 3)
652s ***** error<gumbelcdf: invalid value for alpha.> [p, plo, pup] = ...
652s  gumbelcdf (1, 2, 3, [1, 0; 0, 1], 0)
652s ***** error<gumbelcdf: invalid value for alpha.> [p, plo, pup] = ...
652s  gumbelcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
652s ***** error<gumbelcdf: invalid value for alpha.> [p, plo, pup] = ...
652s  gumbelcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
652s ***** error<gumbelcdf: X, MU, and BETA must not be complex.> gumbelcdf (i, 2, 2)
652s ***** error<gumbelcdf: X, MU, and BETA must not be complex.> gumbelcdf (2, i, 2)
652s ***** error<gumbelcdf: X, MU, and BETA must not be complex.> gumbelcdf (2, 2, i)
652s ***** error<gumbelcdf: bad covariance matrix.> ...
652s  [p, plo, pup] = gumbelcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
652s 24 tests, 24 passed, 0 known failure, 0 skipped
652s [inst/dist_fun/loglinv.m]
652s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/loglinv.m
652s ***** demo
652s  ## Plot various iCDFs from the log-logistic distribution
652s  p = 0.001:0.001:0.999;
652s  x1 = loglinv (p, log (1), 1/0.5);
652s  x2 = loglinv (p, log (1), 1);
652s  x3 = loglinv (p, log (1), 1/2);
652s  x4 = loglinv (p, log (1), 1/4);
652s  x5 = loglinv (p, log (1), 1/8);
652s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
652s  ylim ([0, 20])
652s  grid on
652s  legend ({"σ = 2 (β = 0.5)", "σ = 1 (β = 1)", "σ = 0.5 (β = 2)", ...
652s           "σ = 0.25 (β = 4)", "σ = 0.125 (β = 8)"}, "location", "northwest")
652s  title ("Log-logistic iCDF")
652s  xlabel ("probability")
652s  ylabel ("x")
652s  text (0.03, 12.5, "μ = 0 (α = 1), values of σ (β) as shown in legend")
652s ***** shared p, out1, out2
652s  p = [-1, 0, 0.2, 0.5, 0.8, 0.95, 1, 2];
652s  out1 = [NaN, 0, 0.25, 1, 4, 19, Inf, NaN];
652s  out2 = [NaN, 0, 0.0424732, 2.718282, 173.970037, 18644.695061, Inf, NaN];
652s ***** assert (loglinv (p, 0, 1), out1, 1e-8)
652s ***** assert (loglinv (p, 0, 1), out1, 1e-8)
652s ***** assert (loglinv (p, 1, 3), out2, 1e-6)
652s ***** assert (class (loglinv (single (1), 2, 3)), "single")
652s ***** assert (class (loglinv (1, single (2), 3)), "single")
652s ***** assert (class (loglinv (1, 2, single (3))), "single")
652s ***** error<loglinv: function called with too few input arguments.> loglinv (1)
652s ***** error<loglinv: function called with too few input arguments.> loglinv (1, 2)
652s ***** error<loglinv: P, MU, and SIGMA must be of common size or scalars.> ...
652s  loglinv (1, ones (2), ones (3))
652s ***** error<loglinv: P, MU, and SIGMA must be of common size or scalars.> ...
652s  loglinv (ones (2), 1, ones (3))
652s ***** error<loglinv: P, MU, and SIGMA must be of common size or scalars.> ...
652s  loglinv (ones (2), ones (3), 1)
652s ***** error<loglinv: P, MU, and SIGMA must not be complex.> loglinv (i, 2, 3)
652s ***** error<loglinv: P, MU, and SIGMA must not be complex.> loglinv (1, i, 3)
652s ***** error<loglinv: P, MU, and SIGMA must not be complex.> loglinv (1, 2, i)
652s 14 tests, 14 passed, 0 known failure, 0 skipped
652s [inst/dist_fun/bisainv.m]
652s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/bisainv.m
652s ***** demo
652s  ## Plot various iCDFs from the Birnbaum-Saunders distribution
652s  p = 0.001:0.001:0.999;
652s  x1 = bisainv (p, 1, 0.5);
652s  x2 = bisainv (p, 1, 1);
652s  x3 = bisainv (p, 1, 2);
652s  x4 = bisainv (p, 1, 5);
652s  x5 = bisainv (p, 1, 10);
652s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
652s  grid on
652s  ylim ([0, 10])
652s  legend ({"β = 1, γ = 0.5", "β = 1, γ = 1", "β = 1, γ = 2", ...
652s           "β = 1, γ = 5", "β = 1, γ = 10"}, "location", "northwest")
652s  title ("Birnbaum-Saunders iCDF")
652s  xlabel ("probability")
652s  ylabel ("values in x")
652s ***** demo
652s  ## Plot various iCDFs from the Birnbaum-Saunders distribution
652s  p = 0.001:0.001:0.999;
652s  x1 = bisainv (p, 1, 0.3);
652s  x2 = bisainv (p, 2, 0.3);
652s  x3 = bisainv (p, 1, 0.5);
652s  x4 = bisainv (p, 3, 0.5);
652s  x5 = bisainv (p, 5, 0.5);
652s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
652s  grid on
652s  ylim ([0, 10])
652s  legend ({"β = 1, γ = 0.3", "β = 2, γ = 0.3", "β = 1, γ = 0.5", ...
652s           "β = 3, γ = 0.5", "β = 5, γ = 0.5"}, "location", "northwest")
652s  title ("Birnbaum-Saunders iCDF")
652s  xlabel ("probability")
652s  ylabel ("values in x")
652s ***** shared p, y, f
652s  f = @(p,b,c) (b * (c * norminv (p) + sqrt (4 + (c * norminv(p))^2))^2) / 4;
652s  p = [-1, 0, 1/4, 1/2, 1, 2];
652s  y = [NaN, 0, f(1/4, 1, 1), 1, Inf, NaN];
652s ***** assert (bisainv (p, ones (1,6), ones (1,6)), y)
652s ***** assert (bisainv (p, 1, ones (1,6)), y)
652s ***** assert (bisainv (p, ones (1,6), 1), y)
652s ***** assert (bisainv (p, 1, 1), y)
652s ***** assert (bisainv (p, 1, [1, 1, 1, NaN, 1, 1]), [y(1:3), NaN, y(5:6)])
652s ***** assert (bisainv (p, [1, 1, 1, NaN, 1, 1], 1), [y(1:3), NaN, y(5:6)])
652s ***** assert (bisainv ([p, NaN], 1, 1), [y, NaN])
652s ***** assert (bisainv (single ([p, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
652s ***** assert (bisainv ([p, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
652s ***** assert (bisainv ([p, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
652s ***** error<bisainv: function called with too few input arguments.> bisainv ()
652s ***** error<bisainv: function called with too few input arguments.> bisainv (1)
652s ***** error<bisainv: function called with too few input arguments.> bisainv (1, 2)
652s ***** error<bisainv: function called with too many inputs> bisainv (1, 2, 3, 4)
652s ***** error<bisainv: P, BETA, and GAMMA must be of common size or scalars.> ...
652s  bisainv (ones (3), ones (2), ones(2))
652s ***** error<bisainv: P, BETA, and GAMMA must be of common size or scalars.> ...
652s  bisainv (ones (2), ones (3), ones(2))
652s ***** error<bisainv: P, BETA, and GAMMA must be of common size or scalars.> ...
652s  bisainv (ones (2), ones (2), ones(3))
652s ***** error<bisainv: P, BETA, and GAMMA must not be complex.> bisainv (i, 4, 3)
652s ***** error<bisainv: P, BETA, and GAMMA must not be complex.> bisainv (1, i, 3)
652s ***** error<bisainv: P, BETA, and GAMMA must not be complex.> bisainv (1, 4, i)
652s 20 tests, 20 passed, 0 known failure, 0 skipped
652s [inst/dist_fun/vminv.m]
652s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/vminv.m
652s ***** demo
652s  ## Plot various iCDFs from the von Mises distribution
652s  p1 = [0,0.005,0.01:0.01:0.1,0.15,0.2:0.1:0.8,0.85,0.9:0.01:0.99,0.995,1];
652s  x1 = vminv (p1, 0, 0.5);
652s  x2 = vminv (p1, 0, 1);
652s  x3 = vminv (p1, 0, 2);
652s  x4 = vminv (p1, 0, 4);
652s  plot (p1, x1, "-r", p1, x2, "-g", p1, x3, "-b", p1, x4, "-c")
652s  grid on
652s  ylim ([-pi, pi])
652s  legend ({"μ = 0, k = 0.5", "μ = 0, k = 1", ...
652s           "μ = 0, k = 2", "μ = 0, k = 4"}, "location", "northwest")
652s  title ("Von Mises iCDF")
652s  xlabel ("probability")
652s  ylabel ("values in x")
652s ***** shared x, p0, p1
652s  x = [-pi:pi/2:pi];
652s  p0 = [0, 0.10975, 0.5, 0.89025, 1];
652s  p1 = [0, 0.03752, 0.5, 0.99622, 1];
652s ***** assert (vminv (p0, 0, 1), x, 5e-5)
652s ***** assert (vminv (p0, zeros (1,5), ones (1,5)), x, 5e-5)
652s ***** assert (vminv (p1, 0, [1 2 3 4 5]), x, [5e-5, 5e-4, 5e-5, 5e-4, 5e-5])
653s ***** error<vminv: function called with too few input arguments.> vminv ()
653s ***** error<vminv: function called with too few input arguments.> vminv (1)
653s ***** error<vminv: function called with too few input arguments.> vminv (1, 2)
653s ***** error<vminv: P, MU, and K must be of common size or scalars.> ...
653s  vminv (ones (3), ones (2), ones (2))
653s ***** error<vminv: P, MU, and K must be of common size or scalars.> ...
653s  vminv (ones (2), ones (3), ones (2))
653s ***** error<vminv: P, MU, and K must be of common size or scalars.> ...
653s  vminv (ones (2), ones (2), ones (3))
653s ***** error<vminv: P, MU, and K must not be complex.> vminv (i, 2, 2)
653s ***** error<vminv: P, MU, and K must not be complex.> vminv (2, i, 2)
653s ***** error<vminv: P, MU, and K must not be complex.> vminv (2, 2, i)
653s 12 tests, 12 passed, 0 known failure, 0 skipped
653s [inst/dist_fun/finv.m]
653s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/finv.m
653s ***** demo
653s  ## Plot various iCDFs from the F distribution
653s  p = 0.001:0.001:0.999;
653s  x1 = finv (p, 1, 1);
653s  x2 = finv (p, 2, 1);
653s  x3 = finv (p, 5, 2);
653s  x4 = finv (p, 10, 1);
653s  x5 = finv (p, 100, 100);
653s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
653s  grid on
653s  ylim ([0, 4])
653s  legend ({"df1 = 1, df2 = 2", "df1 = 2, df2 = 1", ...
653s           "df1 = 5, df2 = 2", "df1 = 10, df2 = 1", ...
653s           "df1 = 100, df2 = 100"}, "location", "northwest")
653s  title ("F iCDF")
653s  xlabel ("probability")
653s  ylabel ("values in x")
653s ***** shared p
653s  p = [-1 0 0.5 1 2];
653s ***** assert (finv (p, 2*ones (1,5), 2*ones (1,5)), [NaN 0 1 Inf NaN])
653s ***** assert (finv (p, 2, 2*ones (1,5)), [NaN 0 1 Inf NaN])
653s ***** assert (finv (p, 2*ones (1,5), 2), [NaN 0 1 Inf NaN])
653s ***** assert (finv (p, [2 -Inf NaN Inf 2], 2), [NaN NaN NaN NaN NaN])
653s ***** assert (finv (p, 2, [2 -Inf NaN Inf 2]), [NaN NaN NaN NaN NaN])
653s ***** assert (finv ([p(1:2) NaN p(4:5)], 2, 2), [NaN 0 NaN Inf NaN])
653s ***** assert (finv (0.025, 10, 1e6), 0.3247, 1e-4)
653s ***** assert (finv (0.025, 10, 1e7), 0.3247, 1e-4)
653s ***** assert (finv (0.025, 10, 1e10), 0.3247, 1e-4)
653s ***** assert (finv (0.025, 10, 1e255), 0.3247, 1e-4)
653s ***** assert (finv (0.025, 10, Inf), 0.3247, 1e-4)
653s ***** assert (finv ([p, NaN], 2, 2), [NaN 0 1 Inf NaN NaN])
653s ***** assert (finv (single ([p, NaN]), 2, 2), single ([NaN 0 1 Inf NaN NaN]))
653s ***** assert (finv ([p, NaN], single (2), 2), single ([NaN 0 1 Inf NaN NaN]))
653s ***** assert (finv ([p, NaN], 2, single (2)), single ([NaN 0 1 Inf NaN NaN]))
653s ***** error<finv: function called with too few input arguments.> finv ()
653s ***** error<finv: function called with too few input arguments.> finv (1)
653s ***** error<finv: function called with too few input arguments.> finv (1,2)
653s ***** error<finv: P, DF1, and DF2 must be of common size or scalars.> ...
653s  finv (ones (3), ones (2), ones (2))
653s ***** error<finv: P, DF1, and DF2 must be of common size or scalars.> ...
653s  finv (ones (2), ones (3), ones (2))
653s ***** error<finv: P, DF1, and DF2 must be of common size or scalars.> ...
653s  finv (ones (2), ones (2), ones (3))
653s ***** error<finv: P, DF1, and DF2 must not be complex.> finv (i, 2, 2)
653s ***** error<finv: P, DF1, and DF2 must not be complex.> finv (2, i, 2)
653s ***** error<finv: P, DF1, and DF2 must not be complex.> finv (2, 2, i)
653s 24 tests, 24 passed, 0 known failure, 0 skipped
653s [inst/dist_fun/chi2cdf.m]
653s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/chi2cdf.m
653s ***** demo
653s  ## Plot various CDFs from the chi-squared distribution
653s  x = 0:0.01:8;
653s  p1 = chi2cdf (x, 1);
653s  p2 = chi2cdf (x, 2);
653s  p3 = chi2cdf (x, 3);
653s  p4 = chi2cdf (x, 4);
653s  p5 = chi2cdf (x, 6);
653s  p6 = chi2cdf (x, 9);
653s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", ...
653s        x, p4, "-c", x, p5, "-m", x, p6, "-y")
653s  grid on
653s  xlim ([0, 8])
653s  legend ({"df = 1", "df = 2", "df = 3", ...
653s           "df = 4", "df = 6", "df = 9"}, "location", "southeast")
653s  title ("Chi-squared CDF")
653s  xlabel ("values in x")
653s  ylabel ("probability")
653s ***** shared x, p, u
653s  x = [-1, 0, 0.5, 1, 2];
653s  p = [0, (1 - exp (-x(2:end) / 2))];
653s  u = [1, 0, NaN, 0.3934693402873666, 0.6321205588285577];
653s ***** assert (chi2cdf (x, 2 * ones (1,5)), p, eps)
653s ***** assert (chi2cdf (x, 2), p, eps)
653s ***** assert (chi2cdf (x, 2 * [1, 0, NaN, 1, 1]), [p(1), 1, NaN, p(4:5)], eps)
653s ***** assert (chi2cdf (x, 2 * [1, 0, NaN, 1, 1], "upper"), ...
653s                         [p(1), 1, NaN, u(4:5)], eps)
653s ***** assert (chi2cdf ([x(1:2), NaN, x(4:5)], 2), [p(1:2), NaN, p(4:5)], eps)
653s ***** assert (chi2cdf ([x, NaN], 2), [p, NaN], eps)
653s ***** assert (chi2cdf (single ([x, NaN]), 2), single ([p, NaN]), eps ("single"))
653s ***** assert (chi2cdf ([x, NaN], single (2)), single ([p, NaN]), eps ("single"))
653s ***** error<chi2cdf: function called with too few input arguments.> chi2cdf ()
653s ***** error<chi2cdf: function called with too few input arguments.> chi2cdf (1)
653s ***** error<chi2cdf: function called with too many inputs> chi2cdf (1, 2, 3, 4)
653s ***** error<chi2cdf: invalid argument for upper tail.> chi2cdf (1, 2, 3)
653s ***** error<chi2cdf: invalid argument for upper tail.> chi2cdf (1, 2, "uper")
653s ***** error<chi2cdf: X and DF must be of common size or scalars.> ...
653s  chi2cdf (ones (3), ones (2))
653s ***** error<chi2cdf: X and DF must be of common size or scalars.> ...
653s  chi2cdf (ones (2), ones (3))
653s ***** error<chi2cdf: X and DF must not be complex.> chi2cdf (i, 2)
653s ***** error<chi2cdf: X and DF must not be complex.> chi2cdf (2, i)
653s 17 tests, 17 passed, 0 known failure, 0 skipped
653s [inst/dist_fun/exprnd.m]
653s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/exprnd.m
653s ***** assert (size (exprnd (2)), [1, 1])
653s ***** assert (size (exprnd (ones (2,1))), [2, 1])
653s ***** assert (size (exprnd (ones (2,2))), [2, 2])
653s ***** assert (size (exprnd (1, 3)), [3, 3])
653s ***** assert (size (exprnd (1, [4 1])), [4, 1])
653s ***** assert (size (exprnd (1, 4, 1)), [4, 1])
653s ***** assert (size (exprnd (1, 4, 1)), [4, 1])
653s ***** assert (size (exprnd (1, 4, 1, 5)), [4, 1, 5])
653s ***** assert (size (exprnd (1, 0, 1)), [0, 1])
653s ***** assert (size (exprnd (1, 1, 0)), [1, 0])
653s ***** assert (size (exprnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
653s ***** assert (class (exprnd (2)), "double")
653s ***** assert (class (exprnd (single (2))), "single")
653s ***** assert (class (exprnd (single ([2 2]))), "single")
653s ***** error<exprnd: function called with too few input arguments.> exprnd ()
653s ***** error<exprnd: MU must not be complex.> exprnd (i)
653s ***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
653s  exprnd (1, -1)
653s ***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
653s  exprnd (1, 1.2)
653s ***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
653s  exprnd (1, ones (2))
653s ***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
653s  exprnd (1, [2 -1 2])
653s ***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
653s  exprnd (1, [2 0 2.5])
653s ***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
653s  exprnd (ones (2), ones (2))
653s ***** error<exprnd: dimensions must be non-negative integers.> ...
653s  exprnd (1, 2, -1, 5)
653s ***** error<exprnd: dimensions must be non-negative integers.> ...
653s  exprnd (1, 2, 1.5, 5)
653s ***** error<exprnd: MU must be scalar or of size SZ.> exprnd (ones (2,2), 3)
653s ***** error<exprnd: MU must be scalar or of size SZ.> exprnd (ones (2,2), [3, 2])
653s ***** error<exprnd: MU must be scalar or of size SZ.> exprnd (ones (2,2), 2, 3)
653s 27 tests, 27 passed, 0 known failure, 0 skipped
653s [inst/dist_fun/nctpdf.m]
653s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/nctpdf.m
653s ***** demo
653s  ## Plot various PDFs from the noncentral T distribution
653s  x = -5:0.01:10;
653s  y1 = nctpdf (x, 1, 0);
653s  y2 = nctpdf (x, 4, 0);
653s  y3 = nctpdf (x, 1, 2);
653s  y4 = nctpdf (x, 4, 2);
653s  plot (x, y1, "-r", x, y2, "-g", x, y3, "-k", x, y4, "-m")
653s  grid on
653s  xlim ([-5, 10])
653s  ylim ([0, 0.4])
653s  legend ({"df = 1, μ = 0", "df = 4, μ = 0", ...
653s           "df = 1, μ = 2", "df = 4, μ = 2"}, "location", "northeast")
653s  title ("Noncentral T PDF")
653s  xlabel ("values in x")
653s  ylabel ("density")
653s ***** demo
653s  ## Compare the noncentral T PDF with MU = 1 to the T PDF
653s  ## with the same number of degrees of freedom (10).
653s 
653s  x = -5:0.1:5;
653s  y1 = nctpdf (x, 10, 1);
653s  y2 = tpdf (x, 10);
653s  plot (x, y1, "-", x, y2, "-");
653s  grid on
653s  xlim ([-5, 5])
653s  ylim ([0, 0.4])
653s  legend ({"Noncentral χ^2(4,2)", "χ^2(4)"}, "location", "northwest")
653s  title ("Noncentral T vs T PDFs")
653s  xlabel ("values in x")
653s  ylabel ("density")
653s ***** shared x1, df, mu
653s  x1 = [-Inf, 2, NaN, 4, Inf];
653s  df = [2, 0, -1, 1, 4];
653s  mu = [1, NaN, 3, -1, 2];
653s ***** assert (nctpdf (x1, df, mu), [0, NaN, NaN, 0.00401787561306999, 0], 1e-14);
653s ***** assert (nctpdf (x1, df, 1), [0, NaN, NaN, 0.0482312135423008, 0], 1e-14);
653s ***** assert (nctpdf (x1, df, 3), [0, NaN, NaN, 0.1048493126401585, 0], 1e-14);
653s ***** assert (nctpdf (x1, df, 2), [0, NaN, NaN, 0.08137377919890307, 0], 1e-14);
653s ***** assert (nctpdf (x1, 3, mu), [0, NaN, NaN, 0.001185305171654381, 0], 1e-14);
653s ***** assert (nctpdf (2, df, mu), [0.1791097459405861, NaN, NaN, ...
653s                              0.0146500727180389, 0.3082302682110299], 1e-14);
653s ***** assert (nctpdf (4, df, mu), [0.04467929612254971, NaN, NaN, ...
653s                              0.00401787561306999, 0.0972086534042828], 1e-14);
653s ***** error<nctpdf: function called with too few input arguments.> nctpdf ()
653s ***** error<nctpdf: function called with too few input arguments.> nctpdf (1)
653s ***** error<nctpdf: function called with too few input arguments.> nctpdf (1, 2)
653s ***** error<nctpdf: X, DF, and MU must be of common size or scalars.> ...
653s  nctpdf (ones (3), ones (2), ones (2))
653s ***** error<nctpdf: X, DF, and MU must be of common size or scalars.> ...
653s  nctpdf (ones (2), ones (3), ones (2))
653s ***** error<nctpdf: X, DF, and MU must be of common size or scalars.> ...
653s  nctpdf (ones (2), ones (2), ones (3))
653s ***** error<nctpdf: X, DF, and MU must not be complex.> nctpdf (i, 2, 2)
653s ***** error<nctpdf: X, DF, and MU must not be complex.> nctpdf (2, i, 2)
653s ***** error<nctpdf: X, DF, and MU must not be complex.> nctpdf (2, 2, i)
653s 16 tests, 16 passed, 0 known failure, 0 skipped
653s [inst/dist_fun/binopdf.m]
653s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/binopdf.m
653s ***** demo
653s  ## Plot various PDFs from the binomial distribution
653s  x = 0:40;
653s  y1 = binopdf (x, 20, 0.5);
653s  y2 = binopdf (x, 20, 0.7);
653s  y3 = binopdf (x, 40, 0.5);
653s  plot (x, y1, "*b", x, y2, "*g", x, y3, "*r")
653s  grid on
653s  ylim ([0, 0.25])
653s  legend ({"n = 20, ps = 0.5", "n = 20, ps = 0.7", ...
653s           "n = 40, ps = 0.5"}, "location", "northeast")
653s  title ("Binomial PDF")
653s  xlabel ("values in x (number of successes)")
653s  ylabel ("density")
653s ***** shared x, y
653s  x = [-1 0 1 2 3];
653s  y = [0 1/4 1/2 1/4 0];
653s ***** assert (binopdf (x, 2 * ones (1, 5), 0.5 * ones (1, 5)), y, eps)
653s ***** assert (binopdf (x, 2, 0.5 * ones (1, 5)), y, eps)
653s ***** assert (binopdf (x, 2 * ones (1, 5), 0.5), y, eps)
653s ***** assert (binopdf (x, 2 * [0 -1 NaN 1.1 1], 0.5), [0 NaN NaN NaN 0])
653s ***** assert (binopdf (x, 2, 0.5 * [0 -1 NaN 3 1]), [0 NaN NaN NaN 0])
653s ***** assert (binopdf ([x, NaN], 2, 0.5), [y, NaN], eps)
653s ***** assert (binopdf (cat (3, x, x), 2, 0.5), cat (3, y, y), eps)
653s ***** assert (binopdf (1, 1, 1), 1)
653s ***** assert (binopdf (0, 3, 0), 1)
653s ***** assert (binopdf (2, 2, 1), 1)
653s ***** assert (binopdf (1, 2, 1), 0)
653s ***** assert (binopdf (0, 1.1, 0), NaN)
653s ***** assert (binopdf (1, 2, -1), NaN)
653s ***** assert (binopdf (1, 2, 1.5), NaN)
653s ***** assert (binopdf ([], 1, 1), [])
653s ***** assert (binopdf (1, [], 1), [])
653s ***** assert (binopdf (1, 1, []), [])
653s ***** assert (binopdf (ones (1, 0), 2, .5), ones(1, 0))
653s ***** assert (binopdf (ones (0, 1), 2, .5), ones(0, 1))
653s ***** assert (binopdf (ones (0, 1, 2), 2, .5), ones(0, 1, 2))
653s ***** assert (binopdf (1, ones (0, 1, 2), .5), ones(0, 1, 2))
653s ***** assert (binopdf (1, 2, ones (0, 1, 2)), ones(0, 1, 2))
653s ***** assert (binopdf (ones (1, 0, 2), 2, .5), ones(1, 0, 2))
653s ***** assert (binopdf (ones (1, 2, 0), 2, .5), ones(1, 2, 0))
653s ***** assert (binopdf (ones (0, 1, 2), NaN, .5), ones(0, 1, 2))
653s ***** assert (binopdf (ones (0, 1, 2), 2, NaN), ones(0, 1, 2))
653s ***** assert (binopdf (single ([x, NaN]), 2, 0.5), single ([y, NaN]))
653s ***** assert (binopdf ([x, NaN], single (2), 0.5), single ([y, NaN]))
653s ***** assert (binopdf ([x, NaN], 2, single (0.5)), single ([y, NaN]))
653s ***** error<binopdf: function called with too few input arguments.> binopdf ()
653s ***** error<binopdf: function called with too few input arguments.> binopdf (1)
653s ***** error<binopdf: function called with too few input arguments.> binopdf (1, 2)
653s ***** error<binopdf: function called with too many inputs> binopdf (1, 2, 3, 4)
653s ***** error<binopdf: X, N, and PS must be of common size or scalars.> ...
653s  binopdf (ones (3), ones (2), ones (2))
653s ***** error<binopdf: X, N, and PS must be of common size or scalars.> ...
653s  binopdf (ones (2), ones (3), ones (2))
653s ***** error<binopdf: X, N, and PS must be of common size or scalars.> ...
653s  binopdf (ones (2), ones (2), ones (3))
653s ***** error<binopdf: X, N, and PS must not be complex.> binopdf (i, 2, 2)
653s ***** error<binopdf: X, N, and PS must not be complex.> binopdf (2, i, 2)
653s ***** error<binopdf: X, N, and PS must not be complex.> binopdf (2, 2, i)
653s 39 tests, 39 passed, 0 known failure, 0 skipped
653s [inst/dist_fun/bisapdf.m]
653s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/bisapdf.m
653s ***** demo
653s  ## Plot various PDFs from the Birnbaum-Saunders distribution
653s  x = 0.01:0.01:4;
653s  y1 = bisapdf (x, 1, 0.5);
653s  y2 = bisapdf (x, 1, 1);
653s  y3 = bisapdf (x, 1, 2);
653s  y4 = bisapdf (x, 1, 5);
653s  y5 = bisapdf (x, 1, 10);
653s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
653s  grid on
653s  ylim ([0, 1.5])
653s  legend ({"β = 1 ,γ = 0.5", "β = 1, γ = 1", "β = 1, γ = 2", ...
653s           "β = 1, γ = 5", "β = 1, γ = 10"}, "location", "northeast")
653s  title ("Birnbaum-Saunders PDF")
653s  xlabel ("values in x")
653s  ylabel ("density")
653s ***** demo
653s  ## Plot various PDFs from the Birnbaum-Saunders distribution
653s  x = 0.01:0.01:6;
653s  y1 = bisapdf (x, 1, 0.3);
653s  y2 = bisapdf (x, 2, 0.3);
653s  y3 = bisapdf (x, 1, 0.5);
653s  y4 = bisapdf (x, 3, 0.5);
653s  y5 = bisapdf (x, 5, 0.5);
653s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
653s  grid on
653s  ylim ([0, 1.5])
653s  legend ({"β = 1, γ = 0.3", "β = 2, γ = 0.3", "β = 1, γ = 0.5", ...
653s           "β = 3, γ = 0.5", "β = 5, γ = 0.5"}, "location", "northeast")
653s  title ("Birnbaum-Saunders CDF")
653s  xlabel ("values in x")
653s  ylabel ("density")
653s ***** shared x, y
653s  x = [-1, 0, 1, 2, Inf];
653s  y = [0, 0, 0.3989422804014327, 0.1647717335503959, 0];
653s ***** assert (bisapdf (x, ones (1,5), ones (1,5)), y, eps)
653s ***** assert (bisapdf (x, 1, 1), y, eps)
653s ***** assert (bisapdf (x, 1, ones (1,5)), y, eps)
653s ***** assert (bisapdf (x, ones (1,5), 1), y, eps)
653s ***** assert (bisapdf (x, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)], eps)
653s ***** assert (bisapdf (x, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)], eps)
653s ***** assert (bisapdf ([x, NaN], 1, 1), [y, NaN], eps)
653s ***** assert (bisapdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
653s ***** assert (bisapdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
653s ***** assert (bisapdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
653s ***** error<bisapdf: function called with too few input arguments.> bisapdf ()
653s ***** error<bisapdf: function called with too few input arguments.> bisapdf (1)
653s ***** error<bisapdf: function called with too few input arguments.> bisapdf (1, 2)
653s ***** error<bisapdf: function called with too many inputs> bisapdf (1, 2, 3, 4)
653s ***** error<bisapdf: X, BETA, and GAMMA must be of common size or scalars.> ...
653s  bisapdf (ones (3), ones (2), ones(2))
653s ***** error<bisapdf: X, BETA, and GAMMA must be of common size or scalars.> ...
653s  bisapdf (ones (2), ones (3), ones(2))
653s ***** error<bisapdf: X, BETA, and GAMMA must be of common size or scalars.> ...
653s  bisapdf (ones (2), ones (2), ones(3))
653s ***** error<bisapdf: X, BETA, and GAMMA must not be complex.> bisapdf (i, 4, 3)
653s ***** error<bisapdf: X, BETA, and GAMMA must not be complex.> bisapdf (1, i, 3)
653s ***** error<bisapdf: X, BETA, and GAMMA must not be complex.> bisapdf (1, 4, i)
653s 20 tests, 20 passed, 0 known failure, 0 skipped
653s [inst/dist_fun/hncdf.m]
653s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/hncdf.m
653s ***** demo
653s  ## Plot various CDFs from the half-normal distribution
653s  x = 0:0.001:10;
653s  p1 = hncdf (x, 0, 1);
653s  p2 = hncdf (x, 0, 2);
653s  p3 = hncdf (x, 0, 3);
653s  p4 = hncdf (x, 0, 5);
653s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
653s  grid on
653s  xlim ([0, 10])
653s  legend ({"μ = 0, σ = 1", "μ = 0, σ = 2", ...
653s           "μ = 0, σ = 3", "μ = 0, σ = 5"}, "location", "southeast")
653s  title ("Half-normal CDF")
653s  xlabel ("values in x")
653s  ylabel ("probability")
653s ***** demo
653s  ## Plot half-normal against normal cumulative distribution function
653s  x = -5:0.001:5;
653s  p1 = hncdf (x, 0, 1);
653s  p2 = normcdf (x);
653s  plot (x, p1, "-b", x, p2, "-g")
653s  grid on
653s  xlim ([-5, 5])
653s  legend ({"half-normal with μ = 0, σ = 1", ...
653s           "standart normal (μ = 0, σ = 1)"}, "location", "southeast")
653s  title ("Half-normal against standard normal CDF")
653s  xlabel ("values in x")
653s  ylabel ("probability")
653s ***** shared x, p1, p1u, y2, y2u, y3, y3u
653s  x = [-Inf, -1, 0, 1/2, 1, Inf];
653s  p1 = [0, 0, 0, 0.3829, 0.6827, 1];
653s  p1u = [1, 1, 1, 0.6171, 0.3173, 0];
653s ***** assert (hncdf (x, zeros (1,6), ones (1,6)), p1, 1e-4)
653s ***** assert (hncdf (x, 0, 1), p1, 1e-4)
653s ***** assert (hncdf (x, 0, ones (1,6)), p1, 1e-4)
653s ***** assert (hncdf (x, zeros (1,6), 1), p1, 1e-4)
653s ***** assert (hncdf (x, 0, [1, 1, 1, NaN, 1, 1]), [p1(1:3), NaN, p1(5:6)], 1e-4)
653s ***** assert (hncdf (x, [0, 0, 0, NaN, 0, 0], 1), [p1(1:3), NaN, p1(5:6)], 1e-4)
653s ***** assert (hncdf ([x(1:3), NaN, x(5:6)], 0, 1), [p1(1:3), NaN, p1(5:6)], 1e-4)
653s ***** assert (hncdf (x, zeros (1,6), ones (1,6), "upper"), p1u, 1e-4)
653s ***** assert (hncdf (x, 0, 1, "upper"), p1u, 1e-4)
653s ***** assert (hncdf (x, 0, ones (1,6), "upper"), p1u, 1e-4)
653s ***** assert (hncdf (x, zeros (1,6), 1, "upper"), p1u, 1e-4)
653s ***** assert (class (hncdf (single ([x, NaN]), 0, 1)), "single")
653s ***** assert (class (hncdf ([x, NaN], 0, single (1))), "single")
653s ***** assert (class (hncdf ([x, NaN], single (0), 1)), "single")
653s ***** error<hncdf: function called with too few input arguments.> hncdf ()
653s ***** error<hncdf: function called with too few input arguments.> hncdf (1)
653s ***** error<hncdf: function called with too few input arguments.> hncdf (1, 2)
653s ***** error<hncdf: invalid argument for upper tail.> hncdf (1, 2, 3, "tail")
653s ***** error<hncdf: invalid argument for upper tail.> hncdf (1, 2, 3, 5)
653s ***** error<hncdf: X, MU, and SIGMA must be of common size or scalars.> ...
653s  hncdf (ones (3), ones (2), ones(2))
653s ***** error<hncdf: X, MU, and SIGMA must be of common size or scalars.> ...
653s  hncdf (ones (2), ones (3), ones(2))
653s ***** error<hncdf: X, MU, and SIGMA must be of common size or scalars.> ...
653s  hncdf (ones (2), ones (2), ones(3))
653s ***** error<hncdf: X, MU, and SIGMA must not be complex.> hncdf (i, 2, 3)
653s ***** error<hncdf: X, MU, and SIGMA must not be complex.> hncdf (1, i, 3)
653s ***** error<hncdf: X, MU, and SIGMA must not be complex.> hncdf (1, 2, i)
653s 25 tests, 25 passed, 0 known failure, 0 skipped
653s [inst/dist_fun/gampdf.m]
653s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/gampdf.m
653s ***** demo
653s  ## Plot various PDFs from the Gamma distribution
653s  x = 0:0.01:20;
653s  y1 = gampdf (x, 1, 2);
653s  y2 = gampdf (x, 2, 2);
653s  y3 = gampdf (x, 3, 2);
653s  y4 = gampdf (x, 5, 1);
653s  y5 = gampdf (x, 9, 0.5);
653s  y6 = gampdf (x, 7.5, 1);
653s  y7 = gampdf (x, 0.5, 1);
653s  plot (x, y1, "-r", x, y2, "-g", x, y3, "-y", x, y4, "-m", ...
653s        x, y5, "-k", x, y6, "-b", x, y7, "-c")
653s  grid on
653s  ylim ([0,0.5])
653s  legend ({"α = 1, β = 2", "α = 2, β = 2", "α = 3, β = 2", ...
653s           "α = 5, β = 1", "α = 9, β = 0.5", "α = 7.5, β = 1", ...
653s           "α = 0.5, β = 1"}, "location", "northeast")
653s  title ("Gamma PDF")
653s  xlabel ("values in x")
653s  ylabel ("density")
653s ***** shared x, y
653s  x = [-1 0 0.5 1 Inf];
653s  y = [0 exp(-x(2:end))];
653s ***** assert (gampdf (x, ones (1,5), ones (1,5)), y)
653s ***** assert (gampdf (x, 1, ones (1,5)), y)
653s ***** assert (gampdf (x, ones (1,5), 1), y)
653s ***** assert (gampdf (x, [0 -Inf NaN Inf 1], 1), [NaN NaN NaN NaN y(5)])
653s ***** assert (gampdf (x, 1, [0 -Inf NaN Inf 1]), [NaN NaN NaN 0 y(5)])
653s ***** assert (gampdf ([x, NaN], 1, 1), [y, NaN])
653s ***** assert (gampdf (single ([x, NaN]), 1, 1), single ([y, NaN]))
653s ***** assert (gampdf ([x, NaN], single (1), 1), single ([y, NaN]))
653s ***** assert (gampdf ([x, NaN], 1, single (1)), single ([y, NaN]))
653s ***** error<gampdf: function called with too few input arguments.> gampdf ()
653s ***** error<gampdf: function called with too few input arguments.> gampdf (1)
653s ***** error<gampdf: function called with too few input arguments.> gampdf (1,2)
653s ***** error<gampdf: X, A, and B must be of common size or scalars.> ...
653s  gampdf (ones (3), ones (2), ones (2))
653s ***** error<gampdf: X, A, and B must be of common size or scalars.> ...
653s  gampdf (ones (2), ones (3), ones (2))
653s ***** error<gampdf: X, A, and B must be of common size or scalars.> ...
653s  gampdf (ones (2), ones (2), ones (3))
653s ***** error<gampdf: X, A, and B must not be complex.> gampdf (i, 2, 2)
653s ***** error<gampdf: X, A, and B must not be complex.> gampdf (2, i, 2)
653s ***** error<gampdf: X, A, and B must not be complex.> gampdf (2, 2, i)
653s 18 tests, 18 passed, 0 known failure, 0 skipped
653s [inst/dist_fun/unidcdf.m]
653s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/unidcdf.m
653s ***** demo
653s  ## Plot various CDFs from the discrete uniform distribution
653s  x = 0:10;
653s  p1 = unidcdf (x, 5);
653s  p2 = unidcdf (x, 9);
653s  plot (x, p1, "*b", x, p2, "*g")
653s  grid on
653s  xlim ([0, 10])
653s  ylim ([0, 1])
653s  legend ({"N = 5", "N = 9"}, "location", "southeast")
653s  title ("Discrete uniform CDF")
653s  xlabel ("values in x")
653s  ylabel ("probability")
653s ***** shared x, y
653s  x = [0 1 2.5 10 11];
653s  y = [0, 0.1 0.2 1.0 1.0];
653s ***** assert (unidcdf (x, 10*ones (1,5)), y)
653s ***** assert (unidcdf (x, 10*ones (1,5), "upper"), 1 - y)
653s ***** assert (unidcdf (x, 10), y)
653s ***** assert (unidcdf (x, 10, "upper"), 1 - y)
653s ***** assert (unidcdf (x, 10*[0 1 NaN 1 1]), [NaN 0.1 NaN y(4:5)])
653s ***** assert (unidcdf ([x(1:2) NaN Inf x(5)], 10), [y(1:2) NaN 1 y(5)])
653s ***** assert (unidcdf ([x, NaN], 10), [y, NaN])
653s ***** assert (unidcdf (single ([x, NaN]), 10), single ([y, NaN]))
653s ***** assert (unidcdf ([x, NaN], single (10)), single ([y, NaN]))
653s ***** error<unidcdf: function called with too few input arguments.> unidcdf ()
653s ***** error<unidcdf: function called with too few input arguments.> unidcdf (1)
653s ***** error<unidcdf: invalid argument for upper tail.> unidcdf (1, 2, 3)
653s ***** error<unidcdf: invalid argument for upper tail.> unidcdf (1, 2, "tail")
653s ***** error<unidcdf: X and N must be of common size or scalars.> ...
653s  unidcdf (ones (3), ones (2))
653s ***** error<unidcdf: X and N must be of common size or scalars.> ...
653s  unidcdf (ones (2), ones (3))
653s ***** error<unidcdf: X and N must not be complex.> unidcdf (i, 2)
653s ***** error<unidcdf: X and N must not be complex.> unidcdf (2, i)
653s 17 tests, 17 passed, 0 known failure, 0 skipped
653s [inst/dist_fun/wishpdf.m]
653s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/wishpdf.m
653s ***** assert(wishpdf(4, 3, 3.1), 0.07702496, 1E-7);
653s ***** assert(wishpdf([2 -0.3;-0.3 4], [1 0.3;0.3 1], 4), 0.004529741, 1E-7);
653s ***** assert(wishpdf([6 2 5; 2 10 -5; 5 -5 25], [9 5 5; 5 10 -8; 5 -8 22], 5.1), 4.474865e-10, 1E-15);
653s ***** error wishpdf ()
654s ***** error wishpdf (1, 2)
654s ***** error wishpdf (1, 2, 0)
654s ***** error wishpdf (1, 2)
654s 7 tests, 7 passed, 0 known failure, 0 skipped
654s [inst/dist_fun/raylinv.m]
654s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/raylinv.m
654s ***** demo
654s  ## Plot various iCDFs from the Rayleigh distribution
654s  p = 0.001:0.001:0.999;
654s  x1 = raylinv (p, 0.5);
654s  x2 = raylinv (p, 1);
654s  x3 = raylinv (p, 2);
654s  x4 = raylinv (p, 3);
654s  x5 = raylinv (p, 4);
654s  plot (p, x1, "-b", p, x2, "g", p, x3, "-r", p, x4, "-m", p, x5, "-k")
654s  grid on
654s  ylim ([0, 10])
654s  legend ({"σ = 0,5", "σ = 1", "σ = 2", ...
654s           "σ = 3", "σ = 4"}, "location", "northwest")
654s  title ("Rayleigh iCDF")
654s  xlabel ("probability")
654s  ylabel ("values in x")
654s ***** test
654s  p = 0:0.1:0.5;
654s  sigma = 1:6;
654s  x = raylinv (p, sigma);
654s  expected_x = [0.0000, 0.9181, 2.0041, 3.3784, 5.0538, 7.0645];
654s  assert (x, expected_x, 0.001);
654s ***** test
654s  p = 0:0.1:0.5;
654s  x = raylinv (p, 0.5);
654s  expected_x = [0.0000, 0.2295, 0.3340, 0.4223, 0.5054, 0.5887];
654s  assert (x, expected_x, 0.001);
654s ***** error<raylinv: function called with too few input arguments.> raylinv ()
654s ***** error<raylinv: function called with too few input arguments.> raylinv (1)
654s ***** error<raylinv: P and SIGMA must be of common size or scalars.> ...
654s  raylinv (ones (3), ones (2))
654s ***** error<raylinv: P and SIGMA must be of common size or scalars.> ...
654s  raylinv (ones (2), ones (3))
654s ***** error<raylinv: P and SIGMA must not be complex.> raylinv (i, 2)
654s ***** error<raylinv: P and SIGMA must not be complex.> raylinv (2, i)
654s 8 tests, 8 passed, 0 known failure, 0 skipped
654s [inst/dist_fun/evpdf.m]
654s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/evpdf.m
654s ***** demo
654s  ## Plot various PDFs from the Extreme value distribution
654s  x = -10:0.001:10;
654s  y1 = evpdf (x, 0.5, 2);
654s  y2 = evpdf (x, 1.0, 2);
654s  y3 = evpdf (x, 1.5, 3);
654s  y4 = evpdf (x, 3.0, 4);
654s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
654s  grid on
654s  ylim ([0, 0.2])
654s  legend ({"μ = 0.5, σ = 2", "μ = 1.0, σ = 2", ...
654s           "μ = 1.5, σ = 3", "μ = 3.0, σ = 4"}, "location", "northeast")
654s  title ("Extreme value PDF")
654s  xlabel ("values in x")
654s  ylabel ("density")
654s ***** shared x, y0, y1
654s  x = [-5, 0, 1, 2, 3];
654s  y0 = [0.0067, 0.3679, 0.1794, 0.0046, 0];
654s  y1 = [0.0025, 0.2546, 0.3679, 0.1794, 0.0046];
654s ***** assert (evpdf (x), y0, 1e-4)
654s ***** assert (evpdf (x, zeros (1,5), ones (1,5)), y0, 1e-4)
654s ***** assert (evpdf (x, ones (1,5), ones (1,5)), y1, 1e-4)
654s ***** error<evpdf: function called with too few input arguments.> evpdf ()
654s ***** error<evpdf: X, MU, and SIGMA must be of common size or scalars.> ...
654s  evpdf (ones (3), ones (2), ones (2))
654s ***** error<evpdf: X, MU, and SIGMA must not be complex.> evpdf (i, 2, 2)
654s ***** error<evpdf: X, MU, and SIGMA must not be complex.> evpdf (2, i, 2)
654s ***** error<evpdf: X, MU, and SIGMA must not be complex.> evpdf (2, 2, i)
654s 8 tests, 8 passed, 0 known failure, 0 skipped
654s [inst/dist_fun/laplacecdf.m]
654s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/laplacecdf.m
654s ***** demo
654s  ## Plot various CDFs from the Laplace distribution
654s  x = -10:0.01:10;
654s  p1 = laplacecdf (x, 0, 1);
654s  p2 = laplacecdf (x, 0, 2);
654s  p3 = laplacecdf (x, 0, 4);
654s  p4 = laplacecdf (x, -5, 4);
654s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
654s  grid on
654s  xlim ([-10, 10])
654s  legend ({"μ = 0, β = 1", "μ = 0, β = 2", ...
654s           "μ = 0, β = 4", "μ = -5, β = 4"}, "location", "southeast")
654s  title ("Laplace CDF")
654s  xlabel ("values in x")
654s  ylabel ("probability")
654s ***** shared x, y
654s  x = [-Inf, -log(2), 0, log(2), Inf];
654s  y = [0, 1/4, 1/2, 3/4, 1];
654s ***** assert (laplacecdf ([x, NaN], 0, 1), [y, NaN])
654s ***** assert (laplacecdf (x, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), 0.75, 1])
654s ***** assert (laplacecdf (single ([x, NaN]), 0, 1), single ([y, NaN]), eps ("single"))
654s ***** assert (laplacecdf ([x, NaN], single (0), 1), single ([y, NaN]), eps ("single"))
654s ***** assert (laplacecdf ([x, NaN], 0, single (1)), single ([y, NaN]), eps ("single"))
654s ***** error<laplacecdf: function called with too few input arguments.> laplacecdf ()
654s ***** error<laplacecdf: function called with too few input arguments.> laplacecdf (1)
654s ***** error<laplacecdf: function called with too few input arguments.> ...
654s  laplacecdf (1, 2)
654s ***** error<laplacecdf: function called with too many inputs> ...
654s  laplacecdf (1, 2, 3, 4, 5)
654s ***** error<laplacecdf: invalid argument for upper tail.> laplacecdf (1, 2, 3, "tail")
654s ***** error<laplacecdf: invalid argument for upper tail.> laplacecdf (1, 2, 3, 4)
654s ***** error<laplacecdf: X, MU, and BETA must be of common size or scalars.> ...
654s  laplacecdf (ones (3), ones (2), ones (2))
654s ***** error<laplacecdf: X, MU, and BETA must be of common size or scalars.> ...
654s  laplacecdf (ones (2), ones (3), ones (2))
654s ***** error<laplacecdf: X, MU, and BETA must be of common size or scalars.> ...
654s  laplacecdf (ones (2), ones (2), ones (3))
654s ***** error<laplacecdf: X, MU, and BETA must not be complex.> laplacecdf (i, 2, 2)
654s ***** error<laplacecdf: X, MU, and BETA must not be complex.> laplacecdf (2, i, 2)
654s ***** error<laplacecdf: X, MU, and BETA must not be complex.> laplacecdf (2, 2, i)
654s 17 tests, 17 passed, 0 known failure, 0 skipped
654s [inst/dist_fun/trirnd.m]
654s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/trirnd.m
654s ***** assert (size (trirnd (1, 1.5, 2)), [1, 1])
654s ***** assert (size (trirnd (1 * ones (2, 1), 1.5, 2)), [2, 1])
654s ***** assert (size (trirnd (1 * ones (2, 2), 1.5, 2)), [2, 2])
654s ***** assert (size (trirnd (1, 1.5 * ones (2, 1), 2)), [2, 1])
654s ***** assert (size (trirnd (1, 1.5 * ones (2, 2), 2)), [2, 2])
654s ***** assert (size (trirnd (1, 1.5, 2 * ones (2, 1))), [2, 1])
654s ***** assert (size (trirnd (1, 1.5, 2 * ones (2, 2))), [2, 2])
654s ***** assert (size (trirnd (1, 1.5, 2, 3)), [3, 3])
654s ***** assert (size (trirnd (1, 1.5, 2, [4, 1])), [4, 1])
654s ***** assert (size (trirnd (1, 1.5, 2, 4, 1)), [4, 1])
654s ***** assert (class (trirnd (1, 1.5, 2)), "double")
654s ***** assert (class (trirnd (single (1), 1.5, 2)), "single")
654s ***** assert (class (trirnd (single ([1, 1]), 1.5, 2)), "single")
654s ***** assert (class (trirnd (1, single (1.5), 2)), "single")
654s ***** assert (class (trirnd (1, single ([1.5, 1.5]), 2)), "single")
654s ***** assert (class (trirnd (1, 1.5, single (1.5))), "single")
654s ***** assert (class (trirnd (1, 1.5, single ([2, 2]))), "single")
654s ***** error<trirnd: function called with too few input arguments.> trirnd ()
654s ***** error<trirnd: function called with too few input arguments.> trirnd (1)
654s ***** error<trirnd: function called with too few input arguments.> trirnd (1, 2)
654s ***** error<trirnd: A, B, and C must be of common size or scalars.> ...
654s  trirnd (ones (3), 5 * ones (2), ones (2))
654s ***** error<trirnd: A, B, and C must be of common size or scalars.> ...
654s  trirnd (ones (2), 5 * ones (3), ones (2))
654s ***** error<trirnd: A, B, and C must be of common size or scalars.> ...
654s  trirnd (ones (2), 5 * ones (2), ones (3))
654s ***** error<trirnd: A, B, and C must not be complex.> trirnd (i, 5, 3)
654s ***** error<trirnd: A, B, and C must not be complex.> trirnd (1, 5+i, 3)
654s ***** error<trirnd: A, B, and C must not be complex.> trirnd (1, 5, i)
654s ***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
654s  trirnd (1, 5, 3, -1)
654s ***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
654s  trirnd (1, 5, 3, 1.2)
654s ***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
654s  trirnd (1, 5, 3, ones (2))
654s ***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
654s  trirnd (1, 5, 3, [2 -1 2])
654s ***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
654s  trirnd (1, 5, 3, [2 0 2.5])
654s ***** error<trirnd: dimensions must be non-negative integers.> ...
654s  trirnd (1, 5, 3, 2, -1, 5)
654s ***** error<trirnd: dimensions must be non-negative integers.> ...
654s  trirnd (1, 5, 3, 2, 1.5, 5)
654s ***** error<trirnd: A, B, and C must be scalar or of size SZ.> ...
654s  trirnd (2, 5 * ones (2), 2, 3)
654s ***** error<trirnd: A, B, and C must be scalar or of size SZ.> ...
654s  trirnd (2, 5 * ones (2), 2, [3, 2])
654s ***** error<trirnd: A, B, and C must be scalar or of size SZ.> ...
654s  trirnd (2, 5 * ones (2), 2, 3, 2)
654s 36 tests, 36 passed, 0 known failure, 0 skipped
654s [inst/dist_fun/nakacdf.m]
654s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/nakacdf.m
654s ***** demo
654s  ## Plot various CDFs from the Nakagami distribution
654s  x = 0:0.01:3;
654s  p1 = nakacdf (x, 0.5, 1);
654s  p2 = nakacdf (x, 1, 1);
654s  p3 = nakacdf (x, 1, 2);
654s  p4 = nakacdf (x, 1, 3);
654s  p5 = nakacdf (x, 2, 1);
654s  p6 = nakacdf (x, 2, 2);
654s  p7 = nakacdf (x, 5, 1);
654s  plot (x, p1, "-r", x, p2, "-g", x, p3, "-y", x, p4, "-m", ...
654s        x, p5, "-k", x, p6, "-b", x, p7, "-c")
654s  grid on
654s  xlim ([0, 3])
654s  legend ({"μ = 0.5, ω = 1", "μ = 1, ω = 1", "μ = 1, ω = 2", ...
654s           "μ = 1, ω = 3", "μ = 2, ω = 1", "μ = 2, ω = 2", ...
654s           "μ = 5, ω = 1"}, "location", "southeast")
654s  title ("Nakagami CDF")
654s  xlabel ("values in x")
654s  ylabel ("probability")
654s ***** shared x, y
654s  x = [-1, 0, 1, 2, Inf];
654s  y = [0, 0, 0.63212055882855778, 0.98168436111126578, 1];
654s ***** assert (nakacdf (x, ones (1,5), ones (1,5)), y, eps)
654s ***** assert (nakacdf (x, 1, 1), y, eps)
654s ***** assert (nakacdf (x, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)])
654s ***** assert (nakacdf (x, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)])
654s ***** assert (nakacdf ([x, NaN], 1, 1), [y, NaN], eps)
654s ***** assert (nakacdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps("single"))
654s ***** assert (nakacdf ([x, NaN], single (1), 1), single ([y, NaN]), eps("single"))
654s ***** assert (nakacdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps("single"))
654s ***** error<nakacdf: function called with too few input arguments.> nakacdf ()
654s ***** error<nakacdf: function called with too few input arguments.> nakacdf (1)
654s ***** error<nakacdf: function called with too few input arguments.> nakacdf (1, 2)
654s ***** error<nakacdf: invalid argument for upper tail.> nakacdf (1, 2, 3, "tail")
654s ***** error<nakacdf: invalid argument for upper tail.> nakacdf (1, 2, 3, 4)
654s ***** error<nakacdf: X, MU, and OMEGA must be of common size or scalars.> ...
654s  nakacdf (ones (3), ones (2), ones (2))
654s ***** error<nakacdf: X, MU, and OMEGA must be of common size or scalars.> ...
654s  nakacdf (ones (2), ones (3), ones (2))
654s ***** error<nakacdf: X, MU, and OMEGA must be of common size or scalars.> ...
654s  nakacdf (ones (2), ones (2), ones (3))
654s ***** error<nakacdf: X, MU, and OMEGA must not be complex.> nakacdf (i, 2, 2)
654s ***** error<nakacdf: X, MU, and OMEGA must not be complex.> nakacdf (2, i, 2)
654s ***** error<nakacdf: X, MU, and OMEGA must not be complex.> nakacdf (2, 2, i)
654s 19 tests, 19 passed, 0 known failure, 0 skipped
654s [inst/dist_fun/tcdf.m]
654s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/tcdf.m
654s ***** demo
654s  ## Plot various CDFs from the Student's T distribution
654s  x = -5:0.01:5;
654s  p1 = tcdf (x, 1);
654s  p2 = tcdf (x, 2);
654s  p3 = tcdf (x, 5);
654s  p4 = tcdf (x, Inf);
654s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-m")
654s  grid on
654s  xlim ([-5, 5])
654s  ylim ([0, 1])
654s  legend ({"df = 1", "df = 2", ...
654s           "df = 5", 'df = \infty'}, "location", "southeast")
654s  title ("Student's T CDF")
654s  xlabel ("values in x")
654s  ylabel ("probability")
654s ***** shared x,y
654s  x = [-Inf 0 1 Inf];
654s  y = [0 1/2 3/4 1];
654s ***** assert (tcdf (x, ones (1,4)), y, eps)
654s ***** assert (tcdf (x, 1), y, eps)
654s ***** assert (tcdf (x, [0 1 NaN 1]), [NaN 1/2 NaN 1], eps)
654s ***** assert (tcdf ([x(1:2) NaN x(4)], 1), [y(1:2) NaN y(4)], eps)
654s ***** assert (tcdf (2, 3, "upper"), 0.0697, 1e-4)
654s ***** assert (tcdf (205, 5, "upper"), 2.6206e-11, 1e-14)
654s ***** assert (tcdf ([x, NaN], 1), [y, NaN], eps)
654s ***** assert (tcdf (single ([x, NaN]), 1), single ([y, NaN]), eps ("single"))
654s ***** assert (tcdf ([x, NaN], single (1)), single ([y, NaN]), eps ("single"))
654s ***** error<tcdf: function called with too few input arguments.> tcdf ()
654s ***** error<tcdf: function called with too few input arguments.> tcdf (1)
654s ***** error<tcdf: invalid argument for upper tail.> tcdf (1, 2, "uper")
654s ***** error<tcdf: invalid argument for upper tail.> tcdf (1, 2, 3)
654s ***** error<tcdf: X and DF must be of common size or scalars.> ...
654s  tcdf (ones (3), ones (2))
654s ***** error<tcdf: X and DF must be of common size or scalars.> ...
654s  tcdf (ones (3), ones (2))
654s ***** error<tcdf: X and DF must be of common size or scalars.> ...
654s  tcdf (ones (3), ones (2), "upper")
654s ***** error<tcdf: X and DF must not be complex.> tcdf (i, 2)
654s ***** error<tcdf: X and DF must not be complex.> tcdf (2, i)
654s ***** shared tol_rel
654s  tol_rel = 10 * eps;
654s ***** assert (tcdf (10^(-10), 2.5), 0.50000000003618087, -tol_rel)
654s ***** assert (tcdf (10^(-11), 2.5), 0.50000000000361809, -tol_rel)
654s ***** assert (tcdf (10^(-12), 2.5), 0.50000000000036181, -tol_rel)
654s ***** assert (tcdf (10^(-13), 2.5), 0.50000000000003618, -tol_rel)
654s ***** assert (tcdf (10^(-14), 2.5), 0.50000000000000362, -tol_rel)
654s ***** assert (tcdf (10^(-15), 2.5), 0.50000000000000036, -tol_rel)
654s ***** assert (tcdf (10^(-16), 2.5), 0.50000000000000004, -tol_rel)
654s ***** assert (tcdf (-10^1, 2.5), 2.2207478836537124e-03, -tol_rel)
654s ***** assert (tcdf (-10^2, 2.5), 7.1916492116661878e-06, -tol_rel)
654s ***** assert (tcdf (-10^3, 2.5), 2.2747463948307452e-08, -tol_rel)
654s ***** assert (tcdf (-10^4, 2.5), 7.1933970159922115e-11, -tol_rel)
654s ***** assert (tcdf (-10^5, 2.5), 2.2747519231756221e-13, -tol_rel)
654s 30 tests, 30 passed, 0 known failure, 0 skipped
654s [inst/dist_fun/chi2inv.m]
654s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/chi2inv.m
654s ***** demo
654s  ## Plot various iCDFs from the chi-squared distribution
654s  p = 0.001:0.001:0.999;
654s  x1 = chi2inv (p, 1);
654s  x2 = chi2inv (p, 2);
654s  x3 = chi2inv (p, 3);
654s  x4 = chi2inv (p, 4);
654s  x5 = chi2inv (p, 6);
654s  x6 = chi2inv (p, 9);
654s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", ...
654s        p, x4, "-c", p, x5, "-m", p, x6, "-y")
654s  grid on
654s  ylim ([0, 8])
654s  legend ({"df = 1", "df = 2", "df = 3", ...
654s           "df = 4", "df = 6", "df = 9"}, "location", "northwest")
654s  title ("Chi-squared iCDF")
654s  xlabel ("probability")
654s  ylabel ("values in x")
654s ***** shared p
654s  p = [-1 0 0.3934693402873666 1 2];
654s ***** assert (chi2inv (p, 2*ones (1,5)), [NaN 0 1 Inf NaN], 5*eps)
654s ***** assert (chi2inv (p, 2), [NaN 0 1 Inf NaN], 5*eps)
654s ***** assert (chi2inv (p, 2*[0 1 NaN 1 1]), [NaN 0 NaN Inf NaN], 5*eps)
654s ***** assert (chi2inv ([p(1:2) NaN p(4:5)], 2), [NaN 0 NaN Inf NaN], 5*eps)
654s ***** assert (chi2inv ([p, NaN], 2), [NaN 0 1 Inf NaN NaN], 5*eps)
654s ***** assert (chi2inv (single ([p, NaN]), 2), single ([NaN 0 1 Inf NaN NaN]), 5*eps ("single"))
654s ***** assert (chi2inv ([p, NaN], single (2)), single ([NaN 0 1 Inf NaN NaN]), 5*eps ("single"))
654s ***** error<chi2inv: function called with too few input arguments.> chi2inv ()
654s ***** error<chi2inv: function called with too few input arguments.> chi2inv (1)
654s ***** error<chi2inv: function called with too many inputs> chi2inv (1,2,3)
654s ***** error<chi2inv: P and DF must be of common size or scalars.> ...
654s  chi2inv (ones (3), ones (2))
654s ***** error<chi2inv: P and DF must be of common size or scalars.> ...
654s  chi2inv (ones (2), ones (3))
654s ***** error<chi2inv: P and DF must not be complex.> chi2inv (i, 2)
654s ***** error<chi2inv: P and DF must not be complex.> chi2inv (2, i)
654s 14 tests, 14 passed, 0 known failure, 0 skipped
654s [inst/dist_fun/wblinv.m]
654s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/wblinv.m
654s ***** demo
654s  ## Plot various iCDFs from the Weibull distribution
654s  p = 0.001:0.001:0.999;
654s  x1 = wblinv (p, 1, 0.5);
654s  x2 = wblinv (p, 1, 1);
654s  x3 = wblinv (p, 1, 1.5);
654s  x4 = wblinv (p, 1, 5);
654s  plot (p, x1, "-b", p, x2, "-r", p, x3, "-m", p, x4, "-g")
654s  ylim ([0, 2.5])
654s  grid on
654s  legend ({"λ = 1, k = 0.5", "λ = 1, k = 1",  ...
654s           "λ = 1, k = 1.5", "λ = 1, k = 5"}, "location", "northwest")
654s  title ("Weibull iCDF")
654s  xlabel ("probability")
654s  ylabel ("x")
654s ***** shared p
654s  p = [-1 0 0.63212055882855778 1 2];
654s ***** assert (wblinv (p, ones (1,5), ones (1,5)), [NaN 0 1 Inf NaN], eps)
654s ***** assert (wblinv (p, 1, ones (1,5)), [NaN 0 1 Inf NaN], eps)
654s ***** assert (wblinv (p, ones (1,5), 1), [NaN 0 1 Inf NaN], eps)
654s ***** assert (wblinv (p, [1 -1 NaN Inf 1], 1), [NaN NaN NaN NaN NaN])
654s ***** assert (wblinv (p, 1, [1 -1 NaN Inf 1]), [NaN NaN NaN NaN NaN])
654s ***** assert (wblinv ([p(1:2) NaN p(4:5)], 1, 1), [NaN 0 NaN Inf NaN])
654s ***** assert (wblinv ([p, NaN], 1, 1), [NaN 0 1 Inf NaN NaN], eps)
654s ***** assert (wblinv (single ([p, NaN]), 1, 1), single ([NaN 0 1 Inf NaN NaN]), eps ("single"))
654s ***** assert (wblinv ([p, NaN], single (1), 1), single ([NaN 0 1 Inf NaN NaN]), eps ("single"))
654s ***** assert (wblinv ([p, NaN], 1, single (1)), single ([NaN 0 1 Inf NaN NaN]), eps ("single"))
654s ***** error<wblinv: invalid number of input arguments.> wblinv ()
654s ***** error<wblinv: invalid number of input arguments.> wblinv (1,2,3,4)
654s ***** error<wblinv: P, LAMBDA, and K must be of common size or scalars.> ...
654s  wblinv (ones (3), ones (2), ones (2))
654s ***** error<wblinv: P, LAMBDA, and K must be of common size or scalars.> ...
654s  wblinv (ones (2), ones (3), ones (2))
654s ***** error<wblinv: P, LAMBDA, and K must be of common size or scalars.> ...
654s  wblinv (ones (2), ones (2), ones (3))
654s ***** error<wblinv: P, LAMBDA, and K must not be complex.> wblinv (i, 2, 2)
654s ***** error<wblinv: P, LAMBDA, and K must not be complex.> wblinv (2, i, 2)
654s ***** error<wblinv: P, LAMBDA, and K must not be complex.> wblinv (2, 2, i)
654s 18 tests, 18 passed, 0 known failure, 0 skipped
654s [inst/dist_fun/burrinv.m]
654s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/burrinv.m
654s ***** demo
654s  ## Plot various iCDFs from the Burr type XII distribution
654s  p = 0.001:0.001:0.999;
654s  x1 = burrinv (p, 1, 1, 1);
654s  x2 = burrinv (p, 1, 1, 2);
654s  x3 = burrinv (p, 1, 1, 3);
654s  x4 = burrinv (p, 1, 2, 1);
654s  x5 = burrinv (p, 1, 3, 1);
654s  x6 = burrinv (p, 1, 0.5, 2);
654s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", ...
654s        p, x4, "-c", p, x5, "-m", p, x6, "-k")
654s  grid on
654s  ylim ([0, 5])
654s  legend ({"λ = 1, c = 1, k = 1", "λ = 1, c = 1, k = 2", ...
654s           "λ = 1, c = 1, k = 3", "λ = 1, c = 2, k = 1", ...
654s           "λ = 1, c = 3, k = 1", "λ = 1, c = 0.5, k = 2"}, ...
654s          "location", "northwest")
654s  title ("Burr type XII iCDF")
654s  xlabel ("probability")
654s  ylabel ("values in x")
654s ***** shared p, y
654s  p = [-Inf, -1, 0, 1/2, 1, 2, Inf];
654s  y = [NaN, NaN, 0, 1 , Inf, NaN, NaN];
654s ***** assert (burrinv (p, ones (1,7), ones (1,7), ones(1,7)), y, eps)
654s ***** assert (burrinv (p, 1, 1, 1), y, eps)
654s ***** assert (burrinv (p, [1, 1, 1, NaN, 1, 1, 1], 1, 1), [y(1:3), NaN, y(5:7)], eps)
654s ***** assert (burrinv (p, 1, [1, 1, 1, NaN, 1, 1, 1], 1), [y(1:3), NaN, y(5:7)], eps)
654s ***** assert (burrinv (p, 1, 1, [1, 1, 1, NaN, 1, 1, 1]), [y(1:3), NaN, y(5:7)], eps)
654s ***** assert (burrinv ([p, NaN], 1, 1, 1), [y, NaN], eps)
654s ***** assert (burrinv (single ([p, NaN]), 1, 1, 1), single ([y, NaN]), eps("single"))
654s ***** assert (burrinv ([p, NaN], single (1), 1, 1), single ([y, NaN]), eps("single"))
654s ***** assert (burrinv ([p, NaN], 1, single (1), 1), single ([y, NaN]), eps("single"))
654s ***** assert (burrinv ([p, NaN], 1, 1, single (1)), single ([y, NaN]), eps("single"))
654s ***** error<burrinv: function called with too few input arguments.> burrinv ()
654s ***** error<burrinv: function called with too few input arguments.> burrinv (1)
654s ***** error<burrinv: function called with too few input arguments.> burrinv (1, 2)
654s ***** error<burrinv: function called with too few input arguments.> burrinv (1, 2, 3)
654s ***** error<burrinv: function called with too many inputs> ...
654s  burrinv (1, 2, 3, 4, 5)
654s ***** error<burrinv: P, LAMBDA, C, and K must be of common size or scalars.> ...
654s  burrinv (ones (3), ones (2), ones(2), ones(2))
654s ***** error<burrinv: P, LAMBDA, C, and K must be of common size or scalars.> ...
654s  burrinv (ones (2), ones (3), ones(2), ones(2))
654s ***** error<burrinv: P, LAMBDA, C, and K must be of common size or scalars.> ...
654s  burrinv (ones (2), ones (2), ones(3), ones(2))
654s ***** error<burrinv: P, LAMBDA, C, and K must be of common size or scalars.> ...
654s  burrinv (ones (2), ones (2), ones(2), ones(3))
654s ***** error<burrinv: P, LAMBDA, C, and K must not be complex.> burrinv (i, 2, 3, 4)
654s ***** error<burrinv: P, LAMBDA, C, and K must not be complex.> burrinv (1, i, 3, 4)
654s ***** error<burrinv: P, LAMBDA, C, and K must not be complex.> burrinv (1, 2, i, 4)
654s ***** error<burrinv: P, LAMBDA, C, and K must not be complex.> burrinv (1, 2, 3, i)
654s 23 tests, 23 passed, 0 known failure, 0 skipped
654s [inst/dist_fun/ncfpdf.m]
654s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/ncfpdf.m
654s ***** demo
654s  ## Plot various PDFs from the noncentral F distribution
654s  x = 0:0.01:5;
654s  y1 = ncfpdf (x, 2, 5, 1);
654s  y2 = ncfpdf (x, 2, 5, 2);
654s  y3 = ncfpdf (x, 5, 10, 1);
654s  y4 = ncfpdf (x, 10, 20, 10);
654s  plot (x, y1, "-r", x, y2, "-g", x, y3, "-k", x, y4, "-m")
654s  grid on
654s  xlim ([0, 5])
654s  ylim ([0, 0.8])
654s  legend ({"df1 = 2, df2 = 5, λ = 1", "df1 = 2, df2 = 5, λ = 2", ...
654s           "df1 = 5, df2 = 10, λ = 1", "df1 = 10, df2 = 20, λ = 10"}, ...
654s          "location", "northeast")
654s  title ("Noncentral F PDF")
654s  xlabel ("values in x")
654s  ylabel ("density")
654s ***** demo
654s  ## Compare the noncentral F PDF with LAMBDA = 10 to the F PDF with the
654s  ## same number of numerator and denominator degrees of freedom (5, 20)
654s 
654s  x = 0.01:0.1:10.01;
654s  y1 = ncfpdf (x, 5, 20, 10);
654s  y2 = fpdf (x, 5, 20);
654s  plot (x, y1, "-", x, y2, "-");
654s  grid on
654s  xlim ([0, 10])
654s  ylim ([0, 0.8])
654s  legend ({"Noncentral F(5,20,10)", "F(5,20)"}, "location", "northeast")
654s  title ("Noncentral F vs F PDFs")
654s  xlabel ("values in x")
654s  ylabel ("density")
654s ***** shared x1, df1, df2, lambda
654s  x1 = [-Inf, 2, NaN, 4, Inf];
654s  df1 = [2, 0, -1, 1, 4];
654s  df2 = [2, 4, 5, 6, 8];
654s  lambda = [1, NaN, 3, -1, 2];
654s ***** assert (ncfpdf (x1, df1, df2, lambda), [0, NaN, NaN, NaN, NaN]);
654s ***** assert (ncfpdf (x1, df1, df2, 1), [0, NaN, NaN, ...
654s                                    0.05607937264237208, NaN], 1e-14);
654s ***** assert (ncfpdf (x1, df1, df2, 3), [0, NaN, NaN, ...
654s                                    0.080125760971946518, NaN], 1e-14);
654s ***** assert (ncfpdf (x1, df1, df2, 2), [0, NaN, NaN, ...
654s                                    0.0715902008258656, NaN], 1e-14);
654s ***** assert (ncfpdf (x1, 3, 5, lambda), [0, NaN, NaN, NaN, NaN]);
654s ***** assert (ncfpdf (2, df1, df2, lambda), [0.1254046999837947, NaN, NaN, ...
654s                                       NaN, 0.2152571783045893], 1e-14);
654s ***** assert (ncfpdf (4, df1, df2, lambda), [0.05067089541001374, NaN, NaN, ...
654s                                       NaN, 0.05560846335398539], 1e-14);
654s ***** error<ncfpdf: function called with too few input arguments.> ncfpdf ()
654s ***** error<ncfpdf: function called with too few input arguments.> ncfpdf (1)
654s ***** error<ncfpdf: function called with too few input arguments.> ncfpdf (1, 2)
654s ***** error<ncfpdf: function called with too few input arguments.> ncfpdf (1, 2, 3)
654s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
654s  ncfpdf (ones (3), ones (2), ones (2), ones (2))
654s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
654s  ncfpdf (ones (2), ones (3), ones (2), ones (2))
654s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
654s  ncfpdf (ones (2), ones (2), ones (3), ones (2))
654s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
654s  ncfpdf (ones (2), ones (2), ones (2), ones (3))
654s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfpdf (i, 2, 2, 2)
654s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfpdf (2, i, 2, 2)
654s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfpdf (2, 2, i, 2)
654s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfpdf (2, 2, 2, i)
654s 19 tests, 19 passed, 0 known failure, 0 skipped
654s [inst/dist_fun/gumbelrnd.m]
654s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/gumbelrnd.m
654s ***** assert (size (gumbelrnd (1, 1)), [1 1])
654s ***** assert (size (gumbelrnd (1, ones (2,1))), [2, 1])
654s ***** assert (size (gumbelrnd (1, ones (2,2))), [2, 2])
654s ***** assert (size (gumbelrnd (ones (2,1), 1)), [2, 1])
654s ***** assert (size (gumbelrnd (ones (2,2), 1)), [2, 2])
654s ***** assert (size (gumbelrnd (1, 1, 3)), [3, 3])
654s ***** assert (size (gumbelrnd (1, 1, [4, 1])), [4, 1])
654s ***** assert (size (gumbelrnd (1, 1, 4, 1)), [4, 1])
654s ***** assert (size (gumbelrnd (1, 1, 4, 1, 5)), [4, 1, 5])
654s ***** assert (size (gumbelrnd (1, 1, 0, 1)), [0, 1])
654s ***** assert (size (gumbelrnd (1, 1, 1, 0)), [1, 0])
654s ***** assert (size (gumbelrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
654s ***** assert (class (gumbelrnd (1, 1)), "double")
654s ***** assert (class (gumbelrnd (1, single (1))), "single")
654s ***** assert (class (gumbelrnd (1, single ([1, 1]))), "single")
654s ***** assert (class (gumbelrnd (single (1), 1)), "single")
654s ***** assert (class (gumbelrnd (single ([1, 1]), 1)), "single")
654s ***** error<gumbelrnd: function called with too few input arguments.> gumbelrnd ()
654s ***** error<gumbelrnd: function called with too few input arguments.> gumbelrnd (1)
654s ***** error<gumbelrnd: MU and BETA must be of common size or scalars.> ...
654s  gumbelrnd (ones (3), ones (2))
654s ***** error<gumbelrnd: MU and BETA must be of common size or scalars.> ...
654s  gumbelrnd (ones (2), ones (3))
654s ***** error<gumbelrnd: MU and BETA must not be complex.> gumbelrnd (i, 2, 3)
654s ***** error<gumbelrnd: MU and BETA must not be complex.> gumbelrnd (1, i, 3)
654s ***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
654s  gumbelrnd (1, 2, -1)
654s ***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
654s  gumbelrnd (1, 2, 1.2)
654s ***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
654s  gumbelrnd (1, 2, ones (2))
654s ***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
654s  gumbelrnd (1, 2, [2 -1 2])
654s ***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
654s  gumbelrnd (1, 2, [2 0 2.5])
654s ***** error<gumbelrnd: dimensions must be non-negative integers.> ...
654s  gumbelrnd (1, 2, 2, -1, 5)
654s ***** error<gumbelrnd: dimensions must be non-negative integers.> ...
654s  gumbelrnd (1, 2, 2, 1.5, 5)
654s ***** error<gumbelrnd: MU and BETA must be scalars or of size SZ.> ...
654s  gumbelrnd (2, ones (2), 3)
654s ***** error<gumbelrnd: MU and BETA must be scalars or of size SZ.> ...
654s  gumbelrnd (2, ones (2), [3, 2])
654s ***** error<gumbelrnd: MU and BETA must be scalars or of size SZ.> ...
654s  gumbelrnd (2, ones (2), 3, 2)
654s 33 tests, 33 passed, 0 known failure, 0 skipped
654s [inst/dist_fun/plcdf.m]
654s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/plcdf.m
654s ***** demo
654s  ## Plot various CDFs from the Piecewise linear distribution
654s  data = 0:0.01:10;
654s  x1 = [0, 1, 3, 4, 7, 10];
654s  Fx1 = [0, 0.2, 0.5, 0.6, 0.7, 1];
654s  x2 = [0, 2, 5, 6, 7, 8];
654s  Fx2 = [0, 0.1, 0.3, 0.6, 0.9, 1];
654s  p1 = plcdf (data, x1, Fx1);
654s  p2 = plcdf (data, x2, Fx2);
654s  plot (data, p1, "-b", data, p2, "g")
654s  grid on
654s  ylim ([0, 1])
654s  xlim ([0, 10])
654s  legend ({"x1, Fx1", "x2, Fx2"}, "location", "southeast")
654s  title ("Piecewise linear CDF")
654s  xlabel ("values in data")
654s  ylabel ("probability")
654s ***** test
654s  data = 0:0.2:1;
654s  p = plcdf (data, [0, 1], [0, 1]);
654s  assert (p, data);
654s ***** test
654s  data = 0:0.2:1;
654s  p = plcdf (data, [0, 2], [0, 1]);
654s  assert (p, 0.5 * data);
654s ***** test
654s  data = 0:0.2:1;
654s  p = plcdf (data, [0, 1], [0, 0.5]);
654s  assert (p, 0.5 * data);
654s ***** test
654s  data = 0:0.2:1;
654s  p = plcdf (data, [0, 0.5], [0, 1]);
654s  assert (p, [0, 0.4, 0.8, 1, 1, 1]);
654s ***** test
654s  data = 0:0.2:1;
654s  p = plcdf (data, [0, 1], [0, 1], "upper");
654s  assert (p, 1 - data);
654s ***** error<plcdf: function called with too few input arguments.> plcdf ()
654s ***** error<plcdf: function called with too few input arguments.> plcdf (1)
654s ***** error<plcdf: function called with too few input arguments.> plcdf (1, 2)
654s ***** error<plcdf: invalid argument for upper tail.> plcdf (1, 2, 3, "uper")
654s ***** error<plcdf: invalid argument for upper tail.> plcdf (1, 2, 3, 4)
654s ***** error<plcdf: X and FX must be vectors of equal size.> ...
654s  plcdf (1, [0, 1, 2], [0, 1])
654s ***** error<plcdf: X and FX must be at least two-elements long.> ...
654s  plcdf (1, [0], [1])
654s ***** error<plcdf: FX must be bounded in the range> ...
654s  plcdf (1, [0, 1, 2], [0, 1, 1.5])
654s ***** error<plcdf: FX must be bounded in the range> ...
654s  plcdf (1, [0, 1, 2], [0, i, 1])
654s ***** error<plcdf: DATA, X, and FX must not be complex.> ...
654s  plcdf (i, [0, 1, 2], [0, 0.5, 1])
654s ***** error<plcdf: DATA, X, and FX must not be complex.> ...
654s  plcdf (1, [0, i, 2], [0, 0.5, 1])
654s ***** error<plcdf: DATA, X, and FX must not be complex.> ...
654s  plcdf (1, [0, 1, 2], [0, 0.5i, 1])
654s 17 tests, 17 passed, 0 known failure, 0 skipped
654s [inst/dist_fun/nbininv.m]
654s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/nbininv.m
654s ***** demo
654s  ## Plot various iCDFs from the negative binomial distribution
654s  p = 0.001:0.001:0.999;
654s  x1 = nbininv (p, 2, 0.15);
654s  x2 = nbininv (p, 5, 0.2);
654s  x3 = nbininv (p, 4, 0.4);
654s  x4 = nbininv (p, 10, 0.3);
654s  plot (p, x1, "-r", p, x2, "-g", p, x3, "-k", p, x4, "-m")
654s  grid on
654s  ylim ([0, 40])
654s  legend ({"r = 2, ps = 0.15", "r = 5, ps = 0.2", "r = 4, p = 0.4", ...
654s           "r = 10, ps = 0.3"}, "location", "northwest")
654s  title ("Negative binomial iCDF")
654s  xlabel ("probability")
654s  ylabel ("values in x (number of failures)")
654s ***** shared p
654s  p = [-1 0 3/4 1 2];
654s ***** assert (nbininv (p, ones (1,5), 0.5*ones (1,5)), [NaN 0 1 Inf NaN])
654s ***** assert (nbininv (p, 1, 0.5*ones (1,5)), [NaN 0 1 Inf NaN])
654s ***** assert (nbininv (p, ones (1,5), 0.5), [NaN 0 1 Inf NaN])
654s ***** assert (nbininv (p, [1 0 NaN Inf 1], 0.5), [NaN NaN NaN NaN NaN])
654s ***** assert (nbininv (p, [1 0 1.5 Inf 1], 0.5), [NaN NaN 2 NaN NaN])
654s ***** assert (nbininv (p, 1, 0.5*[1 -Inf NaN Inf 1]), [NaN NaN NaN NaN NaN])
654s ***** assert (nbininv ([p(1:2) NaN p(4:5)], 1, 0.5), [NaN 0 NaN Inf NaN])
654s ***** assert (nbininv ([p, NaN], 1, 0.5), [NaN 0 1 Inf NaN NaN])
654s ***** assert (nbininv (single ([p, NaN]), 1, 0.5), single ([NaN 0 1 Inf NaN NaN]))
654s ***** assert (nbininv ([p, NaN], single (1), 0.5), single ([NaN 0 1 Inf NaN NaN]))
654s ***** assert (nbininv ([p, NaN], 1, single (0.5)), single ([NaN 0 1 Inf NaN NaN]))
654s ***** shared y, tol
654s  y = magic (3) + 1;
654s  tol = 1;
654s ***** assert (nbininv (nbincdf (1:10, 3, 0.1), 3, 0.1), 1:10, tol)
654s ***** assert (nbininv (nbincdf (1:10, 3./(1:10), 0.1), 3./(1:10), 0.1), 1:10, tol)
654s ***** assert (nbininv (nbincdf (y, 3./y, 1./y), 3./y, 1./y), y, tol)
654s ***** error<nbininv: function called with too few input arguments.> nbininv ()
654s ***** error<nbininv: function called with too few input arguments.> nbininv (1)
654s ***** error<nbininv: function called with too few input arguments.> nbininv (1, 2)
654s ***** error<nbininv: P, R, and PS must be of common size or scalars.> ...
654s  nbininv (ones (3), ones (2), ones (2))
654s ***** error<nbininv: P, R, and PS must be of common size or scalars.> ...
654s  nbininv (ones (2), ones (3), ones (2))
654s ***** error<nbininv: P, R, and PS must be of common size or scalars.> ...
654s  nbininv (ones (2), ones (2), ones (3))
654s ***** error<nbininv: P, R, and PS must not be complex.> nbininv (i, 2, 2)
654s ***** error<nbininv: P, R, and PS must not be complex.> nbininv (2, i, 2)
654s ***** error<nbininv: P, R, and PS must not be complex.> nbininv (2, 2, i)
654s 23 tests, 23 passed, 0 known failure, 0 skipped
654s [inst/dist_fun/gevinv.m]
654s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/gevinv.m
654s ***** demo
654s  ## Plot various iCDFs from the generalized extreme value distribution
654s  p = 0.001:0.001:0.999;
654s  x1 = gevinv (p, 1, 1, 1);
654s  x2 = gevinv (p, 0.5, 1, 1);
654s  x3 = gevinv (p, 1, 1, 5);
654s  x4 = gevinv (p, 1, 2, 5);
654s  x5 = gevinv (p, 1, 5, 5);
654s  x6 = gevinv (p, 1, 0.5, 5);
654s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", ...
654s        p, x4, "-c", p, x5, "-m", p, x6, "-k")
654s  grid on
654s  ylim ([-1, 10])
654s  legend ({"k = 1, σ = 1, μ = 1", "k = 0.5, σ = 1, μ = 1", ...
654s           "k = 1, σ = 1, μ = 5", "k = 1, σ = 2, μ = 5", ...
654s           "k = 1, σ = 5, μ = 5", "k = 1, σ = 0.5, μ = 5"}, ...
654s          "location", "northwest")
654s  title ("Generalized extreme value iCDF")
654s  xlabel ("probability")
654s  ylabel ("values in x")
654s ***** test
654s  p = 0.1:0.1:0.9;
654s  k = 0;
654s  sigma = 1;
654s  mu = 0;
654s  x = gevinv (p, k, sigma, mu);
654s  c = gevcdf(x, k, sigma, mu);
654s  assert (c, p, 0.001);
654s ***** test
654s  p = 0.1:0.1:0.9;
654s  k = 1;
654s  sigma = 1;
654s  mu = 0;
654s  x = gevinv (p, k, sigma, mu);
654s  c = gevcdf(x, k, sigma, mu);
654s  assert (c, p, 0.001);
654s ***** test
654s  p = 0.1:0.1:0.9;
654s  k = 0.3;
654s  sigma = 1;
654s  mu = 0;
654s  x = gevinv (p, k, sigma, mu);
654s  c = gevcdf(x, k, sigma, mu);
654s  assert (c, p, 0.001);
654s ***** error<gevinv: function called with too few input arguments.> gevinv ()
654s ***** error<gevinv: function called with too few input arguments.> gevinv (1)
654s ***** error<gevinv: function called with too few input arguments.> gevinv (1, 2)
654s ***** error<gevinv: function called with too few input arguments.> gevinv (1, 2, 3)
654s ***** error<gevinv: P, K, SIGMA, and MU must be of common size or scalars.> ...
654s  gevinv (ones (3), ones (2), ones(2), ones(2))
654s ***** error<gevinv: P, K, SIGMA, and MU must be of common size or scalars.> ...
654s  gevinv (ones (2), ones (3), ones(2), ones(2))
655s ***** error<gevinv: P, K, SIGMA, and MU must be of common size or scalars.> ...
655s  gevinv (ones (2), ones (2), ones(3), ones(2))
655s ***** error<gevinv: P, K, SIGMA, and MU must be of common size or scalars.> ...
655s  gevinv (ones (2), ones (2), ones(2), ones(3))
655s ***** error<gevinv: P, K, SIGMA, and MU must not be complex.> gevinv (i, 2, 3, 4)
655s ***** error<gevinv: P, K, SIGMA, and MU must not be complex.> gevinv (1, i, 3, 4)
655s ***** error<gevinv: P, K, SIGMA, and MU must not be complex.> gevinv (1, 2, i, 4)
655s ***** error<gevinv: P, K, SIGMA, and MU must not be complex.> gevinv (1, 2, 3, i)
655s 15 tests, 15 passed, 0 known failure, 0 skipped
655s [inst/dist_fun/gumbelpdf.m]
655s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/gumbelpdf.m
655s ***** demo
655s  ## Plot various PDFs from the Extreme value distribution
655s  x = -5:0.001:20;
655s  y1 = gumbelpdf (x, 0.5, 2);
655s  y2 = gumbelpdf (x, 1.0, 2);
655s  y3 = gumbelpdf (x, 1.5, 3);
655s  y4 = gumbelpdf (x, 3.0, 4);
655s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
655s  grid on
655s  ylim ([0, 0.2])
655s  legend ({"μ = 0.5, β = 2", "μ = 1.0, β = 2", ...
655s           "μ = 1.5, β = 3", "μ = 3.0, β = 4"}, "location", "northeast")
655s  title ("Extreme value PDF")
655s  xlabel ("values in x")
655s  ylabel ("density")
655s ***** shared x, y0, y1
655s  x = [-5, 0, 1, 2, 3];
655s  y0 = [0, 0.3679, 0.2547, 0.1182, 0.0474];
655s  y1 = [0, 0.1794, 0.3679, 0.2547, 0.1182];
655s ***** assert (gumbelpdf (x), y0, 1e-4)
655s ***** assert (gumbelpdf (x, zeros (1,5), ones (1,5)), y0, 1e-4)
655s ***** assert (gumbelpdf (x, ones (1,5), ones (1,5)), y1, 1e-4)
655s ***** error<gumbelpdf: too few input arguments.> gumbelpdf ()
655s ***** error<gumbelpdf: X, MU, and BETA must be of common size or scalars.> ...
655s  gumbelpdf (ones (3), ones (2), ones (2))
655s ***** error<gumbelpdf: X, MU, and BETA must not be complex.> gumbelpdf (i, 2, 2)
655s ***** error<gumbelpdf: X, MU, and BETA must not be complex.> gumbelpdf (2, i, 2)
655s ***** error<gumbelpdf: X, MU, and BETA must not be complex.> gumbelpdf (2, 2, i)
655s 8 tests, 8 passed, 0 known failure, 0 skipped
655s [inst/dist_fun/burrcdf.m]
655s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/burrcdf.m
655s ***** demo
655s  ## Plot various CDFs from the Burr type XII distribution
655s  x = 0.001:0.001:5;
655s  p1 = burrcdf (x, 1, 1, 1);
655s  p2 = burrcdf (x, 1, 1, 2);
655s  p3 = burrcdf (x, 1, 1, 3);
655s  p4 = burrcdf (x, 1, 2, 1);
655s  p5 = burrcdf (x, 1, 3, 1);
655s  p6 = burrcdf (x, 1, 0.5, 2);
655s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", ...
655s        x, p4, "-c", x, p5, "-m", x, p6, "-k")
655s  grid on
655s  legend ({"λ = 1, c = 1, k = 1", "λ = 1, c = 1, k = 2", ...
655s           "λ = 1, c = 1, k = 3", "λ = 1, c = 2, k = 1", ...
655s           "λ = 1, c = 3, k = 1", "λ = 1, c = 0.5, k = 2"}, ...
655s          "location", "southeast")
655s  title ("Burr type XII CDF")
655s  xlabel ("values in x")
655s  ylabel ("probability")
655s ***** shared x, y
655s  x = [-1, 0, 1, 2, Inf];
655s  y = [0, 0, 1/2, 2/3, 1];
655s ***** assert (burrcdf (x, ones(1,5), ones (1,5), ones (1,5)), y, eps)
655s ***** assert (burrcdf (x, 1, 1, 1), y, eps)
655s ***** assert (burrcdf (x, [1, 1, NaN, 1, 1], 1, 1), [y(1:2), NaN, y(4:5)], eps)
655s ***** assert (burrcdf (x, 1, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)], eps)
655s ***** assert (burrcdf (x, 1, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)], eps)
655s ***** assert (burrcdf ([x, NaN], 1, 1, 1), [y, NaN], eps)
655s ***** assert (burrcdf (single ([x, NaN]), 1, 1, 1), single ([y, NaN]), eps("single"))
655s ***** assert (burrcdf ([x, NaN], single (1), 1, 1), single ([y, NaN]), eps("single"))
655s ***** assert (burrcdf ([x, NaN], 1, single (1), 1), single ([y, NaN]), eps("single"))
655s ***** assert (burrcdf ([x, NaN], 1, 1, single (1)), single ([y, NaN]), eps("single"))
655s ***** error<burrcdf: function called with too few input arguments.> burrcdf ()
655s ***** error<burrcdf: function called with too few input arguments.> burrcdf (1)
655s ***** error<burrcdf: function called with too few input arguments.> burrcdf (1, 2)
655s ***** error<burrcdf: function called with too few input arguments.> burrcdf (1, 2, 3)
655s ***** error<burrcdf: function called with too many inputs> ...
655s  burrcdf (1, 2, 3, 4, 5, 6)
655s ***** error<burrcdf: invalid argument for upper tail.> burrcdf (1, 2, 3, 4, "tail")
655s ***** error<burrcdf: invalid argument for upper tail.> burrcdf (1, 2, 3, 4, 5)
655s ***** error<burrcdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
655s  burrcdf (ones (3), ones (2), ones(2), ones(2))
655s ***** error<burrcdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
655s  burrcdf (ones (2), ones (3), ones(2), ones(2))
655s ***** error<burrcdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
655s  burrcdf (ones (2), ones (2), ones(3), ones(2))
655s ***** error<burrcdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
655s  burrcdf (ones (2), ones (2), ones(2), ones(3))
655s ***** error<burrcdf: X, LAMBDA, C, and K must not be complex.> burrcdf (i, 2, 3, 4)
655s ***** error<burrcdf: X, LAMBDA, C, and K must not be complex.> burrcdf (1, i, 3, 4)
655s ***** error<burrcdf: X, LAMBDA, C, and K must not be complex.> burrcdf (1, 2, i, 4)
655s ***** error<burrcdf: X, LAMBDA, C, and K must not be complex.> burrcdf (1, 2, 3, i)
655s 25 tests, 25 passed, 0 known failure, 0 skipped
655s [inst/dist_fun/invgrnd.m]
655s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/invgrnd.m
655s ***** assert (size (invgrnd (1, 1, 1)), [1, 1])
655s ***** assert (size (invgrnd (1, 1, 2)), [2, 2])
655s ***** assert (size (invgrnd (1, 1, [2, 1])), [2, 1])
655s ***** assert (size (invgrnd (1, zeros (2, 2))), [2, 2])
655s ***** assert (size (invgrnd (1, ones (2, 1))), [2, 1])
655s ***** assert (size (invgrnd (1, ones (2, 2))), [2, 2])
655s ***** assert (size (invgrnd (ones (2, 1), 1)), [2, 1])
655s ***** assert (size (invgrnd (ones (2, 2), 1)), [2, 2])
655s ***** assert (size (invgrnd (1, 1, 3)), [3, 3])
655s ***** assert (size (invgrnd (1, 1, [4 1])), [4, 1])
655s ***** assert (size (invgrnd (1, 1, 4, 1)), [4, 1])
655s ***** test
655s  r =  invgrnd (1, [1, 0, -1]);
655s  assert (r([2:3]), [NaN, NaN])
655s ***** assert (class (invgrnd (1, 0)), "double")
655s ***** assert (class (invgrnd (1, single (0))), "single")
655s ***** assert (class (invgrnd (1, single ([0 0]))), "single")
655s ***** assert (class (invgrnd (1, single (1))), "single")
655s ***** assert (class (invgrnd (1, single ([1 1]))), "single")
655s ***** assert (class (invgrnd (single (1), 1)), "single")
655s ***** assert (class (invgrnd (single ([1 1]), 1)), "single")
655s ***** error<invgrnd: function called with too few input arguments.> invgrnd ()
655s ***** error<invgrnd: function called with too few input arguments.> invgrnd (1)
655s ***** error<invgrnd: MU and LAMBDA must be of common size or scalars.> ...
655s  invgrnd (ones (3), ones (2))
655s ***** error<invgrnd: MU and LAMBDA must be of common size or scalars.> ...
655s  invgrnd (ones (2), ones (3))
655s ***** error<invgrnd: MU and LAMBDA must not be complex.> invgrnd (i, 2, 3)
655s ***** error<invgrnd: MU and LAMBDA must not be complex.> invgrnd (1, i, 3)
655s ***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
655s  invgrnd (1, 2, -1)
655s ***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
655s  invgrnd (1, 2, 1.2)
655s ***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
655s  invgrnd (1, 2, ones (2))
655s ***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
655s  invgrnd (1, 2, [2 -1 2])
655s ***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
655s  invgrnd (1, 2, [2 0 2.5])
655s ***** error<invgrnd: dimensions must be non-negative integers.> ...
655s  invgrnd (1, 2, 2, -1, 5)
655s ***** error<invgrnd: dimensions must be non-negative integers.> ...
655s  invgrnd (1, 2, 2, 1.5, 5)
655s ***** error<invgrnd: MU and LAMBDA must be scalars or of size SZ.> ...
655s  invgrnd (2, ones (2), 3)
655s ***** error<invgrnd: MU and LAMBDA must be scalars or of size SZ.> ...
655s  invgrnd (2, ones (2), [3, 2])
655s ***** error<invgrnd: MU and LAMBDA must be scalars or of size SZ.> ...
655s  invgrnd (2, ones (2), 3, 2)
655s 35 tests, 35 passed, 0 known failure, 0 skipped
655s [inst/dist_fun/bisarnd.m]
655s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/bisarnd.m
655s ***** assert (size (bisarnd (1, 1)), [1 1])
655s ***** assert (size (bisarnd (1, ones (2,1))), [2, 1])
655s ***** assert (size (bisarnd (1, ones (2,2))), [2, 2])
655s ***** assert (size (bisarnd (ones (2,1), 1)), [2, 1])
655s ***** assert (size (bisarnd (ones (2,2), 1)), [2, 2])
655s ***** assert (size (bisarnd (1, 1, 3)), [3, 3])
655s ***** assert (size (bisarnd (1, 1, [4, 1])), [4, 1])
655s ***** assert (size (bisarnd (1, 1, 4, 1)), [4, 1])
655s ***** assert (size (bisarnd (1, 1, 4, 1, 5)), [4, 1, 5])
655s ***** assert (size (bisarnd (1, 1, 0, 1)), [0, 1])
655s ***** assert (size (bisarnd (1, 1, 1, 0)), [1, 0])
655s ***** assert (size (bisarnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
655s ***** assert (class (bisarnd (1, 1)), "double")
655s ***** assert (class (bisarnd (1, single (1))), "single")
655s ***** assert (class (bisarnd (1, single ([1, 1]))), "single")
655s ***** assert (class (bisarnd (single (1), 1)), "single")
655s ***** assert (class (bisarnd (single ([1, 1]), 1)), "single")
655s ***** error<bisarnd: function called with too few input arguments.> bisarnd ()
655s ***** error<bisarnd: function called with too few input arguments.> bisarnd (1)
655s ***** error<bisarnd: BETA and GAMMA must be of common size or scalars.> ...
655s  bisarnd (ones (3), ones (2))
655s ***** error<bisarnd: BETA and GAMMA must be of common size or scalars.> ...
655s  bisarnd (ones (2), ones (3))
655s ***** error<bisarnd: BETA and GAMMA must not be complex.> bisarnd (i, 2, 3)
655s ***** error<bisarnd: BETA and GAMMA must not be complex.> bisarnd (1, i, 3)
655s ***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
655s  bisarnd (1, 2, -1)
655s ***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
655s  bisarnd (1, 2, 1.2)
655s ***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
655s  bisarnd (1, 2, ones (2))
655s ***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
655s  bisarnd (1, 2, [2 -1 2])
655s ***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
655s  bisarnd (1, 2, [2 0 2.5])
655s ***** error<bisarnd: dimensions must be non-negative integers.> ...
655s  bisarnd (1, 2, 2, -1, 5)
655s ***** error<bisarnd: dimensions must be non-negative integers.> ...
655s  bisarnd (1, 2, 2, 1.5, 5)
655s ***** error<bisarnd: BETA and GAMMA must be scalars or of size SZ.> ...
655s  bisarnd (2, ones (2), 3)
655s ***** error<bisarnd: BETA and GAMMA must be scalars or of size SZ.> ...
655s  bisarnd (2, ones (2), [3, 2])
655s ***** error<bisarnd: BETA and GAMMA must be scalars or of size SZ.> ...
655s  bisarnd (2, ones (2), 3, 2)
655s 33 tests, 33 passed, 0 known failure, 0 skipped
655s [inst/dist_fun/lognpdf.m]
655s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/lognpdf.m
655s ***** demo
655s  ## Plot various PDFs from the log-normal distribution
655s  x = 0:0.01:5;
655s  y1 = lognpdf (x, 0, 1);
655s  y2 = lognpdf (x, 0, 0.5);
655s  y3 = lognpdf (x, 0, 0.25);
655s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r")
655s  grid on
655s  ylim ([0, 2])
655s  legend ({"μ = 0, σ = 1", "μ = 0, σ = 0.5", "μ = 0, σ = 0.25"}, ...
655s           "location", "northeast")
655s  title ("Log-normal PDF")
655s  xlabel ("values in x")
655s  ylabel ("density")
655s ***** shared x, y
655s  x = [-1 0 e Inf];
655s  y = [0, 0, 1/(e*sqrt(2*pi)) * exp(-1/2), 0];
655s ***** assert (lognpdf (x, zeros (1,4), ones (1,4)), y, eps)
655s ***** assert (lognpdf (x, 0, ones (1,4)), y, eps)
655s ***** assert (lognpdf (x, zeros (1,4), 1), y, eps)
655s ***** assert (lognpdf (x, [0 1 NaN 0], 1), [0 0 NaN y(4)], eps)
655s ***** assert (lognpdf (x, 0, [0 NaN Inf 1]), [NaN NaN NaN y(4)], eps)
655s ***** assert (lognpdf ([x, NaN], 0, 1), [y, NaN], eps)
655s ***** assert (lognpdf (single ([x, NaN]), 0, 1), single ([y, NaN]), eps ("single"))
655s ***** assert (lognpdf ([x, NaN], single (0), 1), single ([y, NaN]), eps ("single"))
655s ***** assert (lognpdf ([x, NaN], 0, single (1)), single ([y, NaN]), eps ("single"))
655s ***** error lognpdf ()
655s ***** error lognpdf (1,2,3,4)
655s ***** error lognpdf (ones (3), ones (2), ones (2))
655s ***** error lognpdf (ones (2), ones (3), ones (2))
655s ***** error lognpdf (ones (2), ones (2), ones (3))
655s ***** error lognpdf (i, 2, 2)
655s ***** error lognpdf (2, i, 2)
655s ***** error lognpdf (2, 2, i)
655s 17 tests, 17 passed, 0 known failure, 0 skipped
655s [inst/dist_fun/evinv.m]
655s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/evinv.m
655s ***** demo
655s  ## Plot various iCDFs from the extreme value distribution
655s  p = 0.001:0.001:0.999;
655s  x1 = evinv (p, 0.5, 2);
655s  x2 = evinv (p, 1.0, 2);
655s  x3 = evinv (p, 1.5, 3);
655s  x4 = evinv (p, 3.0, 4);
655s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
655s  grid on
655s  ylim ([-10, 10])
655s  legend ({"μ = 0.5, σ = 2", "μ = 1.0, σ = 2", ...
655s           "μ = 1.5, σ = 3", "μ = 3.0, σ = 4"}, "location", "northwest")
655s  title ("Extreme value iCDF")
655s  xlabel ("probability")
655s  ylabel ("values in x")
655s ***** shared p, x
655s  p = [0, 0.05, 0.5 0.95];
655s  x = [-Inf, -2.9702, -0.3665, 1.0972];
655s ***** assert (evinv (p), x, 1e-4)
655s ***** assert (evinv (p, zeros (1,4), ones (1,4)), x, 1e-4)
655s ***** assert (evinv (p, 0, ones (1,4)), x, 1e-4)
655s ***** assert (evinv (p, zeros (1,4), 1), x, 1e-4)
655s ***** assert (evinv (p, [0, -Inf, NaN, Inf], 1), [-Inf, -Inf, NaN, Inf], 1e-4)
655s ***** assert (evinv (p, 0, [Inf, NaN, -1, 0]), [-Inf, NaN, NaN, NaN], 1e-4)
655s ***** assert (evinv ([p(1:2), NaN, p(4)], 0, 1), [x(1:2), NaN, x(4)], 1e-4)
655s ***** assert (evinv ([p, NaN], 0, 1), [x, NaN], 1e-4)
655s ***** assert (evinv (single ([p, NaN]), 0, 1), single ([x, NaN]), 1e-4)
655s ***** assert (evinv ([p, NaN], single (0), 1), single ([x, NaN]), 1e-4)
655s ***** assert (evinv ([p, NaN], 0, single (1)), single ([x, NaN]), 1e-4)
655s ***** error<evinv: invalid number of input arguments.> evinv ()
655s ***** error evinv (1,2,3,4,5,6)
655s ***** error<evinv: P, MU, and SIGMA must be of common size or scalars.> ...
655s  evinv (ones (3), ones (2), ones (2))
655s ***** error<evinv: invalid size of covariance matrix.> ...
655s  [p, plo, pup] = evinv (2, 3, 4, [1, 2])
655s ***** error<evinv: covariance matrix is required for confidence bounds.> ...
655s  [p, plo, pup] = evinv (1, 2, 3)
655s ***** error<evinv: invalid value for alpha.> [p, plo, pup] = ...
655s  evinv (1, 2, 3, [1, 0; 0, 1], 0)
655s ***** error<evinv: invalid value for alpha.> [p, plo, pup] = ...
655s  evinv (1, 2, 3, [1, 0; 0, 1], 1.22)
655s ***** error<evinv: P, MU, and SIGMA must not be complex.> evinv (i, 2, 2)
655s ***** error<evinv: P, MU, and SIGMA must not be complex.> evinv (2, i, 2)
655s ***** error<evinv: P, MU, and SIGMA must not be complex.> evinv (2, 2, i)
655s ***** error<evinv: bad covariance matrix.> ...
655s  [p, plo, pup] = evinv (1, 2, 3, [-1, -10; -Inf, -Inf], 0.04)
655s 22 tests, 22 passed, 0 known failure, 0 skipped
655s [inst/dist_fun/iwishrnd.m]
655s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/iwishrnd.m
655s ***** assert(size (iwishrnd (1,2,1)), [1, 1]);
655s ***** assert(size (iwishrnd ([],2,1)), [1, 1]);
655s ***** assert(size (iwishrnd ([3 1; 1 3], 2.00001, [], 1)), [2, 2]);
655s ***** assert(size (iwishrnd (eye(2), 2, [], 3)), [2, 2, 3]);
655s ***** error iwishrnd ()
655s ***** error iwishrnd (1)
655s ***** error iwishrnd ([-3 1; 1 3],1)
655s ***** error iwishrnd ([1; 1],1)
655s 8 tests, 8 passed, 0 known failure, 0 skipped
655s [inst/dist_fun/geopdf.m]
655s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/geopdf.m
655s ***** demo
655s  ## Plot various PDFs from the geometric distribution
655s  x = 0:10;
655s  y1 = geopdf (x, 0.2);
655s  y2 = geopdf (x, 0.5);
655s  y3 = geopdf (x, 0.7);
655s  plot (x, y1, "*b", x, y2, "*g", x, y3, "*r")
655s  grid on
655s  ylim ([0, 0.8])
655s  legend ({"ps = 0.2", "ps = 0.5", "ps = 0.7"}, "location", "northeast")
655s  title ("Geometric PDF")
655s  xlabel ("values in x (number of failures)")
655s  ylabel ("density")
655s ***** shared x, y
655s  x = [-1 0 1 Inf];
655s  y = [0, 1/2, 1/4, NaN];
655s ***** assert (geopdf (x, 0.5*ones (1,4)), y)
655s ***** assert (geopdf (x, 0.5), y)
655s ***** assert (geopdf (x, 0.5*[-1 NaN 4 1]), [NaN NaN NaN y(4)])
655s ***** assert (geopdf ([x, NaN], 0.5), [y, NaN])
655s ***** assert (geopdf (single ([x, NaN]), 0.5), single ([y, NaN]), 5*eps ("single"))
655s ***** assert (geopdf ([x, NaN], single (0.5)), single ([y, NaN]), 5*eps ("single"))
655s ***** error geopdf ()
655s ***** error geopdf (1)
655s ***** error geopdf (1,2,3)
655s ***** error geopdf (ones (3), ones (2))
655s ***** error geopdf (ones (2), ones (3))
655s ***** error geopdf (i, 2)
655s ***** error geopdf (2, i)
655s 13 tests, 13 passed, 0 known failure, 0 skipped
655s [inst/dist_fun/tlsrnd.m]
655s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/tlsrnd.m
655s ***** assert (size (tlsrnd (1, 2, 3)), [1, 1])
655s ***** assert (size (tlsrnd (ones (2,1), 2, 3)), [2, 1])
655s ***** assert (size (tlsrnd (ones (2,2), 2, 3)), [2, 2])
655s ***** assert (size (tlsrnd (1, 2, 3, 3)), [3, 3])
655s ***** assert (size (tlsrnd (1, 2, 3, [4 1])), [4, 1])
655s ***** assert (size (tlsrnd (1, 2, 3, 4, 1)), [4, 1])
655s ***** assert (size (tlsrnd (1, 2, 3, 4, 1)), [4, 1])
655s ***** assert (size (tlsrnd (1, 2, 3, 4, 1, 5)), [4, 1, 5])
655s ***** assert (size (tlsrnd (1, 2, 3, 0, 1)), [0, 1])
655s ***** assert (size (tlsrnd (1, 2, 3, 1, 0)), [1, 0])
655s ***** assert (size (tlsrnd (1, 2, 3, 1, 2, 0, 5)), [1, 2, 0, 5])
655s ***** assert (tlsrnd (1, 2, 0, 1, 1), NaN)
655s ***** assert (tlsrnd (1, 2, [0, 0, 0], [1, 3]), [NaN, NaN, NaN])
655s ***** assert (class (tlsrnd (1, 2, 3)), "double")
655s ***** assert (class (tlsrnd (single (1), 2, 3)), "single")
655s ***** assert (class (tlsrnd (single ([1, 1]), 2, 3)), "single")
655s ***** assert (class (tlsrnd (1, single (2), 3)), "single")
655s ***** assert (class (tlsrnd (1, single ([2, 2]), 3)), "single")
655s ***** assert (class (tlsrnd (1, 2, single (3))), "single")
655s ***** assert (class (tlsrnd (1, 2, single ([3, 3]))), "single")
655s ***** error<tlsrnd: function called with too few input arguments.> tlsrnd ()
655s ***** error<tlsrnd: function called with too few input arguments.> tlsrnd (1)
655s ***** error<tlsrnd: function called with too few input arguments.> tlsrnd (1, 2)
655s ***** error<tlsrnd: MU, SIGMA, and NU must be of common size or scalars.> ...
655s  tlsrnd (ones (3), ones (2), 1)
655s ***** error<tlsrnd: MU, SIGMA, and NU must be of common size or scalars.> ...
655s  tlsrnd (ones (2), 1, ones (3))
655s ***** error<tlsrnd: MU, SIGMA, and NU must be of common size or scalars.> ...
655s  tlsrnd (1, ones (2), ones (3))
655s ***** error<tlsrnd: MU, SIGMA, and NU must not be complex.> tlsrnd (i, 2, 3)
655s ***** error<tlsrnd: MU, SIGMA, and NU must not be complex.> tlsrnd (1, i, 3)
655s ***** error<tlsrnd: MU, SIGMA, and NU must not be complex.> tlsrnd (1, 2, i)
655s ***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
655s  tlsrnd (1, 2, 3, -1)
655s ***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
655s  tlsrnd (1, 2, 3, 1.2)
655s ***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
655s  tlsrnd (1, 2, 3, ones (2))
655s ***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
655s  tlsrnd (1, 2, 3, [2 -1 2])
655s ***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
655s  tlsrnd (1, 2, 3, [2 0 2.5])
655s ***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
655s  tlsrnd (ones (2), 2, 3, ones (2))
655s ***** error<tlsrnd: dimensions must be non-negative integers.> ...
655s  tlsrnd (1, 2, 3, 2, -1, 5)
655s ***** error<tlsrnd: dimensions must be non-negative integers.> ...
655s  tlsrnd (1, 2, 3, 2, 1.5, 5)
655s ***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
655s  tlsrnd (ones (2,2), 2, 3, 3)
655s ***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
655s  tlsrnd (1, ones (2,2), 3, 3)
655s ***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
655s  tlsrnd (1, 2, ones (2,2), 3)
655s ***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
655s  tlsrnd (1, 2, ones (2,2), [3, 3])
655s ***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
655s  tlsrnd (1, 2, ones (2,2), 2, 3)
655s 42 tests, 42 passed, 0 known failure, 0 skipped
655s [inst/dist_fun/invginv.m]
655s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/invginv.m
655s ***** demo
655s  ## Plot various iCDFs from the inverse Gaussian distribution
655s  p = 0.001:0.001:0.999;
655s  x1 = invginv (p, 1, 0.2);
655s  x2 = invginv (p, 1, 1);
655s  x3 = invginv (p, 1, 3);
655s  x4 = invginv (p, 3, 0.2);
655s  x5 = invginv (p, 3, 1);
655s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-y")
655s  grid on
655s  ylim ([0, 3])
655s  legend ({"μ = 1, σ = 0.2", "μ = 1, σ = 1", "μ = 1, σ = 3", ...
655s           "μ = 3, σ = 0.2", "μ = 3, σ = 1"}, "location", "northwest")
655s  title ("Inverse Gaussian iCDF")
655s  xlabel ("probability")
655s  ylabel ("x")
655s ***** shared p, x
655s  p = [0, 0.3829, 0.6827, 1];
655s  x = [0, 0.5207, 1.0376, Inf];
655s ***** assert (invginv (p, 1, 1), x, 1e-4);
655s ***** assert (invginv (p, 1, ones (1,4)), x, 1e-4);
655s ***** assert (invginv (p, 1, [-1, 0, 1, 1]), [NaN, NaN, x(3:4)], 1e-4)
655s ***** assert (invginv (p, [-1, 0, 1, 1], 1), [NaN, NaN, x(3:4)], 1e-4)
655s ***** assert (class (invginv (single ([p, NaN]), 0, 1)), "single")
655s ***** assert (class (invginv ([p, NaN], single (0), 1)), "single")
655s ***** assert (class (invginv ([p, NaN], 0, single (1))), "single")
655s ***** error<invginv: function called with too few input arguments.> invginv (1)
655s ***** error<invginv: function called with too few input arguments.> invginv (1, 2)
655s ***** error<invginv: P, MU, and LAMBDA must be of common size or scalars.> ...
655s  invginv (1, ones (2), ones (3))
655s ***** error<invginv: P, MU, and LAMBDA must be of common size or scalars.> ...
655s  invginv (ones (2), 1, ones (3))
655s ***** error<invginv: P, MU, and LAMBDA must be of common size or scalars.> ...
655s  invginv (ones (2), ones (3), 1)
655s ***** error<invginv: P, MU, and LAMBDA must not be complex.> invginv (i, 2, 3)
655s ***** error<invginv: P, MU, and LAMBDA must not be complex.> invginv (1, i, 3)
655s ***** error<invginv: P, MU, and LAMBDA must not be complex.> invginv (1, 2, i)
655s 15 tests, 15 passed, 0 known failure, 0 skipped
655s [inst/dist_fun/loglcdf.m]
655s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/loglcdf.m
655s ***** demo
655s  ## Plot various CDFs from the log-logistic distribution
655s  x = 0:0.001:2;
655s  p1 = loglcdf (x, log (1), 1/0.5);
655s  p2 = loglcdf (x, log (1), 1);
655s  p3 = loglcdf (x, log (1), 1/2);
655s  p4 = loglcdf (x, log (1), 1/4);
655s  p5 = loglcdf (x, log (1), 1/8);
655s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
655s  legend ({"σ = 2 (β = 0.5)", "σ = 1 (β = 1)", "σ = 0.5 (β = 2)", ...
655s           "σ = 0.25 (β = 4)", "σ = 0.125 (β = 8)"}, "location", "northwest")
655s  grid on
655s  title ("Log-logistic CDF")
655s  xlabel ("values in x")
655s  ylabel ("probability")
655s  text (0.05, 0.64, "μ = 0 (α = 1), values of σ (β) as shown in legend")
655s ***** shared out1, out2
655s  out1 = [0, 0.5, 0.66666667, 0.75, 0.8, 0.83333333];
655s  out2 = [0, 0.4174, 0.4745, 0.5082, 0.5321, 0.5506];
655s ***** assert (loglcdf ([0:5], 0, 1), out1, 1e-8)
655s ***** assert (loglcdf ([0:5], 0, 1, "upper"), 1 - out1, 1e-8)
655s ***** assert (loglcdf ([0:5], 0, 1), out1, 1e-8)
655s ***** assert (loglcdf ([0:5], 0, 1, "upper"), 1 - out1, 1e-8)
655s ***** assert (loglcdf ([0:5], 1, 3), out2, 1e-4)
655s ***** assert (loglcdf ([0:5], 1, 3, "upper"), 1 - out2, 1e-4)
655s ***** assert (class (loglcdf (single (1), 2, 3)), "single")
655s ***** assert (class (loglcdf (1, single (2), 3)), "single")
655s ***** assert (class (loglcdf (1, 2, single (3))), "single")
655s ***** error<loglcdf: function called with too few input arguments.> loglcdf (1)
655s ***** error<loglcdf: function called with too few input arguments.> loglcdf (1, 2)
655s ***** error<loglcdf: invalid argument for upper tail.> ...
655s  loglcdf (1, 2, 3, 4)
655s ***** error<loglcdf: invalid argument for upper tail.> ...
655s  loglcdf (1, 2, 3, "uper")
655s ***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
655s  loglcdf (1, ones (2), ones (3))
655s ***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
655s  loglcdf (1, ones (2), ones (3), "upper")
655s ***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
655s  loglcdf (ones (2), 1, ones (3))
655s ***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
655s  loglcdf (ones (2), 1, ones (3), "upper")
655s ***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
655s  loglcdf (ones (2), ones (3), 1)
655s ***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
655s  loglcdf (ones (2), ones (3), 1, "upper")
655s ***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (i, 2, 3)
655s ***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (i, 2, 3, "upper")
655s ***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (1, i, 3)
655s ***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (1, i, 3, "upper")
655s ***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (1, 2, i)
655s ***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (1, 2, i, "upper")
655s 25 tests, 25 passed, 0 known failure, 0 skipped
655s [inst/dist_fun/cauchycdf.m]
655s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/cauchycdf.m
655s ***** demo
655s  ## Plot various CDFs from the Cauchy distribution
655s  x = -5:0.01:5;
655s  p1 = cauchycdf (x, 0, 0.5);
655s  p2 = cauchycdf (x, 0, 1);
655s  p3 = cauchycdf (x, 0, 2);
655s  p4 = cauchycdf (x, -2, 1);
655s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
655s  grid on
655s  xlim ([-5, 5])
655s  legend ({"x0 = 0, γ = 0.5", "x0 = 0, γ = 1", ...
655s           "x0 = 0, γ = 2", "x0 = -2, γ = 1"}, "location", "southeast")
655s  title ("Cauchy CDF")
655s  xlabel ("values in x")
655s  ylabel ("probability")
655s ***** shared x, y
655s  x = [-1 0 0.5 1 2];
655s  y = 1/pi * atan ((x-1) / 2) + 1/2;
655s ***** assert (cauchycdf (x, ones (1,5), 2*ones (1,5)), y)
655s ***** assert (cauchycdf (x, 1, 2*ones (1,5)), y)
655s ***** assert (cauchycdf (x, ones (1,5), 2), y)
655s ***** assert (cauchycdf (x, [-Inf 1 NaN 1 Inf], 2), [NaN y(2) NaN y(4) NaN])
655s ***** assert (cauchycdf (x, 1, 2*[0 1 NaN 1 Inf]), [NaN y(2) NaN y(4) NaN])
655s ***** assert (cauchycdf ([x(1:2) NaN x(4:5)], 1, 2), [y(1:2) NaN y(4:5)])
655s ***** assert (cauchycdf ([x, NaN], 1, 2), [y, NaN])
655s ***** assert (cauchycdf (single ([x, NaN]), 1, 2), single ([y, NaN]), eps ("single"))
655s ***** assert (cauchycdf ([x, NaN], single (1), 2), single ([y, NaN]), eps ("single"))
655s ***** assert (cauchycdf ([x, NaN], 1, single (2)), single ([y, NaN]), eps ("single"))
655s ***** error<cauchycdf: function called with too few input arguments.> cauchycdf ()
655s ***** error<cauchycdf: function called with too few input arguments.> cauchycdf (1)
655s ***** error<cauchycdf: function called with too few input arguments.> ...
655s  cauchycdf (1, 2)
655s ***** error<cauchycdf: function called with too many inputs> ...
655s  cauchycdf (1, 2, 3, 4, 5)
655s ***** error<cauchycdf: invalid argument for upper tail.> cauchycdf (1, 2, 3, "tail")
655s ***** error<cauchycdf: invalid argument for upper tail.> cauchycdf (1, 2, 3, 4)
655s ***** error<cauchycdf: X, X0, and GAMMA must be of common size or scalars.> ...
655s  cauchycdf (ones (3), ones (2), ones (2))
655s ***** error<cauchycdf: X, X0, and GAMMA must be of common size or scalars.> ...
655s  cauchycdf (ones (2), ones (3), ones (2))
655s ***** error<cauchycdf: X, X0, and GAMMA must be of common size or scalars.> ...
655s  cauchycdf (ones (2), ones (2), ones (3))
655s ***** error<cauchycdf: X, X0, and GAMMA must not be complex.> cauchycdf (i, 2, 2)
655s ***** error<cauchycdf: X, X0, and GAMMA must not be complex.> cauchycdf (2, i, 2)
655s ***** error<cauchycdf: X, X0, and GAMMA must not be complex.> cauchycdf (2, 2, i)
655s 22 tests, 22 passed, 0 known failure, 0 skipped
655s [inst/dist_fun/mvtrnd.m]
655s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/mvtrnd.m
655s ***** test
655s  rho = [1, 0.5; 0.5, 1];
655s  df = 3;
655s  n = 10;
655s  r = mvtrnd (rho, df, n);
655s  assert (size (r), [10, 2]);
655s ***** test
655s  rho = [1, 0.5; 0.5, 1];
655s  df = [2; 3];
655s  n = 2;
655s  r = mvtrnd (rho, df, 2);
655s  assert (size (r), [2, 2]);
655s 2 tests, 2 passed, 0 known failure, 0 skipped
655s [inst/dist_fun/logiinv.m]
655s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/logiinv.m
655s ***** demo
655s  ## Plot various iCDFs from the logistic distribution
655s  p = 0.001:0.001:0.999;
655s  x1 = logiinv (p, 5, 2);
655s  x2 = logiinv (p, 9, 3);
655s  x3 = logiinv (p, 9, 4);
655s  x4 = logiinv (p, 6, 2);
655s  x5 = logiinv (p, 2, 1);
655s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
655s  grid on
655s  legend ({"μ = 5, σ = 2", "μ = 9, σ = 3", "μ = 9, σ = 4", ...
655s           "μ = 6, σ = 2", "μ = 2, σ = 1"}, "location", "southeast")
655s  title ("Logistic iCDF")
655s  xlabel ("probability")
655s  ylabel ("x")
655s ***** test
655s  p = [0.01:0.01:0.99];
655s  assert (logiinv (p, 0, 1), log (p ./ (1-p)), 25*eps);
655s ***** shared p
655s  p = [-1 0 0.5 1 2];
655s ***** assert (logiinv (p, 0, 1), [NaN -Inf 0 Inf NaN])
655s ***** assert (logiinv (p, 0, [-1, 0, 1, 2, 3]), [NaN NaN 0 Inf NaN])
655s ***** assert (logiinv ([p, NaN], 0, 1), [NaN -Inf 0 Inf NaN NaN])
655s ***** assert (logiinv (single ([p, NaN]), 0, 1), single ([NaN -Inf 0 Inf NaN NaN]))
655s ***** assert (logiinv ([p, NaN], single (0), 1), single ([NaN -Inf 0 Inf NaN NaN]))
655s ***** assert (logiinv ([p, NaN], 0, single (1)), single ([NaN -Inf 0 Inf NaN NaN]))
655s ***** error<logiinv: function called with too few input arguments.> logiinv ()
655s ***** error<logiinv: function called with too few input arguments.> logiinv (1)
655s ***** error<logiinv: function called with too few input arguments.> ...
655s  logiinv (1, 2)
655s ***** error<logiinv: P, MU, and SIGMA must be of common size or scalars.> ...
655s  logiinv (1, ones (2), ones (3))
655s ***** error<logiinv: P, MU, and SIGMA must be of common size or scalars.> ...
655s  logiinv (ones (2), 1, ones (3))
655s ***** error<logiinv: P, MU, and SIGMA must be of common size or scalars.> ...
655s  logiinv (ones (2), ones (3), 1)
655s ***** error<logiinv: P, MU, and SIGMA must not be complex.> logiinv (i, 2, 3)
655s ***** error<logiinv: P, MU, and SIGMA must not be complex.> logiinv (1, i, 3)
655s ***** error<logiinv: P, MU, and SIGMA must not be complex.> logiinv (1, 2, i)
655s 16 tests, 16 passed, 0 known failure, 0 skipped
655s [inst/dist_fun/bisacdf.m]
655s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/bisacdf.m
655s ***** demo
655s  ## Plot various CDFs from the Birnbaum-Saunders distribution
655s  x = 0.01:0.01:4;
655s  p1 = bisacdf (x, 1, 0.5);
655s  p2 = bisacdf (x, 1, 1);
655s  p3 = bisacdf (x, 1, 2);
655s  p4 = bisacdf (x, 1, 5);
655s  p5 = bisacdf (x, 1, 10);
655s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
655s  grid on
655s  legend ({"β = 1, γ = 0.5", "β = 1, γ = 1", "β = 1, γ = 2", ...
655s           "β = 1, γ = 5", "β = 1, γ = 10"}, "location", "southeast")
655s  title ("Birnbaum-Saunders CDF")
655s  xlabel ("values in x")
655s  ylabel ("probability")
655s ***** demo
655s  ## Plot various CDFs from the Birnbaum-Saunders distribution
655s  x = 0.01:0.01:6;
655s  p1 = bisacdf (x, 1, 0.3);
655s  p2 = bisacdf (x, 2, 0.3);
655s  p3 = bisacdf (x, 1, 0.5);
655s  p4 = bisacdf (x, 3, 0.5);
655s  p5 = bisacdf (x, 5, 0.5);
655s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
655s  grid on
655s  legend ({"β = 1, γ = 0.3", "β = 2, γ = 0.3", "β = 1, γ = 0.5", ...
655s           "β = 3, γ = 0.5", "β = 5, γ = 0.5"}, "location", "southeast")
655s  title ("Birnbaum-Saunders CDF")
655s  xlabel ("values in x")
655s  ylabel ("probability")
655s ***** shared x, y
655s  x = [-1, 0, 1, 2, Inf];
655s  y = [0, 0, 1/2, 0.76024993890652337, 1];
655s ***** assert (bisacdf (x, ones (1,5), ones (1,5)), y, eps)
655s ***** assert (bisacdf (x, 1, 1), y, eps)
655s ***** assert (bisacdf (x, 1, ones (1,5)), y, eps)
655s ***** assert (bisacdf (x, ones (1,5), 1), y, eps)
655s ***** assert (bisacdf (x, 1, 1), y, eps)
655s ***** assert (bisacdf (x, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)], eps)
655s ***** assert (bisacdf (x, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)], eps)
655s ***** assert (bisacdf ([x, NaN], 1, 1), [y, NaN], eps)
655s ***** assert (bisacdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
655s ***** assert (bisacdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
655s ***** assert (bisacdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
655s ***** error<bisacdf: function called with too few input arguments.> bisacdf ()
655s ***** error<bisacdf: function called with too few input arguments.> bisacdf (1)
655s ***** error<bisacdf: function called with too few input arguments.> bisacdf (1, 2)
655s ***** error<bisacdf: function called with too many inputs> ...
655s  bisacdf (1, 2, 3, 4, 5)
655s ***** error<bisacdf: invalid argument for upper tail.> bisacdf (1, 2, 3, "tail")
655s ***** error<bisacdf: invalid argument for upper tail.> bisacdf (1, 2, 3, 4)
655s ***** error<bisacdf: X, BETA, and GAMMA must be of common size or scalars.> ...
655s  bisacdf (ones (3), ones (2), ones(2))
655s ***** error<bisacdf: X, BETA, and GAMMA must be of common size or scalars.> ...
655s  bisacdf (ones (2), ones (3), ones(2))
655s ***** error<bisacdf: X, BETA, and GAMMA must be of common size or scalars.> ...
655s  bisacdf (ones (2), ones (2), ones(3))
655s ***** error<bisacdf: X, BETA, and GAMMA must not be complex.> bisacdf (i, 4, 3)
655s ***** error<bisacdf: X, BETA, and GAMMA must not be complex.> bisacdf (1, i, 3)
655s ***** error<bisacdf: X, BETA, and GAMMA must not be complex.> bisacdf (1, 4, i)
655s 23 tests, 23 passed, 0 known failure, 0 skipped
655s [inst/dist_fun/triinv.m]
655s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/triinv.m
655s ***** demo
655s  ## Plot various iCDFs from the triangular distribution
655s  p = 0.001:0.001:0.999;
655s  x1 = triinv (p, 3, 6, 4);
655s  x2 = triinv (p, 1, 5, 2);
655s  x3 = triinv (p, 2, 9, 3);
655s  x4 = triinv (p, 2, 9, 5);
655s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
655s  grid on
655s  ylim ([0, 10])
655s  legend ({"a = 3, b = 6, c = 4", "a = 1, b = 5, c = 2", ...
655s           "a = 2, b = 9, c = 3", "a = 2, b = 9, c = 5"}, ...
655s          "location", "northwest")
655s  title ("Triangular CDF")
655s  xlabel ("probability")
655s  ylabel ("values in x")
655s ***** shared p, y
655s  p = [-1, 0, 0.02, 0.5, 0.98, 1, 2];
655s  y = [NaN, 0, 0.1, 0.5, 0.9, 1, NaN] + 1;
655s ***** assert (triinv (p, ones (1, 7), 1.5 * ones (1, 7), 2 * ones (1, 7)), y, eps)
655s ***** assert (triinv (p, 1 * ones (1, 7), 1.5, 2), y, eps)
655s ***** assert (triinv (p, 1, 1.5, 2 * ones (1, 7)), y, eps)
655s ***** assert (triinv (p, 1, 1.5*ones (1,7), 2), y, eps)
655s ***** assert (triinv (p, 1, 1.5, 2), y, eps)
655s ***** assert (triinv (p, [1, 1, NaN, 1, 1, 1, 1], 1.5, 2), [y(1:2), NaN, y(4:7)], eps)
655s ***** assert (triinv (p, 1, 1.5 * [1, 1, NaN, 1, 1, 1, 1], 2), [y(1:2), NaN, y(4:7)], eps)
655s ***** assert (triinv (p, 1, 1.5, 2 * [1, 1, NaN, 1, 1, 1, 1]), [y(1:2), NaN, y(4:7)], eps)
655s ***** assert (triinv ([p, NaN], 1, 1.5, 2), [y, NaN], eps)
655s ***** assert (triinv (single ([p, NaN]), 1, 1.5, 2), single ([y, NaN]), eps('single'))
655s ***** assert (triinv ([p, NaN], single (1), 1.5, 2), single ([y, NaN]), eps('single'))
655s ***** assert (triinv ([p, NaN], 1, single (1.5), 2), single ([y, NaN]), eps('single'))
655s ***** assert (triinv ([p, NaN], 1, 1.5, single (2)), single ([y, NaN]), eps('single'))
655s ***** error<triinv: function called with too few input arguments.> triinv ()
655s ***** error<triinv: function called with too few input arguments.> triinv (1)
655s ***** error<triinv: function called with too few input arguments.> triinv (1, 2)
655s ***** error<triinv: function called with too few input arguments.> triinv (1, 2, 3)
655s ***** error<triinv: function called with too many inputs> ...
655s  triinv (1, 2, 3, 4, 5)
655s ***** error<triinv: P, A, B, and C must be of common size or scalars.> ...
655s  triinv (ones (3), ones (2), ones(2), ones(2))
655s ***** error<triinv: P, A, B, and C must be of common size or scalars.> ...
655s  triinv (ones (2), ones (3), ones(2), ones(2))
655s ***** error<triinv: P, A, B, and C must be of common size or scalars.> ...
655s  triinv (ones (2), ones (2), ones(3), ones(2))
655s ***** error<triinv: P, A, B, and C must be of common size or scalars.> ...
655s  triinv (ones (2), ones (2), ones(2), ones(3))
655s ***** error<triinv: P, A, B, and C must not be complex.> triinv (i, 2, 3, 4)
655s ***** error<triinv: P, A, B, and C must not be complex.> triinv (1, i, 3, 4)
655s ***** error<triinv: P, A, B, and C must not be complex.> triinv (1, 2, i, 4)
655s ***** error<triinv: P, A, B, and C must not be complex.> triinv (1, 2, 3, i)
655s 26 tests, 26 passed, 0 known failure, 0 skipped
655s [inst/dist_fun/betapdf.m]
656s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/betapdf.m
656s ***** demo
656s  ## Plot various PDFs from the Beta distribution
656s  x = 0.001:0.001:0.999;
656s  y1 = betapdf (x, 0.5, 0.5);
656s  y2 = betapdf (x, 5, 1);
656s  y3 = betapdf (x, 1, 3);
656s  y4 = betapdf (x, 2, 2);
656s  y5 = betapdf (x, 2, 5);
656s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
656s  grid on
656s  ylim ([0, 2.5])
656s  legend ({"α = β = 0.5", "α = 5, β = 1", "α = 1, β = 3", ...
656s           "α = 2, β = 2", "α = 2, β = 5"}, "location", "north")
656s  title ("Beta PDF")
656s  xlabel ("values in x")
656s  ylabel ("density")
656s ***** shared x, y
656s  x = [-1 0 0.5 1 2];
656s  y = [0 2 1 0 0];
656s ***** assert (betapdf (x, ones (1, 5), 2 * ones (1, 5)), y)
656s ***** assert (betapdf (x, 1, 2 * ones (1, 5)), y)
656s ***** assert (betapdf (x, ones (1, 5), 2), y)
656s ***** assert (betapdf (x, [0 NaN 1 1 1], 2), [NaN NaN y(3:5)])
656s ***** assert (betapdf (x, 1, 2 * [0 NaN 1 1 1]), [NaN NaN y(3:5)])
656s ***** assert (betapdf ([x, NaN], 1, 2), [y, NaN])
656s ***** assert (betapdf (single ([x, NaN]), 1, 2), single ([y, NaN]))
656s ***** assert (betapdf ([x, NaN], single (1), 2), single ([y, NaN]))
656s ***** assert (betapdf ([x, NaN], 1, single (2)), single ([y, NaN]))
656s ***** test
656s  x = rand (10,1);
656s  y = 1 ./ (pi * sqrt (x .* (1 - x)));
656s  assert (betapdf (x, 1/2, 1/2), y, 1e-12);
656s ***** assert (betapdf (0.5, 1000, 1000), 35.678, 1e-3)
656s ***** error<betapdf: function called with too few input arguments.> betapdf ()
656s ***** error<betapdf: function called with too few input arguments.> betapdf (1)
656s ***** error<betapdf: function called with too few input arguments.> betapdf (1,2)
656s ***** error<betapdf: function called with too many inputs> betapdf (1,2,3,4)
656s ***** error<betapdf: X, A, and B must be of common size or scalars.> ...
656s  betapdf (ones (3), ones (2), ones (2))
656s ***** error<betapdf: X, A, and B must be of common size or scalars.> ...
656s  betapdf (ones (2), ones (3), ones (2))
656s ***** error<betapdf: X, A, and B must be of common size or scalars.> ...
656s  betapdf (ones (2), ones (2), ones (3))
656s ***** error<betapdf: X, A, and B must not be complex.> betapdf (i, 2, 2)
656s ***** error<betapdf: X, A, and B must not be complex.> betapdf (2, i, 2)
656s ***** error<betapdf: X, A, and B must not be complex.> betapdf (2, 2, i)
656s 21 tests, 21 passed, 0 known failure, 0 skipped
656s [inst/dist_fun/wienrnd.m]
656s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/wienrnd.m
656s ***** error wienrnd (0)
656s ***** error wienrnd (1, 3, -50)
656s ***** error wienrnd (5, 0)
656s ***** error wienrnd (0.4, 3, 5)
656s ***** error wienrnd ([1 4], 3, 5)
656s 5 tests, 5 passed, 0 known failure, 0 skipped
656s [inst/dist_fun/expcdf.m]
656s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/expcdf.m
656s ***** demo
656s  ## Plot various CDFs from the exponential distribution
656s  x = 0:0.01:5;
656s  p1 = expcdf (x, 2/3);
656s  p2 = expcdf (x, 1.0);
656s  p3 = expcdf (x, 2.0);
656s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r")
656s  grid on
656s  legend ({"μ = 2/3", "μ = 1", "μ = 2"}, "location", "southeast")
656s  title ("Exponential CDF")
656s  xlabel ("values in x")
656s  ylabel ("probability")
656s ***** shared x, p
656s  x = [-1 0 0.5 1 Inf];
656s  p = [0, 1 - exp(-x(2:end)/2)];
656s ***** assert (expcdf (x, 2 * ones (1, 5)), p, 1e-16)
656s ***** assert (expcdf (x, 2), p, 1e-16)
656s ***** assert (expcdf (x, 2 * [1, 0, NaN, 1, 1]), [0, NaN, NaN, p(4:5)], 1e-16)
656s ***** assert (expcdf ([x, NaN], 2), [p, NaN], 1e-16)
656s ***** assert (expcdf (single ([x, NaN]), 2), single ([p, NaN]))
656s ***** assert (expcdf ([x, NaN], single (2)), single ([p, NaN]))
656s ***** test
656s  [p, plo, pup] = expcdf (1, 2, 3);
656s  assert (p, 0.39346934028737, 1e-14);
656s  assert (plo, 0.08751307220484, 1e-14);
656s  assert (pup, 0.93476821257933, 1e-14);
656s ***** test
656s  [p, plo, pup] = expcdf (1, 2, 2, 0.1);
656s  assert (p, 0.39346934028737, 1e-14);
656s  assert (plo, 0.14466318041675, 1e-14);
656s  assert (pup, 0.79808291849140, 1e-14);
656s ***** test
656s  [p, plo, pup] = expcdf (1, 2, 2, 0.1, "upper");
656s  assert (p, 0.60653065971263, 1e-14);
656s  assert (plo, 0.20191708150860, 1e-14);
656s  assert (pup, 0.85533681958325, 1e-14);
656s ***** error<expcdf: invalid number of input arguments.> expcdf ()
656s ***** error<expcdf: invalid number of input arguments.> expcdf (1, 2 ,3 ,4 ,5, 6)
656s ***** error<expcdf: invalid argument for upper tail.> expcdf (1, 2, 3, 4, "uper")
656s ***** error<expcdf: X and MU must be of common size or scalars.> ...
656s  expcdf (ones (3), ones (2))
656s ***** error<expcdf: invalid size of variance, PCOV must be a scalar.> ...
656s  expcdf (2, 3, [1, 2])
656s ***** error<expcdf: variance, PCOV, is required for confidence bounds.> ...
656s  [p, plo, pup] = expcdf (1, 2)
656s ***** error<expcdf: invalid value for alpha.> [p, plo, pup] = ...
656s  expcdf (1, 2, 3, 0)
656s ***** error<expcdf: invalid value for alpha.> [p, plo, pup] = ...
656s  expcdf (1, 2, 3, 1.22)
656s ***** error<expcdf: invalid value for alpha.> [p, plo, pup] = ...
656s  expcdf (1, 2, 3, "alpha", "upper")
656s ***** error<expcdf: X and MU must not be complex.> expcdf (i, 2)
656s ***** error<expcdf: X and MU must not be complex.> expcdf (2, i)
656s ***** error<expcdf: variance, PCOV, cannot be negative.> ...
656s  [p, plo, pup] = expcdf (1, 2, -1, 0.04)
656s 21 tests, 21 passed, 0 known failure, 0 skipped
656s [inst/dist_fun/ncfinv.m]
656s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/ncfinv.m
656s ***** demo
656s  ## Plot various iCDFs from the noncentral F distribution
656s  p = 0.001:0.001:0.999;
656s  x1 = ncfinv (p, 2, 5, 1);
656s  x2 = ncfinv (p, 2, 5, 2);
656s  x3 = ncfinv (p, 5, 10, 1);
656s  x4 = ncfinv (p, 10, 20, 10);
656s  plot (p, x1, "-r", p, x2, "-g", p, x3, "-k", p, x4, "-m")
656s  grid on
656s  ylim ([0, 5])
656s  legend ({"df1 = 2, df2 = 5, λ = 1", "df1 = 2, df2 = 5, λ = 2", ...
656s           "df1 = 5, df2 = 10, λ = 1", "df1 = 10, df2 = 20, λ = 10"}, ...
656s          "location", "northwest")
656s  title ("Noncentral F iCDF")
656s  xlabel ("probability")
656s  ylabel ("values in x")
656s ***** demo
656s  ## Compare the noncentral F iCDF with LAMBDA = 10 to the F iCDF with the
656s  ## same number of numerator and denominator degrees of freedom (5, 20)
656s 
656s  p = 0.001:0.001:0.999;
656s  x1 = ncfinv (p, 5, 20, 10);
656s  x2 = finv (p, 5, 20);
656s  plot (p, x1, "-", p, x2, "-");
656s  grid on
656s  ylim ([0, 10])
656s  legend ({"Noncentral F(5,20,10)", "F(5,20)"}, "location", "northwest")
656s  title ("Noncentral F vs F quantile functions")
656s  xlabel ("probability")
656s  ylabel ("values in x")
656s ***** test
656s  x = [0,0.1775,0.3864,0.6395,0.9564,1.3712,1.9471,2.8215,4.3679,8.1865,Inf];
656s  assert (ncfinv ([0:0.1:1], 2, 3, 1), x, 1e-4);
656s ***** test
656s  x = [0,0.7492,1.3539,2.0025,2.7658,3.7278,5.0324,6.9826,10.3955,18.7665,Inf];
656s  assert (ncfinv ([0:0.1:1], 2, 3, 5), x, 1e-4);
656s ***** test
656s  x = [0,0.2890,0.8632,1.5653,2.4088,3.4594,4.8442,6.8286,10.0983,17.3736,Inf];
656s  assert (ncfinv ([0:0.1:1], 1, 4, 3), x, 1e-4);
656s ***** test
656s  x = [0.078410, 0.212716, 0.288618, 0.335752, 0.367963, 0.391460];
656s  assert (ncfinv (0.05, [1, 2, 3, 4, 5, 6], 10, 3), x, 1e-6);
656s ***** test
656s  x = [0.2574, 0.2966, 0.3188, 0.3331, 0.3432, 0.3507];
656s  assert (ncfinv (0.05, 5, [1, 2, 3, 4, 5, 6], 3), x, 1e-4);
656s ***** test
656s  x = [1.6090, 1.8113, 1.9215, 1.9911, NaN, 2.0742];
656s  assert (ncfinv (0.05, 1, [1, 2, 3, 4, -1, 6], 10), x, 1e-4);
656s ***** test
656s  assert (ncfinv (0.996, 3, 5, 8), 58.0912074080671, 4e-12);
656s ***** error<ncfinv: function called with too few input arguments.> ncfinv ()
656s ***** error<ncfinv: function called with too few input arguments.> ncfinv (1)
656s ***** error<ncfinv: function called with too few input arguments.> ncfinv (1, 2)
656s ***** error<ncfinv: function called with too few input arguments.> ncfinv (1, 2, 3)
656s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
656s  ncfinv (ones (3), ones (2), ones (2), ones (2))
656s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
656s  ncfinv (ones (2), ones (3), ones (2), ones (2))
656s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
656s  ncfinv (ones (2), ones (2), ones (3), ones (2))
656s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
656s  ncfinv (ones (2), ones (2), ones (2), ones (3))
656s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must not be complex.> ncfinv (i, 2, 2, 2)
656s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must not be complex.> ncfinv (2, i, 2, 2)
656s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must not be complex.> ncfinv (2, 2, i, 2)
656s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must not be complex.> ncfinv (2, 2, 2, i)
656s 19 tests, 19 passed, 0 known failure, 0 skipped
656s [inst/dist_fun/unidrnd.m]
656s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/unidrnd.m
656s ***** assert (size (unidrnd (2)), [1, 1])
656s ***** assert (size (unidrnd (ones (2,1))), [2, 1])
656s ***** assert (size (unidrnd (ones (2,2))), [2, 2])
656s ***** assert (size (unidrnd (1, 3)), [3, 3])
656s ***** assert (size (unidrnd (1, [4 1])), [4, 1])
656s ***** assert (size (unidrnd (1, 4, 1)), [4, 1])
656s ***** assert (size (unidrnd (1, 4, 1)), [4, 1])
656s ***** assert (size (unidrnd (1, 4, 1, 5)), [4, 1, 5])
656s ***** assert (size (unidrnd (1, 0, 1)), [0, 1])
656s ***** assert (size (unidrnd (1, 1, 0)), [1, 0])
656s ***** assert (size (unidrnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
656s ***** assert (unidrnd (0, 1, 1), NaN)
656s ***** assert (unidrnd ([0, 0, 0], [1, 3]), [NaN, NaN, NaN])
656s ***** assert (class (unidrnd (2)), "double")
656s ***** assert (class (unidrnd (single (2))), "single")
656s ***** assert (class (unidrnd (single ([2 2]))), "single")
656s ***** error<unidrnd: function called with too few input arguments.> unidrnd ()
656s ***** error<unidrnd: N must not be complex.> unidrnd (i)
656s ***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
656s  unidrnd (1, -1)
656s ***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
656s  unidrnd (1, 1.2)
656s ***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
656s  unidrnd (1, ones (2))
656s ***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
656s  unidrnd (1, [2 -1 2])
656s ***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
656s  unidrnd (1, [2 0 2.5])
656s ***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
656s  unidrnd (ones (2), ones (2))
656s ***** error<unidrnd: dimensions must be non-negative integers.> ...
656s  unidrnd (1, 2, -1, 5)
656s ***** error<unidrnd: dimensions must be non-negative integers.> ...
656s  unidrnd (1, 2, 1.5, 5)
656s ***** error<unidrnd: N must be scalar or of size SZ.> unidrnd (ones (2,2), 3)
656s ***** error<unidrnd: N must be scalar or of size SZ.> unidrnd (ones (2,2), [3, 2])
656s ***** error<unidrnd: N must be scalar or of size SZ.> unidrnd (ones (2,2), 2, 3)
656s 29 tests, 29 passed, 0 known failure, 0 skipped
656s [inst/dist_fun/riceinv.m]
656s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/riceinv.m
656s ***** demo
656s  ## Plot various iCDFs from the Rician distribution
656s  p = 0.001:0.001:0.999;
656s  x1 = riceinv (p, 0, 1);
656s  x2 = riceinv (p, 0.5, 1);
656s  x3 = riceinv (p, 1, 1);
656s  x4 = riceinv (p, 2, 1);
656s  x5 = riceinv (p, 4, 1);
656s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-m", p, x5, "-k")
656s  grid on
656s  legend ({"s = 0, σ = 1", "s = 0.5, σ = 1", "s = 1, σ = 1", ...
656s           "s = 2, σ = 1", "s = 4, σ = 1"}, "location", "northwest")
656s  title ("Rician iCDF")
656s  xlabel ("probability")
656s  ylabel ("values in x")
656s ***** shared p
656s  p = [-1 0 0.75 1 2];
656s ***** assert (riceinv (p, ones (1,5), 2*ones (1,5)), [NaN 0 3.5354 Inf NaN], 1e-4)
656s ***** assert (riceinv (p, 1, 2*ones (1,5)), [NaN 0 3.5354 Inf NaN], 1e-4)
656s ***** assert (riceinv (p, ones (1,5), 2), [NaN 0 3.5354 Inf NaN], 1e-4)
656s ***** assert (riceinv (p, [1 0 NaN 1 1], 2), [NaN 0 NaN Inf NaN])
656s ***** assert (riceinv (p, 1, 2*[1 0 NaN 1 1]), [NaN NaN NaN Inf NaN])
656s ***** assert (riceinv ([p(1:2) NaN p(4:5)], 1, 2), [NaN 0 NaN Inf NaN])
656s ***** assert (riceinv ([p, NaN], 1, 2), [NaN 0 3.5354 Inf NaN NaN], 1e-4)
656s ***** assert (riceinv (single ([p, NaN]), 1, 2), ...
656s         single ([NaN 0 3.5354 Inf NaN NaN]), 1e-4)
656s ***** assert (riceinv ([p, NaN], single (1), 2), ...
656s         single ([NaN 0 3.5354 Inf NaN NaN]), 1e-4)
656s ***** assert (riceinv ([p, NaN], 1, single (2)), ...
656s         single ([NaN 0 3.5354 Inf NaN NaN]), 1e-4)
656s ***** error<riceinv: function called with too few input arguments.> riceinv ()
656s ***** error<riceinv: function called with too few input arguments.> riceinv (1)
656s ***** error<riceinv: function called with too few input arguments.> riceinv (1,2)
656s ***** error<riceinv: function called with too many inputs> riceinv (1,2,3,4)
656s ***** error<riceinv: P, S, and B must be of common size or scalars.> ...
656s  riceinv (ones (3), ones (2), ones (2))
656s ***** error<riceinv: P, S, and B must be of common size or scalars.> ...
656s  riceinv (ones (2), ones (3), ones (2))
656s ***** error<riceinv: P, S, and B must be of common size or scalars.> ...
656s  riceinv (ones (2), ones (2), ones (3))
656s ***** error<riceinv: P, S, and B must not be complex.> riceinv (i, 2, 2)
656s ***** error<riceinv: P, S, and B must not be complex.> riceinv (2, i, 2)
656s ***** error<riceinv: P, S, and B must not be complex.> riceinv (2, 2, i)
656s 20 tests, 20 passed, 0 known failure, 0 skipped
656s [inst/dist_fun/cauchyrnd.m]
656s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/cauchyrnd.m
656s ***** assert (size (cauchyrnd (1, 1)), [1 1])
656s ***** assert (size (cauchyrnd (1, ones (2,1))), [2, 1])
656s ***** assert (size (cauchyrnd (1, ones (2,2))), [2, 2])
656s ***** assert (size (cauchyrnd (ones (2,1), 1)), [2, 1])
656s ***** assert (size (cauchyrnd (ones (2,2), 1)), [2, 2])
656s ***** assert (size (cauchyrnd (1, 1, 3)), [3, 3])
656s ***** assert (size (cauchyrnd (1, 1, [4, 1])), [4, 1])
656s ***** assert (size (cauchyrnd (1, 1, 4, 1)), [4, 1])
656s ***** assert (size (cauchyrnd (1, 1, 4, 1, 5)), [4, 1, 5])
656s ***** assert (size (cauchyrnd (1, 1, 0, 1)), [0, 1])
656s ***** assert (size (cauchyrnd (1, 1, 1, 0)), [1, 0])
656s ***** assert (size (cauchyrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
656s ***** assert (class (cauchyrnd (1, 1)), "double")
656s ***** assert (class (cauchyrnd (1, single (1))), "single")
656s ***** assert (class (cauchyrnd (1, single ([1, 1]))), "single")
656s ***** assert (class (cauchyrnd (single (1), 1)), "single")
656s ***** assert (class (cauchyrnd (single ([1, 1]), 1)), "single")
656s ***** error<cauchyrnd: function called with too few input arguments.> cauchyrnd ()
656s ***** error<cauchyrnd: function called with too few input arguments.> cauchyrnd (1)
656s ***** error<cauchyrnd: X0 and GAMMA must be of common size or scalars.> ...
656s  cauchyrnd (ones (3), ones (2))
656s ***** error<cauchyrnd: X0 and GAMMA must be of common size or scalars.> ...
656s  cauchyrnd (ones (2), ones (3))
656s ***** error<cauchyrnd: X0 and GAMMA must not be complex.> cauchyrnd (i, 2, 3)
656s ***** error<cauchyrnd: X0 and GAMMA must not be complex.> cauchyrnd (1, i, 3)
656s ***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
656s  cauchyrnd (1, 2, -1)
656s ***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
656s  cauchyrnd (1, 2, 1.2)
656s ***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
656s  cauchyrnd (1, 2, ones (2))
656s ***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
656s  cauchyrnd (1, 2, [2 -1 2])
656s ***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
656s  cauchyrnd (1, 2, [2 0 2.5])
656s ***** error<cauchyrnd: dimensions must be non-negative integers.> ...
656s  cauchyrnd (1, 2, 2, -1, 5)
656s ***** error<cauchyrnd: dimensions must be non-negative integers.> ...
656s  cauchyrnd (1, 2, 2, 1.5, 5)
656s ***** error<cauchyrnd: X0 and GAMMA must be scalars or of size SZ.> ...
656s  cauchyrnd (2, ones (2), 3)
656s ***** error<cauchyrnd: X0 and GAMMA must be scalars or of size SZ.> ...
656s  cauchyrnd (2, ones (2), [3, 2])
656s ***** error<cauchyrnd: X0 and GAMMA must be scalars or of size SZ.> ...
656s  cauchyrnd (2, ones (2), 3, 2)
656s 33 tests, 33 passed, 0 known failure, 0 skipped
656s [inst/dist_fun/ncx2rnd.m]
656s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/ncx2rnd.m
656s ***** assert (size (ncx2rnd (1, 1)), [1 1])
656s ***** assert (size (ncx2rnd (1, ones (2,1))), [2, 1])
656s ***** assert (size (ncx2rnd (1, ones (2,2))), [2, 2])
656s ***** assert (size (ncx2rnd (ones (2,1), 1)), [2, 1])
656s ***** assert (size (ncx2rnd (ones (2,2), 1)), [2, 2])
656s ***** assert (size (ncx2rnd (1, 1, 3)), [3, 3])
656s ***** assert (size (ncx2rnd (1, 1, [4, 1])), [4, 1])
656s ***** assert (size (ncx2rnd (1, 1, 4, 1)), [4, 1])
656s ***** assert (size (ncx2rnd (1, 1, 4, 1, 5)), [4, 1, 5])
656s ***** assert (size (ncx2rnd (1, 1, 0, 1)), [0, 1])
656s ***** assert (size (ncx2rnd (1, 1, 1, 0)), [1, 0])
656s ***** assert (size (ncx2rnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
656s ***** assert (class (ncx2rnd (1, 1)), "double")
656s ***** assert (class (ncx2rnd (1, single (1))), "single")
656s ***** assert (class (ncx2rnd (1, single ([1, 1]))), "single")
656s ***** assert (class (ncx2rnd (single (1), 1)), "single")
656s ***** assert (class (ncx2rnd (single ([1, 1]), 1)), "single")
656s ***** error<ncx2rnd: function called with too few input arguments.> ncx2rnd ()
656s ***** error<ncx2rnd: function called with too few input arguments.> ncx2rnd (1)
656s ***** error<ncx2rnd: DF and LAMBDA must be of common size or scalars.> ...
656s  ncx2rnd (ones (3), ones (2))
656s ***** error<ncx2rnd: DF and LAMBDA must be of common size or scalars.> ...
656s  ncx2rnd (ones (2), ones (3))
656s ***** error<ncx2rnd: DF and LAMBDA must not be complex.> ncx2rnd (i, 2)
656s ***** error<ncx2rnd: DF and LAMBDA must not be complex.> ncx2rnd (1, i)
656s ***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
656s  ncx2rnd (1, 2, -1)
656s ***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
656s  ncx2rnd (1, 2, 1.2)
656s ***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
656s  ncx2rnd (1, 2, ones (2))
656s ***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
656s  ncx2rnd (1, 2, [2 -1 2])
656s ***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
656s  ncx2rnd (1, 2, [2 0 2.5])
656s ***** error<ncx2rnd: dimensions must be non-negative integers.> ...
656s  ncx2rnd (1, 2, 2, -1, 5)
656s ***** error<ncx2rnd: dimensions must be non-negative integers.> ...
656s  ncx2rnd (1, 2, 2, 1.5, 5)
656s ***** error<ncx2rnd: DF and LAMBDA must be scalars or of size SZ.> ...
656s  ncx2rnd (2, ones (2), 3)
656s ***** error<ncx2rnd: DF and LAMBDA must be scalars or of size SZ.> ...
656s  ncx2rnd (2, ones (2), [3, 2])
656s ***** error<ncx2rnd: DF and LAMBDA must be scalars or of size SZ.> ...
656s  ncx2rnd (2, ones (2), 3, 2)
656s 33 tests, 33 passed, 0 known failure, 0 skipped
656s [inst/dist_fun/binoinv.m]
656s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/binoinv.m
656s ***** demo
656s  ## Plot various iCDFs from the binomial distribution
656s  p = 0.001:0.001:0.999;
656s  x1 = binoinv (p, 20, 0.5);
656s  x2 = binoinv (p, 20, 0.7);
656s  x3 = binoinv (p, 40, 0.5);
656s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
656s  grid on
656s  legend ({"n = 20, ps = 0.5", "n = 20, ps = 0.7", ...
656s           "n = 40, ps = 0.5"}, "location", "southeast")
656s  title ("Binomial iCDF")
656s  xlabel ("probability")
656s  ylabel ("values in x (number of successes)")
656s ***** shared p
656s  p = [-1 0 0.5 1 2];
656s ***** assert (binoinv (p, 2*ones (1,5), 0.5*ones (1,5)), [NaN 0 1 2 NaN])
656s ***** assert (binoinv (p, 2, 0.5*ones (1,5)), [NaN 0 1 2 NaN])
656s ***** assert (binoinv (p, 2*ones (1,5), 0.5), [NaN 0 1 2 NaN])
656s ***** assert (binoinv (p, 2*[0 -1 NaN 1.1 1], 0.5), [NaN NaN NaN NaN NaN])
656s ***** assert (binoinv (p, 2, 0.5*[0 -1 NaN 3 1]), [NaN NaN NaN NaN NaN])
656s ***** assert (binoinv ([p(1:2) NaN p(4:5)], 2, 0.5), [NaN 0 NaN 2 NaN])
656s ***** assert (binoinv ([p, NaN], 2, 0.5), [NaN 0 1 2 NaN NaN])
656s ***** assert (binoinv (single ([p, NaN]), 2, 0.5), single ([NaN 0 1 2 NaN NaN]))
656s ***** assert (binoinv ([p, NaN], single (2), 0.5), single ([NaN 0 1 2 NaN NaN]))
656s ***** assert (binoinv ([p, NaN], 2, single (0.5)), single ([NaN 0 1 2 NaN NaN]))
656s ***** shared x, tol
656s  x = magic (3) + 1;
656s  tol = 1;
656s ***** assert (binoinv (binocdf (1:10, 11, 0.1), 11, 0.1), 1:10, tol)
656s ***** assert (binoinv (binocdf (1:10, 2*(1:10), 0.1), 2*(1:10), 0.1), 1:10, tol)
656s ***** assert (binoinv (binocdf (x, 2*x, 1./x), 2*x, 1./x), x, tol)
656s ***** error<binoinv: function called with too few input arguments.> binoinv ()
656s ***** error<binoinv: function called with too few input arguments.> binoinv (1)
656s ***** error<binoinv: function called with too few input arguments.> binoinv (1,2)
656s ***** error<binoinv: function called with too many inputs> binoinv (1,2,3,4)
656s ***** error<binoinv: P, N, and PS must be of common size or scalars.> ...
656s  binoinv (ones (3), ones (2), ones (2))
656s ***** error<binoinv: P, N, and PS must be of common size or scalars.> ...
656s  binoinv (ones (2), ones (3), ones (2))
656s ***** error<binoinv: P, N, and PS must be of common size or scalars.> ...
656s  binoinv (ones (2), ones (2), ones (3))
656s ***** error<binoinv: P, N, and PS must not be complex.> binoinv (i, 2, 2)
656s ***** error<binoinv: P, N, and PS must not be complex.> binoinv (2, i, 2)
656s ***** error<binoinv: P, N, and PS must not be complex.> binoinv (2, 2, i)
656s 23 tests, 23 passed, 0 known failure, 0 skipped
656s [inst/dist_fun/unifrnd.m]
656s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/unifrnd.m
656s ***** assert (size (unifrnd (1, 1)), [1 1])
656s ***** assert (size (unifrnd (1, ones (2,1))), [2, 1])
656s ***** assert (size (unifrnd (1, ones (2,2))), [2, 2])
656s ***** assert (size (unifrnd (ones (2,1), 1)), [2, 1])
656s ***** assert (size (unifrnd (ones (2,2), 1)), [2, 2])
656s ***** assert (size (unifrnd (1, 1, 3)), [3, 3])
656s ***** assert (size (unifrnd (1, 1, [4, 1])), [4, 1])
656s ***** assert (size (unifrnd (1, 1, 4, 1)), [4, 1])
656s ***** assert (size (unifrnd (1, 1, 4, 1, 5)), [4, 1, 5])
656s ***** assert (size (unifrnd (1, 1, 0, 1)), [0, 1])
656s ***** assert (size (unifrnd (1, 1, 1, 0)), [1, 0])
656s ***** assert (size (unifrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
656s ***** assert (class (unifrnd (1, 1)), "double")
656s ***** assert (class (unifrnd (1, single (1))), "single")
656s ***** assert (class (unifrnd (1, single ([1, 1]))), "single")
656s ***** assert (class (unifrnd (single (1), 1)), "single")
656s ***** assert (class (unifrnd (single ([1, 1]), 1)), "single")
656s ***** error<unifrnd: function called with too few input arguments.> unifrnd ()
656s ***** error<unifrnd: function called with too few input arguments.> unifrnd (1)
656s ***** error<unifrnd: A and B must be of common size or scalars.> ...
656s  unifrnd (ones (3), ones (2))
656s ***** error<unifrnd: A and B must be of common size or scalars.> ...
656s  unifrnd (ones (2), ones (3))
656s ***** error<unifrnd: A and B must not be complex.> unifrnd (i, 2, 3)
656s ***** error<unifrnd: A and B must not be complex.> unifrnd (1, i, 3)
656s ***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
656s  unifrnd (1, 2, -1)
656s ***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
656s  unifrnd (1, 2, 1.2)
656s ***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
656s  unifrnd (1, 2, ones (2))
656s ***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
656s  unifrnd (1, 2, [2 -1 2])
656s ***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
656s  unifrnd (1, 2, [2 0 2.5])
656s ***** error<unifrnd: dimensions must be non-negative integers.> ...
656s  unifrnd (1, 2, 2, -1, 5)
656s ***** error<unifrnd: dimensions must be non-negative integers.> ...
656s  unifrnd (1, 2, 2, 1.5, 5)
656s ***** error<unifrnd: A and B must be scalars or of size SZ.> ...
656s  unifrnd (2, ones (2), 3)
656s ***** error<unifrnd: A and B must be scalars or of size SZ.> ...
656s  unifrnd (2, ones (2), [3, 2])
656s ***** error<unifrnd: A and B must be scalars or of size SZ.> ...
656s  unifrnd (2, ones (2), 3, 2)
656s 33 tests, 33 passed, 0 known failure, 0 skipped
656s [inst/dist_fun/mvtpdf.m]
656s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/mvtpdf.m
656s ***** demo
656s  ## Compute the pdf of a multivariate t distribution with correlation
656s  ## parameters rho = [1 .4; .4 1] and 2 degrees of freedom.
656s 
656s  rho = [1, 0.4; 0.4, 1];
656s  df = 2;
656s  [X1, X2] = meshgrid (linspace (-2, 2, 25)', linspace (-2, 2, 25)');
656s  X = [X1(:), X2(:)];
656s  y = mvtpdf (X, rho, df);
656s  surf (X1, X2, reshape (y, 25, 25));
656s  title ("Bivariate Student's t probability density function");
656s ***** assert (mvtpdf ([0 0], eye(2), 1), 0.1591549, 1E-7)
656s ***** assert (mvtpdf ([1 0], [1 0.5; 0.5 1], 2), 0.06615947, 1E-7)
656s ***** assert (mvtpdf ([1 0.4 0; 1.2 0.5 0.5; 1.4 0.6 1], ...
656s  [1 0.5 0.3; 0.5 1 0.6; 0.3 0.6 1], [5 6 7]), ...
656s  [0.04713313 0.03722421 0.02069011]', 1E-7)
656s 3 tests, 3 passed, 0 known failure, 0 skipped
656s [inst/dist_fun/ncx2cdf.m]
656s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/ncx2cdf.m
656s ***** demo
656s  ## Plot various CDFs from the noncentral chi-squared distribution
656s  x = 0:0.1:10;
656s  p1 = ncx2cdf (x, 2, 1);
656s  p2 = ncx2cdf (x, 2, 2);
656s  p3 = ncx2cdf (x, 2, 3);
656s  p4 = ncx2cdf (x, 4, 1);
656s  p5 = ncx2cdf (x, 4, 2);
656s  p6 = ncx2cdf (x, 4, 3);
656s  plot (x, p1, "-r", x, p2, "-g", x, p3, "-k", ...
656s        x, p4, "-m", x, p5, "-c", x, p6, "-y")
656s  grid on
656s  xlim ([0, 10])
656s  legend ({"df = 2, λ = 1", "df = 2, λ = 2", ...
656s           "df = 2, λ = 3", "df = 4, λ = 1", ...
656s           "df = 4, λ = 2", "df = 4, λ = 3"}, "location", "southeast")
656s  title ("Noncentral chi-squared CDF")
656s  xlabel ("values in x")
656s  ylabel ("probability")
656s ***** demo
656s  ## Compare the noncentral chi-squared CDF with LAMBDA = 2 to the
656s  ## chi-squared CDF with the same number of degrees of freedom (4).
656s 
656s  x = 0:0.1:10;
656s  p1 = ncx2cdf (x, 4, 2);
656s  p2 = chi2cdf (x, 4);
656s  plot (x, p1, "-", x, p2, "-")
656s  grid on
656s  xlim ([0, 10])
656s  legend ({"Noncentral χ^2(4,2)", "χ^2(4)"}, "location", "northwest")
656s  title ("Noncentral chi-squared vs chi-squared CDFs")
656s  xlabel ("values in x")
656s  ylabel ("probability")
656s ***** test
656s  x = -2:0.1:2;
656s  p = ncx2cdf (x, 10, 1);
656s  assert (p([1:21]), zeros (1, 21), 3e-84);
656s  assert (p(22), 1.521400636466575e-09, 1e-14);
656s  assert (p(30), 6.665480510026046e-05, 1e-14);
656s  assert (p(41), 0.002406447308399836, 1e-14);
656s ***** test
656s  p = ncx2cdf (12, 10, 3);
656s  assert (p, 0.4845555602398649, 1e-14);
656s ***** test
656s  p = ncx2cdf (2, 3, 2);
656s  assert (p, 0.2207330870741212, 1e-14);
656s ***** test
656s  p = ncx2cdf (2, 3, 2, "upper");
656s  assert (p, 0.7792669129258789, 1e-14);
656s ***** test
656s  p = ncx2cdf ([3, 6], 3, 2, "upper");
656s  assert (p, [0.6423318186400054, 0.3152299878943012], 1e-14);
656s ***** error<ncx2cdf: function called with too few input arguments.> ncx2cdf ()
656s ***** error<ncx2cdf: function called with too few input arguments.> ncx2cdf (1)
656s ***** error<ncx2cdf: function called with too few input arguments.> ncx2cdf (1, 2)
656s ***** error<ncx2cdf: invalid argument for upper tail.> ncx2cdf (1, 2, 3, "tail")
656s ***** error<ncx2cdf: invalid argument for upper tail.> ncx2cdf (1, 2, 3, 4)
656s ***** error<ncx2cdf: X, DF, and LAMBDA must be of common size or scalars.> ...
656s  ncx2cdf (ones (3), ones (2), ones (2))
656s ***** error<ncx2cdf: X, DF, and LAMBDA must be of common size or scalars.> ...
656s  ncx2cdf (ones (2), ones (3), ones (2))
656s ***** error<ncx2cdf: X, DF, and LAMBDA must be of common size or scalars.> ...
656s  ncx2cdf (ones (2), ones (2), ones (3))
656s ***** error<ncx2cdf: X, DF, and LAMBDA must not be complex.> ncx2cdf (i, 2, 2)
656s ***** error<ncx2cdf: X, DF, and LAMBDA must not be complex.> ncx2cdf (2, i, 2)
656s ***** error<ncx2cdf: X, DF, and LAMBDA must not be complex.> ncx2cdf (2, 2, i)
656s 16 tests, 16 passed, 0 known failure, 0 skipped
656s [inst/dist_fun/geocdf.m]
656s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/geocdf.m
656s ***** demo
656s  ## Plot various CDFs from the geometric distribution
656s  x = 0:10;
656s  p1 = geocdf (x, 0.2);
656s  p2 = geocdf (x, 0.5);
656s  p3 = geocdf (x, 0.7);
656s  plot (x, p1, "*b", x, p2, "*g", x, p3, "*r")
656s  grid on
656s  xlim ([0, 10])
656s  legend ({"ps = 0.2", "ps = 0.5", "ps = 0.7"}, "location", "southeast")
656s  title ("Geometric CDF")
656s  xlabel ("values in x (number of failures)")
656s  ylabel ("probability")
656s ***** test
656s  p = geocdf ([1, 2, 3, 4], 0.25);
656s  assert (p(1), 0.4375000000, 1e-14);
656s  assert (p(2), 0.5781250000, 1e-14);
656s  assert (p(3), 0.6835937500, 1e-14);
656s  assert (p(4), 0.7626953125, 1e-14);
656s ***** test
656s  p = geocdf ([1, 2, 3, 4], 0.25, "upper");
656s  assert (p(1), 0.5625000000, 1e-14);
656s  assert (p(2), 0.4218750000, 1e-14);
656s  assert (p(3), 0.3164062500, 1e-14);
656s  assert (p(4), 0.2373046875, 1e-14);
656s ***** shared x, p
656s  x = [-1 0 1 Inf];
656s  p = [0 0.5 0.75 1];
656s ***** assert (geocdf (x, 0.5*ones (1,4)), p)
656s ***** assert (geocdf (x, 0.5), p)
656s ***** assert (geocdf (x, 0.5*[-1 NaN 4 1]), [NaN NaN NaN p(4)])
656s ***** assert (geocdf ([x(1:2) NaN x(4)], 0.5), [p(1:2) NaN p(4)])
656s ***** assert (geocdf ([x, NaN], 0.5), [p, NaN])
656s ***** assert (geocdf (single ([x, NaN]), 0.5), single ([p, NaN]))
656s ***** assert (geocdf ([x, NaN], single (0.5)), single ([p, NaN]))
656s ***** error<geocdf: function called with too few input arguments.> geocdf ()
656s ***** error<geocdf: function called with too few input arguments.> geocdf (1)
656s ***** error<geocdf: X and PS must be of common size or scalars.> ...
656s  geocdf (ones (3), ones (2))
656s ***** error<geocdf: X and PS must be of common size or scalars.> ...
656s  geocdf (ones (2), ones (3))
656s ***** error<geocdf: X and PS must not be complex.> geocdf (i, 2)
656s ***** error<geocdf: X and PS must not be complex.> geocdf (2, i)
656s ***** error<geocdf: invalid argument for upper tail.> geocdf (2, 3, "tail")
656s ***** error<geocdf: invalid argument for upper tail.> geocdf (2, 3, 5)
656s 17 tests, 17 passed, 0 known failure, 0 skipped
656s [inst/dist_fun/plpdf.m]
656s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/plpdf.m
656s ***** demo
656s  ## Plot various PDFs from the Piecewise linear distribution
656s  data = 0:0.01:10;
656s  x1 = [0, 1, 3, 4, 7, 10];
656s  Fx1 = [0, 0.2, 0.5, 0.6, 0.7, 1];
656s  x2 = [0, 2, 5, 6, 7, 8];
656s  Fx2 = [0, 0.1, 0.3, 0.6, 0.9, 1];
656s  y1 = plpdf (data, x1, Fx1);
656s  y2 = plpdf (data, x2, Fx2);
656s  plot (data, y1, "-b", data, y2, "g")
656s  grid on
656s  ylim ([0, 0.6])
656s  xlim ([0, 10])
656s  legend ({"x1, Fx1", "x2, Fx2"}, "location", "northeast")
656s  title ("Piecewise linear CDF")
656s  xlabel ("values in data")
656s  ylabel ("density")
656s ***** shared x, Fx
656s  x = [0, 1, 3, 4, 7, 10];
656s  Fx = [0, 0.2, 0.5, 0.6, 0.7, 1];
656s ***** assert (plpdf (0.5, x, Fx), 0.2, eps);
656s ***** assert (plpdf (1.5, x, Fx), 0.15, eps);
656s ***** assert (plpdf (3.5, x, Fx), 0.1, eps);
656s ***** assert (plpdf (5, x, Fx), 0.1/3, eps);
656s ***** assert (plpdf (8, x, Fx), 0.1, eps);
656s ***** error<plpdf: function called with too few input arguments.> plpdf ()
656s ***** error<plpdf: function called with too few input arguments.> plpdf (1)
656s ***** error<plpdf: function called with too few input arguments.> plpdf (1, 2)
656s ***** error<plpdf: X and FX must be vectors of equal size.> ...
656s  plpdf (1, [0, 1, 2], [0, 1])
656s ***** error<plpdf: X and FX must be at least two-elements long.> ...
656s  plpdf (1, [0], [1])
656s ***** error<plpdf: FX must be bounded in the range> ...
656s  plpdf (1, [0, 1, 2], [0, 1, 1.5])
656s ***** error<plpdf: FX must be bounded in the range> ...
656s  plpdf (1, [0, 1, 2], [0, i, 1])
656s ***** error<plpdf: DATA, X, and FX must not be complex.> ...
656s  plpdf (i, [0, 1, 2], [0, 0.5, 1])
656s ***** error<plpdf: DATA, X, and FX must not be complex.> ...
656s  plpdf (1, [0, i, 2], [0, 0.5, 1])
656s ***** error<plpdf: DATA, X, and FX must not be complex.> ...
656s  plpdf (1, [0, 1, 2], [0, 0.5i, 1])
656s 15 tests, 15 passed, 0 known failure, 0 skipped
656s [inst/dist_fun/gumbelinv.m]
656s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/gumbelinv.m
656s ***** demo
656s  ## Plot various iCDFs from the Gumbel distribution
656s  p = 0.001:0.001:0.999;
656s  x1 = gumbelinv (p, 0.5, 2);
656s  x2 = gumbelinv (p, 1.0, 2);
656s  x3 = gumbelinv (p, 1.5, 3);
656s  x4 = gumbelinv (p, 3.0, 4);
656s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
656s  grid on
656s  ylim ([-5, 20])
656s  legend ({"μ = 0.5, β = 2", "μ = 1.0, β = 2", ...
656s           "μ = 1.5, β = 3", "μ = 3.0, β = 4"}, "location", "northwest")
656s  title ("Gumbel iCDF")
656s  xlabel ("probability")
656s  ylabel ("values in x")
656s ***** shared p, x
656s  p = [0, 0.05, 0.5 0.95];
656s  x = [-Inf, -1.0972, 0.3665, 2.9702];
656s ***** assert (gumbelinv (p), x, 1e-4)
656s ***** assert (gumbelinv (p, zeros (1,4), ones (1,4)), x, 1e-4)
656s ***** assert (gumbelinv (p, 0, ones (1,4)), x, 1e-4)
656s ***** assert (gumbelinv (p, zeros (1,4), 1), x, 1e-4)
656s ***** assert (gumbelinv (p, [0, -Inf, NaN, Inf], 1), [-Inf, -Inf, NaN, Inf], 1e-4)
656s ***** assert (gumbelinv (p, 0, [Inf, NaN, -1, 0]), [-Inf, NaN, NaN, NaN], 1e-4)
656s ***** assert (gumbelinv ([p(1:2), NaN, p(4)], 0, 1), [x(1:2), NaN, x(4)], 1e-4)
656s ***** assert (gumbelinv ([p, NaN], 0, 1), [x, NaN], 1e-4)
656s ***** assert (gumbelinv (single ([p, NaN]), 0, 1), single ([x, NaN]), 1e-4)
656s ***** assert (gumbelinv ([p, NaN], single (0), 1), single ([x, NaN]), 1e-4)
656s ***** assert (gumbelinv ([p, NaN], 0, single (1)), single ([x, NaN]), 1e-4)
656s  p = [0.05, 0.5, 0.95];
656s  x = gumbelinv(p);
656s ***** assert (gumbelcdf(x), p, 1e-4)
656s ***** error<gumbelinv: invalid number of input arguments.> gumbelinv ()
656s ***** error gumbelinv (1,2,3,4,5,6)
656s ***** error<gumbelinv: P, MU, and BETA must be of common size or scalars.> ...
656s  gumbelinv (ones (3), ones (2), ones (2))
656s ***** error<gumbelinv: invalid size of covariance matrix.> ...
656s  [p, plo, pup] = gumbelinv (2, 3, 4, [1, 2])
656s ***** error<gumbelinv: covariance matrix is required for confidence bounds.> ...
656s  [p, plo, pup] = gumbelinv (1, 2, 3)
656s ***** error<gumbelinv: invalid value for alpha.> [p, plo, pup] = ...
656s  gumbelinv (1, 2, 3, [1, 0; 0, 1], 0)
656s ***** error<gumbelinv: invalid value for alpha.> [p, plo, pup] = ...
656s  gumbelinv (1, 2, 3, [1, 0; 0, 1], 1.22)
656s ***** error<gumbelinv: P, MU, and BETA must not be complex.> gumbelinv (i, 2, 2)
656s ***** error<gumbelinv: P, MU, and BETA must not be complex.> gumbelinv (2, i, 2)
656s ***** error<gumbelinv: P, MU, and BETA must not be complex.> gumbelinv (2, 2, i)
656s ***** error<gumbelinv: bad covariance matrix.> ...
656s  [p, plo, pup] = gumbelinv (1, 2, 3, [-1, 10; -Inf, -Inf], 0.04)
656s 23 tests, 23 passed, 0 known failure, 0 skipped
656s [inst/dist_fun/tlscdf.m]
656s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/tlscdf.m
656s ***** demo
656s  ## Plot various CDFs from the location-scale Student's T distribution
656s  x = -8:0.01:8;
656s  p1 = tlscdf (x, 0, 1, 1);
656s  p2 = tlscdf (x, 0, 2, 2);
656s  p3 = tlscdf (x, 3, 2, 5);
656s  p4 = tlscdf (x, -1, 3, Inf);
656s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-m")
656s  grid on
656s  xlim ([-8, 8])
656s  ylim ([0, 1])
656s  legend ({"mu = 0, sigma = 1, nu = 1", "mu = 0, sigma = 2, nu = 2", ...
656s           "mu = 3, sigma = 2, nu = 5", 'mu = -1, sigma = 3, nu = \infty'}, ...
656s          "location", "northwest")
656s  title ("Location-scale Student's T CDF")
656s  xlabel ("values in x")
656s  ylabel ("probability")
656s ***** shared x,y
656s  x = [-Inf 0 1 Inf];
656s  y = [0 1/2 3/4 1];
656s ***** assert (tlscdf (x, 0, 1, ones (1,4)), y, eps)
656s ***** assert (tlscdf (x, 0, 1, 1), y, eps)
657s ***** assert (tlscdf (x, 0, 1, [0 1 NaN 1]), [NaN 1/2 NaN 1], eps)
657s ***** assert (tlscdf ([x(1:2) NaN x(4)], 0, 1, 1), [y(1:2) NaN y(4)], eps)
657s ***** assert (tlscdf (2, 0, 1, 3, "upper"), 0.0697, 1e-4)
657s ***** assert (tlscdf (205, 0, 1, 5, "upper"), 2.6206e-11, 1e-14)
657s ***** assert (tlscdf ([x, NaN], 0, 1, 1), [y, NaN], eps)
657s ***** assert (tlscdf (single ([x, NaN]), 0, 1, 1), single ([y, NaN]), eps ("single"))
657s ***** assert (tlscdf ([x, NaN], single (0), 1, 1), single ([y, NaN]), eps ("single"))
657s ***** assert (tlscdf ([x, NaN], 0, single (1), 1), single ([y, NaN]), eps ("single"))
657s ***** assert (tlscdf ([x, NaN], 0, 1, single (1)), single ([y, NaN]), eps ("single"))
657s ***** error<tlscdf: function called with too few input arguments.> tlscdf ()
657s ***** error<tlscdf: function called with too few input arguments.> tlscdf (1)
657s ***** error<tlscdf: function called with too few input arguments.> tlscdf (1, 2)
657s ***** error<tlscdf: function called with too few input arguments.> tlscdf (1, 2, 3)
657s ***** error<tlscdf: invalid argument for upper tail.> tlscdf (1, 2, 3, 4, "uper")
657s ***** error<tlscdf: invalid argument for upper tail.> tlscdf (1, 2, 3, 4, 5)
657s ***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
657s  tlscdf (ones (3), ones (2), 1, 1)
657s ***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
657s  tlscdf (ones (3), 1, ones (2), 1)
657s ***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
657s  tlscdf (ones (3), 1, 1, ones (2))
657s ***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
657s  tlscdf (ones (3), ones (2), 1, 1, "upper")
657s ***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
657s  tlscdf (ones (3), 1, ones (2), 1, "upper")
657s ***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
657s  tlscdf (ones (3), 1, 1, ones (2), "upper")
657s ***** error<tlscdf: X, MU, SIGMA, and NU must not be complex.> tlscdf (i, 2, 1, 1)
657s ***** error<tlscdf: X, MU, SIGMA, and NU must not be complex.> tlscdf (2, i, 1, 1)
657s ***** error<tlscdf: X, MU, SIGMA, and NU must not be complex.> tlscdf (2, 1, i, 1)
657s ***** error<tlscdf: X, MU, SIGMA, and NU must not be complex.> tlscdf (2, 1, 1, i)
657s 27 tests, 27 passed, 0 known failure, 0 skipped
657s [inst/dist_fun/vmrnd.m]
657s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/vmrnd.m
657s ***** assert (size (vmrnd (1, 1)), [1 1])
657s ***** assert (size (vmrnd (1, ones (2,1))), [2, 1])
657s ***** assert (size (vmrnd (1, ones (2,2))), [2, 2])
657s ***** assert (size (vmrnd (ones (2,1), 1)), [2, 1])
657s ***** assert (size (vmrnd (ones (2,2), 1)), [2, 2])
657s ***** assert (size (vmrnd (1, 1, 3)), [3, 3])
657s ***** assert (size (vmrnd (1, 1, [4, 1])), [4, 1])
657s ***** assert (size (vmrnd (1, 1, 4, 1)), [4, 1])
657s ***** assert (size (vmrnd (1, 1, 4, 1, 5)), [4, 1, 5])
657s ***** assert (size (vmrnd (1, 1, 0, 1)), [0, 1])
657s ***** assert (size (vmrnd (1, 1, 1, 0)), [1, 0])
657s ***** assert (size (vmrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
657s ***** assert (class (vmrnd (1, 1)), "double")
657s ***** assert (class (vmrnd (1, single (1))), "single")
657s ***** assert (class (vmrnd (1, single ([1, 1]))), "single")
657s ***** assert (class (vmrnd (single (1), 1)), "single")
657s ***** assert (class (vmrnd (single ([1, 1]), 1)), "single")
657s ***** error<vmrnd: function called with too few input arguments.> vmrnd ()
657s ***** error<vmrnd: function called with too few input arguments.> vmrnd (1)
657s ***** error<vmrnd: MU and K must be of common size or scalars.> ...
657s  vmrnd (ones (3), ones (2))
657s ***** error<vmrnd: MU and K must be of common size or scalars.> ...
657s  vmrnd (ones (2), ones (3))
657s ***** error<vmrnd: MU and K must not be complex.> vmrnd (i, 2, 3)
657s ***** error<vmrnd: MU and K must not be complex.> vmrnd (1, i, 3)
657s ***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
657s  vmrnd (1, 2, -1)
657s ***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
657s  vmrnd (1, 2, 1.2)
657s ***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
657s  vmrnd (1, 2, ones (2))
657s ***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
657s  vmrnd (1, 2, [2 -1 2])
657s ***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
657s  vmrnd (1, 2, [2 0 2.5])
657s ***** error<vmrnd: dimensions must be non-negative integers.> ...
657s  vmrnd (1, 2, 2, -1, 5)
657s ***** error<vmrnd: dimensions must be non-negative integers.> ...
657s  vmrnd (1, 2, 2, 1.5, 5)
657s ***** error<vmrnd: MU and K must be scalars or of size SZ.> ...
657s  vmrnd (2, ones (2), 3)
657s ***** error<vmrnd: MU and K must be scalars or of size SZ.> ...
657s  vmrnd (2, ones (2), [3, 2])
657s ***** error<vmrnd: MU and K must be scalars or of size SZ.> ...
657s  vmrnd (2, ones (2), 3, 2)
657s 33 tests, 33 passed, 0 known failure, 0 skipped
657s [inst/dist_fun/nctrnd.m]
657s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/nctrnd.m
657s ***** assert (size (nctrnd (1, 1)), [1 1])
657s ***** assert (size (nctrnd (1, ones (2,1))), [2, 1])
657s ***** assert (size (nctrnd (1, ones (2,2))), [2, 2])
657s ***** assert (size (nctrnd (ones (2,1), 1)), [2, 1])
657s ***** assert (size (nctrnd (ones (2,2), 1)), [2, 2])
657s ***** assert (size (nctrnd (1, 1, 3)), [3, 3])
657s ***** assert (size (nctrnd (1, 1, [4, 1])), [4, 1])
657s ***** assert (size (nctrnd (1, 1, 4, 1)), [4, 1])
657s ***** assert (size (nctrnd (1, 1, 4, 1, 5)), [4, 1, 5])
657s ***** assert (size (nctrnd (1, 1, 0, 1)), [0, 1])
657s ***** assert (size (nctrnd (1, 1, 1, 0)), [1, 0])
657s ***** assert (size (nctrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
657s ***** assert (class (nctrnd (1, 1)), "double")
657s ***** assert (class (nctrnd (1, single (1))), "single")
657s ***** assert (class (nctrnd (1, single ([1, 1]))), "single")
657s ***** assert (class (nctrnd (single (1), 1)), "single")
657s ***** assert (class (nctrnd (single ([1, 1]), 1)), "single")
657s ***** error<nctrnd: function called with too few input arguments.> nctrnd ()
657s ***** error<nctrnd: function called with too few input arguments.> nctrnd (1)
657s ***** error<nctrnd: DF and MU must be of common size or scalars.> ...
657s  nctrnd (ones (3), ones (2))
657s ***** error<nctrnd: DF and MU must be of common size or scalars.> ...
657s  nctrnd (ones (2), ones (3))
657s ***** error<nctrnd: DF and MU must not be complex.> nctrnd (i, 2)
657s ***** error<nctrnd: DF and MU must not be complex.> nctrnd (1, i)
657s ***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
657s  nctrnd (1, 2, -1)
657s ***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
657s  nctrnd (1, 2, 1.2)
657s ***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
657s  nctrnd (1, 2, ones (2))
657s ***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
657s  nctrnd (1, 2, [2 -1 2])
657s ***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
657s  nctrnd (1, 2, [2 0 2.5])
657s ***** error<nctrnd: dimensions must be non-negative integers.> ...
657s  nctrnd (1, 2, 2, -1, 5)
657s ***** error<nctrnd: dimensions must be non-negative integers.> ...
657s  nctrnd (1, 2, 2, 1.5, 5)
657s ***** error<nctrnd: DF and MU must be scalars or of size SZ.> ...
657s  nctrnd (2, ones (2), 3)
657s ***** error<nctrnd: DF and MU must be scalars or of size SZ.> ...
657s  nctrnd (2, ones (2), [3, 2])
657s ***** error<nctrnd: DF and MU must be scalars or of size SZ.> ...
657s  nctrnd (2, ones (2), 3, 2)
657s 33 tests, 33 passed, 0 known failure, 0 skipped
657s [inst/dist_fun/geoinv.m]
657s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/geoinv.m
657s ***** demo
657s  ## Plot various iCDFs from the geometric distribution
657s  p = 0.001:0.001:0.999;
657s  x1 = geoinv (p, 0.2);
657s  x2 = geoinv (p, 0.5);
657s  x3 = geoinv (p, 0.7);
657s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
657s  grid on
657s  ylim ([0, 10])
657s  legend ({"ps = 0.2", "ps = 0.5", "ps = 0.7"}, "location", "northwest")
657s  title ("Geometric iCDF")
657s  xlabel ("probability")
657s  ylabel ("values in x (number of failures)")
657s ***** shared p
657s  p = [-1 0 0.75 1 2];
657s ***** assert (geoinv (p, 0.5*ones (1,5)), [NaN 0 1 Inf NaN])
657s ***** assert (geoinv (p, 0.5), [NaN 0 1 Inf NaN])
657s ***** assert (geoinv (p, 0.5*[1 -1 NaN 4 1]), [NaN NaN NaN NaN NaN])
657s ***** assert (geoinv ([p(1:2) NaN p(4:5)], 0.5), [NaN 0 NaN Inf NaN])
657s ***** assert (geoinv ([p, NaN], 0.5), [NaN 0 1 Inf NaN NaN])
657s ***** assert (geoinv (single ([p, NaN]), 0.5), single ([NaN 0 1 Inf NaN NaN]))
657s ***** assert (geoinv ([p, NaN], single (0.5)), single ([NaN 0 1 Inf NaN NaN]))
657s ***** error<geoinv: function called with too few input arguments.> geoinv ()
657s ***** error<geoinv: function called with too few input arguments.> geoinv (1)
657s ***** error<geoinv: P and PS must be of common size or scalars.> ...
657s  geoinv (ones (3), ones (2))
657s ***** error<geoinv: P and PS must be of common size or scalars.> ...
657s  geoinv (ones (2), ones (3))
657s ***** error<geoinv: P and PS must not be complex.> ...
657s  geoinv (i, 2)
657s ***** error<geoinv: P and PS must not be complex.> ...
657s  geoinv (2, i)
657s 13 tests, 13 passed, 0 known failure, 0 skipped
657s [inst/dist_fun/copulacdf.m]
657s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/copulacdf.m
657s ***** test
657s  x = [0.2:0.2:0.6; 0.2:0.2:0.6];
657s  theta = [1; 2];
657s  p = copulacdf ("Clayton", x, theta);
657s  expected_p = [0.1395; 0.1767];
657s  assert (p, expected_p, 0.001);
657s ***** test
657s  x = [0.2:0.2:0.6; 0.2:0.2:0.6];
657s  p = copulacdf ("Gumbel", x, 2);
657s  expected_p = [0.1464; 0.1464];
657s  assert (p, expected_p, 0.001);
657s ***** test
657s  x = [0.2:0.2:0.6; 0.2:0.2:0.6];
657s  theta = [1; 2];
657s  p = copulacdf ("Frank", x, theta);
657s  expected_p = [0.0699; 0.0930];
657s  assert (p, expected_p, 0.001);
657s ***** test
657s  x = [0.2:0.2:0.6; 0.2:0.2:0.6];
657s  theta = [0.3; 0.7];
657s  p = copulacdf ("AMH", x, theta);
657s  expected_p = [0.0629; 0.0959];
657s  assert (p, expected_p, 0.001);
657s ***** test
657s  x = [0.2:0.2:0.6; 0.2:0.1:0.4];
657s  theta = [0.2, 0.1, 0.1, 0.05];
657s  p = copulacdf ("FGM", x, theta);
657s  expected_p = [0.0558; 0.0293];
657s  assert (p, expected_p, 0.001);
657s 5 tests, 5 passed, 0 known failure, 0 skipped
657s [inst/dist_fun/nbinrnd.m]
657s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/nbinrnd.m
657s ***** assert (size (nbinrnd (1, 0.5)), [1 1])
657s ***** assert (size (nbinrnd (1, 0.5 * ones (2,1))), [2, 1])
657s ***** assert (size (nbinrnd (1, 0.5 * ones (2,2))), [2, 2])
657s ***** assert (size (nbinrnd (ones (2,1), 0.5)), [2, 1])
657s ***** assert (size (nbinrnd (ones (2,2), 0.5)), [2, 2])
657s ***** assert (size (nbinrnd (1, 0.5, 3)), [3, 3])
657s ***** assert (size (nbinrnd (1, 0.5, [4, 1])), [4, 1])
657s ***** assert (size (nbinrnd (1, 0.5, 4, 1)), [4, 1])
657s ***** assert (size (nbinrnd (1, 0.5, 4, 1, 5)), [4, 1, 5])
657s ***** assert (size (nbinrnd (1, 0.5, 0, 1)), [0, 1])
657s ***** assert (size (nbinrnd (1, 0.5, 1, 0)), [1, 0])
657s ***** assert (size (nbinrnd (1, 0.5, 1, 2, 0, 5)), [1, 2, 0, 5])
657s ***** assert (class (nbinrnd (1, 0.5)), "double")
657s ***** assert (class (nbinrnd (1, single (0.5))), "single")
657s ***** assert (class (nbinrnd (1, single ([0.5, 0.5]))), "single")
657s ***** assert (class (nbinrnd (single (1), 0.5)), "single")
657s ***** assert (class (nbinrnd (single ([1, 1]), 0.5)), "single")
657s ***** error<nbinrnd: function called with too few input arguments.> nbinrnd ()
657s ***** error<nbinrnd: function called with too few input arguments.> nbinrnd (1)
657s ***** error<nbinrnd: R and PS must be of common size or scalars.> ...
657s  nbinrnd (ones (3), ones (2))
657s ***** error<nbinrnd: R and PS must be of common size or scalars.> ...
657s  nbinrnd (ones (2), ones (3))
657s ***** error<nbinrnd: R and PS must not be complex.> nbinrnd (i, 2, 3)
657s ***** error<nbinrnd: R and PS must not be complex.> nbinrnd (1, i, 3)
657s ***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
657s  nbinrnd (1, 2, -1)
657s ***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
657s  nbinrnd (1, 2, 1.2)
657s ***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
657s  nbinrnd (1, 2, ones (2))
657s ***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
657s  nbinrnd (1, 2, [2 -1 2])
657s ***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
657s  nbinrnd (1, 2, [2 0 2.5])
657s ***** error<nbinrnd: dimensions must be non-negative integers.> ...
657s  nbinrnd (1, 2, 2, -1, 5)
657s ***** error<nbinrnd: dimensions must be non-negative integers.> ...
657s  nbinrnd (1, 2, 2, 1.5, 5)
657s ***** error<nbinrnd: R and PS must be scalars or of size SZ.> ...
657s  nbinrnd (2, ones (2), 3)
657s ***** error<nbinrnd: R and PS must be scalars or of size SZ.> ...
657s  nbinrnd (2, ones (2), [3, 2])
657s ***** error<nbinrnd: R and PS must be scalars or of size SZ.> ...
657s  nbinrnd (2, ones (2), 3, 2)
657s 33 tests, 33 passed, 0 known failure, 0 skipped
657s [inst/dist_fun/nbinpdf.m]
657s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/nbinpdf.m
657s ***** demo
657s  ## Plot various PDFs from the negative binomial distribution
657s  x = 0:40;
657s  y1 = nbinpdf (x, 2, 0.15);
657s  y2 = nbinpdf (x, 5, 0.2);
657s  y3 = nbinpdf (x, 4, 0.4);
657s  y4 = nbinpdf (x, 10, 0.3);
657s  plot (x, y1, "*r", x, y2, "*g", x, y3, "*k", x, y4, "*m")
657s  grid on
657s  xlim ([0, 40])
657s  ylim ([0, 0.12])
657s  legend ({"r = 2, ps = 0.15", "r = 5, ps = 0.2", "r = 4, p = 0.4", ...
657s           "r = 10, ps = 0.3"}, "location", "northeast")
657s  title ("Negative binomial PDF")
657s  xlabel ("values in x (number of failures)")
657s  ylabel ("density")
657s ***** shared x, y
657s  x = [-1 0 1 2 Inf];
657s  y = [0 1/2 1/4 1/8 NaN];
657s ***** assert (nbinpdf (x, ones (1,5), 0.5*ones (1,5)), y)
657s ***** assert (nbinpdf (x, 1, 0.5*ones (1,5)), y)
657s ***** assert (nbinpdf (x, ones (1,5), 0.5), y)
657s ***** assert (nbinpdf (x, [0 1 NaN 1.5 Inf], 0.5), [NaN 1/2 NaN 1.875*0.5^1.5/4 NaN], eps)
657s ***** assert (nbinpdf (x, 1, 0.5*[-1 NaN 4 1 1]), [NaN NaN NaN y(4:5)])
657s ***** assert (nbinpdf ([x, NaN], 1, 0.5), [y, NaN])
657s ***** assert (nbinpdf (single ([x, NaN]), 1, 0.5), single ([y, NaN]))
657s ***** assert (nbinpdf ([x, NaN], single (1), 0.5), single ([y, NaN]))
657s ***** assert (nbinpdf ([x, NaN], 1, single (0.5)), single ([y, NaN]))
657s ***** error<nbinpdf: function called with too few input arguments.> nbinpdf ()
657s ***** error<nbinpdf: function called with too few input arguments.> nbinpdf (1)
657s ***** error<nbinpdf: function called with too few input arguments.> nbinpdf (1, 2)
657s ***** error<nbinpdf: X, R, and PS must be of common size or scalars.> ...
657s  nbinpdf (ones (3), ones (2), ones (2))
657s ***** error<nbinpdf: X, R, and PS must be of common size or scalars.> ...
657s  nbinpdf (ones (2), ones (3), ones (2))
657s ***** error<nbinpdf: X, R, and PS must be of common size or scalars.> ...
657s  nbinpdf (ones (2), ones (2), ones (3))
657s ***** error<nbinpdf: X, R, and PS must not be complex.> nbinpdf (i, 2, 2)
657s ***** error<nbinpdf: X, R, and PS must not be complex.> nbinpdf (2, i, 2)
657s ***** error<nbinpdf: X, R, and PS must not be complex.> nbinpdf (2, 2, i)
657s 18 tests, 18 passed, 0 known failure, 0 skipped
657s [inst/dist_fun/mvnpdf.m]
657s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/mvnpdf.m
657s ***** demo
657s  mu = [1, -1];
657s  sigma = [0.9, 0.4; 0.4, 0.3];
657s  [X1, X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
657s  x = [X1(:), X2(:)];
657s  p = mvnpdf (x, mu, sigma);
657s  surf (X1, X2, reshape (p, 25, 25));
657s ***** error<mvnpdf: too few input arguments.> y = mvnpdf ();
657s ***** error<mvnpdf: too few dimensions in X.> y = mvnpdf ([]);
657s ***** error<mvnpdf: wrong dimensions in X.> y = mvnpdf (ones (3,3,3));
657s ***** error<mvnpdf: columns in X and MU mismatch.> ...
657s  y = mvnpdf (ones (10, 2), [4, 2, 3]);
657s ***** error<mvnpdf: rows in X and MU mismatch.> ...
657s  y = mvnpdf (ones (10, 2), [4, 2; 3, 2]);
657s ***** error<mvnpdf: wrong size of MU.> ...
657s  y = mvnpdf (ones (10, 2), ones (3, 3, 3));
657s ***** shared x, mu, sigma
657s  x = [1, 2, 5, 4, 6];
657s  mu = [2, 0, -1, 1, 4];
657s  sigma = [2, 2, 2, 2, 2];
657s ***** assert (mvnpdf (x), 1.579343404440977e-20, 1e-30);
657s ***** assert (mvnpdf (x, mu), 1.899325144348102e-14, 1e-25);
657s ***** assert (mvnpdf (x, mu, sigma), 2.449062307156273e-09, 1e-20);
657s 9 tests, 9 passed, 0 known failure, 0 skipped
657s [inst/dist_fun/gevcdf.m]
657s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/gevcdf.m
657s ***** demo
657s  ## Plot various CDFs from the generalized extreme value distribution
657s  x = -1:0.001:10;
657s  p1 = gevcdf (x, 1, 1, 1);
657s  p2 = gevcdf (x, 0.5, 1, 1);
657s  p3 = gevcdf (x, 1, 1, 5);
657s  p4 = gevcdf (x, 1, 2, 5);
657s  p5 = gevcdf (x, 1, 5, 5);
657s  p6 = gevcdf (x, 1, 0.5, 5);
657s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", ...
657s        x, p4, "-c", x, p5, "-m", x, p6, "-k")
657s  grid on
657s  xlim ([-1, 10])
657s  legend ({"k = 1, σ = 1, μ = 1", "k = 0.5, σ = 1, μ = 1", ...
657s           "k = 1, σ = 1, μ = 5", "k = 1, σ = 2, μ = 5", ...
657s           "k = 1, σ = 5, μ = 5", "k = 1, σ = 0.5, μ = 5"}, ...
657s          "location", "southeast")
657s  title ("Generalized extreme value CDF")
657s  xlabel ("values in x")
657s  ylabel ("probability")
657s ***** test
657s  x = 0:0.5:2.5;
657s  sigma = 1:6;
657s  k = 1;
657s  mu = 0;
657s  p = gevcdf (x, k, sigma, mu);
657s  expected_p = [0.36788, 0.44933, 0.47237, 0.48323, 0.48954, 0.49367];
657s  assert (p, expected_p, 0.001);
657s ***** test
657s  x = -0.5:0.5:2.5;
657s  sigma = 0.5;
657s  k = 1;
657s  mu = 0;
657s  p = gevcdf (x, k, sigma, mu);
657s  expected_p = [0, 0.36788, 0.60653, 0.71653, 0.77880, 0.81873, 0.84648];
657s  assert (p, expected_p, 0.001);
657s ***** test # check for continuity for k near 0
657s  x = 1;
657s  sigma = 0.5;
657s  k = -0.03:0.01:0.03;
657s  mu = 0;
657s  p = gevcdf (x, k, sigma, mu);
657s  expected_p = [0.88062, 0.87820, 0.87580, 0.87342, 0.87107, 0.86874, 0.86643];
657s  assert (p, expected_p, 0.001);
657s ***** error<gevcdf: function called with too few input arguments.> gevcdf ()
657s ***** error<gevcdf: function called with too few input arguments.> gevcdf (1)
657s ***** error<gevcdf: function called with too few input arguments.> gevcdf (1, 2)
657s ***** error<gevcdf: function called with too few input arguments.> gevcdf (1, 2, 3)
657s ***** error<gevcdf: function called with too many inputs> ...
657s  gevcdf (1, 2, 3, 4, 5, 6)
657s ***** error<gevcdf: invalid argument for upper tail.> gevcdf (1, 2, 3, 4, "tail")
657s ***** error<gevcdf: invalid argument for upper tail.> gevcdf (1, 2, 3, 4, 5)
657s ***** error<gevcdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
657s  gevcdf (ones (3), ones (2), ones(2), ones(2))
657s ***** error<gevcdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
657s  gevcdf (ones (2), ones (3), ones(2), ones(2))
657s ***** error<gevcdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
657s  gevcdf (ones (2), ones (2), ones(3), ones(2))
657s ***** error<gevcdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
657s  gevcdf (ones (2), ones (2), ones(2), ones(3))
657s ***** error<gevcdf: X, K, SIGMA, and MU must not be complex.> gevcdf (i, 2, 3, 4)
657s ***** error<gevcdf: X, K, SIGMA, and MU must not be complex.> gevcdf (1, i, 3, 4)
657s ***** error<gevcdf: X, K, SIGMA, and MU must not be complex.> gevcdf (1, 2, i, 4)
657s ***** error<gevcdf: X, K, SIGMA, and MU must not be complex.> gevcdf (1, 2, 3, i)
657s 18 tests, 18 passed, 0 known failure, 0 skipped
657s [inst/dist_fun/unidpdf.m]
657s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/unidpdf.m
657s ***** demo
657s  ## Plot various PDFs from the discrete uniform distribution
657s  x = 0:10;
657s  y1 = unidpdf (x, 5);
657s  y2 = unidpdf (x, 9);
657s  plot (x, y1, "*b", x, y2, "*g")
657s  grid on
657s  xlim ([0, 10])
657s  ylim ([0, 0.25])
657s  legend ({"N = 5", "N = 9"}, "location", "northeast")
657s  title ("Descrete uniform PDF")
657s  xlabel ("values in x")
657s  ylabel ("density")
657s ***** shared x, y
657s  x = [-1 0 1 2 10 11];
657s  y = [0 0 0.1 0.1 0.1 0];
657s ***** assert (unidpdf (x, 10*ones (1,6)), y)
657s ***** assert (unidpdf (x, 10), y)
657s ***** assert (unidpdf (x, 10*[0 NaN 1 1 1 1]), [NaN NaN y(3:6)])
657s ***** assert (unidpdf ([x, NaN], 10), [y, NaN])
657s ***** assert (unidpdf (single ([x, NaN]), 10), single ([y, NaN]))
657s ***** assert (unidpdf ([x, NaN], single (10)), single ([y, NaN]))
657s ***** error<unidpdf: function called with too few input arguments.> unidpdf ()
657s ***** error<unidpdf: function called with too few input arguments.> unidpdf (1)
657s ***** error<unidpdf: X and N must be of common size or scalars.> ...
657s  unidpdf (ones (3), ones (2))
657s ***** error<unidpdf: X and N must be of common size or scalars.> ...
657s  unidpdf (ones (2), ones (3))
657s ***** error<unidpdf: X and N must not be complex.> unidpdf (i, 2)
657s ***** error<unidpdf: X and N must not be complex.> unidpdf (2, i)
657s 12 tests, 12 passed, 0 known failure, 0 skipped
657s [inst/dist_fun/tpdf.m]
657s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/tpdf.m
657s ***** demo
657s  ## Plot various PDFs from the Student's T distribution
657s  x = -5:0.01:5;
657s  y1 = tpdf (x, 1);
657s  y2 = tpdf (x, 2);
657s  y3 = tpdf (x, 5);
657s  y4 = tpdf (x, Inf);
657s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-m")
657s  grid on
657s  xlim ([-5, 5])
657s  ylim ([0, 0.41])
657s  legend ({"df = 1", "df = 2", ...
657s           "df = 5", 'df = \infty'}, "location", "northeast")
657s  title ("Student's T PDF")
657s  xlabel ("values in x")
657s  ylabel ("density")
657s ***** test
657s  x = rand (10,1);
657s  y = 1./(pi * (1 + x.^2));
657s  assert (tpdf (x, 1), y, 5*eps);
657s ***** shared x, y
657s  x = [-Inf 0 0.5 1 Inf];
657s  y = 1./(pi * (1 + x.^2));
657s ***** assert (tpdf (x, ones (1,5)), y, eps)
657s ***** assert (tpdf (x, 1), y, eps)
657s ***** assert (tpdf (x, [0 NaN 1 1 1]), [NaN NaN y(3:5)], eps)
657s ***** assert (tpdf (x, Inf), normpdf (x))
657s ***** assert (tpdf ([x, NaN], 1), [y, NaN], eps)
657s ***** assert (tpdf (single ([x, NaN]), 1), single ([y, NaN]), eps ("single"))
657s ***** assert (tpdf ([x, NaN], single (1)), single ([y, NaN]), eps ("single"))
657s ***** error<tpdf: function called with too few input arguments.> tpdf ()
657s ***** error<tpdf: function called with too few input arguments.> tpdf (1)
657s ***** error<tpdf: X and DF must be of common size or scalars.> ...
657s  tpdf (ones (3), ones (2))
657s ***** error<tpdf: X and DF must be of common size or scalars.> ...
657s  tpdf (ones (2), ones (3))
657s ***** error<tpdf: X and DF must not be complex.> tpdf (i, 2)
657s ***** error<tpdf: X and DF must not be complex.> tpdf (2, i)
657s 14 tests, 14 passed, 0 known failure, 0 skipped
657s [inst/dist_fun/copularnd.m]
657s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/copularnd.m
657s ***** test
657s  theta = 0.5;
657s  r = copularnd ("Gaussian", theta);
657s  assert (size (r), [1, 2]);
657s  assert (all ((r >= 0) & (r <= 1)));
657s ***** test
657s  theta = 0.5;
657s  df = 2;
657s  r = copularnd ("t", theta, df);
657s  assert (size (r), [1, 2]);
657s  assert (all ((r >= 0) & (r <= 1)));
657s ***** test
657s  theta = 0.5;
657s  r = copularnd ("Clayton", theta);
657s  assert (size (r), [1, 2]);
657s  assert (all ((r >= 0) & (r <= 1)));
657s ***** test
657s  theta = 0.5;
657s  n = 2;
657s  r = copularnd ("Clayton", theta, n);
657s  assert (size (r), [n, 2]);
657s  assert (all ((r >= 0) & (r <= 1)));
657s ***** test
657s  theta = [1; 2];
657s  n = 2;
657s  d = 3;
657s  r = copularnd ("Clayton", theta, n, d);
657s  assert (size (r), [n, d]);
657s  assert (all ((r >= 0) & (r <= 1)));
657s 5 tests, 5 passed, 0 known failure, 0 skipped
657s [inst/dist_fun/laplacepdf.m]
657s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/laplacepdf.m
657s ***** demo
657s  ## Plot various PDFs from the Laplace distribution
657s  x = -10:0.01:10;
657s  y1 = laplacepdf (x, 0, 1);
657s  y2 = laplacepdf (x, 0, 2);
657s  y3 = laplacepdf (x, 0, 4);
657s  y4 = laplacepdf (x, -5, 4);
657s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
657s  grid on
657s  xlim ([-10, 10])
657s  ylim ([0, 0.6])
657s  legend ({"μ = 0, β = 1", "μ = 0, β = 2", ...
657s           "μ = 0, β = 4", "μ = -5, β = 4"}, "location", "northeast")
657s  title ("Laplace PDF")
657s  xlabel ("values in x")
657s  ylabel ("density")
657s ***** shared x, y
657s  x = [-Inf -log(2) 0 log(2) Inf];
657s  y = [0, 1/4, 1/2, 1/4, 0];
657s ***** assert (laplacepdf ([x, NaN], 0, 1), [y, NaN])
657s ***** assert (laplacepdf (x, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), 0.25, 0])
657s ***** assert (laplacepdf (single ([x, NaN]), 0, 1), single ([y, NaN]))
657s ***** assert (laplacepdf ([x, NaN], single (0), 1), single ([y, NaN]))
657s ***** assert (laplacepdf ([x, NaN], 0, single (1)), single ([y, NaN]))
657s ***** error<laplacepdf: function called with too few input arguments.> laplacepdf ()
657s ***** error<laplacepdf: function called with too few input arguments.> laplacepdf (1)
657s ***** error<laplacepdf: function called with too few input arguments.> ...
657s  laplacepdf (1, 2)
657s ***** error<laplacepdf: function called with too many inputs> laplacepdf (1, 2, 3, 4)
657s ***** error<laplacepdf: X, MU, and BETA must be of common size or scalars.> ...
657s  laplacepdf (1, ones (2), ones (3))
657s ***** error<laplacepdf: X, MU, and BETA must be of common size or scalars.> ...
657s  laplacepdf (ones (2), 1, ones (3))
657s ***** error<laplacepdf: X, MU, and BETA must be of common size or scalars.> ...
657s  laplacepdf (ones (2), ones (3), 1)
657s ***** error<laplacepdf: X, MU, and BETA must not be complex.> laplacepdf (i, 2, 3)
657s ***** error<laplacepdf: X, MU, and BETA must not be complex.> laplacepdf (1, i, 3)
657s ***** error<laplacepdf: X, MU, and BETA must not be complex.> laplacepdf (1, 2, i)
657s 15 tests, 15 passed, 0 known failure, 0 skipped
657s [inst/dist_fun/raylpdf.m]
657s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/raylpdf.m
657s ***** demo
657s  ## Plot various PDFs from the Rayleigh distribution
657s  x = 0:0.01:10;
657s  y1 = raylpdf (x, 0.5);
657s  y2 = raylpdf (x, 1);
657s  y3 = raylpdf (x, 2);
657s  y4 = raylpdf (x, 3);
657s  y5 = raylpdf (x, 4);
657s  plot (x, y1, "-b", x, y2, "g", x, y3, "-r", x, y4, "-m", x, y5, "-k")
657s  grid on
657s  ylim ([0, 1.25])
657s  legend ({"σ = 0,5", "σ = 1", "σ = 2", ...
657s           "σ = 3", "σ = 4"}, "location", "northeast")
657s  title ("Rayleigh PDF")
657s  xlabel ("values in x")
657s  ylabel ("density")
657s ***** test
657s  x = 0:0.5:2.5;
657s  sigma = 1:6;
657s  y = raylpdf (x, sigma);
657s  expected_y = [0.0000, 0.1212, 0.1051, 0.0874, 0.0738, 0.0637];
657s  assert (y, expected_y, 0.001);
657s ***** test
657s  x = 0:0.5:2.5;
657s  y = raylpdf (x, 0.5);
657s  expected_y = [0.0000, 1.2131, 0.5413, 0.0667, 0.0027, 0.0000];
657s  assert (y, expected_y, 0.001);
657s ***** error<raylpdf: function called with too few input arguments.> raylpdf ()
657s ***** error<raylpdf: function called with too few input arguments.> raylpdf (1)
657s ***** error<raylpdf: X and SIGMA must be of common size or scalars.> ...
657s  raylpdf (ones (3), ones (2))
657s ***** error<raylpdf: X and SIGMA must be of common size or scalars.> ...
657s  raylpdf (ones (2), ones (3))
657s ***** error<raylpdf: X and SIGMA must not be complex.> raylpdf (i, 2)
657s ***** error<raylpdf: X and SIGMA must not be complex.> raylpdf (2, i)
657s 8 tests, 8 passed, 0 known failure, 0 skipped
657s [inst/dist_fun/chi2rnd.m]
657s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/chi2rnd.m
657s ***** assert (size (chi2rnd (2)), [1, 1])
657s ***** assert (size (chi2rnd (ones (2,1))), [2, 1])
657s ***** assert (size (chi2rnd (ones (2,2))), [2, 2])
657s ***** assert (size (chi2rnd (1, 3)), [3, 3])
657s ***** assert (size (chi2rnd (1, [4 1])), [4, 1])
657s ***** assert (size (chi2rnd (1, 4, 1)), [4, 1])
657s ***** assert (size (chi2rnd (1, 4, 1)), [4, 1])
657s ***** assert (size (chi2rnd (1, 4, 1, 5)), [4, 1, 5])
657s ***** assert (size (chi2rnd (1, 0, 1)), [0, 1])
657s ***** assert (size (chi2rnd (1, 1, 0)), [1, 0])
657s ***** assert (size (chi2rnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
657s ***** assert (class (chi2rnd (2)), "double")
657s ***** assert (class (chi2rnd (single (2))), "single")
657s ***** assert (class (chi2rnd (single ([2 2]))), "single")
657s ***** error<chi2rnd: function called with too few input arguments.> chi2rnd ()
657s ***** error<chi2rnd: DF must not be complex.> chi2rnd (i)
657s ***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
657s  chi2rnd (1, -1)
657s ***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
657s  chi2rnd (1, 1.2)
657s ***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
657s  chi2rnd (1, ones (2))
657s ***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
657s  chi2rnd (1, [2 -1 2])
657s ***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
657s  chi2rnd (1, [2 0 2.5])
657s ***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
657s  chi2rnd (ones (2), ones (2))
657s ***** error<chi2rnd: dimensions must be non-negative integers.> ...
657s  chi2rnd (1, 2, -1, 5)
657s ***** error<chi2rnd: dimensions must be non-negative integers.> ...
657s  chi2rnd (1, 2, 1.5, 5)
657s ***** error<chi2rnd: DF must be scalar or of size SZ.> chi2rnd (ones (2,2), 3)
657s ***** error<chi2rnd: DF must be scalar or of size SZ.> chi2rnd (ones (2,2), [3, 2])
657s ***** error<chi2rnd: DF must be scalar or of size SZ.> chi2rnd (ones (2,2), 2, 3)
657s 27 tests, 27 passed, 0 known failure, 0 skipped
657s [inst/dist_fun/frnd.m]
657s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/frnd.m
657s ***** assert (size (frnd (1, 1)), [1 1])
657s ***** assert (size (frnd (1, ones (2,1))), [2, 1])
657s ***** assert (size (frnd (1, ones (2,2))), [2, 2])
657s ***** assert (size (frnd (ones (2,1), 1)), [2, 1])
657s ***** assert (size (frnd (ones (2,2), 1)), [2, 2])
657s ***** assert (size (frnd (1, 1, 3)), [3, 3])
657s ***** assert (size (frnd (1, 1, [4, 1])), [4, 1])
657s ***** assert (size (frnd (1, 1, 4, 1)), [4, 1])
657s ***** assert (size (frnd (1, 1, 4, 1, 5)), [4, 1, 5])
657s ***** assert (size (frnd (1, 1, 0, 1)), [0, 1])
657s ***** assert (size (frnd (1, 1, 1, 0)), [1, 0])
657s ***** assert (size (frnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
657s ***** assert (class (frnd (1, 1)), "double")
657s ***** assert (class (frnd (1, single (1))), "single")
657s ***** assert (class (frnd (1, single ([1, 1]))), "single")
657s ***** assert (class (frnd (single (1), 1)), "single")
657s ***** assert (class (frnd (single ([1, 1]), 1)), "single")
657s ***** error<frnd: function called with too few input arguments.> frnd ()
657s ***** error<frnd: function called with too few input arguments.> frnd (1)
657s ***** error<frnd: DF1 and DF2 must be of common size or scalars.> ...
657s  frnd (ones (3), ones (2))
657s ***** error<frnd: DF1 and DF2 must be of common size or scalars.> ...
657s  frnd (ones (2), ones (3))
657s ***** error<frnd: DF1 and DF2 must not be complex.> frnd (i, 2, 3)
657s ***** error<frnd: DF1 and DF2 must not be complex.> frnd (1, i, 3)
657s ***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
657s  frnd (1, 2, -1)
657s ***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
657s  frnd (1, 2, 1.2)
657s ***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
657s  frnd (1, 2, ones (2))
657s ***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
657s  frnd (1, 2, [2 -1 2])
657s ***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
657s  frnd (1, 2, [2 0 2.5])
657s ***** error<frnd: dimensions must be non-negative integers.> ...
657s  frnd (1, 2, 2, -1, 5)
657s ***** error<frnd: dimensions must be non-negative integers.> ...
657s  frnd (1, 2, 2, 1.5, 5)
657s ***** error<frnd: DF1 and DF2 must be scalars or of size SZ.> ...
657s  frnd (2, ones (2), 3)
657s ***** error<frnd: DF1 and DF2 must be scalars or of size SZ.> ...
657s  frnd (2, ones (2), [3, 2])
657s ***** error<frnd: DF1 and DF2 must be scalars or of size SZ.> ...
657s  frnd (2, ones (2), 3, 2)
657s 33 tests, 33 passed, 0 known failure, 0 skipped
657s [inst/dist_fun/binocdf.m]
657s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/binocdf.m
657s ***** demo
657s  ## Plot various CDFs from the binomial distribution
657s  x = 0:40;
657s  p1 = binocdf (x, 20, 0.5);
657s  p2 = binocdf (x, 20, 0.7);
657s  p3 = binocdf (x, 40, 0.5);
657s  plot (x, p1, "*b", x, p2, "*g", x, p3, "*r")
657s  grid on
657s  legend ({"n = 20, ps = 0.5", "n = 20, ps = 0.7", ...
657s           "n = 40, ps = 0.5"}, "location", "southeast")
657s  title ("Binomial CDF")
657s  xlabel ("values in x (number of successes)")
657s  ylabel ("probability")
657s ***** shared x, p, p1
657s  x = [-1 0 1 2 3];
657s  p = [0 1/4 3/4 1 1];
657s  p1 = 1 - p;
657s ***** assert (binocdf (x, 2 * ones (1, 5), 0.5 * ones (1, 5)), p, eps)
657s ***** assert (binocdf (x, 2, 0.5 * ones (1, 5)), p, eps)
657s ***** assert (binocdf (x, 2 * ones (1, 5), 0.5), p, eps)
657s ***** assert (binocdf (x, 2 * [0 -1 NaN 1.1 1], 0.5), [0 NaN NaN NaN 1])
657s ***** assert (binocdf (x, 2, 0.5 * [0 -1 NaN 3 1]), [0 NaN NaN NaN 1])
657s ***** assert (binocdf ([x(1:2) NaN x(4:5)], 2, 0.5), [p(1:2) NaN p(4:5)], eps)
657s ***** assert (binocdf (99, 100, 0.1, "upper"), 1e-100, 1e-112);
657s ***** assert (binocdf (x, 2 * ones (1, 5), 0.5*ones (1,5), "upper"), p1, eps)
657s ***** assert (binocdf (x, 2, 0.5 * ones (1, 5), "upper"), p1, eps)
657s ***** assert (binocdf (x, 2 * ones (1, 5), 0.5, "upper"), p1, eps)
657s ***** assert (binocdf (x, 2 * [0 -1 NaN 1.1 1], 0.5, "upper"), [1 NaN NaN NaN 0])
657s ***** assert (binocdf (x, 2, 0.5 * [0 -1 NaN 3 1], "upper"), [1 NaN NaN NaN 0])
657s ***** assert (binocdf ([x(1:2) NaN x(4:5)], 2, 0.5, "upper"), [p1(1:2) NaN p1(4:5)])
657s ***** assert (binocdf ([x, NaN], 2, 0.5), [p, NaN], eps)
657s ***** assert (binocdf (single ([x, NaN]), 2, 0.5), single ([p, NaN]))
657s ***** assert (binocdf ([x, NaN], single (2), 0.5), single ([p, NaN]))
657s ***** assert (binocdf ([x, NaN], 2, single (0.5)), single ([p, NaN]))
657s ***** error<binocdf: function called with too few input arguments.> binocdf ()
657s ***** error<binocdf: function called with too few input arguments.> binocdf (1)
657s ***** error<binocdf: function called with too few input arguments.> binocdf (1, 2)
657s ***** error<binocdf: function called with too many inputs> binocdf (1, 2, 3, 4, 5)
657s ***** error<binocdf: invalid argument for upper tail.> binocdf (1, 2, 3, "tail")
657s ***** error<binocdf: invalid argument for upper tail.> binocdf (1, 2, 3, 4)
657s ***** error<binocdf: X, N, and PS must be of common size or scalars.> ...
657s  binocdf (ones (3), ones (2), ones (2))
657s ***** error<binocdf: X, N, and PS must be of common size or scalars.> ...
657s  binocdf (ones (2), ones (3), ones (2))
657s ***** error<binocdf: X, N, and PS must be of common size or scalars.> ...
657s  binocdf (ones (2), ones (2), ones (3))
657s ***** error<binocdf: X, N, and PS must not be complex.> binocdf (i, 2, 2)
657s ***** error<binocdf: X, N, and PS must not be complex.> binocdf (2, i, 2)
657s ***** error<binocdf: X, N, and PS must not be complex.> binocdf (2, 2, i)
657s 29 tests, 29 passed, 0 known failure, 0 skipped
657s [inst/dist_fun/raylcdf.m]
657s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/raylcdf.m
657s ***** demo
657s  ## Plot various CDFs from the Rayleigh distribution
657s  x = 0:0.01:10;
657s  p1 = raylcdf (x, 0.5);
657s  p2 = raylcdf (x, 1);
657s  p3 = raylcdf (x, 2);
657s  p4 = raylcdf (x, 3);
657s  p5 = raylcdf (x, 4);
657s  plot (x, p1, "-b", x, p2, "g", x, p3, "-r", x, p4, "-m", x, p5, "-k")
657s  grid on
657s  ylim ([0, 1])
657s  legend ({"σ = 0.5", "σ = 1", "σ = 2", ...
657s           "σ = 3", "σ = 4"}, "location", "southeast")
657s  title ("Rayleigh CDF")
657s  xlabel ("values in x")
657s  ylabel ("probability")
657s ***** test
657s  x = 0:0.5:2.5;
657s  sigma = 1:6;
657s  p = raylcdf (x, sigma);
657s  expected_p = [0.0000, 0.0308, 0.0540, 0.0679, 0.0769, 0.0831];
657s  assert (p, expected_p, 0.001);
657s ***** test
657s  x = 0:0.5:2.5;
657s  p = raylcdf (x, 0.5);
657s  expected_p = [0.0000, 0.3935, 0.8647, 0.9889, 0.9997, 1.0000];
657s  assert (p, expected_p, 0.001);
657s ***** shared x, p
657s  x = [-1, 0, 1, 2, Inf];
657s  p = [0, 0, 0.39346934028737, 0.86466471676338, 1];
657s ***** assert (raylcdf (x, 1), p, 1e-14)
657s ***** assert (raylcdf (x, 1, "upper"), 1 - p, 1e-14)
657s ***** error<raylcdf: function called with too few input arguments.> raylcdf ()
657s ***** error<raylcdf: function called with too few input arguments.> raylcdf (1)
657s ***** error<raylcdf: invalid argument for upper tail.> raylcdf (1, 2, "uper")
657s ***** error<raylcdf: invalid argument for upper tail.> raylcdf (1, 2, 3)
657s ***** error<raylcdf: X and SIGMA must be of common size or scalars.> ...
657s  raylcdf (ones (3), ones (2))
657s ***** error<raylcdf: X and SIGMA must be of common size or scalars.> ...
657s  raylcdf (ones (2), ones (3))
657s ***** error<raylcdf: X and SIGMA must not be complex.> raylcdf (i, 2)
657s ***** error<raylcdf: X and SIGMA must not be complex.> raylcdf (2, i)
657s 12 tests, 12 passed, 0 known failure, 0 skipped
657s [inst/dist_fun/hnpdf.m]
657s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/hnpdf.m
657s ***** demo
657s  ## Plot various PDFs from the half-normal distribution
657s  x = 0:0.001:10;
657s  y1 = hnpdf (x, 0, 1);
657s  y2 = hnpdf (x, 0, 2);
657s  y3 = hnpdf (x, 0, 3);
657s  y4 = hnpdf (x, 0, 5);
657s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
657s  grid on
657s  xlim ([0, 10])
657s  ylim ([0, 0.9])
657s  legend ({"μ = 0, σ = 1", "μ = 0, σ = 2", ...
657s           "μ = 0, σ = 3", "μ = 0, σ = 5"}, "location", "northeast")
657s  title ("Half-normal PDF")
657s  xlabel ("values in x")
657s  ylabel ("density")
657s ***** demo
657s  ## Plot half-normal against normal probability density function
657s  x = -5:0.001:5;
657s  y1 = hnpdf (x, 0, 1);
657s  y2 = normpdf (x);
657s  plot (x, y1, "-b", x, y2, "-g")
657s  grid on
657s  xlim ([-5, 5])
657s  ylim ([0, 0.9])
657s  legend ({"half-normal with μ = 0, σ = 1", ...
657s           "standart normal (μ = 0, σ = 1)"}, "location", "northeast")
657s  title ("Half-normal against standard normal PDF")
657s  xlabel ("values in x")
657s  ylabel ("density")
657s ***** shared x, y
657s  x = [-Inf, -1, 0, 1/2, 1, Inf];
657s  y = [0, 0, 0.7979, 0.7041, 0.4839, 0];
657s ***** assert (hnpdf ([x, NaN], 0, 1), [y, NaN], 1e-4)
657s ***** assert (hnpdf (x, 0, [-2, -1, 0, 1, 1, 1]), [nan(1,3), y([4:6])], 1e-4)
657s ***** assert (class (hncdf (single ([x, NaN]), 0, 1)), "single")
657s ***** assert (class (hncdf ([x, NaN], 0, single (1))), "single")
657s ***** assert (class (hncdf ([x, NaN], single (0), 1)), "single")
657s ***** error<hnpdf: function called with too few input arguments.> hnpdf ()
657s ***** error<hnpdf: function called with too few input arguments.> hnpdf (1)
657s ***** error<hnpdf: function called with too few input arguments.> hnpdf (1, 2)
657s ***** error<hnpdf: X, MU, and SIGMA must be of common size or scalars.> ...
657s  hnpdf (1, ones (2), ones (3))
657s ***** error<hnpdf: X, MU, and SIGMA must be of common size or scalars.> ...
657s  hnpdf (ones (2), 1, ones (3))
657s ***** error<hnpdf: X, MU, and SIGMA must be of common size or scalars.> ...
657s  hnpdf (ones (2), ones (3), 1)
657s ***** error<hnpdf: X, MU, and SIGMA must not be complex.> hnpdf (i, 2, 3)
657s ***** error<hnpdf: X, MU, and SIGMA must not be complex.> hnpdf (1, i, 3)
657s ***** error<hnpdf: X, MU, and SIGMA must not be complex.> hnpdf (1, 2, i)
657s 14 tests, 14 passed, 0 known failure, 0 skipped
657s [inst/dist_fun/vmcdf.m]
657s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/vmcdf.m
657s ***** demo
657s  ## Plot various CDFs from the von Mises distribution
657s  x1 = [-pi:0.1:pi];
657s  p1 = vmcdf (x1, 0, 0.5);
657s  p2 = vmcdf (x1, 0, 1);
657s  p3 = vmcdf (x1, 0, 2);
657s  p4 = vmcdf (x1, 0, 4);
657s  plot (x1, p1, "-r", x1, p2, "-g", x1, p3, "-b", x1, p4, "-c")
657s  grid on
657s  xlim ([-pi, pi])
657s  legend ({"μ = 0, k = 0.5", "μ = 0, k = 1", ...
657s           "μ = 0, k = 2", "μ = 0, k = 4"}, "location", "northwest")
657s  title ("Von Mises CDF")
657s  xlabel ("values in x")
657s  ylabel ("probability")
657s ***** shared x, p0, p1
657s  x = [-pi:pi/2:pi];
657s  p0 = [0, 0.10975, 0.5, 0.89025, 1];
657s  p1 = [0, 0.03752, 0.5, 0.99622, 1];
657s ***** assert (vmcdf (x, 0, 1), p0, 1e-5)
657s ***** assert (vmcdf (x, 0, 1, "upper"), 1 - p0, 1e-5)
657s ***** assert (vmcdf (x, zeros (1,5), ones (1,5)), p0, 1e-5)
657s ***** assert (vmcdf (x, zeros (1,5), ones (1,5), "upper"), 1 - p0, 1e-5)
657s ***** assert (vmcdf (x, 0, [1 2 3 4 5]), p1, 1e-5)
658s ***** assert (vmcdf (x, 0, [1 2 3 4 5], "upper"), 1 - p1, 1e-5)
658s ***** assert (isa (vmcdf (single (pi), 0, 1), "single"), true)
658s ***** assert (isa (vmcdf (pi, single (0), 1), "single"), true)
658s ***** assert (isa (vmcdf (pi, 0, single (1)), "single"), true)
658s ***** error<vmcdf: function called with too few input arguments.> vmcdf ()
658s ***** error<vmcdf: function called with too few input arguments.> vmcdf (1)
658s ***** error<vmcdf: function called with too few input arguments.> vmcdf (1, 2)
658s ***** error<vmcdf: invalid argument for upper tail.> vmcdf (1, 2, 3, "tail")
658s ***** error<vmcdf: invalid argument for upper tail.> vmcdf (1, 2, 3, 4)
658s ***** error<vmcdf: X, MU, and K must be of common size or scalars.> ...
658s  vmcdf (ones (3), ones (2), ones (2))
658s ***** error<vmcdf: X, MU, and K must be of common size or scalars.> ...
658s  vmcdf (ones (2), ones (3), ones (2))
658s ***** error<vmcdf: X, MU, and K must be of common size or scalars.> ...
658s  vmcdf (ones (2), ones (2), ones (3))
658s ***** error<vmcdf: X, MU, and K must not be complex.> vmcdf (i, 2, 2)
658s ***** error<vmcdf: X, MU, and K must not be complex.> vmcdf (2, i, 2)
658s ***** error<vmcdf: X, MU, and K must not be complex.> vmcdf (2, 2, i)
658s 20 tests, 20 passed, 0 known failure, 0 skipped
658s [inst/dist_fun/vmpdf.m]
658s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/vmpdf.m
658s ***** demo
658s  ## Plot various PDFs from the von Mises distribution
658s  x1 = [-pi:0.1:pi];
658s  y1 = vmpdf (x1, 0, 0.5);
658s  y2 = vmpdf (x1, 0, 1);
658s  y3 = vmpdf (x1, 0, 2);
658s  y4 = vmpdf (x1, 0, 4);
658s  plot (x1, y1, "-r", x1, y2, "-g", x1, y3, "-b", x1, y4, "-c")
658s  grid on
658s  xlim ([-pi, pi])
658s  ylim ([0, 0.8])
658s  legend ({"μ = 0, k = 0.5", "μ = 0, k = 1", ...
658s           "μ = 0, k = 2", "μ = 0, k = 4"}, "location", "northwest")
658s  title ("Von Mises PDF")
658s  xlabel ("values in x")
658s  ylabel ("density")
658s ***** shared x, y0, y1
658s  x = [-pi:pi/2:pi];
658s  y0 = [0.046245, 0.125708, 0.341710, 0.125708, 0.046245];
658s  y1 = [0.046245, 0.069817, 0.654958, 0.014082, 0.000039];
658s ***** assert (vmpdf (x, 0, 1), y0, 1e-5)
658s ***** assert (vmpdf (x, zeros (1,5), ones (1,5)), y0, 1e-6)
658s ***** assert (vmpdf (x, 0, [1 2 3 4 5]), y1, 1e-6)
658s ***** assert (isa (vmpdf (single (pi), 0, 1), "single"), true)
658s ***** assert (isa (vmpdf (pi, single (0), 1), "single"), true)
658s ***** assert (isa (vmpdf (pi, 0, single (1)), "single"), true)
658s ***** error<vmpdf: function called with too few input arguments.> vmpdf ()
658s ***** error<vmpdf: function called with too few input arguments.> vmpdf (1)
658s ***** error<vmpdf: function called with too few input arguments.> vmpdf (1, 2)
658s ***** error<vmpdf: X, MU, and K must be of common size or scalars.> ...
658s  vmpdf (ones (3), ones (2), ones (2))
658s ***** error<vmpdf: X, MU, and K must be of common size or scalars.> ...
658s  vmpdf (ones (2), ones (3), ones (2))
658s ***** error<vmpdf: X, MU, and K must be of common size or scalars.> ...
658s  vmpdf (ones (2), ones (2), ones (3))
658s ***** error<vmpdf: X, MU, and K must not be complex.> vmpdf (i, 2, 2)
658s ***** error<vmpdf: X, MU, and K must not be complex.> vmpdf (2, i, 2)
658s ***** error<vmpdf: X, MU, and K must not be complex.> vmpdf (2, 2, i)
658s 15 tests, 15 passed, 0 known failure, 0 skipped
658s [inst/dist_fun/fpdf.m]
658s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/fpdf.m
658s ***** demo
658s  ## Plot various PDFs from the F distribution
658s  x = 0.01:0.01:4;
658s  y1 = fpdf (x, 1, 1);
658s  y2 = fpdf (x, 2, 1);
658s  y3 = fpdf (x, 5, 2);
658s  y4 = fpdf (x, 10, 1);
658s  y5 = fpdf (x, 100, 100);
658s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
658s  grid on
658s  ylim ([0, 2.5])
658s  legend ({"df1 = 1, df2 = 2", "df1 = 2, df2 = 1", ...
658s           "df1 = 5, df2 = 2", "df1 = 10, df2 = 1", ...
658s           "df1 = 100, df2 = 100"}, "location", "northeast")
658s  title ("F PDF")
658s  xlabel ("values in x")
658s  ylabel ("density")
658s ***** shared x, y
658s  x = [-1 0 0.5 1 2];
658s  y = [0 0 4/9 1/4 1/9];
658s ***** assert (fpdf (x, 2*ones (1,5), 2*ones (1,5)), y, eps)
658s ***** assert (fpdf (x, 2, 2*ones (1,5)), y, eps)
658s ***** assert (fpdf (x, 2*ones (1,5), 2), y, eps)
658s ***** assert (fpdf (x, [0 NaN Inf 2 2], 2), [NaN NaN NaN y(4:5)], eps)
658s ***** assert (fpdf (x, 2, [0 NaN Inf 2 2]), [NaN NaN NaN y(4:5)], eps)
658s ***** assert (fpdf ([x, NaN], 2, 2), [y, NaN], eps)
658s ***** test #F (x, 1, df1) == T distribution (sqrt (x), df1) / sqrt (x)
658s  rand ("seed", 1234);    # for reproducibility
658s  xr = rand (10,1);
658s  xr = xr(x > 0.1 & x < 0.9);
658s  yr = tpdf (sqrt (xr), 2) ./ sqrt (xr);
658s  assert (fpdf (xr, 1, 2), yr, 5*eps);
658s ***** assert (fpdf (single ([x, NaN]), 2, 2), single ([y, NaN]), eps ("single"))
658s ***** assert (fpdf ([x, NaN], single (2), 2), single ([y, NaN]), eps ("single"))
658s ***** assert (fpdf ([x, NaN], 2, single (2)), single ([y, NaN]), eps ("single"))
658s ***** error<fpdf: function called with too few input arguments.> fpdf ()
658s ***** error<fpdf: function called with too few input arguments.> fpdf (1)
658s ***** error<fpdf: function called with too few input arguments.> fpdf (1,2)
658s ***** error<fpdf: X, DF1, and DF2 must be of common size or scalars.> ...
658s  fpdf (ones (3), ones (2), ones (2))
658s ***** error<fpdf: X, DF1, and DF2 must be of common size or scalars.> ...
658s  fpdf (ones (2), ones (3), ones (2))
658s ***** error<fpdf: X, DF1, and DF2 must be of common size or scalars.> ...
658s  fpdf (ones (2), ones (2), ones (3))
658s ***** error<fpdf: X, DF1, and DF2 must not be complex.> fpdf (i, 2, 2)
658s ***** error<fpdf: X, DF1, and DF2 must not be complex.> fpdf (2, i, 2)
658s ***** error<fpdf: X, DF1, and DF2 must not be complex.> fpdf (2, 2, i)
658s 19 tests, 19 passed, 0 known failure, 0 skipped
658s [inst/dist_fun/betainv.m]
658s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/betainv.m
658s ***** demo
658s  ## Plot various iCDFs from the Beta distribution
658s  p = 0.001:0.001:0.999;
658s  x1 = betainv (p, 0.5, 0.5);
658s  x2 = betainv (p, 5, 1);
658s  x3 = betainv (p, 1, 3);
658s  x4 = betainv (p, 2, 2);
658s  x5 = betainv (p, 2, 5);
658s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
658s  grid on
658s  legend ({"α = β = 0.5", "α = 5, β = 1", "α = 1, β = 3", ...
658s           "α = 2, β = 2", "α = 2, β = 5"}, "location", "southeast")
658s  title ("Beta iCDF")
658s  xlabel ("probability")
658s  ylabel ("values in x")
658s ***** shared p
658s  p = [-1 0 0.75 1 2];
658s ***** assert (betainv (p, ones (1,5), 2*ones (1,5)), [NaN 0 0.5 1 NaN], eps)
658s ***** assert (betainv (p, 1, 2*ones (1,5)), [NaN 0 0.5 1 NaN], eps)
658s ***** assert (betainv (p, ones (1,5), 2), [NaN 0 0.5 1 NaN], eps)
658s ***** assert (betainv (p, [1 0 NaN 1 1], 2), [NaN NaN NaN 1 NaN])
658s ***** assert (betainv (p, 1, 2*[1 0 NaN 1 1]), [NaN NaN NaN 1 NaN])
658s ***** assert (betainv ([p(1:2) NaN p(4:5)], 1, 2), [NaN 0 NaN 1 NaN])
658s ***** assert (betainv ([p, NaN], 1, 2), [NaN 0 0.5 1 NaN NaN], eps)
658s ***** assert (betainv (single ([p, NaN]), 1, 2), single ([NaN 0 0.5 1 NaN NaN]))
658s ***** assert (betainv ([p, NaN], single (1), 2), single ([NaN 0 0.5 1 NaN NaN]), eps("single"))
658s ***** assert (betainv ([p, NaN], 1, single (2)), single ([NaN 0 0.5 1 NaN NaN]), eps("single"))
658s ***** error<betainv: function called with too few input arguments.> betainv ()
658s ***** error<betainv: function called with too few input arguments.> betainv (1)
658s ***** error<betainv: function called with too few input arguments.> betainv (1,2)
658s ***** error<betainv: function called with too many inputs> betainv (1,2,3,4)
658s ***** error<betainv: P, A, and B must be of common size or scalars.> ...
658s  betainv (ones (3), ones (2), ones (2))
658s ***** error<betainv: P, A, and B must be of common size or scalars.> ...
658s  betainv (ones (2), ones (3), ones (2))
658s ***** error<betainv: P, A, and B must be of common size or scalars.> ...
658s  betainv (ones (2), ones (2), ones (3))
658s ***** error<betainv: P, A, and B must not be complex.> betainv (i, 2, 2)
658s ***** error<betainv: P, A, and B must not be complex.> betainv (2, i, 2)
658s ***** error<betainv: P, A, and B must not be complex.> betainv (2, 2, i)
658s 20 tests, 20 passed, 0 known failure, 0 skipped
658s [inst/dist_fun/gaminv.m]
658s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/gaminv.m
658s ***** demo
658s  ## Plot various iCDFs from the Gamma distribution
658s  p = 0.001:0.001:0.999;
658s  x1 = gaminv (p, 1, 2);
658s  x2 = gaminv (p, 2, 2);
658s  x3 = gaminv (p, 3, 2);
658s  x4 = gaminv (p, 5, 1);
658s  x5 = gaminv (p, 9, 0.5);
658s  x6 = gaminv (p, 7.5, 1);
658s  x7 = gaminv (p, 0.5, 1);
658s  plot (p, x1, "-r", p, x2, "-g", p, x3, "-y", p, x4, "-m", ...
658s        p, x5, "-k", p, x6, "-b", p, x7, "-c")
658s  ylim ([0, 20])
658s  grid on
658s  legend ({"α = 1, β = 2", "α = 2, β = 2", "α = 3, β = 2", ...
658s           "α = 5, β = 1", "α = 9, β = 0.5", "α = 7.5, β = 1", ...
658s           "α = 0.5, β = 1"}, "location", "northwest")
658s  title ("Gamma iCDF")
658s  xlabel ("probability")
658s  ylabel ("x")
658s ***** shared p
658s  p = [-1 0 0.63212055882855778 1 2];
658s ***** assert (gaminv (p, ones (1,5), ones (1,5)), [NaN 0 1 Inf NaN], eps)
658s ***** assert (gaminv (p, 1, ones (1,5)), [NaN 0 1 Inf NaN], eps)
658s ***** assert (gaminv (p, ones (1,5), 1), [NaN 0 1 Inf NaN], eps)
658s ***** assert (gaminv (p, [1 -Inf NaN Inf 1], 1), [NaN NaN NaN NaN NaN])
658s ***** assert (gaminv (p, 1, [1 -Inf NaN Inf 1]), [NaN NaN NaN NaN NaN])
658s ***** assert (gaminv ([p(1:2) NaN p(4:5)], 1, 1), [NaN 0 NaN Inf NaN])
658s ***** assert (gaminv ([p(1:2) NaN p(4:5)], 1, 1), [NaN 0 NaN Inf NaN])
658s ***** assert (gaminv (1e-16, 1, 1), 1e-16, eps)
658s ***** assert (gaminv (1e-16, 1, 2), 2e-16, eps)
658s ***** assert (gaminv (1e-20, 3, 5), 1.957434012161815e-06, eps)
658s ***** assert (gaminv (1e-15, 1, 1), 1e-15, eps)
658s ***** assert (gaminv (1e-35, 1, 1), 1e-35, eps)
658s ***** assert (gaminv ([p, NaN], 1, 1), [NaN 0 1 Inf NaN NaN], eps)
658s ***** assert (gaminv (single ([p, NaN]), 1, 1), single ([NaN 0 1 Inf NaN NaN]), ...
658s  eps ("single"))
658s ***** assert (gaminv ([p, NaN], single (1), 1), single ([NaN 0 1 Inf NaN NaN]), ...
658s  eps ("single"))
658s ***** assert (gaminv ([p, NaN], 1, single (1)), single ([NaN 0 1 Inf NaN NaN]), ...
658s  eps ("single"))
658s ***** error<gaminv: function called with too few input arguments.> gaminv ()
658s ***** error<gaminv: function called with too few input arguments.> gaminv (1)
658s ***** error<gaminv: function called with too few input arguments.> gaminv (1,2)
658s ***** error<gaminv: P, Α, and Β must be of common size or scalars.> ...
658s  gaminv (ones (3), ones (2), ones (2))
658s ***** error<gaminv: P, Α, and Β must be of common size or scalars.> ...
658s  gaminv (ones (2), ones (3), ones (2))
658s ***** error<gaminv: P, Α, and Β must be of common size or scalars.> ...
658s  gaminv (ones (2), ones (2), ones (3))
658s ***** error<gaminv: P, Α, and Β must not be complex.> gaminv (i, 2, 2)
658s ***** error<gaminv: P, Α, and Β must not be complex.> gaminv (2, i, 2)
658s ***** error<gaminv: P, Α, and Β must not be complex.> gaminv (2, 2, i)
658s 25 tests, 25 passed, 0 known failure, 0 skipped
658s [inst/dist_fun/normcdf.m]
658s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/normcdf.m
658s ***** demo
658s  ## Plot various CDFs from the normal distribution
658s  x = -5:0.01:5;
658s  p1 = normcdf (x, 0, 0.5);
658s  p2 = normcdf (x, 0, 1);
658s  p3 = normcdf (x, 0, 2);
658s  p4 = normcdf (x, -2, 0.8);
658s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
658s  grid on
658s  xlim ([-5, 5])
658s  legend ({"μ = 0, σ = 0.5", "μ = 0, σ = 1", ...
658s           "μ = 0, σ = 2", "μ = -2, σ = 0.8"}, "location", "southeast")
658s  title ("Normal CDF")
658s  xlabel ("values in x")
658s  ylabel ("probability")
658s ***** shared x, y
658s  x = [-Inf 1 2 Inf];
658s  y = [0, 0.5, 1/2*(1+erf(1/sqrt(2))), 1];
658s ***** assert (normcdf (x, ones (1,4), ones (1,4)), y)
658s ***** assert (normcdf (x, 1, ones (1,4)), y)
658s ***** assert (normcdf (x, ones (1,4), 1), y)
658s ***** assert (normcdf (x, [0, -Inf, NaN, Inf], 1), [0, 1, NaN, NaN])
658s ***** assert (normcdf (x, 1, [Inf, NaN, -1, 0]), [NaN, NaN, NaN, 1])
658s ***** assert (normcdf ([x(1:2), NaN, x(4)], 1, 1), [y(1:2), NaN, y(4)])
658s ***** assert (normcdf (x, "upper"), [1, 0.1587, 0.0228, 0], 1e-4)
658s ***** assert (normcdf ([x, NaN], 1, 1), [y, NaN])
658s ***** assert (normcdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
658s ***** assert (normcdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
658s ***** assert (normcdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
658s ***** error<normcdf: invalid number of input arguments.> normcdf ()
658s ***** error<normcdf: invalid number of input arguments.> normcdf (1,2,3,4,5,6,7)
658s ***** error<normcdf: invalid argument for upper tail.> normcdf (1, 2, 3, 4, "uper")
658s ***** error<normcdf: X, MU, and SIGMA must be of common size or scalars.> ...
658s  normcdf (ones (3), ones (2), ones (2))
658s ***** error<normcdf: invalid size of covariance matrix.> normcdf (2, 3, 4, [1, 2])
658s ***** error<normcdf: covariance matrix is required for confidence bounds.> ...
658s  [p, plo, pup] = normcdf (1, 2, 3)
658s ***** error<normcdf: invalid value for alpha.> [p, plo, pup] = ...
658s  normcdf (1, 2, 3, [1, 0; 0, 1], 0)
658s ***** error<normcdf: invalid value for alpha.> [p, plo, pup] = ...
658s  normcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
658s ***** error<normcdf: invalid value for alpha.> [p, plo, pup] = ...
658s  normcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
658s ***** error<normcdf: X, MU, and SIGMA must not be complex.> normcdf (i, 2, 2)
658s ***** error<normcdf: X, MU, and SIGMA must not be complex.> normcdf (2, i, 2)
658s ***** error<normcdf: X, MU, and SIGMA must not be complex.> normcdf (2, 2, i)
658s ***** error<normcdf: bad covariance matrix.> ...
658s  [p, plo, pup] =normcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
658s 24 tests, 24 passed, 0 known failure, 0 skipped
658s [inst/dist_fun/ricepdf.m]
658s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/ricepdf.m
658s ***** demo
658s  ## Plot various PDFs from the Rician distribution
658s  x = 0:0.01:8;
658s  y1 = ricepdf (x, 0, 1);
658s  y2 = ricepdf (x, 0.5, 1);
658s  y3 = ricepdf (x, 1, 1);
658s  y4 = ricepdf (x, 2, 1);
658s  y5 = ricepdf (x, 4, 1);
658s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-m", x, y5, "-k")
658s  grid on
658s  ylim ([0, 0.65])
658s  xlim ([0, 8])
658s  legend ({"s = 0, σ = 1", "s = 0.5, σ = 1", "s = 1, σ = 1", ...
658s           "s = 2, σ = 1", "s = 4, σ = 1"}, "location", "northeast")
658s  title ("Rician PDF")
658s  xlabel ("values in x")
658s  ylabel ("density")
658s ***** shared x, y
658s  x = [-1 0 0.5 1 2];
658s  y = [0 0 0.1073 0.1978 0.2846];
658s ***** assert (ricepdf (x, ones (1, 5), 2 * ones (1, 5)), y, 1e-4)
658s ***** assert (ricepdf (x, 1, 2 * ones (1, 5)), y, 1e-4)
658s ***** assert (ricepdf (x, ones (1, 5), 2), y, 1e-4)
658s ***** assert (ricepdf (x, [0 NaN 1 1 1], 2), [0 NaN y(3:5)], 1e-4)
658s ***** assert (ricepdf (x, 1, 2 * [0 NaN 1 1 1]), [0 NaN y(3:5)], 1e-4)
658s ***** assert (ricepdf ([x, NaN], 1, 2), [y, NaN], 1e-4)
658s ***** assert (ricepdf (single ([x, NaN]), 1, 2), single ([y, NaN]), 1e-4)
658s ***** assert (ricepdf ([x, NaN], single (1), 2), single ([y, NaN]), 1e-4)
658s ***** assert (ricepdf ([x, NaN], 1, single (2)), single ([y, NaN]), 1e-4)
658s ***** error<ricepdf: function called with too few input arguments.> ricepdf ()
658s ***** error<ricepdf: function called with too few input arguments.> ricepdf (1)
658s ***** error<ricepdf: function called with too few input arguments.> ricepdf (1,2)
658s ***** error<ricepdf: function called with too many inputs> ricepdf (1,2,3,4)
658s ***** error<ricepdf: X, S, and SIGMA must be of common size or scalars.> ...
658s  ricepdf (ones (3), ones (2), ones (2))
658s ***** error<ricepdf: X, S, and SIGMA must be of common size or scalars.> ...
658s  ricepdf (ones (2), ones (3), ones (2))
658s ***** error<ricepdf: X, S, and SIGMA must be of common size or scalars.> ...
658s  ricepdf (ones (2), ones (2), ones (3))
658s ***** error<ricepdf: X, S, and SIGMA must not be complex.> ricepdf (i, 2, 2)
658s ***** error<ricepdf: X, S, and SIGMA must not be complex.> ricepdf (2, i, 2)
658s ***** error<ricepdf: X, S, and SIGMA must not be complex.> ricepdf (2, 2, i)
658s 19 tests, 19 passed, 0 known failure, 0 skipped
658s [inst/dist_fun/ncfcdf.m]
658s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/ncfcdf.m
658s ***** demo
658s  ## Plot various CDFs from the noncentral F distribution
658s  x = 0:0.01:5;
658s  p1 = ncfcdf (x, 2, 5, 1);
658s  p2 = ncfcdf (x, 2, 5, 2);
658s  p3 = ncfcdf (x, 5, 10, 1);
658s  p4 = ncfcdf (x, 10, 20, 10);
658s  plot (x, p1, "-r", x, p2, "-g", x, p3, "-k", x, p4, "-m")
658s  grid on
658s  xlim ([0, 5])
658s  legend ({"df1 = 2, df2 = 5, λ = 1", "df1 = 2, df2 = 5, λ = 2", ...
658s           "df1 = 5, df2 = 10, λ = 1", "df1 = 10, df2 = 20, λ = 10"}, ...
658s          "location", "southeast")
658s  title ("Noncentral F CDF")
658s  xlabel ("values in x")
658s  ylabel ("probability")
658s ***** demo
658s  ## Compare the noncentral F CDF with LAMBDA = 10 to the F CDF with the
658s  ## same number of numerator and denominator degrees of freedom (5, 20)
658s 
658s  x = 0.01:0.1:10.01;
658s  p1 = ncfcdf (x, 5, 20, 10);
658s  p2 = fcdf (x, 5, 20);
658s  plot (x, p1, "-", x, p2, "-");
658s  grid on
658s  xlim ([0, 10])
658s  legend ({"Noncentral F(5,20,10)", "F(5,20)"}, "location", "southeast")
658s  title ("Noncentral F vs F CDFs")
658s  xlabel ("values in x")
658s  ylabel ("probability")
658s ***** test
658s  x = -2:0.1:2;
658s  p = ncfcdf (x, 10, 1, 3);
658s  assert (p([1:21]), zeros (1, 21), 1e-76);
658s  assert (p(22), 0.004530737275319753, 1e-14);
658s  assert (p(30), 0.255842099135669, 1e-14);
658s  assert (p(41), 0.4379890998457305, 1e-14);
658s ***** test
658s  p = ncfcdf (12, 10, 3, 2);
658s  assert (p, 0.9582287900447416, 1e-14);
658s ***** test
658s  p = ncfcdf (2, 3, 2, 1);
658s  assert (p, 0.5731985522994989, 1e-14);
658s ***** test
658s  p = ncfcdf (2, 3, 2, 1, "upper");
658s  assert (p, 0.4268014477004823, 1e-14);
658s ***** test
658s  p = ncfcdf ([3, 6], 3, 2, 5, "upper");
658s  assert (p, [0.530248523596927, 0.3350482341323044], 1e-14);
658s ***** error<ncfcdf: function called with too few input arguments.> ncfcdf ()
658s ***** error<ncfcdf: function called with too few input arguments.> ncfcdf (1)
658s ***** error<ncfcdf: function called with too few input arguments.> ncfcdf (1, 2)
658s ***** error<ncfcdf: function called with too few input arguments.> ncfcdf (1, 2, 3)
658s ***** error<ncfcdf: invalid argument for upper tail.> ncfcdf (1, 2, 3, 4, "tail")
658s ***** error<ncfcdf: invalid argument for upper tail.> ncfcdf (1, 2, 3, 4, 5)
658s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
658s  ncfcdf (ones (3), ones (2), ones (2), ones (2))
658s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
658s  ncfcdf (ones (2), ones (3), ones (2), ones (2))
658s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
658s  ncfcdf (ones (2), ones (2), ones (3), ones (2))
658s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
658s  ncfcdf (ones (2), ones (2), ones (2), ones (3))
658s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfcdf (i, 2, 2, 2)
658s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfcdf (2, i, 2, 2)
658s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfcdf (2, 2, i, 2)
658s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfcdf (2, 2, 2, i)
658s 19 tests, 19 passed, 0 known failure, 0 skipped
658s [inst/dist_fun/plinv.m]
658s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/plinv.m
658s ***** demo
658s  ## Plot various iCDFs from the Piecewise linear distribution
658s  p = 0.001:0.001:0.999;
658s  x1 = [0, 1, 3, 4, 7, 10];
658s  Fx1 = [0, 0.2, 0.5, 0.6, 0.7, 1];
658s  x2 = [0, 2, 5, 6, 7, 8];
658s  Fx2 = [0, 0.1, 0.3, 0.6, 0.9, 1];
658s  data1 = plinv (p, x1, Fx1);
658s  data2 = plinv (p, x2, Fx2);
658s  plot (p, data1, "-b", p, data2, "-g")
658s  grid on
658s  legend ({"x1, Fx1", "x2, Fx2"}, "location", "northwest")
658s  title ("Piecewise linear iCDF")
658s  xlabel ("probability")
658s  ylabel ("values in data")
658s ***** test
658s  p = 0:0.2:1;
658s  data = plinv (p, [0, 1], [0, 1]);
658s  assert (data, p);
658s ***** test
658s  p = 0:0.2:1;
658s  data = plinv (p, [0, 2], [0, 1]);
658s  assert (data, 2 * p);
658s ***** test
658s  p = 0:0.2:1;
658s  data_out = 1:6;
658s  data = plinv (p, [0, 1], [0, 0.5]);
658s  assert (data, [0, 0.4, 0.8, NA, NA, NA]);
658s ***** test
658s  p = 0:0.2:1;
658s  data_out = 1:6;
658s  data = plinv (p, [0, 0.5], [0, 1]);
658s  assert (data, [0:0.1:0.5]);
658s ***** error<plinv: function called with too few input arguments.> plinv ()
658s ***** error<plinv: function called with too few input arguments.> plinv (1)
658s ***** error<plinv: function called with too few input arguments.> plinv (1, 2)
658s ***** error<plinv: X and FX must be vectors of equal size.> ...
658s  plinv (1, [0, 1, 2], [0, 1])
658s ***** error<plinv: X and FX must be at least two-elements long.> ...
658s  plinv (1, [0], [1])
658s ***** error<plinv: FX must be bounded in the range> ...
658s  plinv (1, [0, 1, 2], [0, 1, 1.5])
658s ***** error<plinv: FX must be bounded in the range> ...
658s  plinv (1, [0, 1, 2], [0, i, 1])
658s ***** error<plinv: P, X, and FX must not be complex.> ...
658s  plinv (i, [0, 1, 2], [0, 0.5, 1])
658s ***** error<plinv: P, X, and FX must not be complex.> ...
658s  plinv (1, [0, i, 2], [0, 0.5, 1])
658s ***** error<plinv: P, X, and FX must not be complex.> ...
658s  plinv (1, [0, 1, 2], [0, 0.5i, 1])
658s 14 tests, 14 passed, 0 known failure, 0 skipped
658s [inst/dist_fun/evcdf.m]
658s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/evcdf.m
658s ***** demo
658s  ## Plot various CDFs from the extreme value distribution
658s  x = -10:0.01:10;
658s  p1 = evcdf (x, 0.5, 2);
658s  p2 = evcdf (x, 1.0, 2);
658s  p3 = evcdf (x, 1.5, 3);
658s  p4 = evcdf (x, 3.0, 4);
658s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
658s  grid on
658s  legend ({"μ = 0.5, σ = 2", "μ = 1.0, σ = 2", ...
658s           "μ = 1.5, σ = 3", "μ = 3.0, σ = 4"}, "location", "southeast")
658s  title ("Extreme value CDF")
658s  xlabel ("values in x")
658s  ylabel ("probability")
658s ***** shared x, y
658s  x = [-Inf, 1, 2, Inf];
658s  y = [0, 0.6321, 0.9340, 1];
658s ***** assert (evcdf (x, ones (1,4), ones (1,4)), y, 1e-4)
658s ***** assert (evcdf (x, 1, ones (1,4)), y, 1e-4)
658s ***** assert (evcdf (x, ones (1,4), 1), y, 1e-4)
658s ***** assert (evcdf (x, [0, -Inf, NaN, Inf], 1), [0, 1, NaN, NaN], 1e-4)
658s ***** assert (evcdf (x, 1, [Inf, NaN, -1, 0]), [NaN, NaN, NaN, NaN], 1e-4)
658s ***** assert (evcdf ([x(1:2), NaN, x(4)], 1, 1), [y(1:2), NaN, y(4)], 1e-4)
658s ***** assert (evcdf (x, "upper"), [1, 0.0660, 0.0006, 0], 1e-4)
658s ***** assert (evcdf ([x, NaN], 1, 1), [y, NaN], 1e-4)
658s ***** assert (evcdf (single ([x, NaN]), 1, 1), single ([y, NaN]), 1e-4)
658s ***** assert (evcdf ([x, NaN], single (1), 1), single ([y, NaN]), 1e-4)
658s ***** assert (evcdf ([x, NaN], 1, single (1)), single ([y, NaN]), 1e-4)
658s ***** error<evcdf: invalid number of input arguments.> evcdf ()
658s ***** error<evcdf: invalid number of input arguments.> evcdf (1,2,3,4,5,6,7)
658s ***** error<evcdf: invalid argument for upper tail.> evcdf (1, 2, 3, 4, "uper")
658s ***** error<evcdf: X, MU, and SIGMA must be of common size or scalars.> ...
658s  evcdf (ones (3), ones (2), ones (2))
658s ***** error<evcdf: invalid size of covariance matrix.> evcdf (2, 3, 4, [1, 2])
658s ***** error<evcdf: covariance matrix is required for confidence bounds.> ...
658s  [p, plo, pup] = evcdf (1, 2, 3)
658s ***** error<evcdf: invalid value for alpha.> [p, plo, pup] = ...
658s  evcdf (1, 2, 3, [1, 0; 0, 1], 0)
658s ***** error<evcdf: invalid value for alpha.> [p, plo, pup] = ...
658s  evcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
658s ***** error<evcdf: invalid value for alpha.> [p, plo, pup] = ...
658s  evcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
658s ***** error<evcdf: X, MU, and SIGMA must not be complex.> evcdf (i, 2, 2)
658s ***** error<evcdf: X, MU, and SIGMA must not be complex.> evcdf (2, i, 2)
658s ***** error<evcdf: X, MU, and SIGMA must not be complex.> evcdf (2, 2, i)
658s ***** error<evcdf: bad covariance matrix.> ...
658s  [p, plo, pup] = evcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
658s 24 tests, 24 passed, 0 known failure, 0 skipped
658s [inst/dist_fun/mvtcdfqmc.m]
658s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/mvtcdfqmc.m
658s ***** error mvtcdfqmc (1, 2, 3);
658s ***** error mvtcdfqmc (1, 2, 3, 4, 5, 6, 7, 8);
658s 2 tests, 2 passed, 0 known failure, 0 skipped
658s [inst/dist_fun/poissrnd.m]
658s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/poissrnd.m
658s ***** assert (size (poissrnd (2)), [1, 1])
658s ***** assert (size (poissrnd (ones (2,1))), [2, 1])
658s ***** assert (size (poissrnd (ones (2,2))), [2, 2])
658s ***** assert (size (poissrnd (1, 3)), [3, 3])
658s ***** assert (size (poissrnd (1, [4 1])), [4, 1])
658s ***** assert (size (poissrnd (1, 4, 1)), [4, 1])
658s ***** assert (size (poissrnd (1, 4, 1)), [4, 1])
658s ***** assert (size (poissrnd (1, 4, 1, 5)), [4, 1, 5])
658s ***** assert (size (poissrnd (1, 0, 1)), [0, 1])
658s ***** assert (size (poissrnd (1, 1, 0)), [1, 0])
658s ***** assert (size (poissrnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
658s ***** assert (poissrnd (0, 1, 1), 0)
658s ***** assert (poissrnd ([0, 0, 0], [1, 3]), [0 0 0])
658s ***** assert (class (poissrnd (2)), "double")
658s ***** assert (class (poissrnd (single (2))), "single")
658s ***** assert (class (poissrnd (single ([2 2]))), "single")
658s ***** error<poissrnd: function called with too few input arguments.> poissrnd ()
658s ***** error<poissrnd: LAMBDA must not be complex.> poissrnd (i)
658s ***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
658s  poissrnd (1, -1)
658s ***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
658s  poissrnd (1, 1.2)
658s ***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
658s  poissrnd (1, ones (2))
658s ***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
658s  poissrnd (1, [2 -1 2])
658s ***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
658s  poissrnd (1, [2 0 2.5])
658s ***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
658s  poissrnd (ones (2), ones (2))
658s ***** error<poissrnd: dimensions must be non-negative integers.> ...
658s  poissrnd (1, 2, -1, 5)
658s ***** error<poissrnd: dimensions must be non-negative integers.> ...
658s  poissrnd (1, 2, 1.5, 5)
658s ***** error<poissrnd: LAMBDA must be scalar or of size SZ.> poissrnd (ones (2,2), 3)
658s ***** error<poissrnd: LAMBDA must be scalar or of size SZ.> poissrnd (ones (2,2), [3, 2])
658s ***** error<poissrnd: LAMBDA must be scalar or of size SZ.> poissrnd (ones (2,2), 2, 3)
658s 29 tests, 29 passed, 0 known failure, 0 skipped
658s [inst/dist_fun/logninv.m]
658s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/logninv.m
658s ***** demo
658s  ## Plot various iCDFs from the log-normal distribution
658s  p = 0.001:0.001:0.999;
658s  x1 = logninv (p, 0, 1);
658s  x2 = logninv (p, 0, 0.5);
658s  x3 = logninv (p, 0, 0.25);
658s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
658s  grid on
658s  ylim ([0, 3])
658s  legend ({"μ = 0, σ = 1", "μ = 0, σ = 0.5", "μ = 0, σ = 0.25"}, ...
658s          "location", "northwest")
658s  title ("Log-normal iCDF")
658s  xlabel ("probability")
658s  ylabel ("values in x")
658s ***** shared p
658s  p = [-1 0 0.5 1 2];
658s ***** assert (logninv (p, ones (1,5), ones (1,5)), [NaN 0 e Inf NaN])
658s ***** assert (logninv (p, 1, ones (1,5)), [NaN 0 e Inf NaN])
658s ***** assert (logninv (p, ones (1,5), 1), [NaN 0 e Inf NaN])
658s ***** assert (logninv (p, [1 1 NaN 0 1], 1), [NaN 0 NaN Inf NaN])
658s ***** assert (logninv (p, 1, [1 0 NaN Inf 1]), [NaN NaN NaN NaN NaN])
658s ***** assert (logninv ([p(1:2) NaN p(4:5)], 1, 2), [NaN 0 NaN Inf NaN])
658s ***** assert (logninv ([p, NaN], 1, 1), [NaN 0 e Inf NaN NaN])
658s ***** assert (logninv (single ([p, NaN]), 1, 1), single ([NaN 0 e Inf NaN NaN]))
658s ***** assert (logninv ([p, NaN], single (1), 1), single ([NaN 0 e Inf NaN NaN]))
658s ***** assert (logninv ([p, NaN], 1, single (1)), single ([NaN 0 e Inf NaN NaN]))
658s ***** error logninv ()
658s ***** error logninv (1,2,3,4)
658s ***** error logninv (ones (3), ones (2), ones (2))
658s ***** error logninv (ones (2), ones (3), ones (2))
658s ***** error logninv (ones (2), ones (2), ones (3))
658s ***** error logninv (i, 2, 2)
658s ***** error logninv (2, i, 2)
658s ***** error logninv (2, 2, i)
658s 18 tests, 18 passed, 0 known failure, 0 skipped
658s [inst/dist_fun/cauchyinv.m]
658s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/cauchyinv.m
658s ***** demo
658s  ## Plot various iCDFs from the Cauchy distribution
658s  p = 0.001:0.001:0.999;
658s  x1 = cauchyinv (p, 0, 0.5);
658s  x2 = cauchyinv (p, 0, 1);
658s  x3 = cauchyinv (p, 0, 2);
658s  x4 = cauchyinv (p, -2, 1);
658s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
658s  grid on
658s  ylim ([-5, 5])
658s  legend ({"x0 = 0, γ = 0.5", "x0 = 0, γ = 1", ...
658s           "x0 = 0, γ = 2", "x0 = -2, γ = 1"}, "location", "northwest")
658s  title ("Cauchy iCDF")
658s  xlabel ("probability")
658s  ylabel ("values in x")
658s ***** shared p
658s  p = [-1 0 0.5 1 2];
658s ***** assert (cauchyinv (p, ones (1,5), 2 * ones (1,5)), [NaN -Inf 1 Inf NaN], eps)
658s ***** assert (cauchyinv (p, 1, 2 * ones (1,5)), [NaN -Inf 1 Inf NaN], eps)
658s ***** assert (cauchyinv (p, ones (1,5), 2), [NaN -Inf 1 Inf NaN], eps)
658s ***** assert (cauchyinv (p, [1 -Inf NaN Inf 1], 2), [NaN NaN NaN NaN NaN])
658s ***** assert (cauchyinv (p, 1, 2 * [1 0 NaN Inf 1]), [NaN NaN NaN NaN NaN])
658s ***** assert (cauchyinv ([p(1:2) NaN p(4:5)], 1, 2), [NaN -Inf NaN Inf NaN])
658s ***** assert (cauchyinv ([p, NaN], 1, 2), [NaN -Inf 1 Inf NaN NaN], eps)
658s ***** assert (cauchyinv (single ([p, NaN]), 1, 2), ...
658s  single ([NaN -Inf 1 Inf NaN NaN]), eps ("single"))
658s ***** assert (cauchyinv ([p, NaN], single (1), 2), ...
658s  single ([NaN -Inf 1 Inf NaN NaN]), eps ("single"))
658s ***** assert (cauchyinv ([p, NaN], 1, single (2)), ...
658s  single ([NaN -Inf 1 Inf NaN NaN]), eps ("single"))
658s ***** error<cauchyinv: function called with too few input arguments.> cauchyinv ()
658s ***** error<cauchyinv: function called with too few input arguments.> cauchyinv (1)
658s ***** error<cauchyinv: function called with too few input arguments.> ...
658s  cauchyinv (1, 2)
658s ***** error<cauchyinv: function called with too many inputs> cauchyinv (1, 2, 3, 4)
658s ***** error<cauchyinv: P, X0, and GAMMA must be of common size or scalars.> ...
658s  cauchyinv (ones (3), ones (2), ones(2))
658s ***** error<cauchyinv: P, X0, and GAMMA must be of common size or scalars.> ...
658s  cauchyinv (ones (2), ones (3), ones(2))
658s ***** error<cauchyinv: P, X0, and GAMMA must be of common size or scalars.> ...
658s  cauchyinv (ones (2), ones (2), ones(3))
658s ***** error<cauchyinv: P, X0, and GAMMA must not be complex.> cauchyinv (i, 4, 3)
658s ***** error<cauchyinv: P, X0, and GAMMA must not be complex.> cauchyinv (1, i, 3)
658s ***** error<cauchyinv: P, X0, and GAMMA must not be complex.> cauchyinv (1, 4, i)
658s 20 tests, 20 passed, 0 known failure, 0 skipped
658s [inst/dist_fun/gamrnd.m]
658s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/gamrnd.m
658s ***** assert (size (gamrnd (1, 1)), [1 1])
658s ***** assert (size (gamrnd (1, ones (2,1))), [2, 1])
658s ***** assert (size (gamrnd (1, ones (2,2))), [2, 2])
658s ***** assert (size (gamrnd (ones (2,1), 1)), [2, 1])
658s ***** assert (size (gamrnd (ones (2,2), 1)), [2, 2])
658s ***** assert (size (gamrnd (1, 1, 3)), [3, 3])
658s ***** assert (size (gamrnd (1, 1, [4, 1])), [4, 1])
658s ***** assert (size (gamrnd (1, 1, 4, 1)), [4, 1])
658s ***** assert (size (gamrnd (1, 1, 4, 1, 5)), [4, 1, 5])
658s ***** assert (size (gamrnd (1, 1, 0, 1)), [0, 1])
658s ***** assert (size (gamrnd (1, 1, 1, 0)), [1, 0])
658s ***** assert (size (gamrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
658s ***** assert (class (gamrnd (1, 1)), "double")
658s ***** assert (class (gamrnd (1, single (1))), "single")
658s ***** assert (class (gamrnd (1, single ([1, 1]))), "single")
658s ***** assert (class (gamrnd (single (1), 1)), "single")
658s ***** assert (class (gamrnd (single ([1, 1]), 1)), "single")
658s ***** error<gamrnd: function called with too few input arguments.> gamrnd ()
658s ***** error<gamrnd: function called with too few input arguments.> gamrnd (1)
658s ***** error<gamrnd: A and B must be of common size or scalars.> ...
658s  gamrnd (ones (3), ones (2))
658s ***** error<gamrnd: A and B must be of common size or scalars.> ...
658s  gamrnd (ones (2), ones (3))
658s ***** error<gamrnd: A and B must not be complex.> gamrnd (i, 2, 3)
658s ***** error<gamrnd: A and B must not be complex.> gamrnd (1, i, 3)
658s ***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
658s  gamrnd (1, 2, -1)
658s ***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
658s  gamrnd (1, 2, 1.2)
658s ***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
658s  gamrnd (1, 2, ones (2))
658s ***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
658s  gamrnd (1, 2, [2 -1 2])
658s ***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
658s  gamrnd (1, 2, [2 0 2.5])
658s ***** error<gamrnd: dimensions must be non-negative integers.> ...
658s  gamrnd (1, 2, 2, -1, 5)
658s ***** error<gamrnd: dimensions must be non-negative integers.> ...
658s  gamrnd (1, 2, 2, 1.5, 5)
658s ***** error<gamrnd: A and B must be scalars or of size SZ.> ...
658s  gamrnd (2, ones (2), 3)
658s ***** error<gamrnd: A and B must be scalars or of size SZ.> ...
658s  gamrnd (2, ones (2), [3, 2])
658s ***** error<gamrnd: A and B must be scalars or of size SZ.> ...
658s  gamrnd (2, ones (2), 3, 2)
658s 33 tests, 33 passed, 0 known failure, 0 skipped
658s [inst/dist_fun/betacdf.m]
658s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/betacdf.m
658s ***** demo
658s  ## Plot various CDFs from the Beta distribution
658s  x = 0:0.005:1;
658s  p1 = betacdf (x, 0.5, 0.5);
658s  p2 = betacdf (x, 5, 1);
658s  p3 = betacdf (x, 1, 3);
658s  p4 = betacdf (x, 2, 2);
658s  p5 = betacdf (x, 2, 5);
658s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
658s  grid on
658s  legend ({"α = β = 0.5", "α = 5, β = 1", "α = 1, β = 3", ...
658s           "α = 2, β = 2", "α = 2, β = 5"}, "location", "northwest")
658s  title ("Beta CDF")
658s  xlabel ("values in x")
658s  ylabel ("probability")
658s ***** shared x, y, x1, x2
658s  x = [-1 0 0.5 1 2];
658s  y = [0 0 0.75 1 1];
658s ***** assert (betacdf (x, ones (1, 5), 2 * ones (1, 5)), y)
658s ***** assert (betacdf (x, 1, 2 * ones (1, 5)), y)
658s ***** assert (betacdf (x, ones (1, 5), 2), y)
658s ***** assert (betacdf (x, [0 1 NaN 1 1], 2), [NaN 0 NaN 1 1])
658s ***** assert (betacdf (x, 1, 2 * [0 1 NaN 1 1]), [NaN 0 NaN 1 1])
658s ***** assert (betacdf ([x(1:2) NaN x(4:5)], 1, 2), [y(1:2) NaN y(4:5)])
658s  x1 = [0.1:0.2:0.9];
658s ***** assert (betacdf (x1, 2, 2), [0.028, 0.216, 0.5, 0.784, 0.972], 1e-14);
658s ***** assert (betacdf (x1, 2, 2, "upper"), 1 - [0.028, 0.216, 0.5, 0.784, 0.972],...
658s         1e-14);
658s  x2 = [1, 2, 3];
658s ***** assert (betacdf (0.5, x2, x2), [0.5, 0.5, 0.5], 1e-14);
658s ***** assert (betacdf ([x, NaN], 1, 2), [y, NaN])
658s ***** assert (betacdf (single ([x, NaN]), 1, 2), single ([y, NaN]))
658s ***** assert (betacdf ([x, NaN], single (1), 2), single ([y, NaN]))
658s ***** assert (betacdf ([x, NaN], 1, single (2)), single ([y, NaN]))
658s ***** error<betacdf: function called with too few input arguments.> betacdf ()
658s ***** error<betacdf: function called with too few input arguments.> betacdf (1)
658s ***** error<betacdf: function called with too few input arguments.> betacdf (1, 2)
658s ***** error<betacdf: function called with too many inputs> betacdf (1, 2, 3, 4, 5)
658s ***** error<betacdf: invalid argument for upper tail.> betacdf (1, 2, 3, "tail")
658s ***** error<betacdf: invalid argument for upper tail.> betacdf (1, 2, 3, 4)
658s ***** error<betacdf: X, A, and B must be of common size or scalars.> ...
658s  betacdf (ones (3), ones (2), ones (2))
658s ***** error<betacdf: X, A, and B must be of common size or scalars.> ...
658s  betacdf (ones (2), ones (3), ones (2))
658s ***** error<betacdf: X, A, and B must be of common size or scalars.> ...
658s  betacdf (ones (2), ones (2), ones (3))
658s ***** error<betacdf: X, A, and B must not be complex.> betacdf (i, 2, 2)
658s ***** error<betacdf: X, A, and B must not be complex.> betacdf (2, i, 2)
658s ***** error<betacdf: X, A, and B must not be complex.> betacdf (2, 2, i)
658s 25 tests, 25 passed, 0 known failure, 0 skipped
658s [inst/dist_fun/norminv.m]
658s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/norminv.m
658s ***** demo
658s  ## Plot various iCDFs from the normal distribution
658s  p = 0.001:0.001:0.999;
658s  x1 = norminv (p, 0, 0.5);
658s  x2 = norminv (p, 0, 1);
658s  x3 = norminv (p, 0, 2);
658s  x4 = norminv (p, -2, 0.8);
658s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
658s  grid on
658s  ylim ([-5, 5])
658s  legend ({"μ = 0, σ = 0.5", "μ = 0, σ = 1", ...
658s           "μ = 0, σ = 2", "μ = -2, σ = 0.8"}, "location", "northwest")
658s  title ("Normal iCDF")
658s  xlabel ("probability")
658s  ylabel ("values in x")
658s ***** shared p
658s  p = [-1 0 0.5 1 2];
658s ***** assert (norminv (p, ones (1,5), ones (1,5)), [NaN -Inf 1 Inf NaN])
658s ***** assert (norminv (p, 1, ones (1,5)), [NaN -Inf 1 Inf NaN])
658s ***** assert (norminv (p, ones (1,5), 1), [NaN -Inf 1 Inf NaN])
658s ***** assert (norminv (p, [1 -Inf NaN Inf 1], 1), [NaN NaN NaN NaN NaN])
658s ***** assert (norminv (p, 1, [1 0 NaN Inf 1]), [NaN NaN NaN NaN NaN])
658s ***** assert (norminv ([p(1:2) NaN p(4:5)], 1, 1), [NaN -Inf NaN Inf NaN])
658s ***** assert (norminv (p), probit (p))
658s ***** assert (norminv (0.31254), probit (0.31254))
658s ***** assert (norminv ([p, NaN], 1, 1), [NaN -Inf 1 Inf NaN NaN])
658s ***** assert (norminv (single ([p, NaN]), 1, 1), single ([NaN -Inf 1 Inf NaN NaN]))
658s ***** assert (norminv ([p, NaN], single (1), 1), single ([NaN -Inf 1 Inf NaN NaN]))
658s ***** assert (norminv ([p, NaN], 1, single (1)), single ([NaN -Inf 1 Inf NaN NaN]))
658s ***** error<norminv: function called with too few input arguments.> norminv ()
658s ***** error<norminv: P, MU, and SIGMA must be of common size or scalars.> ...
658s  norminv (ones (3), ones (2), ones (2))
658s ***** error<norminv: P, MU, and SIGMA must be of common size or scalars.> ...
658s  norminv (ones (2), ones (3), ones (2))
658s ***** error<norminv: P, MU, and SIGMA must be of common size or scalars.> ...
658s  norminv (ones (2), ones (2), ones (3))
658s ***** error<norminv: P, MU, and SIGMA must not be complex.> norminv (i, 2, 2)
658s ***** error<norminv: P, MU, and SIGMA must not be complex.> norminv (2, i, 2)
658s ***** error<norminv: P, MU, and SIGMA must not be complex.> norminv (2, 2, i)
658s 19 tests, 19 passed, 0 known failure, 0 skipped
658s [inst/dist_fun/mvnrnd.m]
658s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/mvnrnd.m
658s ***** error<mvnrnd: too few input arguments.> mvnrnd ()
658s ***** error<mvnrnd: too few input arguments.> mvnrnd ([2, 3, 4])
658s ***** error<mvnrnd: wrong size of MU.> mvnrnd (ones (2, 2, 2), ones (1, 2, 3, 4))
658s ***** error<mvnrnd: wrong size of SIGMA.> mvnrnd (ones (1, 3), ones (1, 2, 3, 4))
658s ***** assert (size (mvnrnd ([2, 3, 4], [2, 2, 2])), [1, 3])
658s ***** assert (size (mvnrnd ([2, 3, 4], [2, 2, 2], 10)), [10, 3])
658s 6 tests, 6 passed, 0 known failure, 0 skipped
658s [inst/dist_fun/trnd.m]
658s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/trnd.m
658s ***** assert (size (trnd (2)), [1, 1])
658s ***** assert (size (trnd (ones (2,1))), [2, 1])
658s ***** assert (size (trnd (ones (2,2))), [2, 2])
658s ***** assert (size (trnd (1, 3)), [3, 3])
658s ***** assert (size (trnd (1, [4 1])), [4, 1])
658s ***** assert (size (trnd (1, 4, 1)), [4, 1])
658s ***** assert (size (trnd (1, 4, 1)), [4, 1])
658s ***** assert (size (trnd (1, 4, 1, 5)), [4, 1, 5])
658s ***** assert (size (trnd (1, 0, 1)), [0, 1])
658s ***** assert (size (trnd (1, 1, 0)), [1, 0])
658s ***** assert (size (trnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
658s ***** assert (trnd (0, 1, 1), NaN)
658s ***** assert (trnd ([0, 0, 0], [1, 3]), [NaN, NaN, NaN])
658s ***** assert (class (trnd (2)), "double")
658s ***** assert (class (trnd (single (2))), "single")
658s ***** assert (class (trnd (single ([2 2]))), "single")
658s ***** error<trnd: function called with too few input arguments.> trnd ()
658s ***** error<trnd: DF must not be complex.> trnd (i)
658s ***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
658s  trnd (1, -1)
658s ***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
658s  trnd (1, 1.2)
658s ***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
658s  trnd (1, ones (2))
658s ***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
658s  trnd (1, [2 -1 2])
658s ***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
658s  trnd (1, [2 0 2.5])
658s ***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
658s  trnd (ones (2), ones (2))
658s ***** error<trnd: dimensions must be non-negative integers.> ...
658s  trnd (1, 2, -1, 5)
658s ***** error<trnd: dimensions must be non-negative integers.> ...
658s  trnd (1, 2, 1.5, 5)
658s ***** error<trnd: DF must be scalar or of size SZ.> trnd (ones (2,2), 3)
658s ***** error<trnd: DF must be scalar or of size SZ.> trnd (ones (2,2), [3, 2])
658s ***** error<trnd: DF must be scalar or of size SZ.> trnd (ones (2,2), 2, 3)
658s 29 tests, 29 passed, 0 known failure, 0 skipped
658s [inst/dist_fun/hygecdf.m]
658s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/hygecdf.m
658s ***** demo
658s  ## Plot various CDFs from the hypergeometric distribution
658s  x = 0:60;
658s  p1 = hygecdf (x, 500, 50, 100);
658s  p2 = hygecdf (x, 500, 60, 200);
658s  p3 = hygecdf (x, 500, 70, 300);
658s  plot (x, p1, "*b", x, p2, "*g", x, p3, "*r")
658s  grid on
658s  xlim ([0, 60])
658s  legend ({"m = 500, k = 50, n = 100", "m = 500, k = 60, n = 200", ...
658s           "m = 500, k = 70, n = 300"}, "location", "southeast")
658s  title ("Hypergeometric CDF")
658s  xlabel ("values in x (number of successes)")
658s  ylabel ("probability")
658s ***** shared x, y
658s  x = [-1 0 1 2 3];
658s  y = [0 1/6 5/6 1 1];
658s ***** assert (hygecdf (x, 4*ones (1,5), 2, 2), y, 5*eps)
658s ***** assert (hygecdf (x, 4, 2*ones (1,5), 2), y, 5*eps)
658s ***** assert (hygecdf (x, 4, 2, 2*ones (1,5)), y, 5*eps)
658s ***** assert (hygecdf (x, 4*[1 -1 NaN 1.1 1], 2, 2), [y(1) NaN NaN NaN y(5)], 5*eps)
658s ***** assert (hygecdf (x, 4*[1 -1 NaN 1.1 1], 2, 2, "upper"), ...
658s  [y(5) NaN NaN NaN y(1)], 5*eps)
658s ***** assert (hygecdf (x, 4, 2*[1 -1 NaN 1.1 1], 2), [y(1) NaN NaN NaN y(5)], 5*eps)
658s ***** assert (hygecdf (x, 4, 2*[1 -1 NaN 1.1 1], 2, "upper"), ...
658s  [y(5) NaN NaN NaN y(1)], 5*eps)
658s ***** assert (hygecdf (x, 4, 5, 2), [NaN NaN NaN NaN NaN])
658s ***** assert (hygecdf (x, 4, 2, 2*[1 -1 NaN 1.1 1]), [y(1) NaN NaN NaN y(5)], 5*eps)
658s ***** assert (hygecdf (x, 4, 2, 2*[1 -1 NaN 1.1 1], "upper"), ...
658s  [y(5) NaN NaN NaN y(1)], 5*eps)
658s ***** assert (hygecdf (x, 4, 2, 5), [NaN NaN NaN NaN NaN])
658s ***** assert (hygecdf ([x(1:2) NaN x(4:5)], 4, 2, 2), [y(1:2) NaN y(4:5)], 5*eps)
658s ***** test
658s  p = hygecdf (x, 10, [1 2 3 4 5], 2, "upper");
658s  assert (p, [1, 34/90, 2/30, 0, 0], 10*eps);
658s ***** test
658s  p = hygecdf (2*x, 10, [1 2 3 4 5], 2, "upper");
658s  assert (p, [1, 34/90, 0, 0, 0], 10*eps);
658s ***** assert (hygecdf ([x, NaN], 4, 2, 2), [y, NaN], 5*eps)
658s ***** assert (hygecdf (single ([x, NaN]), 4, 2, 2), single ([y, NaN]), ...
658s  eps ("single"))
658s ***** assert (hygecdf ([x, NaN], single (4), 2, 2), single ([y, NaN]), ...
658s  eps ("single"))
658s ***** assert (hygecdf ([x, NaN], 4, single (2), 2), single ([y, NaN]), ...
658s  eps ("single"))
659s ***** assert (hygecdf ([x, NaN], 4, 2, single (2)), single ([y, NaN]), ...
659s  eps ("single"))
659s ***** error<hygecdf: function called with too few input arguments.> hygecdf ()
659s ***** error<hygecdf: function called with too few input arguments.> hygecdf (1)
659s ***** error<hygecdf: function called with too few input arguments.> hygecdf (1,2)
659s ***** error<hygecdf: function called with too few input arguments.> hygecdf (1,2,3)
659s ***** error<hygecdf: invalid argument for upper tail.> hygecdf (1,2,3,4,5)
659s ***** error<hygecdf: invalid argument for upper tail.> hygecdf (1,2,3,4,"uper")
659s ***** error<hygecdf: X, T, k, and N must be of common size or scalars.> ...
659s  hygecdf (ones (2), ones (3), 1, 1)
659s ***** error<hygecdf: X, T, k, and N must be of common size or scalars.> ...
659s  hygecdf (1, ones (2), ones (3), 1)
659s ***** error<hygecdf: X, T, k, and N must be of common size or scalars.> ...
659s  hygecdf (1, 1, ones (2), ones (3))
659s ***** error<hygecdf: X, T, k, and N must not be complex.> hygecdf (i, 2, 2, 2)
659s ***** error<hygecdf: X, T, k, and N must not be complex.> hygecdf (2, i, 2, 2)
659s ***** error<hygecdf: X, T, k, and N must not be complex.> hygecdf (2, 2, i, 2)
659s ***** error<hygecdf: X, T, k, and N must not be complex.> hygecdf (2, 2, 2, i)
659s 32 tests, 32 passed, 0 known failure, 0 skipped
659s [inst/dist_fun/nctcdf.m]
659s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/nctcdf.m
659s ***** demo
659s  ## Plot various CDFs from the noncentral Τ distribution
659s  x = -5:0.01:5;
659s  p1 = nctcdf (x, 1, 0);
659s  p2 = nctcdf (x, 4, 0);
659s  p3 = nctcdf (x, 1, 2);
659s  p4 = nctcdf (x, 4, 2);
659s  plot (x, p1, "-r", x, p2, "-g", x, p3, "-k", x, p4, "-m")
659s  grid on
659s  xlim ([-5, 5])
659s  legend ({"df = 1, μ = 0", "df = 4, μ = 0", ...
659s           "df = 1, μ = 2", "df = 4, μ = 2"}, "location", "southeast")
659s  title ("Noncentral Τ CDF")
659s  xlabel ("values in x")
659s  ylabel ("probability")
659s ***** demo
659s  ## Compare the noncentral T CDF with MU = 1 to the T CDF
659s  ## with the same number of degrees of freedom (10).
659s 
659s  x = -5:0.1:5;
659s  p1 = nctcdf (x, 10, 1);
659s  p2 = tcdf (x, 10);
659s  plot (x, p1, "-", x, p2, "-")
659s  grid on
659s  xlim ([-5, 5])
659s  legend ({"Noncentral T(10,1)", "T(10)"}, "location", "southeast")
659s  title ("Noncentral T vs T CDFs")
659s  xlabel ("values in x")
659s  ylabel ("probability")
659s ***** test
659s  x = -2:0.1:2;
659s  p = nctcdf (x, 10, 1);
659s  assert (p(1), 0.003302485766631558, 1e-14);
659s  assert (p(2), 0.004084668193532631, 1e-14);
659s  assert (p(3), 0.005052800319478737, 1e-14);
659s  assert (p(41), 0.8076115625303751, 1e-14);
659s ***** test
659s  p = nctcdf (12, 10, 3);
659s  assert (p, 0.9997719343243797, 1e-14);
659s ***** test
659s  p = nctcdf (2, 3, 2);
659s  assert (p, 0.4430757822176028, 1e-14);
659s ***** test
659s  p = nctcdf (2, 3, 2, "upper");
659s  assert (p, 0.5569242177823971, 1e-14);
659s ***** test
659s  p = nctcdf ([3, 6], 3, 2, "upper");
659s  assert (p, [0.3199728259444777, 0.07064855592441913], 1e-14);
659s ***** error<nctcdf: function called with too few input arguments.> nctcdf ()
659s ***** error<nctcdf: function called with too few input arguments.> nctcdf (1)
659s ***** error<nctcdf: function called with too few input arguments.> nctcdf (1, 2)
659s ***** error<nctcdf: invalid argument for upper tail.> nctcdf (1, 2, 3, "tail")
659s ***** error<nctcdf: invalid argument for upper tail.> nctcdf (1, 2, 3, 4)
659s ***** error<nctcdf: X, DF, and MU must be of common size or scalars.> ...
659s  nctcdf (ones (3), ones (2), ones (2))
659s ***** error<nctcdf: X, DF, and MU must be of common size or scalars.> ...
659s  nctcdf (ones (2), ones (3), ones (2))
659s ***** error<nctcdf: X, DF, and MU must be of common size or scalars.> ...
659s  nctcdf (ones (2), ones (2), ones (3))
659s ***** error<nctcdf: X, DF, and MU must not be complex.> nctcdf (i, 2, 2)
659s ***** error<nctcdf: X, DF, and MU must not be complex.> nctcdf (2, i, 2)
659s ***** error<nctcdf: X, DF, and MU must not be complex.> nctcdf (2, 2, i)
659s 16 tests, 16 passed, 0 known failure, 0 skipped
659s [inst/dist_fun/nakainv.m]
659s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/nakainv.m
659s ***** demo
659s  ## Plot various iCDFs from the Nakagami distribution
659s  p = 0.001:0.001:0.999;
659s  x1 = nakainv (p, 0.5, 1);
659s  x2 = nakainv (p, 1, 1);
659s  x3 = nakainv (p, 1, 2);
659s  x4 = nakainv (p, 1, 3);
659s  x5 = nakainv (p, 2, 1);
659s  x6 = nakainv (p, 2, 2);
659s  x7 = nakainv (p, 5, 1);
659s  plot (p, x1, "-r", p, x2, "-g", p, x3, "-y", p, x4, "-m", ...
659s        p, x5, "-k", p, x6, "-b", p, x7, "-c")
659s  grid on
659s  ylim ([0, 3])
659s  legend ({"μ = 0.5, ω = 1", "μ = 1, ω = 1", "μ = 1, ω = 2", ...
659s           "μ = 1, ω = 3", "μ = 2, ω = 1", "μ = 2, ω = 2", ...
659s           "μ = 5, ω = 1"}, "location", "northwest")
659s  title ("Nakagami iCDF")
659s  xlabel ("probability")
659s  ylabel ("values in x")
659s ***** shared p, y
659s  p = [-Inf, -1, 0, 1/2, 1, 2, Inf];
659s  y = [NaN, NaN, 0, 0.83255461115769769, Inf, NaN, NaN];
659s ***** assert (nakainv (p, ones (1,7), ones (1,7)), y, eps)
659s ***** assert (nakainv (p, 1, 1), y, eps)
659s ***** assert (nakainv (p, [1, 1, 1, NaN, 1, 1, 1], 1), [y(1:3), NaN, y(5:7)], eps)
659s ***** assert (nakainv (p, 1, [1, 1, 1, NaN, 1, 1, 1]), [y(1:3), NaN, y(5:7)], eps)
659s ***** assert (nakainv ([p, NaN], 1, 1), [y, NaN], eps)
659s ***** assert (nakainv (single ([p, NaN]), 1, 1), single ([y, NaN]))
659s ***** assert (nakainv ([p, NaN], single (1), 1), single ([y, NaN]))
659s ***** assert (nakainv ([p, NaN], 1, single (1)), single ([y, NaN]))
659s ***** error<nakainv: function called with too few input arguments.> nakainv ()
659s ***** error<nakainv: function called with too few input arguments.> nakainv (1)
659s ***** error<nakainv: function called with too few input arguments.> nakainv (1, 2)
659s ***** error<nakainv: P, MU, and OMEGA must be of common size or scalars.> ...
659s  nakainv (ones (3), ones (2), ones(2))
659s ***** error<nakainv: P, MU, and OMEGA must be of common size or scalars.> ...
659s  nakainv (ones (2), ones (3), ones(2))
659s ***** error<nakainv: P, MU, and OMEGA must be of common size or scalars.> ...
659s  nakainv (ones (2), ones (2), ones(3))
659s ***** error<nakainv: P, MU, and OMEGA must not be complex.> nakainv (i, 4, 3)
659s ***** error<nakainv: P, MU, and OMEGA must not be complex.> nakainv (1, i, 3)
659s ***** error<nakainv: P, MU, and OMEGA must not be complex.> nakainv (1, 4, i)
659s 17 tests, 17 passed, 0 known failure, 0 skipped
659s [inst/dist_fun/gpcdf.m]
659s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/gpcdf.m
659s ***** demo
659s  ## Plot various CDFs from the generalized Pareto distribution
659s  x = 0:0.001:5;
659s  p1 = gpcdf (x, 1, 1, 0);
659s  p2 = gpcdf (x, 5, 1, 0);
659s  p3 = gpcdf (x, 20, 1, 0);
659s  p4 = gpcdf (x, 1, 2, 0);
659s  p5 = gpcdf (x, 5, 2, 0);
659s  p6 = gpcdf (x, 20, 2, 0);
659s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", ...
659s        x, p4, "-c", x, p5, "-m", x, p6, "-k")
659s  grid on
659s  xlim ([0, 5])
659s  legend ({"k = 1, σ = 1, θ = 0", "k = 5, σ = 1, θ = 0", ...
659s           "k = 20, σ = 1, θ = 0", "k = 1, σ = 2, θ = 0", ...
659s           "k = 5, σ = 2, θ = 0", "k = 20, σ = 2, θ = 0"}, ...
659s          "location", "northwest")
659s  title ("Generalized Pareto CDF")
659s  xlabel ("values in x")
659s  ylabel ("probability")
659s ***** shared x, y1, y1u, y2, y2u, y3, y3u
659s  x = [-Inf, -1, 0, 1/2, 1, Inf];
659s  y1 = [0, 0, 0, 0.3934693402873666, 0.6321205588285577, 1];
659s  y1u = [1, 1, 1, 0.6065306597126334, 0.3678794411714423, 0];
659s  y2 = [0, 0, 0, 1/3, 1/2, 1];
659s  y2u = [1, 1, 1, 2/3, 1/2, 0];
659s  y3 = [0, 0, 0, 1/2, 1, 1];
659s  y3u = [1, 1, 1, 1/2, 0, 0];
659s ***** assert (gpcdf (x, zeros (1,6), ones (1,6), zeros (1,6)), y1, eps)
659s ***** assert (gpcdf (x, 0, 1, zeros (1,6)), y1, eps)
659s ***** assert (gpcdf (x, 0, ones (1,6), 0), y1, eps)
659s ***** assert (gpcdf (x, zeros (1,6), 1, 0), y1, eps)
659s ***** assert (gpcdf (x, 0, 1, 0), y1, eps)
659s ***** assert (gpcdf (x, 0, 1, [0, 0, 0, NaN, 0, 0]), [y1(1:3), NaN, y1(5:6)], eps)
659s ***** assert (gpcdf (x, 0, [1, 1, 1, NaN, 1, 1], 0), [y1(1:3), NaN, y1(5:6)], eps)
659s ***** assert (gpcdf (x, [0, 0, 0, NaN, 0, 0], 1, 0), [y1(1:3), NaN, y1(5:6)], eps)
659s ***** assert (gpcdf ([x(1:3), NaN, x(5:6)], 0, 1, 0), [y1(1:3), NaN, y1(5:6)], eps)
659s ***** assert (gpcdf (x, zeros (1,6), ones (1,6), zeros (1,6), "upper"), y1u, eps)
659s ***** assert (gpcdf (x, 0, 1, zeros (1,6), "upper"), y1u, eps)
659s ***** assert (gpcdf (x, 0, ones (1,6), 0, "upper"), y1u, eps)
659s ***** assert (gpcdf (x, zeros (1,6), 1, 0, "upper"), y1u, eps)
659s ***** assert (gpcdf (x, 0, 1, 0, "upper"), y1u, eps)
659s ***** assert (gpcdf (x, ones (1,6), ones (1,6), zeros (1,6)), y2, eps)
659s ***** assert (gpcdf (x, 1, 1, zeros (1,6)), y2, eps)
659s ***** assert (gpcdf (x, 1, ones (1,6), 0), y2, eps)
659s ***** assert (gpcdf (x, ones (1,6), 1, 0), y2, eps)
659s ***** assert (gpcdf (x, 1, 1, 0), y2, eps)
659s ***** assert (gpcdf (x, 1, 1, [0, 0, 0, NaN, 0, 0]), [y2(1:3), NaN, y2(5:6)], eps)
659s ***** assert (gpcdf (x, 1, [1, 1, 1, NaN, 1, 1], 0), [y2(1:3), NaN, y2(5:6)], eps)
659s ***** assert (gpcdf (x, [1, 1, 1, NaN, 1, 1], 1, 0), [y2(1:3), NaN, y2(5:6)], eps)
659s ***** assert (gpcdf ([x(1:3), NaN, x(5:6)], 1, 1, 0), [y2(1:3), NaN, y2(5:6)], eps)
659s ***** assert (gpcdf (x, ones (1,6), ones (1,6), zeros (1,6), "upper"), y2u, eps)
659s ***** assert (gpcdf (x, 1, 1, zeros (1,6), "upper"), y2u, eps)
659s ***** assert (gpcdf (x, 1, ones (1,6), 0, "upper"), y2u, eps)
659s ***** assert (gpcdf (x, ones (1,6), 1, 0, "upper"), y2u, eps)
659s ***** assert (gpcdf (x, 1, 1, 0, "upper"), y2u, eps)
659s ***** assert (gpcdf (x, 1, 1, [0, 0, 0, NaN, 0, 0], "upper"), ...
659s                         [y2u(1:3), NaN, y2u(5:6)], eps)
659s ***** assert (gpcdf (x, 1, [1, 1, 1, NaN, 1, 1], 0, "upper"), ...
659s                         [y2u(1:3), NaN, y2u(5:6)], eps)
659s ***** assert (gpcdf (x, [1, 1, 1, NaN, 1, 1], 1, 0, "upper"), ...
659s                         [y2u(1:3), NaN, y2u(5:6)], eps)
659s ***** assert (gpcdf ([x(1:3), NaN, x(5:6)], 1, 1, 0, "upper"), ...
659s                         [y2u(1:3), NaN, y2u(5:6)], eps)
659s ***** assert (gpcdf (x, -ones (1,6), ones (1,6), zeros (1,6)), y3, eps)
659s ***** assert (gpcdf (x, -1, 1, zeros (1,6)), y3, eps)
659s ***** assert (gpcdf (x, -1, ones (1,6), 0), y3, eps)
659s ***** assert (gpcdf (x, -ones (1,6), 1, 0), y3, eps)
659s ***** assert (gpcdf (x, -1, 1, 0), y3, eps)
659s ***** assert (gpcdf (x, -1, 1, [0, 0, 0, NaN, 0, 0]), [y3(1:3), NaN, y3(5:6)], eps)
659s ***** assert (gpcdf (x, -1, [1, 1, 1, NaN, 1, 1], 0), [y3(1:3), NaN, y3(5:6)], eps)
659s ***** assert (gpcdf (x, [-1, -1, -1, NaN, -1, -1], 1, 0), [y3(1:3), NaN, y3(5:6)], eps)
659s ***** assert (gpcdf ([x(1:3), NaN, x(5:6)], -1, 1, 0), [y3(1:3), NaN, y3(5:6)], eps)
659s ***** assert (gpcdf (x, -ones (1,6), ones (1,6), zeros (1,6), "upper"), y3u, eps)
659s ***** assert (gpcdf (x, -1, 1, zeros (1,6), "upper"), y3u, eps)
659s ***** assert (gpcdf (x, -1, ones (1,6), 0, "upper"), y3u, eps)
659s ***** assert (gpcdf (x, -ones (1,6), 1, 0, "upper"), y3u, eps)
659s ***** assert (gpcdf (x, -1, 1, 0, "upper"), y3u, eps)
659s ***** assert (gpcdf (x, -1, 1, [0, 0, 0, NaN, 0, 0], "upper"), ...
659s                          [y3u(1:3), NaN, y3u(5:6)], eps)
659s ***** assert (gpcdf (x, -1, [1, 1, 1, NaN, 1, 1], 0, "upper"), ...
659s                           [y3u(1:3), NaN, y3u(5:6)], eps)
659s ***** assert (gpcdf (x, [-1, -1, -1, NaN, -1, -1], 1, 0, "upper"), ...
659s                           [y3u(1:3), NaN, y3u(5:6)], eps)
659s ***** assert (gpcdf ([x(1:3), NaN, x(5:6)], -1, 1, 0, "upper"), ...
659s                           [y3u(1:3), NaN, y3u(5:6)], eps)
659s ***** assert (gpcdf (single ([x, NaN]), 0, 1, 0), single ([y1, NaN]), eps("single"))
659s ***** assert (gpcdf ([x, NaN], 0, 1, single (0)), single ([y1, NaN]), eps("single"))
659s ***** assert (gpcdf ([x, NaN], 0, single (1), 0), single ([y1, NaN]), eps("single"))
659s ***** assert (gpcdf ([x, NaN], single (0), 1, 0), single ([y1, NaN]), eps("single"))
659s ***** assert (gpcdf (single ([x, NaN]), 1, 1, 0), single ([y2, NaN]), eps("single"))
659s ***** assert (gpcdf ([x, NaN], 1, 1, single (0)), single ([y2, NaN]), eps("single"))
659s ***** assert (gpcdf ([x, NaN], 1, single (1), 0), single ([y2, NaN]), eps("single"))
659s ***** assert (gpcdf ([x, NaN], single (1), 1, 0), single ([y2, NaN]), eps("single"))
659s ***** assert (gpcdf (single ([x, NaN]), -1, 1, 0), single ([y3, NaN]), eps("single"))
659s ***** assert (gpcdf ([x, NaN], -1, 1, single (0)), single ([y3, NaN]), eps("single"))
659s ***** assert (gpcdf ([x, NaN], -1, single (1), 0), single ([y3, NaN]), eps("single"))
659s ***** assert (gpcdf ([x, NaN], single (-1), 1, 0), single ([y3, NaN]), eps("single"))
659s ***** error<gpcdf: function called with too few input arguments.> gpcdf ()
659s ***** error<gpcdf: function called with too few input arguments.> gpcdf (1)
659s ***** error<gpcdf: function called with too few input arguments.> gpcdf (1, 2)
659s ***** error<gpcdf: function called with too few input arguments.> gpcdf (1, 2, 3)
659s ***** error<gpcdf: invalid argument for upper tail.> gpcdf (1, 2, 3, 4, "tail")
659s ***** error<gpcdf: invalid argument for upper tail.> gpcdf (1, 2, 3, 4, 5)
659s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
659s  gpcdf (ones (3), ones (2), ones(2), ones(2))
659s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
659s  gpcdf (ones (2), ones (3), ones(2), ones(2))
659s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
659s  gpcdf (ones (2), ones (2), ones(3), ones(2))
659s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
659s  gpcdf (ones (2), ones (2), ones(2), ones(3))
659s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (i, 2, 3, 4)
659s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, i, 3, 4)
659s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, 2, i, 4)
659s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, 2, 3, i)
659s 76 tests, 76 passed, 0 known failure, 0 skipped
659s [inst/dist_fun/logipdf.m]
659s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/logipdf.m
659s ***** demo
659s  ## Plot various PDFs from the logistic distribution
659s  x = -5:0.01:20;
659s  y1 = logipdf (x, 5, 2);
659s  y2 = logipdf (x, 9, 3);
659s  y3 = logipdf (x, 9, 4);
659s  y4 = logipdf (x, 6, 2);
659s  y5 = logipdf (x, 2, 1);
659s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
659s  grid on
659s  ylim ([0, 0.3])
659s  legend ({"μ = 5, σ = 2", "μ = 9, σ = 3", "μ = 9, σ = 4", ...
659s           "μ = 6, σ = 2", "μ = 2, σ = 1"}, "location", "northeast")
659s  title ("Logistic PDF")
659s  xlabel ("values in x")
659s  ylabel ("density")
659s ***** shared x, y
659s  x = [-Inf -log(4) 0 log(4) Inf];
659s  y = [0, 0.16, 1/4, 0.16, 0];
659s ***** assert (logipdf ([x, NaN], 0, 1), [y, NaN], eps)
659s ***** assert (logipdf (x, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), y([4:5])], eps)
659s ***** assert (logipdf (single ([x, NaN]), 0, 1), single ([y, NaN]), eps ("single"))
659s ***** assert (logipdf ([x, NaN], single (0), 1), single ([y, NaN]), eps ("single"))
659s ***** assert (logipdf ([x, NaN], 0, single (1)), single ([y, NaN]), eps ("single"))
659s ***** error<logipdf: function called with too few input arguments.> logipdf ()
659s ***** error<logipdf: function called with too few input arguments.> logipdf (1)
659s ***** error<logipdf: function called with too few input arguments.> ...
659s  logipdf (1, 2)
659s ***** error<logipdf: X, MU, and SIGMA must be of common size or scalars.> ...
659s  logipdf (1, ones (2), ones (3))
659s ***** error<logipdf: X, MU, and SIGMA must be of common size or scalars.> ...
659s  logipdf (ones (2), 1, ones (3))
659s ***** error<logipdf: X, MU, and SIGMA must be of common size or scalars.> ...
659s  logipdf (ones (2), ones (3), 1)
659s ***** error<logipdf: X, MU, and SIGMA must not be complex.> logipdf (i, 2, 3)
659s ***** error<logipdf: X, MU, and SIGMA must not be complex.> logipdf (1, i, 3)
659s ***** error<logipdf: X, MU, and SIGMA must not be complex.> logipdf (1, 2, i)
659s 14 tests, 14 passed, 0 known failure, 0 skipped
659s [inst/dist_fun/geornd.m]
659s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/geornd.m
659s ***** assert (size (geornd (0.5)), [1, 1])
659s ***** assert (size (geornd (0.5*ones (2,1))), [2, 1])
659s ***** assert (size (geornd (0.5*ones (2,2))), [2, 2])
659s ***** assert (size (geornd (0.5, 3)), [3, 3])
659s ***** assert (size (geornd (0.5, [4 1])), [4, 1])
659s ***** assert (size (geornd (0.5, 4, 1)), [4, 1])
659s ***** assert (class (geornd (0.5)), "double")
659s ***** assert (class (geornd (single (0.5))), "single")
659s ***** assert (class (geornd (single ([0.5 0.5]))), "single")
659s ***** assert (class (geornd (single (0))), "single")
659s ***** assert (class (geornd (single (1))), "single")
659s ***** error<geornd: function called with too few input arguments.> geornd ()
659s ***** error<geornd: PS must not be complex.> geornd (i)
659s ***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
659s  geornd (1, -1)
659s ***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
659s  geornd (1, 1.2)
659s ***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
659s  geornd (1, ones (2))
659s ***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
659s  geornd (1, [2 -1 2])
659s ***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
659s  geornd (1, [2 0 2.5])
659s ***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
659s  geornd (ones (2), ones (2))
659s ***** error<geornd: dimensions must be non-negative integers.> ...
659s  geornd (1, 2, -1, 5)
659s ***** error<geornd: dimensions must be non-negative integers.> ...
659s  geornd (1, 2, 1.5, 5)
659s ***** error<geornd: PS must be scalar or of size SZ.> geornd (ones (2,2), 3)
659s ***** error<geornd: PS must be scalar or of size SZ.> geornd (ones (2,2), [3, 2])
659s ***** error<geornd: PS must be scalar or of size SZ.> geornd (ones (2,2), 2, 3)
659s 24 tests, 24 passed, 0 known failure, 0 skipped
659s [inst/dist_fun/ricernd.m]
659s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/ricernd.m
659s ***** assert (size (ricernd (2, 1/2)), [1, 1])
659s ***** assert (size (ricernd (2 * ones (2, 1), 1/2)), [2, 1])
659s ***** assert (size (ricernd (2 * ones (2, 2), 1/2)), [2, 2])
659s ***** assert (size (ricernd (2, 1/2 * ones (2, 1))), [2, 1])
659s ***** assert (size (ricernd (1, 1/2 * ones (2, 2))), [2, 2])
659s ***** assert (size (ricernd (ones (2, 1), 1)), [2, 1])
659s ***** assert (size (ricernd (ones (2, 2), 1)), [2, 2])
659s ***** assert (size (ricernd (2, 1/2, 3)), [3, 3])
659s ***** assert (size (ricernd (1, 1, [4, 1])), [4, 1])
659s ***** assert (size (ricernd (1, 1, 4, 1)), [4, 1])
659s ***** assert (size (ricernd (1, 1, 4, 1, 5)), [4, 1, 5])
659s ***** assert (size (ricernd (1, 1, 0, 1)), [0, 1])
659s ***** assert (size (ricernd (1, 1, 1, 0)), [1, 0])
659s ***** assert (size (ricernd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
659s ***** assert (class (ricernd (1, 1)), "double")
659s ***** assert (class (ricernd (1, single (0))), "single")
659s ***** assert (class (ricernd (1, single ([0, 0]))), "single")
659s ***** assert (class (ricernd (1, single (1), 2)), "single")
659s ***** assert (class (ricernd (1, single ([1, 1]), 1, 2)), "single")
659s ***** assert (class (ricernd (single (1), 1, 2)), "single")
659s ***** assert (class (ricernd (single ([1, 1]), 1, 1, 2)), "single")
659s ***** error<ricernd: function called with too few input arguments.> ricernd ()
659s ***** error<ricernd: function called with too few input arguments.> ricernd (1)
659s ***** error<ricernd: S and SIGMA must be of common size or scalars.> ...
659s  ricernd (ones (3), ones (2))
659s ***** error<ricernd: S and SIGMA must be of common size or scalars.> ...
659s  ricernd (ones (2), ones (3))
659s ***** error<ricernd: S and SIGMA must not be complex.> ricernd (i, 2)
659s ***** error<ricernd: S and SIGMA must not be complex.> ricernd (1, i)
659s ***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
659s  ricernd (1, 1/2, -1)
659s ***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
659s  ricernd (1, 1/2, 1.2)
659s ***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
659s  ricernd (1, 1/2, ones (2))
659s ***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
659s  ricernd (1, 1/2, [2 -1 2])
659s ***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
659s  ricernd (1, 1/2, [2 0 2.5])
659s ***** error<ricernd: dimensions must be non-negative integers.> ...
659s  ricernd (1, 1/2, 2, -1, 5)
659s ***** error<ricernd: dimensions must be non-negative integers.> ...
659s  ricernd (1, 1/2, 2, 1.5, 5)
659s ***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
659s  ricernd (2, 1/2 * ones (2), 3)
659s ***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
659s  ricernd (2, 1/2 * ones (2), [3, 2])
659s ***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
659s  ricernd (2, 1/2 * ones (2), 3, 2)
659s ***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
659s  ricernd (2 * ones (2), 1/2, 3)
659s ***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
659s  ricernd (2 * ones (2), 1/2, [3, 2])
659s ***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
659s  ricernd (2 * ones (2), 1/2, 3, 2)
659s 40 tests, 40 passed, 0 known failure, 0 skipped
659s [inst/dist_fun/unifinv.m]
659s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/unifinv.m
659s ***** demo
659s  ## Plot various iCDFs from the continuous uniform distribution
659s  p = 0.001:0.001:0.999;
659s  x1 = unifinv (p, 2, 5);
659s  x2 = unifinv (p, 3, 9);
659s  plot (p, x1, "-b", p, x2, "-g")
659s  grid on
659s  xlim ([0, 1])
659s  ylim ([0, 10])
659s  legend ({"a = 2, b = 5", "a = 3, b = 9"}, "location", "northwest")
659s  title ("Continuous uniform iCDF")
659s  xlabel ("probability")
659s  ylabel ("values in x")
659s ***** shared p
659s  p = [-1 0 0.5 1 2];
659s ***** assert (unifinv (p, ones (1,5), 2*ones (1,5)), [NaN 1 1.5 2 NaN])
659s ***** assert (unifinv (p, 0, 1), [NaN 1 1.5 2 NaN] - 1)
659s ***** assert (unifinv (p, 1, 2*ones (1,5)), [NaN 1 1.5 2 NaN])
659s ***** assert (unifinv (p, ones (1,5), 2), [NaN 1 1.5 2 NaN])
659s ***** assert (unifinv (p, [1 2 NaN 1 1], 2), [NaN NaN NaN 2 NaN])
659s ***** assert (unifinv (p, 1, 2*[1 0 NaN 1 1]), [NaN NaN NaN 2 NaN])
659s ***** assert (unifinv ([p(1:2) NaN p(4:5)], 1, 2), [NaN 1 NaN 2 NaN])
659s ***** assert (unifinv ([p, NaN], 1, 2), [NaN 1 1.5 2 NaN NaN])
659s ***** assert (unifinv (single ([p, NaN]), 1, 2), single ([NaN 1 1.5 2 NaN NaN]))
659s ***** assert (unifinv ([p, NaN], single (1), 2), single ([NaN 1 1.5 2 NaN NaN]))
659s ***** assert (unifinv ([p, NaN], 1, single (2)), single ([NaN 1 1.5 2 NaN NaN]))
659s ***** error<unifinv: function called with too few input arguments.> unifinv ()
659s ***** error<unifinv: function called with too few input arguments.> unifinv (1, 2)
659s ***** error<unifinv: P, A, and B must be of common size or scalars.> ...
659s  unifinv (ones (3), ones (2), ones (2))
659s ***** error<unifinv: P, A, and B must be of common size or scalars.> ...
659s  unifinv (ones (2), ones (3), ones (2))
659s ***** error<unifinv: P, A, and B must be of common size or scalars.> ...
659s  unifinv (ones (2), ones (2), ones (3))
659s ***** error<unifinv: P, A, and B must not be complex.> unifinv (i, 2, 2)
659s ***** error<unifinv: P, A, and B must not be complex.> unifinv (2, i, 2)
659s ***** error<unifinv: P, A, and B must not be complex.> unifinv (2, 2, i)
659s 19 tests, 19 passed, 0 known failure, 0 skipped
659s [inst/dist_fun/mvncdf.m]
659s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/mvncdf.m
659s ***** demo
659s  mu = [1, -1];
659s  Sigma = [0.9, 0.4; 0.4, 0.3];
659s  [X1, X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
659s  X = [X1(:), X2(:)];
659s  p = mvncdf (X, mu, Sigma);
659s  Z = reshape (p, 25, 25);
659s  surf (X1, X2, Z);
659s  title ("Bivariate Normal Distribution");
659s  ylabel "X1"
659s  xlabel "X2"
659s ***** demo
659s  mu = [0, 0];
659s  Sigma = [0.25, 0.3; 0.3, 1];
659s  p = mvncdf ([0 0], [1 1], mu, Sigma);
659s  x1 = -3:.2:3;
659s  x2 = -3:.2:3;
659s  [X1, X2] = meshgrid (x1, x2);
659s  X = [X1(:), X2(:)];
659s  p = mvnpdf (X, mu, Sigma);
659s  p = reshape (p, length (x2), length (x1));
659s  contour (x1, x2, p, [0.0001, 0.001, 0.01, 0.05, 0.15, 0.25, 0.35]);
659s  xlabel ("x");
659s  ylabel ("p");
659s  title ("Probability over Rectangular Region");
659s  line ([0, 0, 1, 1, 0], [1, 0, 0, 1, 1], "Linestyle", "--", "Color", "k");
659s ***** test
659s  fD = (-2:2)';
659s  X = repmat (fD, 1, 4);
659s  p = mvncdf (X);
659s  assert (p, [0; 0.0006; 0.0625; 0.5011; 0.9121], ones (5, 1) * 1e-4);
659s ***** test
659s  mu = [1, -1];
659s  Sigma = [0.9, 0.4; 0.4, 0.3];
659s  [X1,X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
659s  X = [X1(:), X2(:)];
659s  p = mvncdf (X, mu, Sigma);
659s  p_out = [0.00011878988774500, 0.00034404112322371, ...
659s           0.00087682502191813, 0.00195221905058185, ...
659s           0.00378235566873474, 0.00638175749734415, ...
659s           0.00943764224329656, 0.01239164888125426, ...
659s           0.01472750274376648, 0.01623228313374828]';
659s  assert (p([1:10]), p_out, 1e-16);
659s ***** test
659s  mu = [1, -1];
659s  Sigma = [0.9, 0.4; 0.4, 0.3];
659s  [X1,X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
659s  X = [X1(:), X2(:)];
659s  p = mvncdf (X, mu, Sigma);
659s  p_out = [0.8180695783608276, 0.8854485749482751, ...
659s           0.9308108777385832, 0.9579855743025508, ...
659s           0.9722897881414742, 0.9788150170059926, ...
659s           0.9813597788804785, 0.9821977956568989, ...
659s           0.9824283794464095, 0.9824809345614861]';
659s  assert (p([616:625]), p_out, 3e-16);
659s ***** test
659s  mu = [0, 0];
659s  Sigma = [0.25, 0.3; 0.3, 1];
659s  [p, err] = mvncdf ([0, 0], [1, 1], mu, Sigma);
659s  assert (p, 0.2097424404755626, 1e-16);
659s  assert (err, 1e-08);
659s ***** test
659s  x = [1 2];
659s  mu = [0.5 1.5];
659s  sigma = [1.0, 0.5; 0.5, 1.0];
659s  p = mvncdf (x, mu, sigma);
659s  assert (p, 0.546244443857090, 1e-15);
659s ***** test
659s  x = [1 2];
659s  mu = [0.5 1.5];
659s  sigma = [1.0, 0.5; 0.5, 1.0];
659s  a = [-inf 0];
659s  p = mvncdf (a, x, mu, sigma);
659s  assert (p, 0.482672935215631, 1e-15);
659s ***** error p = mvncdf (randn (25,26), [], eye (26));
659s ***** error p = mvncdf (randn (25,8), [], eye (9));
659s ***** error p = mvncdf (randn (25,4), randn (25,5), [], eye (4));
659s ***** error p = mvncdf (randn (25,4), randn (25,4), [2, 3; 2, 3], eye (4));
659s ***** error p = mvncdf (randn (25,4), randn (25,4), ones (1, 5), eye (4));
659s ***** error p = mvncdf ([-inf, 0], [1, 2], [0.5, 1.5], [1.0, 0.5; 0.5, 1.0], option)
659s 12 tests, 12 passed, 0 known failure, 0 skipped
659s [inst/dist_fun/mvtcdf.m]
659s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/mvtcdf.m
659s ***** demo
659s  ## Compute the cdf of a multivariate Student's t distribution with
659s  ## correlation parameters rho = [1, 0.4; 0.4, 1] and 2 degrees of freedom.
659s 
659s  rho = [1, 0.4; 0.4, 1];
659s  df = 2;
659s  [X1, X2] = meshgrid (linspace (-2, 2, 25)', linspace (-2, 2, 25)');
659s  X = [X1(:), X2(:)];
659s  p = mvtcdf (X, rho, df);
659s  surf (X1, X2, reshape (p, 25, 25));
659s  title ("Bivariate Student's t cummulative distribution function");
659s ***** test
659s  x = [1, 2];
659s  rho = [1, 0.5; 0.5, 1];
659s  df = 4;
659s  a = [-1, 0];
659s  assert (mvtcdf(a, x, rho, df), 0.294196905339283, 1e-14);
659s ***** test
659s  x = [1, 2;2, 4;1, 5];
659s  rho = [1, 0.5; 0.5, 1];
659s  df = 4;
659s  p =[0.790285178602166; 0.938703291727784; 0.81222737321336];
659s  assert (mvtcdf(x, rho, df), p, 1e-14);
659s ***** test
659s  x = [1, 2, 2, 4, 1, 5];
659s  rho = eye (6);
659s  rho(rho == 0) = 0.5;
659s  df = 4;
659s  assert (mvtcdf(x, rho, df), 0.6874, 1e-4);
666s ***** error mvtcdf (1)
666s ***** error mvtcdf (1, 2)
666s ***** error<mvtcdf: correlation matrix RHO does not match dimensions in data.> ...
666s  mvtcdf (1, [2, 3; 3, 2], 1)
666s ***** error<mvtcdf: correlation matrix RHO does not match dimensions in data.> ...
666s  mvtcdf ([2, 3, 4], ones (2), 1)
666s ***** error<mvtcdf: X_LO and X_UP must be of the same size.> ...
666s  mvtcdf ([1, 2, 3], [2, 3], ones (2), 1)
666s ***** error<mvtcdf: correlation matrix RHO must be positive semi-definite.> ...
666s  mvtcdf ([2, 3], ones (2), [1, 2, 3])
666s ***** error<mvtcdf: DF must be a scalar or a vector with the same samples as in> ...
666s  mvtcdf ([2, 3], [1, 0.5; 0.5, 1], [1, 2, 3])
666s 10 tests, 10 passed, 0 known failure, 0 skipped
666s [inst/dist_fun/expinv.m]
666s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/expinv.m
666s ***** demo
666s  ## Plot various iCDFs from the exponential distribution
666s  p = 0.001:0.001:0.999;
666s  x1 = expinv (p, 2/3);
666s  x2 = expinv (p, 1.0);
666s  x3 = expinv (p, 2.0);
666s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
666s  grid on
666s  ylim ([0, 5])
666s  legend ({"μ = 2/3", "μ = 1", "μ = 2"}, "location", "northwest")
666s  title ("Exponential iCDF")
666s  xlabel ("probability")
666s  ylabel ("values in x")
666s ***** shared p
666s  p = [-1 0 0.3934693402873666 1 2];
666s ***** assert (expinv (p, 2*ones (1,5)), [NaN 0 1 Inf NaN], eps)
666s ***** assert (expinv (p, 2), [NaN 0 1 Inf NaN], eps)
666s ***** assert (expinv (p, 2*[1 0 NaN 1 1]), [NaN NaN NaN Inf NaN], eps)
666s ***** assert (expinv ([p(1:2) NaN p(4:5)], 2), [NaN 0 NaN Inf NaN], eps)
666s ***** assert (expinv ([p, NaN], 2), [NaN 0 1 Inf NaN NaN], eps)
666s ***** assert (expinv (single ([p, NaN]), 2), single ([NaN 0 1 Inf NaN NaN]), eps)
666s ***** assert (expinv ([p, NaN], single (2)), single ([NaN 0 1 Inf NaN NaN]), eps)
666s ***** error<expinv: invalid number of input arguments.> expinv ()
666s ***** error<expinv: invalid number of input arguments.> expinv (1, 2 ,3 ,4 ,5)
666s ***** error<expinv: P and MU must be of common size or scalars.> ...
666s  expinv (ones (3), ones (2))
666s ***** error<expinv: invalid size of variance, PCOV must be a scalar.> ...
666s  expinv (2, 3, [1, 2])
666s ***** error<expinv: variance, PCOV, is required for confidence bounds.> ...
666s  [x, xlo, xup] = expinv (1, 2)
666s ***** error<expinv: invalid value for alpha.> [x, xlo, xup] = ...
666s  expinv (1, 2, 3, 0)
666s ***** error<expinv: invalid value for alpha.> [x, xlo, xup] = ...
666s  expinv (1, 2, 3, 1.22)
666s ***** error<expinv: invalid value for alpha.> [x, xlo, xup] = ...
666s  expinv (1, 2, 3, [0.05, 0.1])
666s ***** error<expinv: P and MU must not be complex.> expinv (i, 2)
666s ***** error<expinv: P and MU must not be complex.> expinv (2, i)
666s ***** error<expinv: variance, PCOV, cannot be negative.> ...
666s  [x, xlo, xup] = expinv (1, 2, -1, 0.04)
666s 18 tests, 18 passed, 0 known failure, 0 skipped
666s [inst/dist_fun/unifcdf.m]
666s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/unifcdf.m
666s ***** demo
666s  ## Plot various CDFs from the continuous uniform distribution
666s  x = 0:0.1:10;
666s  p1 = unifcdf (x, 2, 5);
666s  p2 = unifcdf (x, 3, 9);
666s  plot (x, p1, "-b", x, p2, "-g")
666s  grid on
666s  xlim ([0, 10])
666s  ylim ([0, 1])
666s  legend ({"a = 2, b = 5", "a = 3, b = 9"}, "location", "southeast")
666s  title ("Continuous uniform CDF")
666s  xlabel ("values in x")
666s  ylabel ("probability")
666s ***** shared x, y
666s  x = [-1 0 0.5 1 2] + 1;
666s  y = [0 0 0.5 1 1];
666s ***** assert (unifcdf (x, ones (1,5), 2*ones (1,5)), y)
666s ***** assert (unifcdf (x, ones (1,5), 2*ones (1,5), "upper"), 1 - y)
666s ***** assert (unifcdf (x, 1, 2*ones (1,5)), y)
666s ***** assert (unifcdf (x, 1, 2*ones (1,5), "upper"), 1 - y)
666s ***** assert (unifcdf (x, ones (1,5), 2), y)
666s ***** assert (unifcdf (x, ones (1,5), 2, "upper"), 1 - y)
666s ***** assert (unifcdf (x, [2 1 NaN 1 1], 2), [NaN 0 NaN 1 1])
666s ***** assert (unifcdf (x, [2 1 NaN 1 1], 2, "upper"), 1 - [NaN 0 NaN 1 1])
666s ***** assert (unifcdf (x, 1, 2*[0 1 NaN 1 1]), [NaN 0 NaN 1 1])
666s ***** assert (unifcdf (x, 1, 2*[0 1 NaN 1 1], "upper"), 1 - [NaN 0 NaN 1 1])
666s ***** assert (unifcdf ([x(1:2) NaN x(4:5)], 1, 2), [y(1:2) NaN y(4:5)])
666s ***** assert (unifcdf ([x(1:2) NaN x(4:5)], 1, 2, "upper"), 1 - [y(1:2) NaN y(4:5)])
666s ***** assert (unifcdf ([x, NaN], 1, 2), [y, NaN])
666s ***** assert (unifcdf (single ([x, NaN]), 1, 2), single ([y, NaN]))
666s ***** assert (unifcdf ([x, NaN], single (1), 2), single ([y, NaN]))
666s ***** assert (unifcdf ([x, NaN], 1, single (2)), single ([y, NaN]))
666s ***** error<unifcdf: function called with too few input arguments.> unifcdf ()
666s ***** error<unifcdf: function called with too few input arguments.> unifcdf (1)
666s ***** error<unifcdf: function called with too few input arguments.> unifcdf (1, 2)
667s ***** error<unifcdf: invalid argument for upper tail.> unifcdf (1, 2, 3, 4)
667s ***** error<unifcdf: invalid argument for upper tail.> unifcdf (1, 2, 3, "tail")
667s ***** error<unifcdf: X, A, and B must be of common size or scalars.> ...
667s  unifcdf (ones (3), ones (2), ones (2))
667s ***** error<unifcdf: X, A, and B must be of common size or scalars.> ...
667s  unifcdf (ones (2), ones (3), ones (2))
667s ***** error<unifcdf: X, A, and B must be of common size or scalars.> ...
667s  unifcdf (ones (2), ones (2), ones (3))
667s ***** error<unifcdf: X, A, and B must not be complex.> unifcdf (i, 2, 2)
667s ***** error<unifcdf: X, A, and B must not be complex.> unifcdf (2, i, 2)
667s ***** error<unifcdf: X, A, and B must not be complex.> unifcdf (2, 2, i)
667s 27 tests, 27 passed, 0 known failure, 0 skipped
667s [inst/dist_fun/gevrnd.m]
667s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/gevrnd.m
667s ***** assert(size (gevrnd (1,2,1)), [1, 1]);
667s ***** assert(size (gevrnd (ones(2,1), 2, 1)), [2, 1]);
667s ***** assert(size (gevrnd (ones(2,2), 2, 1)), [2, 2]);
667s ***** assert(size (gevrnd (1, 2*ones(2,1), 1)), [2, 1]);
667s ***** assert(size (gevrnd (1, 2*ones(2,2), 1)), [2, 2]);
667s ***** assert(size (gevrnd (1, 2, 1, 3)), [3, 3]);
667s ***** assert(size (gevrnd (1, 2, 1, [4 1])), [4, 1]);
667s ***** assert(size (gevrnd (1, 2, 1, 4, 1)), [4, 1]);
667s ***** assert (class (gevrnd (1,1,1)), "double")
667s ***** assert (class (gevrnd (single (1),1,1)), "single")
667s ***** assert (class (gevrnd (single ([1 1]),1,1)), "single")
667s ***** assert (class (gevrnd (1,single (1),1)), "single")
667s ***** assert (class (gevrnd (1,single ([1 1]),1)), "single")
667s ***** assert (class (gevrnd (1,1,single (1))), "single")
667s ***** assert (class (gevrnd (1,1,single ([1 1]))), "single")
667s ***** error<gevrnd: function called with too few input arguments.> gevrnd ()
667s ***** error<gevrnd: function called with too few input arguments.> gevrnd (1)
667s ***** error<gevrnd: function called with too few input arguments.> gevrnd (1, 2)
667s ***** error<gevrnd: K, SIGMA, and MU must be of common size or scalars.> ...
667s  gevrnd (ones (3), ones (2), ones (2))
667s ***** error<gevrnd: K, SIGMA, and MU must be of common size or scalars.> ...
667s  gevrnd (ones (2), ones (3), ones (2))
667s ***** error<gevrnd: K, SIGMA, and MU must be of common size or scalars.> ...
667s  gevrnd (ones (2), ones (2), ones (3))
667s ***** error<gevrnd: K, SIGMA, and MU must not be complex.> gevrnd (i, 2, 3)
667s ***** error<gevrnd: K, SIGMA, and MU must not be complex.> gevrnd (1, i, 3)
667s ***** error<gevrnd: K, SIGMA, and MU must not be complex.> gevrnd (1, 2, i)
667s ***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
667s  gevrnd (1, 2, 3, -1)
667s ***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
667s  gevrnd (1, 2, 3, 1.2)
667s ***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
667s  gevrnd (1, 2, 3, ones (2))
667s ***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
667s  gevrnd (1, 2, 3, [2 -1 2])
667s ***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
667s  gevrnd (1, 2, 3, [2 0 2.5])
667s ***** error<gevrnd: dimensions must be non-negative integers.> ...
667s  gevrnd (1, 2, 3, 2, -1, 5)
667s ***** error<gevrnd: dimensions must be non-negative integers.> ...
667s  gevrnd (1, 2, 3, 2, 1.5, 5)
667s ***** error<gevrnd: K, SIGMA, and MU must be scalars or of size SZ.> ...
667s  gevrnd (2, ones (2), 2, 3)
667s ***** error<gevrnd: K, SIGMA, and MU must be scalars or of size SZ.> ...
667s  gevrnd (2, ones (2), 2, [3, 2])
667s ***** error<gevrnd: K, SIGMA, and MU must be scalars or of size SZ.> ...
667s  gevrnd (2, ones (2), 2, 3, 2)
667s 34 tests, 34 passed, 0 known failure, 0 skipped
667s [inst/dist_fun/lognrnd.m]
667s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/lognrnd.m
667s ***** assert (size (lognrnd (1, 1)), [1 1])
667s ***** assert (size (lognrnd (1, ones (2,1))), [2, 1])
667s ***** assert (size (lognrnd (1, ones (2,2))), [2, 2])
667s ***** assert (size (lognrnd (ones (2,1), 1)), [2, 1])
667s ***** assert (size (lognrnd (ones (2,2), 1)), [2, 2])
667s ***** assert (size (lognrnd (1, 1, 3)), [3, 3])
667s ***** assert (size (lognrnd (1, 1, [4, 1])), [4, 1])
667s ***** assert (size (lognrnd (1, 1, 4, 1)), [4, 1])
667s ***** assert (size (lognrnd (1, 1, 4, 1, 5)), [4, 1, 5])
667s ***** assert (size (lognrnd (1, 1, 0, 1)), [0, 1])
667s ***** assert (size (lognrnd (1, 1, 1, 0)), [1, 0])
667s ***** assert (size (lognrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
667s ***** assert (class (lognrnd (1, 1)), "double")
667s ***** assert (class (lognrnd (1, single (1))), "single")
667s ***** assert (class (lognrnd (1, single ([1, 1]))), "single")
667s ***** assert (class (lognrnd (single (1), 1)), "single")
667s ***** assert (class (lognrnd (single ([1, 1]), 1)), "single")
667s ***** error<lognrnd: function called with too few input arguments.> lognrnd ()
667s ***** error<lognrnd: function called with too few input arguments.> lognrnd (1)
667s ***** error<lognrnd: MU and SIGMA must be of common size or scalars.> ...
667s  lognrnd (ones (3), ones (2))
667s ***** error<lognrnd: MU and SIGMA must be of common size or scalars.> ...
667s  lognrnd (ones (2), ones (3))
667s ***** error<lognrnd: MU and SIGMA must not be complex.> lognrnd (i, 2, 3)
667s ***** error<lognrnd: MU and SIGMA must not be complex.> lognrnd (1, i, 3)
667s ***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
667s  lognrnd (1, 2, -1)
667s ***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
667s  lognrnd (1, 2, 1.2)
667s ***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
667s  lognrnd (1, 2, ones (2))
667s ***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
667s  lognrnd (1, 2, [2 -1 2])
667s ***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
667s  lognrnd (1, 2, [2 0 2.5])
667s ***** error<lognrnd: dimensions must be non-negative integers.> ...
667s  lognrnd (1, 2, 2, -1, 5)
667s ***** error<lognrnd: dimensions must be non-negative integers.> ...
667s  lognrnd (1, 2, 2, 1.5, 5)
667s ***** error<lognrnd: MU and SIGMA must be scalars or of size SZ.> ...
667s  lognrnd (2, ones (2), 3)
667s ***** error<lognrnd: MU and SIGMA must be scalars or of size SZ.> ...
667s  lognrnd (2, ones (2), [3, 2])
667s ***** error<lognrnd: MU and SIGMA must be scalars or of size SZ.> ...
667s  lognrnd (2, ones (2), 3, 2)
667s 33 tests, 33 passed, 0 known failure, 0 skipped
667s [inst/dist_fun/ncx2pdf.m]
667s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/ncx2pdf.m
667s ***** demo
667s  ## Plot various PDFs from the noncentral chi-squared distribution
667s  x = 0:0.1:10;
667s  y1 = ncx2pdf (x, 2, 1);
667s  y2 = ncx2pdf (x, 2, 2);
667s  y3 = ncx2pdf (x, 2, 3);
667s  y4 = ncx2pdf (x, 4, 1);
667s  y5 = ncx2pdf (x, 4, 2);
667s  y6 = ncx2pdf (x, 4, 3);
667s  plot (x, y1, "-r", x, y2, "-g", x, y3, "-k", ...
667s        x, y4, "-m", x, y5, "-c", x, y6, "-y")
667s  grid on
667s  xlim ([0, 10])
667s  ylim ([0, 0.32])
667s  legend ({"df = 2, λ = 1", "df = 2, λ = 2", ...
667s           "df = 2, λ = 3", "df = 4, λ = 1", ...
667s           "df = 4, λ = 2", "df = 4, λ = 3"}, "location", "northeast")
667s  title ("Noncentral chi-squared PDF")
667s  xlabel ("values in x")
667s  ylabel ("density")
667s ***** demo
667s  ## Compare the noncentral chi-squared PDF with LAMBDA = 2 to the
667s  ## chi-squared PDF with the same number of degrees of freedom (4).
667s 
667s  x = 0:0.1:10;
667s  y1 = ncx2pdf (x, 4, 2);
667s  y2 = chi2pdf (x, 4);
667s  plot (x, y1, "-", x, y2, "-");
667s  grid on
667s  xlim ([0, 10])
667s  ylim ([0, 0.32])
667s  legend ({"Noncentral T(10,1)", "T(10)"}, "location", "northwest")
667s  title ("Noncentral chi-squared vs chi-squared PDFs")
667s  xlabel ("values in x")
667s  ylabel ("density")
667s ***** shared x1, df, d1
667s  x1 = [-Inf, 2, NaN, 4, Inf];
667s  df = [2, 0, -1, 1, 4];
667s  d1 = [1, NaN, 3, -1, 2];
667s ***** assert (ncx2pdf (x1, df, d1), [0, NaN, NaN, NaN, 0]);
667s ***** assert (ncx2pdf (x1, df, 1), [0, 0.07093996461786045, NaN, ...
667s                               0.06160064323277038, 0], 1e-14);
667s ***** assert (ncx2pdf (x1, df, 3), [0, 0.1208364909271113, NaN, ...
667s                               0.09631299762429098, 0], 1e-14);
667s ***** assert (ncx2pdf (x1, df, 2), [0, 0.1076346446244688, NaN, ...
667s                               0.08430464047296625, 0], 1e-14);
667s ***** assert (ncx2pdf (x1, 2, d1), [0, NaN, NaN, NaN, 0]);
667s ***** assert (ncx2pdf (2, df, d1), [0.1747201674611283, NaN, NaN, ...
667s                               NaN, 0.1076346446244688], 1e-14);
667s ***** assert (ncx2pdf (4, df, d1), [0.09355987820265799, NaN, NaN, ...
667s                               NaN, 0.1192317192431485], 1e-14);
667s ***** error<ncx2pdf: function called with too few input arguments.> ncx2pdf ()
667s ***** error<ncx2pdf: function called with too few input arguments.> ncx2pdf (1)
667s ***** error<ncx2pdf: function called with too few input arguments.> ncx2pdf (1, 2)
667s ***** error<ncx2pdf: X, DF, and LAMBDA must be of common size or scalars.> ...
667s  ncx2pdf (ones (3), ones (2), ones (2))
667s ***** error<ncx2pdf: X, DF, and LAMBDA must be of common size or scalars.> ...
667s  ncx2pdf (ones (2), ones (3), ones (2))
667s ***** error<ncx2pdf: X, DF, and LAMBDA must be of common size or scalars.> ...
667s  ncx2pdf (ones (2), ones (2), ones (3))
667s ***** error<ncx2pdf: X, DF, and LAMBDA must not be complex.> ncx2pdf (i, 2, 2)
667s ***** error<ncx2pdf: X, DF, and LAMBDA must not be complex.> ncx2pdf (2, i, 2)
667s ***** error<ncx2pdf: X, DF, and LAMBDA must not be complex.> ncx2pdf (2, 2, i)
667s 16 tests, 16 passed, 0 known failure, 0 skipped
667s [inst/dist_fun/loglpdf.m]
667s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/loglpdf.m
667s ***** demo
667s  ## Plot various PDFs from the log-logistic distribution
667s  x = 0.001:0.001:2;
667s  y1 = loglpdf (x, log (1), 1/0.5);
667s  y2 = loglpdf (x, log (1), 1);
667s  y3 = loglpdf (x, log (1), 1/2);
667s  y4 = loglpdf (x, log (1), 1/4);
667s  y5 = loglpdf (x, log (1), 1/8);
667s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
667s  grid on
667s  ylim ([0,3])
667s  legend ({"σ = 2 (β = 0.5)", "σ = 1 (β = 1)", "σ = 0.5 (β = 2)", ...
667s           "σ = 0.25 (β = 4)", "σ = 0.125 (β = 8)"}, "location", "northeast")
667s  title ("Log-logistic PDF")
667s  xlabel ("values in x")
667s  ylabel ("density")
667s  text (0.1, 2.8, "μ = 0 (α = 1), values of σ (β) as shown in legend")
667s ***** shared out1, out2
667s  out1 = [0, 0, 1, 0.2500, 0.1111, 0.0625, 0.0400, 0.0278, 0];
667s  out2 = [0, 0, 0.0811, 0.0416, 0.0278, 0.0207, 0.0165, 0];
667s ***** assert (loglpdf ([-1,0,realmin,1:5,Inf], 0, 1), out1, 1e-4)
667s ***** assert (loglpdf ([-1,0,realmin,1:5,Inf], 0, 1), out1, 1e-4)
667s ***** assert (loglpdf ([-1:5,Inf], 1, 3), out2, 1e-4)
667s ***** assert (class (loglpdf (single (1), 2, 3)), "single")
667s ***** assert (class (loglpdf (1, single (2), 3)), "single")
667s ***** assert (class (loglpdf (1, 2, single (3))), "single")
667s ***** error<loglpdf: function called with too few input arguments.> loglpdf (1)
667s ***** error<loglpdf: function called with too few input arguments.> loglpdf (1, 2)
667s ***** error<loglpdf: X, MU, and SIGMA must be of common size or scalars.> ...
667s  loglpdf (1, ones (2), ones (3))
667s ***** error<loglpdf: X, MU, and SIGMA must be of common size or scalars.> ...
667s  loglpdf (ones (2), 1, ones (3))
667s ***** error<loglpdf: X, MU, and SIGMA must be of common size or scalars.> ...
667s  loglpdf (ones (2), ones (3), 1)
667s ***** error<loglpdf: X, MU, and SIGMA must not be complex.> loglpdf (i, 2, 3)
667s ***** error<loglpdf: X, MU, and SIGMA must not be complex.> loglpdf (1, i, 3)
667s ***** error<loglpdf: X, MU, and SIGMA must not be complex.> loglpdf (1, 2, i)
667s 14 tests, 14 passed, 0 known failure, 0 skipped
667s [inst/dist_fun/logicdf.m]
667s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/logicdf.m
667s ***** demo
667s  ## Plot various CDFs from the logistic distribution
667s  x = -5:0.01:20;
667s  p1 = logicdf (x, 5, 2);
667s  p2 = logicdf (x, 9, 3);
667s  p3 = logicdf (x, 9, 4);
667s  p4 = logicdf (x, 6, 2);
667s  p5 = logicdf (x, 2, 1);
667s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
667s  grid on
667s  legend ({"μ = 5, σ = 2", "μ = 9, σ = 3", "μ = 9, σ = 4", ...
667s           "μ = 6, σ = 2", "μ = 2, σ = 1"}, "location", "southeast")
667s  title ("Logistic CDF")
667s  xlabel ("values in x")
667s  ylabel ("probability")
667s ***** shared x, y
667s  x = [-Inf -log(3) 0 log(3) Inf];
667s  y = [0, 1/4, 1/2, 3/4, 1];
667s ***** assert (logicdf ([x, NaN], 0, 1), [y, NaN], eps)
667s ***** assert (logicdf (x, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), 0.75, 1])
667s ***** assert (logicdf (single ([x, NaN]), 0, 1), single ([y, NaN]), eps ("single"))
667s ***** assert (logicdf ([x, NaN], single (0), 1), single ([y, NaN]), eps ("single"))
667s ***** assert (logicdf ([x, NaN], 0, single (1)), single ([y, NaN]), eps ("single"))
667s ***** error<logicdf: function called with too few input arguments.> logicdf ()
667s ***** error<logicdf: function called with too few input arguments.> logicdf (1)
667s ***** error<logicdf: function called with too few input arguments.> ...
667s  logicdf (1, 2)
667s ***** error<logicdf: invalid argument for upper tail.> logicdf (1, 2, 3, "tail")
667s ***** error<logicdf: invalid argument for upper tail.> logicdf (1, 2, 3, 4)
667s ***** error<logicdf: X, MU, and SIGMA must be of common size or scalars.> ...
667s  logicdf (1, ones (2), ones (3))
667s ***** error<logicdf: X, MU, and SIGMA must be of common size or scalars.> ...
667s  logicdf (ones (2), 1, ones (3))
667s ***** error<logicdf: X, MU, and SIGMA must be of common size or scalars.> ...
667s  logicdf (ones (2), ones (3), 1)
667s ***** error<logicdf: X, MU, and SIGMA must not be complex.> logicdf (i, 2, 3)
667s ***** error<logicdf: X, MU, and SIGMA must not be complex.> logicdf (1, i, 3)
667s ***** error<logicdf: X, MU, and SIGMA must not be complex.> logicdf (1, 2, i)
667s 16 tests, 16 passed, 0 known failure, 0 skipped
667s [inst/dist_fun/hygeinv.m]
667s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/hygeinv.m
667s ***** demo
667s  ## Plot various iCDFs from the hypergeometric distribution
667s  p = 0.001:0.001:0.999;
667s  x1 = hygeinv (p, 500, 50, 100);
667s  x2 = hygeinv (p, 500, 60, 200);
667s  x3 = hygeinv (p, 500, 70, 300);
667s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
667s  grid on
667s  ylim ([0, 60])
667s  legend ({"m = 500, k = 50, n = 100", "m = 500, k = 60, n = 200", ...
667s           "m = 500, k = 70, n = 300"}, "location", "northwest")
667s  title ("Hypergeometric iCDF")
667s  xlabel ("probability")
667s  ylabel ("values in p (number of successes)")
667s ***** shared p
667s  p = [-1 0 0.5 1 2];
667s ***** assert (hygeinv (p, 4*ones (1,5), 2*ones (1,5), 2*ones (1,5)), [NaN 0 1 2 NaN])
667s ***** assert (hygeinv (p, 4*ones (1,5), 2, 2), [NaN 0 1 2 NaN])
667s ***** assert (hygeinv (p, 4, 2*ones (1,5), 2), [NaN 0 1 2 NaN])
667s ***** assert (hygeinv (p, 4, 2, 2*ones (1,5)), [NaN 0 1 2 NaN])
667s ***** assert (hygeinv (p, 4*[1 -1 NaN 1.1 1], 2, 2), [NaN NaN NaN NaN NaN])
667s ***** assert (hygeinv (p, 4, 2*[1 -1 NaN 1.1 1], 2), [NaN NaN NaN NaN NaN])
667s ***** assert (hygeinv (p, 4, 5, 2), [NaN NaN NaN NaN NaN])
667s ***** assert (hygeinv (p, 4, 2, 2*[1 -1 NaN 1.1 1]), [NaN NaN NaN NaN NaN])
667s ***** assert (hygeinv (p, 4, 2, 5), [NaN NaN NaN NaN NaN])
667s ***** assert (hygeinv ([p(1:2) NaN p(4:5)], 4, 2, 2), [NaN 0 NaN 2 NaN])
667s ***** assert (hygeinv ([p, NaN], 4, 2, 2), [NaN 0 1 2 NaN NaN])
667s ***** assert (hygeinv (single ([p, NaN]), 4, 2, 2), single ([NaN 0 1 2 NaN NaN]))
667s ***** assert (hygeinv ([p, NaN], single (4), 2, 2), single ([NaN 0 1 2 NaN NaN]))
667s ***** assert (hygeinv ([p, NaN], 4, single (2), 2), single ([NaN 0 1 2 NaN NaN]))
667s ***** assert (hygeinv ([p, NaN], 4, 2, single (2)), single ([NaN 0 1 2 NaN NaN]))
667s ***** error<hygeinv: function called with too few input arguments.> hygeinv ()
667s ***** error<hygeinv: function called with too few input arguments.> hygeinv (1)
667s ***** error<hygeinv: function called with too few input arguments.> hygeinv (1,2)
667s ***** error<hygeinv: function called with too few input arguments.> hygeinv (1,2,3)
667s ***** error<hygeinv: P, T, M, and N must be of common size or scalars.> ...
667s  hygeinv (ones (2), ones (3), 1, 1)
667s ***** error<hygeinv: P, T, M, and N must be of common size or scalars.> ...
667s  hygeinv (1, ones (2), ones (3), 1)
667s ***** error<hygeinv: P, T, M, and N must be of common size or scalars.> ...
667s  hygeinv (1, 1, ones (2), ones (3))
667s ***** error<hygeinv: P, T, M, and N must not be complex.> hygeinv (i, 2, 2, 2)
667s ***** error<hygeinv: P, T, M, and N must not be complex.> hygeinv (2, i, 2, 2)
667s ***** error<hygeinv: P, T, M, and N must not be complex.> hygeinv (2, 2, i, 2)
667s ***** error<hygeinv: P, T, M, and N must not be complex.> hygeinv (2, 2, 2, i)
667s 26 tests, 26 passed, 0 known failure, 0 skipped
667s [inst/dist_fun/gppdf.m]
667s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/gppdf.m
667s ***** demo
667s  ## Plot various PDFs from the generalized Pareto distribution
667s  x = 0:0.001:5;
667s  y1 = gppdf (x, 1, 1, 0);
667s  y2 = gppdf (x, 5, 1, 0);
667s  y3 = gppdf (x, 20, 1, 0);
667s  y4 = gppdf (x, 1, 2, 0);
667s  y5 = gppdf (x, 5, 2, 0);
667s  y6 = gppdf (x, 20, 2, 0);
667s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", ...
667s        x, y4, "-c", x, y5, "-m", x, y6, "-k")
667s  grid on
667s  xlim ([0, 5])
667s  ylim ([0, 1])
667s  legend ({"k = 1, σ = 1, θ = 0", "k = 5, σ = 1, θ = 0", ...
667s           "k = 20, σ = 1, θ = 0", "k = 1, σ = 2, θ = 0", ...
667s           "k = 5, σ = 2, θ = 0", "k = 20, σ = 2, θ = 0"}, ...
667s          "location", "northeast")
667s  title ("Generalized Pareto PDF")
667s  xlabel ("values in x")
667s  ylabel ("density")
667s ***** shared x, y1, y2, y3
667s  x = [-Inf, -1, 0, 1/2, 1, Inf];
667s  y1 = [0, 0, 1, 0.6065306597126334, 0.36787944117144233, 0];
667s  y2 = [0, 0, 1, 4/9, 1/4, 0];
667s  y3 = [0, 0, 1, 1, 1, 0];
667s ***** assert (gppdf (x, zeros (1,6), ones (1,6), zeros (1,6)), y1, eps)
667s ***** assert (gppdf (x, 0, 1, zeros (1,6)), y1, eps)
667s ***** assert (gppdf (x, 0, ones (1,6), 0), y1, eps)
667s ***** assert (gppdf (x, zeros (1,6), 1, 0), y1, eps)
667s ***** assert (gppdf (x, 0, 1, 0), y1, eps)
667s ***** assert (gppdf (x, 0, 1, [0, 0, 0, NaN, 0, 0]), [y1(1:3), NaN, y1(5:6)])
667s ***** assert (gppdf (x, 0, [1, 1, 1, NaN, 1, 1], 0), [y1(1:3), NaN, y1(5:6)])
667s ***** assert (gppdf (x, [0, 0, 0, NaN, 0, 0], 1, 0), [y1(1:3), NaN, y1(5:6)])
667s ***** assert (gppdf ([x(1:3), NaN, x(5:6)], 0, 1, 0), [y1(1:3), NaN, y1(5:6)])
667s ***** assert (gppdf (x, ones (1,6), ones (1,6), zeros (1,6)), y2, eps)
667s ***** assert (gppdf (x, 1, 1, zeros (1,6)), y2, eps)
667s ***** assert (gppdf (x, 1, ones (1,6), 0), y2, eps)
667s ***** assert (gppdf (x, ones (1,6), 1, 0), y2, eps)
667s ***** assert (gppdf (x, 1, 1, 0), y2, eps)
667s ***** assert (gppdf (x, 1, 1, [0, 0, 0, NaN, 0, 0]), [y2(1:3), NaN, y2(5:6)])
667s ***** assert (gppdf (x, 1, [1, 1, 1, NaN, 1, 1], 0), [y2(1:3), NaN, y2(5:6)])
667s ***** assert (gppdf (x, [1, 1, 1, NaN, 1, 1], 1, 0), [y2(1:3), NaN, y2(5:6)])
667s ***** assert (gppdf ([x(1:3), NaN, x(5:6)], 1, 1, 0), [y2(1:3), NaN, y2(5:6)])
667s ***** assert (gppdf (x, -ones (1,6), ones (1,6), zeros (1,6)), y3, eps)
667s ***** assert (gppdf (x, -1, 1, zeros (1,6)), y3, eps)
667s ***** assert (gppdf (x, -1, ones (1,6), 0), y3, eps)
667s ***** assert (gppdf (x, -ones (1,6), 1, 0), y3, eps)
667s ***** assert (gppdf (x, -1, 1, 0), y3, eps)
667s ***** assert (gppdf (x, -1, 1, [0, 0, 0, NaN, 0, 0]), [y3(1:3), NaN, y3(5:6)])
667s ***** assert (gppdf (x, -1, [1, 1, 1, NaN, 1, 1], 0), [y3(1:3), NaN, y3(5:6)])
667s ***** assert (gppdf (x, [-1, -1, -1, NaN, -1, -1], 1, 0), [y3(1:3), NaN, y3(5:6)])
667s ***** assert (gppdf ([x(1:3), NaN, x(5:6)], -1, 1, 0), [y3(1:3), NaN, y3(5:6)])
667s ***** assert (gppdf (single ([x, NaN]), 0, 1, 0), single ([y1, NaN]))
667s ***** assert (gppdf ([x, NaN], 0, 1, single (0)), single ([y1, NaN]))
667s ***** assert (gppdf ([x, NaN], 0, single (1), 0), single ([y1, NaN]))
667s ***** assert (gppdf ([x, NaN], single (0), 1, 0), single ([y1, NaN]))
667s ***** assert (gppdf (single ([x, NaN]), 1, 1, 0), single ([y2, NaN]))
667s ***** assert (gppdf ([x, NaN], 1, 1, single (0)), single ([y2, NaN]))
667s ***** assert (gppdf ([x, NaN], 1, single (1), 0), single ([y2, NaN]))
667s ***** assert (gppdf ([x, NaN], single (1), 1, 0), single ([y2, NaN]))
667s ***** assert (gppdf (single ([x, NaN]), -1, 1, 0), single ([y3, NaN]))
667s ***** assert (gppdf ([x, NaN], -1, 1, single (0)), single ([y3, NaN]))
667s ***** assert (gppdf ([x, NaN], -1, single (1), 0), single ([y3, NaN]))
667s ***** assert (gppdf ([x, NaN], single (-1), 1, 0), single ([y3, NaN]))
667s ***** error<gpcdf: function called with too few input arguments.> gpcdf ()
667s ***** error<gpcdf: function called with too few input arguments.> gpcdf (1)
667s ***** error<gpcdf: function called with too few input arguments.> gpcdf (1, 2)
667s ***** error<gpcdf: function called with too few input arguments.> gpcdf (1, 2, 3)
667s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
667s  gpcdf (ones (3), ones (2), ones(2), ones(2))
667s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
667s  gpcdf (ones (2), ones (3), ones(2), ones(2))
667s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
667s  gpcdf (ones (2), ones (2), ones(3), ones(2))
667s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
667s  gpcdf (ones (2), ones (2), ones(2), ones(3))
667s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (i, 2, 3, 4)
667s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, i, 3, 4)
667s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, 2, i, 4)
667s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, 2, 3, i)
667s 51 tests, 51 passed, 0 known failure, 0 skipped
667s [inst/dist_fun/plrnd.m]
667s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/plrnd.m
667s ***** shared x, Fx
667s  x = [0, 1, 3, 4, 7, 10];
667s  Fx = [0, 0.2, 0.5, 0.6, 0.7, 1];
667s ***** assert (size (plrnd (x, Fx)), [1, 1])
667s ***** assert (size (plrnd (x, Fx, 3)), [3, 3])
667s ***** assert (size (plrnd (x, Fx, [4, 1])), [4, 1])
667s ***** assert (size (plrnd (x, Fx, 4, 1)), [4, 1])
667s ***** assert (size (plrnd (x, Fx, 4, 1, 5)), [4, 1, 5])
667s ***** assert (size (plrnd (x, Fx, 0, 1)), [0, 1])
667s ***** assert (size (plrnd (x, Fx, 1, 0)), [1, 0])
667s ***** assert (size (plrnd (x, Fx, 1, 2, 0, 5)), [1, 2, 0, 5])
667s ***** assert (class (plrnd (x, Fx)), "double")
667s ***** assert (class (plrnd (x, single (Fx))), "single")
667s ***** assert (class (plrnd (single (x), Fx)), "single")
667s ***** error<plrnd: function called with too few input arguments.> plrnd ()
667s ***** error<plrnd: function called with too few input arguments.> plrnd (1)
667s ***** error<plrnd: X and FX must be vectors of equal size.> ...
667s  plrnd ([0, 1, 2], [0, 1])
667s ***** error<plrnd: X and FX must be at least two-elements long.> ...
667s  plrnd ([0], [1])
667s ***** error<plrnd: FX must be bounded in the range> ...
667s  plrnd ([0, 1, 2], [0, 1, 1.5])
667s ***** error<plrnd: FX must be bounded in the range> ...
667s  plrnd ([0, 1, 2], [0, i, 1])
667s ***** error<plrnd: X and FX must not be complex.> ...
667s  plrnd ([0, i, 2], [0, 0.5, 1])
667s ***** error<plrnd: X and FX must not be complex.> ...
667s  plrnd ([0, i, 2], [0, 0.5i, 1])
667s ***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
667s  plrnd (x, Fx, -1)
667s ***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
667s  plrnd (x, Fx, 1.2)
667s ***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
667s  plrnd (x, Fx, ones (2))
667s ***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
667s  plrnd (x, Fx, [2 -1 2])
667s ***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
667s  plrnd (x, Fx, [2 0 2.5])
667s ***** error<plrnd: dimensions must be non-negative integers.> ...
667s  plrnd (x, Fx, 2, -1, 5)
667s ***** error<plrnd: dimensions must be non-negative integers.> ...
667s  plrnd (x, Fx, 2, 1.5, 5)
667s 26 tests, 26 passed, 0 known failure, 0 skipped
667s [inst/dist_fun/unifpdf.m]
667s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/unifpdf.m
667s ***** demo
667s  ## Plot various PDFs from the continuous uniform distribution
667s  x = 0:0.001:10;
667s  y1 = unifpdf (x, 2, 5);
667s  y2 = unifpdf (x, 3, 9);
667s  plot (x, y1, "-b", x, y2, "-g")
667s  grid on
667s  xlim ([0, 10])
667s  ylim ([0, 0.4])
667s  legend ({"a = 2, b = 5", "a = 3, b = 9"}, "location", "northeast")
667s  title ("Continuous uniform PDF")
667s  xlabel ("values in x")
667s  ylabel ("density")
667s ***** shared x, y
667s  x = [-1 0 0.5 1 2] + 1;
667s  y = [0 1 1 1 0];
667s ***** assert (unifpdf (x, ones (1,5), 2*ones (1,5)), y)
667s ***** assert (unifpdf (x, 1, 2*ones (1,5)), y)
667s ***** assert (unifpdf (x, ones (1,5), 2), y)
667s ***** assert (unifpdf (x, [2 NaN 1 1 1], 2), [NaN NaN y(3:5)])
667s ***** assert (unifpdf (x, 1, 2*[0 NaN 1 1 1]), [NaN NaN y(3:5)])
667s ***** assert (unifpdf ([x, NaN], 1, 2), [y, NaN])
667s ***** assert (unifpdf (x, 0, 1), [1 1 0 0 0])
667s ***** assert (unifpdf (single ([x, NaN]), 1, 2), single ([y, NaN]))
667s ***** assert (unifpdf (single ([x, NaN]), single (1), 2), single ([y, NaN]))
667s ***** assert (unifpdf ([x, NaN], 1, single (2)), single ([y, NaN]))
667s ***** error<unifpdf: function called with too few input arguments.> unifpdf ()
667s ***** error<unifpdf: function called with too few input arguments.> unifpdf (1)
667s ***** error<unifpdf: function called with too few input arguments.> unifpdf (1, 2)
667s ***** error<unifpdf: X, A, and B must be of common size or scalars.> ...
667s  unifpdf (ones (3), ones (2), ones (2))
667s ***** error<unifpdf: X, A, and B must be of common size or scalars.> ...
667s  unifpdf (ones (2), ones (3), ones (2))
667s ***** error<unifpdf: X, A, and B must be of common size or scalars.> ...
667s  unifpdf (ones (2), ones (2), ones (3))
667s ***** error<unifpdf: X, A, and B must not be complex.> unifpdf (i, 2, 2)
667s ***** error<unifpdf: X, A, and B must not be complex.> unifpdf (2, i, 2)
667s ***** error<unifpdf: X, A, and B must not be complex.> unifpdf (2, 2, i)
667s 19 tests, 19 passed, 0 known failure, 0 skipped
667s [inst/dist_fun/tricdf.m]
667s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/tricdf.m
667s ***** demo
667s  ## Plot various CDFs from the triangular distribution
667s  x = 0.001:0.001:10;
667s  p1 = tricdf (x, 3, 4, 6);
667s  p2 = tricdf (x, 1, 2, 5);
667s  p3 = tricdf (x, 2, 3, 9);
667s  p4 = tricdf (x, 2, 5, 9);
667s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
667s  grid on
667s  xlim ([0, 10])
667s  legend ({"a = 3, b = 4, c = 6", "a = 1, b = 2, c = 5", ...
667s           "a = 2, b = 3, c = 9", "a = 2, b = 5, c = 9"}, ...
667s          "location", "southeast")
667s  title ("Triangular CDF")
667s  xlabel ("values in x")
667s  ylabel ("probability")
667s ***** shared x, y
667s  x = [-1, 0, 0.1, 0.5, 0.9, 1, 2] + 1;
667s  y = [0, 0, 0.02, 0.5, 0.98, 1 1];
667s ***** assert (tricdf (x, ones (1,7), 1.5 * ones (1, 7), 2 * ones (1, 7)), y, eps)
667s ***** assert (tricdf (x, 1 * ones (1, 7), 1.5, 2), y, eps)
667s ***** assert (tricdf (x, 1 * ones (1, 7), 1.5, 2, "upper"), 1 - y, eps)
667s ***** assert (tricdf (x, 1, 1.5, 2 * ones (1, 7)), y, eps)
667s ***** assert (tricdf (x, 1, 1.5 * ones (1, 7), 2), y, eps)
667s ***** assert (tricdf (x, 1, 1.5, 2), y, eps)
667s ***** assert (tricdf (x, [1, 1, NaN, 1, 1, 1, 1], 1.5, 2), ...
667s  [y(1:2), NaN, y(4:7)], eps)
667s ***** assert (tricdf (x, 1, 1.5, 2*[1, 1, NaN, 1, 1, 1, 1]), ...
667s  [y(1:2), NaN, y(4:7)], eps)
667s ***** assert (tricdf (x, 1, 1.5, 2*[1, 1, NaN, 1, 1, 1, 1]), ...
667s  [y(1:2), NaN, y(4:7)], eps)
667s ***** assert (tricdf ([x, NaN], 1, 1.5, 2), [y, NaN], eps)
667s ***** assert (tricdf (single ([x, NaN]), 1, 1.5, 2), ...
667s  single ([y, NaN]), eps("single"))
667s ***** assert (tricdf ([x, NaN], single (1), 1.5, 2), ...
667s  single ([y, NaN]), eps("single"))
667s ***** assert (tricdf ([x, NaN], 1, single (1.5), 2), ...
667s  single ([y, NaN]), eps("single"))
667s ***** assert (tricdf ([x, NaN], 1, 1.5, single (2)), ...
667s  single ([y, NaN]), eps("single"))
667s ***** error<tricdf: function called with too few input arguments.> tricdf ()
667s ***** error<tricdf: function called with too few input arguments.> tricdf (1)
667s ***** error<tricdf: function called with too few input arguments.> tricdf (1, 2)
667s ***** error<tricdf: function called with too few input arguments.> tricdf (1, 2, 3)
667s ***** error<tricdf: function called with too many inputs> ...
667s  tricdf (1, 2, 3, 4, 5, 6)
667s ***** error<tricdf: invalid argument for upper tail.> tricdf (1, 2, 3, 4, "tail")
667s ***** error<tricdf: invalid argument for upper tail.> tricdf (1, 2, 3, 4, 5)
667s ***** error<tricdf: X, A, B, and C must be of common size or scalars.> ...
667s  tricdf (ones (3), ones (2), ones(2), ones(2))
667s ***** error<tricdf: X, A, B, and C must be of common size or scalars.> ...
667s  tricdf (ones (2), ones (3), ones(2), ones(2))
667s ***** error<tricdf: X, A, B, and C must be of common size or scalars.> ...
667s  tricdf (ones (2), ones (2), ones(3), ones(2))
667s ***** error<tricdf: X, A, B, and C must be of common size or scalars.> ...
667s  tricdf (ones (2), ones (2), ones(2), ones(3))
667s ***** error<tricdf: X, A, B, and C must not be complex.> tricdf (i, 2, 3, 4)
667s ***** error<tricdf: X, A, B, and C must not be complex.> tricdf (1, i, 3, 4)
667s ***** error<tricdf: X, A, B, and C must not be complex.> tricdf (1, 2, i, 4)
667s ***** error<tricdf: X, A, B, and C must not be complex.> tricdf (1, 2, 3, i)
667s 29 tests, 29 passed, 0 known failure, 0 skipped
667s [inst/dist_fun/hygepdf.m]
667s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/hygepdf.m
667s ***** demo
667s  ## Plot various PDFs from the hypergeometric distribution
667s  x = 0:60;
667s  y1 = hygepdf (x, 500, 50, 100);
667s  y2 = hygepdf (x, 500, 60, 200);
667s  y3 = hygepdf (x, 500, 70, 300);
667s  plot (x, y1, "*b", x, y2, "*g", x, y3, "*r")
667s  grid on
667s  xlim ([0, 60])
667s  ylim ([0, 0.18])
667s  legend ({"m = 500, k = 50, μ = 100", "m = 500, k = 60, μ = 200", ...
667s           "m = 500, k = 70, μ = 300"}, "location", "northeast")
667s  title ("Hypergeometric PDF")
667s  xlabel ("values in x (number of successes)")
667s  ylabel ("density")
667s ***** shared x, y
667s  x = [-1 0 1 2 3];
667s  y = [0 1/6 4/6 1/6 0];
667s ***** assert (hygepdf (x, 4 * ones (1, 5), 2, 2), y, 3 * eps)
667s ***** assert (hygepdf (x, 4, 2 * ones (1, 5), 2), y, 3 * eps)
667s ***** assert (hygepdf (x, 4, 2, 2 * ones (1, 5)), y, 3 * eps)
667s ***** assert (hygepdf (x, 4 * [1, -1, NaN, 1.1, 1], 2, 2), [0, NaN, NaN, NaN, 0])
667s ***** assert (hygepdf (x, 4, 2 * [1, -1, NaN, 1.1, 1], 2), [0, NaN, NaN, NaN, 0])
667s ***** assert (hygepdf (x, 4, 5, 2), [NaN, NaN, NaN, NaN, NaN], 3 * eps)
667s ***** assert (hygepdf (x, 4, 2, 2 * [1, -1, NaN, 1.1, 1]), [0, NaN, NaN, NaN, 0])
667s ***** assert (hygepdf (x, 4, 2, 5), [NaN, NaN, NaN, NaN, NaN], 3 * eps)
667s ***** assert (hygepdf ([x, NaN], 4, 2, 2), [y, NaN], 3 * eps)
667s ***** assert (hygepdf (single ([x, NaN]), 4, 2, 2), single ([y, NaN]), eps ("single"))
667s ***** assert (hygepdf ([x, NaN], single (4), 2, 2), single ([y, NaN]), eps ("single"))
667s ***** assert (hygepdf ([x, NaN], 4, single (2), 2), single ([y, NaN]), eps ("single"))
667s ***** assert (hygepdf ([x, NaN], 4, 2, single (2)), single ([y, NaN]), eps ("single"))
667s ***** test
667s  z = zeros(3,5);
667s  z([4,5,6,8,9,12]) = [1, 0.5, 1/6, 0.5, 2/3, 1/6];
667s  assert (hygepdf (x, 4, [0, 1, 2], 2, "vectorexpand"), z, 3 * eps);
667s  assert (hygepdf (x, 4, [0, 1, 2]', 2, "vectorexpand"), z, 3 * eps);
667s  assert (hygepdf (x', 4, [0, 1, 2], 2, "vectorexpand"), z, 3 * eps);
667s  assert (hygepdf (2, 4, [0 ,1, 2], 2, "vectorexpand"), z(:,4), 3 * eps);
667s  assert (hygepdf (x, 4, 1, 2, "vectorexpand"), z(2,:), 3 *eps);
667s  assert (hygepdf ([NaN, x], 4, [0 1 2]', 2, "vectorexpand"), [NaN(3, 1), z], 3 * eps);
667s ***** error<hygepdf: function called with too few input arguments.> hygepdf ()
667s ***** error<hygepdf: function called with too few input arguments.> hygepdf (1)
667s ***** error<hygepdf: function called with too few input arguments.> hygepdf (1,2)
667s ***** error<hygepdf: function called with too few input arguments.> hygepdf (1,2,3)
667s ***** error<hygepdf: X, T, M, and N must be of common size or scalars.> ...
667s  hygepdf (1, ones (3), ones (2), ones (2))
667s ***** error<hygepdf: X, T, M, and N must be of common size or scalars.> ...
667s  hygepdf (1, ones (2), ones (3), ones (2))
667s ***** error<hygepdf: X, T, M, and N must be of common size or scalars.> ...
667s  hygepdf (1, ones (2), ones (2), ones (3))
667s ***** error<hygepdf: X, T, M, and N must not be complex.> hygepdf (i, 2, 2, 2)
667s ***** error<hygepdf: X, T, M, and N must not be complex.> hygepdf (2, i, 2, 2)
667s ***** error<hygepdf: X, T, M, and N must not be complex.> hygepdf (2, 2, i, 2)
667s ***** error<hygepdf: X, T, M, and N must not be complex.> hygepdf (2, 2, 2, i)
667s 25 tests, 25 passed, 0 known failure, 0 skipped
667s [inst/dist_fun/raylrnd.m]
667s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/raylrnd.m
667s ***** assert (size (raylrnd (2)), [1, 1])
667s ***** assert (size (raylrnd (ones (2,1))), [2, 1])
667s ***** assert (size (raylrnd (ones (2,2))), [2, 2])
667s ***** assert (size (raylrnd (1, 3)), [3, 3])
667s ***** assert (size (raylrnd (1, [4 1])), [4, 1])
667s ***** assert (size (raylrnd (1, 4, 1)), [4, 1])
667s ***** assert (size (raylrnd (1, 4, 1)), [4, 1])
667s ***** assert (size (raylrnd (1, 4, 1, 5)), [4, 1, 5])
667s ***** assert (size (raylrnd (1, 0, 1)), [0, 1])
667s ***** assert (size (raylrnd (1, 1, 0)), [1, 0])
667s ***** assert (size (raylrnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
667s ***** assert (raylrnd (0, 1, 1), NaN)
667s ***** assert (raylrnd ([0, 0, 0], [1, 3]), [NaN, NaN, NaN])
667s ***** assert (class (raylrnd (2)), "double")
667s ***** assert (class (raylrnd (single (2))), "single")
667s ***** assert (class (raylrnd (single ([2 2]))), "single")
667s ***** error<raylrnd: function called with too few input arguments.> raylrnd ()
667s ***** error<raylrnd: SIGMA must not be complex.> raylrnd (i)
667s ***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
667s  raylrnd (1, -1)
667s ***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
667s  raylrnd (1, 1.2)
667s ***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
667s  raylrnd (1, ones (2))
667s ***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
667s  raylrnd (1, [2 -1 2])
667s ***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
667s  raylrnd (1, [2 0 2.5])
667s ***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
667s  raylrnd (ones (2), ones (2))
667s ***** error<raylrnd: dimensions must be non-negative integers.> ...
667s  raylrnd (1, 2, -1, 5)
667s ***** error<raylrnd: dimensions must be non-negative integers.> ...
667s  raylrnd (1, 2, 1.5, 5)
667s ***** error<raylrnd: SIGMA must be scalar or of size SZ.> raylrnd (ones (2,2), 3)
667s ***** error<raylrnd: SIGMA must be scalar or of size SZ.> raylrnd (ones (2,2), [3, 2])
667s ***** error<raylrnd: SIGMA must be scalar or of size SZ.> raylrnd (ones (2,2), 2, 3)
667s 29 tests, 29 passed, 0 known failure, 0 skipped
667s [inst/dist_fun/copulapdf.m]
667s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/copulapdf.m
667s ***** test
667s  x = [0.2:0.2:0.6; 0.2:0.2:0.6];
667s  theta = [1; 2];
667s  y = copulapdf ("Clayton", x, theta);
667s  expected_p = [0.9872; 0.7295];
667s  assert (y, expected_p, 0.001);
667s ***** test
667s  x = [0.2:0.2:0.6; 0.2:0.2:0.6];
667s  y = copulapdf ("Gumbel", x, 2);
667s  expected_p = [0.9468; 0.9468];
667s  assert (y, expected_p, 0.001);
667s ***** test
667s  x = [0.2, 0.6; 0.2, 0.6];
667s  theta = [1; 2];
667s  y = copulapdf ("Frank", x, theta);
667s  expected_p = [0.9378; 0.8678];
667s  assert (y, expected_p, 0.001);
667s ***** test
667s  x = [0.2, 0.6; 0.2, 0.6];
667s  theta = [0.3; 0.7];
667s  y = copulapdf ("AMH", x, theta);
667s  expected_p = [0.9540; 0.8577];
667s  assert (y, expected_p, 0.001);
667s 4 tests, 4 passed, 0 known failure, 0 skipped
667s [inst/dist_fun/poisspdf.m]
667s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/poisspdf.m
667s ***** demo
667s  ## Plot various PDFs from the Poisson distribution
667s  x = 0:20;
667s  y1 = poisspdf (x, 1);
667s  y2 = poisspdf (x, 4);
667s  y3 = poisspdf (x, 10);
667s  plot (x, y1, "*b", x, y2, "*g", x, y3, "*r")
667s  grid on
667s  ylim ([0, 0.4])
667s  legend ({"λ = 1", "λ = 4", "λ = 10"}, "location", "northeast")
667s  title ("Poisson PDF")
667s  xlabel ("values in x (number of occurences)")
667s  ylabel ("density")
667s ***** shared x, y
667s  x = [-1 0 1 2 Inf];
667s  y = [0, exp(-1)*[1 1 0.5], 0];
667s ***** assert (poisspdf (x, ones (1,5)), y, eps)
667s ***** assert (poisspdf (x, 1), y, eps)
667s ***** assert (poisspdf (x, [1 0 NaN 1 1]), [y(1) NaN NaN y(4:5)], eps)
667s ***** assert (poisspdf ([x, NaN], 1), [y, NaN], eps)
667s ***** assert (poisspdf (single ([x, NaN]), 1), single ([y, NaN]), eps ("single"))
667s ***** assert (poisspdf ([x, NaN], single (1)), single ([y, NaN]), eps ("single"))
667s ***** error<poisspdf: function called with too few input arguments.> poisspdf ()
667s ***** error<poisspdf: function called with too few input arguments.> poisspdf (1)
667s ***** error<poisspdf: X and LAMBDA must be of common size or scalars.> ...
667s  poisspdf (ones (3), ones (2))
667s ***** error<poisspdf: X and LAMBDA must be of common size or scalars.> ...
667s  poisspdf (ones (2), ones (3))
667s ***** error<poisspdf: X and LAMBDA must not be complex.> poisspdf (i, 2)
667s ***** error<poisspdf: X and LAMBDA must not be complex.> poisspdf (2, i)
667s 12 tests, 12 passed, 0 known failure, 0 skipped
667s [inst/dist_fun/normpdf.m]
667s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/normpdf.m
667s ***** demo
667s  ## Plot various PDFs from the normal distribution
667s  x = -5:0.01:5;
667s  y1 = normpdf (x, 0, 0.5);
667s  y2 = normpdf (x, 0, 1);
667s  y3 = normpdf (x, 0, 2);
667s  y4 = normpdf (x, -2, 0.8);
667s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
667s  grid on
667s  xlim ([-5, 5])
667s  ylim ([0, 0.9])
667s  legend ({"μ = 0, σ = 0.5", "μ = 0, σ = 1", ...
667s           "μ = 0, σ = 2", "μ = -2, σ = 0.8"}, "location", "northeast")
667s  title ("Normal PDF")
667s  xlabel ("values in x")
667s  ylabel ("density")
667s ***** shared x, y
667s  x = [-Inf, 1, 2, Inf];
667s  y = 1 / sqrt (2 * pi) * exp (-(x - 1) .^ 2 / 2);
667s ***** assert (normpdf (x, ones (1,4), ones (1,4)), y, eps)
667s ***** assert (normpdf (x, 1, ones (1,4)), y, eps)
667s ***** assert (normpdf (x, ones (1,4), 1), y, eps)
667s ***** assert (normpdf (x, [0 -Inf NaN Inf], 1), [y(1) NaN NaN NaN], eps)
667s ***** assert (normpdf (x, 1, [Inf NaN -1 0]), [NaN NaN NaN NaN], eps)
667s ***** assert (normpdf ([x, NaN], 1, 1), [y, NaN], eps)
667s ***** assert (normpdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
667s ***** assert (normpdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
667s ***** assert (normpdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
667s ***** error<normpdf: function called with too few input arguments.> normpdf ()
667s ***** error<normpdf: X, MU, and SIGMA must be of common size or scalars.> ...
667s  normpdf (ones (3), ones (2), ones (2))
667s ***** error<normpdf: X, MU, and SIGMA must be of common size or scalars.> ...
667s  normpdf (ones (2), ones (3), ones (2))
667s ***** error<normpdf: X, MU, and SIGMA must be of common size or scalars.> ...
667s  normpdf (ones (2), ones (2), ones (3))
667s ***** error<normpdf: X, MU, and SIGMA must not be complex.> normpdf (i, 2, 2)
667s ***** error<normpdf: X, MU, and SIGMA must not be complex.> normpdf (2, i, 2)
667s ***** error<normpdf: X, MU, and SIGMA must not be complex.> normpdf (2, 2, i)
667s 16 tests, 16 passed, 0 known failure, 0 skipped
667s [inst/dist_fun/nakapdf.m]
667s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/nakapdf.m
667s ***** demo
667s  ## Plot various PDFs from the Nakagami distribution
667s  x = 0:0.01:3;
667s  y1 = nakapdf (x, 0.5, 1);
667s  y2 = nakapdf (x, 1, 1);
667s  y3 = nakapdf (x, 1, 2);
667s  y4 = nakapdf (x, 1, 3);
667s  y5 = nakapdf (x, 2, 1);
667s  y6 = nakapdf (x, 2, 2);
667s  y7 = nakapdf (x, 5, 1);
667s  plot (x, y1, "-r", x, y2, "-g", x, y3, "-y", x, y4, "-m", ...
667s        x, y5, "-k", x, y6, "-b", x, y7, "-c")
667s  grid on
667s  xlim ([0, 3])
667s  ylim ([0, 2])
667s  legend ({"μ = 0.5, ω = 1", "μ = 1, ω = 1", "μ = 1, ω = 2", ...
667s           "μ = 1, ω = 3", "μ = 2, ω = 1", "μ = 2, ω = 2", ...
667s           "μ = 5, ω = 1"}, "location", "northeast")
667s  title ("Nakagami PDF")
667s  xlabel ("values in x")
667s  ylabel ("density")
667s ***** shared x, y
667s  x = [-1, 0, 1, 2, Inf];
667s  y = [0, 0, 0.73575888234288467, 0.073262555554936715, 0];
667s ***** assert (nakapdf (x, ones (1,5), ones (1,5)), y, eps)
667s ***** assert (nakapdf (x, 1, 1), y, eps)
667s ***** assert (nakapdf (x, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)], eps)
667s ***** assert (nakapdf (x, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)], eps)
667s ***** assert (nakapdf ([x, NaN], 1, 1), [y, NaN], eps)
667s ***** assert (nakapdf (single ([x, NaN]), 1, 1), single ([y, NaN]))
667s ***** assert (nakapdf ([x, NaN], single (1), 1), single ([y, NaN]))
667s ***** assert (nakapdf ([x, NaN], 1, single (1)), single ([y, NaN]))
667s ***** error<nakapdf: function called with too few input arguments.> nakapdf ()
667s ***** error<nakapdf: function called with too few input arguments.> nakapdf (1)
667s ***** error<nakapdf: function called with too few input arguments.> nakapdf (1, 2)
667s ***** error<nakapdf: X, MU, and OMEGA must be of common size or scalars.> ...
667s  nakapdf (ones (3), ones (2), ones(2))
667s ***** error<nakapdf: X, MU, and OMEGA must be of common size or scalars.> ...
667s  nakapdf (ones (2), ones (3), ones(2))
667s ***** error<nakapdf: X, MU, and OMEGA must be of common size or scalars.> ...
667s  nakapdf (ones (2), ones (2), ones(3))
667s ***** error<nakapdf: X, MU, and OMEGA must not be complex.> nakapdf (i, 4, 3)
667s ***** error<nakapdf: X, MU, and OMEGA must not be complex.> nakapdf (1, i, 3)
667s ***** error<nakapdf: X, MU, and OMEGA must not be complex.> nakapdf (1, 4, i)
667s 17 tests, 17 passed, 0 known failure, 0 skipped
667s [inst/dist_fun/nctinv.m]
667s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/nctinv.m
667s ***** demo
667s  ## Plot various iCDFs from the noncentral T distribution
667s  p = 0.001:0.001:0.999;
667s  x1 = nctinv (p, 1, 0);
667s  x2 = nctinv (p, 4, 0);
667s  x3 = nctinv (p, 1, 2);
667s  x4 = nctinv (p, 4, 2);
667s  plot (p, x1, "-r", p, x2, "-g", p, x3, "-k", p, x4, "-m")
667s  grid on
667s  ylim ([-5, 5])
667s  legend ({"df = 1, μ = 0", "df = 4, μ = 0", ...
667s           "df = 1, μ = 2", "df = 4, μ = 2"}, "location", "northwest")
667s  title ("Noncentral T iCDF")
667s  xlabel ("probability")
667s  ylabel ("values in x")
667s ***** demo
667s  ## Compare the noncentral T iCDF with MU = 1 to the T iCDF
667s  ## with the same number of degrees of freedom (10).
667s 
667s  p = 0.001:0.001:0.999;
667s  x1 = nctinv (p, 10, 1);
667s  x2 = tinv (p, 10);
667s  plot (p, x1, "-", p, x2, "-");
667s  grid on
667s  ylim ([-5, 5])
667s  legend ({"Noncentral T(10,1)", "T(10)"}, "location", "northwest")
667s  title ("Noncentral T vs T quantile functions")
667s  xlabel ("probability")
667s  ylabel ("values in x")
667s ***** test
667s  x = [-Inf,-0.3347,0.1756,0.5209,0.8279,1.1424,1.5021,1.9633,2.6571,4.0845,Inf];
667s  assert (nctinv ([0:0.1:1], 2, 1), x, 1e-4);
667s ***** test
667s  x = [-Inf,1.5756,2.0827,2.5343,3.0043,3.5406,4.2050,5.1128,6.5510,9.6442,Inf];
667s  assert (nctinv ([0:0.1:1], 2, 3), x, 1e-4);
668s ***** test
668s  x = [-Inf,2.2167,2.9567,3.7276,4.6464,5.8455,7.5619,10.3327,15.7569,31.8159,Inf];
668s  assert (nctinv ([0:0.1:1], 1, 4), x, 1e-4);
668s ***** test
668s  x = [1.7791   1.9368   2.0239   2.0801   2.1195   2.1489];
668s  assert (nctinv (0.05, [1, 2, 3, 4, 5, 6], 4), x, 1e-4);
669s ***** test
669s  x = [-0.7755, 0.3670, 1.2554, 2.0239, 2.7348, 3.4154];
669s  assert (nctinv (0.05, 3, [1, 2, 3, 4, 5, 6]), x, 1e-4);
669s ***** test
669s  x = [-0.7183, 0.3624, 1.2878, 2.1195, -3.5413, 3.6430];
669s  assert (nctinv (0.05, 5, [1, 2, 3, 4, -1, 6]), x, 1e-4);
670s ***** test
670s  assert (nctinv (0.996, 5, 8), 30.02610554063658, 2e-11);
671s ***** error<nctinv: function called with too few input arguments.> nctinv ()
671s ***** error<nctinv: function called with too few input arguments.> nctinv (1)
671s ***** error<nctinv: function called with too few input arguments.> nctinv (1, 2)
671s ***** error<nctinv: P, DF, and MU must be of common size or scalars.> ...
671s  nctinv (ones (3), ones (2), ones (2))
671s ***** error<nctinv: P, DF, and MU must be of common size or scalars.> ...
671s  nctinv (ones (2), ones (3), ones (2))
671s ***** error<nctinv: P, DF, and MU must be of common size or scalars.> ...
671s  nctinv (ones (2), ones (2), ones (3))
671s ***** error<nctinv: P, DF, and MU must not be complex.> nctinv (i, 2, 2)
671s ***** error<nctinv: P, DF, and MU must not be complex.> nctinv (2, i, 2)
671s ***** error<nctinv: P, DF, and MU must not be complex.> nctinv (2, 2, i)
671s 16 tests, 16 passed, 0 known failure, 0 skipped
671s [inst/dist_fun/chi2pdf.m]
671s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/chi2pdf.m
671s ***** demo
671s  ## Plot various PDFs from the chi-squared distribution
671s  x = 0:0.01:8;
671s  y1 = chi2pdf (x, 1);
671s  y2 = chi2pdf (x, 2);
671s  y3 = chi2pdf (x, 3);
671s  y4 = chi2pdf (x, 4);
671s  y5 = chi2pdf (x, 6);
671s  y6 = chi2pdf (x, 9);
671s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", ...
671s        x, y4, "-c", x, y5, "-m", x, y6, "-y")
671s  grid on
671s  xlim ([0, 8])
671s  ylim ([0, 0.5])
671s  legend ({"df = 1", "df = 2", "df = 3", ...
671s           "df = 4", "df = 6", "df = 9"}, "location", "northeast")
671s  title ("Chi-squared PDF")
671s  xlabel ("values in x")
671s  ylabel ("density")
671s ***** shared x, y
671s  x = [-1 0 0.5 1 Inf];
671s  y = [0, 1/2 * exp(-x(2:5)/2)];
671s ***** assert (chi2pdf (x, 2*ones (1,5)), y)
671s ***** assert (chi2pdf (x, 2), y)
671s ***** assert (chi2pdf (x, 2*[1 0 NaN 1 1]), [y(1) NaN NaN y(4:5)])
671s ***** assert (chi2pdf ([x, NaN], 2), [y, NaN])
671s ***** assert (chi2pdf (single ([x, NaN]), 2), single ([y, NaN]))
671s ***** assert (chi2pdf ([x, NaN], single (2)), single ([y, NaN]))
671s ***** error<chi2pdf: function called with too few input arguments.> chi2pdf ()
671s ***** error<chi2pdf: function called with too few input arguments.> chi2pdf (1)
671s ***** error<chi2pdf: function called with too many inputs> chi2pdf (1,2,3)
671s ***** error<chi2pdf: X and DF must be of common size or scalars.> ...
671s  chi2pdf (ones (3), ones (2))
671s ***** error<chi2pdf: X and DF must be of common size or scalars.> ...
671s  chi2pdf (ones (2), ones (3))
671s ***** error<chi2pdf: X and DF must not be complex.> chi2pdf (i, 2)
671s ***** error<chi2pdf: X and DF must not be complex.> chi2pdf (2, i)
671s 13 tests, 13 passed, 0 known failure, 0 skipped
671s [inst/dist_fun/bvtcdf.m]
671s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/bvtcdf.m
671s ***** test
671s  x = [1, 2];
671s  rho = [1, 0.5; 0.5, 1];
671s  df = 4;
671s  assert (bvtcdf(x, rho(2), df), mvtcdf(x, rho, df), 1e-14);
671s ***** test
671s  x = [3, 2;2, 4;1, 5];
671s  rho = [1, 0.5; 0.5, 1];
671s  df = 4;
671s  assert (bvtcdf(x, rho(2), df), mvtcdf(x, rho, df), 1e-14);
671s 2 tests, 2 passed, 0 known failure, 0 skipped
671s [inst/dist_fun/unidinv.m]
671s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/unidinv.m
671s ***** demo
671s  ## Plot various iCDFs from the discrete uniform distribution
671s  p = 0.001:0.001:0.999;
671s  x1 = unidinv (p, 5);
671s  x2 = unidinv (p, 9);
671s  plot (p, x1, "-b", p, x2, "-g")
671s  grid on
671s  xlim ([0, 1])
671s  ylim ([0, 10])
671s  legend ({"N = 5", "N = 9"}, "location", "northwest")
671s  title ("Discrete uniform iCDF")
671s  xlabel ("probability")
671s  ylabel ("values in x")
671s ***** shared p
671s  p = [-1 0 0.5 1 2];
671s ***** assert (unidinv (p, 10*ones (1,5)), [NaN NaN 5 10 NaN], eps)
671s ***** assert (unidinv (p, 10), [NaN NaN 5 10 NaN], eps)
671s ***** assert (unidinv (p, 10*[0 1 NaN 1 1]), [NaN NaN NaN 10 NaN], eps)
671s ***** assert (unidinv ([p(1:2) NaN p(4:5)], 10), [NaN NaN NaN 10 NaN], eps)
671s ***** assert (unidinv ([p, NaN], 10), [NaN NaN 5 10 NaN NaN], eps)
671s ***** assert (unidinv (single ([p, NaN]), 10), single ([NaN NaN 5 10 NaN NaN]), eps)
671s ***** assert (unidinv ([p, NaN], single (10)), single ([NaN NaN 5 10 NaN NaN]), eps)
671s ***** error<unidinv: function called with too few input arguments.> unidinv ()
671s ***** error<unidinv: function called with too few input arguments.> unidinv (1)
671s ***** error<unidinv: P and N must be of common size or scalars.> ...
671s  unidinv (ones (3), ones (2))
671s ***** error<unidinv: P and N must be of common size or scalars.> ...
671s  unidinv (ones (2), ones (3))
671s ***** error<unidinv: P and N must not be complex.> unidinv (i, 2)
671s ***** error<unidinv: P and N must not be complex.> unidinv (2, i)
671s 13 tests, 13 passed, 0 known failure, 0 skipped
671s [inst/dist_fun/exppdf.m]
671s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/exppdf.m
671s ***** demo
671s  ## Plot various PDFs from the exponential distribution
671s  x = 0:0.01:5;
671s  y1 = exppdf (x, 2/3);
671s  y2 = exppdf (x, 1.0);
671s  y3 = exppdf (x, 2.0);
671s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r")
671s  grid on
671s  ylim ([0, 1.5])
671s  legend ({"μ = 2/3", "μ = 1", "μ = 2"}, "location", "northeast")
671s  title ("Exponential PDF")
671s  xlabel ("values in x")
671s  ylabel ("density")
671s ***** shared x,y
671s  x = [-1 0 0.5 1 Inf];
671s  y = gampdf (x, 1, 2);
671s ***** assert (exppdf (x, 2*ones (1,5)), y)
671s ***** assert (exppdf (x, 2*[1 0 NaN 1 1]), [y(1) NaN NaN y(4:5)])
671s ***** assert (exppdf ([x, NaN], 2), [y, NaN])
671s ***** assert (exppdf (single ([x, NaN]), 2), single ([y, NaN]))
671s ***** assert (exppdf ([x, NaN], single (2)), single ([y, NaN]))
671s ***** error<exppdf: function called with too few input arguments.> exppdf ()
671s ***** error<exppdf: function called with too many inputs> exppdf (1,2,3)
671s ***** error<exppdf: X and MU must be of common size or scalars.> ...
671s  exppdf (ones (3), ones (2))
671s ***** error<exppdf: X and MU must be of common size or scalars.> ...
671s  exppdf (ones (2), ones (3))
671s ***** error<exppdf: X and MU must not be complex.> exppdf (i, 2)
671s ***** error<exppdf: X and MU must not be complex.> exppdf (2, i)
671s 11 tests, 11 passed, 0 known failure, 0 skipped
671s [inst/dist_fun/gamcdf.m]
671s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/gamcdf.m
671s ***** demo
671s  ## Plot various CDFs from the Gamma distribution
671s  x = 0:0.01:20;
671s  p1 = gamcdf (x, 1, 2);
671s  p2 = gamcdf (x, 2, 2);
671s  p3 = gamcdf (x, 3, 2);
671s  p4 = gamcdf (x, 5, 1);
671s  p5 = gamcdf (x, 9, 0.5);
671s  p6 = gamcdf (x, 7.5, 1);
671s  p7 = gamcdf (x, 0.5, 1);
671s  plot (x, p1, "-r", x, p2, "-g", x, p3, "-y", x, p4, "-m", ...
671s        x, p5, "-k", x, p6, "-b", x, p7, "-c")
671s  grid on
671s  legend ({"α = 1, β = 2", "α = 2, β = 2", "α = 3, β = 2", ...
671s           "α = 5, β = 1", "α = 9, β = 0.5", "α = 7.5, β = 1", ...
671s           "α = 0.5, β = 1"}, "location", "southeast")
671s  title ("Gamma CDF")
671s  xlabel ("values in x")
671s  ylabel ("probability")
671s ***** shared x, y, u
671s  x = [-1, 0, 0.5, 1, 2, Inf];
671s  y = [0, gammainc(x(2:end), 1)];
671s  u = [0, NaN, NaN, 1, 0.1353352832366127, 0];
671s ***** assert (gamcdf (x, ones (1,6), ones (1,6)), y, eps)
671s ***** assert (gamcdf (x, ones (1,6), ones (1,6), []), y, eps)
671s ***** assert (gamcdf (x, 1, ones (1,6)), y, eps)
671s ***** assert (gamcdf (x, ones (1,6), 1), y, eps)
671s ***** assert (gamcdf (x, [0, -Inf, NaN, Inf, 1, 1], 1), [1, NaN, NaN, 0, y(5:6)], eps)
671s ***** assert (gamcdf (x, [0, -Inf, NaN, Inf, 1, 1], 1, "upper"), u, eps)
671s ***** assert (gamcdf (x, 1, [0, -Inf, NaN, Inf, 1, 1]), [NaN, NaN, NaN, 0, y(5:6)], eps)
671s ***** assert (gamcdf ([x(1:2), NaN, x(4:6)], 1, 1), [y(1:2), NaN, y(4:6)], eps)
671s ***** assert (gamcdf ([x, NaN], 1, 1), [y, NaN])
671s ***** assert (gamcdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
671s ***** assert (gamcdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
671s ***** assert (gamcdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
671s ***** error<gamcdf: invalid number of input arguments.> gamcdf ()
671s ***** error<gamcdf: invalid number of input arguments.> gamcdf (1)
671s ***** error<gamcdf: invalid number of input arguments.> gamcdf (1, 2, 3, 4, 5, 6, 7)
671s ***** error<gamcdf: invalid argument for upper tail.> gamcdf (1, 2, 3, "uper")
671s ***** error<gamcdf: invalid argument for upper tail.> gamcdf (1, 2, 3, 4, 5, "uper")
671s ***** error<gamcdf: invalid size of covariance matrix.> gamcdf (2, 3, 4, [1, 2])
671s ***** error<gamcdf: covariance matrix is required for confidence bounds.> ...
671s  [p, plo, pup] = gamcdf (1, 2, 3)
671s ***** error<gamcdf: covariance matrix is required for confidence bounds.> ...
671s  [p, plo, pup] = gamcdf (1, 2, 3, "upper")
671s ***** error<gamcdf: invalid value for alpha.> [p, plo, pup] = ...
671s  gamcdf (1, 2, 3, [1, 0; 0, 1], 0)
671s ***** error<gamcdf: invalid value for alpha.> [p, plo, pup] = ...
671s  gamcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
671s ***** error<gamcdf: invalid value for alpha.> [p, plo, pup] = ...
671s  gamcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
671s ***** error<gamcdf: X, A, and B must be of common size or scalars.> ...
671s  gamcdf (ones (3), ones (2), ones (2))
671s ***** error<gamcdf: X, A, and B must be of common size or scalars.> ...
671s  gamcdf (ones (2), ones (3), ones (2))
671s ***** error<gamcdf: X, A, and B must be of common size or scalars.> ...
671s  gamcdf (ones (2), ones (2), ones (3))
671s ***** error<gamcdf: X, A, and B must not be complex.> gamcdf (i, 2, 2)
671s ***** error<gamcdf: X, A, and B must not be complex.> gamcdf (2, i, 2)
671s ***** error<gamcdf: X, A, and B must not be complex.> gamcdf (2, 2, i)
671s ***** error<gamcdf: bad covariance matrix.> ...
671s  [p, plo, pup] = gamcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
671s 30 tests, 30 passed, 0 known failure, 0 skipped
671s [inst/dist_fun/normrnd.m]
671s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/normrnd.m
671s ***** assert (size (normrnd (1, 1)), [1 1])
671s ***** assert (size (normrnd (1, ones (2,1))), [2, 1])
671s ***** assert (size (normrnd (1, ones (2,2))), [2, 2])
671s ***** assert (size (normrnd (ones (2,1), 1)), [2, 1])
671s ***** assert (size (normrnd (ones (2,2), 1)), [2, 2])
671s ***** assert (size (normrnd (1, 1, 3)), [3, 3])
671s ***** assert (size (normrnd (1, 1, [4, 1])), [4, 1])
671s ***** assert (size (normrnd (1, 1, 4, 1)), [4, 1])
671s ***** assert (size (normrnd (1, 1, 4, 1, 5)), [4, 1, 5])
671s ***** assert (size (normrnd (1, 1, 0, 1)), [0, 1])
671s ***** assert (size (normrnd (1, 1, 1, 0)), [1, 0])
671s ***** assert (size (normrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
671s ***** assert (class (normrnd (1, 1)), "double")
671s ***** assert (class (normrnd (1, single (1))), "single")
671s ***** assert (class (normrnd (1, single ([1, 1]))), "single")
671s ***** assert (class (normrnd (single (1), 1)), "single")
671s ***** assert (class (normrnd (single ([1, 1]), 1)), "single")
671s ***** error<normrnd: function called with too few input arguments.> normrnd ()
671s ***** error<normrnd: function called with too few input arguments.> normrnd (1)
671s ***** error<normrnd: MU and SIGMA must be of common size or scalars.> ...
671s  normrnd (ones (3), ones (2))
671s ***** error<normrnd: MU and SIGMA must be of common size or scalars.> ...
671s  normrnd (ones (2), ones (3))
671s ***** error<normrnd: MU and SIGMA must not be complex.> normrnd (i, 2, 3)
671s ***** error<normrnd: MU and SIGMA must not be complex.> normrnd (1, i, 3)
671s ***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
671s  normrnd (1, 2, -1)
671s ***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
671s  normrnd (1, 2, 1.2)
671s ***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
671s  normrnd (1, 2, ones (2))
671s ***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
671s  normrnd (1, 2, [2 -1 2])
671s ***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
671s  normrnd (1, 2, [2 0 2.5])
671s ***** error<normrnd: dimensions must be non-negative integers.> ...
671s  normrnd (1, 2, 2, -1, 5)
671s ***** error<normrnd: dimensions must be non-negative integers.> ...
671s  normrnd (1, 2, 2, 1.5, 5)
671s ***** error<normrnd: MU and SIGMA must be scalars or of size SZ.> ...
671s  normrnd (2, ones (2), 3)
671s ***** error<normrnd: MU and SIGMA must be scalars or of size SZ.> ...
671s  normrnd (2, ones (2), [3, 2])
671s ***** error<normrnd: MU and SIGMA must be scalars or of size SZ.> ...
671s  normrnd (2, ones (2), 3, 2)
671s 33 tests, 33 passed, 0 known failure, 0 skipped
671s [inst/dist_fun/burrrnd.m]
671s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/burrrnd.m
671s ***** assert (size (burrrnd (1, 1, 1)), [1 1])
671s ***** assert (size (burrrnd (ones (2,1), 1, 1)), [2, 1])
671s ***** assert (size (burrrnd (ones (2,2), 1, 1)), [2, 2])
671s ***** assert (size (burrrnd (1, ones (2,1), 1)), [2, 1])
671s ***** assert (size (burrrnd (1, ones (2,2), 1)), [2, 2])
671s ***** assert (size (burrrnd (1, 1, ones (2,1))), [2, 1])
671s ***** assert (size (burrrnd (1, 1, ones (2,2))), [2, 2])
671s ***** assert (size (burrrnd (1, 1, 1, 3)), [3, 3])
671s ***** assert (size (burrrnd (1, 1, 1, [4 1])), [4, 1])
671s ***** assert (size (burrrnd (1, 1, 1, 4, 1)), [4, 1])
671s ***** assert (class (burrrnd (1,1,1)), "double")
671s ***** assert (class (burrrnd (single (1),1,1)), "single")
671s ***** assert (class (burrrnd (single ([1 1]),1,1)), "single")
671s ***** assert (class (burrrnd (1,single (1),1)), "single")
671s ***** assert (class (burrrnd (1,single ([1 1]),1)), "single")
671s ***** assert (class (burrrnd (1,1,single (1))), "single")
671s ***** assert (class (burrrnd (1,1,single ([1 1]))), "single")
671s ***** error<burrrnd: function called with too few input arguments.> burrrnd ()
671s ***** error<burrrnd: function called with too few input arguments.> burrrnd (1)
671s ***** error<burrrnd: function called with too few input arguments.> burrrnd (1, 2)
671s ***** error<burrrnd: LAMBDA, C, and K must be of common size or scalars.> ...
671s  burrrnd (ones (3), ones (2), ones (2))
671s ***** error<burrrnd: LAMBDA, C, and K must be of common size or scalars.> ...
671s  burrrnd (ones (2), ones (3), ones (2))
671s ***** error<burrrnd: LAMBDA, C, and K must be of common size or scalars.> ...
671s  burrrnd (ones (2), ones (2), ones (3))
671s ***** error<burrrnd: LAMBDA, C, and K must not be complex.> burrrnd (i, 2, 3)
671s ***** error<burrrnd: LAMBDA, C, and K must not be complex.> burrrnd (1, i, 3)
671s ***** error<burrrnd: LAMBDA, C, and K must not be complex.> burrrnd (1, 2, i)
671s ***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
671s  burrrnd (1, 2, 3, -1)
671s ***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
671s  burrrnd (1, 2, 3, 1.2)
671s ***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
671s  burrrnd (1, 2, 3, ones (2))
671s ***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
671s  burrrnd (1, 2, 3, [2 -1 2])
671s ***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
671s  burrrnd (1, 2, 3, [2 0 2.5])
671s ***** error<burrrnd: dimensions must be non-negative integers.> ...
671s  burrrnd (1, 2, 3, 2, -1, 5)
671s ***** error<burrrnd: dimensions must be non-negative integers.> ...
671s  burrrnd (1, 2, 3, 2, 1.5, 5)
671s ***** error<burrrnd: LAMBDA, C, and K must be scalars or of size SZ.> ...
671s  burrrnd (2, ones (2), 2, 3)
671s ***** error<burrrnd: LAMBDA, C, and K must be scalars or of size SZ.> ...
671s  burrrnd (2, ones (2), 2, [3, 2])
671s ***** error<burrrnd: LAMBDA, C, and K must be scalars or of size SZ.> ...
671s  burrrnd (2, ones (2), 2, 3, 2)
671s 36 tests, 36 passed, 0 known failure, 0 skipped
671s [inst/dist_fun/binornd.m]
671s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/binornd.m
671s ***** assert (size (binornd (2, 1/2)), [1 1])
671s ***** assert (size (binornd (2 * ones (2, 1), 1/2)), [2, 1])
671s ***** assert (size (binornd (2 * ones (2, 2), 1/2)), [2, 2])
671s ***** assert (size (binornd (2, 1/2 * ones (2, 1))), [2, 1])
671s ***** assert (size (binornd (1, 1/2 * ones (2, 2))), [2, 2])
671s ***** assert (size (binornd (ones (2, 1), 1)), [2, 1])
671s ***** assert (size (binornd (ones (2, 2), 1)), [2, 2])
671s ***** assert (size (binornd (2, 1/2, 3)), [3, 3])
671s ***** assert (size (binornd (1, 1, [4, 1])), [4, 1])
671s ***** assert (size (binornd (1, 1, 4, 1)), [4, 1])
671s ***** assert (size (binornd (1, 1, 4, 1, 5)), [4, 1, 5])
671s ***** assert (size (binornd (1, 1, 0, 1)), [0, 1])
671s ***** assert (size (binornd (1, 1, 1, 0)), [1, 0])
671s ***** assert (size (binornd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
671s ***** assert (class (binornd (1, 1)), "double")
671s ***** assert (class (binornd (1, single (0))), "single")
671s ***** assert (class (binornd (1, single ([0, 0]))), "single")
671s ***** assert (class (binornd (1, single (1), 2)), "single")
671s ***** assert (class (binornd (1, single ([1, 1]), 1, 2)), "single")
671s ***** assert (class (binornd (single (1), 1, 2)), "single")
671s ***** assert (class (binornd (single ([1, 1]), 1, 1, 2)), "single")
671s ***** error<binornd: function called with too few input arguments.> binornd ()
671s ***** error<binornd: function called with too few input arguments.> binornd (1)
671s ***** error<binornd: N and PS must be of common size or scalars.> ...
671s  binornd (ones (3), ones (2))
671s ***** error<binornd: N and PS must be of common size or scalars.> ...
671s  binornd (ones (2), ones (3))
671s ***** error<binornd: N and PS must not be complex.> binornd (i, 2)
671s ***** error<binornd: N and PS must not be complex.> binornd (1, i)
671s ***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
671s  binornd (1, 1/2, -1)
671s ***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
671s  binornd (1, 1/2, 1.2)
671s ***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
671s  binornd (1, 1/2, ones (2))
671s ***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
671s  binornd (1, 1/2, [2 -1 2])
671s ***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
671s  binornd (1, 1/2, [2 0 2.5])
671s ***** error<binornd: dimensions must be non-negative integers.> ...
671s  binornd (1, 1/2, 2, -1, 5)
671s ***** error<binornd: dimensions must be non-negative integers.> ...
671s  binornd (1, 1/2, 2, 1.5, 5)
671s ***** error<binornd: N and PS must be scalars or of size SZ.> ...
671s  binornd (2, 1/2 * ones (2), 3)
671s ***** error<binornd: N and PS must be scalars or of size SZ.> ...
671s  binornd (2, 1/2 * ones (2), [3, 2])
671s ***** error<binornd: N and PS must be scalars or of size SZ.> ...
671s  binornd (2, 1/2 * ones (2), 3, 2)
671s ***** error<binornd: N and PS must be scalars or of size SZ.> ...
671s  binornd (2 * ones (2), 1/2, 3)
671s ***** error<binornd: N and PS must be scalars or of size SZ.> ...
671s  binornd (2 * ones (2), 1/2, [3, 2])
671s ***** error<binornd: N and PS must be scalars or of size SZ.> ...
671s  binornd (2 * ones (2), 1/2, 3, 2)
671s 40 tests, 40 passed, 0 known failure, 0 skipped
671s [inst/dist_fun/burrpdf.m]
671s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/dist_fun/burrpdf.m
671s ***** demo
671s  ## Plot various PDFs from the Burr type XII distribution
671s  x = 0.001:0.001:3;
671s  y1 = burrpdf (x, 1, 1, 1);
671s  y2 = burrpdf (x, 1, 1, 2);
671s  y3 = burrpdf (x, 1, 1, 3);
671s  y4 = burrpdf (x, 1, 2, 1);
671s  y5 = burrpdf (x, 1, 3, 1);
671s  y6 = burrpdf (x, 1, 0.5, 2);
671s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", ...
671s        x, y4, "-c", x, y5, "-m", x, y6, "-k")
671s  grid on
671s  ylim ([0, 2])
671s  legend ({"λ = 1, c = 1, k = 1", "λ = 1, c = 1, k = 2", ...
671s           "λ = 1, c = 1, k = 3", "λ = 1, c = 2, k = 1", ...
671s           "λ = 1, c = 3, k = 1", "λ = 1, c = 0.5, k = 2"}, ...
671s          "location", "northeast")
671s  title ("Burr type XII PDF")
671s  xlabel ("values in x")
671s  ylabel ("density")
671s ***** shared x, y
671s  x = [-1, 0, 1, 2, Inf];
671s  y = [0, 1, 1/4, 1/9, 0];
671s ***** assert (burrpdf (x, ones(1,5), ones (1,5), ones (1,5)), y)
671s ***** assert (burrpdf (x, 1, 1, 1), y)
671s ***** assert (burrpdf (x, [1, 1, NaN, 1, 1], 1, 1), [y(1:2), NaN, y(4:5)])
671s ***** assert (burrpdf (x, 1, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)])
671s ***** assert (burrpdf (x, 1, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)])
671s ***** assert (burrpdf ([x, NaN], 1, 1, 1), [y, NaN])
671s ***** assert (burrpdf (single ([x, NaN]), 1, 1, 1), single ([y, NaN]))
671s ***** assert (burrpdf ([x, NaN], single (1), 1, 1), single ([y, NaN]))
671s ***** assert (burrpdf ([x, NaN], 1, single (1), 1), single ([y, NaN]))
671s ***** assert (burrpdf ([x, NaN], 1, 1, single (1)), single ([y, NaN]))
671s ***** error<burrpdf: function called with too few input arguments.> burrpdf ()
671s ***** error<burrpdf: function called with too few input arguments.> burrpdf (1)
671s ***** error<burrpdf: function called with too few input arguments.> burrpdf (1, 2)
671s ***** error<burrpdf: function called with too few input arguments.> burrpdf (1, 2, 3)
671s ***** error<burrpdf: function called with too many inputs> ...
671s  burrpdf (1, 2, 3, 4, 5)
671s ***** error<burrpdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
671s  burrpdf (ones (3), ones (2), ones(2), ones(2))
671s ***** error<burrpdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
671s  burrpdf (ones (2), ones (3), ones(2), ones(2))
671s ***** error<burrpdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
671s  burrpdf (ones (2), ones (2), ones(3), ones(2))
671s ***** error<burrpdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
671s  burrpdf (ones (2), ones (2), ones(2), ones(3))
671s ***** error<burrpdf: X, LAMBDA, C, and K must not be complex.> burrpdf (i, 2, 3, 4)
671s ***** error<burrpdf: X, LAMBDA, C, and K must not be complex.> burrpdf (1, i, 3, 4)
671s ***** error<burrpdf: X, LAMBDA, C, and K must not be complex.> burrpdf (1, 2, i, 4)
671s ***** error<burrpdf: X, LAMBDA, C, and K must not be complex.> burrpdf (1, 2, 3, i)
671s 23 tests, 23 passed, 0 known failure, 0 skipped
671s [inst/anova2.m]
671s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/inst/anova2.m
671s ***** demo
671s 
671s  # Factorial (Crossed) Two-way ANOVA with Interaction
671s 
671s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
671s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
671s 
671s  [p, atab, stats] = anova2(popcorn, 3, "on");
671s ***** demo
671s 
671s  # One-way Repeated Measures ANOVA (Rows are a crossed random factor)
671s 
671s  data = [54, 43, 78, 111;
671s          23, 34, 37, 41;
671s          45, 65, 99, 78;
671s          31, 33, 36, 35;
671s          15, 25, 30, 26];
671s 
671s  [p, atab, stats] = anova2 (data, 1, "on", "linear");
671s ***** demo
671s 
671s  # Balanced Nested One-way ANOVA (Rows are a nested random factor)
671s 
671s  data = [4.5924 7.3809 21.322; -0.5488 9.2085 25.0426; ...
671s          6.1605 13.1147 22.66; 2.3374 15.2654 24.1283; ...
671s          5.1873 12.4188 16.5927; 3.3579 14.3951 10.2129; ...
671s          6.3092 8.5986 9.8934; 3.2831 3.4945 10.0203];
671s 
671s  [p, atab, stats] = anova2 (data, 4, "on", "nested");
671s ***** test
671s  ## Test for anova2 ("interaction")
671s  ## comparison with results from Matlab for column effect
671s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
671s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
671s  [p, atab, stats] = anova2 (popcorn, 3, "off");
671s  assert (p(1), 7.678957383294716e-07, 1e-14);
671s  assert (p(2), 0.0001003738963050171, 1e-14);
671s  assert (p(3), 0.7462153966366274, 1e-14);
671s  assert (atab{2,5}, 56.700, 1e-14);
671s  assert (atab{2,3}, 2, 0);
671s  assert (atab{4,2}, 0.08333333333333348, 1e-14);
671s  assert (atab{5,4}, 0.1388888888888889, 1e-14);
671s  assert (atab{5,2}, 1.666666666666667, 1e-14);
671s  assert (atab{6,2}, 22);
671s  assert (stats.source, "anova2");
671s  assert (stats.colmeans, [6.25, 4.75, 4]);
671s  assert (stats.inter, 1, 0);
671s  assert (stats.pval, 0.7462153966366274, 1e-14);
671s  assert (stats.df, 12);
671s ***** test
671s  ## Test for anova2 ("linear") - comparison with results from GraphPad Prism 8
671s  data = [54, 43, 78, 111;
671s          23, 34, 37, 41;
671s          45, 65, 99, 78;
671s          31, 33, 36, 35;
671s          15, 25, 30, 26];
671s  [p, atab, stats] = anova2 (data, 1, "off", "linear");
671s  assert (atab{2,2}, 2174.95, 1e-10);
671s  assert (atab{3,2}, 8371.7, 1e-10);
671s  assert (atab{4,2}, 2404.3, 1e-10);
671s  assert (atab{5,2}, 12950.95, 1e-10);
671s  assert (atab{2,4}, 724.983333333333, 1e-10);
671s  assert (atab{3,4}, 2092.925, 1e-10);
671s  assert (atab{4,4}, 200.358333333333, 1e-10);
671s  assert (atab{2,5}, 3.61843363972882, 1e-10);
671s  assert (atab{3,5}, 10.445909412303, 1e-10);
671s  assert (atab{2,6}, 0.087266112738617, 1e-10);
671s  assert (atab{3,6}, 0.000698397753556, 1e-10);
671s ***** test
671s  ## Test for anova2 ("nested") - comparison with results from GraphPad Prism 8
671s  data = [4.5924 7.3809 21.322; -0.5488 9.2085 25.0426; ...
671s          6.1605 13.1147 22.66; 2.3374 15.2654 24.1283; ...
671s          5.1873 12.4188 16.5927; 3.3579 14.3951 10.2129; ...
671s          6.3092 8.5986 9.8934; 3.2831 3.4945 10.0203];
671s  [p, atab, stats] = anova2 (data, 4, "off", "nested");
671s  assert (atab{2,2}, 745.360306290833, 1e-10);
671s  assert (atab{3,2}, 278.01854140125, 1e-10);
671s  assert (atab{4,2}, 180.180377467501, 1e-10);
671s  assert (atab{5,2}, 1203.55922515958, 1e-10);
671s  assert (atab{2,4}, 372.680153145417, 1e-10);
671s  assert (atab{3,4}, 92.67284713375, 1e-10);
671s  assert (atab{4,4}, 10.0100209704167, 1e-10);
671s  assert (atab{2,5}, 4.02146005730833, 1e-10);
671s  assert (atab{3,5}, 9.25800729165627, 1e-10);
671s  assert (atab{2,6}, 0.141597630656771, 1e-10);
671s  assert (atab{3,6}, 0.000636643812875719, 1e-10);
671s 3 tests, 3 passed, 0 known failure, 0 skipped
671s Checking C++ files ...
671s [src/libsvmwrite.cc]
671s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/src/libsvmwrite.cc
671s ***** shared L, D
671s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
671s ***** error <libsvmwrite: error opening file for write.> libsvmwrite ("", L, D);
671s ***** error <libsvmwrite: length of label vector does not match instances.> ...
671s  libsvmwrite (tempname (), [L;L], D);
671s ***** error <libsvmwrite: wrong number of output arguments.> ...
671s  OUT = libsvmwrite (tempname (), L, D);
671s ***** error <libsvmwrite: label vector and instance matrix must be double.> ...
671s  libsvmwrite (tempname (), single (L), D);
671s ***** error <libsvmwrite: filename must be a string.> libsvmwrite (13412, L, D);
671s ***** error <libsvmwrite: instance_matrix must be sparse.> ...
671s  libsvmwrite (tempname (), L, full (D));
671s ***** error <libsvmwrite: wrong number of input arguments.> ...
671s  libsvmwrite (tempname (), L, D, D);
671s 7 tests, 7 passed, 0 known failure, 0 skipped
671s [src/svmtrain.cc]
671s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/src/svmtrain.cc
671s ***** test
671s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
671s  model = svmtrain(L, D, '-c 1 -g 0.07');
671s  [predict_label, accuracy, dec_values] = svmpredict(L, D, model);
671s  assert (isstruct (model), true);
671s  assert (isfield (model, "Parameters"), true);
671s  assert (model.totalSV, 130);
671s  assert (model.nr_class, 2);
671s  assert (size (model.Label), [2, 1]);
671s ***** shared L, D
671s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
671s ***** error <svmtrain: wrong number of output arguments.> [L, D] = svmtrain (L, D);
671s ***** error <svmtrain: label vector and instance matrix must be double.> ...
671s  model = svmtrain (single (L), D);
671s ***** error <svmtrain: wrong number of input arguments.> ...
671s  model = svmtrain (L, D, "", "");
671s 4 tests, 4 passed, 0 known failure, 0 skipped
671s [src/svmpredict.cc]
671s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/src/svmpredict.cc
671s ***** test
671s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
671s  model = svmtrain (L, D, '-c 1 -g 0.07');
671s  [predict_label, accuracy, dec_values] = svmpredict (L, D, model);
671s  assert (size (predict_label), size (dec_values));
671s  assert (accuracy, [86.666, 0.533, 0.533]', [1e-3, 1e-3, 1e-3]');
671s  assert (dec_values(1), 1.225836001973273, 1e-14);
671s  assert (dec_values(2), -0.3212992933043805, 1e-14);
671s  assert (predict_label(1), 1);
671s ***** shared L, D, model
671s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
671s  model = svmtrain (L, D, '-c 1 -g 0.07');
671s ***** error <svmpredict: wrong number of output arguments.> ...
671s  [p, a] = svmpredict (L, D, model);
671s ***** error <svmpredict: wrong number of input arguments.> p = svmpredict (L, D);
671s ***** error <svmpredict: label vector and instance matrix must be double.> ...
671s  p = svmpredict (single (L), D, model);
671s ***** error <svmpredict: model should be a struct array.> p = svmpredict (L, D, 123);
671s 5 tests, 5 passed, 0 known failure, 0 skipped
671s [src/editDistance.cc]
671s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/src/editDistance.cc
671s ***** error <editDistance: too many input arguments.> d = editDistance (1, 2, 3, 4);
671s ***** error <editDistance: too many output arguments.> ...
671s  [C, IA, IC, I] = editDistance ({"AS","SD","AD"}, 1);
671s ***** error <editDistance: too many output arguments.> ...
671s  [C, IA] = editDistance ({"AS","SD","AD"});
671s ***** error <editDistance: minDist must be a scalar value.> ...
671s  d = editDistance ({"AS","SD","AD"}, [1, 2]);
671s ***** error <editDistance: minDist must be a nonnegative integer.> ...
671s  d = editDistance ({"AS","SD","AD"}, -2);
671s ***** error <editDistance: minDist must be a nonnegative integer.> ...
671s  d = editDistance ({"AS","SD","AD"}, 1.25);
671s ***** error <editDistance: minDist must be a scalar value.> ...
671s  d = editDistance ({"AS","SD","AD"}, {"AS","SD","AD"}, [1, 2]);
671s ***** error <editDistance: minDist must be a nonnegative integer.> ...
671s  d = editDistance ({"AS","SD","AD"}, {"AS","SD","AD"}, -2);
671s ***** error <editDistance: minDist must be a nonnegative integer.> ...
671s  d = editDistance ({"AS","SD","AD"}, {"AS","SD","AD"}, 1.25);
671s ***** error <editDistance: minDist must be a scalar value.> ...
671s  d = editDistance ("string1", "string2", [1, 2]);
671s ***** error <editDistance: minDist must be a nonnegative integer.> ...
671s  d = editDistance ("string1", "string2", -2);
671s ***** error <editDistance: minDist must be a nonnegative integer.> ...
671s  d = editDistance ("string1", "string2", 1.25);
671s ***** error <editDistance: tokenizedDocument must contain cellstr arrays.> ...
671s  d = editDistance ({{"string1", "string2"}, 2});
671s ***** error <editDistance: tokenizedDocument must contain cellstr arrays.> ...
671s  d = editDistance ({{"string1", "string2"}, 2}, 2);
671s ***** error <editDistance: STR1 must be a cellstr.> ...
671s  d = editDistance ([1, 2, 3]);
671s ***** error <editDistance: STR1 must be a cellstr.> ...
671s  d = editDistance (["AS","SD","AD","AS"]);
671s ***** error <editDistance: STR1 must be a cellstr.> ...
671s  d = editDistance (["AS","SD","AD"], 2);
671s ***** error <editDistance: STR1 and STR2 must be either strings or cellstr.> ...
671s  d = editDistance (logical ([1,2,3]), {"AS","AS","AD"});
671s ***** error <editDistance: STR1 and STR2 must be either strings or cellstr.> ...
671s  d = editDistance ({"AS","SD","AD"}, logical ([1,2,3]));
671s ***** error <editDistance: STR1 and STR2 must be either strings or cellstr.> ...
671s  d = editDistance ([1,2,3], {"AS","AS","AD"});
671s ***** error <editDistance: first tokenizedDocument does not contain cellstr arrays.> ...
671s  d = editDistance ({1,2,3}, {"AS","SD","AD"});
671s ***** error <editDistance: second tokenizedDocument does not contain cellstr arrays.> ...
671s  d = editDistance ({"AS","SD","AD"}, {1,2,3});
671s ***** error <editDistance: cellstr input arguments size mismatch.> ...
671s  d = editDistance ({"AS","SD","AD"}, {"AS", "AS"});
671s ***** test
671s  d = editDistance ({"AS","SD","AD"});
671s  assert (d, [2; 1; 1]);
671s  assert (class (d), "double");
671s ***** test
671s  C = editDistance ({"AS","SD","AD"}, 1);
671s  assert (iscellstr (C), true);
671s  assert (C, {"AS";"SD"});
671s ***** test
671s  [C, IA] = editDistance ({"AS","SD","AD"}, 1);
671s  assert (class (IA), "double");
671s  assert (IA, [1;2]);
671s ***** test
671s  A = {"ASS"; "SDS"; "FDE"; "EDS"; "OPA"};
671s  [C, IA] = editDistance (A, 2, "OutputAllIndices", false);
671s  assert (class (IA), "double");
671s  assert (A(IA), C);
671s ***** test
671s  A = {"ASS"; "SDS"; "FDE"; "EDS"; "OPA"};
671s  [C, IA] = editDistance (A, 2, "OutputAllIndices", true);
671s  assert (class (IA), "cell");
671s  assert (C, {"ASS"; "FDE"; "OPA"});
671s  assert (A(IA{1}), {"ASS"; "SDS"; "EDS"});
671s  assert (A(IA{2}), {"FDE"; "EDS"});
671s  assert (A(IA{3}), {"OPA"});
671s ***** test
671s  A = {"ASS"; "SDS"; "FDE"; "EDS"; "OPA"};
671s  [C, IA, IC] = editDistance (A, 2);
671s  assert (class (IA), "double");
671s  assert (A(IA), C);
671s  assert (IC, [1; 1; 3; 1; 5]);
671s ***** test
671s  d = editDistance ({"AS","SD","AD"}, {"AS", "AD", "SE"});
671s  assert (d, [0; 1; 2]);
671s  assert (class (d), "double");
671s ***** test
671s  d = editDistance ({"AS","SD","AD"}, {"AS"});
671s  assert (d, [0; 2; 1]);
671s  assert (class (d), "double");
671s ***** test
671s  d = editDistance ({"AS"}, {"AS","SD","AD"});
671s  assert (d, [0; 2; 1]);
671s  assert (class (d), "double");
671s ***** test
671s  b = editDistance ("Octave", "octave");
671s  assert (b, 1);
671s  assert (class (b), "double");
671s 33 tests, 33 passed, 0 known failure, 0 skipped
671s [src/fcnntrain.cc]
671s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/src/fcnntrain.cc
671s ***** shared X, Y, MODEL
671s  load fisheriris
671s  X = meas;
671s  Y = grp2idx (species);
671s ***** error <fcnntrain: too few input arguments.> ...
671s  model = fcnntrain (X, Y);
671s ***** error <fcnntrain: too many output arguments.> ...
671s  [Q, W] = fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 50, false);
671s ***** error <fcnntrain: X must be a real numeric matrix.> ...
671s  fcnntrain (complex (X), Y, 10, [1, 1], 1, 0.01, 0.025, 50, false);
671s ***** error <fcnntrain: X must be a real numeric matrix.> ...
671s  fcnntrain ({X}, Y, 10, [1, 1], 1, 0.01, 0.025, 50, false);
671s ***** error <fcnntrain: X cannot be empty.> ...
671s  fcnntrain ([], Y, 10, [1, 1], 1, 0.01, 0.025, 50, false);
671s ***** error <fcnntrain: Y must be a real numeric matrix.> ...
671s  fcnntrain (X, complex (Y), 10, 1, 0.01, [1, 1], 0.025, 50, false);
671s ***** error <fcnntrain: Y must be a real numeric matrix.> ...
671s  fcnntrain (X, {Y}, 10, [1, 1], 1, 0.01, 0.025, 50, false);
671s ***** error <fcnntrain: Y cannot be empty.> ...
671s  fcnntrain (X, [], 10, [1, 1], 1, 0.01, 0.025, 50, false);
671s ***** error <fcnntrain: X and Y must have the same number of rows.> ...
671s  fcnntrain (X, Y([1:50]), 10, [1, 1], 1, 0.01, 0.025, 50, false);
671s ***** error <fcnntrain: labels in Y must be positive integers.> ...
671s  fcnntrain (X, Y - 1, 10, [1, 1], 1, 0.01, 0.025, 50, false);
671s ***** error <fcnntrain: 'LayerSizes' must be a row vector of integer values.> ...
671s  fcnntrain (X, Y, [10; 5], [1, 1, 1], 1, 0.01, 0.025, 50, false);
671s ***** error <fcnntrain: 'LayerSizes' must be a row vector of integer values.> ...
671s  fcnntrain (X, Y, "10", [1, 1], 1, 0.01, 0.025, 50, false);
671s ***** error <fcnntrain: 'LayerSizes' must be a row vector of integer values.> ...
671s  fcnntrain (X, Y, {10}, [1, 1], 1, 0.01, 0.025, 50, false);
671s ***** error <fcnntrain: 'LayerSizes' must be a row vector of integer values.> ...
671s  fcnntrain (X, Y, complex (10), [1, 1], 1, 0.01, 0.025, 50, false);
671s ***** error <fcnntrain: 'Activations' must be a row vector of integer values.> ...
671s  fcnntrain (X, Y, 10, [1; 1], 1, 0.01, 0.025, 50, false);
671s ***** error <fcnntrain: 'Activations' must be a row vector of integer values.> ...
671s  fcnntrain (X, Y, 10, {1, 1}, 1, 0.01, 0.025, 50, false);
671s ***** error <fcnntrain: 'Activations' must be a row vector of integer values.> ...
671s  fcnntrain (X, Y, 10, "1", 1, 0.01, 0.025, 50, false);
671s ***** error <fcnntrain: 'Activations' must be a row vector of integer values.> ...
671s  fcnntrain (X, Y, 10, complex ([1, 1]), 1, 0.01, 0.025, 50, false);
671s ***** error <fcnntrain: 'Activations' do not match LayerSizes.> ...
671s  fcnntrain (X, Y, 10, [1, 1, 1], 1, 0.01, 0.025, 50, false);
671s ***** error <fcnntrain: cannot have a layer of zero size.> ...
671s  fcnntrain (X, Y, [10, 0, 5], [1, 1, 1, 1], 1, 0.01, 0.025, 50, false);
671s ***** error <fcnntrain: invalid 'Activations' code.> ...
671s  fcnntrain (X, Y, 10, [-1, 1], 1, 0.01, 0.025, 50, false);
671s ***** error <fcnntrain: invalid 'Activations' code.> ...
671s  fcnntrain (X, Y, 10, [8, 1], 1, 0.01, 0.025, 50, false);
671s ***** error <fcnntrain: 'NumThreads' must be a positive integer scalar value.> ...
671s  fcnntrain (X, Y, 10, [1, 1], 0, 0.01, 0.025, 50, false);
671s ***** error <fcnntrain: 'Alpha' must be a positive scalar value.> ...
671s  fcnntrain (X, Y, 10, [1, 1], 1, -0.01, 0.025, 50, false);
671s ***** error <fcnntrain: 'LearningRate' must be a positive scalar value.> ...
671s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, -0.025, 50, false);
671s ***** error <fcnntrain: 'LearningRate' must be a positive scalar value.> ...
671s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0, 50, false);
671s ***** error <fcnntrain: 'LearningRate' must be a positive scalar value.> ...
671s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, [0.025, 0.001], 50, false);
671s ***** error <fcnntrain: 'LearningRate' must be a positive scalar value.> ...
671s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, {0.025}, 50, false);
671s ***** error <fcnntrain: 'Epochs' must be a positive scalar value.> ...
671s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 0, false);
671s ***** error <fcnntrain: 'Epochs' must be a positive scalar value.> ...
671s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, [50, 25], false);
671s ***** error <fcnntrain: 'DisplayInfo' must be a boolean scalar.> ...
671s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 50, 0);
671s ***** error <fcnntrain: 'DisplayInfo' must be a boolean scalar.> ...
671s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 50, 1);
671s ***** error <fcnntrain: 'DisplayInfo' must be a boolean scalar.> ...
671s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 50, [false, false]);
671s 33 tests, 33 passed, 0 known failure, 0 skipped
671s [src/fcnnpredict.cc]
671s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/src/fcnnpredict.cc
671s ***** shared X, Y, MODEL
671s  load fisheriris
671s  X = meas;
671s  Y = grp2idx (species);
671s  MODEL = fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 100, false);
671s ***** test
671s  [Y_pred, Y_scores] = fcnnpredict (MODEL, X);
671s  assert (numel (Y_pred), numel (Y));
671s  assert (isequal (size (Y_pred), size (Y)), true);
671s  assert (columns (Y_scores), numel (unique (Y)));
671s  assert (rows (Y_scores), numel (Y));
671s ***** error <fcnnpredict: too few input arguments.> ...
671s  fcnnpredict (MODEL);
671s ***** error <fcnnpredict: too many output arguments.> ...
671s  [Q, W, E] = fcnnpredict (MODEL, X);
671s ***** error <fcnnpredict: first argument must be a scalar structure.> ...
671s  fcnnpredict (1, X);
671s ***** error <fcnnpredict: first argument must be a scalar structure.> ...
671s  fcnnpredict (struct ("L", {1, 2, 3}), X);
671s ***** error <fcnnpredict: model does not have a 'LayerWeights' field.> ...
671s  fcnnpredict (struct ("L", 1), X);
671s ***** error <fcnnpredict: 'LayerWeights' must be a cell row vector.> ...
671s  fcnnpredict (struct ("LayerWeights", 1), X);
671s ***** error <fcnnpredict: 'LayerWeights' must be a cell row vector.> ...
671s  fcnnpredict (struct ("LayerWeights", {1}), X);
671s ***** error <fcnnpredict: 'LayerWeights' must be a cell row vector.> ...
671s  fcnnpredict (struct ("LayerWeights", {{1; 2; 3}}), X);
671s ***** error <fcnnpredict: model does not have an 'Activations' field.> ...
671s  fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, "R", 2), X);
671s ***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
671s  fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
671s                       "Activations", [2]), X);
671s ***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
671s  fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
671s                       "Activations", [2; 2]), X);
671s ***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
671s  fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
671s                       "Activations", {{2, 2}}), X);
671s ***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
671s  fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
671s                       "Activations", {{"sigmoid", "softmax"}}), X);
671s ***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
671s  fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
671s                       "Activations", "sigmoid"), X);
671s ***** error <fcnnpredict: XC must be a real numeric matrix.> ...
671s  fcnnpredict (MODEL, complex (X));
671s ***** error <fcnnpredict: XC must be a real numeric matrix.> ...
671s  fcnnpredict (MODEL, {1, 2, 3, 4});
671s ***** error <fcnnpredict: XC must be a real numeric matrix.> ...
671s  fcnnpredict (MODEL, "asd");
671s ***** error <fcnnpredict: XC cannot be empty.> ...
671s  fcnnpredict (MODEL, []);
671s ***** error <fcnnpredict: the features in XC do not match the trained model.> ...
671s  fcnnpredict (MODEL, X(:,[1:3]));
671s 20 tests, 20 passed, 0 known failure, 0 skipped
671s [src/libsvmread.cc]
671s >>>>> /tmp/autopkgtest.V4q3WS/build.s0V/src/src/libsvmread.cc
671s ***** error <libsvmread: filename must be a string.> [L, D] = libsvmread (24);
671s ***** error <libsvmread: wrong number of input or output arguments.> ...
671s  D = libsvmread ("filename");
671s ***** test
671s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
671s  assert (size (L), [270, 1]);
671s  assert (size (D), [270, 13]);
671s ***** test
671s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
671s  assert (issparse (L), false);
671s  assert (issparse (D), true);
671s 4 tests, 4 passed, 0 known failure, 0 skipped
672s Done running the unit tests.
672s Summary: 11162 tests, 11159 passed, 1 known failures, 2 skipped
672s autopkgtest [10:46:39]: test command1: -----------------------]
673s command1             PASS
673s autopkgtest [10:46:40]: test command1:  - - - - - - - - - - results - - - - - - - - - -
673s autopkgtest [10:46:40]: @@@@@@@@@@@@@@@@@@@@ summary
673s command1             PASS
691s nova [W] Skipping flock for amd64
691s Creating nova instance adt-plucky-amd64-octave-statistics-20250315-103527-juju-7f2275-prod-proposed-migration-environment-20-90633440-8d87-4427-a6b8-514d5af20acd from image adt/ubuntu-plucky-amd64-server-20250304.img (UUID 9c7d4da5-d95f-4c85-ac1f-51eb37e75c4c)...
691s nova [W] Timed out waiting for e2bc00f8-f305-4317-aa90-bc243944e70b to get deleted.