0s autopkgtest [14:51:39]: starting date and time: 2025-03-15 14:51:39+0000
  0s autopkgtest [14:51:39]: git checkout: 325255d2 Merge branch 'pin-any-arch' into 'ubuntu/production'
  0s autopkgtest [14:51:39]: host juju-7f2275-prod-proposed-migration-environment-9; command line: /home/ubuntu/autopkgtest/runner/autopkgtest --output-dir /tmp/autopkgtest-work.y4i3h0gm/out --timeout-copy=6000 --setup-commands 'ln -s /dev/null /etc/systemd/system/bluetooth.service; printf "http_proxy=http://squid.internal:3128\nhttps_proxy=http://squid.internal:3128\nno_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\n" >> /etc/environment' --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 -- lxd -r lxd-armhf-10.145.243.79 lxd-armhf-10.145.243.79:autopkgtest/ubuntu/plucky/armhf
 20s autopkgtest [14:51:59]: testbed dpkg architecture: armhf
 22s autopkgtest [14:52:01]: testbed apt version: 2.9.33
 26s autopkgtest [14:52:05]: @@@@@@@@@@@@@@@@@@@@ test bed setup
 28s autopkgtest [14:52:07]: testbed release detected to be: None
 35s autopkgtest [14:52:14]: updating testbed package index (apt update)
 37s Get:1 http://ftpmaster.internal/ubuntu plucky-proposed InRelease [126 kB]
 38s Get:2 http://ftpmaster.internal/ubuntu plucky InRelease [257 kB]
 38s Get:3 http://ftpmaster.internal/ubuntu plucky-updates InRelease [126 kB]
 38s Get:4 http://ftpmaster.internal/ubuntu plucky-security InRelease [126 kB]
 39s Get:5 http://ftpmaster.internal/ubuntu plucky-proposed/multiverse Sources [15.8 kB]
 39s Get:6 http://ftpmaster.internal/ubuntu plucky-proposed/universe Sources [404 kB]
 39s Get:7 http://ftpmaster.internal/ubuntu plucky-proposed/main Sources [101 kB]
 39s Get:8 http://ftpmaster.internal/ubuntu plucky-proposed/main armhf Packages [81.0 kB]
 39s Get:9 http://ftpmaster.internal/ubuntu plucky-proposed/main armhf c-n-f Metadata [1944 B]
 40s Get:10 http://ftpmaster.internal/ubuntu plucky-proposed/restricted armhf c-n-f Metadata [116 B]
 40s Get:11 http://ftpmaster.internal/ubuntu plucky-proposed/universe armhf Packages [326 kB]
 40s Get:12 http://ftpmaster.internal/ubuntu plucky-proposed/universe armhf c-n-f Metadata [12.1 kB]
 40s Get:13 http://ftpmaster.internal/ubuntu plucky-proposed/multiverse armhf Packages [3472 B]
 40s Get:14 http://ftpmaster.internal/ubuntu plucky-proposed/multiverse armhf c-n-f Metadata [332 B]
 40s Get:15 http://ftpmaster.internal/ubuntu plucky/universe Sources [21.0 MB]
 72s Get:16 http://ftpmaster.internal/ubuntu plucky/main Sources [1400 kB]
 74s Get:17 http://ftpmaster.internal/ubuntu plucky/multiverse Sources [299 kB]
 74s Get:18 http://ftpmaster.internal/ubuntu plucky/main armhf Packages [1378 kB]
 76s Get:19 http://ftpmaster.internal/ubuntu plucky/main armhf c-n-f Metadata [29.4 kB]
 76s Get:20 http://ftpmaster.internal/ubuntu plucky/restricted armhf c-n-f Metadata [108 B]
 76s Get:21 http://ftpmaster.internal/ubuntu plucky/universe armhf Packages [15.1 MB]
 98s Get:22 http://ftpmaster.internal/ubuntu plucky/multiverse armhf Packages [172 kB]
100s Fetched 41.0 MB in 1min 2s (658 kB/s)
101s Reading package lists...
107s autopkgtest [14:53:26]: upgrading testbed (apt dist-upgrade and autopurge)
108s Reading package lists...
109s Building dependency tree...
109s Reading state information...
109s Calculating upgrade...Starting pkgProblemResolver with broken count: 0
109s Starting 2 pkgProblemResolver with broken count: 0
109s Done
110s Entering ResolveByKeep
110s 
110s Calculating upgrade...
111s The following packages will be upgraded:
111s   libc-bin libc6 locales python3-jinja2 sos strace
111s 6 upgraded, 0 newly installed, 0 to remove and 0 not upgraded.
111s Need to get 8642 kB of archives.
111s After this operation, 23.6 kB of additional disk space will be used.
111s Get:1 http://ftpmaster.internal/ubuntu plucky-proposed/main armhf libc6 armhf 2.41-1ubuntu2 [2932 kB]
115s Get:2 http://ftpmaster.internal/ubuntu plucky-proposed/main armhf libc-bin armhf 2.41-1ubuntu2 [545 kB]
115s Get:3 http://ftpmaster.internal/ubuntu plucky-proposed/main armhf locales all 2.41-1ubuntu2 [4246 kB]
120s Get:4 http://ftpmaster.internal/ubuntu plucky/main armhf strace armhf 6.13+ds-1ubuntu1 [445 kB]
120s Get:5 http://ftpmaster.internal/ubuntu plucky/main armhf python3-jinja2 all 3.1.5-2ubuntu1 [109 kB]
121s Get:6 http://ftpmaster.internal/ubuntu plucky/main armhf sos all 4.9.0-5 [365 kB]
121s Preconfiguring packages ...
121s Fetched 8642 kB in 10s (865 kB/s)
122s (Reading database ... 
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(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 64655 files and directories currently installed.)
122s Preparing to unpack .../libc6_2.41-1ubuntu2_armhf.deb ...
122s Unpacking libc6:armhf (2.41-1ubuntu2) over (2.41-1ubuntu1) ...
122s Setting up libc6:armhf (2.41-1ubuntu2) ...
122s (Reading database ... 
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(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 ... 64655 files and directories currently installed.)
122s Preparing to unpack .../libc-bin_2.41-1ubuntu2_armhf.deb ...
122s Unpacking libc-bin (2.41-1ubuntu2) over (2.41-1ubuntu1) ...
122s Setting up libc-bin (2.41-1ubuntu2) ...
122s (Reading database ... 
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(Reading database ... 50%
(Reading database ... 55%
(Reading database ... 60%
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(Reading database ... 70%
(Reading database ... 75%
(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 64655 files and directories currently installed.)
122s Preparing to unpack .../locales_2.41-1ubuntu2_all.deb ...
122s Unpacking locales (2.41-1ubuntu2) over (2.41-1ubuntu1) ...
123s Preparing to unpack .../strace_6.13+ds-1ubuntu1_armhf.deb ...
123s Unpacking strace (6.13+ds-1ubuntu1) over (6.11-0ubuntu1) ...
123s Preparing to unpack .../python3-jinja2_3.1.5-2ubuntu1_all.deb ...
123s Unpacking python3-jinja2 (3.1.5-2ubuntu1) over (3.1.5-2) ...
123s Preparing to unpack .../archives/sos_4.9.0-5_all.deb ...
123s Unpacking sos (4.9.0-5) over (4.9.0-4) ...
123s Setting up sos (4.9.0-5) ...
124s Setting up locales (2.41-1ubuntu2) ...
125s Generating locales (this might take a while)...
126s   en_US.UTF-8... done
126s Generation complete.
126s Setting up python3-jinja2 (3.1.5-2ubuntu1) ...
126s Setting up strace (6.13+ds-1ubuntu1) ...
126s Processing triggers for man-db (2.13.0-1) ...
128s Processing triggers for systemd (257.3-1ubuntu3) ...
130s Reading package lists...
130s Building dependency tree...
130s Reading state information...
131s Starting pkgProblemResolver with broken count: 0
131s Starting 2 pkgProblemResolver with broken count: 0
131s Done
131s Solving dependencies...
132s 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded.
134s autopkgtest [14:53:53]: rebooting testbed after setup commands that affected boot
174s autopkgtest [14:54:33]: testbed running kernel: Linux 6.8.0-52-generic #53~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Wed Jan 15 18:10:51 UTC 2
200s autopkgtest [14:54:59]: @@@@@@@@@@@@@@@@@@@@ apt-source octave-statistics
216s Get:1 http://ftpmaster.internal/ubuntu plucky/universe octave-statistics 1.7.3-2 (dsc) [2285 B]
216s Get:2 http://ftpmaster.internal/ubuntu plucky/universe octave-statistics 1.7.3-2 (tar) [1302 kB]
216s Get:3 http://ftpmaster.internal/ubuntu plucky/universe octave-statistics 1.7.3-2 (diff) [9744 B]
216s gpgv: Signature made Thu Feb 20 15:34:23 2025 UTC
216s gpgv:                using RSA key 53951D95272E0C5B82BE8C4A2CECE9350ECEBE4A
216s gpgv: Can't check signature: No public key
216s dpkg-source: warning: cannot verify inline signature for ./octave-statistics_1.7.3-2.dsc: no acceptable signature found
216s autopkgtest [14:55:15]: testing package octave-statistics version 1.7.3-2
218s autopkgtest [14:55:17]: build not needed
221s autopkgtest [14:55:20]: test command1: preparing testbed
223s Reading package lists...
223s Building dependency tree...
223s Reading state information...
224s Starting pkgProblemResolver with broken count: 0
224s Starting 2 pkgProblemResolver with broken count: 0
224s Done
225s The following NEW packages will be installed:
225s   aglfn appstream autoconf automake autopoint autotools-dev cme comerr-dev cpp
225s   cpp-14 cpp-14-arm-linux-gnueabihf cpp-arm-linux-gnueabihf debhelper
225s   debugedit dh-autoreconf dh-octave dh-octave-autopkgtest
225s   dh-strip-nondeterminism diffstat dwz fontconfig fontconfig-config
225s   fonts-dejavu-core fonts-dejavu-mono fonts-freefont-otf g++ g++-14
225s   g++-14-arm-linux-gnueabihf g++-arm-linux-gnueabihf gcc gcc-14
225s   gcc-14-arm-linux-gnueabihf gcc-arm-linux-gnueabihf gettext gfortran
225s   gfortran-14 gfortran-14-arm-linux-gnueabihf gfortran-arm-linux-gnueabihf
225s   gnuplot-data gnuplot-nox hdf5-helpers intltool-debian krb5-multidev
225s   libaec-dev libaec0 libalgorithm-c3-perl libaliased-perl libamd3 libaom3
225s   libapp-cmd-perl libappstream5 libapt-pkg-perl libarchive-zip-perl
225s   libarpack2t64 libarray-intspan-perl libasan8 libasound2-data libasound2t64
225s   libavahi-client3 libavahi-common-data libavahi-common3
225s   libb-hooks-endofscope-perl libb-hooks-op-check-perl libb2-1
225s   libberkeleydb-perl libblas-dev libblas3 libboolean-perl libbrotli-dev
225s   libc-dev-bin libc6-dev libcairo2 libcamd3 libcapture-tiny-perl
225s   libcarp-assert-more-perl libcc1-0 libccolamd3 libcgi-pm-perl libcholmod5
225s   libclass-c3-perl libclass-data-inheritable-perl libclass-inspector-perl
225s   libclass-load-perl libclass-method-modifiers-perl libclass-xsaccessor-perl
225s   libclone-choose-perl libclone-perl libcolamd3
225s   libconfig-model-backend-yaml-perl libconfig-model-dpkg-perl
225s   libconfig-model-perl libconfig-tiny-perl libconst-fast-perl
225s   libconvert-binhex-perl libcpanel-json-xs-perl libcrypt-dev libcups2t64
225s   libcurl4-openssl-dev libcxsparse4 libdata-dpath-perl
225s   libdata-messagepack-perl libdata-optlist-perl libdata-section-perl
225s   libdata-validate-domain-perl libdata-validate-ip-perl
225s   libdata-validate-uri-perl libdatrie1 libde265-0 libdebhelper-perl
225s   libdeflate0 libdevel-callchecker-perl libdevel-size-perl
225s   libdevel-stacktrace-perl libdouble-conversion3 libduktape207 libdw1t64
225s   libdynaloader-functions-perl libegl-mesa0 libegl1 libemail-address-xs-perl
225s   libencode-locale-perl liberror-perl libevent-2.1-7t64
225s   libexception-class-perl libexporter-lite-perl libexporter-tiny-perl
225s   libfftw3-bin libfftw3-dev libfftw3-double3 libfftw3-single3
225s   libfile-basedir-perl libfile-find-rule-perl libfile-homedir-perl
225s   libfile-listing-perl libfile-sharedir-perl libfile-stripnondeterminism-perl
225s   libfile-which-perl libflac12t64 libfltk-gl1.3t64 libfltk1.3t64
225s   libfont-ttf-perl libfontconfig1 libfontenc1 libfreetype6 libgbm1
225s   libgcc-14-dev libgd3 libgetopt-long-descriptive-perl libgfortran-14-dev
225s   libgfortran5 libgl-dev libgl1 libgl1-mesa-dri libgl2ps1.4 libglpk40
225s   libglu1-mesa libglvnd0 libglx-dev libglx-mesa0 libglx0 libgmp-dev
225s   libgmpxx4ldbl libgnutls-dane0t64 libgnutls-openssl27t64 libgnutls28-dev
225s   libgomp1 libgraphicsmagick++-q16-12t64 libgraphicsmagick-q16-3t64
225s   libgraphite2-3 libgssrpc4t64 libharfbuzz0b libhash-merge-perl libhdf5-310
225s   libhdf5-cpp-310 libhdf5-dev libhdf5-fortran-310 libhdf5-hl-310
225s   libhdf5-hl-cpp-310 libhdf5-hl-fortran-310 libheif-plugin-aomdec
225s   libheif-plugin-libde265 libheif1 libhtml-form-perl
225s   libhtml-html5-entities-perl libhtml-parser-perl libhtml-tagset-perl
225s   libhtml-tokeparser-simple-perl libhtml-tree-perl libhttp-cookies-perl
225s   libhttp-date-perl libhttp-message-perl libhttp-negotiate-perl libice6
225s   libidn2-dev libimagequant0 libimport-into-perl libindirect-perl libinput-bin
225s   libinput10 libio-html-perl libio-interactive-perl libio-socket-ssl-perl
225s   libio-string-perl libio-stringy-perl libio-tiecombine-perl libipc-run3-perl
225s   libipc-system-simple-perl libisl23 libiterator-perl libiterator-util-perl
225s   libjack-jackd2-0 libjbig0 libjpeg-dev libjpeg-turbo8 libjpeg-turbo8-dev
225s   libjpeg8 libjpeg8-dev libjson-maybexs-perl libjson-perl libjxl0.11
225s   libkadm5clnt-mit12 libkadm5srv-mit12 libkdb5-10t64 libkrb5-dev liblapack-dev
225s   liblapack3 liblcms2-2 libldap-dev liblerc4 liblist-compare-perl
225s   liblist-moreutils-perl liblist-moreutils-xs-perl liblist-someutils-perl
225s   liblist-utilsby-perl libllvm19 liblog-any-adapter-screen-perl
225s   liblog-any-perl liblog-log4perl-perl libltdl7 liblua5.4-0
225s   liblwp-mediatypes-perl liblwp-protocol-https-perl libmailtools-perl
225s   libmarkdown2 libmd4c0 libmime-tools-perl libmldbm-perl
225s   libmodule-implementation-perl libmodule-pluggable-perl
225s   libmodule-runtime-perl libmoo-perl libmoox-aliases-perl libmouse-perl
225s   libmousex-nativetraits-perl libmousex-strictconstructor-perl libmp3lame0
225s   libmpc3 libmpg123-0t64 libmro-compat-perl libmtdev1t64
225s   libnamespace-clean-perl libncurses-dev libnet-domain-tld-perl
225s   libnet-http-perl libnet-ipv6addr-perl libnet-netmask-perl
225s   libnet-smtp-ssl-perl libnet-ssleay-perl libnetaddr-ip-perl libnghttp2-dev
225s   libnumber-compare-perl libobject-pad-perl libogg0 libopengl0 libopus0
225s   libp11-kit-dev libpackage-stash-perl libpango-1.0-0 libpangocairo-1.0-0
225s   libpangoft2-1.0-0 libparams-classify-perl libparams-util-perl
225s   libparams-validate-perl libparse-debcontrol-perl libparse-recdescent-perl
225s   libpath-iterator-rule-perl libpath-tiny-perl libpcre2-16-0
225s   libperlio-gzip-perl libperlio-utf8-strict-perl libpixman-1-0 libpkgconf3
225s   libpod-constants-perl libpod-parser-perl libpod-pom-perl libportaudio2
225s   libproc-processtable-perl libproxy1v5 libpsl-dev libqhull-r8.0 libqrupdate1
225s   libqscintilla2-qt6-15 libqscintilla2-qt6-l10n libqt6core5compat6
225s   libqt6core6t64 libqt6dbus6 libqt6gui6 libqt6help6 libqt6network6
225s   libqt6opengl6 libqt6openglwidgets6 libqt6printsupport6 libqt6sql6
225s   libqt6widgets6 libqt6xml6 libraqm0 libreadline-dev libregexp-common-perl
225s   libregexp-pattern-license-perl libregexp-pattern-perl
225s   libregexp-wildcards-perl librole-tiny-perl librtmp-dev libsamplerate0
225s   libsereal-decoder-perl libsereal-encoder-perl libset-intspan-perl
225s   libsharpyuv0 libsm6 libsndfile1 libsoftware-copyright-perl
225s   libsoftware-license-perl libsoftware-licensemoreutils-perl
225s   libsort-versions-perl libspqr4 libssh2-1-dev libssl-dev libstdc++-14-dev
225s   libstemmer0d libstrictures-perl libstring-copyright-perl
225s   libstring-escape-perl libstring-license-perl libstring-rewriteprefix-perl
225s   libsub-exporter-perl libsub-exporter-progressive-perl libsub-identify-perl
225s   libsub-install-perl libsub-name-perl libsub-quote-perl libsub-uplevel-perl
225s   libsuitesparseconfig7 libsyntax-keyword-try-perl libsz2 libtasn1-6-dev
225s   libterm-readkey-perl libtest-exception-perl libtext-autoformat-perl
225s   libtext-glob-perl libtext-levenshtein-damerau-perl
225s   libtext-levenshteinxs-perl libtext-markdown-discount-perl
225s   libtext-reform-perl libtext-template-perl libtext-unidecode-perl
225s   libtext-xslate-perl libthai-data libthai0 libtiff6 libtime-duration-perl
225s   libtime-moment-perl libtimedate-perl libtoml-tiny-perl libtool
225s   libtry-tiny-perl libts0t64 libubsan1 libumfpack6 libunbound8
225s   libunicode-utf8-perl liburi-perl libvariable-magic-perl libvorbis0a
225s   libvorbisenc2 libvulkan1 libwacom-common libwacom9 libwayland-client0
225s   libwayland-server0 libwebp7 libwebpmux3 libwmflite-0.2-7
225s   libwww-mechanize-perl libwww-perl libwww-robotrules-perl libx11-dev
225s   libx11-xcb1 libxau-dev libxaw7 libxcb-cursor0 libxcb-dri3-0 libxcb-glx0
225s   libxcb-icccm4 libxcb-image0 libxcb-keysyms1 libxcb-present0 libxcb-randr0
225s   libxcb-render-util0 libxcb-render0 libxcb-shape0 libxcb-shm0 libxcb-sync1
225s   libxcb-util1 libxcb-xfixes0 libxcb-xinput0 libxcb-xkb1 libxcb1-dev
225s   libxcursor1 libxdmcp-dev libxfixes3 libxfont2 libxft2 libxinerama1
225s   libxkbcommon-x11-0 libxkbfile1 libxml-libxml-perl
225s   libxml-namespacesupport-perl libxml-sax-base-perl libxml-sax-perl libxmu6
225s   libxpm4 libxrandr2 libxrender1 libxs-parse-keyword-perl
225s   libxs-parse-sublike-perl libxshmfence1 libxt6t64 libxxf86vm1
225s   libyaml-libyaml-perl libyaml-pp-perl libyaml-tiny-perl libzstd-dev
225s   licensecheck lintian linux-libc-dev lzip lzop m4 mesa-libgallium nettle-dev
225s   octave octave-common octave-dev octave-io octave-statistics
225s   octave-statistics-common patchutils perl-openssl-defaults pkgconf
225s   pkgconf-bin po-debconf rpcsvc-proto t1utils tex-common texinfo texinfo-lib
225s   unzip x11-common x11-xkb-utils x11proto-dev xorg-sgml-doctools
225s   xserver-common xtrans-dev xvfb zlib1g-dev
225s 0 upgraded, 475 newly installed, 0 to remove and 0 not upgraded.
225s Need to get 190 MB of archives.
225s After this operation, 602 MB of additional disk space will be used.
225s Get:1 http://ftpmaster.internal/ubuntu plucky/main armhf libstemmer0d armhf 2.2.0-4build1 [130 kB]
225s Get:2 http://ftpmaster.internal/ubuntu plucky/main armhf libappstream5 armhf 1.0.4-1 [211 kB]
226s Get:3 http://ftpmaster.internal/ubuntu plucky/main armhf appstream armhf 1.0.4-1 [67.3 kB]
226s Get:4 http://ftpmaster.internal/ubuntu plucky/main armhf m4 armhf 1.4.19-7 [238 kB]
226s Get:5 http://ftpmaster.internal/ubuntu plucky/main armhf autoconf all 2.72-3ubuntu1 [383 kB]
226s Get:6 http://ftpmaster.internal/ubuntu plucky/main armhf autotools-dev all 20220109.1 [44.9 kB]
227s Get:7 http://ftpmaster.internal/ubuntu plucky/main armhf automake all 1:1.17-3ubuntu1 [572 kB]
227s Get:8 http://ftpmaster.internal/ubuntu plucky/main armhf autopoint all 0.23.1-1 [619 kB]
228s Get:9 http://ftpmaster.internal/ubuntu plucky/main armhf libcapture-tiny-perl all 0.50-1 [20.7 kB]
228s Get:10 http://ftpmaster.internal/ubuntu plucky/main armhf libparams-util-perl armhf 1.102-3build1 [20.5 kB]
228s Get:11 http://ftpmaster.internal/ubuntu plucky/main armhf libsub-install-perl all 0.929-1 [9764 B]
228s Get:12 http://ftpmaster.internal/ubuntu plucky/main armhf libdata-optlist-perl all 0.114-1 [9708 B]
228s Get:13 http://ftpmaster.internal/ubuntu plucky/main armhf libb-hooks-op-check-perl armhf 0.22-3build2 [9174 B]
228s Get:14 http://ftpmaster.internal/ubuntu plucky/main armhf libdynaloader-functions-perl all 0.004-1 [11.4 kB]
228s Get:15 http://ftpmaster.internal/ubuntu plucky/main armhf libdevel-callchecker-perl armhf 0.009-1build1 [14.0 kB]
228s Get:16 http://ftpmaster.internal/ubuntu plucky/main armhf libparams-classify-perl armhf 0.015-2build6 [18.8 kB]
228s Get:17 http://ftpmaster.internal/ubuntu plucky/main armhf libmodule-runtime-perl all 0.016-2 [16.4 kB]
228s Get:18 http://ftpmaster.internal/ubuntu plucky/main armhf libtry-tiny-perl all 0.32-1 [21.2 kB]
228s Get:19 http://ftpmaster.internal/ubuntu plucky/main armhf libmodule-implementation-perl all 0.09-2 [12.0 kB]
228s Get:20 http://ftpmaster.internal/ubuntu plucky/main armhf libpackage-stash-perl all 0.40-1 [19.5 kB]
228s Get:21 http://ftpmaster.internal/ubuntu plucky/universe armhf libclass-load-perl all 0.25-2 [12.7 kB]
228s Get:22 http://ftpmaster.internal/ubuntu plucky/main armhf libio-stringy-perl all 2.113-2 [45.3 kB]
228s Get:23 http://ftpmaster.internal/ubuntu plucky/universe armhf libparams-validate-perl armhf 1.31-2build4 [52.9 kB]
228s Get:24 http://ftpmaster.internal/ubuntu plucky/main armhf libsub-exporter-perl all 0.990-1 [49.0 kB]
228s Get:25 http://ftpmaster.internal/ubuntu plucky/universe armhf libgetopt-long-descriptive-perl all 0.116-2 [25.0 kB]
228s Get:26 http://ftpmaster.internal/ubuntu plucky/universe armhf libio-tiecombine-perl all 1.005-3 [9464 B]
228s Get:27 http://ftpmaster.internal/ubuntu plucky/universe armhf libmodule-pluggable-perl all 5.2-5 [19.5 kB]
228s Get:28 http://ftpmaster.internal/ubuntu plucky/universe armhf libstring-rewriteprefix-perl all 0.009-1 [6310 B]
228s Get:29 http://ftpmaster.internal/ubuntu plucky/universe armhf libapp-cmd-perl all 0.337-2 [58.3 kB]
228s Get:30 http://ftpmaster.internal/ubuntu plucky/universe armhf libboolean-perl all 0.46-3 [8430 B]
228s Get:31 http://ftpmaster.internal/ubuntu plucky/universe armhf libsub-uplevel-perl all 0.2800-3 [11.6 kB]
228s Get:32 http://ftpmaster.internal/ubuntu plucky/universe armhf libtest-exception-perl all 0.43-3 [13.4 kB]
229s Get:33 http://ftpmaster.internal/ubuntu plucky/universe armhf libcarp-assert-more-perl all 2.8.0-1 [19.2 kB]
229s Get:34 http://ftpmaster.internal/ubuntu plucky/main armhf libfile-which-perl all 1.27-2 [12.5 kB]
229s Get:35 http://ftpmaster.internal/ubuntu plucky/main armhf libfile-homedir-perl all 1.006-2 [37.0 kB]
229s Get:36 http://ftpmaster.internal/ubuntu plucky/universe armhf libclone-choose-perl all 0.010-2 [7738 B]
229s Get:37 http://ftpmaster.internal/ubuntu plucky/universe armhf libhash-merge-perl all 0.302-1 [13.0 kB]
229s Get:38 http://ftpmaster.internal/ubuntu plucky/main armhf libjson-perl all 4.10000-1 [81.9 kB]
229s Get:39 http://ftpmaster.internal/ubuntu plucky/main armhf libexporter-tiny-perl all 1.006002-1 [36.8 kB]
229s Get:40 http://ftpmaster.internal/ubuntu plucky/universe armhf liblist-moreutils-xs-perl armhf 0.430-4build1 [37.9 kB]
229s Get:41 http://ftpmaster.internal/ubuntu plucky/universe armhf liblist-moreutils-perl all 0.430-2 [38.2 kB]
229s Get:42 http://ftpmaster.internal/ubuntu plucky/universe armhf liblog-log4perl-perl all 1.57-1 [345 kB]
229s Get:43 http://ftpmaster.internal/ubuntu plucky/main armhf libmouse-perl armhf 2.5.11-1build1 [130 kB]
229s Get:44 http://ftpmaster.internal/ubuntu plucky/universe armhf libmousex-nativetraits-perl all 1.09-3 [53.2 kB]
230s Get:45 http://ftpmaster.internal/ubuntu plucky/universe armhf libmousex-strictconstructor-perl all 0.02-3 [4582 B]
230s Get:46 http://ftpmaster.internal/ubuntu plucky/universe armhf libparse-recdescent-perl all 1.967015+dfsg-4 [139 kB]
230s Get:47 http://ftpmaster.internal/ubuntu plucky/main armhf libpath-tiny-perl all 0.146-1 [47.5 kB]
230s Get:48 http://ftpmaster.internal/ubuntu plucky/universe armhf libpod-pom-perl all 2.01-4 [61.3 kB]
230s Get:49 http://ftpmaster.internal/ubuntu plucky/main armhf libregexp-common-perl all 2024080801-1 [162 kB]
230s Get:50 http://ftpmaster.internal/ubuntu plucky/main armhf libyaml-tiny-perl all 1.76-1 [24.2 kB]
230s Get:51 http://ftpmaster.internal/ubuntu plucky/universe armhf libconfig-model-perl all 2.155-1 [356 kB]
231s Get:52 http://ftpmaster.internal/ubuntu plucky/universe armhf libyaml-pp-perl all 0.39.0-1 [107 kB]
231s Get:53 http://ftpmaster.internal/ubuntu plucky/universe armhf cme all 1.041-1 [65.4 kB]
231s Get:54 http://ftpmaster.internal/ubuntu plucky/main armhf libisl23 armhf 0.27-1 [546 kB]
231s Get:55 http://ftpmaster.internal/ubuntu plucky/main armhf libmpc3 armhf 1.3.1-1build2 [47.1 kB]
232s Get:56 http://ftpmaster.internal/ubuntu plucky/main armhf cpp-14-arm-linux-gnueabihf armhf 14.2.0-17ubuntu3 [9220 kB]
243s Get:57 http://ftpmaster.internal/ubuntu plucky/main armhf cpp-14 armhf 14.2.0-17ubuntu3 [1030 B]
243s Get:58 http://ftpmaster.internal/ubuntu plucky/main armhf cpp-arm-linux-gnueabihf armhf 4:14.2.0-1ubuntu1 [5578 B]
243s Get:59 http://ftpmaster.internal/ubuntu plucky/main armhf cpp armhf 4:14.2.0-1ubuntu1 [22.4 kB]
243s Get:60 http://ftpmaster.internal/ubuntu plucky/main armhf libdebhelper-perl all 13.24.1ubuntu2 [95.4 kB]
244s Get:61 http://ftpmaster.internal/ubuntu plucky/main armhf libcc1-0 armhf 15-20250222-0ubuntu1 [38.9 kB]
244s Get:62 http://ftpmaster.internal/ubuntu plucky/main armhf libgomp1 armhf 15-20250222-0ubuntu1 [128 kB]
244s Get:63 http://ftpmaster.internal/ubuntu plucky/main armhf libasan8 armhf 15-20250222-0ubuntu1 [2955 kB]
248s Get:64 http://ftpmaster.internal/ubuntu plucky/main armhf libubsan1 armhf 15-20250222-0ubuntu1 [1191 kB]
249s Get:65 http://ftpmaster.internal/ubuntu plucky/main armhf libgcc-14-dev armhf 14.2.0-17ubuntu3 [897 kB]
250s Get:66 http://ftpmaster.internal/ubuntu plucky/main armhf gcc-14-arm-linux-gnueabihf armhf 14.2.0-17ubuntu3 [18.0 MB]
273s Get:67 http://ftpmaster.internal/ubuntu plucky/main armhf gcc-14 armhf 14.2.0-17ubuntu3 [506 kB]
274s Get:68 http://ftpmaster.internal/ubuntu plucky/main armhf gcc-arm-linux-gnueabihf armhf 4:14.2.0-1ubuntu1 [1218 B]
274s Get:69 http://ftpmaster.internal/ubuntu plucky/main armhf gcc armhf 4:14.2.0-1ubuntu1 [5004 B]
274s Get:70 http://ftpmaster.internal/ubuntu plucky-proposed/main armhf libc-dev-bin armhf 2.41-1ubuntu2 [23.0 kB]
274s Get:71 http://ftpmaster.internal/ubuntu plucky/main armhf linux-libc-dev armhf 6.14.0-10.10 [1683 kB]
276s Get:72 http://ftpmaster.internal/ubuntu plucky/main armhf libcrypt-dev armhf 1:4.4.38-1 [120 kB]
276s Get:73 http://ftpmaster.internal/ubuntu plucky/main armhf rpcsvc-proto armhf 1.4.2-0ubuntu7 [62.2 kB]
276s Get:74 http://ftpmaster.internal/ubuntu plucky-proposed/main armhf libc6-dev armhf 2.41-1ubuntu2 [1396 kB]
278s Get:75 http://ftpmaster.internal/ubuntu plucky/main armhf libtool all 2.5.4-4 [168 kB]
278s Get:76 http://ftpmaster.internal/ubuntu plucky/main armhf dh-autoreconf all 20 [16.1 kB]
278s Get:77 http://ftpmaster.internal/ubuntu plucky/main armhf libarchive-zip-perl all 1.68-1 [90.2 kB]
278s Get:78 http://ftpmaster.internal/ubuntu plucky/main armhf libfile-stripnondeterminism-perl all 1.14.1-2 [20.3 kB]
278s Get:79 http://ftpmaster.internal/ubuntu plucky/main armhf dh-strip-nondeterminism all 1.14.1-2 [5064 B]
278s Get:80 http://ftpmaster.internal/ubuntu plucky/main armhf libdw1t64 armhf 0.192-4 [243 kB]
279s Get:81 http://ftpmaster.internal/ubuntu plucky/main armhf debugedit armhf 1:5.1-2 [46.7 kB]
279s Get:82 http://ftpmaster.internal/ubuntu plucky/main armhf dwz armhf 0.15-1build6 [116 kB]
279s Get:83 http://ftpmaster.internal/ubuntu plucky/main armhf gettext armhf 0.23.1-1 [1053 kB]
280s Get:84 http://ftpmaster.internal/ubuntu plucky/main armhf intltool-debian all 0.35.0+20060710.6 [23.2 kB]
280s Get:85 http://ftpmaster.internal/ubuntu plucky/main armhf po-debconf all 1.0.21+nmu1 [233 kB]
280s Get:86 http://ftpmaster.internal/ubuntu plucky/main armhf debhelper all 13.24.1ubuntu2 [895 kB]
282s Get:87 http://ftpmaster.internal/ubuntu plucky/universe armhf aglfn all 1.7+git20191031.4036a9c-2 [30.6 kB]
282s Get:88 http://ftpmaster.internal/ubuntu plucky/universe armhf gnuplot-data all 6.0.2+dfsg1-1 [75.4 kB]
282s Get:89 http://ftpmaster.internal/ubuntu plucky/main armhf libfreetype6 armhf 2.13.3+dfsg-1 [330 kB]
282s Get:90 http://ftpmaster.internal/ubuntu plucky/main armhf fonts-dejavu-mono all 2.37-8 [502 kB]
283s Get:91 http://ftpmaster.internal/ubuntu plucky/main armhf fonts-dejavu-core all 2.37-8 [835 kB]
284s Get:92 http://ftpmaster.internal/ubuntu plucky/universe armhf fonts-freefont-otf all 20211204+svn4273-2 [4596 kB]
290s Get:93 http://ftpmaster.internal/ubuntu plucky/main armhf fontconfig-config armhf 2.15.0-2ubuntu1 [37.5 kB]
290s Get:94 http://ftpmaster.internal/ubuntu plucky/main armhf libfontconfig1 armhf 2.15.0-2ubuntu1 [114 kB]
290s Get:95 http://ftpmaster.internal/ubuntu plucky/main armhf libpixman-1-0 armhf 0.44.0-3 [183 kB]
290s Get:96 http://ftpmaster.internal/ubuntu plucky/main armhf libxcb-render0 armhf 1.17.0-2 [15.3 kB]
290s Get:97 http://ftpmaster.internal/ubuntu plucky/main armhf libxcb-shm0 armhf 1.17.0-2 [5774 B]
290s Get:98 http://ftpmaster.internal/ubuntu plucky/main armhf libxrender1 armhf 1:0.9.10-1.1build1 [16.0 kB]
290s Get:99 http://ftpmaster.internal/ubuntu plucky/main armhf libcairo2 armhf 1.18.2-2 [484 kB]
291s Get:100 http://ftpmaster.internal/ubuntu plucky/main armhf libsharpyuv0 armhf 1.5.0-0.1 [16.4 kB]
291s Get:101 http://ftpmaster.internal/ubuntu plucky/main armhf libaom3 armhf 3.12.0-1 [1235 kB]
292s Get:102 http://ftpmaster.internal/ubuntu plucky/main armhf libheif-plugin-aomdec armhf 1.19.7-1 [10.9 kB]
292s Get:103 http://ftpmaster.internal/ubuntu plucky/main armhf libde265-0 armhf 1.0.15-1build5 [157 kB]
293s Get:104 http://ftpmaster.internal/ubuntu plucky/main armhf libheif-plugin-libde265 armhf 1.19.7-1 [11.7 kB]
293s Get:105 http://ftpmaster.internal/ubuntu plucky/main armhf libheif1 armhf 1.19.7-1 [479 kB]
294s Get:106 http://ftpmaster.internal/ubuntu plucky/main armhf libimagequant0 armhf 2.18.0-1build1 [31.1 kB]
294s Get:107 http://ftpmaster.internal/ubuntu plucky/main armhf libjpeg-turbo8 armhf 2.1.5-3ubuntu2 [127 kB]
294s Get:108 http://ftpmaster.internal/ubuntu plucky/main armhf libjpeg8 armhf 8c-2ubuntu11 [2148 B]
294s Get:109 http://ftpmaster.internal/ubuntu plucky/main armhf libgraphite2-3 armhf 1.3.14-2ubuntu1 [64.8 kB]
294s Get:110 http://ftpmaster.internal/ubuntu plucky/main armhf libharfbuzz0b armhf 10.2.0-1 [464 kB]
295s Get:111 http://ftpmaster.internal/ubuntu plucky/main armhf libraqm0 armhf 0.10.2-1 [12.4 kB]
295s Get:112 http://ftpmaster.internal/ubuntu plucky/main armhf libdeflate0 armhf 1.23-1 [38.5 kB]
295s Get:113 http://ftpmaster.internal/ubuntu plucky/main armhf libjbig0 armhf 2.1-6.1ubuntu2 [24.9 kB]
296s Get:114 http://ftpmaster.internal/ubuntu plucky/main armhf liblerc4 armhf 4.0.0+ds-5ubuntu1 [160 kB]
296s Get:115 http://ftpmaster.internal/ubuntu plucky/main armhf libwebp7 armhf 1.5.0-0.1 [188 kB]
296s Get:116 http://ftpmaster.internal/ubuntu plucky/main armhf libtiff6 armhf 4.5.1+git230720-4ubuntu4 [179 kB]
297s Get:117 http://ftpmaster.internal/ubuntu plucky/main armhf libxpm4 armhf 1:3.5.17-1build2 [30.1 kB]
297s Get:118 http://ftpmaster.internal/ubuntu plucky/main armhf libgd3 armhf 2.3.3-12ubuntu3 [108 kB]
297s Get:119 http://ftpmaster.internal/ubuntu plucky/main armhf liblua5.4-0 armhf 5.4.7-1 [134 kB]
297s Get:120 http://ftpmaster.internal/ubuntu plucky/main armhf fontconfig armhf 2.15.0-2ubuntu1 [190 kB]
297s Get:121 http://ftpmaster.internal/ubuntu plucky/main armhf libthai-data all 0.1.29-2build1 [158 kB]
298s Get:122 http://ftpmaster.internal/ubuntu plucky/main armhf libdatrie1 armhf 0.2.13-3build1 [15.7 kB]
298s Get:123 http://ftpmaster.internal/ubuntu plucky/main armhf libthai0 armhf 0.1.29-2build1 [15.2 kB]
298s Get:124 http://ftpmaster.internal/ubuntu plucky/main armhf libpango-1.0-0 armhf 1.56.2-1 [216 kB]
298s Get:125 http://ftpmaster.internal/ubuntu plucky/main armhf libpangoft2-1.0-0 armhf 1.56.2-1 [43.6 kB]
298s Get:126 http://ftpmaster.internal/ubuntu plucky/main armhf libpangocairo-1.0-0 armhf 1.56.2-1 [25.1 kB]
298s Get:127 http://ftpmaster.internal/ubuntu plucky/main armhf libwebpmux3 armhf 1.5.0-0.1 [22.4 kB]
298s Get:128 http://ftpmaster.internal/ubuntu plucky/universe armhf gnuplot-nox armhf 6.0.2+dfsg1-1 [909 kB]
300s Get:129 http://ftpmaster.internal/ubuntu plucky/universe armhf dh-octave-autopkgtest all 1.8.0 [10.1 kB]
300s Get:130 http://ftpmaster.internal/ubuntu plucky/main armhf libapt-pkg-perl armhf 0.1.41build1 [66.6 kB]
300s Get:131 http://ftpmaster.internal/ubuntu plucky/main armhf libarray-intspan-perl all 2.004-2 [25.0 kB]
300s Get:132 http://ftpmaster.internal/ubuntu plucky/main armhf libyaml-libyaml-perl armhf 0.903.0+ds-1 [29.3 kB]
300s Get:133 http://ftpmaster.internal/ubuntu plucky/universe armhf libconfig-model-backend-yaml-perl all 2.134-2 [10.5 kB]
300s Get:134 http://ftpmaster.internal/ubuntu plucky/universe armhf libexporter-lite-perl all 0.09-2 [9748 B]
300s Get:135 http://ftpmaster.internal/ubuntu plucky/main armhf libencode-locale-perl all 1.05-3 [11.6 kB]
300s Get:136 http://ftpmaster.internal/ubuntu plucky/main armhf libtimedate-perl all 2.3300-2 [34.0 kB]
300s Get:137 http://ftpmaster.internal/ubuntu plucky/main armhf libhttp-date-perl all 6.06-1 [10.2 kB]
300s Get:138 http://ftpmaster.internal/ubuntu plucky/main armhf libfile-listing-perl all 6.16-1 [11.3 kB]
300s Get:139 http://ftpmaster.internal/ubuntu plucky/main armhf libhtml-tagset-perl all 3.24-1 [14.1 kB]
300s Get:140 http://ftpmaster.internal/ubuntu plucky/main armhf liburi-perl all 5.30-1 [94.4 kB]
301s Get:141 http://ftpmaster.internal/ubuntu plucky/main armhf libhtml-parser-perl armhf 3.83-1build1 [83.1 kB]
301s Get:142 http://ftpmaster.internal/ubuntu plucky/main armhf libhtml-tree-perl all 5.07-3 [200 kB]
301s Get:143 http://ftpmaster.internal/ubuntu plucky/main armhf libclone-perl armhf 0.47-1 [10.0 kB]
301s Get:144 http://ftpmaster.internal/ubuntu plucky/main armhf libio-html-perl all 1.004-3 [15.9 kB]
301s Get:145 http://ftpmaster.internal/ubuntu plucky/main armhf liblwp-mediatypes-perl all 6.04-2 [20.1 kB]
301s Get:146 http://ftpmaster.internal/ubuntu plucky/main armhf libhttp-message-perl all 7.00-2ubuntu1 [75.9 kB]
301s Get:147 http://ftpmaster.internal/ubuntu plucky/main armhf libhttp-cookies-perl all 6.11-1 [18.2 kB]
301s Get:148 http://ftpmaster.internal/ubuntu plucky/main armhf libhttp-negotiate-perl all 6.01-2 [12.4 kB]
301s Get:149 http://ftpmaster.internal/ubuntu plucky/main armhf perl-openssl-defaults armhf 7build3 [6628 B]
301s Get:150 http://ftpmaster.internal/ubuntu plucky/main armhf libnet-ssleay-perl armhf 1.94-3 [298 kB]
302s Get:151 http://ftpmaster.internal/ubuntu plucky/main armhf libio-socket-ssl-perl all 2.089-1 [200 kB]
302s Get:152 http://ftpmaster.internal/ubuntu plucky/main armhf libnet-http-perl all 6.23-1 [22.3 kB]
302s Get:153 http://ftpmaster.internal/ubuntu plucky/main armhf liblwp-protocol-https-perl all 6.14-1 [9040 B]
302s Get:154 http://ftpmaster.internal/ubuntu plucky/main armhf libwww-robotrules-perl all 6.02-1 [12.6 kB]
302s Get:155 http://ftpmaster.internal/ubuntu plucky/main armhf libwww-perl all 6.78-1 [139 kB]
303s Get:156 http://ftpmaster.internal/ubuntu plucky/main armhf liberror-perl all 0.17030-1 [23.5 kB]
303s Get:157 http://ftpmaster.internal/ubuntu plucky/universe armhf libparse-debcontrol-perl all 2.005-6 [20.4 kB]
303s Get:158 http://ftpmaster.internal/ubuntu plucky/universe armhf libsoftware-copyright-perl all 0.014-1 [14.5 kB]
303s Get:159 http://ftpmaster.internal/ubuntu plucky/universe armhf libalgorithm-c3-perl all 0.11-2 [10.2 kB]
303s Get:160 http://ftpmaster.internal/ubuntu plucky/universe armhf libclass-c3-perl all 0.35-2 [18.4 kB]
303s Get:161 http://ftpmaster.internal/ubuntu plucky/universe armhf libmro-compat-perl all 0.15-2 [10.1 kB]
303s Get:162 http://ftpmaster.internal/ubuntu plucky/universe armhf libdata-section-perl all 0.200008-1 [11.6 kB]
303s Get:163 http://ftpmaster.internal/ubuntu plucky/universe armhf libtext-template-perl all 1.61-1 [48.5 kB]
303s Get:164 http://ftpmaster.internal/ubuntu plucky/universe armhf libsoftware-license-perl all 0.104006-1 [117 kB]
303s Get:165 http://ftpmaster.internal/ubuntu plucky/universe armhf libsoftware-licensemoreutils-perl all 1.009-1 [21.5 kB]
303s Get:166 http://ftpmaster.internal/ubuntu plucky/main armhf libsort-versions-perl all 1.62-3 [7378 B]
303s Get:167 http://ftpmaster.internal/ubuntu plucky/universe armhf libtext-reform-perl all 1.20-5 [35.4 kB]
303s Get:168 http://ftpmaster.internal/ubuntu plucky/universe armhf libtext-autoformat-perl all 1.750000-2 [29.8 kB]
303s Get:169 http://ftpmaster.internal/ubuntu plucky/universe armhf libtext-levenshtein-damerau-perl all 0.41-3 [10.8 kB]
303s Get:170 http://ftpmaster.internal/ubuntu plucky/universe armhf libtoml-tiny-perl all 0.19-1 [21.6 kB]
303s Get:171 http://ftpmaster.internal/ubuntu plucky/main armhf libclass-inspector-perl all 1.36-3 [15.4 kB]
304s Get:172 http://ftpmaster.internal/ubuntu plucky/main armhf libfile-sharedir-perl all 1.118-3 [14.0 kB]
304s Get:173 http://ftpmaster.internal/ubuntu plucky/main armhf libindirect-perl armhf 0.39-2build5 [20.6 kB]
304s Get:174 http://ftpmaster.internal/ubuntu plucky/main armhf libxs-parse-keyword-perl armhf 0.48-2 [59.3 kB]
304s Get:175 http://ftpmaster.internal/ubuntu plucky/main armhf libxs-parse-sublike-perl armhf 0.37-1 [42.7 kB]
304s Get:176 http://ftpmaster.internal/ubuntu plucky/main armhf libobject-pad-perl armhf 0.820-1 [121 kB]
304s Get:177 http://ftpmaster.internal/ubuntu plucky/main armhf libsyntax-keyword-try-perl armhf 0.30-1 [24.0 kB]
304s Get:178 http://ftpmaster.internal/ubuntu plucky/main armhf libio-interactive-perl all 1.026-1 [10.8 kB]
304s Get:179 http://ftpmaster.internal/ubuntu plucky/main armhf liblog-any-perl all 1.717-1 [73.2 kB]
304s Get:180 http://ftpmaster.internal/ubuntu plucky/main armhf liblog-any-adapter-screen-perl all 0.141-1 [12.9 kB]
304s Get:181 http://ftpmaster.internal/ubuntu plucky/main armhf libsub-exporter-progressive-perl all 0.001013-3 [6718 B]
304s Get:182 http://ftpmaster.internal/ubuntu plucky/main armhf libvariable-magic-perl armhf 0.64-1build1 [33.4 kB]
304s Get:183 http://ftpmaster.internal/ubuntu plucky/main armhf libb-hooks-endofscope-perl all 0.28-1 [15.8 kB]
305s Get:184 http://ftpmaster.internal/ubuntu plucky/main armhf libsub-identify-perl armhf 0.14-3build4 [9396 B]
305s Get:185 http://ftpmaster.internal/ubuntu plucky/main armhf libsub-name-perl armhf 0.28-1 [10.0 kB]
305s Get:186 http://ftpmaster.internal/ubuntu plucky/main armhf libnamespace-clean-perl all 0.27-2 [14.0 kB]
305s Get:187 http://ftpmaster.internal/ubuntu plucky/main armhf libnumber-compare-perl all 0.03-3 [5974 B]
305s Get:188 http://ftpmaster.internal/ubuntu plucky/main armhf libtext-glob-perl all 0.11-3 [6780 B]
305s Get:189 http://ftpmaster.internal/ubuntu plucky/main armhf libpath-iterator-rule-perl all 1.015-2 [39.9 kB]
305s Get:190 http://ftpmaster.internal/ubuntu plucky/main armhf libpod-parser-perl all 1.67-1 [80.6 kB]
305s Get:191 http://ftpmaster.internal/ubuntu plucky/main armhf libpod-constants-perl all 0.19-2 [16.3 kB]
305s Get:192 http://ftpmaster.internal/ubuntu plucky/main armhf libset-intspan-perl all 1.19-3 [24.8 kB]
305s Get:193 http://ftpmaster.internal/ubuntu plucky/main armhf libstring-copyright-perl all 0.003014-1 [20.5 kB]
305s Get:194 http://ftpmaster.internal/ubuntu plucky/main armhf libstring-escape-perl all 2010.002-3 [16.1 kB]
305s Get:195 http://ftpmaster.internal/ubuntu plucky/main armhf libregexp-pattern-license-perl all 3.11.2-1 [85.3 kB]
305s Get:196 http://ftpmaster.internal/ubuntu plucky/main armhf libregexp-pattern-perl all 0.2.14-2 [17.6 kB]
305s Get:197 http://ftpmaster.internal/ubuntu plucky/main armhf libstring-license-perl all 0.0.11-1ubuntu1 [34.3 kB]
305s Get:198 http://ftpmaster.internal/ubuntu plucky/main armhf licensecheck all 3.3.9-1ubuntu1 [37.7 kB]
305s Get:199 http://ftpmaster.internal/ubuntu plucky/main armhf diffstat armhf 1.67-1 [28.9 kB]
306s Get:200 http://ftpmaster.internal/ubuntu plucky/main armhf libberkeleydb-perl armhf 0.66-1 [109 kB]
306s Get:201 http://ftpmaster.internal/ubuntu plucky/main armhf libclass-xsaccessor-perl armhf 1.19-4build6 [32.4 kB]
306s Get:202 http://ftpmaster.internal/ubuntu plucky/main armhf libconfig-tiny-perl all 2.30-1 [14.7 kB]
306s Get:203 http://ftpmaster.internal/ubuntu plucky/main armhf libconst-fast-perl all 0.014-2 [8034 B]
306s Get:204 http://ftpmaster.internal/ubuntu plucky/main armhf libcpanel-json-xs-perl armhf 4.39-1 [113 kB]
306s Get:205 http://ftpmaster.internal/ubuntu plucky/main armhf libaliased-perl all 0.34-3 [12.8 kB]
306s Get:206 http://ftpmaster.internal/ubuntu plucky/main armhf libclass-data-inheritable-perl all 0.10-1 [8038 B]
306s Get:207 http://ftpmaster.internal/ubuntu plucky/main armhf libdevel-stacktrace-perl all 2.0500-1 [22.1 kB]
306s Get:208 http://ftpmaster.internal/ubuntu plucky/main armhf libexception-class-perl all 1.45-1 [28.6 kB]
306s Get:209 http://ftpmaster.internal/ubuntu plucky/main armhf libiterator-perl all 0.03+ds1-2 [18.8 kB]
306s Get:210 http://ftpmaster.internal/ubuntu plucky/main armhf libiterator-util-perl all 0.02+ds1-2 [14.1 kB]
306s Get:211 http://ftpmaster.internal/ubuntu plucky/main armhf libdata-dpath-perl all 0.60-1 [37.3 kB]
306s Get:212 http://ftpmaster.internal/ubuntu plucky/main armhf libnet-domain-tld-perl all 1.75-4 [29.0 kB]
307s Get:213 http://ftpmaster.internal/ubuntu plucky/main armhf libdata-validate-domain-perl all 0.15-1 [10.4 kB]
307s Get:214 http://ftpmaster.internal/ubuntu plucky/main armhf libnet-ipv6addr-perl all 1.02-1 [21.0 kB]
307s Get:215 http://ftpmaster.internal/ubuntu plucky/main armhf libnet-netmask-perl all 2.0002-2 [24.8 kB]
307s Get:216 http://ftpmaster.internal/ubuntu plucky/main armhf libnetaddr-ip-perl armhf 4.079+dfsg-2build5 [79.0 kB]
307s Get:217 http://ftpmaster.internal/ubuntu plucky/main armhf libdata-validate-ip-perl all 0.31-1 [17.2 kB]
307s Get:218 http://ftpmaster.internal/ubuntu plucky/main armhf libdata-validate-uri-perl all 0.07-3 [10.8 kB]
307s Get:219 http://ftpmaster.internal/ubuntu plucky/main armhf libdevel-size-perl armhf 0.84-1build1 [18.8 kB]
307s Get:220 http://ftpmaster.internal/ubuntu plucky/main armhf libemail-address-xs-perl armhf 1.05-1build5 [26.5 kB]
307s Get:221 http://ftpmaster.internal/ubuntu plucky/main armhf libipc-system-simple-perl all 1.30-2 [22.3 kB]
307s Get:222 http://ftpmaster.internal/ubuntu plucky/main armhf libfile-basedir-perl all 0.09-2 [14.4 kB]
307s Get:223 http://ftpmaster.internal/ubuntu plucky/main armhf libfile-find-rule-perl all 0.34-3 [24.4 kB]
307s Get:224 http://ftpmaster.internal/ubuntu plucky/main armhf libio-string-perl all 1.08-4 [11.1 kB]
307s Get:225 http://ftpmaster.internal/ubuntu plucky/main armhf libfont-ttf-perl all 1.06-2 [323 kB]
307s Get:226 http://ftpmaster.internal/ubuntu plucky/main armhf libhtml-html5-entities-perl all 0.004-3 [21.6 kB]
307s Get:227 http://ftpmaster.internal/ubuntu plucky/main armhf libhtml-tokeparser-simple-perl all 3.16-4 [38.0 kB]
307s Get:228 http://ftpmaster.internal/ubuntu plucky/main armhf libipc-run3-perl all 0.049-1 [28.8 kB]
307s Get:229 http://ftpmaster.internal/ubuntu plucky/main armhf libjson-maybexs-perl all 1.004008-1 [11.1 kB]
307s Get:230 http://ftpmaster.internal/ubuntu plucky/main armhf liblist-compare-perl all 0.55-2 [62.9 kB]
308s Get:231 http://ftpmaster.internal/ubuntu plucky/main armhf liblist-someutils-perl all 0.59-1 [30.4 kB]
308s Get:232 http://ftpmaster.internal/ubuntu plucky/main armhf liblist-utilsby-perl all 0.12-2 [14.9 kB]
308s Get:233 http://ftpmaster.internal/ubuntu plucky/main armhf libmldbm-perl all 2.05-4 [16.0 kB]
308s Get:234 http://ftpmaster.internal/ubuntu plucky/main armhf libclass-method-modifiers-perl all 2.15-1 [16.1 kB]
308s Get:235 http://ftpmaster.internal/ubuntu plucky/main armhf libimport-into-perl all 1.002005-2 [10.7 kB]
308s Get:236 http://ftpmaster.internal/ubuntu plucky/main armhf librole-tiny-perl all 2.002004-1 [16.3 kB]
308s Get:237 http://ftpmaster.internal/ubuntu plucky/main armhf libsub-quote-perl all 2.006008-1ubuntu1 [20.7 kB]
308s Get:238 http://ftpmaster.internal/ubuntu plucky/main armhf libmoo-perl all 2.005005-1 [47.4 kB]
308s Get:239 http://ftpmaster.internal/ubuntu plucky/main armhf libstrictures-perl all 2.000006-1 [16.3 kB]
308s Get:240 http://ftpmaster.internal/ubuntu plucky/main armhf libmoox-aliases-perl all 0.001006-2 [6796 B]
308s Get:241 http://ftpmaster.internal/ubuntu plucky/main armhf libperlio-gzip-perl armhf 0.20-1build5 [13.5 kB]
308s Get:242 http://ftpmaster.internal/ubuntu plucky/main armhf libperlio-utf8-strict-perl armhf 0.010-1build4 [10.5 kB]
308s Get:243 http://ftpmaster.internal/ubuntu plucky/main armhf libproc-processtable-perl armhf 0.636-1build4 [35.6 kB]
308s Get:244 http://ftpmaster.internal/ubuntu plucky/main armhf libregexp-wildcards-perl all 1.05-3 [12.9 kB]
308s Get:245 http://ftpmaster.internal/ubuntu plucky/main armhf libsereal-decoder-perl armhf 5.004+ds-1build4 [94.1 kB]
308s Get:246 http://ftpmaster.internal/ubuntu plucky/main armhf libsereal-encoder-perl armhf 5.004+ds-1build4 [96.1 kB]
308s Get:247 http://ftpmaster.internal/ubuntu plucky/main armhf libterm-readkey-perl armhf 2.38-2build5 [22.6 kB]
308s Get:248 http://ftpmaster.internal/ubuntu plucky/main armhf libtext-levenshteinxs-perl armhf 0.03-5build5 [7482 B]
308s Get:249 http://ftpmaster.internal/ubuntu plucky/main armhf libmarkdown2 armhf 2.2.7-2.1 [31.0 kB]
308s Get:250 http://ftpmaster.internal/ubuntu plucky/main armhf libtext-markdown-discount-perl armhf 0.18-1 [12.0 kB]
308s Get:251 http://ftpmaster.internal/ubuntu plucky/main armhf libdata-messagepack-perl armhf 1.02-1build5 [29.5 kB]
308s Get:252 http://ftpmaster.internal/ubuntu plucky/main armhf libtext-xslate-perl armhf 3.5.9-2build1 [158 kB]
308s Get:253 http://ftpmaster.internal/ubuntu plucky/main armhf libtime-duration-perl all 1.21-2 [12.3 kB]
308s Get:254 http://ftpmaster.internal/ubuntu plucky/main armhf libtime-moment-perl armhf 0.44-2build5 [70.6 kB]
309s Get:255 http://ftpmaster.internal/ubuntu plucky/main armhf libunicode-utf8-perl armhf 0.62-2build4 [17.1 kB]
309s Get:256 http://ftpmaster.internal/ubuntu plucky/main armhf libcgi-pm-perl all 4.67-1 [185 kB]
309s Get:257 http://ftpmaster.internal/ubuntu plucky/main armhf libhtml-form-perl all 6.12-1 [31.1 kB]
309s Get:258 http://ftpmaster.internal/ubuntu plucky/main armhf libwww-mechanize-perl all 2.19-1ubuntu1 [93.3 kB]
309s Get:259 http://ftpmaster.internal/ubuntu plucky/main armhf libxml-namespacesupport-perl all 1.12-2 [13.5 kB]
309s Get:260 http://ftpmaster.internal/ubuntu plucky/main armhf libxml-sax-base-perl all 1.09-3 [18.9 kB]
309s Get:261 http://ftpmaster.internal/ubuntu plucky/main armhf libxml-sax-perl all 1.02+dfsg-4 [52.4 kB]
309s Get:262 http://ftpmaster.internal/ubuntu plucky/main armhf libxml-libxml-perl armhf 2.0207+dfsg+really+2.0134-5build1 [291 kB]
309s Get:263 http://ftpmaster.internal/ubuntu plucky/main armhf lzip armhf 1.25-2 [80.7 kB]
309s Get:264 http://ftpmaster.internal/ubuntu plucky/main armhf lzop armhf 1.04-2build3 [82.4 kB]
310s Get:265 http://ftpmaster.internal/ubuntu plucky/main armhf patchutils armhf 0.4.2-1build3 [73.2 kB]
310s Get:266 http://ftpmaster.internal/ubuntu plucky/main armhf t1utils armhf 1.41-4build3 [51.8 kB]
310s Get:267 http://ftpmaster.internal/ubuntu plucky/main armhf unzip armhf 6.0-28ubuntu6 [167 kB]
310s Get:268 http://ftpmaster.internal/ubuntu plucky/main armhf lintian all 2.121.1+nmu1ubuntu2 [1075 kB]
311s Get:269 http://ftpmaster.internal/ubuntu plucky/universe armhf libconfig-model-dpkg-perl all 3.010 [176 kB]
311s Get:270 http://ftpmaster.internal/ubuntu plucky/main armhf libconvert-binhex-perl all 1.125-3 [27.1 kB]
312s Get:271 http://ftpmaster.internal/ubuntu plucky/main armhf libnet-smtp-ssl-perl all 1.04-2 [6218 B]
312s Get:272 http://ftpmaster.internal/ubuntu plucky/main armhf libmailtools-perl all 2.22-1 [77.1 kB]
312s Get:273 http://ftpmaster.internal/ubuntu plucky/main armhf libmime-tools-perl all 5.515-1 [187 kB]
312s Get:274 http://ftpmaster.internal/ubuntu plucky/main armhf libsuitesparseconfig7 armhf 1:7.8.3+dfsg-3 [12.7 kB]
312s Get:275 http://ftpmaster.internal/ubuntu plucky/universe armhf libamd3 armhf 1:7.8.3+dfsg-3 [29.2 kB]
312s Get:276 http://ftpmaster.internal/ubuntu plucky/main armhf libblas3 armhf 3.12.1-2 [132 kB]
312s Get:277 http://ftpmaster.internal/ubuntu plucky/main armhf libgfortran5 armhf 15-20250222-0ubuntu1 [330 kB]
313s Get:278 http://ftpmaster.internal/ubuntu plucky/main armhf liblapack3 armhf 3.12.1-2 [2091 kB]
316s Get:279 http://ftpmaster.internal/ubuntu plucky/universe armhf libarpack2t64 armhf 3.9.1-4 [86.7 kB]
316s Get:280 http://ftpmaster.internal/ubuntu plucky/universe armhf libccolamd3 armhf 1:7.8.3+dfsg-3 [27.8 kB]
316s Get:281 http://ftpmaster.internal/ubuntu plucky/universe armhf libcamd3 armhf 1:7.8.3+dfsg-3 [26.0 kB]
316s Get:282 http://ftpmaster.internal/ubuntu plucky/main armhf libcolamd3 armhf 1:7.8.3+dfsg-3 [19.8 kB]
316s Get:283 http://ftpmaster.internal/ubuntu plucky/universe armhf libcholmod5 armhf 1:7.8.3+dfsg-3 [675 kB]
317s Get:284 http://ftpmaster.internal/ubuntu plucky/universe armhf libcxsparse4 armhf 1:7.8.3+dfsg-3 [70.1 kB]
317s Get:285 http://ftpmaster.internal/ubuntu plucky/main armhf libfftw3-double3 armhf 3.3.10-2fakesync1build1 [369 kB]
317s Get:286 http://ftpmaster.internal/ubuntu plucky/main armhf libfftw3-single3 armhf 3.3.10-2fakesync1build1 [581 kB]
319s Get:287 http://ftpmaster.internal/ubuntu plucky/main armhf libxfixes3 armhf 1:6.0.0-2build1 [9038 B]
319s Get:288 http://ftpmaster.internal/ubuntu plucky/main armhf libxcursor1 armhf 1:1.2.3-1 [18.0 kB]
319s Get:289 http://ftpmaster.internal/ubuntu plucky/main armhf libxft2 armhf 2.3.6-1build1 [37.4 kB]
319s Get:290 http://ftpmaster.internal/ubuntu plucky/main armhf libxinerama1 armhf 2:1.1.4-3build1 [5866 B]
319s Get:291 http://ftpmaster.internal/ubuntu plucky/universe armhf libfltk1.3t64 armhf 1.3.8-6.1build2 [553 kB]
319s Get:292 http://ftpmaster.internal/ubuntu plucky/main armhf libglvnd0 armhf 1.7.0-1build1 [83.7 kB]
319s Get:293 http://ftpmaster.internal/ubuntu plucky/main armhf libx11-xcb1 armhf 2:1.8.10-2 [7902 B]
319s Get:294 http://ftpmaster.internal/ubuntu plucky/main armhf libxcb-dri3-0 armhf 1.17.0-2 [7120 B]
319s Get:295 http://ftpmaster.internal/ubuntu plucky/main armhf libxcb-glx0 armhf 1.17.0-2 [22.6 kB]
319s Get:296 http://ftpmaster.internal/ubuntu plucky/main armhf libxcb-present0 armhf 1.17.0-2 [5940 B]
319s Get:297 http://ftpmaster.internal/ubuntu plucky/main armhf libxcb-xfixes0 armhf 1.17.0-2 [10.0 kB]
319s Get:298 http://ftpmaster.internal/ubuntu plucky/main armhf libxxf86vm1 armhf 1:1.1.4-1build4 [8068 B]
319s Get:299 http://ftpmaster.internal/ubuntu plucky/main armhf libllvm19 armhf 1:19.1.7-1ubuntu2 [27.8 MB]
365s Get:300 http://ftpmaster.internal/ubuntu plucky/main armhf libxcb-randr0 armhf 1.17.0-2 [17.0 kB]
365s Get:301 http://ftpmaster.internal/ubuntu plucky/main armhf libxcb-sync1 armhf 1.17.0-2 [8732 B]
365s Get:302 http://ftpmaster.internal/ubuntu plucky/main armhf libxshmfence1 armhf 1.3-1build5 [4464 B]
365s Get:303 http://ftpmaster.internal/ubuntu plucky/main armhf mesa-libgallium armhf 25.0.1-2ubuntu1 [8255 kB]
381s Get:304 http://ftpmaster.internal/ubuntu plucky/main armhf libwayland-server0 armhf 1.23.1-3 [30.3 kB]
381s Get:305 http://ftpmaster.internal/ubuntu plucky/main armhf libgbm1 armhf 25.0.1-2ubuntu1 [29.1 kB]
381s Get:306 http://ftpmaster.internal/ubuntu plucky/main armhf libvulkan1 armhf 1.4.304.0-1 [127 kB]
381s Get:307 http://ftpmaster.internal/ubuntu plucky/main armhf libgl1-mesa-dri armhf 25.0.1-2ubuntu1 [31.9 kB]
381s Get:308 http://ftpmaster.internal/ubuntu plucky/main armhf libglx-mesa0 armhf 25.0.1-2ubuntu1 [120 kB]
381s Get:309 http://ftpmaster.internal/ubuntu plucky/main armhf libglx0 armhf 1.7.0-1build1 [39.3 kB]
381s Get:310 http://ftpmaster.internal/ubuntu plucky/main armhf libgl1 armhf 1.7.0-1build1 [105 kB]
382s Get:311 http://ftpmaster.internal/ubuntu plucky/universe armhf libfltk-gl1.3t64 armhf 1.3.8-6.1build2 [36.6 kB]
382s Get:312 http://ftpmaster.internal/ubuntu plucky/universe armhf libgl2ps1.4 armhf 1.4.2+dfsg1-2build1 [37.7 kB]
382s Get:313 http://ftpmaster.internal/ubuntu plucky/main armhf libltdl7 armhf 2.5.4-4 [39.8 kB]
382s Get:314 http://ftpmaster.internal/ubuntu plucky/universe armhf libglpk40 armhf 5.0-1build2 [326 kB]
382s Get:315 http://ftpmaster.internal/ubuntu plucky/main armhf libopengl0 armhf 1.7.0-1build1 [36.2 kB]
382s Get:316 http://ftpmaster.internal/ubuntu plucky/main armhf libglu1-mesa armhf 9.0.2-1.1build1 [153 kB]
383s Get:317 http://ftpmaster.internal/ubuntu plucky/main armhf liblcms2-2 armhf 2.16-2 [137 kB]
383s Get:318 http://ftpmaster.internal/ubuntu plucky/universe armhf libjxl0.11 armhf 0.11.1-1 [1009 kB]
384s Get:319 http://ftpmaster.internal/ubuntu plucky/main armhf libwmflite-0.2-7 armhf 0.2.13-1.1build3 [63.2 kB]
384s Get:320 http://ftpmaster.internal/ubuntu plucky/universe armhf libgraphicsmagick-q16-3t64 armhf 1.4+really1.3.45+hg17689-1 [1230 kB]
386s Get:321 http://ftpmaster.internal/ubuntu plucky/universe armhf libgraphicsmagick++-q16-12t64 armhf 1.4+really1.3.45+hg17689-1 [97.9 kB]
386s Get:322 http://ftpmaster.internal/ubuntu plucky/universe armhf libaec0 armhf 1.1.3-1 [20.8 kB]
386s Get:323 http://ftpmaster.internal/ubuntu plucky/universe armhf libsz2 armhf 1.1.3-1 [5302 B]
386s Get:324 http://ftpmaster.internal/ubuntu plucky/universe armhf libhdf5-310 armhf 1.14.5+repack-3 [1410 kB]
388s Get:325 http://ftpmaster.internal/ubuntu plucky/main armhf libasound2-data all 1.2.13-1build1 [21.1 kB]
388s Get:326 http://ftpmaster.internal/ubuntu plucky/main armhf libasound2t64 armhf 1.2.13-1build1 [347 kB]
389s Get:327 http://ftpmaster.internal/ubuntu plucky/main armhf libopus0 armhf 1.5.2-2 [2875 kB]
392s Get:328 http://ftpmaster.internal/ubuntu plucky/main armhf libsamplerate0 armhf 0.2.2-4build1 [1341 kB]
394s Get:329 http://ftpmaster.internal/ubuntu plucky/main armhf libjack-jackd2-0 armhf 1.9.22~dfsg-4 [247 kB]
395s Get:330 http://ftpmaster.internal/ubuntu plucky/universe armhf libportaudio2 armhf 19.6.0-1.2build3 [60.9 kB]
395s Get:331 http://ftpmaster.internal/ubuntu plucky/universe armhf libqhull-r8.0 armhf 2020.2-6build1 [173 kB]
395s Get:332 http://ftpmaster.internal/ubuntu plucky/universe armhf libqrupdate1 armhf 1.1.5-1 [31.4 kB]
395s Get:333 http://ftpmaster.internal/ubuntu plucky/universe armhf libqscintilla2-qt6-l10n all 2.14.1+dfsg-1build4 [56.4 kB]
395s Get:334 http://ftpmaster.internal/ubuntu plucky/universe armhf libb2-1 armhf 0.98.1-1.1build1 [23.6 kB]
395s Get:335 http://ftpmaster.internal/ubuntu plucky/universe armhf libdouble-conversion3 armhf 3.3.1-1 [39.1 kB]
395s Get:336 http://ftpmaster.internal/ubuntu plucky/main armhf libpcre2-16-0 armhf 10.45-1 [207 kB]
395s Get:337 http://ftpmaster.internal/ubuntu plucky/universe armhf libqt6core6t64 armhf 6.8.2+dfsg-5 [1743 kB]
398s Get:338 http://ftpmaster.internal/ubuntu plucky/main armhf libwayland-client0 armhf 1.23.1-3 [23.3 kB]
398s Get:339 http://ftpmaster.internal/ubuntu plucky/main armhf libegl-mesa0 armhf 25.0.1-2ubuntu1 [100 kB]
398s Get:340 http://ftpmaster.internal/ubuntu plucky/main armhf libegl1 armhf 1.7.0-1build1 [29.8 kB]
398s Get:341 http://ftpmaster.internal/ubuntu plucky/main armhf x11-common all 1:7.7+23ubuntu3 [21.7 kB]
398s Get:342 http://ftpmaster.internal/ubuntu plucky/main armhf libice6 armhf 2:1.1.1-1 [36.5 kB]
398s Get:343 http://ftpmaster.internal/ubuntu plucky/main armhf libmtdev1t64 armhf 1.1.7-1 [13.0 kB]
398s Get:344 http://ftpmaster.internal/ubuntu plucky/main armhf libwacom-common all 2.14.0-1 [103 kB]
398s Get:345 http://ftpmaster.internal/ubuntu plucky/main armhf libwacom9 armhf 2.14.0-1 [23.6 kB]
398s Get:346 http://ftpmaster.internal/ubuntu plucky/main armhf libinput-bin armhf 1.27.1-1 [25.9 kB]
398s Get:347 http://ftpmaster.internal/ubuntu plucky/main armhf libinput10 armhf 1.27.1-1 [124 kB]
398s Get:348 http://ftpmaster.internal/ubuntu plucky/universe armhf libmd4c0 armhf 0.5.2-2 [38.2 kB]
398s Get:349 http://ftpmaster.internal/ubuntu plucky/universe armhf libqt6dbus6 armhf 6.8.2+dfsg-5 [245 kB]
399s Get:350 http://ftpmaster.internal/ubuntu plucky/main armhf libsm6 armhf 2:1.2.4-1 [15.1 kB]
399s Get:351 http://ftpmaster.internal/ubuntu plucky/universe armhf libts0t64 armhf 1.22-1.1build1 [59.4 kB]
399s Get:352 http://ftpmaster.internal/ubuntu plucky/main armhf libxcb-util1 armhf 0.4.1-1 [9468 B]
399s Get:353 http://ftpmaster.internal/ubuntu plucky/main armhf libxcb-image0 armhf 0.4.0-2build1 [9680 B]
399s Get:354 http://ftpmaster.internal/ubuntu plucky/main armhf libxcb-render-util0 armhf 0.3.10-1 [9444 B]
399s Get:355 http://ftpmaster.internal/ubuntu plucky/universe armhf libxcb-cursor0 armhf 0.1.5-1 [9952 B]
399s Get:356 http://ftpmaster.internal/ubuntu plucky/main armhf libxcb-icccm4 armhf 0.4.2-1 [9840 B]
399s Get:357 http://ftpmaster.internal/ubuntu plucky/main armhf libxcb-keysyms1 armhf 0.4.1-1 [8076 B]
399s Get:358 http://ftpmaster.internal/ubuntu plucky/main armhf libxcb-shape0 armhf 1.17.0-2 [6030 B]
399s Get:359 http://ftpmaster.internal/ubuntu plucky/main armhf libxcb-xinput0 armhf 1.17.0-2 [29.5 kB]
399s Get:360 http://ftpmaster.internal/ubuntu plucky/main armhf libxcb-xkb1 armhf 1.17.0-2 [28.8 kB]
399s Get:361 http://ftpmaster.internal/ubuntu plucky/main armhf libxkbcommon-x11-0 armhf 1.7.0-2 [13.6 kB]
399s Get:362 http://ftpmaster.internal/ubuntu plucky/universe armhf libqt6gui6 armhf 6.8.2+dfsg-5 [2994 kB]
403s Get:363 http://ftpmaster.internal/ubuntu plucky/main armhf libavahi-common-data armhf 0.8-16ubuntu1 [30.9 kB]
403s Get:364 http://ftpmaster.internal/ubuntu plucky/main armhf libavahi-common3 armhf 0.8-16ubuntu1 [20.2 kB]
403s Get:365 http://ftpmaster.internal/ubuntu plucky/main armhf libavahi-client3 armhf 0.8-16ubuntu1 [24.1 kB]
403s Get:366 http://ftpmaster.internal/ubuntu plucky/main armhf libcups2t64 armhf 2.4.11-0ubuntu2 [243 kB]
403s Get:367 http://ftpmaster.internal/ubuntu plucky/universe armhf libqt6widgets6 armhf 6.8.2+dfsg-5 [2495 kB]
407s Get:368 http://ftpmaster.internal/ubuntu plucky/universe armhf libqt6printsupport6 armhf 6.8.2+dfsg-5 [208 kB]
407s Get:369 http://ftpmaster.internal/ubuntu plucky/universe armhf libqscintilla2-qt6-15 armhf 2.14.1+dfsg-1build4 [1026 kB]
408s Get:370 http://ftpmaster.internal/ubuntu plucky/universe armhf libqt6core5compat6 armhf 6.8.2-3 [130 kB]
409s Get:371 http://ftpmaster.internal/ubuntu plucky/universe armhf libqt6sql6 armhf 6.8.2+dfsg-5 [128 kB]
409s Get:372 http://ftpmaster.internal/ubuntu plucky/universe armhf libqt6help6 armhf 6.8.2-3 [179 kB]
409s Get:373 http://ftpmaster.internal/ubuntu plucky/main armhf libduktape207 armhf 2.7.0+tests-0ubuntu3 [123 kB]
409s Get:374 http://ftpmaster.internal/ubuntu plucky/main armhf libproxy1v5 armhf 0.5.9-1 [25.1 kB]
409s Get:375 http://ftpmaster.internal/ubuntu plucky/universe armhf libqt6network6 armhf 6.8.2+dfsg-5 [773 kB]
410s Get:376 http://ftpmaster.internal/ubuntu plucky/universe armhf libqt6opengl6 armhf 6.8.2+dfsg-5 [390 kB]
411s Get:377 http://ftpmaster.internal/ubuntu plucky/universe armhf libqt6openglwidgets6 armhf 6.8.2+dfsg-5 [41.1 kB]
411s Get:378 http://ftpmaster.internal/ubuntu plucky/universe armhf libqt6xml6 armhf 6.8.2+dfsg-5 [73.5 kB]
411s Get:379 http://ftpmaster.internal/ubuntu plucky/main armhf libogg0 armhf 1.3.5-3build1 [20.5 kB]
411s Get:380 http://ftpmaster.internal/ubuntu plucky/main armhf libflac12t64 armhf 1.4.3+ds-4 [143 kB]
411s Get:381 http://ftpmaster.internal/ubuntu plucky/main armhf libmp3lame0 armhf 3.100-6build1 [126 kB]
411s Get:382 http://ftpmaster.internal/ubuntu plucky/main armhf libmpg123-0t64 armhf 1.32.10-1 [158 kB]
411s Get:383 http://ftpmaster.internal/ubuntu plucky/main armhf libvorbis0a armhf 1.3.7-2 [86.7 kB]
411s Get:384 http://ftpmaster.internal/ubuntu plucky/main armhf libvorbisenc2 armhf 1.3.7-2 [72.9 kB]
412s Get:385 http://ftpmaster.internal/ubuntu plucky/main armhf libsndfile1 armhf 1.2.2-2 [201 kB]
412s Get:386 http://ftpmaster.internal/ubuntu plucky/universe armhf libspqr4 armhf 1:7.8.3+dfsg-3 [130 kB]
412s Get:387 http://ftpmaster.internal/ubuntu plucky/universe armhf libumfpack6 armhf 1:7.8.3+dfsg-3 [290 kB]
412s Get:388 http://ftpmaster.internal/ubuntu plucky/universe armhf libtext-unidecode-perl all 1.30-3 [105 kB]
413s Get:389 http://ftpmaster.internal/ubuntu plucky/universe armhf texinfo-lib armhf 7.1.1-1 [115 kB]
413s Get:390 http://ftpmaster.internal/ubuntu plucky/universe armhf tex-common all 6.19 [29.8 kB]
413s Get:391 http://ftpmaster.internal/ubuntu plucky/universe armhf texinfo all 7.1.1-1 [1272 kB]
415s Get:392 http://ftpmaster.internal/ubuntu plucky/universe armhf octave-common all 9.4.0-1 [6087 kB]
423s Get:393 http://ftpmaster.internal/ubuntu plucky/universe armhf octave armhf 9.4.0-1 [9025 kB]
436s Get:394 http://ftpmaster.internal/ubuntu plucky/main armhf libncurses-dev armhf 6.5+20250216-2 [345 kB]
436s Get:395 http://ftpmaster.internal/ubuntu plucky/main armhf libreadline-dev armhf 8.2-6 [153 kB]
436s Get:396 http://ftpmaster.internal/ubuntu plucky/universe armhf libhdf5-fortran-310 armhf 1.14.5+repack-3 [95.8 kB]
436s Get:397 http://ftpmaster.internal/ubuntu plucky/universe armhf libhdf5-hl-310 armhf 1.14.5+repack-3 [58.8 kB]
437s Get:398 http://ftpmaster.internal/ubuntu plucky/universe armhf libhdf5-hl-fortran-310 armhf 1.14.5+repack-3 [26.6 kB]
437s Get:399 http://ftpmaster.internal/ubuntu plucky/universe armhf libhdf5-cpp-310 armhf 1.14.5+repack-3 [125 kB]
437s Get:400 http://ftpmaster.internal/ubuntu plucky/universe armhf libhdf5-hl-cpp-310 armhf 1.14.5+repack-3 [11.1 kB]
437s Get:401 http://ftpmaster.internal/ubuntu plucky/main armhf zlib1g-dev armhf 1:1.3.dfsg+really1.3.1-1ubuntu1 [880 kB]
438s Get:402 http://ftpmaster.internal/ubuntu plucky/main armhf libjpeg-turbo8-dev armhf 2.1.5-3ubuntu2 [265 kB]
438s Get:403 http://ftpmaster.internal/ubuntu plucky/main armhf libjpeg8-dev armhf 8c-2ubuntu11 [1484 B]
438s Get:404 http://ftpmaster.internal/ubuntu plucky/main armhf libjpeg-dev armhf 8c-2ubuntu11 [1482 B]
438s Get:405 http://ftpmaster.internal/ubuntu plucky/universe armhf libaec-dev armhf 1.1.3-1 [17.3 kB]
439s Get:406 http://ftpmaster.internal/ubuntu plucky/main armhf libbrotli-dev armhf 1.1.0-2build4 [336 kB]
439s Get:407 http://ftpmaster.internal/ubuntu plucky/main armhf libidn2-dev armhf 2.3.7-2build2 [115 kB]
439s Get:408 http://ftpmaster.internal/ubuntu plucky/main armhf comerr-dev armhf 2.1-1.47.2-1ubuntu1 [44.1 kB]
439s Get:409 http://ftpmaster.internal/ubuntu plucky/main armhf libgssrpc4t64 armhf 1.21.3-4ubuntu2 [51.5 kB]
439s Get:410 http://ftpmaster.internal/ubuntu plucky/main armhf libkadm5clnt-mit12 armhf 1.21.3-4ubuntu2 [35.7 kB]
439s Get:411 http://ftpmaster.internal/ubuntu plucky/main armhf libkdb5-10t64 armhf 1.21.3-4ubuntu2 [35.2 kB]
440s Get:412 http://ftpmaster.internal/ubuntu plucky/main armhf libkadm5srv-mit12 armhf 1.21.3-4ubuntu2 [46.3 kB]
440s Get:413 http://ftpmaster.internal/ubuntu plucky/main armhf krb5-multidev armhf 1.21.3-4ubuntu2 [125 kB]
440s Get:414 http://ftpmaster.internal/ubuntu plucky/main armhf libkrb5-dev armhf 1.21.3-4ubuntu2 [11.9 kB]
440s Get:415 http://ftpmaster.internal/ubuntu plucky/main armhf libldap-dev armhf 2.6.9+dfsg-1~exp2ubuntu1 [289 kB]
440s Get:416 http://ftpmaster.internal/ubuntu plucky/main armhf libpkgconf3 armhf 1.8.1-4 [26.6 kB]
440s Get:417 http://ftpmaster.internal/ubuntu plucky/main armhf pkgconf-bin armhf 1.8.1-4 [21.2 kB]
440s Get:418 http://ftpmaster.internal/ubuntu plucky/main armhf pkgconf armhf 1.8.1-4 [16.8 kB]
440s Get:419 http://ftpmaster.internal/ubuntu plucky/main armhf libnghttp2-dev armhf 1.64.0-1 [106 kB]
440s Get:420 http://ftpmaster.internal/ubuntu plucky/main armhf libpsl-dev armhf 0.21.2-1.1build1 [75.6 kB]
441s Get:421 http://ftpmaster.internal/ubuntu plucky/main armhf libgmpxx4ldbl armhf 2:6.3.0+dfsg-3ubuntu1 [9152 B]
441s Get:422 http://ftpmaster.internal/ubuntu plucky/main armhf libgmp-dev armhf 2:6.3.0+dfsg-3ubuntu1 [290 kB]
441s Get:423 http://ftpmaster.internal/ubuntu plucky/main armhf libevent-2.1-7t64 armhf 2.1.12-stable-10 [127 kB]
441s Get:424 http://ftpmaster.internal/ubuntu plucky/main armhf libunbound8 armhf 1.22.0-1ubuntu1 [423 kB]
442s Get:425 http://ftpmaster.internal/ubuntu plucky/main armhf libgnutls-dane0t64 armhf 3.8.9-2ubuntu2 [34.9 kB]
442s Get:426 http://ftpmaster.internal/ubuntu plucky/main armhf libgnutls-openssl27t64 armhf 3.8.9-2ubuntu2 [34.8 kB]
442s Get:427 http://ftpmaster.internal/ubuntu plucky/main armhf libp11-kit-dev armhf 0.25.5-2ubuntu3 [23.5 kB]
442s Get:428 http://ftpmaster.internal/ubuntu plucky/main armhf libtasn1-6-dev armhf 4.20.0-2 [86.5 kB]
442s Get:429 http://ftpmaster.internal/ubuntu plucky/main armhf nettle-dev armhf 3.10.1-1 [1173 kB]
444s Get:430 http://ftpmaster.internal/ubuntu plucky/main armhf libgnutls28-dev armhf 3.8.9-2ubuntu2 [1023 kB]
445s Get:431 http://ftpmaster.internal/ubuntu plucky/main armhf librtmp-dev armhf 2.4+20151223.gitfa8646d.1-2build7 [62.9 kB]
446s Get:432 http://ftpmaster.internal/ubuntu plucky/main armhf libssl-dev armhf 3.4.1-1ubuntu1 [2381 kB]
449s Get:433 http://ftpmaster.internal/ubuntu plucky/main armhf libssh2-1-dev armhf 1.11.1-1 [270 kB]
449s Get:434 http://ftpmaster.internal/ubuntu plucky/main armhf libzstd-dev armhf 1.5.6+dfsg-2 [332 kB]
450s Get:435 http://ftpmaster.internal/ubuntu plucky/main armhf libcurl4-openssl-dev armhf 8.12.1-3ubuntu1 [462 kB]
450s Get:436 http://ftpmaster.internal/ubuntu plucky/universe armhf hdf5-helpers armhf 1.14.5+repack-3 [17.0 kB]
450s Get:437 http://ftpmaster.internal/ubuntu plucky/universe armhf libhdf5-dev armhf 1.14.5+repack-3 [3333 kB]
455s Get:438 http://ftpmaster.internal/ubuntu plucky/main armhf xorg-sgml-doctools all 1:1.11-1.1 [10.9 kB]
455s Get:439 http://ftpmaster.internal/ubuntu plucky/main armhf x11proto-dev all 2024.1-1 [606 kB]
456s Get:440 http://ftpmaster.internal/ubuntu plucky/main armhf libxau-dev armhf 1:1.0.11-1 [9034 B]
456s Get:441 http://ftpmaster.internal/ubuntu plucky/main armhf libxdmcp-dev armhf 1:1.1.5-1 [25.1 kB]
456s Get:442 http://ftpmaster.internal/ubuntu plucky/main armhf xtrans-dev all 1.4.0-1 [68.9 kB]
456s Get:443 http://ftpmaster.internal/ubuntu plucky/main armhf libxcb1-dev armhf 1.17.0-2 [85.0 kB]
456s Get:444 http://ftpmaster.internal/ubuntu plucky/main armhf libx11-dev armhf 2:1.8.10-2 [674 kB]
457s Get:445 http://ftpmaster.internal/ubuntu plucky/main armhf libglx-dev armhf 1.7.0-1build1 [14.2 kB]
457s Get:446 http://ftpmaster.internal/ubuntu plucky/main armhf libgl-dev armhf 1.7.0-1build1 [102 kB]
457s Get:447 http://ftpmaster.internal/ubuntu plucky/main armhf libblas-dev armhf 3.12.1-2 [141 kB]
457s Get:448 http://ftpmaster.internal/ubuntu plucky/main armhf liblapack-dev armhf 3.12.1-2 [2207 kB]
460s Get:449 http://ftpmaster.internal/ubuntu plucky/main armhf libfftw3-bin armhf 3.3.10-2fakesync1build1 [30.4 kB]
460s Get:450 http://ftpmaster.internal/ubuntu plucky/main armhf libfftw3-dev armhf 3.3.10-2fakesync1build1 [923 kB]
461s Get:451 http://ftpmaster.internal/ubuntu plucky/main armhf libgfortran-14-dev armhf 14.2.0-17ubuntu3 [370 kB]
462s Get:452 http://ftpmaster.internal/ubuntu plucky/main armhf gfortran-14-arm-linux-gnueabihf armhf 14.2.0-17ubuntu3 [9763 kB]
476s Get:453 http://ftpmaster.internal/ubuntu plucky/main armhf gfortran-14 armhf 14.2.0-17ubuntu3 [13.6 kB]
476s Get:454 http://ftpmaster.internal/ubuntu plucky/main armhf gfortran-arm-linux-gnueabihf armhf 4:14.2.0-1ubuntu1 [1026 B]
476s Get:455 http://ftpmaster.internal/ubuntu plucky/main armhf gfortran armhf 4:14.2.0-1ubuntu1 [1166 B]
476s Get:456 http://ftpmaster.internal/ubuntu plucky/main armhf libstdc++-14-dev armhf 14.2.0-17ubuntu3 [2573 kB]
480s Get:457 http://ftpmaster.internal/ubuntu plucky/main armhf g++-14-arm-linux-gnueabihf armhf 14.2.0-17ubuntu3 [10.5 MB]
495s Get:458 http://ftpmaster.internal/ubuntu plucky/main armhf g++-14 armhf 14.2.0-17ubuntu3 [21.8 kB]
495s Get:459 http://ftpmaster.internal/ubuntu plucky/main armhf g++-arm-linux-gnueabihf armhf 4:14.2.0-1ubuntu1 [966 B]
495s Get:460 http://ftpmaster.internal/ubuntu plucky/main armhf g++ armhf 4:14.2.0-1ubuntu1 [1084 B]
495s Get:461 http://ftpmaster.internal/ubuntu plucky/universe armhf octave-dev armhf 9.4.0-1 [457 kB]
495s Get:462 http://ftpmaster.internal/ubuntu plucky/universe armhf dh-octave all 1.8.0 [19.7 kB]
496s Get:463 http://ftpmaster.internal/ubuntu plucky/main armhf libfontenc1 armhf 1:1.1.8-1build1 [11.5 kB]
496s Get:464 http://ftpmaster.internal/ubuntu plucky/main armhf libxt6t64 armhf 1:1.2.1-1.2build1 [145 kB]
496s Get:465 http://ftpmaster.internal/ubuntu plucky/main armhf libxmu6 armhf 2:1.1.3-3build2 [40.4 kB]
496s Get:466 http://ftpmaster.internal/ubuntu plucky/main armhf libxaw7 armhf 2:1.0.16-1 [154 kB]
496s Get:467 http://ftpmaster.internal/ubuntu plucky/main armhf libxfont2 armhf 1:2.0.6-1build1 [80.3 kB]
497s Get:468 http://ftpmaster.internal/ubuntu plucky/main armhf libxkbfile1 armhf 1:1.1.0-1build4 [60.0 kB]
497s Get:469 http://ftpmaster.internal/ubuntu plucky/main armhf libxrandr2 armhf 2:1.5.4-1 [15.8 kB]
497s Get:470 http://ftpmaster.internal/ubuntu plucky/universe armhf octave-io armhf 2.6.4-3build2 [205 kB]
497s Get:471 http://ftpmaster.internal/ubuntu plucky/universe armhf octave-statistics-common all 1.7.3-2 [932 kB]
498s Get:472 http://ftpmaster.internal/ubuntu plucky/universe armhf octave-statistics armhf 1.7.3-2 [134 kB]
499s Get:473 http://ftpmaster.internal/ubuntu plucky/main armhf x11-xkb-utils armhf 7.7+9 [159 kB]
499s Get:474 http://ftpmaster.internal/ubuntu plucky/main armhf xserver-common all 2:21.1.16-1ubuntu1 [34.4 kB]
499s Get:475 http://ftpmaster.internal/ubuntu plucky/universe armhf xvfb armhf 2:21.1.16-1ubuntu1 [732 kB]
501s Fetched 190 MB in 4min 35s (691 kB/s)
501s Selecting previously unselected package libstemmer0d:armhf.
501s (Reading database ... 
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(Reading database ... 64655 files and directories currently installed.)
501s Preparing to unpack .../000-libstemmer0d_2.2.0-4build1_armhf.deb ...
501s Unpacking libstemmer0d:armhf (2.2.0-4build1) ...
501s Selecting previously unselected package libappstream5:armhf.
501s Preparing to unpack .../001-libappstream5_1.0.4-1_armhf.deb ...
501s Unpacking libappstream5:armhf (1.0.4-1) ...
501s Selecting previously unselected package appstream.
501s Preparing to unpack .../002-appstream_1.0.4-1_armhf.deb ...
501s Unpacking appstream (1.0.4-1) ...
501s Selecting previously unselected package m4.
501s Preparing to unpack .../003-m4_1.4.19-7_armhf.deb ...
501s Unpacking m4 (1.4.19-7) ...
501s Selecting previously unselected package autoconf.
501s Preparing to unpack .../004-autoconf_2.72-3ubuntu1_all.deb ...
501s Unpacking autoconf (2.72-3ubuntu1) ...
501s Selecting previously unselected package autotools-dev.
501s Preparing to unpack .../005-autotools-dev_20220109.1_all.deb ...
501s Unpacking autotools-dev (20220109.1) ...
501s Selecting previously unselected package automake.
501s Preparing to unpack .../006-automake_1%3a1.17-3ubuntu1_all.deb ...
501s Unpacking automake (1:1.17-3ubuntu1) ...
501s Selecting previously unselected package autopoint.
501s Preparing to unpack .../007-autopoint_0.23.1-1_all.deb ...
501s Unpacking autopoint (0.23.1-1) ...
501s Selecting previously unselected package libcapture-tiny-perl.
502s Preparing to unpack .../008-libcapture-tiny-perl_0.50-1_all.deb ...
502s Unpacking libcapture-tiny-perl (0.50-1) ...
502s Selecting previously unselected package libparams-util-perl.
502s Preparing to unpack .../009-libparams-util-perl_1.102-3build1_armhf.deb ...
502s Unpacking libparams-util-perl (1.102-3build1) ...
502s Selecting previously unselected package libsub-install-perl.
502s Preparing to unpack .../010-libsub-install-perl_0.929-1_all.deb ...
502s Unpacking libsub-install-perl (0.929-1) ...
502s Selecting previously unselected package libdata-optlist-perl.
502s Preparing to unpack .../011-libdata-optlist-perl_0.114-1_all.deb ...
502s Unpacking libdata-optlist-perl (0.114-1) ...
502s Selecting previously unselected package libb-hooks-op-check-perl:armhf.
502s Preparing to unpack .../012-libb-hooks-op-check-perl_0.22-3build2_armhf.deb ...
502s Unpacking libb-hooks-op-check-perl:armhf (0.22-3build2) ...
502s Selecting previously unselected package libdynaloader-functions-perl.
502s Preparing to unpack .../013-libdynaloader-functions-perl_0.004-1_all.deb ...
502s Unpacking libdynaloader-functions-perl (0.004-1) ...
502s Selecting previously unselected package libdevel-callchecker-perl:armhf.
502s Preparing to unpack .../014-libdevel-callchecker-perl_0.009-1build1_armhf.deb ...
502s Unpacking libdevel-callchecker-perl:armhf (0.009-1build1) ...
502s Selecting previously unselected package libparams-classify-perl:armhf.
502s Preparing to unpack .../015-libparams-classify-perl_0.015-2build6_armhf.deb ...
502s Unpacking libparams-classify-perl:armhf (0.015-2build6) ...
502s Selecting previously unselected package libmodule-runtime-perl.
502s Preparing to unpack .../016-libmodule-runtime-perl_0.016-2_all.deb ...
502s Unpacking libmodule-runtime-perl (0.016-2) ...
502s Selecting previously unselected package libtry-tiny-perl.
502s Preparing to unpack .../017-libtry-tiny-perl_0.32-1_all.deb ...
502s Unpacking libtry-tiny-perl (0.32-1) ...
502s Selecting previously unselected package libmodule-implementation-perl.
502s Preparing to unpack .../018-libmodule-implementation-perl_0.09-2_all.deb ...
502s Unpacking libmodule-implementation-perl (0.09-2) ...
502s Selecting previously unselected package libpackage-stash-perl.
502s Preparing to unpack .../019-libpackage-stash-perl_0.40-1_all.deb ...
502s Unpacking libpackage-stash-perl (0.40-1) ...
502s Selecting previously unselected package libclass-load-perl.
502s Preparing to unpack .../020-libclass-load-perl_0.25-2_all.deb ...
502s Unpacking libclass-load-perl (0.25-2) ...
502s Selecting previously unselected package libio-stringy-perl.
502s Preparing to unpack .../021-libio-stringy-perl_2.113-2_all.deb ...
502s Unpacking libio-stringy-perl (2.113-2) ...
502s Selecting previously unselected package libparams-validate-perl:armhf.
502s Preparing to unpack .../022-libparams-validate-perl_1.31-2build4_armhf.deb ...
502s Unpacking libparams-validate-perl:armhf (1.31-2build4) ...
502s Selecting previously unselected package libsub-exporter-perl.
502s Preparing to unpack .../023-libsub-exporter-perl_0.990-1_all.deb ...
502s Unpacking libsub-exporter-perl (0.990-1) ...
502s Selecting previously unselected package libgetopt-long-descriptive-perl.
502s Preparing to unpack .../024-libgetopt-long-descriptive-perl_0.116-2_all.deb ...
502s Unpacking libgetopt-long-descriptive-perl (0.116-2) ...
502s Selecting previously unselected package libio-tiecombine-perl.
502s Preparing to unpack .../025-libio-tiecombine-perl_1.005-3_all.deb ...
502s Unpacking libio-tiecombine-perl (1.005-3) ...
502s Selecting previously unselected package libmodule-pluggable-perl.
502s Preparing to unpack .../026-libmodule-pluggable-perl_5.2-5_all.deb ...
502s Unpacking libmodule-pluggable-perl (5.2-5) ...
502s Selecting previously unselected package libstring-rewriteprefix-perl.
502s Preparing to unpack .../027-libstring-rewriteprefix-perl_0.009-1_all.deb ...
502s Unpacking libstring-rewriteprefix-perl (0.009-1) ...
502s Selecting previously unselected package libapp-cmd-perl.
502s Preparing to unpack .../028-libapp-cmd-perl_0.337-2_all.deb ...
502s Unpacking libapp-cmd-perl (0.337-2) ...
502s Selecting previously unselected package libboolean-perl.
502s Preparing to unpack .../029-libboolean-perl_0.46-3_all.deb ...
502s Unpacking libboolean-perl (0.46-3) ...
502s Selecting previously unselected package libsub-uplevel-perl.
502s Preparing to unpack .../030-libsub-uplevel-perl_0.2800-3_all.deb ...
502s Unpacking libsub-uplevel-perl (0.2800-3) ...
502s Selecting previously unselected package libtest-exception-perl.
502s Preparing to unpack .../031-libtest-exception-perl_0.43-3_all.deb ...
502s Unpacking libtest-exception-perl (0.43-3) ...
502s Selecting previously unselected package libcarp-assert-more-perl.
502s Preparing to unpack .../032-libcarp-assert-more-perl_2.8.0-1_all.deb ...
502s Unpacking libcarp-assert-more-perl (2.8.0-1) ...
502s Selecting previously unselected package libfile-which-perl.
502s Preparing to unpack .../033-libfile-which-perl_1.27-2_all.deb ...
502s Unpacking libfile-which-perl (1.27-2) ...
502s Selecting previously unselected package libfile-homedir-perl.
502s Preparing to unpack .../034-libfile-homedir-perl_1.006-2_all.deb ...
502s Unpacking libfile-homedir-perl (1.006-2) ...
502s Selecting previously unselected package libclone-choose-perl.
502s Preparing to unpack .../035-libclone-choose-perl_0.010-2_all.deb ...
502s Unpacking libclone-choose-perl (0.010-2) ...
502s Selecting previously unselected package libhash-merge-perl.
502s Preparing to unpack .../036-libhash-merge-perl_0.302-1_all.deb ...
502s Unpacking libhash-merge-perl (0.302-1) ...
503s Selecting previously unselected package libjson-perl.
503s Preparing to unpack .../037-libjson-perl_4.10000-1_all.deb ...
503s Unpacking libjson-perl (4.10000-1) ...
503s Selecting previously unselected package libexporter-tiny-perl.
503s Preparing to unpack .../038-libexporter-tiny-perl_1.006002-1_all.deb ...
503s Unpacking libexporter-tiny-perl (1.006002-1) ...
503s Selecting previously unselected package liblist-moreutils-xs-perl.
503s Preparing to unpack .../039-liblist-moreutils-xs-perl_0.430-4build1_armhf.deb ...
503s Unpacking liblist-moreutils-xs-perl (0.430-4build1) ...
503s Selecting previously unselected package liblist-moreutils-perl.
503s Preparing to unpack .../040-liblist-moreutils-perl_0.430-2_all.deb ...
503s Unpacking liblist-moreutils-perl (0.430-2) ...
503s Selecting previously unselected package liblog-log4perl-perl.
503s Preparing to unpack .../041-liblog-log4perl-perl_1.57-1_all.deb ...
503s Unpacking liblog-log4perl-perl (1.57-1) ...
503s Selecting previously unselected package libmouse-perl:armhf.
503s Preparing to unpack .../042-libmouse-perl_2.5.11-1build1_armhf.deb ...
503s Unpacking libmouse-perl:armhf (2.5.11-1build1) ...
503s Selecting previously unselected package libmousex-nativetraits-perl.
503s Preparing to unpack .../043-libmousex-nativetraits-perl_1.09-3_all.deb ...
503s Unpacking libmousex-nativetraits-perl (1.09-3) ...
503s Selecting previously unselected package libmousex-strictconstructor-perl.
503s Preparing to unpack .../044-libmousex-strictconstructor-perl_0.02-3_all.deb ...
503s Unpacking libmousex-strictconstructor-perl (0.02-3) ...
503s Selecting previously unselected package libparse-recdescent-perl.
503s Preparing to unpack .../045-libparse-recdescent-perl_1.967015+dfsg-4_all.deb ...
503s Unpacking libparse-recdescent-perl (1.967015+dfsg-4) ...
503s Selecting previously unselected package libpath-tiny-perl.
503s Preparing to unpack .../046-libpath-tiny-perl_0.146-1_all.deb ...
503s Unpacking libpath-tiny-perl (0.146-1) ...
503s Selecting previously unselected package libpod-pom-perl.
503s Preparing to unpack .../047-libpod-pom-perl_2.01-4_all.deb ...
503s Unpacking libpod-pom-perl (2.01-4) ...
503s Selecting previously unselected package libregexp-common-perl.
503s Preparing to unpack .../048-libregexp-common-perl_2024080801-1_all.deb ...
503s Unpacking libregexp-common-perl (2024080801-1) ...
503s Selecting previously unselected package libyaml-tiny-perl.
503s Preparing to unpack .../049-libyaml-tiny-perl_1.76-1_all.deb ...
503s Unpacking libyaml-tiny-perl (1.76-1) ...
503s Selecting previously unselected package libconfig-model-perl.
503s Preparing to unpack .../050-libconfig-model-perl_2.155-1_all.deb ...
503s Unpacking libconfig-model-perl (2.155-1) ...
503s Selecting previously unselected package libyaml-pp-perl.
503s Preparing to unpack .../051-libyaml-pp-perl_0.39.0-1_all.deb ...
503s Unpacking libyaml-pp-perl (0.39.0-1) ...
503s Selecting previously unselected package cme.
503s Preparing to unpack .../052-cme_1.041-1_all.deb ...
503s Unpacking cme (1.041-1) ...
503s Selecting previously unselected package libisl23:armhf.
503s Preparing to unpack .../053-libisl23_0.27-1_armhf.deb ...
503s Unpacking libisl23:armhf (0.27-1) ...
503s Selecting previously unselected package libmpc3:armhf.
503s Preparing to unpack .../054-libmpc3_1.3.1-1build2_armhf.deb ...
503s Unpacking libmpc3:armhf (1.3.1-1build2) ...
503s Selecting previously unselected package cpp-14-arm-linux-gnueabihf.
503s Preparing to unpack .../055-cpp-14-arm-linux-gnueabihf_14.2.0-17ubuntu3_armhf.deb ...
503s Unpacking cpp-14-arm-linux-gnueabihf (14.2.0-17ubuntu3) ...
504s Selecting previously unselected package cpp-14.
504s Preparing to unpack .../056-cpp-14_14.2.0-17ubuntu3_armhf.deb ...
504s Unpacking cpp-14 (14.2.0-17ubuntu3) ...
504s Selecting previously unselected package cpp-arm-linux-gnueabihf.
504s Preparing to unpack .../057-cpp-arm-linux-gnueabihf_4%3a14.2.0-1ubuntu1_armhf.deb ...
504s Unpacking cpp-arm-linux-gnueabihf (4:14.2.0-1ubuntu1) ...
504s Selecting previously unselected package cpp.
504s Preparing to unpack .../058-cpp_4%3a14.2.0-1ubuntu1_armhf.deb ...
504s Unpacking cpp (4:14.2.0-1ubuntu1) ...
504s Selecting previously unselected package libdebhelper-perl.
504s Preparing to unpack .../059-libdebhelper-perl_13.24.1ubuntu2_all.deb ...
504s Unpacking libdebhelper-perl (13.24.1ubuntu2) ...
504s Selecting previously unselected package libcc1-0:armhf.
504s Preparing to unpack .../060-libcc1-0_15-20250222-0ubuntu1_armhf.deb ...
504s Unpacking libcc1-0:armhf (15-20250222-0ubuntu1) ...
504s Selecting previously unselected package libgomp1:armhf.
504s Preparing to unpack .../061-libgomp1_15-20250222-0ubuntu1_armhf.deb ...
504s Unpacking libgomp1:armhf (15-20250222-0ubuntu1) ...
504s Selecting previously unselected package libasan8:armhf.
504s Preparing to unpack .../062-libasan8_15-20250222-0ubuntu1_armhf.deb ...
504s Unpacking libasan8:armhf (15-20250222-0ubuntu1) ...
504s Selecting previously unselected package libubsan1:armhf.
504s Preparing to unpack .../063-libubsan1_15-20250222-0ubuntu1_armhf.deb ...
504s Unpacking libubsan1:armhf (15-20250222-0ubuntu1) ...
504s Selecting previously unselected package libgcc-14-dev:armhf.
504s Preparing to unpack .../064-libgcc-14-dev_14.2.0-17ubuntu3_armhf.deb ...
504s Unpacking libgcc-14-dev:armhf (14.2.0-17ubuntu3) ...
504s Selecting previously unselected package gcc-14-arm-linux-gnueabihf.
504s Preparing to unpack .../065-gcc-14-arm-linux-gnueabihf_14.2.0-17ubuntu3_armhf.deb ...
504s Unpacking gcc-14-arm-linux-gnueabihf (14.2.0-17ubuntu3) ...
504s Selecting previously unselected package gcc-14.
504s Preparing to unpack .../066-gcc-14_14.2.0-17ubuntu3_armhf.deb ...
504s Unpacking gcc-14 (14.2.0-17ubuntu3) ...
504s Selecting previously unselected package gcc-arm-linux-gnueabihf.
504s Preparing to unpack .../067-gcc-arm-linux-gnueabihf_4%3a14.2.0-1ubuntu1_armhf.deb ...
504s Unpacking gcc-arm-linux-gnueabihf (4:14.2.0-1ubuntu1) ...
504s Selecting previously unselected package gcc.
504s Preparing to unpack .../068-gcc_4%3a14.2.0-1ubuntu1_armhf.deb ...
504s Unpacking gcc (4:14.2.0-1ubuntu1) ...
504s Selecting previously unselected package libc-dev-bin.
504s Preparing to unpack .../069-libc-dev-bin_2.41-1ubuntu2_armhf.deb ...
504s Unpacking libc-dev-bin (2.41-1ubuntu2) ...
504s Selecting previously unselected package linux-libc-dev:armhf.
504s Preparing to unpack .../070-linux-libc-dev_6.14.0-10.10_armhf.deb ...
504s Unpacking linux-libc-dev:armhf (6.14.0-10.10) ...
505s Selecting previously unselected package libcrypt-dev:armhf.
505s Preparing to unpack .../071-libcrypt-dev_1%3a4.4.38-1_armhf.deb ...
505s Unpacking libcrypt-dev:armhf (1:4.4.38-1) ...
505s Selecting previously unselected package rpcsvc-proto.
505s Preparing to unpack .../072-rpcsvc-proto_1.4.2-0ubuntu7_armhf.deb ...
505s Unpacking rpcsvc-proto (1.4.2-0ubuntu7) ...
505s Selecting previously unselected package libc6-dev:armhf.
505s Preparing to unpack .../073-libc6-dev_2.41-1ubuntu2_armhf.deb ...
505s Unpacking libc6-dev:armhf (2.41-1ubuntu2) ...
505s Selecting previously unselected package libtool.
505s Preparing to unpack .../074-libtool_2.5.4-4_all.deb ...
505s Unpacking libtool (2.5.4-4) ...
505s Selecting previously unselected package dh-autoreconf.
505s Preparing to unpack .../075-dh-autoreconf_20_all.deb ...
505s Unpacking dh-autoreconf (20) ...
505s Selecting previously unselected package libarchive-zip-perl.
505s Preparing to unpack .../076-libarchive-zip-perl_1.68-1_all.deb ...
505s Unpacking libarchive-zip-perl (1.68-1) ...
505s Selecting previously unselected package libfile-stripnondeterminism-perl.
505s Preparing to unpack .../077-libfile-stripnondeterminism-perl_1.14.1-2_all.deb ...
505s Unpacking libfile-stripnondeterminism-perl (1.14.1-2) ...
505s Selecting previously unselected package dh-strip-nondeterminism.
505s Preparing to unpack .../078-dh-strip-nondeterminism_1.14.1-2_all.deb ...
505s Unpacking dh-strip-nondeterminism (1.14.1-2) ...
505s Selecting previously unselected package libdw1t64:armhf.
505s Preparing to unpack .../079-libdw1t64_0.192-4_armhf.deb ...
505s Unpacking libdw1t64:armhf (0.192-4) ...
505s Selecting previously unselected package debugedit.
505s Preparing to unpack .../080-debugedit_1%3a5.1-2_armhf.deb ...
505s Unpacking debugedit (1:5.1-2) ...
505s Selecting previously unselected package dwz.
505s Preparing to unpack .../081-dwz_0.15-1build6_armhf.deb ...
505s Unpacking dwz (0.15-1build6) ...
505s Selecting previously unselected package gettext.
505s Preparing to unpack .../082-gettext_0.23.1-1_armhf.deb ...
505s Unpacking gettext (0.23.1-1) ...
505s Selecting previously unselected package intltool-debian.
505s Preparing to unpack .../083-intltool-debian_0.35.0+20060710.6_all.deb ...
505s Unpacking intltool-debian (0.35.0+20060710.6) ...
505s Selecting previously unselected package po-debconf.
505s Preparing to unpack .../084-po-debconf_1.0.21+nmu1_all.deb ...
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505s Selecting previously unselected package debhelper.
505s Preparing to unpack .../085-debhelper_13.24.1ubuntu2_all.deb ...
505s Unpacking debhelper (13.24.1ubuntu2) ...
505s Selecting previously unselected package aglfn.
505s Preparing to unpack .../086-aglfn_1.7+git20191031.4036a9c-2_all.deb ...
505s Unpacking aglfn (1.7+git20191031.4036a9c-2) ...
505s Selecting previously unselected package gnuplot-data.
505s Preparing to unpack .../087-gnuplot-data_6.0.2+dfsg1-1_all.deb ...
505s Unpacking gnuplot-data (6.0.2+dfsg1-1) ...
506s Selecting previously unselected package libfreetype6:armhf.
506s Preparing to unpack .../088-libfreetype6_2.13.3+dfsg-1_armhf.deb ...
506s Unpacking libfreetype6:armhf (2.13.3+dfsg-1) ...
506s Selecting previously unselected package fonts-dejavu-mono.
506s Preparing to unpack .../089-fonts-dejavu-mono_2.37-8_all.deb ...
506s Unpacking fonts-dejavu-mono (2.37-8) ...
506s Selecting previously unselected package fonts-dejavu-core.
506s Preparing to unpack .../090-fonts-dejavu-core_2.37-8_all.deb ...
506s Unpacking fonts-dejavu-core (2.37-8) ...
506s Selecting previously unselected package fonts-freefont-otf.
506s Preparing to unpack .../091-fonts-freefont-otf_20211204+svn4273-2_all.deb ...
506s Unpacking fonts-freefont-otf (20211204+svn4273-2) ...
506s Selecting previously unselected package fontconfig-config.
506s Preparing to unpack .../092-fontconfig-config_2.15.0-2ubuntu1_armhf.deb ...
506s Unpacking fontconfig-config (2.15.0-2ubuntu1) ...
506s Selecting previously unselected package libfontconfig1:armhf.
506s Preparing to unpack .../093-libfontconfig1_2.15.0-2ubuntu1_armhf.deb ...
506s Unpacking libfontconfig1:armhf (2.15.0-2ubuntu1) ...
506s Selecting previously unselected package libpixman-1-0:armhf.
506s Preparing to unpack .../094-libpixman-1-0_0.44.0-3_armhf.deb ...
506s Unpacking libpixman-1-0:armhf (0.44.0-3) ...
506s Selecting previously unselected package libxcb-render0:armhf.
506s Preparing to unpack .../095-libxcb-render0_1.17.0-2_armhf.deb ...
506s Unpacking libxcb-render0:armhf (1.17.0-2) ...
506s Selecting previously unselected package libxcb-shm0:armhf.
506s Preparing to unpack .../096-libxcb-shm0_1.17.0-2_armhf.deb ...
506s Unpacking libxcb-shm0:armhf (1.17.0-2) ...
506s Selecting previously unselected package libxrender1:armhf.
506s Preparing to unpack .../097-libxrender1_1%3a0.9.10-1.1build1_armhf.deb ...
506s Unpacking libxrender1:armhf (1:0.9.10-1.1build1) ...
506s Selecting previously unselected package libcairo2:armhf.
506s Preparing to unpack .../098-libcairo2_1.18.2-2_armhf.deb ...
506s Unpacking libcairo2:armhf (1.18.2-2) ...
506s Selecting previously unselected package libsharpyuv0:armhf.
506s Preparing to unpack .../099-libsharpyuv0_1.5.0-0.1_armhf.deb ...
506s Unpacking libsharpyuv0:armhf (1.5.0-0.1) ...
506s Selecting previously unselected package libaom3:armhf.
506s Preparing to unpack .../100-libaom3_3.12.0-1_armhf.deb ...
506s Unpacking libaom3:armhf (3.12.0-1) ...
507s Selecting previously unselected package libheif-plugin-aomdec:armhf.
507s Preparing to unpack .../101-libheif-plugin-aomdec_1.19.7-1_armhf.deb ...
507s Unpacking libheif-plugin-aomdec:armhf (1.19.7-1) ...
507s Selecting previously unselected package libde265-0:armhf.
507s Preparing to unpack .../102-libde265-0_1.0.15-1build5_armhf.deb ...
507s Unpacking libde265-0:armhf (1.0.15-1build5) ...
507s Selecting previously unselected package libheif-plugin-libde265:armhf.
507s Preparing to unpack .../103-libheif-plugin-libde265_1.19.7-1_armhf.deb ...
507s Unpacking libheif-plugin-libde265:armhf (1.19.7-1) ...
507s Selecting previously unselected package libheif1:armhf.
507s Preparing to unpack .../104-libheif1_1.19.7-1_armhf.deb ...
507s Unpacking libheif1:armhf (1.19.7-1) ...
507s Selecting previously unselected package libimagequant0:armhf.
507s Preparing to unpack .../105-libimagequant0_2.18.0-1build1_armhf.deb ...
507s Unpacking libimagequant0:armhf (2.18.0-1build1) ...
507s Selecting previously unselected package libjpeg-turbo8:armhf.
507s Preparing to unpack .../106-libjpeg-turbo8_2.1.5-3ubuntu2_armhf.deb ...
507s Unpacking libjpeg-turbo8:armhf (2.1.5-3ubuntu2) ...
507s Selecting previously unselected package libjpeg8:armhf.
507s Preparing to unpack .../107-libjpeg8_8c-2ubuntu11_armhf.deb ...
507s Unpacking libjpeg8:armhf (8c-2ubuntu11) ...
507s Selecting previously unselected package libgraphite2-3:armhf.
507s Preparing to unpack .../108-libgraphite2-3_1.3.14-2ubuntu1_armhf.deb ...
507s Unpacking libgraphite2-3:armhf (1.3.14-2ubuntu1) ...
507s Selecting previously unselected package libharfbuzz0b:armhf.
507s Preparing to unpack .../109-libharfbuzz0b_10.2.0-1_armhf.deb ...
507s Unpacking libharfbuzz0b:armhf (10.2.0-1) ...
507s Selecting previously unselected package libraqm0:armhf.
507s Preparing to unpack .../110-libraqm0_0.10.2-1_armhf.deb ...
507s Unpacking libraqm0:armhf (0.10.2-1) ...
507s Selecting previously unselected package libdeflate0:armhf.
507s Preparing to unpack .../111-libdeflate0_1.23-1_armhf.deb ...
507s Unpacking libdeflate0:armhf (1.23-1) ...
507s Selecting previously unselected package libjbig0:armhf.
507s Preparing to unpack .../112-libjbig0_2.1-6.1ubuntu2_armhf.deb ...
507s Unpacking libjbig0:armhf (2.1-6.1ubuntu2) ...
507s Selecting previously unselected package liblerc4:armhf.
507s Preparing to unpack .../113-liblerc4_4.0.0+ds-5ubuntu1_armhf.deb ...
507s Unpacking liblerc4:armhf (4.0.0+ds-5ubuntu1) ...
507s Selecting previously unselected package libwebp7:armhf.
507s Preparing to unpack .../114-libwebp7_1.5.0-0.1_armhf.deb ...
507s Unpacking libwebp7:armhf (1.5.0-0.1) ...
507s Selecting previously unselected package libtiff6:armhf.
507s Preparing to unpack .../115-libtiff6_4.5.1+git230720-4ubuntu4_armhf.deb ...
507s Unpacking libtiff6:armhf (4.5.1+git230720-4ubuntu4) ...
507s Selecting previously unselected package libxpm4:armhf.
507s Preparing to unpack .../116-libxpm4_1%3a3.5.17-1build2_armhf.deb ...
507s Unpacking libxpm4:armhf (1:3.5.17-1build2) ...
507s Selecting previously unselected package libgd3:armhf.
507s Preparing to unpack .../117-libgd3_2.3.3-12ubuntu3_armhf.deb ...
507s Unpacking libgd3:armhf (2.3.3-12ubuntu3) ...
507s Selecting previously unselected package liblua5.4-0:armhf.
507s Preparing to unpack .../118-liblua5.4-0_5.4.7-1_armhf.deb ...
507s Unpacking liblua5.4-0:armhf (5.4.7-1) ...
507s Selecting previously unselected package fontconfig.
507s Preparing to unpack .../119-fontconfig_2.15.0-2ubuntu1_armhf.deb ...
507s Unpacking fontconfig (2.15.0-2ubuntu1) ...
507s Selecting previously unselected package libthai-data.
507s Preparing to unpack .../120-libthai-data_0.1.29-2build1_all.deb ...
507s Unpacking libthai-data (0.1.29-2build1) ...
507s Selecting previously unselected package libdatrie1:armhf.
507s Preparing to unpack .../121-libdatrie1_0.2.13-3build1_armhf.deb ...
507s Unpacking libdatrie1:armhf (0.2.13-3build1) ...
507s Selecting previously unselected package libthai0:armhf.
507s Preparing to unpack .../122-libthai0_0.1.29-2build1_armhf.deb ...
507s Unpacking libthai0:armhf (0.1.29-2build1) ...
507s Selecting previously unselected package libpango-1.0-0:armhf.
507s Preparing to unpack .../123-libpango-1.0-0_1.56.2-1_armhf.deb ...
507s Unpacking libpango-1.0-0:armhf (1.56.2-1) ...
507s Selecting previously unselected package libpangoft2-1.0-0:armhf.
507s Preparing to unpack .../124-libpangoft2-1.0-0_1.56.2-1_armhf.deb ...
507s Unpacking libpangoft2-1.0-0:armhf (1.56.2-1) ...
507s Selecting previously unselected package libpangocairo-1.0-0:armhf.
507s Preparing to unpack .../125-libpangocairo-1.0-0_1.56.2-1_armhf.deb ...
507s Unpacking libpangocairo-1.0-0:armhf (1.56.2-1) ...
507s Selecting previously unselected package libwebpmux3:armhf.
507s Preparing to unpack .../126-libwebpmux3_1.5.0-0.1_armhf.deb ...
507s Unpacking libwebpmux3:armhf (1.5.0-0.1) ...
508s Selecting previously unselected package gnuplot-nox.
508s Preparing to unpack .../127-gnuplot-nox_6.0.2+dfsg1-1_armhf.deb ...
508s Unpacking gnuplot-nox (6.0.2+dfsg1-1) ...
508s Selecting previously unselected package dh-octave-autopkgtest.
508s Preparing to unpack .../128-dh-octave-autopkgtest_1.8.0_all.deb ...
508s Unpacking dh-octave-autopkgtest (1.8.0) ...
508s Selecting previously unselected package libapt-pkg-perl.
508s Preparing to unpack .../129-libapt-pkg-perl_0.1.41build1_armhf.deb ...
508s Unpacking libapt-pkg-perl (0.1.41build1) ...
508s Selecting previously unselected package libarray-intspan-perl.
508s Preparing to unpack .../130-libarray-intspan-perl_2.004-2_all.deb ...
508s Unpacking libarray-intspan-perl (2.004-2) ...
508s Selecting previously unselected package libyaml-libyaml-perl.
508s Preparing to unpack .../131-libyaml-libyaml-perl_0.903.0+ds-1_armhf.deb ...
508s Unpacking libyaml-libyaml-perl (0.903.0+ds-1) ...
508s Selecting previously unselected package libconfig-model-backend-yaml-perl.
508s Preparing to unpack .../132-libconfig-model-backend-yaml-perl_2.134-2_all.deb ...
508s Unpacking libconfig-model-backend-yaml-perl (2.134-2) ...
508s Selecting previously unselected package libexporter-lite-perl.
508s Preparing to unpack .../133-libexporter-lite-perl_0.09-2_all.deb ...
508s Unpacking libexporter-lite-perl (0.09-2) ...
508s Selecting previously unselected package libencode-locale-perl.
508s Preparing to unpack .../134-libencode-locale-perl_1.05-3_all.deb ...
508s Unpacking libencode-locale-perl (1.05-3) ...
508s Selecting previously unselected package libtimedate-perl.
508s Preparing to unpack .../135-libtimedate-perl_2.3300-2_all.deb ...
508s Unpacking libtimedate-perl (2.3300-2) ...
508s Selecting previously unselected package libhttp-date-perl.
508s Preparing to unpack .../136-libhttp-date-perl_6.06-1_all.deb ...
508s Unpacking libhttp-date-perl (6.06-1) ...
508s Selecting previously unselected package libfile-listing-perl.
508s Preparing to unpack .../137-libfile-listing-perl_6.16-1_all.deb ...
508s Unpacking libfile-listing-perl (6.16-1) ...
508s Selecting previously unselected package libhtml-tagset-perl.
508s Preparing to unpack .../138-libhtml-tagset-perl_3.24-1_all.deb ...
508s Unpacking libhtml-tagset-perl (3.24-1) ...
508s Selecting previously unselected package liburi-perl.
508s Preparing to unpack .../139-liburi-perl_5.30-1_all.deb ...
508s Unpacking liburi-perl (5.30-1) ...
508s Selecting previously unselected package libhtml-parser-perl:armhf.
508s Preparing to unpack .../140-libhtml-parser-perl_3.83-1build1_armhf.deb ...
508s Unpacking libhtml-parser-perl:armhf (3.83-1build1) ...
508s Selecting previously unselected package libhtml-tree-perl.
508s Preparing to unpack .../141-libhtml-tree-perl_5.07-3_all.deb ...
508s Unpacking libhtml-tree-perl (5.07-3) ...
508s Selecting previously unselected package libclone-perl:armhf.
508s Preparing to unpack .../142-libclone-perl_0.47-1_armhf.deb ...
508s Unpacking libclone-perl:armhf (0.47-1) ...
508s Selecting previously unselected package libio-html-perl.
508s Preparing to unpack .../143-libio-html-perl_1.004-3_all.deb ...
508s Unpacking libio-html-perl (1.004-3) ...
508s Selecting previously unselected package liblwp-mediatypes-perl.
508s Preparing to unpack .../144-liblwp-mediatypes-perl_6.04-2_all.deb ...
508s Unpacking liblwp-mediatypes-perl (6.04-2) ...
508s Selecting previously unselected package libhttp-message-perl.
508s Preparing to unpack .../145-libhttp-message-perl_7.00-2ubuntu1_all.deb ...
508s Unpacking libhttp-message-perl (7.00-2ubuntu1) ...
508s Selecting previously unselected package libhttp-cookies-perl.
508s Preparing to unpack .../146-libhttp-cookies-perl_6.11-1_all.deb ...
508s Unpacking libhttp-cookies-perl (6.11-1) ...
508s Selecting previously unselected package libhttp-negotiate-perl.
508s Preparing to unpack .../147-libhttp-negotiate-perl_6.01-2_all.deb ...
508s Unpacking libhttp-negotiate-perl (6.01-2) ...
508s Selecting previously unselected package perl-openssl-defaults:armhf.
508s Preparing to unpack .../148-perl-openssl-defaults_7build3_armhf.deb ...
508s Unpacking perl-openssl-defaults:armhf (7build3) ...
508s Selecting previously unselected package libnet-ssleay-perl:armhf.
508s Preparing to unpack .../149-libnet-ssleay-perl_1.94-3_armhf.deb ...
508s Unpacking libnet-ssleay-perl:armhf (1.94-3) ...
508s Selecting previously unselected package libio-socket-ssl-perl.
509s Preparing to unpack .../150-libio-socket-ssl-perl_2.089-1_all.deb ...
509s Unpacking libio-socket-ssl-perl (2.089-1) ...
509s Selecting previously unselected package libnet-http-perl.
509s Preparing to unpack .../151-libnet-http-perl_6.23-1_all.deb ...
509s Unpacking libnet-http-perl (6.23-1) ...
509s Selecting previously unselected package liblwp-protocol-https-perl.
509s Preparing to unpack .../152-liblwp-protocol-https-perl_6.14-1_all.deb ...
509s Unpacking liblwp-protocol-https-perl (6.14-1) ...
509s Selecting previously unselected package libwww-robotrules-perl.
509s Preparing to unpack .../153-libwww-robotrules-perl_6.02-1_all.deb ...
509s Unpacking libwww-robotrules-perl (6.02-1) ...
509s Selecting previously unselected package libwww-perl.
509s Preparing to unpack .../154-libwww-perl_6.78-1_all.deb ...
509s Unpacking libwww-perl (6.78-1) ...
509s Selecting previously unselected package liberror-perl.
509s Preparing to unpack .../155-liberror-perl_0.17030-1_all.deb ...
509s Unpacking liberror-perl (0.17030-1) ...
509s Selecting previously unselected package libparse-debcontrol-perl.
509s Preparing to unpack .../156-libparse-debcontrol-perl_2.005-6_all.deb ...
509s Unpacking libparse-debcontrol-perl (2.005-6) ...
509s Selecting previously unselected package libsoftware-copyright-perl.
509s Preparing to unpack .../157-libsoftware-copyright-perl_0.014-1_all.deb ...
509s Unpacking libsoftware-copyright-perl (0.014-1) ...
509s Selecting previously unselected package libalgorithm-c3-perl.
509s Preparing to unpack .../158-libalgorithm-c3-perl_0.11-2_all.deb ...
509s Unpacking libalgorithm-c3-perl (0.11-2) ...
509s Selecting previously unselected package libclass-c3-perl.
509s Preparing to unpack .../159-libclass-c3-perl_0.35-2_all.deb ...
509s Unpacking libclass-c3-perl (0.35-2) ...
509s Selecting previously unselected package libmro-compat-perl.
509s Preparing to unpack .../160-libmro-compat-perl_0.15-2_all.deb ...
509s Unpacking libmro-compat-perl (0.15-2) ...
509s Selecting previously unselected package libdata-section-perl.
509s Preparing to unpack .../161-libdata-section-perl_0.200008-1_all.deb ...
509s Unpacking libdata-section-perl (0.200008-1) ...
509s Selecting previously unselected package libtext-template-perl.
509s Preparing to unpack .../162-libtext-template-perl_1.61-1_all.deb ...
509s Unpacking libtext-template-perl (1.61-1) ...
509s Selecting previously unselected package libsoftware-license-perl.
509s Preparing to unpack .../163-libsoftware-license-perl_0.104006-1_all.deb ...
509s Unpacking libsoftware-license-perl (0.104006-1) ...
509s Selecting previously unselected package libsoftware-licensemoreutils-perl.
509s Preparing to unpack .../164-libsoftware-licensemoreutils-perl_1.009-1_all.deb ...
509s Unpacking libsoftware-licensemoreutils-perl (1.009-1) ...
509s Selecting previously unselected package libsort-versions-perl.
509s Preparing to unpack .../165-libsort-versions-perl_1.62-3_all.deb ...
509s Unpacking libsort-versions-perl (1.62-3) ...
509s Selecting previously unselected package libtext-reform-perl.
509s Preparing to unpack .../166-libtext-reform-perl_1.20-5_all.deb ...
509s Unpacking libtext-reform-perl (1.20-5) ...
509s Selecting previously unselected package libtext-autoformat-perl.
509s Preparing to unpack .../167-libtext-autoformat-perl_1.750000-2_all.deb ...
509s Unpacking libtext-autoformat-perl (1.750000-2) ...
509s Selecting previously unselected package libtext-levenshtein-damerau-perl.
509s Preparing to unpack .../168-libtext-levenshtein-damerau-perl_0.41-3_all.deb ...
509s Unpacking libtext-levenshtein-damerau-perl (0.41-3) ...
509s Selecting previously unselected package libtoml-tiny-perl.
509s Preparing to unpack .../169-libtoml-tiny-perl_0.19-1_all.deb ...
509s Unpacking libtoml-tiny-perl (0.19-1) ...
509s Selecting previously unselected package libclass-inspector-perl.
509s Preparing to unpack .../170-libclass-inspector-perl_1.36-3_all.deb ...
509s Unpacking libclass-inspector-perl (1.36-3) ...
509s Selecting previously unselected package libfile-sharedir-perl.
509s Preparing to unpack .../171-libfile-sharedir-perl_1.118-3_all.deb ...
509s Unpacking libfile-sharedir-perl (1.118-3) ...
509s Selecting previously unselected package libindirect-perl.
509s Preparing to unpack .../172-libindirect-perl_0.39-2build5_armhf.deb ...
509s Unpacking libindirect-perl (0.39-2build5) ...
509s Selecting previously unselected package libxs-parse-keyword-perl.
509s Preparing to unpack .../173-libxs-parse-keyword-perl_0.48-2_armhf.deb ...
509s Unpacking libxs-parse-keyword-perl (0.48-2) ...
509s Selecting previously unselected package libxs-parse-sublike-perl:armhf.
509s Preparing to unpack .../174-libxs-parse-sublike-perl_0.37-1_armhf.deb ...
509s Unpacking libxs-parse-sublike-perl:armhf (0.37-1) ...
509s Selecting previously unselected package libobject-pad-perl.
509s Preparing to unpack .../175-libobject-pad-perl_0.820-1_armhf.deb ...
509s Unpacking libobject-pad-perl (0.820-1) ...
509s Selecting previously unselected package libsyntax-keyword-try-perl.
509s Preparing to unpack .../176-libsyntax-keyword-try-perl_0.30-1_armhf.deb ...
509s Unpacking libsyntax-keyword-try-perl (0.30-1) ...
509s Selecting previously unselected package libio-interactive-perl.
510s Preparing to unpack .../177-libio-interactive-perl_1.026-1_all.deb ...
510s Unpacking libio-interactive-perl (1.026-1) ...
510s Selecting previously unselected package liblog-any-perl.
510s Preparing to unpack .../178-liblog-any-perl_1.717-1_all.deb ...
510s Unpacking liblog-any-perl (1.717-1) ...
510s Selecting previously unselected package liblog-any-adapter-screen-perl.
510s Preparing to unpack .../179-liblog-any-adapter-screen-perl_0.141-1_all.deb ...
510s Unpacking liblog-any-adapter-screen-perl (0.141-1) ...
510s Selecting previously unselected package libsub-exporter-progressive-perl.
510s Preparing to unpack .../180-libsub-exporter-progressive-perl_0.001013-3_all.deb ...
510s Unpacking libsub-exporter-progressive-perl (0.001013-3) ...
510s Selecting previously unselected package libvariable-magic-perl.
510s Preparing to unpack .../181-libvariable-magic-perl_0.64-1build1_armhf.deb ...
510s Unpacking libvariable-magic-perl (0.64-1build1) ...
510s Selecting previously unselected package libb-hooks-endofscope-perl.
510s Preparing to unpack .../182-libb-hooks-endofscope-perl_0.28-1_all.deb ...
510s Unpacking libb-hooks-endofscope-perl (0.28-1) ...
510s Selecting previously unselected package libsub-identify-perl.
510s Preparing to unpack .../183-libsub-identify-perl_0.14-3build4_armhf.deb ...
510s Unpacking libsub-identify-perl (0.14-3build4) ...
510s Selecting previously unselected package libsub-name-perl:armhf.
510s Preparing to unpack .../184-libsub-name-perl_0.28-1_armhf.deb ...
510s Unpacking libsub-name-perl:armhf (0.28-1) ...
510s Selecting previously unselected package libnamespace-clean-perl.
510s Preparing to unpack .../185-libnamespace-clean-perl_0.27-2_all.deb ...
510s Unpacking libnamespace-clean-perl (0.27-2) ...
510s Selecting previously unselected package libnumber-compare-perl.
510s Preparing to unpack .../186-libnumber-compare-perl_0.03-3_all.deb ...
510s Unpacking libnumber-compare-perl (0.03-3) ...
510s Selecting previously unselected package libtext-glob-perl.
510s Preparing to unpack .../187-libtext-glob-perl_0.11-3_all.deb ...
510s Unpacking libtext-glob-perl (0.11-3) ...
510s Selecting previously unselected package libpath-iterator-rule-perl.
510s Preparing to unpack .../188-libpath-iterator-rule-perl_1.015-2_all.deb ...
510s Unpacking libpath-iterator-rule-perl (1.015-2) ...
510s Selecting previously unselected package libpod-parser-perl.
510s Preparing to unpack .../189-libpod-parser-perl_1.67-1_all.deb ...
510s Adding 'diversion of /usr/bin/podselect to /usr/bin/podselect.bundled by libpod-parser-perl'
510s Adding 'diversion of /usr/share/man/man1/podselect.1.gz to /usr/share/man/man1/podselect.bundled.1.gz by libpod-parser-perl'
510s Unpacking libpod-parser-perl (1.67-1) ...
510s Selecting previously unselected package libpod-constants-perl.
510s Preparing to unpack .../190-libpod-constants-perl_0.19-2_all.deb ...
510s Unpacking libpod-constants-perl (0.19-2) ...
510s Selecting previously unselected package libset-intspan-perl.
510s Preparing to unpack .../191-libset-intspan-perl_1.19-3_all.deb ...
510s Unpacking libset-intspan-perl (1.19-3) ...
510s Selecting previously unselected package libstring-copyright-perl.
510s Preparing to unpack .../192-libstring-copyright-perl_0.003014-1_all.deb ...
510s Unpacking libstring-copyright-perl (0.003014-1) ...
510s Selecting previously unselected package libstring-escape-perl.
510s Preparing to unpack .../193-libstring-escape-perl_2010.002-3_all.deb ...
510s Unpacking libstring-escape-perl (2010.002-3) ...
510s Selecting previously unselected package libregexp-pattern-license-perl.
510s Preparing to unpack .../194-libregexp-pattern-license-perl_3.11.2-1_all.deb ...
510s Unpacking libregexp-pattern-license-perl (3.11.2-1) ...
510s Selecting previously unselected package libregexp-pattern-perl.
510s Preparing to unpack .../195-libregexp-pattern-perl_0.2.14-2_all.deb ...
510s Unpacking libregexp-pattern-perl (0.2.14-2) ...
510s Selecting previously unselected package libstring-license-perl.
510s Preparing to unpack .../196-libstring-license-perl_0.0.11-1ubuntu1_all.deb ...
510s Unpacking libstring-license-perl (0.0.11-1ubuntu1) ...
510s Selecting previously unselected package licensecheck.
510s Preparing to unpack .../197-licensecheck_3.3.9-1ubuntu1_all.deb ...
510s Unpacking licensecheck (3.3.9-1ubuntu1) ...
510s Selecting previously unselected package diffstat.
510s Preparing to unpack .../198-diffstat_1.67-1_armhf.deb ...
510s Unpacking diffstat (1.67-1) ...
510s Selecting previously unselected package libberkeleydb-perl:armhf.
510s Preparing to unpack .../199-libberkeleydb-perl_0.66-1_armhf.deb ...
510s Unpacking libberkeleydb-perl:armhf (0.66-1) ...
510s Selecting previously unselected package libclass-xsaccessor-perl.
510s Preparing to unpack .../200-libclass-xsaccessor-perl_1.19-4build6_armhf.deb ...
510s Unpacking libclass-xsaccessor-perl (1.19-4build6) ...
510s Selecting previously unselected package libconfig-tiny-perl.
510s Preparing to unpack .../201-libconfig-tiny-perl_2.30-1_all.deb ...
510s Unpacking libconfig-tiny-perl (2.30-1) ...
510s Selecting previously unselected package libconst-fast-perl.
510s Preparing to unpack .../202-libconst-fast-perl_0.014-2_all.deb ...
510s Unpacking libconst-fast-perl (0.014-2) ...
511s Selecting previously unselected package libcpanel-json-xs-perl:armhf.
511s Preparing to unpack .../203-libcpanel-json-xs-perl_4.39-1_armhf.deb ...
511s Unpacking libcpanel-json-xs-perl:armhf (4.39-1) ...
511s Selecting previously unselected package libaliased-perl.
511s Preparing to unpack .../204-libaliased-perl_0.34-3_all.deb ...
511s Unpacking libaliased-perl (0.34-3) ...
511s Selecting previously unselected package libclass-data-inheritable-perl.
511s Preparing to unpack .../205-libclass-data-inheritable-perl_0.10-1_all.deb ...
511s Unpacking libclass-data-inheritable-perl (0.10-1) ...
511s Selecting previously unselected package libdevel-stacktrace-perl.
511s Preparing to unpack .../206-libdevel-stacktrace-perl_2.0500-1_all.deb ...
511s Unpacking libdevel-stacktrace-perl (2.0500-1) ...
511s Selecting previously unselected package libexception-class-perl.
511s Preparing to unpack .../207-libexception-class-perl_1.45-1_all.deb ...
511s Unpacking libexception-class-perl (1.45-1) ...
511s Selecting previously unselected package libiterator-perl.
511s Preparing to unpack .../208-libiterator-perl_0.03+ds1-2_all.deb ...
511s Unpacking libiterator-perl (0.03+ds1-2) ...
511s Selecting previously unselected package libiterator-util-perl.
511s Preparing to unpack .../209-libiterator-util-perl_0.02+ds1-2_all.deb ...
511s Unpacking libiterator-util-perl (0.02+ds1-2) ...
511s Selecting previously unselected package libdata-dpath-perl.
511s Preparing to unpack .../210-libdata-dpath-perl_0.60-1_all.deb ...
511s Unpacking libdata-dpath-perl (0.60-1) ...
511s Selecting previously unselected package libnet-domain-tld-perl.
511s Preparing to unpack .../211-libnet-domain-tld-perl_1.75-4_all.deb ...
511s Unpacking libnet-domain-tld-perl (1.75-4) ...
511s Selecting previously unselected package libdata-validate-domain-perl.
511s Preparing to unpack .../212-libdata-validate-domain-perl_0.15-1_all.deb ...
511s Unpacking libdata-validate-domain-perl (0.15-1) ...
511s Selecting previously unselected package libnet-ipv6addr-perl.
511s Preparing to unpack .../213-libnet-ipv6addr-perl_1.02-1_all.deb ...
511s Unpacking libnet-ipv6addr-perl (1.02-1) ...
511s Selecting previously unselected package libnet-netmask-perl.
511s Preparing to unpack .../214-libnet-netmask-perl_2.0002-2_all.deb ...
511s Unpacking libnet-netmask-perl (2.0002-2) ...
511s Selecting previously unselected package libnetaddr-ip-perl.
511s Preparing to unpack .../215-libnetaddr-ip-perl_4.079+dfsg-2build5_armhf.deb ...
511s Unpacking libnetaddr-ip-perl (4.079+dfsg-2build5) ...
511s Selecting previously unselected package libdata-validate-ip-perl.
511s Preparing to unpack .../216-libdata-validate-ip-perl_0.31-1_all.deb ...
511s Unpacking libdata-validate-ip-perl (0.31-1) ...
511s Selecting previously unselected package libdata-validate-uri-perl.
511s Preparing to unpack .../217-libdata-validate-uri-perl_0.07-3_all.deb ...
511s Unpacking libdata-validate-uri-perl (0.07-3) ...
511s Selecting previously unselected package libdevel-size-perl.
511s Preparing to unpack .../218-libdevel-size-perl_0.84-1build1_armhf.deb ...
511s Unpacking libdevel-size-perl (0.84-1build1) ...
511s Selecting previously unselected package libemail-address-xs-perl.
511s Preparing to unpack .../219-libemail-address-xs-perl_1.05-1build5_armhf.deb ...
511s Unpacking libemail-address-xs-perl (1.05-1build5) ...
511s Selecting previously unselected package libipc-system-simple-perl.
511s Preparing to unpack .../220-libipc-system-simple-perl_1.30-2_all.deb ...
511s Unpacking libipc-system-simple-perl (1.30-2) ...
511s Selecting previously unselected package libfile-basedir-perl.
511s Preparing to unpack .../221-libfile-basedir-perl_0.09-2_all.deb ...
511s Unpacking libfile-basedir-perl (0.09-2) ...
511s Selecting previously unselected package libfile-find-rule-perl.
511s Preparing to unpack .../222-libfile-find-rule-perl_0.34-3_all.deb ...
511s Unpacking libfile-find-rule-perl (0.34-3) ...
511s Selecting previously unselected package libio-string-perl.
511s Preparing to unpack .../223-libio-string-perl_1.08-4_all.deb ...
511s Unpacking libio-string-perl (1.08-4) ...
511s Selecting previously unselected package libfont-ttf-perl.
511s Preparing to unpack .../224-libfont-ttf-perl_1.06-2_all.deb ...
511s Unpacking libfont-ttf-perl (1.06-2) ...
511s Selecting previously unselected package libhtml-html5-entities-perl.
511s Preparing to unpack .../225-libhtml-html5-entities-perl_0.004-3_all.deb ...
511s Unpacking libhtml-html5-entities-perl (0.004-3) ...
512s Selecting previously unselected package libhtml-tokeparser-simple-perl.
512s Preparing to unpack .../226-libhtml-tokeparser-simple-perl_3.16-4_all.deb ...
512s Unpacking libhtml-tokeparser-simple-perl (3.16-4) ...
512s Selecting previously unselected package libipc-run3-perl.
512s Preparing to unpack .../227-libipc-run3-perl_0.049-1_all.deb ...
512s Unpacking libipc-run3-perl (0.049-1) ...
512s Selecting previously unselected package libjson-maybexs-perl.
512s Preparing to unpack .../228-libjson-maybexs-perl_1.004008-1_all.deb ...
512s Unpacking libjson-maybexs-perl (1.004008-1) ...
512s Selecting previously unselected package liblist-compare-perl.
512s Preparing to unpack .../229-liblist-compare-perl_0.55-2_all.deb ...
512s Unpacking liblist-compare-perl (0.55-2) ...
512s Selecting previously unselected package liblist-someutils-perl.
512s Preparing to unpack .../230-liblist-someutils-perl_0.59-1_all.deb ...
512s Unpacking liblist-someutils-perl (0.59-1) ...
512s Selecting previously unselected package liblist-utilsby-perl.
512s Preparing to unpack .../231-liblist-utilsby-perl_0.12-2_all.deb ...
512s Unpacking liblist-utilsby-perl (0.12-2) ...
512s Selecting previously unselected package libmldbm-perl.
512s Preparing to unpack .../232-libmldbm-perl_2.05-4_all.deb ...
512s Unpacking libmldbm-perl (2.05-4) ...
512s Selecting previously unselected package libclass-method-modifiers-perl.
512s Preparing to unpack .../233-libclass-method-modifiers-perl_2.15-1_all.deb ...
512s Unpacking libclass-method-modifiers-perl (2.15-1) ...
512s Selecting previously unselected package libimport-into-perl.
512s Preparing to unpack .../234-libimport-into-perl_1.002005-2_all.deb ...
512s Unpacking libimport-into-perl (1.002005-2) ...
512s Selecting previously unselected package librole-tiny-perl.
512s Preparing to unpack .../235-librole-tiny-perl_2.002004-1_all.deb ...
512s Unpacking librole-tiny-perl (2.002004-1) ...
512s Selecting previously unselected package libsub-quote-perl.
512s Preparing to unpack .../236-libsub-quote-perl_2.006008-1ubuntu1_all.deb ...
512s Unpacking libsub-quote-perl (2.006008-1ubuntu1) ...
512s Selecting previously unselected package libmoo-perl.
512s Preparing to unpack .../237-libmoo-perl_2.005005-1_all.deb ...
512s Unpacking libmoo-perl (2.005005-1) ...
512s Selecting previously unselected package libstrictures-perl.
512s Preparing to unpack .../238-libstrictures-perl_2.000006-1_all.deb ...
512s Unpacking libstrictures-perl (2.000006-1) ...
512s Selecting previously unselected package libmoox-aliases-perl.
512s Preparing to unpack .../239-libmoox-aliases-perl_0.001006-2_all.deb ...
512s Unpacking libmoox-aliases-perl (0.001006-2) ...
512s Selecting previously unselected package libperlio-gzip-perl.
512s Preparing to unpack .../240-libperlio-gzip-perl_0.20-1build5_armhf.deb ...
512s Unpacking libperlio-gzip-perl (0.20-1build5) ...
512s Selecting previously unselected package libperlio-utf8-strict-perl.
512s Preparing to unpack .../241-libperlio-utf8-strict-perl_0.010-1build4_armhf.deb ...
512s Unpacking libperlio-utf8-strict-perl (0.010-1build4) ...
512s Selecting previously unselected package libproc-processtable-perl:armhf.
512s Preparing to unpack .../242-libproc-processtable-perl_0.636-1build4_armhf.deb ...
512s Unpacking libproc-processtable-perl:armhf (0.636-1build4) ...
512s Selecting previously unselected package libregexp-wildcards-perl.
512s Preparing to unpack .../243-libregexp-wildcards-perl_1.05-3_all.deb ...
512s Unpacking libregexp-wildcards-perl (1.05-3) ...
512s Selecting previously unselected package libsereal-decoder-perl.
512s Preparing to unpack .../244-libsereal-decoder-perl_5.004+ds-1build4_armhf.deb ...
512s Unpacking libsereal-decoder-perl (5.004+ds-1build4) ...
512s Selecting previously unselected package libsereal-encoder-perl.
512s Preparing to unpack .../245-libsereal-encoder-perl_5.004+ds-1build4_armhf.deb ...
512s Unpacking libsereal-encoder-perl (5.004+ds-1build4) ...
512s Selecting previously unselected package libterm-readkey-perl.
512s Preparing to unpack .../246-libterm-readkey-perl_2.38-2build5_armhf.deb ...
512s Unpacking libterm-readkey-perl (2.38-2build5) ...
512s Selecting previously unselected package libtext-levenshteinxs-perl.
512s Preparing to unpack .../247-libtext-levenshteinxs-perl_0.03-5build5_armhf.deb ...
512s Unpacking libtext-levenshteinxs-perl (0.03-5build5) ...
512s Selecting previously unselected package libmarkdown2:armhf.
512s Preparing to unpack .../248-libmarkdown2_2.2.7-2.1_armhf.deb ...
512s Unpacking libmarkdown2:armhf (2.2.7-2.1) ...
512s Selecting previously unselected package libtext-markdown-discount-perl.
512s Preparing to unpack .../249-libtext-markdown-discount-perl_0.18-1_armhf.deb ...
512s Unpacking libtext-markdown-discount-perl (0.18-1) ...
513s Selecting previously unselected package libdata-messagepack-perl.
513s Preparing to unpack .../250-libdata-messagepack-perl_1.02-1build5_armhf.deb ...
513s Unpacking libdata-messagepack-perl (1.02-1build5) ...
513s Selecting previously unselected package libtext-xslate-perl:armhf.
513s Preparing to unpack .../251-libtext-xslate-perl_3.5.9-2build1_armhf.deb ...
513s Unpacking libtext-xslate-perl:armhf (3.5.9-2build1) ...
513s Selecting previously unselected package libtime-duration-perl.
513s Preparing to unpack .../252-libtime-duration-perl_1.21-2_all.deb ...
513s Unpacking libtime-duration-perl (1.21-2) ...
513s Selecting previously unselected package libtime-moment-perl.
513s Preparing to unpack .../253-libtime-moment-perl_0.44-2build5_armhf.deb ...
513s Unpacking libtime-moment-perl (0.44-2build5) ...
513s Selecting previously unselected package libunicode-utf8-perl.
513s Preparing to unpack .../254-libunicode-utf8-perl_0.62-2build4_armhf.deb ...
513s Unpacking libunicode-utf8-perl (0.62-2build4) ...
513s Selecting previously unselected package libcgi-pm-perl.
513s Preparing to unpack .../255-libcgi-pm-perl_4.67-1_all.deb ...
513s Unpacking libcgi-pm-perl (4.67-1) ...
513s Selecting previously unselected package libhtml-form-perl.
513s Preparing to unpack .../256-libhtml-form-perl_6.12-1_all.deb ...
513s Unpacking libhtml-form-perl (6.12-1) ...
513s Selecting previously unselected package libwww-mechanize-perl.
513s Preparing to unpack .../257-libwww-mechanize-perl_2.19-1ubuntu1_all.deb ...
513s Unpacking libwww-mechanize-perl (2.19-1ubuntu1) ...
513s Selecting previously unselected package libxml-namespacesupport-perl.
513s Preparing to unpack .../258-libxml-namespacesupport-perl_1.12-2_all.deb ...
513s Unpacking libxml-namespacesupport-perl (1.12-2) ...
513s Selecting previously unselected package libxml-sax-base-perl.
513s Preparing to unpack .../259-libxml-sax-base-perl_1.09-3_all.deb ...
513s Unpacking libxml-sax-base-perl (1.09-3) ...
513s Selecting previously unselected package libxml-sax-perl.
513s Preparing to unpack .../260-libxml-sax-perl_1.02+dfsg-4_all.deb ...
513s Unpacking libxml-sax-perl (1.02+dfsg-4) ...
513s Selecting previously unselected package libxml-libxml-perl.
513s Preparing to unpack .../261-libxml-libxml-perl_2.0207+dfsg+really+2.0134-5build1_armhf.deb ...
513s Unpacking libxml-libxml-perl (2.0207+dfsg+really+2.0134-5build1) ...
513s Selecting previously unselected package lzip.
513s Preparing to unpack .../262-lzip_1.25-2_armhf.deb ...
513s Unpacking lzip (1.25-2) ...
513s Selecting previously unselected package lzop.
513s Preparing to unpack .../263-lzop_1.04-2build3_armhf.deb ...
513s Unpacking lzop (1.04-2build3) ...
513s Selecting previously unselected package patchutils.
513s Preparing to unpack .../264-patchutils_0.4.2-1build3_armhf.deb ...
513s Unpacking patchutils (0.4.2-1build3) ...
513s Selecting previously unselected package t1utils.
513s Preparing to unpack .../265-t1utils_1.41-4build3_armhf.deb ...
513s Unpacking t1utils (1.41-4build3) ...
513s Selecting previously unselected package unzip.
513s Preparing to unpack .../266-unzip_6.0-28ubuntu6_armhf.deb ...
513s Unpacking unzip (6.0-28ubuntu6) ...
513s Selecting previously unselected package lintian.
513s Preparing to unpack .../267-lintian_2.121.1+nmu1ubuntu2_all.deb ...
513s Unpacking lintian (2.121.1+nmu1ubuntu2) ...
514s Selecting previously unselected package libconfig-model-dpkg-perl.
514s Preparing to unpack .../268-libconfig-model-dpkg-perl_3.010_all.deb ...
514s Unpacking libconfig-model-dpkg-perl (3.010) ...
514s Selecting previously unselected package libconvert-binhex-perl.
514s Preparing to unpack .../269-libconvert-binhex-perl_1.125-3_all.deb ...
514s Unpacking libconvert-binhex-perl (1.125-3) ...
514s Selecting previously unselected package libnet-smtp-ssl-perl.
514s Preparing to unpack .../270-libnet-smtp-ssl-perl_1.04-2_all.deb ...
514s Unpacking libnet-smtp-ssl-perl (1.04-2) ...
514s Selecting previously unselected package libmailtools-perl.
514s Preparing to unpack .../271-libmailtools-perl_2.22-1_all.deb ...
514s Unpacking libmailtools-perl (2.22-1) ...
514s Selecting previously unselected package libmime-tools-perl.
514s Preparing to unpack .../272-libmime-tools-perl_5.515-1_all.deb ...
514s Unpacking libmime-tools-perl (5.515-1) ...
514s Selecting previously unselected package libsuitesparseconfig7:armhf.
514s Preparing to unpack .../273-libsuitesparseconfig7_1%3a7.8.3+dfsg-3_armhf.deb ...
514s Unpacking libsuitesparseconfig7:armhf (1:7.8.3+dfsg-3) ...
514s Selecting previously unselected package libamd3:armhf.
514s Preparing to unpack .../274-libamd3_1%3a7.8.3+dfsg-3_armhf.deb ...
514s Unpacking libamd3:armhf (1:7.8.3+dfsg-3) ...
514s Selecting previously unselected package libblas3:armhf.
514s Preparing to unpack .../275-libblas3_3.12.1-2_armhf.deb ...
514s Unpacking libblas3:armhf (3.12.1-2) ...
514s Selecting previously unselected package libgfortran5:armhf.
514s Preparing to unpack .../276-libgfortran5_15-20250222-0ubuntu1_armhf.deb ...
514s Unpacking libgfortran5:armhf (15-20250222-0ubuntu1) ...
514s Selecting previously unselected package liblapack3:armhf.
514s Preparing to unpack .../277-liblapack3_3.12.1-2_armhf.deb ...
514s Unpacking liblapack3:armhf (3.12.1-2) ...
514s Selecting previously unselected package libarpack2t64:armhf.
514s Preparing to unpack .../278-libarpack2t64_3.9.1-4_armhf.deb ...
514s Unpacking libarpack2t64:armhf (3.9.1-4) ...
514s Selecting previously unselected package libccolamd3:armhf.
514s Preparing to unpack .../279-libccolamd3_1%3a7.8.3+dfsg-3_armhf.deb ...
514s Unpacking libccolamd3:armhf (1:7.8.3+dfsg-3) ...
514s Selecting previously unselected package libcamd3:armhf.
514s Preparing to unpack .../280-libcamd3_1%3a7.8.3+dfsg-3_armhf.deb ...
514s Unpacking libcamd3:armhf (1:7.8.3+dfsg-3) ...
514s Selecting previously unselected package libcolamd3:armhf.
514s Preparing to unpack .../281-libcolamd3_1%3a7.8.3+dfsg-3_armhf.deb ...
514s Unpacking libcolamd3:armhf (1:7.8.3+dfsg-3) ...
514s Selecting previously unselected package libcholmod5:armhf.
514s Preparing to unpack .../282-libcholmod5_1%3a7.8.3+dfsg-3_armhf.deb ...
514s Unpacking libcholmod5:armhf (1:7.8.3+dfsg-3) ...
514s Selecting previously unselected package libcxsparse4:armhf.
514s Preparing to unpack .../283-libcxsparse4_1%3a7.8.3+dfsg-3_armhf.deb ...
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514s Selecting previously unselected package libfftw3-double3:armhf.
514s Preparing to unpack .../284-libfftw3-double3_3.3.10-2fakesync1build1_armhf.deb ...
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514s Selecting previously unselected package libfftw3-single3:armhf.
514s Preparing to unpack .../285-libfftw3-single3_3.3.10-2fakesync1build1_armhf.deb ...
514s Unpacking libfftw3-single3:armhf (3.3.10-2fakesync1build1) ...
515s Selecting previously unselected package libxfixes3:armhf.
515s Preparing to unpack .../286-libxfixes3_1%3a6.0.0-2build1_armhf.deb ...
515s Unpacking libxfixes3:armhf (1:6.0.0-2build1) ...
515s Selecting previously unselected package libxcursor1:armhf.
515s Preparing to unpack .../287-libxcursor1_1%3a1.2.3-1_armhf.deb ...
515s Unpacking libxcursor1:armhf (1:1.2.3-1) ...
515s Selecting previously unselected package libxft2:armhf.
515s Preparing to unpack .../288-libxft2_2.3.6-1build1_armhf.deb ...
515s Unpacking libxft2:armhf (2.3.6-1build1) ...
515s Selecting previously unselected package libxinerama1:armhf.
515s Preparing to unpack .../289-libxinerama1_2%3a1.1.4-3build1_armhf.deb ...
515s Unpacking libxinerama1:armhf (2:1.1.4-3build1) ...
515s Selecting previously unselected package libfltk1.3t64:armhf.
515s Preparing to unpack .../290-libfltk1.3t64_1.3.8-6.1build2_armhf.deb ...
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515s Selecting previously unselected package libglvnd0:armhf.
515s Preparing to unpack .../291-libglvnd0_1.7.0-1build1_armhf.deb ...
515s Unpacking libglvnd0:armhf (1.7.0-1build1) ...
515s Selecting previously unselected package libx11-xcb1:armhf.
515s Preparing to unpack .../292-libx11-xcb1_2%3a1.8.10-2_armhf.deb ...
515s Unpacking libx11-xcb1:armhf (2:1.8.10-2) ...
515s Selecting previously unselected package libxcb-dri3-0:armhf.
515s Preparing to unpack .../293-libxcb-dri3-0_1.17.0-2_armhf.deb ...
515s Unpacking libxcb-dri3-0:armhf (1.17.0-2) ...
515s Selecting previously unselected package libxcb-glx0:armhf.
515s Preparing to unpack .../294-libxcb-glx0_1.17.0-2_armhf.deb ...
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515s Selecting previously unselected package libxcb-present0:armhf.
515s Preparing to unpack .../295-libxcb-present0_1.17.0-2_armhf.deb ...
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515s Selecting previously unselected package libxcb-xfixes0:armhf.
515s Preparing to unpack .../296-libxcb-xfixes0_1.17.0-2_armhf.deb ...
515s Unpacking libxcb-xfixes0:armhf (1.17.0-2) ...
515s Selecting previously unselected package libxxf86vm1:armhf.
515s Preparing to unpack .../297-libxxf86vm1_1%3a1.1.4-1build4_armhf.deb ...
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515s Selecting previously unselected package libllvm19:armhf.
515s Preparing to unpack .../298-libllvm19_1%3a19.1.7-1ubuntu2_armhf.deb ...
515s Unpacking libllvm19:armhf (1:19.1.7-1ubuntu2) ...
516s Selecting previously unselected package libxcb-randr0:armhf.
516s Preparing to unpack .../299-libxcb-randr0_1.17.0-2_armhf.deb ...
516s Unpacking libxcb-randr0:armhf (1.17.0-2) ...
516s Selecting previously unselected package libxcb-sync1:armhf.
516s Preparing to unpack .../300-libxcb-sync1_1.17.0-2_armhf.deb ...
516s Unpacking libxcb-sync1:armhf (1.17.0-2) ...
516s Selecting previously unselected package libxshmfence1:armhf.
516s Preparing to unpack .../301-libxshmfence1_1.3-1build5_armhf.deb ...
516s Unpacking libxshmfence1:armhf (1.3-1build5) ...
516s Selecting previously unselected package mesa-libgallium:armhf.
516s Preparing to unpack .../302-mesa-libgallium_25.0.1-2ubuntu1_armhf.deb ...
516s Unpacking mesa-libgallium:armhf (25.0.1-2ubuntu1) ...
516s Selecting previously unselected package libwayland-server0:armhf.
516s Preparing to unpack .../303-libwayland-server0_1.23.1-3_armhf.deb ...
516s Unpacking libwayland-server0:armhf (1.23.1-3) ...
516s Selecting previously unselected package libgbm1:armhf.
516s Preparing to unpack .../304-libgbm1_25.0.1-2ubuntu1_armhf.deb ...
516s Unpacking libgbm1:armhf (25.0.1-2ubuntu1) ...
517s Selecting previously unselected package libvulkan1:armhf.
517s Preparing to unpack .../305-libvulkan1_1.4.304.0-1_armhf.deb ...
517s Unpacking libvulkan1:armhf (1.4.304.0-1) ...
517s Selecting previously unselected package libgl1-mesa-dri:armhf.
517s Preparing to unpack .../306-libgl1-mesa-dri_25.0.1-2ubuntu1_armhf.deb ...
517s Unpacking libgl1-mesa-dri:armhf (25.0.1-2ubuntu1) ...
517s Selecting previously unselected package libglx-mesa0:armhf.
517s Preparing to unpack .../307-libglx-mesa0_25.0.1-2ubuntu1_armhf.deb ...
517s Unpacking libglx-mesa0:armhf (25.0.1-2ubuntu1) ...
517s Selecting previously unselected package libglx0:armhf.
517s Preparing to unpack .../308-libglx0_1.7.0-1build1_armhf.deb ...
517s Unpacking libglx0:armhf (1.7.0-1build1) ...
517s Selecting previously unselected package libgl1:armhf.
517s Preparing to unpack .../309-libgl1_1.7.0-1build1_armhf.deb ...
517s Unpacking libgl1:armhf (1.7.0-1build1) ...
517s Selecting previously unselected package libfltk-gl1.3t64:armhf.
517s Preparing to unpack .../310-libfltk-gl1.3t64_1.3.8-6.1build2_armhf.deb ...
517s Unpacking libfltk-gl1.3t64:armhf (1.3.8-6.1build2) ...
517s Selecting previously unselected package libgl2ps1.4.
517s Preparing to unpack .../311-libgl2ps1.4_1.4.2+dfsg1-2build1_armhf.deb ...
517s Unpacking libgl2ps1.4 (1.4.2+dfsg1-2build1) ...
517s Selecting previously unselected package libltdl7:armhf.
517s Preparing to unpack .../312-libltdl7_2.5.4-4_armhf.deb ...
517s Unpacking libltdl7:armhf (2.5.4-4) ...
517s Selecting previously unselected package libglpk40:armhf.
517s Preparing to unpack .../313-libglpk40_5.0-1build2_armhf.deb ...
517s Unpacking libglpk40:armhf (5.0-1build2) ...
517s Selecting previously unselected package libopengl0:armhf.
517s Preparing to unpack .../314-libopengl0_1.7.0-1build1_armhf.deb ...
517s Unpacking libopengl0:armhf (1.7.0-1build1) ...
517s Selecting previously unselected package libglu1-mesa:armhf.
517s Preparing to unpack .../315-libglu1-mesa_9.0.2-1.1build1_armhf.deb ...
517s Unpacking libglu1-mesa:armhf (9.0.2-1.1build1) ...
517s Selecting previously unselected package liblcms2-2:armhf.
517s Preparing to unpack .../316-liblcms2-2_2.16-2_armhf.deb ...
517s Unpacking liblcms2-2:armhf (2.16-2) ...
517s Selecting previously unselected package libjxl0.11:armhf.
517s Preparing to unpack .../317-libjxl0.11_0.11.1-1_armhf.deb ...
517s Unpacking libjxl0.11:armhf (0.11.1-1) ...
517s Selecting previously unselected package libwmflite-0.2-7:armhf.
517s Preparing to unpack .../318-libwmflite-0.2-7_0.2.13-1.1build3_armhf.deb ...
517s Unpacking libwmflite-0.2-7:armhf (0.2.13-1.1build3) ...
517s Selecting previously unselected package libgraphicsmagick-q16-3t64.
517s Preparing to unpack .../319-libgraphicsmagick-q16-3t64_1.4+really1.3.45+hg17689-1_armhf.deb ...
517s Unpacking libgraphicsmagick-q16-3t64 (1.4+really1.3.45+hg17689-1) ...
517s Selecting previously unselected package libgraphicsmagick++-q16-12t64.
517s Preparing to unpack .../320-libgraphicsmagick++-q16-12t64_1.4+really1.3.45+hg17689-1_armhf.deb ...
517s Unpacking libgraphicsmagick++-q16-12t64 (1.4+really1.3.45+hg17689-1) ...
517s Selecting previously unselected package libaec0:armhf.
517s Preparing to unpack .../321-libaec0_1.1.3-1_armhf.deb ...
517s Unpacking libaec0:armhf (1.1.3-1) ...
517s Selecting previously unselected package libsz2:armhf.
517s Preparing to unpack .../322-libsz2_1.1.3-1_armhf.deb ...
517s Unpacking libsz2:armhf (1.1.3-1) ...
517s Selecting previously unselected package libhdf5-310:armhf.
517s Preparing to unpack .../323-libhdf5-310_1.14.5+repack-3_armhf.deb ...
517s Unpacking libhdf5-310:armhf (1.14.5+repack-3) ...
517s Selecting previously unselected package libasound2-data.
517s Preparing to unpack .../324-libasound2-data_1.2.13-1build1_all.deb ...
517s Unpacking libasound2-data (1.2.13-1build1) ...
517s Selecting previously unselected package libasound2t64:armhf.
517s Preparing to unpack .../325-libasound2t64_1.2.13-1build1_armhf.deb ...
517s Unpacking libasound2t64:armhf (1.2.13-1build1) ...
517s Selecting previously unselected package libopus0:armhf.
517s Preparing to unpack .../326-libopus0_1.5.2-2_armhf.deb ...
517s Unpacking libopus0:armhf (1.5.2-2) ...
518s Selecting previously unselected package libsamplerate0:armhf.
518s Preparing to unpack .../327-libsamplerate0_0.2.2-4build1_armhf.deb ...
518s Unpacking libsamplerate0:armhf (0.2.2-4build1) ...
518s Selecting previously unselected package libjack-jackd2-0:armhf.
518s Preparing to unpack .../328-libjack-jackd2-0_1.9.22~dfsg-4_armhf.deb ...
518s Unpacking libjack-jackd2-0:armhf (1.9.22~dfsg-4) ...
518s Selecting previously unselected package libportaudio2:armhf.
518s Preparing to unpack .../329-libportaudio2_19.6.0-1.2build3_armhf.deb ...
518s Unpacking libportaudio2:armhf (19.6.0-1.2build3) ...
518s Selecting previously unselected package libqhull-r8.0:armhf.
518s Preparing to unpack .../330-libqhull-r8.0_2020.2-6build1_armhf.deb ...
518s Unpacking libqhull-r8.0:armhf (2020.2-6build1) ...
518s Selecting previously unselected package libqrupdate1:armhf.
518s Preparing to unpack .../331-libqrupdate1_1.1.5-1_armhf.deb ...
518s Unpacking libqrupdate1:armhf (1.1.5-1) ...
518s Selecting previously unselected package libqscintilla2-qt6-l10n.
518s Preparing to unpack .../332-libqscintilla2-qt6-l10n_2.14.1+dfsg-1build4_all.deb ...
518s Unpacking libqscintilla2-qt6-l10n (2.14.1+dfsg-1build4) ...
518s Selecting previously unselected package libb2-1:armhf.
518s Preparing to unpack .../333-libb2-1_0.98.1-1.1build1_armhf.deb ...
518s Unpacking libb2-1:armhf (0.98.1-1.1build1) ...
518s Selecting previously unselected package libdouble-conversion3:armhf.
518s Preparing to unpack .../334-libdouble-conversion3_3.3.1-1_armhf.deb ...
518s Unpacking libdouble-conversion3:armhf (3.3.1-1) ...
518s Selecting previously unselected package libpcre2-16-0:armhf.
518s Preparing to unpack .../335-libpcre2-16-0_10.45-1_armhf.deb ...
518s Unpacking libpcre2-16-0:armhf (10.45-1) ...
518s Selecting previously unselected package libqt6core6t64:armhf.
518s Preparing to unpack .../336-libqt6core6t64_6.8.2+dfsg-5_armhf.deb ...
518s Unpacking libqt6core6t64:armhf (6.8.2+dfsg-5) ...
518s Selecting previously unselected package libwayland-client0:armhf.
518s Preparing to unpack .../337-libwayland-client0_1.23.1-3_armhf.deb ...
518s Unpacking libwayland-client0:armhf (1.23.1-3) ...
518s Selecting previously unselected package libegl-mesa0:armhf.
518s Preparing to unpack .../338-libegl-mesa0_25.0.1-2ubuntu1_armhf.deb ...
518s Unpacking libegl-mesa0:armhf (25.0.1-2ubuntu1) ...
518s Selecting previously unselected package libegl1:armhf.
518s Preparing to unpack .../339-libegl1_1.7.0-1build1_armhf.deb ...
518s Unpacking libegl1:armhf (1.7.0-1build1) ...
518s Selecting previously unselected package x11-common.
518s Preparing to unpack .../340-x11-common_1%3a7.7+23ubuntu3_all.deb ...
518s Unpacking x11-common (1:7.7+23ubuntu3) ...
518s Selecting previously unselected package libice6:armhf.
518s Preparing to unpack .../341-libice6_2%3a1.1.1-1_armhf.deb ...
518s Unpacking libice6:armhf (2:1.1.1-1) ...
518s Selecting previously unselected package libmtdev1t64:armhf.
518s Preparing to unpack .../342-libmtdev1t64_1.1.7-1_armhf.deb ...
518s Unpacking libmtdev1t64:armhf (1.1.7-1) ...
518s Selecting previously unselected package libwacom-common.
518s Preparing to unpack .../343-libwacom-common_2.14.0-1_all.deb ...
518s Unpacking libwacom-common (2.14.0-1) ...
518s Selecting previously unselected package libwacom9:armhf.
518s Preparing to unpack .../344-libwacom9_2.14.0-1_armhf.deb ...
518s Unpacking libwacom9:armhf (2.14.0-1) ...
518s Selecting previously unselected package libinput-bin.
518s Preparing to unpack .../345-libinput-bin_1.27.1-1_armhf.deb ...
518s Unpacking libinput-bin (1.27.1-1) ...
518s Selecting previously unselected package libinput10:armhf.
518s Preparing to unpack .../346-libinput10_1.27.1-1_armhf.deb ...
518s Unpacking libinput10:armhf (1.27.1-1) ...
518s Selecting previously unselected package libmd4c0:armhf.
519s Preparing to unpack .../347-libmd4c0_0.5.2-2_armhf.deb ...
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519s Selecting previously unselected package libqt6dbus6:armhf.
519s Preparing to unpack .../348-libqt6dbus6_6.8.2+dfsg-5_armhf.deb ...
519s Unpacking libqt6dbus6:armhf (6.8.2+dfsg-5) ...
519s Selecting previously unselected package libsm6:armhf.
519s Preparing to unpack .../349-libsm6_2%3a1.2.4-1_armhf.deb ...
519s Unpacking libsm6:armhf (2:1.2.4-1) ...
519s Selecting previously unselected package libts0t64:armhf.
519s Preparing to unpack .../350-libts0t64_1.22-1.1build1_armhf.deb ...
519s Unpacking libts0t64:armhf (1.22-1.1build1) ...
519s Selecting previously unselected package libxcb-util1:armhf.
519s Preparing to unpack .../351-libxcb-util1_0.4.1-1_armhf.deb ...
519s Unpacking libxcb-util1:armhf (0.4.1-1) ...
519s Selecting previously unselected package libxcb-image0:armhf.
519s Preparing to unpack .../352-libxcb-image0_0.4.0-2build1_armhf.deb ...
519s Unpacking libxcb-image0:armhf (0.4.0-2build1) ...
519s Selecting previously unselected package libxcb-render-util0:armhf.
519s Preparing to unpack .../353-libxcb-render-util0_0.3.10-1_armhf.deb ...
519s Unpacking libxcb-render-util0:armhf (0.3.10-1) ...
519s Selecting previously unselected package libxcb-cursor0:armhf.
519s Preparing to unpack .../354-libxcb-cursor0_0.1.5-1_armhf.deb ...
519s Unpacking libxcb-cursor0:armhf (0.1.5-1) ...
519s Selecting previously unselected package libxcb-icccm4:armhf.
519s Preparing to unpack .../355-libxcb-icccm4_0.4.2-1_armhf.deb ...
519s Unpacking libxcb-icccm4:armhf (0.4.2-1) ...
519s Selecting previously unselected package libxcb-keysyms1:armhf.
519s Preparing to unpack .../356-libxcb-keysyms1_0.4.1-1_armhf.deb ...
519s Unpacking libxcb-keysyms1:armhf (0.4.1-1) ...
519s Selecting previously unselected package libxcb-shape0:armhf.
519s Preparing to unpack .../357-libxcb-shape0_1.17.0-2_armhf.deb ...
519s Unpacking libxcb-shape0:armhf (1.17.0-2) ...
519s Selecting previously unselected package libxcb-xinput0:armhf.
519s Preparing to unpack .../358-libxcb-xinput0_1.17.0-2_armhf.deb ...
519s Unpacking libxcb-xinput0:armhf (1.17.0-2) ...
519s Selecting previously unselected package libxcb-xkb1:armhf.
519s Preparing to unpack .../359-libxcb-xkb1_1.17.0-2_armhf.deb ...
519s Unpacking libxcb-xkb1:armhf (1.17.0-2) ...
519s Selecting previously unselected package libxkbcommon-x11-0:armhf.
519s Preparing to unpack .../360-libxkbcommon-x11-0_1.7.0-2_armhf.deb ...
519s Unpacking libxkbcommon-x11-0:armhf (1.7.0-2) ...
519s Selecting previously unselected package libqt6gui6:armhf.
519s Preparing to unpack .../361-libqt6gui6_6.8.2+dfsg-5_armhf.deb ...
519s Unpacking libqt6gui6:armhf (6.8.2+dfsg-5) ...
519s Selecting previously unselected package libavahi-common-data:armhf.
519s Preparing to unpack .../362-libavahi-common-data_0.8-16ubuntu1_armhf.deb ...
519s Unpacking libavahi-common-data:armhf (0.8-16ubuntu1) ...
519s Selecting previously unselected package libavahi-common3:armhf.
519s Preparing to unpack .../363-libavahi-common3_0.8-16ubuntu1_armhf.deb ...
519s Unpacking libavahi-common3:armhf (0.8-16ubuntu1) ...
519s Selecting previously unselected package libavahi-client3:armhf.
519s Preparing to unpack .../364-libavahi-client3_0.8-16ubuntu1_armhf.deb ...
519s Unpacking libavahi-client3:armhf (0.8-16ubuntu1) ...
519s Selecting previously unselected package libcups2t64:armhf.
519s Preparing to unpack .../365-libcups2t64_2.4.11-0ubuntu2_armhf.deb ...
519s Unpacking libcups2t64:armhf (2.4.11-0ubuntu2) ...
519s Selecting previously unselected package libqt6widgets6:armhf.
519s Preparing to unpack .../366-libqt6widgets6_6.8.2+dfsg-5_armhf.deb ...
519s Unpacking libqt6widgets6:armhf (6.8.2+dfsg-5) ...
519s Selecting previously unselected package libqt6printsupport6:armhf.
519s Preparing to unpack .../367-libqt6printsupport6_6.8.2+dfsg-5_armhf.deb ...
519s Unpacking libqt6printsupport6:armhf (6.8.2+dfsg-5) ...
519s Selecting previously unselected package libqscintilla2-qt6-15:armhf.
520s Preparing to unpack .../368-libqscintilla2-qt6-15_2.14.1+dfsg-1build4_armhf.deb ...
520s Unpacking libqscintilla2-qt6-15:armhf (2.14.1+dfsg-1build4) ...
520s Selecting previously unselected package libqt6core5compat6:armhf.
520s Preparing to unpack .../369-libqt6core5compat6_6.8.2-3_armhf.deb ...
520s Unpacking libqt6core5compat6:armhf (6.8.2-3) ...
520s Selecting previously unselected package libqt6sql6:armhf.
520s Preparing to unpack .../370-libqt6sql6_6.8.2+dfsg-5_armhf.deb ...
520s Unpacking libqt6sql6:armhf (6.8.2+dfsg-5) ...
520s Selecting previously unselected package libqt6help6:armhf.
520s Preparing to unpack .../371-libqt6help6_6.8.2-3_armhf.deb ...
520s Unpacking libqt6help6:armhf (6.8.2-3) ...
520s Selecting previously unselected package libduktape207:armhf.
520s Preparing to unpack .../372-libduktape207_2.7.0+tests-0ubuntu3_armhf.deb ...
520s Unpacking libduktape207:armhf (2.7.0+tests-0ubuntu3) ...
520s Selecting previously unselected package libproxy1v5:armhf.
520s Preparing to unpack .../373-libproxy1v5_0.5.9-1_armhf.deb ...
520s Unpacking libproxy1v5:armhf (0.5.9-1) ...
520s Selecting previously unselected package libqt6network6:armhf.
520s Preparing to unpack .../374-libqt6network6_6.8.2+dfsg-5_armhf.deb ...
520s Unpacking libqt6network6:armhf (6.8.2+dfsg-5) ...
520s Selecting previously unselected package libqt6opengl6:armhf.
520s Preparing to unpack .../375-libqt6opengl6_6.8.2+dfsg-5_armhf.deb ...
520s Unpacking libqt6opengl6:armhf (6.8.2+dfsg-5) ...
520s Selecting previously unselected package libqt6openglwidgets6:armhf.
520s Preparing to unpack .../376-libqt6openglwidgets6_6.8.2+dfsg-5_armhf.deb ...
520s Unpacking libqt6openglwidgets6:armhf (6.8.2+dfsg-5) ...
520s Selecting previously unselected package libqt6xml6:armhf.
520s Preparing to unpack .../377-libqt6xml6_6.8.2+dfsg-5_armhf.deb ...
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520s Selecting previously unselected package libogg0:armhf.
520s Preparing to unpack .../378-libogg0_1.3.5-3build1_armhf.deb ...
520s Unpacking libogg0:armhf (1.3.5-3build1) ...
520s Selecting previously unselected package libflac12t64:armhf.
520s Preparing to unpack .../379-libflac12t64_1.4.3+ds-4_armhf.deb ...
520s Unpacking libflac12t64:armhf (1.4.3+ds-4) ...
520s Selecting previously unselected package libmp3lame0:armhf.
520s Preparing to unpack .../380-libmp3lame0_3.100-6build1_armhf.deb ...
520s Unpacking libmp3lame0:armhf (3.100-6build1) ...
520s Selecting previously unselected package libmpg123-0t64:armhf.
520s Preparing to unpack .../381-libmpg123-0t64_1.32.10-1_armhf.deb ...
520s Unpacking libmpg123-0t64:armhf (1.32.10-1) ...
520s Selecting previously unselected package libvorbis0a:armhf.
520s Preparing to unpack .../382-libvorbis0a_1.3.7-2_armhf.deb ...
520s Unpacking libvorbis0a:armhf (1.3.7-2) ...
520s Selecting previously unselected package libvorbisenc2:armhf.
520s Preparing to unpack .../383-libvorbisenc2_1.3.7-2_armhf.deb ...
520s Unpacking libvorbisenc2:armhf (1.3.7-2) ...
520s Selecting previously unselected package libsndfile1:armhf.
520s Preparing to unpack .../384-libsndfile1_1.2.2-2_armhf.deb ...
520s Unpacking libsndfile1:armhf (1.2.2-2) ...
520s Selecting previously unselected package libspqr4:armhf.
520s Preparing to unpack .../385-libspqr4_1%3a7.8.3+dfsg-3_armhf.deb ...
520s Unpacking libspqr4:armhf (1:7.8.3+dfsg-3) ...
520s Selecting previously unselected package libumfpack6:armhf.
520s Preparing to unpack .../386-libumfpack6_1%3a7.8.3+dfsg-3_armhf.deb ...
520s Unpacking libumfpack6:armhf (1:7.8.3+dfsg-3) ...
520s Selecting previously unselected package libtext-unidecode-perl.
520s Preparing to unpack .../387-libtext-unidecode-perl_1.30-3_all.deb ...
520s Unpacking libtext-unidecode-perl (1.30-3) ...
520s Selecting previously unselected package texinfo-lib.
520s Preparing to unpack .../388-texinfo-lib_7.1.1-1_armhf.deb ...
520s Unpacking texinfo-lib (7.1.1-1) ...
520s Selecting previously unselected package tex-common.
521s Preparing to unpack .../389-tex-common_6.19_all.deb ...
521s Unpacking tex-common (6.19) ...
521s Selecting previously unselected package texinfo.
521s Preparing to unpack .../390-texinfo_7.1.1-1_all.deb ...
521s Unpacking texinfo (7.1.1-1) ...
521s Selecting previously unselected package octave-common.
521s Preparing to unpack .../391-octave-common_9.4.0-1_all.deb ...
521s Unpacking octave-common (9.4.0-1) ...
521s Selecting previously unselected package octave.
521s Preparing to unpack .../392-octave_9.4.0-1_armhf.deb ...
521s Unpacking octave (9.4.0-1) ...
521s Selecting previously unselected package libncurses-dev:armhf.
521s Preparing to unpack .../393-libncurses-dev_6.5+20250216-2_armhf.deb ...
521s Unpacking libncurses-dev:armhf (6.5+20250216-2) ...
521s Selecting previously unselected package libreadline-dev:armhf.
521s Preparing to unpack .../394-libreadline-dev_8.2-6_armhf.deb ...
521s Unpacking libreadline-dev:armhf (8.2-6) ...
521s Selecting previously unselected package libhdf5-fortran-310:armhf.
521s Preparing to unpack .../395-libhdf5-fortran-310_1.14.5+repack-3_armhf.deb ...
521s Unpacking libhdf5-fortran-310:armhf (1.14.5+repack-3) ...
521s Selecting previously unselected package libhdf5-hl-310:armhf.
521s Preparing to unpack .../396-libhdf5-hl-310_1.14.5+repack-3_armhf.deb ...
521s Unpacking libhdf5-hl-310:armhf (1.14.5+repack-3) ...
521s Selecting previously unselected package libhdf5-hl-fortran-310:armhf.
521s Preparing to unpack .../397-libhdf5-hl-fortran-310_1.14.5+repack-3_armhf.deb ...
521s Unpacking libhdf5-hl-fortran-310:armhf (1.14.5+repack-3) ...
521s Selecting previously unselected package libhdf5-cpp-310:armhf.
522s Preparing to unpack .../398-libhdf5-cpp-310_1.14.5+repack-3_armhf.deb ...
522s Unpacking libhdf5-cpp-310:armhf (1.14.5+repack-3) ...
522s Selecting previously unselected package libhdf5-hl-cpp-310:armhf.
522s Preparing to unpack .../399-libhdf5-hl-cpp-310_1.14.5+repack-3_armhf.deb ...
522s Unpacking libhdf5-hl-cpp-310:armhf (1.14.5+repack-3) ...
522s Selecting previously unselected package zlib1g-dev:armhf.
522s Preparing to unpack .../400-zlib1g-dev_1%3a1.3.dfsg+really1.3.1-1ubuntu1_armhf.deb ...
522s Unpacking zlib1g-dev:armhf (1:1.3.dfsg+really1.3.1-1ubuntu1) ...
522s Selecting previously unselected package libjpeg-turbo8-dev:armhf.
522s Preparing to unpack .../401-libjpeg-turbo8-dev_2.1.5-3ubuntu2_armhf.deb ...
522s Unpacking libjpeg-turbo8-dev:armhf (2.1.5-3ubuntu2) ...
522s Selecting previously unselected package libjpeg8-dev:armhf.
522s Preparing to unpack .../402-libjpeg8-dev_8c-2ubuntu11_armhf.deb ...
522s Unpacking libjpeg8-dev:armhf (8c-2ubuntu11) ...
522s Selecting previously unselected package libjpeg-dev:armhf.
522s Preparing to unpack .../403-libjpeg-dev_8c-2ubuntu11_armhf.deb ...
522s Unpacking libjpeg-dev:armhf (8c-2ubuntu11) ...
522s Selecting previously unselected package libaec-dev:armhf.
522s Preparing to unpack .../404-libaec-dev_1.1.3-1_armhf.deb ...
522s Unpacking libaec-dev:armhf (1.1.3-1) ...
522s Selecting previously unselected package libbrotli-dev:armhf.
522s Preparing to unpack .../405-libbrotli-dev_1.1.0-2build4_armhf.deb ...
522s Unpacking libbrotli-dev:armhf (1.1.0-2build4) ...
522s Selecting previously unselected package libidn2-dev:armhf.
522s Preparing to unpack .../406-libidn2-dev_2.3.7-2build2_armhf.deb ...
522s Unpacking libidn2-dev:armhf (2.3.7-2build2) ...
522s Selecting previously unselected package comerr-dev:armhf.
522s Preparing to unpack .../407-comerr-dev_2.1-1.47.2-1ubuntu1_armhf.deb ...
522s Unpacking comerr-dev:armhf (2.1-1.47.2-1ubuntu1) ...
522s Selecting previously unselected package libgssrpc4t64:armhf.
522s Preparing to unpack .../408-libgssrpc4t64_1.21.3-4ubuntu2_armhf.deb ...
522s Unpacking libgssrpc4t64:armhf (1.21.3-4ubuntu2) ...
522s Selecting previously unselected package libkadm5clnt-mit12:armhf.
522s Preparing to unpack .../409-libkadm5clnt-mit12_1.21.3-4ubuntu2_armhf.deb ...
522s Unpacking libkadm5clnt-mit12:armhf (1.21.3-4ubuntu2) ...
522s Selecting previously unselected package libkdb5-10t64:armhf.
522s Preparing to unpack .../410-libkdb5-10t64_1.21.3-4ubuntu2_armhf.deb ...
522s Unpacking libkdb5-10t64:armhf (1.21.3-4ubuntu2) ...
522s Selecting previously unselected package libkadm5srv-mit12:armhf.
522s Preparing to unpack .../411-libkadm5srv-mit12_1.21.3-4ubuntu2_armhf.deb ...
522s Unpacking libkadm5srv-mit12:armhf (1.21.3-4ubuntu2) ...
522s Selecting previously unselected package krb5-multidev:armhf.
522s Preparing to unpack .../412-krb5-multidev_1.21.3-4ubuntu2_armhf.deb ...
522s Unpacking krb5-multidev:armhf (1.21.3-4ubuntu2) ...
522s Selecting previously unselected package libkrb5-dev:armhf.
522s Preparing to unpack .../413-libkrb5-dev_1.21.3-4ubuntu2_armhf.deb ...
522s Unpacking libkrb5-dev:armhf (1.21.3-4ubuntu2) ...
522s Selecting previously unselected package libldap-dev:armhf.
522s Preparing to unpack .../414-libldap-dev_2.6.9+dfsg-1~exp2ubuntu1_armhf.deb ...
522s Unpacking libldap-dev:armhf (2.6.9+dfsg-1~exp2ubuntu1) ...
522s Selecting previously unselected package libpkgconf3:armhf.
522s Preparing to unpack .../415-libpkgconf3_1.8.1-4_armhf.deb ...
522s Unpacking libpkgconf3:armhf (1.8.1-4) ...
522s Selecting previously unselected package pkgconf-bin.
522s Preparing to unpack .../416-pkgconf-bin_1.8.1-4_armhf.deb ...
522s Unpacking pkgconf-bin (1.8.1-4) ...
522s Selecting previously unselected package pkgconf:armhf.
522s Preparing to unpack .../417-pkgconf_1.8.1-4_armhf.deb ...
522s Unpacking pkgconf:armhf (1.8.1-4) ...
522s Selecting previously unselected package libnghttp2-dev:armhf.
523s Preparing to unpack .../418-libnghttp2-dev_1.64.0-1_armhf.deb ...
523s Unpacking libnghttp2-dev:armhf (1.64.0-1) ...
523s Selecting previously unselected package libpsl-dev:armhf.
523s Preparing to unpack .../419-libpsl-dev_0.21.2-1.1build1_armhf.deb ...
523s Unpacking libpsl-dev:armhf (0.21.2-1.1build1) ...
523s Selecting previously unselected package libgmpxx4ldbl:armhf.
523s Preparing to unpack .../420-libgmpxx4ldbl_2%3a6.3.0+dfsg-3ubuntu1_armhf.deb ...
523s Unpacking libgmpxx4ldbl:armhf (2:6.3.0+dfsg-3ubuntu1) ...
523s Selecting previously unselected package libgmp-dev:armhf.
523s Preparing to unpack .../421-libgmp-dev_2%3a6.3.0+dfsg-3ubuntu1_armhf.deb ...
523s Unpacking libgmp-dev:armhf (2:6.3.0+dfsg-3ubuntu1) ...
523s Selecting previously unselected package libevent-2.1-7t64:armhf.
523s Preparing to unpack .../422-libevent-2.1-7t64_2.1.12-stable-10_armhf.deb ...
523s Unpacking libevent-2.1-7t64:armhf (2.1.12-stable-10) ...
523s Selecting previously unselected package libunbound8:armhf.
523s Preparing to unpack .../423-libunbound8_1.22.0-1ubuntu1_armhf.deb ...
523s Unpacking libunbound8:armhf (1.22.0-1ubuntu1) ...
523s Selecting previously unselected package libgnutls-dane0t64:armhf.
523s Preparing to unpack .../424-libgnutls-dane0t64_3.8.9-2ubuntu2_armhf.deb ...
523s Unpacking libgnutls-dane0t64:armhf (3.8.9-2ubuntu2) ...
523s Selecting previously unselected package libgnutls-openssl27t64:armhf.
523s Preparing to unpack .../425-libgnutls-openssl27t64_3.8.9-2ubuntu2_armhf.deb ...
523s Unpacking libgnutls-openssl27t64:armhf (3.8.9-2ubuntu2) ...
523s Selecting previously unselected package libp11-kit-dev:armhf.
523s Preparing to unpack .../426-libp11-kit-dev_0.25.5-2ubuntu3_armhf.deb ...
523s Unpacking libp11-kit-dev:armhf (0.25.5-2ubuntu3) ...
523s Selecting previously unselected package libtasn1-6-dev:armhf.
523s Preparing to unpack .../427-libtasn1-6-dev_4.20.0-2_armhf.deb ...
523s Unpacking libtasn1-6-dev:armhf (4.20.0-2) ...
523s Selecting previously unselected package nettle-dev:armhf.
523s Preparing to unpack .../428-nettle-dev_3.10.1-1_armhf.deb ...
523s Unpacking nettle-dev:armhf (3.10.1-1) ...
523s Selecting previously unselected package libgnutls28-dev:armhf.
523s Preparing to unpack .../429-libgnutls28-dev_3.8.9-2ubuntu2_armhf.deb ...
523s Unpacking libgnutls28-dev:armhf (3.8.9-2ubuntu2) ...
523s Selecting previously unselected package librtmp-dev:armhf.
523s Preparing to unpack .../430-librtmp-dev_2.4+20151223.gitfa8646d.1-2build7_armhf.deb ...
523s Unpacking librtmp-dev:armhf (2.4+20151223.gitfa8646d.1-2build7) ...
523s Selecting previously unselected package libssl-dev:armhf.
523s Preparing to unpack .../431-libssl-dev_3.4.1-1ubuntu1_armhf.deb ...
523s Unpacking libssl-dev:armhf (3.4.1-1ubuntu1) ...
523s Selecting previously unselected package libssh2-1-dev:armhf.
523s Preparing to unpack .../432-libssh2-1-dev_1.11.1-1_armhf.deb ...
523s Unpacking libssh2-1-dev:armhf (1.11.1-1) ...
523s Selecting previously unselected package libzstd-dev:armhf.
523s Preparing to unpack .../433-libzstd-dev_1.5.6+dfsg-2_armhf.deb ...
523s Unpacking libzstd-dev:armhf (1.5.6+dfsg-2) ...
523s Selecting previously unselected package libcurl4-openssl-dev:armhf.
523s Preparing to unpack .../434-libcurl4-openssl-dev_8.12.1-3ubuntu1_armhf.deb ...
523s Unpacking libcurl4-openssl-dev:armhf (8.12.1-3ubuntu1) ...
524s Selecting previously unselected package hdf5-helpers.
524s Preparing to unpack .../435-hdf5-helpers_1.14.5+repack-3_armhf.deb ...
524s Unpacking hdf5-helpers (1.14.5+repack-3) ...
524s Selecting previously unselected package libhdf5-dev.
524s Preparing to unpack .../436-libhdf5-dev_1.14.5+repack-3_armhf.deb ...
524s Unpacking libhdf5-dev (1.14.5+repack-3) ...
524s Selecting previously unselected package xorg-sgml-doctools.
524s Preparing to unpack .../437-xorg-sgml-doctools_1%3a1.11-1.1_all.deb ...
524s Unpacking xorg-sgml-doctools (1:1.11-1.1) ...
524s Selecting previously unselected package x11proto-dev.
524s Preparing to unpack .../438-x11proto-dev_2024.1-1_all.deb ...
524s Unpacking x11proto-dev (2024.1-1) ...
524s Selecting previously unselected package libxau-dev:armhf.
524s Preparing to unpack .../439-libxau-dev_1%3a1.0.11-1_armhf.deb ...
524s Unpacking libxau-dev:armhf (1:1.0.11-1) ...
524s Selecting previously unselected package libxdmcp-dev:armhf.
524s Preparing to unpack .../440-libxdmcp-dev_1%3a1.1.5-1_armhf.deb ...
524s Unpacking libxdmcp-dev:armhf (1:1.1.5-1) ...
524s Selecting previously unselected package xtrans-dev.
524s Preparing to unpack .../441-xtrans-dev_1.4.0-1_all.deb ...
524s Unpacking xtrans-dev (1.4.0-1) ...
524s Selecting previously unselected package libxcb1-dev:armhf.
524s Preparing to unpack .../442-libxcb1-dev_1.17.0-2_armhf.deb ...
524s Unpacking libxcb1-dev:armhf (1.17.0-2) ...
524s Selecting previously unselected package libx11-dev:armhf.
524s Preparing to unpack .../443-libx11-dev_2%3a1.8.10-2_armhf.deb ...
524s Unpacking libx11-dev:armhf (2:1.8.10-2) ...
524s Selecting previously unselected package libglx-dev:armhf.
524s Preparing to unpack .../444-libglx-dev_1.7.0-1build1_armhf.deb ...
524s Unpacking libglx-dev:armhf (1.7.0-1build1) ...
524s Selecting previously unselected package libgl-dev:armhf.
524s Preparing to unpack .../445-libgl-dev_1.7.0-1build1_armhf.deb ...
524s Unpacking libgl-dev:armhf (1.7.0-1build1) ...
524s Selecting previously unselected package libblas-dev:armhf.
524s Preparing to unpack .../446-libblas-dev_3.12.1-2_armhf.deb ...
524s Unpacking libblas-dev:armhf (3.12.1-2) ...
524s Selecting previously unselected package liblapack-dev:armhf.
524s Preparing to unpack .../447-liblapack-dev_3.12.1-2_armhf.deb ...
524s Unpacking liblapack-dev:armhf (3.12.1-2) ...
524s Selecting previously unselected package libfftw3-bin.
524s Preparing to unpack .../448-libfftw3-bin_3.3.10-2fakesync1build1_armhf.deb ...
524s Unpacking libfftw3-bin (3.3.10-2fakesync1build1) ...
524s Selecting previously unselected package libfftw3-dev:armhf.
524s Preparing to unpack .../449-libfftw3-dev_3.3.10-2fakesync1build1_armhf.deb ...
524s Unpacking libfftw3-dev:armhf (3.3.10-2fakesync1build1) ...
524s Selecting previously unselected package libgfortran-14-dev:armhf.
525s Preparing to unpack .../450-libgfortran-14-dev_14.2.0-17ubuntu3_armhf.deb ...
525s Unpacking libgfortran-14-dev:armhf (14.2.0-17ubuntu3) ...
525s Selecting previously unselected package gfortran-14-arm-linux-gnueabihf.
525s Preparing to unpack .../451-gfortran-14-arm-linux-gnueabihf_14.2.0-17ubuntu3_armhf.deb ...
525s Unpacking gfortran-14-arm-linux-gnueabihf (14.2.0-17ubuntu3) ...
525s Selecting previously unselected package gfortran-14.
525s Preparing to unpack .../452-gfortran-14_14.2.0-17ubuntu3_armhf.deb ...
525s Unpacking gfortran-14 (14.2.0-17ubuntu3) ...
525s Selecting previously unselected package gfortran-arm-linux-gnueabihf.
525s Preparing to unpack .../453-gfortran-arm-linux-gnueabihf_4%3a14.2.0-1ubuntu1_armhf.deb ...
525s Unpacking gfortran-arm-linux-gnueabihf (4:14.2.0-1ubuntu1) ...
525s Selecting previously unselected package gfortran.
525s Preparing to unpack .../454-gfortran_4%3a14.2.0-1ubuntu1_armhf.deb ...
525s Unpacking gfortran (4:14.2.0-1ubuntu1) ...
525s Selecting previously unselected package libstdc++-14-dev:armhf.
525s Preparing to unpack .../455-libstdc++-14-dev_14.2.0-17ubuntu3_armhf.deb ...
525s Unpacking libstdc++-14-dev:armhf (14.2.0-17ubuntu3) ...
525s Selecting previously unselected package g++-14-arm-linux-gnueabihf.
525s Preparing to unpack .../456-g++-14-arm-linux-gnueabihf_14.2.0-17ubuntu3_armhf.deb ...
525s Unpacking g++-14-arm-linux-gnueabihf (14.2.0-17ubuntu3) ...
525s Selecting previously unselected package g++-14.
525s Preparing to unpack .../457-g++-14_14.2.0-17ubuntu3_armhf.deb ...
525s Unpacking g++-14 (14.2.0-17ubuntu3) ...
525s Selecting previously unselected package g++-arm-linux-gnueabihf.
526s Preparing to unpack .../458-g++-arm-linux-gnueabihf_4%3a14.2.0-1ubuntu1_armhf.deb ...
526s Unpacking g++-arm-linux-gnueabihf (4:14.2.0-1ubuntu1) ...
526s Selecting previously unselected package g++.
526s Preparing to unpack .../459-g++_4%3a14.2.0-1ubuntu1_armhf.deb ...
526s Unpacking g++ (4:14.2.0-1ubuntu1) ...
526s Selecting previously unselected package octave-dev.
526s Preparing to unpack .../460-octave-dev_9.4.0-1_armhf.deb ...
526s Unpacking octave-dev (9.4.0-1) ...
526s Selecting previously unselected package dh-octave.
526s Preparing to unpack .../461-dh-octave_1.8.0_all.deb ...
526s Unpacking dh-octave (1.8.0) ...
526s Selecting previously unselected package libfontenc1:armhf.
526s Preparing to unpack .../462-libfontenc1_1%3a1.1.8-1build1_armhf.deb ...
526s Unpacking libfontenc1:armhf (1:1.1.8-1build1) ...
526s Selecting previously unselected package libxt6t64:armhf.
526s Preparing to unpack .../463-libxt6t64_1%3a1.2.1-1.2build1_armhf.deb ...
526s Unpacking libxt6t64:armhf (1:1.2.1-1.2build1) ...
526s Selecting previously unselected package libxmu6:armhf.
526s Preparing to unpack .../464-libxmu6_2%3a1.1.3-3build2_armhf.deb ...
526s Unpacking libxmu6:armhf (2:1.1.3-3build2) ...
526s Selecting previously unselected package libxaw7:armhf.
526s Preparing to unpack .../465-libxaw7_2%3a1.0.16-1_armhf.deb ...
526s Unpacking libxaw7:armhf (2:1.0.16-1) ...
526s Selecting previously unselected package libxfont2:armhf.
526s Preparing to unpack .../466-libxfont2_1%3a2.0.6-1build1_armhf.deb ...
526s Unpacking libxfont2:armhf (1:2.0.6-1build1) ...
526s Selecting previously unselected package libxkbfile1:armhf.
526s Preparing to unpack .../467-libxkbfile1_1%3a1.1.0-1build4_armhf.deb ...
526s Unpacking libxkbfile1:armhf (1:1.1.0-1build4) ...
526s Selecting previously unselected package libxrandr2:armhf.
526s Preparing to unpack .../468-libxrandr2_2%3a1.5.4-1_armhf.deb ...
526s Unpacking libxrandr2:armhf (2:1.5.4-1) ...
526s Selecting previously unselected package octave-io.
526s Preparing to unpack .../469-octave-io_2.6.4-3build2_armhf.deb ...
526s Unpacking octave-io (2.6.4-3build2) ...
526s Selecting previously unselected package octave-statistics-common.
526s Preparing to unpack .../470-octave-statistics-common_1.7.3-2_all.deb ...
526s Unpacking octave-statistics-common (1.7.3-2) ...
526s Selecting previously unselected package octave-statistics.
527s Preparing to unpack .../471-octave-statistics_1.7.3-2_armhf.deb ...
527s Unpacking octave-statistics (1.7.3-2) ...
527s Selecting previously unselected package x11-xkb-utils.
527s Preparing to unpack .../472-x11-xkb-utils_7.7+9_armhf.deb ...
527s Unpacking x11-xkb-utils (7.7+9) ...
527s Selecting previously unselected package xserver-common.
527s Preparing to unpack .../473-xserver-common_2%3a21.1.16-1ubuntu1_all.deb ...
527s Unpacking xserver-common (2:21.1.16-1ubuntu1) ...
527s Selecting previously unselected package xvfb.
527s Preparing to unpack .../474-xvfb_2%3a21.1.16-1ubuntu1_armhf.deb ...
527s Unpacking xvfb (2:21.1.16-1ubuntu1) ...
527s Setting up libapt-pkg-perl (0.1.41build1) ...
527s Setting up libmodule-pluggable-perl (5.2-5) ...
527s Setting up libgraphite2-3:armhf (1.3.14-2ubuntu1) ...
527s Setting up libstring-escape-perl (2010.002-3) ...
527s Setting up libgnutls-openssl27t64:armhf (3.8.9-2ubuntu2) ...
527s Setting up libxcb-dri3-0:armhf (1.17.0-2) ...
527s Setting up liblcms2-2:armhf (2.16-2) ...
527s Setting up libberkeleydb-perl:armhf (0.66-1) ...
527s Setting up libpixman-1-0:armhf (0.44.0-3) ...
527s Setting up libllvm19:armhf (1:19.1.7-1ubuntu2) ...
527s Setting up libsharpyuv0:armhf (1.5.0-0.1) ...
527s Setting up libwayland-server0:armhf (1.23.1-3) ...
527s Setting up libaom3:armhf (3.12.0-1) ...
527s Setting up libx11-xcb1:armhf (2:1.8.10-2) ...
527s Setting up libfile-which-perl (1.27-2) ...
527s Setting up libdouble-conversion3:armhf (3.3.1-1) ...
527s Setting up libunicode-utf8-perl (0.62-2build4) ...
527s Setting up libset-intspan-perl (1.19-3) ...
527s Setting up libxcb-xfixes0:armhf (1.17.0-2) ...
527s Setting up libogg0:armhf (1.3.5-3build1) ...
527s Setting up libmouse-perl:armhf (2.5.11-1build1) ...
527s Setting up libzstd-dev:armhf (1.5.6+dfsg-2) ...
527s Setting up liblerc4:armhf (4.0.0+ds-5ubuntu1) ...
527s Setting up libpod-pom-perl (2.01-4) ...
527s Setting up libxpm4:armhf (1:3.5.17-1build2) ...
527s Setting up hdf5-helpers (1.14.5+repack-3) ...
527s Setting up libwmflite-0.2-7:armhf (0.2.13-1.1build3) ...
527s Setting up libregexp-pattern-perl (0.2.14-2) ...
527s Setting up libdata-messagepack-perl (1.02-1build5) ...
527s Setting up libclass-inspector-perl (1.36-3) ...
527s Setting up libxcb-xinput0:armhf (1.17.0-2) ...
527s Setting up libxrender1:armhf (1:0.9.10-1.1build1) ...
527s Setting up libdynaloader-functions-perl (0.004-1) ...
527s Setting up libdatrie1:armhf (0.2.13-3build1) ...
527s Setting up libtext-glob-perl (0.11-3) ...
527s Setting up libclass-method-modifiers-perl (2.15-1) ...
527s Setting up liblist-compare-perl (0.55-2) ...
527s Setting up libxcb-render0:armhf (1.17.0-2) ...
527s Setting up libclone-perl:armhf (0.47-1) ...
527s Setting up libarchive-zip-perl (1.68-1) ...
527s Setting up libsub-identify-perl (0.14-3build4) ...
527s Setting up libcpanel-json-xs-perl:armhf (4.39-1) ...
527s Setting up libglvnd0:armhf (1.7.0-1build1) ...
527s Setting up libio-stringy-perl (2.113-2) ...
527s Setting up libhtml-tagset-perl (3.24-1) ...
527s Setting up libts0t64:armhf (1.22-1.1build1) ...
527s Setting up liblog-any-perl (1.717-1) ...
527s Setting up libyaml-pp-perl (0.39.0-1) ...
527s Setting up libxcb-glx0:armhf (1.17.0-2) ...
527s Setting up libdevel-size-perl (0.84-1build1) ...
527s Setting up unzip (6.0-28ubuntu6) ...
527s Setting up libdebhelper-perl (13.24.1ubuntu2) ...
527s Setting up libregexp-pattern-license-perl (3.11.2-1) ...
527s Setting up libconvert-binhex-perl (1.125-3) ...
527s Setting up liblwp-mediatypes-perl (6.04-2) ...
527s Setting up libyaml-libyaml-perl (0.903.0+ds-1) ...
527s Setting up fonts-freefont-otf (20211204+svn4273-2) ...
527s Setting up libio-interactive-perl (1.026-1) ...
527s Setting up libxcb-keysyms1:armhf (0.4.1-1) ...
527s Setting up libxcb-shape0:armhf (1.17.0-2) ...
527s Setting up x11-common (1:7.7+23ubuntu3) ...
527s Setting up libtry-tiny-perl (0.32-1) ...
527s Setting up libdeflate0:armhf (1.23-1) ...
527s Setting up perl-openssl-defaults:armhf (7build3) ...
527s Setting up libmldbm-perl (2.05-4) ...
527s Setting up linux-libc-dev:armhf (6.14.0-10.10) ...
527s Setting up libxml-namespacesupport-perl (1.12-2) ...
527s Setting up m4 (1.4.19-7) ...
527s Setting up libevent-2.1-7t64:armhf (2.1.12-stable-10) ...
527s Setting up libclone-choose-perl (0.010-2) ...
527s Setting up libqhull-r8.0:armhf (2020.2-6build1) ...
527s Setting up libxcb-render-util0:armhf (0.3.10-1) ...
527s Setting up libtime-moment-perl (0.44-2build5) ...
527s Setting up libencode-locale-perl (1.05-3) ...
527s Setting up libxcb-shm0:armhf (1.17.0-2) ...
527s Setting up libxcb-icccm4:armhf (0.4.2-1) ...
527s Setting up texinfo-lib (7.1.1-1) ...
527s Setting up libmpg123-0t64:armhf (1.32.10-1) ...
527s Setting up libgomp1:armhf (15-20250222-0ubuntu1) ...
527s Setting up libconfig-tiny-perl (2.30-1) ...
527s Setting up libsereal-encoder-perl (5.004+ds-1build4) ...
527s Setting up liblist-utilsby-perl (0.12-2) ...
527s Setting up libyaml-tiny-perl (1.76-1) ...
527s Setting up libjbig0:armhf (2.1-6.1ubuntu2) ...
527s Setting up octave-common (9.4.0-1) ...
527s Setting up libregexp-common-perl (2024080801-1) ...
527s Setting up libpcre2-16-0:armhf (10.45-1) ...
527s Setting up libaec0:armhf (1.1.3-1) ...
527s Setting up libnet-netmask-perl (2.0002-2) ...
527s Setting up libopengl0:armhf (1.7.0-1build1) ...
527s Setting up libsub-install-perl (0.929-1) ...
527s Setting up libxcb-util1:armhf (0.4.1-1) ...
527s Setting up libpsl-dev:armhf (0.21.2-1.1build1) ...
527s Setting up libxxf86vm1:armhf (1:1.1.4-1build4) ...
527s Setting up libindirect-perl (0.39-2build5) ...
527s Setting up libxcb-xkb1:armhf (1.17.0-2) ...
527s Setting up libxcb-image0:armhf (0.4.0-2build1) ...
527s Setting up libnumber-compare-perl (0.03-3) ...
527s Setting up libdw1t64:armhf (0.192-4) ...
527s Setting up libxcb-present0:armhf (1.17.0-2) ...
527s Setting up liberror-perl (0.17030-1) ...
527s Setting up libasound2-data (1.2.13-1build1) ...
527s Setting up patchutils (0.4.2-1build3) ...
527s Setting up tex-common (6.19) ...
527s update-language: texlive-base not installed and configured, doing nothing!
528s Setting up libjson-maybexs-perl (1.004008-1) ...
528s Setting up libxml-sax-base-perl (1.09-3) ...
528s Setting up libio-string-perl (1.08-4) ...
528s Setting up libboolean-perl (0.46-3) ...
528s Setting up libnetaddr-ip-perl (4.079+dfsg-2build5) ...
528s Setting up xtrans-dev (1.4.0-1) ...
528s Setting up libfontenc1:armhf (1:1.1.8-1build1) ...
528s Setting up autotools-dev (20220109.1) ...
528s Setting up libblas3:armhf (3.12.1-2) ...
528s update-alternatives: using /usr/lib/arm-linux-gnueabihf/blas/libblas.so.3 to provide /usr/lib/arm-linux-gnueabihf/libblas.so.3 (libblas.so.3-arm-linux-gnueabihf) in auto mode
528s Setting up libclass-data-inheritable-perl (0.10-1) ...
528s Setting up libunbound8:armhf (1.22.0-1ubuntu1) ...
528s Setting up libpkgconf3:armhf (1.8.1-4) ...
528s Setting up libgmpxx4ldbl:armhf (2:6.3.0+dfsg-3ubuntu1) ...
528s Setting up libalgorithm-c3-perl (0.11-2) ...
528s Setting up rpcsvc-proto (1.4.2-0ubuntu7) ...
528s Setting up libasound2t64:armhf (1.2.13-1build1) ...
528s Setting up liblog-log4perl-perl (1.57-1) ...
528s Setting up libtext-reform-perl (1.20-5) ...
528s Setting up libgnutls-dane0t64:armhf (3.8.9-2ubuntu2) ...
528s Setting up libfreetype6:armhf (2.13.3+dfsg-1) ...
528s Setting up libfile-find-rule-perl (0.34-3) ...
528s Setting up libxfixes3:armhf (1:6.0.0-2build1) ...
528s Setting up libxcb-sync1:armhf (1.17.0-2) ...
528s Setting up libipc-system-simple-perl (1.30-2) ...
528s Setting up libio-tiecombine-perl (1.005-3) ...
528s Setting up libnet-domain-tld-perl (1.75-4) ...
528s Setting up libgssrpc4t64:armhf (1.21.3-4ubuntu2) ...
528s Setting up libperlio-utf8-strict-perl (0.010-1build4) ...
528s Setting up libldap-dev:armhf (2.6.9+dfsg-1~exp2ubuntu1) ...
528s Setting up aglfn (1.7+git20191031.4036a9c-2) ...
528s Setting up libxcb-cursor0:armhf (0.1.5-1) ...
528s Setting up lzip (1.25-2) ...
528s update-alternatives: using /usr/bin/lzip.lzip to provide /usr/bin/lzip (lzip) in auto mode
528s update-alternatives: using /usr/bin/lzip.lzip to provide /usr/bin/lzip-compressor (lzip-compressor) in auto mode
528s update-alternatives: using /usr/bin/lzip.lzip to provide /usr/bin/lzip-decompressor (lzip-decompressor) in auto mode
528s Setting up libavahi-common-data:armhf (0.8-16ubuntu1) ...
528s Setting up libopus0:armhf (1.5.2-2) ...
528s Setting up t1utils (1.41-4build3) ...
528s Setting up libxinerama1:armhf (2:1.1.4-3build1) ...
528s Setting up diffstat (1.67-1) ...
528s Setting up libimagequant0:armhf (2.18.0-1build1) ...
528s Setting up libxkbcommon-x11-0:armhf (1.7.0-2) ...
528s Setting up fonts-dejavu-mono (2.37-8) ...
528s Setting up libssl-dev:armhf (3.4.1-1ubuntu1) ...
528s Setting up libmpc3:armhf (1.3.1-1build2) ...
528s Setting up libvorbis0a:armhf (1.3.7-2) ...
528s Setting up libvariable-magic-perl (0.64-1build1) ...
528s Setting up libio-html-perl (1.004-3) ...
528s Setting up libxrandr2:armhf (2:1.5.4-1) ...
528s Setting up libtext-template-perl (1.61-1) ...
528s Setting up libpod-parser-perl (1.67-1) ...
528s Setting up autopoint (0.23.1-1) ...
528s Setting up libb-hooks-op-check-perl:armhf (0.22-3build2) ...
528s Setting up fonts-dejavu-core (2.37-8) ...
528s Setting up liblist-moreutils-xs-perl (0.430-4build1) ...
528s Setting up pkgconf-bin (1.8.1-4) ...
528s Setting up libjpeg-turbo8:armhf (2.1.5-3ubuntu2) ...
528s Setting up libqscintilla2-qt6-l10n (2.14.1+dfsg-1build4) ...
528s Setting up libltdl7:armhf (2.5.4-4) ...
528s Setting up libidn2-dev:armhf (2.3.7-2build2) ...
528s Setting up libfftw3-double3:armhf (3.3.10-2fakesync1build1) ...
528s Setting up libparams-util-perl (1.102-3build1) ...
528s Setting up libgfortran5:armhf (15-20250222-0ubuntu1) ...
528s Setting up libvulkan1:armhf (1.4.304.0-1) ...
528s Setting up libtime-duration-perl (1.21-2) ...
528s Setting up autoconf (2.72-3ubuntu1) ...
528s Setting up libtext-xslate-perl:armhf (3.5.9-2build1) ...
528s Setting up libsub-exporter-progressive-perl (0.001013-3) ...
528s Setting up libwebp7:armhf (1.5.0-0.1) ...
528s Setting up libarray-intspan-perl (2.004-2) ...
528s Setting up libcapture-tiny-perl (0.50-1) ...
528s Setting up libtimedate-perl (2.3300-2) ...
528s Setting up libexporter-lite-perl (0.09-2) ...
528s Setting up libubsan1:armhf (15-20250222-0ubuntu1) ...
528s Setting up libsub-name-perl:armhf (0.28-1) ...
528s Setting up dwz (0.15-1build6) ...
528s Setting up libdata-validate-domain-perl (0.15-1) ...
528s Setting up libproc-processtable-perl:armhf (0.636-1build4) ...
528s Setting up libparse-recdescent-perl (1.967015+dfsg-4) ...
528s Setting up libmtdev1t64:armhf (1.1.7-1) ...
528s Setting up libduktape207:armhf (2.7.0+tests-0ubuntu3) ...
528s Setting up libxshmfence1:armhf (1.3-1build5) ...
528s Setting up libcrypt-dev:armhf (1:4.4.38-1) ...
528s Setting up libxcb-randr0:armhf (1.17.0-2) ...
528s Setting up libpath-tiny-perl (0.146-1) ...
528s Setting up libasan8:armhf (15-20250222-0ubuntu1) ...
528s Setting up lzop (1.04-2build3) ...
528s Setting up libjson-perl (4.10000-1) ...
528s Setting up liblog-any-adapter-screen-perl (0.141-1) ...
528s Setting up librole-tiny-perl (2.002004-1) ...
528s Setting up debugedit (1:5.1-2) ...
528s Setting up libipc-run3-perl (0.049-1) ...
528s Setting up libmd4c0:armhf (0.5.2-2) ...
528s Setting up libregexp-wildcards-perl (1.05-3) ...
528s Setting up libmousex-strictconstructor-perl (0.02-3) ...
528s Setting up libfile-sharedir-perl (1.118-3) ...
528s Setting up libsub-uplevel-perl (0.2800-3) ...
528s Setting up libsuitesparseconfig7:armhf (1:7.8.3+dfsg-3) ...
528s Setting up liblua5.4-0:armhf (5.4.7-1) ...
528s Setting up libaliased-perl (0.34-3) ...
528s Setting up libharfbuzz0b:armhf (10.2.0-1) ...
528s Setting up libthai-data (0.1.29-2build1) ...
528s Setting up xorg-sgml-doctools (1:1.11-1.1) ...
528s Setting up libstrictures-perl (2.000006-1) ...
528s Setting up libsub-quote-perl (2.006008-1ubuntu1) ...
528s Setting up libdevel-stacktrace-perl (2.0500-1) ...
528s Setting up libclass-xsaccessor-perl (1.19-4build6) ...
528s Setting up libtext-autoformat-perl (1.750000-2) ...
528s Setting up libglu1-mesa:armhf (9.0.2-1.1build1) ...
528s Setting up libflac12t64:armhf (1.4.3+ds-4) ...
528s Setting up libtoml-tiny-perl (0.19-1) ...
528s Setting up libstemmer0d:armhf (2.2.0-4build1) ...
528s Setting up libxkbfile1:armhf (1:1.1.0-1build4) ...
528s Setting up libsort-versions-perl (1.62-3) ...
528s Setting up libexporter-tiny-perl (1.006002-1) ...
528s Setting up libgcc-14-dev:armhf (14.2.0-17ubuntu3) ...
528s Setting up libterm-readkey-perl (2.38-2build5) ...
528s Setting up libisl23:armhf (0.27-1) ...
528s Setting up libtext-unidecode-perl (1.30-3) ...
528s Setting up libde265-0:armhf (1.0.15-1build5) ...
528s Setting up libc-dev-bin (2.41-1ubuntu2) ...
528s Setting up libfont-ttf-perl (1.06-2) ...
528s Setting up libfile-homedir-perl (1.006-2) ...
528s Setting up libsamplerate0:armhf (0.2.2-4build1) ...
528s Setting up libtasn1-6-dev:armhf (4.20.0-2) ...
528s Setting up libwebpmux3:armhf (1.5.0-0.1) ...
528s Setting up libtext-levenshteinxs-perl (0.03-5build5) ...
528s Setting up libperlio-gzip-perl (0.20-1build5) ...
528s Setting up libjxl0.11:armhf (0.11.1-1) ...
528s Setting up libxfont2:armhf (1:2.0.6-1build1) ...
528s Setting up libhtml-html5-entities-perl (0.004-3) ...
528s Setting up libtext-levenshtein-damerau-perl (0.41-3) ...
528s Setting up libsereal-decoder-perl (5.004+ds-1build4) ...
528s Setting up libmarkdown2:armhf (2.2.7-2.1) ...
528s Setting up libcc1-0:armhf (15-20250222-0ubuntu1) ...
528s Setting up liburi-perl (5.30-1) ...
528s Setting up libnet-ipv6addr-perl (1.02-1) ...
528s Setting up libbrotli-dev:armhf (1.1.0-2build4) ...
528s Setting up libp11-kit-dev:armhf (0.25.5-2ubuntu3) ...
528s Setting up libmp3lame0:armhf (3.100-6build1) ...
528s Setting up libblas-dev:armhf (3.12.1-2) ...
528s update-alternatives: using /usr/lib/arm-linux-gnueabihf/blas/libblas.so to provide /usr/lib/arm-linux-gnueabihf/libblas.so (libblas.so-arm-linux-gnueabihf) in auto mode
528s Setting up libsz2:armhf (1.1.3-1) ...
528s Setting up libvorbisenc2:armhf (1.3.7-2) ...
528s Setting up cpp-14-arm-linux-gnueabihf (14.2.0-17ubuntu3) ...
528s Setting up libkadm5clnt-mit12:armhf (1.21.3-4ubuntu2) ...
528s Setting up libdata-validate-ip-perl (0.31-1) ...
528s Setting up libwacom-common (2.14.0-1) ...
528s Setting up libmousex-nativetraits-perl (1.09-3) ...
528s Setting up libemail-address-xs-perl (1.05-1build5) ...
528s Setting up libwayland-client0:armhf (1.23.1-3) ...
528s Setting up libnet-ssleay-perl:armhf (1.94-3) ...
528s Setting up libjpeg8:armhf (8c-2ubuntu11) ...
528s Setting up automake (1:1.17-3ubuntu1) ...
528s update-alternatives: using /usr/bin/automake-1.17 to provide /usr/bin/automake (automake) in auto mode
528s Setting up libgfortran-14-dev:armhf (14.2.0-17ubuntu3) ...
528s Setting up libb2-1:armhf (0.98.1-1.1build1) ...
528s Setting up x11proto-dev (2024.1-1) ...
528s Setting up libfile-stripnondeterminism-perl (1.14.1-2) ...
528s Setting up gnuplot-data (6.0.2+dfsg1-1) ...
528s Setting up libice6:armhf (2:1.1.1-1) ...
528s Setting up libqt6core6t64:armhf (6.8.2+dfsg-5) ...
528s Setting up libhttp-date-perl (6.06-1) ...
528s Setting up mesa-libgallium:armhf (25.0.1-2ubuntu1) ...
528s Setting up liblapack3:armhf (3.12.1-2) ...
528s update-alternatives: using /usr/lib/arm-linux-gnueabihf/lapack/liblapack.so.3 to provide /usr/lib/arm-linux-gnueabihf/liblapack.so.3 (liblapack.so.3-arm-linux-gnueabihf) in auto mode
528s Setting up libproxy1v5:armhf (0.5.9-1) ...
528s Setting up libfile-basedir-perl (0.09-2) ...
528s Setting up gettext (0.23.1-1) ...
528s Setting up libarpack2t64:armhf (3.9.1-4) ...
528s Setting up libfftw3-single3:armhf (3.3.10-2fakesync1build1) ...
528s Setting up libgmp-dev:armhf (2:6.3.0+dfsg-3ubuntu1) ...
528s Setting up libamd3:armhf (1:7.8.3+dfsg-3) ...
528s Setting up libfile-listing-perl (6.16-1) ...
528s Setting up libxau-dev:armhf (1:1.0.11-1) ...
528s Setting up nettle-dev:armhf (3.10.1-1) ...
528s Setting up libkdb5-10t64:armhf (1.21.3-4ubuntu2) ...
528s Setting up libgbm1:armhf (25.0.1-2ubuntu1) ...
528s Setting up libcolamd3:armhf (1:7.8.3+dfsg-3) ...
528s Setting up libfftw3-bin (3.3.10-2fakesync1build1) ...
528s Setting up libwacom9:armhf (2.14.0-1) ...
528s Setting up fontconfig-config (2.15.0-2ubuntu1) ...
528s Setting up liblist-moreutils-perl (0.430-2) ...
528s Setting up libxcursor1:armhf (1:1.2.3-1) ...
528s Setting up libpod-constants-perl (0.19-2) ...
528s Setting up libgl1-mesa-dri:armhf (25.0.1-2ubuntu1) ...
528s Setting up libhash-merge-perl (0.302-1) ...
528s Setting up libsoftware-copyright-perl (0.014-1) ...
528s Setting up libaec-dev:armhf (1.1.3-1) ...
528s Setting up libavahi-common3:armhf (0.8-16ubuntu1) ...
528s Setting up libcxsparse4:armhf (1:7.8.3+dfsg-3) ...
528s Setting up libnet-http-perl (6.23-1) ...
528s Setting up libpath-iterator-rule-perl (1.015-2) ...
528s Setting up libtext-markdown-discount-perl (0.18-1) ...
528s Setting up gcc-14-arm-linux-gnueabihf (14.2.0-17ubuntu3) ...
528s Setting up libappstream5:armhf (1.0.4-1) ...
528s Setting up libexception-class-perl (1.45-1) ...
528s Setting up libclass-c3-perl (0.35-2) ...
528s Setting up libqrupdate1:armhf (1.1.5-1) ...
528s Setting up libdevel-callchecker-perl:armhf (0.009-1build1) ...
528s Setting up libxml-sax-perl (1.02+dfsg-4) ...
528s update-perl-sax-parsers: Registering Perl SAX parser XML::SAX::PurePerl with priority 10...
528s update-perl-sax-parsers: Updating overall Perl SAX parser modules info file...
528s Creating config file /etc/perl/XML/SAX/ParserDetails.ini with new version
528s Setting up libcamd3:armhf (1:7.8.3+dfsg-3) ...
528s Setting up pkgconf:armhf (1.8.1-4) ...
528s Setting up libinput-bin (1.27.1-1) ...
528s Setting up libxs-parse-sublike-perl:armhf (0.37-1) ...
528s Setting up intltool-debian (0.35.0+20060710.6) ...
528s Setting up libthai0:armhf (0.1.29-2build1) ...
528s Setting up libxdmcp-dev:armhf (1:1.1.5-1) ...
528s Setting up libegl-mesa0:armhf (25.0.1-2ubuntu1) ...
528s Setting up libdata-validate-uri-perl (0.07-3) ...
528s Setting up libxs-parse-keyword-perl (0.48-2) ...
528s Setting up libtest-exception-perl (0.43-3) ...
528s Setting up appstream (1.0.4-1) ...
528s ✔ Metadata cache was updated successfully.
528s Setting up libqt6xml6:armhf (6.8.2+dfsg-5) ...
528s Setting up libglpk40:armhf (5.0-1build2) ...
528s Setting up libqt6sql6:armhf (6.8.2+dfsg-5) ...
528s Setting up libstring-copyright-perl (0.003014-1) ...
528s Setting up libraqm0:armhf (0.10.2-1) ...
528s Setting up liblapack-dev:armhf (3.12.1-2) ...
528s update-alternatives: using /usr/lib/arm-linux-gnueabihf/lapack/liblapack.so to provide /usr/lib/arm-linux-gnueabihf/liblapack.so (liblapack.so-arm-linux-gnueabihf) in auto mode
528s Setting up libdata-optlist-perl (0.114-1) ...
528s Setting up cpp-14 (14.2.0-17ubuntu3) ...
528s Setting up libccolamd3:armhf (1:7.8.3+dfsg-3) ...
528s Setting up libxml-libxml-perl (2.0207+dfsg+really+2.0134-5build1) ...
528s update-perl-sax-parsers: Registering Perl SAX parser XML::LibXML::SAX::Parser with priority 50...
528s update-perl-sax-parsers: Registering Perl SAX parser XML::LibXML::SAX with priority 50...
529s update-perl-sax-parsers: Updating overall Perl SAX parser modules info file...
529s Replacing config file /etc/perl/XML/SAX/ParserDetails.ini with new version
529s Setting up dh-strip-nondeterminism (1.14.1-2) ...
529s Setting up libwww-robotrules-perl (6.02-1) ...
529s Setting up libsyntax-keyword-try-perl (0.30-1) ...
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545s autopkgtest [15:00:43]: test command1: DH_OCTAVE_TEST_ENV="xvfb-run -a" /usr/bin/dh_octave_check --use-installed-package
545s autopkgtest [15:00:43]: test command1: [-----------------------
546s Checking package...
547s Run the unit tests...
547s Checking m files ...
548s [inst/@cvpartition/cvpartition.m]
548s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/@cvpartition/cvpartition.m
548s ***** demo
548s  ## Partition with Fisher iris dataset (n = 150)
548s  ## Stratified by species
548s  load fisheriris
548s  y = species;
548s  ## 10-fold cross-validation partition
548s  c = cvpartition (species, 'KFold', 10)
548s  ## leave-10-out partition
548s  c1 = cvpartition (species, 'HoldOut', 10)
548s  idx1 = test (c, 2);
548s  idx2 = training (c, 2);
548s  ## another leave-10-out partition
548s  c2 = repartition (c1)
548s ***** test
548s  C = cvpartition (ones (10, 1));
548s  assert (isa (C, "cvpartition"), true);
548s ***** test
548s  C = cvpartition (ones (10, 1), "KFold", 5);
548s  assert (get (C, "NumObservations"), 10);
548s  assert (get (C, "NumTestSets"), 5);
548s  assert (get (C, "TrainSize"), ones(5,1) * 8);
548s  assert (get (C, "TestSize"), ones (5,1) * 2);
548s  assert (get (C, "inds"), [1 1 2 2 3 3 4 4 5 5]');
548s  assert (get (C, "Type"), "kfold");
548s ***** test
548s  C = cvpartition (ones (10, 1), "KFold", 2);
548s  assert (get (C, "NumObservations"), 10);
548s  assert (get (C, "NumTestSets"), 2);
548s  assert (get (C, "TrainSize"), [5; 5]);
548s  assert (get (C, "TestSize"), [5; 5]);
548s  assert (get (C, "inds"), [1 1 1 1 1 2 2 2 2 2]');
548s  assert (get (C, "Type"), "kfold");
548s ***** test
548s  C = cvpartition (ones (10, 1), "HoldOut", 5);
548s  assert (get (C, "NumObservations"), 10);
548s  assert (get (C, "NumTestSets"), 1);
548s  assert (get (C, "TrainSize"), 5);
548s  assert (get (C, "TestSize"), 5);
548s  assert (class (get (C, "inds")), "logical");
548s  assert (length (get (C, "inds")), 10);
548s  assert (get (C, "Type"), "holdout");
548s ***** test
548s  C = cvpartition ([1 2 3 4 5 6 7 8 9 10], "LeaveOut", 5);
548s  assert (get (C, "NumObservations"), 10);
548s  assert (get (C, "NumTestSets"), 10);
548s  assert (get (C, "TrainSize"), ones (10, 1));
548s  assert (get (C, "TestSize"), ones (10, 1) * 9);
548s  assert (get (C, "inds"), []);
548s  assert (get (C, "Type"), "leaveout");
548s ***** test
548s  C = cvpartition ([1 2 3 4 5 6 7 8 9 10], "resubstitution", 5);
548s  assert (get (C, "NumObservations"), 10);
548s  assert (get (C, "NumTestSets"), 1);
548s  assert (get (C, "TrainSize"), 10);
548s  assert (get (C, "TestSize"), 10);
548s  assert (get (C, "inds"), []);
548s  assert (get (C, "Type"), "resubstitution");
548s ***** test
548s  C = cvpartition ([1 2 3 4 5 6 7 8 9 10], "Given", 2);
548s  assert (get (C, "NumObservations"), 10);
548s  assert (get (C, "NumTestSets"), 10);
548s  assert (get (C, "TrainSize"), ones (10, 1) * 9);
548s  assert (get (C, "TestSize"), ones (10, 1));
548s  assert (get (C, "inds"), [1:10]');
548s  assert (get (C, "Type"), "given");
548s ***** warning<cvpartition: unrecognized type, using KFold.> ...
548s  C = cvpartition ([1 2 3 4 5 6 7 8 9 10], "some", 2);
548s 8 tests, 8 passed, 0 known failure, 0 skipped
548s [inst/@cvpartition/display.m]
548s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/@cvpartition/display.m
548s ***** test
548s  C = cvpartition (ones (10, 1), "KFold", 5);
548s  s = evalc ("display (C)");
548s  sout = "K-fold cross validation partition";
548s  assert (strcmpi (s(1:length (sout)), sout), true);
548s ***** test
548s  C = cvpartition (ones (10, 1), "HoldOut", 5);
548s  s = evalc ("display (C)");
548s  sout = "HoldOut cross validation partition";
548s  assert (strcmpi (s(1:length (sout)), sout), true);
548s ***** test
548s  C = cvpartition (ones (10, 1), "LeaveOut", 5);
548s  s = evalc ("display (C)");
548s  sout = "Leave-One-Out cross validation partition";
548s  assert (strcmpi (s(1:length (sout)), sout), true);
548s ***** test
548s  C = cvpartition (ones (10, 1), "resubstitution", 5);
548s  s = evalc ("display (C)");
548s  sout = "Resubstitution cross validation partition";
548s  assert (strcmpi (s(1:length (sout)), sout), true);
548s ***** test
548s  C = cvpartition (ones (10, 1), "Given", 5);
548s  s = evalc ("display (C)");
548s  sout = "Given cross validation partition";
548s  assert (strcmpi (s(1:length (sout)), sout), true);
548s ***** error<Invalid call to display.  Correct usage is> display ()
548s 6 tests, 6 passed, 0 known failure, 0 skipped
548s [inst/@cvpartition/get.m]
548s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/@cvpartition/get.m
548s ***** shared C
548s  C = cvpartition (ones (10, 1), "KFold", 5);
548s ***** assert (get (C, "NumObservations"), 10);
548s ***** assert (get (C, "NumTestSets"), 5);
548s ***** assert (get (C, "TrainSize"), ones(5,1) * 8);
548s ***** assert (get (C, "TestSize"), ones (5,1) * 2);
548s ***** assert (get (C, "inds"), [1 1 2 2 3 3 4 4 5 5]');
548s ***** assert (get (C, "Type"), "kfold");
548s ***** error<get: invalid property some.> get (C, "some")
548s ***** error<get: expecting the property to be a string.> get (C, 25)
548s ***** error<get: expecting the property to be a string.> get (C, {25})
548s 9 tests, 9 passed, 0 known failure, 0 skipped
548s [inst/@cvpartition/repartition.m]
548s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/@cvpartition/repartition.m
548s ***** test
548s  C = cvpartition (ones (10, 1), "KFold", 5);
548s  Cnew = repartition (C);
548s  assert (isa (Cnew, "cvpartition"), true);
548s ***** test
548s  C = cvpartition (ones (100, 1), "HoldOut", 5);
548s  Cnew = repartition (C);
548s  indC = get (C, "inds");
548s  indCnew = get (Cnew, "inds");
548s  assert (isequal (indC, indCnew), false);
548s 2 tests, 2 passed, 0 known failure, 0 skipped
548s [inst/@cvpartition/set.m]
548s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/@cvpartition/set.m
548s ***** shared C
548s  C = cvpartition (ones (10, 1), "KFold", 5);
548s ***** test
548s  Cnew = set (C, "inds", [1 2 2 2 3 4 3 4 5 5]');
548s  assert (get (Cnew, "inds"), [1 2 2 2 3 4 3 4 5 5]');
548s ***** error<set: expecting field/value pairs.> set (C)
548s ***** error<set: expecting field/value pairs.> set (C, "NumObservations")
548s ***** error<set: invalid field some.> set (C, "some", 15)
548s ***** error<set: expecting the field to be a string.> set (C, 15, 15)
548s 5 tests, 5 passed, 0 known failure, 0 skipped
548s [inst/@cvpartition/test.m]
548s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/@cvpartition/test.m
548s ***** shared C
548s  C = cvpartition (ones (10, 1), "KFold", 5);
548s ***** assert (test (C, 1), logical ([1 1 0 0 0 0 0 0 0 0]'))
548s ***** assert (test (C, 2), logical ([0 0 1 1 0 0 0 0 0 0]'))
548s ***** assert (test (C, 3), logical ([0 0 0 0 1 1 0 0 0 0]'))
548s ***** assert (test (C, 4), logical ([0 0 0 0 0 0 1 1 0 0]'))
548s ***** assert (test (C, 5), logical ([0 0 0 0 0 0 0 0 1 1]'))
548s ***** test
548s  C = set (C, "inds", [1 2 2 2 3 4 3 4 5 5]');
548s ***** assert (test (C), logical ([1 0 0 0 0 0 0 0 0 0]'))
548s ***** assert (test (C, 2), logical ([0 1 1 1 0 0 0 0 0 0]'))
548s ***** assert (test (C, 3), logical ([0 0 0 0 1 0 1 0 0 0]'))
548s 9 tests, 9 passed, 0 known failure, 0 skipped
548s [inst/@cvpartition/training.m]
548s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/@cvpartition/training.m
548s ***** shared C
548s  C = cvpartition (ones (10, 1), "KFold", 5);
548s ***** assert (training (C, 1), logical ([0 0 1 1 1 1 1 1 1 1]'))
548s ***** assert (training (C, 2), logical ([1 1 0 0 1 1 1 1 1 1]'))
548s ***** assert (training (C, 3), logical ([1 1 1 1 0 0 1 1 1 1]'))
548s ***** assert (training (C, 4), logical ([1 1 1 1 1 1 0 0 1 1]'))
548s ***** assert (training (C, 5), logical ([1 1 1 1 1 1 1 1 0 0]'))
548s ***** test
548s  C = set (C, "inds", [1 2 2 2 3 4 3 4 5 5]');
548s ***** assert (training (C), logical ([0 1 1 1 1 1 1 1 1 1]'))
548s ***** assert (training (C, 2), logical ([1 0 0 0 1 1 1 1 1 1]'))
548s ***** assert (training (C, 3), logical ([1 1 1 1 0 1 0 1 1 1]'))
548s 9 tests, 9 passed, 0 known failure, 0 skipped
548s [inst/Classification/ClassificationDiscriminant.m]
548s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/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)
548s ***** test
548s  load fisheriris
548s  x = meas;
548s  y = species;
548s  Mdl = ClassificationDiscriminant (x, y, "Gamma", 0.5);
548s  sigma = [0.265008, 0.046361, 0.083757, 0.019201; ...
548s           0.046361, 0.115388, 0.027622, 0.016355; ...
548s           0.083757, 0.027622, 0.185188, 0.021333; ...
548s           0.019201, 0.016355, 0.021333, 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}, {0.5, 0})
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, -8.6884, 1e-4)
548s ***** shared X, Y, MODEL
548s  X = rand (10,2);
548s  Y = [ones(5,1);2*ones(5,1)];
548s  MODEL = ClassificationDiscriminant (X, Y);
548s ***** error<ClassificationDiscriminant: too few input arguments.> ClassificationDiscriminant ()
548s ***** error<ClassificationDiscriminant: too few input arguments.> ...
548s  ClassificationDiscriminant (ones(4, 1))
548s ***** error<ClassificationDiscriminant: number of rows in X and Y must be equal.> ...
548s  ClassificationDiscriminant (ones (4,2), ones (1,4))
548s ***** error<ClassificationDiscriminant: 'PredictorNames' must be supplied as a cellstring array.> ...
548s  ClassificationDiscriminant (X, Y, "PredictorNames", ["A"])
548s ***** error<ClassificationDiscriminant: 'PredictorNames' must be supplied as a cellstring array.> ...
548s  ClassificationDiscriminant (X, Y, "PredictorNames", "A")
548s ***** error<ClassificationDiscriminant: 'PredictorNames' must equal the number of columns in X.> ...
548s  ClassificationDiscriminant (X, Y, "PredictorNames", {"A", "B", "C"})
548s ***** error<ClassificationDiscriminant: 'ResponseName' must be a character vector.> ...
548s  ClassificationDiscriminant (X, Y, "ResponseName", {"Y"})
548s ***** error<ClassificationDiscriminant: 'ResponseName' must be a character vector.> ...
548s  ClassificationDiscriminant (X, Y, "ResponseName", 1)
548s ***** error<ClassificationDiscriminant: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
548s  ClassificationDiscriminant (X, Y, "ClassNames", @(x)x)
548s ***** error<ClassificationDiscriminant: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
548s  ClassificationDiscriminant (X, Y, "ClassNames", ['a'])
548s ***** error<ClassificationDiscriminant: not all 'ClassNames' are present in Y.> ...
548s  ClassificationDiscriminant (X, ones (10,1), "ClassNames", [1, 2])
548s ***** error<ClassificationDiscriminant: not all 'ClassNames' are present in Y.> ...
548s  ClassificationDiscriminant ([1;2;3;4;5], {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
548s ***** error<ClassificationDiscriminant: not all 'ClassNames' are present in Y.> ...
548s  ClassificationDiscriminant (X, logical (ones (10,1)), "ClassNames", [true, false])
548s ***** error<ClassificationDiscriminant: 'Prior' must be either a numeric or a character vector.> ...
548s  ClassificationDiscriminant (X, Y, "Prior", {"1", "2"})
548s ***** error<ClassificationDiscriminant: the elements in 'Prior' do not correspond to the selected classes in Y.> ...
548s  ClassificationDiscriminant (X, ones (10,1), "Prior", [1 2])
548s ***** error<ClassificationDiscriminant: 'Cost' must be a numeric square matrix.> ...
548s  ClassificationDiscriminant (X, Y, "Cost", [1, 2])
548s ***** error<ClassificationDiscriminant: 'Cost' must be a numeric square matrix.> ...
548s  ClassificationDiscriminant (X, Y, "Cost", "string")
548s ***** error<ClassificationDiscriminant: 'Cost' must be a numeric square matrix.> ...
548s  ClassificationDiscriminant (X, Y, "Cost", {eye(2)})
548s ***** error<ClassificationDiscriminant: the number of rows and columns in 'Cost' must correspond to selected classes in Y.> ...
548s  ClassificationDiscriminant (X, Y, "Cost", ones (3))
548s ***** error<ClassificationDiscriminant: Predictor 'x1' has zero within-class variance.> ...
548s  ClassificationDiscriminant (ones (5,2), [1; 1; 2; 2; 2])
548s ***** error<ClassificationDiscriminant: Predictor 'A' has zero within-class variance.> ...
548s  ClassificationDiscriminant (ones (5,2), [1; 1; 2; 2; 2], "PredictorNames", {"A", "B"})
548s ***** error<ClassificationDiscriminant: Predictor 'x2' has zero within-class variance.> ...
548s  ClassificationDiscriminant ([1,2;2,2;3,2;4,2;5,2], ones (5, 1))
548s ***** error<ClassificationDiscriminant: Predictor 'B' has zero within-class variance.> ...
548s  ClassificationDiscriminant ([1,2;2,2;3,2;4,2;5,2], ones (5, 1), "PredictorNames", {"A", "B"})
548s ***** test
548s  load fisheriris
548s  x = meas;
548s  y = species;
548s  Mdl = fitcdiscr (meas, species, "Gamma", 0.5);
548s  [label, score, cost] = predict (Mdl, [2, 2, 2, 2]);
548s  assert (label, {'versicolor'})
548s  assert (score, [0, 0.9999, 0.0001], 1e-4)
548s  assert (cost, [1, 0.0001, 0.9999], 1e-4)
548s  [label, score, cost] = predict (Mdl, [2.5, 2.5, 2.5, 2.5]);
548s  assert (label, {'versicolor'})
548s  assert (score, [0, 0.6368, 0.3632], 1e-4)
548s  assert (cost, [1, 0.3632, 0.6368], 1e-4)
548s ***** test
548s  load fisheriris
548s  x = meas;
548s  y = species;
548s  xc = [min(x); mean(x); max(x)];
548s  Mdl = fitcdiscr (x, y);
548s  [label, score, cost] = predict (Mdl, xc);
548s  l = {'setosa'; 'versicolor'; 'virginica'};
548s  s = [1, 0, 0; 0, 1, 0; 0, 0, 1];
548s  c = [0, 1, 1; 1, 0, 1; 1, 1, 0];
548s  assert (label, l)
548s  assert (score, s, 1e-4)
548s  assert (cost, c, 1e-4)
548s ***** error<ClassificationDiscriminant.predict: too few input arguments.> ...
548s  predict (MODEL)
548s ***** error<ClassificationDiscriminant.predict: XC is empty.> ...
548s  predict (MODEL, [])
548s ***** error<ClassificationDiscriminant.predict: XC must have the same number of predictors as the trained model.> ...
548s  predict (MODEL, 1)
548s ***** test
548s  load fisheriris
548s  model = fitcdiscr (meas, species);
548s  x = mean (meas);
548s  y = {'versicolor'};
548s  L = loss (model, x, y);
548s  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")
551s ***** test
551s  partition = cvpartition (y, 'KFold', 3);
551s  CVMdl = crossval (obj, 'cvPartition', partition);
551s  assert (class (CVMdl), "ClassificationPartitionedModel")
551s  assert (CVMdl.KFold == 3)
551s  assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
551s  assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
551s ***** error<ClassificationDiscriminant.crossval: Name-Value arguments must be in pairs.> ...
551s  crossval (obj, "kfold")
551s ***** error<ClassificationDiscriminant.crossval: specify only one of the optional Name-Value paired arguments.>...
551s  crossval (obj, "kfold", 12, "holdout", 0.2)
551s ***** error<ClassificationDiscriminant.crossval: 'KFold' must be an integer value greater than 1.> ...
551s  crossval (obj, "kfold", 'a')
551s ***** error<ClassificationDiscriminant.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
551s  crossval (obj, "holdout", 2)
551s ***** error<ClassificationDiscriminant.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
551s  crossval (obj, "leaveout", 1)
551s ***** error<ClassificationDiscriminant.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
551s  crossval (obj, "cvpartition", 1)
551s 65 tests, 65 passed, 0 known failure, 0 skipped
551s [inst/Classification/ClassificationGAM.m]
551s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/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)
553s ***** test
553s  load fisheriris
553s  inds = strcmp (species,'versicolor') | strcmp (species,'virginica');
553s  X = meas(inds, :);
553s  Y = species(inds, :)';
553s  Y = strcmp (Y, 'virginica')';
553s  a = ClassificationGAM (X, Y, 'Formula', 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3');
553s  assert (class (a), "ClassificationGAM");
553s  assert ({a.X, a.Y, a.NumObservations}, {X, Y, 100})
553s  assert ({a.NumPredictors, a.ResponseName}, {4, "Y"})
553s  assert (a.ClassNames, {'0'; '1'})
553s  assert (a.Formula, 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3')
553s  assert (a.PredictorNames, {'x1', 'x2', 'x3', 'x4'})
553s  assert (a.ModelwInt.Intercept, 0)
559s ***** test
559s  X = [2, 3, 5; 4, 6, 8; 1, 2, 3; 7, 8, 9; 5, 4, 3];
559s  Y = [0; 1; 0; 1; 1];
559s  a = ClassificationGAM (X, Y, 'Knots', [4, 4, 4], 'Order', [3, 3, 3]);
559s  assert (class (a), "ClassificationGAM");
559s  assert ({a.X, a.Y, a.NumObservations}, {X, Y, 5})
559s  assert ({a.NumPredictors, a.ResponseName}, {3, "Y"})
559s  assert (a.ClassNames, {'0'; '1'})
559s  assert (a.PredictorNames, {'x1', 'x2', 'x3'})
559s  assert (a.Knots, [4, 4, 4])
559s  assert (a.Order, [3, 3, 3])
559s  assert (a.DoF, [7, 7, 7])
559s  assert (a.BaseModel.Intercept, 0.4055, 1e-1)
560s ***** error<ClassificationGAM: too few input arguments.> ClassificationGAM ()
560s ***** error<ClassificationGAM: too few input arguments.> ...
560s  ClassificationGAM (ones(4, 1))
560s ***** error<ClassificationGAM: number of rows in X and Y must be equal.> ...
560s  ClassificationGAM (ones (4,2), ones (1,4))
560s ***** error<ClassificationGAM: 'PredictorNames' must be supplied as a cellstring array.> ...
560s  ClassificationGAM (ones (5,2), ones (5,1), "PredictorNames", ["A"])
560s ***** error<ClassificationGAM: 'PredictorNames' must be supplied as a cellstring array.> ...
560s  ClassificationGAM (ones (5,2), ones (5,1), "PredictorNames", "A")
560s ***** error<ClassificationGAM: 'PredictorNames' must equal the number of columns in X.> ...
560s  ClassificationGAM (ones (5,2), ones (5,1), "PredictorNames", {"A", "B", "C"})
560s ***** error<ClassificationGAM: 'ResponseName' must be a character vector.> ...
560s  ClassificationGAM (ones (5,2), ones (5,1), "ResponseName", {"Y"})
560s ***** error<ClassificationGAM: 'ResponseName' must be a character vector.> ...
560s  ClassificationGAM (ones (5,2), ones (5,1), "ResponseName", 1)
560s ***** error<ClassificationGAM: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
560s  ClassificationGAM (ones(10,2), ones (10,1), "ClassNames", @(x)x)
560s ***** error<ClassificationGAM: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
560s  ClassificationGAM (ones(10,2), ones (10,1), "ClassNames", ['a'])
560s ***** error<ClassificationGAM: not all 'ClassNames' are present in Y.> ...
560s  ClassificationGAM (ones(10,2), ones (10,1), "ClassNames", [1, 2])
560s ***** error<ClassificationGAM: not all 'ClassNames' are present in Y.> ...
560s  ClassificationGAM (ones(5,2), {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
560s ***** error<ClassificationGAM: not all 'ClassNames' are present in Y.> ...
560s  ClassificationGAM (ones(10,2), logical (ones (10,1)), "ClassNames", [true, false])
560s ***** error<ClassificationGAM: 'Cost' must be a numeric square matrix.> ...
560s  ClassificationGAM (ones (5,2), ones (5,1), "Cost", [1, 2])
560s ***** error<ClassificationGAM: 'Cost' must be a numeric square matrix.> ...
560s  ClassificationGAM (ones (5,2), ones (5,1), "Cost", "string")
560s ***** error<ClassificationGAM: 'Cost' must be a numeric square matrix.> ...
560s  ClassificationGAM (ones (5,2), ones (5,1), "Cost", {eye(2)})
560s ***** test
560s  x = [1, 2; 3, 4; 5, 6; 7, 8; 9, 10];
560s  y = [1; 0; 1; 0; 1];
560s  a = ClassificationGAM (x, y, "interactions", "all");
560s  l = {'0'; '0'; '0'; '0'; '0'};
560s  s = [0.3760, 0.6240; 0.4259, 0.5741; 0.3760, 0.6240; ...
560s       0.4259, 0.5741; 0.3760, 0.6240];
560s  [labels, scores] = predict (a, x);
560s  assert (class (a), "ClassificationGAM");
560s  assert ({a.X, a.Y, a.NumObservations}, {x, y, 5})
560s  assert ({a.NumPredictors, a.ResponseName}, {2, "Y"})
560s  assert (a.ClassNames, {'1'; '0'})
560s  assert (a.PredictorNames, {'x1', 'x2'})
560s  assert (a.ModelwInt.Intercept, 0.4055, 1e-1)
560s  assert (labels, l)
560s  assert (scores, s, 1e-1)
563s ***** test
563s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
563s  y = [0; 0; 1; 1];
563s  interactions = [false, true, false; true, false, true; false, true, false];
563s  a = fitcgam (x, y, "learningrate", 0.2, "interactions", interactions);
563s  [label, score] = predict (a, x, "includeinteractions", true);
563s  l = {'0'; '0'; '1'; '1'};
563s  s = [0.5106, 0.4894; 0.5135, 0.4865; 0.4864, 0.5136; 0.4847, 0.5153];
563s  assert (class (a), "ClassificationGAM");
563s  assert ({a.X, a.Y, a.NumObservations}, {x, y, 4})
563s  assert ({a.NumPredictors, a.ResponseName}, {3, "Y"})
563s  assert (a.ClassNames, {'0'; '1'})
563s  assert (a.PredictorNames, {'x1', 'x2', 'x3'})
563s  assert (a.ModelwInt.Intercept, 0)
563s  assert (label, l)
563s  assert (score, s, 1e-1)
568s ***** error<ClassificationGAM.predict: too few input arguments.> ...
568s  predict (ClassificationGAM (ones (4,2), ones (4,1)))
572s ***** error<ClassificationGAM.predict: XC is empty.> ...
572s  predict (ClassificationGAM (ones (4,2), ones (4,1)), [])
572s ***** error<ClassificationGAM.predict: XC must have the same number of predictors as the trained model.> ...
572s  predict (ClassificationGAM (ones (4,2), ones (4,1)), 1)
572s ***** shared x, y, obj
572s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
572s  y = [0; 0; 1; 1];
572s  obj = fitcgam (x, y);
573s ***** test
573s  CVMdl = crossval (obj);
573s  assert (class (CVMdl), "ClassificationPartitionedModel")
573s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
573s  assert (CVMdl.KFold == 10)
573s  assert (class (CVMdl.Trained{1}), "CompactClassificationGAM")
573s  assert (CVMdl.CrossValidatedModel, "ClassificationGAM")
590s ***** test
590s  CVMdl = crossval (obj, "KFold", 5);
590s  assert (class (CVMdl), "ClassificationPartitionedModel")
590s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
590s  assert (CVMdl.KFold == 5)
590s  assert (class (CVMdl.Trained{1}), "CompactClassificationGAM")
590s  assert (CVMdl.CrossValidatedModel, "ClassificationGAM")
598s ***** test
598s  CVMdl = crossval (obj, "HoldOut", 0.2);
598s  assert (class (CVMdl), "ClassificationPartitionedModel")
598s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
598s  assert (class (CVMdl.Trained{1}), "CompactClassificationGAM")
598s  assert (CVMdl.CrossValidatedModel, "ClassificationGAM")
600s ***** test
600s  partition = cvpartition (y, 'KFold', 3);
600s  CVMdl = crossval (obj, 'cvPartition', partition);
600s  assert (class (CVMdl), "ClassificationPartitionedModel")
600s  assert (CVMdl.KFold == 3)
600s  assert (class (CVMdl.Trained{1}), "CompactClassificationGAM")
600s  assert (CVMdl.CrossValidatedModel, "ClassificationGAM")
605s ***** error<ClassificationGAM.crossval: Name-Value arguments must be in pairs.> ...
605s  crossval (obj, "kfold")
605s ***** error<ClassificationGAM.crossval: specify only one of the optional Name-Value paired arguments.>...
605s  crossval (obj, "kfold", 12, "holdout", 0.2)
605s ***** error<ClassificationGAM.crossval: 'KFold' must be an integer value greater than 1.> ...
605s  crossval (obj, "kfold", 'a')
605s ***** error<ClassificationGAM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
605s  crossval (obj, "holdout", 2)
605s ***** error<ClassificationGAM.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
605s  crossval (obj, "leaveout", 1)
605s ***** error<ClassificationGAM.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
605s  crossval (obj, "cvpartition", 1)
605s 34 tests, 34 passed, 0 known failure, 0 skipped
605s [inst/Classification/ClassificationKNN.m]
605s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/Classification/ClassificationKNN.m
605s ***** demo
605s  ## Create a k-nearest neighbor classifier for Fisher's iris data with k = 5.
605s  ## Evaluate some model predictions on new data.
605s 
605s  load fisheriris
605s  x = meas;
605s  y = species;
605s  xc = [min(x); mean(x); max(x)];
605s  obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);
605s  [label, score, cost] = predict (obj, xc)
605s ***** demo
605s  load fisheriris
605s  x = meas;
605s  y = species;
605s  obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);
605s 
605s  ## Create a cross-validated model
605s  CVMdl = crossval (obj)
605s ***** demo
605s  load fisheriris
605s  x = meas;
605s  y = species;
605s  covMatrix = cov (x);
605s 
605s  ## Fit the k-NN model using the 'mahalanobis' distance
605s  ## and the custom covariance matrix
605s  obj = fitcknn(x, y, 'NumNeighbors', 5, 'Distance','mahalanobis', ...
605s  'Cov', covMatrix);
605s 
605s  ## Create a partition model using cvpartition
605s  Partition = cvpartition (size (x, 1), 'kfold', 12);
605s 
605s  ## Create cross-validated model using 'cvPartition' name-value argument
605s  CVMdl = crossval (obj, 'cvPartition', Partition)
605s 
605s  ## Access the trained model from first fold of cross-validation
605s  CVMdl.Trained{1}
605s ***** demo
605s  X = [1, 2; 3, 4; 5, 6];
605s  Y = {'A'; 'B'; 'A'};
605s  model = fitcknn (X, Y);
605s  customLossFun = @(C, S, W, Cost) sum (W .* sum (abs (C - S), 2));
605s  ## Calculate loss using custom loss function
605s  L = loss (model, X, Y, 'LossFun', customLossFun)
605s ***** demo
605s  X = [1, 2; 3, 4; 5, 6];
605s  Y = {'A'; 'B'; 'A'};
605s  model = fitcknn (X, Y);
605s  ## Calculate loss using 'mincost' loss function
605s  L = loss (model, X, Y, 'LossFun', 'mincost')
605s ***** demo
605s  X = [1, 2; 3, 4; 5, 6];
605s  Y = ['1'; '2'; '3'];
605s  model = fitcknn (X, Y);
605s  X_test = [3, 3; 5, 7];
605s  Y_test = ['1'; '2'];
605s  ## Specify custom Weights
605s  W = [1; 2];
605s  L = loss (model, X_test, Y_test, 'LossFun', 'logit', 'Weights', W);
605s ***** demo
605s  load fisheriris
605s  mdl = fitcknn (meas, species);
605s  X = mean (meas);
605s  Y = {'versicolor'};
605s  m = margin (mdl, X, Y)
605s ***** demo
605s  X = [1, 2; 4, 5; 7, 8; 3, 2];
605s  Y = [2; 1; 3; 2];
605s  ## Train the model
605s  mdl = fitcknn (X, Y);
605s  ## Specify Vars and Labels
605s  Vars = 1;
605s  Labels = 2;
605s  ## Calculate partialDependence
605s  [pd, x, y] = partialDependence (mdl, Vars, Labels);
605s ***** demo
605s  X = [1, 2; 4, 5; 7, 8; 3, 2];
605s  Y = [2; 1; 3; 2];
605s  ## Train the model
605s  mdl = fitcknn (X, Y);
605s  ## Specify Vars and Labels
605s  Vars = 1;
605s  Labels = 1;
605s  queryPoints = [linspace(0, 1, 3)', linspace(0, 1, 3)'];
605s  ## Calculate partialDependence using queryPoints
605s  [pd, x, y] = partialDependence (mdl, Vars, Labels, 'QueryPoints', ...
605s  queryPoints)
605s ***** test
605s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
605s  y = ["a"; "a"; "b"; "b"];
605s  a = ClassificationKNN (x, y);
605s  assert (class (a), "ClassificationKNN");
605s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
605s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
605s  assert ({a.BucketSize}, {50})
605s ***** test
605s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
605s  y = ["a"; "a"; "b"; "b"];
605s  a = ClassificationKNN (x, y, "NSMethod", "exhaustive");
605s  assert (class (a), "ClassificationKNN");
605s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
605s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
605s  assert ({a.BucketSize}, {50})
605s ***** test
605s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
605s  y = ["a"; "a"; "b"; "b"];
605s  k = 10;
605s  a = ClassificationKNN (x, y, "NumNeighbors" ,k);
605s  assert (class (a), "ClassificationKNN");
605s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
605s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
605s  assert ({a.BucketSize}, {50})
605s ***** test
605s  x = ones (4, 11);
605s  y = ["a"; "a"; "b"; "b"];
605s  k = 10;
605s  a = ClassificationKNN (x, y, "NumNeighbors" ,k);
605s  assert (class (a), "ClassificationKNN");
605s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
605s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
605s  assert ({a.BucketSize}, {50})
605s ***** test
605s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
605s  y = ["a"; "a"; "b"; "b"];
605s  k = 10;
605s  a = ClassificationKNN (x, y, "NumNeighbors" ,k, "NSMethod", "exhaustive");
605s  assert (class (a), "ClassificationKNN");
605s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
605s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
605s  assert ({a.BucketSize}, {50})
605s ***** test
605s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
605s  y = ["a"; "a"; "b"; "b"];
605s  k = 10;
605s  a = ClassificationKNN (x, y, "NumNeighbors" ,k, "Distance", "hamming");
605s  assert (class (a), "ClassificationKNN");
605s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
605s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "hamming"})
605s  assert ({a.BucketSize}, {50})
605s ***** test
605s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
605s  y = ["a"; "a"; "b"; "b"];
605s  weights = ones (4,1);
605s  a = ClassificationKNN (x, y, "Standardize", 1);
605s  assert (class (a), "ClassificationKNN");
605s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
605s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
605s  assert ({a.Standardize}, {true})
605s  assert ({a.Sigma}, {std(x, [], 1)})
605s  assert ({a.Mu}, {[3.75, 4.25, 4.75]})
605s ***** test
605s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
605s  y = ["a"; "a"; "b"; "b"];
605s  weights = ones (4,1);
605s  a = ClassificationKNN (x, y, "Standardize", false);
605s  assert (class (a), "ClassificationKNN");
605s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
605s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
605s  assert ({a.Standardize}, {false})
605s  assert ({a.Sigma}, {[]})
605s  assert ({a.Mu}, {[]})
605s ***** test
605s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
605s  y = ["a"; "a"; "b"; "b"];
605s  s = ones (1, 3);
605s  a = ClassificationKNN (x, y, "Scale" , s, "Distance", "seuclidean");
605s  assert (class (a), "ClassificationKNN");
605s  assert ({a.DistParameter}, {s})
605s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "seuclidean"})
605s  assert ({a.BucketSize}, {50})
605s ***** test
605s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
605s  y = ["a"; "a"; "b"; "b"];
605s  a = ClassificationKNN (x, y, "Exponent" , 5, "Distance", "minkowski");
605s  assert (class (a), "ClassificationKNN");
605s  assert (a.DistParameter, 5)
605s  assert ({a.NSMethod, a.Distance}, {"kdtree", "minkowski"})
605s ***** test
605s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
605s  y = ["a"; "a"; "b"; "b"];
605s  a = ClassificationKNN (x, y, "Exponent" , 5, "Distance", "minkowski", ...
605s                         "NSMethod", "exhaustive");
605s  assert (class (a), "ClassificationKNN");
605s  assert (a.DistParameter, 5)
605s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "minkowski"})
605s ***** test
605s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
605s  y = ["a"; "a"; "b"; "b"];
605s  a = ClassificationKNN (x, y, "BucketSize" , 20, "distance", "mahalanobis");
605s  assert (class (a), "ClassificationKNN");
605s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "mahalanobis"})
605s  assert ({a.BucketSize}, {20})
605s ***** test
605s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
605s  y = ["a"; "a"; "b"; "b"];
605s  a = ClassificationKNN (x, y, "IncludeTies", true);
605s  assert (class (a), "ClassificationKNN");
605s  assert (a.IncludeTies, true);
605s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
605s ***** test
605s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
605s  y = ["a"; "a"; "b"; "b"];
605s  a = ClassificationKNN (x, y);
605s  assert (class (a), "ClassificationKNN");
605s  assert (a.IncludeTies, false);
605s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
605s ***** test
605s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
605s  y = ["a"; "a"; "b"; "b"];
605s  a = ClassificationKNN (x, y);
605s  assert (class (a), "ClassificationKNN")
605s  assert (a.Prior, [0.5; 0.5])
605s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
605s  assert ({a.BucketSize}, {50})
605s ***** test
605s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
605s  y = ["a"; "a"; "b"; "b"];
605s  prior = [0.5; 0.5];
605s  a = ClassificationKNN (x, y, "Prior", "empirical");
605s  assert (class (a), "ClassificationKNN")
605s  assert (a.Prior, prior)
605s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
605s  assert ({a.BucketSize}, {50})
605s ***** test
605s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
605s  y = ["a"; "a"; "a"; "b"];
605s  prior = [0.75; 0.25];
605s  a = ClassificationKNN (x, y, "Prior", "empirical");
605s  assert (class (a), "ClassificationKNN")
605s  assert (a.Prior, prior)
605s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
605s  assert ({a.BucketSize}, {50})
605s ***** test
605s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
605s  y = ["a"; "a"; "a"; "b"];
605s  prior = [0.5; 0.5];
605s  a = ClassificationKNN (x, y, "Prior", "uniform");
605s  assert (class (a), "ClassificationKNN")
605s  assert (a.Prior, prior)
605s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
605s  assert ({a.BucketSize}, {50})
605s ***** test
605s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
605s  y = ["a"; "a"; "b"; "b"];
605s  cost = eye (2);
605s  a = ClassificationKNN (x, y, "Cost", cost);
605s  assert (class (a), "ClassificationKNN")
605s  assert (a.Cost, [1, 0; 0, 1])
605s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
605s  assert ({a.BucketSize}, {50})
605s ***** test
605s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
605s  y = ["a"; "a"; "b"; "b"];
605s  cost = eye (2);
605s  a = ClassificationKNN (x, y, "Cost", cost, "Distance", "hamming" );
605s  assert (class (a), "ClassificationKNN")
605s  assert (a.Cost, [1, 0; 0, 1])
605s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "hamming"})
605s  assert ({a.BucketSize}, {50})
605s ***** test
605s  x = [1, 2; 3, 4; 5,6; 5, 8];
605s  y = {'9'; '9'; '6'; '7'};
605s  a = ClassificationKNN (x, y);
605s  assert (a.Prior, [0.5; 0.25; 0.25])
605s ***** test
605s  load fisheriris
605s  x = meas;
605s  y = species;
605s  ClassNames = {'setosa', 'versicolor', 'virginica'};
605s  a = ClassificationKNN (x, y, 'ClassNames', ClassNames);
605s  assert (a.ClassNames, ClassNames')
605s ***** error<ClassificationKNN: too few input arguments.> ClassificationKNN ()
605s ***** error<ClassificationKNN: too few input arguments.> ...
605s  ClassificationKNN (ones(4, 1))
605s ***** error<ClassificationKNN: number of rows in X and Y must be equal.> ...
605s  ClassificationKNN (ones (4,2), ones (1,4))
605s ***** error<ClassificationKNN: 'Standardize' must be either true or false.> ...
605s  ClassificationKNN (ones (5,3), ones (5,1), "standardize", "a")
605s ***** error<ClassificationKNN: 'Standardize' cannot simultaneously be specified with> ...
605s  ClassificationKNN (ones (5,2), ones (5,1), "scale", [1 1], "standardize", true)
605s ***** error<ClassificationKNN: 'PredictorNames' must be supplied as a cellstring array.> ...
605s  ClassificationKNN (ones (5,2), ones (5,1), "PredictorNames", ["A"])
605s ***** error<ClassificationKNN: 'PredictorNames' must be supplied as a cellstring array.> ...
605s  ClassificationKNN (ones (5,2), ones (5,1), "PredictorNames", "A")
605s ***** error<ClassificationKNN: 'PredictorNames' must have the same number of columns as X.> ...
605s  ClassificationKNN (ones (5,2), ones (5,1), "PredictorNames", {"A", "B", "C"})
605s ***** error<ClassificationKNN: 'ResponseName' must be a character vector.> ...
605s  ClassificationKNN (ones (5,2), ones (5,1), "ResponseName", {"Y"})
605s ***** error<ClassificationKNN: 'ResponseName' must be a character vector.> ...
605s  ClassificationKNN (ones (5,2), ones (5,1), "ResponseName", 1)
605s ***** error<ClassificationKNN: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
605s  ClassificationKNN (ones(10,2), ones (10,1), "ClassNames", @(x)x)
605s ***** error<ClassificationKNN: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
605s  ClassificationKNN (ones(10,2), ones (10,1), "ClassNames", ['a'])
605s ***** error<ClassificationKNN: not all 'ClassNames' are present in Y.> ...
605s  ClassificationKNN (ones(10,2), ones (10,1), "ClassNames", [1, 2])
605s ***** error<ClassificationKNN: not all 'ClassNames' are present in Y.> ...
605s  ClassificationKNN (ones(5,2), {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
605s ***** error<ClassificationKNN: not all 'ClassNames' are present in Y.> ...
605s  ClassificationKNN (ones(10,2), logical (ones (10,1)), "ClassNames", [true, false])
605s ***** error<ClassificationKNN: 'BreakTies' must be a character vector.> ...
605s  ClassificationKNN (ones (5,2), ones (5,1), "BreakTies", 1)
605s ***** error<ClassificationKNN: 'BreakTies' must be a character vector.> ...
605s  ClassificationKNN (ones (5,2), ones (5,1), "BreakTies", {"1"})
605s ***** error<ClassificationKNN: invalid value for 'BreakTies'.> ...
605s  ClassificationKNN (ones (5,2), ones (5,1), "BreakTies", "some")
605s ***** error<ClassificationKNN: 'Prior' must be either a numeric vector or a character vector.> ...
605s  ClassificationKNN (ones (5,2), ones (5,1), "Prior", {"1", "2"})
605s ***** error<ClassificationKNN: 'Cost' must be a numeric square matrix.> ...
605s  ClassificationKNN (ones (5,2), ones (5,1), "Cost", [1, 2])
605s ***** error<ClassificationKNN: 'Cost' must be a numeric square matrix.> ...
605s  ClassificationKNN (ones (5,2), ones (5,1), "Cost", "string")
605s ***** error<ClassificationKNN: 'Cost' must be a numeric square matrix.> ...
605s  ClassificationKNN (ones (5,2), ones (5,1), "Cost", {eye(2)})
605s ***** error<ClassificationKNN: 'NumNeighbors' must be a positive integer.> ...
605s  ClassificationKNN (ones (5,2), ones (5,1), "NumNeighbors", 0)
605s ***** error<ClassificationKNN: 'NumNeighbors' must be a positive integer.> ...
605s  ClassificationKNN (ones (5,2), ones (5,1), "NumNeighbors", 15.2)
605s ***** error<ClassificationKNN: 'NumNeighbors' must be a positive integer.> ...
605s  ClassificationKNN (ones (5,2), ones (5,1), "NumNeighbors", "asd")
605s ***** error<ClassificationKNN: unsupported distance metric.> ...
605s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", "somemetric")
605s ***** error<ClassificationKNN: invalid function handle for distance metric.> ...
605s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", ...
605s                     @(v,m)sqrt(repmat(v,rows(m),1)-m,2))
606s ***** error<ClassificationKNN: custom distance function produces wrong output size.> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", ...
606s                     @(v,m)sqrt(sum(sumsq(repmat(v,rows(m),1)-m,2))))
606s ***** error<ClassificationKNN: invalid distance metric.> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", [1 2 3])
606s ***** error<ClassificationKNN: invalid distance metric.> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", {"mahalanobis"})
606s ***** error<ClassificationKNN: invalid distance metric.> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", logical (5))
606s ***** error<ClassificationKNN: function handle for distance weight must return the> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "DistanceWeight", @(x)sum(x))
606s ***** error<ClassificationKNN: invalid distance weight.> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "DistanceWeight", "text")
606s ***** error<ClassificationKNN: invalid distance weight.> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "DistanceWeight", [1 2 3])
606s ***** error<ClassificationKNN: 'Scale' must be a numeric vector.> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "Scale", "scale")
606s ***** error<ClassificationKNN: 'Scale' must be a numeric vector.> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "Scale", {[1 2 3]})
606s ***** error<ClassificationKNN: 'Scale' cannot simultaneously be specified with> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "standardize", true, "scale", [1 1])
606s ***** error<ClassificationKNN: 'Cov' must be a symmetric positive definite matrix.> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "Cov", ones (2), "Distance", "mahalanobis")
606s ***** error<ClassificationKNN: 'Cov' cannot simultaneously be specified with> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "scale", [1 1], "Cov", ones (2))
606s ***** error<ClassificationKNN: 'Exponent' must be a positive integer.> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "Exponent", 12.5)
606s ***** error<ClassificationKNN: 'Exponent' must be a positive integer.> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "Exponent", -3)
606s ***** error<ClassificationKNN: 'Exponent' must be a positive integer.> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "Exponent", "three")
606s ***** error<ClassificationKNN: 'Exponent' must be a positive integer.> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "Exponent", {3})
606s ***** error<ClassificationKNN: 'NSMethod' must be a character vector.> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "NSMethod", {"kdtree"})
606s ***** error<ClassificationKNN: 'NSMethod' must be a character vector.> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "NSMethod", 3)
606s ***** error<ClassificationKNN: 'NSMethod' must be either 'kdtree' or 'exhaustive'.> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "NSMethod", "some")
606s ***** error<ClassificationKNN: 'IncludeTies' must be either true or false.> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "IncludeTies", "some")
606s ***** error<ClassificationKNN: 'BucketSize' must be a positive integer.> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "BucketSize", 42.5)
606s ***** error<ClassificationKNN: 'BucketSize' must be a positive integer.> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "BucketSize", -50)
606s ***** error<ClassificationKNN: 'BucketSize' must be a positive integer.> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "BucketSize", "some")
606s ***** error<ClassificationKNN: 'BucketSize' must be a positive integer.> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "BucketSize", {50})
606s ***** error<ClassificationKNN: invalid parameter name in optional pair arguments.> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "some", "some")
606s ***** error<ClassificationKNN: invalid values in X.> ...
606s  ClassificationKNN ([1;2;3;'a';4], ones (5,1))
606s ***** error<ClassificationKNN: invalid values in X.> ...
606s  ClassificationKNN ([1;2;3;Inf;4], ones (5,1))
606s ***** error<ClassificationKNN: the elements in 'Prior' do not correspond to selected classes in Y.> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "Prior", [1 2])
606s ***** error<ClassificationKNN: the number of rows and columns in 'Cost' must correspond to selected classes in Y.> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "Cost", [1 2; 1 3])
606s ***** error<ClassificationKNN: 'Scale' is only valid when distance metric is seuclidean.> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "Scale", [1 1])
606s ***** error<ClassificationKNN: 'Scale' vector must have equal length to the number of columns in X.> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "Scale", [1 1 1], "Distance", "seuclidean")
606s ***** error<ClassificationKNN: 'Scale' vector must contain nonnegative scalar values.> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "Scale", [1 -1], "Distance", "seuclidean")
606s ***** error<ClassificationKNN: 'Cov' is only valid when distance metric is 'mahalanobis'.> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "Cov", eye (2))
606s ***** error<ClassificationKNN: 'Cov' matrix must have equal columns as X.> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "Cov", eye (3), "Distance", "mahalanobis")
606s ***** error<ClassificationKNN: 'Exponent' is only valid when distance metric is 'minkowski'.> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "Exponent", 3)
606s ***** error<ClassificationKNN: 'kdtree' method is only valid for 'euclidean', 'cityblock', 'manhattan', 'minkowski', and 'chebychev' distance metrics.> ...
606s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", "hamming", "NSMethod", "kdtree")
606s ***** shared x, y
606s  load fisheriris
606s  x = meas;
606s  y = species;
606s ***** test
606s  xc = [min(x); mean(x); max(x)];
606s  obj = fitcknn (x, y, "NumNeighbors", 5);
606s  [l, s, c] = predict (obj, xc);
606s  assert (l, {"setosa"; "versicolor"; "virginica"})
606s  assert (s, [1, 0, 0; 0, 1, 0; 0, 0, 1])
606s  assert (c, [0, 1, 1; 1, 0, 1; 1, 1, 0])
606s ***** test
606s  xc = [min(x); mean(x); max(x)];
606s  obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);
606s  [l, s, c] = predict (obj, xc);
606s  assert (l, {"versicolor"; "versicolor"; "virginica"})
606s  assert (s, [0.4, 0.6, 0; 0, 1, 0; 0, 0, 1])
606s  assert (c, [0.6, 0.4, 1; 1, 0, 1; 1, 1, 0])
606s ***** test
606s  xc = [min(x); mean(x); max(x)];
606s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "mahalanobis");
606s  [l, s, c] = predict (obj, xc);
606s  assert (s, [0.3, 0.7, 0; 0, 0.9, 0.1; 0.2, 0.2, 0.6], 1e-4)
606s  assert (c, [0.7, 0.3, 1; 1, 0.1, 0.9; 0.8, 0.8, 0.4], 1e-4)
606s ***** test
606s  xc = [min(x); mean(x); max(x)];
606s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "cosine");
606s  [l, s, c] = predict (obj, xc);
606s  assert (l, {"setosa"; "versicolor"; "virginica"})
606s  assert (s, [1, 0, 0; 0, 1, 0; 0, 0.3, 0.7], 1e-4)
606s  assert (c, [0, 1, 1; 1, 0, 1; 1, 0.7, 0.3], 1e-4)
606s ***** test
606s  xc = [5.2, 4.1, 1.5, 0.1; 5.1, 3.8, 1.9, 0.4; ...
606s          5.1, 3.8, 1.5, 0.3; 4.9, 3.6, 1.4, 0.1];
606s  obj = fitcknn (x, y, "NumNeighbors", 5);
606s  [l, s, c] = predict (obj, xc);
606s  assert (l, {"setosa"; "setosa"; "setosa"; "setosa"})
606s  assert (s, [1, 0, 0; 1, 0, 0; 1, 0, 0; 1, 0, 0])
606s  assert (c, [0, 1, 1; 0, 1, 1; 0, 1, 1; 0, 1, 1])
606s ***** test
606s  xc = [5, 3, 5, 1.45];
606s  obj = fitcknn (x, y, "NumNeighbors", 5);
606s  [l, s, c] = predict (obj, xc);
606s  assert (l, {"versicolor"})
606s  assert (s, [0, 0.6, 0.4], 1e-4)
606s  assert (c, [1, 0.4, 0.6], 1e-4)
606s ***** test
606s  xc = [5, 3, 5, 1.45];
606s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "minkowski", "Exponent", 5);
606s  [l, s, c] = predict (obj, xc);
606s  assert (l, {"versicolor"})
606s  assert (s, [0, 0.5, 0.5], 1e-4)
606s  assert (c, [1, 0.5, 0.5], 1e-4)
606s ***** test
606s  xc = [5, 3, 5, 1.45];
606s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "jaccard");
606s  [l, s, c] = predict (obj, xc);
606s  assert (l, {"setosa"})
606s  assert (s, [0.9, 0.1, 0], 1e-4)
606s  assert (c, [0.1, 0.9, 1], 1e-4)
606s ***** test
606s  xc = [5, 3, 5, 1.45];
606s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "mahalanobis");
606s  [l, s, c] = predict (obj, xc);
606s  assert (l, {"versicolor"})
606s  assert (s, [0.1000, 0.5000, 0.4000], 1e-4)
606s  assert (c, [0.9000, 0.5000, 0.6000], 1e-4)
606s ***** test
606s  xc = [5, 3, 5, 1.45];
606s  obj = fitcknn (x, y, "NumNeighbors", 5, "distance", "jaccard");
606s  [l, s, c] = predict (obj, xc);
606s  assert (l, {"setosa"})
606s  assert (s, [0.8, 0.2, 0], 1e-4)
606s  assert (c, [0.2, 0.8, 1], 1e-4)
606s ***** test
606s  xc = [5, 3, 5, 1.45];
606s  obj = fitcknn (x, y, "NumNeighbors", 5, "distance", "seuclidean");
606s  [l, s, c] = predict (obj, xc);
606s  assert (l, {"versicolor"})
606s  assert (s, [0, 1, 0], 1e-4)
606s  assert (c, [1, 0, 1], 1e-4)
606s ***** test
606s  xc = [5, 3, 5, 1.45];
606s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "chebychev");
606s  [l, s, c] = predict (obj, xc);
606s  assert (l, {"versicolor"})
606s  assert (s, [0, 0.7, 0.3], 1e-4)
606s  assert (c, [1, 0.3, 0.7], 1e-4)
606s ***** test
606s  xc = [5, 3, 5, 1.45];
606s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "cityblock");
606s  [l, s, c] = predict (obj, xc);
606s  assert (l, {"versicolor"})
606s  assert (s, [0, 0.6, 0.4], 1e-4)
606s  assert (c, [1, 0.4, 0.6], 1e-4)
606s ***** test
606s  xc = [5, 3, 5, 1.45];
606s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "cosine");
606s  [l, s, c] = predict (obj, xc);
606s  assert (l, {"virginica"})
606s  assert (s, [0, 0.1, 0.9], 1e-4)
606s  assert (c, [1, 0.9, 0.1], 1e-4)
606s ***** test
606s  xc = [5, 3, 5, 1.45];
606s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "correlation");
606s  [l, s, c] = predict (obj, xc);
606s  assert (l, {"virginica"})
606s  assert (s, [0, 0.1, 0.9], 1e-4)
606s  assert (c, [1, 0.9, 0.1], 1e-4)
606s ***** test
606s  xc = [5, 3, 5, 1.45];
606s  obj = fitcknn (x, y, "NumNeighbors", 30, "distance", "spearman");
606s  [l, s, c] = predict (obj, xc);
606s  assert (l, {"versicolor"})
606s  assert (s, [0, 1, 0], 1e-4)
606s  assert (c, [1, 0, 1], 1e-4)
606s ***** test
606s  xc = [5, 3, 5, 1.45];
606s  obj = fitcknn (x, y, "NumNeighbors", 30, "distance", "hamming");
606s  [l, s, c] = predict (obj, xc);
606s  assert (l, {"setosa"})
606s  assert (s, [0.4333, 0.3333, 0.2333], 1e-4)
606s  assert (c, [0.5667, 0.6667, 0.7667], 1e-4)
606s ***** test
606s  xc = [5, 3, 5, 1.45];
606s  obj = fitcknn (x, y, "NumNeighbors", 5, "distance", "hamming");
606s  [l, s, c] = predict (obj, xc);
606s  assert (l, {"setosa"})
606s  assert (s, [0.8, 0.2, 0], 1e-4)
606s  assert (c, [0.2, 0.8, 1], 1e-4)
606s ***** test
606s  xc = [min(x); mean(x); max(x)];
606s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "correlation");
606s  [l, s, c] = predict (obj, xc);
606s  assert (l, {"setosa"; "versicolor"; "virginica"})
606s  assert (s, [1, 0, 0; 0, 1, 0; 0, 0.4, 0.6], 1e-4)
606s  assert (c, [0, 1, 1; 1, 0, 1; 1, 0.6, 0.4], 1e-4)
606s ***** test
606s  xc = [min(x); mean(x); max(x)];
606s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "hamming");
606s  [l, s, c] = predict (obj, xc);
606s  assert (l, {"setosa";"setosa";"setosa"})
606s  assert (s, [0.9, 0.1, 0; 1, 0, 0; 0.5, 0, 0.5], 1e-4)
606s  assert (c, [0.1, 0.9, 1; 0, 1, 1; 0.5, 1, 0.5], 1e-4)
606s ***** error<ClassificationKNN.predict: too few input arguments.> ...
606s  predict (ClassificationKNN (ones (4,2), ones (4,1)))
606s ***** error<ClassificationKNN.predict: XC is empty.> ...
606s  predict (ClassificationKNN (ones (4,2), ones (4,1)), [])
606s ***** error<ClassificationKNN.predict: XC must have the same number of predictors as the trained model.> ...
606s  predict (ClassificationKNN (ones (4,2), ones (4,1)), 1)
606s ***** test
606s  load fisheriris
606s  model = fitcknn (meas, species, 'NumNeighbors', 5);
606s  X = mean (meas);
606s  Y = {'versicolor'};
606s  L = loss (model, X, Y);
606s  assert (L, 0)
606s ***** test
606s  X = [1, 2; 3, 4; 5, 6];
606s  Y = {'A'; 'B'; 'A'};
606s  model = fitcknn (X, Y);
606s  X_test = [1, 6; 3, 3];
606s  Y_test = {'A'; 'B'};
606s  L = loss (model, X_test, Y_test);
606s  assert (abs (L - 0.6667) > 1e-5)
606s ***** test
606s  X = [1, 2; 3, 4; 5, 6];
606s  Y = {'A'; 'B'; 'A'};
606s  model = fitcknn (X, Y);
606s  X_with_nan = [1, 2; NaN, 4];
606s  Y_test = {'A'; 'B'};
606s  L = loss (model, X_with_nan, Y_test);
606s  assert (abs (L - 0.3333) < 1e-4)
606s ***** test
606s  X = [1, 2; 3, 4; 5, 6];
606s  Y = {'A'; 'B'; 'A'};
606s  model = fitcknn (X, Y);
606s  X_with_nan = [1, 2; NaN, 4];
606s  Y_test = {'A'; 'B'};
606s  L = loss (model, X_with_nan, Y_test, 'LossFun', 'logit');
606s  assert (isnan (L))
606s ***** test
606s  X = [1, 2; 3, 4; 5, 6];
606s  Y = {'A'; 'B'; 'A'};
606s  model = fitcknn (X, Y);
606s  customLossFun = @(C, S, W, Cost) sum (W .* sum (abs (C - S), 2));
606s  L = loss (model, X, Y, 'LossFun', customLossFun);
606s  assert (L, 0)
606s ***** test
606s  X = [1, 2; 3, 4; 5, 6];
606s  Y = [1; 2; 1];
606s  model = fitcknn (X, Y);
606s  L = loss (model, X, Y, 'LossFun', 'classiferror');
606s  assert (L, 0)
606s ***** test
606s  X = [1, 2; 3, 4; 5, 6];
606s  Y = [true; false; true];
606s  model = fitcknn (X, Y);
606s  L = loss (model, X, Y, 'LossFun', 'binodeviance');
606s  assert (abs (L - 0.1269) < 1e-4)
606s ***** test
606s  X = [1, 2; 3, 4; 5, 6];
606s  Y = ['1'; '2'; '1'];
606s  model = fitcknn (X, Y);
606s  L = loss (model, X, Y, 'LossFun', 'classiferror');
606s  assert (L, 0)
606s ***** test
606s  X = [1, 2; 3, 4; 5, 6];
606s  Y = ['1'; '2'; '3'];
606s  model = fitcknn (X, Y);
606s  X_test = [3, 3];
606s  Y_test = ['1'];
606s  L = loss (model, X_test, Y_test, 'LossFun', 'quadratic');
606s  assert (L, 1)
606s ***** test
606s  X = [1, 2; 3, 4; 5, 6];
606s  Y = ['1'; '2'; '3'];
606s  model = fitcknn (X, Y);
606s  X_test = [3, 3; 5, 7];
606s  Y_test = ['1'; '2'];
606s  L = loss (model, X_test, Y_test, 'LossFun', 'classifcost');
606s  assert (L, 1)
606s ***** test
606s  X = [1, 2; 3, 4; 5, 6];
606s  Y = ['1'; '2'; '3'];
606s  model = fitcknn (X, Y);
606s  X_test = [3, 3; 5, 7];
606s  Y_test = ['1'; '2'];
606s  L = loss (model, X_test, Y_test, 'LossFun', 'hinge');
606s  assert (L, 1)
606s ***** test
606s  X = [1, 2; 3, 4; 5, 6];
606s  Y = ['1'; '2'; '3'];
606s  model = fitcknn (X, Y);
606s  X_test = [3, 3; 5, 7];
606s  Y_test = ['1'; '2'];
606s  W = [1; 2];
606s  L = loss (model, X_test, Y_test, 'LossFun', 'logit', 'Weights', W);
606s  assert (abs (L - 0.6931) < 1e-4)
606s ***** error<ClassificationKNN.loss: too few input arguments.> ...
606s  loss (ClassificationKNN (ones (4,2), ones (4,1)))
606s ***** error<ClassificationKNN.loss: too few input arguments.> ...
606s  loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2))
606s ***** error<ClassificationKNN.loss: X is empty.> ...
606s  loss (ClassificationKNN (ones (40,2), randi ([1, 2], 40, 1)), [], zeros (2))
606s ***** error<ClassificationKNN.loss: X must have the same number of predictors as the trained model.> ...
606s  loss (ClassificationKNN (ones (40,2), randi ([1, 2], 40, 1)), 1, zeros (2))
606s ***** error<ClassificationKNN.loss: name-value arguments must be in pairs.> ...
606s  loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ...
606s         ones (4,1), 'LossFun')
606s ***** error<ClassificationKNN.loss: Y must have the same number of rows as X.> ...
606s  loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ones (3,1))
606s ***** error<ClassificationKNN.loss: invalid loss function.> ...
606s  loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ...
606s         ones (4,1), 'LossFun', 'a')
606s ***** error<ClassificationKNN.loss: invalid Weights.> ...
606s  loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ...
606s         ones (4,1), 'Weights', 'w')
606s ***** test
606s  load fisheriris
606s  mdl = fitcknn (meas, species, 'NumNeighbors', 5);
606s  X = mean (meas);
606s  Y = {'versicolor'};
606s  m = margin (mdl, X, Y);
606s  assert (m, 1)
606s ***** test
606s  X = [1, 2; 3, 4; 5, 6];
606s  Y = [1; 2; 3];
606s  mdl = fitcknn (X, Y);
606s  m = margin (mdl, X, Y);
606s  assert (m, [1; 1; 1])
606s ***** test
606s  X = [7, 8; 9, 10];
606s  Y = ['1'; '2'];
606s  mdl = fitcknn (X, Y);
606s  m = margin (mdl, X, Y);
606s  assert (m, [1; 1])
606s ***** test
606s  X = [11, 12];
606s  Y = {'1'};
606s  mdl = fitcknn (X, Y);
606s  m = margin (mdl, X, Y);
606s  assert (isnan (m))
606s ***** test
606s  X = [1, 2; 3, 4; 5, 6];
606s  Y = [1; 2; 3];
606s  mdl = fitcknn (X, Y);
606s  X1 = [15, 16];
606s  Y1 = [1];
606s  m = margin (mdl, X1, Y1);
606s  assert (m, -1)
606s ***** error<ClassificationKNN.margin: too few input arguments.> ...
606s  margin (ClassificationKNN (ones (4,2), ones (4,1)))
606s ***** error<ClassificationKNN.margin: too few input arguments.> ...
606s  margin (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2))
606s ***** error<ClassificationKNN.margin: X is empty.> ...
606s  margin (ClassificationKNN (ones (40,2), randi ([1, 2], 40, 1)), [], zeros (2))
606s ***** error<ClassificationKNN.margin: X must have the same number of predictors as the trained model.> ...
606s  margin (ClassificationKNN (ones (40,2), randi ([1, 2], 40, 1)), 1, zeros (2))
606s ***** error<ClassificationKNN.margin: Y must have the same number of rows as X.> ...
606s  margin (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ones (3,1))
606s ***** shared X, Y, mdl
606s  X = [1, 2; 4, 5; 7, 8; 3, 2];
606s  Y = [2; 1; 3; 2];
606s  mdl = fitcknn (X, Y);
606s ***** test
606s  Vars = 1;
606s  Labels = 2;
606s  [pd, x, y] = partialDependence (mdl, Vars, Labels);
606s  pdm = [0.7500, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
606s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
606s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
606s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
606s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
606s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
606s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
606s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
606s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
606s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
606s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
606s  0.5000, 0.5000];
606s  assert (pd, pdm)
607s ***** test
607s  Vars = 1;
607s  Labels = 2;
607s  [pd, x, y] = partialDependence (mdl, Vars, Labels, ...
607s  'NumObservationsToSample', 5);
607s  pdm = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
607s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
607s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
607s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
607s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0];
607s  assert (abs (pdm - pd) < 1)
607s ***** test
607s  Vars = 1;
607s  Labels = 2;
607s  [pd, x, y] = partialDependence (mdl, Vars, Labels, 'UseParallel', true);
607s  pdm = [0.7500, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
607s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
607s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
607s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
607s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
607s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
607s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
607s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
607s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
607s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
607s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
607s  0.5000, 0.5000];
607s  assert (pd, pdm)
608s ***** test
608s  Vars = [1, 2];
608s  Labels = 1;
608s  queryPoints = {linspace(0, 1, 3)', linspace(0, 1, 3)'};
608s  [pd, x, y] = partialDependence (mdl, Vars, Labels, 'QueryPoints', ...
608s                             queryPoints, 'UseParallel', true);
608s  pdm = [0, 0, 0; 0, 0, 0; 0, 0, 0];
608s  assert (pd, pdm)
608s ***** test
608s  Vars = 1;
608s  Labels = [1; 2];
608s  [pd, x, y] = partialDependence (mdl, Vars, Labels);
608s  pdm = [0.2500, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
608s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
608s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
608s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
608s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
608s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.2500, 0.2500, 0.2500, ...
608s  0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
608s  0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
608s  0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
608s  0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
608s  0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
608s  0.2500, 0.2500; 0.7500, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
608s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
608s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
608s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
608s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
608s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
608s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
608s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
608s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
608s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
608s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
608s  0.5000, 0.5000, 0.5000];
608s  assert (pd, pdm)
609s ***** test
609s  Vars = [1, 2];
609s  Labels = [1; 2];
609s  queryPoints = {linspace(0, 1, 3)', linspace(0, 1, 3)'};
609s  [pd, x, y] = partialDependence (mdl, Vars, Labels, 'QueryPoints', queryPoints);
609s  pdm(:,:,1) = [0, 0, 0; 1, 1, 1];
609s  pdm(:,:,2) = [0, 0, 0; 1, 1, 1];
609s  pdm(:,:,3) = [0, 0, 0; 1, 1, 1];
609s  assert (pd, pdm)
609s ***** test
609s  X1 = [1; 2; 4; 5; 7; 8; 3; 2];
609s  X2 = ['2'; '3'; '1'; '3'; '1'; '3'; '2'; '2'];
609s  X = [X1, double(X2)];
609s  Y = [1; 2; 3; 3; 2; 1; 2; 1];
609s  mdl = fitcknn (X, Y, 'ClassNames', {'1', '2', '3'});
609s  Vars = 1;
609s  Labels = 1;
609s  [pd, x, y] = partialDependence (mdl, Vars, Labels);
609s  pdm = [1.0000, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, ...
609s  0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, ...
609s  0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
609s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
609s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3750, ...
609s  0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, ...
609s  0.3750, 0.3750, 0.3750, 0.3750, 0.7500, 0.7500, 0.7500, 0.7500, 0.7500, ...
609s  0.7500, 0.7500, 0.7500];
609s  assert (pd, pdm)
609s ***** test
609s  X1 = [1; 2; 4; 5; 7; 8; 3; 2];
609s  X2 = ['2'; '3'; '1'; '3'; '1'; '3'; '2'; '2'];
609s  X = [X1, double(X2)];
609s  Y = [1; 2; 3; 3; 2; 1; 2; 1];
609s  predictorNames = {'Feature1', 'Feature2'};
609s  mdl = fitcknn (X, Y, 'PredictorNames', predictorNames);
609s  Vars = 'Feature1';
609s  Labels = 1;
609s  [pd, x, y] = partialDependence (mdl, Vars, Labels);
609s  pdm = [1.0000, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, ...
609s  0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, ...
609s  0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
609s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
609s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3750, ...
609s  0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, ...
609s  0.3750, 0.3750, 0.3750, 0.3750, 0.7500, 0.7500, 0.7500, 0.7500, 0.7500, ...
609s  0.7500, 0.7500, 0.7500];
609s  assert (pd, pdm)
610s ***** test
610s  X1 = [1; 2; 4; 5; 7; 8; 3; 2];
610s  X2 = ['2'; '3'; '1'; '3'; '1'; '3'; '2'; '2'];
610s  X = [X1, double(X2)];
610s  Y = [1; 2; 3; 3; 2; 1; 2; 1];
610s  predictorNames = {'Feature1', 'Feature2'};
610s  mdl = fitcknn (X, Y, 'PredictorNames', predictorNames);
610s  new_X1 = [10; 5; 6; 8; 9; 20; 35; 6];
610s  new_X2 = ['2'; '2'; '1'; '2'; '1'; '3'; '3'; '2'];
610s  new_X = [new_X1, double(new_X2)];
610s  Vars = 'Feature1';
610s  Labels = 1;
610s  [pd, x, y] = partialDependence (mdl, Vars, Labels, new_X);
610s  pdm = [0, 0, 0, 0, 0, 0.2500, 0.2500, 0.2500, 0.2500, 0.7500, 0.7500, ...
610s  0.7500, 0.7500, 0.7500, 0.7500, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, ...
610s  1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, ...
610s  1.0000, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
610s  1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
610s  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];
610s  assert (pd, pdm)
610s ***** error<ClassificationKNN.partialDependence: too few input arguments.> ...
610s  partialDependence (ClassificationKNN (ones (4,2), ones (4,1)))
610s ***** error<ClassificationKNN.partialDependence: too few input arguments.> ...
610s  partialDependence (ClassificationKNN (ones (4,2), ones (4,1)), 1)
610s ***** error<ClassificationKNN.partialDependence: name-value arguments must be in pairs.> ...
610s  partialDependence (ClassificationKNN (ones (4,2), ones (4,1)), 1, ...
610s           ones (4,1), 'NumObservationsToSample')
610s ***** error<ClassificationKNN.partialDependence: name-value arguments must be in pairs.> ...
610s  partialDependence (ClassificationKNN (ones (4,2), ones (4,1)), 1, ...
610s           ones (4,1), 2)
610s ***** shared x, y, obj
610s  load fisheriris
610s  x = meas;
610s  y = species;
610s  covMatrix = cov (x);
610s  obj = fitcknn (x, y, 'NumNeighbors', 5, 'Distance', ...
610s       'mahalanobis', 'Cov', covMatrix);
610s ***** test
610s  CVMdl = crossval (obj);
610s  assert (class (CVMdl), "ClassificationPartitionedModel")
610s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
610s  assert (CVMdl.KFold == 10)
610s  assert (CVMdl.ModelParameters.NumNeighbors == 5)
610s  assert (strcmp (CVMdl.ModelParameters.Distance, "mahalanobis"))
610s  assert (class (CVMdl.Trained{1}), "ClassificationKNN")
610s  assert (!CVMdl.ModelParameters.Standardize)
610s ***** test
610s  CVMdl = crossval (obj, "KFold", 5);
610s  assert (class (CVMdl), "ClassificationPartitionedModel")
610s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
610s  assert (CVMdl.KFold == 5)
610s  assert (CVMdl.ModelParameters.NumNeighbors == 5)
610s  assert (strcmp (CVMdl.ModelParameters.Distance, "mahalanobis"))
610s  assert (class (CVMdl.Trained{1}), "ClassificationKNN")
610s  assert (CVMdl.ModelParameters.Standardize == obj.Standardize)
611s ***** test
611s  obj = fitcknn (x, y, "NumNeighbors", 5, "Distance", "cityblock");
611s  CVMdl = crossval (obj, "HoldOut", 0.2);
611s  assert (class (CVMdl), "ClassificationPartitionedModel")
611s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
611s  assert (CVMdl.ModelParameters.NumNeighbors == 5)
611s  assert (strcmp (CVMdl.ModelParameters.Distance, "cityblock"))
611s  assert (class (CVMdl.Trained{1}), "ClassificationKNN")
611s  assert (CVMdl.ModelParameters.Standardize == obj.Standardize)
611s ***** test
611s  obj = fitcknn (x, y, "NumNeighbors", 10, "Distance", "cityblock");
611s  CVMdl = crossval (obj, "LeaveOut", 'on');
611s  assert (class (CVMdl), "ClassificationPartitionedModel")
611s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
611s  assert (CVMdl.ModelParameters.NumNeighbors == 10)
611s  assert (strcmp (CVMdl.ModelParameters.Distance, "cityblock"))
611s  assert (class (CVMdl.Trained{1}), "ClassificationKNN")
611s  assert (CVMdl.ModelParameters.Standardize == obj.Standardize)
612s ***** test
612s  obj = fitcknn (x, y, "NumNeighbors", 10, "Distance", "cityblock");
612s  partition = cvpartition (y, 'KFold', 3);
612s  CVMdl = crossval (obj, 'cvPartition', partition);
612s  assert (class (CVMdl), "ClassificationPartitionedModel")
612s  assert (CVMdl.KFold == 3)
612s  assert (CVMdl.ModelParameters.NumNeighbors == 10)
612s  assert (strcmp (CVMdl.ModelParameters.Distance, "cityblock"))
612s  assert (class (CVMdl.Trained{1}), "ClassificationKNN")
612s  assert (CVMdl.ModelParameters.Standardize == obj.Standardize)
612s ***** error<ClassificationKNN.crossval: Name-Value arguments must be in pairs.> ...
612s  crossval (ClassificationKNN (ones (4,2), ones (4,1)), "kfold")
612s ***** error<ClassificationKNN.crossval: specify only one of the optional Name-Value paired arguments.>...
612s  crossval (ClassificationKNN (ones (4,2), ones (4,1)), "kfold", 12, "holdout", 0.2)
612s ***** error<ClassificationKNN.crossval: 'KFold' must be an integer value greater than 1.> ...
612s  crossval (ClassificationKNN (ones (4,2), ones (4,1)), "kfold", 'a')
612s ***** error<ClassificationKNN.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
612s  crossval (ClassificationKNN (ones (4,2), ones (4,1)), "holdout", 2)
612s ***** error<ClassificationKNN.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
612s  crossval (ClassificationKNN (ones (4,2), ones (4,1)), "leaveout", 1)
612s ***** error<ClassificationKNN.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
612s  crossval (ClassificationKNN (ones (4,2), ones (4,1)), "cvpartition", 1)
612s 162 tests, 162 passed, 0 known failure, 0 skipped
612s [inst/Classification/ClassificationNeuralNetwork.m]
612s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/Classification/ClassificationNeuralNetwork.m
612s ***** error<ClassificationNeuralNetwork: too few input arguments.> ...
612s  ClassificationNeuralNetwork ()
612s ***** error<ClassificationNeuralNetwork: too few input arguments.> ...
612s  ClassificationNeuralNetwork (ones(10,2))
612s ***** error<ClassificationNeuralNetwork: number of rows in X and Y must be equal.> ...
612s  ClassificationNeuralNetwork (ones(10,2), ones (5,1))
612s ***** error<ClassificationNeuralNetwork: 'Standardize' must be either true or false.> ...
612s  ClassificationNeuralNetwork (ones (5,3), ones (5,1), "standardize", "a")
612s ***** error<ClassificationNeuralNetwork: 'PredictorNames' must be supplied as a cellstring array.> ...
612s  ClassificationNeuralNetwork (ones (5,2), ones (5,1), "PredictorNames", ["A"])
612s ***** error<ClassificationNeuralNetwork: 'PredictorNames' must be supplied as a cellstring array.> ...
612s  ClassificationNeuralNetwork (ones (5,2), ones (5,1), "PredictorNames", "A")
612s ***** error<ClassificationNeuralNetwork: 'PredictorNames' must have the same number of columns as X.> ...
612s  ClassificationNeuralNetwork (ones (5,2), ones (5,1), "PredictorNames", {"A", "B", "C"})
612s ***** error<ClassificationNeuralNetwork: 'ResponseName' must be a character vector.> ...
612s  ClassificationNeuralNetwork (ones (5,2), ones (5,1), "ResponseName", {"Y"})
612s ***** error<ClassificationNeuralNetwork: 'ResponseName' must be a character vector.> ...
612s  ClassificationNeuralNetwork (ones (5,2), ones (5,1), "ResponseName", 1)
612s ***** error<ClassificationNeuralNetwork: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
612s  ClassificationNeuralNetwork (ones(10,2), ones (10,1), "ClassNames", @(x)x)
612s ***** error<ClassificationNeuralNetwork: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
612s  ClassificationNeuralNetwork (ones(10,2), ones (10,1), "ClassNames", ['a'])
612s ***** error<ClassificationNeuralNetwork: not all 'ClassNames' are present in Y.> ...
612s  ClassificationNeuralNetwork (ones(10,2), ones (10,1), "ClassNames", [1, 2])
612s ***** error<ClassificationNeuralNetwork: not all 'ClassNames' are present in Y.> ...
612s  ClassificationNeuralNetwork (ones(5,2), {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
612s ***** error<ClassificationNeuralNetwork: not all 'ClassNames' are present in Y.> ...
612s  ClassificationNeuralNetwork (ones(10,2), logical (ones (10,1)), "ClassNames", [true, false])
612s ***** error<ClassificationNeuralNetwork: 'LayerSizes' must be a positive integer vector.> ...
612s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", -1)
612s ***** error<ClassificationNeuralNetwork: 'LayerSizes' must be a positive integer vector.> ...
612s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", 0.5)
612s ***** error<ClassificationNeuralNetwork: 'LayerSizes' must be a positive integer vector.> ...
612s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", [1,-2])
612s ***** error<ClassificationNeuralNetwork: 'LayerSizes' must be a positive integer vector.> ...
612s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", [10,20,30.5])
612s ***** error<ClassificationNeuralNetwork: 'LearningRate' must be a positive scalar.> ...
612s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LearningRate", -0.1)
612s ***** error<ClassificationNeuralNetwork: 'LearningRate' must be a positive scalar.> ...
612s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LearningRate", [0.1, 0.01])
612s ***** error<ClassificationNeuralNetwork: 'LearningRate' must be a positive scalar.> ...
612s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LearningRate", "a")
612s ***** error<ClassificationNeuralNetwork: 'Activations' must be a character vector or a cellstring vector.> ...
612s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "Activations", 123)
612s ***** error<ClassificationNeuralNetwork: unsupported 'Activation' function.> ...
612s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "Activations", "unsupported_type")
612s ***** error<ClassificationNeuralNetwork: unsupported 'Activation' functions.> ...
612s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", [10, 5], ...
612s  "Activations", {"sigmoid", "unsupported_type"})
612s ***** error<ClassificationNeuralNetwork: 'Activations' vector does not match the number of layers.> ...
612s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "Activations", {"sigmoid", "relu", "softmax"})
612s ***** error<ClassificationNeuralNetwork: 'OutputLayerActivation' must be a character vector.> ...
612s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "OutputLayerActivation", 123)
612s ***** error<ClassificationNeuralNetwork: unsupported 'OutputLayerActivation' function.> ...
612s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "OutputLayerActivation", "unsupported_type")
612s ***** error<ClassificationNeuralNetwork: 'IterationLimit' must be a positive integer.> ...
612s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "IterationLimit", -1)
612s ***** error<ClassificationNeuralNetwork: 'IterationLimit' must be a positive integer.> ...
612s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "IterationLimit", 0.5)
612s ***** error<ClassificationNeuralNetwork: 'IterationLimit' must be a positive integer.> ...
612s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "IterationLimit", [1,2])
612s ***** error<ClassificationNeuralNetwork: 'ScoreTransform' must be a character vector or a function handle.> ...
612s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "ScoreTransform", [1,2])
612s ***** error<ClassificationNeuralNetwork: unrecognized 'ScoreTransform' function.> ...
612s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "ScoreTransform", "unsupported_type")
612s ***** error<ClassificationNeuralNetwork: invalid parameter name in optional pair arguments.> ...
612s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "some", "some")
612s ***** error<ClassificationNeuralNetwork: invalid values in X.> ...
612s  ClassificationNeuralNetwork ([1;2;3;'a';4], ones (5,1))
612s ***** error<ClassificationNeuralNetwork: invalid values in X.> ...
612s  ClassificationNeuralNetwork ([1;2;3;Inf;4], ones (5,1))
612s ***** shared x, y, objST, Mdl
612s  load fisheriris
612s  x = meas;
612s  y = grp2idx (species);
612s  Mdl = fitcnet (x, y, "IterationLimit", 100);
612s ***** error<ClassificationNeuralNetwork.predict: too few input arguments.> ...
612s  predict (Mdl)
612s ***** error<ClassificationNeuralNetwork.predict: XC is empty.> ...
612s  predict (Mdl, [])
612s ***** error<ClassificationNeuralNetwork.predict: XC must have the same number of predictors as the trained model.> ...
612s  predict (Mdl, 1)
612s ***** test
612s  objST = fitcnet (x, y, "IterationLimit", 100);
612s  objST.ScoreTransform = "a";
612s ***** error<ClassificationNeuralNetwork.predict: 'ScoreTransform' must be a 'function_handle' object.> ...
612s  [labels, scores] = predict (objST, x);
612s ***** error<ClassificationNeuralNetwork.resubPredict: 'ScoreTransform' must be a 'function_handle' object.> ...
612s  [labels, scores] = resubPredict (objST);
612s ***** test
612s  CVMdl = crossval (Mdl, "KFold", 5);
612s  assert (class (CVMdl), "ClassificationPartitionedModel")
612s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
612s  assert (CVMdl.KFold == 5)
612s  assert (class (CVMdl.Trained{1}), "CompactClassificationNeuralNetwork")
612s  assert (CVMdl.CrossValidatedModel, "ClassificationNeuralNetwork")
613s ***** test
613s  CVMdl = crossval (Mdl, "HoldOut", 0.2);
613s  assert (class (CVMdl), "ClassificationPartitionedModel")
613s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
613s  assert (class (CVMdl.Trained{1}), "CompactClassificationNeuralNetwork")
613s  assert (CVMdl.CrossValidatedModel, "ClassificationNeuralNetwork")
613s ***** error<ClassificationNeuralNetwork.crossval: Name-Value arguments must be in pairs.> ...
613s  crossval (Mdl, "KFold")
613s ***** error<ClassificationNeuralNetwork.crossval: specify only one of the optional Name-Value paired arguments.> ...
613s  crossval (Mdl, "KFold", 5, "leaveout", 'on')
613s ***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
613s  crossval (Mdl, "KFold", 'a')
613s ***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
613s  crossval (Mdl, "KFold", 1)
613s ***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
613s  crossval (Mdl, "KFold", -1)
613s ***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
613s  crossval (Mdl, "KFold", 11.5)
613s ***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
613s  crossval (Mdl, "KFold", [1,2])
613s ***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
613s  crossval (Mdl, "Holdout", 'a')
613s ***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
613s  crossval (Mdl, "Holdout", 11.5)
613s ***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
613s  crossval (Mdl, "Holdout", -1)
613s ***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
613s  crossval (Mdl, "Holdout", 0)
613s ***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
613s  crossval (Mdl, "Holdout", 1)
613s ***** error<ClassificationNeuralNetwork.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
613s  crossval (Mdl, "Leaveout", 1)
613s ***** error<ClassificationNeuralNetwork.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
613s  crossval (Mdl, "CVPartition", 1)
613s ***** error<ClassificationNeuralNetwork.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
613s  crossval (Mdl, "CVPartition", 'a')
613s ***** error<ClassificationNeuralNetwork.crossval: invalid parameter name in optional paired arguments> ...
613s  crossval (Mdl, "some", "some")
613s 59 tests, 59 passed, 0 known failure, 0 skipped
613s [inst/Classification/ClassificationPartitionedModel.m]
613s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/Classification/ClassificationPartitionedModel.m
613s ***** demo
613s 
613s  load fisheriris
613s  x = meas;
613s  y = species;
613s 
613s  ## Create a KNN classifier model
613s  obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);
613s 
613s  ## Create a partition for 5-fold cross-validation
613s  partition = cvpartition (y, "KFold", 5);
613s 
613s  ## Create the ClassificationPartitionedModel object
613s  cvModel = crossval (obj, 'cvPartition', partition)
613s ***** demo
613s 
613s  load fisheriris
613s  x = meas;
613s  y = species;
613s 
613s  ## Create a KNN classifier model
613s  obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);
613s 
613s  ## Create the ClassificationPartitionedModel object
613s  cvModel = crossval (obj);
613s 
613s  ## Predict the class labels for the observations not used for training
613s  [label, score, cost] = kfoldPredict (cvModel);
613s  fprintf ("Cross-validated accuracy = %1.2f%% (%d/%d)\n", ...
613s           sum (strcmp (label, y)) / numel (y) *100, ...
613s           sum (strcmp (label, y)), numel (y))
613s ***** test
613s  load fisheriris
613s  a = fitcdiscr (meas, species, "gamma", 0.3);
613s  cvModel = crossval (a, "KFold", 5);
613s  assert (class (cvModel), "ClassificationPartitionedModel");
613s  assert (cvModel.NumObservations, 150);
613s  assert (numel (cvModel.Trained), 5);
613s  assert (class (cvModel.Trained{1}), "CompactClassificationDiscriminant");
613s  assert (cvModel.CrossValidatedModel, "ClassificationDiscriminant");
613s  assert (cvModel.KFold, 5);
613s ***** test
613s  load fisheriris
613s  a = fitcdiscr (meas, species, "gamma", 0.5, "fillcoeffs", "off");
613s  cvModel = crossval (a, "HoldOut", 0.3);
613s  assert (class (cvModel), "ClassificationPartitionedModel");
613s  assert ({cvModel.X, cvModel.Y}, {meas, species});
613s  assert (cvModel.NumObservations, 150);
613s  assert (numel (cvModel.Trained), 1);
613s  assert (class (cvModel.Trained{1}), "CompactClassificationDiscriminant");
613s  assert (cvModel.CrossValidatedModel, "ClassificationDiscriminant");
613s ***** test
613s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
613s  y = ["a"; "a"; "b"; "b"];
613s  a = fitcgam (x, y, "Interactions", "all");
613s  cvModel = crossval (a, "KFold", 5);
613s  assert (class (cvModel), "ClassificationPartitionedModel");
613s  assert (cvModel.NumObservations, 4);
613s  assert (numel (cvModel.Trained), 5);
613s  assert (class (cvModel.Trained{1}), "CompactClassificationGAM");
613s  assert (cvModel.CrossValidatedModel, "ClassificationGAM");
613s  assert (cvModel.KFold, 5);
647s ***** test
647s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
647s  y = ["a"; "a"; "b"; "b"];
647s  a = fitcgam (x, y);
647s  cvModel = crossval (a, "LeaveOut", "on");
647s  assert (class (cvModel), "ClassificationPartitionedModel");
647s  assert ({cvModel.X, cvModel.Y}, {x, y});
647s  assert (cvModel.NumObservations, 4);
647s  assert (numel (cvModel.Trained), 4);
647s  assert (class (cvModel.Trained{1}), "CompactClassificationGAM");
647s  assert (cvModel.CrossValidatedModel, "ClassificationGAM");
656s ***** test
656s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
656s  y = ["a"; "a"; "b"; "b"];
656s  a = fitcknn (x, y);
656s  partition = cvpartition (y, "KFold", 5);
656s  cvModel = ClassificationPartitionedModel (a, partition);
656s  assert (class (cvModel), "ClassificationPartitionedModel");
656s  assert (class (cvModel.Trained{1}), "ClassificationKNN");
656s  assert (cvModel.NumObservations, 4);
656s  assert (cvModel.ModelParameters.NumNeighbors, 1);
656s  assert (cvModel.ModelParameters.NSMethod, "kdtree");
656s  assert (cvModel.ModelParameters.Distance, "euclidean");
656s  assert (! cvModel.ModelParameters.Standardize);
656s ***** test
656s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
656s  y = ["a"; "a"; "b"; "b"];
656s  a = fitcknn (x, y, "NSMethod", "exhaustive");
656s  partition = cvpartition (y, "HoldOut", 0.2);
656s  cvModel = ClassificationPartitionedModel (a, partition);
656s  assert (class (cvModel), "ClassificationPartitionedModel");
656s  assert (class (cvModel.Trained{1}), "ClassificationKNN");
656s  assert ({cvModel.X, cvModel.Y}, {x, y});
656s  assert (cvModel.NumObservations, 4);
656s  assert (cvModel.ModelParameters.NumNeighbors, 1);
656s  assert (cvModel.ModelParameters.NSMethod, "exhaustive");
656s  assert (cvModel.ModelParameters.Distance, "euclidean");
656s  assert (! cvModel.ModelParameters.Standardize);
656s ***** test
656s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
656s  y = ["a"; "a"; "b"; "b"];
656s  k = 3;
656s  a = fitcknn (x, y, "NumNeighbors" ,k);
656s  partition = cvpartition (y, "LeaveOut");
656s  cvModel = ClassificationPartitionedModel (a, partition);
656s  assert (class (cvModel), "ClassificationPartitionedModel");
656s  assert (class (cvModel.Trained{1}), "ClassificationKNN");
656s  assert ({cvModel.X, cvModel.Y}, {x, y});
656s  assert (cvModel.NumObservations, 4);
656s  assert (cvModel.ModelParameters.NumNeighbors, k);
656s  assert (cvModel.ModelParameters.NSMethod, "kdtree");
656s  assert (cvModel.ModelParameters.Distance, "euclidean");
656s  assert (! cvModel.ModelParameters.Standardize);
656s ***** test
656s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
656s  y = {"a"; "a"; "b"; "b"};
656s  a = fitcnet (x, y, "IterationLimit", 50);
656s  cvModel = crossval (a, "KFold", 5);
656s  assert (class (cvModel), "ClassificationPartitionedModel");
656s  assert (cvModel.NumObservations, 4);
656s  assert (numel (cvModel.Trained), 5);
656s  assert (class (cvModel.Trained{1}), "CompactClassificationNeuralNetwork");
656s  assert (cvModel.CrossValidatedModel, "ClassificationNeuralNetwork");
656s  assert (cvModel.KFold, 5);
656s ***** test
656s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
656s  y = {"a"; "a"; "b"; "b"};
656s  a = fitcnet (x, y, "LayerSizes", [5, 3]);
656s  cvModel = crossval (a, "LeaveOut", "on");
656s  assert (class (cvModel), "ClassificationPartitionedModel");
656s  assert ({cvModel.X, cvModel.Y}, {x, y});
656s  assert (cvModel.NumObservations, 4);
656s  assert (numel (cvModel.Trained), 4);
656s  assert (class (cvModel.Trained{1}), "CompactClassificationNeuralNetwork");
656s  assert (cvModel.CrossValidatedModel, "ClassificationNeuralNetwork");
656s ***** test
656s  load fisheriris
656s  inds = ! strcmp (species, 'setosa');
656s  x = meas(inds, 3:4);
656s  y = grp2idx (species(inds));
656s  SVMModel = fitcsvm (x,y);
656s  CVMdl = crossval (SVMModel, "KFold", 5);
656s  assert (class (CVMdl), "ClassificationPartitionedModel")
656s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
656s  assert (CVMdl.KFold == 5)
656s  assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
656s  assert (CVMdl.CrossValidatedModel, "ClassificationSVM");
656s ***** test
656s  load fisheriris
656s  inds = ! strcmp (species, 'setosa');
656s  x = meas(inds, 3:4);
656s  y = grp2idx (species(inds));
656s  obj = fitcsvm (x, y);
656s  CVMdl = crossval (obj, "HoldOut", 0.2);
656s  assert (class (CVMdl), "ClassificationPartitionedModel")
656s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
656s  assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
656s  assert (CVMdl.CrossValidatedModel, "ClassificationSVM");
657s ***** test
657s  load fisheriris
657s  inds = ! strcmp (species, 'setosa');
657s  x = meas(inds, 3:4);
657s  y = grp2idx (species(inds));
657s  obj = fitcsvm (x, y);
657s  CVMdl = crossval (obj, "LeaveOut", 'on');
657s  assert (class (CVMdl), "ClassificationPartitionedModel")
657s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
657s  assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
657s  assert (CVMdl.CrossValidatedModel, "ClassificationSVM");
657s ***** error<ClassificationPartitionedModel: too few input arguments.> ...
657s  ClassificationPartitionedModel ()
657s ***** error<ClassificationPartitionedModel: too few input arguments.> ...
657s  ClassificationPartitionedModel (ClassificationKNN (ones (4,2), ones (4,1)))
657s ***** error<ClassificationPartitionedModel: unsupported model type.> ...
657s  ClassificationPartitionedModel (RegressionGAM (ones (40,2), ...
657s  randi ([1, 2], 40, 1)), cvpartition (randi ([1, 2], 40, 1), 'Holdout', 0.3))
657s ***** error<ClassificationPartitionedModel: invalid 'cvpartition' object.> ...
657s  ClassificationPartitionedModel (ClassificationKNN (ones (4,2), ...
657s  ones (4,1)), 'Holdout')
657s ***** test
657s  load fisheriris
657s  a = fitcdiscr (meas, species, "gamma", 0.5, "fillcoeffs", "off");
657s  cvModel = crossval (a, "Kfold", 4);
657s  [label, score, cost] = kfoldPredict (cvModel);
657s  assert (class(cvModel), "ClassificationPartitionedModel");
657s  assert ({cvModel.X, cvModel.Y}, {meas, species});
657s  assert (cvModel.NumObservations, 150);
658s ***** # assert (label, {"b"; "b"; "a"; "a"});
658s ***** # assert (score, [4.5380e-01, 5.4620e-01; 2.4404e-01, 7.5596e-01; ...
658s ***** #         9.9392e-01, 6.0844e-03; 9.9820e-01, 1.8000e-03], 1e-4);
658s ***** # assert (cost, [5.4620e-01, 4.5380e-01; 7.5596e-01, 2.4404e-01; ...
658s ***** #         6.0844e-03, 9.9392e-01; 1.8000e-03, 9.9820e-01], 1e-4);
658s ***** test
658s  x = ones(4, 11);
658s  y = {"a"; "a"; "b"; "b"};
658s  k = 3;
658s  a = fitcknn (x, y, "NumNeighbors", k);
658s  partition = cvpartition (y, "LeaveOut");
658s  cvModel = ClassificationPartitionedModel (a, partition);
658s  [label, score, cost] = kfoldPredict (cvModel);
658s  assert (class(cvModel), "ClassificationPartitionedModel");
658s  assert ({cvModel.X, cvModel.Y}, {x, y});
658s  assert (cvModel.NumObservations, 4);
658s  assert (cvModel.ModelParameters.NumNeighbors, k);
658s  assert (cvModel.ModelParameters.NSMethod, "exhaustive");
658s  assert (cvModel.ModelParameters.Distance, "euclidean");
658s  assert (! cvModel.ModelParameters.Standardize);
658s  assert (label, {"b"; "b"; "a"; "a"});
658s  assert (score, [0.3333, 0.6667; 0.3333, 0.6667; 0.6667, 0.3333; ...
658s           0.6667, 0.3333], 1e-4);
658s  assert (cost, [0.6667, 0.3333; 0.6667, 0.3333; 0.3333, 0.6667; ...
658s           0.3333, 0.6667], 1e-4);
658s ***** error<ClassificationPartitionedModel.kfoldPredict: 'Cost' output is not supported for ClassificationSVM cross validated models.> ...
658s  [label, score, cost] = kfoldPredict (crossval (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1))))
658s ***** error<ClassificationPartitionedModel.kfoldPredict: 'Cost' output is not supported for ClassificationNeuralNetwork cross validated models.> ...
658s  [label, score, cost] = kfoldPredict (crossval (ClassificationNeuralNetwork (ones (40,2), randi ([1, 2], 40, 1))))
662s 20 tests, 20 passed, 0 known failure, 0 skipped
662s [inst/Classification/ClassificationSVM.m]
662s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/Classification/ClassificationSVM.m
662s ***** demo
662s  ## Create a Support Vector Machine classifier and determine margin for test
662s  ## data.
662s  load fisheriris
662s  rng(1);  ## For reproducibility
662s 
662s  ## Select indices of the non-setosa species
662s  inds = !strcmp(species, 'setosa');
662s 
662s   ## Select features and labels for non-setosa species
662s  X = meas(inds, 3:4);
662s  Y = grp2idx(species(inds));
662s 
662s  ##  Convert labels to +1 and -1
662s  unique_classes = unique(Y);
662s  Y(Y == unique_classes(1)) = -1;
662s  Y(Y == unique_classes(2)) = 1;
662s 
662s  ## Partition data for training and testing
662s  cv = cvpartition(Y, 'HoldOut', 0.15);
662s  X_train = X(training(cv), :);
662s  Y_train = Y(training(cv));
662s  X_test = X(test(cv), :);
662s  Y_test = Y(test(cv));
662s 
662s  ## Train the SVM model
662s  CVSVMModel = fitcsvm(X_train, Y_train);
662s 
662s  ## Calculate margins
662s  m = margin(CVSVMModel, X_test, Y_test);
662s  disp(m);
662s ***** demo
662s  ## Create a Support Vector Machine classifier and determine loss for test
662s  ## data.
662s  load fisheriris
662s  rng(1);  ## For reproducibility
662s 
662s   ## Select indices of the non-setosa species
662s  inds = !strcmp(species, 'setosa');
662s 
662s   ## Select features and labels for non-setosa species
662s  X = meas(inds, 3:4);
662s  Y = grp2idx(species(inds));
662s 
662s  ##  Convert labels to +1 and -1
662s  unique_classes = unique(Y);
662s  Y(Y == unique_classes(1)) = -1;
662s  Y(Y == unique_classes(2)) = 1;
662s 
662s  ## Randomly partition the data into training and testing sets
662s  cv = cvpartition(Y, 'HoldOut', 0.3); # 30% data for testing, 60% for training
662s 
662s  X_train = X(training(cv), :);
662s  Y_train = Y(training(cv));
662s 
662s  X_test = X(test(cv), :);
662s  Y_test = Y(test(cv));
662s 
662s  ## Train the SVM model
662s  SVMModel = fitcsvm(X_train, Y_train);
662s 
662s  ## Calculate loss
662s 
662s  L = loss(SVMModel,X_test,Y_test,'LossFun','binodeviance')
662s  L = loss(SVMModel,X_test,Y_test,'LossFun','classiferror')
662s  L = loss(SVMModel,X_test,Y_test,'LossFun','exponential')
662s  L = loss(SVMModel,X_test,Y_test,'LossFun','hinge')
662s  L = loss(SVMModel,X_test,Y_test,'LossFun','logit')
662s  L = loss(SVMModel,X_test,Y_test,'LossFun','quadratic')
662s ***** test
662s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1; 4, 5, 6; 7, 8, 9; ...
662s  3, 2, 1; 4, 5, 6; 7, 8, 9; 3, 2, 1; 4, 5, 6; 7, 8, 9; 3, 2, 1];
662s  y = [1; 2; 3; 4; 2; 3; 4; 2; 3; 4; 2; 3; 4];
662s  a = ClassificationSVM (x, y, "ClassNames", [1, 2]);
662s  assert (class (a), "ClassificationSVM");
662s  assert (a.RowsUsed, [1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0]');
662s  assert ({a.X, a.Y}, {x, y})
662s  assert (a.NumObservations, 5)
662s  assert ({a.ResponseName, a.PredictorNames}, {"Y", {"x1", "x2", "x3"}})
662s  assert ({a.ClassNames, a.ModelParameters.SVMtype}, {[1; 2], "c_svc"})
662s ***** test
662s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
662s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
662s  a = ClassificationSVM (x, y);
662s  assert (class (a), "ClassificationSVM");
662s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "linear"})
662s  assert (a.ModelParameters.BoxConstraint, 1)
662s  assert (a.ClassNames, [1; -1])
662s  assert (a.ModelParameters.KernelOffset, 0)
662s ***** test
662s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
662s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
662s  a = ClassificationSVM (x, y, "KernelFunction", "rbf", "BoxConstraint", 2, ...
662s  "KernelOffset", 2);
662s  assert (class (a), "ClassificationSVM");
662s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "rbf"})
662s  assert (a.ModelParameters.BoxConstraint, 2)
662s  assert (a.ModelParameters.KernelOffset, 2)
662s ***** test
662s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
662s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
662s  a = ClassificationSVM (x, y, "KernelFunction", "polynomial", ...
662s  "PolynomialOrder", 3);
662s  assert (class (a), "ClassificationSVM");
662s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "polynomial"})
662s  assert (a.ModelParameters.PolynomialOrder, 3)
662s ***** error<ClassificationSVM: too few input arguments.> ClassificationSVM ()
662s ***** error<ClassificationSVM: too few input arguments.> ...
662s  ClassificationSVM (ones(10,2))
662s ***** error<ClassificationSVM: number of rows in X and Y must be equal.> ...
662s  ClassificationSVM (ones(10,2), ones (5,1))
662s ***** error<ClassificationSVM: 'Standardize' must be either true or false.> ...
662s  ClassificationSVM (ones(10,2), ones (10,1), "Standardize", 'a')
662s ***** error<ClassificationSVM: 'PredictorNames' must be supplied as a cellstring array.> ...
662s  ClassificationSVM (ones(10,2), ones (10,1), "PredictorNames", ['x1';'x2'])
662s ***** error<ClassificationSVM: 'PredictorNames' must have the same number of columns as X.> ...
662s  ClassificationSVM (ones(10,2), ones (10,1), "PredictorNames", {'x1','x2','x3'})
662s ***** error<ClassificationSVM: 'ResponseName' must be a character vector.> ...
662s  ClassificationSVM (ones(10,2), ones (10,1), "ResponseName", {'Y'})
662s ***** error<ClassificationSVM: 'ResponseName' must be a character vector.> ...
662s  ClassificationSVM (ones(10,2), ones (10,1), "ResponseName", 21)
662s ***** error<ClassificationSVM: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
662s  ClassificationSVM (ones(10,2), ones (10,1), "ClassNames", @(x)x)
662s ***** error<ClassificationSVM: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
662s  ClassificationSVM (ones(10,2), ones (10,1), "ClassNames", ['a'])
662s ***** error<ClassificationSVM: not all 'ClassNames' are present in Y.> ...
662s  ClassificationSVM (ones(10,2), ones (10,1), "ClassNames", [1, 2])
662s ***** error<ClassificationSVM: not all 'ClassNames' are present in Y.> ...
662s  ClassificationSVM (ones(5,2), {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
662s ***** error<ClassificationSVM: not all 'ClassNames' are present in Y.> ...
662s  ClassificationSVM (ones(10,2), logical (ones (10,1)), "ClassNames", [true, false])
662s ***** error<ClassificationSVM: 'Prior' must be either a numeric vector or a character vector.> ...
662s  ClassificationSVM (ones(10,2), ones(10,1), "Prior", {"asd"})
662s ***** error<ClassificationSVM: 'Prior' must be either a numeric vector or a character vector.> ...
662s  ClassificationSVM (ones(10,2), ones(10,1), "Prior", ones (2))
662s ***** error<ClassificationSVM: 'Cost' must be a numeric square matrix.> ...
662s  ClassificationSVM (ones(10,2), ones(10,1), "Cost", [1:4])
662s ***** error<ClassificationSVM: 'Cost' must be a numeric square matrix.> ...
662s  ClassificationSVM (ones(10,2), ones(10,1), "Cost", {0,1;1,0})
662s ***** error<ClassificationSVM: 'Cost' must be a numeric square matrix.> ...
662s  ClassificationSVM (ones(10,2), ones(10,1), "Cost", 'a')
662s ***** error<ClassificationSVM: 'SVMtype' must be 'c_svc', 'nu_svc', or 'one_class_svm'.> ...
662s  ClassificationSVM (ones(10,2), ones(10,1), "svmtype", 123)
662s ***** error<ClassificationSVM: 'SVMtype' must be 'c_svc', 'nu_svc', or 'one_class_svm'.> ...
662s  ClassificationSVM (ones(10,2), ones(10,1), "svmtype", 'some_type')
662s ***** error<ClassificationSVM: 'OutlierFraction' must be a positive scalar in the range 0 =< OutlierFraction < 1.> ...
662s  ClassificationSVM (ones(10,2), ones(10,1), "OutlierFraction", -1)
662s ***** error<ClassificationSVM: 'KernelFunction' must be a character vector.> ...
662s  ClassificationSVM (ones(10,2), ones(10,1), "KernelFunction", 123)
662s ***** error<ClassificationSVM: unsupported Kernel function.> ...
662s  ClassificationSVM (ones(10,2), ones(10,1), "KernelFunction", "fcn")
662s ***** error<ClassificationSVM: 'PolynomialOrder' must be a positive integer.> ...
662s  ClassificationSVM (ones(10,2), ones(10,1), "PolynomialOrder", -1)
662s ***** error<ClassificationSVM: 'PolynomialOrder' must be a positive integer.> ...
662s  ClassificationSVM (ones(10,2), ones(10,1), "PolynomialOrder", 0.5)
662s ***** error<ClassificationSVM: 'PolynomialOrder' must be a positive integer.> ...
662s  ClassificationSVM (ones(10,2), ones(10,1), "PolynomialOrder", [1,2])
662s ***** error<ClassificationSVM: 'KernelScale' must be a positive scalar.> ...
662s  ClassificationSVM (ones(10,2), ones (10,1), "KernelScale", -1)
662s ***** error<ClassificationSVM: 'KernelScale' must be a positive scalar.> ...
662s  ClassificationSVM (ones(10,2), ones (10,1), "KernelScale", 0)
662s ***** error<ClassificationSVM: 'KernelScale' must be a positive scalar.> ...
662s  ClassificationSVM (ones(10,2), ones (10,1), "KernelScale", [1, 2])
662s ***** error<ClassificationSVM: 'KernelScale' must be a positive scalar.> ...
662s  ClassificationSVM (ones(10,2), ones (10,1), "KernelScale", "invalid")
662s ***** error<ClassificationSVM: 'KernelOffset' must be a non-negative scalar.> ...
662s  ClassificationSVM (ones(10,2), ones(10,1), "KernelOffset", -1)
662s ***** error<ClassificationSVM: 'KernelOffset' must be a non-negative scalar.> ...
662s  ClassificationSVM (ones(10,2), ones(10,1), "KernelOffset", [1,2])
662s ***** error<ClassificationSVM: 'BoxConstraint' must be a positive scalar.> ...
662s  ClassificationSVM (ones(10,2), ones (10,1), "BoxConstraint", -1)
662s ***** error<ClassificationSVM: 'BoxConstraint' must be a positive scalar.> ...
662s  ClassificationSVM (ones(10,2), ones (10,1), "BoxConstraint", 0)
662s ***** error<ClassificationSVM: 'BoxConstraint' must be a positive scalar.> ...
662s  ClassificationSVM (ones(10,2), ones (10,1), "BoxConstraint", [1, 2])
662s ***** error<ClassificationSVM: 'BoxConstraint' must be a positive scalar.> ...
662s  ClassificationSVM (ones(10,2), ones (10,1), "BoxConstraint", "invalid")
662s ***** error<ClassificationSVM: 'Nu' must be a positive scalar in the range 0 < Nu <= 1.> ...
662s  ClassificationSVM (ones(10,2), ones(10,1), "nu", -0.5)
662s ***** error<ClassificationSVM: 'Nu' must be a positive scalar in the range 0 < Nu <= 1.> ...
662s  ClassificationSVM (ones(10,2), ones(10,1), "nu", 0)
662s ***** error<ClassificationSVM: 'Nu' must be a positive scalar in the range 0 < Nu <= 1.> ...
662s  ClassificationSVM (ones(10,2), ones(10,1), "nu", 1.5)
662s ***** error<ClassificationSVM: 'CacheSize' must be a positive scalar.> ...
662s  ClassificationSVM (ones(10,2), ones(10,1), "CacheSize", -1)
662s ***** error<ClassificationSVM: 'CacheSize' must be a positive scalar.> ...
662s  ClassificationSVM (ones(10,2), ones(10,1), "CacheSize", [1,2])
662s ***** error<ClassificationSVM: 'Tolerance' must be a positive scalar.> ...
662s  ClassificationSVM (ones(10,2), ones(10,1), "Tolerance", -0.1)
662s ***** error<ClassificationSVM: 'Tolerance' must be a positive scalar.> ...
662s  ClassificationSVM (ones(10,2), ones(10,1), "Tolerance", [0.1,0.2])
662s ***** error<ClassificationSVM: 'Shrinking' must be either 0 or 1.> ...
662s  ClassificationSVM (ones(10,2), ones(10,1), "shrinking", 2)
662s ***** error<ClassificationSVM: 'Shrinking' must be either 0 or 1.> ...
662s  ClassificationSVM (ones(10,2), ones(10,1), "shrinking", -1)
662s ***** error<ClassificationSVM: 'Shrinking' must be either 0 or 1.> ...
662s  ClassificationSVM (ones(10,2), ones(10,1), "shrinking", [1 0])
662s ***** error<ClassificationSVM: invalid parameter name in optional pair arguments.> ...
662s  ClassificationSVM (ones(10,2), ones(10,1), "invalid_name", 'c_svc')
662s ***** error<ClassificationSVM: cannot train a binary problem with only one class available.> ...
662s  ClassificationSVM (ones(10,2), ones(10,1), "SVMtype", 'c_svc')
662s ***** error<ClassificationSVM: can only be used for one-class or two-class learning.> ...
662s  ClassificationSVM (ones(10,2), [1;1;1;1;2;2;2;2;3;3])
662s ***** error<ClassificationSVM: invalid values in X.> ...
662s  ClassificationSVM ([ones(9,2);2,Inf], ones(10,1))
662s ***** error<ClassificationSVM: the elements in 'Prior' do not correspond to selected classes in Y.> ...
662s  ClassificationSVM (ones (5,2), ones (5,1), "Prior", [0,1])
662s ***** error<ClassificationSVM: the elements in 'Prior' do not correspond to selected classes in Y.> ...
662s  ClassificationSVM (ones (5,2), [1;1;2;2;3], "ClassNames", [1,2], "Prior", [0,0.4,0.6])
662s ***** error<ClassificationSVM: the number of rows and columns in 'Cost' must correspond to the selected classes in Y.> ...
662s  ClassificationSVM (ones (5,2), [1;1;2;2;3], "ClassNames", [1,2], "Cost", ones (3))
662s ***** shared x, y, x_train, x_test, y_train, y_test, objST
662s  load fisheriris
662s  inds = ! strcmp (species, 'setosa');
662s  x = meas(inds, 3:4);
662s  y = grp2idx (species(inds));
662s ***** test
662s  xc = [min(x); mean(x); max(x)];
662s  obj = fitcsvm (x, y, 'KernelFunction', 'rbf', 'Tolerance', 1e-7);
662s  assert (isempty (obj.Alpha), true)
662s  assert (sum (obj.IsSupportVector), numel (obj.Beta))
662s  [label, score] = predict (obj, xc);
662s  assert (label, [1; 2; 2]);
662s  assert (score(:,1), [0.99285; -0.080296; -0.93694], 2e-5);
662s  assert (score(:,1), -score(:,2), eps)
662s  obj = fitPosterior (obj);
662s  [label, probs] = predict (obj, xc);
662s  assert (probs(:,2), [0.97555; 0.428164; 0.030385], 2e-5);
662s  assert (probs(:,1) + probs(:,2), [1; 1; 1], 0.05)
662s ***** test
662s  obj = fitcsvm (x, y);
662s  assert (isempty (obj.Beta), true)
662s  assert (sum (obj.IsSupportVector), numel (obj.Alpha))
662s  assert (numel (obj.Alpha), 24)
662s  assert (obj.Bias, -14.415, 1e-3)
662s  xc = [min(x); mean(x); max(x)];
662s  label = predict (obj, xc);
662s  assert (label, [1; 2; 2]);
662s ***** error<ClassificationSVM.predict: too few input arguments.> ...
662s  predict (ClassificationSVM (ones (40,2), ones (40,1)))
662s ***** error<ClassificationSVM.predict: XC is empty.> ...
662s  predict (ClassificationSVM (ones (40,2), ones (40,1)), [])
662s ***** error<ClassificationSVM.predict: XC must have the same number of predictors as the trained model.> ...
662s  predict (ClassificationSVM (ones (40,2), ones (40,1)), 1)
662s ***** test
662s  objST = fitcsvm (x, y);
662s  objST.ScoreTransform = "a";
662s ***** error<ClassificationSVM.predict: 'ScoreTransform' must be a 'function_handle' object.> ...
662s  [labels, scores] = predict (objST, x);
662s ***** error<ClassificationSVM.resubPredict: 'ScoreTransform' must be a 'function_handle' object.> ...
662s  [labels, scores] = resubPredict (objST);
662s ***** test
662s  rand ("seed", 1);
662s  CVSVMModel = fitcsvm (x, y, 'KernelFunction', 'rbf', 'HoldOut', 0.15, ...
662s                        'Tolerance', 1e-7);
662s  obj = CVSVMModel.Trained{1};
662s  testInds = test (CVSVMModel.Partition);
662s  expected_margin = [2.0000;  0.8579;  1.6690;  3.4141;  3.4552; ...
662s                     2.6605;  3.5251; -4.0000; -6.3411; -6.4511; ...
662s                    -3.0532; -7.5054; -1.6700; -5.6227; -7.3640];
662s  computed_margin = margin (obj, x(testInds,:), y(testInds,:));
662s  assert (computed_margin, expected_margin, 1e-4);
662s ***** error<ClassificationSVM.margin: too few input arguments.> ...
662s  margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)))
662s ***** error<ClassificationSVM.margin: too few input arguments.> ...
662s  margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2))
662s ***** error<ClassificationSVM.margin: X is empty.> ...
662s  margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), [], zeros (2))
662s ***** error<ClassificationSVM.margin: X must have the same number of predictors as the trained model.> ...
662s  margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), 1, zeros (2))
662s ***** error<ClassificationSVM.margin: Y is empty.> ...
662s  margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), [])
662s ***** error<ClassificationSVM.margin: Y must have the same number of rows as X.> ...
662s  margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), 1)
662s ***** test
662s  rand ("seed", 1);
662s  CVSVMModel = fitcsvm (x, y, 'KernelFunction', 'rbf', 'HoldOut', 0.15);
662s  obj = CVSVMModel.Trained{1};
662s  testInds = test (CVSVMModel.Partition);
662s  L1 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'binodeviance');
662s  L2 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'classiferror');
662s  L3 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'exponential');
662s  L4 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'hinge');
662s  L5 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'logit');
662s  L6 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'quadratic');
662s  assert (L1, 2.8711, 1e-4);
662s  assert (L2, 0.5333, 1e-4);
662s  assert (L3, 10.9685, 1e-4);
662s  assert (L4, 1.9827, 1e-4);
662s  assert (L5, 1.5849, 1e-4);
662s  assert (L6, 7.6739, 1e-4);
662s ***** error<ClassificationSVM.loss: too few input arguments.> ...
662s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)))
662s ***** error<ClassificationSVM.loss: too few input arguments.> ...
662s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2))
662s ***** error<ClassificationSVM.loss: Name-Value arguments must be in pairs.> ...
662s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
662s  ones(2,1), "LossFun")
662s ***** error<ClassificationSVM.loss: X is empty.> ...
662s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), [], zeros (2))
662s ***** error<ClassificationSVM.loss: X must have the same number of predictors as the trained model.> ...
662s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), 1, zeros (2))
662s ***** error<ClassificationSVM.loss: Y is empty.> ...
662s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), [])
662s ***** error<ClassificationSVM.loss: Y must have the same number of rows as X.> ...
662s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), 1)
662s ***** error<ClassificationSVM.loss: 'LossFun' must be a character vector.> ...
662s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
662s  ones (2,1), "LossFun", 1)
662s ***** error<ClassificationSVM.loss: unsupported Loss function.> ...
662s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
662s  ones (2,1), "LossFun", "some")
662s ***** error<ClassificationSVM.loss: 'Weights' must be a numeric vector.> ...
662s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
662s  ones (2,1), "Weights", ['a','b'])
662s ***** error<ClassificationSVM.loss: 'Weights' must be a numeric vector.> ...
662s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
662s  ones (2,1), "Weights", 'a')
662s ***** error<ClassificationSVM.loss: size of 'Weights' must be equal to the number> ...
662s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
662s  ones (2,1), "Weights", [1,2,3])
662s ***** error<ClassificationSVM.loss: size of 'Weights' must be equal to the number> ...
662s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
662s  ones (2,1), "Weights", 3)
662s ***** error<ClassificationSVM.loss: invalid parameter name in optional pair arg> ...
662s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
662s  ones (2,1), "some", "some")
662s ***** error<ClassificationSVM.resubLoss: Name-Value arguments must be in pairs.> ...
662s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "LossFun")
662s ***** error<ClassificationSVM.resubLoss: 'LossFun' must be a character vector.> ...
662s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "LossFun", 1)
662s ***** error<ClassificationSVM.resubLoss: unsupported Loss function.> ...
662s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "LossFun", "some")
662s ***** error<ClassificationSVM.resubLoss: 'Weights' must be a numeric vector.> ...
662s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "Weights", ['a','b'])
662s ***** error<ClassificationSVM.resubLoss: 'Weights' must be a numeric vector.> ...
662s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "Weights", 'a')
662s ***** error<ClassificationSVM.resubLoss: size of 'Weights' must be equal to the n> ...
662s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "Weights", [1,2,3])
662s ***** error<ClassificationSVM.resubLoss: size of 'Weights' must be equal to the n> ...
662s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "Weights", 3)
662s ***** error<ClassificationSVM.resubLoss: invalid parameter name in optional pai> ...
662s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "some", "some")
662s ***** test
662s  SVMModel = fitcsvm (x, y);
662s  CVMdl = crossval (SVMModel, "KFold", 5);
662s  assert (class (CVMdl), "ClassificationPartitionedModel")
662s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
662s  assert (CVMdl.KFold == 5)
662s  assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
662s  assert (CVMdl.CrossValidatedModel, "ClassificationSVM")
662s ***** test
662s  obj = fitcsvm (x, y);
662s  CVMdl = crossval (obj, "HoldOut", 0.2);
662s  assert (class (CVMdl), "ClassificationPartitionedModel")
662s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
662s  assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
662s  assert (CVMdl.CrossValidatedModel, "ClassificationSVM")
662s ***** test
662s  obj = fitcsvm (x, y);
662s  CVMdl = crossval (obj, "LeaveOut", 'on');
662s  assert (class (CVMdl), "ClassificationPartitionedModel")
662s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
662s  assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
662s  assert (CVMdl.CrossValidatedModel, "ClassificationSVM")
663s ***** error<ClassificationSVM.crossval: Name-Value arguments must be in pairs.> ...
663s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold")
663s ***** error<ClassificationSVM.crossval: specify only one of the optional Name-Value paired arguments.> ...
663s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), ...
663s  "KFold", 5, "leaveout", 'on')
663s ***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
663s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", 'a')
663s ***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
663s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", 1)
663s ***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
663s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", -1)
663s ***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
663s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", 11.5)
663s ***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
663s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", [1,2])
663s ***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
663s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", 'a')
663s ***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
663s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", 11.5)
663s ***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
663s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", -1)
663s ***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
663s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", 0)
663s ***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
663s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", 1)
663s ***** error<ClassificationSVM.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
663s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Leaveout", 1)
664s ***** error<ClassificationSVM.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
664s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "CVPartition", 1)
664s ***** error<ClassificationSVM.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
664s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "CVPartition", 'a')
664s ***** error<ClassificationSVM.crossval: invalid parameter name in optional paired arguments> ...
664s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "some", "some")
664s 114 tests, 114 passed, 0 known failure, 0 skipped
664s [inst/Classification/CompactClassificationDiscriminant.m]
664s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/Classification/CompactClassificationDiscriminant.m
664s ***** demo
664s  ## Create a discriminant analysis classifier and its compact version
664s  # and compare their size
664s 
664s  load fisheriris
664s  X = meas;
664s  Y = species;
664s 
664s  Mdl = fitcdiscr (X, Y, 'ClassNames', unique (species))
664s  CMdl = crossval (Mdl)
664s ***** test
664s  load fisheriris
664s  x = meas;
664s  y = species;
664s  PredictorNames = {'Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width'};
664s  Mdl = fitcdiscr (x, y, "PredictorNames", PredictorNames);
664s  CMdl = compact (Mdl);
664s  sigma = [0.265008, 0.092721, 0.167514, 0.038401; ...
664s           0.092721, 0.115388, 0.055244, 0.032710; ...
664s           0.167514, 0.055244, 0.185188, 0.042665; ...
664s           0.038401, 0.032710, 0.042665, 0.041882];
664s  mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
664s        5.9360, 2.7700, 4.2600, 1.3260; ...
664s        6.5880, 2.9740, 5.5520, 2.0260];
664s  xCentered = [ 9.4000e-02,  7.2000e-02, -6.2000e-02, -4.6000e-02; ...
664s               -1.0600e-01, -4.2800e-01, -6.2000e-02, -4.6000e-02; ...
664s               -3.0600e-01, -2.2800e-01, -1.6200e-01, -4.6000e-02];
664s  assert (class (CMdl), "CompactClassificationDiscriminant");
664s  assert ({CMdl.DiscrimType, CMdl.ResponseName}, {"linear", "Y"})
664s  assert ({CMdl.Gamma, CMdl.MinGamma}, {0, 0}, 1e-15)
664s  assert (CMdl.ClassNames, unique (species))
664s  assert (CMdl.Sigma, sigma, 1e-6)
664s  assert (CMdl.Mu, mu, 1e-14)
664s  assert (CMdl.XCentered([1:3],:), xCentered, 1e-14)
664s  assert (CMdl.LogDetSigma, -9.9585, 1e-4)
664s  assert (CMdl.PredictorNames, PredictorNames)
664s ***** test
664s  load fisheriris
664s  x = meas;
664s  y = species;
664s  Mdl = fitcdiscr (x, y, "Gamma", 0.5);
664s  CMdl = compact (Mdl);
664s  sigma = [0.265008, 0.046361, 0.083757, 0.019201; ...
664s           0.046361, 0.115388, 0.027622, 0.016355; ...
664s           0.083757, 0.027622, 0.185188, 0.021333; ...
664s           0.019201, 0.016355, 0.021333, 0.041882];
664s  mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
664s        5.9360, 2.7700, 4.2600, 1.3260; ...
664s        6.5880, 2.9740, 5.5520, 2.0260];
664s  xCentered = [ 9.4000e-02,  7.2000e-02, -6.2000e-02, -4.6000e-02; ...
664s               -1.0600e-01, -4.2800e-01, -6.2000e-02, -4.6000e-02; ...
664s               -3.0600e-01, -2.2800e-01, -1.6200e-01, -4.6000e-02];
664s  assert (class (CMdl), "CompactClassificationDiscriminant");
664s  assert ({CMdl.DiscrimType, CMdl.ResponseName}, {"linear", "Y"})
664s  assert ({CMdl.Gamma, CMdl.MinGamma}, {0.5, 0})
664s  assert (CMdl.ClassNames, unique (species))
664s  assert (CMdl.Sigma, sigma, 1e-6)
664s  assert (CMdl.Mu, mu, 1e-14)
664s  assert (CMdl.XCentered([1:3],:), xCentered, 1e-14)
664s  assert (CMdl.LogDetSigma, -8.6884, 1e-4)
664s ***** error<CompactClassificationDiscriminant: invalid classification object.> ...
664s  CompactClassificationDiscriminant (1)
664s ***** test
664s  load fisheriris
664s  x = meas;
664s  y = species;
664s  Mdl = fitcdiscr (meas, species, "Gamma", 0.5);
664s  CMdl = compact (Mdl);
664s  [label, score, cost] = predict (CMdl, [2, 2, 2, 2]);
664s  assert (label, {'versicolor'})
664s  assert (score, [0, 0.9999, 0.0001], 1e-4)
664s  assert (cost, [1, 0.0001, 0.9999], 1e-4)
664s  [label, score, cost] = predict (CMdl, [2.5, 2.5, 2.5, 2.5]);
664s  assert (label, {'versicolor'})
664s  assert (score, [0, 0.6368, 0.3632], 1e-4)
664s  assert (cost, [1, 0.3632, 0.6368], 1e-4)
664s ***** test
664s  load fisheriris
664s  x = meas;
664s  y = species;
664s  xc = [min(x); mean(x); max(x)];
664s  Mdl = fitcdiscr (x, y);
664s  CMdl = compact (Mdl);
664s  [label, score, cost] = predict (CMdl, xc);
664s  l = {'setosa'; 'versicolor'; 'virginica'};
664s  s = [1, 0, 0; 0, 1, 0; 0, 0, 1];
664s  c = [0, 1, 1; 1, 0, 1; 1, 1, 0];
664s  assert (label, l)
664s  assert (score, s, 1e-4)
664s  assert (cost, c, 1e-4)
664s ***** shared MODEL
664s  X = rand (10,2);
664s  Y = [ones(5,1);2*ones(5,1)];
664s  MODEL = compact (ClassificationDiscriminant (X, Y));
664s ***** error<CompactClassificationDiscriminant.predict: too few input arguments.> ...
664s  predict (MODEL)
664s ***** error<CompactClassificationDiscriminant.predict: XC is empty.> ...
664s  predict (MODEL, [])
664s ***** error<CompactClassificationDiscriminant.predict: XC must have the same number of features as the trained model.> ...
664s  predict (MODEL, 1)
664s ***** test
664s  load fisheriris
664s  model = fitcdiscr (meas, species);
664s  x = mean (meas);
664s  y = {'versicolor'};
664s  L = loss (model, x, y);
664s  assert (L, 0)
664s ***** test
664s  x = [1, 2; 3, 4; 5, 6];
664s  y = {'A'; 'B'; 'A'};
664s  model = fitcdiscr (x, y, "Gamma", 0.4);
664s  x_test = [1, 6; 3, 3];
664s  y_test = {'A'; 'B'};
664s  L = loss (model, x_test, y_test);
664s  assert (L, 0.3333, 1e-4)
664s ***** test
664s  x = [1, 2; 3, 4; 5, 6; 7, 8];
664s  y = ['1'; '2'; '3'; '1'];
664s  model = fitcdiscr (x, y, "gamma" , 0.5);
664s  x_test = [3, 3];
664s  y_test = ['1'];
664s  L = loss (model, x_test, y_test, 'LossFun', 'quadratic');
664s  assert (L, 0.2423, 1e-4)
664s ***** test
664s  x = [1, 2; 3, 4; 5, 6; 7, 8];
664s  y = ['1'; '2'; '3'; '1'];
664s  model = fitcdiscr (x, y, "gamma" , 0.5);
664s  x_test = [3, 3; 5, 7];
664s  y_test = ['1'; '2'];
664s  L = loss (model, x_test, y_test, 'LossFun', 'classifcost');
664s  assert (L, 0.3333, 1e-4)
664s ***** test
664s  x = [1, 2; 3, 4; 5, 6; 7, 8];
664s  y = ['1'; '2'; '3'; '1'];
664s  model = fitcdiscr (x, y, "gamma" , 0.5);
664s  x_test = [3, 3; 5, 7];
664s  y_test = ['1'; '2'];
664s  L = loss (model, x_test, y_test, 'LossFun', 'hinge');
664s  assert (L, 0.5886, 1e-4)
664s ***** test
664s  x = [1, 2; 3, 4; 5, 6; 7, 8];
664s  y = ['1'; '2'; '3'; '1'];
664s  model = fitcdiscr (x, y, "gamma" , 0.5);
664s  x_test = [3, 3; 5, 7];
664s  y_test = ['1'; '2'];
664s  W = [1; 2];
664s  L = loss (model, x_test, y_test, 'LossFun', 'logit', 'Weights', W);
664s  assert (L, 0.5107, 1e-4)
664s ***** test
664s  x = [1, 2; 3, 4; 5, 6];
664s  y = {'A'; 'B'; 'A'};
664s  model = fitcdiscr (x, y, "gamma" , 0.5);
664s  x_with_nan = [1, 2; NaN, 4];
664s  y_test = {'A'; 'B'};
664s  L = loss (model, x_with_nan, y_test);
664s  assert (L, 0.3333, 1e-4)
664s ***** test
664s  x = [1, 2; 3, 4; 5, 6];
664s  y = {'A'; 'B'; 'A'};
664s  model = fitcdiscr (x, y);
664s  x_with_nan = [1, 2; NaN, 4];
664s  y_test = {'A'; 'B'};
664s  L = loss (model, x_with_nan, y_test, 'LossFun', 'logit');
664s  assert (isnan (L))
664s ***** test
664s  x = [1, 2; 3, 4; 5, 6];
664s  y = {'A'; 'B'; 'A'};
664s  model = fitcdiscr (x, y);
664s  customLossFun = @(C, S, W, Cost) sum (W .* sum (abs (C - S), 2));
664s  L = loss (model, x, y, 'LossFun', customLossFun);
664s  assert (L, 0.8889, 1e-4)
664s ***** test
664s  x = [1, 2; 3, 4; 5, 6];
664s  y = [1; 2; 1];
664s  model = fitcdiscr (x, y);
664s  L = loss (model, x, y, 'LossFun', 'classiferror');
664s  assert (L, 0.3333, 1e-4)
664s ***** error<CompactClassificationDiscriminant.loss: too few input arguments.> ...
664s  loss (MODEL)
664s ***** error<CompactClassificationDiscriminant.loss: too few input arguments.> ...
664s  loss (MODEL, ones (4,2))
664s ***** error<CompactClassificationDiscriminant.loss: name-value arguments must be in pairs.> ...
664s  loss (MODEL, ones (4,2), ones (4,1), 'LossFun')
664s ***** error<CompactClassificationDiscriminant.loss: Y must have the same number of rows as X.> ...
664s  loss (MODEL, ones (4,2), ones (3,1))
664s ***** error<CompactClassificationDiscriminant.loss: invalid loss function.> ...
664s  loss (MODEL, ones (4,2), ones (4,1), 'LossFun', 'a')
664s ***** error<CompactClassificationDiscriminant.loss: invalid 'Weights'.> ...
664s  loss (MODEL, ones (4,2), ones (4,1), 'Weights', 'w')
664s  load fisheriris
664s  mdl = fitcdiscr (meas, species);
664s  X = mean (meas);
664s  Y = {'versicolor'};
664s  m = margin (mdl, X, Y);
664s  assert (m, 1, 1e-6)
664s ***** test
664s  X = [1, 2; 3, 4; 5, 6];
664s  Y = [1; 2; 1];
664s  mdl = fitcdiscr (X, Y, "gamma", 0.5);
664s  m = margin (mdl, X, Y);
664s  assert (m, [0.3333; -0.3333; 0.3333], 1e-4)
664s ***** error<CompactClassificationDiscriminant.margin: too few input arguments.> ...
664s  margin (MODEL)
664s ***** error<CompactClassificationDiscriminant.margin: too few input arguments.> ...
664s  margin (MODEL, ones (4,2))
664s ***** error<CompactClassificationDiscriminant.margin: Y must have the same number of rows as X.> ...
664s  margin (MODEL, ones (4,2), ones (3,1))
664s 28 tests, 28 passed, 0 known failure, 0 skipped
664s [inst/Classification/CompactClassificationGAM.m]
664s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/Classification/CompactClassificationGAM.m
664s ***** demo
664s  ## Create a generalized additive model classifier and its compact version
664s  # and compare their size
664s 
664s  load fisheriris
664s  X = meas;
664s  Y = species;
664s 
664s  Mdl = fitcdiscr (X, Y, 'ClassNames', unique (species))
664s  CMdl = crossval (Mdl)
664s ***** test
664s  Mdl = CompactClassificationGAM ();
664s  assert (class (Mdl), "CompactClassificationGAM")
664s ***** test
664s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
664s  y = [0; 0; 1; 1];
664s  PredictorNames = {'Feature1', 'Feature2', 'Feature3'};
664s  Mdl = fitcgam (x, y, "PredictorNames", PredictorNames);
664s  CMdl = compact (Mdl);
664s  assert (class (CMdl), "CompactClassificationGAM");
664s  assert ({CMdl.NumPredictors, CMdl.ResponseName}, {3, "Y"})
664s  assert (CMdl.ClassNames, {'0'; '1'})
664s  assert (CMdl.PredictorNames, PredictorNames)
664s  assert (CMdl.BaseModel.Intercept, 0)
666s ***** test
666s  load fisheriris
666s  inds = strcmp (species,'versicolor') | strcmp (species,'virginica');
666s  X = meas(inds, :);
666s  Y = species(inds, :)';
666s  Y = strcmp (Y, 'virginica')';
666s  Mdl = fitcgam (X, Y, 'Formula', 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3');
666s  CMdl = compact (Mdl);
666s  assert (class (CMdl), "CompactClassificationGAM");
666s  assert ({CMdl.NumPredictors, CMdl.ResponseName}, {4, "Y"})
666s  assert (CMdl.ClassNames, {'0'; '1'})
666s  assert (CMdl.Formula, 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3')
666s  assert (CMdl.PredictorNames, {'x1', 'x2', 'x3', 'x4'})
666s  assert (CMdl.ModelwInt.Intercept, 0)
672s ***** test
672s  X = [2, 3, 5; 4, 6, 8; 1, 2, 3; 7, 8, 9; 5, 4, 3];
672s  Y = [0; 1; 0; 1; 1];
672s  Mdl = fitcgam (X, Y, 'Knots', [4, 4, 4], 'Order', [3, 3, 3]);
672s  CMdl = compact (Mdl);
672s  assert (class (CMdl), "CompactClassificationGAM");
672s  assert ({CMdl.NumPredictors, CMdl.ResponseName}, {3, "Y"})
672s  assert (CMdl.ClassNames, {'0'; '1'})
672s  assert (CMdl.PredictorNames, {'x1', 'x2', 'x3'})
672s  assert (CMdl.Knots, [4, 4, 4])
672s  assert (CMdl.Order, [3, 3, 3])
672s  assert (CMdl.DoF, [7, 7, 7])
672s  assert (CMdl.BaseModel.Intercept, 0.4055, 1e-1)
673s ***** error<CompactClassificationGAM: invalid classification object.> ...
673s  CompactClassificationGAM (1)
673s ***** test
673s  x = [1, 2; 3, 4; 5, 6; 7, 8; 9, 10];
673s  y = [1; 0; 1; 0; 1];
673s  Mdl = fitcgam (x, y, "interactions", "all");
673s  CMdl = compact (Mdl);
673s  l = {'0'; '0'; '0'; '0'; '0'};
673s  s = [0.3760, 0.6240; 0.4259, 0.5741; 0.3760, 0.6240; ...
673s       0.4259, 0.5741; 0.3760, 0.6240];
673s  [labels, scores] = predict (CMdl, x);
673s  assert (class (CMdl), "CompactClassificationGAM");
673s  assert ({CMdl.NumPredictors, CMdl.ResponseName}, {2, "Y"})
673s  assert (CMdl.ClassNames, {'1'; '0'})
673s  assert (CMdl.PredictorNames, {'x1', 'x2'})
673s  assert (CMdl.ModelwInt.Intercept, 0.4055, 1e-1)
673s  assert (labels, l)
673s  assert (scores, s, 1e-1)
676s ***** test
676s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
676s  y = [0; 0; 1; 1];
676s  interactions = [false, true, false; true, false, true; false, true, false];
676s  Mdl = fitcgam (x, y, "learningrate", 0.2, "interactions", interactions);
676s  CMdl = compact (Mdl);
676s  [label, score] = predict (CMdl, x, "includeinteractions", true);
676s  l = {'0'; '0'; '1'; '1'};
676s  s = [0.5106, 0.4894; 0.5135, 0.4865; 0.4864, 0.5136; 0.4847, 0.5153];
676s  assert (class (CMdl), "CompactClassificationGAM");
676s  assert ({CMdl.NumPredictors, CMdl.ResponseName}, {3, "Y"})
676s  assert (CMdl.ClassNames, {'0'; '1'})
676s  assert (CMdl.PredictorNames, {'x1', 'x2', 'x3'})
676s  assert (CMdl.ModelwInt.Intercept, 0)
676s  assert (label, l)
676s  assert (score, s, 1e-1)
682s ***** shared CMdl
682s  Mdl = fitcgam (ones (4,2), ones (4,1));
682s  CMdl = compact (Mdl);
683s ***** error<CompactClassificationGAM.predict: too few input arguments.> ...
683s  predict (CMdl)
683s ***** error<CompactClassificationGAM.predict: XC is empty.> ...
683s  predict (CMdl, [])
683s ***** error<CompactClassificationGAM.predict: XC must have the same number of features as the trained model.> ...
683s  predict (CMdl, 1)
683s 10 tests, 10 passed, 0 known failure, 0 skipped
683s [inst/Classification/CompactClassificationNeuralNetwork.m]
683s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/Classification/CompactClassificationNeuralNetwork.m
683s ***** demo
683s  ## Create a neural network classifier and its compact version
683s  # and compare their size
683s 
683s  load fisheriris
683s  X = meas;
683s  Y = species;
683s 
683s  Mdl = fitcnet (X, Y, 'ClassNames', unique (species))
683s  CMdl = crossval (Mdl)
683s ***** error<CompactClassificationDiscriminant: invalid classification object.> ...
683s  CompactClassificationDiscriminant (1)
683s ***** shared x, y, CMdl
683s  load fisheriris
683s  x = meas;
683s  y = grp2idx (species);
683s  Mdl = fitcnet (x, y, "IterationLimit", 100);
683s  CMdl = compact (Mdl);
683s ***** error<CompactClassificationNeuralNetwork.predict: too few input arguments.> ...
683s  predict (CMdl)
683s ***** error<CompactClassificationNeuralNetwork.predict: XC is empty.> ...
683s  predict (CMdl, [])
683s ***** error<CompactClassificationNeuralNetwork.predict: XC must have the same number of predictors as the trained neural network.> ...
683s  predict (CMdl, 1)
683s ***** test
683s  CMdl.ScoreTransform = "a";
683s ***** error<CompactClassificationNeuralNetwork.predict: 'ScoreTransform' must be a 'function_handle' object.> ...
683s  [labels, scores] = predict (CMdl, x);
683s 6 tests, 6 passed, 0 known failure, 0 skipped
683s [inst/Classification/CompactClassificationSVM.m]
683s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/Classification/CompactClassificationSVM.m
683s ***** demo
683s  ## Create a support vectors machine classifier and its compact version
683s  # and compare their size
683s 
683s  load fisheriris
683s  X = meas;
683s  Y = species;
683s 
683s  Mdl = fitcsvm (X, Y, 'ClassNames', unique (species))
683s  CMdl = crossval (Mdl)
683s ***** error<CompactClassificationSVM: invalid classification object.> ...
683s  CompactClassificationSVM (1)
683s ***** shared x, y, CMdl
683s  load fisheriris
683s  inds = ! strcmp (species, 'setosa');
683s  x = meas(inds, 3:4);
683s  y = grp2idx (species(inds));
683s ***** test
683s  xc = [min(x); mean(x); max(x)];
683s  Mdl = fitcsvm (x, y, 'KernelFunction', 'rbf', 'Tolerance', 1e-7);
683s  CMdl = compact (Mdl);
683s  assert (isempty (CMdl.Alpha), true)
683s  assert (sum (CMdl.IsSupportVector), numel (CMdl.Beta))
683s  [label, score] = predict (CMdl, xc);
683s  assert (label, [1; 2; 2]);
683s  assert (score(:,1), [0.99285; -0.080296; -0.93694], 1e-5);
683s  assert (score(:,1), -score(:,2), eps)
683s ***** test
683s  Mdl = fitcsvm (x, y);
683s  CMdl = compact (Mdl);
683s  assert (isempty (CMdl.Beta), true)
683s  assert (sum (CMdl.IsSupportVector), numel (CMdl.Alpha))
683s  assert (numel (CMdl.Alpha), 24)
683s  assert (CMdl.Bias, -14.415, 1e-3)
683s  xc = [min(x); mean(x); max(x)];
683s  label = predict (CMdl, xc);
683s  assert (label, [1; 2; 2]);
683s ***** error<CompactClassificationSVM.predict: too few input arguments.> ...
683s  predict (CMdl)
683s ***** error<CompactClassificationSVM.predict: XC is empty.> ...
683s  predict (CMdl, [])
683s ***** error<CompactClassificationSVM.predict: XC must have the same number of predictors as the trained SVM model.> ...
683s  predict (CMdl, 1)
683s ***** test
683s  CMdl.ScoreTransform = "a";
683s ***** error<CompactClassificationSVM.predict: 'ScoreTransform' must be a 'function_handle' object.> ...
683s  [labels, scores] = predict (CMdl, x);
683s ***** test
683s  rand ("seed", 1);
683s  C = cvpartition (y, 'HoldOut', 0.15);
683s  Mdl = fitcsvm (x(training (C),:), y(training (C)), ...
683s                 'KernelFunction', 'rbf', 'Tolerance', 1e-7);
683s  CMdl = compact (Mdl);
683s  testInds = test (C);
683s  expected_margin = [2.0000;  0.8579;  1.6690;  3.4141;  3.4552; ...
683s                     2.6605;  3.5251; -4.0000; -6.3411; -6.4511; ...
683s                    -3.0532; -7.5054; -1.6700; -5.6227; -7.3640];
683s  computed_margin = margin (CMdl, x(testInds,:), y(testInds,:));
683s  assert (computed_margin, expected_margin, 1e-4);
683s ***** error<CompactClassificationSVM.margin: too few input arguments.> ...
683s  margin (CMdl)
683s ***** error<CompactClassificationSVM.margin: too few input arguments.> ...
683s  margin (CMdl, zeros (2))
683s ***** error<CompactClassificationSVM.margin: X is empty.> ...
683s  margin (CMdl, [], 1)
683s ***** error<CompactClassificationSVM.margin: X must have the same number of predictors as the trained SVM model.> ...
683s  margin (CMdl, 1, 1)
683s ***** error<CompactClassificationSVM.margin: Y is empty.> ...
683s  margin (CMdl, [1, 2], [])
683s ***** error<CompactClassificationSVM.margin: Y must have the same number of rows as X.> ...
683s  margin (CMdl, [1, 2], [1; 2])
683s ***** test
683s  rand ("seed", 1);
683s  C = cvpartition (y, 'HoldOut', 0.15);
683s  Mdl = fitcsvm (x(training (C),:), y(training (C)), ...
683s                 'KernelFunction', 'rbf', 'Tolerance', 1e-7);
683s  CMdl = compact (Mdl);
683s  testInds = test (C);
683s  L1 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'binodeviance');
683s  L2 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'classiferror');
683s  L3 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'exponential');
683s  L4 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'hinge');
683s  L5 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'logit');
683s  L6 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'quadratic');
683s  assert (L1, 2.8711, 1e-4);
683s  assert (L2, 0.5333, 1e-4);
683s  assert (L3, 10.9685, 1e-4);
683s  assert (L4, 1.9827, 1e-4);
683s  assert (L5, 1.5849, 1e-4);
683s  assert (L6, 7.6739, 1e-4);
683s ***** error<CompactClassificationSVM.loss: too few input arguments.> ...
683s  loss (CMdl)
683s ***** error<CompactClassificationSVM.loss: too few input arguments.> ...
683s  loss (CMdl, zeros (2))
683s ***** error<CompactClassificationSVM.loss: Name-Value arguments must be in pairs.> ...
683s  loss (CMdl, [1, 2], 1, "LossFun")
683s ***** error<CompactClassificationSVM.loss: X is empty.> ...
683s  loss (CMdl, [], zeros (2))
683s ***** error<CompactClassificationSVM.loss: X must have the same number of predictors as the trained SVM model.> ...
683s  loss (CMdl, 1, zeros (2))
683s ***** error<CompactClassificationSVM.loss: Y is empty.> ...
683s  loss (CMdl, [1, 2], [])
683s ***** error<CompactClassificationSVM.loss: Y must have the same number of rows as X.> ...
683s  loss (CMdl, [1, 2], [1; 2])
683s ***** error<CompactClassificationSVM.loss: 'LossFun' must be a character vector.> ...
683s  loss (CMdl, [1, 2], 1, "LossFun", 1)
683s ***** error<CompactClassificationSVM.loss: unsupported Loss function.> ...
683s  loss (CMdl, [1, 2], 1, "LossFun", "some")
683s ***** error<CompactClassificationSVM.loss: 'Weights' must be a numeric vector.> ...
683s  loss (CMdl, [1, 2], 1, "Weights", ['a', 'b'])
683s ***** error<CompactClassificationSVM.loss: 'Weights' must be a numeric vector.> ...
683s  loss (CMdl, [1, 2], 1, "Weights", 'a')
683s ***** error<CompactClassificationSVM.loss: size of 'Weights' must be equal to the number of rows in X.> ...
683s  loss (CMdl, [1, 2], 1, "Weights", [1, 2])
683s ***** error<CompactClassificationSVM.loss: invalid parameter name in optional pair arguments.> ...
683s  loss (CMdl, [1, 2], 1, "some", "some")
683s 29 tests, 29 passed, 0 known failure, 0 skipped
683s [inst/Classification/ConfusionMatrixChart.m]
683s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/Classification/ConfusionMatrixChart.m
683s ***** demo
683s  ## Create a simple ConfusionMatrixChart Object
683s 
683s  cm = ConfusionMatrixChart (gca, [1 2; 1 2], {"A","B"}, {"XLabel","LABEL A"})
683s  NormalizedValues = cm.NormalizedValues
683s  ClassLabels = cm.ClassLabels
683s ***** test
683s  hf = figure ("visible", "off");
683s  unwind_protect
683s    cm = ConfusionMatrixChart (gca, [1 2; 1 2], {"A","B"}, {"XLabel","LABEL A"});
683s    assert (isa (cm, "ConfusionMatrixChart"), true);
683s  unwind_protect_cleanup
683s    close (hf);
683s  end_unwind_protect
683s warning: using the gnuplot graphics toolkit is discouraged
683s 
683s The gnuplot graphics toolkit is not actively maintained and has a number
683s of limitations that are unlikely to be fixed.  Communication with gnuplot
683s uses a one-directional pipe and limited information is passed back to the
683s Octave interpreter so most changes made interactively in the plot window
683s will not be reflected in the graphics properties managed by Octave.  For
683s example, if the plot window is closed with a mouse click, Octave will not
683s be notified and will not update its internal list of open figure windows.
683s The qt toolkit is recommended instead.
683s 1 test, 1 passed, 0 known failure, 0 skipped
683s [inst/Clustering/CalinskiHarabaszEvaluation.m]
683s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/Clustering/CalinskiHarabaszEvaluation.m
683s ***** test
683s  load fisheriris
683s  eva = evalclusters (meas, "kmeans", "calinskiharabasz", "KList", [1:6]);
683s  assert (class (eva), "CalinskiHarabaszEvaluation");
684s 1 test, 1 passed, 0 known failure, 0 skipped
684s [inst/Clustering/ClusterCriterion.m]
684s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/Clustering/ClusterCriterion.m
684s ***** error <ClusterCriterion: 'x' must be a numeric matrix> ...
684s  ClusterCriterion ("1", "kmeans", [1:6])
684s ***** error <ClusterCriterion: unknown clustering algorithm 'k'> ...
684s  ClusterCriterion ([1, 2, 1, 3, 2, 4, 3], "k", [1:6])
684s ***** error <ClusterCriterion: invalid matrix of clustering solutions> ...
684s  ClusterCriterion ([1, 2, 1; 3, 2, 4], 1, [1:6])
684s ***** error <ClusterCriterion: invalid argument> ...
684s  ClusterCriterion ([1, 2, 1; 3, 2, 4], ones (2, 2, 2), [1:6])
684s 4 tests, 4 passed, 0 known failure, 0 skipped
684s [inst/Clustering/DaviesBouldinEvaluation.m]
684s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/Clustering/DaviesBouldinEvaluation.m
684s ***** test
684s  load fisheriris
684s  eva = evalclusters (meas, "kmeans", "DaviesBouldin", "KList", [1:6]);
684s  assert (class (eva), "DaviesBouldinEvaluation");
684s 1 test, 1 passed, 0 known failure, 0 skipped
684s [inst/Clustering/GapEvaluation.m]
684s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/Clustering/GapEvaluation.m
684s ***** test
684s  load fisheriris
684s  eva = evalclusters (meas([1:50],:), "kmeans", "gap", "KList", [1:3], ...
684s                      "referencedistribution", "uniform");
684s  assert (class (eva), "GapEvaluation");
688s 1 test, 1 passed, 0 known failure, 0 skipped
688s [inst/Clustering/SilhouetteEvaluation.m]
688s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/Clustering/SilhouetteEvaluation.m
688s ***** test
688s  load fisheriris
688s  eva = evalclusters (meas, "kmeans", "silhouette", "KList", [1:6]);
688s  assert (class (eva), "SilhouetteEvaluation");
689s 1 test, 1 passed, 0 known failure, 0 skipped
689s [inst/Regression/RegressionGAM.m]
689s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/Regression/RegressionGAM.m
689s ***** demo
689s  ## Train a RegressionGAM Model for synthetic values
689s  f1 = @(x) cos (3 * x);
689s  f2 = @(x) x .^ 3;
689s  x1 = 2 * rand (50, 1) - 1;
689s  x2 = 2 * rand (50, 1) - 1;
689s  y = f1(x1) + f2(x2);
689s  y = y + y .* 0.2 .* rand (50,1);
689s  X = [x1, x2];
689s  a = fitrgam (X, y, "tol", 1e-3)
689s ***** demo
689s  ## Declare two different functions
689s  f1 = @(x) cos (3 * x);
689s  f2 = @(x) x .^ 3;
689s 
689s  ## Generate 80 samples for f1 and f2
689s  x = [-4*pi:0.1*pi:4*pi-0.1*pi]';
689s  X1 = f1 (x);
689s  X2 = f2 (x);
689s 
689s  ## Create a synthetic response by adding noise
689s  rand ("seed", 3);
689s  Ytrue = X1 + X2;
689s  Y = Ytrue + Ytrue .* 0.2 .* rand (80,1);
689s 
689s  ## Assemble predictor data
689s  X = [X1, X2];
689s 
689s  ## Train the GAM and test on the same data
689s  a = fitrgam (X, Y, "order", [5, 5]);
689s  [ypred, ySDsd, yInt] = predict (a, X);
689s 
689s  ## Plot the results
689s  figure
689s  [sortedY, indY] = sort (Ytrue);
689s  plot (sortedY, "r-");
689s  xlim ([0, 80]);
689s  hold on
689s  plot (ypred(indY), "g+")
689s  plot (yInt(indY,1), "k:")
689s  plot (yInt(indY,2), "k:")
689s  xlabel ("Predictor samples");
689s  ylabel ("Response");
689s  title ("actual vs predicted values for function f1(x) = cos (3x) ");
689s  legend ({"Theoretical Response", "Predicted Response", "Prediction Intervals"});
689s 
689s  ## Use 30% Holdout partitioning for training and testing data
689s  C = cvpartition (80, "HoldOut", 0.3);
689s  [ypred, ySDsd, yInt] = predict (a, X(test(C),:));
689s 
689s  ## Plot the results
689s  figure
689s  [sortedY, indY] = sort (Ytrue(test(C)));
689s  plot (sortedY, 'r-');
689s  xlim ([0, sum(test(C))]);
689s  hold on
689s  plot (ypred(indY), "g+")
689s  plot (yInt(indY,1),'k:')
689s  plot (yInt(indY,2),'k:')
689s  xlabel ("Predictor samples");
689s  ylabel ("Response");
689s  title ("actual vs predicted values for function f1(x) = cos (3x) ");
689s  legend ({"Theoretical Response", "Predicted Response", "Prediction Intervals"});
689s ***** test
689s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
689s  y = [1; 2; 3; 4];
689s  a = RegressionGAM (x, y);
689s  assert ({a.X, a.Y}, {x, y})
689s  assert ({a.BaseModel.Intercept}, {2.5000})
689s  assert ({a.Knots, a.Order, a.DoF}, {[5, 5, 5], [3, 3, 3], [8, 8, 8]})
689s  assert ({a.NumObservations, a.NumPredictors}, {4, 3})
689s  assert ({a.ResponseName, a.PredictorNames}, {"Y", {"x1", "x2", "x3"}})
689s  assert ({a.Formula}, {[]})
689s ***** test
689s  x = [1, 2, 3, 4; 4, 5, 6, 7; 7, 8, 9, 1; 3, 2, 1, 2];
689s  y = [1; 2; 3; 4];
689s  pnames = {"A", "B", "C", "D"};
689s  formula = "Y ~ A + B + C + D + A:C";
689s  intMat = logical ([1,0,0,0;0,1,0,0;0,0,1,0;0,0,0,1;1,0,1,0]);
689s  a = RegressionGAM (x, y, "predictors", pnames, "formula", formula);
689s  assert ({a.IntMatrix}, {intMat})
689s  assert ({a.ResponseName, a.PredictorNames}, {"Y", pnames})
689s  assert ({a.Formula}, {formula})
689s ***** error<RegressionGAM: too few input arguments.> RegressionGAM ()
689s ***** error<RegressionGAM: too few input arguments.> RegressionGAM (ones(10,2))
689s ***** error<RegressionGAM: number of rows in X and Y must be equal.> ...
689s  RegressionGAM (ones(10,2), ones (5,1))
689s ***** error<RegressionGAM: invalid values in X.> ...
689s  RegressionGAM ([1;2;3;"a";4], ones (5,1))
689s ***** error<RegressionGAM: invalid parameter name in optional pair arguments.> ...
689s  RegressionGAM (ones(10,2), ones (10,1), "some", "some")
689s ***** error<RegressionGAM: Formula must be a string.>
689s  RegressionGAM (ones(10,2), ones (10,1), "formula", {"y~x1+x2"})
689s ***** error<RegressionGAM: Formula must be a string.>
689s  RegressionGAM (ones(10,2), ones (10,1), "formula", [0, 1, 0])
689s ***** error<RegressionGAM: invalid syntax in Formula.> ...
689s  RegressionGAM (ones(10,2), ones (10,1), "formula", "something")
689s ***** error<RegressionGAM: no predictor terms in Formula.> ...
689s  RegressionGAM (ones(10,2), ones (10,1), "formula", "something~")
689s ***** error<RegressionGAM: no predictor terms in Formula.> ...
689s  RegressionGAM (ones(10,2), ones (10,1), "formula", "something~")
689s ***** error<RegressionGAM: some predictors have not been identified> ...
689s  RegressionGAM (ones(10,2), ones (10,1), "formula", "something~x1:")
689s ***** error<RegressionGAM: invalid Interactions parameter.> ...
689s  RegressionGAM (ones(10,2), ones (10,1), "interactions", "some")
689s ***** error<RegressionGAM: invalid Interactions parameter.> ...
689s  RegressionGAM (ones(10,2), ones (10,1), "interactions", -1)
689s ***** error<RegressionGAM: invalid Interactions parameter.> ...
689s  RegressionGAM (ones(10,2), ones (10,1), "interactions", [1 2 3 4])
689s ***** error<RegressionGAM: number of interaction terms requested is larger than> ...
689s  RegressionGAM (ones(10,2), ones (10,1), "interactions", 3)
689s ***** error<RegressionGAM: Formula has been already defined.> ...
689s  RegressionGAM (ones(10,2), ones (10,1), "formula", "y ~ x1 + x2", "interactions", 1)
689s ***** error<RegressionGAM: Interactions have been already defined.> ...
689s  RegressionGAM (ones(10,2), ones (10,1), "interactions", 1, "formula", "y ~ x1 + x2")
689s ***** error<RegressionGAM: invalid value for Knots.> ...
689s  RegressionGAM (ones(10,2), ones (10,1), "knots", "a")
689s ***** error<RegressionGAM: DoF and Order have been set already.> ...
689s  RegressionGAM (ones(10,2), ones (10,1), "order", 3, "dof", 2, "knots", 5)
689s ***** error<RegressionGAM: invalid value for DoF.> ...
689s  RegressionGAM (ones(10,2), ones (10,1), "dof", 'a')
689s ***** error<RegressionGAM: Knots and Order have been set already.> ...
689s  RegressionGAM (ones(10,2), ones (10,1), "knots", 5, "order", 3, "dof", 2)
689s ***** error<RegressionGAM: invalid value for Order.> ...
689s  RegressionGAM (ones(10,2), ones (10,1), "order", 'a')
689s ***** error<RegressionGAM: DoF and Knots have been set already.> ...
689s  RegressionGAM (ones(10,2), ones (10,1), "knots", 5, "dof", 2, "order", 2)
689s ***** error<RegressionGAM: Tolerance must be a Positive scalar.> ...
689s  RegressionGAM (ones(10,2), ones (10,1), "tol", -1)
689s ***** error<RegressionGAM: ResponseName must be a char string.> ...
689s  RegressionGAM (ones(10,2), ones (10,1), "responsename", -1)
689s ***** error<RegressionGAM: PredictorNames must be a cellstring array.> ...
689s  RegressionGAM (ones(10,2), ones (10,1), "predictors", -1)
689s ***** error<RegressionGAM: PredictorNames must be a cellstring array.> ...
689s  RegressionGAM (ones(10,2), ones (10,1), "predictors", ['a','b','c'])
689s ***** error<RegressionGAM: PredictorNames must have same number of columns as X.> ...
689s  RegressionGAM (ones(10,2), ones (10,1), "predictors", {'a','b','c'})
689s ***** error<RegressionGAM.predict: too few arguments.> ...
689s  predict (RegressionGAM (ones(10,1), ones(10,1)))
689s ***** error<RegressionGAM.predict: Xfit is empty.> ...
689s  predict (RegressionGAM (ones(10,1), ones(10,1)), [])
689s ***** error<RegressionGAM.predict: Xfit must have the same number of features> ...
689s  predict (RegressionGAM(ones(10,2), ones(10,1)), 2)
689s ***** error<RegressionGAM.predict: invalid NAME in optional pairs of arguments.> ...
689s  predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "some", "some")
689s ***** error<RegressionGAM.predict: includeinteractions must be a logical value.> ...
689s  predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "includeinteractions", "some")
689s ***** error<RegressionGAM.predict: includeinteractions must be a logical value.> ...
689s  predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "includeinteractions", 5)
689s ***** error<RegressionGAM.predict: alpha must be a scalar value between 0 and 1.> ...
689s  predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "alpha", 5)
689s ***** error<RegressionGAM.predict: alpha must be a scalar value between 0 and 1.> ...
689s  predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "alpha", -1)
689s ***** error<RegressionGAM.predict: alpha must be a scalar value between 0 and 1.> ...
689s  predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "alpha", 'a')
689s 39 tests, 39 passed, 0 known failure, 0 skipped
689s [inst/adtest.m]
689s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/adtest.m
689s ***** error<Invalid call to adtest.  Correct usage is:> adtest ();
689s ***** error<adtest: X must be a vector of real numbers.> adtest (ones (20,2));
689s ***** error<adtest: X must be a vector of real numbers.> adtest ([1+i,0-3i]);
689s ***** error<adtest: invalid distribution family in char string.> ...
689s  adtest (ones (20,1), "Distribution", "normal");
689s ***** error<adtest: invalid distribution family in cell array.> ...
689s  adtest (rand (20,1), "Distribution", {"normal", 5, 3});
689s ***** error<adtest: invalid distribution parameters in cell array.> ...
689s  adtest (rand (20,1), "Distribution", {"norm", 5});
689s ***** error<adtest: invalid distribution parameters in cell array.> ...
689s  adtest (rand (20,1), "Distribution", {"exp", 5, 4});
689s ***** error<adtest: invalid distribution parameters in cell array.> ...
689s  adtest (rand (20,1), "Distribution", {"ev", 5});
689s ***** error<adtest: invalid distribution parameters in cell array.> ...
689s  adtest (rand (20,1), "Distribution", {"logn", 5, 3, 2});
689s ***** error<adtest: invalid distribution parameters in cell array.> ...
689s  adtest (rand (20,1), "Distribution", {"Weibull", 5});
689s ***** error<adtest: invalid distribution option.> ...
689s  adtest (rand (20,1), "Distribution", 35);
689s ***** error<adtest: invalid Name argument.> ...
689s  adtest (rand (20,1), "Name", "norm");
689s ***** error<adtest: invalid Name argument.> ...
689s  adtest (rand (20,1), "Name", {"norm", 75, 10});
689s ***** error<adtest: asymptotic option is not valid for the composite> ...
689s  adtest (rand (20,1), "Distribution", "norm", "Asymptotic", true);
689s ***** error<adtest: asymptotic option is not valid for the composite> ...
689s  adtest (rand (20,1), "MCTol", 0.001, "Asymptotic", true);
689s ***** error<adtest: asymptotic option is not valid for the Monte Carlo> ...
689s  adtest (rand (20,1), "Distribution", {"norm", 5, 3}, "MCTol", 0.001, ...
689s          "Asymptotic", true);
689s ***** error<adtest: out of range invalid alpha - lower limit: 0.0005 upper> ...
689s  [h, pval, ADstat, CV] = adtest (ones (20,1), "Distribution", {"norm",5,3},...
689s                                  "Alpha", 0.000000001);
689s ***** error<adtest: out of range invalid alpha - lower limit: 0.0005 upper> ...
689s  [h, pval, ADstat, CV] = adtest (ones (20,1), "Distribution", {"norm",5,3},...
689s                                  "Alpha", 0.999999999);
689s ***** error<adtest: not enough data for composite testing.> ...
689s  adtest (10);
689s ***** warning<adtest: out of range min p-value:> ...
689s  randn ("seed", 34);
689s  adtest (ones (20,1), "Alpha", 0.000001);
689s ***** warning<adtest: alpha not within the lookup table.> ...
689s  randn ("seed", 34);
689s  adtest (normrnd(0,1,100,1), "Alpha", 0.99999);
689s ***** warning<adtest: alpha not within the lookup table.> ...
689s  randn ("seed", 34);
689s  adtest (normrnd(0,1,100,1), "Alpha", 0.00001);
690s ***** test
690s  load examgrades
690s  x = grades(:,1);
690s  [h, pval, adstat, cv] = adtest (x);
690s  assert (h, false);
690s  assert (pval, 0.1854, 1e-4);
690s  assert (adstat, 0.5194, 1e-4);
690s  assert (cv, 0.7470, 1e-4);
690s ***** test
690s  load examgrades
690s  x = grades(:,1);
690s  [h, pval, adstat, cv] = adtest (x, "Distribution", "ev");
690s  assert (h, false);
690s  assert (pval, 0.071363, 1e-6);
690s ***** test
690s  load examgrades
690s  x = grades(:,1);
690s  [h, pval, adstat, cv] = adtest (x, "Distribution", {"norm", 75, 10});
690s  assert (h, false);
690s  assert (pval, 0.4687, 1e-4);
690s 25 tests, 25 passed, 0 known failure, 0 skipped
690s [inst/anova1.m]
690s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/anova1.m
690s ***** demo
690s  x = meshgrid (1:6);
690s  randn ("seed", 15);    # for reproducibility
690s  x = x + normrnd (0, 1, 6, 6);
690s  anova1 (x, [], 'off');
690s ***** demo
690s  x = meshgrid (1:6);
690s  randn ("seed", 15);    # for reproducibility
690s  x = x + normrnd (0, 1, 6, 6);
690s  [p, atab] = anova1(x);
690s ***** demo
690s  x = ones (50, 4) .* [-2, 0, 1, 5];
690s  randn ("seed", 13);    # for reproducibility
690s  x = x + normrnd (0, 2, 50, 4);
690s  groups = {"A", "B", "C", "D"};
690s  anova1 (x, groups);
690s ***** demo
690s  y = [54 87 45; 23 98 39; 45 64 51; 54 77 49; 45 89 50; 47 NaN 55];
690s  g = [1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ];
690s  anova1 (y(:), g(:), "on", "unequal");
690s ***** test
690s  data = [1.006, 0.996, 0.998, 1.000, 0.992, 0.993, 1.002, 0.999, 0.994, 1.000, ...
690s          0.998, 1.006, 1.000, 1.002, 0.997, 0.998, 0.996, 1.000, 1.006, 0.988, ...
690s          0.991, 0.987, 0.997, 0.999, 0.995, 0.994, 1.000, 0.999, 0.996, 0.996, ...
690s          1.005, 1.002, 0.994, 1.000, 0.995, 0.994, 0.998, 0.996, 1.002, 0.996, ...
690s          0.998, 0.998, 0.982, 0.990, 1.002, 0.984, 0.996, 0.993, 0.980, 0.996, ...
690s          1.009, 1.013, 1.009, 0.997, 0.988, 1.002, 0.995, 0.998, 0.981, 0.996, ...
690s          0.990, 1.004, 0.996, 1.001, 0.998, 1.000, 1.018, 1.010, 0.996, 1.002, ...
690s          0.998, 1.000, 1.006, 1.000, 1.002, 0.996, 0.998, 0.996, 1.002, 1.006, ...
690s          1.002, 0.998, 0.996, 0.995, 0.996, 1.004, 1.004, 0.998, 0.999, 0.991, ...
690s          0.991, 0.995, 0.984, 0.994, 0.997, 0.997, 0.991, 0.998, 1.004, 0.997];
690s  group = [1:10] .* ones (10,10);
690s  group = group(:);
690s  [p, tbl] = anova1 (data, group, "off");
690s  assert (p, 0.022661, 1e-6);
690s  assert (tbl{2,5}, 2.2969, 1e-4);
690s  assert (tbl{2,3}, 9, 0);
690s  assert (tbl{4,2}, 0.003903, 1e-6);
690s  data = reshape (data, 10, 10);
690s  [p, tbl, stats] = anova1 (data, [], "off");
690s  assert (p, 0.022661, 1e-6);
690s  assert (tbl{2,5}, 2.2969, 1e-4);
690s  assert (tbl{2,3}, 9, 0);
690s  assert (tbl{4,2}, 0.003903, 1e-6);
690s  means = [0.998, 0.9991, 0.9954, 0.9982, 0.9919, 0.9988, 1.0015, 1.0004, 0.9983, 0.9948];
690s  N = 10 * ones (1, 10);
690s  assert (stats.means, means, 1e-6);
690s  assert (length (stats.gnames), 10, 0);
690s  assert (stats.n, N, 0);
690s ***** test
690s  y = [54 87 45; 23 98 39; 45 64 51; 54 77 49; 45 89 50; 47 NaN 55];
690s  g = [1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ];
690s  [p, tbl] = anova1 (y(:), g(:), "off", "equal");
690s  assert (p, 0.00004163, 1e-6);
690s  assert (tbl{2,5}, 22.573418, 1e-6);
690s  assert (tbl{2,3}, 2, 0);
690s  assert (tbl{3,3}, 14, 0);
690s  [p, tbl] = anova1 (y(:), g(:), "off", "unequal");
690s  assert (p, 0.00208877, 1e-8);
690s  assert (tbl{2,5}, 15.523192, 1e-6);
690s  assert (tbl{2,3}, 2, 0);
690s  assert (tbl{2,4}, 7.5786897, 1e-6);
690s 2 tests, 2 passed, 0 known failure, 0 skipped
690s [inst/anova2.m]
690s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/anova2.m
690s ***** demo
690s 
690s  # Factorial (Crossed) Two-way ANOVA with Interaction
690s 
690s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
690s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
690s 
690s  [p, atab, stats] = anova2(popcorn, 3, "on");
690s ***** demo
690s 
690s  # One-way Repeated Measures ANOVA (Rows are a crossed random factor)
690s 
690s  data = [54, 43, 78, 111;
690s          23, 34, 37, 41;
690s          45, 65, 99, 78;
690s          31, 33, 36, 35;
690s          15, 25, 30, 26];
690s 
690s  [p, atab, stats] = anova2 (data, 1, "on", "linear");
690s ***** demo
690s 
690s  # Balanced Nested One-way ANOVA (Rows are a nested random factor)
690s 
690s  data = [4.5924 7.3809 21.322; -0.5488 9.2085 25.0426; ...
690s          6.1605 13.1147 22.66; 2.3374 15.2654 24.1283; ...
690s          5.1873 12.4188 16.5927; 3.3579 14.3951 10.2129; ...
690s          6.3092 8.5986 9.8934; 3.2831 3.4945 10.0203];
690s 
690s  [p, atab, stats] = anova2 (data, 4, "on", "nested");
690s ***** test
690s  ## Test for anova2 ("interaction")
690s  ## comparison with results from Matlab for column effect
690s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
690s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
690s  [p, atab, stats] = anova2 (popcorn, 3, "off");
690s  assert (p(1), 7.678957383294716e-07, 1e-14);
690s  assert (p(2), 0.0001003738963050171, 1e-14);
690s  assert (p(3), 0.7462153966366274, 1e-14);
690s  assert (atab{2,5}, 56.700, 1e-14);
690s  assert (atab{2,3}, 2, 0);
690s  assert (atab{4,2}, 0.08333333333333348, 1e-14);
690s  assert (atab{5,4}, 0.1388888888888889, 1e-14);
690s  assert (atab{5,2}, 1.666666666666667, 1e-14);
690s  assert (atab{6,2}, 22);
690s  assert (stats.source, "anova2");
690s  assert (stats.colmeans, [6.25, 4.75, 4]);
690s  assert (stats.inter, 1, 0);
690s  assert (stats.pval, 0.7462153966366274, 1e-14);
690s  assert (stats.df, 12);
690s ***** test
690s  ## Test for anova2 ("linear") - comparison with results from GraphPad Prism 8
690s  data = [54, 43, 78, 111;
690s          23, 34, 37, 41;
690s          45, 65, 99, 78;
690s          31, 33, 36, 35;
690s          15, 25, 30, 26];
690s  [p, atab, stats] = anova2 (data, 1, "off", "linear");
690s  assert (atab{2,2}, 2174.95, 1e-10);
690s  assert (atab{3,2}, 8371.7, 1e-10);
690s  assert (atab{4,2}, 2404.3, 1e-10);
690s  assert (atab{5,2}, 12950.95, 1e-10);
690s  assert (atab{2,4}, 724.983333333333, 1e-10);
690s  assert (atab{3,4}, 2092.925, 1e-10);
690s  assert (atab{4,4}, 200.358333333333, 1e-10);
690s  assert (atab{2,5}, 3.61843363972882, 1e-10);
690s  assert (atab{3,5}, 10.445909412303, 1e-10);
690s  assert (atab{2,6}, 0.087266112738617, 1e-10);
690s  assert (atab{3,6}, 0.000698397753556, 1e-10);
690s ***** test
690s  ## Test for anova2 ("nested") - comparison with results from GraphPad Prism 8
690s  data = [4.5924 7.3809 21.322; -0.5488 9.2085 25.0426; ...
690s          6.1605 13.1147 22.66; 2.3374 15.2654 24.1283; ...
690s          5.1873 12.4188 16.5927; 3.3579 14.3951 10.2129; ...
690s          6.3092 8.5986 9.8934; 3.2831 3.4945 10.0203];
690s  [p, atab, stats] = anova2 (data, 4, "off", "nested");
690s  assert (atab{2,2}, 745.360306290833, 1e-10);
690s  assert (atab{3,2}, 278.01854140125, 1e-10);
690s  assert (atab{4,2}, 180.180377467501, 1e-10);
690s  assert (atab{5,2}, 1203.55922515958, 1e-10);
690s  assert (atab{2,4}, 372.680153145417, 1e-10);
690s  assert (atab{3,4}, 92.67284713375, 1e-10);
690s  assert (atab{4,4}, 10.0100209704167, 1e-10);
690s  assert (atab{2,5}, 4.02146005730833, 1e-10);
690s  assert (atab{3,5}, 9.25800729165627, 1e-10);
690s  assert (atab{2,6}, 0.141597630656771, 1e-10);
690s  assert (atab{3,6}, 0.000636643812875719, 1e-10);
690s 3 tests, 3 passed, 0 known failure, 0 skipped
690s [inst/anovan.m]
690s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/anovan.m
690s ***** demo
690s 
690s  # Two-sample unpaired test on independent samples (equivalent to Student's
690s  # t-test). Note that the absolute value of t-statistic can be obtained by
690s  # taking the square root of the reported F statistic. In this example,
690s  # t = sqrt (1.44) = 1.20.
690s 
690s  score = [54 23 45 54 45 43 34 65 77 46 65]';
690s  gender = {"male" "male" "male" "male" "male" "female" "female" "female" ...
690s            "female" "female" "female"}';
690s 
690s  [P, ATAB, STATS] = anovan (score, gender, "display", "on", "varnames", "gender");
690s ***** demo
690s 
690s  # Two-sample paired test on dependent or matched samples equivalent to a
690s  # paired t-test. As for the first example, the t-statistic can be obtained by
690s  # taking the square root of the reported F statistic. Note that the interaction
690s  # between treatment x subject was dropped from the full model by assigning
690s  # subject as a random factor (').
690s 
690s  score = [4.5 5.6; 3.7 6.4; 5.3 6.4; 5.4 6.0; 3.9 5.7]';
690s  treatment = {"before" "after"; "before" "after"; "before" "after";
690s               "before" "after"; "before" "after"}';
690s  subject = {"GS" "GS"; "JM" "JM"; "HM" "HM"; "JW" "JW"; "PS" "PS"}';
690s 
690s  [P, ATAB, STATS] = anovan (score(:), {treatment(:), subject(:)}, ...
690s                             "model", "full", "random", 2, "sstype", 2, ...
690s                             "varnames", {"treatment", "subject"}, ...
690s                             "display", "on");
690s ***** demo
690s 
690s  # One-way ANOVA on the data from a study on the strength of structural beams,
690s  # in Hogg and Ledolter (1987) Engineering Statistics. New York: MacMillan
690s 
690s  strength = [82 86 79 83 84 85 86 87 74 82 ...
690s             78 75 76 77 79 79 77 78 82 79]';
690s  alloy = {"st","st","st","st","st","st","st","st", ...
690s           "al1","al1","al1","al1","al1","al1", ...
690s           "al2","al2","al2","al2","al2","al2"}';
690s 
690s  [P, ATAB, STATS] = anovan (strength, alloy, "display", "on", ...
690s                             "varnames", "alloy");
690s ***** demo
690s 
690s  # One-way repeated measures ANOVA on the data from a study on the number of
690s  # words recalled by 10 subjects for three time condtions, in Loftus & Masson
690s  # (1994) Psychon Bull Rev. 1(4):476-490, Table 2. Note that the interaction
690s  # between seconds x subject was dropped from the full model by assigning
690s  # subject as a random factor (').
690s 
690s  words = [10 13 13; 6 8 8; 11 14 14; 22 23 25; 16 18 20; ...
690s           15 17 17; 1 1 4; 12 15 17;  9 12 12;  8 9 12];
690s  seconds = [1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5; ...
690s             1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5;];
690s  subject = [ 1  1  1;  2  2  2;  3  3  3;  4  4  4;  5  5  5; ...
690s              6  6  6;  7  7  7;  8  8  8;  9  9  9; 10 10 10];
690s 
690s  [P, ATAB, STATS] = anovan (words(:), {seconds(:), subject(:)}, ...
690s                             "model", "full", "random", 2, "sstype", 2, ...
690s                             "display", "on", "varnames", {"seconds", "subject"});
690s ***** demo
690s 
690s  # Balanced two-way ANOVA with interaction on the data from a study of popcorn
690s  # brands and popper types, in Hogg and Ledolter (1987) Engineering Statistics.
690s  # New York: MacMillan
690s 
690s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
690s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
690s  brands = {"Gourmet", "National", "Generic"; ...
690s            "Gourmet", "National", "Generic"; ...
690s            "Gourmet", "National", "Generic"; ...
690s            "Gourmet", "National", "Generic"; ...
690s            "Gourmet", "National", "Generic"; ...
690s            "Gourmet", "National", "Generic"};
690s  popper = {"oil", "oil", "oil"; "oil", "oil", "oil"; "oil", "oil", "oil"; ...
690s            "air", "air", "air"; "air", "air", "air"; "air", "air", "air"};
690s 
690s  [P, ATAB, STATS] = anovan (popcorn(:), {brands(:), popper(:)}, ...
690s                             "display", "on", "model", "full", ...
690s                             "varnames", {"brands", "popper"});
690s ***** demo
690s 
690s  # Unbalanced two-way ANOVA (2x2) on the data from a study on the effects of
690s  # gender and having a college degree on salaries of company employees,
690s  # in Maxwell, Delaney and Kelly (2018): Chapter 7, Table 15
690s 
690s  salary = [24 26 25 24 27 24 27 23 15 17 20 16, ...
690s            25 29 27 19 18 21 20 21 22 19]';
690s  gender = {"f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f"...
690s            "m" "m" "m" "m" "m" "m" "m" "m" "m" "m"}';
690s  degree = [1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0]';
690s 
690s  [P, ATAB, STATS] = anovan (salary, {gender, degree}, "model", "full", ...
690s                             "sstype", 3, "display", "on", "varnames", ...
690s                             {"gender", "degree"});
690s ***** demo
690s 
690s  # Unbalanced two-way ANOVA (3x2) on the data from a study of the effect of
690s  # adding sugar and/or milk on the tendency of coffee to make people babble,
690s  # in from Navarro (2019): 16.10
690s 
690s  sugar = {"real" "fake" "fake" "real" "real" "real" "none" "none" "none" ...
690s           "fake" "fake" "fake" "real" "real" "real" "none" "none" "fake"}';
690s  milk = {"yes" "no" "no" "yes" "yes" "no" "yes" "yes" "yes" ...
690s          "no" "no" "yes" "no" "no" "no" "no" "no" "yes"}';
690s  babble = [4.6 4.4 3.9 5.6 5.1 5.5 3.9 3.5 3.7...
690s            5.6 4.7 5.9 6.0 5.4 6.6 5.8 5.3 5.7]';
690s 
690s  [P, ATAB, STATS] = anovan (babble, {sugar, milk}, "model", "full",  ...
690s                             "sstype", 3, "display", "on", ...
690s                             "varnames", {"sugar", "milk"});
690s ***** demo
690s 
690s  # Unbalanced three-way ANOVA (3x2x2) on the data from a study of the effects
690s  # of three different drugs, biofeedback and diet on patient blood pressure,
690s  # adapted* from Maxwell, Delaney and Kelly (2018): Chapter 8, Table 12
690s  # * Missing values introduced to make the sample sizes unequal to test the
690s  #   calculation of different types of sums-of-squares
690s 
690s  drug = {"X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" ...
690s          "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X";
690s          "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" ...
690s          "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y";
690s          "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" ...
690s          "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z"};
690s  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;
690s              1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
690s              1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0];
690s  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;
690s          0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1;
690s          0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1];
690s  BP = [170 175 165 180 160 158 161 173 157 152 181 190 ...
690s        173 194 197 190 176 198 164 190 169 164 176 175;
690s        186 194 201 215 219 209 164 166 159 182 187 174 ...
690s        189 194 217 206 199 195 171 173 196 199 180 NaN;
690s        180 187 199 170 204 194 162 184 183 156 180 173 ...
690s        202 228 190 206 224 204 205 199 170 160 NaN NaN];
690s 
690s  [P, ATAB, STATS] = anovan (BP(:), {drug(:), feedback(:), diet(:)}, ...
690s                                     "model", "full", "sstype", 3, ...
690s                                     "display", "on", ...
690s                                     "varnames", {"drug", "feedback", "diet"});
690s ***** demo
690s 
690s  # Balanced three-way ANOVA (2x2x2) with one of the factors being a blocking
690s  # factor. The data is from a randomized block design study on the effects
690s  # of antioxidant treatment on glutathione-S-transferase (GST) levels in
690s  # different mouse strains, from Festing (2014), ILAR Journal, 55(3):427-476.
690s  # Note that all interactions involving block were dropped from the full model
690s  # by assigning block as a random factor (').
690s 
690s  measurement = [444 614 423 625 408  856 447 719 ...
690s                 764 831 586 782 609 1002 606 766]';
690s  strain= {"NIH","NIH","BALB/C","BALB/C","A/J","A/J","129/Ola","129/Ola", ...
690s           "NIH","NIH","BALB/C","BALB/C","A/J","A/J","129/Ola","129/Ola"}';
690s  treatment={"C" "T" "C" "T" "C" "T" "C" "T" "C" "T" "C" "T" "C" "T" "C" "T"}';
690s  block = [1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2]';
690s 
690s  [P, ATAB, STATS] = anovan (measurement/10, {strain, treatment, block}, ...
690s                             "sstype", 2, "model", "full", "random", 3, ...
690s                             "display", "on", ...
690s                             "varnames", {"strain", "treatment", "block"});
690s ***** demo
690s 
690s  # One-way ANCOVA on data from a study of the additive effects of species
690s  # and temperature on chirpy pulses of crickets, from Stitch, The Worst Stats
690s  # Text eveR
690s 
690s  pulse = [67.9 65.1 77.3 78.7 79.4 80.4 85.8 86.6 87.5 89.1 ...
690s           98.6 100.8 99.3 101.7 44.3 47.2 47.6 49.6 50.3 51.8 ...
690s           60 58.5 58.9 60.7 69.8 70.9 76.2 76.1 77 77.7 84.7]';
690s  temp = [20.8 20.8 24 24 24 24 26.2 26.2 26.2 26.2 28.4 ...
690s          29 30.4 30.4 17.2 18.3 18.3 18.3 18.9 18.9 20.4 ...
690s          21 21 22.1 23.5 24.2 25.9 26.5 26.5 26.5 28.6]';
690s  species = {"ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" ...
690s             "ex" "ex" "ex" "niv" "niv" "niv" "niv" "niv" "niv" "niv" ...
690s             "niv" "niv" "niv" "niv" "niv" "niv" "niv" "niv" "niv" "niv"};
690s 
690s  [P, ATAB, STATS] = anovan (pulse, {species, temp}, "model", "linear", ...
690s                            "continuous", 2, "sstype", "h", "display", "on", ...
690s                            "varnames", {"species", "temp"});
690s ***** demo
690s 
690s  # Factorial ANCOVA on data from a study of the effects of treatment and
690s  # exercise on stress reduction score after adjusting for age. Data from R
690s  # datarium package).
690s 
690s  score = [95.6 82.2 97.2 96.4 81.4 83.6 89.4 83.8 83.3 85.7 ...
690s           97.2 78.2 78.9 91.8 86.9 84.1 88.6 89.8 87.3 85.4 ...
690s           81.8 65.8 68.1 70.0 69.9 75.1 72.3 70.9 71.5 72.5 ...
690s           84.9 96.1 94.6 82.5 90.7 87.0 86.8 93.3 87.6 92.4 ...
690s           100. 80.5 92.9 84.0 88.4 91.1 85.7 91.3 92.3 87.9 ...
690s           91.7 88.6 75.8 75.7 75.3 82.4 80.1 86.0 81.8 82.5]';
690s  treatment = {"yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
690s               "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
690s               "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
690s               "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
690s               "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
690s               "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"}';
690s  exercise = {"lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
690s              "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
690s              "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  ...
690s              "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
690s              "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
690s              "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"}';
690s  age = [59 65 70 66 61 65 57 61 58 55 62 61 60 59 55 57 60 63 62 57 ...
690s         58 56 57 59 59 60 55 53 55 58 68 62 61 54 59 63 60 67 60 67 ...
690s         75 54 57 62 65 60 58 61 65 57 56 58 58 58 52 53 60 62 61 61]';
690s 
690s  [P, ATAB, STATS] = anovan (score, {treatment, exercise, age}, ...
690s                             "model", [1 0 0; 0 1 0; 0 0 1; 1 1 0], ...
690s                             "continuous", 3, "sstype", "h", "display", "on", ...
690s                             "varnames", {"treatment", "exercise", "age"});
690s ***** demo
690s 
690s  # Unbalanced one-way ANOVA with custom, orthogonal contrasts. The statistics
690s  # relating to the contrasts are shown in the table of model parameters, and
690s  # can be retrieved from the STATS.coeffs output.
690s 
690s  dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
690s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
690s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
690s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
690s         25.694 ]';
690s  g = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 ...
690s       4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
690s  C = [ 0.4001601  0.3333333  0.5  0.0
690s        0.4001601  0.3333333 -0.5  0.0
690s        0.4001601 -0.6666667  0.0  0.0
690s       -0.6002401  0.0000000  0.0  0.5
690s       -0.6002401  0.0000000  0.0 -0.5];
690s 
690s  [P,ATAB, STATS] = anovan (dv, g, "contrasts", C, "varnames", "score", ...
690s                           "alpha", 0.05, "display", "on");
690s ***** demo
690s 
690s  # One-way ANOVA with the linear model fit by weighted least squares to
690s  # account for heteroskedasticity. In this example, the variance appears
690s  # proportional to the outcome, so weights have been estimated by initially
690s  # fitting the model without weights and regressing the absolute residuals on
690s  # the fitted values. Although this data could have been analysed by Welch's
690s  # ANOVA test, the approach here can generalize to ANOVA models with more than
690s  # one factor.
690s 
690s  g = [1, 1, 1, 1, 1, 1, 1, 1, ...
690s       2, 2, 2, 2, 2, 2, 2, 2, ...
690s       3, 3, 3, 3, 3, 3, 3, 3]';
690s  y = [13, 16, 16,  7, 11,  5,  1,  9, ...
690s       10, 25, 66, 43, 47, 56,  6, 39, ...
690s       11, 39, 26, 35, 25, 14, 24, 17]';
690s 
690s  [P,ATAB,STATS] = anovan(y, g, "display", "off");
690s  fitted = STATS.X * STATS.coeffs(:,1); # fitted values
690s  b = polyfit (fitted, abs (STATS.resid), 1);
690s  v = polyval (b, fitted);  # Variance as a function of the fitted values
690s  figure("Name", "Regression of the absolute residuals on the fitted values");
690s  plot (fitted, abs (STATS.resid),'ob');hold on; plot(fitted,v,'-r'); hold off;
690s  xlabel("Fitted values"); ylabel("Absolute residuals");
690s 
690s  [P,ATAB,STATS] = anovan (y, g, "weights", v.^-1);
690s ***** test
690s  score = [54 23 45 54 45 43 34 65 77 46 65]';
690s  gender = {'male' 'male' 'male' 'male' 'male' 'female' 'female' 'female' ...
690s            'female' 'female' 'female'}';
690s 
690s  [P, T, STATS] = anovan (score,gender,'display','off');
690s  assert (P(1), 0.2612876773271042, 1e-09);              # compared to p calculated by MATLAB anovan
690s  assert (sqrt(T{2,6}), abs(1.198608733288208), 1e-09);  # compared to abs(t) calculated from sqrt(F) by MATLAB anovan
690s  assert (P(1), 0.2612876773271047, 1e-09);              # compared to p calculated by MATLAB ttest2
690s  assert (sqrt(T{2,6}), abs(-1.198608733288208), 1e-09); # compared to abs(t) calculated by MATLAB ttest2
690s ***** test
690s  score = [4.5 5.6; 3.7 6.4; 5.3 6.4; 5.4 6.0; 3.9 5.7]';
690s  treatment = {'before' 'after'; 'before' 'after'; 'before' 'after';
690s               'before' 'after'; 'before' 'after'}';
690s  subject = {'GS' 'GS'; 'JM' 'JM'; 'HM' 'HM'; 'JW' 'JW'; 'PS' 'PS'}';
690s 
690s  [P, ATAB, STATS] = anovan (score(:),{treatment(:),subject(:)},'display','off','sstype',2);
690s  assert (P(1), 0.016004356735364, 1e-09);              # compared to p calculated by MATLAB anovan
690s  assert (sqrt(ATAB{2,6}), abs(4.00941576558195), 1e-09);  # compared to abs(t) calculated from sqrt(F) by MATLAB anovan
690s  assert (P(1), 0.016004356735364, 1e-09);              # compared to p calculated by MATLAB ttest2
690s  assert (sqrt(ATAB{2,6}), abs(-4.00941576558195), 1e-09); # compared to abs(t) calculated by MATLAB ttest2
690s ***** test
690s  strength = [82 86 79 83 84 85 86 87 74 82 ...
690s             78 75 76 77 79 79 77 78 82 79]';
690s  alloy = {'st','st','st','st','st','st','st','st', ...
690s           'al1','al1','al1','al1','al1','al1', ...
690s           'al2','al2','al2','al2','al2','al2'}';
690s 
690s  [P, ATAB, STATS] = anovan (strength,{alloy},'display','off');
690s  assert (P(1), 0.000152643638830491, 1e-09);
690s  assert (ATAB{2,6}, 15.4, 1e-09);
690s ***** test
690s  words = [10 13 13; 6 8 8; 11 14 14; 22 23 25; 16 18 20; ...
690s           15 17 17; 1 1 4; 12 15 17;  9 12 12;  8 9 12];
690s  subject = [ 1  1  1;  2  2  2;  3  3  3;  4  4  4;  5  5  5; ...
690s              6  6  6;  7  7  7;  8  8  8;  9  9  9; 10 10 10];
690s  seconds = [1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5; ...
690s             1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5;];
690s 
690s  [P, ATAB, STATS] = anovan (words(:),{seconds(:),subject(:)},'model','full','random',2,'sstype',2,'display','off');
690s  assert (P(1), 1.51865926758752e-07, 1e-09);
690s  assert (ATAB{2,2}, 52.2666666666667, 1e-09);
690s  assert (ATAB{3,2}, 942.533333333333, 1e-09);
690s  assert (ATAB{4,2}, 11.0666666666667, 1e-09);
690s ***** test
690s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
690s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
690s  brands = {'Gourmet', 'National', 'Generic'; ...
690s            'Gourmet', 'National', 'Generic'; ...
690s            'Gourmet', 'National', 'Generic'; ...
690s            'Gourmet', 'National', 'Generic'; ...
690s            'Gourmet', 'National', 'Generic'; ...
690s            'Gourmet', 'National', 'Generic'};
690s  popper = {'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; ...
690s            'air', 'air', 'air'; 'air', 'air', 'air'; 'air', 'air', 'air'};
690s 
690s  [P, ATAB, STATS] = anovan (popcorn(:),{brands(:),popper(:)},'display','off','model','full');
690s  assert (P(1), 7.67895738278171e-07, 1e-09);
690s  assert (P(2), 0.000100373896304998, 1e-09);
690s  assert (P(3), 0.746215396636649, 1e-09);
690s  assert (ATAB{2,6}, 56.7, 1e-09);
690s  assert (ATAB{3,6}, 32.4, 1e-09);
690s  assert (ATAB{4,6}, 0.29999999999997, 1e-09);
690s ***** test
690s  salary = [24 26 25 24 27 24 27 23 15 17 20 16, ...
690s            25 29 27 19 18 21 20 21 22 19]';
690s  gender = {'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f'...
690s            'm' 'm' 'm' 'm' 'm' 'm' 'm' 'm' 'm' 'm'}';
690s  degree = [1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0]';
690s 
690s  [P, ATAB, STATS] = anovan (salary,{gender,degree},'model','full','sstype',1,'display','off');
690s  assert (P(1), 0.747462549227232, 1e-09);
690s  assert (P(2), 1.03809316857694e-08, 1e-09);
690s  assert (P(3), 0.523689833702691, 1e-09);
690s  assert (ATAB{2,2}, 0.296969696969699, 1e-09);
690s  assert (ATAB{3,2}, 272.391841491841, 1e-09);
690s  assert (ATAB{4,2}, 1.17482517482512, 1e-09);
690s  assert (ATAB{5,2}, 50.0000000000001, 1e-09);
690s  [P, ATAB, STATS] = anovan (salary,{degree,gender},'model','full','sstype',1,'display','off');
690s  assert (P(1), 2.53445097305047e-08, 1e-09);
690s  assert (P(2), 0.00388133678528749, 1e-09);
690s  assert (P(3), 0.523689833702671, 1e-09);
690s  assert (ATAB{2,2}, 242.227272727273, 1e-09);
690s  assert (ATAB{3,2}, 30.4615384615384, 1e-09);
690s  assert (ATAB{4,2}, 1.17482517482523, 1e-09);
690s  assert (ATAB{5,2}, 50.0000000000001, 1e-09);
690s  [P, ATAB, STATS] = anovan (salary,{gender,degree},'model','full','sstype',2,'display','off');
690s  assert (P(1), 0.00388133678528743, 1e-09);
690s  assert (P(2), 1.03809316857694e-08, 1e-09);
690s  assert (P(3), 0.523689833702691, 1e-09);
690s  assert (ATAB{2,2}, 30.4615384615385, 1e-09);
690s  assert (ATAB{3,2}, 272.391841491841, 1e-09);
690s  assert (ATAB{4,2}, 1.17482517482512, 1e-09);
690s  assert (ATAB{5,2}, 50.0000000000001, 1e-09);
690s  [P, ATAB, STATS] = anovan (salary,{gender,degree},'model','full','sstype',3,'display','off');
690s  assert (P(1), 0.00442898146583742, 1e-09);
690s  assert (P(2), 1.30634252053587e-08, 1e-09);
690s  assert (P(3), 0.523689833702691, 1e-09);
690s  assert (ATAB{2,2}, 29.3706293706294, 1e-09);
690s  assert (ATAB{3,2}, 264.335664335664, 1e-09);
690s  assert (ATAB{4,2}, 1.17482517482512, 1e-09);
690s  assert (ATAB{5,2}, 50.0000000000001, 1e-09);
690s ***** test
690s  sugar = {'real' 'fake' 'fake' 'real' 'real' 'real' 'none' 'none' 'none' ...
690s           'fake' 'fake' 'fake' 'real' 'real' 'real' 'none' 'none' 'fake'}';
690s  milk = {'yes' 'no' 'no' 'yes' 'yes' 'no' 'yes' 'yes' 'yes' ...
690s          'no' 'no' 'yes' 'no' 'no' 'no' 'no' 'no' 'yes'}';
690s  babble = [4.6 4.4 3.9 5.6 5.1 5.5 3.9 3.5 3.7...
690s            5.6 4.7 5.9 6.0 5.4 6.6 5.8 5.3 5.7]';
690s 
690s  [P, ATAB, STATS] = anovan (babble,{sugar,milk},'model','full','sstype',1,'display','off');
690s  assert (P(1), 0.0108632139833963, 1e-09);
690s  assert (P(2), 0.0810606976703546, 1e-09);
690s  assert (P(3), 0.00175433329935627, 1e-09);
690s  assert (ATAB{2,2}, 3.55752380952381, 1e-09);
690s  assert (ATAB{3,2}, 0.956108477471702, 1e-09);
690s  assert (ATAB{4,2}, 5.94386771300448, 1e-09);
690s  assert (ATAB{5,2}, 3.1625, 1e-09);
690s  [P, ATAB, STATS] = anovan (babble,{milk,sugar},'model','full','sstype',1,'display','off');
690s  assert (P(1), 0.0373333189297505, 1e-09);
690s  assert (P(2), 0.017075098787169, 1e-09);
690s  assert (P(3), 0.00175433329935627, 1e-09);
690s  assert (ATAB{2,2}, 1.444, 1e-09);
690s  assert (ATAB{3,2}, 3.06963228699552, 1e-09);
690s  assert (ATAB{4,2}, 5.94386771300448, 1e-09);
690s  assert (ATAB{5,2}, 3.1625, 1e-09);
690s  [P, ATAB, STATS] = anovan (babble,{sugar,milk},'model','full','sstype',2,'display','off');
690s  assert (P(1), 0.017075098787169, 1e-09);
690s  assert (P(2), 0.0810606976703546, 1e-09);
690s  assert (P(3), 0.00175433329935627, 1e-09);
690s  assert (ATAB{2,2}, 3.06963228699552, 1e-09);
690s  assert (ATAB{3,2}, 0.956108477471702, 1e-09);
690s  assert (ATAB{4,2}, 5.94386771300448, 1e-09);
690s  assert (ATAB{5,2}, 3.1625,  1e-09);
690s  [P, ATAB, STATS] = anovan (babble,{sugar,milk},'model','full','sstype',3,'display','off');
690s  assert (P(1), 0.0454263063473954, 1e-09);
690s  assert (P(2), 0.0746719907091438, 1e-09);
690s  assert (P(3), 0.00175433329935627, 1e-09);
690s  assert (ATAB{2,2}, 2.13184977578476, 1e-09);
690s  assert (ATAB{3,2}, 1.00413461538462, 1e-09);
690s  assert (ATAB{4,2}, 5.94386771300448, 1e-09);
690s  assert (ATAB{5,2}, 3.1625, 1e-09);
690s ***** test
690s  drug = {'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' ...
690s          'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X';
690s          'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' ...
690s          'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y';
690s          'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' ...
690s          'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z'};
690s  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;
690s              1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
690s              1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0];
690s  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;
690s          0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1;
690s          0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1];
690s  BP = [170 175 165 180 160 158 161 173 157 152 181 190 ...
690s        173 194 197 190 176 198 164 190 169 164 176 175;
690s        186 194 201 215 219 209 164 166 159 182 187 174 ...
690s        189 194 217 206 199 195 171 173 196 199 180 NaN;
690s        180 187 199 170 204 194 162 184 183 156 180 173 ...
690s        202 228 190 206 224 204 205 199 170 160 NaN NaN];
690s 
690s  [P, ATAB, STATS] = anovan (BP(:),{drug(:),feedback(:),diet(:)},'model','full','sstype', 1,'display','off');
690s  assert (P(1), 7.02561843825325e-05, 1e-09);
690s  assert (P(2), 0.000425806013389362, 1e-09);
690s  assert (P(3), 6.16780773446401e-07, 1e-09);
690s  assert (P(4), 0.261347622678438, 1e-09);
690s  assert (P(5), 0.0542278432357043, 1e-09);
690s  assert (P(6), 0.590353225626655, 1e-09);
690s  assert (P(7), 0.0861628249564267, 1e-09);
690s  assert (ATAB{2,2}, 3614.70355731226, 1e-09);
690s  assert (ATAB{3,2}, 2227.46639771024, 1e-09);
690s  assert (ATAB{4,2}, 5008.25614451819, 1e-09);
690s  assert (ATAB{5,2}, 437.066007908781, 1e-09);
690s  assert (ATAB{6,2}, 976.180770397332, 1e-09);
690s  assert (ATAB{7,2}, 46.616653365254, 1e-09);
690s  assert (ATAB{8,2}, 814.345251396648, 1e-09);
690s  assert (ATAB{9,2}, 9065.8,  1e-09);
690s  [P, ATAB, STATS] = anovan (BP(:),{drug(:),feedback(:),diet(:)},'model','full','sstype',2,'display','off');
690s  assert (P(1), 9.4879638470754e-05, 1e-09);
690s  assert (P(2), 0.00124177666315809, 1e-09);
690s  assert (P(3), 6.86162012732911e-07, 1e-09);
690s  assert (P(4), 0.260856132341256, 1e-09);
690s  assert (P(5), 0.0523758623892078, 1e-09);
690s  assert (P(6), 0.590353225626655, 1e-09);
690s  assert (P(7), 0.0861628249564267, 1e-09);
690s  assert (ATAB{2,2}, 3481.72176560122, 1e-09);
690s  assert (ATAB{3,2}, 1837.08812970469, 1e-09);
690s  assert (ATAB{4,2}, 4957.20277938622, 1e-09);
690s  assert (ATAB{5,2}, 437.693674777847, 1e-09);
690s  assert (ATAB{6,2}, 988.431929811402, 1e-09);
690s  assert (ATAB{7,2}, 46.616653365254, 1e-09);
690s  assert (ATAB{8,2}, 814.345251396648, 1e-09);
690s  assert (ATAB{9,2}, 9065.8,  1e-09);
690s  [P, ATAB, STATS] = anovan (BP(:),{drug(:),feedback(:),diet(:)},'model','full','sstype', 3,'display','off');
690s  assert (P(1), 0.000106518678028207, 1e-09);
690s  assert (P(2), 0.00125371366571508, 1e-09);
690s  assert (P(3), 5.30813260778464e-07, 1e-09);
690s  assert (P(4), 0.308353667232981, 1e-09);
690s  assert (P(5), 0.0562901327343161, 1e-09);
690s  assert (P(6), 0.599091042141092, 1e-09);
690s  assert (P(7), 0.0861628249564267, 1e-09);
690s  assert (ATAB{2,2}, 3430.88156424581, 1e-09);
690s  assert (ATAB{3,2}, 1833.68031496063, 1e-09);
690s  assert (ATAB{4,2}, 5080.48346456693, 1e-09);
690s  assert (ATAB{5,2}, 382.07709497207, 1e-09);
690s  assert (ATAB{6,2}, 963.037988826813, 1e-09);
690s  assert (ATAB{7,2}, 44.4519685039322, 1e-09);
690s  assert (ATAB{8,2}, 814.345251396648, 1e-09);
690s  assert (ATAB{9,2}, 9065.8, 1e-09);
690s ***** test
690s  measurement = [444 614 423 625 408  856 447 719 ...
690s                 764 831 586 782 609 1002 606 766]';
690s  strain= {'NIH','NIH','BALB/C','BALB/C','A/J','A/J','129/Ola','129/Ola', ...
690s           'NIH','NIH','BALB/C','BALB/C','A/J','A/J','129/Ola','129/Ola'}';
690s  treatment={'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T'}';
690s  block = [1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2]';
690s 
690s  [P, ATAB, STATS] = anovan (measurement/10,{strain,treatment,block},'model','full','random',3,'display','off');
690s  assert (P(1), 0.0914352969909372, 1e-09);
690s  assert (P(2), 5.04077373924908e-05, 1e-09);
690s  assert (P(4), 0.0283196918836667, 1e-09);
690s  assert (ATAB{2,2}, 286.132500000002, 1e-09);
690s  assert (ATAB{3,2}, 2275.29, 1e-09);
690s  assert (ATAB{4,2}, 1242.5625, 1e-09);
690s  assert (ATAB{5,2}, 495.905000000001, 1e-09);
690s  assert (ATAB{6,2}, 207.007499999999, 1e-09);
690s ***** test
690s  pulse = [67.9 65.1 77.3 78.7 79.4 80.4 85.8 86.6 87.5 89.1 ...
690s           98.6 100.8 99.3 101.7 44.3 47.2 47.6 49.6 50.3 51.8 ...
690s           60 58.5 58.9 60.7 69.8 70.9 76.2 76.1 77 77.7 84.7]';
690s  temp = [20.8 20.8 24 24 24 24 26.2 26.2 26.2 26.2 28.4 ...
690s          29 30.4 30.4 17.2 18.3 18.3 18.3 18.9 18.9 20.4 ...
690s          21 21 22.1 23.5 24.2 25.9 26.5 26.5 26.5 28.6]';
690s  species = {'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' ...
690s             'ex' 'ex' 'ex' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' ...
690s             'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv'};
690s 
690s  [P, ATAB, STATS] = anovan (pulse,{species,temp},'model','linear','continuous',2,'sstype','h','display','off');
690s  assert (P(1), 6.27153318786007e-14, 1e-09);
690s  assert (P(2), 2.48773241196644e-25, 1e-09);
690s  assert (ATAB{2,2}, 598.003953318404, 1e-09);
690s  assert (ATAB{3,2}, 4376.08256843712, 1e-09);
690s  assert (ATAB{4,2}, 89.3498685376726, 1e-09);
690s  assert (ATAB{2,6}, 187.399388123951, 1e-09);
690s  assert (ATAB{3,6}, 1371.35413763454, 1e-09);
690s ***** test
690s  score = [95.6 82.2 97.2 96.4 81.4 83.6 89.4 83.8 83.3 85.7 ...
690s           97.2 78.2 78.9 91.8 86.9 84.1 88.6 89.8 87.3 85.4 ...
690s           81.8 65.8 68.1 70.0 69.9 75.1 72.3 70.9 71.5 72.5 ...
690s           84.9 96.1 94.6 82.5 90.7 87.0 86.8 93.3 87.6 92.4 ...
690s           100. 80.5 92.9 84.0 88.4 91.1 85.7 91.3 92.3 87.9 ...
690s           91.7 88.6 75.8 75.7 75.3 82.4 80.1 86.0 81.8 82.5]';
690s  treatment = {'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' ...
690s               'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' ...
690s               'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' ...
690s               'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  ...
690s               'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  ...
690s               'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'}';
690s  exercise = {'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  ...
690s              'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' ...
690s              'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  ...
690s              'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  ...
690s              'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' ...
690s              'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'}';
690s  age = [59 65 70 66 61 65 57 61 58 55 62 61 60 59 55 57 60 63 62 57 ...
690s         58 56 57 59 59 60 55 53 55 58 68 62 61 54 59 63 60 67 60 67 ...
690s         75 54 57 62 65 60 58 61 65 57 56 58 58 58 52 53 60 62 61 61]';
690s 
690s  [P, ATAB, STATS] = anovan (score,{treatment,exercise,age},'model','full','continuous',3,'sstype','h','display','off');
690s  assert (P(5), 0.9245630968248468, 1e-09);
690s  assert (P(6), 0.791115159521822, 1e-09);
690s  assert (P(7), 0.9296668751457956, 1e-09);
690s  [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');
690s  assert (P(1), 0.00158132928938933, 1e-09);
690s  assert (P(2), 2.12537505039986e-07, 1e-09);
690s  assert (P(3), 0.00390292555160047, 1e-09);
690s  assert (P(4), 0.0164086580775543, 1e-09);
690s  assert (ATAB{2,6}, 11.0956027650549, 1e-09);
690s  assert (ATAB{3,6}, 20.8195665467178, 1e-09);
690s  assert (ATAB{4,6}, 9.10966630720186, 1e-09);
690s  assert (ATAB{5,6}, 4.4457923698584, 1e-09);
691s ***** test
691s  dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
691s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
691s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
691s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
691s         25.694 ]';
691s  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]';
691s  C = [ 0.4001601  0.3333333  0.5  0.0
691s        0.4001601  0.3333333 -0.5  0.0
691s        0.4001601 -0.6666667  0.0  0.0
691s       -0.6002401  0.0000000  0.0  0.5
691s       -0.6002401  0.0000000  0.0 -0.5];
691s 
691s  [P,ATAB,STATS] = anovan (dv,g,'contrasts',{C},'display','off');
691s  assert (STATS.coeffs(1,1), 19.4001, 1e-04);
691s  assert (STATS.coeffs(2,1), -9.3297, 1e-04);
691s  assert (STATS.coeffs(3,1), -5.0000, 1e-04);
691s  assert (STATS.coeffs(4,1), -8.0000, 1e-04);
691s  assert (STATS.coeffs(5,1), -8.0000, 1e-04);
691s  assert (STATS.coeffs(1,2), 0.4831, 1e-04);
691s  assert (STATS.coeffs(2,2), 0.9694, 1e-04);
691s  assert (STATS.coeffs(3,2), 1.3073, 1e-04);
691s  assert (STATS.coeffs(4,2), 1.6411, 1e-04);
691s  assert (STATS.coeffs(5,2), 1.4507, 1e-04);
691s  assert (STATS.coeffs(1,5), 40.161, 1e-03);
691s  assert (STATS.coeffs(2,5), -9.624, 1e-03);
691s  assert (STATS.coeffs(3,5), -3.825, 1e-03);
691s  assert (STATS.coeffs(4,5), -4.875, 1e-03);
691s  assert (STATS.coeffs(5,5), -5.515, 1e-03);
691s  assert (STATS.coeffs(2,6), 5.74e-11, 1e-12);
691s  assert (STATS.coeffs(3,6), 0.000572, 1e-06);
691s  assert (STATS.coeffs(4,6), 2.86e-05, 1e-07);
691s  assert (STATS.coeffs(5,6), 4.44e-06, 1e-08);
691s 12 tests, 12 passed, 0 known failure, 0 skipped
691s [inst/bar3.m]
691s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/bar3.m
691s ***** demo
691s  ## Ploting 5 bars in the same series.
691s 
691s  z = [50; 40; 30; 20; 10];
691s  bar3 (z);
691s ***** demo
691s  ## Ploting 5 bars in different groups.
691s 
691s  z = [50, 40, 30, 20, 10];
691s  bar3 (z);
691s ***** demo
691s  ## A 3D bar graph with each series corresponding to a column in z.
691s 
691s  z = [1, 4, 7; 2, 5, 8; 3, 6, 9; 4, 7, 10];
691s  bar3 (z);
691s ***** demo
691s  ## Specify y-axis locations as tick names. y must be a column vector!
691s 
691s  y = [1950, 1960, 1970, 1980, 1990]';
691s  z = [16, 8, 4, 2, 1]';
691s  bar3 (y, z);
691s ***** demo
691s  ## Plot 3 series as a grouped plot without any space between the grouped bars
691s 
691s  z = [70 50 33 10; 75 55 35 15; 80 60 40 20];
691s  bar3 (z, 1, 'grouped');
691s ***** demo
691s  ## Plot a stacked style 3D bar graph
691s 
691s  z = [19, 30, 21, 30; 40, 16, 32, 12];
691s  b = bar3 (z, 0.5, 'stacked');
691s ***** error <bar3: Z must be numeric.> bar3 ("A")
691s ***** error <bar3: Z must be numeric.> bar3 ({2,3,4,5})
691s ***** error <bar3: inconsistent size in Y and Z input arguments.> ...
691s  bar3 ([1,2,3]', ones (2))
691s ***** error <bar3: WIDTH must be a scalar in the range> ...
691s  bar3 ([1:5], 1.2)
691s ***** error <bar3: WIDTH must be a scalar in the range> ...
691s  bar3 ([1:5]', ones (5), 1.2)
691s ***** error <bar3: numeric COLOR must be a 1x3 vector of an Nx3 matrix> ...
691s  bar3 ([1:5]', ones (5), [0.8, 0.7])
691s ***** error <bar3: missing value for optional argument 'width'.> ...
691s  bar3 (ones (5), 'width')
691s ***** error <bar3: invalid value for optional argument 'width'.> ...
691s  bar3 (ones (5), 'width', 1.2)
691s ***** error <bar3: invalid value for optional argument 'width'.> ...
691s  bar3 (ones (5), 'width', [0.8, 0.8, 0.8])
691s ***** error <bar3: missing value for optional argument 'color'.> ...
691s  bar3 (ones (5), 'color')
691s ***** error <bar3: numeric COLOR must be a 1x3 vector of an Nx3 matrix> ...
691s  bar3 (ones (5), 'color', [0.8, 0.8])
691s ***** error <bar3: invalid value for optional argument 'color'.> ...
691s  bar3 (ones (5), 'color', "brown")
691s ***** error <bar3: invalid value for optional argument 'color'.> ...
691s  bar3 (ones (5), 'color', {"r", "k", "c", "m", "brown"})
691s ***** error <bar3: missing value for optional argument 'xlabel'.> ...
691s  bar3 (ones (5), 'xlabel')
691s ***** error <bar3: invalid value for optional argument 'xlabel'.> ...
691s  bar3 (ones (5), 'xlabel', 4)
691s ***** error <bar3: missing value for optional argument 'ylabel'.> ...
691s  bar3 (ones (5), 'ylabel')
691s ***** error <bar3: invalid value for optional argument 'ylabel'.> ...
691s  bar3 (ones (5), 'ylabel', 4)
691s ***** error <bar3: invalid optional argument.> bar3 (ones (5), 'this', 4)
691s ***** error <bar3: the elements in 'xlabel' must equal the columns in Z.> ...
691s  bar3 (ones (5), 'xlabel', {"A", "B", "C"})
691s ***** error <bar3: the elements in 'ylabel' must equal the rows in Z.> ...
691s  bar3 (ones (5), 'ylabel', {"A", "B", "C"})
691s 20 tests, 20 passed, 0 known failure, 0 skipped
691s [inst/bar3h.m]
691s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/bar3h.m
691s ***** demo
691s  ## Ploting 5 bars in the same series.
691s 
691s  y = [50; 40; 30; 20; 10];
691s  bar3h (y);
691s ***** demo
691s  ## Ploting 5 bars in different groups.
691s 
691s  y = [50, 40, 30, 20, 10];
691s  bar3h (y);
691s ***** demo
691s  ## A 3D bar graph with each series corresponding to a column in y.
691s 
691s  y = [1, 4, 7; 2, 5, 8; 3, 6, 9; 4, 7, 10];
691s  bar3h (y);
691s ***** demo
691s  ## Specify z-axis locations as tick names. z must be a column vector!
691s 
691s  z = [1950, 1960, 1970, 1980, 1990]';
691s  y = [16, 8, 4, 2, 1]';
691s  bar3h (z, y);
691s ***** demo
691s  ## Plot 3 series as a grouped plot without any space between the grouped bars
691s 
691s  y = [70 50 33 10; 75 55 35 15; 80 60 40 20];
691s  bar3h (y, 1, 'grouped');
691s ***** demo
691s  ## Plot a stacked style 3D bar graph
691s 
691s  y = [19, 30, 21, 30; 40, 16, 32, 12];
691s  b = bar3h (y, 0.5, 'stacked');
691s ***** error <bar3h: Z must be numeric.> bar3h ("A")
691s ***** error <bar3h: Z must be numeric.> bar3h ({2,3,4,5})
691s ***** error <bar3h: inconsistent size in Y and Z input arguments.> ...
691s  bar3h ([1,2,3]', ones (2))
691s ***** error <bar3h: WIDTH must be a scalar in the range> ...
691s  bar3h ([1:5], 1.2)
691s ***** error <bar3h: WIDTH must be a scalar in the range> ...
691s  bar3h ([1:5]', ones (5), 1.2)
691s ***** error <bar3h: numeric COLOR must be a 1x3 vector of an Nx3 matrix> ...
691s  bar3h ([1:5]', ones (5), [0.8, 0.7])
691s ***** error <bar3h: missing value for optional argument 'width'.> ...
691s  bar3h (ones (5), 'width')
691s ***** error <bar3h: invalid value for optional argument 'width'.> ...
691s  bar3h (ones (5), 'width', 1.2)
691s ***** error <bar3h: invalid value for optional argument 'width'.> ...
691s  bar3h (ones (5), 'width', [0.8, 0.8, 0.8])
691s ***** error <bar3h: missing value for optional argument 'color'.> ...
691s  bar3h (ones (5), 'color')
691s ***** error <bar3h: numeric COLOR must be a 1x3 vector of an Nx3 matrix> ...
691s  bar3h (ones (5), 'color', [0.8, 0.8])
691s ***** error <bar3h: invalid value for optional argument 'color'.> ...
691s  bar3h (ones (5), 'color', "brown")
691s ***** error <bar3h: invalid value for optional argument 'color'.> ...
691s  bar3h (ones (5), 'color', {"r", "k", "c", "m", "brown"})
691s ***** error <bar3h: missing value for optional argument 'xlabel'.> ...
691s  bar3h (ones (5), 'xlabel')
691s ***** error <bar3h: invalid value for optional argument 'xlabel'.> ...
691s  bar3h (ones (5), 'xlabel', 4)
691s ***** error <bar3h: missing value for optional argument 'zlabel'.> ...
691s  bar3h (ones (5), 'zlabel')
691s ***** error <bar3h: invalid value for optional argument 'zlabel'.> ...
691s  bar3h (ones (5), 'zlabel', 4)
691s ***** error <bar3h: invalid optional argument.> bar3h (ones (5), 'this', 4)
691s ***** error <bar3h: the elements in 'xlabel' must equal the columns in Z.> ...
691s  bar3h (ones (5), 'xlabel', {"A", "B", "C"})
691s ***** error <bar3h: the elements in 'zlabel' must equal the rows in Z.> ...
691s  bar3h (ones (5), 'zlabel', {"A", "B", "C"})
691s 20 tests, 20 passed, 0 known failure, 0 skipped
691s [inst/bartlett_test.m]
691s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/bartlett_test.m
691s ***** error<bartlett_test: invalid number of input arguments.> bartlett_test ()
691s ***** error<bartlett_test: invalid number of input arguments.> ...
691s  bartlett_test (1, 2, 3, 4);
691s ***** error<bartlett_test: wrong value for alpha.> bartlett_test (randn (50, 2), 0);
691s ***** error<bartlett_test: GROUP and X mismatch.> ...
691s  bartlett_test (randn (50, 2), [1, 2, 3]);
691s ***** error<bartlett_test: GROUP and X mismatch.> ...
691s  bartlett_test (randn (50, 1), ones (55, 1));
691s ***** error<bartlett_test: invalid second input argument.> ...
691s  bartlett_test (randn (50, 1), ones (50, 2));
691s ***** error<bartlett_test: wrong value for alpha.> ...
691s  bartlett_test (randn (50, 2), [], 1.2);
691s ***** error<bartlett_test: wrong value for alpha.> ...
691s  bartlett_test (randn (50, 2), [], "alpha");
691s ***** error<bartlett_test: wrong value for alpha.> ...
691s  bartlett_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], 1.2);
691s ***** error<bartlett_test: wrong value for alpha.> ...
691s  bartlett_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], "err");
691s ***** warning<bartlett_test: GROUP> ...
691s  bartlett_test (randn (50, 1), [ones(24, 1); 2*ones(25, 1); 3]);
691s ***** test
691s  load examgrades
691s  [h, pval, chisq, df] = bartlett_test (grades);
691s  assert (h, 1);
691s  assert (pval, 7.908647337018238e-08, 1e-14);
691s  assert (chisq, 38.73324, 1e-5);
691s  assert (df, 4);
691s ***** test
691s  load examgrades
691s  [h, pval, chisq, df] = bartlett_test (grades(:,[2:4]));
691s  assert (h, 1);
691s  assert (pval, 0.01172, 1e-5);
691s  assert (chisq, 8.89274, 1e-5);
691s  assert (df, 2);
691s ***** test
691s  load examgrades
691s  [h, pval, chisq, df] = bartlett_test (grades(:,[1,4]));
691s  assert (h, 0);
691s  assert (pval, 0.88118, 1e-5);
691s  assert (chisq, 0.02234, 1e-5);
691s  assert (df, 1);
691s ***** test
691s  load examgrades
691s  grades = [grades; nan(10, 5)];
691s  [h, pval, chisq, df] = bartlett_test (grades(:,[1,4]));
691s  assert (h, 0);
691s  assert (pval, 0.88118, 1e-5);
691s  assert (chisq, 0.02234, 1e-5);
691s  assert (df, 1);
691s ***** test
691s  load examgrades
691s  [h, pval, chisq, df] = bartlett_test (grades(:,[2,5]), 0.01);
691s  assert (h, 0);
691s  assert (pval, 0.01791, 1e-5);
691s  assert (chisq, 5.60486, 1e-5);
691s  assert (df, 1);
691s 16 tests, 16 passed, 0 known failure, 0 skipped
691s [inst/barttest.m]
691s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/barttest.m
691s ***** error<barttest: invalid number of input arguments.> barttest ()
691s ***** error<barttest: NaN values in input are not allowed.> barttest ([2,NaN;3,4])
691s ***** error<barttest: wrong value for alpha.> barttest (ones (30, 4), "alpha")
691s ***** error<barttest: wrong value for alpha.> barttest (ones (30, 4), 0)
691s ***** error<barttest: wrong value for alpha.> barttest (ones (30, 4), 1.2)
691s ***** error<barttest: wrong value for alpha.> barttest (ones (30, 4), [0.2, 0.05])
691s ***** error<barttest: not enough data in X.> barttest (ones (30, 1))
691s ***** error<barttest: not enough data in X.> barttest (ones (30, 1), 0.05)
691s ***** test
691s  x = [2, 3, 4, 5, 6, 7, 8, 9; 1, 2, 3, 4, 5, 6, 7, 8]';
691s  [ndim, pval, chisq] = barttest (x);
691s  assert (ndim, 2);
691s  assert (pval, 0);
691s  ## assert (chisq, 512.0558, 1e-4); Result differs between octave 6 and 7 ?
691s ***** test
691s  x = [0.53767,  0.62702,   -0.10224,   -0.25485,   1.4193,   1.5237  ; ...
691s       1.8339,   1.6452,    -0.24145,   -0.23444,   0.29158,  0.1634  ; ...
691s      -2.2588,  -2.1351,     0.31286,    0.39396,   0.19781,  0.20995 ; ...
691s       0.86217,  1.0835,     0.31286,    0.46499,   1.5877,   1.495   ; ...
691s       0.31877,  0.38454,   -0.86488,   -0.63839,  -0.80447, -0.7536  ; ...
691s      -1.3077,  -1.1487,    -0.030051,  -0.017629,  0.69662,  0.60497 ; ...
691s      -0.43359, -0.32672,   -0.16488,   -0.37364,   0.83509,  0.89586 ; ...
691s       0.34262,  0.29639,    0.62771,    0.51672,  -0.24372, -0.13698 ; ...
691s       3.5784,   3.5841,     1.0933,     0.93258,   0.21567,  0.455   ; ...
691s       2.7694,   2.6307,     1.1093,     1.4298,   -1.1658,  -1.1816  ; ...
691s      -1.3499,  -1.2111,    -0.86365,   -0.94186,  -1.148,   -1.4381  ; ...
691s       3.0349,   2.8428,     0.077359,   0.18211,   0.10487, -0.014613; ...
691s       0.7254,   0.56737,   -1.2141,    -1.2291,    0.72225,  0.90612 ; ...
691s      -0.063055,-0.17662,   -1.1135,    -0.97701,   2.5855,   2.4084  ; ...
691s       0.71474,  0.29225,   -0.0068493, -0.11468,  -0.66689, -0.52466 ; ...
691s      -0.20497, -7.8874e-06, 1.5326,     1.3195,    0.18733,  0.20296 ; ...
691s      -0.12414, -0.077029,  -0.76967,   -0.96262,  -0.082494, 0.121   ; ...
691s       1.4897,   1.3683,     0.37138,    0.43653,  -1.933,   -2.1903  ; ...
691s       1.409,    1.5882,    -0.22558,   -0.24835,  -0.43897, -0.46247 ; ...
691s       1.4172,   1.1616,     1.1174,     1.0785,   -1.7947,  -1.9471  ];
691s  [ndim, pval, chisq] = barttest (x);
691s  assert (ndim, 3);
691s  assert (pval, [0; 0; 0; 0.52063; 0.34314], 1e-5);
691s  chisq_out = [251.6802; 210.2670; 153.1773; 4.2026; 2.1392];
691s  assert (chisq, chisq_out, 1e-4);
691s 10 tests, 10 passed, 0 known failure, 0 skipped
691s [inst/binotest.m]
691s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/binotest.m
691s ***** demo
691s  % flip a coin 1000 times, showing 475 heads
691s  % Hypothesis: coin is fair, i.e. p=1/2
691s  [h,p_val,ci] = binotest(475,1000,0.5)
691s  % Result: h = 0 : null hypothesis not rejected, coin could be fair
691s  %         P value 0.12, i.e. hypothesis not rejected for alpha up to 12%
691s  %         0.444 <= p <= 0.506 with 95% confidence
691s ***** demo
691s  % flip a coin 100 times, showing 65 heads
691s  % Hypothesis: coin shows less than 50% heads, i.e. p<=1/2
691s  [h,p_val,ci] = binotest(65,100,0.5,'tail','left','alpha',0.01)
691s  % Result: h = 1 : null hypothesis is rejected, i.e. coin shows more heads than tails
691s  %         P value 0.0018, i.e. hypothesis not rejected for alpha up to 0.18%
691s  %         0 <= p <= 0.76 with 99% confidence
691s ***** test #example from https://en.wikipedia.org/wiki/Binomial_test
691s  [h,p_val,ci] = binotest (51,235,1/6);
691s  assert (p_val, 0.0437, 0.00005)
691s  [h,p_val,ci] = binotest (51,235,1/6,'tail','left');
691s  assert (p_val, 0.027, 0.0005)
691s 1 test, 1 passed, 0 known failure, 0 skipped
691s [inst/boxplot.m]
691s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/boxplot.m
691s ***** demo
691s  axis ([0, 3]);
691s  randn ("seed", 1);    # for reproducibility
691s  girls = randn (10, 1) * 5 + 140;
691s  randn ("seed", 2);    # for reproducibility
691s  boys = randn (13, 1) * 8 + 135;
691s  boxplot ({girls, boys});
691s  set (gca (), "xtick", [1 2], "xticklabel", {"girls", "boys"})
691s  title ("Grade 3 heights");
691s ***** demo
691s  randn ("seed", 7);    # for reproducibility
691s  A = randn (10, 1) * 5 + 140;
691s  randn ("seed", 8);    # for reproducibility
691s  B = randn (25, 1) * 8 + 135;
691s  randn ("seed", 9);    # for reproducibility
691s  C = randn (20, 1) * 6 + 165;
691s  data = [A; B; C];
691s  groups = [(ones (10, 1)); (ones (25, 1) * 2); (ones (20, 1) * 3)];
691s  labels = {"Team A", "Team B", "Team C"};
691s  pos = [2, 1, 3];
691s  boxplot (data, groups, "Notch", "on", "Labels", labels, "Positions", pos, ...
691s           "OutlierTags", "on", "BoxStyle", "filled");
691s  title ("Example of Group splitting with paired vectors");
691s ***** demo
691s  randn ("seed", 1);    # for reproducibility
691s  data = randn (100, 9);
691s  boxplot (data, "notch", "on", "boxstyle", "filled", ...
691s           "colors", "ygcwkmb", "whisker", 1.2);
691s  title ("Example of different colors specified with characters");
691s ***** demo
691s  randn ("seed", 5);    # for reproducibility
691s  data = randn (100, 13);
691s  colors = [0.7 0.7 0.7; ...
691s            0.0 0.4 0.9; ...
691s            0.7 0.4 0.3; ...
691s            0.7 0.1 0.7; ...
691s            0.8 0.7 0.4; ...
691s            0.1 0.8 0.5; ...
691s            0.9 0.9 0.2];
691s  boxplot (data, "notch", "on", "boxstyle", "filled", ...
691s           "colors", colors, "whisker", 1.3, "boxwidth", "proportional");
691s  title ("Example of different colors specified as RGB values");
691s ***** error <numerical array or cell array containing> boxplot ("a")
691s ***** error <data cells must contain> boxplot ({[1 2 3], "a"})
691s ***** error <grouping vector may only be passed> boxplot ([1 2 3], 1, {2, 3})
691s ***** error <grouping vector must be numerical> boxplot ([1 2 3], {"a", "b"})
691s ***** error <'Notch' input argument accepts> boxplot ([1:10], "notch", "any")
691s ***** error <illegal Notch value> boxplot ([1:10], "notch", i)
691s ***** error <illegal Notch value> boxplot ([1:10], "notch", {})
691s ***** error <must be character> boxplot (1, "symbol", 1)
691s ***** error <'Orientation' input argument accepts only> boxplot (1, "orientation", "diagonal")
691s ***** error <illegal Orientation value> boxplot (1, "orientation", {})
691s ***** error <'Whisker' input argument accepts only> boxplot (1, "whisker", "a")
691s ***** error <'Whisker' input argument accepts only> boxplot (1, "whisker", [1 3])
691s ***** error <'OutlierTags' input argument accepts only> boxplot (3, "OutlierTags", "maybe")
691s ***** error <illegal OutlierTags value> boxplot (3, "OutlierTags", {})
691s ***** error <'Sample_IDs' input argument accepts only> boxplot (1, "sample_IDs", 1)
691s ***** error <'BoxWidth' input argument accepts only> boxplot (1, "boxwidth", 2)
691s ***** error <'BoxWidth' input argument accepts only> boxplot (1, "boxwidth", "anything")
691s ***** error <'Widths' input argument accepts only> boxplot (5, "widths", "a")
691s ***** error <'Widths' input argument accepts only> boxplot (5, "widths", [1:4])
691s ***** error <'Widths' input argument accepts only> boxplot (5, "widths", [])
691s ***** error <'CapWidths' input argument accepts only> boxplot (5, "capwidths", "a")
691s ***** error <'CapWidths' input argument accepts only> boxplot (5, "capwidths", [1:4])
691s ***** error <'CapWidths' input argument accepts only> boxplot (5, "capwidths", [])
691s ***** error <'BoxStyle' input argument accepts only> boxplot (1, "Boxstyle", 1)
691s ***** error <'BoxStyle' input argument accepts only> boxplot (1, "Boxstyle", "garbage")
691s ***** error <'Positions' input argument accepts only> boxplot (1, "positions", "aa")
691s ***** error <'Labels' input argument accepts only> boxplot (3, "labels", [1 5])
691s ***** error <'Colors' input argument accepts only> boxplot (1, "colors", {})
691s ***** error <'Colors' input argument accepts only> boxplot (2, "colors", [1 2 3 4])
691s ***** error <Sample_IDs must match the data> boxplot (randn (10, 3), 'Sample_IDs', {"a", "b"})
691s ***** error <with the formalism> boxplot (rand (3, 3), [1 2])
691s ***** test
691s  hf = figure ("visible", "off");
691s  unwind_protect
691s    [a, b] = boxplot (rand (10, 3));
691s    assert (size (a), [7, 3]);
691s    assert (numel (b.box), 3);
691s    assert (numel (b.whisker), 12);
691s    assert (numel (b.median), 3);
691s  unwind_protect_cleanup
691s    close (hf);
691s  end_unwind_protect
691s ***** test
691s  hf = figure ("visible", "off");
691s  unwind_protect
691s    [~, b] = boxplot (rand (10, 3), "BoxStyle", "filled", "colors", "ybc");
691s    assert (numel (b.box_fill), 3);
691s  unwind_protect_cleanup
691s    close (hf);
691s  end_unwind_protect
692s ***** test
692s  hf = figure ("visible", "off");
692s  unwind_protect
692s    hold on
692s    [a, b] = boxplot (rand (10, 3));
692s    assert (ishold, true);
692s  unwind_protect_cleanup
692s    close (hf);
692s  end_unwind_protect
692s 34 tests, 34 passed, 0 known failure, 0 skipped
692s [inst/canoncorr.m]
692s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/canoncorr.m
692s ***** shared X,Y,A,B,r,U,V,k
692s  k = 10;
692s  X = [1:k; sin(1:k); cos(1:k)]'; Y = [tan(1:k); tanh((1:k)/k)]';
692s  [A,B,r,U,V,stats] = canoncorr (X,Y);
692s ***** assert (A, [-0.329229   0.072908; 0.074870   1.389318; -0.069302  -0.024109], 1E-6);
692s ***** assert (B, [-0.017086  -0.398402; -4.475049  -0.824538], 1E-6);
692s ***** assert (r, [0.99590   0.26754], 1E-5);
692s ***** assert (U, center(X) * A, 10*eps);
692s ***** assert (V, center(Y) * B, 10*eps);
692s ***** assert (cov(U), eye(size(U, 2)), 10*eps);
692s ***** assert (cov(V), eye(size(V, 2)), 10*eps);
692s  rand ("state", 1); [A,B,r] = canoncorr (rand(5, 10),rand(5, 20));
692s ***** assert (r, ones(1, 5), 10*eps);
692s 8 tests, 8 passed, 0 known failure, 0 skipped
692s [inst/cdfcalc.m]
692s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/cdfcalc.m
692s ***** test
692s  x = [2, 4, 3, 2, 4, 3, 2, 5, 6, 4];
692s  [yCDF, xCDF, n, emsg, eid] = cdfcalc (x);
692s  assert (yCDF, [0, 0.3, 0.5, 0.8, 0.9, 1]');
692s  assert (xCDF, [2, 3, 4, 5, 6]');
692s  assert (n, 10);
692s ***** shared x
692s  x = [2, 4, 3, 2, 4, 3, 2, 5, 6, 4];
692s ***** error yCDF = cdfcalc (x);
692s ***** error [yCDF, xCDF] = cdfcalc ();
692s ***** error [yCDF, xCDF] = cdfcalc (x, x);
692s ***** warning [yCDF, xCDF] = cdfcalc (ones(10,2));
692s 5 tests, 5 passed, 0 known failure, 0 skipped
692s [inst/cdfplot.m]
692s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/cdfplot.m
692s ***** demo
692s  x = randn(100,1);
692s  cdfplot (x);
692s ***** test
692s  hf = figure ("visible", "off");
692s  unwind_protect
692s    x = [2, 4, 3, 2, 4, 3, 2, 5, 6, 4];
692s    [hCDF, stats] = cdfplot (x);
692s    assert (stats.min, 2);
692s    assert (stats.max, 6);
692s    assert (stats.median, 3.5);
692s    assert (stats.std, 1.35400640077266, 1e-14);
692s  unwind_protect_cleanup
692s    close (hf);
692s  end_unwind_protect
692s ***** test
692s  hf = figure ("visible", "off");
692s  unwind_protect
692s    x = randn(100,1);
692s    cdfplot (x);
692s  unwind_protect_cleanup
692s    close (hf);
692s  end_unwind_protect
692s ***** error cdfplot ();
692s ***** error cdfplot ([x',x']);
692s ***** error cdfplot ([NaN, NaN, NaN, NaN]);
692s 5 tests, 5 passed, 0 known failure, 0 skipped
692s [inst/chi2gof.m]
692s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/chi2gof.m
692s ***** demo
692s  x = normrnd (50, 5, 100, 1);
692s  [h, p, stats] = chi2gof (x)
692s  [h, p, stats] = chi2gof (x, "cdf", @(x)normcdf (x, mean(x), std(x)))
692s  [h, p, stats] = chi2gof (x, "cdf", {@normcdf, mean(x), std(x)})
692s ***** demo
692s  x = rand (100,1 );
692s  n = length (x);
692s  binedges = linspace (0, 1, 11);
692s  expectedCounts = n * diff (binedges);
692s  [h, p, stats] = chi2gof (x, "binedges", binedges, "expected", expectedCounts)
692s ***** demo
692s  bins = 0:5;
692s  obsCounts = [6 16 10 12 4 2];
692s  n = sum(obsCounts);
692s  lambdaHat = sum(bins.*obsCounts) / n;
692s  expCounts = n * poisspdf(bins,lambdaHat);
692s  [h, p, stats] = chi2gof (bins, "binctrs", bins, "frequency", obsCounts, ...
692s                           "expected", expCounts, "nparams",1)
692s ***** error chi2gof ()
692s ***** error chi2gof ([2,3;3,4])
692s ***** error chi2gof ([1,2,3,4], "nbins", 3, "ctrs", [2,3,4])
692s ***** error chi2gof ([1,2,3,4], "frequency", [2,3,2])
692s ***** error chi2gof ([1,2,3,4], "frequency", [2,3,2,-2])
692s ***** error chi2gof ([1,2,3,4], "frequency", [2,3,2,2], "nparams", i)
692s ***** error chi2gof ([1,2,3,4], "frequency", [2,3,2,2], "alpha", 1.3)
692s ***** error chi2gof ([1,2,3,4], "expected", [-3,2,2])
692s ***** error chi2gof ([1,2,3,4], "expected", [3,2,2], "nbins", 5)
692s ***** error chi2gof ([1,2,3,4], "cdf", @normcdff)
692s ***** test
692s  x = [1 2 1 3 2 4 3 2 4 3 2 2];
692s  [h, p, stats] = chi2gof (x);
692s  assert (h, 0);
692s  assert (p, NaN);
692s  assert (stats.chi2stat, 0.1205375022748029, 1e-14);
692s  assert (stats.df, 0);
692s  assert (stats.edges, [1, 2.5, 4], 1e-14);
692s  assert (stats.O, [7, 5], 1e-14);
692s  assert (stats.E, [6.399995519909668, 5.600004480090332], 1e-14);
692s 11 tests, 11 passed, 0 known failure, 0 skipped
692s [inst/chi2test.m]
692s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/chi2test.m
692s ***** error chi2test ();
692s ***** error chi2test ([1, 2, 3, 4, 5]);
692s ***** error chi2test ([1, 2; 2, 1+3i]);
692s ***** error chi2test ([NaN, 6; 34, 12]);
692s ***** error<chi2test: optional arguments are not supported for 2-way> ...
692s  p = chi2test (ones (3, 3), "mutual", []);
692s ***** error<chi2test: invalid model name for testing a 3-way table.> ...
692s  p = chi2test (ones (3, 3, 3), "testtype", 2);
692s ***** error<chi2test: optional arguments must be in pairs.> ...
692s  p = chi2test (ones (3, 3, 3), "mutual");
692s ***** error<chi2test: value must be numeric in optional argument> ...
692s  p = chi2test (ones (3, 3, 3), "joint", ["a"]);
692s ***** error<chi2test: value must be empty or scalar in optional argument> ...
692s  p = chi2test (ones (3, 3, 3), "joint", [2, 3]);
692s ***** error<chi2test: optional arguments are not supported for k> ...
692s  p = chi2test (ones (3, 3, 3, 4), "mutual", [])
692s ***** warning<chi2test: Expected values less than 5.> p = chi2test (ones (2));
692s ***** warning<chi2test: Expected values less than 5.> p = chi2test (ones (3, 2));
692s ***** warning<chi2test: Expected values less than 1.> p = chi2test (0.4 * ones (3));
692s ***** test
692s  x = [11, 3, 8; 2, 9, 14; 12, 13, 28];
692s  p = chi2test (x);
692s  assert (p, 0.017787, 1e-6);
692s ***** test
692s  x = [11, 3, 8; 2, 9, 14; 12, 13, 28];
692s  [p, chisq] = chi2test (x);
692s  assert (chisq, 11.9421, 1e-4);
692s ***** test
692s  x = [11, 3, 8; 2, 9, 14; 12, 13, 28];
692s  [p, chisq, df] = chi2test (x);
692s  assert (df, 4);
692s ***** test
692s ***** shared x
692s  x(:,:,1) = [59, 32; 9,16];
692s  x(:,:,2) = [55, 24;12,33];
692s  x(:,:,3) = [107,80;17,56];%!
692s ***** assert (chi2test (x), 2.282063427117009e-11, 1e-14);
692s ***** assert (chi2test (x, "mutual", []), 2.282063427117009e-11, 1e-14);
692s ***** assert (chi2test (x, "joint", 1), 1.164834895206468e-11, 1e-14);
692s ***** assert (chi2test (x, "joint", 2), 7.771350230001417e-11, 1e-14);
692s ***** assert (chi2test (x, "joint", 3), 0.07151361728026107, 1e-14);
692s ***** assert (chi2test (x, "marginal", 1), 0, 1e-14);
692s ***** assert (chi2test (x, "marginal", 2), 6.347555814301131e-11, 1e-14);
692s ***** assert (chi2test (x, "marginal", 3), 0, 1e-14);
692s ***** assert (chi2test (x, "conditional", 1), 0.2303114201312508, 1e-14);
692s ***** assert (chi2test (x, "conditional", 2), 0.0958810684407079, 1e-14);
692s ***** assert (chi2test (x, "conditional", 3), 2.648037344954446e-11, 1e-14);
692s ***** assert (chi2test (x, "homogeneous", []), 0.4485579470993741, 1e-14);
692s ***** test
692s  [pval, chisq, df, E] = chi2test (x);
692s  assert (chisq, 64.0982, 1e-4);
692s  assert (df, 7);
692s  assert (E(:,:,1), [42.903, 39.921; 17.185, 15.991], ones (2, 2) * 1e-3);
692s ***** test
692s  [pval, chisq, df, E] = chi2test (x, "joint", 2);
692s  assert (chisq, 56.0943, 1e-4);
692s  assert (df, 5);
692s  assert (E(:,:,2), [40.922, 23.310; 38.078, 21.690], ones (2, 2) * 1e-3);
692s ***** test
692s  [pval, chisq, df, E] = chi2test (x, "marginal", 3);
692s  assert (chisq, 146.6058, 1e-4);
692s  assert (df, 9);
692s  assert (E(:,1,1), [61.642; 57.358], ones (2, 1) * 1e-3);
692s ***** test
692s  [pval, chisq, df, E] = chi2test (x, "conditional", 3);
692s  assert (chisq, 52.2509, 1e-4);
692s  assert (df, 3);
692s  assert (E(:,:,1), [53.345, 37.655; 14.655, 10.345], ones (2, 2) * 1e-3);
692s ***** test
692s  [pval, chisq, df, E] = chi2test (x, "homogeneous", []);
692s  assert (chisq, 1.6034, 1e-4);
692s  assert (df, 2);
692s  assert (E(:,:,1), [60.827, 31.382; 7.173, 16.618], ones (2, 2) * 1e-3);
692s 34 tests, 34 passed, 0 known failure, 0 skipped
692s [inst/cholcov.m]
692s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/cholcov.m
692s ***** demo
692s  C1 = [2, 1, 1, 2; 1, 2, 1, 2; 1, 1, 2, 2; 2, 2, 2, 3]
692s  T = cholcov (C1)
692s  C2 = T'*T
692s ***** test
692s  C1 = [2, 1, 1, 2; 1, 2, 1, 2; 1, 1, 2, 2; 2, 2, 2, 3];
692s  T = cholcov (C1);
692s  assert (C1, T'*T, 1e-15 * ones (size (C1)));
692s 1 test, 1 passed, 0 known failure, 0 skipped
692s [inst/cl_multinom.m]
692s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/cl_multinom.m
692s ***** demo
692s  CL = cl_multinom ([27; 43; 19; 11], 10000, 0.05)
692s ***** error<Invalid call to cl_multinom.  Correct usage> cl_multinom ();
692s ***** error cl_multinom (1, 2, 3, 4, 5);
692s ***** error<cl_multinom: argument method must be a string.> ...
692s  cl_multinom (1, 2, 3, 4);
692s ***** error<cl_multinom: unknown calculation type.> ...
692s  cl_multinom (1, 2, 3, "some string");
692s 4 tests, 4 passed, 0 known failure, 0 skipped
692s [inst/cluster.m]
692s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/cluster.m
692s ***** error cluster ()
693s ***** error <Z must be .* generated by the linkage .*> cluster ([1 1], "Cutoff", 1)
693s ***** error <unknown option .*> cluster ([1 2 1], "Bogus", 1)
693s ***** error <C must be a positive scalar .*> cluster ([1 2 1], "Cutoff", -1)
693s ***** error <unknown property .*> cluster ([1 2 1], "Cutoff", 1, "Bogus", 1)
693s ***** test
693s 6 tests, 6 passed, 0 known failure, 0 skipped
693s [inst/clusterdata.m]
693s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/clusterdata.m
693s ***** demo
693s  randn ("seed", 1)  # for reproducibility
693s  r1 = randn (10, 2) * 0.25 + 1;
693s  randn ("seed", 5)  # for reproducibility
693s  r2 = randn (20, 2) * 0.5 - 1;
693s  X = [r1; r2];
693s 
693s  wnl = warning ("off", "Octave:linkage_savemem", "local");
693s  T = clusterdata (X, "linkage", "ward", "MaxClust", 2);
693s  scatter (X(:,1), X(:,2), 36, T, "filled");
693s ***** error<clusterdata: function called with too few input arguments.> ...
693s  clusterdata ()
693s ***** error<clusterdata: function called with too few input arguments.> ...
693s  clusterdata (1)
693s ***** error <unknown property .*> clusterdata ([1 1], "Bogus", 1)
693s ***** error <specify .* 'MaxClust' or 'Cutoff' .*> clusterdata ([1 1], "Depth", 1)
693s 4 tests, 4 passed, 0 known failure, 0 skipped
693s [inst/cmdscale.m]
693s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/cmdscale.m
693s ***** shared m, n, X, D
693s  m = randi(100) + 1; n = randi(100) + 1; X = rand(m, n); D = pdist(X);
693s ***** assert(norm(pdist(cmdscale(D))), norm(D), sqrt(eps))
693s ***** assert(norm(pdist(cmdscale(squareform(D)))), norm(D), sqrt(eps))
693s 2 tests, 2 passed, 0 known failure, 0 skipped
693s [inst/combnk.m]
693s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/combnk.m
693s ***** demo
693s  c = combnk (1:5, 2);
693s  disp ("All pairs of integers between 1 and 5:");
693s  disp (c);
693s ***** test
693s  c = combnk (1:3, 2);
693s  assert (c, [1, 2; 1, 3; 2, 3]);
693s ***** test
693s  c = combnk (1:3, 6);
693s  assert (isempty (c));
693s ***** test
693s  c = combnk ({1, 2, 3}, 2);
693s  assert (c, {1, 2; 1, 3; 2, 3});
693s ***** test
693s  c = combnk ("hello", 2);
693s  assert (c, ["lo"; "lo"; "ll"; "eo"; "el"; "el"; "ho"; "hl"; "hl"; "he"]);
693s 4 tests, 4 passed, 0 known failure, 0 skipped
693s [inst/confusionchart.m]
693s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/confusionchart.m
693s ***** demo
693s  ## Setting the chart properties
693s  Yt = [8 5 6 8 5 3 1 6 4 2 5 3 1 4]';
693s  Yp = [8 5 6 8 5 2 3 4 4 5 5 7 2 6]';
693s  confusionchart (Yt, Yp, "Title", ...
693s    "Demonstration with summaries","Normalization",...
693s    "absolute","ColumnSummary", "column-normalized","RowSummary",...
693s    "row-normalized")
693s ***** demo
693s  ## Cellstr as inputs
693s  Yt = {"Positive", "Positive", "Positive", "Negative", "Negative"};
693s  Yp = {"Positive", "Positive", "Negative", "Negative", "Negative"};
693s  m = confusionmat (Yt, Yp);
693s  confusionchart (m, {"Positive", "Negative"});
693s ***** demo
693s  ## Editing the object properties
693s  Yt = {"Positive", "Positive", "Positive", "Negative", "Negative"};
693s  Yp = {"Positive", "Positive", "Negative", "Negative", "Negative"};
693s  cm = confusionchart (Yt, Yp);
693s  cm.Title = "This is an example with a green diagonal";
693s  cm.DiagonalColor = [0.4660, 0.6740, 0.1880];
693s ***** demo
693s  ## Confusion chart in a uipanel
693s  h = uipanel ();
693s  Yt = {"Positive", "Positive", "Positive", "Negative", "Negative"};
693s  Yp = {"Positive", "Positive", "Negative", "Negative", "Negative"};
693s  cm = confusionchart (h, Yt, Yp);
693s ***** demo
693s  ## Sorting classes
693s  Yt = [8 5 6 8 5 3 1 6 4 2 5 3 1 4]';
693s  Yp = [8 5 6 8 5 2 3 4 4 5 5 7 2 6]';
693s  cm = confusionchart (Yt, Yp, "Title", ...
693s    "Classes are sorted in ascending order");
693s  cm = confusionchart (Yt, Yp, "Title", ...
693s    "Classes are sorted according to clusters");
693s  sortClasses (cm, "cluster");
693s ***** shared visibility_setting
693s  visibility_setting = get (0, "DefaultFigureVisible");
693s ***** test
693s  set (0, "DefaultFigureVisible", "off");
693s  fail ("confusionchart ()", "Invalid call");
693s  set (0, "DefaultFigureVisible", visibility_setting);
693s ***** test
693s  set (0, "DefaultFigureVisible", "off");
693s  fail ("confusionchart ([1 1; 2 2; 3 3])", "invalid argument");
693s  set (0, "DefaultFigureVisible", visibility_setting);
693s ***** test
693s  set (0, "DefaultFigureVisible", "off");
693s  fail ("confusionchart ([1 2], [0 1], 'xxx', 1)", "invalid property");
693s  set (0, "DefaultFigureVisible", visibility_setting);
693s ***** test
693s  set (0, "DefaultFigureVisible", "off");
693s  fail ("confusionchart ([1 2], [0 1], 'XLabel', 1)", "XLabel .* string");
693s  set (0, "DefaultFigureVisible", visibility_setting);
693s ***** test
693s  set (0, "DefaultFigureVisible", "off");
693s  fail ("confusionchart ([1 2], [0 1], 'YLabel', [1 0])", ...
693s        ".* YLabel .* string");
693s  set (0, "DefaultFigureVisible", visibility_setting);
693s ***** test
693s  set (0, "DefaultFigureVisible", "off");
693s  fail ("confusionchart ([1 2], [0 1], 'Title', .5)", ".* Title .* string");
693s  set (0, "DefaultFigureVisible", visibility_setting);
693s ***** test
693s  set (0, "DefaultFigureVisible", "off");
693s  fail ("confusionchart ([1 2], [0 1], 'FontName', [])", ...
693s        ".* FontName .* string");
693s  set (0, "DefaultFigureVisible", visibility_setting);
693s ***** test
693s  set (0, "DefaultFigureVisible", "off");
693s  fail ("confusionchart ([1 2], [0 1], 'FontSize', 'b')", ...
693s        ".* FontSize .* numeric");
693s  set (0, "DefaultFigureVisible", visibility_setting);
693s ***** test
693s  set (0, "DefaultFigureVisible", "off");
693s  fail ("confusionchart ([1 2], [0 1], 'DiagonalColor', 'h')", ...
693s        ".* DiagonalColor .* color");
693s  set (0, "DefaultFigureVisible", visibility_setting);
693s ***** test
693s  set (0, "DefaultFigureVisible", "off");
693s  fail ("confusionchart ([1 2], [0 1], 'OffDiagonalColor', [])", ...
693s        ".* OffDiagonalColor .* color");
693s  set (0, "DefaultFigureVisible", visibility_setting);
694s ***** test
694s  set (0, "DefaultFigureVisible", "off");
694s  fail ("confusionchart ([1 2], [0 1], 'Normalization', '')", ...
694s        ".* invalid .* Normalization");
694s  set (0, "DefaultFigureVisible", visibility_setting);
694s ***** test
694s  set (0, "DefaultFigureVisible", "off");
694s  fail ("confusionchart ([1 2], [0 1], 'ColumnSummary', [])", ...
694s        ".* invalid .* ColumnSummary");
694s  set (0, "DefaultFigureVisible", visibility_setting);
694s ***** test
694s  set (0, "DefaultFigureVisible", "off");
694s  fail ("confusionchart ([1 2], [0 1], 'RowSummary', 1)", ...
694s        ".* invalid .* RowSummary");
694s  set (0, "DefaultFigureVisible", visibility_setting);
694s ***** test
694s  set (0, "DefaultFigureVisible", "off");
694s  fail ("confusionchart ([1 2], [0 1], 'GridVisible', .1)", ...
694s        ".* invalid .* GridVisible");
694s  set (0, "DefaultFigureVisible", visibility_setting);
694s ***** test
694s  set (0, "DefaultFigureVisible", "off");
694s  fail ("confusionchart ([1 2], [0 1], 'HandleVisibility', .1)", ...
694s        ".* invalid .* HandleVisibility");
694s  set (0, "DefaultFigureVisible", visibility_setting);
694s ***** test
694s  set (0, "DefaultFigureVisible", "off");
694s  fail ("confusionchart ([1 2], [0 1], 'OuterPosition', .1)", ...
694s        ".* invalid .* OuterPosition");
694s  set (0, "DefaultFigureVisible", visibility_setting);
694s ***** test
694s  set (0, "DefaultFigureVisible", "off");
694s  fail ("confusionchart ([1 2], [0 1], 'Position', .1)", ...
694s        ".* invalid .* Position");
694s  set (0, "DefaultFigureVisible", visibility_setting);
694s ***** test
694s  set (0, "DefaultFigureVisible", "off");
694s  fail ("confusionchart ([1 2], [0 1], 'Units', .1)", ".* invalid .* Units");
694s  set (0, "DefaultFigureVisible", visibility_setting);
694s 18 tests, 18 passed, 0 known failure, 0 skipped
694s [inst/confusionmat.m]
694s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/confusionmat.m
694s ***** test
694s  Yt = [8 5 6 8 5 3 1 6 4 2 5 3 1 4]';
694s  Yp = [8 5 6 8 5 2 3 4 4 5 5 7 2 6]';
694s  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; ...
694s        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];
694s  assert (confusionmat (Yt, Yp), C)
694s 1 test, 1 passed, 0 known failure, 0 skipped
694s [inst/cophenet.m]
694s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/cophenet.m
694s ***** demo
694s  randn ("seed", 5)  # for reproducibility
694s  X = randn (10,2);
694s  y = pdist (X);
694s  Z = linkage (y, "average");
694s  cophenet (Z, y)
694s ***** error<cophenet: function called with too few input arguments.> cophenet ()
694s ***** error<cophenet: function called with too few input arguments.> cophenet (1)
694s ***** error<cophenet: Z must be a matrix as generated by the linkage function.> ...
694s  cophenet (ones (2,2), 1)
694s ***** error<cophenet: Y must be a vector of euclidean distances.> ...
694s  cophenet ([1 2 1], "a")
694s ***** error<cophenet: Y must be a vector of euclidean distances.> ...
694s  cophenet ([1 2 1], [1 2])
694s 5 tests, 5 passed, 0 known failure, 0 skipped
694s [inst/correlation_test.m]
694s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/correlation_test.m
694s ***** error<Invalid call to correlation_test.  Correct usage> correlation_test ();
695s ***** error<Invalid call to correlation_test.  Correct usage> correlation_test (1);
695s ***** error<correlation_test: X must contain finite real numbers.> ...
695s  correlation_test ([1 2 NaN]', [2 3 4]');
695s ***** error<correlation_test: X must contain finite real numbers.> ...
695s  correlation_test ([1 2 Inf]', [2 3 4]');
695s ***** error<correlation_test: X must contain finite real numbers.> ...
695s  correlation_test ([1 2 3+i]', [2 3 4]');
695s ***** error<correlation_test: Y must contain finite real numbers.> ...
695s  correlation_test ([1 2 3]', [2 3 NaN]');
695s ***** error<correlation_test: Y must contain finite real numbers.> ...
695s  correlation_test ([1 2 3]', [2 3 Inf]');
695s ***** error<correlation_test: Y must contain finite real numbers.> ...
695s  correlation_test ([1 2 3]', [3 4 3+i]');
695s ***** error<correlation_test: X and Y must be vectors of equal length.> ...
695s  correlation_test ([1 2 3]', [3 4 4 5]');
695s ***** error<correlation_test: invalid value for alpha.> ...
695s  correlation_test ([1 2 3]', [2 3 4]', "alpha", 0);
695s ***** error<correlation_test: invalid value for alpha.> ...
695s  correlation_test ([1 2 3]', [2 3 4]', "alpha", 1.2);
695s ***** error<correlation_test: invalid value for alpha.> ...
695s  correlation_test ([1 2 3]', [2 3 4]', "alpha", [.02 .1]);
695s ***** error<correlation_test: invalid value for alpha.> ...
695s  correlation_test ([1 2 3]', [2 3 4]', "alpha", "a");
695s ***** error<correlation_test: invalid Name argument.> ...
695s  correlation_test ([1 2 3]', [2 3 4]', "some", 0.05);
695s ***** error<correlation_test: invalid value for tail.>  ...
695s  correlation_test ([1 2 3]', [2 3 4]', "tail", "val");
695s ***** error<correlation_test: invalid value for tail.>  ...
695s  correlation_test ([1 2 3]', [2 3 4]', "alpha", 0.01, "tail", "val");
695s ***** error<correlation_test: invalid value for method.>  ...
695s  correlation_test ([1 2 3]', [2 3 4]', "method", 0.01);
695s ***** error<correlation_test: invalid value for method.>  ...
695s  correlation_test ([1 2 3]', [2 3 4]', "method", "some");
695s ***** test
695s  x = [6 7 7 9 10 12 13 14 15 17];
695s  y = [19 22 27 25 30 28 30 29 25 32];
695s  [h, pval, stats] = correlation_test (x, y);
695s  assert (stats.corrcoef, corr (x', y'), 1e-14);
695s  assert (pval, 0.0223, 1e-4);
695s ***** test
695s  x = [6 7 7 9 10 12 13 14 15 17]';
695s  y = [19 22 27 25 30 28 30 29 25 32]';
695s  [h, pval, stats] = correlation_test (x, y);
695s  assert (stats.corrcoef, corr (x, y), 1e-14);
695s  assert (pval, 0.0223, 1e-4);
695s 20 tests, 20 passed, 0 known failure, 0 skipped
695s [inst/crosstab.m]
695s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/crosstab.m
695s ***** error crosstab ()
695s ***** error crosstab (1)
695s ***** error <crosstab: x1, x2 ... xn must be vectors.> crosstab (ones (2), [1 1])
695s ***** error <crosstab: x1, x2 ... xn must be vectors.> crosstab ([1 1], ones (2))
695s ***** error <x1, x2 .* must be vectors of the same length> crosstab ([1], [1 2])
695s ***** error <x1, x2 .* must be vectors of the same length> crosstab ([1 2], [1])
695s ***** test
695s  load carbig
695s  [table, chisq, p, labels] = crosstab (cyl4, when, org);
695s  assert (table(2,3,1), 38);
695s  assert (labels{3,3}, "Japan");
695s ***** test
695s  load carbig
695s  [table, chisq, p, labels] = crosstab (cyl4, when, org);
695s  assert (table(2,3,2), 17);
695s  assert (labels{1,3}, "USA");
695s 8 tests, 8 passed, 0 known failure, 0 skipped
695s [inst/crossval.m]
695s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/crossval.m
695s ***** test
695s  load fisheriris
695s  y = meas(:, 1);
695s  X = [ones(size(y)) meas(:, 2:4)];
695s  f = @(X1, y1, X2, y2) meansq (y2 - X2*regress(y1, X1));
695s  results0 = crossval (f, X, y);
695s  results1 = crossval (f, X, y, 'KFold', 10);
695s  folds = 5;
695s  results2 = crossval (f, X, y, 'KFold', folds);
695s  results3 = crossval (f, X, y, 'Partition', cvpartition (numel (y), 'KFold', folds));
695s  results4 = crossval (f, X, y, 'LeaveOut', 1);
695s  mcreps = 2; n_holdout = 20;
695s  results5 = crossval (f, X, y, 'HoldOut', n_holdout, 'mcreps', mcreps);
695s 
695s  ## ensure equal representation of iris species in the training set -- tends
695s  ## to slightly reduce cross-validation mean square error
695s  results6 = crossval (f, X, y, 'KFold', 5, 'stratify', grp2idx(species));
695s 
695s  assert (results0, results1, 2e-15);
695s  assert (results2, results3, 5e-17);
695s  assert (size(results4), [1 numel(y)]);
695s  assert (mean(results4), 0.1018, 1e-4);
695s  assert (size(results5), [mcreps 1]);
695s warning: strmatch is obsolete; use strncmp or strcmp instead
695s 1 test, 1 passed, 0 known failure, 0 skipped
695s [inst/datasample.m]
695s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/datasample.m
695s ***** error datasample();
695s ***** error datasample(1);
696s ***** error <data must be a vector or matrix> datasample({1, 2, 3}, 1);
696s ***** error <k must be a positive integer scalar> datasample([1 2], -1);
696s ***** error <k must be a positive integer scalar> datasample([1 2], 1.5);
696s ***** error <k must be a positive integer scalar> datasample([1 2], [1 1]);
696s ***** error <k must be a positive integer scalar> datasample([1 2], 'g', [1 1]);
696s ***** error <DIM must be a positive integer scalar> datasample([1 2], 1, -1);
696s ***** error <DIM must be a positive integer scalar> datasample([1 2], 1, 1.5);
696s ***** error <DIM must be a positive integer scalar> datasample([1 2], 1, [1 1]);
696s ***** error <Replace> datasample([1 2], 1, 1, "Replace", -2);
696s ***** error <weights must be defined> datasample([1 2], 1, 1, "Weights", "abc");
696s ***** error <weights must be defined> datasample([1 2], 1, 1, "Weights", [1 -2 3]);
696s ***** error <weights must be defined> datasample([1 2], 1, 1, "Weights", ones (2));
696s ***** error <weights must be equal> datasample([1 2], 1, 1, "Weights", [1 2 3]);
696s ***** test
696s  dat = randn (10, 4);
696s  assert (size (datasample (dat, 3, 1)), [3 4]);
696s ***** test
696s  dat = randn (10, 4);
696s  assert (size (datasample (dat, 3, 2)), [10 3]);
696s 17 tests, 17 passed, 0 known failure, 0 skipped
696s [inst/dcov.m]
696s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dcov.m
696s ***** demo
696s  base=@(x) (x- min(x))./(max(x)-min(x));
696s  N = 5e2;
696s  x = randn (N,1); x = base (x);
696s  z = randn (N,1); z = base (z);
696s  # Linear relations
696s  cy = [1 0.55 0.3 0 -0.3 -0.55 -1];
696s  ly = x .* cy;
696s  ly(:,[1:3 5:end]) = base (ly(:,[1:3 5:end]));
696s  # Correlated Gaussian
696s  cz = 1 - abs (cy);
696s  gy = base ( ly + cz.*z);
696s  # Shapes
696s  sx      = repmat (x,1,7);
696s  sy      = zeros (size (ly));
696s  v       = 2 * rand (size(x,1),2) - 1;
696s  sx(:,1) = v(:,1); sy(:,1) = cos(2*pi*sx(:,1)) + 0.5*v(:,2).*exp(-sx(:,1).^2/0.5);
696s  R       =@(d) [cosd(d) sind(d); -sind(d) cosd(d)];
696s  tmp     = R(35) * v.';
696s  sx(:,2) = tmp(1,:); sy(:,2) = tmp(2,:);
696s  tmp     = R(45) * v.';
696s  sx(:,3) = tmp(1,:); sy(:,3) = tmp(2,:);
696s  sx(:,4) = v(:,1); sy(:,4) = sx(:,4).^2 + 0.5*v(:,2);
696s  sx(:,5) = v(:,1); sy(:,5) = 3*sign(v(:,2)).*(sx(:,5)).^2  + v(:,2);
696s  sx(:,6) = cos (2*pi*v(:,1)) + 0.5*(x-0.5);
696s  sy(:,6) = sin (2*pi*v(:,1)) + 0.5*(z-0.5);
696s  sx(:,7) = x + sign(v(:,1)); sy(:,7) = z + sign(v(:,2));
696s  sy      = base (sy);
696s  sx      = base (sx);
696s  # scaled shape
696s  sc  = 1/3;
696s  ssy = (sy-0.5) * sc + 0.5;
696s  n = size (ly,2);
696s  ym = 1.2;
696s  xm = 0.5;
696s  fmt={'horizontalalignment','center'};
696s  ff = "% .2f";
696s  figure (1)
696s  for i=1:n
696s    subplot(4,n,i);
696s    plot (x, gy(:,i), '.b');
696s    axis tight
696s    axis off
696s    text (xm,ym,sprintf (ff, dcov (x,gy(:,i))),fmt{:})
696s 
696s    subplot(4,n,i+n);
696s    plot (x, ly(:,i), '.b');
696s    axis tight
696s    axis off
696s    text (xm,ym,sprintf (ff, dcov (x,ly(:,i))),fmt{:})
696s 
696s    subplot(4,n,i+2*n);
696s    plot (sx(:,i), sy(:,i), '.b');
696s    axis tight
696s    axis off
696s    text (xm,ym,sprintf (ff, dcov (sx(:,i),sy(:,i))),fmt{:})
696s    v = axis ();
696s 
696s    subplot(4,n,i+3*n);
696s    plot (sx(:,i), ssy(:,i), '.b');
696s    axis (v)
696s    axis off
696s    text (xm,ym,sprintf (ff, dcov (sx(:,i),ssy(:,i))),fmt{:})
696s  endfor
696s ***** error dcov (randn (30, 5), randn (25,5))
696s 1 test, 1 passed, 0 known failure, 0 skipped
696s [inst/dendrogram.m]
696s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dendrogram.m
696s ***** demo
696s  ## simple dendrogram
696s  y = [4, 5; 2, 6; 3, 7; 8, 9; 1, 10];
696s  y(:,3) = 1:5;
696s  dendrogram (y);
696s  title ("simple dendrogram");
696s ***** demo
696s  ## another simple dendrogram
696s  v = 2 * rand (30, 1) - 1;
696s  d = abs (bsxfun (@minus, v(:, 1), v(:, 1)'));
696s  y = linkage (squareform (d, "tovector"));
696s  dendrogram (y);
696s  title ("another simple dendrogram");
696s ***** demo
696s  ## collapsed tree, find all the leaves of node 5
696s  X = randn (60, 2);
696s  D = pdist (X);
696s  y = linkage (D, "average");
696s  subplot (2, 1, 1);
696s  title ("original tree");
696s  dendrogram (y, 0);
696s  subplot (2, 1, 2);
696s  title ("collapsed tree");
696s  [~, t] = dendrogram (y, 20);
696s  find(t == 5)
696s ***** demo
696s  ## optimal leaf order
696s  X = randn (30, 2);
696s  D = pdist (X);
696s  y = linkage (D, "average");
696s  order = optimalleaforder (y, D);
696s  subplot (2, 1, 1);
696s  title ("original leaf order");
696s  dendrogram (y);
696s  subplot (2, 1, 2);
696s  title ("optimal leaf order");
696s  dendrogram (y, "Reorder", order);
696s ***** demo
696s  ## horizontal orientation and labels
696s  X = randn (8, 2);
696s  D = pdist (X);
696s  L = ["Snow White"; "Doc"; "Grumpy"; "Happy"; "Sleepy"; "Bashful"; ...
696s       "Sneezy"; "Dopey"];
696s  y = linkage (D, "average");
696s  dendrogram (y, "Orientation", "left", "Labels", L);
696s  title ("horizontal orientation and labels");
696s ***** shared visibility_setting
696s  visibility_setting = get (0, "DefaultFigureVisible");
696s ***** test
696s  hf = figure ("visible", "off");
696s  unwind_protect
696s    y = [4, 5; 2, 6; 3, 7; 8, 9; 1, 10];
696s    y(:,3) = 1:5;
696s    dendrogram (y);
696s  unwind_protect_cleanup
696s    close (hf);
696s  end_unwind_protect
696s ***** test
696s  hf = figure ("visible", "off");
696s  unwind_protect
696s    y = [4, 5; 2, 6; 3, 7; 8, 9; 1, 10];
696s    y(:,3) = 1:5;
696s    dendrogram (y);
696s  unwind_protect_cleanup
696s    close (hf);
696s  end_unwind_protect
696s ***** test
696s  hf = figure ("visible", "off");
696s  unwind_protect
696s    v = 2 * rand (30, 1) - 1;
696s    d = abs (bsxfun (@minus, v(:, 1), v(:, 1)'));
696s    y = linkage (squareform (d, "tovector"));
696s    dendrogram (y);
696s  unwind_protect_cleanup
696s    close (hf);
696s  end_unwind_protect
697s ***** test
697s  hf = figure ("visible", "off");
697s  unwind_protect
697s    X = randn (30, 2);
697s    D = pdist (X);
697s    y = linkage (D, "average");
697s    order = optimalleaforder (y, D);
697s    subplot (2, 1, 1);
697s    title ("original leaf order");
697s    dendrogram (y);
697s    subplot (2, 1, 2);
697s    title ("optimal leaf order");
697s    dendrogram (y, "Reorder", order);
697s  unwind_protect_cleanup
697s    close (hf);
697s  end_unwind_protect
698s ***** error dendrogram ();
698s ***** error <tree must be .*> dendrogram (ones (2, 2), 1);
698s ***** error <unknown property .*> dendrogram ([1 2 1], 1, "xxx", "xxx");
698s ***** error <reorder.*> dendrogram ([1 2 1], "Reorder", "xxx");
698s ***** error <reorder.*> dendrogram ([1 2 1], "Reorder", [1 2 3 4]);
698s  fail ('dendrogram ([1 2 1], "Orientation", "north")', "invalid orientation .*")
698s 9 tests, 9 passed, 0 known failure, 0 skipped
698s [inst/dist_fit/betafit.m]
698s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/betafit.m
698s ***** demo
698s  ## Sample 2 populations from different Beta distibutions
698s  randg ("seed", 1);   # for reproducibility
698s  r1 = betarnd (2, 5, 500, 1);
698s  randg ("seed", 2);   # for reproducibility
698s  r2 = betarnd (2, 2, 500, 1);
698s  r = [r1, r2];
698s 
698s  ## Plot them normalized and fix their colors
698s  hist (r, 12, 15);
698s  h = findobj (gca, "Type", "patch");
698s  set (h(1), "facecolor", "c");
698s  set (h(2), "facecolor", "g");
698s  hold on
698s 
698s  ## Estimate their shape parameters
698s  a_b_A = betafit (r(:,1));
698s  a_b_B = betafit (r(:,2));
698s 
698s  ## Plot their estimated PDFs
698s  x = [min(r(:)):0.01:max(r(:))];
698s  y = betapdf (x, a_b_A(1), a_b_A(2));
698s  plot (x, y, "-pr");
698s  y = betapdf (x, a_b_B(1), a_b_B(2));
698s  plot (x, y, "-sg");
698s  ylim ([0, 4])
698s  legend ({"Normalized HIST of sample 1 with α=2 and β=5", ...
698s           "Normalized HIST of sample 2 with α=2 and β=2", ...
698s           sprintf("PDF for sample 1 with estimated α=%0.2f and β=%0.2f", ...
698s                   a_b_A(1), a_b_A(2)), ...
698s           sprintf("PDF for sample 2 with estimated α=%0.2f and β=%0.2f", ...
698s                   a_b_B(1), a_b_B(2))})
698s  title ("Two population samples from different Beta distibutions")
698s  hold off
698s ***** test
698s  x = 0.01:0.02:0.99;
698s  [paramhat, paramci] = betafit (x);
698s  paramhat_out = [1.0199, 1.0199];
698s  paramci_out = [0.6947, 0.6947; 1.4974, 1.4974];
698s  assert (paramhat, paramhat_out, 1e-4);
698s  assert (paramci, paramci_out, 1e-4);
698s ***** test
698s  x = 0.01:0.02:0.99;
698s  [paramhat, paramci] = betafit (x, 0.01);
698s  paramci_out = [0.6157, 0.6157; 1.6895, 1.6895];
698s  assert (paramci, paramci_out, 1e-4);
698s ***** test
698s  x = 0.00:0.02:1;
698s  [paramhat, paramci] = betafit (x);
698s  paramhat_out = [0.0875, 0.1913];
698s  paramci_out = [0.0822, 0.1490; 0.0931, 0.2455];
698s  assert (paramhat, paramhat_out, 1e-4);
698s  assert (paramci, paramci_out, 1e-4);
699s ***** error<betafit: X must be a vector of real values.> betafit ([0.2, 0.5+i]);
699s ***** error<betafit: X must be a vector of real values.> betafit (ones (2,2) * 0.5);
699s ***** error<betafit: X must be in the range> betafit ([0.5, 1.2]);
699s ***** error<betafit: X must contain distinct values.> betafit ([0.1, 0.1]);
699s ***** error<betafit: wrong value for ALPHA.> betafit ([0.01:0.1:0.99], 1.2);
699s ***** error<betafit: X and FREQ vectors mismatch.> ...
699s  betafit ([0.01:0.01:0.05], 0.05, [1, 2, 3, 2]);
699s ***** error<betafit: FREQ must not contain negative values.> ...
699s  betafit ([0.01:0.01:0.05], 0.05, [1, 2, 3, 2, -1]);
699s ***** error<betafit: FREQ must contain integer values.> ...
699s  betafit ([0.01:0.01:0.05], 0.05, [1, 2, 3, 2, 1.5]);
699s ***** error<betafit: 'options' argument must be a structure> ...
699s  betafit ([0.01:0.01:0.05], 0.05, struct ("option", 234));
699s ***** error<betafit: 'options' argument must be a structure> ...
699s  betafit ([0.01:0.01:0.05], 0.05, ones (1,5), struct ("option", 234));
699s 13 tests, 13 passed, 0 known failure, 0 skipped
699s [inst/dist_fit/betalike.m]
699s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/betalike.m
699s ***** test
699s  x = 0.01:0.02:0.99;
699s  [nlogL, avar] = betalike ([2.3, 1.2], x);
699s  avar_out = [0.03691678, 0.02803056; 0.02803056, 0.03965629];
699s  assert (nlogL, 17.873477715879040, 3e-14);
699s  assert (avar, avar_out, 1e-7);
699s ***** test
699s  x = 0.01:0.02:0.99;
699s  [nlogL, avar] = betalike ([1, 4], x);
699s  avar_out = [0.02793282, 0.02717274; 0.02717274, 0.03993361];
699s  assert (nlogL, 79.648061114839550, 1e-13);
699s  assert (avar, avar_out, 1e-7);
699s ***** test
699s  x = 0.00:0.02:1;
699s  [nlogL, avar] = betalike ([1, 4], x);
699s  avar_out = [0.00000801564765, 0.00000131397245; ...
699s              0.00000131397245, 0.00070827639442];
699s  assert (nlogL, 573.2008434477486, 1e-10);
699s  assert (avar, avar_out, 1e-14);
699s ***** error<betalike: function called with too few input arguments.> ...
699s  betalike ([12, 15]);
699s ***** error<betalike: wrong parameters length.> betalike ([12, 15, 3], [1:50]);
699s ***** error<betalike: X and FREQ vectors mismatch.> ...
699s  betalike ([12, 15], ones (10, 1), ones (8,1))
699s ***** error<betalike: FREQ must not contain negative values.> ...
699s  betalike ([12, 15], ones (1, 8), [1 1 1 1 1 1 1 -1])
699s ***** error<betalike: FREQ must contain integer values.> ...
699s  betalike ([12, 15], ones (1, 8), [1 1 1 1 1 1 1 1.5])
699s 8 tests, 8 passed, 0 known failure, 0 skipped
699s [inst/dist_fit/binofit.m]
699s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/binofit.m
699s ***** demo
699s  ## Sample 2 populations from different binomial distibutions
699s  rand ("seed", 1);    # for reproducibility
699s  r1 = binornd (50, 0.15, 1000, 1);
699s  rand ("seed", 2);    # for reproducibility
699s  r2 = binornd (100, 0.5, 1000, 1);
699s  r = [r1, r2];
699s 
699s  ## Plot them normalized and fix their colors
699s  hist (r, 23, 0.35);
699s  h = findobj (gca, "Type", "patch");
699s  set (h(1), "facecolor", "c");
699s  set (h(2), "facecolor", "g");
699s  hold on
699s 
699s  ## Estimate their probability of success
699s  pshatA = binofit (r(:,1), 50);
699s  pshatB = binofit (r(:,2), 100);
699s 
699s  ## Plot their estimated PDFs
699s  x = [min(r(:,1)):max(r(:,1))];
699s  y = binopdf (x, 50, mean (pshatA));
699s  plot (x, y, "-pg");
699s  x = [min(r(:,2)):max(r(:,2))];
699s  y = binopdf (x, 100, mean (pshatB));
699s  plot (x, y, "-sc");
699s  ylim ([0, 0.2])
699s  legend ({"Normalized HIST of sample 1 with ps=0.15", ...
699s           "Normalized HIST of sample 2 with ps=0.50", ...
699s           sprintf("PDF for sample 1 with estimated ps=%0.2f", ...
699s                   mean (pshatA)), ...
699s           sprintf("PDF for sample 2 with estimated ps=%0.2f", ...
699s                   mean (pshatB))})
699s  title ("Two population samples from different binomial distibutions")
699s  hold off
699s ***** test
699s  x = 0:3;
699s  [pshat, psci] = binofit (x, 3);
699s  assert (pshat, [0, 0.3333, 0.6667, 1], 1e-4);
699s  assert (psci(1,:), [0, 0.7076], 1e-4);
699s  assert (psci(2,:), [0.0084, 0.9057], 1e-4);
699s  assert (psci(3,:), [0.0943, 0.9916], 1e-4);
699s  assert (psci(4,:), [0.2924, 1.0000], 1e-4);
699s ***** error<binofit: function called with too few input arguments.> ...
699s  binofit ([1 2 3 4])
699s ***** error<binofit: X cannot have negative values.> ...
699s  binofit ([-1, 4, 3, 2], [1, 2, 3, 3])
699s ***** error<binofit: X must be a vector.> binofit (ones(2), [1, 2, 3, 3])
699s ***** error<binofit: N must be a non-negative integer.> ...
699s  binofit ([1, 4, 3, 2], [1, 2, -1, 3])
699s ***** error<binofit: N must be a scalar or the same size as X.> ...
699s  binofit ([1, 4, 3, 2], [5, 5, 5])
699s ***** error<binofit: N must be at least as large as X.> ...
699s  binofit ([1, 4, 3, 2], [5, 3, 5, 5])
699s ***** error<binofit: wrong value for ALPHA.> binofit ([1, 2, 1], 3, 1.2);
699s ***** error<binofit: wrong value for ALPHA.> binofit ([1, 2, 1], 3, 0);
699s ***** error<binofit: wrong value for ALPHA.> binofit ([1, 2, 1], 3, "alpha");
699s 10 tests, 10 passed, 0 known failure, 0 skipped
699s [inst/dist_fit/binolike.m]
699s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/binolike.m
699s ***** assert (binolike ([3, 0.333], [0:3]), 6.8302, 1e-4)
699s ***** assert (binolike ([3, 0.333], 0), 1.2149, 1e-4)
699s ***** assert (binolike ([3, 0.333], 1), 0.8109, 1e-4)
699s ***** assert (binolike ([3, 0.333], 2), 1.5056, 1e-4)
699s ***** assert (binolike ([3, 0.333], 3), 3.2988, 1e-4)
699s ***** test
699s  [nlogL, acov] = binolike ([3, 0.333], 3);
699s  assert (acov(4), 0.0740, 1e-4)
699s ***** error<binolike: function called with too few input arguments.> binolike (3.25)
699s ***** error<binolike: X must be a vector.> binolike ([5, 0.2], ones (2))
699s ***** error<binolike: PARAMS must be a two-element vector.> ...
699s  binolike ([1, 0.2, 3], [1, 3, 5, 7])
699s ***** error<binolike: number of trials,> binolike ([1.5, 0.2], 1)
699s ***** error<binolike: number of trials,> binolike ([-1, 0.2], 1)
699s ***** error<binolike: number of trials,> binolike ([Inf, 0.2], 1)
699s ***** error<binolike: probability of success,> binolike ([5, 1.2], [3, 5])
699s ***** error<binolike: probability of success,> binolike ([5, -0.2], [3, 5])
699s ***** error<binolike: X and FREQ vectors mismatch.> ...
699s  binolike ([5, 0.5], ones (10, 1), ones (8,1))
699s ***** error<binolike: FREQ must not contain negative values.> ...
699s  binolike ([5, 0.5], ones (1, 8), [1 1 1 1 1 1 1 -1])
699s ***** error<binolike: X cannot have negative values.> binolike ([5, 0.2], [-1, 3])
699s ***** error<binolike: number of successes,> binolike ([5, 0.2], [3, 5, 7])
699s 18 tests, 18 passed, 0 known failure, 0 skipped
699s [inst/dist_fit/bisafit.m]
699s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/bisafit.m
699s ***** demo
699s  ## Sample 3 populations from different Birnbaum-Saunders distibutions
699s  rand ("seed", 5);    # for reproducibility
699s  r1 = bisarnd (1, 0.5, 2000, 1);
699s  rand ("seed", 2);    # for reproducibility
699s  r2 = bisarnd (2, 0.3, 2000, 1);
699s  rand ("seed", 7);    # for reproducibility
699s  r3 = bisarnd (4, 0.5, 2000, 1);
699s  r = [r1, r2, r3];
699s 
699s  ## Plot them normalized and fix their colors
699s  hist (r, 80, 4.2);
699s  h = findobj (gca, "Type", "patch");
699s  set (h(1), "facecolor", "c");
699s  set (h(2), "facecolor", "g");
699s  set (h(3), "facecolor", "r");
699s  ylim ([0, 1.1]);
699s  xlim ([0, 8]);
699s  hold on
699s 
699s  ## Estimate their α and β parameters
699s  beta_gammaA = bisafit (r(:,1));
699s  beta_gammaB = bisafit (r(:,2));
699s  beta_gammaC = bisafit (r(:,3));
699s 
699s  ## Plot their estimated PDFs
699s  x = [0:0.1:8];
699s  y = bisapdf (x, beta_gammaA(1), beta_gammaA(2));
699s  plot (x, y, "-pr");
699s  y = bisapdf (x, beta_gammaB(1), beta_gammaB(2));
699s  plot (x, y, "-sg");
699s  y = bisapdf (x, beta_gammaC(1), beta_gammaC(2));
699s  plot (x, y, "-^c");
699s  hold off
699s  legend ({"Normalized HIST of sample 1 with β=1 and γ=0.5", ...
699s           "Normalized HIST of sample 2 with β=2 and γ=0.3", ...
699s           "Normalized HIST of sample 3 with β=4 and γ=0.5", ...
699s           sprintf("PDF for sample 1 with estimated β=%0.2f and γ=%0.2f", ...
699s                   beta_gammaA(1), beta_gammaA(2)), ...
699s           sprintf("PDF for sample 2 with estimated β=%0.2f and γ=%0.2f", ...
699s                   beta_gammaB(1), beta_gammaB(2)), ...
699s           sprintf("PDF for sample 3 with estimated β=%0.2f and γ=%0.2f", ...
699s                   beta_gammaC(1), beta_gammaC(2))})
699s  title ("Three population samples from different Birnbaum-Saunders distibutions")
699s  hold off
699s ***** test
699s  paramhat = bisafit ([1:50]);
699s  paramhat_out = [16.2649, 1.0156];
699s  assert (paramhat, paramhat_out, 1e-4);
699s ***** test
699s  paramhat = bisafit ([1:5]);
699s  paramhat_out = [2.5585, 0.5839];
699s  assert (paramhat, paramhat_out, 1e-4);
699s ***** error<bisafit: X must be a vector.> bisafit (ones (2,5));
699s ***** error<bisafit: X must contain only positive values.> bisafit ([-1 2 3 4]);
699s ***** error<bisafit: wrong value for ALPHA.> bisafit ([1, 2, 3, 4, 5], 1.2);
699s ***** error<bisafit: wrong value for ALPHA.> bisafit ([1, 2, 3, 4, 5], 0);
699s ***** error<bisafit: wrong value for ALPHA.> bisafit ([1, 2, 3, 4, 5], "alpha");
699s ***** error<bisafit: X and CENSOR vectors mismatch.> ...
699s  bisafit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
699s ***** error<bisafit: X and CENSOR vectors mismatch.> ...
699s  bisafit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
699s ***** error<bisafit: X and FREQ vectors mismatch.> ...
699s  bisafit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
699s ***** error<bisafit: X and FREQ vectors mismatch.> ...
699s  bisafit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
699s ***** error<bisafit: 'options' 5th argument must be a structure> ...
699s  bisafit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
699s 12 tests, 12 passed, 0 known failure, 0 skipped
699s [inst/dist_fit/bisalike.m]
699s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/bisalike.m
699s ***** test
699s  nlogL = bisalike ([16.2649, 1.0156], [1:50]);
699s  assert (nlogL, 215.5905, 1e-4);
699s ***** test
699s  nlogL = bisalike ([2.5585, 0.5839], [1:5]);
699s  assert (nlogL, 8.9950, 1e-4);
699s ***** error<bisalike: function called with too few input arguments.> bisalike (3.25)
699s ***** error<bisalike: X must be a vector.> bisalike ([5, 0.2], ones (2))
699s ***** error<bisalike: X cannot have negative values.> bisalike ([5, 0.2], [-1, 3])
699s ***** error<bisalike: PARAMS must be a two-element vector.> ...
699s  bisalike ([1, 0.2, 3], [1, 3, 5, 7])
699s ***** error<bisalike: X and CENSOR vector mismatch.> ...
699s  bisalike ([1.5, 0.2], [1:5], [0, 0, 0])
699s ***** error<bisalike: X and FREQ vector mismatch.> ...
699s  bisalike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
699s ***** error<bisalike: X and FREQ vector mismatch.> ...
699s  bisalike ([1.5, 0.2], [1:5], [], [1, 1, 1])
699s 9 tests, 9 passed, 0 known failure, 0 skipped
699s [inst/dist_fit/burrfit.m]
699s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/burrfit.m
699s ***** demo
699s  ## Sample 3 populations from different Burr type XII distibutions
699s  rand ("seed", 4);    # for reproducibility
699s  r1 = burrrnd (3.5, 2, 2.5, 10000, 1);
699s  rand ("seed", 2);    # for reproducibility
699s  r2 = burrrnd (1, 3, 1, 10000, 1);
699s  rand ("seed", 9);    # for reproducibility
699s  r3 = burrrnd (0.5, 2, 3, 10000, 1);
699s  r = [r1, r2, r3];
699s 
699s  ## Plot them normalized and fix their colors
699s  hist (r, [0.1:0.2:20], [18, 5, 3]);
699s  h = findobj (gca, "Type", "patch");
699s  set (h(1), "facecolor", "c");
699s  set (h(2), "facecolor", "g");
699s  set (h(3), "facecolor", "r");
699s  ylim ([0, 3]);
699s  xlim ([0, 5]);
699s  hold on
699s 
699s  ## Estimate their α and β parameters
699s  lambda_c_kA = burrfit (r(:,1));
699s  lambda_c_kB = burrfit (r(:,2));
699s  lambda_c_kC = burrfit (r(:,3));
699s 
699s  ## Plot their estimated PDFs
699s  x = [0.01:0.15:15];
699s  y = burrpdf (x, lambda_c_kA(1), lambda_c_kA(2), lambda_c_kA(3));
699s  plot (x, y, "-pr");
699s  y = burrpdf (x, lambda_c_kB(1), lambda_c_kB(2), lambda_c_kB(3));
699s  plot (x, y, "-sg");
699s  y = burrpdf (x, lambda_c_kC(1), lambda_c_kC(2), lambda_c_kC(3));
699s  plot (x, y, "-^c");
699s  hold off
699s  legend ({"Normalized HIST of sample 1 with λ=3.5, c=2, and k=2.5", ...
699s           "Normalized HIST of sample 2 with λ=1, c=3, and k=1", ...
699s           "Normalized HIST of sample 3 with λ=0.5, c=2, and k=3", ...
699s   sprintf("PDF for sample 1 with estimated λ=%0.2f, c=%0.2f, and k=%0.2f", ...
699s           lambda_c_kA(1), lambda_c_kA(2), lambda_c_kA(3)), ...
699s   sprintf("PDF for sample 2 with estimated λ=%0.2f, c=%0.2f, and k=%0.2f", ...
699s           lambda_c_kB(1), lambda_c_kB(2), lambda_c_kB(3)), ...
699s   sprintf("PDF for sample 3 with estimated λ=%0.2f, c=%0.2f, and k=%0.2f", ...
699s           lambda_c_kC(1), lambda_c_kC(2), lambda_c_kC(3))})
699s  title ("Three population samples from different Burr type XII distibutions")
699s  hold off
699s ***** test
699s  l = 1; c = 2; k = 3;
699s  r = burrrnd (l, c, k, 100000, 1);
699s  lambda_c_kA = burrfit (r);
699s  assert (lambda_c_kA(1), l, 0.2);
699s  assert (lambda_c_kA(2), c, 0.2);
699s  assert (lambda_c_kA(3), k, 0.3);
702s ***** test
702s  l = 0.5; c = 1; k = 3;
702s  r = burrrnd (l, c, k, 100000, 1);
702s  lambda_c_kA = burrfit (r);
702s  assert (lambda_c_kA(1), l, 0.2);
702s  assert (lambda_c_kA(2), c, 0.2);
702s  assert (lambda_c_kA(3), k, 0.3);
704s ***** test
704s  l = 1; c = 3; k = 1;
704s  r = burrrnd (l, c, k, 100000, 1);
704s  lambda_c_kA = burrfit (r);
704s  assert (lambda_c_kA(1), l, 0.2);
704s  assert (lambda_c_kA(2), c, 0.2);
704s  assert (lambda_c_kA(3), k, 0.3);
707s ***** test
707s  l = 3; c = 2; k = 1;
707s  r = burrrnd (l, c, k, 100000, 1);
707s  lambda_c_kA = burrfit (r);
707s  assert (lambda_c_kA(1), l, 0.2);
707s  assert (lambda_c_kA(2), c, 0.2);
707s  assert (lambda_c_kA(3), k, 0.3);
711s ***** test
711s  l = 4; c = 2; k = 4;
711s  r = burrrnd (l, c, k, 100000, 1);
711s  lambda_c_kA = burrfit (r);
711s  assert (lambda_c_kA(1), l, 0.2);
711s  assert (lambda_c_kA(2), c, 0.2);
711s  assert (lambda_c_kA(3), k, 0.3);
713s ***** error<burrfit: X must be a vector.> burrfit (ones (2,5));
713s ***** error<burrfit: X must contain only positive values.> burrfit ([-1 2 3 4]);
713s ***** error<burrfit: wrong value for ALPHA.> burrfit ([1, 2, 3, 4, 5], 1.2);
713s ***** error<burrfit: wrong value for ALPHA.> burrfit ([1, 2, 3, 4, 5], 0);
713s ***** error<burrfit: wrong value for ALPHA.> burrfit ([1, 2, 3, 4, 5], "alpha");
713s ***** error<burrfit: X and CENSOR vectors mismatch.> ...
713s  burrfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
713s ***** error<burrfit: X and CENSOR vectors mismatch.> ...
713s  burrfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
713s ***** error<burrfit: X and FREQ vectors mismatch.>
713s  burrfit ([1, 2, 3, 4, 5], 0.05, [], [1, 1, 5])
713s ***** error<burrfit: FREQ must not contain negative values.>
713s  burrfit ([1, 2, 3, 4, 5], 0.05, [], [1, 5, 1, 1, -1])
713s ***** error<burrfit: 'options' 5th argument must be a structure> ...
713s  burrfit ([1:10], 0.05, [], [], 5)
713s 15 tests, 15 passed, 0 known failure, 0 skipped
713s [inst/dist_fit/burrlike.m]
713s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/burrlike.m
713s ***** error<burrlike: function called with too few input arguments.> burrlike (3.25)
713s ***** error<burrlike: X must be a vector.> burrlike ([1, 2, 3], ones (2))
713s ***** error<burrlike: X cannot have negative values.> burrlike ([1, 2, 3], [-1, 3])
713s ***** error<burrlike: PARAMS must be a three-element vector.> ...
713s  burrlike ([1, 2], [1, 3, 5, 7])
713s ***** error<burrlike: PARAMS must be a three-element vector.> ...
713s  burrlike ([1, 2, 3, 4], [1, 3, 5, 7])
713s ***** error<burrlike: X and CENSOR vector mismatch.> ...
713s  burrlike ([1, 2, 3], [1:5], [0, 0, 0])
713s ***** error<burrlike: X and FREQ vector mismatch.> ...
713s  burrlike ([1, 2, 3], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
713s ***** error<burrlike: X and FREQ vector mismatch.> ...
713s  burrlike ([1, 2, 3], [1:5], [], [1, 1, 1])
713s 8 tests, 8 passed, 0 known failure, 0 skipped
713s [inst/dist_fit/evfit.m]
713s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/evfit.m
713s ***** demo
713s  ## Sample 3 populations from different extreme value distibutions
713s  rand ("seed", 1);    # for reproducibility
713s  r1 = evrnd (2, 5, 400, 1);
713s  rand ("seed", 12);    # for reproducibility
713s  r2 = evrnd (-5, 3, 400, 1);
713s  rand ("seed", 13);    # for reproducibility
713s  r3 = evrnd (14, 8, 400, 1);
713s  r = [r1, r2, r3];
713s 
713s  ## Plot them normalized and fix their colors
713s  hist (r, 25, 0.4);
713s  h = findobj (gca, "Type", "patch");
713s  set (h(1), "facecolor", "c");
713s  set (h(2), "facecolor", "g");
713s  set (h(3), "facecolor", "r");
713s  ylim ([0, 0.28])
713s  xlim ([-30, 30]);
713s  hold on
713s 
713s  ## Estimate their MU and SIGMA parameters
713s  mu_sigmaA = evfit (r(:,1));
713s  mu_sigmaB = evfit (r(:,2));
713s  mu_sigmaC = evfit (r(:,3));
713s 
713s  ## Plot their estimated PDFs
713s  x = [min(r(:)):max(r(:))];
713s  y = evpdf (x, mu_sigmaA(1), mu_sigmaA(2));
713s  plot (x, y, "-pr");
713s  y = evpdf (x, mu_sigmaB(1), mu_sigmaB(2));
713s  plot (x, y, "-sg");
713s  y = evpdf (x, mu_sigmaC(1), mu_sigmaC(2));
713s  plot (x, y, "-^c");
713s  legend ({"Normalized HIST of sample 1 with μ=2 and σ=5", ...
713s           "Normalized HIST of sample 2 with μ=-5 and σ=3", ...
713s           "Normalized HIST of sample 3 with μ=14 and σ=8", ...
713s           sprintf("PDF for sample 1 with estimated μ=%0.2f and σ=%0.2f", ...
713s                   mu_sigmaA(1), mu_sigmaA(2)), ...
713s           sprintf("PDF for sample 2 with estimated μ=%0.2f and σ=%0.2f", ...
713s                   mu_sigmaB(1), mu_sigmaB(2)), ...
713s           sprintf("PDF for sample 3 with estimated μ=%0.2f and σ=%0.2f", ...
713s                   mu_sigmaC(1), mu_sigmaC(2))})
713s  title ("Three population samples from different extreme value distibutions")
713s  hold off
713s ***** test
713s  x = 1:50;
713s  [paramhat, paramci] = evfit (x);
713s  paramhat_out = [32.6811, 13.0509];
713s  paramci_out = [28.8504, 10.5294; 36.5118, 16.1763];
713s  assert (paramhat, paramhat_out, 1e-4);
713s  assert (paramci, paramci_out, 1e-4);
713s ***** test
713s  x = 1:50;
713s  [paramhat, paramci] = evfit (x, 0.01);
713s  paramci_out = [27.6468, 9.8426; 37.7155, 17.3051];
713s  assert (paramci, paramci_out, 1e-4);
713s ***** error<evfit: X must be a double-precision vector.> evfit (ones (2,5));
713s ***** error<evfit: X must be a double-precision vector.> evfit (single (ones (1,5)));
713s ***** error<evfit: X must NOT contain missing values> evfit ([1, 2, 3, 4, NaN]);
713s ***** error<evfit: wrong value for ALPHA.> evfit ([1, 2, 3, 4, 5], 1.2);
713s ***** error<evfit: X and FREQ vectors mismatch.>
713s  evfit ([1 2 3], 0.05, [], [1 5])
713s ***** error<evfit: FREQ must not contain negative values.>
713s  evfit ([1 2 3], 0.05, [], [1 5 -1])
713s ***** error<evfit: 'options' 5th argument must be a structure> ...
713s  evfit ([1:10], 0.05, [], [], 5)
713s 9 tests, 9 passed, 0 known failure, 0 skipped
713s [inst/dist_fit/evlike.m]
714s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/evlike.m
714s ***** test
714s  x = 1:50;
714s  [nlogL, acov] = evlike ([2.3, 1.2], x);
714s  avar_out = [-1.2778e-13, 3.1859e-15; 3.1859e-15, -7.9430e-17];
714s  assert (nlogL, 3.242264755689906e+17, 1e-14);
714s  assert (acov, avar_out, 1e-3);
714s ***** test
714s  x = 1:50;
714s  [nlogL, acov] = evlike ([2.3, 1.2], x * 0.5);
714s  avar_out = [-7.6094e-05, 3.9819e-06; 3.9819e-06, -2.0836e-07];
714s  assert (nlogL, 481898704.0472211, 1e-6);
714s  assert (acov, avar_out, 1e-3);
714s ***** test
714s  x = 1:50;
714s  [nlogL, acov] = evlike ([21, 15], x);
714s  avar_out = [11.73913876598908, -5.9546128523121216; ...
714s              -5.954612852312121, 3.708060045170236];
714s  assert (nlogL, 223.7612479380652, 1e-13);
714s  assert (acov, avar_out, 1e-14);
714s ***** error<evlike: function called with too few input arguments.> evlike ([12, 15])
714s ***** error<evlike: wrong parameters length.> evlike ([12, 15, 3], [1:50])
714s ***** error<evlike: X must be a vector.> evlike ([12, 3], ones (10, 2))
714s ***** error<evlike: X and CENSOR vectors mismatch.> ...
714s  evlike ([12, 15], [1:50], [1, 2, 3])
714s ***** error<evlike: X and FREQ vectors mismatch.> ...
714s  evlike ([12, 15], [1:50], [], [1, 2, 3])
714s 8 tests, 8 passed, 0 known failure, 0 skipped
714s [inst/dist_fit/expfit.m]
714s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/expfit.m
714s ***** demo
714s  ## Sample 3 populations from 3 different exponential distibutions
714s  rande ("seed", 1);   # for reproducibility
714s  r1 = exprnd (2, 4000, 1);
714s  rande ("seed", 2);   # for reproducibility
714s  r2 = exprnd (5, 4000, 1);
714s  rande ("seed", 3);   # for reproducibility
714s  r3 = exprnd (12, 4000, 1);
714s  r = [r1, r2, r3];
714s 
714s  ## Plot them normalized and fix their colors
714s  hist (r, 48, 0.52);
714s  h = findobj (gca, "Type", "patch");
714s  set (h(1), "facecolor", "c");
714s  set (h(2), "facecolor", "g");
714s  set (h(3), "facecolor", "r");
714s  hold on
714s 
714s  ## Estimate their mu parameter
714s  muhat = expfit (r);
714s 
714s  ## Plot their estimated PDFs
714s  x = [0:max(r(:))];
714s  y = exppdf (x, muhat(1));
714s  plot (x, y, "-pr");
714s  y = exppdf (x, muhat(2));
714s  plot (x, y, "-sg");
714s  y = exppdf (x, muhat(3));
714s  plot (x, y, "-^c");
714s  ylim ([0, 0.6])
714s  xlim ([0, 40])
714s  legend ({"Normalized HIST of sample 1 with μ=2", ...
714s           "Normalized HIST of sample 2 with μ=5", ...
714s           "Normalized HIST of sample 3 with μ=12", ...
714s           sprintf("PDF for sample 1 with estimated μ=%0.2f", muhat(1)), ...
714s           sprintf("PDF for sample 2 with estimated μ=%0.2f", muhat(2)), ...
714s           sprintf("PDF for sample 3 with estimated μ=%0.2f", muhat(3))})
714s  title ("Three population samples from different exponential distibutions")
714s  hold off
714s ***** assert (expfit (1), 1)
714s ***** assert (expfit (1:3), 2)
714s ***** assert (expfit ([1:3]'), 2)
714s ***** assert (expfit (1:3, []), 2)
714s ***** assert (expfit (1:3, [], [], []), 2)
714s ***** assert (expfit (magic (3)), [5 5 5])
714s ***** assert (expfit (cat (3, magic (3), 2*magic (3))), cat (3,[5 5 5], [10 10 10]))
714s ***** assert (expfit (1:3, 0.1, [0 0 0], [1 1 1]), 2)
714s ***** assert (expfit ([1:3]', 0.1, [0 0 0]', [1 1 1]'), 2)
714s ***** assert (expfit (1:3, 0.1, [0 0 0]', [1 1 1]'), 2)
714s ***** assert (expfit (1:3, 0.1, [1 0 0], [1 1 1]), 3)
714s ***** assert (expfit (1:3, 0.1, [0 0 0], [4 1 1]), 1.5)
714s ***** assert (expfit (1:3, 0.1, [1 0 0], [4 1 1]), 4.5)
714s ***** assert (expfit (1:3, 0.1, [1 0 1], [4 1 1]), 9)
714s ***** assert (expfit (1:3, 0.1, [], [-1 1 1]), 4)
714s ***** assert (expfit (1:3, 0.1, [], [0.5 1 1]), 2.2)
714s ***** assert (expfit (1:3, 0.1, [1 1 1]), NaN)
714s ***** assert (expfit (1:3, 0.1, [], [0 0 0]), NaN)
714s ***** assert (expfit (reshape (1:9, [3 3])), [2 5 8])
714s ***** assert (expfit (reshape (1:9, [3 3]), [], eye(3)), [3 7.5 12])
714s ***** assert (expfit (reshape (1:9, [3 3]), [], 2*eye(3)), [3 7.5 12])
714s ***** assert (expfit (reshape (1:9, [3 3]), [], [], [2 2 2; 1 1 1; 1 1 1]), ...
714s  [1.75 4.75 7.75])
714s ***** assert (expfit (reshape (1:9, [3 3]), [], [], [2 2 2; 1 1 1; 1 1 1]), ...
714s  [1.75 4.75 7.75])
714s ***** assert (expfit (reshape (1:9, [3 3]), [], eye(3), [2 2 2; 1 1 1; 1 1 1]), ...
714s  [3.5 19/3 31/3])
714s ***** assert ([~,muci] = expfit (1:3, 0), [0; Inf])
714s ***** assert ([~,muci] = expfit (1:3, 2), [Inf; 0])
714s ***** assert ([~,muci] = expfit (1:3, 0.1, [1 1 1]), [NaN; NaN])
714s ***** assert ([~,muci] = expfit (1:3, 0.1, [], [0 0 0]), [NaN; NaN])
714s ***** assert ([~,muci] = expfit (1:3, -1), [NaN; NaN])
714s ***** assert ([~,muci] = expfit (1:3, 5), [NaN; NaN])
714s ***** assert ([~,muci] = expfit (1:3), [0.830485728373393; 9.698190330474096], ...
714s              1000*eps)
714s ***** assert ([~,muci] = expfit (1:3, 0.1), ...
714s                           [0.953017262058213; 7.337731146400207], 1000*eps)
714s ***** assert ([~,muci] = expfit ([1:3;2:4]), ...
714s              [0.538440777613095, 0.897401296021825, 1.256361814430554; ...
714s              12.385982973214016, 20.643304955356694, 28.900626937499371], ...
714s              1000*eps)
714s ***** assert ([~,muci] = expfit ([1:3;2:4], [], [1 1 1; 0 0 0]), ...
714s              100*[0.008132550920455, 0.013554251534091, 0.018975952147727; ...
714s              1.184936706156216, 1.974894510260360, 2.764852314364504], ...
714s              1000*eps)
714s ***** assert ([~,muci] = expfit ([1:3;2:4], [], [], [3 3 3; 1 1 1]), ...
714s              [0.570302756652583, 1.026544961974649, 1.482787167296715; ...
714s              4.587722594914109, 8.257900670845396, 11.928078746776684], ...
714s              1000*eps)
714s ***** assert ([~,muci] = expfit ([1:3;2:4], [], [0 0 0; 1 1 1], [3 3 3; 1 1 1]), ...
714s              [0.692071440311161, 1.245728592560089, 1.799385744809018; ...
714s              8.081825275395081, 14.547285495711145, 21.012745716027212], ...
714s              1000*eps)
714s ***** test
714s  x = reshape (1:8, [4 2]);
714s  x(4) = NaN;
714s  [muhat,muci] = expfit (x);
714s  assert ({muhat, muci}, {[NaN, 6.5], ...
714s          [NaN, 2.965574334593430;NaN, 23.856157493553368]}, 1000*eps);
714s ***** test
714s  x = magic (3);
714s  censor = [0 1 0; 0 1 0; 0 1 0];
714s  freq = [1 1 0; 1 1 0; 1 1 0];
714s  [muhat,muci] = expfit (x, [], censor, freq);
714s  assert ({muhat, muci}, {[5 NaN NaN], ...
714s                  [[2.076214320933482; 24.245475826185242],NaN(2)]}, 1000*eps);
714s ***** error expfit ()
714s ***** error expfit (1,2,3,4,5)
714s ***** error [a b censor] = expfit (1)
714s ***** error <ALPHA must be a scalar quantity> expfit (1, [1 2])
714s ***** error <X cannot be negative> expfit ([-1 2 3 4 5])
714s ***** error <CENSOR must be a numeric or logical array> expfit ([1:5], [], "test")
714s ***** error <FREQ must be a numeric or logical array> expfit ([1:5], [], [], "test")
714s ***** error <X and CENSOR vectors mismatch.> expfit ([1:5], [], [0 0 0 0])
714s ***** error <X and FREQ vectors mismatch.> expfit ([1:5], [], [], [1 1 1 1])
714s 47 tests, 47 passed, 0 known failure, 0 skipped
714s [inst/dist_fit/explike.m]
714s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/explike.m
714s ***** test
714s  x = 12;
714s  beta = 5;
714s  [L, V] = explike (beta, x);
714s  expected_L = 4.0094;
714s  expected_V = 6.5789;
714s  assert (L, expected_L, 0.001);
714s  assert (V, expected_V, 0.001);
714s ***** test
714s  x = 1:5;
714s  beta = 2;
714s  [L, V] = explike (beta, x);
714s  expected_L = 10.9657;
714s  expected_V = 0.4;
714s  assert (L, expected_L, 0.001);
714s  assert (V, expected_V, 0.001);
714s ***** error<explike: function called with too few input arguments.> explike ()
714s ***** error<explike: function called with too few input arguments.> explike (2)
714s ***** error<explike: MU must be a scalar.> explike ([12, 3], [1:50])
714s ***** error<explike: X must be a vector.> explike (3, ones (10, 2))
714s ***** error<explike: X and CENSOR vectors mismatch.> ...
714s  explike (3, [1:50], [1, 2, 3])
714s ***** error<explike: X and FREQ vectors mismatch.> ...
714s  explike (3, [1:50], [], [1, 2, 3])
714s 8 tests, 8 passed, 0 known failure, 0 skipped
714s [inst/dist_fit/gamfit.m]
714s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/gamfit.m
714s ***** demo
714s  ## Sample 3 populations from different Gamma distibutions
714s  randg ("seed", 5);    # for reproducibility
714s  r1 = gamrnd (1, 2, 2000, 1);
714s  randg ("seed", 2);    # for reproducibility
714s  r2 = gamrnd (2, 2, 2000, 1);
714s  randg ("seed", 7);    # for reproducibility
714s  r3 = gamrnd (7.5, 1, 2000, 1);
714s  r = [r1, r2, r3];
714s 
714s  ## Plot them normalized and fix their colors
714s  hist (r, 75, 4);
714s  h = findobj (gca, "Type", "patch");
714s  set (h(1), "facecolor", "c");
714s  set (h(2), "facecolor", "g");
714s  set (h(3), "facecolor", "r");
714s  ylim ([0, 0.62]);
714s  xlim ([0, 12]);
714s  hold on
714s 
714s  ## Estimate their α and β parameters
714s  a_bA = gamfit (r(:,1));
714s  a_bB = gamfit (r(:,2));
714s  a_bC = gamfit (r(:,3));
714s 
714s  ## Plot their estimated PDFs
714s  x = [0.01,0.1:0.2:18];
714s  y = gampdf (x, a_bA(1), a_bA(2));
714s  plot (x, y, "-pr");
714s  y = gampdf (x, a_bB(1), a_bB(2));
714s  plot (x, y, "-sg");
714s  y = gampdf (x, a_bC(1), a_bC(2));
714s  plot (x, y, "-^c");
714s  hold off
714s  legend ({"Normalized HIST of sample 1 with α=1 and β=2", ...
714s           "Normalized HIST of sample 2 with α=2 and β=2", ...
714s           "Normalized HIST of sample 3 with α=7.5 and β=1", ...
714s           sprintf("PDF for sample 1 with estimated α=%0.2f and β=%0.2f", ...
714s                   a_bA(1), a_bA(2)), ...
714s           sprintf("PDF for sample 2 with estimated α=%0.2f and β=%0.2f", ...
714s                   a_bB(1), a_bB(2)), ...
714s           sprintf("PDF for sample 3 with estimated α=%0.2f and β=%0.2f", ...
714s                   a_bC(1), a_bC(2))})
714s  title ("Three population samples from different Gamma distibutions")
714s  hold off
714s ***** shared x
714s  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];
714s ***** test
714s  [paramhat, paramci] = gamfit (x);
714s  assert (paramhat, [3.4248, 0.9752], 1e-4);
714s  assert (paramci, [1.7287, 0.4670; 6.7852, 2.0366], 1e-4);
714s ***** test
714s  [paramhat, paramci] = gamfit (x, 0.01);
714s  assert (paramhat, [3.4248, 0.9752], 1e-4);
714s  assert (paramci, [1.3945, 0.3705; 8.4113, 2.5668], 1e-4);
714s ***** test
714s  freq = [1 1 1 1 2 1 1 1 1 2 1 1 1 1 2];
714s  [paramhat, paramci] = gamfit (x, [], [], freq);
714s  assert (paramhat, [3.3025, 1.0615], 1e-4);
714s  assert (paramci, [1.7710, 0.5415; 6.1584, 2.0806], 1e-4);
714s ***** test
714s  [paramhat, paramci] = gamfit (x, [], [], [1:15]);
714s  assert (paramhat, [4.4484, 0.9689], 1e-4);
714s  assert (paramci, [3.4848, 0.7482; 5.6785, 1.2546], 1e-4);
714s ***** test
714s  [paramhat, paramci] = gamfit (x, 0.01, [], [1:15]);
714s  assert (paramhat, [4.4484, 0.9689], 1e-4);
714s  assert (paramci, [3.2275, 0.6899; 6.1312, 1.3608], 1e-4);
714s ***** test
714s  cens = [0 0 0 0 1 0 0 0 0 0 0 0 0 0 0];
714s  [paramhat, paramci] = gamfit (x, [], cens, [1:15]);
714s  assert (paramhat, [4.7537, 0.9308], 1e-4);
714s  assert (paramci, [3.7123, 0.7162; 6.0872, 1.2097], 1e-4);
714s ***** test
714s  cens = [0 0 0 0 1 0 0 0 0 0 0 0 0 0 0];
714s  freq = [1 1 1 1 2 1 1 1 1 2 1 1 1 1 2];
714s  [paramhat, paramci] = gamfit (x, [], cens, freq);
714s  assert (paramhat, [3.4736, 1.0847], 1e-4);
714s  assert (paramci, [1.8286, 0.5359; 6.5982, 2.1956], 1e-4);
715s ***** test
715s  [paramhat, paramci] = gamfit ([1 1 1 1 1 1]);
715s  assert (paramhat, [Inf, 0]);
715s  assert (paramci, [Inf, 0; Inf, 0]);
715s ***** test
715s  [paramhat, paramci] = gamfit ([1 1 1 1 1 1], [], [1 1 1 1 1 1]);
715s  assert (paramhat, [NaN, NaN]);
715s  assert (paramci, [NaN, NaN; NaN, NaN]);
715s ***** test
715s  [paramhat, paramci] = gamfit ([1 1 1 1 1 1], [], [], [1 1 1 1 1 1]);
715s  assert (paramhat, [Inf, 0]);
715s  assert (paramci, [Inf, 0; Inf, 0]);
715s ***** assert (class (gamfit (single (x))), "single")
715s ***** error<gamfit: X must be a vector.> gamfit (ones (2))
715s ***** error<gamfit: wrong value for ALPHA.> gamfit (x, 1)
715s ***** error<gamfit: wrong value for ALPHA.> gamfit (x, -1)
715s ***** error<gamfit: wrong value for ALPHA.> gamfit (x, {0.05})
715s ***** error<gamfit: wrong value for ALPHA.> gamfit (x, "a")
715s ***** error<gamfit: wrong value for ALPHA.> gamfit (x, i)
715s ***** error<gamfit: wrong value for ALPHA.> gamfit (x, [0.01 0.02])
715s ***** error<gamfit: X and FREQ vectors mismatch.>
715s  gamfit ([1 2 3], 0.05, [], [1 5])
715s ***** error<gamfit: FREQ must not contain negative values.>
715s  gamfit ([1 2 3], 0.05, [], [1 5 -1])
715s ***** error<gamfit: 'options' 5th argument must be a structure> ...
715s  gamfit ([1:10], 0.05, [], [], 5)
715s ***** error<gamfit: X cannot contain negative values.> gamfit ([1 2 3 -4])
715s ***** error<gamfit: X must contain positive values when censored.> ...
715s  gamfit ([1 2 0], [], [1 0 0])
715s 23 tests, 23 passed, 0 known failure, 0 skipped
715s [inst/dist_fit/gamlike.m]
715s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/gamlike.m
715s ***** test
715s  [nlogL, acov] = gamlike([2, 3], [2, 3, 4, 5, 6, 7, 8, 9]);
715s  assert (nlogL, 19.4426, 1e-4);
715s  assert (acov, [2.7819, -5.0073; -5.0073, 9.6882], 1e-4);
715s ***** test
715s  [nlogL, acov] = gamlike([2, 3], [5:45]);
715s  assert (nlogL, 305.8070, 1e-4);
715s  assert (acov, [0.0423, -0.0087; -0.0087, 0.0167], 1e-4);
715s ***** test
715s  [nlogL, acov] = gamlike([2, 13], [5:45]);
715s  assert (nlogL, 163.2261, 1e-4);
715s  assert (acov, [0.2362, -1.6631; -1.6631, 13.9440], 1e-4);
715s ***** error<gamlike: function called with too few input arguments.> ...
715s  gamlike ([12, 15])
715s ***** error<gamlike: wrong parameters length.> gamlike ([12, 15, 3], [1:50])
715s ***** error<gamlike: X must be a vector.> gamlike ([12, 3], ones (10, 2))
715s ***** error<gamlike: X and CENSOR vectors mismatch.> ...
715s  gamlike ([12, 15], [1:50], [1, 2, 3])
715s ***** error<gamlike: X and FREQ vectors mismatch.> ...
715s  gamlike ([12, 15], [1:50], [], [1, 2, 3])
715s 8 tests, 8 passed, 0 known failure, 0 skipped
715s [inst/dist_fit/geofit.m]
715s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/geofit.m
715s ***** demo
715s  ## Sample 2 populations from different geometric distibutions
715s  rande ("seed", 1);    # for reproducibility
715s  r1 = geornd (0.15, 1000, 1);
715s  rande ("seed", 2);    # for reproducibility
715s  r2 = geornd (0.5, 1000, 1);
715s  r = [r1, r2];
715s 
715s  ## Plot them normalized and fix their colors
715s  hist (r, 0:0.5:20.5, 1);
715s  h = findobj (gca, "Type", "patch");
715s  set (h(1), "facecolor", "c");
715s  set (h(2), "facecolor", "g");
715s  hold on
715s 
715s  ## Estimate their probability of success
715s  pshatA = geofit (r(:,1));
715s  pshatB = geofit (r(:,2));
715s 
715s  ## Plot their estimated PDFs
715s  x = [0:15];
715s  y = geopdf (x, pshatA);
715s  plot (x, y, "-pg");
715s  y = geopdf (x, pshatB);
715s  plot (x, y, "-sc");
715s  xlim ([0, 15])
715s  ylim ([0, 0.6])
715s  legend ({"Normalized HIST of sample 1 with ps=0.15", ...
715s           "Normalized HIST of sample 2 with ps=0.50", ...
715s           sprintf("PDF for sample 1 with estimated ps=%0.2f", ...
715s                   mean (pshatA)), ...
715s           sprintf("PDF for sample 2 with estimated ps=%0.2f", ...
715s                   mean (pshatB))})
715s  title ("Two population samples from different geometric distibutions")
715s  hold off
715s ***** test
715s  x = 0:5;
715s  [pshat, psci] = geofit (x);
715s  assert (pshat, 0.2857, 1e-4);
715s  assert (psci, [0.092499; 0.478929], 1e-5);
715s ***** test
715s  x = 0:5;
715s  [pshat, psci] = geofit (x, [], [1 1 1 1 1 1]);
715s  assert (pshat, 0.2857, 1e-4);
715s  assert (psci, [0.092499; 0.478929], 1e-5);
715s ***** assert (geofit ([1 1 2 3]), geofit ([1 2 3], [] ,[2 1 1]))
715s ***** error<geofit: function called with too few input arguments.> geofit ()
715s ***** error<geofit: X cannot have negative values.> geofit (-1, [1 2 3 3])
715s ***** error<geofit: wrong value for ALPHA.> geofit (1, 0)
715s ***** error<geofit: wrong value for ALPHA.> geofit (1, 1.2)
715s ***** error<geofit: wrong value for ALPHA.> geofit (1, [0.02 0.05])
715s ***** error<geofit: X and FREQ vector mismatch.> ...
715s  geofit ([1.5, 0.2], [], [0, 0, 0, 0, 0])
715s ***** error<geofit: X and FREQ vector mismatch.> ...
715s  geofit ([1.5, 0.2], [], [1, 1, 1])
715s 10 tests, 10 passed, 0 known failure, 0 skipped
715s [inst/dist_fit/gevfit.m]
715s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/gevfit.m
715s ***** demo
715s  ## Sample 2 populations from 2 different exponential distibutions
715s  rand ("seed", 1);   # for reproducibility
715s  r1 = gevrnd (-0.5, 1, 2, 5000, 1);
715s  rand ("seed", 2);   # for reproducibility
715s  r2 = gevrnd (0, 1, -4, 5000, 1);
715s  r = [r1, r2];
715s 
715s  ## Plot them normalized and fix their colors
715s  hist (r, 50, 5);
715s  h = findobj (gca, "Type", "patch");
715s  set (h(1), "facecolor", "c");
715s  set (h(2), "facecolor", "g");
715s  hold on
715s 
715s  ## Estimate their k, sigma, and mu parameters
715s  k_sigma_muA = gevfit (r(:,1));
715s  k_sigma_muB = gevfit (r(:,2));
715s 
715s  ## Plot their estimated PDFs
715s  x = [-10:0.5:20];
715s  y = gevpdf (x, k_sigma_muA(1), k_sigma_muA(2), k_sigma_muA(3));
715s  plot (x, y, "-pr");
715s  y = gevpdf (x, k_sigma_muB(1), k_sigma_muB(2), k_sigma_muB(3));
715s  plot (x, y, "-sg");
715s  ylim ([0, 0.7])
715s  xlim ([-7, 5])
715s  legend ({"Normalized HIST of sample 1 with k=-0.5, σ=1, μ=2", ...
715s           "Normalized HIST of sample 2 with k=0, σ=1, μ=-4",
715s      sprintf("PDF for sample 1 with estimated k=%0.2f, σ=%0.2f, μ=%0.2f", ...
715s                  k_sigma_muA(1), k_sigma_muA(2), k_sigma_muA(3)), ...
715s      sprintf("PDF for sample 3 with estimated k=%0.2f, σ=%0.2f, μ=%0.2f", ...
715s                  k_sigma_muB(1), k_sigma_muB(2), k_sigma_muB(3))})
715s  title ("Two population samples from different exponential distibutions")
715s  hold off
715s ***** test
715s  x = 1:50;
715s  [pfit, pci] = gevfit (x);
715s  pfit_out = [-0.4407, 15.1923, 21.5309];
715s  pci_out = [-0.7532, 11.5878, 16.5686; -0.1282, 19.9183, 26.4926];
715s  assert (pfit, pfit_out, 1e-3);
715s  assert (pci, pci_out, 1e-3);
715s ***** test
715s  x = 1:2:50;
715s  [pfit, pci] = gevfit (x);
715s  pfit_out = [-0.4434, 15.2024, 21.0532];
715s  pci_out = [-0.8904, 10.3439, 14.0168; 0.0035, 22.3429, 28.0896];
715s  assert (pfit, pfit_out, 1e-3);
715s  assert (pci, pci_out, 1e-3);
715s ***** error<gevfit: X must be a vector.> gevfit (ones (2,5));
715s ***** error<gevfit: wrong value for ALPHA.> gevfit ([1, 2, 3, 4, 5], 1.2);
715s ***** error<gevfit: wrong value for ALPHA.> gevfit ([1, 2, 3, 4, 5], 0);
715s ***** error<gevfit: wrong value for ALPHA.> gevfit ([1, 2, 3, 4, 5], "alpha");
715s ***** error<gevfit: X and FREQ vectors mismatch.> ...
715s  gevfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2]);
715s ***** error<gevfit: FREQ must not contain negative values.> ...
715s  gevfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2, -1]);
715s ***** error<gevfit: FREQ must contain integer values.> ...
715s  gevfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2, 1.5]);
715s ***** error<gevfit: 'options' argument must be a structure> ...
715s  gevfit ([1, 2, 3, 4, 5], 0.05, struct ("option", 234));
715s ***** error<gevfit: 'options' argument must be a structure> ...
715s  gevfit ([1, 2, 3, 4, 5], 0.05, ones (1,5), struct ("option", 234));
715s 11 tests, 11 passed, 0 known failure, 0 skipped
715s [inst/dist_fit/gevfit_lmom.m]
715s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/gevfit_lmom.m
715s ***** xtest <31070>
715s  data = 1:50;
715s  [pfit, pci] = gevfit_lmom (data);
715s  expected_p = [-0.28 15.01 20.22]';
715s  assert (pfit, expected_p, 0.1);
715s 1 test, 1 passed, 0 known failure, 0 skipped
715s [inst/dist_fit/gevlike.m]
715s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/gevlike.m
715s ***** test
715s  x = 1;
715s  k = 0.2;
715s  sigma = 0.3;
715s  mu = 0.5;
715s  [L, C] = gevlike ([k sigma mu], x);
715s  expected_L = 0.75942;
715s  expected_C = [-0.12547 1.77884 1.06731; 1.77884 16.40761 8.48877; 1.06731 8.48877 0.27979];
715s  assert (L, expected_L, 0.001);
715s  assert (C, inv (expected_C), 0.001);
715s ***** test
715s  x = 1;
715s  k = 0;
715s  sigma = 0.3;
715s  mu = 0.5;
715s  [L, C] = gevlike ([k sigma mu], x);
715s  expected_L = 0.65157;
715s  expected_C = [0.090036 3.41229 2.047337; 3.412229 24.760027 12.510190; 2.047337 12.510190 2.098618];
715s  assert (L, expected_L, 0.001);
715s  assert (C, inv (expected_C), 0.001);
715s ***** test
715s  x = -5:-1;
715s  k = -0.2;
715s  sigma = 0.3;
715s  mu = 0.5;
715s  [L, C] = gevlike ([k sigma mu], x);
715s  expected_L = 3786.4;
715s  expected_C = [1.6802e-07, 4.6110e-06, 8.7297e-05; ...
715s                4.6110e-06, 7.5693e-06, 1.2034e-05; ...
715s                8.7297e-05, 1.2034e-05, -0.0019125];
715s  assert (L, expected_L, -0.001);
715s  assert (C, expected_C, -0.001);
715s ***** test
715s  x = -5:0;
715s  k = -0.2;
715s  sigma = 0.3;
715s  mu = 0.5;
715s  [L, C] = gevlike ([k sigma mu], x, [1, 1, 1, 1, 1, 0]);
715s  expected_L = 3786.4;
715s  expected_C = [1.6802e-07, 4.6110e-06, 8.7297e-05; ...
715s                4.6110e-06, 7.5693e-06, 1.2034e-05; ...
715s                8.7297e-05, 1.2034e-05, -0.0019125];
715s  assert (L, expected_L, -0.001);
715s  assert (C, expected_C, -0.001);
715s ***** error<gevlike: function called with too few input arguments.> gevlike (3.25)
715s ***** error<gevlike: X must be a vector.> gevlike ([1, 2, 3], ones (2))
715s ***** error<gevlike: PARAMS must be a three-element vector.> ...
715s  gevlike ([1, 2], [1, 3, 5, 7])
715s ***** error<gevlike: PARAMS must be a three-element vector.> ...
715s  gevlike ([1, 2, 3, 4], [1, 3, 5, 7])
715s ***** error<gevlike: X and FREQ vectors mismatch.> ...
715s  gevlike ([5, 0.2, 1], ones (10, 1), ones (8,1))
715s ***** error<gevlike: FREQ must not contain negative values.> ...
715s  gevlike ([5, 0.2, 1], ones (1, 8), [1 1 1 1 1 1 1 -1])
715s ***** error<gevlike: FREQ must contain integer values.> ...
715s  gevlike ([5, 0.2, 1], ones (1, 8), [1 1 1 1 1 1 1 1.5])
715s 11 tests, 11 passed, 0 known failure, 0 skipped
715s [inst/dist_fit/gpfit.m]
715s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/gpfit.m
715s ***** demo
715s  ## Sample 2 populations from different generalized Pareto distibutions
715s  ## Assume location parameter θ is known
715s  theta = 0;
715s  rand ("seed", 5);    # for reproducibility
715s  r1 = gprnd (1, 2, theta, 20000, 1);
715s  rand ("seed", 2);    # for reproducibility
715s  r2 = gprnd (3, 1, theta, 20000, 1);
715s  r = [r1, r2];
715s 
715s  ## Plot them normalized and fix their colors
715s  hist (r, [0.1:0.2:100], 5);
715s  h = findobj (gca, "Type", "patch");
715s  set (h(1), "facecolor", "r");
715s  set (h(2), "facecolor", "c");
715s  ylim ([0, 1]);
715s  xlim ([0, 5]);
715s  hold on
715s 
715s  ## Estimate their α and β parameters
715s  k_sigmaA = gpfit (r(:,1), theta);
715s  k_sigmaB = gpfit (r(:,2), theta);
715s 
715s  ## Plot their estimated PDFs
715s  x = [0.01, 0.1:0.2:18];
715s  y = gppdf (x, k_sigmaA(1), k_sigmaA(2), theta);
715s  plot (x, y, "-pc");
715s  y = gppdf (x, k_sigmaB(1), k_sigmaB(2), theta);
715s  plot (x, y, "-sr");
715s  hold off
715s  legend ({"Normalized HIST of sample 1 with k=1 and σ=2", ...
715s           "Normalized HIST of sample 2 with k=2 and σ=2", ...
715s           sprintf("PDF for sample 1 with estimated k=%0.2f and σ=%0.2f", ...
715s                   k_sigmaA(1), k_sigmaA(2)), ...
715s           sprintf("PDF for sample 3 with estimated k=%0.2f and σ=%0.2f", ...
715s                   k_sigmaB(1), k_sigmaB(2))})
715s  title ("Three population samples from different generalized Pareto distibutions")
715s  text (2, 0.7, "Known location parameter θ = 0")
715s  hold off
715s ***** test
715s  k = 0.8937; sigma = 1.3230; theta = 1;
715s  x = [2.2196, 11.9301, 4.3673, 1.0949, 6.5626, ...
715s       1.2109, 1.8576, 1.0039, 12.7917, 2.2590];
715s  [hat, ci] = gpfit (x, theta);
715s  assert (hat, [k, sigma, theta], 1e-4);
715s  assert (ci, [-0.7750, 0.2437, 1; 2.5624, 7.1820, 1], 1e-4);
715s ***** error<gpfit: function called with too few input arguments.> gpfit ()
715s ***** error<gpfit: function called with too few input arguments.> gpfit (1)
715s ***** error<gpfit: X must be a vector of real values.> gpfit ([0.2, 0.5+i], 0);
715s ***** error<gpfit: X must be a vector of real values.> gpfit (ones (2,2) * 0.5, 0);
715s ***** error<gpfit: THETA must be a real scalar value.> ...
715s  gpfit ([0.5, 1.2], [0, 1]);
715s ***** error<gpfit: THETA must be a real scalar value.> ...
715s  gpfit ([0.5, 1.2], 5+i);
715s ***** error<gpfit: X cannot contain values less than THETA.> ...
715s  gpfit ([1:5], 2);
715s ***** error<gpfit: wrong value for ALPHA.> gpfit ([0.01:0.1:0.99], 0, 1.2);
715s ***** error<gpfit: wrong value for ALPHA.> gpfit ([0.01:0.1:0.99], 0, i);
715s ***** error<gpfit: wrong value for ALPHA.> gpfit ([0.01:0.1:0.99], 0, -1);
715s ***** error<gpfit: wrong value for ALPHA.> gpfit ([0.01:0.1:0.99], 0, [0.05, 0.01]);
715s ***** error<gpfit: X and FREQ vectors mismatch.>
715s  gpfit ([1 2 3], 0, [], [1 5])
715s ***** error<gpfit: FREQ must not contain negative values.>
715s  gpfit ([1 2 3], 0, [], [1 5 -1])
715s ***** error<gpfit: 'options' 5th argument must be a structure> ...
715s  gpfit ([1:10], 1, 0.05, [], 5)
715s 15 tests, 15 passed, 0 known failure, 0 skipped
715s [inst/dist_fit/gplike.m]
715s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/gplike.m
715s ***** test
715s  k = 0.8937; sigma = 1.3230; theta = 1;
715s  x = [2.2196, 11.9301, 4.3673, 1.0949, 6.5626, ...
715s       1.2109, 1.8576, 1.0039, 12.7917, 2.2590];
715s  [nlogL, acov] = gplike ([k, sigma, theta], x);
715s  assert (nlogL, 21.736, 1e-3);
715s  assert (acov, [0.7249, -0.7351, 0; -0.7351, 1.3040, 0; 0, 0, 0], 1e-4);
715s ***** assert (gplike ([2, 3, 0], 4), 3.047536764863501, 1e-14)
715s ***** assert (gplike ([2, 3, 4], 8), 3.047536764863501, 1e-14)
715s ***** assert (gplike ([1, 2, 0], 4), 2.890371757896165, 1e-14)
715s ***** assert (gplike ([1, 2, 4], 8), 2.890371757896165, 1e-14)
715s ***** assert (gplike ([2, 3, 0], [1:10]), 32.57864322725392, 1e-14)
715s ***** assert (gplike ([2, 3, 2], [1:10] + 2), 32.57864322725392, 1e-14)
715s ***** assert (gplike ([2, 3, 0], [1:10], ones (1,10)), 32.57864322725392, 1e-14)
715s ***** assert (gplike ([1, 2, 0], [1:10]), 31.65666282460443, 1e-14)
715s ***** assert (gplike ([1, 2, 3], [1:10] + 3), 31.65666282460443, 1e-14)
715s ***** assert (gplike ([1, 2, 0], [1:10], ones (1,10)), 31.65666282460443, 1e-14)
715s ***** assert (gplike ([1, NaN, 0], [1:10]), NaN)
715s ***** error<gplike: function called with too few input arguments.> gplike ()
715s ***** error<gplike: function called with too few input arguments.> gplike (1)
715s ***** error<gplike: X must be a vector.> gplike ([1, 2, 0], [])
715s ***** error<gplike: X must be a vector.> gplike ([1, 2, 0], ones (2))
715s ***** error<gplike: PARAMS must be a three-element vector.> gplike (2, [1:10])
715s ***** error<gplike: PARAMS must be a three-element vector.> gplike ([2, 3], [1:10])
715s ***** error<gplike: X and FREQ vectors mismatch.> ...
715s  gplike ([1, 2, 0], ones (10, 1), ones (8,1))
715s ***** error<gplike: FREQ must not contain negative values.> ...
715s  gplike ([1, 2, 0], ones (1, 8), [1 1 1 1 1 1 1 -1])
715s 20 tests, 20 passed, 0 known failure, 0 skipped
715s [inst/dist_fit/gumbelfit.m]
715s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/gumbelfit.m
715s ***** demo
715s  ## Sample 3 populations from different Gumbel distibutions
715s  rand ("seed", 1);    # for reproducibility
715s  r1 = gumbelrnd (2, 5, 400, 1);
715s  rand ("seed", 11);    # for reproducibility
715s  r2 = gumbelrnd (-5, 3, 400, 1);
715s  rand ("seed", 16);    # for reproducibility
715s  r3 = gumbelrnd (14, 8, 400, 1);
715s  r = [r1, r2, r3];
715s 
715s  ## Plot them normalized and fix their colors
715s  hist (r, 25, 0.32);
715s  h = findobj (gca, "Type", "patch");
715s  set (h(1), "facecolor", "c");
715s  set (h(2), "facecolor", "g");
715s  set (h(3), "facecolor", "r");
715s  ylim ([0, 0.28])
715s  xlim ([-11, 50]);
715s  hold on
715s 
715s  ## Estimate their MU and BETA parameters
715s  mu_betaA = gumbelfit (r(:,1));
715s  mu_betaB = gumbelfit (r(:,2));
715s  mu_betaC = gumbelfit (r(:,3));
715s 
715s  ## Plot their estimated PDFs
715s  x = [min(r(:)):max(r(:))];
715s  y = gumbelpdf (x, mu_betaA(1), mu_betaA(2));
715s  plot (x, y, "-pr");
715s  y = gumbelpdf (x, mu_betaB(1), mu_betaB(2));
715s  plot (x, y, "-sg");
715s  y = gumbelpdf (x, mu_betaC(1), mu_betaC(2));
715s  plot (x, y, "-^c");
715s  legend ({"Normalized HIST of sample 1 with μ=2 and β=5", ...
715s           "Normalized HIST of sample 2 with μ=-5 and β=3", ...
715s           "Normalized HIST of sample 3 with μ=14 and β=8", ...
715s           sprintf("PDF for sample 1 with estimated μ=%0.2f and β=%0.2f", ...
715s                   mu_betaA(1), mu_betaA(2)), ...
715s           sprintf("PDF for sample 2 with estimated μ=%0.2f and β=%0.2f", ...
715s                   mu_betaB(1), mu_betaB(2)), ...
715s           sprintf("PDF for sample 3 with estimated μ=%0.2f and β=%0.2f", ...
715s                   mu_betaC(1), mu_betaC(2))})
715s  title ("Three population samples from different Gumbel distibutions")
715s  hold off
715s ***** test
715s  x = 1:50;
715s  [paramhat, paramci] = gumbelfit (x);
715s  paramhat_out = [18.3188, 13.0509];
715s  paramci_out = [14.4882, 10.5294; 22.1495, 16.1763];
715s  assert (paramhat, paramhat_out, 1e-4);
715s  assert (paramci, paramci_out, 1e-4);
715s ***** test
715s  x = 1:50;
715s  [paramhat, paramci] = gumbelfit (x, 0.01);
715s  paramci_out = [13.2845, 9.8426; 23.3532, 17.3051];
715s  assert (paramci, paramci_out, 1e-4);
715s ***** error<gumbelfit: X must be a double-precision vector.> gumbelfit (ones (2,5));
715s ***** error<gumbelfit: X must be a double-precision vector.> ...
715s  gumbelfit (single (ones (1,5)));
715s ***** error<gumbelfit: X must NOT contain missing values> ...
715s  gumbelfit ([1, 2, 3, 4, NaN]);
715s ***** error<gumbelfit: wrong value for ALPHA.> gumbelfit ([1, 2, 3, 4, 5], 1.2);
715s ***** error<gumbelfit: X and CENSOR vectors mismatch.> ...
715s  gumbelfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
715s ***** error<gumbelfit: X and FREQ vectors mismatch.> ...
715s  gumbelfit ([1, 2, 3, 4, 5], 0.05, [], [1 1 0]);
715s ***** error<gamfit: FREQ must not contain negative values.>
715s  gamfit ([1, 2, 3], 0.05, [], [1 5 -1])
715s ***** error<gumbelfit: 'options' 5th argument> ...
715s  gumbelfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
715s 10 tests, 10 passed, 0 known failure, 0 skipped
715s [inst/dist_fit/gumbellike.m]
715s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/gumbellike.m
715s ***** test
715s  x = 1:50;
715s  [nlogL, avar] = gumbellike ([2.3, 1.2], x);
715s  avar_out = [-1.2778e-13, 3.1859e-15; 3.1859e-15, -7.9430e-17];
715s  assert (nlogL, 3.242264755689906e+17, 1e-14);
715s  assert (avar, avar_out, 1e-3);
715s ***** test
715s  x = 1:50;
715s  [nlogL, avar] = gumbellike ([2.3, 1.2], x * 0.5);
715s  avar_out = [-7.6094e-05, 3.9819e-06; 3.9819e-06, -2.0836e-07];
715s  assert (nlogL, 481898704.0472211, 1e-6);
715s  assert (avar, avar_out, 1e-3);
715s ***** test
715s  x = 1:50;
715s  [nlogL, avar] = gumbellike ([21, 15], x);
715s  avar_out = [11.73913876598908, -5.9546128523121216; ...
715s              -5.954612852312121, 3.708060045170236];
715s  assert (nlogL, 223.7612479380652, 1e-13);
715s  assert (avar, avar_out, 1e-14);
715s ***** error<gumbellike: too few input arguments.> gumbellike ([12, 15]);
715s ***** error<gumbellike: wrong parameters length.> gumbellike ([12, 15, 3], [1:50]);
715s ***** error<gumbellike: X must be a vector.> gumbellike ([12, 3], ones (10, 2));
715s ***** error<gumbellike: X and CENSOR> gumbellike ([12, 15], [1:50], [1, 2, 3]);
715s ***** error<gumbellike: X and FREQ> gumbellike ([12, 15], [1:50], [], [1, 2, 3]);
715s 8 tests, 8 passed, 0 known failure, 0 skipped
715s [inst/dist_fit/hnfit.m]
715s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/hnfit.m
715s ***** demo
715s  ## Sample 2 populations from different half-normal distibutions
715s  rand ("seed", 1);   # for reproducibility
715s  r1 = hnrnd (0, 5, 5000, 1);
715s  rand ("seed", 2);   # for reproducibility
715s  r2 = hnrnd (0, 2, 5000, 1);
715s  r = [r1, r2];
715s 
715s  ## Plot them normalized and fix their colors
715s  hist (r, [0.5:20], 1);
715s  h = findobj (gca, "Type", "patch");
715s  set (h(1), "facecolor", "c");
715s  set (h(2), "facecolor", "g");
715s  hold on
715s 
715s  ## Estimate their shape parameters
715s  mu_sigmaA = hnfit (r(:,1), 0);
715s  mu_sigmaB = hnfit (r(:,2), 0);
715s 
715s  ## Plot their estimated PDFs
715s  x = [0:0.2:10];
715s  y = hnpdf (x, mu_sigmaA(1), mu_sigmaA(2));
715s  plot (x, y, "-pr");
715s  y = hnpdf (x, mu_sigmaB(1), mu_sigmaB(2));
715s  plot (x, y, "-sg");
715s  xlim ([0, 10])
715s  ylim ([0, 0.5])
715s  legend ({"Normalized HIST of sample 1 with μ=0 and σ=5", ...
715s           "Normalized HIST of sample 2 with μ=0 and σ=2", ...
715s           sprintf("PDF for sample 1 with estimated μ=%0.2f and σ=%0.2f", ...
715s                   mu_sigmaA(1), mu_sigmaA(2)), ...
715s           sprintf("PDF for sample 2 with estimated μ=%0.2f and σ=%0.2f", ...
715s                   mu_sigmaB(1), mu_sigmaB(2))})
715s  title ("Two population samples from different half-normal distibutions")
715s  hold off
715s ***** test
715s  x = 1:20;
715s  [paramhat, paramci] = hnfit (x, 0);
715s  assert (paramhat, [0, 11.9791], 1e-4);
715s  assert (paramci, [0, 9.1648; 0, 17.2987], 1e-4);
715s ***** test
715s  x = 1:20;
715s  [paramhat, paramci] = hnfit (x, 0, 0.01);
715s  assert (paramci, [0, 8.4709; 0, 19.6487], 1e-4);
715s ***** error<hnfit: function called with too few input arguments.> hnfit ()
715s ***** error<hnfit: function called with too few input arguments.> hnfit (1)
715s ***** error<hnfit: X must be a vector of real values.> hnfit ([0.2, 0.5+i], 0);
715s ***** error<hnfit: X must be a vector of real values.> hnfit (ones (2,2) * 0.5, 0);
715s ***** error<hnfit: MU must be a real scalar value.> ...
715s  hnfit ([0.5, 1.2], [0, 1]);
715s ***** error<hnfit: MU must be a real scalar value.> ...
715s  hnfit ([0.5, 1.2], 5+i);
715s ***** error<hnfit: X cannot contain values less than MU.> ...
715s  hnfit ([1:5], 2);
715s ***** error<hnfit: wrong value for ALPHA.> hnfit ([0.01:0.1:0.99], 0, 1.2);
715s ***** error<hnfit: wrong value for ALPHA.> hnfit ([0.01:0.1:0.99], 0, i);
715s ***** error<hnfit: wrong value for ALPHA.> hnfit ([0.01:0.1:0.99], 0, -1);
715s ***** error<hnfit: wrong value for ALPHA.> hnfit ([0.01:0.1:0.99], 0, [0.05, 0.01]);
715s ***** error<hnfit: X and FREQ vectors mismatch.>
715s  hnfit ([1 2 3], 0, [], [1 5])
715s ***** error<hnfit: FREQ must not contain negative values.>
715s  hnfit ([1 2 3], 0, [], [1 5 -1])
715s 15 tests, 15 passed, 0 known failure, 0 skipped
715s [inst/dist_fit/hnlike.m]
715s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/hnlike.m
715s ***** test
715s  x = 1:20;
715s  paramhat = hnfit (x, 0);
715s  [nlogL, acov] = hnlike (paramhat, x);
715s  assert (nlogL, 64.179177404891300, 1e-14);
715s ***** test
715s  x = 1:20;
715s  paramhat = hnfit (x, 0);
715s  [nlogL, acov] = hnlike (paramhat, x, ones (1, 20));
715s  assert (nlogL, 64.179177404891300, 1e-14);
715s ***** error<hnlike: function called with too few input arguments.> ...
715s  hnlike ([12, 15]);
715s ***** error<hnlike: wrong parameters length.> hnlike ([12, 15, 3], [1:50]);
715s ***** error<hnlike: wrong parameters length.> hnlike ([3], [1:50]);
715s ***** error<hnlike: X must be a vector of real values.> ...
715s  hnlike ([0, 3], ones (2));
715s ***** error<hnlike: X must be a vector of real values.> ...
715s  hnlike ([0, 3], [1, 2, 3, 4, 5+i]);
715s ***** error<hnlike: X and FREQ vectors mismatch.> ...
715s  hnlike ([1, 2], ones (10, 1), ones (8,1))
715s ***** error<hnlike: FREQ must not contain negative values.> ...
715s  hnlike ([1, 2], ones (1, 8), [1 1 1 1 1 1 1 -1])
715s 9 tests, 9 passed, 0 known failure, 0 skipped
715s [inst/dist_fit/invgfit.m]
715s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/invgfit.m
715s ***** demo
715s  ## Sample 3 populations from different inverse Gaussian distibutions
715s  rand ("seed", 5); randn ("seed", 5);   # for reproducibility
715s  r1 = invgrnd (1, 0.2, 2000, 1);
715s  rand ("seed", 2); randn ("seed", 2);   # for reproducibility
715s  r2 = invgrnd (1, 3, 2000, 1);
715s  rand ("seed", 7); randn ("seed", 7);   # for reproducibility
715s  r3 = invgrnd (3, 1, 2000, 1);
715s  r = [r1, r2, r3];
715s 
715s  ## Plot them normalized and fix their colors
715s  hist (r, [0.1:0.1:3.2], 9);
715s  h = findobj (gca, "Type", "patch");
715s  set (h(1), "facecolor", "c");
715s  set (h(2), "facecolor", "g");
715s  set (h(3), "facecolor", "r");
715s  ylim ([0, 3]);
715s  xlim ([0, 3]);
715s  hold on
715s 
715s  ## Estimate their MU and LAMBDA parameters
715s  mu_lambdaA = invgfit (r(:,1));
715s  mu_lambdaB = invgfit (r(:,2));
715s  mu_lambdaC = invgfit (r(:,3));
715s 
715s  ## Plot their estimated PDFs
715s  x = [0:0.1:3];
715s  y = invgpdf (x, mu_lambdaA(1), mu_lambdaA(2));
715s  plot (x, y, "-pr");
715s  y = invgpdf (x, mu_lambdaB(1), mu_lambdaB(2));
715s  plot (x, y, "-sg");
715s  y = invgpdf (x, mu_lambdaC(1), mu_lambdaC(2));
715s  plot (x, y, "-^c");
715s  hold off
715s  legend ({"Normalized HIST of sample 1 with μ=1 and λ=0.5", ...
715s           "Normalized HIST of sample 2 with μ=2 and λ=0.3", ...
715s           "Normalized HIST of sample 3 with μ=4 and λ=0.5", ...
715s           sprintf("PDF for sample 1 with estimated μ=%0.2f and λ=%0.2f", ...
715s                   mu_lambdaA(1), mu_lambdaA(2)), ...
715s           sprintf("PDF for sample 2 with estimated μ=%0.2f and λ=%0.2f", ...
715s                   mu_lambdaB(1), mu_lambdaB(2)), ...
715s           sprintf("PDF for sample 3 with estimated μ=%0.2f and λ=%0.2f", ...
715s                   mu_lambdaC(1), mu_lambdaC(2))})
715s  title ("Three population samples from different inverse Gaussian distibutions")
715s  hold off
715s ***** test
715s  paramhat = invgfit ([1:50]);
715s  paramhat_out = [25.5, 19.6973];
715s  assert (paramhat, paramhat_out, 1e-4);
715s ***** test
715s  paramhat = invgfit ([1:5]);
715s  paramhat_out = [3, 8.1081];
715s  assert (paramhat, paramhat_out, 1e-4);
715s ***** error<invgfit: X must be a vector.> invgfit (ones (2,5));
715s ***** error<invgfit: X must contain only positive values.> invgfit ([-1 2 3 4]);
715s ***** error<invgfit: wrong value for ALPHA.> invgfit ([1, 2, 3, 4, 5], 1.2);
715s ***** error<invgfit: wrong value for ALPHA.> invgfit ([1, 2, 3, 4, 5], 0);
715s ***** error<invgfit: wrong value for ALPHA.> invgfit ([1, 2, 3, 4, 5], "alpha");
715s ***** error<invgfit: X and CENSOR vectors mismatch.> ...
715s  invgfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
715s ***** error<invgfit: X and CENSOR vectors mismatch.> ...
715s  invgfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
715s ***** error<invgfit: X and FREQ vectors mismatch.> ...
715s  invgfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
715s ***** error<invgfit: X and FREQ vectors mismatch.> ...
715s  invgfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
715s ***** error<invgfit: 'options' 5th argument must be a structure> ...
715s  invgfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
715s 12 tests, 12 passed, 0 known failure, 0 skipped
715s [inst/dist_fit/invglike.m]
715s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/invglike.m
715s ***** test
715s  nlogL = invglike ([25.5, 19.6973], [1:50]);
715s  assert (nlogL, 219.1516, 1e-4);
715s ***** test
715s  nlogL = invglike ([3, 8.1081], [1:5]);
715s  assert (nlogL, 9.0438, 1e-4);
715s ***** error<invglike: function called with too few input arguments.> invglike (3.25)
715s ***** error<invglike: X must be a vector.> invglike ([5, 0.2], ones (2))
715s ***** error<invglike: X must have positive values.> invglike ([5, 0.2], [-1, 3])
715s ***** error<invglike: PARAMS must be a two-element vector.> ...
715s  invglike ([1, 0.2, 3], [1, 3, 5, 7])
715s ***** error<invglike: X and CENSOR vector mismatch.> ...
715s  invglike ([1.5, 0.2], [1:5], [0, 0, 0])
715s ***** error<invglike: X and FREQ vector mismatch.> ...
715s  invglike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
715s ***** error<invglike: X and FREQ vector mismatch.> ...
715s  invglike ([1.5, 0.2], [1:5], [], [1, 1, 1])
715s 9 tests, 9 passed, 0 known failure, 0 skipped
715s [inst/dist_fit/logifit.m]
716s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/logifit.m
716s ***** demo
716s  ## Sample 3 populations from different logistic distibutions
716s  rand ("seed", 5)  # for reproducibility
716s  r1 = logirnd (2, 1, 2000, 1);
716s  rand ("seed", 2)   # for reproducibility
716s  r2 = logirnd (5, 2, 2000, 1);
716s  rand ("seed", 7)   # for reproducibility
716s  r3 = logirnd (9, 4, 2000, 1);
716s  r = [r1, r2, r3];
716s 
716s  ## Plot them normalized and fix their colors
716s  hist (r, [-6:20], 1);
716s  h = findobj (gca, "Type", "patch");
716s  set (h(1), "facecolor", "c");
716s  set (h(2), "facecolor", "g");
716s  set (h(3), "facecolor", "r");
716s  ylim ([0, 0.3]);
716s  xlim ([-5, 20]);
716s  hold on
716s 
716s  ## Estimate their MU and LAMBDA parameters
716s  mu_sA = logifit (r(:,1));
716s  mu_sB = logifit (r(:,2));
716s  mu_sC = logifit (r(:,3));
716s 
716s  ## Plot their estimated PDFs
716s  x = [-5:0.5:20];
716s  y = logipdf (x, mu_sA(1), mu_sA(2));
716s  plot (x, y, "-pr");
716s  y = logipdf (x, mu_sB(1), mu_sB(2));
716s  plot (x, y, "-sg");
716s  y = logipdf (x, mu_sC(1), mu_sC(2));
716s  plot (x, y, "-^c");
716s  hold off
716s  legend ({"Normalized HIST of sample 1 with μ=1 and s=0.5", ...
716s           "Normalized HIST of sample 2 with μ=2 and s=0.3", ...
716s           "Normalized HIST of sample 3 with μ=4 and s=0.5", ...
716s           sprintf("PDF for sample 1 with estimated μ=%0.2f and s=%0.2f", ...
716s                   mu_sA(1), mu_sA(2)), ...
716s           sprintf("PDF for sample 2 with estimated μ=%0.2f and s=%0.2f", ...
716s                   mu_sB(1), mu_sB(2)), ...
716s           sprintf("PDF for sample 3 with estimated μ=%0.2f and s=%0.2f", ...
716s                   mu_sC(1), mu_sC(2))})
716s  title ("Three population samples from different logistic distibutions")
716s  hold off
716s ***** test
716s  paramhat = logifit ([1:50]);
716s  paramhat_out = [25.5, 8.7724];
716s  assert (paramhat, paramhat_out, 1e-4);
716s ***** test
716s  paramhat = logifit ([1:5]);
716s  paramhat_out = [3, 0.8645];
716s  assert (paramhat, paramhat_out, 1e-4);
716s ***** test
716s  paramhat = logifit ([1:6], [], [], [1 1 1 1 1 0]);
716s  paramhat_out = [3, 0.8645];
716s  assert (paramhat, paramhat_out, 1e-4);
716s ***** test
716s  paramhat = logifit ([1:5], [], [], [1 1 1 1 2]);
716s  paramhat_out = logifit ([1:5, 5]);
716s  assert (paramhat, paramhat_out, 1e-4);
716s ***** error<logifit: X must be a vector.> logifit (ones (2,5));
716s ***** error<logifit: wrong value for ALPHA.> logifit ([1, 2, 3, 4, 5], 1.2);
716s ***** error<logifit: wrong value for ALPHA.> logifit ([1, 2, 3, 4, 5], 0);
716s ***** error<logifit: wrong value for ALPHA.> logifit ([1, 2, 3, 4, 5], "alpha");
716s ***** error<logifit: X and CENSOR vectors mismatch.> ...
716s  logifit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
716s ***** error<logifit: X and CENSOR vectors mismatch.> ...
716s  logifit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
716s ***** error<logifit: X and FREQ vectors mismatch.> ...
716s  logifit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
716s ***** error<logifit: X and FREQ vectors mismatch.> ...
716s  logifit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
716s ***** error<logifit: 'options' 5th argument must be a structure> ...
716s  logifit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
716s 13 tests, 13 passed, 0 known failure, 0 skipped
716s [inst/dist_fit/logilike.m]
716s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/logilike.m
716s ***** test
716s  nlogL = logilike ([25.5, 8.7725], [1:50]);
716s  assert (nlogL, 206.6769, 1e-4);
716s ***** test
716s  nlogL = logilike ([3, 0.8645], [1:5]);
716s  assert (nlogL, 9.0699, 1e-4);
716s ***** error<logilike: function called with too few input arguments.> logilike (3.25)
716s ***** error<logilike: X must be a vector.> logilike ([5, 0.2], ones (2))
716s ***** error<logilike: PARAMS must be a two-element vector.> ...
716s  logilike ([1, 0.2, 3], [1, 3, 5, 7])
716s ***** error<logilike: X and CENSOR vector mismatch.> ...
716s  logilike ([1.5, 0.2], [1:5], [0, 0, 0])
716s ***** error<logilike: X and FREQ vector mismatch.> ...
716s  logilike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
716s ***** error<logilike: X and FREQ vector mismatch.> ...
716s  logilike ([1.5, 0.2], [1:5], [], [1, 1, 1])
716s 8 tests, 8 passed, 0 known failure, 0 skipped
716s [inst/dist_fit/loglfit.m]
716s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/loglfit.m
716s ***** demo
716s  ## Sample 3 populations from different log-logistic distibutions
716s  rand ("seed", 5)  # for reproducibility
716s  r1 = loglrnd (0, 1, 2000, 1);
716s  rand ("seed", 2)   # for reproducibility
716s  r2 = loglrnd (0, 0.5, 2000, 1);
716s  rand ("seed", 7)   # for reproducibility
716s  r3 = loglrnd (0, 0.125, 2000, 1);
716s  r = [r1, r2, r3];
716s 
716s  ## Plot them normalized and fix their colors
716s  hist (r, [0.05:0.1:2.5], 10);
716s  h = findobj (gca, "Type", "patch");
716s  set (h(1), "facecolor", "c");
716s  set (h(2), "facecolor", "g");
716s  set (h(3), "facecolor", "r");
716s  ylim ([0, 3.5]);
716s  xlim ([0, 2.0]);
716s  hold on
716s 
716s  ## Estimate their MU and LAMBDA parameters
716s  a_bA = loglfit (r(:,1));
716s  a_bB = loglfit (r(:,2));
716s  a_bC = loglfit (r(:,3));
716s 
716s  ## Plot their estimated PDFs
716s  x = [0.01:0.1:2.01];
716s  y = loglpdf (x, a_bA(1), a_bA(2));
716s  plot (x, y, "-pr");
716s  y = loglpdf (x, a_bB(1), a_bB(2));
716s  plot (x, y, "-sg");
716s  y = loglpdf (x, a_bC(1), a_bC(2));
716s  plot (x, y, "-^c");
716s  legend ({"Normalized HIST of sample 1 with α=1 and β=1", ...
716s           "Normalized HIST of sample 2 with α=1 and β=2", ...
716s           "Normalized HIST of sample 3 with α=1 and β=8", ...
716s           sprintf("PDF for sample 1 with estimated α=%0.2f and β=%0.2f", ...
716s                   a_bA(1), a_bA(2)), ...
716s           sprintf("PDF for sample 2 with estimated α=%0.2f and β=%0.2f", ...
716s                   a_bB(1), a_bB(2)), ...
716s           sprintf("PDF for sample 3 with estimated α=%0.2f and β=%0.2f", ...
716s                   a_bC(1), a_bC(2))})
716s  title ("Three population samples from different log-logistic distibutions")
716s  hold off
716s ***** test
716s  [paramhat, paramci] = loglfit ([1:50]);
716s  paramhat_out = [3.09717, 0.468525];
716s  paramci_out = [2.87261, 0.370616; 3.32174, 0.5923];
716s  assert (paramhat, paramhat_out, 1e-5);
716s  assert (paramci, paramci_out, 1e-5);
716s ***** test
716s  paramhat = loglfit ([1:5]);
716s  paramhat_out = [1.01124, 0.336449];
716s  assert (paramhat, paramhat_out, 1e-5);
716s ***** test
716s  paramhat = loglfit ([1:6], [], [], [1 1 1 1 1 0]);
716s  paramhat_out = [1.01124, 0.336449];
716s  assert (paramhat, paramhat_out, 1e-4);
716s ***** test
716s  paramhat = loglfit ([1:5], [], [], [1 1 1 1 2]);
716s  paramhat_out = loglfit ([1:5, 5]);
716s  assert (paramhat, paramhat_out, 1e-4);
716s ***** error<loglfit: X must be a vector.> loglfit (ones (2,5));
716s ***** error<loglfit: wrong value for ALPHA.> loglfit ([1, 2, 3, 4, 5], 1.2);
716s ***** error<loglfit: wrong value for ALPHA.> loglfit ([1, 2, 3, 4, 5], 0);
716s ***** error<loglfit: wrong value for ALPHA.> loglfit ([1, 2, 3, 4, 5], "alpha");
716s ***** error<loglfit: X and CENSOR vectors mismatch.> ...
716s  loglfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
716s ***** error<loglfit: X and CENSOR vectors mismatch.> ...
716s  loglfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
716s ***** error<loglfit: X and FREQ vectors mismatch.> ...
716s  loglfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
716s ***** error<loglfit: X and FREQ vectors mismatch.> ...
716s  loglfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
716s ***** error<loglfit: 'options' 5th argument must be a structure> ...
716s  loglfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
716s 13 tests, 13 passed, 0 known failure, 0 skipped
716s [inst/dist_fit/logllike.m]
716s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/logllike.m
716s ***** test
716s  [nlogL, acov] = logllike ([3.09717, 0.468525], [1:50]);
716s  assert (nlogL, 211.2965, 1e-4);
716s  assert (acov, [0.0131, -0.0007; -0.0007, 0.0031], 1e-4);
716s ***** test
716s  [nlogL, acov] = logllike ([1.01124, 0.336449], [1:5]);
716s  assert (nlogL, 9.2206, 1e-4);
716s  assert (acov, [0.0712, -0.0032; -0.0032, 0.0153], 1e-4);
716s ***** error<logllike: function called with too few input arguments.> logllike (3.25)
716s ***** error<logllike: X must be a vector.> logllike ([5, 0.2], ones (2))
716s ***** error<logllike: PARAMS must be a two-element vector.> ...
716s  logllike ([1, 0.2, 3], [1, 3, 5, 7])
716s ***** error<logllike: X and CENSOR vector mismatch.> ...
716s  logllike ([1.5, 0.2], [1:5], [0, 0, 0])
716s ***** error<logllike: X and FREQ vector mismatch.> ...
716s  logllike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
716s ***** error<logllike: X and FREQ vector mismatch.> ...
716s  logllike ([1.5, 0.2], [1:5], [], [1, 1, 1])
716s 8 tests, 8 passed, 0 known failure, 0 skipped
716s [inst/dist_fit/lognfit.m]
716s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/lognfit.m
716s ***** demo
716s  ## Sample 3 populations from 3 different log-normal distibutions
716s  randn ("seed", 1);    # for reproducibility
716s  r1 = lognrnd (0, 0.25, 1000, 1);
716s  randn ("seed", 2);    # for reproducibility
716s  r2 = lognrnd (0, 0.5, 1000, 1);
716s  randn ("seed", 3);    # for reproducibility
716s  r3 = lognrnd (0, 1, 1000, 1);
716s  r = [r1, r2, r3];
716s 
716s  ## Plot them normalized and fix their colors
716s  hist (r, 30, 2);
716s  h = findobj (gca, "Type", "patch");
716s  set (h(1), "facecolor", "c");
716s  set (h(2), "facecolor", "g");
716s  set (h(3), "facecolor", "r");
716s  hold on
716s 
716s  ## Estimate their mu and sigma parameters
716s  mu_sigmaA = lognfit (r(:,1));
716s  mu_sigmaB = lognfit (r(:,2));
716s  mu_sigmaC = lognfit (r(:,3));
716s 
716s  ## Plot their estimated PDFs
716s  x = [0:0.1:6];
716s  y = lognpdf (x, mu_sigmaA(1), mu_sigmaA(2));
716s  plot (x, y, "-pr");
716s  y = lognpdf (x, mu_sigmaB(1), mu_sigmaB(2));
716s  plot (x, y, "-sg");
716s  y = lognpdf (x, mu_sigmaC(1), mu_sigmaC(2));
716s  plot (x, y, "-^c");
716s  ylim ([0, 2])
716s  xlim ([0, 6])
716s  hold off
716s  legend ({"Normalized HIST of sample 1 with mu=0, σ=0.25", ...
716s           "Normalized HIST of sample 2 with mu=0, σ=0.5", ...
716s           "Normalized HIST of sample 3 with mu=0, σ=1", ...
716s           sprintf("PDF for sample 1 with estimated mu=%0.2f and σ=%0.2f", ...
716s                   mu_sigmaA(1), mu_sigmaA(2)), ...
716s           sprintf("PDF for sample 2 with estimated mu=%0.2f and σ=%0.2f", ...
716s                   mu_sigmaB(1), mu_sigmaB(2)), ...
716s           sprintf("PDF for sample 3 with estimated mu=%0.2f and σ=%0.2f", ...
716s                   mu_sigmaC(1), mu_sigmaC(2))}, "location", "northeast")
716s  title ("Three population samples from different log-normal distibutions")
716s  hold off
716s ***** test
716s  randn ("seed", 1);
716s  x = lognrnd (3, 5, [1000, 1]);
716s  [paramhat, paramci] = lognfit (x, 0.01);
716s  assert (paramci(1,1) < 3);
716s  assert (paramci(1,2) > 3);
716s  assert (paramci(2,1) < 5);
716s  assert (paramci(2,2) > 5);
716s ***** error<lognfit: X must be a numeric vector of positive values.> ...
716s  lognfit (ones (20,3))
716s ***** error<lognfit: X must be a numeric vector of positive values.> ...
716s  lognfit ({1, 2, 3, 4, 5})
716s ***** error<lognfit: X must be a numeric vector of positive values.> ...
716s  lognfit ([-1, 2, 3, 4, 5])
716s ***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), 0)
716s ***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), -0.3)
716s ***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), 1.2)
716s ***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), [0.05,  0.1])
716s ***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), 0.02+i)
716s ***** error<lognfit: X and CENSOR vectors mismatch.> ...
716s  lognfit (ones (20,1), [], zeros(15,1))
716s ***** error<lognfit: X and FREQ vectors mismatch.> ...
716s  lognfit (ones (20,1), [], zeros(20,1), ones(25,1))
716s ***** error<lognfit: > lognfit (ones (20,1), [], zeros(20,1), ones(20,1), "options")
716s 12 tests, 12 passed, 0 known failure, 0 skipped
716s [inst/dist_fit/lognlike.m]
716s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/lognlike.m
716s ***** test
716s  x = 1:50;
716s  [nlogL, avar] = lognlike ([0, 0.25], x);
716s  avar_out = [-5.4749e-03, 2.8308e-04; 2.8308e-04, -1.1916e-05];
716s  assert (nlogL, 3962.330333301793, 1e-10);
716s  assert (avar, avar_out, 1e-7);
716s ***** test
716s  x = 1:50;
716s  [nlogL, avar] = lognlike ([0, 0.25], x * 0.5);
716s  avar_out = [-7.6229e-03, 4.8722e-04; 4.8722e-04, -2.6754e-05];
716s  assert (nlogL, 2473.183051225747, 1e-10);
716s  assert (avar, avar_out, 1e-7);
716s ***** test
716s  x = 1:50;
716s  [nlogL, avar] = lognlike ([0, 0.5], x);
716s  avar_out = [-2.1152e-02, 2.2017e-03; 2.2017e-03, -1.8535e-04];
716s  assert (nlogL, 1119.072424020455, 1e-12);
716s  assert (avar, avar_out, 1e-6);
716s ***** test
716s  x = 1:50;
716s  censor = ones (1, 50);
716s  censor([2, 4, 6, 8, 12, 14]) = 0;
716s  [nlogL, avar] = lognlike ([0, 0.5], x, censor);
716s  avar_out = [-1.9823e-02, 2.0370e-03; 2.0370e-03, -1.6618e-04];
716s  assert (nlogL, 1091.746371145497, 1e-12);
716s  assert (avar, avar_out, 1e-6);
716s ***** test
716s  x = 1:50;
716s  censor = ones (1, 50);
716s  censor([2, 4, 6, 8, 12, 14]) = 0;
716s  [nlogL, avar] = lognlike ([0, 1], x, censor);
716s  avar_out = [-6.8634e-02, 1.3968e-02; 1.3968e-02, -2.1664e-03];
716s  assert (nlogL, 349.3969104144271, 1e-12);
716s  assert (avar, avar_out, 1e-6);
716s ***** error<lognlike: function called with too few input arguments.> ...
716s  lognlike ([12, 15]);
716s ***** error<lognlike: X must be a vector.> lognlike ([12, 15], ones (2));
716s ***** error<lognlike: PARAMS must be a two-element vector.> ...
716s  lognlike ([12, 15, 3], [1:50]);
716s ***** error<lognlike: X and CENSOR vectors mismatch.> ...
716s  lognlike ([12, 15], [1:50], [1, 2, 3]);
716s ***** error<lognlike: X and FREQ vectors mismatch.> ...
716s  lognlike ([12, 15], [1:50], [], [1, 2, 3]);
716s 10 tests, 10 passed, 0 known failure, 0 skipped
716s [inst/dist_fit/nakafit.m]
716s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/nakafit.m
716s ***** demo
716s  ## Sample 3 populations from different Nakagami distibutions
716s  randg ("seed", 5)  # for reproducibility
716s  r1 = nakarnd (0.5, 1, 2000, 1);
716s  randg ("seed", 2)   # for reproducibility
716s  r2 = nakarnd (5, 1, 2000, 1);
716s  randg ("seed", 7)   # for reproducibility
716s  r3 = nakarnd (2, 2, 2000, 1);
716s  r = [r1, r2, r3];
716s 
716s  ## Plot them normalized and fix their colors
716s  hist (r, [0.05:0.1:3.5], 10);
716s  h = findobj (gca, "Type", "patch");
716s  set (h(1), "facecolor", "c");
716s  set (h(2), "facecolor", "g");
716s  set (h(3), "facecolor", "r");
716s  ylim ([0, 2.5]);
716s  xlim ([0, 3.0]);
716s  hold on
716s 
716s  ## Estimate their MU and LAMBDA parameters
716s  mu_omegaA = nakafit (r(:,1));
716s  mu_omegaB = nakafit (r(:,2));
716s  mu_omegaC = nakafit (r(:,3));
716s 
716s  ## Plot their estimated PDFs
716s  x = [0.01:0.1:3.01];
716s  y = nakapdf (x, mu_omegaA(1), mu_omegaA(2));
716s  plot (x, y, "-pr");
716s  y = nakapdf (x, mu_omegaB(1), mu_omegaB(2));
716s  plot (x, y, "-sg");
716s  y = nakapdf (x, mu_omegaC(1), mu_omegaC(2));
716s  plot (x, y, "-^c");
716s  legend ({"Normalized HIST of sample 1 with μ=0.5 and ω=1", ...
716s           "Normalized HIST of sample 2 with μ=5 and ω=1", ...
716s           "Normalized HIST of sample 3 with μ=2 and ω=2", ...
716s           sprintf("PDF for sample 1 with estimated μ=%0.2f and ω=%0.2f", ...
716s                   mu_omegaA(1), mu_omegaA(2)), ...
716s           sprintf("PDF for sample 2 with estimated μ=%0.2f and ω=%0.2f", ...
716s                   mu_omegaB(1), mu_omegaB(2)), ...
716s           sprintf("PDF for sample 3 with estimated μ=%0.2f and ω=%0.2f", ...
716s                   mu_omegaC(1), mu_omegaC(2))})
716s  title ("Three population samples from different Nakagami distibutions")
716s  hold off
716s ***** test
716s  paramhat = nakafit ([1:50]);
716s  paramhat_out = [0.7355, 858.5];
716s  assert (paramhat, paramhat_out, 1e-4);
716s ***** test
716s  paramhat = nakafit ([1:5]);
716s  paramhat_out = [1.1740, 11];
716s  assert (paramhat, paramhat_out, 1e-4);
716s ***** test
716s  paramhat = nakafit ([1:6], [], [], [1 1 1 1 1 0]);
716s  paramhat_out = [1.1740, 11];
716s  assert (paramhat, paramhat_out, 1e-4);
716s ***** test
716s  paramhat = nakafit ([1:5], [], [], [1 1 1 1 2]);
716s  paramhat_out = nakafit ([1:5, 5]);
716s  assert (paramhat, paramhat_out, 1e-4);
716s ***** error<nakafit: X must be a vector.> nakafit (ones (2,5));
716s ***** error<nakafit: wrong value for ALPHA.> nakafit ([1, 2, 3, 4, 5], 1.2);
716s ***** error<nakafit: wrong value for ALPHA.> nakafit ([1, 2, 3, 4, 5], 0);
716s ***** error<nakafit: wrong value for ALPHA.> nakafit ([1, 2, 3, 4, 5], "alpha");
717s ***** error<nakafit: X and CENSOR vectors mismatch.> ...
717s  nakafit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
717s ***** error<nakafit: X and CENSOR vectors mismatch.> ...
717s  nakafit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
717s ***** error<nakafit: X and FREQ vectors mismatch.> ...
717s  nakafit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
717s ***** error<nakafit: X and FREQ vectors mismatch.> ...
717s  nakafit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
717s ***** error<nakafit: FREQ must not contain negative values.> ...
717s  nakafit ([1, 2, 3, 4, 5], [], [], [1 1 -1 1 1]);
717s ***** error<nakafit: FREQ must contain integer values.> ...
717s  nakafit ([1, 2, 3, 4, 5], [], [], [1 1 1.5 1 1]);
717s ***** error<nakafit: 'options' 5th argument must be a structure> ...
717s  nakafit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
717s 15 tests, 15 passed, 0 known failure, 0 skipped
717s [inst/dist_fit/nakalike.m]
717s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/nakalike.m
717s ***** test
717s  nlogL = nakalike ([0.735504, 858.5], [1:50]);
717s  assert (nlogL, 202.8689, 1e-4);
717s ***** test
717s  nlogL = nakalike ([1.17404, 11], [1:5]);
717s  assert (nlogL, 8.6976, 1e-4);
717s ***** test
717s  nlogL = nakalike ([1.17404, 11], [1:5], [], [1, 1, 1, 1, 1]);
717s  assert (nlogL, 8.6976, 1e-4);
717s ***** test
717s  nlogL = nakalike ([1.17404, 11], [1:6], [], [1, 1, 1, 1, 1, 0]);
717s  assert (nlogL, 8.6976, 1e-4);
717s ***** error<nakalike: function called with too few input arguments.> nakalike (3.25)
717s ***** error<nakalike: X must be a vector.> nakalike ([5, 0.2], ones (2))
717s ***** error<nakalike: PARAMS must be a two-element vector.> ...
717s  nakalike ([1, 0.2, 3], [1, 3, 5, 7])
717s ***** error<nakalike: X and CENSOR vector mismatch.> ...
717s  nakalike ([1.5, 0.2], [1:5], [0, 0, 0])
717s ***** error<nakalike: X and FREQ vector mismatch.> ...
717s  nakalike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
717s ***** error<nakalike: X and FREQ vector mismatch.> ...
717s  nakalike ([1.5, 0.2], [1:5], [], [1, 1, 1])
717s ***** error<nakalike: FREQ must not contain negative values.> ...
717s  nakalike ([1.5, 0.2], [1:5], [], [1, 1, 1, 1, -1])
717s 11 tests, 11 passed, 0 known failure, 0 skipped
717s [inst/dist_fit/nbinfit.m]
717s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/nbinfit.m
717s ***** demo
717s  ## Sample 2 populations from different negative binomial distibutions
717s  randp ("seed", 5); randg ("seed", 5);    # for reproducibility
717s  r1 = nbinrnd (2, 0.15, 5000, 1);
717s  randp ("seed", 8); randg ("seed", 8);    # for reproducibility
717s  r2 = nbinrnd (5, 0.2, 5000, 1);
717s  r = [r1, r2];
717s 
717s  ## Plot them normalized and fix their colors
717s  hist (r, [0:51], 1);
717s  h = findobj (gca, "Type", "patch");
717s  set (h(1), "facecolor", "c");
717s  set (h(2), "facecolor", "g");
717s  hold on
717s 
717s  ## Estimate their probability of success
717s  r_psA = nbinfit (r(:,1));
717s  r_psB = nbinfit (r(:,2));
717s 
717s  ## Plot their estimated PDFs
717s  x = [0:40];
717s  y = nbinpdf (x, r_psA(1), r_psA(2));
717s  plot (x, y, "-pg");
717s  x = [min(r(:,2)):max(r(:,2))];
717s  y = nbinpdf (x, r_psB(1), r_psB(2));
717s  plot (x, y, "-sc");
717s  ylim ([0, 0.1])
717s  xlim ([0, 50])
717s  legend ({"Normalized HIST of sample 1 with r=2 and ps=0.15", ...
717s           "Normalized HIST of sample 2 with r=5 and ps=0.2", ...
717s           sprintf("PDF for sample 1 with estimated r=%0.2f and ps=%0.2f", ...
717s                   r_psA(1), r_psA(2)), ...
717s           sprintf("PDF for sample 2 with estimated r=%0.2f and ps=%0.2f", ...
717s                   r_psB(1), r_psB(2))})
717s  title ("Two population samples from negative different binomial distibutions")
717s  hold off
717s ***** test
717s  [paramhat, paramci] = nbinfit ([1:50]);
717s  assert (paramhat, [2.420857, 0.086704], 1e-6);
717s  assert (paramci(:,1), [1.382702; 3.459012], 1e-6);
717s  assert (paramci(:,2), [0.049676; 0.123732], 1e-6);
717s ***** test
717s  [paramhat, paramci] = nbinfit ([1:20]);
717s  assert (paramhat, [3.588233, 0.254697], 1e-6);
717s  assert (paramci(:,1), [0.451693; 6.724774], 1e-6);
717s  assert (paramci(:,2), [0.081143; 0.428251], 1e-6);
717s ***** test
717s  [paramhat, paramci] = nbinfit ([1:10]);
717s  assert (paramhat, [8.8067, 0.6156], 1e-4);
717s  assert (paramci(:,1), [0; 30.7068], 1e-4);
717s  assert (paramci(:,2), [0.0217; 1], 1e-4);
717s ***** test
717s  [paramhat, paramci] = nbinfit ([1:10], 0.05, ones (1, 10));
717s  assert (paramhat, [8.8067, 0.6156], 1e-4);
717s  assert (paramci(:,1), [0; 30.7068], 1e-4);
717s  assert (paramci(:,2), [0.0217; 1], 1e-4);
717s ***** test
717s  [paramhat, paramci] = nbinfit ([1:11], 0.05, [ones(1, 10), 0]);
717s  assert (paramhat, [8.8067, 0.6156], 1e-4);
717s  assert (paramci(:,1), [0; 30.7068], 1e-4);
717s  assert (paramci(:,2), [0.0217; 1], 1e-4);
717s ***** error<nbinfit: X cannot have negative values.> nbinfit ([-1 2 3 3])
717s ***** error<nbinfit: X must be a vector.> nbinfit (ones (2))
717s ***** error<nbinfit: X must be a non-negative integer.> nbinfit ([1 2 1.2 3])
717s ***** error<nbinfit: wrong value for ALPHA.> nbinfit ([1 2 3], 0)
717s ***** error<nbinfit: wrong value for ALPHA.> nbinfit ([1 2 3], 1.2)
717s ***** error<nbinfit: wrong value for ALPHA.> nbinfit ([1 2 3], [0.02 0.05])
717s ***** error<nbinfit: X and FREQ vectors mismatch.> ...
717s  nbinfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2]);
717s ***** error<nbinfit: FREQ must not contain negative values.> ...
717s  nbinfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2, -1]);
717s ***** error<nbinfit: FREQ must contain integer values.> ...
717s  nbinfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2, 1.5]);
717s ***** error<nbinfit: 'options' argument must be a structure> ...
717s  nbinfit ([1, 2, 3, 4, 5], 0.05, struct ("option", 234));
717s ***** error<nbinfit: 'options' argument must be a structure> ...
717s  nbinfit ([1, 2, 3, 4, 5], 0.05, ones (1,5), struct ("option", 234));
717s 16 tests, 16 passed, 0 known failure, 0 skipped
717s [inst/dist_fit/nbinlike.m]
717s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/nbinlike.m
717s ***** assert (nbinlike ([2.42086, 0.0867043], [1:50]), 205.5942, 1e-4)
717s ***** assert (nbinlike ([3.58823, 0.254697], [1:20]), 63.6435, 1e-4)
717s ***** assert (nbinlike ([8.80671, 0.615565], [1:10]), 24.7410, 1e-4)
717s ***** assert (nbinlike ([22.1756, 0.831306], [1:8]), 17.9528, 1e-4)
717s ***** assert (nbinlike ([22.1756, 0.831306], [1:9], [ones(1,8), 0]), 17.9528, 1e-4)
717s ***** error<nbinlike: function called with too few input arguments.> nbinlike (3.25)
717s ***** error<nbinlike: X must be a vector.> nbinlike ([5, 0.2], ones (2))
717s ***** error<nbinlike: X cannot have negative values.> nbinlike ([5, 0.2], [-1, 3])
717s ***** error<nbinlike: PARAMS must be a two-element vector.> ...
717s  nbinlike ([1, 0.2, 3], [1, 3, 5, 7])
717s ***** error<nbinlike: number of successes,> nbinlike ([-5, 0.2], [1:15])
717s ***** error<nbinlike: number of successes,> nbinlike ([0, 0.2], [1:15])
717s ***** error<nbinlike: probability of success,> nbinlike ([5, 1.2], [3, 5])
717s ***** error<nbinlike: probability of success,> nbinlike ([5, -0.2], [3, 5])
717s ***** error<nbinlike: X and FREQ vectors mismatch.> ...
717s  nbinlike ([5, 0.2], ones (10, 1), ones (8,1))
717s ***** error<nbinlike: FREQ must not contain negative values.> ...
717s  nbinlike ([5, 0.2], ones (1, 8), [1 1 1 1 1 1 1 -1])
717s ***** error<nbinlike: FREQ must contain integer values.> ...
717s  nbinlike ([5, 0.2], ones (1, 8), [1 1 1 1 1 1 1 1.5])
717s 16 tests, 16 passed, 0 known failure, 0 skipped
717s [inst/dist_fit/normfit.m]
717s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/normfit.m
717s ***** demo
717s  ## Sample 3 populations from 3 different normal distibutions
717s  randn ("seed", 1);    # for reproducibility
717s  r1 = normrnd (2, 5, 5000, 1);
717s  randn ("seed", 2);    # for reproducibility
717s  r2 = normrnd (5, 2, 5000, 1);
717s  randn ("seed", 3);    # for reproducibility
717s  r3 = normrnd (9, 4, 5000, 1);
717s  r = [r1, r2, r3];
717s 
717s  ## Plot them normalized and fix their colors
717s  hist (r, 15, 0.4);
717s  h = findobj (gca, "Type", "patch");
717s  set (h(1), "facecolor", "c");
717s  set (h(2), "facecolor", "g");
717s  set (h(3), "facecolor", "r");
717s  hold on
717s 
717s  ## Estimate their mu and sigma parameters
717s  [muhat, sigmahat] = normfit (r);
717s 
717s  ## Plot their estimated PDFs
717s  x = [min(r(:)):max(r(:))];
717s  y = normpdf (x, muhat(1), sigmahat(1));
717s  plot (x, y, "-pr");
717s  y = normpdf (x, muhat(2), sigmahat(2));
717s  plot (x, y, "-sg");
717s  y = normpdf (x, muhat(3), sigmahat(3));
717s  plot (x, y, "-^c");
717s  ylim ([0, 0.5])
717s  xlim ([-20, 20])
717s  hold off
717s  legend ({"Normalized HIST of sample 1 with mu=2, σ=5", ...
717s           "Normalized HIST of sample 2 with mu=5, σ=2", ...
717s           "Normalized HIST of sample 3 with mu=9, σ=4", ...
717s           sprintf("PDF for sample 1 with estimated mu=%0.2f and σ=%0.2f", ...
717s                   muhat(1), sigmahat(1)), ...
717s           sprintf("PDF for sample 2 with estimated mu=%0.2f and σ=%0.2f", ...
717s                   muhat(2), sigmahat(2)), ...
717s           sprintf("PDF for sample 3 with estimated mu=%0.2f and σ=%0.2f", ...
717s                   muhat(3), sigmahat(3))}, "location", "northwest")
717s  title ("Three population samples from different normal distibutions")
717s  hold off
717s ***** test
717s  load lightbulb
717s  idx = find (lightbulb(:,2) == 0);
717s  censoring = lightbulb(idx,3) == 1;
717s  [muHat, sigmaHat] = normfit (lightbulb(idx,1), [], censoring);
717s  assert (muHat, 9496.59586737857, 1e-11);
717s  assert (sigmaHat, 3064.021012796456, 2e-12);
717s ***** test
717s  randn ("seed", 234);
717s  x = normrnd (3, 5, [1000, 1]);
717s  [muHat, sigmaHat, muCI, sigmaCI] = normfit (x, 0.01);
717s  assert (muCI(1) < 3);
717s  assert (muCI(2) > 3);
717s  assert (sigmaCI(1) < 5);
717s  assert (sigmaCI(2) > 5);
717s ***** error<normfit: X must not be a multi-dimensional array.> ...
717s  normfit (ones (3,3,3))
717s ***** error<normfit: matrix data acceptable only under 2-arg syntax.> ...
717s  normfit (ones (20,3), [], zeros (20,1))
717s ***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), 0)
717s ***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), -0.3)
717s ***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), 1.2)
717s ***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), [0.05 0.1])
717s ***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), 0.02+i)
717s ***** error<normfit: X and CENSOR vectors mismatch.> ...
717s  normfit (ones (20,1), [], zeros(15,1))
717s ***** error<normfit: X and FREQ vectors mismatch.> ...
717s  normfit (ones (20,1), [], zeros(20,1), ones(25,1))
717s ***** error<normfit: FREQ must not contain negative values.> ...
717s  normfit (ones (5,1), [], zeros(5,1), [1, 2, 1, 2, -1]')
717s ***** error<normfit: > normfit (ones (20,1), [], zeros(20,1), ones(20,1), "options")
717s 13 tests, 13 passed, 0 known failure, 0 skipped
717s [inst/dist_fit/normlike.m]
717s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/normlike.m
717s ***** error<normlike: too few input arguments.> normlike ([12, 15]);
717s ***** error<normlike: X must be a vector.> normlike ([12, 15], ones (2));
717s ***** error<normlike: PARAMS must be a two-element vector.> ...
717s  normlike ([12, 15, 3], [1:50]);
717s ***** error<normlike: X and CENSOR vectors mismatch.> ...
717s  normlike ([12, 15], [1:50], [1, 2, 3]);
717s ***** error<normlike: X and FREQ vectors mismatch.> ...
717s  normlike ([12, 15], [1:50], [], [1, 2, 3]);
717s ***** error<normlike: FREQ must not contain negative values.> ...
717s  normlike ([12, 15], [1:5], [], [1, 2, 3, 2, -1]);
717s ***** test
717s  x = 1:50;
717s  [nlogL, avar] = normlike ([2.3, 1.2], x);
717s  avar_out = [7.5767e-01, -1.8850e-02; -1.8850e-02, 4.8750e-04];
717s  assert (nlogL, 13014.95883783327, 1e-10);
717s  assert (avar, avar_out, 1e-4);
717s ***** test
717s  x = 1:50;
717s  [nlogL, avar] = normlike ([2.3, 1.2], x * 0.5);
717s  avar_out = [3.0501e-01, -1.5859e-02; -1.5859e-02, 9.1057e-04];
717s  assert (nlogL, 2854.802587833265, 1e-10);
717s  assert (avar, avar_out, 1e-4);
717s ***** test
717s  x = 1:50;
717s  [nlogL, avar] = normlike ([21, 15], x);
717s  avar_out = [5.460474308300396, -1.600790513833993; ...
717s              -1.600790513833993, 2.667984189723321];
717s  assert (nlogL, 206.738325604233, 1e-12);
717s  assert (avar, avar_out, 1e-14);
717s ***** test
717s  x = 1:50;
717s  censor = ones (1, 50);
717s  censor([2, 4, 6, 8, 12, 14]) = 0;
717s  [nlogL, avar] = normlike ([2.3, 1.2], x, censor);
717s  avar_out = [3.0501e-01, -1.5859e-02; -1.5859e-02, 9.1057e-04];
717s  assert (nlogL, Inf);
717s  assert (avar, [NaN, NaN; NaN, NaN]);
717s ***** test
717s  x = 1:50;
717s  censor = ones (1, 50);
717s  censor([2, 4, 6, 8, 12, 14]) = 0;
717s  [nlogL, avar] = normlike ([21, 15], x, censor);
717s  avar_out = [24.4824488866131, -10.6649544179636; ...
717s              -10.6649544179636, 6.22827849965737];
717s  assert (nlogL, 86.9254371829733, 1e-12);
717s  assert (avar, avar_out, 8e-14);
717s 11 tests, 11 passed, 0 known failure, 0 skipped
717s [inst/dist_fit/poissfit.m]
717s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/poissfit.m
717s ***** demo
717s  ## Sample 3 populations from 3 different Poisson distibutions
717s  randp ("seed", 2);    # for reproducibility
717s  r1 = poissrnd (1, 1000, 1);
717s  randp ("seed", 2);    # for reproducibility
717s  r2 = poissrnd (4, 1000, 1);
717s  randp ("seed", 3);    # for reproducibility
717s  r3 = poissrnd (10, 1000, 1);
717s  r = [r1, r2, r3];
717s 
717s  ## Plot them normalized and fix their colors
717s  hist (r, [0:20], 1);
717s  h = findobj (gca, "Type", "patch");
717s  set (h(1), "facecolor", "c");
717s  set (h(2), "facecolor", "g");
717s  set (h(3), "facecolor", "r");
717s  hold on
717s 
717s  ## Estimate their lambda parameter
717s  lambdahat = poissfit (r);
717s 
717s  ## Plot their estimated PDFs
717s  x = [0:20];
717s  y = poisspdf (x, lambdahat(1));
717s  plot (x, y, "-pr");
717s  y = poisspdf (x, lambdahat(2));
717s  plot (x, y, "-sg");
717s  y = poisspdf (x, lambdahat(3));
717s  plot (x, y, "-^c");
717s  xlim ([0, 20])
717s  ylim ([0, 0.4])
717s  legend ({"Normalized HIST of sample 1 with λ=1", ...
717s           "Normalized HIST of sample 2 with λ=4", ...
717s           "Normalized HIST of sample 3 with λ=10", ...
717s           sprintf("PDF for sample 1 with estimated λ=%0.2f", ...
717s                   lambdahat(1)), ...
717s           sprintf("PDF for sample 2 with estimated λ=%0.2f", ...
717s                   lambdahat(2)), ...
717s           sprintf("PDF for sample 3 with estimated λ=%0.2f", ...
717s                   lambdahat(3))})
717s  title ("Three population samples from different Poisson distibutions")
717s  hold off
717s ***** test
717s  x = [1 3 2 4 5 4 3 4];
717s  [lhat, lci] = poissfit (x);
717s  assert (lhat, 3.25)
717s  assert (lci, [2.123007901949543; 4.762003010390628], 1e-14)
717s ***** test
717s  x = [1 3 2 4 5 4 3 4];
717s  [lhat, lci] = poissfit (x, 0.01);
717s  assert (lhat, 3.25)
717s  assert (lci, [1.842572740234582; 5.281369033298528], 1e-14)
717s ***** test
717s  x = [1 2 3 4 5];
717s  f = [1 1 2 3 1];
717s  [lhat, lci] = poissfit (x, [], f);
717s  assert (lhat, 3.25)
717s  assert (lci, [2.123007901949543; 4.762003010390628], 1e-14)
717s ***** test
717s  x = [1 2 3 4 5];
717s  f = [1 1 2 3 1];
717s  [lhat, lci] = poissfit (x, 0.01, f);
717s  assert (lhat, 3.25)
717s  assert (lci, [1.842572740234582; 5.281369033298528], 1e-14)
717s ***** error<poissfit: X cannot have negative values.> poissfit ([1 2 -1 3])
717s ***** error<poissfit: wrong value for ALPHA.> poissfit ([1 2 3], 0)
717s ***** error<poissfit: wrong value for ALPHA.> poissfit ([1 2 3], 1.2)
717s ***** error<poissfit: wrong value for ALPHA.> poissfit ([1 2 3], [0.02 0.05])
717s ***** error<poissfit: X and FREQ vectors mismatch.>
717s  poissfit ([1 2 3], [], [1 5])
717s ***** error<poissfit: FREQ must not contain negative values.>
717s  poissfit ([1 2 3], [], [1 5 -1])
717s 10 tests, 10 passed, 0 known failure, 0 skipped
717s [inst/dist_fit/poisslike.m]
717s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/poisslike.m
717s ***** test
717s  x = [1 3 2 4 5 4 3 4];
717s  [nlogL, avar] = poisslike (3.25, x);
717s  assert (nlogL, 13.9533, 1e-4)
717s ***** test
717s  x = [1 2 3 4 5];
717s  f = [1 1 2 3 1];
717s  [nlogL, avar] = poisslike (3.25, x, f);
717s  assert (nlogL, 13.9533, 1e-4)
717s ***** error<poisslike: function called with too few input arguments.> poisslike (1)
717s ***** error<poisslike: LAMBDA must be a positive scalar.> poisslike ([1 2 3], [1 2])
717s ***** error<poisslike: X must be a vector of non-negative values.> ...
717s  poisslike (3.25, ones (10, 2))
717s ***** error<poisslike: X must be a vector of non-negative values.> ...
717s  poisslike (3.25, [1 2 3 -4 5])
717s ***** error<poisslike: X and FREQ vectors mismatch.> ...
717s  poisslike (3.25, ones (10, 1), ones (8,1))
717s ***** error<poisslike: FREQ must not contain negative values.> ...
717s  poisslike (3.25, ones (1, 8), [1 1 1 1 1 1 1 -1])
717s 8 tests, 8 passed, 0 known failure, 0 skipped
717s [inst/dist_fit/raylfit.m]
717s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/raylfit.m
717s ***** demo
717s  ## Sample 3 populations from 3 different Rayleigh distibutions
717s  rand ("seed", 2);    # for reproducibility
717s  r1 = raylrnd (1, 1000, 1);
717s  rand ("seed", 2);    # for reproducibility
717s  r2 = raylrnd (2, 1000, 1);
717s  rand ("seed", 3);    # for reproducibility
717s  r3 = raylrnd (4, 1000, 1);
717s  r = [r1, r2, r3];
717s 
717s  ## Plot them normalized and fix their colors
717s  hist (r, [0.5:0.5:10.5], 2);
717s  h = findobj (gca, "Type", "patch");
717s  set (h(1), "facecolor", "c");
717s  set (h(2), "facecolor", "g");
717s  set (h(3), "facecolor", "r");
717s  hold on
717s 
717s  ## Estimate their lambda parameter
717s  sigmaA = raylfit (r(:,1));
717s  sigmaB = raylfit (r(:,2));
717s  sigmaC = raylfit (r(:,3));
717s 
717s  ## Plot their estimated PDFs
717s  x = [0:0.1:10];
717s  y = raylpdf (x, sigmaA);
717s  plot (x, y, "-pr");
717s  y = raylpdf (x, sigmaB);
717s  plot (x, y, "-sg");
717s  y = raylpdf (x, sigmaC);
717s  plot (x, y, "-^c");
717s  xlim ([0, 10])
717s  ylim ([0, 0.7])
717s  legend ({"Normalized HIST of sample 1 with σ=1", ...
717s           "Normalized HIST of sample 2 with σ=2", ...
717s           "Normalized HIST of sample 3 with σ=4", ...
717s           sprintf("PDF for sample 1 with estimated σ=%0.2f", ...
717s                   sigmaA), ...
717s           sprintf("PDF for sample 2 with estimated σ=%0.2f", ...
717s                   sigmaB), ...
717s           sprintf("PDF for sample 3 with estimated σ=%0.2f", ...
717s                   sigmaC)})
717s  title ("Three population samples from different Rayleigh distibutions")
717s  hold off
717s ***** test
717s  x = [1 3 2 4 5 4 3 4];
717s  [shat, sci] = raylfit (x);
717s  assert (shat, 2.4495, 1e-4)
717s  assert (sci, [1.8243; 3.7279], 1e-4)
717s ***** test
717s  x = [1 3 2 4 5 4 3 4];
717s  [shat, sci] = raylfit (x, 0.01);
717s  assert (shat, 2.4495, 1e-4)
717s  assert (sci, [1.6738; 4.3208], 1e-4)
717s ***** test
717s  x = [1 2 3 4 5];
717s  f = [1 1 2 3 1];
717s  [shat, sci] = raylfit (x, [], [], f);
717s  assert (shat, 2.4495, 1e-4)
717s  assert (sci, [1.8243; 3.7279], 1e-4)
717s ***** test
717s  x = [1 2 3 4 5];
717s  f = [1 1 2 3 1];
717s  [shat, sci] = raylfit (x, 0.01, [], f);
717s  assert (shat, 2.4495, 1e-4)
717s  assert (sci, [1.6738; 4.3208], 1e-4)
717s ***** test
717s  x = [1 2 3 4 5 6];
717s  c = [0 0 0 0 0 1];
717s  f = [1 1 2 3 1 1];
717s  [shat, sci] = raylfit (x, 0.01, c, f);
717s  assert (shat, 2.4495, 1e-4)
717s  assert (sci, [1.6738; 4.3208], 1e-4)
717s ***** error<raylfit: X must be a vector.> raylfit (ones (2,5));
717s ***** error<raylfit: X cannot have negative values.> raylfit ([1 2 -1 3])
717s ***** error<raylfit: wrong value for ALPHA.> raylfit ([1 2 3], 0)
717s ***** error<raylfit: wrong value for ALPHA.> raylfit ([1 2 3], 1.2)
717s ***** error<raylfit: wrong value for ALPHA.> raylfit ([1 2 3], [0.02 0.05])
717s ***** error<raylfit: X and CENSOR vectors mismatch.> ...
717s  raylfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
717s ***** error<raylfit: X and CENSOR vectors mismatch.> ...
717s  raylfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
717s ***** error<raylfit: X and FREQ vectors mismatch.> ...
717s  raylfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
717s ***** error<raylfit: X and FREQ vectors mismatch.> ...
717s  raylfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
717s ***** error<raylfit: X and FREQ vectors mismatch.>
717s  raylfit ([1 2 3], [], [], [1 5])
717s ***** error<raylfit: FREQ must not contain negative values.>
717s  raylfit ([1 2 3], [], [], [1 5 -1])
717s 16 tests, 16 passed, 0 known failure, 0 skipped
717s [inst/dist_fit/rayllike.m]
717s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/rayllike.m
717s ***** test
717s  x = [1 3 2 4 5 4 3 4];
717s  [nlogL, acov] = rayllike (3.25, x);
717s  assert (nlogL, 14.7442, 1e-4)
717s ***** test
717s  x = [1 2 3 4 5];
717s  f = [1 1 2 3 1];
717s  [nlogL, acov] = rayllike (3.25, x, [], f);
717s  assert (nlogL, 14.7442, 1e-4)
717s ***** test
717s  x = [1 2 3 4 5 6];
717s  f = [1 1 2 3 1 0];
717s  [nlogL, acov] = rayllike (3.25, x, [], f);
717s  assert (nlogL, 14.7442, 1e-4)
717s ***** test
717s  x = [1 2 3 4 5 6];
717s  c = [0 0 0 0 0 1];
717s  f = [1 1 2 3 1 0];
717s  [nlogL, acov] = rayllike (3.25, x, c, f);
717s  assert (nlogL, 14.7442, 1e-4)
717s ***** error<rayllike: function called with too few input arguments.> rayllike (1)
717s ***** error<rayllike: SIGMA must be a positive scalar.> rayllike ([1 2 3], [1 2])
717s ***** error<rayllike: X must be a vector of non-negative values.> ...
717s  rayllike (3.25, ones (10, 2))
717s ***** error<rayllike: X must be a vector of non-negative values.> ...
717s  rayllike (3.25, [1 2 3 -4 5])
717s ***** error<rayllike: X and CENSOR vectors mismatch.> ...
717s  rayllike (3.25, [1, 2, 3, 4, 5], [1 1 0]);
717s ***** error<rayllike: X and CENSOR vectors mismatch.> ...
717s  rayllike (3.25, [1, 2, 3, 4, 5], [1 1 0 1 1]');
717s ***** error<rayllike: X and FREQ vectors mismatch.> ...
717s  rayllike (3.25, [1, 2, 3, 4, 5], zeros (1,5), [1 1 0]);
717s ***** error<rayllike: X and FREQ vectors mismatch.> ...
717s  rayllike (3.25, [1, 2, 3, 4, 5], [], [1 1 0 1 1]');
717s ***** error<rayllike: FREQ must not contain negative values.> ...
717s  rayllike (3.25, ones (1, 8), [], [1 1 1 1 1 1 1 -1])
717s 13 tests, 13 passed, 0 known failure, 0 skipped
717s [inst/dist_fit/ricefit.m]
717s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/ricefit.m
717s ***** demo
717s  ## Sample 3 populations from different Gamma distibutions
717s  randg ("seed", 5);    # for reproducibility
717s  randp ("seed", 6);
717s  r1 = ricernd (1, 2, 3000, 1);
717s  randg ("seed", 2);    # for reproducibility
717s  randp ("seed", 8);
717s  r2 = ricernd (2, 4, 3000, 1);
717s  randg ("seed", 7);    # for reproducibility
717s  randp ("seed", 9);
717s  r3 = ricernd (7.5, 1, 3000, 1);
717s  r = [r1, r2, r3];
717s 
717s  ## Plot them normalized and fix their colors
717s  hist (r, 75, 4);
717s  h = findobj (gca, "Type", "patch");
717s  set (h(1), "facecolor", "c");
717s  set (h(2), "facecolor", "g");
717s  set (h(3), "facecolor", "r");
717s  ylim ([0, 0.7]);
717s  xlim ([0, 12]);
717s  hold on
717s 
717s  ## Estimate their α and β parameters
717s  s_sigmaA = ricefit (r(:,1));
717s  s_sigmaB = ricefit (r(:,2));
717s  s_sigmaC = ricefit (r(:,3));
717s 
717s  ## Plot their estimated PDFs
717s  x = [0.01,0.1:0.2:18];
717s  y = ricepdf (x, s_sigmaA(1), s_sigmaA(2));
717s  plot (x, y, "-pr");
717s  y = ricepdf (x, s_sigmaB(1), s_sigmaB(2));
717s  plot (x, y, "-sg");
717s  y = ricepdf (x, s_sigmaC(1), s_sigmaC(2));
717s  plot (x, y, "-^c");
717s  hold off
717s  legend ({"Normalized HIST of sample 1 with s=1 and σ=2", ...
717s           "Normalized HIST of sample 2 with s=2 and σ=4", ...
717s           "Normalized HIST of sample 3 with s=7.5 and σ=1", ...
717s           sprintf("PDF for sample 1 with estimated s=%0.2f and σ=%0.2f", ...
717s                   s_sigmaA(1), s_sigmaA(2)), ...
717s           sprintf("PDF for sample 2 with estimated s=%0.2f and σ=%0.2f", ...
717s                   s_sigmaB(1), s_sigmaB(2)), ...
717s           sprintf("PDF for sample 3 with estimated s=%0.2f and σ=%0.2f", ...
717s                   s_sigmaC(1), s_sigmaC(2))})
717s  title ("Three population samples from different Rician distibutions")
717s  hold off
717s ***** test
717s  [paramhat, paramci] = ricefit ([1:50]);
717s  assert (paramhat, [15.3057, 17.6668], 1e-4);
717s  assert (paramci, [9.5468, 11.7802; 24.5383, 26.4952], 1e-4);
717s ***** test
717s  [paramhat, paramci] = ricefit ([1:50], 0.01);
717s  assert (paramhat, [15.3057, 17.6668], 1e-4);
717s  assert (paramci, [8.2309, 10.3717; 28.4615, 30.0934], 1e-4);
718s ***** test
718s  [paramhat, paramci] = ricefit ([1:5]);
718s  assert (paramhat, [2.3123, 1.6812], 1e-4);
718s  assert (paramci, [1.0819, 0.6376; 4.9424, 4.4331], 1e-4);
718s ***** test
718s  [paramhat, paramci] = ricefit ([1:5], 0.01);
718s  assert (paramhat, [2.3123, 1.6812], 1e-4);
718s  assert (paramci, [0.8521, 0.4702; 6.2747, 6.0120], 1e-4);
718s ***** test
718s  freq = [1 1 1 1 5];
718s  [paramhat, paramci] = ricefit ([1:5], [], [], freq);
718s  assert (paramhat, [3.5181, 1.5565], 1e-4);
718s  assert (paramci, [2.5893, 0.9049; 4.7801, 2.6772], 1e-4);
718s ***** test
718s  censor = [1 0 0 0 0];
718s  [paramhat, paramci] = ricefit ([1:5], [], censor);
718s  assert (paramhat, [3.2978, 1.1527], 1e-4);
718s  assert (paramci, [2.3192, 0.5476; 4.6895, 2.4261], 1e-4);
718s ***** assert (class (ricefit (single ([1:50]))), "single")
718s ***** error<ricefit: X must be a vector.> ricefit (ones (2))
718s ***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], 1)
718s ***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], -1)
718s ***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], {0.05})
718s ***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], "k")
718s ***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], i)
718s ***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], [0.01 0.02])
718s ***** error<ricefit: X and CENSOR vectors mismatch.> ricefit ([1:50], [], [1 1])
718s ***** error<ricefit: X and FREQ vectors mismatch.> ricefit ([1:50], [], [], [1 1])
718s ***** error<ricefit: FREQ cannot have negative values.> ...
718s  ricefit ([1:5], [], [], [1, 1, 2, 1, -1])
718s ***** error<ricefit: X must contain positive values.> ricefit ([1 2 3 -4])
718s ***** error<ricefit: X must contain positive values.> ricefit ([1 2 0], [], [1 0 0])
718s 19 tests, 19 passed, 0 known failure, 0 skipped
718s [inst/dist_fit/ricelike.m]
718s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/ricelike.m
718s ***** test
718s  nlogL = ricelike ([15.3057344, 17.6668458], [1:50]);
718s  assert (nlogL, 204.5230311010569, 1e-12);
718s ***** test
718s  nlogL = ricelike ([2.312346885, 1.681228265], [1:5]);
718s  assert (nlogL, 8.65562164930058, 1e-12);
718s ***** error<ricelike: function called with too few input arguments.> ricelike (3.25)
718s ***** error<ricelike: X must be a vector.> ricelike ([5, 0.2], ones (2))
718s ***** error<ricelike: PARAMS must be a two-element vector.> ...
718s  ricelike ([1, 0.2, 3], [1, 3, 5, 7])
718s ***** error<ricelike: X and CENSOR vector mismatch.> ...
718s  ricelike ([1.5, 0.2], [1:5], [0, 0, 0])
718s ***** error<ricelike: X and FREQ vector mismatch.> ...
718s  ricelike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
718s ***** error<ricelike: X and FREQ vector mismatch.> ...
718s  ricelike ([1.5, 0.2], [1:5], [], [1, 1, 1])
718s ***** error<ricelike: FREQ must not contain negative values.> ...
718s  ricelike ([1.5, 0.2], [1:5], [], [1, 1, 1, 0, -1])
718s 9 tests, 9 passed, 0 known failure, 0 skipped
718s [inst/dist_fit/tlsfit.m]
718s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/tlsfit.m
718s ***** demo
718s  ## Sample 3 populations from 3 different location-scale T distibutions
718s  randn ("seed", 1);    # for reproducibility
718s  randg ("seed", 2);    # for reproducibility
718s  r1 = tlsrnd (-4, 3, 1, 2000, 1);
718s  randn ("seed", 3);    # for reproducibility
718s  randg ("seed", 4);    # for reproducibility
718s  r2 = tlsrnd (0, 3, 1, 2000, 1);
718s  randn ("seed", 5);    # for reproducibility
718s  randg ("seed", 6);    # for reproducibility
718s  r3 = tlsrnd (5, 5, 4, 2000, 1);
718s  r = [r1, r2, r3];
718s 
718s  ## Plot them normalized and fix their colors
718s  hist (r, [-21:21], [1, 1, 1]);
718s  h = findobj (gca, "Type", "patch");
718s  set (h(1), "facecolor", "c");
718s  set (h(2), "facecolor", "g");
718s  set (h(3), "facecolor", "r");
718s  ylim ([0, 0.25]);
718s  xlim ([-20, 20]);
718s  hold on
718s 
718s  ## Estimate their lambda parameter
718s  mu_sigma_nuA = tlsfit (r(:,1));
718s  mu_sigma_nuB = tlsfit (r(:,2));
718s  mu_sigma_nuC = tlsfit (r(:,3));
718s 
718s  ## Plot their estimated PDFs
718s  x = [-20:0.1:20];
718s  y = tlspdf (x, mu_sigma_nuA(1), mu_sigma_nuA(2), mu_sigma_nuA(3));
718s  plot (x, y, "-pr");
718s  y = tlspdf (x, mu_sigma_nuB(1), mu_sigma_nuB(2), mu_sigma_nuB(3));
718s  plot (x, y, "-sg");
718s  y = tlspdf (x, mu_sigma_nuC(1), mu_sigma_nuC(2), mu_sigma_nuC(3));
718s  plot (x, y, "-^c");
718s  hold off
718s  legend ({"Normalized HIST of sample 1 with μ=0, σ=2 and nu=1", ...
718s           "Normalized HIST of sample 2 with μ=5, σ=2 and nu=1", ...
718s           "Normalized HIST of sample 3 with μ=3, σ=4 and nu=3", ...
718s           sprintf("PDF for sample 1 with estimated μ=%0.2f, σ=%0.2f, and ν=%0.2f", ...
718s                   mu_sigma_nuA(1), mu_sigma_nuA(2), mu_sigma_nuA(3)), ...
718s           sprintf("PDF for sample 2 with estimated μ=%0.2f, σ=%0.2f, and ν=%0.2f", ...
718s                   mu_sigma_nuB(1), mu_sigma_nuB(2), mu_sigma_nuB(3)), ...
718s           sprintf("PDF for sample 3 with estimated μ=%0.2f, σ=%0.2f, and ν=%0.2f", ...
718s                   mu_sigma_nuC(1), mu_sigma_nuC(2), mu_sigma_nuC(3))})
718s  title ("Three population samples from different location-scale T distibutions")
718s  hold off
718s ***** test
718s  x = [-1.2352, -0.2741, 0.1726, 7.4356, 1.0392, 16.4165];
718s  [paramhat, paramci] = tlsfit (x);
718s  paramhat_out = [0.035893, 0.862711, 0.649261];
718s  paramci_out = [-0.949034, 0.154655, 0.181080; 1.02082, 4.812444, 2.327914];
718s  assert (paramhat, paramhat_out, 1e-6);
718s  assert (paramci, paramci_out, 1e-5);
718s ***** test
718s  x = [-1.2352, -0.2741, 0.1726, 7.4356, 1.0392, 16.4165];
718s  [paramhat, paramci] = tlsfit (x, 0.01);
718s  paramci_out = [-1.2585, 0.0901, 0.1212; 1.3303, 8.2591, 3.4771];
718s  assert (paramci, paramci_out, 1e-4);
718s ***** error<tlsfit: X must be a vector.> tlsfit (ones (2,5));
718s ***** error<tlsfit: wrong value for ALPHA.> tlsfit ([1, 2, 3, 4, 5], 1.2);
718s ***** error<tlsfit: wrong value for ALPHA.> tlsfit ([1, 2, 3, 4, 5], 0);
718s ***** error<tlsfit: wrong value for ALPHA.> tlsfit ([1, 2, 3, 4, 5], "alpha");
718s ***** error<tlsfit: X and CENSOR vectors mismatch.> ...
718s  tlsfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
718s ***** error<tlsfit: X and CENSOR vectors mismatch.> ...
718s  tlsfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
718s ***** error<tlsfit: X and FREQ vectors mismatch.> ...
718s  tlsfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
718s ***** error<tlsfit: X and FREQ vectors mismatch.> ...
718s  tlsfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
718s ***** error<tlsfit: FREQ cannot have negative values.> ...
718s  tlsfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 -1]);
718s ***** error<tlsfit: 'options' 5th argument must be a structure> ...
718s  tlsfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
718s 12 tests, 12 passed, 0 known failure, 0 skipped
718s [inst/dist_fit/tlslike.m]
718s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/tlslike.m
718s ***** test
718s  x = [-1.2352, -0.2741, 0.1726, 7.4356, 1.0392, 16.4165];
718s  [nlogL, acov] = tlslike ([0.035893, 0.862711, 0.649261], x);
718s  acov_out = [0.2525, 0.0670, 0.0288; ...
718s              0.0670, 0.5724, 0.1786; ...
718s              0.0288, 0.1786, 0.1789];
718s  assert (nlogL, 17.9979636579, 1e-10);
718s  assert (acov, acov_out, 1e-4);
718s ***** error<tlslike: too few input arguments.> tlslike ([12, 15, 1]);
718s ***** error<tlslike: wrong parameters length.> tlslike ([12, 15], [1:50]);
718s ***** error<tlslike: X must be a vector.> tlslike ([12, 3, 1], ones (10, 2));
718s ***** error<tlslike: X and CENSOR> tlslike ([12, 15, 1], [1:50], [1, 2, 3]);
718s ***** error<tlslike: X and FREQ> tlslike ([12, 15, 1], [1:50], [], [1, 2, 3]);
718s ***** error<tlslike: FREQ cannot> tlslike ([12, 15, 1], [1:3], [], [1, 2, -3]);
718s 7 tests, 7 passed, 0 known failure, 0 skipped
718s [inst/dist_fit/unidfit.m]
718s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/unidfit.m
718s ***** demo
718s  ## Sample 2 populations from different discrete uniform distibutions
718s  rand ("seed", 1);    # for reproducibility
718s  r1 = unidrnd (5, 1000, 1);
718s  rand ("seed", 2);    # for reproducibility
718s  r2 = unidrnd (9, 1000, 1);
718s  r = [r1, r2];
718s 
718s  ## Plot them normalized and fix their colors
718s  hist (r, 0:0.5:20.5, 1);
718s  h = findobj (gca, "Type", "patch");
718s  set (h(1), "facecolor", "c");
718s  set (h(2), "facecolor", "g");
718s  hold on
718s 
718s  ## Estimate their probability of success
718s  NhatA = unidfit (r(:,1));
718s  NhatB = unidfit (r(:,2));
718s 
718s  ## Plot their estimated PDFs
718s  x = [0:10];
718s  y = unidpdf (x, NhatA);
718s  plot (x, y, "-pg");
718s  y = unidpdf (x, NhatB);
718s  plot (x, y, "-sc");
718s  xlim ([0, 10])
718s  ylim ([0, 0.4])
718s  legend ({"Normalized HIST of sample 1 with N=5", ...
718s           "Normalized HIST of sample 2 with N=9", ...
718s           sprintf("PDF for sample 1 with estimated N=%0.2f", NhatA), ...
718s           sprintf("PDF for sample 2 with estimated N=%0.2f", NhatB)})
718s  title ("Two population samples from different discrete uniform distibutions")
718s  hold off
718s ***** test
718s  x = 0:5;
718s  [Nhat, Nci] = unidfit (x);
718s  assert (Nhat, 5);
718s  assert (Nci, [5; 9]);
718s ***** test
718s  x = 0:5;
718s  [Nhat, Nci] = unidfit (x, [], [1 1 1 1 1 1]);
718s  assert (Nhat, 5);
718s  assert (Nci, [5; 9]);
718s ***** assert (unidfit ([1 1 2 3]), unidfit ([1 2 3], [] ,[2 1 1]))
718s ***** error<unidfit: function called with too few input arguments.> unidfit ()
718s ***** error<unidfit: X cannot have negative values.> unidfit (-1, [1 2 3 3])
718s ***** error<unidfit: wrong value for ALPHA.> unidfit (1, 0)
718s ***** error<unidfit: wrong value for ALPHA.> unidfit (1, 1.2)
718s ***** error<unidfit: wrong value for ALPHA.> unidfit (1, [0.02 0.05])
718s ***** error<unidfit: X and FREQ vector mismatch.> ...
718s  unidfit ([1.5, 0.2], [], [0, 0, 0, 0, 0])
718s ***** error<unidfit: X and FREQ vector mismatch.> ...
718s  unidfit ([1.5, 0.2], [], [1, 1, 1])
718s ***** error<unidfit: FREQ cannot have negative values.> ...
718s  unidfit ([1.5, 0.2], [], [1, -1])
718s 11 tests, 11 passed, 0 known failure, 0 skipped
718s [inst/dist_fit/unifit.m]
718s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/unifit.m
718s ***** demo
718s  ## Sample 2 populations from different continuous uniform distibutions
718s  rand ("seed", 5);    # for reproducibility
718s  r1 = unifrnd (2, 5, 2000, 1);
718s  rand ("seed", 6);    # for reproducibility
718s  r2 = unifrnd (3, 9, 2000, 1);
718s  r = [r1, r2];
718s 
718s  ## Plot them normalized and fix their colors
718s  hist (r, 0:0.5:10, 2);
718s  h = findobj (gca, "Type", "patch");
718s  set (h(1), "facecolor", "c");
718s  set (h(2), "facecolor", "g");
718s  hold on
718s 
718s  ## Estimate their probability of success
718s  a_bA = unifit (r(:,1));
718s  a_bB = unifit (r(:,2));
718s 
718s  ## Plot their estimated PDFs
718s  x = [0:10];
718s  y = unifpdf (x, a_bA(1), a_bA(2));
718s  plot (x, y, "-pg");
718s  y = unifpdf (x, a_bB(1), a_bB(2));
718s  plot (x, y, "-sc");
718s  xlim ([1, 10])
718s  ylim ([0, 0.5])
718s  legend ({"Normalized HIST of sample 1 with a=2 and b=5", ...
718s           "Normalized HIST of sample 2 with a=3 and b=9", ...
718s           sprintf("PDF for sample 1 with estimated a=%0.2f and b=%0.2f", ...
718s                   a_bA(1), a_bA(2)), ...
718s           sprintf("PDF for sample 2 with estimated a=%0.2f and b=%0.2f", ...
718s                   a_bB(1), a_bB(2))})
718s  title ("Two population samples from different continuous uniform distibutions")
718s  hold off
718s ***** test
718s  x = 0:5;
718s  [paramhat, paramci] = unifit (x);
718s  assert (paramhat, [0, 5]);
718s  assert (paramci, [-3.2377, 8.2377; 0, 5], 1e-4);
718s ***** test
718s  x = 0:5;
718s  [paramhat, paramci] = unifit (x, [], [1 1 1 1 1 1]);
718s  assert (paramhat, [0, 5]);
718s  assert (paramci, [-3.2377, 8.2377; 0, 5], 1e-4);
718s ***** assert (unifit ([1 1 2 3]), unifit ([1 2 3], [] ,[2 1 1]))
718s ***** error<unifit: function called with too few input arguments.> unifit ()
718s ***** error<unifit: X cannot have negative values.> unifit (-1, [1 2 3 3])
718s ***** error<unifit: wrong value for ALPHA.> unifit (1, 0)
718s ***** error<unifit: wrong value for ALPHA.> unifit (1, 1.2)
718s ***** error<unifit: wrong value for ALPHA.> unifit (1, [0.02 0.05])
718s ***** error<unifit: X and FREQ vector mismatch.> ...
718s  unifit ([1.5, 0.2], [], [0, 0, 0, 0, 0])
718s ***** error<unifit: FREQ cannot have negative values.> ...
718s  unifit ([1.5, 0.2], [], [1, -1])
718s ***** error<unifit: X and FREQ vector mismatch.> ...
718s  unifit ([1.5, 0.2], [], [1, 1, 1])
718s 11 tests, 11 passed, 0 known failure, 0 skipped
718s [inst/dist_fit/wblfit.m]
718s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/wblfit.m
718s ***** demo
718s  ## Sample 3 populations from 3 different Weibull distibutions
718s  rande ("seed", 1);    # for reproducibility
718s  r1 = wblrnd(2, 4, 2000, 1);
718s  rande ("seed", 2);    # for reproducibility
718s  r2 = wblrnd(5, 2, 2000, 1);
718s  rande ("seed", 5);    # for reproducibility
718s  r3 = wblrnd(1, 5, 2000, 1);
718s  r = [r1, r2, r3];
718s 
718s  ## Plot them normalized and fix their colors
718s  hist (r, 30, [2.5 2.1 3.2]);
718s  h = findobj (gca, "Type", "patch");
718s  set (h(1), "facecolor", "c");
718s  set (h(2), "facecolor", "g");
718s  set (h(3), "facecolor", "r");
718s  ylim ([0, 2]);
718s  xlim ([0, 10]);
718s  hold on
718s 
718s  ## Estimate their lambda parameter
718s  lambda_kA = wblfit (r(:,1));
718s  lambda_kB = wblfit (r(:,2));
718s  lambda_kC = wblfit (r(:,3));
718s 
718s  ## Plot their estimated PDFs
718s  x = [0:0.1:15];
718s  y = wblpdf (x, lambda_kA(1), lambda_kA(2));
718s  plot (x, y, "-pr");
718s  y = wblpdf (x, lambda_kB(1), lambda_kB(2));
718s  plot (x, y, "-sg");
718s  y = wblpdf (x, lambda_kC(1), lambda_kC(2));
718s  plot (x, y, "-^c");
718s  hold off
718s  legend ({"Normalized HIST of sample 1 with λ=2 and k=4", ...
718s           "Normalized HIST of sample 2 with λ=5 and k=2", ...
718s           "Normalized HIST of sample 3 with λ=1 and k=5", ...
718s           sprintf("PDF for sample 1 with estimated λ=%0.2f and k=%0.2f", ...
718s                   lambda_kA(1), lambda_kA(2)), ...
718s           sprintf("PDF for sample 2 with estimated λ=%0.2f and k=%0.2f", ...
718s                   lambda_kB(1), lambda_kB(2)), ...
718s           sprintf("PDF for sample 3 with estimated λ=%0.2f and k=%0.2f", ...
718s                   lambda_kC(1), lambda_kC(2))})
718s  title ("Three population samples from different Weibull distibutions")
718s  hold off
718s ***** test
718s  x = 1:50;
718s  [paramhat, paramci] = wblfit (x);
718s  paramhat_out = [28.3636, 1.7130];
718s  paramci_out = [23.9531, 1.3551; 33.5861, 2.1655];
718s  assert (paramhat, paramhat_out, 1e-4);
718s  assert (paramci, paramci_out, 1e-4);
718s ***** test
718s  x = 1:50;
718s  [paramhat, paramci] = wblfit (x, 0.01);
718s  paramci_out = [22.7143, 1.2589; 35.4179, 2.3310];
718s  assert (paramci, paramci_out, 1e-4);
719s ***** error<wblfit: X must be a vector.> wblfit (ones (2,5));
719s ***** error<wblfit: X must contain only positive values.> wblfit ([-1 2 3 4]);
719s ***** error<wblfit: wrong value for ALPHA.> wblfit ([1, 2, 3, 4, 5], 1.2);
719s ***** error<wblfit: wrong value for ALPHA.> wblfit ([1, 2, 3, 4, 5], 0);
719s ***** error<wblfit: wrong value for ALPHA.> wblfit ([1, 2, 3, 4, 5], "alpha");
719s ***** error<wblfit: X and CENSOR vectors mismatch.> ...
719s  wblfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
719s ***** error<wblfit: X and CENSOR vectors mismatch.> ...
719s  wblfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
719s ***** error<wblfit: X and FREQ vectors mismatch.> ...
719s  wblfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
719s ***** error<wblfit: FREQ cannot have negative values.> ...
719s  wblfit ([1, 2, 3, 4, 5], [], [], [1 1 0 -1 1]);
719s ***** error<wblfit: X and FREQ vectors mismatch.> ...
719s  wblfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
719s ***** error<wblfit: 'options' 5th argument must be a structure> ...
719s  wblfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
719s 13 tests, 13 passed, 0 known failure, 0 skipped
719s [inst/dist_fit/wbllike.m]
719s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fit/wbllike.m
719s ***** test
719s  x = 1:50;
719s  [nlogL, acov] = wbllike ([2.3, 1.2], x);
719s  avar_out = [0.0250, 0.0062; 0.0062, 0.0017];
719s  assert (nlogL, 945.9589180651594, 1e-12);
719s  assert (acov, avar_out, 1e-4);
719s ***** test
719s  x = 1:50;
719s  [nlogL, acov] = wbllike ([2.3, 1.2], x * 0.5);
719s  avar_out = [-0.3238, -0.1112; -0.1112, -0.0376];
719s  assert (nlogL, 424.9879809704742, 6e-14);
719s  assert (acov, avar_out, 1e-4);
719s ***** test
719s  x = 1:50;
719s  [nlogL, acov] = wbllike ([21, 15], x);
719s  avar_out = [-0.00001236, -0.00001166; -0.00001166, -0.00001009];
719s  assert (nlogL, 1635190.328991511, 1e-8);
719s  assert (acov, avar_out, 1e-8);
719s ***** error<wbllike: too few input arguments.> wbllike ([12, 15]);
719s ***** error<wbllike: wrong parameters length.> wbllike ([12, 15, 3], [1:50]);
719s ***** error<wbllike: X must be a vector.> wbllike ([12, 3], ones (10, 2));
719s ***** error<wbllike: X and CENSOR> wbllike ([12, 15], [1:50], [1, 2, 3]);
719s ***** error<wbllike: X and FREQ> wbllike ([12, 15], [1:50], [], [1, 2, 3]);
719s ***** error<wbllike: FREQ cannot have negative values.> ...
719s  wbllike ([12, 15], [1:5], [], [1, 2, 3, -1, 0]);
719s 9 tests, 9 passed, 0 known failure, 0 skipped
719s [inst/dist_fun/betacdf.m]
719s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/betacdf.m
719s ***** demo
719s  ## Plot various CDFs from the Beta distribution
719s  x = 0:0.005:1;
719s  p1 = betacdf (x, 0.5, 0.5);
719s  p2 = betacdf (x, 5, 1);
719s  p3 = betacdf (x, 1, 3);
719s  p4 = betacdf (x, 2, 2);
719s  p5 = betacdf (x, 2, 5);
719s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
719s  grid on
719s  legend ({"α = β = 0.5", "α = 5, β = 1", "α = 1, β = 3", ...
719s           "α = 2, β = 2", "α = 2, β = 5"}, "location", "northwest")
719s  title ("Beta CDF")
719s  xlabel ("values in x")
719s  ylabel ("probability")
719s ***** shared x, y, x1, x2
719s  x = [-1 0 0.5 1 2];
719s  y = [0 0 0.75 1 1];
719s ***** assert (betacdf (x, ones (1, 5), 2 * ones (1, 5)), y)
719s ***** assert (betacdf (x, 1, 2 * ones (1, 5)), y)
719s ***** assert (betacdf (x, ones (1, 5), 2), y)
719s ***** assert (betacdf (x, [0 1 NaN 1 1], 2), [NaN 0 NaN 1 1])
719s ***** assert (betacdf (x, 1, 2 * [0 1 NaN 1 1]), [NaN 0 NaN 1 1])
719s ***** assert (betacdf ([x(1:2) NaN x(4:5)], 1, 2), [y(1:2) NaN y(4:5)])
719s  x1 = [0.1:0.2:0.9];
719s ***** assert (betacdf (x1, 2, 2), [0.028, 0.216, 0.5, 0.784, 0.972], 1e-14);
719s ***** assert (betacdf (x1, 2, 2, "upper"), 1 - [0.028, 0.216, 0.5, 0.784, 0.972],...
719s         1e-14);
719s  x2 = [1, 2, 3];
719s ***** assert (betacdf (0.5, x2, x2), [0.5, 0.5, 0.5], 1e-14);
719s ***** assert (betacdf ([x, NaN], 1, 2), [y, NaN])
719s ***** assert (betacdf (single ([x, NaN]), 1, 2), single ([y, NaN]))
719s ***** assert (betacdf ([x, NaN], single (1), 2), single ([y, NaN]))
719s ***** assert (betacdf ([x, NaN], 1, single (2)), single ([y, NaN]))
719s ***** error<betacdf: function called with too few input arguments.> betacdf ()
719s ***** error<betacdf: function called with too few input arguments.> betacdf (1)
719s ***** error<betacdf: function called with too few input arguments.> betacdf (1, 2)
719s ***** error<betacdf: function called with too many inputs> betacdf (1, 2, 3, 4, 5)
719s ***** error<betacdf: invalid argument for upper tail.> betacdf (1, 2, 3, "tail")
719s ***** error<betacdf: invalid argument for upper tail.> betacdf (1, 2, 3, 4)
719s ***** error<betacdf: X, A, and B must be of common size or scalars.> ...
719s  betacdf (ones (3), ones (2), ones (2))
719s ***** error<betacdf: X, A, and B must be of common size or scalars.> ...
719s  betacdf (ones (2), ones (3), ones (2))
719s ***** error<betacdf: X, A, and B must be of common size or scalars.> ...
719s  betacdf (ones (2), ones (2), ones (3))
719s ***** error<betacdf: X, A, and B must not be complex.> betacdf (i, 2, 2)
719s ***** error<betacdf: X, A, and B must not be complex.> betacdf (2, i, 2)
719s ***** error<betacdf: X, A, and B must not be complex.> betacdf (2, 2, i)
719s 25 tests, 25 passed, 0 known failure, 0 skipped
719s [inst/dist_fun/betainv.m]
719s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/betainv.m
719s ***** demo
719s  ## Plot various iCDFs from the Beta distribution
719s  p = 0.001:0.001:0.999;
719s  x1 = betainv (p, 0.5, 0.5);
719s  x2 = betainv (p, 5, 1);
719s  x3 = betainv (p, 1, 3);
719s  x4 = betainv (p, 2, 2);
719s  x5 = betainv (p, 2, 5);
719s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
719s  grid on
719s  legend ({"α = β = 0.5", "α = 5, β = 1", "α = 1, β = 3", ...
719s           "α = 2, β = 2", "α = 2, β = 5"}, "location", "southeast")
719s  title ("Beta iCDF")
719s  xlabel ("probability")
719s  ylabel ("values in x")
719s ***** shared p
719s  p = [-1 0 0.75 1 2];
719s ***** assert (betainv (p, ones (1,5), 2*ones (1,5)), [NaN 0 0.5 1 NaN], eps)
719s ***** assert (betainv (p, 1, 2*ones (1,5)), [NaN 0 0.5 1 NaN], eps)
719s ***** assert (betainv (p, ones (1,5), 2), [NaN 0 0.5 1 NaN], eps)
719s ***** assert (betainv (p, [1 0 NaN 1 1], 2), [NaN NaN NaN 1 NaN])
719s ***** assert (betainv (p, 1, 2*[1 0 NaN 1 1]), [NaN NaN NaN 1 NaN])
719s ***** assert (betainv ([p(1:2) NaN p(4:5)], 1, 2), [NaN 0 NaN 1 NaN])
719s ***** assert (betainv ([p, NaN], 1, 2), [NaN 0 0.5 1 NaN NaN], eps)
719s ***** assert (betainv (single ([p, NaN]), 1, 2), single ([NaN 0 0.5 1 NaN NaN]))
719s ***** assert (betainv ([p, NaN], single (1), 2), single ([NaN 0 0.5 1 NaN NaN]), eps("single"))
719s ***** assert (betainv ([p, NaN], 1, single (2)), single ([NaN 0 0.5 1 NaN NaN]), eps("single"))
719s ***** error<betainv: function called with too few input arguments.> betainv ()
719s ***** error<betainv: function called with too few input arguments.> betainv (1)
719s ***** error<betainv: function called with too few input arguments.> betainv (1,2)
719s ***** error<betainv: function called with too many inputs> betainv (1,2,3,4)
719s ***** error<betainv: P, A, and B must be of common size or scalars.> ...
719s  betainv (ones (3), ones (2), ones (2))
719s ***** error<betainv: P, A, and B must be of common size or scalars.> ...
719s  betainv (ones (2), ones (3), ones (2))
719s ***** error<betainv: P, A, and B must be of common size or scalars.> ...
719s  betainv (ones (2), ones (2), ones (3))
719s ***** error<betainv: P, A, and B must not be complex.> betainv (i, 2, 2)
719s ***** error<betainv: P, A, and B must not be complex.> betainv (2, i, 2)
719s ***** error<betainv: P, A, and B must not be complex.> betainv (2, 2, i)
719s 20 tests, 20 passed, 0 known failure, 0 skipped
719s [inst/dist_fun/betapdf.m]
719s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/betapdf.m
719s ***** demo
719s  ## Plot various PDFs from the Beta distribution
719s  x = 0.001:0.001:0.999;
719s  y1 = betapdf (x, 0.5, 0.5);
719s  y2 = betapdf (x, 5, 1);
719s  y3 = betapdf (x, 1, 3);
719s  y4 = betapdf (x, 2, 2);
719s  y5 = betapdf (x, 2, 5);
719s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
719s  grid on
719s  ylim ([0, 2.5])
719s  legend ({"α = β = 0.5", "α = 5, β = 1", "α = 1, β = 3", ...
719s           "α = 2, β = 2", "α = 2, β = 5"}, "location", "north")
719s  title ("Beta PDF")
719s  xlabel ("values in x")
719s  ylabel ("density")
719s ***** shared x, y
719s  x = [-1 0 0.5 1 2];
719s  y = [0 2 1 0 0];
719s ***** assert (betapdf (x, ones (1, 5), 2 * ones (1, 5)), y)
719s ***** assert (betapdf (x, 1, 2 * ones (1, 5)), y)
719s ***** assert (betapdf (x, ones (1, 5), 2), y)
719s ***** assert (betapdf (x, [0 NaN 1 1 1], 2), [NaN NaN y(3:5)])
719s ***** assert (betapdf (x, 1, 2 * [0 NaN 1 1 1]), [NaN NaN y(3:5)])
719s ***** assert (betapdf ([x, NaN], 1, 2), [y, NaN])
719s ***** assert (betapdf (single ([x, NaN]), 1, 2), single ([y, NaN]))
719s ***** assert (betapdf ([x, NaN], single (1), 2), single ([y, NaN]))
719s ***** assert (betapdf ([x, NaN], 1, single (2)), single ([y, NaN]))
719s ***** test
719s  x = rand (10,1);
719s  y = 1 ./ (pi * sqrt (x .* (1 - x)));
719s  assert (betapdf (x, 1/2, 1/2), y, 1e-12);
719s ***** assert (betapdf (0.5, 1000, 1000), 35.678, 1e-3)
719s ***** error<betapdf: function called with too few input arguments.> betapdf ()
719s ***** error<betapdf: function called with too few input arguments.> betapdf (1)
719s ***** error<betapdf: function called with too few input arguments.> betapdf (1,2)
719s ***** error<betapdf: function called with too many inputs> betapdf (1,2,3,4)
719s ***** error<betapdf: X, A, and B must be of common size or scalars.> ...
719s  betapdf (ones (3), ones (2), ones (2))
719s ***** error<betapdf: X, A, and B must be of common size or scalars.> ...
719s  betapdf (ones (2), ones (3), ones (2))
719s ***** error<betapdf: X, A, and B must be of common size or scalars.> ...
719s  betapdf (ones (2), ones (2), ones (3))
719s ***** error<betapdf: X, A, and B must not be complex.> betapdf (i, 2, 2)
719s ***** error<betapdf: X, A, and B must not be complex.> betapdf (2, i, 2)
719s ***** error<betapdf: X, A, and B must not be complex.> betapdf (2, 2, i)
719s 21 tests, 21 passed, 0 known failure, 0 skipped
719s [inst/dist_fun/betarnd.m]
719s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/betarnd.m
719s ***** assert (size (betarnd (2, 1/2)), [1 1])
719s ***** assert (size (betarnd (2 * ones (2, 1), 1/2)), [2, 1])
719s ***** assert (size (betarnd (2 * ones (2, 2), 1/2)), [2, 2])
719s ***** assert (size (betarnd (2, 1/2 * ones (2, 1))), [2, 1])
719s ***** assert (size (betarnd (1, 1/2 * ones (2, 2))), [2, 2])
719s ***** assert (size (betarnd (ones (2, 1), 1)), [2, 1])
719s ***** assert (size (betarnd (ones (2, 2), 1)), [2, 2])
719s ***** assert (size (betarnd (2, 1/2, 3)), [3, 3])
719s ***** assert (size (betarnd (1, 1, [4, 1])), [4, 1])
719s ***** assert (size (betarnd (1, 1, 4, 1)), [4, 1])
719s ***** assert (size (betarnd (1, 1, 4, 1, 5)), [4, 1, 5])
719s ***** assert (size (betarnd (1, 1, 0, 1)), [0, 1])
719s ***** assert (size (betarnd (1, 1, 1, 0)), [1, 0])
719s ***** assert (size (betarnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
719s ***** assert (class (betarnd (1, 1)), "double")
719s ***** assert (class (betarnd (1, single (0))), "single")
719s ***** assert (class (betarnd (1, single ([0, 0]))), "single")
719s ***** assert (class (betarnd (1, single (1), 2)), "single")
719s ***** assert (class (betarnd (1, single ([1, 1]), 1, 2)), "single")
719s ***** assert (class (betarnd (single (1), 1, 2)), "single")
719s ***** assert (class (betarnd (single ([1, 1]), 1, 1, 2)), "single")
719s ***** error<betarnd: function called with too few input arguments.> betarnd ()
719s ***** error<betarnd: function called with too few input arguments.> betarnd (1)
719s ***** error<betarnd: A and B must be of common size or scalars.> ...
719s  betarnd (ones (3), ones (2))
719s ***** error<betarnd: A and B must be of common size or scalars.> ...
719s  betarnd (ones (2), ones (3))
719s ***** error<betarnd: A and B must not be complex.> betarnd (i, 2)
719s ***** error<betarnd: A and B must not be complex.> betarnd (1, i)
719s ***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
719s  betarnd (1, 1/2, -1)
719s ***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
719s  betarnd (1, 1/2, 1.2)
719s ***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
719s  betarnd (1, 1/2, ones (2))
719s ***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
719s  betarnd (1, 1/2, [2 -1 2])
719s ***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
719s  betarnd (1, 1/2, [2 0 2.5])
719s ***** error<betarnd: dimensions must be non-negative integers.> ...
719s  betarnd (1, 1/2, 2, -1, 5)
719s ***** error<betarnd: dimensions must be non-negative integers.> ...
719s  betarnd (1, 1/2, 2, 1.5, 5)
719s ***** error<betarnd: A and B must be scalars or of size SZ.> ...
719s  betarnd (2, 1/2 * ones (2), 3)
719s ***** error<betarnd: A and B must be scalars or of size SZ.> ...
719s  betarnd (2, 1/2 * ones (2), [3, 2])
719s ***** error<betarnd: A and B must be scalars or of size SZ.> ...
719s  betarnd (2, 1/2 * ones (2), 3, 2)
719s ***** error<betarnd: A and B must be scalars or of size SZ.> ...
719s  betarnd (2 * ones (2), 1/2, 3)
719s ***** error<betarnd: A and B must be scalars or of size SZ.> ...
719s  betarnd (2 * ones (2), 1/2, [3, 2])
719s ***** error<betarnd: A and B must be scalars or of size SZ.> ...
719s  betarnd (2 * ones (2), 1/2, 3, 2)
719s 40 tests, 40 passed, 0 known failure, 0 skipped
719s [inst/dist_fun/binocdf.m]
719s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/binocdf.m
719s ***** demo
719s  ## Plot various CDFs from the binomial distribution
719s  x = 0:40;
719s  p1 = binocdf (x, 20, 0.5);
719s  p2 = binocdf (x, 20, 0.7);
719s  p3 = binocdf (x, 40, 0.5);
719s  plot (x, p1, "*b", x, p2, "*g", x, p3, "*r")
719s  grid on
719s  legend ({"n = 20, ps = 0.5", "n = 20, ps = 0.7", ...
719s           "n = 40, ps = 0.5"}, "location", "southeast")
719s  title ("Binomial CDF")
719s  xlabel ("values in x (number of successes)")
719s  ylabel ("probability")
719s ***** shared x, p, p1
719s  x = [-1 0 1 2 3];
719s  p = [0 1/4 3/4 1 1];
719s  p1 = 1 - p;
719s ***** assert (binocdf (x, 2 * ones (1, 5), 0.5 * ones (1, 5)), p, eps)
719s ***** assert (binocdf (x, 2, 0.5 * ones (1, 5)), p, eps)
719s ***** assert (binocdf (x, 2 * ones (1, 5), 0.5), p, eps)
719s ***** assert (binocdf (x, 2 * [0 -1 NaN 1.1 1], 0.5), [0 NaN NaN NaN 1])
719s ***** assert (binocdf (x, 2, 0.5 * [0 -1 NaN 3 1]), [0 NaN NaN NaN 1])
719s ***** assert (binocdf ([x(1:2) NaN x(4:5)], 2, 0.5), [p(1:2) NaN p(4:5)], eps)
719s ***** assert (binocdf (99, 100, 0.1, "upper"), 1e-100, 1e-112);
719s ***** assert (binocdf (x, 2 * ones (1, 5), 0.5*ones (1,5), "upper"), p1, eps)
719s ***** assert (binocdf (x, 2, 0.5 * ones (1, 5), "upper"), p1, eps)
719s ***** assert (binocdf (x, 2 * ones (1, 5), 0.5, "upper"), p1, eps)
719s ***** assert (binocdf (x, 2 * [0 -1 NaN 1.1 1], 0.5, "upper"), [1 NaN NaN NaN 0])
719s ***** assert (binocdf (x, 2, 0.5 * [0 -1 NaN 3 1], "upper"), [1 NaN NaN NaN 0])
719s ***** assert (binocdf ([x(1:2) NaN x(4:5)], 2, 0.5, "upper"), [p1(1:2) NaN p1(4:5)])
719s ***** assert (binocdf ([x, NaN], 2, 0.5), [p, NaN], eps)
719s ***** assert (binocdf (single ([x, NaN]), 2, 0.5), single ([p, NaN]))
719s ***** assert (binocdf ([x, NaN], single (2), 0.5), single ([p, NaN]))
719s ***** assert (binocdf ([x, NaN], 2, single (0.5)), single ([p, NaN]))
719s ***** error<binocdf: function called with too few input arguments.> binocdf ()
719s ***** error<binocdf: function called with too few input arguments.> binocdf (1)
719s ***** error<binocdf: function called with too few input arguments.> binocdf (1, 2)
719s ***** error<binocdf: function called with too many inputs> binocdf (1, 2, 3, 4, 5)
719s ***** error<binocdf: invalid argument for upper tail.> binocdf (1, 2, 3, "tail")
719s ***** error<binocdf: invalid argument for upper tail.> binocdf (1, 2, 3, 4)
719s ***** error<binocdf: X, N, and PS must be of common size or scalars.> ...
719s  binocdf (ones (3), ones (2), ones (2))
719s ***** error<binocdf: X, N, and PS must be of common size or scalars.> ...
719s  binocdf (ones (2), ones (3), ones (2))
719s ***** error<binocdf: X, N, and PS must be of common size or scalars.> ...
719s  binocdf (ones (2), ones (2), ones (3))
719s ***** error<binocdf: X, N, and PS must not be complex.> binocdf (i, 2, 2)
719s ***** error<binocdf: X, N, and PS must not be complex.> binocdf (2, i, 2)
719s ***** error<binocdf: X, N, and PS must not be complex.> binocdf (2, 2, i)
719s 29 tests, 29 passed, 0 known failure, 0 skipped
719s [inst/dist_fun/binoinv.m]
719s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/binoinv.m
719s ***** demo
719s  ## Plot various iCDFs from the binomial distribution
719s  p = 0.001:0.001:0.999;
719s  x1 = binoinv (p, 20, 0.5);
719s  x2 = binoinv (p, 20, 0.7);
719s  x3 = binoinv (p, 40, 0.5);
719s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
719s  grid on
719s  legend ({"n = 20, ps = 0.5", "n = 20, ps = 0.7", ...
719s           "n = 40, ps = 0.5"}, "location", "southeast")
719s  title ("Binomial iCDF")
719s  xlabel ("probability")
719s  ylabel ("values in x (number of successes)")
719s ***** shared p
719s  p = [-1 0 0.5 1 2];
719s ***** assert (binoinv (p, 2*ones (1,5), 0.5*ones (1,5)), [NaN 0 1 2 NaN])
719s ***** assert (binoinv (p, 2, 0.5*ones (1,5)), [NaN 0 1 2 NaN])
719s ***** assert (binoinv (p, 2*ones (1,5), 0.5), [NaN 0 1 2 NaN])
719s ***** assert (binoinv (p, 2*[0 -1 NaN 1.1 1], 0.5), [NaN NaN NaN NaN NaN])
719s ***** assert (binoinv (p, 2, 0.5*[0 -1 NaN 3 1]), [NaN NaN NaN NaN NaN])
719s ***** assert (binoinv ([p(1:2) NaN p(4:5)], 2, 0.5), [NaN 0 NaN 2 NaN])
719s ***** assert (binoinv ([p, NaN], 2, 0.5), [NaN 0 1 2 NaN NaN])
719s ***** assert (binoinv (single ([p, NaN]), 2, 0.5), single ([NaN 0 1 2 NaN NaN]))
719s ***** assert (binoinv ([p, NaN], single (2), 0.5), single ([NaN 0 1 2 NaN NaN]))
719s ***** assert (binoinv ([p, NaN], 2, single (0.5)), single ([NaN 0 1 2 NaN NaN]))
719s ***** shared x, tol
719s  x = magic (3) + 1;
719s  tol = 1;
719s ***** assert (binoinv (binocdf (1:10, 11, 0.1), 11, 0.1), 1:10, tol)
719s ***** assert (binoinv (binocdf (1:10, 2*(1:10), 0.1), 2*(1:10), 0.1), 1:10, tol)
719s ***** assert (binoinv (binocdf (x, 2*x, 1./x), 2*x, 1./x), x, tol)
719s ***** error<binoinv: function called with too few input arguments.> binoinv ()
719s ***** error<binoinv: function called with too few input arguments.> binoinv (1)
719s ***** error<binoinv: function called with too few input arguments.> binoinv (1,2)
719s ***** error<binoinv: function called with too many inputs> binoinv (1,2,3,4)
719s ***** error<binoinv: P, N, and PS must be of common size or scalars.> ...
719s  binoinv (ones (3), ones (2), ones (2))
719s ***** error<binoinv: P, N, and PS must be of common size or scalars.> ...
719s  binoinv (ones (2), ones (3), ones (2))
719s ***** error<binoinv: P, N, and PS must be of common size or scalars.> ...
719s  binoinv (ones (2), ones (2), ones (3))
719s ***** error<binoinv: P, N, and PS must not be complex.> binoinv (i, 2, 2)
719s ***** error<binoinv: P, N, and PS must not be complex.> binoinv (2, i, 2)
719s ***** error<binoinv: P, N, and PS must not be complex.> binoinv (2, 2, i)
719s 23 tests, 23 passed, 0 known failure, 0 skipped
719s [inst/dist_fun/binopdf.m]
719s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/binopdf.m
719s ***** demo
719s  ## Plot various PDFs from the binomial distribution
719s  x = 0:40;
719s  y1 = binopdf (x, 20, 0.5);
719s  y2 = binopdf (x, 20, 0.7);
719s  y3 = binopdf (x, 40, 0.5);
719s  plot (x, y1, "*b", x, y2, "*g", x, y3, "*r")
719s  grid on
719s  ylim ([0, 0.25])
719s  legend ({"n = 20, ps = 0.5", "n = 20, ps = 0.7", ...
719s           "n = 40, ps = 0.5"}, "location", "northeast")
719s  title ("Binomial PDF")
719s  xlabel ("values in x (number of successes)")
719s  ylabel ("density")
719s ***** shared x, y
719s  x = [-1 0 1 2 3];
719s  y = [0 1/4 1/2 1/4 0];
719s ***** assert (binopdf (x, 2 * ones (1, 5), 0.5 * ones (1, 5)), y, eps)
719s ***** assert (binopdf (x, 2, 0.5 * ones (1, 5)), y, eps)
719s ***** assert (binopdf (x, 2 * ones (1, 5), 0.5), y, eps)
719s ***** assert (binopdf (x, 2 * [0 -1 NaN 1.1 1], 0.5), [0 NaN NaN NaN 0])
719s ***** assert (binopdf (x, 2, 0.5 * [0 -1 NaN 3 1]), [0 NaN NaN NaN 0])
719s ***** assert (binopdf ([x, NaN], 2, 0.5), [y, NaN], eps)
719s ***** assert (binopdf (cat (3, x, x), 2, 0.5), cat (3, y, y), eps)
719s ***** assert (binopdf (1, 1, 1), 1)
719s ***** assert (binopdf (0, 3, 0), 1)
719s ***** assert (binopdf (2, 2, 1), 1)
719s ***** assert (binopdf (1, 2, 1), 0)
719s ***** assert (binopdf (0, 1.1, 0), NaN)
719s ***** assert (binopdf (1, 2, -1), NaN)
719s ***** assert (binopdf (1, 2, 1.5), NaN)
719s ***** assert (binopdf ([], 1, 1), [])
719s ***** assert (binopdf (1, [], 1), [])
719s ***** assert (binopdf (1, 1, []), [])
719s ***** assert (binopdf (ones (1, 0), 2, .5), ones(1, 0))
719s ***** assert (binopdf (ones (0, 1), 2, .5), ones(0, 1))
719s ***** assert (binopdf (ones (0, 1, 2), 2, .5), ones(0, 1, 2))
719s ***** assert (binopdf (1, ones (0, 1, 2), .5), ones(0, 1, 2))
719s ***** assert (binopdf (1, 2, ones (0, 1, 2)), ones(0, 1, 2))
719s ***** assert (binopdf (ones (1, 0, 2), 2, .5), ones(1, 0, 2))
719s ***** assert (binopdf (ones (1, 2, 0), 2, .5), ones(1, 2, 0))
719s ***** assert (binopdf (ones (0, 1, 2), NaN, .5), ones(0, 1, 2))
719s ***** assert (binopdf (ones (0, 1, 2), 2, NaN), ones(0, 1, 2))
719s ***** assert (binopdf (single ([x, NaN]), 2, 0.5), single ([y, NaN]))
719s ***** assert (binopdf ([x, NaN], single (2), 0.5), single ([y, NaN]))
719s ***** assert (binopdf ([x, NaN], 2, single (0.5)), single ([y, NaN]))
719s ***** error<binopdf: function called with too few input arguments.> binopdf ()
719s ***** error<binopdf: function called with too few input arguments.> binopdf (1)
719s ***** error<binopdf: function called with too few input arguments.> binopdf (1, 2)
719s ***** error<binopdf: function called with too many inputs> binopdf (1, 2, 3, 4)
719s ***** error<binopdf: X, N, and PS must be of common size or scalars.> ...
719s  binopdf (ones (3), ones (2), ones (2))
719s ***** error<binopdf: X, N, and PS must be of common size or scalars.> ...
719s  binopdf (ones (2), ones (3), ones (2))
719s ***** error<binopdf: X, N, and PS must be of common size or scalars.> ...
719s  binopdf (ones (2), ones (2), ones (3))
719s ***** error<binopdf: X, N, and PS must not be complex.> binopdf (i, 2, 2)
719s ***** error<binopdf: X, N, and PS must not be complex.> binopdf (2, i, 2)
719s ***** error<binopdf: X, N, and PS must not be complex.> binopdf (2, 2, i)
719s 39 tests, 39 passed, 0 known failure, 0 skipped
719s [inst/dist_fun/binornd.m]
719s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/binornd.m
719s ***** assert (size (binornd (2, 1/2)), [1 1])
719s ***** assert (size (binornd (2 * ones (2, 1), 1/2)), [2, 1])
719s ***** assert (size (binornd (2 * ones (2, 2), 1/2)), [2, 2])
719s ***** assert (size (binornd (2, 1/2 * ones (2, 1))), [2, 1])
719s ***** assert (size (binornd (1, 1/2 * ones (2, 2))), [2, 2])
719s ***** assert (size (binornd (ones (2, 1), 1)), [2, 1])
719s ***** assert (size (binornd (ones (2, 2), 1)), [2, 2])
719s ***** assert (size (binornd (2, 1/2, 3)), [3, 3])
719s ***** assert (size (binornd (1, 1, [4, 1])), [4, 1])
719s ***** assert (size (binornd (1, 1, 4, 1)), [4, 1])
719s ***** assert (size (binornd (1, 1, 4, 1, 5)), [4, 1, 5])
719s ***** assert (size (binornd (1, 1, 0, 1)), [0, 1])
719s ***** assert (size (binornd (1, 1, 1, 0)), [1, 0])
719s ***** assert (size (binornd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
719s ***** assert (class (binornd (1, 1)), "double")
719s ***** assert (class (binornd (1, single (0))), "single")
719s ***** assert (class (binornd (1, single ([0, 0]))), "single")
719s ***** assert (class (binornd (1, single (1), 2)), "single")
719s ***** assert (class (binornd (1, single ([1, 1]), 1, 2)), "single")
719s ***** assert (class (binornd (single (1), 1, 2)), "single")
719s ***** assert (class (binornd (single ([1, 1]), 1, 1, 2)), "single")
719s ***** error<binornd: function called with too few input arguments.> binornd ()
719s ***** error<binornd: function called with too few input arguments.> binornd (1)
719s ***** error<binornd: N and PS must be of common size or scalars.> ...
719s  binornd (ones (3), ones (2))
719s ***** error<binornd: N and PS must be of common size or scalars.> ...
719s  binornd (ones (2), ones (3))
719s ***** error<binornd: N and PS must not be complex.> binornd (i, 2)
719s ***** error<binornd: N and PS must not be complex.> binornd (1, i)
719s ***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
719s  binornd (1, 1/2, -1)
719s ***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
719s  binornd (1, 1/2, 1.2)
719s ***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
719s  binornd (1, 1/2, ones (2))
719s ***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
719s  binornd (1, 1/2, [2 -1 2])
719s ***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
719s  binornd (1, 1/2, [2 0 2.5])
719s ***** error<binornd: dimensions must be non-negative integers.> ...
719s  binornd (1, 1/2, 2, -1, 5)
719s ***** error<binornd: dimensions must be non-negative integers.> ...
719s  binornd (1, 1/2, 2, 1.5, 5)
719s ***** error<binornd: N and PS must be scalars or of size SZ.> ...
719s  binornd (2, 1/2 * ones (2), 3)
719s ***** error<binornd: N and PS must be scalars or of size SZ.> ...
719s  binornd (2, 1/2 * ones (2), [3, 2])
719s ***** error<binornd: N and PS must be scalars or of size SZ.> ...
719s  binornd (2, 1/2 * ones (2), 3, 2)
719s ***** error<binornd: N and PS must be scalars or of size SZ.> ...
719s  binornd (2 * ones (2), 1/2, 3)
719s ***** error<binornd: N and PS must be scalars or of size SZ.> ...
719s  binornd (2 * ones (2), 1/2, [3, 2])
719s ***** error<binornd: N and PS must be scalars or of size SZ.> ...
719s  binornd (2 * ones (2), 1/2, 3, 2)
719s 40 tests, 40 passed, 0 known failure, 0 skipped
719s [inst/dist_fun/bisacdf.m]
719s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/bisacdf.m
719s ***** demo
719s  ## Plot various CDFs from the Birnbaum-Saunders distribution
719s  x = 0.01:0.01:4;
719s  p1 = bisacdf (x, 1, 0.5);
719s  p2 = bisacdf (x, 1, 1);
719s  p3 = bisacdf (x, 1, 2);
719s  p4 = bisacdf (x, 1, 5);
719s  p5 = bisacdf (x, 1, 10);
719s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
719s  grid on
719s  legend ({"β = 1, γ = 0.5", "β = 1, γ = 1", "β = 1, γ = 2", ...
719s           "β = 1, γ = 5", "β = 1, γ = 10"}, "location", "southeast")
719s  title ("Birnbaum-Saunders CDF")
719s  xlabel ("values in x")
719s  ylabel ("probability")
719s ***** demo
719s  ## Plot various CDFs from the Birnbaum-Saunders distribution
719s  x = 0.01:0.01:6;
719s  p1 = bisacdf (x, 1, 0.3);
719s  p2 = bisacdf (x, 2, 0.3);
719s  p3 = bisacdf (x, 1, 0.5);
719s  p4 = bisacdf (x, 3, 0.5);
719s  p5 = bisacdf (x, 5, 0.5);
719s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
719s  grid on
719s  legend ({"β = 1, γ = 0.3", "β = 2, γ = 0.3", "β = 1, γ = 0.5", ...
719s           "β = 3, γ = 0.5", "β = 5, γ = 0.5"}, "location", "southeast")
719s  title ("Birnbaum-Saunders CDF")
719s  xlabel ("values in x")
719s  ylabel ("probability")
719s ***** shared x, y
719s  x = [-1, 0, 1, 2, Inf];
719s  y = [0, 0, 1/2, 0.76024993890652337, 1];
719s ***** assert (bisacdf (x, ones (1,5), ones (1,5)), y, eps)
719s ***** assert (bisacdf (x, 1, 1), y, eps)
719s ***** assert (bisacdf (x, 1, ones (1,5)), y, eps)
719s ***** assert (bisacdf (x, ones (1,5), 1), y, eps)
719s ***** assert (bisacdf (x, 1, 1), y, eps)
719s ***** assert (bisacdf (x, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)], eps)
719s ***** assert (bisacdf (x, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)], eps)
719s ***** assert (bisacdf ([x, NaN], 1, 1), [y, NaN], eps)
719s ***** assert (bisacdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
719s ***** assert (bisacdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
719s ***** assert (bisacdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
719s ***** error<bisacdf: function called with too few input arguments.> bisacdf ()
719s ***** error<bisacdf: function called with too few input arguments.> bisacdf (1)
719s ***** error<bisacdf: function called with too few input arguments.> bisacdf (1, 2)
719s ***** error<bisacdf: function called with too many inputs> ...
719s  bisacdf (1, 2, 3, 4, 5)
719s ***** error<bisacdf: invalid argument for upper tail.> bisacdf (1, 2, 3, "tail")
719s ***** error<bisacdf: invalid argument for upper tail.> bisacdf (1, 2, 3, 4)
719s ***** error<bisacdf: X, BETA, and GAMMA must be of common size or scalars.> ...
719s  bisacdf (ones (3), ones (2), ones(2))
719s ***** error<bisacdf: X, BETA, and GAMMA must be of common size or scalars.> ...
719s  bisacdf (ones (2), ones (3), ones(2))
719s ***** error<bisacdf: X, BETA, and GAMMA must be of common size or scalars.> ...
719s  bisacdf (ones (2), ones (2), ones(3))
719s ***** error<bisacdf: X, BETA, and GAMMA must not be complex.> bisacdf (i, 4, 3)
719s ***** error<bisacdf: X, BETA, and GAMMA must not be complex.> bisacdf (1, i, 3)
719s ***** error<bisacdf: X, BETA, and GAMMA must not be complex.> bisacdf (1, 4, i)
719s 23 tests, 23 passed, 0 known failure, 0 skipped
719s [inst/dist_fun/bisainv.m]
719s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/bisainv.m
719s ***** demo
719s  ## Plot various iCDFs from the Birnbaum-Saunders distribution
719s  p = 0.001:0.001:0.999;
719s  x1 = bisainv (p, 1, 0.5);
719s  x2 = bisainv (p, 1, 1);
719s  x3 = bisainv (p, 1, 2);
719s  x4 = bisainv (p, 1, 5);
719s  x5 = bisainv (p, 1, 10);
719s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
719s  grid on
719s  ylim ([0, 10])
719s  legend ({"β = 1, γ = 0.5", "β = 1, γ = 1", "β = 1, γ = 2", ...
719s           "β = 1, γ = 5", "β = 1, γ = 10"}, "location", "northwest")
719s  title ("Birnbaum-Saunders iCDF")
719s  xlabel ("probability")
719s  ylabel ("values in x")
719s ***** demo
719s  ## Plot various iCDFs from the Birnbaum-Saunders distribution
719s  p = 0.001:0.001:0.999;
719s  x1 = bisainv (p, 1, 0.3);
719s  x2 = bisainv (p, 2, 0.3);
719s  x3 = bisainv (p, 1, 0.5);
719s  x4 = bisainv (p, 3, 0.5);
719s  x5 = bisainv (p, 5, 0.5);
719s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
719s  grid on
719s  ylim ([0, 10])
719s  legend ({"β = 1, γ = 0.3", "β = 2, γ = 0.3", "β = 1, γ = 0.5", ...
719s           "β = 3, γ = 0.5", "β = 5, γ = 0.5"}, "location", "northwest")
719s  title ("Birnbaum-Saunders iCDF")
719s  xlabel ("probability")
719s  ylabel ("values in x")
719s ***** shared p, y, f
719s  f = @(p,b,c) (b * (c * norminv (p) + sqrt (4 + (c * norminv(p))^2))^2) / 4;
719s  p = [-1, 0, 1/4, 1/2, 1, 2];
719s  y = [NaN, 0, f(1/4, 1, 1), 1, Inf, NaN];
719s ***** assert (bisainv (p, ones (1,6), ones (1,6)), y)
719s ***** assert (bisainv (p, 1, ones (1,6)), y)
719s ***** assert (bisainv (p, ones (1,6), 1), y)
719s ***** assert (bisainv (p, 1, 1), y)
719s ***** assert (bisainv (p, 1, [1, 1, 1, NaN, 1, 1]), [y(1:3), NaN, y(5:6)])
719s ***** assert (bisainv (p, [1, 1, 1, NaN, 1, 1], 1), [y(1:3), NaN, y(5:6)])
720s ***** assert (bisainv ([p, NaN], 1, 1), [y, NaN])
720s ***** assert (bisainv (single ([p, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
720s ***** assert (bisainv ([p, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
720s ***** assert (bisainv ([p, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
720s ***** error<bisainv: function called with too few input arguments.> bisainv ()
720s ***** error<bisainv: function called with too few input arguments.> bisainv (1)
720s ***** error<bisainv: function called with too few input arguments.> bisainv (1, 2)
720s ***** error<bisainv: function called with too many inputs> bisainv (1, 2, 3, 4)
720s ***** error<bisainv: P, BETA, and GAMMA must be of common size or scalars.> ...
720s  bisainv (ones (3), ones (2), ones(2))
720s ***** error<bisainv: P, BETA, and GAMMA must be of common size or scalars.> ...
720s  bisainv (ones (2), ones (3), ones(2))
720s ***** error<bisainv: P, BETA, and GAMMA must be of common size or scalars.> ...
720s  bisainv (ones (2), ones (2), ones(3))
720s ***** error<bisainv: P, BETA, and GAMMA must not be complex.> bisainv (i, 4, 3)
720s ***** error<bisainv: P, BETA, and GAMMA must not be complex.> bisainv (1, i, 3)
720s ***** error<bisainv: P, BETA, and GAMMA must not be complex.> bisainv (1, 4, i)
720s 20 tests, 20 passed, 0 known failure, 0 skipped
720s [inst/dist_fun/bisapdf.m]
720s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/bisapdf.m
720s ***** demo
720s  ## Plot various PDFs from the Birnbaum-Saunders distribution
720s  x = 0.01:0.01:4;
720s  y1 = bisapdf (x, 1, 0.5);
720s  y2 = bisapdf (x, 1, 1);
720s  y3 = bisapdf (x, 1, 2);
720s  y4 = bisapdf (x, 1, 5);
720s  y5 = bisapdf (x, 1, 10);
720s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
720s  grid on
720s  ylim ([0, 1.5])
720s  legend ({"β = 1 ,γ = 0.5", "β = 1, γ = 1", "β = 1, γ = 2", ...
720s           "β = 1, γ = 5", "β = 1, γ = 10"}, "location", "northeast")
720s  title ("Birnbaum-Saunders PDF")
720s  xlabel ("values in x")
720s  ylabel ("density")
720s ***** demo
720s  ## Plot various PDFs from the Birnbaum-Saunders distribution
720s  x = 0.01:0.01:6;
720s  y1 = bisapdf (x, 1, 0.3);
720s  y2 = bisapdf (x, 2, 0.3);
720s  y3 = bisapdf (x, 1, 0.5);
720s  y4 = bisapdf (x, 3, 0.5);
720s  y5 = bisapdf (x, 5, 0.5);
720s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
720s  grid on
720s  ylim ([0, 1.5])
720s  legend ({"β = 1, γ = 0.3", "β = 2, γ = 0.3", "β = 1, γ = 0.5", ...
720s           "β = 3, γ = 0.5", "β = 5, γ = 0.5"}, "location", "northeast")
720s  title ("Birnbaum-Saunders CDF")
720s  xlabel ("values in x")
720s  ylabel ("density")
720s ***** shared x, y
720s  x = [-1, 0, 1, 2, Inf];
720s  y = [0, 0, 0.3989422804014327, 0.1647717335503959, 0];
720s ***** assert (bisapdf (x, ones (1,5), ones (1,5)), y, eps)
720s ***** assert (bisapdf (x, 1, 1), y, eps)
720s ***** assert (bisapdf (x, 1, ones (1,5)), y, eps)
720s ***** assert (bisapdf (x, ones (1,5), 1), y, eps)
720s ***** assert (bisapdf (x, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)], eps)
720s ***** assert (bisapdf (x, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)], eps)
720s ***** assert (bisapdf ([x, NaN], 1, 1), [y, NaN], eps)
720s ***** assert (bisapdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
720s ***** assert (bisapdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
720s ***** assert (bisapdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
720s ***** error<bisapdf: function called with too few input arguments.> bisapdf ()
720s ***** error<bisapdf: function called with too few input arguments.> bisapdf (1)
720s ***** error<bisapdf: function called with too few input arguments.> bisapdf (1, 2)
720s ***** error<bisapdf: function called with too many inputs> bisapdf (1, 2, 3, 4)
720s ***** error<bisapdf: X, BETA, and GAMMA must be of common size or scalars.> ...
720s  bisapdf (ones (3), ones (2), ones(2))
720s ***** error<bisapdf: X, BETA, and GAMMA must be of common size or scalars.> ...
720s  bisapdf (ones (2), ones (3), ones(2))
720s ***** error<bisapdf: X, BETA, and GAMMA must be of common size or scalars.> ...
720s  bisapdf (ones (2), ones (2), ones(3))
720s ***** error<bisapdf: X, BETA, and GAMMA must not be complex.> bisapdf (i, 4, 3)
720s ***** error<bisapdf: X, BETA, and GAMMA must not be complex.> bisapdf (1, i, 3)
720s ***** error<bisapdf: X, BETA, and GAMMA must not be complex.> bisapdf (1, 4, i)
720s 20 tests, 20 passed, 0 known failure, 0 skipped
720s [inst/dist_fun/bisarnd.m]
720s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/bisarnd.m
720s ***** assert (size (bisarnd (1, 1)), [1 1])
720s ***** assert (size (bisarnd (1, ones (2,1))), [2, 1])
720s ***** assert (size (bisarnd (1, ones (2,2))), [2, 2])
720s ***** assert (size (bisarnd (ones (2,1), 1)), [2, 1])
720s ***** assert (size (bisarnd (ones (2,2), 1)), [2, 2])
720s ***** assert (size (bisarnd (1, 1, 3)), [3, 3])
720s ***** assert (size (bisarnd (1, 1, [4, 1])), [4, 1])
720s ***** assert (size (bisarnd (1, 1, 4, 1)), [4, 1])
720s ***** assert (size (bisarnd (1, 1, 4, 1, 5)), [4, 1, 5])
720s ***** assert (size (bisarnd (1, 1, 0, 1)), [0, 1])
720s ***** assert (size (bisarnd (1, 1, 1, 0)), [1, 0])
720s ***** assert (size (bisarnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
720s ***** assert (class (bisarnd (1, 1)), "double")
720s ***** assert (class (bisarnd (1, single (1))), "single")
720s ***** assert (class (bisarnd (1, single ([1, 1]))), "single")
720s ***** assert (class (bisarnd (single (1), 1)), "single")
720s ***** assert (class (bisarnd (single ([1, 1]), 1)), "single")
720s ***** error<bisarnd: function called with too few input arguments.> bisarnd ()
720s ***** error<bisarnd: function called with too few input arguments.> bisarnd (1)
720s ***** error<bisarnd: BETA and GAMMA must be of common size or scalars.> ...
720s  bisarnd (ones (3), ones (2))
720s ***** error<bisarnd: BETA and GAMMA must be of common size or scalars.> ...
720s  bisarnd (ones (2), ones (3))
720s ***** error<bisarnd: BETA and GAMMA must not be complex.> bisarnd (i, 2, 3)
720s ***** error<bisarnd: BETA and GAMMA must not be complex.> bisarnd (1, i, 3)
720s ***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
720s  bisarnd (1, 2, -1)
720s ***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
720s  bisarnd (1, 2, 1.2)
720s ***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
720s  bisarnd (1, 2, ones (2))
720s ***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
720s  bisarnd (1, 2, [2 -1 2])
720s ***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
720s  bisarnd (1, 2, [2 0 2.5])
720s ***** error<bisarnd: dimensions must be non-negative integers.> ...
720s  bisarnd (1, 2, 2, -1, 5)
720s ***** error<bisarnd: dimensions must be non-negative integers.> ...
720s  bisarnd (1, 2, 2, 1.5, 5)
720s ***** error<bisarnd: BETA and GAMMA must be scalars or of size SZ.> ...
720s  bisarnd (2, ones (2), 3)
720s ***** error<bisarnd: BETA and GAMMA must be scalars or of size SZ.> ...
720s  bisarnd (2, ones (2), [3, 2])
720s ***** error<bisarnd: BETA and GAMMA must be scalars or of size SZ.> ...
720s  bisarnd (2, ones (2), 3, 2)
720s 33 tests, 33 passed, 0 known failure, 0 skipped
720s [inst/dist_fun/burrcdf.m]
720s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/burrcdf.m
720s ***** demo
720s  ## Plot various CDFs from the Burr type XII distribution
720s  x = 0.001:0.001:5;
720s  p1 = burrcdf (x, 1, 1, 1);
720s  p2 = burrcdf (x, 1, 1, 2);
720s  p3 = burrcdf (x, 1, 1, 3);
720s  p4 = burrcdf (x, 1, 2, 1);
720s  p5 = burrcdf (x, 1, 3, 1);
720s  p6 = burrcdf (x, 1, 0.5, 2);
720s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", ...
720s        x, p4, "-c", x, p5, "-m", x, p6, "-k")
720s  grid on
720s  legend ({"λ = 1, c = 1, k = 1", "λ = 1, c = 1, k = 2", ...
720s           "λ = 1, c = 1, k = 3", "λ = 1, c = 2, k = 1", ...
720s           "λ = 1, c = 3, k = 1", "λ = 1, c = 0.5, k = 2"}, ...
720s          "location", "southeast")
720s  title ("Burr type XII CDF")
720s  xlabel ("values in x")
720s  ylabel ("probability")
720s ***** shared x, y
720s  x = [-1, 0, 1, 2, Inf];
720s  y = [0, 0, 1/2, 2/3, 1];
720s ***** assert (burrcdf (x, ones(1,5), ones (1,5), ones (1,5)), y, eps)
720s ***** assert (burrcdf (x, 1, 1, 1), y, eps)
720s ***** assert (burrcdf (x, [1, 1, NaN, 1, 1], 1, 1), [y(1:2), NaN, y(4:5)], eps)
720s ***** assert (burrcdf (x, 1, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)], eps)
720s ***** assert (burrcdf (x, 1, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)], eps)
720s ***** assert (burrcdf ([x, NaN], 1, 1, 1), [y, NaN], eps)
720s ***** assert (burrcdf (single ([x, NaN]), 1, 1, 1), single ([y, NaN]), eps("single"))
720s ***** assert (burrcdf ([x, NaN], single (1), 1, 1), single ([y, NaN]), eps("single"))
720s ***** assert (burrcdf ([x, NaN], 1, single (1), 1), single ([y, NaN]), eps("single"))
720s ***** assert (burrcdf ([x, NaN], 1, 1, single (1)), single ([y, NaN]), eps("single"))
720s ***** error<burrcdf: function called with too few input arguments.> burrcdf ()
720s ***** error<burrcdf: function called with too few input arguments.> burrcdf (1)
720s ***** error<burrcdf: function called with too few input arguments.> burrcdf (1, 2)
720s ***** error<burrcdf: function called with too few input arguments.> burrcdf (1, 2, 3)
720s ***** error<burrcdf: function called with too many inputs> ...
720s  burrcdf (1, 2, 3, 4, 5, 6)
720s ***** error<burrcdf: invalid argument for upper tail.> burrcdf (1, 2, 3, 4, "tail")
720s ***** error<burrcdf: invalid argument for upper tail.> burrcdf (1, 2, 3, 4, 5)
720s ***** error<burrcdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
720s  burrcdf (ones (3), ones (2), ones(2), ones(2))
720s ***** error<burrcdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
720s  burrcdf (ones (2), ones (3), ones(2), ones(2))
720s ***** error<burrcdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
720s  burrcdf (ones (2), ones (2), ones(3), ones(2))
720s ***** error<burrcdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
720s  burrcdf (ones (2), ones (2), ones(2), ones(3))
720s ***** error<burrcdf: X, LAMBDA, C, and K must not be complex.> burrcdf (i, 2, 3, 4)
720s ***** error<burrcdf: X, LAMBDA, C, and K must not be complex.> burrcdf (1, i, 3, 4)
720s ***** error<burrcdf: X, LAMBDA, C, and K must not be complex.> burrcdf (1, 2, i, 4)
720s ***** error<burrcdf: X, LAMBDA, C, and K must not be complex.> burrcdf (1, 2, 3, i)
720s 25 tests, 25 passed, 0 known failure, 0 skipped
720s [inst/dist_fun/burrinv.m]
720s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/burrinv.m
720s ***** demo
720s  ## Plot various iCDFs from the Burr type XII distribution
720s  p = 0.001:0.001:0.999;
720s  x1 = burrinv (p, 1, 1, 1);
720s  x2 = burrinv (p, 1, 1, 2);
720s  x3 = burrinv (p, 1, 1, 3);
720s  x4 = burrinv (p, 1, 2, 1);
720s  x5 = burrinv (p, 1, 3, 1);
720s  x6 = burrinv (p, 1, 0.5, 2);
720s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", ...
720s        p, x4, "-c", p, x5, "-m", p, x6, "-k")
720s  grid on
720s  ylim ([0, 5])
720s  legend ({"λ = 1, c = 1, k = 1", "λ = 1, c = 1, k = 2", ...
720s           "λ = 1, c = 1, k = 3", "λ = 1, c = 2, k = 1", ...
720s           "λ = 1, c = 3, k = 1", "λ = 1, c = 0.5, k = 2"}, ...
720s          "location", "northwest")
720s  title ("Burr type XII iCDF")
720s  xlabel ("probability")
720s  ylabel ("values in x")
720s ***** shared p, y
720s  p = [-Inf, -1, 0, 1/2, 1, 2, Inf];
720s  y = [NaN, NaN, 0, 1 , Inf, NaN, NaN];
720s ***** assert (burrinv (p, ones (1,7), ones (1,7), ones(1,7)), y, eps)
720s ***** assert (burrinv (p, 1, 1, 1), y, eps)
720s ***** assert (burrinv (p, [1, 1, 1, NaN, 1, 1, 1], 1, 1), [y(1:3), NaN, y(5:7)], eps)
720s ***** assert (burrinv (p, 1, [1, 1, 1, NaN, 1, 1, 1], 1), [y(1:3), NaN, y(5:7)], eps)
720s ***** assert (burrinv (p, 1, 1, [1, 1, 1, NaN, 1, 1, 1]), [y(1:3), NaN, y(5:7)], eps)
720s ***** assert (burrinv ([p, NaN], 1, 1, 1), [y, NaN], eps)
720s ***** assert (burrinv (single ([p, NaN]), 1, 1, 1), single ([y, NaN]), eps("single"))
720s ***** assert (burrinv ([p, NaN], single (1), 1, 1), single ([y, NaN]), eps("single"))
720s ***** assert (burrinv ([p, NaN], 1, single (1), 1), single ([y, NaN]), eps("single"))
720s ***** assert (burrinv ([p, NaN], 1, 1, single (1)), single ([y, NaN]), eps("single"))
720s ***** error<burrinv: function called with too few input arguments.> burrinv ()
720s ***** error<burrinv: function called with too few input arguments.> burrinv (1)
720s ***** error<burrinv: function called with too few input arguments.> burrinv (1, 2)
720s ***** error<burrinv: function called with too few input arguments.> burrinv (1, 2, 3)
720s ***** error<burrinv: function called with too many inputs> ...
720s  burrinv (1, 2, 3, 4, 5)
720s ***** error<burrinv: P, LAMBDA, C, and K must be of common size or scalars.> ...
720s  burrinv (ones (3), ones (2), ones(2), ones(2))
720s ***** error<burrinv: P, LAMBDA, C, and K must be of common size or scalars.> ...
720s  burrinv (ones (2), ones (3), ones(2), ones(2))
720s ***** error<burrinv: P, LAMBDA, C, and K must be of common size or scalars.> ...
720s  burrinv (ones (2), ones (2), ones(3), ones(2))
720s ***** error<burrinv: P, LAMBDA, C, and K must be of common size or scalars.> ...
720s  burrinv (ones (2), ones (2), ones(2), ones(3))
720s ***** error<burrinv: P, LAMBDA, C, and K must not be complex.> burrinv (i, 2, 3, 4)
720s ***** error<burrinv: P, LAMBDA, C, and K must not be complex.> burrinv (1, i, 3, 4)
720s ***** error<burrinv: P, LAMBDA, C, and K must not be complex.> burrinv (1, 2, i, 4)
720s ***** error<burrinv: P, LAMBDA, C, and K must not be complex.> burrinv (1, 2, 3, i)
720s 23 tests, 23 passed, 0 known failure, 0 skipped
720s [inst/dist_fun/burrpdf.m]
720s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/burrpdf.m
720s ***** demo
720s  ## Plot various PDFs from the Burr type XII distribution
720s  x = 0.001:0.001:3;
720s  y1 = burrpdf (x, 1, 1, 1);
720s  y2 = burrpdf (x, 1, 1, 2);
720s  y3 = burrpdf (x, 1, 1, 3);
720s  y4 = burrpdf (x, 1, 2, 1);
720s  y5 = burrpdf (x, 1, 3, 1);
720s  y6 = burrpdf (x, 1, 0.5, 2);
720s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", ...
720s        x, y4, "-c", x, y5, "-m", x, y6, "-k")
720s  grid on
720s  ylim ([0, 2])
720s  legend ({"λ = 1, c = 1, k = 1", "λ = 1, c = 1, k = 2", ...
720s           "λ = 1, c = 1, k = 3", "λ = 1, c = 2, k = 1", ...
720s           "λ = 1, c = 3, k = 1", "λ = 1, c = 0.5, k = 2"}, ...
720s          "location", "northeast")
720s  title ("Burr type XII PDF")
720s  xlabel ("values in x")
720s  ylabel ("density")
720s ***** shared x, y
720s  x = [-1, 0, 1, 2, Inf];
720s  y = [0, 1, 1/4, 1/9, 0];
720s ***** assert (burrpdf (x, ones(1,5), ones (1,5), ones (1,5)), y)
720s ***** assert (burrpdf (x, 1, 1, 1), y)
720s ***** assert (burrpdf (x, [1, 1, NaN, 1, 1], 1, 1), [y(1:2), NaN, y(4:5)])
720s ***** assert (burrpdf (x, 1, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)])
720s ***** assert (burrpdf (x, 1, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)])
720s ***** assert (burrpdf ([x, NaN], 1, 1, 1), [y, NaN])
720s ***** assert (burrpdf (single ([x, NaN]), 1, 1, 1), single ([y, NaN]))
720s ***** assert (burrpdf ([x, NaN], single (1), 1, 1), single ([y, NaN]))
720s ***** assert (burrpdf ([x, NaN], 1, single (1), 1), single ([y, NaN]))
720s ***** assert (burrpdf ([x, NaN], 1, 1, single (1)), single ([y, NaN]))
720s ***** error<burrpdf: function called with too few input arguments.> burrpdf ()
720s ***** error<burrpdf: function called with too few input arguments.> burrpdf (1)
720s ***** error<burrpdf: function called with too few input arguments.> burrpdf (1, 2)
720s ***** error<burrpdf: function called with too few input arguments.> burrpdf (1, 2, 3)
720s ***** error<burrpdf: function called with too many inputs> ...
720s  burrpdf (1, 2, 3, 4, 5)
720s ***** error<burrpdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
720s  burrpdf (ones (3), ones (2), ones(2), ones(2))
720s ***** error<burrpdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
720s  burrpdf (ones (2), ones (3), ones(2), ones(2))
720s ***** error<burrpdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
720s  burrpdf (ones (2), ones (2), ones(3), ones(2))
720s ***** error<burrpdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
720s  burrpdf (ones (2), ones (2), ones(2), ones(3))
720s ***** error<burrpdf: X, LAMBDA, C, and K must not be complex.> burrpdf (i, 2, 3, 4)
720s ***** error<burrpdf: X, LAMBDA, C, and K must not be complex.> burrpdf (1, i, 3, 4)
720s ***** error<burrpdf: X, LAMBDA, C, and K must not be complex.> burrpdf (1, 2, i, 4)
720s ***** error<burrpdf: X, LAMBDA, C, and K must not be complex.> burrpdf (1, 2, 3, i)
720s 23 tests, 23 passed, 0 known failure, 0 skipped
720s [inst/dist_fun/burrrnd.m]
720s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/burrrnd.m
720s ***** assert (size (burrrnd (1, 1, 1)), [1 1])
720s ***** assert (size (burrrnd (ones (2,1), 1, 1)), [2, 1])
720s ***** assert (size (burrrnd (ones (2,2), 1, 1)), [2, 2])
720s ***** assert (size (burrrnd (1, ones (2,1), 1)), [2, 1])
720s ***** assert (size (burrrnd (1, ones (2,2), 1)), [2, 2])
720s ***** assert (size (burrrnd (1, 1, ones (2,1))), [2, 1])
720s ***** assert (size (burrrnd (1, 1, ones (2,2))), [2, 2])
720s ***** assert (size (burrrnd (1, 1, 1, 3)), [3, 3])
720s ***** assert (size (burrrnd (1, 1, 1, [4 1])), [4, 1])
720s ***** assert (size (burrrnd (1, 1, 1, 4, 1)), [4, 1])
720s ***** assert (class (burrrnd (1,1,1)), "double")
720s ***** assert (class (burrrnd (single (1),1,1)), "single")
720s ***** assert (class (burrrnd (single ([1 1]),1,1)), "single")
720s ***** assert (class (burrrnd (1,single (1),1)), "single")
720s ***** assert (class (burrrnd (1,single ([1 1]),1)), "single")
720s ***** assert (class (burrrnd (1,1,single (1))), "single")
720s ***** assert (class (burrrnd (1,1,single ([1 1]))), "single")
720s ***** error<burrrnd: function called with too few input arguments.> burrrnd ()
720s ***** error<burrrnd: function called with too few input arguments.> burrrnd (1)
720s ***** error<burrrnd: function called with too few input arguments.> burrrnd (1, 2)
720s ***** error<burrrnd: LAMBDA, C, and K must be of common size or scalars.> ...
720s  burrrnd (ones (3), ones (2), ones (2))
720s ***** error<burrrnd: LAMBDA, C, and K must be of common size or scalars.> ...
720s  burrrnd (ones (2), ones (3), ones (2))
720s ***** error<burrrnd: LAMBDA, C, and K must be of common size or scalars.> ...
720s  burrrnd (ones (2), ones (2), ones (3))
720s ***** error<burrrnd: LAMBDA, C, and K must not be complex.> burrrnd (i, 2, 3)
720s ***** error<burrrnd: LAMBDA, C, and K must not be complex.> burrrnd (1, i, 3)
720s ***** error<burrrnd: LAMBDA, C, and K must not be complex.> burrrnd (1, 2, i)
720s ***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
720s  burrrnd (1, 2, 3, -1)
720s ***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
720s  burrrnd (1, 2, 3, 1.2)
720s ***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
720s  burrrnd (1, 2, 3, ones (2))
720s ***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
720s  burrrnd (1, 2, 3, [2 -1 2])
720s ***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
720s  burrrnd (1, 2, 3, [2 0 2.5])
720s ***** error<burrrnd: dimensions must be non-negative integers.> ...
720s  burrrnd (1, 2, 3, 2, -1, 5)
720s ***** error<burrrnd: dimensions must be non-negative integers.> ...
720s  burrrnd (1, 2, 3, 2, 1.5, 5)
720s ***** error<burrrnd: LAMBDA, C, and K must be scalars or of size SZ.> ...
720s  burrrnd (2, ones (2), 2, 3)
720s ***** error<burrrnd: LAMBDA, C, and K must be scalars or of size SZ.> ...
720s  burrrnd (2, ones (2), 2, [3, 2])
720s ***** error<burrrnd: LAMBDA, C, and K must be scalars or of size SZ.> ...
720s  burrrnd (2, ones (2), 2, 3, 2)
720s 36 tests, 36 passed, 0 known failure, 0 skipped
720s [inst/dist_fun/bvncdf.m]
720s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/bvncdf.m
720s ***** demo
720s  mu = [1, -1];
720s  sigma = [0.9, 0.4; 0.4, 0.3];
720s  [X1, X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
720s  x = [X1(:), X2(:)];
720s  p = bvncdf (x, mu, sigma);
720s  Z = reshape (p, 25, 25);
720s  surf (X1, X2, Z);
720s  title ("Bivariate Normal Distribution");
720s  ylabel "X1"
720s  xlabel "X2"
720s ***** test
720s  mu = [1, -1];
720s  sigma = [0.9, 0.4; 0.4, 0.3];
720s  [X1,X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
720s  x = [X1(:), X2(:)];
720s  p = bvncdf (x, mu, sigma);
720s  p_out = [0.00011878988774500, 0.00034404112322371, ...
720s           0.00087682502191813, 0.00195221905058185, ...
720s           0.00378235566873474, 0.00638175749734415, ...
720s           0.00943764224329656, 0.01239164888125426, ...
720s           0.01472750274376648, 0.01623228313374828]';
720s  assert (p([1:10]), p_out, 1e-16);
720s ***** test
720s  mu = [1, -1];
720s  sigma = [0.9, 0.4; 0.4, 0.3];
720s  [X1,X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
720s  x = [X1(:), X2(:)];
720s  p = bvncdf (x, mu, sigma);
720s  p_out = [0.8180695783608276, 0.8854485749482751, ...
720s           0.9308108777385832, 0.9579855743025508, ...
720s           0.9722897881414742, 0.9788150170059926, ...
720s           0.9813597788804785, 0.9821977956568989, ...
720s           0.9824283794464095, 0.9824809345614861]';
720s  assert (p([616:625]), p_out, 3e-16);
720s ***** error bvncdf (randn (25,3), [], [1, 1; 1, 1]);
720s ***** error bvncdf (randn (25,2), [], [1, 1; 1, 1]);
720s ***** error bvncdf (randn (25,2), [], ones (3, 2));
720s 5 tests, 5 passed, 0 known failure, 0 skipped
720s [inst/dist_fun/bvtcdf.m]
720s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/bvtcdf.m
720s ***** test
720s  x = [1, 2];
720s  rho = [1, 0.5; 0.5, 1];
720s  df = 4;
720s  assert (bvtcdf(x, rho(2), df), mvtcdf(x, rho, df), 1e-14);
720s ***** test
720s  x = [3, 2;2, 4;1, 5];
720s  rho = [1, 0.5; 0.5, 1];
720s  df = 4;
720s  assert (bvtcdf(x, rho(2), df), mvtcdf(x, rho, df), 1e-14);
720s 2 tests, 2 passed, 0 known failure, 0 skipped
720s [inst/dist_fun/cauchycdf.m]
720s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/cauchycdf.m
720s ***** demo
720s  ## Plot various CDFs from the Cauchy distribution
720s  x = -5:0.01:5;
720s  p1 = cauchycdf (x, 0, 0.5);
720s  p2 = cauchycdf (x, 0, 1);
720s  p3 = cauchycdf (x, 0, 2);
720s  p4 = cauchycdf (x, -2, 1);
720s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
720s  grid on
720s  xlim ([-5, 5])
720s  legend ({"x0 = 0, γ = 0.5", "x0 = 0, γ = 1", ...
720s           "x0 = 0, γ = 2", "x0 = -2, γ = 1"}, "location", "southeast")
720s  title ("Cauchy CDF")
720s  xlabel ("values in x")
720s  ylabel ("probability")
720s ***** shared x, y
720s  x = [-1 0 0.5 1 2];
720s  y = 1/pi * atan ((x-1) / 2) + 1/2;
720s ***** assert (cauchycdf (x, ones (1,5), 2*ones (1,5)), y)
720s ***** assert (cauchycdf (x, 1, 2*ones (1,5)), y)
720s ***** assert (cauchycdf (x, ones (1,5), 2), y)
720s ***** assert (cauchycdf (x, [-Inf 1 NaN 1 Inf], 2), [NaN y(2) NaN y(4) NaN])
720s ***** assert (cauchycdf (x, 1, 2*[0 1 NaN 1 Inf]), [NaN y(2) NaN y(4) NaN])
720s ***** assert (cauchycdf ([x(1:2) NaN x(4:5)], 1, 2), [y(1:2) NaN y(4:5)])
720s ***** assert (cauchycdf ([x, NaN], 1, 2), [y, NaN])
720s ***** assert (cauchycdf (single ([x, NaN]), 1, 2), single ([y, NaN]), eps ("single"))
720s ***** assert (cauchycdf ([x, NaN], single (1), 2), single ([y, NaN]), eps ("single"))
720s ***** assert (cauchycdf ([x, NaN], 1, single (2)), single ([y, NaN]), eps ("single"))
720s ***** error<cauchycdf: function called with too few input arguments.> cauchycdf ()
720s ***** error<cauchycdf: function called with too few input arguments.> cauchycdf (1)
720s ***** error<cauchycdf: function called with too few input arguments.> ...
720s  cauchycdf (1, 2)
720s ***** error<cauchycdf: function called with too many inputs> ...
720s  cauchycdf (1, 2, 3, 4, 5)
720s ***** error<cauchycdf: invalid argument for upper tail.> cauchycdf (1, 2, 3, "tail")
720s ***** error<cauchycdf: invalid argument for upper tail.> cauchycdf (1, 2, 3, 4)
720s ***** error<cauchycdf: X, X0, and GAMMA must be of common size or scalars.> ...
720s  cauchycdf (ones (3), ones (2), ones (2))
720s ***** error<cauchycdf: X, X0, and GAMMA must be of common size or scalars.> ...
720s  cauchycdf (ones (2), ones (3), ones (2))
720s ***** error<cauchycdf: X, X0, and GAMMA must be of common size or scalars.> ...
720s  cauchycdf (ones (2), ones (2), ones (3))
720s ***** error<cauchycdf: X, X0, and GAMMA must not be complex.> cauchycdf (i, 2, 2)
720s ***** error<cauchycdf: X, X0, and GAMMA must not be complex.> cauchycdf (2, i, 2)
720s ***** error<cauchycdf: X, X0, and GAMMA must not be complex.> cauchycdf (2, 2, i)
720s 22 tests, 22 passed, 0 known failure, 0 skipped
720s [inst/dist_fun/cauchyinv.m]
720s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/cauchyinv.m
720s ***** demo
720s  ## Plot various iCDFs from the Cauchy distribution
720s  p = 0.001:0.001:0.999;
720s  x1 = cauchyinv (p, 0, 0.5);
720s  x2 = cauchyinv (p, 0, 1);
720s  x3 = cauchyinv (p, 0, 2);
720s  x4 = cauchyinv (p, -2, 1);
720s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
720s  grid on
720s  ylim ([-5, 5])
720s  legend ({"x0 = 0, γ = 0.5", "x0 = 0, γ = 1", ...
720s           "x0 = 0, γ = 2", "x0 = -2, γ = 1"}, "location", "northwest")
720s  title ("Cauchy iCDF")
720s  xlabel ("probability")
720s  ylabel ("values in x")
720s ***** shared p
720s  p = [-1 0 0.5 1 2];
720s ***** assert (cauchyinv (p, ones (1,5), 2 * ones (1,5)), [NaN -Inf 1 Inf NaN], eps)
720s ***** assert (cauchyinv (p, 1, 2 * ones (1,5)), [NaN -Inf 1 Inf NaN], eps)
720s ***** assert (cauchyinv (p, ones (1,5), 2), [NaN -Inf 1 Inf NaN], eps)
720s ***** assert (cauchyinv (p, [1 -Inf NaN Inf 1], 2), [NaN NaN NaN NaN NaN])
720s ***** assert (cauchyinv (p, 1, 2 * [1 0 NaN Inf 1]), [NaN NaN NaN NaN NaN])
720s ***** assert (cauchyinv ([p(1:2) NaN p(4:5)], 1, 2), [NaN -Inf NaN Inf NaN])
720s ***** assert (cauchyinv ([p, NaN], 1, 2), [NaN -Inf 1 Inf NaN NaN], eps)
720s ***** assert (cauchyinv (single ([p, NaN]), 1, 2), ...
720s  single ([NaN -Inf 1 Inf NaN NaN]), eps ("single"))
720s ***** assert (cauchyinv ([p, NaN], single (1), 2), ...
720s  single ([NaN -Inf 1 Inf NaN NaN]), eps ("single"))
720s ***** assert (cauchyinv ([p, NaN], 1, single (2)), ...
720s  single ([NaN -Inf 1 Inf NaN NaN]), eps ("single"))
720s ***** error<cauchyinv: function called with too few input arguments.> cauchyinv ()
720s ***** error<cauchyinv: function called with too few input arguments.> cauchyinv (1)
720s ***** error<cauchyinv: function called with too few input arguments.> ...
720s  cauchyinv (1, 2)
720s ***** error<cauchyinv: function called with too many inputs> cauchyinv (1, 2, 3, 4)
720s ***** error<cauchyinv: P, X0, and GAMMA must be of common size or scalars.> ...
720s  cauchyinv (ones (3), ones (2), ones(2))
720s ***** error<cauchyinv: P, X0, and GAMMA must be of common size or scalars.> ...
720s  cauchyinv (ones (2), ones (3), ones(2))
720s ***** error<cauchyinv: P, X0, and GAMMA must be of common size or scalars.> ...
720s  cauchyinv (ones (2), ones (2), ones(3))
720s ***** error<cauchyinv: P, X0, and GAMMA must not be complex.> cauchyinv (i, 4, 3)
720s ***** error<cauchyinv: P, X0, and GAMMA must not be complex.> cauchyinv (1, i, 3)
720s ***** error<cauchyinv: P, X0, and GAMMA must not be complex.> cauchyinv (1, 4, i)
720s 20 tests, 20 passed, 0 known failure, 0 skipped
720s [inst/dist_fun/cauchypdf.m]
720s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/cauchypdf.m
720s ***** demo
720s  ## Plot various PDFs from the Cauchy distribution
720s  x = -5:0.01:5;
720s  y1 = cauchypdf (x, 0, 0.5);
720s  y2 = cauchypdf (x, 0, 1);
720s  y3 = cauchypdf (x, 0, 2);
720s  y4 = cauchypdf (x, -2, 1);
720s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
720s  grid on
720s  xlim ([-5, 5])
720s  ylim ([0, 0.7])
720s  legend ({"x0 = 0, γ = 0.5", "x0 = 0, γ = 1", ...
720s           "x0 = 0, γ = 2", "x0 = -2, γ = 1"}, "location", "northeast")
720s  title ("Cauchy PDF")
720s  xlabel ("values in x")
720s  ylabel ("density")
720s ***** shared x, y
720s  x = [-1 0 0.5 1 2];
720s  y = 1/pi * ( 2 ./ ((x-1).^2 + 2^2) );
720s ***** assert (cauchypdf (x, ones (1,5), 2*ones (1,5)), y)
720s ***** assert (cauchypdf (x, 1, 2*ones (1,5)), y)
720s ***** assert (cauchypdf (x, ones (1,5), 2), y)
720s ***** assert (cauchypdf (x, [-Inf 1 NaN 1 Inf], 2), [NaN y(2) NaN y(4) NaN])
720s ***** assert (cauchypdf (x, 1, 2*[0 1 NaN 1 Inf]), [NaN y(2) NaN y(4) NaN])
720s ***** assert (cauchypdf ([x, NaN], 1, 2), [y, NaN])
720s ***** assert (cauchypdf (single ([x, NaN]), 1, 2), single ([y, NaN]), eps ("single"))
720s ***** assert (cauchypdf ([x, NaN], single (1), 2), single ([y, NaN]), eps ("single"))
720s ***** assert (cauchypdf ([x, NaN], 1, single (2)), single ([y, NaN]), eps ("single"))
720s ***** test
720s  x = rand (10, 1);
720s  assert (cauchypdf (x, 0, 1), tpdf (x, 1), eps);
720s ***** error<cauchypdf: function called with too few input arguments.> cauchypdf ()
720s ***** error<cauchypdf: function called with too few input arguments.> cauchypdf (1)
720s ***** error<cauchypdf: function called with too few input arguments.> ...
720s  cauchypdf (1, 2)
720s ***** error<cauchypdf: function called with too many inputs> cauchypdf (1, 2, 3, 4)
720s ***** error<cauchypdf: X, X0, and GAMMA must be of common size or scalars.> ...
720s  cauchypdf (ones (3), ones (2), ones(2))
720s ***** error<cauchypdf: X, X0, and GAMMA must be of common size or scalars.> ...
720s  cauchypdf (ones (2), ones (3), ones(2))
720s ***** error<cauchypdf: X, X0, and GAMMA must be of common size or scalars.> ...
720s  cauchypdf (ones (2), ones (2), ones(3))
720s ***** error<cauchypdf: X, X0, and GAMMA must not be complex.> cauchypdf (i, 4, 3)
720s ***** error<cauchypdf: X, X0, and GAMMA must not be complex.> cauchypdf (1, i, 3)
720s ***** error<cauchypdf: X, X0, and GAMMA must not be complex.> cauchypdf (1, 4, i)
720s 20 tests, 20 passed, 0 known failure, 0 skipped
720s [inst/dist_fun/cauchyrnd.m]
720s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/cauchyrnd.m
720s ***** assert (size (cauchyrnd (1, 1)), [1 1])
720s ***** assert (size (cauchyrnd (1, ones (2,1))), [2, 1])
720s ***** assert (size (cauchyrnd (1, ones (2,2))), [2, 2])
720s ***** assert (size (cauchyrnd (ones (2,1), 1)), [2, 1])
720s ***** assert (size (cauchyrnd (ones (2,2), 1)), [2, 2])
720s ***** assert (size (cauchyrnd (1, 1, 3)), [3, 3])
720s ***** assert (size (cauchyrnd (1, 1, [4, 1])), [4, 1])
720s ***** assert (size (cauchyrnd (1, 1, 4, 1)), [4, 1])
720s ***** assert (size (cauchyrnd (1, 1, 4, 1, 5)), [4, 1, 5])
720s ***** assert (size (cauchyrnd (1, 1, 0, 1)), [0, 1])
720s ***** assert (size (cauchyrnd (1, 1, 1, 0)), [1, 0])
720s ***** assert (size (cauchyrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
720s ***** assert (class (cauchyrnd (1, 1)), "double")
720s ***** assert (class (cauchyrnd (1, single (1))), "single")
720s ***** assert (class (cauchyrnd (1, single ([1, 1]))), "single")
720s ***** assert (class (cauchyrnd (single (1), 1)), "single")
720s ***** assert (class (cauchyrnd (single ([1, 1]), 1)), "single")
720s ***** error<cauchyrnd: function called with too few input arguments.> cauchyrnd ()
720s ***** error<cauchyrnd: function called with too few input arguments.> cauchyrnd (1)
720s ***** error<cauchyrnd: X0 and GAMMA must be of common size or scalars.> ...
720s  cauchyrnd (ones (3), ones (2))
720s ***** error<cauchyrnd: X0 and GAMMA must be of common size or scalars.> ...
720s  cauchyrnd (ones (2), ones (3))
720s ***** error<cauchyrnd: X0 and GAMMA must not be complex.> cauchyrnd (i, 2, 3)
720s ***** error<cauchyrnd: X0 and GAMMA must not be complex.> cauchyrnd (1, i, 3)
720s ***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
720s  cauchyrnd (1, 2, -1)
720s ***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
720s  cauchyrnd (1, 2, 1.2)
720s ***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
720s  cauchyrnd (1, 2, ones (2))
720s ***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
720s  cauchyrnd (1, 2, [2 -1 2])
720s ***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
720s  cauchyrnd (1, 2, [2 0 2.5])
720s ***** error<cauchyrnd: dimensions must be non-negative integers.> ...
720s  cauchyrnd (1, 2, 2, -1, 5)
720s ***** error<cauchyrnd: dimensions must be non-negative integers.> ...
720s  cauchyrnd (1, 2, 2, 1.5, 5)
720s ***** error<cauchyrnd: X0 and GAMMA must be scalars or of size SZ.> ...
720s  cauchyrnd (2, ones (2), 3)
720s ***** error<cauchyrnd: X0 and GAMMA must be scalars or of size SZ.> ...
720s  cauchyrnd (2, ones (2), [3, 2])
720s ***** error<cauchyrnd: X0 and GAMMA must be scalars or of size SZ.> ...
720s  cauchyrnd (2, ones (2), 3, 2)
720s 33 tests, 33 passed, 0 known failure, 0 skipped
720s [inst/dist_fun/chi2cdf.m]
720s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/chi2cdf.m
720s ***** demo
720s  ## Plot various CDFs from the chi-squared distribution
720s  x = 0:0.01:8;
720s  p1 = chi2cdf (x, 1);
720s  p2 = chi2cdf (x, 2);
720s  p3 = chi2cdf (x, 3);
720s  p4 = chi2cdf (x, 4);
720s  p5 = chi2cdf (x, 6);
720s  p6 = chi2cdf (x, 9);
720s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", ...
720s        x, p4, "-c", x, p5, "-m", x, p6, "-y")
720s  grid on
720s  xlim ([0, 8])
720s  legend ({"df = 1", "df = 2", "df = 3", ...
720s           "df = 4", "df = 6", "df = 9"}, "location", "southeast")
720s  title ("Chi-squared CDF")
720s  xlabel ("values in x")
720s  ylabel ("probability")
720s ***** shared x, p, u
720s  x = [-1, 0, 0.5, 1, 2];
720s  p = [0, (1 - exp (-x(2:end) / 2))];
720s  u = [1, 0, NaN, 0.3934693402873666, 0.6321205588285577];
720s ***** assert (chi2cdf (x, 2 * ones (1,5)), p, eps)
720s ***** assert (chi2cdf (x, 2), p, eps)
720s ***** assert (chi2cdf (x, 2 * [1, 0, NaN, 1, 1]), [p(1), 1, NaN, p(4:5)], eps)
720s ***** assert (chi2cdf (x, 2 * [1, 0, NaN, 1, 1], "upper"), ...
720s                         [p(1), 1, NaN, u(4:5)], eps)
720s ***** assert (chi2cdf ([x(1:2), NaN, x(4:5)], 2), [p(1:2), NaN, p(4:5)], eps)
720s ***** assert (chi2cdf ([x, NaN], 2), [p, NaN], eps)
720s ***** assert (chi2cdf (single ([x, NaN]), 2), single ([p, NaN]), eps ("single"))
720s ***** assert (chi2cdf ([x, NaN], single (2)), single ([p, NaN]), eps ("single"))
720s ***** error<chi2cdf: function called with too few input arguments.> chi2cdf ()
720s ***** error<chi2cdf: function called with too few input arguments.> chi2cdf (1)
720s ***** error<chi2cdf: function called with too many inputs> chi2cdf (1, 2, 3, 4)
720s ***** error<chi2cdf: invalid argument for upper tail.> chi2cdf (1, 2, 3)
720s ***** error<chi2cdf: invalid argument for upper tail.> chi2cdf (1, 2, "uper")
720s ***** error<chi2cdf: X and DF must be of common size or scalars.> ...
720s  chi2cdf (ones (3), ones (2))
720s ***** error<chi2cdf: X and DF must be of common size or scalars.> ...
720s  chi2cdf (ones (2), ones (3))
720s ***** error<chi2cdf: X and DF must not be complex.> chi2cdf (i, 2)
720s ***** error<chi2cdf: X and DF must not be complex.> chi2cdf (2, i)
720s 17 tests, 17 passed, 0 known failure, 0 skipped
720s [inst/dist_fun/chi2inv.m]
720s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/chi2inv.m
720s ***** demo
720s  ## Plot various iCDFs from the chi-squared distribution
720s  p = 0.001:0.001:0.999;
720s  x1 = chi2inv (p, 1);
720s  x2 = chi2inv (p, 2);
720s  x3 = chi2inv (p, 3);
720s  x4 = chi2inv (p, 4);
720s  x5 = chi2inv (p, 6);
720s  x6 = chi2inv (p, 9);
720s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", ...
720s        p, x4, "-c", p, x5, "-m", p, x6, "-y")
720s  grid on
720s  ylim ([0, 8])
720s  legend ({"df = 1", "df = 2", "df = 3", ...
720s           "df = 4", "df = 6", "df = 9"}, "location", "northwest")
720s  title ("Chi-squared iCDF")
720s  xlabel ("probability")
720s  ylabel ("values in x")
720s ***** shared p
720s  p = [-1 0 0.3934693402873666 1 2];
720s ***** assert (chi2inv (p, 2*ones (1,5)), [NaN 0 1 Inf NaN], 5*eps)
720s ***** assert (chi2inv (p, 2), [NaN 0 1 Inf NaN], 5*eps)
720s ***** assert (chi2inv (p, 2*[0 1 NaN 1 1]), [NaN 0 NaN Inf NaN], 5*eps)
720s ***** assert (chi2inv ([p(1:2) NaN p(4:5)], 2), [NaN 0 NaN Inf NaN], 5*eps)
720s ***** assert (chi2inv ([p, NaN], 2), [NaN 0 1 Inf NaN NaN], 5*eps)
720s ***** assert (chi2inv (single ([p, NaN]), 2), single ([NaN 0 1 Inf NaN NaN]), 5*eps ("single"))
720s ***** assert (chi2inv ([p, NaN], single (2)), single ([NaN 0 1 Inf NaN NaN]), 5*eps ("single"))
721s ***** error<chi2inv: function called with too few input arguments.> chi2inv ()
721s ***** error<chi2inv: function called with too few input arguments.> chi2inv (1)
721s ***** error<chi2inv: function called with too many inputs> chi2inv (1,2,3)
721s ***** error<chi2inv: P and DF must be of common size or scalars.> ...
721s  chi2inv (ones (3), ones (2))
721s ***** error<chi2inv: P and DF must be of common size or scalars.> ...
721s  chi2inv (ones (2), ones (3))
721s ***** error<chi2inv: P and DF must not be complex.> chi2inv (i, 2)
721s ***** error<chi2inv: P and DF must not be complex.> chi2inv (2, i)
721s 14 tests, 14 passed, 0 known failure, 0 skipped
721s [inst/dist_fun/chi2pdf.m]
721s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/chi2pdf.m
721s ***** demo
721s  ## Plot various PDFs from the chi-squared distribution
721s  x = 0:0.01:8;
721s  y1 = chi2pdf (x, 1);
721s  y2 = chi2pdf (x, 2);
721s  y3 = chi2pdf (x, 3);
721s  y4 = chi2pdf (x, 4);
721s  y5 = chi2pdf (x, 6);
721s  y6 = chi2pdf (x, 9);
721s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", ...
721s        x, y4, "-c", x, y5, "-m", x, y6, "-y")
721s  grid on
721s  xlim ([0, 8])
721s  ylim ([0, 0.5])
721s  legend ({"df = 1", "df = 2", "df = 3", ...
721s           "df = 4", "df = 6", "df = 9"}, "location", "northeast")
721s  title ("Chi-squared PDF")
721s  xlabel ("values in x")
721s  ylabel ("density")
721s ***** shared x, y
721s  x = [-1 0 0.5 1 Inf];
721s  y = [0, 1/2 * exp(-x(2:5)/2)];
721s ***** assert (chi2pdf (x, 2*ones (1,5)), y)
721s ***** assert (chi2pdf (x, 2), y)
721s ***** assert (chi2pdf (x, 2*[1 0 NaN 1 1]), [y(1) NaN NaN y(4:5)])
721s ***** assert (chi2pdf ([x, NaN], 2), [y, NaN])
721s ***** assert (chi2pdf (single ([x, NaN]), 2), single ([y, NaN]))
721s ***** assert (chi2pdf ([x, NaN], single (2)), single ([y, NaN]))
721s ***** error<chi2pdf: function called with too few input arguments.> chi2pdf ()
721s ***** error<chi2pdf: function called with too few input arguments.> chi2pdf (1)
721s ***** error<chi2pdf: function called with too many inputs> chi2pdf (1,2,3)
721s ***** error<chi2pdf: X and DF must be of common size or scalars.> ...
721s  chi2pdf (ones (3), ones (2))
721s ***** error<chi2pdf: X and DF must be of common size or scalars.> ...
721s  chi2pdf (ones (2), ones (3))
721s ***** error<chi2pdf: X and DF must not be complex.> chi2pdf (i, 2)
721s ***** error<chi2pdf: X and DF must not be complex.> chi2pdf (2, i)
721s 13 tests, 13 passed, 0 known failure, 0 skipped
721s [inst/dist_fun/chi2rnd.m]
721s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/chi2rnd.m
721s ***** assert (size (chi2rnd (2)), [1, 1])
721s ***** assert (size (chi2rnd (ones (2,1))), [2, 1])
721s ***** assert (size (chi2rnd (ones (2,2))), [2, 2])
721s ***** assert (size (chi2rnd (1, 3)), [3, 3])
721s ***** assert (size (chi2rnd (1, [4 1])), [4, 1])
721s ***** assert (size (chi2rnd (1, 4, 1)), [4, 1])
721s ***** assert (size (chi2rnd (1, 4, 1)), [4, 1])
721s ***** assert (size (chi2rnd (1, 4, 1, 5)), [4, 1, 5])
721s ***** assert (size (chi2rnd (1, 0, 1)), [0, 1])
721s ***** assert (size (chi2rnd (1, 1, 0)), [1, 0])
721s ***** assert (size (chi2rnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
721s ***** assert (class (chi2rnd (2)), "double")
721s ***** assert (class (chi2rnd (single (2))), "single")
721s ***** assert (class (chi2rnd (single ([2 2]))), "single")
721s ***** error<chi2rnd: function called with too few input arguments.> chi2rnd ()
721s ***** error<chi2rnd: DF must not be complex.> chi2rnd (i)
721s ***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
721s  chi2rnd (1, -1)
721s ***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
721s  chi2rnd (1, 1.2)
721s ***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
721s  chi2rnd (1, ones (2))
721s ***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
721s  chi2rnd (1, [2 -1 2])
721s ***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
721s  chi2rnd (1, [2 0 2.5])
721s ***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
721s  chi2rnd (ones (2), ones (2))
721s ***** error<chi2rnd: dimensions must be non-negative integers.> ...
721s  chi2rnd (1, 2, -1, 5)
721s ***** error<chi2rnd: dimensions must be non-negative integers.> ...
721s  chi2rnd (1, 2, 1.5, 5)
721s ***** error<chi2rnd: DF must be scalar or of size SZ.> chi2rnd (ones (2,2), 3)
721s ***** error<chi2rnd: DF must be scalar or of size SZ.> chi2rnd (ones (2,2), [3, 2])
721s ***** error<chi2rnd: DF must be scalar or of size SZ.> chi2rnd (ones (2,2), 2, 3)
721s 27 tests, 27 passed, 0 known failure, 0 skipped
721s [inst/dist_fun/copulacdf.m]
721s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/copulacdf.m
721s ***** test
721s  x = [0.2:0.2:0.6; 0.2:0.2:0.6];
721s  theta = [1; 2];
721s  p = copulacdf ("Clayton", x, theta);
721s  expected_p = [0.1395; 0.1767];
721s  assert (p, expected_p, 0.001);
721s ***** test
721s  x = [0.2:0.2:0.6; 0.2:0.2:0.6];
721s  p = copulacdf ("Gumbel", x, 2);
721s  expected_p = [0.1464; 0.1464];
721s  assert (p, expected_p, 0.001);
721s ***** test
721s  x = [0.2:0.2:0.6; 0.2:0.2:0.6];
721s  theta = [1; 2];
721s  p = copulacdf ("Frank", x, theta);
721s  expected_p = [0.0699; 0.0930];
721s  assert (p, expected_p, 0.001);
721s ***** test
721s  x = [0.2:0.2:0.6; 0.2:0.2:0.6];
721s  theta = [0.3; 0.7];
721s  p = copulacdf ("AMH", x, theta);
721s  expected_p = [0.0629; 0.0959];
721s  assert (p, expected_p, 0.001);
721s ***** test
721s  x = [0.2:0.2:0.6; 0.2:0.1:0.4];
721s  theta = [0.2, 0.1, 0.1, 0.05];
721s  p = copulacdf ("FGM", x, theta);
721s  expected_p = [0.0558; 0.0293];
721s  assert (p, expected_p, 0.001);
721s 5 tests, 5 passed, 0 known failure, 0 skipped
721s [inst/dist_fun/copulapdf.m]
721s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/copulapdf.m
721s ***** test
721s  x = [0.2:0.2:0.6; 0.2:0.2:0.6];
721s  theta = [1; 2];
721s  y = copulapdf ("Clayton", x, theta);
721s  expected_p = [0.9872; 0.7295];
721s  assert (y, expected_p, 0.001);
721s ***** test
721s  x = [0.2:0.2:0.6; 0.2:0.2:0.6];
721s  y = copulapdf ("Gumbel", x, 2);
721s  expected_p = [0.9468; 0.9468];
721s  assert (y, expected_p, 0.001);
721s ***** test
721s  x = [0.2, 0.6; 0.2, 0.6];
721s  theta = [1; 2];
721s  y = copulapdf ("Frank", x, theta);
721s  expected_p = [0.9378; 0.8678];
721s  assert (y, expected_p, 0.001);
721s ***** test
721s  x = [0.2, 0.6; 0.2, 0.6];
721s  theta = [0.3; 0.7];
721s  y = copulapdf ("AMH", x, theta);
721s  expected_p = [0.9540; 0.8577];
721s  assert (y, expected_p, 0.001);
721s 4 tests, 4 passed, 0 known failure, 0 skipped
721s [inst/dist_fun/copularnd.m]
721s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/copularnd.m
721s ***** test
721s  theta = 0.5;
721s  r = copularnd ("Gaussian", theta);
721s  assert (size (r), [1, 2]);
721s  assert (all ((r >= 0) & (r <= 1)));
721s ***** test
721s  theta = 0.5;
721s  df = 2;
721s  r = copularnd ("t", theta, df);
721s  assert (size (r), [1, 2]);
721s  assert (all ((r >= 0) & (r <= 1)));
721s ***** test
721s  theta = 0.5;
721s  r = copularnd ("Clayton", theta);
721s  assert (size (r), [1, 2]);
721s  assert (all ((r >= 0) & (r <= 1)));
721s ***** test
721s  theta = 0.5;
721s  n = 2;
721s  r = copularnd ("Clayton", theta, n);
721s  assert (size (r), [n, 2]);
721s  assert (all ((r >= 0) & (r <= 1)));
721s ***** test
721s  theta = [1; 2];
721s  n = 2;
721s  d = 3;
721s  r = copularnd ("Clayton", theta, n, d);
721s  assert (size (r), [n, d]);
721s  assert (all ((r >= 0) & (r <= 1)));
721s 5 tests, 5 passed, 0 known failure, 0 skipped
721s [inst/dist_fun/evcdf.m]
721s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/evcdf.m
721s ***** demo
721s  ## Plot various CDFs from the extreme value distribution
721s  x = -10:0.01:10;
721s  p1 = evcdf (x, 0.5, 2);
721s  p2 = evcdf (x, 1.0, 2);
721s  p3 = evcdf (x, 1.5, 3);
721s  p4 = evcdf (x, 3.0, 4);
721s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
721s  grid on
721s  legend ({"μ = 0.5, σ = 2", "μ = 1.0, σ = 2", ...
721s           "μ = 1.5, σ = 3", "μ = 3.0, σ = 4"}, "location", "southeast")
721s  title ("Extreme value CDF")
721s  xlabel ("values in x")
721s  ylabel ("probability")
721s ***** shared x, y
721s  x = [-Inf, 1, 2, Inf];
721s  y = [0, 0.6321, 0.9340, 1];
721s ***** assert (evcdf (x, ones (1,4), ones (1,4)), y, 1e-4)
721s ***** assert (evcdf (x, 1, ones (1,4)), y, 1e-4)
721s ***** assert (evcdf (x, ones (1,4), 1), y, 1e-4)
721s ***** assert (evcdf (x, [0, -Inf, NaN, Inf], 1), [0, 1, NaN, NaN], 1e-4)
721s ***** assert (evcdf (x, 1, [Inf, NaN, -1, 0]), [NaN, NaN, NaN, NaN], 1e-4)
721s ***** assert (evcdf ([x(1:2), NaN, x(4)], 1, 1), [y(1:2), NaN, y(4)], 1e-4)
721s ***** assert (evcdf (x, "upper"), [1, 0.0660, 0.0006, 0], 1e-4)
721s ***** assert (evcdf ([x, NaN], 1, 1), [y, NaN], 1e-4)
721s ***** assert (evcdf (single ([x, NaN]), 1, 1), single ([y, NaN]), 1e-4)
721s ***** assert (evcdf ([x, NaN], single (1), 1), single ([y, NaN]), 1e-4)
721s ***** assert (evcdf ([x, NaN], 1, single (1)), single ([y, NaN]), 1e-4)
721s ***** error<evcdf: invalid number of input arguments.> evcdf ()
721s ***** error<evcdf: invalid number of input arguments.> evcdf (1,2,3,4,5,6,7)
721s ***** error<evcdf: invalid argument for upper tail.> evcdf (1, 2, 3, 4, "uper")
721s ***** error<evcdf: X, MU, and SIGMA must be of common size or scalars.> ...
721s  evcdf (ones (3), ones (2), ones (2))
721s ***** error<evcdf: invalid size of covariance matrix.> evcdf (2, 3, 4, [1, 2])
721s ***** error<evcdf: covariance matrix is required for confidence bounds.> ...
721s  [p, plo, pup] = evcdf (1, 2, 3)
721s ***** error<evcdf: invalid value for alpha.> [p, plo, pup] = ...
721s  evcdf (1, 2, 3, [1, 0; 0, 1], 0)
721s ***** error<evcdf: invalid value for alpha.> [p, plo, pup] = ...
721s  evcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
721s ***** error<evcdf: invalid value for alpha.> [p, plo, pup] = ...
721s  evcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
721s ***** error<evcdf: X, MU, and SIGMA must not be complex.> evcdf (i, 2, 2)
721s ***** error<evcdf: X, MU, and SIGMA must not be complex.> evcdf (2, i, 2)
721s ***** error<evcdf: X, MU, and SIGMA must not be complex.> evcdf (2, 2, i)
721s ***** error<evcdf: bad covariance matrix.> ...
721s  [p, plo, pup] = evcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
721s 24 tests, 24 passed, 0 known failure, 0 skipped
721s [inst/dist_fun/evinv.m]
721s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/evinv.m
721s ***** demo
721s  ## Plot various iCDFs from the extreme value distribution
721s  p = 0.001:0.001:0.999;
721s  x1 = evinv (p, 0.5, 2);
721s  x2 = evinv (p, 1.0, 2);
721s  x3 = evinv (p, 1.5, 3);
721s  x4 = evinv (p, 3.0, 4);
721s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
721s  grid on
721s  ylim ([-10, 10])
721s  legend ({"μ = 0.5, σ = 2", "μ = 1.0, σ = 2", ...
721s           "μ = 1.5, σ = 3", "μ = 3.0, σ = 4"}, "location", "northwest")
721s  title ("Extreme value iCDF")
721s  xlabel ("probability")
721s  ylabel ("values in x")
721s ***** shared p, x
721s  p = [0, 0.05, 0.5 0.95];
721s  x = [-Inf, -2.9702, -0.3665, 1.0972];
721s ***** assert (evinv (p), x, 1e-4)
721s ***** assert (evinv (p, zeros (1,4), ones (1,4)), x, 1e-4)
721s ***** assert (evinv (p, 0, ones (1,4)), x, 1e-4)
721s ***** assert (evinv (p, zeros (1,4), 1), x, 1e-4)
721s ***** assert (evinv (p, [0, -Inf, NaN, Inf], 1), [-Inf, -Inf, NaN, Inf], 1e-4)
721s ***** assert (evinv (p, 0, [Inf, NaN, -1, 0]), [-Inf, NaN, NaN, NaN], 1e-4)
721s ***** assert (evinv ([p(1:2), NaN, p(4)], 0, 1), [x(1:2), NaN, x(4)], 1e-4)
721s ***** assert (evinv ([p, NaN], 0, 1), [x, NaN], 1e-4)
721s ***** assert (evinv (single ([p, NaN]), 0, 1), single ([x, NaN]), 1e-4)
721s ***** assert (evinv ([p, NaN], single (0), 1), single ([x, NaN]), 1e-4)
721s ***** assert (evinv ([p, NaN], 0, single (1)), single ([x, NaN]), 1e-4)
721s ***** error<evinv: invalid number of input arguments.> evinv ()
721s ***** error evinv (1,2,3,4,5,6)
721s ***** error<evinv: P, MU, and SIGMA must be of common size or scalars.> ...
721s  evinv (ones (3), ones (2), ones (2))
721s ***** error<evinv: invalid size of covariance matrix.> ...
721s  [p, plo, pup] = evinv (2, 3, 4, [1, 2])
721s ***** error<evinv: covariance matrix is required for confidence bounds.> ...
721s  [p, plo, pup] = evinv (1, 2, 3)
721s ***** error<evinv: invalid value for alpha.> [p, plo, pup] = ...
721s  evinv (1, 2, 3, [1, 0; 0, 1], 0)
721s ***** error<evinv: invalid value for alpha.> [p, plo, pup] = ...
721s  evinv (1, 2, 3, [1, 0; 0, 1], 1.22)
721s ***** error<evinv: P, MU, and SIGMA must not be complex.> evinv (i, 2, 2)
721s ***** error<evinv: P, MU, and SIGMA must not be complex.> evinv (2, i, 2)
721s ***** error<evinv: P, MU, and SIGMA must not be complex.> evinv (2, 2, i)
721s ***** error<evinv: bad covariance matrix.> ...
721s  [p, plo, pup] = evinv (1, 2, 3, [-1, -10; -Inf, -Inf], 0.04)
721s 22 tests, 22 passed, 0 known failure, 0 skipped
721s [inst/dist_fun/evpdf.m]
721s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/evpdf.m
721s ***** demo
721s  ## Plot various PDFs from the Extreme value distribution
721s  x = -10:0.001:10;
721s  y1 = evpdf (x, 0.5, 2);
721s  y2 = evpdf (x, 1.0, 2);
721s  y3 = evpdf (x, 1.5, 3);
721s  y4 = evpdf (x, 3.0, 4);
721s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
721s  grid on
721s  ylim ([0, 0.2])
721s  legend ({"μ = 0.5, σ = 2", "μ = 1.0, σ = 2", ...
721s           "μ = 1.5, σ = 3", "μ = 3.0, σ = 4"}, "location", "northeast")
721s  title ("Extreme value PDF")
721s  xlabel ("values in x")
721s  ylabel ("density")
721s ***** shared x, y0, y1
721s  x = [-5, 0, 1, 2, 3];
721s  y0 = [0.0067, 0.3679, 0.1794, 0.0046, 0];
721s  y1 = [0.0025, 0.2546, 0.3679, 0.1794, 0.0046];
721s ***** assert (evpdf (x), y0, 1e-4)
721s ***** assert (evpdf (x, zeros (1,5), ones (1,5)), y0, 1e-4)
721s ***** assert (evpdf (x, ones (1,5), ones (1,5)), y1, 1e-4)
721s ***** error<evpdf: function called with too few input arguments.> evpdf ()
721s ***** error<evpdf: X, MU, and SIGMA must be of common size or scalars.> ...
721s  evpdf (ones (3), ones (2), ones (2))
721s ***** error<evpdf: X, MU, and SIGMA must not be complex.> evpdf (i, 2, 2)
721s ***** error<evpdf: X, MU, and SIGMA must not be complex.> evpdf (2, i, 2)
721s ***** error<evpdf: X, MU, and SIGMA must not be complex.> evpdf (2, 2, i)
721s 8 tests, 8 passed, 0 known failure, 0 skipped
721s [inst/dist_fun/evrnd.m]
721s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/evrnd.m
721s ***** assert (size (evrnd (1, 1)), [1 1])
721s ***** assert (size (evrnd (1, ones (2,1))), [2, 1])
721s ***** assert (size (evrnd (1, ones (2,2))), [2, 2])
721s ***** assert (size (evrnd (ones (2,1), 1)), [2, 1])
721s ***** assert (size (evrnd (ones (2,2), 1)), [2, 2])
721s ***** assert (size (evrnd (1, 1, 3)), [3, 3])
721s ***** assert (size (evrnd (1, 1, [4, 1])), [4, 1])
721s ***** assert (size (evrnd (1, 1, 4, 1)), [4, 1])
721s ***** assert (size (evrnd (1, 1, 4, 1, 5)), [4, 1, 5])
721s ***** assert (size (evrnd (1, 1, 0, 1)), [0, 1])
721s ***** assert (size (evrnd (1, 1, 1, 0)), [1, 0])
721s ***** assert (size (evrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
721s ***** assert (class (evrnd (1, 1)), "double")
721s ***** assert (class (evrnd (1, single (1))), "single")
721s ***** assert (class (evrnd (1, single ([1, 1]))), "single")
721s ***** assert (class (evrnd (single (1), 1)), "single")
721s ***** assert (class (evrnd (single ([1, 1]), 1)), "single")
721s ***** error<evrnd: function called with too few input arguments.> evrnd ()
721s ***** error<evrnd: function called with too few input arguments.> evrnd (1)
721s ***** error<evrnd: MU and SIGMA must be of common size or scalars.> ...
721s  evrnd (ones (3), ones (2))
721s ***** error<evrnd: MU and SIGMA must be of common size or scalars.> ...
721s  evrnd (ones (2), ones (3))
721s ***** error<evrnd: MU and SIGMA must not be complex.> evrnd (i, 2, 3)
721s ***** error<evrnd: MU and SIGMA must not be complex.> evrnd (1, i, 3)
721s ***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
721s  evrnd (1, 2, -1)
721s ***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
721s  evrnd (1, 2, 1.2)
721s ***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
721s  evrnd (1, 2, ones (2))
721s ***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
721s  evrnd (1, 2, [2 -1 2])
721s ***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
721s  evrnd (1, 2, [2 0 2.5])
721s ***** error<evrnd: dimensions must be non-negative integers.> ...
721s  evrnd (1, 2, 2, -1, 5)
721s ***** error<evrnd: dimensions must be non-negative integers.> ...
721s  evrnd (1, 2, 2, 1.5, 5)
721s ***** error<evrnd: MU and SIGMA must be scalars or of size SZ.> ...
721s  evrnd (2, ones (2), 3)
721s ***** error<evrnd: MU and SIGMA must be scalars or of size SZ.> ...
721s  evrnd (2, ones (2), [3, 2])
721s ***** error<evrnd: MU and SIGMA must be scalars or of size SZ.> ...
721s  evrnd (2, ones (2), 3, 2)
721s 33 tests, 33 passed, 0 known failure, 0 skipped
721s [inst/dist_fun/expcdf.m]
721s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/expcdf.m
721s ***** demo
721s  ## Plot various CDFs from the exponential distribution
721s  x = 0:0.01:5;
721s  p1 = expcdf (x, 2/3);
721s  p2 = expcdf (x, 1.0);
721s  p3 = expcdf (x, 2.0);
721s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r")
721s  grid on
721s  legend ({"μ = 2/3", "μ = 1", "μ = 2"}, "location", "southeast")
721s  title ("Exponential CDF")
721s  xlabel ("values in x")
721s  ylabel ("probability")
721s ***** shared x, p
721s  x = [-1 0 0.5 1 Inf];
721s  p = [0, 1 - exp(-x(2:end)/2)];
721s ***** assert (expcdf (x, 2 * ones (1, 5)), p, 1e-16)
721s ***** assert (expcdf (x, 2), p, 1e-16)
721s ***** assert (expcdf (x, 2 * [1, 0, NaN, 1, 1]), [0, NaN, NaN, p(4:5)], 1e-16)
721s ***** assert (expcdf ([x, NaN], 2), [p, NaN], 1e-16)
721s ***** assert (expcdf (single ([x, NaN]), 2), single ([p, NaN]))
721s ***** assert (expcdf ([x, NaN], single (2)), single ([p, NaN]))
721s ***** test
721s  [p, plo, pup] = expcdf (1, 2, 3);
721s  assert (p, 0.39346934028737, 1e-14);
721s  assert (plo, 0.08751307220484, 1e-14);
721s  assert (pup, 0.93476821257933, 1e-14);
721s ***** test
721s  [p, plo, pup] = expcdf (1, 2, 2, 0.1);
721s  assert (p, 0.39346934028737, 1e-14);
721s  assert (plo, 0.14466318041675, 1e-14);
721s  assert (pup, 0.79808291849140, 1e-14);
721s ***** test
721s  [p, plo, pup] = expcdf (1, 2, 2, 0.1, "upper");
721s  assert (p, 0.60653065971263, 1e-14);
721s  assert (plo, 0.20191708150860, 1e-14);
721s  assert (pup, 0.85533681958325, 1e-14);
721s ***** error<expcdf: invalid number of input arguments.> expcdf ()
721s ***** error<expcdf: invalid number of input arguments.> expcdf (1, 2 ,3 ,4 ,5, 6)
721s ***** error<expcdf: invalid argument for upper tail.> expcdf (1, 2, 3, 4, "uper")
721s ***** error<expcdf: X and MU must be of common size or scalars.> ...
721s  expcdf (ones (3), ones (2))
721s ***** error<expcdf: invalid size of variance, PCOV must be a scalar.> ...
721s  expcdf (2, 3, [1, 2])
721s ***** error<expcdf: variance, PCOV, is required for confidence bounds.> ...
721s  [p, plo, pup] = expcdf (1, 2)
721s ***** error<expcdf: invalid value for alpha.> [p, plo, pup] = ...
721s  expcdf (1, 2, 3, 0)
721s ***** error<expcdf: invalid value for alpha.> [p, plo, pup] = ...
721s  expcdf (1, 2, 3, 1.22)
721s ***** error<expcdf: invalid value for alpha.> [p, plo, pup] = ...
721s  expcdf (1, 2, 3, "alpha", "upper")
721s ***** error<expcdf: X and MU must not be complex.> expcdf (i, 2)
721s ***** error<expcdf: X and MU must not be complex.> expcdf (2, i)
721s ***** error<expcdf: variance, PCOV, cannot be negative.> ...
721s  [p, plo, pup] = expcdf (1, 2, -1, 0.04)
721s 21 tests, 21 passed, 0 known failure, 0 skipped
721s [inst/dist_fun/expinv.m]
721s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/expinv.m
721s ***** demo
721s  ## Plot various iCDFs from the exponential distribution
721s  p = 0.001:0.001:0.999;
721s  x1 = expinv (p, 2/3);
721s  x2 = expinv (p, 1.0);
721s  x3 = expinv (p, 2.0);
721s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
721s  grid on
721s  ylim ([0, 5])
721s  legend ({"μ = 2/3", "μ = 1", "μ = 2"}, "location", "northwest")
721s  title ("Exponential iCDF")
721s  xlabel ("probability")
721s  ylabel ("values in x")
721s ***** shared p
721s  p = [-1 0 0.3934693402873666 1 2];
721s ***** assert (expinv (p, 2*ones (1,5)), [NaN 0 1 Inf NaN], eps)
721s ***** assert (expinv (p, 2), [NaN 0 1 Inf NaN], eps)
721s ***** assert (expinv (p, 2*[1 0 NaN 1 1]), [NaN NaN NaN Inf NaN], eps)
721s ***** assert (expinv ([p(1:2) NaN p(4:5)], 2), [NaN 0 NaN Inf NaN], eps)
721s ***** assert (expinv ([p, NaN], 2), [NaN 0 1 Inf NaN NaN], eps)
721s ***** assert (expinv (single ([p, NaN]), 2), single ([NaN 0 1 Inf NaN NaN]), eps)
721s ***** assert (expinv ([p, NaN], single (2)), single ([NaN 0 1 Inf NaN NaN]), eps)
721s ***** error<expinv: invalid number of input arguments.> expinv ()
721s ***** error<expinv: invalid number of input arguments.> expinv (1, 2 ,3 ,4 ,5)
721s ***** error<expinv: P and MU must be of common size or scalars.> ...
721s  expinv (ones (3), ones (2))
721s ***** error<expinv: invalid size of variance, PCOV must be a scalar.> ...
721s  expinv (2, 3, [1, 2])
721s ***** error<expinv: variance, PCOV, is required for confidence bounds.> ...
721s  [x, xlo, xup] = expinv (1, 2)
721s ***** error<expinv: invalid value for alpha.> [x, xlo, xup] = ...
721s  expinv (1, 2, 3, 0)
721s ***** error<expinv: invalid value for alpha.> [x, xlo, xup] = ...
721s  expinv (1, 2, 3, 1.22)
721s ***** error<expinv: invalid value for alpha.> [x, xlo, xup] = ...
721s  expinv (1, 2, 3, [0.05, 0.1])
721s ***** error<expinv: P and MU must not be complex.> expinv (i, 2)
721s ***** error<expinv: P and MU must not be complex.> expinv (2, i)
721s ***** error<expinv: variance, PCOV, cannot be negative.> ...
721s  [x, xlo, xup] = expinv (1, 2, -1, 0.04)
721s 18 tests, 18 passed, 0 known failure, 0 skipped
721s [inst/dist_fun/exppdf.m]
721s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/exppdf.m
721s ***** demo
721s  ## Plot various PDFs from the exponential distribution
721s  x = 0:0.01:5;
721s  y1 = exppdf (x, 2/3);
721s  y2 = exppdf (x, 1.0);
721s  y3 = exppdf (x, 2.0);
721s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r")
721s  grid on
721s  ylim ([0, 1.5])
721s  legend ({"μ = 2/3", "μ = 1", "μ = 2"}, "location", "northeast")
721s  title ("Exponential PDF")
721s  xlabel ("values in x")
721s  ylabel ("density")
721s ***** shared x,y
721s  x = [-1 0 0.5 1 Inf];
721s  y = gampdf (x, 1, 2);
721s ***** assert (exppdf (x, 2*ones (1,5)), y)
721s ***** assert (exppdf (x, 2*[1 0 NaN 1 1]), [y(1) NaN NaN y(4:5)])
721s ***** assert (exppdf ([x, NaN], 2), [y, NaN])
721s ***** assert (exppdf (single ([x, NaN]), 2), single ([y, NaN]))
721s ***** assert (exppdf ([x, NaN], single (2)), single ([y, NaN]))
721s ***** error<exppdf: function called with too few input arguments.> exppdf ()
721s ***** error<exppdf: function called with too many inputs> exppdf (1,2,3)
721s ***** error<exppdf: X and MU must be of common size or scalars.> ...
721s  exppdf (ones (3), ones (2))
721s ***** error<exppdf: X and MU must be of common size or scalars.> ...
721s  exppdf (ones (2), ones (3))
721s ***** error<exppdf: X and MU must not be complex.> exppdf (i, 2)
721s ***** error<exppdf: X and MU must not be complex.> exppdf (2, i)
721s 11 tests, 11 passed, 0 known failure, 0 skipped
721s [inst/dist_fun/exprnd.m]
721s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/exprnd.m
721s ***** assert (size (exprnd (2)), [1, 1])
721s ***** assert (size (exprnd (ones (2,1))), [2, 1])
721s ***** assert (size (exprnd (ones (2,2))), [2, 2])
721s ***** assert (size (exprnd (1, 3)), [3, 3])
721s ***** assert (size (exprnd (1, [4 1])), [4, 1])
721s ***** assert (size (exprnd (1, 4, 1)), [4, 1])
721s ***** assert (size (exprnd (1, 4, 1)), [4, 1])
721s ***** assert (size (exprnd (1, 4, 1, 5)), [4, 1, 5])
721s ***** assert (size (exprnd (1, 0, 1)), [0, 1])
721s ***** assert (size (exprnd (1, 1, 0)), [1, 0])
721s ***** assert (size (exprnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
721s ***** assert (class (exprnd (2)), "double")
721s ***** assert (class (exprnd (single (2))), "single")
721s ***** assert (class (exprnd (single ([2 2]))), "single")
721s ***** error<exprnd: function called with too few input arguments.> exprnd ()
721s ***** error<exprnd: MU must not be complex.> exprnd (i)
721s ***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
721s  exprnd (1, -1)
721s ***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
721s  exprnd (1, 1.2)
721s ***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
721s  exprnd (1, ones (2))
721s ***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
721s  exprnd (1, [2 -1 2])
721s ***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
721s  exprnd (1, [2 0 2.5])
721s ***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
721s  exprnd (ones (2), ones (2))
721s ***** error<exprnd: dimensions must be non-negative integers.> ...
721s  exprnd (1, 2, -1, 5)
721s ***** error<exprnd: dimensions must be non-negative integers.> ...
721s  exprnd (1, 2, 1.5, 5)
721s ***** error<exprnd: MU must be scalar or of size SZ.> exprnd (ones (2,2), 3)
721s ***** error<exprnd: MU must be scalar or of size SZ.> exprnd (ones (2,2), [3, 2])
721s ***** error<exprnd: MU must be scalar or of size SZ.> exprnd (ones (2,2), 2, 3)
721s 27 tests, 27 passed, 0 known failure, 0 skipped
721s [inst/dist_fun/fcdf.m]
721s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/fcdf.m
721s ***** demo
721s  ## Plot various CDFs from the F distribution
721s  x = 0.01:0.01:4;
721s  p1 = fcdf (x, 1, 2);
721s  p2 = fcdf (x, 2, 1);
721s  p3 = fcdf (x, 5, 2);
721s  p4 = fcdf (x, 10, 1);
721s  p5 = fcdf (x, 100, 100);
721s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
721s  grid on
721s  legend ({"df1 = 1, df2 = 2", "df1 = 2, df2 = 1", ...
721s           "df1 = 5, df2 = 2", "df1 = 10, df2 = 1", ...
721s           "df1 = 100, df2 = 100"}, "location", "southeast")
721s  title ("F CDF")
721s  xlabel ("values in x")
721s  ylabel ("probability")
721s ***** shared x, y
721s  x = [-1, 0, 0.5, 1, 2, Inf];
721s  y = [0, 0, 1/3, 1/2, 2/3, 1];
721s ***** assert (fcdf (x, 2*ones (1,6), 2*ones (1,6)), y, eps)
721s ***** assert (fcdf (x, 2, 2*ones (1,6)), y, eps)
721s ***** assert (fcdf (x, 2*ones (1,6), 2), y, eps)
721s ***** assert (fcdf (x, [0 NaN Inf 2 2 2], 2), [NaN NaN 0.1353352832366127 y(4:6)], eps)
721s ***** assert (fcdf (x, 2, [0 NaN Inf 2 2 2]), [NaN NaN 0.3934693402873666 y(4:6)], eps)
721s ***** assert (fcdf ([x(1:2) NaN x(4:6)], 2, 2), [y(1:2) NaN y(4:6)], eps)
721s ***** assert (fcdf ([x, NaN], 2, 2), [y, NaN], eps)
721s ***** assert (fcdf (single ([x, NaN]), 2, 2), single ([y, NaN]), eps ("single"))
721s ***** assert (fcdf ([x, NaN], single (2), 2), single ([y, NaN]), eps ("single"))
721s ***** assert (fcdf ([x, NaN], 2, single (2)), single ([y, NaN]), eps ("single"))
721s ***** error<fcdf: function called with too few input arguments.> fcdf ()
721s ***** error<fcdf: function called with too few input arguments.> fcdf (1)
721s ***** error<fcdf: function called with too few input arguments.> fcdf (1, 2)
721s ***** error<fcdf: invalid argument for upper tail.> fcdf (1, 2, 3, 4)
721s ***** error<fcdf: invalid argument for upper tail.> fcdf (1, 2, 3, "tail")
721s ***** error<fcdf: X, DF1, and DF2 must be of common size or scalars.> ...
721s  fcdf (ones (3), ones (2), ones (2))
721s ***** error<fcdf: X, DF1, and DF2 must be of common size or scalars.> ...
721s  fcdf (ones (2), ones (3), ones (2))
721s ***** error<fcdf: X, DF1, and DF2 must be of common size or scalars.> ...
721s  fcdf (ones (2), ones (2), ones (3))
721s ***** error<fcdf: X, DF1, and DF2 must not be complex.> fcdf (i, 2, 2)
721s ***** error<fcdf: X, DF1, and DF2 must not be complex.> fcdf (2, i, 2)
721s ***** error<fcdf: X, DF1, and DF2 must not be complex.> fcdf (2, 2, i)
721s 21 tests, 21 passed, 0 known failure, 0 skipped
721s [inst/dist_fun/finv.m]
721s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/finv.m
721s ***** demo
721s  ## Plot various iCDFs from the F distribution
721s  p = 0.001:0.001:0.999;
721s  x1 = finv (p, 1, 1);
721s  x2 = finv (p, 2, 1);
721s  x3 = finv (p, 5, 2);
721s  x4 = finv (p, 10, 1);
721s  x5 = finv (p, 100, 100);
721s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
721s  grid on
721s  ylim ([0, 4])
721s  legend ({"df1 = 1, df2 = 2", "df1 = 2, df2 = 1", ...
721s           "df1 = 5, df2 = 2", "df1 = 10, df2 = 1", ...
721s           "df1 = 100, df2 = 100"}, "location", "northwest")
721s  title ("F iCDF")
721s  xlabel ("probability")
721s  ylabel ("values in x")
721s ***** shared p
721s  p = [-1 0 0.5 1 2];
721s ***** assert (finv (p, 2*ones (1,5), 2*ones (1,5)), [NaN 0 1 Inf NaN])
721s ***** assert (finv (p, 2, 2*ones (1,5)), [NaN 0 1 Inf NaN])
721s ***** assert (finv (p, 2*ones (1,5), 2), [NaN 0 1 Inf NaN])
721s ***** assert (finv (p, [2 -Inf NaN Inf 2], 2), [NaN NaN NaN NaN NaN])
721s ***** assert (finv (p, 2, [2 -Inf NaN Inf 2]), [NaN NaN NaN NaN NaN])
721s ***** assert (finv ([p(1:2) NaN p(4:5)], 2, 2), [NaN 0 NaN Inf NaN])
721s ***** assert (finv (0.025, 10, 1e6), 0.3247, 1e-4)
721s ***** assert (finv (0.025, 10, 1e7), 0.3247, 1e-4)
721s ***** assert (finv (0.025, 10, 1e10), 0.3247, 1e-4)
721s ***** assert (finv (0.025, 10, 1e255), 0.3247, 1e-4)
721s ***** assert (finv (0.025, 10, Inf), 0.3247, 1e-4)
721s ***** assert (finv ([p, NaN], 2, 2), [NaN 0 1 Inf NaN NaN])
721s ***** assert (finv (single ([p, NaN]), 2, 2), single ([NaN 0 1 Inf NaN NaN]))
721s ***** assert (finv ([p, NaN], single (2), 2), single ([NaN 0 1 Inf NaN NaN]))
721s ***** assert (finv ([p, NaN], 2, single (2)), single ([NaN 0 1 Inf NaN NaN]))
721s ***** error<finv: function called with too few input arguments.> finv ()
721s ***** error<finv: function called with too few input arguments.> finv (1)
721s ***** error<finv: function called with too few input arguments.> finv (1,2)
721s ***** error<finv: P, DF1, and DF2 must be of common size or scalars.> ...
721s  finv (ones (3), ones (2), ones (2))
721s ***** error<finv: P, DF1, and DF2 must be of common size or scalars.> ...
721s  finv (ones (2), ones (3), ones (2))
721s ***** error<finv: P, DF1, and DF2 must be of common size or scalars.> ...
721s  finv (ones (2), ones (2), ones (3))
721s ***** error<finv: P, DF1, and DF2 must not be complex.> finv (i, 2, 2)
721s ***** error<finv: P, DF1, and DF2 must not be complex.> finv (2, i, 2)
721s ***** error<finv: P, DF1, and DF2 must not be complex.> finv (2, 2, i)
721s 24 tests, 24 passed, 0 known failure, 0 skipped
721s [inst/dist_fun/fpdf.m]
721s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/fpdf.m
721s ***** demo
721s  ## Plot various PDFs from the F distribution
721s  x = 0.01:0.01:4;
721s  y1 = fpdf (x, 1, 1);
721s  y2 = fpdf (x, 2, 1);
721s  y3 = fpdf (x, 5, 2);
721s  y4 = fpdf (x, 10, 1);
721s  y5 = fpdf (x, 100, 100);
721s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
721s  grid on
721s  ylim ([0, 2.5])
721s  legend ({"df1 = 1, df2 = 2", "df1 = 2, df2 = 1", ...
721s           "df1 = 5, df2 = 2", "df1 = 10, df2 = 1", ...
721s           "df1 = 100, df2 = 100"}, "location", "northeast")
721s  title ("F PDF")
721s  xlabel ("values in x")
721s  ylabel ("density")
721s ***** shared x, y
721s  x = [-1 0 0.5 1 2];
721s  y = [0 0 4/9 1/4 1/9];
721s ***** assert (fpdf (x, 2*ones (1,5), 2*ones (1,5)), y, eps)
722s ***** assert (fpdf (x, 2, 2*ones (1,5)), y, eps)
722s ***** assert (fpdf (x, 2*ones (1,5), 2), y, eps)
722s ***** assert (fpdf (x, [0 NaN Inf 2 2], 2), [NaN NaN NaN y(4:5)], eps)
722s ***** assert (fpdf (x, 2, [0 NaN Inf 2 2]), [NaN NaN NaN y(4:5)], eps)
722s ***** assert (fpdf ([x, NaN], 2, 2), [y, NaN], eps)
722s ***** test #F (x, 1, df1) == T distribution (sqrt (x), df1) / sqrt (x)
722s  rand ("seed", 1234);    # for reproducibility
722s  xr = rand (10,1);
722s  xr = xr(x > 0.1 & x < 0.9);
722s  yr = tpdf (sqrt (xr), 2) ./ sqrt (xr);
722s  assert (fpdf (xr, 1, 2), yr, 5*eps);
722s ***** assert (fpdf (single ([x, NaN]), 2, 2), single ([y, NaN]), eps ("single"))
722s ***** assert (fpdf ([x, NaN], single (2), 2), single ([y, NaN]), eps ("single"))
722s ***** assert (fpdf ([x, NaN], 2, single (2)), single ([y, NaN]), eps ("single"))
722s ***** error<fpdf: function called with too few input arguments.> fpdf ()
722s ***** error<fpdf: function called with too few input arguments.> fpdf (1)
722s ***** error<fpdf: function called with too few input arguments.> fpdf (1,2)
722s ***** error<fpdf: X, DF1, and DF2 must be of common size or scalars.> ...
722s  fpdf (ones (3), ones (2), ones (2))
722s ***** error<fpdf: X, DF1, and DF2 must be of common size or scalars.> ...
722s  fpdf (ones (2), ones (3), ones (2))
722s ***** error<fpdf: X, DF1, and DF2 must be of common size or scalars.> ...
722s  fpdf (ones (2), ones (2), ones (3))
722s ***** error<fpdf: X, DF1, and DF2 must not be complex.> fpdf (i, 2, 2)
722s ***** error<fpdf: X, DF1, and DF2 must not be complex.> fpdf (2, i, 2)
722s ***** error<fpdf: X, DF1, and DF2 must not be complex.> fpdf (2, 2, i)
722s 19 tests, 19 passed, 0 known failure, 0 skipped
722s [inst/dist_fun/frnd.m]
722s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/frnd.m
722s ***** assert (size (frnd (1, 1)), [1 1])
722s ***** assert (size (frnd (1, ones (2,1))), [2, 1])
722s ***** assert (size (frnd (1, ones (2,2))), [2, 2])
722s ***** assert (size (frnd (ones (2,1), 1)), [2, 1])
722s ***** assert (size (frnd (ones (2,2), 1)), [2, 2])
722s ***** assert (size (frnd (1, 1, 3)), [3, 3])
722s ***** assert (size (frnd (1, 1, [4, 1])), [4, 1])
722s ***** assert (size (frnd (1, 1, 4, 1)), [4, 1])
722s ***** assert (size (frnd (1, 1, 4, 1, 5)), [4, 1, 5])
722s ***** assert (size (frnd (1, 1, 0, 1)), [0, 1])
722s ***** assert (size (frnd (1, 1, 1, 0)), [1, 0])
722s ***** assert (size (frnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
722s ***** assert (class (frnd (1, 1)), "double")
722s ***** assert (class (frnd (1, single (1))), "single")
722s ***** assert (class (frnd (1, single ([1, 1]))), "single")
722s ***** assert (class (frnd (single (1), 1)), "single")
722s ***** assert (class (frnd (single ([1, 1]), 1)), "single")
722s ***** error<frnd: function called with too few input arguments.> frnd ()
722s ***** error<frnd: function called with too few input arguments.> frnd (1)
722s ***** error<frnd: DF1 and DF2 must be of common size or scalars.> ...
722s  frnd (ones (3), ones (2))
722s ***** error<frnd: DF1 and DF2 must be of common size or scalars.> ...
722s  frnd (ones (2), ones (3))
722s ***** error<frnd: DF1 and DF2 must not be complex.> frnd (i, 2, 3)
722s ***** error<frnd: DF1 and DF2 must not be complex.> frnd (1, i, 3)
722s ***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
722s  frnd (1, 2, -1)
722s ***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
722s  frnd (1, 2, 1.2)
722s ***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
722s  frnd (1, 2, ones (2))
722s ***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
722s  frnd (1, 2, [2 -1 2])
722s ***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
722s  frnd (1, 2, [2 0 2.5])
722s ***** error<frnd: dimensions must be non-negative integers.> ...
722s  frnd (1, 2, 2, -1, 5)
722s ***** error<frnd: dimensions must be non-negative integers.> ...
722s  frnd (1, 2, 2, 1.5, 5)
722s ***** error<frnd: DF1 and DF2 must be scalars or of size SZ.> ...
722s  frnd (2, ones (2), 3)
722s ***** error<frnd: DF1 and DF2 must be scalars or of size SZ.> ...
722s  frnd (2, ones (2), [3, 2])
722s ***** error<frnd: DF1 and DF2 must be scalars or of size SZ.> ...
722s  frnd (2, ones (2), 3, 2)
722s 33 tests, 33 passed, 0 known failure, 0 skipped
722s [inst/dist_fun/gamcdf.m]
722s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/gamcdf.m
722s ***** demo
722s  ## Plot various CDFs from the Gamma distribution
722s  x = 0:0.01:20;
722s  p1 = gamcdf (x, 1, 2);
722s  p2 = gamcdf (x, 2, 2);
722s  p3 = gamcdf (x, 3, 2);
722s  p4 = gamcdf (x, 5, 1);
722s  p5 = gamcdf (x, 9, 0.5);
722s  p6 = gamcdf (x, 7.5, 1);
722s  p7 = gamcdf (x, 0.5, 1);
722s  plot (x, p1, "-r", x, p2, "-g", x, p3, "-y", x, p4, "-m", ...
722s        x, p5, "-k", x, p6, "-b", x, p7, "-c")
722s  grid on
722s  legend ({"α = 1, β = 2", "α = 2, β = 2", "α = 3, β = 2", ...
722s           "α = 5, β = 1", "α = 9, β = 0.5", "α = 7.5, β = 1", ...
722s           "α = 0.5, β = 1"}, "location", "southeast")
722s  title ("Gamma CDF")
722s  xlabel ("values in x")
722s  ylabel ("probability")
722s ***** shared x, y, u
722s  x = [-1, 0, 0.5, 1, 2, Inf];
722s  y = [0, gammainc(x(2:end), 1)];
722s  u = [0, NaN, NaN, 1, 0.1353352832366127, 0];
722s ***** assert (gamcdf (x, ones (1,6), ones (1,6)), y, eps)
722s ***** assert (gamcdf (x, ones (1,6), ones (1,6), []), y, eps)
722s ***** assert (gamcdf (x, 1, ones (1,6)), y, eps)
722s ***** assert (gamcdf (x, ones (1,6), 1), y, eps)
722s ***** assert (gamcdf (x, [0, -Inf, NaN, Inf, 1, 1], 1), [1, NaN, NaN, 0, y(5:6)], eps)
722s ***** assert (gamcdf (x, [0, -Inf, NaN, Inf, 1, 1], 1, "upper"), u, eps)
722s ***** assert (gamcdf (x, 1, [0, -Inf, NaN, Inf, 1, 1]), [NaN, NaN, NaN, 0, y(5:6)], eps)
722s ***** assert (gamcdf ([x(1:2), NaN, x(4:6)], 1, 1), [y(1:2), NaN, y(4:6)], eps)
722s ***** assert (gamcdf ([x, NaN], 1, 1), [y, NaN])
722s ***** assert (gamcdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
722s ***** assert (gamcdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
722s ***** assert (gamcdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
722s ***** error<gamcdf: invalid number of input arguments.> gamcdf ()
722s ***** error<gamcdf: invalid number of input arguments.> gamcdf (1)
722s ***** error<gamcdf: invalid number of input arguments.> gamcdf (1, 2, 3, 4, 5, 6, 7)
722s ***** error<gamcdf: invalid argument for upper tail.> gamcdf (1, 2, 3, "uper")
722s ***** error<gamcdf: invalid argument for upper tail.> gamcdf (1, 2, 3, 4, 5, "uper")
722s ***** error<gamcdf: invalid size of covariance matrix.> gamcdf (2, 3, 4, [1, 2])
722s ***** error<gamcdf: covariance matrix is required for confidence bounds.> ...
722s  [p, plo, pup] = gamcdf (1, 2, 3)
722s ***** error<gamcdf: covariance matrix is required for confidence bounds.> ...
722s  [p, plo, pup] = gamcdf (1, 2, 3, "upper")
722s ***** error<gamcdf: invalid value for alpha.> [p, plo, pup] = ...
722s  gamcdf (1, 2, 3, [1, 0; 0, 1], 0)
722s ***** error<gamcdf: invalid value for alpha.> [p, plo, pup] = ...
722s  gamcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
722s ***** error<gamcdf: invalid value for alpha.> [p, plo, pup] = ...
722s  gamcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
722s ***** error<gamcdf: X, A, and B must be of common size or scalars.> ...
722s  gamcdf (ones (3), ones (2), ones (2))
722s ***** error<gamcdf: X, A, and B must be of common size or scalars.> ...
722s  gamcdf (ones (2), ones (3), ones (2))
722s ***** error<gamcdf: X, A, and B must be of common size or scalars.> ...
722s  gamcdf (ones (2), ones (2), ones (3))
722s ***** error<gamcdf: X, A, and B must not be complex.> gamcdf (i, 2, 2)
722s ***** error<gamcdf: X, A, and B must not be complex.> gamcdf (2, i, 2)
722s ***** error<gamcdf: X, A, and B must not be complex.> gamcdf (2, 2, i)
722s ***** error<gamcdf: bad covariance matrix.> ...
722s  [p, plo, pup] = gamcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
722s 30 tests, 30 passed, 0 known failure, 0 skipped
722s [inst/dist_fun/gaminv.m]
722s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/gaminv.m
722s ***** demo
722s  ## Plot various iCDFs from the Gamma distribution
722s  p = 0.001:0.001:0.999;
722s  x1 = gaminv (p, 1, 2);
722s  x2 = gaminv (p, 2, 2);
722s  x3 = gaminv (p, 3, 2);
722s  x4 = gaminv (p, 5, 1);
722s  x5 = gaminv (p, 9, 0.5);
722s  x6 = gaminv (p, 7.5, 1);
722s  x7 = gaminv (p, 0.5, 1);
722s  plot (p, x1, "-r", p, x2, "-g", p, x3, "-y", p, x4, "-m", ...
722s        p, x5, "-k", p, x6, "-b", p, x7, "-c")
722s  ylim ([0, 20])
722s  grid on
722s  legend ({"α = 1, β = 2", "α = 2, β = 2", "α = 3, β = 2", ...
722s           "α = 5, β = 1", "α = 9, β = 0.5", "α = 7.5, β = 1", ...
722s           "α = 0.5, β = 1"}, "location", "northwest")
722s  title ("Gamma iCDF")
722s  xlabel ("probability")
722s  ylabel ("x")
722s ***** shared p
722s  p = [-1 0 0.63212055882855778 1 2];
722s ***** assert (gaminv (p, ones (1,5), ones (1,5)), [NaN 0 1 Inf NaN], eps)
722s ***** assert (gaminv (p, 1, ones (1,5)), [NaN 0 1 Inf NaN], eps)
722s ***** assert (gaminv (p, ones (1,5), 1), [NaN 0 1 Inf NaN], eps)
722s ***** assert (gaminv (p, [1 -Inf NaN Inf 1], 1), [NaN NaN NaN NaN NaN])
722s ***** assert (gaminv (p, 1, [1 -Inf NaN Inf 1]), [NaN NaN NaN NaN NaN])
722s ***** assert (gaminv ([p(1:2) NaN p(4:5)], 1, 1), [NaN 0 NaN Inf NaN])
722s ***** assert (gaminv ([p(1:2) NaN p(4:5)], 1, 1), [NaN 0 NaN Inf NaN])
722s ***** assert (gaminv (1e-16, 1, 1), 1e-16, eps)
722s ***** assert (gaminv (1e-16, 1, 2), 2e-16, eps)
722s ***** assert (gaminv (1e-20, 3, 5), 1.957434012161815e-06, eps)
722s ***** assert (gaminv (1e-15, 1, 1), 1e-15, eps)
722s ***** assert (gaminv (1e-35, 1, 1), 1e-35, eps)
722s ***** assert (gaminv ([p, NaN], 1, 1), [NaN 0 1 Inf NaN NaN], eps)
722s ***** assert (gaminv (single ([p, NaN]), 1, 1), single ([NaN 0 1 Inf NaN NaN]), ...
722s  eps ("single"))
722s ***** assert (gaminv ([p, NaN], single (1), 1), single ([NaN 0 1 Inf NaN NaN]), ...
722s  eps ("single"))
722s ***** assert (gaminv ([p, NaN], 1, single (1)), single ([NaN 0 1 Inf NaN NaN]), ...
722s  eps ("single"))
722s ***** error<gaminv: function called with too few input arguments.> gaminv ()
722s ***** error<gaminv: function called with too few input arguments.> gaminv (1)
722s ***** error<gaminv: function called with too few input arguments.> gaminv (1,2)
722s ***** error<gaminv: P, Α, and Β must be of common size or scalars.> ...
722s  gaminv (ones (3), ones (2), ones (2))
722s ***** error<gaminv: P, Α, and Β must be of common size or scalars.> ...
722s  gaminv (ones (2), ones (3), ones (2))
722s ***** error<gaminv: P, Α, and Β must be of common size or scalars.> ...
722s  gaminv (ones (2), ones (2), ones (3))
722s ***** error<gaminv: P, Α, and Β must not be complex.> gaminv (i, 2, 2)
722s ***** error<gaminv: P, Α, and Β must not be complex.> gaminv (2, i, 2)
722s ***** error<gaminv: P, Α, and Β must not be complex.> gaminv (2, 2, i)
722s 25 tests, 25 passed, 0 known failure, 0 skipped
722s [inst/dist_fun/gampdf.m]
722s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/gampdf.m
722s ***** demo
722s  ## Plot various PDFs from the Gamma distribution
722s  x = 0:0.01:20;
722s  y1 = gampdf (x, 1, 2);
722s  y2 = gampdf (x, 2, 2);
722s  y3 = gampdf (x, 3, 2);
722s  y4 = gampdf (x, 5, 1);
722s  y5 = gampdf (x, 9, 0.5);
722s  y6 = gampdf (x, 7.5, 1);
722s  y7 = gampdf (x, 0.5, 1);
722s  plot (x, y1, "-r", x, y2, "-g", x, y3, "-y", x, y4, "-m", ...
722s        x, y5, "-k", x, y6, "-b", x, y7, "-c")
722s  grid on
722s  ylim ([0,0.5])
722s  legend ({"α = 1, β = 2", "α = 2, β = 2", "α = 3, β = 2", ...
722s           "α = 5, β = 1", "α = 9, β = 0.5", "α = 7.5, β = 1", ...
722s           "α = 0.5, β = 1"}, "location", "northeast")
722s  title ("Gamma PDF")
722s  xlabel ("values in x")
722s  ylabel ("density")
722s ***** shared x, y
722s  x = [-1 0 0.5 1 Inf];
722s  y = [0 exp(-x(2:end))];
722s ***** assert (gampdf (x, ones (1,5), ones (1,5)), y)
722s ***** assert (gampdf (x, 1, ones (1,5)), y)
722s ***** assert (gampdf (x, ones (1,5), 1), y)
722s ***** assert (gampdf (x, [0 -Inf NaN Inf 1], 1), [NaN NaN NaN NaN y(5)])
722s ***** assert (gampdf (x, 1, [0 -Inf NaN Inf 1]), [NaN NaN NaN 0 y(5)])
722s ***** assert (gampdf ([x, NaN], 1, 1), [y, NaN])
722s ***** assert (gampdf (single ([x, NaN]), 1, 1), single ([y, NaN]))
722s ***** assert (gampdf ([x, NaN], single (1), 1), single ([y, NaN]))
722s ***** assert (gampdf ([x, NaN], 1, single (1)), single ([y, NaN]))
722s ***** error<gampdf: function called with too few input arguments.> gampdf ()
722s ***** error<gampdf: function called with too few input arguments.> gampdf (1)
722s ***** error<gampdf: function called with too few input arguments.> gampdf (1,2)
722s ***** error<gampdf: X, A, and B must be of common size or scalars.> ...
722s  gampdf (ones (3), ones (2), ones (2))
722s ***** error<gampdf: X, A, and B must be of common size or scalars.> ...
722s  gampdf (ones (2), ones (3), ones (2))
722s ***** error<gampdf: X, A, and B must be of common size or scalars.> ...
722s  gampdf (ones (2), ones (2), ones (3))
722s ***** error<gampdf: X, A, and B must not be complex.> gampdf (i, 2, 2)
722s ***** error<gampdf: X, A, and B must not be complex.> gampdf (2, i, 2)
722s ***** error<gampdf: X, A, and B must not be complex.> gampdf (2, 2, i)
722s 18 tests, 18 passed, 0 known failure, 0 skipped
722s [inst/dist_fun/gamrnd.m]
722s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/gamrnd.m
722s ***** assert (size (gamrnd (1, 1)), [1 1])
722s ***** assert (size (gamrnd (1, ones (2,1))), [2, 1])
722s ***** assert (size (gamrnd (1, ones (2,2))), [2, 2])
722s ***** assert (size (gamrnd (ones (2,1), 1)), [2, 1])
722s ***** assert (size (gamrnd (ones (2,2), 1)), [2, 2])
722s ***** assert (size (gamrnd (1, 1, 3)), [3, 3])
722s ***** assert (size (gamrnd (1, 1, [4, 1])), [4, 1])
722s ***** assert (size (gamrnd (1, 1, 4, 1)), [4, 1])
722s ***** assert (size (gamrnd (1, 1, 4, 1, 5)), [4, 1, 5])
722s ***** assert (size (gamrnd (1, 1, 0, 1)), [0, 1])
722s ***** assert (size (gamrnd (1, 1, 1, 0)), [1, 0])
722s ***** assert (size (gamrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
722s ***** assert (class (gamrnd (1, 1)), "double")
722s ***** assert (class (gamrnd (1, single (1))), "single")
722s ***** assert (class (gamrnd (1, single ([1, 1]))), "single")
722s ***** assert (class (gamrnd (single (1), 1)), "single")
722s ***** assert (class (gamrnd (single ([1, 1]), 1)), "single")
722s ***** error<gamrnd: function called with too few input arguments.> gamrnd ()
722s ***** error<gamrnd: function called with too few input arguments.> gamrnd (1)
722s ***** error<gamrnd: A and B must be of common size or scalars.> ...
722s  gamrnd (ones (3), ones (2))
722s ***** error<gamrnd: A and B must be of common size or scalars.> ...
722s  gamrnd (ones (2), ones (3))
722s ***** error<gamrnd: A and B must not be complex.> gamrnd (i, 2, 3)
722s ***** error<gamrnd: A and B must not be complex.> gamrnd (1, i, 3)
722s ***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
722s  gamrnd (1, 2, -1)
722s ***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
722s  gamrnd (1, 2, 1.2)
722s ***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
722s  gamrnd (1, 2, ones (2))
722s ***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
722s  gamrnd (1, 2, [2 -1 2])
722s ***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
722s  gamrnd (1, 2, [2 0 2.5])
722s ***** error<gamrnd: dimensions must be non-negative integers.> ...
722s  gamrnd (1, 2, 2, -1, 5)
722s ***** error<gamrnd: dimensions must be non-negative integers.> ...
722s  gamrnd (1, 2, 2, 1.5, 5)
722s ***** error<gamrnd: A and B must be scalars or of size SZ.> ...
722s  gamrnd (2, ones (2), 3)
722s ***** error<gamrnd: A and B must be scalars or of size SZ.> ...
722s  gamrnd (2, ones (2), [3, 2])
722s ***** error<gamrnd: A and B must be scalars or of size SZ.> ...
722s  gamrnd (2, ones (2), 3, 2)
722s 33 tests, 33 passed, 0 known failure, 0 skipped
722s [inst/dist_fun/geocdf.m]
722s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/geocdf.m
722s ***** demo
722s  ## Plot various CDFs from the geometric distribution
722s  x = 0:10;
722s  p1 = geocdf (x, 0.2);
722s  p2 = geocdf (x, 0.5);
722s  p3 = geocdf (x, 0.7);
722s  plot (x, p1, "*b", x, p2, "*g", x, p3, "*r")
722s  grid on
722s  xlim ([0, 10])
722s  legend ({"ps = 0.2", "ps = 0.5", "ps = 0.7"}, "location", "southeast")
722s  title ("Geometric CDF")
722s  xlabel ("values in x (number of failures)")
722s  ylabel ("probability")
722s ***** test
722s  p = geocdf ([1, 2, 3, 4], 0.25);
722s  assert (p(1), 0.4375000000, 1e-14);
722s  assert (p(2), 0.5781250000, 1e-14);
722s  assert (p(3), 0.6835937500, 1e-14);
722s  assert (p(4), 0.7626953125, 1e-14);
722s ***** test
722s  p = geocdf ([1, 2, 3, 4], 0.25, "upper");
722s  assert (p(1), 0.5625000000, 1e-14);
722s  assert (p(2), 0.4218750000, 1e-14);
722s  assert (p(3), 0.3164062500, 1e-14);
722s  assert (p(4), 0.2373046875, 1e-14);
722s ***** shared x, p
722s  x = [-1 0 1 Inf];
722s  p = [0 0.5 0.75 1];
722s ***** assert (geocdf (x, 0.5*ones (1,4)), p)
722s ***** assert (geocdf (x, 0.5), p)
722s ***** assert (geocdf (x, 0.5*[-1 NaN 4 1]), [NaN NaN NaN p(4)])
722s ***** assert (geocdf ([x(1:2) NaN x(4)], 0.5), [p(1:2) NaN p(4)])
722s ***** assert (geocdf ([x, NaN], 0.5), [p, NaN])
722s ***** assert (geocdf (single ([x, NaN]), 0.5), single ([p, NaN]))
722s ***** assert (geocdf ([x, NaN], single (0.5)), single ([p, NaN]))
722s ***** error<geocdf: function called with too few input arguments.> geocdf ()
722s ***** error<geocdf: function called with too few input arguments.> geocdf (1)
722s ***** error<geocdf: X and PS must be of common size or scalars.> ...
722s  geocdf (ones (3), ones (2))
722s ***** error<geocdf: X and PS must be of common size or scalars.> ...
722s  geocdf (ones (2), ones (3))
722s ***** error<geocdf: X and PS must not be complex.> geocdf (i, 2)
722s ***** error<geocdf: X and PS must not be complex.> geocdf (2, i)
722s ***** error<geocdf: invalid argument for upper tail.> geocdf (2, 3, "tail")
722s ***** error<geocdf: invalid argument for upper tail.> geocdf (2, 3, 5)
722s 17 tests, 17 passed, 0 known failure, 0 skipped
722s [inst/dist_fun/geoinv.m]
722s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/geoinv.m
722s ***** demo
722s  ## Plot various iCDFs from the geometric distribution
722s  p = 0.001:0.001:0.999;
722s  x1 = geoinv (p, 0.2);
722s  x2 = geoinv (p, 0.5);
722s  x3 = geoinv (p, 0.7);
722s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
722s  grid on
722s  ylim ([0, 10])
722s  legend ({"ps = 0.2", "ps = 0.5", "ps = 0.7"}, "location", "northwest")
722s  title ("Geometric iCDF")
722s  xlabel ("probability")
722s  ylabel ("values in x (number of failures)")
722s ***** shared p
722s  p = [-1 0 0.75 1 2];
722s ***** assert (geoinv (p, 0.5*ones (1,5)), [NaN 0 1 Inf NaN])
722s ***** assert (geoinv (p, 0.5), [NaN 0 1 Inf NaN])
722s ***** assert (geoinv (p, 0.5*[1 -1 NaN 4 1]), [NaN NaN NaN NaN NaN])
722s ***** assert (geoinv ([p(1:2) NaN p(4:5)], 0.5), [NaN 0 NaN Inf NaN])
722s ***** assert (geoinv ([p, NaN], 0.5), [NaN 0 1 Inf NaN NaN])
722s ***** assert (geoinv (single ([p, NaN]), 0.5), single ([NaN 0 1 Inf NaN NaN]))
722s ***** assert (geoinv ([p, NaN], single (0.5)), single ([NaN 0 1 Inf NaN NaN]))
722s ***** error<geoinv: function called with too few input arguments.> geoinv ()
722s ***** error<geoinv: function called with too few input arguments.> geoinv (1)
722s ***** error<geoinv: P and PS must be of common size or scalars.> ...
722s  geoinv (ones (3), ones (2))
722s ***** error<geoinv: P and PS must be of common size or scalars.> ...
722s  geoinv (ones (2), ones (3))
722s ***** error<geoinv: P and PS must not be complex.> ...
722s  geoinv (i, 2)
722s ***** error<geoinv: P and PS must not be complex.> ...
722s  geoinv (2, i)
722s 13 tests, 13 passed, 0 known failure, 0 skipped
722s [inst/dist_fun/geopdf.m]
722s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/geopdf.m
722s ***** demo
722s  ## Plot various PDFs from the geometric distribution
722s  x = 0:10;
722s  y1 = geopdf (x, 0.2);
722s  y2 = geopdf (x, 0.5);
722s  y3 = geopdf (x, 0.7);
722s  plot (x, y1, "*b", x, y2, "*g", x, y3, "*r")
722s  grid on
722s  ylim ([0, 0.8])
722s  legend ({"ps = 0.2", "ps = 0.5", "ps = 0.7"}, "location", "northeast")
722s  title ("Geometric PDF")
722s  xlabel ("values in x (number of failures)")
722s  ylabel ("density")
722s ***** shared x, y
722s  x = [-1 0 1 Inf];
722s  y = [0, 1/2, 1/4, NaN];
722s ***** assert (geopdf (x, 0.5*ones (1,4)), y)
722s ***** assert (geopdf (x, 0.5), y)
722s ***** assert (geopdf (x, 0.5*[-1 NaN 4 1]), [NaN NaN NaN y(4)])
722s ***** assert (geopdf ([x, NaN], 0.5), [y, NaN])
722s ***** assert (geopdf (single ([x, NaN]), 0.5), single ([y, NaN]), 5*eps ("single"))
722s ***** assert (geopdf ([x, NaN], single (0.5)), single ([y, NaN]), 5*eps ("single"))
722s ***** error geopdf ()
722s ***** error geopdf (1)
722s ***** error geopdf (1,2,3)
722s ***** error geopdf (ones (3), ones (2))
722s ***** error geopdf (ones (2), ones (3))
722s ***** error geopdf (i, 2)
722s ***** error geopdf (2, i)
722s 13 tests, 13 passed, 0 known failure, 0 skipped
722s [inst/dist_fun/geornd.m]
722s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/geornd.m
722s ***** assert (size (geornd (0.5)), [1, 1])
722s ***** assert (size (geornd (0.5*ones (2,1))), [2, 1])
722s ***** assert (size (geornd (0.5*ones (2,2))), [2, 2])
722s ***** assert (size (geornd (0.5, 3)), [3, 3])
722s ***** assert (size (geornd (0.5, [4 1])), [4, 1])
722s ***** assert (size (geornd (0.5, 4, 1)), [4, 1])
722s ***** assert (class (geornd (0.5)), "double")
722s ***** assert (class (geornd (single (0.5))), "single")
722s ***** assert (class (geornd (single ([0.5 0.5]))), "single")
722s ***** assert (class (geornd (single (0))), "single")
722s ***** assert (class (geornd (single (1))), "single")
722s ***** error<geornd: function called with too few input arguments.> geornd ()
722s ***** error<geornd: PS must not be complex.> geornd (i)
722s ***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
722s  geornd (1, -1)
722s ***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
722s  geornd (1, 1.2)
722s ***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
722s  geornd (1, ones (2))
722s ***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
722s  geornd (1, [2 -1 2])
722s ***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
722s  geornd (1, [2 0 2.5])
722s ***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
722s  geornd (ones (2), ones (2))
722s ***** error<geornd: dimensions must be non-negative integers.> ...
722s  geornd (1, 2, -1, 5)
722s ***** error<geornd: dimensions must be non-negative integers.> ...
722s  geornd (1, 2, 1.5, 5)
722s ***** error<geornd: PS must be scalar or of size SZ.> geornd (ones (2,2), 3)
722s ***** error<geornd: PS must be scalar or of size SZ.> geornd (ones (2,2), [3, 2])
722s ***** error<geornd: PS must be scalar or of size SZ.> geornd (ones (2,2), 2, 3)
722s 24 tests, 24 passed, 0 known failure, 0 skipped
722s [inst/dist_fun/gevcdf.m]
722s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/gevcdf.m
722s ***** demo
722s  ## Plot various CDFs from the generalized extreme value distribution
722s  x = -1:0.001:10;
722s  p1 = gevcdf (x, 1, 1, 1);
722s  p2 = gevcdf (x, 0.5, 1, 1);
722s  p3 = gevcdf (x, 1, 1, 5);
722s  p4 = gevcdf (x, 1, 2, 5);
722s  p5 = gevcdf (x, 1, 5, 5);
722s  p6 = gevcdf (x, 1, 0.5, 5);
722s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", ...
722s        x, p4, "-c", x, p5, "-m", x, p6, "-k")
722s  grid on
722s  xlim ([-1, 10])
722s  legend ({"k = 1, σ = 1, μ = 1", "k = 0.5, σ = 1, μ = 1", ...
722s           "k = 1, σ = 1, μ = 5", "k = 1, σ = 2, μ = 5", ...
722s           "k = 1, σ = 5, μ = 5", "k = 1, σ = 0.5, μ = 5"}, ...
722s          "location", "southeast")
722s  title ("Generalized extreme value CDF")
722s  xlabel ("values in x")
722s  ylabel ("probability")
722s ***** test
722s  x = 0:0.5:2.5;
722s  sigma = 1:6;
722s  k = 1;
722s  mu = 0;
722s  p = gevcdf (x, k, sigma, mu);
722s  expected_p = [0.36788, 0.44933, 0.47237, 0.48323, 0.48954, 0.49367];
722s  assert (p, expected_p, 0.001);
722s ***** test
722s  x = -0.5:0.5:2.5;
722s  sigma = 0.5;
722s  k = 1;
722s  mu = 0;
722s  p = gevcdf (x, k, sigma, mu);
722s  expected_p = [0, 0.36788, 0.60653, 0.71653, 0.77880, 0.81873, 0.84648];
722s  assert (p, expected_p, 0.001);
722s ***** test # check for continuity for k near 0
722s  x = 1;
722s  sigma = 0.5;
722s  k = -0.03:0.01:0.03;
722s  mu = 0;
722s  p = gevcdf (x, k, sigma, mu);
722s  expected_p = [0.88062, 0.87820, 0.87580, 0.87342, 0.87107, 0.86874, 0.86643];
722s  assert (p, expected_p, 0.001);
722s ***** error<gevcdf: function called with too few input arguments.> gevcdf ()
722s ***** error<gevcdf: function called with too few input arguments.> gevcdf (1)
722s ***** error<gevcdf: function called with too few input arguments.> gevcdf (1, 2)
722s ***** error<gevcdf: function called with too few input arguments.> gevcdf (1, 2, 3)
722s ***** error<gevcdf: function called with too many inputs> ...
722s  gevcdf (1, 2, 3, 4, 5, 6)
722s ***** error<gevcdf: invalid argument for upper tail.> gevcdf (1, 2, 3, 4, "tail")
722s ***** error<gevcdf: invalid argument for upper tail.> gevcdf (1, 2, 3, 4, 5)
722s ***** error<gevcdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
722s  gevcdf (ones (3), ones (2), ones(2), ones(2))
722s ***** error<gevcdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
722s  gevcdf (ones (2), ones (3), ones(2), ones(2))
722s ***** error<gevcdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
722s  gevcdf (ones (2), ones (2), ones(3), ones(2))
722s ***** error<gevcdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
722s  gevcdf (ones (2), ones (2), ones(2), ones(3))
722s ***** error<gevcdf: X, K, SIGMA, and MU must not be complex.> gevcdf (i, 2, 3, 4)
722s ***** error<gevcdf: X, K, SIGMA, and MU must not be complex.> gevcdf (1, i, 3, 4)
722s ***** error<gevcdf: X, K, SIGMA, and MU must not be complex.> gevcdf (1, 2, i, 4)
722s ***** error<gevcdf: X, K, SIGMA, and MU must not be complex.> gevcdf (1, 2, 3, i)
722s 18 tests, 18 passed, 0 known failure, 0 skipped
722s [inst/dist_fun/gevinv.m]
722s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/gevinv.m
722s ***** demo
722s  ## Plot various iCDFs from the generalized extreme value distribution
722s  p = 0.001:0.001:0.999;
722s  x1 = gevinv (p, 1, 1, 1);
722s  x2 = gevinv (p, 0.5, 1, 1);
722s  x3 = gevinv (p, 1, 1, 5);
722s  x4 = gevinv (p, 1, 2, 5);
722s  x5 = gevinv (p, 1, 5, 5);
722s  x6 = gevinv (p, 1, 0.5, 5);
722s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", ...
722s        p, x4, "-c", p, x5, "-m", p, x6, "-k")
722s  grid on
722s  ylim ([-1, 10])
722s  legend ({"k = 1, σ = 1, μ = 1", "k = 0.5, σ = 1, μ = 1", ...
722s           "k = 1, σ = 1, μ = 5", "k = 1, σ = 2, μ = 5", ...
722s           "k = 1, σ = 5, μ = 5", "k = 1, σ = 0.5, μ = 5"}, ...
722s          "location", "northwest")
722s  title ("Generalized extreme value iCDF")
722s  xlabel ("probability")
722s  ylabel ("values in x")
722s ***** test
722s  p = 0.1:0.1:0.9;
722s  k = 0;
722s  sigma = 1;
722s  mu = 0;
722s  x = gevinv (p, k, sigma, mu);
722s  c = gevcdf(x, k, sigma, mu);
722s  assert (c, p, 0.001);
722s ***** test
722s  p = 0.1:0.1:0.9;
722s  k = 1;
722s  sigma = 1;
722s  mu = 0;
722s  x = gevinv (p, k, sigma, mu);
722s  c = gevcdf(x, k, sigma, mu);
722s  assert (c, p, 0.001);
722s ***** test
722s  p = 0.1:0.1:0.9;
722s  k = 0.3;
722s  sigma = 1;
722s  mu = 0;
722s  x = gevinv (p, k, sigma, mu);
722s  c = gevcdf(x, k, sigma, mu);
722s  assert (c, p, 0.001);
722s ***** error<gevinv: function called with too few input arguments.> gevinv ()
722s ***** error<gevinv: function called with too few input arguments.> gevinv (1)
722s ***** error<gevinv: function called with too few input arguments.> gevinv (1, 2)
722s ***** error<gevinv: function called with too few input arguments.> gevinv (1, 2, 3)
722s ***** error<gevinv: P, K, SIGMA, and MU must be of common size or scalars.> ...
722s  gevinv (ones (3), ones (2), ones(2), ones(2))
722s ***** error<gevinv: P, K, SIGMA, and MU must be of common size or scalars.> ...
722s  gevinv (ones (2), ones (3), ones(2), ones(2))
722s ***** error<gevinv: P, K, SIGMA, and MU must be of common size or scalars.> ...
722s  gevinv (ones (2), ones (2), ones(3), ones(2))
722s ***** error<gevinv: P, K, SIGMA, and MU must be of common size or scalars.> ...
722s  gevinv (ones (2), ones (2), ones(2), ones(3))
722s ***** error<gevinv: P, K, SIGMA, and MU must not be complex.> gevinv (i, 2, 3, 4)
722s ***** error<gevinv: P, K, SIGMA, and MU must not be complex.> gevinv (1, i, 3, 4)
722s ***** error<gevinv: P, K, SIGMA, and MU must not be complex.> gevinv (1, 2, i, 4)
722s ***** error<gevinv: P, K, SIGMA, and MU must not be complex.> gevinv (1, 2, 3, i)
722s 15 tests, 15 passed, 0 known failure, 0 skipped
722s [inst/dist_fun/gevpdf.m]
722s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/gevpdf.m
722s ***** demo
722s  ## Plot various PDFs from the generalized extreme value distribution
722s  x = -1:0.001:10;
722s  y1 = gevpdf (x, 1, 1, 1);
722s  y2 = gevpdf (x, 0.5, 1, 1);
722s  y3 = gevpdf (x, 1, 1, 5);
722s  y4 = gevpdf (x, 1, 2, 5);
722s  y5 = gevpdf (x, 1, 5, 5);
722s  y6 = gevpdf (x, 1, 0.5, 5);
722s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", ...
722s        x, y4, "-c", x, y5, "-m", x, y6, "-k")
722s  grid on
722s  xlim ([-1, 10])
722s  ylim ([0, 1.1])
722s  legend ({"k = 1, σ = 1, μ = 1", "k = 0.5, σ = 1, μ = 1", ...
722s           "k = 1, σ = 1, μ = 5", "k = 1, σ = 2, μ = 5", ...
722s           "k = 1, σ = 5, μ = 5", "k = 1, σ = 0.5, μ = 5"}, ...
722s          "location", "northeast")
722s  title ("Generalized extreme value PDF")
722s  xlabel ("values in x")
722s  ylabel ("density")
722s ***** test
722s  x = 0:0.5:2.5;
722s  sigma = 1:6;
722s  k = 1;
722s  mu = 0;
722s  y = gevpdf (x, k, sigma, mu);
722s  expected_y = [0.367879   0.143785   0.088569   0.063898   0.049953   0.040997];
722s  assert (y, expected_y, 0.001);
722s ***** test
722s  x = -0.5:0.5:2.5;
722s  sigma = 0.5;
722s  k = 1;
722s  mu = 0;
722s  y = gevpdf (x, k, sigma, mu);
722s  expected_y = [0 0.735759   0.303265   0.159229   0.097350   0.065498   0.047027];
722s  assert (y, expected_y, 0.001);
722s ***** test # check for continuity for k near 0
722s  x = 1;
722s  sigma = 0.5;
722s  k = -0.03:0.01:0.03;
722s  mu = 0;
722s  y = gevpdf (x, k, sigma, mu);
722s  expected_y = [0.23820   0.23764   0.23704   0.23641   0.23576   0.23508   0.23438];
722s  assert (y, expected_y, 0.001);
722s ***** error<gevpdf: function called with too few input arguments.> gevpdf ()
722s ***** error<gevpdf: function called with too few input arguments.> gevpdf (1)
722s ***** error<gevpdf: function called with too few input arguments.> gevpdf (1, 2)
722s ***** error<gevpdf: function called with too few input arguments.> gevpdf (1, 2, 3)
722s ***** error<gevpdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
722s  gevpdf (ones (3), ones (2), ones(2), ones(2))
722s ***** error<gevpdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
722s  gevpdf (ones (2), ones (3), ones(2), ones(2))
722s ***** error<gevpdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
722s  gevpdf (ones (2), ones (2), ones(3), ones(2))
722s ***** error<gevpdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
722s  gevpdf (ones (2), ones (2), ones(2), ones(3))
722s ***** error<gevpdf: X, K, SIGMA, and MU must not be complex.> gevpdf (i, 2, 3, 4)
722s ***** error<gevpdf: X, K, SIGMA, and MU must not be complex.> gevpdf (1, i, 3, 4)
722s ***** error<gevpdf: X, K, SIGMA, and MU must not be complex.> gevpdf (1, 2, i, 4)
722s ***** error<gevpdf: X, K, SIGMA, and MU must not be complex.> gevpdf (1, 2, 3, i)
722s 15 tests, 15 passed, 0 known failure, 0 skipped
722s [inst/dist_fun/gevrnd.m]
722s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/gevrnd.m
722s ***** assert(size (gevrnd (1,2,1)), [1, 1]);
722s ***** assert(size (gevrnd (ones(2,1), 2, 1)), [2, 1]);
722s ***** assert(size (gevrnd (ones(2,2), 2, 1)), [2, 2]);
722s ***** assert(size (gevrnd (1, 2*ones(2,1), 1)), [2, 1]);
722s ***** assert(size (gevrnd (1, 2*ones(2,2), 1)), [2, 2]);
722s ***** assert(size (gevrnd (1, 2, 1, 3)), [3, 3]);
722s ***** assert(size (gevrnd (1, 2, 1, [4 1])), [4, 1]);
722s ***** assert(size (gevrnd (1, 2, 1, 4, 1)), [4, 1]);
722s ***** assert (class (gevrnd (1,1,1)), "double")
722s ***** assert (class (gevrnd (single (1),1,1)), "single")
722s ***** assert (class (gevrnd (single ([1 1]),1,1)), "single")
722s ***** assert (class (gevrnd (1,single (1),1)), "single")
722s ***** assert (class (gevrnd (1,single ([1 1]),1)), "single")
722s ***** assert (class (gevrnd (1,1,single (1))), "single")
722s ***** assert (class (gevrnd (1,1,single ([1 1]))), "single")
722s ***** error<gevrnd: function called with too few input arguments.> gevrnd ()
722s ***** error<gevrnd: function called with too few input arguments.> gevrnd (1)
722s ***** error<gevrnd: function called with too few input arguments.> gevrnd (1, 2)
722s ***** error<gevrnd: K, SIGMA, and MU must be of common size or scalars.> ...
722s  gevrnd (ones (3), ones (2), ones (2))
722s ***** error<gevrnd: K, SIGMA, and MU must be of common size or scalars.> ...
722s  gevrnd (ones (2), ones (3), ones (2))
722s ***** error<gevrnd: K, SIGMA, and MU must be of common size or scalars.> ...
722s  gevrnd (ones (2), ones (2), ones (3))
722s ***** error<gevrnd: K, SIGMA, and MU must not be complex.> gevrnd (i, 2, 3)
722s ***** error<gevrnd: K, SIGMA, and MU must not be complex.> gevrnd (1, i, 3)
722s ***** error<gevrnd: K, SIGMA, and MU must not be complex.> gevrnd (1, 2, i)
722s ***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
722s  gevrnd (1, 2, 3, -1)
722s ***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
722s  gevrnd (1, 2, 3, 1.2)
722s ***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
722s  gevrnd (1, 2, 3, ones (2))
722s ***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
722s  gevrnd (1, 2, 3, [2 -1 2])
722s ***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
722s  gevrnd (1, 2, 3, [2 0 2.5])
722s ***** error<gevrnd: dimensions must be non-negative integers.> ...
722s  gevrnd (1, 2, 3, 2, -1, 5)
722s ***** error<gevrnd: dimensions must be non-negative integers.> ...
722s  gevrnd (1, 2, 3, 2, 1.5, 5)
722s ***** error<gevrnd: K, SIGMA, and MU must be scalars or of size SZ.> ...
722s  gevrnd (2, ones (2), 2, 3)
722s ***** error<gevrnd: K, SIGMA, and MU must be scalars or of size SZ.> ...
722s  gevrnd (2, ones (2), 2, [3, 2])
722s ***** error<gevrnd: K, SIGMA, and MU must be scalars or of size SZ.> ...
722s  gevrnd (2, ones (2), 2, 3, 2)
722s 34 tests, 34 passed, 0 known failure, 0 skipped
722s [inst/dist_fun/gpcdf.m]
722s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/gpcdf.m
722s ***** demo
722s  ## Plot various CDFs from the generalized Pareto distribution
722s  x = 0:0.001:5;
722s  p1 = gpcdf (x, 1, 1, 0);
722s  p2 = gpcdf (x, 5, 1, 0);
722s  p3 = gpcdf (x, 20, 1, 0);
722s  p4 = gpcdf (x, 1, 2, 0);
722s  p5 = gpcdf (x, 5, 2, 0);
722s  p6 = gpcdf (x, 20, 2, 0);
722s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", ...
722s        x, p4, "-c", x, p5, "-m", x, p6, "-k")
722s  grid on
722s  xlim ([0, 5])
722s  legend ({"k = 1, σ = 1, θ = 0", "k = 5, σ = 1, θ = 0", ...
722s           "k = 20, σ = 1, θ = 0", "k = 1, σ = 2, θ = 0", ...
722s           "k = 5, σ = 2, θ = 0", "k = 20, σ = 2, θ = 0"}, ...
722s          "location", "northwest")
722s  title ("Generalized Pareto CDF")
722s  xlabel ("values in x")
722s  ylabel ("probability")
722s ***** shared x, y1, y1u, y2, y2u, y3, y3u
722s  x = [-Inf, -1, 0, 1/2, 1, Inf];
722s  y1 = [0, 0, 0, 0.3934693402873666, 0.6321205588285577, 1];
722s  y1u = [1, 1, 1, 0.6065306597126334, 0.3678794411714423, 0];
722s  y2 = [0, 0, 0, 1/3, 1/2, 1];
722s  y2u = [1, 1, 1, 2/3, 1/2, 0];
722s  y3 = [0, 0, 0, 1/2, 1, 1];
722s  y3u = [1, 1, 1, 1/2, 0, 0];
722s ***** assert (gpcdf (x, zeros (1,6), ones (1,6), zeros (1,6)), y1, eps)
722s ***** assert (gpcdf (x, 0, 1, zeros (1,6)), y1, eps)
722s ***** assert (gpcdf (x, 0, ones (1,6), 0), y1, eps)
723s ***** assert (gpcdf (x, zeros (1,6), 1, 0), y1, eps)
723s ***** assert (gpcdf (x, 0, 1, 0), y1, eps)
723s ***** assert (gpcdf (x, 0, 1, [0, 0, 0, NaN, 0, 0]), [y1(1:3), NaN, y1(5:6)], eps)
723s ***** assert (gpcdf (x, 0, [1, 1, 1, NaN, 1, 1], 0), [y1(1:3), NaN, y1(5:6)], eps)
723s ***** assert (gpcdf (x, [0, 0, 0, NaN, 0, 0], 1, 0), [y1(1:3), NaN, y1(5:6)], eps)
723s ***** assert (gpcdf ([x(1:3), NaN, x(5:6)], 0, 1, 0), [y1(1:3), NaN, y1(5:6)], eps)
723s ***** assert (gpcdf (x, zeros (1,6), ones (1,6), zeros (1,6), "upper"), y1u, eps)
723s ***** assert (gpcdf (x, 0, 1, zeros (1,6), "upper"), y1u, eps)
723s ***** assert (gpcdf (x, 0, ones (1,6), 0, "upper"), y1u, eps)
723s ***** assert (gpcdf (x, zeros (1,6), 1, 0, "upper"), y1u, eps)
723s ***** assert (gpcdf (x, 0, 1, 0, "upper"), y1u, eps)
723s ***** assert (gpcdf (x, ones (1,6), ones (1,6), zeros (1,6)), y2, eps)
723s ***** assert (gpcdf (x, 1, 1, zeros (1,6)), y2, eps)
723s ***** assert (gpcdf (x, 1, ones (1,6), 0), y2, eps)
723s ***** assert (gpcdf (x, ones (1,6), 1, 0), y2, eps)
723s ***** assert (gpcdf (x, 1, 1, 0), y2, eps)
723s ***** assert (gpcdf (x, 1, 1, [0, 0, 0, NaN, 0, 0]), [y2(1:3), NaN, y2(5:6)], eps)
723s ***** assert (gpcdf (x, 1, [1, 1, 1, NaN, 1, 1], 0), [y2(1:3), NaN, y2(5:6)], eps)
723s ***** assert (gpcdf (x, [1, 1, 1, NaN, 1, 1], 1, 0), [y2(1:3), NaN, y2(5:6)], eps)
723s ***** assert (gpcdf ([x(1:3), NaN, x(5:6)], 1, 1, 0), [y2(1:3), NaN, y2(5:6)], eps)
723s ***** assert (gpcdf (x, ones (1,6), ones (1,6), zeros (1,6), "upper"), y2u, eps)
723s ***** assert (gpcdf (x, 1, 1, zeros (1,6), "upper"), y2u, eps)
723s ***** assert (gpcdf (x, 1, ones (1,6), 0, "upper"), y2u, eps)
723s ***** assert (gpcdf (x, ones (1,6), 1, 0, "upper"), y2u, eps)
723s ***** assert (gpcdf (x, 1, 1, 0, "upper"), y2u, eps)
723s ***** assert (gpcdf (x, 1, 1, [0, 0, 0, NaN, 0, 0], "upper"), ...
723s                         [y2u(1:3), NaN, y2u(5:6)], eps)
723s ***** assert (gpcdf (x, 1, [1, 1, 1, NaN, 1, 1], 0, "upper"), ...
723s                         [y2u(1:3), NaN, y2u(5:6)], eps)
723s ***** assert (gpcdf (x, [1, 1, 1, NaN, 1, 1], 1, 0, "upper"), ...
723s                         [y2u(1:3), NaN, y2u(5:6)], eps)
723s ***** assert (gpcdf ([x(1:3), NaN, x(5:6)], 1, 1, 0, "upper"), ...
723s                         [y2u(1:3), NaN, y2u(5:6)], eps)
723s ***** assert (gpcdf (x, -ones (1,6), ones (1,6), zeros (1,6)), y3, eps)
723s ***** assert (gpcdf (x, -1, 1, zeros (1,6)), y3, eps)
723s ***** assert (gpcdf (x, -1, ones (1,6), 0), y3, eps)
723s ***** assert (gpcdf (x, -ones (1,6), 1, 0), y3, eps)
723s ***** assert (gpcdf (x, -1, 1, 0), y3, eps)
723s ***** assert (gpcdf (x, -1, 1, [0, 0, 0, NaN, 0, 0]), [y3(1:3), NaN, y3(5:6)], eps)
723s ***** assert (gpcdf (x, -1, [1, 1, 1, NaN, 1, 1], 0), [y3(1:3), NaN, y3(5:6)], eps)
723s ***** assert (gpcdf (x, [-1, -1, -1, NaN, -1, -1], 1, 0), [y3(1:3), NaN, y3(5:6)], eps)
723s ***** assert (gpcdf ([x(1:3), NaN, x(5:6)], -1, 1, 0), [y3(1:3), NaN, y3(5:6)], eps)
723s ***** assert (gpcdf (x, -ones (1,6), ones (1,6), zeros (1,6), "upper"), y3u, eps)
723s ***** assert (gpcdf (x, -1, 1, zeros (1,6), "upper"), y3u, eps)
723s ***** assert (gpcdf (x, -1, ones (1,6), 0, "upper"), y3u, eps)
723s ***** assert (gpcdf (x, -ones (1,6), 1, 0, "upper"), y3u, eps)
723s ***** assert (gpcdf (x, -1, 1, 0, "upper"), y3u, eps)
723s ***** assert (gpcdf (x, -1, 1, [0, 0, 0, NaN, 0, 0], "upper"), ...
723s                          [y3u(1:3), NaN, y3u(5:6)], eps)
723s ***** assert (gpcdf (x, -1, [1, 1, 1, NaN, 1, 1], 0, "upper"), ...
723s                           [y3u(1:3), NaN, y3u(5:6)], eps)
723s ***** assert (gpcdf (x, [-1, -1, -1, NaN, -1, -1], 1, 0, "upper"), ...
723s                           [y3u(1:3), NaN, y3u(5:6)], eps)
723s ***** assert (gpcdf ([x(1:3), NaN, x(5:6)], -1, 1, 0, "upper"), ...
723s                           [y3u(1:3), NaN, y3u(5:6)], eps)
723s ***** assert (gpcdf (single ([x, NaN]), 0, 1, 0), single ([y1, NaN]), eps("single"))
723s ***** assert (gpcdf ([x, NaN], 0, 1, single (0)), single ([y1, NaN]), eps("single"))
723s ***** assert (gpcdf ([x, NaN], 0, single (1), 0), single ([y1, NaN]), eps("single"))
723s ***** assert (gpcdf ([x, NaN], single (0), 1, 0), single ([y1, NaN]), eps("single"))
723s ***** assert (gpcdf (single ([x, NaN]), 1, 1, 0), single ([y2, NaN]), eps("single"))
723s ***** assert (gpcdf ([x, NaN], 1, 1, single (0)), single ([y2, NaN]), eps("single"))
723s ***** assert (gpcdf ([x, NaN], 1, single (1), 0), single ([y2, NaN]), eps("single"))
723s ***** assert (gpcdf ([x, NaN], single (1), 1, 0), single ([y2, NaN]), eps("single"))
723s ***** assert (gpcdf (single ([x, NaN]), -1, 1, 0), single ([y3, NaN]), eps("single"))
723s ***** assert (gpcdf ([x, NaN], -1, 1, single (0)), single ([y3, NaN]), eps("single"))
723s ***** assert (gpcdf ([x, NaN], -1, single (1), 0), single ([y3, NaN]), eps("single"))
723s ***** assert (gpcdf ([x, NaN], single (-1), 1, 0), single ([y3, NaN]), eps("single"))
723s ***** error<gpcdf: function called with too few input arguments.> gpcdf ()
723s ***** error<gpcdf: function called with too few input arguments.> gpcdf (1)
723s ***** error<gpcdf: function called with too few input arguments.> gpcdf (1, 2)
723s ***** error<gpcdf: function called with too few input arguments.> gpcdf (1, 2, 3)
723s ***** error<gpcdf: invalid argument for upper tail.> gpcdf (1, 2, 3, 4, "tail")
723s ***** error<gpcdf: invalid argument for upper tail.> gpcdf (1, 2, 3, 4, 5)
723s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
723s  gpcdf (ones (3), ones (2), ones(2), ones(2))
723s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
723s  gpcdf (ones (2), ones (3), ones(2), ones(2))
723s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
723s  gpcdf (ones (2), ones (2), ones(3), ones(2))
723s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
723s  gpcdf (ones (2), ones (2), ones(2), ones(3))
723s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (i, 2, 3, 4)
723s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, i, 3, 4)
723s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, 2, i, 4)
723s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, 2, 3, i)
723s 76 tests, 76 passed, 0 known failure, 0 skipped
723s [inst/dist_fun/gpinv.m]
723s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/gpinv.m
723s ***** demo
723s  ## Plot various iCDFs from the generalized Pareto distribution
723s  p = 0.001:0.001:0.999;
723s  x1 = gpinv (p, 1, 1, 0);
723s  x2 = gpinv (p, 5, 1, 0);
723s  x3 = gpinv (p, 20, 1, 0);
723s  x4 = gpinv (p, 1, 2, 0);
723s  x5 = gpinv (p, 5, 2, 0);
723s  x6 = gpinv (p, 20, 2, 0);
723s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", ...
723s        p, x4, "-c", p, x5, "-m", p, x6, "-k")
723s  grid on
723s  ylim ([0, 5])
723s  legend ({"k = 1, σ = 1, θ = 0", "k = 5, σ = 1, θ = 0", ...
723s           "k = 20, σ = 1, θ = 0", "k = 1, σ = 2, θ = 0", ...
723s           "k = 5, σ = 2, θ = 0", "k = 20, σ = 2, θ = 0"}, ...
723s          "location", "southeast")
723s  title ("Generalized Pareto iCDF")
723s  xlabel ("probability")
723s  ylabel ("values in x")
723s ***** shared p, y1, y2, y3
723s  p = [-1, 0, 1/2, 1, 2];
723s  y1 = [NaN, 0, 0.6931471805599453, Inf, NaN];
723s  y2 = [NaN, 0, 1, Inf, NaN];
723s  y3 = [NaN, 0, 1/2, 1, NaN];
723s ***** assert (gpinv (p, zeros (1,5), ones (1,5), zeros (1,5)), y1)
723s ***** assert (gpinv (p, 0, 1, zeros (1,5)), y1)
723s ***** assert (gpinv (p, 0, ones (1,5), 0), y1)
723s ***** assert (gpinv (p, zeros (1,5), 1, 0), y1)
723s ***** assert (gpinv (p, 0, 1, 0), y1)
723s ***** assert (gpinv (p, 0, 1, [0, 0, NaN, 0, 0]), [y1(1:2), NaN, y1(4:5)])
723s ***** assert (gpinv (p, 0, [1, 1, NaN, 1, 1], 0), [y1(1:2), NaN, y1(4:5)])
723s ***** assert (gpinv (p, [0, 0, NaN, 0, 0], 1, 0), [y1(1:2), NaN, y1(4:5)])
723s ***** assert (gpinv ([p(1:2), NaN, p(4:5)], 0, 1, 0), [y1(1:2), NaN, y1(4:5)])
723s ***** assert (gpinv (p, ones (1,5), ones (1,5), zeros (1,5)), y2)
723s ***** assert (gpinv (p, 1, 1, zeros (1,5)), y2)
723s ***** assert (gpinv (p, 1, ones (1,5), 0), y2)
723s ***** assert (gpinv (p, ones (1,5), 1, 0), y2)
723s ***** assert (gpinv (p, 1, 1, 0), y2)
723s ***** assert (gpinv (p, 1, 1, [0, 0, NaN, 0, 0]), [y2(1:2), NaN, y2(4:5)])
723s ***** assert (gpinv (p, 1, [1, 1, NaN, 1, 1], 0), [y2(1:2), NaN, y2(4:5)])
723s ***** assert (gpinv (p, [1, 1, NaN, 1, 1], 1, 0), [y2(1:2), NaN, y2(4:5)])
723s ***** assert (gpinv ([p(1:2), NaN, p(4:5)], 1, 1, 0), [y2(1:2), NaN, y2(4:5)])
723s ***** assert (gpinv (p, -ones (1,5), ones (1,5), zeros (1,5)), y3)
723s ***** assert (gpinv (p, -1, 1, zeros (1,5)), y3)
723s ***** assert (gpinv (p, -1, ones (1,5), 0), y3)
723s ***** assert (gpinv (p, -ones (1,5), 1, 0), y3)
723s ***** assert (gpinv (p, -1, 1, 0), y3)
723s ***** assert (gpinv (p, -1, 1, [0, 0, NaN, 0, 0]), [y3(1:2), NaN, y3(4:5)])
723s ***** assert (gpinv (p, -1, [1, 1, NaN, 1, 1], 0), [y3(1:2), NaN, y3(4:5)])
723s ***** assert (gpinv (p, -[1, 1, NaN, 1, 1], 1, 0), [y3(1:2), NaN, y3(4:5)])
723s ***** assert (gpinv ([p(1:2), NaN, p(4:5)], -1, 1, 0), [y3(1:2), NaN, y3(4:5)])
723s ***** assert (gpinv (single ([p, NaN]), 0, 1, 0), single ([y1, NaN]))
723s ***** assert (gpinv ([p, NaN], 0, 1, single (0)), single ([y1, NaN]))
723s ***** assert (gpinv ([p, NaN], 0, single (1), 0), single ([y1, NaN]))
723s ***** assert (gpinv ([p, NaN], single (0), 1, 0), single ([y1, NaN]))
723s ***** assert (gpinv (single ([p, NaN]), 1, 1, 0), single ([y2, NaN]))
723s ***** assert (gpinv ([p, NaN], 1, 1, single (0)), single ([y2, NaN]))
723s ***** assert (gpinv ([p, NaN], 1, single (1), 0), single ([y2, NaN]))
723s ***** assert (gpinv ([p, NaN], single (1), 1, 0), single ([y2, NaN]))
723s ***** assert (gpinv (single ([p, NaN]), -1, 1, 0), single ([y3, NaN]))
723s ***** assert (gpinv ([p, NaN], -1, 1, single (0)), single ([y3, NaN]))
723s ***** assert (gpinv ([p, NaN], -1, single (1), 0), single ([y3, NaN]))
723s ***** assert (gpinv ([p, NaN], single (-1), 1, 0), single ([y3, NaN]))
723s ***** error<gpinv: function called with too few input arguments.> gpinv ()
723s ***** error<gpinv: function called with too few input arguments.> gpinv (1)
723s ***** error<gpinv: function called with too few input arguments.> gpinv (1, 2)
723s ***** error<gpinv: function called with too few input arguments.> gpinv (1, 2, 3)
723s ***** error<gpinv: P, K, SIGMA, and THETA must be of common size or scalars.> ...
723s  gpinv (ones (3), ones (2), ones(2), ones(2))
723s ***** error<gpinv: P, K, SIGMA, and THETA must be of common size or scalars.> ...
723s  gpinv (ones (2), ones (3), ones(2), ones(2))
723s ***** error<gpinv: P, K, SIGMA, and THETA must be of common size or scalars.> ...
723s  gpinv (ones (2), ones (2), ones(3), ones(2))
723s ***** error<gpinv: P, K, SIGMA, and THETA must be of common size or scalars.> ...
723s  gpinv (ones (2), ones (2), ones(2), ones(3))
723s ***** error<gpinv: P, K, SIGMA, and THETA must not be complex.> gpinv (i, 2, 3, 4)
723s ***** error<gpinv: P, K, SIGMA, and THETA must not be complex.> gpinv (1, i, 3, 4)
723s ***** error<gpinv: P, K, SIGMA, and THETA must not be complex.> gpinv (1, 2, i, 4)
723s ***** error<gpinv: P, K, SIGMA, and THETA must not be complex.> gpinv (1, 2, 3, i)
723s 51 tests, 51 passed, 0 known failure, 0 skipped
723s [inst/dist_fun/gppdf.m]
723s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/gppdf.m
723s ***** demo
723s  ## Plot various PDFs from the generalized Pareto distribution
723s  x = 0:0.001:5;
723s  y1 = gppdf (x, 1, 1, 0);
723s  y2 = gppdf (x, 5, 1, 0);
723s  y3 = gppdf (x, 20, 1, 0);
723s  y4 = gppdf (x, 1, 2, 0);
723s  y5 = gppdf (x, 5, 2, 0);
723s  y6 = gppdf (x, 20, 2, 0);
723s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", ...
723s        x, y4, "-c", x, y5, "-m", x, y6, "-k")
723s  grid on
723s  xlim ([0, 5])
723s  ylim ([0, 1])
723s  legend ({"k = 1, σ = 1, θ = 0", "k = 5, σ = 1, θ = 0", ...
723s           "k = 20, σ = 1, θ = 0", "k = 1, σ = 2, θ = 0", ...
723s           "k = 5, σ = 2, θ = 0", "k = 20, σ = 2, θ = 0"}, ...
723s          "location", "northeast")
723s  title ("Generalized Pareto PDF")
723s  xlabel ("values in x")
723s  ylabel ("density")
723s ***** shared x, y1, y2, y3
723s  x = [-Inf, -1, 0, 1/2, 1, Inf];
723s  y1 = [0, 0, 1, 0.6065306597126334, 0.36787944117144233, 0];
723s  y2 = [0, 0, 1, 4/9, 1/4, 0];
723s  y3 = [0, 0, 1, 1, 1, 0];
723s ***** assert (gppdf (x, zeros (1,6), ones (1,6), zeros (1,6)), y1, eps)
723s ***** assert (gppdf (x, 0, 1, zeros (1,6)), y1, eps)
723s ***** assert (gppdf (x, 0, ones (1,6), 0), y1, eps)
723s ***** assert (gppdf (x, zeros (1,6), 1, 0), y1, eps)
723s ***** assert (gppdf (x, 0, 1, 0), y1, eps)
723s ***** assert (gppdf (x, 0, 1, [0, 0, 0, NaN, 0, 0]), [y1(1:3), NaN, y1(5:6)])
723s ***** assert (gppdf (x, 0, [1, 1, 1, NaN, 1, 1], 0), [y1(1:3), NaN, y1(5:6)])
723s ***** assert (gppdf (x, [0, 0, 0, NaN, 0, 0], 1, 0), [y1(1:3), NaN, y1(5:6)])
723s ***** assert (gppdf ([x(1:3), NaN, x(5:6)], 0, 1, 0), [y1(1:3), NaN, y1(5:6)])
723s ***** assert (gppdf (x, ones (1,6), ones (1,6), zeros (1,6)), y2, eps)
723s ***** assert (gppdf (x, 1, 1, zeros (1,6)), y2, eps)
723s ***** assert (gppdf (x, 1, ones (1,6), 0), y2, eps)
723s ***** assert (gppdf (x, ones (1,6), 1, 0), y2, eps)
723s ***** assert (gppdf (x, 1, 1, 0), y2, eps)
723s ***** assert (gppdf (x, 1, 1, [0, 0, 0, NaN, 0, 0]), [y2(1:3), NaN, y2(5:6)])
723s ***** assert (gppdf (x, 1, [1, 1, 1, NaN, 1, 1], 0), [y2(1:3), NaN, y2(5:6)])
723s ***** assert (gppdf (x, [1, 1, 1, NaN, 1, 1], 1, 0), [y2(1:3), NaN, y2(5:6)])
723s ***** assert (gppdf ([x(1:3), NaN, x(5:6)], 1, 1, 0), [y2(1:3), NaN, y2(5:6)])
723s ***** assert (gppdf (x, -ones (1,6), ones (1,6), zeros (1,6)), y3, eps)
723s ***** assert (gppdf (x, -1, 1, zeros (1,6)), y3, eps)
723s ***** assert (gppdf (x, -1, ones (1,6), 0), y3, eps)
723s ***** assert (gppdf (x, -ones (1,6), 1, 0), y3, eps)
723s ***** assert (gppdf (x, -1, 1, 0), y3, eps)
723s ***** assert (gppdf (x, -1, 1, [0, 0, 0, NaN, 0, 0]), [y3(1:3), NaN, y3(5:6)])
723s ***** assert (gppdf (x, -1, [1, 1, 1, NaN, 1, 1], 0), [y3(1:3), NaN, y3(5:6)])
723s ***** assert (gppdf (x, [-1, -1, -1, NaN, -1, -1], 1, 0), [y3(1:3), NaN, y3(5:6)])
723s ***** assert (gppdf ([x(1:3), NaN, x(5:6)], -1, 1, 0), [y3(1:3), NaN, y3(5:6)])
723s ***** assert (gppdf (single ([x, NaN]), 0, 1, 0), single ([y1, NaN]))
723s ***** assert (gppdf ([x, NaN], 0, 1, single (0)), single ([y1, NaN]))
723s ***** assert (gppdf ([x, NaN], 0, single (1), 0), single ([y1, NaN]))
723s ***** assert (gppdf ([x, NaN], single (0), 1, 0), single ([y1, NaN]))
723s ***** assert (gppdf (single ([x, NaN]), 1, 1, 0), single ([y2, NaN]))
723s ***** assert (gppdf ([x, NaN], 1, 1, single (0)), single ([y2, NaN]))
723s ***** assert (gppdf ([x, NaN], 1, single (1), 0), single ([y2, NaN]))
723s ***** assert (gppdf ([x, NaN], single (1), 1, 0), single ([y2, NaN]))
723s ***** assert (gppdf (single ([x, NaN]), -1, 1, 0), single ([y3, NaN]))
723s ***** assert (gppdf ([x, NaN], -1, 1, single (0)), single ([y3, NaN]))
723s ***** assert (gppdf ([x, NaN], -1, single (1), 0), single ([y3, NaN]))
723s ***** assert (gppdf ([x, NaN], single (-1), 1, 0), single ([y3, NaN]))
723s ***** error<gpcdf: function called with too few input arguments.> gpcdf ()
723s ***** error<gpcdf: function called with too few input arguments.> gpcdf (1)
723s ***** error<gpcdf: function called with too few input arguments.> gpcdf (1, 2)
723s ***** error<gpcdf: function called with too few input arguments.> gpcdf (1, 2, 3)
723s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
723s  gpcdf (ones (3), ones (2), ones(2), ones(2))
723s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
723s  gpcdf (ones (2), ones (3), ones(2), ones(2))
723s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
723s  gpcdf (ones (2), ones (2), ones(3), ones(2))
723s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
723s  gpcdf (ones (2), ones (2), ones(2), ones(3))
723s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (i, 2, 3, 4)
723s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, i, 3, 4)
723s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, 2, i, 4)
723s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, 2, 3, i)
723s 51 tests, 51 passed, 0 known failure, 0 skipped
723s [inst/dist_fun/gprnd.m]
723s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/gprnd.m
723s ***** assert (size (gprnd (0, 1, 0)), [1, 1])
723s ***** assert (size (gprnd (0, 1, zeros (2,1))), [2, 1])
723s ***** assert (size (gprnd (0, 1, zeros (2,2))), [2, 2])
723s ***** assert (size (gprnd (0, ones (2,1), 0)), [2, 1])
723s ***** assert (size (gprnd (0, ones (2,2), 0)), [2, 2])
723s ***** assert (size (gprnd (zeros (2,1), 1, 0)), [2, 1])
723s ***** assert (size (gprnd (zeros (2,2), 1, 0)), [2, 2])
723s ***** assert (size (gprnd (0, 1, 0, 3)), [3, 3])
723s ***** assert (size (gprnd (0, 1, 0, [4 1])), [4, 1])
723s ***** assert (size (gprnd (0, 1, 0, 4, 1)), [4, 1])
723s ***** assert (size (gprnd (1,1,0)), [1, 1])
723s ***** assert (size (gprnd (1, 1, zeros (2,1))), [2, 1])
723s ***** assert (size (gprnd (1, 1, zeros (2,2))), [2, 2])
723s ***** assert (size (gprnd (1, ones (2,1), 0)), [2, 1])
723s ***** assert (size (gprnd (1, ones (2,2), 0)), [2, 2])
723s ***** assert (size (gprnd (ones (2,1), 1, 0)), [2, 1])
723s ***** assert (size (gprnd (ones (2,2), 1, 0)), [2, 2])
723s ***** assert (size (gprnd (1, 1, 0, 3)), [3, 3])
723s ***** assert (size (gprnd (1, 1, 0, [4 1])), [4, 1])
723s ***** assert (size (gprnd (1, 1, 0, 4, 1)), [4, 1])
723s ***** assert (size (gprnd (-1, 1, 0)), [1, 1])
723s ***** assert (size (gprnd (-1, 1, zeros (2,1))), [2, 1])
723s ***** assert (size (gprnd (1, -1, zeros (2,2))), [2, 2])
723s ***** assert (size (gprnd (-1, ones (2,1), 0)), [2, 1])
723s ***** assert (size (gprnd (-1, ones (2,2), 0)), [2, 2])
723s ***** assert (size (gprnd (-ones (2,1), 1, 0)), [2, 1])
723s ***** assert (size (gprnd (-ones (2,2), 1, 0)), [2, 2])
723s ***** assert (size (gprnd (-1, 1, 0, 3)), [3, 3])
723s ***** assert (size (gprnd (-1, 1, 0, [4, 1])), [4, 1])
723s ***** assert (size (gprnd (-1, 1, 0, 4, 1)), [4, 1])
723s ***** assert (class (gprnd (0, 1, 0)), "double")
723s ***** assert (class (gprnd (0, 1, single (0))), "single")
723s ***** assert (class (gprnd (0, 1, single ([0, 0]))), "single")
723s ***** assert (class (gprnd (0, single (1),0)), "single")
723s ***** assert (class (gprnd (0, single ([1, 1]),0)), "single")
723s ***** assert (class (gprnd (single (0), 1, 0)), "single")
723s ***** assert (class (gprnd (single ([0, 0]), 1, 0)), "single")
723s ***** error<gprnd: function called with too few input arguments.> gprnd ()
723s ***** error<gprnd: function called with too few input arguments.> gprnd (1)
723s ***** error<gprnd: function called with too few input arguments.> gprnd (1, 2)
723s ***** error<gprnd: K, SIGMA, and THETA must be of common size or scalars.> ...
723s  gprnd (ones (3), ones (2), ones (2))
723s ***** error<gprnd: K, SIGMA, and THETA must be of common size or scalars.> ...
723s  gprnd (ones (2), ones (3), ones (2))
723s ***** error<gprnd: K, SIGMA, and THETA must be of common size or scalars.> ...
723s  gprnd (ones (2), ones (2), ones (3))
723s ***** error<gprnd: K, SIGMA, and THETA must not be complex.> gprnd (i, 2, 3)
723s ***** error<gprnd: K, SIGMA, and THETA must not be complex.> gprnd (1, i, 3)
723s ***** error<gprnd: K, SIGMA, and THETA must not be complex.> gprnd (1, 2, i)
723s ***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
723s  gprnd (1, 2, 3, -1)
723s ***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
723s  gprnd (1, 2, 3, 1.2)
723s ***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
723s  gprnd (1, 2, 3, ones (2))
723s ***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
723s  gprnd (1, 2, 3, [2 -1 2])
723s ***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
723s  gprnd (1, 2, 3, [2 0 2.5])
723s ***** error<gprnd: dimensions must be non-negative integers.> ...
723s  gprnd (1, 2, 3, 2, -1, 5)
723s ***** error<gprnd: dimensions must be non-negative integers.> ...
723s  gprnd (1, 2, 3, 2, 1.5, 5)
723s ***** error<gprnd: K, SIGMA, and THETA must be scalars or of size SZ.> ...
723s  gprnd (2, ones (2), 2, 3)
723s ***** error<gprnd: K, SIGMA, and THETA must be scalars or of size SZ.> ...
723s  gprnd (2, ones (2), 2, [3, 2])
723s ***** error<gprnd: K, SIGMA, and THETA must be scalars or of size SZ.> ...
723s  gprnd (2, ones (2), 2, 3, 2)
723s 56 tests, 56 passed, 0 known failure, 0 skipped
723s [inst/dist_fun/gumbelcdf.m]
723s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/gumbelcdf.m
723s ***** demo
723s  ## Plot various CDFs from the Gumbel distribution
723s  x = -5:0.01:20;
723s  p1 = gumbelcdf (x, 0.5, 2);
723s  p2 = gumbelcdf (x, 1.0, 2);
723s  p3 = gumbelcdf (x, 1.5, 3);
723s  p4 = gumbelcdf (x, 3.0, 4);
723s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
723s  grid on
723s  legend ({"μ = 0.5, β = 2", "μ = 1.0, β = 2", ...
723s           "μ = 1.5, β = 3", "μ = 3.0, β = 4"}, "location", "southeast")
723s  title ("Gumbel CDF")
723s  xlabel ("values in x")
723s  ylabel ("probability")
723s ***** shared x, y
723s  x = [-Inf, 1, 2, Inf];
723s  y = [0, 0.3679, 0.6922, 1];
723s ***** assert (gumbelcdf (x, ones (1,4), ones (1,4)), y, 1e-4)
723s ***** assert (gumbelcdf (x, 1, ones (1,4)), y, 1e-4)
723s ***** assert (gumbelcdf (x, ones (1,4), 1), y, 1e-4)
723s ***** assert (gumbelcdf (x, [0, -Inf, NaN, Inf], 1), [0, 1, NaN, NaN], 1e-4)
723s ***** assert (gumbelcdf (x, 1, [Inf, NaN, -1, 0]), [NaN, NaN, NaN, NaN], 1e-4)
723s ***** assert (gumbelcdf ([x(1:2), NaN, x(4)], 1, 1), [y(1:2), NaN, y(4)], 1e-4)
723s ***** assert (gumbelcdf (x, "upper"), [1, 0.3078, 0.1266, 0], 1e-4)
723s ***** assert (gumbelcdf ([x, NaN], 1, 1), [y, NaN], 1e-4)
723s ***** assert (gumbelcdf (single ([x, NaN]), 1, 1), single ([y, NaN]), 1e-4)
723s ***** assert (gumbelcdf ([x, NaN], single (1), 1), single ([y, NaN]), 1e-4)
723s ***** assert (gumbelcdf ([x, NaN], 1, single (1)), single ([y, NaN]), 1e-4)
723s ***** error<gumbelcdf: invalid number of input arguments.> gumbelcdf ()
723s ***** error<gumbelcdf: invalid number of input arguments.> gumbelcdf (1,2,3,4,5,6,7)
723s ***** error<gumbelcdf: invalid argument for upper tail.> gumbelcdf (1, 2, 3, 4, "uper")
723s ***** error<gumbelcdf: X, MU, and BETA must be of common size or scalars.> ...
723s  gumbelcdf (ones (3), ones (2), ones (2))
723s ***** error<gumbelcdf: invalid size of covariance matrix.> gumbelcdf (2, 3, 4, [1, 2])
723s ***** error<gumbelcdf: covariance matrix is required for confidence bounds.> ...
723s  [p, plo, pup] = gumbelcdf (1, 2, 3)
723s ***** error<gumbelcdf: invalid value for alpha.> [p, plo, pup] = ...
723s  gumbelcdf (1, 2, 3, [1, 0; 0, 1], 0)
723s ***** error<gumbelcdf: invalid value for alpha.> [p, plo, pup] = ...
723s  gumbelcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
723s ***** error<gumbelcdf: invalid value for alpha.> [p, plo, pup] = ...
723s  gumbelcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
723s ***** error<gumbelcdf: X, MU, and BETA must not be complex.> gumbelcdf (i, 2, 2)
723s ***** error<gumbelcdf: X, MU, and BETA must not be complex.> gumbelcdf (2, i, 2)
723s ***** error<gumbelcdf: X, MU, and BETA must not be complex.> gumbelcdf (2, 2, i)
723s ***** error<gumbelcdf: bad covariance matrix.> ...
723s  [p, plo, pup] = gumbelcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
723s 24 tests, 24 passed, 0 known failure, 0 skipped
723s [inst/dist_fun/gumbelinv.m]
723s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/gumbelinv.m
723s ***** demo
723s  ## Plot various iCDFs from the Gumbel distribution
723s  p = 0.001:0.001:0.999;
723s  x1 = gumbelinv (p, 0.5, 2);
723s  x2 = gumbelinv (p, 1.0, 2);
723s  x3 = gumbelinv (p, 1.5, 3);
723s  x4 = gumbelinv (p, 3.0, 4);
723s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
723s  grid on
723s  ylim ([-5, 20])
723s  legend ({"μ = 0.5, β = 2", "μ = 1.0, β = 2", ...
723s           "μ = 1.5, β = 3", "μ = 3.0, β = 4"}, "location", "northwest")
723s  title ("Gumbel iCDF")
723s  xlabel ("probability")
723s  ylabel ("values in x")
723s ***** shared p, x
723s  p = [0, 0.05, 0.5 0.95];
723s  x = [-Inf, -1.0972, 0.3665, 2.9702];
723s ***** assert (gumbelinv (p), x, 1e-4)
723s ***** assert (gumbelinv (p, zeros (1,4), ones (1,4)), x, 1e-4)
723s ***** assert (gumbelinv (p, 0, ones (1,4)), x, 1e-4)
723s ***** assert (gumbelinv (p, zeros (1,4), 1), x, 1e-4)
723s ***** assert (gumbelinv (p, [0, -Inf, NaN, Inf], 1), [-Inf, -Inf, NaN, Inf], 1e-4)
723s ***** assert (gumbelinv (p, 0, [Inf, NaN, -1, 0]), [-Inf, NaN, NaN, NaN], 1e-4)
723s ***** assert (gumbelinv ([p(1:2), NaN, p(4)], 0, 1), [x(1:2), NaN, x(4)], 1e-4)
723s ***** assert (gumbelinv ([p, NaN], 0, 1), [x, NaN], 1e-4)
723s ***** assert (gumbelinv (single ([p, NaN]), 0, 1), single ([x, NaN]), 1e-4)
723s ***** assert (gumbelinv ([p, NaN], single (0), 1), single ([x, NaN]), 1e-4)
723s ***** assert (gumbelinv ([p, NaN], 0, single (1)), single ([x, NaN]), 1e-4)
723s  p = [0.05, 0.5, 0.95];
723s  x = gumbelinv(p);
723s ***** assert (gumbelcdf(x), p, 1e-4)
723s ***** error<gumbelinv: invalid number of input arguments.> gumbelinv ()
723s ***** error gumbelinv (1,2,3,4,5,6)
723s ***** error<gumbelinv: P, MU, and BETA must be of common size or scalars.> ...
723s  gumbelinv (ones (3), ones (2), ones (2))
723s ***** error<gumbelinv: invalid size of covariance matrix.> ...
723s  [p, plo, pup] = gumbelinv (2, 3, 4, [1, 2])
723s ***** error<gumbelinv: covariance matrix is required for confidence bounds.> ...
723s  [p, plo, pup] = gumbelinv (1, 2, 3)
723s ***** error<gumbelinv: invalid value for alpha.> [p, plo, pup] = ...
723s  gumbelinv (1, 2, 3, [1, 0; 0, 1], 0)
723s ***** error<gumbelinv: invalid value for alpha.> [p, plo, pup] = ...
723s  gumbelinv (1, 2, 3, [1, 0; 0, 1], 1.22)
723s ***** error<gumbelinv: P, MU, and BETA must not be complex.> gumbelinv (i, 2, 2)
723s ***** error<gumbelinv: P, MU, and BETA must not be complex.> gumbelinv (2, i, 2)
723s ***** error<gumbelinv: P, MU, and BETA must not be complex.> gumbelinv (2, 2, i)
723s ***** error<gumbelinv: bad covariance matrix.> ...
723s  [p, plo, pup] = gumbelinv (1, 2, 3, [-1, 10; -Inf, -Inf], 0.04)
723s 23 tests, 23 passed, 0 known failure, 0 skipped
723s [inst/dist_fun/gumbelpdf.m]
723s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/gumbelpdf.m
723s ***** demo
723s  ## Plot various PDFs from the Extreme value distribution
723s  x = -5:0.001:20;
723s  y1 = gumbelpdf (x, 0.5, 2);
723s  y2 = gumbelpdf (x, 1.0, 2);
723s  y3 = gumbelpdf (x, 1.5, 3);
723s  y4 = gumbelpdf (x, 3.0, 4);
723s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
723s  grid on
723s  ylim ([0, 0.2])
723s  legend ({"μ = 0.5, β = 2", "μ = 1.0, β = 2", ...
723s           "μ = 1.5, β = 3", "μ = 3.0, β = 4"}, "location", "northeast")
723s  title ("Extreme value PDF")
723s  xlabel ("values in x")
723s  ylabel ("density")
723s ***** shared x, y0, y1
723s  x = [-5, 0, 1, 2, 3];
723s  y0 = [0, 0.3679, 0.2547, 0.1182, 0.0474];
723s  y1 = [0, 0.1794, 0.3679, 0.2547, 0.1182];
723s ***** assert (gumbelpdf (x), y0, 1e-4)
723s ***** assert (gumbelpdf (x, zeros (1,5), ones (1,5)), y0, 1e-4)
723s ***** assert (gumbelpdf (x, ones (1,5), ones (1,5)), y1, 1e-4)
723s ***** error<gumbelpdf: too few input arguments.> gumbelpdf ()
723s ***** error<gumbelpdf: X, MU, and BETA must be of common size or scalars.> ...
723s  gumbelpdf (ones (3), ones (2), ones (2))
723s ***** error<gumbelpdf: X, MU, and BETA must not be complex.> gumbelpdf (i, 2, 2)
723s ***** error<gumbelpdf: X, MU, and BETA must not be complex.> gumbelpdf (2, i, 2)
723s ***** error<gumbelpdf: X, MU, and BETA must not be complex.> gumbelpdf (2, 2, i)
723s 8 tests, 8 passed, 0 known failure, 0 skipped
723s [inst/dist_fun/gumbelrnd.m]
723s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/gumbelrnd.m
723s ***** assert (size (gumbelrnd (1, 1)), [1 1])
723s ***** assert (size (gumbelrnd (1, ones (2,1))), [2, 1])
723s ***** assert (size (gumbelrnd (1, ones (2,2))), [2, 2])
723s ***** assert (size (gumbelrnd (ones (2,1), 1)), [2, 1])
723s ***** assert (size (gumbelrnd (ones (2,2), 1)), [2, 2])
723s ***** assert (size (gumbelrnd (1, 1, 3)), [3, 3])
723s ***** assert (size (gumbelrnd (1, 1, [4, 1])), [4, 1])
723s ***** assert (size (gumbelrnd (1, 1, 4, 1)), [4, 1])
723s ***** assert (size (gumbelrnd (1, 1, 4, 1, 5)), [4, 1, 5])
723s ***** assert (size (gumbelrnd (1, 1, 0, 1)), [0, 1])
724s ***** assert (size (gumbelrnd (1, 1, 1, 0)), [1, 0])
724s ***** assert (size (gumbelrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
724s ***** assert (class (gumbelrnd (1, 1)), "double")
724s ***** assert (class (gumbelrnd (1, single (1))), "single")
724s ***** assert (class (gumbelrnd (1, single ([1, 1]))), "single")
724s ***** assert (class (gumbelrnd (single (1), 1)), "single")
724s ***** assert (class (gumbelrnd (single ([1, 1]), 1)), "single")
724s ***** error<gumbelrnd: function called with too few input arguments.> gumbelrnd ()
724s ***** error<gumbelrnd: function called with too few input arguments.> gumbelrnd (1)
724s ***** error<gumbelrnd: MU and BETA must be of common size or scalars.> ...
724s  gumbelrnd (ones (3), ones (2))
724s ***** error<gumbelrnd: MU and BETA must be of common size or scalars.> ...
724s  gumbelrnd (ones (2), ones (3))
724s ***** error<gumbelrnd: MU and BETA must not be complex.> gumbelrnd (i, 2, 3)
724s ***** error<gumbelrnd: MU and BETA must not be complex.> gumbelrnd (1, i, 3)
724s ***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
724s  gumbelrnd (1, 2, -1)
724s ***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
724s  gumbelrnd (1, 2, 1.2)
724s ***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
724s  gumbelrnd (1, 2, ones (2))
724s ***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
724s  gumbelrnd (1, 2, [2 -1 2])
724s ***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
724s  gumbelrnd (1, 2, [2 0 2.5])
724s ***** error<gumbelrnd: dimensions must be non-negative integers.> ...
724s  gumbelrnd (1, 2, 2, -1, 5)
724s ***** error<gumbelrnd: dimensions must be non-negative integers.> ...
724s  gumbelrnd (1, 2, 2, 1.5, 5)
724s ***** error<gumbelrnd: MU and BETA must be scalars or of size SZ.> ...
724s  gumbelrnd (2, ones (2), 3)
724s ***** error<gumbelrnd: MU and BETA must be scalars or of size SZ.> ...
724s  gumbelrnd (2, ones (2), [3, 2])
724s ***** error<gumbelrnd: MU and BETA must be scalars or of size SZ.> ...
724s  gumbelrnd (2, ones (2), 3, 2)
724s 33 tests, 33 passed, 0 known failure, 0 skipped
724s [inst/dist_fun/hncdf.m]
724s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/hncdf.m
724s ***** demo
724s  ## Plot various CDFs from the half-normal distribution
724s  x = 0:0.001:10;
724s  p1 = hncdf (x, 0, 1);
724s  p2 = hncdf (x, 0, 2);
724s  p3 = hncdf (x, 0, 3);
724s  p4 = hncdf (x, 0, 5);
724s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
724s  grid on
724s  xlim ([0, 10])
724s  legend ({"μ = 0, σ = 1", "μ = 0, σ = 2", ...
724s           "μ = 0, σ = 3", "μ = 0, σ = 5"}, "location", "southeast")
724s  title ("Half-normal CDF")
724s  xlabel ("values in x")
724s  ylabel ("probability")
724s ***** demo
724s  ## Plot half-normal against normal cumulative distribution function
724s  x = -5:0.001:5;
724s  p1 = hncdf (x, 0, 1);
724s  p2 = normcdf (x);
724s  plot (x, p1, "-b", x, p2, "-g")
724s  grid on
724s  xlim ([-5, 5])
724s  legend ({"half-normal with μ = 0, σ = 1", ...
724s           "standart normal (μ = 0, σ = 1)"}, "location", "southeast")
724s  title ("Half-normal against standard normal CDF")
724s  xlabel ("values in x")
724s  ylabel ("probability")
724s ***** shared x, p1, p1u, y2, y2u, y3, y3u
724s  x = [-Inf, -1, 0, 1/2, 1, Inf];
724s  p1 = [0, 0, 0, 0.3829, 0.6827, 1];
724s  p1u = [1, 1, 1, 0.6171, 0.3173, 0];
724s ***** assert (hncdf (x, zeros (1,6), ones (1,6)), p1, 1e-4)
724s ***** assert (hncdf (x, 0, 1), p1, 1e-4)
724s ***** assert (hncdf (x, 0, ones (1,6)), p1, 1e-4)
724s ***** assert (hncdf (x, zeros (1,6), 1), p1, 1e-4)
724s ***** assert (hncdf (x, 0, [1, 1, 1, NaN, 1, 1]), [p1(1:3), NaN, p1(5:6)], 1e-4)
724s ***** assert (hncdf (x, [0, 0, 0, NaN, 0, 0], 1), [p1(1:3), NaN, p1(5:6)], 1e-4)
724s ***** assert (hncdf ([x(1:3), NaN, x(5:6)], 0, 1), [p1(1:3), NaN, p1(5:6)], 1e-4)
724s ***** assert (hncdf (x, zeros (1,6), ones (1,6), "upper"), p1u, 1e-4)
724s ***** assert (hncdf (x, 0, 1, "upper"), p1u, 1e-4)
724s ***** assert (hncdf (x, 0, ones (1,6), "upper"), p1u, 1e-4)
724s ***** assert (hncdf (x, zeros (1,6), 1, "upper"), p1u, 1e-4)
724s ***** assert (class (hncdf (single ([x, NaN]), 0, 1)), "single")
724s ***** assert (class (hncdf ([x, NaN], 0, single (1))), "single")
724s ***** assert (class (hncdf ([x, NaN], single (0), 1)), "single")
724s ***** error<hncdf: function called with too few input arguments.> hncdf ()
724s ***** error<hncdf: function called with too few input arguments.> hncdf (1)
724s ***** error<hncdf: function called with too few input arguments.> hncdf (1, 2)
724s ***** error<hncdf: invalid argument for upper tail.> hncdf (1, 2, 3, "tail")
724s ***** error<hncdf: invalid argument for upper tail.> hncdf (1, 2, 3, 5)
724s ***** error<hncdf: X, MU, and SIGMA must be of common size or scalars.> ...
724s  hncdf (ones (3), ones (2), ones(2))
724s ***** error<hncdf: X, MU, and SIGMA must be of common size or scalars.> ...
724s  hncdf (ones (2), ones (3), ones(2))
724s ***** error<hncdf: X, MU, and SIGMA must be of common size or scalars.> ...
724s  hncdf (ones (2), ones (2), ones(3))
724s ***** error<hncdf: X, MU, and SIGMA must not be complex.> hncdf (i, 2, 3)
724s ***** error<hncdf: X, MU, and SIGMA must not be complex.> hncdf (1, i, 3)
724s ***** error<hncdf: X, MU, and SIGMA must not be complex.> hncdf (1, 2, i)
724s 25 tests, 25 passed, 0 known failure, 0 skipped
724s [inst/dist_fun/hninv.m]
724s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/hninv.m
724s ***** demo
724s  ## Plot various iCDFs from the half-normal distribution
724s  p = 0.001:0.001:0.999;
724s  x1 = hninv (p, 0, 1);
724s  x2 = hninv (p, 0, 2);
724s  x3 = hninv (p, 0, 3);
724s  x4 = hninv (p, 0, 5);
724s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
724s  grid on
724s  ylim ([0, 10])
724s  legend ({"μ = 0, σ = 1", "μ = 0, σ = 2", ...
724s           "μ = 0, σ = 3", "μ = 0, σ = 5"}, "location", "northwest")
724s  title ("Half-normal iCDF")
724s  xlabel ("probability")
724s  ylabel ("x")
724s ***** shared p, x
724s  p = [0, 0.3829, 0.6827, 1];
724s  x = [0, 1/2, 1, Inf];
724s ***** assert (hninv (p, 0, 1), x, 1e-4);
724s ***** assert (hninv (p, 5, 1), x + 5, 1e-4);
724s ***** assert (hninv (p, 0, ones (1,4)), x, 1e-4);
724s ***** assert (hninv (p, 0, [-1, 0, 1, 1]), [NaN, NaN, x(3:4)], 1e-4)
724s ***** assert (class (hninv (single ([p, NaN]), 0, 1)), "single")
724s ***** assert (class (hninv ([p, NaN], single (0), 1)), "single")
724s ***** assert (class (hninv ([p, NaN], 0, single (1))), "single")
724s ***** error<hninv: function called with too few input arguments.> hninv (1)
724s ***** error<hninv: function called with too few input arguments.> hninv (1, 2)
724s ***** error<hninv: P, MU, and SIGMA must be of common size or scalars.> ...
724s  hninv (1, ones (2), ones (3))
724s ***** error<hninv: P, MU, and SIGMA must be of common size or scalars.> ...
724s  hninv (ones (2), 1, ones (3))
724s ***** error<hninv: P, MU, and SIGMA must be of common size or scalars.> ...
724s  hninv (ones (2), ones (3), 1)
724s ***** error<hninv: P, MU, and SIGMA must not be complex.> hninv (i, 2, 3)
724s ***** error<hninv: P, MU, and SIGMA must not be complex.> hninv (1, i, 3)
724s ***** error<hninv: P, MU, and SIGMA must not be complex.> hninv (1, 2, i)
724s 15 tests, 15 passed, 0 known failure, 0 skipped
724s [inst/dist_fun/hnpdf.m]
724s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/hnpdf.m
724s ***** demo
724s  ## Plot various PDFs from the half-normal distribution
724s  x = 0:0.001:10;
724s  y1 = hnpdf (x, 0, 1);
724s  y2 = hnpdf (x, 0, 2);
724s  y3 = hnpdf (x, 0, 3);
724s  y4 = hnpdf (x, 0, 5);
724s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
724s  grid on
724s  xlim ([0, 10])
724s  ylim ([0, 0.9])
724s  legend ({"μ = 0, σ = 1", "μ = 0, σ = 2", ...
724s           "μ = 0, σ = 3", "μ = 0, σ = 5"}, "location", "northeast")
724s  title ("Half-normal PDF")
724s  xlabel ("values in x")
724s  ylabel ("density")
724s ***** demo
724s  ## Plot half-normal against normal probability density function
724s  x = -5:0.001:5;
724s  y1 = hnpdf (x, 0, 1);
724s  y2 = normpdf (x);
724s  plot (x, y1, "-b", x, y2, "-g")
724s  grid on
724s  xlim ([-5, 5])
724s  ylim ([0, 0.9])
724s  legend ({"half-normal with μ = 0, σ = 1", ...
724s           "standart normal (μ = 0, σ = 1)"}, "location", "northeast")
724s  title ("Half-normal against standard normal PDF")
724s  xlabel ("values in x")
724s  ylabel ("density")
724s ***** shared x, y
724s  x = [-Inf, -1, 0, 1/2, 1, Inf];
724s  y = [0, 0, 0.7979, 0.7041, 0.4839, 0];
724s ***** assert (hnpdf ([x, NaN], 0, 1), [y, NaN], 1e-4)
724s ***** assert (hnpdf (x, 0, [-2, -1, 0, 1, 1, 1]), [nan(1,3), y([4:6])], 1e-4)
724s ***** assert (class (hncdf (single ([x, NaN]), 0, 1)), "single")
724s ***** assert (class (hncdf ([x, NaN], 0, single (1))), "single")
724s ***** assert (class (hncdf ([x, NaN], single (0), 1)), "single")
724s ***** error<hnpdf: function called with too few input arguments.> hnpdf ()
724s ***** error<hnpdf: function called with too few input arguments.> hnpdf (1)
724s ***** error<hnpdf: function called with too few input arguments.> hnpdf (1, 2)
724s ***** error<hnpdf: X, MU, and SIGMA must be of common size or scalars.> ...
724s  hnpdf (1, ones (2), ones (3))
724s ***** error<hnpdf: X, MU, and SIGMA must be of common size or scalars.> ...
724s  hnpdf (ones (2), 1, ones (3))
724s ***** error<hnpdf: X, MU, and SIGMA must be of common size or scalars.> ...
724s  hnpdf (ones (2), ones (3), 1)
724s ***** error<hnpdf: X, MU, and SIGMA must not be complex.> hnpdf (i, 2, 3)
724s ***** error<hnpdf: X, MU, and SIGMA must not be complex.> hnpdf (1, i, 3)
724s ***** error<hnpdf: X, MU, and SIGMA must not be complex.> hnpdf (1, 2, i)
724s 14 tests, 14 passed, 0 known failure, 0 skipped
724s [inst/dist_fun/hnrnd.m]
724s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/hnrnd.m
724s ***** assert (size (hnrnd (1, 1, 1)), [1, 1])
724s ***** assert (size (hnrnd (1, 1, 2)), [2, 2])
724s ***** assert (size (hnrnd (1, 1, [2, 1])), [2, 1])
724s ***** assert (size (hnrnd (1, zeros (2, 2))), [2, 2])
724s ***** assert (size (hnrnd (1, ones (2, 1))), [2, 1])
724s ***** assert (size (hnrnd (1, ones (2, 2))), [2, 2])
724s ***** assert (size (hnrnd (ones (2, 1), 1)), [2, 1])
724s ***** assert (size (hnrnd (ones (2, 2), 1)), [2, 2])
724s ***** assert (size (hnrnd (1, 1, 3)), [3, 3])
724s ***** assert (size (hnrnd (1, 1, [4 1])), [4, 1])
724s ***** assert (size (hnrnd (1, 1, 4, 1)), [4, 1])
724s ***** test
724s  r =  hnrnd (1, [1, 0, -1]);
724s  assert (r([2:3]), [NaN, NaN])
724s ***** assert (class (hnrnd (1, 0)), "double")
724s ***** assert (class (hnrnd (1, single (0))), "single")
724s ***** assert (class (hnrnd (1, single ([0 0]))), "single")
724s ***** assert (class (hnrnd (1, single (1))), "single")
724s ***** assert (class (hnrnd (1, single ([1 1]))), "single")
724s ***** assert (class (hnrnd (single (1), 1)), "single")
724s ***** assert (class (hnrnd (single ([1 1]), 1)), "single")
724s ***** error<hnrnd: function called with too few input arguments.> hnrnd ()
724s ***** error<hnrnd: function called with too few input arguments.> hnrnd (1)
724s ***** error<hnrnd: MU and SIGMA must be of common size or scalars.> ...
724s  hnrnd (ones (3), ones (2))
724s ***** error<hnrnd: MU and SIGMA must be of common size or scalars.> ...
724s  hnrnd (ones (2), ones (3))
724s ***** error<hnrnd: MU and SIGMA must not be complex.> hnrnd (i, 2, 3)
724s ***** error<hnrnd: MU and SIGMA must not be complex.> hnrnd (1, i, 3)
724s ***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
724s  hnrnd (1, 2, -1)
724s ***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
724s  hnrnd (1, 2, 1.2)
724s ***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
724s  hnrnd (1, 2, ones (2))
724s ***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
724s  hnrnd (1, 2, [2 -1 2])
724s ***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
724s  hnrnd (1, 2, [2 0 2.5])
724s ***** error<hnrnd: dimensions must be non-negative integers.> ...
724s  hnrnd (1, 2, 2, -1, 5)
724s ***** error<hnrnd: dimensions must be non-negative integers.> ...
724s  hnrnd (1, 2, 2, 1.5, 5)
724s ***** error<hnrnd: MU and SIGMA must be scalars or of size SZ.> ...
724s  hnrnd (2, ones (2), 3)
724s ***** error<hnrnd: MU and SIGMA must be scalars or of size SZ.> ...
724s  hnrnd (2, ones (2), [3, 2])
724s ***** error<hnrnd: MU and SIGMA must be scalars or of size SZ.> ...
724s  hnrnd (2, ones (2), 3, 2)
724s 35 tests, 35 passed, 0 known failure, 0 skipped
724s [inst/dist_fun/hygecdf.m]
724s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/hygecdf.m
724s ***** demo
724s  ## Plot various CDFs from the hypergeometric distribution
724s  x = 0:60;
724s  p1 = hygecdf (x, 500, 50, 100);
724s  p2 = hygecdf (x, 500, 60, 200);
724s  p3 = hygecdf (x, 500, 70, 300);
724s  plot (x, p1, "*b", x, p2, "*g", x, p3, "*r")
724s  grid on
724s  xlim ([0, 60])
724s  legend ({"m = 500, k = 50, n = 100", "m = 500, k = 60, n = 200", ...
724s           "m = 500, k = 70, n = 300"}, "location", "southeast")
724s  title ("Hypergeometric CDF")
724s  xlabel ("values in x (number of successes)")
724s  ylabel ("probability")
724s ***** shared x, y
724s  x = [-1 0 1 2 3];
724s  y = [0 1/6 5/6 1 1];
724s ***** assert (hygecdf (x, 4*ones (1,5), 2, 2), y, 5*eps)
724s ***** assert (hygecdf (x, 4, 2*ones (1,5), 2), y, 5*eps)
724s ***** assert (hygecdf (x, 4, 2, 2*ones (1,5)), y, 5*eps)
724s ***** assert (hygecdf (x, 4*[1 -1 NaN 1.1 1], 2, 2), [y(1) NaN NaN NaN y(5)], 5*eps)
724s ***** assert (hygecdf (x, 4*[1 -1 NaN 1.1 1], 2, 2, "upper"), ...
724s  [y(5) NaN NaN NaN y(1)], 5*eps)
724s ***** assert (hygecdf (x, 4, 2*[1 -1 NaN 1.1 1], 2), [y(1) NaN NaN NaN y(5)], 5*eps)
724s ***** assert (hygecdf (x, 4, 2*[1 -1 NaN 1.1 1], 2, "upper"), ...
724s  [y(5) NaN NaN NaN y(1)], 5*eps)
724s ***** assert (hygecdf (x, 4, 5, 2), [NaN NaN NaN NaN NaN])
724s ***** assert (hygecdf (x, 4, 2, 2*[1 -1 NaN 1.1 1]), [y(1) NaN NaN NaN y(5)], 5*eps)
724s ***** assert (hygecdf (x, 4, 2, 2*[1 -1 NaN 1.1 1], "upper"), ...
724s  [y(5) NaN NaN NaN y(1)], 5*eps)
724s ***** assert (hygecdf (x, 4, 2, 5), [NaN NaN NaN NaN NaN])
724s ***** assert (hygecdf ([x(1:2) NaN x(4:5)], 4, 2, 2), [y(1:2) NaN y(4:5)], 5*eps)
724s ***** test
724s  p = hygecdf (x, 10, [1 2 3 4 5], 2, "upper");
724s  assert (p, [1, 34/90, 2/30, 0, 0], 10*eps);
724s ***** test
724s  p = hygecdf (2*x, 10, [1 2 3 4 5], 2, "upper");
724s  assert (p, [1, 34/90, 0, 0, 0], 10*eps);
724s ***** assert (hygecdf ([x, NaN], 4, 2, 2), [y, NaN], 5*eps)
724s ***** assert (hygecdf (single ([x, NaN]), 4, 2, 2), single ([y, NaN]), ...
724s  eps ("single"))
724s ***** assert (hygecdf ([x, NaN], single (4), 2, 2), single ([y, NaN]), ...
724s  eps ("single"))
724s ***** assert (hygecdf ([x, NaN], 4, single (2), 2), single ([y, NaN]), ...
724s  eps ("single"))
724s ***** assert (hygecdf ([x, NaN], 4, 2, single (2)), single ([y, NaN]), ...
724s  eps ("single"))
724s ***** error<hygecdf: function called with too few input arguments.> hygecdf ()
724s ***** error<hygecdf: function called with too few input arguments.> hygecdf (1)
724s ***** error<hygecdf: function called with too few input arguments.> hygecdf (1,2)
724s ***** error<hygecdf: function called with too few input arguments.> hygecdf (1,2,3)
724s ***** error<hygecdf: invalid argument for upper tail.> hygecdf (1,2,3,4,5)
724s ***** error<hygecdf: invalid argument for upper tail.> hygecdf (1,2,3,4,"uper")
724s ***** error<hygecdf: X, T, k, and N must be of common size or scalars.> ...
724s  hygecdf (ones (2), ones (3), 1, 1)
724s ***** error<hygecdf: X, T, k, and N must be of common size or scalars.> ...
724s  hygecdf (1, ones (2), ones (3), 1)
724s ***** error<hygecdf: X, T, k, and N must be of common size or scalars.> ...
724s  hygecdf (1, 1, ones (2), ones (3))
724s ***** error<hygecdf: X, T, k, and N must not be complex.> hygecdf (i, 2, 2, 2)
724s ***** error<hygecdf: X, T, k, and N must not be complex.> hygecdf (2, i, 2, 2)
724s ***** error<hygecdf: X, T, k, and N must not be complex.> hygecdf (2, 2, i, 2)
724s ***** error<hygecdf: X, T, k, and N must not be complex.> hygecdf (2, 2, 2, i)
724s 32 tests, 32 passed, 0 known failure, 0 skipped
724s [inst/dist_fun/hygeinv.m]
724s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/hygeinv.m
724s ***** demo
724s  ## Plot various iCDFs from the hypergeometric distribution
724s  p = 0.001:0.001:0.999;
724s  x1 = hygeinv (p, 500, 50, 100);
724s  x2 = hygeinv (p, 500, 60, 200);
724s  x3 = hygeinv (p, 500, 70, 300);
724s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
724s  grid on
724s  ylim ([0, 60])
724s  legend ({"m = 500, k = 50, n = 100", "m = 500, k = 60, n = 200", ...
724s           "m = 500, k = 70, n = 300"}, "location", "northwest")
724s  title ("Hypergeometric iCDF")
724s  xlabel ("probability")
724s  ylabel ("values in p (number of successes)")
724s ***** shared p
724s  p = [-1 0 0.5 1 2];
724s ***** assert (hygeinv (p, 4*ones (1,5), 2*ones (1,5), 2*ones (1,5)), [NaN 0 1 2 NaN])
724s ***** assert (hygeinv (p, 4*ones (1,5), 2, 2), [NaN 0 1 2 NaN])
724s ***** assert (hygeinv (p, 4, 2*ones (1,5), 2), [NaN 0 1 2 NaN])
724s ***** assert (hygeinv (p, 4, 2, 2*ones (1,5)), [NaN 0 1 2 NaN])
724s ***** assert (hygeinv (p, 4*[1 -1 NaN 1.1 1], 2, 2), [NaN NaN NaN NaN NaN])
724s ***** assert (hygeinv (p, 4, 2*[1 -1 NaN 1.1 1], 2), [NaN NaN NaN NaN NaN])
724s ***** assert (hygeinv (p, 4, 5, 2), [NaN NaN NaN NaN NaN])
724s ***** assert (hygeinv (p, 4, 2, 2*[1 -1 NaN 1.1 1]), [NaN NaN NaN NaN NaN])
724s ***** assert (hygeinv (p, 4, 2, 5), [NaN NaN NaN NaN NaN])
724s ***** assert (hygeinv ([p(1:2) NaN p(4:5)], 4, 2, 2), [NaN 0 NaN 2 NaN])
724s ***** assert (hygeinv ([p, NaN], 4, 2, 2), [NaN 0 1 2 NaN NaN])
724s ***** assert (hygeinv (single ([p, NaN]), 4, 2, 2), single ([NaN 0 1 2 NaN NaN]))
724s ***** assert (hygeinv ([p, NaN], single (4), 2, 2), single ([NaN 0 1 2 NaN NaN]))
724s ***** assert (hygeinv ([p, NaN], 4, single (2), 2), single ([NaN 0 1 2 NaN NaN]))
724s ***** assert (hygeinv ([p, NaN], 4, 2, single (2)), single ([NaN 0 1 2 NaN NaN]))
724s ***** error<hygeinv: function called with too few input arguments.> hygeinv ()
724s ***** error<hygeinv: function called with too few input arguments.> hygeinv (1)
724s ***** error<hygeinv: function called with too few input arguments.> hygeinv (1,2)
724s ***** error<hygeinv: function called with too few input arguments.> hygeinv (1,2,3)
724s ***** error<hygeinv: P, T, M, and N must be of common size or scalars.> ...
724s  hygeinv (ones (2), ones (3), 1, 1)
724s ***** error<hygeinv: P, T, M, and N must be of common size or scalars.> ...
724s  hygeinv (1, ones (2), ones (3), 1)
724s ***** error<hygeinv: P, T, M, and N must be of common size or scalars.> ...
724s  hygeinv (1, 1, ones (2), ones (3))
724s ***** error<hygeinv: P, T, M, and N must not be complex.> hygeinv (i, 2, 2, 2)
724s ***** error<hygeinv: P, T, M, and N must not be complex.> hygeinv (2, i, 2, 2)
724s ***** error<hygeinv: P, T, M, and N must not be complex.> hygeinv (2, 2, i, 2)
724s ***** error<hygeinv: P, T, M, and N must not be complex.> hygeinv (2, 2, 2, i)
724s 26 tests, 26 passed, 0 known failure, 0 skipped
724s [inst/dist_fun/hygepdf.m]
724s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/hygepdf.m
724s ***** demo
724s  ## Plot various PDFs from the hypergeometric distribution
724s  x = 0:60;
724s  y1 = hygepdf (x, 500, 50, 100);
724s  y2 = hygepdf (x, 500, 60, 200);
724s  y3 = hygepdf (x, 500, 70, 300);
724s  plot (x, y1, "*b", x, y2, "*g", x, y3, "*r")
724s  grid on
724s  xlim ([0, 60])
724s  ylim ([0, 0.18])
724s  legend ({"m = 500, k = 50, μ = 100", "m = 500, k = 60, μ = 200", ...
724s           "m = 500, k = 70, μ = 300"}, "location", "northeast")
724s  title ("Hypergeometric PDF")
724s  xlabel ("values in x (number of successes)")
724s  ylabel ("density")
724s ***** shared x, y
724s  x = [-1 0 1 2 3];
724s  y = [0 1/6 4/6 1/6 0];
724s ***** assert (hygepdf (x, 4 * ones (1, 5), 2, 2), y, 3 * eps)
724s ***** assert (hygepdf (x, 4, 2 * ones (1, 5), 2), y, 3 * eps)
724s ***** assert (hygepdf (x, 4, 2, 2 * ones (1, 5)), y, 3 * eps)
724s ***** assert (hygepdf (x, 4 * [1, -1, NaN, 1.1, 1], 2, 2), [0, NaN, NaN, NaN, 0])
724s ***** assert (hygepdf (x, 4, 2 * [1, -1, NaN, 1.1, 1], 2), [0, NaN, NaN, NaN, 0])
724s ***** assert (hygepdf (x, 4, 5, 2), [NaN, NaN, NaN, NaN, NaN], 3 * eps)
724s ***** assert (hygepdf (x, 4, 2, 2 * [1, -1, NaN, 1.1, 1]), [0, NaN, NaN, NaN, 0])
724s ***** assert (hygepdf (x, 4, 2, 5), [NaN, NaN, NaN, NaN, NaN], 3 * eps)
724s ***** assert (hygepdf ([x, NaN], 4, 2, 2), [y, NaN], 3 * eps)
724s ***** assert (hygepdf (single ([x, NaN]), 4, 2, 2), single ([y, NaN]), eps ("single"))
724s ***** assert (hygepdf ([x, NaN], single (4), 2, 2), single ([y, NaN]), eps ("single"))
724s ***** assert (hygepdf ([x, NaN], 4, single (2), 2), single ([y, NaN]), eps ("single"))
724s ***** assert (hygepdf ([x, NaN], 4, 2, single (2)), single ([y, NaN]), eps ("single"))
724s ***** test
724s  z = zeros(3,5);
724s  z([4,5,6,8,9,12]) = [1, 0.5, 1/6, 0.5, 2/3, 1/6];
724s  assert (hygepdf (x, 4, [0, 1, 2], 2, "vectorexpand"), z, 3 * eps);
724s  assert (hygepdf (x, 4, [0, 1, 2]', 2, "vectorexpand"), z, 3 * eps);
724s  assert (hygepdf (x', 4, [0, 1, 2], 2, "vectorexpand"), z, 3 * eps);
724s  assert (hygepdf (2, 4, [0 ,1, 2], 2, "vectorexpand"), z(:,4), 3 * eps);
724s  assert (hygepdf (x, 4, 1, 2, "vectorexpand"), z(2,:), 3 *eps);
724s  assert (hygepdf ([NaN, x], 4, [0 1 2]', 2, "vectorexpand"), [NaN(3, 1), z], 3 * eps);
724s ***** error<hygepdf: function called with too few input arguments.> hygepdf ()
724s ***** error<hygepdf: function called with too few input arguments.> hygepdf (1)
724s ***** error<hygepdf: function called with too few input arguments.> hygepdf (1,2)
724s ***** error<hygepdf: function called with too few input arguments.> hygepdf (1,2,3)
724s ***** error<hygepdf: X, T, M, and N must be of common size or scalars.> ...
724s  hygepdf (1, ones (3), ones (2), ones (2))
724s ***** error<hygepdf: X, T, M, and N must be of common size or scalars.> ...
724s  hygepdf (1, ones (2), ones (3), ones (2))
724s ***** error<hygepdf: X, T, M, and N must be of common size or scalars.> ...
724s  hygepdf (1, ones (2), ones (2), ones (3))
724s ***** error<hygepdf: X, T, M, and N must not be complex.> hygepdf (i, 2, 2, 2)
724s ***** error<hygepdf: X, T, M, and N must not be complex.> hygepdf (2, i, 2, 2)
724s ***** error<hygepdf: X, T, M, and N must not be complex.> hygepdf (2, 2, i, 2)
724s ***** error<hygepdf: X, T, M, and N must not be complex.> hygepdf (2, 2, 2, i)
724s 25 tests, 25 passed, 0 known failure, 0 skipped
724s [inst/dist_fun/hygernd.m]
724s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/hygernd.m
724s ***** assert (size (hygernd (4,2,2)), [1, 1])
724s ***** assert (size (hygernd (4*ones (2,1), 2,2)), [2, 1])
724s ***** assert (size (hygernd (4*ones (2,2), 2,2)), [2, 2])
724s ***** assert (size (hygernd (4, 2*ones (2,1), 2)), [2, 1])
724s ***** assert (size (hygernd (4, 2*ones (2,2), 2)), [2, 2])
724s ***** assert (size (hygernd (4, 2, 2*ones (2,1))), [2, 1])
724s ***** assert (size (hygernd (4, 2, 2*ones (2,2))), [2, 2])
724s ***** assert (size (hygernd (4, 2, 2, 3)), [3, 3])
724s ***** assert (size (hygernd (4, 2, 2, [4 1])), [4, 1])
724s ***** assert (size (hygernd (4, 2, 2, 4, 1)), [4, 1])
724s ***** assert (class (hygernd (4,2,2)), "double")
724s ***** assert (class (hygernd (single (4),2,2)), "single")
724s ***** assert (class (hygernd (single ([4 4]),2,2)), "single")
724s ***** assert (class (hygernd (4,single (2),2)), "single")
724s ***** assert (class (hygernd (4,single ([2 2]),2)), "single")
724s ***** assert (class (hygernd (4,2,single (2))), "single")
724s ***** assert (class (hygernd (4,2,single ([2 2]))), "single")
724s ***** error<hygernd: function called with too few input arguments.> hygernd ()
724s ***** error<hygernd: function called with too few input arguments.> hygernd (1)
724s ***** error<hygernd: function called with too few input arguments.> hygernd (1, 2)
724s ***** error<hygernd: T, M, and N must be of common size or scalars.> ...
724s  hygernd (ones (3), ones (2), ones (2))
724s ***** error<hygernd: T, M, and N must be of common size or scalars.> ...
724s  hygernd (ones (2), ones (3), ones (2))
724s ***** error<hygernd: T, M, and N must be of common size or scalars.> ...
724s  hygernd (ones (2), ones (2), ones (3))
724s ***** error<hygernd: T, M, and N must not be complex.> hygernd (i, 2, 3)
724s ***** error<hygernd: T, M, and N must not be complex.> hygernd (1, i, 3)
724s ***** error<hygernd: T, M, and N must not be complex.> hygernd (1, 2, i)
724s ***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
724s  hygernd (1, 2, 3, -1)
724s ***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
724s  hygernd (1, 2, 3, 1.2)
724s ***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
724s  hygernd (1, 2, 3, ones (2))
724s ***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
724s  hygernd (1, 2, 3, [2 -1 2])
724s ***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
724s  hygernd (1, 2, 3, [2 0 2.5])
724s ***** error<hygernd: dimensions must be non-negative integers.> ...
724s  hygernd (1, 2, 3, 2, -1, 5)
724s ***** error<hygernd: dimensions must be non-negative integers.> ...
724s  hygernd (1, 2, 3, 2, 1.5, 5)
724s ***** error<hygernd: T, M, and N must be scalars or of size SZ.> ...
724s  hygernd (2, ones (2), 2, 3)
724s ***** error<hygernd: T, M, and N must be scalars or of size SZ.> ...
724s  hygernd (2, ones (2), 2, [3, 2])
724s ***** error<hygernd: T, M, and N must be scalars or of size SZ.> ...
724s  hygernd (2, ones (2), 2, 3, 2)
724s 36 tests, 36 passed, 0 known failure, 0 skipped
724s [inst/dist_fun/invgcdf.m]
724s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/invgcdf.m
724s ***** demo
724s  ## Plot various CDFs from the inverse Gaussian distribution
724s  x = 0:0.001:3;
724s  p1 = invgcdf (x, 1, 0.2);
724s  p2 = invgcdf (x, 1, 1);
724s  p3 = invgcdf (x, 1, 3);
724s  p4 = invgcdf (x, 3, 0.2);
724s  p5 = invgcdf (x, 3, 1);
724s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-y")
724s  grid on
724s  xlim ([0, 3])
724s  legend ({"μ = 1, σ = 0.2", "μ = 1, σ = 1", "μ = 1, σ = 3", ...
724s           "μ = 3, σ = 0.2", "μ = 3, σ = 1"}, "location", "southeast")
724s  title ("Inverse Gaussian CDF")
724s  xlabel ("values in x")
724s  ylabel ("probability")
724s ***** shared x, p1, p1u, y2, y2u, y3, y3u
724s  x = [-Inf, -1, 0, 1/2, 1, Inf];
724s  p1 = [0, 0, 0, 0.3650, 0.6681, 1];
724s  p1u = [1, 1, 1, 0.6350, 0.3319, 0];
724s ***** assert (invgcdf (x, ones (1,6), ones (1,6)), p1, 1e-4)
724s ***** assert (invgcdf (x, 1, 1), p1, 1e-4)
724s ***** assert (invgcdf (x, 1, ones (1,6)), p1, 1e-4)
724s ***** assert (invgcdf (x, ones (1,6), 1), p1, 1e-4)
724s ***** assert (invgcdf (x, 1, [1, 1, 1, NaN, 1, 1]), [p1(1:3), NaN, p1(5:6)], 1e-4)
724s ***** assert (invgcdf (x, [1, 1, 1, NaN, 1, 1], 1), [p1(1:3), NaN, p1(5:6)], 1e-4)
724s ***** assert (invgcdf ([x(1:3), NaN, x(5:6)], 1, 1), [p1(1:3), NaN, p1(5:6)], 1e-4)
724s ***** assert (invgcdf (x, ones (1,6), ones (1,6), "upper"), p1u, 1e-4)
724s ***** assert (invgcdf (x, 1, 1, "upper"), p1u, 1e-4)
724s ***** assert (invgcdf (x, 1, ones (1,6), "upper"), p1u, 1e-4)
724s ***** assert (invgcdf (x, ones (1,6), 1, "upper"), p1u, 1e-4)
724s ***** assert (class (invgcdf (single ([x, NaN]), 1, 1)), "single")
724s ***** assert (class (invgcdf ([x, NaN], 1, single (1))), "single")
724s ***** assert (class (invgcdf ([x, NaN], single (1), 1)), "single")
724s ***** error<invgcdf: function called with too few input arguments.> invgcdf ()
724s ***** error<invgcdf: function called with too few input arguments.> invgcdf (1)
724s ***** error<invgcdf: function called with too few input arguments.> invgcdf (1, 2)
724s ***** error<invgcdf: invalid argument for upper tail.> invgcdf (1, 2, 3, "tail")
724s ***** error<invgcdf: invalid argument for upper tail.> invgcdf (1, 2, 3, 5)
724s ***** error<invgcdf: X, MU, and LAMBDA must be of common size or scalars.> ...
724s  invgcdf (ones (3), ones (2), ones(2))
724s ***** error<invgcdf: X, MU, and LAMBDA must be of common size or scalars.> ...
724s  invgcdf (ones (2), ones (3), ones(2))
724s ***** error<invgcdf: X, MU, and LAMBDA must be of common size or scalars.> ...
724s  invgcdf (ones (2), ones (2), ones(3))
724s ***** error<invgcdf: X, MU, and LAMBDA must not be complex.> invgcdf (i, 2, 3)
724s ***** error<invgcdf: X, MU, and LAMBDA must not be complex.> invgcdf (1, i, 3)
724s ***** error<invgcdf: X, MU, and LAMBDA must not be complex.> invgcdf (1, 2, i)
724s 25 tests, 25 passed, 0 known failure, 0 skipped
724s [inst/dist_fun/invginv.m]
724s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/invginv.m
724s ***** demo
724s  ## Plot various iCDFs from the inverse Gaussian distribution
724s  p = 0.001:0.001:0.999;
724s  x1 = invginv (p, 1, 0.2);
724s  x2 = invginv (p, 1, 1);
724s  x3 = invginv (p, 1, 3);
724s  x4 = invginv (p, 3, 0.2);
724s  x5 = invginv (p, 3, 1);
724s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-y")
724s  grid on
724s  ylim ([0, 3])
724s  legend ({"μ = 1, σ = 0.2", "μ = 1, σ = 1", "μ = 1, σ = 3", ...
724s           "μ = 3, σ = 0.2", "μ = 3, σ = 1"}, "location", "northwest")
724s  title ("Inverse Gaussian iCDF")
724s  xlabel ("probability")
724s  ylabel ("x")
724s ***** shared p, x
724s  p = [0, 0.3829, 0.6827, 1];
724s  x = [0, 0.5207, 1.0376, Inf];
724s ***** assert (invginv (p, 1, 1), x, 1e-4);
724s ***** assert (invginv (p, 1, ones (1,4)), x, 1e-4);
724s ***** assert (invginv (p, 1, [-1, 0, 1, 1]), [NaN, NaN, x(3:4)], 1e-4)
724s ***** assert (invginv (p, [-1, 0, 1, 1], 1), [NaN, NaN, x(3:4)], 1e-4)
724s ***** assert (class (invginv (single ([p, NaN]), 0, 1)), "single")
724s ***** assert (class (invginv ([p, NaN], single (0), 1)), "single")
724s ***** assert (class (invginv ([p, NaN], 0, single (1))), "single")
724s ***** error<invginv: function called with too few input arguments.> invginv (1)
724s ***** error<invginv: function called with too few input arguments.> invginv (1, 2)
724s ***** error<invginv: P, MU, and LAMBDA must be of common size or scalars.> ...
724s  invginv (1, ones (2), ones (3))
724s ***** error<invginv: P, MU, and LAMBDA must be of common size or scalars.> ...
724s  invginv (ones (2), 1, ones (3))
724s ***** error<invginv: P, MU, and LAMBDA must be of common size or scalars.> ...
724s  invginv (ones (2), ones (3), 1)
724s ***** error<invginv: P, MU, and LAMBDA must not be complex.> invginv (i, 2, 3)
724s ***** error<invginv: P, MU, and LAMBDA must not be complex.> invginv (1, i, 3)
724s ***** error<invginv: P, MU, and LAMBDA must not be complex.> invginv (1, 2, i)
724s 15 tests, 15 passed, 0 known failure, 0 skipped
724s [inst/dist_fun/invgpdf.m]
724s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/invgpdf.m
724s ***** demo
724s  ## Plot various PDFs from the inverse Gaussian distribution
724s  x = 0:0.001:3;
724s  y1 = invgpdf (x, 1, 0.2);
724s  y2 = invgpdf (x, 1, 1);
724s  y3 = invgpdf (x, 1, 3);
724s  y4 = invgpdf (x, 3, 0.2);
724s  y5 = invgpdf (x, 3, 1);
724s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-y")
724s  grid on
724s  xlim ([0, 3])
724s  ylim ([0, 3])
724s  legend ({"μ = 1, σ = 0.2", "μ = 1, σ = 1", "μ = 1, σ = 3", ...
724s           "μ = 3, σ = 0.2", "μ = 3, σ = 1"}, "location", "northeast")
724s  title ("Inverse Gaussian PDF")
724s  xlabel ("values in x")
724s  ylabel ("density")
724s ***** shared x, y
724s  x = [-Inf, -1, 0, 1/2, 1, Inf];
724s  y = [0, 0, 0, 0.8788, 0.3989, 0];
724s ***** assert (invgpdf ([x, NaN], 1, 1), [y, NaN], 1e-4)
724s ***** assert (invgpdf (x, 1, [-2, -1, 0, 1, 1, 1]), [nan(1,3), y([4:6])], 1e-4)
724s ***** assert (class (hncdf (single ([x, NaN]), 1, 1)), "single")
724s ***** assert (class (hncdf ([x, NaN], 1, single (1))), "single")
724s ***** assert (class (hncdf ([x, NaN], single (1), 1)), "single")
724s ***** error<invgpdf: function called with too few input arguments.> invgpdf ()
724s ***** error<invgpdf: function called with too few input arguments.> invgpdf (1)
724s ***** error<invgpdf: function called with too few input arguments.> invgpdf (1, 2)
724s ***** error<invgpdf: X, MU, and LAMBDA must be of common size or scalars.> ...
724s  invgpdf (1, ones (2), ones (3))
724s ***** error<invgpdf: X, MU, and LAMBDA must be of common size or scalars.> ...
724s  invgpdf (ones (2), 1, ones (3))
724s ***** error<invgpdf: X, MU, and LAMBDA must be of common size or scalars.> ...
724s  invgpdf (ones (2), ones (3), 1)
724s ***** error<invgpdf: X, MU, and LAMBDA must not be complex.> invgpdf (i, 2, 3)
724s ***** error<invgpdf: X, MU, and LAMBDA must not be complex.> invgpdf (1, i, 3)
724s ***** error<invgpdf: X, MU, and LAMBDA must not be complex.> invgpdf (1, 2, i)
724s 14 tests, 14 passed, 0 known failure, 0 skipped
724s [inst/dist_fun/invgrnd.m]
724s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/invgrnd.m
724s ***** assert (size (invgrnd (1, 1, 1)), [1, 1])
724s ***** assert (size (invgrnd (1, 1, 2)), [2, 2])
724s ***** assert (size (invgrnd (1, 1, [2, 1])), [2, 1])
724s ***** assert (size (invgrnd (1, zeros (2, 2))), [2, 2])
724s ***** assert (size (invgrnd (1, ones (2, 1))), [2, 1])
724s ***** assert (size (invgrnd (1, ones (2, 2))), [2, 2])
724s ***** assert (size (invgrnd (ones (2, 1), 1)), [2, 1])
724s ***** assert (size (invgrnd (ones (2, 2), 1)), [2, 2])
724s ***** assert (size (invgrnd (1, 1, 3)), [3, 3])
724s ***** assert (size (invgrnd (1, 1, [4 1])), [4, 1])
724s ***** assert (size (invgrnd (1, 1, 4, 1)), [4, 1])
724s ***** test
724s  r =  invgrnd (1, [1, 0, -1]);
724s  assert (r([2:3]), [NaN, NaN])
724s ***** assert (class (invgrnd (1, 0)), "double")
724s ***** assert (class (invgrnd (1, single (0))), "single")
724s ***** assert (class (invgrnd (1, single ([0 0]))), "single")
724s ***** assert (class (invgrnd (1, single (1))), "single")
724s ***** assert (class (invgrnd (1, single ([1 1]))), "single")
724s ***** assert (class (invgrnd (single (1), 1)), "single")
724s ***** assert (class (invgrnd (single ([1 1]), 1)), "single")
724s ***** error<invgrnd: function called with too few input arguments.> invgrnd ()
724s ***** error<invgrnd: function called with too few input arguments.> invgrnd (1)
724s ***** error<invgrnd: MU and LAMBDA must be of common size or scalars.> ...
724s  invgrnd (ones (3), ones (2))
724s ***** error<invgrnd: MU and LAMBDA must be of common size or scalars.> ...
724s  invgrnd (ones (2), ones (3))
724s ***** error<invgrnd: MU and LAMBDA must not be complex.> invgrnd (i, 2, 3)
724s ***** error<invgrnd: MU and LAMBDA must not be complex.> invgrnd (1, i, 3)
724s ***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
724s  invgrnd (1, 2, -1)
724s ***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
724s  invgrnd (1, 2, 1.2)
725s ***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
725s  invgrnd (1, 2, ones (2))
725s ***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
725s  invgrnd (1, 2, [2 -1 2])
725s ***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
725s  invgrnd (1, 2, [2 0 2.5])
725s ***** error<invgrnd: dimensions must be non-negative integers.> ...
725s  invgrnd (1, 2, 2, -1, 5)
725s ***** error<invgrnd: dimensions must be non-negative integers.> ...
725s  invgrnd (1, 2, 2, 1.5, 5)
725s ***** error<invgrnd: MU and LAMBDA must be scalars or of size SZ.> ...
725s  invgrnd (2, ones (2), 3)
725s ***** error<invgrnd: MU and LAMBDA must be scalars or of size SZ.> ...
725s  invgrnd (2, ones (2), [3, 2])
725s ***** error<invgrnd: MU and LAMBDA must be scalars or of size SZ.> ...
725s  invgrnd (2, ones (2), 3, 2)
725s 35 tests, 35 passed, 0 known failure, 0 skipped
725s [inst/dist_fun/iwishpdf.m]
725s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/iwishpdf.m
725s ***** assert(iwishpdf(4, 3, 3.1), 0.04226595, 1E-7);
725s ***** assert(iwishpdf([2 -0.3;-0.3 4], [1 0.3;0.3 1], 4), 1.60166e-05, 1E-10);
725s ***** assert(iwishpdf([6 2 5; 2 10 -5; 5 -5 25], ...
725s  [9 5 5; 5 10 -8; 5 -8 22], 5.1), 4.946831e-12, 1E-17);
725s ***** error iwishpdf ()
725s ***** error iwishpdf (1, 2)
725s ***** error iwishpdf (1, 2, 0)
725s 6 tests, 6 passed, 0 known failure, 0 skipped
725s [inst/dist_fun/iwishrnd.m]
725s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/iwishrnd.m
725s ***** assert(size (iwishrnd (1,2,1)), [1, 1]);
725s ***** assert(size (iwishrnd ([],2,1)), [1, 1]);
725s ***** assert(size (iwishrnd ([3 1; 1 3], 2.00001, [], 1)), [2, 2]);
725s ***** assert(size (iwishrnd (eye(2), 2, [], 3)), [2, 2, 3]);
725s ***** error iwishrnd ()
725s ***** error iwishrnd (1)
725s ***** error iwishrnd ([-3 1; 1 3],1)
725s ***** error iwishrnd ([1; 1],1)
725s 8 tests, 8 passed, 0 known failure, 0 skipped
725s [inst/dist_fun/jsucdf.m]
725s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/jsucdf.m
725s ***** error jsucdf ()
725s ***** error jsucdf (1, 2, 3, 4)
725s ***** error<jsucdf: X, ALPHA1, and ALPHA2 must be of common size or scalars.> ...
725s  jsucdf (1, ones (2), ones (3))
725s 3 tests, 3 passed, 0 known failure, 0 skipped
725s [inst/dist_fun/jsupdf.m]
725s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/jsupdf.m
725s ***** error jsupdf ()
725s ***** error jsupdf (1, 2, 3, 4)
725s ***** error<jsupdf: X, ALPHA1, and ALPHA2 must be of common size or scalars.> ...
725s  jsupdf (1, ones (2), ones (3))
725s 3 tests, 3 passed, 0 known failure, 0 skipped
725s [inst/dist_fun/laplacecdf.m]
725s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/laplacecdf.m
725s ***** demo
725s  ## Plot various CDFs from the Laplace distribution
725s  x = -10:0.01:10;
725s  p1 = laplacecdf (x, 0, 1);
725s  p2 = laplacecdf (x, 0, 2);
725s  p3 = laplacecdf (x, 0, 4);
725s  p4 = laplacecdf (x, -5, 4);
725s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
725s  grid on
725s  xlim ([-10, 10])
725s  legend ({"μ = 0, β = 1", "μ = 0, β = 2", ...
725s           "μ = 0, β = 4", "μ = -5, β = 4"}, "location", "southeast")
725s  title ("Laplace CDF")
725s  xlabel ("values in x")
725s  ylabel ("probability")
725s ***** shared x, y
725s  x = [-Inf, -log(2), 0, log(2), Inf];
725s  y = [0, 1/4, 1/2, 3/4, 1];
725s ***** assert (laplacecdf ([x, NaN], 0, 1), [y, NaN])
725s ***** assert (laplacecdf (x, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), 0.75, 1])
725s ***** assert (laplacecdf (single ([x, NaN]), 0, 1), single ([y, NaN]), eps ("single"))
726s ***** assert (laplacecdf ([x, NaN], single (0), 1), single ([y, NaN]), eps ("single"))
726s ***** assert (laplacecdf ([x, NaN], 0, single (1)), single ([y, NaN]), eps ("single"))
726s ***** error<laplacecdf: function called with too few input arguments.> laplacecdf ()
726s ***** error<laplacecdf: function called with too few input arguments.> laplacecdf (1)
726s ***** error<laplacecdf: function called with too few input arguments.> ...
726s  laplacecdf (1, 2)
726s ***** error<laplacecdf: function called with too many inputs> ...
726s  laplacecdf (1, 2, 3, 4, 5)
726s ***** error<laplacecdf: invalid argument for upper tail.> laplacecdf (1, 2, 3, "tail")
726s ***** error<laplacecdf: invalid argument for upper tail.> laplacecdf (1, 2, 3, 4)
726s ***** error<laplacecdf: X, MU, and BETA must be of common size or scalars.> ...
726s  laplacecdf (ones (3), ones (2), ones (2))
726s ***** error<laplacecdf: X, MU, and BETA must be of common size or scalars.> ...
726s  laplacecdf (ones (2), ones (3), ones (2))
726s ***** error<laplacecdf: X, MU, and BETA must be of common size or scalars.> ...
726s  laplacecdf (ones (2), ones (2), ones (3))
726s ***** error<laplacecdf: X, MU, and BETA must not be complex.> laplacecdf (i, 2, 2)
726s ***** error<laplacecdf: X, MU, and BETA must not be complex.> laplacecdf (2, i, 2)
726s ***** error<laplacecdf: X, MU, and BETA must not be complex.> laplacecdf (2, 2, i)
726s 17 tests, 17 passed, 0 known failure, 0 skipped
726s [inst/dist_fun/laplaceinv.m]
726s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/laplaceinv.m
726s ***** demo
726s  ## Plot various iCDFs from the Laplace distribution
726s  p = 0.001:0.001:0.999;
726s  x1 = cauchyinv (p, 0, 1);
726s  x2 = cauchyinv (p, 0, 2);
726s  x3 = cauchyinv (p, 0, 4);
726s  x4 = cauchyinv (p, -5, 4);
726s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
726s  grid on
726s  ylim ([-10, 10])
726s  legend ({"μ = 0, β = 1", "μ = 0, β = 2", ...
726s           "μ = 0, β = 4", "μ = -5, β = 4"}, "location", "northwest")
726s  title ("Laplace iCDF")
726s  xlabel ("probability")
726s  ylabel ("values in x")
726s ***** shared p, x
726s  p = [-1 0 0.5 1 2];
726s  x = [NaN, -Inf, 0, Inf, NaN];
726s ***** assert (laplaceinv (p, 0, 1), x)
726s ***** assert (laplaceinv (p, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), Inf, NaN])
726s ***** assert (laplaceinv ([p, NaN], 0, 1), [x, NaN])
726s ***** assert (laplaceinv (single ([p, NaN]), 0, 1), single ([x, NaN]))
726s ***** assert (laplaceinv ([p, NaN], single (0), 1), single ([x, NaN]))
726s ***** assert (laplaceinv ([p, NaN], 0, single (1)), single ([x, NaN]))
726s ***** error<laplaceinv: function called with too few input arguments.> laplaceinv ()
726s ***** error<laplaceinv: function called with too few input arguments.> laplaceinv (1)
726s ***** error<laplaceinv: function called with too few input arguments.> ...
726s  laplaceinv (1, 2)
726s ***** error<laplaceinv: function called with too many inputs> laplaceinv (1, 2, 3, 4)
726s ***** error<laplaceinv: P, MU, and BETA must be of common size or scalars.> ...
726s  laplaceinv (1, ones (2), ones (3))
726s ***** error<laplaceinv: P, MU, and BETA must be of common size or scalars.> ...
726s  laplaceinv (ones (2), 1, ones (3))
726s ***** error<laplaceinv: P, MU, and BETA must be of common size or scalars.> ...
726s  laplaceinv (ones (2), ones (3), 1)
726s ***** error<laplaceinv: P, MU, and BETA must not be complex.> laplaceinv (i, 2, 3)
726s ***** error<laplaceinv: P, MU, and BETA must not be complex.> laplaceinv (1, i, 3)
726s ***** error<laplaceinv: P, MU, and BETA must not be complex.> laplaceinv (1, 2, i)
726s 16 tests, 16 passed, 0 known failure, 0 skipped
726s [inst/dist_fun/laplacepdf.m]
726s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/laplacepdf.m
726s ***** demo
726s  ## Plot various PDFs from the Laplace distribution
726s  x = -10:0.01:10;
726s  y1 = laplacepdf (x, 0, 1);
726s  y2 = laplacepdf (x, 0, 2);
726s  y3 = laplacepdf (x, 0, 4);
726s  y4 = laplacepdf (x, -5, 4);
726s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
726s  grid on
726s  xlim ([-10, 10])
726s  ylim ([0, 0.6])
726s  legend ({"μ = 0, β = 1", "μ = 0, β = 2", ...
726s           "μ = 0, β = 4", "μ = -5, β = 4"}, "location", "northeast")
726s  title ("Laplace PDF")
726s  xlabel ("values in x")
726s  ylabel ("density")
726s ***** shared x, y
726s  x = [-Inf -log(2) 0 log(2) Inf];
726s  y = [0, 1/4, 1/2, 1/4, 0];
726s ***** assert (laplacepdf ([x, NaN], 0, 1), [y, NaN])
726s ***** assert (laplacepdf (x, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), 0.25, 0])
726s ***** assert (laplacepdf (single ([x, NaN]), 0, 1), single ([y, NaN]))
726s ***** assert (laplacepdf ([x, NaN], single (0), 1), single ([y, NaN]))
726s ***** assert (laplacepdf ([x, NaN], 0, single (1)), single ([y, NaN]))
726s ***** error<laplacepdf: function called with too few input arguments.> laplacepdf ()
726s ***** error<laplacepdf: function called with too few input arguments.> laplacepdf (1)
726s ***** error<laplacepdf: function called with too few input arguments.> ...
726s  laplacepdf (1, 2)
726s ***** error<laplacepdf: function called with too many inputs> laplacepdf (1, 2, 3, 4)
726s ***** error<laplacepdf: X, MU, and BETA must be of common size or scalars.> ...
726s  laplacepdf (1, ones (2), ones (3))
726s ***** error<laplacepdf: X, MU, and BETA must be of common size or scalars.> ...
726s  laplacepdf (ones (2), 1, ones (3))
726s ***** error<laplacepdf: X, MU, and BETA must be of common size or scalars.> ...
726s  laplacepdf (ones (2), ones (3), 1)
726s ***** error<laplacepdf: X, MU, and BETA must not be complex.> laplacepdf (i, 2, 3)
726s ***** error<laplacepdf: X, MU, and BETA must not be complex.> laplacepdf (1, i, 3)
726s ***** error<laplacepdf: X, MU, and BETA must not be complex.> laplacepdf (1, 2, i)
726s 15 tests, 15 passed, 0 known failure, 0 skipped
726s [inst/dist_fun/laplacernd.m]
726s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/laplacernd.m
726s ***** assert (size (laplacernd (1, 1)), [1 1])
726s ***** assert (size (laplacernd (1, ones (2,1))), [2, 1])
726s ***** assert (size (laplacernd (1, ones (2,2))), [2, 2])
726s ***** assert (size (laplacernd (ones (2,1), 1)), [2, 1])
726s ***** assert (size (laplacernd (ones (2,2), 1)), [2, 2])
726s ***** assert (size (laplacernd (1, 1, 3)), [3, 3])
726s ***** assert (size (laplacernd (1, 1, [4, 1])), [4, 1])
726s ***** assert (size (laplacernd (1, 1, 4, 1)), [4, 1])
726s ***** assert (size (laplacernd (1, 1, 4, 1, 5)), [4, 1, 5])
726s ***** assert (size (laplacernd (1, 1, 0, 1)), [0, 1])
726s ***** assert (size (laplacernd (1, 1, 1, 0)), [1, 0])
726s ***** assert (size (laplacernd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
726s ***** assert (class (laplacernd (1, 1)), "double")
726s ***** assert (class (laplacernd (1, single (1))), "single")
726s ***** assert (class (laplacernd (1, single ([1, 1]))), "single")
726s ***** assert (class (laplacernd (single (1), 1)), "single")
726s ***** assert (class (laplacernd (single ([1, 1]), 1)), "single")
726s ***** error<laplacernd: function called with too few input arguments.> laplacernd ()
726s ***** error<laplacernd: function called with too few input arguments.> laplacernd (1)
726s ***** error<laplacernd: MU and BETA must be of common size or scalars.> ...
726s  laplacernd (ones (3), ones (2))
726s ***** error<laplacernd: MU and BETA must be of common size or scalars.> ...
726s  laplacernd (ones (2), ones (3))
726s ***** error<laplacernd: MU and BETA must not be complex.> laplacernd (i, 2, 3)
726s ***** error<laplacernd: MU and BETA must not be complex.> laplacernd (1, i, 3)
726s ***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
726s  laplacernd (1, 2, -1)
726s ***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
726s  laplacernd (1, 2, 1.2)
726s ***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
726s  laplacernd (1, 2, ones (2))
726s ***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
726s  laplacernd (1, 2, [2 -1 2])
726s ***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
726s  laplacernd (1, 2, [2 0 2.5])
726s ***** error<laplacernd: dimensions must be non-negative integers.> ...
726s  laplacernd (1, 2, 2, -1, 5)
726s ***** error<laplacernd: dimensions must be non-negative integers.> ...
726s  laplacernd (1, 2, 2, 1.5, 5)
726s ***** error<laplacernd: MU and BETA must be scalars or of size SZ.> ...
726s  laplacernd (2, ones (2), 3)
726s ***** error<laplacernd: MU and BETA must be scalars or of size SZ.> ...
726s  laplacernd (2, ones (2), [3, 2])
726s ***** error<laplacernd: MU and BETA must be scalars or of size SZ.> ...
726s  laplacernd (2, ones (2), 3, 2)
726s 33 tests, 33 passed, 0 known failure, 0 skipped
726s [inst/dist_fun/logicdf.m]
726s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/logicdf.m
726s ***** demo
726s  ## Plot various CDFs from the logistic distribution
726s  x = -5:0.01:20;
726s  p1 = logicdf (x, 5, 2);
726s  p2 = logicdf (x, 9, 3);
726s  p3 = logicdf (x, 9, 4);
726s  p4 = logicdf (x, 6, 2);
726s  p5 = logicdf (x, 2, 1);
726s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
726s  grid on
726s  legend ({"μ = 5, σ = 2", "μ = 9, σ = 3", "μ = 9, σ = 4", ...
726s           "μ = 6, σ = 2", "μ = 2, σ = 1"}, "location", "southeast")
726s  title ("Logistic CDF")
726s  xlabel ("values in x")
726s  ylabel ("probability")
726s ***** shared x, y
726s  x = [-Inf -log(3) 0 log(3) Inf];
726s  y = [0, 1/4, 1/2, 3/4, 1];
726s ***** assert (logicdf ([x, NaN], 0, 1), [y, NaN], eps)
726s ***** assert (logicdf (x, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), 0.75, 1])
726s ***** assert (logicdf (single ([x, NaN]), 0, 1), single ([y, NaN]), eps ("single"))
726s ***** assert (logicdf ([x, NaN], single (0), 1), single ([y, NaN]), eps ("single"))
726s ***** assert (logicdf ([x, NaN], 0, single (1)), single ([y, NaN]), eps ("single"))
726s ***** error<logicdf: function called with too few input arguments.> logicdf ()
726s ***** error<logicdf: function called with too few input arguments.> logicdf (1)
726s ***** error<logicdf: function called with too few input arguments.> ...
726s  logicdf (1, 2)
726s ***** error<logicdf: invalid argument for upper tail.> logicdf (1, 2, 3, "tail")
726s ***** error<logicdf: invalid argument for upper tail.> logicdf (1, 2, 3, 4)
726s ***** error<logicdf: X, MU, and SIGMA must be of common size or scalars.> ...
726s  logicdf (1, ones (2), ones (3))
726s ***** error<logicdf: X, MU, and SIGMA must be of common size or scalars.> ...
726s  logicdf (ones (2), 1, ones (3))
726s ***** error<logicdf: X, MU, and SIGMA must be of common size or scalars.> ...
726s  logicdf (ones (2), ones (3), 1)
726s ***** error<logicdf: X, MU, and SIGMA must not be complex.> logicdf (i, 2, 3)
726s ***** error<logicdf: X, MU, and SIGMA must not be complex.> logicdf (1, i, 3)
726s ***** error<logicdf: X, MU, and SIGMA must not be complex.> logicdf (1, 2, i)
726s 16 tests, 16 passed, 0 known failure, 0 skipped
726s [inst/dist_fun/logiinv.m]
726s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/logiinv.m
726s ***** demo
726s  ## Plot various iCDFs from the logistic distribution
726s  p = 0.001:0.001:0.999;
726s  x1 = logiinv (p, 5, 2);
726s  x2 = logiinv (p, 9, 3);
726s  x3 = logiinv (p, 9, 4);
726s  x4 = logiinv (p, 6, 2);
726s  x5 = logiinv (p, 2, 1);
726s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
726s  grid on
726s  legend ({"μ = 5, σ = 2", "μ = 9, σ = 3", "μ = 9, σ = 4", ...
726s           "μ = 6, σ = 2", "μ = 2, σ = 1"}, "location", "southeast")
726s  title ("Logistic iCDF")
726s  xlabel ("probability")
726s  ylabel ("x")
726s ***** test
726s  p = [0.01:0.01:0.99];
726s  assert (logiinv (p, 0, 1), log (p ./ (1-p)), 25*eps);
726s ***** shared p
726s  p = [-1 0 0.5 1 2];
726s ***** assert (logiinv (p, 0, 1), [NaN -Inf 0 Inf NaN])
726s ***** assert (logiinv (p, 0, [-1, 0, 1, 2, 3]), [NaN NaN 0 Inf NaN])
726s ***** assert (logiinv ([p, NaN], 0, 1), [NaN -Inf 0 Inf NaN NaN])
726s ***** assert (logiinv (single ([p, NaN]), 0, 1), single ([NaN -Inf 0 Inf NaN NaN]))
726s ***** assert (logiinv ([p, NaN], single (0), 1), single ([NaN -Inf 0 Inf NaN NaN]))
726s ***** assert (logiinv ([p, NaN], 0, single (1)), single ([NaN -Inf 0 Inf NaN NaN]))
726s ***** error<logiinv: function called with too few input arguments.> logiinv ()
726s ***** error<logiinv: function called with too few input arguments.> logiinv (1)
726s ***** error<logiinv: function called with too few input arguments.> ...
726s  logiinv (1, 2)
726s ***** error<logiinv: P, MU, and SIGMA must be of common size or scalars.> ...
726s  logiinv (1, ones (2), ones (3))
726s ***** error<logiinv: P, MU, and SIGMA must be of common size or scalars.> ...
726s  logiinv (ones (2), 1, ones (3))
726s ***** error<logiinv: P, MU, and SIGMA must be of common size or scalars.> ...
726s  logiinv (ones (2), ones (3), 1)
726s ***** error<logiinv: P, MU, and SIGMA must not be complex.> logiinv (i, 2, 3)
726s ***** error<logiinv: P, MU, and SIGMA must not be complex.> logiinv (1, i, 3)
726s ***** error<logiinv: P, MU, and SIGMA must not be complex.> logiinv (1, 2, i)
726s 16 tests, 16 passed, 0 known failure, 0 skipped
726s [inst/dist_fun/logipdf.m]
726s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/logipdf.m
726s ***** demo
726s  ## Plot various PDFs from the logistic distribution
726s  x = -5:0.01:20;
726s  y1 = logipdf (x, 5, 2);
726s  y2 = logipdf (x, 9, 3);
726s  y3 = logipdf (x, 9, 4);
726s  y4 = logipdf (x, 6, 2);
726s  y5 = logipdf (x, 2, 1);
726s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
726s  grid on
726s  ylim ([0, 0.3])
726s  legend ({"μ = 5, σ = 2", "μ = 9, σ = 3", "μ = 9, σ = 4", ...
726s           "μ = 6, σ = 2", "μ = 2, σ = 1"}, "location", "northeast")
726s  title ("Logistic PDF")
726s  xlabel ("values in x")
726s  ylabel ("density")
726s ***** shared x, y
726s  x = [-Inf -log(4) 0 log(4) Inf];
726s  y = [0, 0.16, 1/4, 0.16, 0];
726s ***** assert (logipdf ([x, NaN], 0, 1), [y, NaN], eps)
726s ***** assert (logipdf (x, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), y([4:5])], eps)
726s ***** assert (logipdf (single ([x, NaN]), 0, 1), single ([y, NaN]), eps ("single"))
726s ***** assert (logipdf ([x, NaN], single (0), 1), single ([y, NaN]), eps ("single"))
726s ***** assert (logipdf ([x, NaN], 0, single (1)), single ([y, NaN]), eps ("single"))
726s ***** error<logipdf: function called with too few input arguments.> logipdf ()
726s ***** error<logipdf: function called with too few input arguments.> logipdf (1)
726s ***** error<logipdf: function called with too few input arguments.> ...
726s  logipdf (1, 2)
726s ***** error<logipdf: X, MU, and SIGMA must be of common size or scalars.> ...
726s  logipdf (1, ones (2), ones (3))
726s ***** error<logipdf: X, MU, and SIGMA must be of common size or scalars.> ...
726s  logipdf (ones (2), 1, ones (3))
726s ***** error<logipdf: X, MU, and SIGMA must be of common size or scalars.> ...
726s  logipdf (ones (2), ones (3), 1)
726s ***** error<logipdf: X, MU, and SIGMA must not be complex.> logipdf (i, 2, 3)
726s ***** error<logipdf: X, MU, and SIGMA must not be complex.> logipdf (1, i, 3)
726s ***** error<logipdf: X, MU, and SIGMA must not be complex.> logipdf (1, 2, i)
726s 14 tests, 14 passed, 0 known failure, 0 skipped
726s [inst/dist_fun/logirnd.m]
726s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/logirnd.m
726s ***** assert (size (logirnd (1, 1)), [1 1])
726s ***** assert (size (logirnd (1, ones (2,1))), [2, 1])
726s ***** assert (size (logirnd (1, ones (2,2))), [2, 2])
726s ***** assert (size (logirnd (ones (2,1), 1)), [2, 1])
726s ***** assert (size (logirnd (ones (2,2), 1)), [2, 2])
726s ***** assert (size (logirnd (1, 1, 3)), [3, 3])
726s ***** assert (size (logirnd (1, 1, [4, 1])), [4, 1])
726s ***** assert (size (logirnd (1, 1, 4, 1)), [4, 1])
726s ***** assert (size (logirnd (1, 1, 4, 1, 5)), [4, 1, 5])
726s ***** assert (size (logirnd (1, 1, 0, 1)), [0, 1])
726s ***** assert (size (logirnd (1, 1, 1, 0)), [1, 0])
726s ***** assert (size (logirnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
726s ***** assert (class (logirnd (1, 1)), "double")
726s ***** assert (class (logirnd (1, single (1))), "single")
726s ***** assert (class (logirnd (1, single ([1, 1]))), "single")
726s ***** assert (class (logirnd (single (1), 1)), "single")
726s ***** assert (class (logirnd (single ([1, 1]), 1)), "single")
726s ***** error<logirnd: function called with too few input arguments.> logirnd ()
726s ***** error<logirnd: function called with too few input arguments.> logirnd (1)
726s ***** error<logirnd: MU and SIGMA must be of common size or scalars.> ...
726s  logirnd (ones (3), ones (2))
726s ***** error<logirnd: MU and SIGMA must be of common size or scalars.> ...
726s  logirnd (ones (2), ones (3))
726s ***** error<logirnd: MU and SIGMA must not be complex.> logirnd (i, 2, 3)
726s ***** error<logirnd: MU and SIGMA must not be complex.> logirnd (1, i, 3)
726s ***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
726s  logirnd (1, 2, -1)
726s ***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
726s  logirnd (1, 2, 1.2)
726s ***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
726s  logirnd (1, 2, ones (2))
726s ***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
726s  logirnd (1, 2, [2 -1 2])
726s ***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
726s  logirnd (1, 2, [2 0 2.5])
726s ***** error<logirnd: dimensions must be non-negative integers.> ...
726s  logirnd (1, 2, 2, -1, 5)
726s ***** error<logirnd: dimensions must be non-negative integers.> ...
726s  logirnd (1, 2, 2, 1.5, 5)
726s ***** error<logirnd: MU and SIGMA must be scalars or of size SZ.> ...
726s  logirnd (2, ones (2), 3)
726s ***** error<logirnd: MU and SIGMA must be scalars or of size SZ.> ...
726s  logirnd (2, ones (2), [3, 2])
726s ***** error<logirnd: MU and SIGMA must be scalars or of size SZ.> ...
726s  logirnd (2, ones (2), 3, 2)
726s 33 tests, 33 passed, 0 known failure, 0 skipped
726s [inst/dist_fun/loglcdf.m]
726s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/loglcdf.m
726s ***** demo
726s  ## Plot various CDFs from the log-logistic distribution
726s  x = 0:0.001:2;
726s  p1 = loglcdf (x, log (1), 1/0.5);
726s  p2 = loglcdf (x, log (1), 1);
726s  p3 = loglcdf (x, log (1), 1/2);
726s  p4 = loglcdf (x, log (1), 1/4);
726s  p5 = loglcdf (x, log (1), 1/8);
726s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
726s  legend ({"σ = 2 (β = 0.5)", "σ = 1 (β = 1)", "σ = 0.5 (β = 2)", ...
726s           "σ = 0.25 (β = 4)", "σ = 0.125 (β = 8)"}, "location", "northwest")
726s  grid on
726s  title ("Log-logistic CDF")
726s  xlabel ("values in x")
726s  ylabel ("probability")
726s  text (0.05, 0.64, "μ = 0 (α = 1), values of σ (β) as shown in legend")
726s ***** shared out1, out2
726s  out1 = [0, 0.5, 0.66666667, 0.75, 0.8, 0.83333333];
726s  out2 = [0, 0.4174, 0.4745, 0.5082, 0.5321, 0.5506];
726s ***** assert (loglcdf ([0:5], 0, 1), out1, 1e-8)
726s ***** assert (loglcdf ([0:5], 0, 1, "upper"), 1 - out1, 1e-8)
726s ***** assert (loglcdf ([0:5], 0, 1), out1, 1e-8)
726s ***** assert (loglcdf ([0:5], 0, 1, "upper"), 1 - out1, 1e-8)
726s ***** assert (loglcdf ([0:5], 1, 3), out2, 1e-4)
726s ***** assert (loglcdf ([0:5], 1, 3, "upper"), 1 - out2, 1e-4)
726s ***** assert (class (loglcdf (single (1), 2, 3)), "single")
726s ***** assert (class (loglcdf (1, single (2), 3)), "single")
726s ***** assert (class (loglcdf (1, 2, single (3))), "single")
726s ***** error<loglcdf: function called with too few input arguments.> loglcdf (1)
726s ***** error<loglcdf: function called with too few input arguments.> loglcdf (1, 2)
726s ***** error<loglcdf: invalid argument for upper tail.> ...
726s  loglcdf (1, 2, 3, 4)
726s ***** error<loglcdf: invalid argument for upper tail.> ...
726s  loglcdf (1, 2, 3, "uper")
726s ***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
726s  loglcdf (1, ones (2), ones (3))
726s ***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
726s  loglcdf (1, ones (2), ones (3), "upper")
726s ***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
726s  loglcdf (ones (2), 1, ones (3))
726s ***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
726s  loglcdf (ones (2), 1, ones (3), "upper")
726s ***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
726s  loglcdf (ones (2), ones (3), 1)
726s ***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
726s  loglcdf (ones (2), ones (3), 1, "upper")
726s ***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (i, 2, 3)
726s ***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (i, 2, 3, "upper")
726s ***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (1, i, 3)
726s ***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (1, i, 3, "upper")
726s ***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (1, 2, i)
726s ***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (1, 2, i, "upper")
726s 25 tests, 25 passed, 0 known failure, 0 skipped
726s [inst/dist_fun/loglinv.m]
726s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/loglinv.m
726s ***** demo
726s  ## Plot various iCDFs from the log-logistic distribution
726s  p = 0.001:0.001:0.999;
726s  x1 = loglinv (p, log (1), 1/0.5);
726s  x2 = loglinv (p, log (1), 1);
726s  x3 = loglinv (p, log (1), 1/2);
726s  x4 = loglinv (p, log (1), 1/4);
726s  x5 = loglinv (p, log (1), 1/8);
726s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
726s  ylim ([0, 20])
726s  grid on
726s  legend ({"σ = 2 (β = 0.5)", "σ = 1 (β = 1)", "σ = 0.5 (β = 2)", ...
726s           "σ = 0.25 (β = 4)", "σ = 0.125 (β = 8)"}, "location", "northwest")
726s  title ("Log-logistic iCDF")
726s  xlabel ("probability")
726s  ylabel ("x")
726s  text (0.03, 12.5, "μ = 0 (α = 1), values of σ (β) as shown in legend")
726s ***** shared p, out1, out2
726s  p = [-1, 0, 0.2, 0.5, 0.8, 0.95, 1, 2];
726s  out1 = [NaN, 0, 0.25, 1, 4, 19, Inf, NaN];
726s  out2 = [NaN, 0, 0.0424732, 2.718282, 173.970037, 18644.695061, Inf, NaN];
726s ***** assert (loglinv (p, 0, 1), out1, 1e-8)
726s ***** assert (loglinv (p, 0, 1), out1, 1e-8)
726s ***** assert (loglinv (p, 1, 3), out2, 1e-6)
726s ***** assert (class (loglinv (single (1), 2, 3)), "single")
726s ***** assert (class (loglinv (1, single (2), 3)), "single")
726s ***** assert (class (loglinv (1, 2, single (3))), "single")
726s ***** error<loglinv: function called with too few input arguments.> loglinv (1)
726s ***** error<loglinv: function called with too few input arguments.> loglinv (1, 2)
726s ***** error<loglinv: P, MU, and SIGMA must be of common size or scalars.> ...
726s  loglinv (1, ones (2), ones (3))
726s ***** error<loglinv: P, MU, and SIGMA must be of common size or scalars.> ...
726s  loglinv (ones (2), 1, ones (3))
726s ***** error<loglinv: P, MU, and SIGMA must be of common size or scalars.> ...
726s  loglinv (ones (2), ones (3), 1)
726s ***** error<loglinv: P, MU, and SIGMA must not be complex.> loglinv (i, 2, 3)
726s ***** error<loglinv: P, MU, and SIGMA must not be complex.> loglinv (1, i, 3)
726s ***** error<loglinv: P, MU, and SIGMA must not be complex.> loglinv (1, 2, i)
726s 14 tests, 14 passed, 0 known failure, 0 skipped
726s [inst/dist_fun/loglpdf.m]
726s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/loglpdf.m
726s ***** demo
726s  ## Plot various PDFs from the log-logistic distribution
726s  x = 0.001:0.001:2;
726s  y1 = loglpdf (x, log (1), 1/0.5);
726s  y2 = loglpdf (x, log (1), 1);
726s  y3 = loglpdf (x, log (1), 1/2);
726s  y4 = loglpdf (x, log (1), 1/4);
726s  y5 = loglpdf (x, log (1), 1/8);
726s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
726s  grid on
726s  ylim ([0,3])
726s  legend ({"σ = 2 (β = 0.5)", "σ = 1 (β = 1)", "σ = 0.5 (β = 2)", ...
726s           "σ = 0.25 (β = 4)", "σ = 0.125 (β = 8)"}, "location", "northeast")
726s  title ("Log-logistic PDF")
726s  xlabel ("values in x")
726s  ylabel ("density")
726s  text (0.1, 2.8, "μ = 0 (α = 1), values of σ (β) as shown in legend")
726s ***** shared out1, out2
726s  out1 = [0, 0, 1, 0.2500, 0.1111, 0.0625, 0.0400, 0.0278, 0];
726s  out2 = [0, 0, 0.0811, 0.0416, 0.0278, 0.0207, 0.0165, 0];
726s ***** assert (loglpdf ([-1,0,realmin,1:5,Inf], 0, 1), out1, 1e-4)
726s ***** assert (loglpdf ([-1,0,realmin,1:5,Inf], 0, 1), out1, 1e-4)
726s ***** assert (loglpdf ([-1:5,Inf], 1, 3), out2, 1e-4)
726s ***** assert (class (loglpdf (single (1), 2, 3)), "single")
726s ***** assert (class (loglpdf (1, single (2), 3)), "single")
726s ***** assert (class (loglpdf (1, 2, single (3))), "single")
726s ***** error<loglpdf: function called with too few input arguments.> loglpdf (1)
726s ***** error<loglpdf: function called with too few input arguments.> loglpdf (1, 2)
726s ***** error<loglpdf: X, MU, and SIGMA must be of common size or scalars.> ...
726s  loglpdf (1, ones (2), ones (3))
726s ***** error<loglpdf: X, MU, and SIGMA must be of common size or scalars.> ...
726s  loglpdf (ones (2), 1, ones (3))
726s ***** error<loglpdf: X, MU, and SIGMA must be of common size or scalars.> ...
726s  loglpdf (ones (2), ones (3), 1)
726s ***** error<loglpdf: X, MU, and SIGMA must not be complex.> loglpdf (i, 2, 3)
726s ***** error<loglpdf: X, MU, and SIGMA must not be complex.> loglpdf (1, i, 3)
726s ***** error<loglpdf: X, MU, and SIGMA must not be complex.> loglpdf (1, 2, i)
726s 14 tests, 14 passed, 0 known failure, 0 skipped
726s [inst/dist_fun/loglrnd.m]
726s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/loglrnd.m
726s ***** assert (size (loglrnd (1, 1)), [1 1])
726s ***** assert (size (loglrnd (1, ones (2,1))), [2, 1])
726s ***** assert (size (loglrnd (1, ones (2,2))), [2, 2])
726s ***** assert (size (loglrnd (ones (2,1), 1)), [2, 1])
726s ***** assert (size (loglrnd (ones (2,2), 1)), [2, 2])
726s ***** assert (size (loglrnd (1, 1, 3)), [3, 3])
726s ***** assert (size (loglrnd (1, 1, [4, 1])), [4, 1])
726s ***** assert (size (loglrnd (1, 1, 4, 1)), [4, 1])
726s ***** assert (size (loglrnd (1, 1, 4, 1, 5)), [4, 1, 5])
726s ***** assert (size (loglrnd (1, 1, 0, 1)), [0, 1])
726s ***** assert (size (loglrnd (1, 1, 1, 0)), [1, 0])
726s ***** assert (size (loglrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
726s ***** assert (class (loglrnd (1, 1)), "double")
726s ***** assert (class (loglrnd (1, single (1))), "single")
726s ***** assert (class (loglrnd (1, single ([1, 1]))), "single")
726s ***** assert (class (loglrnd (single (1), 1)), "single")
726s ***** assert (class (loglrnd (single ([1, 1]), 1)), "single")
726s ***** error<loglrnd: function called with too few input arguments.> loglrnd ()
726s ***** error<loglrnd: function called with too few input arguments.> loglrnd (1)
726s ***** error<loglrnd: MU and SIGMA must be of common size or scalars.> ...
726s  loglrnd (ones (3), ones (2))
726s ***** error<loglrnd: MU and SIGMA must be of common size or scalars.> ...
726s  loglrnd (ones (2), ones (3))
726s ***** error<loglrnd: MU and SIGMA must not be complex.> loglrnd (i, 2, 3)
726s ***** error<loglrnd: MU and SIGMA must not be complex.> loglrnd (1, i, 3)
726s ***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
726s  loglrnd (1, 2, -1)
726s ***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
726s  loglrnd (1, 2, 1.2)
726s ***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
726s  loglrnd (1, 2, ones (2))
726s ***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
726s  loglrnd (1, 2, [2 -1 2])
726s ***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
726s  loglrnd (1, 2, [2 0 2.5])
726s ***** error<loglrnd: dimensions must be non-negative integers.> ...
726s  loglrnd (1, 2, 2, -1, 5)
726s ***** error<loglrnd: dimensions must be non-negative integers.> ...
726s  loglrnd (1, 2, 2, 1.5, 5)
726s ***** error<loglrnd: MU and SIGMA must be scalars or of size SZ.> ...
726s  loglrnd (2, ones (2), 3)
726s ***** error<loglrnd: MU and SIGMA must be scalars or of size SZ.> ...
726s  loglrnd (2, ones (2), [3, 2])
726s ***** error<loglrnd: MU and SIGMA must be scalars or of size SZ.> ...
726s  loglrnd (2, ones (2), 3, 2)
726s 33 tests, 33 passed, 0 known failure, 0 skipped
726s [inst/dist_fun/logncdf.m]
726s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/logncdf.m
726s ***** demo
726s  ## Plot various CDFs from the log-normal distribution
726s  x = 0:0.01:3;
726s  p1 = logncdf (x, 0, 1);
726s  p2 = logncdf (x, 0, 0.5);
726s  p3 = logncdf (x, 0, 0.25);
726s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r")
726s  grid on
726s  legend ({"μ = 0, σ = 1", "μ = 0, σ = 0.5", "μ = 0, σ = 0.25"}, ...
726s          "location", "southeast")
726s  title ("Log-normal CDF")
726s  xlabel ("values in x")
726s  ylabel ("probability")
726s ***** shared x, y
726s  x = [-1, 0, 1, e, Inf];
726s  y = [0, 0, 0.5, 1/2+1/2*erf(1/2), 1];
726s ***** assert (logncdf (x, zeros (1,5), sqrt(2)*ones (1,5)), y, eps)
726s ***** assert (logncdf (x, zeros (1,5), sqrt(2)*ones (1,5), []), y, eps)
726s ***** assert (logncdf (x, 0, sqrt(2)*ones (1,5)), y, eps)
726s ***** assert (logncdf (x, zeros (1,5), sqrt(2)), y, eps)
726s ***** assert (logncdf (x, [0 1 NaN 0 1], sqrt(2)), [0 0 NaN y(4:5)], eps)
726s ***** assert (logncdf (x, 0, sqrt(2)*[0 NaN Inf 1 1]), [NaN NaN y(3:5)], eps)
726s ***** assert (logncdf ([x(1:3) NaN x(5)], 0, sqrt(2)), [y(1:3) NaN y(5)], eps)
726s ***** assert (logncdf ([x, NaN], 0, sqrt(2)), [y, NaN], eps)
726s ***** assert (logncdf (single ([x, NaN]), 0, sqrt(2)), single ([y, NaN]), eps ("single"))
726s ***** assert (logncdf ([x, NaN], single (0), sqrt(2)), single ([y, NaN]), eps ("single"))
726s ***** assert (logncdf ([x, NaN], 0, single (sqrt(2))), single ([y, NaN]), eps ("single"))
726s ***** error<logncdf: invalid number of input arguments.> logncdf ()
726s ***** error<logncdf: invalid number of input arguments.> logncdf (1,2,3,4,5,6,7)
726s ***** error<logncdf: invalid argument for upper tail.> logncdf (1, 2, 3, 4, "uper")
726s ***** error<logncdf: X, MU, and SIGMA must be of common size or scalars.> ...
726s  logncdf (ones (3), ones (2), ones (2))
726s ***** error<logncdf: invalid size of covariance matrix.> logncdf (2, 3, 4, [1, 2])
726s ***** error<logncdf: covariance matrix is required for confidence bounds.> ...
726s  [p, plo, pup] = logncdf (1, 2, 3)
726s ***** error<logncdf: invalid value for alpha.> [p, plo, pup] = ...
726s  logncdf (1, 2, 3, [1, 0; 0, 1], 0)
726s ***** error<logncdf: invalid value for alpha.> [p, plo, pup] = ...
726s  logncdf (1, 2, 3, [1, 0; 0, 1], 1.22)
726s ***** error<logncdf: invalid value for alpha.> [p, plo, pup] = ...
726s  logncdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
726s ***** error<logncdf: X, MU, and SIGMA must not be complex.> logncdf (i, 2, 2)
726s ***** error<logncdf: X, MU, and SIGMA must not be complex.> logncdf (2, i, 2)
726s ***** error<logncdf: X, MU, and SIGMA must not be complex.> logncdf (2, 2, i)
726s ***** error<logncdf: bad covariance matrix.> ...
726s  [p, plo, pup] =logncdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
726s 24 tests, 24 passed, 0 known failure, 0 skipped
726s [inst/dist_fun/logninv.m]
726s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/logninv.m
726s ***** demo
726s  ## Plot various iCDFs from the log-normal distribution
726s  p = 0.001:0.001:0.999;
726s  x1 = logninv (p, 0, 1);
726s  x2 = logninv (p, 0, 0.5);
726s  x3 = logninv (p, 0, 0.25);
726s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
726s  grid on
726s  ylim ([0, 3])
726s  legend ({"μ = 0, σ = 1", "μ = 0, σ = 0.5", "μ = 0, σ = 0.25"}, ...
726s          "location", "northwest")
726s  title ("Log-normal iCDF")
726s  xlabel ("probability")
726s  ylabel ("values in x")
726s ***** shared p
726s  p = [-1 0 0.5 1 2];
726s ***** assert (logninv (p, ones (1,5), ones (1,5)), [NaN 0 e Inf NaN])
726s ***** assert (logninv (p, 1, ones (1,5)), [NaN 0 e Inf NaN])
726s ***** assert (logninv (p, ones (1,5), 1), [NaN 0 e Inf NaN])
726s ***** assert (logninv (p, [1 1 NaN 0 1], 1), [NaN 0 NaN Inf NaN])
726s ***** assert (logninv (p, 1, [1 0 NaN Inf 1]), [NaN NaN NaN NaN NaN])
726s ***** assert (logninv ([p(1:2) NaN p(4:5)], 1, 2), [NaN 0 NaN Inf NaN])
726s ***** assert (logninv ([p, NaN], 1, 1), [NaN 0 e Inf NaN NaN])
726s ***** assert (logninv (single ([p, NaN]), 1, 1), single ([NaN 0 e Inf NaN NaN]))
726s ***** assert (logninv ([p, NaN], single (1), 1), single ([NaN 0 e Inf NaN NaN]))
726s ***** assert (logninv ([p, NaN], 1, single (1)), single ([NaN 0 e Inf NaN NaN]))
726s ***** error logninv ()
726s ***** error logninv (1,2,3,4)
726s ***** error logninv (ones (3), ones (2), ones (2))
726s ***** error logninv (ones (2), ones (3), ones (2))
726s ***** error logninv (ones (2), ones (2), ones (3))
726s ***** error logninv (i, 2, 2)
726s ***** error logninv (2, i, 2)
726s ***** error logninv (2, 2, i)
727s 18 tests, 18 passed, 0 known failure, 0 skipped
727s [inst/dist_fun/lognpdf.m]
727s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/lognpdf.m
727s ***** demo
727s  ## Plot various PDFs from the log-normal distribution
727s  x = 0:0.01:5;
727s  y1 = lognpdf (x, 0, 1);
727s  y2 = lognpdf (x, 0, 0.5);
727s  y3 = lognpdf (x, 0, 0.25);
727s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r")
727s  grid on
727s  ylim ([0, 2])
727s  legend ({"μ = 0, σ = 1", "μ = 0, σ = 0.5", "μ = 0, σ = 0.25"}, ...
727s           "location", "northeast")
727s  title ("Log-normal PDF")
727s  xlabel ("values in x")
727s  ylabel ("density")
727s ***** shared x, y
727s  x = [-1 0 e Inf];
727s  y = [0, 0, 1/(e*sqrt(2*pi)) * exp(-1/2), 0];
727s ***** assert (lognpdf (x, zeros (1,4), ones (1,4)), y, eps)
727s ***** assert (lognpdf (x, 0, ones (1,4)), y, eps)
727s ***** assert (lognpdf (x, zeros (1,4), 1), y, eps)
727s ***** assert (lognpdf (x, [0 1 NaN 0], 1), [0 0 NaN y(4)], eps)
727s ***** assert (lognpdf (x, 0, [0 NaN Inf 1]), [NaN NaN NaN y(4)], eps)
727s ***** assert (lognpdf ([x, NaN], 0, 1), [y, NaN], eps)
727s ***** assert (lognpdf (single ([x, NaN]), 0, 1), single ([y, NaN]), eps ("single"))
727s ***** assert (lognpdf ([x, NaN], single (0), 1), single ([y, NaN]), eps ("single"))
727s ***** assert (lognpdf ([x, NaN], 0, single (1)), single ([y, NaN]), eps ("single"))
727s ***** error lognpdf ()
727s ***** error lognpdf (1,2,3,4)
727s ***** error lognpdf (ones (3), ones (2), ones (2))
727s ***** error lognpdf (ones (2), ones (3), ones (2))
727s ***** error lognpdf (ones (2), ones (2), ones (3))
727s ***** error lognpdf (i, 2, 2)
727s ***** error lognpdf (2, i, 2)
727s ***** error lognpdf (2, 2, i)
727s 17 tests, 17 passed, 0 known failure, 0 skipped
727s [inst/dist_fun/lognrnd.m]
727s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/lognrnd.m
727s ***** assert (size (lognrnd (1, 1)), [1 1])
727s ***** assert (size (lognrnd (1, ones (2,1))), [2, 1])
727s ***** assert (size (lognrnd (1, ones (2,2))), [2, 2])
727s ***** assert (size (lognrnd (ones (2,1), 1)), [2, 1])
727s ***** assert (size (lognrnd (ones (2,2), 1)), [2, 2])
727s ***** assert (size (lognrnd (1, 1, 3)), [3, 3])
727s ***** assert (size (lognrnd (1, 1, [4, 1])), [4, 1])
727s ***** assert (size (lognrnd (1, 1, 4, 1)), [4, 1])
727s ***** assert (size (lognrnd (1, 1, 4, 1, 5)), [4, 1, 5])
727s ***** assert (size (lognrnd (1, 1, 0, 1)), [0, 1])
727s ***** assert (size (lognrnd (1, 1, 1, 0)), [1, 0])
727s ***** assert (size (lognrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
727s ***** assert (class (lognrnd (1, 1)), "double")
727s ***** assert (class (lognrnd (1, single (1))), "single")
727s ***** assert (class (lognrnd (1, single ([1, 1]))), "single")
727s ***** assert (class (lognrnd (single (1), 1)), "single")
727s ***** assert (class (lognrnd (single ([1, 1]), 1)), "single")
727s ***** error<lognrnd: function called with too few input arguments.> lognrnd ()
727s ***** error<lognrnd: function called with too few input arguments.> lognrnd (1)
727s ***** error<lognrnd: MU and SIGMA must be of common size or scalars.> ...
727s  lognrnd (ones (3), ones (2))
727s ***** error<lognrnd: MU and SIGMA must be of common size or scalars.> ...
727s  lognrnd (ones (2), ones (3))
727s ***** error<lognrnd: MU and SIGMA must not be complex.> lognrnd (i, 2, 3)
727s ***** error<lognrnd: MU and SIGMA must not be complex.> lognrnd (1, i, 3)
727s ***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
727s  lognrnd (1, 2, -1)
727s ***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
727s  lognrnd (1, 2, 1.2)
727s ***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
727s  lognrnd (1, 2, ones (2))
727s ***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
727s  lognrnd (1, 2, [2 -1 2])
727s ***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
727s  lognrnd (1, 2, [2 0 2.5])
727s ***** error<lognrnd: dimensions must be non-negative integers.> ...
727s  lognrnd (1, 2, 2, -1, 5)
727s ***** error<lognrnd: dimensions must be non-negative integers.> ...
727s  lognrnd (1, 2, 2, 1.5, 5)
727s ***** error<lognrnd: MU and SIGMA must be scalars or of size SZ.> ...
727s  lognrnd (2, ones (2), 3)
727s ***** error<lognrnd: MU and SIGMA must be scalars or of size SZ.> ...
727s  lognrnd (2, ones (2), [3, 2])
727s ***** error<lognrnd: MU and SIGMA must be scalars or of size SZ.> ...
727s  lognrnd (2, ones (2), 3, 2)
727s 33 tests, 33 passed, 0 known failure, 0 skipped
727s [inst/dist_fun/mnpdf.m]
727s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/mnpdf.m
727s ***** test
727s  x = [1, 4, 2];
727s  pk = [0.2, 0.5, 0.3];
727s  y = mnpdf (x, pk);
727s  assert (y, 0.11812, 0.001);
727s ***** test
727s  x = [1, 4, 2; 1, 0, 9];
727s  pk = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8];
727s  y = mnpdf (x, pk);
727s  assert (y, [0.11812; 0.13422], 0.001);
727s 2 tests, 2 passed, 0 known failure, 0 skipped
727s [inst/dist_fun/mnrnd.m]
727s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/mnrnd.m
727s ***** test
727s  n = 10;
727s  pk = [0.2, 0.5, 0.3];
727s  r = mnrnd (n, pk);
727s  assert (size (r), size (pk));
727s  assert (all (r >= 0));
727s  assert (all (round (r) == r));
727s  assert (sum (r) == n);
727s ***** test
727s  n = 10 * ones (3, 1);
727s  pk = [0.2, 0.5, 0.3];
727s  r = mnrnd (n, pk);
727s  assert (size (r), [length(n), length(pk)]);
727s  assert (all (r >= 0));
727s  assert (all (round (r) == r));
727s  assert (all (sum (r, 2) == n));
727s ***** test
727s  n = (1:2)';
727s  pk = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8];
727s  r = mnrnd (n, pk);
727s  assert (size (r), size (pk));
727s  assert (all (r >= 0));
727s  assert (all (round (r) == r));
727s  assert (all (sum (r, 2) == n));
727s 3 tests, 3 passed, 0 known failure, 0 skipped
727s [inst/dist_fun/mvncdf.m]
727s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/mvncdf.m
727s ***** demo
727s  mu = [1, -1];
727s  Sigma = [0.9, 0.4; 0.4, 0.3];
727s  [X1, X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
727s  X = [X1(:), X2(:)];
727s  p = mvncdf (X, mu, Sigma);
727s  Z = reshape (p, 25, 25);
727s  surf (X1, X2, Z);
727s  title ("Bivariate Normal Distribution");
727s  ylabel "X1"
727s  xlabel "X2"
727s ***** demo
727s  mu = [0, 0];
727s  Sigma = [0.25, 0.3; 0.3, 1];
727s  p = mvncdf ([0 0], [1 1], mu, Sigma);
727s  x1 = -3:.2:3;
727s  x2 = -3:.2:3;
727s  [X1, X2] = meshgrid (x1, x2);
727s  X = [X1(:), X2(:)];
727s  p = mvnpdf (X, mu, Sigma);
727s  p = reshape (p, length (x2), length (x1));
727s  contour (x1, x2, p, [0.0001, 0.001, 0.01, 0.05, 0.15, 0.25, 0.35]);
727s  xlabel ("x");
727s  ylabel ("p");
727s  title ("Probability over Rectangular Region");
727s  line ([0, 0, 1, 1, 0], [1, 0, 0, 1, 1], "Linestyle", "--", "Color", "k");
727s ***** test
727s  fD = (-2:2)';
727s  X = repmat (fD, 1, 4);
727s  p = mvncdf (X);
727s  assert (p, [0; 0.0006; 0.0625; 0.5011; 0.9121], ones (5, 1) * 1e-4);
727s ***** test
727s  mu = [1, -1];
727s  Sigma = [0.9, 0.4; 0.4, 0.3];
727s  [X1,X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
727s  X = [X1(:), X2(:)];
727s  p = mvncdf (X, mu, Sigma);
727s  p_out = [0.00011878988774500, 0.00034404112322371, ...
727s           0.00087682502191813, 0.00195221905058185, ...
727s           0.00378235566873474, 0.00638175749734415, ...
727s           0.00943764224329656, 0.01239164888125426, ...
727s           0.01472750274376648, 0.01623228313374828]';
727s  assert (p([1:10]), p_out, 1e-16);
727s ***** test
727s  mu = [1, -1];
727s  Sigma = [0.9, 0.4; 0.4, 0.3];
727s  [X1,X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
727s  X = [X1(:), X2(:)];
727s  p = mvncdf (X, mu, Sigma);
727s  p_out = [0.8180695783608276, 0.8854485749482751, ...
727s           0.9308108777385832, 0.9579855743025508, ...
727s           0.9722897881414742, 0.9788150170059926, ...
727s           0.9813597788804785, 0.9821977956568989, ...
727s           0.9824283794464095, 0.9824809345614861]';
727s  assert (p([616:625]), p_out, 3e-16);
727s ***** test
727s  mu = [0, 0];
727s  Sigma = [0.25, 0.3; 0.3, 1];
727s  [p, err] = mvncdf ([0, 0], [1, 1], mu, Sigma);
727s  assert (p, 0.2097424404755626, 1e-16);
727s  assert (err, 1e-08);
727s ***** test
727s  x = [1 2];
727s  mu = [0.5 1.5];
727s  sigma = [1.0, 0.5; 0.5, 1.0];
727s  p = mvncdf (x, mu, sigma);
727s  assert (p, 0.546244443857090, 1e-15);
727s ***** test
727s  x = [1 2];
727s  mu = [0.5 1.5];
727s  sigma = [1.0, 0.5; 0.5, 1.0];
727s  a = [-inf 0];
727s  p = mvncdf (a, x, mu, sigma);
727s  assert (p, 0.482672935215631, 1e-15);
727s ***** error p = mvncdf (randn (25,26), [], eye (26));
727s ***** error p = mvncdf (randn (25,8), [], eye (9));
727s ***** error p = mvncdf (randn (25,4), randn (25,5), [], eye (4));
727s ***** error p = mvncdf (randn (25,4), randn (25,4), [2, 3; 2, 3], eye (4));
727s ***** error p = mvncdf (randn (25,4), randn (25,4), ones (1, 5), eye (4));
727s ***** error p = mvncdf ([-inf, 0], [1, 2], [0.5, 1.5], [1.0, 0.5; 0.5, 1.0], option)
727s 12 tests, 12 passed, 0 known failure, 0 skipped
727s [inst/dist_fun/mvnpdf.m]
727s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/mvnpdf.m
727s ***** demo
727s  mu = [1, -1];
727s  sigma = [0.9, 0.4; 0.4, 0.3];
727s  [X1, X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
727s  x = [X1(:), X2(:)];
727s  p = mvnpdf (x, mu, sigma);
727s  surf (X1, X2, reshape (p, 25, 25));
727s ***** error<mvnpdf: too few input arguments.> y = mvnpdf ();
727s ***** error<mvnpdf: too few dimensions in X.> y = mvnpdf ([]);
727s ***** error<mvnpdf: wrong dimensions in X.> y = mvnpdf (ones (3,3,3));
727s ***** error<mvnpdf: columns in X and MU mismatch.> ...
727s  y = mvnpdf (ones (10, 2), [4, 2, 3]);
727s ***** error<mvnpdf: rows in X and MU mismatch.> ...
727s  y = mvnpdf (ones (10, 2), [4, 2; 3, 2]);
727s ***** error<mvnpdf: wrong size of MU.> ...
727s  y = mvnpdf (ones (10, 2), ones (3, 3, 3));
727s ***** shared x, mu, sigma
727s  x = [1, 2, 5, 4, 6];
727s  mu = [2, 0, -1, 1, 4];
727s  sigma = [2, 2, 2, 2, 2];
727s ***** assert (mvnpdf (x), 1.579343404440977e-20, 1e-30);
727s ***** assert (mvnpdf (x, mu), 1.899325144348102e-14, 1e-25);
727s ***** assert (mvnpdf (x, mu, sigma), 2.449062307156273e-09, 1e-20);
727s 9 tests, 9 passed, 0 known failure, 0 skipped
727s [inst/dist_fun/mvnrnd.m]
727s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/mvnrnd.m
727s ***** error<mvnrnd: too few input arguments.> mvnrnd ()
727s ***** error<mvnrnd: too few input arguments.> mvnrnd ([2, 3, 4])
727s ***** error<mvnrnd: wrong size of MU.> mvnrnd (ones (2, 2, 2), ones (1, 2, 3, 4))
727s ***** error<mvnrnd: wrong size of SIGMA.> mvnrnd (ones (1, 3), ones (1, 2, 3, 4))
727s ***** assert (size (mvnrnd ([2, 3, 4], [2, 2, 2])), [1, 3])
727s ***** assert (size (mvnrnd ([2, 3, 4], [2, 2, 2], 10)), [10, 3])
727s 6 tests, 6 passed, 0 known failure, 0 skipped
727s [inst/dist_fun/mvtcdf.m]
727s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/mvtcdf.m
727s ***** demo
727s  ## Compute the cdf of a multivariate Student's t distribution with
727s  ## correlation parameters rho = [1, 0.4; 0.4, 1] and 2 degrees of freedom.
727s 
727s  rho = [1, 0.4; 0.4, 1];
727s  df = 2;
727s  [X1, X2] = meshgrid (linspace (-2, 2, 25)', linspace (-2, 2, 25)');
727s  X = [X1(:), X2(:)];
727s  p = mvtcdf (X, rho, df);
727s  surf (X1, X2, reshape (p, 25, 25));
727s  title ("Bivariate Student's t cummulative distribution function");
727s ***** test
727s  x = [1, 2];
727s  rho = [1, 0.5; 0.5, 1];
727s  df = 4;
727s  a = [-1, 0];
727s  assert (mvtcdf(a, x, rho, df), 0.294196905339283, 1e-14);
727s ***** test
727s  x = [1, 2;2, 4;1, 5];
727s  rho = [1, 0.5; 0.5, 1];
727s  df = 4;
727s  p =[0.790285178602166; 0.938703291727784; 0.81222737321336];
727s  assert (mvtcdf(x, rho, df), p, 1e-14);
727s ***** test
727s  x = [1, 2, 2, 4, 1, 5];
727s  rho = eye (6);
727s  rho(rho == 0) = 0.5;
727s  df = 4;
727s  assert (mvtcdf(x, rho, df), 0.6874, 1e-4);
742s ***** error mvtcdf (1)
742s ***** error mvtcdf (1, 2)
742s ***** error<mvtcdf: correlation matrix RHO does not match dimensions in data.> ...
742s  mvtcdf (1, [2, 3; 3, 2], 1)
742s ***** error<mvtcdf: correlation matrix RHO does not match dimensions in data.> ...
742s  mvtcdf ([2, 3, 4], ones (2), 1)
742s ***** error<mvtcdf: X_LO and X_UP must be of the same size.> ...
742s  mvtcdf ([1, 2, 3], [2, 3], ones (2), 1)
742s ***** error<mvtcdf: correlation matrix RHO must be positive semi-definite.> ...
742s  mvtcdf ([2, 3], ones (2), [1, 2, 3])
742s ***** error<mvtcdf: DF must be a scalar or a vector with the same samples as in> ...
742s  mvtcdf ([2, 3], [1, 0.5; 0.5, 1], [1, 2, 3])
742s 10 tests, 10 passed, 0 known failure, 0 skipped
742s [inst/dist_fun/mvtcdfqmc.m]
742s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/mvtcdfqmc.m
742s ***** error mvtcdfqmc (1, 2, 3);
742s ***** error mvtcdfqmc (1, 2, 3, 4, 5, 6, 7, 8);
742s 2 tests, 2 passed, 0 known failure, 0 skipped
742s [inst/dist_fun/mvtpdf.m]
742s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/mvtpdf.m
742s ***** demo
742s  ## Compute the pdf of a multivariate t distribution with correlation
742s  ## parameters rho = [1 .4; .4 1] and 2 degrees of freedom.
742s 
742s  rho = [1, 0.4; 0.4, 1];
742s  df = 2;
742s  [X1, X2] = meshgrid (linspace (-2, 2, 25)', linspace (-2, 2, 25)');
742s  X = [X1(:), X2(:)];
742s  y = mvtpdf (X, rho, df);
742s  surf (X1, X2, reshape (y, 25, 25));
742s  title ("Bivariate Student's t probability density function");
742s ***** assert (mvtpdf ([0 0], eye(2), 1), 0.1591549, 1E-7)
742s ***** assert (mvtpdf ([1 0], [1 0.5; 0.5 1], 2), 0.06615947, 1E-7)
742s ***** assert (mvtpdf ([1 0.4 0; 1.2 0.5 0.5; 1.4 0.6 1], ...
742s  [1 0.5 0.3; 0.5 1 0.6; 0.3 0.6 1], [5 6 7]), ...
742s  [0.04713313 0.03722421 0.02069011]', 1E-7)
742s 3 tests, 3 passed, 0 known failure, 0 skipped
742s [inst/dist_fun/mvtrnd.m]
742s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/mvtrnd.m
742s ***** test
742s  rho = [1, 0.5; 0.5, 1];
742s  df = 3;
742s  n = 10;
742s  r = mvtrnd (rho, df, n);
742s  assert (size (r), [10, 2]);
742s ***** test
742s  rho = [1, 0.5; 0.5, 1];
742s  df = [2; 3];
742s  n = 2;
742s  r = mvtrnd (rho, df, 2);
742s  assert (size (r), [2, 2]);
742s 2 tests, 2 passed, 0 known failure, 0 skipped
742s [inst/dist_fun/nakacdf.m]
742s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/nakacdf.m
742s ***** demo
742s  ## Plot various CDFs from the Nakagami distribution
742s  x = 0:0.01:3;
742s  p1 = nakacdf (x, 0.5, 1);
742s  p2 = nakacdf (x, 1, 1);
742s  p3 = nakacdf (x, 1, 2);
742s  p4 = nakacdf (x, 1, 3);
742s  p5 = nakacdf (x, 2, 1);
742s  p6 = nakacdf (x, 2, 2);
742s  p7 = nakacdf (x, 5, 1);
742s  plot (x, p1, "-r", x, p2, "-g", x, p3, "-y", x, p4, "-m", ...
742s        x, p5, "-k", x, p6, "-b", x, p7, "-c")
742s  grid on
742s  xlim ([0, 3])
742s  legend ({"μ = 0.5, ω = 1", "μ = 1, ω = 1", "μ = 1, ω = 2", ...
742s           "μ = 1, ω = 3", "μ = 2, ω = 1", "μ = 2, ω = 2", ...
742s           "μ = 5, ω = 1"}, "location", "southeast")
742s  title ("Nakagami CDF")
742s  xlabel ("values in x")
742s  ylabel ("probability")
742s ***** shared x, y
742s  x = [-1, 0, 1, 2, Inf];
742s  y = [0, 0, 0.63212055882855778, 0.98168436111126578, 1];
742s ***** assert (nakacdf (x, ones (1,5), ones (1,5)), y, eps)
742s ***** assert (nakacdf (x, 1, 1), y, eps)
742s ***** assert (nakacdf (x, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)])
742s ***** assert (nakacdf (x, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)])
742s ***** assert (nakacdf ([x, NaN], 1, 1), [y, NaN], eps)
742s ***** assert (nakacdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps("single"))
742s ***** assert (nakacdf ([x, NaN], single (1), 1), single ([y, NaN]), eps("single"))
742s ***** assert (nakacdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps("single"))
742s ***** error<nakacdf: function called with too few input arguments.> nakacdf ()
742s ***** error<nakacdf: function called with too few input arguments.> nakacdf (1)
742s ***** error<nakacdf: function called with too few input arguments.> nakacdf (1, 2)
742s ***** error<nakacdf: invalid argument for upper tail.> nakacdf (1, 2, 3, "tail")
742s ***** error<nakacdf: invalid argument for upper tail.> nakacdf (1, 2, 3, 4)
742s ***** error<nakacdf: X, MU, and OMEGA must be of common size or scalars.> ...
742s  nakacdf (ones (3), ones (2), ones (2))
742s ***** error<nakacdf: X, MU, and OMEGA must be of common size or scalars.> ...
742s  nakacdf (ones (2), ones (3), ones (2))
742s ***** error<nakacdf: X, MU, and OMEGA must be of common size or scalars.> ...
742s  nakacdf (ones (2), ones (2), ones (3))
742s ***** error<nakacdf: X, MU, and OMEGA must not be complex.> nakacdf (i, 2, 2)
742s ***** error<nakacdf: X, MU, and OMEGA must not be complex.> nakacdf (2, i, 2)
742s ***** error<nakacdf: X, MU, and OMEGA must not be complex.> nakacdf (2, 2, i)
742s 19 tests, 19 passed, 0 known failure, 0 skipped
742s [inst/dist_fun/nakainv.m]
742s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/nakainv.m
742s ***** demo
742s  ## Plot various iCDFs from the Nakagami distribution
742s  p = 0.001:0.001:0.999;
742s  x1 = nakainv (p, 0.5, 1);
742s  x2 = nakainv (p, 1, 1);
742s  x3 = nakainv (p, 1, 2);
742s  x4 = nakainv (p, 1, 3);
742s  x5 = nakainv (p, 2, 1);
742s  x6 = nakainv (p, 2, 2);
742s  x7 = nakainv (p, 5, 1);
742s  plot (p, x1, "-r", p, x2, "-g", p, x3, "-y", p, x4, "-m", ...
742s        p, x5, "-k", p, x6, "-b", p, x7, "-c")
742s  grid on
742s  ylim ([0, 3])
742s  legend ({"μ = 0.5, ω = 1", "μ = 1, ω = 1", "μ = 1, ω = 2", ...
742s           "μ = 1, ω = 3", "μ = 2, ω = 1", "μ = 2, ω = 2", ...
742s           "μ = 5, ω = 1"}, "location", "northwest")
742s  title ("Nakagami iCDF")
742s  xlabel ("probability")
742s  ylabel ("values in x")
742s ***** shared p, y
742s  p = [-Inf, -1, 0, 1/2, 1, 2, Inf];
742s  y = [NaN, NaN, 0, 0.83255461115769769, Inf, NaN, NaN];
742s ***** assert (nakainv (p, ones (1,7), ones (1,7)), y, eps)
742s ***** assert (nakainv (p, 1, 1), y, eps)
742s ***** assert (nakainv (p, [1, 1, 1, NaN, 1, 1, 1], 1), [y(1:3), NaN, y(5:7)], eps)
742s ***** assert (nakainv (p, 1, [1, 1, 1, NaN, 1, 1, 1]), [y(1:3), NaN, y(5:7)], eps)
742s ***** assert (nakainv ([p, NaN], 1, 1), [y, NaN], eps)
742s ***** assert (nakainv (single ([p, NaN]), 1, 1), single ([y, NaN]))
742s ***** assert (nakainv ([p, NaN], single (1), 1), single ([y, NaN]))
742s ***** assert (nakainv ([p, NaN], 1, single (1)), single ([y, NaN]))
742s ***** error<nakainv: function called with too few input arguments.> nakainv ()
742s ***** error<nakainv: function called with too few input arguments.> nakainv (1)
742s ***** error<nakainv: function called with too few input arguments.> nakainv (1, 2)
742s ***** error<nakainv: P, MU, and OMEGA must be of common size or scalars.> ...
742s  nakainv (ones (3), ones (2), ones(2))
742s ***** error<nakainv: P, MU, and OMEGA must be of common size or scalars.> ...
742s  nakainv (ones (2), ones (3), ones(2))
742s ***** error<nakainv: P, MU, and OMEGA must be of common size or scalars.> ...
742s  nakainv (ones (2), ones (2), ones(3))
742s ***** error<nakainv: P, MU, and OMEGA must not be complex.> nakainv (i, 4, 3)
742s ***** error<nakainv: P, MU, and OMEGA must not be complex.> nakainv (1, i, 3)
742s ***** error<nakainv: P, MU, and OMEGA must not be complex.> nakainv (1, 4, i)
742s 17 tests, 17 passed, 0 known failure, 0 skipped
742s [inst/dist_fun/nakapdf.m]
742s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/nakapdf.m
742s ***** demo
742s  ## Plot various PDFs from the Nakagami distribution
742s  x = 0:0.01:3;
742s  y1 = nakapdf (x, 0.5, 1);
742s  y2 = nakapdf (x, 1, 1);
742s  y3 = nakapdf (x, 1, 2);
742s  y4 = nakapdf (x, 1, 3);
742s  y5 = nakapdf (x, 2, 1);
742s  y6 = nakapdf (x, 2, 2);
742s  y7 = nakapdf (x, 5, 1);
742s  plot (x, y1, "-r", x, y2, "-g", x, y3, "-y", x, y4, "-m", ...
742s        x, y5, "-k", x, y6, "-b", x, y7, "-c")
742s  grid on
742s  xlim ([0, 3])
742s  ylim ([0, 2])
742s  legend ({"μ = 0.5, ω = 1", "μ = 1, ω = 1", "μ = 1, ω = 2", ...
742s           "μ = 1, ω = 3", "μ = 2, ω = 1", "μ = 2, ω = 2", ...
742s           "μ = 5, ω = 1"}, "location", "northeast")
742s  title ("Nakagami PDF")
742s  xlabel ("values in x")
742s  ylabel ("density")
742s ***** shared x, y
742s  x = [-1, 0, 1, 2, Inf];
742s  y = [0, 0, 0.73575888234288467, 0.073262555554936715, 0];
742s ***** assert (nakapdf (x, ones (1,5), ones (1,5)), y, eps)
742s ***** assert (nakapdf (x, 1, 1), y, eps)
742s ***** assert (nakapdf (x, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)], eps)
742s ***** assert (nakapdf (x, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)], eps)
742s ***** assert (nakapdf ([x, NaN], 1, 1), [y, NaN], eps)
742s ***** assert (nakapdf (single ([x, NaN]), 1, 1), single ([y, NaN]))
742s ***** assert (nakapdf ([x, NaN], single (1), 1), single ([y, NaN]))
742s ***** assert (nakapdf ([x, NaN], 1, single (1)), single ([y, NaN]))
742s ***** error<nakapdf: function called with too few input arguments.> nakapdf ()
742s ***** error<nakapdf: function called with too few input arguments.> nakapdf (1)
742s ***** error<nakapdf: function called with too few input arguments.> nakapdf (1, 2)
742s ***** error<nakapdf: X, MU, and OMEGA must be of common size or scalars.> ...
742s  nakapdf (ones (3), ones (2), ones(2))
742s ***** error<nakapdf: X, MU, and OMEGA must be of common size or scalars.> ...
742s  nakapdf (ones (2), ones (3), ones(2))
742s ***** error<nakapdf: X, MU, and OMEGA must be of common size or scalars.> ...
742s  nakapdf (ones (2), ones (2), ones(3))
742s ***** error<nakapdf: X, MU, and OMEGA must not be complex.> nakapdf (i, 4, 3)
742s ***** error<nakapdf: X, MU, and OMEGA must not be complex.> nakapdf (1, i, 3)
742s ***** error<nakapdf: X, MU, and OMEGA must not be complex.> nakapdf (1, 4, i)
742s 17 tests, 17 passed, 0 known failure, 0 skipped
742s [inst/dist_fun/nakarnd.m]
742s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/nakarnd.m
742s ***** assert (size (nakarnd (1, 1)), [1 1])
742s ***** assert (size (nakarnd (1, ones (2,1))), [2, 1])
742s ***** assert (size (nakarnd (1, ones (2,2))), [2, 2])
742s ***** assert (size (nakarnd (ones (2,1), 1)), [2, 1])
742s ***** assert (size (nakarnd (ones (2,2), 1)), [2, 2])
742s ***** assert (size (nakarnd (1, 1, 3)), [3, 3])
742s ***** assert (size (nakarnd (1, 1, [4, 1])), [4, 1])
742s ***** assert (size (nakarnd (1, 1, 4, 1)), [4, 1])
742s ***** assert (size (nakarnd (1, 1, 4, 1, 5)), [4, 1, 5])
742s ***** assert (size (nakarnd (1, 1, 0, 1)), [0, 1])
742s ***** assert (size (nakarnd (1, 1, 1, 0)), [1, 0])
742s ***** assert (size (nakarnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
742s ***** assert (class (nakarnd (1, 1)), "double")
742s ***** assert (class (nakarnd (1, single (1))), "single")
742s ***** assert (class (nakarnd (1, single ([1, 1]))), "single")
742s ***** assert (class (nakarnd (single (1), 1)), "single")
742s ***** assert (class (nakarnd (single ([1, 1]), 1)), "single")
742s ***** error<nakarnd: function called with too few input arguments.> nakarnd ()
742s ***** error<nakarnd: function called with too few input arguments.> nakarnd (1)
742s ***** error<nakarnd: MU and OMEGA must be of common size or scalars.> ...
742s  nakarnd (ones (3), ones (2))
742s ***** error<nakarnd: MU and OMEGA must be of common size or scalars.> ...
742s  nakarnd (ones (2), ones (3))
742s ***** error<nakarnd: MU and OMEGA must not be complex.> nakarnd (i, 2, 3)
742s ***** error<nakarnd: MU and OMEGA must not be complex.> nakarnd (1, i, 3)
742s ***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
742s  nakarnd (1, 2, -1)
742s ***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
742s  nakarnd (1, 2, 1.2)
742s ***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
742s  nakarnd (1, 2, ones (2))
742s ***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
742s  nakarnd (1, 2, [2 -1 2])
742s ***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
742s  nakarnd (1, 2, [2 0 2.5])
742s ***** error<nakarnd: dimensions must be non-negative integers.> ...
742s  nakarnd (1, 2, 2, -1, 5)
742s ***** error<nakarnd: dimensions must be non-negative integers.> ...
742s  nakarnd (1, 2, 2, 1.5, 5)
742s ***** error<nakarnd: MU and OMEGA must be scalars or of size SZ.> ...
742s  nakarnd (2, ones (2), 3)
742s ***** error<nakarnd: MU and OMEGA must be scalars or of size SZ.> ...
742s  nakarnd (2, ones (2), [3, 2])
742s ***** error<nakarnd: MU and OMEGA must be scalars or of size SZ.> ...
742s  nakarnd (2, ones (2), 3, 2)
742s 33 tests, 33 passed, 0 known failure, 0 skipped
742s [inst/dist_fun/nbincdf.m]
742s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/nbincdf.m
742s ***** demo
742s  ## Plot various CDFs from the negative binomial distribution
742s  x = 0:50;
742s  p1 = nbincdf (x, 2, 0.15);
742s  p2 = nbincdf (x, 5, 0.2);
742s  p3 = nbincdf (x, 4, 0.4);
742s  p4 = nbincdf (x, 10, 0.3);
742s  plot (x, p1, "*r", x, p2, "*g", x, p3, "*k", x, p4, "*m")
742s  grid on
742s  xlim ([0, 40])
742s  legend ({"r = 2, ps = 0.15", "r = 5, ps = 0.2", "r = 4, p = 0.4", ...
742s           "r = 10, ps = 0.3"}, "location", "southeast")
742s  title ("Negative binomial CDF")
742s  xlabel ("values in x (number of failures)")
742s  ylabel ("probability")
742s ***** shared x, y
742s  x = [-1 0 1 2 Inf];
742s  y = [0 1/2 3/4 7/8 1];
742s ***** assert (nbincdf (x, ones (1,5), 0.5*ones (1,5)), y)
742s ***** assert (nbincdf (x, 1, 0.5*ones (1,5)), y)
742s ***** assert (nbincdf (x, ones (1,5), 0.5), y)
742s ***** assert (nbincdf (x, ones (1,5), 0.5, "upper"), 1 - y, eps)
742s ***** assert (nbincdf ([x(1:3) 0 x(5)], [0 1 NaN 1.5 Inf], 0.5), ...
742s  [NaN 1/2 NaN nbinpdf(0,1.5,0.5) NaN], eps)
742s ***** assert (nbincdf (x, 1, 0.5*[-1 NaN 4 1 1]), [NaN NaN NaN y(4:5)])
742s ***** assert (nbincdf ([x(1:2) NaN x(4:5)], 1, 0.5), [y(1:2) NaN y(4:5)])
742s ***** assert (nbincdf ([x, NaN], 1, 0.5), [y, NaN])
742s ***** assert (nbincdf (single ([x, NaN]), 1, 0.5), single ([y, NaN]))
742s ***** assert (nbincdf ([x, NaN], single (1), 0.5), single ([y, NaN]))
742s ***** assert (nbincdf ([x, NaN], 1, single (0.5)), single ([y, NaN]))
742s ***** error<nbincdf: function called with too few input arguments.> nbincdf ()
742s ***** error<nbincdf: function called with too few input arguments.> nbincdf (1)
742s ***** error<nbincdf: function called with too few input arguments.> nbincdf (1, 2)
742s ***** error<nbincdf: invalid argument for upper tail.> nbincdf (1, 2, 3, 4)
742s ***** error<nbincdf: invalid argument for upper tail.> nbincdf (1, 2, 3, "some")
742s ***** error<nbincdf: X, R, and PS must be of common size or scalars.> ...
742s  nbincdf (ones (3), ones (2), ones (2))
742s ***** error<nbincdf: X, R, and PS must be of common size or scalars.> ...
742s  nbincdf (ones (2), ones (3), ones (2))
742s ***** error<nbincdf: X, R, and PS must be of common size or scalars.> ...
742s  nbincdf (ones (2), ones (2), ones (3))
742s ***** error<nbincdf: X, R, and PS must not be complex.> nbincdf (i, 2, 2)
742s ***** error<nbincdf: X, R, and PS must not be complex.> nbincdf (2, i, 2)
742s ***** error<nbincdf: X, R, and PS must not be complex.> nbincdf (2, 2, i)
742s 22 tests, 22 passed, 0 known failure, 0 skipped
742s [inst/dist_fun/nbininv.m]
742s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/nbininv.m
742s ***** demo
742s  ## Plot various iCDFs from the negative binomial distribution
742s  p = 0.001:0.001:0.999;
742s  x1 = nbininv (p, 2, 0.15);
742s  x2 = nbininv (p, 5, 0.2);
742s  x3 = nbininv (p, 4, 0.4);
742s  x4 = nbininv (p, 10, 0.3);
742s  plot (p, x1, "-r", p, x2, "-g", p, x3, "-k", p, x4, "-m")
742s  grid on
742s  ylim ([0, 40])
742s  legend ({"r = 2, ps = 0.15", "r = 5, ps = 0.2", "r = 4, p = 0.4", ...
742s           "r = 10, ps = 0.3"}, "location", "northwest")
742s  title ("Negative binomial iCDF")
742s  xlabel ("probability")
742s  ylabel ("values in x (number of failures)")
742s ***** shared p
742s  p = [-1 0 3/4 1 2];
742s ***** assert (nbininv (p, ones (1,5), 0.5*ones (1,5)), [NaN 0 1 Inf NaN])
742s ***** assert (nbininv (p, 1, 0.5*ones (1,5)), [NaN 0 1 Inf NaN])
742s ***** assert (nbininv (p, ones (1,5), 0.5), [NaN 0 1 Inf NaN])
742s ***** assert (nbininv (p, [1 0 NaN Inf 1], 0.5), [NaN NaN NaN NaN NaN])
742s ***** assert (nbininv (p, [1 0 1.5 Inf 1], 0.5), [NaN NaN 2 NaN NaN])
742s ***** assert (nbininv (p, 1, 0.5*[1 -Inf NaN Inf 1]), [NaN NaN NaN NaN NaN])
742s ***** assert (nbininv ([p(1:2) NaN p(4:5)], 1, 0.5), [NaN 0 NaN Inf NaN])
742s ***** assert (nbininv ([p, NaN], 1, 0.5), [NaN 0 1 Inf NaN NaN])
743s ***** assert (nbininv (single ([p, NaN]), 1, 0.5), single ([NaN 0 1 Inf NaN NaN]))
743s ***** assert (nbininv ([p, NaN], single (1), 0.5), single ([NaN 0 1 Inf NaN NaN]))
743s ***** assert (nbininv ([p, NaN], 1, single (0.5)), single ([NaN 0 1 Inf NaN NaN]))
743s ***** shared y, tol
743s  y = magic (3) + 1;
743s  tol = 1;
743s ***** assert (nbininv (nbincdf (1:10, 3, 0.1), 3, 0.1), 1:10, tol)
743s ***** assert (nbininv (nbincdf (1:10, 3./(1:10), 0.1), 3./(1:10), 0.1), 1:10, tol)
743s ***** assert (nbininv (nbincdf (y, 3./y, 1./y), 3./y, 1./y), y, tol)
743s ***** error<nbininv: function called with too few input arguments.> nbininv ()
743s ***** error<nbininv: function called with too few input arguments.> nbininv (1)
743s ***** error<nbininv: function called with too few input arguments.> nbininv (1, 2)
743s ***** error<nbininv: P, R, and PS must be of common size or scalars.> ...
743s  nbininv (ones (3), ones (2), ones (2))
743s ***** error<nbininv: P, R, and PS must be of common size or scalars.> ...
743s  nbininv (ones (2), ones (3), ones (2))
743s ***** error<nbininv: P, R, and PS must be of common size or scalars.> ...
743s  nbininv (ones (2), ones (2), ones (3))
743s ***** error<nbininv: P, R, and PS must not be complex.> nbininv (i, 2, 2)
743s ***** error<nbininv: P, R, and PS must not be complex.> nbininv (2, i, 2)
743s ***** error<nbininv: P, R, and PS must not be complex.> nbininv (2, 2, i)
743s 23 tests, 23 passed, 0 known failure, 0 skipped
743s [inst/dist_fun/nbinpdf.m]
743s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/nbinpdf.m
743s ***** demo
743s  ## Plot various PDFs from the negative binomial distribution
743s  x = 0:40;
743s  y1 = nbinpdf (x, 2, 0.15);
743s  y2 = nbinpdf (x, 5, 0.2);
743s  y3 = nbinpdf (x, 4, 0.4);
743s  y4 = nbinpdf (x, 10, 0.3);
743s  plot (x, y1, "*r", x, y2, "*g", x, y3, "*k", x, y4, "*m")
743s  grid on
743s  xlim ([0, 40])
743s  ylim ([0, 0.12])
743s  legend ({"r = 2, ps = 0.15", "r = 5, ps = 0.2", "r = 4, p = 0.4", ...
743s           "r = 10, ps = 0.3"}, "location", "northeast")
743s  title ("Negative binomial PDF")
743s  xlabel ("values in x (number of failures)")
743s  ylabel ("density")
743s ***** shared x, y
743s  x = [-1 0 1 2 Inf];
743s  y = [0 1/2 1/4 1/8 NaN];
743s ***** assert (nbinpdf (x, ones (1,5), 0.5*ones (1,5)), y)
743s ***** assert (nbinpdf (x, 1, 0.5*ones (1,5)), y)
743s ***** assert (nbinpdf (x, ones (1,5), 0.5), y)
743s ***** assert (nbinpdf (x, [0 1 NaN 1.5 Inf], 0.5), [NaN 1/2 NaN 1.875*0.5^1.5/4 NaN], eps)
743s ***** assert (nbinpdf (x, 1, 0.5*[-1 NaN 4 1 1]), [NaN NaN NaN y(4:5)])
743s ***** assert (nbinpdf ([x, NaN], 1, 0.5), [y, NaN])
743s ***** assert (nbinpdf (single ([x, NaN]), 1, 0.5), single ([y, NaN]))
743s ***** assert (nbinpdf ([x, NaN], single (1), 0.5), single ([y, NaN]))
743s ***** assert (nbinpdf ([x, NaN], 1, single (0.5)), single ([y, NaN]))
743s ***** error<nbinpdf: function called with too few input arguments.> nbinpdf ()
743s ***** error<nbinpdf: function called with too few input arguments.> nbinpdf (1)
743s ***** error<nbinpdf: function called with too few input arguments.> nbinpdf (1, 2)
743s ***** error<nbinpdf: X, R, and PS must be of common size or scalars.> ...
743s  nbinpdf (ones (3), ones (2), ones (2))
743s ***** error<nbinpdf: X, R, and PS must be of common size or scalars.> ...
743s  nbinpdf (ones (2), ones (3), ones (2))
743s ***** error<nbinpdf: X, R, and PS must be of common size or scalars.> ...
743s  nbinpdf (ones (2), ones (2), ones (3))
743s ***** error<nbinpdf: X, R, and PS must not be complex.> nbinpdf (i, 2, 2)
743s ***** error<nbinpdf: X, R, and PS must not be complex.> nbinpdf (2, i, 2)
743s ***** error<nbinpdf: X, R, and PS must not be complex.> nbinpdf (2, 2, i)
743s 18 tests, 18 passed, 0 known failure, 0 skipped
743s [inst/dist_fun/nbinrnd.m]
743s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/nbinrnd.m
743s ***** assert (size (nbinrnd (1, 0.5)), [1 1])
743s ***** assert (size (nbinrnd (1, 0.5 * ones (2,1))), [2, 1])
743s ***** assert (size (nbinrnd (1, 0.5 * ones (2,2))), [2, 2])
743s ***** assert (size (nbinrnd (ones (2,1), 0.5)), [2, 1])
743s ***** assert (size (nbinrnd (ones (2,2), 0.5)), [2, 2])
743s ***** assert (size (nbinrnd (1, 0.5, 3)), [3, 3])
743s ***** assert (size (nbinrnd (1, 0.5, [4, 1])), [4, 1])
743s ***** assert (size (nbinrnd (1, 0.5, 4, 1)), [4, 1])
743s ***** assert (size (nbinrnd (1, 0.5, 4, 1, 5)), [4, 1, 5])
743s ***** assert (size (nbinrnd (1, 0.5, 0, 1)), [0, 1])
743s ***** assert (size (nbinrnd (1, 0.5, 1, 0)), [1, 0])
743s ***** assert (size (nbinrnd (1, 0.5, 1, 2, 0, 5)), [1, 2, 0, 5])
743s ***** assert (class (nbinrnd (1, 0.5)), "double")
743s ***** assert (class (nbinrnd (1, single (0.5))), "single")
743s ***** assert (class (nbinrnd (1, single ([0.5, 0.5]))), "single")
743s ***** assert (class (nbinrnd (single (1), 0.5)), "single")
743s ***** assert (class (nbinrnd (single ([1, 1]), 0.5)), "single")
743s ***** error<nbinrnd: function called with too few input arguments.> nbinrnd ()
743s ***** error<nbinrnd: function called with too few input arguments.> nbinrnd (1)
743s ***** error<nbinrnd: R and PS must be of common size or scalars.> ...
743s  nbinrnd (ones (3), ones (2))
743s ***** error<nbinrnd: R and PS must be of common size or scalars.> ...
743s  nbinrnd (ones (2), ones (3))
743s ***** error<nbinrnd: R and PS must not be complex.> nbinrnd (i, 2, 3)
743s ***** error<nbinrnd: R and PS must not be complex.> nbinrnd (1, i, 3)
743s ***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
743s  nbinrnd (1, 2, -1)
743s ***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
743s  nbinrnd (1, 2, 1.2)
743s ***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
743s  nbinrnd (1, 2, ones (2))
743s ***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
743s  nbinrnd (1, 2, [2 -1 2])
743s ***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
743s  nbinrnd (1, 2, [2 0 2.5])
743s ***** error<nbinrnd: dimensions must be non-negative integers.> ...
743s  nbinrnd (1, 2, 2, -1, 5)
743s ***** error<nbinrnd: dimensions must be non-negative integers.> ...
743s  nbinrnd (1, 2, 2, 1.5, 5)
743s ***** error<nbinrnd: R and PS must be scalars or of size SZ.> ...
743s  nbinrnd (2, ones (2), 3)
743s ***** error<nbinrnd: R and PS must be scalars or of size SZ.> ...
743s  nbinrnd (2, ones (2), [3, 2])
743s ***** error<nbinrnd: R and PS must be scalars or of size SZ.> ...
743s  nbinrnd (2, ones (2), 3, 2)
743s 33 tests, 33 passed, 0 known failure, 0 skipped
743s [inst/dist_fun/ncfcdf.m]
743s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/ncfcdf.m
743s ***** demo
743s  ## Plot various CDFs from the noncentral F distribution
743s  x = 0:0.01:5;
743s  p1 = ncfcdf (x, 2, 5, 1);
743s  p2 = ncfcdf (x, 2, 5, 2);
743s  p3 = ncfcdf (x, 5, 10, 1);
743s  p4 = ncfcdf (x, 10, 20, 10);
743s  plot (x, p1, "-r", x, p2, "-g", x, p3, "-k", x, p4, "-m")
743s  grid on
743s  xlim ([0, 5])
743s  legend ({"df1 = 2, df2 = 5, λ = 1", "df1 = 2, df2 = 5, λ = 2", ...
743s           "df1 = 5, df2 = 10, λ = 1", "df1 = 10, df2 = 20, λ = 10"}, ...
743s          "location", "southeast")
743s  title ("Noncentral F CDF")
743s  xlabel ("values in x")
743s  ylabel ("probability")
743s ***** demo
743s  ## Compare the noncentral F CDF with LAMBDA = 10 to the F CDF with the
743s  ## same number of numerator and denominator degrees of freedom (5, 20)
743s 
743s  x = 0.01:0.1:10.01;
743s  p1 = ncfcdf (x, 5, 20, 10);
743s  p2 = fcdf (x, 5, 20);
743s  plot (x, p1, "-", x, p2, "-");
743s  grid on
743s  xlim ([0, 10])
743s  legend ({"Noncentral F(5,20,10)", "F(5,20)"}, "location", "southeast")
743s  title ("Noncentral F vs F CDFs")
743s  xlabel ("values in x")
743s  ylabel ("probability")
743s ***** test
743s  x = -2:0.1:2;
743s  p = ncfcdf (x, 10, 1, 3);
743s  assert (p([1:21]), zeros (1, 21), 1e-76);
743s  assert (p(22), 0.004530737275319753, 1e-14);
743s  assert (p(30), 0.255842099135669, 1e-14);
743s  assert (p(41), 0.4379890998457305, 1e-14);
743s ***** test
743s  p = ncfcdf (12, 10, 3, 2);
743s  assert (p, 0.9582287900447416, 1e-14);
743s ***** test
743s  p = ncfcdf (2, 3, 2, 1);
743s  assert (p, 0.5731985522994989, 1e-14);
743s ***** test
743s  p = ncfcdf (2, 3, 2, 1, "upper");
743s  assert (p, 0.4268014477004823, 1e-14);
743s ***** test
743s  p = ncfcdf ([3, 6], 3, 2, 5, "upper");
743s  assert (p, [0.530248523596927, 0.3350482341323044], 1e-14);
743s ***** error<ncfcdf: function called with too few input arguments.> ncfcdf ()
743s ***** error<ncfcdf: function called with too few input arguments.> ncfcdf (1)
743s ***** error<ncfcdf: function called with too few input arguments.> ncfcdf (1, 2)
743s ***** error<ncfcdf: function called with too few input arguments.> ncfcdf (1, 2, 3)
743s ***** error<ncfcdf: invalid argument for upper tail.> ncfcdf (1, 2, 3, 4, "tail")
743s ***** error<ncfcdf: invalid argument for upper tail.> ncfcdf (1, 2, 3, 4, 5)
743s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
743s  ncfcdf (ones (3), ones (2), ones (2), ones (2))
743s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
743s  ncfcdf (ones (2), ones (3), ones (2), ones (2))
743s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
743s  ncfcdf (ones (2), ones (2), ones (3), ones (2))
743s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
743s  ncfcdf (ones (2), ones (2), ones (2), ones (3))
743s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfcdf (i, 2, 2, 2)
743s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfcdf (2, i, 2, 2)
743s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfcdf (2, 2, i, 2)
743s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfcdf (2, 2, 2, i)
743s 19 tests, 19 passed, 0 known failure, 0 skipped
743s [inst/dist_fun/ncfinv.m]
743s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/ncfinv.m
743s ***** demo
743s  ## Plot various iCDFs from the noncentral F distribution
743s  p = 0.001:0.001:0.999;
743s  x1 = ncfinv (p, 2, 5, 1);
743s  x2 = ncfinv (p, 2, 5, 2);
743s  x3 = ncfinv (p, 5, 10, 1);
743s  x4 = ncfinv (p, 10, 20, 10);
743s  plot (p, x1, "-r", p, x2, "-g", p, x3, "-k", p, x4, "-m")
743s  grid on
743s  ylim ([0, 5])
743s  legend ({"df1 = 2, df2 = 5, λ = 1", "df1 = 2, df2 = 5, λ = 2", ...
743s           "df1 = 5, df2 = 10, λ = 1", "df1 = 10, df2 = 20, λ = 10"}, ...
743s          "location", "northwest")
743s  title ("Noncentral F iCDF")
743s  xlabel ("probability")
743s  ylabel ("values in x")
743s ***** demo
743s  ## Compare the noncentral F iCDF with LAMBDA = 10 to the F iCDF with the
743s  ## same number of numerator and denominator degrees of freedom (5, 20)
743s 
743s  p = 0.001:0.001:0.999;
743s  x1 = ncfinv (p, 5, 20, 10);
743s  x2 = finv (p, 5, 20);
743s  plot (p, x1, "-", p, x2, "-");
743s  grid on
743s  ylim ([0, 10])
743s  legend ({"Noncentral F(5,20,10)", "F(5,20)"}, "location", "northwest")
743s  title ("Noncentral F vs F quantile functions")
743s  xlabel ("probability")
743s  ylabel ("values in x")
743s ***** test
743s  x = [0,0.1775,0.3864,0.6395,0.9564,1.3712,1.9471,2.8215,4.3679,8.1865,Inf];
743s  assert (ncfinv ([0:0.1:1], 2, 3, 1), x, 1e-4);
743s ***** test
743s  x = [0,0.7492,1.3539,2.0025,2.7658,3.7278,5.0324,6.9826,10.3955,18.7665,Inf];
743s  assert (ncfinv ([0:0.1:1], 2, 3, 5), x, 1e-4);
743s ***** test
743s  x = [0,0.2890,0.8632,1.5653,2.4088,3.4594,4.8442,6.8286,10.0983,17.3736,Inf];
743s  assert (ncfinv ([0:0.1:1], 1, 4, 3), x, 1e-4);
743s ***** test
743s  x = [0.078410, 0.212716, 0.288618, 0.335752, 0.367963, 0.391460];
743s  assert (ncfinv (0.05, [1, 2, 3, 4, 5, 6], 10, 3), x, 1e-6);
743s ***** test
743s  x = [0.2574, 0.2966, 0.3188, 0.3331, 0.3432, 0.3507];
743s  assert (ncfinv (0.05, 5, [1, 2, 3, 4, 5, 6], 3), x, 1e-4);
743s ***** test
743s  x = [1.6090, 1.8113, 1.9215, 1.9911, NaN, 2.0742];
743s  assert (ncfinv (0.05, 1, [1, 2, 3, 4, -1, 6], 10), x, 1e-4);
743s ***** test
743s  assert (ncfinv (0.996, 3, 5, 8), 58.0912074080671, 4e-12);
743s ***** error<ncfinv: function called with too few input arguments.> ncfinv ()
743s ***** error<ncfinv: function called with too few input arguments.> ncfinv (1)
743s ***** error<ncfinv: function called with too few input arguments.> ncfinv (1, 2)
743s ***** error<ncfinv: function called with too few input arguments.> ncfinv (1, 2, 3)
743s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
743s  ncfinv (ones (3), ones (2), ones (2), ones (2))
743s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
743s  ncfinv (ones (2), ones (3), ones (2), ones (2))
743s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
743s  ncfinv (ones (2), ones (2), ones (3), ones (2))
743s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
743s  ncfinv (ones (2), ones (2), ones (2), ones (3))
743s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must not be complex.> ncfinv (i, 2, 2, 2)
743s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must not be complex.> ncfinv (2, i, 2, 2)
743s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must not be complex.> ncfinv (2, 2, i, 2)
743s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must not be complex.> ncfinv (2, 2, 2, i)
743s 19 tests, 19 passed, 0 known failure, 0 skipped
743s [inst/dist_fun/ncfpdf.m]
743s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/ncfpdf.m
743s ***** demo
743s  ## Plot various PDFs from the noncentral F distribution
743s  x = 0:0.01:5;
743s  y1 = ncfpdf (x, 2, 5, 1);
743s  y2 = ncfpdf (x, 2, 5, 2);
743s  y3 = ncfpdf (x, 5, 10, 1);
743s  y4 = ncfpdf (x, 10, 20, 10);
743s  plot (x, y1, "-r", x, y2, "-g", x, y3, "-k", x, y4, "-m")
743s  grid on
743s  xlim ([0, 5])
743s  ylim ([0, 0.8])
743s  legend ({"df1 = 2, df2 = 5, λ = 1", "df1 = 2, df2 = 5, λ = 2", ...
743s           "df1 = 5, df2 = 10, λ = 1", "df1 = 10, df2 = 20, λ = 10"}, ...
743s          "location", "northeast")
743s  title ("Noncentral F PDF")
743s  xlabel ("values in x")
743s  ylabel ("density")
743s ***** demo
743s  ## Compare the noncentral F PDF with LAMBDA = 10 to the F PDF with the
743s  ## same number of numerator and denominator degrees of freedom (5, 20)
743s 
743s  x = 0.01:0.1:10.01;
743s  y1 = ncfpdf (x, 5, 20, 10);
743s  y2 = fpdf (x, 5, 20);
743s  plot (x, y1, "-", x, y2, "-");
743s  grid on
743s  xlim ([0, 10])
743s  ylim ([0, 0.8])
743s  legend ({"Noncentral F(5,20,10)", "F(5,20)"}, "location", "northeast")
743s  title ("Noncentral F vs F PDFs")
743s  xlabel ("values in x")
743s  ylabel ("density")
743s ***** shared x1, df1, df2, lambda
743s  x1 = [-Inf, 2, NaN, 4, Inf];
743s  df1 = [2, 0, -1, 1, 4];
743s  df2 = [2, 4, 5, 6, 8];
743s  lambda = [1, NaN, 3, -1, 2];
743s ***** assert (ncfpdf (x1, df1, df2, lambda), [0, NaN, NaN, NaN, NaN]);
743s ***** assert (ncfpdf (x1, df1, df2, 1), [0, NaN, NaN, ...
743s                                    0.05607937264237208, NaN], 1e-14);
743s ***** assert (ncfpdf (x1, df1, df2, 3), [0, NaN, NaN, ...
743s                                    0.080125760971946518, NaN], 1e-14);
743s ***** assert (ncfpdf (x1, df1, df2, 2), [0, NaN, NaN, ...
743s                                    0.0715902008258656, NaN], 1e-14);
743s ***** assert (ncfpdf (x1, 3, 5, lambda), [0, NaN, NaN, NaN, NaN]);
743s ***** assert (ncfpdf (2, df1, df2, lambda), [0.1254046999837947, NaN, NaN, ...
743s                                       NaN, 0.2152571783045893], 1e-14);
743s ***** assert (ncfpdf (4, df1, df2, lambda), [0.05067089541001374, NaN, NaN, ...
743s                                       NaN, 0.05560846335398539], 1e-14);
743s ***** error<ncfpdf: function called with too few input arguments.> ncfpdf ()
743s ***** error<ncfpdf: function called with too few input arguments.> ncfpdf (1)
743s ***** error<ncfpdf: function called with too few input arguments.> ncfpdf (1, 2)
743s ***** error<ncfpdf: function called with too few input arguments.> ncfpdf (1, 2, 3)
743s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
743s  ncfpdf (ones (3), ones (2), ones (2), ones (2))
743s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
743s  ncfpdf (ones (2), ones (3), ones (2), ones (2))
743s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
743s  ncfpdf (ones (2), ones (2), ones (3), ones (2))
743s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
743s  ncfpdf (ones (2), ones (2), ones (2), ones (3))
744s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfpdf (i, 2, 2, 2)
744s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfpdf (2, i, 2, 2)
744s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfpdf (2, 2, i, 2)
744s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfpdf (2, 2, 2, i)
744s 19 tests, 19 passed, 0 known failure, 0 skipped
744s [inst/dist_fun/ncfrnd.m]
744s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/ncfrnd.m
744s ***** assert (size (ncfrnd (1, 1, 1)), [1 1])
744s ***** assert (size (ncfrnd (1, ones (2,1), 1)), [2, 1])
744s ***** assert (size (ncfrnd (1, ones (2,2), 1)), [2, 2])
744s ***** assert (size (ncfrnd (ones (2,1), 1, 1)), [2, 1])
744s ***** assert (size (ncfrnd (ones (2,2), 1, 1)), [2, 2])
744s ***** assert (size (ncfrnd (1, 1, 1, 3)), [3, 3])
744s ***** assert (size (ncfrnd (1, 1, 1, [4, 1])), [4, 1])
744s ***** assert (size (ncfrnd (1, 1, 1, 4, 1)), [4, 1])
744s ***** assert (size (ncfrnd (1, 1, 1, 4, 1, 5)), [4, 1, 5])
744s ***** assert (size (ncfrnd (1, 1, 1, 0, 1)), [0, 1])
744s ***** assert (size (ncfrnd (1, 1, 1, 1, 0)), [1, 0])
744s ***** assert (size (ncfrnd (1, 1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
744s ***** assert (class (ncfrnd (1, 1, 1)), "double")
744s ***** assert (class (ncfrnd (1, single (1), 1)), "single")
744s ***** assert (class (ncfrnd (1, 1, single (1))), "single")
744s ***** assert (class (ncfrnd (1, single ([1, 1]), 1)), "single")
744s ***** assert (class (ncfrnd (1, 1, single ([1, 1]))), "single")
744s ***** assert (class (ncfrnd (single (1), 1, 1)), "single")
744s ***** assert (class (ncfrnd (single ([1, 1]), 1, 1)), "single")
744s ***** error<ncfrnd: function called with too few input arguments.> ncfrnd ()
744s ***** error<ncfrnd: function called with too few input arguments.> ncfrnd (1)
744s ***** error<ncfrnd: function called with too few input arguments.> ncfrnd (1, 2)
744s ***** error<ncfrnd: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
744s  ncfrnd (ones (3), ones (2), ones (2))
744s ***** error<ncfrnd: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
744s  ncfrnd (ones (2), ones (3), ones (2))
744s ***** error<ncfrnd: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
744s  ncfrnd (ones (2), ones (2), ones (3))
744s ***** error<ncfrnd: DF1, DF2, and LAMBDA must not be complex.> ncfrnd (i, 2, 3)
744s ***** error<ncfrnd: DF1, DF2, and LAMBDA must not be complex.> ncfrnd (1, i, 3)
744s ***** error<ncfrnd: DF1, DF2, and LAMBDA must not be complex.> ncfrnd (1, 2, i)
744s ***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
744s  ncfrnd (1, 2, 3, -1)
744s ***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
744s  ncfrnd (1, 2, 3, 1.2)
744s ***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
744s  ncfrnd (1, 2, 3, ones (2))
744s ***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
744s  ncfrnd (1, 2, 3, [2 -1 2])
744s ***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
744s  ncfrnd (1, 2, 3, [2 0 2.5])
744s ***** error<ncfrnd: dimensions must be non-negative integers.> ...
744s  ncfrnd (1, 2, 3, 2, -1, 5)
744s ***** error<ncfrnd: dimensions must be non-negative integers.> ...
744s  ncfrnd (1, 2, 3, 2, 1.5, 5)
744s ***** error<ncfrnd: DF1, DF2, and LAMBDA must be scalars or of size SZ.> ...
744s  ncfrnd (2, ones (2), 2, 3)
744s ***** error<ncfrnd: DF1, DF2, and LAMBDA must be scalars or of size SZ.> ...
744s  ncfrnd (2, ones (2), 2, [3, 2])
744s ***** error<ncfrnd: DF1, DF2, and LAMBDA must be scalars or of size SZ.> ...
744s  ncfrnd (2, ones (2), 2, 3, 2)
744s 38 tests, 38 passed, 0 known failure, 0 skipped
744s [inst/dist_fun/nctcdf.m]
744s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/nctcdf.m
744s ***** demo
744s  ## Plot various CDFs from the noncentral Τ distribution
744s  x = -5:0.01:5;
744s  p1 = nctcdf (x, 1, 0);
744s  p2 = nctcdf (x, 4, 0);
744s  p3 = nctcdf (x, 1, 2);
744s  p4 = nctcdf (x, 4, 2);
744s  plot (x, p1, "-r", x, p2, "-g", x, p3, "-k", x, p4, "-m")
744s  grid on
744s  xlim ([-5, 5])
744s  legend ({"df = 1, μ = 0", "df = 4, μ = 0", ...
744s           "df = 1, μ = 2", "df = 4, μ = 2"}, "location", "southeast")
744s  title ("Noncentral Τ CDF")
744s  xlabel ("values in x")
744s  ylabel ("probability")
744s ***** demo
744s  ## Compare the noncentral T CDF with MU = 1 to the T CDF
744s  ## with the same number of degrees of freedom (10).
744s 
744s  x = -5:0.1:5;
744s  p1 = nctcdf (x, 10, 1);
744s  p2 = tcdf (x, 10);
744s  plot (x, p1, "-", x, p2, "-")
744s  grid on
744s  xlim ([-5, 5])
744s  legend ({"Noncentral T(10,1)", "T(10)"}, "location", "southeast")
744s  title ("Noncentral T vs T CDFs")
744s  xlabel ("values in x")
744s  ylabel ("probability")
744s ***** test
744s  x = -2:0.1:2;
744s  p = nctcdf (x, 10, 1);
744s  assert (p(1), 0.003302485766631558, 1e-14);
744s  assert (p(2), 0.004084668193532631, 1e-14);
744s  assert (p(3), 0.005052800319478737, 1e-14);
744s  assert (p(41), 0.8076115625303751, 1e-14);
744s ***** test
744s  p = nctcdf (12, 10, 3);
744s  assert (p, 0.9997719343243797, 1e-14);
744s ***** test
744s  p = nctcdf (2, 3, 2);
744s  assert (p, 0.4430757822176028, 1e-14);
744s ***** test
744s  p = nctcdf (2, 3, 2, "upper");
744s  assert (p, 0.5569242177823971, 1e-14);
744s ***** test
744s  p = nctcdf ([3, 6], 3, 2, "upper");
744s  assert (p, [0.3199728259444777, 0.07064855592441913], 1e-14);
744s ***** error<nctcdf: function called with too few input arguments.> nctcdf ()
744s ***** error<nctcdf: function called with too few input arguments.> nctcdf (1)
744s ***** error<nctcdf: function called with too few input arguments.> nctcdf (1, 2)
744s ***** error<nctcdf: invalid argument for upper tail.> nctcdf (1, 2, 3, "tail")
744s ***** error<nctcdf: invalid argument for upper tail.> nctcdf (1, 2, 3, 4)
744s ***** error<nctcdf: X, DF, and MU must be of common size or scalars.> ...
744s  nctcdf (ones (3), ones (2), ones (2))
744s ***** error<nctcdf: X, DF, and MU must be of common size or scalars.> ...
744s  nctcdf (ones (2), ones (3), ones (2))
744s ***** error<nctcdf: X, DF, and MU must be of common size or scalars.> ...
744s  nctcdf (ones (2), ones (2), ones (3))
744s ***** error<nctcdf: X, DF, and MU must not be complex.> nctcdf (i, 2, 2)
744s ***** error<nctcdf: X, DF, and MU must not be complex.> nctcdf (2, i, 2)
744s ***** error<nctcdf: X, DF, and MU must not be complex.> nctcdf (2, 2, i)
744s 16 tests, 16 passed, 0 known failure, 0 skipped
744s [inst/dist_fun/nctinv.m]
744s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/nctinv.m
744s ***** demo
744s  ## Plot various iCDFs from the noncentral T distribution
744s  p = 0.001:0.001:0.999;
744s  x1 = nctinv (p, 1, 0);
744s  x2 = nctinv (p, 4, 0);
744s  x3 = nctinv (p, 1, 2);
744s  x4 = nctinv (p, 4, 2);
744s  plot (p, x1, "-r", p, x2, "-g", p, x3, "-k", p, x4, "-m")
744s  grid on
744s  ylim ([-5, 5])
744s  legend ({"df = 1, μ = 0", "df = 4, μ = 0", ...
744s           "df = 1, μ = 2", "df = 4, μ = 2"}, "location", "northwest")
744s  title ("Noncentral T iCDF")
744s  xlabel ("probability")
744s  ylabel ("values in x")
744s ***** demo
744s  ## Compare the noncentral T iCDF with MU = 1 to the T iCDF
744s  ## with the same number of degrees of freedom (10).
744s 
744s  p = 0.001:0.001:0.999;
744s  x1 = nctinv (p, 10, 1);
744s  x2 = tinv (p, 10);
744s  plot (p, x1, "-", p, x2, "-");
744s  grid on
744s  ylim ([-5, 5])
744s  legend ({"Noncentral T(10,1)", "T(10)"}, "location", "northwest")
744s  title ("Noncentral T vs T quantile functions")
744s  xlabel ("probability")
744s  ylabel ("values in x")
744s ***** test
744s  x = [-Inf,-0.3347,0.1756,0.5209,0.8279,1.1424,1.5021,1.9633,2.6571,4.0845,Inf];
744s  assert (nctinv ([0:0.1:1], 2, 1), x, 1e-4);
744s ***** test
744s  x = [-Inf,1.5756,2.0827,2.5343,3.0043,3.5406,4.2050,5.1128,6.5510,9.6442,Inf];
744s  assert (nctinv ([0:0.1:1], 2, 3), x, 1e-4);
745s ***** test
745s  x = [-Inf,2.2167,2.9567,3.7276,4.6464,5.8455,7.5619,10.3327,15.7569,31.8159,Inf];
745s  assert (nctinv ([0:0.1:1], 1, 4), x, 1e-4);
746s ***** test
746s  x = [1.7791   1.9368   2.0239   2.0801   2.1195   2.1489];
746s  assert (nctinv (0.05, [1, 2, 3, 4, 5, 6], 4), x, 1e-4);
747s ***** test
747s  x = [-0.7755, 0.3670, 1.2554, 2.0239, 2.7348, 3.4154];
747s  assert (nctinv (0.05, 3, [1, 2, 3, 4, 5, 6]), x, 1e-4);
748s ***** test
748s  x = [-0.7183, 0.3624, 1.2878, 2.1195, -3.5413, 3.6430];
748s  assert (nctinv (0.05, 5, [1, 2, 3, 4, -1, 6]), x, 1e-4);
749s ***** test
749s  assert (nctinv (0.996, 5, 8), 30.02610554063658, 2e-11);
751s ***** error<nctinv: function called with too few input arguments.> nctinv ()
751s ***** error<nctinv: function called with too few input arguments.> nctinv (1)
751s ***** error<nctinv: function called with too few input arguments.> nctinv (1, 2)
751s ***** error<nctinv: P, DF, and MU must be of common size or scalars.> ...
751s  nctinv (ones (3), ones (2), ones (2))
751s ***** error<nctinv: P, DF, and MU must be of common size or scalars.> ...
751s  nctinv (ones (2), ones (3), ones (2))
751s ***** error<nctinv: P, DF, and MU must be of common size or scalars.> ...
751s  nctinv (ones (2), ones (2), ones (3))
751s ***** error<nctinv: P, DF, and MU must not be complex.> nctinv (i, 2, 2)
751s ***** error<nctinv: P, DF, and MU must not be complex.> nctinv (2, i, 2)
751s ***** error<nctinv: P, DF, and MU must not be complex.> nctinv (2, 2, i)
751s 16 tests, 16 passed, 0 known failure, 0 skipped
751s [inst/dist_fun/nctpdf.m]
751s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/nctpdf.m
751s ***** demo
751s  ## Plot various PDFs from the noncentral T distribution
751s  x = -5:0.01:10;
751s  y1 = nctpdf (x, 1, 0);
751s  y2 = nctpdf (x, 4, 0);
751s  y3 = nctpdf (x, 1, 2);
751s  y4 = nctpdf (x, 4, 2);
751s  plot (x, y1, "-r", x, y2, "-g", x, y3, "-k", x, y4, "-m")
751s  grid on
751s  xlim ([-5, 10])
751s  ylim ([0, 0.4])
751s  legend ({"df = 1, μ = 0", "df = 4, μ = 0", ...
751s           "df = 1, μ = 2", "df = 4, μ = 2"}, "location", "northeast")
751s  title ("Noncentral T PDF")
751s  xlabel ("values in x")
751s  ylabel ("density")
751s ***** demo
751s  ## Compare the noncentral T PDF with MU = 1 to the T PDF
751s  ## with the same number of degrees of freedom (10).
751s 
751s  x = -5:0.1:5;
751s  y1 = nctpdf (x, 10, 1);
751s  y2 = tpdf (x, 10);
751s  plot (x, y1, "-", x, y2, "-");
751s  grid on
751s  xlim ([-5, 5])
751s  ylim ([0, 0.4])
751s  legend ({"Noncentral χ^2(4,2)", "χ^2(4)"}, "location", "northwest")
751s  title ("Noncentral T vs T PDFs")
751s  xlabel ("values in x")
751s  ylabel ("density")
751s ***** shared x1, df, mu
751s  x1 = [-Inf, 2, NaN, 4, Inf];
751s  df = [2, 0, -1, 1, 4];
751s  mu = [1, NaN, 3, -1, 2];
751s ***** assert (nctpdf (x1, df, mu), [0, NaN, NaN, 0.00401787561306999, 0], 1e-14);
751s ***** assert (nctpdf (x1, df, 1), [0, NaN, NaN, 0.0482312135423008, 0], 1e-14);
751s ***** assert (nctpdf (x1, df, 3), [0, NaN, NaN, 0.1048493126401585, 0], 1e-14);
751s ***** assert (nctpdf (x1, df, 2), [0, NaN, NaN, 0.08137377919890307, 0], 1e-14);
751s ***** assert (nctpdf (x1, 3, mu), [0, NaN, NaN, 0.001185305171654381, 0], 1e-14);
751s ***** assert (nctpdf (2, df, mu), [0.1791097459405861, NaN, NaN, ...
751s                              0.0146500727180389, 0.3082302682110299], 1e-14);
751s ***** assert (nctpdf (4, df, mu), [0.04467929612254971, NaN, NaN, ...
751s                              0.00401787561306999, 0.0972086534042828], 1e-14);
751s ***** error<nctpdf: function called with too few input arguments.> nctpdf ()
751s ***** error<nctpdf: function called with too few input arguments.> nctpdf (1)
751s ***** error<nctpdf: function called with too few input arguments.> nctpdf (1, 2)
751s ***** error<nctpdf: X, DF, and MU must be of common size or scalars.> ...
751s  nctpdf (ones (3), ones (2), ones (2))
751s ***** error<nctpdf: X, DF, and MU must be of common size or scalars.> ...
751s  nctpdf (ones (2), ones (3), ones (2))
751s ***** error<nctpdf: X, DF, and MU must be of common size or scalars.> ...
751s  nctpdf (ones (2), ones (2), ones (3))
751s ***** error<nctpdf: X, DF, and MU must not be complex.> nctpdf (i, 2, 2)
751s ***** error<nctpdf: X, DF, and MU must not be complex.> nctpdf (2, i, 2)
751s ***** error<nctpdf: X, DF, and MU must not be complex.> nctpdf (2, 2, i)
752s 16 tests, 16 passed, 0 known failure, 0 skipped
752s [inst/dist_fun/nctrnd.m]
752s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/nctrnd.m
752s ***** assert (size (nctrnd (1, 1)), [1 1])
752s ***** assert (size (nctrnd (1, ones (2,1))), [2, 1])
752s ***** assert (size (nctrnd (1, ones (2,2))), [2, 2])
752s ***** assert (size (nctrnd (ones (2,1), 1)), [2, 1])
752s ***** assert (size (nctrnd (ones (2,2), 1)), [2, 2])
752s ***** assert (size (nctrnd (1, 1, 3)), [3, 3])
752s ***** assert (size (nctrnd (1, 1, [4, 1])), [4, 1])
752s ***** assert (size (nctrnd (1, 1, 4, 1)), [4, 1])
752s ***** assert (size (nctrnd (1, 1, 4, 1, 5)), [4, 1, 5])
752s ***** assert (size (nctrnd (1, 1, 0, 1)), [0, 1])
752s ***** assert (size (nctrnd (1, 1, 1, 0)), [1, 0])
752s ***** assert (size (nctrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
752s ***** assert (class (nctrnd (1, 1)), "double")
752s ***** assert (class (nctrnd (1, single (1))), "single")
752s ***** assert (class (nctrnd (1, single ([1, 1]))), "single")
752s ***** assert (class (nctrnd (single (1), 1)), "single")
752s ***** assert (class (nctrnd (single ([1, 1]), 1)), "single")
752s ***** error<nctrnd: function called with too few input arguments.> nctrnd ()
752s ***** error<nctrnd: function called with too few input arguments.> nctrnd (1)
752s ***** error<nctrnd: DF and MU must be of common size or scalars.> ...
752s  nctrnd (ones (3), ones (2))
752s ***** error<nctrnd: DF and MU must be of common size or scalars.> ...
752s  nctrnd (ones (2), ones (3))
752s ***** error<nctrnd: DF and MU must not be complex.> nctrnd (i, 2)
752s ***** error<nctrnd: DF and MU must not be complex.> nctrnd (1, i)
752s ***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
752s  nctrnd (1, 2, -1)
752s ***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
752s  nctrnd (1, 2, 1.2)
752s ***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
752s  nctrnd (1, 2, ones (2))
752s ***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
752s  nctrnd (1, 2, [2 -1 2])
752s ***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
752s  nctrnd (1, 2, [2 0 2.5])
752s ***** error<nctrnd: dimensions must be non-negative integers.> ...
752s  nctrnd (1, 2, 2, -1, 5)
752s ***** error<nctrnd: dimensions must be non-negative integers.> ...
752s  nctrnd (1, 2, 2, 1.5, 5)
752s ***** error<nctrnd: DF and MU must be scalars or of size SZ.> ...
752s  nctrnd (2, ones (2), 3)
752s ***** error<nctrnd: DF and MU must be scalars or of size SZ.> ...
752s  nctrnd (2, ones (2), [3, 2])
752s ***** error<nctrnd: DF and MU must be scalars or of size SZ.> ...
752s  nctrnd (2, ones (2), 3, 2)
752s 33 tests, 33 passed, 0 known failure, 0 skipped
752s [inst/dist_fun/ncx2cdf.m]
752s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/ncx2cdf.m
752s ***** demo
752s  ## Plot various CDFs from the noncentral chi-squared distribution
752s  x = 0:0.1:10;
752s  p1 = ncx2cdf (x, 2, 1);
752s  p2 = ncx2cdf (x, 2, 2);
752s  p3 = ncx2cdf (x, 2, 3);
752s  p4 = ncx2cdf (x, 4, 1);
752s  p5 = ncx2cdf (x, 4, 2);
752s  p6 = ncx2cdf (x, 4, 3);
752s  plot (x, p1, "-r", x, p2, "-g", x, p3, "-k", ...
752s        x, p4, "-m", x, p5, "-c", x, p6, "-y")
752s  grid on
752s  xlim ([0, 10])
752s  legend ({"df = 2, λ = 1", "df = 2, λ = 2", ...
752s           "df = 2, λ = 3", "df = 4, λ = 1", ...
752s           "df = 4, λ = 2", "df = 4, λ = 3"}, "location", "southeast")
752s  title ("Noncentral chi-squared CDF")
752s  xlabel ("values in x")
752s  ylabel ("probability")
752s ***** demo
752s  ## Compare the noncentral chi-squared CDF with LAMBDA = 2 to the
752s  ## chi-squared CDF with the same number of degrees of freedom (4).
752s 
752s  x = 0:0.1:10;
752s  p1 = ncx2cdf (x, 4, 2);
752s  p2 = chi2cdf (x, 4);
752s  plot (x, p1, "-", x, p2, "-")
752s  grid on
752s  xlim ([0, 10])
752s  legend ({"Noncentral χ^2(4,2)", "χ^2(4)"}, "location", "northwest")
752s  title ("Noncentral chi-squared vs chi-squared CDFs")
752s  xlabel ("values in x")
752s  ylabel ("probability")
752s ***** test
752s  x = -2:0.1:2;
752s  p = ncx2cdf (x, 10, 1);
752s  assert (p([1:21]), zeros (1, 21), 3e-84);
752s  assert (p(22), 1.521400636466575e-09, 1e-14);
752s  assert (p(30), 6.665480510026046e-05, 1e-14);
752s  assert (p(41), 0.002406447308399836, 1e-14);
752s ***** test
752s  p = ncx2cdf (12, 10, 3);
752s  assert (p, 0.4845555602398649, 1e-14);
752s ***** test
752s  p = ncx2cdf (2, 3, 2);
752s  assert (p, 0.2207330870741212, 1e-14);
752s ***** test
752s  p = ncx2cdf (2, 3, 2, "upper");
752s  assert (p, 0.7792669129258789, 1e-14);
752s ***** test
752s  p = ncx2cdf ([3, 6], 3, 2, "upper");
752s  assert (p, [0.6423318186400054, 0.3152299878943012], 1e-14);
752s ***** error<ncx2cdf: function called with too few input arguments.> ncx2cdf ()
752s ***** error<ncx2cdf: function called with too few input arguments.> ncx2cdf (1)
752s ***** error<ncx2cdf: function called with too few input arguments.> ncx2cdf (1, 2)
752s ***** error<ncx2cdf: invalid argument for upper tail.> ncx2cdf (1, 2, 3, "tail")
752s ***** error<ncx2cdf: invalid argument for upper tail.> ncx2cdf (1, 2, 3, 4)
752s ***** error<ncx2cdf: X, DF, and LAMBDA must be of common size or scalars.> ...
752s  ncx2cdf (ones (3), ones (2), ones (2))
752s ***** error<ncx2cdf: X, DF, and LAMBDA must be of common size or scalars.> ...
752s  ncx2cdf (ones (2), ones (3), ones (2))
752s ***** error<ncx2cdf: X, DF, and LAMBDA must be of common size or scalars.> ...
752s  ncx2cdf (ones (2), ones (2), ones (3))
752s ***** error<ncx2cdf: X, DF, and LAMBDA must not be complex.> ncx2cdf (i, 2, 2)
752s ***** error<ncx2cdf: X, DF, and LAMBDA must not be complex.> ncx2cdf (2, i, 2)
752s ***** error<ncx2cdf: X, DF, and LAMBDA must not be complex.> ncx2cdf (2, 2, i)
752s 16 tests, 16 passed, 0 known failure, 0 skipped
752s [inst/dist_fun/ncx2inv.m]
752s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/ncx2inv.m
752s ***** demo
752s  ## Plot various iCDFs from the noncentral chi-squared distribution
752s  p = 0.001:0.001:0.999;
752s  x1 = ncx2inv (p, 2, 1);
752s  x2 = ncx2inv (p, 2, 2);
752s  x3 = ncx2inv (p, 2, 3);
752s  x4 = ncx2inv (p, 4, 1);
752s  x5 = ncx2inv (p, 4, 2);
752s  x6 = ncx2inv (p, 4, 3);
752s  plot (p, x1, "-r", p, x2, "-g", p, x3, "-k", ...
752s        p, x4, "-m", p, x5, "-c", p, x6, "-y")
752s  grid on
752s  ylim ([0, 10])
752s  legend ({"df = 2, λ = 1", "df = 2, λ = 2", ...
752s           "df = 2, λ = 3", "df = 4, λ = 1", ...
752s           "df = 4, λ = 2", "df = 4, λ = 3"}, "location", "northwest")
752s  title ("Noncentral chi-squared iCDF")
752s  xlabel ("probability")
752s  ylabel ("values in x")
752s ***** demo
752s  ## Compare the noncentral chi-squared CDF with LAMBDA = 2 to the
752s  ## chi-squared CDF with the same number of degrees of freedom (4).
752s 
752s  p = 0.001:0.001:0.999;
752s  x1 = ncx2inv (p, 4, 2);
752s  x2 = chi2inv (p, 4);
752s  plot (p, x1, "-", p, x2, "-");
752s  grid on
752s  ylim ([0, 10])
752s  legend ({"Noncentral χ^2(4,2)", "χ^2(4)"}, "location", "northwest")
752s  title ("Noncentral chi-squared vs chi-squared quantile functions")
752s  xlabel ("probability")
752s  ylabel ("values in x")
752s ***** test
752s  x = [0,0.3443,0.7226,1.1440,1.6220,2.1770,2.8436,3.6854,4.8447,6.7701,Inf];
752s  assert (ncx2inv ([0:0.1:1], 2, 1), x, 1e-4);
752s ***** test
752s  x = [0,0.8295,1.6001,2.3708,3.1785,4.0598,5.0644,6.2765,7.8763,10.4199,Inf];
752s  assert (ncx2inv ([0:0.1:1], 2, 3), x, 1e-4);
752s ***** test
752s  x = [0,0.5417,1.3483,2.1796,3.0516,4.0003,5.0777,6.3726,8.0748,10.7686,Inf];
752s  assert (ncx2inv ([0:0.1:1], 1, 4), x, 1e-4);
752s ***** test
752s  x = [0.1808, 0.6456, 1.1842, 1.7650, 2.3760, 3.0105];
752s  assert (ncx2inv (0.05, [1, 2, 3, 4, 5, 6], 4), x, 1e-4);
752s ***** test
752s  x = [0.4887, 0.6699, 0.9012, 1.1842, 1.5164, 1.8927];
752s  assert (ncx2inv (0.05, 3, [1, 2, 3, 4, 5, 6]), x, 1e-4);
753s ***** test
753s  x = [1.3941, 1.6824, 2.0103, 2.3760, NaN, 3.2087];
753s  assert (ncx2inv (0.05, 5, [1, 2, 3, 4, -1, 6]), x, 1e-4);
753s ***** test
753s  assert (ncx2inv (0.996, 5, 8), 35.51298862765576, 3e-13);
753s ***** error<ncx2inv: function called with too few input arguments.> ncx2inv ()
753s ***** error<ncx2inv: function called with too few input arguments.> ncx2inv (1)
753s ***** error<ncx2inv: function called with too few input arguments.> ncx2inv (1, 2)
753s ***** error<ncx2inv: P, DF, and LAMBDA must be of common size or scalars.> ...
753s  ncx2inv (ones (3), ones (2), ones (2))
753s ***** error<ncx2inv: P, DF, and LAMBDA must be of common size or scalars.> ...
753s  ncx2inv (ones (2), ones (3), ones (2))
753s ***** error<ncx2inv: P, DF, and LAMBDA must be of common size or scalars.> ...
753s  ncx2inv (ones (2), ones (2), ones (3))
753s ***** error<ncx2inv: P, DF, and LAMBDA must not be complex.> ncx2inv (i, 2, 2)
753s ***** error<ncx2inv: P, DF, and LAMBDA must not be complex.> ncx2inv (2, i, 2)
753s ***** error<ncx2inv: P, DF, and LAMBDA must not be complex.> ncx2inv (2, 2, i)
753s 16 tests, 16 passed, 0 known failure, 0 skipped
753s [inst/dist_fun/ncx2pdf.m]
753s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/ncx2pdf.m
753s ***** demo
753s  ## Plot various PDFs from the noncentral chi-squared distribution
753s  x = 0:0.1:10;
753s  y1 = ncx2pdf (x, 2, 1);
753s  y2 = ncx2pdf (x, 2, 2);
753s  y3 = ncx2pdf (x, 2, 3);
753s  y4 = ncx2pdf (x, 4, 1);
753s  y5 = ncx2pdf (x, 4, 2);
753s  y6 = ncx2pdf (x, 4, 3);
753s  plot (x, y1, "-r", x, y2, "-g", x, y3, "-k", ...
753s        x, y4, "-m", x, y5, "-c", x, y6, "-y")
753s  grid on
753s  xlim ([0, 10])
753s  ylim ([0, 0.32])
753s  legend ({"df = 2, λ = 1", "df = 2, λ = 2", ...
753s           "df = 2, λ = 3", "df = 4, λ = 1", ...
753s           "df = 4, λ = 2", "df = 4, λ = 3"}, "location", "northeast")
753s  title ("Noncentral chi-squared PDF")
753s  xlabel ("values in x")
753s  ylabel ("density")
753s ***** demo
753s  ## Compare the noncentral chi-squared PDF with LAMBDA = 2 to the
753s  ## chi-squared PDF with the same number of degrees of freedom (4).
753s 
753s  x = 0:0.1:10;
753s  y1 = ncx2pdf (x, 4, 2);
753s  y2 = chi2pdf (x, 4);
753s  plot (x, y1, "-", x, y2, "-");
753s  grid on
753s  xlim ([0, 10])
753s  ylim ([0, 0.32])
753s  legend ({"Noncentral T(10,1)", "T(10)"}, "location", "northwest")
753s  title ("Noncentral chi-squared vs chi-squared PDFs")
753s  xlabel ("values in x")
753s  ylabel ("density")
753s ***** shared x1, df, d1
753s  x1 = [-Inf, 2, NaN, 4, Inf];
753s  df = [2, 0, -1, 1, 4];
753s  d1 = [1, NaN, 3, -1, 2];
753s ***** assert (ncx2pdf (x1, df, d1), [0, NaN, NaN, NaN, 0]);
753s ***** assert (ncx2pdf (x1, df, 1), [0, 0.07093996461786045, NaN, ...
753s                               0.06160064323277038, 0], 1e-14);
753s ***** assert (ncx2pdf (x1, df, 3), [0, 0.1208364909271113, NaN, ...
753s                               0.09631299762429098, 0], 1e-14);
753s ***** assert (ncx2pdf (x1, df, 2), [0, 0.1076346446244688, NaN, ...
753s                               0.08430464047296625, 0], 1e-14);
753s ***** assert (ncx2pdf (x1, 2, d1), [0, NaN, NaN, NaN, 0]);
753s ***** assert (ncx2pdf (2, df, d1), [0.1747201674611283, NaN, NaN, ...
753s                               NaN, 0.1076346446244688], 1e-14);
753s ***** assert (ncx2pdf (4, df, d1), [0.09355987820265799, NaN, NaN, ...
753s                               NaN, 0.1192317192431485], 1e-14);
753s ***** error<ncx2pdf: function called with too few input arguments.> ncx2pdf ()
753s ***** error<ncx2pdf: function called with too few input arguments.> ncx2pdf (1)
753s ***** error<ncx2pdf: function called with too few input arguments.> ncx2pdf (1, 2)
753s ***** error<ncx2pdf: X, DF, and LAMBDA must be of common size or scalars.> ...
753s  ncx2pdf (ones (3), ones (2), ones (2))
753s ***** error<ncx2pdf: X, DF, and LAMBDA must be of common size or scalars.> ...
753s  ncx2pdf (ones (2), ones (3), ones (2))
753s ***** error<ncx2pdf: X, DF, and LAMBDA must be of common size or scalars.> ...
753s  ncx2pdf (ones (2), ones (2), ones (3))
753s ***** error<ncx2pdf: X, DF, and LAMBDA must not be complex.> ncx2pdf (i, 2, 2)
753s ***** error<ncx2pdf: X, DF, and LAMBDA must not be complex.> ncx2pdf (2, i, 2)
753s ***** error<ncx2pdf: X, DF, and LAMBDA must not be complex.> ncx2pdf (2, 2, i)
753s 16 tests, 16 passed, 0 known failure, 0 skipped
753s [inst/dist_fun/ncx2rnd.m]
753s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/ncx2rnd.m
753s ***** assert (size (ncx2rnd (1, 1)), [1 1])
753s ***** assert (size (ncx2rnd (1, ones (2,1))), [2, 1])
753s ***** assert (size (ncx2rnd (1, ones (2,2))), [2, 2])
753s ***** assert (size (ncx2rnd (ones (2,1), 1)), [2, 1])
753s ***** assert (size (ncx2rnd (ones (2,2), 1)), [2, 2])
753s ***** assert (size (ncx2rnd (1, 1, 3)), [3, 3])
753s ***** assert (size (ncx2rnd (1, 1, [4, 1])), [4, 1])
753s ***** assert (size (ncx2rnd (1, 1, 4, 1)), [4, 1])
753s ***** assert (size (ncx2rnd (1, 1, 4, 1, 5)), [4, 1, 5])
753s ***** assert (size (ncx2rnd (1, 1, 0, 1)), [0, 1])
753s ***** assert (size (ncx2rnd (1, 1, 1, 0)), [1, 0])
753s ***** assert (size (ncx2rnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
753s ***** assert (class (ncx2rnd (1, 1)), "double")
753s ***** assert (class (ncx2rnd (1, single (1))), "single")
753s ***** assert (class (ncx2rnd (1, single ([1, 1]))), "single")
753s ***** assert (class (ncx2rnd (single (1), 1)), "single")
753s ***** assert (class (ncx2rnd (single ([1, 1]), 1)), "single")
753s ***** error<ncx2rnd: function called with too few input arguments.> ncx2rnd ()
753s ***** error<ncx2rnd: function called with too few input arguments.> ncx2rnd (1)
753s ***** error<ncx2rnd: DF and LAMBDA must be of common size or scalars.> ...
753s  ncx2rnd (ones (3), ones (2))
753s ***** error<ncx2rnd: DF and LAMBDA must be of common size or scalars.> ...
753s  ncx2rnd (ones (2), ones (3))
753s ***** error<ncx2rnd: DF and LAMBDA must not be complex.> ncx2rnd (i, 2)
753s ***** error<ncx2rnd: DF and LAMBDA must not be complex.> ncx2rnd (1, i)
753s ***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
753s  ncx2rnd (1, 2, -1)
753s ***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
753s  ncx2rnd (1, 2, 1.2)
753s ***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
753s  ncx2rnd (1, 2, ones (2))
753s ***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
753s  ncx2rnd (1, 2, [2 -1 2])
753s ***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
753s  ncx2rnd (1, 2, [2 0 2.5])
753s ***** error<ncx2rnd: dimensions must be non-negative integers.> ...
753s  ncx2rnd (1, 2, 2, -1, 5)
753s ***** error<ncx2rnd: dimensions must be non-negative integers.> ...
753s  ncx2rnd (1, 2, 2, 1.5, 5)
753s ***** error<ncx2rnd: DF and LAMBDA must be scalars or of size SZ.> ...
753s  ncx2rnd (2, ones (2), 3)
754s ***** error<ncx2rnd: DF and LAMBDA must be scalars or of size SZ.> ...
754s  ncx2rnd (2, ones (2), [3, 2])
754s ***** error<ncx2rnd: DF and LAMBDA must be scalars or of size SZ.> ...
754s  ncx2rnd (2, ones (2), 3, 2)
754s 33 tests, 33 passed, 0 known failure, 0 skipped
754s [inst/dist_fun/normcdf.m]
754s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/normcdf.m
754s ***** demo
754s  ## Plot various CDFs from the normal distribution
754s  x = -5:0.01:5;
754s  p1 = normcdf (x, 0, 0.5);
754s  p2 = normcdf (x, 0, 1);
754s  p3 = normcdf (x, 0, 2);
754s  p4 = normcdf (x, -2, 0.8);
754s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
754s  grid on
754s  xlim ([-5, 5])
754s  legend ({"μ = 0, σ = 0.5", "μ = 0, σ = 1", ...
754s           "μ = 0, σ = 2", "μ = -2, σ = 0.8"}, "location", "southeast")
754s  title ("Normal CDF")
754s  xlabel ("values in x")
754s  ylabel ("probability")
754s ***** shared x, y
754s  x = [-Inf 1 2 Inf];
754s  y = [0, 0.5, 1/2*(1+erf(1/sqrt(2))), 1];
754s ***** assert (normcdf (x, ones (1,4), ones (1,4)), y)
754s ***** assert (normcdf (x, 1, ones (1,4)), y)
754s ***** assert (normcdf (x, ones (1,4), 1), y)
754s ***** assert (normcdf (x, [0, -Inf, NaN, Inf], 1), [0, 1, NaN, NaN])
754s ***** assert (normcdf (x, 1, [Inf, NaN, -1, 0]), [NaN, NaN, NaN, 1])
754s ***** assert (normcdf ([x(1:2), NaN, x(4)], 1, 1), [y(1:2), NaN, y(4)])
754s ***** assert (normcdf (x, "upper"), [1, 0.1587, 0.0228, 0], 1e-4)
754s ***** assert (normcdf ([x, NaN], 1, 1), [y, NaN])
754s ***** assert (normcdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
754s ***** assert (normcdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
754s ***** assert (normcdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
754s ***** error<normcdf: invalid number of input arguments.> normcdf ()
754s ***** error<normcdf: invalid number of input arguments.> normcdf (1,2,3,4,5,6,7)
754s ***** error<normcdf: invalid argument for upper tail.> normcdf (1, 2, 3, 4, "uper")
754s ***** error<normcdf: X, MU, and SIGMA must be of common size or scalars.> ...
754s  normcdf (ones (3), ones (2), ones (2))
754s ***** error<normcdf: invalid size of covariance matrix.> normcdf (2, 3, 4, [1, 2])
754s ***** error<normcdf: covariance matrix is required for confidence bounds.> ...
754s  [p, plo, pup] = normcdf (1, 2, 3)
754s ***** error<normcdf: invalid value for alpha.> [p, plo, pup] = ...
754s  normcdf (1, 2, 3, [1, 0; 0, 1], 0)
754s ***** error<normcdf: invalid value for alpha.> [p, plo, pup] = ...
754s  normcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
754s ***** error<normcdf: invalid value for alpha.> [p, plo, pup] = ...
754s  normcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
754s ***** error<normcdf: X, MU, and SIGMA must not be complex.> normcdf (i, 2, 2)
754s ***** error<normcdf: X, MU, and SIGMA must not be complex.> normcdf (2, i, 2)
754s ***** error<normcdf: X, MU, and SIGMA must not be complex.> normcdf (2, 2, i)
754s ***** error<normcdf: bad covariance matrix.> ...
754s  [p, plo, pup] =normcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
754s 24 tests, 24 passed, 0 known failure, 0 skipped
754s [inst/dist_fun/norminv.m]
754s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/norminv.m
754s ***** demo
754s  ## Plot various iCDFs from the normal distribution
754s  p = 0.001:0.001:0.999;
754s  x1 = norminv (p, 0, 0.5);
754s  x2 = norminv (p, 0, 1);
754s  x3 = norminv (p, 0, 2);
754s  x4 = norminv (p, -2, 0.8);
754s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
754s  grid on
754s  ylim ([-5, 5])
754s  legend ({"μ = 0, σ = 0.5", "μ = 0, σ = 1", ...
754s           "μ = 0, σ = 2", "μ = -2, σ = 0.8"}, "location", "northwest")
754s  title ("Normal iCDF")
754s  xlabel ("probability")
754s  ylabel ("values in x")
754s ***** shared p
754s  p = [-1 0 0.5 1 2];
754s ***** assert (norminv (p, ones (1,5), ones (1,5)), [NaN -Inf 1 Inf NaN])
754s ***** assert (norminv (p, 1, ones (1,5)), [NaN -Inf 1 Inf NaN])
754s ***** assert (norminv (p, ones (1,5), 1), [NaN -Inf 1 Inf NaN])
754s ***** assert (norminv (p, [1 -Inf NaN Inf 1], 1), [NaN NaN NaN NaN NaN])
754s ***** assert (norminv (p, 1, [1 0 NaN Inf 1]), [NaN NaN NaN NaN NaN])
754s ***** assert (norminv ([p(1:2) NaN p(4:5)], 1, 1), [NaN -Inf NaN Inf NaN])
754s ***** assert (norminv (p), probit (p))
754s ***** assert (norminv (0.31254), probit (0.31254))
754s ***** assert (norminv ([p, NaN], 1, 1), [NaN -Inf 1 Inf NaN NaN])
754s ***** assert (norminv (single ([p, NaN]), 1, 1), single ([NaN -Inf 1 Inf NaN NaN]))
754s ***** assert (norminv ([p, NaN], single (1), 1), single ([NaN -Inf 1 Inf NaN NaN]))
754s ***** assert (norminv ([p, NaN], 1, single (1)), single ([NaN -Inf 1 Inf NaN NaN]))
754s ***** error<norminv: function called with too few input arguments.> norminv ()
754s ***** error<norminv: P, MU, and SIGMA must be of common size or scalars.> ...
754s  norminv (ones (3), ones (2), ones (2))
754s ***** error<norminv: P, MU, and SIGMA must be of common size or scalars.> ...
754s  norminv (ones (2), ones (3), ones (2))
754s ***** error<norminv: P, MU, and SIGMA must be of common size or scalars.> ...
754s  norminv (ones (2), ones (2), ones (3))
754s ***** error<norminv: P, MU, and SIGMA must not be complex.> norminv (i, 2, 2)
754s ***** error<norminv: P, MU, and SIGMA must not be complex.> norminv (2, i, 2)
754s ***** error<norminv: P, MU, and SIGMA must not be complex.> norminv (2, 2, i)
754s 19 tests, 19 passed, 0 known failure, 0 skipped
754s [inst/dist_fun/normpdf.m]
754s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/normpdf.m
754s ***** demo
754s  ## Plot various PDFs from the normal distribution
754s  x = -5:0.01:5;
754s  y1 = normpdf (x, 0, 0.5);
754s  y2 = normpdf (x, 0, 1);
754s  y3 = normpdf (x, 0, 2);
754s  y4 = normpdf (x, -2, 0.8);
754s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
754s  grid on
754s  xlim ([-5, 5])
754s  ylim ([0, 0.9])
754s  legend ({"μ = 0, σ = 0.5", "μ = 0, σ = 1", ...
754s           "μ = 0, σ = 2", "μ = -2, σ = 0.8"}, "location", "northeast")
754s  title ("Normal PDF")
754s  xlabel ("values in x")
754s  ylabel ("density")
754s ***** shared x, y
754s  x = [-Inf, 1, 2, Inf];
754s  y = 1 / sqrt (2 * pi) * exp (-(x - 1) .^ 2 / 2);
754s ***** assert (normpdf (x, ones (1,4), ones (1,4)), y, eps)
754s ***** assert (normpdf (x, 1, ones (1,4)), y, eps)
754s ***** assert (normpdf (x, ones (1,4), 1), y, eps)
754s ***** assert (normpdf (x, [0 -Inf NaN Inf], 1), [y(1) NaN NaN NaN], eps)
754s ***** assert (normpdf (x, 1, [Inf NaN -1 0]), [NaN NaN NaN NaN], eps)
754s ***** assert (normpdf ([x, NaN], 1, 1), [y, NaN], eps)
754s ***** assert (normpdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
754s ***** assert (normpdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
754s ***** assert (normpdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
754s ***** error<normpdf: function called with too few input arguments.> normpdf ()
754s ***** error<normpdf: X, MU, and SIGMA must be of common size or scalars.> ...
754s  normpdf (ones (3), ones (2), ones (2))
754s ***** error<normpdf: X, MU, and SIGMA must be of common size or scalars.> ...
754s  normpdf (ones (2), ones (3), ones (2))
754s ***** error<normpdf: X, MU, and SIGMA must be of common size or scalars.> ...
754s  normpdf (ones (2), ones (2), ones (3))
754s ***** error<normpdf: X, MU, and SIGMA must not be complex.> normpdf (i, 2, 2)
754s ***** error<normpdf: X, MU, and SIGMA must not be complex.> normpdf (2, i, 2)
754s ***** error<normpdf: X, MU, and SIGMA must not be complex.> normpdf (2, 2, i)
754s 16 tests, 16 passed, 0 known failure, 0 skipped
754s [inst/dist_fun/normrnd.m]
754s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/normrnd.m
754s ***** assert (size (normrnd (1, 1)), [1 1])
754s ***** assert (size (normrnd (1, ones (2,1))), [2, 1])
754s ***** assert (size (normrnd (1, ones (2,2))), [2, 2])
754s ***** assert (size (normrnd (ones (2,1), 1)), [2, 1])
754s ***** assert (size (normrnd (ones (2,2), 1)), [2, 2])
754s ***** assert (size (normrnd (1, 1, 3)), [3, 3])
754s ***** assert (size (normrnd (1, 1, [4, 1])), [4, 1])
754s ***** assert (size (normrnd (1, 1, 4, 1)), [4, 1])
754s ***** assert (size (normrnd (1, 1, 4, 1, 5)), [4, 1, 5])
754s ***** assert (size (normrnd (1, 1, 0, 1)), [0, 1])
754s ***** assert (size (normrnd (1, 1, 1, 0)), [1, 0])
754s ***** assert (size (normrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
754s ***** assert (class (normrnd (1, 1)), "double")
754s ***** assert (class (normrnd (1, single (1))), "single")
754s ***** assert (class (normrnd (1, single ([1, 1]))), "single")
754s ***** assert (class (normrnd (single (1), 1)), "single")
754s ***** assert (class (normrnd (single ([1, 1]), 1)), "single")
754s ***** error<normrnd: function called with too few input arguments.> normrnd ()
754s ***** error<normrnd: function called with too few input arguments.> normrnd (1)
754s ***** error<normrnd: MU and SIGMA must be of common size or scalars.> ...
754s  normrnd (ones (3), ones (2))
754s ***** error<normrnd: MU and SIGMA must be of common size or scalars.> ...
754s  normrnd (ones (2), ones (3))
754s ***** error<normrnd: MU and SIGMA must not be complex.> normrnd (i, 2, 3)
754s ***** error<normrnd: MU and SIGMA must not be complex.> normrnd (1, i, 3)
754s ***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
754s  normrnd (1, 2, -1)
754s ***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
754s  normrnd (1, 2, 1.2)
754s ***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
754s  normrnd (1, 2, ones (2))
754s ***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
754s  normrnd (1, 2, [2 -1 2])
754s ***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
754s  normrnd (1, 2, [2 0 2.5])
754s ***** error<normrnd: dimensions must be non-negative integers.> ...
754s  normrnd (1, 2, 2, -1, 5)
754s ***** error<normrnd: dimensions must be non-negative integers.> ...
754s  normrnd (1, 2, 2, 1.5, 5)
754s ***** error<normrnd: MU and SIGMA must be scalars or of size SZ.> ...
754s  normrnd (2, ones (2), 3)
754s ***** error<normrnd: MU and SIGMA must be scalars or of size SZ.> ...
754s  normrnd (2, ones (2), [3, 2])
754s ***** error<normrnd: MU and SIGMA must be scalars or of size SZ.> ...
754s  normrnd (2, ones (2), 3, 2)
754s 33 tests, 33 passed, 0 known failure, 0 skipped
754s [inst/dist_fun/plcdf.m]
754s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/plcdf.m
754s ***** demo
754s  ## Plot various CDFs from the Piecewise linear distribution
754s  data = 0:0.01:10;
754s  x1 = [0, 1, 3, 4, 7, 10];
754s  Fx1 = [0, 0.2, 0.5, 0.6, 0.7, 1];
754s  x2 = [0, 2, 5, 6, 7, 8];
754s  Fx2 = [0, 0.1, 0.3, 0.6, 0.9, 1];
754s  p1 = plcdf (data, x1, Fx1);
754s  p2 = plcdf (data, x2, Fx2);
754s  plot (data, p1, "-b", data, p2, "g")
754s  grid on
754s  ylim ([0, 1])
754s  xlim ([0, 10])
754s  legend ({"x1, Fx1", "x2, Fx2"}, "location", "southeast")
754s  title ("Piecewise linear CDF")
754s  xlabel ("values in data")
754s  ylabel ("probability")
754s ***** test
754s  data = 0:0.2:1;
754s  p = plcdf (data, [0, 1], [0, 1]);
754s  assert (p, data);
754s ***** test
754s  data = 0:0.2:1;
754s  p = plcdf (data, [0, 2], [0, 1]);
754s  assert (p, 0.5 * data);
754s ***** test
754s  data = 0:0.2:1;
754s  p = plcdf (data, [0, 1], [0, 0.5]);
754s  assert (p, 0.5 * data);
754s ***** test
754s  data = 0:0.2:1;
754s  p = plcdf (data, [0, 0.5], [0, 1]);
754s  assert (p, [0, 0.4, 0.8, 1, 1, 1]);
754s ***** test
754s  data = 0:0.2:1;
754s  p = plcdf (data, [0, 1], [0, 1], "upper");
754s  assert (p, 1 - data);
754s ***** error<plcdf: function called with too few input arguments.> plcdf ()
754s ***** error<plcdf: function called with too few input arguments.> plcdf (1)
754s ***** error<plcdf: function called with too few input arguments.> plcdf (1, 2)
754s ***** error<plcdf: invalid argument for upper tail.> plcdf (1, 2, 3, "uper")
754s ***** error<plcdf: invalid argument for upper tail.> plcdf (1, 2, 3, 4)
754s ***** error<plcdf: X and FX must be vectors of equal size.> ...
754s  plcdf (1, [0, 1, 2], [0, 1])
754s ***** error<plcdf: X and FX must be at least two-elements long.> ...
754s  plcdf (1, [0], [1])
754s ***** error<plcdf: FX must be bounded in the range> ...
754s  plcdf (1, [0, 1, 2], [0, 1, 1.5])
754s ***** error<plcdf: FX must be bounded in the range> ...
754s  plcdf (1, [0, 1, 2], [0, i, 1])
754s ***** error<plcdf: DATA, X, and FX must not be complex.> ...
754s  plcdf (i, [0, 1, 2], [0, 0.5, 1])
754s ***** error<plcdf: DATA, X, and FX must not be complex.> ...
754s  plcdf (1, [0, i, 2], [0, 0.5, 1])
754s ***** error<plcdf: DATA, X, and FX must not be complex.> ...
754s  plcdf (1, [0, 1, 2], [0, 0.5i, 1])
754s 17 tests, 17 passed, 0 known failure, 0 skipped
754s [inst/dist_fun/plinv.m]
754s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/plinv.m
754s ***** demo
754s  ## Plot various iCDFs from the Piecewise linear distribution
754s  p = 0.001:0.001:0.999;
754s  x1 = [0, 1, 3, 4, 7, 10];
754s  Fx1 = [0, 0.2, 0.5, 0.6, 0.7, 1];
754s  x2 = [0, 2, 5, 6, 7, 8];
754s  Fx2 = [0, 0.1, 0.3, 0.6, 0.9, 1];
754s  data1 = plinv (p, x1, Fx1);
754s  data2 = plinv (p, x2, Fx2);
754s  plot (p, data1, "-b", p, data2, "-g")
754s  grid on
754s  legend ({"x1, Fx1", "x2, Fx2"}, "location", "northwest")
754s  title ("Piecewise linear iCDF")
754s  xlabel ("probability")
754s  ylabel ("values in data")
754s ***** test
754s  p = 0:0.2:1;
754s  data = plinv (p, [0, 1], [0, 1]);
754s  assert (data, p);
754s ***** test
754s  p = 0:0.2:1;
754s  data = plinv (p, [0, 2], [0, 1]);
754s  assert (data, 2 * p);
754s ***** test
754s  p = 0:0.2:1;
754s  data_out = 1:6;
754s  data = plinv (p, [0, 1], [0, 0.5]);
754s  assert (data, [0, 0.4, 0.8, NA, NA, NA]);
754s ***** test
754s  p = 0:0.2:1;
754s  data_out = 1:6;
754s  data = plinv (p, [0, 0.5], [0, 1]);
754s  assert (data, [0:0.1:0.5]);
754s ***** error<plinv: function called with too few input arguments.> plinv ()
754s ***** error<plinv: function called with too few input arguments.> plinv (1)
754s ***** error<plinv: function called with too few input arguments.> plinv (1, 2)
754s ***** error<plinv: X and FX must be vectors of equal size.> ...
754s  plinv (1, [0, 1, 2], [0, 1])
754s ***** error<plinv: X and FX must be at least two-elements long.> ...
754s  plinv (1, [0], [1])
754s ***** error<plinv: FX must be bounded in the range> ...
754s  plinv (1, [0, 1, 2], [0, 1, 1.5])
754s ***** error<plinv: FX must be bounded in the range> ...
754s  plinv (1, [0, 1, 2], [0, i, 1])
754s ***** error<plinv: P, X, and FX must not be complex.> ...
754s  plinv (i, [0, 1, 2], [0, 0.5, 1])
754s ***** error<plinv: P, X, and FX must not be complex.> ...
754s  plinv (1, [0, i, 2], [0, 0.5, 1])
754s ***** error<plinv: P, X, and FX must not be complex.> ...
754s  plinv (1, [0, 1, 2], [0, 0.5i, 1])
754s 14 tests, 14 passed, 0 known failure, 0 skipped
754s [inst/dist_fun/plpdf.m]
754s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/plpdf.m
754s ***** demo
754s  ## Plot various PDFs from the Piecewise linear distribution
754s  data = 0:0.01:10;
754s  x1 = [0, 1, 3, 4, 7, 10];
754s  Fx1 = [0, 0.2, 0.5, 0.6, 0.7, 1];
754s  x2 = [0, 2, 5, 6, 7, 8];
754s  Fx2 = [0, 0.1, 0.3, 0.6, 0.9, 1];
754s  y1 = plpdf (data, x1, Fx1);
754s  y2 = plpdf (data, x2, Fx2);
754s  plot (data, y1, "-b", data, y2, "g")
754s  grid on
754s  ylim ([0, 0.6])
754s  xlim ([0, 10])
754s  legend ({"x1, Fx1", "x2, Fx2"}, "location", "northeast")
754s  title ("Piecewise linear CDF")
754s  xlabel ("values in data")
754s  ylabel ("density")
754s ***** shared x, Fx
754s  x = [0, 1, 3, 4, 7, 10];
754s  Fx = [0, 0.2, 0.5, 0.6, 0.7, 1];
754s ***** assert (plpdf (0.5, x, Fx), 0.2, eps);
754s ***** assert (plpdf (1.5, x, Fx), 0.15, eps);
754s ***** assert (plpdf (3.5, x, Fx), 0.1, eps);
754s ***** assert (plpdf (5, x, Fx), 0.1/3, eps);
754s ***** assert (plpdf (8, x, Fx), 0.1, eps);
754s ***** error<plpdf: function called with too few input arguments.> plpdf ()
754s ***** error<plpdf: function called with too few input arguments.> plpdf (1)
754s ***** error<plpdf: function called with too few input arguments.> plpdf (1, 2)
754s ***** error<plpdf: X and FX must be vectors of equal size.> ...
754s  plpdf (1, [0, 1, 2], [0, 1])
754s ***** error<plpdf: X and FX must be at least two-elements long.> ...
754s  plpdf (1, [0], [1])
754s ***** error<plpdf: FX must be bounded in the range> ...
754s  plpdf (1, [0, 1, 2], [0, 1, 1.5])
754s ***** error<plpdf: FX must be bounded in the range> ...
754s  plpdf (1, [0, 1, 2], [0, i, 1])
754s ***** error<plpdf: DATA, X, and FX must not be complex.> ...
754s  plpdf (i, [0, 1, 2], [0, 0.5, 1])
754s ***** error<plpdf: DATA, X, and FX must not be complex.> ...
754s  plpdf (1, [0, i, 2], [0, 0.5, 1])
754s ***** error<plpdf: DATA, X, and FX must not be complex.> ...
754s  plpdf (1, [0, 1, 2], [0, 0.5i, 1])
754s 15 tests, 15 passed, 0 known failure, 0 skipped
754s [inst/dist_fun/plrnd.m]
754s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/plrnd.m
754s ***** shared x, Fx
754s  x = [0, 1, 3, 4, 7, 10];
754s  Fx = [0, 0.2, 0.5, 0.6, 0.7, 1];
754s ***** assert (size (plrnd (x, Fx)), [1, 1])
754s ***** assert (size (plrnd (x, Fx, 3)), [3, 3])
754s ***** assert (size (plrnd (x, Fx, [4, 1])), [4, 1])
754s ***** assert (size (plrnd (x, Fx, 4, 1)), [4, 1])
754s ***** assert (size (plrnd (x, Fx, 4, 1, 5)), [4, 1, 5])
754s ***** assert (size (plrnd (x, Fx, 0, 1)), [0, 1])
754s ***** assert (size (plrnd (x, Fx, 1, 0)), [1, 0])
754s ***** assert (size (plrnd (x, Fx, 1, 2, 0, 5)), [1, 2, 0, 5])
754s ***** assert (class (plrnd (x, Fx)), "double")
754s ***** assert (class (plrnd (x, single (Fx))), "single")
754s ***** assert (class (plrnd (single (x), Fx)), "single")
754s ***** error<plrnd: function called with too few input arguments.> plrnd ()
754s ***** error<plrnd: function called with too few input arguments.> plrnd (1)
754s ***** error<plrnd: X and FX must be vectors of equal size.> ...
754s  plrnd ([0, 1, 2], [0, 1])
754s ***** error<plrnd: X and FX must be at least two-elements long.> ...
754s  plrnd ([0], [1])
754s ***** error<plrnd: FX must be bounded in the range> ...
754s  plrnd ([0, 1, 2], [0, 1, 1.5])
754s ***** error<plrnd: FX must be bounded in the range> ...
754s  plrnd ([0, 1, 2], [0, i, 1])
754s ***** error<plrnd: X and FX must not be complex.> ...
754s  plrnd ([0, i, 2], [0, 0.5, 1])
754s ***** error<plrnd: X and FX must not be complex.> ...
754s  plrnd ([0, i, 2], [0, 0.5i, 1])
754s ***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
754s  plrnd (x, Fx, -1)
754s ***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
754s  plrnd (x, Fx, 1.2)
754s ***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
754s  plrnd (x, Fx, ones (2))
754s ***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
754s  plrnd (x, Fx, [2 -1 2])
754s ***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
754s  plrnd (x, Fx, [2 0 2.5])
754s ***** error<plrnd: dimensions must be non-negative integers.> ...
754s  plrnd (x, Fx, 2, -1, 5)
754s ***** error<plrnd: dimensions must be non-negative integers.> ...
754s  plrnd (x, Fx, 2, 1.5, 5)
754s 26 tests, 26 passed, 0 known failure, 0 skipped
754s [inst/dist_fun/poisscdf.m]
754s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/poisscdf.m
754s ***** demo
754s  ## Plot various CDFs from the Poisson distribution
754s  x = 0:20;
754s  p1 = poisscdf (x, 1);
754s  p2 = poisscdf (x, 4);
754s  p3 = poisscdf (x, 10);
754s  plot (x, p1, "*b", x, p2, "*g", x, p3, "*r")
754s  grid on
754s  ylim ([0, 1])
754s  legend ({"λ = 1", "λ = 4", "λ = 10"}, "location", "southeast")
754s  title ("Poisson CDF")
754s  xlabel ("values in x (number of occurences)")
754s  ylabel ("probability")
754s ***** shared x, y
754s  x = [-1 0 1 2 Inf];
754s  y = [0, gammainc(1, (x(2:4) +1), "upper"), 1];
754s ***** assert (poisscdf (x, ones (1,5)), y)
754s ***** assert (poisscdf (x, 1), y)
754s ***** assert (poisscdf (x, [1 0 NaN 1 1]), [y(1) 1 NaN y(4:5)])
754s ***** assert (poisscdf ([x(1:2) NaN Inf x(5)], 1), [y(1:2) NaN 1 y(5)])
754s ***** assert (poisscdf ([x, NaN], 1), [y, NaN])
754s ***** assert (poisscdf (single ([x, NaN]), 1), single ([y, NaN]), eps ("single"))
754s ***** assert (poisscdf ([x, NaN], single (1)), single ([y, NaN]), eps ("single"))
754s ***** error<poisscdf: function called with too few input arguments.> poisscdf ()
754s ***** error<poisscdf: function called with too few input arguments.> poisscdf (1)
754s ***** error<poisscdf: invalid argument for upper tail.> poisscdf (1, 2, 3)
754s ***** error<poisscdf: invalid argument for upper tail.> poisscdf (1, 2, "tail")
754s ***** error<poisscdf: X and LAMBDA must be of common size or scalars.> ...
754s  poisscdf (ones (3), ones (2))
754s ***** error<poisscdf: X and LAMBDA must be of common size or scalars.> ...
754s  poisscdf (ones (2), ones (3))
754s ***** error<poisscdf: X and LAMBDA must not be complex.> poisscdf (i, 2)
754s ***** error<poisscdf: X and LAMBDA must not be complex.> poisscdf (2, i)
754s 15 tests, 15 passed, 0 known failure, 0 skipped
754s [inst/dist_fun/poissinv.m]
754s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/poissinv.m
754s ***** demo
754s  ## Plot various iCDFs from the Poisson distribution
754s  p = 0.001:0.001:0.999;
754s  x1 = poissinv (p, 13);
754s  x2 = poissinv (p, 4);
754s  x3 = poissinv (p, 10);
754s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
754s  grid on
754s  ylim ([0, 20])
754s  legend ({"λ = 1", "λ = 4", "λ = 10"}, "location", "northwest")
754s  title ("Poisson iCDF")
754s  xlabel ("probability")
754s  ylabel ("values in x (number of occurences)")
754s ***** shared p
754s  p = [-1 0 0.5 1 2];
754s ***** assert (poissinv (p, ones (1,5)), [NaN 0 1 Inf NaN])
754s ***** assert (poissinv (p, 1), [NaN 0 1 Inf NaN])
754s ***** assert (poissinv (p, [1 0 NaN 1 1]), [NaN NaN NaN Inf NaN])
754s ***** assert (poissinv ([p(1:2) NaN p(4:5)], 1), [NaN 0 NaN Inf NaN])
754s ***** assert (poissinv ([p, NaN], 1), [NaN 0 1 Inf NaN NaN])
754s ***** assert (poissinv (single ([p, NaN]), 1), single ([NaN 0 1 Inf NaN NaN]))
754s ***** assert (poissinv ([p, NaN], single (1)), single ([NaN 0 1 Inf NaN NaN]))
754s ***** error<poissinv: function called with too few input arguments.> poissinv ()
754s ***** error<poissinv: function called with too few input arguments.> poissinv (1)
754s ***** error<poissinv: P and LAMBDA must be of common size or scalars.> ...
754s  poissinv (ones (3), ones (2))
754s ***** error<poissinv: P and LAMBDA must be of common size or scalars.> ...
754s  poissinv (ones (2), ones (3))
754s ***** error<poissinv: P and LAMBDA must not be complex.> poissinv (i, 2)
754s ***** error<poissinv: P and LAMBDA must not be complex.> poissinv (2, i)
754s 13 tests, 13 passed, 0 known failure, 0 skipped
754s [inst/dist_fun/poisspdf.m]
754s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/poisspdf.m
754s ***** demo
754s  ## Plot various PDFs from the Poisson distribution
754s  x = 0:20;
754s  y1 = poisspdf (x, 1);
754s  y2 = poisspdf (x, 4);
754s  y3 = poisspdf (x, 10);
754s  plot (x, y1, "*b", x, y2, "*g", x, y3, "*r")
754s  grid on
754s  ylim ([0, 0.4])
754s  legend ({"λ = 1", "λ = 4", "λ = 10"}, "location", "northeast")
754s  title ("Poisson PDF")
754s  xlabel ("values in x (number of occurences)")
754s  ylabel ("density")
754s ***** shared x, y
754s  x = [-1 0 1 2 Inf];
754s  y = [0, exp(-1)*[1 1 0.5], 0];
754s ***** assert (poisspdf (x, ones (1,5)), y, eps)
754s ***** assert (poisspdf (x, 1), y, eps)
754s ***** assert (poisspdf (x, [1 0 NaN 1 1]), [y(1) NaN NaN y(4:5)], eps)
754s ***** assert (poisspdf ([x, NaN], 1), [y, NaN], eps)
754s ***** assert (poisspdf (single ([x, NaN]), 1), single ([y, NaN]), eps ("single"))
754s ***** assert (poisspdf ([x, NaN], single (1)), single ([y, NaN]), eps ("single"))
754s ***** error<poisspdf: function called with too few input arguments.> poisspdf ()
754s ***** error<poisspdf: function called with too few input arguments.> poisspdf (1)
754s ***** error<poisspdf: X and LAMBDA must be of common size or scalars.> ...
754s  poisspdf (ones (3), ones (2))
754s ***** error<poisspdf: X and LAMBDA must be of common size or scalars.> ...
754s  poisspdf (ones (2), ones (3))
754s ***** error<poisspdf: X and LAMBDA must not be complex.> poisspdf (i, 2)
754s ***** error<poisspdf: X and LAMBDA must not be complex.> poisspdf (2, i)
754s 12 tests, 12 passed, 0 known failure, 0 skipped
754s [inst/dist_fun/poissrnd.m]
754s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/poissrnd.m
754s ***** assert (size (poissrnd (2)), [1, 1])
754s ***** assert (size (poissrnd (ones (2,1))), [2, 1])
754s ***** assert (size (poissrnd (ones (2,2))), [2, 2])
754s ***** assert (size (poissrnd (1, 3)), [3, 3])
754s ***** assert (size (poissrnd (1, [4 1])), [4, 1])
754s ***** assert (size (poissrnd (1, 4, 1)), [4, 1])
754s ***** assert (size (poissrnd (1, 4, 1)), [4, 1])
754s ***** assert (size (poissrnd (1, 4, 1, 5)), [4, 1, 5])
754s ***** assert (size (poissrnd (1, 0, 1)), [0, 1])
754s ***** assert (size (poissrnd (1, 1, 0)), [1, 0])
754s ***** assert (size (poissrnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
754s ***** assert (poissrnd (0, 1, 1), 0)
754s ***** assert (poissrnd ([0, 0, 0], [1, 3]), [0 0 0])
754s ***** assert (class (poissrnd (2)), "double")
754s ***** assert (class (poissrnd (single (2))), "single")
754s ***** assert (class (poissrnd (single ([2 2]))), "single")
754s ***** error<poissrnd: function called with too few input arguments.> poissrnd ()
754s ***** error<poissrnd: LAMBDA must not be complex.> poissrnd (i)
754s ***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
754s  poissrnd (1, -1)
754s ***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
754s  poissrnd (1, 1.2)
754s ***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
754s  poissrnd (1, ones (2))
754s ***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
754s  poissrnd (1, [2 -1 2])
754s ***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
754s  poissrnd (1, [2 0 2.5])
754s ***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
754s  poissrnd (ones (2), ones (2))
754s ***** error<poissrnd: dimensions must be non-negative integers.> ...
754s  poissrnd (1, 2, -1, 5)
754s ***** error<poissrnd: dimensions must be non-negative integers.> ...
754s  poissrnd (1, 2, 1.5, 5)
754s ***** error<poissrnd: LAMBDA must be scalar or of size SZ.> poissrnd (ones (2,2), 3)
754s ***** error<poissrnd: LAMBDA must be scalar or of size SZ.> poissrnd (ones (2,2), [3, 2])
754s ***** error<poissrnd: LAMBDA must be scalar or of size SZ.> poissrnd (ones (2,2), 2, 3)
754s 29 tests, 29 passed, 0 known failure, 0 skipped
754s [inst/dist_fun/raylcdf.m]
754s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/raylcdf.m
754s ***** demo
754s  ## Plot various CDFs from the Rayleigh distribution
754s  x = 0:0.01:10;
754s  p1 = raylcdf (x, 0.5);
754s  p2 = raylcdf (x, 1);
754s  p3 = raylcdf (x, 2);
754s  p4 = raylcdf (x, 3);
754s  p5 = raylcdf (x, 4);
754s  plot (x, p1, "-b", x, p2, "g", x, p3, "-r", x, p4, "-m", x, p5, "-k")
754s  grid on
754s  ylim ([0, 1])
754s  legend ({"σ = 0.5", "σ = 1", "σ = 2", ...
754s           "σ = 3", "σ = 4"}, "location", "southeast")
754s  title ("Rayleigh CDF")
754s  xlabel ("values in x")
754s  ylabel ("probability")
754s ***** test
754s  x = 0:0.5:2.5;
754s  sigma = 1:6;
754s  p = raylcdf (x, sigma);
754s  expected_p = [0.0000, 0.0308, 0.0540, 0.0679, 0.0769, 0.0831];
754s  assert (p, expected_p, 0.001);
754s ***** test
754s  x = 0:0.5:2.5;
754s  p = raylcdf (x, 0.5);
754s  expected_p = [0.0000, 0.3935, 0.8647, 0.9889, 0.9997, 1.0000];
754s  assert (p, expected_p, 0.001);
754s ***** shared x, p
754s  x = [-1, 0, 1, 2, Inf];
754s  p = [0, 0, 0.39346934028737, 0.86466471676338, 1];
754s ***** assert (raylcdf (x, 1), p, 1e-14)
754s ***** assert (raylcdf (x, 1, "upper"), 1 - p, 1e-14)
754s ***** error<raylcdf: function called with too few input arguments.> raylcdf ()
754s ***** error<raylcdf: function called with too few input arguments.> raylcdf (1)
754s ***** error<raylcdf: invalid argument for upper tail.> raylcdf (1, 2, "uper")
754s ***** error<raylcdf: invalid argument for upper tail.> raylcdf (1, 2, 3)
754s ***** error<raylcdf: X and SIGMA must be of common size or scalars.> ...
754s  raylcdf (ones (3), ones (2))
754s ***** error<raylcdf: X and SIGMA must be of common size or scalars.> ...
754s  raylcdf (ones (2), ones (3))
754s ***** error<raylcdf: X and SIGMA must not be complex.> raylcdf (i, 2)
754s ***** error<raylcdf: X and SIGMA must not be complex.> raylcdf (2, i)
754s 12 tests, 12 passed, 0 known failure, 0 skipped
754s [inst/dist_fun/raylinv.m]
754s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/raylinv.m
754s ***** demo
754s  ## Plot various iCDFs from the Rayleigh distribution
754s  p = 0.001:0.001:0.999;
754s  x1 = raylinv (p, 0.5);
754s  x2 = raylinv (p, 1);
754s  x3 = raylinv (p, 2);
754s  x4 = raylinv (p, 3);
754s  x5 = raylinv (p, 4);
754s  plot (p, x1, "-b", p, x2, "g", p, x3, "-r", p, x4, "-m", p, x5, "-k")
754s  grid on
754s  ylim ([0, 10])
754s  legend ({"σ = 0,5", "σ = 1", "σ = 2", ...
754s           "σ = 3", "σ = 4"}, "location", "northwest")
754s  title ("Rayleigh iCDF")
754s  xlabel ("probability")
754s  ylabel ("values in x")
754s ***** test
754s  p = 0:0.1:0.5;
754s  sigma = 1:6;
754s  x = raylinv (p, sigma);
754s  expected_x = [0.0000, 0.9181, 2.0041, 3.3784, 5.0538, 7.0645];
754s  assert (x, expected_x, 0.001);
754s ***** test
754s  p = 0:0.1:0.5;
754s  x = raylinv (p, 0.5);
754s  expected_x = [0.0000, 0.2295, 0.3340, 0.4223, 0.5054, 0.5887];
754s  assert (x, expected_x, 0.001);
754s ***** error<raylinv: function called with too few input arguments.> raylinv ()
754s ***** error<raylinv: function called with too few input arguments.> raylinv (1)
754s ***** error<raylinv: P and SIGMA must be of common size or scalars.> ...
754s  raylinv (ones (3), ones (2))
754s ***** error<raylinv: P and SIGMA must be of common size or scalars.> ...
754s  raylinv (ones (2), ones (3))
754s ***** error<raylinv: P and SIGMA must not be complex.> raylinv (i, 2)
754s ***** error<raylinv: P and SIGMA must not be complex.> raylinv (2, i)
754s 8 tests, 8 passed, 0 known failure, 0 skipped
754s [inst/dist_fun/raylpdf.m]
754s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/raylpdf.m
754s ***** demo
754s  ## Plot various PDFs from the Rayleigh distribution
754s  x = 0:0.01:10;
754s  y1 = raylpdf (x, 0.5);
754s  y2 = raylpdf (x, 1);
754s  y3 = raylpdf (x, 2);
754s  y4 = raylpdf (x, 3);
754s  y5 = raylpdf (x, 4);
754s  plot (x, y1, "-b", x, y2, "g", x, y3, "-r", x, y4, "-m", x, y5, "-k")
754s  grid on
754s  ylim ([0, 1.25])
754s  legend ({"σ = 0,5", "σ = 1", "σ = 2", ...
754s           "σ = 3", "σ = 4"}, "location", "northeast")
754s  title ("Rayleigh PDF")
754s  xlabel ("values in x")
754s  ylabel ("density")
754s ***** test
754s  x = 0:0.5:2.5;
754s  sigma = 1:6;
754s  y = raylpdf (x, sigma);
754s  expected_y = [0.0000, 0.1212, 0.1051, 0.0874, 0.0738, 0.0637];
754s  assert (y, expected_y, 0.001);
754s ***** test
754s  x = 0:0.5:2.5;
754s  y = raylpdf (x, 0.5);
754s  expected_y = [0.0000, 1.2131, 0.5413, 0.0667, 0.0027, 0.0000];
754s  assert (y, expected_y, 0.001);
754s ***** error<raylpdf: function called with too few input arguments.> raylpdf ()
754s ***** error<raylpdf: function called with too few input arguments.> raylpdf (1)
754s ***** error<raylpdf: X and SIGMA must be of common size or scalars.> ...
754s  raylpdf (ones (3), ones (2))
754s ***** error<raylpdf: X and SIGMA must be of common size or scalars.> ...
754s  raylpdf (ones (2), ones (3))
754s ***** error<raylpdf: X and SIGMA must not be complex.> raylpdf (i, 2)
754s ***** error<raylpdf: X and SIGMA must not be complex.> raylpdf (2, i)
754s 8 tests, 8 passed, 0 known failure, 0 skipped
754s [inst/dist_fun/raylrnd.m]
754s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/raylrnd.m
754s ***** assert (size (raylrnd (2)), [1, 1])
754s ***** assert (size (raylrnd (ones (2,1))), [2, 1])
754s ***** assert (size (raylrnd (ones (2,2))), [2, 2])
754s ***** assert (size (raylrnd (1, 3)), [3, 3])
754s ***** assert (size (raylrnd (1, [4 1])), [4, 1])
754s ***** assert (size (raylrnd (1, 4, 1)), [4, 1])
754s ***** assert (size (raylrnd (1, 4, 1)), [4, 1])
754s ***** assert (size (raylrnd (1, 4, 1, 5)), [4, 1, 5])
755s ***** assert (size (raylrnd (1, 0, 1)), [0, 1])
755s ***** assert (size (raylrnd (1, 1, 0)), [1, 0])
755s ***** assert (size (raylrnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
755s ***** assert (raylrnd (0, 1, 1), NaN)
755s ***** assert (raylrnd ([0, 0, 0], [1, 3]), [NaN, NaN, NaN])
755s ***** assert (class (raylrnd (2)), "double")
755s ***** assert (class (raylrnd (single (2))), "single")
755s ***** assert (class (raylrnd (single ([2 2]))), "single")
755s ***** error<raylrnd: function called with too few input arguments.> raylrnd ()
755s ***** error<raylrnd: SIGMA must not be complex.> raylrnd (i)
755s ***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
755s  raylrnd (1, -1)
755s ***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
755s  raylrnd (1, 1.2)
755s ***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
755s  raylrnd (1, ones (2))
755s ***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
755s  raylrnd (1, [2 -1 2])
755s ***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
755s  raylrnd (1, [2 0 2.5])
755s ***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
755s  raylrnd (ones (2), ones (2))
755s ***** error<raylrnd: dimensions must be non-negative integers.> ...
755s  raylrnd (1, 2, -1, 5)
755s ***** error<raylrnd: dimensions must be non-negative integers.> ...
755s  raylrnd (1, 2, 1.5, 5)
755s ***** error<raylrnd: SIGMA must be scalar or of size SZ.> raylrnd (ones (2,2), 3)
755s ***** error<raylrnd: SIGMA must be scalar or of size SZ.> raylrnd (ones (2,2), [3, 2])
755s ***** error<raylrnd: SIGMA must be scalar or of size SZ.> raylrnd (ones (2,2), 2, 3)
755s 29 tests, 29 passed, 0 known failure, 0 skipped
755s [inst/dist_fun/ricecdf.m]
755s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/ricecdf.m
755s ***** demo
755s  ## Plot various CDFs from the Rician distribution
755s  x = 0:0.01:10;
755s  p1 = ricecdf (x, 0, 1);
755s  p2 = ricecdf (x, 0.5, 1);
755s  p3 = ricecdf (x, 1, 1);
755s  p4 = ricecdf (x, 2, 1);
755s  p5 = ricecdf (x, 4, 1);
755s  plot (x, p1, "-b", x, p2, "g", x, p3, "-r", x, p4, "-m", x, p5, "-k")
755s  grid on
755s  ylim ([0, 1])
755s  xlim ([0, 8])
755s  legend ({"s = 0, σ = 1", "s = 0.5, σ = 1", "s = 1, σ = 1", ...
755s           "s = 2, σ = 1", "s = 4, σ = 1"}, "location", "southeast")
755s  title ("Rician CDF")
755s  xlabel ("values in x")
755s  ylabel ("probability")
755s ***** demo
755s  ## Plot various CDFs from the Rician distribution
755s  x = 0:0.01:10;
755s  p1 = ricecdf (x, 0, 0.5);
755s  p2 = ricecdf (x, 0, 2);
755s  p3 = ricecdf (x, 0, 3);
755s  p4 = ricecdf (x, 2, 2);
755s  p5 = ricecdf (x, 4, 2);
755s  plot (x, p1, "-b", x, p2, "g", x, p3, "-r", x, p4, "-m", x, p5, "-k")
755s  grid on
755s  ylim ([0, 1])
755s  xlim ([0, 8])
755s  legend ({"ν = 0, σ = 0.5", "ν = 0, σ = 2", "ν = 0, σ = 3", ...
755s           "ν = 2, σ = 2", "ν = 4, σ = 2"}, "location", "southeast")
755s  title ("Rician CDF")
755s  xlabel ("values in x")
755s  ylabel ("probability")
755s ***** test
755s  x = 0:0.5:2.5;
755s  s = 1:6;
755s  p = ricecdf (x, s, 1);
755s  expected_p = [0.0000, 0.0179, 0.0108, 0.0034, 0.0008, 0.0001];
755s  assert (p, expected_p, 0.001);
755s ***** test
755s  x = 0:0.5:2.5;
755s  sigma = 1:6;
755s  p = ricecdf (x, 1, sigma);
755s  expected_p = [0.0000, 0.0272, 0.0512, 0.0659, 0.0754, 0.0820];
755s  assert (p, expected_p, 0.001);
755s ***** test
755s  x = 0:0.5:2.5;
755s  p = ricecdf (x, 0, 1);
755s  expected_p = [0.0000, 0.1175, 0.3935, 0.6753, 0.8647, 0.9561];
755s  assert (p, expected_p, 0.001);
755s ***** test
755s  x = 0:0.5:2.5;
755s  p = ricecdf (x, 1, 1);
755s  expected_p = [0.0000, 0.0735, 0.2671, 0.5120, 0.7310, 0.8791];
755s  assert (p, expected_p, 0.001);
755s ***** shared x, p
755s  x = [-1, 0, 1, 2, Inf];
755s  p = [0, 0, 0.26712019620318, 0.73098793996409, 1];
755s ***** assert (ricecdf (x, 1, 1), p, 1e-14)
755s ***** assert (ricecdf (x, 1, 1, "upper"), 1 - p, 1e-14)
755s ***** error<ricecdf: function called with too few input arguments.> ricecdf ()
755s ***** error<ricecdf: function called with too few input arguments.> ricecdf (1)
755s ***** error<ricecdf: function called with too few input arguments.> ricecdf (1, 2)
755s ***** error<ricecdf: invalid argument for upper tail.> ricecdf (1, 2, 3, "uper")
755s ***** error<ricecdf: invalid argument for upper tail.> ricecdf (1, 2, 3, 4)
755s ***** error<ricecdf: X, S, and SIGMA must be of common size or scalars.> ...
755s  ricecdf (ones (3), ones (2), ones (2))
755s ***** error<ricecdf: X, S, and SIGMA must be of common size or scalars.> ...
755s  ricecdf (ones (2), ones (3), ones (2))
755s ***** error<ricecdf: X, S, and SIGMA must be of common size or scalars.> ...
755s  ricecdf (ones (2), ones (2), ones (3))
755s ***** error<ricecdf: X, S, and SIGMA must not be complex.> ricecdf (i, 2, 3)
755s ***** error<ricecdf: X, S, and SIGMA must not be complex.> ricecdf (2, i, 3)
755s ***** error<ricecdf: X, S, and SIGMA must not be complex.> ricecdf (2, 2, i)
755s 17 tests, 17 passed, 0 known failure, 0 skipped
755s [inst/dist_fun/riceinv.m]
755s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/riceinv.m
755s ***** demo
755s  ## Plot various iCDFs from the Rician distribution
755s  p = 0.001:0.001:0.999;
755s  x1 = riceinv (p, 0, 1);
755s  x2 = riceinv (p, 0.5, 1);
755s  x3 = riceinv (p, 1, 1);
755s  x4 = riceinv (p, 2, 1);
755s  x5 = riceinv (p, 4, 1);
755s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-m", p, x5, "-k")
755s  grid on
755s  legend ({"s = 0, σ = 1", "s = 0.5, σ = 1", "s = 1, σ = 1", ...
755s           "s = 2, σ = 1", "s = 4, σ = 1"}, "location", "northwest")
755s  title ("Rician iCDF")
755s  xlabel ("probability")
755s  ylabel ("values in x")
755s ***** shared p
755s  p = [-1 0 0.75 1 2];
755s ***** assert (riceinv (p, ones (1,5), 2*ones (1,5)), [NaN 0 3.5354 Inf NaN], 1e-4)
755s ***** assert (riceinv (p, 1, 2*ones (1,5)), [NaN 0 3.5354 Inf NaN], 1e-4)
755s ***** assert (riceinv (p, ones (1,5), 2), [NaN 0 3.5354 Inf NaN], 1e-4)
755s ***** assert (riceinv (p, [1 0 NaN 1 1], 2), [NaN 0 NaN Inf NaN])
755s ***** assert (riceinv (p, 1, 2*[1 0 NaN 1 1]), [NaN NaN NaN Inf NaN])
755s ***** assert (riceinv ([p(1:2) NaN p(4:5)], 1, 2), [NaN 0 NaN Inf NaN])
755s ***** assert (riceinv ([p, NaN], 1, 2), [NaN 0 3.5354 Inf NaN NaN], 1e-4)
755s ***** assert (riceinv (single ([p, NaN]), 1, 2), ...
755s         single ([NaN 0 3.5354 Inf NaN NaN]), 1e-4)
755s ***** assert (riceinv ([p, NaN], single (1), 2), ...
755s         single ([NaN 0 3.5354 Inf NaN NaN]), 1e-4)
755s ***** assert (riceinv ([p, NaN], 1, single (2)), ...
755s         single ([NaN 0 3.5354 Inf NaN NaN]), 1e-4)
755s ***** error<riceinv: function called with too few input arguments.> riceinv ()
755s ***** error<riceinv: function called with too few input arguments.> riceinv (1)
755s ***** error<riceinv: function called with too few input arguments.> riceinv (1,2)
755s ***** error<riceinv: function called with too many inputs> riceinv (1,2,3,4)
755s ***** error<riceinv: P, S, and B must be of common size or scalars.> ...
755s  riceinv (ones (3), ones (2), ones (2))
755s ***** error<riceinv: P, S, and B must be of common size or scalars.> ...
755s  riceinv (ones (2), ones (3), ones (2))
755s ***** error<riceinv: P, S, and B must be of common size or scalars.> ...
755s  riceinv (ones (2), ones (2), ones (3))
755s ***** error<riceinv: P, S, and B must not be complex.> riceinv (i, 2, 2)
755s ***** error<riceinv: P, S, and B must not be complex.> riceinv (2, i, 2)
755s ***** error<riceinv: P, S, and B must not be complex.> riceinv (2, 2, i)
755s 20 tests, 20 passed, 0 known failure, 0 skipped
755s [inst/dist_fun/ricepdf.m]
755s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/ricepdf.m
755s ***** demo
755s  ## Plot various PDFs from the Rician distribution
755s  x = 0:0.01:8;
755s  y1 = ricepdf (x, 0, 1);
755s  y2 = ricepdf (x, 0.5, 1);
755s  y3 = ricepdf (x, 1, 1);
755s  y4 = ricepdf (x, 2, 1);
755s  y5 = ricepdf (x, 4, 1);
755s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-m", x, y5, "-k")
755s  grid on
755s  ylim ([0, 0.65])
755s  xlim ([0, 8])
755s  legend ({"s = 0, σ = 1", "s = 0.5, σ = 1", "s = 1, σ = 1", ...
755s           "s = 2, σ = 1", "s = 4, σ = 1"}, "location", "northeast")
755s  title ("Rician PDF")
755s  xlabel ("values in x")
755s  ylabel ("density")
755s ***** shared x, y
755s  x = [-1 0 0.5 1 2];
755s  y = [0 0 0.1073 0.1978 0.2846];
755s ***** assert (ricepdf (x, ones (1, 5), 2 * ones (1, 5)), y, 1e-4)
755s ***** assert (ricepdf (x, 1, 2 * ones (1, 5)), y, 1e-4)
755s ***** assert (ricepdf (x, ones (1, 5), 2), y, 1e-4)
755s ***** assert (ricepdf (x, [0 NaN 1 1 1], 2), [0 NaN y(3:5)], 1e-4)
755s ***** assert (ricepdf (x, 1, 2 * [0 NaN 1 1 1]), [0 NaN y(3:5)], 1e-4)
755s ***** assert (ricepdf ([x, NaN], 1, 2), [y, NaN], 1e-4)
755s ***** assert (ricepdf (single ([x, NaN]), 1, 2), single ([y, NaN]), 1e-4)
755s ***** assert (ricepdf ([x, NaN], single (1), 2), single ([y, NaN]), 1e-4)
755s ***** assert (ricepdf ([x, NaN], 1, single (2)), single ([y, NaN]), 1e-4)
755s ***** error<ricepdf: function called with too few input arguments.> ricepdf ()
755s ***** error<ricepdf: function called with too few input arguments.> ricepdf (1)
755s ***** error<ricepdf: function called with too few input arguments.> ricepdf (1,2)
755s ***** error<ricepdf: function called with too many inputs> ricepdf (1,2,3,4)
755s ***** error<ricepdf: X, S, and SIGMA must be of common size or scalars.> ...
755s  ricepdf (ones (3), ones (2), ones (2))
755s ***** error<ricepdf: X, S, and SIGMA must be of common size or scalars.> ...
755s  ricepdf (ones (2), ones (3), ones (2))
755s ***** error<ricepdf: X, S, and SIGMA must be of common size or scalars.> ...
755s  ricepdf (ones (2), ones (2), ones (3))
755s ***** error<ricepdf: X, S, and SIGMA must not be complex.> ricepdf (i, 2, 2)
755s ***** error<ricepdf: X, S, and SIGMA must not be complex.> ricepdf (2, i, 2)
755s ***** error<ricepdf: X, S, and SIGMA must not be complex.> ricepdf (2, 2, i)
755s 19 tests, 19 passed, 0 known failure, 0 skipped
755s [inst/dist_fun/ricernd.m]
755s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/ricernd.m
755s ***** assert (size (ricernd (2, 1/2)), [1, 1])
755s ***** assert (size (ricernd (2 * ones (2, 1), 1/2)), [2, 1])
755s ***** assert (size (ricernd (2 * ones (2, 2), 1/2)), [2, 2])
755s ***** assert (size (ricernd (2, 1/2 * ones (2, 1))), [2, 1])
755s ***** assert (size (ricernd (1, 1/2 * ones (2, 2))), [2, 2])
755s ***** assert (size (ricernd (ones (2, 1), 1)), [2, 1])
755s ***** assert (size (ricernd (ones (2, 2), 1)), [2, 2])
755s ***** assert (size (ricernd (2, 1/2, 3)), [3, 3])
755s ***** assert (size (ricernd (1, 1, [4, 1])), [4, 1])
755s ***** assert (size (ricernd (1, 1, 4, 1)), [4, 1])
755s ***** assert (size (ricernd (1, 1, 4, 1, 5)), [4, 1, 5])
755s ***** assert (size (ricernd (1, 1, 0, 1)), [0, 1])
755s ***** assert (size (ricernd (1, 1, 1, 0)), [1, 0])
755s ***** assert (size (ricernd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
755s ***** assert (class (ricernd (1, 1)), "double")
755s ***** assert (class (ricernd (1, single (0))), "single")
755s ***** assert (class (ricernd (1, single ([0, 0]))), "single")
755s ***** assert (class (ricernd (1, single (1), 2)), "single")
755s ***** assert (class (ricernd (1, single ([1, 1]), 1, 2)), "single")
755s ***** assert (class (ricernd (single (1), 1, 2)), "single")
755s ***** assert (class (ricernd (single ([1, 1]), 1, 1, 2)), "single")
755s ***** error<ricernd: function called with too few input arguments.> ricernd ()
755s ***** error<ricernd: function called with too few input arguments.> ricernd (1)
755s ***** error<ricernd: S and SIGMA must be of common size or scalars.> ...
755s  ricernd (ones (3), ones (2))
755s ***** error<ricernd: S and SIGMA must be of common size or scalars.> ...
755s  ricernd (ones (2), ones (3))
755s ***** error<ricernd: S and SIGMA must not be complex.> ricernd (i, 2)
755s ***** error<ricernd: S and SIGMA must not be complex.> ricernd (1, i)
755s ***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
755s  ricernd (1, 1/2, -1)
755s ***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
755s  ricernd (1, 1/2, 1.2)
755s ***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
755s  ricernd (1, 1/2, ones (2))
755s ***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
755s  ricernd (1, 1/2, [2 -1 2])
755s ***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
755s  ricernd (1, 1/2, [2 0 2.5])
755s ***** error<ricernd: dimensions must be non-negative integers.> ...
755s  ricernd (1, 1/2, 2, -1, 5)
755s ***** error<ricernd: dimensions must be non-negative integers.> ...
755s  ricernd (1, 1/2, 2, 1.5, 5)
755s ***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
755s  ricernd (2, 1/2 * ones (2), 3)
755s ***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
755s  ricernd (2, 1/2 * ones (2), [3, 2])
755s ***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
755s  ricernd (2, 1/2 * ones (2), 3, 2)
755s ***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
755s  ricernd (2 * ones (2), 1/2, 3)
755s ***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
755s  ricernd (2 * ones (2), 1/2, [3, 2])
755s ***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
755s  ricernd (2 * ones (2), 1/2, 3, 2)
755s 40 tests, 40 passed, 0 known failure, 0 skipped
755s [inst/dist_fun/tcdf.m]
755s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/tcdf.m
755s ***** demo
755s  ## Plot various CDFs from the Student's T distribution
755s  x = -5:0.01:5;
755s  p1 = tcdf (x, 1);
755s  p2 = tcdf (x, 2);
755s  p3 = tcdf (x, 5);
755s  p4 = tcdf (x, Inf);
755s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-m")
755s  grid on
755s  xlim ([-5, 5])
755s  ylim ([0, 1])
755s  legend ({"df = 1", "df = 2", ...
755s           "df = 5", 'df = \infty'}, "location", "southeast")
755s  title ("Student's T CDF")
755s  xlabel ("values in x")
755s  ylabel ("probability")
755s ***** shared x,y
755s  x = [-Inf 0 1 Inf];
755s  y = [0 1/2 3/4 1];
755s ***** assert (tcdf (x, ones (1,4)), y, eps)
755s ***** assert (tcdf (x, 1), y, eps)
755s ***** assert (tcdf (x, [0 1 NaN 1]), [NaN 1/2 NaN 1], eps)
755s ***** assert (tcdf ([x(1:2) NaN x(4)], 1), [y(1:2) NaN y(4)], eps)
755s ***** assert (tcdf (2, 3, "upper"), 0.0697, 1e-4)
755s ***** assert (tcdf (205, 5, "upper"), 2.6206e-11, 1e-14)
755s ***** assert (tcdf ([x, NaN], 1), [y, NaN], eps)
755s ***** assert (tcdf (single ([x, NaN]), 1), single ([y, NaN]), eps ("single"))
755s ***** assert (tcdf ([x, NaN], single (1)), single ([y, NaN]), eps ("single"))
755s ***** error<tcdf: function called with too few input arguments.> tcdf ()
755s ***** error<tcdf: function called with too few input arguments.> tcdf (1)
755s ***** error<tcdf: invalid argument for upper tail.> tcdf (1, 2, "uper")
755s ***** error<tcdf: invalid argument for upper tail.> tcdf (1, 2, 3)
755s ***** error<tcdf: X and DF must be of common size or scalars.> ...
755s  tcdf (ones (3), ones (2))
755s ***** error<tcdf: X and DF must be of common size or scalars.> ...
755s  tcdf (ones (3), ones (2))
755s ***** error<tcdf: X and DF must be of common size or scalars.> ...
755s  tcdf (ones (3), ones (2), "upper")
755s ***** error<tcdf: X and DF must not be complex.> tcdf (i, 2)
755s ***** error<tcdf: X and DF must not be complex.> tcdf (2, i)
755s ***** shared tol_rel
755s  tol_rel = 10 * eps;
755s ***** assert (tcdf (10^(-10), 2.5), 0.50000000003618087, -tol_rel)
755s ***** assert (tcdf (10^(-11), 2.5), 0.50000000000361809, -tol_rel)
755s ***** assert (tcdf (10^(-12), 2.5), 0.50000000000036181, -tol_rel)
755s ***** assert (tcdf (10^(-13), 2.5), 0.50000000000003618, -tol_rel)
755s ***** assert (tcdf (10^(-14), 2.5), 0.50000000000000362, -tol_rel)
755s ***** assert (tcdf (10^(-15), 2.5), 0.50000000000000036, -tol_rel)
755s ***** assert (tcdf (10^(-16), 2.5), 0.50000000000000004, -tol_rel)
755s ***** assert (tcdf (-10^1, 2.5), 2.2207478836537124e-03, -tol_rel)
755s ***** assert (tcdf (-10^2, 2.5), 7.1916492116661878e-06, -tol_rel)
755s ***** assert (tcdf (-10^3, 2.5), 2.2747463948307452e-08, -tol_rel)
755s ***** assert (tcdf (-10^4, 2.5), 7.1933970159922115e-11, -tol_rel)
755s ***** assert (tcdf (-10^5, 2.5), 2.2747519231756221e-13, -tol_rel)
755s 30 tests, 30 passed, 0 known failure, 0 skipped
755s [inst/dist_fun/tinv.m]
755s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/tinv.m
755s ***** demo
755s  ## Plot various iCDFs from the Student's T distribution
755s  p = 0.001:0.001:0.999;
755s  x1 = tinv (p, 1);
755s  x2 = tinv (p, 2);
755s  x3 = tinv (p, 5);
755s  x4 = tinv (p, Inf);
755s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-m")
755s  grid on
755s  xlim ([0, 1])
755s  ylim ([-5, 5])
755s  legend ({"df = 1", "df = 2", ...
755s           "df = 5", 'df = \infty'}, "location", "northwest")
755s  title ("Student's T iCDF")
755s  xlabel ("probability")
755s  ylabel ("values in x")
755s ***** shared p
755s  p = [-1 0 0.5 1 2];
755s ***** assert (tinv (p, ones (1,5)), [NaN -Inf 0 Inf NaN])
755s ***** assert (tinv (p, 1), [NaN -Inf 0 Inf NaN], eps)
755s ***** assert (tinv (p, [1 0 NaN 1 1]), [NaN NaN NaN Inf NaN], eps)
755s ***** assert (tinv ([p(1:2) NaN p(4:5)], 1), [NaN -Inf NaN Inf NaN])
755s ***** assert (tinv ([p, NaN], 1), [NaN -Inf 0 Inf NaN NaN], eps)
755s ***** assert (tinv (single ([p, NaN]), 1), single ([NaN -Inf 0 Inf NaN NaN]), eps ("single"))
755s ***** assert (tinv ([p, NaN], single (1)), single ([NaN -Inf 0 Inf NaN NaN]), eps ("single"))
755s ***** error<tinv: function called with too few input arguments.> tinv ()
755s ***** error<tinv: function called with too few input arguments.> tinv (1)
755s ***** error<tinv: P and DF must be of common size or scalars.> ...
755s  tinv (ones (3), ones (2))
755s ***** error<tinv: P and DF must be of common size or scalars.> ...
755s  tinv (ones (2), ones (3))
755s ***** error<tinv: P and DF must not be complex.> tinv (i, 2)
755s ***** error<tinv: P and DF must not be complex.> tinv (2, i)
755s 13 tests, 13 passed, 0 known failure, 0 skipped
755s [inst/dist_fun/tlscdf.m]
755s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/tlscdf.m
755s ***** demo
755s  ## Plot various CDFs from the location-scale Student's T distribution
755s  x = -8:0.01:8;
755s  p1 = tlscdf (x, 0, 1, 1);
755s  p2 = tlscdf (x, 0, 2, 2);
755s  p3 = tlscdf (x, 3, 2, 5);
755s  p4 = tlscdf (x, -1, 3, Inf);
755s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-m")
755s  grid on
755s  xlim ([-8, 8])
755s  ylim ([0, 1])
755s  legend ({"mu = 0, sigma = 1, nu = 1", "mu = 0, sigma = 2, nu = 2", ...
755s           "mu = 3, sigma = 2, nu = 5", 'mu = -1, sigma = 3, nu = \infty'}, ...
755s          "location", "northwest")
755s  title ("Location-scale Student's T CDF")
755s  xlabel ("values in x")
755s  ylabel ("probability")
755s ***** shared x,y
755s  x = [-Inf 0 1 Inf];
755s  y = [0 1/2 3/4 1];
755s ***** assert (tlscdf (x, 0, 1, ones (1,4)), y, eps)
755s ***** assert (tlscdf (x, 0, 1, 1), y, eps)
755s ***** assert (tlscdf (x, 0, 1, [0 1 NaN 1]), [NaN 1/2 NaN 1], eps)
755s ***** assert (tlscdf ([x(1:2) NaN x(4)], 0, 1, 1), [y(1:2) NaN y(4)], eps)
755s ***** assert (tlscdf (2, 0, 1, 3, "upper"), 0.0697, 1e-4)
755s ***** assert (tlscdf (205, 0, 1, 5, "upper"), 2.6206e-11, 1e-14)
756s ***** assert (tlscdf ([x, NaN], 0, 1, 1), [y, NaN], eps)
756s ***** assert (tlscdf (single ([x, NaN]), 0, 1, 1), single ([y, NaN]), eps ("single"))
756s ***** assert (tlscdf ([x, NaN], single (0), 1, 1), single ([y, NaN]), eps ("single"))
756s ***** assert (tlscdf ([x, NaN], 0, single (1), 1), single ([y, NaN]), eps ("single"))
756s ***** assert (tlscdf ([x, NaN], 0, 1, single (1)), single ([y, NaN]), eps ("single"))
756s ***** error<tlscdf: function called with too few input arguments.> tlscdf ()
756s ***** error<tlscdf: function called with too few input arguments.> tlscdf (1)
756s ***** error<tlscdf: function called with too few input arguments.> tlscdf (1, 2)
756s ***** error<tlscdf: function called with too few input arguments.> tlscdf (1, 2, 3)
756s ***** error<tlscdf: invalid argument for upper tail.> tlscdf (1, 2, 3, 4, "uper")
756s ***** error<tlscdf: invalid argument for upper tail.> tlscdf (1, 2, 3, 4, 5)
756s ***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
756s  tlscdf (ones (3), ones (2), 1, 1)
756s ***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
756s  tlscdf (ones (3), 1, ones (2), 1)
756s ***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
756s  tlscdf (ones (3), 1, 1, ones (2))
756s ***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
756s  tlscdf (ones (3), ones (2), 1, 1, "upper")
756s ***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
756s  tlscdf (ones (3), 1, ones (2), 1, "upper")
756s ***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
756s  tlscdf (ones (3), 1, 1, ones (2), "upper")
756s ***** error<tlscdf: X, MU, SIGMA, and NU must not be complex.> tlscdf (i, 2, 1, 1)
756s ***** error<tlscdf: X, MU, SIGMA, and NU must not be complex.> tlscdf (2, i, 1, 1)
756s ***** error<tlscdf: X, MU, SIGMA, and NU must not be complex.> tlscdf (2, 1, i, 1)
756s ***** error<tlscdf: X, MU, SIGMA, and NU must not be complex.> tlscdf (2, 1, 1, i)
756s 27 tests, 27 passed, 0 known failure, 0 skipped
756s [inst/dist_fun/tlsinv.m]
756s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/tlsinv.m
756s ***** demo
756s  ## Plot various iCDFs from the location-scale Student's T distribution
756s  p = 0.001:0.001:0.999;
756s  x1 = tlsinv (p, 0, 1, 1);
756s  x2 = tlsinv (p, 0, 2, 2);
756s  x3 = tlsinv (p, 3, 2, 5);
756s  x4 = tlsinv (p, -1, 3, Inf);
756s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-m")
756s  grid on
756s  xlim ([0, 1])
756s  ylim ([-8, 8])
756s  legend ({"mu = 0, sigma = 1, nu = 1", "mu = 0, sigma = 2, nu = 2", ...
756s           "mu = 3, sigma = 2, nu = 5", 'mu = -1, sigma = 3, nu = \infty'}, ...
756s          "location", "southeast")
756s  title ("Location-scale Student's T iCDF")
756s  xlabel ("probability")
756s  ylabel ("values in x")
756s ***** shared p
756s  p = [-1 0 0.5 1 2];
756s ***** assert (tlsinv (p, 0, 1, ones (1,5)), [NaN -Inf 0 Inf NaN])
756s ***** assert (tlsinv (p, 0, 1, 1), [NaN -Inf 0 Inf NaN], eps)
756s ***** assert (tlsinv (p, 0, 1, [1 0 NaN 1 1]), [NaN NaN NaN Inf NaN], eps)
756s ***** assert (tlsinv ([p(1:2) NaN p(4:5)], 0, 1, 1), [NaN -Inf NaN Inf NaN])
756s ***** assert (class (tlsinv ([p, NaN], 0, 1, 1)), "double")
756s ***** assert (class (tlsinv (single ([p, NaN]), 0, 1, 1)), "single")
756s ***** assert (class (tlsinv ([p, NaN], single (0), 1, 1)), "single")
756s ***** assert (class (tlsinv ([p, NaN], 0, single (1), 1)), "single")
756s ***** assert (class (tlsinv ([p, NaN], 0, 1, single (1))), "single")
756s ***** error<tlsinv: function called with too few input arguments.> tlsinv ()
756s ***** error<tlsinv: function called with too few input arguments.> tlsinv (1)
756s ***** error<tlsinv: function called with too few input arguments.> tlsinv (1, 2)
756s ***** error<tlsinv: function called with too few input arguments.> tlsinv (1, 2, 3)
756s ***** error<tlsinv: P, MU, SIGMA, and NU must be of common size or scalars.> ...
756s  tlsinv (ones (3), ones (2), 1, 1)
756s ***** error<tlsinv: P, MU, SIGMA, and NU must be of common size or scalars.> ...
756s  tlsinv (ones (2), 1, ones (3), 1)
756s ***** error<tlsinv: P, MU, SIGMA, and NU must be of common size or scalars.> ...
756s  tlsinv (ones (2), 1, 1, ones (3))
756s ***** error<tlsinv: P, MU, SIGMA, and NU must not be complex.> tlsinv (i, 2, 3, 4)
756s ***** error<tlsinv: P, MU, SIGMA, and NU must not be complex.> tlsinv (2, i, 3, 4)
756s ***** error<tlsinv: P, MU, SIGMA, and NU must not be complex.> tlsinv (2, 2, i, 4)
756s ***** error<tlsinv: P, MU, SIGMA, and NU must not be complex.> tlsinv (2, 2, 3, i)
756s 20 tests, 20 passed, 0 known failure, 0 skipped
756s [inst/dist_fun/tlspdf.m]
756s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/tlspdf.m
756s ***** demo
756s  ## Plot various PDFs from the Student's T distribution
756s  x = -8:0.01:8;
756s  y1 = tlspdf (x, 0, 1, 1);
756s  y2 = tlspdf (x, 0, 2, 2);
756s  y3 = tlspdf (x, 3, 2, 5);
756s  y4 = tlspdf (x, -1, 3, Inf);
756s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-m")
756s  grid on
756s  xlim ([-8, 8])
756s  ylim ([0, 0.41])
756s  legend ({"mu = 0, sigma = 1, nu = 1", "mu = 0, sigma = 2, nu = 2", ...
756s           "mu = 3, sigma = 2, nu = 5", 'mu = -1, sigma = 3, nu = \infty'}, ...
756s          "location", "northwest")
756s  title ("Location-scale Student's T PDF")
756s  xlabel ("values in x")
756s  ylabel ("density")
756s ***** test
756s  x = rand (10,1);
756s  y = 1./(pi * (1 + x.^2));
756s  assert (tlspdf (x, 0, 1, 1), y, 5*eps);
756s  assert (tlspdf (x+5, 5, 1, 1), y, 5*eps);
756s  assert (tlspdf (x.*2, 0, 2, 1), y./2, 5*eps);
756s ***** shared x, y
756s  x = [-Inf 0 0.5 1 Inf];
756s  y = 1./(pi * (1 + x.^2));
756s ***** assert (tlspdf (x, 0, 1, ones (1,5)), y, eps)
756s ***** assert (tlspdf (x, 0, 1, 1), y, eps)
756s ***** assert (tlspdf (x, 0, 1, [0 NaN 1 1 1]), [NaN NaN y(3:5)], eps)
756s ***** assert (tlspdf (x, 0, 1, Inf), normpdf (x))
756s ***** assert (class (tlspdf ([x, NaN], 1, 1, 1)), "double")
756s ***** assert (class (tlspdf (single ([x, NaN]), 1, 1, 1)), "single")
756s ***** assert (class (tlspdf ([x, NaN], single (1), 1, 1)), "single")
756s ***** assert (class (tlspdf ([x, NaN], 1, single (1), 1)), "single")
756s ***** assert (class (tlspdf ([x, NaN], 1, 1, single (1))), "single")
756s ***** error<tlspdf: function called with too few input arguments.> tlspdf ()
756s ***** error<tlspdf: function called with too few input arguments.> tlspdf (1)
756s ***** error<tlspdf: function called with too few input arguments.> tlspdf (1, 2)
756s ***** error<tlspdf: function called with too few input arguments.> tlspdf (1, 2, 3)
756s ***** error<tlspdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
756s  tlspdf (ones (3), ones (2), 1, 1)
756s ***** error<tlspdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
756s  tlspdf (ones (2), 1, ones (3), 1)
756s ***** error<tlspdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
756s  tlspdf (ones (2), 1, 1, ones (3))
756s ***** error<tlspdf: X, MU, SIGMA, and NU must not be complex.> tlspdf (i, 2, 1, 1)
756s ***** error<tlspdf: X, MU, SIGMA, and NU must not be complex.> tlspdf (2, i, 1, 1)
756s ***** error<tlspdf: X, MU, SIGMA, and NU must not be complex.> tlspdf (2, 1, i, 1)
756s ***** error<tlspdf: X, MU, SIGMA, and NU must not be complex.> tlspdf (2, 1, 1, i)
756s 21 tests, 21 passed, 0 known failure, 0 skipped
756s [inst/dist_fun/tlsrnd.m]
756s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/tlsrnd.m
756s ***** assert (size (tlsrnd (1, 2, 3)), [1, 1])
756s ***** assert (size (tlsrnd (ones (2,1), 2, 3)), [2, 1])
756s ***** assert (size (tlsrnd (ones (2,2), 2, 3)), [2, 2])
756s ***** assert (size (tlsrnd (1, 2, 3, 3)), [3, 3])
756s ***** assert (size (tlsrnd (1, 2, 3, [4 1])), [4, 1])
756s ***** assert (size (tlsrnd (1, 2, 3, 4, 1)), [4, 1])
756s ***** assert (size (tlsrnd (1, 2, 3, 4, 1)), [4, 1])
756s ***** assert (size (tlsrnd (1, 2, 3, 4, 1, 5)), [4, 1, 5])
756s ***** assert (size (tlsrnd (1, 2, 3, 0, 1)), [0, 1])
756s ***** assert (size (tlsrnd (1, 2, 3, 1, 0)), [1, 0])
756s ***** assert (size (tlsrnd (1, 2, 3, 1, 2, 0, 5)), [1, 2, 0, 5])
756s ***** assert (tlsrnd (1, 2, 0, 1, 1), NaN)
756s ***** assert (tlsrnd (1, 2, [0, 0, 0], [1, 3]), [NaN, NaN, NaN])
756s ***** assert (class (tlsrnd (1, 2, 3)), "double")
756s ***** assert (class (tlsrnd (single (1), 2, 3)), "single")
756s ***** assert (class (tlsrnd (single ([1, 1]), 2, 3)), "single")
756s ***** assert (class (tlsrnd (1, single (2), 3)), "single")
756s ***** assert (class (tlsrnd (1, single ([2, 2]), 3)), "single")
756s ***** assert (class (tlsrnd (1, 2, single (3))), "single")
756s ***** assert (class (tlsrnd (1, 2, single ([3, 3]))), "single")
756s ***** error<tlsrnd: function called with too few input arguments.> tlsrnd ()
756s ***** error<tlsrnd: function called with too few input arguments.> tlsrnd (1)
756s ***** error<tlsrnd: function called with too few input arguments.> tlsrnd (1, 2)
756s ***** error<tlsrnd: MU, SIGMA, and NU must be of common size or scalars.> ...
756s  tlsrnd (ones (3), ones (2), 1)
756s ***** error<tlsrnd: MU, SIGMA, and NU must be of common size or scalars.> ...
756s  tlsrnd (ones (2), 1, ones (3))
756s ***** error<tlsrnd: MU, SIGMA, and NU must be of common size or scalars.> ...
756s  tlsrnd (1, ones (2), ones (3))
756s ***** error<tlsrnd: MU, SIGMA, and NU must not be complex.> tlsrnd (i, 2, 3)
756s ***** error<tlsrnd: MU, SIGMA, and NU must not be complex.> tlsrnd (1, i, 3)
756s ***** error<tlsrnd: MU, SIGMA, and NU must not be complex.> tlsrnd (1, 2, i)
756s ***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
756s  tlsrnd (1, 2, 3, -1)
756s ***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
756s  tlsrnd (1, 2, 3, 1.2)
756s ***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
756s  tlsrnd (1, 2, 3, ones (2))
756s ***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
756s  tlsrnd (1, 2, 3, [2 -1 2])
756s ***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
756s  tlsrnd (1, 2, 3, [2 0 2.5])
756s ***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
756s  tlsrnd (ones (2), 2, 3, ones (2))
756s ***** error<tlsrnd: dimensions must be non-negative integers.> ...
756s  tlsrnd (1, 2, 3, 2, -1, 5)
756s ***** error<tlsrnd: dimensions must be non-negative integers.> ...
756s  tlsrnd (1, 2, 3, 2, 1.5, 5)
756s ***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
756s  tlsrnd (ones (2,2), 2, 3, 3)
756s ***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
756s  tlsrnd (1, ones (2,2), 3, 3)
756s ***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
756s  tlsrnd (1, 2, ones (2,2), 3)
756s ***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
756s  tlsrnd (1, 2, ones (2,2), [3, 3])
756s ***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
756s  tlsrnd (1, 2, ones (2,2), 2, 3)
756s 42 tests, 42 passed, 0 known failure, 0 skipped
756s [inst/dist_fun/tpdf.m]
756s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/tpdf.m
756s ***** demo
756s  ## Plot various PDFs from the Student's T distribution
756s  x = -5:0.01:5;
756s  y1 = tpdf (x, 1);
756s  y2 = tpdf (x, 2);
756s  y3 = tpdf (x, 5);
756s  y4 = tpdf (x, Inf);
756s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-m")
756s  grid on
756s  xlim ([-5, 5])
756s  ylim ([0, 0.41])
756s  legend ({"df = 1", "df = 2", ...
756s           "df = 5", 'df = \infty'}, "location", "northeast")
756s  title ("Student's T PDF")
756s  xlabel ("values in x")
756s  ylabel ("density")
756s ***** test
756s  x = rand (10,1);
756s  y = 1./(pi * (1 + x.^2));
756s  assert (tpdf (x, 1), y, 5*eps);
756s ***** shared x, y
756s  x = [-Inf 0 0.5 1 Inf];
756s  y = 1./(pi * (1 + x.^2));
756s ***** assert (tpdf (x, ones (1,5)), y, eps)
756s ***** assert (tpdf (x, 1), y, eps)
756s ***** assert (tpdf (x, [0 NaN 1 1 1]), [NaN NaN y(3:5)], eps)
756s ***** assert (tpdf (x, Inf), normpdf (x))
756s ***** assert (tpdf ([x, NaN], 1), [y, NaN], eps)
756s ***** assert (tpdf (single ([x, NaN]), 1), single ([y, NaN]), eps ("single"))
756s ***** assert (tpdf ([x, NaN], single (1)), single ([y, NaN]), eps ("single"))
756s ***** error<tpdf: function called with too few input arguments.> tpdf ()
756s ***** error<tpdf: function called with too few input arguments.> tpdf (1)
756s ***** error<tpdf: X and DF must be of common size or scalars.> ...
756s  tpdf (ones (3), ones (2))
756s ***** error<tpdf: X and DF must be of common size or scalars.> ...
756s  tpdf (ones (2), ones (3))
756s ***** error<tpdf: X and DF must not be complex.> tpdf (i, 2)
756s ***** error<tpdf: X and DF must not be complex.> tpdf (2, i)
756s 14 tests, 14 passed, 0 known failure, 0 skipped
756s [inst/dist_fun/tricdf.m]
756s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/tricdf.m
756s ***** demo
756s  ## Plot various CDFs from the triangular distribution
756s  x = 0.001:0.001:10;
756s  p1 = tricdf (x, 3, 4, 6);
756s  p2 = tricdf (x, 1, 2, 5);
756s  p3 = tricdf (x, 2, 3, 9);
756s  p4 = tricdf (x, 2, 5, 9);
756s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
756s  grid on
756s  xlim ([0, 10])
756s  legend ({"a = 3, b = 4, c = 6", "a = 1, b = 2, c = 5", ...
756s           "a = 2, b = 3, c = 9", "a = 2, b = 5, c = 9"}, ...
756s          "location", "southeast")
756s  title ("Triangular CDF")
756s  xlabel ("values in x")
756s  ylabel ("probability")
756s ***** shared x, y
756s  x = [-1, 0, 0.1, 0.5, 0.9, 1, 2] + 1;
756s  y = [0, 0, 0.02, 0.5, 0.98, 1 1];
756s ***** assert (tricdf (x, ones (1,7), 1.5 * ones (1, 7), 2 * ones (1, 7)), y, eps)
756s ***** assert (tricdf (x, 1 * ones (1, 7), 1.5, 2), y, eps)
756s ***** assert (tricdf (x, 1 * ones (1, 7), 1.5, 2, "upper"), 1 - y, eps)
756s ***** assert (tricdf (x, 1, 1.5, 2 * ones (1, 7)), y, eps)
756s ***** assert (tricdf (x, 1, 1.5 * ones (1, 7), 2), y, eps)
756s ***** assert (tricdf (x, 1, 1.5, 2), y, eps)
756s ***** assert (tricdf (x, [1, 1, NaN, 1, 1, 1, 1], 1.5, 2), ...
756s  [y(1:2), NaN, y(4:7)], eps)
756s ***** assert (tricdf (x, 1, 1.5, 2*[1, 1, NaN, 1, 1, 1, 1]), ...
756s  [y(1:2), NaN, y(4:7)], eps)
756s ***** assert (tricdf (x, 1, 1.5, 2*[1, 1, NaN, 1, 1, 1, 1]), ...
756s  [y(1:2), NaN, y(4:7)], eps)
756s ***** assert (tricdf ([x, NaN], 1, 1.5, 2), [y, NaN], eps)
756s ***** assert (tricdf (single ([x, NaN]), 1, 1.5, 2), ...
756s  single ([y, NaN]), eps("single"))
756s ***** assert (tricdf ([x, NaN], single (1), 1.5, 2), ...
756s  single ([y, NaN]), eps("single"))
756s ***** assert (tricdf ([x, NaN], 1, single (1.5), 2), ...
756s  single ([y, NaN]), eps("single"))
756s ***** assert (tricdf ([x, NaN], 1, 1.5, single (2)), ...
756s  single ([y, NaN]), eps("single"))
756s ***** error<tricdf: function called with too few input arguments.> tricdf ()
756s ***** error<tricdf: function called with too few input arguments.> tricdf (1)
756s ***** error<tricdf: function called with too few input arguments.> tricdf (1, 2)
756s ***** error<tricdf: function called with too few input arguments.> tricdf (1, 2, 3)
756s ***** error<tricdf: function called with too many inputs> ...
756s  tricdf (1, 2, 3, 4, 5, 6)
756s ***** error<tricdf: invalid argument for upper tail.> tricdf (1, 2, 3, 4, "tail")
756s ***** error<tricdf: invalid argument for upper tail.> tricdf (1, 2, 3, 4, 5)
756s ***** error<tricdf: X, A, B, and C must be of common size or scalars.> ...
756s  tricdf (ones (3), ones (2), ones(2), ones(2))
756s ***** error<tricdf: X, A, B, and C must be of common size or scalars.> ...
756s  tricdf (ones (2), ones (3), ones(2), ones(2))
756s ***** error<tricdf: X, A, B, and C must be of common size or scalars.> ...
756s  tricdf (ones (2), ones (2), ones(3), ones(2))
756s ***** error<tricdf: X, A, B, and C must be of common size or scalars.> ...
756s  tricdf (ones (2), ones (2), ones(2), ones(3))
756s ***** error<tricdf: X, A, B, and C must not be complex.> tricdf (i, 2, 3, 4)
756s ***** error<tricdf: X, A, B, and C must not be complex.> tricdf (1, i, 3, 4)
756s ***** error<tricdf: X, A, B, and C must not be complex.> tricdf (1, 2, i, 4)
756s ***** error<tricdf: X, A, B, and C must not be complex.> tricdf (1, 2, 3, i)
756s 29 tests, 29 passed, 0 known failure, 0 skipped
756s [inst/dist_fun/triinv.m]
756s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/triinv.m
756s ***** demo
756s  ## Plot various iCDFs from the triangular distribution
756s  p = 0.001:0.001:0.999;
756s  x1 = triinv (p, 3, 6, 4);
756s  x2 = triinv (p, 1, 5, 2);
756s  x3 = triinv (p, 2, 9, 3);
756s  x4 = triinv (p, 2, 9, 5);
756s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
756s  grid on
756s  ylim ([0, 10])
756s  legend ({"a = 3, b = 6, c = 4", "a = 1, b = 5, c = 2", ...
756s           "a = 2, b = 9, c = 3", "a = 2, b = 9, c = 5"}, ...
756s          "location", "northwest")
756s  title ("Triangular CDF")
756s  xlabel ("probability")
756s  ylabel ("values in x")
756s ***** shared p, y
756s  p = [-1, 0, 0.02, 0.5, 0.98, 1, 2];
756s  y = [NaN, 0, 0.1, 0.5, 0.9, 1, NaN] + 1;
756s ***** assert (triinv (p, ones (1, 7), 1.5 * ones (1, 7), 2 * ones (1, 7)), y, eps)
756s ***** assert (triinv (p, 1 * ones (1, 7), 1.5, 2), y, eps)
756s ***** assert (triinv (p, 1, 1.5, 2 * ones (1, 7)), y, eps)
756s ***** assert (triinv (p, 1, 1.5*ones (1,7), 2), y, eps)
756s ***** assert (triinv (p, 1, 1.5, 2), y, eps)
756s ***** assert (triinv (p, [1, 1, NaN, 1, 1, 1, 1], 1.5, 2), [y(1:2), NaN, y(4:7)], eps)
756s ***** assert (triinv (p, 1, 1.5 * [1, 1, NaN, 1, 1, 1, 1], 2), [y(1:2), NaN, y(4:7)], eps)
756s ***** assert (triinv (p, 1, 1.5, 2 * [1, 1, NaN, 1, 1, 1, 1]), [y(1:2), NaN, y(4:7)], eps)
756s ***** assert (triinv ([p, NaN], 1, 1.5, 2), [y, NaN], eps)
756s ***** assert (triinv (single ([p, NaN]), 1, 1.5, 2), single ([y, NaN]), eps('single'))
756s ***** assert (triinv ([p, NaN], single (1), 1.5, 2), single ([y, NaN]), eps('single'))
756s ***** assert (triinv ([p, NaN], 1, single (1.5), 2), single ([y, NaN]), eps('single'))
756s ***** assert (triinv ([p, NaN], 1, 1.5, single (2)), single ([y, NaN]), eps('single'))
756s ***** error<triinv: function called with too few input arguments.> triinv ()
756s ***** error<triinv: function called with too few input arguments.> triinv (1)
756s ***** error<triinv: function called with too few input arguments.> triinv (1, 2)
756s ***** error<triinv: function called with too few input arguments.> triinv (1, 2, 3)
756s ***** error<triinv: function called with too many inputs> ...
756s  triinv (1, 2, 3, 4, 5)
756s ***** error<triinv: P, A, B, and C must be of common size or scalars.> ...
756s  triinv (ones (3), ones (2), ones(2), ones(2))
756s ***** error<triinv: P, A, B, and C must be of common size or scalars.> ...
756s  triinv (ones (2), ones (3), ones(2), ones(2))
756s ***** error<triinv: P, A, B, and C must be of common size or scalars.> ...
756s  triinv (ones (2), ones (2), ones(3), ones(2))
756s ***** error<triinv: P, A, B, and C must be of common size or scalars.> ...
756s  triinv (ones (2), ones (2), ones(2), ones(3))
756s ***** error<triinv: P, A, B, and C must not be complex.> triinv (i, 2, 3, 4)
756s ***** error<triinv: P, A, B, and C must not be complex.> triinv (1, i, 3, 4)
756s ***** error<triinv: P, A, B, and C must not be complex.> triinv (1, 2, i, 4)
756s ***** error<triinv: P, A, B, and C must not be complex.> triinv (1, 2, 3, i)
756s 26 tests, 26 passed, 0 known failure, 0 skipped
756s [inst/dist_fun/tripdf.m]
756s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/tripdf.m
756s ***** demo
756s  ## Plot various CDFs from the triangular distribution
756s  x = 0.001:0.001:10;
756s  y1 = tripdf (x, 3, 4, 6);
756s  y2 = tripdf (x, 1, 2, 5);
756s  y3 = tripdf (x, 2, 3, 9);
756s  y4 = tripdf (x, 2, 5, 9);
756s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
756s  grid on
756s  xlim ([0, 10])
756s  legend ({"a = 3, b = 4, c = 6", "a = 1, b = 2, c = 5", ...
756s           "a = 2, b = 3, c = 9", "a = 2, b = 5, c = 9"}, ...
756s          "location", "northeast")
756s  title ("Triangular CDF")
756s  xlabel ("values in x")
756s  ylabel ("probability")
756s ***** shared x, y, deps
756s  x = [-1, 0, 0.1, 0.5, 0.9, 1, 2] + 1;
756s  y = [0, 0, 0.4, 2, 0.4, 0, 0];
756s  deps = 2*eps;
756s ***** assert (tripdf (x, ones (1,7), 1.5*ones (1,7), 2*ones (1,7)), y, deps)
756s ***** assert (tripdf (x, 1*ones (1,7), 1.5, 2), y, deps)
756s ***** assert (tripdf (x, 1, 1.5, 2*ones (1,7)), y, deps)
756s ***** assert (tripdf (x, 1, 1.5*ones (1,7), 2), y, deps)
756s ***** assert (tripdf (x, 1, 1.5, 2), y, deps)
756s ***** assert (tripdf (x, [1, 1, NaN, 1, 1, 1, 1], 1.5, 2), [y(1:2), NaN, y(4:7)], deps)
756s ***** assert (tripdf (x, 1, 1.5, 2*[1, 1, NaN, 1, 1, 1, 1]), [y(1:2), NaN, y(4:7)], deps)
756s ***** assert (tripdf (x, 1, 1.5*[1, 1, NaN, 1, 1, 1, 1], 2), [y(1:2), NaN, y(4:7)], deps)
756s ***** assert (tripdf ([x, NaN], 1, 1.5, 2), [y, NaN], deps)
756s ***** assert (tripdf (single ([x, NaN]), 1, 1.5, 2), single ([y, NaN]), eps("single"))
756s ***** assert (tripdf ([x, NaN], single (1), 1.5, 2), single ([y, NaN]), eps("single"))
756s ***** assert (tripdf ([x, NaN], 1, 1.5, single (2)), single ([y, NaN]), eps("single"))
756s ***** assert (tripdf ([x, NaN], 1, single (1.5), 2), single ([y, NaN]), eps("single"))
756s ***** error<tripdf: function called with too few input arguments.> tripdf ()
756s ***** error<tripdf: function called with too few input arguments.> tripdf (1)
756s ***** error<tripdf: function called with too few input arguments.> tripdf (1, 2)
756s ***** error<tripdf: function called with too few input arguments.> tripdf (1, 2, 3)
756s ***** error<tripdf: function called with too many inputs> ...
756s  tripdf (1, 2, 3, 4, 5)
756s ***** error<tripdf: X, A, B, and C must be of common size or scalars.> ...
756s  tripdf (ones (3), ones (2), ones(2), ones(2))
756s ***** error<tripdf: X, A, B, and C must be of common size or scalars.> ...
756s  tripdf (ones (2), ones (3), ones(2), ones(2))
756s ***** error<tripdf: X, A, B, and C must be of common size or scalars.> ...
756s  tripdf (ones (2), ones (2), ones(3), ones(2))
756s ***** error<tripdf: X, A, B, and C must be of common size or scalars.> ...
756s  tripdf (ones (2), ones (2), ones(2), ones(3))
756s ***** error<tripdf: X, A, B, and C must not be complex.> tripdf (i, 2, 3, 4)
756s ***** error<tripdf: X, A, B, and C must not be complex.> tripdf (1, i, 3, 4)
756s ***** error<tripdf: X, A, B, and C must not be complex.> tripdf (1, 2, i, 4)
756s ***** error<tripdf: X, A, B, and C must not be complex.> tripdf (1, 2, 3, i)
756s 26 tests, 26 passed, 0 known failure, 0 skipped
756s [inst/dist_fun/trirnd.m]
756s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/trirnd.m
756s ***** assert (size (trirnd (1, 1.5, 2)), [1, 1])
756s ***** assert (size (trirnd (1 * ones (2, 1), 1.5, 2)), [2, 1])
756s ***** assert (size (trirnd (1 * ones (2, 2), 1.5, 2)), [2, 2])
756s ***** assert (size (trirnd (1, 1.5 * ones (2, 1), 2)), [2, 1])
756s ***** assert (size (trirnd (1, 1.5 * ones (2, 2), 2)), [2, 2])
756s ***** assert (size (trirnd (1, 1.5, 2 * ones (2, 1))), [2, 1])
756s ***** assert (size (trirnd (1, 1.5, 2 * ones (2, 2))), [2, 2])
756s ***** assert (size (trirnd (1, 1.5, 2, 3)), [3, 3])
756s ***** assert (size (trirnd (1, 1.5, 2, [4, 1])), [4, 1])
756s ***** assert (size (trirnd (1, 1.5, 2, 4, 1)), [4, 1])
756s ***** assert (class (trirnd (1, 1.5, 2)), "double")
756s ***** assert (class (trirnd (single (1), 1.5, 2)), "single")
756s ***** assert (class (trirnd (single ([1, 1]), 1.5, 2)), "single")
756s ***** assert (class (trirnd (1, single (1.5), 2)), "single")
756s ***** assert (class (trirnd (1, single ([1.5, 1.5]), 2)), "single")
756s ***** assert (class (trirnd (1, 1.5, single (1.5))), "single")
756s ***** assert (class (trirnd (1, 1.5, single ([2, 2]))), "single")
756s ***** error<trirnd: function called with too few input arguments.> trirnd ()
756s ***** error<trirnd: function called with too few input arguments.> trirnd (1)
756s ***** error<trirnd: function called with too few input arguments.> trirnd (1, 2)
756s ***** error<trirnd: A, B, and C must be of common size or scalars.> ...
756s  trirnd (ones (3), 5 * ones (2), ones (2))
756s ***** error<trirnd: A, B, and C must be of common size or scalars.> ...
756s  trirnd (ones (2), 5 * ones (3), ones (2))
756s ***** error<trirnd: A, B, and C must be of common size or scalars.> ...
756s  trirnd (ones (2), 5 * ones (2), ones (3))
756s ***** error<trirnd: A, B, and C must not be complex.> trirnd (i, 5, 3)
756s ***** error<trirnd: A, B, and C must not be complex.> trirnd (1, 5+i, 3)
756s ***** error<trirnd: A, B, and C must not be complex.> trirnd (1, 5, i)
756s ***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
756s  trirnd (1, 5, 3, -1)
756s ***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
756s  trirnd (1, 5, 3, 1.2)
756s ***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
756s  trirnd (1, 5, 3, ones (2))
756s ***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
756s  trirnd (1, 5, 3, [2 -1 2])
756s ***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
756s  trirnd (1, 5, 3, [2 0 2.5])
756s ***** error<trirnd: dimensions must be non-negative integers.> ...
756s  trirnd (1, 5, 3, 2, -1, 5)
756s ***** error<trirnd: dimensions must be non-negative integers.> ...
756s  trirnd (1, 5, 3, 2, 1.5, 5)
756s ***** error<trirnd: A, B, and C must be scalar or of size SZ.> ...
756s  trirnd (2, 5 * ones (2), 2, 3)
756s ***** error<trirnd: A, B, and C must be scalar or of size SZ.> ...
756s  trirnd (2, 5 * ones (2), 2, [3, 2])
756s ***** error<trirnd: A, B, and C must be scalar or of size SZ.> ...
756s  trirnd (2, 5 * ones (2), 2, 3, 2)
756s 36 tests, 36 passed, 0 known failure, 0 skipped
756s [inst/dist_fun/trnd.m]
756s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/trnd.m
756s ***** assert (size (trnd (2)), [1, 1])
756s ***** assert (size (trnd (ones (2,1))), [2, 1])
756s ***** assert (size (trnd (ones (2,2))), [2, 2])
756s ***** assert (size (trnd (1, 3)), [3, 3])
756s ***** assert (size (trnd (1, [4 1])), [4, 1])
756s ***** assert (size (trnd (1, 4, 1)), [4, 1])
756s ***** assert (size (trnd (1, 4, 1)), [4, 1])
756s ***** assert (size (trnd (1, 4, 1, 5)), [4, 1, 5])
756s ***** assert (size (trnd (1, 0, 1)), [0, 1])
756s ***** assert (size (trnd (1, 1, 0)), [1, 0])
756s ***** assert (size (trnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
756s ***** assert (trnd (0, 1, 1), NaN)
756s ***** assert (trnd ([0, 0, 0], [1, 3]), [NaN, NaN, NaN])
756s ***** assert (class (trnd (2)), "double")
756s ***** assert (class (trnd (single (2))), "single")
756s ***** assert (class (trnd (single ([2 2]))), "single")
756s ***** error<trnd: function called with too few input arguments.> trnd ()
756s ***** error<trnd: DF must not be complex.> trnd (i)
756s ***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
756s  trnd (1, -1)
756s ***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
756s  trnd (1, 1.2)
756s ***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
756s  trnd (1, ones (2))
756s ***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
756s  trnd (1, [2 -1 2])
756s ***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
756s  trnd (1, [2 0 2.5])
756s ***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
756s  trnd (ones (2), ones (2))
756s ***** error<trnd: dimensions must be non-negative integers.> ...
756s  trnd (1, 2, -1, 5)
756s ***** error<trnd: dimensions must be non-negative integers.> ...
756s  trnd (1, 2, 1.5, 5)
756s ***** error<trnd: DF must be scalar or of size SZ.> trnd (ones (2,2), 3)
756s ***** error<trnd: DF must be scalar or of size SZ.> trnd (ones (2,2), [3, 2])
756s ***** error<trnd: DF must be scalar or of size SZ.> trnd (ones (2,2), 2, 3)
756s 29 tests, 29 passed, 0 known failure, 0 skipped
756s [inst/dist_fun/unidcdf.m]
756s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/unidcdf.m
756s ***** demo
756s  ## Plot various CDFs from the discrete uniform distribution
756s  x = 0:10;
756s  p1 = unidcdf (x, 5);
756s  p2 = unidcdf (x, 9);
756s  plot (x, p1, "*b", x, p2, "*g")
756s  grid on
756s  xlim ([0, 10])
756s  ylim ([0, 1])
756s  legend ({"N = 5", "N = 9"}, "location", "southeast")
756s  title ("Discrete uniform CDF")
756s  xlabel ("values in x")
756s  ylabel ("probability")
756s ***** shared x, y
756s  x = [0 1 2.5 10 11];
756s  y = [0, 0.1 0.2 1.0 1.0];
756s ***** assert (unidcdf (x, 10*ones (1,5)), y)
756s ***** assert (unidcdf (x, 10*ones (1,5), "upper"), 1 - y)
756s ***** assert (unidcdf (x, 10), y)
756s ***** assert (unidcdf (x, 10, "upper"), 1 - y)
756s ***** assert (unidcdf (x, 10*[0 1 NaN 1 1]), [NaN 0.1 NaN y(4:5)])
756s ***** assert (unidcdf ([x(1:2) NaN Inf x(5)], 10), [y(1:2) NaN 1 y(5)])
756s ***** assert (unidcdf ([x, NaN], 10), [y, NaN])
756s ***** assert (unidcdf (single ([x, NaN]), 10), single ([y, NaN]))
756s ***** assert (unidcdf ([x, NaN], single (10)), single ([y, NaN]))
756s ***** error<unidcdf: function called with too few input arguments.> unidcdf ()
756s ***** error<unidcdf: function called with too few input arguments.> unidcdf (1)
756s ***** error<unidcdf: invalid argument for upper tail.> unidcdf (1, 2, 3)
756s ***** error<unidcdf: invalid argument for upper tail.> unidcdf (1, 2, "tail")
756s ***** error<unidcdf: X and N must be of common size or scalars.> ...
756s  unidcdf (ones (3), ones (2))
756s ***** error<unidcdf: X and N must be of common size or scalars.> ...
756s  unidcdf (ones (2), ones (3))
756s ***** error<unidcdf: X and N must not be complex.> unidcdf (i, 2)
756s ***** error<unidcdf: X and N must not be complex.> unidcdf (2, i)
756s 17 tests, 17 passed, 0 known failure, 0 skipped
756s [inst/dist_fun/unidinv.m]
756s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/unidinv.m
756s ***** demo
756s  ## Plot various iCDFs from the discrete uniform distribution
756s  p = 0.001:0.001:0.999;
756s  x1 = unidinv (p, 5);
756s  x2 = unidinv (p, 9);
756s  plot (p, x1, "-b", p, x2, "-g")
756s  grid on
756s  xlim ([0, 1])
756s  ylim ([0, 10])
756s  legend ({"N = 5", "N = 9"}, "location", "northwest")
756s  title ("Discrete uniform iCDF")
756s  xlabel ("probability")
756s  ylabel ("values in x")
756s ***** shared p
756s  p = [-1 0 0.5 1 2];
756s ***** assert (unidinv (p, 10*ones (1,5)), [NaN NaN 5 10 NaN], eps)
756s ***** assert (unidinv (p, 10), [NaN NaN 5 10 NaN], eps)
756s ***** assert (unidinv (p, 10*[0 1 NaN 1 1]), [NaN NaN NaN 10 NaN], eps)
756s ***** assert (unidinv ([p(1:2) NaN p(4:5)], 10), [NaN NaN NaN 10 NaN], eps)
756s ***** assert (unidinv ([p, NaN], 10), [NaN NaN 5 10 NaN NaN], eps)
756s ***** assert (unidinv (single ([p, NaN]), 10), single ([NaN NaN 5 10 NaN NaN]), eps)
756s ***** assert (unidinv ([p, NaN], single (10)), single ([NaN NaN 5 10 NaN NaN]), eps)
756s ***** error<unidinv: function called with too few input arguments.> unidinv ()
756s ***** error<unidinv: function called with too few input arguments.> unidinv (1)
756s ***** error<unidinv: P and N must be of common size or scalars.> ...
756s  unidinv (ones (3), ones (2))
756s ***** error<unidinv: P and N must be of common size or scalars.> ...
756s  unidinv (ones (2), ones (3))
756s ***** error<unidinv: P and N must not be complex.> unidinv (i, 2)
756s ***** error<unidinv: P and N must not be complex.> unidinv (2, i)
756s 13 tests, 13 passed, 0 known failure, 0 skipped
756s [inst/dist_fun/unidpdf.m]
757s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/unidpdf.m
757s ***** demo
757s  ## Plot various PDFs from the discrete uniform distribution
757s  x = 0:10;
757s  y1 = unidpdf (x, 5);
757s  y2 = unidpdf (x, 9);
757s  plot (x, y1, "*b", x, y2, "*g")
757s  grid on
757s  xlim ([0, 10])
757s  ylim ([0, 0.25])
757s  legend ({"N = 5", "N = 9"}, "location", "northeast")
757s  title ("Descrete uniform PDF")
757s  xlabel ("values in x")
757s  ylabel ("density")
757s ***** shared x, y
757s  x = [-1 0 1 2 10 11];
757s  y = [0 0 0.1 0.1 0.1 0];
757s ***** assert (unidpdf (x, 10*ones (1,6)), y)
757s ***** assert (unidpdf (x, 10), y)
757s ***** assert (unidpdf (x, 10*[0 NaN 1 1 1 1]), [NaN NaN y(3:6)])
757s ***** assert (unidpdf ([x, NaN], 10), [y, NaN])
757s ***** assert (unidpdf (single ([x, NaN]), 10), single ([y, NaN]))
757s ***** assert (unidpdf ([x, NaN], single (10)), single ([y, NaN]))
757s ***** error<unidpdf: function called with too few input arguments.> unidpdf ()
757s ***** error<unidpdf: function called with too few input arguments.> unidpdf (1)
757s ***** error<unidpdf: X and N must be of common size or scalars.> ...
757s  unidpdf (ones (3), ones (2))
757s ***** error<unidpdf: X and N must be of common size or scalars.> ...
757s  unidpdf (ones (2), ones (3))
757s ***** error<unidpdf: X and N must not be complex.> unidpdf (i, 2)
757s ***** error<unidpdf: X and N must not be complex.> unidpdf (2, i)
757s 12 tests, 12 passed, 0 known failure, 0 skipped
757s [inst/dist_fun/unidrnd.m]
757s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/unidrnd.m
757s ***** assert (size (unidrnd (2)), [1, 1])
757s ***** assert (size (unidrnd (ones (2,1))), [2, 1])
757s ***** assert (size (unidrnd (ones (2,2))), [2, 2])
757s ***** assert (size (unidrnd (1, 3)), [3, 3])
757s ***** assert (size (unidrnd (1, [4 1])), [4, 1])
757s ***** assert (size (unidrnd (1, 4, 1)), [4, 1])
757s ***** assert (size (unidrnd (1, 4, 1)), [4, 1])
757s ***** assert (size (unidrnd (1, 4, 1, 5)), [4, 1, 5])
757s ***** assert (size (unidrnd (1, 0, 1)), [0, 1])
757s ***** assert (size (unidrnd (1, 1, 0)), [1, 0])
757s ***** assert (size (unidrnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
757s ***** assert (unidrnd (0, 1, 1), NaN)
757s ***** assert (unidrnd ([0, 0, 0], [1, 3]), [NaN, NaN, NaN])
757s ***** assert (class (unidrnd (2)), "double")
757s ***** assert (class (unidrnd (single (2))), "single")
757s ***** assert (class (unidrnd (single ([2 2]))), "single")
757s ***** error<unidrnd: function called with too few input arguments.> unidrnd ()
757s ***** error<unidrnd: N must not be complex.> unidrnd (i)
757s ***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
757s  unidrnd (1, -1)
757s ***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
757s  unidrnd (1, 1.2)
757s ***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
757s  unidrnd (1, ones (2))
757s ***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
757s  unidrnd (1, [2 -1 2])
757s ***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
757s  unidrnd (1, [2 0 2.5])
757s ***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
757s  unidrnd (ones (2), ones (2))
757s ***** error<unidrnd: dimensions must be non-negative integers.> ...
757s  unidrnd (1, 2, -1, 5)
757s ***** error<unidrnd: dimensions must be non-negative integers.> ...
757s  unidrnd (1, 2, 1.5, 5)
757s ***** error<unidrnd: N must be scalar or of size SZ.> unidrnd (ones (2,2), 3)
757s ***** error<unidrnd: N must be scalar or of size SZ.> unidrnd (ones (2,2), [3, 2])
757s ***** error<unidrnd: N must be scalar or of size SZ.> unidrnd (ones (2,2), 2, 3)
757s 29 tests, 29 passed, 0 known failure, 0 skipped
757s [inst/dist_fun/unifcdf.m]
757s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/unifcdf.m
757s ***** demo
757s  ## Plot various CDFs from the continuous uniform distribution
757s  x = 0:0.1:10;
757s  p1 = unifcdf (x, 2, 5);
757s  p2 = unifcdf (x, 3, 9);
757s  plot (x, p1, "-b", x, p2, "-g")
757s  grid on
757s  xlim ([0, 10])
757s  ylim ([0, 1])
757s  legend ({"a = 2, b = 5", "a = 3, b = 9"}, "location", "southeast")
757s  title ("Continuous uniform CDF")
757s  xlabel ("values in x")
757s  ylabel ("probability")
757s ***** shared x, y
757s  x = [-1 0 0.5 1 2] + 1;
757s  y = [0 0 0.5 1 1];
757s ***** assert (unifcdf (x, ones (1,5), 2*ones (1,5)), y)
757s ***** assert (unifcdf (x, ones (1,5), 2*ones (1,5), "upper"), 1 - y)
757s ***** assert (unifcdf (x, 1, 2*ones (1,5)), y)
757s ***** assert (unifcdf (x, 1, 2*ones (1,5), "upper"), 1 - y)
757s ***** assert (unifcdf (x, ones (1,5), 2), y)
757s ***** assert (unifcdf (x, ones (1,5), 2, "upper"), 1 - y)
757s ***** assert (unifcdf (x, [2 1 NaN 1 1], 2), [NaN 0 NaN 1 1])
757s ***** assert (unifcdf (x, [2 1 NaN 1 1], 2, "upper"), 1 - [NaN 0 NaN 1 1])
757s ***** assert (unifcdf (x, 1, 2*[0 1 NaN 1 1]), [NaN 0 NaN 1 1])
757s ***** assert (unifcdf (x, 1, 2*[0 1 NaN 1 1], "upper"), 1 - [NaN 0 NaN 1 1])
757s ***** assert (unifcdf ([x(1:2) NaN x(4:5)], 1, 2), [y(1:2) NaN y(4:5)])
757s ***** assert (unifcdf ([x(1:2) NaN x(4:5)], 1, 2, "upper"), 1 - [y(1:2) NaN y(4:5)])
757s ***** assert (unifcdf ([x, NaN], 1, 2), [y, NaN])
757s ***** assert (unifcdf (single ([x, NaN]), 1, 2), single ([y, NaN]))
757s ***** assert (unifcdf ([x, NaN], single (1), 2), single ([y, NaN]))
757s ***** assert (unifcdf ([x, NaN], 1, single (2)), single ([y, NaN]))
757s ***** error<unifcdf: function called with too few input arguments.> unifcdf ()
757s ***** error<unifcdf: function called with too few input arguments.> unifcdf (1)
757s ***** error<unifcdf: function called with too few input arguments.> unifcdf (1, 2)
757s ***** error<unifcdf: invalid argument for upper tail.> unifcdf (1, 2, 3, 4)
757s ***** error<unifcdf: invalid argument for upper tail.> unifcdf (1, 2, 3, "tail")
757s ***** error<unifcdf: X, A, and B must be of common size or scalars.> ...
757s  unifcdf (ones (3), ones (2), ones (2))
757s ***** error<unifcdf: X, A, and B must be of common size or scalars.> ...
757s  unifcdf (ones (2), ones (3), ones (2))
757s ***** error<unifcdf: X, A, and B must be of common size or scalars.> ...
757s  unifcdf (ones (2), ones (2), ones (3))
757s ***** error<unifcdf: X, A, and B must not be complex.> unifcdf (i, 2, 2)
757s ***** error<unifcdf: X, A, and B must not be complex.> unifcdf (2, i, 2)
757s ***** error<unifcdf: X, A, and B must not be complex.> unifcdf (2, 2, i)
757s 27 tests, 27 passed, 0 known failure, 0 skipped
757s [inst/dist_fun/unifinv.m]
757s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/unifinv.m
757s ***** demo
757s  ## Plot various iCDFs from the continuous uniform distribution
757s  p = 0.001:0.001:0.999;
757s  x1 = unifinv (p, 2, 5);
757s  x2 = unifinv (p, 3, 9);
757s  plot (p, x1, "-b", p, x2, "-g")
757s  grid on
757s  xlim ([0, 1])
757s  ylim ([0, 10])
757s  legend ({"a = 2, b = 5", "a = 3, b = 9"}, "location", "northwest")
757s  title ("Continuous uniform iCDF")
757s  xlabel ("probability")
757s  ylabel ("values in x")
757s ***** shared p
757s  p = [-1 0 0.5 1 2];
757s ***** assert (unifinv (p, ones (1,5), 2*ones (1,5)), [NaN 1 1.5 2 NaN])
757s ***** assert (unifinv (p, 0, 1), [NaN 1 1.5 2 NaN] - 1)
757s ***** assert (unifinv (p, 1, 2*ones (1,5)), [NaN 1 1.5 2 NaN])
757s ***** assert (unifinv (p, ones (1,5), 2), [NaN 1 1.5 2 NaN])
757s ***** assert (unifinv (p, [1 2 NaN 1 1], 2), [NaN NaN NaN 2 NaN])
757s ***** assert (unifinv (p, 1, 2*[1 0 NaN 1 1]), [NaN NaN NaN 2 NaN])
757s ***** assert (unifinv ([p(1:2) NaN p(4:5)], 1, 2), [NaN 1 NaN 2 NaN])
757s ***** assert (unifinv ([p, NaN], 1, 2), [NaN 1 1.5 2 NaN NaN])
757s ***** assert (unifinv (single ([p, NaN]), 1, 2), single ([NaN 1 1.5 2 NaN NaN]))
757s ***** assert (unifinv ([p, NaN], single (1), 2), single ([NaN 1 1.5 2 NaN NaN]))
757s ***** assert (unifinv ([p, NaN], 1, single (2)), single ([NaN 1 1.5 2 NaN NaN]))
757s ***** error<unifinv: function called with too few input arguments.> unifinv ()
757s ***** error<unifinv: function called with too few input arguments.> unifinv (1, 2)
757s ***** error<unifinv: P, A, and B must be of common size or scalars.> ...
757s  unifinv (ones (3), ones (2), ones (2))
757s ***** error<unifinv: P, A, and B must be of common size or scalars.> ...
757s  unifinv (ones (2), ones (3), ones (2))
757s ***** error<unifinv: P, A, and B must be of common size or scalars.> ...
757s  unifinv (ones (2), ones (2), ones (3))
757s ***** error<unifinv: P, A, and B must not be complex.> unifinv (i, 2, 2)
757s ***** error<unifinv: P, A, and B must not be complex.> unifinv (2, i, 2)
757s ***** error<unifinv: P, A, and B must not be complex.> unifinv (2, 2, i)
757s 19 tests, 19 passed, 0 known failure, 0 skipped
757s [inst/dist_fun/unifpdf.m]
757s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/unifpdf.m
757s ***** demo
757s  ## Plot various PDFs from the continuous uniform distribution
757s  x = 0:0.001:10;
757s  y1 = unifpdf (x, 2, 5);
757s  y2 = unifpdf (x, 3, 9);
757s  plot (x, y1, "-b", x, y2, "-g")
757s  grid on
757s  xlim ([0, 10])
757s  ylim ([0, 0.4])
757s  legend ({"a = 2, b = 5", "a = 3, b = 9"}, "location", "northeast")
757s  title ("Continuous uniform PDF")
757s  xlabel ("values in x")
757s  ylabel ("density")
757s ***** shared x, y
757s  x = [-1 0 0.5 1 2] + 1;
757s  y = [0 1 1 1 0];
757s ***** assert (unifpdf (x, ones (1,5), 2*ones (1,5)), y)
757s ***** assert (unifpdf (x, 1, 2*ones (1,5)), y)
757s ***** assert (unifpdf (x, ones (1,5), 2), y)
757s ***** assert (unifpdf (x, [2 NaN 1 1 1], 2), [NaN NaN y(3:5)])
757s ***** assert (unifpdf (x, 1, 2*[0 NaN 1 1 1]), [NaN NaN y(3:5)])
757s ***** assert (unifpdf ([x, NaN], 1, 2), [y, NaN])
757s ***** assert (unifpdf (x, 0, 1), [1 1 0 0 0])
757s ***** assert (unifpdf (single ([x, NaN]), 1, 2), single ([y, NaN]))
757s ***** assert (unifpdf (single ([x, NaN]), single (1), 2), single ([y, NaN]))
757s ***** assert (unifpdf ([x, NaN], 1, single (2)), single ([y, NaN]))
757s ***** error<unifpdf: function called with too few input arguments.> unifpdf ()
757s ***** error<unifpdf: function called with too few input arguments.> unifpdf (1)
757s ***** error<unifpdf: function called with too few input arguments.> unifpdf (1, 2)
757s ***** error<unifpdf: X, A, and B must be of common size or scalars.> ...
757s  unifpdf (ones (3), ones (2), ones (2))
757s ***** error<unifpdf: X, A, and B must be of common size or scalars.> ...
757s  unifpdf (ones (2), ones (3), ones (2))
757s ***** error<unifpdf: X, A, and B must be of common size or scalars.> ...
757s  unifpdf (ones (2), ones (2), ones (3))
757s ***** error<unifpdf: X, A, and B must not be complex.> unifpdf (i, 2, 2)
757s ***** error<unifpdf: X, A, and B must not be complex.> unifpdf (2, i, 2)
757s ***** error<unifpdf: X, A, and B must not be complex.> unifpdf (2, 2, i)
757s 19 tests, 19 passed, 0 known failure, 0 skipped
757s [inst/dist_fun/unifrnd.m]
757s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/unifrnd.m
757s ***** assert (size (unifrnd (1, 1)), [1 1])
757s ***** assert (size (unifrnd (1, ones (2,1))), [2, 1])
757s ***** assert (size (unifrnd (1, ones (2,2))), [2, 2])
757s ***** assert (size (unifrnd (ones (2,1), 1)), [2, 1])
757s ***** assert (size (unifrnd (ones (2,2), 1)), [2, 2])
757s ***** assert (size (unifrnd (1, 1, 3)), [3, 3])
757s ***** assert (size (unifrnd (1, 1, [4, 1])), [4, 1])
757s ***** assert (size (unifrnd (1, 1, 4, 1)), [4, 1])
757s ***** assert (size (unifrnd (1, 1, 4, 1, 5)), [4, 1, 5])
757s ***** assert (size (unifrnd (1, 1, 0, 1)), [0, 1])
757s ***** assert (size (unifrnd (1, 1, 1, 0)), [1, 0])
757s ***** assert (size (unifrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
757s ***** assert (class (unifrnd (1, 1)), "double")
757s ***** assert (class (unifrnd (1, single (1))), "single")
757s ***** assert (class (unifrnd (1, single ([1, 1]))), "single")
757s ***** assert (class (unifrnd (single (1), 1)), "single")
757s ***** assert (class (unifrnd (single ([1, 1]), 1)), "single")
757s ***** error<unifrnd: function called with too few input arguments.> unifrnd ()
757s ***** error<unifrnd: function called with too few input arguments.> unifrnd (1)
757s ***** error<unifrnd: A and B must be of common size or scalars.> ...
757s  unifrnd (ones (3), ones (2))
757s ***** error<unifrnd: A and B must be of common size or scalars.> ...
757s  unifrnd (ones (2), ones (3))
757s ***** error<unifrnd: A and B must not be complex.> unifrnd (i, 2, 3)
757s ***** error<unifrnd: A and B must not be complex.> unifrnd (1, i, 3)
757s ***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
757s  unifrnd (1, 2, -1)
757s ***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
757s  unifrnd (1, 2, 1.2)
757s ***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
757s  unifrnd (1, 2, ones (2))
757s ***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
757s  unifrnd (1, 2, [2 -1 2])
757s ***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
757s  unifrnd (1, 2, [2 0 2.5])
757s ***** error<unifrnd: dimensions must be non-negative integers.> ...
757s  unifrnd (1, 2, 2, -1, 5)
757s ***** error<unifrnd: dimensions must be non-negative integers.> ...
757s  unifrnd (1, 2, 2, 1.5, 5)
757s ***** error<unifrnd: A and B must be scalars or of size SZ.> ...
757s  unifrnd (2, ones (2), 3)
757s ***** error<unifrnd: A and B must be scalars or of size SZ.> ...
757s  unifrnd (2, ones (2), [3, 2])
757s ***** error<unifrnd: A and B must be scalars or of size SZ.> ...
757s  unifrnd (2, ones (2), 3, 2)
757s 33 tests, 33 passed, 0 known failure, 0 skipped
757s [inst/dist_fun/vmcdf.m]
757s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/vmcdf.m
757s ***** demo
757s  ## Plot various CDFs from the von Mises distribution
757s  x1 = [-pi:0.1:pi];
757s  p1 = vmcdf (x1, 0, 0.5);
757s  p2 = vmcdf (x1, 0, 1);
757s  p3 = vmcdf (x1, 0, 2);
757s  p4 = vmcdf (x1, 0, 4);
757s  plot (x1, p1, "-r", x1, p2, "-g", x1, p3, "-b", x1, p4, "-c")
757s  grid on
757s  xlim ([-pi, pi])
757s  legend ({"μ = 0, k = 0.5", "μ = 0, k = 1", ...
757s           "μ = 0, k = 2", "μ = 0, k = 4"}, "location", "northwest")
757s  title ("Von Mises CDF")
757s  xlabel ("values in x")
757s  ylabel ("probability")
757s ***** shared x, p0, p1
757s  x = [-pi:pi/2:pi];
757s  p0 = [0, 0.10975, 0.5, 0.89025, 1];
757s  p1 = [0, 0.03752, 0.5, 0.99622, 1];
757s ***** assert (vmcdf (x, 0, 1), p0, 1e-5)
757s ***** assert (vmcdf (x, 0, 1, "upper"), 1 - p0, 1e-5)
757s ***** assert (vmcdf (x, zeros (1,5), ones (1,5)), p0, 1e-5)
758s ***** assert (vmcdf (x, zeros (1,5), ones (1,5), "upper"), 1 - p0, 1e-5)
758s ***** assert (vmcdf (x, 0, [1 2 3 4 5]), p1, 1e-5)
758s ***** assert (vmcdf (x, 0, [1 2 3 4 5], "upper"), 1 - p1, 1e-5)
758s ***** assert (isa (vmcdf (single (pi), 0, 1), "single"), true)
758s ***** assert (isa (vmcdf (pi, single (0), 1), "single"), true)
758s ***** assert (isa (vmcdf (pi, 0, single (1)), "single"), true)
758s ***** error<vmcdf: function called with too few input arguments.> vmcdf ()
758s ***** error<vmcdf: function called with too few input arguments.> vmcdf (1)
758s ***** error<vmcdf: function called with too few input arguments.> vmcdf (1, 2)
758s ***** error<vmcdf: invalid argument for upper tail.> vmcdf (1, 2, 3, "tail")
758s ***** error<vmcdf: invalid argument for upper tail.> vmcdf (1, 2, 3, 4)
758s ***** error<vmcdf: X, MU, and K must be of common size or scalars.> ...
758s  vmcdf (ones (3), ones (2), ones (2))
758s ***** error<vmcdf: X, MU, and K must be of common size or scalars.> ...
758s  vmcdf (ones (2), ones (3), ones (2))
758s ***** error<vmcdf: X, MU, and K must be of common size or scalars.> ...
758s  vmcdf (ones (2), ones (2), ones (3))
759s ***** error<vmcdf: X, MU, and K must not be complex.> vmcdf (i, 2, 2)
759s ***** error<vmcdf: X, MU, and K must not be complex.> vmcdf (2, i, 2)
759s ***** error<vmcdf: X, MU, and K must not be complex.> vmcdf (2, 2, i)
759s 20 tests, 20 passed, 0 known failure, 0 skipped
759s [inst/dist_fun/vminv.m]
759s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/vminv.m
759s ***** demo
759s  ## Plot various iCDFs from the von Mises distribution
759s  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];
759s  x1 = vminv (p1, 0, 0.5);
759s  x2 = vminv (p1, 0, 1);
759s  x3 = vminv (p1, 0, 2);
759s  x4 = vminv (p1, 0, 4);
759s  plot (p1, x1, "-r", p1, x2, "-g", p1, x3, "-b", p1, x4, "-c")
759s  grid on
759s  ylim ([-pi, pi])
759s  legend ({"μ = 0, k = 0.5", "μ = 0, k = 1", ...
759s           "μ = 0, k = 2", "μ = 0, k = 4"}, "location", "northwest")
759s  title ("Von Mises iCDF")
759s  xlabel ("probability")
759s  ylabel ("values in x")
759s ***** shared x, p0, p1
759s  x = [-pi:pi/2:pi];
759s  p0 = [0, 0.10975, 0.5, 0.89025, 1];
759s  p1 = [0, 0.03752, 0.5, 0.99622, 1];
759s ***** assert (vminv (p0, 0, 1), x, 5e-5)
759s ***** assert (vminv (p0, zeros (1,5), ones (1,5)), x, 5e-5)
760s ***** assert (vminv (p1, 0, [1 2 3 4 5]), x, [5e-5, 5e-4, 5e-5, 5e-4, 5e-5])
762s ***** error<vminv: function called with too few input arguments.> vminv ()
762s ***** error<vminv: function called with too few input arguments.> vminv (1)
762s ***** error<vminv: function called with too few input arguments.> vminv (1, 2)
762s ***** error<vminv: P, MU, and K must be of common size or scalars.> ...
762s  vminv (ones (3), ones (2), ones (2))
762s ***** error<vminv: P, MU, and K must be of common size or scalars.> ...
762s  vminv (ones (2), ones (3), ones (2))
762s ***** error<vminv: P, MU, and K must be of common size or scalars.> ...
762s  vminv (ones (2), ones (2), ones (3))
762s ***** error<vminv: P, MU, and K must not be complex.> vminv (i, 2, 2)
762s ***** error<vminv: P, MU, and K must not be complex.> vminv (2, i, 2)
762s ***** error<vminv: P, MU, and K must not be complex.> vminv (2, 2, i)
762s 12 tests, 12 passed, 0 known failure, 0 skipped
762s [inst/dist_fun/vmpdf.m]
762s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/vmpdf.m
762s ***** demo
762s  ## Plot various PDFs from the von Mises distribution
762s  x1 = [-pi:0.1:pi];
762s  y1 = vmpdf (x1, 0, 0.5);
762s  y2 = vmpdf (x1, 0, 1);
762s  y3 = vmpdf (x1, 0, 2);
762s  y4 = vmpdf (x1, 0, 4);
762s  plot (x1, y1, "-r", x1, y2, "-g", x1, y3, "-b", x1, y4, "-c")
762s  grid on
762s  xlim ([-pi, pi])
762s  ylim ([0, 0.8])
762s  legend ({"μ = 0, k = 0.5", "μ = 0, k = 1", ...
762s           "μ = 0, k = 2", "μ = 0, k = 4"}, "location", "northwest")
762s  title ("Von Mises PDF")
762s  xlabel ("values in x")
762s  ylabel ("density")
762s ***** shared x, y0, y1
762s  x = [-pi:pi/2:pi];
762s  y0 = [0.046245, 0.125708, 0.341710, 0.125708, 0.046245];
762s  y1 = [0.046245, 0.069817, 0.654958, 0.014082, 0.000039];
762s ***** assert (vmpdf (x, 0, 1), y0, 1e-5)
762s ***** assert (vmpdf (x, zeros (1,5), ones (1,5)), y0, 1e-6)
762s ***** assert (vmpdf (x, 0, [1 2 3 4 5]), y1, 1e-6)
762s ***** assert (isa (vmpdf (single (pi), 0, 1), "single"), true)
762s ***** assert (isa (vmpdf (pi, single (0), 1), "single"), true)
762s ***** assert (isa (vmpdf (pi, 0, single (1)), "single"), true)
762s ***** error<vmpdf: function called with too few input arguments.> vmpdf ()
762s ***** error<vmpdf: function called with too few input arguments.> vmpdf (1)
762s ***** error<vmpdf: function called with too few input arguments.> vmpdf (1, 2)
762s ***** error<vmpdf: X, MU, and K must be of common size or scalars.> ...
762s  vmpdf (ones (3), ones (2), ones (2))
762s ***** error<vmpdf: X, MU, and K must be of common size or scalars.> ...
762s  vmpdf (ones (2), ones (3), ones (2))
762s ***** error<vmpdf: X, MU, and K must be of common size or scalars.> ...
762s  vmpdf (ones (2), ones (2), ones (3))
762s ***** error<vmpdf: X, MU, and K must not be complex.> vmpdf (i, 2, 2)
762s ***** error<vmpdf: X, MU, and K must not be complex.> vmpdf (2, i, 2)
762s ***** error<vmpdf: X, MU, and K must not be complex.> vmpdf (2, 2, i)
762s 15 tests, 15 passed, 0 known failure, 0 skipped
762s [inst/dist_fun/vmrnd.m]
762s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/vmrnd.m
762s ***** assert (size (vmrnd (1, 1)), [1 1])
762s ***** assert (size (vmrnd (1, ones (2,1))), [2, 1])
762s ***** assert (size (vmrnd (1, ones (2,2))), [2, 2])
762s ***** assert (size (vmrnd (ones (2,1), 1)), [2, 1])
762s ***** assert (size (vmrnd (ones (2,2), 1)), [2, 2])
762s ***** assert (size (vmrnd (1, 1, 3)), [3, 3])
762s ***** assert (size (vmrnd (1, 1, [4, 1])), [4, 1])
762s ***** assert (size (vmrnd (1, 1, 4, 1)), [4, 1])
762s ***** assert (size (vmrnd (1, 1, 4, 1, 5)), [4, 1, 5])
762s ***** assert (size (vmrnd (1, 1, 0, 1)), [0, 1])
762s ***** assert (size (vmrnd (1, 1, 1, 0)), [1, 0])
762s ***** assert (size (vmrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
762s ***** assert (class (vmrnd (1, 1)), "double")
762s ***** assert (class (vmrnd (1, single (1))), "single")
762s ***** assert (class (vmrnd (1, single ([1, 1]))), "single")
762s ***** assert (class (vmrnd (single (1), 1)), "single")
762s ***** assert (class (vmrnd (single ([1, 1]), 1)), "single")
762s ***** error<vmrnd: function called with too few input arguments.> vmrnd ()
762s ***** error<vmrnd: function called with too few input arguments.> vmrnd (1)
762s ***** error<vmrnd: MU and K must be of common size or scalars.> ...
762s  vmrnd (ones (3), ones (2))
762s ***** error<vmrnd: MU and K must be of common size or scalars.> ...
762s  vmrnd (ones (2), ones (3))
762s ***** error<vmrnd: MU and K must not be complex.> vmrnd (i, 2, 3)
762s ***** error<vmrnd: MU and K must not be complex.> vmrnd (1, i, 3)
762s ***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
762s  vmrnd (1, 2, -1)
762s ***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
762s  vmrnd (1, 2, 1.2)
762s ***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
762s  vmrnd (1, 2, ones (2))
762s ***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
762s  vmrnd (1, 2, [2 -1 2])
762s ***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
762s  vmrnd (1, 2, [2 0 2.5])
762s ***** error<vmrnd: dimensions must be non-negative integers.> ...
762s  vmrnd (1, 2, 2, -1, 5)
762s ***** error<vmrnd: dimensions must be non-negative integers.> ...
762s  vmrnd (1, 2, 2, 1.5, 5)
762s ***** error<vmrnd: MU and K must be scalars or of size SZ.> ...
762s  vmrnd (2, ones (2), 3)
762s ***** error<vmrnd: MU and K must be scalars or of size SZ.> ...
762s  vmrnd (2, ones (2), [3, 2])
762s ***** error<vmrnd: MU and K must be scalars or of size SZ.> ...
762s  vmrnd (2, ones (2), 3, 2)
762s 33 tests, 33 passed, 0 known failure, 0 skipped
762s [inst/dist_fun/wblcdf.m]
762s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/wblcdf.m
762s ***** demo
762s  ## Plot various CDFs from the Weibull distribution
762s  x = 0:0.001:2.5;
762s  p1 = wblcdf (x, 1, 0.5);
762s  p2 = wblcdf (x, 1, 1);
762s  p3 = wblcdf (x, 1, 1.5);
762s  p4 = wblcdf (x, 1, 5);
762s  plot (x, p1, "-b", x, p2, "-r", x, p3, "-m", x, p4, "-g")
762s  grid on
762s  legend ({"λ = 1, k = 0.5", "λ = 1, k = 1", ...
762s           "λ = 1, k = 1.5", "λ = 1, k = 5"}, "location", "southeast")
762s  title ("Weibull CDF")
762s  xlabel ("values in x")
762s  ylabel ("probability")
762s ***** shared x, y
762s  x = [-1 0 0.5 1 Inf];
762s  y = [0, 1-exp(-x(2:4)), 1];
762s ***** assert (wblcdf (x, ones (1,5), ones (1,5)), y, 1e-16)
762s ***** assert (wblcdf (x, ones (1,5), ones (1,5), "upper"), 1 - y)
762s ***** assert (wblcdf (x, "upper"), 1 - y)
762s ***** assert (wblcdf (x, 1, ones (1,5)), y, 1e-16)
762s ***** assert (wblcdf (x, ones (1,5), 1), y, 1e-16)
762s ***** assert (wblcdf (x, [0 1 NaN Inf 1], 1), [NaN 0 NaN 0 1])
762s ***** assert (wblcdf (x, [0 1 NaN Inf 1], 1, "upper"), 1 - [NaN 0 NaN 0 1])
762s ***** assert (wblcdf (x, 1, [0 1 NaN Inf 1]), [NaN 0 NaN y(4:5)])
762s ***** assert (wblcdf (x, 1, [0 1 NaN Inf 1], "upper"), 1 - [NaN 0 NaN y(4:5)])
762s ***** assert (wblcdf ([x(1:2) NaN x(4:5)], 1, 1), [y(1:2) NaN y(4:5)])
762s ***** assert (wblcdf ([x(1:2) NaN x(4:5)], 1, 1, "upper"), 1 - [y(1:2) NaN y(4:5)])
762s ***** assert (wblcdf ([x, NaN], 1, 1), [y, NaN], 1e-16)
762s ***** assert (wblcdf (single ([x, NaN]), 1, 1), single ([y, NaN]))
762s ***** assert (wblcdf ([x, NaN], single (1), 1), single ([y, NaN]))
762s ***** assert (wblcdf ([x, NaN], 1, single (1)), single ([y, NaN]))
762s ***** error<wblcdf: invalid number of input arguments.> wblcdf ()
762s ***** error<wblcdf: invalid number of input arguments.> wblcdf (1,2,3,4,5,6,7)
762s ***** error<wblcdf: invalid argument for upper tail.> wblcdf (1, 2, 3, 4, "uper")
762s ***** error<wblcdf: X, LAMBDA, and K must be of common size or scalars.> ...
762s  wblcdf (ones (3), ones (2), ones (2))
762s ***** error<wblcdf: invalid size of covariance matrix.> wblcdf (2, 3, 4, [1, 2])
762s ***** error<wblcdf: covariance matrix is required for confidence bounds.> ...
762s  [p, plo, pup] = wblcdf (1, 2, 3)
762s ***** error<wblcdf: invalid value for alpha.> [p, plo, pup] = ...
762s  wblcdf (1, 2, 3, [1, 0; 0, 1], 0)
762s ***** error<wblcdf: invalid value for alpha.> [p, plo, pup] = ...
762s  wblcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
762s ***** error<wblcdf: invalid value for alpha.> [p, plo, pup] = ...
762s  wblcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
762s ***** error<wblcdf: X, LAMBDA, and K must not be complex.> wblcdf (i, 2, 2)
762s ***** error<wblcdf: X, LAMBDA, and K must not be complex.> wblcdf (2, i, 2)
762s ***** error<wblcdf: X, LAMBDA, and K must not be complex.> wblcdf (2, 2, i)
762s ***** error<wblcdf: bad covariance matrix.> ...
762s  [p, plo, pup] =wblcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
762s 28 tests, 28 passed, 0 known failure, 0 skipped
762s [inst/dist_fun/wblinv.m]
762s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/wblinv.m
762s ***** demo
762s  ## Plot various iCDFs from the Weibull distribution
762s  p = 0.001:0.001:0.999;
762s  x1 = wblinv (p, 1, 0.5);
762s  x2 = wblinv (p, 1, 1);
762s  x3 = wblinv (p, 1, 1.5);
762s  x4 = wblinv (p, 1, 5);
762s  plot (p, x1, "-b", p, x2, "-r", p, x3, "-m", p, x4, "-g")
762s  ylim ([0, 2.5])
762s  grid on
762s  legend ({"λ = 1, k = 0.5", "λ = 1, k = 1",  ...
762s           "λ = 1, k = 1.5", "λ = 1, k = 5"}, "location", "northwest")
762s  title ("Weibull iCDF")
762s  xlabel ("probability")
762s  ylabel ("x")
762s ***** shared p
762s  p = [-1 0 0.63212055882855778 1 2];
762s ***** assert (wblinv (p, ones (1,5), ones (1,5)), [NaN 0 1 Inf NaN], eps)
762s ***** assert (wblinv (p, 1, ones (1,5)), [NaN 0 1 Inf NaN], eps)
762s ***** assert (wblinv (p, ones (1,5), 1), [NaN 0 1 Inf NaN], eps)
762s ***** assert (wblinv (p, [1 -1 NaN Inf 1], 1), [NaN NaN NaN NaN NaN])
762s ***** assert (wblinv (p, 1, [1 -1 NaN Inf 1]), [NaN NaN NaN NaN NaN])
762s ***** assert (wblinv ([p(1:2) NaN p(4:5)], 1, 1), [NaN 0 NaN Inf NaN])
762s ***** assert (wblinv ([p, NaN], 1, 1), [NaN 0 1 Inf NaN NaN], eps)
763s ***** assert (wblinv (single ([p, NaN]), 1, 1), single ([NaN 0 1 Inf NaN NaN]), eps ("single"))
763s ***** assert (wblinv ([p, NaN], single (1), 1), single ([NaN 0 1 Inf NaN NaN]), eps ("single"))
763s ***** assert (wblinv ([p, NaN], 1, single (1)), single ([NaN 0 1 Inf NaN NaN]), eps ("single"))
763s ***** error<wblinv: invalid number of input arguments.> wblinv ()
763s ***** error<wblinv: invalid number of input arguments.> wblinv (1,2,3,4)
763s ***** error<wblinv: P, LAMBDA, and K must be of common size or scalars.> ...
763s  wblinv (ones (3), ones (2), ones (2))
763s ***** error<wblinv: P, LAMBDA, and K must be of common size or scalars.> ...
763s  wblinv (ones (2), ones (3), ones (2))
763s ***** error<wblinv: P, LAMBDA, and K must be of common size or scalars.> ...
763s  wblinv (ones (2), ones (2), ones (3))
763s ***** error<wblinv: P, LAMBDA, and K must not be complex.> wblinv (i, 2, 2)
763s ***** error<wblinv: P, LAMBDA, and K must not be complex.> wblinv (2, i, 2)
763s ***** error<wblinv: P, LAMBDA, and K must not be complex.> wblinv (2, 2, i)
763s 18 tests, 18 passed, 0 known failure, 0 skipped
763s [inst/dist_fun/wblpdf.m]
763s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/wblpdf.m
763s ***** demo
763s  ## Plot various PDFs from the Weibul distribution
763s  x = 0:0.001:2.5;
763s  y1 = wblpdf (x, 1, 0.5);
763s  y2 = wblpdf (x, 1, 1);
763s  y3 = wblpdf (x, 1, 1.5);
763s  y4 = wblpdf (x, 1, 5);
763s  plot (x, y1, "-b", x, y2, "-r", x, y3, "-m", x, y4, "-g")
763s  grid on
763s  ylim ([0, 2.5])
763s  legend ({"λ = 5, k = 0.5", "λ = 9, k = 1", ...
763s           "λ = 6, k = 1.5", "λ = 2, k = 5"}, "location", "northeast")
763s  title ("Weibul PDF")
763s  xlabel ("values in x")
763s  ylabel ("density")
763s ***** shared x,y
763s  x = [-1 0 0.5 1 Inf];
763s  y = [0, exp(-x(2:4)), NaN];
763s ***** assert (wblpdf (x, ones (1,5), ones (1,5)), y)
763s ***** assert (wblpdf (x, 1, ones (1,5)), y)
763s ***** assert (wblpdf (x, ones (1,5), 1), y)
763s ***** assert (wblpdf (x, [0 NaN Inf 1 1], 1), [NaN NaN NaN y(4:5)])
763s ***** assert (wblpdf (x, 1, [0 NaN Inf 1 1]), [NaN NaN NaN y(4:5)])
763s ***** assert (wblpdf ([x, NaN], 1, 1), [y, NaN])
763s ***** assert (wblpdf (single ([x, NaN]), 1, 1), single ([y, NaN]))
763s ***** assert (wblpdf ([x, NaN], single (1), 1), single ([y, NaN]))
763s ***** assert (wblpdf ([x, NaN], 1, single (1)), single ([y, NaN]))
763s ***** error wblpdf ()
763s ***** error wblpdf (1,2,3,4)
763s ***** error wblpdf (ones (3), ones (2), ones (2))
763s ***** error wblpdf (ones (2), ones (3), ones (2))
763s ***** error wblpdf (ones (2), ones (2), ones (3))
763s ***** error wblpdf (i, 2, 2)
763s ***** error wblpdf (2, i, 2)
763s ***** error wblpdf (2, 2, i)
763s 17 tests, 17 passed, 0 known failure, 0 skipped
763s [inst/dist_fun/wblrnd.m]
763s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/wblrnd.m
763s ***** assert (size (wblrnd (1, 1)), [1 1])
763s ***** assert (size (wblrnd (1, ones (2,1))), [2, 1])
763s ***** assert (size (wblrnd (1, ones (2,2))), [2, 2])
763s ***** assert (size (wblrnd (ones (2,1), 1)), [2, 1])
763s ***** assert (size (wblrnd (ones (2,2), 1)), [2, 2])
763s ***** assert (size (wblrnd (1, 1, 3)), [3, 3])
763s ***** assert (size (wblrnd (1, 1, [4, 1])), [4, 1])
763s ***** assert (size (wblrnd (1, 1, 4, 1)), [4, 1])
763s ***** assert (size (wblrnd (1, 1, 4, 1, 5)), [4, 1, 5])
763s ***** assert (size (wblrnd (1, 1, 0, 1)), [0, 1])
763s ***** assert (size (wblrnd (1, 1, 1, 0)), [1, 0])
763s ***** assert (size (wblrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
763s ***** assert (class (wblrnd (1, 1)), "double")
763s ***** assert (class (wblrnd (1, single (1))), "single")
763s ***** assert (class (wblrnd (1, single ([1, 1]))), "single")
763s ***** assert (class (wblrnd (single (1), 1)), "single")
763s ***** assert (class (wblrnd (single ([1, 1]), 1)), "single")
763s ***** error<wblrnd: function called with too few input arguments.> wblrnd ()
763s ***** error<wblrnd: function called with too few input arguments.> wblrnd (1)
763s ***** error<wblrnd: LAMBDA and K must be of common size or scalars.> ...
763s  wblrnd (ones (3), ones (2))
763s ***** error<wblrnd: LAMBDA and K must be of common size or scalars.> ...
763s  wblrnd (ones (2), ones (3))
763s ***** error<wblrnd: LAMBDA and K must not be complex.> wblrnd (i, 2, 3)
763s ***** error<wblrnd: LAMBDA and K must not be complex.> wblrnd (1, i, 3)
763s ***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
763s  wblrnd (1, 2, -1)
763s ***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
763s  wblrnd (1, 2, 1.2)
763s ***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
763s  wblrnd (1, 2, ones (2))
763s ***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
763s  wblrnd (1, 2, [2 -1 2])
763s ***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
763s  wblrnd (1, 2, [2 0 2.5])
763s ***** error<wblrnd: dimensions must be non-negative integers.> ...
763s  wblrnd (1, 2, 2, -1, 5)
763s ***** error<wblrnd: dimensions must be non-negative integers.> ...
763s  wblrnd (1, 2, 2, 1.5, 5)
763s ***** error<wblrnd: LAMBDA and K must be scalar or of size SZ.> ...
763s  wblrnd (2, ones (2), 3)
763s ***** error<wblrnd: LAMBDA and K must be scalar or of size SZ.> ...
763s  wblrnd (2, ones (2), [3, 2])
763s ***** error<wblrnd: LAMBDA and K must be scalar or of size SZ.> ...
763s  wblrnd (2, ones (2), 3, 2)
763s 33 tests, 33 passed, 0 known failure, 0 skipped
763s [inst/dist_fun/wienrnd.m]
763s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/wienrnd.m
763s ***** error wienrnd (0)
763s ***** error wienrnd (1, 3, -50)
763s ***** error wienrnd (5, 0)
763s ***** error wienrnd (0.4, 3, 5)
763s ***** error wienrnd ([1 4], 3, 5)
763s 5 tests, 5 passed, 0 known failure, 0 skipped
763s [inst/dist_fun/wishpdf.m]
763s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/wishpdf.m
763s ***** assert(wishpdf(4, 3, 3.1), 0.07702496, 1E-7);
763s ***** assert(wishpdf([2 -0.3;-0.3 4], [1 0.3;0.3 1], 4), 0.004529741, 1E-7);
763s ***** 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);
763s ***** error wishpdf ()
763s ***** error wishpdf (1, 2)
763s ***** error wishpdf (1, 2, 0)
763s ***** error wishpdf (1, 2)
763s 7 tests, 7 passed, 0 known failure, 0 skipped
763s [inst/dist_fun/wishrnd.m]
763s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_fun/wishrnd.m
763s ***** assert(size (wishrnd (1,2)), [1, 1]);
763s ***** assert(size (wishrnd (1,2,[])), [1, 1]);
763s ***** assert(size (wishrnd (1,2,1)), [1, 1]);
763s ***** assert(size (wishrnd ([],2,1)), [1, 1]);
763s ***** assert(size (wishrnd ([3 1; 1 3], 2.00001, [], 1)), [2, 2]);
763s ***** assert(size (wishrnd (eye(2), 2, [], 3)), [2, 2, 3]);
763s ***** error wishrnd ()
763s ***** error wishrnd (1)
764s ***** error wishrnd ([1; 1], 2)
764s 9 tests, 9 passed, 0 known failure, 0 skipped
764s [inst/dist_obj/BetaDistribution.m]
764s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_obj/BetaDistribution.m
764s ***** demo
764s  ## Generate a data set of 5000 random samples from a Beta distribution with
764s  ## parameters a = 2 and b = 4.  Fit a Beta distribution to this data and plot
764s  ## a PDF of the fitted distribution superimposed on a histogram of the data
764s 
764s  pd = makedist ("Beta", "a", 2, "b", 4)
764s  randg ("seed", 21);
764s  data = random (pd, 5000, 1);
764s  pd = fitdist (data, "Beta")
764s  plot (pd)
764s  title (sprintf ("Fitted Beta distribution with a = %0.2f and b = %0.2f", ...
764s                  pd.a, pd.b))
764s ***** demo
764s  ## Plot the PDF of a Beta distribution, with parameters a = 2 and b = 4,
764s  ## truncated at [0.1, 0.8] intervals.  Generate 10000 random samples from
764s  ## this truncated distribution and superimpose a histogram with 100 bins
764s  ## scaled accordingly
764s 
764s  pd = makedist ("Beta", "a", 2, "b", 4)
764s  t = truncate (pd, 0.1, 0.8)
764s  randg ("seed", 21);
764s  data = random (t, 10000, 1);
764s  plot (t)
764s  title ("Beta distribution (a = 2, b = 4) truncated at [0.1, 0.8]")
764s  hold on
764s  hist (data, 100, 140)
764s  hold off
764s ***** demo
764s  ## Generate a data set of 100 random samples from a Beta distribution with
764s  ## parameters a = 2 and b = 4.  Fit a Beta distribution to this data and plot
764s  ## its CDF superimposed over an empirical CDF of the data
764s 
764s  pd = makedist ("Beta", "a", 2, "b", 4)
764s  randg ("seed", 21);
764s  data = random (pd, 100, 1);
764s  pd = fitdist (data, "Beta")
764s  plot (pd, "plottype", "cdf")
764s  title (sprintf ("Fitted Beta distribution with a = %0.2f and b = %0.2f", ...
764s                  pd.a, pd.b))
764s  legend ({"empirical CDF", "fitted CDF"}, "location", "east")
764s ***** demo
764s  ## Generate a data set of 200 random samples from a Beta distribution with
764s  ## parameters a = 2 and b = 4.  Display a probability plot for the Beta
764s  ## distribution fit to the data.
764s 
764s  pd = makedist ("Beta", "a", 2, "b", 4)
764s  randg ("seed", 21);
764s  data = random (pd, 200, 1);
764s  pd = fitdist (data, "Beta")
764s  plot (pd, "plottype", "probability")
764s  title (sprintf ("Probability plot of a fitted Beta distribution with a = %0.2f and b = %0.2f", ...
764s                  pd.a, pd.b))
764s  legend ({"empirical CDF", "fitted CDF"}, "location", "southeast")
764s ***** shared pd, t
764s  pd = BetaDistribution;
764s  t = truncate (pd, 0.2, 0.8);
764s ***** assert (cdf (pd, [0:0.2:1]), [0, 0.2, 0.4, 0.6, 0.8, 1], 1e-4);
764s ***** assert (cdf (t, [0:0.2:1]), [0, 0, 0.3333, 0.6667, 1, 1], 1e-4);
764s ***** assert (cdf (pd, [-1, 1, NaN]), [0, 1, NaN], 1e-4);
764s ***** assert (cdf (t, [-1, 1, NaN]), [0, 1, NaN], 1e-4);
764s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.2, 0.4, 0.6, 0.8, 1], 1e-4);
764s ***** assert (icdf (t, [0:0.2:1]), [0.2, 0.32, 0.44, 0.56, 0.68, 0.8], 1e-4);
764s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.4, 0.6, 0.8, 1, NaN], 1e-4);
764s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 0.44, 0.56, 0.68, 0.8, NaN], 1e-4);
764s ***** assert (iqr (pd), 0.5, 1e-4);
764s ***** assert (iqr (t), 0.3, 1e-4);
764s ***** assert (mean (pd), 0.5);
764s ***** assert (mean (t), 0.5, 1e-6);
764s ***** assert (median (pd), 0.5);
764s ***** assert (median (t), 0.5, 1e-6);
764s ***** assert (pdf (pd, [0:0.2:1]), [1, 1, 1, 1, 1, 1], 1e-4);
764s ***** assert (pdf (t, [0:0.2:1]), [0, 1.6667, 1.6667, 1.6667, 1.6667, 0], 1e-4);
764s ***** assert (pdf (pd, [-1, 1, NaN]), [0, 1, NaN], 1e-4);
764s ***** assert (pdf (t, [-1, 1, NaN]), [0, 0, NaN], 1e-4);
764s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
764s ***** assert (any (random (t, 1000, 1) < 0.2), false);
764s ***** assert (any (random (t, 1000, 1) > 0.8), false);
764s ***** assert (std (pd), 0.2887, 1e-4);
764s ***** assert (std (t), 0.1732, 1e-4);
764s ***** assert (var (pd), 0.0833, 1e-4);
764s ***** assert (var (t), 0.0300, 1e-4);
764s ***** error <BetaDistribution: A must be a positive real scalar.> ...
764s  BetaDistribution(0, 1)
764s ***** error <BetaDistribution: A must be a positive real scalar.> ...
764s  BetaDistribution(Inf, 1)
764s ***** error <BetaDistribution: A must be a positive real scalar.> ...
764s  BetaDistribution(i, 1)
764s ***** error <BetaDistribution: A must be a positive real scalar.> ...
764s  BetaDistribution("a", 1)
764s ***** error <BetaDistribution: A must be a positive real scalar.> ...
764s  BetaDistribution([1, 2], 1)
764s ***** error <BetaDistribution: A must be a positive real scalar.> ...
764s  BetaDistribution(NaN, 1)
764s ***** error <BetaDistribution: B must be a positive real scalar.> ...
764s  BetaDistribution(1, 0)
764s ***** error <BetaDistribution: B must be a positive real scalar.> ...
764s  BetaDistribution(1, -1)
764s ***** error <BetaDistribution: B must be a positive real scalar.> ...
764s  BetaDistribution(1, Inf)
764s ***** error <BetaDistribution: B must be a positive real scalar.> ...
764s  BetaDistribution(1, i)
764s ***** error <BetaDistribution: B must be a positive real scalar.> ...
764s  BetaDistribution(1, "a")
764s ***** error <BetaDistribution: B must be a positive real scalar.> ...
764s  BetaDistribution(1, [1, 2])
764s ***** error <BetaDistribution: B must be a positive real scalar.> ...
764s  BetaDistribution(1, NaN)
764s ***** error <cdf: invalid argument for upper tail.> ...
764s  cdf (BetaDistribution, 2, "uper")
764s ***** error <cdf: invalid argument for upper tail.> ...
764s  cdf (BetaDistribution, 2, 3)
764s ***** shared x
764s  randg ("seed", 1);
764s  x = betarnd (1, 1, [100, 1]);
764s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
764s  paramci (BetaDistribution.fit (x), "alpha")
765s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
765s  paramci (BetaDistribution.fit (x), "alpha", 0)
765s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
765s  paramci (BetaDistribution.fit (x), "alpha", 1)
765s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
765s  paramci (BetaDistribution.fit (x), "alpha", [0.5 2])
765s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
765s  paramci (BetaDistribution.fit (x), "alpha", "")
765s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
765s  paramci (BetaDistribution.fit (x), "alpha", {0.05})
765s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
765s  paramci (BetaDistribution.fit (x), "parameter", "a", "alpha", {0.05})
765s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
765s  paramci (BetaDistribution.fit (x), "parameter", {"a", "b", "param"})
765s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
765s  paramci (BetaDistribution.fit (x), "alpha", 0.01, ...
765s           "parameter", {"a", "b", "param"})
765s ***** error <paramci: unknown distribution parameter.> ...
765s  paramci (BetaDistribution.fit (x), "parameter", "param")
765s ***** error <paramci: unknown distribution parameter.> ...
765s  paramci (BetaDistribution.fit (x), "alpha", 0.01, "parameter", "param")
765s ***** error <paramci: invalid NAME for optional argument.> ...
765s  paramci (BetaDistribution.fit (x), "NAME", "value")
765s ***** error <paramci: invalid NAME for optional argument.> ...
765s  paramci (BetaDistribution.fit (x), "alpha", 0.01, "NAME", "value")
765s ***** error <paramci: invalid NAME for optional argument.> ...
765s  paramci (BetaDistribution.fit (x), "alpha", 0.01, "parameter", "a", ...
765s           "NAME", "value")
765s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
765s  plot (BetaDistribution, "Parent")
765s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
765s  plot (BetaDistribution, "PlotType", 12)
765s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
765s  plot (BetaDistribution, "PlotType", {"pdf", "cdf"})
765s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
765s  plot (BetaDistribution, "PlotType", "pdfcdf")
765s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
765s  plot (BetaDistribution, "Discrete", "pdfcdf")
765s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
765s  plot (BetaDistribution, "Discrete", [1, 0])
765s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
765s  plot (BetaDistribution, "Discrete", {true})
765s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
765s  plot (BetaDistribution, "Parent", 12)
765s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
765s  plot (BetaDistribution, "Parent", "hax")
765s ***** error <plot: invalid NAME for optional argument.> ...
765s  plot (BetaDistribution, "invalidNAME", "pdf")
765s ***** error <plot: no fitted DATA to plot a probability plot.> ...
765s  plot (BetaDistribution, "PlotType", "probability")
765s ***** error <proflik: no fitted data available.> ...
765s  proflik (BetaDistribution, 2)
765s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
765s  proflik (BetaDistribution.fit (x), 3)
765s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
765s  proflik (BetaDistribution.fit (x), [1, 2])
765s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
765s  proflik (BetaDistribution.fit (x), {1})
765s ***** error <proflik: SETPARAM must be a numeric vector.> ...
765s  proflik (BetaDistribution.fit (x), 1, ones (2))
765s ***** error <proflik: missing VALUE for 'Display' argument.> ...
765s  proflik (BetaDistribution.fit (x), 1, "Display")
765s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
765s  proflik (BetaDistribution.fit (x), 1, "Display", 1)
765s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
765s  proflik (BetaDistribution.fit (x), 1, "Display", {1})
765s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
765s  proflik (BetaDistribution.fit (x), 1, "Display", {"on"})
765s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
765s  proflik (BetaDistribution.fit (x), 1, "Display", ["on"; "on"])
765s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
765s  proflik (BetaDistribution.fit (x), 1, "Display", "onnn")
765s ***** error <proflik: invalid NAME for optional arguments.> ...
765s  proflik (BetaDistribution.fit (x), 1, "NAME", "on")
765s ***** error <proflik: invalid optional argument.> ...
765s  proflik (BetaDistribution.fit (x), 1, {"NAME"}, "on")
765s ***** error <proflik: invalid optional argument.> ...
765s  proflik (BetaDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
765s ***** error <truncate: missing input argument.> ...
765s  truncate (BetaDistribution)
765s ***** error <truncate: missing input argument.> ...
765s  truncate (BetaDistribution, 2)
765s ***** error <truncate: invalid lower upper limits.> ...
765s  truncate (BetaDistribution, 4, 2)
765s ***** shared pd
765s  pd = BetaDistribution(1, 1);
765s  pd(2) = BetaDistribution(1, 3);
765s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
765s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
765s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
765s ***** error <mean: requires a scalar probability distribution.> mean (pd)
765s ***** error <median: requires a scalar probability distribution.> median (pd)
765s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
765s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
765s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
765s ***** error <plot: requires a scalar probability distribution.> plot (pd)
765s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
765s ***** error <random: requires a scalar probability distribution.> random (pd)
765s ***** error <std: requires a scalar probability distribution.> std (pd)
765s ***** error <truncate: requires a scalar probability distribution.> ...
765s  truncate (pd, 2, 4)
765s ***** error <var: requires a scalar probability distribution.> var (pd)
765s 96 tests, 96 passed, 0 known failure, 0 skipped
765s [inst/dist_obj/BinomialDistribution.m]
765s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_obj/BinomialDistribution.m
765s ***** shared pd, t, t_inf
765s  pd = BinomialDistribution (5, 0.5);
765s  t = truncate (pd, 2, 4);
765s  t_inf = truncate (pd, 2, Inf);
765s ***** assert (cdf (pd, [0:5]), [0.0312, 0.1875, 0.5, 0.8125, 0.9688, 1], 1e-4);
765s ***** assert (cdf (t, [0:5]), [0, 0, 0.4, 0.8, 1, 1], 1e-4);
765s ***** assert (cdf (t_inf, [0:5]), [0, 0, 0.3846, 0.7692, 0.9615, 1], 1e-4);
765s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.1875, 0.5, 0.8125, 0.9688, NaN], 1e-4);
765s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0.4, 0.8, 1, NaN], 1e-4);
765s ***** assert (icdf (pd, [0:0.2:1]), [0, 2, 2, 3, 3, 5], 1e-4);
765s ***** assert (icdf (t, [0:0.2:1]), [2, 2, 2, 3, 3, 4], 1e-4);
765s ***** assert (icdf (t_inf, [0:0.2:1]), [2, 2, 3, 3, 4, 5], 1e-4);
765s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 2, 3, 3, 5, NaN], 1e-4);
765s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2, 3, 3, 4, NaN], 1e-4);
765s ***** assert (iqr (pd), 1);
765s ***** assert (iqr (t), 1);
765s ***** assert (mean (pd), 2.5, 1e-10);
765s ***** assert (mean (t), 2.8, 1e-10);
765s ***** assert (mean (t_inf), 2.8846, 1e-4);
765s ***** assert (median (pd), 2.5);
765s ***** assert (median (t), 3);
765s ***** assert (pdf (pd, [0:5]), [0.0312, 0.1562, 0.3125, 0.3125, 0.1562, 0.0312], 1e-4);
765s ***** assert (pdf (t, [0:5]), [0, 0, 0.4, 0.4, 0.2, 0], 1e-4);
765s ***** assert (pdf (t_inf, [0:5]), [0, 0, 0.3846, 0.3846, 0.1923, 0.0385], 1e-4);
765s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
765s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
765s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
765s ***** assert (any (random (t, 1000, 1) < 2), false);
765s ***** assert (any (random (t, 1000, 1) > 4), false);
765s ***** assert (std (pd), 1.1180, 1e-4);
765s ***** assert (std (t), 0.7483, 1e-4);
765s ***** assert (std (t_inf), 0.8470, 1e-4);
765s ***** assert (var (pd), 1.2500, 1e-4);
765s ***** assert (var (t), 0.5600, 1e-4);
765s ***** assert (var (t_inf), 0.7175, 1e-4);
765s ***** error <BinomialDistribution: N must be a positive integer scalar.> ...
765s  BinomialDistribution(Inf, 0.5)
765s ***** error <BinomialDistribution: N must be a positive integer scalar.> ...
765s  BinomialDistribution(i, 0.5)
765s ***** error <BinomialDistribution: N must be a positive integer scalar.> ...
765s  BinomialDistribution("a", 0.5)
765s ***** error <BinomialDistribution: N must be a positive integer scalar.> ...
765s  BinomialDistribution([1, 2], 0.5)
765s ***** error <BinomialDistribution: N must be a positive integer scalar.> ...
765s  BinomialDistribution(NaN, 0.5)
765s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
765s  BinomialDistribution(1, 1.01)
765s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
765s  BinomialDistribution(1, -0.01)
765s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
765s  BinomialDistribution(1, Inf)
765s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
765s  BinomialDistribution(1, i)
765s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
765s  BinomialDistribution(1, "a")
765s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
765s  BinomialDistribution(1, [1, 2])
765s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
765s  BinomialDistribution(1, NaN)
765s ***** error <cdf: invalid argument for upper tail.> ...
765s  cdf (BinomialDistribution, 2, "uper")
765s ***** error <cdf: invalid argument for upper tail.> ...
765s  cdf (BinomialDistribution, 2, 3)
765s ***** shared x
765s  rand ("seed", 2);
765s  x = binornd (5, 0.5, [1, 100]);
766s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
766s  paramci (BinomialDistribution.fit (x, 6), "alpha")
766s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
766s  paramci (BinomialDistribution.fit (x, 6), "alpha", 0)
766s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
766s  paramci (BinomialDistribution.fit (x, 6), "alpha", 1)
766s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
766s  paramci (BinomialDistribution.fit (x, 6), "alpha", [0.5 2])
766s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
766s  paramci (BinomialDistribution.fit (x, 6), "alpha", "")
766s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
766s  paramci (BinomialDistribution.fit (x, 6), "alpha", {0.05})
766s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
766s  paramci (BinomialDistribution.fit (x, 6), "parameter", "p", ...
766s           "alpha", {0.05})
766s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
766s  paramci (BinomialDistribution.fit (x, 6), ...
766s           "parameter", {"N", "p", "param"})
766s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
766s  paramci (BinomialDistribution.fit (x, 6), "alpha", 0.01, ...
766s           "parameter", {"N", "p", "param"})
766s ***** error <paramci: unknown distribution parameter.> ...
766s  paramci (BinomialDistribution.fit (x, 6), "parameter", "param")
766s ***** error <paramci: unknown distribution parameter.> ...
766s  paramci (BinomialDistribution.fit (x, 6), "parameter", "N")
766s ***** error <paramci: unknown distribution parameter.> ...
766s  paramci (BinomialDistribution.fit (x, 6), "alpha", 0.01, ...
766s           "parameter", "param")
766s ***** error <paramci: invalid NAME for optional argument.> ...
766s  paramci (BinomialDistribution.fit (x, 6), "NAME", "value")
766s ***** error <paramci: invalid NAME for optional argument.> ...
766s  paramci (BinomialDistribution.fit (x, 6), "alpha", 0.01, ...
766s           "NAME", "value")
766s ***** error <paramci: invalid NAME for optional argument.> ...
766s  paramci (BinomialDistribution.fit (x, 6), "alpha", 0.01, ...
766s           "parameter", "p", "NAME", "value")
766s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
766s  plot (BinomialDistribution, "Parent")
766s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
766s  plot (BinomialDistribution, "PlotType", 12)
766s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
766s  plot (BinomialDistribution, "PlotType", {"pdf", "cdf"})
766s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
766s  plot (BinomialDistribution, "PlotType", "pdfcdf")
766s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
766s  plot (BinomialDistribution, "Discrete", "pdfcdf")
766s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
766s  plot (BinomialDistribution, "Discrete", [1, 0])
766s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
766s  plot (BinomialDistribution, "Discrete", {true})
766s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
766s  plot (BinomialDistribution, "Parent", 12)
766s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
766s  plot (BinomialDistribution, "Parent", "hax")
766s ***** error <plot: invalid NAME for optional argument.> ...
766s  plot (BinomialDistribution, "invalidNAME", "pdf")
766s ***** error <plot: no fitted DATA to plot a probability plot.> ...
766s  plot (BinomialDistribution, "PlotType", "probability")
766s ***** error <proflik: no fitted data available.> ...
766s  proflik (BinomialDistribution, 2)
766s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
766s  proflik (BinomialDistribution.fit (x, 6), 3)
766s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
766s  proflik (BinomialDistribution.fit (x, 6), [1, 2])
766s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
766s  proflik (BinomialDistribution.fit (x, 6), {1})
766s ***** error <proflik: SETPARAM must be a numeric vector.> ...
766s  proflik (BinomialDistribution.fit (x, 6), 2, ones (2))
766s ***** error <proflik: missing VALUE for 'Display' argument.> ...
766s  proflik (BinomialDistribution.fit (x, 6), 2, "Display")
766s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
766s  proflik (BinomialDistribution.fit (x, 6), 2, "Display", 1)
766s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
766s  proflik (BinomialDistribution.fit (x, 6), 2, "Display", {1})
766s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
766s  proflik (BinomialDistribution.fit (x, 6), 2, "Display", {"on"})
766s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
766s  proflik (BinomialDistribution.fit (x, 6), 2, "Display", ["on"; "on"])
766s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
766s  proflik (BinomialDistribution.fit (x, 6), 2, "Display", "onnn")
766s ***** error <proflik: invalid NAME for optional arguments.> ...
766s  proflik (BinomialDistribution.fit (x, 6), 2, "NAME", "on")
766s ***** error <proflik: invalid optional argument.> ...
766s  proflik (BinomialDistribution.fit (x, 6), 2, {"NAME"}, "on")
766s ***** error <proflik: invalid optional argument.> ...
766s  proflik (BinomialDistribution.fit (x, 6), 2, {[1 2 3]}, "Display", "on")
766s ***** error <truncate: missing input argument.> ...
766s  truncate (BinomialDistribution)
766s ***** error <truncate: missing input argument.> ...
766s  truncate (BinomialDistribution, 2)
766s ***** error <truncate: invalid lower upper limits.> ...
766s  truncate (BinomialDistribution, 4, 2)
766s ***** shared pd
766s  pd = BinomialDistribution(1, 0.5);
766s  pd(2) = BinomialDistribution(1, 0.6);
766s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
766s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
766s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
766s ***** error <mean: requires a scalar probability distribution.> mean (pd)
766s ***** error <median: requires a scalar probability distribution.> median (pd)
766s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
766s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
766s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
766s ***** error <plot: requires a scalar probability distribution.> plot (pd)
766s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
766s ***** error <random: requires a scalar probability distribution.> random (pd)
766s ***** error <std: requires a scalar probability distribution.> std (pd)
766s ***** error <truncate: requires a scalar probability distribution.> ...
766s  truncate (pd, 2, 4)
766s ***** error <var: requires a scalar probability distribution.> var (pd)
766s 102 tests, 102 passed, 0 known failure, 0 skipped
766s [inst/dist_obj/BirnbaumSaundersDistribution.m]
766s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_obj/BirnbaumSaundersDistribution.m
766s ***** demo
766s  ## Generate a data set of 5000 random samples from a Birnbaum-Saunders
766s  ## distribution with parameters β = 1 and γ = 0.5.  Fit a Birnbaum-Saunders
766s  ## distribution to this data and plot a PDF of the fitted distribution
766s  ## superimposed on a histogram of the data
766s 
766s  pd = makedist ("BirnbaumSaunders", "beta", 1, "gamma", 0.5)
766s  randg ("seed", 21);
766s  data = random (pd, 5000, 1);
766s  pd = fitdist (data, "BirnbaumSaunders")
766s  plot (pd)
766s  msg = "Fitted Birnbaum-Saunders distribution with a = %0.2f and b = %0.2f";
766s  title (sprintf (msg, pd.beta, pd.gamma))
766s ***** demo
766s  ## Plot the PDF of a Birnbaum-Saunders distribution, with parameters beta = 1
766s  ## and gamma = 0.5, truncated at [0, 2] intervals.  Generate 10000 random
766s  ## samples from this truncated distribution and superimpose a histogram with
766s  ## 100 bins scaled accordingly
766s 
766s  pd = makedist ("BirnbaumSaunders", "beta", 1, "gamma", 0.5)
766s  t = truncate (pd, 0, 2)
766s  randg ("seed", 21);
766s  data = random (t, 10000, 1);
766s  plot (t)
766s  title ("Birnbaum-Saunders distribution (a = 2, b = 4) truncated at [0.1, 0.8]")
766s  hold on
766s  hist (data, 100, 50)
766s  hold off
766s ***** demo
766s  ## Generate a data set of 100 random samples from a Birnbaum-Saunders
766s  ## distribution with parameters β = 1 and γ = 0.5.  Fit a Birnbaum-Saunders
766s  ## distribution to this data and plot its CDF superimposed over an empirical
766s  ## CDF of the data
766s 
766s  pd = makedist ("BirnbaumSaunders", "beta", 1, "gamma", 0.5)
766s  randg ("seed", 21);
766s  data = random (pd, 100, 1);
766s  pd = fitdist (data, "BirnbaumSaunders")
766s  plot (pd, "plottype", "cdf")
766s  title (sprintf ("Fitted Beta distribution with a = %0.2f and b = %0.2f", ...
766s                  pd.beta, pd.gamma))
766s  legend ({"empirical CDF", "fitted CDF"}, "location", "east")
766s ***** shared pd, t
766s  pd = BirnbaumSaundersDistribution;
766s  t = truncate (pd, 2, 4);
766s ***** assert (cdf (pd, [0:5]), [0, 0.5, 0.7602, 0.8759, 0.9332, 0.9632], 1e-4);
766s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.6687, 1, 1], 1e-4);
766s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.6585, 0.7602, 0.8759, 0.9332, NaN], 1e-4);
766s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.6687, 1, NaN], 1e-4);
766s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.4411, 0.7767, 1.2875, 2.2673, Inf], 1e-4);
766s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2293, 2.5073, 2.8567, 3.3210, 4], 1e-4);
766s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.7767, 1.2875, 2.2673, Inf, NaN], 1e-4);
766s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5073, 2.8567, 3.3210, 4, NaN], 1e-4);
766s ***** assert (iqr (pd), 1.4236, 1e-4);
766s ***** assert (iqr (t), 0.8968, 1e-4);
766s ***** assert (mean (pd), 1.5, eps);
766s ***** assert (mean (t), 2.7723, 1e-4);
766s ***** assert (median (pd), 1, 1e-4);
766s ***** assert (median (t), 2.6711, 1e-4);
766s ***** assert (pdf (pd, [0:5]), [0, 0.3989, 0.1648, 0.0788, 0.0405, 0.0216], 1e-4);
766s ***** assert (pdf (t, [0:5]), [0, 0, 0.9528, 0.4559, 0.2340, 0], 1e-4);
766s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.2497, NaN], 1e-4);
766s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
766s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
766s ***** assert (any (random (t, 1000, 1) < 2), false);
766s ***** assert (any (random (t, 1000, 1) > 4), false);
766s ***** assert (std (pd), 1.5, eps);
766s ***** assert (std (t), 0.5528, 1e-4);
766s ***** assert (var (pd), 2.25, eps);
766s ***** assert (var (t), 0.3056, 1e-4);
766s ***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
766s  BirnbaumSaundersDistribution(0, 1)
766s ***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
766s  BirnbaumSaundersDistribution(Inf, 1)
766s ***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
766s  BirnbaumSaundersDistribution(i, 1)
766s ***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
766s  BirnbaumSaundersDistribution("beta", 1)
766s ***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
766s  BirnbaumSaundersDistribution([1, 2], 1)
766s ***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
766s  BirnbaumSaundersDistribution(NaN, 1)
766s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
766s  BirnbaumSaundersDistribution(1, 0)
766s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
766s  BirnbaumSaundersDistribution(1, -1)
766s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
766s  BirnbaumSaundersDistribution(1, Inf)
766s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
766s  BirnbaumSaundersDistribution(1, i)
766s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
766s  BirnbaumSaundersDistribution(1, "beta")
766s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
766s  BirnbaumSaundersDistribution(1, [1, 2])
766s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
766s  BirnbaumSaundersDistribution(1, NaN)
766s ***** error <cdf: invalid argument for upper tail.> ...
766s  cdf (BirnbaumSaundersDistribution, 2, "uper")
766s ***** error <cdf: invalid argument for upper tail.> ...
766s  cdf (BirnbaumSaundersDistribution, 2, 3)
766s ***** shared x
766s  rand ("seed", 5);
766s  x = bisarnd (1, 1, [100, 1]);
766s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
766s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha")
766s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
766s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0)
766s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
766s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 1)
767s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
767s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", [0.5 2])
767s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
767s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", "")
767s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
767s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", {0.05})
767s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
767s  paramci (BirnbaumSaundersDistribution.fit (x), "parameter", ...
767s           "beta", "alpha", {0.05})
767s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
767s  paramci (BirnbaumSaundersDistribution.fit (x), ...
767s           "parameter", {"beta", "gamma", "param"})
767s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
767s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0.01, ...
767s           "parameter", {"beta", "gamma", "param"})
767s ***** error <paramci: unknown distribution parameter.> ...
767s  paramci (BirnbaumSaundersDistribution.fit (x), "parameter", "param")
767s ***** error <paramci: unknown distribution parameter.> ...
767s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0.01, ...
767s           "parameter", "param")
767s ***** error <paramci: invalid NAME for optional argument.> ...
767s  paramci (BirnbaumSaundersDistribution.fit (x), "NAME", "value")
767s ***** error <paramci: invalid NAME for optional argument.> ...
767s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0.01, ...
767s           "NAME", "value")
767s ***** error <paramci: invalid NAME for optional argument.> ...
767s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0.01, ...
767s           "parameter", "beta", "NAME", "value")
767s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
767s  plot (BirnbaumSaundersDistribution, "Parent")
767s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
767s  plot (BirnbaumSaundersDistribution, "PlotType", 12)
767s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
767s  plot (BirnbaumSaundersDistribution, "PlotType", {"pdf", "cdf"})
767s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
767s  plot (BirnbaumSaundersDistribution, "PlotType", "pdfcdf")
767s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
767s  plot (BirnbaumSaundersDistribution, "Discrete", "pdfcdf")
767s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
767s  plot (BirnbaumSaundersDistribution, "Discrete", [1, 0])
767s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
767s  plot (BirnbaumSaundersDistribution, "Discrete", {true})
767s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
767s  plot (BirnbaumSaundersDistribution, "Parent", 12)
767s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
767s  plot (BirnbaumSaundersDistribution, "Parent", "hax")
767s ***** error <plot: invalid NAME for optional argument.> ...
767s  plot (BirnbaumSaundersDistribution, "invalidNAME", "pdf")
767s ***** error <plot: no fitted DATA to plot a probability plot.> ...
767s  plot (BirnbaumSaundersDistribution, "PlotType", "probability")
767s ***** error <proflik: no fitted data available.> ...
767s  proflik (BirnbaumSaundersDistribution, 2)
767s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
767s  proflik (BirnbaumSaundersDistribution.fit (x), 3)
767s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
767s  proflik (BirnbaumSaundersDistribution.fit (x), [1, 2])
767s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
767s  proflik (BirnbaumSaundersDistribution.fit (x), {1})
767s ***** error <proflik: SETPARAM must be a numeric vector.> ...
767s  proflik (BirnbaumSaundersDistribution.fit (x), 1, ones (2))
767s ***** error <proflik: missing VALUE for 'Display' argument.> ...
767s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display")
767s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
767s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", 1)
767s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
767s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", {1})
767s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
767s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", {"on"})
768s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
768s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", ["on"; "on"])
768s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
768s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", "onnn")
768s ***** error <proflik: invalid NAME for optional arguments.> ...
768s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "NAME", "on")
768s ***** error <proflik: invalid optional argument.> ...
768s  proflik (BirnbaumSaundersDistribution.fit (x), 1, {"NAME"}, "on")
768s ***** error <proflik: invalid optional argument.> ...
768s  proflik (BirnbaumSaundersDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
768s ***** error <truncate: missing input argument.> ...
768s  truncate (BirnbaumSaundersDistribution)
768s ***** error <truncate: missing input argument.> ...
768s  truncate (BirnbaumSaundersDistribution, 2)
768s ***** error <truncate: invalid lower upper limits.> ...
768s  truncate (BirnbaumSaundersDistribution, 4, 2)
768s ***** shared pd
768s  pd = BirnbaumSaundersDistribution(1, 1);
768s  pd(2) = BirnbaumSaundersDistribution(1, 3);
768s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
768s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
768s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
768s ***** error <mean: requires a scalar probability distribution.> mean (pd)
768s ***** error <median: requires a scalar probability distribution.> median (pd)
768s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
768s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
768s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
768s ***** error <plot: requires a scalar probability distribution.> plot (pd)
768s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
768s ***** error <random: requires a scalar probability distribution.> random (pd)
768s ***** error <std: requires a scalar probability distribution.> std (pd)
768s ***** error <truncate: requires a scalar probability distribution.> ...
768s  truncate (pd, 2, 4)
768s ***** error <var: requires a scalar probability distribution.> var (pd)
768s 96 tests, 96 passed, 0 known failure, 0 skipped
768s [inst/dist_obj/BurrDistribution.m]
768s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_obj/BurrDistribution.m
768s ***** demo
768s  ## Generate a data set of 5000 random samples from a Burr type XII
768s  ## distribution with parameters alpha = 1, c = 2, and k = 1.  Fit a Burr type
768s  ## XII distribution to this data and plot a PDF of the fitted distribution
768s  ## superimposed on a histogram of the data
768s 
768s  pd = makedist ("Burr", "alpha", 1, "c", 2, "k", 1)
768s  rand ("seed", 21);
768s  data = random (pd, 5000, 1);
768s  pd = fitdist (data, "Burr")
768s  plot (pd)
768s  msg = strcat (["Fitted Burr type XII distribution with"], ...
768s                 [" alpha = %0.2f, c =  %0.2f, and k = %0.2f"]);
768s  title (sprintf (msg, pd.alpha, pd.c, pd.k))
768s ***** demo
768s  ## Plot the PDF of a Burr type XII distribution, with parameters alpha = 1,
768s  ## c = 2, and k = 1, truncated at [0, 2] intervals. Generate 10000 random
768s  ## samples from this truncated distribution and superimpose a histogram with
768s  ## 100 bins scaled accordingly
768s 
768s  pd = makedist ("Burr", "alpha", 1, "c", 2, "k", 1)
768s  t = truncate (pd, 0.5, 2.5)
768s  rand ("seed", 21);
768s  data = random (t, 10000, 1);
768s  plot (t)
768s  title ("Burr type XII distribution (alpha = 1, c = 2, k = 1) truncated at [0.5, 2.5]")
768s  hold on
768s  hist (data, 100, 50)
768s  hold off
768s ***** demo
768s  ## Generate a data set of 100 random samples from a Burr type XII
768s  ## distribution with parameters alpha = 1, c = 2, and k = 1.  Fit a Burr type
768s  ## XII  distribution to this data and plot its CDF superimposed over an
768s  ## empirical CDF of the data
768s 
768s  pd = makedist ("Burr", "alpha", 1, "c", 2, "k", 1)
768s  rand ("seed", 21);
768s  data = random (pd, 100, 1);
768s  pd = fitdist (data, "Burr")
768s  plot (pd, "plottype", "cdf")
768s  msg = strcat (["Fitted Burr type XII distribution with"], ...
768s                 [" alpha = %0.2f, c =  %0.2f, and k = %0.2f"]);
768s  title (sprintf (msg, pd.alpha, pd.c, pd.k))
768s  legend ({"empirical CDF", "fitted CDF"}, "location", "east")
768s ***** shared pd, t
768s  pd = BurrDistribution;
768s  t = truncate (pd, 2, 4);
768s ***** assert (cdf (pd, [0:5]), [0, 0.5, 0.6667, 0.75, 0.8, 0.8333], 1e-4);
768s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.625, 1, 1], 1e-4);
768s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.6, 0.6667, 0.75, 0.8], 1e-4);
768s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.625, 1], 1e-4);
768s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.25, 0.6667, 1.5, 4, Inf], 1e-4);
768s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2609, 2.5714, 2.9474, 3.4118, 4], 1e-4);
768s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.6667, 1.5, 4, Inf, NaN], 1e-4);
768s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5714, 2.9474, 3.4118, 4, NaN], 1e-4);
768s ***** assert (iqr (pd), 2.6667, 1e-4);
768s ***** assert (iqr (t), 0.9524, 1e-4);
768s ***** assert (mean (pd), Inf);
768s ***** assert (mean (t), 2.8312, 1e-4);
768s ***** assert (median (pd), 1, 1e-4);
768s ***** assert (median (t), 2.75, 1e-4);
768s ***** assert (pdf (pd, [0:5]), [1, 0.25, 0.1111, 0.0625, 0.04, 0.0278], 1e-4);
768s ***** assert (pdf (t, [0:5]), [0, 0, 0.8333, 0.4687, 0.3, 0], 1e-4);
768s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.25, 0.1111, 0.0625, 0.04, NaN], 1e-4);
768s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.8333, 0.4687, 0.3, NaN], 1e-4);
768s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
768s ***** assert (any (random (t, 1000, 1) < 2), false);
768s ***** assert (any (random (t, 1000, 1) > 4), false);
768s ***** assert (std (pd), Inf);
768s ***** assert (std (t), 0.5674, 1e-4);
768s ***** assert (var (pd), Inf);
768s ***** assert (var (t), 0.3220, 1e-4);
768s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
768s  BurrDistribution(0, 1, 1)
768s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
768s  BurrDistribution(-1, 1, 1)
768s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
768s  BurrDistribution(Inf, 1, 1)
768s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
768s  BurrDistribution(i, 1, 1)
768s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
768s  BurrDistribution("a", 1, 1)
768s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
768s  BurrDistribution([1, 2], 1, 1)
768s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
768s  BurrDistribution(NaN, 1, 1)
768s ***** error <BurrDistribution: C must be a positive real scalar.> ...
768s  BurrDistribution(1, 0, 1)
768s ***** error <BurrDistribution: C must be a positive real scalar.> ...
768s  BurrDistribution(1, -1, 1)
768s ***** error <BurrDistribution: C must be a positive real scalar.> ...
768s  BurrDistribution(1, Inf, 1)
768s ***** error <BurrDistribution: C must be a positive real scalar.> ...
768s  BurrDistribution(1, i, 1)
768s ***** error <BurrDistribution: C must be a positive real scalar.> ...
768s  BurrDistribution(1, "a", 1)
768s ***** error <BurrDistribution: C must be a positive real scalar.> ...
768s  BurrDistribution(1, [1, 2], 1)
768s ***** error <BurrDistribution: C must be a positive real scalar.> ...
768s  BurrDistribution(1, NaN, 1)
768s ***** error <BurrDistribution: K must be a positive real scalar.> ...
768s  BurrDistribution(1, 1, 0)
768s ***** error <BurrDistribution: K must be a positive real scalar.> ...
768s  BurrDistribution(1, 1, -1)
768s ***** error <BurrDistribution: K must be a positive real scalar.> ...
768s  BurrDistribution(1, 1, Inf)
768s ***** error <BurrDistribution: K must be a positive real scalar.> ...
768s  BurrDistribution(1, 1, i)
768s ***** error <BurrDistribution: K must be a positive real scalar.> ...
768s  BurrDistribution(1, 1, "a")
768s ***** error <BurrDistribution: K must be a positive real scalar.> ...
768s  BurrDistribution(1, 1, [1, 2])
768s ***** error <BurrDistribution: K must be a positive real scalar.> ...
768s  BurrDistribution(1, 1, NaN)
768s ***** error <cdf: invalid argument for upper tail.> ...
768s  cdf (BurrDistribution, 2, "uper")
768s ***** error <cdf: invalid argument for upper tail.> ...
768s  cdf (BurrDistribution, 2, 3)
768s ***** shared x
768s  rand ("seed", 4);
768s  x = burrrnd (1, 1, 1, [1, 100]);
768s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
768s  paramci (BurrDistribution.fit (x), "alpha")
768s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
768s  paramci (BurrDistribution.fit (x), "alpha", 0)
768s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
768s  paramci (BurrDistribution.fit (x), "alpha", 1)
768s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
768s  paramci (BurrDistribution.fit (x), "alpha", [0.5 2])
768s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
768s  paramci (BurrDistribution.fit (x), "alpha", "")
769s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
769s  paramci (BurrDistribution.fit (x), "alpha", {0.05})
769s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
769s  paramci (BurrDistribution.fit (x), "parameter", "c", "alpha", {0.05})
769s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
769s  paramci (BurrDistribution.fit (x), "parameter", {"alpha", "c", "k", "param"})
769s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
769s  paramci (BurrDistribution.fit (x), "alpha", 0.01, ...
769s           "parameter", {"alpha", "c", "k", "param"})
769s ***** error <paramci: unknown distribution parameter.> ...
769s  paramci (BurrDistribution.fit (x), "parameter", "param")
769s ***** error <paramci: unknown distribution parameter.> ...
769s  paramci (BurrDistribution.fit (x), "alpha", 0.01, "parameter", "param")
769s ***** error <paramci: invalid NAME for optional argument.> ...
769s  paramci (BurrDistribution.fit (x), "NAME", "value")
769s ***** error <paramci: invalid NAME for optional argument.> ...
769s  paramci (BurrDistribution.fit (x), "alpha", 0.01, "NAME", "value")
769s ***** error <paramci: invalid NAME for optional argument.> ...
769s  paramci (BurrDistribution.fit (x), "alpha", 0.01, "parameter", "c", ...
769s           "NAME", "value")
770s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
770s  plot (BurrDistribution, "Parent")
770s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
770s  plot (BurrDistribution, "PlotType", 12)
770s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
770s  plot (BurrDistribution, "PlotType", {"pdf", "cdf"})
770s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
770s  plot (BurrDistribution, "PlotType", "pdfcdf")
770s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
770s  plot (BurrDistribution, "Discrete", "pdfcdf")
770s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
770s  plot (BurrDistribution, "Discrete", [1, 0])
770s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
770s  plot (BurrDistribution, "Discrete", {true})
770s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
770s  plot (BurrDistribution, "Parent", 12)
770s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
770s  plot (BurrDistribution, "Parent", "hax")
770s ***** error <plot: invalid NAME for optional argument.> ...
770s  plot (BurrDistribution, "invalidNAME", "pdf")
770s ***** error <plot: no fitted DATA to plot a probability plot.> ...
770s  plot (BurrDistribution, "PlotType", "probability")
770s ***** error <proflik: no fitted data available.> ...
770s  proflik (BurrDistribution, 2)
770s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
770s  proflik (BurrDistribution.fit (x), 4)
770s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
770s  proflik (BurrDistribution.fit (x), [1, 2])
770s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
770s  proflik (BurrDistribution.fit (x), {1})
770s ***** error <proflik: SETPARAM must be a numeric vector.> ...
770s  proflik (BurrDistribution.fit (x), 1, ones (2))
770s ***** error <proflik: missing VALUE for 'Display' argument.> ...
770s  proflik (BurrDistribution.fit (x), 1, "Display")
770s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
770s  proflik (BurrDistribution.fit (x), 1, "Display", 1)
770s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
770s  proflik (BurrDistribution.fit (x), 1, "Display", {1})
770s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
770s  proflik (BurrDistribution.fit (x), 1, "Display", {"on"})
770s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
770s  proflik (BurrDistribution.fit (x), 1, "Display", ["on"; "on"])
770s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
770s  proflik (BurrDistribution.fit (x), 1, "Display", "onnn")
771s ***** error <proflik: invalid NAME for optional arguments.> ...
771s  proflik (BurrDistribution.fit (x), 1, "NAME", "on")
771s ***** error <proflik: invalid optional argument.> ...
771s  proflik (BurrDistribution.fit (x), 1, {"NAME"}, "on")
771s ***** error <proflik: invalid optional argument.> ...
771s  proflik (BurrDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
771s ***** error <truncate: missing input argument.> ...
771s  truncate (BurrDistribution)
771s ***** error <truncate: missing input argument.> ...
771s  truncate (BurrDistribution, 2)
771s ***** error <truncate: invalid lower upper limits.> ...
771s  truncate (BurrDistribution, 4, 2)
771s ***** shared pd
771s  pd = BurrDistribution(1, 1, 1);
771s  pd(2) = BurrDistribution(1, 3, 1);
771s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
771s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
771s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
771s ***** error <mean: requires a scalar probability distribution.> mean (pd)
771s ***** error <median: requires a scalar probability distribution.> median (pd)
771s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
771s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
771s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
771s ***** error <plot: requires a scalar probability distribution.> plot (pd)
771s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
771s ***** error <random: requires a scalar probability distribution.> random (pd)
771s ***** error <std: requires a scalar probability distribution.> std (pd)
771s ***** error <truncate: requires a scalar probability distribution.> ...
771s  truncate (pd, 2, 4)
771s ***** error <var: requires a scalar probability distribution.> var (pd)
771s 104 tests, 104 passed, 0 known failure, 0 skipped
771s [inst/dist_obj/ExponentialDistribution.m]
771s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_obj/ExponentialDistribution.m
771s ***** shared pd, t
771s  pd = ExponentialDistribution (1);
771s  t = truncate (pd, 2, 4);
771s ***** assert (cdf (pd, [0:5]), [0, 0.6321, 0.8647, 0.9502, 0.9817, 0.9933], 1e-4);
771s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7311, 1, 1], 1e-4);
771s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.7769, 0.8647, 0.9502, 0.9817], 1e-4);
771s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7311, 1], 1e-4);
771s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.2231, 0.5108, 0.9163, 1.6094, Inf], 1e-4);
771s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1899, 2.4244, 2.7315, 3.1768, 4], 1e-4);
771s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5108, 0.9163, 1.6094, Inf, NaN], 1e-4);
771s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4244, 2.7315, 3.1768, 4, NaN], 1e-4);
771s ***** assert (iqr (pd), 1.0986, 1e-4);
771s ***** assert (iqr (t), 0.8020, 1e-4);
771s ***** assert (mean (pd), 1);
771s ***** assert (mean (t), 2.6870, 1e-4);
771s ***** assert (median (pd), 0.6931, 1e-4);
771s ***** assert (median (t), 2.5662, 1e-4);
771s ***** assert (pdf (pd, [0:5]), [1, 0.3679, 0.1353, 0.0498, 0.0183, 0.0067], 1e-4);
771s ***** assert (pdf (t, [0:5]), [0, 0, 1.1565, 0.4255, 0.1565, 0], 1e-4);
771s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3679, 0.1353, 0.0498, 0.0183, NaN], 1e-4);
771s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.1565, 0.4255, 0.1565, NaN], 1e-4);
771s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
771s ***** assert (any (random (t, 1000, 1) < 2), false);
771s ***** assert (any (random (t, 1000, 1) > 4), false);
771s ***** assert (std (pd), 1);
771s ***** assert (std (t), 0.5253, 1e-4);
771s ***** assert (var (pd), 1);
771s ***** assert (var (t), 0.2759, 1e-4);
771s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
771s  ExponentialDistribution(0)
771s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
771s  ExponentialDistribution(-1)
771s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
771s  ExponentialDistribution(Inf)
771s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
771s  ExponentialDistribution(i)
771s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
771s  ExponentialDistribution("a")
771s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
771s  ExponentialDistribution([1, 2])
771s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
771s  ExponentialDistribution(NaN)
771s ***** error <cdf: invalid argument for upper tail.> ...
771s  cdf (ExponentialDistribution, 2, "uper")
771s ***** error <cdf: invalid argument for upper tail.> ...
771s  cdf (ExponentialDistribution, 2, 3)
771s ***** shared x
771s  x = exprnd (1, [100, 1]);
771s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
771s  paramci (ExponentialDistribution.fit (x), "alpha")
771s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
771s  paramci (ExponentialDistribution.fit (x), "alpha", 0)
771s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
771s  paramci (ExponentialDistribution.fit (x), "alpha", 1)
771s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
771s  paramci (ExponentialDistribution.fit (x), "alpha", [0.5 2])
771s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
771s  paramci (ExponentialDistribution.fit (x), "alpha", "")
771s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
771s  paramci (ExponentialDistribution.fit (x), "alpha", {0.05})
771s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
771s  paramci (ExponentialDistribution.fit (x), "parameter", "mu", ...
771s           "alpha", {0.05})
771s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
771s  paramci (ExponentialDistribution.fit (x), "parameter", {"mu", "param"})
771s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
771s  paramci (ExponentialDistribution.fit (x), "alpha", 0.01, ...
771s           "parameter", {"mu", "param"})
771s ***** error <paramci: unknown distribution parameter.> ...
771s  paramci (ExponentialDistribution.fit (x), "parameter", "param")
771s ***** error <paramci: unknown distribution parameter.> ...
771s  paramci (ExponentialDistribution.fit (x), "alpha", 0.01, "parameter", "parm")
771s ***** error <paramci: invalid NAME for optional argument.> ...
771s  paramci (ExponentialDistribution.fit (x), "NAME", "value")
771s ***** error <paramci: invalid NAME for optional argument.> ...
771s  paramci (ExponentialDistribution.fit (x), "alpha", 0.01, "NAME", "value")
771s ***** error <paramci: invalid NAME for optional argument.> ...
771s  paramci (ExponentialDistribution.fit (x), "alpha", 0.01, ...
771s           "parameter", "mu", "NAME", "value")
771s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
771s  plot (ExponentialDistribution, "Parent")
771s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
771s  plot (ExponentialDistribution, "PlotType", 12)
771s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
771s  plot (ExponentialDistribution, "PlotType", {"pdf", "cdf"})
771s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
771s  plot (ExponentialDistribution, "PlotType", "pdfcdf")
771s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
771s  plot (ExponentialDistribution, "Discrete", "pdfcdf")
771s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
771s  plot (ExponentialDistribution, "Discrete", [1, 0])
771s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
771s  plot (ExponentialDistribution, "Discrete", {true})
771s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
771s  plot (ExponentialDistribution, "Parent", 12)
771s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
771s  plot (ExponentialDistribution, "Parent", "hax")
771s ***** error <plot: invalid NAME for optional argument.> ...
771s  plot (ExponentialDistribution, "invalidNAME", "pdf")
771s ***** error <plot: no fitted DATA to plot a probability plot.> ...
771s  plot (ExponentialDistribution, "PlotType", "probability")
771s ***** error <proflik: no fitted data available.> ...
771s  proflik (ExponentialDistribution, 2)
772s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
772s  proflik (ExponentialDistribution.fit (x), 3)
772s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
772s  proflik (ExponentialDistribution.fit (x), [1, 2])
772s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
772s  proflik (ExponentialDistribution.fit (x), {1})
772s ***** error <proflik: SETPARAM must be a numeric vector.> ...
772s  proflik (ExponentialDistribution.fit (x), 1, ones (2))
772s ***** error <proflik: missing VALUE for 'Display' argument.> ...
772s  proflik (ExponentialDistribution.fit (x), 1, "Display")
772s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
772s  proflik (ExponentialDistribution.fit (x), 1, "Display", 1)
772s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
772s  proflik (ExponentialDistribution.fit (x), 1, "Display", {1})
772s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
772s  proflik (ExponentialDistribution.fit (x), 1, "Display", {"on"})
772s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
772s  proflik (ExponentialDistribution.fit (x), 1, "Display", ["on"; "on"])
772s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
772s  proflik (ExponentialDistribution.fit (x), 1, "Display", "onnn")
772s ***** error <proflik: invalid NAME for optional arguments.> ...
772s  proflik (ExponentialDistribution.fit (x), 1, "NAME", "on")
772s ***** error <proflik: invalid optional argument.> ...
772s  proflik (ExponentialDistribution.fit (x), 1, {"NAME"}, "on")
772s ***** error <proflik: invalid optional argument.> ...
772s  proflik (ExponentialDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
772s ***** error <truncate: missing input argument.> ...
772s  truncate (ExponentialDistribution)
772s ***** error <truncate: missing input argument.> ...
772s  truncate (ExponentialDistribution, 2)
772s ***** error <truncate: invalid lower upper limits.> ...
772s  truncate (ExponentialDistribution, 4, 2)
772s ***** shared pd
772s  pd = ExponentialDistribution(1);
772s  pd(2) = ExponentialDistribution(3);
772s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
772s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
772s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
772s ***** error <mean: requires a scalar probability distribution.> mean (pd)
772s ***** error <median: requires a scalar probability distribution.> median (pd)
772s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
772s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
772s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
772s ***** error <plot: requires a scalar probability distribution.> plot (pd)
772s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
772s ***** error <random: requires a scalar probability distribution.> random (pd)
772s ***** error <std: requires a scalar probability distribution.> std (pd)
772s ***** error <truncate: requires a scalar probability distribution.> ...
772s  truncate (pd, 2, 4)
772s ***** error <var: requires a scalar probability distribution.> var (pd)
772s 90 tests, 90 passed, 0 known failure, 0 skipped
772s [inst/dist_obj/ExtremeValueDistribution.m]
772s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_obj/ExtremeValueDistribution.m
772s ***** shared pd, t
772s  pd = ExtremeValueDistribution (0, 1);
772s  t = truncate (pd, 2, 4);
772s ***** assert (cdf (pd, [0:5]), [0.6321, 0.9340, 0.9994, 1, 1, 1], 1e-4);
772s ***** assert (cdf (t, [0:5]), [0, 0, 0, 1, 1, 1], 1e-4);
772s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.9887, 0.9994, 1, 1], 1e-4);
772s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 1, 1], 1e-4);
772s ***** assert (icdf (pd, [0:0.2:1]), [-Inf, -1.4999, -0.6717, -0.0874, 0.4759, Inf], 1e-4);
772s ***** assert (icdf (t, [0:0.2:1]), [2, 2.0298, 2.0668, 2.1169, 2.1971, 4], 1e-4);
772s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, -0.6717, -0.0874, 0.4759, Inf, NaN], 1e-4);
772s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.0668, 2.1169, 2.1971, 4, NaN], 1e-4);
772s ***** assert (iqr (pd), 1.5725, 1e-4);
772s ***** assert (iqr (t), 0.1338, 1e-4);
772s ***** assert (mean (pd), -0.5772, 1e-4);
772s ***** assert (mean (t), 2.1206, 1e-4);
772s ***** assert (median (pd), -0.3665, 1e-4);
772s ***** assert (median (t), 2.0897, 1e-4);
772s ***** assert (pdf (pd, [0:5]), [0.3679, 0.1794, 0.0046, 0, 0, 0], 1e-4);
772s ***** assert (pdf (t, [0:5]), [0, 0, 7.3891, 0.0001, 0, 0], 1e-4);
772s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0.2546, 0.1794, 0.0046, 0, 0, NaN], 1e-4);
772s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 7.3891, 0.0001, 0, NaN], 1e-4);
772s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
772s ***** assert (any (random (t, 1000, 1) < 2), false);
772s ***** assert (any (random (t, 1000, 1) > 4), false);
773s ***** assert (std (pd), 1.2825, 1e-4);
773s ***** assert (std (t), 0.1091, 1e-4);
773s ***** assert (var (pd), 1.6449, 1e-4);
773s ***** assert (var (t), 0.0119, 1e-4);
773s ***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
773s  ExtremeValueDistribution(Inf, 1)
773s ***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
773s  ExtremeValueDistribution(i, 1)
773s ***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
773s  ExtremeValueDistribution("a", 1)
773s ***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
773s  ExtremeValueDistribution([1, 2], 1)
773s ***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
773s  ExtremeValueDistribution(NaN, 1)
773s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
773s  ExtremeValueDistribution(1, 0)
773s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
773s  ExtremeValueDistribution(1, -1)
773s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
773s  ExtremeValueDistribution(1, Inf)
773s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
773s  ExtremeValueDistribution(1, i)
773s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
773s  ExtremeValueDistribution(1, "a")
773s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
773s  ExtremeValueDistribution(1, [1, 2])
773s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
773s  ExtremeValueDistribution(1, NaN)
773s ***** error <cdf: invalid argument for upper tail.> ...
773s  cdf (ExtremeValueDistribution, 2, "uper")
773s ***** error <cdf: invalid argument for upper tail.> ...
773s  cdf (ExtremeValueDistribution, 2, 3)
773s ***** shared x
773s  rand ("seed", 1);
773s  x = evrnd (1, 1, [1000, 1]);
773s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
773s  paramci (ExtremeValueDistribution.fit (x), "alpha")
773s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
773s  paramci (ExtremeValueDistribution.fit (x), "alpha", 0)
773s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
773s  paramci (ExtremeValueDistribution.fit (x), "alpha", 1)
773s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
773s  paramci (ExtremeValueDistribution.fit (x), "alpha", [0.5 2])
773s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
773s  paramci (ExtremeValueDistribution.fit (x), "alpha", "")
773s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
773s  paramci (ExtremeValueDistribution.fit (x), "alpha", {0.05})
773s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
773s  paramci (ExtremeValueDistribution.fit (x), ...
773s           "parameter", "mu", "alpha", {0.05})
773s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
773s  paramci (ExtremeValueDistribution.fit (x), ...
773s           "parameter", {"mu", "sigma", "param"})
773s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
773s  paramci (ExtremeValueDistribution.fit (x), "alpha", 0.01, ...
773s           "parameter", {"mu", "sigma", "param"})
773s ***** error <paramci: unknown distribution parameter.> ...
773s  paramci (ExtremeValueDistribution.fit (x), "parameter", "param")
773s ***** error <paramci: unknown distribution parameter.> ...
773s  paramci (ExtremeValueDistribution.fit (x), "alpha", 0.01, ...
773s           "parameter", "param")
773s ***** error <paramci: invalid NAME for optional argument.> ...
773s  paramci (ExtremeValueDistribution.fit (x), "NAME", "value")
773s ***** error <paramci: invalid NAME for optional argument.> ...
773s  paramci (ExtremeValueDistribution.fit (x), "alpha", 0.01, "NAME", "value")
773s ***** error <paramci: invalid NAME for optional argument.> ...
773s  paramci (ExtremeValueDistribution.fit (x), "alpha", 0.01, ...
773s           "parameter", "mu", "NAME", "value")
773s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
773s  plot (ExtremeValueDistribution, "Parent")
773s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
773s  plot (ExtremeValueDistribution, "PlotType", 12)
773s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
773s  plot (ExtremeValueDistribution, "PlotType", {"pdf", "cdf"})
773s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
773s  plot (ExtremeValueDistribution, "PlotType", "pdfcdf")
773s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
773s  plot (ExtremeValueDistribution, "Discrete", "pdfcdf")
773s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
773s  plot (ExtremeValueDistribution, "Discrete", [1, 0])
773s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
773s  plot (ExtremeValueDistribution, "Discrete", {true})
773s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
773s  plot (ExtremeValueDistribution, "Parent", 12)
773s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
773s  plot (ExtremeValueDistribution, "Parent", "hax")
773s ***** error <plot: invalid NAME for optional argument.> ...
773s  plot (ExtremeValueDistribution, "invalidNAME", "pdf")
773s ***** error <plot: no fitted DATA to plot a probability plot.> ...
773s  plot (ExtremeValueDistribution, "PlotType", "probability")
773s ***** error <proflik: no fitted data available.> ...
773s  proflik (ExtremeValueDistribution, 2)
773s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
773s  proflik (ExtremeValueDistribution.fit (x), 3)
773s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
773s  proflik (ExtremeValueDistribution.fit (x), [1, 2])
773s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
773s  proflik (ExtremeValueDistribution.fit (x), {1})
773s ***** error <proflik: SETPARAM must be a numeric vector.> ...
773s  proflik (ExtremeValueDistribution.fit (x), 1, ones (2))
773s ***** error <proflik: missing VALUE for 'Display' argument.> ...
773s  proflik (ExtremeValueDistribution.fit (x), 1, "Display")
773s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
773s  proflik (ExtremeValueDistribution.fit (x), 1, "Display", 1)
773s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
773s  proflik (ExtremeValueDistribution.fit (x), 1, "Display", {1})
773s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
773s  proflik (ExtremeValueDistribution.fit (x), 1, "Display", {"on"})
773s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
773s  proflik (ExtremeValueDistribution.fit (x), 1, "Display", ["on"; "on"])
773s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
773s  proflik (ExtremeValueDistribution.fit (x), 1, "Display", "onnn")
773s ***** error <proflik: invalid NAME for optional arguments.> ...
773s  proflik (ExtremeValueDistribution.fit (x), 1, "NAME", "on")
773s ***** error <proflik: invalid optional argument.> ...
773s  proflik (ExtremeValueDistribution.fit (x), 1, {"NAME"}, "on")
773s ***** error <proflik: invalid optional argument.> ...
773s  proflik (ExtremeValueDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
773s ***** error <truncate: missing input argument.> ...
773s  truncate (ExtremeValueDistribution)
773s ***** error <truncate: missing input argument.> ...
773s  truncate (ExtremeValueDistribution, 2)
773s ***** error <truncate: invalid lower upper limits.> ...
773s  truncate (ExtremeValueDistribution, 4, 2)
773s ***** shared pd
773s  pd = ExtremeValueDistribution(1, 1);
773s  pd(2) = ExtremeValueDistribution(1, 3);
773s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
773s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
773s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
773s ***** error <mean: requires a scalar probability distribution.> mean (pd)
773s ***** error <median: requires a scalar probability distribution.> median (pd)
773s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
773s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
773s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
773s ***** error <plot: requires a scalar probability distribution.> plot (pd)
773s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
773s ***** error <random: requires a scalar probability distribution.> random (pd)
773s ***** error <std: requires a scalar probability distribution.> std (pd)
773s ***** error <truncate: requires a scalar probability distribution.> ...
773s  truncate (pd, 2, 4)
773s ***** error <var: requires a scalar probability distribution.> var (pd)
773s 95 tests, 95 passed, 0 known failure, 0 skipped
773s [inst/dist_obj/GammaDistribution.m]
773s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_obj/GammaDistribution.m
773s ***** shared pd, t
773s  pd = GammaDistribution (1, 1);
773s  t = truncate (pd, 2, 4);
773s ***** assert (cdf (pd, [0:5]), [0, 0.6321, 0.8647, 0.9502, 0.9817, 0.9933], 1e-4);
773s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7311, 1, 1], 1e-4);
773s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.7769, 0.8647, 0.9502, 0.9817], 1e-4);
773s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7311, 1], 1e-4);
773s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.2231, 0.5108, 0.9163, 1.6094, Inf], 1e-4);
773s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1899, 2.4244, 2.7315, 3.1768, 4], 1e-4);
773s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5108, 0.9163, 1.6094, Inf, NaN], 1e-4);
773s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4244, 2.7315, 3.1768, 4, NaN], 1e-4);
773s ***** assert (iqr (pd), 1.0986, 1e-4);
773s ***** assert (iqr (t), 0.8020, 1e-4);
773s ***** assert (mean (pd), 1);
773s ***** assert (mean (t), 2.6870, 1e-4);
773s ***** assert (median (pd), 0.6931, 1e-4);
773s ***** assert (median (t), 2.5662, 1e-4);
773s ***** assert (pdf (pd, [0:5]), [1, 0.3679, 0.1353, 0.0498, 0.0183, 0.0067], 1e-4);
773s ***** assert (pdf (t, [0:5]), [0, 0, 1.1565, 0.4255, 0.1565, 0], 1e-4);
773s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3679, 0.1353, 0.0498, 0.0183, NaN], 1e-4);
773s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.1565, 0.4255, 0.1565, NaN], 1e-4);
773s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
773s ***** assert (any (random (t, 1000, 1) < 2), false);
773s ***** assert (any (random (t, 1000, 1) > 4), false);
773s ***** assert (std (pd), 1);
773s ***** assert (std (t), 0.5253, 1e-4);
773s ***** assert (var (pd), 1);
773s ***** assert (var (t), 0.2759, 1e-4);
773s ***** error <GammaDistribution: A must be a positive real scalar.> ...
773s  GammaDistribution(0, 1)
773s ***** error <GammaDistribution: A must be a positive real scalar.> ...
773s  GammaDistribution(Inf, 1)
773s ***** error <GammaDistribution: A must be a positive real scalar.> ...
773s  GammaDistribution(i, 1)
773s ***** error <GammaDistribution: A must be a positive real scalar.> ...
773s  GammaDistribution("a", 1)
773s ***** error <GammaDistribution: A must be a positive real scalar.> ...
773s  GammaDistribution([1, 2], 1)
773s ***** error <GammaDistribution: A must be a positive real scalar.> ...
773s  GammaDistribution(NaN, 1)
773s ***** error <GammaDistribution: B must be a positive real scalar.> ...
773s  GammaDistribution(1, 0)
773s ***** error <GammaDistribution: B must be a positive real scalar.> ...
773s  GammaDistribution(1, -1)
773s ***** error <GammaDistribution: B must be a positive real scalar.> ...
773s  GammaDistribution(1, Inf)
773s ***** error <GammaDistribution: B must be a positive real scalar.> ...
773s  GammaDistribution(1, i)
773s ***** error <GammaDistribution: B must be a positive real scalar.> ...
773s  GammaDistribution(1, "a")
773s ***** error <GammaDistribution: B must be a positive real scalar.> ...
773s  GammaDistribution(1, [1, 2])
773s ***** error <GammaDistribution: B must be a positive real scalar.> ...
773s  GammaDistribution(1, NaN)
773s ***** error <cdf: invalid argument for upper tail.> ...
773s  cdf (GammaDistribution, 2, "uper")
773s ***** error <cdf: invalid argument for upper tail.> ...
773s  cdf (GammaDistribution, 2, 3)
773s ***** shared x
773s  x = gamrnd (1, 1, [100, 1]);
773s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
773s  paramci (GammaDistribution.fit (x), "alpha")
773s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
773s  paramci (GammaDistribution.fit (x), "alpha", 0)
773s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
773s  paramci (GammaDistribution.fit (x), "alpha", 1)
773s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
773s  paramci (GammaDistribution.fit (x), "alpha", [0.5 2])
773s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
773s  paramci (GammaDistribution.fit (x), "alpha", "")
773s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
773s  paramci (GammaDistribution.fit (x), "alpha", {0.05})
773s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
773s  paramci (GammaDistribution.fit (x), "parameter", "a", "alpha", {0.05})
773s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
773s  paramci (GammaDistribution.fit (x), "parameter", {"a", "b", "param"})
773s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
773s  paramci (GammaDistribution.fit (x), "alpha", 0.01, ...
773s           "parameter", {"a", "b", "param"})
773s ***** error <paramci: unknown distribution parameter.> ...
773s  paramci (GammaDistribution.fit (x), "parameter", "param")
774s ***** error <paramci: unknown distribution parameter.> ...
774s  paramci (GammaDistribution.fit (x), "alpha", 0.01, "parameter", "param")
774s ***** error <paramci: invalid NAME for optional argument.> ...
774s  paramci (GammaDistribution.fit (x), "NAME", "value")
774s ***** error <paramci: invalid NAME for optional argument.> ...
774s  paramci (GammaDistribution.fit (x), "alpha", 0.01, "NAME", "value")
774s ***** error <paramci: invalid NAME for optional argument.> ...
774s  paramci (GammaDistribution.fit (x), "alpha", 0.01, "parameter", "a", ...
774s           "NAME", "value")
774s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
774s  plot (GammaDistribution, "Parent")
774s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
774s  plot (GammaDistribution, "PlotType", 12)
774s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
774s  plot (GammaDistribution, "PlotType", {"pdf", "cdf"})
774s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
774s  plot (GammaDistribution, "PlotType", "pdfcdf")
774s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
774s  plot (GammaDistribution, "Discrete", "pdfcdf")
774s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
774s  plot (GammaDistribution, "Discrete", [1, 0])
774s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
774s  plot (GammaDistribution, "Discrete", {true})
774s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
774s  plot (GammaDistribution, "Parent", 12)
774s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
774s  plot (GammaDistribution, "Parent", "hax")
774s ***** error <plot: invalid NAME for optional argument.> ...
774s  plot (GammaDistribution, "invalidNAME", "pdf")
774s ***** error <plot: no fitted DATA to plot a probability plot.> ...
774s  plot (GammaDistribution, "PlotType", "probability")
774s ***** error <proflik: no fitted data available.> ...
774s  proflik (GammaDistribution, 2)
774s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
774s  proflik (GammaDistribution.fit (x), 3)
774s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
774s  proflik (GammaDistribution.fit (x), [1, 2])
774s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
774s  proflik (GammaDistribution.fit (x), {1})
774s ***** error <proflik: SETPARAM must be a numeric vector.> ...
774s  proflik (GammaDistribution.fit (x), 1, ones (2))
774s ***** error <proflik: missing VALUE for 'Display' argument.> ...
774s  proflik (GammaDistribution.fit (x), 1, "Display")
774s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
774s  proflik (GammaDistribution.fit (x), 1, "Display", 1)
774s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
774s  proflik (GammaDistribution.fit (x), 1, "Display", {1})
774s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
774s  proflik (GammaDistribution.fit (x), 1, "Display", {"on"})
774s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
774s  proflik (GammaDistribution.fit (x), 1, "Display", ["on"; "on"])
774s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
774s  proflik (GammaDistribution.fit (x), 1, "Display", "onnn")
774s ***** error <proflik: invalid NAME for optional arguments.> ...
774s  proflik (GammaDistribution.fit (x), 1, "NAME", "on")
774s ***** error <proflik: invalid optional argument.> ...
774s  proflik (GammaDistribution.fit (x), 1, {"NAME"}, "on")
774s ***** error <proflik: invalid optional argument.> ...
774s  proflik (GammaDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
774s ***** error <truncate: missing input argument.> ...
774s  truncate (GammaDistribution)
774s ***** error <truncate: missing input argument.> ...
774s  truncate (GammaDistribution, 2)
774s ***** error <truncate: invalid lower upper limits.> ...
774s  truncate (GammaDistribution, 4, 2)
774s ***** shared pd
774s  pd = GammaDistribution(1, 1);
774s  pd(2) = GammaDistribution(1, 3);
774s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
774s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
774s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
774s ***** error <mean: requires a scalar probability distribution.> mean (pd)
774s ***** error <median: requires a scalar probability distribution.> median (pd)
774s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
774s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
774s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
774s ***** error <plot: requires a scalar probability distribution.> plot (pd)
774s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
774s ***** error <random: requires a scalar probability distribution.> random (pd)
774s ***** error <std: requires a scalar probability distribution.> std (pd)
774s ***** error <truncate: requires a scalar probability distribution.> ...
774s  truncate (pd, 2, 4)
774s ***** error <var: requires a scalar probability distribution.> var (pd)
774s 96 tests, 96 passed, 0 known failure, 0 skipped
774s [inst/dist_obj/GeneralizedExtremeValueDistribution.m]
774s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_obj/GeneralizedExtremeValueDistribution.m
774s ***** shared pd, t
774s  pd = GeneralizedExtremeValueDistribution;
774s  t = truncate (pd, 2, 4);
774s ***** assert (cdf (pd, [0:5]), [0.3679, 0.6922, 0.8734, 0.9514, 0.9819, 0.9933], 1e-4);
774s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7195, 1, 1], 1e-4);
774s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8, 0.8734, 0.9514, 0.9819], 1e-4);
774s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7195, 1], 1e-4);
774s ***** assert (icdf (pd, [0:0.2:1]), [-Inf, -0.4759, 0.0874, 0.6717, 1.4999, Inf], 1e-4);
774s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1999, 2.4433, 2.7568, 3.2028, 4], 1e-4);
774s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.0874, 0.6717, 1.4999, Inf, NaN], 1e-4);
774s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4433, 2.7568, 3.2028, 4, NaN], 1e-4);
774s ***** assert (iqr (pd), 1.5725, 1e-4);
774s ***** assert (iqr (t), 0.8164, 1e-4);
774s ***** assert (mean (pd), 0.5772, 1e-4);
774s ***** assert (mean (t), 2.7043, 1e-4);
774s ***** assert (median (pd), 0.3665, 1e-4);
774s ***** assert (median (t), 2.5887, 1e-4);
774s ***** assert (pdf (pd, [0:5]), [0.3679, 0.2546, 0.1182, 0.0474, 0.0180, 0.0067], 1e-4);
774s ***** assert (pdf (t, [0:5]), [0, 0, 1.0902, 0.4369, 0.1659, 0], 1e-4);
774s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0.1794, 0.2546, 0.1182, 0.0474, 0.0180, NaN], 1e-4);
774s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.0902, 0.4369, 0.1659, NaN], 1e-4);
774s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
774s ***** assert (any (random (t, 1000, 1) < 2), false);
774s ***** assert (any (random (t, 1000, 1) > 4), false);
774s ***** assert (std (pd), 1.2825, 1e-4);
774s ***** assert (std (t), 0.5289, 1e-4);
774s ***** assert (var (pd), 1.6449, 1e-4);
774s ***** assert (var (t), 0.2798, 1e-4);
774s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
774s  GeneralizedExtremeValueDistribution(Inf, 1, 1)
774s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
774s  GeneralizedExtremeValueDistribution(i, 1, 1)
774s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
774s  GeneralizedExtremeValueDistribution("a", 1, 1)
774s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
774s  GeneralizedExtremeValueDistribution([1, 2], 1, 1)
774s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
774s  GeneralizedExtremeValueDistribution(NaN, 1, 1)
774s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
774s  GeneralizedExtremeValueDistribution(1, 0, 1)
774s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
774s  GeneralizedExtremeValueDistribution(1, -1, 1)
774s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
774s  GeneralizedExtremeValueDistribution(1, Inf, 1)
774s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
774s  GeneralizedExtremeValueDistribution(1, i, 1)
774s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
774s  GeneralizedExtremeValueDistribution(1, "a", 1)
774s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
774s  GeneralizedExtremeValueDistribution(1, [1, 2], 1)
774s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
774s  GeneralizedExtremeValueDistribution(1, NaN, 1)
774s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
774s  GeneralizedExtremeValueDistribution(1, 1, Inf)
774s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
774s  GeneralizedExtremeValueDistribution(1, 1, i)
774s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
774s  GeneralizedExtremeValueDistribution(1, 1, "a")
774s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
774s  GeneralizedExtremeValueDistribution(1, 1, [1, 2])
774s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
774s  GeneralizedExtremeValueDistribution(1, 1, NaN)
774s ***** error <cdf: invalid argument for upper tail.> ...
774s  cdf (GeneralizedExtremeValueDistribution, 2, "uper")
774s ***** error <cdf: invalid argument for upper tail.> ...
774s  cdf (GeneralizedExtremeValueDistribution, 2, 3)
774s ***** shared x
774s  x = gevrnd (1, 1, 1, [1, 100]);
774s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
774s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha")
774s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
774s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0)
775s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
775s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 1)
775s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
775s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", [0.5 2])
775s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
775s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", "")
775s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
775s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", {0.05})
775s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
775s  paramci (GeneralizedExtremeValueDistribution.fit (x), ...
775s           "parameter", "sigma", "alpha", {0.05})
775s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
775s  paramci (GeneralizedExtremeValueDistribution.fit (x), ...
775s           "parameter", {"k", "sigma", "mu", "param"})
775s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
775s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0.01, ...
775s           "parameter", {"k", "sigma", "mu", "param"})
776s ***** error <paramci: unknown distribution parameter.> ...
776s  paramci (GeneralizedExtremeValueDistribution.fit (x), "parameter", "param")
776s ***** error <paramci: unknown distribution parameter.> ...
776s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0.01, ...
776s           "parameter", "param")
776s ***** error <paramci: invalid NAME for optional argument.> ...
776s  paramci (GeneralizedExtremeValueDistribution.fit (x), "NAME", "value")
776s ***** error <paramci: invalid NAME for optional argument.> ...
776s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0.01, ...
776s           "NAME", "value")
776s ***** error <paramci: invalid NAME for optional argument.> ...
776s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0.01, ...
776s           "parameter", "sigma", "NAME", "value")
776s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
776s  plot (GeneralizedExtremeValueDistribution, "Parent")
776s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
776s  plot (GeneralizedExtremeValueDistribution, "PlotType", 12)
776s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
776s  plot (GeneralizedExtremeValueDistribution, "PlotType", {"pdf", "cdf"})
776s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
776s  plot (GeneralizedExtremeValueDistribution, "PlotType", "pdfcdf")
776s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
776s  plot (GeneralizedExtremeValueDistribution, "Discrete", "pdfcdf")
776s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
776s  plot (GeneralizedExtremeValueDistribution, "Discrete", [1, 0])
776s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
776s  plot (GeneralizedExtremeValueDistribution, "Discrete", {true})
776s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
776s  plot (GeneralizedExtremeValueDistribution, "Parent", 12)
776s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
776s  plot (GeneralizedExtremeValueDistribution, "Parent", "hax")
776s ***** error <plot: invalid NAME for optional argument.> ...
776s  plot (GeneralizedExtremeValueDistribution, "invalidNAME", "pdf")
776s ***** error <plot: no fitted DATA to plot a probability plot.> ...
776s  plot (GeneralizedExtremeValueDistribution, "PlotType", "probability")
776s ***** error <proflik: no fitted data available.> ...
776s  proflik (GeneralizedExtremeValueDistribution, 2)
776s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
776s  proflik (GeneralizedExtremeValueDistribution.fit (x), 4)
777s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
777s  proflik (GeneralizedExtremeValueDistribution.fit (x), [1, 2])
777s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
777s  proflik (GeneralizedExtremeValueDistribution.fit (x), {1})
777s ***** error <proflik: SETPARAM must be a numeric vector.> ...
777s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, ones (2))
777s ***** error <proflik: missing VALUE for 'Display' argument.> ...
777s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display")
777s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
777s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display", 1)
777s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
777s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display", {1})
777s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
777s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display", {"on"})
778s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
778s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, ...
778s           "Display", ["on"; "on"])
778s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
778s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display", "onnn")
778s ***** error <proflik: invalid NAME for optional arguments.> ...
778s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "NAME", "on")
778s ***** error <proflik: invalid optional argument.> ...
778s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, {"NAME"}, "on")
778s ***** error <proflik: invalid optional argument.> ...
778s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, {[1 2 3 4]}, ...
778s           "Display", "on")
778s ***** error <truncate: missing input argument.> ...
778s  truncate (GeneralizedExtremeValueDistribution)
778s ***** error <truncate: missing input argument.> ...
778s  truncate (GeneralizedExtremeValueDistribution, 2)
778s ***** error <truncate: invalid lower upper limits.> ...
778s  truncate (GeneralizedExtremeValueDistribution, 4, 2)
778s ***** shared pd
778s  pd = GeneralizedExtremeValueDistribution(1, 1, 1);
778s  pd(2) = GeneralizedExtremeValueDistribution(1, 3, 1);
778s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
778s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
778s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
778s ***** error <mean: requires a scalar probability distribution.> mean (pd)
778s ***** error <median: requires a scalar probability distribution.> median (pd)
778s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
778s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
778s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
778s ***** error <plot: requires a scalar probability distribution.> plot (pd)
778s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
778s ***** error <random: requires a scalar probability distribution.> random (pd)
778s ***** error <std: requires a scalar probability distribution.> std (pd)
778s ***** error <truncate: requires a scalar probability distribution.> ...
778s  truncate (pd, 2, 4)
778s ***** error <var: requires a scalar probability distribution.> var (pd)
778s 100 tests, 100 passed, 0 known failure, 0 skipped
778s [inst/dist_obj/GeneralizedParetoDistribution.m]
778s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_obj/GeneralizedParetoDistribution.m
778s ***** shared pd, t
778s  pd = GeneralizedParetoDistribution (1, 1, 1);
778s  t = truncate (pd, 2, 4);
778s ***** assert (cdf (pd, [0:5]), [0, 0, 0.5, 0.6667, 0.75, 0.8], 1e-4);
778s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.6667, 1, 1], 1e-4);
778s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.3333, 0.5, 0.6667, 0.75], 1e-4);
778s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.6667, 1], 1e-4);
779s ***** assert (icdf (pd, [0:0.2:1]), [1, 1.25, 1.6667, 2.5, 5, Inf], 1e-4);
779s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2222, 2.5, 2.8571, 3.3333, 4], 1e-4);
779s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1.6667, 2.5, 5, Inf, NaN], 1e-4);
779s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5, 2.8571, 3.3333, 4, NaN], 1e-4);
779s ***** assert (iqr (pd), 2.6667, 1e-4);
779s ***** assert (iqr (t), 0.9143, 1e-4);
779s ***** assert (mean (pd), Inf);
779s ***** assert (mean (t), 2.7726, 1e-4);
779s ***** assert (median (pd), 2);
779s ***** assert (median (t), 2.6667, 1e-4);
779s ***** assert (pdf (pd, [0:5]), [0, 1, 0.25, 0.1111, 0.0625, 0.04], 1e-4);
779s ***** assert (pdf (t, [0:5]), [0, 0, 1, 0.4444, 0.25, 0], 1e-4);
779s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 1, 0.25, 0.1111, 0.0625, NaN], 1e-4);
779s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1, 0.4444, 0.25, NaN], 1e-4);
779s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
779s ***** assert (any (random (t, 1000, 1) < 2), false);
779s ***** assert (any (random (t, 1000, 1) > 4), false);
779s ***** assert (std (pd), Inf);
779s ***** assert (std (t), 0.5592, 1e-4);
779s ***** assert (var (pd), Inf);
779s ***** assert (var (t), 0.3128, 1e-4);
779s ***** error <GeneralizedParetoDistribution: MU must be a real scalar.> ...
779s  GeneralizedParetoDistribution(Inf, 1, 1)
779s ***** error <GeneralizedParetoDistribution: MU must be a real scalar.> ...
779s  GeneralizedParetoDistribution(i, 1, 1)
779s ***** error <GeneralizedParetoDistribution: MU must be a real scalar.> ...
779s  GeneralizedParetoDistribution("a", 1, 1)
779s ***** error <GeneralizedParetoDistribution: MU must be a real scalar.> ...
779s  GeneralizedParetoDistribution([1, 2], 1, 1)
779s ***** error <GeneralizedParetoDistribution: MU must be a real scalar.> ...
779s  GeneralizedParetoDistribution(NaN, 1, 1)
779s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
779s  GeneralizedParetoDistribution(1, 0, 1)
779s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
779s  GeneralizedParetoDistribution(1, -1, 1)
779s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
779s  GeneralizedParetoDistribution(1, Inf, 1)
779s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
779s  GeneralizedParetoDistribution(1, i, 1)
779s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
779s  GeneralizedParetoDistribution(1, "a", 1)
779s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
779s  GeneralizedParetoDistribution(1, [1, 2], 1)
779s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
779s  GeneralizedParetoDistribution(1, NaN, 1)
779s ***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
779s  GeneralizedParetoDistribution(1, 1, Inf)
779s ***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
779s  GeneralizedParetoDistribution(1, 1, i)
779s ***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
779s  GeneralizedParetoDistribution(1, 1, "a")
779s ***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
779s  GeneralizedParetoDistribution(1, 1, [1, 2])
779s ***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
779s  GeneralizedParetoDistribution(1, 1, NaN)
779s ***** error <cdf: invalid argument for upper tail.> ...
779s  cdf (GeneralizedParetoDistribution, 2, "uper")
779s ***** error <cdf: invalid argument for upper tail.> ...
779s  cdf (GeneralizedParetoDistribution, 2, 3)
779s ***** shared x
779s  x = gprnd (1, 1, 1, [1, 100]);
779s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
779s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha")
779s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
779s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0)
779s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
779s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 1)
779s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
779s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", [0.5 2])
779s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
779s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", "")
779s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
779s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", {0.05})
779s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
779s  paramci (GeneralizedParetoDistribution.fit (x, 1), ...
779s           "parameter", "sigma", "alpha", {0.05})
779s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
779s  paramci (GeneralizedParetoDistribution.fit (x, 1), ...
779s           "parameter", {"k", "sigma", "param"})
779s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
779s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0.01, ...
779s           "parameter", {"k", "sigma", "param"})
779s ***** error <paramci: unknown distribution parameter.> ...
779s  paramci (GeneralizedParetoDistribution.fit (x, 1), "parameter", "param")
779s ***** error <paramci: unknown distribution parameter.> ...
779s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0.01, ...
779s           "parameter", "param")
779s ***** error <paramci: invalid NAME for optional argument.> ...
779s  paramci (GeneralizedParetoDistribution.fit (x, 1), "NAME", "value")
779s ***** error <paramci: invalid NAME for optional argument.> ...
779s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0.01, ...
779s           "NAME", "value")
779s ***** error <paramci: invalid NAME for optional argument.> ...
779s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0.01, ...
779s           "parameter", "sigma", "NAME", "value")
779s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
779s  plot (GeneralizedParetoDistribution, "Parent")
779s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
779s  plot (GeneralizedParetoDistribution, "PlotType", 12)
779s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
779s  plot (GeneralizedParetoDistribution, "PlotType", {"pdf", "cdf"})
779s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
779s  plot (GeneralizedParetoDistribution, "PlotType", "pdfcdf")
779s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
779s  plot (GeneralizedParetoDistribution, "Discrete", "pdfcdf")
779s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
779s  plot (GeneralizedParetoDistribution, "Discrete", [1, 0])
779s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
779s  plot (GeneralizedParetoDistribution, "Discrete", {true})
779s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
779s  plot (GeneralizedParetoDistribution, "Parent", 12)
779s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
779s  plot (GeneralizedParetoDistribution, "Parent", "hax")
779s ***** error <plot: invalid NAME for optional argument.> ...
779s  plot (GeneralizedParetoDistribution, "invalidNAME", "pdf")
779s ***** error <plot: no fitted DATA to plot a probability plot.> ...
779s  plot (GeneralizedParetoDistribution, "PlotType", "probability")
779s ***** error <proflik: no fitted data available.> ...
779s  proflik (GeneralizedParetoDistribution, 2)
779s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
779s  proflik (GeneralizedParetoDistribution.fit (x, 1), 3)
779s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
779s  proflik (GeneralizedParetoDistribution.fit (x, 1), [1, 2])
779s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
779s  proflik (GeneralizedParetoDistribution.fit (x, 1), {1})
779s ***** error <proflik: SETPARAM must be a numeric vector.> ...
779s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, ones (2))
779s ***** error <proflik: missing VALUE for 'Display' argument.> ...
779s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display")
779s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
779s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display", 1)
779s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
779s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display", {1})
779s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
779s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display", {"on"})
779s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
779s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, ...
779s           "Display", ["on"; "on"])
779s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
779s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display", "onnn")
779s ***** error <proflik: invalid NAME for optional arguments.> ...
779s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "NAME", "on")
779s ***** error <proflik: invalid optional argument.> ...
779s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, {"NAME"}, "on")
779s ***** error <proflik: invalid optional argument.> ...
779s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, {[1 2 3 4]}, ...
779s           "Display", "on")
779s ***** error <truncate: missing input argument.> ...
779s  truncate (GeneralizedParetoDistribution)
779s ***** error <truncate: missing input argument.> ...
779s  truncate (GeneralizedParetoDistribution, 2)
779s ***** error <truncate: invalid lower upper limits.> ...
779s  truncate (GeneralizedParetoDistribution, 4, 2)
779s ***** shared pd
779s  pd = GeneralizedParetoDistribution(1, 1, 1);
779s  pd(2) = GeneralizedParetoDistribution(1, 3, 1);
779s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
779s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
779s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
779s ***** error <mean: requires a scalar probability distribution.> mean (pd)
779s ***** error <median: requires a scalar probability distribution.> median (pd)
779s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
779s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
779s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
779s ***** error <plot: requires a scalar probability distribution.> plot (pd)
779s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
779s ***** error <random: requires a scalar probability distribution.> random (pd)
779s ***** error <std: requires a scalar probability distribution.> std (pd)
779s ***** error <truncate: requires a scalar probability distribution.> ...
779s  truncate (pd, 2, 4)
779s ***** error <var: requires a scalar probability distribution.> var (pd)
779s 100 tests, 100 passed, 0 known failure, 0 skipped
779s [inst/dist_obj/HalfNormalDistribution.m]
779s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_obj/HalfNormalDistribution.m
779s ***** shared pd, t
779s  pd = HalfNormalDistribution (0, 1);
779s  t = truncate (pd, 2, 4);
779s ***** assert (cdf (pd, [0:5]), [0, 0.6827, 0.9545, 0.9973, 0.9999, 1], 1e-4);
779s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.9420, 1, 1], 1e-4);
779s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8664, 0.9545, 0.9973, 0.9999], 1e-4);
779s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.9420, 1], 1e-4);
779s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.2533, 0.5244, 0.8416, 1.2816, Inf], 1e-4);
779s ***** assert (icdf (t, [0:0.2:1]), [2, 2.0923, 2.2068, 2.3607, 2.6064, 4], 1e-4);
779s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5244, 0.8416, 1.2816, Inf, NaN], 1e-4);
779s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.2068, 2.3607, 2.6064, 4, NaN], 1e-4);
779s ***** assert (iqr (pd), 0.8317, 1e-4);
779s ***** assert (iqr (t), 0.4111, 1e-4);
779s ***** assert (mean (pd), 0.7979, 1e-4);
779s ***** assert (mean (t), 2.3706, 1e-4);
779s ***** assert (median (pd), 0.6745, 1e-4);
779s ***** assert (median (t), 2.2771, 1e-4);
779s ***** assert (pdf (pd, [0:5]), [0.7979, 0.4839, 0.1080, 0.0089, 0.0003, 0], 1e-4);
779s ***** assert (pdf (t, [0:5]), [0, 0, 2.3765, 0.1951, 0.0059, 0], 1e-4);
779s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.4839, 0.1080, 0.0089, 0.0003, NaN], 1e-4);
779s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 2.3765, 0.1951, 0.0059, NaN], 1e-4);
779s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
779s ***** assert (any (random (t, 1000, 1) < 2), false);
779s ***** assert (any (random (t, 1000, 1) > 4), false);
780s ***** assert (std (pd), 0.6028, 1e-4);
780s ***** assert (std (t), 0.3310, 1e-4);
780s ***** assert (var (pd), 0.3634, 1e-4);
780s ***** assert (var (t), 0.1096, 1e-4);
780s ***** error <HalfNormalDistribution: MU must be a real scalar.> ...
780s  HalfNormalDistribution(Inf, 1)
780s ***** error <HalfNormalDistribution: MU must be a real scalar.> ...
780s  HalfNormalDistribution(i, 1)
780s ***** error <HalfNormalDistribution: MU must be a real scalar.> ...
780s  HalfNormalDistribution("a", 1)
780s ***** error <HalfNormalDistribution: MU must be a real scalar.> ...
780s  HalfNormalDistribution([1, 2], 1)
780s ***** error <HalfNormalDistribution: MU must be a real scalar.> ...
780s  HalfNormalDistribution(NaN, 1)
780s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
780s  HalfNormalDistribution(1, 0)
780s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
780s  HalfNormalDistribution(1, -1)
780s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
780s  HalfNormalDistribution(1, Inf)
780s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
780s  HalfNormalDistribution(1, i)
780s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
780s  HalfNormalDistribution(1, "a")
780s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
780s  HalfNormalDistribution(1, [1, 2])
780s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
780s  HalfNormalDistribution(1, NaN)
780s ***** error <cdf: invalid argument for upper tail.> ...
780s  cdf (HalfNormalDistribution, 2, "uper")
780s ***** error <cdf: invalid argument for upper tail.> ...
780s  cdf (HalfNormalDistribution, 2, 3)
780s ***** shared x
780s  x = hnrnd (1, 1, [1, 100]);
780s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
780s  paramci (HalfNormalDistribution.fit (x, 1), "alpha")
780s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
780s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0)
780s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
780s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", 1)
780s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
780s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", [0.5 2])
780s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
780s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", "")
780s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
780s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", {0.05})
780s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
780s  paramci (HalfNormalDistribution.fit (x, 1), "parameter", "sigma", ...
780s           "alpha", {0.05})
780s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
780s  paramci (HalfNormalDistribution.fit (x, 1), ...
780s           "parameter", {"mu", "sigma", "param"})
780s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
780s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0.01, ...
780s           "parameter", {"mu", "sigma", "param"})
780s ***** error <paramci: unknown distribution parameter.> ...
780s  paramci (HalfNormalDistribution.fit (x, 1), "parameter", "param")
780s ***** error <paramci: unknown distribution parameter.> ...
780s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0.01, ...
780s           "parameter", "param")
780s ***** error <paramci: invalid NAME for optional argument.> ...
780s  paramci (HalfNormalDistribution.fit (x, 1),"NAME", "value")
780s ***** error <paramci: invalid NAME for optional argument.> ...
780s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0.01, ...
780s           "NAME", "value")
780s ***** error <paramci: invalid NAME for optional argument.> ...
780s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0.01, ...
780s           "parameter", "sigma", "NAME", "value")
780s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
780s  plot (HalfNormalDistribution, "Parent")
780s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
780s  plot (HalfNormalDistribution, "PlotType", 12)
780s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
780s  plot (HalfNormalDistribution, "PlotType", {"pdf", "cdf"})
780s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
780s  plot (HalfNormalDistribution, "PlotType", "pdfcdf")
780s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
780s  plot (HalfNormalDistribution, "Discrete", "pdfcdf")
780s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
780s  plot (HalfNormalDistribution, "Discrete", [1, 0])
780s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
780s  plot (HalfNormalDistribution, "Discrete", {true})
780s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
780s  plot (HalfNormalDistribution, "Parent", 12)
780s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
780s  plot (HalfNormalDistribution, "Parent", "hax")
780s ***** error <plot: invalid NAME for optional argument.> ...
780s  plot (HalfNormalDistribution, "invalidNAME", "pdf")
780s ***** error <plot: no fitted DATA to plot a probability plot.> ...
780s  plot (HalfNormalDistribution, "PlotType", "probability")
780s ***** error <proflik: no fitted data available.> ...
780s  proflik (HalfNormalDistribution, 2)
780s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
780s  proflik (HalfNormalDistribution.fit (x, 1), 3)
780s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
780s  proflik (HalfNormalDistribution.fit (x, 1), [1, 2])
780s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
780s  proflik (HalfNormalDistribution.fit (x, 1), {1})
780s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
780s  proflik (HalfNormalDistribution.fit (x, 1), 1)
780s ***** error <proflik: SETPARAM must be a numeric vector.> ...
780s  proflik (HalfNormalDistribution.fit (x, 1), 2, ones (2))
781s ***** error <proflik: missing VALUE for 'Display' argument.> ...
781s  proflik (HalfNormalDistribution.fit (x, 1), 2, "Display")
781s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
781s  proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", 1)
781s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
781s  proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", {1})
781s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
781s  proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", {"on"})
781s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
781s  proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", ["on"; "on"])
781s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
781s  proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", "onnn")
781s ***** error <proflik: invalid NAME for optional arguments.> ...
781s  proflik (HalfNormalDistribution.fit (x, 1), 2, "NAME", "on")
781s ***** error <proflik: invalid optional argument.> ...
781s  proflik (HalfNormalDistribution.fit (x, 1), 2, {"NAME"}, "on")
781s ***** error <proflik: invalid optional argument.> ...
781s  proflik (HalfNormalDistribution.fit (x, 1), 2, {[1 2 3 4]}, ...
781s           "Display", "on")
781s ***** error <truncate: missing input argument.> ...
781s  truncate (HalfNormalDistribution)
781s ***** error <truncate: missing input argument.> ...
781s  truncate (HalfNormalDistribution, 2)
781s ***** error <truncate: invalid lower upper limits.> ...
781s  truncate (HalfNormalDistribution, 4, 2)
781s ***** shared pd
781s  pd = HalfNormalDistribution(1, 1);
781s  pd(2) = HalfNormalDistribution(1, 3);
781s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
781s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
781s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
781s ***** error <mean: requires a scalar probability distribution.> mean (pd)
781s ***** error <median: requires a scalar probability distribution.> median (pd)
781s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
781s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
781s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
781s ***** error <plot: requires a scalar probability distribution.> plot (pd)
781s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
781s ***** error <random: requires a scalar probability distribution.> random (pd)
781s ***** error <std: requires a scalar probability distribution.> std (pd)
781s ***** error <truncate: requires a scalar probability distribution.> ...
781s  truncate (pd, 2, 4)
781s ***** error <var: requires a scalar probability distribution.> var (pd)
781s 96 tests, 96 passed, 0 known failure, 0 skipped
781s [inst/dist_obj/InverseGaussianDistribution.m]
781s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_obj/InverseGaussianDistribution.m
781s ***** shared pd, t
781s  pd = InverseGaussianDistribution (1, 1);
781s  t = truncate (pd, 2, 4);
781s ***** assert (cdf (pd, [0:5]), [0, 0.6681, 0.8855, 0.9532, 0.9791, 0.9901], 1e-4);
781s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7234, 1, 1], 1e-4);
781s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8108, 0.8855, 0.9532, 0.9791], 1e-4);
781s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7234, 1], 1e-4);
781s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.3320, 0.5411, 0.8483, 1.4479, Inf], 1e-4);
781s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1889, 2.4264, 2.7417, 3.1993, 4], 1e-4);
781s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5411, 0.8483, 1.4479, Inf, NaN], 1e-4);
781s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4264, 2.7417, 3.1993, 4, NaN], 1e-4);
781s ***** assert (iqr (pd), 0.8643, 1e-4);
781s ***** assert (iqr (t), 0.8222, 1e-4);
781s ***** assert (mean (pd), 1);
781s ***** assert (mean (t), 2.6953, 1e-4);
781s ***** assert (median (pd), 0.6758, 1e-4);
781s ***** assert (median (t), 2.5716, 1e-4);
781s ***** assert (pdf (pd, [0:5]), [0, 0.3989, 0.1098, 0.0394, 0.0162, 0.0072], 1e-4);
781s ***** assert (pdf (t, [0:5]), [0, 0, 1.1736, 0.4211, 0.1730, 0], 1e-4);
781s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3989, 0.1098, 0.0394, 0.0162, NaN], 1e-4);
781s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.1736, 0.4211, 0.1730, NaN], 1e-4);
781s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
781s ***** assert (any (random (t, 1000, 1) < 2), false);
781s ***** assert (any (random (t, 1000, 1) > 4), false);
781s ***** assert (std (pd), 1);
781s ***** assert (std (t), 0.5332, 1e-4);
781s ***** assert (var (pd), 1);
781s ***** assert (var (t), 0.2843, 1e-4);
781s ***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
781s  InverseGaussianDistribution(0, 1)
781s ***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
781s  InverseGaussianDistribution(Inf, 1)
781s ***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
781s  InverseGaussianDistribution(i, 1)
781s ***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
781s  InverseGaussianDistribution("a", 1)
781s ***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
781s  InverseGaussianDistribution([1, 2], 1)
781s ***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
781s  InverseGaussianDistribution(NaN, 1)
781s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
781s  InverseGaussianDistribution(1, 0)
781s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
781s  InverseGaussianDistribution(1, -1)
781s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
781s  InverseGaussianDistribution(1, Inf)
781s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
781s  InverseGaussianDistribution(1, i)
781s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
781s  InverseGaussianDistribution(1, "a")
781s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
781s  InverseGaussianDistribution(1, [1, 2])
781s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
781s  InverseGaussianDistribution(1, NaN)
781s ***** error <cdf: invalid argument for upper tail.> ...
781s  cdf (InverseGaussianDistribution, 2, "uper")
781s ***** error <cdf: invalid argument for upper tail.> ...
781s  cdf (InverseGaussianDistribution, 2, 3)
781s ***** shared x
781s  x = invgrnd (1, 1, [1, 100]);
781s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
781s  paramci (InverseGaussianDistribution.fit (x), "alpha")
781s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
781s  paramci (InverseGaussianDistribution.fit (x), "alpha", 0)
781s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
781s  paramci (InverseGaussianDistribution.fit (x), "alpha", 1)
781s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
781s  paramci (InverseGaussianDistribution.fit (x), "alpha", [0.5 2])
781s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
781s  paramci (InverseGaussianDistribution.fit (x), "alpha", "")
781s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
781s  paramci (InverseGaussianDistribution.fit (x), "alpha", {0.05})
781s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
781s  paramci (InverseGaussianDistribution.fit (x), "parameter", "mu", ...
781s           "alpha", {0.05})
781s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
781s  paramci (InverseGaussianDistribution.fit (x), ...
781s           "parameter", {"mu", "lambda", "param"})
781s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
781s  paramci (InverseGaussianDistribution.fit (x), "alpha", 0.01, ...
781s           "parameter", {"mu", "lambda", "param"})
781s ***** error <paramci: unknown distribution parameter.> ...
781s  paramci (InverseGaussianDistribution.fit (x), "parameter", "param")
781s ***** error <paramci: unknown distribution parameter.> ...
781s  paramci (InverseGaussianDistribution.fit (x), "alpha", 0.01, ...
781s           "parameter", "param")
781s ***** error <paramci: invalid NAME for optional argument.> ...
781s  paramci (InverseGaussianDistribution.fit (x), "NAME", "value")
781s ***** error <paramci: invalid NAME for optional argument.> ...
781s  paramci (InverseGaussianDistribution.fit (x), "alpha", 0.01, "NAME", "value")
782s ***** error <paramci: invalid NAME for optional argument.> ...
782s  paramci (InverseGaussianDistribution.fit (x), "alpha", 0.01, ...
782s           "parameter", "mu", "NAME", "value")
782s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
782s  plot (InverseGaussianDistribution, "Parent")
782s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
782s  plot (InverseGaussianDistribution, "PlotType", 12)
782s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
782s  plot (InverseGaussianDistribution, "PlotType", {"pdf", "cdf"})
782s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
782s  plot (InverseGaussianDistribution, "PlotType", "pdfcdf")
782s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
782s  plot (InverseGaussianDistribution, "Discrete", "pdfcdf")
782s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
782s  plot (InverseGaussianDistribution, "Discrete", [1, 0])
782s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
782s  plot (InverseGaussianDistribution, "Discrete", {true})
782s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
782s  plot (InverseGaussianDistribution, "Parent", 12)
782s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
782s  plot (InverseGaussianDistribution, "Parent", "hax")
782s ***** error <plot: invalid NAME for optional argument.> ...
782s  plot (InverseGaussianDistribution, "invalidNAME", "pdf")
782s ***** error <plot: no fitted DATA to plot a probability plot.> ...
782s  plot (InverseGaussianDistribution, "PlotType", "probability")
782s ***** error <proflik: no fitted data available.> ...
782s  proflik (InverseGaussianDistribution, 2)
782s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
782s  proflik (InverseGaussianDistribution.fit (x), 3)
782s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
782s  proflik (InverseGaussianDistribution.fit (x), [1, 2])
782s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
782s  proflik (InverseGaussianDistribution.fit (x), {1})
782s ***** error <proflik: SETPARAM must be a numeric vector.> ...
782s  proflik (InverseGaussianDistribution.fit (x), 1, ones (2))
782s ***** error <proflik: missing VALUE for 'Display' argument.> ...
782s  proflik (InverseGaussianDistribution.fit (x), 1, "Display")
782s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
782s  proflik (InverseGaussianDistribution.fit (x), 1, "Display", 1)
782s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
782s  proflik (InverseGaussianDistribution.fit (x), 1, "Display", {1})
782s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
782s  proflik (InverseGaussianDistribution.fit (x), 1, "Display", {"on"})
782s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
782s  proflik (InverseGaussianDistribution.fit (x), 1, "Display", ["on"; "on"])
782s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
782s  proflik (InverseGaussianDistribution.fit (x), 1, "Display", "onnn")
782s ***** error <proflik: invalid NAME for optional arguments.> ...
782s  proflik (InverseGaussianDistribution.fit (x), 1, "NAME", "on")
782s ***** error <proflik: invalid optional argument.> ...
782s  proflik (InverseGaussianDistribution.fit (x), 1, {"NAME"}, "on")
782s ***** error <proflik: invalid optional argument.> ...
782s  proflik (InverseGaussianDistribution.fit (x), 1, {[1 2 3]}, "Display", "on")
782s ***** error <truncate: missing input argument.> ...
782s  truncate (InverseGaussianDistribution)
782s ***** error <truncate: missing input argument.> ...
782s  truncate (InverseGaussianDistribution, 2)
782s ***** error <truncate: invalid lower upper limits.> ...
782s  truncate (InverseGaussianDistribution, 4, 2)
782s ***** shared pd
782s  pd = InverseGaussianDistribution(1, 1);
782s  pd(2) = InverseGaussianDistribution(1, 3);
782s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
782s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
782s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
782s ***** error <mean: requires a scalar probability distribution.> mean (pd)
782s ***** error <median: requires a scalar probability distribution.> median (pd)
782s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
782s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
782s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
782s ***** error <plot: requires a scalar probability distribution.> plot (pd)
782s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
782s ***** error <random: requires a scalar probability distribution.> random (pd)
782s ***** error <std: requires a scalar probability distribution.> std (pd)
782s ***** error <truncate: requires a scalar probability distribution.> ...
782s  truncate (pd, 2, 4)
782s ***** error <var: requires a scalar probability distribution.> var (pd)
782s 96 tests, 96 passed, 0 known failure, 0 skipped
782s [inst/dist_obj/LogisticDistribution.m]
782s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_obj/LogisticDistribution.m
782s ***** shared pd, t
782s  pd = LogisticDistribution (0, 1);
782s  t = truncate (pd, 2, 4);
782s ***** assert (cdf (pd, [0:5]), [0.5, 0.7311, 0.8808, 0.9526, 0.9820, 0.9933], 1e-4);
782s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7091, 1, 1], 1e-4);
782s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8176, 0.8808, 0.9526, 0.9820], 1e-4);
782s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7091, 1], 1e-4);
782s ***** assert (icdf (pd, [0:0.2:1]), [-Inf, -1.3863, -0.4055, 0.4055, 1.3863, Inf], 1e-4);
782s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2088, 2.4599, 2.7789, 3.2252, 4], 1e-4);
782s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, -0.4055, 0.4055, 1.3863, Inf, NaN], 1e-4);
782s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4599, 2.7789, 3.2252, 4, NaN], 1e-4);
782s ***** assert (iqr (pd), 2.1972, 1e-4);
782s ***** assert (iqr (t), 0.8286, 1e-4);
782s ***** assert (mean (pd), 0, 1e-4);
782s ***** assert (mean (t), 2.7193, 1e-4);
782s ***** assert (median (pd), 0);
782s ***** assert (median (t), 2.6085, 1e-4);
782s ***** assert (pdf (pd, [0:5]), [0.25, 0.1966, 0.1050, 0.0452, 0.0177, 0.0066], 1e-4);
782s ***** assert (pdf (t, [0:5]), [0, 0, 1.0373, 0.4463, 0.1745, 0], 1e-4);
782s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0.1966, 0.1966, 0.1050, 0.0452, 0.0177, NaN], 1e-4);
782s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.0373, 0.4463, 0.1745, NaN], 1e-4);
782s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
782s ***** assert (any (random (t, 1000, 1) < 2), false);
782s ***** assert (any (random (t, 1000, 1) > 4), false);
782s ***** assert (std (pd), 1.8138, 1e-4);
782s ***** assert (std (t), 0.5320, 1e-4);
782s ***** assert (var (pd), 3.2899, 1e-4);
782s ***** assert (var (t), 0.2830, 1e-4);
782s ***** error <LogisticDistribution: MU must be a nonnegative real scalar.> ...
782s  LogisticDistribution(Inf, 1)
782s ***** error <LogisticDistribution: MU must be a nonnegative real scalar.> ...
782s  LogisticDistribution(i, 1)
782s ***** error <LogisticDistribution: MU must be a nonnegative real scalar.> ...
782s  LogisticDistribution("a", 1)
782s ***** error <LogisticDistribution: MU must be a nonnegative real scalar.> ...
782s  LogisticDistribution([1, 2], 1)
782s ***** error <LogisticDistribution: MU must be a nonnegative real scalar.> ...
782s  LogisticDistribution(NaN, 1)
782s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
782s  LogisticDistribution(1, 0)
782s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
782s  LogisticDistribution(1, -1)
782s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
782s  LogisticDistribution(1, Inf)
782s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
782s  LogisticDistribution(1, i)
782s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
782s  LogisticDistribution(1, "a")
782s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
782s  LogisticDistribution(1, [1, 2])
782s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
782s  LogisticDistribution(1, NaN)
782s ***** error <cdf: invalid argument for upper tail.> ...
782s  cdf (LogisticDistribution, 2, "uper")
782s ***** error <cdf: invalid argument for upper tail.> ...
782s  cdf (LogisticDistribution, 2, 3)
782s ***** shared x
782s  x = logirnd (1, 1, [1, 100]);
782s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
782s  paramci (LogisticDistribution.fit (x), "alpha")
782s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
782s  paramci (LogisticDistribution.fit (x), "alpha", 0)
782s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
782s  paramci (LogisticDistribution.fit (x), "alpha", 1)
782s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
782s  paramci (LogisticDistribution.fit (x), "alpha", [0.5 2])
782s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
782s  paramci (LogisticDistribution.fit (x), "alpha", "")
782s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
782s  paramci (LogisticDistribution.fit (x), "alpha", {0.05})
782s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
782s  paramci (LogisticDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
782s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
782s  paramci (LogisticDistribution.fit (x), "parameter", {"mu", "sigma", "param"})
782s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
782s  paramci (LogisticDistribution.fit (x), "alpha", 0.01, ...
782s           "parameter", {"mu", "sigma", "param"})
782s ***** error <paramci: unknown distribution parameter.> ...
782s  paramci (LogisticDistribution.fit (x), "parameter", "param")
783s ***** error <paramci: unknown distribution parameter.> ...
783s  paramci (LogisticDistribution.fit (x), "alpha", 0.01, "parameter", "param")
783s ***** error <paramci: invalid NAME for optional argument.> ...
783s  paramci (LogisticDistribution.fit (x), "NAME", "value")
783s ***** error <paramci: invalid NAME for optional argument.> ...
783s  paramci (LogisticDistribution.fit (x), "alpha", 0.01, "NAME", "value")
783s ***** error <paramci: invalid NAME for optional argument.> ...
783s  paramci (LogisticDistribution.fit (x), "alpha", 0.01, "parameter", "mu", ...
783s           "NAME", "value")
783s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
783s  plot (LogisticDistribution, "Parent")
783s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
783s  plot (LogisticDistribution, "PlotType", 12)
783s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
783s  plot (LogisticDistribution, "PlotType", {"pdf", "cdf"})
783s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
783s  plot (LogisticDistribution, "PlotType", "pdfcdf")
783s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
783s  plot (LogisticDistribution, "Discrete", "pdfcdf")
783s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
783s  plot (LogisticDistribution, "Discrete", [1, 0])
783s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
783s  plot (LogisticDistribution, "Discrete", {true})
783s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
783s  plot (LogisticDistribution, "Parent", 12)
783s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
783s  plot (LogisticDistribution, "Parent", "hax")
783s ***** error <plot: invalid NAME for optional argument.> ...
783s  plot (LogisticDistribution, "invalidNAME", "pdf")
783s ***** error <plot: no fitted DATA to plot a probability plot.> ...
783s  plot (LogisticDistribution, "PlotType", "probability")
783s ***** error <proflik: no fitted data available.> ...
783s  proflik (LogisticDistribution, 2)
783s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
783s  proflik (LogisticDistribution.fit (x), 3)
783s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
783s  proflik (LogisticDistribution.fit (x), [1, 2])
783s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
783s  proflik (LogisticDistribution.fit (x), {1})
783s ***** error <proflik: SETPARAM must be a numeric vector.> ...
783s  proflik (LogisticDistribution.fit (x), 1, ones (2))
783s ***** error <proflik: missing VALUE for 'Display' argument.> ...
783s  proflik (LogisticDistribution.fit (x), 1, "Display")
783s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
783s  proflik (LogisticDistribution.fit (x), 1, "Display", 1)
783s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
783s  proflik (LogisticDistribution.fit (x), 1, "Display", {1})
783s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
783s  proflik (LogisticDistribution.fit (x), 1, "Display", {"on"})
783s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
783s  proflik (LogisticDistribution.fit (x), 1, "Display", ["on"; "on"])
783s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
783s  proflik (LogisticDistribution.fit (x), 1, "Display", "onnn")
783s ***** error <proflik: invalid NAME for optional arguments.> ...
783s  proflik (LogisticDistribution.fit (x), 1, "NAME", "on")
783s ***** error <proflik: invalid optional argument.> ...
783s  proflik (LogisticDistribution.fit (x), 1, {"NAME"}, "on")
783s ***** error <proflik: invalid optional argument.> ...
783s  proflik (LogisticDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
783s ***** error <truncate: missing input argument.> ...
783s  truncate (LogisticDistribution)
783s ***** error <truncate: missing input argument.> ...
783s  truncate (LogisticDistribution, 2)
783s ***** error <truncate: invalid lower upper limits.> ...
783s  truncate (LogisticDistribution, 4, 2)
783s ***** shared pd
783s  pd = LogisticDistribution(1, 1);
783s  pd(2) = LogisticDistribution(1, 3);
783s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
783s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
783s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
783s ***** error <mean: requires a scalar probability distribution.> mean (pd)
783s ***** error <median: requires a scalar probability distribution.> median (pd)
783s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
783s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
783s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
783s ***** error <plot: requires a scalar probability distribution.> plot (pd)
783s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
783s ***** error <random: requires a scalar probability distribution.> random (pd)
783s ***** error <std: requires a scalar probability distribution.> std (pd)
783s ***** error <truncate: requires a scalar probability distribution.> ...
783s  truncate (pd, 2, 4)
783s ***** error <var: requires a scalar probability distribution.> var (pd)
783s 95 tests, 95 passed, 0 known failure, 0 skipped
783s [inst/dist_obj/LoglogisticDistribution.m]
784s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_obj/LoglogisticDistribution.m
784s ***** shared pd, t
784s  pd = LoglogisticDistribution;
784s  t = truncate (pd, 2, 4);
784s ***** assert (cdf (pd, [0:5]), [0, 0.5, 0.6667, 0.75, 0.8, 0.8333], 1e-4);
784s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.625, 1, 1], 1e-4);
784s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.6, 0.6667, 0.75, 0.8], 1e-4);
784s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.625, 1], 1e-4);
784s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.25, 0.6667, 1.5, 4, Inf], 1e-4);
784s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2609, 2.5714, 2.9474, 3.4118, 4], 1e-4);
784s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.6667, 1.5, 4, Inf, NaN], 1e-4);
784s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5714, 2.9474, 3.4118, 4, NaN], 1e-4);
784s ***** assert (iqr (pd), 2.6667, 1e-4);
784s ***** assert (iqr (t), 0.9524, 1e-4);
784s ***** assert (mean (pd), Inf);
784s ***** assert (mean (t), 2.8312, 1e-4);
784s ***** assert (median (pd), 1, 1e-4);
784s ***** assert (median (t), 2.75, 1e-4);
784s ***** assert (pdf (pd, [0:5]), [0, 0.25, 0.1111, 0.0625, 0.04, 0.0278], 1e-4);
784s ***** assert (pdf (t, [0:5]), [0, 0, 0.8333, 0.4687, 0.3, 0], 1e-4);
784s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.25, 0.1111, 0.0625, 0.04, NaN], 1e-4);
784s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.8333, 0.4687, 0.3, NaN], 1e-4);
784s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
784s ***** assert (any (random (t, 1000, 1) < 2), false);
784s ***** assert (any (random (t, 1000, 1) > 4), false);
784s ***** assert (std (pd), Inf);
784s ***** assert (std (t), 0.5674, 1e-4);
784s ***** assert (var (pd), Inf);
784s ***** assert (var (t), 0.3220, 1e-4);
784s ***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
784s  LoglogisticDistribution(Inf, 1)
784s ***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
784s  LoglogisticDistribution(i, 1)
784s ***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
784s  LoglogisticDistribution("a", 1)
784s ***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
784s  LoglogisticDistribution([1, 2], 1)
784s ***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
784s  LoglogisticDistribution(NaN, 1)
784s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
784s  LoglogisticDistribution(1, 0)
784s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
784s  LoglogisticDistribution(1, -1)
784s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
784s  LoglogisticDistribution(1, Inf)
784s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
784s  LoglogisticDistribution(1, i)
784s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
784s  LoglogisticDistribution(1, "a")
784s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
784s  LoglogisticDistribution(1, [1, 2])
784s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
784s  LoglogisticDistribution(1, NaN)
784s ***** error <cdf: invalid argument for upper tail.> ...
784s  cdf (LoglogisticDistribution, 2, "uper")
784s ***** error <cdf: invalid argument for upper tail.> ...
784s  cdf (LoglogisticDistribution, 2, 3)
784s ***** shared x
784s  x = loglrnd (1, 1, [1, 100]);
784s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
784s  paramci (LoglogisticDistribution.fit (x), "alpha")
784s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
784s  paramci (LoglogisticDistribution.fit (x), "alpha", 0)
784s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
784s  paramci (LoglogisticDistribution.fit (x), "alpha", 1)
784s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
784s  paramci (LoglogisticDistribution.fit (x), "alpha", [0.5 2])
784s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
784s  paramci (LoglogisticDistribution.fit (x), "alpha", "")
784s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
784s  paramci (LoglogisticDistribution.fit (x), "alpha", {0.05})
784s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
784s  paramci (LoglogisticDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
784s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
784s  paramci (LoglogisticDistribution.fit (x), "parameter", {"mu", "sigma", "pa"})
784s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
784s  paramci (LoglogisticDistribution.fit (x), "alpha", 0.01, ...
784s           "parameter", {"mu", "sigma", "param"})
784s ***** error <paramci: unknown distribution parameter.> ...
784s  paramci (LoglogisticDistribution.fit (x), "parameter", "param")
784s ***** error <paramci: unknown distribution parameter.> ...
784s  paramci (LoglogisticDistribution.fit (x), "alpha", 0.01, "parameter", "parm")
784s ***** error <paramci: invalid NAME for optional argument.> ...
784s  paramci (LoglogisticDistribution.fit (x), "NAME", "value")
784s ***** error <paramci: invalid NAME for optional argument.> ...
784s  paramci (LoglogisticDistribution.fit (x), "alpha", 0.01, "NAME", "value")
784s ***** error <paramci: invalid NAME for optional argument.> ...
784s  paramci (LoglogisticDistribution.fit (x), "alpha", 0.01, ...
784s           "parameter", "mu", "NAME", "value")
784s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
784s  plot (LoglogisticDistribution, "Parent")
784s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
784s  plot (LoglogisticDistribution, "PlotType", 12)
784s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
784s  plot (LoglogisticDistribution, "PlotType", {"pdf", "cdf"})
784s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
784s  plot (LoglogisticDistribution, "PlotType", "pdfcdf")
784s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
784s  plot (LoglogisticDistribution, "Discrete", "pdfcdf")
784s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
784s  plot (LoglogisticDistribution, "Discrete", [1, 0])
784s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
784s  plot (LoglogisticDistribution, "Discrete", {true})
784s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
784s  plot (LoglogisticDistribution, "Parent", 12)
784s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
784s  plot (LoglogisticDistribution, "Parent", "hax")
784s ***** error <plot: invalid NAME for optional argument.> ...
784s  plot (LoglogisticDistribution, "invalidNAME", "pdf")
784s ***** error <plot: no fitted DATA to plot a probability plot.> ...
784s  plot (LoglogisticDistribution, "PlotType", "probability")
784s ***** error <proflik: no fitted data available.> ...
784s  proflik (LoglogisticDistribution, 2)
784s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
784s  proflik (LoglogisticDistribution.fit (x), 3)
785s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
785s  proflik (LoglogisticDistribution.fit (x), [1, 2])
785s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
785s  proflik (LoglogisticDistribution.fit (x), {1})
785s ***** error <proflik: SETPARAM must be a numeric vector.> ...
785s  proflik (LoglogisticDistribution.fit (x), 1, ones (2))
785s ***** error <proflik: missing VALUE for 'Display' argument.> ...
785s  proflik (LoglogisticDistribution.fit (x), 1, "Display")
785s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
785s  proflik (LoglogisticDistribution.fit (x), 1, "Display", 1)
785s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
785s  proflik (LoglogisticDistribution.fit (x), 1, "Display", {1})
785s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
785s  proflik (LoglogisticDistribution.fit (x), 1, "Display", {"on"})
785s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
785s  proflik (LoglogisticDistribution.fit (x), 1, "Display", ["on"; "on"])
785s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
785s  proflik (LoglogisticDistribution.fit (x), 1, "Display", "onnn")
785s ***** error <proflik: invalid NAME for optional arguments.> ...
785s  proflik (LoglogisticDistribution.fit (x), 1, "NAME", "on")
785s ***** error <proflik: invalid optional argument.> ...
785s  proflik (LoglogisticDistribution.fit (x), 1, {"NAME"}, "on")
785s ***** error <proflik: invalid optional argument.> ...
785s  proflik (LoglogisticDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
785s ***** error <truncate: missing input argument.> ...
785s  truncate (LoglogisticDistribution)
785s ***** error <truncate: missing input argument.> ...
785s  truncate (LoglogisticDistribution, 2)
785s ***** error <truncate: invalid lower upper limits.> ...
785s  truncate (LoglogisticDistribution, 4, 2)
785s ***** shared pd
785s  pd = LoglogisticDistribution(1, 1);
785s  pd(2) = LoglogisticDistribution(1, 3);
785s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
785s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
785s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
785s ***** error <mean: requires a scalar probability distribution.> mean (pd)
785s ***** error <median: requires a scalar probability distribution.> median (pd)
785s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
785s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
785s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
785s ***** error <plot: requires a scalar probability distribution.> plot (pd)
785s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
785s ***** error <random: requires a scalar probability distribution.> random (pd)
785s ***** error <std: requires a scalar probability distribution.> std (pd)
785s ***** error <truncate: requires a scalar probability distribution.> ...
785s  truncate (pd, 2, 4)
785s ***** error <var: requires a scalar probability distribution.> var (pd)
785s 95 tests, 95 passed, 0 known failure, 0 skipped
785s [inst/dist_obj/LognormalDistribution.m]
785s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_obj/LognormalDistribution.m
785s ***** shared pd, t
785s  pd = LognormalDistribution;
785s  t = truncate (pd, 2, 4);
785s ***** assert (cdf (pd, [0:5]), [0, 0.5, 0.7559, 0.8640, 0.9172, 0.9462], 1e-4);
785s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.6705, 1, 1], 1e-4);
785s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.6574, 0.7559, 0.8640, 0.9172], 1e-4);
785s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.6705, 1], 1e-4);
785s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.4310, 0.7762, 1.2883, 2.3201, Inf], 1e-4);
785s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2256, 2.5015, 2.8517, 3.3199, 4], 1e-4);
785s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.7762, 1.2883, 2.3201, Inf, NaN], 1e-4);
785s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5015, 2.8517, 3.3199, 4, NaN], 1e-4);
785s ***** assert (iqr (pd), 1.4536, 1e-4);
785s ***** assert (iqr (t), 0.8989, 1e-4);
785s ***** assert (mean (pd), 1.6487, 1e-4);
785s ***** assert (mean (t), 2.7692, 1e-4);
785s ***** assert (median (pd), 1, 1e-4);
785s ***** assert (median (t), 2.6653, 1e-4);
785s ***** assert (pdf (pd, [0:5]), [0, 0.3989, 0.1569, 0.0727, 0.0382, 0.0219], 1e-4);
785s ***** assert (pdf (t, [0:5]), [0, 0, 0.9727, 0.4509, 0.2366, 0], 1e-4);
785s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3989, 0.1569, 0.0727, 0.0382, NaN], 1e-4);
785s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.9727, 0.4509, 0.2366, NaN], 1e-4);
785s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
785s ***** assert (any (random (t, 1000, 1) < 2), false);
785s ***** assert (any (random (t, 1000, 1) > 4), false);
785s ***** assert (std (pd), 2.1612, 1e-4);
785s ***** assert (std (t), 0.5540, 1e-4);
785s ***** assert (var (pd), 4.6708, 1e-4);
785s ***** assert (var (t), 0.3069, 1e-4);
785s ***** error <LognormalDistribution: MU must be a real scalar.> ...
785s  LognormalDistribution(Inf, 1)
785s ***** error <LognormalDistribution: MU must be a real scalar.> ...
785s  LognormalDistribution(i, 1)
785s ***** error <LognormalDistribution: MU must be a real scalar.> ...
785s  LognormalDistribution("a", 1)
785s ***** error <LognormalDistribution: MU must be a real scalar.> ...
785s  LognormalDistribution([1, 2], 1)
785s ***** error <LognormalDistribution: MU must be a real scalar.> ...
785s  LognormalDistribution(NaN, 1)
785s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
785s  LognormalDistribution(1, 0)
785s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
785s  LognormalDistribution(1, -1)
785s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
785s  LognormalDistribution(1, Inf)
785s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
785s  LognormalDistribution(1, i)
785s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
785s  LognormalDistribution(1, "a")
785s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
785s  LognormalDistribution(1, [1, 2])
785s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
785s  LognormalDistribution(1, NaN)
785s ***** error <cdf: invalid argument for upper tail.> ...
785s  cdf (LognormalDistribution, 2, "uper")
785s ***** error <cdf: invalid argument for upper tail.> ...
785s  cdf (LognormalDistribution, 2, 3)
785s ***** shared x
785s  randn ("seed", 1);
785s  x = lognrnd (1, 1, [1, 100]);
785s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
785s  paramci (LognormalDistribution.fit (x), "alpha")
786s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
786s  paramci (LognormalDistribution.fit (x), "alpha", 0)
786s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
786s  paramci (LognormalDistribution.fit (x), "alpha", 1)
786s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
786s  paramci (LognormalDistribution.fit (x), "alpha", [0.5 2])
786s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
786s  paramci (LognormalDistribution.fit (x), "alpha", "")
786s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
786s  paramci (LognormalDistribution.fit (x), "alpha", {0.05})
786s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
786s  paramci (LognormalDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
786s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
786s  paramci (LognormalDistribution.fit (x), "parameter", {"mu", "sigma", "parm"})
786s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
786s  paramci (LognormalDistribution.fit (x), "alpha", 0.01, ...
786s           "parameter", {"mu", "sigma", "param"})
786s ***** error <paramci: unknown distribution parameter.> ...
786s  paramci (LognormalDistribution.fit (x), "parameter", "param")
786s ***** error <paramci: unknown distribution parameter.> ...
786s  paramci (LognormalDistribution.fit (x), "alpha", 0.01, "parameter", "param")
787s ***** error <paramci: invalid NAME for optional argument.> ...
787s  paramci (LognormalDistribution.fit (x), "NAME", "value")
787s ***** error <paramci: invalid NAME for optional argument.> ...
787s  paramci (LognormalDistribution.fit (x), "alpha", 0.01, "NAME", "value")
787s ***** error <paramci: invalid NAME for optional argument.> ...
787s  paramci (LognormalDistribution.fit (x), "alpha", 0.01, "parameter", "mu", ...
787s           "NAME", "value")
787s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
787s  plot (LognormalDistribution, "Parent")
787s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
787s  plot (LognormalDistribution, "PlotType", 12)
787s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
787s  plot (LognormalDistribution, "PlotType", {"pdf", "cdf"})
787s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
787s  plot (LognormalDistribution, "PlotType", "pdfcdf")
787s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
787s  plot (LognormalDistribution, "Discrete", "pdfcdf")
787s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
787s  plot (LognormalDistribution, "Discrete", [1, 0])
787s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
787s  plot (LognormalDistribution, "Discrete", {true})
787s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
787s  plot (LognormalDistribution, "Parent", 12)
787s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
787s  plot (LognormalDistribution, "Parent", "hax")
787s ***** error <plot: invalid NAME for optional argument.> ...
787s  plot (LognormalDistribution, "invalidNAME", "pdf")
787s ***** error <plot: no fitted DATA to plot a probability plot.> ...
787s  plot (LognormalDistribution, "PlotType", "probability")
787s ***** error <proflik: no fitted data available.> ...
787s  proflik (LognormalDistribution, 2)
787s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
787s  proflik (LognormalDistribution.fit (x), 3)
787s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
787s  proflik (LognormalDistribution.fit (x), [1, 2])
787s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
787s  proflik (LognormalDistribution.fit (x), {1})
787s ***** error <proflik: SETPARAM must be a numeric vector.> ...
787s  proflik (LognormalDistribution.fit (x), 1, ones (2))
787s ***** error <proflik: missing VALUE for 'Display' argument.> ...
787s  proflik (LognormalDistribution.fit (x), 1, "Display")
787s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
787s  proflik (LognormalDistribution.fit (x), 1, "Display", 1)
787s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
787s  proflik (LognormalDistribution.fit (x), 1, "Display", {1})
788s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
788s  proflik (LognormalDistribution.fit (x), 1, "Display", {"on"})
788s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
788s  proflik (LognormalDistribution.fit (x), 1, "Display", ["on"; "on"])
788s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
788s  proflik (LognormalDistribution.fit (x), 1, "Display", "onnn")
788s ***** error <proflik: invalid NAME for optional arguments.> ...
788s  proflik (LognormalDistribution.fit (x), 1, "NAME", "on")
788s ***** error <proflik: invalid optional argument.> ...
788s  proflik (LognormalDistribution.fit (x), 1, {"NAME"}, "on")
788s ***** error <proflik: invalid optional argument.> ...
788s  proflik (LognormalDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
788s ***** error <truncate: missing input argument.> ...
788s  truncate (LognormalDistribution)
788s ***** error <truncate: missing input argument.> ...
788s  truncate (LognormalDistribution, 2)
788s ***** error <truncate: invalid lower upper limits.> ...
788s  truncate (LognormalDistribution, 4, 2)
788s ***** shared pd
788s  pd = LognormalDistribution(1, 1);
788s  pd(2) = LognormalDistribution(1, 3);
788s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
788s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
788s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
788s ***** error <mean: requires a scalar probability distribution.> mean (pd)
788s ***** error <median: requires a scalar probability distribution.> median (pd)
788s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
788s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
788s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
788s ***** error <plot: requires a scalar probability distribution.> plot (pd)
788s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
788s ***** error <random: requires a scalar probability distribution.> random (pd)
788s ***** error <std: requires a scalar probability distribution.> std (pd)
788s ***** error <truncate: requires a scalar probability distribution.> ...
788s  truncate (pd, 2, 4)
788s ***** error <var: requires a scalar probability distribution.> var (pd)
788s 95 tests, 95 passed, 0 known failure, 0 skipped
788s [inst/dist_obj/LoguniformDistribution.m]
788s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_obj/LoguniformDistribution.m
788s ***** shared pd, t
788s  pd = LoguniformDistribution (1, 4);
788s  t = truncate (pd, 2, 4);
788s ***** assert (cdf (pd, [0, 1, 2, 3, 4, 5]), [0, 0, 0.5, 0.7925, 1, 1], 1e-4);
788s ***** assert (cdf (t, [0, 1, 2, 3, 4, 5]), [0, 0, 0, 0.5850, 1, 1], 1e-4);
788s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.2925, 0.5, 0.7925, 1], 1e-4);
788s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.5850, 1], 1e-4);
788s ***** assert (icdf (pd, [0:0.2:1]), [1, 1.3195, 1.7411, 2.2974, 3.0314, 4], 1e-4);
788s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2974, 2.6390, 3.0314, 3.4822, 4], 1e-4);
788s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1.7411, 2.2974, 3.0314, 4, NaN], 1e-4);
788s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.6390, 3.0314, 3.4822, 4, NaN], 1e-4);
788s ***** assert (iqr (pd), 1.4142, 1e-4);
788s ***** assert (iqr (t), 0.9852, 1e-4);
788s ***** assert (mean (pd), 2.1640, 1e-4);
788s ***** assert (mean (t), 2.8854, 1e-4);
788s ***** assert (median (pd), 2);
788s ***** assert (median (t), 2.8284, 1e-4);
788s ***** assert (pdf (pd, [0, 1, 2, 3, 4, 5]), [0, 0.7213, 0.3607, 0.2404, 0.1803, 0], 1e-4);
788s ***** assert (pdf (t, [0, 1, 2, 3, 4, 5]), [0, 0, 0.7213, 0.4809, 0.3607, 0], 1e-4);
788s ***** assert (pdf (pd, [-1, 1, 2, 3, 4, NaN]), [0, 0.7213, 0.3607, 0.2404, 0.1803, NaN], 1e-4);
788s ***** assert (pdf (t, [-1, 1, 2, 3, 4, NaN]), [0, 0, 0.7213, 0.4809, 0.3607, NaN], 1e-4);
788s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
788s ***** assert (any (random (pd, 1000, 1) < 1), false);
788s ***** assert (any (random (pd, 1000, 1) > 4), false);
788s ***** assert (any (random (t, 1000, 1) < 2), false);
788s ***** assert (any (random (t, 1000, 1) > 4), false);
788s ***** assert (std (pd), 0.8527, 1e-4);
788s ***** assert (std (t), 0.5751, 1e-4);
788s ***** assert (var (pd), 0.7270, 1e-4);
788s ***** assert (var (t), 0.3307, 1e-4);
788s ***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
788s  LoguniformDistribution (i, 1)
788s ***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
788s  LoguniformDistribution (Inf, 1)
788s ***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
788s  LoguniformDistribution ([1, 2], 1)
788s ***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
788s  LoguniformDistribution ("a", 1)
788s ***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
788s  LoguniformDistribution (NaN, 1)
788s ***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
788s  LoguniformDistribution (1, i)
788s ***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
788s  LoguniformDistribution (1, Inf)
788s ***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
788s  LoguniformDistribution (1, [1, 2])
788s ***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
788s  LoguniformDistribution (1, "a")
788s ***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
788s  LoguniformDistribution (1, NaN)
788s ***** error <LoguniformDistribution: LOWER must be less than UPPER.> ...
788s  LoguniformDistribution (2, 1)
788s ***** error <cdf: invalid argument for upper tail.> ...
788s  cdf (LoguniformDistribution, 2, "uper")
788s ***** error <cdf: invalid argument for upper tail.> ...
788s  cdf (LoguniformDistribution, 2, 3)
788s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
788s  plot (LoguniformDistribution, "Parent")
788s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
788s  plot (LoguniformDistribution, "PlotType", 12)
788s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
788s  plot (LoguniformDistribution, "PlotType", {"pdf", "cdf"})
788s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
788s  plot (LoguniformDistribution, "PlotType", "pdfcdf")
788s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
788s  plot (LoguniformDistribution, "Discrete", "pdfcdf")
788s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
788s  plot (LoguniformDistribution, "Discrete", [1, 0])
788s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
788s  plot (LoguniformDistribution, "Discrete", {true})
788s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
788s  plot (LoguniformDistribution, "Parent", 12)
788s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
788s  plot (LoguniformDistribution, "Parent", "hax")
788s ***** error <plot: invalid NAME for optional argument.> ...
788s  plot (LoguniformDistribution, "invalidNAME", "pdf")
788s ***** error <plot: 'probability' PlotType is not supported for 'LoguniformDistribution'.> ...
788s  plot (LoguniformDistribution, "PlotType", "probability")
788s ***** error <truncate: missing input argument.> ...
788s  truncate (LoguniformDistribution)
788s ***** error <truncate: missing input argument.> ...
788s  truncate (LoguniformDistribution, 2)
788s ***** error <truncate: invalid lower upper limits.> ...
788s  truncate (LoguniformDistribution, 4, 2)
788s ***** shared pd
788s  pd = LoguniformDistribution(1, 4);
788s  pd(2) = LoguniformDistribution(2, 5);
788s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
788s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
788s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
788s ***** error <mean: requires a scalar probability distribution.> mean (pd)
788s ***** error <median: requires a scalar probability distribution.> median (pd)
788s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
788s ***** error <plot: requires a scalar probability distribution.> plot (pd)
788s ***** error <random: requires a scalar probability distribution.> random (pd)
788s ***** error <std: requires a scalar probability distribution.> std (pd)
788s ***** error <truncate: requires a scalar probability distribution.> ...
788s  truncate (pd, 2, 4)
788s ***** error <var: requires a scalar probability distribution.> var (pd)
788s 65 tests, 65 passed, 0 known failure, 0 skipped
788s [inst/dist_obj/MultinomialDistribution.m]
788s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_obj/MultinomialDistribution.m
788s ***** shared pd, t
788s  pd = MultinomialDistribution ([0.1, 0.2, 0.3, 0.2, 0.1, 0.1]);
788s  t = truncate (pd, 2, 4);
788s ***** assert (cdf (pd, [2, 3, 4]), [0.3, 0.6, 0.8], eps);
788s ***** assert (cdf (t, [2, 3, 4]), [0.2857, 0.7143, 1], 1e-4);
788s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.1, 0.3, 0.6, 0.8], eps);
788s ***** assert (cdf (pd, [1.5, 2-eps, 3, 4]), [0.1, 0.1, 0.6, 0.8], eps);
788s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0.2857, 0.7143, 1], 1e-4);
788s ***** assert (cdf (t, [1.5, 2-eps, 3, 4]), [0, 0, 0.7143, 1], 1e-4);
788s ***** assert (cdf (pd, [1, 2.5, 4, 6]), [0.1, 0.3, 0.8, 1], eps);
788s ***** assert (icdf (pd, [0, 0.2857, 0.7143, 1]), [1, 2, 4, 6]);
788s ***** assert (icdf (t, [0, 0.2857, 0.7143, 1]), [2, 2, 4, 4]);
788s ***** assert (icdf (t, [0, 0.35, 0.7143, 1]), [2, 3, 4, 4]);
788s ***** assert (icdf (t, [0, 0.35, 0.7143, 1, NaN]), [2, 3, 4, 4, NaN]);
788s ***** assert (icdf (t, [-0.5, 0, 0.35, 0.7143, 1, NaN]), [NaN, 2, 3, 4, 4, NaN]);
789s ***** assert (icdf (pd, [-0.5, 0, 0.35, 0.7143, 1, NaN]), [NaN, 1, 3, 4, 6, NaN]);
789s ***** assert (iqr (pd), 2);
789s ***** assert (iqr (t), 2);
789s ***** assert (mean (pd), 3.3, 1e-14);
789s ***** assert (mean (t), 3, eps);
789s ***** assert (median (pd), 3);
789s ***** assert (median (t), 3);
789s ***** assert (pdf (pd, [-5, 1, 2.5, 4, 6, NaN, 9]), [0, 0.1, 0, 0.2, 0.1, NaN, 0]);
789s ***** assert (pdf (pd, [-5, 1, 2, 3, 4, 6, NaN, 9]), ...
789s  [0, 0.1, 0.2, 0.3, 0.2, 0.1, NaN, 0]);
789s ***** assert (pdf (t, [-5, 1, 2, 3, 4, 6, NaN, 0]), ...
789s  [0, 0, 0.2857, 0.4286, 0.2857, 0, NaN, 0], 1e-4);
789s ***** assert (pdf (t, [-5, 1, 2, 4, 6, NaN, 0]), ...
789s  [0, 0, 0.2857, 0.2857, 0, NaN, 0], 1e-4);
789s ***** assert (unique (random (pd, 1000, 5)), [1, 2, 3, 4, 5, 6]');
789s ***** assert (unique (random (t, 1000, 5)), [2, 3, 4]');
789s ***** assert (std (pd), 1.4177, 1e-4);
789s ***** assert (std (t), 0.7559, 1e-4);
789s ***** assert (var (pd), 2.0100, 1e-4);
789s ***** assert (var (t), 0.5714, 1e-4);
789s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
789s  MultinomialDistribution(0)
789s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
789s  MultinomialDistribution(-1)
789s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
789s  MultinomialDistribution(Inf)
789s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
789s  MultinomialDistribution(i)
789s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
789s  MultinomialDistribution("a")
789s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
789s  MultinomialDistribution([1, 2])
789s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
789s  MultinomialDistribution(NaN)
789s ***** error <cdf: invalid argument for upper tail.> ...
789s  cdf (MultinomialDistribution, 2, "uper")
789s ***** error <cdf: invalid argument for upper tail.> ...
789s  cdf (MultinomialDistribution, 2, 3)
789s ***** error <cdf: X must be real.> ...
789s  cdf (MultinomialDistribution, i)
789s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
789s  plot (MultinomialDistribution, "Parent")
789s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
789s  plot (MultinomialDistribution, "PlotType", 12)
789s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
789s  plot (MultinomialDistribution, "PlotType", {"pdf", "cdf"})
789s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
789s  plot (MultinomialDistribution, "PlotType", "pdfcdf")
789s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
789s  plot (MultinomialDistribution, "Discrete", "pdfcdf")
789s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
789s  plot (MultinomialDistribution, "Discrete", [1, 0])
789s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
789s  plot (MultinomialDistribution, "Discrete", {true})
789s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
789s  plot (MultinomialDistribution, "Parent", 12)
789s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
789s  plot (MultinomialDistribution, "Parent", "hax")
789s ***** error <plot: invalid NAME for optional argument.> ...
789s  plot (MultinomialDistribution, "invalidNAME", "pdf")
789s ***** error <plot: 'probability' PlotType is not supported for 'MultinomialDistribution'.> ...
789s  plot (MultinomialDistribution, "PlotType", "probability")
789s ***** error <truncate: is_nan input argument.> ...
789s  truncate (MultinomialDistribution)
789s ***** error <truncate: is_nan input argument.> ...
789s  truncate (MultinomialDistribution, 2)
789s ***** error <truncate: invalid lower upper limits.> ...
789s  truncate (MultinomialDistribution, 4, 2)
789s ***** shared pd
789s  pd = MultinomialDistribution([0.1, 0.2, 0.3, 0.4]);
789s  pd(2) = MultinomialDistribution([0.1, 0.2, 0.3, 0.4]);
789s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
789s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
789s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
789s ***** error <mean: requires a scalar probability distribution.> mean (pd)
789s ***** error <median: requires a scalar probability distribution.> median (pd)
789s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
789s ***** error <plot: requires a scalar probability distribution.> plot (pd)
789s ***** error <random: requires a scalar probability distribution.> random (pd)
789s ***** error <std: requires a scalar probability distribution.> std (pd)
789s ***** error <truncate: requires a scalar probability distribution.> ...
789s  truncate (pd, 2, 4)
789s ***** error <var: requires a scalar probability distribution.> var (pd)
789s 64 tests, 64 passed, 0 known failure, 0 skipped
789s [inst/dist_obj/NakagamiDistribution.m]
789s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_obj/NakagamiDistribution.m
789s ***** shared pd, t
789s  pd = NakagamiDistribution;
789s  t = truncate (pd, 2, 4);
789s ***** assert (cdf (pd, [0:5]), [0, 0.6321, 0.9817, 0.9999, 1, 1], 1e-4);
789s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.9933, 1, 1], 1e-4);
789s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8946, 0.9817, 0.9999, 1], 1e-4);
789s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.9933, 1], 1e-4);
789s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.4724, 0.7147, 0.9572, 1.2686, Inf], 1e-4);
789s ***** assert (icdf (t, [0:0.2:1]), [2, 2.0550, 2.1239, 2.2173, 2.3684, 4], 1e-4);
789s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.7147, 0.9572, 1.2686, Inf, NaN], 1e-4);
789s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.1239, 2.2173, 2.3684, 4, NaN], 1e-4);
789s ***** assert (iqr (pd), 0.6411, 1e-4);
789s ***** assert (iqr (t), 0.2502, 1e-4);
789s ***** assert (mean (pd), 0.8862, 1e-4);
789s ***** assert (mean (t), 2.2263, 1e-4);
789s ***** assert (median (pd), 0.8326, 1e-4);
789s ***** assert (median (t), 2.1664, 1e-4);
789s ***** assert (pdf (pd, [0:5]), [0, 0.7358, 0.0733, 0.0007, 0, 0], 1e-4);
789s ***** assert (pdf (t, [0:5]), [0, 0, 4, 0.0404, 0, 0], 1e-4);
789s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.7358, 0.0733, 0.0007, 0, NaN], 1e-4);
789s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 4, 0.0404, 0, NaN], 1e-4);
789s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
789s ***** assert (any (random (t, 1000, 1) < 2), false);
789s ***** assert (any (random (t, 1000, 1) > 4), false);
789s ***** assert (std (pd), 0.4633, 1e-4);
789s ***** assert (std (t), 0.2083, 1e-4);
789s ***** assert (var (pd), 0.2146, 1e-4);
789s ***** assert (var (t), 0.0434, 1e-4);
789s ***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
789s  NakagamiDistribution(Inf, 1)
789s ***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
789s  NakagamiDistribution(i, 1)
789s ***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
789s  NakagamiDistribution("a", 1)
789s ***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
789s  NakagamiDistribution([1, 2], 1)
789s ***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
789s  NakagamiDistribution(NaN, 1)
789s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
789s  NakagamiDistribution(1, 0)
789s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
789s  NakagamiDistribution(1, -1)
789s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
789s  NakagamiDistribution(1, Inf)
789s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
789s  NakagamiDistribution(1, i)
789s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
789s  NakagamiDistribution(1, "a")
789s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
789s  NakagamiDistribution(1, [1, 2])
789s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
789s  NakagamiDistribution(1, NaN)
789s ***** error <cdf: invalid argument for upper tail.> ...
789s  cdf (NakagamiDistribution, 2, "uper")
789s ***** error <cdf: invalid argument for upper tail.> ...
789s  cdf (NakagamiDistribution, 2, 3)
789s ***** shared x
789s  x = nakarnd (1, 0.5, [1, 100]);
789s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
789s  paramci (NakagamiDistribution.fit (x), "alpha")
789s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
789s  paramci (NakagamiDistribution.fit (x), "alpha", 0)
789s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
789s  paramci (NakagamiDistribution.fit (x), "alpha", 1)
789s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
789s  paramci (NakagamiDistribution.fit (x), "alpha", [0.5 2])
789s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
789s  paramci (NakagamiDistribution.fit (x), "alpha", "")
789s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
789s  paramci (NakagamiDistribution.fit (x), "alpha", {0.05})
789s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
789s  paramci (NakagamiDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
789s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
789s  paramci (NakagamiDistribution.fit (x), "parameter", {"mu", "omega", "param"})
789s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
789s  paramci (NakagamiDistribution.fit (x), "alpha", 0.01, ...
789s           "parameter", {"mu", "omega", "param"})
789s ***** error <paramci: unknown distribution parameter.> ...
789s  paramci (NakagamiDistribution.fit (x), "parameter", "param")
789s ***** error <paramci: unknown distribution parameter.> ...
789s  paramci (NakagamiDistribution.fit (x), "alpha", 0.01, "parameter", "param")
789s ***** error <paramci: invalid NAME for optional argument.> ...
789s  paramci (NakagamiDistribution.fit (x), "NAME", "value")
789s ***** error <paramci: invalid NAME for optional argument.> ...
789s  paramci (NakagamiDistribution.fit (x), "alpha", 0.01, "NAME", "value")
789s ***** error <paramci: invalid NAME for optional argument.> ...
789s  paramci (NakagamiDistribution.fit (x), "alpha", 0.01, "parameter", "mu", ...
789s           "NAME", "value")
789s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
789s  plot (NakagamiDistribution, "Parent")
789s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
789s  plot (NakagamiDistribution, "PlotType", 12)
789s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
789s  plot (NakagamiDistribution, "PlotType", {"pdf", "cdf"})
789s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
789s  plot (NakagamiDistribution, "PlotType", "pdfcdf")
789s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
789s  plot (NakagamiDistribution, "Discrete", "pdfcdf")
789s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
789s  plot (NakagamiDistribution, "Discrete", [1, 0])
789s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
789s  plot (NakagamiDistribution, "Discrete", {true})
789s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
789s  plot (NakagamiDistribution, "Parent", 12)
789s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
789s  plot (NakagamiDistribution, "Parent", "hax")
789s ***** error <plot: invalid NAME for optional argument.> ...
789s  plot (NakagamiDistribution, "invalidNAME", "pdf")
789s ***** error <plot: no fitted DATA to plot a probability plot.> ...
789s  plot (NakagamiDistribution, "PlotType", "probability")
789s ***** error <proflik: no fitted data available.> ...
789s  proflik (NakagamiDistribution, 2)
789s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
789s  proflik (NakagamiDistribution.fit (x), 3)
789s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
789s  proflik (NakagamiDistribution.fit (x), [1, 2])
789s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
789s  proflik (NakagamiDistribution.fit (x), {1})
789s ***** error <proflik: SETPARAM must be a numeric vector.> ...
789s  proflik (NakagamiDistribution.fit (x), 1, ones (2))
789s ***** error <proflik: missing VALUE for 'Display' argument.> ...
789s  proflik (NakagamiDistribution.fit (x), 1, "Display")
789s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
789s  proflik (NakagamiDistribution.fit (x), 1, "Display", 1)
789s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
789s  proflik (NakagamiDistribution.fit (x), 1, "Display", {1})
789s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
789s  proflik (NakagamiDistribution.fit (x), 1, "Display", {"on"})
790s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
790s  proflik (NakagamiDistribution.fit (x), 1, "Display", ["on"; "on"])
790s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
790s  proflik (NakagamiDistribution.fit (x), 1, "Display", "onnn")
790s ***** error <proflik: invalid NAME for optional arguments.> ...
790s  proflik (NakagamiDistribution.fit (x), 1, "NAME", "on")
790s ***** error <proflik: invalid optional argument.> ...
790s  proflik (NakagamiDistribution.fit (x), 1, {"NAME"}, "on")
790s ***** error <proflik: invalid optional argument.> ...
790s  proflik (NakagamiDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
790s ***** error <truncate: missing input argument.> ...
790s  truncate (NakagamiDistribution)
790s ***** error <truncate: missing input argument.> ...
790s  truncate (NakagamiDistribution, 2)
790s ***** error <truncate: invalid lower upper limits.> ...
790s  truncate (NakagamiDistribution, 4, 2)
790s ***** shared pd
790s  pd = NakagamiDistribution(1, 0.5);
790s  pd(2) = NakagamiDistribution(1, 0.6);
790s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
790s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
790s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
790s ***** error <mean: requires a scalar probability distribution.> mean (pd)
790s ***** error <median: requires a scalar probability distribution.> median (pd)
790s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
790s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
790s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
790s ***** error <plot: requires a scalar probability distribution.> plot (pd)
790s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
790s ***** error <random: requires a scalar probability distribution.> random (pd)
790s ***** error <std: requires a scalar probability distribution.> std (pd)
790s ***** error <truncate: requires a scalar probability distribution.> ...
790s  truncate (pd, 2, 4)
790s ***** error <var: requires a scalar probability distribution.> var (pd)
790s 95 tests, 95 passed, 0 known failure, 0 skipped
790s [inst/dist_obj/NegativeBinomialDistribution.m]
790s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_obj/NegativeBinomialDistribution.m
790s ***** shared pd, t, t_inf
790s  pd = NegativeBinomialDistribution (5, 0.5);
790s  t = truncate (pd, 2, 4);
790s  t_inf = truncate (pd, 2, Inf);
790s ***** assert (cdf (pd, [0:5]), [0.0312, 0.1094, 0.2266, 0.3633, 0.5, 0.6230], 1e-4);
790s ***** assert (cdf (t, [0:5]), [0, 0, 0.3, 0.65, 1, 1], 1e-4);
790s ***** assert (cdf (t_inf, [0:5]), [0, 0, 0.1316, 0.2851, 0.4386, 0.5768], 1e-4);
790s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.1094, 0.2266, 0.3633, 0.5000], 1e-4);
790s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0.3, 0.65, 1], 1e-4);
790s ***** assert (icdf (pd, [0:0.2:1]), [0, 2, 4, 5, 7, Inf], 1e-4);
790s ***** assert (icdf (t, [0:0.2:1]), [2, 2, 3, 3, 4, 4], 1e-4);
790s ***** assert (icdf (t_inf, [0:0.2:1]), [2, 3, 4, 6, 8, Inf], 1e-4);
790s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 4, 5, 7, Inf, NaN], 1e-4);
790s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 3, 3, 4, 4, NaN], 1e-4);
790s ***** assert (iqr (pd), 4);
790s ***** assert (iqr (t), 2);
790s ***** assert (mean (pd), 5);
790s ***** assert (mean (t), 3.0500, 1e-4);
790s ***** assert (mean (t_inf), 5.5263, 1e-4);
790s ***** assert (median (pd), 4);
790s ***** assert (median (t), 3);
790s ***** assert (pdf (pd, [0:5]), [0.0312, 0.0781, 0.1172, 0.1367, 0.1367, 0.1230], 1e-4);
790s ***** assert (pdf (t, [0:5]), [0, 0, 0.3, 0.35, 0.35, 0], 1e-4);
790s ***** assert (pdf (t_inf, [0:5]), [0, 0, 0.1316, 0.1535, 0.1535, 0.1382], 1e-4);
790s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.0781, 0.1172, 0.1367, 0.1367, NaN], 1e-4);
790s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.3, 0.35, 0.35, NaN], 1e-4);
790s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
790s ***** assert (any (random (t, 1000, 1) < 2), false);
790s ***** assert (any (random (t, 1000, 1) > 4), false);
790s ***** assert (std (pd), 3.1623, 1e-4);
790s ***** assert (std (t), 0.8047, 1e-4);
790s ***** assert (std (t_inf), 2.9445, 1e-4);
790s ***** assert (var (pd), 10);
790s ***** assert (var (t), 0.6475, 1e-4);
790s ***** assert (var (t_inf), 8.6704, 1e-4);
790s ***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
790s  NegativeBinomialDistribution(Inf, 1)
790s ***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
790s  NegativeBinomialDistribution(i, 1)
790s ***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
790s  NegativeBinomialDistribution("a", 1)
790s ***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
790s  NegativeBinomialDistribution([1, 2], 1)
790s ***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
790s  NegativeBinomialDistribution(NaN, 1)
790s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
790s  NegativeBinomialDistribution(1, 0)
790s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
790s  NegativeBinomialDistribution(1, -1)
790s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
790s  NegativeBinomialDistribution(1, Inf)
790s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
790s  NegativeBinomialDistribution(1, i)
790s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
790s  NegativeBinomialDistribution(1, "a")
790s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
790s  NegativeBinomialDistribution(1, [1, 2])
790s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
790s  NegativeBinomialDistribution(1, NaN)
790s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
790s  NegativeBinomialDistribution(1, 1.2)
790s ***** error <cdf: invalid argument for upper tail.> ...
790s  cdf (NegativeBinomialDistribution, 2, "uper")
790s ***** error <cdf: invalid argument for upper tail.> ...
790s  cdf (NegativeBinomialDistribution, 2, 3)
790s ***** shared x
790s  x = nbinrnd (1, 0.5, [1, 100]);
790s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
790s  paramci (NegativeBinomialDistribution.fit (x), "alpha")
790s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
790s  paramci (NegativeBinomialDistribution.fit (x), "alpha", 0)
790s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
790s  paramci (NegativeBinomialDistribution.fit (x), "alpha", 1)
790s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
790s  paramci (NegativeBinomialDistribution.fit (x), "alpha", [0.5 2])
790s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
790s  paramci (NegativeBinomialDistribution.fit (x), "alpha", "")
790s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
790s  paramci (NegativeBinomialDistribution.fit (x), "alpha", {0.05})
790s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
790s  paramci (NegativeBinomialDistribution.fit (x), "parameter", "R", ...
790s           "alpha", {0.05})
790s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
790s  paramci (NegativeBinomialDistribution.fit (x), ...
790s           "parameter", {"R", "P", "param"})
790s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
790s  paramci (NegativeBinomialDistribution.fit (x), "alpha", 0.01, ...
790s           "parameter", {"R", "P", "param"})
790s ***** error <paramci: unknown distribution parameter.> ...
790s  paramci (NegativeBinomialDistribution.fit (x), "parameter", "param")
790s ***** error <paramci: unknown distribution parameter.> ...
790s  paramci (NegativeBinomialDistribution.fit (x), "alpha", 0.01, ...
790s           "parameter", "param")
790s ***** error <paramci: invalid NAME for optional argument.> ...
790s  paramci (NegativeBinomialDistribution.fit (x), "NAME", "value")
790s ***** error <paramci: invalid NAME for optional argument.> ...
790s  paramci (NegativeBinomialDistribution.fit (x), "alpha", 0.01, ...
790s           "NAME", "value")
790s ***** error <paramci: invalid NAME for optional argument.> ...
790s  paramci (NegativeBinomialDistribution.fit (x), "alpha", 0.01, ...
790s           "parameter", "R", "NAME", "value")
790s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
790s  plot (NegativeBinomialDistribution, "Parent")
790s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
790s  plot (NegativeBinomialDistribution, "PlotType", 12)
790s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
790s  plot (NegativeBinomialDistribution, "PlotType", {"pdf", "cdf"})
790s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
790s  plot (NegativeBinomialDistribution, "PlotType", "pdfcdf")
790s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
790s  plot (NegativeBinomialDistribution, "Discrete", "pdfcdf")
790s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
790s  plot (NegativeBinomialDistribution, "Discrete", [1, 0])
790s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
790s  plot (NegativeBinomialDistribution, "Discrete", {true})
790s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
790s  plot (NegativeBinomialDistribution, "Parent", 12)
790s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
790s  plot (NegativeBinomialDistribution, "Parent", "hax")
790s ***** error <plot: invalid NAME for optional argument.> ...
790s  plot (NegativeBinomialDistribution, "invalidNAME", "pdf")
790s ***** error <plot: no fitted DATA to plot a probability plot.> ...
790s  plot (NegativeBinomialDistribution, "PlotType", "probability")
790s ***** error <proflik: no fitted data available.> ...
790s  proflik (NegativeBinomialDistribution, 2)
790s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
790s  proflik (NegativeBinomialDistribution.fit (x), 3)
790s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
790s  proflik (NegativeBinomialDistribution.fit (x), [1, 2])
790s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
790s  proflik (NegativeBinomialDistribution.fit (x), {1})
790s ***** error <proflik: SETPARAM must be a numeric vector.> ...
790s  proflik (NegativeBinomialDistribution.fit (x), 1, ones (2))
790s ***** error <proflik: missing VALUE for 'Display' argument.> ...
790s  proflik (NegativeBinomialDistribution.fit (x), 1, "Display")
790s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
790s  proflik (NegativeBinomialDistribution.fit (x), 1, "Display", 1)
790s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
790s  proflik (NegativeBinomialDistribution.fit (x), 1, "Display", {1})
790s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
790s  proflik (NegativeBinomialDistribution.fit (x), 1, "Display", {"on"})
790s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
790s  proflik (NegativeBinomialDistribution.fit (x), 1, "Display", ["on"; "on"])
790s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
790s  proflik (NegativeBinomialDistribution.fit (x), 1, "Display", "onnn")
790s ***** error <proflik: invalid NAME for optional arguments.> ...
790s  proflik (NegativeBinomialDistribution.fit (x), 1, "NAME", "on")
790s ***** error <proflik: invalid optional argument.> ...
790s  proflik (NegativeBinomialDistribution.fit (x), 1, {"NAME"}, "on")
790s ***** error <proflik: invalid optional argument.> ...
790s  proflik (NegativeBinomialDistribution.fit (x), 1, {[1 2 3]}, "Display", "on")
790s ***** error <truncate: missing input argument.> ...
790s  truncate (NegativeBinomialDistribution)
790s ***** error <truncate: missing input argument.> ...
790s  truncate (NegativeBinomialDistribution, 2)
790s ***** error <truncate: invalid lower upper limits.> ...
790s  truncate (NegativeBinomialDistribution, 4, 2)
790s ***** shared pd
790s  pd = NegativeBinomialDistribution(1, 0.5);
790s  pd(2) = NegativeBinomialDistribution(1, 0.6);
790s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
790s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
790s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
790s ***** error <mean: requires a scalar probability distribution.> mean (pd)
790s ***** error <median: requires a scalar probability distribution.> median (pd)
790s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
790s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
790s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
791s ***** error <plot: requires a scalar probability distribution.> plot (pd)
791s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
791s ***** error <random: requires a scalar probability distribution.> random (pd)
791s ***** error <std: requires a scalar probability distribution.> std (pd)
791s ***** error <truncate: requires a scalar probability distribution.> ...
791s  truncate (pd, 2, 4)
791s ***** error <var: requires a scalar probability distribution.> var (pd)
791s 102 tests, 102 passed, 0 known failure, 0 skipped
791s [inst/dist_obj/NormalDistribution.m]
791s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_obj/NormalDistribution.m
791s ***** shared pd, t
791s  pd = NormalDistribution;
791s  t = truncate (pd, -2, 2);
791s ***** assert (cdf (pd, [0:5]), [0.5, 0.8413, 0.9772, 0.9987, 1, 1], 1e-4);
791s ***** assert (cdf (t, [0:5]), [0.5, 0.8576, 1, 1, 1, 1], 1e-4);
791s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.9332, 0.9772, 0.9987, 1], 1e-4);
791s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0.9538, 1, 1, 1], 1e-4);
791s ***** assert (icdf (pd, [0:0.2:1]), [-Inf, -0.8416, -0.2533, 0.2533, 0.8416, Inf], 1e-4);
791s ***** assert (icdf (t, [0:0.2:1]), [-2, -0.7938, -0.2416, 0.2416, 0.7938, 2], 1e-4);
791s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, -0.2533, 0.2533, 0.8416, Inf, NaN], 1e-4);
791s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, -0.2416, 0.2416, 0.7938, 2, NaN], 1e-4);
791s ***** assert (iqr (pd), 1.3490, 1e-4);
791s ***** assert (iqr (t), 1.2782, 1e-4);
791s ***** assert (mean (pd), 0);
791s ***** assert (mean (t), 0, 3e-16);
791s ***** assert (median (pd), 0);
791s ***** assert (median (t), 0, 3e-16);
791s ***** assert (pdf (pd, [0:5]), [0.3989, 0.2420, 0.0540, 0.0044, 0.0001, 0], 1e-4);
791s ***** assert (pdf (t, [0:5]), [0.4180, 0.2535, 0.0566, 0, 0, 0], 1e-4);
791s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0.2420, 0.2420, 0.0540, 0.0044, 0.0001, NaN], 1e-4);
791s ***** assert (pdf (t, [-1, 1:4, NaN]), [0.2535, 0.2535, 0.0566, 0, 0, NaN], 1e-4);
791s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
791s ***** assert (any (random (t, 1000, 1) < -2), false);
791s ***** assert (any (random (t, 1000, 1) > 2), false);
791s ***** assert (std (pd), 1);
791s ***** assert (std (t), 0.8796, 1e-4);
791s ***** assert (var (pd), 1);
791s ***** assert (var (t), 0.7737, 1e-4);
791s ***** error <NormalDistribution: MU must be a real scalar.> ...
791s  NormalDistribution(Inf, 1)
791s ***** error <NormalDistribution: MU must be a real scalar.> ...
791s  NormalDistribution(i, 1)
791s ***** error <NormalDistribution: MU must be a real scalar.> ...
791s  NormalDistribution("a", 1)
791s ***** error <NormalDistribution: MU must be a real scalar.> ...
791s  NormalDistribution([1, 2], 1)
791s ***** error <NormalDistribution: MU must be a real scalar.> ...
791s  NormalDistribution(NaN, 1)
791s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
791s  NormalDistribution(1, 0)
791s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
791s  NormalDistribution(1, -1)
791s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
791s  NormalDistribution(1, Inf)
791s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
791s  NormalDistribution(1, i)
791s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
791s  NormalDistribution(1, "a")
791s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
791s  NormalDistribution(1, [1, 2])
791s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
791s  NormalDistribution(1, NaN)
791s ***** error <cdf: invalid argument for upper tail.> ...
791s  cdf (NormalDistribution, 2, "uper")
791s ***** error <cdf: invalid argument for upper tail.> ...
791s  cdf (NormalDistribution, 2, 3)
791s ***** shared x
791s  x = normrnd (1, 1, [1, 100]);
791s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
791s  paramci (NormalDistribution.fit (x), "alpha")
791s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
791s  paramci (NormalDistribution.fit (x), "alpha", 0)
791s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
791s  paramci (NormalDistribution.fit (x), "alpha", 1)
791s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
791s  paramci (NormalDistribution.fit (x), "alpha", [0.5 2])
791s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
791s  paramci (NormalDistribution.fit (x), "alpha", "")
791s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
791s  paramci (NormalDistribution.fit (x), "alpha", {0.05})
791s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
791s  paramci (NormalDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
791s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
791s  paramci (NormalDistribution.fit (x), "parameter", {"mu", "sigma", "param"})
792s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
792s  paramci (NormalDistribution.fit (x), "alpha", 0.01, ...
792s           "parameter", {"mu", "sigma", "param"})
792s ***** error <paramci: unknown distribution parameter.> ...
792s  paramci (NormalDistribution.fit (x), "parameter", "param")
792s ***** error <paramci: unknown distribution parameter.> ...
792s  paramci (NormalDistribution.fit (x), "alpha", 0.01, "parameter", "param")
792s ***** error <paramci: invalid NAME for optional argument.> ...
792s  paramci (NormalDistribution.fit (x), "NAME", "value")
792s ***** error <paramci: invalid NAME for optional argument.> ...
792s  paramci (NormalDistribution.fit (x), "alpha", 0.01, "NAME", "value")
792s ***** error <paramci: invalid NAME for optional argument.> ...
792s  paramci (NormalDistribution.fit (x), "alpha", 0.01, "parameter", "mu", ...
792s           "NAME", "value")
792s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
792s  plot (NormalDistribution, "Parent")
792s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
792s  plot (NormalDistribution, "PlotType", 12)
792s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
792s  plot (NormalDistribution, "PlotType", {"pdf", "cdf"})
792s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
792s  plot (NormalDistribution, "PlotType", "pdfcdf")
792s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
792s  plot (NormalDistribution, "Discrete", "pdfcdf")
792s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
792s  plot (NormalDistribution, "Discrete", [1, 0])
792s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
792s  plot (NormalDistribution, "Discrete", {true})
792s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
792s  plot (NormalDistribution, "Parent", 12)
792s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
792s  plot (NormalDistribution, "Parent", "hax")
792s ***** error <plot: invalid NAME for optional argument.> ...
792s  plot (NormalDistribution, "invalidNAME", "pdf")
792s ***** error <plot: no fitted DATA to plot a probability plot.> ...
792s  plot (NormalDistribution, "PlotType", "probability")
792s ***** error <proflik: no fitted data available.> ...
792s  proflik (NormalDistribution, 2)
792s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
792s  proflik (NormalDistribution.fit (x), 3)
792s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
792s  proflik (NormalDistribution.fit (x), [1, 2])
792s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
792s  proflik (NormalDistribution.fit (x), {1})
792s ***** error <proflik: SETPARAM must be a numeric vector.> ...
792s  proflik (NormalDistribution.fit (x), 1, ones (2))
793s ***** error <proflik: missing VALUE for 'Display' argument.> ...
793s  proflik (NormalDistribution.fit (x), 1, "Display")
793s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
793s  proflik (NormalDistribution.fit (x), 1, "Display", 1)
793s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
793s  proflik (NormalDistribution.fit (x), 1, "Display", {1})
793s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
793s  proflik (NormalDistribution.fit (x), 1, "Display", {"on"})
793s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
793s  proflik (NormalDistribution.fit (x), 1, "Display", ["on"; "on"])
793s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
793s  proflik (NormalDistribution.fit (x), 1, "Display", "onnn")
793s ***** error <proflik: invalid NAME for optional arguments.> ...
793s  proflik (NormalDistribution.fit (x), 1, "NAME", "on")
793s ***** error <proflik: invalid optional argument.> ...
793s  proflik (NormalDistribution.fit (x), 1, {"NAME"}, "on")
793s ***** error <proflik: invalid optional argument.> ...
793s  proflik (NormalDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
793s ***** error <truncate: missing input argument.> ...
793s  truncate (NormalDistribution)
793s ***** error <truncate: missing input argument.> ...
793s  truncate (NormalDistribution, 2)
793s ***** error <truncate: invalid lower upper limits.> ...
793s  truncate (NormalDistribution, 4, 2)
793s ***** shared pd
793s  pd = NormalDistribution(1, 1);
793s  pd(2) = NormalDistribution(1, 3);
793s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
793s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
794s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
794s ***** error <mean: requires a scalar probability distribution.> mean (pd)
794s ***** error <median: requires a scalar probability distribution.> median (pd)
794s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
794s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
794s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
794s ***** error <plot: requires a scalar probability distribution.> plot (pd)
794s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
794s ***** error <random: requires a scalar probability distribution.> random (pd)
794s ***** error <std: requires a scalar probability distribution.> std (pd)
794s ***** error <truncate: requires a scalar probability distribution.> ...
794s  truncate (pd, 2, 4)
794s ***** error <var: requires a scalar probability distribution.> var (pd)
794s 95 tests, 95 passed, 0 known failure, 0 skipped
794s [inst/dist_obj/PiecewiseLinearDistribution.m]
794s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_obj/PiecewiseLinearDistribution.m
794s ***** shared pd, t
794s  load patients
794s  [f, x] = ecdf (Weight);
794s  f = f(1:5:end);
794s  x = x(1:5:end);
794s  pd = PiecewiseLinearDistribution (x, f);
794s  t = truncate (pd, 130, 180);
794s ***** assert (cdf (pd, [120, 130, 140, 150, 200]), [0.0767, 0.25, 0.4629, 0.5190, 0.9908], 1e-4);
794s ***** assert (cdf (t, [120, 130, 140, 150, 200]), [0, 0, 0.4274, 0.5403, 1], 1e-4);
794s ***** assert (cdf (pd, [100, 250, NaN]), [0, 1, NaN], 1e-4);
794s ***** assert (cdf (t, [115, 290, NaN]), [0, 1, NaN], 1e-4);
794s ***** assert (icdf (pd, [0:0.2:1]), [111, 127.5, 136.62, 169.67, 182.17, 202], 1e-2);
794s ***** assert (icdf (t, [0:0.2:1]), [130, 134.15, 139.26, 162.5, 173.99, 180], 1e-2);
794s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NA, 136.62, 169.67, 182.17, 202, NA], 1e-2);
794s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NA, 139.26, 162.5, 173.99, 180, NA], 1e-2);
794s ***** assert (iqr (pd), 50.0833, 1e-4);
794s ***** assert (iqr (t), 36.8077, 1e-4);
794s ***** assert (mean (pd), 153.61, 1e-10);
794s ***** assert (mean (t), 152.311, 1e-3);
800s ***** assert (median (pd), 142, 1e-10);
800s ***** assert (median (t), 141.9462, 1e-4);
800s ***** assert (pdf (pd, [120, 130, 140, 150, 200]), [0.0133, 0.0240, 0.0186, 0.0024, 0.0046], 1e-4);
800s ***** assert (pdf (t, [120, 130, 140, 150, 200]), [0, 0.0482, 0.0373, 0.0048, 0], 1e-4);
800s ***** assert (pdf (pd, [100, 250, NaN]), [0, 0, NaN], 1e-4);
800s ***** assert (pdf (t, [100, 250, NaN]), [0, 0, NaN], 1e-4);
800s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
800s ***** assert (any (random (t, 1000, 1) < 130), false);
800s ***** assert (any (random (t, 1000, 1) > 180), false);
800s ***** assert (std (pd), 26.5196, 1e-4);
800s ***** assert (std (t), 18.2941, 1e-4);
812s ***** assert (var (pd), 703.2879, 1e-4);
812s ***** assert (var (t), 334.6757, 1e-4);
825s ***** error <PiecewiseLinearDistribution: X must be a real vector.> ...
825s  PiecewiseLinearDistribution ([0, i], [0, 1])
825s ***** error <PiecewiseLinearDistribution: X must be a real vector.> ...
825s  PiecewiseLinearDistribution ([0, Inf], [0, 1])
825s ***** error <PiecewiseLinearDistribution: X must be a real vector.> ...
825s  PiecewiseLinearDistribution (["a", "c"], [0, 1])
825s ***** error <PiecewiseLinearDistribution: X must be a real vector.> ...
825s  PiecewiseLinearDistribution ([NaN, 1], [0, 1])
825s ***** error <PiecewiseLinearDistribution: Fx must be a real vector.> ...
825s  PiecewiseLinearDistribution ([0, 1], [0, i])
825s ***** error <PiecewiseLinearDistribution: Fx must be a real vector.> ...
825s  PiecewiseLinearDistribution ([0, 1], [0, Inf])
825s ***** error <PiecewiseLinearDistribution: Fx must be a real vector.> ...
825s  PiecewiseLinearDistribution ([0, 1], ["a", "c"])
825s ***** error <PiecewiseLinearDistribution: Fx must be a real vector.> ...
825s  PiecewiseLinearDistribution ([0, 1], [NaN, 1])
825s ***** error <PiecewiseLinearDistribution: X and FX must be vectors of equal size.> ...
825s  PiecewiseLinearDistribution ([0, 1], [0, 0.5, 1])
825s ***** error <PiecewiseLinearDistribution: X and FX must be at least two-elements long.> ...
825s  PiecewiseLinearDistribution ([0], [1])
825s ***** error <PiecewiseLinearDistribution: FX must be bounded in the range> ...
825s  PiecewiseLinearDistribution ([0, 0.5, 1], [0, 1, 1.5])
825s ***** error <cdf: invalid argument for upper tail.> ...
825s  cdf (PiecewiseLinearDistribution, 2, "uper")
825s ***** error <cdf: invalid argument for upper tail.> ...
825s  cdf (PiecewiseLinearDistribution, 2, 3)
825s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
825s  plot (PiecewiseLinearDistribution, "Parent")
825s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
825s  plot (PiecewiseLinearDistribution, "PlotType", 12)
825s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
825s  plot (PiecewiseLinearDistribution, "PlotType", {"pdf", "cdf"})
825s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
825s  plot (PiecewiseLinearDistribution, "PlotType", "pdfcdf")
825s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
825s  plot (PiecewiseLinearDistribution, "Discrete", "pdfcdf")
825s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
825s  plot (PiecewiseLinearDistribution, "Discrete", [1, 0])
825s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
825s  plot (PiecewiseLinearDistribution, "Discrete", {true})
825s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
825s  plot (PiecewiseLinearDistribution, "Parent", 12)
825s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
825s  plot (PiecewiseLinearDistribution, "Parent", "hax")
825s ***** error <plot: invalid NAME for optional argument.> ...
825s  plot (PiecewiseLinearDistribution, "invalidNAME", "pdf")
825s ***** error <plot: 'probability' PlotType is not supported for 'PiecewiseLinearDistribution'.> ...
825s  plot (PiecewiseLinearDistribution, "PlotType", "probability")
825s ***** error <truncate: missing input argument.> ...
825s  truncate (PiecewiseLinearDistribution)
825s ***** error <truncate: missing input argument.> ...
825s  truncate (PiecewiseLinearDistribution, 2)
825s ***** error <truncate: invalid lower upper limits.> ...
825s  truncate (PiecewiseLinearDistribution, 4, 2)
825s ***** shared pd
825s  pd = PiecewiseLinearDistribution ();
825s  pd(2) = PiecewiseLinearDistribution ();
825s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
825s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
825s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
825s ***** error <mean: requires a scalar probability distribution.> mean (pd)
825s ***** error <median: requires a scalar probability distribution.> median (pd)
825s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
825s ***** error <plot: requires a scalar probability distribution.> plot (pd)
825s ***** error <random: requires a scalar probability distribution.> random (pd)
825s ***** error <std: requires a scalar probability distribution.> std (pd)
825s ***** error <truncate: requires a scalar probability distribution.> ...
825s  truncate (pd, 2, 4)
825s ***** error <var: requires a scalar probability distribution.> var (pd)
825s 63 tests, 63 passed, 0 known failure, 0 skipped
825s [inst/dist_obj/PoissonDistribution.m]
825s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_obj/PoissonDistribution.m
825s ***** shared pd, t, t_inf
825s  pd = PoissonDistribution;
825s  t = truncate (pd, 2, 4);
825s  t_inf = truncate (pd, 2, Inf);
825s ***** assert (cdf (pd, [0:5]), [0.3679, 0.7358, 0.9197, 0.9810, 0.9963, 0.9994], 1e-4);
825s ***** assert (cdf (t, [0:5]), [0, 0, 0.7059, 0.9412, 1, 1], 1e-4);
825s ***** assert (cdf (t_inf, [0:5]), [0, 0, 0.6961, 0.9281, 0.9861, 0.9978], 1e-4);
825s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.7358, 0.9197, 0.9810, 0.9963], 1e-4);
825s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0.7059, 0.9412, 1], 1e-4);
825s ***** assert (icdf (pd, [0:0.2:1]), [0, 0, 1, 1, 2, Inf], 1e-4);
825s ***** assert (icdf (t, [0:0.2:1]), [2, 2, 2, 2, 3, 4], 1e-4);
825s ***** assert (icdf (t_inf, [0:0.2:1]), [2, 2, 2, 2, 3, Inf], 1e-4);
825s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1, 1, 2, Inf, NaN], 1e-4);
825s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2, 2, 3, 4, NaN], 1e-4);
825s ***** assert (iqr (pd), 2);
825s ***** assert (iqr (t), 1);
825s ***** assert (mean (pd), 1);
825s ***** assert (mean (t), 2.3529, 1e-4);
825s ***** assert (mean (t_inf), 2.3922, 1e-4);
825s ***** assert (median (pd), 1);
825s ***** assert (median (t), 2);
825s ***** assert (median (t_inf), 2);
825s ***** assert (pdf (pd, [0:5]), [0.3679, 0.3679, 0.1839, 0.0613, 0.0153, 0.0031], 1e-4);
825s ***** assert (pdf (t, [0:5]), [0, 0, 0.7059, 0.2353, 0.0588, 0], 1e-4);
825s ***** assert (pdf (t_inf, [0:5]), [0, 0, 0.6961, 0.2320, 0.0580, 0.0116], 1e-4);
825s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3679, 0.1839, 0.0613, 0.0153, NaN], 1e-4);
825s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.7059, 0.2353, 0.0588, NaN], 1e-4);
825s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
825s ***** assert (any (random (t, 1000, 1) < 2), false);
825s ***** assert (any (random (t, 1000, 1) > 4), false);
825s ***** assert (std (pd), 1);
825s ***** assert (std (t), 0.5882, 1e-4);
825s ***** assert (std (t_inf), 0.6738, 1e-4);
825s ***** assert (var (pd), 1);
825s ***** assert (var (t), 0.3460, 1e-4);
825s ***** assert (var (t_inf), 0.4540, 1e-4);
825s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
825s  PoissonDistribution(0)
825s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
825s  PoissonDistribution(-1)
825s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
825s  PoissonDistribution(Inf)
825s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
825s  PoissonDistribution(i)
825s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
825s  PoissonDistribution("a")
825s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
825s  PoissonDistribution([1, 2])
825s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
825s  PoissonDistribution(NaN)
825s ***** error <cdf: invalid argument for upper tail.> ...
825s  cdf (PoissonDistribution, 2, "uper")
825s ***** error <cdf: invalid argument for upper tail.> ...
825s  cdf (PoissonDistribution, 2, 3)
825s ***** shared x
825s  x = poissrnd (1, [1, 100]);
825s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
825s  paramci (PoissonDistribution.fit (x), "alpha")
825s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
825s  paramci (PoissonDistribution.fit (x), "alpha", 0)
825s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
825s  paramci (PoissonDistribution.fit (x), "alpha", 1)
825s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
825s  paramci (PoissonDistribution.fit (x), "alpha", [0.5 2])
825s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
825s  paramci (PoissonDistribution.fit (x), "alpha", "")
825s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
825s  paramci (PoissonDistribution.fit (x), "alpha", {0.05})
825s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
825s  paramci (PoissonDistribution.fit (x), "parameter", "lambda", "alpha", {0.05})
825s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
825s  paramci (PoissonDistribution.fit (x), "parameter", {"lambda", "param"})
825s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
825s  paramci (PoissonDistribution.fit (x), "alpha", 0.01, ...
825s           "parameter", {"lambda", "param"})
825s ***** error <paramci: unknown distribution parameter.> ...
825s  paramci (PoissonDistribution.fit (x), "parameter", "param")
825s ***** error <paramci: unknown distribution parameter.> ...
825s  paramci (PoissonDistribution.fit (x), "alpha", 0.01, "parameter", "param")
825s ***** error <paramci: invalid NAME for optional argument.> ...
825s  paramci (PoissonDistribution.fit (x), "NAME", "value")
826s ***** error <paramci: invalid NAME for optional argument.> ...
826s  paramci (PoissonDistribution.fit (x), "alpha", 0.01, "NAME", "value")
826s ***** error <paramci: invalid NAME for optional argument.> ...
826s  paramci (PoissonDistribution.fit (x), "alpha", 0.01, ...
826s           "parameter", "lambda", "NAME", "value")
826s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
826s  plot (PoissonDistribution, "Parent")
826s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
826s  plot (PoissonDistribution, "PlotType", 12)
826s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
826s  plot (PoissonDistribution, "PlotType", {"pdf", "cdf"})
826s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
826s  plot (PoissonDistribution, "PlotType", "pdfcdf")
826s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
826s  plot (PoissonDistribution, "Discrete", "pdfcdf")
826s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
826s  plot (PoissonDistribution, "Discrete", [1, 0])
826s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
826s  plot (PoissonDistribution, "Discrete", {true})
826s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
826s  plot (PoissonDistribution, "Parent", 12)
826s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
826s  plot (PoissonDistribution, "Parent", "hax")
826s ***** error <plot: invalid NAME for optional argument.> ...
826s  plot (PoissonDistribution, "invalidNAME", "pdf")
826s ***** error <plot: no fitted DATA to plot a probability plot.> ...
826s  plot (PoissonDistribution, "PlotType", "probability")
826s ***** error <proflik: no fitted data available.> ...
826s  proflik (PoissonDistribution, 2)
826s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
826s  proflik (PoissonDistribution.fit (x), 3)
826s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
826s  proflik (PoissonDistribution.fit (x), [1, 2])
826s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
826s  proflik (PoissonDistribution.fit (x), {1})
826s ***** error <proflik: SETPARAM must be a numeric vector.> ...
826s  proflik (PoissonDistribution.fit (x), 1, ones (2))
826s ***** error <proflik: missing VALUE for 'Display' argument.> ...
826s  proflik (PoissonDistribution.fit (x), 1, "Display")
826s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
826s  proflik (PoissonDistribution.fit (x), 1, "Display", 1)
826s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
826s  proflik (PoissonDistribution.fit (x), 1, "Display", {1})
826s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
826s  proflik (PoissonDistribution.fit (x), 1, "Display", {"on"})
826s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
826s  proflik (PoissonDistribution.fit (x), 1, "Display", ["on"; "on"])
826s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
826s  proflik (PoissonDistribution.fit (x), 1, "Display", "onnn")
826s ***** error <proflik: invalid NAME for optional arguments.> ...
826s  proflik (PoissonDistribution.fit (x), 1, "NAME", "on")
826s ***** error <proflik: invalid optional argument.> ...
826s  proflik (PoissonDistribution.fit (x), 1, {"NAME"}, "on")
826s ***** error <proflik: invalid optional argument.> ...
826s  proflik (PoissonDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
826s ***** error <truncate: missing input argument.> ...
826s  truncate (PoissonDistribution)
826s ***** error <truncate: missing input argument.> ...
826s  truncate (PoissonDistribution, 2)
826s ***** error <truncate: invalid lower upper limits.> ...
826s  truncate (PoissonDistribution, 4, 2)
826s ***** shared pd
826s  pd = PoissonDistribution(1);
826s  pd(2) = PoissonDistribution(3);
826s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
826s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
826s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
826s ***** error <mean: requires a scalar probability distribution.> mean (pd)
826s ***** error <median: requires a scalar probability distribution.> median (pd)
826s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
826s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
826s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
826s ***** error <plot: requires a scalar probability distribution.> plot (pd)
826s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
826s ***** error <random: requires a scalar probability distribution.> random (pd)
826s ***** error <std: requires a scalar probability distribution.> std (pd)
826s ***** error <truncate: requires a scalar probability distribution.> ...
826s  truncate (pd, 2, 4)
826s ***** error <var: requires a scalar probability distribution.> var (pd)
826s 97 tests, 97 passed, 0 known failure, 0 skipped
826s [inst/dist_obj/RayleighDistribution.m]
826s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_obj/RayleighDistribution.m
826s ***** shared pd, t
826s  pd = RayleighDistribution;
826s  t = truncate (pd, 2, 4);
826s ***** assert (cdf (pd, [0:5]), [0, 0.3935, 0.8647, 0.9889, 0.9997, 1], 1e-4);
826s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.9202, 1, 1], 1e-4);
826s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.6753, 0.8647, 0.9889, 0.9997, NaN], 1e-4);
826s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.9202, 1, NaN], 1e-4);
826s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.6680, 1.0108, 1.3537, 1.7941, Inf], 1e-4);
826s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1083, 2.2402, 2.4135, 2.6831, 4], 1e-4);
826s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1.0108, 1.3537, 1.7941, Inf, NaN], 1e-4);
826s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.2402, 2.4135, 2.6831, 4, NaN], 1e-4);
826s ***** assert (iqr (pd), 0.9066, 1e-4);
826s ***** assert (iqr (t), 0.4609, 1e-4);
826s ***** assert (mean (pd), 1.2533, 1e-4);
826s ***** assert (mean (t), 2.4169, 1e-4);
826s ***** assert (median (pd), 1.1774, 1e-4);
826s ***** assert (median (t), 2.3198, 1e-4);
826s ***** assert (pdf (pd, [0:5]), [0, 0.6065, 0.2707, 0.0333, 0.0013, 0], 1e-4);
826s ***** assert (pdf (t, [0:5]), [0, 0, 2.0050, 0.2469, 0.0099, 0], 1e-4);
826s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.4870, NaN], 1e-4);
826s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
826s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
826s ***** assert (any (random (t, 1000, 1) < 2), false);
826s ***** assert (any (random (t, 1000, 1) > 4), false);
826s ***** assert (std (pd), 0.6551, 1e-4);
826s ***** assert (std (t), 0.3591, 1e-4);
826s ***** assert (var (pd), 0.4292, 1e-4);
826s ***** assert (var (t), 0.1290, 1e-4);
826s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
826s  RayleighDistribution(0)
826s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
826s  RayleighDistribution(-1)
826s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
826s  RayleighDistribution(Inf)
826s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
826s  RayleighDistribution(i)
826s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
826s  RayleighDistribution("a")
826s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
826s  RayleighDistribution([1, 2])
826s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
826s  RayleighDistribution(NaN)
826s ***** error <cdf: invalid argument for upper tail.> ...
826s  cdf (RayleighDistribution, 2, "uper")
826s ***** error <cdf: invalid argument for upper tail.> ...
826s  cdf (RayleighDistribution, 2, 3)
826s ***** shared x
826s  x = raylrnd (1, [1, 100]);
826s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
826s  paramci (RayleighDistribution.fit (x), "alpha")
826s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
826s  paramci (RayleighDistribution.fit (x), "alpha", 0)
826s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
826s  paramci (RayleighDistribution.fit (x), "alpha", 1)
826s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
826s  paramci (RayleighDistribution.fit (x), "alpha", [0.5 2])
826s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
826s  paramci (RayleighDistribution.fit (x), "alpha", "")
826s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
826s  paramci (RayleighDistribution.fit (x), "alpha", {0.05})
826s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
826s  paramci (RayleighDistribution.fit (x), "parameter", "sigma", "alpha", {0.05})
826s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
826s  paramci (RayleighDistribution.fit (x), "parameter", {"sigma", "param"})
826s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
826s  paramci (RayleighDistribution.fit (x), "alpha", 0.01, ...
826s           "parameter", {"sigma", "param"})
826s ***** error <paramci: unknown distribution parameter.> ...
826s  paramci (RayleighDistribution.fit (x), "parameter", "param")
827s ***** error <paramci: unknown distribution parameter.> ...
827s  paramci (RayleighDistribution.fit (x), "alpha", 0.01, "parameter", "param")
827s ***** error <paramci: invalid NAME for optional argument.> ...
827s  paramci (RayleighDistribution.fit (x), "NAME", "value")
827s ***** error <paramci: invalid NAME for optional argument.> ...
827s  paramci (RayleighDistribution.fit (x), "alpha", 0.01, "NAME", "value")
827s ***** error <paramci: invalid NAME for optional argument.> ...
827s  paramci (RayleighDistribution.fit (x), "alpha", 0.01, ...
827s           "parameter", "sigma", "NAME", "value")
827s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
827s  plot (RayleighDistribution, "Parent")
827s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
827s  plot (RayleighDistribution, "PlotType", 12)
827s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
827s  plot (RayleighDistribution, "PlotType", {"pdf", "cdf"})
827s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
827s  plot (RayleighDistribution, "PlotType", "pdfcdf")
827s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
827s  plot (RayleighDistribution, "Discrete", "pdfcdf")
827s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
827s  plot (RayleighDistribution, "Discrete", [1, 0])
827s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
827s  plot (RayleighDistribution, "Discrete", {true})
827s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
827s  plot (RayleighDistribution, "Parent", 12)
827s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
827s  plot (RayleighDistribution, "Parent", "hax")
827s ***** error <plot: invalid NAME for optional argument.> ...
827s  plot (RayleighDistribution, "invalidNAME", "pdf")
827s ***** error <plot: no fitted DATA to plot a probability plot.> ...
827s  plot (RayleighDistribution, "PlotType", "probability")
827s ***** error <proflik: no fitted data available.> ...
827s  proflik (RayleighDistribution, 2)
827s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
827s  proflik (RayleighDistribution.fit (x), 3)
827s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
827s  proflik (RayleighDistribution.fit (x), [1, 2])
827s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
827s  proflik (RayleighDistribution.fit (x), {1})
827s ***** error <proflik: SETPARAM must be a numeric vector.> ...
827s  proflik (RayleighDistribution.fit (x), 1, ones (2))
827s ***** error <proflik: missing VALUE for 'Display' argument.> ...
827s  proflik (RayleighDistribution.fit (x), 1, "Display")
827s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
827s  proflik (RayleighDistribution.fit (x), 1, "Display", 1)
827s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
827s  proflik (RayleighDistribution.fit (x), 1, "Display", {1})
827s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
827s  proflik (RayleighDistribution.fit (x), 1, "Display", {"on"})
827s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
827s  proflik (RayleighDistribution.fit (x), 1, "Display", ["on"; "on"])
827s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
827s  proflik (RayleighDistribution.fit (x), 1, "Display", "onnn")
827s ***** error <proflik: invalid NAME for optional arguments.> ...
827s  proflik (RayleighDistribution.fit (x), 1, "NAME", "on")
827s ***** error <proflik: invalid optional argument.> ...
827s  proflik (RayleighDistribution.fit (x), 1, {"NAME"}, "on")
827s ***** error <proflik: invalid optional argument.> ...
827s  proflik (RayleighDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
827s ***** error <truncate: missing input argument.> ...
827s  truncate (RayleighDistribution)
827s ***** error <truncate: missing input argument.> ...
827s  truncate (RayleighDistribution, 2)
827s ***** error <truncate: invalid lower upper limits.> ...
827s  truncate (RayleighDistribution, 4, 2)
827s ***** shared pd
827s  pd = RayleighDistribution(1);
827s  pd(2) = RayleighDistribution(3);
827s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
827s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
827s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
827s ***** error <mean: requires a scalar probability distribution.> mean (pd)
827s ***** error <median: requires a scalar probability distribution.> median (pd)
827s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
827s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
827s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
827s ***** error <plot: requires a scalar probability distribution.> plot (pd)
827s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
827s ***** error <random: requires a scalar probability distribution.> random (pd)
827s ***** error <std: requires a scalar probability distribution.> std (pd)
827s ***** error <truncate: requires a scalar probability distribution.> ...
827s  truncate (pd, 2, 4)
827s ***** error <var: requires a scalar probability distribution.> var (pd)
827s 90 tests, 90 passed, 0 known failure, 0 skipped
827s [inst/dist_obj/RicianDistribution.m]
827s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_obj/RicianDistribution.m
827s ***** shared pd, t
827s  pd = RicianDistribution;
827s  t = truncate (pd, 2, 4);
827s ***** assert (cdf (pd, [0:5]), [0, 0.2671, 0.7310, 0.9563, 0.9971, 0.9999], 1e-4);
827s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.8466, 1, 1], 1e-4);
827s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.5120, 0.7310, 0.9563, 0.9971, NaN], 1e-4);
827s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.8466, 1, NaN], 1e-4);
827s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.8501, 1.2736, 1.6863, 2.2011, Inf], 1e-4);
827s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1517, 2.3296, 2.5545, 2.8868, 4], 1e-4);
827s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1.2736, 1.6863, 2.2011, Inf, NaN], 1e-4);
827s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.3296, 2.5545, 2.8868, 4, NaN], 1e-4);
828s ***** assert (iqr (pd), 1.0890, 1e-4);
828s ***** assert (iqr (t), 0.5928, 1e-4);
828s ***** assert (mean (pd), 1.5486, 1e-4);
828s ***** assert (mean (t), 2.5380, 1e-4);
828s ***** assert (median (pd), 1.4755, 1e-4);
828s ***** assert (median (t), 2.4341, 1e-4);
828s ***** assert (pdf (pd, [0:5]), [0, 0.4658, 0.3742, 0.0987, 0.0092, 0.0003], 1e-4);
828s ***** assert (pdf (t, [0:5]), [0, 0, 1.4063, 0.3707, 0.0346, 0], 1e-4);
828s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.4864, NaN], 1e-4);
828s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
828s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
828s ***** assert (any (random (t, 1000, 1) < 2), false);
828s ***** assert (any (random (t, 1000, 1) > 4), false);
828s ***** assert (std (pd), 0.7758, 1e-4);
828s ***** assert (std (t), 0.4294, 1e-4);
828s ***** assert (var (pd), 0.6019, 1e-4);
828s ***** assert (var (t), 0.1844, 1e-4);
828s ***** error <RicianDistribution: NU must be a non-negative real scalar.> ...
828s  RicianDistribution(-eps, 1)
828s ***** error <RicianDistribution: NU must be a non-negative real scalar.> ...
828s  RicianDistribution(-1, 1)
828s ***** error <RicianDistribution: NU must be a non-negative real scalar.> ...
828s  RicianDistribution(Inf, 1)
828s ***** error <RicianDistribution: NU must be a non-negative real scalar.> ...
828s  RicianDistribution(i, 1)
828s ***** error <RicianDistribution: NU must be a non-negative real scalar.> ...
828s  RicianDistribution("a", 1)
828s ***** error <RicianDistribution: NU must be a non-negative real scalar.> ...
828s  RicianDistribution([1, 2], 1)
828s ***** error <RicianDistribution: NU must be a non-negative real scalar.> ...
828s  RicianDistribution(NaN, 1)
828s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
828s  RicianDistribution(1, 0)
828s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
828s  RicianDistribution(1, -1)
828s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
828s  RicianDistribution(1, Inf)
828s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
828s  RicianDistribution(1, i)
828s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
828s  RicianDistribution(1, "a")
828s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
828s  RicianDistribution(1, [1, 2])
828s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
828s  RicianDistribution(1, NaN)
828s ***** error <cdf: invalid argument for upper tail.> ...
828s  cdf (RicianDistribution, 2, "uper")
828s ***** error <cdf: invalid argument for upper tail.> ...
828s  cdf (RicianDistribution, 2, 3)
828s ***** shared x
828s  x = gevrnd (1, 1, 1, [1, 100]);
828s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
828s  paramci (RicianDistribution.fit (x), "alpha")
828s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
828s  paramci (RicianDistribution.fit (x), "alpha", 0)
828s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
828s  paramci (RicianDistribution.fit (x), "alpha", 1)
828s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
828s  paramci (RicianDistribution.fit (x), "alpha", [0.5 2])
828s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
828s  paramci (RicianDistribution.fit (x), "alpha", "")
828s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
828s  paramci (RicianDistribution.fit (x), "alpha", {0.05})
828s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
828s  paramci (RicianDistribution.fit (x), "parameter", "s", "alpha", {0.05})
829s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
829s  paramci (RicianDistribution.fit (x), "parameter", {"s", "sigma", "param"})
829s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
829s  paramci (RicianDistribution.fit (x), "alpha", 0.01, ...
829s           "parameter", {"s", "sigma", "param"})
829s ***** error <paramci: unknown distribution parameter.> ...
829s  paramci (RicianDistribution.fit (x), "parameter", "param")
829s ***** error <paramci: unknown distribution parameter.> ...
829s  paramci (RicianDistribution.fit (x), "alpha", 0.01, "parameter", "param")
829s ***** error <paramci: invalid NAME for optional argument.> ...
829s  paramci (RicianDistribution.fit (x), "NAME", "value")
829s ***** error <paramci: invalid NAME for optional argument.> ...
829s  paramci (RicianDistribution.fit (x), "alpha", 0.01, "NAME", "value")
829s ***** error <paramci: invalid NAME for optional argument.> ...
829s  paramci (RicianDistribution.fit (x), "alpha", 0.01, "parameter", "s", ...
829s           "NAME", "value")
829s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
829s  plot (RicianDistribution, "Parent")
829s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
829s  plot (RicianDistribution, "PlotType", 12)
829s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
829s  plot (RicianDistribution, "PlotType", {"pdf", "cdf"})
829s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
829s  plot (RicianDistribution, "PlotType", "pdfcdf")
829s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
829s  plot (RicianDistribution, "Discrete", "pdfcdf")
829s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
829s  plot (RicianDistribution, "Discrete", [1, 0])
829s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
829s  plot (RicianDistribution, "Discrete", {true})
829s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
829s  plot (RicianDistribution, "Parent", 12)
829s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
829s  plot (RicianDistribution, "Parent", "hax")
829s ***** error <plot: invalid NAME for optional argument.> ...
829s  plot (RicianDistribution, "invalidNAME", "pdf")
829s ***** error <plot: no fitted DATA to plot a probability plot.> ...
829s  plot (RicianDistribution, "PlotType", "probability")
829s ***** error <proflik: no fitted data available.> ...
829s  proflik (RicianDistribution, 2)
829s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
829s  proflik (RicianDistribution.fit (x), 3)
829s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
829s  proflik (RicianDistribution.fit (x), [1, 2])
829s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
829s  proflik (RicianDistribution.fit (x), {1})
829s ***** error <proflik: SETPARAM must be a numeric vector.> ...
829s  proflik (RicianDistribution.fit (x), 1, ones (2))
829s ***** error <proflik: missing VALUE for 'Display' argument.> ...
829s  proflik (RicianDistribution.fit (x), 1, "Display")
830s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
830s  proflik (RicianDistribution.fit (x), 1, "Display", 1)
830s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
830s  proflik (RicianDistribution.fit (x), 1, "Display", {1})
830s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
830s  proflik (RicianDistribution.fit (x), 1, "Display", {"on"})
830s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
830s  proflik (RicianDistribution.fit (x), 1, "Display", ["on"; "on"])
830s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
830s  proflik (RicianDistribution.fit (x), 1, "Display", "onnn")
830s ***** error <proflik: invalid NAME for optional arguments.> ...
830s  proflik (RicianDistribution.fit (x), 1, "NAME", "on")
830s ***** error <proflik: invalid optional argument.> ...
830s  proflik (RicianDistribution.fit (x), 1, {"NAME"}, "on")
830s ***** error <proflik: invalid optional argument.> ...
830s  proflik (RicianDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
830s ***** error <truncate: missing input argument.> ...
830s  truncate (RicianDistribution)
830s ***** error <truncate: missing input argument.> ...
830s  truncate (RicianDistribution, 2)
830s ***** error <truncate: invalid lower upper limits.> ...
830s  truncate (RicianDistribution, 4, 2)
830s ***** shared pd
830s  pd = RicianDistribution(1, 1);
830s  pd(2) = RicianDistribution(1, 3);
830s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
830s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
830s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
830s ***** error <mean: requires a scalar probability distribution.> mean (pd)
830s ***** error <median: requires a scalar probability distribution.> median (pd)
830s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
830s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
830s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
830s ***** error <plot: requires a scalar probability distribution.> plot (pd)
830s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
830s ***** error <random: requires a scalar probability distribution.> random (pd)
830s ***** error <std: requires a scalar probability distribution.> std (pd)
830s ***** error <truncate: requires a scalar probability distribution.> ...
830s  truncate (pd, 2, 4)
830s ***** error <var: requires a scalar probability distribution.> var (pd)
830s 97 tests, 97 passed, 0 known failure, 0 skipped
830s [inst/dist_obj/TriangularDistribution.m]
830s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_obj/TriangularDistribution.m
830s ***** shared pd, t
830s  pd = TriangularDistribution (0, 3, 5);
830s  t = truncate (pd, 2, 4);
830s ***** assert (cdf (pd, [0:5]), [0, 0.0667, 0.2667, 0.6000, 0.9000, 1], 1e-4);
830s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.5263, 1, 1], 1e-4);
830s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.1500, 0.2667, 0.6, 0.9, NaN], 1e-4);
830s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.5263, 1, NaN], 1e-4);
830s ***** assert (icdf (pd, [0:0.2:1]), [0, 1.7321, 2.4495, 3, 3.5858, 5], 1e-4);
830s ***** assert (icdf (t, [0:0.2:1]), [2, 2.4290, 2.7928, 3.1203, 3.4945, 4], 1e-4);
830s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4495, 3, 3.5858, 5, NaN], 1e-4);
830s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.7928, 3.1203, 3.4945, 4, NaN], 1e-4);
830s ***** assert (iqr (pd), 1.4824, 1e-4);
830s ***** assert (iqr (t), 0.8678, 1e-4);
830s ***** assert (mean (pd), 2.6667, 1e-4);
830s ***** assert (mean (t), 2.9649, 1e-4);
830s ***** assert (median (pd), 2.7386, 1e-4);
830s ***** assert (median (t), 2.9580, 1e-4);
830s ***** assert (pdf (pd, [0:5]), [0, 0.1333, 0.2667, 0.4, 0.2, 0], 1e-4);
830s ***** assert (pdf (t, [0:5]), [0, 0, 0.4211, 0.6316, 0.3158, 0], 1e-4);
830s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.2, NaN], 1e-4);
830s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
830s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
830s ***** assert (any (random (t, 1000, 1) < 2), false);
830s ***** assert (any (random (t, 1000, 1) > 4), false);
830s ***** assert (std (pd), 1.0274, 1e-4);
830s ***** assert (std (t), 0.5369, 1e-4);
830s ***** assert (var (pd), 1.0556, 1e-4);
830s ***** assert (var (t), 0.2882, 1e-4);
830s ***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
830s  TriangularDistribution (i, 1, 2)
830s ***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
830s  TriangularDistribution (Inf, 1, 2)
830s ***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
830s  TriangularDistribution ([1, 2], 1, 2)
830s ***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
830s  TriangularDistribution ("a", 1, 2)
830s ***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
830s  TriangularDistribution (NaN, 1, 2)
830s ***** error <TriangularDistribution: mode B must be a real scalar.> ...
830s  TriangularDistribution (1, i, 2)
830s ***** error <TriangularDistribution: mode B must be a real scalar.> ...
830s  TriangularDistribution (1, Inf, 2)
830s ***** error <TriangularDistribution: mode B must be a real scalar.> ...
830s  TriangularDistribution (1, [1, 2], 2)
830s ***** error <TriangularDistribution: mode B must be a real scalar.> ...
830s  TriangularDistribution (1, "a", 2)
830s ***** error <TriangularDistribution: mode B must be a real scalar.> ...
830s  TriangularDistribution (1, NaN, 2)
830s ***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
830s  TriangularDistribution (1, 2, i)
830s ***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
830s  TriangularDistribution (1, 2, Inf)
830s ***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
830s  TriangularDistribution (1, 2, [1, 2])
830s ***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
830s  TriangularDistribution (1, 2, "a")
830s ***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
830s  TriangularDistribution (1, 2, NaN)
830s ***** error <TriangularDistribution: lower limit A must be less than upper limit C.> ...
830s  TriangularDistribution (1, 1, 1)
830s ***** error <TriangularDistribution: mode B must be within lower limit A and upper limit C.> ...
830s  TriangularDistribution (1, 0.5, 2)
830s ***** error <cdf: invalid argument for upper tail.> ...
830s  cdf (TriangularDistribution, 2, "uper")
830s ***** error <cdf: invalid argument for upper tail.> ...
830s  cdf (TriangularDistribution, 2, 3)
830s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
830s  plot (TriangularDistribution, "Parent")
830s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
830s  plot (TriangularDistribution, "PlotType", 12)
830s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
830s  plot (TriangularDistribution, "PlotType", {"pdf", "cdf"})
830s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
830s  plot (TriangularDistribution, "PlotType", "pdfcdf")
830s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
830s  plot (TriangularDistribution, "Discrete", "pdfcdf")
830s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
830s  plot (TriangularDistribution, "Discrete", [1, 0])
830s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
830s  plot (TriangularDistribution, "Discrete", {true})
830s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
830s  plot (TriangularDistribution, "Parent", 12)
830s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
830s  plot (TriangularDistribution, "Parent", "hax")
830s ***** error <plot: invalid NAME for optional argument.> ...
830s  plot (TriangularDistribution, "invalidNAME", "pdf")
830s ***** error <'probability' PlotType is not supported for 'TriangularDistribution'.> ...
830s  plot (TriangularDistribution, "PlotType", "probability")
830s ***** error <truncate: missing input argument.> ...
830s  truncate (TriangularDistribution)
830s ***** error <truncate: missing input argument.> ...
830s  truncate (TriangularDistribution, 2)
830s ***** error <truncate: invalid lower upper limits.> ...
830s  truncate (TriangularDistribution, 4, 2)
830s ***** shared pd
830s  pd = TriangularDistribution (0, 1, 2);
830s  pd(2) = TriangularDistribution (0, 1, 2);
830s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
830s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
830s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
830s ***** error <mean: requires a scalar probability distribution.> mean (pd)
830s ***** error <median: requires a scalar probability distribution.> median (pd)
830s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
830s ***** error <plot: requires a scalar probability distribution.> plot (pd)
830s ***** error <random: requires a scalar probability distribution.> random (pd)
830s ***** error <std: requires a scalar probability distribution.> std (pd)
830s ***** error <truncate: requires a scalar probability distribution.> ...
830s  truncate (pd, 2, 4)
830s ***** error <var: requires a scalar probability distribution.> var (pd)
830s 69 tests, 69 passed, 0 known failure, 0 skipped
830s [inst/dist_obj/UniformDistribution.m]
830s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_obj/UniformDistribution.m
830s ***** shared pd, t
830s  pd = UniformDistribution (0, 5);
830s  t = truncate (pd, 2, 4);
830s ***** assert (cdf (pd, [0:5]), [0, 0.2, 0.4, 0.6, 0.8, 1], 1e-4);
830s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.5, 1, 1], 1e-4);
831s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.3, 0.4, 0.6, 0.8, NaN], 1e-4);
831s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.5, 1, NaN], 1e-4);
831s ***** assert (icdf (pd, [0:0.2:1]), [0, 1, 2, 3, 4, 5], 1e-4);
831s ***** assert (icdf (t, [0:0.2:1]), [2, 2.4, 2.8, 3.2, 3.6, 4], 1e-4);
831s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 2, 3, 4, 5, NaN], 1e-4);
831s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.8, 3.2, 3.6, 4, NaN], 1e-4);
831s ***** assert (iqr (pd), 2.5, 1e-14);
831s ***** assert (iqr (t), 1, 1e-14);
831s ***** assert (mean (pd), 2.5, 1e-14);
831s ***** assert (mean (t), 3, 1e-14);
831s ***** assert (median (pd), 2.5, 1e-14);
831s ***** assert (median (t), 3, 1e-14);
831s ***** assert (pdf (pd, [0:5]), [0.2, 0.2, 0.2, 0.2, 0.2, 0.2], 1e-4);
831s ***** assert (pdf (t, [0:5]), [0, 0, 0.5, 0.5, 0.5, 0], 1e-4);
831s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.2, NaN], 1e-4);
831s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
831s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
831s ***** assert (any (random (t, 1000, 1) < 2), false);
831s ***** assert (any (random (t, 1000, 1) > 4), false);
831s ***** assert (std (pd), 1.4434, 1e-4);
831s ***** assert (std (t), 0.5774, 1e-4);
831s ***** assert (var (pd), 2.0833, 1e-4);
831s ***** assert (var (t), 0.3333, 1e-4);
831s ***** error <UniformDistribution: LOWER must be a real scalar.> ...
831s  UniformDistribution (i, 1)
831s ***** error <UniformDistribution: LOWER must be a real scalar.> ...
831s  UniformDistribution (Inf, 1)
831s ***** error <UniformDistribution: LOWER must be a real scalar.> ...
831s  UniformDistribution ([1, 2], 1)
831s ***** error <UniformDistribution: LOWER must be a real scalar.> ...
831s  UniformDistribution ("a", 1)
831s ***** error <UniformDistribution: LOWER must be a real scalar.> ...
831s  UniformDistribution (NaN, 1)
831s ***** error <UniformDistribution: UPPER must be a real scalar.> ...
831s  UniformDistribution (1, i)
831s ***** error <UniformDistribution: UPPER must be a real scalar.> ...
831s  UniformDistribution (1, Inf)
831s ***** error <UniformDistribution: UPPER must be a real scalar.> ...
831s  UniformDistribution (1, [1, 2])
831s ***** error <UniformDistribution: UPPER must be a real scalar.> ...
831s  UniformDistribution (1, "a")
831s ***** error <UniformDistribution: UPPER must be a real scalar.> ...
831s  UniformDistribution (1, NaN)
831s ***** error <UniformDistribution: LOWER must be less than UPPER.> ...
831s  UniformDistribution (2, 1)
831s ***** error <cdf: invalid argument for upper tail.> ...
831s  cdf (UniformDistribution, 2, "uper")
831s ***** error <cdf: invalid argument for upper tail.> ...
831s  cdf (UniformDistribution, 2, 3)
831s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
831s  plot (UniformDistribution, "Parent")
831s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
831s  plot (UniformDistribution, "PlotType", 12)
831s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
831s  plot (UniformDistribution, "PlotType", {"pdf", "cdf"})
831s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
831s  plot (UniformDistribution, "PlotType", "pdfcdf")
831s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
831s  plot (UniformDistribution, "Discrete", "pdfcdf")
831s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
831s  plot (UniformDistribution, "Discrete", [1, 0])
831s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
831s  plot (UniformDistribution, "Discrete", {true})
831s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
831s  plot (UniformDistribution, "Parent", 12)
831s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
831s  plot (UniformDistribution, "Parent", "hax")
831s ***** error <plot: invalid NAME for optional argument.> ...
831s  plot (UniformDistribution, "invalidNAME", "pdf")
831s ***** error <plot: 'probability' PlotType is not supported for 'UniformDistribution'.> ...
831s  plot (UniformDistribution, "PlotType", "probability")
831s ***** error <truncate: missing input argument.> ...
831s  truncate (UniformDistribution)
831s ***** error <truncate: missing input argument.> ...
831s  truncate (UniformDistribution, 2)
831s ***** error <truncate: invalid lower upper limits.> ...
831s  truncate (UniformDistribution, 4, 2)
831s ***** shared pd
831s  pd = UniformDistribution (0, 1);
831s  pd(2) = UniformDistribution (0, 2);
831s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
831s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
831s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
831s ***** error <mean: requires a scalar probability distribution.> mean (pd)
831s ***** error <median: requires a scalar probability distribution.> median (pd)
831s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
831s ***** error <plot: requires a scalar probability distribution.> plot (pd)
831s ***** error <random: requires a scalar probability distribution.> random (pd)
831s ***** error <std: requires a scalar probability distribution.> std (pd)
831s ***** error <truncate: requires a scalar probability distribution.> ...
831s  truncate (pd, 2, 4)
831s ***** error <var: requires a scalar probability distribution.> var (pd)
831s 63 tests, 63 passed, 0 known failure, 0 skipped
831s [inst/dist_obj/WeibullDistribution.m]
831s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_obj/WeibullDistribution.m
831s ***** shared pd, t
831s  pd = WeibullDistribution;
831s  t = truncate (pd, 2, 4);
831s ***** assert (cdf (pd, [0:5]), [0, 0.6321, 0.8647, 0.9502, 0.9817, 0.9933], 1e-4);
831s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7311, 1, 1], 1e-4);
831s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.7769, 0.8647, 0.9502, 0.9817, NaN], 1e-4);
831s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.7311, 1, NaN], 1e-4);
831s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.2231, 0.5108, 0.9163, 1.6094, Inf], 1e-4);
831s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1899, 2.4244, 2.7315, 3.1768, 4], 1e-4);
831s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5108, 0.9163, 1.6094, Inf, NaN], 1e-4);
831s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4244, 2.7315, 3.1768, 4, NaN], 1e-4);
831s ***** assert (iqr (pd), 1.0986, 1e-4);
831s ***** assert (iqr (t), 0.8020, 1e-4);
831s ***** assert (mean (pd), 1, 1e-14);
831s ***** assert (mean (t), 2.6870, 1e-4);
831s ***** assert (median (pd), 0.6931, 1e-4);
831s ***** assert (median (t), 2.5662, 1e-4);
831s ***** assert (pdf (pd, [0:5]), [1, 0.3679, 0.1353, 0.0498, 0.0183, 0.0067], 1e-4);
831s ***** assert (pdf (t, [0:5]), [0, 0, 1.1565, 0.4255, 0.1565, 0], 1e-4);
831s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.2231, NaN], 1e-4);
831s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
831s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
831s ***** assert (any (random (t, 1000, 1) < 2), false);
831s ***** assert (any (random (t, 1000, 1) > 4), false);
831s ***** assert (std (pd), 1, 1e-14);
831s ***** assert (std (t), 0.5253, 1e-4);
831s ***** assert (var (pd), 1, 1e-14);
831s ***** assert (var (t), 0.2759, 1e-4);
831s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
831s  WeibullDistribution(0, 1)
831s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
831s  WeibullDistribution(-1, 1)
831s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
831s  WeibullDistribution(Inf, 1)
831s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
831s  WeibullDistribution(i, 1)
831s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
831s  WeibullDistribution("a", 1)
831s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
831s  WeibullDistribution([1, 2], 1)
831s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
831s  WeibullDistribution(NaN, 1)
831s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
831s  WeibullDistribution(1, 0)
831s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
831s  WeibullDistribution(1, -1)
831s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
831s  WeibullDistribution(1, Inf)
831s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
831s  WeibullDistribution(1, i)
831s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
831s  WeibullDistribution(1, "a")
831s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
831s  WeibullDistribution(1, [1, 2])
831s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
831s  WeibullDistribution(1, NaN)
831s ***** error <cdf: invalid argument for upper tail.> ...
831s  cdf (WeibullDistribution, 2, "uper")
831s ***** error <cdf: invalid argument for upper tail.> ...
831s  cdf (WeibullDistribution, 2, 3)
831s ***** shared x
831s  x = wblrnd (1, 1, [1, 100]);
831s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
831s  paramci (WeibullDistribution.fit (x), "alpha")
831s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
831s  paramci (WeibullDistribution.fit (x), "alpha", 0)
831s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
831s  paramci (WeibullDistribution.fit (x), "alpha", 1)
831s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
831s  paramci (WeibullDistribution.fit (x), "alpha", [0.5 2])
831s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
831s  paramci (WeibullDistribution.fit (x), "alpha", "")
831s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
831s  paramci (WeibullDistribution.fit (x), "alpha", {0.05})
831s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
831s  paramci (WeibullDistribution.fit (x), "parameter", "k", "alpha", {0.05})
831s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
831s  paramci (WeibullDistribution.fit (x), "parameter", {"lambda", "k", "param"})
831s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
831s  paramci (WeibullDistribution.fit (x), "alpha", 0.01, ...
831s           "parameter", {"lambda", "k", "param"})
831s ***** error <paramci: unknown distribution parameter.> ...
831s  paramci (WeibullDistribution.fit (x), "parameter", "param")
831s ***** error <paramci: unknown distribution parameter.> ...
831s  paramci (WeibullDistribution.fit (x), "alpha", 0.01, "parameter", "param")
831s ***** error <paramci: invalid NAME for optional argument.> ...
831s  paramci (WeibullDistribution.fit (x), "NAME", "value")
831s ***** error <paramci: invalid NAME for optional argument.> ...
831s  paramci (WeibullDistribution.fit (x), "alpha", 0.01, "NAME", "value")
831s ***** error <paramci: invalid NAME for optional argument.> ...
831s  paramci (WeibullDistribution.fit (x), "alpha", 0.01, "parameter", "k", ...
831s           "NAME", "value")
831s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
831s  plot (WeibullDistribution, "Parent")
831s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
831s  plot (WeibullDistribution, "PlotType", 12)
831s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
831s  plot (WeibullDistribution, "PlotType", {"pdf", "cdf"})
831s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
831s  plot (WeibullDistribution, "PlotType", "pdfcdf")
831s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
831s  plot (WeibullDistribution, "Discrete", "pdfcdf")
831s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
831s  plot (WeibullDistribution, "Discrete", [1, 0])
831s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
831s  plot (WeibullDistribution, "Discrete", {true})
831s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
831s  plot (WeibullDistribution, "Parent", 12)
831s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
831s  plot (WeibullDistribution, "Parent", "hax")
831s ***** error <plot: invalid NAME for optional argument.> ...
831s  plot (WeibullDistribution, "invalidNAME", "pdf")
831s ***** error <plot: no fitted DATA to plot a probability plot.> ...
831s  plot (WeibullDistribution, "PlotType", "probability")
831s ***** error <proflik: no fitted data available.> ...
831s  proflik (WeibullDistribution, 2)
831s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
831s  proflik (WeibullDistribution.fit (x), 3)
831s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
831s  proflik (WeibullDistribution.fit (x), [1, 2])
831s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
831s  proflik (WeibullDistribution.fit (x), {1})
831s ***** error <proflik: SETPARAM must be a numeric vector.> ...
831s  proflik (WeibullDistribution.fit (x), 1, ones (2))
831s ***** error <proflik: missing VALUE for 'Display' argument.> ...
831s  proflik (WeibullDistribution.fit (x), 1, "Display")
831s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
831s  proflik (WeibullDistribution.fit (x), 1, "Display", 1)
831s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
831s  proflik (WeibullDistribution.fit (x), 1, "Display", {1})
831s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
831s  proflik (WeibullDistribution.fit (x), 1, "Display", {"on"})
831s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
831s  proflik (WeibullDistribution.fit (x), 1, "Display", ["on"; "on"])
831s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
831s  proflik (WeibullDistribution.fit (x), 1, "Display", "onnn")
831s ***** error <proflik: invalid NAME for optional arguments.> ...
831s  proflik (WeibullDistribution.fit (x), 1, "NAME", "on")
831s ***** error <proflik: invalid optional argument.> ...
831s  proflik (WeibullDistribution.fit (x), 1, {"NAME"}, "on")
831s ***** error <proflik: invalid optional argument.> ...
831s  proflik (WeibullDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
831s ***** error <truncate: missing input argument.> ...
831s  truncate (WeibullDistribution)
831s ***** error <truncate: missing input argument.> ...
831s  truncate (WeibullDistribution, 2)
831s ***** error <truncate: invalid lower upper limits.> ...
831s  truncate (WeibullDistribution, 4, 2)
831s ***** shared pd
831s  pd = WeibullDistribution(1, 1);
831s  pd(2) = WeibullDistribution(1, 3);
831s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
831s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
831s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
831s ***** error <mean: requires a scalar probability distribution.> mean (pd)
831s ***** error <median: requires a scalar probability distribution.> median (pd)
831s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
831s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
831s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
831s ***** error <plot: requires a scalar probability distribution.> plot (pd)
831s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
831s ***** error <random: requires a scalar probability distribution.> random (pd)
831s ***** error <std: requires a scalar probability distribution.> std (pd)
831s ***** error <truncate: requires a scalar probability distribution.> ...
831s  truncate (pd, 2, 4)
831s ***** error <var: requires a scalar probability distribution.> var (pd)
831s 97 tests, 97 passed, 0 known failure, 0 skipped
831s [inst/dist_obj/tLocationScaleDistribution.m]
831s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_obj/tLocationScaleDistribution.m
831s ***** shared pd, t
831s  pd = tLocationScaleDistribution;
831s  t = truncate (pd, 2, 4);
831s ***** assert (cdf (pd, [0:5]), [0.5, 0.8184, 0.9490, 0.9850, 0.9948, 0.9979], 1e-4);
831s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7841, 1, 1], 1e-4);
831s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.9030, 0.9490, 0.9850, 0.9948, NaN], 1e-4);
831s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.7841, 1, NaN], 1e-4);
831s ***** assert (icdf (pd, [0:0.2:1]), [-Inf, -0.9195, -0.2672, 0.2672, 0.9195, Inf], 1e-4);
831s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1559, 2.3533, 2.6223, 3.0432, 4], 1e-4);
831s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, -0.2672, 0.2672, 0.9195, Inf, NaN], 1e-4);
831s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.3533, 2.6223, 3.0432, 4, NaN], 1e-4);
831s ***** assert (iqr (pd), 1.4534, 1e-4);
831s ***** assert (iqr (t), 0.7139, 1e-4);
831s ***** assert (mean (pd), 0, eps);
831s ***** assert (mean (t), 2.6099, 1e-4);
831s ***** assert (median (pd), 0, eps);
831s ***** assert (median (t), 2.4758, 1e-4);
831s ***** assert (pdf (pd, [0:5]), [0.3796, 0.2197, 0.0651, 0.0173, 0.0051, 0.0018], 1e-4);
831s ***** assert (pdf (t, [0:5]), [0, 0, 1.4209, 0.3775, 0.1119, 0], 1e-4);
831s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0.2197, 0.1245, NaN], 1e-4);
831s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
831s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
831s ***** assert (any (random (t, 1000, 1) < 2), false);
831s ***** assert (any (random (t, 1000, 1) > 4), false);
831s ***** assert (std (pd), 1.2910, 1e-4);
832s ***** assert (std (t), 0.4989, 1e-4);
832s ***** assert (var (pd), 1.6667, 1e-4);
832s ***** assert (var (t), 0.2489, 1e-4);
832s ***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
832s  tLocationScaleDistribution(i, 1, 1)
832s ***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
832s  tLocationScaleDistribution(Inf, 1, 1)
832s ***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
832s  tLocationScaleDistribution([1, 2], 1, 1)
832s ***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
832s  tLocationScaleDistribution("a", 1, 1)
832s ***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
832s  tLocationScaleDistribution(NaN, 1, 1)
832s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
832s  tLocationScaleDistribution(0, 0, 1)
832s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
832s  tLocationScaleDistribution(0, -1, 1)
832s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
832s  tLocationScaleDistribution(0, Inf, 1)
832s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
832s  tLocationScaleDistribution(0, i, 1)
832s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
832s  tLocationScaleDistribution(0, "a", 1)
832s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
832s  tLocationScaleDistribution(0, [1, 2], 1)
832s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
832s  tLocationScaleDistribution(0, NaN, 1)
832s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
832s  tLocationScaleDistribution(0, 1, 0)
832s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
832s  tLocationScaleDistribution(0, 1, -1)
832s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
832s  tLocationScaleDistribution(0, 1, Inf)
832s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
832s  tLocationScaleDistribution(0, 1, i)
832s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
832s  tLocationScaleDistribution(0, 1, "a")
832s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
832s  tLocationScaleDistribution(0, 1, [1, 2])
832s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
832s  tLocationScaleDistribution(0, 1, NaN)
832s ***** error <cdf: invalid argument for upper tail.> ...
832s  cdf (tLocationScaleDistribution, 2, "uper")
832s ***** error <cdf: invalid argument for upper tail.> ...
832s  cdf (tLocationScaleDistribution, 2, 3)
832s ***** shared x
832s  x = tlsrnd (0, 1, 1, [1, 100]);
832s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
832s  paramci (tLocationScaleDistribution.fit (x), "alpha")
832s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
832s  paramci (tLocationScaleDistribution.fit (x), "alpha", 0)
832s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
832s  paramci (tLocationScaleDistribution.fit (x), "alpha", 1)
832s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
832s  paramci (tLocationScaleDistribution.fit (x), "alpha", [0.5 2])
832s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
832s  paramci (tLocationScaleDistribution.fit (x), "alpha", "")
832s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
832s  paramci (tLocationScaleDistribution.fit (x), "alpha", {0.05})
832s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
832s  paramci (tLocationScaleDistribution.fit (x), "parameter", "mu", ...
832s           "alpha", {0.05})
832s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
832s  paramci (tLocationScaleDistribution.fit (x), ...
832s           "parameter", {"mu", "sigma", "nu", "param"})
832s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
832s  paramci (tLocationScaleDistribution.fit (x), "alpha", 0.01, ...
832s           "parameter", {"mu", "sigma", "nu", "param"})
833s ***** error <paramci: unknown distribution parameter.> ...
833s  paramci (tLocationScaleDistribution.fit (x), "parameter", "param")
833s ***** error <paramci: unknown distribution parameter.> ...
833s  paramci (tLocationScaleDistribution.fit (x), "alpha", 0.01, ...
833s           "parameter", "param")
833s ***** error <paramci: invalid NAME for optional argument.> ...
833s  paramci (tLocationScaleDistribution.fit (x), "NAME", "value")
833s ***** error <paramci: invalid NAME for optional argument.> ...
833s  paramci (tLocationScaleDistribution.fit (x), "alpha", 0.01, "NAME", "value")
833s ***** error <paramci: invalid NAME for optional argument.> ...
833s  paramci (tLocationScaleDistribution.fit (x), "alpha", 0.01, ...
833s           "parameter", "mu", "NAME", "value")
833s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
833s  plot (tLocationScaleDistribution, "Parent")
833s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
833s  plot (tLocationScaleDistribution, "PlotType", 12)
833s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
833s  plot (tLocationScaleDistribution, "PlotType", {"pdf", "cdf"})
833s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
833s  plot (tLocationScaleDistribution, "PlotType", "pdfcdf")
833s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
833s  plot (tLocationScaleDistribution, "Discrete", "pdfcdf")
833s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
833s  plot (tLocationScaleDistribution, "Discrete", [1, 0])
833s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
833s  plot (tLocationScaleDistribution, "Discrete", {true})
833s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
833s  plot (tLocationScaleDistribution, "Parent", 12)
833s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
833s  plot (tLocationScaleDistribution, "Parent", "hax")
833s ***** error <plot: invalid NAME for optional argument.> ...
833s  plot (tLocationScaleDistribution, "invalidNAME", "pdf")
833s ***** error <plot: no fitted DATA to plot a probability plot.> ...
833s  plot (tLocationScaleDistribution, "PlotType", "probability")
833s ***** error <proflik: no fitted data available.> ...
833s  proflik (tLocationScaleDistribution, 2)
833s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
833s  proflik (tLocationScaleDistribution.fit (x), 4)
833s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
833s  proflik (tLocationScaleDistribution.fit (x), [1, 2])
833s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
833s  proflik (tLocationScaleDistribution.fit (x), {1})
833s ***** error <proflik: SETPARAM must be a numeric vector.> ...
833s  proflik (tLocationScaleDistribution.fit (x), 1, ones (2))
833s ***** error <proflik: missing VALUE for 'Display' argument.> ...
833s  proflik (tLocationScaleDistribution.fit (x), 1, "Display")
834s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
834s  proflik (tLocationScaleDistribution.fit (x), 1, "Display", 1)
834s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
834s  proflik (tLocationScaleDistribution.fit (x), 1, "Display", {1})
834s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
834s  proflik (tLocationScaleDistribution.fit (x), 1, "Display", {"on"})
834s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
834s  proflik (tLocationScaleDistribution.fit (x), 1, "Display", ["on"; "on"])
834s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
834s  proflik (tLocationScaleDistribution.fit (x), 1, "Display", "onnn")
834s ***** error <proflik: invalid NAME for optional arguments.> ...
834s  proflik (tLocationScaleDistribution.fit (x), 1, "NAME", "on")
834s ***** error <proflik: invalid optional argument.> ...
834s  proflik (tLocationScaleDistribution.fit (x), 1, {"NAME"}, "on")
834s ***** error <proflik: invalid optional argument.> ...
834s  proflik (tLocationScaleDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
834s ***** error <truncate: missing input argument.> ...
834s  truncate (tLocationScaleDistribution)
834s ***** error <truncate: missing input argument.> ...
834s  truncate (tLocationScaleDistribution, 2)
834s ***** error <truncate: invalid lower upper limits.> ...
834s  truncate (tLocationScaleDistribution, 4, 2)
834s ***** shared pd
834s  pd = tLocationScaleDistribution (0, 1, 1);
834s  pd(2) = tLocationScaleDistribution (0, 1, 3);
834s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
834s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
834s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
834s ***** error <mean: requires a scalar probability distribution.> mean (pd)
834s ***** error <median: requires a scalar probability distribution.> median (pd)
834s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
834s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
834s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
834s ***** error <plot: requires a scalar probability distribution.> plot (pd)
834s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
834s ***** error <random: requires a scalar probability distribution.> random (pd)
834s ***** error <std: requires a scalar probability distribution.> std (pd)
834s ***** error <truncate: requires a scalar probability distribution.> ...
834s  truncate (pd, 2, 4)
834s ***** error <var: requires a scalar probability distribution.> var (pd)
834s 102 tests, 102 passed, 0 known failure, 0 skipped
834s [inst/dist_stat/betastat.m]
834s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/betastat.m
834s ***** error<betastat: function called with too few input arguments.> betastat ()
834s ***** error<betastat: function called with too few input arguments.> betastat (1)
834s ***** error<betastat: A and B must be numeric.> betastat ({}, 2)
834s ***** error<betastat: A and B must be numeric.> betastat (1, "")
834s ***** error<betastat: A and B must not be complex.> betastat (i, 2)
834s ***** error<betastat: A and B must not be complex.> betastat (1, i)
834s ***** error<betastat: A and B must be of common size or scalars.> ...
834s  betastat (ones (3), ones (2))
834s ***** error<betastat: A and B must be of common size or scalars.> ...
834s  betastat (ones (2), ones (3))
834s ***** test
834s  a = -2:6;
834s  b = 0.4:0.2:2;
834s  [m, v] = betastat (a, b);
834s  expected_m = [NaN NaN NaN 1/2 2/3.2 3/4.4 4/5.6 5/6.8 6/8];
834s  expected_v = [NaN NaN NaN 0.0833, 0.0558, 0.0402, 0.0309, 0.0250, 0.0208];
834s  assert (m, expected_m, eps*100);
834s  assert (v, expected_v, 0.001);
834s ***** test
834s  a = -2:1:6;
834s  [m, v] = betastat (a, 1.5);
834s  expected_m = [NaN NaN NaN 1/2.5 2/3.5 3/4.5 4/5.5 5/6.5 6/7.5];
834s  expected_v = [NaN NaN NaN 0.0686, 0.0544, 0.0404, 0.0305, 0.0237, 0.0188];
834s  assert (m, expected_m);
834s  assert (v, expected_v, 0.001);
834s ***** test
834s  a = [14  Inf   10  NaN  10];
834s  b = [12    9  NaN  Inf  12];
834s  [m, v] = betastat (a, b);
834s  expected_m = [14/26 NaN NaN NaN 10/22];
834s  expected_v = [168/18252 NaN NaN NaN 120/11132];
834s  assert (m, expected_m);
834s  assert (v, expected_v);
834s ***** assert (nthargout (1:2, @betastat, 5, []), {[], []})
834s ***** assert (nthargout (1:2, @betastat, [], 5), {[], []})
834s ***** assert (size (betastat (rand (10, 5, 4), rand (10, 5, 4))), [10 5 4])
834s ***** assert (size (betastat (rand (10, 5, 4), 7)), [10 5 4])
834s 15 tests, 15 passed, 0 known failure, 0 skipped
834s [inst/dist_stat/binostat.m]
834s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/binostat.m
834s ***** error<binostat: function called with too few input arguments.> binostat ()
834s ***** error<binostat: function called with too few input arguments.> binostat (1)
834s ***** error<binostat: N and PS must be numeric.> binostat ({}, 2)
834s ***** error<binostat: N and PS must be numeric.> binostat (1, "")
834s ***** error<binostat: N and PS must not be complex.> binostat (i, 2)
834s ***** error<binostat: N and PS must not be complex.> binostat (1, i)
834s ***** error<binostat: N and PS must be of common size or scalars.> ...
834s  binostat (ones (3), ones (2))
835s ***** error<binostat: N and PS must be of common size or scalars.> ...
835s  binostat (ones (2), ones (3))
835s ***** test
835s  n = 1:6;
835s  ps = 0:0.2:1;
835s  [m, v] = binostat (n, ps);
835s  expected_m = [0.00, 0.40, 1.20, 2.40, 4.00, 6.00];
835s  expected_v = [0.00, 0.32, 0.72, 0.96, 0.80, 0.00];
835s  assert (m, expected_m, 0.001);
835s  assert (v, expected_v, 0.001);
835s ***** test
835s  n = 1:6;
835s  [m, v] = binostat (n, 0.5);
835s  expected_m = [0.50, 1.00, 1.50, 2.00, 2.50, 3.00];
835s  expected_v = [0.25, 0.50, 0.75, 1.00, 1.25, 1.50];
835s  assert (m, expected_m, 0.001);
835s  assert (v, expected_v, 0.001);
835s ***** test
835s  n = [-Inf -3 5 0.5 3 NaN 100, Inf];
835s  [m, v] = binostat (n, 0.5);
835s  assert (isnan (m), [true true false true false true false false])
835s  assert (isnan (v), [true true false true false true false false])
835s  assert (m(end), Inf);
835s  assert (v(end), Inf);
835s ***** assert (nthargout (1:2, @binostat, 5, []), {[], []})
835s ***** assert (nthargout (1:2, @binostat, [], 5), {[], []})
835s ***** assert (size (binostat (randi (100, 10, 5, 4), rand (10, 5, 4))), [10 5 4])
835s ***** assert (size (binostat (randi (100, 10, 5, 4), 7)), [10 5 4])
835s 15 tests, 15 passed, 0 known failure, 0 skipped
835s [inst/dist_stat/bisastat.m]
835s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/bisastat.m
835s ***** error<bisastat: function called with too few input arguments.> bisastat ()
835s ***** error<bisastat: function called with too few input arguments.> bisastat (1)
835s ***** error<bisastat: BETA and GAMMA must be numeric.> bisastat ({}, 2)
835s ***** error<bisastat: BETA and GAMMA must be numeric.> bisastat (1, "")
835s ***** error<bisastat: BETA and GAMMA must not be complex.> bisastat (i, 2)
835s ***** error<bisastat: BETA and GAMMA must not be complex.> bisastat (1, i)
835s ***** error<bisastat: BETA and GAMMA must be of common size or scalars.> ...
835s  bisastat (ones (3), ones (2))
835s ***** error<bisastat: BETA and GAMMA must be of common size or scalars.> ...
835s  bisastat (ones (2), ones (3))
835s ***** test
835s  beta = 1:6;
835s  gamma = 1:0.2:2;
835s  [m, v] = bisastat (beta, gamma);
835s  expected_m = [1.50, 3.44, 5.94,  9.12,  13.10, 18];
835s  expected_v = [2.25, 16.128, 60.858, 172.032, 409.050, 864];
835s  assert (m, expected_m, 1e-2);
835s  assert (v, expected_v, 1e-3);
835s ***** test
835s  beta = 1:6;
835s  [m, v] = bisastat (beta, 1.5);
835s  expected_m = [2.125, 4.25, 6.375, 8.5, 10.625, 12.75];
835s  expected_v = [8.5781, 34.3125, 77.2031, 137.2500, 214.4531, 308.8125];
835s  assert (m, expected_m, 1e-3);
835s  assert (v, expected_v, 1e-4);
835s 10 tests, 10 passed, 0 known failure, 0 skipped
835s [inst/dist_stat/burrstat.m]
835s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/burrstat.m
835s ***** error<burrstat: function called with too few input arguments.> burrstat ()
835s ***** error<burrstat: function called with too few input arguments.> burrstat (1)
835s ***** error<burrstat: function called with too few input arguments.> burrstat (1, 2)
835s ***** error<burrstat: LAMBDA, C, and K must be numeric.> burrstat ({}, 2, 3)
835s ***** error<burrstat: LAMBDA, C, and K must be numeric.> burrstat (1, "", 3)
835s ***** error<burrstat: LAMBDA, C, and K must be numeric.> burrstat (1, 2, "")
835s ***** error<burrstat: LAMBDA, C, and K must not be complex.> burrstat (i, 2, 3)
835s ***** error<burrstat: LAMBDA, C, and K must not be complex.> burrstat (1, i, 3)
835s ***** error<burrstat: LAMBDA, C, and K must not be complex.> burrstat (1, 2, i)
835s ***** error<burrstat: LAMBDA, C, and K must be of common size or scalars.> ...
835s  burrstat (ones (3), ones (2), 3)
835s ***** error<burrstat: LAMBDA, C, and K must be of common size or scalars.> ...
835s  burrstat (ones (2), 2, ones (3))
835s ***** error<burrstat: LAMBDA, C, and K must be of common size or scalars.> ...
835s  burrstat (1, ones (2), ones (3))
835s ***** test
835s  [m, v] = burrstat (1, 2, 5);
835s  assert (m, 0.4295, 1e-4);
835s  assert (v, 0.0655, 1e-4);
835s ***** test
835s  [m, v] = burrstat (1, 1, 1);
835s  assert (m, Inf);
835s  assert (v, Inf);
835s ***** test
835s  [m, v] = burrstat (2, 4, 1);
835s  assert (m, 2.2214, 1e-4);
835s  assert (v, 1.3484, 1e-4);
835s 15 tests, 15 passed, 0 known failure, 0 skipped
835s [inst/dist_stat/chi2stat.m]
835s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/chi2stat.m
835s ***** error<chi2stat: function called with too few input arguments.> chi2stat ()
835s ***** error<chi2stat: DF must be numeric.> chi2stat ({})
835s ***** error<chi2stat: DF must be numeric.> chi2stat ("")
835s ***** error<chi2stat: DF must not be complex.> chi2stat (i)
835s ***** test
835s  df = 1:6;
835s  [m, v] = chi2stat (df);
835s  assert (m, df);
835s  assert (v, [2, 4, 6, 8, 10, 12], 0.001);
835s 5 tests, 5 passed, 0 known failure, 0 skipped
835s [inst/dist_stat/evstat.m]
835s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/evstat.m
835s ***** error<evstat: function called with too few input arguments.> evstat ()
835s ***** error<evstat: function called with too few input arguments.> evstat (1)
835s ***** error<evstat: MU and SIGMA must be numeric.> evstat ({}, 2)
835s ***** error<evstat: MU and SIGMA must be numeric.> evstat (1, "")
835s ***** error<evstat: MU and SIGMA must not be complex.> evstat (i, 2)
835s ***** error<evstat: MU and SIGMA must not be complex.> evstat (1, i)
835s ***** error<evstat: MU and SIGMA must be of common size or scalars.> ...
835s  evstat (ones (3), ones (2))
835s ***** error<evstat: MU and SIGMA must be of common size or scalars.> ...
835s  evstat (ones (2), ones (3))
835s ***** shared x, y0, y1
835s  x = [-5, 0, 1, 2, 3];
835s  y0 = [NaN, NaN, 0.4228, 0.8456, 1.2684];
835s  y1 = [-5.5772, -3.4633, -3.0405, -2.6177, -2.1949];
835s ***** assert (evstat (x, x), y0, 1e-4)
835s ***** assert (evstat (x, x+6), y1, 1e-4)
835s ***** assert (evstat (x, x-6), NaN (1,5))
835s 11 tests, 11 passed, 0 known failure, 0 skipped
835s [inst/dist_stat/expstat.m]
835s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/expstat.m
835s ***** error<expstat: function called with too few input arguments.> expstat ()
835s ***** error<expstat: MU must be numeric.> expstat ({})
835s ***** error<expstat: MU must be numeric.> expstat ("")
835s ***** error<expstat: MU must not be complex.> expstat (i)
835s ***** test
835s  mu = 1:6;
835s  [m, v] = expstat (mu);
835s  assert (m, [1, 2, 3, 4, 5, 6], 0.001);
835s  assert (v, [1, 4, 9, 16, 25, 36], 0.001);
835s 5 tests, 5 passed, 0 known failure, 0 skipped
835s [inst/dist_stat/fstat.m]
835s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/fstat.m
835s ***** error<fstat: function called with too few input arguments.> fstat ()
835s ***** error<fstat: function called with too few input arguments.> fstat (1)
835s ***** error<fstat: DF1 and DF2 must be numeric.> fstat ({}, 2)
835s ***** error<fstat: DF1 and DF2 must be numeric.> fstat (1, "")
835s ***** error<fstat: DF1 and DF2 must not be complex.> fstat (i, 2)
835s ***** error<fstat: DF1 and DF2 must not be complex.> fstat (1, i)
835s ***** error<fstat: DF1 and DF2 must be of common size or scalars.> ...
835s  fstat (ones (3), ones (2))
835s ***** error<fstat: DF1 and DF2 must be of common size or scalars.> ...
835s  fstat (ones (2), ones (3))
835s ***** test
835s  df1 = 1:6;
835s  df2 = 5:10;
835s  [m, v] = fstat (df1, df2);
835s  expected_mn = [1.6667, 1.5000, 1.4000, 1.3333, 1.2857, 1.2500];
835s  expected_v = [22.2222, 6.7500, 3.4844, 2.2222, 1.5869, 1.2153];
835s  assert (m, expected_mn, 0.001);
835s  assert (v, expected_v, 0.001);
835s ***** test
835s  df1 = 1:6;
835s  [m, v] = fstat (df1, 5);
835s  expected_mn = [1.6667, 1.6667, 1.6667, 1.6667, 1.6667, 1.6667];
835s  expected_v = [22.2222, 13.8889, 11.1111, 9.7222, 8.8889, 8.3333];
835s  assert (m, expected_mn, 0.001);
835s  assert (v, expected_v, 0.001);
835s 10 tests, 10 passed, 0 known failure, 0 skipped
835s [inst/dist_stat/gamstat.m]
835s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/gamstat.m
835s ***** error<gamstat: function called with too few input arguments.> gamstat ()
835s ***** error<gamstat: function called with too few input arguments.> gamstat (1)
835s ***** error<gamstat: A and B must be numeric.> gamstat ({}, 2)
835s ***** error<gamstat: A and B must be numeric.> gamstat (1, "")
835s ***** error<gamstat: A and B must not be complex.> gamstat (i, 2)
835s ***** error<gamstat: A and B must not be complex.> gamstat (1, i)
835s ***** error<gamstat: A and B must be of common size or scalars.> ...
835s  gamstat (ones (3), ones (2))
835s ***** error<gamstat: A and B must be of common size or scalars.> ...
835s  gamstat (ones (2), ones (3))
835s ***** test
835s  a = 1:6;
835s  b = 1:0.2:2;
835s  [m, v] = gamstat (a, b);
835s  expected_m = [1.00, 2.40, 4.20,  6.40,  9.00, 12.00];
835s  expected_v = [1.00, 2.88, 5.88, 10.24, 16.20, 24.00];
835s  assert (m, expected_m, 0.001);
835s  assert (v, expected_v, 0.001);
835s ***** test
835s  a = 1:6;
835s  [m, v] = gamstat (a, 1.5);
835s  expected_m = [1.50, 3.00, 4.50, 6.00,  7.50,  9.00];
835s  expected_v = [2.25, 4.50, 6.75, 9.00, 11.25, 13.50];
835s  assert (m, expected_m, 0.001);
835s  assert (v, expected_v, 0.001);
835s 10 tests, 10 passed, 0 known failure, 0 skipped
835s [inst/dist_stat/geostat.m]
835s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/geostat.m
835s ***** error<geostat: function called with too few input arguments.> geostat ()
835s ***** error<geostat: PS must be numeric.> geostat ({})
835s ***** error<geostat: PS must be numeric.> geostat ("")
835s ***** error<geostat: PS must not be complex.> geostat (i)
835s ***** test
835s  ps = 1 ./ (1:6);
835s  [m, v] = geostat (ps);
835s  assert (m, [0, 1, 2, 3, 4, 5], 0.001);
835s  assert (v, [0, 2, 6, 12, 20, 30], 0.001);
835s 5 tests, 5 passed, 0 known failure, 0 skipped
835s [inst/dist_stat/gevstat.m]
835s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/gevstat.m
835s ***** error<gevstat: function called with too few input arguments.> gevstat ()
835s ***** error<gevstat: function called with too few input arguments.> gevstat (1)
835s ***** error<gevstat: function called with too few input arguments.> gevstat (1, 2)
835s ***** error<gevstat: K, SIGMA, and MU must be numeric.> gevstat ({}, 2, 3)
835s ***** error<gevstat: K, SIGMA, and MU must be numeric.> gevstat (1, "", 3)
835s ***** error<gevstat: K, SIGMA, and MU must be numeric.> gevstat (1, 2, "")
835s ***** error<gevstat: K, SIGMA, and MU must not be complex.> gevstat (i, 2, 3)
835s ***** error<gevstat: K, SIGMA, and MU must not be complex.> gevstat (1, i, 3)
835s ***** error<gevstat: K, SIGMA, and MU must not be complex.> gevstat (1, 2, i)
835s ***** error<gevstat: K, SIGMA, and MU must be of common size or scalars.> ...
835s  gevstat (ones (3), ones (2), 3)
835s ***** error<gevstat: K, SIGMA, and MU must be of common size or scalars.> ...
835s  gevstat (ones (2), 2, ones (3))
835s ***** error<gevstat: K, SIGMA, and MU must be of common size or scalars.> ...
835s  gevstat (1, ones (2), ones (3))
835s ***** test
835s  k = [-1, -0.5, 0, 0.2, 0.4, 0.5, 1];
835s  sigma = 2;
835s  mu = 1;
835s  [m, v] = gevstat (k, sigma, mu);
835s  expected_m = [1, 1.4551, 2.1544, 2.6423, 3.4460, 4.0898, Inf];
835s  expected_v = [4, 3.4336, 6.5797, 13.3761, 59.3288, Inf, Inf];
835s  assert (m, expected_m, -0.001);
835s  assert (v, expected_v, -0.001);
835s 13 tests, 13 passed, 0 known failure, 0 skipped
835s [inst/dist_stat/gpstat.m]
835s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/gpstat.m
835s ***** error<gpstat: function called with too few input arguments.> gpstat ()
835s ***** error<gpstat: function called with too few input arguments.> gpstat (1)
835s ***** error<gpstat: function called with too few input arguments.> gpstat (1, 2)
835s ***** error<gpstat: K, SIGMA, and MU must be numeric.> gpstat ({}, 2, 3)
835s ***** error<gpstat: K, SIGMA, and MU must be numeric.> gpstat (1, "", 3)
835s ***** error<gpstat: K, SIGMA, and MU must be numeric.> gpstat (1, 2, "")
835s ***** error<gpstat: K, SIGMA, and MU must not be complex.> gpstat (i, 2, 3)
835s ***** error<gpstat: K, SIGMA, and MU must not be complex.> gpstat (1, i, 3)
835s ***** error<gpstat: K, SIGMA, and MU must not be complex.> gpstat (1, 2, i)
835s ***** error<gpstat: K, SIGMA, and MU must be of common size or scalars.> ...
835s  gpstat (ones (3), ones (2), 3)
835s ***** error<gpstat: K, SIGMA, and MU must be of common size or scalars.> ...
835s  gpstat (ones (2), 2, ones (3))
835s ***** error<gpstat: K, SIGMA, and MU must be of common size or scalars.> ...
835s  gpstat (1, ones (2), ones (3))
835s ***** shared x, y
835s  x = [-Inf, -1, 0, 1/2, 1, Inf];
835s  y = [0, 0.5, 1, 2, Inf, Inf];
835s ***** assert (gpstat (x, ones (1,6), zeros (1,6)), y, eps)
835s ***** assert (gpstat (single (x), 1, 0), single (y), eps("single"))
835s ***** assert (gpstat (x, single (1), 0), single (y), eps("single"))
835s ***** assert (gpstat (x, 1, single (0)), single (y), eps("single"))
835s ***** assert (gpstat (single ([x, NaN]), 1, 0), single ([y, NaN]), eps("single"))
835s ***** assert (gpstat ([x, NaN], single (1), 0), single ([y, NaN]), eps("single"))
835s ***** assert (gpstat ([x, NaN], 1, single (0)), single ([y, NaN]), eps("single"))
835s 19 tests, 19 passed, 0 known failure, 0 skipped
835s [inst/dist_stat/hnstat.m]
835s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/hnstat.m
835s ***** error<hnstat: function called with too few input arguments.> hnstat ()
835s ***** error<hnstat: function called with too few input arguments.> hnstat (1)
835s ***** error<hnstat: MU and SIGMA must be numeric.> hnstat ({}, 2)
835s ***** error<hnstat: MU and SIGMA must be numeric.> hnstat (1, "")
835s ***** error<hnstat: MU and SIGMA must not be complex.> hnstat (i, 2)
835s ***** error<hnstat: MU and SIGMA must not be complex.> hnstat (1, i)
835s ***** error<hnstat: MU and SIGMA must be of common size or scalars.> ...
835s  hnstat (ones (3), ones (2))
835s ***** error<hnstat: MU and SIGMA must be of common size or scalars.> ...
835s  hnstat (ones (2), ones (3))
835s ***** test
835s  [m, v] = hnstat (0, 1);
835s  assert (m, 0.7979, 1e-4);
835s  assert (v, 0.3634, 1e-4);
835s ***** test
835s  [m, v] = hnstat (2, 1);
835s  assert (m, 2.7979, 1e-4);
835s  assert (v, 0.3634, 1e-4);
835s ***** test
835s  [m, v] = hnstat (2, 2);
835s  assert (m, 3.5958, 1e-4);
835s  assert (v, 1.4535, 1e-4);
835s ***** test
835s  [m, v] = hnstat (2, 2.5);
835s  assert (m, 3.9947, 1e-4);
835s  assert (v, 2.2711, 1e-4);
835s ***** test
835s  [m, v] = hnstat (1.5, 0.5);
835s  assert (m, 1.8989, 1e-4);
835s  assert (v, 0.0908, 1e-4);
835s ***** test
835s  [m, v] = hnstat (-1.5, 0.5);
835s  assert (m, -1.1011, 1e-4);
835s  assert (v, 0.0908, 1e-4);
835s 14 tests, 14 passed, 0 known failure, 0 skipped
835s [inst/dist_stat/hygestat.m]
835s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/hygestat.m
835s ***** error<hygestat: function called with too few input arguments.> hygestat ()
835s ***** error<hygestat: function called with too few input arguments.> hygestat (1)
835s ***** error<hygestat: function called with too few input arguments.> hygestat (1, 2)
835s ***** error<hygestat: M, K, and N must be numeric.> hygestat ({}, 2, 3)
835s ***** error<hygestat: M, K, and N must be numeric.> hygestat (1, "", 3)
835s ***** error<hygestat: M, K, and N must be numeric.> hygestat (1, 2, "")
835s ***** error<hygestat: M, K, and N must not be complex.> hygestat (i, 2, 3)
835s ***** error<hygestat: M, K, and N must not be complex.> hygestat (1, i, 3)
835s ***** error<hygestat: M, K, and N must not be complex.> hygestat (1, 2, i)
835s ***** error<hygestat: M, K, and N must be of common size or scalars.> ...
835s  hygestat (ones (3), ones (2), 3)
835s ***** error<hygestat: M, K, and N must be of common size or scalars.> ...
835s  hygestat (ones (2), 2, ones (3))
835s ***** error<hygestat: M, K, and N must be of common size or scalars.> ...
835s  hygestat (1, ones (2), ones (3))
835s ***** test
835s  m = 4:9;
835s  k = 0:5;
835s  n = 1:6;
835s  [mn, v] = hygestat (m, k, n);
835s  expected_mn = [0.0000, 0.4000, 1.0000, 1.7143, 2.5000, 3.3333];
835s  expected_v = [0.0000, 0.2400, 0.4000, 0.4898, 0.5357, 0.5556];
835s  assert (mn, expected_mn, 0.001);
835s  assert (v, expected_v, 0.001);
835s ***** test
835s  m = 4:9;
835s  k = 0:5;
835s  [mn, v] = hygestat (m, k, 2);
835s  expected_mn = [0.0000, 0.4000, 0.6667, 0.8571, 1.0000, 1.1111];
835s  expected_v = [0.0000, 0.2400, 0.3556, 0.4082, 0.4286, 0.4321];
835s  assert (mn, expected_mn, 0.001);
835s  assert (v, expected_v, 0.001);
835s 14 tests, 14 passed, 0 known failure, 0 skipped
835s [inst/dist_stat/invgstat.m]
835s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/invgstat.m
835s ***** error<invgstat: function called with too few input arguments.> invgstat ()
835s ***** error<invgstat: function called with too few input arguments.> invgstat (1)
835s ***** error<invgstat: MU and LAMBDA must be numeric.> invgstat ({}, 2)
835s ***** error<invgstat: MU and LAMBDA must be numeric.> invgstat (1, "")
835s ***** error<invgstat: MU and LAMBDA must not be complex.> invgstat (i, 2)
835s ***** error<invgstat: MU and LAMBDA must not be complex.> invgstat (1, i)
835s ***** error<invgstat: MU and LAMBDA must be of common size or scalars.> ...
835s  invgstat (ones (3), ones (2))
835s ***** error<invgstat: MU and LAMBDA must be of common size or scalars.> ...
835s  invgstat (ones (2), ones (3))
835s ***** test
835s  [m, v] = invgstat (1, 1);
835s  assert (m, 1);
835s  assert (v, 1);
835s ***** test
835s  [m, v] = invgstat (2, 1);
835s  assert (m, 2);
835s  assert (v, 8);
835s ***** test
835s  [m, v] = invgstat (2, 2);
835s  assert (m, 2);
835s  assert (v, 4);
835s ***** test
835s  [m, v] = invgstat (2, 2.5);
835s  assert (m, 2);
835s  assert (v, 3.2);
835s ***** test
835s  [m, v] = invgstat (1.5, 0.5);
835s  assert (m, 1.5);
835s  assert (v, 6.75);
835s 13 tests, 13 passed, 0 known failure, 0 skipped
835s [inst/dist_stat/logistat.m]
835s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/logistat.m
835s ***** error<logistat: function called with too few input arguments.> logistat ()
835s ***** error<logistat: function called with too few input arguments.> logistat (1)
835s ***** error<logistat: MU and SIGMA must be numeric.> logistat ({}, 2)
835s ***** error<logistat: MU and SIGMA must be numeric.> logistat (1, "")
835s ***** error<logistat: MU and SIGMA must not be complex.> logistat (i, 2)
835s ***** error<logistat: MU and SIGMA must not be complex.> logistat (1, i)
835s ***** error<logistat: MU and SIGMA must be of common size or scalars.> ...
835s  logistat (ones (3), ones (2))
835s ***** error<logistat: MU and SIGMA must be of common size or scalars.> ...
835s  logistat (ones (2), ones (3))
835s ***** test
835s  [m, v] = logistat (0, 1);
835s  assert (m, 0);
835s  assert (v, 3.2899, 0.001);
835s ***** test
835s  [m, v] = logistat (0, 0.8);
835s  assert (m, 0);
835s  assert (v, 2.1055, 0.001);
835s ***** test
835s  [m, v] = logistat (1, 0.6);
835s  assert (m, 1);
835s  assert (v, 1.1844, 0.001);
835s ***** test
835s  [m, v] = logistat (0, 0.4);
835s  assert (m, 0);
835s  assert (v, 0.5264, 0.001);
835s ***** test
835s  [m, v] = logistat (-1, 0.2);
835s  assert (m, -1);
835s  assert (v, 0.1316, 0.001);
835s 13 tests, 13 passed, 0 known failure, 0 skipped
835s [inst/dist_stat/loglstat.m]
835s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/loglstat.m
835s ***** error<loglstat: function called with too few input arguments.> loglstat ()
835s ***** error<loglstat: function called with too few input arguments.> loglstat (1)
835s ***** error<loglstat: MU and SIGMA must be numeric.> loglstat ({}, 2)
835s ***** error<loglstat: MU and SIGMA must be numeric.> loglstat (1, "")
835s ***** error<loglstat: MU and SIGMA must not be complex.> loglstat (i, 2)
835s ***** error<loglstat: MU and SIGMA must not be complex.> loglstat (1, i)
835s ***** error<loglstat: MU and SIGMA must be of common size or scalars.> ...
835s  loglstat (ones (3), ones (2))
835s ***** error<loglstat: MU and SIGMA must be of common size or scalars.> ...
835s  loglstat (ones (2), ones (3))
835s ***** test
835s  [m, v] = loglstat (0, 1);
835s  assert (m, Inf, 0.001);
835s  assert (v, Inf, 0.001);
835s ***** test
835s  [m, v] = loglstat (0, 0.8);
835s  assert (m, 4.2758, 0.001);
835s  assert (v, Inf, 0.001);
835s ***** test
835s  [m, v] = loglstat (0, 0.6);
835s  assert (m, 1.9820, 0.001);
835s  assert (v, Inf, 0.001);
835s ***** test
835s  [m, v] = loglstat (0, 0.4);
835s  assert (m, 1.3213, 0.001);
835s  assert (v, 2.5300, 0.001);
835s ***** test
835s  [m, v] = loglstat (0, 0.2);
835s  assert (m, 1.0690, 0.001);
835s  assert (v, 0.1786, 0.001);
835s 13 tests, 13 passed, 0 known failure, 0 skipped
835s [inst/dist_stat/lognstat.m]
835s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/lognstat.m
835s ***** error<lognstat: function called with too few input arguments.> lognstat ()
835s ***** error<lognstat: function called with too few input arguments.> lognstat (1)
835s ***** error<lognstat: MU and SIGMA must be numeric.> lognstat ({}, 2)
835s ***** error<lognstat: MU and SIGMA must be numeric.> lognstat (1, "")
835s ***** error<lognstat: MU and SIGMA must not be complex.> lognstat (i, 2)
835s ***** error<lognstat: MU and SIGMA must not be complex.> lognstat (1, i)
835s ***** error<lognstat: MU and SIGMA must be of common size or scalars.> ...
835s  lognstat (ones (3), ones (2))
835s ***** error<lognstat: MU and SIGMA must be of common size or scalars.> ...
835s  lognstat (ones (2), ones (3))
835s ***** test
835s  mu = 0:0.2:1;
835s  sigma = 0.2:0.2:1.2;
835s  [m, v] = lognstat (mu, sigma);
835s  expected_m = [1.0202, 1.3231, 1.7860, 2.5093,  3.6693,   5.5845];
835s  expected_v = [0.0425, 0.3038, 1.3823, 5.6447, 23.1345, 100.4437];
835s  assert (m, expected_m, 0.001);
835s  assert (v, expected_v, 0.001);
835s ***** test
835s  sigma = 0.2:0.2:1.2;
835s  [m, v] = lognstat (0, sigma);
835s  expected_m = [1.0202, 1.0833, 1.1972, 1.3771, 1.6487,  2.0544];
835s  expected_v = [0.0425, 0.2036, 0.6211, 1.7002, 4.6708, 13.5936];
835s  assert (m, expected_m, 0.001);
835s  assert (v, expected_v, 0.001);
835s 10 tests, 10 passed, 0 known failure, 0 skipped
835s [inst/dist_stat/nakastat.m]
835s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/nakastat.m
835s ***** error<nakastat: function called with too few input arguments.> nakastat ()
835s ***** error<nakastat: function called with too few input arguments.> nakastat (1)
835s ***** error<nakastat: MU and OMEGA must be numeric.> nakastat ({}, 2)
835s ***** error<nakastat: MU and OMEGA must be numeric.> nakastat (1, "")
835s ***** error<nakastat: MU and OMEGA must not be complex.> nakastat (i, 2)
835s ***** error<nakastat: MU and OMEGA must not be complex.> nakastat (1, i)
835s ***** error<nakastat: MU and OMEGA must be of common size or scalars.> ...
835s  nakastat (ones (3), ones (2))
835s ***** error<nakastat: MU and OMEGA must be of common size or scalars.> ...
835s  nakastat (ones (2), ones (3))
835s ***** test
835s  [m, v] = nakastat (1, 1);
835s  assert (m, 0.8862269254, 1e-10);
835s  assert (v, 0.2146018366, 1e-10);
835s ***** test
835s  [m, v] = nakastat (1, 2);
835s  assert (m, 1.25331413731, 1e-10);
835s  assert (v, 0.42920367321, 1e-10);
835s ***** test
835s  [m, v] = nakastat (2, 1);
835s  assert (m, 0.93998560299, 1e-10);
835s  assert (v, 0.11642706618, 1e-10);
835s 11 tests, 11 passed, 0 known failure, 0 skipped
835s [inst/dist_stat/nbinstat.m]
835s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/nbinstat.m
835s ***** error<nbinstat: function called with too few input arguments.> nbinstat ()
835s ***** error<nbinstat: function called with too few input arguments.> nbinstat (1)
835s ***** error<nbinstat: R and PS must be numeric.> nbinstat ({}, 2)
835s ***** error<nbinstat: R and PS must be numeric.> nbinstat (1, "")
835s ***** error<nbinstat: R and PS must not be complex.> nbinstat (i, 2)
835s ***** error<nbinstat: R and PS must not be complex.> nbinstat (1, i)
835s ***** error<nbinstat: R and PS must be of common size or scalars.> ...
835s  nbinstat (ones (3), ones (2))
835s ***** error<nbinstat: R and PS must be of common size or scalars.> ...
835s  nbinstat (ones (2), ones (3))
835s ***** test
835s  r = 1:4;
835s  ps = 0.2:0.2:0.8;
835s  [m, v] = nbinstat (r, ps);
835s  expected_m = [ 4.0000, 3.0000, 2.0000, 1.0000];
835s  expected_v = [20.0000, 7.5000, 3.3333, 1.2500];
835s  assert (m, expected_m, 0.001);
835s  assert (v, expected_v, 0.001);
835s ***** test
835s  r = 1:4;
835s  [m, v] = nbinstat (r, 0.5);
835s  expected_m = [1, 2, 3, 4];
835s  expected_v = [2, 4, 6, 8];
835s  assert (m, expected_m, 0.001);
835s  assert (v, expected_v, 0.001);
835s 10 tests, 10 passed, 0 known failure, 0 skipped
835s [inst/dist_stat/ncfstat.m]
835s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/ncfstat.m
835s ***** error<ncfstat: function called with too few input arguments.> ncfstat ()
835s ***** error<ncfstat: function called with too few input arguments.> ncfstat (1)
835s ***** error<ncfstat: function called with too few input arguments.> ncfstat (1, 2)
835s ***** error<ncfstat: DF1, DF2, and LAMBDA must be numeric.> ncfstat ({}, 2, 3)
835s ***** error<ncfstat: DF1, DF2, and LAMBDA must be numeric.> ncfstat (1, "", 3)
835s ***** error<ncfstat: DF1, DF2, and LAMBDA must be numeric.> ncfstat (1, 2, "")
835s ***** error<ncfstat: DF1, DF2, and LAMBDA must not be complex.> ncfstat (i, 2, 3)
835s ***** error<ncfstat: DF1, DF2, and LAMBDA must not be complex.> ncfstat (1, i, 3)
835s ***** error<ncfstat: DF1, DF2, and LAMBDA must not be complex.> ncfstat (1, 2, i)
835s ***** error<ncfstat: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
835s  ncfstat (ones (3), ones (2), 3)
835s ***** error<ncfstat: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
835s  ncfstat (ones (2), 2, ones (3))
835s ***** error<ncfstat: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
835s  ncfstat (1, ones (2), ones (3))
835s ***** shared df1, df2, lambda
835s  df1 = [2, 0, -1, 1, 4, 5];
835s  df2 = [2, 4, -1, 5, 6, 7];
835s  lambda = [1, NaN, 3, 0, 2, -1];
835s ***** assert (ncfstat (df1, df2, lambda), [NaN, NaN, NaN, 1.6667, 2.25, 1.12], 1e-4);
835s ***** assert (ncfstat (df1(4:6), df2(4:6), 1), [3.3333, 1.8750, 1.6800], 1e-4);
835s ***** assert (ncfstat (df1(4:6), df2(4:6), 2), [5.0000, 2.2500, 1.9600], 1e-4);
835s ***** assert (ncfstat (df1(4:6), df2(4:6), 3), [6.6667, 2.6250, 2.2400], 1e-4);
835s ***** assert (ncfstat (2, [df2(1), df2(4:6)], 5), [NaN,5.8333,5.2500,4.9000], 1e-4);
835s ***** assert (ncfstat (0, [df2(1), df2(4:6)], 5), [NaN, Inf, Inf, Inf]);
835s ***** assert (ncfstat (1, [df2(1), df2(4:6)], 5), [NaN, 10, 9, 8.4], 1e-14);
835s ***** assert (ncfstat (4, [df2(1), df2(4:6)], 5), [NaN, 3.75, 3.375, 3.15], 1e-14);
835s 20 tests, 20 passed, 0 known failure, 0 skipped
835s [inst/dist_stat/nctstat.m]
835s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/nctstat.m
835s ***** error<nctstat: function called with too few input arguments.> nctstat ()
835s ***** error<nctstat: function called with too few input arguments.> nctstat (1)
835s ***** error<nctstat: DF and MU must be numeric.> nctstat ({}, 2)
835s ***** error<nctstat: DF and MU must be numeric.> nctstat (1, "")
835s ***** error<nctstat: DF and MU must not be complex.> nctstat (i, 2)
835s ***** error<nctstat: DF and MU must not be complex.> nctstat (1, i)
835s ***** error<nctstat: DF and MU must be of common size or scalars.> ...
835s  nctstat (ones (3), ones (2))
835s ***** error<nctstat: DF and MU must be of common size or scalars.> ...
835s  nctstat (ones (2), ones (3))
835s ***** shared df, mu
835s  df = [2, 0, -1, 1, 4];
835s  mu = [1, NaN, 3, -1, 2];
835s ***** assert (nctstat (df, mu), [1.7725, NaN, NaN, NaN, 2.5066], 1e-4);
835s ***** assert (nctstat ([df(1:2), df(4:5)], 1), [1.7725, NaN, NaN, 1.2533], 1e-4);
835s ***** assert (nctstat ([df(1:2), df(4:5)], 3), [5.3174, NaN, NaN, 3.7599], 1e-4);
835s ***** assert (nctstat ([df(1:2), df(4:5)], 2), [3.5449, NaN, NaN, 2.5066], 1e-4);
835s ***** assert (nctstat (2, [mu(1), mu(3:5)]), [1.7725,5.3174,-1.7725,3.5449], 1e-4);
835s ***** assert (nctstat (0, [mu(1), mu(3:5)]), [NaN, NaN, NaN, NaN]);
835s ***** assert (nctstat (1, [mu(1), mu(3:5)]), [NaN, NaN, NaN, NaN]);
835s ***** assert (nctstat (4, [mu(1), mu(3:5)]), [1.2533,3.7599,-1.2533,2.5066], 1e-4);
835s 16 tests, 16 passed, 0 known failure, 0 skipped
835s [inst/dist_stat/ncx2stat.m]
835s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/ncx2stat.m
835s ***** error<ncx2stat: function called with too few input arguments.> ncx2stat ()
835s ***** error<ncx2stat: function called with too few input arguments.> ncx2stat (1)
835s ***** error<ncx2stat: DF and LAMBDA must be numeric.> ncx2stat ({}, 2)
835s ***** error<ncx2stat: DF and LAMBDA must be numeric.> ncx2stat (1, "")
835s ***** error<ncx2stat: DF and LAMBDA must not be complex.> ncx2stat (i, 2)
835s ***** error<ncx2stat: DF and LAMBDA must not be complex.> ncx2stat (1, i)
835s ***** error<ncx2stat: DF and LAMBDA must be of common size or scalars.> ...
835s  ncx2stat (ones (3), ones (2))
835s ***** error<ncx2stat: DF and LAMBDA must be of common size or scalars.> ...
835s  ncx2stat (ones (2), ones (3))
835s ***** shared df, d1
835s  df = [2, 0, -1, 1, 4];
835s  d1 = [1, NaN, 3, -1, 2];
835s ***** assert (ncx2stat (df, d1), [3, NaN, NaN, NaN, 6]);
835s ***** assert (ncx2stat ([df(1:2), df(4:5)], 1), [3, NaN, 2, 5]);
835s ***** assert (ncx2stat ([df(1:2), df(4:5)], 3), [5, NaN, 4, 7]);
835s ***** assert (ncx2stat ([df(1:2), df(4:5)], 2), [4, NaN, 3, 6]);
835s ***** assert (ncx2stat (2, [d1(1), d1(3:5)]), [3, 5, NaN, 4]);
835s ***** assert (ncx2stat (0, [d1(1), d1(3:5)]), [NaN, NaN, NaN, NaN]);
835s ***** assert (ncx2stat (1, [d1(1), d1(3:5)]), [2, 4, NaN, 3]);
835s ***** assert (ncx2stat (4, [d1(1), d1(3:5)]), [5, 7, NaN, 6]);
835s 16 tests, 16 passed, 0 known failure, 0 skipped
835s [inst/dist_stat/normstat.m]
835s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/normstat.m
835s ***** error<normstat: function called with too few input arguments.> normstat ()
835s ***** error<normstat: function called with too few input arguments.> normstat (1)
835s ***** error<normstat: MU and SIGMA must be numeric.> normstat ({}, 2)
835s ***** error<normstat: MU and SIGMA must be numeric.> normstat (1, "")
835s ***** error<normstat: MU and SIGMA must not be complex.> normstat (i, 2)
835s ***** error<normstat: MU and SIGMA must not be complex.> normstat (1, i)
835s ***** error<normstat: MU and SIGMA must be of common size or scalars.> ...
835s  normstat (ones (3), ones (2))
835s ***** error<normstat: MU and SIGMA must be of common size or scalars.> ...
835s  normstat (ones (2), ones (3))
836s ***** test
836s  mu = 1:6;
836s  sigma = 0.2:0.2:1.2;
836s  [m, v] = normstat (mu, sigma);
836s  expected_v = [0.0400, 0.1600, 0.3600, 0.6400, 1.0000, 1.4400];
836s  assert (m, mu);
836s  assert (v, expected_v, 0.001);
836s ***** test
836s  sigma = 0.2:0.2:1.2;
836s  [m, v] = normstat (0, sigma);
836s  expected_mn = [0, 0, 0, 0, 0, 0];
836s  expected_v = [0.0400, 0.1600, 0.3600, 0.6400, 1.0000, 1.4400];
836s  assert (m, expected_mn, 0.001);
836s  assert (v, expected_v, 0.001);
836s 10 tests, 10 passed, 0 known failure, 0 skipped
836s [inst/dist_stat/plstat.m]
836s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/plstat.m
836s ***** shared x, Fx
836s  x = [0, 1, 3, 4, 7, 10];
836s  Fx = [0, 0.2, 0.5, 0.6, 0.7, 1];
836s ***** assert (plstat (x, Fx), 4.15)
836s ***** test
836s  [m, v] = plstat (x, Fx);
836s  assert (v, 10.3775, 1e-14)
836s ***** error<plstat: function called with too few input arguments.> plstat ()
836s ***** error<plstat: function called with too few input arguments.> plstat (1)
836s ***** error<plstat: X and FX must be vectors of equal size.> ...
836s  plstat ([0, 1, 2], [0, 1])
836s ***** error<plstat: X and FX must be at least two-elements long.> ...
836s  plstat ([0], [1])
836s ***** error<plstat: FX must be bounded in the range> ...
836s  plstat ([0, 1, 2], [0, 1, 1.5])
836s ***** error<plstat: FX must be bounded in the range> ...
836s  plstat ([0, 1, 2], [0, i, 1])
836s ***** error<plstat: X and FX must not be complex.> ...
836s  plstat ([0, i, 2], [0, 0.5, 1])
836s ***** error<plstat: X and FX must not be complex.> ...
836s  plstat ([0, i, 2], [0, 0.5i, 1])
836s 10 tests, 10 passed, 0 known failure, 0 skipped
836s [inst/dist_stat/poisstat.m]
836s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/poisstat.m
836s ***** error<poisstat: function called with too few input arguments.> poisstat ()
836s ***** error<poisstat: SIGMA must be numeric.> poisstat ({})
836s ***** error<poisstat: SIGMA must be numeric.> poisstat ("")
836s ***** error<poisstat: SIGMA must not be complex.> poisstat (i)
836s ***** test
836s  lambda = 1 ./ (1:6);
836s  [m, v] = poisstat (lambda);
836s  assert (m, lambda);
836s  assert (v, lambda);
836s 5 tests, 5 passed, 0 known failure, 0 skipped
836s [inst/dist_stat/raylstat.m]
836s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/raylstat.m
836s ***** error<raylstat: function called with too few input arguments.> raylstat ()
836s ***** error<raylstat: SIGMA must be numeric.> raylstat ({})
836s ***** error<raylstat: SIGMA must be numeric.> raylstat ("")
836s ***** error<raylstat: SIGMA must not be complex.> raylstat (i)
836s ***** test
836s  sigma = 1:6;
836s  [m, v] = raylstat (sigma);
836s  expected_m = [1.2533, 2.5066, 3.7599, 5.0133, 6.2666, 7.5199];
836s  expected_v = [0.4292, 1.7168, 3.8628, 6.8673, 10.7301, 15.4513];
836s  assert (m, expected_m, 0.001);
836s  assert (v, expected_v, 0.001);
836s 5 tests, 5 passed, 0 known failure, 0 skipped
836s [inst/dist_stat/ricestat.m]
836s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/ricestat.m
836s ***** error<ricestat: function called with too few input arguments.> ricestat ()
836s ***** error<ricestat: function called with too few input arguments.> ricestat (1)
836s ***** error<ricestat: S and SIGMA must be numeric.> ricestat ({}, 2)
836s ***** error<ricestat: S and SIGMA must be numeric.> ricestat (1, "")
836s ***** error<ricestat: S and SIGMA must not be complex.> ricestat (i, 2)
836s ***** error<ricestat: S and SIGMA must not be complex.> ricestat (1, i)
836s ***** error<ricestat: S and SIGMA must be of common size or scalars.> ...
836s  ricestat (ones (3), ones (2))
836s ***** error<ricestat: S and SIGMA must be of common size or scalars.> ...
836s  ricestat (ones (2), ones (3))
836s ***** shared s, sigma
836s  s = [2, 0, -1, 1, 4];
836s  sigma = [1, NaN, 3, -1, 2];
836s ***** assert (ricestat (s, sigma), [2.2724, NaN, NaN, NaN, 4.5448], 1e-4);
836s ***** assert (ricestat ([s(1:2), s(4:5)], 1), [2.2724, 1.2533, 1.5486, 4.1272], 1e-4);
836s ***** assert (ricestat ([s(1:2), s(4:5)], 3), [4.1665, 3.7599, 3.8637, 5.2695], 1e-4);
836s ***** assert (ricestat ([s(1:2), s(4:5)], 2), [3.0971, 2.5066, 2.6609, 4.5448], 1e-4);
836s ***** assert (ricestat (2, [sigma(1), sigma(3:5)]), [2.2724, 4.1665, NaN, 3.0971], 1e-4);
836s ***** assert (ricestat (0, [sigma(1), sigma(3:5)]), [1.2533, 3.7599, NaN, 2.5066], 1e-4);
836s ***** assert (ricestat (1, [sigma(1), sigma(3:5)]), [1.5486, 3.8637, NaN, 2.6609], 1e-4);
836s ***** assert (ricestat (4, [sigma(1), sigma(3:5)]), [4.1272, 5.2695, NaN, 4.5448], 1e-4);
836s 16 tests, 16 passed, 0 known failure, 0 skipped
836s [inst/dist_stat/tlsstat.m]
836s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/tlsstat.m
836s ***** error<tlsstat: function called with too few input arguments.> tlsstat ()
836s ***** error<tlsstat: function called with too few input arguments.> tlsstat (1)
836s ***** error<tlsstat: function called with too few input arguments.> tlsstat (1, 2)
836s ***** error<tlsstat: MU, SIGMA, and NU must be numeric.> tlsstat ({}, 2, 3)
836s ***** error<tlsstat: MU, SIGMA, and NU must be numeric.> tlsstat (1, "", 3)
836s ***** error<tlsstat: MU, SIGMA, and NU must be numeric.> tlsstat (1, 2, ["d"])
836s ***** error<tlsstat: MU, SIGMA, and NU must not be complex.> tlsstat (i, 2, 3)
836s ***** error<tlsstat: MU, SIGMA, and NU must not be complex.> tlsstat (1, i, 3)
836s ***** error<tlsstat: MU, SIGMA, and NU must not be complex.> tlsstat (1, 2, i)
836s ***** error<tlsstat: MU, SIGMA, and NU must be of common size or scalars.> ...
836s  tlsstat (ones (3), ones (2), 1)
836s ***** error<tlsstat: MU, SIGMA, and NU must be of common size or scalars.> ...
836s  tlsstat (ones (2), 1, ones (3))
836s ***** error<tlsstat: MU, SIGMA, and NU must be of common size or scalars.> ...
836s  tlsstat (1, ones (2), ones (3))
836s ***** test
836s  [m, v] = tlsstat (0, 1, 0);
836s  assert (m, NaN);
836s  assert (v, NaN);
836s ***** test
836s  [m, v] = tlsstat (0, 1, 1);
836s  assert (m, NaN);
836s  assert (v, NaN);
836s ***** test
836s  [m, v] = tlsstat (2, 1, 1);
836s  assert (m, NaN);
836s  assert (v, NaN);
836s ***** test
836s  [m, v] = tlsstat (-2, 1, 1);
836s  assert (m, NaN);
836s  assert (v, NaN);
836s ***** test
836s  [m, v] = tlsstat (0, 1, 2);
836s  assert (m, 0);
836s  assert (v, NaN);
836s ***** test
836s  [m, v] = tlsstat (2, 1, 2);
836s  assert (m, 2);
836s  assert (v, NaN);
836s ***** test
836s  [m, v] = tlsstat (-2, 1, 2);
836s  assert (m, -2);
836s  assert (v, NaN);
836s ***** test
836s  [m, v] = tlsstat (0, 2, 2);
836s  assert (m, 0);
836s  assert (v, NaN);
836s ***** test
836s  [m, v] = tlsstat (2, 2, 2);
836s  assert (m, 2);
836s  assert (v, NaN);
836s ***** test
836s  [m, v] = tlsstat (-2, 2, 2);
836s  assert (m, -2);
836s  assert (v, NaN);
836s ***** test
836s  [m, v] = tlsstat (0, 1, 3);
836s  assert (m, 0);
836s  assert (v, 3);
836s ***** test
836s  [m, v] = tlsstat (0, 2, 3);
836s  assert (m, 0);
836s  assert (v, 6);
836s ***** test
836s  [m, v] = tlsstat (2, 1, 3);
836s  assert (m, 2);
836s  assert (v, 3);
836s ***** test
836s  [m, v] = tlsstat (2, 2, 3);
836s  assert (m, 2);
836s  assert (v, 6);
836s ***** test
836s  [m, v] = tlsstat (-2, 1, 3);
836s  assert (m, -2);
836s  assert (v, 3);
836s ***** test
836s  [m, v] = tlsstat (-2, 2, 3);
836s  assert (m, -2);
836s  assert (v, 6);
836s 28 tests, 28 passed, 0 known failure, 0 skipped
836s [inst/dist_stat/tristat.m]
836s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/tristat.m
836s ***** error<tristat: function called with too few input arguments.> tristat ()
836s ***** error<tristat: function called with too few input arguments.> tristat (1)
836s ***** error<tristat: function called with too few input arguments.> tristat (1, 2)
836s ***** error<tristat: A, B, and C must be numeric.> tristat ("i", 2, 1)
836s ***** error<tristat: A, B, and C must be numeric.> tristat (0, "d", 1)
836s ***** error<tristat: A, B, and C must be numeric.> tristat (0, 3, {})
836s ***** error<tristat: A, B, and C must be real.> tristat (i, 2, 1)
836s ***** error<tristat: A, B, and C must be real.> tristat (0, i, 1)
836s ***** error<tristat: A, B, and C must be real.> tristat (0, 3, i)
836s ***** test
836s  a = 1:5;
836s  b = 3:7;
836s  c = 5:9;
836s  [m, v] = tristat (a, b, c);
836s  expected_m = [3, 4, 5, 6, 7];
836s  assert (m, expected_m);
836s  assert (v, ones (1, 5) * (2/3));
836s 10 tests, 10 passed, 0 known failure, 0 skipped
836s [inst/dist_stat/tstat.m]
836s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/tstat.m
836s ***** error<tstat: function called with too few input arguments.> tstat ()
836s ***** error<tstat: DF must be numeric.> tstat ({})
836s ***** error<tstat: DF must be numeric.> tstat ("")
836s ***** error<tstat: DF must not be complex.> tstat (i)
836s ***** test
836s  df = 3:8;
836s  [m, v] = tstat (df);
836s  expected_m = [0, 0, 0, 0, 0, 0];
836s  expected_v = [3.0000, 2.0000, 1.6667, 1.5000, 1.4000, 1.3333];
836s  assert (m, expected_m);
836s  assert (v, expected_v, 0.001);
836s 5 tests, 5 passed, 0 known failure, 0 skipped
836s [inst/dist_stat/unidstat.m]
836s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/unidstat.m
836s ***** error<unidstat: function called with too few input arguments.> unidstat ()
836s ***** error<unidstat: N must be numeric.> unidstat ({})
836s ***** error<unidstat: N must be numeric.> unidstat ("")
836s ***** error<unidstat: N must not be complex.> unidstat (i)
836s ***** test
836s  N = 1:6;
836s  [m, v] = unidstat (N);
836s  expected_m = [1.0000, 1.5000, 2.0000, 2.5000, 3.0000, 3.5000];
836s  expected_v = [0.0000, 0.2500, 0.6667, 1.2500, 2.0000, 2.9167];
836s  assert (m, expected_m, 0.001);
836s  assert (v, expected_v, 0.001);
836s 5 tests, 5 passed, 0 known failure, 0 skipped
836s [inst/dist_stat/unifstat.m]
836s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/unifstat.m
836s ***** error<unifstat: function called with too few input arguments.> unifstat ()
836s ***** error<unifstat: function called with too few input arguments.> unifstat (1)
836s ***** error<unifstat: A and B must be numeric.> unifstat ({}, 2)
836s ***** error<unifstat: A and B must be numeric.> unifstat (1, "")
836s ***** error<unifstat: A and B must not be complex.> unifstat (i, 2)
836s ***** error<unifstat: A and B must not be complex.> unifstat (1, i)
836s ***** error<unifstat: A and B must be of common size or scalars.> ...
836s  unifstat (ones (3), ones (2))
836s ***** error<unifstat: A and B must be of common size or scalars.> ...
836s  unifstat (ones (2), ones (3))
836s ***** test
836s  a = 1:6;
836s  b = 2:2:12;
836s  [m, v] = unifstat (a, b);
836s  expected_m = [1.5000, 3.0000, 4.5000, 6.0000, 7.5000, 9.0000];
836s  expected_v = [0.0833, 0.3333, 0.7500, 1.3333, 2.0833, 3.0000];
836s  assert (m, expected_m, 0.001);
836s  assert (v, expected_v, 0.001);
836s ***** test
836s  a = 1:6;
836s  [m, v] = unifstat (a, 10);
836s  expected_m = [5.5000, 6.0000, 6.5000, 7.0000, 7.5000, 8.0000];
836s  expected_v = [6.7500, 5.3333, 4.0833, 3.0000, 2.0833, 1.3333];
836s  assert (m, expected_m, 0.001);
836s  assert (v, expected_v, 0.001);
836s 10 tests, 10 passed, 0 known failure, 0 skipped
836s [inst/dist_stat/wblstat.m]
836s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_stat/wblstat.m
836s ***** error<wblstat: function called with too few input arguments.> wblstat ()
836s ***** error<wblstat: function called with too few input arguments.> wblstat (1)
836s ***** error<wblstat: LAMBDA and K must be numeric.> wblstat ({}, 2)
836s ***** error<wblstat: LAMBDA and K must be numeric.> wblstat (1, "")
836s ***** error<wblstat: LAMBDA and K must not be complex.> wblstat (i, 2)
836s ***** error<wblstat: LAMBDA and K must not be complex.> wblstat (1, i)
836s ***** error<wblstat: LAMBDA and K must be of common size or scalars.> ...
836s  wblstat (ones (3), ones (2))
836s ***** error<wblstat: LAMBDA and K must be of common size or scalars.> ...
836s  wblstat (ones (2), ones (3))
836s ***** test
836s  lambda = 3:8;
836s  k = 1:6;
836s  [m, v] = wblstat (lambda, k);
836s  expected_m = [3.0000, 3.5449, 4.4649, 5.4384, 6.4272, 7.4218];
836s  expected_v = [9.0000, 3.4336, 2.6333, 2.3278, 2.1673, 2.0682];
836s  assert (m, expected_m, 0.001);
836s  assert (v, expected_v, 0.001);
836s ***** test
836s  k = 1:6;
836s  [m, v] = wblstat (6, k);
836s  expected_m = [ 6.0000, 5.3174, 5.3579, 5.4384, 5.5090, 5.5663];
836s  expected_v = [36.0000, 7.7257, 3.7920, 2.3278, 1.5923, 1.1634];
836s  assert (m, expected_m, 0.001);
836s  assert (v, expected_v, 0.001);
836s 10 tests, 10 passed, 0 known failure, 0 skipped
836s [inst/dist_wrap/cdf.m]
836s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_wrap/cdf.m
836s ***** shared x
836s  x = [1:5];
836s ***** assert (cdf ("Beta", x, 5, 2), betacdf (x, 5, 2))
836s ***** assert (cdf ("beta", x, 5, 2, "upper"), betacdf (x, 5, 2, "upper"))
836s ***** assert (cdf ("Binomial", x, 5, 2), binocdf (x, 5, 2))
836s ***** assert (cdf ("bino", x, 5, 2, "upper"), binocdf (x, 5, 2, "upper"))
836s ***** assert (cdf ("Birnbaum-Saunders", x, 5, 2), bisacdf (x, 5, 2))
836s ***** assert (cdf ("bisa", x, 5, 2, "upper"), bisacdf (x, 5, 2, "upper"))
836s ***** assert (cdf ("Burr", x, 5, 2, 2), burrcdf (x, 5, 2, 2))
836s ***** assert (cdf ("burr", x, 5, 2, 2, "upper"), burrcdf (x, 5, 2, 2, "upper"))
836s ***** assert (cdf ("Cauchy", x, 5, 2), cauchycdf (x, 5, 2))
836s ***** assert (cdf ("cauchy", x, 5, 2, "upper"), cauchycdf (x, 5, 2, "upper"))
836s ***** assert (cdf ("Chi-squared", x, 5), chi2cdf (x, 5))
836s ***** assert (cdf ("chi2", x, 5, "upper"), chi2cdf (x, 5, "upper"))
836s ***** assert (cdf ("Extreme Value", x, 5, 2), evcdf (x, 5, 2))
836s ***** assert (cdf ("ev", x, 5, 2, "upper"), evcdf (x, 5, 2, "upper"))
836s ***** assert (cdf ("Exponential", x, 5), expcdf (x, 5))
836s ***** assert (cdf ("exp", x, 5, "upper"), expcdf (x, 5, "upper"))
836s ***** assert (cdf ("F-Distribution", x, 5, 2), fcdf (x, 5, 2))
836s ***** assert (cdf ("f", x, 5, 2, "upper"), fcdf (x, 5, 2, "upper"))
836s ***** assert (cdf ("Gamma", x, 5, 2), gamcdf (x, 5, 2))
836s ***** assert (cdf ("gam", x, 5, 2, "upper"), gamcdf (x, 5, 2, "upper"))
836s ***** assert (cdf ("Geometric", x, 5), geocdf (x, 5))
836s ***** assert (cdf ("geo", x, 5, "upper"), geocdf (x, 5, "upper"))
836s ***** assert (cdf ("Generalized Extreme Value", x, 5, 2, 2), gevcdf (x, 5, 2, 2))
836s ***** assert (cdf ("gev", x, 5, 2, 2, "upper"), gevcdf (x, 5, 2, 2, "upper"))
836s ***** assert (cdf ("Generalized Pareto", x, 5, 2, 2), gpcdf (x, 5, 2, 2))
836s ***** assert (cdf ("gp", x, 5, 2, 2, "upper"), gpcdf (x, 5, 2, 2, "upper"))
836s ***** assert (cdf ("Gumbel", x, 5, 2), gumbelcdf (x, 5, 2))
836s ***** assert (cdf ("gumbel", x, 5, 2, "upper"), gumbelcdf (x, 5, 2, "upper"))
836s ***** assert (cdf ("Half-normal", x, 5, 2), hncdf (x, 5, 2))
836s ***** assert (cdf ("hn", x, 5, 2, "upper"), hncdf (x, 5, 2, "upper"))
836s ***** assert (cdf ("Hypergeometric", x, 5, 2, 2), hygecdf (x, 5, 2, 2))
836s ***** assert (cdf ("hyge", x, 5, 2, 2, "upper"), hygecdf (x, 5, 2, 2, "upper"))
836s ***** assert (cdf ("Inverse Gaussian", x, 5, 2), invgcdf (x, 5, 2))
836s ***** assert (cdf ("invg", x, 5, 2, "upper"), invgcdf (x, 5, 2, "upper"))
836s ***** assert (cdf ("Laplace", x, 5, 2), laplacecdf (x, 5, 2))
836s ***** assert (cdf ("laplace", x, 5, 2, "upper"), laplacecdf (x, 5, 2, "upper"))
836s ***** assert (cdf ("Logistic", x, 5, 2), logicdf (x, 5, 2))
836s ***** assert (cdf ("logi", x, 5, 2, "upper"), logicdf (x, 5, 2, "upper"))
836s ***** assert (cdf ("Log-Logistic", x, 5, 2), loglcdf (x, 5, 2))
836s ***** assert (cdf ("logl", x, 5, 2, "upper"), loglcdf (x, 5, 2, "upper"))
836s ***** assert (cdf ("Lognormal", x, 5, 2), logncdf (x, 5, 2))
836s ***** assert (cdf ("logn", x, 5, 2, "upper"), logncdf (x, 5, 2, "upper"))
836s ***** assert (cdf ("Nakagami", x, 5, 2), nakacdf (x, 5, 2))
836s ***** assert (cdf ("naka", x, 5, 2, "upper"), nakacdf (x, 5, 2, "upper"))
836s ***** assert (cdf ("Negative Binomial", x, 5, 2), nbincdf (x, 5, 2))
836s ***** assert (cdf ("nbin", x, 5, 2, "upper"), nbincdf (x, 5, 2, "upper"))
836s ***** assert (cdf ("Noncentral F-Distribution", x, 5, 2, 2), ncfcdf (x, 5, 2, 2))
836s ***** assert (cdf ("ncf", x, 5, 2, 2, "upper"), ncfcdf (x, 5, 2, 2, "upper"))
836s ***** assert (cdf ("Noncentral Student T", x, 5, 2), nctcdf (x, 5, 2))
836s ***** assert (cdf ("nct", x, 5, 2, "upper"), nctcdf (x, 5, 2, "upper"))
836s ***** assert (cdf ("Noncentral Chi-Squared", x, 5, 2), ncx2cdf (x, 5, 2))
836s ***** assert (cdf ("ncx2", x, 5, 2, "upper"), ncx2cdf (x, 5, 2, "upper"))
836s ***** assert (cdf ("Normal", x, 5, 2), normcdf (x, 5, 2))
836s ***** assert (cdf ("norm", x, 5, 2, "upper"), normcdf (x, 5, 2, "upper"))
836s ***** assert (cdf ("Poisson", x, 5), poisscdf (x, 5))
836s ***** assert (cdf ("poiss", x, 5, "upper"), poisscdf (x, 5, "upper"))
837s ***** assert (cdf ("Rayleigh", x, 5), raylcdf (x, 5))
837s ***** assert (cdf ("rayl", x, 5, "upper"), raylcdf (x, 5, "upper"))
837s ***** assert (cdf ("Rician", x, 5, 1), ricecdf (x, 5, 1))
837s ***** assert (cdf ("rice", x, 5, 1, "upper"), ricecdf (x, 5, 1, "upper"))
837s ***** assert (cdf ("Student T", x, 5), tcdf (x, 5))
837s ***** assert (cdf ("t", x, 5, "upper"), tcdf (x, 5, "upper"))
837s ***** assert (cdf ("location-scale T", x, 5, 1, 2), tlscdf (x, 5, 1, 2))
837s ***** assert (cdf ("tls", x, 5, 1, 2, "upper"), tlscdf (x, 5, 1, 2, "upper"))
837s ***** assert (cdf ("Triangular", x, 5, 2, 2), tricdf (x, 5, 2, 2))
837s ***** assert (cdf ("tri", x, 5, 2, 2, "upper"), tricdf (x, 5, 2, 2, "upper"))
837s ***** assert (cdf ("Discrete Uniform", x, 5), unidcdf (x, 5))
837s ***** assert (cdf ("unid", x, 5, "upper"), unidcdf (x, 5, "upper"))
837s ***** assert (cdf ("Uniform", x, 5, 2), unifcdf (x, 5, 2))
837s ***** assert (cdf ("unif", x, 5, 2, "upper"), unifcdf (x, 5, 2, "upper"))
837s ***** assert (cdf ("Von Mises", x, 5, 2), vmcdf (x, 5, 2))
837s ***** assert (cdf ("vm", x, 5, 2, "upper"), vmcdf (x, 5, 2, "upper"))
837s ***** assert (cdf ("Weibull", x, 5, 2), wblcdf (x, 5, 2))
837s ***** assert (cdf ("wbl", x, 5, 2, "upper"), wblcdf (x, 5, 2, "upper"))
837s ***** error<cdf: distribution NAME must a char string.> cdf (1)
838s ***** error<cdf: distribution NAME must a char string.> cdf ({"beta"})
838s ***** error<cdf: X must be numeric.> cdf ("beta", {[1 2 3 4 5]})
838s ***** error<cdf: X must be numeric.> cdf ("beta", "text")
838s ***** error<cdf: values in X must be real.> cdf ("beta", 1+i)
838s ***** error<cdf: distribution parameters must be numeric.> ...
838s  cdf ("Beta", x, "a", 2)
838s ***** error<cdf: distribution parameters must be numeric.> ...
838s  cdf ("Beta", x, 5, "")
838s ***** error<cdf: distribution parameters must be numeric.> ...
838s  cdf ("Beta", x, 5, {2})
838s ***** error<cdf: chi2 distribution requires 1 parameter.> cdf ("chi2", x)
838s ***** error<cdf: Beta distribution requires 2 parameters.> cdf ("Beta", x, 5)
838s ***** error<cdf: Burr distribution requires 3 parameters.> cdf ("Burr", x, 5)
838s ***** error<cdf: Burr distribution requires 3 parameters.> cdf ("Burr", x, 5, 2)
838s 86 tests, 86 passed, 0 known failure, 0 skipped
838s [inst/dist_wrap/icdf.m]
838s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_wrap/icdf.m
838s ***** shared p
838s  p = [0.05:0.05:0.5];
838s ***** assert (icdf ("Beta", p, 5, 2), betainv (p, 5, 2))
838s ***** assert (icdf ("beta", p, 5, 2), betainv (p, 5, 2))
838s ***** assert (icdf ("Binomial", p, 5, 2), binoinv (p, 5, 2))
838s ***** assert (icdf ("bino", p, 5, 2), binoinv (p, 5, 2))
838s ***** assert (icdf ("Birnbaum-Saunders", p, 5, 2), bisainv (p, 5, 2))
838s ***** assert (icdf ("bisa", p, 5, 2), bisainv (p, 5, 2))
838s ***** assert (icdf ("Burr", p, 5, 2, 2), burrinv (p, 5, 2, 2))
838s ***** assert (icdf ("burr", p, 5, 2, 2), burrinv (p, 5, 2, 2))
838s ***** assert (icdf ("Cauchy", p, 5, 2), cauchyinv (p, 5, 2))
838s ***** assert (icdf ("cauchy", p, 5, 2), cauchyinv (p, 5, 2))
838s ***** assert (icdf ("Chi-squared", p, 5), chi2inv (p, 5))
838s ***** assert (icdf ("chi2", p, 5), chi2inv (p, 5))
838s ***** assert (icdf ("Extreme Value", p, 5, 2), evinv (p, 5, 2))
838s ***** assert (icdf ("ev", p, 5, 2), evinv (p, 5, 2))
838s ***** assert (icdf ("Exponential", p, 5), expinv (p, 5))
838s ***** assert (icdf ("exp", p, 5), expinv (p, 5))
838s ***** assert (icdf ("F-Distribution", p, 5, 2), finv (p, 5, 2))
838s ***** assert (icdf ("f", p, 5, 2), finv (p, 5, 2))
838s ***** assert (icdf ("Gamma", p, 5, 2), gaminv (p, 5, 2))
838s ***** assert (icdf ("gam", p, 5, 2), gaminv (p, 5, 2))
838s ***** assert (icdf ("Geometric", p, 5), geoinv (p, 5))
838s ***** assert (icdf ("geo", p, 5), geoinv (p, 5))
838s ***** assert (icdf ("Generalized Extreme Value", p, 5, 2, 2), gevinv (p, 5, 2, 2))
838s ***** assert (icdf ("gev", p, 5, 2, 2), gevinv (p, 5, 2, 2))
838s ***** assert (icdf ("Generalized Pareto", p, 5, 2, 2), gpinv (p, 5, 2, 2))
838s ***** assert (icdf ("gp", p, 5, 2, 2), gpinv (p, 5, 2, 2))
838s ***** assert (icdf ("Gumbel", p, 5, 2), gumbelinv (p, 5, 2))
838s ***** assert (icdf ("gumbel", p, 5, 2), gumbelinv (p, 5, 2))
838s ***** assert (icdf ("Half-normal", p, 5, 2), hninv (p, 5, 2))
838s ***** assert (icdf ("hn", p, 5, 2), hninv (p, 5, 2))
838s ***** assert (icdf ("Hypergeometric", p, 5, 2, 2), hygeinv (p, 5, 2, 2))
838s ***** assert (icdf ("hyge", p, 5, 2, 2), hygeinv (p, 5, 2, 2))
838s ***** assert (icdf ("Inverse Gaussian", p, 5, 2), invginv (p, 5, 2))
838s ***** assert (icdf ("invg", p, 5, 2), invginv (p, 5, 2))
838s ***** assert (icdf ("Laplace", p, 5, 2), laplaceinv (p, 5, 2))
838s ***** assert (icdf ("laplace", p, 5, 2), laplaceinv (p, 5, 2))
838s ***** assert (icdf ("Logistic", p, 5, 2), logiinv (p, 5, 2))
838s ***** assert (icdf ("logi", p, 5, 2), logiinv (p, 5, 2))
838s ***** assert (icdf ("Log-Logistic", p, 5, 2), loglinv (p, 5, 2))
838s ***** assert (icdf ("logl", p, 5, 2), loglinv (p, 5, 2))
838s ***** assert (icdf ("Lognormal", p, 5, 2), logninv (p, 5, 2))
838s ***** assert (icdf ("logn", p, 5, 2), logninv (p, 5, 2))
838s ***** assert (icdf ("Nakagami", p, 5, 2), nakainv (p, 5, 2))
838s ***** assert (icdf ("naka", p, 5, 2), nakainv (p, 5, 2))
838s ***** assert (icdf ("Negative Binomial", p, 5, 2), nbininv (p, 5, 2))
838s ***** assert (icdf ("nbin", p, 5, 2), nbininv (p, 5, 2))
838s ***** assert (icdf ("Noncentral F-Distribution", p, 5, 2, 2), ncfinv (p, 5, 2, 2))
838s ***** assert (icdf ("ncf", p, 5, 2, 2), ncfinv (p, 5, 2, 2))
838s ***** assert (icdf ("Noncentral Student T", p, 5, 2), nctinv (p, 5, 2))
839s ***** assert (icdf ("nct", p, 5, 2), nctinv (p, 5, 2))
839s ***** assert (icdf ("Noncentral Chi-Squared", p, 5, 2), ncx2inv (p, 5, 2))
840s ***** assert (icdf ("ncx2", p, 5, 2), ncx2inv (p, 5, 2))
840s ***** assert (icdf ("Normal", p, 5, 2), norminv (p, 5, 2))
840s ***** assert (icdf ("norm", p, 5, 2), norminv (p, 5, 2))
840s ***** assert (icdf ("Poisson", p, 5), poissinv (p, 5))
840s ***** assert (icdf ("poiss", p, 5), poissinv (p, 5))
840s ***** assert (icdf ("Rayleigh", p, 5), raylinv (p, 5))
840s ***** assert (icdf ("rayl", p, 5), raylinv (p, 5))
840s ***** assert (icdf ("Rician", p, 5, 1), riceinv (p, 5, 1))
841s ***** assert (icdf ("rice", p, 5, 1), riceinv (p, 5, 1))
842s ***** assert (icdf ("Student T", p, 5), tinv (p, 5))
842s ***** assert (icdf ("t", p, 5), tinv (p, 5))
842s ***** assert (icdf ("location-scale T", p, 5, 1, 2), tlsinv (p, 5, 1, 2))
842s ***** assert (icdf ("tls", p, 5, 1, 2), tlsinv (p, 5, 1, 2))
842s ***** assert (icdf ("Triangular", p, 5, 2, 2), triinv (p, 5, 2, 2))
842s ***** assert (icdf ("tri", p, 5, 2, 2), triinv (p, 5, 2, 2))
842s ***** assert (icdf ("Discrete Uniform", p, 5), unidinv (p, 5))
842s ***** assert (icdf ("unid", p, 5), unidinv (p, 5))
842s ***** assert (icdf ("Uniform", p, 5, 2), unifinv (p, 5, 2))
842s ***** assert (icdf ("unif", p, 5, 2), unifinv (p, 5, 2))
842s ***** assert (icdf ("Von Mises", p, 5, 2), vminv (p, 5, 2))
853s ***** assert (icdf ("vm", p, 5, 2), vminv (p, 5, 2))
864s ***** assert (icdf ("Weibull", p, 5, 2), wblinv (p, 5, 2))
864s ***** assert (icdf ("wbl", p, 5, 2), wblinv (p, 5, 2))
864s ***** error<icdf: distribution NAME must a char string.> icdf (1)
864s ***** error<icdf: distribution NAME must a char string.> icdf ({"beta"})
864s ***** error<icdf: P must be numeric.> icdf ("beta", {[1 2 3 4 5]})
864s ***** error<icdf: P must be numeric.> icdf ("beta", "text")
865s ***** error<icdf: values in P must be real.> icdf ("beta", 1+i)
865s ***** error<icdf: distribution parameters must be numeric.> ...
865s  icdf ("Beta", p, "a", 2)
865s ***** error<icdf: distribution parameters must be numeric.> ...
865s  icdf ("Beta", p, 5, "")
865s ***** error<icdf: distribution parameters must be numeric.> ...
865s  icdf ("Beta", p, 5, {2})
865s ***** error<icdf: chi2 distribution requires 1 parameter.> icdf ("chi2", p)
865s ***** error<icdf: Beta distribution requires 2 parameters.> icdf ("Beta", p, 5)
865s ***** error<icdf: Burr distribution requires 3 parameters.> icdf ("Burr", p, 5)
865s ***** error<icdf: Burr distribution requires 3 parameters.> icdf ("Burr", p, 5, 2)
865s 86 tests, 86 passed, 0 known failure, 0 skipped
865s [inst/dist_wrap/makedist.m]
865s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_wrap/makedist.m
865s ***** test
865s  pd = makedist ("beta");
865s  assert (class (pd), "BetaDistribution");
865s  assert (pd.a, 1);
865s  assert (pd.b, 1);
865s ***** test
865s  pd = makedist ("beta", "a", 5);
865s  assert (pd.a, 5);
865s  assert (pd.b, 1);
865s ***** test
865s  pd = makedist ("beta", "b", 5);
865s  assert (pd.a, 1);
865s  assert (pd.b, 5);
865s ***** test
865s  pd = makedist ("beta", "a", 3, "b", 5);
865s  assert (pd.a, 3);
865s  assert (pd.b, 5);
865s ***** test
865s  pd = makedist ("binomial");
865s  assert (class (pd), "BinomialDistribution");
865s  assert (pd.N, 1);
865s  assert (pd.p, 0.5);
865s ***** test
865s  pd = makedist ("binomial", "N", 5);
865s  assert (pd.N, 5);
865s  assert (pd.p, 0.5);
865s ***** test
865s  pd = makedist ("binomial", "p", 0.2);
865s  assert (pd.N, 1);
865s  assert (pd.p, 0.2);
865s ***** test
865s  pd = makedist ("binomial", "N", 3, "p", 0.3);
865s  assert (pd.N, 3);
865s  assert (pd.p, 0.3);
865s ***** test
865s  pd = makedist ("birnbaumsaunders");
865s  assert (class (pd), "BirnbaumSaundersDistribution");
865s  assert (pd.beta, 1);
865s  assert (pd.gamma, 1);
865s ***** test
865s  pd = makedist ("birnbaumsaunders", "beta", 5);
865s  assert (pd.beta, 5);
865s  assert (pd.gamma, 1);
865s ***** test
865s  pd = makedist ("birnbaumsaunders", "gamma", 5);
865s  assert (pd.beta, 1);
865s  assert (pd.gamma, 5);
865s ***** test
865s  pd = makedist ("birnbaumsaunders", "beta", 3, "gamma", 5);
865s  assert (pd.beta, 3);
865s  assert (pd.gamma, 5);
865s ***** test
865s  pd = makedist ("burr");
865s  assert (class (pd), "BurrDistribution");
865s  assert (pd.alpha, 1);
865s  assert (pd.c, 1);
865s  assert (pd.k, 1);
865s ***** test
865s  pd = makedist ("burr", "k", 5);
865s  assert (pd.alpha, 1);
865s  assert (pd.c, 1);
865s  assert (pd.k, 5);
865s ***** test
865s  pd = makedist ("burr", "c", 5);
865s  assert (pd.alpha, 1);
865s  assert (pd.c, 5);
865s  assert (pd.k, 1);
865s ***** test
865s  pd = makedist ("burr", "alpha", 3, "c", 5);
865s  assert (pd.alpha, 3);
865s  assert (pd.c, 5);
865s  assert (pd.k, 1);
865s ***** test
865s  pd = makedist ("burr", "k", 3, "c", 5);
865s  assert (pd.alpha, 1);
865s  assert (pd.c, 5);
865s  assert (pd.k, 3);
865s ***** test
865s  pd = makedist ("exponential");
865s  assert (class (pd), "ExponentialDistribution");
865s  assert (pd.mu, 1);
865s ***** test
865s  pd = makedist ("exponential", "mu", 5);
865s  assert (pd.mu, 5);
865s ***** test
865s  pd = makedist ("extremevalue");
865s  assert (class (pd), "ExtremeValueDistribution");
865s  assert (pd.mu, 0);
865s  assert (pd.sigma, 1);
865s ***** test
865s  pd = makedist ("extremevalue", "mu", 5);
865s  assert (class (pd), "ExtremeValueDistribution");
865s  assert (pd.mu, 5);
865s  assert (pd.sigma, 1);
865s ***** test
865s  pd = makedist ("ev", "sigma", 5);
865s  assert (class (pd), "ExtremeValueDistribution");
865s  assert (pd.mu, 0);
865s  assert (pd.sigma, 5);
865s ***** test
865s  pd = makedist ("ev", "mu", -3, "sigma", 5);
865s  assert (class (pd), "ExtremeValueDistribution");
865s  assert (pd.mu, -3);
865s  assert (pd.sigma, 5);
865s ***** test
865s  pd = makedist ("gamma");
865s  assert (class (pd), "GammaDistribution");
865s  assert (pd.a, 1);
865s  assert (pd.b, 1);
865s ***** test
865s  pd = makedist ("gamma", "a", 5);
865s  assert (pd.a, 5);
865s  assert (pd.b, 1);
865s ***** test
865s  pd = makedist ("gamma", "b", 5);
865s  assert (pd.a, 1);
865s  assert (pd.b, 5);
865s ***** test
865s  pd = makedist ("gamma", "a", 3, "b", 5);
865s  assert (pd.a, 3);
865s  assert (pd.b, 5);
865s ***** test
865s  pd = makedist ("GeneralizedExtremeValue");
865s  assert (class (pd), "GeneralizedExtremeValueDistribution");
865s  assert (pd.k, 0);
865s  assert (pd.sigma, 1);
865s  assert (pd.mu, 0);
865s ***** test
865s  pd = makedist ("GeneralizedExtremeValue", "k", 5);
865s  assert (pd.k, 5);
865s  assert (pd.sigma, 1);
865s  assert (pd.mu, 0);
865s ***** test
865s  pd = makedist ("GeneralizedExtremeValue", "sigma", 5);
865s  assert (pd.k, 0);
865s  assert (pd.sigma, 5);
865s  assert (pd.mu, 0);
865s ***** test
865s  pd = makedist ("GeneralizedExtremeValue", "k", 3, "sigma", 5);
865s  assert (pd.k, 3);
865s  assert (pd.sigma, 5);
865s  assert (pd.mu, 0);
865s ***** test
865s  pd = makedist ("GeneralizedExtremeValue", "mu", 3, "sigma", 5);
865s  assert (pd.k, 0);
865s  assert (pd.sigma, 5);
865s  assert (pd.mu, 3);
865s ***** test
865s  pd = makedist ("GeneralizedPareto");
865s  assert (class (pd), "GeneralizedParetoDistribution");
865s  assert (pd.k, 1);
865s  assert (pd.sigma, 1);
865s  assert (pd.theta, 1);
865s ***** test
865s  pd = makedist ("GeneralizedPareto", "k", 5);
865s  assert (pd.k, 5);
865s  assert (pd.sigma, 1);
865s  assert (pd.theta, 1);
865s ***** test
865s  pd = makedist ("GeneralizedPareto", "sigma", 5);
865s  assert (pd.k, 1);
865s  assert (pd.sigma, 5);
865s  assert (pd.theta, 1);
865s ***** test
865s  pd = makedist ("GeneralizedPareto", "k", 3, "sigma", 5);
865s  assert (pd.k, 3);
865s  assert (pd.sigma, 5);
865s  assert (pd.theta, 1);
865s ***** test
865s  pd = makedist ("GeneralizedPareto", "theta", 3, "sigma", 5);
865s  assert (pd.k, 1);
865s  assert (pd.sigma, 5);
865s  assert (pd.theta, 3);
865s ***** test
865s  pd = makedist ("HalfNormal");
865s  assert (class (pd), "HalfNormalDistribution");
865s  assert (pd.mu, 0);
865s  assert (pd.sigma, 1);
865s ***** test
865s  pd = makedist ("HalfNormal", "mu", 5);
865s  assert (pd.mu, 5);
865s  assert (pd.sigma, 1);
865s ***** test
865s  pd = makedist ("HalfNormal", "sigma", 5);
865s  assert (pd.mu, 0);
865s  assert (pd.sigma, 5);
865s ***** test
865s  pd = makedist ("HalfNormal", "mu", 3, "sigma", 5);
865s  assert (pd.mu, 3);
865s  assert (pd.sigma, 5);
865s ***** test
865s  pd = makedist ("InverseGaussian");
865s  assert (class (pd), "InverseGaussianDistribution");
865s  assert (pd.mu, 1);
865s  assert (pd.lambda, 1);
865s ***** test
865s  pd = makedist ("InverseGaussian", "mu", 5);
865s  assert (pd.mu, 5);
865s  assert (pd.lambda, 1);
865s ***** test
865s  pd = makedist ("InverseGaussian", "lambda", 5);
865s  assert (pd.mu, 1);
865s  assert (pd.lambda, 5);
865s ***** test
865s  pd = makedist ("InverseGaussian", "mu", 3, "lambda", 5);
865s  assert (pd.mu, 3);
865s  assert (pd.lambda, 5);
865s ***** test
865s  pd = makedist ("logistic");
865s  assert (class (pd), "LogisticDistribution");
865s  assert (pd.mu, 0);
865s  assert (pd.sigma, 1);
865s ***** test
865s  pd = makedist ("logistic", "mu", 5);
865s  assert (pd.mu, 5);
865s  assert (pd.sigma, 1);
865s ***** test
865s  pd = makedist ("logistic", "sigma", 5);
865s  assert (pd.mu, 0);
865s  assert (pd.sigma, 5);
865s ***** test
865s  pd = makedist ("logistic", "mu", 3, "sigma", 5);
865s  assert (pd.mu, 3);
865s  assert (pd.sigma, 5);
865s ***** test
865s  pd = makedist ("loglogistic");
865s  assert (class (pd), "LoglogisticDistribution");
865s  assert (pd.mu, 0);
865s  assert (pd.sigma, 1);
865s ***** test
865s  pd = makedist ("loglogistic", "mu", 5);
865s  assert (pd.mu, 5);
865s  assert (pd.sigma, 1);
865s ***** test
865s  pd = makedist ("loglogistic", "sigma", 5);
865s  assert (pd.mu, 0);
865s  assert (pd.sigma, 5);
865s ***** test
865s  pd = makedist ("loglogistic", "mu", 3, "sigma", 5);
865s  assert (pd.mu, 3);
865s  assert (pd.sigma, 5);
865s ***** test
865s  pd = makedist ("Lognormal");
865s  assert (class (pd), "LognormalDistribution");
865s  assert (pd.mu, 0);
865s  assert (pd.sigma, 1);
865s ***** test
865s  pd = makedist ("Lognormal", "mu", 5);
865s  assert (pd.mu, 5);
865s  assert (pd.sigma, 1);
865s ***** test
865s  pd = makedist ("Lognormal", "sigma", 5);
865s  assert (pd.mu, 0);
865s  assert (pd.sigma, 5);
865s ***** test
865s  pd = makedist ("Lognormal", "mu", -3, "sigma", 5);
865s  assert (pd.mu, -3);
865s  assert (pd.sigma, 5);
865s ***** test
865s  pd = makedist ("Loguniform");
865s  assert (class (pd), "LoguniformDistribution");
865s  assert (pd.Lower, 1);
865s  assert (pd.Upper, 4);
865s ***** test
865s  pd = makedist ("Loguniform", "Lower", 2);
865s  assert (pd.Lower, 2);
865s  assert (pd.Upper, 4);
865s ***** test
865s  pd = makedist ("Loguniform", "Lower", 1, "Upper", 3);
865s  assert (pd.Lower, 1);
865s  assert (pd.Upper, 3);
865s ***** test
865s  pd = makedist ("Multinomial");
865s  assert (class (pd), "MultinomialDistribution");
865s  assert (pd.Probabilities, [0.5, 0.5]);
865s ***** test
865s  pd = makedist ("Multinomial", "Probabilities", [0.2, 0.3, 0.1, 0.4]);
865s  assert (class (pd), "MultinomialDistribution");
865s  assert (pd.Probabilities, [0.2, 0.3, 0.1, 0.4]);
865s ***** test
865s  pd = makedist ("Nakagami");
865s  assert (class (pd), "NakagamiDistribution");
865s  assert (pd.mu, 1);
865s  assert (pd.omega, 1);
865s ***** test
865s  pd = makedist ("Nakagami", "mu", 5);
865s  assert (class (pd), "NakagamiDistribution");
865s  assert (pd.mu, 5);
865s  assert (pd.omega, 1);
865s ***** test
865s  pd = makedist ("Nakagami", "omega", 0.3);
865s  assert (class (pd), "NakagamiDistribution");
865s  assert (pd.mu, 1);
865s  assert (pd.omega, 0.3);
865s ***** test
865s  pd = makedist ("NegativeBinomial");
865s  assert (class (pd), "NegativeBinomialDistribution");
865s  assert (pd.R, 1);
865s  assert (pd.P, 0.5);
865s ***** test
865s  pd = makedist ("NegativeBinomial", "R", 5);
865s  assert (class (pd), "NegativeBinomialDistribution");
865s  assert (pd.R, 5);
865s  assert (pd.P, 0.5);
865s ***** test
865s  pd = makedist ("NegativeBinomial", "p", 0.3);
865s  assert (class (pd), "NegativeBinomialDistribution");
865s  assert (pd.R, 1);
865s  assert (pd.P, 0.3);
865s ***** test
865s  pd = makedist ("Normal");
865s  assert (class (pd), "NormalDistribution");
865s  assert (pd.mu, 0);
865s  assert (pd.sigma, 1);
865s ***** test
865s  pd = makedist ("Normal", "mu", 5);
865s  assert (class (pd), "NormalDistribution");
865s  assert (pd.mu, 5);
865s  assert (pd.sigma, 1);
865s ***** test
865s  pd = makedist ("Normal", "sigma", 5);
865s  assert (class (pd), "NormalDistribution");
865s  assert (pd.mu, 0);
865s  assert (pd.sigma, 5);
865s ***** test
865s  pd = makedist ("Normal", "mu", -3, "sigma", 5);
865s  assert (class (pd), "NormalDistribution");
865s  assert (pd.mu, -3);
865s  assert (pd.sigma, 5);
865s ***** test
865s  pd = makedist ("PiecewiseLinear");
865s  assert (class (pd), "PiecewiseLinearDistribution");
865s  assert (pd.x, [0; 1]);
865s  assert (pd.Fx, [0; 1]);
865s ***** test
865s  pd = makedist ("PiecewiseLinear", "x", [0, 1, 2], "Fx", [0, 0.5, 1]);
865s  assert (pd.x, [0; 1; 2]);
865s  assert (pd.Fx, [0; 0.5; 1]);
865s ***** test
865s  pd = makedist ("Poisson");
865s  assert (class (pd), "PoissonDistribution");
865s  assert (pd.lambda, 1);
865s ***** test
865s  pd = makedist ("Poisson", "lambda", 5);
865s  assert (pd.lambda, 5);
865s ***** test
865s  pd = makedist ("Rayleigh");
865s  assert (class (pd), "RayleighDistribution");
865s  assert (pd.sigma, 1);
865s ***** test
865s  pd = makedist ("Rayleigh", "sigma", 5);
865s  assert (pd.sigma, 5);
865s ***** test
865s  pd = makedist ("Rician");
865s  assert (class (pd), "RicianDistribution");
865s  assert (pd.s, 1);
865s  assert (pd.sigma, 1);
865s ***** test
865s  pd = makedist ("Rician", "s", 3);
865s  assert (pd.s, 3);
865s  assert (pd.sigma, 1);
865s ***** test
865s  pd = makedist ("Rician", "sigma", 3);
865s  assert (pd.s, 1);
865s  assert (pd.sigma, 3);
865s ***** test
865s  pd = makedist ("Rician", "s", 2, "sigma", 3);
865s  assert (pd.s, 2);
865s  assert (pd.sigma, 3);
865s ***** warning
865s  pd = makedist ("stable");
865s  assert (class (pd), "double");
865s  assert (isempty (pd), true);
865s ***** test
865s  pd = makedist ("tlocationscale");
865s  assert (class (pd), "tLocationScaleDistribution");
865s  assert (pd.mu, 0);
865s  assert (pd.sigma, 1);
865s  assert (pd.nu, 5);
865s ***** test
865s  pd = makedist ("tlocationscale", "mu", 5);
865s  assert (pd.mu, 5);
865s  assert (pd.sigma, 1);
865s  assert (pd.nu, 5);
865s ***** test
865s  pd = makedist ("tlocationscale", "sigma", 2);
865s  assert (pd.mu, 0);
865s  assert (pd.sigma, 2);
865s  assert (pd.nu, 5);
865s ***** test
865s  pd = makedist ("tlocationscale", "mu", 5, "sigma", 2);
865s  assert (pd.mu, 5);
865s  assert (pd.sigma, 2);
865s  assert (pd.nu, 5);
865s ***** test
865s  pd = makedist ("tlocationscale", "nu", 1, "sigma", 2);
865s  assert (pd.mu, 0);
865s  assert (pd.sigma, 2);
865s  assert (pd.nu, 1);
865s ***** test
865s  pd = makedist ("tlocationscale", "mu", -2, "sigma", 3, "nu", 1);
865s  assert (pd.mu, -2);
865s  assert (pd.sigma, 3);
865s  assert (pd.nu, 1);
865s ***** test
865s  pd = makedist ("Triangular");
865s  assert (class (pd), "TriangularDistribution");
865s  assert (pd.A, 0);
865s  assert (pd.B, 0.5);
865s  assert (pd.C, 1);
865s ***** test
865s  pd = makedist ("Triangular", "A", -2);
865s  assert (pd.A, -2);
865s  assert (pd.B, 0.5);
865s  assert (pd.C, 1);
865s ***** test
865s  pd = makedist ("Triangular", "A", 0.5, "B", 0.9);
865s  assert (pd.A, 0.5);
865s  assert (pd.B, 0.9);
865s  assert (pd.C, 1);
865s ***** test
865s  pd = makedist ("Triangular", "A", 1, "B", 2, "C", 5);
865s  assert (pd.A, 1);
865s  assert (pd.B, 2);
865s  assert (pd.C, 5);
865s ***** test
865s  pd = makedist ("Uniform");
865s  assert (class (pd), "UniformDistribution");
865s  assert (pd.Lower, 0);
865s  assert (pd.Upper, 1);
865s ***** test
865s  pd = makedist ("Uniform", "Lower", -2);
865s  assert (pd.Lower, -2);
865s  assert (pd.Upper, 1);
865s ***** test
865s  pd = makedist ("Uniform", "Lower", 1, "Upper", 3);
865s  assert (pd.Lower, 1);
865s  assert (pd.Upper, 3);
865s ***** test
865s  pd = makedist ("Weibull");
865s  assert (class (pd), "WeibullDistribution");
865s  assert (pd.lambda, 1);
865s  assert (pd.k, 1);
865s ***** test
865s  pd = makedist ("Weibull", "lambda", 3);
865s  assert (pd.lambda, 3);
865s  assert (pd.k, 1);
865s ***** test
865s  pd = makedist ("Weibull", "lambda", 3, "k", 2);
865s  assert (pd.lambda, 3);
865s  assert (pd.k, 2);
865s ***** error <makedist: DISTNAME must be a character vector.> makedist (1)
865s ***** error <makedist: DISTNAME must be a character vector.> makedist (["as";"sd"])
865s ***** error <makedist: unrecognized distribution name.> makedist ("some")
865s ***** error <makedist: optional arguments must be in NAME-VALUE pairs.> ...
865s  makedist ("Beta", "a")
865s ***** error <makedist: unknown parameter for 'Beta' distribution.> ...
865s  makedist ("Beta", "a", 1, "Q", 23)
865s ***** error <makedist: unknown parameter for 'Binomial' distribution.> ...
865s  makedist ("Binomial", "N", 1, "Q", 23)
865s ***** error <makedist: unknown parameter for 'BirnbaumSaunders' distribution.> ...
865s  makedist ("BirnbaumSaunders", "N", 1)
865s ***** error <makedist: unknown parameter for 'Burr' distribution.> ...
865s  makedist ("Burr", "lambda", 1, "sdfs", 34)
865s ***** error <makedist: unknown parameter for 'ExtremeValue' distribution.> ...
865s  makedist ("extremevalue", "mu", 1, "sdfs", 34)
865s ***** error <makedist: unknown parameter for 'Exponential' distribution.> ...
865s  makedist ("exponential", "mu", 1, "sdfs", 34)
865s ***** error <makedist: unknown parameter for 'Gamma' distribution.> ...
865s  makedist ("Gamma", "k", 1, "sdfs", 34)
865s ***** error <makedist: unknown parameter for 'GeneralizedExtremeValue' distribution.> ...
865s  makedist ("GeneralizedExtremeValue", "k", 1, "sdfs", 34)
865s ***** error <makedist: unknown parameter for 'GeneralizedPareto' distribution.> ...
865s  makedist ("GeneralizedPareto", "k", 1, "sdfs", 34)
865s ***** error <makedist: unknown parameter for 'HalfNormal' distribution.> ...
865s  makedist ("HalfNormal", "k", 1, "sdfs", 34)
865s ***** error <makedist: unknown parameter for 'InverseGaussian' distribution.> ...
865s  makedist ("InverseGaussian", "k", 1, "sdfs", 34)
865s ***** error <makedist: unknown parameter for 'Logistic' distribution.> ...
865s  makedist ("Logistic", "k", 1, "sdfs", 34)
865s ***** error <makedist: unknown parameter for 'Loglogistic' distribution.> ...
865s  makedist ("Loglogistic", "k", 1, "sdfs", 34)
865s ***** error <makedist: unknown parameter for 'Lognormal' distribution.> ...
865s  makedist ("Lognormal", "k", 1, "sdfs", 34)
865s ***** error <makedist: unknown parameter for 'Loguniform' distribution.> ...
865s  makedist ("Loguniform", "k", 1, "sdfs", 34)
865s ***** error <makedist: unknown parameter for 'Multinomial' distribution.> ...
865s  makedist ("Multinomial", "k", 1, "sdfs", 34)
865s ***** error <makedist: unknown parameter for 'Nakagami' distribution.> ...
865s  makedist ("Nakagami", "mu", 1, "sdfs", 34)
865s ***** error <makedist: unknown parameter for 'NegativeBinomial' distribution.> ...
865s  makedist ("NegativeBinomial", "mu", 1, "sdfs", 34)
865s ***** error <makedist: unknown parameter for 'Normal' distribution.> ...
865s  makedist ("Normal", "mu", 1, "sdfs", 34)
865s ***** error <makedist: unknown parameter for 'PiecewiseLinear' distribution.> ...
865s  makedist ("PiecewiseLinear", "mu", 1, "sdfs", 34)
865s ***** error <makedist: unknown parameter for 'Poisson' distribution.> ...
865s  makedist ("Poisson", "mu", 1, "sdfs", 34)
865s ***** error <makedist: unknown parameter for 'Rayleigh' distribution.> ...
865s  makedist ("Rayleigh", "mu", 1, "sdfs", 34)
865s ***** error <makedist: unknown parameter for 'Rician' distribution.> ...
865s  makedist ("Rician", "mu", 1, "sdfs", 34)
865s ***** error <makedist: unknown parameter for 'Stable' distribution.> ...
865s  makedist ("Stable", "mu", 1, "sdfs", 34)
865s ***** error <makedist: unknown parameter for 'tLocationScale' distribution.> ...
865s  makedist ("tLocationScale", "mu", 1, "sdfs", 34)
865s ***** error <makedist: unknown parameter for 'Triangular' distribution.> ...
865s  makedist ("Triangular", "mu", 1, "sdfs", 34)
865s ***** error <makedist: unknown parameter for 'Uniform' distribution.> ...
865s  makedist ("Uniform", "mu", 1, "sdfs", 34)
865s ***** error <makedist: unknown parameter for 'Weibull' distribution.> ...
865s  makedist ("Weibull", "mu", 1, "sdfs", 34)
865s 131 tests, 131 passed, 0 known failure, 0 skipped
865s [inst/dist_wrap/mle.m]
865s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_wrap/mle.m
865s ***** error <mle: X must be a numeric vector of real values.> mle (ones (2))
865s ***** error <mle: X must be a numeric vector of real values.> mle ("text")
865s ***** error <mle: X must be a numeric vector of real values.> mle ([1, 2, 3, i, 5])
865s ***** error <mle: optional arguments must be in NAME-VALUE pairs.> ...
865s  mle ([1:50], "distribution")
865s ***** error <mle: 'censoring' argument must have the same size as the input data in X.> ...
865s  mle ([1:50], "censoring", logical ([1,0,1,0]))
865s ***** error <mle: 'frequency' argument must have the same size as the input data in X.> ...
865s  mle ([1:50], "frequency", [1,0,1,0])
865s ***** error <mle: 'frequency' argument must contain non-negative integer values.> ...
865s  mle ([1 0 1 0], "frequency", [-1 1 0 0])
865s ***** error <mle: 'frequency' argument must contain non-negative integer values.> ...
865s  mle ([1 0 1 0], "distribution", "nbin", "frequency", [-1 1 0 0])
865s ***** error <mle: invalid value for 'alpha' argument.> mle ([1:50], "alpha", [0.05, 0.01])
865s ***** error <mle: invalid value for 'alpha' argument.> mle ([1:50], "alpha", 1)
865s ***** error <mle: invalid value for 'alpha' argument.> mle ([1:50], "alpha", -1)
865s ***** error <mle: invalid value for 'alpha' argument.> mle ([1:50], "alpha", i)
865s ***** error <mle: 'ntrials' argument must be a positive integer scalar value.> ...
865s  mle ([1:50], "ntrials", -1)
865s ***** error <mle: 'ntrials' argument must be a positive integer scalar value.> ...
865s  mle ([1:50], "ntrials", [20, 50])
865s ***** error <mle: 'ntrials' argument must be a positive integer scalar value.> ...
865s  mle ([1:50], "ntrials", [20.3])
865s ***** error <mle: 'ntrials' argument must be a positive integer scalar value.> ...
865s  mle ([1:50], "ntrials", 3i)
865s ***** error <mle: 'options' argument must be a structure compatible for 'fminsearch'.> ...
865s  mle ([1:50], "options", 4)
865s ***** error <mle: 'options' argument must be a structure compatible for 'fminsearch'.> ...
865s  mle ([1:50], "options", struct ("x", 3))
865s ***** error <mle: unknown parameter name.> mle ([1:50], "NAME", "value")
865s ***** error <mle: censoring is not supported for the Bernoulli distribution.> ...
865s  mle ([1 0 1 0], "distribution", "bernoulli", "censoring", [1 1 0 0])
865s ***** error <mle: invalid data for the Bernoulli distribution.> ...
865s  mle ([1 2 1 0], "distribution", "bernoulli")
865s ***** error <mle: censoring is not supported for the Beta distribution.> ...
865s  mle ([1 0 1 0], "distribution", "beta", "censoring", [1 1 0 0])
865s ***** error <mle: censoring is not supported for the Binomial distribution.> ...
865s  mle ([1 0 1 0], "distribution", "bino", "censoring", [1 1 0 0])
865s ***** error <mle: 'Ntrials' parameter is required for the Binomial distribution.> ...
865s  mle ([1 0 1 0], "distribution", "bino")
865s ***** error <mle: censoring is not supported for the Geometric distribution.> ...
865s  mle ([1 0 1 0], "distribution", "geo", "censoring", [1 1 0 0])
865s ***** error <mle: censoring is not supported for the Generalized Extreme Value distribution.> ...
865s  mle ([1 0 1 0], "distribution", "gev", "censoring", [1 1 0 0])
865s ***** error <mle: censoring is not supported for the Generalized Pareto distribution.> ...
865s  mle ([1 0 1 0], "distribution", "gp", "censoring", [1 1 0 0])
865s ***** error <mle: invalid 'theta' location parameter for the Generalized Pareto distribution.> ...
865s  mle ([1 0 -1 0], "distribution", "gp")
865s ***** error <mle: censoring is not supported for the Half Normal distribution.> ...
865s  mle ([1 0 1 0], "distribution", "hn", "censoring", [1 1 0 0])
865s ***** error <mle: invalid 'mu' location parameter for the Half Normal distribution.> ...
865s  mle ([1 0 -1 0], "distribution", "hn")
865s ***** error <mle: censoring is not supported for the Negative Binomial distribution.> ...
865s  mle ([1 0 1 0], "distribution", "nbin", "censoring", [1 1 0 0])
865s ***** error <mle: censoring is not supported for the Poisson distribution.> ...
865s  mle ([1 0 1 0], "distribution", "poisson", "censoring", [1 1 0 0])
865s ***** error <mle: censoring is not supported for the Discrete Uniform distribution.> ...
865s  mle ([1 0 1 0], "distribution", "unid", "censoring", [1 1 0 0])
865s ***** error <mle: censoring is not supported for the Continuous Uniform distribution.> ...
865s  mle ([1 0 1 0], "distribution", "unif", "censoring", [1 1 0 0])
865s ***** error <mle: unrecognized distribution name.> mle ([1:50], "distribution", "value")
865s ***** error <mle: censoring is not supported for the Continuous Uniform distribution.> ...
865s  mle ([1 0 1 0], "distribution", "unif", "censoring", [1 1 0 0])
865s 36 tests, 36 passed, 0 known failure, 0 skipped
865s [inst/dist_wrap/pdf.m]
865s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_wrap/pdf.m
865s ***** shared x
865s  x = [1:5];
865s ***** assert (pdf ("Beta", x, 5, 2), betapdf (x, 5, 2))
865s ***** assert (pdf ("beta", x, 5, 2), betapdf (x, 5, 2))
865s ***** assert (pdf ("Binomial", x, 5, 2), binopdf (x, 5, 2))
865s ***** assert (pdf ("bino", x, 5, 2), binopdf (x, 5, 2))
865s ***** assert (pdf ("Birnbaum-Saunders", x, 5, 2), bisapdf (x, 5, 2))
865s ***** assert (pdf ("bisa", x, 5, 2), bisapdf (x, 5, 2))
865s ***** assert (pdf ("Burr", x, 5, 2, 2), burrpdf (x, 5, 2, 2))
865s ***** assert (pdf ("burr", x, 5, 2, 2), burrpdf (x, 5, 2, 2))
865s ***** assert (pdf ("Cauchy", x, 5, 2), cauchypdf (x, 5, 2))
865s ***** assert (pdf ("cauchy", x, 5, 2), cauchypdf (x, 5, 2))
865s ***** assert (pdf ("Chi-squared", x, 5), chi2pdf (x, 5))
865s ***** assert (pdf ("chi2", x, 5), chi2pdf (x, 5))
865s ***** assert (pdf ("Extreme Value", x, 5, 2), evpdf (x, 5, 2))
865s ***** assert (pdf ("ev", x, 5, 2), evpdf (x, 5, 2))
865s ***** assert (pdf ("Exponential", x, 5), exppdf (x, 5))
865s ***** assert (pdf ("exp", x, 5), exppdf (x, 5))
865s ***** assert (pdf ("F-Distribution", x, 5, 2), fpdf (x, 5, 2))
865s ***** assert (pdf ("f", x, 5, 2), fpdf (x, 5, 2))
865s ***** assert (pdf ("Gamma", x, 5, 2), gampdf (x, 5, 2))
865s ***** assert (pdf ("gam", x, 5, 2), gampdf (x, 5, 2))
865s ***** assert (pdf ("Geometric", x, 5), geopdf (x, 5))
865s ***** assert (pdf ("geo", x, 5), geopdf (x, 5))
865s ***** assert (pdf ("Generalized Extreme Value", x, 5, 2, 2), gevpdf (x, 5, 2, 2))
865s ***** assert (pdf ("gev", x, 5, 2, 2), gevpdf (x, 5, 2, 2))
865s ***** assert (pdf ("Generalized Pareto", x, 5, 2, 2), gppdf (x, 5, 2, 2))
865s ***** assert (pdf ("gp", x, 5, 2, 2), gppdf (x, 5, 2, 2))
865s ***** assert (pdf ("Gumbel", x, 5, 2), gumbelpdf (x, 5, 2))
865s ***** assert (pdf ("gumbel", x, 5, 2), gumbelpdf (x, 5, 2))
865s ***** assert (pdf ("Half-normal", x, 5, 2), hnpdf (x, 5, 2))
866s ***** assert (pdf ("hn", x, 5, 2), hnpdf (x, 5, 2))
866s ***** assert (pdf ("Hypergeometric", x, 5, 2, 2), hygepdf (x, 5, 2, 2))
866s ***** assert (pdf ("hyge", x, 5, 2, 2), hygepdf (x, 5, 2, 2))
866s ***** assert (pdf ("Inverse Gaussian", x, 5, 2), invgpdf (x, 5, 2))
866s ***** assert (pdf ("invg", x, 5, 2), invgpdf (x, 5, 2))
866s ***** assert (pdf ("Laplace", x, 5, 2), laplacepdf (x, 5, 2))
866s ***** assert (pdf ("laplace", x, 5, 2), laplacepdf (x, 5, 2))
866s ***** assert (pdf ("Logistic", x, 5, 2), logipdf (x, 5, 2))
866s ***** assert (pdf ("logi", x, 5, 2), logipdf (x, 5, 2))
866s ***** assert (pdf ("Log-Logistic", x, 5, 2), loglpdf (x, 5, 2))
866s ***** assert (pdf ("logl", x, 5, 2), loglpdf (x, 5, 2))
866s ***** assert (pdf ("Lognormal", x, 5, 2), lognpdf (x, 5, 2))
866s ***** assert (pdf ("logn", x, 5, 2), lognpdf (x, 5, 2))
866s ***** assert (pdf ("Nakagami", x, 5, 2), nakapdf (x, 5, 2))
866s ***** assert (pdf ("naka", x, 5, 2), nakapdf (x, 5, 2))
866s ***** assert (pdf ("Negative Binomial", x, 5, 2), nbinpdf (x, 5, 2))
866s ***** assert (pdf ("nbin", x, 5, 2), nbinpdf (x, 5, 2))
866s ***** assert (pdf ("Noncentral F-Distribution", x, 5, 2, 2), ncfpdf (x, 5, 2, 2))
866s ***** assert (pdf ("ncf", x, 5, 2, 2), ncfpdf (x, 5, 2, 2))
866s ***** assert (pdf ("Noncentral Student T", x, 5, 2), nctpdf (x, 5, 2))
866s ***** assert (pdf ("nct", x, 5, 2), nctpdf (x, 5, 2))
866s ***** assert (pdf ("Noncentral Chi-Squared", x, 5, 2), ncx2pdf (x, 5, 2))
866s ***** assert (pdf ("ncx2", x, 5, 2), ncx2pdf (x, 5, 2))
866s ***** assert (pdf ("Normal", x, 5, 2), normpdf (x, 5, 2))
866s ***** assert (pdf ("norm", x, 5, 2), normpdf (x, 5, 2))
866s ***** assert (pdf ("Poisson", x, 5), poisspdf (x, 5))
866s ***** assert (pdf ("poiss", x, 5), poisspdf (x, 5))
866s ***** assert (pdf ("Rayleigh", x, 5), raylpdf (x, 5))
866s ***** assert (pdf ("rayl", x, 5), raylpdf (x, 5))
866s ***** assert (pdf ("Rician", x, 5, 1), ricepdf (x, 5, 1))
866s ***** assert (pdf ("rice", x, 5, 1), ricepdf (x, 5, 1))
866s ***** assert (pdf ("Student T", x, 5), tpdf (x, 5))
866s ***** assert (pdf ("t", x, 5), tpdf (x, 5))
866s ***** assert (pdf ("location-scale T", x, 5, 1, 2), tlspdf (x, 5, 1, 2))
866s ***** assert (pdf ("tls", x, 5, 1, 2), tlspdf (x, 5, 1, 2))
866s ***** assert (pdf ("Triangular", x, 5, 2, 2), tripdf (x, 5, 2, 2))
866s ***** assert (pdf ("tri", x, 5, 2, 2), tripdf (x, 5, 2, 2))
866s ***** assert (pdf ("Discrete Uniform", x, 5), unidpdf (x, 5))
866s ***** assert (pdf ("unid", x, 5), unidpdf (x, 5))
866s ***** assert (pdf ("Uniform", x, 5, 2), unifpdf (x, 5, 2))
866s ***** assert (pdf ("unif", x, 5, 2), unifpdf (x, 5, 2))
866s ***** assert (pdf ("Von Mises", x, 5, 2), vmpdf (x, 5, 2))
866s ***** assert (pdf ("vm", x, 5, 2), vmpdf (x, 5, 2))
866s ***** assert (pdf ("Weibull", x, 5, 2), wblpdf (x, 5, 2))
866s ***** assert (pdf ("wbl", x, 5, 2), wblpdf (x, 5, 2))
866s ***** error<pdf: distribution NAME must a char string.> pdf (1)
866s ***** error<pdf: distribution NAME must a char string.> pdf ({"beta"})
866s ***** error<pdf: X must be numeric.> pdf ("beta", {[1 2 3 4 5]})
866s ***** error<pdf: X must be numeric.> pdf ("beta", "text")
866s ***** error<pdf: values in X must be real.> pdf ("beta", 1+i)
866s ***** error<pdf: distribution parameters must be numeric.> ...
866s  pdf ("Beta", x, "a", 2)
866s ***** error<pdf: distribution parameters must be numeric.> ...
866s  pdf ("Beta", x, 5, "")
866s ***** error<pdf: distribution parameters must be numeric.> ...
866s  pdf ("Beta", x, 5, {2})
866s ***** error<pdf: chi2 distribution requires 1 parameter.> pdf ("chi2", x)
866s ***** error<pdf: Beta distribution requires 2 parameters.> pdf ("Beta", x, 5)
866s ***** error<pdf: Burr distribution requires 3 parameters.> pdf ("Burr", x, 5)
866s ***** error<pdf: Burr distribution requires 3 parameters.> pdf ("Burr", x, 5, 2)
866s 86 tests, 86 passed, 0 known failure, 0 skipped
866s [inst/dist_wrap/random.m]
866s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_wrap/random.m
866s ***** assert (size (random ("Beta", 5, 2, 2, 10)), size (betarnd (5, 2, 2, 10)))
866s ***** assert (size (random ("beta", 5, 2, 2, 10)), size (betarnd (5, 2, 2, 10)))
866s ***** assert (size (random ("Binomial", 5, 2, [10, 20])), size (binornd (5, 2, 10, 20)))
866s ***** assert (size (random ("bino", 5, 2, [10, 20])), size (binornd (5, 2, 10, 20)))
866s ***** assert (size (random ("Birnbaum-Saunders", 5, 2, [10, 20])), size (bisarnd (5, 2, 10, 20)))
866s ***** assert (size (random ("bisa", 5, 2, [10, 20])), size (bisarnd (5, 2, 10, 20)))
866s ***** assert (size (random ("Burr", 5, 2, 2, [10, 20])), size (burrrnd (5, 2, 2, 10, 20)))
866s ***** assert (size (random ("burr", 5, 2, 2, [10, 20])), size (burrrnd (5, 2, 2, 10, 20)))
866s ***** assert (size (random ("Cauchy", 5, 2, [10, 20])), size (cauchyrnd (5, 2, 10, 20)))
866s ***** assert (size (random ("cauchy", 5, 2, [10, 20])), size (cauchyrnd (5, 2, 10, 20)))
866s ***** assert (size (random ("Chi-squared", 5, [10, 20])), size (chi2rnd (5, 10, 20)))
866s ***** assert (size (random ("chi2", 5, [10, 20])), size (chi2rnd (5, 10, 20)))
866s ***** assert (size (random ("Extreme Value", 5, 2, [10, 20])), size (evrnd (5, 2, 10, 20)))
866s ***** assert (size (random ("ev", 5, 2, [10, 20])), size (evrnd (5, 2, 10, 20)))
866s ***** assert (size (random ("Exponential", 5, [10, 20])), size (exprnd (5, 10, 20)))
866s ***** assert (size (random ("exp", 5, [10, 20])), size (exprnd (5, 10, 20)))
866s ***** assert (size (random ("F-Distribution", 5, 2, [10, 20])), size (frnd (5, 2, 10, 20)))
866s ***** assert (size (random ("f", 5, 2, [10, 20])), size (frnd (5, 2, 10, 20)))
866s ***** assert (size (random ("Gamma", 5, 2, [10, 20])), size (gamrnd (5, 2, 10, 20)))
866s ***** assert (size (random ("gam", 5, 2, [10, 20])), size (gamrnd (5, 2, 10, 20)))
866s ***** assert (size (random ("Geometric", 5, [10, 20])), size (geornd (5, 10, 20)))
866s ***** assert (size (random ("geo", 5, [10, 20])), size (geornd (5, 10, 20)))
866s ***** assert (size (random ("Generalized Extreme Value", 5, 2, 2, [10, 20])), size (gevrnd (5, 2, 2, 10, 20)))
866s ***** assert (size (random ("gev", 5, 2, 2, [10, 20])), size (gevrnd (5, 2, 2, 10, 20)))
866s ***** assert (size (random ("Generalized Pareto", 5, 2, 2, [10, 20])), size (gprnd (5, 2, 2, 10, 20)))
866s ***** assert (size (random ("gp", 5, 2, 2, [10, 20])), size (gprnd (5, 2, 2, 10, 20)))
866s ***** assert (size (random ("Gumbel", 5, 2, [10, 20])), size (gumbelrnd (5, 2, 10, 20)))
866s ***** assert (size (random ("gumbel", 5, 2, [10, 20])), size (gumbelrnd (5, 2, 10, 20)))
866s ***** assert (size (random ("Half-normal", 5, 2, [10, 20])), size (hnrnd (5, 2, 10, 20)))
866s ***** assert (size (random ("hn", 5, 2, [10, 20])), size (hnrnd (5, 2, 10, 20)))
866s ***** assert (size (random ("Hypergeometric", 5, 2, 2, [10, 20])), size (hygernd (5, 2, 2, 10, 20)))
866s ***** assert (size (random ("hyge", 5, 2, 2, [10, 20])), size (hygernd (5, 2, 2, 10, 20)))
866s ***** assert (size (random ("Inverse Gaussian", 5, 2, [10, 20])), size (invgrnd (5, 2, 10, 20)))
866s ***** assert (size (random ("invg", 5, 2, [10, 20])), size (invgrnd (5, 2, 10, 20)))
866s ***** assert (size (random ("Laplace", 5, 2, [10, 20])), size (laplacernd (5, 2, 10, 20)))
866s ***** assert (size (random ("laplace", 5, 2, [10, 20])), size (laplacernd (5, 2, 10, 20)))
866s ***** assert (size (random ("Logistic", 5, 2, [10, 20])), size (logirnd (5, 2, 10, 20)))
866s ***** assert (size (random ("logi", 5, 2, [10, 20])), size (logirnd (5, 2, 10, 20)))
866s ***** assert (size (random ("Log-Logistic", 5, 2, [10, 20])), size (loglrnd (5, 2, 10, 20)))
866s ***** assert (size (random ("logl", 5, 2, [10, 20])), size (loglrnd (5, 2, 10, 20)))
866s ***** assert (size (random ("Lognormal", 5, 2, [10, 20])), size (lognrnd (5, 2, 10, 20)))
866s ***** assert (size (random ("logn", 5, 2, [10, 20])), size (lognrnd (5, 2, 10, 20)))
866s ***** assert (size (random ("Nakagami", 5, 2, [10, 20])), size (nakarnd (5, 2, 10, 20)))
866s ***** assert (size (random ("naka", 5, 2, [10, 20])), size (nakarnd (5, 2, 10, 20)))
866s ***** assert (size (random ("Negative Binomial", 5, 2, [10, 20])), size (nbinrnd (5, 2, 10, 20)))
866s ***** assert (size (random ("nbin", 5, 2, [10, 20])), size (nbinrnd (5, 2, 10, 20)))
866s ***** assert (size (random ("Noncentral F-Distribution", 5, 2, 2, [10, 20])), size (ncfrnd (5, 2, 2, 10, 20)))
866s ***** assert (size (random ("ncf", 5, 2, 2, [10, 20])), size (ncfrnd (5, 2, 2, 10, 20)))
866s ***** assert (size (random ("Noncentral Student T", 5, 2, [10, 20])), size (nctrnd (5, 2, 10, 20)))
866s ***** assert (size (random ("nct", 5, 2, [10, 20])), size (nctrnd (5, 2, 10, 20)))
866s ***** assert (size (random ("Noncentral Chi-Squared", 5, 2, [10, 20])), size (ncx2rnd (5, 2, 10, 20)))
866s ***** assert (size (random ("ncx2", 5, 2, [10, 20])), size (ncx2rnd (5, 2, 10, 20)))
866s ***** assert (size (random ("Normal", 5, 2, [10, 20])), size (normrnd (5, 2, 10, 20)))
866s ***** assert (size (random ("norm", 5, 2, [10, 20])), size (normrnd (5, 2, 10, 20)))
866s ***** assert (size (random ("Poisson", 5, [10, 20])), size (poissrnd (5, 10, 20)))
866s ***** assert (size (random ("poiss", 5, [10, 20])), size (poissrnd (5, 10, 20)))
866s ***** assert (size (random ("Rayleigh", 5, [10, 20])), size (raylrnd (5, 10, 20)))
866s ***** assert (size (random ("rayl", 5, [10, 20])), size (raylrnd (5, 10, 20)))
866s ***** assert (size (random ("Rician", 5, 1, [10, 20])), size (ricernd (5, 1, 10, 20)))
866s ***** assert (size (random ("rice", 5, 1, [10, 20])), size (ricernd (5, 1, 10, 20)))
866s ***** assert (size (random ("Student T", 5, [10, 20])), size (trnd (5, 10, 20)))
866s ***** assert (size (random ("t", 5, [10, 20])), size (trnd (5, 10, 20)))
866s ***** assert (size (random ("location-scale T", 5, 1, 2, [10, 20])), size (tlsrnd (5, 1, 2, 10, 20)))
866s ***** assert (size (random ("tls", 5, 1, 2, [10, 20])), size (tlsrnd (5, 1, 2, 10, 20)))
866s ***** assert (size (random ("Triangular", 5, 2, 2, [10, 20])), size (trirnd (5, 2, 2, 10, 20)))
866s ***** assert (size (random ("tri", 5, 2, 2, [10, 20])), size (trirnd (5, 2, 2, 10, 20)))
866s ***** assert (size (random ("Discrete Uniform", 5, [10, 20])), size (unidrnd (5, 10, 20)))
866s ***** assert (size (random ("unid", 5, [10, 20])), size (unidrnd (5, 10, 20)))
866s ***** assert (size (random ("Uniform", 5, 2, [10, 20])), size (unifrnd (5, 2, 10, 20)))
866s ***** assert (size (random ("unif", 5, 2, [10, 20])), size (unifrnd (5, 2, 10, 20)))
866s ***** assert (size (random ("Von Mises", 5, 2, [10, 20])), size (vmrnd (5, 2, 10, 20)))
866s ***** assert (size (random ("vm", 5, 2, [10, 20])), size (vmrnd (5, 2, 10, 20)))
866s ***** assert (size (random ("Weibull", 5, 2, [10, 20])), size (wblrnd (5, 2, 10, 20)))
866s ***** assert (size (random ("wbl", 5, 2, [10, 20])), size (wblrnd (5, 2, 10, 20)))
866s ***** error<random: distribution NAME must a char string.> random (1)
866s ***** error<random: distribution NAME must a char string.> random ({"beta"})
866s ***** error<random: distribution parameters must be numeric.> ...
866s  random ("Beta", "a", 2)
866s ***** error<random: distribution parameters must be numeric.> ...
866s  random ("Beta", 5, "")
866s ***** error<random: distribution parameters must be numeric.> ...
866s  random ("Beta", 5, {2})
866s ***** error<random: distribution parameters must be numeric.> ...
866s  random ("Beta", "a", 2, 2, 10)
866s ***** error<random: distribution parameters must be numeric.> ...
866s  random ("Beta", 5, "", 2, 10)
866s ***** error<random: distribution parameters must be numeric.> ...
866s  random ("Beta", 5, {2}, 2, 10)
866s ***** error<random: distribution parameters must be numeric.> ...
866s  random ("Beta", 5, "", 2, 10)
866s ***** error<random: chi2 distribution requires 1 parameter.> random ("chi2")
866s ***** error<random: Beta distribution requires 2 parameters.> random ("Beta", 5)
866s ***** error<random: Burr distribution requires 3 parameters.> random ("Burr", 5)
866s ***** error<random: Burr distribution requires 3 parameters.> random ("Burr", 5, 2)
866s 87 tests, 87 passed, 0 known failure, 0 skipped
866s [inst/dist_wrap/fitdist.m]
866s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/dist_wrap/fitdist.m
866s ***** test
866s  x = betarnd (1, 1, 100, 1);
866s  pd = fitdist (x, "Beta");
866s  [phat, pci] = betafit (x);
866s  assert ([pd.a, pd.b], phat);
866s  assert (paramci (pd), pci);
867s ***** test
867s  x1 = betarnd (1, 1, 100, 1);
867s  x2 = betarnd (5, 2, 100, 1);
867s  pd = fitdist ([x1; x2], "Beta", "By", [ones(100,1); 2*ones(100,1)]);
867s  [phat, pci] = betafit (x1);
867s  assert ([pd(1).a, pd(1).b], phat);
867s  assert (paramci (pd(1)), pci);
867s  [phat, pci] = betafit (x2);
867s  assert ([pd(2).a, pd(2).b], phat);
867s  assert (paramci (pd(2)), pci);
867s ***** test
867s  N = 1;
867s  x = binornd (N, 0.5, 100, 1);
867s  pd = fitdist (x, "binomial");
867s  [phat, pci] = binofit (sum (x), numel (x));
867s  assert ([pd.N, pd.p], [N, phat]);
867s  assert (paramci (pd), pci);
867s ***** test
867s  N = 3;
867s  x = binornd (N, 0.4, 100, 1);
867s  pd = fitdist (x, "binomial", "ntrials", N);
867s  [phat, pci] = binofit (sum (x), numel (x) * N);
867s  assert ([pd.N, pd.p], [N, phat]);
867s  assert (paramci (pd), pci);
867s ***** test
867s  N = 1;
867s  x1 = binornd (N, 0.5, 100, 1);
867s  x2 = binornd (N, 0.7, 100, 1);
867s  pd = fitdist ([x1; x2], "binomial", "By", [ones(100,1); 2*ones(100,1)]);
867s  [phat, pci] = binofit (sum (x1), numel (x1));
867s  assert ([pd(1).N, pd(1).p], [N, phat]);
867s  assert (paramci (pd(1)), pci);
867s  [phat, pci] = binofit (sum (x2), numel (x2));
867s  assert ([pd(2).N, pd(2).p], [N, phat]);
867s  assert (paramci (pd(2)), pci);
867s ***** test
867s  N = 5;
867s  x1 = binornd (N, 0.5, 100, 1);
867s  x2 = binornd (N, 0.8, 100, 1);
867s  pd = fitdist ([x1; x2], "binomial", "ntrials", N, ...
867s                "By", [ones(100,1); 2*ones(100,1)]);
867s  [phat, pci] = binofit (sum (x1), numel (x1) * N);
867s  assert ([pd(1).N, pd(1).p], [N, phat]);
867s  assert (paramci (pd(1)), pci);
867s  [phat, pci] = binofit (sum (x2), numel (x2) * N);
867s  assert ([pd(2).N, pd(2).p], [N, phat]);
867s  assert (paramci (pd(2)), pci);
867s ***** test
867s  x = bisarnd (1, 1, 100, 1);
867s  pd = fitdist (x, "BirnbaumSaunders");
867s  [phat, pci] = bisafit (x);
867s  assert ([pd.beta, pd.gamma], phat);
867s  assert (paramci (pd), pci);
867s ***** test
867s  x1 = bisarnd (1, 1, 100, 1);
867s  x2 = bisarnd (5, 2, 100, 1);
867s  pd = fitdist ([x1; x2], "bisa", "By", [ones(100,1); 2*ones(100,1)]);
867s  [phat, pci] = bisafit (x1);
867s  assert ([pd(1).beta, pd(1).gamma], phat);
867s  assert (paramci (pd(1)), pci);
867s  [phat, pci] = bisafit (x2);
867s  assert ([pd(2).beta, pd(2).gamma], phat);
867s  assert (paramci (pd(2)), pci);
867s ***** test
867s  x = burrrnd (1, 2, 1, 100, 1);
867s  pd = fitdist (x, "Burr");
867s  [phat, pci] = burrfit (x);
867s  assert ([pd.alpha, pd.c, pd.k], phat);
867s  assert (paramci (pd), pci);
868s ***** xtest
868s  x1 = burrrnd (1, 2, 1, 100, 1);
868s  x2 = burrrnd (1, 0.5, 2, 100, 1);
868s  pd = fitdist ([x1; x2], "burr", "By", [ones(100,1); 2*ones(100,1)]);
868s  [phat, pci] = burrfit (x1);
868s  assert ([pd(1).alpha, pd(1).c, pd(1).k], phat);
868s  assert (paramci (pd(1)), pci);
868s  [phat, pci] = burrfit (x2);
868s  assert ([pd(2).alpha, pd(2).c, pd(2).k], phat);
868s  assert (paramci (pd(2)), pci);
869s ***** test
869s  x = exprnd (1, 100, 1);
869s  pd = fitdist (x, "exponential");
869s  [muhat, muci] = expfit (x);
869s  assert ([pd.mu], muhat);
869s  assert (paramci (pd), muci);
869s ***** test
869s  x1 = exprnd (1, 100, 1);
869s  x2 = exprnd (5, 100, 1);
869s  pd = fitdist ([x1; x2], "exponential", "By", [ones(100,1); 2*ones(100,1)]);
869s  [muhat, muci] = expfit (x1);
869s  assert ([pd(1).mu], muhat);
869s  assert (paramci (pd(1)), muci);
869s  [muhat, muci] = expfit (x2);
869s  assert ([pd(2).mu], muhat);
869s  assert (paramci (pd(2)), muci);
869s ***** test
869s  x = evrnd (1, 1, 100, 1);
869s  pd = fitdist (x, "ev");
869s  [phat, pci] = evfit (x);
869s  assert ([pd.mu, pd.sigma], phat);
869s  assert (paramci (pd), pci);
869s ***** test
869s  x1 = evrnd (1, 1, 100, 1);
869s  x2 = evrnd (5, 2, 100, 1);
869s  pd = fitdist ([x1; x2], "extremevalue", "By", [ones(100,1); 2*ones(100,1)]);
869s  [phat, pci] = evfit (x1);
869s  assert ([pd(1).mu, pd(1).sigma], phat);
869s  assert (paramci (pd(1)), pci);
869s  [phat, pci] = evfit (x2);
869s  assert ([pd(2).mu, pd(2).sigma], phat);
869s  assert (paramci (pd(2)), pci);
869s ***** test
869s  x = gamrnd (1, 1, 100, 1);
869s  pd = fitdist (x, "Gamma");
869s  [phat, pci] = gamfit (x);
869s  assert ([pd.a, pd.b], phat);
869s  assert (paramci (pd), pci);
869s ***** test
869s  x1 = gamrnd (1, 1, 100, 1);
869s  x2 = gamrnd (5, 2, 100, 1);
869s  pd = fitdist ([x1; x2], "Gamma", "By", [ones(100,1); 2*ones(100,1)]);
869s  [phat, pci] = gamfit (x1);
869s  assert ([pd(1).a, pd(1).b], phat);
869s  assert (paramci (pd(1)), pci);
869s  [phat, pci] = gamfit (x2);
869s  assert ([pd(2).a, pd(2).b], phat);
869s  assert (paramci (pd(2)), pci);
869s ***** test
869s  rand ("seed", 4);   # for reproducibility
869s  x = gevrnd (-0.5, 1, 2, 1000, 1);
869s  pd = fitdist (x, "generalizedextremevalue");
869s  [phat, pci] = gevfit (x);
869s  assert ([pd.k, pd.sigma, pd.mu], phat);
869s  assert (paramci (pd), pci);
869s ***** test
869s  rand ("seed", 5);   # for reproducibility
869s  x1 = gevrnd (-0.5, 1, 2, 1000, 1);
869s  rand ("seed", 9);   # for reproducibility
869s  x2 = gevrnd (0, 1, -4, 1000, 1);
869s  pd = fitdist ([x1; x2], "gev", "By", [ones(1000,1); 2*ones(1000,1)]);
869s  [phat, pci] = gevfit (x1);
869s  assert ([pd(1).k, pd(1).sigma, pd(1).mu], phat);
869s  assert (paramci (pd(1)), pci);
869s  [phat, pci] = gevfit (x2);
869s  assert ([pd(2).k, pd(2).sigma, pd(2).mu], phat);
869s  assert (paramci (pd(2)), pci);
870s ***** test
870s  x = gprnd (1, 1, 1, 100, 1);
870s  pd = fitdist (x, "GeneralizedPareto");
870s  [phat, pci] = gpfit (x, 1);
870s  assert ([pd.k, pd.sigma, pd.theta], phat);
870s  assert (paramci (pd), pci);
870s ***** test
870s  x = gprnd (1, 1, 2, 100, 1);
870s  pd = fitdist (x, "GeneralizedPareto", "theta", 2);
870s  [phat, pci] = gpfit (x, 2);
870s  assert ([pd.k, pd.sigma, pd.theta], phat);
870s  assert (paramci (pd), pci);
870s ***** test
870s  x1 = gprnd (1, 1, 1, 100, 1);
870s  x2 = gprnd (0, 2, 1, 100, 1);
870s  pd = fitdist ([x1; x2], "gp", "By", [ones(100,1); 2*ones(100,1)]);
870s  [phat, pci] = gpfit (x1, 1);
870s  assert ([pd(1).k, pd(1).sigma, pd(1).theta], phat);
870s  assert (paramci (pd(1)), pci);
870s  [phat, pci] = gpfit (x2, 1);
870s  assert ([pd(2).k, pd(2).sigma, pd(2).theta], phat);
870s  assert (paramci (pd(2)), pci);
871s ***** test
871s  x1 = gprnd (3, 2, 2, 100, 1);
871s  x2 = gprnd (2, 3, 2, 100, 1);
871s  pd = fitdist ([x1; x2], "GeneralizedPareto", "theta", 2, ...
871s                "By", [ones(100,1); 2*ones(100,1)]);
871s  [phat, pci] = gpfit (x1, 2);
871s  assert ([pd(1).k, pd(1).sigma, pd(1).theta], phat);
871s  assert (paramci (pd(1)), pci);
871s  [phat, pci] = gpfit (x2, 2);
871s  assert ([pd(2).k, pd(2).sigma, pd(2).theta], phat);
871s  assert (paramci (pd(2)), pci);
871s ***** test
871s  x = hnrnd (0, 1, 100, 1);
871s  pd = fitdist (x, "HalfNormal");
871s  [phat, pci] = hnfit (x, 0);
871s  assert ([pd.mu, pd.sigma], phat);
871s  assert (paramci (pd), pci);
871s ***** test
871s  x = hnrnd (1, 1, 100, 1);
871s  pd = fitdist (x, "HalfNormal", "mu", 1);
871s  [phat, pci] = hnfit (x, 1);
871s  assert ([pd.mu, pd.sigma], phat);
871s  assert (paramci (pd), pci);
871s ***** test
871s  x1 = hnrnd (0, 1, 100, 1);
871s  x2 = hnrnd (0, 2, 100, 1);
871s  pd = fitdist ([x1; x2], "HalfNormal", "By", [ones(100,1); 2*ones(100,1)]);
871s  [phat, pci] = hnfit (x1, 0);
871s  assert ([pd(1).mu, pd(1).sigma], phat);
871s  assert (paramci (pd(1)), pci);
871s  [phat, pci] = hnfit (x2, 0);
871s  assert ([pd(2).mu, pd(2).sigma], phat);
871s  assert (paramci (pd(2)), pci);
871s ***** test
871s  x1 = hnrnd (2, 1, 100, 1);
871s  x2 = hnrnd (2, 2, 100, 1);
871s  pd = fitdist ([x1; x2], "HalfNormal", "mu", 2, ...
871s                "By", [ones(100,1); 2*ones(100,1)]);
871s  [phat, pci] = hnfit (x1, 2);
871s  assert ([pd(1).mu, pd(1).sigma], phat);
871s  assert (paramci (pd(1)), pci);
871s  [phat, pci] = hnfit (x2, 2);
871s  assert ([pd(2).mu, pd(2).sigma], phat);
871s  assert (paramci (pd(2)), pci);
872s ***** test
872s  x = invgrnd (1, 1, 100, 1);
872s  pd = fitdist (x, "InverseGaussian");
872s  [phat, pci] = invgfit (x);
872s  assert ([pd.mu, pd.lambda], phat);
872s  assert (paramci (pd), pci);
872s ***** test
872s  x1 = invgrnd (1, 1, 100, 1);
872s  x2 = invgrnd (5, 2, 100, 1);
872s  pd = fitdist ([x1; x2], "InverseGaussian", "By", [ones(100,1); 2*ones(100,1)]);
872s  [phat, pci] = invgfit (x1);
872s  assert ([pd(1).mu, pd(1).lambda], phat);
872s  assert (paramci (pd(1)), pci);
872s  [phat, pci] = invgfit (x2);
872s  assert ([pd(2).mu, pd(2).lambda], phat);
872s  assert (paramci (pd(2)), pci);
872s ***** test
872s  x = logirnd (1, 1, 100, 1);
872s  pd = fitdist (x, "logistic");
872s  [phat, pci] = logifit (x);
872s  assert ([pd.mu, pd.sigma], phat);
872s  assert (paramci (pd), pci);
872s ***** test
872s  x1 = logirnd (1, 1, 100, 1);
872s  x2 = logirnd (5, 2, 100, 1);
872s  pd = fitdist ([x1; x2], "logistic", "By", [ones(100,1); 2*ones(100,1)]);
872s  [phat, pci] = logifit (x1);
872s  assert ([pd(1).mu, pd(1).sigma], phat);
872s  assert (paramci (pd(1)), pci);
872s  [phat, pci] = logifit (x2);
872s  assert ([pd(2).mu, pd(2).sigma], phat);
872s  assert (paramci (pd(2)), pci);
872s ***** test
872s  x = loglrnd (1, 1, 100, 1);
872s  pd = fitdist (x, "loglogistic");
872s  [phat, pci] = loglfit (x);
872s  assert ([pd.mu, pd.sigma], phat);
872s  assert (paramci (pd), pci);
872s ***** test
872s  x1 = loglrnd (1, 1, 100, 1);
872s  x2 = loglrnd (5, 2, 100, 1);
872s  pd = fitdist ([x1; x2], "loglogistic", "By", [ones(100,1); 2*ones(100,1)]);
872s  [phat, pci] = loglfit (x1);
872s  assert ([pd(1).mu, pd(1).sigma], phat);
872s  assert (paramci (pd(1)), pci);
872s  [phat, pci] = loglfit (x2);
872s  assert ([pd(2).mu, pd(2).sigma], phat);
872s  assert (paramci (pd(2)), pci);
873s ***** test
873s  x = lognrnd (1, 1, 100, 1);
873s  pd = fitdist (x, "lognormal");
873s  [phat, pci] = lognfit (x);
873s  assert ([pd.mu, pd.sigma], phat);
873s  assert (paramci (pd), pci);
873s ***** test
873s  x1 = lognrnd (1, 1, 100, 1);
873s  x2 = lognrnd (5, 2, 100, 1);
873s  pd = fitdist ([x1; x2], "lognormal", "By", [ones(100,1); 2*ones(100,1)]);
873s  [phat, pci] = lognfit (x1);
873s  assert ([pd(1).mu, pd(1).sigma], phat);
873s  assert (paramci (pd(1)), pci);
873s  [phat, pci] = lognfit (x2);
873s  assert ([pd(2).mu, pd(2).sigma], phat);
873s  assert (paramci (pd(2)), pci);
873s ***** test
873s  x = nakarnd (2, 0.5, 100, 1);
873s  pd = fitdist (x, "Nakagami");
873s  [phat, pci] = nakafit (x);
873s  assert ([pd.mu, pd.omega], phat);
873s  assert (paramci (pd), pci);
874s ***** test
874s  x1 = nakarnd (2, 0.5, 100, 1);
874s  x2 = nakarnd (5, 0.8, 100, 1);
874s  pd = fitdist ([x1; x2], "Nakagami", "By", [ones(100,1); 2*ones(100,1)]);
874s  [phat, pci] = nakafit (x1);
874s  assert ([pd(1).mu, pd(1).omega], phat);
874s  assert (paramci (pd(1)), pci);
874s  [phat, pci] = nakafit (x2);
874s  assert ([pd(2).mu, pd(2).omega], phat);
874s  assert (paramci (pd(2)), pci);
874s ***** test
874s  randp ("seed", 123);
874s  randg ("seed", 321);
874s  x = nbinrnd (2, 0.5, 100, 1);
874s  pd = fitdist (x, "negativebinomial");
874s  [phat, pci] = nbinfit (x);
874s  assert ([pd.R, pd.P], phat);
874s  assert (paramci (pd), pci);
874s ***** test
874s  randp ("seed", 345);
874s  randg ("seed", 543);
874s  x1 = nbinrnd (2, 0.5, 100, 1);
874s  randp ("seed", 432);
874s  randg ("seed", 234);
874s  x2 = nbinrnd (5, 0.8, 100, 1);
874s  pd = fitdist ([x1; x2], "nbin", "By", [ones(100,1); 2*ones(100,1)]);
874s  [phat, pci] = nbinfit (x1);
874s  assert ([pd(1).R, pd(1).P], phat);
874s  assert (paramci (pd(1)), pci);
874s  [phat, pci] = nbinfit (x2);
874s  assert ([pd(2).R, pd(2).P], phat);
874s  assert (paramci (pd(2)), pci);
874s ***** test
874s  x = normrnd (1, 1, 100, 1);
874s  pd = fitdist (x, "normal");
874s  [muhat, sigmahat, muci, sigmaci] = normfit (x);
874s  assert ([pd.mu, pd.sigma], [muhat, sigmahat]);
874s  assert (paramci (pd), [muci, sigmaci]);
874s ***** test
874s  x1 = normrnd (1, 1, 100, 1);
874s  x2 = normrnd (5, 2, 100, 1);
874s  pd = fitdist ([x1; x2], "normal", "By", [ones(100,1); 2*ones(100,1)]);
874s  [muhat, sigmahat, muci, sigmaci] = normfit (x1);
874s  assert ([pd(1).mu, pd(1).sigma], [muhat, sigmahat]);
874s  assert (paramci (pd(1)), [muci, sigmaci]);
874s  [muhat, sigmahat, muci, sigmaci] = normfit (x2);
874s  assert ([pd(2).mu, pd(2).sigma], [muhat, sigmahat]);
874s  assert (paramci (pd(2)), [muci, sigmaci]);
875s ***** test
875s  x = poissrnd (1, 100, 1);
875s  pd = fitdist (x, "poisson");
875s  [phat, pci] = poissfit (x);
875s  assert (pd.lambda, phat);
875s  assert (paramci (pd), pci);
875s ***** test
875s  x1 = poissrnd (1, 100, 1);
875s  x2 = poissrnd (5, 100, 1);
875s  pd = fitdist ([x1; x2], "poisson", "By", [ones(100,1); 2*ones(100,1)]);
875s  [phat, pci] = poissfit (x1);
875s  assert (pd(1).lambda, phat);
875s  assert (paramci (pd(1)), pci);
875s  [phat, pci] = poissfit (x2);
875s  assert (pd(2).lambda, phat);
875s  assert (paramci (pd(2)), pci);
875s ***** test
875s  x = raylrnd (1, 100, 1);
875s  pd = fitdist (x, "rayleigh");
875s  [phat, pci] = raylfit (x);
875s  assert (pd.sigma, phat);
875s  assert (paramci (pd), pci);
875s ***** test
875s  x1 = raylrnd (1, 100, 1);
875s  x2 = raylrnd (5, 100, 1);
875s  pd = fitdist ([x1; x2], "rayleigh", "By", [ones(100,1); 2*ones(100,1)]);
875s  [phat, pci] = raylfit (x1);
875s  assert ( pd(1).sigma, phat);
875s  assert (paramci (pd(1)), pci);
875s  [phat, pci] = raylfit (x2);
875s  assert (pd(2).sigma, phat);
875s  assert (paramci (pd(2)), pci);
875s ***** test
875s  x = ricernd (1, 1, 100, 1);
875s  pd = fitdist (x, "rician");
875s  [phat, pci] = ricefit (x);
875s  assert ([pd.s, pd.sigma], phat);
875s  assert (paramci (pd), pci);
875s ***** test
875s  x1 = ricernd (1, 1, 100, 1);
875s  x2 = ricernd (5, 2, 100, 1);
875s  pd = fitdist ([x1; x2], "rician", "By", [ones(100,1); 2*ones(100,1)]);
875s  [phat, pci] = ricefit (x1);
875s  assert ([pd(1).s, pd(1).sigma], phat);
875s  assert (paramci (pd(1)), pci);
875s  [phat, pci] = ricefit (x2);
875s  assert ([pd(2).s, pd(2).sigma], phat);
875s  assert (paramci (pd(2)), pci);
876s ***** warning <fitdist: 'Stable' distribution not supported yet.> ...
876s  fitdist ([1 2 3 4 5], "Stable");
876s ***** test
876s  x = tlsrnd (0, 1, 1, 100, 1);
876s  pd = fitdist (x, "tlocationscale");
876s  [phat, pci] = tlsfit (x);
876s  assert ([pd.mu, pd.sigma, pd.nu], phat);
876s  assert (paramci (pd), pci);
876s ***** test
876s  x1 = tlsrnd (0, 1, 1, 100, 1);
876s  x2 = tlsrnd (5, 2, 1, 100, 1);
876s  pd = fitdist ([x1; x2], "tlocationscale", "By", [ones(100,1); 2*ones(100,1)]);
876s  [phat, pci] = tlsfit (x1);
876s  assert ([pd(1).mu, pd(1).sigma, pd(1).nu], phat);
876s  assert (paramci (pd(1)), pci);
876s  [phat, pci] = tlsfit (x2);
876s  assert ([pd(2).mu, pd(2).sigma, pd(2).nu], phat);
876s  assert (paramci (pd(2)), pci);
877s ***** test
877s  x = [1 2 3 4 5];
877s  pd = fitdist (x, "weibull");
877s  [phat, pci] = wblfit (x);
877s  assert ([pd.lambda, pd.k], phat);
877s  assert (paramci (pd), pci);
877s ***** test
877s  x = [1 2 3 4 5 6 7 8 9 10];
877s  pd = fitdist (x, "weibull", "By", [1 1 1 1 1 2 2 2 2 2]);
877s  [phat, pci] = wblfit (x(1:5));
877s  assert ([pd(1).lambda, pd(1).k], phat);
877s  assert (paramci (pd(1)), pci);
877s  [phat, pci] = wblfit (x(6:10));
877s  assert ([pd(2).lambda, pd(2).k], phat);
877s  assert (paramci (pd(2)), pci);
877s ***** error <fitdist: DISTNAME is required.> fitdist (1)
877s ***** error <fitdist: DISTNAME must be a character vector.> fitdist (1, ["as";"sd"])
877s ***** error <fitdist: unrecognized distribution name.> fitdist (1, "some")
877s ***** error <fitdist: X must be a numeric vector of real values.> ...
877s  fitdist (ones (2), "normal")
877s ***** error <fitdist: X must be a numeric vector of real values.> ...
877s  fitdist ([i, 2, 3], "normal")
877s ***** error <fitdist: X must be a numeric vector of real values.> ...
877s  fitdist (["a", "s", "d"], "normal")
877s ***** error <fitdist: optional arguments must be in NAME-VALUE pairs.> ...
877s  fitdist ([1, 2, 3], "normal", "By")
877s ***** error <fitdist: GROUPVAR argument must have the same size as the input data in X.> ...
877s  fitdist ([1, 2, 3], "normal", "By", [1, 2])
877s ***** error <fitdist: 'censoring' argument must have the same size as the input data in X.> ...
877s  fitdist ([1, 2, 3], "normal", "Censoring", [1, 2])
877s ***** error <fitdist: 'frequency' argument must have the same size as the input data in X.> ...
877s  fitdist ([1, 2, 3], "normal", "frequency", [1, 2])
877s ***** error <fitdist: 'frequency' argument must contain non-negative integer values.> ...
877s  fitdist ([1, 2, 3], "negativebinomial", "frequency", [1, -2, 3])
877s ***** error <fitdist: invalid value for 'alpha' argument.> ...
877s  fitdist ([1, 2, 3], "normal", "alpha", [1, 2])
877s ***** error <fitdist: invalid value for 'alpha' argument.> ...
877s  fitdist ([1, 2, 3], "normal", "alpha", i)
877s ***** error <fitdist: invalid value for 'alpha' argument.> ...
877s  fitdist ([1, 2, 3], "normal", "alpha", -0.5)
877s ***** error <fitdist: invalid value for 'alpha' argument.> ...
877s  fitdist ([1, 2, 3], "normal", "alpha", 1.5)
877s ***** error <fitdist: 'ntrials' argument must be a positive integer scalar value.> ...
877s  fitdist ([1, 2, 3], "normal", "ntrials", [1, 2])
877s ***** error <fitdist: 'ntrials' argument must be a positive integer scalar value.> ...
877s  fitdist ([1, 2, 3], "normal", "ntrials", 0)
877s ***** error <fitdist: 'options' argument must be a structure compatible for 'fminsearch'.> ...
877s  fitdist ([1, 2, 3], "normal", "options", 0)
877s ***** error <fitdist: 'options' argument must be a structure compatible for 'fminsearch'.> ...
877s  fitdist ([1, 2, 3], "normal", "options", struct ("options", 1))
877s ***** warning fitdist ([1, 2, 3], "kernel", "kernel", "normal");
877s ***** warning fitdist ([1, 2, 3], "kernel", "support", "positive");
877s ***** warning fitdist ([1, 2, 3], "kernel", "width", 1);
877s ***** error <fitdist: unknown parameter name.> ...
877s  fitdist ([1, 2, 3], "normal", "param", struct ("options", 1))
877s ***** error <fitdist: must define GROUPVAR for more than one output arguments.> ...
877s  [pdca, gn, gl] = fitdist ([1, 2, 3], "normal");
877s ***** error <fitdist: invalid THETA value for generalized Pareto distribution.> ...
877s  fitdist ([1, 2, 3], "generalizedpareto", "theta", 2);
877s ***** error <fitdist: invalid MU value for half-normal distribution.> ...
877s  fitdist ([1, 2, 3], "halfnormal", "mu", 2);
877s 77 tests, 77 passed, 0 known failure, 0 skipped
877s [inst/ecdf.m]
877s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/ecdf.m
877s ***** demo
877s  y = exprnd (10, 50, 1);    ## random failure times are exponential(10)
877s  d = exprnd (20, 50, 1);    ## drop-out times are exponential(20)
877s  t = min (y, d);            ## we observe the minimum of these times
877s  censored = (y > d);        ## we also observe whether the subject failed
877s 
877s  ## Calculate and plot the empirical cdf and confidence bounds
877s  [f, x, flo, fup] = ecdf (t, "censoring", censored);
877s  stairs (x, f);
877s  hold on;
877s  stairs (x, flo, "r:"); stairs (x, fup, "r:");
877s 
877s  ## Superimpose a plot of the known true cdf
877s  xx = 0:.1:max (t); yy = 1 - exp (-xx / 10); plot (xx, yy, "g-");
877s  hold off;
877s ***** demo
877s  R = wblrnd (100, 2, 100, 1);
877s  ecdf (R, "Function", "survivor", "Alpha", 0.01, "Bounds", "on");
877s  hold on
877s  x = 1:1:250;
877s  wblsurv = 1 - cdf ("weibull", x, 100, 2);
877s  plot (x, wblsurv, "g-", "LineWidth", 2)
877s  legend ("Empirical survivor function", "Lower confidence bound", ...
877s          "Upper confidence bound", "Weibull survivor function", ...
877s          "Location", "northeast");
877s  hold off
877s ***** error ecdf ();
877s ***** error ecdf (randi (15,2));
877s ***** error ecdf ([3,2,4,3+2i,5]);
877s ***** error kstest ([2,3,4,5,6],"tail");
877s ***** error kstest ([2,3,4,5,6],"tail", "whatever");
877s ***** error kstest ([2,3,4,5,6],"function", "");
877s ***** error kstest ([2,3,4,5,6],"badoption", 0.51);
877s ***** error kstest ([2,3,4,5,6],"tail", 0);
877s ***** error kstest ([2,3,4,5,6],"alpha", 0);
877s ***** error kstest ([2,3,4,5,6],"alpha", NaN);
877s ***** error kstest ([NaN,NaN,NaN,NaN,NaN],"tail", "unequal");
877s ***** error kstest ([2,3,4,5,6],"alpha", 0.05, "CDF", [2,3,4;1,3,4;1,2,1]);
877s ***** test
877s  hf = figure ("visible", "off");
877s  unwind_protect
877s    x = [2, 3, 4, 3, 5, 4, 6, 5, 8, 3, 7, 8, 9, 0];
877s    [F, x, Flo, Fup] = ecdf (x);
877s    F_out = [0; 0.0714; 0.1429; 0.3571; 0.5; 0.6429; 0.7143; 0.7857; 0.9286; 1];
877s    assert (F, F_out, ones (10,1) * 1e-4);
877s    x_out = [0 0 2 3 4 5 6 7 8 9]';
877s    assert (x, x_out);
877s    Flo_out = [NaN, 0, 0, 0.1061, 0.2381, 0.3919, 0.4776, 0.5708, 0.7937, NaN]';
877s    assert (Flo, Flo_out, ones (10,1) * 1e-4);
877s    Fup_out = [NaN, 0.2063, 0.3262, 0.6081, 0.7619, 0.8939, 0.9509, 1, 1, NaN]';
877s    assert (Fup, Fup_out, ones (10,1) * 1e-4);
877s  unwind_protect_cleanup
877s    close (hf);
877s  end_unwind_protect
877s ***** test
877s  hf = figure ("visible", "off");
877s  unwind_protect
877s    x = [2, 3, 4, 3, 5, 4, 6, 5, 8, 3, 7, 8, 9, 0];
877s    ecdf (x);
877s  unwind_protect_cleanup
877s    close (hf);
877s  end_unwind_protect
877s 14 tests, 14 passed, 0 known failure, 0 skipped
877s [inst/einstein.m]
877s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/einstein.m
877s ***** demo
877s  einstein (0.4, 0.6)
877s ***** demo
877s  einstein (0.2, 0.5)
877s ***** demo
877s  einstein (0.6, 0.1)
877s ***** test
877s  hf = figure ("visible", "off");
877s  unwind_protect
877s    tiles = einstein (0.4, 0.6);
877s    assert (isstruct (tiles), true);
877s  unwind_protect_cleanup
877s    close (hf);
877s  end_unwind_protect
877s ***** error<Invalid call to einstein.  Correct usage is> einstein
877s ***** error<Invalid call to einstein.  Correct usage is> einstein (0.5)
877s ***** error<einstein: A and B must be within the open interval> einstein (0, 0.9)
877s ***** error<einstein: A and B must be within the open interval> einstein (0.4, 1)
877s ***** error<einstein: A and B must be within the open interval> einstein (-0.4, 1)
877s 6 tests, 6 passed, 0 known failure, 0 skipped
877s [inst/evalclusters.m]
877s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/evalclusters.m
877s ***** demo
877s  load fisheriris;
877s  eva = evalclusters (meas, "kmeans", "calinskiharabasz", "KList", [1:6])
877s  plot (eva)
877s ***** error evalclusters ()
878s ***** error evalclusters ([1 1;0 1])
878s ***** error evalclusters ([1 1;0 1], "kmeans")
878s ***** error <'x' must be a numeric*> evalclusters ("abc", "kmeans", "gap")
878s ***** error <unknown clustering*> evalclusters ([1 1;0 1], "xxx", "gap")
878s ***** error <invalid matrix*> evalclusters ([1 1;0 1], [1 2], "gap")
878s ***** error <invalid argument*> evalclusters ([1 1;0 1], 1.2, "gap")
878s ***** error <invalid criterion*> evalclusters ([1 1;0 1], [1; 2], 123)
878s ***** error <unknown criterion*> evalclusters ([1 1;0 1], [1; 2], "xxx")
878s ***** error <'KList' can be empty*> evalclusters ([1 1;0 1], "kmeans", "gap")
878s ***** error <invalid parameter*> evalclusters ([1 1;0 1], [1; 2], "gap", 1)
878s ***** error <invalid property*> evalclusters ([1 1;0 1], [1; 2], "gap", 1, 1)
878s ***** error <unknown property*> evalclusters ([1 1;0 1], [1; 2], "gap", "xxx", 1)
878s ***** error <'KList'*> evalclusters ([1 1;0 1], [1; 2], "gap", "KList", [-1 0])
878s ***** error <'KList'*> evalclusters ([1 1;0 1], [1; 2], "gap", "KList", [1 .5])
878s ***** error <'KList'*> evalclusters ([1 1;0 1], [1; 2], "gap", "KList", [1 1; 1 1])
878s ***** error <unknown distance*> evalclusters ([1 1;0 1], [1; 2], "gap", ...
878s                                         "distance", "a")
878s ***** error <distance metric*> evalclusters ([1 1;0 1], [1; 2], "daviesbouldin", ...
878s                                        "distance", "a")
878s ***** error <cluster prior*> evalclusters ([1 1;0 1], [1; 2], "gap", ...
878s                                      "clusterpriors", "equal")
878s ***** error <invalid cluster prior*> evalclusters ([1 1;0 1], [1; 2], ...
878s                                          "silhouette", "clusterpriors", "xxx")
878s ***** error <'clust' must be a clustering*> evalclusters ([1 1;0 1], [1; 2], "gap")
878s ***** test
878s  load fisheriris;
878s  eva = evalclusters (meas, "kmeans", "calinskiharabasz", "KList", [1:6]);
878s  assert (isa (eva, "CalinskiHarabaszEvaluation"));
878s  assert (eva.NumObservations, 150);
878s  assert (eva.OptimalK, 3);
878s  assert (eva.InspectedK, [1 2 3 4 5 6]);
878s 22 tests, 22 passed, 0 known failure, 0 skipped
878s [inst/ff2n.m]
878s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/ff2n.m
878s ***** error ff2n ();
878s ***** error ff2n (2, 5);
878s ***** error ff2n (2.5);
878s ***** error ff2n (0);
878s ***** error ff2n (-3);
878s ***** error ff2n (3+2i);
878s ***** error ff2n (Inf);
878s ***** error ff2n (NaN);
878s ***** test
878s  A = ff2n (3);
878s  assert (A, fullfact (3));
878s ***** test
878s  A = ff2n (8);
878s  assert (A, fullfact (8));
878s 10 tests, 10 passed, 0 known failure, 0 skipped
878s [inst/fillmissing.m]
878s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/fillmissing.m
878s ***** assert (fillmissing ([1, 2, 3], "constant", 99), [1, 2, 3])
878s ***** assert (fillmissing ([1, 2, NaN], "constant", 99), [1, 2, 99])
878s ***** assert (fillmissing ([NaN, 2, NaN], "constant", 99), [99, 2, 99])
878s ***** assert (fillmissing ([1, 2, 3]', "constant", 99), [1, 2, 3]')
878s ***** assert (fillmissing ([1, 2, NaN]', "constant", 99), [1, 2, 99]')
878s ***** assert (fillmissing ([1, 2, 3; 4, 5, 6], "constant", 99), [1, 2, 3; 4, 5, 6])
878s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "constant", 99), [1, 2, 99; 4, 99, 6])
878s ***** assert (fillmissing ([NaN, 2, NaN; 4, NaN, 6], "constant", [97, 98, 99]), [97, 2, 99; 4, 98, 6])
878s ***** test
878s  x = cat (3, [1, 2, NaN; 4, NaN, 6], [NaN, 2, 3; 4, 5, NaN]);
878s  y = cat (3, [1, 2, 99; 4, 99, 6], [99, 2, 3; 4, 5, 99]);
878s  assert (fillmissing (x, "constant", 99), y);
878s  y = cat (3, [1, 2, 96; 4, 95, 6], [97, 2, 3; 4, 5, 99]);
878s  assert (fillmissing (x, "constant", [94:99]), y);
878s  assert (fillmissing (x, "constant", [94:99]'), y);
878s  assert (fillmissing (x, "constant", permute ([94:99], [1 3 2])), y);
878s  assert (fillmissing (x, "constant", [94, 96, 98; 95, 97, 99]), y);
878s  assert (fillmissing (x, "constant", [94:99], 1), y);
878s  y = cat (3, [1, 2, 96; 4, 97, 6], [98, 2, 3; 4, 5, 99]);
878s  assert (fillmissing (x, "constant", [96:99], 2), y);
878s  y = cat (3, [1, 2, 98; 4, 97, 6], [94, 2, 3; 4, 5, 99]);
878s  assert (fillmissing (x, "constant", [94:99], 3), y);
878s  y = cat (3, [1, 2, 92; 4, 91, 6], [94, 2, 3; 4, 5, 99]);
878s  assert (fillmissing (x, "constant", [88:99], 99), y);
878s ***** test
878s  x = reshape ([1:24], 4, 3, 2);
878s  x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
878s  y = x;
878s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [94, 95, 95, 96, 96, 97, 97, 98, 99, 99];
878s  assert (fillmissing (x, "constant", [94:99], 1), y);
878s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [92, 93, 94, 92, 95, 97, 99, 98, 97, 98];
878s  assert (fillmissing (x, "constant", [92:99], 2), y);
878s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [88, 93, 94, 96, 99, 89, 91, 94, 97, 98];
878s  assert (fillmissing (x, "constant", [88:99], 3), y);
878s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [76, 81, 82, 84, 87, 89, 91, 94, 97, 98];
878s  assert (fillmissing (x, "constant", [76:99], 99), y);
878s ***** assert (fillmissing ([1, 2, 3], "constant", 99, "endvalues", 88), [1, 2, 3])
878s ***** assert (fillmissing ([1, NaN, 3], "constant", 99, "endvalues", 88), [1, 99, 3])
878s ***** assert (fillmissing ([1, 2, NaN], "constant", 99, "endvalues", 88), [1, 2, 88])
878s ***** assert (fillmissing ([NaN, 2, 3], "constant", 99, "endvalues", 88), [88, 2, 3])
878s ***** assert (fillmissing ([NaN, NaN, 3], "constant", 99, "endvalues", 88), [88, 88, 3])
878s ***** assert (fillmissing ([1, NaN, NaN], "constant", 99, "endvalues", 88), [1, 88, 88])
878s ***** assert (fillmissing ([NaN, 2, NaN], "constant", 99, "endvalues", 88), [88, 2, 88])
878s ***** assert (fillmissing ([NaN, 2, NaN]', "constant", 99, "endvalues", 88), [88, 2, 88]')
878s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", 99, "endvalues", 88), [1, 99, 3, 99, 5])
878s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "constant", 99, "endvalues", 88), [1, 99, 99, 99, 5])
878s ***** assert (fillmissing ([NaN, NaN, NaN, NaN, 5], "constant", 99, "endvalues", 88), [88, 88, 88, 88, 5])
878s ***** assert (fillmissing ([1, NaN, 3, 4, NaN], "constant", 99, "endvalues", 88), [1, 99, 3, 4, 88])
878s ***** assert (fillmissing ([1, NaN, 3, 4, NaN], "constant", 99, 1, "endvalues", 88), [1, 88, 3, 4, 88])
878s ***** assert (fillmissing ([1, NaN, 3, 4, NaN], "constant", 99, 1, "endvalues", "extrap"), [1, 99, 3, 4, 99])
878s ***** test
878s  x = reshape ([1:24], 3, 4, 2);
878s  y = x;
878s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
878s  y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 88;
878s  y([8]) = 99;
878s  assert (fillmissing (x, "constant", 99, "endvalues", 88), y);
878s  assert (fillmissing (x, "constant", 99, 1, "endvalues", 88), y);
878s  y = x;
878s  y([1, 2, 5, 8, 10, 13, 16, 19, 22]) = 88;
878s  y([6, 18, 20, 21]) = 99;
878s  assert (fillmissing (x, "constant", 99, 2, "endvalues", 88), y);
878s  y(y == 99) = 88;
878s  assert (fillmissing (x, "constant", 99, 3, "endvalues", 88), y);
878s  assert (fillmissing (x, "constant", 99, 4, "endvalues", 88), y);
878s  assert (fillmissing (x, "constant", 99, 99, "endvalues", 88), y);
878s  y([8]) = 94;
878s  assert (fillmissing (x, "constant", [92:99], 1, "endvalues", 88), y);
878s  y([6, 8, 18, 20, 21]) = [96, 88, 99, 98, 99];
878s  assert (fillmissing (x, "constant", [94:99], 2, "endvalues", 88), y);
878s  y = x;
878s  y(isnan (y)) = 88;
878s  assert (fillmissing (x, "constant", [88:99], 3, "endvalues", 88), y);
878s  y = x;
878s  y(isnan (y)) = [82, 82, 83, 83, 94, 85, 86, 87, 87, 88, 88, 88, 89];
878s  assert (fillmissing (x, "constant", [92:99], 1, "endvalues", [82:89]), y);
878s  y = x;
878s  y(isnan (y)) = [84, 85, 85, 96, 85, 84, 87, 87, 99, 87, 98, 99, 87];
878s  assert (fillmissing (x, "constant", [94:99], 2, "endvalues", [84:89]), y);
878s  y = x;
878s  y(isnan (y)) = [68, 69, 72, 73, 75, 77, 68, 71, 73, 74, 75, 76, 77];
878s  assert (fillmissing (x, "constant", [88:99], 3, "endvalues", [68:79]), y);
878s  assert (fillmissing (x, "constant", [88:93; 94:99]', 3, "endvalues", [68:73; 74:79]'), y)
878s ***** test
878s  x = reshape ([1:24],4,3,2);
878s  x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
878s  y = x;
878s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [94, 95, 95, 96, 96, 97, 97, 98, 99, 99];
878s  assert (fillmissing (x, "constant", [94:99], 1), y);
878s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [92, 93, 94, 92, 95, 97, 99, 98, 97, 98];
878s  assert (fillmissing (x, "constant", [92:99], 2), y);
878s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [88, 93, 94, 96, 99, 89, 91, 94, 97, 98];
878s  assert (fillmissing (x, "constant", [88:99], 3), y);
878s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [76, 81, 82, 84, 87, 89, 91, 94, 97, 98];
878s  assert (fillmissing (x, "constant", [76:99], 99), y);
878s ***** assert (fillmissing ([1, 2, 3], "previous"), [1, 2, 3])
878s ***** assert (fillmissing ([1, 2, 3], "next"), [1, 2, 3])
878s ***** assert (fillmissing ([1, 2, 3]', "previous"), [1, 2, 3]')
878s ***** assert (fillmissing ([1, 2, 3]', "next"), [1, 2, 3]')
878s ***** assert (fillmissing ([1, 2, NaN], "previous"), [1, 2, 2])
878s ***** assert (fillmissing ([1, 2, NaN], "next"), [1, 2, NaN])
878s ***** assert (fillmissing ([NaN, 2, NaN], "previous"), [NaN, 2, 2])
878s ***** assert (fillmissing ([NaN, 2, NaN], "next"), [2, 2, NaN])
878s ***** assert (fillmissing ([1, NaN, 3], "previous"), [1, 1, 3])
878s ***** assert (fillmissing ([1, NaN, 3], "next"), [1, 3, 3])
878s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "previous", 1), [1, 2, NaN; 4, 2, 6])
878s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "previous", 2), [1, 2, 2; 4, 4, 6])
878s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "previous", 3), [1, 2, NaN; 4, NaN, 6])
878s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "next", 1), [1, 2, 6; 4, NaN, 6])
878s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "next", 2), [1, 2, NaN; 4, 6, 6])
878s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "next", 3), [1, 2, NaN; 4, NaN, 6])
878s ***** test
878s  x = reshape ([1:24], 4, 3, 2);
878s  x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
878s  y = x;
878s  y([1, 6, 7, 9, 14, 19, 22, 23]) = [2, 8, 8, 10, 15, 20, 24, 24];
878s  assert (fillmissing (x, "next", 1), y);
878s  y = x;
878s  y([1, 6, 7, 14, 16]) = [5, 10, 11, 18, 20];
878s  assert (fillmissing (x, "next", 2), y);
878s  y = x;
878s  y([1, 6, 9, 12]) = [13, 18, 21, 24];
878s  assert (fillmissing (x, "next", 3), y);
878s  assert (fillmissing (x, "next", 99), x);
878s  y = x;
878s  y([6, 7, 12, 14, 16, 19, 22, 23]) = [5, 5, 11, 13, 15, 18, 21, 21];
878s  assert (fillmissing (x, "previous", 1), y);
878s  y = x;
878s  y([6, 7, 9, 12, 19, 22, 23]) = [2, 3, 5, 8, 15, 18, 15];
878s  assert (fillmissing (x, "previous", 2), y);
878s  y = x;
878s  y([14, 16, 22, 23]) = [2, 4, 10, 11];
878s  assert (fillmissing (x, "previous", 3), y);
878s  assert (fillmissing (x, "previous", 99), x);
878s ***** assert (fillmissing ([1, 2, 3], "constant", 0, "endvalues", "previous"), [1, 2, 3])
878s ***** assert (fillmissing ([1, 2, 3], "constant", 0, "endvalues", "next"), [1, 2, 3])
878s ***** assert (fillmissing ([1, NaN, 3], "constant", 0, "endvalues", "previous"), [1, 0, 3])
878s ***** assert (fillmissing ([1, NaN, 3], "constant", 0, "endvalues", "next"), [1, 0, 3])
879s ***** assert (fillmissing ([1, 2, NaN], "constant", 0, "endvalues", "previous"), [1, 2, 2])
879s ***** assert (fillmissing ([1, 2, NaN], "constant", 0, "endvalues", "next"), [1, 2, NaN])
879s ***** assert (fillmissing ([1, NaN, NaN], "constant", 0, "endvalues", "previous"), [1, 1, 1])
879s ***** assert (fillmissing ([1, NaN, NaN], "constant", 0, "endvalues", "next"), [1, NaN, NaN])
879s ***** assert (fillmissing ([NaN, 2, 3], "constant", 0, "endvalues", "previous"), [NaN, 2, 3])
879s ***** assert (fillmissing ([NaN, 2, 3], "constant", 0, "endvalues", "next"), [2, 2, 3])
879s ***** assert (fillmissing ([NaN, NaN, 3], "constant", 0, "endvalues", "previous"), [NaN, NaN, 3])
879s ***** assert (fillmissing ([NaN, NaN, 3], "constant", 0, "endvalues", "next"), [3, 3, 3])
879s ***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "endvalues", "previous"), [NaN, NaN, NaN])
879s ***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "endvalues", "next"), [NaN, NaN, NaN])
879s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, "endvalues", "previous"), [NaN, 2, 0, 4, 4])
879s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, "endvalues", "next"), [2, 2, 0, 4, NaN])
879s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 1, "endvalues", "previous"), [NaN, 2, NaN, 4, NaN])
879s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 1, "endvalues", "next"), [NaN, 2, NaN, 4, NaN])
879s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 2, "endvalues", "previous"), [NaN, 2, 0, 4, 4])
879s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 2, "endvalues", "next"), [2, 2, 0, 4, NaN])
879s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 3, "endvalues", "previous"), [NaN, 2, NaN, 4, NaN])
879s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 3, "endvalues", "next"), [NaN, 2, NaN, 4, NaN])
879s ***** test
879s  x = reshape ([1:24], 3, 4, 2);
879s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
879s  y = x;
879s  y([5, 6, 8, 18]) = [4, 4, 0, 17];
879s  assert (fillmissing (x, "constant", 0, "endvalues", "previous"), y);
879s  assert (fillmissing (x, "constant", 0, 1, "endvalues", "previous"), y);
879s  y = x;
879s  y([6, 10, 18, 20, 21]) = [0, 7, 0, 0, 0];
879s  assert (fillmissing (x, "constant", 0, 2, "endvalues", "previous"), y);
879s  y = x;
879s  y([16, 19, 21]) = [4, 7, 9];
879s  assert (fillmissing (x, "constant", 0, 3, "endvalues", "previous"), y);
879s  assert (fillmissing (x, "constant", 0, 4, "endvalues", "previous"), x);
879s  assert (fillmissing (x, "constant", 0, 99, "endvalues", "previous"), x);
879s  y = x;
879s  y([1, 2, 8, 10, 13, 16, 22]) = [3, 3, 0, 11, 14, 17, 23];
879s  assert (fillmissing (x, "constant", 0, "endvalues", "next"), y);
879s  assert (fillmissing (x, "constant", 0, 1, "endvalues", "next"), y);
879s  y = x;
879s  y([1, 2, 5, 6, 8, 18, 20, 21]) = [4, 11, 11, 0, 11, 0, 0, 0];
879s  assert (fillmissing (x, "constant", 0, 2, "endvalues", "next"), y);
879s  y = x;
879s  y([2, 5]) = [14, 17];
879s  assert (fillmissing (x, "constant", 0, 3, "endvalues", "next"), y);
879s  assert (fillmissing (x, "constant", 0, 4, "endvalues", "next"), x);
879s  assert (fillmissing (x, "constant", 0, 99, "endvalues", "next"), x);
879s ***** assert (fillmissing ([1, 2, 3], "nearest"), [1, 2, 3])
879s ***** assert (fillmissing ([1, 2, 3]', "nearest"), [1, 2, 3]')
879s ***** assert (fillmissing ([1, 2, NaN], "nearest"), [1, 2, 2])
879s ***** assert (fillmissing ([NaN, 2, NaN], "nearest"), [2, 2, 2])
879s ***** assert (fillmissing ([1, NaN, 3], "nearest"), [1, 3, 3])
879s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "nearest", 1), [1, 2, 6; 4, 2, 6])
879s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "nearest", 2), [1, 2, 2; 4, 6, 6])
879s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "nearest", 3), [1, 2, NaN; 4, NaN, 6])
879s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "nearest"), [1, 3, 3, 5, 5])
879s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "nearest", "samplepoints", [0, 1, 2, 3, 4]), [1, 3, 3, 5, 5])
879s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "nearest", "samplepoints", [0.5, 1, 2, 3, 5]), [1, 1, 3, 3, 5])
879s ***** test
879s  x = reshape ([1:24], 4, 3, 2);
879s  x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
879s  y = x;
879s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [2, 5, 8, 10, 11, 15, 15, 20, 21, 24];
879s  assert (fillmissing (x, "nearest", 1), y);
879s  y = x;
879s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [5, 10, 11, 5, 8, 18, 20, 15, 18, 15];
879s  assert (fillmissing (x, "nearest", 2), y);
879s  y = x;
879s  y([1, 6, 9, 12, 14, 16, 22, 23]) = [13, 18, 21, 24, 2, 4, 10, 11];
879s  assert (fillmissing (x, "nearest", 3), y);
879s  assert (fillmissing (x, "nearest", 99), x);
879s ***** assert (fillmissing ([1, 2, 3], "constant", 0, "endvalues", "nearest"), [1, 2, 3])
879s ***** assert (fillmissing ([1, NaN, 3], "constant", 0, "endvalues", "nearest"), [1 0 3])
879s ***** assert (fillmissing ([1, 2, NaN], "constant", 0, "endvalues", "nearest"), [1, 2, 2])
879s ***** assert (fillmissing ([1, NaN, NaN], "constant", 0, "endvalues", "nearest"), [1, 1, 1])
879s ***** assert (fillmissing ([NaN, 2, 3], "constant", 0, "endvalues", "nearest"), [2, 2, 3])
879s ***** assert (fillmissing ([NaN, NaN, 3], "constant", 0, "endvalues", "nearest"), [3, 3, 3])
879s ***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "endvalues", "nearest"), [NaN, NaN, NaN])
879s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, "endvalues", "nearest"), [2, 2, 0, 4, 4])
879s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 1, "endvalues", "nearest"), [NaN, 2, NaN, 4, NaN])
879s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 2, "endvalues", "nearest"), [2, 2, 0, 4, 4])
879s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 3, "endvalues", "nearest"), [NaN, 2, NaN, 4, NaN])
879s ***** test
879s  x = reshape ([1:24], 3, 4, 2);
879s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
879s  y = x;
879s  y([1, 2, 5, 6, 8, 10, 13, 16, 18, 22]) = [3, 3, 4, 4, 0, 11, 14, 17, 17, 23];
879s  assert (fillmissing (x, "constant", 0, "endvalues", "nearest"), y);
879s  assert (fillmissing (x, "constant", 0, 1, "endvalues", "nearest"), y);
879s  y = x;
879s  y([1, 2, 5, 6, 8, 10, 18, 20, 21]) = [4, 11, 11, 0, 11, 7, 0, 0, 0];
879s  assert (fillmissing (x, "constant", 0, 2, "endvalues", "nearest"), y);
879s  y = x;
879s  y([2, 5, 16, 19, 21]) = [14, 17, 4, 7, 9];
879s  assert (fillmissing (x, "constant", 0, 3, "endvalues", "nearest"), y);
879s  assert (fillmissing (x, "constant", 0, 99, "endvalues", "nearest"), x);
879s ***** assert (fillmissing ([1, 2, 3], "linear"), [1, 2, 3])
879s ***** assert (fillmissing ([1, 2, 3]', "linear"), [1, 2, 3]')
879s ***** assert (fillmissing ([1, 2, NaN], "linear"), [1, 2, 3])
879s ***** assert (fillmissing ([NaN, 2, NaN], "linear"), [NaN, 2, NaN])
879s ***** assert (fillmissing ([1, NaN, 3], "linear"), [1, 2, 3])
879s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "linear", 1), [1, 2, NaN; 4, NaN, 6])
879s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "linear", 2), [1, 2, 3; 4, 5, 6])
879s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "linear", 3), [1, 2, NaN; 4, NaN, 6])
879s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "linear"), [1, 2, 3, 4, 5])
879s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "linear", "samplepoints", [0, 1, 2, 3, 4]), [1, 2, 3, 4, 5])
879s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "linear", "samplepoints", [0, 1.5, 2, 5, 14]), [1, 2.5, 3, 3.5, 5], eps)
879s ***** test
879s  x = reshape ([1:24], 4, 3, 2);
879s  x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
879s  assert (fillmissing (x, "linear", 1), reshape ([1:24], 4, 3, 2));
879s  y = reshape ([1:24], 4, 3, 2);
879s  y([1, 9, 14, 19, 22, 23]) = NaN;
879s  assert (fillmissing (x, "linear", 2), y);
879s  y = reshape ([1:24], 4, 3, 2);
879s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
879s  assert (fillmissing (x, "linear", 3), y);
879s  assert (fillmissing (x, "linear", 99), x);
879s ***** assert (fillmissing ([1, 2, 3], "linear", "endvalues", 0), [1, 2, 3])
879s ***** assert (fillmissing ([1, NaN, 3], "linear", "endvalues", 0), [1, 2, 3])
879s ***** assert (fillmissing ([1, 2, NaN], "linear", "endvalues", 0), [1, 2, 0])
879s ***** assert (fillmissing ([1, NaN, NaN], "linear", "endvalues", 0), [1, 0, 0])
879s ***** assert (fillmissing ([NaN, 2, 3], "linear", "endvalues", 0), [0, 2, 3])
879s ***** assert (fillmissing ([NaN, NaN, 3], "linear", "endvalues", 0), [0, 0, 3])
879s ***** assert (fillmissing ([NaN, NaN, NaN], "linear", "endvalues", 0), [0, 0, 0])
879s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "linear", "endvalues", 0), [0, 2, 3, 4, 0])
879s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "linear", 1, "endvalues", 0), [0, 2, 0, 4, 0])
879s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "linear", 2, "endvalues", 0), [0, 2, 3, 4, 0])
879s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "linear", 3, "endvalues", 0), [0, 2, 0, 4, 0])
879s ***** test
879s  x = reshape ([1:24], 3, 4, 2);
879s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
879s  y = x;
879s  y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 0;
879s  y(8) = 8;
879s  assert (fillmissing (x, "linear", "endvalues", 0), y);
879s  assert (fillmissing (x, "linear", 1, "endvalues", 0), y);
879s  y = x;
879s  y([1, 2, 5, 8, 10, 13, 16, 19, 22]) = 0;
879s  y([6, 18, 20, 21]) = [6, 18, 20, 21];
879s  assert (fillmissing (x, "linear", 2, "endvalues", 0), y);
879s  y = x;
879s  y(isnan(y)) = 0;
879s  assert (fillmissing (x, "linear", 3, "endvalues", 0), y);
879s  assert (fillmissing (x, "linear", 99, "endvalues", 0), y);
879s ***** assert (fillmissing ([1, 2, 3], "constant", 99, "endvalues", "linear"), [1, 2, 3])
879s ***** assert (fillmissing ([1, NaN, 3], "constant", 99, "endvalues", "linear"), [1, 99, 3])
879s ***** assert (fillmissing ([1, NaN, 3, NaN], "constant", 99, "endvalues", "linear"), [1, 99, 3, 4])
879s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "linear"), [1, 2, 99, 4, 5])
879s ***** assert (fillmissing ([NaN, 2, NaN, NaN], "constant", 99, "endvalues", "linear"), [NaN, 2, NaN, NaN])
879s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "linear", "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 99, 4, 5])
879s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "linear", "samplepoints", [0, 2, 3, 4, 10]), [0, 2, 99, 4, 10])
879s ***** test
879s  x = reshape ([1:24], 3, 4, 2);
879s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
879s  y = x;
879s  y([1, 6, 10, 18, 20, 21]) = [2.5, 5, 8.5, 17.25, 21, 21.75];
879s  assert (fillmissing (x, "linear", 2, "samplepoints", [2 4 8 10]), y, eps);
879s  y([1, 6, 10, 18, 20, 21]) = [2.5, 4.5, 8.5, 17.25, 21.5, 21.75];
879s  assert (fillmissing (x, "spline", 2, "samplepoints", [2, 4, 8, 10]), y, eps);
879s  y([1, 6, 10, 18, 20, 21]) = [2.5, 4.559386973180077, 8.5, 17.25, 21.440613026819925, 21.75];
879s  assert (fillmissing (x, "pchip", 2, "samplepoints", [2, 4, 8, 10]), y, 10*eps);
879s ***** test <60965>
879s  x = reshape ([1:24], 3, 4, 2);
879s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
879s  y = x;
879s  y([1, 6, 10, 18, 20, 21]) = [2.5, 4.609523809523809, 8.5, 17.25, 21.390476190476186, 21.75];
879s  assert (fillmissing (x, "makima", 2, "samplepoints", [2, 4, 8, 10]), y, 10*eps);
879s !!!!! known bug: https://octave.org/testfailure/?60965
879s interp1: invalid METHOD 'makima'
879s ***** assert (fillmissing ([1, 2, 3], "constant", 99, "endvalues", "spline"), [1, 2, 3])
879s ***** assert (fillmissing ([1, NaN, 3], "constant", 99, "endvalues", "spline"), [1, 99, 3])
879s ***** assert (fillmissing ([1, NaN, 3, NaN], "constant", 99, "endvalues", "spline"), [1, 99, 3, 4])
879s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "spline"), [1, 2, 99, 4, 5])
879s ***** assert (fillmissing ([NaN, 2, NaN, NaN], "constant", 99, "endvalues", "spline"), [NaN, 2, NaN, NaN])
879s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "spline", "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 99, 4, 5])
879s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "spline", "samplepoints", [0, 2, 3, 4, 10]), [0, 2, 99, 4, 10])
879s ***** assert (fillmissing ([1, 2, 3], "movmean", 1), [1, 2, 3])
879s ***** assert (fillmissing ([1, 2, NaN], "movmean", 1), [1, 2, NaN])
879s ***** assert (fillmissing ([1, 2, 3], "movmean", 2), [1, 2, 3])
879s ***** assert (fillmissing ([1, 2, 3], "movmean", [1, 0]), [1, 2, 3])
879s ***** assert (fillmissing ([1, 2, 3]', "movmean", 2), [1, 2, 3]')
879s ***** assert (fillmissing ([1, 2, NaN], "movmean", 2), [1, 2, 2])
879s ***** assert (fillmissing ([1, 2, NaN], "movmean", [1, 0]), [1, 2, 2])
879s ***** assert (fillmissing ([1, 2, NaN], "movmean", [1, 0]'), [1, 2, 2])
879s ***** assert (fillmissing ([NaN, 2, NaN], "movmean", 2), [NaN, 2, 2])
879s ***** assert (fillmissing ([NaN, 2, NaN], "movmean", [1, 0]), [NaN, 2, 2])
879s ***** assert (fillmissing ([NaN, 2, NaN], "movmean", [0, 1]), [2, 2, NaN])
879s ***** assert (fillmissing ([NaN, 2, NaN], "movmean", [0, 1.1]), [2, 2, NaN])
879s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmean", [3, 0]), [1, 1, 3, 2, 5])
879s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmean", 3, 1), [1, 2, 6; 4, 2, 6])
879s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmean", 3, 2), [1, 2, 2; 4, 5, 6])
879s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmean", 3, 3), [1, 2, NaN; 4, NaN, 6])
879s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmean", 99), [1, 3, 3, 3, 5])
879s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmean", 99, 1), [1, NaN, 3, NaN, 5])
879s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "movmean", 99, 1), [1, 3, 3, 3, 5]')
879s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmean", 99, 2), [1, 3, 3, 3, 5])
879s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "movmean", 99, 2), [1, NaN, 3, NaN, 5]')
879s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 3, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
879s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1, 1], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
879s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1.5, 1.5], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
879s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 4, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 1, 5, 5])
879s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [2, 2], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 3, 5, 5])
879s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 4.0001, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 3, 5, 5])
879s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 3, "samplepoints", [1.5, 2, 3, 4, 5]), [1, 1, 1, 5, 5])
879s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 3, "samplepoints", [1 2, 3, 4, 4.5]), [1, 1, NaN, 5, 5])
879s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 3, "samplepoints", [1.5, 2, 3, 4, 4.5]), [1, 1, 1, 5, 5])
879s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1.5, 1.5], "samplepoints", [1.5, 2, 3, 4, 5]), [1, 1, 1, 5, 5])
879s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1.5, 1.5], "samplepoints", [1, 2, 3, 4, 4.5]), [1, 1, 5, 5, 5])
879s ***** 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])
879s ***** test
879s  x = reshape ([1:24], 3, 4, 2);
879s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
879s  y = x;
879s  y([2, 5, 8, 10, 13, 16, 18, 22]) = [3, 4, 8, 11, 14, 17, 17, 23];
879s  assert (fillmissing (x, "movmean", 3), y);
879s  assert (fillmissing (x, "movmean", [1, 1]), y);
879s  assert (fillmissing (x, "movmean", 3, "endvalues", "extrap"), y);
879s  assert (fillmissing (x, "movmean", 3, "samplepoints", [1, 2, 3]), y);
879s  y = x;
879s  y([1, 6, 8, 10, 18, 20, 21]) = [4, 6, 11, 7, 15, 20, 24];
879s  assert (fillmissing (x, "movmean", 3, 2), y);
879s  assert (fillmissing (x, "movmean", [1, 1], 2), y);
879s  assert (fillmissing (x, "movmean", 3, 2, "endvalues", "extrap"), y);
879s  assert (fillmissing (x, "movmean", 3, 2, "samplepoints", [1, 2, 3, 4]), y);
879s  y([1, 18]) = NaN;
879s  y(6) = 9;
879s  assert (fillmissing (x, "movmean", 3, 2, "samplepoints", [0, 2, 3, 4]), y);
879s  y = x;
879s  y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 99;
879s  y(8) = 8;
879s  assert (fillmissing (x, "movmean", 3, "endvalues", 99), y);
879s  y = x;
879s  y([1, 2, 5, 8, 10, 13, 16, 19, 22]) = 99;
879s  y([6, 18, 20, 21]) = [6, 15, 20, 24];
879s  assert (fillmissing (x, "movmean", 3, 2, "endvalues", 99), y);
879s ***** assert (fillmissing ([1, 2, 3], "movmedian", 1), [1, 2, 3])
879s ***** assert (fillmissing ([1, 2, NaN], "movmedian", 1), [1, 2, NaN])
879s ***** assert (fillmissing ([1, 2, 3], "movmedian", 2), [1, 2, 3])
879s ***** assert (fillmissing ([1, 2, 3], "movmedian", [1, 0]), [1, 2, 3])
879s ***** assert (fillmissing ([1, 2, 3]', "movmedian", 2), [1, 2, 3]')
879s ***** assert (fillmissing ([1, 2, NaN], "movmedian", 2), [1, 2, 2])
879s ***** assert (fillmissing ([1, 2, NaN], "movmedian", [1, 0]), [1, 2, 2])
879s ***** assert (fillmissing ([1, 2, NaN], "movmedian", [1, 0]'), [1, 2, 2])
879s ***** assert (fillmissing ([NaN, 2, NaN], "movmedian", 2), [NaN, 2, 2])
879s ***** assert (fillmissing ([NaN, 2, NaN], "movmedian", [1, 0]), [NaN, 2, 2])
879s ***** assert (fillmissing ([NaN, 2, NaN], "movmedian", [0, 1]), [2, 2, NaN])
879s ***** assert (fillmissing ([NaN, 2, NaN], "movmedian", [0, 1.1]), [2, 2, NaN])
879s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmedian", [3, 0]), [1, 1, 3, 2, 5])
879s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmedian", 3, 1), [1, 2, 6; 4, 2, 6])
879s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmedian", 3, 2), [1, 2, 2; 4, 5, 6])
879s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmedian", 3, 3), [1, 2, NaN; 4, NaN, 6])
879s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmedian", 99), [1, 3, 3, 3, 5])
879s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmedian", 99, 1), [1, NaN, 3, NaN, 5])
879s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "movmedian", 99, 1), [1, 3, 3, 3, 5]')
879s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmedian", 99, 2), [1, 3, 3, 3, 5])
879s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "movmedian", 99, 2), [1, NaN, 3, NaN, 5]')
879s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 3, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
879s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1, 1], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
879s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1.5, 1.5], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
879s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 4, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 1, 5, 5])
879s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [2, 2], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 3, 5, 5])
879s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 4.0001, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 3, 5, 5])
879s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 3, "samplepoints", [1.5 2 3 4 5]), [1, 1, 1, 5, 5])
879s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 3, "samplepoints", [1 2 3 4 4.5]), [1, 1, NaN, 5, 5])
879s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 3, "samplepoints", [1.5 2 3 4 4.5]), [1, 1, 1, 5, 5])
879s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1.5, 1.5], "samplepoints", [1.5 2 3 4 5]), [1, 1, 1, 5, 5])
879s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1.5, 1.5], "samplepoints", [1 2 3 4 4.5]), [1, 1, 5, 5, 5])
879s ***** 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])
879s ***** test
879s  x = reshape ([1:24], 3, 4, 2);
879s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
879s  y = x;
879s  y([2, 5, 8, 10, 13, 16, 18, 22]) = [3, 4, 8, 11, 14, 17, 17, 23];
879s  assert (fillmissing (x, "movmedian", 3), y);
879s  assert (fillmissing (x, "movmedian", [1, 1]), y);
879s  assert (fillmissing (x, "movmedian", 3, "endvalues", "extrap"), y);
879s  assert (fillmissing (x, "movmedian", 3, "samplepoints", [1, 2, 3]), y);
879s  y = x;
879s  y([1, 6, 8, 10, 18, 20, 21]) = [4, 6, 11, 7, 15, 20, 24];
879s  assert (fillmissing (x, "movmedian", 3, 2), y);
879s  assert (fillmissing (x, "movmedian", [1, 1], 2), y);
879s  assert (fillmissing (x, "movmedian", 3, 2, "endvalues", "extrap"), y);
879s  assert (fillmissing (x, "movmedian", 3, 2, "samplepoints", [1, 2, 3, 4]), y);
879s  y([1,18]) = NaN;
879s  y(6) = 9;
879s  assert (fillmissing (x, "movmedian", 3, 2, "samplepoints", [0, 2, 3, 4]), y);
879s  y = x;
879s  y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 99;
879s  y(8) = 8;
879s  assert (fillmissing (x, "movmedian", 3, "endvalues", 99), y);
879s  y = x;
879s  y([1, 2, 5, 8, 10, 13, 16, 19, 22]) = 99;
879s  y([6, 18, 20, 21]) = [6, 15, 20, 24];
879s  assert (fillmissing (x, "movmedian", 3, 2, "endvalues", 99), y);
879s ***** assert (fillmissing ([1, 2, 3], @(x,y,z) x+y+z, 2), [1, 2, 3])
879s ***** assert (fillmissing ([1, 2, NaN], @(x,y,z) x+y+z, 1), [1, 2, NaN])
879s ***** assert (fillmissing ([1, 2, 3], @(x,y,z) x+y+z, 2), [1, 2, 3])
879s ***** assert (fillmissing ([1, 2, 3], @(x,y,z) x+y+z, [1, 0]), [1, 2, 3])
879s ***** assert (fillmissing ([1, 2, 3]', @(x,y,z) x+y+z, 2), [1, 2, 3]')
879s ***** assert (fillmissing ([1, 2, NaN], @(x,y,z) x+y+z, 2), [1, 2, 7])
879s ***** assert (fillmissing ([1, 2, NaN], @(x,y,z) x+y+z, [1, 0]), [1, 2, 7])
879s ***** assert (fillmissing ([1, 2, NaN], @(x,y,z) x+y+z, [1, 0]'), [1, 2, 7])
879s ***** assert (fillmissing ([NaN, 2, NaN], @(x,y,z) x+y+z, 2), [5, 2, 7])
879s ***** assert (fillmissing ([NaN, 2, NaN], @(x,y,z) x+y+z, [1, 0]), [NaN, 2, 7])
879s ***** assert (fillmissing ([NaN, 2, NaN], @(x,y,z) x+y+z, [0, 1]), [5, 2, NaN])
879s ***** assert (fillmissing ([NaN, 2, NaN], @(x,y,z) x+y+z, [0, 1.1]), [5, 2, NaN])
879s ***** assert (fillmissing ([1, 2, NaN, NaN, 3, 4], @(x,y,z) x+y+z, 2), [1, 2, 7, 12, 3, 4])
879s ***** assert (fillmissing ([1, 2, NaN, NaN, 3, 4], @(x,y,z) x+y+z, 0.5), [1, 2, NaN, NaN, 3, 4])
879s ***** function A = testfcn (x, y, z)
879s   if (isempty (y))
879s     A = z;
879s   elseif (numel (y) == 1)
879s     A = repelem (x(1), numel(z));
879s   else
879s     A = interp1 (y, x, z, "linear", "extrap");
879s   endif
879s ***** endfunction
879s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, [3, 0]), [1, 1, 3, NaN, 5])
879s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], @testfcn, 3, 1), [1, 2, 6; 4, 2, 6])
879s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], @testfcn, 3, 2), [1, 2, 2; 4, 5, 6])
879s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], @testfcn, 3, 3), [1, 2, NaN; 4, NaN, 6])
879s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, 99), [1, 2, 3, 4, 5])
879s ***** 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]
879s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', @testfcn, 99, 1), [1, 2, 3, 4, 5]')
879s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, 99, 2), [1, 2, 3, 4, 5])
879s ***** 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]'
879s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, 99, 3), [1, NaN, 3, NaN, 5])
879s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', @testfcn, 99, 3), [1, NaN, 3, NaN, 5]')
880s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, 3, "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
880s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, [1, 1], "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
880s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, [1.5, 1.5], "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
880s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, 4, "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
880s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, [2, 2], "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
880s ***** 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)
880s ***** 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]
880s ***** test
880s ***** function A = testfcn (x, y, z)
880s   if (isempty (y))
880s     A = z;
880s   elseif (numel (y) == 1)
880s     A = repelem (x(1), numel(z));
880s   else
880s     A = interp1 (y, x, z, "linear", "extrap");
880s   endif
880s ***** endfunction
880s  x = reshape ([1:24], 3, 4, 2);
880s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
880s  y = x;
880s  y([1, 2, 5, 6, 8, 10, 13, 16, 18, 22]) = [3, 3, 4, 4, 8, 11, 14, 17, 17, 23];
880s  assert (fillmissing (x, @testfcn, 3), y);
880s  assert (fillmissing (x, @testfcn, [1, 1]), y);
880s  assert (fillmissing (x, @testfcn, 3, "endvalues", "extrap"), y);
880s  assert (fillmissing (x, @testfcn, 3, "samplepoints", [1, 2, 3]), y);
880s  y= x;
880s  y(isnan (x)) = 99;
880s  y(8) = 8;
880s  assert (fillmissing (x, @testfcn, 3, "endvalues", 99), y)
880s  y = x;
880s  y([1, 2, 5, 6, 8, 10, 18, 20, 21]) = [4, 11, 11, 6, 11, 7, 18, 20, 21];
880s  assert (fillmissing (x, @testfcn, 3, 2), y);
880s  assert (fillmissing (x, @testfcn, [1, 1], 2), y);
880s  assert (fillmissing (x, @testfcn, 3, 2, "endvalues", "extrap"), y);
880s  assert (fillmissing (x, @testfcn, 3, 2, "samplepoints", [1, 2, 3, 4]), y);
880s  y(1) = NaN;
880s  y([6, 18, 21]) = [9, 24, 24];
880s  assert (fillmissing (x, @testfcn, 3, 2, "samplepoints", [0, 2, 3, 4]), y);
880s  y = x;
880s  y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 99;
880s  y(8) = 8;
880s  assert (fillmissing (x, @testfcn, 3, "endvalues", 99), y);
880s  y([6, 18, 20, 21]) = [6, 18, 20, 21];
880s  y(8) = 99;
880s  assert (fillmissing (x, @testfcn, 3, 2, "endvalues", 99), y);
880s  y([6, 18, 20, 21]) = 99;
880s  assert (fillmissing (x, @testfcn, 3, 3, "endvalues", 99), y);
880s ***** assert (fillmissing ([1, 2, 3], "constant", 0, "maxgap", 1), [1, 2, 3])
880s ***** assert (fillmissing ([1, 2, 3], "constant", 0, "maxgap", 99), [1, 2, 3])
880s ***** assert (fillmissing ([1, NaN, 3], "constant", 0, "maxgap", 1), [1, NaN, 3])
880s ***** assert (fillmissing ([1, NaN, 3], "constant", 0, "maxgap", 1.999), [1, NaN, 3])
880s ***** assert (fillmissing ([1, NaN, 3], "constant", 0, "maxgap", 2), [1, 0, 3])
880s ***** assert (fillmissing ([1, NaN, NaN, 4], "constant", 0, "maxgap", 2), [1, NaN, NaN, 4])
880s ***** assert (fillmissing ([1, NaN, NaN, 4], "constant", 0, "maxgap", 3), [1, 0, 0, 4])
880s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", 0, "maxgap", 2), [1, 0, 3, 0, 5])
880s ***** assert (fillmissing ([NaN, 2, NaN], "constant", 0, "maxgap", 0.999), [NaN, 2, NaN])
880s ***** assert (fillmissing ([NaN, 2, NaN], "constant", 0, "maxgap", 1), [0, 2, 0])
880s ***** assert (fillmissing ([NaN, 2, NaN, NaN], "constant", 0, "maxgap", 1), [0, 2, NaN, NaN])
880s ***** assert (fillmissing ([NaN, 2, NaN, NaN], "constant", 0, "maxgap", 2), [0, 2, 0, 0])
880s ***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "maxgap", 1), [NaN, NaN, NaN])
880s ***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "maxgap", 3), [NaN, NaN, NaN])
880s ***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "maxgap", 999), [NaN, NaN, NaN])
880s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", 0, "maxgap", 2, "samplepoints", [0, 1, 2, 3, 5]), [1, 0, 3, NaN, 5])
880s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "constant", 0, "maxgap", 2, "samplepoints", [0, 1, 2, 3, 5]), [1, 0, 3, NaN, 5]')
880s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", 0, "maxgap", 2, "samplepoints", [0, 2, 3, 4, 5]), [1, NaN, 3, 0, 5])
880s ***** 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])
880s ***** test
880s  x = cat (3, [1, 2, NaN; 4, NaN, NaN], [NaN, 2, 3; 4, 5, NaN]);
880s  assert (fillmissing (x, "constant", 0, "maxgap", 0.1), x);
880s  y = x;
880s  y([4, 7, 12]) = 0;
880s  assert (fillmissing (x, "constant", 0, "maxgap", 1), y);
880s  assert (fillmissing (x, "constant", 0, 1, "maxgap", 1), y);
880s  y = x;
880s  y([5, 7, 12]) = 0;
880s  assert (fillmissing (x, "constant", 0, 2, "maxgap", 1), y);
880s  y = x;
880s  y([4, 5, 7]) = 0;
880s  assert (fillmissing (x, "constant", 0, 3, "maxgap", 1), y);
880s ***** test
880s  x = cat (3, [1, 2, NaN; 4, NaN, NaN], [NaN, 2, 3; 4, 5, NaN]);
880s  [~, idx] = fillmissing (x, "constant", 0, "maxgap", 1);
880s  assert (idx, logical (cat (3, [0, 0, 0; 0, 1, 0], [1, 0, 0; 0, 0, 1])));
880s  [~, idx] = fillmissing (x, "constant", 0, 1, "maxgap", 1);
880s  assert (idx, logical (cat (3, [0, 0, 0; 0, 1, 0], [1, 0, 0; 0, 0, 1])));
880s  [~, idx] = fillmissing (x, "constant", 0, 2, "maxgap", 1);
880s  assert (idx, logical (cat (3, [0, 0, 1; 0, 0, 0], [1, 0, 0; 0, 0, 1])));
880s  [~, idx] = fillmissing (x, "constant", 0, 3, "maxgap", 1);
880s  assert (idx, logical (cat (3, [0, 0, 1; 0, 1, 0], [1, 0, 0; 0, 0, 0])));
880s ***** test
880s  x = [NaN, 2, 3];
880s  [~, idx] = fillmissing (x, "previous");
880s  assert (idx, logical ([0, 0, 0]));
880s  [~, idx] = fillmissing (x, "movmean", 1);
880s  assert (idx, logical ([0, 0, 0]));
880s  x = [1:3; 4:6; 7:9];
880s  x([2, 4, 7, 9]) = NaN;
880s  [~, idx] = fillmissing (x, "linear");
880s  assert (idx, logical ([0, 1, 0; 1, 0, 0; 0, 0, 0]));
880s  [~, idx] = fillmissing (x, "movmean", 2);
880s  assert (idx, logical ([0, 0, 0; 1, 0, 0; 0, 0, 1]));
880s  [A, idx] = fillmissing ([1, 2, 3, NaN, NaN], "movmean",2);
880s  assert (A, [1, 2, 3, 3, NaN]);
880s  assert (idx, logical ([0, 0, 0, 1, 0]));
880s  [A, idx] = fillmissing ([1, 2, 3, NaN, NaN], "movmean",3);
880s  assert (A, [1, 2, 3, 3, NaN]);
880s  assert (idx, logical ([0, 0, 0, 1, 0]));
880s  [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmedian", 2);
880s  assert (A, [1, 2, 2, NaN, NaN]);
880s  assert (idx, logical ([0, 0, 1, 0, 0]));
880s  [A, idx] = fillmissing ([1, 2, 3, NaN, NaN], "movmedian", 3);
880s  assert (A, [1, 2, 3, 3, NaN]);
880s  assert (idx, logical ([0, 0, 0, 1, 0]));
880s  [A, idx] = fillmissing ([1, NaN, 1, NaN, 1],  @(x,y,z) z, 3);
880s  assert (A, [1, 2, 1, 4, 1]);
880s  assert (idx, logical ([0, 1, 0, 1, 0]));
880s  [A, idx] = fillmissing ([1, NaN, 1, NaN, 1],  @(x,y,z) NaN (size (z)), 3);
880s  assert (A, [1, NaN, 1, NaN, 1]);
880s  assert (idx, logical ([0, 0, 0, 0, 0]));
880s ***** assert (fillmissing ([1, 2, 3], "constant", 99, "missinglocations", logical ([0, 0, 0])), [1, 2, 3])
880s ***** assert (fillmissing ([1, 2, 3], "constant", 99, "missinglocations", logical ([1, 1, 1])), [99, 99, 99])
880s ***** assert (fillmissing ([1, NaN, 2, 3, NaN], "constant", 99, "missinglocations", logical ([1, 0, 1, 0, 1])), [99, NaN, 99, 3, 99])
880s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", NaN, "missinglocations", logical ([0, 1, 1, 1, 0])), [1, NaN, NaN, NaN, 5])
880s ***** assert (fillmissing (["foo "; " bar"], "constant", "X", "missinglocations", logical ([0, 0, 0, 0; 0, 0, 0, 0])), ["foo "; " bar"])
880s ***** assert (fillmissing (["foo "; " bar"], "constant", "X", "missinglocations", logical ([1, 0, 1, 0; 0, 1, 1, 0])), ["XoX "; " XXr"])
880s ***** assert (fillmissing ({"foo", "", "bar"}, "constant", "X", "missinglocations", logical ([0, 0, 0])), {"foo", "", "bar"})
880s ***** assert (fillmissing ({"foo", "", "bar"}, "constant", "X", "missinglocations", logical ([1, 1, 0])), {"X", "X", "bar"})
880s ***** test
880s  [~, idx] = fillmissing ([1, NaN, 3, NaN, 5], "constant", NaN);
880s  assert (idx, logical ([0, 0, 0, 0, 0]));
880s  [~, idx] = fillmissing ([1 NaN 3 NaN 5], "constant", NaN, "missinglocations", logical ([0, 1, 1, 1, 0]));
880s  assert (idx, logical ([0, 1, 1, 1, 0]));
880s  [A, idx] = fillmissing ([1, 2, NaN, 1, NaN], "movmean", 3.1, "missinglocations", logical ([0, 0, 1, 1, 0]));
880s  assert (A, [1, 2, 2, NaN, NaN]);
880s  assert (idx, logical ([0, 0, 1, 0, 0]));
880s  [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmean", 2, "missinglocations", logical ([0, 0, 1, 1, 0]));
880s  assert (A, [1, 2, 2, NaN, NaN]);
880s  assert (idx, logical ([0, 0, 1, 0, 0]));
880s  [A, idx] = fillmissing ([1, 2, NaN, 1, NaN], "movmean", 3, "missinglocations", logical ([0, 0, 1, 1, 0]));
880s  assert (A, [1, 2, 2, NaN, NaN]);
880s  assert (idx, logical ([0, 0, 1, 0, 0]));
880s  [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmean", 3, "missinglocations", logical ([0, 0, 1, 1, 0]));
880s  assert (A, [1, 2, 2, NaN, NaN]);
880s  assert (idx, logical ([0, 0, 1, 0, 0]));
880s  [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmedian", 2, "missinglocations", logical ([0, 0, 1, 1, 0]));
880s  assert (A, [1, 2, 2, NaN, NaN]);
880s  assert (idx, logical ([0, 0, 1, 0, 0]));
880s  [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmedian", 3, "missinglocations", logical ([0, 0, 1, 1, 0]));
880s  assert (A, [1, 2, 2, NaN, NaN]);
880s  assert (idx, logical ([0, 0, 1, 0, 0]));
880s  [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmedian", 3.1, "missinglocations", logical ([0, 0, 1, 1, 0]));
880s  assert (A, [1, 2, 2, NaN, NaN]);
880s  assert (idx, logical ([0, 0, 1, 0, 0]));
880s  [A, idx] = fillmissing ([1, NaN, 1, NaN, 1],  @(x,y,z) ones (size (z)), 3, "missinglocations", logical ([0, 1, 0, 1, 1]));
880s  assert (A, [1, 1, 1, 1, 1]);
880s  assert (idx, logical ([0, 1, 0, 1, 1]));
880s  [A, idx] = fillmissing ([1, NaN, 1, NaN, 1],  @(x,y,z) NaN (size (z)), 3, "missinglocations", logical ([0, 1, 0, 1, 1]));
880s  assert (A, [1, NaN, 1, NaN, NaN]);
880s  assert (idx, logical ([0, 0, 0, 0, 0]));
880s ***** test
880s  [A, idx] = fillmissing ([1, 2, 5], "movmedian", 3, "missinglocations", logical ([0, 1, 0]));
880s  assert (A, [1, 3, 5]);
880s  assert (idx, logical ([0, 1, 0]));
880s ***** assert (fillmissing (" foo bar ", "constant", "X"), "XfooXbarX")
880s ***** assert (fillmissing ([" foo"; "bar "], "constant", "X"), ["Xfoo"; "barX"])
880s ***** assert (fillmissing ([" foo"; "bar "], "next"), ["bfoo"; "bar "])
880s ***** assert (fillmissing ([" foo"; "bar "], "next", 1), ["bfoo"; "bar "])
880s ***** assert (fillmissing ([" foo"; "bar "], "previous"), [" foo"; "baro"])
880s ***** assert (fillmissing ([" foo"; "bar "], "previous", 1), [" foo"; "baro"])
880s ***** assert (fillmissing ([" foo"; "bar "], "nearest"), ["bfoo"; "baro"])
880s ***** assert (fillmissing ([" foo"; "bar "], "nearest", 1), ["bfoo"; "baro"])
880s ***** assert (fillmissing ([" foo"; "bar "], "next", 2), ["ffoo"; "bar "])
880s ***** assert (fillmissing ([" foo"; "bar "], "previous", 2), [" foo"; "barr"])
880s ***** assert (fillmissing ([" foo"; "bar "], "nearest", 2), ["ffoo"; "barr"])
880s ***** assert (fillmissing ([" foo"; "bar "], "next", 3), [" foo"; "bar "])
880s ***** assert (fillmissing ([" foo"; "bar "], "previous", 3), [" foo"; "bar "])
880s ***** assert (fillmissing ([" foo"; "bar "], "nearest", 3), [" foo"; "bar "])
880s ***** assert (fillmissing ({"foo", "bar"}, "constant", "a"), {"foo", "bar"})
880s ***** assert (fillmissing ({"foo", "bar"}, "constant", {"a"}), {"foo", "bar"})
880s ***** assert (fillmissing ({"foo", "", "bar"}, "constant", "a"), {"foo", "a", "bar"})
880s ***** assert (fillmissing ({"foo", "", "bar"}, "constant", {"a"}), {"foo", "a", "bar"})
880s ***** assert (fillmissing ({"foo", "", "bar"}, "previous"), {"foo", "foo", "bar"})
880s ***** assert (fillmissing ({"foo", "", "bar"}, "next"), {"foo", "bar", "bar"})
880s ***** assert (fillmissing ({"foo", "", "bar"}, "nearest"), {"foo", "bar", "bar"})
880s ***** assert (fillmissing ({"foo", "", "bar"}, "previous", 2), {"foo", "foo", "bar"})
880s ***** assert (fillmissing ({"foo", "", "bar"}, "next", 2), {"foo", "bar", "bar"})
880s ***** assert (fillmissing ({"foo", "", "bar"}, "nearest", 2), {"foo", "bar", "bar"})
880s ***** assert (fillmissing ({"foo", "", "bar"}, "previous", 1), {"foo", "", "bar"})
880s ***** assert (fillmissing ({"foo", "", "bar"}, "previous", 1), {"foo", "", "bar"})
880s ***** assert (fillmissing ({"foo", "", "bar"}, "next", 1), {"foo", "", "bar"})
880s ***** assert (fillmissing ({"foo", "", "bar"}, "nearest", 1), {"foo", "", "bar"})
880s ***** assert (fillmissing ("abc ", @(x,y,z) x+y+z, 2), "abcj")
880s ***** assert (fillmissing ({"foo", "", "bar"}, @(x,y,z) x(1), 3), {"foo", "foo", "bar"})
880s ***** test
880s  [A, idx] = fillmissing (" a b c", "constant", " ");
880s  assert (A, " a b c");
880s  assert (idx, logical ([0, 0, 0, 0, 0, 0]));
880s  [A, idx] = fillmissing ({"foo", "", "bar", ""}, "constant", "");
880s  assert (A, {"foo", "", "bar", ""});
880s  assert (idx, logical ([0, 0, 0, 0]));
880s  [A, idx] = fillmissing ({"foo", "", "bar", ""}, "constant", {""});
880s  assert (A, {"foo", "", "bar", ""});
880s  assert (idx, logical ([0, 0, 0, 0]));
880s  [A,idx] = fillmissing (" f o o ", @(x,y,z) repelem ("a", numel (z)), 3);
880s  assert (A, "afaoaoa");
880s  assert (idx, logical ([1, 0, 1, 0, 1, 0, 1]));
880s  [A,idx] = fillmissing (" f o o ", @(x,y,z) repelem (" ", numel (z)), 3);
880s  assert (A, " f o o ");
880s  assert (idx, logical ([0, 0, 0, 0, 0, 0, 0]));
880s  [A,idx] = fillmissing ({"", "foo", ""}, @(x,y,z) repelem ({"a"}, numel (z)), 3);
880s  assert (A, {"a", "foo", "a"});
880s  assert (idx, logical ([1, 0, 1]));
880s  [A,idx] = fillmissing ({"", "foo", ""}, @(x,y,z) repelem ({""}, numel (z)), 3);
880s  assert (A, {"", "foo", ""});
880s  assert (idx, logical ([0, 0, 0]));
880s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "constant", true), logical ([1, 0, 1, 0, 1]))
880s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "constant", false, "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([0, 0, 0, 0, 0]))
880s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "previous",  "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([1, 0, 0, 0, 0]))
880s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "next",  "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([0, 0, 0, 0, 1]))
880s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "nearest", "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([0, 0, 0, 0, 0]))
880s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]),  @(x,y,z) false(size(z)), 3), logical ([1, 0, 1, 0, 1]))
880s ***** 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]))
880s ***** 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]))
880s ***** test
880s  x = logical ([1, 0, 1, 0, 1]);
880s  [~, idx] = fillmissing (x, "constant", true);
880s  assert (idx, logical ([0, 0, 0, 0, 0]));
880s  [~, idx] = fillmissing (x, "constant", false, "missinglocations", logical ([1, 0, 1, 0, 1]));
880s  assert (idx, logical ([1, 0, 1, 0, 1]));
880s  [~, idx] = fillmissing (x, "constant", true, "missinglocations", logical ([1, 0, 1, 0, 1]));
880s  assert (idx, logical ([1, 0, 1, 0, 1]));
880s  [~, idx] = fillmissing (x, "previous", "missinglocations", logical ([1, 0, 1, 0, 1]));
880s  assert (idx, logical ([0, 0, 1, 0, 1]));
880s  [~, idx] = fillmissing (x, "next",  "missinglocations", logical ([1, 0, 1, 0, 1]));
880s  assert (idx, logical ([1, 0, 1, 0, 0]));
880s  [~, idx] = fillmissing (x, "nearest", "missinglocations", logical ([1, 0, 1, 0, 1]));
880s  assert (idx, logical ([1, 0, 1, 0, 1]));
880s  [~, idx] = fillmissing (x, @(x,y,z) false(size(z)), 3);
880s  assert (idx, logical ([0, 0, 0, 0, 0]))
880s  [~, idx] = fillmissing (x, @(x,y,z) false(size(z)), 3, "missinglocations", logical ([1, 0, 1, 0, 1]));
880s  assert (idx, logical ([1, 0, 1, 0, 1]))
880s  [~, idx] = fillmissing (x, @(x,y,z) false(size(z)), [2 0], "missinglocations", logical ([1, 0, 1, 0, 1]));
880s  assert (idx, logical ([0, 0, 1, 0, 1]))
880s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "constant", 0), int32 ([1, 2, 3, 4, 5]))
880s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "constant", 0, "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([0, 2, 0, 4, 0]))
880s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "previous", "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([1, 2, 2, 4, 4]))
880s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "next", "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([2, 2, 4, 4, 5]))
880s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "nearest", "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([2, 2, 4, 4, 4]))
880s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), @(x,y,z) z+10, 3), int32 ([1, 2, 3, 4, 5]))
880s ***** 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]))
880s ***** 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]))
880s ***** test
880s  x = int32 ([1, 2, 3, 4, 5]);
880s  [~, idx] = fillmissing (x, "constant", 0);
880s  assert (idx, logical ([0, 0, 0, 0, 0]));
880s  [~, idx] = fillmissing (x, "constant", 0, "missinglocations", logical ([1, 0, 1, 0, 1]));
880s  assert (idx, logical ([1, 0, 1, 0, 1]));
880s  [~, idx] = fillmissing (x, "constant", 3, "missinglocations", logical ([0, 0, 1, 0, 0]));
880s  assert (idx, logical ([0, 0, 1, 0, 0]));
880s  [~, idx] = fillmissing (x, "previous", "missinglocations", logical ([1, 0, 1, 0, 1]));
880s  assert (idx, logical ([0, 0, 1, 0, 1]));
880s  [~, idx] = fillmissing (x, "next", "missinglocations", logical ([1, 0, 1, 0, 1]));
880s  assert (idx, logical ([1, 0, 1, 0, 0]));
880s  [~, idx] = fillmissing (x, "nearest", "missinglocations", logical ([1, 0, 1, 0, 1]));
880s  assert (idx, logical ([1, 0, 1, 0, 1]));
880s  [~, idx] = fillmissing (x, @(x,y,z) z+10, 3);
880s  assert (idx, logical ([0, 0, 0, 0, 0]));
880s  [~, idx] = fillmissing (x, @(x,y,z) z+10, 3, "missinglocations", logical ([1, 0, 1, 0, 1]));
880s  assert (idx, logical ([1, 0, 1, 0, 1]));
880s  [~, idx] = fillmissing (x, @(x,y,z) z+10, [2 0], "missinglocations", logical ([1, 0, 1, 0, 1]));
880s  assert (idx, logical ([0, 0, 1, 0, 1]));
880s ***** test
880s  [A, idx] = fillmissing ([struct, struct], "constant", 1);
880s  assert (A, [struct, struct])
880s  assert (idx, [false, false])
880s ***** error <Invalid call> fillmissing ()
880s ***** error <Invalid call> fillmissing (1)
880s ***** error <Invalid call> fillmissing (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
881s ***** error <second input must be a> fillmissing (1, 2)
881s ***** error <unknown fill method 'foo'> fillmissing (1, "foo")
881s ***** error <fill function must accept at least> fillmissing (1, @(x) x, 1)
881s ***** error <fill function must accept at least> fillmissing (1, @(x,y) x+y, 1)
881s ***** error <interpolation methods only valid for numeric> fillmissing ("a b c", "linear")
881s ***** error <interpolation methods only valid for numeric> fillmissing ({"a", "b"}, "linear")
881s ***** error <'movmean' and 'movmedian' methods only valid for numeric> fillmissing ("a b c", "movmean", 2)
881s ***** error <'movmean' and 'movmedian' methods only valid for numeric> fillmissing ({"a", "b"}, "movmean", 2)
881s ***** error <'constant' method must be followed by> fillmissing (1, "constant")
881s ***** error <a numeric fill value cannot be emtpy> fillmissing (1, "constant", [])
881s ***** error <fill value must be the same data type> fillmissing (1, "constant", "a")
881s ***** error <fill value must be the same data type> fillmissing ("a", "constant", 1)
881s ***** error <fill value must be the same data type> fillmissing ("a", "constant", {"foo"})
881s ***** error <fill value must be the same data type> fillmissing ({"foo"}, "constant", 1)
881s ***** error <moving window method must be followed by> fillmissing (1, "movmean")
881s ***** error <moving window method must be followed by> fillmissing (1, "movmedian")
881s ***** error <DIM must be a positive scalar> fillmissing (1, "constant", 1, 0)
881s ***** error <DIM must be a positive scalar> fillmissing (1, "constant", 1, -1)
881s ***** error <DIM must be a positive scalar> fillmissing (1, "constant", 1, [1, 2])
881s ***** error <properties must be given as> fillmissing (1, "constant", 1, "samplepoints")
881s ***** error <properties must be given as> fillmissing (1, "constant", 1, "foo")
881s ***** error <properties must be given as> fillmissing (1, "constant", 1, 1, "foo")
881s ***** error <invalid parameter name specified> fillmissing (1, "constant", 1, 2, {1}, 4)
881s ***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", [1, 2])
881s ***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", [3, 1, 2])
881s ***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", [1, 1, 2])
881s ***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", "abc")
881s ***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", logical ([1, 1, 1]))
881s ***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 1, "samplepoints", [1, 2, 3])
881s ***** error <EndValues method 'constant' only valid> fillmissing ("foo", "next", "endvalues", 1)
881s ***** error <invalid EndValues method 'foo'> fillmissing (1, "constant", 1, 1, "endvalues", "foo")
881s ***** error <EndValues must be a scalar or a 1 element array> fillmissing ([1, 2, 3], "constant", 1, 2, "endvalues", [1, 2, 3])
881s ***** error <EndValues must be a scalar or a 3 element array> fillmissing ([1, 2, 3], "constant", 1, 1, "endvalues", [1, 2])
881s ***** error <EndValues must be a scalar or a 12 element array> fillmissing (randi(5,4,3,2), "constant", 1, 3, "endvalues", [1, 2])
881s ***** error <EndValues must be numeric or a> fillmissing (1, "constant", 1, 1, "endvalues", {1})
881s ***** error <invalid parameter name 'foo'> fillmissing (1, "constant", 1, 2, "foo", 4)
881s ***** error <MissingLocations option is not compatible with> fillmissing (struct, "constant", 1, "missinglocations", false)
881s ***** error <MissingLocations and MaxGap options> fillmissing (1, "constant", 1, 2, "maxgap", 1, "missinglocations", false)
881s ***** error <MissingLocations and MaxGap options> fillmissing (1, "constant", 1, 2, "missinglocations", false, "maxgap", 1)
881s ***** error <the 'replacevalues' option has not> fillmissing (1, "constant", 1, "replacevalues", true)
881s ***** error <the 'datavariables' option has not> fillmissing (1, "constant", 1, "datavariables", "Varname")
881s ***** error <MissingLocations must be a> fillmissing (1, "constant", 1, 2, "missinglocations", 1)
881s ***** error <MissingLocations must be a> fillmissing (1, "constant", 1, 2, "missinglocations", "a")
881s ***** error <MissingLocations must be a> fillmissing (1, "constant", 1, 2, "missinglocations", [true, false])
881s ***** error <MissingLocations cannot be used with method> fillmissing (true, "linear", "missinglocations", true)
881s ***** error <MissingLocations cannot be used with method> fillmissing (int8 (1), "linear", "missinglocations", true)
881s ***** error <MissingLocations cannot be used with EndValues method> fillmissing (true, "next", "missinglocations", true, "EndValues", "linear")
881s ***** error <MissingLocations cannot be used with EndValues method> fillmissing (true, "next", "EndValues", "linear", "missinglocations", true)
881s ***** error <MissingLocations cannot be used with EndValues method> fillmissing (int8 (1), "next", "missinglocations", true, "EndValues", "linear")
881s ***** error <MissingLocations cannot be used with EndValues method> fillmissing (int8 (1), "next", "EndValues", "linear", "missinglocations", true)
881s ***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", true)
881s ***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", "a")
881s ***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", [1, 2])
881s ***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", 0)
881s ***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", -1)
881s ***** error <fill value 'V' must be a scalar or a 1> fillmissing ([1, 2, 3], "constant", [1, 2, 3])
881s ***** error <fill value 'V' must be a scalar or a 1> fillmissing ([1, 2, 3]', "constant", [1, 2, 3])
881s ***** error <fill value 'V' must be a scalar or a 1> fillmissing ([1, 2, 3]', "constant", [1, 2, 3], 1)
881s ***** error <fill value 'V' must be a scalar or a 1> fillmissing ([1, 2, 3], "constant", [1, 2, 3], 2)
881s ***** error <fill value 'V' must be a scalar or a 6> fillmissing (randi (5, 4, 3, 2), "constant", [1, 2], 1)
881s ***** error <fill value 'V' must be a scalar or a 8> fillmissing (randi (5, 4, 3, 2), "constant", [1, 2], 2)
881s ***** error <fill value 'V' must be a scalar or a 12> fillmissing (randi (5, 4, 3, 2), "constant", [1, 2], 3)
881s ***** error <fill function handle must be followed by> fillmissing (1, @(x,y,z) x+y+z)
881s ***** error <fill function return values must be the same size> fillmissing ([1, NaN, 2], @(x,y,z) [1, 2], 2)
881s 380 tests, 379 passed, 0 known failure, 1 skipped
881s [inst/fishertest.m]
881s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/fishertest.m
881s ***** demo
881s  ## A Fisher's exact test example
881s 
881s  x = [3, 1; 1, 3]
881s  [h, p, stats] = fishertest(x)
881s ***** assert (fishertest ([3, 4; 5, 7]), false);
881s ***** assert (isa (fishertest ([3, 4; 5, 7]), "logical"), true);
881s ***** test
881s  [h, pval, stats] = fishertest ([3, 4; 5, 7]);
881s  assert (pval, 1, 1e-14);
881s  assert (stats.OddsRatio, 1.05);
881s  CI = [0.159222057151289, 6.92429189601808];
881s  assert (stats.ConfidenceInterval, CI, 1e-14)
881s ***** test
881s  [h, pval, stats] = fishertest ([3, 4; 5, 0]);
881s  assert (pval, 0.08080808080808080, 1e-14);
881s  assert (stats.OddsRatio, 0);
881s  assert (stats.ConfidenceInterval, [-Inf, Inf])
881s ***** error fishertest ();
881s ***** error fishertest (1, 2, 3, 4, 5, 6);
881s ***** error<fishertest: X must be a 2-dimensional matrix.> ...
881s  fishertest (ones (2, 2, 2));
881s ***** error<fishertest: X must contain only non-negative real integers.> ...
881s  fishertest ([1, 2; -3, 4]);
881s ***** error<fishertest: X must contain only non-negative real integers.> ...
881s  fishertest ([1, 2; 3, 4+i]);
881s ***** error<fishertest: X must contain only non-negative real integers.> ...
881s  fishertest ([1, 2; 3, 4.2]);
881s ***** error<fishertest: X must contain only non-negative real integers.> ...
881s  fishertest ([NaN, 2; 3, 4]);
881s ***** error<fishertest: X must contain only non-negative real integers.> ...
881s  fishertest ([1, Inf; 3, 4]);
881s ***** error<fishertest: cannot handle large entries> ...
881s  fishertest (ones (2) * 1e8);
881s ***** error<fishertest: invalid value for alpha.> ...
881s  fishertest ([1, 2; 3, 4], "alpha", 0);
881s ***** error<fishertest: invalid value for alpha.> ...
881s  fishertest ([1, 2; 3, 4], "alpha", 1.2);
881s ***** error<fishertest: invalid value for alpha.> ...
881s  fishertest ([1, 2; 3, 4], "alpha", "val");
881s ***** error<fishertest: invalid value for tail.>  ...
881s  fishertest ([1, 2; 3, 4], "tail", "val");
881s ***** error<fishertest: invalid value for tail.>  ...
881s  fishertest ([1, 2; 3, 4], "alpha", 0.01, "tail", "val");
881s ***** error<fishertest: invalid name for optional arguments.> ...
881s  fishertest ([1, 2; 3, 4], "alpha", 0.01, "badoption", 3);
881s 19 tests, 19 passed, 0 known failure, 0 skipped
881s [inst/fitcdiscr.m]
881s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/fitcdiscr.m
881s ***** demo
881s  ## Train a linear discriminant classifier for Gamma = 0.5
881s  ## and plot the decision boundaries.
881s 
881s  load fisheriris
881s  idx = ! strcmp (species, "setosa");
881s  X = meas(idx,3:4);
881s  Y = cast (strcmpi (species(idx), "virginica"), "double");
881s  obj = fitcdiscr (X, Y, "Gamma", 0.5)
881s  x1 = [min(X(:,1)):0.03:max(X(:,1))];
881s  x2 = [min(X(:,2)):0.02:max(X(:,2))];
881s  [x1G, x2G] = meshgrid (x1, x2);
881s  XGrid = [x1G(:), x2G(:)];
881s  pred = predict (obj, XGrid);
881s  gidx = logical (str2num (cell2mat (pred)));
881s 
881s  figure
881s  scatter (XGrid(gidx,1), XGrid(gidx,2), "markerfacecolor", "magenta");
881s  hold on
881s  scatter (XGrid(!gidx,1), XGrid(!gidx,2), "markerfacecolor", "red");
881s  plot (X(Y == 0, 1), X(Y == 0, 2), "ko", X(Y == 1, 1), X(Y == 1, 2), "kx");
881s  xlabel ("Petal length (cm)");
881s  ylabel ("Petal width (cm)");
881s  title ("Linear Discriminant Analysis Decision Boundary");
881s  legend ({"Versicolor Region", "Virginica Region", ...
881s          "Sampled Versicolor", "Sampled Virginica"}, ...
881s          "location", "northwest")
881s  axis tight
881s  hold off
881s ***** test
881s  load fisheriris
881s  Mdl = fitcdiscr (meas, species, "Gamma", 0.5);
881s  [label, score, cost] = predict (Mdl, [2, 2, 2, 2]);
881s  assert (label, {'versicolor'})
881s  assert (score, [0, 0.9999, 0.0001], 1e-4)
881s  assert (cost, [1, 0.0001, 0.9999], 1e-4)
881s  [label, score, cost] = predict (Mdl, [2.5, 2.5, 2.5, 2.5]);
881s  assert (label, {'versicolor'})
881s  assert (score, [0, 0.6368, 0.3632], 1e-4)
881s  assert (cost, [1, 0.3632, 0.6368], 1e-4)
881s  assert (class (Mdl), "ClassificationDiscriminant");
881s  assert ({Mdl.X, Mdl.Y, Mdl.NumObservations}, {meas, species, 150})
881s  assert ({Mdl.DiscrimType, Mdl.ResponseName}, {"linear", "Y"})
881s  assert ({Mdl.Gamma, Mdl.MinGamma}, {0.5, 0})
881s  assert (Mdl.ClassNames, unique (species))
881s  sigma = [0.265008, 0.046361, 0.083757, 0.019201; ...
881s           0.046361, 0.115388, 0.027622, 0.016355; ...
881s           0.083757, 0.027622, 0.185188, 0.021333; ...
881s           0.019201, 0.016355, 0.021333, 0.041882];
881s  assert (Mdl.Sigma, sigma, 1e-6)
881s  mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
881s        5.9360, 2.7700, 4.2600, 1.3260; ...
881s        6.5880, 2.9740, 5.5520, 2.0260];
881s  assert (Mdl.Mu, mu, 1e-14)
881s  assert (Mdl.LogDetSigma, -8.6884, 1e-4)
881s ***** error<fitcdiscr: too few arguments.> fitcdiscr ()
881s ***** error<fitcdiscr: too few arguments.> fitcdiscr (ones (4,1))
881s ***** error<fitcdiscr: name-value arguments must be in pairs.>
881s  fitcdiscr (ones (4,2), ones (4, 1), "K")
881s ***** error<fitcdiscr: number of rows in X and Y must be equal.>
881s  fitcdiscr (ones (4,2), ones (3, 1))
881s ***** error<fitcdiscr: number of rows in X and Y must be equal.>
881s  fitcdiscr (ones (4,2), ones (3, 1), "K", 2)
881s 6 tests, 6 passed, 0 known failure, 0 skipped
881s [inst/fitcgam.m]
881s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/fitcgam.m
881s ***** demo
881s  ## Train a GAM classifier for binary classification
881s  ## using specific data and plot the decision boundaries.
881s 
881s  ## Define specific data
881s  X = [1, 2; 2, 3; 3, 3; 4, 5; 5, 5; ...
881s      6, 7; 7, 8; 8, 8; 9, 9; 10, 10];
881s  Y = [0; 0; 0; 0; 0; ...
881s      1; 1; 1; 1; 1];
881s 
881s  ## Train the GAM model
881s  obj = fitcgam (X, Y, "Interactions", "all");
881s 
881s  ## Create a grid of values for prediction
881s  x1 = [min(X(:,1)):0.1:max(X(:,1))];
881s  x2 = [min(X(:,2)):0.1:max(X(:,2))];
881s  [x1G, x2G] = meshgrid (x1, x2);
881s  XGrid = [x1G(:), x2G(:)];
881s  pred = predict (obj, XGrid);
881s 
881s  ## Plot decision boundaries and data points
881s  predNumeric = str2double (pred);
881s  gidx = predNumeric > 0.5;
881s 
881s  figure
881s  scatter(XGrid(gidx,1), XGrid(gidx,2), "markerfacecolor", "magenta");
881s  hold on
881s  scatter(XGrid(!gidx,1), XGrid(!gidx,2), "markerfacecolor", "red");
881s  plot(X(Y == 0, 1), X(Y == 0, 2), "ko", X(Y == 1, 1), X(Y == 1, 2), "kx");
881s  xlabel("Feature 1");
881s  ylabel("Feature 2");
881s  title("Generalized Additive Model (GAM) Decision Boundary");
881s  legend({"Class 1 Region", "Class 0 Region", ...
881s        "Class 1 Samples", "Class 0 Samples"}, ...
881s        "location", "northwest")
881s  axis tight
881s  hold off
881s ***** test
881s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
881s  y = [0; 0; 1; 1];
881s  PredictorNames = {'Feature1', 'Feature2', 'Feature3'};
881s  a = fitcgam (x, y, "PredictorNames", PredictorNames);
881s  assert (class (a), "ClassificationGAM");
881s  assert ({a.X, a.Y, a.NumObservations}, {x, y, 4})
881s  assert ({a.NumPredictors, a.ResponseName}, {3, "Y"})
881s  assert (a.ClassNames, {'0'; '1'})
881s  assert (a.PredictorNames, PredictorNames)
881s  assert (a.BaseModel.Intercept, 0)
883s ***** test
883s  x = [1, 2; 3, 4; 5, 6; 7, 8; 9, 10];
883s  y = [1; 0; 1; 0; 1];
883s  a = fitcgam (x, y, "interactions", "all");
883s  assert (class (a), "ClassificationGAM");
883s  assert ({a.X, a.Y, a.NumObservations}, {x, y, 5})
883s  assert ({a.NumPredictors, a.ResponseName}, {2, "Y"})
883s  assert (a.ClassNames, {'1'; '0'})
883s  assert (a.PredictorNames, {'x1', 'x2'})
883s  assert (a.ModelwInt.Intercept, 0.4055, 1e-1)
886s ***** test
886s  load fisheriris
886s  inds = strcmp (species,'versicolor') | strcmp (species,'virginica');
886s  X = meas(inds, :);
886s  Y = species(inds, :)';
886s  Y = strcmp (Y, 'virginica')';
886s  a = fitcgam (X, Y, 'Formula', 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3');
886s  assert (class (a), "ClassificationGAM");
886s  assert ({a.X, a.Y, a.NumObservations}, {X, Y, 100})
886s  assert ({a.NumPredictors, a.ResponseName}, {4, "Y"})
886s  assert (a.ClassNames, {'0'; '1'})
886s  assert (a.Formula, 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3')
886s  assert (a.PredictorNames, {'x1', 'x2', 'x3', 'x4'})
886s  assert (a.ModelwInt.Intercept, 0)
892s ***** error<fitcgam: too few arguments.> fitcgam ()
892s ***** error<fitcgam: too few arguments.> fitcgam (ones (4,1))
892s ***** error<fitcgam: name-value arguments must be in pairs.>
892s  fitcgam (ones (4,2), ones (4, 1), "K")
892s ***** error<fitcgam: number of rows in X and Y must be equal.>
892s  fitcgam (ones (4,2), ones (3, 1))
892s ***** error<fitcgam: number of rows in X and Y must be equal.>
892s  fitcgam (ones (4,2), ones (3, 1), "K", 2)
892s 8 tests, 8 passed, 0 known failure, 0 skipped
892s [inst/fitcknn.m]
892s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/fitcknn.m
892s ***** demo
892s  ## Train a k-nearest neighbor classifier for k = 10
892s  ## and plot the decision boundaries.
892s 
892s  load fisheriris
892s  idx = ! strcmp (species, "setosa");
892s  X = meas(idx,3:4);
892s  Y = cast (strcmpi (species(idx), "virginica"), "double");
892s  obj = fitcknn (X, Y, "Standardize", 1, "NumNeighbors", 10, "NSMethod", "exhaustive")
892s  x1 = [min(X(:,1)):0.03:max(X(:,1))];
892s  x2 = [min(X(:,2)):0.02:max(X(:,2))];
892s  [x1G, x2G] = meshgrid (x1, x2);
892s  XGrid = [x1G(:), x2G(:)];
892s  pred = predict (obj, XGrid);
892s  gidx = logical (str2num (cell2mat (pred)));
892s 
892s  figure
892s  scatter (XGrid(gidx,1), XGrid(gidx,2), "markerfacecolor", "magenta");
892s  hold on
892s  scatter (XGrid(!gidx,1), XGrid(!gidx,2), "markerfacecolor", "red");
892s  plot (X(Y == 0, 1), X(Y == 0, 2), "ko", X(Y == 1, 1), X(Y == 1, 2), "kx");
892s  xlabel ("Petal length (cm)");
892s  ylabel ("Petal width (cm)");
892s  title ("5-Nearest Neighbor Classifier Decision Boundary");
892s  legend ({"Versicolor Region", "Virginica Region", ...
892s          "Sampled Versicolor", "Sampled Virginica"}, ...
892s          "location", "northwest")
892s  axis tight
892s  hold off
892s ***** test
892s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
892s  y = ["a"; "a"; "b"; "b"];
892s  a = fitcknn (x, y);
892s  assert (class (a), "ClassificationKNN");
892s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
892s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
892s  assert ({a.BucketSize}, {50})
892s ***** test
892s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
892s  y = ["a"; "a"; "b"; "b"];
892s  a = fitcknn (x, y, "NSMethod", "exhaustive");
892s  assert (class (a), "ClassificationKNN");
892s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
892s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
892s  assert ({a.BucketSize}, {50})
892s ***** test
892s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
892s  y = ["a"; "a"; "b"; "b"];
892s  k = 10;
892s  a = fitcknn (x, y, "NumNeighbors" ,k);
892s  assert (class (a), "ClassificationKNN");
892s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
892s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
892s  assert ({a.BucketSize}, {50})
892s ***** test
892s  x = ones (4, 11);
892s  y = ["a"; "a"; "b"; "b"];
892s  k = 10;
892s  a = fitcknn (x, y, "NumNeighbors" ,k);
892s  assert (class (a), "ClassificationKNN");
892s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
892s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
892s  assert ({a.BucketSize}, {50})
892s ***** test
892s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
892s  y = ["a"; "a"; "b"; "b"];
892s  k = 10;
892s  a = fitcknn (x, y, "NumNeighbors" ,k, "NSMethod", "exhaustive");
892s  assert (class (a), "ClassificationKNN");
892s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
892s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
892s  assert ({a.BucketSize}, {50})
892s ***** test
892s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
892s  y = ["a"; "a"; "b"; "b"];
892s  k = 10;
892s  a = fitcknn (x, y, "NumNeighbors" ,k, "Distance", "hamming");
892s  assert (class (a), "ClassificationKNN");
892s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
892s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "hamming"})
892s  assert ({a.BucketSize}, {50})
892s ***** test
892s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
892s  y = ["a"; "a"; "b"; "b"];
892s  weights = ones (4,1);
892s  a = fitcknn (x, y, "Standardize", 1);
892s  assert (class (a), "ClassificationKNN");
892s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
892s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
892s  assert ({a.Standardize}, {true})
892s  assert ({a.Sigma}, {std(x, [], 1)})
892s  assert ({a.Mu}, {[3.75, 4.25, 4.75]})
892s ***** test
892s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
892s  y = ["a"; "a"; "b"; "b"];
892s  weights = ones (4,1);
892s  a = fitcknn (x, y, "Standardize", false);
892s  assert (class (a), "ClassificationKNN");
892s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
892s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
892s  assert ({a.Standardize}, {false})
892s  assert ({a.Sigma}, {[]})
892s  assert ({a.Mu}, {[]})
892s ***** test
892s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
892s  y = ["a"; "a"; "b"; "b"];
892s  s = ones (1, 3);
892s  a = fitcknn (x, y, "Scale" , s, "Distance", "seuclidean");
892s  assert (class (a), "ClassificationKNN");
892s  assert ({a.DistParameter}, {s})
892s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "seuclidean"})
892s  assert ({a.BucketSize}, {50})
892s ***** test
892s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
892s  y = ["a"; "a"; "b"; "b"];
892s  a = fitcknn (x, y, "Exponent" , 5, "Distance", "minkowski");
892s  assert (class (a), "ClassificationKNN");
892s  assert (a.DistParameter, 5)
892s  assert ({a.NSMethod, a.Distance}, {"kdtree", "minkowski"})
892s ***** test
892s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
892s  y = ["a"; "a"; "b"; "b"];
892s  a = fitcknn (x, y, "Exponent" , 5, "Distance", "minkowski", ...
892s                     "NSMethod", "exhaustive");
892s  assert (class (a), "ClassificationKNN");
892s  assert (a.DistParameter, 5)
892s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "minkowski"})
892s ***** test
892s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
892s  y = ["a"; "a"; "b"; "b"];
892s  a = fitcknn (x, y, "BucketSize" , 20, "distance", "mahalanobis");
892s  assert (class (a), "ClassificationKNN");
892s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "mahalanobis"})
892s  assert ({a.BucketSize}, {20})
892s ***** test
892s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
892s  y = ["a"; "a"; "b"; "b"];
892s  a = fitcknn (x, y, "IncludeTies", true);
892s  assert (class (a), "ClassificationKNN");
892s  assert (a.IncludeTies, true);
892s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
892s ***** test
892s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
892s  y = ["a"; "a"; "b"; "b"];
892s  a = fitcknn (x, y);
892s  assert (class (a), "ClassificationKNN");
892s  assert (a.IncludeTies, false);
892s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
892s ***** test
892s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
892s  y = ["a"; "a"; "b"; "b"];
892s  a = fitcknn (x, y);
892s  assert (class (a), "ClassificationKNN")
892s  assert (a.Prior, [0.5; 0.5])
892s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
892s  assert ({a.BucketSize}, {50})
892s ***** test
892s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
892s  y = ["a"; "a"; "b"; "b"];
892s  prior = [0.5; 0.5];
892s  a = fitcknn (x, y, "Prior", "empirical");
892s  assert (class (a), "ClassificationKNN")
892s  assert (a.Prior, prior)
892s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
892s  assert ({a.BucketSize}, {50})
892s ***** test
892s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
892s  y = ["a"; "a"; "a"; "b"];
892s  prior = [0.75; 0.25];
892s  a = fitcknn (x, y, "Prior", "empirical");
892s  assert (class (a), "ClassificationKNN")
892s  assert (a.Prior, prior)
892s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
892s  assert ({a.BucketSize}, {50})
892s ***** test
892s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
892s  y = ["a"; "a"; "a"; "b"];
892s  prior = [0.5; 0.5];
892s  a = fitcknn (x, y, "Prior", "uniform");
892s  assert (class (a), "ClassificationKNN")
892s  assert (a.Prior, prior)
892s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
892s  assert ({a.BucketSize}, {50})
892s ***** test
892s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
892s  y = ["a"; "a"; "b"; "b"];
892s  cost = eye (2);
892s  a = fitcknn (x, y, "Cost", cost);
892s  assert (class (a), "ClassificationKNN")
892s  assert (a.Cost, [1, 0; 0, 1])
892s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
892s  assert ({a.BucketSize}, {50})
892s ***** test
892s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
892s  y = ["a"; "a"; "b"; "b"];
892s  cost = eye (2);
892s  a = fitcknn (x, y, "Cost", cost, "Distance", "hamming" );
892s  assert (class (a), "ClassificationKNN")
892s  assert (a.Cost, [1, 0; 0, 1])
892s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "hamming"})
892s  assert ({a.BucketSize}, {50})
892s ***** test
892s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
892s  y = ["a"; "a"; "b"; "b"];
892s  a = fitcknn (x, y, "NSMethod", "exhaustive", "CrossVal", "on");
892s  assert (class (a), "ClassificationPartitionedModel");
892s  assert ({a.X, a.Y, a.Trained{1}.NumNeighbors}, {x, y, 1})
892s  assert (a.ModelParameters.NSMethod, "exhaustive")
892s  assert (a.ModelParameters.Distance, "euclidean")
892s  assert ({a.Trained{1}.BucketSize}, {50})
892s ***** error<fitcknn: too few arguments.> fitcknn ()
892s ***** error<fitcknn: too few arguments.> fitcknn (ones (4,1))
892s ***** error<fitcknn: Name-Value arguments must be in pairs.>
892s  fitcknn (ones (4,2), ones (4, 1), "K")
892s ***** error<fitcknn: number of rows in X and Y must be equal.>
892s  fitcknn (ones (4,2), ones (3, 1))
892s ***** error<fitcknn: number of rows in X and Y must be equal.>
892s  fitcknn (ones (4,2), ones (3, 1), "K", 2)
892s ***** error <fitcknn: 'CrossVal' must be either 'off' or 'on'.>
892s  fitcknn (ones (4,2), ones (4, 1), "CrossVal", 2)
892s ***** error <fitcknn: 'CrossVal' must be either 'off' or 'on'.>
892s  fitcknn (ones (4,2), ones (4, 1), "CrossVal", 'a')
892s ***** error <fitcknn: You can use only one cross-validation name-value pair argument> ...
892s  fitcknn (ones (4,2), ones (4, 1), "KFold", 10, "Holdout", 0.3)
892s 29 tests, 29 passed, 0 known failure, 0 skipped
892s [inst/fitcnet.m]
892s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/fitcnet.m
892s ***** demo
892s  ## Train a Neural Network on the Fisher's Iris data set and display
892s  ## a confusion chart with the classification results.
892s 
892s  load fisheriris
892s  Mdl = fitcnet (meas, species);
892s  pred_species = resubPredict (Mdl);
892s  confusionchart (species, pred_species);
892s ***** test
892s  load fisheriris
892s  x = meas;
892s  y = grp2idx (species);
892s  Mdl = fitcnet (x, y, "IterationLimit", 50);
892s  assert (class (Mdl), "ClassificationNeuralNetwork");
892s  assert (numel (Mdl.ModelParameters.LayerWeights), 2);
892s  assert (size (Mdl.ModelParameters.LayerWeights{1}), [10, 5]);
892s  assert (size (Mdl.ModelParameters.LayerWeights{2}), [3, 11]);
892s ***** error<fitcnet: too few arguments.> fitcnet ()
892s ***** error<fitcnet: too few arguments.> fitcnet (ones (4,1))
892s ***** error<fitcnet: Name-Value arguments must be in pairs.>
892s  fitcnet (ones (4,2), ones (4, 1), 'LayerSizes')
892s ***** error<fitcnet: number of rows in X and Y must be equal.>
892s  fitcnet (ones (4,2), ones (3, 1))
892s ***** error<fitcnet: number of rows in X and Y must be equal.>
892s  fitcnet (ones (4,2), ones (3, 1), 'LayerSizes', 2)
892s 6 tests, 6 passed, 0 known failure, 0 skipped
892s [inst/fitcsvm.m]
892s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/fitcsvm.m
892s ***** demo
892s  ## Use a subset of Fisher's iris data set
892s 
892s  load fisheriris
892s  inds = ! strcmp (species, 'setosa');
892s  X = meas(inds, [3,4]);
892s  Y = species(inds);
892s 
892s  ## Train a linear SVM classifier
892s  SVMModel = fitcsvm (X, Y)
892s 
892s  ## Plot a scatter diagram of the data and circle the support vectors.
892s  sv = SVMModel.SupportVectors;
892s  figure
892s  gscatter (X(:,1), X(:,2), Y)
892s  hold on
892s  plot (sv(:,1), sv(:,2), 'ko', 'MarkerSize', 10)
892s  legend ('versicolor', 'virginica', 'Support Vector')
892s  hold off
892s ***** test
892s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
892s  y = {"a"; "a"; "b"; "b"};
892s  a = fitcsvm (x, y);
892s  assert (class (a), "ClassificationSVM");
892s  assert ({a.X, a.Y}, {x, y})
892s  assert (a.NumObservations, 4)
892s  assert ({a.ResponseName, a.PredictorNames}, {"Y", {"x1", "x2", "x3"}})
892s  assert (a.ModelParameters.SVMtype, "c_svc")
892s  assert (a.ClassNames, {"a"; "b"})
892s ***** test
892s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
892s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
892s  a = fitcsvm (x, y);
892s  assert (class (a), "ClassificationSVM");
892s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "linear"})
892s  assert (a.ModelParameters.BoxConstraint, 1)
892s  assert (a.ModelParameters.KernelOffset, 0)
892s  assert (a.ClassNames, [1; -1])
893s ***** test
893s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
893s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
893s  a = fitcsvm (x, y, "KernelFunction", "rbf", "BoxConstraint", 2, ...
893s  "KernelOffset", 2);
893s  assert (class (a), "ClassificationSVM");
893s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "rbf"})
893s  assert (a.ModelParameters.BoxConstraint, 2)
893s  assert (a.ModelParameters.KernelOffset, 2)
893s  assert (isempty (a.Alpha), true)
893s  assert (isempty (a.Beta), false)
893s ***** test
893s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
893s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
893s  a = fitcsvm (x, y, "KernelFunction", "polynomial", "PolynomialOrder", 3);
893s  assert (class (a), "ClassificationSVM");
893s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "polynomial"})
893s  assert (a.ModelParameters.PolynomialOrder, 3)
893s  assert (isempty (a.Alpha), true)
893s  assert (isempty (a.Beta), false)
893s ***** test
893s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
893s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
893s  a = fitcsvm (x, y, "KernelFunction", "linear", "PolynomialOrder", 3);
893s  assert (class (a), "ClassificationSVM");
893s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "linear"})
893s  assert (a.ModelParameters.PolynomialOrder, 3)
893s  assert (isempty (a.Alpha), false)
893s  assert (isempty (a.Beta), true)
893s ***** test
893s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
893s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
893s  a = fitcsvm (x, y, "KernelFunction", "linear", "CrossVal", 'on');
893s  assert (class (a), "ClassificationPartitionedModel");
893s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "linear"})
893s  assert (a.ModelParameters.PolynomialOrder, 3)
893s  assert (isempty (a.Trained{1}.Alpha), false)
893s  assert (isempty (a.Trained{1}.Beta), true)
893s ***** error<fitcsvm: too few arguments.> fitcsvm ()
893s ***** error<fitcsvm: too few arguments.> fitcsvm (ones (4,1))
893s ***** error<fitcsvm: Name-Value arguments must be in pairs.>
893s  fitcsvm (ones (4,2), ones (4, 1), 'KFold')
893s ***** error<fitcsvm: number of rows in X and Y must be equal.>
893s  fitcsvm (ones (4,2), ones (3, 1))
893s ***** error<fitcsvm: number of rows in X and Y must be equal.>
893s  fitcsvm (ones (4,2), ones (3, 1), 'KFold', 2)
893s ***** error <fitcsvm: 'CrossVal' must be either 'off' or 'on'.>
893s  fitcsvm (ones (4,2), ones (4, 1), "CrossVal", 2)
893s ***** error <fitcsvm: 'CrossVal' must be either 'off' or 'on'.>
893s  fitcsvm (ones (4,2), ones (4, 1), "CrossVal", 'a')
893s ***** error <fitcsvm: You can use only one cross-validation name-value pair argument> ...
893s  fitcsvm (ones (4,2), ones (4, 1), "KFold", 10, "Holdout", 0.3)
893s 14 tests, 14 passed, 0 known failure, 0 skipped
893s [inst/fitgmdist.m]
893s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/fitgmdist.m
893s ***** demo
893s  ## Generate a two-cluster problem
893s  C1 = randn (100, 2) + 2;
893s  C2 = randn (100, 2) - 2;
893s  data = [C1; C2];
893s 
893s  ## Perform clustering
893s  GMModel = fitgmdist (data, 2);
893s 
893s  ## Plot the result
893s  figure
893s  [heights, bins] = hist3([C1; C2]);
893s  [xx, yy] = meshgrid(bins{1}, bins{2});
893s  bbins = [xx(:), yy(:)];
893s  contour (reshape (GMModel.pdf (bbins), size (heights)));
893s ***** demo
893s  Angle_Theta = [ 30 + 10 * randn(1, 10),  60 + 10 * randn(1, 10) ]';
893s  nbOrientations = 2;
893s  initial_orientations = [38.0; 18.0];
893s  initial_weights = ones (1, nbOrientations) / nbOrientations;
893s  initial_Sigma = 10 * ones (1, 1, nbOrientations);
893s  start = struct ("mu", initial_orientations, "Sigma", initial_Sigma, ...
893s                  "ComponentProportion", initial_weights);
893s  GMModel_Theta = fitgmdist (Angle_Theta, nbOrientations, "Start", start , ...
893s                             "RegularizationValue", 0.0001)
893s ***** test
893s  load fisheriris
893s  classes = unique (species);
893s  [~, score] = pca (meas, "NumComponents", 2);
893s  options.MaxIter = 1000;
893s  options.TolFun = 1e-6;
893s  options.Display = "off";
893s  GMModel = fitgmdist (score, 2, "Options", options);
893s  assert (isa (GMModel, "gmdistribution"), true);
893s  assert (GMModel.mu, [1.3212, -0.0954; -2.6424, 0.1909], 1e-4);
893s 1 test, 1 passed, 0 known failure, 0 skipped
893s [inst/fitlm.m]
893s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/fitlm.m
893s ***** demo
893s  y =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
893s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
893s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
893s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
893s         25.694 ]';
893s  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]';
893s 
893s  [TAB,STATS] = fitlm (X,y,"linear","CategoricalVars",1,"display","on");
893s ***** demo
893s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
893s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
893s  brands = {'Gourmet', 'National', 'Generic'; ...
893s            'Gourmet', 'National', 'Generic'; ...
893s            'Gourmet', 'National', 'Generic'; ...
893s            'Gourmet', 'National', 'Generic'; ...
893s            'Gourmet', 'National', 'Generic'; ...
893s            'Gourmet', 'National', 'Generic'};
893s  popper = {'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; ...
893s            'air', 'air', 'air'; 'air', 'air', 'air'; 'air', 'air', 'air'};
893s 
893s  [TAB, STATS] = fitlm ({brands(:),popper(:)},popcorn(:),"interactions",...
893s                           "CategoricalVars",[1,2],"display","on");
893s ***** test
893s  y =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
893s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
893s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
893s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
893s         25.694 ]';
893s  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]';
893s  [TAB,STATS] = fitlm (X,y,"continuous",[],"display","off");
893s  [TAB,STATS] = fitlm (X,y,"CategoricalVars",1,"display","off");
893s  [TAB,STATS] = fitlm (X,y,"constant","categorical",1,"display","off");
893s  [TAB,STATS] = fitlm (X,y,"linear","categorical",1,"display","off");
893s  [TAB,STATS] = fitlm (X,y,[0,0;1,0],"categorical",1,"display","off");
893s  assert (TAB{2,2}, 10, 1e-04);
893s  assert (TAB{3,2}, 7.99999999999999, 1e-09);
893s  assert (TAB{4,2}, 8.99999999999999, 1e-09);
893s  assert (TAB{5,2}, 11.0001428571429, 1e-09);
893s  assert (TAB{6,2}, 19.0001111111111, 1e-09);
893s  assert (TAB{2,3}, 1.01775379540949, 1e-09);
893s  assert (TAB{3,3}, 1.64107868458008, 1e-09);
893s  assert (TAB{4,3}, 1.43932122062479, 1e-09);
893s  assert (TAB{5,3}, 1.48983900477565, 1e-09);
893s  assert (TAB{6,3}, 1.3987687997822, 1e-09);
893s  assert (TAB{2,6}, 9.82555903510687, 1e-09);
893s  assert (TAB{3,6}, 4.87484242844031, 1e-09);
893s  assert (TAB{4,6}, 6.25294748040552, 1e-09);
893s  assert (TAB{5,6}, 7.38344399756088, 1e-09);
893s  assert (TAB{6,6}, 13.5834536158296, 1e-09);
893s  assert (TAB{3,7}, 2.85812420217862e-05, 1e-12);
893s  assert (TAB{4,7}, 5.22936741204002e-07, 1e-06);
893s  assert (TAB{5,7}, 2.12794763209106e-08, 1e-07);
893s  assert (TAB{6,7}, 7.82091664406755e-15, 1e-08);
893s ***** test
893s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
893s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
893s  brands = bsxfun (@times, ones(6,1), [1,2,3]);
893s  popper = bsxfun (@times, [1;1;1;2;2;2], ones(1,3));
893s 
893s  [TAB, STATS] = fitlm ({brands(:),popper(:)},popcorn(:),"interactions",...
893s                           "categoricalvars",[1,2],"display","off");
893s  assert (TAB{2,2}, 5.66666666666667, 1e-09);
893s  assert (TAB{3,2}, -1.33333333333333, 1e-09);
893s  assert (TAB{4,2}, -2.16666666666667, 1e-09);
893s  assert (TAB{5,2}, 1.16666666666667, 1e-09);
893s  assert (TAB{6,2}, -0.333333333333334, 1e-09);
893s  assert (TAB{7,2}, -0.166666666666667, 1e-09);
893s  assert (TAB{2,3}, 0.215165741455965, 1e-09);
893s  assert (TAB{3,3}, 0.304290309725089, 1e-09);
893s  assert (TAB{4,3}, 0.304290309725089, 1e-09);
893s  assert (TAB{5,3}, 0.304290309725089, 1e-09);
893s  assert (TAB{6,3}, 0.43033148291193, 1e-09);
893s  assert (TAB{7,3}, 0.43033148291193, 1e-09);
893s  assert (TAB{2,6}, 26.3362867542108, 1e-09);
893s  assert (TAB{3,6}, -4.38178046004138, 1e-09);
893s  assert (TAB{4,6}, -7.12039324756724, 1e-09);
893s  assert (TAB{5,6}, 3.83405790253621, 1e-09);
893s  assert (TAB{6,6}, -0.774596669241495, 1e-09);
893s  assert (TAB{7,6}, -0.387298334620748, 1e-09);
893s  assert (TAB{2,7}, 5.49841502258254e-12, 1e-09);
893s  assert (TAB{3,7}, 0.000893505495903642, 1e-09);
893s  assert (TAB{4,7}, 1.21291454302428e-05, 1e-09);
893s  assert (TAB{5,7}, 0.00237798044119407, 1e-09);
893s  assert (TAB{6,7}, 0.453570536021938, 1e-09);
893s  assert (TAB{7,7}, 0.705316781644046, 1e-09);
893s  ## Test with string ids for categorical variables
893s  brands = {'Gourmet', 'National', 'Generic'; ...
893s            'Gourmet', 'National', 'Generic'; ...
893s            'Gourmet', 'National', 'Generic'; ...
893s            'Gourmet', 'National', 'Generic'; ...
893s            'Gourmet', 'National', 'Generic'; ...
893s            'Gourmet', 'National', 'Generic'};
893s  popper = {'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; ...
893s            'air', 'air', 'air'; 'air', 'air', 'air'; 'air', 'air', 'air'};
893s  [TAB, STATS] = fitlm ({brands(:),popper(:)},popcorn(:),"interactions",...
893s                           "categoricalvars",[1,2],"display","off");
893s ***** test
893s  load carsmall
893s  X = [Weight,Horsepower,Acceleration];
893s  [TAB, STATS] = fitlm (X, MPG,"constant","display","off");
893s  [TAB, STATS] = fitlm (X, MPG,"linear","display","off");
893s  assert (TAB{2,2}, 47.9767628118615, 1e-09);
893s  assert (TAB{3,2}, -0.00654155878851796, 1e-09);
893s  assert (TAB{4,2}, -0.0429433065881864, 1e-09);
893s  assert (TAB{5,2}, -0.0115826516894871, 1e-09);
893s  assert (TAB{2,3}, 3.87851641748551, 1e-09);
893s  assert (TAB{3,3}, 0.00112741016370336, 1e-09);
893s  assert (TAB{4,3}, 0.0243130608813806, 1e-09);
893s  assert (TAB{5,3}, 0.193325043113178, 1e-09);
893s  assert (TAB{2,6}, 12.369874881944, 1e-09);
893s  assert (TAB{3,6}, -5.80228828790225, 1e-09);
893s  assert (TAB{4,6}, -1.76626492228599, 1e-09);
893s  assert (TAB{5,6}, -0.0599128364487485, 1e-09);
893s  assert (TAB{2,7}, 4.89570341688996e-21, 1e-09);
893s  assert (TAB{3,7}, 9.87424814144e-08, 1e-09);
893s  assert (TAB{4,7}, 0.0807803098213114, 1e-09);
893s  assert (TAB{5,7}, 0.952359384151778, 1e-09);
893s 3 tests, 3 passed, 0 known failure, 0 skipped
893s [inst/fitrgam.m]
893s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/fitrgam.m
893s ***** demo
893s  # Train a RegressionGAM Model for synthetic values
893s 
893s  f1 = @(x) cos (3 *x);
893s  f2 = @(x) x .^ 3;
893s 
893s  # generate x1 and x2 for f1 and f2
893s  x1 = 2 * rand (50, 1) - 1;
893s  x2 = 2 * rand (50, 1) - 1;
893s 
893s  # calculate y
893s  y = f1(x1) + f2(x2);
893s 
893s  # add noise
893s  y = y + y .* 0.2 .* rand (50,1);
893s  X = [x1, x2];
893s 
893s  # create an object
893s  a = fitrgam (X, y, "tol", 1e-3)
893s ***** test
893s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
893s  y = [1; 2; 3; 4];
893s  a = fitrgam (x, y);
893s  assert ({a.X, a.Y}, {x, y})
893s  assert ({a.BaseModel.Intercept}, {2.5000})
893s  assert ({a.Knots, a.Order, a.DoF}, {[5, 5, 5], [3, 3, 3], [8, 8, 8]})
893s  assert ({a.NumObservations, a.NumPredictors}, {4, 3})
893s  assert ({a.ResponseName, a.PredictorNames}, {"Y", {"x1", "x2", "x3"}})
893s  assert ({a.Formula}, {[]})
893s ***** test
893s  x = [1, 2, 3, 4; 4, 5, 6, 7; 7, 8, 9, 1; 3, 2, 1, 2];
893s  y = [1; 2; 3; 4];
893s  pnames = {"A", "B", "C", "D"};
893s  formula = "Y ~ A + B + C + D + A:C";
893s  intMat = logical ([1,0,0,0;0,1,0,0;0,0,1,0;0,0,0,1;1,0,1,0]);
893s  a = fitrgam (x, y, "predictors", pnames, "formula", formula);
893s  assert ({a.IntMatrix}, {intMat})
893s  assert ({a.ResponseName, a.PredictorNames}, {"Y", pnames})
893s  assert ({a.Formula}, {formula})
893s ***** error<fitrgam: too few arguments.> fitrgam ()
893s ***** error<fitrgam: too few arguments.> fitrgam (ones(10,2))
893s ***** error<fitrgam: Name-Value arguments must be in pairs.>
893s  fitrgam (ones (4,2), ones (4, 1), "K")
893s ***** error<fitrgam: number of rows in X and Y must be equal.>
893s  fitrgam (ones (4,2), ones (3, 1))
893s ***** error<fitrgam: number of rows in X and Y must be equal.>
893s  fitrgam (ones (4,2), ones (3, 1), "K", 2)
893s 7 tests, 7 passed, 0 known failure, 0 skipped
893s [inst/friedman.m]
893s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/friedman.m
893s ***** demo
893s  load popcorn;
893s  friedman (popcorn, 3);
893s ***** demo
893s  load popcorn;
893s  [p, atab] = friedman (popcorn, 3, "off");
893s  disp (p);
893s ***** test
893s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
893s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
893s  [p, atab] = friedman (popcorn, 3, "off");
893s  assert (p, 0.001028853354594794, 1e-14);
893s  assert (atab{2,2}, 99.75, 1e-14);
893s  assert (atab{2,3}, 2, 0);
893s  assert (atab{2,4}, 49.875, 1e-14);
893s  assert (atab{2,5}, 13.75862068965517, 1e-14);
893s  assert (atab{2,6}, 0.001028853354594794, 1e-14);
893s  assert (atab{3,2}, 0.08333333333333215, 1e-14);
893s  assert (atab{3,4}, 0.04166666666666607, 1e-14);
893s  assert (atab{4,3}, 12, 0);
893s ***** test
893s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
893s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
893s  [p, atab, stats] = friedman (popcorn, 3, "off");
893s  assert (atab{5,2}, 116, 0);
893s  assert (atab{5,3}, 17, 0);
893s  assert (stats.source, "friedman");
893s  assert (stats.n, 2);
893s  assert (stats.meanranks, [8, 4.75, 2.25], 0);
893s  assert (stats.sigma, 2.692582403567252, 1e-14);
893s 2 tests, 2 passed, 0 known failure, 0 skipped
893s [inst/fullfact.m]
893s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/fullfact.m
893s ***** demo
893s  ## Full factorial design with 3 binary variables
893s  fullfact (3)
893s ***** demo
893s  ## Full factorial design with 3 ordinal variables
893s  fullfact ([2, 3, 4])
893s ***** error fullfact ();
893s ***** error fullfact (2, 5);
893s ***** error fullfact (2.5);
893s ***** error fullfact (0);
893s ***** error fullfact (-3);
893s ***** error fullfact (3+2i);
893s ***** error fullfact (Inf);
893s ***** error fullfact (NaN);
893s ***** error fullfact ([1, 2, -3]);
893s ***** error fullfact ([0, 1, 2]);
893s ***** error fullfact ([1, 2, NaN]);
893s ***** error fullfact ([1, 2, Inf]);
893s ***** test
893s  A = fullfact (2);
893s  assert (A, [0, 0; 0, 1; 1, 0; 1, 1]);
893s ***** test
893s  A = fullfact ([1, 2]);
893s  assert (A, [1, 1; 1, 2]);
893s ***** test
893s  A = fullfact ([1, 2, 4]);
893s  A_out = [1, 1, 1; 1, 1, 2; 1, 1, 3; 1, 1, 4; ...
893s           1, 2, 1; 1, 2, 2; 1, 2, 3; 1, 2, 4];
893s  assert (A, A_out);
893s 15 tests, 15 passed, 0 known failure, 0 skipped
893s [inst/geomean.m]
893s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/geomean.m
893s ***** test
893s  x = [0:10];
893s  y = [x;x+5;x+10];
893s  assert (geomean (x), 0);
893s  m = [0 9.462942809849169 14.65658770861967];
893s  assert (geomean (y, 2), m', 4e-14);
893s  assert (geomean (y, "all"), 0);
893s  y(2,4) = NaN;
893s  m(2) = 9.623207231679554;
893s  assert (geomean (y, 2), [0 NaN m(3)]', 4e-14);
893s  assert (geomean (y', "omitnan"), m, 4e-14);
893s  z = y + 20;
893s  assert (geomean (z, "all"), NaN);
893s  assert (geomean (z, "all", "includenan"), NaN);
893s  assert (geomean (z, "all", "omitnan"), 29.59298474535024, 4e-14);
893s  m = [24.79790781765634 NaN 34.85638839503932];
893s  assert (geomean (z'), m, 4e-14);
893s  assert (geomean (z', "includenan"), m, 4e-14);
893s  m(2) = 30.02181156156319;
893s  assert (geomean (z', "omitnan"), m, 4e-14);
893s  assert (geomean (z, 2, "omitnan"), m', 4e-14);
893s ***** test
893s  x = repmat ([1:20;6:25], [5 2 6 3]);
893s  assert (size (geomean (x, [3 2])), [10 1 1 3]);
893s  assert (size (geomean (x, [1 2])), [1 1 6 3]);
893s  assert (size (geomean (x, [1 2 4])), [1 1 6]);
893s  assert (size (geomean (x, [1 4 3])), [1 40]);
893s  assert (size (geomean (x, [1 2 3 4])), [1 1]);
893s ***** test
893s  x = repmat ([1:20;6:25], [5 2 6 3]);
893s  m = repmat ([8.304361203739333;14.3078118884256], [5 1 1 3]);
893s  assert (geomean (x, [3 2]), m, 4e-13);
893s  x(2,5,6,3) = NaN;
893s  m(2,3) = NaN;
893s  assert (geomean (x, [3 2]), m, 4e-13);
893s  m(2,3) = 14.3292729579901;
893s  assert (geomean (x, [3 2], "omitnan"), m, 4e-13);
893s ***** error <geomean: X must contain real nonnegative values.> geomean ("char")
893s ***** error <geomean: X must contain real nonnegative values.> geomean ([1 -1 3])
893s ***** error <geomean: DIM must be a positive integer scalar or vector.> ...
893s  geomean (repmat ([1:20;6:25], [5 2 6 3 5]), -1)
893s ***** error <geomean: DIM must be a positive integer scalar or vector.> ...
893s  geomean (repmat ([1:20;6:25], [5 2 6 3 5]), 0)
893s ***** error <geomean: VECDIM must contain non-repeating positive integers.> ...
893s  geomean (repmat ([1:20;6:25], [5 2 6 3 5]), [1 1])
893s 8 tests, 8 passed, 0 known failure, 0 skipped
893s [inst/glmfit.m]
893s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/glmfit.m
893s ***** demo
893s  x = [210, 230, 250, 270, 290, 310, 330, 350, 370, 390, 410, 430]';
893s  n = [48, 42, 31, 34, 31, 21, 23, 23, 21, 16, 17, 21]';
893s  y = [1, 2, 0, 3, 8, 8, 14, 17, 19, 15, 17, 21]';
893s  b = glmfit (x, [y n], "binomial", "Link", "probit");
893s  yfit = glmval (b, x, "probit", "Size", n);
893s  plot (x, y./n, 'o', x, yfit ./ n, '-')
893s ***** demo
893s  load fisheriris
893s  X = meas (51:end, :);
893s  y = strcmp ("versicolor", species(51:end));
893s  b = glmfit (X, y, "binomial", "link", "logit")
893s ***** test
893s  load fisheriris;
893s  X = meas(51:end,:);
893s  y = strcmp ("versicolor", species(51:end));
893s  b = glmfit (X, y, "binomial", "link", "logit");
893s  assert (b, [42.6379; 2.4652; 6.6809; -9.4294; -18.2861], 1e-4);
894s ***** test
894s  X = [1.2, 2.3, 3.4, 4.5, 5.6, 6.7, 7.8, 8.9, 9.0, 10.1]';
894s  y = [0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4]';
894s  [Bnew, dev] = glmfit (X, y, "gamma", "link", "log");
894s  b_matlab = [-0.7631; 0.1113];
894s  dev_matlab = 0.0111;
894s  assert (Bnew, b_matlab, 0.001);
894s  assert (dev, dev_matlab, 0.001);
894s ***** test
894s  X = [1.2, 2.3, 3.4, 4.5, 5.6, 6.7, 7.8, 8.9, 9.0, 10.1]';
894s  y = [0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4]';
894s  p_input = 1;
894s  [Bnew, dev] = glmfit (X, y, "inverse gaussian", "link", p_input);
894s  b_matlab = [0.3813; 0.0950];
894s  dev_matlab = 0.0051;
894s  assert (Bnew, b_matlab, 0.001);
894s  assert (dev, dev_matlab, 0.001);
894s ***** error <glmfit: too few input arguments.> glmfit ()
894s ***** error <glmfit: too few input arguments.> glmfit (1)
894s ***** error <glmfit: too few input arguments.> glmfit (1, 2)
894s ***** error <glmfit: Name-Value arguments must be in pairs.> ...
894s  glmfit (rand (6, 1), rand (6, 1), 'poisson', 'link')
894s ***** error <glmfit: X must be a numeric matrix.> ...
894s  glmfit ('abc', rand (6, 1), 'poisson')
894s ***** error <glmfit: X must be a numeric matrix.> ...
894s  glmfit ([], rand (6, 1), 'poisson')
894s ***** error <glmfit: Y must be either a numeric matrix or a logical vector.> ...
894s  glmfit (rand (5, 2), 'abc', 'poisson')
894s ***** error <glmfit: Y must be either a numeric matrix or a logical vector.> ...
894s  glmfit (rand (5, 2), [], 'poisson')
894s ***** error <glmfit: X and Y must have the same number of observations.> ...
894s  glmfit (rand (5, 2), rand (6, 1), 'poisson')
894s ***** error <glmfit: DISTRIBUTION must be a character vector.> ...
894s  glmfit (rand (6, 2), rand (6, 1), 3)
894s ***** error <glmfit: DISTRIBUTION must be a character vector.> ...
894s  glmfit (rand (6, 2), rand (6, 1), {'poisson'})
894s ***** error <glmfit: for a 'binomial' distribution, Y must be an n-by-1 or n-by-2 matrix.> ...
894s  glmfit (rand (5, 2), rand (5, 3), 'binomial')
894s ***** error <glmfit: n-by-2 matrix Y for 'binomial' distribution must be numeric.> ...
894s  glmfit (rand (2, 2), [true, true; false, false], 'binomial')
894s ***** error <glmfit: for distributions other than 'binomial', Y must be an n-by-1 column vector> ...
894s  glmfit (rand (5, 2), rand (5, 2), 'normal')
894s ***** error <glmfit: unsupported distribution.> ...
894s  glmfit (rand (5, 2), rand (5, 1), 'chebychev')
894s ***** error <glmfit: 'B0' must be a numeric vector of the same size as Y.> ...
894s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'B0', [1; 2; 3; 4])
894s ***** error <glmfit: 'Constant' should be either 'on' or 'off'.> ...
894s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'constant', 1)
894s ***** error <glmfit: 'Constant' should be either 'on' or 'off'.> ...
894s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'constant', 'o')
894s ***** error <glmfit: 'Constant' should be either 'on' or 'off'.> ...
894s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'constant', true)
894s ***** error <glmfit: 'EstDisp' should be either 'on' or 'off'.> ...
894s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'estdisp', 1)
894s ***** error <glmfit: 'EstDisp' should be either 'on' or 'off'.> ...
894s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'estdisp', 'o')
894s ***** error <glmfit: 'EstDisp' should be either 'on' or 'off'.> ...
894s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'estdisp', true)
894s ***** error <glmfit: structure with custom link functions must be a scalar.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", {1, 2}))
894s ***** error <glmfit: structure with custom link functions requires the fields 'Link', 'Derivative', and 'Inverse'.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", "norminv"))
894s ***** error <glmfit: bad 'Link' function in custom link function structure.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", "some", "Derivative", @(x)x, "Inverse", "normcdf"))
894s ***** error <glmfit: bad 'Link' function in custom link function structure.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", 1, "Derivative", @(x)x, "Inverse", "normcdf"))
894s ***** error <glmfit: custom 'Link' function must return an output of the same size as input.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x) [x, x], "Derivative", @(x)x, "Inverse", "normcdf"))
894s ***** error <glmfit: invalid custom 'Link' function.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", "what", "Derivative", @(x)x, "Inverse", "normcdf"))
894s ***** error <glmfit: bad 'Derivative' function in custom link function structure.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "some", "Inverse", "normcdf"))
894s ***** error <glmfit: bad 'Derivative' function in custom link function structure.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", 1, "Inverse", "normcdf"))
894s ***** error <glmfit: custom 'Derivative' function must return an output of the same size as input.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", @(x) [x, x], "Inverse", "normcdf"))
894s ***** error <glmfit: invalid custom 'Derivative' function.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "what", "Inverse", "normcdf"))
894s ***** error <glmfit: bad 'Inverse' function in custom link function structure.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", "some"))
894s ***** error <glmfit: bad 'Inverse' function in custom link function structure.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", 1))
894s ***** error <glmfit: custom 'Inverse' function must return an output of the same size as input.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", @(x) [x, x]))
894s ***** error <glmfit: invalid custom 'Inverse' function.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", "what"))
894s ***** error <glmfit: cell array with custom link functions must have three elements.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {'log'})
894s ***** error <glmfit: cell array with custom link functions must have three elements.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {'log', 'hijy'})
894s ***** error <glmfit: cell array with custom link functions must have three elements.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {1, 2, 3, 4})
894s ***** error <glmfit: bad 'Link' function in custom link function cell array.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {"log", "dfv", "dfgvd"})
894s ***** error <glmfit: custom 'Link' function must return an output of the same size as input.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) [x, x], "dfv", "dfgvd"})
894s ***** error <glmfit: invalid custom 'Link' function.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) what (x), "dfv", "dfgvd"})
894s ***** error <glmfit: bad 'Derivative' function in custom link function cell array.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, "dfv", "dfgvd"})
894s ***** error <glmfit: custom 'Derivative' function must return an output of the same size as input.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, @(x) [x, x], "dfgvd"})
894s ***** error <glmfit: invalid custom 'Derivative' function.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, @(x) what (x), "dfgvd"})
894s ***** error <glmfit: bad 'Inverse' function in custom link function cell array.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, @(x) x, "dfgvd"})
894s ***** error <glmfit: custom 'Inverse' function must return an output of the same size as input.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, @(x) x, @(x) [x, x]})
894s ***** error <glmfit: invalid custom 'Inverse' function.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, @(x) x, @(x) what (x)})
894s ***** error <glmfit: numeric input for custom link function must be a finite real scalar value.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', NaN)
894s ***** error <glmfit: numeric input for custom link function must be a finite real scalar value.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', [1, 2])
894s ***** error <glmfit: numeric input for custom link function must be a finite real scalar value.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', [1i])
894s ***** error <glmfit: canonical link function name must be a character vector.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', ["log"; "log1"])
894s ***** error <glmfit: canonical link function 'somelinkfunction' is not supported.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', 'somelinkfunction')
894s ***** error <glmfit: invalid value for custom link function.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', true)
894s ***** error <glmfit: 'Options' must be a structure containing the fields 'MaxIter', and 'TolX'.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', true)
894s ***** error <glmfit: 'Options' must be a structure containing the fields 'MaxIter', and 'TolX'.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 100))
894s ***** error <glmfit: 'MaxIter' in 'Options' structure must be a positive integer.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 4.5, "TolX", 1e-6))
894s ***** error <glmfit: 'MaxIter' in 'Options' structure must be a positive integer.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 0, "TolX", 1e-6))
894s ***** error <glmfit: 'MaxIter' in 'Options' structure must be a positive integer.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", -100, "TolX", 1e-6))
894s ***** error <glmfit: 'MaxIter' in 'Options' structure must be a positive integer.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", [50 ,50], "TolX", 1e-6))
894s ***** error <glmfit: 'TolX' in 'Options' structure must be a positive scalar.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 100, "TolX", 0))
894s ***** error <glmfit: 'TolX' in 'Options' structure must be a positive scalar.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 100, "TolX", -1e-6))
894s ***** error <glmfit: 'TolX' in 'Options' structure must be a positive scalar.> ...
894s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 100, "TolX", [1e-6, 1e-6]))
894s ***** error <glmfit: 'Offset' must be a numeric vector of the same size as Y.> ...
894s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'offset', [1; 2; 3; 4])
894s ***** error <glmfit: 'Offset' must be a numeric vector of the same size as Y.> ...
894s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'offset', 'asdfg')
894s ***** error <glmfit: 'Weights' must be a numeric vector of the same size as Y.> ...
894s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'weights', [1; 2; 3; 4])
894s ***** error <glmfit: 'Weights' must be a numeric vector of the same size as Y.> ...
894s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'weights', 'asdfg')
894s 70 tests, 70 passed, 0 known failure, 0 skipped
894s [inst/glmval.m]
894s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/glmval.m
894s ***** demo
894s  x = [210, 230, 250, 270, 290, 310, 330, 350, 370, 390, 410, 430]';
894s  n = [48, 42, 31, 34, 31, 21, 23, 23, 21, 16, 17, 21]';
894s  y = [1, 2, 0, 3, 8, 8, 14, 17, 19, 15, 17, 21]';
894s  b = glmfit (x, [y n], "binomial", "Link", "probit");
894s  yfit = glmval (b, x, "probit", "Size", n);
894s  plot (x, y./n, 'o', x, yfit ./ n, '-')
894s ***** error <glmval: too few input arguments.> glmval ()
894s ***** error <glmval: too few input arguments.> glmval (1)
894s ***** error <glmval: too few input arguments.> glmval (1, 2)
894s ***** error <glmval: B must be a numeric vector of coefficient estimates.> ...
894s  glmval ("asd", [1; 1; 1], 'probit')
894s ***** error <glmval: B must be a numeric vector of coefficient estimates.> ...
894s  glmval ([], [1; 1; 1], 'probit')
894s ***** error <glmval: X must be a numeric matrix.> ...
894s  glmval ([0.1; 0.3; 0.4], [], 'probit')
894s ***** error <glmval: X must be a numeric matrix.> ...
894s  glmval ([0.1; 0.3; 0.4], "asd", 'probit')
894s ***** error <glmval: structure with custom link functions must be a scalar.> ...
894s  glmval (rand (3,1), rand (5,2), struct ("Link", {1, 2}))
894s ***** error <glmval: structure with custom link functions requires the fields 'Link', 'Derivative', and 'Inverse'.> ...
894s  glmval (rand (3,1), rand (5,2), struct ("Link", "norminv"))
894s ***** error <glmval: bad 'Link' function in custom link function structure.> ...
894s  glmval (rand (3,1), rand (5,2), struct ("Link", "some", "Derivative", @(x)x, "Inverse", "normcdf"))
894s ***** error <glmval: bad 'Link' function in custom link function structure.> ...
894s  glmval (rand (3,1), rand (5,2), struct ("Link", 1, "Derivative", @(x)x, "Inverse", "normcdf"))
894s ***** error <glmval: custom 'Link' function must return an output of the same size as input.> ...
894s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x) [x, x], "Derivative", @(x)x, "Inverse", "normcdf"))
894s ***** error <glmval: invalid custom 'Link' function.> ...
894s  glmval (rand (3,1), rand (5,2), struct ("Link", "what", "Derivative", @(x)x, "Inverse", "normcdf"))
894s ***** error <glmval: bad 'Derivative' function in custom link function structure.> ...
894s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "some", "Inverse", "normcdf"))
894s ***** error <glmval: bad 'Derivative' function in custom link function structure.> ...
894s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", 1, "Inverse", "normcdf"))
894s ***** error <glmval: custom 'Derivative' function must return an output of the same size as input.> ...
894s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", @(x) [x, x], "Inverse", "normcdf"))
894s ***** error <glmval: invalid custom 'Derivative' function.> ...
894s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "what", "Inverse", "normcdf"))
894s ***** error <glmval: bad 'Inverse' function in custom link function structure.> ...
894s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", "some"))
894s ***** error <glmval: bad 'Inverse' function in custom link function structure.> ...
894s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", 1))
894s ***** error <glmval: custom 'Inverse' function must return an output of the same size as input.> ...
894s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", @(x) [x, x]))
894s ***** error <glmval: invalid custom 'Inverse' function.> ...
894s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", "what"))
894s ***** error <glmval: cell array with custom link functions must have three elements.> ...
894s  glmval (rand (3,1), rand (5,2), {'log'})
894s ***** error <glmval: cell array with custom link functions must have three elements.> ...
894s  glmval (rand (3,1), rand (5,2), {'log', 'hijy'})
894s ***** error <glmval: cell array with custom link functions must have three elements.> ...
894s  glmval (rand (3,1), rand (5,2), {1, 2, 3, 4})
894s ***** error <glmval: bad 'Link' function in custom link function cell array.> ...
894s  glmval (rand (3,1), rand (5,2), {"log", "dfv", "dfgvd"})
894s ***** error <glmval: custom 'Link' function must return an output of the same size as input.> ...
894s  glmval (rand (3,1), rand (5,2), {@(x) [x, x], "dfv", "dfgvd"})
894s ***** error <glmval: invalid custom 'Link' function.> ...
894s  glmval (rand (3,1), rand (5,2), {@(x) what (x), "dfv", "dfgvd"})
894s ***** error <glmval: bad 'Derivative' function in custom link function cell array.> ...
894s  glmval (rand (3,1), rand (5,2), {@(x) x, "dfv", "dfgvd"})
894s ***** error <glmval: custom 'Derivative' function must return an output of the same size as input.> ...
894s  glmval (rand (3,1), rand (5,2), {@(x) x, @(x) [x, x], "dfgvd"})
894s ***** error <glmval: invalid custom 'Derivative' function.> ...
894s  glmval (rand (3,1), rand (5,2), {@(x) x, @(x) what (x), "dfgvd"})
894s ***** error <glmval: bad 'Inverse' function in custom link function cell array.> ...
894s  glmval (rand (3,1), rand (5,2), {@(x) x, @(x) x, "dfgvd"})
894s ***** error <glmval: custom 'Inverse' function must return an output of the same size as input.> ...
894s  glmval (rand (3,1), rand (5,2), {@(x) x, @(x) x, @(x) [x, x]})
894s ***** error <glmval: invalid custom 'Inverse' function.> ...
894s  glmval (rand (3,1), rand (5,2), {@(x) x, @(x) x, @(x) what (x)})
894s ***** error <glmval: numeric input for custom link function must be a finite real scalar value.> ...
894s  glmval (rand (3,1), rand (5,2), NaN)
894s ***** error <glmval: numeric input for custom link function must be a finite real scalar value.> ...
894s  glmval (rand (3,1), rand (5,2), [1, 2])
894s ***** error <glmval: numeric input for custom link function must be a finite real scalar value.> ...
894s  glmval (rand (3,1), rand (5,2), [1i])
894s ***** error <glmval: canonical link function name must be a character vector.> ...
894s  glmval (rand (3,1), rand (5,2), ["log"; "log1"])
894s ***** error <glmval: canonical link function 'somelinkfunction' is not supported.> ...
894s  glmval (rand (3,1), rand (5,2), 'somelinkfunction')
894s ***** error <glmval: invalid value for custom link function.> ...
894s  glmval (rand (3,1), rand (5,2), true)
894s ***** error <glmval: invalid 'stats' structure.> ...
894s  glmval (rand (3,1), rand (5,2), 'probit', struct ("s", 1))
894s ***** error <glmval: Name-Value arguments must be in pairs.> ...
894s  glmval (rand (3,1), rand (5,2), 'probit', 'confidence')
894s ***** error <glmval: 'Confidence' must be a scalar between 0 and 1.> ...
894s  glmval (rand (3,1), rand (5,2), 'probit', 'confidence', 0)
894s ***** error <glmval: 'Confidence' must be a scalar between 0 and 1.> ...
894s  glmval (rand (3,1), rand (5,2), 'probit', 'confidence', 1.2)
894s ***** error <glmval: 'Confidence' must be a scalar between 0 and 1.> ...
894s  glmval (rand (3,1), rand (5,2), 'probit', 'confidence', [0.9, 0.95])
894s ***** error <glmval: 'Constant' should be either 'on' or 'off'.> ...
894s  glmval (rand (3, 1), rand (5, 2), 'probit', 'constant', 1)
894s ***** error <glmval: 'Constant' should be either 'on' or 'off'.> ...
894s  glmval (rand (3, 1), rand (5, 2), 'probit', 'constant', 'o')
894s ***** error <glmval: 'Constant' should be either 'on' or 'off'.> ...
894s  glmval (rand (3, 1), rand (5, 2), 'probit', 'constant', true)
894s ***** error <glmval: 'Offset' must be a numeric vector of the same length as the rows in X.> ...
894s  glmval (rand (3, 1), rand (5, 2), 'probit', 'offset', [1; 2; 3; 4])
894s ***** error <glmval: 'Offset' must be a numeric vector of the same length as the rows in X.> ...
894s  glmval (rand (3, 1), rand (5, 2), 'probit', 'offset', 'asdfg')
894s ***** error <glmval: 'simultaneous' must be a boolean scalar.> ...
894s  glmval (rand (3, 1), rand (5, 2), 'probit', 'simultaneous', 'asdfg')
894s ***** error <glmval: 'simultaneous' must be a boolean scalar.> ...
894s  glmval (rand (3, 1), rand (5, 2), 'probit', 'simultaneous', [true, false])
894s ***** error <glmval: 'size' must be a scalar or a vector with one value for each row of X.> ...
894s  glmval (rand (3, 1), rand (5, 2), 'probit', 'size', "asd")
894s ***** error <glmval: 'size' must be a scalar or a vector with one value for each row of X.> ...
894s  glmval (rand (3, 1), rand (5, 2), 'probit', 'size', [2, 3, 4])
894s ***** error <glmval: 'size' must be a scalar or a vector with one value for each row of X.> ...
894s  glmval (rand (3, 1), rand (5, 2), 'probit', 'size', [2; 3; 4])
894s ***** error <glmval: 'size' must be a scalar or a vector with one value for each row of X.> ...
894s  glmval (rand (3, 1), rand (5, 2), 'probit', 'size', ones (3))
894s ***** error <glmval: unknown parameter name.> ...
894s  glmval (rand (3, 1), rand (5, 2), 'probit', 'someparam', 4)
894s ***** error <glmval: cannot compute confidence intervals without STATS structure.> ...
894s  [y,lo,hi] = glmval (rand (3, 1), rand (5, 2), 'probit')
894s 57 tests, 57 passed, 0 known failure, 0 skipped
894s [inst/gmdistribution.m]
894s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/gmdistribution.m
894s ***** test
894s  mu = eye(2);
894s  Sigma = eye(2);
894s  GM = gmdistribution (mu, Sigma);
894s  density = GM.pdf ([0 0; 1 1]);
894s  assert (density(1) - density(2), 0, 1e-6);
894s 
894s  [idx, nlogl, P, logpdf,M] = cluster (GM, eye(2));
894s  assert (idx, [1; 2]);
894s  [idx2,nlogl2,P2,logpdf2] = GM.cluster (eye(2));
894s  assert (nlogl - nlogl2, 0, 1e-6);
894s  [idx3,nlogl3,P3] = cluster (GM, eye(2));
894s  assert (P - P3, zeros (2), 1e-6);
894s  [idx4,nlogl4] = cluster (GM, eye(2));
894s  assert (size (nlogl4), [1 1]);
894s  idx5 = cluster (GM, eye(2));
894s  assert (idx - idx5, zeros (2,1));
894s 
894s  D = GM.mahal ([1;0]);
894s  assert (D - M(1,:), zeros (1,2), 1e-6);
894s 
894s  P = GM.posterior ([0 1]);
894s  assert (P - P2(2,:), zeros (1,2), 1e-6);
894s 
894s  R = GM.random(20);
894s  assert (size(R), [20, 2]);
894s 
894s  R = GM.random();
894s  assert (size(R), [1, 2]);
894s 1 test, 1 passed, 0 known failure, 0 skipped
894s [inst/grp2idx.m]
894s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/grp2idx.m
894s ***** test
894s  in = [true false false true];
894s  out = {[1; 2; 2; 1] {"1"; "0"} [true; false]};
894s  assert (nthargout (1:3, @grp2idx, in), out)
894s  assert (nthargout (1:3, @grp2idx, in), nthargout (1:3, @grp2idx, in'))
894s ***** test
894s  assert (nthargout (1:3, @grp2idx, [false, true]),
894s          {[1; 2] {"0"; "1"} [false; true]});
894s  assert (nthargout (1:3, @grp2idx, [true, false]),
894s          {[1; 2] {"1"; "0"} [true; false]});
894s ***** assert (nthargout (1:3, @grp2idx, ["oct"; "sci"; "oct"; "oct"; "sci"]),
894s         {[1; 2; 1; 1; 2] {"oct"; "sci"} ["oct"; "sci"]});
894s ***** assert (nthargout (1:3, @grp2idx, {"oct"; "sci"; "oct"; "oct"; "sci"}),
894s         {[1; 2; 1; 1; 2] {"oct"; "sci"} {"oct"; "sci"}});
894s ***** assert (nthargout (1:3, @grp2idx, [ 1 -3 -2 -3 -3  2  1 -1  3 -3]),
894s         {[1; 2; 3; 2; 2; 4; 1; 5; 6; 2], {"1"; "-3"; "-2"; "2"; "-1"; "3"}, ...
894s          [1; -3; -2; 2; -1; 3]});
894s ***** assert (nthargout (1:3, @grp2idx, [2 2 3 NaN 2 3]),
894s         {[1; 1; 2; NaN; 1; 2] {"2"; "3"} [2; 3]})
894s ***** assert (nthargout (1:3, @grp2idx, {"et" "sa" "sa" "" "et"}),
894s         {[1; 2; 2; NaN; 1] {"et"; "sa"} {"et"; "sa"}})
894s ***** test assert (nthargout (1:3, @grp2idx, ["sci"; "oct"; "sci"; "oct"; "oct"]),
894s         {[1; 2; 1; 2; 2] {"sci"; "oct"} ["sci"; "oct"]});
894s ***** test assert (nthargout (1:3, @grp2idx, {"sci"; "oct"; "sci"; "oct"; "oct"}),
894s         {[1; 2; 1; 2; 2] {"sci"; "oct"} {"sci"; "oct"}});
894s ***** test assert (nthargout (1:3, @grp2idx, {"sa" "et" "et" "" "sa"}),
894s         {[1; 2; 2; NaN; 1] {"sa"; "et"} {"sa"; "et"}})
894s 10 tests, 10 passed, 0 known failure, 0 skipped
894s [inst/grpstats.m]
894s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/grpstats.m
894s ***** demo
894s  load carsmall;
894s  [m,p,g] = grpstats (Weight, Model_Year, {"mean", "predci", "gname"})
894s  n = length(m);
894s  errorbar((1:n)',m,p(:,2)-m);
894s  set (gca, "xtick", 1:n, "xticklabel", g);
894s  title ("95% prediction intervals for mean weight by year");
894s ***** demo
894s  load carsmall;
894s  [m,p,g] = grpstats ([Acceleration,Weight/1000],Cylinders, ...
894s                      {"mean", "meanci", "gname"}, 0.05)
894s  [c,r] = size (m);
894s  errorbar((1:c)'.*ones(c,r),m,p(:,[(1:r)])-m);
894s  set (gca, "xtick", 1:c, "xticklabel", g);
894s  title ("95% prediction intervals for mean weight by year");
894s ***** test
894s  load carsmall
894s  means = grpstats (Acceleration, Origin);
894s  assert (means, [14.4377; 18.0500; 15.8867; 16.3778; 16.6000; 15.5000], 0.001);
894s ***** test
894s  load carsmall
894s  [grpMin,grpMax,grp] = grpstats (Acceleration, Origin, {"min","max","gname"});
894s  assert (grpMin, [8.0; 15.3; 13.9; 12.2; 15.7; 15.5]);
894s  assert (grpMax, [22.2; 21.9; 18.2; 24.6; 17.5; 15.5]);
894s ***** test
894s  load carsmall
894s  [grpMin,grpMax,grp] = grpstats (Acceleration, Origin, {"min","max","gname"});
894s  assert (grp', {"USA", "France", "Japan", "Germany", "Sweden", "Italy"});
894s ***** test
894s  load carsmall
894s  [m,p,g] = grpstats ([Acceleration,Weight/1000], Cylinders, ...
894s                      {"mean", "meanci", "gname"}, 0.05);
894s  assert (p(:,1), [11.17621760075134, 16.13845847655224, 16.16222663683362]', ...
894s                  [1e-14, 2e-14, 1e-14]');
894s 4 tests, 4 passed, 0 known failure, 0 skipped
894s [inst/gscatter.m]
894s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/gscatter.m
894s ***** demo
894s  load fisheriris;
894s  X = meas(:,3:4);
894s  cidcs = kmeans (X, 3, "Replicates", 5);
894s  gscatter (X(:,1), X(:,2), cidcs, [.75 .75 0; 0 .75 .75; .75 0 .75], "os^");
894s  title ("Fisher's iris data");
894s ***** shared visibility_setting
894s  visibility_setting = get (0, "DefaultFigureVisible");
894s ***** test
894s  hf = figure ("visible", "off");
894s  unwind_protect
894s    load fisheriris;
894s    X = meas(:,3:4);
894s    cidcs = kmeans (X, 3, "Replicates", 5);
894s    gscatter (X(:,1), X(:,2), cidcs, [.75 .75 0; 0 .75 .75; .75 0 .75], "os^");
894s    title ("Fisher's iris data");
894s  unwind_protect_cleanup
894s    close (hf);
894s  end_unwind_protect
894s warning: legend: 'best' not yet implemented for location specifier, using 'northeast' instead
895s ***** error gscatter ();
895s ***** error gscatter ([1]);
895s ***** error gscatter ([1], [2]);
895s ***** error <x must be a numeric vector> gscatter ('abc', [1 2 3], [1]);
895s ***** error <x and y must have the same size> gscatter ([1 2 3], [1 2], [1]);
895s ***** error <y must be a numeric vector> gscatter ([1 2 3], 'abc', [1]);
895s ***** error <g must have the same size as x and y> gscatter ([1 2], [1 2], [1]);
895s ***** error <invalid dolegend> gscatter ([1 2], [1 2], [1 2], 'rb', 'so', 12, 'xxx');
895s 9 tests, 9 passed, 0 known failure, 0 skipped
895s [inst/harmmean.m]
895s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/harmmean.m
895s ***** test
895s  x = [0:10];
895s  y = [x;x+5;x+10];
895s  assert (harmmean (x), 0);
895s  m = [0 8.907635160795225 14.30854471766802];
895s  assert (harmmean (y, 2), m', 4e-14);
895s  assert (harmmean (y, "all"), 0);
895s  y(2,4) = NaN;
895s  m(2) = 9.009855936313949;
895s  assert (harmmean (y, 2), [0 NaN m(3)]', 4e-14);
895s  assert (harmmean (y', "omitnan"), m, 4e-14);
895s  z = y + 20;
895s  assert (harmmean (z, "all"), NaN);
895s  assert (harmmean (z, "all", "includenan"), NaN);
895s  assert (harmmean (z, "all", "omitnan"), 29.1108719858295, 4e-14);
895s  m = [24.59488458841874 NaN 34.71244385944397];
895s  assert (harmmean (z'), m, 4e-14);
895s  assert (harmmean (z', "includenan"), m, 4e-14);
895s  m(2) = 29.84104075528277;
895s  assert (harmmean (z', "omitnan"), m, 4e-14);
895s  assert (harmmean (z, 2, "omitnan"), m', 4e-14);
895s ***** test
895s  x = repmat ([1:20;6:25], [5 2 6 3]);
895s  assert (size (harmmean (x, [3 2])), [10 1 1 3]);
895s  assert (size (harmmean (x, [1 2])), [1 1 6 3]);
895s  assert (size (harmmean (x, [1 2 4])), [1 1 6]);
895s  assert (size (harmmean (x, [1 4 3])), [1 40]);
895s  assert (size (harmmean (x, [1 2 3 4])), [1 1]);
895s ***** test
895s  x = repmat ([1:20;6:25], [5 2 6 3]);
895s  m = repmat ([5.559045930488016;13.04950789021461], [5 1 1 3]);
895s  assert (harmmean (x, [3 2]), m, 4e-14);
895s  x(2,5,6,3) = NaN;
895s  m(2,3) = NaN;
895s  assert (harmmean (x, [3 2]), m, 4e-14);
895s  m(2,3) = 13.06617961315406;
895s  assert (harmmean (x, [3 2], "omitnan"), m, 4e-14);
895s ***** error <harmmean: X must contain real nonnegative values.> harmmean ("char")
895s ***** error <harmmean: X must contain real nonnegative values.> harmmean ([1 -1 3])
895s ***** error <harmmean: DIM must be a positive integer scalar or vector.> ...
895s  harmmean (repmat ([1:20;6:25], [5 2 6 3 5]), -1)
895s ***** error <harmmean: DIM must be a positive integer scalar or vector.> ...
895s  harmmean (repmat ([1:20;6:25], [5 2 6 3 5]), 0)
895s ***** error <harmmean: VECDIM must contain non-repeating positive integers.> ...
895s  harmmean (repmat ([1:20;6:25], [5 2 6 3 5]), [1 1])
895s 8 tests, 8 passed, 0 known failure, 0 skipped
895s [inst/hist3.m]
895s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/hist3.m
895s ***** demo
895s  X = [
895s     1    1
895s     1    1
895s     1   10
895s     1   10
895s     5    5
895s     5    5
895s     5    5
895s     5    5
895s     5    5
895s     7    3
895s     7    3
895s     7    3
895s    10   10
895s    10   10];
895s  hist3 (X)
895s ***** test
895s  N_exp = [ 0  0  0  5 20
895s            0  0 10 15  0
895s            0 15 10  0  0
895s           20  5  0  0  0];
895s 
895s  n = 100;
895s  x = [1:n]';
895s  y = [n:-1:1]';
895s  D = [x y];
895s  N = hist3 (D, [4 5]);
895s  assert (N, N_exp);
895s ***** test
895s  N_exp = [0  0  0  0  1
895s           0  0  0  0  1
895s           0  0  0  0  1
895s           1  1  1  1 93];
895s 
895s  n = 100;
895s  x = [1:n]';
895s  y = [n:-1:1]';
895s  D = [x y];
895s  C{1} = [1 1.7 3 4];
895s  C{2} = [1:5];
895s  N = hist3 (D, C);
895s  assert (N, N_exp);
895s ***** test
895s  D = [1 1; 3 1; 3 3; 3 1];
895s  [c, nn] = hist3 (D, {0:4, 0:4});
895s  exp_c = zeros (5);
895s  exp_c([7 9 19]) = [1 2 1];
895s  assert (c, exp_c);
895s  assert (nn, {0:4, 0:4});
895s ***** test
895s  for i = 10
895s    assert (size (hist3 (rand (9, 2), "Edges", {[0:.2:1]; [0:.2:1]})), [6 6])
895s  endfor
895s ***** test
895s  edge_1 = linspace (0, 10, 10);
895s  edge_2 = linspace (0, 50, 10);
895s  [c, nn] = hist3 ([1:10; 1:5:50]', "Edges", {edge_1, edge_2});
895s  exp_c = zeros (10, 10);
895s  exp_c([1 12 13 24 35 46 57 68 79 90]) = 1;
895s  assert (c, exp_c);
895s 
895s  assert (nn{1}, edge_1 + edge_1(2)/2, eps*10^4)
895s  assert (nn{2}, edge_2 + edge_2(2)/2, eps*10^4)
895s ***** shared X
895s  X = [
895s   5  2
895s   5  3
895s   1  4
895s   5  3
895s   4  4
895s   1  2
895s   2  3
895s   3  3
895s   5  4
895s   5  3];
895s ***** test
895s  N = zeros (10);
895s  N([1 10 53 56 60 91 98 100]) = [1 1 1 1 3 1 1 1];
895s  C = {(1.2:0.4:4.8), (2.1:0.2:3.9)};
895s  assert (nthargout ([1 2], @hist3, X), {N C}, eps*10^3)
895s ***** test
895s  N = zeros (5, 7);
895s  N([1 5 17 18 20 31 34 35]) = [1 1 1 1 3 1 1 1];
895s  C = {(1.4:0.8:4.6), ((2+(1/7)):(2/7):(4-(1/7)))};
895s  assert (nthargout ([1 2], @hist3, X, [5 7]), {N C}, eps*10^3)
895s  assert (nthargout ([1 2], @hist3, X, "Nbins", [5 7]), {N C}, eps*10^3)
895s ***** test
895s  N = [0 1 0; 0 1 0; 0 0 1; 0 0 0];
895s  C = {(2:5), (2.5:1:4.5)};
895s  assert (nthargout ([1 2], @hist3, X, "Edges", {(1.5:4.5), (2:4)}), {N C})
895s ***** test
895s  N = [0 0 1 0 1 0; 0 0 0 1 0 0; 0 0 1 4 2 0];
895s  C = {(1.2:3.2), (0:5)};
895s  assert (nthargout ([1 2], @hist3, X, "Ctrs", C), {N C})
895s  assert (nthargout ([1 2], @hist3, X, C), {N C})
895s ***** test
895s  [~, C] = hist3 (rand (10, 2), "Edges", {[0 .05 .15 .35 .55 .95],
895s                                          [-1 .05 .07 .2 .3 .5 .89 1.2]});
895s  C_exp = {[ 0.025  0.1   0.25   0.45  0.75  1.15], ...
895s           [-0.475  0.06  0.135  0.25  0.4   0.695  1.045  1.355]};
895s  assert (C, C_exp, eps*10^2)
895s ***** test
895s  Xv = repmat ([1:10]', [1 2]);
895s 
895s  ## Test Centers
895s  assert (hist3 (Xv, "Ctrs", {1:10, 1:10}), eye (10))
895s 
895s  N_exp = eye (6);
895s  N_exp([1 end]) = 3;
895s  assert (hist3 (Xv, "Ctrs", {3:8, 3:8}), N_exp)
895s 
895s  N_exp = zeros (8, 6);
895s  N_exp([1 2 11 20 29 38 47 48]) = [2 1 1 1 1 1 1 2];
895s  assert (hist3 (Xv, "Ctrs", {2:9, 3:8}), N_exp)
895s 
895s  ## Test Edges
895s  assert (hist3 (Xv, "Edges", {1:10, 1:10}), eye (10))
895s  assert (hist3 (Xv, "Edges", {3:8, 3:8}), eye (6))
895s  assert (hist3 (Xv, "Edges", {2:9, 3:8}), [zeros(1, 6); eye(6); zeros(1, 6)])
895s 
895s  N_exp = zeros (14);
895s  N_exp(3:12, 3:12) = eye (10);
895s  assert (hist3 (Xv, "Edges", {-1:12, -1:12}), N_exp)
895s 
895s  ## Test for Nbins
895s  assert (hist3 (Xv), eye (10))
895s  assert (hist3 (Xv, [10 10]), eye (10))
895s  assert (hist3 (Xv, "nbins", [10 10]), eye (10))
895s  assert (hist3 (Xv, [5 5]), eye (5) * 2)
895s 
895s  N_exp = zeros (7, 5);
895s  N_exp([1 9 10 18 26 27 35]) = [2 1 1 2 1 1 2];
895s  assert (hist3 (Xv, [7 5]), N_exp)
895s ***** test # bug #51059
895s  D = [1 1; NaN 2; 3 1; 3 3; 1 NaN; 3 1];
895s  [c, nn] = hist3 (D, {0:4, 0:4});
895s  exp_c = zeros (5);
895s  exp_c([7 9 19]) = [1 2 1];
895s  assert (c, exp_c)
895s  assert (nn, {0:4, 0:4})
895s ***** test
895s  [c, nn] = hist3 ([1 8]);
895s  exp_c = zeros (10, 10);
895s  exp_c(6, 6) = 1;
895s  exp_nn = {-4:5, 3:12};
895s  assert (c, exp_c)
895s  assert (nn, exp_nn, eps)
895s 
895s  [c, nn] = hist3 ([1 8], [10 11]);
895s  exp_c = zeros (10, 11);
895s  exp_c(6, 6) = 1;
895s  exp_nn = {-4:5, 3:13};
895s  assert (c, exp_c)
895s  assert (nn, exp_nn, eps)
895s ***** test
895s  [c, nn] = hist3 ([1 NaN; 2 3; 6 9; 8 NaN]);
895s  exp_c = zeros (10, 10);
895s  exp_c(2, 1) = 1;
895s  exp_c(8, 10) = 1;
895s  exp_nn = {linspace(1.35, 7.65, 10) linspace(3.3, 8.7, 10)};
895s  assert (c, exp_c)
895s  assert (nn, exp_nn, eps*100)
895s ***** test
895s  [c, nn] = hist3 ([1 NaN; 2 NaN; 6 NaN; 8 NaN]);
895s  exp_c = zeros (10, 10);
895s  exp_nn = {linspace(1.35, 7.65, 10) NaN(1, 10)};
895s  assert (c, exp_c)
895s  assert (nn, exp_nn, eps*100)
895s ***** test
895s  [c, nn] = hist3 ([1 NaN; NaN 3; NaN 9; 8 NaN]);
895s  exp_c = zeros (10, 10);
895s  exp_nn = {linspace(1.35, 7.65, 10) linspace(3.3, 8.7, 10)};
895s  assert (c, exp_c)
895s  assert (nn, exp_nn, eps*100)
895s 16 tests, 16 passed, 0 known failure, 0 skipped
895s [inst/histfit.m]
895s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/histfit.m
895s ***** demo
895s  histfit (randn (100, 1))
895s ***** demo
895s  histfit (poissrnd (2, 1000, 1), 10, "Poisson")
895s ***** demo
895s  histfit (betarnd (3, 10, 1000, 1), 10, "beta")
895s ***** test
895s  hf = figure ("visible", "off");
895s  unwind_protect
895s    x = [2, 4, 3, 2, 4, 3, 2, 5, 6, 4, 7, 5, 9, 8, 10, 4, 11];
895s    histfit (x);
895s  unwind_protect_cleanup
895s    close (hf);
895s  end_unwind_protect
895s ***** test
895s  hf = figure ("visible", "off");
895s  unwind_protect
895s    x = [2, 4, 3, 2, NaN, 3, 2, 5, 6, 4, 7, 5, 9, 8, 10, 4, 11];
895s    histfit (x);
895s  unwind_protect_cleanup
895s    close (hf);
895s  end_unwind_protect
896s ***** test
896s  hf = figure ("visible", "off");
896s  unwind_protect
896s    x = [2, 4, 3, 2, NaN, 3, 2, 5, 6, 4, 7, 5, 9, 8, 10, 4, 11];
896s    histfit (x, 3);
896s  unwind_protect_cleanup
896s    close (hf);
896s  end_unwind_protect
896s ***** test
896s  hf = figure ("visible", "off");
896s  unwind_protect
896s    histfit (randn (100, 1));
896s  unwind_protect_cleanup
896s    close (hf);
896s  end_unwind_protect
896s ***** test
896s  hf = figure ("visible", "off");
896s  unwind_protect
896s    histfit (poissrnd (2, 1000, 1), 10, "Poisson");
896s  unwind_protect_cleanup
896s    close (hf);
896s  end_unwind_protect
896s ***** test
896s  hf = figure ("visible", "off");
896s  unwind_protect
896s    histfit (betarnd (3, 10, 1000, 1), 10, "beta");
896s  unwind_protect_cleanup
896s    close (hf);
896s  end_unwind_protect
896s ***** test
896s  hf = figure ("visible", "off");
896s  unwind_protect
896s    ax = gca ();
896s    histfit (ax, randn (100, 1));
896s  unwind_protect_cleanup
896s    close (hf);
896s  end_unwind_protect
896s ***** test
896s  hf = figure ("visible", "off");
896s  unwind_protect
896s    ax = gca ();
896s    histfit (ax, poissrnd (2, 1000, 1), 10, "Poisson");
896s  unwind_protect_cleanup
896s    close (hf);
896s  end_unwind_protect
897s ***** test
897s  hf = figure ("visible", "off");
897s  unwind_protect
897s    ax = gca ();
897s    histfit (ax, betarnd (3, 10, 1000, 1), 10, "beta");
897s  unwind_protect_cleanup
897s    close (hf);
897s  end_unwind_protect
897s ***** test
897s  hf = figure ("visible", "off");
897s  unwind_protect
897s    ax = axes ("parent", hf);
897s    fail ("histfit (ax)", "histfit: too few input arguments.");
897s  unwind_protect_cleanup
897s    close (hf);
897s  end_unwind_protect
897s ***** error<histfit: X must be a numeric vector of real numbers.> ...
897s  histfit ('wer')
897s ***** error<histfit: no data in X.> histfit ([NaN, NaN, NaN]);
897s ***** error<histfit: NBINS must be a real scalar integer value.> ...
897s  histfit (randn (100, 1), 5.6)
897s ***** error<histfit: DISTNAME must be a character vector.> ...
897s  histfit (randn (100, 1), 8, 5)
897s ***** error<histfit: DISTNAME must be a character vector.> ...
897s  histfit (randn (100, 1), 8, {'normal'})
897s ***** error<histfit: 'Kernel' distribution is not supported yet.> ...
897s  histfit (randn (100, 1), 8, 'Kernel')
897s ***** error<histfit: unrecognized distribution name.> ...
897s  histfit (randn (100, 1), 8, 'ASDASDASD')
897s 17 tests, 17 passed, 0 known failure, 0 skipped
897s [inst/hmmestimate.m]
897s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/hmmestimate.m
897s ***** test
897s  sequence = [1, 2, 1, 1, 1, 2, 2, 1, 2, 3, 3, ...
897s              3, 3, 2, 3, 1, 1, 1, 1, 3, 3, 2, 3, 1, 3];
897s  states =   [1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, ...
897s              1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1];
897s  [transprobest, outprobest] = hmmestimate (sequence, states);
897s  expectedtransprob = [0.88889, 0.11111; 0.28571, 0.71429];
897s  expectedoutprob = [0.16667, 0.33333, 0.50000; 1.00000, 0.00000, 0.00000];
897s  assert (transprobest, expectedtransprob, 0.001);
897s  assert (outprobest, expectedoutprob, 0.001);
897s ***** test
897s  sequence = {"A", "B", "A", "A", "A", "B", "B", "A", "B", "C", "C", "C", ...
897s              "C", "B", "C", "A", "A", "A", "A", "C", "C", "B", "C", "A", "C"};
897s  states = {"One", "One", "Two", "Two", "Two", "One", "One", "One", "One", ...
897s            "One", "One", "One", "One", "One", "One", "Two", "Two", "Two", ...
897s            "Two", "One", "One", "One", "One", "One", "One"};
897s  symbols = {"A", "B", "C"};
897s  statenames = {"One", "Two"};
897s  [transprobest, outprobest] = hmmestimate (sequence, states, "symbols", ...
897s                                            symbols, "statenames", statenames);
897s  expectedtransprob = [0.88889, 0.11111; 0.28571, 0.71429];
897s  expectedoutprob = [0.16667, 0.33333, 0.50000; 1.00000, 0.00000, 0.00000];
897s  assert (transprobest, expectedtransprob, 0.001);
897s  assert (outprobest, expectedoutprob, 0.001);
897s ***** test
897s  sequence = [1, 2, 1, 1, 1, 2, 2, 1, 2, 3, 3, 3, ...
897s              3, 2, 3, 1, 1, 1, 1, 3, 3, 2, 3, 1, 3];
897s  states =   [1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, ...
897s              1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1];
897s  pseudotransitions = [8, 2; 4, 6];
897s  pseudoemissions = [2, 4, 4; 7, 2, 1];
897s  [transprobest, outprobest] = hmmestimate (sequence, states, ...
897s   "pseudotransitions", pseudotransitions, "pseudoemissions", pseudoemissions);
897s  expectedtransprob = [0.85714, 0.14286; 0.35294, 0.64706];
897s  expectedoutprob = [0.178571, 0.357143, 0.464286; ...
897s                     0.823529, 0.117647, 0.058824];
897s  assert (transprobest, expectedtransprob, 0.001);
897s  assert (outprobest, expectedoutprob, 0.001);
897s 3 tests, 3 passed, 0 known failure, 0 skipped
897s [inst/hmmgenerate.m]
897s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/hmmgenerate.m
897s ***** test
897s  len = 25;
897s  transprob = [0.8, 0.2; 0.4, 0.6];
897s  outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
897s  [sequence, states] = hmmgenerate (len, transprob, outprob);
897s  assert (length (sequence), len);
897s  assert (length (states), len);
897s  assert (min (sequence) >= 1);
897s  assert (max (sequence) <= columns (outprob));
897s  assert (min (states) >= 1);
897s  assert (max (states) <= rows (transprob));
897s ***** test
897s  len = 25;
897s  transprob = [0.8, 0.2; 0.4, 0.6];
897s  outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
897s  symbols = {"A", "B", "C"};
897s  statenames = {"One", "Two"};
897s  [sequence, states] = hmmgenerate (len, transprob, outprob, ...
897s                       "symbols", symbols, "statenames", statenames);
897s  assert (length (sequence), len);
897s  assert (length (states), len);
897s  assert (strcmp (sequence, "A") + strcmp (sequence, "B") + ...
897s                                   strcmp (sequence, "C") == ones (1, len));
897s  assert (strcmp (states, "One") + strcmp (states, "Two") == ones (1, len));
897s 2 tests, 2 passed, 0 known failure, 0 skipped
897s [inst/hmmviterbi.m]
897s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/hmmviterbi.m
897s ***** test
897s  sequence = [1, 2, 1, 1, 1, 2, 2, 1, 2, 3, 3, 3, ...
897s              3, 2, 3, 1, 1, 1, 1, 3, 3, 2, 3, 1, 3];
897s  transprob = [0.8, 0.2; 0.4, 0.6];
897s  outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
897s  vpath = hmmviterbi (sequence, transprob, outprob);
897s  expected = [1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, ...
897s              1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1];
897s  assert (vpath, expected);
897s ***** test
897s  sequence = {"A", "B", "A", "A", "A", "B", "B", "A", "B", "C", "C", "C", ...
897s              "C", "B", "C", "A", "A", "A", "A", "C", "C", "B", "C", "A", "C"};
897s  transprob = [0.8, 0.2; 0.4, 0.6];
897s  outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
897s  symbols = {"A", "B", "C"};
897s  statenames = {"One", "Two"};
897s  vpath = hmmviterbi (sequence, transprob, outprob, "symbols", symbols, ...
897s                                                    "statenames", statenames);
897s  expected = {"One", "One", "Two", "Two", "Two", "One", "One", "One", ...
897s              "One", "One", "One", "One", "One", "One", "One", "Two", ...
897s              "Two", "Two", "Two", "One", "One", "One", "One", "One", "One"};
897s  assert (vpath, expected);
897s 2 tests, 2 passed, 0 known failure, 0 skipped
897s [inst/hotelling_t2test.m]
897s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/hotelling_t2test.m
897s ***** error<Invalid call to hotelling_t2test.  Correct usage> hotelling_t2test ();
897s ***** error<hotelling_t2test: X must be a vector or a 2D matrix.> ...
897s  hotelling_t2test (1);
897s ***** error<hotelling_t2test: X must be a vector or a 2D matrix.> ...
897s  hotelling_t2test (ones(2,2,2));
897s ***** error<hotelling_t2test: invalid value for alpha.> ...
897s  hotelling_t2test (ones(20,2), [0, 0], "alpha", 1);
897s ***** error<hotelling_t2test: invalid value for alpha.> ...
897s  hotelling_t2test (ones(20,2), [0, 0], "alpha", -0.2);
897s ***** error<hotelling_t2test: invalid value for alpha.> ...
897s  hotelling_t2test (ones(20,2), [0, 0], "alpha", "a");
897s ***** error<hotelling_t2test: invalid value for alpha.> ...
897s  hotelling_t2test (ones(20,2), [0, 0], "alpha", [0.01, 0.05]);
897s ***** error<hotelling_t2test: invalid Name argument.> ...
897s  hotelling_t2test (ones(20,2), [0, 0], "name", 0.01);
897s ***** error<hotelling_t2test: if X is a vector, M must be a scalar.> ...
897s  hotelling_t2test (ones(20,1), [0, 0]);
897s ***** error<hotelling_t2test: X must have more rows than columns.> ...
897s  hotelling_t2test (ones(4,5), [0, 0, 0, 0, 0]);
897s ***** error<hotelling_t2test: if X is a matrix, M must be a vector of length> ...
897s  hotelling_t2test (ones(20,5), [0, 0, 0, 0]);
897s ***** test
897s  randn ("seed", 1);
897s  x = randn (50000, 5);
897s  [h, pval, stats] = hotelling_t2test (x);
897s  assert (h, 0);
897s  assert (stats.df1, 5);
897s  assert (stats.df2, 49995);
897s ***** test
897s  randn ("seed", 1);
897s  x = randn (50000, 5);
897s  [h, pval, stats] = hotelling_t2test (x, ones (1, 5) * 10);
897s  assert (h, 1);
897s  assert (stats.df1, 5);
897s  assert (stats.df2, 49995);
897s 13 tests, 13 passed, 0 known failure, 0 skipped
897s [inst/hotelling_t2test2.m]
897s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/hotelling_t2test2.m
897s ***** error<Invalid call to hotelling_t2test2.  Correct usage> hotelling_t2test2 ();
897s ***** error<Invalid call to hotelling_t2test2.  Correct usage> ...
897s  hotelling_t2test2 ([2, 3, 4, 5, 6]);
897s ***** error<hotelling_t2test2: X must be a vector or a 2D matrix.> ...
897s  hotelling_t2test2 (1, [2, 3, 4, 5, 6]);
897s ***** error<hotelling_t2test2: X must be a vector or a 2D matrix.> ...
897s  hotelling_t2test2 (ones (2,2,2), [2, 3, 4, 5, 6]);
897s ***** error<hotelling_t2test2: Y must be a vector or a 2D matrix.> ...
897s  hotelling_t2test2 ([2, 3, 4, 5, 6], 2);
897s ***** error<hotelling_t2test2: Y must be a vector or a 2D matrix.> ...
897s  hotelling_t2test2 ([2, 3, 4, 5, 6], ones (2,2,2));
897s ***** error<hotelling_t2test2: invalid value for alpha.> ...
897s  hotelling_t2test2 (ones (20,2), ones (20,2), "alpha", 1);
897s ***** error<hotelling_t2test2: invalid value for alpha.> ...
897s  hotelling_t2test2 (ones (20,2), ones (20,2), "alpha", -0.2);
897s ***** error<hotelling_t2test2: invalid value for alpha.> ...
897s  hotelling_t2test2 (ones (20,2), ones (20,2), "alpha", "a");
897s ***** error<hotelling_t2test2: invalid value for alpha.> ...
897s  hotelling_t2test2 (ones (20,2), ones (20,2), "alpha", [0.01, 0.05]);
897s ***** error<hotelling_t2test2: invalid Name argument.> ...
897s  hotelling_t2test2 (ones (20,2), ones (20,2), "name", 0.01);
897s ***** error<hotelling_t2test2: if X is a vector, Y must also be a vector.> ...
897s  hotelling_t2test2 (ones (20,1), ones (20,2));
897s ***** error<hotelling_t2test2: X and Y must have the same number of columns.> ...
897s  hotelling_t2test2 (ones (20,2), ones (25,3));
897s ***** test
897s  randn ("seed", 1);
897s  x1 = randn (60000, 5);
897s  randn ("seed", 5);
897s  x2 = randn (30000, 5);
897s  [h, pval, stats] = hotelling_t2test2 (x1, x2);
897s  assert (h, 0);
897s  assert (stats.df1, 5);
897s  assert (stats.df2, 89994);
898s 14 tests, 14 passed, 0 known failure, 0 skipped
898s [inst/inconsistent.m]
898s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/inconsistent.m
898s ***** error inconsistent ()
898s ***** error inconsistent ([1 2 1], 2, 3)
898s ***** error <Z must be .* generated by the linkage .*> inconsistent (ones (2, 2))
898s ***** error <d must be a positive integer scalar> inconsistent ([1 2 1], -1)
898s ***** error <d must be a positive integer scalar> inconsistent ([1 2 1], 1.3)
898s ***** error <d must be a positive integer scalar> inconsistent ([1 2 1], [1 1])
898s ***** error <Z must be .* generated by the linkage .*> inconsistent (ones (2, 3))
898s ***** test
898s  load fisheriris;
898s  Z = linkage(meas, 'average', 'chebychev');
898s  assert (cond (inconsistent (Z)), 39.9, 1e-3);
898s 8 tests, 8 passed, 0 known failure, 0 skipped
898s [inst/ismissing.m]
898s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/ismissing.m
898s ***** assert (ismissing ([1,NaN,3]), [false,true,false])
898s ***** assert (ismissing ('abcd f'), [false,false,false,false,true,false])
898s ***** assert (ismissing ({'xxx','','xyz'}), [false,true,false])
898s ***** assert (ismissing ({'x','','y'}), [false,true,false])
898s ***** assert (ismissing ({'x','','y';'z','a',''}), logical([0,1,0;0,0,1]))
898s ***** assert (ismissing ([1,2;NaN,2]), [false,false;true,false])
898s ***** assert (ismissing ([1,2;NaN,2], 2), [false,true;false,true])
898s ***** assert (ismissing ([1,2;NaN,2], [1 2]), [true,true;false,true])
898s ***** assert (ismissing ([1,2;NaN,2], NaN), [false,false;true,false])
898s ***** assert (ismissing (cat(3,magic(2),magic(2))), logical (zeros (2,2,2)))
898s ***** assert (ismissing (cat(3,magic(2),[1 2;3 NaN])), logical (cat(3,[0,0;0,0],[0,0;0,1])))
898s ***** assert (ismissing ([1 2; 3 4], [5 1; 2 0]), logical([1 1; 0 0]))
898s ***** assert (ismissing (cat(3,'f oo','ba r')), logical(cat(3,[0 1 0 0],[0 0 1 0])))
898s ***** assert (ismissing (cat(3,{'foo'},{''},{'bar'})), logical(cat(3,0,1,0)))
898s ***** assert (ismissing (double (NaN)), true)
898s ***** assert (ismissing (single (NaN)), true)
898s ***** assert (ismissing (' '), true)
898s ***** assert (ismissing ({''}), true)
898s ***** assert (ismissing ({' '}), false)
898s ***** assert (ismissing (double (eye(3)), single (1)), logical(eye(3)))
898s ***** assert (ismissing (double (eye(3)), true), logical(eye(3)))
898s ***** assert (ismissing (double (eye(3)), int32 (1)), logical(eye(3)))
898s ***** assert (ismissing (single (eye(3)), true), logical(eye(3)))
898s ***** assert (ismissing (single (eye(3)), double (1)), logical(eye(3)))
898s ***** assert (ismissing (single(eye(3)), int32 (1)), logical(eye(3)))
898s ***** assert (ismissing ({'123', '', 123}), [false false false])
898s ***** assert (ismissing (logical ([1 0 1])), [false false false])
898s ***** assert (ismissing (int32 ([1 2 3])), [false false false])
898s ***** assert (ismissing (uint32 ([1 2 3])), [false false false])
898s ***** assert (ismissing ({1, 2, 3}), [false false false])
898s ***** assert (ismissing ([struct struct struct]), [false false false])
898s ***** assert (ismissing (logical (eye(3)), true), logical(eye(3)))
898s ***** assert (ismissing (logical (eye(3)), double (1)), logical(eye(3)))
898s ***** assert (ismissing (logical (eye(3)), single (1)), logical(eye(3)))
898s ***** assert (ismissing (logical (eye(3)), int32 (1)), logical(eye(3)))
898s ***** assert (ismissing (int32 (eye(3)), int32 (1)), logical(eye(3)))
898s ***** assert (ismissing (int32 (eye(3)), true), logical(eye(3)))
898s ***** assert (ismissing (int32 (eye(3)), double (1)), logical(eye(3)))
898s ***** assert (ismissing (int32 (eye(3)), single (1)), logical(eye(3)))
898s ***** assert (ismissing ([]), logical([]))
898s ***** assert (ismissing (''), logical([]))
898s ***** assert (ismissing (ones (0,1)), logical(ones(0,1)))
898s ***** assert (ismissing (ones (1,0)), logical(ones(1,0)))
898s ***** assert (ismissing (ones (1,2,0)), logical(ones(1,2,0)))
898s ***** error ismissing ()
898s ***** error <'indicator' and 'A' must have the same> ismissing ([1 2; 3 4], "abc")
898s ***** error <'indicator' and 'A' must have the same> ismissing ({"", "", ""}, 1)
898s ***** error <'indicator' and 'A' must have the same> ismissing (1, struct)
898s ***** error <indicators not supported for data type> ismissing (struct, 1)
898s 49 tests, 49 passed, 0 known failure, 0 skipped
898s [inst/isoutlier.m]
898s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/isoutlier.m
898s ***** demo
898s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
898s  TF = isoutlier (A, "mean")
898s ***** demo
898s  ## Use a moving detection method to detect local outliers in a sine wave
898s 
898s  x = -2*pi:0.1:2*pi;
898s  A = sin(x);
898s  A(47) = 0;
898s  time = datenum (2023,1,1,0,0,0) + (1/24)*[0:length(x)-1] - 730485;
898s  TF = isoutlier (A, "movmedian", 5*(1/24), "SamplePoints", time);
898s  plot (time, A)
898s  hold on
898s  plot (time(TF), A(TF), "x")
898s  datetick ('x', 20, 'keepticks')
898s  legend ("Original Data", "Outlier Data")
898s ***** demo
898s  ## Locate an outlier in a vector of data and visualize the outlier
898s 
898s  x = 1:10;
898s  A = [60 59 49 49 58 100 61 57 48 58];
898s  [TF, L, U, C] = isoutlier (A);
898s  plot (x, A);
898s  hold on
898s  plot (x(TF), A(TF), "x");
898s  xlim ([1,10]);
898s  line ([1,10], [L, L], "Linestyle", ":");
898s  text (1.1, L-2, "Lower Threshold");
898s  line ([1,10], [U, U], "Linestyle", ":");
898s  text (1.1, U-2, "Upper Threshold");
898s  line ([1,10], [C, C], "Linestyle", ":");
898s  text (1.1, C-3, "Center Value");
898s  legend ("Original Data", "Outlier Data");
898s ***** test
898s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
898s  assert (isoutlier (A, "mean"), logical([zeros(1,8) 1 zeros(1,6)]))
898s  assert (isoutlier (A, "median"), ...
898s  logical([zeros(1,3) 1 zeros(1,4) 1 zeros(1,6)]))
898s ***** test
898s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
898s  [TF, L, U, C] = isoutlier (A, "mean");
898s  assert (L, -109.2459044922864, 1e-12)
898s  assert (U, 264.9792378256198, 1e-12)
898s  assert (C, 77.8666666666666, 1e-12)
898s ***** test
898s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
898s  [TF, L, U, C] = isoutlier (A, "median");
898s  assert (L, 50.104386688966386, 1e-12)
898s  assert (U, 67.895613311033610, 1e-12)
898s  assert (C, 59)
898s ***** test
898s  A = magic(5) + diag(200*ones(1,5));
898s  T = logical (eye (5));
898s  assert (isoutlier (A, 2), T)
898s ***** test
898s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
898s  [TF, L, U, C] = isoutlier (A, "movmedian", 5);
898s  l = [54.5522, 52.8283, 54.5522, 54.5522, 54.5522, 53.5522, 53.5522, ...
898s       53.5522, 47.6566, 56.5522, 57.5522, 56.5522, 51.1044, 52.3283, 53.5522];
898s  u = [63.4478, 66.1717, 63.4478, 63.4478, 63.4478, 62.4478, 62.4478, ...
898s       62.4478, 74.3434, 65.4478, 66.4478, 65.4478, 68.8956, 65.6717, 62.4478];
898s  c = [59, 59.5, 59, 59, 59, 58, 58, 58, 61, 61, 62, 61, 60, 59, 58];
898s  assert (L, l, 1e-4)
898s  assert (U, u, 1e-4)
898s  assert (C, c)
898s ***** test
898s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
898s  [TF, L, U, C] = isoutlier (A, "movmedian", 5, "SamplePoints", [1:15]);
898s  l = [54.5522, 52.8283, 54.5522, 54.5522, 54.5522, 53.5522, 53.5522, ...
898s       53.5522, 47.6566, 56.5522, 57.5522, 56.5522, 51.1044, 52.3283, 53.5522];
898s  u = [63.4478, 66.1717, 63.4478, 63.4478, 63.4478, 62.4478, 62.4478, ...
898s       62.4478, 74.3434, 65.4478, 66.4478, 65.4478, 68.8956, 65.6717, 62.4478];
898s  c = [59, 59.5, 59, 59, 59, 58, 58, 58, 61, 61, 62, 61, 60, 59, 58];
898s  assert (L, l, 1e-4)
898s  assert (U, u, 1e-4)
898s  assert (C, c)
898s ***** test
898s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
898s  [TF, L, U, C] = isoutlier (A, "movmean", 5);
898s  l = [54.0841,  6.8872, 11.5608, 12.1518, 11.0210, 10.0112, -218.2840, ...
898s       -217.2375, -215.1239, -213.4890, -211.3264, 55.5800, 52.9589, ...
898s       52.5979, 51.0627];
898s  u = [63.2492, 131.1128, 122.4392, 122.2482, 122.5790, 122.7888, 431.0840, ...
898s       430.8375, 430.3239, 429.8890, 429.3264, 65.6200, 66.6411, 65.9021, ...
898s       66.9373];
898s  c = [58.6667, 69, 67, 67.2, 66.8, 66.4, 106.4, 106.8, 107.6, 108.2, 109, ...
898s       60.6, 59.8, 59.25, 59];
898s  assert (L, l, 1e-4)
898s  assert (U, u, 1e-4)
898s  assert (C, c, 1e-4)
898s ***** test
898s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
898s  [TF, L, U, C] = isoutlier (A, "movmean", 5, "SamplePoints", [1:15]);
898s  l = [54.0841,  6.8872, 11.5608, 12.1518, 11.0210, 10.0112, -218.2840, ...
898s       -217.2375, -215.1239, -213.4890, -211.3264, 55.5800, 52.9589, ...
898s       52.5979, 51.0627];
898s  u = [63.2492, 131.1128, 122.4392, 122.2482, 122.5790, 122.7888, 431.0840, ...
898s       430.8375, 430.3239, 429.8890, 429.3264, 65.6200, 66.6411, 65.9021, ...
898s       66.9373];
898s  c = [58.6667, 69, 67, 67.2, 66.8, 66.4, 106.4, 106.8, 107.6, 108.2, 109, ...
898s       60.6, 59.8, 59.25, 59];
898s  assert (L, l, 1e-4)
898s  assert (U, u, 1e-4)
898s  assert (C, c, 1e-4)
898s ***** test
898s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
898s  [TF, L, U, C] = isoutlier (A, "gesd");
898s  assert (TF, logical ([0 0 0 1 0 0 0 0 1 0 0 0 0 0 0]))
898s  assert (L, 34.235977035439944, 1e-12)
898s  assert (U, 89.764022964560060, 1e-12)
898s  assert (C, 62)
898s ***** test
898s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
898s  [TF, L, U, C] = isoutlier (A, "gesd", "ThresholdFactor", 0.01);
898s  assert (TF, logical ([0 0 0 1 0 0 0 0 1 0 0 0 0 0 0]))
898s  assert (L, 31.489256770616173, 1e-12)
898s  assert (U, 92.510743229383820, 1e-12)
898s  assert (C, 62)
898s ***** test
898s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
898s  [TF, L, U, C] = isoutlier (A, "gesd", "ThresholdFactor", 5e-10);
898s  assert (TF, logical ([0 0 0 0 0 0 0 0 1 0 0 0 0 0 0]))
898s  assert (L, 23.976664158788935, 1e-12)
898s  assert (U, 100.02333584121110, 1e-12)
898s  assert (C, 62)
898s ***** test
898s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
898s  [TF, L, U, C] = isoutlier (A, "grubbs");
898s  assert (TF, logical ([0 0 0 1 0 0 0 0 1 0 0 0 0 0 0]))
898s  assert (L, 54.642809574646606, 1e-12)
898s  assert (U, 63.511036579199555, 1e-12)
898s  assert (C, 59.076923076923080, 1e-12)
898s ***** test
898s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
898s  [TF, L, U, C] = isoutlier (A, "grubbs", "ThresholdFactor", 0.01);
898s  assert (TF, logical ([0 0 0 1 0 0 0 0 1 0 0 0 0 0 0]))
898s  assert (L, 54.216083184201850, 1e-12)
898s  assert (U, 63.937762969644310, 1e-12)
898s  assert (C, 59.076923076923080, 1e-12)
899s ***** test
899s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
899s  [TF, L, U, C] = isoutlier (A,  "percentiles", [10 90]);
899s  assert (TF, logical ([0 0 0 0 0 0 0 0 1 0 0 0 0 0 0]))
899s  assert (L, 57)
899s  assert (U, 100)
899s  assert (C, 78.5)
899s ***** test
899s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
899s  [TF, L, U, C] = isoutlier (A,  "percentiles", [20 80]);
899s  assert (TF, logical ([1 0 0 1 0 0 1 0 1 0 0 0 0 0 1]))
899s  assert (L, 57.5)
899s  assert (U, 62)
899s  assert (C, 59.75)
899s ***** shared A
899s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
899s ***** error<isoutlier: WINDOW must be a positive scalar> ...
899s  isoutlier (A, "movmedian", 0);
899s ***** error<isoutlier: WINDOW must be a positive scalar> ...
899s  isoutlier (A, "movmedian", []);
899s ***** error<isoutlier: WINDOW must be a positive scalar> ...
899s  isoutlier (A, "movmedian", [2 3 4]);
899s ***** error<isoutlier: WINDOW must be a positive integer> ...
899s  isoutlier (A, "movmedian", 1.4);
899s ***** error<isoutlier: WINDOW must be a positive scalar> ...
899s  isoutlier (A, "movmedian", [0 1]);
899s ***** error<isoutlier: WINDOW must be a positive scalar> ...
899s  isoutlier (A, "movmedian", [2 -1]);
899s ***** error<isoutlier: WINDOW must be a positive scalar> ...
899s  isoutlier (A, "movmedian", {2 3});
899s ***** error<isoutlier: WINDOW must be a positive scalar> ...
899s  isoutlier (A, "movmedian", "char");
899s 
899s ***** error<isoutlier: WINDOW must be a positive scalar> ...
899s  isoutlier (A, "movmean", 0);
899s ***** error<isoutlier: WINDOW must be a positive scalar> ...
899s  isoutlier (A, "movmean", []);
899s ***** error<isoutlier: WINDOW must be a positive scalar> ...
899s  isoutlier (A, "movmean", [2 3 4]);
899s ***** error<isoutlier: WINDOW must be a positive integer> ...
899s  isoutlier (A, "movmean", 1.4);
899s ***** error<isoutlier: WINDOW must be a positive scalar> ...
899s  isoutlier (A, "movmean", [0 1]);
899s ***** error<isoutlier: WINDOW must be a positive scalar> ...
899s  isoutlier (A, "movmean", [2 -1]);
899s ***** error<isoutlier: WINDOW must be a positive scalar> ...
899s  isoutlier (A, "movmean", {2 3});
899s ***** error<isoutlier: WINDOW must be a positive scalar> ...
899s  isoutlier (A, "movmean", "char");
899s 
899s ***** error<isoutlier: THRESHOLD must be a two-element> ...
899s  isoutlier (A, "percentiles", [-1 90]);
899s ***** error<isoutlier: THRESHOLD must be a two-element> ...
899s  isoutlier (A, "percentiles", [10 -90]);
899s ***** error<isoutlier: THRESHOLD must be a two-element> ...
899s  isoutlier (A, "percentiles", [90]);
899s ***** error<isoutlier: THRESHOLD must be a two-element> ...
899s  isoutlier (A, "percentiles", [90 20]);
899s ***** error<isoutlier: THRESHOLD must be a two-element> ...
899s  isoutlier (A, "percentiles", [90 20]);
899s ***** error<isoutlier: THRESHOLD must be a two-element> ...
899s  isoutlier (A, "percentiles", [10 20 90]);
899s ***** error<isoutlier: THRESHOLD must be a two-element> ...
899s  isoutlier (A, "percentiles", {10 90});
899s ***** error<isoutlier: THRESHOLD must be a two-element> ...
899s  isoutlier (A, "percentiles", "char");
899s 
899s ***** error<isoutlier: sample points must be a vector.> ...
899s  isoutlier (A, "movmean", 5, "SamplePoints", ones(3,15));
899s ***** error<isoutlier: sample points must be a vector.> ...
899s  isoutlier (A, "movmean", 5, "SamplePoints", 15);
899s ***** error<isoutlier: sample points must be unique.> ...
899s  isoutlier (A, "movmean", 5, "SamplePoints", [1,1:14]);
899s ***** error<isoutlier: sample points must be sorted.> ...
899s  isoutlier (A, "movmean", 5, "SamplePoints", [2,1,3:15]);
899s ***** error<isoutlier: sample points must have the same size> ...
899s  isoutlier (A, "movmean", 5, "SamplePoints", [1:14]);
899s 
899s ***** error<isoutlier: threshold factor must be a nonnegative scalar.> ...
899s  isoutlier (A, "movmean", 5, "ThresholdFactor", [1:14]);
899s ***** error<isoutlier: threshold factor must be a nonnegative scalar.> ...
899s  isoutlier (A, "movmean", 5, "ThresholdFactor", -1);
899s ***** error<isoutlier: threshold factor must must be in> ...
899s  isoutlier (A, "gesd", "ThresholdFactor", 3);
899s ***** error<isoutlier: threshold factor must must be in> ...
899s  isoutlier (A, "grubbs", "ThresholdFactor", 3);
899s 
899s ***** error<isoutlier: maximum outlier count must be a positive integer scalar.> ...
899s  isoutlier (A, "movmean", 5, "MaxNumOutliers", [1:14]);
899s ***** error<isoutlier: maximum outlier count must be a positive integer scalar.> ...
899s  isoutlier (A, "movmean", 5, "MaxNumOutliers", -1);
899s ***** error<isoutlier: maximum outlier count must be a positive integer scalar.> ...
899s  isoutlier (A, "movmean", 5, "MaxNumOutliers", 0);
899s ***** error<isoutlier: maximum outlier count must be a positive integer scalar.> ...
899s  isoutlier (A, "movmean", 5, "MaxNumOutliers", 1.5);
899s 
899s ***** error<isoutlier: invalid input argument.> ...
899s  isoutlier (A, {"movmean"}, 5, "SamplePoints", [1:15]);
899s ***** error<isoutlier: invalid input argument.> isoutlier (A, {1});
899s ***** error<isoutlier: invalid input argument.> isoutlier (A, true);
899s ***** error<isoutlier: invalid input argument.> isoutlier (A, false);
899s ***** error<isoutlier: DIM must be a positive integer scalar.> isoutlier (A, 0);
899s ***** error<isoutlier: DIM must be a positive integer scalar.> isoutlier (A, [1 2]);
899s ***** error<isoutlier: DIM must be a positive integer scalar.> isoutlier (A, -2);
899s 59 tests, 59 passed, 0 known failure, 0 skipped
899s [inst/jackknife.m]
899s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/jackknife.m
899s ***** demo
899s  for k = 1:1000
899s    rand ("seed", k);  # for reproducibility
899s    x = rand (10, 1);
899s    s(k) = std (x);
899s    jackstat = jackknife (@std, x);
899s    j(k) = 10 * std (x) - 9 * mean (jackstat);
899s  endfor
899s  figure();
899s  hist ([s', j'], 0:sqrt(1/12)/10:2*sqrt(1/12))
899s ***** demo
899s  for k = 1:1000
899s    randn ("seed", k); # for reproducibility
899s    x = randn (1, 50);
899s    rand ("seed", k);  # for reproducibility
899s    y = rand (1, 50);
899s    jackstat = jackknife (@(x) std(x{1})/std(x{2}), y, x);
899s    j(k) = 50 * std (y) / std (x) - 49 * mean (jackstat);
899s    v(k) = sumsq ((50 * std (y) / std (x) - 49 * jackstat) - j(k)) / (50 * 49);
899s  endfor
899s  t = (j - sqrt (1 / 12)) ./ sqrt (v);
899s  figure();
899s  plot (sort (tcdf (t, 49)), ...
899s        "-;Almost linear mapping indicates good fit with t-distribution.;")
899s ***** test
899s  ##Example from Quenouille, Table 1
899s  d=[0.18 4.00 1.04 0.85 2.14 1.01 3.01 2.33 1.57 2.19];
899s  jackstat = jackknife ( @(x) 1/mean(x), d );
899s  assert ( 10 / mean(d) - 9 * mean(jackstat), 0.5240, 1e-5 );
899s 1 test, 1 passed, 0 known failure, 0 skipped
899s [inst/kmeans.m]
899s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/kmeans.m
899s ***** demo
899s  ## Generate a two-cluster problem
899s  randn ("seed", 31)  # for reproducibility
899s  C1 = randn (100, 2) + 1;
899s  randn ("seed", 32)  # for reproducibility
899s  C2 = randn (100, 2) - 1;
899s  data = [C1; C2];
899s 
899s  ## Perform clustering
899s  rand ("seed", 1)  # for reproducibility
899s  [idx, centers] = kmeans (data, 2);
899s 
899s  ## Plot the result
899s  figure;
899s  plot (data (idx==1, 1), data (idx==1, 2), "ro");
899s  hold on;
899s  plot (data (idx==2, 1), data (idx==2, 2), "bs");
899s  plot (centers (:, 1), centers (:, 2), "kv", "markersize", 10);
899s  hold off;
899s ***** demo
899s  ## Cluster data using k-means clustering, then plot the cluster regions
899s  ## Load Fisher's iris data set and use the petal lengths and widths as
899s  ## predictors
899s 
899s  load fisheriris
899s  X = meas(:,3:4);
899s 
899s  figure;
899s  plot (X(:,1), X(:,2), "k*", "MarkerSize", 5);
899s  title ("Fisher's Iris Data");
899s  xlabel ("Petal Lengths (cm)");
899s  ylabel ("Petal Widths (cm)");
899s 
899s  ## Cluster the data. Specify k = 3 clusters
899s  rand ("seed", 1)  # for reproducibility
899s  [idx, C] = kmeans (X, 3);
899s  x1 = min (X(:,1)):0.01:max (X(:,1));
899s  x2 = min (X(:,2)):0.01:max (X(:,2));
899s  [x1G, x2G] = meshgrid (x1, x2);
899s  XGrid = [x1G(:), x2G(:)];
899s 
899s  idx2Region = kmeans (XGrid, 3, "MaxIter", 1, "Start", C);
899s  figure;
899s  gscatter (XGrid(:,1), XGrid(:,2), idx2Region, ...
899s            [0, 0.75, 0.75; 0.75, 0, 0.75; 0.75, 0.75, 0], "..");
899s  hold on;
899s  plot (X(:,1), X(:,2), "k*", "MarkerSize", 5);
899s  title ("Fisher's Iris Data");
899s  xlabel ("Petal Lengths (cm)");
899s  ylabel ("Petal Widths (cm)");
899s  legend ("Region 1", "Region 2", "Region 3", "Data", "Location", "SouthEast");
899s  hold off
899s ***** demo
899s  ## Partition Data into Two Clusters
899s 
899s  randn ("seed", 1)  # for reproducibility
899s  r1 = randn (100, 2) * 0.75 + ones (100, 2);
899s  randn ("seed", 2)  # for reproducibility
899s  r2 = randn (100, 2) * 0.5 - ones (100, 2);
899s  X = [r1; r2];
899s 
899s  figure;
899s  plot (X(:,1), X(:,2), ".");
899s  title ("Randomly Generated Data");
899s  rand ("seed", 1)  # for reproducibility
899s  [idx, C] = kmeans (X, 2, "Distance", "cityblock", ...
899s                           "Replicates", 5, "Display", "final");
899s  figure;
899s  plot (X(idx==1,1), X(idx==1,2), "r.", "MarkerSize", 12);
899s  hold on
899s  plot(X(idx==2,1), X(idx==2,2), "b.", "MarkerSize", 12);
899s  plot (C(:,1), C(:,2), "kx", "MarkerSize", 15, "LineWidth", 3);
899s  legend ("Cluster 1", "Cluster 2", "Centroids", "Location", "NorthWest");
899s  title ("Cluster Assignments and Centroids");
899s  hold off
899s ***** demo
899s  ## Assign New Data to Existing Clusters
899s 
899s  ## Generate a training data set using three distributions
899s  randn ("seed", 5)  # for reproducibility
899s  r1 = randn (100, 2) * 0.75 + ones (100, 2);
899s  randn ("seed", 7)  # for reproducibility
899s  r2 = randn (100, 2) * 0.5 - ones (100, 2);
899s  randn ("seed", 9)  # for reproducibility
899s  r3 = randn (100, 2) * 0.75;
899s  X = [r1; r2; r3];
899s 
899s  ## Partition the training data into three clusters by using kmeans
899s 
899s  rand ("seed", 1)  # for reproducibility
899s  [idx, C] = kmeans (X, 3);
899s 
899s  ## Plot the clusters and the cluster centroids
899s 
899s  figure
899s  gscatter (X(:,1), X(:,2), idx, "bgm", "***");
899s  hold on
899s  plot (C(:,1), C(:,2), "kx");
899s  legend ("Cluster 1", "Cluster 2", "Cluster 3", "Cluster Centroid")
899s 
899s  ## Generate a test data set
899s  randn ("seed", 25)  # for reproducibility
899s  r1 = randn (100, 2) * 0.75 + ones (100, 2);
899s  randn ("seed", 27)  # for reproducibility
899s  r2 = randn (100, 2) * 0.5 - ones (100, 2);
899s  randn ("seed", 29)  # for reproducibility
899s  r3 = randn (100, 2) * 0.75;
899s  Xtest = [r1; r2; r3];
899s 
899s  ## Classify the test data set using the existing clusters
899s  ## Find the nearest centroid from each test data point by using pdist2
899s 
899s  D = pdist2 (C, Xtest, "euclidean");
899s  [group, ~] = find (D == min (D));
899s 
899s  ## Plot the test data and label the test data using idx_test with gscatter
899s 
899s  gscatter (Xtest(:,1), Xtest(:,2), group, "bgm", "ooo");
899s  legend ("Cluster 1", "Cluster 2", "Cluster 3", "Cluster Centroid", ...
899s          "Data classified to Cluster 1", "Data classified to Cluster 2", ...
899s          "Data classified to Cluster 3", "Location", "NorthWest");
899s  title ("Assign New Data to Existing Clusters");
899s ***** test
899s  samples = 4;
899s  dims = 3;
899s  k = 2;
899s  [cls, c, d, z] = kmeans (rand (samples,dims), k, "start", rand (k,dims, 5),
899s                           "emptyAction", "singleton");
899s  assert (size (cls), [samples, 1]);
899s  assert (size (c), [k, dims]);
899s  assert (size (d), [k, 1]);
899s  assert (size (z), [samples, k]);
899s ***** test
899s  samples = 4;
899s  dims = 3;
899s  k = 2;
899s  [cls, c, d, z] = kmeans (rand (samples,dims), [], "start", rand (k,dims, 5),
899s                           "emptyAction", "singleton");
899s  assert (size (cls), [samples, 1]);
899s  assert (size (c), [k, dims]);
899s  assert (size (d), [k, 1]);
899s  assert (size (z), [samples, k]);
899s ***** test
899s  [cls, c] = kmeans ([1 0; 2 0], 2, "start", [8,0;0,8], "emptyaction", "drop");
899s  assert (cls, [1; 1]);
899s  assert (c, [1.5, 0; NA, NA]);
899s ***** test
899s  kmeans (rand (4,3), 2, "start", rand (2,3, 5), "replicates", 5,
899s          "emptyAction", "singleton");
899s ***** test
899s  kmeans (rand (3,4), 2, "start", "sample", "emptyAction", "singleton");
899s ***** test
899s  kmeans (rand (3,4), 2, "start", "plus", "emptyAction", "singleton");
899s ***** test
899s  kmeans (rand (3,4), 2, "start", "cluster", "emptyAction", "singleton");
899s ***** test
899s  kmeans (rand (3,4), 2, "start", "uniform", "emptyAction", "singleton");
899s ***** test
899s  kmeans (rand (4,3), 2, "distance", "sqeuclidean", "emptyAction", "singleton");
899s ***** test
899s  kmeans (rand (4,3), 2, "distance", "cityblock", "emptyAction", "singleton");
899s ***** test
899s  kmeans (rand (4,3), 2, "distance", "cosine", "emptyAction", "singleton");
899s ***** test
899s  kmeans (rand (4,3), 2, "distance", "correlation", "emptyAction", "singleton");
899s ***** test
899s  kmeans (rand (4,3), 2, "distance", "hamming", "emptyAction", "singleton");
899s ***** test
899s  kmeans ([1 0; 1.1 0], 2, "start", eye(2), "emptyaction", "singleton");
899s ***** error kmeans (rand (3,2), 4);
899s ***** error kmeans ([1 0; 1.1 0], 2, "start", eye(2), "emptyaction", "panic");
899s ***** error kmeans (rand (4,3), 2, "start", rand (2,3, 5), "replicates", 1);
899s ***** error kmeans (rand (4,3), 2, "start", rand (2,2));
899s ***** error kmeans (rand (4,3), 2, "distance", "manhattan");
899s ***** error kmeans (rand (3,4), 2, "start", "normal");
899s ***** error kmeans (rand (4,3), 2, "replicates", i);
899s ***** error kmeans (rand (4,3), 2, "replicates", -1);
899s ***** error kmeans (rand (4,3), 2, "replicates", []);
899s ***** error kmeans (rand (4,3), 2, "replicates", [1 2]);
899s ***** error kmeans (rand (4,3), 2, "replicates", "one");
899s ***** error kmeans (rand (4,3), 2, "MAXITER", i);
899s ***** error kmeans (rand (4,3), 2, "MaxIter", -1);
899s ***** error kmeans (rand (4,3), 2, "maxiter", []);
899s ***** error kmeans (rand (4,3), 2, "maxiter", [1 2]);
899s ***** error kmeans (rand (4,3), 2, "maxiter", "one");
899s ***** error <empty cluster created> kmeans ([1 0; 1.1 0], 2, "start", eye(2), "emptyaction", "error");
899s 31 tests, 31 passed, 0 known failure, 0 skipped
899s [inst/knnsearch.m]
899s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/knnsearch.m
899s ***** demo
899s  ## find 10 nearest neighbour of a point using different distance metrics
899s  ## and compare the results by plotting
899s  load fisheriris
899s  X = meas(:,3:4);
899s  Y = species;
899s  point = [5, 1.45];
899s 
899s  ## calculate 10 nearest-neighbours by minkowski distance
899s  [id, d] = knnsearch (X, point, "K", 10);
899s 
899s  ## calculate 10 nearest-neighbours by minkowski distance
899s  [idm, dm] = knnsearch (X, point, "K", 10, "distance", "minkowski", "p", 5);
899s 
899s  ## calculate 10 nearest-neighbours by chebychev distance
899s  [idc, dc] = knnsearch (X, point, "K", 10, "distance", "chebychev");
899s 
899s  ## plotting the results
899s  gscatter (X(:,1), X(:,2), species, [.75 .75 0; 0 .75 .75; .75 0 .75], ".", 20);
899s  title ("Fisher's Iris Data - Nearest Neighbors with different types of distance metrics");
899s  xlabel("Petal length (cm)");
899s  ylabel("Petal width (cm)");
899s 
899s  line (point(1), point(2), "marker", "X", "color", "k", ...
899s        "linewidth", 2, "displayname", "query point")
899s  line (X(id,1), X(id,2), "color", [0.5 0.5 0.5], "marker", "o", ...
899s        "linestyle", "none", "markersize", 10, "displayname", "eulcidean")
899s  line (X(idm,1), X(idm,2), "color", [0.5 0.5 0.5], "marker", "d", ...
899s        "linestyle", "none", "markersize", 10, "displayname", "Minkowski")
899s  line (X(idc,1), X(idc,2), "color", [0.5 0.5 0.5], "marker", "p", ...
899s        "linestyle", "none", "markersize", 10, "displayname", "chebychev")
899s  xlim ([4.5 5.5]);
899s  ylim ([1 2]);
899s  axis square;
899s ***** demo
899s  ## knnsearch on iris dataset using kdtree method
899s  load fisheriris
899s  X = meas(:,3:4);
899s  gscatter (X(:,1), X(:,2), species, [.75 .75 0; 0 .75 .75; .75 0 .75], ".", 20);
899s  title ("Fisher's iris dataset : Nearest Neighbors with kdtree search");
899s 
899s  ## new point to be predicted
899s  point = [5 1.45];
899s 
899s  line (point(1), point(2), "marker", "X", "color", "k", ...
899s        "linewidth", 2, "displayname", "query point")
899s 
899s  ## knnsearch using kdtree method
899s  [idx, d] = knnsearch (X, point, "K", 10, "NSMethod", "kdtree");
899s 
899s  ## plotting predicted neighbours
899s  line (X(idx,1), X(idx,2), "color", [0.5 0.5 0.5], "marker", "o", ...
899s        "linestyle", "none", "markersize", 10, ...
899s        "displayname", "nearest neighbour")
899s  xlim ([4 6])
899s  ylim ([1 3])
899s  axis square
899s  ## details of predicted labels
899s  tabulate (species(idx))
899s 
899s  ctr = point - d(end);
899s  diameter = 2 * d(end);
899s  ##  Draw a circle around the 10 nearest neighbors.
899s  h = rectangle ("position", [ctr, diameter, diameter], "curvature", [1 1]);
899s 
899s  ## here only 8 neighbours are plotted instead of 10 since the dataset
899s  ## contains duplicate values
899s ***** shared X, Y
899s  X = [1, 2, 3, 4; 2, 3, 4, 5; 3, 4, 5, 6];
899s  Y = [1, 2, 2, 3; 2, 3, 3, 4];
899s ***** test
899s  [idx, D] = knnsearch (X, Y, "Distance", "euclidean");
899s  assert (idx, [1; 1]);
899s  assert (D, ones (2, 1) * sqrt (2));
899s ***** test
899s  eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
899s  [idx, D] = knnsearch (X, Y, "Distance", eucldist);
899s  assert (idx, [1; 1]);
899s  assert (D, ones (2, 1) * sqrt (2));
899s ***** test
899s  [idx, D] = knnsearch (X, Y, "Distance", "euclidean", "includeties", true);
899s  assert (iscell (idx), true);
899s  assert (iscell (D), true)
899s  assert (idx {1}, [1]);
899s  assert (idx {2}, [1, 2]);
899s  assert (D{1}, ones (1, 1) * sqrt (2));
899s  assert (D{2}, ones (1, 2) * sqrt (2));
899s ***** test
899s  [idx, D] = knnsearch (X, Y, "Distance", "euclidean", "k", 2);
899s  assert (idx, [1, 2; 1, 2]);
899s  assert (D, [sqrt(2), 3.162277660168380; sqrt(2), sqrt(2)], 1e-14);
899s ***** test
899s  [idx, D] = knnsearch (X, Y, "Distance", "seuclidean");
899s  assert (idx, [1; 1]);
899s  assert (D, ones (2, 1) * sqrt (2));
899s ***** test
899s  [idx, D] = knnsearch (X, Y, "Distance", "seuclidean", "k", 2);
899s  assert (idx, [1, 2; 1, 2]);
899s  assert (D, [sqrt(2), 3.162277660168380; sqrt(2), sqrt(2)], 1e-14);
899s ***** test
899s  xx = [1, 2; 1, 3; 2, 4; 3, 6];
899s  yy = [2, 4; 2, 6];
899s  [idx, D] = knnsearch (xx, yy, "Distance", "mahalanobis");
899s  assert (idx, [3; 2]);
899s  assert (D, [0; 3.162277660168377], 1e-14);
899s ***** test
899s  [idx, D] = knnsearch (X, Y, "Distance", "minkowski");
899s  assert (idx, [1; 1]);
899s  assert (D, ones (2, 1) * sqrt (2));
899s ***** test
899s  [idx, D] = knnsearch (X, Y, "Distance", "minkowski", "p", 3);
899s  assert (idx, [1; 1]);
899s  assert (D, ones (2, 1) * 1.259921049894873, 1e-14);
899s ***** test
899s  [idx, D] = knnsearch (X, Y, "Distance", "cityblock");
899s  assert (idx, [1; 1]);
899s  assert (D, [2; 2]);
899s ***** test
899s  [idx, D] = knnsearch (X, Y, "Distance", "chebychev");
899s  assert (idx, [1; 1]);
899s  assert (D, [1; 1]);
899s ***** test
899s  [idx, D] = knnsearch (X, Y, "Distance", "cosine");
899s  assert (idx, [2; 3]);
899s  assert (D, [0.005674536395645; 0.002911214328620], 1e-14);
899s ***** test
899s  [idx, D] = knnsearch (X, Y, "Distance", "correlation");
899s  assert (idx, [1; 1]);
899s  assert (D, ones (2, 1) * 0.051316701949486, 1e-14);
899s ***** test
899s  [idx, D] = knnsearch (X, Y, "Distance", "spearman");
899s  assert (idx, [1; 1]);
899s  assert (D, ones (2, 1) * 0.051316701949486, 1e-14);
899s ***** test
899s  [idx, D] = knnsearch (X, Y, "Distance", "hamming");
899s  assert (idx, [1; 1]);
899s  assert (D, [0.5; 0.5]);
899s ***** test
899s  [idx, D] = knnsearch (X, Y, "Distance", "jaccard");
899s  assert (idx, [1; 1]);
899s  assert (D, [0.5; 0.5]);
899s ***** test
899s  [idx, D] = knnsearch (X, Y, "Distance", "jaccard", "k", 2);
899s  assert (idx, [1, 2; 1, 2]);
899s  assert (D, [0.5, 1; 0.5, 0.5]);
899s ***** test
899s  a = [1, 5; 1, 2; 2, 2; 1.5, 1.5; 5, 1; 2 -1.34; 1, -3; 4, -4; -3, 1; 8, 9];
899s  b = [1, 1];
899s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree", "includeties", true);
899s  assert (iscell (idx), true);
899s  assert (iscell (D), true)
899s  assert (cell2mat (idx), [4, 2, 3, 6, 1, 5, 7, 9]);
899s  assert (cell2mat (D), [0.7071, 1.0000, 1.4142, 2.5447, 4.0000, 4.0000, 4.0000, 4.0000],1e-4);
899s ***** test
899s  a = [1, 5; 1, 2; 2, 2; 1.5, 1.5; 5, 1; 2 -1.34; 1, -3; 4, -4; -3, 1; 8, 9];
899s  b = [1, 1];
899s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "exhaustive", "includeties", true);
899s  assert (iscell (idx), true);
899s  assert (iscell (D), true)
899s  assert (cell2mat (idx), [4, 2, 3, 6, 1, 5, 7, 9]);
899s  assert (cell2mat (D), [0.7071, 1.0000, 1.4142, 2.5447, 4.0000, 4.0000, 4.0000, 4.0000],1e-4);
899s ***** test
899s  a = [1, 5; 1, 2; 2, 2; 1.5, 1.5; 5, 1; 2 -1.34; 1, -3; 4, -4; -3, 1; 8, 9];
899s  b = [1, 1];
899s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree", "includeties", false);
899s  assert (iscell (idx), false);
899s  assert (iscell (D), false)
899s  assert (idx, [4, 2, 3, 6, 1]);
899s  assert (D, [0.7071, 1.0000, 1.4142, 2.5447, 4.0000],1e-4);
899s ***** test
899s  a = [1, 5; 1, 2; 2, 2; 1.5, 1.5; 5, 1; 2 -1.34; 1, -3; 4, -4; -3, 1; 8, 9];
899s  b = [1, 1];
899s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "exhaustive", "includeties", false);
899s  assert (iscell (idx), false);
899s  assert (iscell (D), false)
899s  assert (idx, [4, 2, 3, 6, 1]);
899s  assert (D, [0.7071, 1.0000, 1.4142, 2.5447, 4.0000],1e-4);
899s ***** test
899s  load fisheriris
899s  a = meas;
899s  b = min(meas);
899s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree");
899s  assert (idx, [42, 9, 14, 39, 13]);
899s  assert (D, [0.5099, 0.9950, 1.0050, 1.0536, 1.1874],1e-4);
899s ***** test
899s  load fisheriris
899s  a = meas;
899s  b = mean(meas);
899s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree");
899s  assert (idx, [65, 83, 89, 72, 100]);
899s  assert (D, [0.3451, 0.3869, 0.4354, 0.4481, 0.4625],1e-4);
900s ***** test
900s  load fisheriris
900s  a = meas;
900s  b = max(meas);
900s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree");
900s  assert (idx, [118, 132, 110, 106, 136]);
900s  assert (D, [0.7280, 0.9274, 1.3304, 1.5166, 1.6371],1e-4);
900s 
900s ***** test
900s  load fisheriris
900s  a = meas;
900s  b = max(meas);
900s  [idx, D] = knnsearch (a, b, "K", 5, "includeties", true);
900s  assert ( iscell (idx), true);
900s  assert ( iscell (D), true);
900s  assert (cell2mat (idx), [118, 132, 110, 106, 136]);
900s  assert (cell2mat (D), [0.7280, 0.9274, 1.3304, 1.5166, 1.6371],1e-4);
900s ***** error<knnsearch: too few input arguments.> knnsearch (1)
900s ***** error<knnsearch: number of columns in X and Y must match.> ...
900s  knnsearch (ones (4, 5), ones (4))
900s ***** error<knnsearch: invalid NAME in optional pairs of arguments.> ...
900s  knnsearch (ones (4, 2), ones (3, 2), "Distance", "euclidean", "some", "some")
900s ***** error<knnsearch: only a single distance parameter can be defined.> ...
900s  knnsearch (ones (4, 5), ones (1, 5), "scale", ones (1, 5), "P", 3)
900s ***** error<knnsearch: invalid value of K.> ...
900s  knnsearch (ones (4, 5), ones (1, 5), "K", 0)
900s ***** error<knnsearch: invalid value of Minkowski Exponent.> ...
900s  knnsearch (ones (4, 5), ones (1, 5), "P",-2)
900s ***** error<knnsearch: invalid value in Scale or the size of Scale.> ...
900s  knnsearch (ones (4, 5), ones (1, 5), "scale", ones(4,5), "distance", "euclidean")
900s ***** error<knnsearch: invalid value in Cov, Cov can only be given for mahalanobis distance.> ...
900s  knnsearch (ones (4, 5), ones (1, 5), "cov", ["some" "some"])
900s ***** error<knnsearch: invalid value in Cov, Cov can only be given for mahalanobis distance.> ...
900s  knnsearch (ones (4, 5), ones (1, 5), "cov", ones(4,5), "distance", "euclidean")
900s ***** error<knnsearch: invalid value of bucketsize.> ...
900s  knnsearch (ones (4, 5), ones (1, 5), "bucketsize", -1)
900s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
900s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "cosine")
900s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
900s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "mahalanobis")
900s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
900s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "correlation")
900s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
900s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "seuclidean")
900s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
900s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "spearman")
900s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
900s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "hamming")
900s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
900s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "jaccard")
900s 42 tests, 42 passed, 0 known failure, 0 skipped
900s [inst/kruskalwallis.m]
900s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/kruskalwallis.m
900s ***** demo
900s  x = meshgrid (1:6);
900s  x = x + normrnd (0, 1, 6, 6);
900s  kruskalwallis (x, [], 'off');
900s ***** demo
900s  x = meshgrid (1:6);
900s  x = x + normrnd (0, 1, 6, 6);
900s  [p, atab] = kruskalwallis(x);
900s ***** demo
900s  x = ones (30, 4) .* [-2, 0, 1, 5];
900s  x = x + normrnd (0, 2, 30, 4);
900s  group = {"A", "B", "C", "D"};
900s  kruskalwallis (x, group);
900s ***** test
900s  data = [1.006, 0.996, 0.998, 1.000, 0.992, 0.993, 1.002, 0.999, 0.994, 1.000, ...
900s          0.998, 1.006, 1.000, 1.002, 0.997, 0.998, 0.996, 1.000, 1.006, 0.988, ...
900s          0.991, 0.987, 0.997, 0.999, 0.995, 0.994, 1.000, 0.999, 0.996, 0.996, ...
900s          1.005, 1.002, 0.994, 1.000, 0.995, 0.994, 0.998, 0.996, 1.002, 0.996, ...
900s          0.998, 0.998, 0.982, 0.990, 1.002, 0.984, 0.996, 0.993, 0.980, 0.996, ...
900s          1.009, 1.013, 1.009, 0.997, 0.988, 1.002, 0.995, 0.998, 0.981, 0.996, ...
900s          0.990, 1.004, 0.996, 1.001, 0.998, 1.000, 1.018, 1.010, 0.996, 1.002, ...
900s          0.998, 1.000, 1.006, 1.000, 1.002, 0.996, 0.998, 0.996, 1.002, 1.006, ...
900s          1.002, 0.998, 0.996, 0.995, 0.996, 1.004, 1.004, 0.998, 0.999, 0.991, ...
900s          0.991, 0.995, 0.984, 0.994, 0.997, 0.997, 0.991, 0.998, 1.004, 0.997];
900s  group = [1:10] .* ones (10,10);
900s  group = group(:);
900s  [p, tbl] = kruskalwallis (data, group, "off");
900s  assert (p, 0.048229, 1e-6);
900s  assert (tbl{2,5}, 17.03124, 1e-5);
900s  assert (tbl{2,3}, 9, 0);
900s  assert (tbl{4,2}, 82655.5, 1e-16);
900s  data = reshape (data, 10, 10);
900s  [p, tbl, stats] = kruskalwallis (data, [], "off");
900s  assert (p, 0.048229, 1e-6);
900s  assert (tbl{2,5}, 17.03124, 1e-5);
900s  assert (tbl{2,3}, 9, 0);
900s  assert (tbl{4,2}, 82655.5, 1e-16);
900s  means = [51.85, 60.45, 37.6, 51.1, 29.5, 54.25, 64.55, 66.7, 53.65, 35.35];
900s  N = 10 * ones (1, 10);
900s  assert (stats.meanranks, means, 1e-6);
900s  assert (length (stats.gnames), 10, 0);
900s  assert (stats.n, N, 0);
900s 1 test, 1 passed, 0 known failure, 0 skipped
900s [inst/kstest.m]
900s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/kstest.m
900s ***** demo
900s  ## Use the stock return data set to test the null hypothesis that the data
900s  ## come from a standard normal distribution against the alternative
900s  ## hypothesis that the population CDF of the data is larger that the
900s  ## standard normal CDF.
900s 
900s  load stockreturns;
900s  x = stocks(:,2);
900s  [h, p, k, c] = kstest (x, "Tail", "larger")
900s 
900s  ## Compute the empirical CDF and plot against the standard normal CDF
900s  [f, x_values] = ecdf (x);
900s  h1 = plot (x_values, f);
900s  hold on;
900s  h2 = plot (x_values, normcdf (x_values), 'r--');
900s  set (h1, "LineWidth", 2);
900s  set (h2, "LineWidth", 2);
900s  legend ([h1, h2], "Empirical CDF", "Standard Normal CDF", ...
900s          "Location", "southeast");
900s  title ("Empirical CDF of stock return data against standard normal CDF")
900s ***** error<kstest: too few inputs.> kstest ()
900s ***** error<kstest: X must be a vector of real numbers.> kstest (ones (2, 4))
900s ***** error<kstest: X must be a vector of real numbers.> kstest ([2, 3, 5, 3+3i])
900s ***** error<kstest: unknown option 'opt'.> kstest ([2, 3, 4, 5, 6], "opt", 0.51)
900s ***** error<kstest: optional parameters must be in name/value pairs.> ...
900s  kstest ([2, 3, 4, 5, 6], "tail")
900s ***** error<kstest: alpha must be a numeric scalar in the range> ...
900s  kstest ([2,3,4,5,6],"alpha", [0.05, 0.05])
900s ***** error<kstest: alpha must be a numeric scalar in the range> ...
900s  kstest ([2, 3, 4, 5, 6], "alpha", NaN)
900s ***** error<kstest: tail argument must be a string.> ...
900s  kstest ([2, 3, 4, 5, 6], "tail", 0)
900s ***** error<kstest: tail value must be either 'both', right' or 'left'.> ...
900s  kstest ([2,3,4,5,6], "tail", "whatever")
900s ***** error<kstest: invalid function handle.> ...
900s  kstest ([1, 2, 3, 4, 5], "CDF", @(x) repmat (x, 2, 3))
900s ***** error<kstest: 'somedist' is not a supported distribution.> ...
900s  kstest ([1, 2, 3, 4, 5], "CDF", "somedist")
900s ***** error<kstest: 'CDF' must be a probability distribution object.> ...
900s  kstest ([1, 2, 3, 4, 5], "CDF", cvpartition (5))
900s ***** error<kstest: numerical CDF should have only 2 columns.> ...
900s  kstest ([2, 3, 4, 5, 6], "alpha", 0.05, "CDF", [2, 3, 4; 1, 3, 4; 1, 2, 1])
900s ***** error<kstest: numerical CDF should have at least one row.> ...
900s  kstest ([2, 3, 4, 5, 6], "alpha", 0.05, "CDF", nan (5, 2))
900s ***** error<kstest: non-incrementing numerical CDF.> ...
900s  kstest ([2, 3, 4, 5, 6], "CDF", [2, 3; 1, 4; 3, 2])
900s ***** error<kstest: wrong duplicates in numerical CDF.> ...
900s  kstest ([2, 3, 4, 5, 6], "CDF", [2, 3; 2, 4; 3, 5])
900s ***** error<kstest: invalid value parsed as CDF optinonal argument.> ...
900s  kstest ([2, 3, 4, 5, 6], "CDF", {1, 2, 3, 4, 5})
900s ***** test
900s  load examgrades
900s  [h, p] = kstest (grades(:,1));
900s  assert (h, true);
900s  assert (p, 7.58603305206105e-107, 1e-14);
900s ***** test
900s  load examgrades
900s  [h, p] = kstest (grades(:,1), "CDF", @(x) normcdf(x, 75, 10));
900s  assert (h, false);
900s  assert (p, 0.5612, 1e-4);
900s ***** test
900s  load examgrades
900s  x = grades(:,1);
900s  test_cdf = makedist ("tlocationscale", "mu", 75, "sigma", 10, "nu", 1);
900s  [h, p] = kstest (x, "alpha", 0.01, "CDF", test_cdf);
900s  assert (h, true);
900s  assert (p, 0.0021, 1e-4);
900s ***** test
900s  load stockreturns
900s  x = stocks(:,3);
900s  [h,p,k,c] = kstest (x, "Tail", "larger");
900s  assert (h, true);
900s  assert (p, 5.085438806199252e-05, 1e-14);
900s  assert (k, 0.2197, 1e-4);
900s  assert (c, 0.1207, 1e-4);
900s 21 tests, 21 passed, 0 known failure, 0 skipped
900s [inst/kstest2.m]
900s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/kstest2.m
900s ***** error kstest2 ([1,2,3,4,5,5])
900s ***** error kstest2 (ones(2,4), [1,2,3,4,5,5])
900s ***** error kstest2 ([2,3,5,7,3+3i], [1,2,3,4,5,5])
900s ***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"tail")
900s ***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"tail", "whatever")
900s ***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"badoption", 0.51)
900s ***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"tail", 0)
900s ***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"alpha", 0)
900s ***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"alpha", NaN)
900s ***** error kstest2 ([NaN,NaN,NaN,NaN,NaN],[3;5;7;8;7;6;5],"tail", "unequal")
900s ***** test
900s  load examgrades
900s  [h, p] = kstest2 (grades(:,1), grades(:,2));
900s  assert (h, false);
900s  assert (p, 0.1222791870137312, 1e-14);
900s ***** test
900s  load examgrades
900s  [h, p] = kstest2 (grades(:,1), grades(:,2), "tail", "larger");
900s  assert (h, false);
900s  assert (p, 0.1844421391011258, 1e-14);
900s ***** test
900s  load examgrades
900s  [h, p] = kstest2 (grades(:,1), grades(:,2), "tail", "smaller");
900s  assert (h, false);
900s  assert (p, 0.06115357930171663, 1e-14);
900s ***** test
900s  load examgrades
900s  [h, p] = kstest2 (grades(:,1), grades(:,2), "tail", "smaller", "alpha", 0.1);
900s  assert (h, true);
900s  assert (p, 0.06115357930171663, 1e-14);
900s 14 tests, 14 passed, 0 known failure, 0 skipped
900s [inst/levene_test.m]
900s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/levene_test.m
900s ***** error<levene_test: invalid number of input arguments.> levene_test ()
900s ***** error<levene_test: invalid number of input arguments.> ...
900s  levene_test (1, 2, 3, 4, 5);
900s ***** error<levene_test: wrong value for alpha.> levene_test (randn (50, 2), 0);
900s ***** error<levene_test: GROUP and X mismatch.> ...
900s  levene_test (randn (50, 2), [1, 2, 3]);
900s ***** error<levene_test: GROUP and X mismatch.> ...
900s  levene_test (randn (50, 1), ones (55, 1));
900s ***** error<levene_test: invalid second input argument.> ...
900s  levene_test (randn (50, 1), ones (50, 2));
900s ***** error<levene_test: wrong value for alpha.> ...
900s  levene_test (randn (50, 2), [], 1.2);
900s ***** error<levene_test: GROUP and X mismatch.> ...
900s  levene_test (randn (50, 2), "some_string");
900s ***** error<levene_test: invalid third input argument.> ...
900s  levene_test (randn (50, 2), [], "alpha");
900s ***** error<levene_test: wrong value for alpha.> ...
900s  levene_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], 1.2);
900s ***** error<levene_test: invalid third input argument.> ...
900s  levene_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], "err");
900s ***** error<levene_test: invalid option for TESTTYPE as 4th argument.> ...
900s  levene_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], 0.05, "type");
900s ***** warning<levene_test: GROUP> ...
900s  levene_test (randn (50, 1), [ones(24, 1); 2*ones(25, 1); 3]);
900s ***** test
900s  load examgrades
900s  [h, pval, W, df] = levene_test (grades);
900s  assert (h, 1);
900s  assert (pval, 9.523239714592791e-07, 1e-14);
900s  assert (W, 8.59529, 1e-5);
900s  assert (df, [4, 595]);
900s ***** test
900s  load examgrades
900s  [h, pval, W, df] = levene_test (grades, [], "quadratic");
900s  assert (h, 1);
900s  assert (pval, 9.523239714592791e-07, 1e-14);
900s  assert (W, 8.59529, 1e-5);
900s  assert (df, [4, 595]);
900s ***** test
900s  load examgrades
900s  [h, pval, W, df] = levene_test (grades, [], "median");
900s  assert (h, 1);
900s  assert (pval, 1.312093241723211e-06, 1e-14);
900s  assert (W, 8.415969, 1e-6);
900s  assert (df, [4, 595]);
900s ***** test
900s  load examgrades
900s  [h, pval, W, df] = levene_test (grades(:,[1:3]));
900s  assert (h, 1);
900s  assert (pval, 0.004349390980463497, 1e-14);
900s  assert (W, 5.52139, 1e-5);
900s  assert (df, [2, 357]);
900s ***** test
900s  load examgrades
900s  [h, pval, W, df] = levene_test (grades(:,[1:3]), "median");
900s  assert (h, 1);
900s  assert (pval, 0.004355216763951453, 1e-14);
900s  assert (W, 5.52001, 1e-5);
900s  assert (df, [2, 357]);
900s ***** test
900s  load examgrades
900s  [h, pval, W, df] = levene_test (grades(:,[3,4]), "quadratic");
900s  assert (h, 0);
900s  assert (pval, 0.1807494957440653, 2e-14);
900s  assert (W, 1.80200, 1e-5);
900s  assert (df, [1, 238]);
901s ***** test
901s  load examgrades
901s  [h, pval, W, df] = levene_test (grades(:,[3,4]), "median");
901s  assert (h, 0);
901s  assert (pval, 0.1978225622063785, 2e-14);
901s  assert (W, 1.66768, 1e-5);
901s  assert (df, [1, 238]);
901s 20 tests, 20 passed, 0 known failure, 0 skipped
901s [inst/linkage.m]
901s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/linkage.m
901s ***** shared x, t
901s  x = reshape (mod (magic (6),5), [], 3);
901s  t = 1e-6;
901s ***** assert (cond (linkage (pdist (x))),                   34.119045, t);
901s ***** assert (cond (linkage (pdist (x), "complete")),       21.793345, t);
901s ***** assert (cond (linkage (pdist (x), "average")),        27.045012, t);
901s ***** assert (cond (linkage (pdist (x), "weighted")),       27.412889, t);
901s  lastwarn(); # Clear last warning before the test
901s ***** warning <cluster distances> linkage (pdist (x), "centroid");
901s ***** test
901s  warning off Octave:clustering
901s  assert (cond (linkage (pdist (x), "centroid")),      27.457477, t);
901s  warning on Octave:clustering
901s ***** warning <cluster distances> linkage (pdist (x), "median");
901s ***** test
901s  warning off Octave:clustering
901s  assert (cond (linkage (pdist (x), "median")),        27.683325, t);
901s  warning on Octave:clustering
901s ***** assert (cond (linkage (pdist (x), "ward")),           17.195198, t);
901s ***** assert (cond (linkage (x, "ward", "euclidean")),      17.195198, t);
901s ***** assert (cond (linkage (x, "ward", {"euclidean"})),    17.195198, t);
901s ***** assert (cond (linkage (x, "ward", {"minkowski", 2})), 17.195198, t);
901s 12 tests, 12 passed, 0 known failure, 0 skipped
901s [inst/loadmodel.m]
901s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/loadmodel.m
901s ***** error<loadmodel: too few arguments.> loadmodel ()
901s ***** error<loadmodel: 'fisheriris.mat' does not contain a Classification or Regression object.> ...
901s  loadmodel ("fisheriris.mat")
901s ***** error<loadmodel: 'ClassificationModel' is not supported.> ...
901s  loadmodel ("fail_loadmodel.mdl")
901s ***** error<ClassificationKNN.load_model: invalid model in 'fail_load_model.mdl'.> ...
901s  loadmodel ("fail_load_model.mdl")
901s 4 tests, 4 passed, 0 known failure, 0 skipped
901s [inst/logistic_regression.m]
901s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/logistic_regression.m
901s ***** test
901s  # Output compared to following MATLAB commands
901s  # [B, DEV, STATS] = mnrfit(X,Y+1,'model','ordinal');
901s  # P = mnrval(B,X)
901s  X = [1.489381332449196, 1.1534152241851305; ...
901s       1.8110085304863965, 0.9449666896938425; ...
901s       -0.04453299665130296, 0.34278203449678646; ...
901s       -0.36616019468850347, 1.130254275908322; ...
901s        0.15339143291005095, -0.7921044310668951; ...
901s       -1.6031878794469698, -1.8343471035233376; ...
901s       -0.14349521143198166, -0.6762996896828459; ...
901s       -0.4403818557740143, -0.7921044310668951; ...
901s       -0.7372685001160434, -0.027793137932169563; ...
901s       -0.11875465773681024, 0.5512305689880763];
901s  Y = [1,1,1,1,1,0,0,0,0,0]';
901s  [INTERCEPT, SLOPE, DEV, DL, D2L, P] = logistic_regression (Y, X, false);
901s ***** test
901s  # Output compared to following MATLAB commands
901s  # [B, DEV, STATS] = mnrfit(X,Y+1,'model','ordinal');
901s  load carbig
901s  X = [Acceleration Displacement Horsepower Weight];
901s  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, ...
901s           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, ...
901s           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, ...
901s           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, ...
901s           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, ...
901s           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, ...
901s           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, ...
901s           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, ...
901s           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, ...
901s           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, ...
901s           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, ...
901s           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, ...
901s           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]';
901s  [INTERCEPT, SLOPE, DEV, DL, D2L, P] = logistic_regression (miles, X, false);
901s  assert (DEV, 433.197174495549, 1e-05);
901s  assert (INTERCEPT(1), -16.6895155618903, 1e-05);
901s  assert (INTERCEPT(2), -11.7207818178493, 1e-05);
901s  assert (INTERCEPT(3), -8.0605768506075, 1e-05);
901s  assert (SLOPE(1), 0.104762463756714, 1e-05);
901s  assert (SLOPE(2), 0.0103357623191891, 1e-05);
901s  assert (SLOPE(3), 0.0645199313242276, 1e-05);
901s  assert (SLOPE(4), 0.00166377028388103, 1e-05);
901s 2 tests, 2 passed, 0 known failure, 0 skipped
901s [inst/logit.m]
901s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/logit.m
901s ***** test
901s  p = [0.01:0.01:0.99];
901s  assert (logit (p), log (p ./ (1-p)), 25*eps);
901s ***** assert (logit ([-1, 0, 0.5, 1, 2]), [NaN, -Inf, 0, +Inf, NaN])
901s ***** error logit ()
901s ***** error logit (1, 2)
901s 4 tests, 4 passed, 0 known failure, 0 skipped
901s [inst/mahal.m]
901s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/mahal.m
901s ***** error mahal ()
901s ***** error mahal (1, 2, 3)
901s ***** error mahal ("A", "B")
901s ***** error <must be numeric> mahal ([1, 2], ["A", "B"])
901s ***** error mahal (ones (2, 2, 2))
901s ***** error <must be 2-D matrices> mahal (ones (2, 2), ones (2, 2, 2))
901s ***** error <same number of columns> mahal (ones (2, 2), ones (2, 3))
901s ***** test
901s  X = [1 0; 0 1; 1 1; 0 0];
901s  assert (mahal (X, X), [1.5; 1.5; 1.5; 1.5], 10*eps)
901s  assert (mahal (X, X+1), [7.5; 7.5; 1.5; 13.5], 10*eps)
901s ***** assert (mahal ([true; true], [false; true]), [0.5; 0.5], eps)
901s 9 tests, 9 passed, 0 known failure, 0 skipped
901s [inst/manova1.m]
901s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/manova1.m
901s ***** demo
901s  load carbig
901s  [d,p] = manova1([MPG, Acceleration, Weight, Displacement], Origin)
901s ***** test
901s  load carbig
901s  [d,p] = manova1([MPG, Acceleration, Weight, Displacement], Origin);
901s  assert (d, 3);
901s  assert (p, [0, 3.140583347827075e-07, 0.007510999577743149, ...
901s              0.1934100745898493]', [1e-12, 1e-12, 1e-12, 1e-12]');
901s ***** test
901s  load carbig
901s  [d,p] = manova1([MPG, Acceleration, Weight], Origin);
901s  assert (d, 2);
901s  assert (p, [0, 0.00516082975137544, 0.1206528056514453]', ...
901s             [1e-12, 1e-12, 1e-12]');
901s 2 tests, 2 passed, 0 known failure, 0 skipped
901s [inst/manovacluster.m]
901s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/manovacluster.m
901s ***** demo
901s  load carbig
901s  X = [MPG Acceleration Weight Displacement];
901s  [d, p, stats] = manova1 (X, Origin);
901s  manovacluster (stats)
901s ***** test
901s  hf = figure ("visible", "off");
901s  unwind_protect
901s    load carbig
901s    X = [MPG Acceleration Weight Displacement];
901s    [d, p, stats] = manova1 (X, Origin);
901s    manovacluster (stats);
901s  unwind_protect_cleanup
901s    close (hf);
901s  end_unwind_protect
901s ***** error manovacluster (stats, "some");
902s 2 tests, 2 passed, 0 known failure, 0 skipped
902s [inst/mcnemar_test.m]
902s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/mcnemar_test.m
902s ***** test
902s  [h, pval, chisq] = mcnemar_test ([101,121;59,33]);
902s  assert (h, 1);
902s  assert (pval, 3.8151e-06, 1e-10);
902s  assert (chisq, 21.356, 1e-3);
902s ***** test
902s  [h, pval, chisq] = mcnemar_test ([59,6;16,80]);
902s  assert (h, 1);
902s  assert (pval, 0.034690, 1e-6);
902s  assert (isempty (chisq), true);
902s ***** test
902s  [h, pval, chisq] = mcnemar_test ([59,6;16,80], 0.01);
902s  assert (h, 0);
902s  assert (pval, 0.034690, 1e-6);
902s  assert (isempty (chisq), true);
902s ***** test
902s  [h, pval, chisq] = mcnemar_test ([59,6;16,80], "mid-p");
902s  assert (h, 1);
902s  assert (pval, 0.034690, 1e-6);
902s  assert (isempty (chisq), true);
902s ***** test
902s  [h, pval, chisq] = mcnemar_test ([59,6;16,80], "asymptotic");
902s  assert (h, 1);
902s  assert (pval, 0.033006, 1e-6);
902s  assert (chisq, 4.5455, 1e-4);
902s ***** test
902s  [h, pval, chisq] = mcnemar_test ([59,6;16,80], "exact");
902s  assert (h, 0);
902s  assert (pval, 0.052479, 1e-6);
902s  assert (isempty (chisq), true);
902s ***** test
902s  [h, pval, chisq] = mcnemar_test ([59,6;16,80], "corrected");
902s  assert (h, 0);
902s  assert (pval, 0.055009, 1e-6);
902s  assert (chisq, 3.6818, 1e-4);
902s ***** test
902s  [h, pval, chisq] = mcnemar_test ([59,6;16,80], 0.1, "corrected");
902s  assert (h, 1);
902s  assert (pval, 0.055009, 1e-6);
902s  assert (chisq, 3.6818, 1e-4);
902s ***** error<mcnemar_test: too many input arguments.> mcnemar_test (59, 6, 16, 80)
902s ***** error<mcnemar_test: X must be a 2x2 matrix.> mcnemar_test (ones (3, 3))
902s ***** error<mcnemar_test: all entries of X must be non-negative integers.> ...
902s  mcnemar_test ([59,6;16,-80])
902s ***** error<mcnemar_test: all entries of X must be non-negative integers.> ...
902s  mcnemar_test ([59,6;16,4.5])
902s ***** error<mcnemar_test: invalid 2nd input argument.> ...
902s  mcnemar_test ([59,6;16,80], {""})
902s ***** error<mcnemar_test: invalid value for ALPHA.> ...
902s  mcnemar_test ([59,6;16,80], -0.2)
902s ***** error<mcnemar_test: invalid value for ALPHA.> ...
902s  mcnemar_test ([59,6;16,80], [0.05, 0.1])
902s ***** error<mcnemar_test: invalid value for ALPHA.> ...
902s  mcnemar_test ([59,6;16,80], 1)
902s ***** error<mcnemar_test: invalid value for TESTTYPE.> ...
902s  mcnemar_test ([59,6;16,80], "")
902s 17 tests, 17 passed, 0 known failure, 0 skipped
902s [inst/mhsample.m]
902s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/mhsample.m
902s ***** demo
902s  ## Define function to sample
902s  d = 2;
902s  mu = [-1; 2];
902s  rand ("seed", 5)  # for reproducibility
902s  Sigma = rand (d);
902s  Sigma = (Sigma + Sigma');
902s  Sigma += eye (d) * abs (eigs (Sigma, 1, "sa")) * 1.1;
902s  pdf = @(x)(2*pi)^(-d/2)*det(Sigma)^-.5*exp(-.5*sum((x.'-mu).*(Sigma\(x.'-mu)),1));
902s  ## Inputs
902s  start = ones (1, 2);
902s  nsamples = 500;
902s  sym = true;
902s  K = 500;
902s  m = 10;
902s  rand ("seed", 8)  # for reproducibility
902s  proprnd = @(x) (rand (size (x)) - .5) * 3 + x;
902s  [smpl, accept] = mhsample (start, nsamples, "pdf", pdf, "proprnd", proprnd, ...
902s                             "symmetric", sym, "burnin", K, "thin", m);
902s  figure;
902s  hold on;
902s  plot (smpl(:, 1), smpl(:, 2), 'x');
902s  [x, y] = meshgrid (linspace (-6, 4), linspace(-3, 7));
902s  z = reshape (pdf ([x(:), y(:)]), size(x));
902s  mesh (x, y, z, "facecolor", "None");
902s  ## Using sample points to find the volume of half a sphere with radius of .5
902s  f = @(x) ((.25-(x(:,1)+1).^2-(x(:,2)-2).^2).^.5.*(((x(:,1)+1).^2+(x(:,2)-2).^2)<.25)).';
902s  int = mean (f (smpl) ./ pdf (smpl));
902s  errest = std (f (smpl) ./ pdf (smpl)) / nsamples ^ .5;
902s  trueerr = abs (2 / 3 * pi * .25 ^ (3 / 2) - int);
902s  printf ("Monte Carlo integral estimate int f(x) dx = %f\n", int);
902s  printf ("Monte Carlo integral error estimate %f\n", errest);
902s  printf ("The actual error %f\n", trueerr);
902s  mesh (x, y, reshape (f([x(:), y(:)]), size(x)), "facecolor", "None");
902s ***** demo
902s  ## Integrate truncated normal distribution to find normilization constant
902s  pdf = @(x) exp (-.5*x.^2)/(pi^.5*2^.5);
902s  nsamples = 1e3;
902s  rand ("seed", 5)  # for reproducibility
902s  proprnd = @(x) (rand (size (x)) - .5) * 3 + x;
902s  [smpl, accept] = mhsample (1, nsamples, "pdf", pdf, "proprnd", proprnd, ...
902s                             "symmetric", true, "thin", 4);
902s  f = @(x) exp(-.5 * x .^ 2) .* (x >= -2 & x <= 2);
902s  x = linspace (-3, 3, 1000);
902s  area(x, f(x));
902s  xlabel ('x');
902s  ylabel ('f(x)');
902s  int = mean (f (smpl) ./ pdf (smpl));
902s  errest = std (f (smpl) ./ pdf (smpl)) / nsamples^ .5;
902s  trueerr = abs (erf (2 ^ .5) * 2 ^ .5 * pi ^ .5 - int);
902s  printf ("Monte Carlo integral estimate int f(x) dx = %f\n", int);
902s  printf ("Monte Carlo integral error estimate %f\n", errest);
902s  printf ("The actual error %f\n", trueerr);
902s ***** test
902s  nchain = 1e4;
902s  start = rand (nchain, 1);
902s  nsamples = 1e3;
902s  pdf = @(x) exp (-.5*(x-1).^2)/(2*pi)^.5;
902s  proppdf = @(x, y) 1/3;
902s  proprnd = @(x) 3 * (rand (size (x)) - .5) + x;
902s  [smpl, accept] = mhsample (start, nsamples, "pdf", pdf, "proppdf", proppdf, ...
902s                             "proprnd", proprnd, "thin", 2, "nchain", nchain, ...
902s                             "burnin", 0);
902s  assert (mean (mean (smpl, 1), 3), 1, .01);
902s  assert (mean (var (smpl, 1), 3), 1, .01)
907s ***** error mhsample ();
907s ***** error mhsample (1);
907s ***** error mhsample (1, 1);
907s ***** error mhsample (1, 1, "pdf", @(x)x);
907s ***** error mhsample (1, 1, "pdf", @(x)x, "proprnd", @(x)x+rand(size(x)));
907s 6 tests, 6 passed, 0 known failure, 0 skipped
907s [inst/mnrfit.m]
908s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/mnrfit.m
908s ***** error<mnrfit: too few input arguments.> mnrfit (ones (50,1))
908s ***** error<mnrfit: Predictors must be numeric.> ...
908s  mnrfit ({1 ;2 ;3 ;4 ;5}, ones (5,1))
908s ***** error<mnrfit: Predictors must be a vector or a 2D matrix.> ...
908s  mnrfit (ones (50, 4, 2), ones (50, 1))
908s ***** error<mnrfit: Response must be a vector or a 2D matrix.> ...
908s  mnrfit (ones (50, 4), ones (50, 1, 3))
908s ***** error<mnrfit: Y must have the same number of rows as X.> ...
908s  mnrfit (ones (50, 4), ones (45,1))
908s ***** error<mnrfit: Response labels must be a character array, a cell vector> ...
908s  mnrfit (ones (5, 4), {1 ;2 ;3 ;4 ;5})
908s ***** error<mnrfit: optional arguments must be in pairs.> ...
908s  mnrfit (ones (5, 4), ones (5, 1), "model")
908s ***** error<mnrfit: Y must be a column vector when given as cellstr.> ...
908s  mnrfit (ones (5, 4), {"q","q";"w","w";"q","q";"w","w";"q","q"})
908s ***** error<mnrfit: Y must contain only 1 and 0 when given as a 2D matrix.> ...
908s  mnrfit (ones (5, 4), [1, 2; 1, 2; 1, 2; 1, 2; 1, 2])
908s ***** error<mnrfit: Y must contain positive integer category numbers.> ...
908s  mnrfit (ones (5, 4), [1; -1; 1; 2; 1])
908s ***** error<mnrfit: fitting more than 2 nominal responses not supported.> ...
908s  mnrfit (ones (5, 4), [1; 2; 3; 2; 1], "model", "nominal")
908s ***** error<mnrfit: fitting hierarchical responses not supported.> ...
908s  mnrfit (ones (5, 4), [1; 2; 3; 2; 1], "model", "hierarchical")
908s ***** error<mnrfit: model type not recognised.> ...
908s  mnrfit (ones (5, 4), [1; 2; 3; 2; 1], "model", "whatever")
908s 13 tests, 13 passed, 0 known failure, 0 skipped
908s [inst/monotone_smooth.m]
908s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/monotone_smooth.m
908s ***** error<Invalid call to monotone_smooth.  Correct usage is> ...
908s  monotone_smooth (1)
908s ***** error<monotone_smooth: X must be a numeric vector.> ...
908s  monotone_smooth ("char", 1)
908s ***** error<monotone_smooth: X must be a numeric vector.> ...
908s  monotone_smooth ({1,2,3}, 1)
908s ***** error<monotone_smooth: X must be a numeric vector.> ...
908s  monotone_smooth (ones(20,3), 1)
908s ***** error<monotone_smooth: Y must be a numeric vector.> ...
908s  monotone_smooth (1, "char")
908s ***** error<monotone_smooth: Y must be a numeric vector.> ...
908s  monotone_smooth (1, {1,2,3})
908s ***** error<monotone_smooth: Y must be a numeric vector.> ...
908s  monotone_smooth (1, ones(20,3))
908s ***** error<monotone_smooth: H>  monotone_smooth (ones (10,1), ones(10,1), [1, 2])
908s ***** error<monotone_smooth: H>  monotone_smooth (ones (10,1), ones(10,1), {2})
908s ***** error<monotone_smooth: H>  monotone_smooth (ones (10,1), ones(10,1), "char")
908s 10 tests, 10 passed, 0 known failure, 0 skipped
908s [inst/multcompare.m]
908s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/multcompare.m
908s ***** demo
908s 
908s  ## Demonstration using balanced one-way ANOVA from anova1
908s 
908s  x = ones (50, 4) .* [-2, 0, 1, 5];
908s  randn ("seed", 1);    # for reproducibility
908s  x = x + normrnd (0, 2, 50, 4);
908s  groups = {"A", "B", "C", "D"};
908s  [p, tbl, stats] = anova1 (x, groups, "off");
908s  multcompare (stats);
908s ***** demo
908s 
908s  ## Demonstration using unbalanced one-way ANOVA example from anovan
908s 
908s  dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
908s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
908s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
908s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
908s         25.694 ]';
908s  g = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 ...
908s       4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
908s 
908s  [P,ATAB, STATS] = anovan (dv, g, "varnames", "score", "display", "off");
908s 
908s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "holm", ...
908s                                   "ControlGroup", 1, "display", "on")
908s 
908s ***** demo
908s 
908s  ## Demonstration using factorial ANCOVA example from anovan
908s 
908s  score = [95.6 82.2 97.2 96.4 81.4 83.6 89.4 83.8 83.3 85.7 ...
908s  97.2 78.2 78.9 91.8 86.9 84.1 88.6 89.8 87.3 85.4 ...
908s  81.8 65.8 68.1 70.0 69.9 75.1 72.3 70.9 71.5 72.5 ...
908s  84.9 96.1 94.6 82.5 90.7 87.0 86.8 93.3 87.6 92.4 ...
908s  100. 80.5 92.9 84.0 88.4 91.1 85.7 91.3 92.3 87.9 ...
908s  91.7 88.6 75.8 75.7 75.3 82.4 80.1 86.0 81.8 82.5]';
908s  treatment = {"yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
908s               "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
908s               "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
908s               "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
908s               "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
908s               "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"}';
908s  exercise = {"lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
908s              "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
908s              "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  ...
908s              "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
908s              "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
908s              "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"}';
908s  age = [59 65 70 66 61 65 57 61 58 55 62 61 60 59 55 57 60 63 62 57 ...
908s  58 56 57 59 59 60 55 53 55 58 68 62 61 54 59 63 60 67 60 67 ...
908s  75 54 57 62 65 60 58 61 65 57 56 58 58 58 52 53 60 62 61 61]';
908s 
908s  [P, ATAB, STATS] = anovan (score, {treatment, exercise, age}, "model", ...
908s                             [1 0 0; 0 1 0; 0 0 1; 1 1 0], "continuous", 3, ...
908s                             "sstype", "h", "display", "off", "contrasts", ...
908s                             {"simple","poly",""});
908s 
908s  [C, M, H, GNAMES] = multcompare (STATS, "dim", [1 2], "ctype", "holm", ...
908s                                   "display", "on")
908s 
908s ***** demo
908s 
908s  ## Demonstration using one-way ANOVA from anovan, with fit by weighted least
908s  ## squares to account for heteroskedasticity.
908s 
908s  g = [1, 1, 1, 1, 1, 1, 1, 1, ...
908s       2, 2, 2, 2, 2, 2, 2, 2, ...
908s       3, 3, 3, 3, 3, 3, 3, 3]';
908s 
908s  y = [13, 16, 16,  7, 11,  5,  1,  9, ...
908s       10, 25, 66, 43, 47, 56,  6, 39, ...
908s       11, 39, 26, 35, 25, 14, 24, 17]';
908s 
908s  [P,ATAB,STATS] = anovan(y, g, "display", "off");
908s  fitted = STATS.X * STATS.coeffs(:,1); # fitted values
908s  b = polyfit (fitted, abs (STATS.resid), 1);
908s  v = polyval (b, fitted);  # Variance as a function of the fitted values
908s  [P,ATAB,STATS] = anovan (y, g, "weights", v.^-1, "display", "off");
908s  [C, M] =  multcompare (STATS, "display", "on", "ctype", "mvt")
908s ***** demo
908s 
908s  ## Demonstration of p-value adjustments to control the false discovery rate
908s  ## Data from Westfall (1997) JASA. 92(437):299-306
908s 
908s  p = [.005708; .023544; .024193; .044895; ...
908s        .048805; .221227; .395867; .693051; .775755];
908s 
908s  padj = multcompare(p,'ctype','fdr')
908s ***** test
908s 
908s  ## Tests using unbalanced one-way ANOVA example from anovan and anova1
908s 
908s  ## Test for anovan - compare pairwise comparisons with matlab for CTYPE "lsd"
908s 
908s  dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
908s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
908s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
908s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
908s         25.694 ]';
908s  g = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 ...
908s       4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
908s 
908s  [P, ATAB, STATS] = anovan (dv, g, "varnames", "score", "display", "off");
908s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "lsd", ...
908s                                   "display", "off");
908s  assert (C(1,6), 2.85812420217898e-05, 1e-09);
908s  assert (C(2,6), 5.22936741204085e-07, 1e-09);
908s  assert (C(3,6), 2.12794763209146e-08, 1e-09);
908s  assert (C(4,6), 7.82091664406946e-15, 1e-09);
908s  assert (C(5,6), 0.546591417210693, 1e-09);
908s  assert (C(6,6), 0.0845897945254446, 1e-09);
908s  assert (C(7,6), 9.47436557975328e-08, 1e-09);
908s  assert (C(8,6), 0.188873478781067, 1e-09);
908s  assert (C(9,6), 4.08974010364197e-08, 1e-09);
908s  assert (C(10,6), 4.44427348175241e-06, 1e-09);
908s  assert (M(1,1), 10, 1e-09);
908s  assert (M(2,1), 18, 1e-09);
908s  assert (M(3,1), 19, 1e-09);
908s  assert (M(4,1), 21.0001428571429, 1e-09);
908s  assert (M(5,1), 29.0001111111111, 1e-09);
908s  assert (M(1,2), 1.0177537954095, 1e-09);
908s  assert (M(2,2), 1.28736803631001, 1e-09);
908s  assert (M(3,2), 1.0177537954095, 1e-09);
908s  assert (M(4,2), 1.0880245732889, 1e-09);
908s  assert (M(5,2), 0.959547480416536, 1e-09);
908s 
908s  ## Compare "fdr" adjusted p-values to those obtained using p.adjust in R
908s 
908s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "fdr", ...
908s                                   "display", "off");
908s  assert (C(1,6), 4.08303457454140e-05, 1e-09);
908s  assert (C(2,6), 1.04587348240817e-06, 1e-09);
908s  assert (C(3,6), 1.06397381604573e-07, 1e-09);
908s  assert (C(4,6), 7.82091664406946e-14, 1e-09);
908s  assert (C(5,6), 5.46591417210693e-01, 1e-09);
908s  assert (C(6,6), 1.05737243156806e-01, 1e-09);
908s  assert (C(7,6), 2.36859139493832e-07, 1e-09);
908s  assert (C(8,6), 2.09859420867852e-01, 1e-09);
908s  assert (C(9,6), 1.36324670121399e-07, 1e-09);
908s  assert (C(10,6), 7.40712246958735e-06, 1e-09);
908s 
908s  ## Compare "hochberg" adjusted p-values to those obtained using p.adjust in R
908s 
908s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "hochberg", ...
908s                                   "display", "off");
908s  assert (C(1,6), 1.14324968087159e-04, 1e-09);
908s  assert (C(2,6), 3.13762044722451e-06, 1e-09);
908s  assert (C(3,6), 1.91515286888231e-07, 1e-09);
908s  assert (C(4,6), 7.82091664406946e-14, 1e-09);
908s  assert (C(5,6), 5.46591417210693e-01, 1e-09);
908s  assert (C(6,6), 2.53769383576334e-01, 1e-09);
908s  assert (C(7,6), 6.63205590582730e-07, 1e-09);
908s  assert (C(8,6), 3.77746957562134e-01, 1e-09);
908s  assert (C(9,6), 3.27179208291358e-07, 1e-09);
908s  assert (C(10,6), 2.22213674087620e-05, 1e-09);
908s 
908s  ## Compare "holm" adjusted p-values to those obtained using p.adjust in R
908s 
908s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "holm", ...
908s                                   "display", "off");
908s  assert (C(1,6), 1.14324968087159e-04, 1e-09);
908s  assert (C(2,6), 3.13762044722451e-06, 1e-09);
908s  assert (C(3,6), 1.91515286888231e-07, 1e-09);
908s  assert (C(4,6), 7.82091664406946e-14, 1e-09);
908s  assert (C(5,6), 5.46591417210693e-01, 1e-09);
908s  assert (C(6,6), 2.53769383576334e-01, 1e-09);
908s  assert (C(7,6), 6.63205590582730e-07, 1e-09);
908s  assert (C(8,6), 3.77746957562134e-01, 1e-09);
908s  assert (C(9,6), 3.27179208291358e-07, 1e-09);
908s  assert (C(10,6), 2.22213674087620e-05, 1e-09);
908s 
908s  ## Compare "scheffe" adjusted p-values to those obtained using 'scheffe' in Matlab
908s 
908s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "scheffe", ...
908s                                   "display", "off");
908s  assert (C(1,6), 0.00108105386141085, 1e-09);
908s  assert (C(2,6), 2.7779386789517e-05, 1e-09);
908s  assert (C(3,6), 1.3599854038198e-06, 1e-09);
908s  assert (C(4,6), 7.58830197867751e-13, 1e-09);
908s  assert (C(5,6), 0.984039948220281, 1e-09);
908s  assert (C(6,6), 0.539077018557706, 1e-09);
908s  assert (C(7,6), 5.59475764460574e-06, 1e-09);
908s  assert (C(8,6), 0.771173490574105, 1e-09);
908s  assert (C(9,6), 2.52838425729905e-06, 1e-09);
908s  assert (C(10,6), 0.000200719143889168, 1e-09);
908s 
908s  ## Compare "bonferroni" adjusted p-values to those obtained using p.adjust in R
908s 
908s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "bonferroni", ...
908s                                   "display", "off");
908s  assert (C(1,6), 2.85812420217898e-04, 1e-09);
908s  assert (C(2,6), 5.22936741204085e-06, 1e-09);
908s  assert (C(3,6), 2.12794763209146e-07, 1e-09);
908s  assert (C(4,6), 7.82091664406946e-14, 1e-09);
908s  assert (C(5,6), 1.00000000000000e+00, 1e-09);
908s  assert (C(6,6), 8.45897945254446e-01, 1e-09);
908s  assert (C(7,6), 9.47436557975328e-07, 1e-09);
908s  assert (C(8,6), 1.00000000000000e+00, 1e-09);
908s  assert (C(9,6), 4.08974010364197e-07, 1e-09);
908s  assert (C(10,6), 4.44427348175241e-05, 1e-09);
908s 
908s  ## Test for anova1 ("equal")- comparison of results from Matlab
908s 
908s  [P, ATAB, STATS] = anova1 (dv, g, "off", "equal");
908s  [C, M, H, GNAMES] = multcompare (STATS, "ctype", "lsd", "display", "off");
908s  assert (C(1,6), 2.85812420217898e-05, 1e-09);
908s  assert (C(2,6), 5.22936741204085e-07, 1e-09);
908s  assert (C(3,6), 2.12794763209146e-08, 1e-09);
908s  assert (C(4,6), 7.82091664406946e-15, 1e-09);
908s  assert (C(5,6), 0.546591417210693, 1e-09);
908s  assert (C(6,6), 0.0845897945254446, 1e-09);
908s  assert (C(7,6), 9.47436557975328e-08, 1e-09);
908s  assert (C(8,6), 0.188873478781067, 1e-09);
908s  assert (C(9,6), 4.08974010364197e-08, 1e-09);
908s  assert (C(10,6), 4.44427348175241e-06, 1e-09);
908s  assert (M(1,1), 10, 1e-09);
908s  assert (M(2,1), 18, 1e-09);
908s  assert (M(3,1), 19, 1e-09);
908s  assert (M(4,1), 21.0001428571429, 1e-09);
908s  assert (M(5,1), 29.0001111111111, 1e-09);
908s  assert (M(1,2), 1.0177537954095, 1e-09);
908s  assert (M(2,2), 1.28736803631001, 1e-09);
908s  assert (M(3,2), 1.0177537954095, 1e-09);
908s  assert (M(4,2), 1.0880245732889, 1e-09);
908s  assert (M(5,2), 0.959547480416536, 1e-09);
908s 
908s  ## Test for anova1 ("unequal") - comparison with results from GraphPad Prism 8
908s  [P, ATAB, STATS] = anova1 (dv, g, "off", "unequal");
908s  [C, M, H, GNAMES] = multcompare (STATS, "ctype", "lsd", "display", "off");
908s  assert (C(1,6), 0.001247025266382, 1e-09);
908s  assert (C(2,6), 0.000018037115146, 1e-09);
908s  assert (C(3,6), 0.000002974595187, 1e-09);
908s  assert (C(4,6), 0.000000000786046, 1e-09);
908s  assert (C(5,6), 0.5693192886650109, 1e-09);
908s  assert (C(6,6), 0.110501699029776, 1e-09);
908s  assert (C(7,6), 0.000131226488700, 1e-09);
908s  assert (C(8,6), 0.1912101409715992, 1e-09);
908s  assert (C(9,6), 0.000005385256394, 1e-09);
908s  assert (C(10,6), 0.000074089106171, 1e-09);
908s ***** test
908s 
908s  ## Test for anova2 ("interaction") - comparison with results from Matlab for column effect
908s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
908s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
908s  [P, ATAB, STATS] = anova2 (popcorn, 3, "off");
908s  [C, M, H, GNAMES] = multcompare (STATS, "estimate", "column",...
908s                                   "ctype", "lsd", "display", "off");
908s  assert (C(1,6), 1.49311100811177e-05, 1e-09);
908s  assert (C(2,6), 2.20506904243535e-07, 1e-09);
908s  assert (C(3,6), 0.00449897860490058, 1e-09);
908s  assert (M(1,1), 6.25, 1e-09);
908s  assert (M(2,1), 4.75, 1e-09);
908s  assert (M(3,1), 4, 1e-09);
908s  assert (M(1,2), 0.152145154862547, 1e-09);
908s  assert (M(2,2), 0.152145154862547, 1e-09);
908s  assert (M(3,2), 0.152145154862547, 1e-09);
908s ***** test
908s 
908s  ## Test for anova2 ("linear") - comparison with results from GraphPad Prism 8
908s  words = [10 13 13; 6 8 8; 11 14 14; 22 23 25; 16 18 20; ...
908s           15 17 17; 1 1 4; 12 15 17;  9 12 12;  8 9 12];
908s  [P, ATAB, STATS] = anova2 (words, 1, "off", "linear");
908s  [C, M, H, GNAMES] = multcompare (STATS, "estimate", "column",...
908s                                   "ctype", "lsd", "display", "off");
908s  assert (C(1,6), 0.000020799832702, 1e-09);
908s  assert (C(2,6), 0.000000035812410, 1e-09);
908s  assert (C(3,6), 0.003038942449215, 1e-09);
908s ***** test
908s 
908s  ## Test for anova2 ("nested") - comparison with results from GraphPad Prism 8
908s  data = [4.5924 7.3809 21.322; -0.5488 9.2085 25.0426; ...
908s          6.1605 13.1147 22.66; 2.3374 15.2654 24.1283; ...
908s          5.1873 12.4188 16.5927; 3.3579 14.3951 10.2129; ...
908s          6.3092 8.5986 9.8934; 3.2831 3.4945 10.0203];
908s  [P, ATAB, STATS] = anova2 (data, 4, "off", "nested");
908s  [C, M, H, GNAMES] = multcompare (STATS, "estimate", "column",...
908s                                   "ctype", "lsd", "display", "off");
908s  assert (C(1,6), 0.261031111511073, 1e-09);
908s  assert (C(2,6), 0.065879755907745, 1e-09);
908s  assert (C(3,6), 0.241874613529270, 1e-09);
908s ***** shared visibility_setting
908s  visibility_setting = get (0, "DefaultFigureVisible");
908s ***** test
908s  set (0, "DefaultFigureVisible", "off");
908s 
908s  ## Test for kruskalwallis - comparison with results from MATLAB
908s  data = [3,2,4; 5,4,4; 4,2,4; 4,2,4; 4,1,5; ...
908s          4,2,3; 4,3,5; 4,2,4; 5,2,4; 5,3,3];
908s  group = [1:3] .* ones (10,3);
908s  [P, ATAB, STATS] = kruskalwallis (data(:), group(:), "off");
908s  C = multcompare (STATS, "ctype", "lsd", "display", "off");
908s  assert (C(1,6), 0.000163089828959986, 1e-09);
908s  assert (C(2,6), 0.630298044801257, 1e-09);
908s  assert (C(3,6), 0.00100567660695682, 1e-09);
908s  C = multcompare (STATS, "ctype", "bonferroni", "display", "off");
908s  assert (C(1,6), 0.000489269486879958, 1e-09);
908s  assert (C(2,6), 1, 1e-09);
908s  assert (C(3,6), 0.00301702982087047, 1e-09);
908s  C = multcompare(STATS, "ctype", "scheffe", "display", "off");
908s  assert (C(1,6), 0.000819054880289573, 1e-09);
908s  assert (C(2,6), 0.890628039849261, 1e-09);
908s  assert (C(3,6), 0.00447816059021654, 1e-09);
908s  set (0, "DefaultFigureVisible", visibility_setting);
908s ***** test
908s  set (0, "DefaultFigureVisible", "off");
908s  ## Test for friedman - comparison with results from MATLAB
908s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
908s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
908s  [P, ATAB, STATS] = friedman (popcorn, 3, "off");
908s  C = multcompare(STATS, "ctype", "lsd", "display", "off");
908s  assert (C(1,6), 0.227424558028569, 1e-09);
908s  assert (C(2,6), 0.0327204848315735, 1e-09);
908s  assert (C(3,6), 0.353160353315988, 1e-09);
908s  C = multcompare(STATS, "ctype", "bonferroni", "display", "off");
908s  assert (C(1,6), 0.682273674085708, 1e-09);
908s  assert (C(2,6), 0.0981614544947206, 1e-09);
908s  assert (C(3,6), 1, 1e-09);
908s  C = multcompare(STATS, "ctype", "scheffe", "display", "off");
908s  assert (C(1,6), 0.482657360384373, 1e-09);
908s  assert (C(2,6), 0.102266573027672, 1e-09);
908s  assert (C(3,6), 0.649836502233148, 1e-09);
908s  set (0, "DefaultFigureVisible", visibility_setting);
908s ***** test
908s  set (0, "DefaultFigureVisible", "off");
908s  ## Test for fitlm - same comparisons as for first anovan example
908s  y =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
908s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
908s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
908s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
908s         25.694 ]';
908s  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]';
908s  [TAB,STATS] = fitlm (X,y,"linear","categorical",1,"display","off",...
908s                       "contrasts","simple");
908s  [C, M] = multcompare(STATS, "ctype", "lsd", "display", "off");
908s  assert (C(1,6), 2.85812420217898e-05, 1e-09);
908s  assert (C(2,6), 5.22936741204085e-07, 1e-09);
908s  assert (C(3,6), 2.12794763209146e-08, 1e-09);
908s  assert (C(4,6), 7.82091664406946e-15, 1e-09);
908s  assert (C(5,6), 0.546591417210693, 1e-09);
908s  assert (C(6,6), 0.0845897945254446, 1e-09);
908s  assert (C(7,6), 9.47436557975328e-08, 1e-09);
908s  assert (C(8,6), 0.188873478781067, 1e-09);
908s  assert (C(9,6), 4.08974010364197e-08, 1e-09);
908s  assert (C(10,6), 4.44427348175241e-06, 1e-09);
908s  assert (M(1,1), 10, 1e-09);
908s  assert (M(2,1), 18, 1e-09);
908s  assert (M(3,1), 19, 1e-09);
908s  assert (M(4,1), 21.0001428571429, 1e-09);
908s  assert (M(5,1), 29.0001111111111, 1e-09);
908s  assert (M(1,2), 1.0177537954095, 1e-09);
908s  assert (M(2,2), 1.28736803631001, 1e-09);
908s  assert (M(3,2), 1.0177537954095, 1e-09);
908s  assert (M(4,2), 1.0880245732889, 1e-09);
908s  assert (M(5,2), 0.959547480416536, 1e-09);
908s  set (0, "DefaultFigureVisible", visibility_setting);
908s ***** test
908s  ## Test p-value adjustments compared to R stats package function p.adjust
908s  ## Data from Westfall (1997) JASA. 92(437):299-306
908s  p = [.005708; .023544; .024193; .044895; ...
908s        .048805; .221227; .395867; .693051; .775755];
908s  padj = multcompare (p);
908s  assert (padj(1), 0.051372, 1e-06);
908s  assert (padj(2), 0.188352, 1e-06);
908s  assert (padj(3), 0.188352, 1e-06);
908s  assert (padj(4), 0.269370, 1e-06);
908s  assert (padj(5), 0.269370, 1e-06);
908s  assert (padj(6), 0.884908, 1e-06);
908s  assert (padj(7), 1.000000, 1e-06);
908s  assert (padj(8), 1.000000, 1e-06);
908s  assert (padj(9), 1.000000, 1e-06);
908s  padj = multcompare(p,'ctype','holm');
908s  assert (padj(1), 0.051372, 1e-06);
908s  assert (padj(2), 0.188352, 1e-06);
908s  assert (padj(3), 0.188352, 1e-06);
908s  assert (padj(4), 0.269370, 1e-06);
908s  assert (padj(5), 0.269370, 1e-06);
908s  assert (padj(6), 0.884908, 1e-06);
908s  assert (padj(7), 1.000000, 1e-06);
908s  assert (padj(8), 1.000000, 1e-06);
908s  assert (padj(9), 1.000000, 1e-06);
908s  padj = multcompare(p,'ctype','hochberg');
908s  assert (padj(1), 0.051372, 1e-06);
908s  assert (padj(2), 0.169351, 1e-06);
908s  assert (padj(3), 0.169351, 1e-06);
908s  assert (padj(4), 0.244025, 1e-06);
908s  assert (padj(5), 0.244025, 1e-06);
908s  assert (padj(6), 0.775755, 1e-06);
908s  assert (padj(7), 0.775755, 1e-06);
908s  assert (padj(8), 0.775755, 1e-06);
908s  assert (padj(9), 0.775755, 1e-06);
908s  padj = multcompare(p,'ctype','fdr');
908s  assert (padj(1), 0.0513720, 1e-07);
908s  assert (padj(2), 0.0725790, 1e-07);
908s  assert (padj(3), 0.0725790, 1e-07);
908s  assert (padj(4), 0.0878490, 1e-07);
908s  assert (padj(5), 0.0878490, 1e-07);
908s  assert (padj(6), 0.3318405, 1e-07);
908s  assert (padj(7), 0.5089719, 1e-07);
908s  assert (padj(8), 0.7757550, 1e-07);
908s  assert (padj(9), 0.7757550, 1e-07);
908s 8 tests, 8 passed, 0 known failure, 0 skipped
908s [inst/nanmax.m]
908s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/nanmax.m
908s ***** demo
908s  ## Find the column maximum values and their indices
908s  ## for matrix data with missing values.
908s 
908s  x = magic (3);
908s  x([1, 6:9]) = NaN
908s  [y, ind] = nanmax (x)
908s ***** demo
908s  ## Find the maximum of all the values in an array, ignoring missing values.
908s  ## Create a 2-by-5-by-3 array x with some missing values.
908s 
908s  x = reshape (1:30, [2, 5, 3]);
908s  x([10:12, 25]) = NaN
908s 
908s  ## Find the maximum of the elements of x.
908s 
908s  y = nanmax (x, [], 'all')
908s ***** assert (nanmax ([2, 4, NaN, 7]), 7)
909s ***** assert (nanmax ([2, 4, NaN, Inf]), Inf)
909s ***** assert (nanmax ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN]), [7, 8, 6])
909s ***** assert (nanmax ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN]'), [3, 6, 8])
909s ***** assert (nanmax (single ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN])), single ([7, 8, 6]))
909s ***** shared x, y
909s  x(:,:,1) = [1.77, -0.005, NaN, -2.95; NaN, 0.34, NaN, 0.19];
909s  x(:,:,2) = [1.77, -0.005, NaN, -2.95; NaN, 0.34, NaN, 0.19] + 5;
909s  y = x;
909s  y(2,3,1) = 0.51;
909s ***** assert (nanmax (x, [], [1, 2])(:), [1.77;6.77])
909s ***** assert (nanmax (x, [], [1, 3])(:), [6.77;5.34;NaN;5.19])
909s ***** assert (nanmax (x, [], [2, 3])(:), [6.77;5.34])
909s ***** assert (nanmax (x, [], [1, 2, 3]), 6.77)
909s ***** assert (nanmax (x, [], 'all'), 6.77)
909s ***** assert (nanmax (y, [], [1, 3])(:), [6.77;5.34;0.51;5.19])
909s ***** assert (nanmax (x(1,:,1), x(2,:,1)), [1.77, 0.34, NaN, 0.19])
909s ***** assert (nanmax (x(1,:,2), x(2,:,2)), [6.77, 5.34, NaN, 5.19])
909s ***** assert (nanmax (y(1,:,1), y(2,:,1)), [1.77, 0.34, 0.51, 0.19])
909s ***** assert (nanmax (y(1,:,2), y(2,:,2)), [6.77, 5.34, NaN, 5.19])
909s ***** test
909s  xx = repmat ([1:20;6:25], [5 2 6 3]);
909s  assert (size (nanmax (xx, [], [3, 2])), [10, 1, 1, 3]);
909s  assert (size (nanmax (xx, [], [1, 2])), [1, 1, 6, 3]);
909s  assert (size (nanmax (xx, [], [1, 2, 4])), [1, 1, 6]);
909s  assert (size (nanmax (xx, [], [1, 4, 3])), [1, 40]);
909s  assert (size (nanmax (xx, [], [1, 2, 3, 4])), [1, 1]);
909s ***** assert (nanmax (ones (2), [], 3), ones (2, 2))
909s ***** assert (nanmax (ones (2, 2, 2), [], 99), ones (2, 2, 2))
909s ***** assert (nanmax (magic (3), [], 3), magic (3))
909s ***** assert (nanmax (magic (3), [], [1, 3]), [8, 9, 7])
909s ***** assert (nanmax (magic (3), [], [1, 99]), [8, 9, 7])
909s ***** assert (nanmax (ones (2), 3), 3 * ones (2,2))
909s ***** error <nanmax: VECDIM must contain non-repeating positive integers.> ...
909s  nanmax (y, [], [1, 1, 2])
909s ***** error <nanmax: a second output is not supported with this syntax.> ...
909s  [v, idx] = nanmax(x, y, [1 2])
909s 24 tests, 24 passed, 0 known failure, 0 skipped
909s [inst/nanmin.m]
909s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/nanmin.m
909s ***** demo
909s  ## Find the column minimum values and their indices
909s  ## for matrix data with missing values.
909s 
909s  x = magic (3);
909s  x([1, 6:9]) = NaN
909s  [y, ind] = nanmin (x)
909s ***** demo
909s  ## Find the minimum of all the values in an array, ignoring missing values.
909s  ## Create a 2-by-5-by-3 array x with some missing values.
909s 
909s  x = reshape (1:30, [2, 5, 3]);
909s  x([10:12, 25]) = NaN
909s 
909s  ## Find the minimum of the elements of x.
909s 
909s  y = nanmin (x, [], 'all')
909s ***** assert (nanmin ([2, 4, NaN, 7]), 2)
909s ***** assert (nanmin ([2, 4, NaN, -Inf]), -Inf)
909s ***** assert (nanmin ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN]), [1, 5, 3])
909s ***** assert (nanmin ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN]'), [1, 5, 7])
909s ***** assert (nanmin (single ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN])), single ([1, 5, 3]))
909s ***** shared x, y
909s  x(:,:,1) = [1.77, -0.005, NaN, -2.95; NaN, 0.34, NaN, 0.19];
909s  x(:,:,2) = [1.77, -0.005, NaN, -2.95; NaN, 0.34, NaN, 0.19] + 5;
909s  y = x;
909s  y(2,3,1) = 0.51;
909s ***** assert (nanmin (x, [], [1, 2])(:), [-2.95; 2.05])
909s ***** assert (nanmin (x, [], [1, 3])(:), [1.77; -0.005; NaN; -2.95])
909s ***** assert (nanmin (x, [], [2, 3])(:), [-2.95; 0.19])
909s ***** assert (nanmin (x, [], [1, 2, 3]), -2.95)
909s ***** assert (nanmin (x, [], 'all'), -2.95)
909s ***** assert (nanmin (y, [], [1, 3])(:), [1.77; -0.005; 0.51; -2.95])
909s ***** assert (nanmin (x(1,:,1), x(2,:,1)), [1.77, -0.005, NaN, -2.95])
909s ***** assert (nanmin (x(1,:,2), x(2,:,2)), [6.77, 4.995, NaN, 2.05])
909s ***** assert (nanmin (y(1,:,1), y(2,:,1)), [1.77, -0.005, 0.51, -2.95])
909s ***** assert (nanmin (y(1,:,2), y(2,:,2)), [6.77, 4.995, NaN, 2.05])
909s ***** test
909s  xx = repmat ([1:20;6:25], [5 2 6 3]);
909s  assert (size (nanmin (xx, [], [3, 2])), [10, 1, 1, 3]);
909s  assert (size (nanmin (xx, [], [1, 2])), [1, 1, 6, 3]);
909s  assert (size (nanmin (xx, [], [1, 2, 4])), [1, 1, 6]);
909s  assert (size (nanmin (xx, [], [1, 4, 3])), [1, 40]);
909s  assert (size (nanmin (xx, [], [1, 2, 3, 4])), [1, 1]);
909s ***** assert (nanmin (ones (2), [], 3), ones (2, 2))
909s ***** assert (nanmin (ones (2, 2, 2), [], 99), ones (2, 2, 2))
909s ***** assert (nanmin (magic (3), [], 3), magic (3))
909s ***** assert (nanmin (magic (3), [], [1, 3]), [3, 1, 2])
909s ***** assert (nanmin (magic (3), [], [1, 99]), [3, 1, 2])
909s ***** assert (nanmin (ones (2), 3), ones (2,2))
909s ***** error <nanmin: VECDIM must contain non-repeating positive integers.> ...
909s  nanmin (y, [], [1, 1, 2])
909s ***** error <nanmin: a second output is not supported with this syntax.> ...
909s  [v, idx] = nanmin(x, y, [1 2])
909s 24 tests, 24 passed, 0 known failure, 0 skipped
909s [inst/nansum.m]
909s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/nansum.m
909s ***** assert (nansum ([2 4 NaN 7]), 13)
909s ***** assert (nansum ([2 4 NaN Inf]), Inf)
909s ***** assert (nansum ([1 NaN 3; NaN 5 6; 7 8 NaN]), [8 13 9])
909s ***** assert (nansum ([1 NaN 3; NaN 5 6; 7 8 NaN], 2), [4; 11; 15])
909s ***** assert (nansum (single ([1 NaN 3; NaN 5 6; 7 8 NaN])), single ([8 13 9]))
909s ***** assert (nansum (single ([1 NaN 3; NaN 5 6; 7 8 NaN]), "double"), [8 13 9])
909s ***** assert (nansum (uint8 ([2 4 1 7])), 14)
909s ***** assert (nansum (uint8 ([2 4 1 7]), "native"), uint8 (14))
909s ***** assert (nansum (uint8 ([2 4 1 7])), 14)
909s 9 tests, 9 passed, 0 known failure, 0 skipped
909s [inst/normalise_distribution.m]
909s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/normalise_distribution.m
909s ***** test
909s  v = normalise_distribution ([1 2 3], [], 1);
909s  assert (v, [0 0 0])
909s ***** test
909s  v = normalise_distribution ([1 2 3], [], 2);
909s  assert (v, norminv ([1 3 5] / 6), 3 * eps)
909s ***** test
909s  v = normalise_distribution ([1 2 3]', [], 2);
909s  assert (v, [0 0 0]')
909s ***** test
909s  v = normalise_distribution ([1 2 3]', [], 1);
909s  assert (v, norminv ([1 3 5]' / 6), 3 * eps)
909s ***** test
909s  v = normalise_distribution ([1 1 2 2 3 3], [], 2);
909s  assert (v, norminv ([3 3 7 7 11 11] / 12), 3 * eps)
909s ***** test
909s  v = normalise_distribution ([1 1 2 2 3 3]', [], 1);
909s  assert (v, norminv ([3 3 7 7 11 11]' / 12), 3 * eps)
909s ***** test
909s  A = randn ( 10 );
909s  N = normalise_distribution (A, @normcdf);
909s  assert (A, N, 10000 * eps)
909s ***** test
909s  A = exprnd (1, 100);
909s  N = normalise_distribution (A, @(x)(expcdf (x, 1)));
909s  assert (mean (vec (N)), 0, 0.1)
909s  assert (std (vec (N)), 1, 0.1)
909s ***** test
909s  A = rand (1000,1);
909s  N = normalise_distribution (A, {@(x)(unifcdf (x, 0, 1))});
909s  assert (mean (vec (N)), 0, 0.2)
909s  assert (std (vec (N)), 1, 0.1)
909s ***** test
909s  A = [rand(1000,1), randn(1000, 1)];
909s  N = normalise_distribution (A, {@(x)(unifcdf (x, 0, 1)), @normcdf});
909s  assert (mean (N), [0, 0], 0.2)
909s  assert (std (N), [1, 1], 0.1)
909s ***** test
909s  A = [rand(1000,1), randn(1000, 1), exprnd(1, 1000, 1)]';
909s  N = normalise_distribution  (A, {@(x)(unifcdf (x, 0, 1)); @normcdf; @(x)(expcdf (x, 1))}, 2);
909s  assert (mean (N, 2), [0, 0, 0]', 0.2);
909s  assert (std (N, [], 2), [1, 1, 1]', 0.1);
909s ***** xtest
909s  A = exprnd (1, 1000, 9); A (300:500, 4:6) = 17;
909s  N = normalise_distribution (A);
909s  assert (mean (N), [0 0 0 0.38 0.38 0.38 0 0 0], 0.1);
909s  assert (var (N), [1 1 1 2.59 2.59 2.59 1 1 1], 0.1);
909s ***** test
909s ***** error normalise_distribution (zeros (3, 4), ...
909s  {@(x)(unifcdf (x, 0, 1)); @normcdf; @(x)(expcdf (x,1))});
909s 14 tests, 14 passed, 0 known failure, 0 skipped
909s [inst/normplot.m]
909s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/normplot.m
909s ***** demo
909s  h = normplot([1:20]);
909s ***** demo
909s  h = normplot([1:20;5:2:44]');
909s ***** demo
909s  ax = newplot();
909s  h = normplot(ax, [1:20]);
909s  ax = gca;
909s  h = normplot(ax, [-10:10]);
909s  set (ax, "xlim", [-11, 21]);
909s ***** error normplot ();
909s ***** error normplot (23);
909s ***** error normplot (23, [1:20]);
909s ***** error normplot (ones(3,4,5));
909s ***** test
909s  hf = figure ("visible", "off");
909s  unwind_protect
909s    ax = newplot (hf);
909s    h = normplot (ax, [1:20]);
909s    ax = gca;
909s    h = normplot(ax, [-10:10]);
909s    set (ax, "xlim", [-11, 21]);
909s  unwind_protect_cleanup
909s    close (hf);
909s  end_unwind_protect
909s ***** test
909s  hf = figure ("visible", "off");
909s  unwind_protect
909s    h = normplot([1:20;5:2:44]');
909s  unwind_protect_cleanup
909s    close (hf);
909s  end_unwind_protect
909s 6 tests, 6 passed, 0 known failure, 0 skipped
909s [inst/optimalleaforder.m]
909s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/optimalleaforder.m
909s ***** demo
909s  randn ("seed", 5)  # for reproducibility
909s  X = randn (10, 2);
909s  D = pdist (X);
909s  tree = linkage(D, 'average');
909s  optimalleaforder (tree, D, 'Transformation', 'linear')
909s ***** error optimalleaforder ()
909s ***** error optimalleaforder (1)
910s ***** error <tree must be .*> optimalleaforder (ones (2, 2), 1)
910s ***** error <character inputs expected> optimalleaforder ([1 2 3], [1 2; 3 4], "criteria", 5)
910s ***** error <D must be .*> optimalleaforder ([1 2 1], [1 2 3])
910s ***** error <unknown property .*> optimalleaforder ([1 2 1], 1, "xxx", "xxx")
910s ***** error optimalleaforder ([1 2 1], 1, "Transformation", "xxx")
910s 7 tests, 7 passed, 0 known failure, 0 skipped
910s [inst/pca.m]
910s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/pca.m
910s ***** shared COEFF,SCORE,latent,tsquare,m,x,R,V,lambda,i,S,F
910s ***** test
910s  x=[7 4 3
910s     4 1 8
910s     6 3 5
910s     8 6 1
910s     8 5 7
910s     7 2 9
910s     5 3 3
910s     9 5 8
910s     7 4 5
910s     8 2 2];
910s  R = corrcoef (x);
910s  [V, lambda] = eig (R);
910s  [~, i] = sort(diag(lambda), "descend"); #arrange largest PC first
910s  S = V(:, i) * diag(sqrt(diag(lambda)(i)));
910s ***** 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
910s  B = V(:, i) * diag( 1./ sqrt(diag(lambda)(i)));
910s  F = zscore(x)*B;
910s  [COEFF,SCORE,latent,tsquare] = pca(zscore(x, 1));
910s ***** assert(tsquare,sumsq(F, 2),1E4*eps);
910s ***** test
910s  x=[1,2,3;2,1,3]';
910s  [COEFF,SCORE,latent,tsquare] = pca(x, "Economy", false);
910s  m=[sqrt(2),sqrt(2);sqrt(2),-sqrt(2);-2*sqrt(2),0]/2;
910s  m(:,1) = m(:,1)*sign(COEFF(1,1));
910s  m(:,2) = m(:,2)*sign(COEFF(1,2));
910s ***** assert(COEFF,m(1:2,:),10*eps);
910s ***** assert(SCORE,-m,10*eps);
910s ***** assert(latent,[1.5;.5],10*eps);
910s ***** assert(tsquare,[4;4;4]/3,10*eps);
910s  [COEFF,SCORE,latent,tsquare] = pca(x, "Economy", false, "weights", [1 2 1], "variableweights", "variance");
910s ***** assert(COEFF, [0.632455532033676 -0.632455532033676; 0.741619848709566 0.741619848709566], 10*eps);
910s ***** assert(SCORE, [-0.622019449426284 0.959119380657905; -0.505649896847432 -0.505649896847431; 1.633319243121148 0.052180413036957], 10*eps);
910s ***** assert(latent, [1.783001790889027; 0.716998209110974], 10*eps);
910s ***** xtest assert(tsquare, [1.5; 0.5; 1.5], 10*eps);  #currently, [4; 2; 4]/3 is actually returned; see comments above
910s !!!!! known failure
910s ASSERT errors for:  assert (tsquare,[1.5; 0.5; 1.5],10 * eps)
910s 
910s   Location  |  Observed  |  Expected  |  Reason
910s     (1)         1.3333        1.5        Abs err 0.16667 exceeds tol 2.2204e-15 by 0.2
910s     (2)        0.66667        0.5        Abs err 0.16667 exceeds tol 2.2204e-15 by 0.2
910s     (3)         1.3333        1.5        Abs err 0.16667 exceeds tol 2.2204e-15 by 0.2
910s ***** test
910s  x=x';
910s  [COEFF,SCORE,latent,tsquare] = pca(x, "Economy", false);
910s  m=[sqrt(2),sqrt(2),0;-sqrt(2),sqrt(2),0;0,0,2]/2;
910s  m(:,1) = m(:,1)*sign(COEFF(1,1));
910s  m(:,2) = m(:,2)*sign(COEFF(1,2));
910s  m(:,3) = m(:,3)*sign(COEFF(3,3));
910s ***** assert(COEFF,m,10*eps);
910s ***** assert(SCORE(:,1),-m(1:2,1),10*eps);
910s ***** assert(SCORE(:,2:3),zeros(2),10*eps);
910s ***** assert(latent,[1;0;0],10*eps);
910s ***** assert(tsquare,[0.5;0.5],10*eps)
910s ***** test
910s  [COEFF,SCORE,latent,tsquare] = pca(x);
910s ***** assert(COEFF,m(:, 1),10*eps);
910s ***** assert(SCORE,-m(1:2,1),10*eps);
910s ***** assert(latent,[1],10*eps);
910s ***** assert(tsquare,[0.5;0.5],10*eps)
910s ***** error <invalid algorithm> pca([1 2; 3 4], "Algorithm", "xxx")
910s ***** error <'centered' requires a boolean value> pca([1 2; 3 4], "Centered", "xxx")
910s ***** error <must be a positive integer> pca([1 2; 3 4], "NumComponents", -4)
910s ***** error <invalid value for rows> pca([1 2; 3 4], "Rows", 1)
910s ***** error <weights must be> pca([1 2; 3 4], "Weights", [1 2 3])
910s ***** error <weights must be> pca([1 2; 3 4], "Weights", [-1 2])
910s ***** error <variable weights must be> pca([1 2; 3 4], "VariableWeights", [-1 2])
910s ***** error <variable weights must be> pca([1 2; 3 4], "VariableWeights", "xxx")
910s ***** error <unknown property> pca([1 2; 3 4], "XXX", 1)
910s 32 tests, 31 passed, 1 known failure, 0 skipped
910s [inst/pcacov.m]
910s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/pcacov.m
910s ***** demo
910s  x = [ 7    26     6    60;
910s        1    29    15    52;
910s       11    56     8    20;
910s       11    31     8    47;
910s        7    52     6    33;
910s       11    55     9    22;
910s        3    71    17     6;
910s        1    31    22    44;
910s        2    54    18    22;
910s       21    47     4    26;
910s        1    40    23    34;
910s       11    66     9    12;
910s       10    68     8    12
910s      ];
910s  Kxx = cov (x);
910s  [coeff, latent, explained] = pcacov (Kxx)
910s ***** test
910s  load hald
910s  Kxx = cov (ingredients);
910s  [coeff,latent,explained] = pcacov(Kxx);
910s  c_out = [-0.0678, -0.6460,  0.5673, 0.5062; ...
910s           -0.6785, -0.0200, -0.5440, 0.4933; ...
910s            0.0290,  0.7553,  0.4036, 0.5156; ...
910s            0.7309, -0.1085, -0.4684, 0.4844];
910s  l_out = [517.7969; 67.4964; 12.4054; 0.2372];
910s  e_out = [ 86.5974; 11.2882;  2.0747; 0.0397];
910s  assert (coeff, c_out, 1e-4);
910s  assert (latent, l_out, 1e-4);
910s  assert (explained, e_out, 1e-4);
910s ***** error<pcacov: K must be a square matrix.> pcacov (ones (2,3))
910s ***** error<pcacov: K must be a square matrix.> pcacov (ones (3,3,3))
910s 3 tests, 3 passed, 0 known failure, 0 skipped
910s [inst/pcares.m]
910s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/pcares.m
910s ***** demo
910s  x = [ 7    26     6    60;
910s        1    29    15    52;
910s       11    56     8    20;
910s       11    31     8    47;
910s        7    52     6    33;
910s       11    55     9    22;
910s        3    71    17     6;
910s        1    31    22    44;
910s        2    54    18    22;
910s       21    47     4    26;
910s        1    40    23    34;
910s       11    66     9    12;
910s       10    68     8    12];
910s 
910s  ## As we increase the number of principal components, the norm
910s  ## of the residuals matrix will decrease
910s  r1 = pcares (x,1);
910s  n1 = norm (r1)
910s  r2 = pcares (x,2);
910s  n2 = norm (r2)
910s  r3 = pcares (x,3);
910s  n3 = norm (r3)
910s  r4 = pcares (x,4);
910s  n4 = norm (r4)
910s ***** test
910s  load hald
910s  r1 = pcares (ingredients,1);
910s  r2 = pcares (ingredients,2);
910s  r3 = pcares (ingredients,3);
910s  assert (r1(1,:), [2.0350,  2.8304, -6.8378, 3.0879], 1e-4);
910s  assert (r2(1,:), [-2.4037, 2.6930, -1.6482, 2.3425], 1e-4);
910s  assert (r3(1,:), [ 0.2008, 0.1957,  0.2045, 0.1921], 1e-4);
910s ***** error<pcares: too few input arguments.> pcares (ones (20, 3))
910s ***** error<pcares: NDIM must be less than or equal to the column of X.> ...
910s  pcares (ones (30, 2), 3)
910s 3 tests, 3 passed, 0 known failure, 0 skipped
910s [inst/pdist.m]
910s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/pdist.m
910s ***** shared xy, t, eucl, x
910s  xy = [0 1; 0 2; 7 6; 5 6];
910s  t = 1e-3;
910s  eucl = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
910s  x = [1 2 3; 4 5 6; 7 8 9; 3 2 1];
910s ***** assert (pdist (xy),                 [1.000 8.602 7.071 8.062 6.403 2.000], t);
910s ***** assert (pdist (xy, eucl),           [1.000 8.602 7.071 8.062 6.403 2.000], t);
910s ***** assert (pdist (xy, "euclidean"),    [1.000 8.602 7.071 8.062 6.403 2.000], t);
910s ***** assert (pdist (xy, "seuclidean"),   [0.380 2.735 2.363 2.486 2.070 0.561], t);
910s ***** assert (pdist (xy, "mahalanobis"),  [1.384 1.967 2.446 2.384 1.535 2.045], t);
910s ***** assert (pdist (xy, "cityblock"),    [1.000 12.00 10.00 11.00 9.000 2.000], t);
910s ***** assert (pdist (xy, "minkowski"),    [1.000 8.602 7.071 8.062 6.403 2.000], t);
910s ***** assert (pdist (xy, "minkowski", 3), [1.000 7.763 6.299 7.410 5.738 2.000], t);
910s ***** assert (pdist (xy, "cosine"),       [0.000 0.349 0.231 0.349 0.231 0.013], t);
910s ***** assert (pdist (xy, "correlation"),  [0.000 2.000 0.000 2.000 0.000 2.000], t);
910s ***** assert (pdist (xy, "spearman"),     [0.000 2.000 0.000 2.000 0.000 2.000], t);
910s ***** assert (pdist (xy, "hamming"),      [0.500 1.000 1.000 1.000 1.000 0.500], t);
910s ***** assert (pdist (xy, "jaccard"),      [1.000 1.000 1.000 1.000 1.000 0.500], t);
910s ***** assert (pdist (xy, "chebychev"),    [1.000 7.000 5.000 7.000 5.000 2.000], t);
910s ***** assert (pdist (x), [5.1962, 10.3923, 2.8284, 5.1962, 5.9161, 10.7703], 1e-4);
910s ***** assert (pdist (x, "euclidean"), ...
910s         [5.1962, 10.3923, 2.8284, 5.1962, 5.9161, 10.7703], 1e-4);
910s ***** assert (pdist (x, eucl), ...
910s         [5.1962, 10.3923, 2.8284, 5.1962, 5.9161, 10.7703], 1e-4);
910s ***** assert (pdist (x, "squaredeuclidean"), [27, 108, 8, 27, 35, 116]);
910s ***** assert (pdist (x, "seuclidean"), ...
910s         [1.8071, 3.6142, 0.9831, 1.8071, 1.8143, 3.4854], 1e-4);
910s ***** warning<pdist: matrix is close to singular> ...
910s  pdist (x, "mahalanobis");
910s ***** assert (pdist (x, "cityblock"), [9, 18, 4, 9, 9, 18]);
910s ***** assert (pdist (x, "minkowski"), ...
910s         [5.1962, 10.3923, 2.8284, 5.1962, 5.9161, 10.7703], 1e-4);
910s ***** assert (pdist (x, "minkowski", 3), ...
910s         [4.3267, 8.6535, 2.5198, 4.3267, 5.3485, 9.2521], 1e-4);
910s ***** assert (pdist (x, "cosine"), ...
910s         [0.0254, 0.0406, 0.2857, 0.0018, 0.1472, 0.1173], 1e-4);
910s ***** assert (pdist (x, "correlation"), [0, 0, 2, 0, 2, 2], 1e-14);
910s ***** assert (pdist (x, "spearman"), [0, 0, 2, 0, 2, 2], 1e-14);
910s ***** assert (pdist (x, "hamming"), [1, 1, 2/3, 1, 1, 1]);
910s ***** assert (pdist (x, "jaccard"), [1, 1, 2/3, 1, 1, 1]);
910s ***** assert (pdist (x, "chebychev"), [3, 6, 2, 3, 5, 8]);
910s 29 tests, 29 passed, 0 known failure, 0 skipped
910s [inst/pdist2.m]
910s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/pdist2.m
910s ***** shared x, y, xx
910s  x = [1, 1, 1; 2, 2, 2; 3, 3, 3];
910s  y = [0, 0, 0; 1, 2, 3; 0, 2, 4; 4, 7, 1];
910s  xx = [1 2 3; 4 5 6; 7 8 9; 3 2 1];
910s ***** test
910s  d = sqrt([3, 5, 11, 45; 12, 2, 8, 30; 27, 5, 11, 21]);
910s  assert (pdist2 (x, y), d);
910s ***** test
910s  d = [5.1962, 2.2361, 3.3166, 6.7082; ...
910s       3.4641, 2.2361, 3.3166, 5.4772];
910s  i = [3, 1, 1, 1; 2, 3, 3, 2];
910s  [D, I] = pdist2 (x, y, "euclidean", "largest", 2);
910s  assert ({D, I}, {d, i}, 1e-4);
910s ***** test
910s  d = [1.7321, 1.4142, 2.8284, 4.5826; ...
910s       3.4641, 2.2361, 3.3166, 5.4772];
910s  i = [1, 2, 2, 3;2, 1, 1, 2];
910s  [D, I] = pdist2 (x, y, "euclidean", "smallest", 2);
910s  assert ({D, I}, {d, i}, 1e-4);
910s ***** test
910s  yy = [1 2 3;5 6 7;9 5 1];
910s  d = [0, 6.1644, 5.3852; 1.4142, 6.9282, 8.7750; ...
910s       3.7417, 7.0711, 9.9499; 6.1644, 10.4881, 10.3441];
910s  i = [2, 4, 4; 3, 2, 2; 1, 3, 3; 4, 1, 1];
910s  [D, I] = pdist2 (y, yy, "euclidean", "smallest", 4);
910s  assert ({D, I}, {d, i}, 1e-4);
910s ***** test
910s  yy = [1 2 3;5 6 7;9 5 1];
910s  d = [0, 38, 29; 2, 48, 77; 14, 50, 99; 38, 110, 107];
910s  i = [2, 4, 4; 3, 2, 2; 1, 3, 3; 4, 1, 1];
910s  [D, I] = pdist2 (y, yy, "squaredeuclidean", "smallest", 4);
910s  assert ({D, I}, {d, i}, 1e-4);
910s ***** test
910s  yy = [1 2 3;5 6 7;9 5 1];
910s  d = [0, 3.3256, 2.7249; 0.7610, 3.3453, 4.4799; ...
910s       1.8514, 3.3869, 5.0703; 2.5525, 5.0709, 5.1297];
910s  i = [2, 2, 4; 3, 4, 2; 1, 3, 1; 4, 1, 3];
910s  [D, I] = pdist2 (y, yy, "seuclidean", "smallest", 4);
910s  assert ({D, I}, {d, i}, 1e-4);
910s ***** test
910s  d = [2.1213, 4.2426, 6.3640; 1.2247, 2.4495, 4.4159; ...
910s       3.2404, 4.8990, 6.8191; 2.7386, 4.2426, 6.1237];
910s  assert (pdist2 (y, x, "mahalanobis"), d, 1e-4);
910s ***** test
910s  xx = [1, 3, 4; 3, 5, 4; 8, 7, 6];
910s  d = [1.3053, 1.8257, 15.0499; 1.3053, 3.3665, 16.5680];
910s  i = [2, 2, 2; 3, 4, 4];
910s  [D, I] = pdist2 (y, xx, "mahalanobis", "smallest", 2);
910s  assert ({D, I}, {d, i}, 1e-4);
910s ***** test
910s  d = [2.5240, 4.1633, 17.3638; 2.0905, 3.9158, 17.0147];
910s  i = [1, 1, 3; 4, 3, 1];
910s  [D, I] = pdist2 (y, xx, "mahalanobis", "largest", 2);
910s  assert ({D, I}, {d, i}, 1e-4);
910s ***** test
910s  d = [3, 3, 5, 9; 6, 2, 4, 8; 9, 3, 5, 7];
910s  assert (pdist2 (x, y, "cityblock"), d);
910s ***** test
910s  d = [1, 2, 3, 6; 2, 1, 2, 5; 3, 2, 3, 4];
910s  assert (pdist2 (x, y, "chebychev"), d);
910s ***** test
910s  d = repmat ([NaN, 0.0742, 0.2254, 0.1472], [3, 1]);
910s  assert (pdist2 (x, y, "cosine"), d, 1e-4);
910s ***** test
910s  yy = [1 2 3;5 6 7;9 5 1];
910s  d = [0, 0, 0.5; 0, 0, 2; 1.5, 1.5, 2; NaN, NaN, NaN];
910s  i = [2, 2, 4; 3, 3, 2; 4, 4, 3; 1, 1, 1];
910s  [D, I] = pdist2 (y, yy, "correlation", "smallest", 4);
910s  assert ({D, I}, {d, i}, eps);
910s  [D, I] = pdist2 (y, yy, "spearman", "smallest", 4);
910s  assert ({D, I}, {d, i}, eps);
910s ***** test
910s  d = [1, 2/3, 1, 1; 1, 2/3, 1, 1; 1, 2/3, 2/3, 2/3];
910s  i = [1, 1, 1, 2; 2, 2, 3, 3; 3, 3, 2, 1];
910s  [D, I] = pdist2 (x, y, "hamming", "largest", 4);
910s  assert ({D, I}, {d, i}, eps);
910s  [D, I] = pdist2 (x, y, "jaccard", "largest", 4);
910s  assert ({D, I}, {d, i}, eps);
910s ***** test
910s  xx = [1, 2, 3, 4; 2, 3, 4, 5; 3, 4, 5, 6];
910s  yy = [1, 2, 2, 3; 2, 3, 3, 4];
910s  [D, I] = pdist2 (x, y, "euclidean", "Smallest", 4);
910s  eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
910s  [d, i] = pdist2 (x, y, eucldist, "Smallest", 4);
910s  assert ({D, I}, {d, i});
910s ***** warning<pdist2: matrix is close to singular> ...
910s  pdist2 (xx, xx, "mahalanobis");
910s ***** error<pdist2: too few input arguments.> pdist2 (1)
910s ***** error<pdist2: X and Y must have equal number of columns.> ...
910s  pdist2 (ones (4, 5), ones (4))
910s ***** error<pdist2: X and Y must be 2 dimensional matrices.> ...
910s  pdist2 (ones (4, 2, 3), ones (3, 2))
910s ***** error<pdist2: missing value in optional name/value paired arguments.> ...
910s  pdist2 (ones (3), ones (3), "euclidean", "Largest")
910s ***** error<pdist2: missing value in optional name/value paired arguments.> ...
910s  pdist2 (ones (3), ones (3), "minkowski", 3, "Largest")
910s ***** error<pdist2: invalid NAME in optional pairs of arguments.> ...
910s  pdist2 (ones (3), ones (3), "minkowski", 3, "large", 4)
910s ***** error<pdist2: you can only use either Smallest or Largest.> ...
910s  pdist2 (ones (3), ones (3), "minkowski", 3, "Largest", 4, "smallest", 5)
910s ***** error<pdist2: Smallest or Largest must be specified to compute second output.> ...
910s  [d, i] = pdist2(ones (3), ones (3), "minkowski", 3)
910s ***** error<pdist2: DistParameter for standardized euclidean must be a vector of> ...
910s  pdist2 (ones (3), ones (3), "seuclidean", 3)
910s ***** error<pdist2: DistParameter for standardized euclidean must be a nonnegative> ...
910s  pdist2 (ones (3), ones (3), "seuclidean", [1, -1, 3])
910s ***** error<pdist2: DistParameter for mahalanobis distance must be a covariance> ...
910s  pdist2 (ones (3), eye (3), "mahalanobis", eye(2))
910s ***** error<pdist2: covariance matrix for mahalanobis distance must be symmetric> ...
910s  pdist2 (ones (3), eye (3), "mahalanobis", ones(3))
910s ***** error<pdist2: DistParameter for minkowski distance must be a positive scalar.> ...
910s  pdist2 (ones (3), eye (3), "minkowski", 0)
910s ***** error<pdist2: DistParameter for minkowski distance must be a positive scalar.> ...
910s  pdist2 (ones (3), eye (3), "minkowski", -5)
910s ***** error<pdist2: DistParameter for minkowski distance must be a positive scalar.> ...
910s  pdist2 (ones (3), eye (3), "minkowski", [1, 2])
910s ***** error<pdist2: invalid function handle for distance metric.> ...
910s  pdist2 (ones (3), ones (3), @(v,m) sqrt(repmat(v,rows(m),1)-m,2))
910s ***** error<pdist2: custom distance function produces wrong output size.> ...
910s  pdist2 (ones (3), ones (3), @(v,m) sqrt(sum(sumsq(repmat(v,rows(m),1)-m,2))))
910s 33 tests, 33 passed, 0 known failure, 0 skipped
910s [inst/plsregress.m]
910s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/plsregress.m
910s ***** demo
910s  ## Perform Partial Least-Squares Regression
910s 
910s  ## Load the spectra data set and use the near infrared (NIR) spectral
910s  ## intensities (NIR) as the predictor and the corresponding octave
910s  ## ratings (octave) as the response.
910s  load spectra
910s 
910s  ## Perform PLS regression with 10 components
910s  [xload, yload, xscore, yscore, coef, ptcVar] = plsregress (NIR, octane, 10);
910s 
910s  ## Plot the percentage of explained variance in the response variable
910s  ## (PCTVAR) as a function of the number of components.
910s  plot (1:10, cumsum (100 * ptcVar(2,:)), "-ro");
910s  xlim ([1, 10]);
910s  xlabel ("Number of PLS components");
910s  ylabel ("Percentage of Explained Variance in octane");
910s  title ("Explained Variance per PLS components");
910s 
910s  ## Compute the fitted response and display the residuals.
910s  octane_fitted = [ones(size(NIR,1),1), NIR] * coef;
910s  residuals = octane - octane_fitted;
910s  figure
910s  stem (residuals, "color", "r", "markersize", 4, "markeredgecolor", "r")
910s  xlabel ("Observations");
910s  ylabel ("Residuals");
910s  title ("Residuals in octane's fitted responce");
910s ***** demo
910s  ## Calculate Variable Importance in Projection (VIP) for PLS Regression
910s 
910s  ## Load the spectra data set and use the near infrared (NIR) spectral
910s  ## intensities (NIR) as the predictor and the corresponding octave
910s  ## ratings (octave) as the response.  Variables with a VIP score greater than
910s  ## 1 are considered important for the projection of the PLS regression model.
910s  load spectra
910s 
910s  ## Perform PLS regression with 10 components
910s  [xload, yload, xscore, yscore, coef, pctVar, mse, stats] = ...
910s                                                  plsregress (NIR, octane, 10);
910s 
910s  ## Calculate the normalized PLS weights
910s  W0 = stats.W ./ sqrt(sum(stats.W.^2,1));
910s 
910s  ## Calculate the VIP scores for 10 components
910s  nobs = size (xload, 1);
910s  SS = sum (xscore .^ 2, 1) .* sum (yload .^ 2, 1);
910s  VIPscore = sqrt (nobs * sum (SS .* (W0 .^ 2), 2) ./ sum (SS, 2));
910s 
910s  ## Find variables with a VIP score greater than or equal to 1
910s  VIPidx = find (VIPscore >= 1);
910s 
910s  ## Plot the VIP scores
910s  scatter (1:length (VIPscore), VIPscore, "xb");
910s  hold on
910s  scatter (VIPidx, VIPscore (VIPidx), "xr");
910s  plot ([1, length(VIPscore)], [1, 1], "--k");
910s  hold off
910s  axis ("tight");
910s  xlabel ("Predictor Variables");
910s  ylabel ("VIP scores");
910s  title ("VIP scores for each predictror variable with 10 components");
910s ***** test
910s  load spectra
910s  [xload, yload, xscore, yscore, coef, pctVar] = plsregress (NIR, octane, 10);
910s  xload1_out = [-0.0170, 0.0039, 0.0095,  0.0258, 0.0025, ...
910s                -0.0075, 0.0000, 0.0018, -0.0027, 0.0020];
910s  yload_out = [6.6384, 9.3106, 2.0505, 0.6471, 0.9625, ...
910s               0.5905, 0.4244, 0.2437, 0.3516, 0.2548];
910s  xscore1_out = [-0.0401, -0.1764, -0.0340, 0.1669,  0.1041, ...
910s                 -0.2067,  0.0457,  0.1565, 0.0706, -0.1471];
910s  yscore1_out = [-12.4635, -15.0003,  0.0638,  0.0652, -0.0070, ...
910s                  -0.0634,   0.0062, -0.0012, -0.0151, -0.0173];
910s  assert (xload(1,:), xload1_out, 1e-4);
910s  assert (yload, yload_out, 1e-4);
910s  assert (xscore(1,:), xscore1_out, 1e-4);
910s  assert (yscore(1,:), yscore1_out, 1e-4);
910s ***** test
910s  load spectra
910s  [xload, yload, xscore, yscore, coef, pctVar] = plsregress (NIR, octane, 5);
910s  xload1_out = [-0.0170, 0.0039, 0.0095, 0.0258, 0.0025];
910s  yload_out = [6.6384, 9.3106, 2.0505, 0.6471, 0.9625];
910s  xscore1_out = [-0.0401, -0.1764, -0.0340, 0.1669, 0.1041];
910s  yscore1_out = [-12.4635, -15.0003, 0.0638, 0.0652, -0.0070];
910s  assert (xload(1,:), xload1_out, 1e-4);
910s  assert (yload, yload_out, 1e-4);
910s  assert (xscore(1,:), xscore1_out, 1e-4);
910s  assert (yscore(1,:), yscore1_out, 1e-4);
910s ***** error<plsregress: function called with too few input arguments.>
910s  plsregress (1)
910s ***** error<plsregress: X and Y must be real matrices.> plsregress (1, "asd")
910s ***** error<plsregress: X and Y must be real matrices.> plsregress (1, {1,2,3})
910s ***** error<plsregress: X and Y must be real matrices.> plsregress ("asd", 1)
910s ***** error<plsregress: X and Y must be real matrices.> plsregress ({1,2,3}, 1)
910s ***** error<plsregress: X and Y observations mismatch.> ...
910s  plsregress (ones (20,3), ones (15,1))
910s ***** error<plsregress: invalid value for NCOMP.> ...
910s  plsregress (ones (20,3), ones (20,1), 0)
910s ***** error<plsregress: invalid value for NCOMP.> ...
910s  plsregress (ones (20,3), ones (20,1), -5)
910s ***** error<plsregress: invalid value for NCOMP.> ...
910s  plsregress (ones (20,3), ones (20,1), 3.2)
910s ***** error<plsregress: invalid value for NCOMP.> ...
910s  plsregress (ones (20,3), ones (20,1), [2, 3])
910s ***** error<plsregress: NCOMP exceeds maximum components for X.> ...
910s  plsregress (ones (20,3), ones (20,1), 4)
910s ***** error<plsregress: invalid VALUE for 'cv' optional argument.> ...
910s  plsregress (ones (20,3), ones (20,1), 3, "cv", 4.5)
910s ***** error<plsregress: invalid VALUE for 'cv' optional argument.> ...
910s  plsregress (ones (20,3), ones (20,1), 3, "cv", -1)
910s ***** error<plsregress: invalid VALUE for 'cv' optional argument.> ...
910s  plsregress (ones (20,3), ones (20,1), 3, "cv", "somestring")
910s ***** error<plsregress: invalid VALUE for 'mcreps' optional argument.> ...
910s  plsregress (ones (20,3), ones (20,1), 3, "cv", 3, "mcreps", 2.2)
910s ***** error<plsregress: invalid VALUE for 'mcreps' optional argument.> ...
910s  plsregress (ones (20,3), ones (20,1), 3, "cv", 3, "mcreps", -2)
910s ***** error<plsregress: invalid VALUE for 'mcreps' optional argument.> ...
910s  plsregress (ones (20,3), ones (20,1), 3, "cv", 3, "mcreps", [1, 2])
910s ***** error<plsregress: invalid NAME argument.> ...
910s  plsregress (ones (20,3), ones (20,1), 3, "Name", 3, "mcreps", 1)
910s ***** error<plsregress: invalid NAME argument.> ...
910s  plsregress (ones (20,3), ones (20,1), 3, "cv", 3, "Name", 1)
910s ***** error<plsregress: 'mcreps' must be 1 when 'resubstitution' is specified> ...
910s  plsregress (ones (20,3), ones (20,1), 3, "mcreps", 2)
910s ***** error<plsregress: 'mcreps' must be 1 when 'resubstitution' is specified> ...
910s  plsregress (ones (20,3), ones (20,1), 3, "cv", "resubstitution", "mcreps", 2)
910s ***** error<Invalid call to plsregress.  Correct usage is:> plsregress (1, 2)
911s 24 tests, 24 passed, 0 known failure, 0 skipped
911s [inst/ppplot.m]
911s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/ppplot.m
911s ***** test
911s  hf = figure ("visible", "off");
911s  unwind_protect
911s    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]);
911s  unwind_protect_cleanup
911s    close (hf);
911s  end_unwind_protect
911s ***** error ppplot ()
911s ***** error <ppplot: X must be a numeric vector> ppplot (ones (2,2))
911s ***** error <ppplot: X must be a numeric vector> ppplot (1, 2)
911s ***** error <ppplot: DIST must be a strin> ppplot ([1 2 3 4], 2)
911s 5 tests, 5 passed, 0 known failure, 0 skipped
911s [inst/princomp.m]
911s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/princomp.m
911s ***** shared COEFF,SCORE,latent,tsquare,m,x,R,V,lambda,i,S,F
911s ***** test
911s  x=[7 4 3
911s     4 1 8
911s     6 3 5
911s     8 6 1
911s     8 5 7
911s     7 2 9
911s     5 3 3
911s     9 5 8
911s     7 4 5
911s     8 2 2];
911s  R = corrcoef (x);
911s  [V, lambda] = eig (R);
911s  [~, i] = sort(diag(lambda), "descend"); #arrange largest PC first
911s  S = V(:, i) * diag(sqrt(diag(lambda)(i)));
911s  ## contribution of first 2 PCs to each original variable
911s ***** assert(diag(S(:, 1:2)*S(:, 1:2)'), [0.8662; 0.8420; 0.9876], 1E-4);
911s  B = V(:, i) * diag( 1./ sqrt(diag(lambda)(i)));
911s  F = zscore(x)*B;
911s  [COEFF,SCORE,latent,tsquare] = princomp(zscore(x, 1));
911s ***** assert(tsquare,sumsq(F, 2),1E4*eps);
911s ***** test
911s  x=[1,2,3;2,1,3]';
911s  [COEFF,SCORE,latent,tsquare] = princomp(x);
911s  m=[sqrt(2),sqrt(2);sqrt(2),-sqrt(2);-2*sqrt(2),0]/2;
911s  m(:,1) = m(:,1)*sign(COEFF(1,1));
911s  m(:,2) = m(:,2)*sign(COEFF(1,2));
911s ***** assert(COEFF,m(1:2,:),10*eps);
911s ***** assert(SCORE,-m,10*eps);
911s ***** assert(latent,[1.5;.5],10*eps);
911s ***** assert(tsquare,[4;4;4]/3,10*eps);
911s ***** test
911s  x=x';
911s  [COEFF,SCORE,latent,tsquare] = princomp(x);
911s  m=[sqrt(2),sqrt(2),0;-sqrt(2),sqrt(2),0;0,0,2]/2;
911s  m(:,1) = m(:,1)*sign(COEFF(1,1));
911s  m(:,2) = m(:,2)*sign(COEFF(1,2));
911s  m(:,3) = m(:,3)*sign(COEFF(3,3));
911s ***** assert(COEFF,m,10*eps);
911s ***** assert(SCORE(:,1),-m(1:2,1),10*eps);
911s ***** assert(SCORE(:,2:3),zeros(2),10*eps);
911s ***** assert(latent,[1;0;0],10*eps);
911s ***** assert(tsquare,[0.5;0.5],10*eps)
911s ***** test
911s  [COEFF,SCORE,latent,tsquare] = princomp(x, "econ");
911s ***** assert(COEFF,m(:, 1),10*eps);
911s ***** assert(SCORE,-m(1:2,1),10*eps);
911s ***** assert(latent,[1],10*eps);
911s ***** assert(tsquare,[0.5;0.5],10*eps)
911s 19 tests, 19 passed, 0 known failure, 0 skipped
911s [inst/probit.m]
911s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/probit.m
911s ***** assert (probit ([-1, 0, 0.5, 1, 2]), [NaN, -Inf, 0, Inf, NaN])
911s ***** assert (probit ([0.2, 0.99]), norminv ([0.2, 0.99]))
911s ***** error probit ()
911s ***** error probit (1, 2)
911s 4 tests, 4 passed, 0 known failure, 0 skipped
911s [inst/procrustes.m]
911s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/procrustes.m
911s ***** demo
911s  ## Create some random points in two dimensions
911s  n = 10;
911s  randn ("seed", 1);
911s  X = normrnd (0, 1, [n, 2]);
911s 
911s  ## Those same points, rotated, scaled, translated, plus some noise
911s  S = [0.5, -sqrt(3)/2; sqrt(3)/2, 0.5]; # rotate 60 degrees
911s  Y = normrnd (0.5*X*S + 2, 0.05, n, 2);
911s 
911s  ## Conform Y to X, plot original X and Y, and transformed Y
911s  [d, Z] = procrustes (X, Y);
911s  plot (X(:,1), X(:,2), "rx", Y(:,1), Y(:,2), "b.", Z(:,1), Z(:,2), "bx");
911s ***** demo
911s  ## Find Procrustes distance and plot superimposed shape
911s 
911s  X = [40 88; 51 88; 35 78; 36 75; 39 72; 44 71; 48 71; 52 74; 55 77];
911s  Y = [36 43; 48 42; 31 26; 33 28; 37 30; 40 31; 45 30; 48 28; 51 24];
911s  plot (X(:,1),X(:,2),"x");
911s  hold on
911s  plot (Y(:,1),Y(:,2),"o");
911s  xlim ([0 100]);
911s  ylim ([0 100]);
911s  legend ("Target shape (X)", "Source shape (Y)");
911s  [d, Z] = procrustes (X, Y)
911s  plot (Z(:,1), Z(:,2), "s");
911s  legend ("Target shape (X)", "Source shape (Y)", "Transformed shape (Z)");
911s  hold off
911s ***** demo
911s  ## Apply Procrustes transformation to larger set of points
911s 
911s  ## Create matrices with landmark points for two triangles
911s  X = [5, 0; 5, 5; 8, 5];   # target
911s  Y = [0, 0; 1, 0; 1, 1];   # source
911s 
911s  ## Create a matrix with more points on the source triangle
911s  Y_mp = [linspace(Y(1,1),Y(2,1),10)', linspace(Y(1,2),Y(2,2),10)'; ...
911s          linspace(Y(2,1),Y(3,1),10)', linspace(Y(2,2),Y(3,2),10)'; ...
911s          linspace(Y(3,1),Y(1,1),10)', linspace(Y(3,2),Y(1,2),10)'];
911s 
911s  ## Plot both shapes, including the larger set of points for the source shape
911s  plot ([X(:,1); X(1,1)], [X(:,2); X(1,2)], "bx-");
911s  hold on
911s  plot ([Y(:,1); Y(1,1)], [Y(:,2); Y(1,2)], "ro-", "MarkerFaceColor", "r");
911s  plot (Y_mp(:,1), Y_mp(:,2), "ro");
911s  xlim ([-1 10]);
911s  ylim ([-1 6]);
911s  legend ("Target shape (X)", "Source shape (Y)", ...
911s          "More points on Y", "Location", "northwest");
911s  hold off
911s 
911s  ## Obtain the Procrustes transformation
911s  [d, Z, transform] = procrustes (X, Y)
911s 
911s  ## Use the Procrustes transformation to superimpose the more points (Y_mp)
911s  ## on the source shape onto the target shape, and then visualize the results.
911s  Z_mp = transform.b * Y_mp * transform.T + transform.c(1,:);
911s  figure
911s  plot ([X(:,1); X(1,1)], [X(:,2); X(1,2)], "bx-");
911s  hold on
911s  plot ([Y(:,1); Y(1,1)], [Y(:,2); Y(1,2)], "ro-", "MarkerFaceColor", "r");
911s  plot (Y_mp(:,1), Y_mp(:,2), "ro");
911s  xlim ([-1 10]);
911s  ylim ([-1 6]);
911s  plot ([Z(:,1); Z(1,1)],[Z(:,2); Z(1,2)],"ks-","MarkerFaceColor","k");
911s  plot (Z_mp(:,1),Z_mp(:,2),"ks");
911s  legend ("Target shape (X)", "Source shape (Y)", ...
911s          "More points on Y", "Transformed source shape (Z)", ...
911s          "Transformed additional points", "Location", "northwest");
911s  hold off
911s ***** demo
911s  ## Compare shapes without reflection
911s 
911s  T = [33, 93; 33, 87; 33, 80; 31, 72; 32, 65; 32, 58; 30, 72; ...
911s       28, 72; 25, 69; 22, 64; 23, 59; 26, 57; 30, 57];
911s  S = [48, 83; 48, 77; 48, 70; 48, 65; 49, 59; 49, 56; 50, 66; ...
911s       52, 66; 56, 65; 58, 61; 57, 57; 54, 56; 51, 55];
911s  plot (T(:,1), T(:,2), "x-");
911s  hold on
911s  plot (S(:,1), S(:,2), "o-");
911s  legend ("Target shape (d)", "Source shape (b)");
911s  hold off
911s  d_false = procrustes (T, S, "reflection", false);
911s  printf ("Procrustes distance without reflection: %f\n", d_false);
911s  d_true = procrustes (T, S, "reflection", true);
911s  printf ("Procrustes distance with reflection: %f\n", d_true);
911s  d_best = procrustes (T, S, "reflection", "best");
911s  printf ("Procrustes distance with best fit: %f\n", d_true);
911s ***** error procrustes ();
911s ***** error procrustes (1, 2, 3, 4, 5, 6);
911s ***** error<procrustes: X and Y must be 2-dimensional matrices.> ...
911s  procrustes (ones (2, 2, 2), ones (2, 2, 2));
911s ***** error<procrustes: values in X and Y must be real.> ...
911s  procrustes ([1, 2; -3, 4; 2, 3], [1, 2; -3, 4; 2, 3+i]);
911s ***** error<procrustes: values in X and Y must be real.> ...
911s  procrustes ([1, 2; -3, 4; 2, 3], [1, 2; -3, 4; 2, NaN]);
911s ***** error<procrustes: values in X and Y must be real.> ...
911s  procrustes ([1, 2; -3, 4; 2, 3], [1, 2; -3, 4; 2, Inf]);
911s ***** error<procrustes: X and Y must have equal number of rows.> ...
911s  procrustes (ones (10 ,3), ones (11, 3));
911s ***** error<procrustes: X must have at least as many columns as Y.> ...
911s  procrustes (ones (10 ,3), ones (10, 4));
911s ***** error<procrustes: optional arguments must be in Name-Value pairs.> ...
911s  procrustes (ones (10 ,3), ones (10, 3), "reflection");
911s ***** error<procrustes: optional arguments must be in Name-Value pairs.> ...
911s  procrustes (ones (10 ,3), ones (10, 3), true);
911s ***** error<procrustes: invalid value for scaling.> ...
911s  procrustes (ones (10 ,3), ones (10, 3), "scaling", 0);
911s ***** error<procrustes: invalid value for scaling.> ...
911s  procrustes (ones (10 ,3), ones (10, 3), "scaling", [true true]);
911s ***** error<procrustes: invalid value for reflection.> ...
911s  procrustes (ones (10 ,3), ones (10, 3), "reflection", 1);
911s ***** error<procrustes: invalid value for reflection.> ...
911s  procrustes (ones (10 ,3), ones (10, 3), "reflection", "some");
911s ***** error<procrustes: invalid name for optional arguments.> ...
911s  procrustes (ones (10 ,3), ones (10, 3), "param1", "some");
911s 15 tests, 15 passed, 0 known failure, 0 skipped
911s [inst/qqplot.m]
911s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/qqplot.m
911s ***** test
911s  hf = figure ("visible", "off");
911s  unwind_protect
911s    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]);
911s  unwind_protect_cleanup
911s    close (hf);
911s  end_unwind_protect
911s ***** error qqplot ()
911s ***** error <qqplot: X must be a numeric vector> qqplot ({1})
911s ***** error <qqplot: X must be a numeric vector> qqplot (ones (2,2))
911s ***** error <qqplot: X must be a numeric vecto> qqplot (1, "foobar")
911s ***** error <qqplot: no inverse CDF found> qqplot ([1 2 3], "foobar")
911s 6 tests, 6 passed, 0 known failure, 0 skipped
911s [inst/qrandn.m]
911s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/qrandn.m
911s ***** demo
911s  z = qrandn (-5, 5e6);
911s  [c x] = hist (z,linspace(-1.5,1.5,200),1);
911s  figure(1)
911s  plot(x,c,"r."); axis tight; axis([-1.5,1.5]);
911s 
911s  z = qrandn (-0.14286, 5e6);
911s  [c x] = hist (z,linspace(-2,2,200),1);
911s  figure(2)
911s  plot(x,c,"r."); axis tight; axis([-2,2]);
911s 
911s  z = qrandn (2.75, 5e6);
911s  [c x] = hist (z,linspace(-1e3,1e3,1e3),1);
911s  figure(3)
911s  semilogy(x,c,"r."); axis tight; axis([-100,100]);
911s 
911s  # ---------
911s  # Figures from the reference paper.
911s ***** error<qrandn: the parameter q must be a scalar.> qrandn ([1 2], 1)
911s ***** error<qrandn: the parameter q must be lower than 3.> qrandn (4, 1)
911s ***** error<qrandn: the parameter q must be lower than 3.> qrandn (3, 1)
911s ***** error qrandn (2.5, 1, 2, 3)
911s ***** error qrandn (2.5)
911s ***** test
911s  q = 1.5;
911s  s = [2, 3];
911s  z = qrandn (q, s);
911s  assert (isnumeric (z) && isequal (size (z), s));
911s 6 tests, 6 passed, 0 known failure, 0 skipped
911s [inst/randsample.m]
911s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/randsample.m
911s ***** test
911s  n = 20;
911s  k = 5;
911s  x = randsample(n, k);
911s  assert (size(x), [1 k]);
911s  x = randsample(n, k, true);
911s  assert (size(x), [1 k]);
911s  x = randsample(n, k, false);
911s  assert (size(x), [1 k]);
911s  x = randsample(n, k, true, ones(n, 1));
911s  assert (size(x), [1 k]);
911s  x = randsample(1:n, k);
911s  assert (size(x), [1 k]);
911s  x = randsample(1:n, k, true);
911s  assert (size(x), [1 k]);
911s  x = randsample(1:n, k, false);
911s  assert (size(x), [1 k]);
911s  x = randsample(1:n, k, true, ones(n, 1));
911s  assert (size(x), [1 k]);
911s  x = randsample((1:n)', k);
911s  assert (size(x), [k 1]);
911s  x = randsample((1:n)', k, true);
911s  assert (size(x), [k 1]);
911s  x = randsample((1:n)', k, false);
911s  assert (size(x), [k 1]);
911s  x = randsample((1:n)', k, true, ones(n, 1));
911s  assert (size(x), [k 1]);
911s  n = 10;
911s  k = 100;
911s  x = randsample(n, k, true, 1:n);
911s  assert (size(x), [1 k]);
911s  x = randsample((1:n)', k, true);
911s  assert (size(x), [k 1]);
911s  x = randsample(k, k, false, 1:k);
911s  assert (size(x), [1 k]);
912s 1 test, 1 passed, 0 known failure, 0 skipped
912s [inst/rangesearch.m]
912s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/rangesearch.m
912s ***** demo
912s  ## Generate 1000 random 2D points from each of five distinct multivariate
912s  ## normal distributions that form five separate classes
912s  N = 1000;
912s  d = 10;
912s  randn ("seed", 5);
912s  X1 = mvnrnd (d * [0, 0], eye (2), 1000);
912s  randn ("seed", 6);
912s  X2 = mvnrnd (d * [1, 1], eye (2), 1000);
912s  randn ("seed", 7);
912s  X3 = mvnrnd (d * [-1, -1], eye (2), 1000);
912s  randn ("seed", 8);
912s  X4 = mvnrnd (d * [1, -1], eye (2), 1000);
912s  randn ("seed", 8);
912s  X5 = mvnrnd (d * [-1, 1], eye (2), 1000);
912s  X = [X1; X2; X3; X4; X5];
912s 
912s  ## For each point in X, find the points in X that are within a radius d
912s  ## away from the points in X.
912s  Idx = rangesearch (X, X, d, "NSMethod", "exhaustive");
912s 
912s  ## Select the first point in X (corresponding to the first class) and find
912s  ## its nearest neighbors within the radius d.  Display these points in
912s  ## one color and the remaining points in a different color.
912s  x = X(1,:);
912s  nearestPoints = X (Idx{1},:);
912s  nonNearestIdx = true (size (X, 1), 1);
912s  nonNearestIdx(Idx{1}) = false;
912s 
912s  scatter (X(nonNearestIdx,1), X(nonNearestIdx,2))
912s  hold on
912s  scatter (nearestPoints(:,1),nearestPoints(:,2))
912s  scatter (x(1), x(2), "black", "filled")
912s  hold off
912s 
912s  ## Select the last point in X (corresponding to the fifth class) and find
912s  ## its nearest neighbors within the radius d.  Display these points in
912s  ## one color and the remaining points in a different color.
912s  x = X(end,:);
912s  nearestPoints = X (Idx{1},:);
912s  nonNearestIdx = true (size (X, 1), 1);
912s  nonNearestIdx(Idx{1}) = false;
912s 
912s  figure
912s  scatter (X(nonNearestIdx,1), X(nonNearestIdx,2))
912s  hold on
912s  scatter (nearestPoints(:,1),nearestPoints(:,2))
912s  scatter (x(1), x(2), "black", "filled")
912s  hold off
912s ***** shared x, y, X, Y
912s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
912s  y = [2, 3, 4; 1, 4, 3];
912s  X = [1, 2, 3, 4; 2, 3, 4, 5; 3, 4, 5, 6];
912s  Y = [1, 2, 2, 3; 2, 3, 3, 4];
912s ***** test
912s  [idx, D] = rangesearch (x, y, 4);
912s  assert (idx, {[1, 4, 2]; [1, 4]});
912s  assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
912s ***** test
912s  [idx, D] = rangesearch (x, y, 4, "NSMethod", "exhaustive");
912s  assert (idx, {[1, 4, 2]; [1, 4]});
912s  assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
912s ***** test
912s  [idx, D] = rangesearch (x, y, 4, "NSMethod", "kdtree");
912s  assert (idx, {[1, 4, 2]; [1, 4]});
912s  assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
912s ***** test
912s  [idx, D] = rangesearch (x, y, 4, "SortIndices", true);
912s  assert (idx, {[1, 4, 2]; [1, 4]});
912s  assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
912s ***** test
912s  [idx, D] = rangesearch (x, y, 4, "SortIndices", false);
912s  assert (idx, {[1, 2, 4]; [1, 4]});
912s  assert (D, {[1.7321, 3.4641, 3.3166]; [2, 3.4641]}, 1e-4);
912s ***** test
912s  [idx, D] = rangesearch (x, y, 4, "NSMethod", "exhaustive", ...
912s                          "SortIndices", false);
912s  assert (idx, {[1, 2, 4]; [1, 4]});
912s  assert (D, {[1.7321, 3.4641, 3.3166]; [2, 3.4641]}, 1e-4);
912s ***** test
912s  eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
912s  [idx, D] = rangesearch (x, y, 4, "Distance", eucldist);
912s  assert (idx, {[1, 4, 2]; [1, 4]});
912s  assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
912s ***** test
912s  eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
912s  [idx, D] = rangesearch (x, y, 4, "Distance", eucldist, ...
912s                          "NSMethod", "exhaustive");
912s  assert (idx, {[1, 4, 2]; [1, 4]});
912s  assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
912s ***** test
912s  [idx, D] = rangesearch (x, y, 1.5, "Distance", "seuclidean", ...
912s                          "NSMethod", "exhaustive");
912s  assert (idx, {[1, 4, 2]; [1, 4]});
912s  assert (D, {[0.6024, 1.0079, 1.2047]; [0.6963, 1.2047]}, 1e-4);
912s ***** test
912s  [idx, D] = rangesearch (x, y, 1.5, "Distance", "seuclidean", ...
912s                          "NSMethod", "exhaustive", "SortIndices", false);
912s  assert (idx, {[1, 2, 4]; [1, 4]});
912s  assert (D, {[0.6024, 1.2047, 1.0079]; [0.6963, 1.2047]}, 1e-4);
912s ***** test
912s  [idx, D] = rangesearch (X, Y, 4);
912s  assert (idx, {[1, 2]; [1, 2, 3]});
912s  assert (D, {[1.4142, 3.1623]; [1.4142, 1.4142, 3.1623]}, 1e-4);
912s ***** test
912s  [idx, D] = rangesearch (X, Y, 2);
912s  assert (idx, {[1]; [1, 2]});
912s  assert (D, {[1.4142]; [1.4142, 1.4142]}, 1e-4);
912s ***** test
912s  eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
912s  [idx, D] = rangesearch (X, Y, 4, "Distance", eucldist);
912s  assert (idx, {[1, 2]; [1, 2, 3]});
912s  assert (D, {[1.4142, 3.1623]; [1.4142, 1.4142, 3.1623]}, 1e-4);
912s ***** test
912s  [idx, D] = rangesearch (X, Y, 4, "SortIndices", false);
912s  assert (idx, {[1, 2]; [1, 2, 3]});
912s  assert (D, {[1.4142, 3.1623]; [1.4142, 1.4142, 3.1623]}, 1e-4);
912s ***** test
912s  [idx, D] = rangesearch (X, Y, 4, "Distance", "seuclidean", ...
912s                          "NSMethod", "exhaustive");
912s  assert (idx, {[1, 2]; [1, 2, 3]});
912s  assert (D, {[1.4142, 3.1623]; [1.4142, 1.4142, 3.1623]}, 1e-4);
912s ***** error<rangesearch: too few input arguments.> rangesearch (1)
912s ***** error<rangesearch: number of columns in X and Y must match.> ...
912s  rangesearch (ones (4, 5), ones (4))
912s ***** error<rangesearch: invalid NAME in optional pairs of arguments.> ...
912s  rangesearch (ones (4, 2), ones (3, 2), 1, "Distance", "euclidean", "some", "some")
912s ***** error<rangesearch: only a single distance parameter can be defined.> ...
912s  rangesearch (ones (4, 5), ones (1, 5), 1, "scale", ones (1, 5), "P", 3)
912s ***** error<rangesearch: invalid value of Minkowski Exponent.> ...
912s  rangesearch (ones (4, 5), ones (1, 5), 1, "P",-2)
912s ***** error<rangesearch: invalid value in Scale or the size of Scale.> ...
912s  rangesearch (ones (4, 5), ones (1, 5), 1, "scale", ones(4,5), "distance", "euclidean")
912s ***** error<rangesearch: invalid value in Cov, Cov can only be given for mahalanobis distance.> ...
912s  rangesearch (ones (4, 5), ones (1, 5), 1, "cov", ["some" "some"])
912s ***** error<rangesearch: invalid value in Cov, Cov can only be given for mahalanobis distance.> ...
912s  rangesearch (ones (4, 5), ones (1, 5), 1, "cov", ones(4,5), "distance", "euclidean")
912s ***** error<rangesearch: invalid value of bucketsize.> ...
912s  rangesearch (ones (4, 5), ones (1, 5), 1, "bucketsize", -1)
912s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
912s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "cosine")
912s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
912s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "mahalanobis")
912s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
912s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "correlation")
912s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
912s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "seuclidean")
912s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
912s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "spearman")
912s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
912s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "hamming")
912s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
912s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "jaccard")
912s 31 tests, 31 passed, 0 known failure, 0 skipped
912s [inst/ranksum.m]
912s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/ranksum.m
912s ***** test
912s  mileage = [33.3, 34.5, 37.4; 33.4, 34.8, 36.8; ...
912s             32.9, 33.8, 37.6; 32.6, 33.4, 36.6; ...
912s             32.5, 33.7, 37.0; 33.0, 33.9, 36.7];
912s  [p,h,stats] = ranksum(mileage(:,1),mileage(:,2));
912s  assert (p, 0.004329004329004329, 1e-14);
912s  assert (h, true);
912s  assert (stats.ranksum, 21.5);
912s ***** test
912s  year1 = [51 52 62 62 52 52 51 53 59 63 59 56 63 74 68 86 82 70 69 75 73 ...
912s           49 47 50 60 59 60 62 61 71]';
912s  year2 = [54 53 64 66 57 53 54 54 62 66 59 59 67 76 75 86 82 67 74 80 75 ...
912s           54 50 53 62 62 62 72 60 67]';
912s  [p,h,stats] = ranksum(year1, year2, "alpha", 0.01, "tail", "left");
912s  assert (p, 0.1270832752950605, 1e-14);
912s  assert (h, false);
912s  assert (stats.ranksum, 837.5);
912s  assert (stats.zval, -1.140287483634606, 1e-14);
912s  [p,h,stats] = ranksum(year1, year2, "alpha", 0.01, "tail", "left", ...
912s                        "method", "exact");
912s  assert (p, 0.127343916432862, 1e-14);
912s  assert (h, false);
912s  assert (stats.ranksum, 837.5);
924s 2 tests, 2 passed, 0 known failure, 0 skipped
924s [inst/regress.m]
924s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/regress.m
924s ***** test
924s  % Longley data from the NIST Statistical Reference Dataset
924s  Z = [  60323    83.0   234289   2356     1590    107608  1947
924s         61122    88.5   259426   2325     1456    108632  1948
924s         60171    88.2   258054   3682     1616    109773  1949
924s         61187    89.5   284599   3351     1650    110929  1950
924s         63221    96.2   328975   2099     3099    112075  1951
924s         63639    98.1   346999   1932     3594    113270  1952
924s         64989    99.0   365385   1870     3547    115094  1953
924s         63761   100.0   363112   3578     3350    116219  1954
924s         66019   101.2   397469   2904     3048    117388  1955
924s         67857   104.6   419180   2822     2857    118734  1956
924s         68169   108.4   442769   2936     2798    120445  1957
924s         66513   110.8   444546   4681     2637    121950  1958
924s         68655   112.6   482704   3813     2552    123366  1959
924s         69564   114.2   502601   3931     2514    125368  1960
924s         69331   115.7   518173   4806     2572    127852  1961
924s         70551   116.9   554894   4007     2827    130081  1962 ];
924s  % Results certified by NIST using 500 digit arithmetic
924s  % b and standard error in b
924s  V = [  -3482258.63459582         890420.383607373
924s          15.0618722713733         84.9149257747669
924s         -0.358191792925910E-01    0.334910077722432E-01
924s         -2.02022980381683         0.488399681651699
924s         -1.03322686717359         0.214274163161675
924s         -0.511041056535807E-01    0.226073200069370
924s          1829.15146461355         455.478499142212 ];
924s  Rsq = 0.995479004577296;
924s  F = 330.285339234588;
924s  y = Z(:,1); X = [ones(rows(Z),1), Z(:,2:end)];
924s  alpha = 0.05;
924s  [b, bint, r, rint, stats] = regress (y, X, alpha);
924s  assert(b,V(:,1),4e-6);
924s  assert(stats(1),Rsq,1e-12);
924s  assert(stats(2),F,3e-8);
924s  assert(((bint(:,1)-bint(:,2))/2)/tinv(alpha/2,9),V(:,2),-1.e-5);
924s warning: matrix singular to machine precision, rcond = 3.50566e-20
924s warning: called from
924s     regress at line 131 column 7
924s     __test__ at line 33 column 28
924s     test at line 682 column 11
924s     /tmp/tmp.YrM4LcnPyg at line 3366 column 31
924s 
924s 1 test, 1 passed, 0 known failure, 0 skipped
924s [inst/regress_gp.m]
924s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/regress_gp.m
924s ***** demo
924s  ## Linear fitting of 1D Data
924s  rand ("seed", 125);
924s  X = 2 * rand (5, 1) - 1;
924s  randn ("seed", 25);
924s  Y = 2 * X - 1 + 0.3 * randn (5, 1);
924s 
924s  ## Points for interpolation/extrapolation
924s  Xfit = linspace (-2, 2, 10)';
924s 
924s  ## Fit regression model
924s  [Yfit, Yint, m] = regress_gp (X, Y, Xfit);
924s 
924s  ## Plot fitted data
924s  plot (X, Y, "xk", Xfit, Yfit, "r-", Xfit, Yint, "b-");
924s  title ("Gaussian process regression with linear kernel");
924s ***** demo
924s  ## Linear fitting of 2D Data
924s  rand ("seed", 135);
924s  X = 2 * rand (4, 2) - 1;
924s  randn ("seed", 35);
924s  Y = 2 * X(:,1) - 3 * X(:,2) - 1 + 1 * randn (4, 1);
924s 
924s  ## Mesh for interpolation/extrapolation
924s  [x1, x2] = meshgrid (linspace (-1, 1, 10));
924s  Xfit = [x1(:), x2(:)];
924s 
924s  ## Fit regression model
924s  [Ypred, Yint, Ysd] = regress_gp (X, Y, Xfit);
924s  Ypred = reshape (Ypred, 10, 10);
924s  YintU = reshape (Yint(:,1), 10, 10);
924s  YintL = reshape (Yint(:,2), 10, 10);
924s 
924s  ## Plot fitted data
924s  plot3 (X(:,1), X(:,2), Y, ".k", "markersize", 16);
924s  hold on;
924s  h = mesh (x1, x2, Ypred, zeros (10, 10));
924s  set (h, "facecolor", "none", "edgecolor", "yellow");
924s  h = mesh (x1, x2, YintU, ones (10, 10));
924s  set (h, "facecolor", "none", "edgecolor", "cyan");
924s  h = mesh (x1, x2, YintL, ones (10, 10));
924s  set (h, "facecolor", "none", "edgecolor", "cyan");
924s  hold off
924s  axis tight
924s  view (75, 25)
924s  title ("Gaussian process regression with linear kernel");
924s ***** demo
924s  ## Projection over basis function with linear kernel
924s  pp = [2, 2, 0.3, 1];
924s  n = 10;
924s  rand ("seed", 145);
924s  X = 2 * rand (n, 1) - 1;
924s  randn ("seed", 45);
924s  Y = polyval (pp, X) + 0.3 * randn (n, 1);
924s 
924s  ## Powers
924s  px = [sqrt(abs(X)), X, X.^2, X.^3];
924s 
924s  ## Points for interpolation/extrapolation
924s  Xfit = linspace (-1, 1, 100)';
924s  pxi = [sqrt(abs(Xfit)), Xfit, Xfit.^2, Xfit.^3];
924s 
924s  ## Define a prior covariance assuming that the sqrt component is not present
924s  Sp = 100 * eye (size (px, 2) + 1);
924s  Sp(2,2) = 1; # We don't believe the sqrt(abs(X)) is present
924s 
924s  ## Fit regression model
924s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, Sp);
924s 
924s  ## Plot fitted data
924s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
924s                          Xfit, polyval (pp, Xfit), "g-;True;");
924s  axis tight
924s  axis manual
924s  hold on
924s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
924s  hold off
924s  title ("Linear kernel over basis function with prior covariance");
924s ***** demo
924s  ## Projection over basis function with linear kernel
924s  pp = [2, 2, 0.3, 1];
924s  n = 10;
924s  rand ("seed", 145);
924s  X = 2 * rand (n, 1) - 1;
924s  randn ("seed", 45);
924s  Y = polyval (pp, X) + 0.3 * randn (n, 1);
924s 
924s  ## Powers
924s  px = [sqrt(abs(X)), X, X.^2, X.^3];
924s 
924s  ## Points for interpolation/extrapolation
924s  Xfit = linspace (-1, 1, 100)';
924s  pxi = [sqrt(abs(Xfit)), Xfit, Xfit.^2, Xfit.^3];
924s 
924s  ## Fit regression model without any assumption on prior covariance
924s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi);
924s 
924s  ## Plot fitted data
924s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
924s                          Xfit, polyval (pp, Xfit), "g-;True;");
924s  axis tight
924s  axis manual
924s  hold on
924s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
924s  hold off
924s  title ("Linear kernel over basis function without prior covariance");
924s ***** demo
924s  ## Projection over basis function with rbf kernel
924s  pp = [2, 2, 0.3, 1];
924s  n = 10;
924s  rand ("seed", 145);
924s  X = 2 * rand (n, 1) - 1;
924s  randn ("seed", 45);
924s  Y = polyval (pp, X) + 0.3 * randn (n, 1);
924s 
924s  ## Powers
924s  px = [sqrt(abs(X)), X, X.^2, X.^3];
924s 
924s  ## Points for interpolation/extrapolation
924s  Xfit = linspace (-1, 1, 100)';
924s  pxi = [sqrt(abs(Xfit)), Xfit, Xfit.^2, Xfit.^3];
924s 
924s  ## Fit regression model with RBF kernel (standard parameters)
924s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf");
924s 
924s  ## Plot fitted data
924s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
924s                          Xfit, polyval (pp, Xfit), "g-;True;");
924s  axis tight
924s  axis manual
924s  hold on
924s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
924s  hold off
924s  title ("RBF kernel over basis function with standard parameters");
924s  text (-0.5, 4, "theta = 5\n g = 0.01");
924s ***** demo
924s  ## Projection over basis function with rbf kernel
924s  pp = [2, 2, 0.3, 1];
924s  n = 10;
924s  rand ("seed", 145);
924s  X = 2 * rand (n, 1) - 1;
924s  randn ("seed", 45);
924s  Y = polyval (pp, X) + 0.3 * randn (n, 1);
924s 
924s  ## Powers
924s  px = [sqrt(abs(X)), X, X.^2, X.^3];
924s 
924s  ## Points for interpolation/extrapolation
924s  Xfit = linspace (-1, 1, 100)';
924s  pxi = [sqrt(abs(Xfit)), Xfit, Xfit.^2, Xfit.^3];
924s 
924s  ## Fit regression model with RBF kernel with different parameters
924s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf", 10, 0.01);
924s 
924s  ## Plot fitted data
924s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
924s                          Xfit, polyval (pp, Xfit), "g-;True;");
924s  axis tight
924s  axis manual
924s  hold on
924s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
924s  hold off
924s  title ("GP regression with RBF kernel and non default parameters");
924s  text (-0.5, 4, "theta = 10\n g = 0.01");
924s 
924s  ## Fit regression model with RBF kernel with different parameters
924s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf", 50, 0.01);
924s 
924s  ## Plot fitted data
924s  figure
924s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
924s                          Xfit, polyval (pp, Xfit), "g-;True;");
924s  axis tight
924s  axis manual
924s  hold on
924s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
924s  hold off
924s  title ("GP regression with RBF kernel and non default parameters");
924s  text (-0.5, 4, "theta = 50\n g = 0.01");
924s 
924s  ## Fit regression model with RBF kernel with different parameters
924s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf", 50, 0.001);
924s 
924s  ## Plot fitted data
924s  figure
924s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
924s                          Xfit, polyval (pp, Xfit), "g-;True;");
924s  axis tight
924s  axis manual
924s  hold on
924s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
924s  hold off
924s  title ("GP regression with RBF kernel and non default parameters");
924s  text (-0.5, 4, "theta = 50\n g = 0.001");
924s 
924s  ## Fit regression model with RBF kernel with different parameters
924s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf", 50, 0.05);
924s 
924s  ## Plot fitted data
924s  figure
924s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
924s                          Xfit, polyval (pp, Xfit), "g-;True;");
924s  axis tight
924s  axis manual
924s  hold on
924s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
924s  hold off
924s  title ("GP regression with RBF kernel and non default parameters");
924s  text (-0.5, 4, "theta = 50\n g = 0.05");
924s ***** demo
924s  ## RBF fitting on noiseless 1D Data
924s  x = [0:2*pi/7:2*pi]';
924s  y = 5 * sin (x);
924s 
924s  ## Predictive grid of 500 equally spaced locations
924s  xi = [-0.5:(2*pi+1)/499:2*pi+0.5]';
924s 
924s  ## Fit regression model with RBF kernel
924s  [Yfit, Yint, Ysd] = regress_gp (x, y, xi, "rbf");
924s 
924s  ## Plot fitted data
924s  r = mvnrnd (Yfit, diag (Ysd)', 50);
924s  plot (xi, r', "c-");
924s  hold on
924s  plot (xi, Yfit, "r-;Estimation;", xi, Yint, "b-;Confidence interval;");
924s  plot (x, y, ".k;Predictor points;", "markersize", 20)
924s  plot (xi, 5 * sin (xi), "-y;True Function;");
924s  xlim ([-0.5,2*pi+0.5]);
924s  ylim ([-10,10]);
924s  hold off
924s  title ("GP regression with RBF kernel on noiseless 1D data");
924s  text (0, -7, "theta = 5\n g = 0.01");
924s ***** demo
924s  ## RBF fitting on noisy 1D Data
924s  x = [0:2*pi/7:2*pi]';
924s  x = [x; x];
924s  y = 5 * sin (x) + randn (size (x));
924s 
924s  ## Predictive grid of 500 equally spaced locations
924s  xi = [-0.5:(2*pi+1)/499:2*pi+0.5]';
924s 
924s  ## Fit regression model with RBF kernel
924s  [Yfit, Yint, Ysd] = regress_gp (x, y, xi, "rbf");
924s 
924s  ## Plot fitted data
924s  r = mvnrnd (Yfit, diag (Ysd)', 50);
924s  plot (xi, r', "c-");
924s  hold on
924s  plot (xi, Yfit, "r-;Estimation;", xi, Yint, "b-;Confidence interval;");
924s  plot (x, y, ".k;Predictor points;", "markersize", 20)
924s  plot (xi, 5 * sin (xi), "-y;True Function;");
924s  xlim ([-0.5,2*pi+0.5]);
924s  ylim ([-10,10]);
924s  hold off
924s  title ("GP regression with RBF kernel on noisy 1D data");
924s  text (0, -7, "theta = 5\n g = 0.01");
924s ***** error<Invalid call to regress_gp.> regress_gp (ones (20, 2))
924s ***** error<Invalid call to regress_gp.> regress_gp (ones (20, 2), ones (20, 1))
925s ***** error<regress_gp: X must be a 2-D matrix.> ...
925s  regress_gp (ones (20, 2, 3), ones (20, 1), ones (20, 2))
925s ***** error<regress_gp: Y must be a column vector.> ...
925s  regress_gp (ones (20, 2), ones (20, 2), ones (20, 2))
925s ***** error<regress_gp: rows in X must equal the length of Y.> ...
925s  regress_gp (ones (20, 2), ones (15, 1), ones (20, 2))
925s ***** error<regress_gp: X and XI must have the same number of columns.> ...
925s  regress_gp (ones (20, 2), ones (20, 1), ones (20, 3))
925s ***** error<regress_gp: invalid 4th argument.> ...
925s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), {[3]})
925s ***** error<regress_gp: invalid 4th argument.> ...
925s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "kernel")
925s ***** error<regress_gp: theta must be a scalar when using RBF kernel.> ...
925s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", ones (4))
925s ***** error<regress_gp: wrong size for prior covariance matrix Sp.> ...
925s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "linear", 1)
925s ***** error<regress_gp: invalid 5th argument.> ...
925s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", "value")
925s ***** error<regress_gp: invalid 5th argument.> ...
925s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", {5})
925s ***** error<regress_gp: invalid 5th argument.> ...
925s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), ones (3), 5)
925s ***** error<regress_gp: wrong size for prior covariance matrix Sp.> ...
925s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "linear", 5)
925s ***** error<regress_gp: invalid 6th argument.> ...
925s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", 5, {5})
925s ***** error<regress_gp: invalid 6th argument.> ...
925s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", 5, ones (2))
925s ***** error<regress_gp: invalid 6th argument.> ...
925s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), 5, 0.01, [1, 1])
925s ***** error<regress_gp: invalid 6th argument.> ...
925s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), 5, 0.01, "f")
925s ***** error<regress_gp: invalid 6th argument.> ...
925s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), 5, 0.01, "f")
925s ***** error<regress_gp: invalid 7th argument.> ...
925s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", 5, 0.01, "f")
925s ***** error<regress_gp: invalid 7th argument.> ...
925s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", 5, 0.01, [1, 1])
925s ***** error<regress_gp: wrong size for prior covariance matrix Sp.> ...
925s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "linear", 1)
925s 22 tests, 22 passed, 0 known failure, 0 skipped
925s [inst/regression_ftest.m]
925s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/regression_ftest.m
925s ***** error<Invalid call to regression_ftest.  Correct usage> regression_ftest ();
925s ***** error<Invalid call to regression_ftest.  Correct usage> ...
925s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]');
925s ***** error<regression_ftest: Y must contain finite real numbers.> ...
925s  regression_ftest ([1 2 NaN]', [2 3 4; 3 4 5]', [1 0.5]);
925s ***** error<regression_ftest: Y must contain finite real numbers.> ...
925s  regression_ftest ([1 2 Inf]', [2 3 4; 3 4 5]', [1 0.5]);
925s ***** error<regression_ftest: Y must contain finite real numbers.> ...
925s  regression_ftest ([1 2 3+i]', [2 3 4; 3 4 5]', [1 0.5]);
925s ***** error<regression_ftest: X must contain finite real numbers.> ...
925s  regression_ftest ([1 2 3]', [2 3 NaN; 3 4 5]', [1 0.5]);
925s ***** error<regression_ftest: X must contain finite real numbers.> ...
925s  regression_ftest ([1 2 3]', [2 3 Inf; 3 4 5]', [1 0.5]);
925s ***** error<regression_ftest: X must contain finite real numbers.> ...
925s  regression_ftest ([1 2 3]', [2 3 4; 3 4 3+i]', [1 0.5]);
925s ***** error<regression_ftest: invalid value for alpha.> ...
925s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "alpha", 0);
925s ***** error<regression_ftest: invalid value for alpha.> ...
925s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "alpha", 1.2);
925s ***** error<regression_ftest: invalid value for alpha.> ...
925s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "alpha", [.02 .1]);
925s ***** error<regression_ftest: invalid value for alpha.> ...
925s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "alpha", "a");
925s ***** error<regression_ftest: invalid Name argument.> ...
925s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "some", 0.05);
925s ***** error<regression_ftest: Y must be a vector of length> ...
925s  regression_ftest ([1 2 3]', [2 3; 3 4]', [1 0.5]);
925s ***** error<regression_ftest: Y must be a vector of length> ...
925s  regression_ftest ([1 2; 3 4]', [2 3; 3 4]', [1 0.5]);
925s ***** error<regression_ftest: reduced model, RM, must be a numeric vector or> ...
925s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], ones (2));
925s ***** error<regression_ftest: reduced model, RM, must be a numeric vector or> ...
925s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], "alpha");
925s ***** error<regression_ftest: reduced model, RM, must have smaller length than> ...
925s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [1 2]);
925s 18 tests, 18 passed, 0 known failure, 0 skipped
925s [inst/regression_ttest.m]
925s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/regression_ttest.m
925s ***** error<Invalid call to regression_ttest.  Correct usage> regression_ttest ();
925s ***** error<Invalid call to regression_ttest.  Correct usage> regression_ttest (1);
925s ***** error<regression_ttest: Y must contain finite real numbers.> ...
925s  regression_ttest ([1 2 NaN]', [2 3 4]');
925s ***** error<regression_ttest: Y must contain finite real numbers.> ...
925s  regression_ttest ([1 2 Inf]', [2 3 4]');
925s ***** error<regression_ttest: Y must contain finite real numbers.> ...
925s  regression_ttest ([1 2 3+i]', [2 3 4]');
925s ***** error<regression_ttest: X must contain finite real numbers.> ...
925s  regression_ttest ([1 2 3]', [2 3 NaN]');
925s ***** error<regression_ttest: X must contain finite real numbers.> ...
925s  regression_ttest ([1 2 3]', [2 3 Inf]');
925s ***** error<regression_ttest: X must contain finite real numbers.> ...
925s  regression_ttest ([1 2 3]', [3 4 3+i]');
925s ***** error<regression_ttest: Y and X must be vectors of equal length.> ...
925s  regression_ttest ([1 2 3]', [3 4 4 5]');
925s ***** error<regression_ttest: invalid value for alpha.> ...
925s  regression_ttest ([1 2 3]', [2 3 4]', "alpha", 0);
925s ***** error<regression_ttest: invalid value for alpha.> ...
925s  regression_ttest ([1 2 3]', [2 3 4]', "alpha", 1.2);
925s ***** error<regression_ttest: invalid value for alpha.> ...
925s  regression_ttest ([1 2 3]', [2 3 4]', "alpha", [.02 .1]);
925s ***** error<regression_ttest: invalid value for alpha.> ...
925s  regression_ttest ([1 2 3]', [2 3 4]', "alpha", "a");
925s ***** error<regression_ttest: invalid Name argument.> ...
925s  regression_ttest ([1 2 3]', [2 3 4]', "some", 0.05);
925s ***** error<regression_ttest: invalid value for tail.>  ...
925s  regression_ttest ([1 2 3]', [2 3 4]', "tail", "val");
925s ***** error<regression_ttest: invalid value for tail.>  ...
925s  regression_ttest ([1 2 3]', [2 3 4]', "alpha", 0.01, "tail", "val");
925s 16 tests, 16 passed, 0 known failure, 0 skipped
925s [inst/ridge.m]
925s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/ridge.m
925s ***** demo
925s  ## Perform ridge regression for a range of ridge parameters and observe
925s  ## how the coefficient estimates change based on the acetylene dataset.
925s 
925s  load acetylene
925s 
925s  X = [x1, x2, x3];
925s 
925s  x1x2 = x1 .* x2;
925s  x1x3 = x1 .* x3;
925s  x2x3 = x2 .* x3;
925s 
925s  D = [x1, x2, x3, x1x2, x1x3, x2x3];
925s 
925s  k = 0:1e-5:5e-3;
925s 
925s  b = ridge (y, D, k);
925s 
925s  figure
925s  plot (k, b, "LineWidth", 2)
925s  ylim ([-100, 100])
925s  grid on
925s  xlabel ("Ridge Parameter")
925s  ylabel ("Standardized Coefficient")
925s  title ("Ridge Trace")
925s  legend ("x1", "x2", "x3", "x1x2", "x1x3", "x2x3")
925s 
925s ***** demo
925s 
925s  load carbig
925s  X = [Acceleration Weight Displacement Horsepower];
925s  y = MPG;
925s 
925s  n = length(y);
925s 
925s  rand("seed",1); % For reproducibility
925s 
925s  c = cvpartition(n,'HoldOut',0.3);
925s  idxTrain = training(c,1);
925s  idxTest = ~idxTrain;
925s 
925s  idxTrain = training(c,1);
925s  idxTest = ~idxTrain;
925s 
925s  k = 5;
925s  b = ridge(y(idxTrain),X(idxTrain,:),k,0);
925s 
925s  % Predict MPG values for the test data using the model.
925s  yhat = b(1) + X(idxTest,:)*b(2:end);
925s  scatter(y(idxTest),yhat)
925s 
925s  hold on
925s  plot(y(idxTest),y(idxTest),"r")
925s  xlabel('Actual MPG')
925s  ylabel('Predicted MPG')
925s  hold off
925s 
925s ***** test
925s  b = ridge ([1 2 3 4]', [1 2 3 4; 2 3 4 5]', 1);
925s  assert (b, [0.5533; 0.5533], 1e-4);
925s ***** test
925s  b = ridge ([1 2 3 4]', [1 2 3 4; 2 3 4 5]', 2);
925s  assert (b, [0.4841; 0.4841], 1e-4);
925s ***** test
925s  load acetylene
925s  x = [x1, x2, x3];
925s  b = ridge (y, x, 0);
925s  assert (b,[10.2273;1.97128;-0.601818],1e-4);
925s ***** test
925s  load acetylene
925s  x = [x1, x2, x3];
925s  b = ridge (y, x, 0.0005);
925s  assert (b,[10.2233;1.9712;-0.6056],1e-4);
925s ***** test
925s  load acetylene
925s  x = [x1, x2, x3];
925s  b = ridge (y, x, 0.001);
925s  assert (b,[10.2194;1.9711;-0.6094],1e-4);
925s ***** test
925s  load acetylene
925s  x = [x1, x2, x3];
925s  b = ridge (y, x, 0.002);
925s  assert (b,[10.2116;1.9709;-0.6169],1e-4);
925s ***** test
925s  load acetylene
925s  x = [x1, x2, x3];
925s  b = ridge (y, x, 0.005);
925s  assert (b,[10.1882;1.9704;-0.6393],1e-4);
925s ***** test
925s  load acetylene
925s  x = [x1, x2, x3];
925s  b = ridge (y, x, 0.01);
925s  assert (b,[10.1497;1.9695;-0.6761],1e-4);
925s ***** error<ridge: function called with too few input arguments.> ridge (1)
925s ***** error<ridge: function called with too few input arguments.> ridge (1, 2)
925s ***** error<ridge: Y must be a numeric column vector.> ridge (ones (3), ones (3), 2)
925s ***** error<ridge: Y must be a numeric column vector.> ridge ([1, 2], ones (2), 2)
925s ***** error<ridge: Y must be a numeric column vector.> ridge ([], ones (3), 2)
925s ***** error<ridge: X must be a numeric matrix.> ridge (ones (5,1), [], 2)
925s ***** error<ridge: Y and X must contain the same number of rows.> ...
925s  ridge ([1; 2; 3; 4; 5], ones (3), 3)
925s ***** error<ridge: wrong value for SCALED argument.> ...
925s  ridge ([1; 2; 3], ones (3), 3, 2)
925s ***** error<ridge: wrong value for SCALED argument.> ...
925s  ridge ([1; 2; 3], ones (3), 3, "some")
925s 17 tests, 17 passed, 0 known failure, 0 skipped
925s [inst/rmmissing.m]
925s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/rmmissing.m
925s ***** assert (rmmissing ([1,NaN,3]), [1,3])
925s ***** assert (rmmissing ('abcd f'), 'abcdf')
925s ***** assert (rmmissing ({'xxx','','xyz'}), {'xxx','xyz'})
925s ***** assert (rmmissing ({'xxx','';'xyz','yyy'}), {'xyz','yyy'})
925s ***** assert (rmmissing ({'xxx','';'xyz','yyy'}, 2), {'xxx';'xyz'})
925s ***** assert (rmmissing ([1,2;NaN,2]), [1,2])
925s ***** assert (rmmissing ([1,2;NaN,2], 2), [2,2]')
925s ***** assert (rmmissing ([1,2;NaN,4;NaN,NaN],"MinNumMissing", 2), [1,2;NaN,4])
925s ***** test
925s  x = [1:6];
925s  x([2,4]) = NaN;
925s  [~, idx] = rmmissing (x);
925s  assert (idx, logical ([0, 1, 0, 1, 0, 0]));
925s  assert (class(idx), 'logical');
925s  x = reshape (x, [2, 3]);
925s  [~, idx] = rmmissing (x);
925s  assert (idx, logical ([0; 1]));
925s  assert (class(idx), 'logical');
925s  [~, idx] = rmmissing (x, 2);
925s  assert (idx, logical ([1, 1, 0]));
925s  assert (class(idx), 'logical');
925s  [~, idx] = rmmissing (x, 1, "MinNumMissing", 2);
925s  assert (idx, logical ([0; 1]));
925s  assert (class(idx), 'logical');
925s  [~, idx] = rmmissing (x, 2, "MinNumMissing", 2);
925s  assert (idx, logical ([0, 0, 0]));
925s  assert (class(idx), 'logical');
925s ***** assert (rmmissing (single ([1 2 NaN; 3 4 5])), single ([3 4 5]))
925s ***** assert (rmmissing (logical (ones (3))), logical (ones (3)))
925s ***** assert (rmmissing (int32 (ones (3))), int32 (ones (3)))
925s ***** assert (rmmissing (uint32 (ones (3))), uint32 (ones (3)))
925s ***** assert (rmmissing ({1, 2, 3}), {1, 2, 3})
925s ***** assert (rmmissing ([struct, struct, struct]), [struct, struct, struct])
925s ***** assert (rmmissing ([]), [])
925s ***** assert (rmmissing (ones (1,0)), ones (1,0))
925s ***** assert (rmmissing (ones (1,0), 1), ones (1,0))
925s ***** assert (rmmissing (ones (1,0), 2), ones (1,0))
925s ***** assert (rmmissing (ones (0,1)), ones (0,1))
925s ***** assert (rmmissing (ones (0,1), 1), ones (0,1))
925s ***** assert (rmmissing (ones (0,1), 2), ones (0,1))
925s ***** error <input dimension> rmmissing (ones (0,1,2))
925s ***** error rmmissing ()
926s ***** error <input dimension> rmmissing (ones(2,2,2))
926s ***** error <must be either 1 or 2> rmmissing ([1 2; 3 4], 5)
926s ***** error <unknown parameter name> rmmissing ([1 2; 3 4], "XXX", 1)
926s ***** error <'MinNumMissing'> rmmissing ([1 2; 3 4], 2, "MinNumMissing", -2)
926s ***** error <'MinNumMissing'> rmmissing ([1 2; 3 4], "MinNumMissing", 3.8)
926s ***** error <'MinNumMissing'> rmmissing ([1 2; 3 4], "MinNumMissing", [1 2 3])
926s ***** error <'MinNumMissing'> rmmissing ([1 2; 3 4], "MinNumMissing", 'xxx')
926s 31 tests, 31 passed, 0 known failure, 0 skipped
926s [inst/runstest.m]
926s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/runstest.m
926s ***** test
926s  ## NIST beam deflection data
926s  ## http://www.itl.nist.gov/div898/handbook/eda/section4/eda425.htm
926s  data = [-213, -564, -35, -15, 141, 115, -420, -360, 203, -338, -431, ...
926s           194, -220, -513, 154, -125, -559, 92, -21, -579, -52, 99, -543, ...
926s          -175, 162, -457, -346, 204, -300, -474, 164, -107, -572, -8, 83, ...
926s          -541, -224, 180, -420, -374, 201, -236, -531, 83, 27, -564, -112, ...
926s           131, -507, -254, 199, -311, -495, 143, -46, -579, -90, 136, ...
926s          -472, -338, 202, -287, -477, 169, -124, -568, 17, 48, -568, -135, ...
926s           162, -430, -422, 172, -74, -577, -13, 92, -534, -243, 194, -355, ...
926s          -465, 156, -81, -578, -64, 139, -449, -384, 193, -198, -538, 110, ...
926s           -44, -577, -6, 66, -552, -164, 161, -460, -344, 205, -281, -504, ...
926s           134, -28, -576, -118, 156, -437, -381, 200, -220, -540, 83, 11, ...
926s          -568, -160, 172, -414, -408, 188, -125, -572, -32, 139, -492, ...
926s          -321, 205, -262, -504, 142, -83, -574, 0, 48, -571, -106, 137, ...
926s          -501, -266, 190, -391, -406, 194, -186, -553, 83, -13, -577, -49, ...
926s           103, -515, -280, 201, 300, -506, 131, -45, -578, -80, 138, -462, ...
926s          -361, 201, -211, -554, 32, 74, -533, -235, 187, -372, -442, 182, ...
926s          -147, -566, 25, 68, -535, -244, 194, -351, -463, 174, -125, -570, ...
926s            15, 72, -550, -190, 172, -424, -385, 198, -218, -536, 96];
926s  [h, p, stats] = runstest (data, median (data));
926s  expected_h = 1;
926s  expected_p = 0.008562;
926s  expected_z = 2.6229;
926s  assert (h, expected_h);
926s  assert (p, expected_p, 1E-6);
926s  assert (stats.z, expected_z, 1E-4);
926s ***** shared x
926s  x = [45, -60, 1.225, 55.4, -9 27];
926s ***** test
926s  [h, p, stats] = runstest (x);
926s  assert (h, 0);
926s  assert (p, 0.6, 1e-14);
926s  assert (stats.nruns, 5);
926s  assert (stats.n1, 3);
926s  assert (stats.n0, 3);
926s  assert (stats.z, 0.456435464587638, 1e-14);
926s ***** test
926s  [h, p, stats] = runstest (x, [], "method", "approximate");
926s  assert (h, 0);
926s  assert (p, 0.6481, 1e-4);
926s  assert (stats.z, 0.456435464587638, 1e-14);
926s ***** test
926s  [h, p, stats] = runstest (x, [], "tail", "left");
926s  assert (h, 0);
926s  assert (p, 0.9, 1e-14);
926s  assert (stats.z, 1.369306393762915, 1e-14);
926s ***** error<runstest: X must be a vector a scalar values.> runstest (ones (2,20))
926s ***** error<runstest: X must be a vector a scalar values.> runstest (["asdasda"])
926s ***** error<runstest: V must be either a scalar number or> ...
926s  runstest ([2 3 4 3 2 3 4], "updown")
926s ***** error<runstest: invalid value for alpha.> ...
926s  runstest ([2 3 4 3 2 3 4], [], "alpha", 0)
926s ***** error<runstest: invalid value for alpha.> ...
926s  runstest ([2 3 4 3 2 3 4], [], "alpha", [0.02 0.2])
926s ***** error<runstest: invalid value for alpha.> ...
926s  runstest ([2 3 4 3 2 3 4], [], "alpha", 1.2)
926s ***** error<runstest: invalid value for alpha.> ...
926s  runstest ([2 3 4 3 2 3 4], [], "alpha", -0.05)
926s ***** error<runstest: invalid value for method.> ...
926s  runstest ([2 3 4 3 2 3 4], [], "method", "some")
926s ***** error<runstest: invalid value for tail.> ...
926s  runstest ([2 3 4 3 2 3 4], [], "tail", "some")
926s ***** error<runstest: invalid optional argument.> ...
926s  runstest ([2 3 4 3 2 3 4], [], "option", "some")
926s 14 tests, 14 passed, 0 known failure, 0 skipped
926s [inst/sampsizepwr.m]
926s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/sampsizepwr.m
926s ***** demo
926s  ## Compute the mean closest to 100 that can be determined to be
926s  ## significantly different from 100 using a t-test with a sample size
926s  ## of 60 and a power of 0.8.
926s  mu1 = sampsizepwr ("t", [100, 10], [], 0.8, 60);
926s  disp (mu1);
926s ***** demo
926s  ## Compute the sample sizes required to distinguish mu0 = 100 from
926s  ## mu1 = 110 by a two-sample t-test with a ratio of the larger and the
926s  ## smaller sample sizes of 1.5 and a power of 0.6.
926s  [N1,N2] = sampsizepwr ("t2", [100, 10], 110, 0.6, [], "ratio", 1.5)
926s ***** demo
926s  ## Compute the sample size N required to distinguish p=.26 from p=.2
926s  ## with a binomial test.  The result is approximate, so make a plot to
926s  ## see if any smaller N values also have the required power of 0.6.
926s  Napprox = sampsizepwr ("p", 0.2, 0.26, 0.6);
926s  nn = 1:250;
926s  pwr = sampsizepwr ("p", 0.2, 0.26, [], nn);
926s  Nexact = min (nn(pwr >= 0.6));
926s  plot(nn,pwr,'b-', [Napprox Nexact],pwr([Napprox Nexact]),'ro');
926s  grid on
926s ***** demo
926s  ## The company must test 52 bottles to detect the difference between a mean
926s  ## volume of 100 mL and 102 mL with a power of 0.80.  Generate a power curve
926s  ## to visualize how the sample size affects the power of the test.
926s 
926s  nout = sampsizepwr('t',[100 5],102,0.80);
926s  nn = 1:100;
926s  pwrout = sampsizepwr('t',[100 5],102,[],nn);
926s 
926s  figure;
926s  plot (nn, pwrout, "b-", nout, 0.8, "ro")
926s  title ("Power versus Sample Size")
926s  xlabel ("Sample Size")
926s  ylabel ("Power")
926s ***** error<sampsizepwr: test type must be a non-empty string.> ...
926s  out = sampsizepwr ([], [100, 10], [], 0.8, 60);
926s ***** error<sampsizepwr: test type must be a non-empty string.> ...
926s  out = sampsizepwr (3, [100, 10], [], 0.8, 60);
926s ***** error<sampsizepwr: test type must be a non-empty string.> ...
926s  out = sampsizepwr ({"t", "t2"}, [100, 10], [], 0.8, 60);
926s ***** error<sampsizepwr: invalid test type.> ...
926s  out = sampsizepwr ("reg", [100, 10], [], 0.8, 60);
926s ***** error<sampsizepwr: parameters must be numeric.> ...
926s  out = sampsizepwr ("t", ["a", "e"], [], 0.8, 60);
926s ***** error<sampsizepwr: invalid size of parameters for this test type.> ...
926s  out = sampsizepwr ("z", 100, [], 0.8, 60);
926s ***** error<sampsizepwr: invalid size of parameters for this test type.> ...
926s  out = sampsizepwr ("t", 100, [], 0.8, 60);
926s ***** error<sampsizepwr: invalid size of parameters for this test type.> ...
926s  out = sampsizepwr ("t2", 60, [], 0.8, 60);
926s ***** error<sampsizepwr: invalid size of parameters for this test type.> ...
926s  out = sampsizepwr ("var", [100, 10], [], 0.8, 60);
926s ***** error<sampsizepwr: invalid size of parameters for this test type.> ...
926s  out = sampsizepwr ("p", [100, 10], [], 0.8, 60);
926s ***** error<sampsizepwr: invalid size of parameters for this test type.> ...
926s  out = sampsizepwr ("r", [100, 10], [], 0.8, 60);
926s ***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
926s  [out, N1] = sampsizepwr ("z", [100, 10], [], 0.8, 60);
926s ***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
926s  [out, N1] = sampsizepwr ("t", [100, 10], [], 0.8, 60);
926s ***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
926s  [out, N1] = sampsizepwr ("var", 2, [], 0.8, 60);
926s ***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
926s  [out, N1] = sampsizepwr ("p", 0.1, [], 0.8, 60);
926s ***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
926s  [out, N1] = sampsizepwr ("r", 0.5, [], 0.8, 60);
926s ***** error<sampsizepwr: negative or zero variance.> ...
926s  out = sampsizepwr ("z", [100, 0], [], 0.8, 60);
926s ***** error<sampsizepwr: negative or zero variance.> ...
926s  out = sampsizepwr ("z", [100, -5], [], 0.8, 60);
926s ***** error<sampsizepwr: negative or zero variance.> ...
926s  out = sampsizepwr ("t", [100, 0], [], 0.8, 60);
926s ***** error<sampsizepwr: negative or zero variance.> ...
926s  out = sampsizepwr ("t", [100, -5], [], 0.8, 60);
926s ***** error<sampsizepwr: negative or zero variance.> ...
926s  [out, N1] = sampsizepwr ("t2", [100, 0], [], 0.8, 60);
926s ***** error<sampsizepwr: negative or zero variance.> ...
926s  [out, N1] = sampsizepwr ("t2", [100, -5], [], 0.8, 60);
926s ***** error<sampsizepwr: negative or zero variance.> ...
926s  out = sampsizepwr ("var", 0, [], 0.8, 60);
926s ***** error<sampsizepwr: negative or zero variance.> ...
926s  out = sampsizepwr ("var", -5, [], 0.8, 60);
926s ***** error<sampsizepwr: out of range probability.> ...
926s  out = sampsizepwr ("p", 0, [], 0.8, 60);
926s ***** error<sampsizepwr: out of range probability.> ...
926s  out = sampsizepwr ("p", 1.2, [], 0.8, 60);
926s ***** error<sampsizepwr: out of range regression coefficient.> ...
926s  out = sampsizepwr ("r", -1.5, [], 0.8, 60);
926s ***** error<sampsizepwr: out of range regression coefficient.> ...
926s  out = sampsizepwr ("r", -1, [], 0.8, 60);
926s ***** error<sampsizepwr: out of range regression coefficient.> ...
926s  out = sampsizepwr ("r", 1.2, [], 0.8, 60);
926s ***** error<sampsizepwr: regression coefficient must not be 0.> ...
926s  out = sampsizepwr ("r", 0, [], 0.8, 60);
926s ***** error<sampsizepwr: invalid value for 'alpha' parameter.> ...
926s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "alpha", -0.2);
926s ***** error<sampsizepwr: invalid value for 'alpha' parameter.> ...
926s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "alpha", 0);
926s ***** error<sampsizepwr: invalid value for 'alpha' parameter.> ...
926s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "alpha", 1.5);
926s ***** error<sampsizepwr: invalid value for 'alpha' parameter.> ...
926s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "alpha", "zero");
926s ***** error<sampsizepwr: 'tail' parameter must be a non-empty string.> ...
926s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "tail", 1.5);
926s ***** error<sampsizepwr: 'tail' parameter must be a non-empty string.> ...
926s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "tail", {"both", "left"});
926s ***** error<sampsizepwr: invalid value for 'tail' parameter.> ...
926s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "tail", "other");
926s ***** error<sampsizepwr: invalid value for 'ratio' parameter.> ...
926s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "ratio", "some");
926s ***** error<sampsizepwr: invalid value for 'ratio' parameter.> ...
926s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "ratio", 0.5);
926s ***** error<sampsizepwr: invalid value for 'ratio' parameter.> ...
926s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "ratio", [2, 1.3, 0.3]);
926s ***** error<sampsizepwr: only one of either p1, power, or n must be missing.> ...
926s  out = sampsizepwr ("z", [100, 5], [], [], 60);
926s ***** error<sampsizepwr: only one of either p1, power, or n must be missing.> ...
926s  out = sampsizepwr ("z", [100, 5], 110, [], []);
926s ***** error<sampsizepwr: only one of either p1, power, or n must be missing.> ...
926s  out = sampsizepwr ("z", [100, 5], [], 0.8, []);
926s ***** error<sampsizepwr: only one of either p1, power, or n must be missing.> ...
926s  out = sampsizepwr ("z", [100, 5], 110, 0.8, 60);
926s ***** error<sampsizepwr: alternative hypothesis parameter must be numeric.> ...
926s  out = sampsizepwr ("z", [100, 5], "mu", [], 60);
926s ***** error<sampsizepwr: alternative hypothesis parameter out of range.> ...
926s  out = sampsizepwr ("var", 5, -1, [], 60);
926s ***** error<sampsizepwr: alternative hypothesis parameter out of range.> ...
926s  out = sampsizepwr ("p", 0.8, 1.2, [], 60, "tail", "right");
926s ***** error<sampsizepwr: alternative hypothesis parameter out of range.> ...
926s  out = sampsizepwr ("r", 0.8, 1.2, [], 60);
926s ***** error<sampsizepwr: alternative hypothesis parameter out of range.> ...
926s  out = sampsizepwr ("r", 0.8, -1.2, [], 60);
926s ***** error<sampsizepwr: invalid value for POWER.> ...
926s  out = sampsizepwr ("z", [100, 5], 110, 1.2);
926s ***** error<sampsizepwr: invalid value for POWER.> ...
926s  out = sampsizepwr ("z", [100, 5], 110, 0);
926s ***** error<sampsizepwr: Cannot compute N or P1 unless POWER> ...
926s  out = sampsizepwr ("z", [100, 5], 110, 0.05, [], "alpha", 0.1);
926s ***** error<sampsizepwr: input arguments size mismatch.> ...
926s  out = sampsizepwr ("z", [100, 5], [], [0.8, 0.7], [60, 80, 100]);
926s ***** error<sampsizepwr: Same value for null and alternative hypothesis.> ...
926s  out = sampsizepwr ("t", [100, 5], 100, 0.8, []);
926s ***** error<sampsizepwr: Invalid P1 for testing left tail.> ...
926s  out = sampsizepwr ("t", [100, 5], 110, 0.8, [], "tail", "left");
926s ***** error<sampsizepwr: Invalid P1 for testing right tail.> ...
926s  out = sampsizepwr ("t", [100, 5], 90, 0.8, [], "tail", "right");
926s ***** warning<sampsizepwr: approximate N.> ...
926s  Napprox = sampsizepwr ("p", 0.2, 0.26, 0.6);
926s ***** warning<sampsizepwr: approximate N.> ...
926s  Napprox = sampsizepwr ("p", 0.30, 0.36, 0.8);
926s ***** test
926s  mu1 = sampsizepwr ("t", [100, 10], [], 0.8, 60);
926s  assert (mu1, 103.67704316, 1e-8);
927s ***** test
927s  [N1,N2] = sampsizepwr ("t2", [100, 10], 110, 0.6, [], "ratio", 1.5);
927s  assert (N1, 9);
927s  assert (N2, 14);
927s ***** test
927s  nn = 1:250;
927s  pwr = sampsizepwr ("p", 0.2, 0.26, [], nn);
927s  pwr_out = [0, 0.0676, 0.0176, 0.0566, 0.0181, 0.0431, 0.0802, 0.0322];
927s  assert (pwr([1:8]), pwr_out, 1e-4 * ones (1,8));
927s  pwr_out = [0.59275, 0.6073, 0.62166, 0.6358, 0.6497, 0.6087, 0.6229, 0.6369];
927s  assert (pwr([243:end]), pwr_out, 1e-4 * ones (1,8));
927s ***** test
927s  nout = sampsizepwr ("t", [100, 5], 102, 0.80);
927s  assert (nout, 52);
927s ***** test
927s  power = sampsizepwr ("t", [20, 5], 25, [], 5, "Tail", "right");
927s  assert (power, 0.5797373588621888, 1e-14);
927s ***** test
927s  nout = sampsizepwr ("t", [20, 5], 25, 0.99, [], "Tail", "right");
927s  assert (nout, 18);
928s ***** test
928s  p1out = sampsizepwr ("t", [20, 5], [], 0.95, 10, "Tail", "right");
928s  assert (p1out, 25.65317979360237, 1e-14);
928s ***** test
928s  pwr = sampsizepwr ("t2", [1.4, 0.2], 1.7, [], 5, "Ratio", 2);
928s  assert (pwr, 0.716504004686586, 1e-14);
928s ***** test
928s  n = sampsizepwr ("t2", [1.4, 0.2], 1.7, 0.9, []);
928s  assert (n, 11);
929s ***** test
929s  [n1, n2] = sampsizepwr ("t2", [1.4, 0.2], 1.7, 0.9, [], "Ratio", 2);
929s  assert ([n1, n2], [8, 16]);
929s 68 tests, 68 passed, 0 known failure, 0 skipped
929s [inst/shadow9/mean.m]
929s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/shadow9/mean.m
929s ***** test
929s  x = -10:10;
929s  y = x';
929s  z = [y, y+10];
929s  assert (mean (x), 0);
929s  assert (mean (y), 0);
929s  assert (mean (z), [0, 10]);
929s ***** assert (mean (magic (3), 1), [5, 5, 5])
929s ***** assert (mean (magic (3), 2), [5; 5; 5])
929s ***** assert (mean (logical ([1 0 1 1])), 0.75)
929s ***** assert (mean (single ([1 0 1 1])), single (0.75))
929s ***** assert (mean ([1 2], 3), [1 2])
929s ***** test
929s  in = [1 2 3];
929s  out = 2;
929s  assert (mean (in, "default"), mean (in));
929s  assert (mean (in, "default"), out);
929s  assert (mean (in, "double"), out);
929s  assert (mean (in, "native"), out);
929s ***** test
929s  in = single ([1 2 3]);
929s  out = 2;
929s  assert (mean (in, "default"), mean (in));
929s  assert (mean (in, "default"), single (out));
929s  assert (mean (in, "double"), out);
929s  assert (mean (in, "native"), single (out));
929s ***** test
929s  in = logical ([1 0 1]);
929s  out = 2/3;
929s  assert (mean (in, "default"), mean (in), eps);
929s  assert (mean (in, "default"), out, eps);
929s  assert (mean (in, "double"), out, eps);
929s  assert (mean (in, "native"), out, eps);
929s ***** test
929s  in = char ("ab");
929s  out = 97.5;
929s  assert (mean (in, "default"), mean (in), eps);
929s  assert (mean (in, "default"), out, eps);
929s  assert (mean (in, "double"), out, eps);
929s ***** test
929s  in = uint8 ([1 2 3]);
929s  out = 2;
929s  assert (mean (in, "default"), mean (in));
929s  assert (mean (in, "default"), out);
929s  assert (mean (in, "double"), out);
929s  assert (mean (in, "native"), uint8 (out));
929s ***** test
929s  in = uint8 ([0 1 2 3]);
929s  out = 1.5;
929s  out_u8 = 2;
929s  assert (mean (in, "default"), mean (in), eps);
929s  assert (mean (in, "default"), out, eps);
929s  assert (mean (in, "double"), out, eps);
929s  assert (mean (in, "native"), uint8 (out_u8));
929s  assert (class (mean (in, "native")), "uint8");
929s ***** test # internal sum exceeding intmax
929s  in = uint8 ([3 141 141 255]);
929s  out = 135;
929s  assert (mean (in, "default"), mean (in));
929s  assert (mean (in, "default"), out);
929s  assert (mean (in, "double"), out);
929s  assert (mean (in, "native"), uint8 (out));
929s  assert (class (mean (in, "native")), "uint8");
929s ***** test # fractional answer with internal sum exceeding intmax
929s  in = uint8 ([1 141 141 255]);
929s  out = 134.5;
929s  out_u8 = 135;
929s  assert (mean (in, "default"), mean (in));
929s  assert (mean (in, "default"), out);
929s  assert (mean (in, "double"), out);
929s  assert (mean (in, "native"), uint8 (out_u8));
929s  assert (class (mean (in, "native")), "uint8");
929s ***** test <54567> # large int64 sum exceeding intmax and double precision limit
929s  in_same = uint64 ([intmax("uint64") intmax("uint64")-2]);
929s  out_same = intmax ("uint64")-1;
929s  in_opp = int64 ([intmin("int64"), intmax("int64")-1]);
929s  out_opp = -1;
929s  in_neg = int64 ([intmin("int64") intmin("int64")+2]);
929s  out_neg = intmin ("int64")+1;
929s 
929s  ## both positive
929s  assert (mean (in_same, "default"), mean (in_same));
929s  assert (mean (in_same, "default"), double (out_same));
929s  assert (mean (in_same, "double"), double (out_same));
929s  assert (mean (in_same, "native"), uint64 (out_same));
929s  assert (class (mean (in_same, "native")), "uint64");
929s 
929s  ## opposite signs
929s  assert (mean (in_opp, "default"), mean (in_opp));
929s  assert (mean (in_opp, "default"), double (out_opp));
929s  assert (mean (in_opp, "double"), double (out_opp));
929s  assert (mean (in_opp, "native"), int64 (out_opp));
929s  assert (class (mean (in_opp, "native")), "int64");
929s 
929s  ## both negative
929s  assert (mean (in_neg, "default"), mean (in_neg));
929s  assert (mean (in_neg, "default"), double(out_neg));
929s  assert (mean (in_neg, "double"), double(out_neg));
929s  assert (mean (in_neg, "native"), int64(out_neg));
929s  assert (class (mean (in_neg, "native")), "int64");
930s ***** test <54567>
930s  in = [(intmin('int64')+5), (intmax('int64'))-5];
930s  assert (mean (in, "native"), int64(-1));
930s  assert (class (mean (in, "native")), "int64");
930s  assert (mean (double(in)), double(0) );
930s  assert (mean (in), double(-0.5) );
930s  assert (mean (in, "default"), double(-0.5) );
930s  assert (mean (in, "double"), double(-0.5) );
930s  assert (mean (in, "all", "native"), int64(-1));
930s  assert (mean (in, 2, "native"), int64(-1));
930s  assert (mean (in, [1 2], "native"), int64(-1));
930s  assert (mean (in, [2 3], "native"), int64(-1));
930s  assert (mean ([intmin("int64"), in, intmax("int64")]), double(-0.5))
930s  assert (mean ([in; int64([1 3])], 2, "native"), int64([-1; 2]));
930s ***** test
930s  x = [-10:10];
930s  y = [x;x+5;x-5];
930s  assert (mean (x), 0);
930s  assert (mean (y, 2), [0, 5, -5]');
930s  assert (mean (y, "all"), 0);
930s  y(2,4) = NaN;
930s  assert (mean (y', "omitnan"), [0 5.35 -5]);
930s  z = y + 20;
930s  assert (mean (z, "all"), NaN);
930s  assert (mean (z, "all", "includenan"), NaN);
930s  assert (mean (z, "all", "omitnan"), 20.03225806451613, 4e-14);
930s  m = [20 NaN 15];
930s  assert (mean (z'), m);
930s  assert (mean (z', "includenan"), m);
930s  m = [20 25.35 15];
930s  assert (mean (z', "omitnan"), m);
930s  assert (mean (z, 2, "omitnan"), m');
930s  assert (mean (z, 2, "native", "omitnan"), m');
930s  assert (mean (z, 2, "omitnan", "native"), m');
930s ***** test
930s  assert (mean (true, "all"), 1);
930s  assert (mean (false), 0);
930s  assert (mean ([true false true]), 2/3, 4e-14);
930s  assert (mean ([true false true], 1), [1 0 1]);
930s  assert (mean ([true false NaN], 1), [1 0 NaN]);
930s  assert (mean ([true false NaN], 2), NaN);
930s  assert (mean ([true false NaN], 2, "omitnan"), 0.5);
930s  assert (mean ([true false NaN], 2, "omitnan", "native"), 0.5);
930s ***** assert (mean ("abc"), double (98))
930s ***** assert (mean ("ab"), double (97.5), eps)
930s ***** assert (mean ("abc", "double"), double (98))
930s ***** assert (mean ("abc", "default"), double (98))
930s ***** test
930s  x = magic (4);
930s  x([2, 9:12]) = NaN;
930s  assert (mean (x), [NaN 8.5, NaN, 8.5], eps);
930s  assert (mean (x,1), [NaN 8.5, NaN, 8.5], eps);
930s  assert (mean (x,2), NaN(4,1), eps);
930s  assert (mean (x,3), x, eps);
930s  assert (mean (x, 'omitnan'), [29/3, 8.5, NaN, 8.5], eps);
930s  assert (mean (x, 1, 'omitnan'), [29/3, 8.5, NaN, 8.5], eps);
930s  assert (mean (x, 2, 'omitnan'), [31/3; 9.5; 28/3; 19/3], eps);
930s  assert (mean (x, 3, 'omitnan'), x, eps);
930s ***** assert (mean ([]), NaN(1,1))
930s ***** assert (mean (single([])), NaN(1,1,"single"))
930s ***** assert (mean ([], 1), NaN(1,0))
930s ***** assert (mean ([], 2), NaN(0,1))
930s ***** assert (mean ([], 3), NaN(0,0))
930s ***** assert (mean (ones(1,0)), NaN(1,1))
930s ***** assert (mean (ones(1,0), 1), NaN(1,0))
930s ***** assert (mean (ones(1,0), 2), NaN(1,1))
930s ***** assert (mean (ones(1,0), 3), NaN(1,0))
930s ***** assert (mean (ones(0,1)), NaN(1,1))
930s ***** assert (mean (ones(0,1), 1), NaN(1,1))
930s ***** assert (mean (ones(0,1), 2), NaN(0,1))
930s ***** assert (mean (ones(0,1), 3), NaN(0,1))
930s ***** assert (mean (ones(0,1,0)), NaN(1,1,0))
930s ***** assert (mean (ones(0,1,0), 1), NaN(1,1,0))
930s ***** assert (mean (ones(0,1,0), 2), NaN(0,1,0))
930s ***** assert (mean (ones(0,1,0), 3), NaN(0,1,1))
930s ***** assert (mean (ones(0,0,1,0)), NaN(1,0,1,0))
930s ***** assert (mean (ones(0,0,1,0), 1), NaN(1,0,1,0))
930s ***** assert (mean (ones(0,0,1,0), 2), NaN(0,1,1,0))
930s ***** assert (mean (ones(0,0,1,0), 3), NaN(0,0,1,0))
930s ***** test
930s  x = repmat ([1:20;6:25], [5 2 6 3]);
930s  assert (size (mean (x, [3 2])), [10 1 1 3]);
930s  assert (size (mean (x, [1 2])), [1 1 6 3]);
930s  assert (size (mean (x, [1 2 4])), [1 1 6]);
930s  assert (size (mean (x, [1 4 3])), [1 40]);
930s  assert (size (mean (x, [1 2 3 4])), [1 1]);
930s ***** assert (mean (ones (2,2), 3), ones (2,2))
930s ***** assert (mean (ones (2,2,2), 99), ones (2,2,2))
930s ***** assert (mean (magic (3), 3), magic (3))
930s ***** assert (mean (magic (3), [1 3]), [5, 5, 5])
930s ***** assert (mean (magic (3), [1 99]), [5, 5, 5])
930s ***** test
930s  x = repmat ([1:20;6:25], [5 2 6 3]);
930s  m = repmat ([10.5;15.5], [5 1 1 3]);
930s  assert (mean (x, [3 2]), m, 4e-14);
930s  x(2,5,6,3) = NaN;
930s  m(2,1,1,3) = NaN;
930s  assert (mean (x, [3 2]), m, 4e-14);
930s  m(2,1,1,3) = 15.52301255230125;
930s  assert (mean (x, [3 2], "omitnan"), m, 4e-14);
930s ***** assert (mean ([1 2 3], "aLL"), 2)
930s ***** assert (mean ([1 2 3], "OmitNan"), 2)
930s ***** assert (mean ([1 2 3], "DOUBle"), 2)
930s ***** assert <*63848> (mean (ones (80e6, 1, "single")), 1, eps)
930s ***** assert <*63848> (mean (ones (80e6, 1, "single"), "all"), 1, eps)
930s ***** assert <*63848> (mean (ones (80e6, 1, "single"), 1), 1, eps)
930s ***** assert <*63848> (mean (ones (80e6, 1, "single"), [1 2]), 1, eps)
931s ***** assert <*63848> (mean (ones (80e6, 1, "single"), [1 3]), 1, eps)
931s ***** assert <63848> (mean ([flintmax("double"), ones(1, 2^8-1, "double")]), ...
931s                                35184372088833-1/(2^8), eps(35184372088833))
931s !!!!! known bug: https://octave.org/testfailure/?63848
931s ASSERT errors for:  assert (mean ([flintmax("double"), ones(1, 2 ^ 8 - 1, "double")]),35184372088833 - 1 / (2 ^ 8),eps (35184372088833))
931s 
931s   Location  |  Observed  |  Expected  |  Reason
931s      ()      35184372088832 35184372088833   Abs err 1 exceeds tol 0.0078125 by 1
931s ***** error <Invalid call to mean.  Correct usage is> mean ()
931s ***** error <Invalid call to mean.  Correct usage is> mean (1, 2, 3)
932s ***** error <Invalid call to mean.  Correct usage is> mean (1, 2, 3, 4)
932s ***** error <Invalid call to mean.  Correct usage is> mean (1, "all", 3)
932s ***** error <Invalid call to mean.  Correct usage is> mean (1, "b")
932s ***** error <Invalid call to mean.  Correct usage is> mean (1, 1, "foo")
932s ***** error <OUTTYPE 'native' cannot be used with char> mean ("abc", "native")
932s ***** error <X must be either a numeric, boolean, or character> mean ({1:5})
932s ***** error <DIM must be a positive integer> mean (1, ones (2,2))
932s ***** error <DIM must be a positive integer> mean (1, 1.5)
932s ***** error <DIM must be a positive integer> mean (1, 0)
932s ***** error <DIM must be a positive integer> mean (1, [])
932s ***** error <DIM must be a positive integer> mean (1, -1)
932s ***** error <DIM must be a positive integer> mean (1, -1.5)
932s ***** error <DIM must be a positive integer> mean (1, NaN)
932s ***** error <DIM must be a positive integer> mean (1, Inf)
932s ***** error <DIM must be a positive integer> mean (repmat ([1:20;6:25], [5 2]), -1)
932s ***** error <DIM must be a positive integer> mean (repmat ([1:5;5:9], [5 2]), [1 -1])
932s ***** error <DIM must be a positive integer> mean (1, ones(1,0))
932s ***** error <VECDIM must contain non-repeating> mean (1, [2 2])
932s 80 tests, 79 passed, 0 known failure, 1 skipped
932s [inst/shadow9/median.m]
932s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/shadow9/median.m
932s ***** assert (median (1), 1)
932s ***** assert (median ([1,2,3]), 2)
932s ***** assert (median ([1,2,3]'), 2)
932s ***** assert (median (cat(3,3,1,2)), 2)
932s ***** assert (median ([3,1,2]), 2)
932s ***** assert (median ([2,4,6,8]), 5)
932s ***** assert (median ([8,2,6,4]), 5)
932s ***** assert (median (single ([1,2,3])), single (2))
932s ***** assert (median ([1,2], 3), [1,2])
932s ***** test
932s  x = [1, 2, 3, 4, 5, 6];
932s  x2 = x';
932s  y = [1, 2, 3, 4, 5, 6, 7];
932s  y2 = y';
932s 
932s  assert (median (x) == median (x2) && median (x) == 3.5);
932s  assert (median (y) == median (y2) && median (y) == 4);
932s  assert (median ([x2, 2 * x2]), [3.5, 7]);
932s  assert (median ([y2, 3 * y2]), [4, 12]);
932s ***** test
932s  in = [1 2 3];
932s  out = 2;
932s  assert (median (in, "default"), median (in));
932s  assert (median (in, "default"), out);
932s ***** test
932s  in = single ([1 2 3]);
932s  out = 2;
932s  assert (median (in, "default"), single (median (in)));
932s  assert (median (in, "default"), single (out));
932s  assert (median (in, "double"), double (out));
932s  assert (median (in, "native"), single (out));
932s ***** test
932s  in = uint8 ([1 2 3]);
932s  out = 2;
932s  assert (median (in, "default"), double (median (in)));
932s  assert (median (in, "default"), double (out));
932s  assert (median (in, "double"), out);
932s  assert (median (in, "native"), uint8 (out));
932s ***** test
932s  in = logical ([1 0 1]);
932s  out = 1;
932s  assert (median (in, "default"), double (median (in)));
932s  assert (median (in, "default"), double (out));
932s  assert (median (in, "double"), double (out));
932s  assert (median (in, "native"), double (out));
932s ***** test
932s  x = repmat ([2 2.1 2.2 2 NaN; 3 1 2 NaN 5; 1 1.1 1.4 5 3], [1, 1, 4]);
932s  y = repmat ([2 1.1 2 NaN NaN], [1, 1, 4]);
932s  assert (median (x), y);
932s  assert (median (x, 1), y);
932s  y = repmat ([2 1.1 2 3.5 4], [1, 1, 4]);
932s  assert (median (x, "omitnan"), y);
932s  assert (median (x, 1, "omitnan"), y);
932s  y = repmat ([2.05; 2.5; 1.4], [1, 1, 4]);
932s  assert (median (x, 2, "omitnan"), y);
932s  y = repmat ([NaN; NaN; 1.4], [1, 1, 4]);
932s  assert (median (x, 2), y);
932s  assert (median (x, "all"), NaN);
932s  assert (median (x, "all", "omitnan"), 2);
932s ***** assert (median (cat (3, 3, 1, NaN, 2), "omitnan"), 2)
932s ***** assert (median (cat (3, 3, 1, NaN, 2), 3, "omitnan"), 2)
932s ***** test
932s  assert (median (true, "all"), logical (1));
932s  assert (median (false), logical (0));
932s  assert (median ([true false true]), true);
932s  assert (median ([true false true], 2), true);
932s  assert (median ([true false true], 1), logical ([1 0 1]));
932s  assert (median ([true false NaN], 1), [1 0 NaN]);
932s  assert (median ([true false NaN], 2), NaN);
932s  assert (median ([true false NaN], 2, "omitnan"), 0.5);
932s  assert (median ([true false NaN], 2, "omitnan", "native"), double(0.5));
932s ***** test
932s  x = repmat ([1:20;6:25], [5 2 6 3]);
932s  assert (size (median (x, [3 2])), [10 1 1 3]);
932s  assert (size (median (x, [1 2])), [1 1 6 3]);
932s  assert (size (median (x, [1 2 4])), [1 1 6]);
932s  assert (size (median (x, [1 4 3])), [1 40]);
932s  assert (size (median (x, [1 2 3 4])), [1 1]);
933s ***** assert (median (ones (2,2), 3), ones (2,2))
933s ***** assert (median (ones (2,2,2), 99), ones (2,2,2))
933s ***** assert (median (magic (3), 3), magic (3))
933s ***** assert (median (magic (3), [1 3]), [4, 5, 6])
933s ***** assert (median (magic (3), [1 99]), [4, 5, 6])
933s ***** test
933s  x = repmat ([2 2.1 2.2 2 NaN; 3 1 2 NaN 5; 1 1.1 1.4 5 3], [1, 1, 4]);
933s  assert (median (x, [3 2]), [NaN NaN 1.4]');
933s  assert (median (x, [3 2], "omitnan"), [2.05 2.5 1.4]');
933s  assert (median (x, [1 3]), [2 1.1 2 NaN NaN]);
933s  assert (median (x, [1 3], "omitnan"), [2 1.1 2 3.5 4]);
933s ***** assert (median (NaN), NaN)
933s ***** assert (median (NaN, "omitnan"), NaN)
933s ***** assert (median (NaN (2)), [NaN NaN])
933s ***** assert (median (NaN (2), "omitnan"), [NaN NaN])
933s ***** assert (median ([1 NaN 3]), NaN)
933s ***** assert (median ([1 NaN 3], 1), [1 NaN 3])
933s ***** assert (median ([1 NaN 3], 2), NaN)
933s ***** assert (median ([1 NaN 3]'), NaN)
933s ***** assert (median ([1 NaN 3]', 1), NaN)
933s ***** assert (median ([1 NaN 3]', 2), [1; NaN; 3])
933s ***** assert (median ([1 NaN 3], "omitnan"), 2)
933s ***** assert (median ([1 NaN 3]', "omitnan"), 2)
933s ***** assert (median ([1 NaN 3], 1, "omitnan"), [1 NaN 3])
933s ***** assert (median ([1 NaN 3], 2, "omitnan"), 2)
933s ***** assert (median ([1 NaN 3]', 1, "omitnan"), 2)
933s ***** assert (median ([1 NaN 3]', 2, "omitnan"), [1; NaN; 3])
933s ***** assert (median ([1 2 NaN 3]), NaN)
933s ***** assert (median ([1 2 NaN 3], "omitnan"), 2)
933s ***** assert (median ([1,2,NaN;4,5,6;NaN,8,9]), [NaN, 5, NaN])
933s ***** assert <*64011> (median ([1,2,NaN;4,5,6;NaN,8,9], "omitnan"), [2.5, 5, 7.5], eps)
933s ***** assert (median ([1 2 ; NaN 4]), [NaN 3])
933s ***** assert (median ([1 2 ; NaN 4], "omitnan"), [1 3])
933s ***** assert (median ([1 2 ; NaN 4], 1, "omitnan"), [1 3])
933s ***** assert (median ([1 2 ; NaN 4], 2, "omitnan"), [1.5; 4], eps)
933s ***** assert (median ([1 2 ; NaN 4], 3, "omitnan"), [1 2 ; NaN 4])
933s ***** assert (median ([NaN 2 ; NaN 4]), [NaN 3])
933s ***** assert (median ([NaN 2 ; NaN 4], "omitnan"), [NaN 3])
933s ***** assert (median (ones (1, 0, 3)), NaN (1, 1, 3))
933s ***** assert <*65405> (median ([NaN NaN], 1, "omitnan"), [NaN NaN])
933s ***** assert <*65405> (median ([NaN NaN], 2, "omitnan"), NaN)
933s ***** assert <*65405> (median ([NaN NaN]', 1, "omitnan"), NaN)
933s ***** assert <*65405> (median ([NaN NaN]', 2, "omitnan"), [NaN; NaN])
933s ***** assert <*65405> (median ([NaN NaN], "omitnan"), NaN)
933s ***** assert <*65405> (median ([NaN NaN]', "omitnan"), NaN)
933s ***** assert <*65405> (median (NaN(1,9), 1, "omitnan"), NaN(1,9))
933s ***** assert <*65405> (median (NaN(1,9), 2, "omitnan"), NaN)
933s ***** assert <*65405> (median (NaN(1,9), 3, "omitnan"), NaN(1,9))
933s ***** assert <*65405> (median (NaN(9,1), 1, "omitnan"), NaN)
933s ***** assert <*65405> (median (NaN(9,1), 2, "omitnan"), NaN(9,1))
933s ***** assert <*65405> (median (NaN(9,1), 3, "omitnan"), NaN(9,1))
933s ***** assert <*65405> (median (NaN(9,2), 1, "omitnan"), NaN(1,2))
933s ***** assert <*65405> (median (NaN(9,2), 2, "omitnan"), NaN(9,1))
933s ***** assert <*65405> (median (NaN(9,2), "omitnan"), NaN(1,2))
933s ***** assert (median (NaN("single")), NaN("single"))
933s ***** assert (median (NaN("single"), "omitnan"), NaN("single"))
933s ***** assert (median (NaN("single"), "double"), NaN("double"))
933s ***** assert (median (single([1 2 ; NaN 4])), single([NaN 3]))
933s ***** assert (median (single([1 2 ; NaN 4]), "double"), double([NaN 3]))
933s ***** assert (median (single([1 2 ; NaN 4]), "omitnan"), single([1 3]))
933s ***** assert (median (single([1 2 ; NaN 4]), "omitnan", "double"), double([1 3]))
933s ***** assert (median (single([NaN 2 ; NaN 4]), "double"), double([NaN 3]))
933s ***** assert (median (single([NaN 2 ; NaN 4]), "omitnan"), single([NaN 3]))
933s ***** assert (median (single([NaN 2 ; NaN 4]), "omitnan", "double"), double([NaN 3]))
933s ***** test <*64011>
933s  x = [magic(3), magic(3)];
933s  x([3, 7, 11, 12, 16, 17]) = NaN;
933s  ynan = [NaN, 5, NaN, NaN, 5, NaN];
933s  yomitnan = [5.5, 5, 4.5, 8, 5, 2];
933s  assert (median (x), ynan);
933s  assert (median (x, "omitnan"), yomitnan, eps);
933s  assert (median (cat (3, x, x)), cat (3, ynan, ynan));
933s  assert (median (cat (3, x, x), "omitnan"), cat (3, yomitnan, yomitnan), eps);
933s ***** assert (median (Inf), Inf)
933s ***** assert (median (-Inf), -Inf)
933s ***** assert (median ([-Inf Inf]), NaN)
933s ***** assert (median ([3 Inf]), Inf)
933s ***** assert (median ([3 4 Inf]), 4)
933s ***** assert (median ([Inf 3 4]), 4)
933s ***** assert (median ([Inf 3 Inf]), Inf)
933s ***** assert (median ([1, 2, Inf]), 2)
933s ***** assert (median ([1, 2, Inf, Inf]), Inf)
933s ***** assert (median ([1, -Inf, Inf, Inf]), Inf)
933s ***** assert (median ([-Inf, -Inf, Inf, Inf]), NaN)
933s ***** assert (median([-Inf, Inf, Inf, Inf]), Inf)
933s ***** assert (median([-Inf, -Inf, -Inf, Inf]), -Inf)
933s ***** assert (median([-Inf, -Inf, -Inf, 2]), -Inf)
933s ***** assert (median([-Inf, -Inf, 1, 2]), -Inf)
933s ***** assert (median ([]), NaN)
933s ***** assert (median (ones(1,0)), NaN)
933s ***** assert (median (ones(0,1)), NaN)
933s ***** assert (median ([], 1), NaN(1,0))
933s ***** assert (median ([], 2), NaN(0,1))
933s ***** assert (median ([], 3), NaN(0,0))
933s ***** assert (median (ones(1,0), 1), NaN(1,0))
933s ***** assert (median (ones(1,0), 2), NaN(1,1))
933s ***** assert (median (ones(1,0), 3), NaN(1,0))
933s ***** assert (median (ones(0,1), 1), NaN(1,1))
933s ***** assert (median (ones(0,1), 2), NaN(0,1))
933s ***** assert (median (ones(0,1), 3), NaN(0,1))
933s ***** assert (median (ones(0,1,0,1), 1), NaN(1,1,0))
933s ***** assert (median (ones(0,1,0,1), 2), NaN(0,1,0))
933s ***** assert (median (ones(0,1,0,1), 3), NaN(0,1,1))
933s ***** assert (median (ones(0,1,0,1), 4), NaN(0,1,0))
933s ***** assert (median([1 3 3i 2 1i]), 2)
933s ***** assert (median([1 2 4i; 3 2i 4]), [2, 1+1i, 2+2i])
933s ***** shared a, b, x, y
933s  old_state = rand ("state");
933s  restore_state = onCleanup (@() rand ("state", old_state));
933s  rand ("state", 2);
933s  a = rand (2,3,4,5);
933s  b = rand (3,4,6,5);
933s  x = sort (a, 4);
933s  y = sort (b, 3);
933s ***** assert <*35679> (median (a, 4), x(:, :, :, 3))
933s ***** assert <*35679> (median (b, 3), (y(:, :, 3, :) + y(:, :, 4, :))/2)
933s ***** shared   ## Clear shared to prevent variable echo for any later test failures
933s ***** test
933s  x = ones(15,1,4);
933s  x([13,15],1,:) = NaN;
933s  assert (median (x, 1, "omitnan"), ones (1,1,4))
933s ***** assert (median ([true, false]), true)
933s ***** assert (median (logical ([])), false)
933s ***** assert (median (uint8 ([1, 3])), uint8 (2))
933s ***** assert (median (uint8 ([])), uint8 (NaN))
933s ***** assert (median (uint8 ([NaN 10])), uint8 (5))
933s ***** assert (median (int8 ([1, 3, 4])), int8 (3))
933s ***** assert (median (int8 ([])), int8 (NaN))
933s ***** assert (median (single ([1, 3, 4])), single (3))
933s ***** assert (median (single ([1, 3, NaN])), single (NaN))
933s ***** assert <54567> (median (uint8 ([253, 255])), uint8 (254))
933s ***** assert <54567> (median (uint8 ([253, 254])), uint8 (254))
933s ***** assert <54567> (median (int8 ([127, 126, 125, 124; 1 3 5 9])), ...
933s                   int8 ([64 65 65 67]))
933s ***** assert <54567> (median (int8 ([127, 126, 125, 124; 1 3 5 9]), 2), ...
933s                   int8 ([126; 4]))
933s ***** assert <54567> (median (int64 ([intmax("int64"), intmax("int64")-2])), ...
933s                   intmax ("int64") - 1)
933s ***** assert <54567> (median ( ...
933s                  int64 ([intmax("int64"), intmax("int64")-2; 1 2]), 2), ...
933s                  int64([intmax("int64") - 1; 2]))
933s ***** assert <54567> (median (uint64 ([intmax("uint64"), intmax("uint64")-2])), ...
933s                   intmax ("uint64") - 1)
933s ***** assert <54567> (median ( ...
933s                  uint64 ([intmax("uint64"), intmax("uint64")-2; 1 2]), 2), ...
933s                  uint64([intmax("uint64") - 1; 2]))
933s ***** assert <54567> (median (...
933s  [intmin('int8') intmin('int8')+5 intmax('int8')-5 intmax('int8')]), ...
933s  int8(-1))
933s ***** assert <54567> (median ([int8([1 2 3 4]); ...
933s  intmin('int8') intmin('int8')+5 intmax('int8')-5 intmax('int8')], 2), ...
933s  int8([3;-1]))
933s ***** assert <54567> (median (...
933s  [intmin('int64') intmin('int64')+5 intmax('int64')-5 intmax('int64')]), ...
933s  int64(-1))
933s ***** assert <54567> (median ([int64([1 2 3 4]); ...
933s  intmin('int64') intmin('int64')+5 intmax('int64')-5 intmax('int64')], 2), ...
933s  int64([3;-1]))
933s ***** assert <54567> (median ([intmax("uint64"), intmax("uint64")-2]), ...
933s   intmax("uint64")-1)
933s ***** assert <54567> (median ([intmax("uint64"), intmax("uint64")-2], "default"), ...
933s   double(intmax("uint64")-1))
933s ***** assert <54567> (median ([intmax("uint64"), intmax("uint64")-2], "double"), ...
933s   double(intmax("uint64")-1))
933s ***** assert <54567> (median ([intmax("uint64"), intmax("uint64")-2], "native"), ...
933s   intmax("uint64")-1)
933s ***** assert (median ([1 2 3], "aLL"), 2)
933s ***** assert (median ([1 2 3], "OmitNan"), 2)
933s ***** assert (median ([1 2 3], "DOUBle"), 2)
933s ***** error <Invalid call> median ()
933s ***** error <Invalid call> median (1, 2, 3)
933s ***** error <Invalid call> median (1, 2, 3, 4)
933s ***** error <Invalid call> median (1, "all", 3)
934s ***** error <Invalid call> median (1, "b")
934s ***** error <Invalid call> median (1, 1, "foo")
934s ***** error <'all' cannot be used with> median (1, 3, "all")
934s ***** error <'all' cannot be used with> median (1, [2 3], "all")
934s ***** error <X must be either numeric or logical> median ({1:5})
934s ***** error <X must be either numeric or logical> median ("char")
934s ***** error <only one OUTTYPE can be specified> median(1, "double", "native")
934s ***** error <DIM must be a positive integer> median (1, ones (2,2))
934s ***** error <DIM must be a positive integer> median (1, 1.5)
934s ***** error <DIM must be a positive integer> median (1, 0)
934s ***** error <DIM must be a positive integer> median ([1 2 3], [-1 1])
934s ***** error <VECDIM must contain non-repeating> median(1, [1 2 2])
934s 159 tests, 159 passed, 0 known failure, 0 skipped
934s [inst/shadow9/std.m]
934s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/shadow9/std.m
934s ***** assert (std (13), 0)
934s ***** assert (std (single (13)), single (0))
934s ***** assert (std ([1,2,3]), 1)
934s ***** assert (std ([1,2,3], 1), sqrt (2/3), eps)
934s ***** assert (std ([1,2,3], [], 1), [0,0,0])
934s ***** assert (std ([1,2,3], [], 3), [0,0,0])
934s ***** assert (std (5, 99), 0)
934s ***** assert (std (5, 99, 1), 0)
934s ***** assert (std (5, 99, 2), 0)
934s ***** assert (std ([5 3], [99 99], 2), 1)
934s ***** assert (std ([1:7], [1:7]), sqrt (3))
934s ***** assert (std ([eye(3)], [1:3]), sqrt ([5/36, 2/9, 1/4]), eps)
934s ***** assert (std (ones (2,2,2), [1:2], 3), [(zeros (2,2))])
934s ***** assert (std ([1 2; 3 4], 0, 'all'), std ([1:4]))
934s ***** assert (std (reshape ([1:8], 2, 2, 2), 0, [1 3]), sqrt ([17/3 17/3]), eps)
934s ***** assert (std ([1 2 3;1 2 3], [], [1 2]), sqrt (0.8), eps)
934s ***** test
934s  x = [-10:10];
934s  y = [x;x+5;x-5];
934s  assert (std (x), sqrt (38.5), 1e-14);
934s  assert (std (y, [], 2), sqrt ([38.5; 38.5; 38.5]), 1e-14);
934s  assert (std (y, 0, 2), sqrt ([38.5; 38.5; 38.5]), 1e-14);
934s  assert (std (y, 1, 2), ones (3,1) * sqrt (36.66666666666666), 1e-14);
934s  assert (std (y, "all"), sqrt (54.19354838709678), 1e-14);
934s  y(2,4) = NaN;
934s  assert (std (y, "all"), NaN);
934s  assert (std (y, "all", "includenan"), NaN);
934s  assert (std (y, "all", "omitnan"), sqrt (55.01533580116342), 1e-14);
934s  assert (std (y, 0, 2, "includenan"), sqrt ([38.5; NaN; 38.5]), 1e-14);
934s  assert (std (y, [], 2), sqrt ([38.5; NaN; 38.5]), 1e-14);
934s  assert (std (y, [], 2, "omitnan"), ...
934s          sqrt ([38.5; 37.81842105263158; 38.5]), 1e-14);
934s ***** assert (std ([4 NaN 6], [1 2 1], "omitnan"), 1, eps)
934s ***** assert (std ([4 5 6], [1 NaN 1], "omitnan"), 1, eps)
934s ***** assert (std (magic(3), [1 NaN 3], "omitnan"), sqrt(3)*[1 2 1], eps)
934s ***** assert (std ([4 NaN 6], [1 2 1], "omitnan", "all"), 1, eps)
934s ***** assert (std ([4 NaN 6], [1 2 1], "all", "omitnan"), 1, eps)
934s ***** assert (std ([4 5 6], [1 NaN 1], "omitnan", "all"), 1, eps)
934s ***** assert (std ([4 NaN 6], [1 2 1], 2, "omitnan"), 1, eps)
934s ***** assert (std ([4 5 6], [1 NaN 1], 2, "omitnan"), 1, eps)
934s ***** assert (std (magic(3), [1 NaN 3], 1, "omitnan"), sqrt(3)*[1 2 1], eps)
934s ***** assert (std (magic(3), [1 NaN 3], 2, "omitnan"), sqrt(3)*[0.5;1;0.5], eps)
934s ***** assert (std (4*[4 5; 6 7; 8 9], [1 3], 2, 'omitnan'), sqrt(3)*[1;1;1], eps)
934s ***** assert (std ([4 NaN; 6 7; 8 9], [1 1 3], 1, 'omitnan'), [1.6 sqrt(3)/2], eps)
934s ***** assert (std (4*[4 NaN; 6 7; 8 9], [1 3], 2, 'omitnan'), sqrt(3)*[0;1;1], eps)
934s ***** assert (std (3*reshape(1:18, [3 3 2]), [1 2 3], 1, 'omitnan'), ...
934s           sqrt(5)*ones(1,3,2), eps)
934s ***** assert (std (reshape(1:18, [3 3 2]), [1 2 3], 2, 'omitnan'), ...
934s           sqrt(5)*ones(3,1,2), eps)
934s ***** assert (std (3*reshape(1:18, [3 3 2]), ones (3,3,2), [1 2], 'omitnan'), ...
934s           sqrt(60)*ones(1,1,2),eps)
934s ***** assert (std (3*reshape(1:18, [3 3 2]), ones (3,3,2), [1 4], 'omitnan'), ...
934s           sqrt(6)*ones(1,3,2),eps)
934s ***** assert (std (6*reshape(1:18, [3 3 2]), ones (3,3,2), [1:3], 'omitnan'), ...
934s           sqrt(969),eps)
934s ***** test
934s  x = reshape(1:18, [3 3 2]);
934s  x([2, 14]) = NaN;
934s  w = ones (3,3,2);
934s  assert (std (16*x, w, [1:3], 'omitnan'), sqrt(6519), eps);
934s ***** test
934s  x = reshape(1:18, [3 3 2]);
934s  w = ones (3,3,2);
934s  w([2, 14]) = NaN;
934s  assert (std (16*x, w, [1:3], 'omitnan'), sqrt(6519), eps);
934s ***** assert (std ([1 2 3], "aLl"), 1);
934s ***** assert (std ([1 2 3], "OmitNan"), 1);
934s ***** assert (std ([1 2 3], "IncludeNan"), 1);
934s ***** test
934s  x = repmat ([1:20;6:25], [5, 2, 6, 3]);
934s  assert (size (std (x, 0, [3 2])), [10, 1, 1, 3]);
934s  assert (size (std (x, 1, [1 2])), [1, 1, 6, 3]);
934s  assert (size (std (x, [], [1 2 4])), [1, 1, 6]);
934s  assert (size (std (x, 0, [1 4 3])), [1, 40]);
934s  assert (size (std (x, [], [1 2 3 4])), [1, 1]);
934s ***** assert (std (3*magic(3)), sqrt([63 144 63]), eps)
934s ***** assert (std (3*magic(3), 'omitnan'), sqrt([63 144 63]), eps)
934s ***** assert (std (3*magic(3), 1), sqrt([42 96 42]), eps)
934s ***** assert (std (3*magic(3), 1, 'omitnan'), sqrt([42 96 42]), eps)
934s ***** assert (std (3*magic(3), ones(1,3), 1), sqrt([42 96 42]), eps)
934s ***** assert (std (3*magic(3), ones(1,3), 1, 'omitnan'), sqrt([42 96 42]), eps)
934s ***** assert (std (2*magic(3), [1 1 NaN], 1, 'omitnan'), [5 4 1], eps)
934s ***** assert (std (3*magic(3), ones(3,3)), sqrt([42 96 42]), eps)
934s ***** assert (std (3*magic(3), ones(3,3), 'omitnan'), sqrt([42 96 42]), eps)
934s ***** assert (std (3*magic(3), [1 1 1; 1 1 1; 1 NaN 1], 'omitnan'), ...
934s          sqrt([42 36 42]), eps)
934s ***** assert (std (3*magic(3), ones(3,3), 1), sqrt([42 96 42]), eps)
934s ***** assert (std (3*magic(3), ones(3,3), 1, 'omitnan'), sqrt([42 96 42]), eps)
934s ***** assert (std (3*magic(3), [1 1 1; 1 1 1; 1 NaN 1], 1, 'omitnan'), ...
934s          sqrt([42 36 42]), eps)
934s ***** assert (std (3*magic(3), ones(3,3), [1 4]), sqrt([42 96 42]), eps)
934s ***** assert (std (3*magic(3), ones(3,3), [1 4], 'omitnan'), sqrt([42 96 42]), eps)
934s ***** assert (std (3*magic(3), [1 1 1; 1 1 1; 1 NaN 1],[1 4],'omitnan'), ...
934s          sqrt([42 36 42]), eps)
934s ***** test
934s  x = repmat ([1:20;6:25], [5, 2, 6, 3]);
934s  v = repmat (sqrt (33.38912133891213), [10, 1, 1, 3]);
934s  assert (std (x, 0, [3, 2]), v, 1e-14);
934s  v = repmat (sqrt (33.250), [10, 1, 1, 3]);
934s  assert (std (x, 1, [3, 2]), v, 1e-14);
934s  x(2,5,6,3) = NaN;
934s  v(2,1,1,3) = NaN;
934s  assert (std (x, 1, [3, 2]), v, 1e-14);
934s  v = repmat (sqrt (33.38912133891213), [10 1 1 3]);
934s  v(2,1,1,3) = NaN;
934s  assert (std (x, [], [3, 2]), v, 1e-14);
934s  v(2,1,1,3) = sqrt (33.40177912169048);
934s  assert (std (x, [], [3, 2], "omitnan"), v, 1e-14);
934s ***** assert (std (ones (2,2,2), [1:2], 3), [(zeros (2, 2))])
934s ***** assert (std (magic (3), [1:9], "all"), 2.581988897471611, 1e-14)
934s ***** assert (std (ones (2,2), [], 3), zeros (2,2))
934s ***** assert (std (ones (2,2,2), [], 99), zeros (2,2,2))
934s ***** assert (std (magic (3), [], 3), zeros (3,3))
934s ***** assert (std (magic (3), [], 1), sqrt ([7, 16, 7]))
934s ***** assert (std (magic (3), [], [1 3]), sqrt ([7, 16, 7]))
934s ***** assert (std (magic (3), [], [1 99]), sqrt ([7, 16, 7]))
934s ***** assert (std ([]), NaN)
934s ***** assert (class (var (single ([]))), "single")
934s ***** assert (std ([],[],1), NaN(1,0))
934s ***** assert (std ([],[],2), NaN(0,1))
934s ***** assert (std ([],[],3), [])
934s ***** assert (class (var (single ([]), [], 1)), "single")
934s ***** assert (std (ones (1,0)), NaN)
934s ***** assert (std (ones (1,0), [], 1), NaN(1,0))
934s ***** assert (std (ones (1,0), [], 2), NaN)
934s ***** assert (std (ones (1,0), [], 3), NaN(1,0))
934s ***** assert (class (var (ones (1, 0, "single"), [], 1)), "single")
934s ***** assert (std (ones (0,1)), NaN)
934s ***** assert (std (ones (0,1), [], 1), NaN)
934s ***** assert (std (ones (0,1), [], 2), NaN(0,1))
934s ***** assert (std (ones (0,1), [], 3), NaN(0,1))
934s ***** assert (std (ones (1,3,0,2)), NaN(1,1,0,2))
934s ***** assert (std (ones (1,3,0,2), [], 1), NaN(1,3,0,2))
934s ***** assert (std (ones (1,3,0,2), [], 2), NaN(1,1,0,2))
934s ***** assert (std (ones (1,3,0,2), [], 3), NaN(1,3,1,2))
934s ***** assert (std (ones (1,3,0,2), [], 4), NaN(1,3,0))
934s ***** test
934s  [~, m] = std ([]);
934s  assert (m, NaN);
934s ***** test <*62395>
934s  [~, m] = std (13);
934s  assert (m, 13);
934s  [~, m] = std (single(13));
934s  assert (m, single(13));
934s  [~, m] = std ([1, 2, 3; 3 2 1], []);
934s  assert (m, [2 2 2]);
934s  [~, m] = std ([1, 2, 3; 3 2 1], [], 1);
934s  assert (m, [2 2 2]);
934s  [~, m] = std ([1, 2, 3; 3 2 1], [], 2);
934s  assert (m, [2 2]');
934s  [~, m] = std ([1, 2, 3; 3 2 1], [], 3);
934s  assert (m, [1 2 3; 3 2 1]);
935s ***** test <*62395>
935s  [~, m] = std (5,99);
935s  assert (m, 5);
935s  [~, m] = std ([1:7], [1:7]);
935s  assert (m, 5);
935s  [~, m] = std ([eye(3)], [1:3]);
935s  assert (m, [1/6, 1/3, 0.5], eps);
935s  [~, m] = std (ones (2,2,2), [1:2], 3);
935s  assert (m, ones (2,2));
935s  [~, m] = std ([1 2; 3 4], 0, 'all');
935s  assert (m, 2.5, eps);
935s  [~, m] = std (reshape ([1:8], 2, 2, 2), 0, [1 3]);
935s  assert (m, [3.5, 5.5], eps);
935s ***** test
935s  [v, m] = std (4 * eye (2), [1, 3]);
935s  assert (v, sqrt ([3, 3]), 1e-14);
935s  assert (m, [1, 3]);
935s ***** test <*62395>
935s  [~, m] = std ([]);
935s  assert (m, NaN);
935s ***** test
935s  x = repmat ([1:20;6:25], [5, 2, 6, 3]);
935s  [~, m] = std (x, 0, [3 2]);
935s  assert (m, mean (x, [3 2]));
935s  [~, m] = std (x, 0, [1 2]);
935s  assert (m, mean (x, [1 2]));
935s  [~, m] = std (x, 0, [1 3 4]);
935s  assert (m, mean (x, [1 3 4]));
935s ***** test
935s  x = repmat ([1:20;6:25], [5, 2, 6, 3]);
935s  x(2,5,6,3) = NaN;
935s  [~, m] = std (x, 0, [3 2], "omitnan");
935s  assert (m, mean (x, [3 2], "omitnan"));
935s ***** test <*63203>
935s  [v, m] = std (Inf);
935s  assert (v, NaN);
935s  assert (m, Inf);
935s ***** test <*63203>
935s  [v, m] = std (NaN);
935s  assert (v, NaN);
935s  assert (m, NaN);
935s ***** test <*63203>
935s  [v, m] = std ([1, Inf, 3]);
935s  assert (v, NaN);
935s  assert (m, Inf);
935s ***** test <*63203>
935s  [v, m] = std ([1, Inf, 3]');
935s  assert (v, NaN);
935s  assert (m, Inf);
935s ***** test <*63203>
935s  [v, m] = std ([1, NaN, 3]);
935s  assert (v, NaN);
935s  assert (m, NaN);
935s ***** test <*63203>
935s  [v, m] = std ([1, NaN, 3]');
935s  assert (v, NaN);
935s  assert (m, NaN);
935s ***** test <*63203>
935s  [v, m] = std ([1, Inf, 3], [], 1);
935s  assert (v, [0, NaN, 0]);
935s  assert (m, [1, Inf, 3]);
935s ***** test <*63203>
935s  [v, m] = std ([1, Inf, 3], [], 2);
935s  assert (v, NaN);
935s  assert (m, Inf);
935s ***** test <*63203>
935s  [v, m] = std ([1, Inf, 3], [], 3);
935s  assert (v, [0, NaN, 0]);
935s  assert (m, [1, Inf, 3]);
935s ***** test <*63203>
935s  [v, m] = std ([1, NaN, 3], [], 1);
935s  assert (v, [0, NaN, 0]);
935s  assert (m, [1, NaN, 3]);
935s ***** test <*63203>
935s  [v, m] = std ([1, NaN, 3], [], 2);
935s  assert (v, NaN);
935s  assert (m, NaN);
935s ***** test <*63203>
935s  [v, m] = std ([1, NaN, 3], [], 3);
935s  assert (v, [0, NaN, 0]);
935s  assert (m, [1, NaN, 3]);
935s ***** test <*63203>
935s  [v, m] = std ([1, 2, 3; 3, Inf, 5]);
935s  assert (v, sqrt ([2, NaN, 2]));
935s  assert (m, [2, Inf, 4]);
935s ***** test <*63203>
935s  [v, m] = std ([1, Inf, 3; 3, Inf, 5]);
935s  assert (v, sqrt ([2, NaN, 2]));
935s  assert (m, [2, Inf, 4]);
935s ***** test <*63203>
935s  [v, m] = std ([1, 2, 3; 3, NaN, 5]);
935s  assert (v, sqrt ([2, NaN, 2]));
935s  assert (m, [2, NaN, 4]);
935s ***** test <*63203>
935s  [v, m] = std ([1, NaN, 3; 3, NaN, 5]);
935s  assert (v, sqrt ([2, NaN, 2]));
935s  assert (m, [2, NaN, 4]);
935s ***** test <*63203>
935s  [v, m] = std ([Inf, 2, NaN]);
935s  assert (v, NaN);
935s  assert (m, NaN);
935s ***** test <*63203>
935s  [v, m] = std ([Inf, 2, NaN]');
935s  assert (v, NaN);
935s  assert (m, NaN);
935s ***** test <*63203>
935s  [v, m] = std ([NaN, 2, Inf]);
935s  assert (v, NaN);
935s  assert (m, NaN);
935s ***** test <*63203>
935s  [v, m] = std ([NaN, 2, Inf]');
935s  assert (v, NaN);
935s  assert (m, NaN);
935s ***** test <*63203>
935s  [v, m] = std ([Inf, 2, NaN], [], 1);
935s  assert (v, [NaN, 0, NaN]);
935s  assert (m, [Inf, 2, NaN]);
935s ***** test <*63203>
935s  [v, m] = std ([Inf, 2, NaN], [], 2);
935s  assert (v, NaN);
935s  assert (m, NaN);
935s ***** test <*63203>
935s  [v, m] = std ([NaN, 2, Inf], [], 1);
935s  assert (v, [NaN, 0, NaN]);
935s  assert (m, [NaN, 2, Inf]);
935s ***** test <*63203>
935s  [v, m] = std ([NaN, 2, Inf], [], 2);
935s  assert (v, NaN);
935s  assert (m, NaN);
935s ***** test <*63203>
935s  [v, m] = std ([1, 3, NaN; 3, 5, Inf]);
935s  assert (v, sqrt ([2, 2, NaN]));
935s  assert (m, [2, 4, NaN]);
935s ***** test <*63203>
935s  [v, m] = std ([1, 3, Inf; 3, 5, NaN]);
935s  assert (v, sqrt ([2, 2, NaN]));
935s  assert (m, [2, 4, NaN]);
935s ***** test <*63291>
935s  [v, m] = std (2 * eye (2));
935s  assert (v, sqrt ([2, 2]));
935s  assert (m, [1, 1]);
935s ***** test <*63291>
935s  [v, m] = std (4 * eye (2), [1, 3]);
935s  assert (v, sqrt ([3, 3]));
935s  assert (m, [1, 3]);
935s ***** test <*63291>
935s  [v, m] = std (sparse (2 * eye (2)));
935s  assert (full (v), sqrt ([2, 2]));
935s  assert (full (m), [1, 1]);
935s ***** test <*63291>
935s  [v, m] = std (sparse (4 * eye (2)), [1, 3]);
935s  assert (full (v), sqrt ([3, 3]));
935s  assert (full (m), [1, 3]);
935s ***** test <*63291>
935s  [v, m] = std (sparse (eye (2)));
935s  assert (issparse (v));
935s  assert (issparse (m));
935s ***** test <*63291>
935s  [v, m] = std (sparse (eye (2)), [1, 3]);
935s  assert (issparse (v));
935s  assert (issparse (m));
935s ***** error <Invalid call> std ()
935s ***** error <Invalid call> std (1, 2, "omitnan", 3)
935s ***** error <Invalid call> std (1, 2, 3, 4)
935s ***** error <Invalid call> std (1, 2, 3, 4, 5)
935s ***** error <Invalid call> std (1, "foo")
936s ***** error <Invalid call> std (1, [], "foo")
936s ***** error <normalization scalar must be either 0 or 1> std ([1 2 3], 2)
936s ***** error <normalization scalar must be either 0 or 1> std ([1 2], 2, "all")
936s ***** error <normalization scalar must be either 0 or 1> std ([1 2],0.5, "all")
936s ***** error <weights must not contain any negative values> std (1, -1)
936s ***** error <weights must not contain any negative values> std (1, [1 -1])
936s ***** error <weights must not contain any negative values> ...
936s  std ([1 2 3], [1 -1 0])
936s ***** error <X must be a numeric vector or matrix> std ({1:5})
936s ***** error <X must be a numeric vector or matrix> std ("char")
936s ***** error <X must be a numeric vector or matrix> std (['A'; 'B'])
936s ***** error <DIM must be a positive integer> std (1, [], ones (2,2))
936s ***** error <DIM must be a positive integer> std (1, 0, 1.5)
936s ***** error <DIM must be a positive integer> std (1, [], 0)
936s ***** error <DIM must be a positive integer> std (1, [], 1.5)
936s ***** error <DIM must be a positive integer> std ([1 2 3], [], [-1 1])
936s ***** error <VECDIM must contain non-repeating positive integers> ...
936s  std (repmat ([1:20;6:25], [5 2 6 3]), 0, [1 2 2 2])
936s ***** error <weight matrix or array does not match X in size> ...
936s  std ([1 2], eye (2))
936s ***** error <weight matrix or array does not match X in size> ...
936s  std ([1 2 3 4], [1 2; 3 4])
936s ***** error <weight matrix or array does not match X in size> ...
936s  std ([1 2 3 4], [1 2; 3 4], 1)
936s ***** error <weight matrix or array does not match X in size> ...
936s  std ([1 2 3 4], [1 2; 3 4], [2 3])
936s ***** error <weight matrix or array does not match X in size> ...
936s  std (ones (2, 2), [1 2], [1 2])
936s ***** error <weight matrix or array does not match X in size> ...
936s  std ([1 2 3 4; 5 6 7 8], [1 2 1 2 1; 1 2 1 2 1], 1)
936s ***** error <weight matrix or array does not match X in size> ...
936s  std (repmat ([1:20;6:25], [5 2 6 3]), repmat ([1:20;6:25], [5 2 3]), [2 3])
936s ***** error <weight vector length does not match> std ([1 2 3; 2 3 4], [1 3 4])
936s ***** error <weight vector length does not match> std ([1 2], [1 2 3])
936s ***** error <weight vector length does not match> std (1, [1 2])
936s ***** error <weight vector length does not match> std ([1 2 3; 2 3 4], [1 3 4], 1)
936s ***** error <weight vector length does not match> std ([1 2 3; 2 3 4], [1 3], 2)
936s ***** error <weight vector length does not match> std ([1 2], [1 2], 1)
936s ***** error <'all' flag cannot be used with DIM or VECDIM options> ...
936s  std (1, [], 1, "all")
936s ***** error <weight vector element count does not match X> ...
936s  std ([1 2 3; 2 3 4], [1 3], "all")
936s ***** error <weight matrix or array does not match X in size> ...
936s  std (repmat ([1:20;6:25], [5 2 6 3]), repmat ([1:20;6:25], [5 2 3]), "all")
936s 162 tests, 162 passed, 0 known failure, 0 skipped
936s [inst/shadow9/var.m]
936s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/shadow9/var.m
936s ***** assert (var (13), 0)
936s ***** assert (var (single (13)), single (0))
936s ***** assert (var ([1,2,3]), 1)
936s ***** assert (var ([1,2,3], 1), 2/3, eps)
936s ***** assert (var ([1,2,3], [], 1), [0,0,0])
936s ***** assert (var ([1,2,3], [], 3), [0,0,0])
936s ***** assert (var (5, 99), 0)
936s ***** assert (var (5, 99, 1), 0)
936s ***** assert (var (5, 99, 2), 0)
936s ***** assert (var ([5 3], [99 99], 2), 1)
936s ***** assert (var ([1:7], [1:7]), 3)
936s ***** assert (var ([eye(3)], [1:3]), [5/36, 2/9, 1/4], eps)
936s ***** assert (var (ones (2,2,2), [1:2], 3), [(zeros (2,2))])
936s ***** assert (var ([1 2; 3 4], 0, 'all'), var ([1:4]))
936s ***** assert (var (reshape ([1:8], 2, 2, 2), 0, [1 3]), [17/3 17/3], eps)
936s ***** assert (var ([1 2 3;1 2 3], [], [1 2]), 0.8, eps)
936s ***** test
936s  x = [-10:10];
936s  y = [x;x+5;x-5];
936s  assert (var (x), 38.5);
936s  assert (var (y, [], 2), [38.5; 38.5; 38.5]);
936s  assert (var (y, 0, 2), [38.5; 38.5; 38.5]);
936s  assert (var (y, 1, 2), ones (3,1) * 36.66666666666666, 1e-14);
936s  assert (var (y, "all"), 54.19354838709678, 1e-14);
936s  y(2,4) = NaN;
936s  assert (var (y, "all"), NaN);
936s  assert (var (y, "all", "includenan"), NaN);
936s  assert (var (y, "all", "omitnan"), 55.01533580116342, 1e-14);
936s  assert (var (y, 0, 2, "includenan"), [38.5; NaN; 38.5]);
936s  assert (var (y, [], 2), [38.5; NaN; 38.5]);
936s  assert (var (y, [], 2, "omitnan"), [38.5; 37.81842105263158; 38.5], 1e-14);
936s ***** assert (var ([1 NaN 3], [1 2 3], "omitnan"), 0.75, eps)
936s ***** assert (var ([1 2 3], [1 NaN 3], "omitnan"), 0.75, eps)
936s ***** assert (var (magic(3), [1 NaN 3], "omitnan"), [3 12 3], eps)
936s ***** assert (var ([1 NaN 3], [1 2 3], "omitnan", "all"), 0.75, eps)
936s ***** assert (var ([1 NaN 3], [1 2 3], "all", "omitnan"), 0.75, eps)
936s ***** assert (var ([1 2 3], [1 NaN 3], "omitnan", "all"), 0.75, eps)
936s ***** assert (var ([1 NaN 3], [1 2 3], 2, "omitnan"), 0.75, eps)
936s ***** assert (var ([1 2 3], [1 NaN 3], 2, "omitnan"), 0.75, eps)
936s ***** assert (var (magic(3), [1 NaN 3], 1, "omitnan"), [3 12 3], eps)
936s ***** assert (var (magic(3), [1 NaN 3], 2, "omitnan"), [0.75;3;0.75], eps)
936s ***** assert (var ([4 4; 4 6; 6 6], [1 3], 2, 'omitnan'), [0;0.75;0], eps)
936s ***** assert (var ([4 NaN; 4 6; 6 6], [1 2 3], 1, 'omitnan'), [1 0])
936s ***** assert (var ([4 NaN; 4 6; 6 6], [1 3], 2, 'omitnan'), [0;0.75;0], eps)
936s ***** assert (var (3*reshape(1:18, [3 3 2]), [1 2 3], 1, 'omitnan'), ones(1,3,2)*5)
936s ***** assert (var (reshape(1:18, [3 3 2]), [1 2 3], 2, 'omitnan'), 5*ones(3,1,2))
936s ***** assert (var (3*reshape(1:18, [3 3 2]), ones (3,3,2), [1 2], 'omitnan'), ...
936s          60 * ones(1,1,2))
936s ***** assert (var (3*reshape(1:18, [3 3 2]), ones (3,3,2), [1 4], 'omitnan'), ...
936s          6 * ones(1,3,2))
936s ***** assert (var (6*reshape(1:18, [3 3 2]), ones (3,3,2), [1:3], 'omitnan'), 969)
936s ***** test
936s  x = reshape(1:18, [3 3 2]);
936s  x([2, 14]) = NaN;
936s  w = ones (3,3,2);
936s  assert (var (16*x, w, [1:3], 'omitnan'), 6519);
936s ***** test
936s  x = reshape(1:18, [3 3 2]);
936s  w = ones (3,3,2);
936s  w([2, 14]) = NaN;
936s  assert (var (16*x, w, [1:3], 'omitnan'), 6519);
936s ***** assert (var ([1 2 3], "aLl"), 1);
936s ***** assert (var ([1 2 3], "OmitNan"), 1);
936s ***** assert (var ([1 2 3], "IncludeNan"), 1);
936s ***** test
936s  x = repmat ([1:20;6:25], [5, 2, 6, 3]);
936s  assert (size (var (x, 0, [3 2])), [10, 1, 1, 3]);
936s  assert (size (var (x, 1, [1 2])), [1, 1, 6, 3]);
936s  assert (size (var (x, [], [1 2 4])), [1, 1, 6]);
936s  assert (size (var (x, 0, [1 4 3])), [1, 40]);
936s  assert (size (var (x, [], [1 2 3 4])), [1, 1]);
936s ***** assert (var (3*magic(3)), [63 144 63])
936s ***** assert (var (3*magic(3), 'omitnan'), [63 144 63])
936s ***** assert (var (3*magic(3), 1), [42 96 42])
936s ***** assert (var (3*magic(3), 1, 'omitnan'), [42 96 42])
936s ***** assert (var (3*magic(3), ones(1,3), 1), [42 96 42])
936s ***** assert (var (3*magic(3), ones(1,3), 1, 'omitnan'), [42 96 42])
936s ***** assert (var (2*magic(3), [1 1 NaN], 1, 'omitnan'), [25 16 1])
936s ***** assert (var (3*magic(3), ones(3,3)), [42 96 42])
936s ***** assert (var (3*magic(3), ones(3,3), 'omitnan'), [42 96 42])
936s ***** assert (var (3*magic(3), [1 1 1; 1 1 1; 1 NaN 1], 'omitnan'), [42 36 42])
936s ***** assert (var (3*magic(3), ones(3,3), 1), [42 96 42])
936s ***** assert (var (3*magic(3), ones(3,3), 1, 'omitnan'), [42 96 42])
936s ***** assert (var (3*magic(3), [1 1 1; 1 1 1; 1 NaN 1], 1, 'omitnan'), [42 36 42])
936s ***** assert (var (3*magic(3), ones(3,3), [1 4]), [42 96 42])
936s ***** assert (var (3*magic(3), ones(3,3), [1 4], 'omitnan'), [42 96 42])
936s ***** assert (var (3*magic(3), [1 1 1; 1 1 1; 1 NaN 1],[1 4],'omitnan'), [42 36 42])
936s ***** test
936s  x = repmat ([1:20;6:25], [5, 2, 6, 3]);
936s  v = repmat (33.38912133891213, [10, 1, 1, 3]);
936s  assert (var (x, 0, [3, 2]), v, 1e-14);
936s  v = repmat (33.250, [10, 1, 1, 3]);
936s  assert (var (x, 1, [3, 2]), v, 1e-14);
936s  x(2,5,6,3) = NaN;
936s  v(2,1,1,3) = NaN;
936s  assert (var (x, 1, [3, 2]), v, 4e-14);
936s  v = repmat (33.38912133891213, [10 1 1 3]);
936s  v(2,1,1,3) = NaN;
936s  assert (var (x, [], [3, 2]), v, 4e-14);
936s  v(2,1,1,3) = 33.40177912169048;
936s  assert (var (x, [], [3, 2], "omitnan"), v, 4e-14);
936s ***** assert (var (ones (2,2,2), [1:2], 3), [(zeros (2, 2))])
936s ***** assert (var (magic (3), [1:9], "all"), 6.666666666666667, 1e-14)
936s ***** assert (var (ones (2,2), [], 3), zeros (2,2))
936s ***** assert (var (ones (2,2,2), [], 99), zeros (2,2,2))
936s ***** assert (var (magic (3), [], 3), zeros (3,3))
936s ***** assert (var (magic (3), [], 1), [7, 16, 7])
936s ***** assert (var (magic (3), [], [1 3]), [7, 16, 7])
936s ***** assert (var (magic (3), [], [1 99]), [7, 16, 7])
936s ***** assert (var ([]), NaN)
936s ***** assert (class (var (single ([]))), "single")
936s ***** assert (var ([],[],1), NaN(1,0))
936s ***** assert (var ([],[],2), NaN(0,1))
936s ***** assert (var ([],[],3), [])
936s ***** assert (class (var (single ([]), [], 1)), "single")
936s ***** assert (var (ones (1,0)), NaN)
936s ***** assert (var (ones (1,0), [], 1), NaN(1,0))
936s ***** assert (var (ones (1,0), [], 2), NaN)
936s ***** assert (var (ones (1,0), [], 3), NaN(1,0))
936s ***** assert (class (var (ones (1, 0, "single"), [], 1)), "single")
936s ***** assert (var (ones (0,1)), NaN)
936s ***** assert (var (ones (0,1), [], 1), NaN)
936s ***** assert (var (ones (0,1), [], 2), NaN(0,1))
936s ***** assert (var (ones (0,1), [], 3), NaN(0,1))
936s ***** assert (var (ones (1,3,0,2)), NaN(1,1,0,2))
936s ***** assert (var (ones (1,3,0,2), [], 1), NaN(1,3,0,2))
936s ***** assert (var (ones (1,3,0,2), [], 2), NaN(1,1,0,2))
936s ***** assert (var (ones (1,3,0,2), [], 3), NaN(1,3,1,2))
936s ***** assert (var (ones (1,3,0,2), [], 4), NaN(1,3,0))
936s ***** test
936s  [~, m] = var ([]);
936s  assert (m, NaN);
936s ***** test <*62395>
936s  [~, m] = var (13);
936s  assert (m, 13);
936s  [~, m] = var (single(13));
936s  assert (m, single(13));
936s  [~, m] = var ([1, 2, 3; 3 2 1], []);
936s  assert (m, [2 2 2]);
936s  [~, m] = var ([1, 2, 3; 3 2 1], [], 1);
936s  assert (m, [2 2 2]);
936s  [~, m] = var ([1, 2, 3; 3 2 1], [], 2);
936s  assert (m, [2 2]');
936s  [~, m] = var ([1, 2, 3; 3 2 1], [], 3);
936s  assert (m, [1 2 3; 3 2 1]);
936s ***** test <*62395>
936s  [~, m] = var (5,99);
936s  assert (m, 5);
936s  [~, m] = var ([1:7], [1:7]);
936s  assert (m, 5);
936s  [~, m] = var ([eye(3)], [1:3]);
936s  assert (m, [1/6, 1/3, 0.5], eps);
936s  [~, m] = var (ones (2,2,2), [1:2], 3);
936s  assert (m, ones (2,2));
936s  [~, m] = var ([1 2; 3 4], 0, 'all');
936s  assert (m, 2.5, eps);
936s  [~, m] = var (reshape ([1:8], 2, 2, 2), 0, [1 3]);
936s  assert (m, [3.5, 5.5], eps);
936s ***** test
936s  [v, m] = var (4 * eye (2), [1, 3]);
936s  assert (v, [3, 3]);
936s  assert (m, [1, 3]);
936s ***** test <*62395>
936s  [~, m] = var ([]);
936s  assert (m, NaN);
936s ***** test <*62395>
936s  x = repmat ([1:20;6:25], [5, 2, 6, 3]);
936s  [~, m] = var (x, 0, [3 2]);
936s  assert (m, mean (x, [3 2]));
936s  [~, m] = var (x, 0, [1 2]);
936s  assert (m, mean (x, [1 2]));
936s  [~, m] = var (x, 0, [1 3 4]);
936s  assert (m, mean (x, [1 3 4]));
936s ***** test
936s  x = repmat ([1:20;6:25], [5, 2, 6, 3]);
936s  x(2,5,6,3) = NaN;
936s  [~, m] = var (x, 0, [3 2], "omitnan");
936s  assert (m, mean (x, [3 2], "omitnan"));
936s ***** test <*63203>
936s  [v, m] = var (Inf);
936s  assert (v, NaN);
936s  assert (m, Inf);
936s ***** test <*63203>
936s  [v, m] = var (NaN);
936s  assert (v, NaN);
936s  assert (m, NaN);
936s ***** test <*63203>
936s  [v, m] = var ([1, Inf, 3]);
936s  assert (v, NaN);
936s  assert (m, Inf);
936s ***** test <*63203>
936s  [v, m] = var ([1, Inf, 3]');
936s  assert (v, NaN);
936s  assert (m, Inf);
936s ***** test <*63203>
936s  [v, m] = var ([1, NaN, 3]);
936s  assert (v, NaN);
936s  assert (m, NaN);
936s ***** test <*63203>
936s  [v, m] = var ([1, NaN, 3]');
936s  assert (v, NaN);
936s  assert (m, NaN);
936s ***** test <*63203>
936s  [v, m] = var ([1, Inf, 3], [], 1);
936s  assert (v, [0, NaN, 0]);
936s  assert (m, [1, Inf, 3]);
936s ***** test <*63203>
936s  [v, m] = var ([1, Inf, 3], [], 2);
936s  assert (v, NaN);
936s  assert (m, Inf);
936s ***** test <*63203>
936s  [v, m] = var ([1, Inf, 3], [], 3);
936s  assert (v, [0, NaN, 0]);
936s  assert (m, [1, Inf, 3]);
936s ***** test <*63203>
936s  [v, m] = var ([1, NaN, 3], [], 1);
936s  assert (v, [0, NaN, 0]);
936s  assert (m, [1, NaN, 3]);
937s ***** test <*63203>
937s  [v, m] = var ([1, NaN, 3], [], 2);
937s  assert (v, NaN);
937s  assert (m, NaN);
937s ***** test <*63203>
937s  [v, m] = var ([1, NaN, 3], [], 3);
937s  assert (v, [0, NaN, 0]);
937s  assert (m, [1, NaN, 3]);
937s ***** test <*63203>
937s  [v, m] = var ([1, 2, 3; 3, Inf, 5]);
937s  assert (v, [2, NaN, 2]);
937s  assert (m, [2, Inf, 4]);
937s ***** test <*63203>
937s  [v, m] = var ([1, Inf, 3; 3, Inf, 5]);
937s  assert (v, [2, NaN, 2]);
937s  assert (m, [2, Inf, 4]);
937s ***** test <*63203>
937s  [v, m] = var ([1, 2, 3; 3, NaN, 5]);
937s  assert (v, [2, NaN, 2]);
937s  assert (m, [2, NaN, 4]);
937s ***** test <*63203>
937s  [v, m] = var ([1, NaN, 3; 3, NaN, 5]);
937s  assert (v, [2, NaN, 2]);
937s  assert (m, [2, NaN, 4]);
937s ***** test <*63203>
937s  [v, m] = var ([Inf, 2, NaN]);
937s  assert (v, NaN);
937s  assert (m, NaN);
937s ***** test <*63203>
937s  [v, m] = var ([Inf, 2, NaN]');
937s  assert (v, NaN);
937s  assert (m, NaN);
937s ***** test <*63203>
937s  [v, m] = var ([NaN, 2, Inf]);
937s  assert (v, NaN);
937s  assert (m, NaN);
937s ***** test <*63203>
937s  [v, m] = var ([NaN, 2, Inf]');
937s  assert (v, NaN);
937s  assert (m, NaN);
937s ***** test <*63203>
937s  [v, m] = var ([Inf, 2, NaN], [], 1);
937s  assert (v, [NaN, 0, NaN]);
937s  assert (m, [Inf, 2, NaN]);
937s ***** test <*63203>
937s  [v, m] = var ([Inf, 2, NaN], [], 2);
937s  assert (v, NaN);
937s  assert (m, NaN);
937s ***** test <*63203>
937s  [v, m] = var ([NaN, 2, Inf], [], 1);
937s  assert (v, [NaN, 0, NaN]);
937s  assert (m, [NaN, 2, Inf]);
937s ***** test <*63203>
937s  [v, m] = var ([NaN, 2, Inf], [], 2);
937s  assert (v, NaN);
937s  assert (m, NaN);
937s ***** test <*63203>
937s  [v, m] = var ([1, 3, NaN; 3, 5, Inf]);
937s  assert (v, [2, 2, NaN]);
937s  assert (m, [2, 4, NaN]);
937s ***** test <*63203>
937s  [v, m] = var ([1, 3, Inf; 3, 5, NaN]);
937s  assert (v, [2, 2, NaN]);
937s  assert (m, [2, 4, NaN]);
937s ***** test <*63291>
937s  [v, m] = var (2 * eye (2));
937s  assert (v, [2, 2]);
937s  assert (m, [1, 1]);
937s ***** test <*63291>
937s  [v, m] = var (4 * eye (2), [1, 3]);
937s  assert (v, [3, 3]);
937s  assert (m, [1, 3]);
937s ***** test <*63291>
937s  [v, m] = var (sparse (2 * eye (2)));
937s  assert (full (v), [2, 2]);
937s  assert (full (m), [1, 1]);
937s ***** test <*63291>
937s  [v, m] = var (sparse (4 * eye (2)), [1, 3]);
937s  assert (full (v), [3, 3]);
937s  assert (full (m), [1, 3]);
937s ***** test<*63291>
937s  [v, m] = var (sparse (eye (2)));
937s  assert (issparse (v));
937s  assert (issparse (m));
937s ***** test<*63291>
937s  [v, m] = var (sparse (eye (2)), [1, 3]);
937s  assert (issparse (v));
937s  assert (issparse (m));
937s ***** error <Invalid call> var ()
937s ***** error <Invalid call> var (1, 2, "omitnan", 3)
937s ***** error <Invalid call> var (1, 2, 3, 4)
937s ***** error <Invalid call> var (1, 2, 3, 4, 5)
937s ***** error <Invalid call> var (1, "foo")
938s ***** error <Invalid call> var (1, [], "foo")
938s ***** error <normalization scalar must be either 0 or 1> var ([1 2 3], 2)
938s ***** error <normalization scalar must be either 0 or 1> var ([1 2], 2, "all")
938s ***** error <normalization scalar must be either 0 or 1> var ([1 2],0.5, "all")
938s ***** error <weights must not contain any negative values> var (1, -1)
938s ***** error <weights must not contain any negative values> var (1, [1 -1])
938s ***** error <weights must not contain any negative values> ...
938s  var ([1 2 3], [1 -1 0])
938s ***** error <X must be a numeric vector or matrix> var ({1:5})
938s ***** error <X must be a numeric vector or matrix> var ("char")
938s ***** error <X must be a numeric vector or matrix> var (['A'; 'B'])
938s ***** error <DIM must be a positive integer> var (1, [], ones (2,2))
938s ***** error <DIM must be a positive integer> var (1, 0, 1.5)
938s ***** error <DIM must be a positive integer> var (1, [], 0)
938s ***** error <DIM must be a positive integer> var (1, [], 1.5)
938s ***** error <DIM must be a positive integer> var ([1 2 3], [], [-1 1])
938s ***** error <VECDIM must contain non-repeating positive integers> ...
938s  var (repmat ([1:20;6:25], [5 2 6 3]), 0, [1 2 2 2])
938s ***** error <weight matrix or array does not match X in size> ...
938s  var ([1 2], eye (2))
938s ***** error <weight matrix or array does not match X in size> ...
938s  var ([1 2 3 4], [1 2; 3 4])
938s ***** error <weight matrix or array does not match X in size> ...
938s  var ([1 2 3 4], [1 2; 3 4], 1)
938s ***** error <weight matrix or array does not match X in size> ...
938s  var ([1 2 3 4], [1 2; 3 4], [2 3])
938s ***** error <weight matrix or array does not match X in size> ...
938s  var (ones (2, 2), [1 2], [1 2])
938s ***** error <weight matrix or array does not match X in size> ...
938s  var ([1 2 3 4; 5 6 7 8], [1 2 1 2 1; 1 2 1 2 1], 1)
938s ***** error <weight matrix or array does not match X in size> ...
938s  var (repmat ([1:20;6:25], [5 2 6 3]), repmat ([1:20;6:25], [5 2 3]), [2 3])
938s ***** error <weight vector length does not match> var ([1 2 3; 2 3 4], [1 3 4])
938s ***** error <weight vector length does not match> var ([1 2], [1 2 3])
938s ***** error <weight vector length does not match> var (1, [1 2])
938s ***** error <weight vector length does not match> var ([1 2 3; 2 3 4], [1 3 4], 1)
938s ***** error <weight vector length does not match> var ([1 2 3; 2 3 4], [1 3], 2)
938s ***** error <weight vector length does not match> var ([1 2], [1 2], 1)
938s ***** error <'all' flag cannot be used with DIM or VECDIM options> ...
938s  var (1, [], 1, "all")
938s ***** error <weight vector element count does not match X> ...
938s  var ([1 2 3; 2 3 4], [1 3], "all")
938s ***** error <weight matrix or array does not match X in size> ...
938s  var (repmat ([1:20;6:25], [5 2 6 3]), repmat ([1:20;6:25], [5 2 3]), "all")
938s 162 tests, 162 passed, 0 known failure, 0 skipped
938s [inst/sigma_pts.m]
938s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/sigma_pts.m
938s ***** demo
938s  K      = [1 0.5; 0.5 1]; # covaraince matrix
938s  # calculate and build associated ellipse
938s  [R,S,~] = svd (K);
938s  theta   = atan2 (R(2,1), R(1,1));
938s  v       = sqrt (diag (S));
938s  v       = v .* [cos(theta) sin(theta); -sin(theta) cos(theta)];
938s  t       = linspace (0, 2*pi, 100).';
938s  xe      = v(1,1) * cos (t) + v(2,1) * sin (t);
938s  ye      = v(1,2) * cos (t) + v(2,2) * sin (t);
938s 
938s  figure(1); clf; hold on
938s  # Plot ellipse and axes
938s  line ([0 0; v(:,1).'],[0 0; v(:,2).'])
938s  plot (xe,ye,'-r');
938s 
938s  col = 'rgb';
938s  l     = [-1.8 -1 1.5];
938s  for li = 1:3
938s   p     = sigma_pts (2, [], K, l(li));
938s   tmp   = plot (p(2:end,1), p(2:end,2), ['x' col(li)], ...
938s                p(1,1), p(1,2), ['o' col(li)]);
938s   h(li) = tmp(1);
938s  endfor
938s  hold off
938s  axis image
938s  legend (h, arrayfun (@(x) sprintf ("l:%.2g", x), l, "unif", 0));
938s ***** test
938s  p = sigma_pts (5);
938s  assert (mean (p), zeros(1,5), sqrt(eps));
938s  assert (cov (p), eye(5), sqrt(eps));
938s ***** test
938s  m = randn(1, 5);
938s  p = sigma_pts (5, m);
938s  assert (mean (p), m, sqrt(eps));
938s  assert (cov (p), eye(5), sqrt(eps));
938s ***** test
938s  x = linspace (0,1,5);
938s  K = exp (- (x.' - x).^2/ 0.5);
938s  p = sigma_pts (5, [], K);
938s  assert (mean (p), zeros(1,5), sqrt(eps));
938s  assert (cov (p), K, sqrt(eps));
938s ***** error sigma_pts(2,1);
938s ***** error sigma_pts(2,[],1);
938s ***** error sigma_pts(2,1,1);
938s ***** error sigma_pts(2,[0.5 0.5],[-1 0; 0 0]);
938s 7 tests, 7 passed, 0 known failure, 0 skipped
938s [inst/signrank.m]
938s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/signrank.m
938s ***** test
938s  load gradespaired.mat
938s  [p, h, stats] = signrank (gradespaired(:,1), ...
938s                            gradespaired(:,2), 'tail', 'left');
938s  assert (p, 0.0047, 1e-4);
938s  assert (h, true);
938s  assert (stats.zval, -2.5982, 1e-4);
938s  assert (stats.signedrank, 2017.5);
938s ***** test
938s  load ('gradespaired.mat');
938s  [p, h, stats] = signrank (gradespaired(:,1), gradespaired(:,2), ...
938s                            'tail', 'left', 'method', 'exact');
938s  assert (p, 0.0045, 1e-4);
938s  assert (h, true);
938s  assert (stats.zval, NaN);
938s  assert (stats.signedrank, 2017.5);
938s ***** test
938s  load mileage
938s  [p, h, stats] = signrank (mileage(:,2), 33);
938s  assert (p, 0.0312, 1e-4);
938s  assert (h, true);
938s  assert (stats.zval, NaN);
938s  assert (stats.signedrank, 21);
938s ***** test
938s  load mileage
938s  [p, h, stats] = signrank (mileage(:,2), 33, 'tail', 'right');
938s  assert (p, 0.0156, 1e-4);
938s  assert (h, true);
938s  assert (stats.zval, NaN);
938s  assert (stats.signedrank, 21);
938s ***** test
938s  load mileage
938s  [p, h, stats] = signrank (mileage(:,2), 33, 'tail', 'right', ...
938s                            'alpha', 0.01, 'method', 'approximate');
938s  assert (p, 0.0180, 1e-4);
938s  assert (h, false);
938s  assert (stats.zval, 2.0966, 1e-4);
938s  assert (stats.signedrank, 21);
938s ***** error <signrank: X must be a vector.> signrank (ones (2))
938s ***** error <signrank: Y must be either a scalar of a vector.> ...
938s  signrank ([1, 2, 3, 4], ones (2))
938s ***** error <signrank: X and Y vectors have different lengths.> ...
938s  signrank ([1, 2, 3, 4], [1, 2, 3])
938s ***** error <signrank: optional arguments must be in pairs.> ...
938s  signrank ([1, 2, 3, 4], [], 'tail')
938s ***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
938s  signrank ([1, 2, 3, 4], [], 'alpha', 1.2)
938s ***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
938s  signrank ([1, 2, 3, 4], [], 'alpha', 0)
938s ***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
938s  signrank ([1, 2, 3, 4], [], 'alpha', -0.05)
938s ***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
938s  signrank ([1, 2, 3, 4], [], 'alpha', "a")
938s ***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
938s  signrank ([1, 2, 3, 4], [], 'alpha', [0.01, 0.05])
938s ***** error <signrank: 'tail' argument must be a character vector.> ...
938s  signrank ([1, 2, 3, 4], [], 'tail', 0.01)
938s ***** error <signrank: 'tail' argument must be a character vector.> ...
938s  signrank ([1, 2, 3, 4], [], 'tail', {"both"})
938s ***** error <signrank: 'tail' value must be either 'both', right' or 'left'.> ...
938s  signrank ([1, 2, 3, 4], [], 'tail', "some")
938s ***** error <signrank: 'tail' value must be either 'both', right' or 'left'.> ...
938s  signrank ([1, 2, 3, 4], [], 'method', 'exact', 'tail', "some")
938s ***** error <signrank: 'method' argument must be a character vector.> ...
938s  signrank ([1, 2, 3, 4], [], 'method', 0.01)
938s ***** error <signrank: 'method' argument must be a character vector.> ...
938s  signrank ([1, 2, 3, 4], [], 'method', {"exact"})
938s ***** error <signrank: 'method' value must be either 'exact' or 'approximate'.> ...
938s  signrank ([1, 2, 3, 4], [], 'method', "some")
938s ***** error <signrank: 'method' value must be either 'exact' or 'approximate'.> ...
938s  signrank ([1, 2, 3, 4], [], 'tail', "both", 'method', "some")
938s 22 tests, 22 passed, 0 known failure, 0 skipped
938s [inst/signtest.m]
938s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/signtest.m
938s ***** test
938s  [pval, h, stats] = signtest ([-ones(1, 1000) 1], 0, "tail", "left");
938s  assert (pval, 1.091701889420221e-218, 1e-14);
938s  assert (h, 1);
938s  assert (stats.zval, -31.5437631079266, 1e-14);
938s ***** test
938s  [pval, h, stats] = signtest ([-2 -1 0 2 1 3 1], 0);
938s  assert (pval, 0.6875000000000006, 1e-14);
938s  assert (h, 0);
938s  assert (stats.zval, NaN);
938s  assert (stats.sign, 4);
938s ***** test
938s  [pval, h, stats] = signtest ([-2 -1 0 2 1 3 1], 0, "method", "approximate");
938s  assert (pval, 0.6830913983096086, 1e-14);
938s  assert (h, 0);
938s  assert (stats.zval, 0.4082482904638631, 1e-14);
938s  assert (stats.sign, 4);
938s ***** error <signtest: X must be a vector.> signtest (ones (2))
938s ***** error <signtest: Y must be either a scalar of a vector.> ...
938s  signtest ([1, 2, 3, 4], ones (2))
938s ***** error <signtest: X and Y vectors have different lengths.> ...
938s  signtest ([1, 2, 3, 4], [1, 2, 3])
938s ***** error <signtest: optional arguments must be in pairs.> ...
938s  signtest ([1, 2, 3, 4], [], 'tail')
938s ***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
938s  signtest ([1, 2, 3, 4], [], 'alpha', 1.2)
938s ***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
938s  signtest ([1, 2, 3, 4], [], 'alpha', 0)
938s ***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
938s  signtest ([1, 2, 3, 4], [], 'alpha', -0.05)
938s ***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
938s  signtest ([1, 2, 3, 4], [], 'alpha', "a")
938s ***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
938s  signtest ([1, 2, 3, 4], [], 'alpha', [0.01, 0.05])
938s ***** error <signtest: 'tail' argument must be a character vector.> ...
938s  signtest ([1, 2, 3, 4], [], 'tail', 0.01)
938s ***** error <signtest: 'tail' argument must be a character vector.> ...
938s  signtest ([1, 2, 3, 4], [], 'tail', {"both"})
938s ***** error <signtest: 'tail' value must be either 'both', right' or 'left'.> ...
938s  signtest ([1, 2, 3, 4], [], 'tail', "some")
938s ***** error <signtest: 'tail' value must be either 'both', right' or 'left'.> ...
938s  signtest ([1, 2, 3, 4], [], 'method', 'exact', 'tail', "some")
938s ***** error <signtest: 'method' argument must be a character vector.> ...
938s  signtest ([1, 2, 3, 4], [], 'method', 0.01)
938s ***** error <signtest: 'method' argument must be a character vector.> ...
938s  signtest ([1, 2, 3, 4], [], 'method', {"exact"})
938s ***** error <signtest: 'method' value must be either 'exact' or 'approximate'.> ...
938s  signtest ([1, 2, 3, 4], [], 'method', "some")
938s ***** error <signtest: 'method' value must be either 'exact' or 'approximate'.> ...
938s  signtest ([1, 2, 3, 4], [], 'tail', "both", 'method', "some")
938s 20 tests, 20 passed, 0 known failure, 0 skipped
938s [inst/silhouette.m]
938s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/silhouette.m
938s ***** demo
938s  load fisheriris;
938s  X = meas(:,3:4);
938s  cidcs = kmeans (X, 3, "Replicates", 5);
938s  silhouette (X, cidcs);
938s  y_labels(cidcs([1 51 101])) = unique (species);
938s  set (gca, "yticklabel", y_labels);
938s  title ("Fisher's iris data");
938s ***** error silhouette ();
938s ***** error silhouette ([1 2; 1 1]);
938s ***** error <X .* doesn't match .* clust> silhouette ([1 2; 1 1], [1 2 3]');
938s ***** error <invalid metric> silhouette ([1 2; 1 1], [1 2]', "xxx");
938s 4 tests, 4 passed, 0 known failure, 0 skipped
938s [inst/slicesample.m]
938s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/slicesample.m
938s ***** demo
938s  ## Define function to sample
938s  d = 2;
938s  mu = [-1; 2];
938s  rand ("seed", 5)  # for reproducibility
938s  Sigma = rand (d);
938s  Sigma = (Sigma + Sigma');
938s  Sigma += eye (d)*abs (eigs (Sigma, 1, "sa")) * 1.1;
938s  pdf = @(x)(2*pi)^(-d/2)*det(Sigma)^-.5*exp(-.5*sum((x.'-mu).*(Sigma\(x.'-mu)),1));
938s 
938s  ## Inputs
938s  start = ones (1,2);
938s  nsamples = 500;
938s  K = 500;
938s  m = 10;
938s  rande ("seed", 4);  rand ("seed", 5)  # for reproducibility
938s  [smpl, accept] = slicesample (start, nsamples, "pdf", pdf, "burnin", K, "thin", m, "width", [20, 30]);
938s  figure;
938s  hold on;
938s  plot (smpl(:,1), smpl(:,2), 'x');
938s  [x, y] = meshgrid (linspace (-6,4), linspace(-3,7));
938s  z = reshape (pdf ([x(:), y(:)]), size(x));
938s  mesh (x, y, z, "facecolor", "None");
938s 
938s  ## Using sample points to find the volume of half a sphere with radius of .5
938s  f = @(x) ((.25-(x(:,1)+1).^2-(x(:,2)-2).^2).^.5.*(((x(:,1)+1).^2+(x(:,2)-2).^2)<.25)).';
938s  int = mean (f (smpl) ./ pdf (smpl));
938s  errest = std (f (smpl) ./ pdf (smpl)) / nsamples^.5;
938s  trueerr = abs (2/3*pi*.25^(3/2)-int);
938s  fprintf ("Monte Carlo integral estimate int f(x) dx = %f\n", int);
938s  fprintf ("Monte Carlo integral error estimate %f\n", errest);
938s  fprintf ("The actual error %f\n", trueerr);
938s  mesh (x,y,reshape (f([x(:), y(:)]), size(x)), "facecolor", "None");
938s ***** demo
938s  ## Integrate truncated normal distribution to find normilization constant
938s  pdf = @(x) exp (-.5*x.^2)/(pi^.5*2^.5);
938s  nsamples = 1e3;
938s  rande ("seed", 4);  rand ("seed", 5)  # for reproducibility
938s  [smpl, accept] = slicesample (1, nsamples, "pdf", pdf, "thin", 4);
938s  f = @(x) exp (-.5 * x .^ 2) .* (x >= -2 & x <= 2);
938s  x = linspace (-3, 3, 1000);
938s  area (x, f(x));
938s  xlabel ("x");
938s  ylabel ("f(x)");
938s  int = mean (f (smpl) ./ pdf (smpl));
938s  errest = std (f (smpl) ./ pdf (smpl)) / nsamples ^ 0.5;
938s  trueerr = abs (erf (2 ^ 0.5) * 2 ^ 0.5 * pi ^ 0.5 - int);
938s  fprintf("Monte Carlo integral estimate int f(x) dx = %f\n", int);
938s  fprintf("Monte Carlo integral error estimate %f\n", errest);
938s  fprintf("The actual error %f\n", trueerr);
938s ***** test
938s  start = 0.5;
938s  nsamples = 1e3;
938s  pdf = @(x) exp (-.5*(x-1).^2)/(2*pi)^.5;
938s  [smpl, accept] = slicesample (start, nsamples, "pdf", pdf, "thin", 2, "burnin", 0, "width", 5);
938s  assert (mean (smpl, 1), 1, .15);
938s  assert (var (smpl, 1), 1, .25);
939s ***** error slicesample ();
939s ***** error slicesample (1);
939s ***** error slicesample (1, 1);
939s 4 tests, 4 passed, 0 known failure, 0 skipped
939s [inst/squareform.m]
939s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/squareform.m
939s ***** shared v, m
939s  v = 1:6;
939s  m = [0 1 2 3;1 0 4 5;2 4 0 6;3 5 6 0];
939s ***** assert (squareform (v), m)
939s ***** assert (squareform (squareform (v)), v)
939s ***** assert (squareform (m), v)
939s ***** assert (squareform (v'), m)
939s ***** assert (squareform (1), [0 1;1 0])
939s ***** assert (squareform (1, "tomatrix"), [0 1; 1 0])
939s ***** assert (squareform (0, "tovector"), zeros (1, 0))
939s ***** warning <not a symmetric matrix> squareform ([0 1 2; 3 0 4; 5 6 0]);
939s ***** test
939s  for c = {@single, @double, @uint8, @uint32, @uint64}
939s    f = c{1};
939s    assert (squareform (f (v)), f (m))
939s    assert (squareform (f (m)), f (v))
939s  endfor
939s 9 tests, 9 passed, 0 known failure, 0 skipped
939s [inst/standardizeMissing.m]
939s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/standardizeMissing.m
939s ***** assert (standardizeMissing (1, 1), NaN)
939s ***** assert (standardizeMissing (1, 0), 1)
939s ***** assert (standardizeMissing (eye(2), 1), [NaN 0;0 NaN])
939s ***** assert (standardizeMissing ([1:3;4:6], [2 3; 4 5]), [1, NaN, NaN; NaN, NaN, 6])
939s ***** assert (standardizeMissing (cat (3,1,2,3,4), 3), cat (3,1,2,NaN,4))
939s ***** assert (standardizeMissing ('foo', 'a'), 'foo')
939s ***** assert (standardizeMissing ('foo', 'f'), ' oo')
939s ***** assert (standardizeMissing ('foo', 'o'), 'f  ')
939s ***** assert (standardizeMissing ('foo', 'oo'), 'f  ')
939s ***** assert (standardizeMissing ({'foo'}, 'f'), {'foo'})
939s ***** assert (standardizeMissing ({'foo'}, {'f'}), {'foo'})
939s ***** assert (standardizeMissing ({'foo'}, 'test'), {'foo'})
939s ***** assert (standardizeMissing ({'foo'}, {'test'}), {'foo'})
939s ***** assert (standardizeMissing ({'foo'}, 'foo'), {''})
939s ***** assert (standardizeMissing ({'foo'}, {'foo'}), {''})
939s ***** assert (standardizeMissing (['foo';'bar'], 'oar'), ['f  ';'b  '])
939s ***** assert (standardizeMissing (['foo';'bar'], ['o';'a';'r']), ['f  ';'b  '])
939s ***** assert (standardizeMissing (['foo';'bar'], ['o ';'ar']), ['f  ';'b  '])
939s ***** assert (standardizeMissing ({'foo','bar'}, 'foo'), {'','bar'})
939s ***** assert (standardizeMissing ({'foo','bar'}, 'f'), {'foo','bar'})
939s ***** assert (standardizeMissing ({'foo','bar'}, {'foo', 'a'}), {'','bar'})
939s ***** assert (standardizeMissing ({'foo'}, {'f', 'oo'}), {'foo'})
939s ***** assert (standardizeMissing ({'foo','bar'}, {'foo'}), {'','bar'})
939s ***** assert (standardizeMissing ({'foo','bar'}, {'foo', 'a'}), {'','bar'})
939s ***** assert (standardizeMissing (double (1), single (1)), double (NaN))
939s ***** assert (standardizeMissing (single (1), single (1)), single (NaN))
939s ***** assert (standardizeMissing (single (1), double (1)), single (NaN))
939s ***** assert (standardizeMissing (single (1), true), single (NaN))
939s ***** assert (standardizeMissing (double (1), int32(1)), double (NaN))
940s ***** assert (standardizeMissing (true, true), true)
940s ***** assert (standardizeMissing (true, 1), true)
940s ***** assert (standardizeMissing (int32 (1), int32 (1)), int32 (1))
940s ***** assert (standardizeMissing (int32 (1), 1), int32 (1))
940s ***** assert (standardizeMissing (uint32 (1), uint32 (1)), uint32 (1))
940s ***** assert (standardizeMissing (uint32 (1), 1), uint32 (1))
940s ***** error standardizeMissing ();
940s ***** error standardizeMissing (1);
940s ***** error standardizeMissing (1,2,3);
940s ***** error <only cells of strings> standardizeMissing ({'abc', 1}, 1);
940s ***** error <unsupported data type> standardizeMissing (struct ('a','b'), 1);
940s ***** error <'indicator' and 'A' must have > standardizeMissing ([1 2 3], {1});
940s ***** error <'indicator' and 'A' must have > standardizeMissing ([1 2 3], 'a');
940s ***** error <'indicator' and 'A' must have > standardizeMissing ([1 2 3], struct ('a', 1));
940s ***** error <'indicator' and 'A' must have > standardizeMissing ('foo', 1);
940s ***** error <'indicator' and 'A' must have > standardizeMissing ('foo', {1});
940s ***** error <'indicator' and 'A' must have > standardizeMissing ('foo', {'f'});
940s ***** error <'indicator' and 'A' must have > standardizeMissing ('foo', struct ('a', 1));
940s ***** error <'indicator' and 'A' must have > standardizeMissing ({'foo'}, 1);
940s ***** error <'indicator' and 'A' must have > standardizeMissing ({'foo'}, 1);
940s 49 tests, 49 passed, 0 known failure, 0 skipped
940s [inst/stepwisefit.m]
940s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/stepwisefit.m
940s ***** test
940s  % Sample data from Draper and Smith (n = 13, k = 4)
940s  X = [7 1 11 11 7 11 3 1 2 21 1 11 10; ...
940s      26 29 56 31 52 55 71 31 54 47 40 66 68; ...
940s      6 15 8 8 6 9 17 22 18 4 23 9 8; ...
940s      60 52 20 47 33 22 6 44 22 26 34 12 12]';
940s  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]';
940s  [X_use, b, bint, r, rint, stats] = stepwisefit(y, X);
940s  assert(X_use, [4 1])
940s  assert(b, regress(y, [ones(size(y)) X(:, X_use)], 0.05))
940s  [X_use, b, bint, r, rint, stats] = stepwisefit(y, X, 0.05, 0.1, "corr");
940s  assert(X_use, [4 1])
940s  assert(b, regress(y, [ones(size(y)) X(:, X_use)], 0.05))
940s  [X_use, b, bint, r, rint, stats] = stepwisefit(y, X, [], [], "p");
940s  assert(X_use, [4 1])
940s  assert(b, regress(y, [ones(size(y)) X(:, X_use)], 0.05))
940s 1 test, 1 passed, 0 known failure, 0 skipped
940s [inst/tabulate.m]
940s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/tabulate.m
940s ***** demo
940s  ## Generate a frequency table for a vector of data in a cell array
940s  load patients
940s 
940s  ## Display the first seven entries of the Gender variable
940s  gender = Gender(1:7)
940s 
940s  ## Compute the equency table that shows the number and
940s  ## percentage of Male and Female patients
940s  tabulate (Gender)
940s ***** demo
940s  ## Create a frequency table for a vector of positive integers
940s  load patients
940s 
940s  ## Display the first seven entries of the Gender variable
940s  height = Height(1:7)
940s 
940s  ## Create a frequency table that shows, in its second and third columns,
940s  ## the number and percentage of patients with a particular height.
940s  table = tabulate (Height);
940s 
940s  ## Display the first and last seven entries of the frequency table
940s  first = table(1:7,:)
940s 
940s  last = table(end-6:end,:)
940s ***** demo
940s  ## Create a frequency table from a character array
940s  load carsmall
940s 
940s  ## Tabulate the data in the Origin variable, which shows the
940s  ## country of origin of each car in the data set
940s  tabulate (Origin)
940s ***** demo
940s  ## Create a frequency table from a numeric vector with NaN values
940s  load carsmall
940s 
940s  ## The carsmall dataset contains measurements of 100 cars
940s  total_cars = length (MPG)
940s  ## For six cars, the MPG value is missing
940s  missingMPG = length (MPG(isnan (MPG)))
940s 
940s  ## Create a frequency table using MPG
940s  tabulate (MPG)
940s  table = tabulate (MPG);
940s 
940s  ## Only 94 cars were used
940s  valid_cars = sum (table(:,2))
940s ***** test
940s  load patients
940s  table = tabulate (Gender);
940s  assert (table{1,1}, "Male");
940s  assert (table{2,1}, "Female");
940s  assert (table{1,2}, 47);
940s  assert (table{2,2}, 53);
940s ***** test
940s  load patients
940s  table = tabulate (Height);
940s  assert (table(end-4,:), [68, 15, 15]);
940s  assert (table(end-3,:), [69, 8, 8]);
940s  assert (table(end-2,:), [70, 11, 11]);
940s  assert (table(end-1,:), [71, 10, 10]);
940s  assert (table(end,:), [72, 4, 4]);
940s ***** error<tabulate: X must be either a numeric vector> tabulate (ones (3))
940s ***** error<tabulate: X must be either a numeric vector> tabulate ({1, 2, 3, 4})
940s ***** error<tabulate: X must be either a numeric vector> ...
940s  tabulate ({"a", "b"; "a", "c"})
940s 5 tests, 5 passed, 0 known failure, 0 skipped
940s [inst/tiedrank.m]
940s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/tiedrank.m
940s ***** test
940s  [r,tieadj] = tiedrank ([10, 20, 30, 40, 20]);
940s  assert (r, [1, 2.5, 4, 5, 2.5]);
940s  assert (tieadj, 3);
940s ***** test
940s  [r,tieadj] = tiedrank ([10; 20; 30; 40; 20]);
940s  assert (r, [1; 2.5; 4; 5; 2.5]);
940s  assert (tieadj, 3);
940s ***** test
940s  [r,tieadj] = tiedrank ([10, 20, 30, 40, 20], 1);
940s  assert (r, [1, 2.5, 4, 5, 2.5]);
940s  assert (tieadj, [1; 0; 18]);
940s ***** test
940s  [r,tieadj] = tiedrank ([10, 20, 30, 40, 20], 0, 1);
940s  assert (r, [1, 2.5, 2, 1, 2.5]);
940s  assert (tieadj, 3);
940s ***** test
940s  [r,tieadj] = tiedrank ([10, 20, 30, 40, 20], 1, 1);
940s  assert (r, [1, 2.5, 2, 1, 2.5]);
940s  assert (tieadj, [1; 0; 18]);
940s ***** error <tiedrank: X must be a vector.> tiedrank (ones (2))
940s ***** error <tiedrank: TIEFLAG must be a numeric or boolean scalar.> ...
940s  tiedrank ([1, 2, 3, 4, 5], [1, 1])
940s ***** error <tiedrank: TIEFLAG must be a numeric or boolean scalar.> ...
940s  tiedrank ([1, 2, 3, 4, 5], "A")
940s ***** error <tiedrank: TIEFLAG must be a numeric or boolean scalar.> ...
940s  tiedrank ([1, 2, 3, 4, 5], [true, true])
940s ***** error <tiedrank: BIDIR must be a numeric or boolean scalar.> ...
940s  tiedrank ([1, 2, 3, 4, 5], 0, [1, 1])
940s ***** error <tiedrank: BIDIR must be a numeric or boolean scalar.> ...
940s  tiedrank ([1, 2, 3, 4, 5], 0, "A")
940s ***** error <tiedrank: BIDIR must be a numeric or boolean scalar.> ...
940s  tiedrank ([1, 2, 3, 4, 5], 0, [true, true])
940s 12 tests, 12 passed, 0 known failure, 0 skipped
940s [inst/trimmean.m]
940s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/trimmean.m
940s ***** test
940s  x = reshape (1:40, [5, 4, 2]);
940s  x([3, 37]) = -100;
940s  assert (trimmean (x, 10, "all"), 19.4722, 1e-4);
940s ***** test
940s  x = reshape (1:40, [5, 4, 2]);
940s  x([3, 37]) = -100;
940s  out = trimmean (x, 10, [1, 2]);
940s  assert (out(1,1,1), 10.3889, 1e-4);
940s  assert (out(1,1,2), 29.6111, 1e-4);
940s ***** test
940s  x = reshape (1:40, [5, 4, 2]);
940s  x([3, 37]) = -100;
940s  x([4, 38]) = NaN;
940s  assert (trimmean (x, 10, "all"), 19.3824, 1e-4);
940s ***** test
940s  x = reshape (1:40, [5, 4, 2]);
940s  x([3, 37]) = -100;
940s  out = trimmean (x, 10, 1);
940s  assert (out(:,:,1), [-17.6, 8, 13, 18]);
940s  assert (out(:,:,2), [23, 28, 33, 10.6]);
940s ***** test
940s  x = reshape (1:40, [5, 4, 2]);
940s  x([3, 37]) = -100;
940s  x([4, 38]) = NaN;
940s  out = trimmean (x, 10, 1);
940s  assert (out(:,:,1), [-23, 8, 13, 18]);
940s  assert (out(:,:,2), [23, 28, 33, 3.75]);
940s ***** test
940s  x = reshape (1:40, [5, 4, 2]);
940s  x([3, 37]) = -100;
940s  out = trimmean (x, 10, 2);
940s  assert (out(:,:,1), [8.5; 9.5; -15.25; 11.5; 12.5]);
940s  assert (out(:,:,2), [28.5; -4.75; 30.5; 31.5; 32.5]);
940s ***** test
940s  x = reshape (1:40, [5, 4, 2]);
940s  x([3, 37]) = -100;
940s  x([4, 38]) = NaN;
940s  out = trimmean (x, 10, 2);
940s  assert (out(:,:,1), [8.5; 9.5; -15.25; 14; 12.5]);
940s  assert (out(:,:,2), [28.5; -4.75; 28; 31.5; 32.5]);
940s ***** test
940s  x = reshape (1:40, [5, 4, 2]);
940s  x([3, 37]) = -100;
940s  out = trimmean (x, 10, [1, 2, 3]);
940s  assert (out, trimmean (x, 10, "all"));
940s ***** test
940s  x = reshape (1:40, [5, 4, 2]);
940s  x([3, 37]) = -100;
940s  x([4, 38]) = NaN;
940s  out = trimmean (x, 10, [1, 2]);
940s  assert (out(1,1,1), 10.7647, 1e-4);
940s  assert (out(1,1,2), 29.1176, 1e-4);
940s ***** test
940s  x = reshape (1:40, [5, 4, 2]);
940s  x([3, 37]) = -100;
940s  x([4, 38]) = NaN;
940s  out = trimmean (x, 10, [1, 3]);
940s  assert (out, [2.5556, 18, 23, 11.6667], 1e-4);
940s ***** test
940s  x = reshape (1:40, [5, 4, 2]);
940s  x([3, 37]) = -100;
940s  x([4, 38]) = NaN;
940s  out = trimmean (x, 10, [2, 3]);
940s  assert (out, [18.5; 2.3750; 3.2857; 24; 22.5], 1e-4);
940s ***** test
940s  x = reshape (1:40, [5, 4, 2]);
940s  x([3, 37]) = -100;
940s  x([4, 38]) = NaN;
940s  out = trimmean (x, 10, [1, 2, 3]);
940s  assert (out, trimmean (x, 10, "all"));
940s ***** test
940s  x = reshape (1:40, [5, 4, 2]);
940s  x([3, 37]) = -100;
940s  x([4, 38]) = NaN;
940s  out = trimmean (x, 10, [2, 3, 5]);
940s  assert (out, [18.5; 2.3750; 3.2857; 24; 22.5], 1e-4);
940s ***** assert (trimmean (reshape (1:40, [5, 4, 2]), 10, 4), reshape(1:40, [5, 4, 2]))
940s ***** assert (trimmean ([], 10), NaN)
940s ***** assert (trimmean ([1;2;3;4;5], 10, 2), [1;2;3;4;5])
940s ***** error<Invalid call to trimmean.  Correct usage is:> trimmean (1)
941s ***** error<Invalid call to trimmean.  Correct usage is:> trimmean (1,2,3,4,5)
941s ***** error<trimmean: invalid percent.> trimmean ([1 2 3 4], -10)
941s ***** error<trimmean: invalid percent.> trimmean ([1 2 3 4], 100)
941s ***** error<trimmean: invalid FLAG argument.> trimmean ([1 2 3 4], 10, "flag")
941s ***** error<trimmean: invalid FLAG argument.> trimmean ([1 2 3 4], 10, "flag", 1)
941s ***** error<trimmean: DIM must be a positive integer scalar or vector.> ...
941s  trimmean ([1 2 3 4], 10, -1)
941s ***** error<trimmean: DIM must be a positive integer scalar or vector.> ...
941s  trimmean ([1 2 3 4], 10, "floor", -1)
941s ***** error<trimmean: DIM must be a positive integer scalar or vector.> ...
941s  trimmean (reshape (1:40, [5, 4, 2]), 10, [-1, 2])
941s ***** error<trimmean: VECDIM must contain non-repeating positive integers.> ...
941s  trimmean (reshape (1:40, [5, 4, 2]), 10, [1, 2, 2])
941s 26 tests, 26 passed, 0 known failure, 0 skipped
941s [inst/ttest.m]
941s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/ttest.m
941s ***** test
941s  x = 8:0.1:12;
941s  [h, pval, ci] = ttest (x, 10);
941s  assert (h, 0)
941s  assert (pval, 1, 10*eps)
941s  assert (ci, [9.6219 10.3781], 1E-5)
941s  [h, pval, ci0] = ttest (x, 0);
941s  assert (h, 1)
941s  assert (pval, 0)
941s  assert (ci0, ci, 2e-15)
941s  [h, pval, ci] = ttest (x, 10, "tail", "right", "dim", 2, "alpha", 0.05);
941s  assert (h, 0)
941s  assert (pval, 0.5, 10*eps)
941s  assert (ci, [9.68498 Inf], 1E-5)
941s ***** error ttest ([8:0.1:12], 10, "tail", "invalid");
941s ***** error ttest ([8:0.1:12], 10, "tail", 25);
941s 3 tests, 3 passed, 0 known failure, 0 skipped
941s [inst/ttest2.m]
941s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/ttest2.m
941s ***** test
941s  a = 1:5;
941s  b = 6:10;
941s  b(5) = NaN;
941s  [h,p,ci,stats] = ttest2 (a,b);
941s  assert (h, 1);
941s  assert (p, 0.002535996080258229, 1e-14);
941s  assert (ci, [-6.822014919225481, -2.17798508077452], 1e-14);
941s  assert (stats.tstat, -4.582575694955839, 1e-14);
941s  assert (stats.df, 7);
941s  assert (stats.sd, 1.4638501094228, 1e-13);
941s ***** error ttest2 ([8:0.1:12], [8:0.1:12], "tail", "invalid");
941s ***** error ttest2 ([8:0.1:12], [8:0.1:12], "tail", 25);
941s 3 tests, 3 passed, 0 known failure, 0 skipped
941s [inst/vartest.m]
941s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/vartest.m
941s ***** error<vartest: too few input arguments.> vartest ();
941s ***** error<vartest: invalid value for variance.> vartest ([1, 2, 3, 4], -0.5);
941s ***** error<vartest: invalid value for alpha.> ...
941s  vartest ([1, 2, 3, 4], 1, "alpha", 0);
941s ***** error<vartest: invalid value for alpha.> ...
941s  vartest ([1, 2, 3, 4], 1, "alpha", 1.2);
941s ***** error<vartest: invalid value for alpha.> ...
941s  vartest ([1, 2, 3, 4], 1, "alpha", "val");
941s ***** error<vartest: invalid value for tail.>  ...
941s  vartest ([1, 2, 3, 4], 1, "tail", "val");
941s ***** error<vartest: invalid value for tail.>  ...
941s  vartest ([1, 2, 3, 4], 1, "alpha", 0.01, "tail", "val");
941s ***** error<vartest: invalid value for operating dimension.> ...
941s  vartest ([1, 2, 3, 4], 1, "dim", 3);
941s ***** error<vartest: invalid value for operating dimension.> ...
941s  vartest ([1, 2, 3, 4], 1, "alpha", 0.01, "tail", "both", "dim", 3);
941s ***** error<vartest: invalid name for optional arguments.> ...
941s  vartest ([1, 2, 3, 4], 1, "alpha", 0.01, "tail", "both", "badoption", 3);
941s ***** error<vartest: optional arguments must be in name/value pairs.> ...
941s  vartest ([1, 2, 3, 4], 1, "alpha", 0.01, "tail");
941s ***** test
941s  load carsmall
941s  [h, pval, ci] = vartest (MPG, 7^2);
941s  assert (h, 1);
941s  assert (pval, 0.04335086742174443, 1e-14);
941s  assert (ci, [49.397; 88.039], 1e-3);
941s 12 tests, 12 passed, 0 known failure, 0 skipped
941s [inst/vartest2.m]
941s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/vartest2.m
941s ***** error<vartest2: too few input arguments.> vartest2 ();
941s ***** error<vartest2: too few input arguments.> vartest2 (ones (20,1));
941s ***** error<vartest2: X and Y must be vectors or matrices or N-D arrays.> ...
941s  vartest2 (rand (20,1), 5);
941s ***** error<vartest2: invalid value for alpha.> ...
941s  vartest2 (rand (20,1), rand (25,1)*2, "alpha", 0);
941s ***** error<vartest2: invalid value for alpha.> ...
941s  vartest2 (rand (20,1), rand (25,1)*2, "alpha", 1.2);
941s ***** error<vartest2: invalid value for alpha.> ...
941s  vartest2 (rand (20,1), rand (25,1)*2, "alpha", "some");
941s ***** error<vartest2: invalid value for alpha.> ...
941s  vartest2 (rand (20,1), rand (25,1)*2, "alpha", [0.05, 0.001]);
941s ***** error<vartest2: invalid value for tail.> ...
941s  vartest2 (rand (20,1), rand (25,1)*2, "tail", [0.05, 0.001]);
941s ***** error<vartest2: invalid value for tail.> ...
941s  vartest2 (rand (20,1), rand (25,1)*2, "tail", "some");
941s ***** error<vartest2: invalid value for operating dimension.> ...
941s  vartest2 (rand (20,1), rand (25,1)*2, "dim", 3);
941s ***** error<vartest2: invalid value for operating dimension.> ...
941s  vartest2 (rand (20,1), rand (25,1)*2, "alpha", 0.001, "dim", 3);
941s ***** error<vartest2: invalid name for optional arguments.> ...
941s  vartest2 (rand (20,1), rand (25,1)*2, "some", 3);
941s ***** error<vartest2: optional arguments must be in name/value pairs.> ...
941s  vartest2 (rand (20,1), rand (25,1)*2, "some");
941s ***** test
941s  load carsmall
941s  [h, pval, ci, stat] = vartest2 (MPG(Model_Year==82), MPG(Model_Year==76));
941s  assert (h, 0);
941s  assert (pval, 0.6288022362718455, 1e-13);
941s  assert (ci, [0.4139; 1.7193], 1e-4);
941s  assert (stat.fstat, 0.8384, 1e-4);
941s  assert (stat.df1, 30);
941s  assert (stat.df2, 33);
941s 14 tests, 14 passed, 0 known failure, 0 skipped
941s [inst/vartestn.m]
941s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/vartestn.m
941s ***** demo
941s  ## Test the null hypothesis that the variances are equal across the five
941s  ## columns of data in the students’ exam grades matrix, grades.
941s 
941s  load examgrades
941s  vartestn (grades)
941s ***** demo
941s  ## Test the null hypothesis that the variances in miles per gallon (MPG) are
941s  ## equal across different model years.
941s 
941s  load carsmall
941s  vartestn (MPG, Model_Year)
941s ***** demo
941s  ## Use Levene’s test to test the null hypothesis that the variances in miles
941s  ## per gallon (MPG) are equal across different model years.
941s 
941s  load carsmall
941s  p = vartestn (MPG, Model_Year, "TestType", "LeveneAbsolute")
941s ***** demo
941s  ## Test the null hypothesis that the variances are equal across the five
941s  ## columns of data in the students’ exam grades matrix, grades, using the
941s  ## Brown-Forsythe test.  Suppress the display of the summary table of
941s  ## statistics and the box plot.
941s 
941s  load examgrades
941s  [p, stats] = vartestn (grades, "TestType", "BrownForsythe", "Display", "off")
941s ***** error<vartestn: too few input arguments.> vartestn ();
941s ***** error<vartestn: X must be a vector or a matrix.> vartestn (1);
941s ***** error<vartestn: if X is a vector then a group vector is required.> ...
941s  vartestn ([1, 2, 3, 4, 5, 6, 7]);
941s ***** error<vartestn: if X is a vector then a group vector is required.> ...
941s  vartestn ([1, 2, 3, 4, 5, 6, 7], []);
941s ***** error<vartestn: if X is a vector then a group vector is required.> ...
941s  vartestn ([1, 2, 3, 4, 5, 6, 7], "TestType", "LeveneAbsolute");
941s ***** error<vartestn: if X is a vector then a group vector is required.> ...
941s  vartestn ([1, 2, 3, 4, 5, 6, 7], [], "TestType", "LeveneAbsolute");
941s ***** error<vartestn: invalid value for display.> ...
941s  vartestn ([1, 2, 3, 4, 5, 6, 7], [1, 1, 1, 2, 2, 2, 2], "Display", "some");
941s ***** error<vartestn: invalid value for display.> ...
941s  vartestn (ones (50,3), "Display", "some");
941s ***** error<vartestn: invalid value for testtype.> ...
941s  vartestn (ones (50,3), "Display", "off", "testtype", "some");
941s ***** error<vartestn: optional arguments must be in name/value pairs.> ...
941s  vartestn (ones (50,3), [], "som");
941s ***** error<vartestn: invalid name for optional arguments.> ...
941s  vartestn (ones (50,3), [], "some", "some");
941s ***** error<vartestn: columns in X and GROUP length do not match.> ...
941s  vartestn (ones (50,3), [1, 2], "Display", "off");
941s ***** test
941s  load examgrades
941s  [p, stat] = vartestn (grades, "Display", "off");
941s  assert (p, 7.908647337018238e-08, 1e-14);
941s  assert (stat.chisqstat, 38.7332, 1e-4);
941s  assert (stat.df, 4);
941s ***** test
941s  load examgrades
941s  [p, stat] = vartestn (grades, "Display", "off", "TestType", "LeveneAbsolute");
941s  assert (p, 9.523239714592791e-07, 1e-14);
941s  assert (stat.fstat, 8.5953, 1e-4);
941s  assert (stat.df, [4, 595]);
941s ***** test
941s  load examgrades
941s  [p, stat] = vartestn (grades, "Display", "off", "TestType", "LeveneQuadratic");
941s  assert (p, 7.219514351897161e-07, 1e-14);
941s  assert (stat.fstat, 8.7503, 1e-4);
941s  assert (stat.df, [4, 595]);
941s ***** test
941s  load examgrades
941s  [p, stat] = vartestn (grades, "Display", "off", "TestType", "BrownForsythe");
941s  assert (p, 1.312093241723211e-06, 1e-14);
941s  assert (stat.fstat, 8.4160, 1e-4);
941s  assert (stat.df, [4, 595]);
941s ***** test
941s  load examgrades
941s  [p, stat] = vartestn (grades, "Display", "off", "TestType", "OBrien");
941s  assert (p, 8.235660885480556e-07, 1e-14);
941s  assert (stat.fstat, 8.6766, 1e-4);
941s  assert (stat.df, [4, 595]);
941s 17 tests, 17 passed, 0 known failure, 0 skipped
941s [inst/violin.m]
941s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/violin.m
941s ***** demo
941s  clf
941s  x = zeros (9e2, 10);
941s  for i=1:10
941s    x(:,i) = (0.1 * randn (3e2, 3) * (randn (3,1) + 1) + 2 * randn (1,3))(:);
941s  endfor
941s  h = violin (x, "color", "c");
941s  axis tight
941s  set (h.violin, "linewidth", 2);
941s  set (gca, "xgrid", "on");
941s  xlabel ("Variables")
941s  ylabel ("Values")
941s ***** demo
941s  clf
941s  data = {randn(100,1)*5+140, randn(130,1)*8+135};
941s  subplot (1,2,1)
941s  title ("Grade 3 heights - vertical");
941s  set (gca, "xtick", 1:2, "xticklabel", {"girls"; "boys"});
941s  violin (data, "Nbins", 10);
941s  axis tight
941s 
941s  subplot(1,2,2)
941s  title ("Grade 3 heights - horizontal");
941s  set (gca, "ytick", 1:2, "yticklabel", {"girls"; "boys"});
941s  violin (data, "horizontal", "Nbins", 10);
941s  axis tight
941s ***** demo
941s  clf
941s  data = exprnd (0.1, 500,4);
941s  violin (data, "nbins", {5,10,50,100});
941s  axis ([0 5 0 max(data(:))])
941s ***** demo
941s  clf
941s  data = exprnd (0.1, 500,4);
941s  violin (data, "color", jet(4));
941s  axis ([0 5 0 max(data(:))])
941s ***** demo
941s  clf
941s  data = repmat(exprnd (0.1, 500,1), 1, 4);
941s  violin (data, "width", linspace (0.1,0.5,4));
941s  axis ([0 5 0 max(data(:))])
941s ***** demo
941s  clf
941s  data = repmat(exprnd (0.1, 500,1), 1, 4);
941s  violin (data, "nbins", [5,10,50,100], "smoothfactor", [4 4 8 10]);
941s  axis ([0 5 0 max(data(:))])
941s ***** test
941s  hf = figure ("visible", "off");
941s  unwind_protect
941s    data = exprnd (0.1, 500,4);
941s    violin (data, "color", jet(4));
941s    axis ([0 5 0 max(data(:))])
941s  unwind_protect_cleanup
941s    close (hf);
941s  end_unwind_protect
942s ***** test
942s  hf = figure ("visible", "off");
942s  unwind_protect
942s    data = {randn(100,1)*5+140, randn(130,1)*8+135};
942s    subplot (1,2,1)
942s    title ("Grade 3 heights - vertical");
942s    set (gca, "xtick", 1:2, "xticklabel", {"girls"; "boys"});
942s    violin (data, "Nbins", 10);
942s    axis tight
942s  unwind_protect_cleanup
942s    close (hf);
942s  end_unwind_protect
942s ***** test
942s  hf = figure ("visible", "off");
942s  unwind_protect
942s    data = {randn(100,1)*5+140, randn(130,1)*8+135};
942s    subplot (1,2,1)
942s    title ("Grade 3 heights - vertical");
942s    set (gca, "xtick", 1:2, "xticklabel", {"girls"; "boys"});
942s    violin (data, "Nbins", 10);
942s    axis tight
942s    subplot(1,2,2)
942s    title ("Grade 3 heights - horizontal");
942s    set (gca, "ytick", 1:2, "yticklabel", {"girls"; "boys"});
942s    violin (data, "horizontal", "Nbins", 10);
942s    axis tight
942s  unwind_protect_cleanup
942s    close (hf);
942s  end_unwind_protect
943s ***** test
943s  hf = figure ("visible", "off");
943s  unwind_protect
943s    data = repmat(exprnd (0.1, 500,1), 1, 4);
943s    violin (data, "nbins", [5,10,50,100], "smoothfactor", [4 4 8 10]);
943s    axis ([0 5 0 max(data(:))])
943s  unwind_protect_cleanup
943s    close (hf);
943s  end_unwind_protect
943s ***** test
943s  hf = figure ("visible", "off");
943s  unwind_protect
943s    data = repmat(exprnd (0.1, 500,1), 1, 4);
943s    violin (data, "width", linspace (0.1,0.5,4));
943s    axis ([0 5 0 max(data(:))])
943s  unwind_protect_cleanup
943s    close (hf);
943s  end_unwind_protect
944s 5 tests, 5 passed, 0 known failure, 0 skipped
944s [inst/wblplot.m]
944s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/wblplot.m
944s ***** demo
944s  x = [16 34 53 75 93 120];
944s  wblplot (x);
944s ***** demo
944s  x = [2 3 5 7 11 13 17 19 23 29 31 37 41 43 47 53 59 61 67]';
944s  c = [0 1 0 1 0 1 1 1 0 0 1 0 1 0 1 1 0 1 1]';
944s  [h, p] = wblplot (x, c);
944s  p
944s ***** demo
944s  x = [16, 34, 53, 75, 93, 120, 150, 191, 240 ,339];
944s  [h, p] = wblplot (x, [], [], 0.05);
944s  p
944s  ## Benchmark Reliasoft eta = 146.2545 beta 1.1973 rho = 0.9999
944s ***** demo
944s  x = [46 64 83 105 123 150 150];
944s  c = [0 0 0 0 0 0 1];
944s  f = [1 1 1 1 1 1 4];
944s  wblplot (x, c, f, 0.05);
944s ***** demo
944s  x = [46 64 83 105 123 150 150];
944s  c = [0 0 0 0 0 0 1];
944s  f = [1 1 1 1 1 1 4];
944s  ## Subtract 30.92 from x to simulate a 3 parameter wbl with gamma = 30.92
944s  wblplot (x - 30.92, c, f, 0.05);
944s ***** test
944s  hf = figure ("visible", "off");
944s  unwind_protect
944s    x = [16, 34, 53, 75, 93, 120, 150, 191, 240 ,339];
944s    [h, p] = wblplot (x, [], [], 0.05);
944s    assert (numel (h), 4)
944s    assert (p(1), 146.2545, 1E-4)
944s    assert (p(2), 1.1973, 1E-4)
944s    assert (p(3), 0.9999, 5E-5)
944s  unwind_protect_cleanup
944s    close (hf);
944s  end_unwind_protect
945s 1 test, 1 passed, 0 known failure, 0 skipped
945s [inst/x2fx.m]
945s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/x2fx.m
945s ***** test
945s  X = [1, 10; 2, 20; 3, 10; 4, 20; 5, 15; 6, 15];
945s  D = x2fx(X,'quadratic');
945s  assert (D(1,:) , [1, 1, 10, 10, 1, 100]);
945s  assert (D(2,:) , [1, 2, 20, 40, 4, 400]);
945s ***** test
945s  X = [1, 10; 2, 20; 3, 10; 4, 20; 5, 15; 6, 15];
945s  model = [0, 0; 1, 0; 0, 1; 1, 1; 2, 0];
945s  D = x2fx(X,model);
945s  assert (D(1,:) , [1, 1, 10, 10, 1]);
945s  assert (D(2,:) , [1, 2, 20, 40, 4]);
945s  assert (D(4,:) , [1, 4, 20, 80, 16]);
945s ***** error x2fx ([1, 10; 2, 20; 3, 10], [0; 1]);
945s ***** error x2fx ([1, 10, 15; 2, 20, 40; 3, 10, 25], [0, 0; 1, 0; 0, 1; 1, 1; 2, 0]);
945s ***** error x2fx ([1, 10; 2, 20; 3, 10], "whatever");
945s 5 tests, 5 passed, 0 known failure, 0 skipped
945s [inst/ztest.m]
945s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/ztest.m
945s ***** error<ztest: too few input arguments.> ztest ();
945s ***** error<ztest: invalid value for standard deviation.> ...
945s  ztest ([1, 2, 3, 4], 2, -0.5);
945s ***** error<ztest: invalid VALUE for alpha.> ...
945s  ztest ([1, 2, 3, 4], 1, 2, "alpha", 0);
945s ***** error<ztest: invalid VALUE for alpha.> ...
945s  ztest ([1, 2, 3, 4], 1, 2, "alpha", 1.2);
945s ***** error<ztest: invalid VALUE for alpha.> ...
945s  ztest ([1, 2, 3, 4], 1, 2, "alpha", "val");
945s ***** error<ztest: invalid VALUE for tail.>  ...
945s  ztest ([1, 2, 3, 4], 1, 2, "tail", "val");
945s ***** error<ztest: invalid VALUE for tail.>  ...
945s  ztest ([1, 2, 3, 4], 1, 2, "alpha", 0.01, "tail", "val");
945s ***** error<ztest: invalid VALUE for operating dimension.> ...
945s  ztest ([1, 2, 3, 4], 1, 2, "dim", 3);
945s ***** error<ztest: invalid VALUE for operating dimension.> ...
945s  ztest ([1, 2, 3, 4], 1, 2, "alpha", 0.01, "tail", "both", "dim", 3);
945s ***** error<ztest: invalid NAME for optional arguments.> ...
945s  ztest ([1, 2, 3, 4], 1, 2, "alpha", 0.01, "tail", "both", "badoption", 3);
945s ***** test
945s  load carsmall
945s  [h, pval, ci] = ztest (MPG, mean (MPG, "omitnan"), std (MPG, "omitnan"));
945s  assert (h, 0);
945s  assert (pval, 1, 1e-14);
945s  assert (ci, [22.094; 25.343], 1e-3);
945s ***** test
945s  load carsmall
945s  [h, pval, ci] = ztest (MPG, 26, 8);
945s  assert (h, 1);
945s  assert (pval, 0.00568359158544743, 1e-14);
945s  assert (ci, [22.101; 25.335], 1e-3);
945s ***** test
945s  load carsmall
945s  [h, pval, ci] = ztest (MPG, 26, 4);
945s  assert (h, 1);
945s  assert (pval, 3.184168011941316e-08, 1e-14);
945s  assert (ci, [22.909; 24.527], 1e-3);
945s 13 tests, 13 passed, 0 known failure, 0 skipped
945s [inst/ztest2.m]
945s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/inst/ztest2.m
945s ***** error ztest2 ();
945s ***** error ztest2 (1);
945s ***** error ztest2 (1, 2);
945s ***** error ztest2 (1, 2, 3);
945s ***** error<ztest2: optional arguments must be in NAME-VALUE pairs.> ...
945s  ztest2 (1, 2, 3, 4, "alpha")
945s ***** error<ztest2: invalid VALUE for alpha.> ...
945s  ztest2 (1, 2, 3, 4, "alpha", 0);
945s ***** error<ztest2: invalid VALUE for alpha.> ...
945s  ztest2 (1, 2, 3, 4, "alpha", 1.2);
945s ***** error<ztest2: invalid VALUE for alpha.> ...
945s  ztest2 (1, 2, 3, 4, "alpha", "val");
945s ***** error<ztest2: invalid VALUE for tail.>  ...
945s  ztest2 (1, 2, 3, 4, "tail", "val");
945s ***** error<ztest2: invalid VALUE for tail.>  ...
945s  ztest2 (1, 2, 3, 4, "alpha", 0.01, "tail", "val");
945s ***** error<ztest2: invalid NAME for optional arguments.> ...
945s  ztest2 (1, 2, 3, 4, "alpha", 0.01, "tail", "both", "badoption", 3);
945s 11 tests, 11 passed, 0 known failure, 0 skipped
945s Checking C++ files ...
945s [src/editDistance.cc]
945s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/src/editDistance.cc
945s ***** error <editDistance: too many input arguments.> d = editDistance (1, 2, 3, 4);
945s ***** error <editDistance: too many output arguments.> ...
945s  [C, IA, IC, I] = editDistance ({"AS","SD","AD"}, 1);
945s ***** error <editDistance: too many output arguments.> ...
945s  [C, IA] = editDistance ({"AS","SD","AD"});
945s ***** error <editDistance: minDist must be a scalar value.> ...
945s  d = editDistance ({"AS","SD","AD"}, [1, 2]);
945s ***** error <editDistance: minDist must be a nonnegative integer.> ...
945s  d = editDistance ({"AS","SD","AD"}, -2);
945s ***** error <editDistance: minDist must be a nonnegative integer.> ...
945s  d = editDistance ({"AS","SD","AD"}, 1.25);
945s ***** error <editDistance: minDist must be a scalar value.> ...
945s  d = editDistance ({"AS","SD","AD"}, {"AS","SD","AD"}, [1, 2]);
945s ***** error <editDistance: minDist must be a nonnegative integer.> ...
945s  d = editDistance ({"AS","SD","AD"}, {"AS","SD","AD"}, -2);
945s ***** error <editDistance: minDist must be a nonnegative integer.> ...
945s  d = editDistance ({"AS","SD","AD"}, {"AS","SD","AD"}, 1.25);
945s ***** error <editDistance: minDist must be a scalar value.> ...
945s  d = editDistance ("string1", "string2", [1, 2]);
945s ***** error <editDistance: minDist must be a nonnegative integer.> ...
945s  d = editDistance ("string1", "string2", -2);
945s ***** error <editDistance: minDist must be a nonnegative integer.> ...
945s  d = editDistance ("string1", "string2", 1.25);
945s ***** error <editDistance: tokenizedDocument must contain cellstr arrays.> ...
945s  d = editDistance ({{"string1", "string2"}, 2});
945s ***** error <editDistance: tokenizedDocument must contain cellstr arrays.> ...
945s  d = editDistance ({{"string1", "string2"}, 2}, 2);
945s ***** error <editDistance: STR1 must be a cellstr.> ...
945s  d = editDistance ([1, 2, 3]);
945s ***** error <editDistance: STR1 must be a cellstr.> ...
945s  d = editDistance (["AS","SD","AD","AS"]);
945s ***** error <editDistance: STR1 must be a cellstr.> ...
945s  d = editDistance (["AS","SD","AD"], 2);
945s ***** error <editDistance: STR1 and STR2 must be either strings or cellstr.> ...
945s  d = editDistance (logical ([1,2,3]), {"AS","AS","AD"});
945s ***** error <editDistance: STR1 and STR2 must be either strings or cellstr.> ...
945s  d = editDistance ({"AS","SD","AD"}, logical ([1,2,3]));
945s ***** error <editDistance: STR1 and STR2 must be either strings or cellstr.> ...
945s  d = editDistance ([1,2,3], {"AS","AS","AD"});
945s ***** error <editDistance: first tokenizedDocument does not contain cellstr arrays.> ...
945s  d = editDistance ({1,2,3}, {"AS","SD","AD"});
945s ***** error <editDistance: second tokenizedDocument does not contain cellstr arrays.> ...
945s  d = editDistance ({"AS","SD","AD"}, {1,2,3});
945s ***** error <editDistance: cellstr input arguments size mismatch.> ...
945s  d = editDistance ({"AS","SD","AD"}, {"AS", "AS"});
945s ***** test
945s  d = editDistance ({"AS","SD","AD"});
945s  assert (d, [2; 1; 1]);
945s  assert (class (d), "double");
945s ***** test
945s  C = editDistance ({"AS","SD","AD"}, 1);
945s  assert (iscellstr (C), true);
945s  assert (C, {"AS";"SD"});
945s ***** test
945s  [C, IA] = editDistance ({"AS","SD","AD"}, 1);
945s  assert (class (IA), "double");
945s  assert (IA, [1;2]);
945s ***** test
945s  A = {"ASS"; "SDS"; "FDE"; "EDS"; "OPA"};
945s  [C, IA] = editDistance (A, 2, "OutputAllIndices", false);
945s  assert (class (IA), "double");
945s  assert (A(IA), C);
945s ***** test
945s  A = {"ASS"; "SDS"; "FDE"; "EDS"; "OPA"};
945s  [C, IA] = editDistance (A, 2, "OutputAllIndices", true);
945s  assert (class (IA), "cell");
945s  assert (C, {"ASS"; "FDE"; "OPA"});
945s  assert (A(IA{1}), {"ASS"; "SDS"; "EDS"});
945s  assert (A(IA{2}), {"FDE"; "EDS"});
945s  assert (A(IA{3}), {"OPA"});
945s ***** test
945s  A = {"ASS"; "SDS"; "FDE"; "EDS"; "OPA"};
945s  [C, IA, IC] = editDistance (A, 2);
945s  assert (class (IA), "double");
945s  assert (A(IA), C);
945s  assert (IC, [1; 1; 3; 1; 5]);
945s ***** test
945s  d = editDistance ({"AS","SD","AD"}, {"AS", "AD", "SE"});
945s  assert (d, [0; 1; 2]);
945s  assert (class (d), "double");
945s ***** test
945s  d = editDistance ({"AS","SD","AD"}, {"AS"});
945s  assert (d, [0; 2; 1]);
945s  assert (class (d), "double");
945s ***** test
945s  d = editDistance ({"AS"}, {"AS","SD","AD"});
945s  assert (d, [0; 2; 1]);
945s  assert (class (d), "double");
945s ***** test
945s  b = editDistance ("Octave", "octave");
945s  assert (b, 1);
945s  assert (class (b), "double");
945s 33 tests, 33 passed, 0 known failure, 0 skipped
945s [src/fcnnpredict.cc]
946s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/src/fcnnpredict.cc
946s ***** shared X, Y, MODEL
946s  load fisheriris
946s  X = meas;
946s  Y = grp2idx (species);
946s  MODEL = fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 100, false);
946s ***** test
946s  [Y_pred, Y_scores] = fcnnpredict (MODEL, X);
946s  assert (numel (Y_pred), numel (Y));
946s  assert (isequal (size (Y_pred), size (Y)), true);
946s  assert (columns (Y_scores), numel (unique (Y)));
946s  assert (rows (Y_scores), numel (Y));
946s ***** error <fcnnpredict: too few input arguments.> ...
946s  fcnnpredict (MODEL);
946s ***** error <fcnnpredict: too many output arguments.> ...
946s  [Q, W, E] = fcnnpredict (MODEL, X);
946s ***** error <fcnnpredict: first argument must be a scalar structure.> ...
946s  fcnnpredict (1, X);
946s ***** error <fcnnpredict: first argument must be a scalar structure.> ...
946s  fcnnpredict (struct ("L", {1, 2, 3}), X);
946s ***** error <fcnnpredict: model does not have a 'LayerWeights' field.> ...
946s  fcnnpredict (struct ("L", 1), X);
946s ***** error <fcnnpredict: 'LayerWeights' must be a cell row vector.> ...
946s  fcnnpredict (struct ("LayerWeights", 1), X);
946s ***** error <fcnnpredict: 'LayerWeights' must be a cell row vector.> ...
946s  fcnnpredict (struct ("LayerWeights", {1}), X);
946s ***** error <fcnnpredict: 'LayerWeights' must be a cell row vector.> ...
946s  fcnnpredict (struct ("LayerWeights", {{1; 2; 3}}), X);
946s ***** error <fcnnpredict: model does not have an 'Activations' field.> ...
946s  fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, "R", 2), X);
946s ***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
946s  fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
946s                       "Activations", [2]), X);
946s ***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
946s  fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
946s                       "Activations", [2; 2]), X);
946s ***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
946s  fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
946s                       "Activations", {{2, 2}}), X);
946s ***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
946s  fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
946s                       "Activations", {{"sigmoid", "softmax"}}), X);
946s ***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
946s  fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
946s                       "Activations", "sigmoid"), X);
946s ***** error <fcnnpredict: XC must be a real numeric matrix.> ...
946s  fcnnpredict (MODEL, complex (X));
946s ***** error <fcnnpredict: XC must be a real numeric matrix.> ...
946s  fcnnpredict (MODEL, {1, 2, 3, 4});
946s ***** error <fcnnpredict: XC must be a real numeric matrix.> ...
946s  fcnnpredict (MODEL, "asd");
946s ***** error <fcnnpredict: XC cannot be empty.> ...
946s  fcnnpredict (MODEL, []);
946s ***** error <fcnnpredict: the features in XC do not match the trained model.> ...
946s  fcnnpredict (MODEL, X(:,[1:3]));
946s 20 tests, 20 passed, 0 known failure, 0 skipped
946s [src/fcnntrain.cc]
946s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/src/fcnntrain.cc
946s ***** shared X, Y, MODEL
946s  load fisheriris
946s  X = meas;
946s  Y = grp2idx (species);
946s ***** error <fcnntrain: too few input arguments.> ...
946s  model = fcnntrain (X, Y);
946s ***** error <fcnntrain: too many output arguments.> ...
946s  [Q, W] = fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 50, false);
946s ***** error <fcnntrain: X must be a real numeric matrix.> ...
946s  fcnntrain (complex (X), Y, 10, [1, 1], 1, 0.01, 0.025, 50, false);
946s ***** error <fcnntrain: X must be a real numeric matrix.> ...
946s  fcnntrain ({X}, Y, 10, [1, 1], 1, 0.01, 0.025, 50, false);
946s ***** error <fcnntrain: X cannot be empty.> ...
946s  fcnntrain ([], Y, 10, [1, 1], 1, 0.01, 0.025, 50, false);
946s ***** error <fcnntrain: Y must be a real numeric matrix.> ...
946s  fcnntrain (X, complex (Y), 10, 1, 0.01, [1, 1], 0.025, 50, false);
946s ***** error <fcnntrain: Y must be a real numeric matrix.> ...
946s  fcnntrain (X, {Y}, 10, [1, 1], 1, 0.01, 0.025, 50, false);
946s ***** error <fcnntrain: Y cannot be empty.> ...
946s  fcnntrain (X, [], 10, [1, 1], 1, 0.01, 0.025, 50, false);
946s ***** error <fcnntrain: X and Y must have the same number of rows.> ...
946s  fcnntrain (X, Y([1:50]), 10, [1, 1], 1, 0.01, 0.025, 50, false);
946s ***** error <fcnntrain: labels in Y must be positive integers.> ...
946s  fcnntrain (X, Y - 1, 10, [1, 1], 1, 0.01, 0.025, 50, false);
946s ***** error <fcnntrain: 'LayerSizes' must be a row vector of integer values.> ...
946s  fcnntrain (X, Y, [10; 5], [1, 1, 1], 1, 0.01, 0.025, 50, false);
946s ***** error <fcnntrain: 'LayerSizes' must be a row vector of integer values.> ...
946s  fcnntrain (X, Y, "10", [1, 1], 1, 0.01, 0.025, 50, false);
946s ***** error <fcnntrain: 'LayerSizes' must be a row vector of integer values.> ...
946s  fcnntrain (X, Y, {10}, [1, 1], 1, 0.01, 0.025, 50, false);
946s ***** error <fcnntrain: 'LayerSizes' must be a row vector of integer values.> ...
946s  fcnntrain (X, Y, complex (10), [1, 1], 1, 0.01, 0.025, 50, false);
946s ***** error <fcnntrain: 'Activations' must be a row vector of integer values.> ...
946s  fcnntrain (X, Y, 10, [1; 1], 1, 0.01, 0.025, 50, false);
946s ***** error <fcnntrain: 'Activations' must be a row vector of integer values.> ...
946s  fcnntrain (X, Y, 10, {1, 1}, 1, 0.01, 0.025, 50, false);
946s ***** error <fcnntrain: 'Activations' must be a row vector of integer values.> ...
946s  fcnntrain (X, Y, 10, "1", 1, 0.01, 0.025, 50, false);
946s ***** error <fcnntrain: 'Activations' must be a row vector of integer values.> ...
946s  fcnntrain (X, Y, 10, complex ([1, 1]), 1, 0.01, 0.025, 50, false);
946s ***** error <fcnntrain: 'Activations' do not match LayerSizes.> ...
946s  fcnntrain (X, Y, 10, [1, 1, 1], 1, 0.01, 0.025, 50, false);
946s ***** error <fcnntrain: cannot have a layer of zero size.> ...
946s  fcnntrain (X, Y, [10, 0, 5], [1, 1, 1, 1], 1, 0.01, 0.025, 50, false);
946s ***** error <fcnntrain: invalid 'Activations' code.> ...
946s  fcnntrain (X, Y, 10, [-1, 1], 1, 0.01, 0.025, 50, false);
946s ***** error <fcnntrain: invalid 'Activations' code.> ...
946s  fcnntrain (X, Y, 10, [8, 1], 1, 0.01, 0.025, 50, false);
946s ***** error <fcnntrain: 'NumThreads' must be a positive integer scalar value.> ...
946s  fcnntrain (X, Y, 10, [1, 1], 0, 0.01, 0.025, 50, false);
946s ***** error <fcnntrain: 'Alpha' must be a positive scalar value.> ...
946s  fcnntrain (X, Y, 10, [1, 1], 1, -0.01, 0.025, 50, false);
946s ***** error <fcnntrain: 'LearningRate' must be a positive scalar value.> ...
946s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, -0.025, 50, false);
946s ***** error <fcnntrain: 'LearningRate' must be a positive scalar value.> ...
946s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0, 50, false);
946s ***** error <fcnntrain: 'LearningRate' must be a positive scalar value.> ...
946s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, [0.025, 0.001], 50, false);
946s ***** error <fcnntrain: 'LearningRate' must be a positive scalar value.> ...
946s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, {0.025}, 50, false);
946s ***** error <fcnntrain: 'Epochs' must be a positive scalar value.> ...
946s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 0, false);
946s ***** error <fcnntrain: 'Epochs' must be a positive scalar value.> ...
946s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, [50, 25], false);
946s ***** error <fcnntrain: 'DisplayInfo' must be a boolean scalar.> ...
946s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 50, 0);
946s ***** error <fcnntrain: 'DisplayInfo' must be a boolean scalar.> ...
946s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 50, 1);
946s ***** error <fcnntrain: 'DisplayInfo' must be a boolean scalar.> ...
946s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 50, [false, false]);
946s 33 tests, 33 passed, 0 known failure, 0 skipped
946s [src/libsvmread.cc]
946s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/src/libsvmread.cc
946s ***** error <libsvmread: filename must be a string.> [L, D] = libsvmread (24);
946s ***** error <libsvmread: wrong number of input or output arguments.> ...
946s  D = libsvmread ("filename");
946s ***** test
946s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
946s  assert (size (L), [270, 1]);
946s  assert (size (D), [270, 13]);
946s ***** test
946s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
946s  assert (issparse (L), false);
946s  assert (issparse (D), true);
946s 4 tests, 4 passed, 0 known failure, 0 skipped
946s [src/libsvmwrite.cc]
946s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/src/libsvmwrite.cc
946s ***** shared L, D
946s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
946s ***** error <libsvmwrite: error opening file for write.> libsvmwrite ("", L, D);
946s ***** error <libsvmwrite: length of label vector does not match instances.> ...
946s  libsvmwrite (tempname (), [L;L], D);
946s ***** error <libsvmwrite: wrong number of output arguments.> ...
946s  OUT = libsvmwrite (tempname (), L, D);
946s ***** error <libsvmwrite: label vector and instance matrix must be double.> ...
946s  libsvmwrite (tempname (), single (L), D);
946s ***** error <libsvmwrite: filename must be a string.> libsvmwrite (13412, L, D);
946s ***** error <libsvmwrite: instance_matrix must be sparse.> ...
946s  libsvmwrite (tempname (), L, full (D));
946s ***** error <libsvmwrite: wrong number of input arguments.> ...
946s  libsvmwrite (tempname (), L, D, D);
946s 7 tests, 7 passed, 0 known failure, 0 skipped
946s [src/svmpredict.cc]
946s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/src/svmpredict.cc
946s ***** test
946s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
946s  model = svmtrain (L, D, '-c 1 -g 0.07');
946s  [predict_label, accuracy, dec_values] = svmpredict (L, D, model);
946s  assert (size (predict_label), size (dec_values));
946s  assert (accuracy, [86.666, 0.533, 0.533]', [1e-3, 1e-3, 1e-3]');
946s  assert (dec_values(1), 1.225836001973273, 1e-14);
946s  assert (dec_values(2), -0.3212992933043805, 1e-14);
946s  assert (predict_label(1), 1);
946s ***** shared L, D, model
946s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
946s  model = svmtrain (L, D, '-c 1 -g 0.07');
946s ***** error <svmpredict: wrong number of output arguments.> ...
946s  [p, a] = svmpredict (L, D, model);
946s ***** error <svmpredict: wrong number of input arguments.> p = svmpredict (L, D);
946s ***** error <svmpredict: label vector and instance matrix must be double.> ...
946s  p = svmpredict (single (L), D, model);
946s ***** error <svmpredict: model should be a struct array.> p = svmpredict (L, D, 123);
946s 5 tests, 5 passed, 0 known failure, 0 skipped
946s [src/svmtrain.cc]
946s >>>>> /tmp/autopkgtest.EemJoQ/build.10v/src/src/svmtrain.cc
946s ***** test
946s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
946s  model = svmtrain(L, D, '-c 1 -g 0.07');
946s  [predict_label, accuracy, dec_values] = svmpredict(L, D, model);
946s  assert (isstruct (model), true);
946s  assert (isfield (model, "Parameters"), true);
946s  assert (model.totalSV, 130);
946s  assert (model.nr_class, 2);
946s  assert (size (model.Label), [2, 1]);
946s ***** shared L, D
946s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
946s ***** error <svmtrain: wrong number of output arguments.> [L, D] = svmtrain (L, D);
946s ***** error <svmtrain: label vector and instance matrix must be double.> ...
946s  model = svmtrain (single (L), D);
946s ***** error <svmtrain: wrong number of input arguments.> ...
946s  model = svmtrain (L, D, "", "");
946s 4 tests, 4 passed, 0 known failure, 0 skipped
946s Done running the unit tests.
946s Summary: 11162 tests, 11159 passed, 1 known failures, 2 skipped
947s autopkgtest [15:07:26]: test command1: -----------------------]
951s autopkgtest [15:07:30]: test command1:  - - - - - - - - - - results - - - - - - - - - -
951s command1             PASS
955s autopkgtest [15:07:34]: @@@@@@@@@@@@@@@@@@@@ summary
955s command1             PASS