0s autopkgtest [07:12:54]: starting date and time: 2026-02-05 07:12:54+0000
  0s autopkgtest [07:12:54]: git checkout: 508d4a25 a-v-ssh wait_for_ssh: demote "ssh connection failed" to a debug message
  0s autopkgtest [07:12:54]: host juju-7f2275-prod-proposed-migration-environment-9; command line: /home/ubuntu/autopkgtest/runner/autopkgtest --output-dir /tmp/autopkgtest-work.ziup2ndl/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,localhost,localdomain,internal,login.ubuntu.com,archive.ubuntu.com,ports.ubuntu.com,security.ubuntu.com,ddebs.ubuntu.com,changelogs.ubuntu.com,keyserver.ubuntu.com,launchpadlibrarian.net,launchpadcontent.net,launchpad.net,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/2.42-2ubuntu5 -- lxd -r lxd-armhf-10.145.243.52 lxd-armhf-10.145.243.52:autopkgtest/ubuntu/resolute/armhf
 21s autopkgtest [07:13:15]: testbed dpkg architecture: armhf
 23s autopkgtest [07:13:17]: testbed apt version: 3.1.14
 27s autopkgtest [07:13:21]: @@@@@@@@@@@@@@@@@@@@ test bed setup
 29s autopkgtest [07:13:23]: testbed release detected to be: None
 37s autopkgtest [07:13:31]: updating testbed package index (apt update)
 39s Get:1 http://ftpmaster.internal/ubuntu resolute-proposed InRelease [124 kB]
 39s Get:2 http://ftpmaster.internal/ubuntu resolute InRelease [124 kB]
 40s Hit:3 http://ftpmaster.internal/ubuntu resolute-updates InRelease
 40s Hit:4 http://ftpmaster.internal/ubuntu resolute-security InRelease
 40s Get:5 http://ftpmaster.internal/ubuntu resolute-proposed/main Sources [228 kB]
 40s Get:6 http://ftpmaster.internal/ubuntu resolute-proposed/restricted Sources [5260 B]
 40s Get:7 http://ftpmaster.internal/ubuntu resolute-proposed/multiverse Sources [35.4 kB]
 40s Get:8 http://ftpmaster.internal/ubuntu resolute-proposed/universe Sources [1719 kB]
 40s Get:9 http://ftpmaster.internal/ubuntu resolute-proposed/main armhf Packages [253 kB]
 40s Get:10 http://ftpmaster.internal/ubuntu resolute-proposed/universe armhf Packages [1418 kB]
 40s Get:11 http://ftpmaster.internal/ubuntu resolute-proposed/multiverse armhf Packages [9664 B]
 40s Get:12 http://ftpmaster.internal/ubuntu resolute/universe Sources [21.3 MB]
 41s Get:13 http://ftpmaster.internal/ubuntu resolute/main Sources [1400 kB]
 41s Get:14 http://ftpmaster.internal/ubuntu resolute/main armhf Packages [1374 kB]
 41s Get:15 http://ftpmaster.internal/ubuntu resolute/universe armhf Packages [15.1 MB]
 45s Fetched 43.1 MB in 6s (7781 kB/s)
 46s Reading package lists...
 54s autopkgtest [07:13:48]: upgrading testbed (apt dist-upgrade and autopurge)
 56s Reading package lists...
 56s Building dependency tree...
 56s Reading state information...
 57s Calculating upgrade...
 57s The following packages will be upgraded:
 57s   libc-bin libc-gconv-modules-extra libc6 locales
 58s 4 upgraded, 0 newly installed, 0 to remove and 0 not upgraded.
 58s Need to get 7850 kB of archives.
 58s After this operation, 0 B of additional disk space will be used.
 58s Get:1 http://ftpmaster.internal/ubuntu resolute-proposed/main armhf libc-gconv-modules-extra armhf 2.42-2ubuntu5 [1417 kB]
 58s Get:2 http://ftpmaster.internal/ubuntu resolute-proposed/main armhf libc6 armhf 2.42-2ubuntu5 [1632 kB]
 58s Get:3 http://ftpmaster.internal/ubuntu resolute-proposed/main armhf libc-bin armhf 2.42-2ubuntu5 [545 kB]
 58s Get:4 http://ftpmaster.internal/ubuntu resolute-proposed/main armhf locales all 2.42-2ubuntu5 [4255 kB]
 59s Preconfiguring packages ...
 59s Fetched 7850 kB in 1s (9147 kB/s)
 59s (Reading database ... 
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(Reading database ... 68395 files and directories currently installed.)
 59s Preparing to unpack .../libc-gconv-modules-extra_2.42-2ubuntu5_armhf.deb ...
 59s Unpacking libc-gconv-modules-extra:armhf (2.42-2ubuntu5) over (2.42-2ubuntu4) ...
 59s Setting up libc-gconv-modules-extra:armhf (2.42-2ubuntu5) ...
 60s (Reading database ... 
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(Reading database ... 68395 files and directories currently installed.)
 60s Preparing to unpack .../libc6_2.42-2ubuntu5_armhf.deb ...
 60s readlink: /usr/lib/libeatmydata: No such file or directory
 60s Unpacking libc6:armhf (2.42-2ubuntu5) over (2.42-2ubuntu4) ...
 60s Setting up libc6:armhf (2.42-2ubuntu5) ...
 60s (Reading database ... 
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(Reading database ... 100%
(Reading database ... 68395 files and directories currently installed.)
 60s Preparing to unpack .../libc-bin_2.42-2ubuntu5_armhf.deb ...
 60s Unpacking libc-bin (2.42-2ubuntu5) over (2.42-2ubuntu4) ...
 60s Setting up libc-bin (2.42-2ubuntu5) ...
 61s (Reading database ... 
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(Reading database ... 95%
(Reading database ... 100%
(Reading database ... 68395 files and directories currently installed.)
 61s Preparing to unpack .../locales_2.42-2ubuntu5_all.deb ...
 61s Unpacking locales (2.42-2ubuntu5) over (2.42-2ubuntu4) ...
 61s Setting up locales (2.42-2ubuntu5) ...
 63s Generating locales (this might take a while)...
 66s   en_US.UTF-8... done
 66s Generation complete.
 66s Processing triggers for systemd (259-1ubuntu3) ...
 66s Processing triggers for man-db (2.13.1-1) ...
 70s Reading package lists...
 70s Building dependency tree...
 70s Reading state information...
 71s Solving dependencies...
 72s 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded.
 74s autopkgtest [07:14:08]: rebooting testbed after setup commands that affected boot
117s autopkgtest [07:14:51]: testbed running kernel: Linux 6.8.0-87-generic #88~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Tue Oct 14 14:00:09 UTC 2
145s autopkgtest [07:15:19]: @@@@@@@@@@@@@@@@@@@@ apt-source octave-statistics
162s Get:1 http://ftpmaster.internal/ubuntu resolute/universe octave-statistics 1.7.6-2 (dsc) [2289 B]
162s Get:2 http://ftpmaster.internal/ubuntu resolute/universe octave-statistics 1.7.6-2 (tar) [1424 kB]
162s Get:3 http://ftpmaster.internal/ubuntu resolute/universe octave-statistics 1.7.6-2 (diff) [10.3 kB]
162s gpgv: Signature made Mon Oct 27 09:10:08 2025 UTC
162s gpgv:                using RSA key 53951D95272E0C5B82BE8C4A2CECE9350ECEBE4A
162s gpgv: Can't check signature: No public key
162s dpkg-source: warning: cannot verify inline signature for ./octave-statistics_1.7.6-2.dsc: no acceptable signature found
162s autopkgtest [07:15:36]: testing package octave-statistics version 1.7.6-2
166s autopkgtest [07:15:40]: build not needed
168s autopkgtest [07:15:42]: test command1: preparing testbed
171s Reading package lists...
171s Building dependency tree...
171s Reading state information...
172s Solving dependencies...
173s The following NEW packages will be installed:
173s   aglfn appstream autoconf automake autopoint autotools-dev cme comerr-dev cpp
173s   cpp-15 cpp-15-arm-linux-gnueabihf cpp-arm-linux-gnueabihf debhelper
173s   debugedit dh-autoreconf dh-octave dh-octave-autopkgtest
173s   dh-strip-nondeterminism diffstat dwz fontconfig fontconfig-config
173s   fonts-freefont-otf g++ g++-15 g++-15-arm-linux-gnueabihf
173s   g++-arm-linux-gnueabihf gcc gcc-15 gcc-15-arm-linux-gnueabihf
173s   gcc-arm-linux-gnueabihf gettext gfortran gfortran-15
173s   gfortran-15-arm-linux-gnueabihf gfortran-arm-linux-gnueabihf gnuplot-data
173s   gnuplot-nox hdf5-helpers intltool-debian krb5-multidev libaec-dev libaec0
173s   libalgorithm-c3-perl libaliased-perl libamd3 libaom3 libapp-cmd-perl
173s   libappstream5 libapt-pkg-perl libarchive-zip-perl libarpack2t64
173s   libarray-intspan-perl libasan8 libasound2-data libasound2t64 libasyncns0
173s   libavahi-client3 libavahi-common-data libavahi-common3
173s   libb-hooks-endofscope-perl libb-hooks-op-check-perl libb-keywords-perl
173s   libb2-1 libberkeleydb-perl libblas-dev libblas3 libboolean-perl
173s   libbrotli-dev libc-dev-bin libc6-dev libcairo2 libcamd3 libcapture-tiny-perl
173s   libcarp-assert-more-perl libcc1-0 libccolamd3 libcgi-pm-perl libcholmod5
173s   libclass-c3-perl libclass-data-inheritable-perl libclass-inspector-perl
173s   libclass-load-perl libclass-method-modifiers-perl libclass-tiny-perl
173s   libclass-xsaccessor-perl libclone-choose-perl libclone-perl libcolamd3
173s   libconfig-inifiles-perl libconfig-model-backend-yaml-perl
173s   libconfig-model-dpkg-perl libconfig-model-perl libconfig-tiny-perl
173s   libconst-fast-perl libconvert-binhex-perl libcpanel-json-xs-perl
173s   libcrypt-dev libcups2t64 libcurl4-openssl-dev libcxsparse4
173s   libdata-dpath-perl libdata-messagepack-perl libdata-optlist-perl
173s   libdata-section-perl libdata-validate-domain-perl libdata-validate-ip-perl
173s   libdata-validate-uri-perl libdatrie1 libde265-0 libdebhelper-perl
173s   libdeflate0 libdevel-callchecker-perl libdevel-size-perl
173s   libdevel-stacktrace-perl libdouble-conversion3 libduktape207 libdw1t64
173s   libdynaloader-functions-perl libegl-mesa0 libegl1 libemail-address-xs-perl
173s   libencode-locale-perl liberror-perl libevent-2.1-7t64
173s   libexception-class-perl libexporter-lite-perl libexporter-tiny-perl
173s   libfftw3-bin libfftw3-dev libfftw3-double3 libfftw3-single3
173s   libfile-basedir-perl libfile-find-rule-perl libfile-homedir-perl
173s   libfile-listing-perl libfile-sharedir-perl libfile-stripnondeterminism-perl
173s   libfile-which-perl libflac14 libfltk-gl1.3t64 libfltk1.3t64 libfont-ttf-perl
173s   libfontconfig1 libfontenc1 libfreetype6 libfyaml0 libgbm1 libgcc-15-dev
173s   libgd3 libgetopt-long-descriptive-perl libgfortran-15-dev libgfortran5
173s   libgl-dev libgl1 libgl1-mesa-dri libgl2ps1.4 libglpk40 libglu1-mesa
173s   libglvnd0 libglx-dev libglx-mesa0 libglx0 libgmp-dev libgmpxx4ldbl
173s   libgnutls-dane0t64 libgnutls-openssl27t64 libgnutls28-dev libgomp1
173s   libgraphicsmagick++-q16-12t64 libgraphicsmagick-q16-3t64 libgraphite2-3
173s   libgssrpc4t64 libharfbuzz0b libhash-merge-perl libhdf5-310 libhdf5-cpp-310
173s   libhdf5-dev libhdf5-fortran-310 libhdf5-hl-310 libhdf5-hl-cpp-310
173s   libhdf5-hl-fortran-310 libheif-plugin-aomdec libheif-plugin-libde265
173s   libheif1 libhtml-form-perl libhtml-html5-entities-perl libhtml-parser-perl
173s   libhtml-tagset-perl libhtml-tokeparser-simple-perl libhtml-tree-perl
173s   libhttp-cookies-perl libhttp-date-perl libhttp-message-perl
173s   libhttp-negotiate-perl libice6 libidn2-dev libimagequant0
173s   libimport-into-perl libindirect-perl libinput-bin libinput10 libintl-perl
173s   libio-html-perl libio-interactive-perl libio-socket-ssl-perl
173s   libio-string-perl libio-stringy-perl libio-tiecombine-perl libipc-run3-perl
173s   libipc-system-simple-perl libisl23 libiterator-perl libiterator-util-perl
173s   libjack-jackd2-0 libjbig0 libjpeg-dev libjpeg-turbo8 libjpeg-turbo8-dev
173s   libjpeg8 libjpeg8-dev libjson-maybexs-perl libjson-perl libjxl0.11
173s   libkadm5clnt-mit12 libkadm5srv-mit12 libkdb5-10t64 libkrb5-dev liblapack-dev
173s   liblapack3 liblcms2-2 libldap-dev liblerc4 liblingua-en-inflect-perl
173s   liblist-compare-perl liblist-moreutils-perl liblist-moreutils-xs-perl
173s   liblist-someutils-perl liblist-utilsby-perl libllvm21
173s   liblog-any-adapter-screen-perl liblog-any-perl liblog-log4perl-perl libltdl7
173s   liblua5.4-0 liblwp-mediatypes-perl liblwp-protocol-https-perl
173s   libmailtools-perl libmarkdown2 libmd4c0 libmime-tools-perl libmldbm-perl
173s   libmodule-implementation-perl libmodule-pluggable-perl
173s   libmodule-runtime-perl libmoo-perl libmoox-aliases-perl libmouse-perl
173s   libmousex-nativetraits-perl libmousex-strictconstructor-perl libmp3lame0
173s   libmpc3 libmpg123-0t64 libmro-compat-perl libmtdev1t64
173s   libnamespace-clean-perl libncurses-dev libnet-domain-tld-perl
173s   libnet-http-perl libnet-ipv6addr-perl libnet-netmask-perl
173s   libnet-smtp-ssl-perl libnet-ssleay-perl libnetaddr-ip-perl libnghttp2-dev
173s   libnumber-compare-perl libobject-pad-perl libogg0 libopengl0 libopus0
173s   libp11-kit-dev libpackage-stash-perl libpango-1.0-0 libpangocairo-1.0-0
173s   libpangoft2-1.0-0 libparams-classify-perl libparams-util-perl
173s   libparams-validate-perl libparse-debcontrol-perl libparse-recdescent-perl
173s   libpath-iterator-rule-perl libpath-tiny-perl libpcre2-16-0
173s   libperl-critic-perl libperlio-gzip-perl libperlio-utf8-strict-perl
173s   libpixman-1-0 libpkgconf3 libpod-constants-perl libpod-parser-perl
173s   libpod-pom-perl libpod-spell-perl libportaudio2 libppi-perl
173s   libppix-quotelike-perl libppix-regexp-perl libppix-utils-perl
173s   libproc-processtable-perl libproxy1v5 libpsl-dev libpulse0 libqhull-r8.0
173s   libqrupdate1 libqscintilla2-qt6-15 libqscintilla2-qt6-l10n
173s   libqt6core5compat6 libqt6core6t64 libqt6dbus6 libqt6gui6 libqt6help6
173s   libqt6network6 libqt6opengl6 libqt6openglwidgets6 libqt6printsupport6
173s   libqt6sql6 libqt6widgets6 libqt6xml6 libreadline-dev libreadonly-perl
173s   libregexp-common-perl libregexp-pattern-license-perl libregexp-pattern-perl
173s   libregexp-wildcards-perl librole-tiny-perl librtmp-dev libsafe-isa-perl
173s   libsamplerate0 libsereal-decoder-perl libsereal-encoder-perl
173s   libset-intspan-perl libsharpyuv0 libsm6 libsndfile1
173s   libsoftware-copyright-perl libsoftware-license-perl
173s   libsoftware-licensemoreutils-perl libsort-versions-perl libspqr4
173s   libssh2-1-dev libssl-dev libstdc++-15-dev libstemmer0d libstrictures-perl
173s   libstring-copyright-perl libstring-escape-perl libstring-format-perl
173s   libstring-license-perl libstring-rewriteprefix-perl libsub-exporter-perl
173s   libsub-exporter-progressive-perl libsub-identify-perl libsub-install-perl
173s   libsub-name-perl libsub-quote-perl libsub-uplevel-perl libsuitesparseconfig7
173s   libsyntax-keyword-try-perl libsz2 libtask-weaken-perl libtasn1-6-dev
173s   libterm-readkey-perl libtest-exception-perl libtext-autoformat-perl
173s   libtext-glob-perl libtext-levenshtein-damerau-perl
173s   libtext-levenshteinxs-perl libtext-markdown-discount-perl
173s   libtext-reform-perl libtext-template-perl libtext-unidecode-perl
173s   libtext-wrapper-perl libtext-xslate-perl libthai-data libthai0 libtiff6
173s   libtime-duration-perl libtime-moment-perl libtimedate-perl libtoml-tiny-perl
173s   libtool libtry-tiny-perl libts0t64 libubsan1 libumfpack6 libunbound8
173s   libunicode-utf8-perl liburi-perl libvariable-magic-perl libvorbis0a
173s   libvorbisenc2 libvulkan1 libwacom-common libwacom9 libwayland-client0
173s   libwebp7 libwebpmux3 libwmflite-0.2-7 libwww-mechanize-perl libwww-perl
173s   libwww-robotrules-perl libx11-dev libx11-xcb1 libxau-dev libxaw7
173s   libxcb-cursor0 libxcb-dri3-0 libxcb-glx0 libxcb-icccm4 libxcb-image0
173s   libxcb-keysyms1 libxcb-present0 libxcb-randr0 libxcb-render-util0
173s   libxcb-render0 libxcb-shape0 libxcb-shm0 libxcb-sync1 libxcb-util1
173s   libxcb-xfixes0 libxcb-xinput0 libxcb-xkb1 libxcb1-dev libxcursor1
173s   libxdmcp-dev libxfixes3 libxfont2 libxft2 libxinerama1 libxkbcommon-x11-0
173s   libxkbfile1 libxml-libxml-perl libxml-namespacesupport-perl
173s   libxml-sax-base-perl libxml-sax-perl libxmu6 libxpm4 libxrandr2 libxrender1
173s   libxs-parse-keyword-perl libxs-parse-sublike-perl libxshmfence1 libxt6t64
173s   libxxf86vm1 libyaml-libyaml-perl libyaml-pp-perl libyaml-tiny-perl
173s   libzstd-dev licensecheck lintian linux-libc-dev lzip lzop m4 mesa-libgallium
173s   nettle-dev octave octave-common octave-dev octave-io octave-statistics
173s   octave-statistics-common patchutils perl-openssl-defaults perltidy pkgconf
173s   pkgconf-bin po-debconf rpcsvc-proto t1utils tex-common texinfo texinfo-lib
173s   unzip x11-common x11-xkb-utils x11proto-dev xorg-sgml-doctools
173s   xserver-common xtrans-dev xvfb zlib1g-dev
173s 0 upgraded, 491 newly installed, 0 to remove and 0 not upgraded.
173s Need to get 202 MB of archives.
173s After this operation, 645 MB of additional disk space will be used.
173s Get:1 http://ftpmaster.internal/ubuntu resolute/main armhf libfyaml0 armhf 0.9.3-1 [243 kB]
173s Get:2 http://ftpmaster.internal/ubuntu resolute/main armhf libstemmer0d armhf 3.0.1-1 [138 kB]
173s Get:3 http://ftpmaster.internal/ubuntu resolute/main armhf libappstream5 armhf 1.1.1-1 [212 kB]
174s Get:4 http://ftpmaster.internal/ubuntu resolute/main armhf appstream armhf 1.1.1-1 [66.3 kB]
174s Get:5 http://ftpmaster.internal/ubuntu resolute/main armhf m4 armhf 1.4.20-2 [212 kB]
174s Get:6 http://ftpmaster.internal/ubuntu resolute/main armhf autoconf all 2.72-3.1ubuntu1 [384 kB]
174s Get:7 http://ftpmaster.internal/ubuntu resolute/main armhf autotools-dev all 20240727.1 [43.4 kB]
174s Get:8 http://ftpmaster.internal/ubuntu resolute/main armhf automake all 1:1.18.1-3build1 [582 kB]
174s Get:9 http://ftpmaster.internal/ubuntu resolute/main armhf autopoint all 0.23.2-1 [620 kB]
174s Get:10 http://ftpmaster.internal/ubuntu resolute/main armhf libcapture-tiny-perl all 0.50-1 [20.7 kB]
174s Get:11 http://ftpmaster.internal/ubuntu resolute/main armhf libparams-util-perl armhf 1.102-3build1 [20.5 kB]
174s Get:12 http://ftpmaster.internal/ubuntu resolute/main armhf libsub-install-perl all 0.929-1 [9764 B]
174s Get:13 http://ftpmaster.internal/ubuntu resolute/main armhf libdata-optlist-perl all 0.114-1 [9708 B]
174s Get:14 http://ftpmaster.internal/ubuntu resolute/main armhf libb-hooks-op-check-perl armhf 0.22-3build2 [9174 B]
174s Get:15 http://ftpmaster.internal/ubuntu resolute/main armhf libdynaloader-functions-perl all 0.004-2 [11.5 kB]
174s Get:16 http://ftpmaster.internal/ubuntu resolute/main armhf libdevel-callchecker-perl armhf 0.009-2 [13.8 kB]
174s Get:17 http://ftpmaster.internal/ubuntu resolute/main armhf libparams-classify-perl armhf 0.015-2build6 [18.8 kB]
174s Get:18 http://ftpmaster.internal/ubuntu resolute/main armhf libmodule-runtime-perl all 0.018-1 [15.2 kB]
174s Get:19 http://ftpmaster.internal/ubuntu resolute/main armhf libtry-tiny-perl all 0.32-1 [21.2 kB]
174s Get:20 http://ftpmaster.internal/ubuntu resolute/main armhf libmodule-implementation-perl all 0.09-2 [12.0 kB]
174s Get:21 http://ftpmaster.internal/ubuntu resolute/main armhf libpackage-stash-perl all 0.40-1 [19.5 kB]
174s Get:22 http://ftpmaster.internal/ubuntu resolute/universe armhf libclass-load-perl all 0.25-2 [12.7 kB]
174s Get:23 http://ftpmaster.internal/ubuntu resolute/main armhf libio-stringy-perl all 2.113-2 [45.3 kB]
174s Get:24 http://ftpmaster.internal/ubuntu resolute/universe armhf libparams-validate-perl armhf 1.31-2build4 [52.9 kB]
174s Get:25 http://ftpmaster.internal/ubuntu resolute/main armhf libsub-exporter-perl all 0.990-1 [49.0 kB]
174s Get:26 http://ftpmaster.internal/ubuntu resolute/universe armhf libgetopt-long-descriptive-perl all 0.116-2 [25.0 kB]
174s Get:27 http://ftpmaster.internal/ubuntu resolute/universe armhf libio-tiecombine-perl all 1.005-3 [9464 B]
174s Get:28 http://ftpmaster.internal/ubuntu resolute/universe armhf libmodule-pluggable-perl all 5.2-5 [19.5 kB]
174s Get:29 http://ftpmaster.internal/ubuntu resolute/universe armhf libstring-rewriteprefix-perl all 0.009-1 [6310 B]
174s Get:30 http://ftpmaster.internal/ubuntu resolute/universe armhf libapp-cmd-perl all 0.338-1 [58.5 kB]
174s Get:31 http://ftpmaster.internal/ubuntu resolute/universe armhf libboolean-perl all 0.46-3 [8430 B]
174s Get:32 http://ftpmaster.internal/ubuntu resolute/universe armhf libsub-uplevel-perl all 0.2800-3 [11.6 kB]
174s Get:33 http://ftpmaster.internal/ubuntu resolute/universe armhf libtest-exception-perl all 0.43-3 [13.4 kB]
174s Get:34 http://ftpmaster.internal/ubuntu resolute/universe armhf libcarp-assert-more-perl all 2.9.0-1 [19.4 kB]
174s Get:35 http://ftpmaster.internal/ubuntu resolute/main armhf libfile-which-perl all 1.27-2 [12.5 kB]
174s Get:36 http://ftpmaster.internal/ubuntu resolute/main armhf libfile-homedir-perl all 1.006-2 [37.0 kB]
174s Get:37 http://ftpmaster.internal/ubuntu resolute/universe armhf libclone-choose-perl all 0.010-2 [7738 B]
174s Get:38 http://ftpmaster.internal/ubuntu resolute/universe armhf libhash-merge-perl all 0.302-1 [13.0 kB]
174s Get:39 http://ftpmaster.internal/ubuntu resolute/main armhf libjson-perl all 4.10000-1 [81.9 kB]
174s Get:40 http://ftpmaster.internal/ubuntu resolute/main armhf libexporter-tiny-perl all 1.006003-1 [35.5 kB]
174s Get:41 http://ftpmaster.internal/ubuntu resolute/universe armhf liblist-moreutils-xs-perl armhf 0.430-4build1 [37.9 kB]
174s Get:42 http://ftpmaster.internal/ubuntu resolute/universe armhf liblist-moreutils-perl all 0.430-2 [38.2 kB]
174s Get:43 http://ftpmaster.internal/ubuntu resolute/universe armhf liblog-log4perl-perl all 1.57-1 [345 kB]
174s Get:44 http://ftpmaster.internal/ubuntu resolute/main armhf libmouse-perl armhf 2.6.1-1 [129 kB]
174s Get:45 http://ftpmaster.internal/ubuntu resolute/universe armhf libmousex-nativetraits-perl all 1.09-3 [53.2 kB]
174s Get:46 http://ftpmaster.internal/ubuntu resolute/universe armhf libmousex-strictconstructor-perl all 0.02-3 [4582 B]
174s Get:47 http://ftpmaster.internal/ubuntu resolute/universe armhf libparse-recdescent-perl all 1.967015+dfsg-4 [139 kB]
174s Get:48 http://ftpmaster.internal/ubuntu resolute/main armhf libpath-tiny-perl all 0.148-1 [47.9 kB]
174s Get:49 http://ftpmaster.internal/ubuntu resolute/universe armhf libpod-pom-perl all 2.01-4 [61.3 kB]
174s Get:50 http://ftpmaster.internal/ubuntu resolute/main armhf libregexp-common-perl all 2024080801-1 [162 kB]
174s Get:51 http://ftpmaster.internal/ubuntu resolute/main armhf libyaml-tiny-perl all 1.76-1 [24.2 kB]
174s Get:52 http://ftpmaster.internal/ubuntu resolute/universe armhf libconfig-model-perl all 2.155-1 [356 kB]
174s Get:53 http://ftpmaster.internal/ubuntu resolute/universe armhf libyaml-pp-perl all 0.39.0-1 [107 kB]
174s Get:54 http://ftpmaster.internal/ubuntu resolute/universe armhf cme all 1.044-2 [68.0 kB]
175s Get:55 http://ftpmaster.internal/ubuntu resolute/main armhf libisl23 armhf 0.27-1build1 [553 kB]
175s Get:56 http://ftpmaster.internal/ubuntu resolute/main armhf libmpc3 armhf 1.3.1-2 [47.0 kB]
175s Get:57 http://ftpmaster.internal/ubuntu resolute/main armhf cpp-15-arm-linux-gnueabihf armhf 15.2.0-12ubuntu1 [10.1 MB]
175s Get:58 http://ftpmaster.internal/ubuntu resolute/main armhf cpp-15 armhf 15.2.0-12ubuntu1 [1032 B]
175s Get:59 http://ftpmaster.internal/ubuntu resolute/main armhf cpp-arm-linux-gnueabihf armhf 4:15.2.0-4ubuntu1 [5756 B]
175s Get:60 http://ftpmaster.internal/ubuntu resolute/main armhf cpp armhf 4:15.2.0-4ubuntu1 [22.4 kB]
175s Get:61 http://ftpmaster.internal/ubuntu resolute/main armhf libdebhelper-perl all 13.28ubuntu1 [97.4 kB]
175s Get:62 http://ftpmaster.internal/ubuntu resolute/main armhf libcc1-0 armhf 15.2.0-12ubuntu1 [43.5 kB]
175s Get:63 http://ftpmaster.internal/ubuntu resolute/main armhf libgomp1 armhf 15.2.0-12ubuntu1 [129 kB]
175s Get:64 http://ftpmaster.internal/ubuntu resolute/main armhf libasan8 armhf 15.2.0-12ubuntu1 [2949 kB]
176s Get:65 http://ftpmaster.internal/ubuntu resolute/main armhf libubsan1 armhf 15.2.0-12ubuntu1 [1187 kB]
176s Get:66 http://ftpmaster.internal/ubuntu resolute/main armhf libgcc-15-dev armhf 15.2.0-12ubuntu1 [898 kB]
176s Get:67 http://ftpmaster.internal/ubuntu resolute/main armhf gcc-15-arm-linux-gnueabihf armhf 15.2.0-12ubuntu1 [19.5 MB]
176s Get:68 http://ftpmaster.internal/ubuntu resolute/main armhf gcc-15 armhf 15.2.0-12ubuntu1 [499 kB]
176s Get:69 http://ftpmaster.internal/ubuntu resolute/main armhf gcc-arm-linux-gnueabihf armhf 4:15.2.0-4ubuntu1 [1220 B]
176s Get:70 http://ftpmaster.internal/ubuntu resolute/main armhf gcc armhf 4:15.2.0-4ubuntu1 [5022 B]
176s Get:71 http://ftpmaster.internal/ubuntu resolute-proposed/main armhf libc-dev-bin armhf 2.42-2ubuntu5 [21.8 kB]
176s Get:72 http://ftpmaster.internal/ubuntu resolute/main armhf linux-libc-dev armhf 6.18.0-9.9 [1819 kB]
176s Get:73 http://ftpmaster.internal/ubuntu resolute/main armhf libcrypt-dev armhf 1:4.5.1-1 [128 kB]
177s Get:74 http://ftpmaster.internal/ubuntu resolute/main armhf rpcsvc-proto armhf 1.4.3-1build1 [62.6 kB]
177s Get:75 http://ftpmaster.internal/ubuntu resolute-proposed/main armhf libc6-dev armhf 2.42-2ubuntu5 [1416 kB]
177s Get:76 http://ftpmaster.internal/ubuntu resolute/main armhf libtool all 2.5.4-9 [169 kB]
177s Get:77 http://ftpmaster.internal/ubuntu resolute/main armhf dh-autoreconf all 21 [12.5 kB]
177s Get:78 http://ftpmaster.internal/ubuntu resolute/main armhf libarchive-zip-perl all 1.68-1 [90.2 kB]
177s Get:79 http://ftpmaster.internal/ubuntu resolute/main armhf libfile-stripnondeterminism-perl all 1.15.0-1build1 [20.6 kB]
177s Get:80 http://ftpmaster.internal/ubuntu resolute/main armhf dh-strip-nondeterminism all 1.15.0-1build1 [5110 B]
177s Get:81 http://ftpmaster.internal/ubuntu resolute/main armhf libdw1t64 armhf 0.194-1 [254 kB]
177s Get:82 http://ftpmaster.internal/ubuntu resolute/main armhf debugedit armhf 1:5.2-3build1 [49.0 kB]
177s Get:83 http://ftpmaster.internal/ubuntu resolute/main armhf dwz armhf 0.16-2 [114 kB]
177s Get:84 http://ftpmaster.internal/ubuntu resolute/main armhf gettext armhf 0.23.2-1 [1059 kB]
177s Get:85 http://ftpmaster.internal/ubuntu resolute/main armhf intltool-debian all 0.35.0+20060710.6build1 [24.1 kB]
177s Get:86 http://ftpmaster.internal/ubuntu resolute/main armhf po-debconf all 1.0.22 [215 kB]
177s Get:87 http://ftpmaster.internal/ubuntu resolute/main armhf debhelper all 13.28ubuntu1 [916 kB]
177s Get:88 http://ftpmaster.internal/ubuntu resolute/universe armhf aglfn all 1.7+git20191031.4036a9c-2build1 [33.2 kB]
177s Get:89 http://ftpmaster.internal/ubuntu resolute/universe armhf gnuplot-data all 6.0.2+dfsg1-2ubuntu1 [76.0 kB]
177s Get:90 http://ftpmaster.internal/ubuntu resolute/main armhf libfreetype6 armhf 2.14.1+dfsg-2 [345 kB]
177s Get:91 http://ftpmaster.internal/ubuntu resolute/universe armhf fonts-freefont-otf all 20211204+svn4273-4build1 [4594 kB]
177s Get:92 http://ftpmaster.internal/ubuntu resolute/main armhf fontconfig-config armhf 2.17.1-3ubuntu1 [38.5 kB]
177s Get:93 http://ftpmaster.internal/ubuntu resolute/main armhf libfontconfig1 armhf 2.17.1-3ubuntu1 [117 kB]
177s Get:94 http://ftpmaster.internal/ubuntu resolute/main armhf libpixman-1-0 armhf 0.46.4-1 [196 kB]
177s Get:95 http://ftpmaster.internal/ubuntu resolute/main armhf libxcb-render0 armhf 1.17.0-2ubuntu1 [15.5 kB]
177s Get:96 http://ftpmaster.internal/ubuntu resolute/main armhf libxcb-shm0 armhf 1.17.0-2ubuntu1 [5956 B]
177s Get:97 http://ftpmaster.internal/ubuntu resolute/main armhf libxrender1 armhf 1:0.9.12-1 [16.6 kB]
177s Get:98 http://ftpmaster.internal/ubuntu resolute/main armhf libcairo2 armhf 1.18.4-3 [489 kB]
177s Get:99 http://ftpmaster.internal/ubuntu resolute/main armhf libsharpyuv0 armhf 1.5.0-0.1build1 [16.3 kB]
177s Get:100 http://ftpmaster.internal/ubuntu resolute/main armhf libaom3 armhf 3.13.1-2 [1258 kB]
177s Get:101 http://ftpmaster.internal/ubuntu resolute/main armhf libheif-plugin-aomdec armhf 1.21.2-1 [13.0 kB]
177s Get:102 http://ftpmaster.internal/ubuntu resolute/main armhf libde265-0 armhf 1.0.16-1build1 [159 kB]
178s Get:103 http://ftpmaster.internal/ubuntu resolute/main armhf libheif-plugin-libde265 armhf 1.21.2-1 [13.6 kB]
178s Get:104 http://ftpmaster.internal/ubuntu resolute/main armhf libheif1 armhf 1.21.2-1 [597 kB]
178s Get:105 http://ftpmaster.internal/ubuntu resolute/main armhf libimagequant0 armhf 2.18.0-1build1 [31.1 kB]
178s Get:106 http://ftpmaster.internal/ubuntu resolute/main armhf libjpeg-turbo8 armhf 2.1.5-4ubuntu2 [127 kB]
178s Get:107 http://ftpmaster.internal/ubuntu resolute/main armhf libjpeg8 armhf 8c-2ubuntu11 [2148 B]
178s Get:108 http://ftpmaster.internal/ubuntu resolute/main armhf libdeflate0 armhf 1.23-2 [38.7 kB]
178s Get:109 http://ftpmaster.internal/ubuntu resolute/main armhf libjbig0 armhf 2.1-6.1ubuntu3 [25.3 kB]
179s Get:110 http://ftpmaster.internal/ubuntu resolute/main armhf liblerc4 armhf 4.0.0+ds-5ubuntu2 [162 kB]
179s Get:111 http://ftpmaster.internal/ubuntu resolute/main armhf libwebp7 armhf 1.5.0-0.1build1 [189 kB]
179s Get:112 http://ftpmaster.internal/ubuntu resolute/main armhf libtiff6 armhf 4.7.0-3ubuntu3 [188 kB]
179s Get:113 http://ftpmaster.internal/ubuntu resolute/main armhf libxpm4 armhf 1:3.5.17-1build3 [31.1 kB]
179s Get:114 http://ftpmaster.internal/ubuntu resolute/main armhf libgd3 armhf 2.3.3-13ubuntu1 [109 kB]
179s Get:115 http://ftpmaster.internal/ubuntu resolute/main armhf liblua5.4-0 armhf 5.4.8-1 [134 kB]
179s Get:116 http://ftpmaster.internal/ubuntu resolute/main armhf fontconfig armhf 2.17.1-3ubuntu1 [180 kB]
179s Get:117 http://ftpmaster.internal/ubuntu resolute/main armhf libgraphite2-3 armhf 1.3.14-11ubuntu1 [65.2 kB]
179s Get:118 http://ftpmaster.internal/ubuntu resolute/main armhf libharfbuzz0b armhf 12.3.2-1 [501 kB]
179s Get:119 http://ftpmaster.internal/ubuntu resolute/main armhf libthai-data all 0.1.30-1 [155 kB]
179s Get:120 http://ftpmaster.internal/ubuntu resolute/main armhf libdatrie1 armhf 0.2.14-1 [16.4 kB]
179s Get:121 http://ftpmaster.internal/ubuntu resolute/main armhf libthai0 armhf 0.1.30-1 [15.4 kB]
179s Get:122 http://ftpmaster.internal/ubuntu resolute/main armhf libpango-1.0-0 armhf 1.57.0-1 [218 kB]
180s Get:123 http://ftpmaster.internal/ubuntu resolute/main armhf libpangoft2-1.0-0 armhf 1.57.0-1 [45.2 kB]
180s Get:124 http://ftpmaster.internal/ubuntu resolute/main armhf libpangocairo-1.0-0 armhf 1.57.0-1 [25.3 kB]
180s Get:125 http://ftpmaster.internal/ubuntu resolute/main armhf libwebpmux3 armhf 1.5.0-0.1build1 [22.4 kB]
180s Get:126 http://ftpmaster.internal/ubuntu resolute/universe armhf gnuplot-nox armhf 6.0.2+dfsg1-2ubuntu1 [915 kB]
180s Get:127 http://ftpmaster.internal/ubuntu resolute/universe armhf dh-octave-autopkgtest all 1.14.0 [11.9 kB]
180s Get:128 http://ftpmaster.internal/ubuntu resolute/main armhf libapt-pkg-perl armhf 0.1.43 [66.6 kB]
180s Get:129 http://ftpmaster.internal/ubuntu resolute/main armhf libarray-intspan-perl all 2.004-2 [25.0 kB]
180s Get:130 http://ftpmaster.internal/ubuntu resolute/main armhf libconfig-inifiles-perl all 3.000003-4 [38.5 kB]
180s Get:131 http://ftpmaster.internal/ubuntu resolute/main armhf libyaml-libyaml-perl armhf 0.904.0+ds-1 [40.4 kB]
180s Get:132 http://ftpmaster.internal/ubuntu resolute/universe armhf libconfig-model-backend-yaml-perl all 2.134-2 [10.5 kB]
180s Get:133 http://ftpmaster.internal/ubuntu resolute/universe armhf libexporter-lite-perl all 0.09-2 [9748 B]
180s Get:134 http://ftpmaster.internal/ubuntu resolute/main armhf libencode-locale-perl all 1.05-3 [11.6 kB]
180s Get:135 http://ftpmaster.internal/ubuntu resolute/main armhf libtimedate-perl all 2.3300-2 [34.0 kB]
180s Get:136 http://ftpmaster.internal/ubuntu resolute/main armhf libhttp-date-perl all 6.06-1 [10.2 kB]
180s Get:137 http://ftpmaster.internal/ubuntu resolute/main armhf libfile-listing-perl all 6.16-1 [11.3 kB]
180s Get:138 http://ftpmaster.internal/ubuntu resolute/main armhf libhtml-tagset-perl all 3.24-1 [14.1 kB]
180s Get:139 http://ftpmaster.internal/ubuntu resolute/main armhf liburi-perl all 5.34-2build1 [100 kB]
180s Get:140 http://ftpmaster.internal/ubuntu resolute/main armhf libhtml-parser-perl armhf 3.83-1build1 [83.1 kB]
180s Get:141 http://ftpmaster.internal/ubuntu resolute/main armhf libhtml-tree-perl all 5.07-3 [200 kB]
180s Get:142 http://ftpmaster.internal/ubuntu resolute/main armhf libclone-perl armhf 0.47-1 [10.0 kB]
180s Get:143 http://ftpmaster.internal/ubuntu resolute/main armhf libio-html-perl all 1.004-3 [15.9 kB]
180s Get:144 http://ftpmaster.internal/ubuntu resolute/main armhf liblwp-mediatypes-perl all 6.04-2 [20.1 kB]
180s Get:145 http://ftpmaster.internal/ubuntu resolute/main armhf libhttp-message-perl all 7.01-1ubuntu1 [76.1 kB]
180s Get:146 http://ftpmaster.internal/ubuntu resolute/main armhf libhttp-cookies-perl all 6.11-1 [18.2 kB]
180s Get:147 http://ftpmaster.internal/ubuntu resolute/main armhf libhttp-negotiate-perl all 6.01-2 [12.4 kB]
180s Get:148 http://ftpmaster.internal/ubuntu resolute/main armhf perl-openssl-defaults armhf 7build4 [6710 B]
180s Get:149 http://ftpmaster.internal/ubuntu resolute/main armhf libnet-ssleay-perl armhf 1.94-3 [298 kB]
181s Get:150 http://ftpmaster.internal/ubuntu resolute/main armhf libio-socket-ssl-perl all 2.098-1 [205 kB]
181s Get:151 http://ftpmaster.internal/ubuntu resolute/main armhf libnet-http-perl all 6.24-1build1 [21.7 kB]
181s Get:152 http://ftpmaster.internal/ubuntu resolute/main armhf liblwp-protocol-https-perl all 6.14-1 [9040 B]
181s Get:153 http://ftpmaster.internal/ubuntu resolute/main armhf libwww-robotrules-perl all 6.02-1build1 [12.4 kB]
181s Get:154 http://ftpmaster.internal/ubuntu resolute/main armhf libwww-perl all 6.81-1build1 [141 kB]
181s Get:155 http://ftpmaster.internal/ubuntu resolute/main armhf liberror-perl all 0.17030-1 [23.5 kB]
181s Get:156 http://ftpmaster.internal/ubuntu resolute/universe armhf libparse-debcontrol-perl all 2.005-6 [20.4 kB]
181s Get:157 http://ftpmaster.internal/ubuntu resolute/universe armhf libsoftware-copyright-perl all 0.015-1 [14.4 kB]
181s Get:158 http://ftpmaster.internal/ubuntu resolute/universe armhf libalgorithm-c3-perl all 0.11-2 [10.2 kB]
181s Get:159 http://ftpmaster.internal/ubuntu resolute/universe armhf libclass-c3-perl all 0.35-2 [18.4 kB]
181s Get:160 http://ftpmaster.internal/ubuntu resolute/universe armhf libmro-compat-perl all 0.15-2 [10.1 kB]
181s Get:161 http://ftpmaster.internal/ubuntu resolute/universe armhf libdata-section-perl all 0.200008-1 [11.6 kB]
181s Get:162 http://ftpmaster.internal/ubuntu resolute/universe armhf libtext-template-perl all 1.61-1 [48.5 kB]
181s Get:163 http://ftpmaster.internal/ubuntu resolute/universe armhf libsoftware-license-perl all 0.104007-1 [123 kB]
181s Get:164 http://ftpmaster.internal/ubuntu resolute/universe armhf libsoftware-licensemoreutils-perl all 1.009-1 [21.5 kB]
181s Get:165 http://ftpmaster.internal/ubuntu resolute/main armhf libsort-versions-perl all 1.62-3 [7378 B]
181s Get:166 http://ftpmaster.internal/ubuntu resolute/universe armhf libtext-reform-perl all 1.20-5 [35.4 kB]
181s Get:167 http://ftpmaster.internal/ubuntu resolute/universe armhf libtext-autoformat-perl all 1.750000-2 [29.8 kB]
181s Get:168 http://ftpmaster.internal/ubuntu resolute/universe armhf libtext-levenshtein-damerau-perl all 0.41-3 [10.8 kB]
181s Get:169 http://ftpmaster.internal/ubuntu resolute/universe armhf libtoml-tiny-perl all 0.20-1 [21.8 kB]
181s Get:170 http://ftpmaster.internal/ubuntu resolute/main armhf libclass-inspector-perl all 1.36-3 [15.4 kB]
181s Get:171 http://ftpmaster.internal/ubuntu resolute/main armhf libfile-sharedir-perl all 1.118-3 [14.0 kB]
181s Get:172 http://ftpmaster.internal/ubuntu resolute/main armhf libindirect-perl armhf 0.39-2build5 [20.6 kB]
181s Get:173 http://ftpmaster.internal/ubuntu resolute/main armhf libxs-parse-keyword-perl armhf 0.49-1 [59.5 kB]
181s Get:174 http://ftpmaster.internal/ubuntu resolute/main armhf libxs-parse-sublike-perl armhf 0.41-1 [45.8 kB]
181s Get:175 http://ftpmaster.internal/ubuntu resolute/main armhf libobject-pad-perl armhf 0.823-2 [123 kB]
181s Get:176 http://ftpmaster.internal/ubuntu resolute/main armhf libsyntax-keyword-try-perl armhf 0.31-1 [23.8 kB]
181s Get:177 http://ftpmaster.internal/ubuntu resolute/main armhf libio-interactive-perl all 1.027-1 [10.8 kB]
181s Get:178 http://ftpmaster.internal/ubuntu resolute/main armhf liblog-any-perl all 1.718-1build1 [69.6 kB]
181s Get:179 http://ftpmaster.internal/ubuntu resolute/main armhf liblog-any-adapter-screen-perl all 0.141-1 [12.9 kB]
181s Get:180 http://ftpmaster.internal/ubuntu resolute/main armhf libsub-exporter-progressive-perl all 0.001013-3 [6718 B]
181s Get:181 http://ftpmaster.internal/ubuntu resolute/main armhf libvariable-magic-perl armhf 0.64-1build1 [33.4 kB]
181s Get:182 http://ftpmaster.internal/ubuntu resolute/main armhf libb-hooks-endofscope-perl all 0.28-2 [15.8 kB]
181s Get:183 http://ftpmaster.internal/ubuntu resolute/main armhf libsub-identify-perl armhf 0.14-4 [9632 B]
181s Get:184 http://ftpmaster.internal/ubuntu resolute/main armhf libsub-name-perl armhf 0.28-1 [10.0 kB]
181s Get:185 http://ftpmaster.internal/ubuntu resolute/main armhf libnamespace-clean-perl all 0.27-2 [14.0 kB]
181s Get:186 http://ftpmaster.internal/ubuntu resolute/main armhf libnumber-compare-perl all 0.03-3 [5974 B]
181s Get:187 http://ftpmaster.internal/ubuntu resolute/main armhf libtext-glob-perl all 0.11-3 [6780 B]
181s Get:188 http://ftpmaster.internal/ubuntu resolute/main armhf libpath-iterator-rule-perl all 1.015-2 [39.9 kB]
181s Get:189 http://ftpmaster.internal/ubuntu resolute/main armhf libpod-parser-perl all 1.67-1 [80.6 kB]
182s Get:190 http://ftpmaster.internal/ubuntu resolute/main armhf libpod-constants-perl all 0.19-2 [16.3 kB]
182s Get:191 http://ftpmaster.internal/ubuntu resolute/main armhf libset-intspan-perl all 1.19-3 [24.8 kB]
182s Get:192 http://ftpmaster.internal/ubuntu resolute/main armhf libstring-copyright-perl all 0.003014-1 [20.5 kB]
182s Get:193 http://ftpmaster.internal/ubuntu resolute/main armhf libstring-escape-perl all 2010.002-3 [16.1 kB]
182s Get:194 http://ftpmaster.internal/ubuntu resolute/main armhf libregexp-pattern-license-perl all 3.11.2-1 [85.3 kB]
182s Get:195 http://ftpmaster.internal/ubuntu resolute/main armhf libregexp-pattern-perl all 0.2.14-2 [17.6 kB]
182s Get:196 http://ftpmaster.internal/ubuntu resolute/main armhf libstring-license-perl all 0.0.11-1ubuntu1 [34.3 kB]
182s Get:197 http://ftpmaster.internal/ubuntu resolute/main armhf licensecheck all 3.3.9-1ubuntu2 [37.2 kB]
182s Get:198 http://ftpmaster.internal/ubuntu resolute/main armhf diffstat armhf 1.68-1 [28.5 kB]
182s Get:199 http://ftpmaster.internal/ubuntu resolute/main armhf libberkeleydb-perl armhf 0.66-2 [109 kB]
182s Get:200 http://ftpmaster.internal/ubuntu resolute/main armhf libclass-xsaccessor-perl armhf 1.19-4build6 [32.4 kB]
182s Get:201 http://ftpmaster.internal/ubuntu resolute/main armhf libconfig-tiny-perl all 2.30-1 [14.7 kB]
182s Get:202 http://ftpmaster.internal/ubuntu resolute/main armhf libconst-fast-perl all 0.014-2 [8034 B]
182s Get:203 http://ftpmaster.internal/ubuntu resolute/main armhf libcpanel-json-xs-perl armhf 4.40-1 [114 kB]
182s Get:204 http://ftpmaster.internal/ubuntu resolute/main armhf libaliased-perl all 0.34-3 [12.8 kB]
182s Get:205 http://ftpmaster.internal/ubuntu resolute/main armhf libclass-data-inheritable-perl all 0.10-1 [8038 B]
182s Get:206 http://ftpmaster.internal/ubuntu resolute/main armhf libdevel-stacktrace-perl all 2.0500-1 [22.1 kB]
182s Get:207 http://ftpmaster.internal/ubuntu resolute/main armhf libexception-class-perl all 1.45-1 [28.6 kB]
182s Get:208 http://ftpmaster.internal/ubuntu resolute/main armhf libiterator-perl all 0.03+ds1-2 [18.8 kB]
182s Get:209 http://ftpmaster.internal/ubuntu resolute/main armhf libiterator-util-perl all 0.02+ds1-2 [14.1 kB]
182s Get:210 http://ftpmaster.internal/ubuntu resolute/main armhf libdata-dpath-perl all 0.60-1 [37.3 kB]
182s Get:211 http://ftpmaster.internal/ubuntu resolute/main armhf libnet-domain-tld-perl all 1.75-4 [29.0 kB]
182s Get:212 http://ftpmaster.internal/ubuntu resolute/main armhf libdata-validate-domain-perl all 0.15-1 [10.4 kB]
182s Get:213 http://ftpmaster.internal/ubuntu resolute/main armhf libnet-ipv6addr-perl all 1.02-1 [21.0 kB]
182s Get:214 http://ftpmaster.internal/ubuntu resolute/main armhf libnet-netmask-perl all 2.0003-1build1 [24.8 kB]
182s Get:215 http://ftpmaster.internal/ubuntu resolute/main armhf libnetaddr-ip-perl armhf 4.079+dfsg-2build5 [79.0 kB]
182s Get:216 http://ftpmaster.internal/ubuntu resolute/main armhf libdata-validate-ip-perl all 0.31-1 [17.2 kB]
182s Get:217 http://ftpmaster.internal/ubuntu resolute/main armhf libdata-validate-uri-perl all 0.07-3 [10.8 kB]
182s Get:218 http://ftpmaster.internal/ubuntu resolute/main armhf libdevel-size-perl armhf 0.85-1 [18.4 kB]
182s Get:219 http://ftpmaster.internal/ubuntu resolute/main armhf libemail-address-xs-perl armhf 1.05-1build5 [26.5 kB]
182s Get:220 http://ftpmaster.internal/ubuntu resolute/main armhf libipc-system-simple-perl all 1.30-2 [22.3 kB]
182s Get:221 http://ftpmaster.internal/ubuntu resolute/main armhf libfile-basedir-perl all 0.09-2 [14.4 kB]
182s Get:222 http://ftpmaster.internal/ubuntu resolute/main armhf libfile-find-rule-perl all 0.35-1build1 [24.0 kB]
182s Get:223 http://ftpmaster.internal/ubuntu resolute/main armhf libio-string-perl all 1.08-4 [11.1 kB]
183s Get:224 http://ftpmaster.internal/ubuntu resolute/main armhf libfont-ttf-perl all 1.06-2 [323 kB]
183s Get:225 http://ftpmaster.internal/ubuntu resolute/main armhf libhtml-html5-entities-perl all 0.004-3 [21.6 kB]
183s Get:226 http://ftpmaster.internal/ubuntu resolute/main armhf libhtml-tokeparser-simple-perl all 3.16-4 [38.0 kB]
183s Get:227 http://ftpmaster.internal/ubuntu resolute/main armhf libipc-run3-perl all 0.049-1 [28.8 kB]
183s Get:228 http://ftpmaster.internal/ubuntu resolute/main armhf libjson-maybexs-perl all 1.004008-1 [11.1 kB]
183s Get:229 http://ftpmaster.internal/ubuntu resolute/main armhf liblist-compare-perl all 0.55-2 [62.9 kB]
183s Get:230 http://ftpmaster.internal/ubuntu resolute/main armhf liblist-someutils-perl all 0.59-1 [30.4 kB]
183s Get:231 http://ftpmaster.internal/ubuntu resolute/main armhf liblist-utilsby-perl all 0.12-2 [14.9 kB]
183s Get:232 http://ftpmaster.internal/ubuntu resolute/main armhf libmldbm-perl all 2.05-4 [16.0 kB]
183s Get:233 http://ftpmaster.internal/ubuntu resolute/main armhf libclass-method-modifiers-perl all 2.15-1 [16.1 kB]
183s Get:234 http://ftpmaster.internal/ubuntu resolute/main armhf libimport-into-perl all 1.002005-2 [10.7 kB]
183s Get:235 http://ftpmaster.internal/ubuntu resolute/main armhf librole-tiny-perl all 2.002004-1 [16.3 kB]
183s Get:236 http://ftpmaster.internal/ubuntu resolute/main armhf libsub-quote-perl all 2.006009-1ubuntu1 [20.3 kB]
183s Get:237 http://ftpmaster.internal/ubuntu resolute/main armhf libmoo-perl all 2.005005-1 [47.4 kB]
183s Get:238 http://ftpmaster.internal/ubuntu resolute/main armhf libstrictures-perl all 2.000006-1build1 [15.2 kB]
183s Get:239 http://ftpmaster.internal/ubuntu resolute/main armhf libmoox-aliases-perl all 0.001006-2 [6796 B]
183s Get:240 http://ftpmaster.internal/ubuntu resolute/main armhf libperlio-gzip-perl armhf 0.20-1build5 [13.5 kB]
183s Get:241 http://ftpmaster.internal/ubuntu resolute/main armhf libperlio-utf8-strict-perl armhf 0.010-1build4 [10.5 kB]
183s Get:242 http://ftpmaster.internal/ubuntu resolute/main armhf libproc-processtable-perl armhf 0.637-1 [35.3 kB]
183s Get:243 http://ftpmaster.internal/ubuntu resolute/main armhf libregexp-wildcards-perl all 1.05-3 [12.9 kB]
183s Get:244 http://ftpmaster.internal/ubuntu resolute/main armhf libsereal-decoder-perl armhf 5.004+ds-1build5 [94.4 kB]
183s Get:245 http://ftpmaster.internal/ubuntu resolute/main armhf libsereal-encoder-perl armhf 5.004+ds-1build4 [96.1 kB]
183s Get:246 http://ftpmaster.internal/ubuntu resolute/main armhf libterm-readkey-perl armhf 2.38-2build5 [22.6 kB]
183s Get:247 http://ftpmaster.internal/ubuntu resolute/main armhf libtext-levenshteinxs-perl armhf 0.03-5build5 [7482 B]
183s Get:248 http://ftpmaster.internal/ubuntu resolute/main armhf libmarkdown2 armhf 2.2.7-2.1build1 [31.6 kB]
183s Get:249 http://ftpmaster.internal/ubuntu resolute/main armhf libtext-markdown-discount-perl armhf 0.18-1 [12.0 kB]
183s Get:250 http://ftpmaster.internal/ubuntu resolute/main armhf libdata-messagepack-perl armhf 1.02-3 [29.5 kB]
183s Get:251 http://ftpmaster.internal/ubuntu resolute/main armhf libtext-xslate-perl armhf 3.5.9-2build1 [158 kB]
183s Get:252 http://ftpmaster.internal/ubuntu resolute/main armhf libtime-duration-perl all 1.21-2 [12.3 kB]
183s Get:253 http://ftpmaster.internal/ubuntu resolute/main armhf libtime-moment-perl armhf 0.46-1 [75.9 kB]
183s Get:254 http://ftpmaster.internal/ubuntu resolute/main armhf libunicode-utf8-perl armhf 0.63-1 [17.4 kB]
183s Get:255 http://ftpmaster.internal/ubuntu resolute/main armhf libcgi-pm-perl all 4.71-1build1 [185 kB]
183s Get:256 http://ftpmaster.internal/ubuntu resolute/main armhf libhtml-form-perl all 6.13-1build1 [31.3 kB]
183s Get:257 http://ftpmaster.internal/ubuntu resolute/main armhf libwww-mechanize-perl all 2.20-1ubuntu1 [95.5 kB]
183s Get:258 http://ftpmaster.internal/ubuntu resolute/main armhf libxml-namespacesupport-perl all 1.12-2 [13.5 kB]
183s Get:259 http://ftpmaster.internal/ubuntu resolute/main armhf libxml-sax-base-perl all 1.09-3 [18.9 kB]
183s Get:260 http://ftpmaster.internal/ubuntu resolute/main armhf libxml-sax-perl all 1.02+dfsg-4 [52.4 kB]
183s Get:261 http://ftpmaster.internal/ubuntu resolute/main armhf libxml-libxml-perl armhf 2.0207+dfsg+really+2.0207-0ubuntu7 [293 kB]
183s Get:262 http://ftpmaster.internal/ubuntu resolute/main armhf lzip armhf 1.25-4 [81.6 kB]
183s Get:263 http://ftpmaster.internal/ubuntu resolute/main armhf lzop armhf 1.04-2build4 [82.7 kB]
183s Get:264 http://ftpmaster.internal/ubuntu resolute/main armhf patchutils armhf 0.4.3-1 [77.8 kB]
183s Get:265 http://ftpmaster.internal/ubuntu resolute/main armhf t1utils armhf 1.41-4build4 [52.4 kB]
183s Get:266 http://ftpmaster.internal/ubuntu resolute/main armhf unzip armhf 6.0-29ubuntu1 [167 kB]
183s Get:267 http://ftpmaster.internal/ubuntu resolute/main armhf lintian all 2.127.0ubuntu1 [1079 kB]
184s Get:268 http://ftpmaster.internal/ubuntu resolute/universe armhf libconfig-model-dpkg-perl all 3.016 [194 kB]
184s Get:269 http://ftpmaster.internal/ubuntu resolute/main armhf libconvert-binhex-perl all 1.125-3 [27.1 kB]
184s Get:270 http://ftpmaster.internal/ubuntu resolute/main armhf libnet-smtp-ssl-perl all 1.04-2 [6218 B]
184s Get:271 http://ftpmaster.internal/ubuntu resolute/main armhf libmailtools-perl all 2.22-1 [77.1 kB]
184s Get:272 http://ftpmaster.internal/ubuntu resolute/main armhf libmime-tools-perl all 5.515-1 [187 kB]
184s Get:273 http://ftpmaster.internal/ubuntu resolute/universe armhf libb-keywords-perl all 1.29-1 [10.4 kB]
184s Get:274 http://ftpmaster.internal/ubuntu resolute/universe armhf libclass-tiny-perl all 1.008-2 [16.4 kB]
184s Get:275 http://ftpmaster.internal/ubuntu resolute/universe armhf liblingua-en-inflect-perl all 1.905-2 [50.8 kB]
184s Get:276 http://ftpmaster.internal/ubuntu resolute/universe armhf libpod-spell-perl all 1.27-1 [30.4 kB]
184s Get:277 http://ftpmaster.internal/ubuntu resolute/universe armhf libsafe-isa-perl all 1.000010-1build1 [7334 B]
184s Get:278 http://ftpmaster.internal/ubuntu resolute/universe armhf libtask-weaken-perl all 1.06-2 [7924 B]
184s Get:279 http://ftpmaster.internal/ubuntu resolute/universe armhf libppi-perl all 1.284-1 [281 kB]
184s Get:280 http://ftpmaster.internal/ubuntu resolute/universe armhf libreadonly-perl all 2.050-3 [19.9 kB]
184s Get:281 http://ftpmaster.internal/ubuntu resolute/universe armhf libppix-quotelike-perl all 0.023-1 [71.6 kB]
184s Get:282 http://ftpmaster.internal/ubuntu resolute/universe armhf libppix-regexp-perl all 0.091-1 [234 kB]
184s Get:283 http://ftpmaster.internal/ubuntu resolute/universe armhf libppix-utils-perl all 0.003-2 [28.4 kB]
184s Get:284 http://ftpmaster.internal/ubuntu resolute/universe armhf libstring-format-perl all 1.18-1build1 [8824 B]
184s Get:285 http://ftpmaster.internal/ubuntu resolute/universe armhf perltidy all 20250105-1build1 [645 kB]
184s Get:286 http://ftpmaster.internal/ubuntu resolute/universe armhf libperl-critic-perl all 1.156-1 [654 kB]
184s Get:287 http://ftpmaster.internal/ubuntu resolute/universe armhf libtext-wrapper-perl all 1.05-4 [9820 B]
184s Get:288 http://ftpmaster.internal/ubuntu resolute/main armhf libsuitesparseconfig7 armhf 1:7.12.1+dfsg-1 [21.4 kB]
184s Get:289 http://ftpmaster.internal/ubuntu resolute/universe armhf libamd3 armhf 1:7.12.1+dfsg-1 [37.5 kB]
184s Get:290 http://ftpmaster.internal/ubuntu resolute/main armhf libblas3 armhf 3.12.1-7ubuntu1 [133 kB]
184s Get:291 http://ftpmaster.internal/ubuntu resolute/main armhf libgfortran5 armhf 15.2.0-12ubuntu1 [334 kB]
184s Get:292 http://ftpmaster.internal/ubuntu resolute/main armhf liblapack3 armhf 3.12.1-7ubuntu1 [2090 kB]
184s Get:293 http://ftpmaster.internal/ubuntu resolute/universe armhf libarpack2t64 armhf 3.9.1-6 [88.7 kB]
184s Get:294 http://ftpmaster.internal/ubuntu resolute/universe armhf libccolamd3 armhf 1:7.12.1+dfsg-1 [36.1 kB]
184s Get:295 http://ftpmaster.internal/ubuntu resolute/universe armhf libcamd3 armhf 1:7.12.1+dfsg-1 [34.5 kB]
184s Get:296 http://ftpmaster.internal/ubuntu resolute/main armhf libcolamd3 armhf 1:7.12.1+dfsg-1 [28.2 kB]
184s Get:297 http://ftpmaster.internal/ubuntu resolute/universe armhf libcholmod5 armhf 1:7.12.1+dfsg-1 [684 kB]
184s Get:298 http://ftpmaster.internal/ubuntu resolute/universe armhf libcxsparse4 armhf 1:7.12.1+dfsg-1 [79.7 kB]
184s Get:299 http://ftpmaster.internal/ubuntu resolute/main armhf libfftw3-double3 armhf 3.3.10-2fakesync1build2 [368 kB]
184s Get:300 http://ftpmaster.internal/ubuntu resolute/main armhf libfftw3-single3 armhf 3.3.10-2fakesync1build2 [583 kB]
185s Get:301 http://ftpmaster.internal/ubuntu resolute/main armhf libxfixes3 armhf 1:6.0.0-2build2 [9382 B]
185s Get:302 http://ftpmaster.internal/ubuntu resolute/main armhf libxcursor1 armhf 1:1.2.3-1build1 [18.5 kB]
185s Get:303 http://ftpmaster.internal/ubuntu resolute/main armhf libxft2 armhf 2.3.6-1build2 [37.2 kB]
185s Get:304 http://ftpmaster.internal/ubuntu resolute/main armhf libxinerama1 armhf 2:1.1.4-3build2 [5932 B]
185s Get:305 http://ftpmaster.internal/ubuntu resolute/universe armhf libfltk1.3t64 armhf 1.3.11-3 [557 kB]
185s Get:306 http://ftpmaster.internal/ubuntu resolute/main armhf libglvnd0 armhf 1.7.0-3 [71.3 kB]
185s Get:307 http://ftpmaster.internal/ubuntu resolute/main armhf libllvm21 armhf 1:21.1.8-1ubuntu1 [30.3 MB]
190s Get:308 http://ftpmaster.internal/ubuntu resolute/main armhf libx11-xcb1 armhf 2:1.8.12-1build1 [7920 B]
190s Get:309 http://ftpmaster.internal/ubuntu resolute/main armhf libxcb-dri3-0 armhf 1.17.0-2ubuntu1 [7342 B]
190s Get:310 http://ftpmaster.internal/ubuntu resolute/main armhf libxcb-present0 armhf 1.17.0-2ubuntu1 [6080 B]
190s Get:311 http://ftpmaster.internal/ubuntu resolute/main armhf libxcb-randr0 armhf 1.17.0-2ubuntu1 [16.9 kB]
190s Get:312 http://ftpmaster.internal/ubuntu resolute/main armhf libxcb-sync1 armhf 1.17.0-2ubuntu1 [8840 B]
190s Get:313 http://ftpmaster.internal/ubuntu resolute/main armhf libxcb-xfixes0 armhf 1.17.0-2ubuntu1 [10.2 kB]
190s Get:314 http://ftpmaster.internal/ubuntu resolute/main armhf libxshmfence1 armhf 1.3.3-1build1 [5000 B]
190s Get:315 http://ftpmaster.internal/ubuntu resolute/main armhf mesa-libgallium armhf 25.3.3-1ubuntu1 [8874 kB]
192s Get:316 http://ftpmaster.internal/ubuntu resolute/main armhf libgbm1 armhf 25.3.3-1ubuntu1 [30.0 kB]
192s Get:317 http://ftpmaster.internal/ubuntu resolute/main armhf libvulkan1 armhf 1.4.335.0-1 [137 kB]
192s Get:318 http://ftpmaster.internal/ubuntu resolute/main armhf libgl1-mesa-dri armhf 25.3.3-1ubuntu1 [32.7 kB]
192s Get:319 http://ftpmaster.internal/ubuntu resolute/main armhf libxcb-glx0 armhf 1.17.0-2ubuntu1 [22.8 kB]
192s Get:320 http://ftpmaster.internal/ubuntu resolute/main armhf libxxf86vm1 armhf 1:1.1.4-2 [9110 B]
192s Get:321 http://ftpmaster.internal/ubuntu resolute/main armhf libglx-mesa0 armhf 25.3.3-1ubuntu1 [87.3 kB]
192s Get:322 http://ftpmaster.internal/ubuntu resolute/main armhf libglx0 armhf 1.7.0-3 [37.9 kB]
192s Get:323 http://ftpmaster.internal/ubuntu resolute/main armhf libgl1 armhf 1.7.0-3 [102 kB]
192s Get:324 http://ftpmaster.internal/ubuntu resolute/universe armhf libfltk-gl1.3t64 armhf 1.3.11-3 [36.7 kB]
192s Get:325 http://ftpmaster.internal/ubuntu resolute/universe armhf libgl2ps1.4 armhf 1.4.2+dfsg1-4 [41.1 kB]
192s Get:326 http://ftpmaster.internal/ubuntu resolute/main armhf libltdl7 armhf 2.5.4-9 [40.4 kB]
192s Get:327 http://ftpmaster.internal/ubuntu resolute/universe armhf libglpk40 armhf 5.0-2 [325 kB]
192s Get:328 http://ftpmaster.internal/ubuntu resolute/main armhf libopengl0 armhf 1.7.0-3 [35.6 kB]
192s Get:329 http://ftpmaster.internal/ubuntu resolute/universe armhf libglu1-mesa armhf 9.0.2-1.1build2 [157 kB]
192s Get:330 http://ftpmaster.internal/ubuntu resolute/main armhf liblcms2-2 armhf 2.17-1 [139 kB]
192s Get:331 http://ftpmaster.internal/ubuntu resolute/main armhf libjxl0.11 armhf 0.11.1-6ubuntu1 [1108 kB]
192s Get:332 http://ftpmaster.internal/ubuntu resolute/main armhf libwmflite-0.2-7 armhf 0.2.13-2 [63.1 kB]
192s Get:333 http://ftpmaster.internal/ubuntu resolute/universe armhf libgraphicsmagick-q16-3t64 armhf 1.4+really1.3.45+hg17696-1build1 [1230 kB]
192s Get:334 http://ftpmaster.internal/ubuntu resolute/universe armhf libgraphicsmagick++-q16-12t64 armhf 1.4+really1.3.45+hg17696-1build1 [107 kB]
192s Get:335 http://ftpmaster.internal/ubuntu resolute/universe armhf libsz2 armhf 1.1.5-1 [16.3 kB]
192s Get:336 http://ftpmaster.internal/ubuntu resolute/universe armhf libhdf5-310 armhf 1.14.6+repack-2 [1423 kB]
192s Get:337 http://ftpmaster.internal/ubuntu resolute/main armhf libasound2-data all 1.2.15.3-1ubuntu1 [21.4 kB]
192s Get:338 http://ftpmaster.internal/ubuntu resolute/main armhf libasound2t64 armhf 1.2.15.3-1ubuntu1 [362 kB]
192s Get:339 http://ftpmaster.internal/ubuntu resolute/main armhf libopus0 armhf 1.6.1-1 [3510 kB]
193s Get:340 http://ftpmaster.internal/ubuntu resolute/main armhf libsamplerate0 armhf 0.2.2-4build2 [1340 kB]
193s Get:341 http://ftpmaster.internal/ubuntu resolute/main armhf libjack-jackd2-0 armhf 1.9.22~dfsg-5 [247 kB]
193s Get:342 http://ftpmaster.internal/ubuntu resolute/main armhf libasyncns0 armhf 0.8-7 [10.8 kB]
193s Get:343 http://ftpmaster.internal/ubuntu resolute/main armhf libogg0 armhf 1.3.6-2 [20.6 kB]
193s Get:344 http://ftpmaster.internal/ubuntu resolute/main armhf libflac14 armhf 1.5.0+ds-5 [136 kB]
193s Get:345 http://ftpmaster.internal/ubuntu resolute/main armhf libmp3lame0 armhf 3.100-6build2 [126 kB]
193s Get:346 http://ftpmaster.internal/ubuntu resolute/main armhf libmpg123-0t64 armhf 1.33.3-2 [160 kB]
193s Get:347 http://ftpmaster.internal/ubuntu resolute/main armhf libvorbis0a armhf 1.3.7-3build1 [86.6 kB]
193s Get:348 http://ftpmaster.internal/ubuntu resolute/main armhf libvorbisenc2 armhf 1.3.7-3build1 [72.7 kB]
193s Get:349 http://ftpmaster.internal/ubuntu resolute/main armhf libsndfile1 armhf 1.2.2-4 [201 kB]
193s Get:350 http://ftpmaster.internal/ubuntu resolute/main armhf libpulse0 armhf 1:17.0+dfsg1-2ubuntu4 [280 kB]
193s Get:351 http://ftpmaster.internal/ubuntu resolute/universe armhf libportaudio2 armhf 19.7.0+git20251227.3270c9ae-0ubuntu1 [70.2 kB]
193s Get:352 http://ftpmaster.internal/ubuntu resolute/universe armhf libqhull-r8.0 armhf 2020.2-8 [175 kB]
193s Get:353 http://ftpmaster.internal/ubuntu resolute/universe armhf libqrupdate1 armhf 1.1.5-3 [30.8 kB]
193s Get:354 http://ftpmaster.internal/ubuntu resolute/universe armhf libqscintilla2-qt6-l10n all 2.14.1+dfsg-2 [55.5 kB]
193s Get:355 http://ftpmaster.internal/ubuntu resolute/universe armhf libb2-1 armhf 0.98.1-1.1build2 [26.2 kB]
193s Get:356 http://ftpmaster.internal/ubuntu resolute/universe armhf libdouble-conversion3 armhf 3.4.0-1 [39.3 kB]
193s Get:357 http://ftpmaster.internal/ubuntu resolute/main armhf libpcre2-16-0 armhf 10.46-1 [206 kB]
193s Get:358 http://ftpmaster.internal/ubuntu resolute/universe armhf libqt6core6t64 armhf 6.9.2+dfsg-3ubuntu2 [1791 kB]
193s Get:359 http://ftpmaster.internal/ubuntu resolute/main armhf libwayland-client0 armhf 1.24.0-2 [24.6 kB]
193s Get:360 http://ftpmaster.internal/ubuntu resolute/main armhf libegl-mesa0 armhf 25.3.3-1ubuntu1 [96.0 kB]
193s Get:361 http://ftpmaster.internal/ubuntu resolute/main armhf libegl1 armhf 1.7.0-3 [30.2 kB]
193s Get:362 http://ftpmaster.internal/ubuntu resolute/main armhf x11-common all 1:7.7+24ubuntu1 [22.4 kB]
193s Get:363 http://ftpmaster.internal/ubuntu resolute/main armhf libice6 armhf 2:1.1.1-1build1 [37.5 kB]
193s Get:364 http://ftpmaster.internal/ubuntu resolute/main armhf libmtdev1t64 armhf 1.1.7-1build1 [13.2 kB]
193s Get:365 http://ftpmaster.internal/ubuntu resolute/main armhf libwacom-common all 2.16.1-1 [113 kB]
193s Get:366 http://ftpmaster.internal/ubuntu resolute/main armhf libwacom9 armhf 2.16.1-1 [24.7 kB]
193s Get:367 http://ftpmaster.internal/ubuntu resolute/main armhf libinput-bin armhf 1.30.1-1 [27.7 kB]
193s Get:368 http://ftpmaster.internal/ubuntu resolute/main armhf libinput10 armhf 1.30.1-1 [144 kB]
193s Get:369 http://ftpmaster.internal/ubuntu resolute/universe armhf libmd4c0 armhf 0.5.2-2build1 [38.6 kB]
193s Get:370 http://ftpmaster.internal/ubuntu resolute/universe armhf libqt6dbus6 armhf 6.9.2+dfsg-3ubuntu2 [247 kB]
193s Get:371 http://ftpmaster.internal/ubuntu resolute/main armhf libsm6 armhf 2:1.2.6-1 [15.3 kB]
193s Get:372 http://ftpmaster.internal/ubuntu resolute/universe armhf libts0t64 armhf 1.22-1.1build2 [60.9 kB]
193s Get:373 http://ftpmaster.internal/ubuntu resolute/main armhf libxcb-util1 armhf 0.4.1-1build1 [9628 B]
193s Get:374 http://ftpmaster.internal/ubuntu resolute/universe armhf libxcb-image0 armhf 0.4.0-2build2 [9776 B]
193s Get:375 http://ftpmaster.internal/ubuntu resolute/universe armhf libxcb-render-util0 armhf 0.3.10-1build1 [9460 B]
193s Get:376 http://ftpmaster.internal/ubuntu resolute/universe armhf libxcb-cursor0 armhf 0.1.5-1build1 [9986 B]
193s Get:377 http://ftpmaster.internal/ubuntu resolute/universe armhf libxcb-icccm4 armhf 0.4.2-1build1 [9914 B]
193s Get:378 http://ftpmaster.internal/ubuntu resolute/universe armhf libxcb-keysyms1 armhf 0.4.1-1build1 [8090 B]
193s Get:379 http://ftpmaster.internal/ubuntu resolute/main armhf libxcb-shape0 armhf 1.17.0-2ubuntu1 [6186 B]
193s Get:380 http://ftpmaster.internal/ubuntu resolute/main armhf libxcb-xinput0 armhf 1.17.0-2ubuntu1 [29.2 kB]
193s Get:381 http://ftpmaster.internal/ubuntu resolute/main armhf libxcb-xkb1 armhf 1.17.0-2ubuntu1 [28.4 kB]
193s Get:382 http://ftpmaster.internal/ubuntu resolute/main armhf libxkbcommon-x11-0 armhf 1.12.3-1 [14.8 kB]
193s Get:383 http://ftpmaster.internal/ubuntu resolute/universe armhf libqt6gui6 armhf 6.9.2+dfsg-3ubuntu2 [3070 kB]
194s Get:384 http://ftpmaster.internal/ubuntu resolute/main armhf libavahi-common-data armhf 0.8-17ubuntu2 [31.5 kB]
194s Get:385 http://ftpmaster.internal/ubuntu resolute/main armhf libavahi-common3 armhf 0.8-17ubuntu2 [20.4 kB]
194s Get:386 http://ftpmaster.internal/ubuntu resolute/main armhf libavahi-client3 armhf 0.8-17ubuntu2 [24.4 kB]
194s Get:387 http://ftpmaster.internal/ubuntu resolute/main armhf libcups2t64 armhf 2.4.16-1ubuntu1 [242 kB]
194s Get:388 http://ftpmaster.internal/ubuntu resolute/universe armhf libqt6widgets6 armhf 6.9.2+dfsg-3ubuntu2 [2549 kB]
194s Get:389 http://ftpmaster.internal/ubuntu resolute/universe armhf libqt6printsupport6 armhf 6.9.2+dfsg-3ubuntu2 [210 kB]
194s Get:390 http://ftpmaster.internal/ubuntu resolute/universe armhf libqscintilla2-qt6-15 armhf 2.14.1+dfsg-2 [1057 kB]
194s Get:391 http://ftpmaster.internal/ubuntu resolute/universe armhf libqt6core5compat6 armhf 6.9.2-3build1 [131 kB]
194s Get:392 http://ftpmaster.internal/ubuntu resolute/universe armhf libqt6sql6 armhf 6.9.2+dfsg-3ubuntu2 [129 kB]
194s Get:393 http://ftpmaster.internal/ubuntu resolute/universe armhf libqt6help6 armhf 6.9.2-5 [178 kB]
194s Get:394 http://ftpmaster.internal/ubuntu resolute/main armhf libduktape207 armhf 2.7.0+tests-0ubuntu4 [123 kB]
194s Get:395 http://ftpmaster.internal/ubuntu resolute/main armhf libproxy1v5 armhf 0.5.12-1 [25.7 kB]
194s Get:396 http://ftpmaster.internal/ubuntu resolute/universe armhf libqt6network6 armhf 6.9.2+dfsg-3ubuntu2 [768 kB]
194s Get:397 http://ftpmaster.internal/ubuntu resolute/universe armhf libqt6opengl6 armhf 6.9.2+dfsg-3ubuntu2 [391 kB]
194s Get:398 http://ftpmaster.internal/ubuntu resolute/universe armhf libqt6openglwidgets6 armhf 6.9.2+dfsg-3ubuntu2 [41.6 kB]
194s Get:399 http://ftpmaster.internal/ubuntu resolute/universe armhf libqt6xml6 armhf 6.9.2+dfsg-3ubuntu2 [74.0 kB]
194s Get:400 http://ftpmaster.internal/ubuntu resolute/universe armhf libspqr4 armhf 1:7.12.1+dfsg-1 [139 kB]
194s Get:401 http://ftpmaster.internal/ubuntu resolute/universe armhf libumfpack6 armhf 1:7.12.1+dfsg-1 [300 kB]
194s Get:402 http://ftpmaster.internal/ubuntu resolute/universe armhf libtext-unidecode-perl all 1.30-3 [105 kB]
194s Get:403 http://ftpmaster.internal/ubuntu resolute/main armhf libintl-perl all 1.35-1 [701 kB]
194s Get:404 http://ftpmaster.internal/ubuntu resolute/universe armhf texinfo-lib armhf 7.2-5 [409 kB]
194s Get:405 http://ftpmaster.internal/ubuntu resolute/universe armhf tex-common all 6.20 [30.0 kB]
194s Get:406 http://ftpmaster.internal/ubuntu resolute/universe armhf texinfo all 7.2-5 [1217 kB]
195s Get:407 http://ftpmaster.internal/ubuntu resolute/universe armhf octave-common all 10.3.0-3 [6088 kB]
196s Get:408 http://ftpmaster.internal/ubuntu resolute/universe armhf octave armhf 10.3.0-3 [9155 kB]
196s Get:409 http://ftpmaster.internal/ubuntu resolute/main armhf libncurses-dev armhf 6.6+20251231-1 [348 kB]
196s Get:410 http://ftpmaster.internal/ubuntu resolute/main armhf libreadline-dev armhf 8.3-3 [165 kB]
196s Get:411 http://ftpmaster.internal/ubuntu resolute/universe armhf libhdf5-fortran-310 armhf 1.14.6+repack-2 [95.9 kB]
196s Get:412 http://ftpmaster.internal/ubuntu resolute/universe armhf libhdf5-hl-310 armhf 1.14.6+repack-2 [58.9 kB]
196s Get:413 http://ftpmaster.internal/ubuntu resolute/universe armhf libhdf5-hl-fortran-310 armhf 1.14.6+repack-2 [26.8 kB]
196s Get:414 http://ftpmaster.internal/ubuntu resolute/universe armhf libhdf5-cpp-310 armhf 1.14.6+repack-2 [125 kB]
196s Get:415 http://ftpmaster.internal/ubuntu resolute/universe armhf libhdf5-hl-cpp-310 armhf 1.14.6+repack-2 [11.1 kB]
196s Get:416 http://ftpmaster.internal/ubuntu resolute/main armhf zlib1g-dev armhf 1:1.3.dfsg+really1.3.1-1ubuntu2 [881 kB]
197s Get:417 http://ftpmaster.internal/ubuntu resolute/main armhf libjpeg-turbo8-dev armhf 2.1.5-4ubuntu2 [264 kB]
197s Get:418 http://ftpmaster.internal/ubuntu resolute/main armhf libjpeg8-dev armhf 8c-2ubuntu11 [1484 B]
197s Get:419 http://ftpmaster.internal/ubuntu resolute/main armhf libjpeg-dev armhf 8c-2ubuntu11 [1482 B]
197s Get:420 http://ftpmaster.internal/ubuntu resolute/universe armhf libaec0 armhf 1.1.5-1 [20.8 kB]
197s Get:421 http://ftpmaster.internal/ubuntu resolute/universe armhf libaec-dev armhf 1.1.5-1 [21.3 kB]
197s Get:422 http://ftpmaster.internal/ubuntu resolute/main armhf libbrotli-dev armhf 1.1.0-2build6 [337 kB]
197s Get:423 http://ftpmaster.internal/ubuntu resolute/main armhf libidn2-dev armhf 2.3.8-4 [116 kB]
197s Get:424 http://ftpmaster.internal/ubuntu resolute/main armhf comerr-dev armhf 2.1-1.47.2-3ubuntu2 [44.1 kB]
197s Get:425 http://ftpmaster.internal/ubuntu resolute/main armhf libgssrpc4t64 armhf 1.22.1-2 [51.8 kB]
197s Get:426 http://ftpmaster.internal/ubuntu resolute/main armhf libkadm5clnt-mit12 armhf 1.22.1-2 [35.7 kB]
197s Get:427 http://ftpmaster.internal/ubuntu resolute/main armhf libkdb5-10t64 armhf 1.22.1-2 [36.0 kB]
197s Get:428 http://ftpmaster.internal/ubuntu resolute/main armhf libkadm5srv-mit12 armhf 1.22.1-2 [46.7 kB]
197s Get:429 http://ftpmaster.internal/ubuntu resolute/main armhf krb5-multidev armhf 1.22.1-2 [126 kB]
197s Get:430 http://ftpmaster.internal/ubuntu resolute/main armhf libkrb5-dev armhf 1.22.1-2 [11.9 kB]
197s Get:431 http://ftpmaster.internal/ubuntu resolute/main armhf libldap-dev armhf 2.6.10+dfsg-1ubuntu5 [290 kB]
197s Get:432 http://ftpmaster.internal/ubuntu resolute/main armhf libpkgconf3 armhf 1.8.1-4build1 [26.6 kB]
197s Get:433 http://ftpmaster.internal/ubuntu resolute/main armhf pkgconf-bin armhf 1.8.1-4build1 [21.4 kB]
197s Get:434 http://ftpmaster.internal/ubuntu resolute/main armhf pkgconf armhf 1.8.1-4build1 [16.8 kB]
197s Get:435 http://ftpmaster.internal/ubuntu resolute/main armhf libnghttp2-dev armhf 1.64.0-1.1ubuntu1 [107 kB]
197s Get:436 http://ftpmaster.internal/ubuntu resolute/main armhf libpsl-dev armhf 0.21.2-1.1build2 [77.8 kB]
197s Get:437 http://ftpmaster.internal/ubuntu resolute/main armhf libgmpxx4ldbl armhf 2:6.3.0+dfsg-5ubuntu1 [9224 B]
197s Get:438 http://ftpmaster.internal/ubuntu resolute/main armhf libgmp-dev armhf 2:6.3.0+dfsg-5ubuntu1 [292 kB]
197s Get:439 http://ftpmaster.internal/ubuntu resolute/main armhf libevent-2.1-7t64 armhf 2.1.12-stable-10build1 [130 kB]
197s Get:440 http://ftpmaster.internal/ubuntu resolute/main armhf libunbound8 armhf 1.24.2-1ubuntu1 [433 kB]
197s Get:441 http://ftpmaster.internal/ubuntu resolute/main armhf libgnutls-dane0t64 armhf 3.8.10-3ubuntu1 [32.6 kB]
197s Get:442 http://ftpmaster.internal/ubuntu resolute/main armhf libgnutls-openssl27t64 armhf 3.8.10-3ubuntu1 [32.3 kB]
197s Get:443 http://ftpmaster.internal/ubuntu resolute/main armhf libp11-kit-dev armhf 0.25.10-1 [29.3 kB]
197s Get:444 http://ftpmaster.internal/ubuntu resolute/main armhf libtasn1-6-dev armhf 4.21.0-2 [86.7 kB]
197s Get:445 http://ftpmaster.internal/ubuntu resolute/main armhf nettle-dev armhf 3.10.2-1 [1174 kB]
197s Get:446 http://ftpmaster.internal/ubuntu resolute/main armhf libgnutls28-dev armhf 3.8.10-3ubuntu1 [1043 kB]
197s Get:447 http://ftpmaster.internal/ubuntu resolute/main armhf librtmp-dev armhf 2.4+20151223.gitfa8646d.1-3 [63.4 kB]
197s Get:448 http://ftpmaster.internal/ubuntu resolute/main armhf libssl-dev armhf 3.5.3-1ubuntu2 [2550 kB]
197s Get:449 http://ftpmaster.internal/ubuntu resolute/main armhf libssh2-1-dev armhf 1.11.1-1build1 [270 kB]
197s Get:450 http://ftpmaster.internal/ubuntu resolute/main armhf libzstd-dev armhf 1.5.7+dfsg-3 [342 kB]
197s Get:451 http://ftpmaster.internal/ubuntu resolute/main armhf libcurl4-openssl-dev armhf 8.18.0-1ubuntu1 [505 kB]
197s Get:452 http://ftpmaster.internal/ubuntu resolute/universe armhf hdf5-helpers armhf 1.14.6+repack-2 [17.3 kB]
197s Get:453 http://ftpmaster.internal/ubuntu resolute/universe armhf libhdf5-dev armhf 1.14.6+repack-2 [3352 kB]
197s Get:454 http://ftpmaster.internal/ubuntu resolute/main armhf xorg-sgml-doctools all 1:1.11-1.1build1 [10.3 kB]
197s Get:455 http://ftpmaster.internal/ubuntu resolute/main armhf x11proto-dev all 2025.1-1 [608 kB]
197s Get:456 http://ftpmaster.internal/ubuntu resolute/main armhf libxau-dev armhf 1:1.0.11-1build1 [9048 B]
197s Get:457 http://ftpmaster.internal/ubuntu resolute/main armhf libxdmcp-dev armhf 1:1.1.5-2 [24.7 kB]
197s Get:458 http://ftpmaster.internal/ubuntu resolute/main armhf xtrans-dev all 1.6.0-1build1 [66.2 kB]
197s Get:459 http://ftpmaster.internal/ubuntu resolute/main armhf libxcb1-dev armhf 1.17.0-2ubuntu1 [85.8 kB]
197s Get:460 http://ftpmaster.internal/ubuntu resolute/main armhf libx11-dev armhf 2:1.8.12-1build1 [678 kB]
197s Get:461 http://ftpmaster.internal/ubuntu resolute/main armhf libglx-dev armhf 1.7.0-3 [14.1 kB]
197s Get:462 http://ftpmaster.internal/ubuntu resolute/main armhf libgl-dev armhf 1.7.0-3 [103 kB]
197s Get:463 http://ftpmaster.internal/ubuntu resolute/main armhf libblas-dev armhf 3.12.1-7ubuntu1 [141 kB]
197s Get:464 http://ftpmaster.internal/ubuntu resolute/main armhf liblapack-dev armhf 3.12.1-7ubuntu1 [2207 kB]
198s Get:465 http://ftpmaster.internal/ubuntu resolute/main armhf libfftw3-bin armhf 3.3.10-2fakesync1build2 [30.2 kB]
198s Get:466 http://ftpmaster.internal/ubuntu resolute/main armhf libfftw3-dev armhf 3.3.10-2fakesync1build2 [926 kB]
198s Get:467 http://ftpmaster.internal/ubuntu resolute/main armhf libgfortran-15-dev armhf 15.2.0-12ubuntu1 [380 kB]
198s Get:468 http://ftpmaster.internal/ubuntu resolute/main armhf gfortran-15-arm-linux-gnueabihf armhf 15.2.0-12ubuntu1 [10.7 MB]
198s Get:469 http://ftpmaster.internal/ubuntu resolute/main armhf gfortran-15 armhf 15.2.0-12ubuntu1 [18.1 kB]
198s Get:470 http://ftpmaster.internal/ubuntu resolute/main armhf gfortran-arm-linux-gnueabihf armhf 4:15.2.0-4ubuntu1 [1020 B]
198s Get:471 http://ftpmaster.internal/ubuntu resolute/main armhf gfortran armhf 4:15.2.0-4ubuntu1 [1162 B]
198s Get:472 http://ftpmaster.internal/ubuntu resolute/main armhf libstdc++-15-dev armhf 15.2.0-12ubuntu1 [2638 kB]
198s Get:473 http://ftpmaster.internal/ubuntu resolute/main armhf g++-15-arm-linux-gnueabihf armhf 15.2.0-12ubuntu1 [11.4 MB]
199s Get:474 http://ftpmaster.internal/ubuntu resolute/main armhf g++-15 armhf 15.2.0-12ubuntu1 [25.3 kB]
199s Get:475 http://ftpmaster.internal/ubuntu resolute/main armhf g++-arm-linux-gnueabihf armhf 4:15.2.0-4ubuntu1 [968 B]
199s Get:476 http://ftpmaster.internal/ubuntu resolute/main armhf g++ armhf 4:15.2.0-4ubuntu1 [1086 B]
199s Get:477 http://ftpmaster.internal/ubuntu resolute/universe armhf octave-dev armhf 10.3.0-3 [464 kB]
199s Get:478 http://ftpmaster.internal/ubuntu resolute/universe armhf dh-octave all 1.14.0 [21.1 kB]
199s Get:479 http://ftpmaster.internal/ubuntu resolute/main armhf libfontenc1 armhf 1:1.1.8-1build2 [11.7 kB]
199s Get:480 http://ftpmaster.internal/ubuntu resolute/main armhf libxt6t64 armhf 1:1.2.1-1.3 [145 kB]
199s Get:481 http://ftpmaster.internal/ubuntu resolute/main armhf libxmu6 armhf 2:1.1.3-4 [41.2 kB]
199s Get:482 http://ftpmaster.internal/ubuntu resolute/main armhf libxaw7 armhf 2:1.0.16-1build1 [156 kB]
199s Get:483 http://ftpmaster.internal/ubuntu resolute/main armhf libxfont2 armhf 1:2.0.6-2 [82.7 kB]
199s Get:484 http://ftpmaster.internal/ubuntu resolute/main armhf libxkbfile1 armhf 1:1.1.0-1build5 [60.6 kB]
199s Get:485 http://ftpmaster.internal/ubuntu resolute/main armhf libxrandr2 armhf 2:1.5.4-1build1 [15.6 kB]
199s Get:486 http://ftpmaster.internal/ubuntu resolute/universe armhf octave-io armhf 2.7.0-3 [239 kB]
199s Get:487 http://ftpmaster.internal/ubuntu resolute/universe armhf octave-statistics-common all 1.7.6-2 [1008 kB]
199s Get:488 http://ftpmaster.internal/ubuntu resolute/universe armhf octave-statistics armhf 1.7.6-2 [131 kB]
199s Get:489 http://ftpmaster.internal/ubuntu resolute/main armhf x11-xkb-utils armhf 7.7+9build1 [159 kB]
199s Get:490 http://ftpmaster.internal/ubuntu resolute/main armhf xserver-common all 2:21.1.21-1ubuntu1 [34.8 kB]
199s Get:491 http://ftpmaster.internal/ubuntu resolute/universe armhf xvfb armhf 2:21.1.21-1ubuntu1 [744 kB]
201s Fetched 202 MB in 26s (7789 kB/s)
201s Selecting previously unselected package libfyaml0:armhf.
201s (Reading database ... 
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(Reading database ... 60%
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(Reading database ... 80%
(Reading database ... 85%
(Reading database ... 90%
(Reading database ... 95%
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(Reading database ... 68395 files and directories currently installed.)
201s Preparing to unpack .../000-libfyaml0_0.9.3-1_armhf.deb ...
201s Unpacking libfyaml0:armhf (0.9.3-1) ...
201s Selecting previously unselected package libstemmer0d:armhf.
201s Preparing to unpack .../001-libstemmer0d_3.0.1-1_armhf.deb ...
201s Unpacking libstemmer0d:armhf (3.0.1-1) ...
201s Selecting previously unselected package libappstream5:armhf.
201s Preparing to unpack .../002-libappstream5_1.1.1-1_armhf.deb ...
201s Unpacking libappstream5:armhf (1.1.1-1) ...
201s Selecting previously unselected package appstream.
201s Preparing to unpack .../003-appstream_1.1.1-1_armhf.deb ...
201s Unpacking appstream (1.1.1-1) ...
201s Selecting previously unselected package m4.
201s Preparing to unpack .../004-m4_1.4.20-2_armhf.deb ...
201s Unpacking m4 (1.4.20-2) ...
201s Selecting previously unselected package autoconf.
201s Preparing to unpack .../005-autoconf_2.72-3.1ubuntu1_all.deb ...
201s Unpacking autoconf (2.72-3.1ubuntu1) ...
201s Selecting previously unselected package autotools-dev.
201s Preparing to unpack .../006-autotools-dev_20240727.1_all.deb ...
201s Unpacking autotools-dev (20240727.1) ...
201s Selecting previously unselected package automake.
201s Preparing to unpack .../007-automake_1%3a1.18.1-3build1_all.deb ...
201s Unpacking automake (1:1.18.1-3build1) ...
201s Selecting previously unselected package autopoint.
201s Preparing to unpack .../008-autopoint_0.23.2-1_all.deb ...
201s Unpacking autopoint (0.23.2-1) ...
201s Selecting previously unselected package libcapture-tiny-perl.
201s Preparing to unpack .../009-libcapture-tiny-perl_0.50-1_all.deb ...
201s Unpacking libcapture-tiny-perl (0.50-1) ...
201s Selecting previously unselected package libparams-util-perl.
201s Preparing to unpack .../010-libparams-util-perl_1.102-3build1_armhf.deb ...
201s Unpacking libparams-util-perl (1.102-3build1) ...
201s Selecting previously unselected package libsub-install-perl.
201s Preparing to unpack .../011-libsub-install-perl_0.929-1_all.deb ...
201s Unpacking libsub-install-perl (0.929-1) ...
201s Selecting previously unselected package libdata-optlist-perl.
201s Preparing to unpack .../012-libdata-optlist-perl_0.114-1_all.deb ...
201s Unpacking libdata-optlist-perl (0.114-1) ...
201s Selecting previously unselected package libb-hooks-op-check-perl:armhf.
201s Preparing to unpack .../013-libb-hooks-op-check-perl_0.22-3build2_armhf.deb ...
201s Unpacking libb-hooks-op-check-perl:armhf (0.22-3build2) ...
201s Selecting previously unselected package libdynaloader-functions-perl.
201s Preparing to unpack .../014-libdynaloader-functions-perl_0.004-2_all.deb ...
201s Unpacking libdynaloader-functions-perl (0.004-2) ...
201s Selecting previously unselected package libdevel-callchecker-perl:armhf.
201s Preparing to unpack .../015-libdevel-callchecker-perl_0.009-2_armhf.deb ...
201s Unpacking libdevel-callchecker-perl:armhf (0.009-2) ...
202s Selecting previously unselected package libparams-classify-perl:armhf.
202s Preparing to unpack .../016-libparams-classify-perl_0.015-2build6_armhf.deb ...
202s Unpacking libparams-classify-perl:armhf (0.015-2build6) ...
202s Selecting previously unselected package libmodule-runtime-perl.
202s Preparing to unpack .../017-libmodule-runtime-perl_0.018-1_all.deb ...
202s Unpacking libmodule-runtime-perl (0.018-1) ...
202s Selecting previously unselected package libtry-tiny-perl.
202s Preparing to unpack .../018-libtry-tiny-perl_0.32-1_all.deb ...
202s Unpacking libtry-tiny-perl (0.32-1) ...
202s Selecting previously unselected package libmodule-implementation-perl.
202s Preparing to unpack .../019-libmodule-implementation-perl_0.09-2_all.deb ...
202s Unpacking libmodule-implementation-perl (0.09-2) ...
202s Selecting previously unselected package libpackage-stash-perl.
202s Preparing to unpack .../020-libpackage-stash-perl_0.40-1_all.deb ...
202s Unpacking libpackage-stash-perl (0.40-1) ...
202s Selecting previously unselected package libclass-load-perl.
202s Preparing to unpack .../021-libclass-load-perl_0.25-2_all.deb ...
202s Unpacking libclass-load-perl (0.25-2) ...
202s Selecting previously unselected package libio-stringy-perl.
202s Preparing to unpack .../022-libio-stringy-perl_2.113-2_all.deb ...
202s Unpacking libio-stringy-perl (2.113-2) ...
202s Selecting previously unselected package libparams-validate-perl:armhf.
202s Preparing to unpack .../023-libparams-validate-perl_1.31-2build4_armhf.deb ...
202s Unpacking libparams-validate-perl:armhf (1.31-2build4) ...
202s Selecting previously unselected package libsub-exporter-perl.
202s Preparing to unpack .../024-libsub-exporter-perl_0.990-1_all.deb ...
202s Unpacking libsub-exporter-perl (0.990-1) ...
202s Selecting previously unselected package libgetopt-long-descriptive-perl.
202s Preparing to unpack .../025-libgetopt-long-descriptive-perl_0.116-2_all.deb ...
202s Unpacking libgetopt-long-descriptive-perl (0.116-2) ...
202s Selecting previously unselected package libio-tiecombine-perl.
202s Preparing to unpack .../026-libio-tiecombine-perl_1.005-3_all.deb ...
202s Unpacking libio-tiecombine-perl (1.005-3) ...
202s Selecting previously unselected package libmodule-pluggable-perl.
202s Preparing to unpack .../027-libmodule-pluggable-perl_5.2-5_all.deb ...
202s Unpacking libmodule-pluggable-perl (5.2-5) ...
202s Selecting previously unselected package libstring-rewriteprefix-perl.
202s Preparing to unpack .../028-libstring-rewriteprefix-perl_0.009-1_all.deb ...
202s Unpacking libstring-rewriteprefix-perl (0.009-1) ...
202s Selecting previously unselected package libapp-cmd-perl.
202s Preparing to unpack .../029-libapp-cmd-perl_0.338-1_all.deb ...
202s Unpacking libapp-cmd-perl (0.338-1) ...
202s Selecting previously unselected package libboolean-perl.
202s Preparing to unpack .../030-libboolean-perl_0.46-3_all.deb ...
202s Unpacking libboolean-perl (0.46-3) ...
202s Selecting previously unselected package libsub-uplevel-perl.
202s Preparing to unpack .../031-libsub-uplevel-perl_0.2800-3_all.deb ...
202s Unpacking libsub-uplevel-perl (0.2800-3) ...
202s Selecting previously unselected package libtest-exception-perl.
202s Preparing to unpack .../032-libtest-exception-perl_0.43-3_all.deb ...
202s Unpacking libtest-exception-perl (0.43-3) ...
202s Selecting previously unselected package libcarp-assert-more-perl.
202s Preparing to unpack .../033-libcarp-assert-more-perl_2.9.0-1_all.deb ...
202s Unpacking libcarp-assert-more-perl (2.9.0-1) ...
202s Selecting previously unselected package libfile-which-perl.
202s Preparing to unpack .../034-libfile-which-perl_1.27-2_all.deb ...
202s Unpacking libfile-which-perl (1.27-2) ...
202s Selecting previously unselected package libfile-homedir-perl.
202s Preparing to unpack .../035-libfile-homedir-perl_1.006-2_all.deb ...
202s Unpacking libfile-homedir-perl (1.006-2) ...
202s Selecting previously unselected package libclone-choose-perl.
202s Preparing to unpack .../036-libclone-choose-perl_0.010-2_all.deb ...
202s Unpacking libclone-choose-perl (0.010-2) ...
202s Selecting previously unselected package libhash-merge-perl.
202s Preparing to unpack .../037-libhash-merge-perl_0.302-1_all.deb ...
202s Unpacking libhash-merge-perl (0.302-1) ...
203s Selecting previously unselected package libjson-perl.
203s Preparing to unpack .../038-libjson-perl_4.10000-1_all.deb ...
203s Unpacking libjson-perl (4.10000-1) ...
203s Selecting previously unselected package libexporter-tiny-perl.
203s Preparing to unpack .../039-libexporter-tiny-perl_1.006003-1_all.deb ...
203s Unpacking libexporter-tiny-perl (1.006003-1) ...
203s Selecting previously unselected package liblist-moreutils-xs-perl.
203s Preparing to unpack .../040-liblist-moreutils-xs-perl_0.430-4build1_armhf.deb ...
203s Unpacking liblist-moreutils-xs-perl (0.430-4build1) ...
203s Selecting previously unselected package liblist-moreutils-perl.
203s Preparing to unpack .../041-liblist-moreutils-perl_0.430-2_all.deb ...
203s Unpacking liblist-moreutils-perl (0.430-2) ...
203s Selecting previously unselected package liblog-log4perl-perl.
203s Preparing to unpack .../042-liblog-log4perl-perl_1.57-1_all.deb ...
203s Unpacking liblog-log4perl-perl (1.57-1) ...
203s Selecting previously unselected package libmouse-perl:armhf.
203s Preparing to unpack .../043-libmouse-perl_2.6.1-1_armhf.deb ...
203s Unpacking libmouse-perl:armhf (2.6.1-1) ...
203s Selecting previously unselected package libmousex-nativetraits-perl.
203s Preparing to unpack .../044-libmousex-nativetraits-perl_1.09-3_all.deb ...
203s Unpacking libmousex-nativetraits-perl (1.09-3) ...
203s Selecting previously unselected package libmousex-strictconstructor-perl.
203s Preparing to unpack .../045-libmousex-strictconstructor-perl_0.02-3_all.deb ...
203s Unpacking libmousex-strictconstructor-perl (0.02-3) ...
203s Selecting previously unselected package libparse-recdescent-perl.
203s Preparing to unpack .../046-libparse-recdescent-perl_1.967015+dfsg-4_all.deb ...
203s Unpacking libparse-recdescent-perl (1.967015+dfsg-4) ...
203s Selecting previously unselected package libpath-tiny-perl.
203s Preparing to unpack .../047-libpath-tiny-perl_0.148-1_all.deb ...
203s Unpacking libpath-tiny-perl (0.148-1) ...
203s Selecting previously unselected package libpod-pom-perl.
203s Preparing to unpack .../048-libpod-pom-perl_2.01-4_all.deb ...
203s Unpacking libpod-pom-perl (2.01-4) ...
203s Selecting previously unselected package libregexp-common-perl.
203s Preparing to unpack .../049-libregexp-common-perl_2024080801-1_all.deb ...
203s Unpacking libregexp-common-perl (2024080801-1) ...
203s Selecting previously unselected package libyaml-tiny-perl.
203s Preparing to unpack .../050-libyaml-tiny-perl_1.76-1_all.deb ...
203s Unpacking libyaml-tiny-perl (1.76-1) ...
203s Selecting previously unselected package libconfig-model-perl.
203s Preparing to unpack .../051-libconfig-model-perl_2.155-1_all.deb ...
203s Unpacking libconfig-model-perl (2.155-1) ...
203s Selecting previously unselected package libyaml-pp-perl.
203s Preparing to unpack .../052-libyaml-pp-perl_0.39.0-1_all.deb ...
203s Unpacking libyaml-pp-perl (0.39.0-1) ...
203s Selecting previously unselected package cme.
203s Preparing to unpack .../053-cme_1.044-2_all.deb ...
203s Unpacking cme (1.044-2) ...
203s Selecting previously unselected package libisl23:armhf.
203s Preparing to unpack .../054-libisl23_0.27-1build1_armhf.deb ...
203s Unpacking libisl23:armhf (0.27-1build1) ...
203s Selecting previously unselected package libmpc3:armhf.
204s Preparing to unpack .../055-libmpc3_1.3.1-2_armhf.deb ...
204s Unpacking libmpc3:armhf (1.3.1-2) ...
204s Selecting previously unselected package cpp-15-arm-linux-gnueabihf.
204s Preparing to unpack .../056-cpp-15-arm-linux-gnueabihf_15.2.0-12ubuntu1_armhf.deb ...
204s Unpacking cpp-15-arm-linux-gnueabihf (15.2.0-12ubuntu1) ...
204s Selecting previously unselected package cpp-15.
204s Preparing to unpack .../057-cpp-15_15.2.0-12ubuntu1_armhf.deb ...
204s Unpacking cpp-15 (15.2.0-12ubuntu1) ...
204s Selecting previously unselected package cpp-arm-linux-gnueabihf.
204s Preparing to unpack .../058-cpp-arm-linux-gnueabihf_4%3a15.2.0-4ubuntu1_armhf.deb ...
204s Unpacking cpp-arm-linux-gnueabihf (4:15.2.0-4ubuntu1) ...
204s Selecting previously unselected package cpp.
204s Preparing to unpack .../059-cpp_4%3a15.2.0-4ubuntu1_armhf.deb ...
204s Unpacking cpp (4:15.2.0-4ubuntu1) ...
204s Selecting previously unselected package libdebhelper-perl.
204s Preparing to unpack .../060-libdebhelper-perl_13.28ubuntu1_all.deb ...
204s Unpacking libdebhelper-perl (13.28ubuntu1) ...
204s Selecting previously unselected package libcc1-0:armhf.
204s Preparing to unpack .../061-libcc1-0_15.2.0-12ubuntu1_armhf.deb ...
204s Unpacking libcc1-0:armhf (15.2.0-12ubuntu1) ...
204s Selecting previously unselected package libgomp1:armhf.
204s Preparing to unpack .../062-libgomp1_15.2.0-12ubuntu1_armhf.deb ...
204s Unpacking libgomp1:armhf (15.2.0-12ubuntu1) ...
204s Selecting previously unselected package libasan8:armhf.
204s Preparing to unpack .../063-libasan8_15.2.0-12ubuntu1_armhf.deb ...
204s Unpacking libasan8:armhf (15.2.0-12ubuntu1) ...
204s Selecting previously unselected package libubsan1:armhf.
204s Preparing to unpack .../064-libubsan1_15.2.0-12ubuntu1_armhf.deb ...
204s Unpacking libubsan1:armhf (15.2.0-12ubuntu1) ...
204s Selecting previously unselected package libgcc-15-dev:armhf.
204s Preparing to unpack .../065-libgcc-15-dev_15.2.0-12ubuntu1_armhf.deb ...
204s Unpacking libgcc-15-dev:armhf (15.2.0-12ubuntu1) ...
205s Selecting previously unselected package gcc-15-arm-linux-gnueabihf.
205s Preparing to unpack .../066-gcc-15-arm-linux-gnueabihf_15.2.0-12ubuntu1_armhf.deb ...
205s Unpacking gcc-15-arm-linux-gnueabihf (15.2.0-12ubuntu1) ...
205s Selecting previously unselected package gcc-15.
205s Preparing to unpack .../067-gcc-15_15.2.0-12ubuntu1_armhf.deb ...
205s Unpacking gcc-15 (15.2.0-12ubuntu1) ...
205s Selecting previously unselected package gcc-arm-linux-gnueabihf.
205s Preparing to unpack .../068-gcc-arm-linux-gnueabihf_4%3a15.2.0-4ubuntu1_armhf.deb ...
205s Unpacking gcc-arm-linux-gnueabihf (4:15.2.0-4ubuntu1) ...
205s Selecting previously unselected package gcc.
205s Preparing to unpack .../069-gcc_4%3a15.2.0-4ubuntu1_armhf.deb ...
205s Unpacking gcc (4:15.2.0-4ubuntu1) ...
205s Selecting previously unselected package libc-dev-bin.
205s Preparing to unpack .../070-libc-dev-bin_2.42-2ubuntu5_armhf.deb ...
205s Unpacking libc-dev-bin (2.42-2ubuntu5) ...
205s Selecting previously unselected package linux-libc-dev:armhf.
205s Preparing to unpack .../071-linux-libc-dev_6.18.0-9.9_armhf.deb ...
205s Unpacking linux-libc-dev:armhf (6.18.0-9.9) ...
206s Selecting previously unselected package libcrypt-dev:armhf.
206s Preparing to unpack .../072-libcrypt-dev_1%3a4.5.1-1_armhf.deb ...
206s Unpacking libcrypt-dev:armhf (1:4.5.1-1) ...
206s Selecting previously unselected package rpcsvc-proto.
206s Preparing to unpack .../073-rpcsvc-proto_1.4.3-1build1_armhf.deb ...
206s Unpacking rpcsvc-proto (1.4.3-1build1) ...
206s Selecting previously unselected package libc6-dev:armhf.
206s Preparing to unpack .../074-libc6-dev_2.42-2ubuntu5_armhf.deb ...
206s Unpacking libc6-dev:armhf (2.42-2ubuntu5) ...
206s Selecting previously unselected package libtool.
206s Preparing to unpack .../075-libtool_2.5.4-9_all.deb ...
206s Unpacking libtool (2.5.4-9) ...
206s Selecting previously unselected package dh-autoreconf.
206s Preparing to unpack .../076-dh-autoreconf_21_all.deb ...
206s Unpacking dh-autoreconf (21) ...
206s Selecting previously unselected package libarchive-zip-perl.
206s Preparing to unpack .../077-libarchive-zip-perl_1.68-1_all.deb ...
206s Unpacking libarchive-zip-perl (1.68-1) ...
206s Selecting previously unselected package libfile-stripnondeterminism-perl.
206s Preparing to unpack .../078-libfile-stripnondeterminism-perl_1.15.0-1build1_all.deb ...
206s Unpacking libfile-stripnondeterminism-perl (1.15.0-1build1) ...
206s Selecting previously unselected package dh-strip-nondeterminism.
206s Preparing to unpack .../079-dh-strip-nondeterminism_1.15.0-1build1_all.deb ...
206s Unpacking dh-strip-nondeterminism (1.15.0-1build1) ...
206s Selecting previously unselected package libdw1t64:armhf.
206s Preparing to unpack .../080-libdw1t64_0.194-1_armhf.deb ...
206s Unpacking libdw1t64:armhf (0.194-1) ...
206s Selecting previously unselected package debugedit.
206s Preparing to unpack .../081-debugedit_1%3a5.2-3build1_armhf.deb ...
206s Unpacking debugedit (1:5.2-3build1) ...
206s Selecting previously unselected package dwz.
206s Preparing to unpack .../082-dwz_0.16-2_armhf.deb ...
206s Unpacking dwz (0.16-2) ...
206s Selecting previously unselected package gettext.
206s Preparing to unpack .../083-gettext_0.23.2-1_armhf.deb ...
206s Unpacking gettext (0.23.2-1) ...
206s Selecting previously unselected package intltool-debian.
206s Preparing to unpack .../084-intltool-debian_0.35.0+20060710.6build1_all.deb ...
206s Unpacking intltool-debian (0.35.0+20060710.6build1) ...
206s Selecting previously unselected package po-debconf.
206s Preparing to unpack .../085-po-debconf_1.0.22_all.deb ...
206s Unpacking po-debconf (1.0.22) ...
207s Selecting previously unselected package debhelper.
207s Preparing to unpack .../086-debhelper_13.28ubuntu1_all.deb ...
207s Unpacking debhelper (13.28ubuntu1) ...
207s Selecting previously unselected package aglfn.
207s Preparing to unpack .../087-aglfn_1.7+git20191031.4036a9c-2build1_all.deb ...
207s Unpacking aglfn (1.7+git20191031.4036a9c-2build1) ...
207s Selecting previously unselected package gnuplot-data.
207s Preparing to unpack .../088-gnuplot-data_6.0.2+dfsg1-2ubuntu1_all.deb ...
207s Unpacking gnuplot-data (6.0.2+dfsg1-2ubuntu1) ...
207s Selecting previously unselected package libfreetype6:armhf.
207s Preparing to unpack .../089-libfreetype6_2.14.1+dfsg-2_armhf.deb ...
207s Unpacking libfreetype6:armhf (2.14.1+dfsg-2) ...
207s Selecting previously unselected package fonts-freefont-otf.
207s Preparing to unpack .../090-fonts-freefont-otf_20211204+svn4273-4build1_all.deb ...
207s Unpacking fonts-freefont-otf (20211204+svn4273-4build1) ...
207s Selecting previously unselected package fontconfig-config.
207s Preparing to unpack .../091-fontconfig-config_2.17.1-3ubuntu1_armhf.deb ...
207s Unpacking fontconfig-config (2.17.1-3ubuntu1) ...
207s Selecting previously unselected package libfontconfig1:armhf.
207s Preparing to unpack .../092-libfontconfig1_2.17.1-3ubuntu1_armhf.deb ...
208s Unpacking libfontconfig1:armhf (2.17.1-3ubuntu1) ...
208s Selecting previously unselected package libpixman-1-0:armhf.
208s Preparing to unpack .../093-libpixman-1-0_0.46.4-1_armhf.deb ...
208s Unpacking libpixman-1-0:armhf (0.46.4-1) ...
208s Selecting previously unselected package libxcb-render0:armhf.
208s Preparing to unpack .../094-libxcb-render0_1.17.0-2ubuntu1_armhf.deb ...
208s Unpacking libxcb-render0:armhf (1.17.0-2ubuntu1) ...
208s Selecting previously unselected package libxcb-shm0:armhf.
208s Preparing to unpack .../095-libxcb-shm0_1.17.0-2ubuntu1_armhf.deb ...
208s Unpacking libxcb-shm0:armhf (1.17.0-2ubuntu1) ...
208s Selecting previously unselected package libxrender1:armhf.
208s Preparing to unpack .../096-libxrender1_1%3a0.9.12-1_armhf.deb ...
208s Unpacking libxrender1:armhf (1:0.9.12-1) ...
208s Selecting previously unselected package libcairo2:armhf.
208s Preparing to unpack .../097-libcairo2_1.18.4-3_armhf.deb ...
208s Unpacking libcairo2:armhf (1.18.4-3) ...
208s Selecting previously unselected package libsharpyuv0:armhf.
208s Preparing to unpack .../098-libsharpyuv0_1.5.0-0.1build1_armhf.deb ...
208s Unpacking libsharpyuv0:armhf (1.5.0-0.1build1) ...
208s Selecting previously unselected package libaom3:armhf.
208s Preparing to unpack .../099-libaom3_3.13.1-2_armhf.deb ...
208s Unpacking libaom3:armhf (3.13.1-2) ...
208s Selecting previously unselected package libheif-plugin-aomdec:armhf.
208s Preparing to unpack .../100-libheif-plugin-aomdec_1.21.2-1_armhf.deb ...
208s Unpacking libheif-plugin-aomdec:armhf (1.21.2-1) ...
208s Selecting previously unselected package libde265-0:armhf.
208s Preparing to unpack .../101-libde265-0_1.0.16-1build1_armhf.deb ...
208s Unpacking libde265-0:armhf (1.0.16-1build1) ...
208s Selecting previously unselected package libheif-plugin-libde265:armhf.
208s Preparing to unpack .../102-libheif-plugin-libde265_1.21.2-1_armhf.deb ...
208s Unpacking libheif-plugin-libde265:armhf (1.21.2-1) ...
208s Selecting previously unselected package libheif1:armhf.
208s Preparing to unpack .../103-libheif1_1.21.2-1_armhf.deb ...
208s Unpacking libheif1:armhf (1.21.2-1) ...
208s Selecting previously unselected package libimagequant0:armhf.
208s Preparing to unpack .../104-libimagequant0_2.18.0-1build1_armhf.deb ...
208s Unpacking libimagequant0:armhf (2.18.0-1build1) ...
208s Selecting previously unselected package libjpeg-turbo8:armhf.
208s Preparing to unpack .../105-libjpeg-turbo8_2.1.5-4ubuntu2_armhf.deb ...
208s Unpacking libjpeg-turbo8:armhf (2.1.5-4ubuntu2) ...
208s Selecting previously unselected package libjpeg8:armhf.
208s Preparing to unpack .../106-libjpeg8_8c-2ubuntu11_armhf.deb ...
208s Unpacking libjpeg8:armhf (8c-2ubuntu11) ...
208s Selecting previously unselected package libdeflate0:armhf.
208s Preparing to unpack .../107-libdeflate0_1.23-2_armhf.deb ...
208s Unpacking libdeflate0:armhf (1.23-2) ...
208s Selecting previously unselected package libjbig0:armhf.
208s Preparing to unpack .../108-libjbig0_2.1-6.1ubuntu3_armhf.deb ...
208s Unpacking libjbig0:armhf (2.1-6.1ubuntu3) ...
208s Selecting previously unselected package liblerc4:armhf.
208s Preparing to unpack .../109-liblerc4_4.0.0+ds-5ubuntu2_armhf.deb ...
208s Unpacking liblerc4:armhf (4.0.0+ds-5ubuntu2) ...
208s Selecting previously unselected package libwebp7:armhf.
208s Preparing to unpack .../110-libwebp7_1.5.0-0.1build1_armhf.deb ...
208s Unpacking libwebp7:armhf (1.5.0-0.1build1) ...
208s Selecting previously unselected package libtiff6:armhf.
209s Preparing to unpack .../111-libtiff6_4.7.0-3ubuntu3_armhf.deb ...
209s Unpacking libtiff6:armhf (4.7.0-3ubuntu3) ...
209s Selecting previously unselected package libxpm4:armhf.
209s Preparing to unpack .../112-libxpm4_1%3a3.5.17-1build3_armhf.deb ...
209s Unpacking libxpm4:armhf (1:3.5.17-1build3) ...
209s Selecting previously unselected package libgd3:armhf.
209s Preparing to unpack .../113-libgd3_2.3.3-13ubuntu1_armhf.deb ...
209s Unpacking libgd3:armhf (2.3.3-13ubuntu1) ...
209s Selecting previously unselected package liblua5.4-0:armhf.
209s Preparing to unpack .../114-liblua5.4-0_5.4.8-1_armhf.deb ...
209s Unpacking liblua5.4-0:armhf (5.4.8-1) ...
209s Selecting previously unselected package fontconfig.
209s Preparing to unpack .../115-fontconfig_2.17.1-3ubuntu1_armhf.deb ...
209s Unpacking fontconfig (2.17.1-3ubuntu1) ...
209s Selecting previously unselected package libgraphite2-3:armhf.
209s Preparing to unpack .../116-libgraphite2-3_1.3.14-11ubuntu1_armhf.deb ...
209s Unpacking libgraphite2-3:armhf (1.3.14-11ubuntu1) ...
209s Selecting previously unselected package libharfbuzz0b:armhf.
209s Preparing to unpack .../117-libharfbuzz0b_12.3.2-1_armhf.deb ...
209s Unpacking libharfbuzz0b:armhf (12.3.2-1) ...
209s Selecting previously unselected package libthai-data.
209s Preparing to unpack .../118-libthai-data_0.1.30-1_all.deb ...
209s Unpacking libthai-data (0.1.30-1) ...
209s Selecting previously unselected package libdatrie1:armhf.
209s Preparing to unpack .../119-libdatrie1_0.2.14-1_armhf.deb ...
209s Unpacking libdatrie1:armhf (0.2.14-1) ...
209s Selecting previously unselected package libthai0:armhf.
209s Preparing to unpack .../120-libthai0_0.1.30-1_armhf.deb ...
209s Unpacking libthai0:armhf (0.1.30-1) ...
209s Selecting previously unselected package libpango-1.0-0:armhf.
209s Preparing to unpack .../121-libpango-1.0-0_1.57.0-1_armhf.deb ...
209s Unpacking libpango-1.0-0:armhf (1.57.0-1) ...
209s Selecting previously unselected package libpangoft2-1.0-0:armhf.
209s Preparing to unpack .../122-libpangoft2-1.0-0_1.57.0-1_armhf.deb ...
209s Unpacking libpangoft2-1.0-0:armhf (1.57.0-1) ...
209s Selecting previously unselected package libpangocairo-1.0-0:armhf.
209s Preparing to unpack .../123-libpangocairo-1.0-0_1.57.0-1_armhf.deb ...
209s Unpacking libpangocairo-1.0-0:armhf (1.57.0-1) ...
209s Selecting previously unselected package libwebpmux3:armhf.
209s Preparing to unpack .../124-libwebpmux3_1.5.0-0.1build1_armhf.deb ...
209s Unpacking libwebpmux3:armhf (1.5.0-0.1build1) ...
209s Selecting previously unselected package gnuplot-nox.
209s Preparing to unpack .../125-gnuplot-nox_6.0.2+dfsg1-2ubuntu1_armhf.deb ...
209s Unpacking gnuplot-nox (6.0.2+dfsg1-2ubuntu1) ...
209s Selecting previously unselected package dh-octave-autopkgtest.
209s Preparing to unpack .../126-dh-octave-autopkgtest_1.14.0_all.deb ...
209s Unpacking dh-octave-autopkgtest (1.14.0) ...
209s Selecting previously unselected package libapt-pkg-perl.
209s Preparing to unpack .../127-libapt-pkg-perl_0.1.43_armhf.deb ...
209s Unpacking libapt-pkg-perl (0.1.43) ...
209s Selecting previously unselected package libarray-intspan-perl.
209s Preparing to unpack .../128-libarray-intspan-perl_2.004-2_all.deb ...
209s Unpacking libarray-intspan-perl (2.004-2) ...
209s Selecting previously unselected package libconfig-inifiles-perl.
209s Preparing to unpack .../129-libconfig-inifiles-perl_3.000003-4_all.deb ...
209s Unpacking libconfig-inifiles-perl (3.000003-4) ...
210s Selecting previously unselected package libyaml-libyaml-perl.
210s Preparing to unpack .../130-libyaml-libyaml-perl_0.904.0+ds-1_armhf.deb ...
210s Unpacking libyaml-libyaml-perl (0.904.0+ds-1) ...
210s Selecting previously unselected package libconfig-model-backend-yaml-perl.
210s Preparing to unpack .../131-libconfig-model-backend-yaml-perl_2.134-2_all.deb ...
210s Unpacking libconfig-model-backend-yaml-perl (2.134-2) ...
210s Selecting previously unselected package libexporter-lite-perl.
210s Preparing to unpack .../132-libexporter-lite-perl_0.09-2_all.deb ...
210s Unpacking libexporter-lite-perl (0.09-2) ...
210s Selecting previously unselected package libencode-locale-perl.
210s Preparing to unpack .../133-libencode-locale-perl_1.05-3_all.deb ...
210s Unpacking libencode-locale-perl (1.05-3) ...
210s Selecting previously unselected package libtimedate-perl.
210s Preparing to unpack .../134-libtimedate-perl_2.3300-2_all.deb ...
210s Unpacking libtimedate-perl (2.3300-2) ...
210s Selecting previously unselected package libhttp-date-perl.
210s Preparing to unpack .../135-libhttp-date-perl_6.06-1_all.deb ...
210s Unpacking libhttp-date-perl (6.06-1) ...
210s Selecting previously unselected package libfile-listing-perl.
210s Preparing to unpack .../136-libfile-listing-perl_6.16-1_all.deb ...
210s Unpacking libfile-listing-perl (6.16-1) ...
210s Selecting previously unselected package libhtml-tagset-perl.
210s Preparing to unpack .../137-libhtml-tagset-perl_3.24-1_all.deb ...
210s Unpacking libhtml-tagset-perl (3.24-1) ...
210s Selecting previously unselected package liburi-perl.
210s Preparing to unpack .../138-liburi-perl_5.34-2build1_all.deb ...
210s Unpacking liburi-perl (5.34-2build1) ...
210s Selecting previously unselected package libhtml-parser-perl:armhf.
210s Preparing to unpack .../139-libhtml-parser-perl_3.83-1build1_armhf.deb ...
210s Unpacking libhtml-parser-perl:armhf (3.83-1build1) ...
210s Selecting previously unselected package libhtml-tree-perl.
210s Preparing to unpack .../140-libhtml-tree-perl_5.07-3_all.deb ...
210s Unpacking libhtml-tree-perl (5.07-3) ...
210s Selecting previously unselected package libclone-perl:armhf.
210s Preparing to unpack .../141-libclone-perl_0.47-1_armhf.deb ...
210s Unpacking libclone-perl:armhf (0.47-1) ...
210s Selecting previously unselected package libio-html-perl.
210s Preparing to unpack .../142-libio-html-perl_1.004-3_all.deb ...
210s Unpacking libio-html-perl (1.004-3) ...
210s Selecting previously unselected package liblwp-mediatypes-perl.
210s Preparing to unpack .../143-liblwp-mediatypes-perl_6.04-2_all.deb ...
210s Unpacking liblwp-mediatypes-perl (6.04-2) ...
210s Selecting previously unselected package libhttp-message-perl.
210s Preparing to unpack .../144-libhttp-message-perl_7.01-1ubuntu1_all.deb ...
210s Unpacking libhttp-message-perl (7.01-1ubuntu1) ...
210s Selecting previously unselected package libhttp-cookies-perl.
210s Preparing to unpack .../145-libhttp-cookies-perl_6.11-1_all.deb ...
210s Unpacking libhttp-cookies-perl (6.11-1) ...
210s Selecting previously unselected package libhttp-negotiate-perl.
210s Preparing to unpack .../146-libhttp-negotiate-perl_6.01-2_all.deb ...
210s Unpacking libhttp-negotiate-perl (6.01-2) ...
210s Selecting previously unselected package perl-openssl-defaults:armhf.
210s Preparing to unpack .../147-perl-openssl-defaults_7build4_armhf.deb ...
210s Unpacking perl-openssl-defaults:armhf (7build4) ...
210s Selecting previously unselected package libnet-ssleay-perl:armhf.
211s Preparing to unpack .../148-libnet-ssleay-perl_1.94-3_armhf.deb ...
211s Unpacking libnet-ssleay-perl:armhf (1.94-3) ...
211s Selecting previously unselected package libio-socket-ssl-perl.
211s Preparing to unpack .../149-libio-socket-ssl-perl_2.098-1_all.deb ...
211s Unpacking libio-socket-ssl-perl (2.098-1) ...
211s Selecting previously unselected package libnet-http-perl.
211s Preparing to unpack .../150-libnet-http-perl_6.24-1build1_all.deb ...
211s Unpacking libnet-http-perl (6.24-1build1) ...
211s Selecting previously unselected package liblwp-protocol-https-perl.
211s Preparing to unpack .../151-liblwp-protocol-https-perl_6.14-1_all.deb ...
211s Unpacking liblwp-protocol-https-perl (6.14-1) ...
211s Selecting previously unselected package libwww-robotrules-perl.
211s Preparing to unpack .../152-libwww-robotrules-perl_6.02-1build1_all.deb ...
211s Unpacking libwww-robotrules-perl (6.02-1build1) ...
211s Selecting previously unselected package libwww-perl.
211s Preparing to unpack .../153-libwww-perl_6.81-1build1_all.deb ...
211s Unpacking libwww-perl (6.81-1build1) ...
211s Selecting previously unselected package liberror-perl.
211s Preparing to unpack .../154-liberror-perl_0.17030-1_all.deb ...
211s Unpacking liberror-perl (0.17030-1) ...
211s Selecting previously unselected package libparse-debcontrol-perl.
211s Preparing to unpack .../155-libparse-debcontrol-perl_2.005-6_all.deb ...
211s Unpacking libparse-debcontrol-perl (2.005-6) ...
211s Selecting previously unselected package libsoftware-copyright-perl.
211s Preparing to unpack .../156-libsoftware-copyright-perl_0.015-1_all.deb ...
211s Unpacking libsoftware-copyright-perl (0.015-1) ...
211s Selecting previously unselected package libalgorithm-c3-perl.
211s Preparing to unpack .../157-libalgorithm-c3-perl_0.11-2_all.deb ...
211s Unpacking libalgorithm-c3-perl (0.11-2) ...
211s Selecting previously unselected package libclass-c3-perl.
211s Preparing to unpack .../158-libclass-c3-perl_0.35-2_all.deb ...
211s Unpacking libclass-c3-perl (0.35-2) ...
211s Selecting previously unselected package libmro-compat-perl.
211s Preparing to unpack .../159-libmro-compat-perl_0.15-2_all.deb ...
211s Unpacking libmro-compat-perl (0.15-2) ...
211s Selecting previously unselected package libdata-section-perl.
211s Preparing to unpack .../160-libdata-section-perl_0.200008-1_all.deb ...
211s Unpacking libdata-section-perl (0.200008-1) ...
211s Selecting previously unselected package libtext-template-perl.
211s Preparing to unpack .../161-libtext-template-perl_1.61-1_all.deb ...
211s Unpacking libtext-template-perl (1.61-1) ...
211s Selecting previously unselected package libsoftware-license-perl.
211s Preparing to unpack .../162-libsoftware-license-perl_0.104007-1_all.deb ...
211s Unpacking libsoftware-license-perl (0.104007-1) ...
211s Selecting previously unselected package libsoftware-licensemoreutils-perl.
211s Preparing to unpack .../163-libsoftware-licensemoreutils-perl_1.009-1_all.deb ...
211s Unpacking libsoftware-licensemoreutils-perl (1.009-1) ...
211s Selecting previously unselected package libsort-versions-perl.
211s Preparing to unpack .../164-libsort-versions-perl_1.62-3_all.deb ...
211s Unpacking libsort-versions-perl (1.62-3) ...
211s Selecting previously unselected package libtext-reform-perl.
211s Preparing to unpack .../165-libtext-reform-perl_1.20-5_all.deb ...
211s Unpacking libtext-reform-perl (1.20-5) ...
211s Selecting previously unselected package libtext-autoformat-perl.
211s Preparing to unpack .../166-libtext-autoformat-perl_1.750000-2_all.deb ...
211s Unpacking libtext-autoformat-perl (1.750000-2) ...
212s Selecting previously unselected package libtext-levenshtein-damerau-perl.
212s Preparing to unpack .../167-libtext-levenshtein-damerau-perl_0.41-3_all.deb ...
212s Unpacking libtext-levenshtein-damerau-perl (0.41-3) ...
212s Selecting previously unselected package libtoml-tiny-perl.
212s Preparing to unpack .../168-libtoml-tiny-perl_0.20-1_all.deb ...
212s Unpacking libtoml-tiny-perl (0.20-1) ...
212s Selecting previously unselected package libclass-inspector-perl.
212s Preparing to unpack .../169-libclass-inspector-perl_1.36-3_all.deb ...
212s Unpacking libclass-inspector-perl (1.36-3) ...
212s Selecting previously unselected package libfile-sharedir-perl.
212s Preparing to unpack .../170-libfile-sharedir-perl_1.118-3_all.deb ...
212s Unpacking libfile-sharedir-perl (1.118-3) ...
212s Selecting previously unselected package libindirect-perl.
212s Preparing to unpack .../171-libindirect-perl_0.39-2build5_armhf.deb ...
212s Unpacking libindirect-perl (0.39-2build5) ...
212s Selecting previously unselected package libxs-parse-keyword-perl.
212s Preparing to unpack .../172-libxs-parse-keyword-perl_0.49-1_armhf.deb ...
212s Unpacking libxs-parse-keyword-perl (0.49-1) ...
212s Selecting previously unselected package libxs-parse-sublike-perl:armhf.
212s Preparing to unpack .../173-libxs-parse-sublike-perl_0.41-1_armhf.deb ...
212s Unpacking libxs-parse-sublike-perl:armhf (0.41-1) ...
212s Selecting previously unselected package libobject-pad-perl.
212s Preparing to unpack .../174-libobject-pad-perl_0.823-2_armhf.deb ...
212s Unpacking libobject-pad-perl (0.823-2) ...
212s Selecting previously unselected package libsyntax-keyword-try-perl.
212s Preparing to unpack .../175-libsyntax-keyword-try-perl_0.31-1_armhf.deb ...
212s Unpacking libsyntax-keyword-try-perl (0.31-1) ...
212s Selecting previously unselected package libio-interactive-perl.
212s Preparing to unpack .../176-libio-interactive-perl_1.027-1_all.deb ...
212s Unpacking libio-interactive-perl (1.027-1) ...
212s Selecting previously unselected package liblog-any-perl.
212s Preparing to unpack .../177-liblog-any-perl_1.718-1build1_all.deb ...
212s Unpacking liblog-any-perl (1.718-1build1) ...
212s Selecting previously unselected package liblog-any-adapter-screen-perl.
212s Preparing to unpack .../178-liblog-any-adapter-screen-perl_0.141-1_all.deb ...
212s Unpacking liblog-any-adapter-screen-perl (0.141-1) ...
212s Selecting previously unselected package libsub-exporter-progressive-perl.
212s Preparing to unpack .../179-libsub-exporter-progressive-perl_0.001013-3_all.deb ...
212s Unpacking libsub-exporter-progressive-perl (0.001013-3) ...
212s Selecting previously unselected package libvariable-magic-perl.
212s Preparing to unpack .../180-libvariable-magic-perl_0.64-1build1_armhf.deb ...
212s Unpacking libvariable-magic-perl (0.64-1build1) ...
212s Selecting previously unselected package libb-hooks-endofscope-perl.
212s Preparing to unpack .../181-libb-hooks-endofscope-perl_0.28-2_all.deb ...
212s Unpacking libb-hooks-endofscope-perl (0.28-2) ...
212s Selecting previously unselected package libsub-identify-perl.
212s Preparing to unpack .../182-libsub-identify-perl_0.14-4_armhf.deb ...
212s Unpacking libsub-identify-perl (0.14-4) ...
212s Selecting previously unselected package libsub-name-perl:armhf.
212s Preparing to unpack .../183-libsub-name-perl_0.28-1_armhf.deb ...
212s Unpacking libsub-name-perl:armhf (0.28-1) ...
212s Selecting previously unselected package libnamespace-clean-perl.
212s Preparing to unpack .../184-libnamespace-clean-perl_0.27-2_all.deb ...
212s Unpacking libnamespace-clean-perl (0.27-2) ...
212s Selecting previously unselected package libnumber-compare-perl.
212s Preparing to unpack .../185-libnumber-compare-perl_0.03-3_all.deb ...
212s Unpacking libnumber-compare-perl (0.03-3) ...
212s Selecting previously unselected package libtext-glob-perl.
212s Preparing to unpack .../186-libtext-glob-perl_0.11-3_all.deb ...
212s Unpacking libtext-glob-perl (0.11-3) ...
213s Selecting previously unselected package libpath-iterator-rule-perl.
213s Preparing to unpack .../187-libpath-iterator-rule-perl_1.015-2_all.deb ...
213s Unpacking libpath-iterator-rule-perl (1.015-2) ...
213s Selecting previously unselected package libpod-parser-perl.
213s Preparing to unpack .../188-libpod-parser-perl_1.67-1_all.deb ...
213s Adding 'diversion of /usr/bin/podselect to /usr/bin/podselect.bundled by libpod-parser-perl'
213s Adding 'diversion of /usr/share/man/man1/podselect.1.gz to /usr/share/man/man1/podselect.bundled.1.gz by libpod-parser-perl'
213s Unpacking libpod-parser-perl (1.67-1) ...
213s Selecting previously unselected package libpod-constants-perl.
213s Preparing to unpack .../189-libpod-constants-perl_0.19-2_all.deb ...
213s Unpacking libpod-constants-perl (0.19-2) ...
213s Selecting previously unselected package libset-intspan-perl.
213s Preparing to unpack .../190-libset-intspan-perl_1.19-3_all.deb ...
213s Unpacking libset-intspan-perl (1.19-3) ...
213s Selecting previously unselected package libstring-copyright-perl.
213s Preparing to unpack .../191-libstring-copyright-perl_0.003014-1_all.deb ...
213s Unpacking libstring-copyright-perl (0.003014-1) ...
213s Selecting previously unselected package libstring-escape-perl.
213s Preparing to unpack .../192-libstring-escape-perl_2010.002-3_all.deb ...
213s Unpacking libstring-escape-perl (2010.002-3) ...
213s Selecting previously unselected package libregexp-pattern-license-perl.
213s Preparing to unpack .../193-libregexp-pattern-license-perl_3.11.2-1_all.deb ...
213s Unpacking libregexp-pattern-license-perl (3.11.2-1) ...
213s Selecting previously unselected package libregexp-pattern-perl.
213s Preparing to unpack .../194-libregexp-pattern-perl_0.2.14-2_all.deb ...
213s Unpacking libregexp-pattern-perl (0.2.14-2) ...
213s Selecting previously unselected package libstring-license-perl.
213s Preparing to unpack .../195-libstring-license-perl_0.0.11-1ubuntu1_all.deb ...
213s Unpacking libstring-license-perl (0.0.11-1ubuntu1) ...
213s Selecting previously unselected package licensecheck.
213s Preparing to unpack .../196-licensecheck_3.3.9-1ubuntu2_all.deb ...
213s Unpacking licensecheck (3.3.9-1ubuntu2) ...
213s Selecting previously unselected package diffstat.
214s Preparing to unpack .../197-diffstat_1.68-1_armhf.deb ...
214s Unpacking diffstat (1.68-1) ...
214s Selecting previously unselected package libberkeleydb-perl:armhf.
214s Preparing to unpack .../198-libberkeleydb-perl_0.66-2_armhf.deb ...
214s Unpacking libberkeleydb-perl:armhf (0.66-2) ...
214s Selecting previously unselected package libclass-xsaccessor-perl.
214s Preparing to unpack .../199-libclass-xsaccessor-perl_1.19-4build6_armhf.deb ...
214s Unpacking libclass-xsaccessor-perl (1.19-4build6) ...
214s Selecting previously unselected package libconfig-tiny-perl.
214s Preparing to unpack .../200-libconfig-tiny-perl_2.30-1_all.deb ...
214s Unpacking libconfig-tiny-perl (2.30-1) ...
214s Selecting previously unselected package libconst-fast-perl.
214s Preparing to unpack .../201-libconst-fast-perl_0.014-2_all.deb ...
214s Unpacking libconst-fast-perl (0.014-2) ...
214s Selecting previously unselected package libcpanel-json-xs-perl:armhf.
214s Preparing to unpack .../202-libcpanel-json-xs-perl_4.40-1_armhf.deb ...
214s Unpacking libcpanel-json-xs-perl:armhf (4.40-1) ...
214s Selecting previously unselected package libaliased-perl.
214s Preparing to unpack .../203-libaliased-perl_0.34-3_all.deb ...
214s Unpacking libaliased-perl (0.34-3) ...
214s Selecting previously unselected package libclass-data-inheritable-perl.
214s Preparing to unpack .../204-libclass-data-inheritable-perl_0.10-1_all.deb ...
214s Unpacking libclass-data-inheritable-perl (0.10-1) ...
214s Selecting previously unselected package libdevel-stacktrace-perl.
214s Preparing to unpack .../205-libdevel-stacktrace-perl_2.0500-1_all.deb ...
214s Unpacking libdevel-stacktrace-perl (2.0500-1) ...
214s Selecting previously unselected package libexception-class-perl.
214s Preparing to unpack .../206-libexception-class-perl_1.45-1_all.deb ...
214s Unpacking libexception-class-perl (1.45-1) ...
214s Selecting previously unselected package libiterator-perl.
214s Preparing to unpack .../207-libiterator-perl_0.03+ds1-2_all.deb ...
214s Unpacking libiterator-perl (0.03+ds1-2) ...
214s Selecting previously unselected package libiterator-util-perl.
214s Preparing to unpack .../208-libiterator-util-perl_0.02+ds1-2_all.deb ...
214s Unpacking libiterator-util-perl (0.02+ds1-2) ...
214s Selecting previously unselected package libdata-dpath-perl.
214s Preparing to unpack .../209-libdata-dpath-perl_0.60-1_all.deb ...
214s Unpacking libdata-dpath-perl (0.60-1) ...
214s Selecting previously unselected package libnet-domain-tld-perl.
214s Preparing to unpack .../210-libnet-domain-tld-perl_1.75-4_all.deb ...
214s Unpacking libnet-domain-tld-perl (1.75-4) ...
214s Selecting previously unselected package libdata-validate-domain-perl.
214s Preparing to unpack .../211-libdata-validate-domain-perl_0.15-1_all.deb ...
214s Unpacking libdata-validate-domain-perl (0.15-1) ...
214s Selecting previously unselected package libnet-ipv6addr-perl.
214s Preparing to unpack .../212-libnet-ipv6addr-perl_1.02-1_all.deb ...
214s Unpacking libnet-ipv6addr-perl (1.02-1) ...
214s Selecting previously unselected package libnet-netmask-perl.
214s Preparing to unpack .../213-libnet-netmask-perl_2.0003-1build1_all.deb ...
214s Unpacking libnet-netmask-perl (2.0003-1build1) ...
214s Selecting previously unselected package libnetaddr-ip-perl.
214s Preparing to unpack .../214-libnetaddr-ip-perl_4.079+dfsg-2build5_armhf.deb ...
214s Unpacking libnetaddr-ip-perl (4.079+dfsg-2build5) ...
214s Selecting previously unselected package libdata-validate-ip-perl.
214s Preparing to unpack .../215-libdata-validate-ip-perl_0.31-1_all.deb ...
214s Unpacking libdata-validate-ip-perl (0.31-1) ...
215s Selecting previously unselected package libdata-validate-uri-perl.
215s Preparing to unpack .../216-libdata-validate-uri-perl_0.07-3_all.deb ...
215s Unpacking libdata-validate-uri-perl (0.07-3) ...
215s Selecting previously unselected package libdevel-size-perl.
215s Preparing to unpack .../217-libdevel-size-perl_0.85-1_armhf.deb ...
215s Unpacking libdevel-size-perl (0.85-1) ...
215s Selecting previously unselected package libemail-address-xs-perl.
215s Preparing to unpack .../218-libemail-address-xs-perl_1.05-1build5_armhf.deb ...
215s Unpacking libemail-address-xs-perl (1.05-1build5) ...
215s Selecting previously unselected package libipc-system-simple-perl.
215s Preparing to unpack .../219-libipc-system-simple-perl_1.30-2_all.deb ...
215s Unpacking libipc-system-simple-perl (1.30-2) ...
215s Selecting previously unselected package libfile-basedir-perl.
215s Preparing to unpack .../220-libfile-basedir-perl_0.09-2_all.deb ...
215s Unpacking libfile-basedir-perl (0.09-2) ...
215s Selecting previously unselected package libfile-find-rule-perl.
215s Preparing to unpack .../221-libfile-find-rule-perl_0.35-1build1_all.deb ...
215s Unpacking libfile-find-rule-perl (0.35-1build1) ...
215s Selecting previously unselected package libio-string-perl.
215s Preparing to unpack .../222-libio-string-perl_1.08-4_all.deb ...
215s Unpacking libio-string-perl (1.08-4) ...
215s Selecting previously unselected package libfont-ttf-perl.
215s Preparing to unpack .../223-libfont-ttf-perl_1.06-2_all.deb ...
215s Unpacking libfont-ttf-perl (1.06-2) ...
215s Selecting previously unselected package libhtml-html5-entities-perl.
215s Preparing to unpack .../224-libhtml-html5-entities-perl_0.004-3_all.deb ...
215s Unpacking libhtml-html5-entities-perl (0.004-3) ...
215s Selecting previously unselected package libhtml-tokeparser-simple-perl.
215s Preparing to unpack .../225-libhtml-tokeparser-simple-perl_3.16-4_all.deb ...
215s Unpacking libhtml-tokeparser-simple-perl (3.16-4) ...
215s Selecting previously unselected package libipc-run3-perl.
215s Preparing to unpack .../226-libipc-run3-perl_0.049-1_all.deb ...
215s Unpacking libipc-run3-perl (0.049-1) ...
215s Selecting previously unselected package libjson-maybexs-perl.
215s Preparing to unpack .../227-libjson-maybexs-perl_1.004008-1_all.deb ...
215s Unpacking libjson-maybexs-perl (1.004008-1) ...
215s Selecting previously unselected package liblist-compare-perl.
215s Preparing to unpack .../228-liblist-compare-perl_0.55-2_all.deb ...
215s Unpacking liblist-compare-perl (0.55-2) ...
215s Selecting previously unselected package liblist-someutils-perl.
215s Preparing to unpack .../229-liblist-someutils-perl_0.59-1_all.deb ...
215s Unpacking liblist-someutils-perl (0.59-1) ...
215s Selecting previously unselected package liblist-utilsby-perl.
215s Preparing to unpack .../230-liblist-utilsby-perl_0.12-2_all.deb ...
215s Unpacking liblist-utilsby-perl (0.12-2) ...
215s Selecting previously unselected package libmldbm-perl.
215s Preparing to unpack .../231-libmldbm-perl_2.05-4_all.deb ...
215s Unpacking libmldbm-perl (2.05-4) ...
215s Selecting previously unselected package libclass-method-modifiers-perl.
215s Preparing to unpack .../232-libclass-method-modifiers-perl_2.15-1_all.deb ...
215s Unpacking libclass-method-modifiers-perl (2.15-1) ...
215s Selecting previously unselected package libimport-into-perl.
215s Preparing to unpack .../233-libimport-into-perl_1.002005-2_all.deb ...
215s Unpacking libimport-into-perl (1.002005-2) ...
215s Selecting previously unselected package librole-tiny-perl.
216s Preparing to unpack .../234-librole-tiny-perl_2.002004-1_all.deb ...
216s Unpacking librole-tiny-perl (2.002004-1) ...
216s Selecting previously unselected package libsub-quote-perl.
216s Preparing to unpack .../235-libsub-quote-perl_2.006009-1ubuntu1_all.deb ...
216s Unpacking libsub-quote-perl (2.006009-1ubuntu1) ...
216s Selecting previously unselected package libmoo-perl.
216s Preparing to unpack .../236-libmoo-perl_2.005005-1_all.deb ...
216s Unpacking libmoo-perl (2.005005-1) ...
216s Selecting previously unselected package libstrictures-perl.
216s Preparing to unpack .../237-libstrictures-perl_2.000006-1build1_all.deb ...
216s Unpacking libstrictures-perl (2.000006-1build1) ...
216s Selecting previously unselected package libmoox-aliases-perl.
216s Preparing to unpack .../238-libmoox-aliases-perl_0.001006-2_all.deb ...
216s Unpacking libmoox-aliases-perl (0.001006-2) ...
216s Selecting previously unselected package libperlio-gzip-perl.
216s Preparing to unpack .../239-libperlio-gzip-perl_0.20-1build5_armhf.deb ...
216s Unpacking libperlio-gzip-perl (0.20-1build5) ...
216s Selecting previously unselected package libperlio-utf8-strict-perl.
216s Preparing to unpack .../240-libperlio-utf8-strict-perl_0.010-1build4_armhf.deb ...
216s Unpacking libperlio-utf8-strict-perl (0.010-1build4) ...
216s Selecting previously unselected package libproc-processtable-perl:armhf.
216s Preparing to unpack .../241-libproc-processtable-perl_0.637-1_armhf.deb ...
216s Unpacking libproc-processtable-perl:armhf (0.637-1) ...
216s Selecting previously unselected package libregexp-wildcards-perl.
216s Preparing to unpack .../242-libregexp-wildcards-perl_1.05-3_all.deb ...
216s Unpacking libregexp-wildcards-perl (1.05-3) ...
216s Selecting previously unselected package libsereal-decoder-perl.
216s Preparing to unpack .../243-libsereal-decoder-perl_5.004+ds-1build5_armhf.deb ...
216s Unpacking libsereal-decoder-perl (5.004+ds-1build5) ...
216s Selecting previously unselected package libsereal-encoder-perl.
216s Preparing to unpack .../244-libsereal-encoder-perl_5.004+ds-1build4_armhf.deb ...
216s Unpacking libsereal-encoder-perl (5.004+ds-1build4) ...
216s Selecting previously unselected package libterm-readkey-perl.
216s Preparing to unpack .../245-libterm-readkey-perl_2.38-2build5_armhf.deb ...
216s Unpacking libterm-readkey-perl (2.38-2build5) ...
216s Selecting previously unselected package libtext-levenshteinxs-perl.
216s Preparing to unpack .../246-libtext-levenshteinxs-perl_0.03-5build5_armhf.deb ...
216s Unpacking libtext-levenshteinxs-perl (0.03-5build5) ...
216s Selecting previously unselected package libmarkdown2:armhf.
216s Preparing to unpack .../247-libmarkdown2_2.2.7-2.1build1_armhf.deb ...
216s Unpacking libmarkdown2:armhf (2.2.7-2.1build1) ...
216s Selecting previously unselected package libtext-markdown-discount-perl.
216s Preparing to unpack .../248-libtext-markdown-discount-perl_0.18-1_armhf.deb ...
216s Unpacking libtext-markdown-discount-perl (0.18-1) ...
216s Selecting previously unselected package libdata-messagepack-perl.
216s Preparing to unpack .../249-libdata-messagepack-perl_1.02-3_armhf.deb ...
216s Unpacking libdata-messagepack-perl (1.02-3) ...
216s Selecting previously unselected package libtext-xslate-perl:armhf.
216s Preparing to unpack .../250-libtext-xslate-perl_3.5.9-2build1_armhf.deb ...
216s Unpacking libtext-xslate-perl:armhf (3.5.9-2build1) ...
216s Selecting previously unselected package libtime-duration-perl.
216s Preparing to unpack .../251-libtime-duration-perl_1.21-2_all.deb ...
216s Unpacking libtime-duration-perl (1.21-2) ...
216s Selecting previously unselected package libtime-moment-perl.
216s Preparing to unpack .../252-libtime-moment-perl_0.46-1_armhf.deb ...
216s Unpacking libtime-moment-perl (0.46-1) ...
216s Selecting previously unselected package libunicode-utf8-perl.
217s Preparing to unpack .../253-libunicode-utf8-perl_0.63-1_armhf.deb ...
217s Unpacking libunicode-utf8-perl (0.63-1) ...
217s Selecting previously unselected package libcgi-pm-perl.
217s Preparing to unpack .../254-libcgi-pm-perl_4.71-1build1_all.deb ...
217s Unpacking libcgi-pm-perl (4.71-1build1) ...
217s Selecting previously unselected package libhtml-form-perl.
217s Preparing to unpack .../255-libhtml-form-perl_6.13-1build1_all.deb ...
217s Unpacking libhtml-form-perl (6.13-1build1) ...
217s Selecting previously unselected package libwww-mechanize-perl.
217s Preparing to unpack .../256-libwww-mechanize-perl_2.20-1ubuntu1_all.deb ...
217s Unpacking libwww-mechanize-perl (2.20-1ubuntu1) ...
217s Selecting previously unselected package libxml-namespacesupport-perl.
217s Preparing to unpack .../257-libxml-namespacesupport-perl_1.12-2_all.deb ...
217s Unpacking libxml-namespacesupport-perl (1.12-2) ...
217s Selecting previously unselected package libxml-sax-base-perl.
217s Preparing to unpack .../258-libxml-sax-base-perl_1.09-3_all.deb ...
217s Unpacking libxml-sax-base-perl (1.09-3) ...
217s Selecting previously unselected package libxml-sax-perl.
217s Preparing to unpack .../259-libxml-sax-perl_1.02+dfsg-4_all.deb ...
217s Unpacking libxml-sax-perl (1.02+dfsg-4) ...
217s Selecting previously unselected package libxml-libxml-perl.
217s Preparing to unpack .../260-libxml-libxml-perl_2.0207+dfsg+really+2.0207-0ubuntu7_armhf.deb ...
217s Unpacking libxml-libxml-perl (2.0207+dfsg+really+2.0207-0ubuntu7) ...
217s Selecting previously unselected package lzip.
217s Preparing to unpack .../261-lzip_1.25-4_armhf.deb ...
217s Unpacking lzip (1.25-4) ...
217s Selecting previously unselected package lzop.
217s Preparing to unpack .../262-lzop_1.04-2build4_armhf.deb ...
217s Unpacking lzop (1.04-2build4) ...
217s Selecting previously unselected package patchutils.
217s Preparing to unpack .../263-patchutils_0.4.3-1_armhf.deb ...
217s Unpacking patchutils (0.4.3-1) ...
217s Selecting previously unselected package t1utils.
217s Preparing to unpack .../264-t1utils_1.41-4build4_armhf.deb ...
217s Unpacking t1utils (1.41-4build4) ...
217s Selecting previously unselected package unzip.
217s Preparing to unpack .../265-unzip_6.0-29ubuntu1_armhf.deb ...
217s Unpacking unzip (6.0-29ubuntu1) ...
217s Selecting previously unselected package lintian.
217s Preparing to unpack .../266-lintian_2.127.0ubuntu1_all.deb ...
217s Unpacking lintian (2.127.0ubuntu1) ...
218s Selecting previously unselected package libconfig-model-dpkg-perl.
218s Preparing to unpack .../267-libconfig-model-dpkg-perl_3.016_all.deb ...
218s Unpacking libconfig-model-dpkg-perl (3.016) ...
218s Selecting previously unselected package libconvert-binhex-perl.
218s Preparing to unpack .../268-libconvert-binhex-perl_1.125-3_all.deb ...
218s Unpacking libconvert-binhex-perl (1.125-3) ...
218s Selecting previously unselected package libnet-smtp-ssl-perl.
218s Preparing to unpack .../269-libnet-smtp-ssl-perl_1.04-2_all.deb ...
218s Unpacking libnet-smtp-ssl-perl (1.04-2) ...
218s Selecting previously unselected package libmailtools-perl.
218s Preparing to unpack .../270-libmailtools-perl_2.22-1_all.deb ...
218s Unpacking libmailtools-perl (2.22-1) ...
218s Selecting previously unselected package libmime-tools-perl.
218s Preparing to unpack .../271-libmime-tools-perl_5.515-1_all.deb ...
218s Unpacking libmime-tools-perl (5.515-1) ...
218s Selecting previously unselected package libb-keywords-perl.
218s Preparing to unpack .../272-libb-keywords-perl_1.29-1_all.deb ...
218s Unpacking libb-keywords-perl (1.29-1) ...
218s Selecting previously unselected package libclass-tiny-perl.
218s Preparing to unpack .../273-libclass-tiny-perl_1.008-2_all.deb ...
218s Unpacking libclass-tiny-perl (1.008-2) ...
218s Selecting previously unselected package liblingua-en-inflect-perl.
218s Preparing to unpack .../274-liblingua-en-inflect-perl_1.905-2_all.deb ...
218s Unpacking liblingua-en-inflect-perl (1.905-2) ...
218s Selecting previously unselected package libpod-spell-perl.
218s Preparing to unpack .../275-libpod-spell-perl_1.27-1_all.deb ...
218s Unpacking libpod-spell-perl (1.27-1) ...
218s Selecting previously unselected package libsafe-isa-perl.
218s Preparing to unpack .../276-libsafe-isa-perl_1.000010-1build1_all.deb ...
218s Unpacking libsafe-isa-perl (1.000010-1build1) ...
219s Selecting previously unselected package libtask-weaken-perl.
219s Preparing to unpack .../277-libtask-weaken-perl_1.06-2_all.deb ...
219s Unpacking libtask-weaken-perl (1.06-2) ...
219s Selecting previously unselected package libppi-perl.
219s Preparing to unpack .../278-libppi-perl_1.284-1_all.deb ...
219s Unpacking libppi-perl (1.284-1) ...
219s Selecting previously unselected package libreadonly-perl.
219s Preparing to unpack .../279-libreadonly-perl_2.050-3_all.deb ...
219s Unpacking libreadonly-perl (2.050-3) ...
219s Selecting previously unselected package libppix-quotelike-perl.
219s Preparing to unpack .../280-libppix-quotelike-perl_0.023-1_all.deb ...
219s Unpacking libppix-quotelike-perl (0.023-1) ...
219s Selecting previously unselected package libppix-regexp-perl.
219s Preparing to unpack .../281-libppix-regexp-perl_0.091-1_all.deb ...
219s Unpacking libppix-regexp-perl (0.091-1) ...
219s Selecting previously unselected package libppix-utils-perl.
219s Preparing to unpack .../282-libppix-utils-perl_0.003-2_all.deb ...
219s Unpacking libppix-utils-perl (0.003-2) ...
219s Selecting previously unselected package libstring-format-perl.
219s Preparing to unpack .../283-libstring-format-perl_1.18-1build1_all.deb ...
219s Unpacking libstring-format-perl (1.18-1build1) ...
219s Selecting previously unselected package perltidy.
219s Preparing to unpack .../284-perltidy_20250105-1build1_all.deb ...
219s Unpacking perltidy (20250105-1build1) ...
219s Selecting previously unselected package libperl-critic-perl.
219s Preparing to unpack .../285-libperl-critic-perl_1.156-1_all.deb ...
219s Unpacking libperl-critic-perl (1.156-1) ...
219s Selecting previously unselected package libtext-wrapper-perl.
219s Preparing to unpack .../286-libtext-wrapper-perl_1.05-4_all.deb ...
219s Unpacking libtext-wrapper-perl (1.05-4) ...
219s Selecting previously unselected package libsuitesparseconfig7:armhf.
219s Preparing to unpack .../287-libsuitesparseconfig7_1%3a7.12.1+dfsg-1_armhf.deb ...
219s Unpacking libsuitesparseconfig7:armhf (1:7.12.1+dfsg-1) ...
219s Selecting previously unselected package libamd3:armhf.
219s Preparing to unpack .../288-libamd3_1%3a7.12.1+dfsg-1_armhf.deb ...
219s Unpacking libamd3:armhf (1:7.12.1+dfsg-1) ...
219s Selecting previously unselected package libblas3:armhf.
219s Preparing to unpack .../289-libblas3_3.12.1-7ubuntu1_armhf.deb ...
219s Unpacking libblas3:armhf (3.12.1-7ubuntu1) ...
219s Selecting previously unselected package libgfortran5:armhf.
219s Preparing to unpack .../290-libgfortran5_15.2.0-12ubuntu1_armhf.deb ...
219s Unpacking libgfortran5:armhf (15.2.0-12ubuntu1) ...
219s Selecting previously unselected package liblapack3:armhf.
219s Preparing to unpack .../291-liblapack3_3.12.1-7ubuntu1_armhf.deb ...
219s Unpacking liblapack3:armhf (3.12.1-7ubuntu1) ...
220s Selecting previously unselected package libarpack2t64:armhf.
220s Preparing to unpack .../292-libarpack2t64_3.9.1-6_armhf.deb ...
220s Unpacking libarpack2t64:armhf (3.9.1-6) ...
220s Selecting previously unselected package libccolamd3:armhf.
220s Preparing to unpack .../293-libccolamd3_1%3a7.12.1+dfsg-1_armhf.deb ...
220s Unpacking libccolamd3:armhf (1:7.12.1+dfsg-1) ...
220s Selecting previously unselected package libcamd3:armhf.
220s Preparing to unpack .../294-libcamd3_1%3a7.12.1+dfsg-1_armhf.deb ...
220s Unpacking libcamd3:armhf (1:7.12.1+dfsg-1) ...
220s Selecting previously unselected package libcolamd3:armhf.
220s Preparing to unpack .../295-libcolamd3_1%3a7.12.1+dfsg-1_armhf.deb ...
220s Unpacking libcolamd3:armhf (1:7.12.1+dfsg-1) ...
220s Selecting previously unselected package libcholmod5:armhf.
220s Preparing to unpack .../296-libcholmod5_1%3a7.12.1+dfsg-1_armhf.deb ...
220s Unpacking libcholmod5:armhf (1:7.12.1+dfsg-1) ...
220s Selecting previously unselected package libcxsparse4:armhf.
220s Preparing to unpack .../297-libcxsparse4_1%3a7.12.1+dfsg-1_armhf.deb ...
220s Unpacking libcxsparse4:armhf (1:7.12.1+dfsg-1) ...
220s Selecting previously unselected package libfftw3-double3:armhf.
220s Preparing to unpack .../298-libfftw3-double3_3.3.10-2fakesync1build2_armhf.deb ...
220s Unpacking libfftw3-double3:armhf (3.3.10-2fakesync1build2) ...
220s Selecting previously unselected package libfftw3-single3:armhf.
220s Preparing to unpack .../299-libfftw3-single3_3.3.10-2fakesync1build2_armhf.deb ...
220s Unpacking libfftw3-single3:armhf (3.3.10-2fakesync1build2) ...
220s Selecting previously unselected package libxfixes3:armhf.
220s Preparing to unpack .../300-libxfixes3_1%3a6.0.0-2build2_armhf.deb ...
220s Unpacking libxfixes3:armhf (1:6.0.0-2build2) ...
220s Selecting previously unselected package libxcursor1:armhf.
220s Preparing to unpack .../301-libxcursor1_1%3a1.2.3-1build1_armhf.deb ...
220s Unpacking libxcursor1:armhf (1:1.2.3-1build1) ...
220s Selecting previously unselected package libxft2:armhf.
220s Preparing to unpack .../302-libxft2_2.3.6-1build2_armhf.deb ...
220s Unpacking libxft2:armhf (2.3.6-1build2) ...
220s Selecting previously unselected package libxinerama1:armhf.
220s Preparing to unpack .../303-libxinerama1_2%3a1.1.4-3build2_armhf.deb ...
220s Unpacking libxinerama1:armhf (2:1.1.4-3build2) ...
220s Selecting previously unselected package libfltk1.3t64:armhf.
220s Preparing to unpack .../304-libfltk1.3t64_1.3.11-3_armhf.deb ...
220s Unpacking libfltk1.3t64:armhf (1.3.11-3) ...
220s Selecting previously unselected package libglvnd0:armhf.
220s Preparing to unpack .../305-libglvnd0_1.7.0-3_armhf.deb ...
220s Unpacking libglvnd0:armhf (1.7.0-3) ...
220s Selecting previously unselected package libllvm21:armhf.
220s Preparing to unpack .../306-libllvm21_1%3a21.1.8-1ubuntu1_armhf.deb ...
220s Unpacking libllvm21:armhf (1:21.1.8-1ubuntu1) ...
222s Selecting previously unselected package libx11-xcb1:armhf.
222s Preparing to unpack .../307-libx11-xcb1_2%3a1.8.12-1build1_armhf.deb ...
222s Unpacking libx11-xcb1:armhf (2:1.8.12-1build1) ...
222s Selecting previously unselected package libxcb-dri3-0:armhf.
222s Preparing to unpack .../308-libxcb-dri3-0_1.17.0-2ubuntu1_armhf.deb ...
222s Unpacking libxcb-dri3-0:armhf (1.17.0-2ubuntu1) ...
222s Selecting previously unselected package libxcb-present0:armhf.
222s Preparing to unpack .../309-libxcb-present0_1.17.0-2ubuntu1_armhf.deb ...
222s Unpacking libxcb-present0:armhf (1.17.0-2ubuntu1) ...
222s Selecting previously unselected package libxcb-randr0:armhf.
222s Preparing to unpack .../310-libxcb-randr0_1.17.0-2ubuntu1_armhf.deb ...
222s Unpacking libxcb-randr0:armhf (1.17.0-2ubuntu1) ...
222s Selecting previously unselected package libxcb-sync1:armhf.
222s Preparing to unpack .../311-libxcb-sync1_1.17.0-2ubuntu1_armhf.deb ...
222s Unpacking libxcb-sync1:armhf (1.17.0-2ubuntu1) ...
222s Selecting previously unselected package libxcb-xfixes0:armhf.
222s Preparing to unpack .../312-libxcb-xfixes0_1.17.0-2ubuntu1_armhf.deb ...
222s Unpacking libxcb-xfixes0:armhf (1.17.0-2ubuntu1) ...
222s Selecting previously unselected package libxshmfence1:armhf.
222s Preparing to unpack .../313-libxshmfence1_1.3.3-1build1_armhf.deb ...
222s Unpacking libxshmfence1:armhf (1.3.3-1build1) ...
222s Selecting previously unselected package mesa-libgallium:armhf.
222s Preparing to unpack .../314-mesa-libgallium_25.3.3-1ubuntu1_armhf.deb ...
222s Unpacking mesa-libgallium:armhf (25.3.3-1ubuntu1) ...
222s Selecting previously unselected package libgbm1:armhf.
222s Preparing to unpack .../315-libgbm1_25.3.3-1ubuntu1_armhf.deb ...
222s Unpacking libgbm1:armhf (25.3.3-1ubuntu1) ...
222s Selecting previously unselected package libvulkan1:armhf.
222s Preparing to unpack .../316-libvulkan1_1.4.335.0-1_armhf.deb ...
222s Unpacking libvulkan1:armhf (1.4.335.0-1) ...
222s Selecting previously unselected package libgl1-mesa-dri:armhf.
223s Preparing to unpack .../317-libgl1-mesa-dri_25.3.3-1ubuntu1_armhf.deb ...
223s Unpacking libgl1-mesa-dri:armhf (25.3.3-1ubuntu1) ...
223s Selecting previously unselected package libxcb-glx0:armhf.
223s Preparing to unpack .../318-libxcb-glx0_1.17.0-2ubuntu1_armhf.deb ...
223s Unpacking libxcb-glx0:armhf (1.17.0-2ubuntu1) ...
223s Selecting previously unselected package libxxf86vm1:armhf.
223s Preparing to unpack .../319-libxxf86vm1_1%3a1.1.4-2_armhf.deb ...
223s Unpacking libxxf86vm1:armhf (1:1.1.4-2) ...
223s Selecting previously unselected package libglx-mesa0:armhf.
223s Preparing to unpack .../320-libglx-mesa0_25.3.3-1ubuntu1_armhf.deb ...
223s Unpacking libglx-mesa0:armhf (25.3.3-1ubuntu1) ...
223s Selecting previously unselected package libglx0:armhf.
223s Preparing to unpack .../321-libglx0_1.7.0-3_armhf.deb ...
223s Unpacking libglx0:armhf (1.7.0-3) ...
223s Selecting previously unselected package libgl1:armhf.
223s Preparing to unpack .../322-libgl1_1.7.0-3_armhf.deb ...
223s Unpacking libgl1:armhf (1.7.0-3) ...
223s Selecting previously unselected package libfltk-gl1.3t64:armhf.
223s Preparing to unpack .../323-libfltk-gl1.3t64_1.3.11-3_armhf.deb ...
223s Unpacking libfltk-gl1.3t64:armhf (1.3.11-3) ...
223s Selecting previously unselected package libgl2ps1.4:armhf.
223s Preparing to unpack .../324-libgl2ps1.4_1.4.2+dfsg1-4_armhf.deb ...
223s Unpacking libgl2ps1.4:armhf (1.4.2+dfsg1-4) ...
223s Selecting previously unselected package libltdl7:armhf.
223s Preparing to unpack .../325-libltdl7_2.5.4-9_armhf.deb ...
223s Unpacking libltdl7:armhf (2.5.4-9) ...
223s Selecting previously unselected package libglpk40:armhf.
223s Preparing to unpack .../326-libglpk40_5.0-2_armhf.deb ...
223s Unpacking libglpk40:armhf (5.0-2) ...
223s Selecting previously unselected package libopengl0:armhf.
223s Preparing to unpack .../327-libopengl0_1.7.0-3_armhf.deb ...
223s Unpacking libopengl0:armhf (1.7.0-3) ...
223s Selecting previously unselected package libglu1-mesa:armhf.
223s Preparing to unpack .../328-libglu1-mesa_9.0.2-1.1build2_armhf.deb ...
223s Unpacking libglu1-mesa:armhf (9.0.2-1.1build2) ...
223s Selecting previously unselected package liblcms2-2:armhf.
223s Preparing to unpack .../329-liblcms2-2_2.17-1_armhf.deb ...
223s Unpacking liblcms2-2:armhf (2.17-1) ...
223s Selecting previously unselected package libjxl0.11:armhf.
223s Preparing to unpack .../330-libjxl0.11_0.11.1-6ubuntu1_armhf.deb ...
223s Unpacking libjxl0.11:armhf (0.11.1-6ubuntu1) ...
223s Selecting previously unselected package libwmflite-0.2-7:armhf.
223s Preparing to unpack .../331-libwmflite-0.2-7_0.2.13-2_armhf.deb ...
223s Unpacking libwmflite-0.2-7:armhf (0.2.13-2) ...
223s Selecting previously unselected package libgraphicsmagick-q16-3t64.
223s Preparing to unpack .../332-libgraphicsmagick-q16-3t64_1.4+really1.3.45+hg17696-1build1_armhf.deb ...
223s Unpacking libgraphicsmagick-q16-3t64 (1.4+really1.3.45+hg17696-1build1) ...
224s Selecting previously unselected package libgraphicsmagick++-q16-12t64.
224s Preparing to unpack .../333-libgraphicsmagick++-q16-12t64_1.4+really1.3.45+hg17696-1build1_armhf.deb ...
224s Unpacking libgraphicsmagick++-q16-12t64 (1.4+really1.3.45+hg17696-1build1) ...
224s Selecting previously unselected package libsz2:armhf.
224s Preparing to unpack .../334-libsz2_1.1.5-1_armhf.deb ...
224s Unpacking libsz2:armhf (1.1.5-1) ...
224s Selecting previously unselected package libhdf5-310:armhf.
224s Preparing to unpack .../335-libhdf5-310_1.14.6+repack-2_armhf.deb ...
224s Unpacking libhdf5-310:armhf (1.14.6+repack-2) ...
224s Selecting previously unselected package libasound2-data.
224s Preparing to unpack .../336-libasound2-data_1.2.15.3-1ubuntu1_all.deb ...
224s Unpacking libasound2-data (1.2.15.3-1ubuntu1) ...
224s Selecting previously unselected package libasound2t64:armhf.
224s Preparing to unpack .../337-libasound2t64_1.2.15.3-1ubuntu1_armhf.deb ...
224s Unpacking libasound2t64:armhf (1.2.15.3-1ubuntu1) ...
224s Selecting previously unselected package libopus0:armhf.
224s Preparing to unpack .../338-libopus0_1.6.1-1_armhf.deb ...
224s Unpacking libopus0:armhf (1.6.1-1) ...
224s Selecting previously unselected package libsamplerate0:armhf.
224s Preparing to unpack .../339-libsamplerate0_0.2.2-4build2_armhf.deb ...
224s Unpacking libsamplerate0:armhf (0.2.2-4build2) ...
224s Selecting previously unselected package libjack-jackd2-0:armhf.
224s Preparing to unpack .../340-libjack-jackd2-0_1.9.22~dfsg-5_armhf.deb ...
224s Unpacking libjack-jackd2-0:armhf (1.9.22~dfsg-5) ...
224s Selecting previously unselected package libasyncns0:armhf.
224s Preparing to unpack .../341-libasyncns0_0.8-7_armhf.deb ...
224s Unpacking libasyncns0:armhf (0.8-7) ...
224s Selecting previously unselected package libogg0:armhf.
224s Preparing to unpack .../342-libogg0_1.3.6-2_armhf.deb ...
224s Unpacking libogg0:armhf (1.3.6-2) ...
224s Selecting previously unselected package libflac14:armhf.
224s Preparing to unpack .../343-libflac14_1.5.0+ds-5_armhf.deb ...
224s Unpacking libflac14:armhf (1.5.0+ds-5) ...
224s Selecting previously unselected package libmp3lame0:armhf.
224s Preparing to unpack .../344-libmp3lame0_3.100-6build2_armhf.deb ...
224s Unpacking libmp3lame0:armhf (3.100-6build2) ...
224s Selecting previously unselected package libmpg123-0t64:armhf.
224s Preparing to unpack .../345-libmpg123-0t64_1.33.3-2_armhf.deb ...
224s Unpacking libmpg123-0t64:armhf (1.33.3-2) ...
224s Selecting previously unselected package libvorbis0a:armhf.
224s Preparing to unpack .../346-libvorbis0a_1.3.7-3build1_armhf.deb ...
224s Unpacking libvorbis0a:armhf (1.3.7-3build1) ...
224s Selecting previously unselected package libvorbisenc2:armhf.
225s Preparing to unpack .../347-libvorbisenc2_1.3.7-3build1_armhf.deb ...
225s Unpacking libvorbisenc2:armhf (1.3.7-3build1) ...
225s Selecting previously unselected package libsndfile1:armhf.
225s Preparing to unpack .../348-libsndfile1_1.2.2-4_armhf.deb ...
225s Unpacking libsndfile1:armhf (1.2.2-4) ...
225s Selecting previously unselected package libpulse0:armhf.
225s Preparing to unpack .../349-libpulse0_1%3a17.0+dfsg1-2ubuntu4_armhf.deb ...
225s Unpacking libpulse0:armhf (1:17.0+dfsg1-2ubuntu4) ...
225s Selecting previously unselected package libportaudio2:armhf.
225s Preparing to unpack .../350-libportaudio2_19.7.0+git20251227.3270c9ae-0ubuntu1_armhf.deb ...
225s Unpacking libportaudio2:armhf (19.7.0+git20251227.3270c9ae-0ubuntu1) ...
225s Selecting previously unselected package libqhull-r8.0:armhf.
225s Preparing to unpack .../351-libqhull-r8.0_2020.2-8_armhf.deb ...
225s Unpacking libqhull-r8.0:armhf (2020.2-8) ...
225s Selecting previously unselected package libqrupdate1:armhf.
225s Preparing to unpack .../352-libqrupdate1_1.1.5-3_armhf.deb ...
225s Unpacking libqrupdate1:armhf (1.1.5-3) ...
225s Selecting previously unselected package libqscintilla2-qt6-l10n.
225s Preparing to unpack .../353-libqscintilla2-qt6-l10n_2.14.1+dfsg-2_all.deb ...
225s Unpacking libqscintilla2-qt6-l10n (2.14.1+dfsg-2) ...
225s Selecting previously unselected package libb2-1:armhf.
225s Preparing to unpack .../354-libb2-1_0.98.1-1.1build2_armhf.deb ...
225s Unpacking libb2-1:armhf (0.98.1-1.1build2) ...
225s Selecting previously unselected package libdouble-conversion3:armhf.
225s Preparing to unpack .../355-libdouble-conversion3_3.4.0-1_armhf.deb ...
225s Unpacking libdouble-conversion3:armhf (3.4.0-1) ...
225s Selecting previously unselected package libpcre2-16-0:armhf.
225s Preparing to unpack .../356-libpcre2-16-0_10.46-1_armhf.deb ...
225s Unpacking libpcre2-16-0:armhf (10.46-1) ...
225s Selecting previously unselected package libqt6core6t64:armhf.
225s Preparing to unpack .../357-libqt6core6t64_6.9.2+dfsg-3ubuntu2_armhf.deb ...
225s Unpacking libqt6core6t64:armhf (6.9.2+dfsg-3ubuntu2) ...
225s Selecting previously unselected package libwayland-client0:armhf.
225s Preparing to unpack .../358-libwayland-client0_1.24.0-2_armhf.deb ...
225s Unpacking libwayland-client0:armhf (1.24.0-2) ...
225s Selecting previously unselected package libegl-mesa0:armhf.
225s Preparing to unpack .../359-libegl-mesa0_25.3.3-1ubuntu1_armhf.deb ...
225s Unpacking libegl-mesa0:armhf (25.3.3-1ubuntu1) ...
225s Selecting previously unselected package libegl1:armhf.
225s Preparing to unpack .../360-libegl1_1.7.0-3_armhf.deb ...
225s Unpacking libegl1:armhf (1.7.0-3) ...
225s Selecting previously unselected package x11-common.
225s Preparing to unpack .../361-x11-common_1%3a7.7+24ubuntu1_all.deb ...
225s Unpacking x11-common (1:7.7+24ubuntu1) ...
225s Selecting previously unselected package libice6:armhf.
225s Preparing to unpack .../362-libice6_2%3a1.1.1-1build1_armhf.deb ...
225s Unpacking libice6:armhf (2:1.1.1-1build1) ...
225s Selecting previously unselected package libmtdev1t64:armhf.
225s Preparing to unpack .../363-libmtdev1t64_1.1.7-1build1_armhf.deb ...
225s Unpacking libmtdev1t64:armhf (1.1.7-1build1) ...
226s Selecting previously unselected package libwacom-common.
226s Preparing to unpack .../364-libwacom-common_2.16.1-1_all.deb ...
226s Unpacking libwacom-common (2.16.1-1) ...
226s Selecting previously unselected package libwacom9:armhf.
226s Preparing to unpack .../365-libwacom9_2.16.1-1_armhf.deb ...
226s Unpacking libwacom9:armhf (2.16.1-1) ...
226s Selecting previously unselected package libinput-bin.
226s Preparing to unpack .../366-libinput-bin_1.30.1-1_armhf.deb ...
226s Unpacking libinput-bin (1.30.1-1) ...
226s Selecting previously unselected package libinput10:armhf.
226s Preparing to unpack .../367-libinput10_1.30.1-1_armhf.deb ...
226s Unpacking libinput10:armhf (1.30.1-1) ...
226s Selecting previously unselected package libmd4c0:armhf.
226s Preparing to unpack .../368-libmd4c0_0.5.2-2build1_armhf.deb ...
226s Unpacking libmd4c0:armhf (0.5.2-2build1) ...
226s Selecting previously unselected package libqt6dbus6:armhf.
226s Preparing to unpack .../369-libqt6dbus6_6.9.2+dfsg-3ubuntu2_armhf.deb ...
226s Unpacking libqt6dbus6:armhf (6.9.2+dfsg-3ubuntu2) ...
226s Selecting previously unselected package libsm6:armhf.
226s Preparing to unpack .../370-libsm6_2%3a1.2.6-1_armhf.deb ...
226s Unpacking libsm6:armhf (2:1.2.6-1) ...
226s Selecting previously unselected package libts0t64:armhf.
226s Preparing to unpack .../371-libts0t64_1.22-1.1build2_armhf.deb ...
226s Unpacking libts0t64:armhf (1.22-1.1build2) ...
226s Selecting previously unselected package libxcb-util1:armhf.
226s Preparing to unpack .../372-libxcb-util1_0.4.1-1build1_armhf.deb ...
226s Unpacking libxcb-util1:armhf (0.4.1-1build1) ...
226s Selecting previously unselected package libxcb-image0:armhf.
226s Preparing to unpack .../373-libxcb-image0_0.4.0-2build2_armhf.deb ...
226s Unpacking libxcb-image0:armhf (0.4.0-2build2) ...
226s Selecting previously unselected package libxcb-render-util0:armhf.
226s Preparing to unpack .../374-libxcb-render-util0_0.3.10-1build1_armhf.deb ...
226s Unpacking libxcb-render-util0:armhf (0.3.10-1build1) ...
226s Selecting previously unselected package libxcb-cursor0:armhf.
226s Preparing to unpack .../375-libxcb-cursor0_0.1.5-1build1_armhf.deb ...
226s Unpacking libxcb-cursor0:armhf (0.1.5-1build1) ...
226s Selecting previously unselected package libxcb-icccm4:armhf.
226s Preparing to unpack .../376-libxcb-icccm4_0.4.2-1build1_armhf.deb ...
226s Unpacking libxcb-icccm4:armhf (0.4.2-1build1) ...
226s Selecting previously unselected package libxcb-keysyms1:armhf.
226s Preparing to unpack .../377-libxcb-keysyms1_0.4.1-1build1_armhf.deb ...
226s Unpacking libxcb-keysyms1:armhf (0.4.1-1build1) ...
227s Selecting previously unselected package libxcb-shape0:armhf.
227s Preparing to unpack .../378-libxcb-shape0_1.17.0-2ubuntu1_armhf.deb ...
227s Unpacking libxcb-shape0:armhf (1.17.0-2ubuntu1) ...
227s Selecting previously unselected package libxcb-xinput0:armhf.
227s Preparing to unpack .../379-libxcb-xinput0_1.17.0-2ubuntu1_armhf.deb ...
227s Unpacking libxcb-xinput0:armhf (1.17.0-2ubuntu1) ...
227s Selecting previously unselected package libxcb-xkb1:armhf.
227s Preparing to unpack .../380-libxcb-xkb1_1.17.0-2ubuntu1_armhf.deb ...
227s Unpacking libxcb-xkb1:armhf (1.17.0-2ubuntu1) ...
227s Selecting previously unselected package libxkbcommon-x11-0:armhf.
227s Preparing to unpack .../381-libxkbcommon-x11-0_1.12.3-1_armhf.deb ...
227s Unpacking libxkbcommon-x11-0:armhf (1.12.3-1) ...
227s Selecting previously unselected package libqt6gui6:armhf.
227s Preparing to unpack .../382-libqt6gui6_6.9.2+dfsg-3ubuntu2_armhf.deb ...
227s Unpacking libqt6gui6:armhf (6.9.2+dfsg-3ubuntu2) ...
227s Selecting previously unselected package libavahi-common-data:armhf.
227s Preparing to unpack .../383-libavahi-common-data_0.8-17ubuntu2_armhf.deb ...
227s Unpacking libavahi-common-data:armhf (0.8-17ubuntu2) ...
227s Selecting previously unselected package libavahi-common3:armhf.
227s Preparing to unpack .../384-libavahi-common3_0.8-17ubuntu2_armhf.deb ...
227s Unpacking libavahi-common3:armhf (0.8-17ubuntu2) ...
227s Selecting previously unselected package libavahi-client3:armhf.
227s Preparing to unpack .../385-libavahi-client3_0.8-17ubuntu2_armhf.deb ...
227s Unpacking libavahi-client3:armhf (0.8-17ubuntu2) ...
227s Selecting previously unselected package libcups2t64:armhf.
227s Preparing to unpack .../386-libcups2t64_2.4.16-1ubuntu1_armhf.deb ...
227s Unpacking libcups2t64:armhf (2.4.16-1ubuntu1) ...
227s Selecting previously unselected package libqt6widgets6:armhf.
227s Preparing to unpack .../387-libqt6widgets6_6.9.2+dfsg-3ubuntu2_armhf.deb ...
227s Unpacking libqt6widgets6:armhf (6.9.2+dfsg-3ubuntu2) ...
227s Selecting previously unselected package libqt6printsupport6:armhf.
227s Preparing to unpack .../388-libqt6printsupport6_6.9.2+dfsg-3ubuntu2_armhf.deb ...
227s Unpacking libqt6printsupport6:armhf (6.9.2+dfsg-3ubuntu2) ...
227s Selecting previously unselected package libqscintilla2-qt6-15:armhf.
227s Preparing to unpack .../389-libqscintilla2-qt6-15_2.14.1+dfsg-2_armhf.deb ...
227s Unpacking libqscintilla2-qt6-15:armhf (2.14.1+dfsg-2) ...
228s Selecting previously unselected package libqt6core5compat6:armhf.
228s Preparing to unpack .../390-libqt6core5compat6_6.9.2-3build1_armhf.deb ...
228s Unpacking libqt6core5compat6:armhf (6.9.2-3build1) ...
228s Selecting previously unselected package libqt6sql6:armhf.
228s Preparing to unpack .../391-libqt6sql6_6.9.2+dfsg-3ubuntu2_armhf.deb ...
228s Unpacking libqt6sql6:armhf (6.9.2+dfsg-3ubuntu2) ...
228s Selecting previously unselected package libqt6help6:armhf.
228s Preparing to unpack .../392-libqt6help6_6.9.2-5_armhf.deb ...
228s Unpacking libqt6help6:armhf (6.9.2-5) ...
228s Selecting previously unselected package libduktape207:armhf.
228s Preparing to unpack .../393-libduktape207_2.7.0+tests-0ubuntu4_armhf.deb ...
228s Unpacking libduktape207:armhf (2.7.0+tests-0ubuntu4) ...
228s Selecting previously unselected package libproxy1v5:armhf.
228s Preparing to unpack .../394-libproxy1v5_0.5.12-1_armhf.deb ...
228s Unpacking libproxy1v5:armhf (0.5.12-1) ...
228s Selecting previously unselected package libqt6network6:armhf.
228s Preparing to unpack .../395-libqt6network6_6.9.2+dfsg-3ubuntu2_armhf.deb ...
228s Unpacking libqt6network6:armhf (6.9.2+dfsg-3ubuntu2) ...
228s Selecting previously unselected package libqt6opengl6:armhf.
228s Preparing to unpack .../396-libqt6opengl6_6.9.2+dfsg-3ubuntu2_armhf.deb ...
228s Unpacking libqt6opengl6:armhf (6.9.2+dfsg-3ubuntu2) ...
228s Selecting previously unselected package libqt6openglwidgets6:armhf.
228s Preparing to unpack .../397-libqt6openglwidgets6_6.9.2+dfsg-3ubuntu2_armhf.deb ...
228s Unpacking libqt6openglwidgets6:armhf (6.9.2+dfsg-3ubuntu2) ...
229s Selecting previously unselected package libqt6xml6:armhf.
229s Preparing to unpack .../398-libqt6xml6_6.9.2+dfsg-3ubuntu2_armhf.deb ...
229s Unpacking libqt6xml6:armhf (6.9.2+dfsg-3ubuntu2) ...
229s Selecting previously unselected package libspqr4:armhf.
229s Preparing to unpack .../399-libspqr4_1%3a7.12.1+dfsg-1_armhf.deb ...
229s Unpacking libspqr4:armhf (1:7.12.1+dfsg-1) ...
229s Selecting previously unselected package libumfpack6:armhf.
229s Preparing to unpack .../400-libumfpack6_1%3a7.12.1+dfsg-1_armhf.deb ...
229s Unpacking libumfpack6:armhf (1:7.12.1+dfsg-1) ...
229s Selecting previously unselected package libtext-unidecode-perl.
229s Preparing to unpack .../401-libtext-unidecode-perl_1.30-3_all.deb ...
229s Unpacking libtext-unidecode-perl (1.30-3) ...
229s Selecting previously unselected package libintl-perl.
229s Preparing to unpack .../402-libintl-perl_1.35-1_all.deb ...
229s Unpacking libintl-perl (1.35-1) ...
229s Selecting previously unselected package texinfo-lib.
229s Preparing to unpack .../403-texinfo-lib_7.2-5_armhf.deb ...
229s Unpacking texinfo-lib (7.2-5) ...
229s Selecting previously unselected package tex-common.
229s Preparing to unpack .../404-tex-common_6.20_all.deb ...
229s Unpacking tex-common (6.20) ...
229s Selecting previously unselected package texinfo.
229s Preparing to unpack .../405-texinfo_7.2-5_all.deb ...
229s Unpacking texinfo (7.2-5) ...
229s Selecting previously unselected package octave-common.
229s Preparing to unpack .../406-octave-common_10.3.0-3_all.deb ...
229s Unpacking octave-common (10.3.0-3) ...
230s Selecting previously unselected package octave.
230s Preparing to unpack .../407-octave_10.3.0-3_armhf.deb ...
230s Unpacking octave (10.3.0-3) ...
230s Selecting previously unselected package libncurses-dev:armhf.
230s Preparing to unpack .../408-libncurses-dev_6.6+20251231-1_armhf.deb ...
230s Unpacking libncurses-dev:armhf (6.6+20251231-1) ...
230s Selecting previously unselected package libreadline-dev:armhf.
230s Preparing to unpack .../409-libreadline-dev_8.3-3_armhf.deb ...
230s Unpacking libreadline-dev:armhf (8.3-3) ...
230s Selecting previously unselected package libhdf5-fortran-310:armhf.
230s Preparing to unpack .../410-libhdf5-fortran-310_1.14.6+repack-2_armhf.deb ...
230s Unpacking libhdf5-fortran-310:armhf (1.14.6+repack-2) ...
230s Selecting previously unselected package libhdf5-hl-310:armhf.
230s Preparing to unpack .../411-libhdf5-hl-310_1.14.6+repack-2_armhf.deb ...
230s Unpacking libhdf5-hl-310:armhf (1.14.6+repack-2) ...
230s Selecting previously unselected package libhdf5-hl-fortran-310:armhf.
230s Preparing to unpack .../412-libhdf5-hl-fortran-310_1.14.6+repack-2_armhf.deb ...
230s Unpacking libhdf5-hl-fortran-310:armhf (1.14.6+repack-2) ...
230s Selecting previously unselected package libhdf5-cpp-310:armhf.
230s Preparing to unpack .../413-libhdf5-cpp-310_1.14.6+repack-2_armhf.deb ...
230s Unpacking libhdf5-cpp-310:armhf (1.14.6+repack-2) ...
230s Selecting previously unselected package libhdf5-hl-cpp-310:armhf.
230s Preparing to unpack .../414-libhdf5-hl-cpp-310_1.14.6+repack-2_armhf.deb ...
230s Unpacking libhdf5-hl-cpp-310:armhf (1.14.6+repack-2) ...
230s Selecting previously unselected package zlib1g-dev:armhf.
230s Preparing to unpack .../415-zlib1g-dev_1%3a1.3.dfsg+really1.3.1-1ubuntu2_armhf.deb ...
230s Unpacking zlib1g-dev:armhf (1:1.3.dfsg+really1.3.1-1ubuntu2) ...
230s Selecting previously unselected package libjpeg-turbo8-dev:armhf.
231s Preparing to unpack .../416-libjpeg-turbo8-dev_2.1.5-4ubuntu2_armhf.deb ...
231s Unpacking libjpeg-turbo8-dev:armhf (2.1.5-4ubuntu2) ...
231s Selecting previously unselected package libjpeg8-dev:armhf.
231s Preparing to unpack .../417-libjpeg8-dev_8c-2ubuntu11_armhf.deb ...
231s Unpacking libjpeg8-dev:armhf (8c-2ubuntu11) ...
231s Selecting previously unselected package libjpeg-dev:armhf.
231s Preparing to unpack .../418-libjpeg-dev_8c-2ubuntu11_armhf.deb ...
231s Unpacking libjpeg-dev:armhf (8c-2ubuntu11) ...
231s Selecting previously unselected package libaec0:armhf.
231s Preparing to unpack .../419-libaec0_1.1.5-1_armhf.deb ...
231s Unpacking libaec0:armhf (1.1.5-1) ...
231s Selecting previously unselected package libaec-dev:armhf.
231s Preparing to unpack .../420-libaec-dev_1.1.5-1_armhf.deb ...
231s Unpacking libaec-dev:armhf (1.1.5-1) ...
231s Selecting previously unselected package libbrotli-dev:armhf.
231s Preparing to unpack .../421-libbrotli-dev_1.1.0-2build6_armhf.deb ...
231s Unpacking libbrotli-dev:armhf (1.1.0-2build6) ...
231s Selecting previously unselected package libidn2-dev:armhf.
231s Preparing to unpack .../422-libidn2-dev_2.3.8-4_armhf.deb ...
231s Unpacking libidn2-dev:armhf (2.3.8-4) ...
231s Selecting previously unselected package comerr-dev:armhf.
231s Preparing to unpack .../423-comerr-dev_2.1-1.47.2-3ubuntu2_armhf.deb ...
231s Unpacking comerr-dev:armhf (2.1-1.47.2-3ubuntu2) ...
231s Selecting previously unselected package libgssrpc4t64:armhf.
231s Preparing to unpack .../424-libgssrpc4t64_1.22.1-2_armhf.deb ...
231s Unpacking libgssrpc4t64:armhf (1.22.1-2) ...
231s Selecting previously unselected package libkadm5clnt-mit12:armhf.
231s Preparing to unpack .../425-libkadm5clnt-mit12_1.22.1-2_armhf.deb ...
231s Unpacking libkadm5clnt-mit12:armhf (1.22.1-2) ...
231s Selecting previously unselected package libkdb5-10t64:armhf.
231s Preparing to unpack .../426-libkdb5-10t64_1.22.1-2_armhf.deb ...
231s Unpacking libkdb5-10t64:armhf (1.22.1-2) ...
231s Selecting previously unselected package libkadm5srv-mit12:armhf.
231s Preparing to unpack .../427-libkadm5srv-mit12_1.22.1-2_armhf.deb ...
231s Unpacking libkadm5srv-mit12:armhf (1.22.1-2) ...
231s Selecting previously unselected package krb5-multidev:armhf.
231s Preparing to unpack .../428-krb5-multidev_1.22.1-2_armhf.deb ...
231s Unpacking krb5-multidev:armhf (1.22.1-2) ...
231s Selecting previously unselected package libkrb5-dev:armhf.
231s Preparing to unpack .../429-libkrb5-dev_1.22.1-2_armhf.deb ...
231s Unpacking libkrb5-dev:armhf (1.22.1-2) ...
231s Selecting previously unselected package libldap-dev:armhf.
231s Preparing to unpack .../430-libldap-dev_2.6.10+dfsg-1ubuntu5_armhf.deb ...
231s Unpacking libldap-dev:armhf (2.6.10+dfsg-1ubuntu5) ...
231s Selecting previously unselected package libpkgconf3:armhf.
231s Preparing to unpack .../431-libpkgconf3_1.8.1-4build1_armhf.deb ...
231s Unpacking libpkgconf3:armhf (1.8.1-4build1) ...
232s Selecting previously unselected package pkgconf-bin.
232s Preparing to unpack .../432-pkgconf-bin_1.8.1-4build1_armhf.deb ...
232s Unpacking pkgconf-bin (1.8.1-4build1) ...
232s Selecting previously unselected package pkgconf:armhf.
232s Preparing to unpack .../433-pkgconf_1.8.1-4build1_armhf.deb ...
232s Unpacking pkgconf:armhf (1.8.1-4build1) ...
232s Selecting previously unselected package libnghttp2-dev:armhf.
232s Preparing to unpack .../434-libnghttp2-dev_1.64.0-1.1ubuntu1_armhf.deb ...
232s Unpacking libnghttp2-dev:armhf (1.64.0-1.1ubuntu1) ...
232s Selecting previously unselected package libpsl-dev:armhf.
232s Preparing to unpack .../435-libpsl-dev_0.21.2-1.1build2_armhf.deb ...
232s Unpacking libpsl-dev:armhf (0.21.2-1.1build2) ...
232s Selecting previously unselected package libgmpxx4ldbl:armhf.
232s Preparing to unpack .../436-libgmpxx4ldbl_2%3a6.3.0+dfsg-5ubuntu1_armhf.deb ...
232s Unpacking libgmpxx4ldbl:armhf (2:6.3.0+dfsg-5ubuntu1) ...
232s Selecting previously unselected package libgmp-dev:armhf.
232s Preparing to unpack .../437-libgmp-dev_2%3a6.3.0+dfsg-5ubuntu1_armhf.deb ...
232s Unpacking libgmp-dev:armhf (2:6.3.0+dfsg-5ubuntu1) ...
232s Selecting previously unselected package libevent-2.1-7t64:armhf.
232s Preparing to unpack .../438-libevent-2.1-7t64_2.1.12-stable-10build1_armhf.deb ...
232s Unpacking libevent-2.1-7t64:armhf (2.1.12-stable-10build1) ...
232s Selecting previously unselected package libunbound8:armhf.
232s Preparing to unpack .../439-libunbound8_1.24.2-1ubuntu1_armhf.deb ...
232s Unpacking libunbound8:armhf (1.24.2-1ubuntu1) ...
232s Selecting previously unselected package libgnutls-dane0t64:armhf.
232s Preparing to unpack .../440-libgnutls-dane0t64_3.8.10-3ubuntu1_armhf.deb ...
232s Unpacking libgnutls-dane0t64:armhf (3.8.10-3ubuntu1) ...
232s Selecting previously unselected package libgnutls-openssl27t64:armhf.
232s Preparing to unpack .../441-libgnutls-openssl27t64_3.8.10-3ubuntu1_armhf.deb ...
232s Unpacking libgnutls-openssl27t64:armhf (3.8.10-3ubuntu1) ...
232s Selecting previously unselected package libp11-kit-dev:armhf.
232s Preparing to unpack .../442-libp11-kit-dev_0.25.10-1_armhf.deb ...
232s Unpacking libp11-kit-dev:armhf (0.25.10-1) ...
232s Selecting previously unselected package libtasn1-6-dev:armhf.
232s Preparing to unpack .../443-libtasn1-6-dev_4.21.0-2_armhf.deb ...
232s Unpacking libtasn1-6-dev:armhf (4.21.0-2) ...
232s Selecting previously unselected package nettle-dev:armhf.
232s Preparing to unpack .../444-nettle-dev_3.10.2-1_armhf.deb ...
232s Unpacking nettle-dev:armhf (3.10.2-1) ...
232s Selecting previously unselected package libgnutls28-dev:armhf.
232s Preparing to unpack .../445-libgnutls28-dev_3.8.10-3ubuntu1_armhf.deb ...
232s Unpacking libgnutls28-dev:armhf (3.8.10-3ubuntu1) ...
232s Selecting previously unselected package librtmp-dev:armhf.
232s Preparing to unpack .../446-librtmp-dev_2.4+20151223.gitfa8646d.1-3_armhf.deb ...
232s Unpacking librtmp-dev:armhf (2.4+20151223.gitfa8646d.1-3) ...
233s Selecting previously unselected package libssl-dev:armhf.
233s Preparing to unpack .../447-libssl-dev_3.5.3-1ubuntu2_armhf.deb ...
233s Unpacking libssl-dev:armhf (3.5.3-1ubuntu2) ...
233s Selecting previously unselected package libssh2-1-dev:armhf.
233s Preparing to unpack .../448-libssh2-1-dev_1.11.1-1build1_armhf.deb ...
233s Unpacking libssh2-1-dev:armhf (1.11.1-1build1) ...
233s Selecting previously unselected package libzstd-dev:armhf.
233s Preparing to unpack .../449-libzstd-dev_1.5.7+dfsg-3_armhf.deb ...
233s Unpacking libzstd-dev:armhf (1.5.7+dfsg-3) ...
233s Selecting previously unselected package libcurl4-openssl-dev:armhf.
233s Preparing to unpack .../450-libcurl4-openssl-dev_8.18.0-1ubuntu1_armhf.deb ...
233s Unpacking libcurl4-openssl-dev:armhf (8.18.0-1ubuntu1) ...
233s Selecting previously unselected package hdf5-helpers.
233s Preparing to unpack .../451-hdf5-helpers_1.14.6+repack-2_armhf.deb ...
233s Unpacking hdf5-helpers (1.14.6+repack-2) ...
233s Selecting previously unselected package libhdf5-dev.
233s Preparing to unpack .../452-libhdf5-dev_1.14.6+repack-2_armhf.deb ...
233s Unpacking libhdf5-dev (1.14.6+repack-2) ...
233s Selecting previously unselected package xorg-sgml-doctools.
233s Preparing to unpack .../453-xorg-sgml-doctools_1%3a1.11-1.1build1_all.deb ...
233s Unpacking xorg-sgml-doctools (1:1.11-1.1build1) ...
233s Selecting previously unselected package x11proto-dev.
233s Preparing to unpack .../454-x11proto-dev_2025.1-1_all.deb ...
233s Unpacking x11proto-dev (2025.1-1) ...
233s Selecting previously unselected package libxau-dev:armhf.
233s Preparing to unpack .../455-libxau-dev_1%3a1.0.11-1build1_armhf.deb ...
233s Unpacking libxau-dev:armhf (1:1.0.11-1build1) ...
233s Selecting previously unselected package libxdmcp-dev:armhf.
233s Preparing to unpack .../456-libxdmcp-dev_1%3a1.1.5-2_armhf.deb ...
233s Unpacking libxdmcp-dev:armhf (1:1.1.5-2) ...
233s Selecting previously unselected package xtrans-dev.
233s Preparing to unpack .../457-xtrans-dev_1.6.0-1build1_all.deb ...
233s Unpacking xtrans-dev (1.6.0-1build1) ...
234s Selecting previously unselected package libxcb1-dev:armhf.
234s Preparing to unpack .../458-libxcb1-dev_1.17.0-2ubuntu1_armhf.deb ...
234s Unpacking libxcb1-dev:armhf (1.17.0-2ubuntu1) ...
234s Selecting previously unselected package libx11-dev:armhf.
234s Preparing to unpack .../459-libx11-dev_2%3a1.8.12-1build1_armhf.deb ...
234s Unpacking libx11-dev:armhf (2:1.8.12-1build1) ...
234s Selecting previously unselected package libglx-dev:armhf.
234s Preparing to unpack .../460-libglx-dev_1.7.0-3_armhf.deb ...
234s Unpacking libglx-dev:armhf (1.7.0-3) ...
234s Selecting previously unselected package libgl-dev:armhf.
234s Preparing to unpack .../461-libgl-dev_1.7.0-3_armhf.deb ...
234s Unpacking libgl-dev:armhf (1.7.0-3) ...
234s Selecting previously unselected package libblas-dev:armhf.
234s Preparing to unpack .../462-libblas-dev_3.12.1-7ubuntu1_armhf.deb ...
234s Unpacking libblas-dev:armhf (3.12.1-7ubuntu1) ...
234s Selecting previously unselected package liblapack-dev:armhf.
234s Preparing to unpack .../463-liblapack-dev_3.12.1-7ubuntu1_armhf.deb ...
234s Unpacking liblapack-dev:armhf (3.12.1-7ubuntu1) ...
234s Selecting previously unselected package libfftw3-bin.
234s Preparing to unpack .../464-libfftw3-bin_3.3.10-2fakesync1build2_armhf.deb ...
234s Unpacking libfftw3-bin (3.3.10-2fakesync1build2) ...
234s Selecting previously unselected package libfftw3-dev:armhf.
234s Preparing to unpack .../465-libfftw3-dev_3.3.10-2fakesync1build2_armhf.deb ...
234s Unpacking libfftw3-dev:armhf (3.3.10-2fakesync1build2) ...
234s Selecting previously unselected package libgfortran-15-dev:armhf.
234s Preparing to unpack .../466-libgfortran-15-dev_15.2.0-12ubuntu1_armhf.deb ...
234s Unpacking libgfortran-15-dev:armhf (15.2.0-12ubuntu1) ...
234s Selecting previously unselected package gfortran-15-arm-linux-gnueabihf.
234s Preparing to unpack .../467-gfortran-15-arm-linux-gnueabihf_15.2.0-12ubuntu1_armhf.deb ...
234s Unpacking gfortran-15-arm-linux-gnueabihf (15.2.0-12ubuntu1) ...
235s Selecting previously unselected package gfortran-15.
235s Preparing to unpack .../468-gfortran-15_15.2.0-12ubuntu1_armhf.deb ...
235s Unpacking gfortran-15 (15.2.0-12ubuntu1) ...
235s Selecting previously unselected package gfortran-arm-linux-gnueabihf.
235s Preparing to unpack .../469-gfortran-arm-linux-gnueabihf_4%3a15.2.0-4ubuntu1_armhf.deb ...
235s Unpacking gfortran-arm-linux-gnueabihf (4:15.2.0-4ubuntu1) ...
235s Selecting previously unselected package gfortran.
235s Preparing to unpack .../470-gfortran_4%3a15.2.0-4ubuntu1_armhf.deb ...
235s Unpacking gfortran (4:15.2.0-4ubuntu1) ...
235s Selecting previously unselected package libstdc++-15-dev:armhf.
235s Preparing to unpack .../471-libstdc++-15-dev_15.2.0-12ubuntu1_armhf.deb ...
235s Unpacking libstdc++-15-dev:armhf (15.2.0-12ubuntu1) ...
235s Selecting previously unselected package g++-15-arm-linux-gnueabihf.
235s Preparing to unpack .../472-g++-15-arm-linux-gnueabihf_15.2.0-12ubuntu1_armhf.deb ...
235s Unpacking g++-15-arm-linux-gnueabihf (15.2.0-12ubuntu1) ...
236s Selecting previously unselected package g++-15.
236s Preparing to unpack .../473-g++-15_15.2.0-12ubuntu1_armhf.deb ...
236s Unpacking g++-15 (15.2.0-12ubuntu1) ...
236s Selecting previously unselected package g++-arm-linux-gnueabihf.
236s Preparing to unpack .../474-g++-arm-linux-gnueabihf_4%3a15.2.0-4ubuntu1_armhf.deb ...
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236s Selecting previously unselected package octave-dev.
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236s Selecting previously unselected package libxaw7:armhf.
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236s Selecting previously unselected package libxfont2:armhf.
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237s Setting up libconfig-inifiles-perl (3.000003-4) ...
237s Setting up libapt-pkg-perl (0.1.43) ...
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237s Setting up libgraphite2-3:armhf (1.3.14-11ubuntu1) ...
237s Setting up libstring-escape-perl (2010.002-3) ...
237s Setting up libgnutls-openssl27t64:armhf (3.8.10-3ubuntu1) ...
237s Setting up libxcb-dri3-0:armhf (1.17.0-2ubuntu1) ...
237s Setting up liblcms2-2:armhf (2.17-1) ...
237s Setting up libberkeleydb-perl:armhf (0.66-2) ...
237s Setting up libpixman-1-0:armhf (0.46.4-1) ...
237s Setting up libsharpyuv0:armhf (1.5.0-0.1build1) ...
237s Setting up libaom3:armhf (3.13.1-2) ...
237s Setting up liblingua-en-inflect-perl (1.905-2) ...
237s Setting up libx11-xcb1:armhf (2:1.8.12-1build1) ...
237s Setting up libfile-which-perl (1.27-2) ...
237s Setting up libdouble-conversion3:armhf (3.4.0-1) ...
237s Setting up libsafe-isa-perl (1.000010-1build1) ...
237s Setting up libtask-weaken-perl (1.06-2) ...
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237s Setting up libogg0:armhf (1.3.6-2) ...
237s Setting up libmouse-perl:armhf (2.6.1-1) ...
237s Setting up libzstd-dev:armhf (1.5.7+dfsg-3) ...
237s Setting up liblerc4:armhf (4.0.0+ds-5ubuntu2) ...
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238s Setting up libxcb-render-util0:armhf (0.3.10-1build1) ...
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238s Setting up octave-common (10.3.0-3) ...
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238s Setting up libaec0:armhf (1.1.5-1) ...
238s Setting up libnet-netmask-perl (2.0003-1build1) ...
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238s update-language: texlive-base not installed and configured, doing nothing!
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238s Setting up xtrans-dev (1.6.0-1build1) ...
238s Setting up libfontenc1:armhf (1:1.1.8-1build2) ...
238s Setting up autotools-dev (20240727.1) ...
238s Setting up libblas3:armhf (3.12.1-7ubuntu1) ...
238s 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
238s Setting up libclass-data-inheritable-perl (0.10-1) ...
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238s Setting up libxfixes3:armhf (1:6.0.0-2build2) ...
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238s Setting up libgssrpc4t64:armhf (1.22.1-2) ...
238s Setting up libperlio-utf8-strict-perl (0.010-1build4) ...
238s Setting up libldap-dev:armhf (2.6.10+dfsg-1ubuntu5) ...
238s Setting up libllvm21:armhf (1:21.1.8-1ubuntu1) ...
238s Setting up aglfn (1.7+git20191031.4036a9c-2build1) ...
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238s Setting up libxcb-cursor0:armhf (0.1.5-1build1) ...
238s Setting up lzip (1.25-4) ...
238s update-alternatives: using /usr/bin/lzip.lzip to provide /usr/bin/lzip (lzip) in auto mode
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238s update-alternatives: using /usr/bin/lzip.lzip to provide /usr/bin/lzip-decompressor (lzip-decompressor) in auto mode
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238s Setting up t1utils (1.41-4build4) ...
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238s Setting up libxkbcommon-x11-0:armhf (1.12.3-1) ...
238s Setting up libssl-dev:armhf (3.5.3-1ubuntu2) ...
238s Setting up libmpc3:armhf (1.3.1-2) ...
238s Setting up libvorbis0a:armhf (1.3.7-3build1) ...
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238s Setting up libio-html-perl (1.004-3) ...
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238s Setting up libflac14:armhf (1.5.0+ds-5) ...
238s Setting up liblist-moreutils-xs-perl (0.430-4build1) ...
238s Setting up pkgconf-bin (1.8.1-4build1) ...
238s Setting up libjpeg-turbo8:armhf (2.1.5-4ubuntu2) ...
238s Setting up libqscintilla2-qt6-l10n (2.14.1+dfsg-2) ...
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238s Setting up libidn2-dev:armhf (2.3.8-4) ...
238s Setting up libfftw3-double3:armhf (3.3.10-2fakesync1build2) ...
238s Setting up libb-keywords-perl (1.29-1) ...
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238s Setting up libgfortran5:armhf (15.2.0-12ubuntu1) ...
238s Setting up libvulkan1:armhf (1.4.335.0-1) ...
238s Setting up libtime-duration-perl (1.21-2) ...
238s Setting up autoconf (2.72-3.1ubuntu1) ...
238s Setting up libtext-xslate-perl:armhf (3.5.9-2build1) ...
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238s Setting up libwebp7:armhf (1.5.0-0.1build1) ...
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238s Setting up dwz (0.16-2) ...
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238s Setting up libproc-processtable-perl:armhf (0.637-1) ...
238s Setting up libparse-recdescent-perl (1.967015+dfsg-4) ...
238s Setting up libmtdev1t64:armhf (1.1.7-1build1) ...
238s Setting up libduktape207:armhf (2.7.0+tests-0ubuntu4) ...
238s Setting up libasyncns0:armhf (0.8-7) ...
238s Setting up libxshmfence1:armhf (1.3.3-1build1) ...
238s Setting up libcrypt-dev:armhf (1:4.5.1-1) ...
238s Setting up libxcb-randr0:armhf (1.17.0-2ubuntu1) ...
238s Setting up libpath-tiny-perl (0.148-1) ...
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238s Setting up lzop (1.04-2build4) ...
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238s Setting up libmd4c0:armhf (0.5.2-2build1) ...
238s Setting up libregexp-wildcards-perl (1.05-3) ...
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238s Setting up liblua5.4-0:armhf (5.4.8-1) ...
238s Setting up libaliased-perl (0.34-3) ...
238s Setting up libharfbuzz0b:armhf (12.3.2-1) ...
238s Setting up libthai-data (0.1.30-1) ...
238s Setting up xorg-sgml-doctools (1:1.11-1.1build1) ...
238s Setting up libstrictures-perl (2.000006-1build1) ...
238s Setting up libsub-quote-perl (2.006009-1ubuntu1) ...
238s Setting up libdevel-stacktrace-perl (2.0500-1) ...
238s Setting up libclass-xsaccessor-perl (1.19-4build6) ...
238s Setting up libpod-spell-perl (1.27-1) ...
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238s Setting up libglu1-mesa:armhf (9.0.2-1.1build2) ...
238s Setting up libtoml-tiny-perl (0.20-1) ...
238s Setting up libstemmer0d:armhf (3.0.1-1) ...
238s Setting up libxkbfile1:armhf (1:1.1.0-1build5) ...
238s Setting up libsort-versions-perl (1.62-3) ...
238s Setting up libexporter-tiny-perl (1.006003-1) ...
238s Setting up libterm-readkey-perl (2.38-2build5) ...
238s Setting up libisl23:armhf (0.27-1build1) ...
238s Setting up libtext-unidecode-perl (1.30-3) ...
238s Setting up libde265-0:armhf (1.0.16-1build1) ...
238s Setting up libc-dev-bin (2.42-2ubuntu5) ...
238s Setting up libfont-ttf-perl (1.06-2) ...
238s Setting up libfile-homedir-perl (1.006-2) ...
238s Setting up libsamplerate0:armhf (0.2.2-4build2) ...
238s Setting up libtasn1-6-dev:armhf (4.21.0-2) ...
238s Setting up libwebpmux3:armhf (1.5.0-0.1build1) ...
238s Setting up libtext-levenshteinxs-perl (0.03-5build5) ...
238s Setting up libperlio-gzip-perl (0.20-1build5) ...
238s Setting up cpp-15-arm-linux-gnueabihf (15.2.0-12ubuntu1) ...
238s Setting up libjxl0.11:armhf (0.11.1-6ubuntu1) ...
238s Setting up libxfont2:armhf (1:2.0.6-2) ...
238s Setting up libhtml-html5-entities-perl (0.004-3) ...
238s Setting up libtext-levenshtein-damerau-perl (0.41-3) ...
238s Setting up libsereal-decoder-perl (5.004+ds-1build5) ...
238s Setting up libmarkdown2:armhf (2.2.7-2.1build1) ...
238s Setting up libcc1-0:armhf (15.2.0-12ubuntu1) ...
238s Setting up liburi-perl (5.34-2build1) ...
238s Setting up libnet-ipv6addr-perl (1.02-1) ...
238s Setting up libbrotli-dev:armhf (1.1.0-2build6) ...
238s Setting up cpp-arm-linux-gnueabihf (4:15.2.0-4ubuntu1) ...
238s Setting up perltidy (20250105-1build1) ...
238s Setting up libp11-kit-dev:armhf (0.25.10-1) ...
238s Setting up libmp3lame0:armhf (3.100-6build2) ...
238s Setting up libblas-dev:armhf (3.12.1-7ubuntu1) ...
238s 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
238s Setting up libsz2:armhf (1.1.5-1) ...
238s Setting up libvorbisenc2:armhf (1.3.7-3build1) ...
238s Setting up libkadm5clnt-mit12:armhf (1.22.1-2) ...
238s Setting up libdata-validate-ip-perl (0.31-1) ...
238s Setting up libwacom-common (2.16.1-1) ...
238s Setting up libmousex-nativetraits-perl (1.09-3) ...
238s Setting up libemail-address-xs-perl (1.05-1build5) ...
238s Setting up libwayland-client0:armhf (1.24.0-2) ...
238s Setting up libnet-ssleay-perl:armhf (1.94-3) ...
238s Setting up libjpeg8:armhf (8c-2ubuntu11) ...
238s Setting up automake (1:1.18.1-3build1) ...
238s update-alternatives: using /usr/bin/automake-1.18 to provide /usr/bin/automake (automake) in auto mode
238s Setting up libb2-1:armhf (0.98.1-1.1build2) ...
238s Setting up x11proto-dev (2025.1-1) ...
238s Setting up libfile-stripnondeterminism-perl (1.15.0-1build1) ...
238s Setting up gnuplot-data (6.0.2+dfsg1-2ubuntu1) ...
238s Setting up libice6:armhf (2:1.1.1-1build1) ...
238s Setting up libqt6core6t64:armhf (6.9.2+dfsg-3ubuntu2) ...
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258s autopkgtest [07:17:12]: test command1: [-----------------------
260s Checking package...
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264s [inst/Classification/ClassificationDiscriminant.m]
264s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/Classification/ClassificationDiscriminant.m
264s ***** demo
264s  ## Create discriminant classifier
264s  ## Evaluate some model predictions on new data.
264s 
264s  load fisheriris
264s  x = meas;
264s  y = species;
264s  xc = [min(x); mean(x); max(x)];
264s  obj = fitcdiscr (x, y);
264s  [label, score, cost] = predict (obj, xc);
264s ***** demo
264s  load fisheriris
264s  model = fitcdiscr (meas, species);
264s  X = mean (meas);
264s  Y = {'versicolor'};
264s  ## Compute loss for discriminant model
264s  L = loss (model, X, Y)
264s ***** demo
264s  load fisheriris
264s  mdl = fitcdiscr (meas, species);
264s  X = mean (meas);
264s  Y = {'versicolor'};
264s  ## Margin for discriminant model
264s  m = margin (mdl, X, Y)
264s ***** demo
264s  load fisheriris
264s  x = meas;
264s  y = species;
264s  obj = fitcdiscr (x, y, "gamma", 0.4);
264s  ## Cross-validation for discriminant model
264s  CVMdl = crossval (obj)
264s ***** test
264s  load fisheriris
264s  x = meas;
264s  y = species;
264s  PredictorNames = {'Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width'};
264s  Mdl = ClassificationDiscriminant (x, y, "PredictorNames", PredictorNames);
264s  sigma = [0.265008, 0.092721, 0.167514, 0.038401; ...
264s           0.092721, 0.115388, 0.055244, 0.032710; ...
264s           0.167514, 0.055244, 0.185188, 0.042665; ...
264s           0.038401, 0.032710, 0.042665, 0.041882];
264s  mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
264s        5.9360, 2.7700, 4.2600, 1.3260; ...
264s        6.5880, 2.9740, 5.5520, 2.0260];
264s  xCentered = [ 9.4000e-02,  7.2000e-02, -6.2000e-02, -4.6000e-02; ...
264s               -1.0600e-01, -4.2800e-01, -6.2000e-02, -4.6000e-02; ...
264s               -3.0600e-01, -2.2800e-01, -1.6200e-01, -4.6000e-02];
264s  assert (class (Mdl), "ClassificationDiscriminant");
264s  assert ({Mdl.X, Mdl.Y, Mdl.NumObservations}, {x, y, 150})
264s  assert ({Mdl.DiscrimType, Mdl.ResponseName}, {"linear", "Y"})
264s  assert ({Mdl.Gamma, Mdl.MinGamma}, {0, 0}, 1e-15)
264s  assert (Mdl.ClassNames, unique (species))
264s  assert (Mdl.Sigma, sigma, 1e-6)
264s  assert (Mdl.Mu, mu, 1e-14)
264s  assert (Mdl.XCentered([1:3],:), xCentered, 1e-14)
264s  assert (Mdl.LogDetSigma, -9.9585, 1e-4)
264s  assert (Mdl.PredictorNames, PredictorNames)
264s ***** test
264s  load fisheriris
264s  x = meas;
264s  y = species;
264s  Mdl = ClassificationDiscriminant (x, y, "Gamma", 0.5);
264s  sigma = [0.265008, 0.046361, 0.083757, 0.019201; ...
264s           0.046361, 0.115388, 0.027622, 0.016355; ...
264s           0.083757, 0.027622, 0.185188, 0.021333; ...
264s           0.019201, 0.016355, 0.021333, 0.041882];
264s  mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
264s        5.9360, 2.7700, 4.2600, 1.3260; ...
264s        6.5880, 2.9740, 5.5520, 2.0260];
264s  xCentered = [ 9.4000e-02,  7.2000e-02, -6.2000e-02, -4.6000e-02; ...
264s               -1.0600e-01, -4.2800e-01, -6.2000e-02, -4.6000e-02; ...
264s               -3.0600e-01, -2.2800e-01, -1.6200e-01, -4.6000e-02];
264s  assert (class (Mdl), "ClassificationDiscriminant");
264s  assert ({Mdl.X, Mdl.Y, Mdl.NumObservations}, {x, y, 150})
264s  assert ({Mdl.DiscrimType, Mdl.ResponseName}, {"linear", "Y"})
264s  assert ({Mdl.Gamma, Mdl.MinGamma}, {0.5, 0})
264s  assert (Mdl.ClassNames, unique (species))
264s  assert (Mdl.Sigma, sigma, 1e-6)
264s  assert (Mdl.Mu, mu, 1e-14)
264s  assert (Mdl.XCentered([1:3],:), xCentered, 1e-14)
264s  assert (Mdl.LogDetSigma, -8.6884, 1e-4)
264s ***** shared X, Y, MODEL
264s  X = rand (10,2);
264s  Y = [ones(5,1);2*ones(5,1)];
264s  MODEL = ClassificationDiscriminant (X, Y);
264s ***** error<ClassificationDiscriminant: too few input arguments.> ClassificationDiscriminant ()
264s ***** error<ClassificationDiscriminant: too few input arguments.> ...
264s  ClassificationDiscriminant (ones(4, 1))
264s ***** error<ClassificationDiscriminant: Name-Value arguments must be in pairs.> ...
264s  ClassificationDiscriminant (X, Y, "prior")
264s ***** error<ClassificationDiscriminant: number of rows in X and Y must be equal.> ...
264s  ClassificationDiscriminant (ones (4,2), ones (1,4))
264s ***** error<ClassificationDiscriminant: 'PredictorNames' must be supplied as a cellstring array.> ...
264s  ClassificationDiscriminant (X, Y, "PredictorNames", ["A"])
264s ***** error<ClassificationDiscriminant: 'PredictorNames' must be supplied as a cellstring array.> ...
264s  ClassificationDiscriminant (X, Y, "PredictorNames", "A")
264s ***** error<ClassificationDiscriminant: 'PredictorNames' must equal the number of columns in X.> ...
264s  ClassificationDiscriminant (X, Y, "PredictorNames", {"A", "B", "C"})
264s ***** error<ClassificationDiscriminant: 'ResponseName' must be a character vector.> ...
264s  ClassificationDiscriminant (X, Y, "ResponseName", {"Y"})
264s ***** error<ClassificationDiscriminant: 'ResponseName' must be a character vector.> ...
264s  ClassificationDiscriminant (X, Y, "ResponseName", 1)
264s ***** error<ClassificationDiscriminant: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
264s  ClassificationDiscriminant (X, Y, "ClassNames", @(x)x)
264s ***** error<ClassificationDiscriminant: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
264s  ClassificationDiscriminant (X, Y, "ClassNames", {1})
264s ***** error<ClassificationDiscriminant: not all 'ClassNames' are present in Y.> ...
264s  ClassificationDiscriminant (X, ones (10,1), "ClassNames", [1, 2])
264s ***** error<ClassificationDiscriminant: not all 'ClassNames' are present in Y.> ...
264s  ClassificationDiscriminant ([1;2;3;4;5], ['a';'b';'a';'a';'b'], "ClassNames", ['a';'c'])
264s ***** error<ClassificationDiscriminant: not all 'ClassNames' are present in Y.> ...
264s  ClassificationDiscriminant ([1;2;3;4;5], {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
264s ***** error<ClassificationDiscriminant: not all 'ClassNames' are present in Y.> ...
264s  ClassificationDiscriminant (X, logical (ones (10,1)), "ClassNames", [true, false])
264s ***** error<ClassificationDiscriminant: 'Prior' must be either a numeric or a character vector.> ...
264s  ClassificationDiscriminant (X, Y, "Prior", {"1", "2"})
264s ***** error<ClassificationDiscriminant: the elements in 'Prior' do not correspond to the selected classes in Y.> ...
264s  ClassificationDiscriminant (X, ones (10,1), "Prior", [1 2])
264s ***** error<ClassificationDiscriminant: 'Cost' must be a numeric square matrix.> ...
264s  ClassificationDiscriminant (X, Y, "Cost", [1, 2])
264s ***** error<ClassificationDiscriminant: 'Cost' must be a numeric square matrix.> ...
264s  ClassificationDiscriminant (X, Y, "Cost", "string")
264s ***** error<ClassificationDiscriminant: 'Cost' must be a numeric square matrix.> ...
264s  ClassificationDiscriminant (X, Y, "Cost", {eye(2)})
264s ***** error<ClassificationDiscriminant: the number of rows and columns in 'Cost' must correspond to selected classes in Y.> ...
264s  ClassificationDiscriminant (X, Y, "Cost", ones (3))
264s ***** error<ClassificationDiscriminant: Predictor 'x1' has zero within-class variance.> ...
264s  ClassificationDiscriminant (ones (5,2), [1; 1; 2; 2; 2])
264s ***** error<ClassificationDiscriminant: Predictor 'A' has zero within-class variance.> ...
264s  ClassificationDiscriminant (ones (5,2), [1; 1; 2; 2; 2], "PredictorNames", {"A", "B"})
264s ***** error<ClassificationDiscriminant: Predictor 'x2' has zero within-class variance.> ...
264s  ClassificationDiscriminant ([1,2;2,2;3,2;4,2;5,2], ones (5, 1))
264s ***** error<ClassificationDiscriminant: Predictor 'B' has zero within-class variance.> ...
264s  ClassificationDiscriminant ([1,2;2,2;3,2;4,2;5,2], ones (5, 1), "PredictorNames", {"A", "B"})
264s ***** test
264s  load fisheriris
264s  x = meas;
264s  y = species;
264s  Mdl = fitcdiscr (meas, species, "Gamma", 0.5);
264s  [label, score, cost] = predict (Mdl, [2, 2, 2, 2]);
264s  assert (label, {'versicolor'})
264s  assert (score, [0, 0.9999, 0.0001], 1e-4)
264s  assert (cost, [1, 0.0001, 0.9999], 1e-4)
264s  [label, score, cost] = predict (Mdl, [2.5, 2.5, 2.5, 2.5]);
264s  assert (label, {'versicolor'})
264s  assert (score, [0, 0.6368, 0.3632], 1e-4)
264s  assert (cost, [1, 0.3632, 0.6368], 1e-4)
264s ***** test
264s  load fisheriris
264s  x = meas;
264s  y = species;
264s  xc = [min(x); mean(x); max(x)];
264s  Mdl = fitcdiscr (x, y);
264s  [label, score, cost] = predict (Mdl, xc);
264s  l = {'setosa'; 'versicolor'; 'virginica'};
264s  s = [1, 0, 0; 0, 1, 0; 0, 0, 1];
264s  c = [0, 1, 1; 1, 0, 1; 1, 1, 0];
264s  assert (label, l)
264s  assert (score, s, 1e-4)
264s  assert (cost, c, 1e-4)
264s ***** error<ClassificationDiscriminant.predict: too few input arguments.> ...
264s  predict (MODEL)
264s ***** error<ClassificationDiscriminant.predict: XC is empty.> ...
264s  predict (MODEL, [])
264s ***** error<ClassificationDiscriminant.predict: XC must have the same number of predictors as the trained model.> ...
264s  predict (MODEL, 1)
264s ***** test
264s  load fisheriris
264s  model = fitcdiscr (meas, species);
264s  x = mean (meas);
264s  y = {'versicolor'};
264s  L = loss (model, x, y);
264s  assert (L, 0)
264s ***** test
264s  x = [1, 2; 3, 4; 5, 6];
264s  y = {'A'; 'B'; 'A'};
264s  model = fitcdiscr (x, y, "Gamma", 0.4);
264s  x_test = [1, 6; 3, 3];
264s  y_test = {'A'; 'B'};
264s  L = loss (model, x_test, y_test);
264s  assert (L, 0.3333, 1e-4)
264s ***** test
264s  x = [1, 2; 3, 4; 5, 6; 7, 8];
264s  y = ['1'; '2'; '3'; '1'];
264s  model = fitcdiscr (x, y, "gamma" , 0.5);
264s  x_test = [3, 3];
264s  y_test = ['1'];
264s  L = loss (model, x_test, y_test, 'LossFun', 'quadratic');
264s  assert (L, 0.2423, 1e-4)
264s ***** test
264s  x = [1, 2; 3, 4; 5, 6; 7, 8];
264s  y = ['1'; '2'; '3'; '1'];
264s  model = fitcdiscr (x, y, "gamma" , 0.5);
264s  x_test = [3, 3; 5, 7];
264s  y_test = ['1'; '2'];
264s  L = loss (model, x_test, y_test, 'LossFun', 'classifcost');
264s  assert (L, 0.3333, 1e-4)
264s ***** test
264s  x = [1, 2; 3, 4; 5, 6; 7, 8];
264s  y = ['1'; '2'; '3'; '1'];
264s  model = fitcdiscr (x, y, "gamma" , 0.5);
264s  x_test = [3, 3; 5, 7];
264s  y_test = ['1'; '2'];
264s  L = loss (model, x_test, y_test, 'LossFun', 'hinge');
264s  assert (L, 0.5886, 1e-4)
264s ***** test
264s  x = [1, 2; 3, 4; 5, 6; 7, 8];
264s  y = ['1'; '2'; '3'; '1'];
264s  model = fitcdiscr (x, y, "gamma" , 0.5);
264s  x_test = [3, 3; 5, 7];
264s  y_test = ['1'; '2'];
264s  W = [1; 2];
264s  L = loss (model, x_test, y_test, 'LossFun', 'logit', 'Weights', W);
264s  assert (L, 0.5107, 1e-4)
264s ***** test
264s  x = [1, 2; 3, 4; 5, 6];
264s  y = {'A'; 'B'; 'A'};
264s  model = fitcdiscr (x, y, "gamma" , 0.5);
264s  x_with_nan = [1, 2; NaN, 4];
264s  y_test = {'A'; 'B'};
264s  L = loss (model, x_with_nan, y_test);
264s  assert (L, 0.3333, 1e-4)
264s ***** test
264s  x = [1, 2; 3, 4; 5, 6];
264s  y = {'A'; 'B'; 'A'};
264s  model = fitcdiscr (x, y);
264s  x_with_nan = [1, 2; NaN, 4];
264s  y_test = {'A'; 'B'};
264s  L = loss (model, x_with_nan, y_test, 'LossFun', 'logit');
264s  assert (isnan (L))
264s ***** test
264s  x = [1, 2; 3, 4; 5, 6];
264s  y = {'A'; 'B'; 'A'};
264s  model = fitcdiscr (x, y);
264s  customLossFun = @(C, S, W, Cost) sum (W .* sum (abs (C - S), 2));
264s  L = loss (model, x, y, 'LossFun', customLossFun);
264s  assert (L, 0.8889, 1e-4)
264s ***** test
264s  x = [1, 2; 3, 4; 5, 6];
264s  y = [1; 2; 1];
264s  model = fitcdiscr (x, y);
264s  L = loss (model, x, y, 'LossFun', 'classiferror');
264s  assert (L, 0.3333, 1e-4)
264s ***** error<ClassificationDiscriminant.loss: too few input arguments.> ...
264s  loss (MODEL)
264s ***** error<ClassificationDiscriminant.loss: too few input arguments.> ...
264s  loss (MODEL, ones (4,2))
264s ***** error<ClassificationDiscriminant.loss: X is empty.> ...
264s  loss (MODEL, [], zeros (2))
264s ***** error<ClassificationDiscriminant.loss: X must have the same number of predictors as the trained model.> ...
264s  loss (MODEL, 1, zeros (2))
264s ***** error<ClassificationDiscriminant.loss: name-value arguments must be in pairs.> ...
264s  loss (MODEL, ones (4,2), ones (4,1), 'LossFun')
264s ***** error<ClassificationDiscriminant.loss: Y must have the same number of rows as X.> ...
264s  loss (MODEL, ones (4,2), ones (3,1))
264s ***** error<ClassificationDiscriminant.loss: invalid loss function.> ...
264s  loss (MODEL, ones (4,2), ones (4,1), 'LossFun', 'a')
264s ***** error<ClassificationDiscriminant.loss: invalid 'Weights'.> ...
264s  loss (MODEL, ones (4,2), ones (4,1), 'Weights', 'w')
264s  load fisheriris
264s  mdl = fitcdiscr (meas, species);
264s  X = mean (meas);
264s  Y = {'versicolor'};
264s  m = margin (mdl, X, Y);
264s  assert (m, 1, 1e-6)
264s ***** test
264s  X = [1, 2; 3, 4; 5, 6];
264s  Y = [1; 2; 1];
264s  mdl = fitcdiscr (X, Y, "gamma", 0.5);
264s  m = margin (mdl, X, Y);
264s  assert (m, [0.3333; -0.3333; 0.3333], 1e-4)
264s ***** error<ClassificationDiscriminant.margin: too few input arguments.> ...
264s  margin (MODEL)
264s ***** error<ClassificationDiscriminant.margin: too few input arguments.> ...
264s  margin (MODEL, ones (4,2))
264s ***** error<ClassificationDiscriminant.margin: X is empty.> ...
264s  margin (MODEL, [], zeros (2))
264s ***** error<ClassificationDiscriminant.margin: X must have the same number of predictors as the trained model.> ...
264s  margin (MODEL, 1, zeros (2))
264s ***** error<ClassificationDiscriminant.margin: Y must have the same number of rows as X.> ...
264s  margin (MODEL, ones (4,2), ones (3,1))
264s ***** shared x, y, obj
264s  load fisheriris
264s  x = meas;
264s  y = species;
264s  obj = fitcdiscr (x, y, "gamma", 0.4);
265s ***** test
265s  CVMdl = crossval (obj);
265s  assert (class (CVMdl), "ClassificationPartitionedModel")
265s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
265s  assert (CVMdl.KFold == 10)
265s  assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
265s  assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
266s ***** test
266s  CVMdl = crossval (obj, "KFold", 3);
266s  assert (class (CVMdl), "ClassificationPartitionedModel")
266s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
266s  assert (CVMdl.KFold == 3)
266s  assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
266s  assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
266s ***** test
266s  CVMdl = crossval (obj, "HoldOut", 0.2);
266s  assert (class (CVMdl), "ClassificationPartitionedModel")
266s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
266s  assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
266s  assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
266s ***** test
266s  CVMdl = crossval (obj, "LeaveOut", 'on');
266s  assert (class (CVMdl), "ClassificationPartitionedModel")
266s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
266s  assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
266s  assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
267s ***** test
267s  partition = cvpartition (y, 'KFold', 3);
267s  CVMdl = crossval (obj, 'cvPartition', partition);
267s  assert (class (CVMdl), "ClassificationPartitionedModel")
267s  assert (CVMdl.KFold == 3)
267s  assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
267s  assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
267s ***** error<ClassificationDiscriminant.crossval: Name-Value arguments must be in pairs.> ...
267s  crossval (obj, "kfold")
267s ***** error<ClassificationDiscriminant.crossval: specify only one of the optional Name-Value paired arguments.>...
267s  crossval (obj, "kfold", 12, "holdout", 0.2)
267s ***** error<ClassificationDiscriminant.crossval: 'KFold' must be an integer value greater than 1.> ...
267s  crossval (obj, "kfold", 'a')
267s ***** error<ClassificationDiscriminant.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
267s  crossval (obj, "holdout", 2)
267s ***** error<ClassificationDiscriminant.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
267s  crossval (obj, "leaveout", 1)
267s ***** error<ClassificationDiscriminant.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
267s  crossval (obj, "cvpartition", 1)
267s 67 tests, 67 passed, 0 known failure, 0 skipped
267s [inst/Classification/ClassificationGAM.m]
267s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/Classification/ClassificationGAM.m
267s ***** demo
267s  ## Train a GAM classifier for binary classification
267s  ## using specific data and plot the decision boundaries.
267s 
267s  ## Define specific data
267s  X = [1, 2; 2, 3; 3, 3; 4, 5; 5, 5; ...
267s      6, 7; 7, 8; 8, 8; 9, 9; 10, 10];
267s  Y = [0; 0; 0; 0; 0; ...
267s      1; 1; 1; 1; 1];
267s 
267s  ## Train the GAM model
267s  obj = fitcgam (X, Y, "Interactions", "all")
267s 
267s  ## Create a grid of values for prediction
267s  x1 = [min(X(:,1)):0.1:max(X(:,1))];
267s  x2 = [min(X(:,2)):0.1:max(X(:,2))];
267s  [x1G, x2G] = meshgrid (x1, x2);
267s  XGrid = [x1G(:), x2G(:)];
267s  [labels, score] = predict (obj, XGrid);
267s ***** test
267s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
267s  y = [0; 0; 1; 1];
267s  PredictorNames = {'Feature1', 'Feature2', 'Feature3'};
267s  a = ClassificationGAM (x, y, "PredictorNames", PredictorNames);
267s  assert (class (a), "ClassificationGAM");
267s  assert ({a.X, a.Y, a.NumObservations}, {x, y, 4})
267s  assert ({a.NumPredictors, a.ResponseName}, {3, "Y"})
267s  assert (a.ClassNames, {'0'; '1'})
267s  assert (a.PredictorNames, PredictorNames)
267s  assert (a.BaseModel.Intercept, 0)
269s ***** test
269s  load fisheriris
269s  inds = strcmp (species,'versicolor') | strcmp (species,'virginica');
269s  X = meas(inds, :);
269s  Y = species(inds, :)';
269s  Y = strcmp (Y, 'virginica')';
269s  a = ClassificationGAM (X, Y, 'Formula', 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3');
269s  assert (class (a), "ClassificationGAM");
269s  assert ({a.X, a.Y, a.NumObservations}, {X, Y, 100})
269s  assert ({a.NumPredictors, a.ResponseName}, {4, "Y"})
269s  assert (a.ClassNames, {'0'; '1'})
269s  assert (a.Formula, 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3')
269s  assert (a.PredictorNames, {'x1', 'x2', 'x3', 'x4'})
269s  assert (a.ModelwInt.Intercept, 0)
276s ***** test
276s  X = [2, 3, 5; 4, 6, 8; 1, 2, 3; 7, 8, 9; 5, 4, 3];
276s  Y = [0; 1; 0; 1; 1];
276s  a = ClassificationGAM (X, Y, 'Knots', [4, 4, 4], 'Order', [3, 3, 3]);
276s  assert (class (a), "ClassificationGAM");
276s  assert ({a.X, a.Y, a.NumObservations}, {X, Y, 5})
276s  assert ({a.NumPredictors, a.ResponseName}, {3, "Y"})
276s  assert (a.ClassNames, {'0'; '1'})
276s  assert (a.PredictorNames, {'x1', 'x2', 'x3'})
276s  assert (a.Knots, [4, 4, 4])
276s  assert (a.Order, [3, 3, 3])
276s  assert (a.DoF, [7, 7, 7])
276s  assert (a.BaseModel.Intercept, 0.4055, 1e-1)
278s ***** error<ClassificationGAM: too few input arguments.> ClassificationGAM ()
278s ***** error<ClassificationGAM: too few input arguments.> ...
278s  ClassificationGAM (ones(4, 1))
278s ***** error<ClassificationGAM: number of rows in X and Y must be equal.> ...
278s  ClassificationGAM (ones (4,2), ones (1,4))
278s ***** error<ClassificationGAM: 'PredictorNames' must be supplied as a cellstring array.> ...
278s  ClassificationGAM (ones (5,2), ones (5,1), "PredictorNames", ["A"])
278s ***** error<ClassificationGAM: 'PredictorNames' must be supplied as a cellstring array.> ...
278s  ClassificationGAM (ones (5,2), ones (5,1), "PredictorNames", "A")
278s ***** error<ClassificationGAM: 'PredictorNames' must equal the number of columns in X.> ...
278s  ClassificationGAM (ones (5,2), ones (5,1), "PredictorNames", {"A", "B", "C"})
279s ***** error<ClassificationGAM: 'ResponseName' must be a character vector.> ...
279s  ClassificationGAM (ones (5,2), ones (5,1), "ResponseName", {"Y"})
279s ***** error<ClassificationGAM: 'ResponseName' must be a character vector.> ...
279s  ClassificationGAM (ones (5,2), ones (5,1), "ResponseName", 1)
279s ***** error<ClassificationGAM: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
279s  ClassificationGAM (ones(10,2), ones (10,1), "ClassNames", @(x)x)
279s ***** error<ClassificationGAM: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
279s  ClassificationGAM (ones(10,2), ones (10,1), "ClassNames", {1})
279s ***** error<ClassificationGAM: not all 'ClassNames' are present in Y.> ...
279s  ClassificationGAM (ones(10,2), ones (10,1), "ClassNames", [1, 2])
279s ***** error<ClassificationGAM: not all 'ClassNames' are present in Y.> ...
279s  ClassificationGAM (ones(5,2), ['a';'b';'a';'a';'b'], "ClassNames", ['a';'c'])
279s ***** error<ClassificationGAM: not all 'ClassNames' are present in Y.> ...
279s  ClassificationGAM (ones(5,2), {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
279s ***** error<ClassificationGAM: not all 'ClassNames' are present in Y.> ...
279s  ClassificationGAM (ones(10,2), logical (ones (10,1)), "ClassNames", [true, false])
279s ***** error<ClassificationGAM: 'Cost' must be a numeric square matrix.> ...
279s  ClassificationGAM (ones (5,2), ones (5,1), "Cost", [1, 2])
279s ***** error<ClassificationGAM: 'Cost' must be a numeric square matrix.> ...
279s  ClassificationGAM (ones (5,2), ones (5,1), "Cost", "string")
279s ***** error<ClassificationGAM: 'Cost' must be a numeric square matrix.> ...
279s  ClassificationGAM (ones (5,2), ones (5,1), "Cost", {eye(2)})
279s ***** test
279s  x = [1, 2; 3, 4; 5, 6; 7, 8; 9, 10];
279s  y = [1; 0; 1; 0; 1];
279s  a = ClassificationGAM (x, y, "interactions", "all");
279s  l = {'0'; '0'; '0'; '0'; '0'};
279s  s = [0.3760, 0.6240; 0.4259, 0.5741; 0.3760, 0.6240; ...
279s       0.4259, 0.5741; 0.3760, 0.6240];
279s  [labels, scores] = predict (a, x);
279s  assert (class (a), "ClassificationGAM");
279s  assert ({a.X, a.Y, a.NumObservations}, {x, y, 5})
279s  assert ({a.NumPredictors, a.ResponseName}, {2, "Y"})
279s  assert (a.ClassNames, {'1'; '0'})
279s  assert (a.PredictorNames, {'x1', 'x2'})
279s  assert (a.ModelwInt.Intercept, 0.4055, 1e-1)
279s  assert (labels, l)
279s  assert (scores, s, 1e-1)
282s ***** test
282s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
282s  y = [0; 0; 1; 1];
282s  interactions = [false, true, false; true, false, true; false, true, false];
282s  a = fitcgam (x, y, "learningrate", 0.2, "interactions", interactions);
282s  [label, score] = predict (a, x, "includeinteractions", true);
282s  l = {'0'; '0'; '1'; '1'};
282s  s = [0.5106, 0.4894; 0.5135, 0.4865; 0.4864, 0.5136; 0.4847, 0.5153];
282s  assert (class (a), "ClassificationGAM");
282s  assert ({a.X, a.Y, a.NumObservations}, {x, y, 4})
282s  assert ({a.NumPredictors, a.ResponseName}, {3, "Y"})
282s  assert (a.ClassNames, {'0'; '1'})
282s  assert (a.PredictorNames, {'x1', 'x2', 'x3'})
282s  assert (a.ModelwInt.Intercept, 0)
282s  assert (label, l)
282s  assert (score, s, 1e-1)
289s ***** error<ClassificationGAM.predict: too few input arguments.> ...
289s  predict (ClassificationGAM (ones (4,2), ones (4,1)))
290s ***** error<ClassificationGAM.predict: XC is empty.> ...
290s  predict (ClassificationGAM (ones (4,2), ones (4,1)), [])
292s ***** error<ClassificationGAM.predict: XC must have the same number of predictors as the trained model.> ...
292s  predict (ClassificationGAM (ones (4,2), ones (4,1)), 1)
293s ***** shared x, y, obj
293s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1; 4, 5, 6];
293s  y = [0; 0; 1; 1; 0];
293s  obj = fitcgam (x, y);
296s ***** test
296s  CVMdl = crossval (obj);
296s  assert (class (CVMdl), "ClassificationPartitionedModel")
296s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
296s  assert (CVMdl.KFold == 4)
296s  assert (class (CVMdl.Trained{1}), "CompactClassificationGAM")
296s  assert (CVMdl.CrossValidatedModel, "ClassificationGAM")
296s warning: One or more of the unique class values in the stratification variable is not present in one or more folds.
296s warning: called from
296s     cvpartition at line 764 column 19
296s     crossval at line 792 column 9
296s     __test__ at line 3 column 2
296s     test at line 685 column 11
296s     /tmp/tmp.scCa8Po4At at line 22 column 2
296s 
305s ***** test
305s  CVMdl = crossval (obj, "KFold", 2);
305s  assert (class (CVMdl), "ClassificationPartitionedModel")
305s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
305s  assert (CVMdl.KFold == 2)
305s  assert (class (CVMdl.Trained{1}), "CompactClassificationGAM")
305s  assert (CVMdl.CrossValidatedModel, "ClassificationGAM")
309s ***** test
309s  CVMdl = crossval (obj, "HoldOut", 0.2);
309s  assert (class (CVMdl), "ClassificationPartitionedModel")
309s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
309s  assert (class (CVMdl.Trained{1}), "CompactClassificationGAM")
309s  assert (CVMdl.CrossValidatedModel, "ClassificationGAM")
311s ***** test
311s  partition = cvpartition (y, 'KFold', 3);
311s  CVMdl = crossval (obj, 'cvPartition', partition);
311s  assert (class (CVMdl), "ClassificationPartitionedModel")
311s  assert (CVMdl.KFold == 3)
311s  assert (class (CVMdl.Trained{1}), "CompactClassificationGAM")
311s  assert (CVMdl.CrossValidatedModel, "ClassificationGAM")
311s warning: One or more of the unique class values in the stratification variable is not present in one or more folds.
311s warning: called from
311s     cvpartition at line 764 column 19
311s     __test__ at line 3 column 2
311s     test at line 685 column 11
311s     /tmp/tmp.scCa8Po4At at line 22 column 2
311s 
317s ***** error<ClassificationGAM.crossval: Name-Value arguments must be in pairs.> ...
317s  crossval (obj, "kfold")
317s ***** error<ClassificationGAM.crossval: specify only one of the optional Name-Value paired arguments.>...
317s  crossval (obj, "kfold", 12, "holdout", 0.2)
317s ***** error<ClassificationGAM.crossval: 'KFold' must be an integer value greater than 1.> ...
317s  crossval (obj, "kfold", 'a')
317s ***** error<ClassificationGAM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
317s  crossval (obj, "holdout", 2)
317s ***** error<ClassificationGAM.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
317s  crossval (obj, "leaveout", 1)
317s ***** error<ClassificationGAM.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
317s  crossval (obj, "cvpartition", 1)
317s 35 tests, 35 passed, 0 known failure, 0 skipped
317s [inst/Classification/ClassificationKNN.m]
317s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/Classification/ClassificationKNN.m
317s ***** demo
317s  ## Create a k-nearest neighbor classifier for Fisher's iris data with k = 5.
317s  ## Evaluate some model predictions on new data.
317s 
317s  load fisheriris
317s  x = meas;
317s  y = species;
317s  xc = [min(x); mean(x); max(x)];
317s  obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);
317s  [label, score, cost] = predict (obj, xc)
317s ***** demo
317s  load fisheriris
317s  x = meas;
317s  y = species;
317s  obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);
317s 
317s  ## Create a cross-validated model
317s  CVMdl = crossval (obj)
317s ***** demo
317s  load fisheriris
317s  x = meas;
317s  y = species;
317s  covMatrix = cov (x);
317s 
317s  ## Fit the k-NN model using the 'mahalanobis' distance
317s  ## and the custom covariance matrix
317s  obj = fitcknn(x, y, 'NumNeighbors', 5, 'Distance','mahalanobis', ...
317s  'Cov', covMatrix);
317s 
317s  ## Create a partition model using cvpartition
317s  Partition = cvpartition (size (x, 1), 'kfold', 12);
317s 
317s  ## Create cross-validated model using 'cvPartition' name-value argument
317s  CVMdl = crossval (obj, 'cvPartition', Partition)
317s 
317s  ## Access the trained model from first fold of cross-validation
317s  CVMdl.Trained{1}
317s ***** demo
317s  X = [1, 2; 3, 4; 5, 6];
317s  Y = {'A'; 'B'; 'A'};
317s  model = fitcknn (X, Y);
317s  customLossFun = @(C, S, W, Cost) sum (W .* sum (abs (C - S), 2));
317s  ## Calculate loss using custom loss function
317s  L = loss (model, X, Y, 'LossFun', customLossFun)
317s ***** demo
317s  X = [1, 2; 3, 4; 5, 6];
317s  Y = {'A'; 'B'; 'A'};
317s  model = fitcknn (X, Y);
317s  ## Calculate loss using 'mincost' loss function
317s  L = loss (model, X, Y, 'LossFun', 'mincost')
317s ***** demo
317s  X = [1, 2; 3, 4; 5, 6];
317s  Y = ['1'; '2'; '3'];
317s  model = fitcknn (X, Y);
317s  X_test = [3, 3; 5, 7];
317s  Y_test = ['1'; '2'];
317s  ## Specify custom Weights
317s  W = [1; 2];
317s  L = loss (model, X_test, Y_test, 'LossFun', 'logit', 'Weights', W);
317s ***** demo
317s  load fisheriris
317s  mdl = fitcknn (meas, species);
317s  X = mean (meas);
317s  Y = {'versicolor'};
317s  m = margin (mdl, X, Y)
317s ***** demo
317s  X = [1, 2; 4, 5; 7, 8; 3, 2];
317s  Y = [2; 1; 3; 2];
317s  ## Train the model
317s  mdl = fitcknn (X, Y);
317s  ## Specify Vars and Labels
317s  Vars = 1;
317s  Labels = 2;
317s  ## Calculate partialDependence
317s  [pd, x, y] = partialDependence (mdl, Vars, Labels);
317s ***** demo
317s  X = [1, 2; 4, 5; 7, 8; 3, 2];
317s  Y = [2; 1; 3; 2];
317s  ## Train the model
317s  mdl = fitcknn (X, Y);
317s  ## Specify Vars and Labels
317s  Vars = 1;
317s  Labels = 1;
317s  queryPoints = [linspace(0, 1, 3)', linspace(0, 1, 3)'];
317s  ## Calculate partialDependence using queryPoints
317s  [pd, x, y] = partialDependence (mdl, Vars, Labels, 'QueryPoints', ...
317s  queryPoints)
317s ***** test
317s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
317s  y = ["a"; "a"; "b"; "b"];
317s  a = ClassificationKNN (x, y);
317s  assert (class (a), "ClassificationKNN");
317s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
317s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
317s  assert ({a.BucketSize}, {50})
318s ***** test
318s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
318s  y = ["a"; "a"; "b"; "b"];
318s  a = ClassificationKNN (x, y, "NSMethod", "exhaustive");
318s  assert (class (a), "ClassificationKNN");
318s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
318s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
318s  assert ({a.BucketSize}, {50})
318s ***** test
318s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
318s  y = ["a"; "a"; "b"; "b"];
318s  k = 10;
318s  a = ClassificationKNN (x, y, "NumNeighbors" ,k);
318s  assert (class (a), "ClassificationKNN");
318s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
318s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
318s  assert ({a.BucketSize}, {50})
318s ***** test
318s  x = ones (4, 11);
318s  y = ["a"; "a"; "b"; "b"];
318s  k = 10;
318s  a = ClassificationKNN (x, y, "NumNeighbors" ,k);
318s  assert (class (a), "ClassificationKNN");
318s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
318s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
318s  assert ({a.BucketSize}, {50})
318s ***** test
318s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
318s  y = ["a"; "a"; "b"; "b"];
318s  k = 10;
318s  a = ClassificationKNN (x, y, "NumNeighbors" ,k, "NSMethod", "exhaustive");
318s  assert (class (a), "ClassificationKNN");
318s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
318s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
318s  assert ({a.BucketSize}, {50})
318s ***** test
318s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
318s  y = ["a"; "a"; "b"; "b"];
318s  k = 10;
318s  a = ClassificationKNN (x, y, "NumNeighbors" ,k, "Distance", "hamming");
318s  assert (class (a), "ClassificationKNN");
318s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
318s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "hamming"})
318s  assert ({a.BucketSize}, {50})
318s ***** test
318s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
318s  y = ["a"; "a"; "b"; "b"];
318s  weights = ones (4,1);
318s  a = ClassificationKNN (x, y, "Standardize", 1);
318s  assert (class (a), "ClassificationKNN");
318s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
318s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
318s  assert ({a.Standardize}, {true})
318s  assert ({a.Sigma}, {std(x, [], 1)})
318s  assert ({a.Mu}, {[3.75, 4.25, 4.75]})
318s ***** test
318s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
318s  y = ["a"; "a"; "b"; "b"];
318s  weights = ones (4,1);
318s  a = ClassificationKNN (x, y, "Standardize", false);
318s  assert (class (a), "ClassificationKNN");
318s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
318s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
318s  assert ({a.Standardize}, {false})
318s  assert ({a.Sigma}, {[]})
318s  assert ({a.Mu}, {[]})
318s ***** test
318s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
318s  y = ["a"; "a"; "b"; "b"];
318s  s = ones (1, 3);
318s  a = ClassificationKNN (x, y, "Scale" , s, "Distance", "seuclidean");
318s  assert (class (a), "ClassificationKNN");
318s  assert ({a.DistParameter}, {s})
318s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "seuclidean"})
318s  assert ({a.BucketSize}, {50})
318s ***** test
318s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
318s  y = ["a"; "a"; "b"; "b"];
318s  a = ClassificationKNN (x, y, "Exponent" , 5, "Distance", "minkowski");
318s  assert (class (a), "ClassificationKNN");
318s  assert (a.DistParameter, 5)
318s  assert ({a.NSMethod, a.Distance}, {"kdtree", "minkowski"})
318s ***** test
318s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
318s  y = ["a"; "a"; "b"; "b"];
318s  a = ClassificationKNN (x, y, "Exponent" , 5, "Distance", "minkowski", ...
318s                         "NSMethod", "exhaustive");
318s  assert (class (a), "ClassificationKNN");
318s  assert (a.DistParameter, 5)
318s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "minkowski"})
318s ***** test
318s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
318s  y = ["a"; "a"; "b"; "b"];
318s  a = ClassificationKNN (x, y, "BucketSize" , 20, "distance", "mahalanobis");
318s  assert (class (a), "ClassificationKNN");
318s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "mahalanobis"})
318s  assert ({a.BucketSize}, {20})
318s ***** test
318s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
318s  y = ["a"; "a"; "b"; "b"];
318s  a = ClassificationKNN (x, y, "IncludeTies", true);
318s  assert (class (a), "ClassificationKNN");
318s  assert (a.IncludeTies, true);
318s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
318s ***** test
318s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
318s  y = ["a"; "a"; "b"; "b"];
318s  a = ClassificationKNN (x, y);
318s  assert (class (a), "ClassificationKNN");
318s  assert (a.IncludeTies, false);
318s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
318s ***** test
318s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
318s  y = ["a"; "a"; "b"; "b"];
318s  a = ClassificationKNN (x, y);
318s  assert (class (a), "ClassificationKNN")
318s  assert (a.Prior, [0.5; 0.5])
318s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
318s  assert ({a.BucketSize}, {50})
318s ***** test
318s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
318s  y = ["a"; "a"; "b"; "b"];
318s  prior = [0.5; 0.5];
318s  a = ClassificationKNN (x, y, "Prior", "empirical");
318s  assert (class (a), "ClassificationKNN")
318s  assert (a.Prior, prior)
318s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
318s  assert ({a.BucketSize}, {50})
318s ***** test
318s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
318s  y = ["a"; "a"; "a"; "b"];
318s  prior = [0.75; 0.25];
318s  a = ClassificationKNN (x, y, "Prior", "empirical");
318s  assert (class (a), "ClassificationKNN")
318s  assert (a.Prior, prior)
318s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
318s  assert ({a.BucketSize}, {50})
318s ***** test
318s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
318s  y = ["a"; "a"; "a"; "b"];
318s  prior = [0.5; 0.5];
318s  a = ClassificationKNN (x, y, "Prior", "uniform");
318s  assert (class (a), "ClassificationKNN")
318s  assert (a.Prior, prior)
318s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
318s  assert ({a.BucketSize}, {50})
318s ***** test
318s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
318s  y = ["a"; "a"; "b"; "b"];
318s  cost = eye (2);
318s  a = ClassificationKNN (x, y, "Cost", cost);
318s  assert (class (a), "ClassificationKNN")
318s  assert (a.Cost, [1, 0; 0, 1])
318s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
318s  assert ({a.BucketSize}, {50})
318s ***** test
318s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
318s  y = ["a"; "a"; "b"; "b"];
318s  cost = eye (2);
318s  a = ClassificationKNN (x, y, "Cost", cost, "Distance", "hamming" );
318s  assert (class (a), "ClassificationKNN")
318s  assert (a.Cost, [1, 0; 0, 1])
318s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "hamming"})
318s  assert ({a.BucketSize}, {50})
318s ***** test
318s  x = [1, 2; 3, 4; 5,6; 5, 8];
318s  y = {'9'; '9'; '6'; '7'};
318s  a = ClassificationKNN (x, y);
318s  assert (a.Prior, [0.25; 0.25; 0.5])
318s ***** test
318s  load fisheriris
318s  x = meas;
318s  y = species;
318s  ClassNames = {'setosa', 'versicolor', 'virginica'};
318s  a = ClassificationKNN (x, y, 'ClassNames', ClassNames);
318s  assert (a.ClassNames, ClassNames')
318s ***** error<ClassificationKNN: too few input arguments.> ClassificationKNN ()
318s ***** error<ClassificationKNN: too few input arguments.> ...
318s  ClassificationKNN (ones(4, 1))
318s ***** error<ClassificationKNN: number of rows in X and Y must be equal.> ...
318s  ClassificationKNN (ones (4,2), ones (1,4))
318s ***** error<ClassificationKNN: 'Standardize' must be either true or false.> ...
318s  ClassificationKNN (ones (5,3), ones (5,1), "standardize", "a")
318s ***** error<ClassificationKNN: 'Standardize' cannot simultaneously be specified with> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "scale", [1 1], "standardize", true)
318s ***** error<ClassificationKNN: 'PredictorNames' must be supplied as a cellstring array.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "PredictorNames", ["A"])
318s ***** error<ClassificationKNN: 'PredictorNames' must be supplied as a cellstring array.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "PredictorNames", "A")
318s ***** error<ClassificationKNN: 'PredictorNames' must have the same number of columns as X.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "PredictorNames", {"A", "B", "C"})
318s ***** error<ClassificationKNN: 'ResponseName' must be a character vector.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "ResponseName", {"Y"})
318s ***** error<ClassificationKNN: 'ResponseName' must be a character vector.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "ResponseName", 1)
318s ***** error<ClassificationKNN: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
318s  ClassificationKNN (ones(10,2), ones (10,1), "ClassNames", @(x)x)
318s ***** error<ClassificationKNN: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
318s  ClassificationKNN (ones(10,2), ones (10,1), "ClassNames", {1})
318s ***** error<ClassificationKNN: not all 'ClassNames' are present in Y.> ...
318s  ClassificationKNN (ones(10,2), ones (10,1), "ClassNames", [1, 2])
318s ***** error<ClassificationKNN: not all 'ClassNames' are present in Y.> ...
318s  ClassificationKNN (ones(5,2), ['a';'b';'a';'a';'b'], "ClassNames", ['a';'c'])
318s ***** error<ClassificationKNN: not all 'ClassNames' are present in Y.> ...
318s  ClassificationKNN (ones(5,2), {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
318s ***** error<ClassificationKNN: not all 'ClassNames' are present in Y.> ...
318s  ClassificationKNN (ones(10,2), logical (ones (10,1)), "ClassNames", [true, false])
318s ***** error<ClassificationKNN: 'BreakTies' must be a character vector.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "BreakTies", 1)
318s ***** error<ClassificationKNN: 'BreakTies' must be a character vector.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "BreakTies", {"1"})
318s ***** error<ClassificationKNN: invalid value for 'BreakTies'.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "BreakTies", "some")
318s ***** error<ClassificationKNN: 'Prior' must be either a numeric vector or a character vector.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "Prior", {"1", "2"})
318s ***** error<ClassificationKNN: 'Cost' must be a numeric square matrix.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "Cost", [1, 2])
318s ***** error<ClassificationKNN: 'Cost' must be a numeric square matrix.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "Cost", "string")
318s ***** error<ClassificationKNN: 'Cost' must be a numeric square matrix.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "Cost", {eye(2)})
318s ***** error<ClassificationKNN: 'NumNeighbors' must be a positive integer.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "NumNeighbors", 0)
318s ***** error<ClassificationKNN: 'NumNeighbors' must be a positive integer.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "NumNeighbors", 15.2)
318s ***** error<ClassificationKNN: 'NumNeighbors' must be a positive integer.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "NumNeighbors", "asd")
318s ***** error<ClassificationKNN: unsupported distance metric.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", "somemetric")
318s ***** error<ClassificationKNN: invalid function handle for distance metric.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", ...
318s                     @(v,m)sqrt(repmat(v,rows(m),1)-m,2))
318s ***** error<ClassificationKNN: custom distance function produces wrong output size.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", ...
318s                     @(v,m)sqrt(sum(sumsq(repmat(v,rows(m),1)-m,2))))
318s ***** error<ClassificationKNN: invalid distance metric.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", [1 2 3])
318s ***** error<ClassificationKNN: invalid distance metric.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", {"mahalanobis"})
318s ***** error<ClassificationKNN: invalid distance metric.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", logical (5))
318s ***** error<ClassificationKNN: function handle for distance weight must return the> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "DistanceWeight", @(x)sum(x))
318s ***** error<ClassificationKNN: invalid distance weight.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "DistanceWeight", "text")
318s ***** error<ClassificationKNN: invalid distance weight.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "DistanceWeight", [1 2 3])
318s ***** error<ClassificationKNN: 'Scale' must be a numeric vector.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "Scale", "scale")
318s ***** error<ClassificationKNN: 'Scale' must be a numeric vector.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "Scale", {[1 2 3]})
318s ***** error<ClassificationKNN: 'Scale' cannot simultaneously be specified with> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "standardize", true, "scale", [1 1])
318s ***** error<ClassificationKNN: 'Cov' must be a symmetric positive definite matrix.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "Cov", ones (2), "Distance", "mahalanobis")
318s ***** error<ClassificationKNN: 'Cov' cannot simultaneously be specified with> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "scale", [1 1], "Cov", ones (2))
318s ***** error<ClassificationKNN: 'Exponent' must be a positive integer.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "Exponent", 12.5)
318s ***** error<ClassificationKNN: 'Exponent' must be a positive integer.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "Exponent", -3)
318s ***** error<ClassificationKNN: 'Exponent' must be a positive integer.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "Exponent", "three")
318s ***** error<ClassificationKNN: 'Exponent' must be a positive integer.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "Exponent", {3})
318s ***** error<ClassificationKNN: 'NSMethod' must be a character vector.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "NSMethod", {"kdtree"})
318s ***** error<ClassificationKNN: 'NSMethod' must be a character vector.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "NSMethod", 3)
318s ***** error<ClassificationKNN: 'NSMethod' must be either 'kdtree' or 'exhaustive'.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "NSMethod", "some")
318s ***** error<ClassificationKNN: 'IncludeTies' must be either true or false.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "IncludeTies", "some")
318s ***** error<ClassificationKNN: 'BucketSize' must be a positive integer.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "BucketSize", 42.5)
318s ***** error<ClassificationKNN: 'BucketSize' must be a positive integer.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "BucketSize", -50)
318s ***** error<ClassificationKNN: 'BucketSize' must be a positive integer.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "BucketSize", "some")
318s ***** error<ClassificationKNN: 'BucketSize' must be a positive integer.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "BucketSize", {50})
318s ***** error<ClassificationKNN: invalid parameter name in optional pair arguments.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "some", "some")
318s ***** error<ClassificationKNN: invalid values in X.> ...
318s  ClassificationKNN ([1;2;3;'a';4], ones (5,1))
318s ***** error<ClassificationKNN: invalid values in X.> ...
318s  ClassificationKNN ([1;2;3;Inf;4], ones (5,1))
318s ***** error<ClassificationKNN: the elements in 'Prior' do not correspond to selected classes in Y.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "Prior", [1 2])
318s ***** error<ClassificationKNN: the number of rows and columns in 'Cost' must correspond to selected classes in Y.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "Cost", [1 2; 1 3])
318s ***** error<ClassificationKNN: 'Scale' is only valid when distance metric is seuclidean.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "Scale", [1 1])
318s ***** error<ClassificationKNN: 'Scale' vector must have equal length to the number of columns in X.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "Scale", [1 1 1], "Distance", "seuclidean")
318s ***** error<ClassificationKNN: 'Scale' vector must contain nonnegative scalar values.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "Scale", [1 -1], "Distance", "seuclidean")
318s ***** error<ClassificationKNN: 'Cov' is only valid when distance metric is 'mahalanobis'.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "Cov", eye (2))
318s ***** error<ClassificationKNN: 'Cov' matrix must have equal columns as X.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "Cov", eye (3), "Distance", "mahalanobis")
318s ***** error<ClassificationKNN: 'Exponent' is only valid when distance metric is 'minkowski'.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "Exponent", 3)
318s ***** error<ClassificationKNN: 'kdtree' method is only valid for 'euclidean', 'cityblock', 'manhattan', 'minkowski', and 'chebychev' distance metrics.> ...
318s  ClassificationKNN (ones (5,2), ones (5,1), "Distance", "hamming", "NSMethod", "kdtree")
318s ***** shared x, y
318s  load fisheriris
318s  x = meas;
318s  y = species;
318s ***** test
318s  xc = [min(x); mean(x); max(x)];
318s  obj = fitcknn (x, y, "NumNeighbors", 5);
318s  [l, s, c] = predict (obj, xc);
318s  assert (l, {"setosa"; "versicolor"; "virginica"})
318s  assert (s, [1, 0, 0; 0, 1, 0; 0, 0, 1])
318s  assert (c, [0, 1, 1; 1, 0, 1; 1, 1, 0])
318s ***** test
318s  xc = [min(x); mean(x); max(x)];
318s  obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);
318s  [l, s, c] = predict (obj, xc);
318s  assert (l, {"versicolor"; "versicolor"; "virginica"})
318s  assert (s, [0.4, 0.6, 0; 0, 1, 0; 0, 0, 1])
318s  assert (c, [0.6, 0.4, 1; 1, 0, 1; 1, 1, 0])
318s ***** test
318s  xc = [min(x); mean(x); max(x)];
318s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "mahalanobis");
318s  [l, s, c] = predict (obj, xc);
318s  assert (s, [0.3, 0.7, 0; 0, 0.9, 0.1; 0.2, 0.2, 0.6], 1e-4)
318s  assert (c, [0.7, 0.3, 1; 1, 0.1, 0.9; 0.8, 0.8, 0.4], 1e-4)
318s ***** test
318s  xc = [min(x); mean(x); max(x)];
318s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "cosine");
318s  [l, s, c] = predict (obj, xc);
318s  assert (l, {"setosa"; "versicolor"; "virginica"})
318s  assert (s, [1, 0, 0; 0, 1, 0; 0, 0.3, 0.7], 1e-4)
318s  assert (c, [0, 1, 1; 1, 0, 1; 1, 0.7, 0.3], 1e-4)
318s ***** test
318s  xc = [5.2, 4.1, 1.5, 0.1; 5.1, 3.8, 1.9, 0.4; ...
318s          5.1, 3.8, 1.5, 0.3; 4.9, 3.6, 1.4, 0.1];
318s  obj = fitcknn (x, y, "NumNeighbors", 5);
318s  [l, s, c] = predict (obj, xc);
318s  assert (l, {"setosa"; "setosa"; "setosa"; "setosa"})
318s  assert (s, [1, 0, 0; 1, 0, 0; 1, 0, 0; 1, 0, 0])
318s  assert (c, [0, 1, 1; 0, 1, 1; 0, 1, 1; 0, 1, 1])
318s ***** test
318s  xc = [5, 3, 5, 1.45];
318s  obj = fitcknn (x, y, "NumNeighbors", 5);
318s  [l, s, c] = predict (obj, xc);
318s  assert (l, {"versicolor"})
318s  assert (s, [0, 0.6, 0.4], 1e-4)
318s  assert (c, [1, 0.4, 0.6], 1e-4)
318s ***** test
318s  xc = [5, 3, 5, 1.45];
318s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "minkowski", "Exponent", 5);
318s  [l, s, c] = predict (obj, xc);
318s  assert (l, {"versicolor"})
318s  assert (s, [0, 0.5, 0.5], 1e-4)
318s  assert (c, [1, 0.5, 0.5], 1e-4)
318s ***** test
318s  xc = [5, 3, 5, 1.45];
318s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "jaccard");
318s  [l, s, c] = predict (obj, xc);
318s  assert (l, {"setosa"})
318s  assert (s, [0.9, 0.1, 0], 1e-4)
318s  assert (c, [0.1, 0.9, 1], 1e-4)
319s ***** test
319s  xc = [5, 3, 5, 1.45];
319s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "mahalanobis");
319s  [l, s, c] = predict (obj, xc);
319s  assert (l, {"versicolor"})
319s  assert (s, [0.1000, 0.5000, 0.4000], 1e-4)
319s  assert (c, [0.9000, 0.5000, 0.6000], 1e-4)
319s ***** test
319s  xc = [5, 3, 5, 1.45];
319s  obj = fitcknn (x, y, "NumNeighbors", 5, "distance", "jaccard");
319s  [l, s, c] = predict (obj, xc);
319s  assert (l, {"setosa"})
319s  assert (s, [0.8, 0.2, 0], 1e-4)
319s  assert (c, [0.2, 0.8, 1], 1e-4)
319s ***** test
319s  xc = [5, 3, 5, 1.45];
319s  obj = fitcknn (x, y, "NumNeighbors", 5, "distance", "seuclidean");
319s  [l, s, c] = predict (obj, xc);
319s  assert (l, {"versicolor"})
319s  assert (s, [0, 1, 0], 1e-4)
319s  assert (c, [1, 0, 1], 1e-4)
319s ***** test
319s  xc = [5, 3, 5, 1.45];
319s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "chebychev");
319s  [l, s, c] = predict (obj, xc);
319s  assert (l, {"versicolor"})
319s  assert (s, [0, 0.7, 0.3], 1e-4)
319s  assert (c, [1, 0.3, 0.7], 1e-4)
319s ***** test
319s  xc = [5, 3, 5, 1.45];
319s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "cityblock");
319s  [l, s, c] = predict (obj, xc);
319s  assert (l, {"versicolor"})
319s  assert (s, [0, 0.6, 0.4], 1e-4)
319s  assert (c, [1, 0.4, 0.6], 1e-4)
319s ***** test
319s  xc = [5, 3, 5, 1.45];
319s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "cosine");
319s  [l, s, c] = predict (obj, xc);
319s  assert (l, {"virginica"})
319s  assert (s, [0, 0.1, 0.9], 1e-4)
319s  assert (c, [1, 0.9, 0.1], 1e-4)
319s ***** test
319s  xc = [5, 3, 5, 1.45];
319s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "correlation");
319s  [l, s, c] = predict (obj, xc);
319s  assert (l, {"virginica"})
319s  assert (s, [0, 0.1, 0.9], 1e-4)
319s  assert (c, [1, 0.9, 0.1], 1e-4)
319s ***** test
319s  xc = [5, 3, 5, 1.45];
319s  obj = fitcknn (x, y, "NumNeighbors", 30, "distance", "spearman");
319s  [l, s, c] = predict (obj, xc);
319s  assert (l, {"versicolor"})
319s  assert (s, [0, 1, 0], 1e-4)
319s  assert (c, [1, 0, 1], 1e-4)
319s ***** test
319s  xc = [5, 3, 5, 1.45];
319s  obj = fitcknn (x, y, "NumNeighbors", 30, "distance", "hamming");
319s  [l, s, c] = predict (obj, xc);
319s  assert (l, {"setosa"})
319s  assert (s, [0.4333, 0.3333, 0.2333], 1e-4)
319s  assert (c, [0.5667, 0.6667, 0.7667], 1e-4)
319s ***** test
319s  xc = [5, 3, 5, 1.45];
319s  obj = fitcknn (x, y, "NumNeighbors", 5, "distance", "hamming");
319s  [l, s, c] = predict (obj, xc);
319s  assert (l, {"setosa"})
319s  assert (s, [0.8, 0.2, 0], 1e-4)
319s  assert (c, [0.2, 0.8, 1], 1e-4)
319s ***** test
319s  xc = [min(x); mean(x); max(x)];
319s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "correlation");
319s  [l, s, c] = predict (obj, xc);
319s  assert (l, {"setosa"; "versicolor"; "virginica"})
319s  assert (s, [1, 0, 0; 0, 1, 0; 0, 0.4, 0.6], 1e-4)
319s  assert (c, [0, 1, 1; 1, 0, 1; 1, 0.6, 0.4], 1e-4)
319s ***** test
319s  xc = [min(x); mean(x); max(x)];
319s  obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "hamming");
319s  [l, s, c] = predict (obj, xc);
319s  assert (l, {"setosa";"setosa";"setosa"})
319s  assert (s, [0.9, 0.1, 0; 1, 0, 0; 0.5, 0, 0.5], 1e-4)
319s  assert (c, [0.1, 0.9, 1; 0, 1, 1; 0.5, 1, 0.5], 1e-4)
319s ***** error<ClassificationKNN.predict: too few input arguments.> ...
319s  predict (ClassificationKNN (ones (4,2), ones (4,1)))
319s ***** error<ClassificationKNN.predict: XC is empty.> ...
319s  predict (ClassificationKNN (ones (4,2), ones (4,1)), [])
319s ***** error<ClassificationKNN.predict: XC must have the same number of predictors as the trained model.> ...
319s  predict (ClassificationKNN (ones (4,2), ones (4,1)), 1)
319s ***** test
319s  load fisheriris
319s  model = fitcknn (meas, species, 'NumNeighbors', 5);
319s  X = mean (meas);
319s  Y = {'versicolor'};
319s  L = loss (model, X, Y);
319s  assert (L, 0)
319s ***** test
319s  load fisheriris
319s  model = fitcknn (meas, species, 'NumNeighbors', 5);
319s  L = loss (model, meas, species, 'LossFun', 'binodeviance');
319s  assert (L, 0.1413, 1e-4)
319s ***** test
319s  load fisheriris
319s  model = fitcknn (meas, species);
319s  L = loss (model, meas, species, 'LossFun', 'binodeviance');
319s  assert (L, 0.1269, 1e-4)
319s ***** test
319s  X = [1, 2; 3, 4; 5, 6];
319s  Y = {'A'; 'B'; 'A'};
319s  model = fitcknn (X, Y);
319s  X_test = [1, 6; 3, 3];
319s  Y_test = {'A'; 'B'};
319s  L = loss (model, X_test, Y_test);
319s  assert (abs (L - 0.6667) > 1e-5)
319s ***** test
319s  X = [1, 2; 3, 4; 5, 6];
319s  Y = {'A'; 'B'; 'A'};
319s  model = fitcknn (X, Y);
319s  X_with_nan = [1, 2; NaN, 4];
319s  Y_test = {'A'; 'B'};
319s  L = loss (model, X_with_nan, Y_test);
319s  assert (abs (L - 0.3333) < 1e-4)
319s ***** test
319s  X = [1, 2; 3, 4; 5, 6];
319s  Y = {'A'; 'B'; 'A'};
319s  model = fitcknn (X, Y);
319s  X_with_nan = [1, 2; NaN, 4];
319s  Y_test = {'A'; 'B'};
319s  L = loss (model, X_with_nan, Y_test, 'LossFun', 'logit');
319s  assert (isnan (L))
319s ***** test
319s  X = [1, 2; 3, 4; 5, 6];
319s  Y = {'A'; 'B'; 'A'};
319s  model = fitcknn (X, Y);
319s  customLossFun = @(C, S, W, Cost) sum (W .* sum (abs (C - S), 2));
319s  L = loss (model, X, Y, 'LossFun', customLossFun);
319s  assert (L, 0)
319s ***** test
319s  X = [1, 2; 3, 4; 5, 6];
319s  Y = [1; 2; 1];
319s  model = fitcknn (X, Y);
319s  L = loss (model, X, Y, 'LossFun', 'classiferror');
319s  assert (L, 0)
320s ***** test
320s  X = [1, 2; 3, 4; 5, 6];
320s  Y = [true; false; true];
320s  model = fitcknn (X, Y);
320s  L = loss (model, X, Y, 'LossFun', 'binodeviance');
320s  assert (abs (L - 0.1269) < 1e-4)
320s ***** test
320s  X = [1, 2; 3, 4; 5, 6];
320s  Y = ['1'; '2'; '1'];
320s  model = fitcknn (X, Y);
320s  L = loss (model, X, Y, 'LossFun', 'classiferror');
320s  assert (L, 0)
320s ***** test
320s  X = [1, 2; 3, 4; 5, 6];
320s  Y = ['1'; '2'; '3'];
320s  model = fitcknn (X, Y);
320s  X_test = [3, 3];
320s  Y_test = ['1'];
320s  L = loss (model, X_test, Y_test, 'LossFun', 'quadratic');
320s  assert (L, 1)
320s ***** test
320s  X = [1, 2; 3, 4; 5, 6];
320s  Y = ['1'; '2'; '3'];
320s  model = fitcknn (X, Y);
320s  X_test = [3, 3; 5, 7];
320s  Y_test = ['1'; '2'];
320s  L = loss (model, X_test, Y_test, 'LossFun', 'classifcost');
320s  assert (L, 1)
320s ***** test
320s  X = [1, 2; 3, 4; 5, 6];
320s  Y = ['1'; '2'; '3'];
320s  model = fitcknn (X, Y);
320s  X_test = [3, 3; 5, 7];
320s  Y_test = ['1'; '2'];
320s  L = loss (model, X_test, Y_test, 'LossFun', 'hinge');
320s  assert (L, 1)
320s ***** test
320s  X = [1, 2; 3, 4; 5, 6];
320s  Y = ['1'; '2'; '3'];
320s  model = fitcknn (X, Y);
320s  X_test = [3, 3; 5, 7];
320s  Y_test = ['1'; '2'];
320s  W = [1; 2];
320s  L = loss (model, X_test, Y_test, 'LossFun', 'logit', 'Weights', W);
320s  assert (abs (L - 0.6931) < 1e-4)
320s ***** error<ClassificationKNN.loss: too few input arguments.> ...
320s  loss (ClassificationKNN (ones (4,2), ones (4,1)))
320s ***** error<ClassificationKNN.loss: too few input arguments.> ...
320s  loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2))
320s ***** error<ClassificationKNN.loss: X is empty.> ...
320s  loss (ClassificationKNN (ones (40,2), randi ([1, 2], 40, 1)), [], zeros (2))
320s ***** error<ClassificationKNN.loss: X must have the same number of predictors as the trained model.> ...
320s  loss (ClassificationKNN (ones (40,2), randi ([1, 2], 40, 1)), 1, zeros (2))
320s ***** error<ClassificationKNN.loss: name-value arguments must be in pairs.> ...
320s  loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ...
320s         ones (4,1), 'LossFun')
320s ***** error<ClassificationKNN.loss: Y must have the same number of rows as X.> ...
320s  loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ones (3,1))
320s ***** error<ClassificationKNN.loss: invalid loss function.> ...
320s  loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ...
320s         ones (4,1), 'LossFun', 'a')
320s ***** error<ClassificationKNN.loss: invalid Weights.> ...
320s  loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ...
320s         ones (4,1), 'Weights', 'w')
320s ***** test
320s  load fisheriris
320s  mdl = fitcknn (meas, species, 'NumNeighbors', 5);
320s  X = mean (meas);
320s  Y = {'versicolor'};
320s  m = margin (mdl, X, Y);
320s  assert (m, 1)
320s ***** test
320s  X = [1, 2; 3, 4; 5, 6];
320s  Y = [1; 2; 3];
320s  mdl = fitcknn (X, Y);
320s  m = margin (mdl, X, Y);
320s  assert (m, [1; 1; 1])
320s ***** test
320s  X = [7, 8; 9, 10];
320s  Y = ['1'; '2'];
320s  mdl = fitcknn (X, Y);
320s  m = margin (mdl, X, Y);
320s  assert (m, [1; 1])
320s ***** test
320s  X = [11, 12];
320s  Y = {'1'};
320s  mdl = fitcknn (X, Y);
320s  m = margin (mdl, X, Y);
320s  assert (isnan (m))
320s ***** test
320s  X = [1, 2; 3, 4; 5, 6];
320s  Y = [1; 2; 3];
320s  mdl = fitcknn (X, Y);
320s  X1 = [15, 16];
320s  Y1 = [1];
320s  m = margin (mdl, X1, Y1);
320s  assert (m, -1)
320s ***** error<ClassificationKNN.margin: too few input arguments.> ...
320s  margin (ClassificationKNN (ones (4,2), ones (4,1)))
320s ***** error<ClassificationKNN.margin: too few input arguments.> ...
320s  margin (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2))
320s ***** error<ClassificationKNN.margin: X is empty.> ...
320s  margin (ClassificationKNN (ones (40,2), randi ([1, 2], 40, 1)), [], zeros (2))
320s ***** error<ClassificationKNN.margin: X must have the same number of predictors as the trained model.> ...
320s  margin (ClassificationKNN (ones (40,2), randi ([1, 2], 40, 1)), 1, zeros (2))
320s ***** error<ClassificationKNN.margin: Y must have the same number of rows as X.> ...
320s  margin (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ones (3,1))
320s ***** shared X, Y, mdl
320s  X = [1, 2; 4, 5; 7, 8; 3, 2];
320s  Y = [2; 1; 3; 2];
320s  mdl = fitcknn (X, Y);
320s ***** test
320s  Vars = 1;
320s  Labels = 2;
320s  [pd, x, y] = partialDependence (mdl, Vars, Labels);
320s  pdm = [0.7500, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
320s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
320s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
320s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
320s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
320s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
320s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
320s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
320s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
320s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
320s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
320s  0.5000, 0.5000];
320s  assert (pd, pdm)
321s ***** test
321s  Vars = 1;
321s  Labels = 2;
321s  [pd, x, y] = partialDependence (mdl, Vars, Labels, ...
321s  'NumObservationsToSample', 5);
321s  pdm = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
321s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
321s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
321s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
321s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0];
321s  assert (abs (pdm - pd) < 1)
321s ***** test
321s  Vars = 1;
321s  Labels = 2;
321s  [pd, x, y] = partialDependence (mdl, Vars, Labels, 'UseParallel', true);
321s  pdm = [0.7500, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
321s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
321s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
321s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
321s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
321s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
321s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
321s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
321s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
321s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
321s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
321s  0.5000, 0.5000];
321s  assert (pd, pdm)
322s ***** test
322s  Vars = [1, 2];
322s  Labels = 1;
322s  queryPoints = {linspace(0, 1, 3)', linspace(0, 1, 3)'};
322s  [pd, x, y] = partialDependence (mdl, Vars, Labels, 'QueryPoints', ...
322s                             queryPoints, 'UseParallel', true);
322s  pdm = [0, 0, 0; 0, 0, 0; 0, 0, 0];
322s  assert (pd, pdm)
322s ***** test
322s  Vars = 1;
322s  Labels = [1; 2];
322s  [pd, x, y] = partialDependence (mdl, Vars, Labels);
322s  pdm = [0.2500, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
322s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
322s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
322s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
322s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
322s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.2500, 0.2500, 0.2500, ...
322s  0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
322s  0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
322s  0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
322s  0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
322s  0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
322s  0.2500, 0.2500; 0.7500, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
322s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
322s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
322s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
322s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
322s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
322s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
322s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
322s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
322s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
322s  0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
322s  0.5000, 0.5000, 0.5000];
322s  assert (pd, pdm)
322s ***** test
322s  Vars = [1, 2];
322s  Labels = [1; 2];
322s  queryPoints = {linspace(0, 1, 3)', linspace(0, 1, 3)'};
322s  [pd, x, y] = partialDependence (mdl, Vars, Labels, 'QueryPoints', queryPoints);
322s  pdm(:,:,1) = [0, 0, 0; 1, 1, 1];
322s  pdm(:,:,2) = [0, 0, 0; 1, 1, 1];
322s  pdm(:,:,3) = [0, 0, 0; 1, 1, 1];
322s  assert (pd, pdm)
323s ***** test
323s  X1 = [1; 2; 4; 5; 7; 8; 3; 2];
323s  X2 = ['2'; '3'; '1'; '3'; '1'; '3'; '2'; '2'];
323s  X = [X1, double(X2)];
323s  Y = [1; 2; 3; 3; 2; 1; 2; 1];
323s  mdl = fitcknn (X, Y, 'ClassNames', {'1', '2', '3'});
323s  Vars = 1;
323s  Labels = 1;
323s  [pd, x, y] = partialDependence (mdl, Vars, Labels);
323s  pdm = [1.0000, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, ...
323s  0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, ...
323s  0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
323s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
323s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3750, ...
323s  0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, ...
323s  0.3750, 0.3750, 0.3750, 0.3750, 0.7500, 0.7500, 0.7500, 0.7500, 0.7500, ...
323s  0.7500, 0.7500, 0.7500];
323s  assert (pd, pdm)
324s ***** test
324s  X1 = [1; 2; 4; 5; 7; 8; 3; 2];
324s  X2 = ['2'; '3'; '1'; '3'; '1'; '3'; '2'; '2'];
324s  X = [X1, double(X2)];
324s  Y = [1; 2; 3; 3; 2; 1; 2; 1];
324s  predictorNames = {'Feature1', 'Feature2'};
324s  mdl = fitcknn (X, Y, 'PredictorNames', predictorNames);
324s  Vars = 'Feature1';
324s  Labels = 1;
324s  [pd, x, y] = partialDependence (mdl, Vars, Labels);
324s  pdm = [1.0000, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, ...
324s  0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, ...
324s  0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
324s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
324s  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3750, ...
324s  0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, ...
324s  0.3750, 0.3750, 0.3750, 0.3750, 0.7500, 0.7500, 0.7500, 0.7500, 0.7500, ...
324s  0.7500, 0.7500, 0.7500];
324s  assert (pd, pdm)
325s ***** test
325s  X1 = [1; 2; 4; 5; 7; 8; 3; 2];
325s  X2 = ['2'; '3'; '1'; '3'; '1'; '3'; '2'; '2'];
325s  X = [X1, double(X2)];
325s  Y = [1; 2; 3; 3; 2; 1; 2; 1];
325s  predictorNames = {'Feature1', 'Feature2'};
325s  mdl = fitcknn (X, Y, 'PredictorNames', predictorNames);
325s  new_X1 = [10; 5; 6; 8; 9; 20; 35; 6];
325s  new_X2 = ['2'; '2'; '1'; '2'; '1'; '3'; '3'; '2'];
325s  new_X = [new_X1, double(new_X2)];
325s  Vars = 'Feature1';
325s  Labels = 1;
325s  [pd, x, y] = partialDependence (mdl, Vars, Labels, new_X);
325s  pdm = [0, 0, 0, 0, 0, 0.2500, 0.2500, 0.2500, 0.2500, 0.7500, 0.7500, ...
325s  0.7500, 0.7500, 0.7500, 0.7500, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, ...
325s  1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, ...
325s  1.0000, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
325s  1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
325s  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];
325s  assert (pd, pdm)
326s ***** error<ClassificationKNN.partialDependence: too few input arguments.> ...
326s  partialDependence (ClassificationKNN (ones (4,2), ones (4,1)))
326s ***** error<ClassificationKNN.partialDependence: too few input arguments.> ...
326s  partialDependence (ClassificationKNN (ones (4,2), ones (4,1)), 1)
326s ***** error<ClassificationKNN.partialDependence: name-value arguments must be in pairs.> ...
326s  partialDependence (ClassificationKNN (ones (4,2), ones (4,1)), 1, ...
326s  ones (4,1), 'NumObservationsToSample')
326s ***** error<ClassificationKNN.partialDependence: name-value arguments must be in pairs.> ...
326s  partialDependence (ClassificationKNN (ones (4,2), ones (4,1)), 1, ...
326s  ones (4,1), 2)
326s ***** shared x, y, obj
326s  load fisheriris
326s  x = meas;
326s  y = species;
326s  covMatrix = cov (x);
326s  obj = fitcknn (x, y, 'NumNeighbors', 5, 'Distance', ...
326s       'mahalanobis', 'Cov', covMatrix);
326s ***** test
326s  CVMdl = crossval (obj);
326s  assert (class (CVMdl), "ClassificationPartitionedModel")
326s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
326s  assert (CVMdl.KFold == 10)
326s  assert (CVMdl.ModelParameters.NumNeighbors == 5)
326s  assert (strcmp (CVMdl.ModelParameters.Distance, "mahalanobis"))
326s  assert (class (CVMdl.Trained{1}), "ClassificationKNN")
326s  assert (!CVMdl.ModelParameters.Standardize)
326s ***** test
326s  CVMdl = crossval (obj, "KFold", 5);
326s  assert (class (CVMdl), "ClassificationPartitionedModel")
326s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
326s  assert (CVMdl.KFold == 5)
326s  assert (CVMdl.ModelParameters.NumNeighbors == 5)
326s  assert (strcmp (CVMdl.ModelParameters.Distance, "mahalanobis"))
326s  assert (class (CVMdl.Trained{1}), "ClassificationKNN")
326s  assert (CVMdl.ModelParameters.Standardize == obj.Standardize)
326s ***** test
326s  obj = fitcknn (x, y, "NumNeighbors", 5, "Distance", "cityblock");
326s  CVMdl = crossval (obj, "HoldOut", 0.2);
326s  assert (class (CVMdl), "ClassificationPartitionedModel")
326s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
326s  assert (CVMdl.ModelParameters.NumNeighbors == 5)
326s  assert (strcmp (CVMdl.ModelParameters.Distance, "cityblock"))
326s  assert (class (CVMdl.Trained{1}), "ClassificationKNN")
326s  assert (CVMdl.ModelParameters.Standardize == obj.Standardize)
326s ***** test
326s  obj = fitcknn (x, y, "NumNeighbors", 10, "Distance", "cityblock");
326s  CVMdl = crossval (obj, "LeaveOut", 'on');
326s  assert (class (CVMdl), "ClassificationPartitionedModel")
326s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
326s  assert (CVMdl.ModelParameters.NumNeighbors == 10)
326s  assert (strcmp (CVMdl.ModelParameters.Distance, "cityblock"))
326s  assert (class (CVMdl.Trained{1}), "ClassificationKNN")
326s  assert (CVMdl.ModelParameters.Standardize == obj.Standardize)
327s ***** test
327s  obj = fitcknn (x, y, "NumNeighbors", 10, "Distance", "cityblock");
327s  partition = cvpartition (y, 'KFold', 3);
327s  CVMdl = crossval (obj, 'cvPartition', partition);
327s  assert (class (CVMdl), "ClassificationPartitionedModel")
327s  assert (CVMdl.KFold == 3)
327s  assert (CVMdl.ModelParameters.NumNeighbors == 10)
327s  assert (strcmp (CVMdl.ModelParameters.Distance, "cityblock"))
327s  assert (class (CVMdl.Trained{1}), "ClassificationKNN")
327s  assert (CVMdl.ModelParameters.Standardize == obj.Standardize)
327s ***** error<ClassificationKNN.crossval: Name-Value arguments must be in pairs.> ...
327s  crossval (ClassificationKNN (ones (4,2), ones (4,1)), "kfold")
327s ***** error<ClassificationKNN.crossval: specify only one of the optional Name-Value paired arguments.>...
327s  crossval (ClassificationKNN (ones (4,2), ones (4,1)), "kfold", 12, "holdout", 0.2)
327s ***** error<ClassificationKNN.crossval: 'KFold' must be an integer value greater than 1.> ...
327s  crossval (ClassificationKNN (ones (4,2), ones (4,1)), "kfold", 'a')
327s ***** error<ClassificationKNN.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
327s  crossval (ClassificationKNN (ones (4,2), ones (4,1)), "holdout", 2)
327s ***** error<ClassificationKNN.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
327s  crossval (ClassificationKNN (ones (4,2), ones (4,1)), "leaveout", 1)
327s ***** error<ClassificationKNN.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
327s  crossval (ClassificationKNN (ones (4,2), ones (4,1)), "cvpartition", 1)
327s 165 tests, 165 passed, 0 known failure, 0 skipped
327s [inst/Classification/ClassificationNeuralNetwork.m]
327s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/Classification/ClassificationNeuralNetwork.m
327s ***** error<ClassificationNeuralNetwork: too few input arguments.> ...
327s  ClassificationNeuralNetwork ()
327s ***** error<ClassificationNeuralNetwork: too few input arguments.> ...
327s  ClassificationNeuralNetwork (ones(10,2))
327s ***** error<ClassificationNeuralNetwork: number of rows in X and Y must be equal.> ...
327s  ClassificationNeuralNetwork (ones(10,2), ones (5,1))
327s ***** error<ClassificationNeuralNetwork: 'Standardize' must be either true or false.> ...
327s  ClassificationNeuralNetwork (ones (5,3), ones (5,1), "standardize", "a")
327s ***** error<ClassificationNeuralNetwork: 'PredictorNames' must be supplied as a cellstring array.> ...
327s  ClassificationNeuralNetwork (ones (5,2), ones (5,1), "PredictorNames", ["A"])
327s ***** error<ClassificationNeuralNetwork: 'PredictorNames' must be supplied as a cellstring array.> ...
327s  ClassificationNeuralNetwork (ones (5,2), ones (5,1), "PredictorNames", "A")
327s ***** error<ClassificationNeuralNetwork: 'PredictorNames' must have the same number of columns as X.> ...
327s  ClassificationNeuralNetwork (ones (5,2), ones (5,1), "PredictorNames", {"A", "B", "C"})
327s ***** error<ClassificationNeuralNetwork: 'ResponseName' must be a character vector.> ...
327s  ClassificationNeuralNetwork (ones (5,2), ones (5,1), "ResponseName", {"Y"})
327s ***** error<ClassificationNeuralNetwork: 'ResponseName' must be a character vector.> ...
327s  ClassificationNeuralNetwork (ones (5,2), ones (5,1), "ResponseName", 1)
327s ***** error<ClassificationNeuralNetwork: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
327s  ClassificationNeuralNetwork (ones(10,2), ones (10,1), "ClassNames", @(x)x)
327s ***** error<ClassificationNeuralNetwork: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
327s  ClassificationNeuralNetwork (ones(10,2), ones (10,1), "ClassNames", {1})
327s ***** error<ClassificationNeuralNetwork: not all 'ClassNames' are present in Y.> ...
327s  ClassificationNeuralNetwork (ones(10,2), ones (10,1), "ClassNames", [1, 2])
327s ***** error<ClassificationNeuralNetwork: not all 'ClassNames' are present in Y.> ...
327s  ClassificationNeuralNetwork (ones(5,2), ['a';'b';'a';'a';'b'], "ClassNames", ['a';'c'])
327s ***** error<ClassificationNeuralNetwork: not all 'ClassNames' are present in Y.> ...
327s  ClassificationNeuralNetwork (ones(5,2), {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
327s ***** error<ClassificationNeuralNetwork: not all 'ClassNames' are present in Y.> ...
327s  ClassificationNeuralNetwork (ones(10,2), logical (ones (10,1)), "ClassNames", [true, false])
327s ***** error<ClassificationNeuralNetwork: 'LayerSizes' must be a positive integer vector.> ...
327s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", -1)
327s ***** error<ClassificationNeuralNetwork: 'LayerSizes' must be a positive integer vector.> ...
327s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", 0.5)
327s ***** error<ClassificationNeuralNetwork: 'LayerSizes' must be a positive integer vector.> ...
327s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", [1,-2])
327s ***** error<ClassificationNeuralNetwork: 'LayerSizes' must be a positive integer vector.> ...
327s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", [10,20,30.5])
327s ***** error<ClassificationNeuralNetwork: 'LearningRate' must be a positive scalar.> ...
327s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LearningRate", -0.1)
327s ***** error<ClassificationNeuralNetwork: 'LearningRate' must be a positive scalar.> ...
327s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LearningRate", [0.1, 0.01])
327s ***** error<ClassificationNeuralNetwork: 'LearningRate' must be a positive scalar.> ...
327s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LearningRate", "a")
327s ***** error<ClassificationNeuralNetwork: 'Activations' must be a character vector or a cellstring vector.> ...
327s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "Activations", 123)
327s ***** error<ClassificationNeuralNetwork: unsupported 'Activation' function.> ...
327s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "Activations", "unsupported_type")
327s ***** error<ClassificationNeuralNetwork: unsupported 'Activation' functions.> ...
327s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", [10, 5], ...
327s  "Activations", {"sigmoid", "unsupported_type"})
327s ***** error<ClassificationNeuralNetwork: 'Activations' vector does not match the number of layers.> ...
327s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "Activations", {"sigmoid", "relu", "softmax"})
327s ***** error<ClassificationNeuralNetwork: 'OutputLayerActivation' must be a character vector.> ...
327s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "OutputLayerActivation", 123)
327s ***** error<ClassificationNeuralNetwork: unsupported 'OutputLayerActivation' function.> ...
327s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "OutputLayerActivation", "unsupported_type")
327s ***** error<ClassificationNeuralNetwork: 'IterationLimit' must be a positive integer.> ...
327s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "IterationLimit", -1)
327s ***** error<ClassificationNeuralNetwork: 'IterationLimit' must be a positive integer.> ...
327s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "IterationLimit", 0.5)
327s ***** error<ClassificationNeuralNetwork: 'IterationLimit' must be a positive integer.> ...
327s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "IterationLimit", [1,2])
327s ***** error<ClassificationNeuralNetwork: 'ScoreTransform' must be a character vector or a function handle.> ...
327s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "ScoreTransform", [1,2])
327s ***** error<ClassificationNeuralNetwork: unrecognized 'ScoreTransform' function.> ...
327s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "ScoreTransform", "unsupported_type")
327s ***** error<ClassificationNeuralNetwork: invalid parameter name in optional pair arguments.> ...
327s  ClassificationNeuralNetwork (ones(10,2), ones(10,1), "some", "some")
327s ***** error<ClassificationNeuralNetwork: invalid values in X.> ...
327s  ClassificationNeuralNetwork ([1;2;3;'a';4], ones (5,1))
327s ***** error<ClassificationNeuralNetwork: invalid values in X.> ...
327s  ClassificationNeuralNetwork ([1;2;3;Inf;4], ones (5,1))
327s ***** shared x, y, objST, Mdl
327s  load fisheriris
327s  x = meas;
327s  y = grp2idx (species);
327s  Mdl = fitcnet (x, y, "IterationLimit", 100);
327s ***** error<ClassificationNeuralNetwork: unrecognized 'ScoreTransform' function.> ...
327s  Mdl.ScoreTransform = "a";
327s ***** error<ClassificationNeuralNetwork.predict: too few input arguments.> ...
327s  predict (Mdl)
327s ***** error<ClassificationNeuralNetwork.predict: XC is empty.> ...
327s  predict (Mdl, [])
327s ***** error<ClassificationNeuralNetwork.predict: XC must have the same number of predictors as the trained model.> ...
327s  predict (Mdl, 1)
327s ***** test
327s  CVMdl = crossval (Mdl, "KFold", 5);
327s  assert (class (CVMdl), "ClassificationPartitionedModel")
327s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
327s  assert (CVMdl.KFold == 5)
327s  assert (class (CVMdl.Trained{1}), "CompactClassificationNeuralNetwork")
327s  assert (CVMdl.CrossValidatedModel, "ClassificationNeuralNetwork")
328s ***** test
328s  CVMdl = crossval (Mdl, "HoldOut", 0.2);
328s  assert (class (CVMdl), "ClassificationPartitionedModel")
328s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
328s  assert (class (CVMdl.Trained{1}), "CompactClassificationNeuralNetwork")
328s  assert (CVMdl.CrossValidatedModel, "ClassificationNeuralNetwork")
328s ***** error<ClassificationNeuralNetwork.crossval: Name-Value arguments must be in pairs.> ...
328s  crossval (Mdl, "KFold")
328s ***** error<ClassificationNeuralNetwork.crossval: specify only one of the optional Name-Value paired arguments.> ...
328s  crossval (Mdl, "KFold", 5, "leaveout", 'on')
328s ***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
328s  crossval (Mdl, "KFold", 'a')
328s ***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
328s  crossval (Mdl, "KFold", 1)
328s ***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
328s  crossval (Mdl, "KFold", -1)
328s ***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
328s  crossval (Mdl, "KFold", 11.5)
328s ***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
328s  crossval (Mdl, "KFold", [1,2])
328s ***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
328s  crossval (Mdl, "Holdout", 'a')
328s ***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
328s  crossval (Mdl, "Holdout", 11.5)
328s ***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
328s  crossval (Mdl, "Holdout", -1)
328s ***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
328s  crossval (Mdl, "Holdout", 0)
328s ***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
328s  crossval (Mdl, "Holdout", 1)
328s ***** error<ClassificationNeuralNetwork.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
328s  crossval (Mdl, "Leaveout", 1)
328s ***** error<ClassificationNeuralNetwork.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
328s  crossval (Mdl, "CVPartition", 1)
328s ***** error<ClassificationNeuralNetwork.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
328s  crossval (Mdl, "CVPartition", 'a')
328s ***** error<ClassificationNeuralNetwork.crossval: invalid parameter name in optional paired arguments> ...
328s  crossval (Mdl, "some", "some")
328s 58 tests, 58 passed, 0 known failure, 0 skipped
328s [inst/Classification/ClassificationPartitionedModel.m]
328s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/Classification/ClassificationPartitionedModel.m
328s ***** demo
328s 
328s  load fisheriris
328s  x = meas;
328s  y = species;
328s 
328s  ## Create a KNN classifier model
328s  obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);
328s 
328s  ## Create a partition for 5-fold cross-validation
328s  partition = cvpartition (y, "KFold", 5);
328s 
328s  ## Create the ClassificationPartitionedModel object
328s  cvModel = crossval (obj, 'cvPartition', partition)
328s ***** demo
328s 
328s  load fisheriris
328s  x = meas;
328s  y = species;
328s 
328s  ## Create a KNN classifier model
328s  obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);
328s 
328s  ## Create the ClassificationPartitionedModel object
328s  cvModel = crossval (obj);
328s 
328s  ## Predict the class labels for the observations not used for training
328s  [label, score, cost] = kfoldPredict (cvModel);
328s  fprintf ("Cross-validated accuracy = %1.2f%% (%d/%d)\n", ...
328s           sum (strcmp (label, y)) / numel (y) *100, ...
328s           sum (strcmp (label, y)), numel (y))
328s ***** test
328s  load fisheriris
328s  a = fitcdiscr (meas, species, "gamma", 0.3);
328s  cvModel = crossval (a, "KFold", 5);
328s  assert (class (cvModel), "ClassificationPartitionedModel");
328s  assert (cvModel.NumObservations, 150);
328s  assert (numel (cvModel.Trained), 5);
328s  assert (class (cvModel.Trained{1}), "CompactClassificationDiscriminant");
328s  assert (cvModel.CrossValidatedModel, "ClassificationDiscriminant");
328s  assert (cvModel.KFold, 5);
328s ***** test
328s  load fisheriris
328s  a = fitcdiscr (meas, species, "gamma", 0.5, "fillcoeffs", "off");
328s  cvModel = crossval (a, "HoldOut", 0.3);
328s  assert (class (cvModel), "ClassificationPartitionedModel");
328s  assert ({cvModel.X, cvModel.Y}, {meas, species});
328s  assert (cvModel.NumObservations, 150);
328s  assert (numel (cvModel.Trained), 1);
328s  assert (class (cvModel.Trained{1}), "CompactClassificationDiscriminant");
328s  assert (cvModel.CrossValidatedModel, "ClassificationDiscriminant");
328s ***** test
328s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
328s  y = ["a"; "a"; "b"; "b"];
328s  a = fitcgam (x, y, "Interactions", "all");
328s  cvModel = crossval (a, "KFold", 2);
328s  assert (class (cvModel), "ClassificationPartitionedModel");
328s  assert (cvModel.NumObservations, 4);
328s  assert (numel (cvModel.Trained), 2);
328s  assert (class (cvModel.Trained{1}), "CompactClassificationGAM");
328s  assert (cvModel.CrossValidatedModel, "ClassificationGAM");
328s  assert (cvModel.KFold, 2);
346s ***** test
346s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
346s  y = ["a"; "a"; "b"; "b"];
346s  a = fitcgam (x, y);
346s  cvModel = crossval (a, "LeaveOut", "on");
346s  assert (class (cvModel), "ClassificationPartitionedModel");
346s  assert ({cvModel.X, cvModel.Y}, {x, y});
346s  assert (cvModel.NumObservations, 4);
346s  assert (numel (cvModel.Trained), 4);
346s  assert (class (cvModel.Trained{1}), "CompactClassificationGAM");
346s  assert (cvModel.CrossValidatedModel, "ClassificationGAM");
358s ***** test
358s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
358s  y = ["a"; "a"; "b"; "b"];
358s  a = fitcknn (x, y);
358s  partition = cvpartition (y, "KFold", 2);
358s  cvModel = ClassificationPartitionedModel (a, partition);
358s  assert (class (cvModel), "ClassificationPartitionedModel");
358s  assert (class (cvModel.Trained{1}), "ClassificationKNN");
358s  assert (cvModel.NumObservations, 4);
358s  assert (cvModel.ModelParameters.NumNeighbors, 1);
358s  assert (cvModel.ModelParameters.NSMethod, "kdtree");
358s  assert (cvModel.ModelParameters.Distance, "euclidean");
358s  assert (! cvModel.ModelParameters.Standardize);
358s ***** test
358s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
358s  y = ["a"; "a"; "b"; "b"];
358s  a = fitcknn (x, y, "NSMethod", "exhaustive");
358s  partition = cvpartition (y, "HoldOut", 0.2);
358s  cvModel = ClassificationPartitionedModel (a, partition);
358s  assert (class (cvModel), "ClassificationPartitionedModel");
358s  assert (class (cvModel.Trained{1}), "ClassificationKNN");
358s  assert ({cvModel.X, cvModel.Y}, {x, y});
358s  assert (cvModel.NumObservations, 4);
358s  assert (cvModel.ModelParameters.NumNeighbors, 1);
358s  assert (cvModel.ModelParameters.NSMethod, "exhaustive");
358s  assert (cvModel.ModelParameters.Distance, "euclidean");
358s  assert (! cvModel.ModelParameters.Standardize);
358s ***** test
358s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
358s  y = ["a"; "a"; "b"; "b"];
358s  k = 2;
358s  a = fitcknn (x, y, "NumNeighbors" ,k);
358s  partition = cvpartition (numel (y), "LeaveOut");
358s  cvModel = ClassificationPartitionedModel (a, partition);
358s  assert (class (cvModel), "ClassificationPartitionedModel");
358s  assert (class (cvModel.Trained{1}), "ClassificationKNN");
358s  assert ({cvModel.X, cvModel.Y}, {x, y});
358s  assert (cvModel.NumObservations, 4);
358s  assert (cvModel.ModelParameters.NumNeighbors, k);
358s  assert (cvModel.ModelParameters.NSMethod, "kdtree");
358s  assert (cvModel.ModelParameters.Distance, "euclidean");
358s  assert (! cvModel.ModelParameters.Standardize);
358s ***** test
358s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
358s  y = {"a"; "a"; "b"; "b"};
358s  a = fitcnet (x, y, "IterationLimit", 50);
358s  cvModel = crossval (a, "KFold", 2);
358s  assert (class (cvModel), "ClassificationPartitionedModel");
358s  assert (cvModel.NumObservations, 4);
358s  assert (numel (cvModel.Trained), 2);
358s  assert (class (cvModel.Trained{1}), "CompactClassificationNeuralNetwork");
358s  assert (cvModel.CrossValidatedModel, "ClassificationNeuralNetwork");
358s  assert (cvModel.KFold, 2);
358s ***** test
358s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
358s  y = {"a"; "a"; "b"; "b"};
358s  a = fitcnet (x, y, "LayerSizes", [5, 3]);
358s  cvModel = crossval (a, "LeaveOut", "on");
358s  assert (class (cvModel), "ClassificationPartitionedModel");
358s  assert ({cvModel.X, cvModel.Y}, {x, y});
358s  assert (cvModel.NumObservations, 4);
358s  assert (numel (cvModel.Trained), 4);
358s  assert (class (cvModel.Trained{1}), "CompactClassificationNeuralNetwork");
358s  assert (cvModel.CrossValidatedModel, "ClassificationNeuralNetwork");
358s ***** test
358s  load fisheriris
358s  inds = ! strcmp (species, 'setosa');
358s  x = meas(inds, 3:4);
358s  y = grp2idx (species(inds));
358s  SVMModel = fitcsvm (x,y);
358s  CVMdl = crossval (SVMModel, "KFold", 5);
358s  assert (class (CVMdl), "ClassificationPartitionedModel")
358s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
358s  assert (CVMdl.KFold == 5)
358s  assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
358s  assert (CVMdl.CrossValidatedModel, "ClassificationSVM");
358s ***** test
358s  load fisheriris
358s  inds = ! strcmp (species, 'setosa');
358s  x = meas(inds, 3:4);
358s  y = grp2idx (species(inds));
358s  obj = fitcsvm (x, y);
358s  CVMdl = crossval (obj, "HoldOut", 0.2);
358s  assert (class (CVMdl), "ClassificationPartitionedModel")
358s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
358s  assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
358s  assert (CVMdl.CrossValidatedModel, "ClassificationSVM");
358s ***** test
358s  load fisheriris
358s  inds = ! strcmp (species, 'setosa');
358s  x = meas(inds, 3:4);
358s  y = grp2idx (species(inds));
358s  obj = fitcsvm (x, y);
358s  CVMdl = crossval (obj, "LeaveOut", 'on');
358s  assert (class (CVMdl), "ClassificationPartitionedModel")
358s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
358s  assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
358s  assert (CVMdl.CrossValidatedModel, "ClassificationSVM");
359s ***** error<ClassificationPartitionedModel: too few input arguments.> ...
359s  ClassificationPartitionedModel ()
359s ***** error<ClassificationPartitionedModel: too few input arguments.> ...
359s  ClassificationPartitionedModel (ClassificationKNN (ones (4,2), ones (4,1)))
359s ***** error<ClassificationPartitionedModel: unsupported model type.> ...
359s  ClassificationPartitionedModel (RegressionGAM (ones (40,2), ...
359s  randi ([1, 2], 40, 1)), cvpartition (randi ([1, 2], 40, 1), 'Holdout', 0.3))
360s ***** error<ClassificationPartitionedModel: invalid 'cvpartition' object.> ...
360s  ClassificationPartitionedModel (ClassificationKNN (ones (4,2), ...
360s  ones (4,1)), 'Holdout')
360s ***** test
360s  load fisheriris
360s  a = fitcdiscr (meas, species, "gamma", 0.5, "fillcoeffs", "off");
360s  cvModel = crossval (a, "Kfold", 4);
360s  [label, score, cost] = kfoldPredict (cvModel);
360s  assert (class(cvModel), "ClassificationPartitionedModel");
360s  assert ({cvModel.X, cvModel.Y}, {meas, species});
360s  assert (cvModel.NumObservations, 150);
360s ***** # assert (label, {"b"; "b"; "a"; "a"});
360s ***** # assert (score, [4.5380e-01, 5.4620e-01; 2.4404e-01, 7.5596e-01; ...
360s ***** #         9.9392e-01, 6.0844e-03; 9.9820e-01, 1.8000e-03], 1e-4);
360s ***** # assert (cost, [5.4620e-01, 4.5380e-01; 7.5596e-01, 2.4404e-01; ...
360s ***** #         6.0844e-03, 9.9392e-01; 1.8000e-03, 9.9820e-01], 1e-4);
360s ***** test
360s  x = ones(4, 11);
360s  y = {"a"; "a"; "b"; "b"};
360s  k = 3;
360s  a = fitcknn (x, y, "NumNeighbors", k);
360s  partition = cvpartition (numel (y), "LeaveOut");
360s  cvModel = ClassificationPartitionedModel (a, partition);
360s  [label, score, cost] = kfoldPredict (cvModel);
360s  assert (class(cvModel), "ClassificationPartitionedModel");
360s  assert ({cvModel.X, cvModel.Y}, {x, y});
360s  assert (cvModel.NumObservations, 4);
360s  assert (cvModel.ModelParameters.NumNeighbors, k);
360s  assert (cvModel.ModelParameters.NSMethod, "exhaustive");
360s  assert (cvModel.ModelParameters.Distance, "euclidean");
360s  assert (! cvModel.ModelParameters.Standardize);
360s  assert (label, {"b"; "b"; "a"; "a"});
360s  assert (score, [0.3333, 0.6667; 0.3333, 0.6667; 0.6667, 0.3333; ...
360s           0.6667, 0.3333], 1e-4);
360s  assert (cost, [0.6667, 0.3333; 0.6667, 0.3333; 0.3333, 0.6667; ...
360s           0.3333, 0.6667], 1e-4);
360s ***** error<ClassificationPartitionedModel.kfoldPredict: 'Cost' output is not supported for ClassificationSVM cross validated models.> ...
360s  [label, score, cost] = kfoldPredict (crossval (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1))))
360s ***** error<ClassificationPartitionedModel.kfoldPredict: 'Cost' output is not supported for ClassificationNeuralNetwork cross validated models.> ...
360s  [label, score, cost] = kfoldPredict (crossval (ClassificationNeuralNetwork (ones (40,2), randi ([1, 2], 40, 1))))
362s 20 tests, 20 passed, 0 known failure, 0 skipped
362s [inst/Classification/ClassificationSVM.m]
363s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/Classification/ClassificationSVM.m
363s ***** demo
363s  ## Create a Support Vector Machine classifier and determine margin for test
363s  ## data.
363s  load fisheriris
363s  rng(1);  ## For reproducibility
363s 
363s  ## Select indices of the non-setosa species
363s  inds = !strcmp(species, 'setosa');
363s 
363s   ## Select features and labels for non-setosa species
363s  X = meas(inds, 3:4);
363s  Y = grp2idx(species(inds));
363s 
363s  ##  Convert labels to +1 and -1
363s  unique_classes = unique(Y);
363s  Y(Y == unique_classes(1)) = -1;
363s  Y(Y == unique_classes(2)) = 1;
363s 
363s  ## Partition data for training and testing
363s  cv = cvpartition(Y, 'HoldOut', 0.15);
363s  X_train = X(training(cv), :);
363s  Y_train = Y(training(cv));
363s  X_test = X(test(cv), :);
363s  Y_test = Y(test(cv));
363s 
363s  ## Train the SVM model
363s  CVSVMModel = fitcsvm(X_train, Y_train);
363s 
363s  ## Calculate margins
363s  m = margin(CVSVMModel, X_test, Y_test);
363s  disp(m);
363s ***** demo
363s  ## Create a Support Vector Machine classifier and determine loss for test
363s  ## data.
363s  load fisheriris
363s  rng(1);  ## For reproducibility
363s 
363s   ## Select indices of the non-setosa species
363s  inds = !strcmp(species, 'setosa');
363s 
363s   ## Select features and labels for non-setosa species
363s  X = meas(inds, 3:4);
363s  Y = grp2idx(species(inds));
363s 
363s  ##  Convert labels to +1 and -1
363s  unique_classes = unique(Y);
363s  Y(Y == unique_classes(1)) = -1;
363s  Y(Y == unique_classes(2)) = 1;
363s 
363s  ## Randomly partition the data into training and testing sets
363s  cv = cvpartition(Y, 'HoldOut', 0.3); # 30% data for testing, 60% for training
363s 
363s  X_train = X(training(cv), :);
363s  Y_train = Y(training(cv));
363s 
363s  X_test = X(test(cv), :);
363s  Y_test = Y(test(cv));
363s 
363s  ## Train the SVM model
363s  SVMModel = fitcsvm(X_train, Y_train);
363s 
363s  ## Calculate loss
363s 
363s  L = loss(SVMModel,X_test,Y_test,'LossFun','binodeviance')
363s  L = loss(SVMModel,X_test,Y_test,'LossFun','classiferror')
363s  L = loss(SVMModel,X_test,Y_test,'LossFun','exponential')
363s  L = loss(SVMModel,X_test,Y_test,'LossFun','hinge')
363s  L = loss(SVMModel,X_test,Y_test,'LossFun','logit')
363s  L = loss(SVMModel,X_test,Y_test,'LossFun','quadratic')
363s ***** test
363s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1; 4, 5, 6; 7, 8, 9; ...
363s  3, 2, 1; 4, 5, 6; 7, 8, 9; 3, 2, 1; 4, 5, 6; 7, 8, 9; 3, 2, 1];
363s  y = [1; 2; 3; 4; 2; 3; 4; 2; 3; 4; 2; 3; 4];
363s  a = ClassificationSVM (x, y, "ClassNames", [1, 2]);
363s  assert (class (a), "ClassificationSVM");
363s  assert (a.RowsUsed, [1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0]');
363s  assert ({a.X, a.Y}, {x, y})
363s  assert (a.NumObservations, 5)
363s  assert ({a.ResponseName, a.PredictorNames}, {"Y", {"x1", "x2", "x3"}})
363s  assert ({a.ClassNames, a.ModelParameters.SVMtype}, {[1; 2], "c_svc"})
363s ***** test
363s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
363s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
363s  a = ClassificationSVM (x, y);
363s  assert (class (a), "ClassificationSVM");
363s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "linear"})
363s  assert (a.ModelParameters.BoxConstraint, 1)
363s  assert (a.ClassNames, [1; -1])
363s  assert (a.ModelParameters.KernelOffset, 0)
363s ***** test
363s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
363s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
363s  a = ClassificationSVM (x, y, "KernelFunction", "rbf", "BoxConstraint", 2, ...
363s  "KernelOffset", 2);
363s  assert (class (a), "ClassificationSVM");
363s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "rbf"})
363s  assert (a.ModelParameters.BoxConstraint, 2)
363s  assert (a.ModelParameters.KernelOffset, 2)
363s ***** test
363s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
363s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
363s  a = ClassificationSVM (x, y, "KernelFunction", "polynomial", ...
363s  "PolynomialOrder", 3);
363s  assert (class (a), "ClassificationSVM");
363s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "polynomial"})
363s  assert (a.ModelParameters.PolynomialOrder, 3)
363s ***** error<ClassificationSVM: too few input arguments.> ClassificationSVM ()
363s ***** error<ClassificationSVM: too few input arguments.> ...
363s  ClassificationSVM (ones(10,2))
363s ***** error<ClassificationSVM: number of rows in X and Y must be equal.> ...
363s  ClassificationSVM (ones(10,2), ones (5,1))
363s ***** error<ClassificationSVM: 'Standardize' must be either true or false.> ...
363s  ClassificationSVM (ones(10,2), ones (10,1), "Standardize", 'a')
363s ***** error<ClassificationSVM: 'PredictorNames' must be supplied as a cellstring array.> ...
363s  ClassificationSVM (ones(10,2), ones (10,1), "PredictorNames", ['x1';'x2'])
363s ***** error<ClassificationSVM: 'PredictorNames' must have the same number of columns as X.> ...
363s  ClassificationSVM (ones(10,2), ones (10,1), "PredictorNames", {'x1','x2','x3'})
363s ***** error<ClassificationSVM: 'ResponseName' must be a character vector.> ...
363s  ClassificationSVM (ones(10,2), ones (10,1), "ResponseName", {'Y'})
363s ***** error<ClassificationSVM: 'ResponseName' must be a character vector.> ...
363s  ClassificationSVM (ones(10,2), ones (10,1), "ResponseName", 21)
363s ***** error<ClassificationSVM: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
363s  ClassificationSVM (ones(10,2), ones (10,1), "ClassNames", @(x)x)
363s ***** error<ClassificationSVM: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
363s  ClassificationSVM (ones(10,2), ones (10,1), "ClassNames", {1})
363s ***** error<ClassificationSVM: not all 'ClassNames' are present in Y.> ...
363s  ClassificationSVM (ones(10,2), ones (10,1), "ClassNames", [1, 2])
363s ***** error<ClassificationSVM: not all 'ClassNames' are present in Y.> ...
363s  ClassificationSVM (ones(5,2), ['a';'b';'a';'a';'b'], "ClassNames", ['a';'c'])
363s ***** error<ClassificationSVM: not all 'ClassNames' are present in Y.> ...
363s  ClassificationSVM (ones(5,2), {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
363s ***** error<ClassificationSVM: not all 'ClassNames' are present in Y.> ...
363s  ClassificationSVM (ones(10,2), logical (ones (10,1)), "ClassNames", [true, false])
363s ***** error<ClassificationSVM: 'Prior' must be either a numeric vector or a character vector.> ...
363s  ClassificationSVM (ones(10,2), ones(10,1), "Prior", {"asd"})
363s ***** error<ClassificationSVM: 'Prior' must be either a numeric vector or a character vector.> ...
363s  ClassificationSVM (ones(10,2), ones(10,1), "Prior", ones (2))
363s ***** error<ClassificationSVM: 'Cost' must be a numeric square matrix.> ...
363s  ClassificationSVM (ones(10,2), ones(10,1), "Cost", [1:4])
363s ***** error<ClassificationSVM: 'Cost' must be a numeric square matrix.> ...
363s  ClassificationSVM (ones(10,2), ones(10,1), "Cost", {0,1;1,0})
363s ***** error<ClassificationSVM: 'Cost' must be a numeric square matrix.> ...
363s  ClassificationSVM (ones(10,2), ones(10,1), "Cost", 'a')
363s ***** error<ClassificationSVM: 'SVMtype' must be 'c_svc', 'nu_svc', or 'one_class_svm'.> ...
363s  ClassificationSVM (ones(10,2), ones(10,1), "svmtype", 123)
363s ***** error<ClassificationSVM: 'SVMtype' must be 'c_svc', 'nu_svc', or 'one_class_svm'.> ...
363s  ClassificationSVM (ones(10,2), ones(10,1), "svmtype", 'some_type')
363s ***** error<ClassificationSVM: 'OutlierFraction' must be a positive scalar in the range 0 =< OutlierFraction < 1.> ...
363s  ClassificationSVM (ones(10,2), ones(10,1), "OutlierFraction", -1)
363s ***** error<ClassificationSVM: 'KernelFunction' must be a character vector.> ...
363s  ClassificationSVM (ones(10,2), ones(10,1), "KernelFunction", 123)
363s ***** error<ClassificationSVM: unsupported Kernel function.> ...
363s  ClassificationSVM (ones(10,2), ones(10,1), "KernelFunction", "fcn")
363s ***** error<ClassificationSVM: 'PolynomialOrder' must be a positive integer.> ...
363s  ClassificationSVM (ones(10,2), ones(10,1), "PolynomialOrder", -1)
363s ***** error<ClassificationSVM: 'PolynomialOrder' must be a positive integer.> ...
363s  ClassificationSVM (ones(10,2), ones(10,1), "PolynomialOrder", 0.5)
363s ***** error<ClassificationSVM: 'PolynomialOrder' must be a positive integer.> ...
363s  ClassificationSVM (ones(10,2), ones(10,1), "PolynomialOrder", [1,2])
363s ***** error<ClassificationSVM: 'KernelScale' must be a positive scalar.> ...
363s  ClassificationSVM (ones(10,2), ones (10,1), "KernelScale", -1)
363s ***** error<ClassificationSVM: 'KernelScale' must be a positive scalar.> ...
363s  ClassificationSVM (ones(10,2), ones (10,1), "KernelScale", 0)
363s ***** error<ClassificationSVM: 'KernelScale' must be a positive scalar.> ...
363s  ClassificationSVM (ones(10,2), ones (10,1), "KernelScale", [1, 2])
363s ***** error<ClassificationSVM: 'KernelScale' must be a positive scalar.> ...
363s  ClassificationSVM (ones(10,2), ones (10,1), "KernelScale", "invalid")
363s ***** error<ClassificationSVM: 'KernelOffset' must be a non-negative scalar.> ...
363s  ClassificationSVM (ones(10,2), ones(10,1), "KernelOffset", -1)
363s ***** error<ClassificationSVM: 'KernelOffset' must be a non-negative scalar.> ...
363s  ClassificationSVM (ones(10,2), ones(10,1), "KernelOffset", [1,2])
363s ***** error<ClassificationSVM: 'BoxConstraint' must be a positive scalar.> ...
363s  ClassificationSVM (ones(10,2), ones (10,1), "BoxConstraint", -1)
363s ***** error<ClassificationSVM: 'BoxConstraint' must be a positive scalar.> ...
363s  ClassificationSVM (ones(10,2), ones (10,1), "BoxConstraint", 0)
363s ***** error<ClassificationSVM: 'BoxConstraint' must be a positive scalar.> ...
363s  ClassificationSVM (ones(10,2), ones (10,1), "BoxConstraint", [1, 2])
363s ***** error<ClassificationSVM: 'BoxConstraint' must be a positive scalar.> ...
363s  ClassificationSVM (ones(10,2), ones (10,1), "BoxConstraint", "invalid")
363s ***** error<ClassificationSVM: 'Nu' must be a positive scalar in the range 0 < Nu <= 1.> ...
363s  ClassificationSVM (ones(10,2), ones(10,1), "nu", -0.5)
363s ***** error<ClassificationSVM: 'Nu' must be a positive scalar in the range 0 < Nu <= 1.> ...
363s  ClassificationSVM (ones(10,2), ones(10,1), "nu", 0)
363s ***** error<ClassificationSVM: 'Nu' must be a positive scalar in the range 0 < Nu <= 1.> ...
363s  ClassificationSVM (ones(10,2), ones(10,1), "nu", 1.5)
363s ***** error<ClassificationSVM: 'CacheSize' must be a positive scalar.> ...
363s  ClassificationSVM (ones(10,2), ones(10,1), "CacheSize", -1)
363s ***** error<ClassificationSVM: 'CacheSize' must be a positive scalar.> ...
363s  ClassificationSVM (ones(10,2), ones(10,1), "CacheSize", [1,2])
363s ***** error<ClassificationSVM: 'Tolerance' must be a positive scalar.> ...
363s  ClassificationSVM (ones(10,2), ones(10,1), "Tolerance", -0.1)
363s ***** error<ClassificationSVM: 'Tolerance' must be a positive scalar.> ...
363s  ClassificationSVM (ones(10,2), ones(10,1), "Tolerance", [0.1,0.2])
363s ***** error<ClassificationSVM: 'Shrinking' must be either 0 or 1.> ...
363s  ClassificationSVM (ones(10,2), ones(10,1), "shrinking", 2)
363s ***** error<ClassificationSVM: 'Shrinking' must be either 0 or 1.> ...
363s  ClassificationSVM (ones(10,2), ones(10,1), "shrinking", -1)
363s ***** error<ClassificationSVM: 'Shrinking' must be either 0 or 1.> ...
363s  ClassificationSVM (ones(10,2), ones(10,1), "shrinking", [1 0])
363s ***** error<ClassificationSVM: invalid parameter name in optional pair arguments.> ...
363s  ClassificationSVM (ones(10,2), ones(10,1), "invalid_name", 'c_svc')
363s ***** error<ClassificationSVM: cannot train a binary problem with only one class available.> ...
363s  ClassificationSVM (ones(10,2), ones(10,1), "SVMtype", 'c_svc')
363s ***** error<ClassificationSVM: can only be used for one-class or two-class learning.> ...
363s  ClassificationSVM (ones(10,2), [1;1;1;1;2;2;2;2;3;3])
363s ***** error<ClassificationSVM: invalid values in X.> ...
363s  ClassificationSVM ([ones(9,2);2,Inf], ones(10,1))
363s ***** error<ClassificationSVM: the elements in 'Prior' do not correspond to selected classes in Y.> ...
363s  ClassificationSVM (ones (5,2), ones (5,1), "Prior", [0,1])
363s ***** error<ClassificationSVM: the elements in 'Prior' do not correspond to selected classes in Y.> ...
363s  ClassificationSVM (ones (5,2), [1;1;2;2;3], "ClassNames", [1,2], "Prior", [0,0.4,0.6])
363s ***** error<ClassificationSVM: the number of rows and columns in 'Cost' must correspond to the selected classes in Y.> ...
363s  ClassificationSVM (ones (5,2), [1;1;2;2;3], "ClassNames", [1,2], "Cost", ones (3))
363s ***** shared x, y, x_train, x_test, y_train, y_test, objST
363s  load fisheriris
363s  inds = ! strcmp (species, 'setosa');
363s  x = meas(inds, 3:4);
363s  y = grp2idx (species(inds));
363s ***** test
363s  xc = [min(x); mean(x); max(x)];
363s  obj = fitcsvm (x, y, 'KernelFunction', 'rbf', 'Tolerance', 1e-7);
363s  assert (isempty (obj.Alpha), true)
363s  assert (sum (obj.IsSupportVector), numel (obj.Beta))
363s  [label, score] = predict (obj, xc);
363s  assert (label, [1; 2; 2]);
363s  assert (score(:,1), [0.99285; -0.080296; -0.93694], 2e-5);
363s  assert (score(:,1), -score(:,2), eps)
363s  obj = fitPosterior (obj);
363s  [label, probs] = predict (obj, xc);
363s  assert (probs(:,2), [0.97555; 0.428164; 0.030385], 3e-2);
363s  assert (probs(:,1) + probs(:,2), [1; 1; 1], 0.05)
363s ***** test
363s  obj = fitcsvm (x, y);
363s  assert (isempty (obj.Beta), true)
363s  assert (sum (obj.IsSupportVector), numel (obj.Alpha))
363s  assert (numel (obj.Alpha), 24)
363s  assert (obj.Bias, -14.415, 1e-3)
363s  xc = [min(x); mean(x); max(x)];
363s  label = predict (obj, xc);
363s  assert (label, [1; 2; 2]);
363s ***** error<ClassificationSVM.predict: too few input arguments.> ...
363s  predict (ClassificationSVM (ones (40,2), ones (40,1)))
363s ***** error<ClassificationSVM.predict: XC is empty.> ...
363s  predict (ClassificationSVM (ones (40,2), ones (40,1)), [])
363s ***** error<ClassificationSVM.predict: XC must have the same number of predictors as the trained model.> ...
363s  predict (ClassificationSVM (ones (40,2), ones (40,1)), 1)
363s ***** test
363s  objST = fitcsvm (x, y);
363s  objST.ScoreTransform = "a";
363s ***** error<ClassificationSVM.predict: 'ScoreTransform' must be a 'function_handle' object.> ...
363s  [labels, scores] = predict (objST, x);
363s ***** error<ClassificationSVM.resubPredict: 'ScoreTransform' must be a 'function_handle' object.> ...
363s  [labels, scores] = resubPredict (objST);
363s ***** test
363s  rand ("seed", 1);
363s  CVSVMModel = fitcsvm (x, y, 'KernelFunction', 'rbf', 'HoldOut', 0.15, ...
363s                        'Tolerance', 1e-7);
363s  obj = CVSVMModel.Trained{1};
363s  testInds = test (CVSVMModel.Partition);
363s  expected_margin = [2.0000;  0.8579;  1.6690;  3.4141;  3.4552; ...
363s                     2.6605;  3.5251; -4.0000; -6.3411; -6.4511; ...
363s                    -3.0532; -7.5054; -1.6700; -5.6227; -7.3640];
363s  computed_margin = margin (obj, x(testInds,:), y(testInds,:));
363s  assert (computed_margin, expected_margin, 1e-4);
363s ***** error<ClassificationSVM.margin: too few input arguments.> ...
363s  margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)))
363s ***** error<ClassificationSVM.margin: too few input arguments.> ...
363s  margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2))
363s ***** error<ClassificationSVM.margin: X is empty.> ...
363s  margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), [], zeros (2))
363s ***** error<ClassificationSVM.margin: X must have the same number of predictors as the trained model.> ...
363s  margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), 1, zeros (2))
363s ***** error<ClassificationSVM.margin: Y is empty.> ...
363s  margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), [])
363s ***** error<ClassificationSVM.margin: Y must have the same number of rows as X.> ...
363s  margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), 1)
363s ***** test
363s  rand ("seed", 1);
363s  CVSVMModel = fitcsvm (x, y, 'KernelFunction', 'rbf', 'HoldOut', 0.15);
363s  obj = CVSVMModel.Trained{1};
363s  testInds = test (CVSVMModel.Partition);
363s  L1 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'binodeviance');
363s  L2 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'classiferror');
363s  L3 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'exponential');
363s  L4 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'hinge');
363s  L5 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'logit');
363s  L6 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'quadratic');
363s  assert (L1, 2.8711, 1e-4);
363s  assert (L2, 0.5333, 1e-4);
363s  assert (L3, 10.9685, 1e-4);
363s  assert (L4, 1.9827, 1e-4);
363s  assert (L5, 1.5849, 1e-4);
363s  assert (L6, 7.6739, 1e-4);
363s ***** error<ClassificationSVM.loss: too few input arguments.> ...
363s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)))
363s ***** error<ClassificationSVM.loss: too few input arguments.> ...
363s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2))
363s ***** error<ClassificationSVM.loss: Name-Value arguments must be in pairs.> ...
363s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
363s  ones(2,1), "LossFun")
363s ***** error<ClassificationSVM.loss: X is empty.> ...
363s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), [], zeros (2))
363s ***** error<ClassificationSVM.loss: X must have the same number of predictors as the trained model.> ...
363s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), 1, zeros (2))
363s ***** error<ClassificationSVM.loss: Y is empty.> ...
363s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), [])
364s ***** error<ClassificationSVM.loss: Y must have the same number of rows as X.> ...
364s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), 1)
364s ***** error<ClassificationSVM.loss: 'LossFun' must be a character vector.> ...
364s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
364s  ones (2,1), "LossFun", 1)
364s ***** error<ClassificationSVM.loss: unsupported Loss function.> ...
364s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
364s  ones (2,1), "LossFun", "some")
364s ***** error<ClassificationSVM.loss: 'Weights' must be a numeric vector.> ...
364s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
364s  ones (2,1), "Weights", ['a','b'])
364s ***** error<ClassificationSVM.loss: 'Weights' must be a numeric vector.> ...
364s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
364s  ones (2,1), "Weights", 'a')
364s ***** error<ClassificationSVM.loss: size of 'Weights' must be equal to the number> ...
364s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
364s  ones (2,1), "Weights", [1,2,3])
364s ***** error<ClassificationSVM.loss: size of 'Weights' must be equal to the number> ...
364s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
364s  ones (2,1), "Weights", 3)
364s ***** error<ClassificationSVM.loss: invalid parameter name in optional pair arg> ...
364s  loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
364s  ones (2,1), "some", "some")
364s ***** error<ClassificationSVM.resubLoss: Name-Value arguments must be in pairs.> ...
364s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "LossFun")
364s ***** error<ClassificationSVM.resubLoss: 'LossFun' must be a character vector.> ...
364s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "LossFun", 1)
364s ***** error<ClassificationSVM.resubLoss: unsupported Loss function.> ...
364s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "LossFun", "some")
364s ***** error<ClassificationSVM.resubLoss: 'Weights' must be a numeric vector.> ...
364s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "Weights", ['a','b'])
364s ***** error<ClassificationSVM.resubLoss: 'Weights' must be a numeric vector.> ...
364s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "Weights", 'a')
364s ***** error<ClassificationSVM.resubLoss: size of 'Weights' must be equal to the n> ...
364s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "Weights", [1,2,3])
364s ***** error<ClassificationSVM.resubLoss: size of 'Weights' must be equal to the n> ...
364s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "Weights", 3)
364s ***** error<ClassificationSVM.resubLoss: invalid parameter name in optional pai> ...
364s  resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "some", "some")
364s ***** test
364s  SVMModel = fitcsvm (x, y);
364s  CVMdl = crossval (SVMModel, "KFold", 5);
364s  assert (class (CVMdl), "ClassificationPartitionedModel")
364s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
364s  assert (CVMdl.KFold == 5)
364s  assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
364s  assert (CVMdl.CrossValidatedModel, "ClassificationSVM")
364s ***** test
364s  obj = fitcsvm (x, y);
364s  CVMdl = crossval (obj, "HoldOut", 0.2);
364s  assert (class (CVMdl), "ClassificationPartitionedModel")
364s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
364s  assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
364s  assert (CVMdl.CrossValidatedModel, "ClassificationSVM")
364s ***** test
364s  obj = fitcsvm (x, y);
364s  CVMdl = crossval (obj, "LeaveOut", 'on');
364s  assert (class (CVMdl), "ClassificationPartitionedModel")
364s  assert ({CVMdl.X, CVMdl.Y}, {x, y})
364s  assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
364s  assert (CVMdl.CrossValidatedModel, "ClassificationSVM")
365s ***** error<ClassificationSVM.crossval: Name-Value arguments must be in pairs.> ...
365s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold")
365s ***** error<ClassificationSVM.crossval: specify only one of the optional Name-Value paired arguments.> ...
365s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), ...
365s  "KFold", 5, "leaveout", 'on')
365s ***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
365s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", 'a')
365s ***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
365s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", 1)
365s ***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
365s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", -1)
365s ***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
365s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", 11.5)
365s ***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
365s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", [1,2])
365s ***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
365s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", 'a')
365s ***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
365s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", 11.5)
365s ***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
365s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", -1)
365s ***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
365s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", 0)
365s ***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
365s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", 1)
365s ***** error<ClassificationSVM.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
365s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Leaveout", 1)
365s ***** error<ClassificationSVM.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
365s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "CVPartition", 1)
365s ***** error<ClassificationSVM.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
365s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "CVPartition", 'a')
365s ***** error<ClassificationSVM.crossval: invalid parameter name in optional paired arguments> ...
365s  crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "some", "some")
365s 115 tests, 115 passed, 0 known failure, 0 skipped
365s [inst/Classification/CompactClassificationDiscriminant.m]
365s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/Classification/CompactClassificationDiscriminant.m
365s ***** demo
365s  ## Create a discriminant analysis classifier and its compact version
365s  # and compare their size
365s 
365s  load fisheriris
365s  X = meas;
365s  Y = species;
365s 
365s  Mdl = fitcdiscr (X, Y, 'ClassNames', unique (species))
365s  CMdl = crossval (Mdl)
365s ***** test
365s  load fisheriris
365s  x = meas;
365s  y = species;
365s  PredictorNames = {'Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width'};
365s  Mdl = fitcdiscr (x, y, "PredictorNames", PredictorNames);
365s  CMdl = compact (Mdl);
365s  sigma = [0.265008, 0.092721, 0.167514, 0.038401; ...
365s           0.092721, 0.115388, 0.055244, 0.032710; ...
365s           0.167514, 0.055244, 0.185188, 0.042665; ...
365s           0.038401, 0.032710, 0.042665, 0.041882];
365s  mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
365s        5.9360, 2.7700, 4.2600, 1.3260; ...
365s        6.5880, 2.9740, 5.5520, 2.0260];
365s  xCentered = [ 9.4000e-02,  7.2000e-02, -6.2000e-02, -4.6000e-02; ...
365s               -1.0600e-01, -4.2800e-01, -6.2000e-02, -4.6000e-02; ...
365s               -3.0600e-01, -2.2800e-01, -1.6200e-01, -4.6000e-02];
365s  assert (class (CMdl), "CompactClassificationDiscriminant");
365s  assert ({CMdl.DiscrimType, CMdl.ResponseName}, {"linear", "Y"})
365s  assert ({CMdl.Gamma, CMdl.MinGamma}, {0, 0}, 1e-15)
365s  assert (CMdl.ClassNames, unique (species))
365s  assert (CMdl.Sigma, sigma, 1e-6)
365s  assert (CMdl.Mu, mu, 1e-14)
365s  assert (CMdl.LogDetSigma, -9.9585, 1e-4)
365s  assert (CMdl.PredictorNames, PredictorNames)
366s ***** test
366s  load fisheriris
366s  x = meas;
366s  y = species;
366s  Mdl = fitcdiscr (x, y, "Gamma", 0.5);
366s  CMdl = compact (Mdl);
366s  sigma = [0.265008, 0.046361, 0.083757, 0.019201; ...
366s           0.046361, 0.115388, 0.027622, 0.016355; ...
366s           0.083757, 0.027622, 0.185188, 0.021333; ...
366s           0.019201, 0.016355, 0.021333, 0.041882];
366s  mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
366s        5.9360, 2.7700, 4.2600, 1.3260; ...
366s        6.5880, 2.9740, 5.5520, 2.0260];
366s  xCentered = [ 9.4000e-02,  7.2000e-02, -6.2000e-02, -4.6000e-02; ...
366s               -1.0600e-01, -4.2800e-01, -6.2000e-02, -4.6000e-02; ...
366s               -3.0600e-01, -2.2800e-01, -1.6200e-01, -4.6000e-02];
366s  assert (class (CMdl), "CompactClassificationDiscriminant");
366s  assert ({CMdl.DiscrimType, CMdl.ResponseName}, {"linear", "Y"})
366s  assert ({CMdl.Gamma, CMdl.MinGamma}, {0.5, 0})
366s  assert (CMdl.ClassNames, unique (species))
366s  assert (CMdl.Sigma, sigma, 1e-6)
366s  assert (CMdl.Mu, mu, 1e-14)
366s  assert (CMdl.LogDetSigma, -8.6884, 1e-4)
366s ***** error<CompactClassificationDiscriminant: invalid classification object.> ...
366s  CompactClassificationDiscriminant (1)
366s ***** test
366s  load fisheriris
366s  x = meas;
366s  y = species;
366s  Mdl = fitcdiscr (meas, species, "Gamma", 0.5);
366s  CMdl = compact (Mdl);
366s  [label, score, cost] = predict (CMdl, [2, 2, 2, 2]);
366s  assert (label, {'versicolor'})
366s  assert (score, [0, 0.9999, 0.0001], 1e-4)
366s  assert (cost, [1, 0.0001, 0.9999], 1e-4)
366s  [label, score, cost] = predict (CMdl, [2.5, 2.5, 2.5, 2.5]);
366s  assert (label, {'versicolor'})
366s  assert (score, [0, 0.6368, 0.3632], 1e-4)
366s  assert (cost, [1, 0.3632, 0.6368], 1e-4)
366s ***** test
366s  load fisheriris
366s  x = meas;
366s  y = species;
366s  xc = [min(x); mean(x); max(x)];
366s  Mdl = fitcdiscr (x, y);
366s  CMdl = compact (Mdl);
366s  [label, score, cost] = predict (CMdl, xc);
366s  l = {'setosa'; 'versicolor'; 'virginica'};
366s  s = [1, 0, 0; 0, 1, 0; 0, 0, 1];
366s  c = [0, 1, 1; 1, 0, 1; 1, 1, 0];
366s  assert (label, l)
366s  assert (score, s, 1e-4)
366s  assert (cost, c, 1e-4)
366s ***** shared MODEL
366s  X = rand (10,2);
366s  Y = [ones(5,1);2*ones(5,1)];
366s  MODEL = compact (ClassificationDiscriminant (X, Y));
366s ***** error<CompactClassificationDiscriminant.predict: too few input arguments.> ...
366s  predict (MODEL)
366s ***** error<CompactClassificationDiscriminant.predict: XC is empty.> ...
366s  predict (MODEL, [])
366s ***** error<CompactClassificationDiscriminant.predict: XC must have the same number of features as the trained model.> ...
366s  predict (MODEL, 1)
366s ***** test
366s  load fisheriris
366s  model = fitcdiscr (meas, species);
366s  x = mean (meas);
366s  y = {'versicolor'};
366s  L = loss (model, x, y);
366s  assert (L, 0)
366s ***** test
366s  x = [1, 2; 3, 4; 5, 6];
366s  y = {'A'; 'B'; 'A'};
366s  model = fitcdiscr (x, y, "Gamma", 0.4);
366s  x_test = [1, 6; 3, 3];
366s  y_test = {'A'; 'B'};
366s  L = loss (model, x_test, y_test);
366s  assert (L, 0.3333, 1e-4)
366s ***** test
366s  x = [1, 2; 3, 4; 5, 6; 7, 8];
366s  y = ['1'; '2'; '3'; '1'];
366s  model = fitcdiscr (x, y, "gamma" , 0.5);
366s  x_test = [3, 3];
366s  y_test = ['1'];
366s  L = loss (model, x_test, y_test, 'LossFun', 'quadratic');
366s  assert (L, 0.2423, 1e-4)
366s ***** test
366s  x = [1, 2; 3, 4; 5, 6; 7, 8];
366s  y = ['1'; '2'; '3'; '1'];
366s  model = fitcdiscr (x, y, "gamma" , 0.5);
366s  x_test = [3, 3; 5, 7];
366s  y_test = ['1'; '2'];
366s  L = loss (model, x_test, y_test, 'LossFun', 'classifcost');
366s  assert (L, 0.3333, 1e-4)
366s ***** test
366s  x = [1, 2; 3, 4; 5, 6; 7, 8];
366s  y = ['1'; '2'; '3'; '1'];
366s  model = fitcdiscr (x, y, "gamma" , 0.5);
366s  x_test = [3, 3; 5, 7];
366s  y_test = ['1'; '2'];
366s  L = loss (model, x_test, y_test, 'LossFun', 'hinge');
366s  assert (L, 0.5886, 1e-4)
366s ***** test
366s  x = [1, 2; 3, 4; 5, 6; 7, 8];
366s  y = ['1'; '2'; '3'; '1'];
366s  model = fitcdiscr (x, y, "gamma" , 0.5);
366s  x_test = [3, 3; 5, 7];
366s  y_test = ['1'; '2'];
366s  W = [1; 2];
366s  L = loss (model, x_test, y_test, 'LossFun', 'logit', 'Weights', W);
366s  assert (L, 0.5107, 1e-4)
366s ***** test
366s  x = [1, 2; 3, 4; 5, 6];
366s  y = {'A'; 'B'; 'A'};
366s  model = fitcdiscr (x, y, "gamma" , 0.5);
366s  x_with_nan = [1, 2; NaN, 4];
366s  y_test = {'A'; 'B'};
366s  L = loss (model, x_with_nan, y_test);
366s  assert (L, 0.3333, 1e-4)
366s ***** test
366s  x = [1, 2; 3, 4; 5, 6];
366s  y = {'A'; 'B'; 'A'};
366s  model = fitcdiscr (x, y);
366s  x_with_nan = [1, 2; NaN, 4];
366s  y_test = {'A'; 'B'};
366s  L = loss (model, x_with_nan, y_test, 'LossFun', 'logit');
366s  assert (isnan (L))
366s ***** test
366s  x = [1, 2; 3, 4; 5, 6];
366s  y = {'A'; 'B'; 'A'};
366s  model = fitcdiscr (x, y);
366s  customLossFun = @(C, S, W, Cost) sum (W .* sum (abs (C - S), 2));
366s  L = loss (model, x, y, 'LossFun', customLossFun);
366s  assert (L, 0.8889, 1e-4)
366s ***** test
366s  x = [1, 2; 3, 4; 5, 6];
366s  y = [1; 2; 1];
366s  model = fitcdiscr (x, y);
366s  L = loss (model, x, y, 'LossFun', 'classiferror');
366s  assert (L, 0.3333, 1e-4)
366s ***** error<CompactClassificationDiscriminant.loss: too few input arguments.> ...
366s  loss (MODEL)
366s ***** error<CompactClassificationDiscriminant.loss: too few input arguments.> ...
366s  loss (MODEL, ones (4,2))
366s ***** error<CompactClassificationDiscriminant.loss: name-value arguments must be in pairs.> ...
366s  loss (MODEL, ones (4,2), ones (4,1), 'LossFun')
366s ***** error<CompactClassificationDiscriminant.loss: Y must have the same number of rows as X.> ...
366s  loss (MODEL, ones (4,2), ones (3,1))
366s ***** error<CompactClassificationDiscriminant.loss: invalid loss function.> ...
366s  loss (MODEL, ones (4,2), ones (4,1), 'LossFun', 'a')
366s ***** error<CompactClassificationDiscriminant.loss: invalid 'Weights'.> ...
366s  loss (MODEL, ones (4,2), ones (4,1), 'Weights', 'w')
366s  load fisheriris
366s  mdl = fitcdiscr (meas, species);
366s  X = mean (meas);
366s  Y = {'versicolor'};
366s  m = margin (mdl, X, Y);
366s  assert (m, 1, 1e-6)
366s ***** test
366s  X = [1, 2; 3, 4; 5, 6];
366s  Y = [1; 2; 1];
366s  mdl = fitcdiscr (X, Y, "gamma", 0.5);
366s  m = margin (mdl, X, Y);
366s  assert (m, [0.3333; -0.3333; 0.3333], 1e-4)
366s ***** error<CompactClassificationDiscriminant.margin: too few input arguments.> ...
366s  margin (MODEL)
366s ***** error<CompactClassificationDiscriminant.margin: too few input arguments.> ...
366s  margin (MODEL, ones (4,2))
366s ***** error<CompactClassificationDiscriminant.margin: Y must have the same number of rows as X.> ...
366s  margin (MODEL, ones (4,2), ones (3,1))
366s 28 tests, 28 passed, 0 known failure, 0 skipped
366s [inst/Classification/CompactClassificationGAM.m]
366s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/Classification/CompactClassificationGAM.m
366s ***** demo
366s  ## Create a generalized additive model classifier and its compact version
366s  # and compare their size
366s 
366s  load fisheriris
366s  X = meas;
366s  Y = species;
366s 
366s  Mdl = fitcdiscr (X, Y, 'ClassNames', unique (species))
366s  CMdl = crossval (Mdl)
366s ***** test
366s  Mdl = CompactClassificationGAM ();
366s  assert (class (Mdl), "CompactClassificationGAM")
366s ***** test
366s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
366s  y = [0; 0; 1; 1];
366s  PredictorNames = {'Feature1', 'Feature2', 'Feature3'};
366s  Mdl = fitcgam (x, y, "PredictorNames", PredictorNames);
366s  CMdl = compact (Mdl);
366s  assert (class (CMdl), "CompactClassificationGAM");
366s  assert ({CMdl.NumPredictors, CMdl.ResponseName}, {3, "Y"})
366s  assert (CMdl.ClassNames, {'0'; '1'})
366s  assert (CMdl.PredictorNames, PredictorNames)
366s  assert (CMdl.BaseModel.Intercept, 0)
368s ***** test
368s  load fisheriris
368s  inds = strcmp (species,'versicolor') | strcmp (species,'virginica');
368s  X = meas(inds, :);
368s  Y = species(inds, :)';
368s  Y = strcmp (Y, 'virginica')';
368s  Mdl = fitcgam (X, Y, 'Formula', 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3');
368s  CMdl = compact (Mdl);
368s  assert (class (CMdl), "CompactClassificationGAM");
368s  assert ({CMdl.NumPredictors, CMdl.ResponseName}, {4, "Y"})
368s  assert (CMdl.ClassNames, {'0'; '1'})
368s  assert (CMdl.Formula, 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3')
368s  assert (CMdl.PredictorNames, {'x1', 'x2', 'x3', 'x4'})
368s  assert (CMdl.ModelwInt.Intercept, 0)
376s ***** test
376s  X = [2, 3, 5; 4, 6, 8; 1, 2, 3; 7, 8, 9; 5, 4, 3];
376s  Y = [0; 1; 0; 1; 1];
376s  Mdl = fitcgam (X, Y, 'Knots', [4, 4, 4], 'Order', [3, 3, 3]);
376s  CMdl = compact (Mdl);
376s  assert (class (CMdl), "CompactClassificationGAM");
376s  assert ({CMdl.NumPredictors, CMdl.ResponseName}, {3, "Y"})
376s  assert (CMdl.ClassNames, {'0'; '1'})
376s  assert (CMdl.PredictorNames, {'x1', 'x2', 'x3'})
376s  assert (CMdl.Knots, [4, 4, 4])
376s  assert (CMdl.Order, [3, 3, 3])
376s  assert (CMdl.DoF, [7, 7, 7])
376s  assert (CMdl.BaseModel.Intercept, 0.4055, 1e-1)
378s ***** error<CompactClassificationGAM: invalid classification object.> ...
378s  CompactClassificationGAM (1)
378s ***** test
378s  x = [1, 2; 3, 4; 5, 6; 7, 8; 9, 10];
378s  y = [1; 0; 1; 0; 1];
378s  Mdl = fitcgam (x, y, "interactions", "all");
378s  CMdl = compact (Mdl);
378s  l = {'0'; '0'; '0'; '0'; '0'};
378s  s = [0.3760, 0.6240; 0.4259, 0.5741; 0.3760, 0.6240; ...
378s       0.4259, 0.5741; 0.3760, 0.6240];
378s  [labels, scores] = predict (CMdl, x);
378s  assert (class (CMdl), "CompactClassificationGAM");
378s  assert ({CMdl.NumPredictors, CMdl.ResponseName}, {2, "Y"})
378s  assert (CMdl.ClassNames, {'1'; '0'})
378s  assert (CMdl.PredictorNames, {'x1', 'x2'})
378s  assert (CMdl.ModelwInt.Intercept, 0.4055, 1e-1)
378s  assert (labels, l)
378s  assert (scores, s, 1e-1)
382s ***** test
382s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
382s  y = [0; 0; 1; 1];
382s  interactions = [false, true, false; true, false, true; false, true, false];
382s  Mdl = fitcgam (x, y, "learningrate", 0.2, "interactions", interactions);
382s  CMdl = compact (Mdl);
382s  [label, score] = predict (CMdl, x, "includeinteractions", true);
382s  l = {'0'; '0'; '1'; '1'};
382s  s = [0.5106, 0.4894; 0.5135, 0.4865; 0.4864, 0.5136; 0.4847, 0.5153];
382s  assert (class (CMdl), "CompactClassificationGAM");
382s  assert ({CMdl.NumPredictors, CMdl.ResponseName}, {3, "Y"})
382s  assert (CMdl.ClassNames, {'0'; '1'})
382s  assert (CMdl.PredictorNames, {'x1', 'x2', 'x3'})
382s  assert (CMdl.ModelwInt.Intercept, 0)
382s  assert (label, l)
382s  assert (score, s, 1e-1)
389s ***** shared CMdl
389s  Mdl = fitcgam (ones (4,2), ones (4,1));
389s  CMdl = compact (Mdl);
391s ***** error<CompactClassificationGAM.predict: too few input arguments.> ...
391s  predict (CMdl)
391s ***** error<CompactClassificationGAM.predict: XC is empty.> ...
391s  predict (CMdl, [])
391s ***** error<CompactClassificationGAM.predict: XC must have the same number of features as the trained model.> ...
391s  predict (CMdl, 1)
391s 10 tests, 10 passed, 0 known failure, 0 skipped
391s [inst/Classification/CompactClassificationNeuralNetwork.m]
391s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/Classification/CompactClassificationNeuralNetwork.m
391s ***** demo
391s  ## Create a neural network classifier and its compact version
391s  # and compare their size
391s 
391s  load fisheriris
391s  X = meas;
391s  Y = species;
391s 
391s  Mdl = fitcnet (X, Y, 'ClassNames', unique (species))
391s  CMdl = crossval (Mdl)
391s ***** error<CompactClassificationDiscriminant: invalid classification object.> ...
391s  CompactClassificationDiscriminant (1)
391s ***** shared x, y, CMdl
391s  load fisheriris
391s  x = meas;
391s  y = grp2idx (species);
391s  Mdl = fitcnet (x, y, "IterationLimit", 100);
391s  CMdl = compact (Mdl);
391s ***** error<CompactClassificationNeuralNetwork.predict: too few input arguments.> ...
391s  predict (CMdl)
391s ***** error<CompactClassificationNeuralNetwork.predict: XC is empty.> ...
391s  predict (CMdl, [])
391s ***** error<CompactClassificationNeuralNetwork.predict: XC must have the same number of predictors as the trained neural network.> ...
391s  predict (CMdl, 1)
391s ***** error<CompactClassificationNeuralNetwork: unrecognized 'ScoreTransform' function.> ...
391s  CMdl.ScoreTransform = "a";
391s 5 tests, 5 passed, 0 known failure, 0 skipped
391s [inst/Classification/CompactClassificationSVM.m]
391s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/Classification/CompactClassificationSVM.m
391s ***** demo
391s  ## Create a support vectors machine classifier and its compact version
391s  # and compare their size
391s 
391s  load fisheriris
391s  X = meas;
391s  Y = species;
391s 
391s  Mdl = fitcsvm (X, Y, 'ClassNames', unique (species))
391s  CMdl = crossval (Mdl)
391s ***** error<CompactClassificationSVM: invalid classification object.> ...
391s  CompactClassificationSVM (1)
391s ***** shared x, y, CMdl
391s  load fisheriris
391s  inds = ! strcmp (species, 'setosa');
391s  x = meas(inds, 3:4);
391s  y = grp2idx (species(inds));
391s ***** test
391s  xc = [min(x); mean(x); max(x)];
391s  Mdl = fitcsvm (x, y, 'KernelFunction', 'rbf', 'Tolerance', 1e-7);
391s  CMdl = compact (Mdl);
391s  assert (isempty (CMdl.Alpha), true)
391s  assert (sum (CMdl.IsSupportVector), numel (CMdl.Beta))
391s  [label, score] = predict (CMdl, xc);
391s  assert (label, [1; 2; 2]);
391s  assert (score(:,1), [0.99285; -0.080296; -0.93694], 1e-5);
391s  assert (score(:,1), -score(:,2), eps)
391s ***** test
391s  Mdl = fitcsvm (x, y);
391s  CMdl = compact (Mdl);
391s  assert (isempty (CMdl.Beta), true)
391s  assert (sum (CMdl.IsSupportVector), numel (CMdl.Alpha))
391s  assert (numel (CMdl.Alpha), 24)
391s  assert (CMdl.Bias, -14.415, 1e-3)
391s  xc = [min(x); mean(x); max(x)];
391s  label = predict (CMdl, xc);
391s  assert (label, [1; 2; 2]);
391s ***** error<CompactClassificationSVM.predict: too few input arguments.> ...
391s  predict (CMdl)
391s ***** error<CompactClassificationSVM.predict: XC is empty.> ...
391s  predict (CMdl, [])
391s ***** error<CompactClassificationSVM.predict: XC must have the same number of predictors as the trained SVM model.> ...
391s  predict (CMdl, 1)
391s ***** test
391s  CMdl.ScoreTransform = "a";
391s ***** error<CompactClassificationSVM.predict: 'ScoreTransform' must be a 'function_handle' object.> ...
391s  [labels, scores] = predict (CMdl, x);
391s ***** test
391s  rand ("seed", 1);
391s  C = cvpartition (y, 'HoldOut', 0.15);
391s  Mdl = fitcsvm (x(training (C),:), y(training (C)), ...
391s                 'KernelFunction', 'rbf', 'Tolerance', 1e-7);
391s  CMdl = compact (Mdl);
391s  testInds = test (C);
391s  expected_margin = [2.0000;  0.8579;  1.6690;  3.4141;  3.4552; ...
391s                     2.6605;  3.5251; -4.0000; -6.3411; -6.4511; ...
391s                    -3.0532; -7.5054; -1.6700; -5.6227; -7.3640];
391s  computed_margin = margin (CMdl, x(testInds,:), y(testInds,:));
391s  assert (computed_margin, expected_margin, 1e-4);
391s ***** error<CompactClassificationSVM.margin: too few input arguments.> ...
391s  margin (CMdl)
391s ***** error<CompactClassificationSVM.margin: too few input arguments.> ...
391s  margin (CMdl, zeros (2))
391s ***** error<CompactClassificationSVM.margin: X is empty.> ...
391s  margin (CMdl, [], 1)
391s ***** error<CompactClassificationSVM.margin: X must have the same number of predictors as the trained SVM model.> ...
391s  margin (CMdl, 1, 1)
391s ***** error<CompactClassificationSVM.margin: Y is empty.> ...
391s  margin (CMdl, [1, 2], [])
391s ***** error<CompactClassificationSVM.margin: Y must have the same number of rows as X.> ...
391s  margin (CMdl, [1, 2], [1; 2])
391s ***** test
391s  rand ("seed", 1);
391s  C = cvpartition (y, 'HoldOut', 0.15);
391s  Mdl = fitcsvm (x(training (C),:), y(training (C)), ...
391s                 'KernelFunction', 'rbf', 'Tolerance', 1e-7);
391s  CMdl = compact (Mdl);
391s  testInds = test (C);
391s  L1 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'binodeviance');
391s  L2 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'classiferror');
391s  L3 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'exponential');
391s  L4 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'hinge');
391s  L5 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'logit');
391s  L6 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'quadratic');
391s  assert (L1, 2.8711, 1e-4);
391s  assert (L2, 0.5333, 1e-4);
391s  assert (L3, 10.9685, 1e-4);
391s  assert (L4, 1.9827, 1e-4);
391s  assert (L5, 1.5849, 1e-4);
391s  assert (L6, 7.6739, 1e-4);
391s ***** error<CompactClassificationSVM.loss: too few input arguments.> ...
391s  loss (CMdl)
391s ***** error<CompactClassificationSVM.loss: too few input arguments.> ...
391s  loss (CMdl, zeros (2))
391s ***** error<CompactClassificationSVM.loss: Name-Value arguments must be in pairs.> ...
391s  loss (CMdl, [1, 2], 1, "LossFun")
391s ***** error<CompactClassificationSVM.loss: X is empty.> ...
391s  loss (CMdl, [], zeros (2))
391s ***** error<CompactClassificationSVM.loss: X must have the same number of predictors as the trained SVM model.> ...
391s  loss (CMdl, 1, zeros (2))
391s ***** error<CompactClassificationSVM.loss: Y is empty.> ...
391s  loss (CMdl, [1, 2], [])
391s ***** error<CompactClassificationSVM.loss: Y must have the same number of rows as X.> ...
391s  loss (CMdl, [1, 2], [1; 2])
391s ***** error<CompactClassificationSVM.loss: 'LossFun' must be a character vector.> ...
391s  loss (CMdl, [1, 2], 1, "LossFun", 1)
391s ***** error<CompactClassificationSVM.loss: unsupported Loss function.> ...
391s  loss (CMdl, [1, 2], 1, "LossFun", "some")
391s ***** error<CompactClassificationSVM.loss: 'Weights' must be a numeric vector.> ...
391s  loss (CMdl, [1, 2], 1, "Weights", ['a', 'b'])
391s ***** error<CompactClassificationSVM.loss: 'Weights' must be a numeric vector.> ...
391s  loss (CMdl, [1, 2], 1, "Weights", 'a')
391s ***** error<CompactClassificationSVM.loss: size of 'Weights' must be equal to the number of rows in X.> ...
391s  loss (CMdl, [1, 2], 1, "Weights", [1, 2])
391s ***** error<CompactClassificationSVM.loss: invalid parameter name in optional pair arguments.> ...
391s  loss (CMdl, [1, 2], 1, "some", "some")
391s 29 tests, 29 passed, 0 known failure, 0 skipped
391s [inst/Classification/ConfusionMatrixChart.m]
391s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/Classification/ConfusionMatrixChart.m
391s ***** demo
391s  ## Create a simple ConfusionMatrixChart Object
391s 
391s  cm = ConfusionMatrixChart (gca, [1 2; 1 2], {"A","B"}, {"XLabel","LABEL A"})
391s  NormalizedValues = cm.NormalizedValues
391s  ClassLabels = cm.ClassLabels
391s ***** test
391s  hf = figure ("visible", "off");
391s  unwind_protect
391s    cm = ConfusionMatrixChart (gca, [1 2; 1 2], {"A","B"}, {"XLabel","LABEL A"});
391s    assert (isa (cm, "ConfusionMatrixChart"), true);
391s  unwind_protect_cleanup
391s    close (hf);
391s  end_unwind_protect
391s warning: using the gnuplot graphics toolkit is discouraged
391s 
391s The gnuplot graphics toolkit is not actively maintained and has a number
391s of limitations that are unlikely to be fixed.  Communication with gnuplot
391s uses a one-directional pipe and limited information is passed back to the
391s Octave interpreter so most changes made interactively in the plot window
391s will not be reflected in the graphics properties managed by Octave.  For
391s example, if the plot window is closed with a mouse click, Octave will not
391s be notified and will not update its internal list of open figure windows.
391s The qt toolkit is recommended instead.
391s 1 test, 1 passed, 0 known failure, 0 skipped
391s [inst/Clustering/CalinskiHarabaszEvaluation.m]
391s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/Clustering/CalinskiHarabaszEvaluation.m
391s ***** test
391s  load fisheriris
391s  eva = evalclusters (meas, "kmeans", "calinskiharabasz", "KList", [1:6]);
391s  assert (class (eva), "CalinskiHarabaszEvaluation");
392s 1 test, 1 passed, 0 known failure, 0 skipped
392s [inst/Clustering/ClusterCriterion.m]
392s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/Clustering/ClusterCriterion.m
392s ***** error <ClusterCriterion: X must be a numeric matrix.> ...
392s  ClusterCriterion ("1", "kmeans", [1:6])
392s ***** error <ClusterCriterion: unknown clustering algorithm 'k'.> ...
392s  ClusterCriterion ([1, 2, 1, 3, 2, 4, 3], "k", [1:6])
392s ***** error <ClusterCriterion: invalid matrix of clustering solutions.> ...
392s  ClusterCriterion ([1, 2, 1; 3, 2, 4], 1, [1:6])
392s ***** error <ClusterCriterion: invalid argument.> ...
392s  ClusterCriterion ([1, 2, 1; 3, 2, 4], ones (2, 2, 2), [1:6])
392s 4 tests, 4 passed, 0 known failure, 0 skipped
392s [inst/Clustering/DaviesBouldinEvaluation.m]
392s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/Clustering/DaviesBouldinEvaluation.m
392s ***** test
392s  load fisheriris
392s  eva = evalclusters (meas, "kmeans", "DaviesBouldin", "KList", [1:6]);
392s  assert (class (eva), "DaviesBouldinEvaluation");
392s 1 test, 1 passed, 0 known failure, 0 skipped
392s [inst/Clustering/ExhaustiveSearcher.m]
392s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/Clustering/ExhaustiveSearcher.m
392s ***** demo
392s  ## Demo to verify implementation using fisheriris dataset
392s  load fisheriris
392s  rng('default');
392s  numSamples = size (meas, 1);
392s  queryIndices = [20, 95, 123, 136, 138];
392s  dataPoints = meas(~ismember (1:numSamples, queryIndices), :);
392s  queryPoints = meas(queryIndices, :);
392s  searchModel = ExhaustiveSearcher (dataPoints, 'Distance', 'mahalanobis')
392s  mahalanobisParam = searchModel.DistParameter
392s  searchRadius = 3;
392s  nearestNeighbors = knnsearch (searchModel, queryPoints, "K", 2)
392s  neighborsInRange = rangesearch (searchModel, queryPoints, searchRadius)
392s ***** demo
392s  ## Create an ExhaustiveSearcher with Euclidean distance
392s  X = [1, 2; 3, 4; 5, 6];
392s  obj = ExhaustiveSearcher (X);
392s  ## Find the nearest neighbor to [2, 3]
392s  Y = [2, 3];
392s  [idx, D] = knnsearch (obj, Y);
392s  disp ("Nearest neighbor index:"); disp (idx);
392s  disp ("Distance:"); disp (D);
392s  ## Find all points within radius 2
392s  [idx, D] = rangesearch (obj, Y, 2);
392s  disp ("Indices within radius:"); disp (idx);
392s  disp ("Distances:"); disp (D);
392s ***** demo
392s  ## Create an ExhaustiveSearcher with Minkowski distance (P=1)
392s  X = [0, 0; 1, 0; 0, 1];
392s  obj = ExhaustiveSearcher (X, "Distance", "minkowski", "P", 1);
392s  ## Find the 2 nearest neighbors to [0.5, 0.5]
392s  Y = [0.5, 0.5];
392s  [idx, D] = knnsearch (obj, Y, "K", 2);
392s  disp ("Nearest neighbor indices:"); disp (idx);
392s  disp ("Distances:"); disp (D);
392s ***** demo
392s  rng(42);
392s  disp('Demonstrating ExhaustiveSearcher');
392s 
392s  n = 100;
392s  mu1 = [0.3, 0.3];
392s  mu2 = [0.7, 0.7];
392s  sigma = 0.1;
392s  X1 = mu1 + sigma * randn(n/2, 2);
392s  X2 = mu2 + sigma * randn(n/2, 2);
392s  X = [X1; X2];
392s 
392s  obj = ExhaustiveSearcher(X);
392s 
392s  Y = [0.3, 0.3; 0.7, 0.7; 0.5, 0.5];
392s 
392s  K = 5;
392s  [idx, D] = knnsearch(obj, Y, "K", K);
392s 
392s  disp('For the first query point:');
392s  disp(['Query point: ', num2str(Y(1,:))]);
392s  disp('Indices of nearest neighbors:');
392s  disp(idx(1,:));
392s  disp('Distances:');
392s  disp(D(1,:));
392s 
392s  figure;
392s  scatter(X(:,1), X(:,2), 36, 'b', 'filled'); % Training points
392s  hold on;
392s  scatter(Y(:,1), Y(:,2), 36, 'r', 'filled'); % Query points
392s  for i = 1:size(Y,1)
392s      query = Y(i,:);
392s      neighbors = X(idx(i,:), :);
392s      for j = 1:K
392s          plot([query(1), neighbors(j,1)], [query(2), neighbors(j,2)], 'k-');
392s      end
392s  end
392s  hold off;
392s  title('K Nearest Neighbors with ExhaustiveSearcher');
392s  xlabel('X1');
392s  ylabel('X2');
392s 
392s  r = 0.15;
392s  [idx, D] = rangesearch(obj, Y, r);
392s 
392s  disp('For the first query point in rangesearch:');
392s  disp(['Query point: ', num2str(Y(1,:))]);
392s  disp('Indices of points within radius:');
392s  disp(idx{1});
392s  disp('Distances:');
392s  disp(D{1});
392s 
392s  figure;
392s  scatter(X(:,1), X(:,2), 36, 'b', 'filled');
392s  hold on;
392s  scatter(Y(:,1), Y(:,2), 36, 'r', 'filled');
392s  theta = linspace(0, 2*pi, 100);
392s  for i = 1:size(Y,1)
392s      center = Y(i,:);
392s      x_circle = center(1) + r * cos(theta);
392s      y_circle = center(2) + r * sin(theta);
392s      plot(x_circle, y_circle, 'g-');
392s      % Highlight points within radius
392s      if ~isempty(idx{i})
392s          in_radius = X(idx{i}, :);
392s          scatter(in_radius(:,1), in_radius(:,2), 36, 'g', 'filled');
392s      end
392s  end
392s  hold off;
392s  title('Points within Radius with ExhaustiveSearcher');
392s  xlabel('X1');
392s  ylabel('X2');
392s ***** test
392s  ## Basic constructor with default Euclidean
392s  X = [1, 2; 3, 4; 5, 6];
392s  obj = ExhaustiveSearcher (X);
392s  assert (obj.X, X)
392s  assert (obj.Distance, "euclidean")
392s  assert (isempty (obj.DistParameter))
392s ***** test
392s  ## Minkowski distance with custom P
392s  X = [1, 2; 3, 4];
392s  obj = ExhaustiveSearcher (X, "Distance", "minkowski", "P", 3);
392s  assert (obj.Distance, "minkowski")
392s  assert (obj.DistParameter, 3)
392s ***** test
392s  ## Seuclidean distance with custom Scale
392s  X = [1, 2; 3, 4; 5, 6];
392s  S = [1, 2];
392s  obj = ExhaustiveSearcher (X, "Distance", "seuclidean", "Scale", S);
392s  assert (obj.Distance, "seuclidean")
392s  assert (obj.DistParameter, S)
392s ***** test
392s  ## Mahalanobis distance with custom Cov
392s  X = [1, 2; 3, 4; 5, 6];
392s  C = [1, 0; 0, 1];
392s  obj = ExhaustiveSearcher (X, "Distance", "mahalanobis", "Cov", C);
392s  assert (obj.Distance, "mahalanobis")
392s  assert (obj.DistParameter, C)
392s ***** test
392s  ## knnsearch with Euclidean distance
392s  X = [1, 2; 3, 4; 5, 6];
392s  obj = ExhaustiveSearcher (X);
392s  Y = [2, 3];
392s  [idx, D] = knnsearch (obj, Y, "K", 1);
392s  assert (idx, 1)
392s  assert (D, sqrt(2), 1e-10)
392s ***** test
392s  ## knnsearch with Cityblock distance
392s  X = [0, 0; 1, 1; 2, 2];
392s  obj = ExhaustiveSearcher (X, "Distance", "cityblock");
392s  Y = [1, 0];
392s  [idx, D] = knnsearch (obj, Y, "K", 1);
392s  assert (idx, 1)
392s  assert (D, 1, 1e-10)
392s ***** test
392s  ## knnsearch with Chebychev distance
392s  X = [1, 1; 2, 3; 4, 2];
392s  obj = ExhaustiveSearcher (X, "Distance", "chebychev");
392s  Y = [2, 2];
392s  [idx, D] = knnsearch (obj, Y);
392s  assert (idx, 1)
392s  assert (D, 1, 1e-10)
392s ***** test
392s  ## knnsearch with Cosine distance
392s  X = [1, 0; 0, 1; 1, 1];
392s  obj = ExhaustiveSearcher (X, "Distance", "cosine");
392s  Y = [1, 0.5];
392s  [idx, D] = knnsearch (obj, Y);
392s  assert (idx, 3)
392s  assert (D < 0.1, true)
392s ***** test
392s  ## knnsearch with Minkowski P=1 (Manhattan)
392s  X = [0, 0; 1, 0; 0, 1];
392s  obj = ExhaustiveSearcher (X, "Distance", "minkowski", "P", 1);
392s  Y = [0.5, 0.5];
392s  [idx, D] = knnsearch (obj, Y, "K", 2, "IncludeTies", true);
392s  assert (iscell (idx))
392s  assert (idx{1}, [1, 2, 3])
392s  assert (D{1}, [1, 1, 1], 1e-10)
392s ***** test
392s  ## rangesearch with Seuclidean
392s  X = [1, 1; 2, 2; 3, 3];
392s  S = [1, 1];
392s  obj = ExhaustiveSearcher (X, "Distance", "seuclidean", "Scale", S);
392s  Y = [0, 0];
392s  [idx, D] = rangesearch (obj, Y, 2);
392s  assert (idx{1}, [1])
392s  assert (D{1}, [sqrt(2)], 1e-10)
392s ***** test
392s  ## rangesearch with Mahalanobis
392s  X = [1, 1; 2, 2; 3, 3];
392s  C = [1, 0; 0, 1];
392s  obj = ExhaustiveSearcher (X, "Distance", "mahalanobis", "Cov", C);
392s  Y = [0, 0];
392s  [idx, D] = rangesearch (obj, Y, 3, "SortIndices", false);
392s  assert (idx{1}, [1, 2])
392s  assert (D{1}, [sqrt(2), sqrt(8)], 1e-10)
392s ***** test
392s  ## rangesearch with Hamming distance
392s  X = [0, 1; 1, 0; 1, 1];
392s  obj = ExhaustiveSearcher (X, "Distance", "hamming");
392s  Y = [0, 0];
392s  [idx, D] = rangesearch (obj, Y, 0.5);
392s  assert (idx{1}, [1, 2])
392s  assert (D{1}, [0.5, 0.5], 1e-10)
392s ***** test
392s  ## Custom distance function
392s  X = [1, 2; 3, 4];
392s  custom_dist = @(x, y) sum(abs(x - y));
392s  obj = ExhaustiveSearcher (X, "Distance", custom_dist);
392s  Y = [2, 3];
392s  [idx, D] = knnsearch (obj, Y);
392s  assert (idx, 1)
392s  assert (D, 2, 1e-10)
392s ***** test
392s  ## Euclidean with high-dimensional data
392s  X = [1, 2, 3; 4, 5, 6; 7, 8, 9; 10, 11, 12];
392s  obj = ExhaustiveSearcher (X);
392s  Y = [5, 6, 7];
392s  [idx, D] = knnsearch (obj, Y);
392s  assert (idx, 2)
392s  assert (D, sqrt(3), 1e-10)
392s ***** test
392s  ## Minkowski P=3 with scaled data
392s  X = [0, 1; 2, 3; 4, 5] * 10;
392s  obj = ExhaustiveSearcher (X, "Distance", "minkowski", "P", 3);
392s  Y = [20, 30];
392s  [idx, D] = knnsearch (obj, Y);
392s  assert (idx, 2)
392s  assert (D, 0, 1e-10)
392s ***** test
392s  ## Seuclidean with custom scales on diverse data
392s  X = [1, 10; 2, 20; 3, 30];
392s  S = [1, 5];
392s  obj = ExhaustiveSearcher (X, "Distance", "seuclidean", "Scale", S);
392s  Y = [1.5, 15];
392s  [idx, D] = knnsearch (obj, Y);
392s  assert (idx, 1)
392s  assert (D, sqrt((0.5/1)^2 + (5/5)^2), 1e-10)
392s ***** test
392s  ## Mahalanobis with correlated data
392s  X = [1, 1; 2, 1.5; 3, 2];
392s  C = [1, 0.5; 0.5, 1];
392s  obj = ExhaustiveSearcher (X, "Distance", "mahalanobis", "Cov", C);
392s  Y = [2, 1.5];
392s  [idx, D] = knnsearch (obj, Y);
392s  assert (idx, 2)
392s  assert (D, 0, 1e-10)
392s ***** test
392s  ## Cityblock with sparse data
392s  X = [0, 0, 1; 1, 0, 0; 0, 1, 0];
392s  obj = ExhaustiveSearcher (X, "Distance", "cityblock");
392s  Y = [0, 0, 0];
392s  [idx, D] = rangesearch (obj, Y, 1);
392s  assert (idx{1}, [1, 2, 3])
392s  assert (D{1}, [1, 1, 1], 1e-10)
392s ***** test
392s  ## Chebychev with extreme values
392s  X = [0, 100; 50, 50; 100, 0];
392s  obj = ExhaustiveSearcher (X, "Distance", "chebychev");
392s  Y = [60, 60];
392s  [idx, D] = knnsearch (obj, Y);
392s  assert (idx, 2)
392s  assert (D, 10, 1e-10)
392s ***** test
392s  ## Cosine with normalized data
392s  X = [1, 0; 0, 1; 1/sqrt(2), 1/sqrt(2)];
392s  obj = ExhaustiveSearcher (X, "Distance", "cosine");
392s  Y = [1, 1];
392s  [idx, D] = knnsearch (obj, Y);
392s  assert (idx, 3)
392s  assert (D < 0.1, true)
392s ***** test
392s  ## Correlation with time-series-like data
392s  X = [1, 2, 3; 2, 4, 6; 1, 1, 1];
392s  obj = ExhaustiveSearcher (X, "Distance", "correlation");
392s  Y = [1.5, 3, 4.5];
392s  [idx, D] = knnsearch (obj, Y);
392s  assert (idx, 1)
392s  assert (D < 0.1, true)
392s ***** test
392s  ## Spearman with ranked data
392s  X = [1, 2, 3; 3, 2, 1; 2, 1, 3];
392s  obj = ExhaustiveSearcher (X, "Distance", "spearman");
392s  Y = [1, 2, 3];
392s  [idx, D] = knnsearch (obj, Y);
392s  assert (idx, 1)
392s  assert (D, 0, 1e-10)
392s ***** test
392s  ## Jaccard with binary sparse data
392s  X = [1, 0, 0; 0, 1, 0; 1, 1, 0];
392s  obj = ExhaustiveSearcher (X, "Distance", "jaccard");
392s  Y = [1, 0, 0];
392s  [idx, D] = knnsearch (obj, Y);
392s  assert (idx, 1)
392s  assert (D, 0, 1e-10)
392s ***** test
392s  obj = ExhaustiveSearcher (ones(3,2));
392s  assert (obj.X, ones(3,2))
392s  assert (obj.Distance, "euclidean")
392s  assert (isempty (obj.DistParameter))
392s ***** test
392s  obj = ExhaustiveSearcher (ones(3,2));
392s  obj.Distance = "minkowski";
392s  assert (obj.Distance, "minkowski")
392s ***** test
392s  obj = ExhaustiveSearcher (ones(3,2), "Distance", "minkowski");
392s  obj.DistParameter = 3;
392s  assert (obj.DistParameter, 3)
392s ***** test
392s  obj = ExhaustiveSearcher (ones(3,2), "Distance", "seuclidean");
392s  obj.DistParameter = [1, 2];
392s  assert (obj.DistParameter, [1, 2])
392s ***** test
392s  obj = ExhaustiveSearcher (ones(3,2), "Distance", "mahalanobis");
392s  obj.DistParameter = eye(2);
392s  assert (obj.DistParameter, eye(2))
392s ***** error<ExhaustiveSearcher: too few input arguments.> ...
392s  ExhaustiveSearcher ()
392s ***** error<ExhaustiveSearcher: Name-Value arguments must be in pairs.> ...
392s  ExhaustiveSearcher (ones(3,2), "Distance")
392s ***** error<ExhaustiveSearcher: X must be a finite numeric matrix.> ...
392s  ExhaustiveSearcher ("abc")
392s ***** error<ExhaustiveSearcher: X must be a finite numeric matrix.> ...
392s  ExhaustiveSearcher ([1; Inf; 3])
392s ***** error<ExhaustiveSearcher: invalid parameter name: 'foo'.> ...
392s  ExhaustiveSearcher (ones(3,2), "foo", "bar")
392s ***** error<ExhaustiveSearcher: unsupported distance metric 'invalid'.> ...
392s  ExhaustiveSearcher (ones(3,2), "Distance", "invalid")
392s ***** error<ExhaustiveSearcher: invalid distance function handle.> ...
392s  ExhaustiveSearcher (ones(3,2), "Distance", @(x) x)
392s ***** error<ExhaustiveSearcher: Distance must be a string or function handle.> ...
392s  ExhaustiveSearcher (ones(3,2), "Distance", 1)
392s ***** error<ExhaustiveSearcher: P must be a positive finite scalar.> ...
392s  ExhaustiveSearcher (ones(3,2), "Distance", "minkowski", "P", -1)
392s ***** error<ExhaustiveSearcher: Scale must be a nonnegative vector matching X columns.> ...
392s  ExhaustiveSearcher (ones(3,2), "Distance", "seuclidean", "Scale", [-1, 1])
392s ***** error<ExhaustiveSearcher: Cov must be a square matrix matching X columns.> ...
392s  ExhaustiveSearcher (ones(3,2), "Distance", "mahalanobis", "Cov", ones(3,3))
392s ***** error<ExhaustiveSearcher: Cov must be positive definite.> ...
392s  ExhaustiveSearcher (ones(3,2), "Distance", "mahalanobis", "Cov", -eye(2))
392s ***** error<ExhaustiveSearcher.knnsearch: too few input arguments.> ...
392s  knnsearch (ExhaustiveSearcher (ones(3,2)))
392s ***** error<ExhaustiveSearcher.knnsearch: Name-Value arguments must be in pairs.> ...
392s  knnsearch (ExhaustiveSearcher (ones(3,2)), ones(3,2), "IncludeTies")
392s ***** error<ExhaustiveSearcher.knnsearch: Y must be a finite numeric matrix.> ...
392s  knnsearch (ExhaustiveSearcher (ones(3,2)), "abc")
392s ***** error<ExhaustiveSearcher.knnsearch: number of columns in X and Y must match.> ...
392s  knnsearch (ExhaustiveSearcher (ones(3,2)), ones(3,3))
392s ***** error<ExhaustiveSearcher.knnsearch: K must be a positive integer.> ...
392s  knnsearch (ExhaustiveSearcher (ones(3,2)), ones(3,2), "K", 0)
392s ***** error<ExhaustiveSearcher.knnsearch: invalid parameter name: 'foo'.> ...
392s  knnsearch (ExhaustiveSearcher (ones(3,2)), ones(3,2), "foo", "bar")
392s ***** error<ExhaustiveSearcher.knnsearch: IncludeTies must be a logical scalar.> ...
392s  knnsearch (ExhaustiveSearcher (ones(3,2)), ones(3,2), "IncludeTies", 1)
392s ***** error<ExhaustiveSearcher.rangesearch: too few input arguments.> ...
392s  rangesearch (ExhaustiveSearcher (ones(3,2)))
392s ***** error<ExhaustiveSearcher.rangesearch: Name-Value arguments must be in pairs.> ...
392s  rangesearch (ExhaustiveSearcher (ones(3,2)), ones(3,2), 1, "SortIndices")
392s ***** error<ExhaustiveSearcher.rangesearch: Y must be a finite numeric matrix.> ...
392s  rangesearch (ExhaustiveSearcher (ones(3,2)), "abc", 1)
392s ***** error<ExhaustiveSearcher.rangesearch: number of columns in X and Y must match.> ...
392s  rangesearch (ExhaustiveSearcher (ones(3,2)), ones(3,3), 1)
392s ***** error<ExhaustiveSearcher.rangesearch: R must be a nonnegative finite scalar.> ...
392s  rangesearch (ExhaustiveSearcher (ones(3,2)), ones(3,2), -1)
392s ***** error<ExhaustiveSearcher.rangesearch: invalid parameter name: 'foo'.> ...
392s  rangesearch (ExhaustiveSearcher (ones(3,2)), ones(3,2), 1, "foo", "bar")
392s ***** error<ExhaustiveSearcher.rangesearch: SortIndices must be a logical scalar.> ...
392s  rangesearch (ExhaustiveSearcher (ones(3,2)), ones(3,2), 1, "SortIndices", 1)
392s ***** error<ExhaustiveSearcher.subsref: \(\) indexing not supported.> ...
392s  obj = ExhaustiveSearcher (ones(3,2)); obj(1)
392s ***** error<ExhaustiveSearcher.subsref: {} indexing not supported.> ...
392s  obj = ExhaustiveSearcher (ones(3,2)); obj{1}
392s ***** error<dynamic structure field names must be strings> ...
392s  obj = ExhaustiveSearcher (ones(3,2)); obj.(1)
392s ***** error<ExhaustiveSearcher.subsref: unrecognized property: 'invalid'> ...
392s  obj = ExhaustiveSearcher (ones(3,2)); obj.invalid
392s ***** error<ExhaustiveSearcher.subsasgn: \(\) indexing not supported.> ...
392s  obj = ExhaustiveSearcher (ones(3,2)); obj(1) = 1
392s ***** error<ExhaustiveSearcher.subsasgn: {} indexing not supported.> ...
392s  obj = ExhaustiveSearcher (ones(3,2)); obj{1} = 1
392s ***** error<ExhaustiveSearcher.subsasgn: chained subscripts not allowed.> ...
392s  obj = ExhaustiveSearcher (ones(3,2)); obj.X.Y = 1
392s ***** error<dynamic structure field names must be strings> ...
392s  obj = ExhaustiveSearcher (ones(3,2)); obj.(1) = 1
392s ***** error<ExhaustiveSearcher.subsasgn: X is read-only and cannot be modified.> ...
392s  obj = ExhaustiveSearcher (ones(3,2)); obj.X = 1
392s ***** error<ExhaustiveSearcher.subsasgn: unsupported distance metric 'invalid'.> ...
392s  obj = ExhaustiveSearcher (ones(3,2)); obj.Distance = "invalid"
392s ***** error<ExhaustiveSearcher.subsasgn: invalid distance function handle.> ...
392s  obj = ExhaustiveSearcher (ones(3,2)); obj.Distance = @(x) x
392s ***** error<ExhaustiveSearcher.subsasgn: custom distance function output invalid.> ...
392s  obj = ExhaustiveSearcher (ones(3,2)); obj.Distance = @(x, y) [1; 1]
392s ***** error<ExhaustiveSearcher.subsasgn: Distance must be a string or function handle.> ...
392s  obj = ExhaustiveSearcher (ones(3,2)); obj.Distance = 1
392s ***** error<ExhaustiveSearcher.subsasgn: DistParameter must be a positive finite scalar for minkowski.> ...
392s  obj = ExhaustiveSearcher (ones(3,2), "Distance", "minkowski"); obj.DistParameter = -1
392s ***** error<ExhaustiveSearcher.subsasgn: DistParameter must be a nonnegative vector matching X columns.> ...
392s  obj = ExhaustiveSearcher (ones(3,2), "Distance", "seuclidean"); obj.DistParameter = [-1, 1]
392s ***** error<ExhaustiveSearcher.subsasgn: DistParameter must be a square matrix matching X columns.> ...
392s  obj = ExhaustiveSearcher (ones(3,2), "Distance", "mahalanobis"); obj.DistParameter = ones(3,3)
392s ***** error<ExhaustiveSearcher.subsasgn: DistParameter must be positive definite for mahalanobis.> ...
392s  obj = ExhaustiveSearcher (ones(3,2), "Distance", "mahalanobis"); obj.DistParameter = -eye(2)
392s ***** error<ExhaustiveSearcher.subsasgn: DistParameter must be empty for this distance metric.> ...
392s  obj = ExhaustiveSearcher (ones(3,2), "Distance", "euclidean"); obj.DistParameter = 1
392s ***** error<ExhaustiveSearcher.subsasgn: unrecognized property: 'invalid'> ...
392s  obj = ExhaustiveSearcher (ones(3,2)); obj.invalid = 1
392s 73 tests, 73 passed, 0 known failure, 0 skipped
392s [inst/Clustering/GapEvaluation.m]
392s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/Clustering/GapEvaluation.m
392s ***** test
392s  load fisheriris
392s  eva = evalclusters (meas([1:50],:), "kmeans", "gap", "KList", [1:3], ...
392s                      "referencedistribution", "uniform");
392s  assert (class (eva), "GapEvaluation");
397s 1 test, 1 passed, 0 known failure, 0 skipped
397s [inst/Clustering/KDTreeSearcher.m]
397s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/Clustering/KDTreeSearcher.m
397s ***** demo
397s  ## Demo to verify implementation using fisheriris dataset
397s  load fisheriris
397s  numSamples = size (meas, 1);
397s  queryIndices = [1, 23, 46, 63, 109];
397s  dataIndices = ~ismember (1:numSamples, queryIndices);
397s  queryPoints = meas(queryIndices, :);
397s  dataPoints = meas(dataIndices, :);
397s  searchRadius = 0.3;
397s  kdTree = KDTreeSearcher (dataPoints, 'Distance', 'minkowski')
397s  nearestNeighbors = knnsearch (kdTree, queryPoints, "K", 2)
397s  neighborsInRange = rangesearch (kdTree, queryPoints, searchRadius)
397s ***** demo
397s  ## Create a KDTreeSearcher with Euclidean distance
397s  X = [1, 2; 3, 4; 5, 6];
397s  obj = KDTreeSearcher (X);
397s  ## Find the nearest neighbor to [2, 3]
397s  Y = [2, 3];
397s  [idx, D] = knnsearch (obj, Y, "K", 1);
397s  disp ("Nearest neighbor index:");
397s  disp (idx);
397s  disp ("Distance:");
397s  disp (D);
397s  ## Find all points within radius 2
397s  [idx, D] = rangesearch (obj, Y, 2);
397s  disp ("Indices within radius:");
397s  disp (idx);
397s  disp ("Distances:");
397s  disp (D);
397s ***** demo
397s  ## Create a KDTreeSearcher with Minkowski distance (P=3)
397s  X = [0, 0; 1, 0; 2, 0];
397s  obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 3);
397s  ## Find the nearest neighbor to [1, 0]
397s  Y = [1, 0];
397s  [idx, D] = knnsearch (obj, Y, "K", 1);
397s  disp ("Nearest neighbor index:");
397s  disp (idx);
397s  disp ("Distance:");
397s  disp (D);
397s ***** demo
397s  rng(42);
397s  disp('Demonstrating KDTreeSearcher');
397s 
397s  n = 100;
397s  mu1 = [0.3, 0.3];
397s  mu2 = [0.7, 0.7];
397s  sigma = 0.1;
397s  X1 = mu1 + sigma * randn (n / 2, 2);
397s  X2 = mu2 + sigma * randn (n / 2, 2);
397s  X = [X1; X2];
397s 
397s  obj = KDTreeSearcher(X);
397s 
397s  Y = [0.3, 0.3; 0.7, 0.7; 0.5, 0.5];
397s 
397s  K = 5;
397s  [idx, D] = knnsearch (obj, Y, "K", K);
397s 
397s  disp ('For the first query point:');
397s  disp (['Query point: ', num2str(Y(1,:))]);
397s  disp ('Indices of nearest neighbors:');
397s  disp (idx(1,:));
397s  disp ('Distances:');
397s  disp (D(1,:));
397s 
397s  figure;
397s  scatter (X(:,1), X(:,2), 36, 'b', 'filled'); # Training points
397s  hold on;
397s  scatter (Y(:,1), Y(:,2), 36, 'r', 'filled'); # Query points
397s  for i = 1:size (Y, 1)
397s      query = Y(i,:);
397s      neighbors = X(idx(i,:), :);
397s      for j = 1:K
397s          plot ([query(1), neighbors(j,1)], [query(2), neighbors(j,2)], 'k-');
397s      endfor
397s  endfor
397s  hold off;
397s  title ('K Nearest Neighbors with KDTreeSearcher');
397s  xlabel ('X1');
397s  ylabel ('X2');
397s 
397s  r = 0.15;
397s  [idx, D] = rangesearch (obj, Y, r);
397s 
397s  disp ('For the first query point in rangesearch:');
397s  disp (['Query point: ', num2str(Y(1,:))]);
397s  disp ('Indices of points within radius:');
397s  disp (idx{1});
397s  disp ('Distances:');
397s  disp (D{1});
397s 
397s  figure;
397s  scatter (X(:,1), X(:,2), 36, 'b', 'filled');
397s  hold on;
397s  scatter (Y(:,1), Y(:,2), 36, 'r', 'filled');
397s  theta = linspace (0, 2 * pi, 100);
397s  for i = 1:size (Y, 1)
397s      center = Y(i,:);
397s      x_circle = center(1) + r * cos (theta);
397s      y_circle = center(2) + r * sin (theta);
397s      plot (x_circle, y_circle, 'g-');
397s      ## Highlight points within radius
397s      if (! isempty (idx{i}))
397s        in_radius = X(idx{i}, :);
397s        scatter (in_radius(:,1), in_radius(:,2), 36, 'g', 'filled');
397s      endif
397s  endfor
397s  hold off
397s  title ('Points within Radius with KDTreeSearcher');
397s  xlabel ('X1');
397s  ylabel ('X2');
397s ***** test
397s  load fisheriris
397s  X = meas;
397s  obj = KDTreeSearcher (X);
397s  Y = X(1:5,:);
397s  [idx, D] = knnsearch (obj, Y, "K", 3);
397s  assert (idx, [[1, 18, 5]; [2, 35, 46]; [3, 48, 4]; [4, 48, 30]; [5, 38, 1]])
397s  assert (D, [[0, 0.1000, 0.1414]; [0,  0.1414,  0.1414]; [0, 0.1414, 0.2449];
397s              [0, 0.1414, 0.1732]; [0, 0.1414, 0.1414]], 5e-5)
397s ***** test
397s  load fisheriris
397s  X = meas;
397s  obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 3);
397s  Y = X(10:15,:);
397s  [idx, D] = knnsearch (obj, Y, "K", 2);
397s  assert (idx, [[10, 35]; [11, 49]; [12, 30]; [13, 2]; [14, 39]; [15, 34]])
397s  assert (D, [[0, 0.1000]; [0, 0.1000]; [0, 0.2080]; [0, 0.1260]; [0, 0.2154];
397s              [0, 0.3503]], 5e-5)
397s ***** test
397s  load fisheriris
397s  X = meas;
397s  obj = KDTreeSearcher (X, "Distance", "cityblock");
397s  Y = X(20:25,:);
397s  [idx, D] = knnsearch (obj, Y, "K", 1);
397s  assert (idx, [20; 21; 22; 23; 24; 25])
397s  assert (D, [0; 0; 0; 0; 0; 0])
398s ***** test
398s  load fisheriris
398s  X = meas;
398s  obj = KDTreeSearcher (X, "Distance", "chebychev");
398s  Y = X(30:35,:);
398s  [idx, D] = knnsearch (obj, Y, "K", 4);
398s  assert (idx, [[30, 31, 4, 12]; [31, 30, 10, 35]; [32, 21, 37, 28];
398s                [33, 20, 34, 47]; [34, 16, 15, 33]; [35, 10, 2, 26]])
398s  assert (D, [[0, 0.1000, 0.1000, 0.2000]; [0, 0.1000, 0.1000, 0.1000];
398s              [0, 0.2000, 0.2000, 0.2000]; [0, 0.3000, 0.3000, 0.3000];
398s              [0, 0.2000, 0.3000, 0.3000]; [0, 0.1000, 0.1000, 0.1000]], 5e-15)
398s ***** test
398s  load fisheriris
398s  X = meas;
398s  obj = KDTreeSearcher (X, "BucketSize", 20);
398s  Y = X(40:45,:);
398s  [idx, D] = knnsearch (obj, Y, "K", 2);
398s  assert (idx, [[40, 8]; [41, 18]; [42, 9]; [43, 39]; [44, 27]; [45, 47]])
398s  assert (D, [[0, 0.1000]; [0, 0.1414]; [0, 0.6245]; [0, 0.2000]; [0, 0.2236];
398s              [0, 0.3606]], 4.7e-5)
398s ***** test
398s  load fisheriris
398s  X = meas;
398s  obj = KDTreeSearcher (X);
398s  Y = X(50:55,:);
398s  [idx, D] = knnsearch (obj, Y, "K", 3, "IncludeTies", true);
398s  assert (idx, {[50; 8; 40]; [51; 53; 87]; [52; 57; 76]; [53; 51; 87]; [54; ...
398s                 90; 81]; [55; 59; 76]})
398s  assert (D, {[0; 0.1414; 0.1732]; [0; 0.2646; 0.3317]; [0; 0.2646; 0.3162];
398s              [0; 0.2646; 0.2828]; [0; 0.2000; 0.3000]; [0; 0.2449; 0.3162]}, 5e-5)
398s ***** test
398s  load fisheriris
398s  X = meas;
398s  obj = KDTreeSearcher (X);
398s  Y = X(60:65,:);
398s  [idx, D] = rangesearch (obj, Y, 0.4);
398s  assert (idx, {[60; 90]; [61; 94]; [62; 97; 79; 96; 100; 89; 98; 72]; [63];
398s                [64; 92; 74; 79]; [65]})
398s  assert (D, {[0; 0.3873]; [0; 0.3606];
398s              [0; 0.3000; 0.3317; 0.3606; 0.3606; 0.3742; 0.3873; 0.4000]; [0];
398s              [0; 0.1414; 0.2236; 0.2449]; [0]}, 5e-5)
398s ***** test
398s  load fisheriris
398s  X = meas;
398s  obj = KDTreeSearcher (X, "Distance", "cityblock");
398s  Y = X(70:72,:);
398s  [idx, D] = rangesearch (obj, Y, 1.0);
398s  assert (idx, {[70; 81; 90; 82; 83; 93; 54; 68; 95; 80; 91; 100; 60; 65; ...
398s                 89; 63]; [71; 139; 128; 150; 127; 57; 86; 64; 79; 92; 124];
398s                [72; 100; 98; 83; 93; 97; 75; 68; 62; 89; 95; 74; 56; 90; ...
398s                 79; 92; 96; 64; 63; 65]})
398s  assert (D, {[0; 0.3000; 0.4000; 0.5000; 0.5000; 0.5000; 0.6000; 0.7000; ...
398s               0.7000; 0.7000; 0.8000; 0.8000; 0.9000; 0.9000; 0.9000; 0.9000];
398s              [0; 0.3000; 0.5000; 0.5000; 0.7000; 0.8000; 0.8000; 1.0000; ...
398s               1.0000; 1.0000; 1]; [0; 0.5000; 0.5000; 0.6000; 0.6000; ...
398s               0.7000; 0.7000; 0.8000; 0.8000; 0.8000; 0.8000; 0.8000; ...
398s               0.9000; 0.9000; 0.9000; 0.9000; 0.9000; 0.9000; 1.0000; 1]}, 5e-5)
398s ***** test
398s  load fisheriris
398s  X = meas;
398s  obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 3);
398s  Y = X(80:85,:);
398s  [idx, D] = rangesearch (obj, Y, 0.8);
398s  assert (idx, {[80; 82; 81; 65; 70; 83; 93; 90; 54; 63; 68; 72; 100; 60; ...
398s                 89; 99; 95; 94; 97; 96]; [81; 82; 70; 54; 90; 93; 80; 83; ...
398s                 60; 68; 95; 100; 65; 63; 97; 61; 91; 94; 89; 96; 72; 58; ...
398s                 62; 56]; [82; 81; 70; 80; 54; 90; 93; 83; 68; 60; 65; 63; ...
398s                 100; 95; 94; 61; 58; 97; 89; 72; 96; 91; 99]; [83; 93; ...
398s                 100; 68; 70; 72; 95; 90; 97; 65; 89; 96; 81; 82; 80; 62; ...
398s                 54; 98; 63; 91; 56; 60; 79; 67; 75; 88; 85; 92; 69]; [84; ...
398s                 134; 102; 143; 150; 124; 128; 73; 127; 139; 147; 64; 112; ...
398s                 114; 120; 74; 135; 122; 71; 92; 104; 138; 148; 117; 79; ...
398s                 55; 56; 57; 67; 111; 129; 69; 78; 59; 52; 133; 85; 88; ...
398s                 87]; [85; 67; 56; 97; 95; 89; 96; 91; 100; 62; 71; 122; ...
398s                 79; 60; 107; 90; 139; 93; 68; 86; 83; 92; 64; 150; 102; ...
398s                 143; 74; 114; 70; 128; 84; 54; 72]})
398s  assert (D, {[0; 0.2884; 0.3530; 0.3826; 0.4062; 0.4198; 0.5117; 0.5440; 0.5718;
398s               0.6000; 0.6018; 0.6073; 0.6308; 0.6333; 0.6753; 0.7000; 0.7192;
398s               0.7230; 0.7350; 0.7459];
398s              [0; 0.1260; 0.1442; 0.2571; 0.2571; 0.3530; 0.3530; 0.3826; 0.4344;
398s               0.4344; 0.4642; 0.4747; 0.5217; 0.5217; 0.5896; 0.6009; 0.6082;
398s               0.6316; 0.6316; 0.6611; 0.6664; 0.6993; 0.7417; 0.7507];
398s              [0; 0.1260; 0.2224; 0.2884; 0.3803; 0.3803; 0.4121; 0.4121; 0.4905;
398s               0.5013; 0.5360; 0.5429; 0.5463; 0.5646; 0.5749; 0.5819; 0.6542;
398s               0.6581; 0.6753; 0.6938; 0.7094; 0.7107; 0.7423];
398s              [0; 0.1260; 0.2224; 0.2520; 0.2571; 0.3107; 0.3302; 0.3332; 0.3332;
398s               0.3530; 0.3530; 0.3803; 0.3826; 0.4121; 0.4198; 0.4344; 0.4531;
398s               0.5155; 0.5217; 0.5348; 0.6028; 0.6073; 0.6374; 0.6527; 0.6611;
398s               0.6804; 0.6938; 0.7399; 0.7560];
398s              [0; 0.3072; 0.3271; 0.3271; 0.3302; 0.3503; 0.3530; 0.3530; 0.3530;
398s               0.3958; 0.3979; 0.4327; 0.4626; 0.4642; 0.5027; 0.5066; 0.5130;
398s               0.5155; 0.5440; 0.5440; 0.5518; 0.5848; 0.6009; 0.6073; 0.6082;
398s               0.6316; 0.6471; 0.6746; 0.6753; 0.6797; 0.6804; 0.7047; 0.7192;
398s               0.7218; 0.7405; 0.7405; 0.7719; 0.7725; 0.7786];
398s              [0; 0.2000; 0.3503; 0.3979; 0.4121; 0.4309; 0.4327; 0.4531; 0.4747;
398s               0.5337; 0.5718; 0.5896; 0.6009; 0.6316; 0.6366; 0.6374; 0.6463;
398s               0.6542; 0.6542; 0.6550; 0.6938; 0.7014; 0.7067; 0.7166; 0.7186;
398s               0.7186; 0.7281; 0.7380; 0.7447; 0.7571; 0.7719; 0.7813; 0.7851]}, 5e-5)
398s ***** test
398s  load fisheriris
398s  X = meas;
398s  obj = KDTreeSearcher (X, "Distance", "chebychev");
398s  Y = X(90,:);
398s  [idx, D] = rangesearch (obj, Y, 0.7);
398s  assert (idx, {[90; 70; 54; 81; 95; 60; 83; 93; 100; 68; 82; 65; 97; 91; ...
398s                 56; 61; 62; 63; 67; 79; 80; 85; 89; 96; 72; 92; 107]})
398s  assert (D, {[0; 0.2000; 0.2000; 0.2000; 0.2000; 0.3000; 0.3000; 0.3000; ...
398s              0.3000; 0.3000; 0.3000; 0.4000; 0.4000; 0.4000; 0.5000; ...
398s              0.5000; 0.5000; 0.5000; 0.5000; 0.5000; 0.5000; 0.5000; ...
398s              0.5000; 0.5000; 0.6000; 0.6000; 0.6000]}, 5e-16)
398s ***** test
398s  ## Constructor with single-point dataset
398s  X = [0, 0];
398s  obj = KDTreeSearcher (X);
398s  assert (obj.X, X);
398s  assert (obj.Distance, "euclidean");
398s  assert (isempty (obj.DistParameter));
398s  assert (obj.BucketSize, 50);
398s ***** test
398s  ## Constructor with duplicate points
398s  X = [0, 0; 0, 0; 1, 0];
398s  obj = KDTreeSearcher (X, "Distance", "cityblock");
398s  assert (obj.X, X);
398s  assert (obj.Distance, "cityblock");
398s ***** test
398s  ## Constructor with 3D data
398s  X = [0, 0, 0; 1, 0, 0; 0, 1, 0];
398s  obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 3);
398s  assert (obj.X, X);
398s  assert (obj.DistParameter, 3);
398s ***** test
398s  ## knnsearch with grid, K = 1
398s  X = [0, 0; 0, 1; 1, 0; 1, 1];
398s  obj = KDTreeSearcher (X, "Distance", "euclidean");
398s  Y = [0.5, 0.5];
398s  [idx, D] = knnsearch (obj, Y, "K", 1);
398s  D_true = pdist2 (X, Y, "euclidean");
398s  assert (D, min (D_true), 1e-10);
398s  assert (any (idx == find (D_true == min (D_true))));
398s ***** test
398s  ## knnsearch with IncludeTies, all points equidistant
398s  X = [0, 0; 0, 1; 1, 0; 1, 1];
398s  obj = KDTreeSearcher (X);
398s  Y = [0.5, 0.5];
398s  [idx, D] = knnsearch (obj, Y, "K", 1, "IncludeTies", true);
398s  D_true = pdist2 (X, Y, "euclidean");
398s  expected_idx = find (D_true == min (D_true));
398s  assert (sort (idx{1}(:)), sort (expected_idx));
398s  assert (D{1}(:)', repmat (min (D_true), 1, 4), 1e-10);
398s ***** test
398s  ## rangesearch with line dataset
398s  X = [0, 0; 1, 0; 2, 0; 3, 0];
398s  obj = KDTreeSearcher (X);
398s  Y = [1.5, 0];
398s  r = 1;
398s  [idx, D] = rangesearch (obj, Y, r);
398s  D_true = pdist2 (X, Y, "euclidean");
398s  expected_idx = find (D_true <= r);
398s  assert (sort (idx{1}(:)), sort (expected_idx));
398s  assert (D{1}, sort (D_true(expected_idx)), 1e-10);
398s ***** test
398s  ## knnsearch with duplicates
398s  X = [0, 0; 0, 0; 1, 0];
398s  obj = KDTreeSearcher (X, "Distance", "cityblock");
398s  Y = [0, 0];
398s  [idx, D] = knnsearch (obj, Y, "K", 1, "IncludeTies", true);
398s  assert (sort (idx{1}(:))', [1, 2]);
398s  assert (D{1}', [0, 0], 1e-10);
398s ***** test
398s  ## rangesearch with 3D data
398s  X = [0, 0, 0; 1, 0, 0; 0, 1, 0];
398s  obj = KDTreeSearcher (X, "Distance", "cityblock");
398s  Y = [0, 0, 0];
398s  r = 1;
398s  [idx, D] = rangesearch (obj, Y, r);
398s  assert (sort (idx{1}(:))', [1, 2, 3]);
398s  assert (D{1}', [0, 1, 1], 1e-10);
398s ***** test
398s  ## knnsearch with P = 2 (Euclidean equivalent)
398s  X = [0, 0; 1, 1];
398s  obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 2);
398s  Y = [0, 1];
398s  [idx, D] = knnsearch (obj, Y, "K", 1);
398s  assert (idx, 1);
398s  assert (D, 1, 1e-10);
398s ***** test
398s  ## rangesearch with P = 3
398s  X = [0, 0; 1, 0; 0, 1];
398s  obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 3);
398s  Y = [0.5, 0.5];
398s  r = 0.8;
398s  [idx, D] = rangesearch (obj, Y, r);
398s  D_true = pdist2 (X, Y, "minkowski", 3);
398s  expected_idx = find (D_true <= r);
398s  assert (sort (idx{1}(:)), sort (expected_idx));
398s  assert (D{1}, sort (D_true(expected_idx)), 1e-10);
398s ***** test
398s  ## knnsearch with P = 4, random data
398s  X = rand (5, 2);
398s  obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 4);
398s  Y = rand (1, 2);
398s  [idx, D] = knnsearch (obj, Y, "K", 3);
398s  D_true = pdist2 (X, Y, "minkowski", 4);
398s  [sorted_D, sort_idx] = sort (D_true);
398s  assert (idx', sort_idx(1:3));
398s  assert (D', sorted_D(1:3), 1e-10);
398s ***** test
398s  ## knnsearch with all same points
398s  X = [1, 1; 1, 1; 1, 1];
398s  obj = KDTreeSearcher (X, "Distance", "chebychev");
398s  Y = [1, 1];
398s  [idx, D] = knnsearch (obj, Y, "K", 1, "IncludeTies", true);
398s  assert (sort (idx{1}(:))', [1, 2, 3]);
398s  assert (D{1}', [0, 0, 0], 1e-10);
398s ***** test
398s  ## rangesearch with grid
398s  X = [0, 0; 0, 1; 1, 0; 1, 1];
398s  obj = KDTreeSearcher (X, "Distance", "chebychev");
398s  Y = [0.5, 0.5];
398s  r = 0.5;
398s  [idx, D] = rangesearch (obj, Y, r);
398s  D_true = pdist2 (X, Y, "chebychev");
398s  expected_idx = find (D_true <= r);
398s  assert (sort (idx{1}(:)), sort (expected_idx));
398s  assert (D{1}, D_true(expected_idx), 1e-10);
398s ***** test
398s  ## Changing Distance and verifying search
398s  X = [0,0; 1,0];
398s  obj = KDTreeSearcher(X, "Distance", "euclidean");
398s  Y = [0,1];
398s  [idx, D] = knnsearch(obj, Y, "K", 1);
398s  assert(D, 1, 1e-10);
398s  obj.Distance = "chebychev";
398s  [idx, D] = knnsearch(obj, Y, "K", 1);
398s  assert(D, 1, 1e-10);
398s ***** test
398s  ## Changing DistParameter for minkowski
398s  X = [0,0; 1,0];
398s  obj = KDTreeSearcher(X, "Distance", "minkowski", "P", 1);
398s  Y = [0,1];
398s  [idx, D] = knnsearch(obj, Y, "K", 1);
398s  assert(D, 1, 1e-10);
398s  obj.DistParameter = 3;
398s  [idx, D] = knnsearch(obj, Y, "K", 1);
398s  assert(D, 1, 1e-10);
398s ***** test
398s  ## Different BucketSize values
398s  X = rand(20,2);
398s  obj1 = KDTreeSearcher(X, "BucketSize", 5);
398s  obj2 = KDTreeSearcher(X, "BucketSize", 15);
398s  Y = rand(1,2);
398s  [idx1, D1] = knnsearch(obj1, Y, "K", 3);
398s  [idx2, D2] = knnsearch(obj2, Y, "K", 3);
398s  assert(idx1, idx2);
398s  assert(D1, D2, 1e-10);
398s ***** test
398s  ## Basic constructor with default Euclidean
398s  X = [1, 2; 3, 4; 5, 6];
398s  obj = KDTreeSearcher (X);
398s  assert (obj.X, X);
398s  assert (obj.Distance, "euclidean");
398s  assert (isempty (obj.DistParameter));
398s  assert (obj.BucketSize, 50);
398s ***** test
398s  ## Minkowski distance with custom P
398s  X = [0, 0; 1, 1; 2, 2];
398s  obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 3);
398s  assert (obj.Distance, "minkowski");
398s  assert (obj.DistParameter, 3);
398s ***** test
398s  ## Cityblock distance
398s  X = [0, 0; 1, 0; 0, 1];
398s  obj = KDTreeSearcher (X, "Distance", "cityblock");
398s  assert (obj.Distance, "cityblock");
398s  assert (isempty (obj.DistParameter));
398s ***** test
398s  ## Chebychev distance
398s  X = [1, 1; 2, 3; 4, 2];
398s  obj = KDTreeSearcher (X, "Distance", "chebychev");
398s  assert (obj.Distance, "chebychev");
398s  assert (isempty (obj.DistParameter));
398s ***** test
398s  ## knnsearch with Euclidean distance
398s  X = [1, 2; 3, 4; 5, 6];
398s  obj = KDTreeSearcher (X);
398s  Y = [2, 3];
398s  [idx, D] = knnsearch (obj, Y, "K", 1);
398s  assert (idx, 1);
398s  assert (D, sqrt(2), 1e-10);
398s ***** test
398s  ## knnsearch with Cityblock distance
398s  X = [0, 0; 1, 1; 2, 2];
398s  obj = KDTreeSearcher (X, "Distance", "cityblock");
398s  Y = [1, 0];
398s  [idx, D] = knnsearch (obj, Y, "K", 1);
398s  assert (ismember (idx, [1, 2]));
398s  assert (D, 1, 1e-10);
398s ***** test
398s  ## knnsearch with Chebychev distance
398s  X = [1, 1; 2, 3; 4, 2];
398s  obj = KDTreeSearcher (X, "Distance", "chebychev");
398s  Y = [2, 2];
398s  [idx, D] = knnsearch (obj, Y, "K", 1);
398s  assert (ismember (idx, [1, 2]));
398s  assert (D, 1, 1e-10);
398s ***** test
398s  ## knnsearch with Minkowski P=3
398s  X = [0, 0; 1, 0; 2, 0];
398s  obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 3);
398s  Y = [1, 0];
398s  [idx, D] = knnsearch (obj, Y, "K", 1);
398s  assert (idx, 2);
398s  assert (D, 0, 1e-10);
398s ***** test
398s  ## knnsearch with IncludeTies
398s  X = [0, 0; 1, 0; 0, 1];
398s  obj = KDTreeSearcher (X);
398s  Y = [0.5, 0];
398s  [idx, D] = knnsearch (obj, Y, "K", 1, "IncludeTies", true);
398s  assert (iscell (idx));
398s  assert (sort (idx{1}(:))', [1, 2]);
398s  assert (sort (D{1}(:)), [0.5; 0.5], 1e-10);
398s ***** test
398s  ## rangesearch with Euclidean
398s  X = [1, 1; 2, 2; 3, 3];
398s  obj = KDTreeSearcher (X);
398s  Y = [0, 0];
398s  [idx, D] = rangesearch (obj, Y, 2);
398s  assert (idx{1}, [1]);
398s  assert (D{1}, [sqrt(2)], 1e-10);
398s ***** test
398s  ## rangesearch with Cityblock
398s  X = [0, 0; 1, 1; 2, 2];
398s  obj = KDTreeSearcher (X, "Distance", "cityblock");
398s  Y = [0, 0];
398s  [idx, D] = rangesearch (obj, Y, 1);
398s  assert (idx{1}, [1]);
398s  assert (D{1}, [0], 1e-10);
398s ***** test
398s  ## rangesearch with Chebychev
398s  X = [1, 1; 2, 3; 4, 2];
398s  obj = KDTreeSearcher (X, "Distance", "chebychev");
398s  Y = [2, 2];
398s  [idx, D] = rangesearch (obj, Y, 1);
398s  assert (sort (idx{1}(:))', [1, 2]);
398s  assert (sort (D{1}(:))', [1, 1], 1e-10);
398s ***** test
398s  ## rangesearch with Minkowski P=3
398s  X = [0, 0; 1, 0; 2, 0];
398s  obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 3);
398s  Y = [1, 0];
398s  [idx, D] = rangesearch (obj, Y, 1);
398s  assert (sort (idx{1}(:))', [1, 2, 3]);
398s  assert (sort (D{1}(:))', [0, 1, 1], 1e-10);
398s ***** test
398s  ## Diverse dataset with Euclidean
398s  X = [0, 10; 5, 5; 10, 0];
398s  obj = KDTreeSearcher (X);
398s  Y = [5, 5];
398s  [idx, D] = knnsearch (obj, Y, "K", 1);
398s  assert (idx, 2);
398s  assert (D, 0, 1e-10);
398s ***** test
398s  ## High-dimensional data with Cityblock
398s  X = [1, 2, 3; 4, 5, 6; 7, 8, 9];
398s  obj = KDTreeSearcher (X, "Distance", "cityblock");
398s  Y = [4, 5, 6];
398s  [idx, D] = knnsearch (obj, Y, "K", 1);
398s  assert (idx, 2);
398s  assert (D, 0, 1e-10);
398s ***** error<KDTreeSearcher: too few input arguments.> ...
398s  KDTreeSearcher ()
398s ***** error<KDTreeSearcher: Name-Value arguments must be in pairs.> ...
398s  KDTreeSearcher (ones(3,2), "Distance")
398s ***** error<KDTreeSearcher: X must be a finite numeric matrix.> ...
398s  KDTreeSearcher ("abc")
398s ***** error<KDTreeSearcher: X must be a finite numeric matrix.> ...
398s  KDTreeSearcher ([1; Inf; 3])
398s ***** error<KDTreeSearcher: invalid parameter name: 'foo'.> ...
398s  KDTreeSearcher (ones(3,2), "foo", "bar")
398s ***** error<KDTreeSearcher: unsupported distance metric 'invalid'.> ...
398s  KDTreeSearcher (ones(3,2), "Distance", "invalid")
398s ***** error<KDTreeSearcher: Distance must be a string.> ...
398s  KDTreeSearcher (ones(3,2), "Distance", 1)
398s ***** error<KDTreeSearcher: P must be a positive finite scalar.> ...
398s  KDTreeSearcher (ones(3,2), "Distance", "minkowski", "P", -1)
398s ***** error<KDTreeSearcher: BucketSize must be a positive integer.> ...
398s  KDTreeSearcher (ones(3,2), "BucketSize", 0)
398s ***** error<KDTreeSearcher: BucketSize must be a positive integer.> ...
398s  KDTreeSearcher(ones(3,2), "BucketSize", -1)
398s ***** error<KDTreeSearcher.knnsearch: too few input arguments.> ...
398s  knnsearch (KDTreeSearcher (ones(3,2)))
398s ***** error<KDTreeSearcher.knnsearch: Name-Value arguments must be in pairs.> ...
398s  knnsearch (KDTreeSearcher (ones(3,2)), ones(3,2), "K", 1, "IncludeTies")
398s ***** error<KDTreeSearcher.knnsearch: Y must be a finite numeric matrix.> ...
398s  knnsearch (KDTreeSearcher (ones(3,2)), "abc", "K", 1)
398s ***** error<KDTreeSearcher.knnsearch: Y must have the same number of columns as the training data in OBJ.X.> ...
398s  knnsearch (KDTreeSearcher (ones(3,2)), ones(3,3), "K", 1)
398s ***** error<KDTreeSearcher.knnsearch: 'K' must be a positive integer.> ...
398s  knnsearch (KDTreeSearcher (ones(3,2)), ones(3,2), "K", 0)
398s ***** error<KDTreeSearcher.knnsearch: 'K' must be a positive integer.> ...
398s  obj = KDTreeSearcher(ones(3,2)); knnsearch(obj, ones(1,2), "K", Inf)
398s ***** error<KDTreeSearcher.knnsearch: invalid parameter name: 'foo'.> ...
398s  knnsearch (KDTreeSearcher (ones(3,2)), ones(3,2), "K", 1, "foo", "bar")
398s ***** error<KDTreeSearcher.knnsearch: IncludeTies must be a logical scalar.> ...
398s  knnsearch (KDTreeSearcher (ones(3,2)), ones(3,2), "K", 1, "IncludeTies", 1)
398s ***** error<KDTreeSearcher.knnsearch: SortIndices must be a logical scalar.> ...
398s  knnsearch (KDTreeSearcher (ones(3,2)), ones(3,2), "K", 1, "SortIndices", 1)
398s ***** error<KDTreeSearcher.rangesearch: too few input arguments.> ...
398s  rangesearch (KDTreeSearcher (ones(3,2)))
398s ***** error<KDTreeSearcher.rangesearch: Name-Value arguments must be in pairs.> ...
398s  rangesearch (KDTreeSearcher (ones(3,2)), ones(3,2), 1, "SortIndices")
398s ***** error<KDTreeSearcher.rangesearch: Y must be a finite numeric matrix.> ...
398s  rangesearch (KDTreeSearcher (ones(3,2)), "abc", 1)
398s ***** error<KDTreeSearcher.rangesearch: number of columns in X and Y must match.> ...
398s  rangesearch (KDTreeSearcher (ones(3,2)), ones(3,3), 1)
398s ***** error<KDTreeSearcher.rangesearch: R must be a nonnegative finite scalar.> ...
398s  rangesearch (KDTreeSearcher (ones(3,2)), ones(3,2), -1)
398s ***** error<KDTreeSearcher.rangesearch: R must be a nonnegative finite scalar.> ...
398s  obj = KDTreeSearcher(ones(3,2)); rangesearch(obj, ones(1,2), Inf)
398s ***** error<KDTreeSearcher.rangesearch: invalid parameter name: 'foo'.> ...
398s  rangesearch (KDTreeSearcher (ones(3,2)), ones(3,2), 1, "foo", "bar")
398s ***** error<KDTreeSearcher.rangesearch: SortIndices must be a logical scalar.> ...
398s  rangesearch (KDTreeSearcher (ones(3,2)), ones(3,2), 1, "SortIndices", 1)
398s ***** error<KDTreeSearcher.subsref: \(\) indexing not supported.> ...
398s  obj = KDTreeSearcher (ones(3,2)); obj(1)
398s ***** error<KDTreeSearcher.subsref: {} indexing not supported.> ...
398s  obj = KDTreeSearcher (ones(3,2)); obj{1}
398s ***** error<KDTreeSearcher.subsref: unrecognized property: 'invalid'.> ...
398s  obj = KDTreeSearcher (ones(3,2)); obj.invalid
398s ***** error<KDTreeSearcher.subsasgn: \(\) indexing not supported.> ...
398s  obj = KDTreeSearcher (ones(3,2)); obj(1) = 1
398s ***** error<KDTreeSearcher.subsasgn: {} indexing not supported.> ...
398s  obj = KDTreeSearcher (ones(3,2)); obj{1} = 1
398s ***** error<KDTreeSearcher.subsasgn: chained subscripts not allowed.> ...
398s  obj = KDTreeSearcher (ones(3,2)); obj.X.Y = 1
398s ***** error<KDTreeSearcher.subsasgn: 'X' is read-only and cannot be modified.> ...
398s  obj = KDTreeSearcher (ones(3,2)); obj.X = 1
398s ***** error<KDTreeSearcher.subsasgn: 'KDTree' is read-only and cannot be modified.> ...
398s  obj = KDTreeSearcher (ones(3,2)); obj.KDTree = 1
398s ***** error<KDTreeSearcher.subsasgn: unsupported distance metric 'invalid'.> ...
398s  obj = KDTreeSearcher (ones(3,2)); obj.Distance = "invalid"
398s ***** error<KDTreeSearcher.subsasgn: 'Distance' must be a string.> ...
398s  obj = KDTreeSearcher (ones(3,2)); obj.Distance = 1
398s ***** error<KDTreeSearcher.subsasgn: 'DistParameter' must be a positive finite scalar for Minkowski distance.> ...
398s  obj = KDTreeSearcher (ones(3,2), "Distance", "minkowski"); obj.DistParameter = -1
398s ***** error<KDTreeSearcher.subsasgn: 'DistParameter' must be empty for this distance metric.> ...
398s  obj = KDTreeSearcher (ones(3,2)); obj.DistParameter = 1
398s ***** error<KDTreeSearcher.subsasgn: 'BucketSize' is read-only and cannot be modified.> ...
398s  obj = KDTreeSearcher (ones(3,2)); obj.BucketSize = 0
398s ***** error<KDTreeSearcher.subsasgn: 'BucketSize' is read-only and cannot be modified.> ...
398s  obj = KDTreeSearcher(ones(3,2)); obj.BucketSize = -1
398s ***** error<KDTreeSearcher.subsasgn: 'BucketSize' is read-only and cannot be modified.> ...
398s  obj = KDTreeSearcher(ones(3,2)); obj.BucketSize = 1.5
398s ***** error<KDTreeSearcher.subsasgn: unrecognized property: 'invalid'.> ...
398s  obj = KDTreeSearcher (ones(3,2)); obj.invalid = 1
398s 84 tests, 84 passed, 0 known failure, 0 skipped
398s [inst/Clustering/SilhouetteEvaluation.m]
398s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/Clustering/SilhouetteEvaluation.m
398s ***** test
398s  load fisheriris
398s  eva = evalclusters (meas, "kmeans", "silhouette", "KList", [1:6]);
398s  assert (class (eva), "SilhouetteEvaluation");
399s 1 test, 1 passed, 0 known failure, 0 skipped
399s [inst/Clustering/cvpartition.m]
399s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/Clustering/cvpartition.m
399s ***** test
399s  custom = [1, 1, 1, 2, 2, 2, 1, 2, 3, 2, 3, 3, 2, 1, 3]';
399s  cv = cvpartition ('CustomPartition', custom);
399s  assert (cv.Type, 'kfold');
399s  assert (cv.NumObservations, 15);
399s  assert (cv.NumTestSets, 3);
399s  assert (cv.TrainSize, [10, 9, 11]);
399s  assert (cv.TestSize, [5, 6, 4]);
399s  assert (cv.IsCustom, true);
399s  assert (cv.IsGrouped, false);
399s  assert (cv.IsStratified, false);
399s  idx = training (cv, 1);
399s  assert (idx, custom != 1);
399s  idx = test (cv, 1);
399s  assert (idx, custom == 1);
399s  idx = training (cv, 2);
399s  assert (idx, custom != 2);
399s  idx = test (cv, 2);
399s  assert (idx, custom == 2);
399s  idx = training (cv, 3);
399s  assert (idx, custom != 3);
399s  idx = test (cv, 3);
399s  assert (idx, custom == 3);
399s  idx1 = training (cv, 'all');
399s  idx2 = test (cv, 'all');
399s  assert (idx1, ! idx2);
399s ***** test
399s  custom = logical ([1, 1, 1, 0, 0, 0, 1, 0, 1, 1])';
399s  cv = cvpartition ('CustomPartition', custom);
399s  assert (cv.Type, 'holdout');
399s  assert (cv.NumObservations, 10);
399s  assert (cv.NumTestSets, 1);
399s  assert (cv.TrainSize, 4);
399s  assert (cv.TestSize, 6);
399s  assert (cv.IsCustom, true);
399s  assert (cv.IsGrouped, false);
399s  assert (cv.IsStratified, false);
399s  idx = training (cv, 1);
399s  assert (idx, custom != 1);
399s  assert (idx, training (cv, 'all'));
399s  idx = test (cv, 1);
399s  assert (idx, custom == 1);
399s  assert (idx, test (cv, 'all'));
399s ***** test
399s  custom = logical ([1, 0, 0; 0, 1, 0; 1, 0, 0; 0, 0, 1]);
399s  cv = cvpartition ('CustomPartition', custom);
399s  assert (cv.Type, 'kfold');
399s  assert (cv.NumObservations, 4);
399s  assert (cv.NumTestSets, 3);
399s  assert (cv.TrainSize, [2, 3, 3]);
399s  assert (cv.TestSize, [2, 1, 1]);
399s  assert (cv.IsCustom, true);
399s  assert (cv.IsGrouped, false);
399s  assert (cv.IsStratified, false);
399s  idx = training (cv, 1);
399s  assert (idx, custom(:,1) == false);
399s  idx = test (cv, 1);
399s  assert (idx, custom(:,1) == true);
399s  idx = training (cv, 2);
399s  assert (idx, custom(:,2) == false);
399s  idx = test (cv, 2);
399s  assert (idx, custom(:,2) == true);
399s  assert (! custom, training (cv, 'all'));
399s  assert (custom, test (cv, 'all'));
399s ***** test
399s  cv = cvpartition ('CustomPartition', [1:8]);
399s  assert (cv.Type, 'leaveout');
399s  assert (cv.NumObservations, 8);
399s  assert (cv.NumTestSets, 8);
399s  assert (cv.TrainSize, [7, 7, 7, 7, 7, 7, 7, 7]);
399s  assert (cv.TestSize, [1, 1, 1, 1, 1, 1, 1, 1]);
399s  assert (cv.IsCustom, true);
399s  assert (cv.IsGrouped, false);
399s  assert (cv.IsStratified, false);
399s  assert (class (training (cv, 1)), 'logical');
399s  assert (sum (training (cv, 1)), 7);
399s  assert (sum (training (cv, 'all')), cv.TrainSize);
399s  assert (class (test (cv, 1)), 'logical');
399s  assert (sum (test (cv, 1)), 1);
399s  assert (sum (test (cv, 'all')), cv.TestSize);
399s  assert (! training (cv, 'all'), test (cv, 'all'));
399s ***** test
399s  cv = cvpartition ('CustomPartition', logical (eye (8)));
399s  assert (cv.Type, 'leaveout');
399s  assert (cv.NumObservations, 8);
399s  assert (cv.NumTestSets, 8);
399s  assert (cv.TrainSize, [7, 7, 7, 7, 7, 7, 7, 7]);
399s  assert (cv.TestSize, [1, 1, 1, 1, 1, 1, 1, 1]);
399s  assert (cv.IsCustom, true);
399s  assert (cv.IsGrouped, false);
399s  assert (cv.IsStratified, false);
399s  assert (class (training (cv, 1)), 'logical');
399s  assert (sum (training (cv, 1)), 7);
399s  assert (sum (training (cv, 'all')), cv.TrainSize);
399s  assert (class (test (cv, 1)), 'logical');
399s  assert (sum (test (cv, 1)), 1);
399s  assert (sum (test (cv, 'all')), cv.TestSize);
399s  assert (! training (cv, 'all'), test (cv, 'all'));
399s ***** test
399s  cv = cvpartition (10, 'resubstitution');
399s  assert (cv.Type, 'resubstitution');
399s  assert (cv.NumObservations, 10);
399s  assert (cv.NumTestSets, 1);
399s  assert (cv.TrainSize, 10);
399s  assert (cv.TestSize, 10);
399s  assert (cv.IsCustom, false);
399s  assert (cv.IsGrouped, false);
399s  assert (cv.IsStratified, false);
399s  assert (class (training (cv, 1)), 'logical');
399s  assert (sum (training (cv, 1)), 10);
399s  assert (training (cv, 'all'), logical (ones (10, 1)));
399s  assert (class (test (cv, 1)), 'logical');
399s  assert (sum (test (cv, 1)), 10);
399s  assert (test (cv, 'all'), logical (ones (10, 1)));
399s  assert (test (cv), training (cv));
399s ***** test
399s  cv = cvpartition (10, 'leaveout');
399s  assert (cv.Type, 'leaveout');
399s  assert (cv.NumObservations, 10);
399s  assert (cv.NumTestSets, 10);
399s  assert (cv.TrainSize, ones (1, 10) * 9);
399s  assert (cv.TestSize, ones (1, 10));
399s  assert (cv.IsCustom, false);
399s  assert (cv.IsGrouped, false);
399s  assert (cv.IsStratified, false);
399s  assert (class (training (cv, 1)), 'logical');
399s  assert (sum (training (cv, 1)), 9);
399s  assert (training (cv, 'all'), ! logical (eye (10)));
399s  assert (class (test (cv, 1)), 'logical');
399s  assert (sum (test (cv, 1)), 1);
399s  assert (test (cv, 'all'), logical (eye (10)));
399s  assert (test (cv), ! training (cv));
399s  assert (test (cv, 'all'), ! training (cv, 'all'));
399s ***** test
399s  rand ('seed', 5);  # for reproducibility
399s  cv = cvpartition (10, 'holdout', 0.3);
399s  assert (cv.Type, 'holdout');
399s  assert (cv.NumObservations, 10);
399s  assert (cv.NumTestSets, 1);
399s  assert (cv.TrainSize, 7);
399s  assert (cv.TestSize, 3);
399s  assert (cv.IsCustom, false);
399s  assert (cv.IsGrouped, false);
399s  assert (cv.IsStratified, false);
399s  assert (class (training (cv, 1)), 'logical');
399s  assert (sum (training (cv, 1)), 7);
399s  assert (training (cv, 'all'), logical ([1, 0, 1, 1, 0, 1, 1, 1, 0, 1])');
399s  assert (class (test (cv, 1)), 'logical');
399s  assert (sum (test (cv, 1)), 3);
399s  assert (test (cv, 'all'), logical ([0, 1, 0, 0, 1, 0, 0, 0, 1, 0])');
399s  assert (test (cv), ! training (cv));
399s  assert (test (cv, 'all'), ! training (cv, 'all'));
399s ***** test
399s  cv = cvpartition (10, 'holdout', 4);
399s  assert (cv.Type, 'holdout');
399s  assert (cv.NumObservations, 10);
399s  assert (cv.NumTestSets, 1);
399s  assert (cv.TrainSize, 6);
399s  assert (cv.TestSize, 4);
399s  assert (cv.IsCustom, false);
399s  assert (cv.IsGrouped, false);
399s  assert (cv.IsStratified, false);
399s  assert (class (training (cv, 1)), 'logical');
399s  assert (sum (training (cv, 1)), 6);
399s  assert (class (test (cv, 1)), 'logical');
399s  assert (sum (test (cv, 1)), 4);
399s  assert (test (cv), ! training (cv));
399s  assert (test (cv, 'all'), ! training (cv, 'all'));
399s ***** test
399s  cv = cvpartition (5, 'kfold');
399s  assert (cv.Type, 'kfold');
399s  assert (cv.NumObservations, 5);
399s  assert (cv.NumTestSets, 5);
399s ***** test
399s  cv = cvpartition (20, 'kfold');
399s  assert (cv.Type, 'kfold');
399s  assert (cv.NumObservations, 20);
399s  assert (cv.NumTestSets, 10);
399s ***** test
399s  cv = cvpartition (10, 'kfold', 5);
399s  assert (cv.Type, 'kfold');
399s  assert (cv.NumObservations, 10);
399s  assert (cv.NumTestSets, 5);
399s  assert (cv.TrainSize, [8, 8, 8, 8, 8]);
399s  assert (cv.TestSize, [2, 2, 2, 2, 2]);
399s  assert (cv.IsCustom, false);
399s  assert (cv.IsGrouped, false);
399s  assert (cv.IsStratified, false);
399s  assert (test (cv, 1), ! training (cv, 1));
399s  assert (test (cv, 'all'), ! training (cv, 'all'));
399s  assert (size (test (cv, 'all')), [10, 5]);
399s ***** test
399s  grpvar = [1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 5, 5];
399s  rand ('seed', 5);
399s  cv = cvpartition (12, 'kfold', 5, 'GroupingVariables', grpvar);
399s  assert (cv.Type, 'kfold');
399s  assert (cv.NumObservations, 12);
399s  assert (cv.NumTestSets, 5);
399s  assert (cv.TrainSize, [10, 10, 10, 8, 10]);
399s  assert (cv.TestSize, [2, 2, 2, 4, 2]);
399s  assert (cv.IsCustom, false);
399s  assert (cv.IsGrouped, true);
399s  assert (cv.IsStratified, false);
399s  assert (test (cv, 1), ! training (cv, 1));
399s  assert (test (cv, 'all'), ! training (cv, 'all'));
399s  assert (size (test (cv, 'all')), [12, 5]);
399s  assert (sum (test (cv, 'all')), [2, 2, 2, 4, 2]);
399s  assert (sum (training (cv, 'all')), [10, 10, 10, 8, 10]);
399s ***** test
399s  grpvar = [1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 3, 3];
399s  rand ('seed', 5);
399s  cv = cvpartition (12, 'kfold', 3, 'GroupingVariables', grpvar);
399s  assert (cv.Type, 'kfold');
399s  assert (cv.NumObservations, 12);
399s  assert (cv.NumTestSets, 3);
399s  assert (cv.TrainSize, [9, 10, 5]);
399s  assert (cv.TestSize, [3, 2, 7]);
399s  assert (cv.IsCustom, false);
399s  assert (cv.IsGrouped, true);
399s  assert (cv.IsStratified, false);
399s  assert (test (cv, 1), ! training (cv, 1));
399s  assert (test (cv, 'all'), ! training (cv, 'all'));
399s  assert (size (test (cv, 'all')), [12, 3]);
399s  assert (sum (test (cv, 'all')), [3, 2, 7]);
399s  assert (sum (training (cv, 'all')), [9, 10, 5]);
399s ***** test
399s  grpvar = [1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3];
399s  rand ('seed', 5);
399s  cv = cvpartition (12, 'kfold', 2, 'GroupingVariables', grpvar);
399s  assert (cv.Type, 'kfold');
399s  assert (cv.NumObservations, 12);
399s  assert (cv.NumTestSets, 2);
399s  assert (cv.TrainSize, [6, 6]);
399s  assert (cv.TestSize, [6, 6]);
399s  assert (cv.IsCustom, false);
399s  assert (cv.IsGrouped, true);
399s  assert (cv.IsStratified, false);
399s  assert (test (cv, 1), ! training (cv, 1));
399s  assert (test (cv, 'all'), ! training (cv, 'all'));
399s  assert (size (test (cv, 'all')), [12, 2]);
399s  assert (sum (test (cv, 'all')), [6, 6]);
399s  assert (sum (training (cv, 'all')), [6, 6]);
399s ***** test
399s  grpvar = [1, 1, 1, 2, 2, 2, 2, NaN, 2, 3, 3, 3];
399s  rand ('seed', 5);
399s  cv = cvpartition (12, 'kfold', 2, 'GroupingVariables', grpvar);
399s  assert (cv.Type, 'kfold');
399s  assert (cv.NumObservations, 11);
399s  assert (cv.NumTestSets, 2);
399s  assert (cv.TrainSize, [6, 5]);
399s  assert (cv.TestSize, [5, 6]);
399s  assert (cv.IsCustom, false);
399s  assert (cv.IsGrouped, true);
399s  assert (cv.IsStratified, false);
399s  assert (test (cv, 1), ! training (cv, 1));
399s  assert (test (cv, 'all'), ! training (cv, 'all'));
399s  assert (size (test (cv, 'all')), [11, 2]);
399s  assert (sum (test (cv, 'all')), [5, 6]);
399s  assert (sum (training (cv, 'all')), [6, 5]);
399s ***** test
399s  grpvar = [1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 3, 3];
399s  rand ('seed', 5);
399s  cv = cvpartition (12, 'kfold', 2, 'GroupingVariables', grpvar);
399s  assert (cv.Type, 'kfold');
399s  assert (cv.NumObservations, 12);
399s  assert (cv.NumTestSets, 2);
399s  assert (cv.TrainSize, [5, 7]);
399s  assert (cv.TestSize, [7, 5]);
399s  assert (cv.IsCustom, false);
399s  assert (cv.IsGrouped, true);
399s  assert (cv.IsStratified, false);
399s  assert (test (cv, 1), ! training (cv, 1));
399s  assert (test (cv, 'all'), ! training (cv, 'all'));
399s  assert (size (test (cv, 'all')), [12, 2]);
399s  assert (sum (test (cv, 'all')), [7, 5]);
399s  assert (sum (training (cv, 'all')), [5, 7]);
399s  assert (test (cv, 1)', grpvar == 2);
399s  assert (test (cv, 2)', grpvar != 2);
399s ***** test
399s  grpvar = [1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3];
399s  rand ('seed', 5);
399s  cv = cvpartition (12, 'kfold', 2, 'GroupingVariables', grpvar);
399s  assert (cv.Type, 'kfold');
399s  assert (cv.NumObservations, 12);
399s  assert (cv.NumTestSets, 2);
399s  assert (cv.TrainSize, [7, 5]);
399s  assert (cv.TestSize, [5, 7]);
399s  assert (cv.IsCustom, false);
399s  assert (cv.IsGrouped, true);
399s  assert (cv.IsStratified, false);
399s  assert (test (cv, 1), ! training (cv, 1));
399s  assert (test (cv, 'all'), ! training (cv, 'all'));
399s  assert (size (test (cv, 'all')), [12, 2]);
399s  assert (sum (test (cv, 'all')), [5, 7]);
399s  assert (sum (training (cv, 'all')), [7, 5]);
399s  assert (test (cv, 1)', grpvar == 2);
399s  assert (test (cv, 2)', grpvar != 2);
399s ***** test
399s  grpvar = [1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3];
399s  rand ('seed', 5);
399s  cv = cvpartition (12, 'kfold', 2, 'GroupingVariables', grpvar);
399s  assert (cv.Type, 'kfold');
399s  assert (cv.NumObservations, 12);
399s  assert (cv.NumTestSets, 2);
399s  assert (cv.TrainSize, [7, 5]);
399s  assert (cv.TestSize, [5, 7]);
399s  assert (cv.IsCustom, false);
399s  assert (cv.IsGrouped, true);
399s  assert (cv.IsStratified, false);
399s  assert (test (cv, 1), ! training (cv, 1));
399s  assert (test (cv, 'all'), ! training (cv, 'all'));
399s  assert (size (test (cv, 'all')), [12, 2]);
399s  assert (sum (test (cv, 'all')), [5, 7]);
399s  assert (sum (training (cv, 'all')), [7, 5]);
399s  assert (test (cv, 1)', grpvar == 3);
399s  assert (test (cv, 2)', grpvar != 3);
399s ***** test
399s  rand ('seed', 5);
399s  cv = cvpartition ([1, 1, 1, 1, 1, 2, 2, 2, 2, 2], 'holdout', 3);
399s  assert (cv.Type, 'holdout');
399s  assert (cv.NumObservations, 10);
399s  assert (cv.NumTestSets, 1);
399s  assert (cv.TrainSize, 7);
399s  assert (cv.TestSize, 3);
399s  assert (cv.IsCustom, false);
399s  assert (cv.IsGrouped, false);
399s  assert (cv.IsStratified, true);
399s  assert (test (cv, 1), ! training (cv, 1));
399s  assert (test (cv), logical ([0, 0, 0, 0, 1, 0, 1, 0, 0, 1])');
399s ***** test
399s  cv = cvpartition ([1, 1, 1, 1, 1, 2, 2, 2, 2, 2], 'holdout', 4);
399s  assert (cv.Type, 'holdout');
399s  assert (cv.NumObservations, 10);
399s  assert (cv.NumTestSets, 1);
399s  assert (cv.TrainSize, 6);
399s  assert (cv.TestSize, 4);
399s  assert (cv.IsCustom, false);
399s  assert (cv.IsGrouped, false);
399s  assert (cv.IsStratified, true);
399s  assert (test (cv, 1), ! training (cv, 1));
399s  assert (sum (test (cv)(1:5)), 2);
399s  assert (sum (test (cv)(6:10)), 2);
399s ***** test
399s  grpvar = [1, 1, 1, 1, 1, 2, 2, 2, 2, 2];
399s  rand ('seed', 5);
399s  cv = cvpartition (grpvar, 'holdout', 4, 'Stratify', false);
399s  assert (cv.Type, 'holdout');
399s  assert (cv.NumObservations, 10);
399s  assert (cv.NumTestSets, 1);
399s  assert (cv.TrainSize, 6);
399s  assert (cv.TestSize, 4);
399s  assert (cv.IsCustom, false);
399s  assert (cv.IsGrouped, false);
399s  assert (cv.IsStratified, false);
399s  assert (test (cv, 1), ! training (cv, 1));
399s  assert (sum (test (cv)(1:5)), 3);
399s  assert (sum (test (cv)(6:10)), 1);
399s ***** test
399s  cv = cvpartition ([1 1 1 1 1 2 2 2 2 1], 'kfold', 2);
399s  assert (cv.Type, 'kfold');
399s  assert (cv.NumObservations, 10);
399s  assert (cv.NumTestSets, 2);
399s  assert (cv.TrainSize, [5, 5]);
399s  assert (cv.TestSize, [5, 5]);
399s  assert (cv.IsCustom, false);
399s  assert (cv.IsGrouped, false);
399s  assert (cv.IsStratified, true);
399s  assert (test (cv, 1), ! training (cv, 1));
399s  assert (test (cv, 'all'), ! training (cv, 'all'));
399s  assert (sum (test (cv, 1)(1:5)), 3);
399s  assert (sum (test (cv, 2)(1:5)), 2);
399s  assert (sum (test (cv, 1)(6:10)), 2);
399s  assert (sum (test (cv, 2)(6:10)), 3);
399s ***** test
399s  grpvar = [1 1 1 1 1 2 2 2 2 1];
399s  rand ('seed', 5);
399s  cv = cvpartition (grpvar, 'kfold', 2, 'Stratify', false);
399s  assert (cv.Type, 'kfold');
399s  assert (cv.NumObservations, 10);
399s  assert (cv.NumTestSets, 2);
399s  assert (cv.TrainSize, [5, 5]);
399s  assert (cv.TestSize, [5, 5]);
399s  assert (cv.IsCustom, false);
399s  assert (cv.IsGrouped, false);
399s  assert (cv.IsStratified, false);
399s  assert (test (cv, 1), ! training (cv, 1));
399s  assert (test (cv, 'all'), ! training (cv, 'all'));
399s  assert (sum (test (cv, 1)(1:5)), 4);
399s  assert (sum (test (cv, 2)(1:5)), 1);
399s  assert (sum (test (cv, 1)(6:10)), 1);
399s  assert (sum (test (cv, 2)(6:10)), 4);
400s ***** error <cvpartition: too few input arguments.> cvpartition (2)
400s ***** error <cvpartition: too many input arguments.> cvpartition (1, 2, 3, 4, 5, 6)
400s ***** error <cvpartition: TESTSETS must be numeric of logical.> ...
400s  cvpartition ("CustomPartition", 'a')
400s ***** error <cvpartition: TESTSETS must be a numeric vector.> ...
400s  cvpartition ("CustomPartition", [2, 3; 2, 3])
400s ***** error <cvpartition: TESTSETS must be a logical vector or matrix.> ...
400s  cvpartition ("CustomPartition", false (3, 3, 3))
400s ***** error <cvpartition: each observation in TESTSETS must be exactly one in each row.> ...
400s  cvpartition ("CustomPartition", [false, true; true, true; true, false])
400s ***** error <cvpartition: a logical matrix in TESTSETS must not have more columns that rows.> ...
400s  cvpartition ("CustomPartition", false (3, 5))
400s ***** error <cvpartition: X must be a scalar positive integer value.> ...
400s  cvpartition (-20, "LeaveOut")
400s ***** error <cvpartition: X must be a scalar positive integer value.> ...
400s  cvpartition (20.5, "LeaveOut")
400s ***** error <cvpartition: P value for 'holdout' must be a numeric scalar.> ...
400s  cvpartition (20, "HoldOut", [0.2, 0.3])
400s ***** error <cvpartition: P value for 'holdout' must be a numeric scalar.> ...
400s  cvpartition (20, "HoldOut", 'a')
400s ***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, n\).> ...
400s  cvpartition (20, "HoldOut", 0)
400s ***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, n\).> ...
400s  cvpartition (20, "HoldOut", -0.1)
400s ***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, n\).> ...
400s  cvpartition (20, "HoldOut", 21)
400s ***** error <cvpartition: K value for 'kfold' must be a numeric scalar.> ...
400s  cvpartition (20, "kfold", [2, 3])
400s ***** error <cvpartition: K value for 'kfold' must be a numeric scalar.> ...
400s  cvpartition (20, "kfold", 'a')
400s ***** error <cvpartition: K value for 'kfold' must be an integer scalar in the range \[1, n\).> ...
400s  cvpartition (20, "kfold", 2.5)
400s ***** error <cvpartition: K value for 'kfold' must be an integer scalar in the range \[1, n\).> ...
400s  cvpartition (20, "kfold", 21)
400s ***** error <cvpartition: invalid optional paired argument for 'GroupingVariables'.> ...
400s  cvpartition (10, "kfold", 3, "Group")
400s ***** error <cvpartition: missing value for optional paired argument 'GroupingVariables'.> ...
400s  cvpartition (10, "kfold", 3, "GroupingVariables")
400s ***** error <cvpartition: invalid value for optional paired argument 'GroupingVariables'.> ...
400s  cvpartition (10, "kfold", 3, "GroupingVariables", ones (3, 3, 3))
400s ***** error <cvpartition: grouping variable does not match the number of observations.> ...
400s  cvpartition (10, "kfold", 3, "GroupingVariables", {'a', 'a', 'a', 'b', 'b'})
400s ***** warning <cvpartition: number of folds K in greater that the groups in 'GroupingVariables'.> ...
400s  cvpartition (5, "kfold", 3, "GroupingVariables", {'a', 'a', 'a', 'b', 'b'});
400s ***** error <cvpartition: invalid optional paired argument.> ...
400s  cvpartition (20, "some")
400s ***** error <cvpartition: invalid optional paired argument for stratification.> ...
400s  cvpartition ([1, 1, 1, 2, 2], "kfold", 2, "strat")
400s ***** error <cvpartition: missing value for optional paired argument 'stratify'.> ...
400s  cvpartition ([1, 1, 1, 2, 2], "kfold", 2, "stratify")
400s ***** error <cvpartition: invalid value for optional paired argument 'stratify'.> ...
400s  cvpartition ([1, 1, 1, 2, 2], "kfold", 2, "stratify", [true, true])
400s ***** error <cvpartition: invalid value for optional paired argument 'stratify'.> ...
400s  cvpartition ([1, 1, 1, 2, 2], "kfold", 2, "stratify", 'no')
400s ***** error <cvpartition: P value for 'holdout' must be a numeric scalar.> ...
400s  cvpartition ([1, 1, 1, 2, 2], "holdout", 'a')
400s ***** error <cvpartition: P value for 'holdout' must be a numeric scalar.> ...
400s  cvpartition ([1, 1, 1, 2, 2], "holdout", 'a', "stratify", true)
400s ***** error <cvpartition: P value for 'holdout' must be a numeric scalar.> ...
400s  cvpartition ([1, 1, 1, 2, 2], "holdout", [0.2, 0.3])
400s ***** error <cvpartition: P value for 'holdout' must be a numeric scalar.> ...
400s  cvpartition ([1, 1, 1, 2, 2], "holdout", [0.2, 0.3], "stratify", true)
400s ***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, n\).> ...
400s  cvpartition ([1, 1, 1, 2, 2], "holdout", 0)
400s ***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, n\).> ...
400s  cvpartition ([1, 1, 1, 2, 2], "holdout", 0, "stratify", true)
400s ***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, n\).> ...
400s  cvpartition ([1, 1, 1, 2, 2], "holdout", -0.1)
400s ***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, n\).> ...
400s  cvpartition ([1, 1, 1, 2, 2], "holdout", -0.1, "stratify", true)
400s ***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, n\).> ...
400s  cvpartition ([1, 1, 1, 2, 2], "holdout", 1.2)
400s ***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, n\).> ...
400s  cvpartition ([1, 1, 1, 2, 2], "holdout", 1.2, "stratify", false)
400s ***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, n\).> ...
400s  cvpartition ([1, 1, 1, 2, 2], "holdout", 6)
400s ***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, n\).> ...
400s  cvpartition ([1, 1, 1, 2, 2], "holdout", 6, "stratify", false)
400s ***** error <cvpartition: K value for 'kfold' must be a numeric scalar.> ...
400s  cvpartition ([1, 1, 1, 2, 2], "kfold", 'a')
400s ***** error <cvpartition: K value for 'kfold' must be a numeric scalar.> ...
400s  cvpartition ([1, 1, 1, 2, 2], "kfold", 'a', "stratify", true)
400s ***** error <cvpartition: K value for 'kfold' must be a numeric scalar.> ...
400s  cvpartition ([1, 1, 1, 2, 2], "kfold", [2, 3])
400s ***** error <cvpartition: K value for 'kfold' must be a numeric scalar.> ...
400s  cvpartition ([1, 1, 1, 2, 2], "kfold", [2, 3], "stratify", false)
400s ***** error <cvpartition: K value for 'kfold' must be an integer scalar in the range \[1, n\).> ...\
400s  cvpartition ([1, 1, 1, 2, 2], "kfold", 0)
400s ***** error <cvpartition: K value for 'kfold' must be an integer scalar in the range \[1, n\).> ...\
400s  cvpartition ([1, 1, 1, 2, 2], "kfold", 0, "stratify", true)
400s ***** error <cvpartition: K value for 'kfold' must be an integer scalar in the range \[1, n\).> ...\
400s  cvpartition ([1, 1, 1, 2, 2], "kfold", 1.5)
400s ***** error <cvpartition: K value for 'kfold' must be an integer scalar in the range \[1, n\).> ...\
400s  cvpartition ([1, 1, 1, 2, 2], "kfold", 1.5, "stratify", true)
400s ***** error <cvpartition: K value for 'kfold' must be an integer scalar in the range \[1, n\).> ...\
400s  cvpartition ([1, 1, 1, 2, 2], "kfold", 5)
400s ***** error <cvpartition: K value for 'kfold' must be an integer scalar in the range \[1, n\).> ...\
400s  cvpartition ([1, 1, 1, 2, 2], "kfold", 5, "stratify", true)
400s ***** error <cvpartition: invalid optional paired argument.> ...
400s  cvpartition ([1, 1, 1, 2, 2], "leaveout")
400s ***** error <cvpartition: invalid optional paired argument.> ...
400s  cvpartition ([1, 1, 1, 2, 2], "resubstitution")
400s ***** error <cvpartition: invalid optional paired argument.> ...
400s  cvpartition ([1, 1, 1, 2, 2], "some")
400s ***** error <cvpartition: invalid first input argument.> ...
400s  cvpartition ({1, 1; 2, 2}, "kfold")
400s ***** error <cvpartition.repartition: cannot repartition a custom partition.> ...
400s  repartition (cvpartition ('CustomPartition', [1,1,2,2,3,3]))
400s ***** error <cvpartition.repartition: 'legacy' flag does not apply to stratified or grouped 'kfold' partitioned objects.> ...
400s  repartition (cvpartition ([1 1 1 1 1 2 2 2 2 1], 'kfold', 2, 'Stratify', true), 'legacy')
400s ***** error <cvpartition.repartition: 'legacy' flag is only valid for 'kfold' partitioned objects.> ...
400s  repartition (cvpartition (20, 'Leaveout', 0.2), 'legacy')
400s ***** error <cvpartition.repartition: SVAL must be a real scalar or vector.> ...
400s  repartition (cvpartition (20, 'Leaveout', 0.2), 'asd')
400s ***** error <cvpartition.repartition: SVAL must be a real scalar or vector.> ...
400s  repartition (cvpartition (20, 'Leaveout', 0.2), 2+i)
400s ***** error <cvpartition.repartition: SVAL must be a real scalar or vector.> ...
400s  repartition (cvpartition (20, 'KFold', 5), [34, 56; 2, 3])
400s ***** error <cvpartition.test: too many input arguments.> ...
400s  test (cvpartition (20, "kfold"), 2, 3)
400s ***** error <cvpartition.test: set index must be a positive integer vector.> ...
400s  test (cvpartition (20, "kfold"), 0)
400s ***** error <cvpartition.test: set index must be a positive integer vector.> ...
400s  test (cvpartition (20, "kfold"), 1.5)
400s ***** error <cvpartition.test: set index must be a positive integer vector.> ...
400s  test (cvpartition (20, "kfold"), [1, 1.5])
400s ***** error <cvpartition.test: set index must be a positive integer vector.> ...
400s  test (cvpartition (20, "kfold"), [2, 3; 2, 3])
400s ***** error <cvpartition.test: set index exceeds 'NumTestSets'.> ...
400s  test (cvpartition (20, "kfold"), 21)
400s ***** error <cvpartition.test: set index exceeds 'NumTestSets'.> ...
400s  test (cvpartition (20, "kfold"), [18, 21])
400s ***** error <cvpartition.training: too many input arguments.> ...
400s  training (cvpartition (20, "kfold"), 2, 3)
400s ***** error <cvpartition.training: set index must be a positive integer vector.> ...
400s  training (cvpartition (20, "kfold"), 0)
400s ***** error <cvpartition.training: set index must be a positive integer vector.> ...
400s  training (cvpartition (20, "kfold"), 1.5)
400s ***** error <cvpartition.training: set index must be a positive integer vector.> ...
400s  training (cvpartition (20, "kfold"), [1, 1.5])
400s ***** error <cvpartition.training: set index must be a positive integer vector.> ...
400s  training (cvpartition (20, "kfold"), [2, 3; 2, 3])
400s ***** error <cvpartition.training: set index exceeds 'NumTestSets'.> ...
400s  training (cvpartition (20, "kfold"), 21)
400s ***** error <cvpartition.training: set index exceeds 'NumTestSets'.> ...
400s  training (cvpartition (20, "kfold"), [18, 21])
400s 98 tests, 98 passed, 0 known failure, 0 skipped
400s [inst/Clustering/hnswSearcher.m]
400s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/Clustering/hnswSearcher.m
400s ***** demo
400s  ## Create an hnswSearcher with Euclidean distance
400s  X = [1, 2; 3, 4; 5, 6];
400s  obj = hnswSearcher (X);
400s  ## Find the nearest neighbor to [2, 3]
400s  Y = [2, 3];
400s  [idx, D] = knnsearch (obj, Y, "K", 1);
400s  disp ("Nearest neighbor index:");
400s  disp (idx);
400s  disp ("Distance:");
400s  disp (D);
400s ***** demo
400s  ## Create an hnswSearcher with Minkowski distance (P=3)
400s  X = [0, 0; 1, 0; 2, 0];
400s  obj = hnswSearcher (X, "Distance", "minkowski", "P", 3);
400s  ## Find the nearest neighbor to [1, 0]
400s  Y = [1, 0];
400s  [idx, D] = knnsearch (obj, Y, "K", 1);
400s  disp ("Nearest neighbor index:");
400s  disp (idx);
400s  disp ("Distance:");
400s  disp (D);
400s ***** test
400s  load fisheriris
400s  X = meas;
400s  obj = hnswSearcher (X, "Distance", "chebychev");
400s  Y = X(30:35,:);
400s  [idx, D] = knnsearch (obj, Y, "K", 4);
400s  assert (idx, [[30 31 4 12]; [31 30 10 35]; [32 21 37 28]; [33 47 20 34]; ...
400s                [34 16 33 15]; [35 10 26 2]])
400s  assert (D, [[0 0.1000 0.1000 0.2000]; [0 0.1000 0.1000 0.1000]; [0 0.2000 ...
400s               0.2000 0.2000]; [0 0.3000 0.3000 0.3000]; [0 0.2000 0.3000 ...
400s               0.3000]; [0 0.1000 0.1000 0.1000]], 5e-15)
408s ***** test
408s  load fisheriris
408s  X = meas;
408s  C = cov (X);
408s  obj = hnswSearcher (X, "Distance", "mahalanobis", "Cov", C);
408s  Y = X(120:125,:);
408s  [idx, D] = knnsearch (obj, Y, "K", 2);
408s  assert (idx(1, :), [120 82])
408s  assert (idx(4, :), [123 106])
408s  assert (idx(5, :), [124 127])
408s  assert (idx(6, :), [125 57])
408s  assert (D(1, :), [0 0.7734], 1e-4)
408s  assert (D(4, :), [0 0.8452], 1e-4)
408s  assert (D(5, :), [0 0.4152], 1e-4)
408s  assert (D(6, :), [0 0.7322], 1e-4)
418s ***** test
418s  ## Basic constructor with default Euclidean
418s  X = [1, 2; 3, 4; 5, 6];
418s  obj = hnswSearcher (X);
418s  assert (obj.X, X);
418s  assert (obj.Distance, "euclidean");
418s  assert (isempty (obj.DistParameter));
418s ***** test
418s  ## Minkowski distance with custom P
418s  X = [0, 0; 1, 1; 2, 2];
418s  obj = hnswSearcher (X, "Distance", "minkowski", "P", 3);
418s  assert (obj.Distance, "minkowski");
418s  assert (obj.DistParameter, 3);
418s ***** test
418s  ## Seuclidean distance with custom Scale
418s  X = [1, 2; 3, 4; 5, 6];
418s  S = [1, 2];
418s  obj = hnswSearcher (X, "Distance", "seuclidean", "Scale", S);
418s  assert (obj.Distance, "seuclidean");
418s  assert (obj.DistParameter, S);
418s ***** test
418s  ## Mahalanobis distance with custom Cov
418s  X = [1, 2; 3, 4; 5, 6];
418s  C = [1, 0; 0, 1];
418s  obj = hnswSearcher (X, "Distance", "mahalanobis", "Cov", C);
418s  assert (obj.Distance, "mahalanobis");
418s  assert (obj.DistParameter, C);
418s ***** test
418s  ## knnsearch with Euclidean distance
418s  X = [1, 2; 3, 4; 5, 6];
418s  obj = hnswSearcher (X);
418s  Y = [2, 3];
418s  [idx, D] = knnsearch (obj, Y, "K", 1);
418s  assert (ismember (idx, [2]));
418s  assert (abs (D - sqrt(2)) < 1e-2);
418s ***** test
418s  ## knnsearch with Cityblock distance
418s  X = [0, 0; 1, 1; 2, 2];
418s  obj = hnswSearcher (X, "Distance", "cityblock");
418s  Y = [1, 0];
418s  [idx, D] = knnsearch (obj, Y, "K", 1);
418s  assert (ismember (idx, [1, 2]));
418s  assert (abs (D - 1) < 1e-2);
418s ***** test
418s  ## knnsearch with Chebychev distance
418s  X = [1, 1; 2, 3; 4, 2];
418s  obj = hnswSearcher (X, "Distance", "chebychev");
418s  Y = [2, 2];
418s  [idx, D] = knnsearch (obj, Y, "K", 1);
418s  assert (ismember (idx, [1, 2]));
418s  assert (abs (D - 1) < 1e-2);
418s ***** test
418s  ## knnsearch with Minkowski P=3
418s  X = [0, 0; 1, 0; 2, 0];
418s  obj = hnswSearcher (X, "Distance", "minkowski", "P", 3);
418s  Y = [1, 0];
418s  [idx, D] = knnsearch (obj, Y, "K", 1);
418s  assert (ismember (idx, [2]));
418s  assert (abs (D - 0) < 1e-2);
418s ***** test
418s  ## Diverse dataset with Euclidean
418s  X = [0, 10; 5, 5; 10, 0];
418s  obj = hnswSearcher (X);
418s  Y = [5, 5];
418s  [idx, D] = knnsearch (obj, Y, "K", 1);
418s  assert (ismember (idx, [2]));
418s  assert (abs (D - 0) < 1e-2);
418s ***** test
418s  ## High-dimensional data with Cityblock
418s  X = [1, 2, 3; 4, 5, 6; 7, 8, 9];
418s  obj = hnswSearcher (X, "Distance", "cityblock");
418s  Y = [4, 5, 6];
418s  [idx, D] = knnsearch (obj, Y, "K", 1);
418s  assert (ismember (idx, [2]));
418s  assert (abs (D - 0) < 1e-2);
418s ***** error<hnswSearcher: too few input arguments.> ...
418s  hnswSearcher ()
418s ***** error<hnswSearcher: Name-Value arguments must be in pairs.> ...
418s  hnswSearcher (ones(3,2), "Distance")
418s ***** error<hnswSearcher: X cannot be empty.> ...
418s  hnswSearcher ([])
418s ***** error<hnswSearcher: X must be a finite numeric matrix.> ...
418s  hnswSearcher ("abc")
418s ***** error<hnswSearcher: X must be a finite numeric matrix.> ...
418s  hnswSearcher ([1; Inf; 3])
418s ***** error<hnswSearcher: invalid parameter name: 'foo'.> ...
418s  hnswSearcher (ones(3,2), "foo", "bar")
418s ***** error<hnswSearcher: unsupported distance metric 'invalid'.> ...
418s  hnswSearcher (ones(3,2), "Distance", "invalid")
418s ***** error<hnswSearcher: 'Distance' must be a string.> ...
418s  hnswSearcher (ones(3,2), "Distance", 1)
418s ***** error<hnswSearcher: 'P' must be a positive finite scalar.> ...
418s  hnswSearcher (ones(3,2), "Distance", "minkowski", "P", -1)
418s ***** error<hnswSearcher: 'Scale' must be a nonnegative vector matching X columns.> ...
418s  hnswSearcher (ones(3,2), "Distance", "seuclidean", "Scale", [-1, 1])
418s ***** error<hnswSearcher: 'Cov' must be a square matrix matching X columns.> ...
418s  hnswSearcher (ones(3,2), "Distance", "mahalanobis", "Cov", ones(3,3))
418s ***** error<hnswSearcher: 'Cov' must be symmetric for mahalanobis.> ...
418s  hnswSearcher (ones(3,2), "Distance", "mahalanobis", "Cov", [1, 2; 3, 4])
418s ***** error<hnswSearcher: 'Cov' must be positive definite for mahalanobis.> ...
418s  hnswSearcher (ones(3,2), "Distance", "mahalanobis", "Cov", -eye(2))
418s ***** error<hnswSearcher: 'MaxNumLinksPerNode' must be a positive integer.> ...
418s  hnswSearcher (ones(3,2), "MaxNumLinksPerNode", 0)
418s ***** error<hnswSearcher: 'TrainSetSize' must be a positive integer.> ...
418s  hnswSearcher (ones(3,2), "TrainSetSize", -1)
418s ***** error<hnswSearcher: 'TrainSetSize' cannot exceed the number of rows in X.> ...
418s  hnswSearcher (ones(3,2), "TrainSetSize", 4)
418s ***** error<hnswSearcher: 'TrainSetSize' cannot exceed the number of rows in X.> ...
418s  hnswSearcher (ones(3,2), "MaxNumLinksPerNode", 200, "TrainSetSize", 100)
418s ***** error<hnswSearcher.knnsearch: too few input arguments.> ...
418s  knnsearch (hnswSearcher (ones(3,2)))
418s ***** error<hnswSearcher.knnsearch: Name-Value arguments must be in pairs.> ...
418s  knnsearch (hnswSearcher (ones(3,2)), ones(3,2), "K")
418s ***** error<hnswSearcher.knnsearch: Y cannot be empty.> ...
418s  knnsearch (hnswSearcher (ones(3,2)), [])
418s ***** error<hnswSearcher.knnsearch: Y must be a finite numeric matrix.> ...
418s  knnsearch (hnswSearcher (ones(3,2)), "abc")
418s ***** error<hnswSearcher.knnsearch: Y must have the same number of columns as the training data in OBJ.X.> ...
418s  knnsearch (hnswSearcher (ones(3,2)), ones(3,3))
418s ***** error<hnswSearcher.knnsearch: 'K' must be a positive integer.> ...
418s  knnsearch (hnswSearcher (ones(3,2)), ones(3,2), "K", 0)
418s ***** error<hnswSearcher.knnsearch: invalid parameter name: 'foo'.> ...
418s  knnsearch (hnswSearcher (ones(3,2)), ones(3,2), "foo", "bar")
418s ***** error<hnswSearcher.subsref: \(\) indexing not supported.> ...
418s  obj = hnswSearcher (ones(3,2)); obj(1)
418s ***** error<hnswSearcher.subsref: {} indexing not supported.> ...
418s  obj = hnswSearcher (ones(3,2)); obj{1}
418s ***** error<hnswSearcher.subsref: unrecognized property: 'invalid'.> ...
418s  obj = hnswSearcher (ones(3,2)); obj.invalid
418s ***** error<hnswSearcher.subsasgn: \(\) indexing not supported.> ...
418s  obj = hnswSearcher (ones(3,2)); obj(1) = 1
418s ***** error<hnswSearcher.subsasgn: {} indexing not supported.> ...
418s  obj = hnswSearcher (ones(3,2)); obj{1} = 1
418s ***** error<hnswSearcher.subsasgn: 'X' is read-only and cannot be modified.> ...
418s  obj = hnswSearcher (ones(3,2)); obj.X = 1
418s ***** error<hnswSearcher.subsasgn: 'HNSWGraph' is read-only and cannot be modified.> ...
418s  obj = hnswSearcher (ones(3,2)); obj.HNSWGraph = 1
418s ***** error<hnswSearcher.subsasgn: 'Distance' is read-only and cannot be modified.> ...
418s  obj = hnswSearcher (ones(3,2)); obj.Distance = "invalid"
418s ***** error<hnswSearcher.subsasgn: 'Distance' is read-only and cannot be modified.> ...
418s  obj = hnswSearcher (ones(3,2)); obj.Distance = 1
418s ***** error<hnswSearcher.subsasgn: 'DistParameter' is read-only and cannot be modified.> ...
418s  obj = hnswSearcher (ones(3,2), "Distance", "minkowski"); obj.DistParameter = -1
418s ***** error<hnswSearcher.subsasgn: 'DistParameter' is read-only and cannot be modified.> ...
418s  obj = hnswSearcher (ones(3,2), "Distance", "seuclidean"); obj.DistParameter = [-1, 1]
418s ***** error<pdist2: covariance matrix for mahalanobis distance must be symmetric and positive definite.> ...
418s  obj = hnswSearcher (ones(3,2), "Distance", "mahalanobis"); obj.DistParameter = ones(3,3)
418s ***** error<pdist2: covariance matrix for mahalanobis distance must be symmetric and positive definite.> ...
418s  obj = hnswSearcher (ones(3,2), "Distance", "mahalanobis"); obj.DistParameter = -eye(2)
418s ***** error<hnswSearcher.subsasgn: 'DistParameter' is read-only and cannot be modified.> ...
418s  obj = hnswSearcher (ones(3,2)); obj.DistParameter = 1
418s ***** error<hnswSearcher.subsasgn: 'MaxNumLinksPerNode' is read-only and cannot be modified.> ...
418s  obj = hnswSearcher (ones(3,2)); obj.MaxNumLinksPerNode = 0
418s ***** error<hnswSearcher.subsasgn: 'TrainSetSize' is read-only and cannot be modified.> ...
418s  obj = hnswSearcher (ones(3,2)); obj.TrainSetSize = -1
418s ***** error<hnswSearcher.subsasgn: unrecognized property: 'efSearch'.> ...
418s  obj = hnswSearcher (ones(3,2)); obj.efSearch = 1.5
418s ***** error<hnswSearcher.subsasgn: unrecognized property: 'invalid'.> ...
418s  obj = hnswSearcher (ones(3,2)); obj.invalid = 1
418s 54 tests, 54 passed, 0 known failure, 0 skipped
418s [inst/Regression/RegressionGAM.m]
418s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/Regression/RegressionGAM.m
418s ***** demo
418s  ## Train a RegressionGAM Model for synthetic values
418s  f1 = @(x) cos (3 * x);
418s  f2 = @(x) x .^ 3;
418s  x1 = 2 * rand (50, 1) - 1;
418s  x2 = 2 * rand (50, 1) - 1;
418s  y = f1(x1) + f2(x2);
418s  y = y + y .* 0.2 .* rand (50,1);
418s  X = [x1, x2];
418s  a = fitrgam (X, y, "tol", 1e-3)
418s ***** demo
418s  ## Declare two different functions
418s  f1 = @(x) cos (3 * x);
418s  f2 = @(x) x .^ 3;
418s 
418s  ## Generate 80 samples for f1 and f2
418s  x = [-4*pi:0.1*pi:4*pi-0.1*pi]';
418s  X1 = f1 (x);
418s  X2 = f2 (x);
418s 
418s  ## Create a synthetic response by adding noise
418s  rand ("seed", 3);
418s  Ytrue = X1 + X2;
418s  Y = Ytrue + Ytrue .* 0.2 .* rand (80,1);
418s 
418s  ## Assemble predictor data
418s  X = [X1, X2];
418s 
418s  ## Train the GAM and test on the same data
418s  a = fitrgam (X, Y, "order", [5, 5]);
418s  [ypred, ySDsd, yInt] = predict (a, X);
418s 
418s  ## Plot the results
418s  figure
418s  [sortedY, indY] = sort (Ytrue);
418s  plot (sortedY, "r-");
418s  xlim ([0, 80]);
418s  hold on
418s  plot (ypred(indY), "g+")
418s  plot (yInt(indY,1), "k:")
418s  plot (yInt(indY,2), "k:")
418s  xlabel ("Predictor samples");
418s  ylabel ("Response");
418s  title ("actual vs predicted values for function f1(x) = cos (3x) ");
418s  legend ({"Theoretical Response", "Predicted Response", "Prediction Intervals"});
418s 
418s  ## Use 30% Holdout partitioning for training and testing data
418s  C = cvpartition (80, "HoldOut", 0.3);
418s  [ypred, ySDsd, yInt] = predict (a, X(test(C),:));
418s 
418s  ## Plot the results
418s  figure
418s  [sortedY, indY] = sort (Ytrue(test(C)));
418s  plot (sortedY, 'r-');
418s  xlim ([0, sum(test(C))]);
418s  hold on
418s  plot (ypred(indY), "g+")
418s  plot (yInt(indY,1),'k:')
418s  plot (yInt(indY,2),'k:')
418s  xlabel ("Predictor samples");
418s  ylabel ("Response");
418s  title ("actual vs predicted values for function f1(x) = cos (3x) ");
418s  legend ({"Theoretical Response", "Predicted Response", "Prediction Intervals"});
418s ***** test
418s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
418s  y = [1; 2; 3; 4];
418s  a = RegressionGAM (x, y);
418s  assert ({a.X, a.Y}, {x, y})
418s  assert ({a.BaseModel.Intercept}, {2.5000})
418s  assert ({a.Knots, a.Order, a.DoF}, {[5, 5, 5], [3, 3, 3], [8, 8, 8]})
418s  assert ({a.NumObservations, a.NumPredictors}, {4, 3})
418s  assert ({a.ResponseName, a.PredictorNames}, {"Y", {"x1", "x2", "x3"}})
418s  assert ({a.Formula}, {[]})
418s ***** test
418s  x = [1, 2, 3, 4; 4, 5, 6, 7; 7, 8, 9, 1; 3, 2, 1, 2];
418s  y = [1; 2; 3; 4];
418s  pnames = {"A", "B", "C", "D"};
418s  formula = "Y ~ A + B + C + D + A:C";
418s  intMat = logical ([1,0,0,0;0,1,0,0;0,0,1,0;0,0,0,1;1,0,1,0]);
418s  a = RegressionGAM (x, y, "predictors", pnames, "formula", formula);
418s  assert ({a.IntMatrix}, {intMat})
418s  assert ({a.ResponseName, a.PredictorNames}, {"Y", pnames})
418s  assert ({a.Formula}, {formula})
419s ***** error<RegressionGAM: too few input arguments.> RegressionGAM ()
419s ***** error<RegressionGAM: too few input arguments.> RegressionGAM (ones(10,2))
419s ***** error<RegressionGAM: number of rows in X and Y must be equal.> ...
419s  RegressionGAM (ones(10,2), ones (5,1))
419s ***** error<RegressionGAM: invalid values in X.> ...
419s  RegressionGAM ([1;2;3;"a";4], ones (5,1))
419s ***** error<RegressionGAM: invalid parameter name in optional pair arguments.> ...
419s  RegressionGAM (ones(10,2), ones (10,1), "some", "some")
419s ***** error<RegressionGAM: Formula must be a string.>
419s  RegressionGAM (ones(10,2), ones (10,1), "formula", {"y~x1+x2"})
419s ***** error<RegressionGAM: Formula must be a string.>
419s  RegressionGAM (ones(10,2), ones (10,1), "formula", [0, 1, 0])
419s ***** error<RegressionGAM: invalid syntax in Formula.> ...
419s  RegressionGAM (ones(10,2), ones (10,1), "formula", "something")
419s ***** error<RegressionGAM: no predictor terms in Formula.> ...
419s  RegressionGAM (ones(10,2), ones (10,1), "formula", "something~")
419s ***** error<RegressionGAM: no predictor terms in Formula.> ...
419s  RegressionGAM (ones(10,2), ones (10,1), "formula", "something~")
419s ***** error<RegressionGAM: some predictors have not been identified> ...
419s  RegressionGAM (ones(10,2), ones (10,1), "formula", "something~x1:")
419s ***** error<RegressionGAM: invalid Interactions parameter.> ...
419s  RegressionGAM (ones(10,2), ones (10,1), "interactions", "some")
419s ***** error<RegressionGAM: invalid Interactions parameter.> ...
419s  RegressionGAM (ones(10,2), ones (10,1), "interactions", -1)
419s ***** error<RegressionGAM: invalid Interactions parameter.> ...
419s  RegressionGAM (ones(10,2), ones (10,1), "interactions", [1 2 3 4])
419s ***** error<RegressionGAM: number of interaction terms requested is larger than> ...
419s  RegressionGAM (ones(10,2), ones (10,1), "interactions", 3)
419s ***** error<RegressionGAM: Formula has been already defined.> ...
419s  RegressionGAM (ones(10,2), ones (10,1), "formula", "y ~ x1 + x2", "interactions", 1)
419s ***** error<RegressionGAM: Interactions have been already defined.> ...
419s  RegressionGAM (ones(10,2), ones (10,1), "interactions", 1, "formula", "y ~ x1 + x2")
419s ***** error<RegressionGAM: invalid value for Knots.> ...
419s  RegressionGAM (ones(10,2), ones (10,1), "knots", "a")
419s ***** error<RegressionGAM: DoF and Order have been set already.> ...
419s  RegressionGAM (ones(10,2), ones (10,1), "order", 3, "dof", 2, "knots", 5)
419s ***** error<RegressionGAM: invalid value for DoF.> ...
419s  RegressionGAM (ones(10,2), ones (10,1), "dof", 'a')
419s ***** error<RegressionGAM: Knots and Order have been set already.> ...
419s  RegressionGAM (ones(10,2), ones (10,1), "knots", 5, "order", 3, "dof", 2)
419s ***** error<RegressionGAM: invalid value for Order.> ...
419s  RegressionGAM (ones(10,2), ones (10,1), "order", 'a')
419s ***** error<RegressionGAM: DoF and Knots have been set already.> ...
419s  RegressionGAM (ones(10,2), ones (10,1), "knots", 5, "dof", 2, "order", 2)
419s ***** error<RegressionGAM: Tolerance must be a Positive scalar.> ...
419s  RegressionGAM (ones(10,2), ones (10,1), "tol", -1)
419s ***** error<RegressionGAM: ResponseName must be a char string.> ...
419s  RegressionGAM (ones(10,2), ones (10,1), "responsename", -1)
419s ***** error<RegressionGAM: PredictorNames must be a cellstring array.> ...
419s  RegressionGAM (ones(10,2), ones (10,1), "predictors", -1)
419s ***** error<RegressionGAM: PredictorNames must be a cellstring array.> ...
419s  RegressionGAM (ones(10,2), ones (10,1), "predictors", ['a','b','c'])
419s ***** error<RegressionGAM: PredictorNames must have same number of columns as X.> ...
419s  RegressionGAM (ones(10,2), ones (10,1), "predictors", {'a','b','c'})
419s ***** error<RegressionGAM.predict: too few arguments.> ...
419s  predict (RegressionGAM (ones(10,1), ones(10,1)))
419s ***** error<RegressionGAM.predict: Xfit is empty.> ...
419s  predict (RegressionGAM (ones(10,1), ones(10,1)), [])
419s ***** error<RegressionGAM.predict: Xfit must have the same number of features> ...
419s  predict (RegressionGAM(ones(10,2), ones(10,1)), 2)
419s ***** error<RegressionGAM.predict: invalid NAME in optional pairs of arguments.> ...
419s  predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "some", "some")
419s ***** error<RegressionGAM.predict: includeinteractions must be a logical value.> ...
419s  predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "includeinteractions", "some")
419s ***** error<RegressionGAM.predict: includeinteractions must be a logical value.> ...
419s  predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "includeinteractions", 5)
419s ***** error<RegressionGAM.predict: alpha must be a scalar value between 0 and 1.> ...
419s  predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "alpha", 5)
419s ***** error<RegressionGAM.predict: alpha must be a scalar value between 0 and 1.> ...
419s  predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "alpha", -1)
419s ***** error<RegressionGAM.predict: alpha must be a scalar value between 0 and 1.> ...
419s  predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "alpha", 'a')
419s 39 tests, 39 passed, 0 known failure, 0 skipped
419s [inst/adtest.m]
419s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/adtest.m
419s ***** error<Invalid call to adtest.  Correct usage is:> adtest ();
419s ***** error<adtest: X must be a vector of real numbers.> adtest (ones (20,2));
419s ***** error<adtest: X must be a vector of real numbers.> adtest ([1+i,0-3i]);
419s ***** error<adtest: invalid distribution family in char string.> ...
419s  adtest (ones (20,1), "Distribution", "normal");
419s ***** error<adtest: invalid distribution family in cell array.> ...
419s  adtest (rand (20,1), "Distribution", {"normal", 5, 3});
419s ***** error<adtest: invalid distribution parameters in cell array.> ...
419s  adtest (rand (20,1), "Distribution", {"norm", 5});
419s ***** error<adtest: invalid distribution parameters in cell array.> ...
419s  adtest (rand (20,1), "Distribution", {"exp", 5, 4});
419s ***** error<adtest: invalid distribution parameters in cell array.> ...
419s  adtest (rand (20,1), "Distribution", {"ev", 5});
419s ***** error<adtest: invalid distribution parameters in cell array.> ...
419s  adtest (rand (20,1), "Distribution", {"logn", 5, 3, 2});
419s ***** error<adtest: invalid distribution parameters in cell array.> ...
419s  adtest (rand (20,1), "Distribution", {"Weibull", 5});
419s ***** error<adtest: invalid distribution option.> ...
419s  adtest (rand (20,1), "Distribution", 35);
419s ***** error<adtest: invalid Name argument.> ...
419s  adtest (rand (20,1), "Name", "norm");
419s ***** error<adtest: invalid Name argument.> ...
419s  adtest (rand (20,1), "Name", {"norm", 75, 10});
419s ***** error<adtest: asymptotic option is not valid for the composite> ...
419s  adtest (rand (20,1), "Distribution", "norm", "Asymptotic", true);
419s ***** error<adtest: asymptotic option is not valid for the composite> ...
419s  adtest (rand (20,1), "MCTol", 0.001, "Asymptotic", true);
419s ***** error<adtest: asymptotic option is not valid for the Monte Carlo> ...
419s  adtest (rand (20,1), "Distribution", {"norm", 5, 3}, "MCTol", 0.001, ...
419s          "Asymptotic", true);
419s ***** error<adtest: out of range invalid alpha - lower limit: 0.0005 upper> ...
419s  [h, pval, ADstat, CV] = adtest (ones (20,1), "Distribution", {"norm",5,3},...
419s                                  "Alpha", 0.000000001);
419s ***** error<adtest: out of range invalid alpha - lower limit: 0.0005 upper> ...
419s  [h, pval, ADstat, CV] = adtest (ones (20,1), "Distribution", {"norm",5,3},...
419s                                  "Alpha", 0.999999999);
419s ***** error<adtest: not enough data for composite testing.> ...
419s  adtest (10);
419s ***** warning<adtest: out of range min p-value:> ...
419s  randn ("seed", 34);
419s  adtest (ones (20,1), "Alpha", 0.000001);
419s ***** warning<adtest: alpha not within the lookup table.> ...
419s  randn ("seed", 34);
419s  adtest (normrnd(0,1,100,1), "Alpha", 0.99999);
419s ***** warning<adtest: alpha not within the lookup table.> ...
419s  randn ("seed", 34);
419s  adtest (normrnd(0,1,100,1), "Alpha", 0.00001);
419s ***** test
419s  load examgrades
419s  x = grades(:,1);
419s  [h, pval, adstat, cv] = adtest (x);
419s  assert (h, false);
419s  assert (pval, 0.1854, 1e-4);
419s  assert (adstat, 0.5194, 1e-4);
419s  assert (cv, 0.7470, 1e-4);
419s ***** test
419s  load examgrades
419s  x = grades(:,1);
419s  [h, pval, adstat, cv] = adtest (x, "Distribution", "ev");
419s  assert (h, false);
419s  assert (pval, 0.071363, 1e-6);
419s ***** test
419s  load examgrades
419s  x = grades(:,1);
419s  [h, pval, adstat, cv] = adtest (x, "Distribution", {"norm", 75, 10});
419s  assert (h, false);
419s  assert (pval, 0.4687, 1e-4);
419s 25 tests, 25 passed, 0 known failure, 0 skipped
419s [inst/anova1.m]
419s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/anova1.m
419s ***** demo
419s  x = meshgrid (1:6);
419s  randn ("seed", 15);    # for reproducibility
419s  x = x + normrnd (0, 1, 6, 6);
419s  anova1 (x, [], 'off');
419s ***** demo
419s  x = meshgrid (1:6);
419s  randn ("seed", 15);    # for reproducibility
419s  x = x + normrnd (0, 1, 6, 6);
419s  [p, atab] = anova1(x);
419s ***** demo
419s  x = ones (50, 4) .* [-2, 0, 1, 5];
419s  randn ("seed", 13);    # for reproducibility
419s  x = x + normrnd (0, 2, 50, 4);
419s  groups = {"A", "B", "C", "D"};
419s  anova1 (x, groups);
419s ***** demo
419s  y = [54 87 45; 23 98 39; 45 64 51; 54 77 49; 45 89 50; 47 NaN 55];
419s  g = [1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ];
419s  anova1 (y(:), g(:), "on", "unequal");
419s ***** test
419s  data = [1.006, 0.996, 0.998, 1.000, 0.992, 0.993, 1.002, 0.999, 0.994, 1.000, ...
419s          0.998, 1.006, 1.000, 1.002, 0.997, 0.998, 0.996, 1.000, 1.006, 0.988, ...
419s          0.991, 0.987, 0.997, 0.999, 0.995, 0.994, 1.000, 0.999, 0.996, 0.996, ...
419s          1.005, 1.002, 0.994, 1.000, 0.995, 0.994, 0.998, 0.996, 1.002, 0.996, ...
419s          0.998, 0.998, 0.982, 0.990, 1.002, 0.984, 0.996, 0.993, 0.980, 0.996, ...
419s          1.009, 1.013, 1.009, 0.997, 0.988, 1.002, 0.995, 0.998, 0.981, 0.996, ...
419s          0.990, 1.004, 0.996, 1.001, 0.998, 1.000, 1.018, 1.010, 0.996, 1.002, ...
419s          0.998, 1.000, 1.006, 1.000, 1.002, 0.996, 0.998, 0.996, 1.002, 1.006, ...
419s          1.002, 0.998, 0.996, 0.995, 0.996, 1.004, 1.004, 0.998, 0.999, 0.991, ...
419s          0.991, 0.995, 0.984, 0.994, 0.997, 0.997, 0.991, 0.998, 1.004, 0.997];
419s  group = [1:10] .* ones (10,10);
419s  group = group(:);
419s  [p, tbl] = anova1 (data, group, "off");
419s  assert (p, 0.022661, 1e-6);
419s  assert (tbl{2,5}, 2.2969, 1e-4);
419s  assert (tbl{2,3}, 9, 0);
419s  assert (tbl{4,2}, 0.003903, 1e-6);
419s  data = reshape (data, 10, 10);
419s  [p, tbl, stats] = anova1 (data, [], "off");
419s  assert (p, 0.022661, 1e-6);
419s  assert (tbl{2,5}, 2.2969, 1e-4);
419s  assert (tbl{2,3}, 9, 0);
419s  assert (tbl{4,2}, 0.003903, 1e-6);
419s  means = [0.998, 0.9991, 0.9954, 0.9982, 0.9919, 0.9988, 1.0015, 1.0004, 0.9983, 0.9948];
419s  N = 10 * ones (1, 10);
419s  assert (stats.means, means, 1e-6);
419s  assert (length (stats.gnames), 10, 0);
419s  assert (stats.n, N, 0);
419s ***** test
419s  y = [54 87 45; 23 98 39; 45 64 51; 54 77 49; 45 89 50; 47 NaN 55];
419s  g = [1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ];
419s  [p, tbl] = anova1 (y(:), g(:), "off", "equal");
419s  assert (p, 0.00004163, 1e-6);
419s  assert (tbl{2,5}, 22.573418, 1e-6);
419s  assert (tbl{2,3}, 2, 0);
419s  assert (tbl{3,3}, 14, 0);
419s  [p, tbl] = anova1 (y(:), g(:), "off", "unequal");
419s  assert (p, 0.00208877, 1e-8);
419s  assert (tbl{2,5}, 15.523192, 1e-6);
419s  assert (tbl{2,3}, 2, 0);
419s  assert (tbl{2,4}, 7.5786897, 1e-6);
419s 2 tests, 2 passed, 0 known failure, 0 skipped
419s [inst/anova2.m]
419s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/anova2.m
419s ***** demo
419s 
419s  # Factorial (Crossed) Two-way ANOVA with Interaction
419s 
419s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
419s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
419s 
419s  [p, atab, stats] = anova2(popcorn, 3, "on");
419s ***** demo
419s 
419s  # One-way Repeated Measures ANOVA (Rows are a crossed random factor)
419s 
419s  data = [54, 43, 78, 111;
419s          23, 34, 37, 41;
419s          45, 65, 99, 78;
419s          31, 33, 36, 35;
419s          15, 25, 30, 26];
419s 
419s  [p, atab, stats] = anova2 (data, 1, "on", "linear");
419s ***** demo
419s 
419s  # Balanced Nested One-way ANOVA (Rows are a nested random factor)
419s 
419s  data = [4.5924 7.3809 21.322; -0.5488 9.2085 25.0426; ...
419s          6.1605 13.1147 22.66; 2.3374 15.2654 24.1283; ...
419s          5.1873 12.4188 16.5927; 3.3579 14.3951 10.2129; ...
419s          6.3092 8.5986 9.8934; 3.2831 3.4945 10.0203];
419s 
419s  [p, atab, stats] = anova2 (data, 4, "on", "nested");
419s ***** test
419s  ## Test for anova2 ("interaction")
419s  ## comparison with results from Matlab for column effect
419s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
419s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
419s  [p, atab, stats] = anova2 (popcorn, 3, "off");
419s  assert (p(1), 7.678957383294716e-07, 1e-14);
419s  assert (p(2), 0.0001003738963050171, 1e-14);
419s  assert (p(3), 0.7462153966366274, 1e-14);
419s  assert (atab{2,5}, 56.700, 1e-14);
419s  assert (atab{2,3}, 2, 0);
419s  assert (atab{4,2}, 0.08333333333333348, 1e-14);
419s  assert (atab{5,4}, 0.1388888888888889, 1e-14);
419s  assert (atab{5,2}, 1.666666666666667, 1e-14);
419s  assert (atab{6,2}, 22);
419s  assert (stats.source, "anova2");
419s  assert (stats.colmeans, [6.25, 4.75, 4]);
419s  assert (stats.inter, 1, 0);
419s  assert (stats.pval, 0.7462153966366274, 1e-14);
419s  assert (stats.df, 12);
419s ***** test
419s  ## Test for anova2 ("linear") - comparison with results from GraphPad Prism 8
419s  data = [54, 43, 78, 111;
419s          23, 34, 37, 41;
419s          45, 65, 99, 78;
419s          31, 33, 36, 35;
419s          15, 25, 30, 26];
419s  [p, atab, stats] = anova2 (data, 1, "off", "linear");
419s  assert (atab{2,2}, 2174.95, 1e-10);
419s  assert (atab{3,2}, 8371.7, 1e-10);
419s  assert (atab{4,2}, 2404.3, 1e-10);
419s  assert (atab{5,2}, 12950.95, 1e-10);
419s  assert (atab{2,4}, 724.983333333333, 1e-10);
419s  assert (atab{3,4}, 2092.925, 1e-10);
419s  assert (atab{4,4}, 200.358333333333, 1e-10);
419s  assert (atab{2,5}, 3.61843363972882, 1e-10);
419s  assert (atab{3,5}, 10.445909412303, 1e-10);
419s  assert (atab{2,6}, 0.087266112738617, 1e-10);
419s  assert (atab{3,6}, 0.000698397753556, 1e-10);
419s ***** test
419s  ## Test for anova2 ("nested") - comparison with results from GraphPad Prism 8
419s  data = [4.5924 7.3809 21.322; -0.5488 9.2085 25.0426; ...
419s          6.1605 13.1147 22.66; 2.3374 15.2654 24.1283; ...
419s          5.1873 12.4188 16.5927; 3.3579 14.3951 10.2129; ...
419s          6.3092 8.5986 9.8934; 3.2831 3.4945 10.0203];
419s  [p, atab, stats] = anova2 (data, 4, "off", "nested");
419s  assert (atab{2,2}, 745.360306290833, 1e-10);
419s  assert (atab{3,2}, 278.01854140125, 1e-10);
419s  assert (atab{4,2}, 180.180377467501, 1e-10);
419s  assert (atab{5,2}, 1203.55922515958, 1e-10);
419s  assert (atab{2,4}, 372.680153145417, 1e-10);
419s  assert (atab{3,4}, 92.67284713375, 1e-10);
419s  assert (atab{4,4}, 10.0100209704167, 1e-10);
419s  assert (atab{2,5}, 4.02146005730833, 1e-10);
419s  assert (atab{3,5}, 9.25800729165627, 1e-10);
419s  assert (atab{2,6}, 0.141597630656771, 1e-10);
419s  assert (atab{3,6}, 0.000636643812875719, 1e-10);
419s 3 tests, 3 passed, 0 known failure, 0 skipped
419s [inst/anovan.m]
420s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/anovan.m
420s ***** demo
420s 
420s  # Two-sample unpaired test on independent samples (equivalent to Student's
420s  # t-test). Note that the absolute value of t-statistic can be obtained by
420s  # taking the square root of the reported F statistic. In this example,
420s  # t = sqrt (1.44) = 1.20.
420s 
420s  score = [54 23 45 54 45 43 34 65 77 46 65]';
420s  gender = {"male" "male" "male" "male" "male" "female" "female" "female" ...
420s            "female" "female" "female"}';
420s 
420s  [P, ATAB, STATS] = anovan (score, gender, "display", "on", "varnames", "gender");
420s ***** demo
420s 
420s  # Two-sample paired test on dependent or matched samples equivalent to a
420s  # paired t-test. As for the first example, the t-statistic can be obtained by
420s  # taking the square root of the reported F statistic. Note that the interaction
420s  # between treatment x subject was dropped from the full model by assigning
420s  # subject as a random factor (').
420s 
420s  score = [4.5 5.6; 3.7 6.4; 5.3 6.4; 5.4 6.0; 3.9 5.7]';
420s  treatment = {"before" "after"; "before" "after"; "before" "after";
420s               "before" "after"; "before" "after"}';
420s  subject = {"GS" "GS"; "JM" "JM"; "HM" "HM"; "JW" "JW"; "PS" "PS"}';
420s 
420s  [P, ATAB, STATS] = anovan (score(:), {treatment(:), subject(:)}, ...
420s                             "model", "full", "random", 2, "sstype", 2, ...
420s                             "varnames", {"treatment", "subject"}, ...
420s                             "display", "on");
420s ***** demo
420s 
420s  # One-way ANOVA on the data from a study on the strength of structural beams,
420s  # in Hogg and Ledolter (1987) Engineering Statistics. New York: MacMillan
420s 
420s  strength = [82 86 79 83 84 85 86 87 74 82 ...
420s             78 75 76 77 79 79 77 78 82 79]';
420s  alloy = {"st","st","st","st","st","st","st","st", ...
420s           "al1","al1","al1","al1","al1","al1", ...
420s           "al2","al2","al2","al2","al2","al2"}';
420s 
420s  [P, ATAB, STATS] = anovan (strength, alloy, "display", "on", ...
420s                             "varnames", "alloy");
420s ***** demo
420s 
420s  # One-way repeated measures ANOVA on the data from a study on the number of
420s  # words recalled by 10 subjects for three time conditions, in Loftus & Masson
420s  # (1994) Psychon Bull Rev. 1(4):476-490, Table 2. Note that the interaction
420s  # between seconds x subject was dropped from the full model by assigning
420s  # subject as a random factor (').
420s 
420s  words = [10 13 13; 6 8 8; 11 14 14; 22 23 25; 16 18 20; ...
420s           15 17 17; 1 1 4; 12 15 17;  9 12 12;  8 9 12];
420s  seconds = [1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5; ...
420s             1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5;];
420s  subject = [ 1  1  1;  2  2  2;  3  3  3;  4  4  4;  5  5  5; ...
420s              6  6  6;  7  7  7;  8  8  8;  9  9  9; 10 10 10];
420s 
420s  [P, ATAB, STATS] = anovan (words(:), {seconds(:), subject(:)}, ...
420s                             "model", "full", "random", 2, "sstype", 2, ...
420s                             "display", "on", "varnames", {"seconds", "subject"});
420s ***** demo
420s 
420s  # Balanced two-way ANOVA with interaction on the data from a study of popcorn
420s  # brands and popper types, in Hogg and Ledolter (1987) Engineering Statistics.
420s  # New York: MacMillan
420s 
420s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
420s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
420s  brands = {"Gourmet", "National", "Generic"; ...
420s            "Gourmet", "National", "Generic"; ...
420s            "Gourmet", "National", "Generic"; ...
420s            "Gourmet", "National", "Generic"; ...
420s            "Gourmet", "National", "Generic"; ...
420s            "Gourmet", "National", "Generic"};
420s  popper = {"oil", "oil", "oil"; "oil", "oil", "oil"; "oil", "oil", "oil"; ...
420s            "air", "air", "air"; "air", "air", "air"; "air", "air", "air"};
420s 
420s  [P, ATAB, STATS] = anovan (popcorn(:), {brands(:), popper(:)}, ...
420s                             "display", "on", "model", "full", ...
420s                             "varnames", {"brands", "popper"});
420s ***** demo
420s 
420s  # Unbalanced two-way ANOVA (2x2) on the data from a study on the effects of
420s  # gender and having a college degree on salaries of company employees,
420s  # in Maxwell, Delaney and Kelly (2018): Chapter 7, Table 15
420s 
420s  salary = [24 26 25 24 27 24 27 23 15 17 20 16, ...
420s            25 29 27 19 18 21 20 21 22 19]';
420s  gender = {"f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f"...
420s            "m" "m" "m" "m" "m" "m" "m" "m" "m" "m"}';
420s  degree = [1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0]';
420s 
420s  [P, ATAB, STATS] = anovan (salary, {gender, degree}, "model", "full", ...
420s                             "sstype", 3, "display", "on", "varnames", ...
420s                             {"gender", "degree"});
420s ***** demo
420s 
420s  # Unbalanced two-way ANOVA (3x2) on the data from a study of the effect of
420s  # adding sugar and/or milk on the tendency of coffee to make people babble,
420s  # in from Navarro (2019): 16.10
420s 
420s  sugar = {"real" "fake" "fake" "real" "real" "real" "none" "none" "none" ...
420s           "fake" "fake" "fake" "real" "real" "real" "none" "none" "fake"}';
420s  milk = {"yes" "no" "no" "yes" "yes" "no" "yes" "yes" "yes" ...
420s          "no" "no" "yes" "no" "no" "no" "no" "no" "yes"}';
420s  babble = [4.6 4.4 3.9 5.6 5.1 5.5 3.9 3.5 3.7...
420s            5.6 4.7 5.9 6.0 5.4 6.6 5.8 5.3 5.7]';
420s 
420s  [P, ATAB, STATS] = anovan (babble, {sugar, milk}, "model", "full",  ...
420s                             "sstype", 3, "display", "on", ...
420s                             "varnames", {"sugar", "milk"});
420s ***** demo
420s 
420s  # Unbalanced three-way ANOVA (3x2x2) on the data from a study of the effects
420s  # of three different drugs, biofeedback and diet on patient blood pressure,
420s  # adapted* from Maxwell, Delaney and Kelly (2018): Chapter 8, Table 12
420s  # * Missing values introduced to make the sample sizes unequal to test the
420s  #   calculation of different types of sums-of-squares
420s 
420s  drug = {"X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" ...
420s          "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X";
420s          "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" ...
420s          "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y";
420s          "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" ...
420s          "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z"};
420s  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;
420s              1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
420s              1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0];
420s  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;
420s          0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1;
420s          0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1];
420s  BP = [170 175 165 180 160 158 161 173 157 152 181 190 ...
420s        173 194 197 190 176 198 164 190 169 164 176 175;
420s        186 194 201 215 219 209 164 166 159 182 187 174 ...
420s        189 194 217 206 199 195 171 173 196 199 180 NaN;
420s        180 187 199 170 204 194 162 184 183 156 180 173 ...
420s        202 228 190 206 224 204 205 199 170 160 NaN NaN];
420s 
420s  [P, ATAB, STATS] = anovan (BP(:), {drug(:), feedback(:), diet(:)}, ...
420s                                     "model", "full", "sstype", 3, ...
420s                                     "display", "on", ...
420s                                     "varnames", {"drug", "feedback", "diet"});
420s ***** demo
420s 
420s  # Balanced three-way ANOVA (2x2x2) with one of the factors being a blocking
420s  # factor. The data is from a randomized block design study on the effects
420s  # of antioxidant treatment on glutathione-S-transferase (GST) levels in
420s  # different mouse strains, from Festing (2014), ILAR Journal, 55(3):427-476.
420s  # Note that all interactions involving block were dropped from the full model
420s  # by assigning block as a random factor (').
420s 
420s  measurement = [444 614 423 625 408  856 447 719 ...
420s                 764 831 586 782 609 1002 606 766]';
420s  strain= {"NIH","NIH","BALB/C","BALB/C","A/J","A/J","129/Ola","129/Ola", ...
420s           "NIH","NIH","BALB/C","BALB/C","A/J","A/J","129/Ola","129/Ola"}';
420s  treatment={"C" "T" "C" "T" "C" "T" "C" "T" "C" "T" "C" "T" "C" "T" "C" "T"}';
420s  block = [1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2]';
420s 
420s  [P, ATAB, STATS] = anovan (measurement/10, {strain, treatment, block}, ...
420s                             "sstype", 2, "model", "full", "random", 3, ...
420s                             "display", "on", ...
420s                             "varnames", {"strain", "treatment", "block"});
420s ***** demo
420s 
420s  # One-way ANCOVA on data from a study of the additive effects of species
420s  # and temperature on chirpy pulses of crickets, from Stitch, The Worst Stats
420s  # Text eveR
420s 
420s  pulse = [67.9 65.1 77.3 78.7 79.4 80.4 85.8 86.6 87.5 89.1 ...
420s           98.6 100.8 99.3 101.7 44.3 47.2 47.6 49.6 50.3 51.8 ...
420s           60 58.5 58.9 60.7 69.8 70.9 76.2 76.1 77 77.7 84.7]';
420s  temp = [20.8 20.8 24 24 24 24 26.2 26.2 26.2 26.2 28.4 ...
420s          29 30.4 30.4 17.2 18.3 18.3 18.3 18.9 18.9 20.4 ...
420s          21 21 22.1 23.5 24.2 25.9 26.5 26.5 26.5 28.6]';
420s  species = {"ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" ...
420s             "ex" "ex" "ex" "niv" "niv" "niv" "niv" "niv" "niv" "niv" ...
420s             "niv" "niv" "niv" "niv" "niv" "niv" "niv" "niv" "niv" "niv"};
420s 
420s  [P, ATAB, STATS] = anovan (pulse, {species, temp}, "model", "linear", ...
420s                            "continuous", 2, "sstype", "h", "display", "on", ...
420s                            "varnames", {"species", "temp"});
420s ***** demo
420s 
420s  # Factorial ANCOVA on data from a study of the effects of treatment and
420s  # exercise on stress reduction score after adjusting for age. Data from R
420s  # datarium package).
420s 
420s  score = [95.6 82.2 97.2 96.4 81.4 83.6 89.4 83.8 83.3 85.7 ...
420s           97.2 78.2 78.9 91.8 86.9 84.1 88.6 89.8 87.3 85.4 ...
420s           81.8 65.8 68.1 70.0 69.9 75.1 72.3 70.9 71.5 72.5 ...
420s           84.9 96.1 94.6 82.5 90.7 87.0 86.8 93.3 87.6 92.4 ...
420s           100. 80.5 92.9 84.0 88.4 91.1 85.7 91.3 92.3 87.9 ...
420s           91.7 88.6 75.8 75.7 75.3 82.4 80.1 86.0 81.8 82.5]';
420s  treatment = {"yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
420s               "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
420s               "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
420s               "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
420s               "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
420s               "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"}';
420s  exercise = {"lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
420s              "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
420s              "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  ...
420s              "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
420s              "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
420s              "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"}';
420s  age = [59 65 70 66 61 65 57 61 58 55 62 61 60 59 55 57 60 63 62 57 ...
420s         58 56 57 59 59 60 55 53 55 58 68 62 61 54 59 63 60 67 60 67 ...
420s         75 54 57 62 65 60 58 61 65 57 56 58 58 58 52 53 60 62 61 61]';
420s 
420s  [P, ATAB, STATS] = anovan (score, {treatment, exercise, age}, ...
420s                             "model", [1 0 0; 0 1 0; 0 0 1; 1 1 0], ...
420s                             "continuous", 3, "sstype", "h", "display", "on", ...
420s                             "varnames", {"treatment", "exercise", "age"});
420s ***** demo
420s 
420s  # Unbalanced one-way ANOVA with custom, orthogonal contrasts. The statistics
420s  # relating to the contrasts are shown in the table of model parameters, and
420s  # can be retrieved from the STATS.coeffs output.
420s 
420s  dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
420s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
420s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
420s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
420s         25.694 ]';
420s  g = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 ...
420s       4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
420s  C = [ 0.4001601  0.3333333  0.5  0.0
420s        0.4001601  0.3333333 -0.5  0.0
420s        0.4001601 -0.6666667  0.0  0.0
420s       -0.6002401  0.0000000  0.0  0.5
420s       -0.6002401  0.0000000  0.0 -0.5];
420s 
420s  [P,ATAB, STATS] = anovan (dv, g, "contrasts", C, "varnames", "score", ...
420s                           "alpha", 0.05, "display", "on");
420s ***** demo
420s 
420s  # One-way ANOVA with the linear model fit by weighted least squares to
420s  # account for heteroskedasticity. In this example, the variance appears
420s  # proportional to the outcome, so weights have been estimated by initially
420s  # fitting the model without weights and regressing the absolute residuals on
420s  # the fitted values. Although this data could have been analysed by Welch's
420s  # ANOVA test, the approach here can generalize to ANOVA models with more than
420s  # one factor.
420s 
420s  g = [1, 1, 1, 1, 1, 1, 1, 1, ...
420s       2, 2, 2, 2, 2, 2, 2, 2, ...
420s       3, 3, 3, 3, 3, 3, 3, 3]';
420s  y = [13, 16, 16,  7, 11,  5,  1,  9, ...
420s       10, 25, 66, 43, 47, 56,  6, 39, ...
420s       11, 39, 26, 35, 25, 14, 24, 17]';
420s 
420s  [P,ATAB,STATS] = anovan(y, g, "display", "off");
420s  fitted = STATS.X * STATS.coeffs(:,1); # fitted values
420s  b = polyfit (fitted, abs (STATS.resid), 1);
420s  v = polyval (b, fitted);  # Variance as a function of the fitted values
420s  figure("Name", "Regression of the absolute residuals on the fitted values");
420s  plot (fitted, abs (STATS.resid),'ob');hold on; plot(fitted,v,'-r'); hold off;
420s  xlabel("Fitted values"); ylabel("Absolute residuals");
420s 
420s  [P,ATAB,STATS] = anovan (y, g, "weights", v.^-1);
420s ***** test
420s  score = [54 23 45 54 45 43 34 65 77 46 65]';
420s  gender = {'male' 'male' 'male' 'male' 'male' 'female' 'female' 'female' ...
420s            'female' 'female' 'female'}';
420s 
420s  [P, T, STATS] = anovan (score,gender,'display','off');
420s  assert (P(1), 0.2612876773271042, 1e-09);              # compared to p calculated by MATLAB anovan
420s  assert (sqrt(T{2,6}), abs(1.198608733288208), 1e-09);  # compared to abs(t) calculated from sqrt(F) by MATLAB anovan
420s  assert (P(1), 0.2612876773271047, 1e-09);              # compared to p calculated by MATLAB ttest2
420s  assert (sqrt(T{2,6}), abs(-1.198608733288208), 1e-09); # compared to abs(t) calculated by MATLAB ttest2
420s ***** test
420s  score = [4.5 5.6; 3.7 6.4; 5.3 6.4; 5.4 6.0; 3.9 5.7]';
420s  treatment = {'before' 'after'; 'before' 'after'; 'before' 'after';
420s               'before' 'after'; 'before' 'after'}';
420s  subject = {'GS' 'GS'; 'JM' 'JM'; 'HM' 'HM'; 'JW' 'JW'; 'PS' 'PS'}';
420s 
420s  [P, ATAB, STATS] = anovan (score(:),{treatment(:),subject(:)},'display','off','sstype',2);
420s  assert (P(1), 0.016004356735364, 1e-09);              # compared to p calculated by MATLAB anovan
420s  assert (sqrt(ATAB{2,6}), abs(4.00941576558195), 1e-09);  # compared to abs(t) calculated from sqrt(F) by MATLAB anovan
420s  assert (P(1), 0.016004356735364, 1e-09);              # compared to p calculated by MATLAB ttest2
420s  assert (sqrt(ATAB{2,6}), abs(-4.00941576558195), 1e-09); # compared to abs(t) calculated by MATLAB ttest2
420s ***** test
420s  strength = [82 86 79 83 84 85 86 87 74 82 ...
420s             78 75 76 77 79 79 77 78 82 79]';
420s  alloy = {'st','st','st','st','st','st','st','st', ...
420s           'al1','al1','al1','al1','al1','al1', ...
420s           'al2','al2','al2','al2','al2','al2'}';
420s 
420s  [P, ATAB, STATS] = anovan (strength,{alloy},'display','off');
420s  assert (P(1), 0.000152643638830491, 1e-09);
420s  assert (ATAB{2,6}, 15.4, 1e-09);
420s ***** test
420s  words = [10 13 13; 6 8 8; 11 14 14; 22 23 25; 16 18 20; ...
420s           15 17 17; 1 1 4; 12 15 17;  9 12 12;  8 9 12];
420s  subject = [ 1  1  1;  2  2  2;  3  3  3;  4  4  4;  5  5  5; ...
420s              6  6  6;  7  7  7;  8  8  8;  9  9  9; 10 10 10];
420s  seconds = [1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5; ...
420s             1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5;];
420s 
420s  [P, ATAB, STATS] = anovan (words(:),{seconds(:),subject(:)},'model','full','random',2,'sstype',2,'display','off');
420s  assert (P(1), 1.51865926758752e-07, 1e-09);
420s  assert (ATAB{2,2}, 52.2666666666667, 1e-09);
420s  assert (ATAB{3,2}, 942.533333333333, 1e-09);
420s  assert (ATAB{4,2}, 11.0666666666667, 1e-09);
420s ***** test
420s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
420s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
420s  brands = {'Gourmet', 'National', 'Generic'; ...
420s            'Gourmet', 'National', 'Generic'; ...
420s            'Gourmet', 'National', 'Generic'; ...
420s            'Gourmet', 'National', 'Generic'; ...
420s            'Gourmet', 'National', 'Generic'; ...
420s            'Gourmet', 'National', 'Generic'};
420s  popper = {'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; ...
420s            'air', 'air', 'air'; 'air', 'air', 'air'; 'air', 'air', 'air'};
420s 
420s  [P, ATAB, STATS] = anovan (popcorn(:),{brands(:),popper(:)},'display','off','model','full');
420s  assert (P(1), 7.67895738278171e-07, 1e-09);
420s  assert (P(2), 0.000100373896304998, 1e-09);
420s  assert (P(3), 0.746215396636649, 1e-09);
420s  assert (ATAB{2,6}, 56.7, 1e-09);
420s  assert (ATAB{3,6}, 32.4, 1e-09);
420s  assert (ATAB{4,6}, 0.29999999999997, 1e-09);
420s ***** test
420s  salary = [24 26 25 24 27 24 27 23 15 17 20 16, ...
420s            25 29 27 19 18 21 20 21 22 19]';
420s  gender = {'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f'...
420s            'm' 'm' 'm' 'm' 'm' 'm' 'm' 'm' 'm' 'm'}';
420s  degree = [1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0]';
420s 
420s  [P, ATAB, STATS] = anovan (salary,{gender,degree},'model','full','sstype',1,'display','off');
420s  assert (P(1), 0.747462549227232, 1e-09);
420s  assert (P(2), 1.03809316857694e-08, 1e-09);
420s  assert (P(3), 0.523689833702691, 1e-09);
420s  assert (ATAB{2,2}, 0.296969696969699, 1e-09);
420s  assert (ATAB{3,2}, 272.391841491841, 1e-09);
420s  assert (ATAB{4,2}, 1.17482517482512, 1e-09);
420s  assert (ATAB{5,2}, 50.0000000000001, 1e-09);
420s  [P, ATAB, STATS] = anovan (salary,{degree,gender},'model','full','sstype',1,'display','off');
420s  assert (P(1), 2.53445097305047e-08, 1e-09);
420s  assert (P(2), 0.00388133678528749, 1e-09);
420s  assert (P(3), 0.523689833702671, 1e-09);
420s  assert (ATAB{2,2}, 242.227272727273, 1e-09);
420s  assert (ATAB{3,2}, 30.4615384615384, 1e-09);
420s  assert (ATAB{4,2}, 1.17482517482523, 1e-09);
420s  assert (ATAB{5,2}, 50.0000000000001, 1e-09);
420s  [P, ATAB, STATS] = anovan (salary,{gender,degree},'model','full','sstype',2,'display','off');
420s  assert (P(1), 0.00388133678528743, 1e-09);
420s  assert (P(2), 1.03809316857694e-08, 1e-09);
420s  assert (P(3), 0.523689833702691, 1e-09);
420s  assert (ATAB{2,2}, 30.4615384615385, 1e-09);
420s  assert (ATAB{3,2}, 272.391841491841, 1e-09);
420s  assert (ATAB{4,2}, 1.17482517482512, 1e-09);
420s  assert (ATAB{5,2}, 50.0000000000001, 1e-09);
420s  [P, ATAB, STATS] = anovan (salary,{gender,degree},'model','full','sstype',3,'display','off');
420s  assert (P(1), 0.00442898146583742, 1e-09);
420s  assert (P(2), 1.30634252053587e-08, 1e-09);
420s  assert (P(3), 0.523689833702691, 1e-09);
420s  assert (ATAB{2,2}, 29.3706293706294, 1e-09);
420s  assert (ATAB{3,2}, 264.335664335664, 1e-09);
420s  assert (ATAB{4,2}, 1.17482517482512, 1e-09);
420s  assert (ATAB{5,2}, 50.0000000000001, 1e-09);
420s ***** test
420s  sugar = {'real' 'fake' 'fake' 'real' 'real' 'real' 'none' 'none' 'none' ...
420s           'fake' 'fake' 'fake' 'real' 'real' 'real' 'none' 'none' 'fake'}';
420s  milk = {'yes' 'no' 'no' 'yes' 'yes' 'no' 'yes' 'yes' 'yes' ...
420s          'no' 'no' 'yes' 'no' 'no' 'no' 'no' 'no' 'yes'}';
420s  babble = [4.6 4.4 3.9 5.6 5.1 5.5 3.9 3.5 3.7...
420s            5.6 4.7 5.9 6.0 5.4 6.6 5.8 5.3 5.7]';
420s 
420s  [P, ATAB, STATS] = anovan (babble,{sugar,milk},'model','full','sstype',1,'display','off');
420s  assert (P(1), 0.0108632139833963, 1e-09);
420s  assert (P(2), 0.0810606976703546, 1e-09);
420s  assert (P(3), 0.00175433329935627, 1e-09);
420s  assert (ATAB{2,2}, 3.55752380952381, 1e-09);
420s  assert (ATAB{3,2}, 0.956108477471702, 1e-09);
420s  assert (ATAB{4,2}, 5.94386771300448, 1e-09);
420s  assert (ATAB{5,2}, 3.1625, 1e-09);
420s  [P, ATAB, STATS] = anovan (babble,{milk,sugar},'model','full','sstype',1,'display','off');
420s  assert (P(1), 0.0373333189297505, 1e-09);
420s  assert (P(2), 0.017075098787169, 1e-09);
420s  assert (P(3), 0.00175433329935627, 1e-09);
420s  assert (ATAB{2,2}, 1.444, 1e-09);
420s  assert (ATAB{3,2}, 3.06963228699552, 1e-09);
420s  assert (ATAB{4,2}, 5.94386771300448, 1e-09);
420s  assert (ATAB{5,2}, 3.1625, 1e-09);
420s  [P, ATAB, STATS] = anovan (babble,{sugar,milk},'model','full','sstype',2,'display','off');
420s  assert (P(1), 0.017075098787169, 1e-09);
420s  assert (P(2), 0.0810606976703546, 1e-09);
420s  assert (P(3), 0.00175433329935627, 1e-09);
420s  assert (ATAB{2,2}, 3.06963228699552, 1e-09);
420s  assert (ATAB{3,2}, 0.956108477471702, 1e-09);
420s  assert (ATAB{4,2}, 5.94386771300448, 1e-09);
420s  assert (ATAB{5,2}, 3.1625,  1e-09);
420s  [P, ATAB, STATS] = anovan (babble,{sugar,milk},'model','full','sstype',3,'display','off');
420s  assert (P(1), 0.0454263063473954, 1e-09);
420s  assert (P(2), 0.0746719907091438, 1e-09);
420s  assert (P(3), 0.00175433329935627, 1e-09);
420s  assert (ATAB{2,2}, 2.13184977578476, 1e-09);
420s  assert (ATAB{3,2}, 1.00413461538462, 1e-09);
420s  assert (ATAB{4,2}, 5.94386771300448, 1e-09);
420s  assert (ATAB{5,2}, 3.1625, 1e-09);
420s ***** test
420s  drug = {'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' ...
420s          'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X';
420s          'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' ...
420s          'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y';
420s          'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' ...
420s          'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z'};
420s  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;
420s              1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
420s              1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0];
420s  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;
420s          0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1;
420s          0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1];
420s  BP = [170 175 165 180 160 158 161 173 157 152 181 190 ...
420s        173 194 197 190 176 198 164 190 169 164 176 175;
420s        186 194 201 215 219 209 164 166 159 182 187 174 ...
420s        189 194 217 206 199 195 171 173 196 199 180 NaN;
420s        180 187 199 170 204 194 162 184 183 156 180 173 ...
420s        202 228 190 206 224 204 205 199 170 160 NaN NaN];
420s 
420s  [P, ATAB, STATS] = anovan (BP(:),{drug(:),feedback(:),diet(:)},'model','full','sstype', 1,'display','off');
420s  assert (P(1), 7.02561843825325e-05, 1e-09);
420s  assert (P(2), 0.000425806013389362, 1e-09);
420s  assert (P(3), 6.16780773446401e-07, 1e-09);
420s  assert (P(4), 0.261347622678438, 1e-09);
420s  assert (P(5), 0.0542278432357043, 1e-09);
420s  assert (P(6), 0.590353225626655, 1e-09);
420s  assert (P(7), 0.0861628249564267, 1e-09);
420s  assert (ATAB{2,2}, 3614.70355731226, 1e-09);
420s  assert (ATAB{3,2}, 2227.46639771024, 1e-09);
420s  assert (ATAB{4,2}, 5008.25614451819, 1e-09);
420s  assert (ATAB{5,2}, 437.066007908781, 1e-09);
420s  assert (ATAB{6,2}, 976.180770397332, 1e-09);
420s  assert (ATAB{7,2}, 46.616653365254, 1e-09);
420s  assert (ATAB{8,2}, 814.345251396648, 1e-09);
420s  assert (ATAB{9,2}, 9065.8,  1e-09);
420s  [P, ATAB, STATS] = anovan (BP(:),{drug(:),feedback(:),diet(:)},'model','full','sstype',2,'display','off');
420s  assert (P(1), 9.4879638470754e-05, 1e-09);
420s  assert (P(2), 0.00124177666315809, 1e-09);
420s  assert (P(3), 6.86162012732911e-07, 1e-09);
420s  assert (P(4), 0.260856132341256, 1e-09);
420s  assert (P(5), 0.0523758623892078, 1e-09);
420s  assert (P(6), 0.590353225626655, 1e-09);
420s  assert (P(7), 0.0861628249564267, 1e-09);
420s  assert (ATAB{2,2}, 3481.72176560122, 1e-09);
420s  assert (ATAB{3,2}, 1837.08812970469, 1e-09);
420s  assert (ATAB{4,2}, 4957.20277938622, 1e-09);
420s  assert (ATAB{5,2}, 437.693674777847, 1e-09);
420s  assert (ATAB{6,2}, 988.431929811402, 1e-09);
420s  assert (ATAB{7,2}, 46.616653365254, 1e-09);
420s  assert (ATAB{8,2}, 814.345251396648, 1e-09);
420s  assert (ATAB{9,2}, 9065.8,  1e-09);
420s  [P, ATAB, STATS] = anovan (BP(:),{drug(:),feedback(:),diet(:)},'model','full','sstype', 3,'display','off');
420s  assert (P(1), 0.000106518678028207, 1e-09);
420s  assert (P(2), 0.00125371366571508, 1e-09);
420s  assert (P(3), 5.30813260778464e-07, 1e-09);
420s  assert (P(4), 0.308353667232981, 1e-09);
420s  assert (P(5), 0.0562901327343161, 1e-09);
420s  assert (P(6), 0.599091042141092, 1e-09);
420s  assert (P(7), 0.0861628249564267, 1e-09);
420s  assert (ATAB{2,2}, 3430.88156424581, 1e-09);
420s  assert (ATAB{3,2}, 1833.68031496063, 1e-09);
420s  assert (ATAB{4,2}, 5080.48346456693, 1e-09);
420s  assert (ATAB{5,2}, 382.07709497207, 1e-09);
420s  assert (ATAB{6,2}, 963.037988826813, 1e-09);
420s  assert (ATAB{7,2}, 44.4519685039322, 1e-09);
420s  assert (ATAB{8,2}, 814.345251396648, 1e-09);
420s  assert (ATAB{9,2}, 9065.8, 1e-09);
420s ***** test
420s  measurement = [444 614 423 625 408  856 447 719 ...
420s                 764 831 586 782 609 1002 606 766]';
420s  strain= {'NIH','NIH','BALB/C','BALB/C','A/J','A/J','129/Ola','129/Ola', ...
420s           'NIH','NIH','BALB/C','BALB/C','A/J','A/J','129/Ola','129/Ola'}';
420s  treatment={'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T'}';
420s  block = [1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2]';
420s 
420s  [P, ATAB, STATS] = anovan (measurement/10,{strain,treatment,block},'model','full','random',3,'display','off');
420s  assert (P(1), 0.0914352969909372, 1e-09);
420s  assert (P(2), 5.04077373924908e-05, 1e-09);
420s  assert (P(4), 0.0283196918836667, 1e-09);
420s  assert (ATAB{2,2}, 286.132500000002, 1e-09);
420s  assert (ATAB{3,2}, 2275.29, 1e-09);
420s  assert (ATAB{4,2}, 1242.5625, 1e-09);
420s  assert (ATAB{5,2}, 495.905000000001, 1e-09);
420s  assert (ATAB{6,2}, 207.007499999999, 1e-09);
420s ***** test
420s  pulse = [67.9 65.1 77.3 78.7 79.4 80.4 85.8 86.6 87.5 89.1 ...
420s           98.6 100.8 99.3 101.7 44.3 47.2 47.6 49.6 50.3 51.8 ...
420s           60 58.5 58.9 60.7 69.8 70.9 76.2 76.1 77 77.7 84.7]';
420s  temp = [20.8 20.8 24 24 24 24 26.2 26.2 26.2 26.2 28.4 ...
420s          29 30.4 30.4 17.2 18.3 18.3 18.3 18.9 18.9 20.4 ...
420s          21 21 22.1 23.5 24.2 25.9 26.5 26.5 26.5 28.6]';
420s  species = {'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' ...
420s             'ex' 'ex' 'ex' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' ...
420s             'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv'};
420s 
420s  [P, ATAB, STATS] = anovan (pulse,{species,temp},'model','linear','continuous',2,'sstype','h','display','off');
420s  assert (P(1), 6.27153318786007e-14, 1e-09);
420s  assert (P(2), 2.48773241196644e-25, 1e-09);
420s  assert (ATAB{2,2}, 598.003953318404, 1e-09);
420s  assert (ATAB{3,2}, 4376.08256843712, 1e-09);
420s  assert (ATAB{4,2}, 89.3498685376726, 1e-09);
420s  assert (ATAB{2,6}, 187.399388123951, 1e-09);
420s  assert (ATAB{3,6}, 1371.35413763454, 1e-09);
420s ***** test
420s  score = [95.6 82.2 97.2 96.4 81.4 83.6 89.4 83.8 83.3 85.7 ...
420s           97.2 78.2 78.9 91.8 86.9 84.1 88.6 89.8 87.3 85.4 ...
420s           81.8 65.8 68.1 70.0 69.9 75.1 72.3 70.9 71.5 72.5 ...
420s           84.9 96.1 94.6 82.5 90.7 87.0 86.8 93.3 87.6 92.4 ...
420s           100. 80.5 92.9 84.0 88.4 91.1 85.7 91.3 92.3 87.9 ...
420s           91.7 88.6 75.8 75.7 75.3 82.4 80.1 86.0 81.8 82.5]';
420s  treatment = {'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' ...
420s               'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' ...
420s               'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' ...
420s               'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  ...
420s               'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  ...
420s               'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'}';
420s  exercise = {'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  ...
420s              'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' ...
420s              'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  ...
420s              'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  ...
420s              'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' ...
420s              'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'}';
420s  age = [59 65 70 66 61 65 57 61 58 55 62 61 60 59 55 57 60 63 62 57 ...
420s         58 56 57 59 59 60 55 53 55 58 68 62 61 54 59 63 60 67 60 67 ...
420s         75 54 57 62 65 60 58 61 65 57 56 58 58 58 52 53 60 62 61 61]';
420s 
420s  [P, ATAB, STATS] = anovan (score,{treatment,exercise,age},'model','full','continuous',3,'sstype','h','display','off');
420s  assert (P(5), 0.9245630968248468, 1e-09);
420s  assert (P(6), 0.791115159521822, 1e-09);
420s  assert (P(7), 0.9296668751457956, 1e-09);
420s  [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');
420s  assert (P(1), 0.00158132928938933, 1e-09);
420s  assert (P(2), 2.12537505039986e-07, 1e-09);
420s  assert (P(3), 0.00390292555160047, 1e-09);
420s  assert (P(4), 0.0164086580775543, 1e-09);
420s  assert (ATAB{2,6}, 11.0956027650549, 1e-09);
420s  assert (ATAB{3,6}, 20.8195665467178, 1e-09);
420s  assert (ATAB{4,6}, 9.10966630720186, 1e-09);
420s  assert (ATAB{5,6}, 4.4457923698584, 1e-09);
421s ***** test
421s  dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
421s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
421s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
421s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
421s         25.694 ]';
421s  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]';
421s  C = [ 0.4001601  0.3333333  0.5  0.0
421s        0.4001601  0.3333333 -0.5  0.0
421s        0.4001601 -0.6666667  0.0  0.0
421s       -0.6002401  0.0000000  0.0  0.5
421s       -0.6002401  0.0000000  0.0 -0.5];
421s 
421s  [P,ATAB,STATS] = anovan (dv,g,'contrasts',{C},'display','off');
421s  assert (STATS.coeffs(1,1), 19.4001, 1e-04);
421s  assert (STATS.coeffs(2,1), -9.3297, 1e-04);
421s  assert (STATS.coeffs(3,1), -5.0000, 1e-04);
421s  assert (STATS.coeffs(4,1), -8.0000, 1e-04);
421s  assert (STATS.coeffs(5,1), -8.0000, 1e-04);
421s  assert (STATS.coeffs(1,2), 0.4831, 1e-04);
421s  assert (STATS.coeffs(2,2), 0.9694, 1e-04);
421s  assert (STATS.coeffs(3,2), 1.3073, 1e-04);
421s  assert (STATS.coeffs(4,2), 1.6411, 1e-04);
421s  assert (STATS.coeffs(5,2), 1.4507, 1e-04);
421s  assert (STATS.coeffs(1,5), 40.161, 1e-03);
421s  assert (STATS.coeffs(2,5), -9.624, 1e-03);
421s  assert (STATS.coeffs(3,5), -3.825, 1e-03);
421s  assert (STATS.coeffs(4,5), -4.875, 1e-03);
421s  assert (STATS.coeffs(5,5), -5.515, 1e-03);
421s  assert (STATS.coeffs(2,6), 5.74e-11, 1e-12);
421s  assert (STATS.coeffs(3,6), 0.000572, 1e-06);
421s  assert (STATS.coeffs(4,6), 2.86e-05, 1e-07);
421s  assert (STATS.coeffs(5,6), 4.44e-06, 1e-08);
421s 12 tests, 12 passed, 0 known failure, 0 skipped
421s [inst/bar3.m]
421s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/bar3.m
421s ***** demo
421s  ## Plotting 5 bars in the same series.
421s 
421s  z = [50; 40; 30; 20; 10];
421s  bar3 (z);
421s ***** demo
421s  ## Plotting 5 bars in different groups.
421s 
421s  z = [50, 40, 30, 20, 10];
421s  bar3 (z);
421s ***** demo
421s  ## A 3D bar graph with each series corresponding to a column in z.
421s 
421s  z = [1, 4, 7; 2, 5, 8; 3, 6, 9; 4, 7, 10];
421s  bar3 (z);
421s ***** demo
421s  ## Specify y-axis locations as tick names. y must be a column vector!
421s 
421s  y = [1950, 1960, 1970, 1980, 1990]';
421s  z = [16, 8, 4, 2, 1]';
421s  bar3 (y, z);
421s ***** demo
421s  ## Plot 3 series as a grouped plot without any space between the grouped bars
421s 
421s  z = [70 50 33 10; 75 55 35 15; 80 60 40 20];
421s  bar3 (z, 1, 'grouped');
421s ***** demo
421s  ## Plot a stacked style 3D bar graph
421s 
421s  z = [19, 30, 21, 30; 40, 16, 32, 12];
421s  b = bar3 (z, 0.5, 'stacked');
421s ***** error <bar3: Z must be numeric.> bar3 ("A")
421s ***** error <bar3: Z must be numeric.> bar3 ({2,3,4,5})
421s ***** error <bar3: inconsistent size in Y and Z input arguments.> ...
421s  bar3 ([1,2,3]', ones (2))
421s ***** error <bar3: WIDTH must be a scalar in the range> ...
421s  bar3 ([1:5], 1.2)
421s ***** error <bar3: WIDTH must be a scalar in the range> ...
421s  bar3 ([1:5]', ones (5), 1.2)
421s ***** error <bar3: numeric COLOR must be a 1x3 vector of an Nx3 matrix> ...
421s  bar3 ([1:5]', ones (5), [0.8, 0.7])
421s ***** error <bar3: missing value for optional argument 'width'.> ...
421s  bar3 (ones (5), 'width')
421s ***** error <bar3: invalid value for optional argument 'width'.> ...
421s  bar3 (ones (5), 'width', 1.2)
421s ***** error <bar3: invalid value for optional argument 'width'.> ...
421s  bar3 (ones (5), 'width', [0.8, 0.8, 0.8])
421s ***** error <bar3: missing value for optional argument 'color'.> ...
421s  bar3 (ones (5), 'color')
421s ***** error <bar3: numeric COLOR must be a 1x3 vector of an Nx3 matrix> ...
421s  bar3 (ones (5), 'color', [0.8, 0.8])
421s ***** error <bar3: invalid value for optional argument 'color'.> ...
421s  bar3 (ones (5), 'color', "brown")
421s ***** error <bar3: invalid value for optional argument 'color'.> ...
421s  bar3 (ones (5), 'color', {"r", "k", "c", "m", "brown"})
421s ***** error <bar3: missing value for optional argument 'xlabel'.> ...
421s  bar3 (ones (5), 'xlabel')
421s ***** error <bar3: invalid value for optional argument 'xlabel'.> ...
421s  bar3 (ones (5), 'xlabel', 4)
421s ***** error <bar3: missing value for optional argument 'ylabel'.> ...
421s  bar3 (ones (5), 'ylabel')
421s ***** error <bar3: invalid value for optional argument 'ylabel'.> ...
421s  bar3 (ones (5), 'ylabel', 4)
421s ***** error <bar3: invalid optional argument.> bar3 (ones (5), 'this', 4)
421s ***** error <bar3: the elements in 'xlabel' must equal the columns in Z.> ...
421s  bar3 (ones (5), 'xlabel', {"A", "B", "C"})
421s ***** error <bar3: the elements in 'ylabel' must equal the rows in Z.> ...
421s  bar3 (ones (5), 'ylabel', {"A", "B", "C"})
421s 20 tests, 20 passed, 0 known failure, 0 skipped
421s [inst/bar3h.m]
421s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/bar3h.m
421s ***** demo
421s  ## Plotting 5 bars in the same series.
421s 
421s  y = [50; 40; 30; 20; 10];
421s  bar3h (y);
421s ***** demo
421s  ## Plotting 5 bars in different groups.
421s 
421s  y = [50, 40, 30, 20, 10];
421s  bar3h (y);
421s ***** demo
421s  ## A 3D bar graph with each series corresponding to a column in y.
421s 
421s  y = [1, 4, 7; 2, 5, 8; 3, 6, 9; 4, 7, 10];
421s  bar3h (y);
421s ***** demo
421s  ## Specify z-axis locations as tick names. z must be a column vector!
421s 
421s  z = [1950, 1960, 1970, 1980, 1990]';
421s  y = [16, 8, 4, 2, 1]';
421s  bar3h (z, y);
421s ***** demo
421s  ## Plot 3 series as a grouped plot without any space between the grouped bars
421s 
421s  y = [70 50 33 10; 75 55 35 15; 80 60 40 20];
421s  bar3h (y, 1, 'grouped');
421s ***** demo
421s  ## Plot a stacked style 3D bar graph
421s 
421s  y = [19, 30, 21, 30; 40, 16, 32, 12];
421s  b = bar3h (y, 0.5, 'stacked');
421s ***** error <bar3h: Z must be numeric.> bar3h ("A")
421s ***** error <bar3h: Z must be numeric.> bar3h ({2,3,4,5})
421s ***** error <bar3h: inconsistent size in Y and Z input arguments.> ...
421s  bar3h ([1,2,3]', ones (2))
421s ***** error <bar3h: WIDTH must be a scalar in the range> ...
421s  bar3h ([1:5], 1.2)
421s ***** error <bar3h: WIDTH must be a scalar in the range> ...
421s  bar3h ([1:5]', ones (5), 1.2)
421s ***** error <bar3h: numeric COLOR must be a 1x3 vector of an Nx3 matrix> ...
421s  bar3h ([1:5]', ones (5), [0.8, 0.7])
421s ***** error <bar3h: missing value for optional argument 'width'.> ...
421s  bar3h (ones (5), 'width')
421s ***** error <bar3h: invalid value for optional argument 'width'.> ...
421s  bar3h (ones (5), 'width', 1.2)
421s ***** error <bar3h: invalid value for optional argument 'width'.> ...
421s  bar3h (ones (5), 'width', [0.8, 0.8, 0.8])
421s ***** error <bar3h: missing value for optional argument 'color'.> ...
421s  bar3h (ones (5), 'color')
421s ***** error <bar3h: numeric COLOR must be a 1x3 vector of an Nx3 matrix> ...
421s  bar3h (ones (5), 'color', [0.8, 0.8])
421s ***** error <bar3h: invalid value for optional argument 'color'.> ...
421s  bar3h (ones (5), 'color', "brown")
421s ***** error <bar3h: invalid value for optional argument 'color'.> ...
421s  bar3h (ones (5), 'color', {"r", "k", "c", "m", "brown"})
421s ***** error <bar3h: missing value for optional argument 'xlabel'.> ...
421s  bar3h (ones (5), 'xlabel')
421s ***** error <bar3h: invalid value for optional argument 'xlabel'.> ...
421s  bar3h (ones (5), 'xlabel', 4)
421s ***** error <bar3h: missing value for optional argument 'zlabel'.> ...
421s  bar3h (ones (5), 'zlabel')
421s ***** error <bar3h: invalid value for optional argument 'zlabel'.> ...
421s  bar3h (ones (5), 'zlabel', 4)
421s ***** error <bar3h: invalid optional argument.> bar3h (ones (5), 'this', 4)
421s ***** error <bar3h: the elements in 'xlabel' must equal the columns in Z.> ...
421s  bar3h (ones (5), 'xlabel', {"A", "B", "C"})
421s ***** error <bar3h: the elements in 'zlabel' must equal the rows in Z.> ...
421s  bar3h (ones (5), 'zlabel', {"A", "B", "C"})
421s 20 tests, 20 passed, 0 known failure, 0 skipped
421s [inst/bartlett_test.m]
421s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/bartlett_test.m
421s ***** error<bartlett_test: invalid number of input arguments.> bartlett_test ()
421s ***** error<bartlett_test: invalid number of input arguments.> ...
421s  bartlett_test (1, 2, 3, 4);
421s ***** error<bartlett_test: wrong value for alpha.> bartlett_test (randn (50, 2), 0);
421s ***** error<bartlett_test: GROUP and X mismatch.> ...
421s  bartlett_test (randn (50, 2), [1, 2, 3]);
421s ***** error<bartlett_test: GROUP and X mismatch.> ...
421s  bartlett_test (randn (50, 1), ones (55, 1));
421s ***** error<bartlett_test: invalid second input argument.> ...
421s  bartlett_test (randn (50, 1), ones (50, 2));
421s ***** error<bartlett_test: wrong value for alpha.> ...
421s  bartlett_test (randn (50, 2), [], 1.2);
421s ***** error<bartlett_test: wrong value for alpha.> ...
421s  bartlett_test (randn (50, 2), [], "alpha");
421s ***** error<bartlett_test: wrong value for alpha.> ...
421s  bartlett_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], 1.2);
421s ***** error<bartlett_test: wrong value for alpha.> ...
421s  bartlett_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], "err");
421s ***** warning<bartlett_test: GROUP> ...
421s  bartlett_test (randn (50, 1), [ones(24, 1); 2*ones(25, 1); 3]);
421s ***** test
421s  load examgrades
421s  [h, pval, chisq, df] = bartlett_test (grades);
421s  assert (h, 1);
421s  assert (pval, 7.908647337018238e-08, 1e-14);
421s  assert (chisq, 38.73324, 1e-5);
421s  assert (df, 4);
421s ***** test
421s  load examgrades
421s  [h, pval, chisq, df] = bartlett_test (grades(:,[2:4]));
421s  assert (h, 1);
421s  assert (pval, 0.01172, 1e-5);
421s  assert (chisq, 8.89274, 1e-5);
421s  assert (df, 2);
421s ***** test
421s  load examgrades
421s  [h, pval, chisq, df] = bartlett_test (grades(:,[1,4]));
421s  assert (h, 0);
421s  assert (pval, 0.88118, 1e-5);
421s  assert (chisq, 0.02234, 1e-5);
421s  assert (df, 1);
421s ***** test
421s  load examgrades
421s  grades = [grades; nan(10, 5)];
421s  [h, pval, chisq, df] = bartlett_test (grades(:,[1,4]));
421s  assert (h, 0);
421s  assert (pval, 0.88118, 1e-5);
421s  assert (chisq, 0.02234, 1e-5);
421s  assert (df, 1);
421s ***** test
421s  load examgrades
421s  [h, pval, chisq, df] = bartlett_test (grades(:,[2,5]), 0.01);
421s  assert (h, 0);
421s  assert (pval, 0.01791, 1e-5);
421s  assert (chisq, 5.60486, 1e-5);
421s  assert (df, 1);
421s 16 tests, 16 passed, 0 known failure, 0 skipped
421s [inst/barttest.m]
421s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/barttest.m
421s ***** error<barttest: invalid number of input arguments.> barttest ()
421s ***** error<barttest: NaN values in input are not allowed.> barttest ([2,NaN;3,4])
421s ***** error<barttest: wrong value for alpha.> barttest (ones (30, 4), "alpha")
421s ***** error<barttest: wrong value for alpha.> barttest (ones (30, 4), 0)
421s ***** error<barttest: wrong value for alpha.> barttest (ones (30, 4), 1.2)
421s ***** error<barttest: wrong value for alpha.> barttest (ones (30, 4), [0.2, 0.05])
421s ***** error<barttest: not enough data in X.> barttest (ones (30, 1))
421s ***** error<barttest: not enough data in X.> barttest (ones (30, 1), 0.05)
421s ***** test
421s  x = [2, 3, 4, 5, 6, 7, 8, 9; 1, 2, 3, 4, 5, 6, 7, 8]';
421s  [ndim, pval, chisq] = barttest (x);
421s  assert (ndim, 2);
421s  assert (pval, 0);
421s  ## assert (chisq, 512.0558, 1e-4); Result differs between octave 6 and 7 ?
421s ***** test
421s  x = [0.53767,  0.62702,   -0.10224,   -0.25485,   1.4193,   1.5237  ; ...
421s       1.8339,   1.6452,    -0.24145,   -0.23444,   0.29158,  0.1634  ; ...
421s      -2.2588,  -2.1351,     0.31286,    0.39396,   0.19781,  0.20995 ; ...
421s       0.86217,  1.0835,     0.31286,    0.46499,   1.5877,   1.495   ; ...
421s       0.31877,  0.38454,   -0.86488,   -0.63839,  -0.80447, -0.7536  ; ...
421s      -1.3077,  -1.1487,    -0.030051,  -0.017629,  0.69662,  0.60497 ; ...
421s      -0.43359, -0.32672,   -0.16488,   -0.37364,   0.83509,  0.89586 ; ...
421s       0.34262,  0.29639,    0.62771,    0.51672,  -0.24372, -0.13698 ; ...
421s       3.5784,   3.5841,     1.0933,     0.93258,   0.21567,  0.455   ; ...
421s       2.7694,   2.6307,     1.1093,     1.4298,   -1.1658,  -1.1816  ; ...
421s      -1.3499,  -1.2111,    -0.86365,   -0.94186,  -1.148,   -1.4381  ; ...
421s       3.0349,   2.8428,     0.077359,   0.18211,   0.10487, -0.014613; ...
421s       0.7254,   0.56737,   -1.2141,    -1.2291,    0.72225,  0.90612 ; ...
421s      -0.063055,-0.17662,   -1.1135,    -0.97701,   2.5855,   2.4084  ; ...
421s       0.71474,  0.29225,   -0.0068493, -0.11468,  -0.66689, -0.52466 ; ...
421s      -0.20497, -7.8874e-06, 1.5326,     1.3195,    0.18733,  0.20296 ; ...
421s      -0.12414, -0.077029,  -0.76967,   -0.96262,  -0.082494, 0.121   ; ...
421s       1.4897,   1.3683,     0.37138,    0.43653,  -1.933,   -2.1903  ; ...
421s       1.409,    1.5882,    -0.22558,   -0.24835,  -0.43897, -0.46247 ; ...
421s       1.4172,   1.1616,     1.1174,     1.0785,   -1.7947,  -1.9471  ];
421s  [ndim, pval, chisq] = barttest (x);
421s  assert (ndim, 3);
421s  assert (pval, [0; 0; 0; 0.52063; 0.34314], 1e-5);
421s  chisq_out = [251.6802; 210.2670; 153.1773; 4.2026; 2.1392];
421s  assert (chisq, chisq_out, 1e-4);
421s 10 tests, 10 passed, 0 known failure, 0 skipped
421s [inst/binotest.m]
421s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/binotest.m
421s ***** demo
421s  % flip a coin 1000 times, showing 475 heads
421s  % Hypothesis: coin is fair, i.e. p=1/2
421s  [h,p_val,ci] = binotest(475,1000,0.5)
421s  % Result: h = 0 : null hypothesis not rejected, coin could be fair
421s  %         P value 0.12, i.e. hypothesis not rejected for alpha up to 12%
421s  %         0.444 <= p <= 0.506 with 95% confidence
421s ***** demo
421s  % flip a coin 100 times, showing 65 heads
421s  % Hypothesis: coin shows less than 50% heads, i.e. p<=1/2
421s  [h,p_val,ci] = binotest(65,100,0.5,'tail','left','alpha',0.01)
421s  % Result: h = 1 : null hypothesis is rejected, i.e. coin shows more heads than tails
421s  %         P value 0.0018, i.e. hypothesis not rejected for alpha up to 0.18%
421s  %         0 <= p <= 0.76 with 99% confidence
421s ***** test #example from https://en.wikipedia.org/wiki/Binomial_test
421s  [h,p_val,ci] = binotest (51,235,1/6);
421s  assert (p_val, 0.0437, 0.00005)
421s  [h,p_val,ci] = binotest (51,235,1/6,'tail','left');
421s  assert (p_val, 0.027, 0.0005)
421s 1 test, 1 passed, 0 known failure, 0 skipped
421s [inst/boxplot.m]
421s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/boxplot.m
421s ***** demo
421s  axis ([0, 3]);
421s  randn ("seed", 1);    # for reproducibility
421s  girls = randn (10, 1) * 5 + 140;
421s  randn ("seed", 2);    # for reproducibility
421s  boys = randn (13, 1) * 8 + 135;
421s  boxplot ({girls, boys});
421s  set (gca (), "xtick", [1 2], "xticklabel", {"girls", "boys"})
421s  title ("Grade 3 heights");
421s ***** demo
421s  randn ("seed", 7);    # for reproducibility
421s  A = randn (10, 1) * 5 + 140;
421s  randn ("seed", 8);    # for reproducibility
421s  B = randn (25, 1) * 8 + 135;
421s  randn ("seed", 9);    # for reproducibility
421s  C = randn (20, 1) * 6 + 165;
421s  data = [A; B; C];
421s  groups = [(ones (10, 1)); (ones (25, 1) * 2); (ones (20, 1) * 3)];
421s  labels = {"Team A", "Team B", "Team C"};
421s  pos = [2, 1, 3];
421s  boxplot (data, groups, "Notch", "on", "Labels", labels, "Positions", pos, ...
421s           "OutlierTags", "on", "BoxStyle", "filled");
421s  title ("Example of Group splitting with paired vectors");
421s ***** demo
421s  randn ("seed", 1);    # for reproducibility
421s  data = randn (100, 9);
421s  boxplot (data, "notch", "on", "boxstyle", "filled", ...
421s           "colors", "ygcwkmb", "whisker", 1.2);
421s  title ("Example of different colors specified with characters");
421s ***** demo
421s  randn ("seed", 5);    # for reproducibility
421s  data = randn (100, 13);
421s  colors = [0.7 0.7 0.7; ...
421s            0.0 0.4 0.9; ...
421s            0.7 0.4 0.3; ...
421s            0.7 0.1 0.7; ...
421s            0.8 0.7 0.4; ...
421s            0.1 0.8 0.5; ...
421s            0.9 0.9 0.2];
421s  boxplot (data, "notch", "on", "boxstyle", "filled", ...
421s           "colors", colors, "whisker", 1.3, "boxwidth", "proportional");
421s  title ("Example of different colors specified as RGB values");
421s ***** error <numerical array or cell array containing> boxplot ("a")
421s ***** error <data cells must contain> boxplot ({[1 2 3], "a"})
421s ***** error <grouping vector may only be passed> boxplot ([1 2 3], 1, {2, 3})
421s ***** error <grouping vector must be numerical> boxplot ([1 2 3], {"a", "b"})
421s ***** error <'Notch' input argument accepts> boxplot ([1:10], "notch", "any")
421s ***** error <illegal Notch value> boxplot ([1:10], "notch", i)
421s ***** error <illegal Notch value> boxplot ([1:10], "notch", {})
421s ***** error <must be character> boxplot (1, "symbol", 1)
421s ***** error <'Orientation' input argument accepts only> boxplot (1, "orientation", "diagonal")
421s ***** error <illegal Orientation value> boxplot (1, "orientation", {})
421s ***** error <'Whisker' input argument accepts only> boxplot (1, "whisker", "a")
421s ***** error <'Whisker' input argument accepts only> boxplot (1, "whisker", [1 3])
421s ***** error <'OutlierTags' input argument accepts only> boxplot (3, "OutlierTags", "maybe")
421s ***** error <illegal OutlierTags value> boxplot (3, "OutlierTags", {})
421s ***** error <'Sample_IDs' input argument accepts only> boxplot (1, "sample_IDs", 1)
421s ***** error <'BoxWidth' input argument accepts only> boxplot (1, "boxwidth", 2)
421s ***** error <'BoxWidth' input argument accepts only> boxplot (1, "boxwidth", "anything")
421s ***** error <'Widths' input argument accepts only> boxplot (5, "widths", "a")
421s ***** error <'Widths' input argument accepts only> boxplot (5, "widths", [1:4])
421s ***** error <'Widths' input argument accepts only> boxplot (5, "widths", [])
421s ***** error <'CapWidths' input argument accepts only> boxplot (5, "capwidths", "a")
421s ***** error <'CapWidths' input argument accepts only> boxplot (5, "capwidths", [1:4])
421s ***** error <'CapWidths' input argument accepts only> boxplot (5, "capwidths", [])
421s ***** error <'BoxStyle' input argument accepts only> boxplot (1, "Boxstyle", 1)
421s ***** error <'BoxStyle' input argument accepts only> boxplot (1, "Boxstyle", "garbage")
421s ***** error <'Positions' input argument accepts only> boxplot (1, "positions", "aa")
421s ***** error <'Labels' input argument accepts only> boxplot (3, "labels", [1 5])
421s ***** error <'Colors' input argument accepts only> boxplot (1, "colors", {})
421s ***** error <'Colors' input argument accepts only> boxplot (2, "colors", [1 2 3 4])
421s ***** error <Sample_IDs must match the data> boxplot (randn (10, 3), 'Sample_IDs', {"a", "b"})
421s ***** error <with the formalism> boxplot (rand (3, 3), [1 2])
421s ***** test
421s  hf = figure ("visible", "off");
421s  unwind_protect
421s    [a, b] = boxplot (rand (10, 3));
421s    assert (size (a), [7, 3]);
421s    assert (numel (b.box), 3);
421s    assert (numel (b.whisker), 12);
421s    assert (numel (b.median), 3);
421s  unwind_protect_cleanup
421s    close (hf);
421s  end_unwind_protect
421s ***** test
421s  hf = figure ("visible", "off");
421s  unwind_protect
421s    [~, b] = boxplot (rand (10, 3), "BoxStyle", "filled", "colors", "ybc");
421s    assert (numel (b.box_fill), 3);
421s  unwind_protect_cleanup
421s    close (hf);
421s  end_unwind_protect
422s ***** test
422s  hf = figure ("visible", "off");
422s  unwind_protect
422s    hold on
422s    [a, b] = boxplot (rand (10, 3));
422s    assert (ishold, true);
422s  unwind_protect_cleanup
422s    close (hf);
422s  end_unwind_protect
422s 34 tests, 34 passed, 0 known failure, 0 skipped
422s [inst/canoncorr.m]
422s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/canoncorr.m
422s ***** shared X,Y,A,B,r,U,V,k
422s  k = 10;
422s  X = [1:k; sin(1:k); cos(1:k)]'; Y = [tan(1:k); tanh((1:k)/k)]';
422s  [A,B,r,U,V,stats] = canoncorr (X,Y);
422s ***** assert (A, [-0.329229   0.072908; 0.074870   1.389318; -0.069302  -0.024109], 1E-6);
422s ***** assert (B, [-0.017086  -0.398402; -4.475049  -0.824538], 1E-6);
422s ***** assert (r, [0.99590   0.26754], 1E-5);
422s ***** assert (U, center(X) * A, 10*eps);
422s ***** assert (V, center(Y) * B, 10*eps);
422s ***** assert (cov(U), eye(size(U, 2)), 10*eps);
422s ***** assert (cov(V), eye(size(V, 2)), 10*eps);
422s  rand ("state", 1); [A,B,r] = canoncorr (rand(5, 10),rand(5, 20));
422s ***** assert (r, ones(1, 5), 10*eps);
422s 8 tests, 8 passed, 0 known failure, 0 skipped
422s [inst/cdfcalc.m]
422s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/cdfcalc.m
422s ***** test
422s  x = [2, 4, 3, 2, 4, 3, 2, 5, 6, 4];
422s  [yCDF, xCDF, n, emsg, eid] = cdfcalc (x);
422s  assert (yCDF, [0, 0.3, 0.5, 0.8, 0.9, 1]');
422s  assert (xCDF, [2, 3, 4, 5, 6]');
422s  assert (n, 10);
422s ***** shared x
422s  x = [2, 4, 3, 2, 4, 3, 2, 5, 6, 4];
422s ***** error yCDF = cdfcalc (x);
422s ***** error [yCDF, xCDF] = cdfcalc ();
422s ***** error [yCDF, xCDF] = cdfcalc (x, x);
422s ***** warning [yCDF, xCDF] = cdfcalc (ones(10,2));
422s 5 tests, 5 passed, 0 known failure, 0 skipped
422s [inst/cdfplot.m]
422s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/cdfplot.m
422s ***** demo
422s  x = randn(100,1);
422s  cdfplot (x);
422s ***** test
422s  hf = figure ("visible", "off");
422s  unwind_protect
422s    x = [2, 4, 3, 2, 4, 3, 2, 5, 6, 4];
422s    [hCDF, stats] = cdfplot (x);
422s    assert (stats.min, 2);
422s    assert (stats.max, 6);
422s    assert (stats.median, 3.5);
422s    assert (stats.std, 1.35400640077266, 1e-14);
422s  unwind_protect_cleanup
422s    close (hf);
422s  end_unwind_protect
422s ***** test
422s  hf = figure ("visible", "off");
422s  unwind_protect
422s    x = randn(100,1);
422s    cdfplot (x);
422s  unwind_protect_cleanup
422s    close (hf);
422s  end_unwind_protect
422s ***** error cdfplot ();
422s ***** error cdfplot ([x',x']);
422s ***** error cdfplot ([NaN, NaN, NaN, NaN]);
422s 5 tests, 5 passed, 0 known failure, 0 skipped
422s [inst/chi2gof.m]
422s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/chi2gof.m
422s ***** demo
422s  x = normrnd (50, 5, 100, 1);
422s  [h, p, stats] = chi2gof (x)
422s  [h, p, stats] = chi2gof (x, "cdf", @(x)normcdf (x, mean(x), std(x)))
422s  [h, p, stats] = chi2gof (x, "cdf", {@normcdf, mean(x), std(x)})
422s ***** demo
422s  x = rand (100,1 );
422s  n = length (x);
422s  binedges = linspace (0, 1, 11);
422s  expectedCounts = n * diff (binedges);
422s  [h, p, stats] = chi2gof (x, "binedges", binedges, "expected", expectedCounts)
422s ***** demo
422s  bins = 0:5;
422s  obsCounts = [6 16 10 12 4 2];
422s  n = sum(obsCounts);
422s  lambdaHat = sum(bins.*obsCounts) / n;
422s  expCounts = n * poisspdf(bins,lambdaHat);
422s  [h, p, stats] = chi2gof (bins, "binctrs", bins, "frequency", obsCounts, ...
422s                           "expected", expCounts, "nparams",1)
422s ***** error chi2gof ()
422s ***** error chi2gof ([2,3;3,4])
422s ***** error chi2gof ([1,2,3,4], "nbins", 3, "ctrs", [2,3,4])
422s ***** error chi2gof ([1,2,3,4], "frequency", [2,3,2])
422s ***** error chi2gof ([1,2,3,4], "frequency", [2,3,2,-2])
422s ***** error chi2gof ([1,2,3,4], "frequency", [2,3,2,2], "nparams", i)
422s ***** error chi2gof ([1,2,3,4], "frequency", [2,3,2,2], "alpha", 1.3)
422s ***** error chi2gof ([1,2,3,4], "expected", [-3,2,2])
422s ***** error chi2gof ([1,2,3,4], "expected", [3,2,2], "nbins", 5)
422s ***** error chi2gof ([1,2,3,4], "cdf", @normcdff)
422s ***** test
422s  x = [1 2 1 3 2 4 3 2 4 3 2 2];
422s  [h, p, stats] = chi2gof (x);
422s  assert (h, 0);
422s  assert (p, NaN);
422s  assert (stats.chi2stat, 0.1205375022748029, 1e-14);
422s  assert (stats.df, 0);
422s  assert (stats.edges, [1, 2.5, 4], 1e-14);
422s  assert (stats.O, [7, 5], 1e-14);
422s  assert (stats.E, [6.399995519909668, 5.600004480090332], 1e-14);
422s 11 tests, 11 passed, 0 known failure, 0 skipped
422s [inst/chi2test.m]
422s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/chi2test.m
422s ***** error chi2test ();
422s ***** error chi2test ([1, 2, 3, 4, 5]);
422s ***** error chi2test ([1, 2; 2, 1+3i]);
422s ***** error chi2test ([NaN, 6; 34, 12]);
422s ***** error<chi2test: optional arguments are not supported for 2-way> ...
422s  p = chi2test (ones (3, 3), "mutual", []);
422s ***** error<chi2test: invalid model name for testing a 3-way table.> ...
422s  p = chi2test (ones (3, 3, 3), "testtype", 2);
422s ***** error<chi2test: optional arguments must be in pairs.> ...
422s  p = chi2test (ones (3, 3, 3), "mutual");
422s ***** error<chi2test: value must be numeric in optional argument> ...
422s  p = chi2test (ones (3, 3, 3), "joint", ["a"]);
422s ***** error<chi2test: value must be empty or scalar in optional argument> ...
422s  p = chi2test (ones (3, 3, 3), "joint", [2, 3]);
422s ***** error<chi2test: optional arguments are not supported for k> ...
422s  p = chi2test (ones (3, 3, 3, 4), "mutual", [])
422s ***** warning<chi2test: Expected values less than 5.> p = chi2test (ones (2));
422s ***** warning<chi2test: Expected values less than 5.> p = chi2test (ones (3, 2));
422s ***** warning<chi2test: Expected values less than 1.> p = chi2test (0.4 * ones (3));
422s ***** test
422s  x = [11, 3, 8; 2, 9, 14; 12, 13, 28];
422s  p = chi2test (x);
422s  assert (p, 0.017787, 1e-6);
422s ***** test
422s  x = [11, 3, 8; 2, 9, 14; 12, 13, 28];
422s  [p, chisq] = chi2test (x);
422s  assert (chisq, 11.9421, 1e-4);
422s ***** test
422s  x = [11, 3, 8; 2, 9, 14; 12, 13, 28];
422s  [p, chisq, df] = chi2test (x);
422s  assert (df, 4);
422s ***** test
422s ***** shared x
422s  x(:,:,1) = [59, 32; 9,16];
422s  x(:,:,2) = [55, 24;12,33];
422s  x(:,:,3) = [107,80;17,56];%!
422s ***** assert (chi2test (x), 2.282063427117009e-11, 1e-14);
422s ***** assert (chi2test (x, "mutual", []), 2.282063427117009e-11, 1e-14);
422s ***** assert (chi2test (x, "joint", 1), 1.164834895206468e-11, 1e-14);
423s ***** assert (chi2test (x, "joint", 2), 7.771350230001417e-11, 1e-14);
423s ***** assert (chi2test (x, "joint", 3), 0.07151361728026107, 1e-14);
423s ***** assert (chi2test (x, "marginal", 1), 0, 1e-14);
423s ***** assert (chi2test (x, "marginal", 2), 6.347555814301131e-11, 1e-14);
423s ***** assert (chi2test (x, "marginal", 3), 0, 1e-14);
423s ***** assert (chi2test (x, "conditional", 1), 0.2303114201312508, 1e-14);
423s ***** assert (chi2test (x, "conditional", 2), 0.0958810684407079, 1e-14);
423s ***** assert (chi2test (x, "conditional", 3), 2.648037344954446e-11, 1e-14);
423s ***** assert (chi2test (x, "homogeneous", []), 0.4485579470993741, 1e-14);
423s ***** test
423s  [pval, chisq, df, E] = chi2test (x);
423s  assert (chisq, 64.0982, 1e-4);
423s  assert (df, 7);
423s  assert (E(:,:,1), [42.903, 39.921; 17.185, 15.991], ones (2, 2) * 1e-3);
423s ***** test
423s  [pval, chisq, df, E] = chi2test (x, "joint", 2);
423s  assert (chisq, 56.0943, 1e-4);
423s  assert (df, 5);
423s  assert (E(:,:,2), [40.922, 23.310; 38.078, 21.690], ones (2, 2) * 1e-3);
423s ***** test
423s  [pval, chisq, df, E] = chi2test (x, "marginal", 3);
423s  assert (chisq, 146.6058, 1e-4);
423s  assert (df, 9);
423s  assert (E(:,1,1), [61.642; 57.358], ones (2, 1) * 1e-3);
423s ***** test
423s  [pval, chisq, df, E] = chi2test (x, "conditional", 3);
423s  assert (chisq, 52.2509, 1e-4);
423s  assert (df, 3);
423s  assert (E(:,:,1), [53.345, 37.655; 14.655, 10.345], ones (2, 2) * 1e-3);
423s ***** test
423s  [pval, chisq, df, E] = chi2test (x, "homogeneous", []);
423s  assert (chisq, 1.6034, 1e-4);
423s  assert (df, 2);
423s  assert (E(:,:,1), [60.827, 31.382; 7.173, 16.618], ones (2, 2) * 1e-3);
423s 34 tests, 34 passed, 0 known failure, 0 skipped
423s [inst/cholcov.m]
423s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/cholcov.m
423s ***** demo
423s  C1 = [2, 1, 1, 2; 1, 2, 1, 2; 1, 1, 2, 2; 2, 2, 2, 3]
423s  T = cholcov (C1)
423s  C2 = T'*T
423s ***** test
423s  C1 = [2, 1, 1, 2; 1, 2, 1, 2; 1, 1, 2, 2; 2, 2, 2, 3];
423s  T = cholcov (C1);
423s  assert (C1, T'*T, 1e-15 * ones (size (C1)));
423s 1 test, 1 passed, 0 known failure, 0 skipped
423s [inst/cl_multinom.m]
423s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/cl_multinom.m
423s ***** demo
423s  CL = cl_multinom ([27; 43; 19; 11], 10000, 0.05)
423s ***** error<Invalid call to cl_multinom.  Correct usage> cl_multinom ();
423s ***** error cl_multinom (1, 2, 3, 4, 5);
423s ***** error<cl_multinom: argument method must be a string.> ...
423s  cl_multinom (1, 2, 3, 4);
423s ***** error<cl_multinom: unknown calculation type.> ...
423s  cl_multinom (1, 2, 3, "some string");
423s 4 tests, 4 passed, 0 known failure, 0 skipped
423s [inst/cluster.m]
423s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/cluster.m
423s ***** error cluster ()
423s ***** error <Z must be .* generated by the linkage .*> cluster ([1 1], "Cutoff", 1)
423s ***** error <unknown option .*> cluster ([1 2 1], "Bogus", 1)
423s ***** error <C must be a positive scalar .*> cluster ([1 2 1], "Cutoff", -1)
423s ***** error <unknown property .*> cluster ([1 2 1], "Cutoff", 1, "Bogus", 1)
423s ***** test
423s 6 tests, 6 passed, 0 known failure, 0 skipped
423s [inst/clusterdata.m]
423s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/clusterdata.m
423s ***** demo
423s  randn ("seed", 1)  # for reproducibility
423s  r1 = randn (10, 2) * 0.25 + 1;
423s  randn ("seed", 5)  # for reproducibility
423s  r2 = randn (20, 2) * 0.5 - 1;
423s  X = [r1; r2];
423s 
423s  wnl = warning ("off", "Octave:linkage_savemem", "local");
423s  T = clusterdata (X, "linkage", "ward", "MaxClust", 2);
423s  scatter (X(:,1), X(:,2), 36, T, "filled");
423s ***** error<clusterdata: function called with too few input arguments.> ...
423s  clusterdata ()
423s ***** error<clusterdata: function called with too few input arguments.> ...
423s  clusterdata (1)
423s ***** error <unknown property .*> clusterdata ([1 1], "Bogus", 1)
423s ***** error <specify .* 'MaxClust' or 'Cutoff' .*> clusterdata ([1 1], "Depth", 1)
423s 4 tests, 4 passed, 0 known failure, 0 skipped
423s [inst/cmdscale.m]
423s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/cmdscale.m
423s ***** shared m, n, X, D
423s  m = randi(100) + 1; n = randi(100) + 1; X = rand(m, n); D = pdist(X);
423s ***** assert(norm(pdist(cmdscale(D))), norm(D), sqrt(eps))
423s ***** assert(norm(pdist(cmdscale(squareform(D)))), norm(D), sqrt(eps))
423s 2 tests, 2 passed, 0 known failure, 0 skipped
423s [inst/combnk.m]
423s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/combnk.m
423s ***** demo
423s  c = combnk (1:5, 2);
423s  disp ("All pairs of integers between 1 and 5:");
423s  disp (c);
423s ***** test
423s  c = combnk (1:3, 2);
423s  assert (c, [1, 2; 1, 3; 2, 3]);
423s ***** test
423s  c = combnk (1:3, 6);
423s  assert (isempty (c));
423s ***** test
423s  c = combnk ({1, 2, 3}, 2);
423s  assert (c, {1, 2; 1, 3; 2, 3});
423s ***** test
423s  c = combnk ("hello", 2);
423s  assert (c, ["lo"; "lo"; "ll"; "eo"; "el"; "el"; "ho"; "hl"; "hl"; "he"]);
423s 4 tests, 4 passed, 0 known failure, 0 skipped
423s [inst/confusionchart.m]
423s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/confusionchart.m
423s ***** demo
423s  close all
423s  ## Setting the chart properties
423s  Yt = [8 5 6 8 5 3 1 6 4 2 5 3 1 4]';
423s  Yp = [8 5 6 8 5 2 3 4 4 5 5 7 2 6]';
423s  confusionchart (Yt, Yp, "Title", ...
423s    "Demonstration with summaries","Normalization",...
423s    "absolute","ColumnSummary", "column-normalized","RowSummary",...
423s    "row-normalized")
423s ***** demo
423s  close all
423s  ## Cellstr as inputs
423s  Yt = {"Positive", "Positive", "Positive", "Negative", "Negative"};
423s  Yp = {"Positive", "Positive", "Negative", "Negative", "Negative"};
423s  m = confusionmat (Yt, Yp);
423s  confusionchart (m, {"Positive", "Negative"});
423s  hold off
423s ***** demo
423s  close all
423s  ## Editing the object properties
423s  Yt = {"Positive", "Positive", "Positive", "Negative", "Negative"};
423s  Yp = {"Positive", "Positive", "Negative", "Negative", "Negative"};
423s  cm = confusionchart (Yt, Yp);
423s  cm.Title = "This is an example with a green diagonal";
423s  cm.DiagonalColor = [0.4660, 0.6740, 0.1880];
423s  hold off
423s ***** demo
423s  close all
423s  ## Confusion chart in a uipanel
423s  h = uipanel ();
423s  Yt = {"Positive", "Positive", "Positive", "Negative", "Negative"};
423s  Yp = {"Positive", "Positive", "Negative", "Negative", "Negative"};
423s  cm = confusionchart (h, Yt, Yp);
423s  hold off
423s ***** demo
423s  close all
423s  ## Sorting classes
423s  Yt = [8 5 6 8 5 3 1 6 4 2 5 3 1 4]';
423s  Yp = [8 5 6 8 5 2 3 4 4 5 5 7 2 6]';
423s  cm = confusionchart (Yt, Yp, "Title", ...
423s    "Classes are sorted in ascending order");
423s  cm = confusionchart (Yt, Yp, "Title", ...
423s    "Classes are sorted according to clusters");
423s  sortClasses (cm, "cluster");
423s ***** shared visibility_setting
423s  visibility_setting = get (0, "DefaultFigureVisible");
423s ***** test
423s  set (0, "DefaultFigureVisible", "off");
423s  fail ("confusionchart ()", "Invalid call");
423s  set (0, "DefaultFigureVisible", visibility_setting);
423s ***** test
423s  set (0, "DefaultFigureVisible", "off");
423s  fail ("confusionchart ([1 1; 2 2; 3 3])", "invalid argument");
423s  set (0, "DefaultFigureVisible", visibility_setting);
423s ***** test
423s  set (0, "DefaultFigureVisible", "off");
423s  fail ("confusionchart ([1 2], [0 1], 'xxx', 1)", "invalid property");
423s  set (0, "DefaultFigureVisible", visibility_setting);
423s ***** test
423s  set (0, "DefaultFigureVisible", "off");
423s  fail ("confusionchart ([1 2], [0 1], 'XLabel', 1)", "XLabel .* string");
423s  set (0, "DefaultFigureVisible", visibility_setting);
423s ***** test
423s  set (0, "DefaultFigureVisible", "off");
423s  fail ("confusionchart ([1 2], [0 1], 'YLabel', [1 0])", ...
423s        ".* YLabel .* string");
423s  set (0, "DefaultFigureVisible", visibility_setting);
423s ***** test
423s  set (0, "DefaultFigureVisible", "off");
423s  fail ("confusionchart ([1 2], [0 1], 'Title', .5)", ".* Title .* string");
423s  set (0, "DefaultFigureVisible", visibility_setting);
423s ***** test
423s  set (0, "DefaultFigureVisible", "off");
423s  fail ("confusionchart ([1 2], [0 1], 'FontName', [])", ...
423s        ".* FontName .* string");
423s  set (0, "DefaultFigureVisible", visibility_setting);
423s ***** test
423s  set (0, "DefaultFigureVisible", "off");
423s  fail ("confusionchart ([1 2], [0 1], 'FontSize', 'b')", ...
423s        ".* FontSize .* numeric");
423s  set (0, "DefaultFigureVisible", visibility_setting);
424s ***** test
424s  set (0, "DefaultFigureVisible", "off");
424s  fail ("confusionchart ([1 2], [0 1], 'DiagonalColor', 'h')", ...
424s        ".* DiagonalColor .* color");
424s  set (0, "DefaultFigureVisible", visibility_setting);
424s ***** test
424s  set (0, "DefaultFigureVisible", "off");
424s  fail ("confusionchart ([1 2], [0 1], 'OffDiagonalColor', [])", ...
424s        ".* OffDiagonalColor .* color");
424s  set (0, "DefaultFigureVisible", visibility_setting);
424s ***** test
424s  set (0, "DefaultFigureVisible", "off");
424s  fail ("confusionchart ([1 2], [0 1], 'Normalization', '')", ...
424s        ".* invalid .* Normalization");
424s  set (0, "DefaultFigureVisible", visibility_setting);
424s ***** test
424s  set (0, "DefaultFigureVisible", "off");
424s  fail ("confusionchart ([1 2], [0 1], 'ColumnSummary', [])", ...
424s        ".* invalid .* ColumnSummary");
424s  set (0, "DefaultFigureVisible", visibility_setting);
424s ***** test
424s  set (0, "DefaultFigureVisible", "off");
424s  fail ("confusionchart ([1 2], [0 1], 'RowSummary', 1)", ...
424s        ".* invalid .* RowSummary");
424s  set (0, "DefaultFigureVisible", visibility_setting);
424s ***** test
424s  set (0, "DefaultFigureVisible", "off");
424s  fail ("confusionchart ([1 2], [0 1], 'GridVisible', .1)", ...
424s        ".* invalid .* GridVisible");
424s  set (0, "DefaultFigureVisible", visibility_setting);
424s ***** test
424s  set (0, "DefaultFigureVisible", "off");
424s  fail ("confusionchart ([1 2], [0 1], 'HandleVisibility', .1)", ...
424s        ".* invalid .* HandleVisibility");
424s  set (0, "DefaultFigureVisible", visibility_setting);
424s ***** test
424s  set (0, "DefaultFigureVisible", "off");
424s  fail ("confusionchart ([1 2], [0 1], 'OuterPosition', .1)", ...
424s        ".* invalid .* OuterPosition");
424s  set (0, "DefaultFigureVisible", visibility_setting);
425s ***** test
425s  set (0, "DefaultFigureVisible", "off");
425s  fail ("confusionchart ([1 2], [0 1], 'Position', .1)", ...
425s        ".* invalid .* Position");
425s  set (0, "DefaultFigureVisible", visibility_setting);
425s ***** test
425s  set (0, "DefaultFigureVisible", "off");
425s  fail ("confusionchart ([1 2], [0 1], 'Units', .1)", ".* invalid .* Units");
425s  set (0, "DefaultFigureVisible", visibility_setting);
425s 18 tests, 18 passed, 0 known failure, 0 skipped
425s [inst/confusionmat.m]
425s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/confusionmat.m
425s ***** test
425s  Yt = [8 5 6 8 5 3 1 6 4 2 5 3 1 4]';
425s  Yp = [8 5 6 8 5 2 3 4 4 5 5 7 2 6]';
425s  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; ...
425s        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];
425s  assert (confusionmat (Yt, Yp), C)
425s 1 test, 1 passed, 0 known failure, 0 skipped
425s [inst/cophenet.m]
425s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/cophenet.m
425s ***** demo
425s  randn ("seed", 5)  # for reproducibility
425s  X = randn (10,2);
425s  y = pdist (X);
425s  Z = linkage (y, "average");
425s  cophenet (Z, y)
425s ***** error<cophenet: function called with too few input arguments.> cophenet ()
425s ***** error<cophenet: function called with too few input arguments.> cophenet (1)
425s ***** error<cophenet: Z must be a matrix as generated by the linkage function.> ...
425s  cophenet (ones (2,2), 1)
425s ***** error<cophenet: Y must be a vector of euclidean distances.> ...
425s  cophenet ([1 2 1], "a")
425s ***** error<cophenet: Y must be a vector of euclidean distances.> ...
425s  cophenet ([1 2 1], [1 2])
425s 5 tests, 5 passed, 0 known failure, 0 skipped
425s [inst/correlation_test.m]
425s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/correlation_test.m
425s ***** error<Invalid call to correlation_test.  Correct usage> correlation_test ();
425s ***** error<Invalid call to correlation_test.  Correct usage> correlation_test (1);
425s ***** error<correlation_test: X must contain finite real numbers.> ...
425s  correlation_test ([1 2 NaN]', [2 3 4]');
425s ***** error<correlation_test: X must contain finite real numbers.> ...
425s  correlation_test ([1 2 Inf]', [2 3 4]');
425s ***** error<correlation_test: X must contain finite real numbers.> ...
425s  correlation_test ([1 2 3+i]', [2 3 4]');
425s ***** error<correlation_test: Y must contain finite real numbers.> ...
425s  correlation_test ([1 2 3]', [2 3 NaN]');
425s ***** error<correlation_test: Y must contain finite real numbers.> ...
425s  correlation_test ([1 2 3]', [2 3 Inf]');
425s ***** error<correlation_test: Y must contain finite real numbers.> ...
425s  correlation_test ([1 2 3]', [3 4 3+i]');
425s ***** error<correlation_test: X and Y must be vectors of equal length.> ...
425s  correlation_test ([1 2 3]', [3 4 4 5]');
425s ***** error<correlation_test: invalid value for alpha.> ...
425s  correlation_test ([1 2 3]', [2 3 4]', "alpha", 0);
425s ***** error<correlation_test: invalid value for alpha.> ...
425s  correlation_test ([1 2 3]', [2 3 4]', "alpha", 1.2);
425s ***** error<correlation_test: invalid value for alpha.> ...
425s  correlation_test ([1 2 3]', [2 3 4]', "alpha", [.02 .1]);
425s ***** error<correlation_test: invalid value for alpha.> ...
425s  correlation_test ([1 2 3]', [2 3 4]', "alpha", "a");
425s ***** error<correlation_test: invalid Name argument.> ...
425s  correlation_test ([1 2 3]', [2 3 4]', "some", 0.05);
425s ***** error<correlation_test: invalid value for tail.>  ...
425s  correlation_test ([1 2 3]', [2 3 4]', "tail", "val");
425s ***** error<correlation_test: invalid value for tail.>  ...
425s  correlation_test ([1 2 3]', [2 3 4]', "alpha", 0.01, "tail", "val");
425s ***** error<correlation_test: invalid value for method.>  ...
425s  correlation_test ([1 2 3]', [2 3 4]', "method", 0.01);
425s ***** error<correlation_test: invalid value for method.>  ...
425s  correlation_test ([1 2 3]', [2 3 4]', "method", "some");
425s ***** test
425s  x = [6 7 7 9 10 12 13 14 15 17];
425s  y = [19 22 27 25 30 28 30 29 25 32];
425s  [h, pval, stats] = correlation_test (x, y);
425s  assert (stats.corrcoef, corr (x', y'), 1e-14);
425s  assert (pval, 0.0223, 1e-4);
425s ***** test
425s  x = [6 7 7 9 10 12 13 14 15 17]';
425s  y = [19 22 27 25 30 28 30 29 25 32]';
425s  [h, pval, stats] = correlation_test (x, y);
425s  assert (stats.corrcoef, corr (x, y), 1e-14);
425s  assert (pval, 0.0223, 1e-4);
425s 20 tests, 20 passed, 0 known failure, 0 skipped
425s [inst/createns.m]
425s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/createns.m
425s ***** test
425s  ## Default ExhaustiveSearcher
425s  X = [1, 2; 3, 4; 5, 6];
425s  obj = createns (X);
425s  assert (isa (obj, "ExhaustiveSearcher"));
425s  assert (obj.X, X);
425s  assert (obj.Distance, "euclidean");
425s ***** test
425s  ## KDTreeSearcher with default parameters
425s  X = [1, 2; 3, 4; 5, 6];
425s  obj = createns (X, "NSMethod", "kdtree");
425s  assert (isa (obj, "KDTreeSearcher"));
425s  assert (obj.X, X);
425s  assert (obj.Distance, "euclidean");
425s ***** test
425s  ## hnswSearcher with custom parameters
425s  X = [1, 2; 3, 4; 5, 6];
425s  obj = createns (X, "NSMethod", "hnsw", "MaxNumLinksPerNode", 2, "TrainSetSize", 3);
425s  assert (isa (obj, "hnswSearcher"));
425s  assert (obj.X, X);
425s  assert (obj.MaxNumLinksPerNode, 2);
425s  assert (obj.TrainSetSize, 3);
425s ***** test
425s  ## ExhaustiveSearcher with custom distance
425s  X = [1, 2; 3, 4];
425s  obj = createns (X, "NSMethod", "exhaustive", "Distance", "cityblock");
425s  assert (isa (obj, "ExhaustiveSearcher"));
425s  assert (obj.Distance, "cityblock");
425s ***** error<createns: too few input arguments.>
425s  createns ()
425s ***** error<createns: Name-Value arguments must be in pairs.>
425s  X = [1, 2; 3, 4]; createns (X, "NSMethod")
425s ***** error<createns: X must be a finite numeric matrix.>
425s  createns ([1; Inf; 3])
425s ***** error<createns: 'NSMethod' must be a string.>
425s  X = [1, 2; 3, 4]; createns (X, "NSMethod", 1)
425s ***** error<createns: invalid 'NSMethod' value: 'invalid'.>
425s  X = [1, 2; 3, 4]; createns (X, "NSMethod", "invalid")
425s 9 tests, 9 passed, 0 known failure, 0 skipped
425s [inst/crosstab.m]
425s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/crosstab.m
425s ***** error crosstab ()
425s ***** error crosstab (1)
425s ***** error <crosstab: x1, x2 ... xn must be vectors.> crosstab (ones (2), [1 1])
425s ***** error <crosstab: x1, x2 ... xn must be vectors.> crosstab ([1 1], ones (2))
425s ***** error <x1, x2 .* must be vectors of the same length> crosstab ([1], [1 2])
425s ***** error <x1, x2 .* must be vectors of the same length> crosstab ([1 2], [1])
425s ***** test
425s  load carbig
425s  [table, chisq, p, labels] = crosstab (cyl4, when, org);
425s  assert (table(2,3,1), 38);
425s  assert (labels{3,3}, "Japan");
425s ***** test
425s  load carbig
425s  [table, chisq, p, labels] = crosstab (cyl4, when, org);
425s  assert (table(2,3,2), 17);
425s  assert (labels{1,3}, "USA");
425s ***** test
425s  x = [1, 1, 2, 3, 1];
425s  y = [1, 2, 5, 3, 1];
425s  t = crosstab (x, y);
425s  assert (t, [2, 1, 0, 0; 0, 0, 0, 1; 0, 0, 1, 0]);
425s ***** test
425s  x = [1, 1, 2, 3, 1];
425s  y = [1, 2, 3, 5, 1];
425s  t = crosstab (x, y);
425s  assert (t, [2, 1, 0, 0; 0, 0, 1, 0; 0, 0, 0, 1]);
425s ***** test
425s  x1 = [1, 3, 7, 7, 8];
425s  x2 = [4, 2, 1, 1, 1];
425s  x3 = [6, 2, 6, 2, NaN];
425s  T1 = [0, 0, 0; 0, 1, 0; 1, 0, 0; 0, 0, 0];
425s  T2 = [0, 0, 1; 0, 0, 0; 1, 0, 0; 0, 0, 0];
425s  T = zeros (4, 3, 2);
425s  T(:,:,1) = T1;
425s  T(:,:,2) = T2;
425s  t = crosstab (x1, x2, x3);
425s  assert (t, T);
425s ***** test
425s  x = [1, 2, NaN, 1];
425s  y = [1, 2, 3, NaN];
425s  t = crosstab (x, y);
425s  assert (t, [1, 0, 0; 0, 1, 0]);
425s 12 tests, 12 passed, 0 known failure, 0 skipped
425s [inst/crossval.m]
425s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/crossval.m
425s ***** demo
425s  ## Determine the optimal number of clusters using cross-validation
425s 
425s  ## Declare a function to compute the sum of squared distances
425s  ## between data points and a varying number of clusters.
425s  function D = dist2clusters (X, Y, k)
425s    [Z, Zmu, Zstd] = zscore (X);
425s    [~, C] = kmeans (Z, k);
425s    ZY = (Y - Zmu) ./ Zstd;
425s    d = pdist2 (C, ZY, 'euclidean', 'Smallest', 1);
425s    D = sum (d .^ 2);
425s  endfunction
425s 
425s  load fisheriris
425s  for k = 1:8
425s    fcn = @(X, Y) dist2clusters (X, Y, k);
425s    distances = crossval (fcn, meas);
425s    cvdist(k) = sum (distances);
425s  endfor
425s 
425s  plot(cvdist)
425s  xlabel('Number of Clusters')
425s  ylabel('CV Sum of Squared Distances')
425s  xlim ([1,8]);
425s ***** test
425s  function yfit = regf (Xtrain, ytrain, Xtest)
425s    b = regress (ytrain, Xtrain);
425s    yfit = Xtest * b;
425s  endfunction
425s 
425s  load carsmall
425s  data = [Acceleration Horsepower Weight MPG];
425s  data(any(isnan(data),2),:) = [];
425s 
425s  y = data(:,4);
425s  X = [ones(length(y),1) data(:,1:3)];
425s  rand ("seed", 3);
425s  cvMSE = crossval('mse',X,y,'Predfun',@regf);
425s  assert (cvMSE, 18.720, 1e-3);
425s ***** error <crossval: criterion must be 'mse' or 'mcr'.> ...
425s  crossval ('fe', rand (10, 1), rand (10, 1), 1);
425s ***** error <crossval: prediction function handle is required for error evaluation.>  ...
425s  crossval ('mse', rand (10, 1), rand (10, 1), 1);
425s ***** error <crossval: X and Y are required for error evaluation.> ...
425s  crossval ('mse', rand (10, 1), 'Predfun', @(x,y) x + y);
425s ***** error <crossval: bad prediction function handle for error evaluation.> ...
425s  crossval ('mse', rand (10, 3), rand (10, 1), 'Predfun', @(x,y) sum (x + y));
425s ***** error <crossval: prediction function handle must return a column vector with the same rows as XTest.> ...
425s  crossval ('mse', rand (10, 3), rand (10, 1), 'Predfun', @(x,y,z) sum (x + y));
425s ***** error <crossval: X is required for values evaluation.> crossval (@(x) x);
425s ***** error <crossval: bad function handle to cross-validate.> ...
425s  crossval (@(x) x, rand (10, 3), rand (10, 1));
425s ***** error <crossval: function handle must return a scalar or a row vector.> ...
425s  crossval (@(x,y) [x, y], rand (10, 3), rand (10, 1));
426s ***** error <crossval: invalid first input argument.> crossval ({1}, 1, 1);
426s ***** error <crossval: you can only set one cvpartition type in paired arguments.> ...
426s  crossval (@(x,y) sum ([x; y]), rand (10, 3), 'Holdout', 0.1, 'Leaveout', true)
426s ***** error <crossval: you cannot specify both 'Partition' and 'Stratify'.> ...
426s  crossval (@(x,y) sum ([x; y]), rand (10, 3), 'Partition', cvpartition (10, 'Leaveout'), 'Stratify', true)
426s 12 tests, 12 passed, 0 known failure, 0 skipped
426s [inst/datasample.m]
426s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/datasample.m
426s ***** error datasample();
426s ***** error datasample(1);
426s ***** error <data must be a vector or matrix> datasample({1, 2, 3}, 1);
426s ***** error <k must be a positive integer scalar> datasample([1 2], -1);
426s ***** error <k must be a positive integer scalar> datasample([1 2], 1.5);
426s ***** error <k must be a positive integer scalar> datasample([1 2], [1 1]);
426s ***** error <k must be a positive integer scalar> datasample([1 2], 'g', [1 1]);
426s ***** error <DIM must be a positive integer scalar> datasample([1 2], 1, -1);
426s ***** error <DIM must be a positive integer scalar> datasample([1 2], 1, 1.5);
426s ***** error <DIM must be a positive integer scalar> datasample([1 2], 1, [1 1]);
426s ***** error <Replace> datasample([1 2], 1, 1, "Replace", -2);
426s ***** error <weights must be defined> datasample([1 2], 1, 1, "Weights", "abc");
426s ***** error <weights must be defined> datasample([1 2], 1, 1, "Weights", [1 -2 3]);
426s ***** error <weights must be defined> datasample([1 2], 1, 1, "Weights", ones (2));
426s ***** error <weights must be equal> datasample([1 2], 1, 1, "Weights", [1 2 3]);
426s ***** test
426s  dat = randn (10, 4);
426s  assert (size (datasample (dat, 3, 1)), [3 4]);
426s ***** test
426s  dat = randn (10, 4);
426s  assert (size (datasample (dat, 3, 2)), [10 3]);
426s 17 tests, 17 passed, 0 known failure, 0 skipped
426s [inst/dcov.m]
426s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dcov.m
426s ***** demo
426s  base=@(x) (x- min(x))./(max(x)-min(x));
426s  N = 5e2;
426s  x = randn (N,1); x = base (x);
426s  z = randn (N,1); z = base (z);
426s  # Linear relations
426s  cy = [1 0.55 0.3 0 -0.3 -0.55 -1];
426s  ly = x .* cy;
426s  ly(:,[1:3 5:end]) = base (ly(:,[1:3 5:end]));
426s  # Correlated Gaussian
426s  cz = 1 - abs (cy);
426s  gy = base ( ly + cz.*z);
426s  # Shapes
426s  sx      = repmat (x,1,7);
426s  sy      = zeros (size (ly));
426s  v       = 2 * rand (size(x,1),2) - 1;
426s  sx(:,1) = v(:,1); sy(:,1) = cos(2*pi*sx(:,1)) + 0.5*v(:,2).*exp(-sx(:,1).^2/0.5);
426s  R       =@(d) [cosd(d) sind(d); -sind(d) cosd(d)];
426s  tmp     = R(35) * v.';
426s  sx(:,2) = tmp(1,:); sy(:,2) = tmp(2,:);
426s  tmp     = R(45) * v.';
426s  sx(:,3) = tmp(1,:); sy(:,3) = tmp(2,:);
426s  sx(:,4) = v(:,1); sy(:,4) = sx(:,4).^2 + 0.5*v(:,2);
426s  sx(:,5) = v(:,1); sy(:,5) = 3*sign(v(:,2)).*(sx(:,5)).^2  + v(:,2);
426s  sx(:,6) = cos (2*pi*v(:,1)) + 0.5*(x-0.5);
426s  sy(:,6) = sin (2*pi*v(:,1)) + 0.5*(z-0.5);
426s  sx(:,7) = x + sign(v(:,1)); sy(:,7) = z + sign(v(:,2));
426s  sy      = base (sy);
426s  sx      = base (sx);
426s  # scaled shape
426s  sc  = 1/3;
426s  ssy = (sy-0.5) * sc + 0.5;
426s  n = size (ly,2);
426s  ym = 1.2;
426s  xm = 0.5;
426s  fmt={'horizontalalignment','center'};
426s  ff = "% .2f";
426s  figure (1)
426s  for i=1:n
426s    subplot(4,n,i);
426s    plot (x, gy(:,i), '.b');
426s    axis tight
426s    axis off
426s    text (xm,ym,sprintf (ff, dcov (x,gy(:,i))),fmt{:})
426s 
426s    subplot(4,n,i+n);
426s    plot (x, ly(:,i), '.b');
426s    axis tight
426s    axis off
426s    text (xm,ym,sprintf (ff, dcov (x,ly(:,i))),fmt{:})
426s 
426s    subplot(4,n,i+2*n);
426s    plot (sx(:,i), sy(:,i), '.b');
426s    axis tight
426s    axis off
426s    text (xm,ym,sprintf (ff, dcov (sx(:,i),sy(:,i))),fmt{:})
426s    v = axis ();
426s 
426s    subplot(4,n,i+3*n);
426s    plot (sx(:,i), ssy(:,i), '.b');
426s    axis (v)
426s    axis off
426s    text (xm,ym,sprintf (ff, dcov (sx(:,i),ssy(:,i))),fmt{:})
426s  endfor
426s ***** error dcov (randn (30, 5), randn (25,5))
426s 1 test, 1 passed, 0 known failure, 0 skipped
426s [inst/dendrogram.m]
426s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dendrogram.m
426s ***** demo
426s  ## simple dendrogram
426s  y = [4, 5; 2, 6; 3, 7; 8, 9; 1, 10];
426s  y(:,3) = 1:5;
426s  dendrogram (y);
426s  title ("simple dendrogram");
426s ***** demo
426s  ## another simple dendrogram
426s  v = 2 * rand (30, 1) - 1;
426s  d = abs (bsxfun (@minus, v(:, 1), v(:, 1)'));
426s  y = linkage (squareform (d, "tovector"));
426s  dendrogram (y);
426s  title ("another simple dendrogram");
426s ***** demo
426s  ## collapsed tree, find all the leaves of node 5
426s  X = randn (60, 2);
426s  D = pdist (X);
426s  y = linkage (D, "average");
426s  subplot (2, 1, 1);
426s  title ("original tree");
426s  dendrogram (y, 0);
426s  subplot (2, 1, 2);
426s  title ("collapsed tree");
426s  [~, t] = dendrogram (y, 20);
426s  find(t == 5)
426s ***** demo
426s  ## optimal leaf order
426s  X = randn (30, 2);
426s  D = pdist (X);
426s  y = linkage (D, "average");
426s  order = optimalleaforder (y, D);
426s  subplot (2, 1, 1);
426s  title ("original leaf order");
426s  dendrogram (y);
426s  subplot (2, 1, 2);
426s  title ("optimal leaf order");
426s  dendrogram (y, "Reorder", order);
426s ***** demo
426s  ## horizontal orientation and labels
426s  X = randn (8, 2);
426s  D = pdist (X);
426s  L = ["Snow White"; "Doc"; "Grumpy"; "Happy"; "Sleepy"; "Bashful"; ...
426s       "Sneezy"; "Dopey"];
426s  y = linkage (D, "average");
426s  dendrogram (y, "Orientation", "left", "Labels", L);
426s  title ("horizontal orientation and labels");
426s ***** shared visibility_setting
426s  visibility_setting = get (0, "DefaultFigureVisible");
426s ***** test
426s  hf = figure ("visible", "off");
426s  unwind_protect
426s    y = [4, 5; 2, 6; 3, 7; 8, 9; 1, 10];
426s    y(:,3) = 1:5;
426s    dendrogram (y);
426s  unwind_protect_cleanup
426s    close (hf);
426s  end_unwind_protect
426s ***** test
426s  hf = figure ("visible", "off");
426s  unwind_protect
426s    y = [4, 5; 2, 6; 3, 7; 8, 9; 1, 10];
426s    y(:,3) = 1:5;
426s    dendrogram (y);
426s  unwind_protect_cleanup
426s    close (hf);
426s  end_unwind_protect
426s ***** test
426s  hf = figure ("visible", "off");
426s  unwind_protect
426s    v = 2 * rand (30, 1) - 1;
426s    d = abs (bsxfun (@minus, v(:, 1), v(:, 1)'));
426s    y = linkage (squareform (d, "tovector"));
426s    dendrogram (y);
426s  unwind_protect_cleanup
426s    close (hf);
426s  end_unwind_protect
427s ***** test
427s  hf = figure ("visible", "off");
427s  unwind_protect
427s    X = randn (30, 2);
427s    D = pdist (X);
427s    y = linkage (D, "average");
427s    order = optimalleaforder (y, D);
427s    subplot (2, 1, 1);
427s    title ("original leaf order");
427s    dendrogram (y);
427s    subplot (2, 1, 2);
427s    title ("optimal leaf order");
427s    dendrogram (y, "Reorder", order);
427s  unwind_protect_cleanup
427s    close (hf);
427s  end_unwind_protect
429s ***** error dendrogram ();
429s ***** error <tree must be .*> dendrogram (ones (2, 2), 1);
429s ***** error <unknown property .*> dendrogram ([1 2 1], 1, "xxx", "xxx");
429s ***** error <reorder.*> dendrogram ([1 2 1], "Reorder", "xxx");
429s ***** error <reorder.*> dendrogram ([1 2 1], "Reorder", [1 2 3 4]);
429s  fail ('dendrogram ([1 2 1], "Orientation", "north")', "invalid orientation .*")
429s 9 tests, 9 passed, 0 known failure, 0 skipped
429s [inst/dist_fit/betafit.m]
429s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/betafit.m
429s ***** demo
429s  ## Sample 2 populations from different Beta distributions
429s  randg ("seed", 1);   # for reproducibility
429s  r1 = betarnd (2, 5, 500, 1);
429s  randg ("seed", 2);   # for reproducibility
429s  r2 = betarnd (2, 2, 500, 1);
429s  r = [r1, r2];
429s 
429s  ## Plot them normalized and fix their colors
429s  hist (r, 12, 15);
429s  h = findobj (gca, "Type", "patch");
429s  set (h(1), "facecolor", "c");
429s  set (h(2), "facecolor", "g");
429s  hold on
429s 
429s  ## Estimate their shape parameters
429s  a_b_A = betafit (r(:,1));
429s  a_b_B = betafit (r(:,2));
429s 
429s  ## Plot their estimated PDFs
429s  x = [min(r(:)):0.01:max(r(:))];
429s  y = betapdf (x, a_b_A(1), a_b_A(2));
429s  plot (x, y, "-pr");
429s  y = betapdf (x, a_b_B(1), a_b_B(2));
429s  plot (x, y, "-sg");
429s  ylim ([0, 4])
429s  legend ({"Normalized HIST of sample 1 with α=2 and β=5", ...
429s           "Normalized HIST of sample 2 with α=2 and β=2", ...
429s           sprintf("PDF for sample 1 with estimated α=%0.2f and β=%0.2f", ...
429s                   a_b_A(1), a_b_A(2)), ...
429s           sprintf("PDF for sample 2 with estimated α=%0.2f and β=%0.2f", ...
429s                   a_b_B(1), a_b_B(2))})
429s  title ("Two population samples from different Beta distributions")
429s  hold off
429s ***** test
429s  x = 0.01:0.02:0.99;
429s  [paramhat, paramci] = betafit (x);
429s  paramhat_out = [1.0199, 1.0199];
429s  paramci_out = [0.6947, 0.6947; 1.4974, 1.4974];
429s  assert (paramhat, paramhat_out, 1e-4);
429s  assert (paramci, paramci_out, 1e-4);
429s ***** test
429s  x = 0.01:0.02:0.99;
429s  [paramhat, paramci] = betafit (x, 0.01);
429s  paramci_out = [0.6157, 0.6157; 1.6895, 1.6895];
429s  assert (paramci, paramci_out, 1e-4);
429s ***** test
429s  x = 0.00:0.02:1;
429s  [paramhat, paramci] = betafit (x);
429s  paramhat_out = [0.0875, 0.1913];
429s  paramci_out = [0.0822, 0.1490; 0.0931, 0.2455];
429s  assert (paramhat, paramhat_out, 1e-4);
429s  assert (paramci, paramci_out, 1e-4);
429s ***** error<betafit: X must be a vector of real values.> betafit ([0.2, 0.5+i]);
429s ***** error<betafit: X must be a vector of real values.> betafit (ones (2,2) * 0.5);
429s ***** error<betafit: X must be in the range> betafit ([0.5, 1.2]);
429s ***** error<betafit: X must contain distinct values.> betafit ([0.1, 0.1]);
429s ***** error<betafit: wrong value for ALPHA.> betafit ([0.01:0.1:0.99], 1.2);
429s ***** error<betafit: X and FREQ vectors mismatch.> ...
429s  betafit ([0.01:0.01:0.05], 0.05, [1, 2, 3, 2]);
429s ***** error<betafit: FREQ must not contain negative values.> ...
429s  betafit ([0.01:0.01:0.05], 0.05, [1, 2, 3, 2, -1]);
429s ***** error<betafit: FREQ must contain integer values.> ...
429s  betafit ([0.01:0.01:0.05], 0.05, [1, 2, 3, 2, 1.5]);
429s ***** error<betafit: 'options' argument must be a structure> ...
429s  betafit ([0.01:0.01:0.05], 0.05, struct ("option", 234));
429s ***** error<betafit: 'options' argument must be a structure> ...
429s  betafit ([0.01:0.01:0.05], 0.05, ones (1,5), struct ("option", 234));
429s 13 tests, 13 passed, 0 known failure, 0 skipped
429s [inst/dist_fit/betalike.m]
429s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/betalike.m
429s ***** test
429s  x = 0.01:0.02:0.99;
429s  [nlogL, avar] = betalike ([2.3, 1.2], x);
429s  avar_out = [0.03691678, 0.02803056; 0.02803056, 0.03965629];
429s  assert (nlogL, 17.873477715879040, 3e-14);
429s  assert (avar, avar_out, 1e-7);
429s ***** test
429s  x = 0.01:0.02:0.99;
429s  [nlogL, avar] = betalike ([1, 4], x);
429s  avar_out = [0.02793282, 0.02717274; 0.02717274, 0.03993361];
429s  assert (nlogL, 79.648061114839550, 1e-13);
429s  assert (avar, avar_out, 1e-7);
429s ***** test
429s  x = 0.00:0.02:1;
429s  [nlogL, avar] = betalike ([1, 4], x);
429s  avar_out = [0.00000801564765, 0.00000131397245; ...
429s              0.00000131397245, 0.00070827639442];
429s  assert (nlogL, 573.2008434477486, 1e-10);
429s  assert (avar, avar_out, 1e-14);
429s ***** error<betalike: function called with too few input arguments.> ...
429s  betalike ([12, 15]);
429s ***** error<betalike: wrong parameters length.> betalike ([12, 15, 3], [1:50]);
429s ***** error<betalike: X and FREQ vectors mismatch.> ...
429s  betalike ([12, 15], ones (10, 1), ones (8,1))
429s ***** error<betalike: FREQ must not contain negative values.> ...
429s  betalike ([12, 15], ones (1, 8), [1 1 1 1 1 1 1 -1])
429s ***** error<betalike: FREQ must contain integer values.> ...
429s  betalike ([12, 15], ones (1, 8), [1 1 1 1 1 1 1 1.5])
429s 8 tests, 8 passed, 0 known failure, 0 skipped
429s [inst/dist_fit/binofit.m]
429s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/binofit.m
429s ***** demo
429s  ## Sample 2 populations from different binomial distributions
429s  rand ("seed", 1);    # for reproducibility
429s  r1 = binornd (50, 0.15, 1000, 1);
429s  rand ("seed", 2);    # for reproducibility
429s  r2 = binornd (100, 0.5, 1000, 1);
429s  r = [r1, r2];
429s 
429s  ## Plot them normalized and fix their colors
429s  hist (r, 23, 0.35);
429s  h = findobj (gca, "Type", "patch");
429s  set (h(1), "facecolor", "c");
429s  set (h(2), "facecolor", "g");
429s  hold on
429s 
429s  ## Estimate their probability of success
429s  pshatA = binofit (r(:,1), 50);
429s  pshatB = binofit (r(:,2), 100);
429s 
429s  ## Plot their estimated PDFs
429s  x = [min(r(:,1)):max(r(:,1))];
429s  y = binopdf (x, 50, mean (pshatA));
429s  plot (x, y, "-pg");
429s  x = [min(r(:,2)):max(r(:,2))];
429s  y = binopdf (x, 100, mean (pshatB));
429s  plot (x, y, "-sc");
429s  ylim ([0, 0.2])
429s  legend ({"Normalized HIST of sample 1 with ps=0.15", ...
429s           "Normalized HIST of sample 2 with ps=0.50", ...
429s           sprintf("PDF for sample 1 with estimated ps=%0.2f", ...
429s                   mean (pshatA)), ...
429s           sprintf("PDF for sample 2 with estimated ps=%0.2f", ...
429s                   mean (pshatB))})
429s  title ("Two population samples from different binomial distributions")
429s  hold off
429s ***** test
429s  x = 0:3;
429s  [pshat, psci] = binofit (x, 3);
429s  assert (pshat, [0, 0.3333, 0.6667, 1], 1e-4);
429s  assert (psci(1,:), [0, 0.7076], 1e-4);
429s  assert (psci(2,:), [0.0084, 0.9057], 1e-4);
429s  assert (psci(3,:), [0.0943, 0.9916], 1e-4);
429s  assert (psci(4,:), [0.2924, 1.0000], 1e-4);
429s ***** error<binofit: function called with too few input arguments.> ...
429s  binofit ([1 2 3 4])
429s ***** error<binofit: X cannot have negative values.> ...
429s  binofit ([-1, 4, 3, 2], [1, 2, 3, 3])
429s ***** error<binofit: X must be a vector.> binofit (ones(2), [1, 2, 3, 3])
429s ***** error<binofit: N must be a non-negative integer.> ...
429s  binofit ([1, 4, 3, 2], [1, 2, -1, 3])
429s ***** error<binofit: N must be a scalar or the same size as X.> ...
429s  binofit ([1, 4, 3, 2], [5, 5, 5])
429s ***** error<binofit: N must be at least as large as X.> ...
429s  binofit ([1, 4, 3, 2], [5, 3, 5, 5])
429s ***** error<binofit: wrong value for ALPHA.> binofit ([1, 2, 1], 3, 1.2);
429s ***** error<binofit: wrong value for ALPHA.> binofit ([1, 2, 1], 3, 0);
429s ***** error<binofit: wrong value for ALPHA.> binofit ([1, 2, 1], 3, "alpha");
429s 10 tests, 10 passed, 0 known failure, 0 skipped
429s [inst/dist_fit/binolike.m]
429s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/binolike.m
429s ***** assert (binolike ([3, 0.333], [0:3]), 6.8302, 1e-4)
429s ***** assert (binolike ([3, 0.333], 0), 1.2149, 1e-4)
429s ***** assert (binolike ([3, 0.333], 1), 0.8109, 1e-4)
429s ***** assert (binolike ([3, 0.333], 2), 1.5056, 1e-4)
429s ***** assert (binolike ([3, 0.333], 3), 3.2988, 1e-4)
429s ***** test
429s  [nlogL, acov] = binolike ([3, 0.333], 3);
429s  assert (acov(4), 0.0740, 1e-4)
429s ***** error<binolike: function called with too few input arguments.> binolike (3.25)
429s ***** error<binolike: X must be a vector.> binolike ([5, 0.2], ones (2))
429s ***** error<binolike: PARAMS must be a two-element vector.> ...
429s  binolike ([1, 0.2, 3], [1, 3, 5, 7])
429s ***** error<binolike: number of trials,> binolike ([1.5, 0.2], 1)
429s ***** error<binolike: number of trials,> binolike ([-1, 0.2], 1)
429s ***** error<binolike: number of trials,> binolike ([Inf, 0.2], 1)
429s ***** error<binolike: probability of success,> binolike ([5, 1.2], [3, 5])
429s ***** error<binolike: probability of success,> binolike ([5, -0.2], [3, 5])
429s ***** error<binolike: X and FREQ vectors mismatch.> ...
429s  binolike ([5, 0.5], ones (10, 1), ones (8,1))
429s ***** error<binolike: FREQ must not contain negative values.> ...
429s  binolike ([5, 0.5], ones (1, 8), [1 1 1 1 1 1 1 -1])
429s ***** error<binolike: X cannot have negative values.> binolike ([5, 0.2], [-1, 3])
429s ***** error<binolike: number of successes,> binolike ([5, 0.2], [3, 5, 7])
429s 18 tests, 18 passed, 0 known failure, 0 skipped
429s [inst/dist_fit/bisafit.m]
429s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/bisafit.m
429s ***** demo
429s  ## Sample 3 populations from different Birnbaum-Saunders distributions
429s  rand ("seed", 5);    # for reproducibility
429s  r1 = bisarnd (1, 0.5, 2000, 1);
429s  rand ("seed", 2);    # for reproducibility
429s  r2 = bisarnd (2, 0.3, 2000, 1);
429s  rand ("seed", 7);    # for reproducibility
429s  r3 = bisarnd (4, 0.5, 2000, 1);
429s  r = [r1, r2, r3];
429s 
429s  ## Plot them normalized and fix their colors
429s  hist (r, 80, 4.2);
429s  h = findobj (gca, "Type", "patch");
429s  set (h(1), "facecolor", "c");
429s  set (h(2), "facecolor", "g");
429s  set (h(3), "facecolor", "r");
429s  ylim ([0, 1.1]);
429s  xlim ([0, 8]);
429s  hold on
429s 
429s  ## Estimate their α and β parameters
429s  beta_gammaA = bisafit (r(:,1));
429s  beta_gammaB = bisafit (r(:,2));
429s  beta_gammaC = bisafit (r(:,3));
429s 
429s  ## Plot their estimated PDFs
429s  x = [0:0.1:8];
429s  y = bisapdf (x, beta_gammaA(1), beta_gammaA(2));
429s  plot (x, y, "-pr");
429s  y = bisapdf (x, beta_gammaB(1), beta_gammaB(2));
429s  plot (x, y, "-sg");
429s  y = bisapdf (x, beta_gammaC(1), beta_gammaC(2));
429s  plot (x, y, "-^c");
429s  hold off
429s  legend ({"Normalized HIST of sample 1 with β=1 and γ=0.5", ...
429s           "Normalized HIST of sample 2 with β=2 and γ=0.3", ...
429s           "Normalized HIST of sample 3 with β=4 and γ=0.5", ...
429s           sprintf("PDF for sample 1 with estimated β=%0.2f and γ=%0.2f", ...
429s                   beta_gammaA(1), beta_gammaA(2)), ...
429s           sprintf("PDF for sample 2 with estimated β=%0.2f and γ=%0.2f", ...
429s                   beta_gammaB(1), beta_gammaB(2)), ...
429s           sprintf("PDF for sample 3 with estimated β=%0.2f and γ=%0.2f", ...
429s                   beta_gammaC(1), beta_gammaC(2))})
429s  title ("Three population samples from different Birnbaum-Saunders distributions")
429s  hold off
429s ***** test
429s  paramhat = bisafit ([1:50]);
429s  paramhat_out = [16.2649, 1.0156];
429s  assert (paramhat, paramhat_out, 1e-4);
429s ***** test
429s  paramhat = bisafit ([1:5]);
429s  paramhat_out = [2.5585, 0.5839];
429s  assert (paramhat, paramhat_out, 1e-4);
429s ***** error<bisafit: X must be a vector.> bisafit (ones (2,5));
429s ***** error<bisafit: X must contain only positive values.> bisafit ([-1 2 3 4]);
429s ***** error<bisafit: wrong value for ALPHA.> bisafit ([1, 2, 3, 4, 5], 1.2);
429s ***** error<bisafit: wrong value for ALPHA.> bisafit ([1, 2, 3, 4, 5], 0);
429s ***** error<bisafit: wrong value for ALPHA.> bisafit ([1, 2, 3, 4, 5], "alpha");
429s ***** error<bisafit: X and CENSOR vectors mismatch.> ...
429s  bisafit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
429s ***** error<bisafit: X and CENSOR vectors mismatch.> ...
429s  bisafit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
429s ***** error<bisafit: X and FREQ vectors mismatch.> ...
429s  bisafit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
429s ***** error<bisafit: X and FREQ vectors mismatch.> ...
429s  bisafit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
429s ***** error<bisafit: 'options' 5th argument must be a structure> ...
429s  bisafit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
429s 12 tests, 12 passed, 0 known failure, 0 skipped
430s [inst/dist_fit/bisalike.m]
430s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/bisalike.m
430s ***** test
430s  nlogL = bisalike ([16.2649, 1.0156], [1:50]);
430s  assert (nlogL, 215.5905, 1e-4);
430s ***** test
430s  nlogL = bisalike ([2.5585, 0.5839], [1:5]);
430s  assert (nlogL, 8.9950, 1e-4);
430s ***** error<bisalike: function called with too few input arguments.> bisalike (3.25)
430s ***** error<bisalike: X must be a vector.> bisalike ([5, 0.2], ones (2))
430s ***** error<bisalike: X cannot have negative values.> bisalike ([5, 0.2], [-1, 3])
430s ***** error<bisalike: PARAMS must be a two-element vector.> ...
430s  bisalike ([1, 0.2, 3], [1, 3, 5, 7])
430s ***** error<bisalike: X and CENSOR vector mismatch.> ...
430s  bisalike ([1.5, 0.2], [1:5], [0, 0, 0])
430s ***** error<bisalike: X and FREQ vector mismatch.> ...
430s  bisalike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
430s ***** error<bisalike: X and FREQ vector mismatch.> ...
430s  bisalike ([1.5, 0.2], [1:5], [], [1, 1, 1])
430s 9 tests, 9 passed, 0 known failure, 0 skipped
430s [inst/dist_fit/burrfit.m]
430s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/burrfit.m
430s ***** demo
430s  ## Sample 3 populations from different Burr type XII distributions
430s  rand ("seed", 4);    # for reproducibility
430s  r1 = burrrnd (3.5, 2, 2.5, 10000, 1);
430s  rand ("seed", 2);    # for reproducibility
430s  r2 = burrrnd (1, 3, 1, 10000, 1);
430s  rand ("seed", 9);    # for reproducibility
430s  r3 = burrrnd (0.5, 2, 3, 10000, 1);
430s  r = [r1, r2, r3];
430s 
430s  ## Plot them normalized and fix their colors
430s  hist (r, [0.1:0.2:20], [18, 5, 3]);
430s  h = findobj (gca, "Type", "patch");
430s  set (h(1), "facecolor", "c");
430s  set (h(2), "facecolor", "g");
430s  set (h(3), "facecolor", "r");
430s  ylim ([0, 3]);
430s  xlim ([0, 5]);
430s  hold on
430s 
430s  ## Estimate their α and β parameters
430s  lambda_c_kA = burrfit (r(:,1));
430s  lambda_c_kB = burrfit (r(:,2));
430s  lambda_c_kC = burrfit (r(:,3));
430s 
430s  ## Plot their estimated PDFs
430s  x = [0.01:0.15:15];
430s  y = burrpdf (x, lambda_c_kA(1), lambda_c_kA(2), lambda_c_kA(3));
430s  plot (x, y, "-pr");
430s  y = burrpdf (x, lambda_c_kB(1), lambda_c_kB(2), lambda_c_kB(3));
430s  plot (x, y, "-sg");
430s  y = burrpdf (x, lambda_c_kC(1), lambda_c_kC(2), lambda_c_kC(3));
430s  plot (x, y, "-^c");
430s  hold off
430s  legend ({"Normalized HIST of sample 1 with λ=3.5, c=2, and k=2.5", ...
430s           "Normalized HIST of sample 2 with λ=1, c=3, and k=1", ...
430s           "Normalized HIST of sample 3 with λ=0.5, c=2, and k=3", ...
430s   sprintf("PDF for sample 1 with estimated λ=%0.2f, c=%0.2f, and k=%0.2f", ...
430s           lambda_c_kA(1), lambda_c_kA(2), lambda_c_kA(3)), ...
430s   sprintf("PDF for sample 2 with estimated λ=%0.2f, c=%0.2f, and k=%0.2f", ...
430s           lambda_c_kB(1), lambda_c_kB(2), lambda_c_kB(3)), ...
430s   sprintf("PDF for sample 3 with estimated λ=%0.2f, c=%0.2f, and k=%0.2f", ...
430s           lambda_c_kC(1), lambda_c_kC(2), lambda_c_kC(3))})
430s  title ("Three population samples from different Burr type XII distributions")
430s  hold off
430s ***** test
430s  l = 1; c = 2; k = 3;
430s  r = burrrnd (l, c, k, 100000, 1);
430s  lambda_c_kA = burrfit (r);
430s  assert (lambda_c_kA(1), l, 0.2);
430s  assert (lambda_c_kA(2), c, 0.2);
430s  assert (lambda_c_kA(3), k, 0.3);
433s ***** test
433s  l = 0.5; c = 1; k = 3;
433s  r = burrrnd (l, c, k, 100000, 1);
433s  lambda_c_kA = burrfit (r);
433s  assert (lambda_c_kA(1), l, 0.2);
433s  assert (lambda_c_kA(2), c, 0.2);
433s  assert (lambda_c_kA(3), k, 0.3);
437s ***** test
437s  l = 1; c = 3; k = 1;
437s  r = burrrnd (l, c, k, 100000, 1);
437s  lambda_c_kA = burrfit (r);
437s  assert (lambda_c_kA(1), l, 0.2);
437s  assert (lambda_c_kA(2), c, 0.2);
437s  assert (lambda_c_kA(3), k, 0.3);
440s ***** test
440s  l = 3; c = 2; k = 1;
440s  r = burrrnd (l, c, k, 100000, 1);
440s  lambda_c_kA = burrfit (r);
440s  assert (lambda_c_kA(1), l, 0.2);
440s  assert (lambda_c_kA(2), c, 0.2);
440s  assert (lambda_c_kA(3), k, 0.3);
444s ***** test
444s  l = 4; c = 2; k = 4;
444s  r = burrrnd (l, c, k, 100000, 1);
444s  lambda_c_kA = burrfit (r);
444s  assert (lambda_c_kA(1), l, 0.2);
444s  assert (lambda_c_kA(2), c, 0.2);
444s  assert (lambda_c_kA(3), k, 0.3);
447s ***** error<burrfit: X must be a vector.> burrfit (ones (2,5));
447s ***** error<burrfit: X must contain only positive values.> burrfit ([-1 2 3 4]);
447s ***** error<burrfit: wrong value for ALPHA.> burrfit ([1, 2, 3, 4, 5], 1.2);
447s ***** error<burrfit: wrong value for ALPHA.> burrfit ([1, 2, 3, 4, 5], 0);
447s ***** error<burrfit: wrong value for ALPHA.> burrfit ([1, 2, 3, 4, 5], "alpha");
447s ***** error<burrfit: X and CENSOR vectors mismatch.> ...
447s  burrfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
447s ***** error<burrfit: X and CENSOR vectors mismatch.> ...
447s  burrfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
447s ***** error<burrfit: X and FREQ vectors mismatch.>
447s  burrfit ([1, 2, 3, 4, 5], 0.05, [], [1, 1, 5])
447s ***** error<burrfit: FREQ must not contain negative values.>
447s  burrfit ([1, 2, 3, 4, 5], 0.05, [], [1, 5, 1, 1, -1])
447s ***** error<burrfit: 'options' 5th argument must be a structure> ...
447s  burrfit ([1:10], 0.05, [], [], 5)
447s 15 tests, 15 passed, 0 known failure, 0 skipped
447s [inst/dist_fit/burrlike.m]
447s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/burrlike.m
447s ***** error<burrlike: function called with too few input arguments.> burrlike (3.25)
447s ***** error<burrlike: X must be a vector.> burrlike ([1, 2, 3], ones (2))
447s ***** error<burrlike: X cannot have negative values.> burrlike ([1, 2, 3], [-1, 3])
447s ***** error<burrlike: PARAMS must be a three-element vector.> ...
447s  burrlike ([1, 2], [1, 3, 5, 7])
447s ***** error<burrlike: PARAMS must be a three-element vector.> ...
447s  burrlike ([1, 2, 3, 4], [1, 3, 5, 7])
447s ***** error<burrlike: X and CENSOR vector mismatch.> ...
447s  burrlike ([1, 2, 3], [1:5], [0, 0, 0])
447s ***** error<burrlike: X and FREQ vector mismatch.> ...
447s  burrlike ([1, 2, 3], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
447s ***** error<burrlike: X and FREQ vector mismatch.> ...
447s  burrlike ([1, 2, 3], [1:5], [], [1, 1, 1])
447s 8 tests, 8 passed, 0 known failure, 0 skipped
447s [inst/dist_fit/evfit.m]
447s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/evfit.m
447s ***** demo
447s  ## Sample 3 populations from different extreme value distributions
447s  rand ("seed", 1);    # for reproducibility
447s  r1 = evrnd (2, 5, 400, 1);
447s  rand ("seed", 12);    # for reproducibility
447s  r2 = evrnd (-5, 3, 400, 1);
447s  rand ("seed", 13);    # for reproducibility
447s  r3 = evrnd (14, 8, 400, 1);
447s  r = [r1, r2, r3];
447s 
447s  ## Plot them normalized and fix their colors
447s  hist (r, 25, 0.4);
447s  h = findobj (gca, "Type", "patch");
447s  set (h(1), "facecolor", "c");
447s  set (h(2), "facecolor", "g");
447s  set (h(3), "facecolor", "r");
447s  ylim ([0, 0.28])
447s  xlim ([-30, 30]);
447s  hold on
447s 
447s  ## Estimate their MU and SIGMA parameters
447s  mu_sigmaA = evfit (r(:,1));
447s  mu_sigmaB = evfit (r(:,2));
447s  mu_sigmaC = evfit (r(:,3));
447s 
447s  ## Plot their estimated PDFs
447s  x = [min(r(:)):max(r(:))];
447s  y = evpdf (x, mu_sigmaA(1), mu_sigmaA(2));
447s  plot (x, y, "-pr");
447s  y = evpdf (x, mu_sigmaB(1), mu_sigmaB(2));
447s  plot (x, y, "-sg");
447s  y = evpdf (x, mu_sigmaC(1), mu_sigmaC(2));
447s  plot (x, y, "-^c");
447s  legend ({"Normalized HIST of sample 1 with μ=2 and σ=5", ...
447s           "Normalized HIST of sample 2 with μ=-5 and σ=3", ...
447s           "Normalized HIST of sample 3 with μ=14 and σ=8", ...
447s           sprintf("PDF for sample 1 with estimated μ=%0.2f and σ=%0.2f", ...
447s                   mu_sigmaA(1), mu_sigmaA(2)), ...
447s           sprintf("PDF for sample 2 with estimated μ=%0.2f and σ=%0.2f", ...
447s                   mu_sigmaB(1), mu_sigmaB(2)), ...
447s           sprintf("PDF for sample 3 with estimated μ=%0.2f and σ=%0.2f", ...
447s                   mu_sigmaC(1), mu_sigmaC(2))})
447s  title ("Three population samples from different extreme value distributions")
447s  hold off
447s ***** test
447s  x = 1:50;
447s  [paramhat, paramci] = evfit (x);
447s  paramhat_out = [32.6811, 13.0509];
447s  paramci_out = [28.8504, 10.5294; 36.5118, 16.1763];
447s  assert (paramhat, paramhat_out, 1e-4);
447s  assert (paramci, paramci_out, 1e-4);
447s ***** test
447s  x = 1:50;
447s  [paramhat, paramci] = evfit (x, 0.01);
447s  paramci_out = [27.6468, 9.8426; 37.7155, 17.3051];
447s  assert (paramci, paramci_out, 1e-4);
447s ***** error<evfit: X must be a double-precision vector.> evfit (ones (2,5));
447s ***** error<evfit: X must be a double-precision vector.> evfit (single (ones (1,5)));
447s ***** error<evfit: X must NOT contain missing values> evfit ([1, 2, 3, 4, NaN]);
447s ***** error<evfit: wrong value for ALPHA.> evfit ([1, 2, 3, 4, 5], 1.2);
447s ***** error<evfit: X and FREQ vectors mismatch.>
447s  evfit ([1 2 3], 0.05, [], [1 5])
447s ***** error<evfit: FREQ must not contain negative values.>
447s  evfit ([1 2 3], 0.05, [], [1 5 -1])
447s ***** error<evfit: 'options' 5th argument must be a structure> ...
447s  evfit ([1:10], 0.05, [], [], 5)
447s 9 tests, 9 passed, 0 known failure, 0 skipped
447s [inst/dist_fit/evlike.m]
447s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/evlike.m
447s ***** test
447s  x = 1:50;
447s  [nlogL, acov] = evlike ([2.3, 1.2], x);
447s  avar_out = [-1.2778e-13, 3.1859e-15; 3.1859e-15, -7.9430e-17];
447s  assert (nlogL, 3.242264755689906e+17, 1e-14);
447s  assert (acov, avar_out, 1e-3);
447s ***** test
447s  x = 1:50;
447s  [nlogL, acov] = evlike ([2.3, 1.2], x * 0.5);
447s  avar_out = [-7.6094e-05, 3.9819e-06; 3.9819e-06, -2.0836e-07];
447s  assert (nlogL, 481898704.0472211, 1e-6);
447s  assert (acov, avar_out, 1e-3);
447s ***** test
447s  x = 1:50;
447s  [nlogL, acov] = evlike ([21, 15], x);
447s  avar_out = [11.73913876598908, -5.9546128523121216; ...
447s              -5.954612852312121, 3.708060045170236];
447s  assert (nlogL, 223.7612479380652, 1e-13);
447s  assert (acov, avar_out, 1e-14);
447s ***** error<evlike: function called with too few input arguments.> evlike ([12, 15])
447s ***** error<evlike: wrong parameters length.> evlike ([12, 15, 3], [1:50])
447s ***** error<evlike: X must be a vector.> evlike ([12, 3], ones (10, 2))
447s ***** error<evlike: X and CENSOR vectors mismatch.> ...
447s  evlike ([12, 15], [1:50], [1, 2, 3])
447s ***** error<evlike: X and FREQ vectors mismatch.> ...
447s  evlike ([12, 15], [1:50], [], [1, 2, 3])
447s 8 tests, 8 passed, 0 known failure, 0 skipped
447s [inst/dist_fit/expfit.m]
447s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/expfit.m
447s ***** demo
447s  ## Sample 3 populations from 3 different exponential distributions
447s  rande ("seed", 1);   # for reproducibility
447s  r1 = exprnd (2, 4000, 1);
447s  rande ("seed", 2);   # for reproducibility
447s  r2 = exprnd (5, 4000, 1);
447s  rande ("seed", 3);   # for reproducibility
447s  r3 = exprnd (12, 4000, 1);
447s  r = [r1, r2, r3];
447s 
447s  ## Plot them normalized and fix their colors
447s  hist (r, 48, 0.52);
447s  h = findobj (gca, "Type", "patch");
447s  set (h(1), "facecolor", "c");
447s  set (h(2), "facecolor", "g");
447s  set (h(3), "facecolor", "r");
447s  hold on
447s 
447s  ## Estimate their mu parameter
447s  muhat = expfit (r);
447s 
447s  ## Plot their estimated PDFs
447s  x = [0:max(r(:))];
447s  y = exppdf (x, muhat(1));
447s  plot (x, y, "-pr");
447s  y = exppdf (x, muhat(2));
447s  plot (x, y, "-sg");
447s  y = exppdf (x, muhat(3));
447s  plot (x, y, "-^c");
447s  ylim ([0, 0.6])
447s  xlim ([0, 40])
447s  legend ({"Normalized HIST of sample 1 with μ=2", ...
447s           "Normalized HIST of sample 2 with μ=5", ...
447s           "Normalized HIST of sample 3 with μ=12", ...
447s           sprintf("PDF for sample 1 with estimated μ=%0.2f", muhat(1)), ...
447s           sprintf("PDF for sample 2 with estimated μ=%0.2f", muhat(2)), ...
447s           sprintf("PDF for sample 3 with estimated μ=%0.2f", muhat(3))})
447s  title ("Three population samples from different exponential distributions")
447s  hold off
447s ***** assert (expfit (1), 1)
447s ***** assert (expfit (1:3), 2)
447s ***** assert (expfit ([1:3]'), 2)
447s ***** assert (expfit (1:3, []), 2)
447s ***** assert (expfit (1:3, [], [], []), 2)
447s ***** assert (expfit (magic (3)), [5 5 5])
447s ***** assert (expfit (cat (3, magic (3), 2*magic (3))), cat (3,[5 5 5], [10 10 10]))
447s ***** assert (expfit (1:3, 0.1, [0 0 0], [1 1 1]), 2)
447s ***** assert (expfit ([1:3]', 0.1, [0 0 0]', [1 1 1]'), 2)
447s ***** assert (expfit (1:3, 0.1, [0 0 0]', [1 1 1]'), 2)
447s ***** assert (expfit (1:3, 0.1, [1 0 0], [1 1 1]), 3)
447s ***** assert (expfit (1:3, 0.1, [0 0 0], [4 1 1]), 1.5)
447s ***** assert (expfit (1:3, 0.1, [1 0 0], [4 1 1]), 4.5)
447s ***** assert (expfit (1:3, 0.1, [1 0 1], [4 1 1]), 9)
447s ***** assert (expfit (1:3, 0.1, [], [-1 1 1]), 4)
447s ***** assert (expfit (1:3, 0.1, [], [0.5 1 1]), 2.2)
447s ***** assert (expfit (1:3, 0.1, [1 1 1]), NaN)
447s ***** assert (expfit (1:3, 0.1, [], [0 0 0]), NaN)
447s ***** assert (expfit (reshape (1:9, [3 3])), [2 5 8])
447s ***** assert (expfit (reshape (1:9, [3 3]), [], eye(3)), [3 7.5 12])
447s ***** assert (expfit (reshape (1:9, [3 3]), [], 2*eye(3)), [3 7.5 12])
447s ***** assert (expfit (reshape (1:9, [3 3]), [], [], [2 2 2; 1 1 1; 1 1 1]), ...
447s  [1.75 4.75 7.75])
447s ***** assert (expfit (reshape (1:9, [3 3]), [], [], [2 2 2; 1 1 1; 1 1 1]), ...
447s  [1.75 4.75 7.75])
447s ***** assert (expfit (reshape (1:9, [3 3]), [], eye(3), [2 2 2; 1 1 1; 1 1 1]), ...
447s  [3.5 19/3 31/3])
448s ***** assert ([~,muci] = expfit (1:3, 0), [0; Inf])
448s ***** assert ([~,muci] = expfit (1:3, 2), [Inf; 0])
448s ***** assert ([~,muci] = expfit (1:3, 0.1, [1 1 1]), [NaN; NaN])
448s ***** assert ([~,muci] = expfit (1:3, 0.1, [], [0 0 0]), [NaN; NaN])
448s ***** assert ([~,muci] = expfit (1:3, -1), [NaN; NaN])
448s ***** assert ([~,muci] = expfit (1:3, 5), [NaN; NaN])
448s ***** assert ([~,muci] = expfit (1:3), [0.830485728373393; 9.698190330474096], ...
448s              1000*eps)
448s ***** assert ([~,muci] = expfit (1:3, 0.1), ...
448s                           [0.953017262058213; 7.337731146400207], 1000*eps)
448s ***** assert ([~,muci] = expfit ([1:3;2:4]), ...
448s              [0.538440777613095, 0.897401296021825, 1.256361814430554; ...
448s              12.385982973214016, 20.643304955356694, 28.900626937499371], ...
448s              1000*eps)
448s ***** assert ([~,muci] = expfit ([1:3;2:4], [], [1 1 1; 0 0 0]), ...
448s              100*[0.008132550920455, 0.013554251534091, 0.018975952147727; ...
448s              1.184936706156216, 1.974894510260360, 2.764852314364504], ...
448s              1000*eps)
448s ***** assert ([~,muci] = expfit ([1:3;2:4], [], [], [3 3 3; 1 1 1]), ...
448s              [0.570302756652583, 1.026544961974649, 1.482787167296715; ...
448s              4.587722594914109, 8.257900670845396, 11.928078746776684], ...
448s              1000*eps)
448s ***** assert ([~,muci] = expfit ([1:3;2:4], [], [0 0 0; 1 1 1], [3 3 3; 1 1 1]), ...
448s              [0.692071440311161, 1.245728592560089, 1.799385744809018; ...
448s              8.081825275395081, 14.547285495711145, 21.012745716027212], ...
448s              1000*eps)
448s ***** test
448s  x = reshape (1:8, [4 2]);
448s  x(4) = NaN;
448s  [muhat,muci] = expfit (x);
448s  assert ({muhat, muci}, {[NaN, 6.5], ...
448s          [NaN, 2.965574334593430;NaN, 23.856157493553368]}, 1000*eps);
448s ***** test
448s  x = magic (3);
448s  censor = [0 1 0; 0 1 0; 0 1 0];
448s  freq = [1 1 0; 1 1 0; 1 1 0];
448s  [muhat,muci] = expfit (x, [], censor, freq);
448s  assert ({muhat, muci}, {[5 NaN NaN], ...
448s                  [[2.076214320933482; 24.245475826185242],NaN(2)]}, 1000*eps);
448s ***** error expfit ()
448s ***** error expfit (1,2,3,4,5)
448s ***** error [a b censor] = expfit (1)
448s ***** error <ALPHA must be a scalar quantity> expfit (1, [1 2])
448s ***** error <X cannot be negative> expfit ([-1 2 3 4 5])
448s ***** error <CENSOR must be a numeric or logical array> expfit ([1:5], [], "test")
448s ***** error <FREQ must be a numeric or logical array> expfit ([1:5], [], [], "test")
448s ***** error <X and CENSOR vectors mismatch.> expfit ([1:5], [], [0 0 0 0])
448s ***** error <X and FREQ vectors mismatch.> expfit ([1:5], [], [], [1 1 1 1])
448s 47 tests, 47 passed, 0 known failure, 0 skipped
448s [inst/dist_fit/explike.m]
448s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/explike.m
448s ***** test
448s  x = 12;
448s  beta = 5;
448s  [L, V] = explike (beta, x);
448s  expected_L = 4.0094;
448s  expected_V = 6.5789;
448s  assert (L, expected_L, 0.001);
448s  assert (V, expected_V, 0.001);
448s ***** test
448s  x = 1:5;
448s  beta = 2;
448s  [L, V] = explike (beta, x);
448s  expected_L = 10.9657;
448s  expected_V = 0.4;
448s  assert (L, expected_L, 0.001);
448s  assert (V, expected_V, 0.001);
448s ***** error<explike: function called with too few input arguments.> explike ()
448s ***** error<explike: function called with too few input arguments.> explike (2)
448s ***** error<explike: MU must be a scalar.> explike ([12, 3], [1:50])
448s ***** error<explike: X must be a vector.> explike (3, ones (10, 2))
448s ***** error<explike: X and CENSOR vectors mismatch.> ...
448s  explike (3, [1:50], [1, 2, 3])
448s ***** error<explike: X and FREQ vectors mismatch.> ...
448s  explike (3, [1:50], [], [1, 2, 3])
448s 8 tests, 8 passed, 0 known failure, 0 skipped
448s [inst/dist_fit/gamfit.m]
448s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/gamfit.m
448s ***** demo
448s  ## Sample 3 populations from different Gamma distributions
448s  randg ("seed", 5);    # for reproducibility
448s  r1 = gamrnd (1, 2, 2000, 1);
448s  randg ("seed", 2);    # for reproducibility
448s  r2 = gamrnd (2, 2, 2000, 1);
448s  randg ("seed", 7);    # for reproducibility
448s  r3 = gamrnd (7.5, 1, 2000, 1);
448s  r = [r1, r2, r3];
448s 
448s  ## Plot them normalized and fix their colors
448s  hist (r, 75, 4);
448s  h = findobj (gca, "Type", "patch");
448s  set (h(1), "facecolor", "c");
448s  set (h(2), "facecolor", "g");
448s  set (h(3), "facecolor", "r");
448s  ylim ([0, 0.62]);
448s  xlim ([0, 12]);
448s  hold on
448s 
448s  ## Estimate their α and β parameters
448s  a_bA = gamfit (r(:,1));
448s  a_bB = gamfit (r(:,2));
448s  a_bC = gamfit (r(:,3));
448s 
448s  ## Plot their estimated PDFs
448s  x = [0.01,0.1:0.2:18];
448s  y = gampdf (x, a_bA(1), a_bA(2));
448s  plot (x, y, "-pr");
448s  y = gampdf (x, a_bB(1), a_bB(2));
448s  plot (x, y, "-sg");
448s  y = gampdf (x, a_bC(1), a_bC(2));
448s  plot (x, y, "-^c");
448s  hold off
448s  legend ({"Normalized HIST of sample 1 with α=1 and β=2", ...
448s           "Normalized HIST of sample 2 with α=2 and β=2", ...
448s           "Normalized HIST of sample 3 with α=7.5 and β=1", ...
448s           sprintf("PDF for sample 1 with estimated α=%0.2f and β=%0.2f", ...
448s                   a_bA(1), a_bA(2)), ...
448s           sprintf("PDF for sample 2 with estimated α=%0.2f and β=%0.2f", ...
448s                   a_bB(1), a_bB(2)), ...
448s           sprintf("PDF for sample 3 with estimated α=%0.2f and β=%0.2f", ...
448s                   a_bC(1), a_bC(2))})
448s  title ("Three population samples from different Gamma distributions")
448s  hold off
448s ***** shared x
448s  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];
448s ***** test
448s  [paramhat, paramci] = gamfit (x);
448s  assert (paramhat, [3.4248, 0.9752], 1e-4);
448s  assert (paramci, [1.7287, 0.4670; 6.7852, 2.0366], 1e-4);
448s ***** test
448s  [paramhat, paramci] = gamfit (x, 0.01);
448s  assert (paramhat, [3.4248, 0.9752], 1e-4);
448s  assert (paramci, [1.3945, 0.3705; 8.4113, 2.5668], 1e-4);
448s ***** test
448s  freq = [1 1 1 1 2 1 1 1 1 2 1 1 1 1 2];
448s  [paramhat, paramci] = gamfit (x, [], [], freq);
448s  assert (paramhat, [3.3025, 1.0615], 1e-4);
448s  assert (paramci, [1.7710, 0.5415; 6.1584, 2.0806], 1e-4);
448s ***** test
448s  [paramhat, paramci] = gamfit (x, [], [], [1:15]);
448s  assert (paramhat, [4.4484, 0.9689], 1e-4);
448s  assert (paramci, [3.4848, 0.7482; 5.6785, 1.2546], 1e-4);
448s ***** test
448s  [paramhat, paramci] = gamfit (x, 0.01, [], [1:15]);
448s  assert (paramhat, [4.4484, 0.9689], 1e-4);
448s  assert (paramci, [3.2275, 0.6899; 6.1312, 1.3608], 1e-4);
448s ***** test
448s  cens = [0 0 0 0 1 0 0 0 0 0 0 0 0 0 0];
448s  [paramhat, paramci] = gamfit (x, [], cens, [1:15]);
448s  assert (paramhat, [4.7537, 0.9308], 1e-4);
448s  assert (paramci, [3.7123, 0.7162; 6.0872, 1.2097], 1e-4);
448s ***** test
448s  cens = [0 0 0 0 1 0 0 0 0 0 0 0 0 0 0];
448s  freq = [1 1 1 1 2 1 1 1 1 2 1 1 1 1 2];
448s  [paramhat, paramci] = gamfit (x, [], cens, freq);
448s  assert (paramhat, [3.4736, 1.0847], 1e-4);
448s  assert (paramci, [1.8286, 0.5359; 6.5982, 2.1956], 1e-4);
448s ***** test
448s  [paramhat, paramci] = gamfit ([1 1 1 1 1 1]);
448s  assert (paramhat, [Inf, 0]);
448s  assert (paramci, [Inf, 0; Inf, 0]);
448s ***** test
448s  [paramhat, paramci] = gamfit ([1 1 1 1 1 1], [], [1 1 1 1 1 1]);
448s  assert (paramhat, [NaN, NaN]);
448s  assert (paramci, [NaN, NaN; NaN, NaN]);
448s ***** test
448s  [paramhat, paramci] = gamfit ([1 1 1 1 1 1], [], [], [1 1 1 1 1 1]);
448s  assert (paramhat, [Inf, 0]);
448s  assert (paramci, [Inf, 0; Inf, 0]);
448s ***** assert (class (gamfit (single (x))), "single")
448s ***** error<gamfit: X must be a vector.> gamfit (ones (2))
448s ***** error<gamfit: wrong value for ALPHA.> gamfit (x, 1)
448s ***** error<gamfit: wrong value for ALPHA.> gamfit (x, -1)
448s ***** error<gamfit: wrong value for ALPHA.> gamfit (x, {0.05})
448s ***** error<gamfit: wrong value for ALPHA.> gamfit (x, "a")
448s ***** error<gamfit: wrong value for ALPHA.> gamfit (x, i)
448s ***** error<gamfit: wrong value for ALPHA.> gamfit (x, [0.01 0.02])
448s ***** error<gamfit: X and FREQ vectors mismatch.>
448s  gamfit ([1 2 3], 0.05, [], [1 5])
448s ***** error<gamfit: FREQ must not contain negative values.>
448s  gamfit ([1 2 3], 0.05, [], [1 5 -1])
448s ***** error<gamfit: 'options' 5th argument must be a structure> ...
448s  gamfit ([1:10], 0.05, [], [], 5)
448s ***** error<gamfit: X cannot contain negative values.> gamfit ([1 2 3 -4])
448s ***** error<gamfit: X must contain positive values when censored.> ...
448s  gamfit ([1 2 0], [], [1 0 0])
448s 23 tests, 23 passed, 0 known failure, 0 skipped
448s [inst/dist_fit/gamlike.m]
449s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/gamlike.m
449s ***** test
449s  [nlogL, acov] = gamlike([2, 3], [2, 3, 4, 5, 6, 7, 8, 9]);
449s  assert (nlogL, 19.4426, 1e-4);
449s  assert (acov, [2.7819, -5.0073; -5.0073, 9.6882], 1e-4);
449s ***** test
449s  [nlogL, acov] = gamlike([2, 3], [5:45]);
449s  assert (nlogL, 305.8070, 1e-4);
449s  assert (acov, [0.0423, -0.0087; -0.0087, 0.0167], 1e-4);
449s ***** test
449s  [nlogL, acov] = gamlike([2, 13], [5:45]);
449s  assert (nlogL, 163.2261, 1e-4);
449s  assert (acov, [0.2362, -1.6631; -1.6631, 13.9440], 1e-4);
449s ***** error<gamlike: function called with too few input arguments.> ...
449s  gamlike ([12, 15])
449s ***** error<gamlike: wrong parameters length.> gamlike ([12, 15, 3], [1:50])
449s ***** error<gamlike: X must be a vector.> gamlike ([12, 3], ones (10, 2))
449s ***** error<gamlike: X and CENSOR vectors mismatch.> ...
449s  gamlike ([12, 15], [1:50], [1, 2, 3])
449s ***** error<gamlike: X and FREQ vectors mismatch.> ...
449s  gamlike ([12, 15], [1:50], [], [1, 2, 3])
449s 8 tests, 8 passed, 0 known failure, 0 skipped
449s [inst/dist_fit/geofit.m]
449s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/geofit.m
449s ***** demo
449s  ## Sample 2 populations from different geometric distributions
449s  rande ("seed", 1);    # for reproducibility
449s  r1 = geornd (0.15, 1000, 1);
449s  rande ("seed", 2);    # for reproducibility
449s  r2 = geornd (0.5, 1000, 1);
449s  r = [r1, r2];
449s 
449s  ## Plot them normalized and fix their colors
449s  hist (r, 0:0.5:20.5, 1);
449s  h = findobj (gca, "Type", "patch");
449s  set (h(1), "facecolor", "c");
449s  set (h(2), "facecolor", "g");
449s  hold on
449s 
449s  ## Estimate their probability of success
449s  pshatA = geofit (r(:,1));
449s  pshatB = geofit (r(:,2));
449s 
449s  ## Plot their estimated PDFs
449s  x = [0:15];
449s  y = geopdf (x, pshatA);
449s  plot (x, y, "-pg");
449s  y = geopdf (x, pshatB);
449s  plot (x, y, "-sc");
449s  xlim ([0, 15])
449s  ylim ([0, 0.6])
449s  legend ({"Normalized HIST of sample 1 with ps=0.15", ...
449s           "Normalized HIST of sample 2 with ps=0.50", ...
449s           sprintf("PDF for sample 1 with estimated ps=%0.2f", ...
449s                   mean (pshatA)), ...
449s           sprintf("PDF for sample 2 with estimated ps=%0.2f", ...
449s                   mean (pshatB))})
449s  title ("Two population samples from different geometric distributions")
449s  hold off
449s ***** test
449s  x = 0:5;
449s  [pshat, psci] = geofit (x);
449s  assert (pshat, 0.2857, 1e-4);
449s  assert (psci, [0.092499; 0.478929], 1e-5);
449s ***** test
449s  x = 0:5;
449s  [pshat, psci] = geofit (x, [], [1 1 1 1 1 1]);
449s  assert (pshat, 0.2857, 1e-4);
449s  assert (psci, [0.092499; 0.478929], 1e-5);
449s ***** assert (geofit ([1 1 2 3]), geofit ([1 2 3], [] ,[2 1 1]))
449s ***** error<geofit: function called with too few input arguments.> geofit ()
449s ***** error<geofit: X cannot have negative values.> geofit (-1, [1 2 3 3])
449s ***** error<geofit: wrong value for ALPHA.> geofit (1, 0)
449s ***** error<geofit: wrong value for ALPHA.> geofit (1, 1.2)
449s ***** error<geofit: wrong value for ALPHA.> geofit (1, [0.02 0.05])
449s ***** error<geofit: X and FREQ vector mismatch.> ...
449s  geofit ([1.5, 0.2], [], [0, 0, 0, 0, 0])
449s ***** error<geofit: X and FREQ vector mismatch.> ...
449s  geofit ([1.5, 0.2], [], [1, 1, 1])
449s 10 tests, 10 passed, 0 known failure, 0 skipped
449s [inst/dist_fit/gevfit.m]
449s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/gevfit.m
449s ***** demo
449s  ## Sample 2 populations from 2 different exponential distributions
449s  rand ("seed", 1);   # for reproducibility
449s  r1 = gevrnd (-0.5, 1, 2, 5000, 1);
449s  rand ("seed", 2);   # for reproducibility
449s  r2 = gevrnd (0, 1, -4, 5000, 1);
449s  r = [r1, r2];
449s 
449s  ## Plot them normalized and fix their colors
449s  hist (r, 50, 5);
449s  h = findobj (gca, "Type", "patch");
449s  set (h(1), "facecolor", "c");
449s  set (h(2), "facecolor", "g");
449s  hold on
449s 
449s  ## Estimate their k, sigma, and mu parameters
449s  k_sigma_muA = gevfit (r(:,1));
449s  k_sigma_muB = gevfit (r(:,2));
449s 
449s  ## Plot their estimated PDFs
449s  x = [-10:0.5:20];
449s  y = gevpdf (x, k_sigma_muA(1), k_sigma_muA(2), k_sigma_muA(3));
449s  plot (x, y, "-pr");
449s  y = gevpdf (x, k_sigma_muB(1), k_sigma_muB(2), k_sigma_muB(3));
449s  plot (x, y, "-sg");
449s  ylim ([0, 0.7])
449s  xlim ([-7, 5])
449s  legend ({"Normalized HIST of sample 1 with k=-0.5, σ=1, μ=2", ...
449s           "Normalized HIST of sample 2 with k=0, σ=1, μ=-4",
449s      sprintf("PDF for sample 1 with estimated k=%0.2f, σ=%0.2f, μ=%0.2f", ...
449s                  k_sigma_muA(1), k_sigma_muA(2), k_sigma_muA(3)), ...
449s      sprintf("PDF for sample 3 with estimated k=%0.2f, σ=%0.2f, μ=%0.2f", ...
449s                  k_sigma_muB(1), k_sigma_muB(2), k_sigma_muB(3))})
449s  title ("Two population samples from different exponential distributions")
449s  hold off
449s ***** test
449s  x = 1:50;
449s  [pfit, pci] = gevfit (x);
449s  pfit_out = [-0.4407, 15.1923, 21.5309];
449s  pci_out = [-0.7532, 11.5878, 16.5686; -0.1282, 19.9183, 26.4926];
449s  assert (pfit, pfit_out, 1e-3);
449s  assert (pci, pci_out, 1e-3);
449s ***** test
449s  x = 1:2:50;
449s  [pfit, pci] = gevfit (x);
449s  pfit_out = [-0.4434, 15.2024, 21.0532];
449s  pci_out = [-0.8904, 10.3439, 14.0168; 0.0035, 22.3429, 28.0896];
449s  assert (pfit, pfit_out, 1e-3);
449s  assert (pci, pci_out, 1e-3);
449s ***** error<gevfit: X must be a vector.> gevfit (ones (2,5));
449s ***** error<gevfit: wrong value for ALPHA.> gevfit ([1, 2, 3, 4, 5], 1.2);
449s ***** error<gevfit: wrong value for ALPHA.> gevfit ([1, 2, 3, 4, 5], 0);
449s ***** error<gevfit: wrong value for ALPHA.> gevfit ([1, 2, 3, 4, 5], "alpha");
449s ***** error<gevfit: X and FREQ vectors mismatch.> ...
449s  gevfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2]);
449s ***** error<gevfit: FREQ must not contain negative values.> ...
449s  gevfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2, -1]);
449s ***** error<gevfit: FREQ must contain integer values.> ...
449s  gevfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2, 1.5]);
449s ***** error<gevfit: 'options' argument must be a structure> ...
449s  gevfit ([1, 2, 3, 4, 5], 0.05, struct ("option", 234));
449s ***** error<gevfit: 'options' argument must be a structure> ...
449s  gevfit ([1, 2, 3, 4, 5], 0.05, ones (1,5), struct ("option", 234));
449s 11 tests, 11 passed, 0 known failure, 0 skipped
449s [inst/dist_fit/gevfit_lmom.m]
449s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/gevfit_lmom.m
449s ***** xtest <31070>
449s  data = 1:50;
449s  [pfit, pci] = gevfit_lmom (data);
449s  expected_p = [-0.28 15.01 20.22]';
449s  assert (pfit, expected_p, 0.1);
449s 1 test, 1 passed, 0 known failure, 0 skipped
449s [inst/dist_fit/gevlike.m]
449s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/gevlike.m
449s ***** test
449s  x = 1;
449s  k = 0.2;
449s  sigma = 0.3;
449s  mu = 0.5;
449s  [L, C] = gevlike ([k sigma mu], x);
449s  expected_L = 0.75942;
449s  expected_C = [-0.12547 1.77884 1.06731; 1.77884 16.40761 8.48877; 1.06731 8.48877 0.27979];
449s  assert (L, expected_L, 0.001);
449s  assert (C, inv (expected_C), 0.001);
449s ***** test
449s  x = 1;
449s  k = 0;
449s  sigma = 0.3;
449s  mu = 0.5;
449s  [L, C] = gevlike ([k sigma mu], x);
449s  expected_L = 0.65157;
449s  expected_C = [0.090036 3.41229 2.047337; 3.412229 24.760027 12.510190; 2.047337 12.510190 2.098618];
449s  assert (L, expected_L, 0.001);
449s  assert (C, inv (expected_C), 0.001);
449s ***** test
449s  x = -5:-1;
449s  k = -0.2;
449s  sigma = 0.3;
449s  mu = 0.5;
449s  [L, C] = gevlike ([k sigma mu], x);
449s  expected_L = 3786.4;
449s  expected_C = [1.6802e-07, 4.6110e-06, 8.7297e-05; ...
449s                4.6110e-06, 7.5693e-06, 1.2034e-05; ...
449s                8.7297e-05, 1.2034e-05, -0.0019125];
449s  assert (L, expected_L, -0.001);
449s  assert (C, expected_C, -0.001);
449s ***** test
449s  x = -5:0;
449s  k = -0.2;
449s  sigma = 0.3;
449s  mu = 0.5;
449s  [L, C] = gevlike ([k sigma mu], x, [1, 1, 1, 1, 1, 0]);
449s  expected_L = 3786.4;
449s  expected_C = [1.6802e-07, 4.6110e-06, 8.7297e-05; ...
449s                4.6110e-06, 7.5693e-06, 1.2034e-05; ...
449s                8.7297e-05, 1.2034e-05, -0.0019125];
449s  assert (L, expected_L, -0.001);
449s  assert (C, expected_C, -0.001);
449s ***** error<gevlike: function called with too few input arguments.> gevlike (3.25)
449s ***** error<gevlike: X must be a vector.> gevlike ([1, 2, 3], ones (2))
449s ***** error<gevlike: PARAMS must be a three-element vector.> ...
449s  gevlike ([1, 2], [1, 3, 5, 7])
449s ***** error<gevlike: PARAMS must be a three-element vector.> ...
449s  gevlike ([1, 2, 3, 4], [1, 3, 5, 7])
449s ***** error<gevlike: X and FREQ vectors mismatch.> ...
449s  gevlike ([5, 0.2, 1], ones (10, 1), ones (8,1))
449s ***** error<gevlike: FREQ must not contain negative values.> ...
449s  gevlike ([5, 0.2, 1], ones (1, 8), [1 1 1 1 1 1 1 -1])
449s ***** error<gevlike: FREQ must contain integer values.> ...
449s  gevlike ([5, 0.2, 1], ones (1, 8), [1 1 1 1 1 1 1 1.5])
449s 11 tests, 11 passed, 0 known failure, 0 skipped
449s [inst/dist_fit/gpfit.m]
449s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/gpfit.m
449s ***** demo
449s  ## Sample 2 populations from different generalized Pareto distributions
449s  ## Assume location parameter θ is known
449s  theta = 0;
449s  rand ("seed", 5);    # for reproducibility
449s  r1 = gprnd (1, 2, theta, 20000, 1);
449s  rand ("seed", 2);    # for reproducibility
449s  r2 = gprnd (3, 1, theta, 20000, 1);
449s  r = [r1, r2];
449s 
449s  ## Plot them normalized and fix their colors
449s  hist (r, [0.1:0.2:100], 5);
449s  h = findobj (gca, "Type", "patch");
449s  set (h(1), "facecolor", "r");
449s  set (h(2), "facecolor", "c");
449s  ylim ([0, 1]);
449s  xlim ([0, 5]);
449s  hold on
449s 
449s  ## Estimate their α and β parameters
449s  k_sigmaA = gpfit (r(:,1), theta);
449s  k_sigmaB = gpfit (r(:,2), theta);
449s 
449s  ## Plot their estimated PDFs
449s  x = [0.01, 0.1:0.2:18];
449s  y = gppdf (x, k_sigmaA(1), k_sigmaA(2), theta);
449s  plot (x, y, "-pc");
449s  y = gppdf (x, k_sigmaB(1), k_sigmaB(2), theta);
449s  plot (x, y, "-sr");
449s  hold off
449s  legend ({"Normalized HIST of sample 1 with k=1 and σ=2", ...
449s           "Normalized HIST of sample 2 with k=2 and σ=2", ...
449s           sprintf("PDF for sample 1 with estimated k=%0.2f and σ=%0.2f", ...
449s                   k_sigmaA(1), k_sigmaA(2)), ...
449s           sprintf("PDF for sample 3 with estimated k=%0.2f and σ=%0.2f", ...
449s                   k_sigmaB(1), k_sigmaB(2))})
449s  title ("Two population samples from different generalized Pareto distributions")
449s  text (2, 0.7, "Known location parameter θ = 0")
449s  hold off
449s ***** test
449s  k = 0.8937; sigma = 1.3230; theta = 1;
449s  x = [2.2196, 11.9301, 4.3673, 1.0949, 6.5626, ...
449s       1.2109, 1.8576, 1.0039, 12.7917, 2.2590];
449s  [hat, ci] = gpfit (x, theta);
449s  assert (hat, [k, sigma, theta], 1e-4);
449s  assert (ci, [-0.7750, 0.2437, 1; 2.5624, 7.1820, 1], 1e-4);
449s ***** error<gpfit: function called with too few input arguments.> gpfit ()
449s ***** error<gpfit: function called with too few input arguments.> gpfit (1)
449s ***** error<gpfit: X must be a vector of real values.> gpfit ([0.2, 0.5+i], 0);
449s ***** error<gpfit: X must be a vector of real values.> gpfit (ones (2,2) * 0.5, 0);
449s ***** error<gpfit: THETA must be a real scalar value.> ...
449s  gpfit ([0.5, 1.2], [0, 1]);
449s ***** error<gpfit: THETA must be a real scalar value.> ...
449s  gpfit ([0.5, 1.2], 5+i);
449s ***** error<gpfit: X cannot contain values less than THETA.> ...
449s  gpfit ([1:5], 2);
449s ***** error<gpfit: wrong value for ALPHA.> gpfit ([0.01:0.1:0.99], 0, 1.2);
449s ***** error<gpfit: wrong value for ALPHA.> gpfit ([0.01:0.1:0.99], 0, i);
449s ***** error<gpfit: wrong value for ALPHA.> gpfit ([0.01:0.1:0.99], 0, -1);
449s ***** error<gpfit: wrong value for ALPHA.> gpfit ([0.01:0.1:0.99], 0, [0.05, 0.01]);
449s ***** error<gpfit: X and FREQ vectors mismatch.>
449s  gpfit ([1 2 3], 0, [], [1 5])
449s ***** error<gpfit: FREQ must not contain negative values.>
449s  gpfit ([1 2 3], 0, [], [1 5 -1])
449s ***** error<gpfit: 'options' 5th argument must be a structure> ...
449s  gpfit ([1:10], 1, 0.05, [], 5)
449s 15 tests, 15 passed, 0 known failure, 0 skipped
449s [inst/dist_fit/gplike.m]
449s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/gplike.m
449s ***** test
449s  k = 0.8937; sigma = 1.3230; theta = 1;
449s  x = [2.2196, 11.9301, 4.3673, 1.0949, 6.5626, ...
449s       1.2109, 1.8576, 1.0039, 12.7917, 2.2590];
449s  [nlogL, acov] = gplike ([k, sigma, theta], x);
449s  assert (nlogL, 21.736, 1e-3);
449s  assert (acov, [0.7249, -0.7351, 0; -0.7351, 1.3040, 0; 0, 0, 0], 1e-4);
449s ***** assert (gplike ([2, 3, 0], 4), 3.047536764863501, 1e-14)
449s ***** assert (gplike ([2, 3, 4], 8), 3.047536764863501, 1e-14)
449s ***** assert (gplike ([1, 2, 0], 4), 2.890371757896165, 1e-14)
449s ***** assert (gplike ([1, 2, 4], 8), 2.890371757896165, 1e-14)
449s ***** assert (gplike ([2, 3, 0], [1:10]), 32.57864322725392, 1e-14)
449s ***** assert (gplike ([2, 3, 2], [1:10] + 2), 32.57864322725392, 1e-14)
449s ***** assert (gplike ([2, 3, 0], [1:10], ones (1,10)), 32.57864322725392, 1e-14)
449s ***** assert (gplike ([1, 2, 0], [1:10]), 31.65666282460443, 1e-14)
449s ***** assert (gplike ([1, 2, 3], [1:10] + 3), 31.65666282460443, 1e-14)
449s ***** assert (gplike ([1, 2, 0], [1:10], ones (1,10)), 31.65666282460443, 1e-14)
449s ***** assert (gplike ([1, NaN, 0], [1:10]), NaN)
449s ***** error<gplike: function called with too few input arguments.> gplike ()
449s ***** error<gplike: function called with too few input arguments.> gplike (1)
449s ***** error<gplike: X must be a vector.> gplike ([1, 2, 0], [])
449s ***** error<gplike: X must be a vector.> gplike ([1, 2, 0], ones (2))
449s ***** error<gplike: PARAMS must be a three-element vector.> gplike (2, [1:10])
449s ***** error<gplike: PARAMS must be a three-element vector.> gplike ([2, 3], [1:10])
449s ***** error<gplike: X and FREQ vectors mismatch.> ...
449s  gplike ([1, 2, 0], ones (10, 1), ones (8,1))
449s ***** error<gplike: FREQ must not contain negative values.> ...
449s  gplike ([1, 2, 0], ones (1, 8), [1 1 1 1 1 1 1 -1])
449s 20 tests, 20 passed, 0 known failure, 0 skipped
449s [inst/dist_fit/gumbelfit.m]
449s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/gumbelfit.m
449s ***** demo
449s  ## Sample 3 populations from different Gumbel distributions
449s  rand ("seed", 1);    # for reproducibility
449s  r1 = gumbelrnd (2, 5, 400, 1);
449s  rand ("seed", 11);    # for reproducibility
449s  r2 = gumbelrnd (-5, 3, 400, 1);
449s  rand ("seed", 16);    # for reproducibility
449s  r3 = gumbelrnd (14, 8, 400, 1);
449s  r = [r1, r2, r3];
449s 
449s  ## Plot them normalized and fix their colors
449s  hist (r, 25, 0.32);
449s  h = findobj (gca, "Type", "patch");
449s  set (h(1), "facecolor", "c");
449s  set (h(2), "facecolor", "g");
449s  set (h(3), "facecolor", "r");
449s  ylim ([0, 0.28])
449s  xlim ([-11, 50]);
449s  hold on
449s 
449s  ## Estimate their MU and BETA parameters
449s  mu_betaA = gumbelfit (r(:,1));
449s  mu_betaB = gumbelfit (r(:,2));
449s  mu_betaC = gumbelfit (r(:,3));
449s 
449s  ## Plot their estimated PDFs
449s  x = [min(r(:)):max(r(:))];
449s  y = gumbelpdf (x, mu_betaA(1), mu_betaA(2));
449s  plot (x, y, "-pr");
449s  y = gumbelpdf (x, mu_betaB(1), mu_betaB(2));
449s  plot (x, y, "-sg");
449s  y = gumbelpdf (x, mu_betaC(1), mu_betaC(2));
449s  plot (x, y, "-^c");
449s  legend ({"Normalized HIST of sample 1 with μ=2 and β=5", ...
449s           "Normalized HIST of sample 2 with μ=-5 and β=3", ...
449s           "Normalized HIST of sample 3 with μ=14 and β=8", ...
449s           sprintf("PDF for sample 1 with estimated μ=%0.2f and β=%0.2f", ...
449s                   mu_betaA(1), mu_betaA(2)), ...
449s           sprintf("PDF for sample 2 with estimated μ=%0.2f and β=%0.2f", ...
449s                   mu_betaB(1), mu_betaB(2)), ...
449s           sprintf("PDF for sample 3 with estimated μ=%0.2f and β=%0.2f", ...
449s                   mu_betaC(1), mu_betaC(2))})
449s  title ("Three population samples from different Gumbel distributions")
449s  hold off
449s ***** test
449s  x = 1:50;
449s  [paramhat, paramci] = gumbelfit (x);
449s  paramhat_out = [18.3188, 13.0509];
449s  paramci_out = [14.4882, 10.5294; 22.1495, 16.1763];
449s  assert (paramhat, paramhat_out, 1e-4);
449s  assert (paramci, paramci_out, 1e-4);
449s ***** test
449s  x = 1:50;
449s  [paramhat, paramci] = gumbelfit (x, 0.01);
449s  paramci_out = [13.2845, 9.8426; 23.3532, 17.3051];
449s  assert (paramci, paramci_out, 1e-4);
449s ***** error<gumbelfit: X must be a double-precision vector.> gumbelfit (ones (2,5));
449s ***** error<gumbelfit: X must be a double-precision vector.> ...
449s  gumbelfit (single (ones (1,5)));
449s ***** error<gumbelfit: X must NOT contain missing values> ...
449s  gumbelfit ([1, 2, 3, 4, NaN]);
449s ***** error<gumbelfit: wrong value for ALPHA.> gumbelfit ([1, 2, 3, 4, 5], 1.2);
449s ***** error<gumbelfit: X and CENSOR vectors mismatch.> ...
449s  gumbelfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
449s ***** error<gumbelfit: X and FREQ vectors mismatch.> ...
449s  gumbelfit ([1, 2, 3, 4, 5], 0.05, [], [1 1 0]);
449s ***** error<gamfit: FREQ must not contain negative values.>
449s  gamfit ([1, 2, 3], 0.05, [], [1 5 -1])
449s ***** error<gumbelfit: 'options' 5th argument> ...
449s  gumbelfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
449s 10 tests, 10 passed, 0 known failure, 0 skipped
449s [inst/dist_fit/gumbellike.m]
449s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/gumbellike.m
449s ***** test
449s  x = 1:50;
449s  [nlogL, avar] = gumbellike ([2.3, 1.2], x);
449s  avar_out = [-1.2778e-13, 3.1859e-15; 3.1859e-15, -7.9430e-17];
449s  assert (nlogL, 3.242264755689906e+17, 1e-14);
449s  assert (avar, avar_out, 1e-3);
449s ***** test
449s  x = 1:50;
449s  [nlogL, avar] = gumbellike ([2.3, 1.2], x * 0.5);
449s  avar_out = [-7.6094e-05, 3.9819e-06; 3.9819e-06, -2.0836e-07];
449s  assert (nlogL, 481898704.0472211, 1e-6);
449s  assert (avar, avar_out, 1e-3);
449s ***** test
449s  x = 1:50;
449s  [nlogL, avar] = gumbellike ([21, 15], x);
449s  avar_out = [11.73913876598908, -5.9546128523121216; ...
449s              -5.954612852312121, 3.708060045170236];
449s  assert (nlogL, 223.7612479380652, 1e-13);
449s  assert (avar, avar_out, 1e-14);
449s ***** error<gumbellike: too few input arguments.> gumbellike ([12, 15]);
449s ***** error<gumbellike: wrong parameters length.> gumbellike ([12, 15, 3], [1:50]);
449s ***** error<gumbellike: X must be a vector.> gumbellike ([12, 3], ones (10, 2));
449s ***** error<gumbellike: X and CENSOR> gumbellike ([12, 15], [1:50], [1, 2, 3]);
449s ***** error<gumbellike: X and FREQ> gumbellike ([12, 15], [1:50], [], [1, 2, 3]);
449s 8 tests, 8 passed, 0 known failure, 0 skipped
449s [inst/dist_fit/hnfit.m]
449s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/hnfit.m
449s ***** demo
449s  ## Sample 2 populations from different half-normal distributions
449s  rand ("seed", 1);   # for reproducibility
449s  r1 = hnrnd (0, 5, 5000, 1);
449s  rand ("seed", 2);   # for reproducibility
449s  r2 = hnrnd (0, 2, 5000, 1);
449s  r = [r1, r2];
449s 
449s  ## Plot them normalized and fix their colors
449s  hist (r, [0.5:20], 1);
449s  h = findobj (gca, "Type", "patch");
449s  set (h(1), "facecolor", "c");
449s  set (h(2), "facecolor", "g");
449s  hold on
449s 
449s  ## Estimate their shape parameters
449s  mu_sigmaA = hnfit (r(:,1), 0);
449s  mu_sigmaB = hnfit (r(:,2), 0);
449s 
449s  ## Plot their estimated PDFs
449s  x = [0:0.2:10];
449s  y = hnpdf (x, mu_sigmaA(1), mu_sigmaA(2));
449s  plot (x, y, "-pr");
449s  y = hnpdf (x, mu_sigmaB(1), mu_sigmaB(2));
449s  plot (x, y, "-sg");
449s  xlim ([0, 10])
449s  ylim ([0, 0.5])
449s  legend ({"Normalized HIST of sample 1 with μ=0 and σ=5", ...
449s           "Normalized HIST of sample 2 with μ=0 and σ=2", ...
449s           sprintf("PDF for sample 1 with estimated μ=%0.2f and σ=%0.2f", ...
449s                   mu_sigmaA(1), mu_sigmaA(2)), ...
449s           sprintf("PDF for sample 2 with estimated μ=%0.2f and σ=%0.2f", ...
449s                   mu_sigmaB(1), mu_sigmaB(2))})
449s  title ("Two population samples from different half-normal distributions")
449s  hold off
449s ***** test
449s  x = 1:20;
449s  [paramhat, paramci] = hnfit (x, 0);
449s  assert (paramhat, [0, 11.9791], 1e-4);
449s  assert (paramci, [0, 9.1648; 0, 17.2987], 1e-4);
449s ***** test
449s  x = 1:20;
449s  [paramhat, paramci] = hnfit (x, 0, 0.01);
449s  assert (paramci, [0, 8.4709; 0, 19.6487], 1e-4);
449s ***** error<hnfit: function called with too few input arguments.> hnfit ()
449s ***** error<hnfit: function called with too few input arguments.> hnfit (1)
449s ***** error<hnfit: X must be a vector of real values.> hnfit ([0.2, 0.5+i], 0);
449s ***** error<hnfit: X must be a vector of real values.> hnfit (ones (2,2) * 0.5, 0);
449s ***** error<hnfit: MU must be a real scalar value.> ...
449s  hnfit ([0.5, 1.2], [0, 1]);
449s ***** error<hnfit: MU must be a real scalar value.> ...
449s  hnfit ([0.5, 1.2], 5+i);
449s ***** error<hnfit: X cannot contain values less than MU.> ...
449s  hnfit ([1:5], 2);
449s ***** error<hnfit: wrong value for ALPHA.> hnfit ([0.01:0.1:0.99], 0, 1.2);
449s ***** error<hnfit: wrong value for ALPHA.> hnfit ([0.01:0.1:0.99], 0, i);
449s ***** error<hnfit: wrong value for ALPHA.> hnfit ([0.01:0.1:0.99], 0, -1);
449s ***** error<hnfit: wrong value for ALPHA.> hnfit ([0.01:0.1:0.99], 0, [0.05, 0.01]);
449s ***** error<hnfit: X and FREQ vectors mismatch.>
449s  hnfit ([1 2 3], 0, [], [1 5])
449s ***** error<hnfit: FREQ must not contain negative values.>
449s  hnfit ([1 2 3], 0, [], [1 5 -1])
449s 15 tests, 15 passed, 0 known failure, 0 skipped
449s [inst/dist_fit/hnlike.m]
449s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/hnlike.m
449s ***** test
449s  x = 1:20;
449s  paramhat = hnfit (x, 0);
449s  [nlogL, acov] = hnlike (paramhat, x);
449s  assert (nlogL, 64.179177404891300, 1e-14);
449s ***** test
449s  x = 1:20;
449s  paramhat = hnfit (x, 0);
449s  [nlogL, acov] = hnlike (paramhat, x, ones (1, 20));
449s  assert (nlogL, 64.179177404891300, 1e-14);
449s ***** error<hnlike: function called with too few input arguments.> ...
449s  hnlike ([12, 15]);
449s ***** error<hnlike: wrong parameters length.> hnlike ([12, 15, 3], [1:50]);
449s ***** error<hnlike: wrong parameters length.> hnlike ([3], [1:50]);
449s ***** error<hnlike: X must be a vector of real values.> ...
449s  hnlike ([0, 3], ones (2));
449s ***** error<hnlike: X must be a vector of real values.> ...
449s  hnlike ([0, 3], [1, 2, 3, 4, 5+i]);
449s ***** error<hnlike: X and FREQ vectors mismatch.> ...
449s  hnlike ([1, 2], ones (10, 1), ones (8,1))
449s ***** error<hnlike: FREQ must not contain negative values.> ...
449s  hnlike ([1, 2], ones (1, 8), [1 1 1 1 1 1 1 -1])
449s 9 tests, 9 passed, 0 known failure, 0 skipped
449s [inst/dist_fit/invgfit.m]
449s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/invgfit.m
449s ***** demo
449s  ## Sample 3 populations from different inverse Gaussian distributions
449s  rand ("seed", 5); randn ("seed", 5);   # for reproducibility
449s  r1 = invgrnd (1, 0.2, 2000, 1);
449s  rand ("seed", 2); randn ("seed", 2);   # for reproducibility
449s  r2 = invgrnd (1, 3, 2000, 1);
449s  rand ("seed", 7); randn ("seed", 7);   # for reproducibility
449s  r3 = invgrnd (3, 1, 2000, 1);
449s  r = [r1, r2, r3];
449s 
449s  ## Plot them normalized and fix their colors
449s  hist (r, [0.1:0.1:3.2], 9);
449s  h = findobj (gca, "Type", "patch");
449s  set (h(1), "facecolor", "c");
449s  set (h(2), "facecolor", "g");
449s  set (h(3), "facecolor", "r");
449s  ylim ([0, 3]);
449s  xlim ([0, 3]);
449s  hold on
449s 
449s  ## Estimate their MU and LAMBDA parameters
449s  mu_lambdaA = invgfit (r(:,1));
449s  mu_lambdaB = invgfit (r(:,2));
449s  mu_lambdaC = invgfit (r(:,3));
449s 
449s  ## Plot their estimated PDFs
449s  x = [0:0.1:3];
449s  y = invgpdf (x, mu_lambdaA(1), mu_lambdaA(2));
449s  plot (x, y, "-pr");
449s  y = invgpdf (x, mu_lambdaB(1), mu_lambdaB(2));
449s  plot (x, y, "-sg");
449s  y = invgpdf (x, mu_lambdaC(1), mu_lambdaC(2));
449s  plot (x, y, "-^c");
449s  hold off
449s  legend ({"Normalized HIST of sample 1 with μ=1 and λ=0.5", ...
449s           "Normalized HIST of sample 2 with μ=2 and λ=0.3", ...
449s           "Normalized HIST of sample 3 with μ=4 and λ=0.5", ...
449s           sprintf("PDF for sample 1 with estimated μ=%0.2f and λ=%0.2f", ...
449s                   mu_lambdaA(1), mu_lambdaA(2)), ...
449s           sprintf("PDF for sample 2 with estimated μ=%0.2f and λ=%0.2f", ...
449s                   mu_lambdaB(1), mu_lambdaB(2)), ...
449s           sprintf("PDF for sample 3 with estimated μ=%0.2f and λ=%0.2f", ...
449s                   mu_lambdaC(1), mu_lambdaC(2))})
449s  title ("Three population samples from different inverse Gaussian distributions")
449s  hold off
449s ***** test
449s  paramhat = invgfit ([1:50]);
449s  paramhat_out = [25.5, 19.6973];
449s  assert (paramhat, paramhat_out, 1e-4);
449s ***** test
449s  paramhat = invgfit ([1:5]);
449s  paramhat_out = [3, 8.1081];
449s  assert (paramhat, paramhat_out, 1e-4);
449s ***** error<invgfit: X must be a vector.> invgfit (ones (2,5));
449s ***** error<invgfit: X must contain only positive values.> invgfit ([-1 2 3 4]);
449s ***** error<invgfit: wrong value for ALPHA.> invgfit ([1, 2, 3, 4, 5], 1.2);
449s ***** error<invgfit: wrong value for ALPHA.> invgfit ([1, 2, 3, 4, 5], 0);
449s ***** error<invgfit: wrong value for ALPHA.> invgfit ([1, 2, 3, 4, 5], "alpha");
449s ***** error<invgfit: X and CENSOR vectors mismatch.> ...
449s  invgfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
449s ***** error<invgfit: X and CENSOR vectors mismatch.> ...
449s  invgfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
449s ***** error<invgfit: X and FREQ vectors mismatch.> ...
449s  invgfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
449s ***** error<invgfit: X and FREQ vectors mismatch.> ...
449s  invgfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
449s ***** error<invgfit: 'options' 5th argument must be a structure> ...
449s  invgfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
449s 12 tests, 12 passed, 0 known failure, 0 skipped
449s [inst/dist_fit/invglike.m]
449s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/invglike.m
449s ***** test
449s  nlogL = invglike ([25.5, 19.6973], [1:50]);
449s  assert (nlogL, 219.1516, 1e-4);
449s ***** test
449s  nlogL = invglike ([3, 8.1081], [1:5]);
449s  assert (nlogL, 9.0438, 1e-4);
449s ***** error<invglike: function called with too few input arguments.> invglike (3.25)
449s ***** error<invglike: X must be a vector.> invglike ([5, 0.2], ones (2))
449s ***** error<invglike: X must have positive values.> invglike ([5, 0.2], [-1, 3])
449s ***** error<invglike: PARAMS must be a two-element vector.> ...
449s  invglike ([1, 0.2, 3], [1, 3, 5, 7])
449s ***** error<invglike: X and CENSOR vector mismatch.> ...
449s  invglike ([1.5, 0.2], [1:5], [0, 0, 0])
449s ***** error<invglike: X and FREQ vector mismatch.> ...
449s  invglike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
449s ***** error<invglike: X and FREQ vector mismatch.> ...
449s  invglike ([1.5, 0.2], [1:5], [], [1, 1, 1])
450s 9 tests, 9 passed, 0 known failure, 0 skipped
450s [inst/dist_fit/logifit.m]
450s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/logifit.m
450s ***** demo
450s  ## Sample 3 populations from different logistic distributions
450s  rand ("seed", 5)  # for reproducibility
450s  r1 = logirnd (2, 1, 2000, 1);
450s  rand ("seed", 2)   # for reproducibility
450s  r2 = logirnd (5, 2, 2000, 1);
450s  rand ("seed", 7)   # for reproducibility
450s  r3 = logirnd (9, 4, 2000, 1);
450s  r = [r1, r2, r3];
450s 
450s  ## Plot them normalized and fix their colors
450s  hist (r, [-6:20], 1);
450s  h = findobj (gca, "Type", "patch");
450s  set (h(1), "facecolor", "c");
450s  set (h(2), "facecolor", "g");
450s  set (h(3), "facecolor", "r");
450s  ylim ([0, 0.3]);
450s  xlim ([-5, 20]);
450s  hold on
450s 
450s  ## Estimate their MU and LAMBDA parameters
450s  mu_sA = logifit (r(:,1));
450s  mu_sB = logifit (r(:,2));
450s  mu_sC = logifit (r(:,3));
450s 
450s  ## Plot their estimated PDFs
450s  x = [-5:0.5:20];
450s  y = logipdf (x, mu_sA(1), mu_sA(2));
450s  plot (x, y, "-pr");
450s  y = logipdf (x, mu_sB(1), mu_sB(2));
450s  plot (x, y, "-sg");
450s  y = logipdf (x, mu_sC(1), mu_sC(2));
450s  plot (x, y, "-^c");
450s  hold off
450s  legend ({"Normalized HIST of sample 1 with μ=1 and s=0.5", ...
450s           "Normalized HIST of sample 2 with μ=2 and s=0.3", ...
450s           "Normalized HIST of sample 3 with μ=4 and s=0.5", ...
450s           sprintf("PDF for sample 1 with estimated μ=%0.2f and s=%0.2f", ...
450s                   mu_sA(1), mu_sA(2)), ...
450s           sprintf("PDF for sample 2 with estimated μ=%0.2f and s=%0.2f", ...
450s                   mu_sB(1), mu_sB(2)), ...
450s           sprintf("PDF for sample 3 with estimated μ=%0.2f and s=%0.2f", ...
450s                   mu_sC(1), mu_sC(2))})
450s  title ("Three population samples from different logistic distributions")
450s  hold off
450s ***** test
450s  paramhat = logifit ([1:50]);
450s  paramhat_out = [25.5, 8.7724];
450s  assert (paramhat, paramhat_out, 1e-4);
450s ***** test
450s  paramhat = logifit ([1:5]);
450s  paramhat_out = [3, 0.8645];
450s  assert (paramhat, paramhat_out, 1e-4);
450s ***** test
450s  paramhat = logifit ([1:6], [], [], [1 1 1 1 1 0]);
450s  paramhat_out = [3, 0.8645];
450s  assert (paramhat, paramhat_out, 1e-4);
450s ***** test
450s  paramhat = logifit ([1:5], [], [], [1 1 1 1 2]);
450s  paramhat_out = logifit ([1:5, 5]);
450s  assert (paramhat, paramhat_out, 1e-4);
450s ***** error<logifit: X must be a vector.> logifit (ones (2,5));
450s ***** error<logifit: wrong value for ALPHA.> logifit ([1, 2, 3, 4, 5], 1.2);
450s ***** error<logifit: wrong value for ALPHA.> logifit ([1, 2, 3, 4, 5], 0);
450s ***** error<logifit: wrong value for ALPHA.> logifit ([1, 2, 3, 4, 5], "alpha");
450s ***** error<logifit: X and CENSOR vectors mismatch.> ...
450s  logifit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
450s ***** error<logifit: X and CENSOR vectors mismatch.> ...
450s  logifit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
450s ***** error<logifit: X and FREQ vectors mismatch.> ...
450s  logifit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
450s ***** error<logifit: X and FREQ vectors mismatch.> ...
450s  logifit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
450s ***** error<logifit: 'options' 5th argument must be a structure> ...
450s  logifit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
450s 13 tests, 13 passed, 0 known failure, 0 skipped
450s [inst/dist_fit/logilike.m]
450s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/logilike.m
450s ***** test
450s  nlogL = logilike ([25.5, 8.7725], [1:50]);
450s  assert (nlogL, 206.6769, 1e-4);
450s ***** test
450s  nlogL = logilike ([3, 0.8645], [1:5]);
450s  assert (nlogL, 9.0699, 1e-4);
450s ***** error<logilike: function called with too few input arguments.> logilike (3.25)
450s ***** error<logilike: X must be a vector.> logilike ([5, 0.2], ones (2))
450s ***** error<logilike: PARAMS must be a two-element vector.> ...
450s  logilike ([1, 0.2, 3], [1, 3, 5, 7])
450s ***** error<logilike: X and CENSOR vector mismatch.> ...
450s  logilike ([1.5, 0.2], [1:5], [0, 0, 0])
450s ***** error<logilike: X and FREQ vector mismatch.> ...
450s  logilike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
450s ***** error<logilike: X and FREQ vector mismatch.> ...
450s  logilike ([1.5, 0.2], [1:5], [], [1, 1, 1])
450s 8 tests, 8 passed, 0 known failure, 0 skipped
450s [inst/dist_fit/loglfit.m]
450s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/loglfit.m
450s ***** demo
450s  ## Sample 3 populations from different log-logistic distributions
450s  rand ("seed", 5)  # for reproducibility
450s  r1 = loglrnd (0, 1, 2000, 1);
450s  rand ("seed", 2)   # for reproducibility
450s  r2 = loglrnd (0, 0.5, 2000, 1);
450s  rand ("seed", 7)   # for reproducibility
450s  r3 = loglrnd (0, 0.125, 2000, 1);
450s  r = [r1, r2, r3];
450s 
450s  ## Plot them normalized and fix their colors
450s  hist (r, [0.05:0.1:2.5], 10);
450s  h = findobj (gca, "Type", "patch");
450s  set (h(1), "facecolor", "c");
450s  set (h(2), "facecolor", "g");
450s  set (h(3), "facecolor", "r");
450s  ylim ([0, 3.5]);
450s  xlim ([0, 2.0]);
450s  hold on
450s 
450s  ## Estimate their MU and LAMBDA parameters
450s  a_bA = loglfit (r(:,1));
450s  a_bB = loglfit (r(:,2));
450s  a_bC = loglfit (r(:,3));
450s 
450s  ## Plot their estimated PDFs
450s  x = [0.01:0.1:2.01];
450s  y = loglpdf (x, a_bA(1), a_bA(2));
450s  plot (x, y, "-pr");
450s  y = loglpdf (x, a_bB(1), a_bB(2));
450s  plot (x, y, "-sg");
450s  y = loglpdf (x, a_bC(1), a_bC(2));
450s  plot (x, y, "-^c");
450s  legend ({"Normalized HIST of sample 1 with α=1 and β=1", ...
450s           "Normalized HIST of sample 2 with α=1 and β=2", ...
450s           "Normalized HIST of sample 3 with α=1 and β=8", ...
450s           sprintf("PDF for sample 1 with estimated α=%0.2f and β=%0.2f", ...
450s                   a_bA(1), a_bA(2)), ...
450s           sprintf("PDF for sample 2 with estimated α=%0.2f and β=%0.2f", ...
450s                   a_bB(1), a_bB(2)), ...
450s           sprintf("PDF for sample 3 with estimated α=%0.2f and β=%0.2f", ...
450s                   a_bC(1), a_bC(2))})
450s  title ("Three population samples from different log-logistic distributions")
450s  hold off
450s ***** test
450s  [paramhat, paramci] = loglfit ([1:50]);
450s  paramhat_out = [3.09717, 0.468525];
450s  paramci_out = [2.87261, 0.370616; 3.32174, 0.5923];
450s  assert (paramhat, paramhat_out, 1e-5);
450s  assert (paramci, paramci_out, 1e-5);
450s ***** test
450s  paramhat = loglfit ([1:5]);
450s  paramhat_out = [1.01124, 0.336449];
450s  assert (paramhat, paramhat_out, 1e-5);
450s ***** test
450s  paramhat = loglfit ([1:6], [], [], [1 1 1 1 1 0]);
450s  paramhat_out = [1.01124, 0.336449];
450s  assert (paramhat, paramhat_out, 1e-4);
450s ***** test
450s  paramhat = loglfit ([1:5], [], [], [1 1 1 1 2]);
450s  paramhat_out = loglfit ([1:5, 5]);
450s  assert (paramhat, paramhat_out, 1e-4);
450s ***** error<loglfit: X must be a vector.> loglfit (ones (2,5));
450s ***** error<loglfit: wrong value for ALPHA.> loglfit ([1, 2, 3, 4, 5], 1.2);
450s ***** error<loglfit: wrong value for ALPHA.> loglfit ([1, 2, 3, 4, 5], 0);
450s ***** error<loglfit: wrong value for ALPHA.> loglfit ([1, 2, 3, 4, 5], "alpha");
450s ***** error<loglfit: X and CENSOR vectors mismatch.> ...
450s  loglfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
450s ***** error<loglfit: X and CENSOR vectors mismatch.> ...
450s  loglfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
450s ***** error<loglfit: X and FREQ vectors mismatch.> ...
450s  loglfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
450s ***** error<loglfit: X and FREQ vectors mismatch.> ...
450s  loglfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
450s ***** error<loglfit: 'options' 5th argument must be a structure> ...
450s  loglfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
450s 13 tests, 13 passed, 0 known failure, 0 skipped
450s [inst/dist_fit/logllike.m]
450s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/logllike.m
450s ***** test
450s  [nlogL, acov] = logllike ([3.09717, 0.468525], [1:50]);
450s  assert (nlogL, 211.2965, 1e-4);
450s  assert (acov, [0.0131, -0.0007; -0.0007, 0.0031], 1e-4);
450s ***** test
450s  [nlogL, acov] = logllike ([1.01124, 0.336449], [1:5]);
450s  assert (nlogL, 9.2206, 1e-4);
450s  assert (acov, [0.0712, -0.0032; -0.0032, 0.0153], 1e-4);
450s ***** error<logllike: function called with too few input arguments.> logllike (3.25)
450s ***** error<logllike: X must be a vector.> logllike ([5, 0.2], ones (2))
450s ***** error<logllike: PARAMS must be a two-element vector.> ...
450s  logllike ([1, 0.2, 3], [1, 3, 5, 7])
450s ***** error<logllike: X and CENSOR vector mismatch.> ...
450s  logllike ([1.5, 0.2], [1:5], [0, 0, 0])
450s ***** error<logllike: X and FREQ vector mismatch.> ...
450s  logllike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
450s ***** error<logllike: X and FREQ vector mismatch.> ...
450s  logllike ([1.5, 0.2], [1:5], [], [1, 1, 1])
450s 8 tests, 8 passed, 0 known failure, 0 skipped
450s [inst/dist_fit/lognfit.m]
450s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/lognfit.m
450s ***** demo
450s  ## Sample 3 populations from 3 different log-normal distributions
450s  randn ("seed", 1);    # for reproducibility
450s  r1 = lognrnd (0, 0.25, 1000, 1);
450s  randn ("seed", 2);    # for reproducibility
450s  r2 = lognrnd (0, 0.5, 1000, 1);
450s  randn ("seed", 3);    # for reproducibility
450s  r3 = lognrnd (0, 1, 1000, 1);
450s  r = [r1, r2, r3];
450s 
450s  ## Plot them normalized and fix their colors
450s  hist (r, 30, 2);
450s  h = findobj (gca, "Type", "patch");
450s  set (h(1), "facecolor", "c");
450s  set (h(2), "facecolor", "g");
450s  set (h(3), "facecolor", "r");
450s  hold on
450s 
450s  ## Estimate their mu and sigma parameters
450s  mu_sigmaA = lognfit (r(:,1));
450s  mu_sigmaB = lognfit (r(:,2));
450s  mu_sigmaC = lognfit (r(:,3));
450s 
450s  ## Plot their estimated PDFs
450s  x = [0:0.1:6];
450s  y = lognpdf (x, mu_sigmaA(1), mu_sigmaA(2));
450s  plot (x, y, "-pr");
450s  y = lognpdf (x, mu_sigmaB(1), mu_sigmaB(2));
450s  plot (x, y, "-sg");
450s  y = lognpdf (x, mu_sigmaC(1), mu_sigmaC(2));
450s  plot (x, y, "-^c");
450s  ylim ([0, 2])
450s  xlim ([0, 6])
450s  hold off
450s  legend ({"Normalized HIST of sample 1 with mu=0, σ=0.25", ...
450s           "Normalized HIST of sample 2 with mu=0, σ=0.5", ...
450s           "Normalized HIST of sample 3 with mu=0, σ=1", ...
450s           sprintf("PDF for sample 1 with estimated mu=%0.2f and σ=%0.2f", ...
450s                   mu_sigmaA(1), mu_sigmaA(2)), ...
450s           sprintf("PDF for sample 2 with estimated mu=%0.2f and σ=%0.2f", ...
450s                   mu_sigmaB(1), mu_sigmaB(2)), ...
450s           sprintf("PDF for sample 3 with estimated mu=%0.2f and σ=%0.2f", ...
450s                   mu_sigmaC(1), mu_sigmaC(2))}, "location", "northeast")
450s  title ("Three population samples from different log-normal distributions")
450s  hold off
450s ***** test
450s  randn ("seed", 1);
450s  x = lognrnd (3, 5, [1000, 1]);
450s  [paramhat, paramci] = lognfit (x, 0.01);
450s  assert (paramci(1,1) < 3);
450s  assert (paramci(1,2) > 3);
450s  assert (paramci(2,1) < 5);
450s  assert (paramci(2,2) > 5);
450s ***** error<lognfit: X must be a numeric vector of positive values.> ...
450s  lognfit (ones (20,3))
450s ***** error<lognfit: X must be a numeric vector of positive values.> ...
450s  lognfit ({1, 2, 3, 4, 5})
450s ***** error<lognfit: X must be a numeric vector of positive values.> ...
450s  lognfit ([-1, 2, 3, 4, 5])
450s ***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), 0)
450s ***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), -0.3)
450s ***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), 1.2)
450s ***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), [0.05,  0.1])
450s ***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), 0.02+i)
450s ***** error<lognfit: X and CENSOR vectors mismatch.> ...
450s  lognfit (ones (20,1), [], zeros(15,1))
450s ***** error<lognfit: X and FREQ vectors mismatch.> ...
450s  lognfit (ones (20,1), [], zeros(20,1), ones(25,1))
450s ***** error<lognfit: > lognfit (ones (20,1), [], zeros(20,1), ones(20,1), "options")
450s 12 tests, 12 passed, 0 known failure, 0 skipped
450s [inst/dist_fit/lognlike.m]
450s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/lognlike.m
450s ***** test
450s  x = 1:50;
450s  [nlogL, avar] = lognlike ([0, 0.25], x);
450s  avar_out = [-5.4749e-03, 2.8308e-04; 2.8308e-04, -1.1916e-05];
450s  assert (nlogL, 3962.330333301793, 1e-10);
450s  assert (avar, avar_out, 1e-7);
450s ***** test
450s  x = 1:50;
450s  [nlogL, avar] = lognlike ([0, 0.25], x * 0.5);
450s  avar_out = [-7.6229e-03, 4.8722e-04; 4.8722e-04, -2.6754e-05];
450s  assert (nlogL, 2473.183051225747, 1e-10);
450s  assert (avar, avar_out, 1e-7);
450s ***** test
450s  x = 1:50;
450s  [nlogL, avar] = lognlike ([0, 0.5], x);
450s  avar_out = [-2.1152e-02, 2.2017e-03; 2.2017e-03, -1.8535e-04];
450s  assert (nlogL, 1119.072424020455, 1e-12);
450s  assert (avar, avar_out, 1e-6);
450s ***** test
450s  x = 1:50;
450s  censor = ones (1, 50);
450s  censor([2, 4, 6, 8, 12, 14]) = 0;
450s  [nlogL, avar] = lognlike ([0, 0.5], x, censor);
450s  avar_out = [-1.9823e-02, 2.0370e-03; 2.0370e-03, -1.6618e-04];
450s  assert (nlogL, 1091.746371145497, 1e-12);
450s  assert (avar, avar_out, 1e-6);
450s ***** test
450s  x = 1:50;
450s  censor = ones (1, 50);
450s  censor([2, 4, 6, 8, 12, 14]) = 0;
450s  [nlogL, avar] = lognlike ([0, 1], x, censor);
450s  avar_out = [-6.8634e-02, 1.3968e-02; 1.3968e-02, -2.1664e-03];
450s  assert (nlogL, 349.3969104144271, 1e-12);
450s  assert (avar, avar_out, 1e-6);
450s ***** error<lognlike: function called with too few input arguments.> ...
450s  lognlike ([12, 15]);
450s ***** error<lognlike: X must be a vector.> lognlike ([12, 15], ones (2));
450s ***** error<lognlike: PARAMS must be a two-element vector.> ...
450s  lognlike ([12, 15, 3], [1:50]);
451s ***** error<lognlike: X and CENSOR vectors mismatch.> ...
451s  lognlike ([12, 15], [1:50], [1, 2, 3]);
451s ***** error<lognlike: X and FREQ vectors mismatch.> ...
451s  lognlike ([12, 15], [1:50], [], [1, 2, 3]);
451s 10 tests, 10 passed, 0 known failure, 0 skipped
451s [inst/dist_fit/nakafit.m]
451s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/nakafit.m
451s ***** demo
451s  ## Sample 3 populations from different Nakagami distributions
451s  randg ("seed", 5)  # for reproducibility
451s  r1 = nakarnd (0.5, 1, 2000, 1);
451s  randg ("seed", 2)   # for reproducibility
451s  r2 = nakarnd (5, 1, 2000, 1);
451s  randg ("seed", 7)   # for reproducibility
451s  r3 = nakarnd (2, 2, 2000, 1);
451s  r = [r1, r2, r3];
451s 
451s  ## Plot them normalized and fix their colors
451s  hist (r, [0.05:0.1:3.5], 10);
451s  h = findobj (gca, "Type", "patch");
451s  set (h(1), "facecolor", "c");
451s  set (h(2), "facecolor", "g");
451s  set (h(3), "facecolor", "r");
451s  ylim ([0, 2.5]);
451s  xlim ([0, 3.0]);
451s  hold on
451s 
451s  ## Estimate their MU and LAMBDA parameters
451s  mu_omegaA = nakafit (r(:,1));
451s  mu_omegaB = nakafit (r(:,2));
451s  mu_omegaC = nakafit (r(:,3));
451s 
451s  ## Plot their estimated PDFs
451s  x = [0.01:0.1:3.01];
451s  y = nakapdf (x, mu_omegaA(1), mu_omegaA(2));
451s  plot (x, y, "-pr");
451s  y = nakapdf (x, mu_omegaB(1), mu_omegaB(2));
451s  plot (x, y, "-sg");
451s  y = nakapdf (x, mu_omegaC(1), mu_omegaC(2));
451s  plot (x, y, "-^c");
451s  legend ({"Normalized HIST of sample 1 with μ=0.5 and ω=1", ...
451s           "Normalized HIST of sample 2 with μ=5 and ω=1", ...
451s           "Normalized HIST of sample 3 with μ=2 and ω=2", ...
451s           sprintf("PDF for sample 1 with estimated μ=%0.2f and ω=%0.2f", ...
451s                   mu_omegaA(1), mu_omegaA(2)), ...
451s           sprintf("PDF for sample 2 with estimated μ=%0.2f and ω=%0.2f", ...
451s                   mu_omegaB(1), mu_omegaB(2)), ...
451s           sprintf("PDF for sample 3 with estimated μ=%0.2f and ω=%0.2f", ...
451s                   mu_omegaC(1), mu_omegaC(2))})
451s  title ("Three population samples from different Nakagami distributions")
451s  hold off
451s ***** test
451s  paramhat = nakafit ([1:50]);
451s  paramhat_out = [0.7355, 858.5];
451s  assert (paramhat, paramhat_out, 1e-4);
451s ***** test
451s  paramhat = nakafit ([1:5]);
451s  paramhat_out = [1.1740, 11];
451s  assert (paramhat, paramhat_out, 1e-4);
451s ***** test
451s  paramhat = nakafit ([1:6], [], [], [1 1 1 1 1 0]);
451s  paramhat_out = [1.1740, 11];
451s  assert (paramhat, paramhat_out, 1e-4);
451s ***** test
451s  paramhat = nakafit ([1:5], [], [], [1 1 1 1 2]);
451s  paramhat_out = nakafit ([1:5, 5]);
451s  assert (paramhat, paramhat_out, 1e-4);
451s ***** error<nakafit: X must be a vector.> nakafit (ones (2,5));
451s ***** error<nakafit: wrong value for ALPHA.> nakafit ([1, 2, 3, 4, 5], 1.2);
451s ***** error<nakafit: wrong value for ALPHA.> nakafit ([1, 2, 3, 4, 5], 0);
451s ***** error<nakafit: wrong value for ALPHA.> nakafit ([1, 2, 3, 4, 5], "alpha");
451s ***** error<nakafit: X and CENSOR vectors mismatch.> ...
451s  nakafit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
451s ***** error<nakafit: X and CENSOR vectors mismatch.> ...
451s  nakafit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
451s ***** error<nakafit: X and FREQ vectors mismatch.> ...
451s  nakafit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
451s ***** error<nakafit: X and FREQ vectors mismatch.> ...
451s  nakafit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
451s ***** error<nakafit: FREQ must not contain negative values.> ...
451s  nakafit ([1, 2, 3, 4, 5], [], [], [1 1 -1 1 1]);
451s ***** error<nakafit: FREQ must contain integer values.> ...
451s  nakafit ([1, 2, 3, 4, 5], [], [], [1 1 1.5 1 1]);
451s ***** error<nakafit: 'options' 5th argument must be a structure> ...
451s  nakafit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
451s 15 tests, 15 passed, 0 known failure, 0 skipped
451s [inst/dist_fit/nakalike.m]
451s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/nakalike.m
451s ***** test
451s  nlogL = nakalike ([0.735504, 858.5], [1:50]);
451s  assert (nlogL, 202.8689, 1e-4);
451s ***** test
451s  nlogL = nakalike ([1.17404, 11], [1:5]);
451s  assert (nlogL, 8.6976, 1e-4);
451s ***** test
451s  nlogL = nakalike ([1.17404, 11], [1:5], [], [1, 1, 1, 1, 1]);
451s  assert (nlogL, 8.6976, 1e-4);
451s ***** test
451s  nlogL = nakalike ([1.17404, 11], [1:6], [], [1, 1, 1, 1, 1, 0]);
451s  assert (nlogL, 8.6976, 1e-4);
451s ***** error<nakalike: function called with too few input arguments.> nakalike (3.25)
451s ***** error<nakalike: X must be a vector.> nakalike ([5, 0.2], ones (2))
451s ***** error<nakalike: PARAMS must be a two-element vector.> ...
451s  nakalike ([1, 0.2, 3], [1, 3, 5, 7])
451s ***** error<nakalike: X and CENSOR vector mismatch.> ...
451s  nakalike ([1.5, 0.2], [1:5], [0, 0, 0])
451s ***** error<nakalike: X and FREQ vector mismatch.> ...
451s  nakalike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
451s ***** error<nakalike: X and FREQ vector mismatch.> ...
451s  nakalike ([1.5, 0.2], [1:5], [], [1, 1, 1])
451s ***** error<nakalike: FREQ must not contain negative values.> ...
451s  nakalike ([1.5, 0.2], [1:5], [], [1, 1, 1, 1, -1])
451s 11 tests, 11 passed, 0 known failure, 0 skipped
451s [inst/dist_fit/nbinfit.m]
451s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/nbinfit.m
451s ***** demo
451s  ## Sample 2 populations from different negative binomial distributions
451s  randp ("seed", 5); randg ("seed", 5);    # for reproducibility
451s  r1 = nbinrnd (2, 0.15, 5000, 1);
451s  randp ("seed", 8); randg ("seed", 8);    # for reproducibility
451s  r2 = nbinrnd (5, 0.2, 5000, 1);
451s  r = [r1, r2];
451s 
451s  ## Plot them normalized and fix their colors
451s  hist (r, [0:51], 1);
451s  h = findobj (gca, "Type", "patch");
451s  set (h(1), "facecolor", "c");
451s  set (h(2), "facecolor", "g");
451s  hold on
451s 
451s  ## Estimate their probability of success
451s  r_psA = nbinfit (r(:,1));
451s  r_psB = nbinfit (r(:,2));
451s 
451s  ## Plot their estimated PDFs
451s  x = [0:40];
451s  y = nbinpdf (x, r_psA(1), r_psA(2));
451s  plot (x, y, "-pg");
451s  x = [min(r(:,2)):max(r(:,2))];
451s  y = nbinpdf (x, r_psB(1), r_psB(2));
451s  plot (x, y, "-sc");
451s  ylim ([0, 0.1])
451s  xlim ([0, 50])
451s  legend ({"Normalized HIST of sample 1 with r=2 and ps=0.15", ...
451s           "Normalized HIST of sample 2 with r=5 and ps=0.2", ...
451s           sprintf("PDF for sample 1 with estimated r=%0.2f and ps=%0.2f", ...
451s                   r_psA(1), r_psA(2)), ...
451s           sprintf("PDF for sample 2 with estimated r=%0.2f and ps=%0.2f", ...
451s                   r_psB(1), r_psB(2))})
451s  title ("Two population samples from negative different binomial distributions")
451s  hold off
451s ***** test
451s  [paramhat, paramci] = nbinfit ([1:50]);
451s  assert (paramhat, [2.420857, 0.086704], 1e-6);
451s  assert (paramci(:,1), [1.382702; 3.459012], 1e-6);
451s  assert (paramci(:,2), [0.049676; 0.123732], 1e-6);
451s ***** test
451s  [paramhat, paramci] = nbinfit ([1:20]);
451s  assert (paramhat, [3.588233, 0.254697], 1e-6);
451s  assert (paramci(:,1), [0.451693; 6.724774], 1e-6);
451s  assert (paramci(:,2), [0.081143; 0.428251], 1e-6);
451s ***** test
451s  [paramhat, paramci] = nbinfit ([1:10]);
451s  assert (paramhat, [8.8067, 0.6156], 1e-4);
451s  assert (paramci(:,1), [0; 30.7068], 1e-4);
451s  assert (paramci(:,2), [0.0217; 1], 1e-4);
451s ***** test
451s  [paramhat, paramci] = nbinfit ([1:10], 0.05, ones (1, 10));
451s  assert (paramhat, [8.8067, 0.6156], 1e-4);
451s  assert (paramci(:,1), [0; 30.7068], 1e-4);
451s  assert (paramci(:,2), [0.0217; 1], 1e-4);
451s ***** test
451s  [paramhat, paramci] = nbinfit ([1:11], 0.05, [ones(1, 10), 0]);
451s  assert (paramhat, [8.8067, 0.6156], 1e-4);
451s  assert (paramci(:,1), [0; 30.7068], 1e-4);
451s  assert (paramci(:,2), [0.0217; 1], 1e-4);
451s ***** error<nbinfit: X cannot have negative values.> nbinfit ([-1 2 3 3])
451s ***** error<nbinfit: X must be a vector.> nbinfit (ones (2))
451s ***** error<nbinfit: X must be a non-negative integer.> nbinfit ([1 2 1.2 3])
451s ***** error<nbinfit: wrong value for ALPHA.> nbinfit ([1 2 3], 0)
451s ***** error<nbinfit: wrong value for ALPHA.> nbinfit ([1 2 3], 1.2)
451s ***** error<nbinfit: wrong value for ALPHA.> nbinfit ([1 2 3], [0.02 0.05])
451s ***** error<nbinfit: X and FREQ vectors mismatch.> ...
451s  nbinfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2]);
451s ***** error<nbinfit: FREQ must not contain negative values.> ...
451s  nbinfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2, -1]);
451s ***** error<nbinfit: FREQ must contain integer values.> ...
451s  nbinfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2, 1.5]);
451s ***** error<nbinfit: 'options' argument must be a structure> ...
451s  nbinfit ([1, 2, 3, 4, 5], 0.05, struct ("option", 234));
451s ***** error<nbinfit: 'options' argument must be a structure> ...
451s  nbinfit ([1, 2, 3, 4, 5], 0.05, ones (1,5), struct ("option", 234));
451s 16 tests, 16 passed, 0 known failure, 0 skipped
451s [inst/dist_fit/nbinlike.m]
451s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/nbinlike.m
451s ***** assert (nbinlike ([2.42086, 0.0867043], [1:50]), 205.5942, 1e-4)
451s ***** assert (nbinlike ([3.58823, 0.254697], [1:20]), 63.6435, 1e-4)
451s ***** assert (nbinlike ([8.80671, 0.615565], [1:10]), 24.7410, 1e-4)
451s ***** assert (nbinlike ([22.1756, 0.831306], [1:8]), 17.9528, 1e-4)
451s ***** assert (nbinlike ([22.1756, 0.831306], [1:9], [ones(1,8), 0]), 17.9528, 1e-4)
451s ***** error<nbinlike: function called with too few input arguments.> nbinlike (3.25)
451s ***** error<nbinlike: X must be a vector.> nbinlike ([5, 0.2], ones (2))
451s ***** error<nbinlike: X cannot have negative values.> nbinlike ([5, 0.2], [-1, 3])
451s ***** error<nbinlike: PARAMS must be a two-element vector.> ...
451s  nbinlike ([1, 0.2, 3], [1, 3, 5, 7])
451s ***** error<nbinlike: number of successes,> nbinlike ([-5, 0.2], [1:15])
451s ***** error<nbinlike: number of successes,> nbinlike ([0, 0.2], [1:15])
451s ***** error<nbinlike: probability of success,> nbinlike ([5, 1.2], [3, 5])
451s ***** error<nbinlike: probability of success,> nbinlike ([5, -0.2], [3, 5])
451s ***** error<nbinlike: X and FREQ vectors mismatch.> ...
451s  nbinlike ([5, 0.2], ones (10, 1), ones (8,1))
451s ***** error<nbinlike: FREQ must not contain negative values.> ...
451s  nbinlike ([5, 0.2], ones (1, 8), [1 1 1 1 1 1 1 -1])
451s ***** error<nbinlike: FREQ must contain integer values.> ...
451s  nbinlike ([5, 0.2], ones (1, 8), [1 1 1 1 1 1 1 1.5])
451s 16 tests, 16 passed, 0 known failure, 0 skipped
451s [inst/dist_fit/normfit.m]
451s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/normfit.m
451s ***** demo
451s  ## Sample 3 populations from 3 different normal distributions
451s  randn ("seed", 1);    # for reproducibility
451s  r1 = normrnd (2, 5, 5000, 1);
451s  randn ("seed", 2);    # for reproducibility
451s  r2 = normrnd (5, 2, 5000, 1);
451s  randn ("seed", 3);    # for reproducibility
451s  r3 = normrnd (9, 4, 5000, 1);
451s  r = [r1, r2, r3];
451s 
451s  ## Plot them normalized and fix their colors
451s  hist (r, 15, 0.4);
451s  h = findobj (gca, "Type", "patch");
451s  set (h(1), "facecolor", "c");
451s  set (h(2), "facecolor", "g");
451s  set (h(3), "facecolor", "r");
451s  hold on
451s 
451s  ## Estimate their mu and sigma parameters
451s  [muhat, sigmahat] = normfit (r);
451s 
451s  ## Plot their estimated PDFs
451s  x = [min(r(:)):max(r(:))];
451s  y = normpdf (x, muhat(1), sigmahat(1));
451s  plot (x, y, "-pr");
451s  y = normpdf (x, muhat(2), sigmahat(2));
451s  plot (x, y, "-sg");
451s  y = normpdf (x, muhat(3), sigmahat(3));
451s  plot (x, y, "-^c");
451s  ylim ([0, 0.5])
451s  xlim ([-20, 20])
451s  hold off
451s  legend ({"Normalized HIST of sample 1 with mu=2, σ=5", ...
451s           "Normalized HIST of sample 2 with mu=5, σ=2", ...
451s           "Normalized HIST of sample 3 with mu=9, σ=4", ...
451s           sprintf("PDF for sample 1 with estimated mu=%0.2f and σ=%0.2f", ...
451s                   muhat(1), sigmahat(1)), ...
451s           sprintf("PDF for sample 2 with estimated mu=%0.2f and σ=%0.2f", ...
451s                   muhat(2), sigmahat(2)), ...
451s           sprintf("PDF for sample 3 with estimated mu=%0.2f and σ=%0.2f", ...
451s                   muhat(3), sigmahat(3))}, "location", "northwest")
451s  title ("Three population samples from different normal distributions")
451s  hold off
451s ***** test
451s  load lightbulb
451s  idx = find (lightbulb(:,2) == 0);
451s  censoring = lightbulb(idx,3) == 1;
451s  [muHat, sigmaHat] = normfit (lightbulb(idx,1), [], censoring);
451s  assert (muHat, 9496.59586737857, 1e-11);
451s  assert (sigmaHat, 3064.021012796456, 2e-12);
451s ***** test
451s  randn ("seed", 234);
451s  x = normrnd (3, 5, [1000, 1]);
451s  [muHat, sigmaHat, muCI, sigmaCI] = normfit (x, 0.01);
451s  assert (muCI(1) < 3);
451s  assert (muCI(2) > 3);
451s  assert (sigmaCI(1) < 5);
451s  assert (sigmaCI(2) > 5);
451s ***** error<normfit: X must not be a multi-dimensional array.> ...
451s  normfit (ones (3,3,3))
451s ***** error<normfit: matrix data acceptable only under 2-arg syntax.> ...
451s  normfit (ones (20,3), [], zeros (20,1))
451s ***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), 0)
451s ***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), -0.3)
451s ***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), 1.2)
451s ***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), [0.05 0.1])
451s ***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), 0.02+i)
451s ***** error<normfit: X and CENSOR vectors mismatch.> ...
451s  normfit (ones (20,1), [], zeros(15,1))
451s ***** error<normfit: X and FREQ vectors mismatch.> ...
451s  normfit (ones (20,1), [], zeros(20,1), ones(25,1))
451s ***** error<normfit: FREQ must not contain negative values.> ...
451s  normfit (ones (5,1), [], zeros(5,1), [1, 2, 1, 2, -1]')
451s ***** error<normfit: > normfit (ones (20,1), [], zeros(20,1), ones(20,1), "options")
451s 13 tests, 13 passed, 0 known failure, 0 skipped
451s [inst/dist_fit/normlike.m]
451s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/normlike.m
451s ***** error<normlike: too few input arguments.> normlike ([12, 15]);
451s ***** error<normlike: X must be a vector.> normlike ([12, 15], ones (2));
451s ***** error<normlike: PARAMS must be a two-element vector.> ...
451s  normlike ([12, 15, 3], [1:50]);
451s ***** error<normlike: X and CENSOR vectors mismatch.> ...
451s  normlike ([12, 15], [1:50], [1, 2, 3]);
451s ***** error<normlike: X and FREQ vectors mismatch.> ...
451s  normlike ([12, 15], [1:50], [], [1, 2, 3]);
451s ***** error<normlike: FREQ must not contain negative values.> ...
451s  normlike ([12, 15], [1:5], [], [1, 2, 3, 2, -1]);
451s ***** test
451s  x = 1:50;
451s  [nlogL, avar] = normlike ([2.3, 1.2], x);
451s  avar_out = [7.5767e-01, -1.8850e-02; -1.8850e-02, 4.8750e-04];
451s  assert (nlogL, 13014.95883783327, 1e-10);
451s  assert (avar, avar_out, 1e-4);
451s ***** test
451s  x = 1:50;
451s  [nlogL, avar] = normlike ([2.3, 1.2], x * 0.5);
451s  avar_out = [3.0501e-01, -1.5859e-02; -1.5859e-02, 9.1057e-04];
451s  assert (nlogL, 2854.802587833265, 1e-10);
451s  assert (avar, avar_out, 1e-4);
451s ***** test
451s  x = 1:50;
451s  [nlogL, avar] = normlike ([21, 15], x);
451s  avar_out = [5.460474308300396, -1.600790513833993; ...
451s              -1.600790513833993, 2.667984189723321];
451s  assert (nlogL, 206.738325604233, 1e-12);
451s  assert (avar, avar_out, 1e-14);
451s ***** test
451s  x = 1:50;
451s  censor = ones (1, 50);
451s  censor([2, 4, 6, 8, 12, 14]) = 0;
451s  [nlogL, avar] = normlike ([2.3, 1.2], x, censor);
451s  avar_out = [3.0501e-01, -1.5859e-02; -1.5859e-02, 9.1057e-04];
451s  assert (nlogL, Inf);
451s  assert (avar, [NaN, NaN; NaN, NaN]);
451s ***** test
451s  x = 1:50;
451s  censor = ones (1, 50);
451s  censor([2, 4, 6, 8, 12, 14]) = 0;
451s  [nlogL, avar] = normlike ([21, 15], x, censor);
451s  avar_out = [24.4824488866131, -10.6649544179636; ...
451s              -10.6649544179636, 6.22827849965737];
451s  assert (nlogL, 86.9254371829733, 1e-12);
451s  assert (avar, avar_out, 8e-14);
451s 11 tests, 11 passed, 0 known failure, 0 skipped
451s [inst/dist_fit/poissfit.m]
451s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/poissfit.m
451s ***** demo
451s  ## Sample 3 populations from 3 different Poisson distributions
451s  randp ("seed", 2);    # for reproducibility
451s  r1 = poissrnd (1, 1000, 1);
451s  randp ("seed", 2);    # for reproducibility
451s  r2 = poissrnd (4, 1000, 1);
451s  randp ("seed", 3);    # for reproducibility
451s  r3 = poissrnd (10, 1000, 1);
451s  r = [r1, r2, r3];
451s 
451s  ## Plot them normalized and fix their colors
451s  hist (r, [0:20], 1);
451s  h = findobj (gca, "Type", "patch");
451s  set (h(1), "facecolor", "c");
451s  set (h(2), "facecolor", "g");
451s  set (h(3), "facecolor", "r");
451s  hold on
451s 
451s  ## Estimate their lambda parameter
451s  lambdahat = poissfit (r);
451s 
451s  ## Plot their estimated PDFs
451s  x = [0:20];
451s  y = poisspdf (x, lambdahat(1));
451s  plot (x, y, "-pr");
451s  y = poisspdf (x, lambdahat(2));
451s  plot (x, y, "-sg");
451s  y = poisspdf (x, lambdahat(3));
451s  plot (x, y, "-^c");
451s  xlim ([0, 20])
451s  ylim ([0, 0.4])
451s  legend ({"Normalized HIST of sample 1 with λ=1", ...
451s           "Normalized HIST of sample 2 with λ=4", ...
451s           "Normalized HIST of sample 3 with λ=10", ...
451s           sprintf("PDF for sample 1 with estimated λ=%0.2f", ...
451s                   lambdahat(1)), ...
451s           sprintf("PDF for sample 2 with estimated λ=%0.2f", ...
451s                   lambdahat(2)), ...
451s           sprintf("PDF for sample 3 with estimated λ=%0.2f", ...
451s                   lambdahat(3))})
451s  title ("Three population samples from different Poisson distributions")
451s  hold off
451s ***** test
451s  x = [1 3 2 4 5 4 3 4];
451s  [lhat, lci] = poissfit (x);
451s  assert (lhat, 3.25)
451s  assert (lci, [2.123007901949543; 4.762003010390628], 1e-14)
451s ***** test
451s  x = [1 3 2 4 5 4 3 4];
451s  [lhat, lci] = poissfit (x, 0.01);
451s  assert (lhat, 3.25)
451s  assert (lci, [1.842572740234582; 5.281369033298528], 1e-14)
451s ***** test
451s  x = [1 2 3 4 5];
451s  f = [1 1 2 3 1];
451s  [lhat, lci] = poissfit (x, [], f);
451s  assert (lhat, 3.25)
451s  assert (lci, [2.123007901949543; 4.762003010390628], 1e-14)
451s ***** test
451s  x = [1 2 3 4 5];
451s  f = [1 1 2 3 1];
451s  [lhat, lci] = poissfit (x, 0.01, f);
451s  assert (lhat, 3.25)
451s  assert (lci, [1.842572740234582; 5.281369033298528], 1e-14)
451s ***** error<poissfit: X cannot have negative values.> poissfit ([1 2 -1 3])
451s ***** error<poissfit: wrong value for ALPHA.> poissfit ([1 2 3], 0)
451s ***** error<poissfit: wrong value for ALPHA.> poissfit ([1 2 3], 1.2)
451s ***** error<poissfit: wrong value for ALPHA.> poissfit ([1 2 3], [0.02 0.05])
451s ***** error<poissfit: X and FREQ vectors mismatch.>
451s  poissfit ([1 2 3], [], [1 5])
451s ***** error<poissfit: FREQ must not contain negative values.>
451s  poissfit ([1 2 3], [], [1 5 -1])
451s 10 tests, 10 passed, 0 known failure, 0 skipped
451s [inst/dist_fit/poisslike.m]
451s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/poisslike.m
451s ***** test
451s  x = [1 3 2 4 5 4 3 4];
451s  [nlogL, avar] = poisslike (3.25, x);
451s  assert (nlogL, 13.9533, 1e-4)
451s ***** test
451s  x = [1 2 3 4 5];
451s  f = [1 1 2 3 1];
451s  [nlogL, avar] = poisslike (3.25, x, f);
451s  assert (nlogL, 13.9533, 1e-4)
451s ***** error<poisslike: function called with too few input arguments.> poisslike (1)
451s ***** error<poisslike: LAMBDA must be a positive scalar.> poisslike ([1 2 3], [1 2])
451s ***** error<poisslike: X must be a vector of non-negative values.> ...
451s  poisslike (3.25, ones (10, 2))
451s ***** error<poisslike: X must be a vector of non-negative values.> ...
451s  poisslike (3.25, [1 2 3 -4 5])
451s ***** error<poisslike: X and FREQ vectors mismatch.> ...
451s  poisslike (3.25, ones (10, 1), ones (8,1))
451s ***** error<poisslike: FREQ must not contain negative values.> ...
451s  poisslike (3.25, ones (1, 8), [1 1 1 1 1 1 1 -1])
451s 8 tests, 8 passed, 0 known failure, 0 skipped
451s [inst/dist_fit/raylfit.m]
451s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/raylfit.m
451s ***** demo
451s  ## Sample 3 populations from 3 different Rayleigh distributions
451s  rand ("seed", 2);    # for reproducibility
451s  r1 = raylrnd (1, 1000, 1);
451s  rand ("seed", 2);    # for reproducibility
451s  r2 = raylrnd (2, 1000, 1);
451s  rand ("seed", 3);    # for reproducibility
451s  r3 = raylrnd (4, 1000, 1);
451s  r = [r1, r2, r3];
451s 
451s  ## Plot them normalized and fix their colors
451s  hist (r, [0.5:0.5:10.5], 2);
451s  h = findobj (gca, "Type", "patch");
451s  set (h(1), "facecolor", "c");
451s  set (h(2), "facecolor", "g");
451s  set (h(3), "facecolor", "r");
451s  hold on
451s 
451s  ## Estimate their lambda parameter
451s  sigmaA = raylfit (r(:,1));
451s  sigmaB = raylfit (r(:,2));
451s  sigmaC = raylfit (r(:,3));
451s 
451s  ## Plot their estimated PDFs
451s  x = [0:0.1:10];
451s  y = raylpdf (x, sigmaA);
451s  plot (x, y, "-pr");
451s  y = raylpdf (x, sigmaB);
451s  plot (x, y, "-sg");
451s  y = raylpdf (x, sigmaC);
451s  plot (x, y, "-^c");
451s  xlim ([0, 10])
451s  ylim ([0, 0.7])
451s  legend ({"Normalized HIST of sample 1 with σ=1", ...
451s           "Normalized HIST of sample 2 with σ=2", ...
451s           "Normalized HIST of sample 3 with σ=4", ...
451s           sprintf("PDF for sample 1 with estimated σ=%0.2f", ...
451s                   sigmaA), ...
451s           sprintf("PDF for sample 2 with estimated σ=%0.2f", ...
451s                   sigmaB), ...
451s           sprintf("PDF for sample 3 with estimated σ=%0.2f", ...
451s                   sigmaC)})
451s  title ("Three population samples from different Rayleigh distributions")
451s  hold off
451s ***** test
451s  x = [1 3 2 4 5 4 3 4];
451s  [shat, sci] = raylfit (x);
451s  assert (shat, 2.4495, 1e-4)
451s  assert (sci, [1.8243; 3.7279], 1e-4)
451s ***** test
451s  x = [1 3 2 4 5 4 3 4];
451s  [shat, sci] = raylfit (x, 0.01);
451s  assert (shat, 2.4495, 1e-4)
451s  assert (sci, [1.6738; 4.3208], 1e-4)
452s ***** test
452s  x = [1 2 3 4 5];
452s  f = [1 1 2 3 1];
452s  [shat, sci] = raylfit (x, [], [], f);
452s  assert (shat, 2.4495, 1e-4)
452s  assert (sci, [1.8243; 3.7279], 1e-4)
452s ***** test
452s  x = [1 2 3 4 5];
452s  f = [1 1 2 3 1];
452s  [shat, sci] = raylfit (x, 0.01, [], f);
452s  assert (shat, 2.4495, 1e-4)
452s  assert (sci, [1.6738; 4.3208], 1e-4)
452s ***** test
452s  x = [1 2 3 4 5 6];
452s  c = [0 0 0 0 0 1];
452s  f = [1 1 2 3 1 1];
452s  [shat, sci] = raylfit (x, 0.01, c, f);
452s  assert (shat, 2.4495, 1e-4)
452s  assert (sci, [1.6738; 4.3208], 1e-4)
452s ***** error<raylfit: X must be a vector.> raylfit (ones (2,5));
452s ***** error<raylfit: X cannot have negative values.> raylfit ([1 2 -1 3])
452s ***** error<raylfit: wrong value for ALPHA.> raylfit ([1 2 3], 0)
452s ***** error<raylfit: wrong value for ALPHA.> raylfit ([1 2 3], 1.2)
452s ***** error<raylfit: wrong value for ALPHA.> raylfit ([1 2 3], [0.02 0.05])
452s ***** error<raylfit: X and CENSOR vectors mismatch.> ...
452s  raylfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
452s ***** error<raylfit: X and CENSOR vectors mismatch.> ...
452s  raylfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
452s ***** error<raylfit: X and FREQ vectors mismatch.> ...
452s  raylfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
452s ***** error<raylfit: X and FREQ vectors mismatch.> ...
452s  raylfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
452s ***** error<raylfit: X and FREQ vectors mismatch.>
452s  raylfit ([1 2 3], [], [], [1 5])
452s ***** error<raylfit: FREQ must not contain negative values.>
452s  raylfit ([1 2 3], [], [], [1 5 -1])
452s 16 tests, 16 passed, 0 known failure, 0 skipped
452s [inst/dist_fit/rayllike.m]
452s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/rayllike.m
452s ***** test
452s  x = [1 3 2 4 5 4 3 4];
452s  [nlogL, acov] = rayllike (3.25, x);
452s  assert (nlogL, 14.7442, 1e-4)
452s ***** test
452s  x = [1 2 3 4 5];
452s  f = [1 1 2 3 1];
452s  [nlogL, acov] = rayllike (3.25, x, [], f);
452s  assert (nlogL, 14.7442, 1e-4)
452s ***** test
452s  x = [1 2 3 4 5 6];
452s  f = [1 1 2 3 1 0];
452s  [nlogL, acov] = rayllike (3.25, x, [], f);
452s  assert (nlogL, 14.7442, 1e-4)
452s ***** test
452s  x = [1 2 3 4 5 6];
452s  c = [0 0 0 0 0 1];
452s  f = [1 1 2 3 1 0];
452s  [nlogL, acov] = rayllike (3.25, x, c, f);
452s  assert (nlogL, 14.7442, 1e-4)
452s ***** error<rayllike: function called with too few input arguments.> rayllike (1)
452s ***** error<rayllike: SIGMA must be a positive scalar.> rayllike ([1 2 3], [1 2])
452s ***** error<rayllike: X must be a vector of non-negative values.> ...
452s  rayllike (3.25, ones (10, 2))
452s ***** error<rayllike: X must be a vector of non-negative values.> ...
452s  rayllike (3.25, [1 2 3 -4 5])
452s ***** error<rayllike: X and CENSOR vectors mismatch.> ...
452s  rayllike (3.25, [1, 2, 3, 4, 5], [1 1 0]);
452s ***** error<rayllike: X and CENSOR vectors mismatch.> ...
452s  rayllike (3.25, [1, 2, 3, 4, 5], [1 1 0 1 1]');
452s ***** error<rayllike: X and FREQ vectors mismatch.> ...
452s  rayllike (3.25, [1, 2, 3, 4, 5], zeros (1,5), [1 1 0]);
452s ***** error<rayllike: X and FREQ vectors mismatch.> ...
452s  rayllike (3.25, [1, 2, 3, 4, 5], [], [1 1 0 1 1]');
452s ***** error<rayllike: FREQ must not contain negative values.> ...
452s  rayllike (3.25, ones (1, 8), [], [1 1 1 1 1 1 1 -1])
452s 13 tests, 13 passed, 0 known failure, 0 skipped
452s [inst/dist_fit/ricefit.m]
452s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/ricefit.m
452s ***** demo
452s  ## Sample 3 populations from different Gamma distributions
452s  randg ("seed", 5);    # for reproducibility
452s  randp ("seed", 6);
452s  r1 = ricernd (1, 2, 3000, 1);
452s  randg ("seed", 2);    # for reproducibility
452s  randp ("seed", 8);
452s  r2 = ricernd (2, 4, 3000, 1);
452s  randg ("seed", 7);    # for reproducibility
452s  randp ("seed", 9);
452s  r3 = ricernd (7.5, 1, 3000, 1);
452s  r = [r1, r2, r3];
452s 
452s  ## Plot them normalized and fix their colors
452s  hist (r, 75, 4);
452s  h = findobj (gca, "Type", "patch");
452s  set (h(1), "facecolor", "c");
452s  set (h(2), "facecolor", "g");
452s  set (h(3), "facecolor", "r");
452s  ylim ([0, 0.7]);
452s  xlim ([0, 12]);
452s  hold on
452s 
452s  ## Estimate their α and β parameters
452s  s_sigmaA = ricefit (r(:,1));
452s  s_sigmaB = ricefit (r(:,2));
452s  s_sigmaC = ricefit (r(:,3));
452s 
452s  ## Plot their estimated PDFs
452s  x = [0.01,0.1:0.2:18];
452s  y = ricepdf (x, s_sigmaA(1), s_sigmaA(2));
452s  plot (x, y, "-pr");
452s  y = ricepdf (x, s_sigmaB(1), s_sigmaB(2));
452s  plot (x, y, "-sg");
452s  y = ricepdf (x, s_sigmaC(1), s_sigmaC(2));
452s  plot (x, y, "-^c");
452s  hold off
452s  legend ({"Normalized HIST of sample 1 with s=1 and σ=2", ...
452s           "Normalized HIST of sample 2 with s=2 and σ=4", ...
452s           "Normalized HIST of sample 3 with s=7.5 and σ=1", ...
452s           sprintf("PDF for sample 1 with estimated s=%0.2f and σ=%0.2f", ...
452s                   s_sigmaA(1), s_sigmaA(2)), ...
452s           sprintf("PDF for sample 2 with estimated s=%0.2f and σ=%0.2f", ...
452s                   s_sigmaB(1), s_sigmaB(2)), ...
452s           sprintf("PDF for sample 3 with estimated s=%0.2f and σ=%0.2f", ...
452s                   s_sigmaC(1), s_sigmaC(2))})
452s  title ("Three population samples from different Rician distributions")
452s  hold off
452s ***** test
452s  [paramhat, paramci] = ricefit ([1:50]);
452s  assert (paramhat, [15.3057, 17.6668], 1e-4);
452s  assert (paramci, [9.5468, 11.7802; 24.5383, 26.4952], 1e-4);
452s ***** test
452s  [paramhat, paramci] = ricefit ([1:50], 0.01);
452s  assert (paramhat, [15.3057, 17.6668], 1e-4);
452s  assert (paramci, [8.2309, 10.3717; 28.4615, 30.0934], 1e-4);
452s ***** test
452s  [paramhat, paramci] = ricefit ([1:5]);
452s  assert (paramhat, [2.3123, 1.6812], 1e-4);
452s  assert (paramci, [1.0819, 0.6376; 4.9424, 4.4331], 1e-4);
452s ***** test
452s  [paramhat, paramci] = ricefit ([1:5], 0.01);
452s  assert (paramhat, [2.3123, 1.6812], 1e-4);
452s  assert (paramci, [0.8521, 0.4702; 6.2747, 6.0120], 1e-4);
452s ***** test
452s  freq = [1 1 1 1 5];
452s  [paramhat, paramci] = ricefit ([1:5], [], [], freq);
452s  assert (paramhat, [3.5181, 1.5565], 1e-4);
452s  assert (paramci, [2.5893, 0.9049; 4.7801, 2.6772], 1e-4);
452s ***** test
452s  censor = [1 0 0 0 0];
452s  [paramhat, paramci] = ricefit ([1:5], [], censor);
452s  assert (paramhat, [3.2978, 1.1527], 1e-4);
452s  assert (paramci, [2.3192, 0.5476; 4.6895, 2.4261], 1e-4);
452s ***** assert (class (ricefit (single ([1:50]))), "single")
452s ***** error<ricefit: X must be a vector.> ricefit (ones (2))
452s ***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], 1)
452s ***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], -1)
452s ***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], {0.05})
452s ***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], "k")
452s ***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], i)
452s ***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], [0.01 0.02])
452s ***** error<ricefit: X and CENSOR vectors mismatch.> ricefit ([1:50], [], [1 1])
452s ***** error<ricefit: X and FREQ vectors mismatch.> ricefit ([1:50], [], [], [1 1])
452s ***** error<ricefit: FREQ cannot have negative values.> ...
452s  ricefit ([1:5], [], [], [1, 1, 2, 1, -1])
452s ***** error<ricefit: X must contain positive values.> ricefit ([1 2 3 -4])
452s ***** error<ricefit: X must contain positive values.> ricefit ([1 2 0], [], [1 0 0])
452s 19 tests, 19 passed, 0 known failure, 0 skipped
452s [inst/dist_fit/ricelike.m]
452s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/ricelike.m
452s ***** test
452s  nlogL = ricelike ([15.3057344, 17.6668458], [1:50]);
452s  assert (nlogL, 204.5230311010569, 1e-12);
452s ***** test
452s  nlogL = ricelike ([2.312346885, 1.681228265], [1:5]);
452s  assert (nlogL, 8.65562164930058, 1e-12);
452s ***** error<ricelike: function called with too few input arguments.> ricelike (3.25)
452s ***** error<ricelike: X must be a vector.> ricelike ([5, 0.2], ones (2))
452s ***** error<ricelike: PARAMS must be a two-element vector.> ...
452s  ricelike ([1, 0.2, 3], [1, 3, 5, 7])
452s ***** error<ricelike: X and CENSOR vector mismatch.> ...
452s  ricelike ([1.5, 0.2], [1:5], [0, 0, 0])
452s ***** error<ricelike: X and FREQ vector mismatch.> ...
452s  ricelike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
452s ***** error<ricelike: X and FREQ vector mismatch.> ...
452s  ricelike ([1.5, 0.2], [1:5], [], [1, 1, 1])
452s ***** error<ricelike: FREQ must not contain negative values.> ...
452s  ricelike ([1.5, 0.2], [1:5], [], [1, 1, 1, 0, -1])
452s 9 tests, 9 passed, 0 known failure, 0 skipped
452s [inst/dist_fit/tlsfit.m]
452s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/tlsfit.m
452s ***** demo
452s  ## Sample 3 populations from 3 different location-scale T distributions
452s  randn ("seed", 1);    # for reproducibility
452s  randg ("seed", 2);    # for reproducibility
452s  r1 = tlsrnd (-4, 3, 1, 2000, 1);
452s  randn ("seed", 3);    # for reproducibility
452s  randg ("seed", 4);    # for reproducibility
452s  r2 = tlsrnd (0, 3, 1, 2000, 1);
452s  randn ("seed", 5);    # for reproducibility
452s  randg ("seed", 6);    # for reproducibility
452s  r3 = tlsrnd (5, 5, 4, 2000, 1);
452s  r = [r1, r2, r3];
452s 
452s  ## Plot them normalized and fix their colors
452s  hist (r, [-21:21], [1, 1, 1]);
452s  h = findobj (gca, "Type", "patch");
452s  set (h(1), "facecolor", "c");
452s  set (h(2), "facecolor", "g");
452s  set (h(3), "facecolor", "r");
452s  ylim ([0, 0.25]);
452s  xlim ([-20, 20]);
452s  hold on
452s 
452s  ## Estimate their lambda parameter
452s  mu_sigma_nuA = tlsfit (r(:,1));
452s  mu_sigma_nuB = tlsfit (r(:,2));
452s  mu_sigma_nuC = tlsfit (r(:,3));
452s 
452s  ## Plot their estimated PDFs
452s  x = [-20:0.1:20];
452s  y = tlspdf (x, mu_sigma_nuA(1), mu_sigma_nuA(2), mu_sigma_nuA(3));
452s  plot (x, y, "-pr");
452s  y = tlspdf (x, mu_sigma_nuB(1), mu_sigma_nuB(2), mu_sigma_nuB(3));
452s  plot (x, y, "-sg");
452s  y = tlspdf (x, mu_sigma_nuC(1), mu_sigma_nuC(2), mu_sigma_nuC(3));
452s  plot (x, y, "-^c");
452s  hold off
452s  legend ({"Normalized HIST of sample 1 with μ=0, σ=2 and nu=1", ...
452s           "Normalized HIST of sample 2 with μ=5, σ=2 and nu=1", ...
452s           "Normalized HIST of sample 3 with μ=3, σ=4 and nu=3", ...
452s           sprintf("PDF for sample 1 with estimated μ=%0.2f, σ=%0.2f, and ν=%0.2f", ...
452s                   mu_sigma_nuA(1), mu_sigma_nuA(2), mu_sigma_nuA(3)), ...
452s           sprintf("PDF for sample 2 with estimated μ=%0.2f, σ=%0.2f, and ν=%0.2f", ...
452s                   mu_sigma_nuB(1), mu_sigma_nuB(2), mu_sigma_nuB(3)), ...
452s           sprintf("PDF for sample 3 with estimated μ=%0.2f, σ=%0.2f, and ν=%0.2f", ...
452s                   mu_sigma_nuC(1), mu_sigma_nuC(2), mu_sigma_nuC(3))})
452s  title ("Three population samples from different location-scale T distributions")
452s  hold off
452s ***** test
452s  x = [-1.2352, -0.2741, 0.1726, 7.4356, 1.0392, 16.4165];
452s  [paramhat, paramci] = tlsfit (x);
452s  paramhat_out = [0.035893, 0.862711, 0.649261];
452s  paramci_out = [-0.949034, 0.154655, 0.181080; 1.02082, 4.812444, 2.327914];
452s  assert (paramhat, paramhat_out, 1e-6);
452s  assert (paramci, paramci_out, 1e-5);
453s ***** test
453s  x = [-1.2352, -0.2741, 0.1726, 7.4356, 1.0392, 16.4165];
453s  [paramhat, paramci] = tlsfit (x, 0.01);
453s  paramci_out = [-1.2585, 0.0901, 0.1212; 1.3303, 8.2591, 3.4771];
453s  assert (paramci, paramci_out, 1e-4);
453s ***** error<tlsfit: X must be a vector.> tlsfit (ones (2,5));
453s ***** error<tlsfit: wrong value for ALPHA.> tlsfit ([1, 2, 3, 4, 5], 1.2);
453s ***** error<tlsfit: wrong value for ALPHA.> tlsfit ([1, 2, 3, 4, 5], 0);
453s ***** error<tlsfit: wrong value for ALPHA.> tlsfit ([1, 2, 3, 4, 5], "alpha");
453s ***** error<tlsfit: X and CENSOR vectors mismatch.> ...
453s  tlsfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
453s ***** error<tlsfit: X and CENSOR vectors mismatch.> ...
453s  tlsfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
453s ***** error<tlsfit: X and FREQ vectors mismatch.> ...
453s  tlsfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
453s ***** error<tlsfit: X and FREQ vectors mismatch.> ...
453s  tlsfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
453s ***** error<tlsfit: FREQ cannot have negative values.> ...
453s  tlsfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 -1]);
453s ***** error<tlsfit: 'options' 5th argument must be a structure> ...
453s  tlsfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
453s 12 tests, 12 passed, 0 known failure, 0 skipped
453s [inst/dist_fit/tlslike.m]
453s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/tlslike.m
453s ***** test
453s  x = [-1.2352, -0.2741, 0.1726, 7.4356, 1.0392, 16.4165];
453s  [nlogL, acov] = tlslike ([0.035893, 0.862711, 0.649261], x);
453s  acov_out = [0.2525, 0.0670, 0.0288; ...
453s              0.0670, 0.5724, 0.1786; ...
453s              0.0288, 0.1786, 0.1789];
453s  assert (nlogL, 17.9979636579, 1e-10);
453s  assert (acov, acov_out, 1e-4);
453s ***** error<tlslike: too few input arguments.> tlslike ([12, 15, 1]);
453s ***** error<tlslike: wrong parameters length.> tlslike ([12, 15], [1:50]);
453s ***** error<tlslike: X must be a vector.> tlslike ([12, 3, 1], ones (10, 2));
453s ***** error<tlslike: X and CENSOR> tlslike ([12, 15, 1], [1:50], [1, 2, 3]);
453s ***** error<tlslike: X and FREQ> tlslike ([12, 15, 1], [1:50], [], [1, 2, 3]);
453s ***** error<tlslike: FREQ cannot> tlslike ([12, 15, 1], [1:3], [], [1, 2, -3]);
453s 7 tests, 7 passed, 0 known failure, 0 skipped
453s [inst/dist_fit/unidfit.m]
453s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/unidfit.m
453s ***** demo
453s  ## Sample 2 populations from different discrete uniform distributions
453s  rand ("seed", 1);    # for reproducibility
453s  r1 = unidrnd (5, 1000, 1);
453s  rand ("seed", 2);    # for reproducibility
453s  r2 = unidrnd (9, 1000, 1);
453s  r = [r1, r2];
453s 
453s  ## Plot them normalized and fix their colors
453s  hist (r, 0:0.5:20.5, 1);
453s  h = findobj (gca, "Type", "patch");
453s  set (h(1), "facecolor", "c");
453s  set (h(2), "facecolor", "g");
453s  hold on
453s 
453s  ## Estimate their probability of success
453s  NhatA = unidfit (r(:,1));
453s  NhatB = unidfit (r(:,2));
453s 
453s  ## Plot their estimated PDFs
453s  x = [0:10];
453s  y = unidpdf (x, NhatA);
453s  plot (x, y, "-pg");
453s  y = unidpdf (x, NhatB);
453s  plot (x, y, "-sc");
453s  xlim ([0, 10])
453s  ylim ([0, 0.4])
453s  legend ({"Normalized HIST of sample 1 with N=5", ...
453s           "Normalized HIST of sample 2 with N=9", ...
453s           sprintf("PDF for sample 1 with estimated N=%0.2f", NhatA), ...
453s           sprintf("PDF for sample 2 with estimated N=%0.2f", NhatB)})
453s  title ("Two population samples from different discrete uniform distributions")
453s  hold off
453s ***** test
453s  x = 0:5;
453s  [Nhat, Nci] = unidfit (x);
453s  assert (Nhat, 5);
453s  assert (Nci, [5; 9]);
453s ***** test
453s  x = 0:5;
453s  [Nhat, Nci] = unidfit (x, [], [1 1 1 1 1 1]);
453s  assert (Nhat, 5);
453s  assert (Nci, [5; 9]);
453s ***** assert (unidfit ([1 1 2 3]), unidfit ([1 2 3], [] ,[2 1 1]))
453s ***** error<unidfit: function called with too few input arguments.> unidfit ()
453s ***** error<unidfit: X cannot have negative values.> unidfit (-1, [1 2 3 3])
453s ***** error<unidfit: wrong value for ALPHA.> unidfit (1, 0)
453s ***** error<unidfit: wrong value for ALPHA.> unidfit (1, 1.2)
453s ***** error<unidfit: wrong value for ALPHA.> unidfit (1, [0.02 0.05])
453s ***** error<unidfit: X and FREQ vector mismatch.> ...
453s  unidfit ([1.5, 0.2], [], [0, 0, 0, 0, 0])
453s ***** error<unidfit: X and FREQ vector mismatch.> ...
453s  unidfit ([1.5, 0.2], [], [1, 1, 1])
453s ***** error<unidfit: FREQ cannot have negative values.> ...
453s  unidfit ([1.5, 0.2], [], [1, -1])
453s 11 tests, 11 passed, 0 known failure, 0 skipped
453s [inst/dist_fit/unifit.m]
453s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/unifit.m
453s ***** demo
453s  ## Sample 2 populations from different continuous uniform distributions
453s  rand ("seed", 5);    # for reproducibility
453s  r1 = unifrnd (2, 5, 2000, 1);
453s  rand ("seed", 6);    # for reproducibility
453s  r2 = unifrnd (3, 9, 2000, 1);
453s  r = [r1, r2];
453s 
453s  ## Plot them normalized and fix their colors
453s  hist (r, 0:0.5:10, 2);
453s  h = findobj (gca, "Type", "patch");
453s  set (h(1), "facecolor", "c");
453s  set (h(2), "facecolor", "g");
453s  hold on
453s 
453s  ## Estimate their probability of success
453s  a_bA = unifit (r(:,1));
453s  a_bB = unifit (r(:,2));
453s 
453s  ## Plot their estimated PDFs
453s  x = [0:10];
453s  y = unifpdf (x, a_bA(1), a_bA(2));
453s  plot (x, y, "-pg");
453s  y = unifpdf (x, a_bB(1), a_bB(2));
453s  plot (x, y, "-sc");
453s  xlim ([1, 10])
453s  ylim ([0, 0.5])
453s  legend ({"Normalized HIST of sample 1 with a=2 and b=5", ...
453s           "Normalized HIST of sample 2 with a=3 and b=9", ...
453s           sprintf("PDF for sample 1 with estimated a=%0.2f and b=%0.2f", ...
453s                   a_bA(1), a_bA(2)), ...
453s           sprintf("PDF for sample 2 with estimated a=%0.2f and b=%0.2f", ...
453s                   a_bB(1), a_bB(2))})
453s  title ("Two population samples from different continuous uniform distributions")
453s  hold off
453s ***** test
453s  x = 0:5;
453s  [paramhat, paramci] = unifit (x);
453s  assert (paramhat, [0, 5]);
453s  assert (paramci, [-3.2377, 8.2377; 0, 5], 1e-4);
453s ***** test
453s  x = 0:5;
453s  [paramhat, paramci] = unifit (x, [], [1 1 1 1 1 1]);
453s  assert (paramhat, [0, 5]);
453s  assert (paramci, [-3.2377, 8.2377; 0, 5], 1e-4);
453s ***** assert (unifit ([1 1 2 3]), unifit ([1 2 3], [] ,[2 1 1]))
453s ***** error<unifit: function called with too few input arguments.> unifit ()
453s ***** error<unifit: X cannot have negative values.> unifit (-1, [1 2 3 3])
453s ***** error<unifit: wrong value for ALPHA.> unifit (1, 0)
453s ***** error<unifit: wrong value for ALPHA.> unifit (1, 1.2)
453s ***** error<unifit: wrong value for ALPHA.> unifit (1, [0.02 0.05])
453s ***** error<unifit: X and FREQ vector mismatch.> ...
453s  unifit ([1.5, 0.2], [], [0, 0, 0, 0, 0])
453s ***** error<unifit: FREQ cannot have negative values.> ...
453s  unifit ([1.5, 0.2], [], [1, -1])
453s ***** error<unifit: X and FREQ vector mismatch.> ...
453s  unifit ([1.5, 0.2], [], [1, 1, 1])
453s 11 tests, 11 passed, 0 known failure, 0 skipped
453s [inst/dist_fit/wblfit.m]
453s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/wblfit.m
453s ***** demo
453s  ## Sample 3 populations from 3 different Weibull distributions
453s  rande ("seed", 1);    # for reproducibility
453s  r1 = wblrnd(2, 4, 2000, 1);
453s  rande ("seed", 2);    # for reproducibility
453s  r2 = wblrnd(5, 2, 2000, 1);
453s  rande ("seed", 5);    # for reproducibility
453s  r3 = wblrnd(1, 5, 2000, 1);
453s  r = [r1, r2, r3];
453s 
453s  ## Plot them normalized and fix their colors
453s  hist (r, 30, [2.5 2.1 3.2]);
453s  h = findobj (gca, "Type", "patch");
453s  set (h(1), "facecolor", "c");
453s  set (h(2), "facecolor", "g");
453s  set (h(3), "facecolor", "r");
453s  ylim ([0, 2]);
453s  xlim ([0, 10]);
453s  hold on
453s 
453s  ## Estimate their lambda parameter
453s  lambda_kA = wblfit (r(:,1));
453s  lambda_kB = wblfit (r(:,2));
453s  lambda_kC = wblfit (r(:,3));
453s 
453s  ## Plot their estimated PDFs
453s  x = [0:0.1:15];
453s  y = wblpdf (x, lambda_kA(1), lambda_kA(2));
453s  plot (x, y, "-pr");
453s  y = wblpdf (x, lambda_kB(1), lambda_kB(2));
453s  plot (x, y, "-sg");
453s  y = wblpdf (x, lambda_kC(1), lambda_kC(2));
453s  plot (x, y, "-^c");
453s  hold off
453s  legend ({"Normalized HIST of sample 1 with λ=2 and k=4", ...
453s           "Normalized HIST of sample 2 with λ=5 and k=2", ...
453s           "Normalized HIST of sample 3 with λ=1 and k=5", ...
453s           sprintf("PDF for sample 1 with estimated λ=%0.2f and k=%0.2f", ...
453s                   lambda_kA(1), lambda_kA(2)), ...
453s           sprintf("PDF for sample 2 with estimated λ=%0.2f and k=%0.2f", ...
453s                   lambda_kB(1), lambda_kB(2)), ...
453s           sprintf("PDF for sample 3 with estimated λ=%0.2f and k=%0.2f", ...
453s                   lambda_kC(1), lambda_kC(2))})
453s  title ("Three population samples from different Weibull distributions")
453s  hold off
453s ***** test
453s  x = 1:50;
453s  [paramhat, paramci] = wblfit (x);
453s  paramhat_out = [28.3636, 1.7130];
453s  paramci_out = [23.9531, 1.3551; 33.5861, 2.1655];
453s  assert (paramhat, paramhat_out, 1e-4);
453s  assert (paramci, paramci_out, 1e-4);
453s ***** test
453s  x = 1:50;
453s  [paramhat, paramci] = wblfit (x, 0.01);
453s  paramci_out = [22.7143, 1.2589; 35.4179, 2.3310];
453s  assert (paramci, paramci_out, 1e-4);
453s ***** error<wblfit: X must be a vector.> wblfit (ones (2,5));
453s ***** error<wblfit: X must contain only positive values.> wblfit ([-1 2 3 4]);
453s ***** error<wblfit: wrong value for ALPHA.> wblfit ([1, 2, 3, 4, 5], 1.2);
453s ***** error<wblfit: wrong value for ALPHA.> wblfit ([1, 2, 3, 4, 5], 0);
453s ***** error<wblfit: wrong value for ALPHA.> wblfit ([1, 2, 3, 4, 5], "alpha");
453s ***** error<wblfit: X and CENSOR vectors mismatch.> ...
453s  wblfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
453s ***** error<wblfit: X and CENSOR vectors mismatch.> ...
453s  wblfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
453s ***** error<wblfit: X and FREQ vectors mismatch.> ...
453s  wblfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
453s ***** error<wblfit: FREQ cannot have negative values.> ...
453s  wblfit ([1, 2, 3, 4, 5], [], [], [1 1 0 -1 1]);
453s ***** error<wblfit: X and FREQ vectors mismatch.> ...
453s  wblfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
453s ***** error<wblfit: 'options' 5th argument must be a structure> ...
453s  wblfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
453s 13 tests, 13 passed, 0 known failure, 0 skipped
453s [inst/dist_fit/wbllike.m]
453s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fit/wbllike.m
453s ***** test
453s  x = 1:50;
453s  [nlogL, acov] = wbllike ([2.3, 1.2], x);
453s  avar_out = [0.0250, 0.0062; 0.0062, 0.0017];
453s  assert (nlogL, 945.9589180651594, 1e-12);
453s  assert (acov, avar_out, 1e-4);
453s ***** test
453s  x = 1:50;
453s  [nlogL, acov] = wbllike ([2.3, 1.2], x * 0.5);
453s  avar_out = [-0.3238, -0.1112; -0.1112, -0.0376];
453s  assert (nlogL, 424.9879809704742, 6e-14);
453s  assert (acov, avar_out, 1e-4);
453s ***** test
453s  x = 1:50;
453s  [nlogL, acov] = wbllike ([21, 15], x);
453s  avar_out = [-0.00001236, -0.00001166; -0.00001166, -0.00001009];
453s  assert (nlogL, 1635190.328991511, 1e-8);
453s  assert (acov, avar_out, 1e-8);
453s ***** error<wbllike: too few input arguments.> wbllike ([12, 15]);
453s ***** error<wbllike: wrong parameters length.> wbllike ([12, 15, 3], [1:50]);
453s ***** error<wbllike: X must be a vector.> wbllike ([12, 3], ones (10, 2));
453s ***** error<wbllike: X and CENSOR> wbllike ([12, 15], [1:50], [1, 2, 3]);
453s ***** error<wbllike: X and FREQ> wbllike ([12, 15], [1:50], [], [1, 2, 3]);
453s ***** error<wbllike: FREQ cannot have negative values.> ...
453s  wbllike ([12, 15], [1:5], [], [1, 2, 3, -1, 0]);
453s 9 tests, 9 passed, 0 known failure, 0 skipped
453s [inst/dist_fun/betacdf.m]
453s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/betacdf.m
453s ***** demo
453s  ## Plot various CDFs from the Beta distribution
453s  x = 0:0.005:1;
453s  p1 = betacdf (x, 0.5, 0.5);
453s  p2 = betacdf (x, 5, 1);
453s  p3 = betacdf (x, 1, 3);
453s  p4 = betacdf (x, 2, 2);
453s  p5 = betacdf (x, 2, 5);
453s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
453s  grid on
453s  legend ({"α = β = 0.5", "α = 5, β = 1", "α = 1, β = 3", ...
453s           "α = 2, β = 2", "α = 2, β = 5"}, "location", "northwest")
453s  title ("Beta CDF")
453s  xlabel ("values in x")
453s  ylabel ("probability")
453s ***** shared x, y, x1, x2
453s  x = [-1 0 0.5 1 2];
453s  y = [0 0 0.75 1 1];
453s ***** assert (betacdf (x, ones (1, 5), 2 * ones (1, 5)), y)
453s ***** assert (betacdf (x, 1, 2 * ones (1, 5)), y)
453s ***** assert (betacdf (x, ones (1, 5), 2), y)
453s ***** assert (betacdf (x, [0 1 NaN 1 1], 2), [NaN 0 NaN 1 1])
453s ***** assert (betacdf (x, 1, 2 * [0 1 NaN 1 1]), [NaN 0 NaN 1 1])
453s ***** assert (betacdf ([x(1:2) NaN x(4:5)], 1, 2), [y(1:2) NaN y(4:5)])
453s  x1 = [0.1:0.2:0.9];
453s ***** assert (betacdf (x1, 2, 2), [0.028, 0.216, 0.5, 0.784, 0.972], 1e-14);
453s ***** assert (betacdf (x1, 2, 2, "upper"), 1 - [0.028, 0.216, 0.5, 0.784, 0.972],...
453s         1e-14);
453s  x2 = [1, 2, 3];
453s ***** assert (betacdf (0.5, x2, x2), [0.5, 0.5, 0.5], 1e-14);
453s ***** assert (betacdf ([x, NaN], 1, 2), [y, NaN])
453s ***** assert (betacdf (single ([x, NaN]), 1, 2), single ([y, NaN]))
453s ***** assert (betacdf ([x, NaN], single (1), 2), single ([y, NaN]))
453s ***** assert (betacdf ([x, NaN], 1, single (2)), single ([y, NaN]))
453s ***** error<betacdf: function called with too few input arguments.> betacdf ()
453s ***** error<betacdf: function called with too few input arguments.> betacdf (1)
453s ***** error<betacdf: function called with too few input arguments.> betacdf (1, 2)
453s ***** error<betacdf: function called with too many inputs> betacdf (1, 2, 3, 4, 5)
453s ***** error<betacdf: invalid argument for upper tail.> betacdf (1, 2, 3, "tail")
453s ***** error<betacdf: invalid argument for upper tail.> betacdf (1, 2, 3, 4)
453s ***** error<betacdf: X, A, and B must be of common size or scalars.> ...
453s  betacdf (ones (3), ones (2), ones (2))
453s ***** error<betacdf: X, A, and B must be of common size or scalars.> ...
453s  betacdf (ones (2), ones (3), ones (2))
453s ***** error<betacdf: X, A, and B must be of common size or scalars.> ...
453s  betacdf (ones (2), ones (2), ones (3))
453s ***** error<betacdf: X, A, and B must not be complex.> betacdf (i, 2, 2)
453s ***** error<betacdf: X, A, and B must not be complex.> betacdf (2, i, 2)
453s ***** error<betacdf: X, A, and B must not be complex.> betacdf (2, 2, i)
453s 25 tests, 25 passed, 0 known failure, 0 skipped
453s [inst/dist_fun/betainv.m]
453s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/betainv.m
453s ***** demo
453s  ## Plot various iCDFs from the Beta distribution
453s  p = 0.001:0.001:0.999;
453s  x1 = betainv (p, 0.5, 0.5);
453s  x2 = betainv (p, 5, 1);
453s  x3 = betainv (p, 1, 3);
453s  x4 = betainv (p, 2, 2);
453s  x5 = betainv (p, 2, 5);
453s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
453s  grid on
453s  legend ({"α = β = 0.5", "α = 5, β = 1", "α = 1, β = 3", ...
453s           "α = 2, β = 2", "α = 2, β = 5"}, "location", "southeast")
453s  title ("Beta iCDF")
453s  xlabel ("probability")
453s  ylabel ("values in x")
453s ***** shared p
453s  p = [-1 0 0.75 1 2];
453s ***** assert (betainv (p, ones (1,5), 2*ones (1,5)), [NaN 0 0.5 1 NaN], eps)
453s ***** assert (betainv (p, 1, 2*ones (1,5)), [NaN 0 0.5 1 NaN], eps)
453s ***** assert (betainv (p, ones (1,5), 2), [NaN 0 0.5 1 NaN], eps)
453s ***** assert (betainv (p, [1 0 NaN 1 1], 2), [NaN NaN NaN 1 NaN])
453s ***** assert (betainv (p, 1, 2*[1 0 NaN 1 1]), [NaN NaN NaN 1 NaN])
453s ***** assert (betainv ([p(1:2) NaN p(4:5)], 1, 2), [NaN 0 NaN 1 NaN])
453s ***** assert (betainv ([p, NaN], 1, 2), [NaN 0 0.5 1 NaN NaN], eps)
453s ***** assert (betainv (single ([p, NaN]), 1, 2), single ([NaN 0 0.5 1 NaN NaN]))
453s ***** assert (betainv ([p, NaN], single (1), 2), single ([NaN 0 0.5 1 NaN NaN]), eps("single"))
453s ***** assert (betainv ([p, NaN], 1, single (2)), single ([NaN 0 0.5 1 NaN NaN]), eps("single"))
453s ***** error<betainv: function called with too few input arguments.> betainv ()
453s ***** error<betainv: function called with too few input arguments.> betainv (1)
453s ***** error<betainv: function called with too few input arguments.> betainv (1,2)
453s ***** error<betainv: function called with too many inputs> betainv (1,2,3,4)
453s ***** error<betainv: P, A, and B must be of common size or scalars.> ...
453s  betainv (ones (3), ones (2), ones (2))
453s ***** error<betainv: P, A, and B must be of common size or scalars.> ...
453s  betainv (ones (2), ones (3), ones (2))
453s ***** error<betainv: P, A, and B must be of common size or scalars.> ...
453s  betainv (ones (2), ones (2), ones (3))
453s ***** error<betainv: P, A, and B must not be complex.> betainv (i, 2, 2)
453s ***** error<betainv: P, A, and B must not be complex.> betainv (2, i, 2)
453s ***** error<betainv: P, A, and B must not be complex.> betainv (2, 2, i)
453s 20 tests, 20 passed, 0 known failure, 0 skipped
453s [inst/dist_fun/betapdf.m]
453s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/betapdf.m
453s ***** demo
453s  ## Plot various PDFs from the Beta distribution
453s  x = 0.001:0.001:0.999;
453s  y1 = betapdf (x, 0.5, 0.5);
453s  y2 = betapdf (x, 5, 1);
453s  y3 = betapdf (x, 1, 3);
453s  y4 = betapdf (x, 2, 2);
453s  y5 = betapdf (x, 2, 5);
453s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
453s  grid on
453s  ylim ([0, 2.5])
453s  legend ({"α = β = 0.5", "α = 5, β = 1", "α = 1, β = 3", ...
453s           "α = 2, β = 2", "α = 2, β = 5"}, "location", "north")
453s  title ("Beta PDF")
453s  xlabel ("values in x")
453s  ylabel ("density")
453s ***** shared x, y
453s  x = [-1 0 0.5 1 2];
453s  y = [0 2 1 0 0];
453s ***** assert (betapdf (x, ones (1, 5), 2 * ones (1, 5)), y)
453s ***** assert (betapdf (x, 1, 2 * ones (1, 5)), y)
453s ***** assert (betapdf (x, ones (1, 5), 2), y)
453s ***** assert (betapdf (x, [0 NaN 1 1 1], 2), [NaN NaN y(3:5)])
453s ***** assert (betapdf (x, 1, 2 * [0 NaN 1 1 1]), [NaN NaN y(3:5)])
453s ***** assert (betapdf ([x, NaN], 1, 2), [y, NaN])
453s ***** assert (betapdf (single ([x, NaN]), 1, 2), single ([y, NaN]))
453s ***** assert (betapdf ([x, NaN], single (1), 2), single ([y, NaN]))
453s ***** assert (betapdf ([x, NaN], 1, single (2)), single ([y, NaN]))
453s ***** test
453s  x = rand (10,1);
453s  y = 1 ./ (pi * sqrt (x .* (1 - x)));
453s  assert (betapdf (x, 1/2, 1/2), y, 1e-12);
453s ***** assert (betapdf (0.5, 1000, 1000), 35.678, 1e-3)
453s ***** error<betapdf: function called with too few input arguments.> betapdf ()
453s ***** error<betapdf: function called with too few input arguments.> betapdf (1)
453s ***** error<betapdf: function called with too few input arguments.> betapdf (1,2)
453s ***** error<betapdf: function called with too many inputs> betapdf (1,2,3,4)
453s ***** error<betapdf: X, A, and B must be of common size or scalars.> ...
453s  betapdf (ones (3), ones (2), ones (2))
453s ***** error<betapdf: X, A, and B must be of common size or scalars.> ...
453s  betapdf (ones (2), ones (3), ones (2))
453s ***** error<betapdf: X, A, and B must be of common size or scalars.> ...
453s  betapdf (ones (2), ones (2), ones (3))
453s ***** error<betapdf: X, A, and B must not be complex.> betapdf (i, 2, 2)
453s ***** error<betapdf: X, A, and B must not be complex.> betapdf (2, i, 2)
453s ***** error<betapdf: X, A, and B must not be complex.> betapdf (2, 2, i)
453s 21 tests, 21 passed, 0 known failure, 0 skipped
453s [inst/dist_fun/betarnd.m]
453s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/betarnd.m
453s ***** assert (size (betarnd (2, 1/2)), [1 1])
453s ***** assert (size (betarnd (2 * ones (2, 1), 1/2)), [2, 1])
453s ***** assert (size (betarnd (2 * ones (2, 2), 1/2)), [2, 2])
453s ***** assert (size (betarnd (2, 1/2 * ones (2, 1))), [2, 1])
453s ***** assert (size (betarnd (1, 1/2 * ones (2, 2))), [2, 2])
453s ***** assert (size (betarnd (ones (2, 1), 1)), [2, 1])
453s ***** assert (size (betarnd (ones (2, 2), 1)), [2, 2])
453s ***** assert (size (betarnd (2, 1/2, 3)), [3, 3])
453s ***** assert (size (betarnd (1, 1, [4, 1])), [4, 1])
453s ***** assert (size (betarnd (1, 1, 4, 1)), [4, 1])
453s ***** assert (size (betarnd (1, 1, 4, 1, 5)), [4, 1, 5])
453s ***** assert (size (betarnd (1, 1, 0, 1)), [0, 1])
453s ***** assert (size (betarnd (1, 1, 1, 0)), [1, 0])
453s ***** assert (size (betarnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
453s ***** assert (class (betarnd (1, 1)), "double")
453s ***** assert (class (betarnd (1, single (0))), "single")
453s ***** assert (class (betarnd (1, single ([0, 0]))), "single")
453s ***** assert (class (betarnd (1, single (1), 2)), "single")
453s ***** assert (class (betarnd (1, single ([1, 1]), 1, 2)), "single")
454s ***** assert (class (betarnd (single (1), 1, 2)), "single")
454s ***** assert (class (betarnd (single ([1, 1]), 1, 1, 2)), "single")
454s ***** error<betarnd: function called with too few input arguments.> betarnd ()
454s ***** error<betarnd: function called with too few input arguments.> betarnd (1)
454s ***** error<betarnd: A and B must be of common size or scalars.> ...
454s  betarnd (ones (3), ones (2))
454s ***** error<betarnd: A and B must be of common size or scalars.> ...
454s  betarnd (ones (2), ones (3))
454s ***** error<betarnd: A and B must not be complex.> betarnd (i, 2)
454s ***** error<betarnd: A and B must not be complex.> betarnd (1, i)
454s ***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
454s  betarnd (1, 1/2, -1)
454s ***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
454s  betarnd (1, 1/2, 1.2)
454s ***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
454s  betarnd (1, 1/2, ones (2))
454s ***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
454s  betarnd (1, 1/2, [2 -1 2])
454s ***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
454s  betarnd (1, 1/2, [2 0 2.5])
454s ***** error<betarnd: dimensions must be non-negative integers.> ...
454s  betarnd (1, 1/2, 2, -1, 5)
454s ***** error<betarnd: dimensions must be non-negative integers.> ...
454s  betarnd (1, 1/2, 2, 1.5, 5)
454s ***** error<betarnd: A and B must be scalars or of size SZ.> ...
454s  betarnd (2, 1/2 * ones (2), 3)
454s ***** error<betarnd: A and B must be scalars or of size SZ.> ...
454s  betarnd (2, 1/2 * ones (2), [3, 2])
454s ***** error<betarnd: A and B must be scalars or of size SZ.> ...
454s  betarnd (2, 1/2 * ones (2), 3, 2)
454s ***** error<betarnd: A and B must be scalars or of size SZ.> ...
454s  betarnd (2 * ones (2), 1/2, 3)
454s ***** error<betarnd: A and B must be scalars or of size SZ.> ...
454s  betarnd (2 * ones (2), 1/2, [3, 2])
454s ***** error<betarnd: A and B must be scalars or of size SZ.> ...
454s  betarnd (2 * ones (2), 1/2, 3, 2)
454s 40 tests, 40 passed, 0 known failure, 0 skipped
454s [inst/dist_fun/binocdf.m]
454s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/binocdf.m
454s ***** demo
454s  ## Plot various CDFs from the binomial distribution
454s  x = 0:40;
454s  p1 = binocdf (x, 20, 0.5);
454s  p2 = binocdf (x, 20, 0.7);
454s  p3 = binocdf (x, 40, 0.5);
454s  plot (x, p1, "*b", x, p2, "*g", x, p3, "*r")
454s  grid on
454s  legend ({"n = 20, ps = 0.5", "n = 20, ps = 0.7", ...
454s           "n = 40, ps = 0.5"}, "location", "southeast")
454s  title ("Binomial CDF")
454s  xlabel ("values in x (number of successes)")
454s  ylabel ("probability")
454s ***** shared x, p, p1
454s  x = [-1 0 1 2 3];
454s  p = [0 1/4 3/4 1 1];
454s  p1 = 1 - p;
454s ***** assert (binocdf (x, 2 * ones (1, 5), 0.5 * ones (1, 5)), p, eps)
454s ***** assert (binocdf (x, 2, 0.5 * ones (1, 5)), p, eps)
454s ***** assert (binocdf (x, 2 * ones (1, 5), 0.5), p, eps)
454s ***** assert (binocdf (x, 2 * [0 -1 NaN 1.1 1], 0.5), [0 NaN NaN NaN 1])
454s ***** assert (binocdf (x, 2, 0.5 * [0 -1 NaN 3 1]), [0 NaN NaN NaN 1])
454s ***** assert (binocdf ([x(1:2) NaN x(4:5)], 2, 0.5), [p(1:2) NaN p(4:5)], eps)
454s ***** assert (binocdf (99, 100, 0.1, "upper"), 1e-100, 1e-112);
454s ***** assert (binocdf (x, 2 * ones (1, 5), 0.5*ones (1,5), "upper"), p1, eps)
454s ***** assert (binocdf (x, 2, 0.5 * ones (1, 5), "upper"), p1, eps)
454s ***** assert (binocdf (x, 2 * ones (1, 5), 0.5, "upper"), p1, eps)
454s ***** assert (binocdf (x, 2 * [0 -1 NaN 1.1 1], 0.5, "upper"), [1 NaN NaN NaN 0])
454s ***** assert (binocdf (x, 2, 0.5 * [0 -1 NaN 3 1], "upper"), [1 NaN NaN NaN 0])
454s ***** assert (binocdf ([x(1:2) NaN x(4:5)], 2, 0.5, "upper"), [p1(1:2) NaN p1(4:5)])
454s ***** assert (binocdf ([x, NaN], 2, 0.5), [p, NaN], eps)
454s ***** assert (binocdf (single ([x, NaN]), 2, 0.5), single ([p, NaN]))
454s ***** assert (binocdf ([x, NaN], single (2), 0.5), single ([p, NaN]))
454s ***** assert (binocdf ([x, NaN], 2, single (0.5)), single ([p, NaN]))
454s ***** error<binocdf: function called with too few input arguments.> binocdf ()
454s ***** error<binocdf: function called with too few input arguments.> binocdf (1)
454s ***** error<binocdf: function called with too few input arguments.> binocdf (1, 2)
454s ***** error<binocdf: function called with too many inputs> binocdf (1, 2, 3, 4, 5)
454s ***** error<binocdf: invalid argument for upper tail.> binocdf (1, 2, 3, "tail")
454s ***** error<binocdf: invalid argument for upper tail.> binocdf (1, 2, 3, 4)
454s ***** error<binocdf: X, N, and PS must be of common size or scalars.> ...
454s  binocdf (ones (3), ones (2), ones (2))
454s ***** error<binocdf: X, N, and PS must be of common size or scalars.> ...
454s  binocdf (ones (2), ones (3), ones (2))
454s ***** error<binocdf: X, N, and PS must be of common size or scalars.> ...
454s  binocdf (ones (2), ones (2), ones (3))
454s ***** error<binocdf: X, N, and PS must not be complex.> binocdf (i, 2, 2)
454s ***** error<binocdf: X, N, and PS must not be complex.> binocdf (2, i, 2)
454s ***** error<binocdf: X, N, and PS must not be complex.> binocdf (2, 2, i)
454s 29 tests, 29 passed, 0 known failure, 0 skipped
454s [inst/dist_fun/binoinv.m]
454s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/binoinv.m
454s ***** demo
454s  ## Plot various iCDFs from the binomial distribution
454s  p = 0.001:0.001:0.999;
454s  x1 = binoinv (p, 20, 0.5);
454s  x2 = binoinv (p, 20, 0.7);
454s  x3 = binoinv (p, 40, 0.5);
454s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
454s  grid on
454s  legend ({"n = 20, ps = 0.5", "n = 20, ps = 0.7", ...
454s           "n = 40, ps = 0.5"}, "location", "southeast")
454s  title ("Binomial iCDF")
454s  xlabel ("probability")
454s  ylabel ("values in x (number of successes)")
454s ***** shared p
454s  p = [-1 0 0.5 1 2];
454s ***** assert (binoinv (p, 2*ones (1,5), 0.5*ones (1,5)), [NaN 0 1 2 NaN])
454s ***** assert (binoinv (p, 2, 0.5*ones (1,5)), [NaN 0 1 2 NaN])
454s ***** assert (binoinv (p, 2*ones (1,5), 0.5), [NaN 0 1 2 NaN])
454s ***** assert (binoinv (p, 2*[0 -1 NaN 1.1 1], 0.5), [NaN NaN NaN NaN NaN])
454s ***** assert (binoinv (p, 2, 0.5*[0 -1 NaN 3 1]), [NaN NaN NaN NaN NaN])
454s ***** assert (binoinv ([p(1:2) NaN p(4:5)], 2, 0.5), [NaN 0 NaN 2 NaN])
454s ***** assert (binoinv ([p, NaN], 2, 0.5), [NaN 0 1 2 NaN NaN])
454s ***** assert (binoinv (single ([p, NaN]), 2, 0.5), single ([NaN 0 1 2 NaN NaN]))
454s ***** assert (binoinv ([p, NaN], single (2), 0.5), single ([NaN 0 1 2 NaN NaN]))
454s ***** assert (binoinv ([p, NaN], 2, single (0.5)), single ([NaN 0 1 2 NaN NaN]))
454s ***** shared x, tol
454s  x = magic (3) + 1;
454s  tol = 1;
454s ***** assert (binoinv (binocdf (1:10, 11, 0.1), 11, 0.1), 1:10, tol)
454s ***** assert (binoinv (binocdf (1:10, 2*(1:10), 0.1), 2*(1:10), 0.1), 1:10, tol)
454s ***** assert (binoinv (binocdf (x, 2*x, 1./x), 2*x, 1./x), x, tol)
454s ***** error<binoinv: function called with too few input arguments.> binoinv ()
454s ***** error<binoinv: function called with too few input arguments.> binoinv (1)
454s ***** error<binoinv: function called with too few input arguments.> binoinv (1,2)
454s ***** error<binoinv: function called with too many inputs> binoinv (1,2,3,4)
454s ***** error<binoinv: P, N, and PS must be of common size or scalars.> ...
454s  binoinv (ones (3), ones (2), ones (2))
454s ***** error<binoinv: P, N, and PS must be of common size or scalars.> ...
454s  binoinv (ones (2), ones (3), ones (2))
454s ***** error<binoinv: P, N, and PS must be of common size or scalars.> ...
454s  binoinv (ones (2), ones (2), ones (3))
454s ***** error<binoinv: P, N, and PS must not be complex.> binoinv (i, 2, 2)
454s ***** error<binoinv: P, N, and PS must not be complex.> binoinv (2, i, 2)
454s ***** error<binoinv: P, N, and PS must not be complex.> binoinv (2, 2, i)
454s 23 tests, 23 passed, 0 known failure, 0 skipped
454s [inst/dist_fun/binopdf.m]
454s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/binopdf.m
454s ***** demo
454s  ## Plot various PDFs from the binomial distribution
454s  x = 0:40;
454s  y1 = binopdf (x, 20, 0.5);
454s  y2 = binopdf (x, 20, 0.7);
454s  y3 = binopdf (x, 40, 0.5);
454s  plot (x, y1, "*b", x, y2, "*g", x, y3, "*r")
454s  grid on
454s  ylim ([0, 0.25])
454s  legend ({"n = 20, ps = 0.5", "n = 20, ps = 0.7", ...
454s           "n = 40, ps = 0.5"}, "location", "northeast")
454s  title ("Binomial PDF")
454s  xlabel ("values in x (number of successes)")
454s  ylabel ("density")
454s ***** shared x, y
454s  x = [-1 0 1 2 3];
454s  y = [0 1/4 1/2 1/4 0];
454s ***** assert (binopdf (x, 2 * ones (1, 5), 0.5 * ones (1, 5)), y, eps)
454s ***** assert (binopdf (x, 2, 0.5 * ones (1, 5)), y, eps)
454s ***** assert (binopdf (x, 2 * ones (1, 5), 0.5), y, eps)
454s ***** assert (binopdf (x, 2 * [0 -1 NaN 1.1 1], 0.5), [0 NaN NaN NaN 0])
454s ***** assert (binopdf (x, 2, 0.5 * [0 -1 NaN 3 1]), [0 NaN NaN NaN 0])
454s ***** assert (binopdf ([x, NaN], 2, 0.5), [y, NaN], eps)
454s ***** assert (binopdf (cat (3, x, x), 2, 0.5), cat (3, y, y), eps)
454s ***** assert (binopdf (1, 1, 1), 1)
454s ***** assert (binopdf (0, 3, 0), 1)
454s ***** assert (binopdf (2, 2, 1), 1)
454s ***** assert (binopdf (1, 2, 1), 0)
454s ***** assert (binopdf (0, 1.1, 0), NaN)
454s ***** assert (binopdf (1, 2, -1), NaN)
454s ***** assert (binopdf (1, 2, 1.5), NaN)
454s ***** assert (binopdf ([], 1, 1), [])
454s ***** assert (binopdf (1, [], 1), [])
454s ***** assert (binopdf (1, 1, []), [])
454s ***** assert (binopdf (ones (1, 0), 2, .5), ones(1, 0))
454s ***** assert (binopdf (ones (0, 1), 2, .5), ones(0, 1))
454s ***** assert (binopdf (ones (0, 1, 2), 2, .5), ones(0, 1, 2))
454s ***** assert (binopdf (1, ones (0, 1, 2), .5), ones(0, 1, 2))
454s ***** assert (binopdf (1, 2, ones (0, 1, 2)), ones(0, 1, 2))
454s ***** assert (binopdf (ones (1, 0, 2), 2, .5), ones(1, 0, 2))
454s ***** assert (binopdf (ones (1, 2, 0), 2, .5), ones(1, 2, 0))
454s ***** assert (binopdf (ones (0, 1, 2), NaN, .5), ones(0, 1, 2))
454s ***** assert (binopdf (ones (0, 1, 2), 2, NaN), ones(0, 1, 2))
454s ***** assert (binopdf (single ([x, NaN]), 2, 0.5), single ([y, NaN]))
454s ***** assert (binopdf ([x, NaN], single (2), 0.5), single ([y, NaN]))
454s ***** assert (binopdf ([x, NaN], 2, single (0.5)), single ([y, NaN]))
454s ***** error<binopdf: function called with too few input arguments.> binopdf ()
454s ***** error<binopdf: function called with too few input arguments.> binopdf (1)
454s ***** error<binopdf: function called with too few input arguments.> binopdf (1, 2)
454s ***** error<binopdf: function called with too many inputs> binopdf (1, 2, 3, 4)
454s ***** error<binopdf: X, N, and PS must be of common size or scalars.> ...
454s  binopdf (ones (3), ones (2), ones (2))
454s ***** error<binopdf: X, N, and PS must be of common size or scalars.> ...
454s  binopdf (ones (2), ones (3), ones (2))
454s ***** error<binopdf: X, N, and PS must be of common size or scalars.> ...
454s  binopdf (ones (2), ones (2), ones (3))
455s ***** error<binopdf: X, N, and PS must not be complex.> binopdf (i, 2, 2)
455s ***** error<binopdf: X, N, and PS must not be complex.> binopdf (2, i, 2)
455s ***** error<binopdf: X, N, and PS must not be complex.> binopdf (2, 2, i)
455s 39 tests, 39 passed, 0 known failure, 0 skipped
455s [inst/dist_fun/binornd.m]
455s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/binornd.m
455s ***** assert (size (binornd (2, 1/2)), [1 1])
455s ***** assert (size (binornd (2 * ones (2, 1), 1/2)), [2, 1])
455s ***** assert (size (binornd (2 * ones (2, 2), 1/2)), [2, 2])
455s ***** assert (size (binornd (2, 1/2 * ones (2, 1))), [2, 1])
455s ***** assert (size (binornd (1, 1/2 * ones (2, 2))), [2, 2])
455s ***** assert (size (binornd (ones (2, 1), 1)), [2, 1])
455s ***** assert (size (binornd (ones (2, 2), 1)), [2, 2])
455s ***** assert (size (binornd (2, 1/2, 3)), [3, 3])
455s ***** assert (size (binornd (1, 1, [4, 1])), [4, 1])
455s ***** assert (size (binornd (1, 1, 4, 1)), [4, 1])
455s ***** assert (size (binornd (1, 1, 4, 1, 5)), [4, 1, 5])
455s ***** assert (size (binornd (1, 1, 0, 1)), [0, 1])
455s ***** assert (size (binornd (1, 1, 1, 0)), [1, 0])
455s ***** assert (size (binornd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
455s ***** assert (class (binornd (1, 1)), "double")
455s ***** assert (class (binornd (1, single (0))), "single")
455s ***** assert (class (binornd (1, single ([0, 0]))), "single")
455s ***** assert (class (binornd (1, single (1), 2)), "single")
455s ***** assert (class (binornd (1, single ([1, 1]), 1, 2)), "single")
455s ***** assert (class (binornd (single (1), 1, 2)), "single")
455s ***** assert (class (binornd (single ([1, 1]), 1, 1, 2)), "single")
455s ***** error<binornd: function called with too few input arguments.> binornd ()
455s ***** error<binornd: function called with too few input arguments.> binornd (1)
455s ***** error<binornd: N and PS must be of common size or scalars.> ...
455s  binornd (ones (3), ones (2))
455s ***** error<binornd: N and PS must be of common size or scalars.> ...
455s  binornd (ones (2), ones (3))
455s ***** error<binornd: N and PS must not be complex.> binornd (i, 2)
455s ***** error<binornd: N and PS must not be complex.> binornd (1, i)
455s ***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
455s  binornd (1, 1/2, -1)
455s ***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
455s  binornd (1, 1/2, 1.2)
455s ***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
455s  binornd (1, 1/2, ones (2))
455s ***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
455s  binornd (1, 1/2, [2 -1 2])
455s ***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
455s  binornd (1, 1/2, [2 0 2.5])
455s ***** error<binornd: dimensions must be non-negative integers.> ...
455s  binornd (1, 1/2, 2, -1, 5)
455s ***** error<binornd: dimensions must be non-negative integers.> ...
455s  binornd (1, 1/2, 2, 1.5, 5)
455s ***** error<binornd: N and PS must be scalars or of size SZ.> ...
455s  binornd (2, 1/2 * ones (2), 3)
455s ***** error<binornd: N and PS must be scalars or of size SZ.> ...
455s  binornd (2, 1/2 * ones (2), [3, 2])
455s ***** error<binornd: N and PS must be scalars or of size SZ.> ...
455s  binornd (2, 1/2 * ones (2), 3, 2)
455s ***** error<binornd: N and PS must be scalars or of size SZ.> ...
455s  binornd (2 * ones (2), 1/2, 3)
455s ***** error<binornd: N and PS must be scalars or of size SZ.> ...
455s  binornd (2 * ones (2), 1/2, [3, 2])
455s ***** error<binornd: N and PS must be scalars or of size SZ.> ...
455s  binornd (2 * ones (2), 1/2, 3, 2)
455s 40 tests, 40 passed, 0 known failure, 0 skipped
455s [inst/dist_fun/bisacdf.m]
455s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/bisacdf.m
455s ***** demo
455s  ## Plot various CDFs from the Birnbaum-Saunders distribution
455s  x = 0.01:0.01:4;
455s  p1 = bisacdf (x, 1, 0.5);
455s  p2 = bisacdf (x, 1, 1);
455s  p3 = bisacdf (x, 1, 2);
455s  p4 = bisacdf (x, 1, 5);
455s  p5 = bisacdf (x, 1, 10);
455s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
455s  grid on
455s  legend ({"β = 1, γ = 0.5", "β = 1, γ = 1", "β = 1, γ = 2", ...
455s           "β = 1, γ = 5", "β = 1, γ = 10"}, "location", "southeast")
455s  title ("Birnbaum-Saunders CDF")
455s  xlabel ("values in x")
455s  ylabel ("probability")
455s ***** demo
455s  ## Plot various CDFs from the Birnbaum-Saunders distribution
455s  x = 0.01:0.01:6;
455s  p1 = bisacdf (x, 1, 0.3);
455s  p2 = bisacdf (x, 2, 0.3);
455s  p3 = bisacdf (x, 1, 0.5);
455s  p4 = bisacdf (x, 3, 0.5);
455s  p5 = bisacdf (x, 5, 0.5);
455s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
455s  grid on
455s  legend ({"β = 1, γ = 0.3", "β = 2, γ = 0.3", "β = 1, γ = 0.5", ...
455s           "β = 3, γ = 0.5", "β = 5, γ = 0.5"}, "location", "southeast")
455s  title ("Birnbaum-Saunders CDF")
455s  xlabel ("values in x")
455s  ylabel ("probability")
455s ***** shared x, y
455s  x = [-1, 0, 1, 2, Inf];
455s  y = [0, 0, 1/2, 0.76024993890652337, 1];
455s ***** assert (bisacdf (x, ones (1,5), ones (1,5)), y, eps)
455s ***** assert (bisacdf (x, 1, 1), y, eps)
455s ***** assert (bisacdf (x, 1, ones (1,5)), y, eps)
455s ***** assert (bisacdf (x, ones (1,5), 1), y, eps)
455s ***** assert (bisacdf (x, 1, 1), y, eps)
455s ***** assert (bisacdf (x, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)], eps)
455s ***** assert (bisacdf (x, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)], eps)
455s ***** assert (bisacdf ([x, NaN], 1, 1), [y, NaN], eps)
455s ***** assert (bisacdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
455s ***** assert (bisacdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
455s ***** assert (bisacdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
455s ***** error<bisacdf: function called with too few input arguments.> bisacdf ()
455s ***** error<bisacdf: function called with too few input arguments.> bisacdf (1)
455s ***** error<bisacdf: function called with too few input arguments.> bisacdf (1, 2)
455s ***** error<bisacdf: function called with too many inputs> ...
455s  bisacdf (1, 2, 3, 4, 5)
455s ***** error<bisacdf: invalid argument for upper tail.> bisacdf (1, 2, 3, "tail")
455s ***** error<bisacdf: invalid argument for upper tail.> bisacdf (1, 2, 3, 4)
455s ***** error<bisacdf: X, BETA, and GAMMA must be of common size or scalars.> ...
455s  bisacdf (ones (3), ones (2), ones(2))
455s ***** error<bisacdf: X, BETA, and GAMMA must be of common size or scalars.> ...
455s  bisacdf (ones (2), ones (3), ones(2))
455s ***** error<bisacdf: X, BETA, and GAMMA must be of common size or scalars.> ...
455s  bisacdf (ones (2), ones (2), ones(3))
455s ***** error<bisacdf: X, BETA, and GAMMA must not be complex.> bisacdf (i, 4, 3)
455s ***** error<bisacdf: X, BETA, and GAMMA must not be complex.> bisacdf (1, i, 3)
455s ***** error<bisacdf: X, BETA, and GAMMA must not be complex.> bisacdf (1, 4, i)
455s 23 tests, 23 passed, 0 known failure, 0 skipped
455s [inst/dist_fun/bisainv.m]
455s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/bisainv.m
455s ***** demo
455s  ## Plot various iCDFs from the Birnbaum-Saunders distribution
455s  p = 0.001:0.001:0.999;
455s  x1 = bisainv (p, 1, 0.5);
455s  x2 = bisainv (p, 1, 1);
455s  x3 = bisainv (p, 1, 2);
455s  x4 = bisainv (p, 1, 5);
455s  x5 = bisainv (p, 1, 10);
455s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
455s  grid on
455s  ylim ([0, 10])
455s  legend ({"β = 1, γ = 0.5", "β = 1, γ = 1", "β = 1, γ = 2", ...
455s           "β = 1, γ = 5", "β = 1, γ = 10"}, "location", "northwest")
455s  title ("Birnbaum-Saunders iCDF")
455s  xlabel ("probability")
455s  ylabel ("values in x")
455s ***** demo
455s  ## Plot various iCDFs from the Birnbaum-Saunders distribution
455s  p = 0.001:0.001:0.999;
455s  x1 = bisainv (p, 1, 0.3);
455s  x2 = bisainv (p, 2, 0.3);
455s  x3 = bisainv (p, 1, 0.5);
455s  x4 = bisainv (p, 3, 0.5);
455s  x5 = bisainv (p, 5, 0.5);
455s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
455s  grid on
455s  ylim ([0, 10])
455s  legend ({"β = 1, γ = 0.3", "β = 2, γ = 0.3", "β = 1, γ = 0.5", ...
455s           "β = 3, γ = 0.5", "β = 5, γ = 0.5"}, "location", "northwest")
455s  title ("Birnbaum-Saunders iCDF")
455s  xlabel ("probability")
455s  ylabel ("values in x")
455s ***** shared p, y, f
455s  f = @(p,b,c) (b * (c * norminv (p) + sqrt (4 + (c * norminv(p))^2))^2) / 4;
455s  p = [-1, 0, 1/4, 1/2, 1, 2];
455s  y = [NaN, 0, f(1/4, 1, 1), 1, Inf, NaN];
455s ***** assert (bisainv (p, ones (1,6), ones (1,6)), y)
455s ***** assert (bisainv (p, 1, ones (1,6)), y)
455s ***** assert (bisainv (p, ones (1,6), 1), y)
455s ***** assert (bisainv (p, 1, 1), y)
455s ***** assert (bisainv (p, 1, [1, 1, 1, NaN, 1, 1]), [y(1:3), NaN, y(5:6)])
455s ***** assert (bisainv (p, [1, 1, 1, NaN, 1, 1], 1), [y(1:3), NaN, y(5:6)])
455s ***** assert (bisainv ([p, NaN], 1, 1), [y, NaN])
455s ***** assert (bisainv (single ([p, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
455s ***** assert (bisainv ([p, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
455s ***** assert (bisainv ([p, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
455s ***** error<bisainv: function called with too few input arguments.> bisainv ()
455s ***** error<bisainv: function called with too few input arguments.> bisainv (1)
455s ***** error<bisainv: function called with too few input arguments.> bisainv (1, 2)
455s ***** error<bisainv: function called with too many inputs> bisainv (1, 2, 3, 4)
455s ***** error<bisainv: P, BETA, and GAMMA must be of common size or scalars.> ...
455s  bisainv (ones (3), ones (2), ones(2))
455s ***** error<bisainv: P, BETA, and GAMMA must be of common size or scalars.> ...
455s  bisainv (ones (2), ones (3), ones(2))
455s ***** error<bisainv: P, BETA, and GAMMA must be of common size or scalars.> ...
455s  bisainv (ones (2), ones (2), ones(3))
455s ***** error<bisainv: P, BETA, and GAMMA must not be complex.> bisainv (i, 4, 3)
455s ***** error<bisainv: P, BETA, and GAMMA must not be complex.> bisainv (1, i, 3)
455s ***** error<bisainv: P, BETA, and GAMMA must not be complex.> bisainv (1, 4, i)
455s 20 tests, 20 passed, 0 known failure, 0 skipped
455s [inst/dist_fun/bisapdf.m]
455s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/bisapdf.m
455s ***** demo
455s  ## Plot various PDFs from the Birnbaum-Saunders distribution
455s  x = 0.01:0.01:4;
455s  y1 = bisapdf (x, 1, 0.5);
455s  y2 = bisapdf (x, 1, 1);
455s  y3 = bisapdf (x, 1, 2);
455s  y4 = bisapdf (x, 1, 5);
455s  y5 = bisapdf (x, 1, 10);
455s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
455s  grid on
455s  ylim ([0, 1.5])
455s  legend ({"β = 1 ,γ = 0.5", "β = 1, γ = 1", "β = 1, γ = 2", ...
455s           "β = 1, γ = 5", "β = 1, γ = 10"}, "location", "northeast")
455s  title ("Birnbaum-Saunders PDF")
455s  xlabel ("values in x")
455s  ylabel ("density")
455s ***** demo
455s  ## Plot various PDFs from the Birnbaum-Saunders distribution
455s  x = 0.01:0.01:6;
455s  y1 = bisapdf (x, 1, 0.3);
455s  y2 = bisapdf (x, 2, 0.3);
455s  y3 = bisapdf (x, 1, 0.5);
455s  y4 = bisapdf (x, 3, 0.5);
455s  y5 = bisapdf (x, 5, 0.5);
455s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
455s  grid on
455s  ylim ([0, 1.5])
455s  legend ({"β = 1, γ = 0.3", "β = 2, γ = 0.3", "β = 1, γ = 0.5", ...
455s           "β = 3, γ = 0.5", "β = 5, γ = 0.5"}, "location", "northeast")
455s  title ("Birnbaum-Saunders CDF")
455s  xlabel ("values in x")
455s  ylabel ("density")
455s ***** shared x, y
455s  x = [-1, 0, 1, 2, Inf];
455s  y = [0, 0, 0.3989422804014327, 0.1647717335503959, 0];
455s ***** assert (bisapdf (x, ones (1,5), ones (1,5)), y, eps)
455s ***** assert (bisapdf (x, 1, 1), y, eps)
455s ***** assert (bisapdf (x, 1, ones (1,5)), y, eps)
455s ***** assert (bisapdf (x, ones (1,5), 1), y, eps)
455s ***** assert (bisapdf (x, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)], eps)
455s ***** assert (bisapdf (x, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)], eps)
455s ***** assert (bisapdf ([x, NaN], 1, 1), [y, NaN], eps)
455s ***** assert (bisapdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
455s ***** assert (bisapdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
455s ***** assert (bisapdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
455s ***** error<bisapdf: function called with too few input arguments.> bisapdf ()
455s ***** error<bisapdf: function called with too few input arguments.> bisapdf (1)
455s ***** error<bisapdf: function called with too few input arguments.> bisapdf (1, 2)
455s ***** error<bisapdf: function called with too many inputs> bisapdf (1, 2, 3, 4)
455s ***** error<bisapdf: X, BETA, and GAMMA must be of common size or scalars.> ...
455s  bisapdf (ones (3), ones (2), ones(2))
455s ***** error<bisapdf: X, BETA, and GAMMA must be of common size or scalars.> ...
455s  bisapdf (ones (2), ones (3), ones(2))
455s ***** error<bisapdf: X, BETA, and GAMMA must be of common size or scalars.> ...
455s  bisapdf (ones (2), ones (2), ones(3))
455s ***** error<bisapdf: X, BETA, and GAMMA must not be complex.> bisapdf (i, 4, 3)
455s ***** error<bisapdf: X, BETA, and GAMMA must not be complex.> bisapdf (1, i, 3)
455s ***** error<bisapdf: X, BETA, and GAMMA must not be complex.> bisapdf (1, 4, i)
455s 20 tests, 20 passed, 0 known failure, 0 skipped
455s [inst/dist_fun/bisarnd.m]
455s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/bisarnd.m
455s ***** assert (size (bisarnd (1, 1)), [1 1])
455s ***** assert (size (bisarnd (1, ones (2,1))), [2, 1])
455s ***** assert (size (bisarnd (1, ones (2,2))), [2, 2])
455s ***** assert (size (bisarnd (ones (2,1), 1)), [2, 1])
455s ***** assert (size (bisarnd (ones (2,2), 1)), [2, 2])
455s ***** assert (size (bisarnd (1, 1, 3)), [3, 3])
455s ***** assert (size (bisarnd (1, 1, [4, 1])), [4, 1])
455s ***** assert (size (bisarnd (1, 1, 4, 1)), [4, 1])
455s ***** assert (size (bisarnd (1, 1, 4, 1, 5)), [4, 1, 5])
455s ***** assert (size (bisarnd (1, 1, 0, 1)), [0, 1])
455s ***** assert (size (bisarnd (1, 1, 1, 0)), [1, 0])
455s ***** assert (size (bisarnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
455s ***** assert (class (bisarnd (1, 1)), "double")
455s ***** assert (class (bisarnd (1, single (1))), "single")
455s ***** assert (class (bisarnd (1, single ([1, 1]))), "single")
455s ***** assert (class (bisarnd (single (1), 1)), "single")
455s ***** assert (class (bisarnd (single ([1, 1]), 1)), "single")
455s ***** error<bisarnd: function called with too few input arguments.> bisarnd ()
455s ***** error<bisarnd: function called with too few input arguments.> bisarnd (1)
455s ***** error<bisarnd: BETA and GAMMA must be of common size or scalars.> ...
455s  bisarnd (ones (3), ones (2))
455s ***** error<bisarnd: BETA and GAMMA must be of common size or scalars.> ...
455s  bisarnd (ones (2), ones (3))
455s ***** error<bisarnd: BETA and GAMMA must not be complex.> bisarnd (i, 2, 3)
455s ***** error<bisarnd: BETA and GAMMA must not be complex.> bisarnd (1, i, 3)
455s ***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
455s  bisarnd (1, 2, -1)
455s ***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
455s  bisarnd (1, 2, 1.2)
455s ***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
455s  bisarnd (1, 2, ones (2))
455s ***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
455s  bisarnd (1, 2, [2 -1 2])
455s ***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
455s  bisarnd (1, 2, [2 0 2.5])
455s ***** error<bisarnd: dimensions must be non-negative integers.> ...
455s  bisarnd (1, 2, 2, -1, 5)
455s ***** error<bisarnd: dimensions must be non-negative integers.> ...
455s  bisarnd (1, 2, 2, 1.5, 5)
455s ***** error<bisarnd: BETA and GAMMA must be scalars or of size SZ.> ...
455s  bisarnd (2, ones (2), 3)
455s ***** error<bisarnd: BETA and GAMMA must be scalars or of size SZ.> ...
455s  bisarnd (2, ones (2), [3, 2])
455s ***** error<bisarnd: BETA and GAMMA must be scalars or of size SZ.> ...
455s  bisarnd (2, ones (2), 3, 2)
455s 33 tests, 33 passed, 0 known failure, 0 skipped
455s [inst/dist_fun/burrcdf.m]
455s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/burrcdf.m
455s ***** demo
455s  ## Plot various CDFs from the Burr type XII distribution
455s  x = 0.001:0.001:5;
455s  p1 = burrcdf (x, 1, 1, 1);
455s  p2 = burrcdf (x, 1, 1, 2);
455s  p3 = burrcdf (x, 1, 1, 3);
455s  p4 = burrcdf (x, 1, 2, 1);
455s  p5 = burrcdf (x, 1, 3, 1);
455s  p6 = burrcdf (x, 1, 0.5, 2);
455s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", ...
455s        x, p4, "-c", x, p5, "-m", x, p6, "-k")
455s  grid on
455s  legend ({"λ = 1, c = 1, k = 1", "λ = 1, c = 1, k = 2", ...
455s           "λ = 1, c = 1, k = 3", "λ = 1, c = 2, k = 1", ...
455s           "λ = 1, c = 3, k = 1", "λ = 1, c = 0.5, k = 2"}, ...
455s          "location", "southeast")
455s  title ("Burr type XII CDF")
455s  xlabel ("values in x")
455s  ylabel ("probability")
455s ***** shared x, y
455s  x = [-1, 0, 1, 2, Inf];
455s  y = [0, 0, 1/2, 2/3, 1];
455s ***** assert (burrcdf (x, ones(1,5), ones (1,5), ones (1,5)), y, eps)
455s ***** assert (burrcdf (x, 1, 1, 1), y, eps)
455s ***** assert (burrcdf (x, [1, 1, NaN, 1, 1], 1, 1), [y(1:2), NaN, y(4:5)], eps)
455s ***** assert (burrcdf (x, 1, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)], eps)
455s ***** assert (burrcdf (x, 1, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)], eps)
455s ***** assert (burrcdf ([x, NaN], 1, 1, 1), [y, NaN], eps)
455s ***** assert (burrcdf (single ([x, NaN]), 1, 1, 1), single ([y, NaN]), eps("single"))
455s ***** assert (burrcdf ([x, NaN], single (1), 1, 1), single ([y, NaN]), eps("single"))
455s ***** assert (burrcdf ([x, NaN], 1, single (1), 1), single ([y, NaN]), eps("single"))
455s ***** assert (burrcdf ([x, NaN], 1, 1, single (1)), single ([y, NaN]), eps("single"))
455s ***** error<burrcdf: function called with too few input arguments.> burrcdf ()
455s ***** error<burrcdf: function called with too few input arguments.> burrcdf (1)
455s ***** error<burrcdf: function called with too few input arguments.> burrcdf (1, 2)
455s ***** error<burrcdf: function called with too few input arguments.> burrcdf (1, 2, 3)
455s ***** error<burrcdf: function called with too many inputs> ...
455s  burrcdf (1, 2, 3, 4, 5, 6)
455s ***** error<burrcdf: invalid argument for upper tail.> burrcdf (1, 2, 3, 4, "tail")
455s ***** error<burrcdf: invalid argument for upper tail.> burrcdf (1, 2, 3, 4, 5)
455s ***** error<burrcdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
455s  burrcdf (ones (3), ones (2), ones(2), ones(2))
455s ***** error<burrcdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
455s  burrcdf (ones (2), ones (3), ones(2), ones(2))
455s ***** error<burrcdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
455s  burrcdf (ones (2), ones (2), ones(3), ones(2))
455s ***** error<burrcdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
455s  burrcdf (ones (2), ones (2), ones(2), ones(3))
455s ***** error<burrcdf: X, LAMBDA, C, and K must not be complex.> burrcdf (i, 2, 3, 4)
455s ***** error<burrcdf: X, LAMBDA, C, and K must not be complex.> burrcdf (1, i, 3, 4)
455s ***** error<burrcdf: X, LAMBDA, C, and K must not be complex.> burrcdf (1, 2, i, 4)
455s ***** error<burrcdf: X, LAMBDA, C, and K must not be complex.> burrcdf (1, 2, 3, i)
455s 25 tests, 25 passed, 0 known failure, 0 skipped
455s [inst/dist_fun/burrinv.m]
455s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/burrinv.m
455s ***** demo
455s  ## Plot various iCDFs from the Burr type XII distribution
455s  p = 0.001:0.001:0.999;
455s  x1 = burrinv (p, 1, 1, 1);
455s  x2 = burrinv (p, 1, 1, 2);
455s  x3 = burrinv (p, 1, 1, 3);
455s  x4 = burrinv (p, 1, 2, 1);
455s  x5 = burrinv (p, 1, 3, 1);
455s  x6 = burrinv (p, 1, 0.5, 2);
455s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", ...
455s        p, x4, "-c", p, x5, "-m", p, x6, "-k")
455s  grid on
455s  ylim ([0, 5])
455s  legend ({"λ = 1, c = 1, k = 1", "λ = 1, c = 1, k = 2", ...
455s           "λ = 1, c = 1, k = 3", "λ = 1, c = 2, k = 1", ...
455s           "λ = 1, c = 3, k = 1", "λ = 1, c = 0.5, k = 2"}, ...
455s          "location", "northwest")
455s  title ("Burr type XII iCDF")
455s  xlabel ("probability")
455s  ylabel ("values in x")
455s ***** shared p, y
455s  p = [-Inf, -1, 0, 1/2, 1, 2, Inf];
455s  y = [NaN, NaN, 0, 1 , Inf, NaN, NaN];
455s ***** assert (burrinv (p, ones (1,7), ones (1,7), ones(1,7)), y, eps)
455s ***** assert (burrinv (p, 1, 1, 1), y, eps)
455s ***** assert (burrinv (p, [1, 1, 1, NaN, 1, 1, 1], 1, 1), [y(1:3), NaN, y(5:7)], eps)
455s ***** assert (burrinv (p, 1, [1, 1, 1, NaN, 1, 1, 1], 1), [y(1:3), NaN, y(5:7)], eps)
455s ***** assert (burrinv (p, 1, 1, [1, 1, 1, NaN, 1, 1, 1]), [y(1:3), NaN, y(5:7)], eps)
455s ***** assert (burrinv ([p, NaN], 1, 1, 1), [y, NaN], eps)
455s ***** assert (burrinv (single ([p, NaN]), 1, 1, 1), single ([y, NaN]), eps("single"))
455s ***** assert (burrinv ([p, NaN], single (1), 1, 1), single ([y, NaN]), eps("single"))
455s ***** assert (burrinv ([p, NaN], 1, single (1), 1), single ([y, NaN]), eps("single"))
455s ***** assert (burrinv ([p, NaN], 1, 1, single (1)), single ([y, NaN]), eps("single"))
455s ***** error<burrinv: function called with too few input arguments.> burrinv ()
455s ***** error<burrinv: function called with too few input arguments.> burrinv (1)
455s ***** error<burrinv: function called with too few input arguments.> burrinv (1, 2)
455s ***** error<burrinv: function called with too few input arguments.> burrinv (1, 2, 3)
455s ***** error<burrinv: function called with too many inputs> ...
455s  burrinv (1, 2, 3, 4, 5)
455s ***** error<burrinv: P, LAMBDA, C, and K must be of common size or scalars.> ...
455s  burrinv (ones (3), ones (2), ones(2), ones(2))
455s ***** error<burrinv: P, LAMBDA, C, and K must be of common size or scalars.> ...
455s  burrinv (ones (2), ones (3), ones(2), ones(2))
455s ***** error<burrinv: P, LAMBDA, C, and K must be of common size or scalars.> ...
455s  burrinv (ones (2), ones (2), ones(3), ones(2))
455s ***** error<burrinv: P, LAMBDA, C, and K must be of common size or scalars.> ...
455s  burrinv (ones (2), ones (2), ones(2), ones(3))
455s ***** error<burrinv: P, LAMBDA, C, and K must not be complex.> burrinv (i, 2, 3, 4)
455s ***** error<burrinv: P, LAMBDA, C, and K must not be complex.> burrinv (1, i, 3, 4)
455s ***** error<burrinv: P, LAMBDA, C, and K must not be complex.> burrinv (1, 2, i, 4)
455s ***** error<burrinv: P, LAMBDA, C, and K must not be complex.> burrinv (1, 2, 3, i)
455s 23 tests, 23 passed, 0 known failure, 0 skipped
455s [inst/dist_fun/burrpdf.m]
455s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/burrpdf.m
455s ***** demo
455s  ## Plot various PDFs from the Burr type XII distribution
455s  x = 0.001:0.001:3;
455s  y1 = burrpdf (x, 1, 1, 1);
455s  y2 = burrpdf (x, 1, 1, 2);
455s  y3 = burrpdf (x, 1, 1, 3);
455s  y4 = burrpdf (x, 1, 2, 1);
455s  y5 = burrpdf (x, 1, 3, 1);
455s  y6 = burrpdf (x, 1, 0.5, 2);
455s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", ...
455s        x, y4, "-c", x, y5, "-m", x, y6, "-k")
455s  grid on
455s  ylim ([0, 2])
455s  legend ({"λ = 1, c = 1, k = 1", "λ = 1, c = 1, k = 2", ...
455s           "λ = 1, c = 1, k = 3", "λ = 1, c = 2, k = 1", ...
455s           "λ = 1, c = 3, k = 1", "λ = 1, c = 0.5, k = 2"}, ...
455s          "location", "northeast")
455s  title ("Burr type XII PDF")
455s  xlabel ("values in x")
455s  ylabel ("density")
455s ***** shared x, y
455s  x = [-1, 0, 1, 2, Inf];
455s  y = [0, 1, 1/4, 1/9, 0];
455s ***** assert (burrpdf (x, ones(1,5), ones (1,5), ones (1,5)), y)
455s ***** assert (burrpdf (x, 1, 1, 1), y)
455s ***** assert (burrpdf (x, [1, 1, NaN, 1, 1], 1, 1), [y(1:2), NaN, y(4:5)])
455s ***** assert (burrpdf (x, 1, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)])
455s ***** assert (burrpdf (x, 1, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)])
455s ***** assert (burrpdf ([x, NaN], 1, 1, 1), [y, NaN])
455s ***** assert (burrpdf (single ([x, NaN]), 1, 1, 1), single ([y, NaN]))
455s ***** assert (burrpdf ([x, NaN], single (1), 1, 1), single ([y, NaN]))
455s ***** assert (burrpdf ([x, NaN], 1, single (1), 1), single ([y, NaN]))
455s ***** assert (burrpdf ([x, NaN], 1, 1, single (1)), single ([y, NaN]))
455s ***** error<burrpdf: function called with too few input arguments.> burrpdf ()
455s ***** error<burrpdf: function called with too few input arguments.> burrpdf (1)
455s ***** error<burrpdf: function called with too few input arguments.> burrpdf (1, 2)
455s ***** error<burrpdf: function called with too few input arguments.> burrpdf (1, 2, 3)
455s ***** error<burrpdf: function called with too many inputs> ...
455s  burrpdf (1, 2, 3, 4, 5)
455s ***** error<burrpdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
455s  burrpdf (ones (3), ones (2), ones(2), ones(2))
455s ***** error<burrpdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
455s  burrpdf (ones (2), ones (3), ones(2), ones(2))
455s ***** error<burrpdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
455s  burrpdf (ones (2), ones (2), ones(3), ones(2))
455s ***** error<burrpdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
455s  burrpdf (ones (2), ones (2), ones(2), ones(3))
455s ***** error<burrpdf: X, LAMBDA, C, and K must not be complex.> burrpdf (i, 2, 3, 4)
455s ***** error<burrpdf: X, LAMBDA, C, and K must not be complex.> burrpdf (1, i, 3, 4)
455s ***** error<burrpdf: X, LAMBDA, C, and K must not be complex.> burrpdf (1, 2, i, 4)
455s ***** error<burrpdf: X, LAMBDA, C, and K must not be complex.> burrpdf (1, 2, 3, i)
455s 23 tests, 23 passed, 0 known failure, 0 skipped
455s [inst/dist_fun/burrrnd.m]
455s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/burrrnd.m
455s ***** assert (size (burrrnd (1, 1, 1)), [1 1])
455s ***** assert (size (burrrnd (ones (2,1), 1, 1)), [2, 1])
455s ***** assert (size (burrrnd (ones (2,2), 1, 1)), [2, 2])
455s ***** assert (size (burrrnd (1, ones (2,1), 1)), [2, 1])
455s ***** assert (size (burrrnd (1, ones (2,2), 1)), [2, 2])
455s ***** assert (size (burrrnd (1, 1, ones (2,1))), [2, 1])
455s ***** assert (size (burrrnd (1, 1, ones (2,2))), [2, 2])
455s ***** assert (size (burrrnd (1, 1, 1, 3)), [3, 3])
455s ***** assert (size (burrrnd (1, 1, 1, [4 1])), [4, 1])
455s ***** assert (size (burrrnd (1, 1, 1, 4, 1)), [4, 1])
455s ***** assert (class (burrrnd (1,1,1)), "double")
455s ***** assert (class (burrrnd (single (1),1,1)), "single")
455s ***** assert (class (burrrnd (single ([1 1]),1,1)), "single")
455s ***** assert (class (burrrnd (1,single (1),1)), "single")
455s ***** assert (class (burrrnd (1,single ([1 1]),1)), "single")
455s ***** assert (class (burrrnd (1,1,single (1))), "single")
455s ***** assert (class (burrrnd (1,1,single ([1 1]))), "single")
455s ***** error<burrrnd: function called with too few input arguments.> burrrnd ()
455s ***** error<burrrnd: function called with too few input arguments.> burrrnd (1)
455s ***** error<burrrnd: function called with too few input arguments.> burrrnd (1, 2)
455s ***** error<burrrnd: LAMBDA, C, and K must be of common size or scalars.> ...
455s  burrrnd (ones (3), ones (2), ones (2))
455s ***** error<burrrnd: LAMBDA, C, and K must be of common size or scalars.> ...
455s  burrrnd (ones (2), ones (3), ones (2))
455s ***** error<burrrnd: LAMBDA, C, and K must be of common size or scalars.> ...
455s  burrrnd (ones (2), ones (2), ones (3))
455s ***** error<burrrnd: LAMBDA, C, and K must not be complex.> burrrnd (i, 2, 3)
455s ***** error<burrrnd: LAMBDA, C, and K must not be complex.> burrrnd (1, i, 3)
455s ***** error<burrrnd: LAMBDA, C, and K must not be complex.> burrrnd (1, 2, i)
455s ***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
455s  burrrnd (1, 2, 3, -1)
455s ***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
455s  burrrnd (1, 2, 3, 1.2)
455s ***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
455s  burrrnd (1, 2, 3, ones (2))
455s ***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
455s  burrrnd (1, 2, 3, [2 -1 2])
455s ***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
455s  burrrnd (1, 2, 3, [2 0 2.5])
455s ***** error<burrrnd: dimensions must be non-negative integers.> ...
455s  burrrnd (1, 2, 3, 2, -1, 5)
455s ***** error<burrrnd: dimensions must be non-negative integers.> ...
455s  burrrnd (1, 2, 3, 2, 1.5, 5)
455s ***** error<burrrnd: LAMBDA, C, and K must be scalars or of size SZ.> ...
455s  burrrnd (2, ones (2), 2, 3)
455s ***** error<burrrnd: LAMBDA, C, and K must be scalars or of size SZ.> ...
455s  burrrnd (2, ones (2), 2, [3, 2])
455s ***** error<burrrnd: LAMBDA, C, and K must be scalars or of size SZ.> ...
455s  burrrnd (2, ones (2), 2, 3, 2)
455s 36 tests, 36 passed, 0 known failure, 0 skipped
455s [inst/dist_fun/bvncdf.m]
455s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/bvncdf.m
455s ***** demo
455s  mu = [1, -1];
455s  sigma = [0.9, 0.4; 0.4, 0.3];
455s  [X1, X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
455s  x = [X1(:), X2(:)];
455s  p = bvncdf (x, mu, sigma);
455s  Z = reshape (p, 25, 25);
455s  surf (X1, X2, Z);
455s  title ("Bivariate Normal Distribution");
455s  ylabel "X1"
455s  xlabel "X2"
455s ***** test
455s  mu = [1, -1];
455s  sigma = [0.9, 0.4; 0.4, 0.3];
455s  [X1,X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
455s  x = [X1(:), X2(:)];
455s  p = bvncdf (x, mu, sigma);
455s  p_out = [0.00011878988774500, 0.00034404112322371, ...
455s           0.00087682502191813, 0.00195221905058185, ...
455s           0.00378235566873474, 0.00638175749734415, ...
455s           0.00943764224329656, 0.01239164888125426, ...
455s           0.01472750274376648, 0.01623228313374828]';
455s  assert (p([1:10]), p_out, 1e-16);
455s ***** test
455s  mu = [1, -1];
455s  sigma = [0.9, 0.4; 0.4, 0.3];
455s  [X1,X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
455s  x = [X1(:), X2(:)];
455s  p = bvncdf (x, mu, sigma);
455s  p_out = [0.8180695783608276, 0.8854485749482751, ...
455s           0.9308108777385832, 0.9579855743025508, ...
455s           0.9722897881414742, 0.9788150170059926, ...
455s           0.9813597788804785, 0.9821977956568989, ...
455s           0.9824283794464095, 0.9824809345614861]';
455s  assert (p([616:625]), p_out, 3e-16);
455s ***** error bvncdf (randn (25,3), [], [1, 1; 1, 1]);
455s ***** error bvncdf (randn (25,2), [], [1, 1; 1, 1]);
455s ***** error bvncdf (randn (25,2), [], ones (3, 2));
455s 5 tests, 5 passed, 0 known failure, 0 skipped
455s [inst/dist_fun/bvtcdf.m]
455s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/bvtcdf.m
455s ***** test
455s  x = [1, 2];
455s  rho = [1, 0.5; 0.5, 1];
455s  df = 4;
455s  assert (bvtcdf(x, rho(2), df), mvtcdf(x, rho, df), 1e-14);
456s ***** test
456s  x = [3, 2;2, 4;1, 5];
456s  rho = [1, 0.5; 0.5, 1];
456s  df = 4;
456s  assert (bvtcdf(x, rho(2), df), mvtcdf(x, rho, df), 1e-14);
456s 2 tests, 2 passed, 0 known failure, 0 skipped
456s [inst/dist_fun/cauchycdf.m]
456s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/cauchycdf.m
456s ***** demo
456s  ## Plot various CDFs from the Cauchy distribution
456s  x = -5:0.01:5;
456s  p1 = cauchycdf (x, 0, 0.5);
456s  p2 = cauchycdf (x, 0, 1);
456s  p3 = cauchycdf (x, 0, 2);
456s  p4 = cauchycdf (x, -2, 1);
456s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
456s  grid on
456s  xlim ([-5, 5])
456s  legend ({"x0 = 0, γ = 0.5", "x0 = 0, γ = 1", ...
456s           "x0 = 0, γ = 2", "x0 = -2, γ = 1"}, "location", "southeast")
456s  title ("Cauchy CDF")
456s  xlabel ("values in x")
456s  ylabel ("probability")
456s ***** shared x, y
456s  x = [-1 0 0.5 1 2];
456s  y = 1/pi * atan ((x-1) / 2) + 1/2;
456s ***** assert (cauchycdf (x, ones (1,5), 2*ones (1,5)), y)
456s ***** assert (cauchycdf (x, 1, 2*ones (1,5)), y)
456s ***** assert (cauchycdf (x, ones (1,5), 2), y)
456s ***** assert (cauchycdf (x, [-Inf 1 NaN 1 Inf], 2), [NaN y(2) NaN y(4) NaN])
456s ***** assert (cauchycdf (x, 1, 2*[0 1 NaN 1 Inf]), [NaN y(2) NaN y(4) NaN])
456s ***** assert (cauchycdf ([x(1:2) NaN x(4:5)], 1, 2), [y(1:2) NaN y(4:5)])
456s ***** assert (cauchycdf ([x, NaN], 1, 2), [y, NaN])
456s ***** assert (cauchycdf (single ([x, NaN]), 1, 2), single ([y, NaN]), eps ("single"))
456s ***** assert (cauchycdf ([x, NaN], single (1), 2), single ([y, NaN]), eps ("single"))
456s ***** assert (cauchycdf ([x, NaN], 1, single (2)), single ([y, NaN]), eps ("single"))
456s ***** error<cauchycdf: function called with too few input arguments.> cauchycdf ()
456s ***** error<cauchycdf: function called with too few input arguments.> cauchycdf (1)
456s ***** error<cauchycdf: function called with too few input arguments.> ...
456s  cauchycdf (1, 2)
456s ***** error<cauchycdf: function called with too many inputs> ...
456s  cauchycdf (1, 2, 3, 4, 5)
456s ***** error<cauchycdf: invalid argument for upper tail.> cauchycdf (1, 2, 3, "tail")
456s ***** error<cauchycdf: invalid argument for upper tail.> cauchycdf (1, 2, 3, 4)
456s ***** error<cauchycdf: X, X0, and GAMMA must be of common size or scalars.> ...
456s  cauchycdf (ones (3), ones (2), ones (2))
456s ***** error<cauchycdf: X, X0, and GAMMA must be of common size or scalars.> ...
456s  cauchycdf (ones (2), ones (3), ones (2))
456s ***** error<cauchycdf: X, X0, and GAMMA must be of common size or scalars.> ...
456s  cauchycdf (ones (2), ones (2), ones (3))
456s ***** error<cauchycdf: X, X0, and GAMMA must not be complex.> cauchycdf (i, 2, 2)
456s ***** error<cauchycdf: X, X0, and GAMMA must not be complex.> cauchycdf (2, i, 2)
456s ***** error<cauchycdf: X, X0, and GAMMA must not be complex.> cauchycdf (2, 2, i)
456s 22 tests, 22 passed, 0 known failure, 0 skipped
456s [inst/dist_fun/cauchyinv.m]
456s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/cauchyinv.m
456s ***** demo
456s  ## Plot various iCDFs from the Cauchy distribution
456s  p = 0.001:0.001:0.999;
456s  x1 = cauchyinv (p, 0, 0.5);
456s  x2 = cauchyinv (p, 0, 1);
456s  x3 = cauchyinv (p, 0, 2);
456s  x4 = cauchyinv (p, -2, 1);
456s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
456s  grid on
456s  ylim ([-5, 5])
456s  legend ({"x0 = 0, γ = 0.5", "x0 = 0, γ = 1", ...
456s           "x0 = 0, γ = 2", "x0 = -2, γ = 1"}, "location", "northwest")
456s  title ("Cauchy iCDF")
456s  xlabel ("probability")
456s  ylabel ("values in x")
456s ***** shared p
456s  p = [-1 0 0.5 1 2];
456s ***** assert (cauchyinv (p, ones (1,5), 2 * ones (1,5)), [NaN -Inf 1 Inf NaN], eps)
456s ***** assert (cauchyinv (p, 1, 2 * ones (1,5)), [NaN -Inf 1 Inf NaN], eps)
456s ***** assert (cauchyinv (p, ones (1,5), 2), [NaN -Inf 1 Inf NaN], eps)
456s ***** assert (cauchyinv (p, [1 -Inf NaN Inf 1], 2), [NaN NaN NaN NaN NaN])
456s ***** assert (cauchyinv (p, 1, 2 * [1 0 NaN Inf 1]), [NaN NaN NaN NaN NaN])
456s ***** assert (cauchyinv ([p(1:2) NaN p(4:5)], 1, 2), [NaN -Inf NaN Inf NaN])
456s ***** assert (cauchyinv ([p, NaN], 1, 2), [NaN -Inf 1 Inf NaN NaN], eps)
456s ***** assert (cauchyinv (single ([p, NaN]), 1, 2), ...
456s  single ([NaN -Inf 1 Inf NaN NaN]), eps ("single"))
456s ***** assert (cauchyinv ([p, NaN], single (1), 2), ...
456s  single ([NaN -Inf 1 Inf NaN NaN]), eps ("single"))
456s ***** assert (cauchyinv ([p, NaN], 1, single (2)), ...
456s  single ([NaN -Inf 1 Inf NaN NaN]), eps ("single"))
456s ***** error<cauchyinv: function called with too few input arguments.> cauchyinv ()
456s ***** error<cauchyinv: function called with too few input arguments.> cauchyinv (1)
456s ***** error<cauchyinv: function called with too few input arguments.> ...
456s  cauchyinv (1, 2)
456s ***** error<cauchyinv: function called with too many inputs> cauchyinv (1, 2, 3, 4)
456s ***** error<cauchyinv: P, X0, and GAMMA must be of common size or scalars.> ...
456s  cauchyinv (ones (3), ones (2), ones(2))
456s ***** error<cauchyinv: P, X0, and GAMMA must be of common size or scalars.> ...
456s  cauchyinv (ones (2), ones (3), ones(2))
456s ***** error<cauchyinv: P, X0, and GAMMA must be of common size or scalars.> ...
456s  cauchyinv (ones (2), ones (2), ones(3))
456s ***** error<cauchyinv: P, X0, and GAMMA must not be complex.> cauchyinv (i, 4, 3)
456s ***** error<cauchyinv: P, X0, and GAMMA must not be complex.> cauchyinv (1, i, 3)
456s ***** error<cauchyinv: P, X0, and GAMMA must not be complex.> cauchyinv (1, 4, i)
456s 20 tests, 20 passed, 0 known failure, 0 skipped
456s [inst/dist_fun/cauchypdf.m]
456s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/cauchypdf.m
456s ***** demo
456s  ## Plot various PDFs from the Cauchy distribution
456s  x = -5:0.01:5;
456s  y1 = cauchypdf (x, 0, 0.5);
456s  y2 = cauchypdf (x, 0, 1);
456s  y3 = cauchypdf (x, 0, 2);
456s  y4 = cauchypdf (x, -2, 1);
456s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
456s  grid on
456s  xlim ([-5, 5])
456s  ylim ([0, 0.7])
456s  legend ({"x0 = 0, γ = 0.5", "x0 = 0, γ = 1", ...
456s           "x0 = 0, γ = 2", "x0 = -2, γ = 1"}, "location", "northeast")
456s  title ("Cauchy PDF")
456s  xlabel ("values in x")
456s  ylabel ("density")
456s ***** shared x, y
456s  x = [-1 0 0.5 1 2];
456s  y = 1/pi * ( 2 ./ ((x-1).^2 + 2^2) );
456s ***** assert (cauchypdf (x, ones (1,5), 2*ones (1,5)), y)
456s ***** assert (cauchypdf (x, 1, 2*ones (1,5)), y)
456s ***** assert (cauchypdf (x, ones (1,5), 2), y)
456s ***** assert (cauchypdf (x, [-Inf 1 NaN 1 Inf], 2), [NaN y(2) NaN y(4) NaN])
456s ***** assert (cauchypdf (x, 1, 2*[0 1 NaN 1 Inf]), [NaN y(2) NaN y(4) NaN])
456s ***** assert (cauchypdf ([x, NaN], 1, 2), [y, NaN])
456s ***** assert (cauchypdf (single ([x, NaN]), 1, 2), single ([y, NaN]), eps ("single"))
456s ***** assert (cauchypdf ([x, NaN], single (1), 2), single ([y, NaN]), eps ("single"))
456s ***** assert (cauchypdf ([x, NaN], 1, single (2)), single ([y, NaN]), eps ("single"))
456s ***** test
456s  x = rand (10, 1);
456s  assert (cauchypdf (x, 0, 1), tpdf (x, 1), eps);
456s ***** error<cauchypdf: function called with too few input arguments.> cauchypdf ()
456s ***** error<cauchypdf: function called with too few input arguments.> cauchypdf (1)
456s ***** error<cauchypdf: function called with too few input arguments.> ...
456s  cauchypdf (1, 2)
456s ***** error<cauchypdf: function called with too many inputs> cauchypdf (1, 2, 3, 4)
456s ***** error<cauchypdf: X, X0, and GAMMA must be of common size or scalars.> ...
456s  cauchypdf (ones (3), ones (2), ones(2))
456s ***** error<cauchypdf: X, X0, and GAMMA must be of common size or scalars.> ...
456s  cauchypdf (ones (2), ones (3), ones(2))
456s ***** error<cauchypdf: X, X0, and GAMMA must be of common size or scalars.> ...
456s  cauchypdf (ones (2), ones (2), ones(3))
456s ***** error<cauchypdf: X, X0, and GAMMA must not be complex.> cauchypdf (i, 4, 3)
456s ***** error<cauchypdf: X, X0, and GAMMA must not be complex.> cauchypdf (1, i, 3)
456s ***** error<cauchypdf: X, X0, and GAMMA must not be complex.> cauchypdf (1, 4, i)
456s 20 tests, 20 passed, 0 known failure, 0 skipped
456s [inst/dist_fun/cauchyrnd.m]
456s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/cauchyrnd.m
456s ***** assert (size (cauchyrnd (1, 1)), [1 1])
456s ***** assert (size (cauchyrnd (1, ones (2,1))), [2, 1])
456s ***** assert (size (cauchyrnd (1, ones (2,2))), [2, 2])
456s ***** assert (size (cauchyrnd (ones (2,1), 1)), [2, 1])
456s ***** assert (size (cauchyrnd (ones (2,2), 1)), [2, 2])
456s ***** assert (size (cauchyrnd (1, 1, 3)), [3, 3])
456s ***** assert (size (cauchyrnd (1, 1, [4, 1])), [4, 1])
456s ***** assert (size (cauchyrnd (1, 1, 4, 1)), [4, 1])
456s ***** assert (size (cauchyrnd (1, 1, 4, 1, 5)), [4, 1, 5])
456s ***** assert (size (cauchyrnd (1, 1, 0, 1)), [0, 1])
456s ***** assert (size (cauchyrnd (1, 1, 1, 0)), [1, 0])
456s ***** assert (size (cauchyrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
456s ***** assert (class (cauchyrnd (1, 1)), "double")
456s ***** assert (class (cauchyrnd (1, single (1))), "single")
456s ***** assert (class (cauchyrnd (1, single ([1, 1]))), "single")
456s ***** assert (class (cauchyrnd (single (1), 1)), "single")
456s ***** assert (class (cauchyrnd (single ([1, 1]), 1)), "single")
456s ***** error<cauchyrnd: function called with too few input arguments.> cauchyrnd ()
456s ***** error<cauchyrnd: function called with too few input arguments.> cauchyrnd (1)
456s ***** error<cauchyrnd: X0 and GAMMA must be of common size or scalars.> ...
456s  cauchyrnd (ones (3), ones (2))
456s ***** error<cauchyrnd: X0 and GAMMA must be of common size or scalars.> ...
456s  cauchyrnd (ones (2), ones (3))
456s ***** error<cauchyrnd: X0 and GAMMA must not be complex.> cauchyrnd (i, 2, 3)
456s ***** error<cauchyrnd: X0 and GAMMA must not be complex.> cauchyrnd (1, i, 3)
456s ***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
456s  cauchyrnd (1, 2, -1)
456s ***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
456s  cauchyrnd (1, 2, 1.2)
456s ***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
456s  cauchyrnd (1, 2, ones (2))
456s ***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
456s  cauchyrnd (1, 2, [2 -1 2])
456s ***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
456s  cauchyrnd (1, 2, [2 0 2.5])
456s ***** error<cauchyrnd: dimensions must be non-negative integers.> ...
456s  cauchyrnd (1, 2, 2, -1, 5)
456s ***** error<cauchyrnd: dimensions must be non-negative integers.> ...
456s  cauchyrnd (1, 2, 2, 1.5, 5)
456s ***** error<cauchyrnd: X0 and GAMMA must be scalars or of size SZ.> ...
456s  cauchyrnd (2, ones (2), 3)
456s ***** error<cauchyrnd: X0 and GAMMA must be scalars or of size SZ.> ...
456s  cauchyrnd (2, ones (2), [3, 2])
456s ***** error<cauchyrnd: X0 and GAMMA must be scalars or of size SZ.> ...
456s  cauchyrnd (2, ones (2), 3, 2)
456s 33 tests, 33 passed, 0 known failure, 0 skipped
456s [inst/dist_fun/chi2cdf.m]
456s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/chi2cdf.m
456s ***** demo
456s  ## Plot various CDFs from the chi-squared distribution
456s  x = 0:0.01:8;
456s  p1 = chi2cdf (x, 1);
456s  p2 = chi2cdf (x, 2);
456s  p3 = chi2cdf (x, 3);
456s  p4 = chi2cdf (x, 4);
456s  p5 = chi2cdf (x, 6);
456s  p6 = chi2cdf (x, 9);
456s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", ...
456s        x, p4, "-c", x, p5, "-m", x, p6, "-y")
456s  grid on
456s  xlim ([0, 8])
456s  legend ({"df = 1", "df = 2", "df = 3", ...
456s           "df = 4", "df = 6", "df = 9"}, "location", "southeast")
456s  title ("Chi-squared CDF")
456s  xlabel ("values in x")
456s  ylabel ("probability")
456s ***** shared x, p, u
456s  x = [-1, 0, 0.5, 1, 2];
456s  p = [0, (1 - exp (-x(2:end) / 2))];
456s  u = [1, 0, NaN, 0.606530659712633, 0.367879441171442];
456s ***** assert (chi2cdf (x, 2 * ones (1,5)), p, eps)
456s ***** assert (chi2cdf (x, 2), p, eps)
456s ***** assert (chi2cdf (x, 2 * [1, 0, NaN, 1, 1]), [0, 1, NaN, p(4:5)], eps)
456s ***** assert (chi2cdf (x, 2 * [1, 0, NaN, 1, 1], "upper"), u, 3 * eps)
456s ***** assert (chi2cdf ([x(1:2), NaN, x(4:5)], 2), [p(1:2), NaN, p(4:5)], eps)
456s ***** assert (chi2cdf ([x, NaN], 2), [p, NaN], eps)
456s ***** assert (chi2cdf (single ([x, NaN]), 2), single ([p, NaN]), eps ("single"))
456s ***** assert (chi2cdf ([x, NaN], single (2)), single ([p, NaN]), eps ("single"))
456s ***** error<chi2cdf: function called with too few input arguments.> chi2cdf ()
456s ***** error<chi2cdf: function called with too few input arguments.> chi2cdf (1)
456s ***** error<chi2cdf: function called with too many inputs> chi2cdf (1, 2, 3, 4)
456s ***** error<chi2cdf: invalid argument for upper tail.> chi2cdf (1, 2, 3)
456s ***** error<chi2cdf: invalid argument for upper tail.> chi2cdf (1, 2, "uper")
456s ***** error<chi2cdf: X and DF must be of common size or scalars.> ...
456s  chi2cdf (ones (3), ones (2))
456s ***** error<chi2cdf: X and DF must be of common size or scalars.> ...
456s  chi2cdf (ones (2), ones (3))
456s ***** error<chi2cdf: X and DF must not be complex.> chi2cdf (i, 2)
456s ***** error<chi2cdf: X and DF must not be complex.> chi2cdf (2, i)
456s 17 tests, 17 passed, 0 known failure, 0 skipped
456s [inst/dist_fun/chi2inv.m]
456s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/chi2inv.m
456s ***** demo
456s  ## Plot various iCDFs from the chi-squared distribution
456s  p = 0.001:0.001:0.999;
456s  x1 = chi2inv (p, 1);
456s  x2 = chi2inv (p, 2);
456s  x3 = chi2inv (p, 3);
456s  x4 = chi2inv (p, 4);
456s  x5 = chi2inv (p, 6);
456s  x6 = chi2inv (p, 9);
456s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", ...
456s        p, x4, "-c", p, x5, "-m", p, x6, "-y")
456s  grid on
456s  ylim ([0, 8])
456s  legend ({"df = 1", "df = 2", "df = 3", ...
456s           "df = 4", "df = 6", "df = 9"}, "location", "northwest")
456s  title ("Chi-squared iCDF")
456s  xlabel ("probability")
456s  ylabel ("values in x")
456s ***** shared p
456s  p = [-1 0 0.3934693402873666 1 2];
456s ***** assert (chi2inv (p, 2*ones (1,5)), [NaN 0 1 Inf NaN], 5*eps)
456s ***** assert (chi2inv (p, 2), [NaN 0 1 Inf NaN], 5*eps)
456s ***** assert (chi2inv (p, 2*[0 1 NaN 1 1]), [NaN 0 NaN Inf NaN], 5*eps)
456s ***** assert (chi2inv ([p(1:2) NaN p(4:5)], 2), [NaN 0 NaN Inf NaN], 5*eps)
456s ***** assert (chi2inv ([p, NaN], 2), [NaN 0 1 Inf NaN NaN], 5*eps)
456s ***** assert (chi2inv (single ([p, NaN]), 2), single ([NaN 0 1 Inf NaN NaN]), 5*eps ("single"))
456s ***** assert (chi2inv ([p, NaN], single (2)), single ([NaN 0 1 Inf NaN NaN]), 5*eps ("single"))
456s ***** error<chi2inv: function called with too few input arguments.> chi2inv ()
456s ***** error<chi2inv: function called with too few input arguments.> chi2inv (1)
456s ***** error<chi2inv: function called with too many inputs> chi2inv (1,2,3)
456s ***** error<chi2inv: P and DF must be of common size or scalars.> ...
456s  chi2inv (ones (3), ones (2))
456s ***** error<chi2inv: P and DF must be of common size or scalars.> ...
456s  chi2inv (ones (2), ones (3))
456s ***** error<chi2inv: P and DF must not be complex.> chi2inv (i, 2)
456s ***** error<chi2inv: P and DF must not be complex.> chi2inv (2, i)
456s 14 tests, 14 passed, 0 known failure, 0 skipped
456s [inst/dist_fun/chi2pdf.m]
456s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/chi2pdf.m
456s ***** demo
456s  ## Plot various PDFs from the chi-squared distribution
456s  x = 0:0.01:8;
456s  y1 = chi2pdf (x, 1);
456s  y2 = chi2pdf (x, 2);
456s  y3 = chi2pdf (x, 3);
456s  y4 = chi2pdf (x, 4);
456s  y5 = chi2pdf (x, 6);
456s  y6 = chi2pdf (x, 9);
456s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", ...
456s        x, y4, "-c", x, y5, "-m", x, y6, "-y")
456s  grid on
456s  xlim ([0, 8])
456s  ylim ([0, 0.5])
456s  legend ({"df = 1", "df = 2", "df = 3", ...
456s           "df = 4", "df = 6", "df = 9"}, "location", "northeast")
456s  title ("Chi-squared PDF")
456s  xlabel ("values in x")
456s  ylabel ("density")
456s ***** shared x, y
456s  x = [-1 0 0.5 1 Inf];
456s  y = [0, 1/2 * exp(-x(2:5)/2)];
456s ***** assert (chi2pdf (x, 2*ones (1,5)), y)
456s ***** assert (chi2pdf (x, 2), y)
456s ***** assert (chi2pdf (x, 2*[1 0 NaN 1 1]), [y(1) NaN NaN y(4:5)])
456s ***** assert (chi2pdf ([x, NaN], 2), [y, NaN])
456s ***** assert (chi2pdf (2, Inf), 0)
456s ***** assert (chi2pdf (single ([x, NaN]), 2), single ([y, NaN]))
456s ***** assert (chi2pdf ([x, NaN], single (2)), single ([y, NaN]))
456s ***** error<chi2pdf: function called with too few input arguments.> chi2pdf ()
456s ***** error<chi2pdf: function called with too few input arguments.> chi2pdf (1)
456s ***** error<chi2pdf: function called with too many inputs> chi2pdf (1,2,3)
456s ***** error<chi2pdf: X and DF must be of common size or scalars.> ...
456s  chi2pdf (ones (3), ones (2))
456s ***** error<chi2pdf: X and DF must be of common size or scalars.> ...
456s  chi2pdf (ones (2), ones (3))
456s ***** error<chi2pdf: X and DF must not be complex.> chi2pdf (i, 2)
456s ***** error<chi2pdf: X and DF must not be complex.> chi2pdf (2, i)
456s 14 tests, 14 passed, 0 known failure, 0 skipped
456s [inst/dist_fun/chi2rnd.m]
456s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/chi2rnd.m
456s ***** assert (size (chi2rnd (2)), [1, 1])
456s ***** assert (size (chi2rnd (ones (2,1))), [2, 1])
456s ***** assert (size (chi2rnd (ones (2,2))), [2, 2])
456s ***** assert (size (chi2rnd (1, 3)), [3, 3])
456s ***** assert (size (chi2rnd (1, [4 1])), [4, 1])
456s ***** assert (size (chi2rnd (1, 4, 1)), [4, 1])
456s ***** assert (size (chi2rnd (1, 4, 1)), [4, 1])
456s ***** assert (size (chi2rnd (1, 4, 1, 5)), [4, 1, 5])
456s ***** assert (size (chi2rnd (1, 0, 1)), [0, 1])
456s ***** assert (size (chi2rnd (1, 1, 0)), [1, 0])
456s ***** assert (size (chi2rnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
456s ***** assert (class (chi2rnd (2)), "double")
456s ***** assert (class (chi2rnd (single (2))), "single")
456s ***** assert (class (chi2rnd (single ([2 2]))), "single")
456s ***** error<chi2rnd: function called with too few input arguments.> chi2rnd ()
456s ***** error<chi2rnd: DF must not be complex.> chi2rnd (i)
456s ***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
456s  chi2rnd (1, -1)
456s ***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
456s  chi2rnd (1, 1.2)
456s ***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
456s  chi2rnd (1, ones (2))
456s ***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
456s  chi2rnd (1, [2 -1 2])
456s ***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
456s  chi2rnd (1, [2 0 2.5])
456s ***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
456s  chi2rnd (ones (2), ones (2))
456s ***** error<chi2rnd: dimensions must be non-negative integers.> ...
456s  chi2rnd (1, 2, -1, 5)
456s ***** error<chi2rnd: dimensions must be non-negative integers.> ...
456s  chi2rnd (1, 2, 1.5, 5)
456s ***** error<chi2rnd: DF must be scalar or of size SZ.> chi2rnd (ones (2,2), 3)
456s ***** error<chi2rnd: DF must be scalar or of size SZ.> chi2rnd (ones (2,2), [3, 2])
456s ***** error<chi2rnd: DF must be scalar or of size SZ.> chi2rnd (ones (2,2), 2, 3)
456s 27 tests, 27 passed, 0 known failure, 0 skipped
456s [inst/dist_fun/copulacdf.m]
456s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/copulacdf.m
456s ***** test
456s  x = [0.2:0.2:0.6; 0.2:0.2:0.6];
456s  theta = [1; 2];
456s  p = copulacdf ("Clayton", x, theta);
456s  expected_p = [0.1395; 0.1767];
456s  assert (p, expected_p, 0.001);
456s ***** test
456s  x = [0.2:0.2:0.6; 0.2:0.2:0.6];
456s  p = copulacdf ("Gumbel", x, 2);
456s  expected_p = [0.1464; 0.1464];
456s  assert (p, expected_p, 0.001);
456s ***** test
456s  x = [0.2:0.2:0.6; 0.2:0.2:0.6];
456s  theta = [1; 2];
456s  p = copulacdf ("Frank", x, theta);
456s  expected_p = [0.0699; 0.0930];
456s  assert (p, expected_p, 0.001);
456s ***** test
456s  x = [0.2:0.2:0.6; 0.2:0.2:0.6];
456s  theta = [0.3; 0.7];
456s  p = copulacdf ("AMH", x, theta);
456s  expected_p = [0.0629; 0.0959];
456s  assert (p, expected_p, 0.001);
456s ***** test
456s  x = [0.2:0.2:0.6; 0.2:0.1:0.4];
456s  theta = [0.2, 0.1, 0.1, 0.05];
456s  p = copulacdf ("FGM", x, theta);
456s  expected_p = [0.0558; 0.0293];
456s  assert (p, expected_p, 0.001);
456s 5 tests, 5 passed, 0 known failure, 0 skipped
456s [inst/dist_fun/copulapdf.m]
456s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/copulapdf.m
456s ***** test
456s  x = [0.2:0.2:0.6; 0.2:0.2:0.6];
456s  theta = [1; 2];
456s  y = copulapdf ("Clayton", x, theta);
456s  expected_p = [0.9872; 0.7295];
456s  assert (y, expected_p, 0.001);
456s ***** test
456s  x = [0.2:0.2:0.6; 0.2:0.2:0.6];
456s  y = copulapdf ("Gumbel", x, 2);
456s  expected_p = [0.9468; 0.9468];
456s  assert (y, expected_p, 0.001);
456s ***** test
456s  x = [0.2, 0.6; 0.2, 0.6];
456s  theta = [1; 2];
456s  y = copulapdf ("Frank", x, theta);
456s  expected_p = [0.9378; 0.8678];
456s  assert (y, expected_p, 0.001);
456s ***** test
456s  x = [0.2, 0.6; 0.2, 0.6];
456s  theta = [0.3; 0.7];
456s  y = copulapdf ("AMH", x, theta);
456s  expected_p = [0.9540; 0.8577];
456s  assert (y, expected_p, 0.001);
456s 4 tests, 4 passed, 0 known failure, 0 skipped
456s [inst/dist_fun/copularnd.m]
456s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/copularnd.m
456s ***** test
456s  theta = 0.5;
456s  r = copularnd ("Gaussian", theta);
456s  assert (size (r), [1, 2]);
456s  assert (all ((r >= 0) & (r <= 1)));
456s ***** test
456s  theta = 0.5;
456s  df = 2;
456s  r = copularnd ("t", theta, df);
456s  assert (size (r), [1, 2]);
456s  assert (all ((r >= 0) & (r <= 1)));
456s ***** test
456s  theta = 0.5;
456s  r = copularnd ("Clayton", theta);
456s  assert (size (r), [1, 2]);
456s  assert (all ((r >= 0) & (r <= 1)));
456s ***** test
456s  theta = 0.5;
456s  n = 2;
456s  r = copularnd ("Clayton", theta, n);
456s  assert (size (r), [n, 2]);
456s  assert (all ((r >= 0) & (r <= 1)));
456s ***** test
456s  theta = [1; 2];
456s  n = 2;
456s  d = 3;
456s  r = copularnd ("Clayton", theta, n, d);
456s  assert (size (r), [n, d]);
456s  assert (all ((r >= 0) & (r <= 1)));
456s 5 tests, 5 passed, 0 known failure, 0 skipped
456s [inst/dist_fun/evcdf.m]
456s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/evcdf.m
456s ***** demo
456s  ## Plot various CDFs from the extreme value distribution
456s  x = -10:0.01:10;
456s  p1 = evcdf (x, 0.5, 2);
456s  p2 = evcdf (x, 1.0, 2);
456s  p3 = evcdf (x, 1.5, 3);
456s  p4 = evcdf (x, 3.0, 4);
456s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
456s  grid on
456s  legend ({"μ = 0.5, σ = 2", "μ = 1.0, σ = 2", ...
456s           "μ = 1.5, σ = 3", "μ = 3.0, σ = 4"}, "location", "southeast")
456s  title ("Extreme value CDF")
456s  xlabel ("values in x")
456s  ylabel ("probability")
456s ***** shared x, y
456s  x = [-Inf, 1, 2, Inf];
456s  y = [0, 0.6321, 0.9340, 1];
456s ***** assert (evcdf (x, ones (1,4), ones (1,4)), y, 1e-4)
456s ***** assert (evcdf (x, 1, ones (1,4)), y, 1e-4)
456s ***** assert (evcdf (x, ones (1,4), 1), y, 1e-4)
456s ***** assert (evcdf (x, [0, -Inf, NaN, Inf], 1), [0, 1, NaN, NaN], 1e-4)
456s ***** assert (evcdf (x, 1, [Inf, NaN, -1, 0]), [NaN, NaN, NaN, NaN], 1e-4)
456s ***** assert (evcdf ([x(1:2), NaN, x(4)], 1, 1), [y(1:2), NaN, y(4)], 1e-4)
456s ***** assert (evcdf (x, "upper"), [1, 0.0660, 0.0006, 0], 1e-4)
456s ***** assert (evcdf ([x, NaN], 1, 1), [y, NaN], 1e-4)
456s ***** assert (evcdf (single ([x, NaN]), 1, 1), single ([y, NaN]), 1e-4)
456s ***** assert (evcdf ([x, NaN], single (1), 1), single ([y, NaN]), 1e-4)
456s ***** assert (evcdf ([x, NaN], 1, single (1)), single ([y, NaN]), 1e-4)
456s ***** error<evcdf: invalid number of input arguments.> evcdf ()
456s ***** error<evcdf: invalid number of input arguments.> evcdf (1,2,3,4,5,6,7)
456s ***** error<evcdf: invalid argument for upper tail.> evcdf (1, 2, 3, 4, "uper")
456s ***** error<evcdf: X, MU, and SIGMA must be of common size or scalars.> ...
456s  evcdf (ones (3), ones (2), ones (2))
456s ***** error<evcdf: invalid size of covariance matrix.> evcdf (2, 3, 4, [1, 2])
456s ***** error<evcdf: covariance matrix is required for confidence bounds.> ...
456s  [p, plo, pup] = evcdf (1, 2, 3)
456s ***** error<evcdf: invalid value for alpha.> [p, plo, pup] = ...
456s  evcdf (1, 2, 3, [1, 0; 0, 1], 0)
456s ***** error<evcdf: invalid value for alpha.> [p, plo, pup] = ...
456s  evcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
456s ***** error<evcdf: invalid value for alpha.> [p, plo, pup] = ...
456s  evcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
456s ***** error<evcdf: X, MU, and SIGMA must not be complex.> evcdf (i, 2, 2)
456s ***** error<evcdf: X, MU, and SIGMA must not be complex.> evcdf (2, i, 2)
456s ***** error<evcdf: X, MU, and SIGMA must not be complex.> evcdf (2, 2, i)
456s ***** error<evcdf: bad covariance matrix.> ...
456s  [p, plo, pup] = evcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
456s 24 tests, 24 passed, 0 known failure, 0 skipped
456s [inst/dist_fun/evinv.m]
456s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/evinv.m
456s ***** demo
456s  ## Plot various iCDFs from the extreme value distribution
456s  p = 0.001:0.001:0.999;
456s  x1 = evinv (p, 0.5, 2);
456s  x2 = evinv (p, 1.0, 2);
456s  x3 = evinv (p, 1.5, 3);
456s  x4 = evinv (p, 3.0, 4);
456s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
456s  grid on
456s  ylim ([-10, 10])
456s  legend ({"μ = 0.5, σ = 2", "μ = 1.0, σ = 2", ...
456s           "μ = 1.5, σ = 3", "μ = 3.0, σ = 4"}, "location", "northwest")
456s  title ("Extreme value iCDF")
456s  xlabel ("probability")
456s  ylabel ("values in x")
456s ***** shared p, x
456s  p = [0, 0.05, 0.5 0.95];
456s  x = [-Inf, -2.9702, -0.3665, 1.0972];
456s ***** assert (evinv (p), x, 1e-4)
457s ***** assert (evinv (p, zeros (1,4), ones (1,4)), x, 1e-4)
457s ***** assert (evinv (p, 0, ones (1,4)), x, 1e-4)
457s ***** assert (evinv (p, zeros (1,4), 1), x, 1e-4)
457s ***** assert (evinv (p, [0, -Inf, NaN, Inf], 1), [-Inf, -Inf, NaN, Inf], 1e-4)
457s ***** assert (evinv (p, 0, [Inf, NaN, -1, 0]), [-Inf, NaN, NaN, NaN], 1e-4)
457s ***** assert (evinv ([p(1:2), NaN, p(4)], 0, 1), [x(1:2), NaN, x(4)], 1e-4)
457s ***** assert (evinv ([p, NaN], 0, 1), [x, NaN], 1e-4)
457s ***** assert (evinv (single ([p, NaN]), 0, 1), single ([x, NaN]), 1e-4)
457s ***** assert (evinv ([p, NaN], single (0), 1), single ([x, NaN]), 1e-4)
457s ***** assert (evinv ([p, NaN], 0, single (1)), single ([x, NaN]), 1e-4)
457s ***** error<evinv: invalid number of input arguments.> evinv ()
457s ***** error evinv (1,2,3,4,5,6)
457s ***** error<evinv: P, MU, and SIGMA must be of common size or scalars.> ...
457s  evinv (ones (3), ones (2), ones (2))
457s ***** error<evinv: invalid size of covariance matrix.> ...
457s  [p, plo, pup] = evinv (2, 3, 4, [1, 2])
457s ***** error<evinv: covariance matrix is required for confidence bounds.> ...
457s  [p, plo, pup] = evinv (1, 2, 3)
457s ***** error<evinv: invalid value for alpha.> [p, plo, pup] = ...
457s  evinv (1, 2, 3, [1, 0; 0, 1], 0)
457s ***** error<evinv: invalid value for alpha.> [p, plo, pup] = ...
457s  evinv (1, 2, 3, [1, 0; 0, 1], 1.22)
457s ***** error<evinv: P, MU, and SIGMA must not be complex.> evinv (i, 2, 2)
457s ***** error<evinv: P, MU, and SIGMA must not be complex.> evinv (2, i, 2)
457s ***** error<evinv: P, MU, and SIGMA must not be complex.> evinv (2, 2, i)
457s ***** error<evinv: bad covariance matrix.> ...
457s  [p, plo, pup] = evinv (1, 2, 3, [-1, -10; -Inf, -Inf], 0.04)
457s 22 tests, 22 passed, 0 known failure, 0 skipped
457s [inst/dist_fun/evpdf.m]
457s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/evpdf.m
457s ***** demo
457s  ## Plot various PDFs from the Extreme value distribution
457s  x = -10:0.001:10;
457s  y1 = evpdf (x, 0.5, 2);
457s  y2 = evpdf (x, 1.0, 2);
457s  y3 = evpdf (x, 1.5, 3);
457s  y4 = evpdf (x, 3.0, 4);
457s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
457s  grid on
457s  ylim ([0, 0.2])
457s  legend ({"μ = 0.5, σ = 2", "μ = 1.0, σ = 2", ...
457s           "μ = 1.5, σ = 3", "μ = 3.0, σ = 4"}, "location", "northeast")
457s  title ("Extreme value PDF")
457s  xlabel ("values in x")
457s  ylabel ("density")
457s ***** shared x, y0, y1
457s  x = [-5, 0, 1, 2, 3];
457s  y0 = [0.0067, 0.3679, 0.1794, 0.0046, 0];
457s  y1 = [0.0025, 0.2546, 0.3679, 0.1794, 0.0046];
457s ***** assert (evpdf (x), y0, 1e-4)
457s ***** assert (evpdf (x, zeros (1,5), ones (1,5)), y0, 1e-4)
457s ***** assert (evpdf (x, ones (1,5), ones (1,5)), y1, 1e-4)
457s ***** error<evpdf: function called with too few input arguments.> evpdf ()
457s ***** error<evpdf: X, MU, and SIGMA must be of common size or scalars.> ...
457s  evpdf (ones (3), ones (2), ones (2))
457s ***** error<evpdf: X, MU, and SIGMA must not be complex.> evpdf (i, 2, 2)
457s ***** error<evpdf: X, MU, and SIGMA must not be complex.> evpdf (2, i, 2)
457s ***** error<evpdf: X, MU, and SIGMA must not be complex.> evpdf (2, 2, i)
457s 8 tests, 8 passed, 0 known failure, 0 skipped
457s [inst/dist_fun/evrnd.m]
457s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/evrnd.m
457s ***** assert (size (evrnd (1, 1)), [1 1])
457s ***** assert (size (evrnd (1, ones (2,1))), [2, 1])
457s ***** assert (size (evrnd (1, ones (2,2))), [2, 2])
457s ***** assert (size (evrnd (ones (2,1), 1)), [2, 1])
457s ***** assert (size (evrnd (ones (2,2), 1)), [2, 2])
457s ***** assert (size (evrnd (1, 1, 3)), [3, 3])
457s ***** assert (size (evrnd (1, 1, [4, 1])), [4, 1])
457s ***** assert (size (evrnd (1, 1, 4, 1)), [4, 1])
457s ***** assert (size (evrnd (1, 1, 4, 1, 5)), [4, 1, 5])
457s ***** assert (size (evrnd (1, 1, 0, 1)), [0, 1])
457s ***** assert (size (evrnd (1, 1, 1, 0)), [1, 0])
457s ***** assert (size (evrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
457s ***** assert (class (evrnd (1, 1)), "double")
457s ***** assert (class (evrnd (1, single (1))), "single")
457s ***** assert (class (evrnd (1, single ([1, 1]))), "single")
457s ***** assert (class (evrnd (single (1), 1)), "single")
457s ***** assert (class (evrnd (single ([1, 1]), 1)), "single")
457s ***** error<evrnd: function called with too few input arguments.> evrnd ()
457s ***** error<evrnd: function called with too few input arguments.> evrnd (1)
457s ***** error<evrnd: MU and SIGMA must be of common size or scalars.> ...
457s  evrnd (ones (3), ones (2))
457s ***** error<evrnd: MU and SIGMA must be of common size or scalars.> ...
457s  evrnd (ones (2), ones (3))
457s ***** error<evrnd: MU and SIGMA must not be complex.> evrnd (i, 2, 3)
457s ***** error<evrnd: MU and SIGMA must not be complex.> evrnd (1, i, 3)
457s ***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
457s  evrnd (1, 2, -1)
457s ***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
457s  evrnd (1, 2, 1.2)
457s ***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
457s  evrnd (1, 2, ones (2))
457s ***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
457s  evrnd (1, 2, [2 -1 2])
457s ***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
457s  evrnd (1, 2, [2 0 2.5])
457s ***** error<evrnd: dimensions must be non-negative integers.> ...
457s  evrnd (1, 2, 2, -1, 5)
457s ***** error<evrnd: dimensions must be non-negative integers.> ...
457s  evrnd (1, 2, 2, 1.5, 5)
457s ***** error<evrnd: MU and SIGMA must be scalars or of size SZ.> ...
457s  evrnd (2, ones (2), 3)
457s ***** error<evrnd: MU and SIGMA must be scalars or of size SZ.> ...
457s  evrnd (2, ones (2), [3, 2])
457s ***** error<evrnd: MU and SIGMA must be scalars or of size SZ.> ...
457s  evrnd (2, ones (2), 3, 2)
457s 33 tests, 33 passed, 0 known failure, 0 skipped
457s [inst/dist_fun/expcdf.m]
457s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/expcdf.m
457s ***** demo
457s  ## Plot various CDFs from the exponential distribution
457s  x = 0:0.01:5;
457s  p1 = expcdf (x, 2/3);
457s  p2 = expcdf (x, 1.0);
457s  p3 = expcdf (x, 2.0);
457s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r")
457s  grid on
457s  legend ({"μ = 2/3", "μ = 1", "μ = 2"}, "location", "southeast")
457s  title ("Exponential CDF")
457s  xlabel ("values in x")
457s  ylabel ("probability")
457s ***** shared x, p
457s  x = [-1 0 0.5 1 Inf];
457s  p = [0, 1 - exp(-x(2:end)/2)];
457s ***** assert (expcdf (x, 2 * ones (1, 5)), p, 1e-16)
457s ***** assert (expcdf (x, 2), p, 1e-16)
457s ***** assert (expcdf (x, 2 * [1, 0, NaN, 1, 1]), [0, NaN, NaN, p(4:5)], 1e-16)
457s ***** assert (expcdf ([x, NaN], 2), [p, NaN], 1e-16)
457s ***** assert (expcdf (single ([x, NaN]), 2), single ([p, NaN]))
457s ***** assert (expcdf ([x, NaN], single (2)), single ([p, NaN]))
457s ***** test
457s  [p, plo, pup] = expcdf (1, 2, 3);
457s  assert (p, 0.39346934028737, 1e-14);
457s  assert (plo, 0.08751307220484, 1e-14);
457s  assert (pup, 0.93476821257933, 1e-14);
457s ***** test
457s  [p, plo, pup] = expcdf (1, 2, 2, 0.1);
457s  assert (p, 0.39346934028737, 1e-14);
457s  assert (plo, 0.14466318041675, 1e-14);
457s  assert (pup, 0.79808291849140, 1e-14);
457s ***** test
457s  [p, plo, pup] = expcdf (1, 2, 2, 0.1, "upper");
457s  assert (p, 0.60653065971263, 1e-14);
457s  assert (plo, 0.20191708150860, 1e-14);
457s  assert (pup, 0.85533681958325, 1e-14);
457s ***** error<expcdf: invalid number of input arguments.> expcdf ()
457s ***** error<expcdf: invalid number of input arguments.> expcdf (1, 2 ,3 ,4 ,5, 6)
457s ***** error<expcdf: invalid argument for upper tail.> expcdf (1, 2, 3, 4, "uper")
457s ***** error<expcdf: X and MU must be of common size or scalars.> ...
457s  expcdf (ones (3), ones (2))
457s ***** error<expcdf: invalid size of variance, PCOV must be a scalar.> ...
457s  expcdf (2, 3, [1, 2])
457s ***** error<expcdf: variance, PCOV, is required for confidence bounds.> ...
457s  [p, plo, pup] = expcdf (1, 2)
457s ***** error<expcdf: invalid value for alpha.> [p, plo, pup] = ...
457s  expcdf (1, 2, 3, 0)
457s ***** error<expcdf: invalid value for alpha.> [p, plo, pup] = ...
457s  expcdf (1, 2, 3, 1.22)
457s ***** error<expcdf: invalid value for alpha.> [p, plo, pup] = ...
457s  expcdf (1, 2, 3, "alpha", "upper")
457s ***** error<expcdf: X and MU must not be complex.> expcdf (i, 2)
457s ***** error<expcdf: X and MU must not be complex.> expcdf (2, i)
457s ***** error<expcdf: variance, PCOV, cannot be negative.> ...
457s  [p, plo, pup] = expcdf (1, 2, -1, 0.04)
457s 21 tests, 21 passed, 0 known failure, 0 skipped
457s [inst/dist_fun/expinv.m]
457s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/expinv.m
457s ***** demo
457s  ## Plot various iCDFs from the exponential distribution
457s  p = 0.001:0.001:0.999;
457s  x1 = expinv (p, 2/3);
457s  x2 = expinv (p, 1.0);
457s  x3 = expinv (p, 2.0);
457s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
457s  grid on
457s  ylim ([0, 5])
457s  legend ({"μ = 2/3", "μ = 1", "μ = 2"}, "location", "northwest")
457s  title ("Exponential iCDF")
457s  xlabel ("probability")
457s  ylabel ("values in x")
457s ***** shared p
457s  p = [-1 0 0.3934693402873666 1 2];
457s ***** assert (expinv (p, 2*ones (1,5)), [NaN 0 1 Inf NaN], eps)
457s ***** assert (expinv (p, 2), [NaN 0 1 Inf NaN], eps)
457s ***** assert (expinv (p, 2*[1 0 NaN 1 1]), [NaN NaN NaN Inf NaN], eps)
457s ***** assert (expinv ([p(1:2) NaN p(4:5)], 2), [NaN 0 NaN Inf NaN], eps)
457s ***** assert (expinv ([p, NaN], 2), [NaN 0 1 Inf NaN NaN], eps)
457s ***** assert (expinv (single ([p, NaN]), 2), single ([NaN 0 1 Inf NaN NaN]), eps)
457s ***** assert (expinv ([p, NaN], single (2)), single ([NaN 0 1 Inf NaN NaN]), eps)
457s ***** error<expinv: invalid number of input arguments.> expinv ()
457s ***** error<expinv: invalid number of input arguments.> expinv (1, 2 ,3 ,4 ,5)
457s ***** error<expinv: P and MU must be of common size or scalars.> ...
457s  expinv (ones (3), ones (2))
457s ***** error<expinv: invalid size of variance, PCOV must be a scalar.> ...
457s  expinv (2, 3, [1, 2])
457s ***** error<expinv: variance, PCOV, is required for confidence bounds.> ...
457s  [x, xlo, xup] = expinv (1, 2)
457s ***** error<expinv: invalid value for alpha.> [x, xlo, xup] = ...
457s  expinv (1, 2, 3, 0)
457s ***** error<expinv: invalid value for alpha.> [x, xlo, xup] = ...
457s  expinv (1, 2, 3, 1.22)
457s ***** error<expinv: invalid value for alpha.> [x, xlo, xup] = ...
457s  expinv (1, 2, 3, [0.05, 0.1])
457s ***** error<expinv: P and MU must not be complex.> expinv (i, 2)
457s ***** error<expinv: P and MU must not be complex.> expinv (2, i)
457s ***** error<expinv: variance, PCOV, cannot be negative.> ...
457s  [x, xlo, xup] = expinv (1, 2, -1, 0.04)
457s 18 tests, 18 passed, 0 known failure, 0 skipped
457s [inst/dist_fun/exppdf.m]
457s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/exppdf.m
457s ***** demo
457s  ## Plot various PDFs from the exponential distribution
457s  x = 0:0.01:5;
457s  y1 = exppdf (x, 2/3);
457s  y2 = exppdf (x, 1.0);
457s  y3 = exppdf (x, 2.0);
457s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r")
457s  grid on
457s  ylim ([0, 1.5])
457s  legend ({"μ = 2/3", "μ = 1", "μ = 2"}, "location", "northeast")
457s  title ("Exponential PDF")
457s  xlabel ("values in x")
457s  ylabel ("density")
457s ***** shared x,y
457s  x = [-1 0 0.5 1 Inf];
457s  y = gampdf (x, 1, 2);
457s ***** assert (exppdf (x, 2*ones (1,5)), y)
457s ***** assert (exppdf (x, 2*[1 0 NaN 1 1]), [y(1) NaN NaN y(4:5)])
457s ***** assert (exppdf ([x, NaN], 2), [y, NaN])
457s ***** assert (exppdf (single ([x, NaN]), 2), single ([y, NaN]))
457s ***** assert (exppdf ([x, NaN], single (2)), single ([y, NaN]))
457s ***** error<exppdf: function called with too few input arguments.> exppdf ()
457s ***** error<exppdf: function called with too many inputs> exppdf (1,2,3)
457s ***** error<exppdf: X and MU must be of common size or scalars.> ...
457s  exppdf (ones (3), ones (2))
457s ***** error<exppdf: X and MU must be of common size or scalars.> ...
457s  exppdf (ones (2), ones (3))
457s ***** error<exppdf: X and MU must not be complex.> exppdf (i, 2)
457s ***** error<exppdf: X and MU must not be complex.> exppdf (2, i)
457s 11 tests, 11 passed, 0 known failure, 0 skipped
457s [inst/dist_fun/exprnd.m]
457s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/exprnd.m
457s ***** assert (size (exprnd (2)), [1, 1])
457s ***** assert (size (exprnd (ones (2,1))), [2, 1])
457s ***** assert (size (exprnd (ones (2,2))), [2, 2])
457s ***** assert (size (exprnd (1, 3)), [3, 3])
457s ***** assert (size (exprnd (1, [4 1])), [4, 1])
457s ***** assert (size (exprnd (1, 4, 1)), [4, 1])
457s ***** assert (size (exprnd (1, 4, 1)), [4, 1])
457s ***** assert (size (exprnd (1, 4, 1, 5)), [4, 1, 5])
457s ***** assert (size (exprnd (1, 0, 1)), [0, 1])
457s ***** assert (size (exprnd (1, 1, 0)), [1, 0])
457s ***** assert (size (exprnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
457s ***** assert (class (exprnd (2)), "double")
457s ***** assert (class (exprnd (single (2))), "single")
457s ***** assert (class (exprnd (single ([2 2]))), "single")
457s ***** error<exprnd: function called with too few input arguments.> exprnd ()
457s ***** error<exprnd: MU must not be complex.> exprnd (i)
457s ***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
457s  exprnd (1, -1)
457s ***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
457s  exprnd (1, 1.2)
457s ***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
457s  exprnd (1, ones (2))
457s ***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
457s  exprnd (1, [2 -1 2])
457s ***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
457s  exprnd (1, [2 0 2.5])
457s ***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
457s  exprnd (ones (2), ones (2))
457s ***** error<exprnd: dimensions must be non-negative integers.> ...
457s  exprnd (1, 2, -1, 5)
457s ***** error<exprnd: dimensions must be non-negative integers.> ...
457s  exprnd (1, 2, 1.5, 5)
457s ***** error<exprnd: MU must be scalar or of size SZ.> exprnd (ones (2,2), 3)
457s ***** error<exprnd: MU must be scalar or of size SZ.> exprnd (ones (2,2), [3, 2])
457s ***** error<exprnd: MU must be scalar or of size SZ.> exprnd (ones (2,2), 2, 3)
457s 27 tests, 27 passed, 0 known failure, 0 skipped
457s [inst/dist_fun/fcdf.m]
457s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/fcdf.m
457s ***** demo
457s  ## Plot various CDFs from the F distribution
457s  x = 0.01:0.01:4;
457s  p1 = fcdf (x, 1, 2);
457s  p2 = fcdf (x, 2, 1);
457s  p3 = fcdf (x, 5, 2);
457s  p4 = fcdf (x, 10, 1);
457s  p5 = fcdf (x, 100, 100);
457s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
457s  grid on
457s  legend ({"df1 = 1, df2 = 2", "df1 = 2, df2 = 1", ...
457s           "df1 = 5, df2 = 2", "df1 = 10, df2 = 1", ...
457s           "df1 = 100, df2 = 100"}, "location", "southeast")
457s  title ("F CDF")
457s  xlabel ("values in x")
457s  ylabel ("probability")
457s ***** shared x, y
457s  x = [-1, 0, 0.5, 1, 2, Inf];
457s  y = [0, 0, 1/3, 1/2, 2/3, 1];
457s ***** assert (fcdf (x, 2*ones (1,6), 2*ones (1,6)), y, eps)
457s ***** assert (fcdf (x, 2, 2*ones (1,6)), y, eps)
457s ***** assert (fcdf (x, 2*ones (1,6), 2), y, eps)
457s ***** assert (fcdf (x, [0 NaN Inf 2 2 2], 2), [NaN NaN 0.1353352832366127 y(4:6)], eps)
457s ***** assert (fcdf (x, 2, [0 NaN Inf 2 2 2]), [NaN NaN 0.3934693402873666 y(4:6)], eps)
457s ***** assert (fcdf ([x(1:2) NaN x(4:6)], 2, 2), [y(1:2) NaN y(4:6)], eps)
457s ***** assert (fcdf ([x, NaN], 2, 2), [y, NaN], eps)
457s ***** assert (fcdf (single ([x, NaN]), 2, 2), single ([y, NaN]), eps ("single"))
457s ***** assert (fcdf ([x, NaN], single (2), 2), single ([y, NaN]), eps ("single"))
457s ***** assert (fcdf ([x, NaN], 2, single (2)), single ([y, NaN]), eps ("single"))
457s ***** error<fcdf: function called with too few input arguments.> fcdf ()
457s ***** error<fcdf: function called with too few input arguments.> fcdf (1)
457s ***** error<fcdf: function called with too few input arguments.> fcdf (1, 2)
457s ***** error<fcdf: invalid argument for upper tail.> fcdf (1, 2, 3, 4)
457s ***** error<fcdf: invalid argument for upper tail.> fcdf (1, 2, 3, "tail")
457s ***** error<fcdf: X, DF1, and DF2 must be of common size or scalars.> ...
457s  fcdf (ones (3), ones (2), ones (2))
457s ***** error<fcdf: X, DF1, and DF2 must be of common size or scalars.> ...
457s  fcdf (ones (2), ones (3), ones (2))
457s ***** error<fcdf: X, DF1, and DF2 must be of common size or scalars.> ...
457s  fcdf (ones (2), ones (2), ones (3))
457s ***** error<fcdf: X, DF1, and DF2 must not be complex.> fcdf (i, 2, 2)
457s ***** error<fcdf: X, DF1, and DF2 must not be complex.> fcdf (2, i, 2)
457s ***** error<fcdf: X, DF1, and DF2 must not be complex.> fcdf (2, 2, i)
457s 21 tests, 21 passed, 0 known failure, 0 skipped
457s [inst/dist_fun/finv.m]
457s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/finv.m
457s ***** demo
457s  ## Plot various iCDFs from the F distribution
457s  p = 0.001:0.001:0.999;
457s  x1 = finv (p, 1, 1);
457s  x2 = finv (p, 2, 1);
457s  x3 = finv (p, 5, 2);
457s  x4 = finv (p, 10, 1);
457s  x5 = finv (p, 100, 100);
457s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
457s  grid on
457s  ylim ([0, 4])
457s  legend ({"df1 = 1, df2 = 2", "df1 = 2, df2 = 1", ...
457s           "df1 = 5, df2 = 2", "df1 = 10, df2 = 1", ...
457s           "df1 = 100, df2 = 100"}, "location", "northwest")
457s  title ("F iCDF")
457s  xlabel ("probability")
457s  ylabel ("values in x")
457s ***** shared p
457s  p = [-1 0 0.5 1 2];
457s ***** assert (finv (p, 2*ones (1,5), 2*ones (1,5)), [NaN 0 1 Inf NaN])
457s ***** assert (finv (p, 2, 2*ones (1,5)), [NaN 0 1 Inf NaN])
457s ***** assert (finv (p, 2*ones (1,5), 2), [NaN 0 1 Inf NaN])
457s ***** assert (finv (p, [2 -Inf NaN Inf 2], 2), [NaN NaN NaN Inf NaN])
457s ***** assert (finv (p, 2, [2 -Inf NaN Inf 2]), [NaN NaN NaN Inf NaN])
457s ***** assert (finv ([p(1:2) NaN p(4:5)], 2, 2), [NaN 0 NaN Inf NaN])
457s ***** assert (finv (0.025, 10, 1e6), 0.3247, 1e-4)
457s ***** assert (finv (0.025, 10, 1e7), 0.3247, 1e-4)
457s ***** assert (finv (0.025, 10, 1e10), 0.3247, 1e-4)
457s ***** assert (finv (0.025, 10, 1e255), 0.3247, 1e-4)
457s ***** assert (finv (0.025, 10, Inf), 0.3247, 1e-4)
457s ***** test
457s  x = finv (0.35, Inf, 4);
457s  assert (x, 0.9014, 1e-4)
457s ***** test
457s  x = finv (0, Inf, 4);
457s  assert (x, 0)
457s ***** test
457s  x = finv (1, Inf, 4);
457s  assert (x, Inf)
457s ***** test
457s  x = finv (0.35, 4, Inf);
457s  assert (x, 0.6175, 1e-4)
457s ***** test
457s  x = finv (0, 4, Inf);
457s  assert (x, 0)
457s ***** test
457s  x = finv (1, 4, Inf);
457s  assert (x, Inf)
457s ***** test
457s  x = finv ([0, 0.000001, 0.35, 1, 1.2], Inf, Inf);
457s  assert (x, [0, 1, 1, 1, NaN]);
457s ***** assert (finv ([p, NaN], 2, 2), [NaN 0 1 Inf NaN NaN])
457s ***** assert (finv (single ([p, NaN]), 2, 2), single ([NaN 0 1 Inf NaN NaN]))
457s ***** assert (finv ([p, NaN], single (2), 2), single ([NaN 0 1 Inf NaN NaN]))
457s ***** assert (finv ([p, NaN], 2, single (2)), single ([NaN 0 1 Inf NaN NaN]))
457s ***** error<finv: function called with too few input arguments.> finv ()
457s ***** error<finv: function called with too few input arguments.> finv (1)
457s ***** error<finv: function called with too few input arguments.> finv (1,2)
457s ***** error<finv: P, DF1, and DF2 must be of common size or scalars.> ...
457s  finv (ones (3), ones (2), ones (2))
457s ***** error<finv: P, DF1, and DF2 must be of common size or scalars.> ...
457s  finv (ones (2), ones (3), ones (2))
457s ***** error<finv: P, DF1, and DF2 must be of common size or scalars.> ...
457s  finv (ones (2), ones (2), ones (3))
457s ***** error<finv: P, DF1, and DF2 must not be complex.> finv (i, 2, 2)
457s ***** error<finv: P, DF1, and DF2 must not be complex.> finv (2, i, 2)
457s ***** error<finv: P, DF1, and DF2 must not be complex.> finv (2, 2, i)
457s 31 tests, 31 passed, 0 known failure, 0 skipped
457s [inst/dist_fun/fpdf.m]
457s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/fpdf.m
457s ***** demo
457s  ## Plot various PDFs from the F distribution
457s  x = 0.01:0.01:4;
457s  y1 = fpdf (x, 1, 1);
457s  y2 = fpdf (x, 2, 1);
457s  y3 = fpdf (x, 5, 2);
457s  y4 = fpdf (x, 10, 1);
457s  y5 = fpdf (x, 100, 100);
457s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
457s  grid on
457s  ylim ([0, 2.5])
457s  legend ({"df1 = 1, df2 = 2", "df1 = 2, df2 = 1", ...
457s           "df1 = 5, df2 = 2", "df1 = 10, df2 = 1", ...
457s           "df1 = 100, df2 = 100"}, "location", "northeast")
457s  title ("F PDF")
457s  xlabel ("values in x")
457s  ylabel ("density")
457s ***** shared x, y
457s  x = [-1, 0, 0.5, 1, 2];
457s  y = [0, 0, 4/9, 1/4, 1/9];
457s ***** assert (fpdf (x, 2*ones (1,5), 2*ones (1,5)), y, eps)
457s ***** assert (fpdf (x, 2, 2*ones (1,5)), y, eps)
457s ***** assert (fpdf (x, 2*ones (1,5), 2), y, eps)
457s ***** assert (fpdf (x, [0, NaN, Inf, 2, 2], 2), [NaN, NaN, 0.5413, y(4:5)], 1e-4)
457s ***** assert (fpdf (x, 2, [0, NaN, Inf, 2, 2]), [NaN, NaN, 0.6065, y(4:5)], 1e-4)
457s ***** assert (fpdf ([x, NaN], 2, 2), [y, NaN], eps)
457s ***** test #F (x, 1, df1) == T distribution (sqrt (x), df1) / sqrt (x)
457s  rand ("seed", 1234);    # for reproducibility
457s  xr = rand (10,1);
457s  xr = xr(x > 0.1 & x < 0.9);
457s  yr = tpdf (sqrt (xr), 2) ./ sqrt (xr);
457s  assert (fpdf (xr, 1, 2), yr, 5*eps);
457s ***** test
457s  yy = fpdf (2, 4, Inf);
457s  assert (yy, 0.1465, 1e-4)
457s ***** test
457s  yy = fpdf (2, 4, 1000000000000000);
457s  assert (yy, 0.1465, 1e-4)
457s ***** test
457s  yy = fpdf (2, Inf, 4);
457s  assert (yy, 0.1839, 1e-4)
457s ***** test
457s  yy = fpdf (2, 10000000000000000, 4);
457s  assert (yy, 0.1839, 1e-4)
457s ***** test
457s  yy = fpdf (2, Inf, Inf);
457s  assert (yy, 0)
457s ***** test
457s  yy = fpdf (NaN, Inf, Inf);
457s  assert (yy, NaN)
457s ***** assert (fpdf (single ([x, NaN]), 2, 2), single ([y, NaN]), eps ("single"))
457s ***** assert (fpdf ([x, NaN], single (2), 2), single ([y, NaN]), eps ("single"))
457s ***** assert (fpdf ([x, NaN], 2, single (2)), single ([y, NaN]), eps ("single"))
457s ***** error<fpdf: function called with too few input arguments.> fpdf ()
457s ***** error<fpdf: function called with too few input arguments.> fpdf (1)
457s ***** error<fpdf: function called with too few input arguments.> fpdf (1,2)
457s ***** error<fpdf: X, DF1, and DF2 must be of common size or scalars.> ...
457s  fpdf (ones (3), ones (2), ones (2))
457s ***** error<fpdf: X, DF1, and DF2 must be of common size or scalars.> ...
457s  fpdf (ones (2), ones (3), ones (2))
457s ***** error<fpdf: X, DF1, and DF2 must be of common size or scalars.> ...
457s  fpdf (ones (2), ones (2), ones (3))
458s ***** error<fpdf: X, DF1, and DF2 must not be complex.> fpdf (i, 2, 2)
458s ***** error<fpdf: X, DF1, and DF2 must not be complex.> fpdf (2, i, 2)
458s ***** error<fpdf: X, DF1, and DF2 must not be complex.> fpdf (2, 2, i)
458s 25 tests, 25 passed, 0 known failure, 0 skipped
458s [inst/dist_fun/frnd.m]
458s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/frnd.m
458s ***** assert (size (frnd (1, 1)), [1 1])
458s ***** assert (size (frnd (1, ones (2,1))), [2, 1])
458s ***** assert (size (frnd (1, ones (2,2))), [2, 2])
458s ***** assert (size (frnd (ones (2,1), 1)), [2, 1])
458s ***** assert (size (frnd (ones (2,2), 1)), [2, 2])
458s ***** assert (size (frnd (1, 1, 3)), [3, 3])
458s ***** assert (size (frnd (1, 1, [4, 1])), [4, 1])
458s ***** assert (size (frnd (1, 1, 4, 1)), [4, 1])
458s ***** assert (size (frnd (1, 1, 4, 1, 5)), [4, 1, 5])
458s ***** assert (size (frnd (1, 1, 0, 1)), [0, 1])
458s ***** assert (size (frnd (1, 1, 1, 0)), [1, 0])
458s ***** assert (size (frnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
458s ***** assert (class (frnd (1, 1)), "double")
458s ***** assert (class (frnd (1, single (1))), "single")
458s ***** assert (class (frnd (1, single ([1, 1]))), "single")
458s ***** assert (class (frnd (single (1), 1)), "single")
458s ***** assert (class (frnd (single ([1, 1]), 1)), "single")
458s ***** error<frnd: function called with too few input arguments.> frnd ()
458s ***** error<frnd: function called with too few input arguments.> frnd (1)
458s ***** error<frnd: DF1 and DF2 must be of common size or scalars.> ...
458s  frnd (ones (3), ones (2))
458s ***** error<frnd: DF1 and DF2 must be of common size or scalars.> ...
458s  frnd (ones (2), ones (3))
458s ***** error<frnd: DF1 and DF2 must not be complex.> frnd (i, 2, 3)
458s ***** error<frnd: DF1 and DF2 must not be complex.> frnd (1, i, 3)
458s ***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
458s  frnd (1, 2, -1)
458s ***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
458s  frnd (1, 2, 1.2)
458s ***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
458s  frnd (1, 2, ones (2))
458s ***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
458s  frnd (1, 2, [2 -1 2])
458s ***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
458s  frnd (1, 2, [2 0 2.5])
458s ***** error<frnd: dimensions must be non-negative integers.> ...
458s  frnd (1, 2, 2, -1, 5)
458s ***** error<frnd: dimensions must be non-negative integers.> ...
458s  frnd (1, 2, 2, 1.5, 5)
458s ***** error<frnd: DF1 and DF2 must be scalars or of size SZ.> ...
458s  frnd (2, ones (2), 3)
458s ***** error<frnd: DF1 and DF2 must be scalars or of size SZ.> ...
458s  frnd (2, ones (2), [3, 2])
458s ***** error<frnd: DF1 and DF2 must be scalars or of size SZ.> ...
458s  frnd (2, ones (2), 3, 2)
458s 33 tests, 33 passed, 0 known failure, 0 skipped
458s [inst/dist_fun/gamcdf.m]
458s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/gamcdf.m
458s ***** demo
458s  ## Plot various CDFs from the Gamma distribution
458s  x = 0:0.01:20;
458s  p1 = gamcdf (x, 1, 2);
458s  p2 = gamcdf (x, 2, 2);
458s  p3 = gamcdf (x, 3, 2);
458s  p4 = gamcdf (x, 5, 1);
458s  p5 = gamcdf (x, 9, 0.5);
458s  p6 = gamcdf (x, 7.5, 1);
458s  p7 = gamcdf (x, 0.5, 1);
458s  plot (x, p1, "-r", x, p2, "-g", x, p3, "-y", x, p4, "-m", ...
458s        x, p5, "-k", x, p6, "-b", x, p7, "-c")
458s  grid on
458s  legend ({"α = 1, β = 2", "α = 2, β = 2", "α = 3, β = 2", ...
458s           "α = 5, β = 1", "α = 9, β = 0.5", "α = 7.5, β = 1", ...
458s           "α = 0.5, β = 1"}, "location", "southeast")
458s  title ("Gamma CDF")
458s  xlabel ("values in x")
458s  ylabel ("probability")
458s ***** shared x, y, u
458s  x = [-1, 0, 0.5, 1, 2, Inf];
458s  y = [0, gammainc(x(2:end), 1)];
458s  u = [0, NaN, NaN, 1, 0.1353352832366127, 0];
458s ***** assert (gamcdf (x, ones (1,6), ones (1,6)), y, eps)
458s ***** assert (gamcdf (x, ones (1,6), ones (1,6), []), y, eps)
458s ***** assert (gamcdf (x, 1, ones (1,6)), y, eps)
458s ***** assert (gamcdf (x, ones (1,6), 1), y, eps)
458s ***** assert (gamcdf (x, [0, -Inf, NaN, Inf, 1, 1], 1), [1, NaN, NaN, 0, y(5:6)], eps)
458s ***** assert (gamcdf (x, [0, -Inf, NaN, Inf, 1, 1], 1, "upper"), u, eps)
458s ***** assert (gamcdf (x, 1, [0, -Inf, NaN, Inf, 1, 1]), [NaN, NaN, NaN, 0, y(5:6)], eps)
458s ***** assert (gamcdf ([x(1:2), NaN, x(4:6)], 1, 1), [y(1:2), NaN, y(4:6)], eps)
458s ***** assert (gamcdf ([x, NaN], 1, 1), [y, NaN])
458s ***** assert (gamcdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
458s ***** assert (gamcdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
458s ***** assert (gamcdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
458s ***** error<gamcdf: invalid number of input arguments.> gamcdf ()
458s ***** error<gamcdf: invalid number of input arguments.> gamcdf (1)
458s ***** error<gamcdf: invalid number of input arguments.> gamcdf (1, 2, 3, 4, 5, 6, 7)
458s ***** error<gamcdf: invalid argument for upper tail.> gamcdf (1, 2, 3, "uper")
458s ***** error<gamcdf: invalid argument for upper tail.> gamcdf (1, 2, 3, 4, 5, "uper")
458s ***** error<gamcdf: invalid size of covariance matrix.> gamcdf (2, 3, 4, [1, 2])
458s ***** error<gamcdf: covariance matrix is required for confidence bounds.> ...
458s  [p, plo, pup] = gamcdf (1, 2, 3)
458s ***** error<gamcdf: covariance matrix is required for confidence bounds.> ...
458s  [p, plo, pup] = gamcdf (1, 2, 3, "upper")
458s ***** error<gamcdf: invalid value for alpha.> [p, plo, pup] = ...
458s  gamcdf (1, 2, 3, [1, 0; 0, 1], 0)
458s ***** error<gamcdf: invalid value for alpha.> [p, plo, pup] = ...
458s  gamcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
458s ***** error<gamcdf: invalid value for alpha.> [p, plo, pup] = ...
458s  gamcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
458s ***** error<gamcdf: X, A, and B must be of common size or scalars.> ...
458s  gamcdf (ones (3), ones (2), ones (2))
458s ***** error<gamcdf: X, A, and B must be of common size or scalars.> ...
458s  gamcdf (ones (2), ones (3), ones (2))
458s ***** error<gamcdf: X, A, and B must be of common size or scalars.> ...
458s  gamcdf (ones (2), ones (2), ones (3))
458s ***** error<gamcdf: X, A, and B must not be complex.> gamcdf (i, 2, 2)
458s ***** error<gamcdf: X, A, and B must not be complex.> gamcdf (2, i, 2)
458s ***** error<gamcdf: X, A, and B must not be complex.> gamcdf (2, 2, i)
458s ***** error<gamcdf: bad covariance matrix.> ...
458s  [p, plo, pup] = gamcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
458s 30 tests, 30 passed, 0 known failure, 0 skipped
458s [inst/dist_fun/gaminv.m]
458s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/gaminv.m
458s ***** demo
458s  ## Plot various iCDFs from the Gamma distribution
458s  p = 0.001:0.001:0.999;
458s  x1 = gaminv (p, 1, 2);
458s  x2 = gaminv (p, 2, 2);
458s  x3 = gaminv (p, 3, 2);
458s  x4 = gaminv (p, 5, 1);
458s  x5 = gaminv (p, 9, 0.5);
458s  x6 = gaminv (p, 7.5, 1);
458s  x7 = gaminv (p, 0.5, 1);
458s  plot (p, x1, "-r", p, x2, "-g", p, x3, "-y", p, x4, "-m", ...
458s        p, x5, "-k", p, x6, "-b", p, x7, "-c")
458s  ylim ([0, 20])
458s  grid on
458s  legend ({"α = 1, β = 2", "α = 2, β = 2", "α = 3, β = 2", ...
458s           "α = 5, β = 1", "α = 9, β = 0.5", "α = 7.5, β = 1", ...
458s           "α = 0.5, β = 1"}, "location", "northwest")
458s  title ("Gamma iCDF")
458s  xlabel ("probability")
458s  ylabel ("x")
458s ***** shared p
458s  p = [-1 0 0.63212055882855778 1 2];
458s ***** assert (gaminv (p, ones (1,5), ones (1,5)), [NaN 0 1 Inf NaN], eps)
458s ***** assert (gaminv (p, 1, ones (1,5)), [NaN 0 1 Inf NaN], eps)
458s ***** assert (gaminv (p, ones (1,5), 1), [NaN 0 1 Inf NaN], eps)
458s ***** assert (gaminv (p, [1 -Inf NaN Inf 1], 1), [NaN NaN NaN NaN NaN])
458s ***** assert (gaminv (p, 1, [1 -Inf NaN Inf 1]), [NaN NaN NaN NaN NaN])
458s ***** assert (gaminv ([p(1:2) NaN p(4:5)], 1, 1), [NaN 0 NaN Inf NaN])
458s ***** assert (gaminv ([p(1:2) NaN p(4:5)], 1, 1), [NaN 0 NaN Inf NaN])
458s ***** assert (gaminv (1e-16, 1, 1), 1e-16, eps)
458s ***** assert (gaminv (1e-16, 1, 2), 2e-16, eps)
458s ***** assert (gaminv (1e-20, 3, 5), 1.957434012161815e-06, eps)
458s ***** assert (gaminv (1e-15, 1, 1), 1e-15, eps)
458s ***** assert (gaminv (1e-35, 1, 1), 1e-35, eps)
458s ***** assert (gaminv ([p, NaN], 1, 1), [NaN 0 1 Inf NaN NaN], eps)
458s ***** assert (gaminv (single ([p, NaN]), 1, 1), single ([NaN 0 1 Inf NaN NaN]), ...
458s  eps ("single"))
458s ***** assert (gaminv ([p, NaN], single (1), 1), single ([NaN 0 1 Inf NaN NaN]), ...
458s  eps ("single"))
458s ***** assert (gaminv ([p, NaN], 1, single (1)), single ([NaN 0 1 Inf NaN NaN]), ...
458s  eps ("single"))
458s ***** error<gaminv: function called with too few input arguments.> gaminv ()
458s ***** error<gaminv: function called with too few input arguments.> gaminv (1)
458s ***** error<gaminv: function called with too few input arguments.> gaminv (1,2)
458s ***** error<gaminv: P, Α, and Β must be of common size or scalars.> ...
458s  gaminv (ones (3), ones (2), ones (2))
458s ***** error<gaminv: P, Α, and Β must be of common size or scalars.> ...
458s  gaminv (ones (2), ones (3), ones (2))
458s ***** error<gaminv: P, Α, and Β must be of common size or scalars.> ...
458s  gaminv (ones (2), ones (2), ones (3))
458s ***** error<gaminv: P, Α, and Β must not be complex.> gaminv (i, 2, 2)
458s ***** error<gaminv: P, Α, and Β must not be complex.> gaminv (2, i, 2)
458s ***** error<gaminv: P, Α, and Β must not be complex.> gaminv (2, 2, i)
458s 25 tests, 25 passed, 0 known failure, 0 skipped
458s [inst/dist_fun/gampdf.m]
458s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/gampdf.m
458s ***** demo
458s  ## Plot various PDFs from the Gamma distribution
458s  x = 0:0.01:20;
458s  y1 = gampdf (x, 1, 2);
458s  y2 = gampdf (x, 2, 2);
458s  y3 = gampdf (x, 3, 2);
458s  y4 = gampdf (x, 5, 1);
458s  y5 = gampdf (x, 9, 0.5);
458s  y6 = gampdf (x, 7.5, 1);
458s  y7 = gampdf (x, 0.5, 1);
458s  plot (x, y1, "-r", x, y2, "-g", x, y3, "-y", x, y4, "-m", ...
458s        x, y5, "-k", x, y6, "-b", x, y7, "-c")
458s  grid on
458s  ylim ([0,0.5])
458s  legend ({"α = 1, β = 2", "α = 2, β = 2", "α = 3, β = 2", ...
458s           "α = 5, β = 1", "α = 9, β = 0.5", "α = 7.5, β = 1", ...
458s           "α = 0.5, β = 1"}, "location", "northeast")
458s  title ("Gamma PDF")
458s  xlabel ("values in x")
458s  ylabel ("density")
458s ***** shared x, y
458s  x = [-1 0 0.5 1 Inf];
458s  y = [0 exp(-x(2:end))];
458s ***** assert (gampdf (x, ones (1,5), ones (1,5)), y)
458s ***** assert (gampdf (x, 1, ones (1,5)), y)
458s ***** assert (gampdf (x, ones (1,5), 1), y)
458s ***** assert (gampdf (x, [0 -Inf NaN Inf 1], 1), [NaN NaN NaN 0 y(5)])
458s ***** assert (gampdf (x, [0 Inf NaN Inf 1], 1), [NaN 0 NaN 0 y(5)])
458s ***** assert (gampdf (x, 1, [0 -Inf NaN Inf 1]), [NaN NaN NaN 0 y(5)])
458s ***** assert (gampdf ([x, NaN], 1, 1), [y, NaN])
458s ***** assert (gampdf (2, Inf, 4), 0)
458s ***** assert (gampdf (2, 4, Inf), 0)
458s ***** assert (gampdf (2, Inf, Inf), 0)
458s ***** assert (gampdf (single ([x, NaN]), 1, 1), single ([y, NaN]))
458s ***** assert (gampdf ([x, NaN], single (1), 1), single ([y, NaN]))
458s ***** assert (gampdf ([x, NaN], 1, single (1)), single ([y, NaN]))
458s ***** error<gampdf: function called with too few input arguments.> gampdf ()
458s ***** error<gampdf: function called with too few input arguments.> gampdf (1)
458s ***** error<gampdf: function called with too few input arguments.> gampdf (1,2)
458s ***** error<gampdf: X, A, and B must be of common size or scalars.> ...
458s  gampdf (ones (3), ones (2), ones (2))
458s ***** error<gampdf: X, A, and B must be of common size or scalars.> ...
458s  gampdf (ones (2), ones (3), ones (2))
458s ***** error<gampdf: X, A, and B must be of common size or scalars.> ...
458s  gampdf (ones (2), ones (2), ones (3))
458s ***** error<gampdf: X, A, and B must not be complex.> gampdf (i, 2, 2)
458s ***** error<gampdf: X, A, and B must not be complex.> gampdf (2, i, 2)
458s ***** error<gampdf: X, A, and B must not be complex.> gampdf (2, 2, i)
458s 22 tests, 22 passed, 0 known failure, 0 skipped
458s [inst/dist_fun/gamrnd.m]
458s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/gamrnd.m
458s ***** assert (size (gamrnd (1, 1)), [1 1])
458s ***** assert (size (gamrnd (1, ones (2,1))), [2, 1])
458s ***** assert (size (gamrnd (1, ones (2,2))), [2, 2])
458s ***** assert (size (gamrnd (ones (2,1), 1)), [2, 1])
458s ***** assert (size (gamrnd (ones (2,2), 1)), [2, 2])
458s ***** assert (size (gamrnd (1, 1, 3)), [3, 3])
458s ***** assert (size (gamrnd (1, 1, [4, 1])), [4, 1])
458s ***** assert (size (gamrnd (1, 1, 4, 1)), [4, 1])
458s ***** assert (size (gamrnd (1, 1, 4, 1, 5)), [4, 1, 5])
458s ***** assert (size (gamrnd (1, 1, 0, 1)), [0, 1])
458s ***** assert (size (gamrnd (1, 1, 1, 0)), [1, 0])
458s ***** assert (size (gamrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
458s ***** assert (class (gamrnd (1, 1)), "double")
458s ***** assert (class (gamrnd (1, single (1))), "single")
458s ***** assert (class (gamrnd (1, single ([1, 1]))), "single")
458s ***** assert (class (gamrnd (single (1), 1)), "single")
458s ***** assert (class (gamrnd (single ([1, 1]), 1)), "single")
458s ***** error<gamrnd: function called with too few input arguments.> gamrnd ()
458s ***** error<gamrnd: function called with too few input arguments.> gamrnd (1)
458s ***** error<gamrnd: A and B must be of common size or scalars.> ...
458s  gamrnd (ones (3), ones (2))
458s ***** error<gamrnd: A and B must be of common size or scalars.> ...
458s  gamrnd (ones (2), ones (3))
458s ***** error<gamrnd: A and B must not be complex.> gamrnd (i, 2, 3)
458s ***** error<gamrnd: A and B must not be complex.> gamrnd (1, i, 3)
458s ***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
458s  gamrnd (1, 2, -1)
458s ***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
458s  gamrnd (1, 2, 1.2)
458s ***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
458s  gamrnd (1, 2, ones (2))
458s ***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
458s  gamrnd (1, 2, [2 -1 2])
458s ***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
458s  gamrnd (1, 2, [2 0 2.5])
458s ***** error<gamrnd: dimensions must be non-negative integers.> ...
458s  gamrnd (1, 2, 2, -1, 5)
458s ***** error<gamrnd: dimensions must be non-negative integers.> ...
458s  gamrnd (1, 2, 2, 1.5, 5)
458s ***** error<gamrnd: A and B must be scalars or of size SZ.> ...
458s  gamrnd (2, ones (2), 3)
458s ***** error<gamrnd: A and B must be scalars or of size SZ.> ...
458s  gamrnd (2, ones (2), [3, 2])
458s ***** error<gamrnd: A and B must be scalars or of size SZ.> ...
458s  gamrnd (2, ones (2), 3, 2)
458s 33 tests, 33 passed, 0 known failure, 0 skipped
458s [inst/dist_fun/geocdf.m]
458s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/geocdf.m
458s ***** demo
458s  ## Plot various CDFs from the geometric distribution
458s  x = 0:10;
458s  p1 = geocdf (x, 0.2);
458s  p2 = geocdf (x, 0.5);
458s  p3 = geocdf (x, 0.7);
458s  plot (x, p1, "*b", x, p2, "*g", x, p3, "*r")
458s  grid on
458s  xlim ([0, 10])
458s  legend ({"ps = 0.2", "ps = 0.5", "ps = 0.7"}, "location", "southeast")
458s  title ("Geometric CDF")
458s  xlabel ("values in x (number of failures)")
458s  ylabel ("probability")
458s ***** test
458s  p = geocdf ([1, 2, 3, 4], 0.25);
458s  assert (p(1), 0.4375000000, 1e-14);
458s  assert (p(2), 0.5781250000, 1e-14);
458s  assert (p(3), 0.6835937500, 1e-14);
458s  assert (p(4), 0.7626953125, 1e-14);
458s ***** test
458s  p = geocdf ([1, 2, 3, 4], 0.25, "upper");
458s  assert (p(1), 0.5625000000, 1e-14);
458s  assert (p(2), 0.4218750000, 1e-14);
458s  assert (p(3), 0.3164062500, 1e-14);
458s  assert (p(4), 0.2373046875, 1e-14);
458s ***** shared x, p
458s  x = [-1 0 1 Inf];
458s  p = [0 0.5 0.75 1];
458s ***** assert (geocdf (x, 0.5*ones (1,4)), p)
458s ***** assert (geocdf (x, 0.5), p)
458s ***** assert (geocdf (x, 0.5*[-1 NaN 4 1]), [NaN NaN NaN p(4)])
458s ***** assert (geocdf ([x(1:2) NaN x(4)], 0.5), [p(1:2) NaN p(4)])
458s ***** assert (geocdf ([x, NaN], 0.5), [p, NaN])
458s ***** assert (geocdf (single ([x, NaN]), 0.5), single ([p, NaN]))
458s ***** assert (geocdf ([x, NaN], single (0.5)), single ([p, NaN]))
458s ***** error<geocdf: function called with too few input arguments.> geocdf ()
458s ***** error<geocdf: function called with too few input arguments.> geocdf (1)
458s ***** error<geocdf: X and PS must be of common size or scalars.> ...
458s  geocdf (ones (3), ones (2))
458s ***** error<geocdf: X and PS must be of common size or scalars.> ...
458s  geocdf (ones (2), ones (3))
458s ***** error<geocdf: X and PS must not be complex.> geocdf (i, 2)
458s ***** error<geocdf: X and PS must not be complex.> geocdf (2, i)
458s ***** error<geocdf: invalid argument for upper tail.> geocdf (2, 3, "tail")
458s ***** error<geocdf: invalid argument for upper tail.> geocdf (2, 3, 5)
458s 17 tests, 17 passed, 0 known failure, 0 skipped
458s [inst/dist_fun/geoinv.m]
458s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/geoinv.m
458s ***** demo
458s  ## Plot various iCDFs from the geometric distribution
458s  p = 0.001:0.001:0.999;
458s  x1 = geoinv (p, 0.2);
458s  x2 = geoinv (p, 0.5);
458s  x3 = geoinv (p, 0.7);
458s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
458s  grid on
458s  ylim ([0, 10])
458s  legend ({"ps = 0.2", "ps = 0.5", "ps = 0.7"}, "location", "northwest")
458s  title ("Geometric iCDF")
458s  xlabel ("probability")
458s  ylabel ("values in x (number of failures)")
458s ***** shared p
458s  p = [-1 0 0.75 1 2];
458s ***** assert (geoinv (p, 0.5*ones (1,5)), [NaN 0 1 Inf NaN])
458s ***** assert (geoinv (p, 0.5), [NaN 0 1 Inf NaN])
458s ***** assert (geoinv (p, 0.5*[1 -1 NaN 4 1]), [NaN NaN NaN NaN NaN])
458s ***** assert (geoinv ([p(1:2) NaN p(4:5)], 0.5), [NaN 0 NaN Inf NaN])
458s ***** assert (geoinv ([p, NaN], 0.5), [NaN 0 1 Inf NaN NaN])
458s ***** assert (geoinv (single ([p, NaN]), 0.5), single ([NaN 0 1 Inf NaN NaN]))
458s ***** assert (geoinv ([p, NaN], single (0.5)), single ([NaN 0 1 Inf NaN NaN]))
458s ***** error<geoinv: function called with too few input arguments.> geoinv ()
458s ***** error<geoinv: function called with too few input arguments.> geoinv (1)
458s ***** error<geoinv: P and PS must be of common size or scalars.> ...
458s  geoinv (ones (3), ones (2))
458s ***** error<geoinv: P and PS must be of common size or scalars.> ...
458s  geoinv (ones (2), ones (3))
458s ***** error<geoinv: P and PS must not be complex.> ...
458s  geoinv (i, 2)
458s ***** error<geoinv: P and PS must not be complex.> ...
458s  geoinv (2, i)
458s 13 tests, 13 passed, 0 known failure, 0 skipped
458s [inst/dist_fun/geopdf.m]
458s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/geopdf.m
458s ***** demo
458s  ## Plot various PDFs from the geometric distribution
458s  x = 0:10;
458s  y1 = geopdf (x, 0.2);
458s  y2 = geopdf (x, 0.5);
458s  y3 = geopdf (x, 0.7);
458s  plot (x, y1, "*b", x, y2, "*g", x, y3, "*r")
458s  grid on
458s  ylim ([0, 0.8])
458s  legend ({"ps = 0.2", "ps = 0.5", "ps = 0.7"}, "location", "northeast")
458s  title ("Geometric PDF")
458s  xlabel ("values in x (number of failures)")
458s  ylabel ("density")
458s ***** shared x, y
458s  x = [-1 0 1 Inf];
458s  y = [0, 1/2, 1/4, NaN];
458s ***** assert (geopdf (x, 0.5*ones (1,4)), y)
458s ***** assert (geopdf (x, 0.5), y)
458s ***** assert (geopdf (x, 0.5*[-1 NaN 4 1]), [NaN NaN NaN y(4)])
458s ***** assert (geopdf ([x, NaN], 0.5), [y, NaN])
458s ***** assert (geopdf (single ([x, NaN]), 0.5), single ([y, NaN]), 5*eps ("single"))
458s ***** assert (geopdf ([x, NaN], single (0.5)), single ([y, NaN]), 5*eps ("single"))
458s ***** error geopdf ()
458s ***** error geopdf (1)
458s ***** error geopdf (1,2,3)
458s ***** error geopdf (ones (3), ones (2))
458s ***** error geopdf (ones (2), ones (3))
458s ***** error geopdf (i, 2)
458s ***** error geopdf (2, i)
458s 13 tests, 13 passed, 0 known failure, 0 skipped
458s [inst/dist_fun/geornd.m]
458s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/geornd.m
458s ***** assert (size (geornd (0.5)), [1, 1])
458s ***** assert (size (geornd (0.5*ones (2,1))), [2, 1])
458s ***** assert (size (geornd (0.5*ones (2,2))), [2, 2])
458s ***** assert (size (geornd (0.5, 3)), [3, 3])
458s ***** assert (size (geornd (0.5, [4 1])), [4, 1])
458s ***** assert (size (geornd (0.5, 4, 1)), [4, 1])
458s ***** assert (class (geornd (0.5)), "double")
458s ***** assert (class (geornd (single (0.5))), "single")
458s ***** assert (class (geornd (single ([0.5 0.5]))), "single")
458s ***** assert (class (geornd (single (0))), "single")
458s ***** assert (class (geornd (single (1))), "single")
458s ***** error<geornd: function called with too few input arguments.> geornd ()
458s ***** error<geornd: PS must not be complex.> geornd (i)
458s ***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
458s  geornd (1, -1)
458s ***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
458s  geornd (1, 1.2)
458s ***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
458s  geornd (1, ones (2))
458s ***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
458s  geornd (1, [2 -1 2])
458s ***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
458s  geornd (1, [2 0 2.5])
458s ***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
458s  geornd (ones (2), ones (2))
458s ***** error<geornd: dimensions must be non-negative integers.> ...
458s  geornd (1, 2, -1, 5)
458s ***** error<geornd: dimensions must be non-negative integers.> ...
458s  geornd (1, 2, 1.5, 5)
458s ***** error<geornd: PS must be scalar or of size SZ.> geornd (ones (2,2), 3)
458s ***** error<geornd: PS must be scalar or of size SZ.> geornd (ones (2,2), [3, 2])
458s ***** error<geornd: PS must be scalar or of size SZ.> geornd (ones (2,2), 2, 3)
458s 24 tests, 24 passed, 0 known failure, 0 skipped
458s [inst/dist_fun/gevcdf.m]
458s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/gevcdf.m
458s ***** demo
458s  ## Plot various CDFs from the generalized extreme value distribution
458s  x = -1:0.001:10;
458s  p1 = gevcdf (x, 1, 1, 1);
458s  p2 = gevcdf (x, 0.5, 1, 1);
458s  p3 = gevcdf (x, 1, 1, 5);
458s  p4 = gevcdf (x, 1, 2, 5);
458s  p5 = gevcdf (x, 1, 5, 5);
458s  p6 = gevcdf (x, 1, 0.5, 5);
458s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", ...
458s        x, p4, "-c", x, p5, "-m", x, p6, "-k")
458s  grid on
458s  xlim ([-1, 10])
458s  legend ({"k = 1, σ = 1, μ = 1", "k = 0.5, σ = 1, μ = 1", ...
458s           "k = 1, σ = 1, μ = 5", "k = 1, σ = 2, μ = 5", ...
458s           "k = 1, σ = 5, μ = 5", "k = 1, σ = 0.5, μ = 5"}, ...
458s          "location", "southeast")
458s  title ("Generalized extreme value CDF")
458s  xlabel ("values in x")
458s  ylabel ("probability")
458s ***** test
458s  x = 0:0.5:2.5;
458s  sigma = 1:6;
458s  k = 1;
458s  mu = 0;
458s  p = gevcdf (x, k, sigma, mu);
458s  expected_p = [0.36788, 0.44933, 0.47237, 0.48323, 0.48954, 0.49367];
458s  assert (p, expected_p, 0.001);
458s ***** test
458s  x = -0.5:0.5:2.5;
458s  sigma = 0.5;
458s  k = 1;
458s  mu = 0;
458s  p = gevcdf (x, k, sigma, mu);
458s  expected_p = [0, 0.36788, 0.60653, 0.71653, 0.77880, 0.81873, 0.84648];
458s  assert (p, expected_p, 0.001);
458s ***** test # check for continuity for k near 0
458s  x = 1;
458s  sigma = 0.5;
458s  k = -0.03:0.01:0.03;
458s  mu = 0;
458s  p = gevcdf (x, k, sigma, mu);
458s  expected_p = [0.88062, 0.87820, 0.87580, 0.87342, 0.87107, 0.86874, 0.86643];
458s  assert (p, expected_p, 0.001);
458s ***** error<gevcdf: function called with too few input arguments.> gevcdf ()
458s ***** error<gevcdf: function called with too few input arguments.> gevcdf (1)
458s ***** error<gevcdf: function called with too few input arguments.> gevcdf (1, 2)
458s ***** error<gevcdf: function called with too few input arguments.> gevcdf (1, 2, 3)
458s ***** error<gevcdf: function called with too many inputs> ...
458s  gevcdf (1, 2, 3, 4, 5, 6)
458s ***** error<gevcdf: invalid argument for upper tail.> gevcdf (1, 2, 3, 4, "tail")
458s ***** error<gevcdf: invalid argument for upper tail.> gevcdf (1, 2, 3, 4, 5)
458s ***** error<gevcdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
458s  gevcdf (ones (3), ones (2), ones(2), ones(2))
458s ***** error<gevcdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
458s  gevcdf (ones (2), ones (3), ones(2), ones(2))
458s ***** error<gevcdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
458s  gevcdf (ones (2), ones (2), ones(3), ones(2))
459s ***** error<gevcdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
459s  gevcdf (ones (2), ones (2), ones(2), ones(3))
459s ***** error<gevcdf: X, K, SIGMA, and MU must not be complex.> gevcdf (i, 2, 3, 4)
459s ***** error<gevcdf: X, K, SIGMA, and MU must not be complex.> gevcdf (1, i, 3, 4)
459s ***** error<gevcdf: X, K, SIGMA, and MU must not be complex.> gevcdf (1, 2, i, 4)
459s ***** error<gevcdf: X, K, SIGMA, and MU must not be complex.> gevcdf (1, 2, 3, i)
459s 18 tests, 18 passed, 0 known failure, 0 skipped
459s [inst/dist_fun/gevinv.m]
459s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/gevinv.m
459s ***** demo
459s  ## Plot various iCDFs from the generalized extreme value distribution
459s  p = 0.001:0.001:0.999;
459s  x1 = gevinv (p, 1, 1, 1);
459s  x2 = gevinv (p, 0.5, 1, 1);
459s  x3 = gevinv (p, 1, 1, 5);
459s  x4 = gevinv (p, 1, 2, 5);
459s  x5 = gevinv (p, 1, 5, 5);
459s  x6 = gevinv (p, 1, 0.5, 5);
459s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", ...
459s        p, x4, "-c", p, x5, "-m", p, x6, "-k")
459s  grid on
459s  ylim ([-1, 10])
459s  legend ({"k = 1, σ = 1, μ = 1", "k = 0.5, σ = 1, μ = 1", ...
459s           "k = 1, σ = 1, μ = 5", "k = 1, σ = 2, μ = 5", ...
459s           "k = 1, σ = 5, μ = 5", "k = 1, σ = 0.5, μ = 5"}, ...
459s          "location", "northwest")
459s  title ("Generalized extreme value iCDF")
459s  xlabel ("probability")
459s  ylabel ("values in x")
459s ***** test
459s  p = 0.1:0.1:0.9;
459s  k = 0;
459s  sigma = 1;
459s  mu = 0;
459s  x = gevinv (p, k, sigma, mu);
459s  c = gevcdf(x, k, sigma, mu);
459s  assert (c, p, 0.001);
459s ***** test
459s  p = 0.1:0.1:0.9;
459s  k = 1;
459s  sigma = 1;
459s  mu = 0;
459s  x = gevinv (p, k, sigma, mu);
459s  c = gevcdf(x, k, sigma, mu);
459s  assert (c, p, 0.001);
459s ***** test
459s  p = 0.1:0.1:0.9;
459s  k = 0.3;
459s  sigma = 1;
459s  mu = 0;
459s  x = gevinv (p, k, sigma, mu);
459s  c = gevcdf(x, k, sigma, mu);
459s  assert (c, p, 0.001);
459s ***** error<gevinv: function called with too few input arguments.> gevinv ()
459s ***** error<gevinv: function called with too few input arguments.> gevinv (1)
459s ***** error<gevinv: function called with too few input arguments.> gevinv (1, 2)
459s ***** error<gevinv: function called with too few input arguments.> gevinv (1, 2, 3)
459s ***** error<gevinv: P, K, SIGMA, and MU must be of common size or scalars.> ...
459s  gevinv (ones (3), ones (2), ones(2), ones(2))
459s ***** error<gevinv: P, K, SIGMA, and MU must be of common size or scalars.> ...
459s  gevinv (ones (2), ones (3), ones(2), ones(2))
459s ***** error<gevinv: P, K, SIGMA, and MU must be of common size or scalars.> ...
459s  gevinv (ones (2), ones (2), ones(3), ones(2))
459s ***** error<gevinv: P, K, SIGMA, and MU must be of common size or scalars.> ...
459s  gevinv (ones (2), ones (2), ones(2), ones(3))
459s ***** error<gevinv: P, K, SIGMA, and MU must not be complex.> gevinv (i, 2, 3, 4)
459s ***** error<gevinv: P, K, SIGMA, and MU must not be complex.> gevinv (1, i, 3, 4)
459s ***** error<gevinv: P, K, SIGMA, and MU must not be complex.> gevinv (1, 2, i, 4)
459s ***** error<gevinv: P, K, SIGMA, and MU must not be complex.> gevinv (1, 2, 3, i)
459s 15 tests, 15 passed, 0 known failure, 0 skipped
459s [inst/dist_fun/gevpdf.m]
459s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/gevpdf.m
459s ***** demo
459s  ## Plot various PDFs from the generalized extreme value distribution
459s  x = -1:0.001:10;
459s  y1 = gevpdf (x, 1, 1, 1);
459s  y2 = gevpdf (x, 0.5, 1, 1);
459s  y3 = gevpdf (x, 1, 1, 5);
459s  y4 = gevpdf (x, 1, 2, 5);
459s  y5 = gevpdf (x, 1, 5, 5);
459s  y6 = gevpdf (x, 1, 0.5, 5);
459s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", ...
459s        x, y4, "-c", x, y5, "-m", x, y6, "-k")
459s  grid on
459s  xlim ([-1, 10])
459s  ylim ([0, 1.1])
459s  legend ({"k = 1, σ = 1, μ = 1", "k = 0.5, σ = 1, μ = 1", ...
459s           "k = 1, σ = 1, μ = 5", "k = 1, σ = 2, μ = 5", ...
459s           "k = 1, σ = 5, μ = 5", "k = 1, σ = 0.5, μ = 5"}, ...
459s          "location", "northeast")
459s  title ("Generalized extreme value PDF")
459s  xlabel ("values in x")
459s  ylabel ("density")
459s ***** test
459s  x = 0:0.5:2.5;
459s  sigma = 1:6;
459s  k = 1;
459s  mu = 0;
459s  y = gevpdf (x, k, sigma, mu);
459s  expected_y = [0.367879   0.143785   0.088569   0.063898   0.049953   0.040997];
459s  assert (y, expected_y, 0.001);
459s ***** test
459s  x = -0.5:0.5:2.5;
459s  sigma = 0.5;
459s  k = 1;
459s  mu = 0;
459s  y = gevpdf (x, k, sigma, mu);
459s  expected_y = [0 0.735759   0.303265   0.159229   0.097350   0.065498   0.047027];
459s  assert (y, expected_y, 0.001);
459s ***** test # check for continuity for k near 0
459s  x = 1;
459s  sigma = 0.5;
459s  k = -0.03:0.01:0.03;
459s  mu = 0;
459s  y = gevpdf (x, k, sigma, mu);
459s  expected_y = [0.23820   0.23764   0.23704   0.23641   0.23576   0.23508   0.23438];
459s  assert (y, expected_y, 0.001);
459s ***** error<gevpdf: function called with too few input arguments.> gevpdf ()
459s ***** error<gevpdf: function called with too few input arguments.> gevpdf (1)
459s ***** error<gevpdf: function called with too few input arguments.> gevpdf (1, 2)
459s ***** error<gevpdf: function called with too few input arguments.> gevpdf (1, 2, 3)
459s ***** error<gevpdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
459s  gevpdf (ones (3), ones (2), ones(2), ones(2))
459s ***** error<gevpdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
459s  gevpdf (ones (2), ones (3), ones(2), ones(2))
459s ***** error<gevpdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
459s  gevpdf (ones (2), ones (2), ones(3), ones(2))
459s ***** error<gevpdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
459s  gevpdf (ones (2), ones (2), ones(2), ones(3))
459s ***** error<gevpdf: X, K, SIGMA, and MU must not be complex.> gevpdf (i, 2, 3, 4)
459s ***** error<gevpdf: X, K, SIGMA, and MU must not be complex.> gevpdf (1, i, 3, 4)
459s ***** error<gevpdf: X, K, SIGMA, and MU must not be complex.> gevpdf (1, 2, i, 4)
459s ***** error<gevpdf: X, K, SIGMA, and MU must not be complex.> gevpdf (1, 2, 3, i)
459s 15 tests, 15 passed, 0 known failure, 0 skipped
459s [inst/dist_fun/gevrnd.m]
459s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/gevrnd.m
459s ***** assert(size (gevrnd (1,2,1)), [1, 1]);
459s ***** assert(size (gevrnd (ones(2,1), 2, 1)), [2, 1]);
459s ***** assert(size (gevrnd (ones(2,2), 2, 1)), [2, 2]);
459s ***** assert(size (gevrnd (1, 2*ones(2,1), 1)), [2, 1]);
459s ***** assert(size (gevrnd (1, 2*ones(2,2), 1)), [2, 2]);
459s ***** assert(size (gevrnd (1, 2, 1, 3)), [3, 3]);
459s ***** assert(size (gevrnd (1, 2, 1, [4 1])), [4, 1]);
459s ***** assert(size (gevrnd (1, 2, 1, 4, 1)), [4, 1]);
459s ***** assert (class (gevrnd (1,1,1)), "double")
459s ***** assert (class (gevrnd (single (1),1,1)), "single")
459s ***** assert (class (gevrnd (single ([1 1]),1,1)), "single")
459s ***** assert (class (gevrnd (1,single (1),1)), "single")
459s ***** assert (class (gevrnd (1,single ([1 1]),1)), "single")
459s ***** assert (class (gevrnd (1,1,single (1))), "single")
459s ***** assert (class (gevrnd (1,1,single ([1 1]))), "single")
459s ***** error<gevrnd: function called with too few input arguments.> gevrnd ()
459s ***** error<gevrnd: function called with too few input arguments.> gevrnd (1)
459s ***** error<gevrnd: function called with too few input arguments.> gevrnd (1, 2)
459s ***** error<gevrnd: K, SIGMA, and MU must be of common size or scalars.> ...
459s  gevrnd (ones (3), ones (2), ones (2))
459s ***** error<gevrnd: K, SIGMA, and MU must be of common size or scalars.> ...
459s  gevrnd (ones (2), ones (3), ones (2))
459s ***** error<gevrnd: K, SIGMA, and MU must be of common size or scalars.> ...
459s  gevrnd (ones (2), ones (2), ones (3))
459s ***** error<gevrnd: K, SIGMA, and MU must not be complex.> gevrnd (i, 2, 3)
459s ***** error<gevrnd: K, SIGMA, and MU must not be complex.> gevrnd (1, i, 3)
459s ***** error<gevrnd: K, SIGMA, and MU must not be complex.> gevrnd (1, 2, i)
459s ***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
459s  gevrnd (1, 2, 3, -1)
459s ***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
459s  gevrnd (1, 2, 3, 1.2)
459s ***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
459s  gevrnd (1, 2, 3, ones (2))
459s ***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
459s  gevrnd (1, 2, 3, [2 -1 2])
459s ***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
459s  gevrnd (1, 2, 3, [2 0 2.5])
459s ***** error<gevrnd: dimensions must be non-negative integers.> ...
459s  gevrnd (1, 2, 3, 2, -1, 5)
459s ***** error<gevrnd: dimensions must be non-negative integers.> ...
459s  gevrnd (1, 2, 3, 2, 1.5, 5)
459s ***** error<gevrnd: K, SIGMA, and MU must be scalars or of size SZ.> ...
459s  gevrnd (2, ones (2), 2, 3)
459s ***** error<gevrnd: K, SIGMA, and MU must be scalars or of size SZ.> ...
459s  gevrnd (2, ones (2), 2, [3, 2])
459s ***** error<gevrnd: K, SIGMA, and MU must be scalars or of size SZ.> ...
459s  gevrnd (2, ones (2), 2, 3, 2)
459s 34 tests, 34 passed, 0 known failure, 0 skipped
459s [inst/dist_fun/gpcdf.m]
459s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/gpcdf.m
459s ***** demo
459s  ## Plot various CDFs from the generalized Pareto distribution
459s  x = 0:0.001:5;
459s  p1 = gpcdf (x, 1, 1, 0);
459s  p2 = gpcdf (x, 5, 1, 0);
459s  p3 = gpcdf (x, 20, 1, 0);
459s  p4 = gpcdf (x, 1, 2, 0);
459s  p5 = gpcdf (x, 5, 2, 0);
459s  p6 = gpcdf (x, 20, 2, 0);
459s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", ...
459s        x, p4, "-c", x, p5, "-m", x, p6, "-k")
459s  grid on
459s  xlim ([0, 5])
459s  legend ({"k = 1, σ = 1, θ = 0", "k = 5, σ = 1, θ = 0", ...
459s           "k = 20, σ = 1, θ = 0", "k = 1, σ = 2, θ = 0", ...
459s           "k = 5, σ = 2, θ = 0", "k = 20, σ = 2, θ = 0"}, ...
459s          "location", "northwest")
459s  title ("Generalized Pareto CDF")
459s  xlabel ("values in x")
459s  ylabel ("probability")
459s ***** shared x, y1, y1u, y2, y2u, y3, y3u
459s  x = [-Inf, -1, 0, 1/2, 1, Inf];
459s  y1 = [0, 0, 0, 0.3934693402873666, 0.6321205588285577, 1];
459s  y1u = [1, 1, 1, 0.6065306597126334, 0.3678794411714423, 0];
459s  y2 = [0, 0, 0, 1/3, 1/2, 1];
459s  y2u = [1, 1, 1, 2/3, 1/2, 0];
459s  y3 = [0, 0, 0, 1/2, 1, 1];
459s  y3u = [1, 1, 1, 1/2, 0, 0];
459s ***** assert (gpcdf (x, zeros (1,6), ones (1,6), zeros (1,6)), y1, eps)
459s ***** assert (gpcdf (x, 0, 1, zeros (1,6)), y1, eps)
459s ***** assert (gpcdf (x, 0, ones (1,6), 0), y1, eps)
459s ***** assert (gpcdf (x, zeros (1,6), 1, 0), y1, eps)
459s ***** assert (gpcdf (x, 0, 1, 0), y1, eps)
459s ***** assert (gpcdf (x, 0, 1, [0, 0, 0, NaN, 0, 0]), [y1(1:3), NaN, y1(5:6)], eps)
459s ***** assert (gpcdf (x, 0, [1, 1, 1, NaN, 1, 1], 0), [y1(1:3), NaN, y1(5:6)], eps)
459s ***** assert (gpcdf (x, [0, 0, 0, NaN, 0, 0], 1, 0), [y1(1:3), NaN, y1(5:6)], eps)
459s ***** assert (gpcdf ([x(1:3), NaN, x(5:6)], 0, 1, 0), [y1(1:3), NaN, y1(5:6)], eps)
459s ***** assert (gpcdf (x, zeros (1,6), ones (1,6), zeros (1,6), "upper"), y1u, eps)
459s ***** assert (gpcdf (x, 0, 1, zeros (1,6), "upper"), y1u, eps)
459s ***** assert (gpcdf (x, 0, ones (1,6), 0, "upper"), y1u, eps)
459s ***** assert (gpcdf (x, zeros (1,6), 1, 0, "upper"), y1u, eps)
459s ***** assert (gpcdf (x, 0, 1, 0, "upper"), y1u, eps)
459s ***** assert (gpcdf (x, ones (1,6), ones (1,6), zeros (1,6)), y2, eps)
459s ***** assert (gpcdf (x, 1, 1, zeros (1,6)), y2, eps)
459s ***** assert (gpcdf (x, 1, ones (1,6), 0), y2, eps)
459s ***** assert (gpcdf (x, ones (1,6), 1, 0), y2, eps)
459s ***** assert (gpcdf (x, 1, 1, 0), y2, eps)
459s ***** assert (gpcdf (x, 1, 1, [0, 0, 0, NaN, 0, 0]), [y2(1:3), NaN, y2(5:6)], eps)
459s ***** assert (gpcdf (x, 1, [1, 1, 1, NaN, 1, 1], 0), [y2(1:3), NaN, y2(5:6)], eps)
459s ***** assert (gpcdf (x, [1, 1, 1, NaN, 1, 1], 1, 0), [y2(1:3), NaN, y2(5:6)], eps)
459s ***** assert (gpcdf ([x(1:3), NaN, x(5:6)], 1, 1, 0), [y2(1:3), NaN, y2(5:6)], eps)
459s ***** assert (gpcdf (x, ones (1,6), ones (1,6), zeros (1,6), "upper"), y2u, eps)
459s ***** assert (gpcdf (x, 1, 1, zeros (1,6), "upper"), y2u, eps)
459s ***** assert (gpcdf (x, 1, ones (1,6), 0, "upper"), y2u, eps)
459s ***** assert (gpcdf (x, ones (1,6), 1, 0, "upper"), y2u, eps)
459s ***** assert (gpcdf (x, 1, 1, 0, "upper"), y2u, eps)
459s ***** assert (gpcdf (x, 1, 1, [0, 0, 0, NaN, 0, 0], "upper"), ...
459s                         [y2u(1:3), NaN, y2u(5:6)], eps)
459s ***** assert (gpcdf (x, 1, [1, 1, 1, NaN, 1, 1], 0, "upper"), ...
459s                         [y2u(1:3), NaN, y2u(5:6)], eps)
459s ***** assert (gpcdf (x, [1, 1, 1, NaN, 1, 1], 1, 0, "upper"), ...
459s                         [y2u(1:3), NaN, y2u(5:6)], eps)
459s ***** assert (gpcdf ([x(1:3), NaN, x(5:6)], 1, 1, 0, "upper"), ...
459s                         [y2u(1:3), NaN, y2u(5:6)], eps)
459s ***** assert (gpcdf (x, -ones (1,6), ones (1,6), zeros (1,6)), y3, eps)
459s ***** assert (gpcdf (x, -1, 1, zeros (1,6)), y3, eps)
459s ***** assert (gpcdf (x, -1, ones (1,6), 0), y3, eps)
459s ***** assert (gpcdf (x, -ones (1,6), 1, 0), y3, eps)
459s ***** assert (gpcdf (x, -1, 1, 0), y3, eps)
459s ***** assert (gpcdf (x, -1, 1, [0, 0, 0, NaN, 0, 0]), [y3(1:3), NaN, y3(5:6)], eps)
459s ***** assert (gpcdf (x, -1, [1, 1, 1, NaN, 1, 1], 0), [y3(1:3), NaN, y3(5:6)], eps)
459s ***** assert (gpcdf (x, [-1, -1, -1, NaN, -1, -1], 1, 0), [y3(1:3), NaN, y3(5:6)], eps)
459s ***** assert (gpcdf ([x(1:3), NaN, x(5:6)], -1, 1, 0), [y3(1:3), NaN, y3(5:6)], eps)
459s ***** assert (gpcdf (x, -ones (1,6), ones (1,6), zeros (1,6), "upper"), y3u, eps)
459s ***** assert (gpcdf (x, -1, 1, zeros (1,6), "upper"), y3u, eps)
459s ***** assert (gpcdf (x, -1, ones (1,6), 0, "upper"), y3u, eps)
459s ***** assert (gpcdf (x, -ones (1,6), 1, 0, "upper"), y3u, eps)
459s ***** assert (gpcdf (x, -1, 1, 0, "upper"), y3u, eps)
459s ***** assert (gpcdf (x, -1, 1, [0, 0, 0, NaN, 0, 0], "upper"), ...
459s                          [y3u(1:3), NaN, y3u(5:6)], eps)
459s ***** assert (gpcdf (x, -1, [1, 1, 1, NaN, 1, 1], 0, "upper"), ...
459s                           [y3u(1:3), NaN, y3u(5:6)], eps)
459s ***** assert (gpcdf (x, [-1, -1, -1, NaN, -1, -1], 1, 0, "upper"), ...
459s                           [y3u(1:3), NaN, y3u(5:6)], eps)
459s ***** assert (gpcdf ([x(1:3), NaN, x(5:6)], -1, 1, 0, "upper"), ...
459s                           [y3u(1:3), NaN, y3u(5:6)], eps)
459s ***** assert (gpcdf (single ([x, NaN]), 0, 1, 0), single ([y1, NaN]), eps("single"))
459s ***** assert (gpcdf ([x, NaN], 0, 1, single (0)), single ([y1, NaN]), eps("single"))
459s ***** assert (gpcdf ([x, NaN], 0, single (1), 0), single ([y1, NaN]), eps("single"))
459s ***** assert (gpcdf ([x, NaN], single (0), 1, 0), single ([y1, NaN]), eps("single"))
459s ***** assert (gpcdf (single ([x, NaN]), 1, 1, 0), single ([y2, NaN]), eps("single"))
459s ***** assert (gpcdf ([x, NaN], 1, 1, single (0)), single ([y2, NaN]), eps("single"))
459s ***** assert (gpcdf ([x, NaN], 1, single (1), 0), single ([y2, NaN]), eps("single"))
459s ***** assert (gpcdf ([x, NaN], single (1), 1, 0), single ([y2, NaN]), eps("single"))
459s ***** assert (gpcdf (single ([x, NaN]), -1, 1, 0), single ([y3, NaN]), eps("single"))
459s ***** assert (gpcdf ([x, NaN], -1, 1, single (0)), single ([y3, NaN]), eps("single"))
459s ***** assert (gpcdf ([x, NaN], -1, single (1), 0), single ([y3, NaN]), eps("single"))
459s ***** assert (gpcdf ([x, NaN], single (-1), 1, 0), single ([y3, NaN]), eps("single"))
459s ***** error<gpcdf: function called with too few input arguments.> gpcdf ()
459s ***** error<gpcdf: function called with too few input arguments.> gpcdf (1)
459s ***** error<gpcdf: function called with too few input arguments.> gpcdf (1, 2)
459s ***** error<gpcdf: function called with too few input arguments.> gpcdf (1, 2, 3)
459s ***** error<gpcdf: invalid argument for upper tail.> gpcdf (1, 2, 3, 4, "tail")
459s ***** error<gpcdf: invalid argument for upper tail.> gpcdf (1, 2, 3, 4, 5)
459s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
459s  gpcdf (ones (3), ones (2), ones(2), ones(2))
459s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
459s  gpcdf (ones (2), ones (3), ones(2), ones(2))
459s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
459s  gpcdf (ones (2), ones (2), ones(3), ones(2))
459s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
459s  gpcdf (ones (2), ones (2), ones(2), ones(3))
459s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (i, 2, 3, 4)
459s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, i, 3, 4)
459s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, 2, i, 4)
459s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, 2, 3, i)
459s 76 tests, 76 passed, 0 known failure, 0 skipped
459s [inst/dist_fun/gpinv.m]
459s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/gpinv.m
459s ***** demo
459s  ## Plot various iCDFs from the generalized Pareto distribution
459s  p = 0.001:0.001:0.999;
459s  x1 = gpinv (p, 1, 1, 0);
459s  x2 = gpinv (p, 5, 1, 0);
459s  x3 = gpinv (p, 20, 1, 0);
459s  x4 = gpinv (p, 1, 2, 0);
459s  x5 = gpinv (p, 5, 2, 0);
459s  x6 = gpinv (p, 20, 2, 0);
459s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", ...
459s        p, x4, "-c", p, x5, "-m", p, x6, "-k")
459s  grid on
459s  ylim ([0, 5])
459s  legend ({"k = 1, σ = 1, θ = 0", "k = 5, σ = 1, θ = 0", ...
459s           "k = 20, σ = 1, θ = 0", "k = 1, σ = 2, θ = 0", ...
459s           "k = 5, σ = 2, θ = 0", "k = 20, σ = 2, θ = 0"}, ...
459s          "location", "southeast")
459s  title ("Generalized Pareto iCDF")
459s  xlabel ("probability")
459s  ylabel ("values in x")
459s ***** shared p, y1, y2, y3
459s  p = [-1, 0, 1/2, 1, 2];
459s  y1 = [NaN, 0, 0.6931471805599453, Inf, NaN];
459s  y2 = [NaN, 0, 1, Inf, NaN];
459s  y3 = [NaN, 0, 1/2, 1, NaN];
459s ***** assert (gpinv (p, zeros (1,5), ones (1,5), zeros (1,5)), y1)
459s ***** assert (gpinv (p, 0, 1, zeros (1,5)), y1)
459s ***** assert (gpinv (p, 0, ones (1,5), 0), y1)
459s ***** assert (gpinv (p, zeros (1,5), 1, 0), y1)
459s ***** assert (gpinv (p, 0, 1, 0), y1)
459s ***** assert (gpinv (p, 0, 1, [0, 0, NaN, 0, 0]), [y1(1:2), NaN, y1(4:5)])
459s ***** assert (gpinv (p, 0, [1, 1, NaN, 1, 1], 0), [y1(1:2), NaN, y1(4:5)])
459s ***** assert (gpinv (p, [0, 0, NaN, 0, 0], 1, 0), [y1(1:2), NaN, y1(4:5)])
459s ***** assert (gpinv ([p(1:2), NaN, p(4:5)], 0, 1, 0), [y1(1:2), NaN, y1(4:5)])
459s ***** assert (gpinv (p, ones (1,5), ones (1,5), zeros (1,5)), y2)
459s ***** assert (gpinv (p, 1, 1, zeros (1,5)), y2)
459s ***** assert (gpinv (p, 1, ones (1,5), 0), y2)
459s ***** assert (gpinv (p, ones (1,5), 1, 0), y2)
459s ***** assert (gpinv (p, 1, 1, 0), y2)
459s ***** assert (gpinv (p, 1, 1, [0, 0, NaN, 0, 0]), [y2(1:2), NaN, y2(4:5)])
459s ***** assert (gpinv (p, 1, [1, 1, NaN, 1, 1], 0), [y2(1:2), NaN, y2(4:5)])
459s ***** assert (gpinv (p, [1, 1, NaN, 1, 1], 1, 0), [y2(1:2), NaN, y2(4:5)])
459s ***** assert (gpinv ([p(1:2), NaN, p(4:5)], 1, 1, 0), [y2(1:2), NaN, y2(4:5)])
459s ***** assert (gpinv (p, -ones (1,5), ones (1,5), zeros (1,5)), y3)
459s ***** assert (gpinv (p, -1, 1, zeros (1,5)), y3)
459s ***** assert (gpinv (p, -1, ones (1,5), 0), y3)
459s ***** assert (gpinv (p, -ones (1,5), 1, 0), y3)
459s ***** assert (gpinv (p, -1, 1, 0), y3)
459s ***** assert (gpinv (p, -1, 1, [0, 0, NaN, 0, 0]), [y3(1:2), NaN, y3(4:5)])
459s ***** assert (gpinv (p, -1, [1, 1, NaN, 1, 1], 0), [y3(1:2), NaN, y3(4:5)])
459s ***** assert (gpinv (p, -[1, 1, NaN, 1, 1], 1, 0), [y3(1:2), NaN, y3(4:5)])
459s ***** assert (gpinv ([p(1:2), NaN, p(4:5)], -1, 1, 0), [y3(1:2), NaN, y3(4:5)])
459s ***** assert (gpinv (single ([p, NaN]), 0, 1, 0), single ([y1, NaN]))
459s ***** assert (gpinv ([p, NaN], 0, 1, single (0)), single ([y1, NaN]))
459s ***** assert (gpinv ([p, NaN], 0, single (1), 0), single ([y1, NaN]))
459s ***** assert (gpinv ([p, NaN], single (0), 1, 0), single ([y1, NaN]))
459s ***** assert (gpinv (single ([p, NaN]), 1, 1, 0), single ([y2, NaN]))
459s ***** assert (gpinv ([p, NaN], 1, 1, single (0)), single ([y2, NaN]))
459s ***** assert (gpinv ([p, NaN], 1, single (1), 0), single ([y2, NaN]))
459s ***** assert (gpinv ([p, NaN], single (1), 1, 0), single ([y2, NaN]))
459s ***** assert (gpinv (single ([p, NaN]), -1, 1, 0), single ([y3, NaN]))
459s ***** assert (gpinv ([p, NaN], -1, 1, single (0)), single ([y3, NaN]))
459s ***** assert (gpinv ([p, NaN], -1, single (1), 0), single ([y3, NaN]))
459s ***** assert (gpinv ([p, NaN], single (-1), 1, 0), single ([y3, NaN]))
459s ***** error<gpinv: function called with too few input arguments.> gpinv ()
459s ***** error<gpinv: function called with too few input arguments.> gpinv (1)
459s ***** error<gpinv: function called with too few input arguments.> gpinv (1, 2)
459s ***** error<gpinv: function called with too few input arguments.> gpinv (1, 2, 3)
459s ***** error<gpinv: P, K, SIGMA, and THETA must be of common size or scalars.> ...
459s  gpinv (ones (3), ones (2), ones(2), ones(2))
459s ***** error<gpinv: P, K, SIGMA, and THETA must be of common size or scalars.> ...
459s  gpinv (ones (2), ones (3), ones(2), ones(2))
459s ***** error<gpinv: P, K, SIGMA, and THETA must be of common size or scalars.> ...
459s  gpinv (ones (2), ones (2), ones(3), ones(2))
459s ***** error<gpinv: P, K, SIGMA, and THETA must be of common size or scalars.> ...
459s  gpinv (ones (2), ones (2), ones(2), ones(3))
459s ***** error<gpinv: P, K, SIGMA, and THETA must not be complex.> gpinv (i, 2, 3, 4)
459s ***** error<gpinv: P, K, SIGMA, and THETA must not be complex.> gpinv (1, i, 3, 4)
459s ***** error<gpinv: P, K, SIGMA, and THETA must not be complex.> gpinv (1, 2, i, 4)
459s ***** error<gpinv: P, K, SIGMA, and THETA must not be complex.> gpinv (1, 2, 3, i)
459s 51 tests, 51 passed, 0 known failure, 0 skipped
459s [inst/dist_fun/gppdf.m]
459s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/gppdf.m
459s ***** demo
459s  ## Plot various PDFs from the generalized Pareto distribution
459s  x = 0:0.001:5;
459s  y1 = gppdf (x, 1, 1, 0);
459s  y2 = gppdf (x, 5, 1, 0);
459s  y3 = gppdf (x, 20, 1, 0);
459s  y4 = gppdf (x, 1, 2, 0);
459s  y5 = gppdf (x, 5, 2, 0);
459s  y6 = gppdf (x, 20, 2, 0);
459s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", ...
459s        x, y4, "-c", x, y5, "-m", x, y6, "-k")
459s  grid on
459s  xlim ([0, 5])
459s  ylim ([0, 1])
459s  legend ({"k = 1, σ = 1, θ = 0", "k = 5, σ = 1, θ = 0", ...
459s           "k = 20, σ = 1, θ = 0", "k = 1, σ = 2, θ = 0", ...
459s           "k = 5, σ = 2, θ = 0", "k = 20, σ = 2, θ = 0"}, ...
459s          "location", "northeast")
459s  title ("Generalized Pareto PDF")
459s  xlabel ("values in x")
459s  ylabel ("density")
459s ***** shared x, y1, y2, y3
459s  x = [-Inf, -1, 0, 1/2, 1, Inf];
459s  y1 = [0, 0, 1, 0.6065306597126334, 0.36787944117144233, 0];
459s  y2 = [0, 0, 1, 4/9, 1/4, 0];
459s  y3 = [0, 0, 1, 1, 1, 0];
459s ***** assert (gppdf (x, zeros (1,6), ones (1,6), zeros (1,6)), y1, eps)
459s ***** assert (gppdf (x, 0, 1, zeros (1,6)), y1, eps)
459s ***** assert (gppdf (x, 0, ones (1,6), 0), y1, eps)
459s ***** assert (gppdf (x, zeros (1,6), 1, 0), y1, eps)
459s ***** assert (gppdf (x, 0, 1, 0), y1, eps)
459s ***** assert (gppdf (x, 0, 1, [0, 0, 0, NaN, 0, 0]), [y1(1:3), NaN, y1(5:6)])
459s ***** assert (gppdf (x, 0, [1, 1, 1, NaN, 1, 1], 0), [y1(1:3), NaN, y1(5:6)])
459s ***** assert (gppdf (x, [0, 0, 0, NaN, 0, 0], 1, 0), [y1(1:3), NaN, y1(5:6)])
459s ***** assert (gppdf ([x(1:3), NaN, x(5:6)], 0, 1, 0), [y1(1:3), NaN, y1(5:6)])
459s ***** assert (gppdf (x, ones (1,6), ones (1,6), zeros (1,6)), y2, eps)
459s ***** assert (gppdf (x, 1, 1, zeros (1,6)), y2, eps)
459s ***** assert (gppdf (x, 1, ones (1,6), 0), y2, eps)
459s ***** assert (gppdf (x, ones (1,6), 1, 0), y2, eps)
459s ***** assert (gppdf (x, 1, 1, 0), y2, eps)
459s ***** assert (gppdf (x, 1, 1, [0, 0, 0, NaN, 0, 0]), [y2(1:3), NaN, y2(5:6)])
459s ***** assert (gppdf (x, 1, [1, 1, 1, NaN, 1, 1], 0), [y2(1:3), NaN, y2(5:6)])
459s ***** assert (gppdf (x, [1, 1, 1, NaN, 1, 1], 1, 0), [y2(1:3), NaN, y2(5:6)])
459s ***** assert (gppdf ([x(1:3), NaN, x(5:6)], 1, 1, 0), [y2(1:3), NaN, y2(5:6)])
459s ***** assert (gppdf (x, -ones (1,6), ones (1,6), zeros (1,6)), y3, eps)
459s ***** assert (gppdf (x, -1, 1, zeros (1,6)), y3, eps)
459s ***** assert (gppdf (x, -1, ones (1,6), 0), y3, eps)
459s ***** assert (gppdf (x, -ones (1,6), 1, 0), y3, eps)
459s ***** assert (gppdf (x, -1, 1, 0), y3, eps)
459s ***** assert (gppdf (x, -1, 1, [0, 0, 0, NaN, 0, 0]), [y3(1:3), NaN, y3(5:6)])
459s ***** assert (gppdf (x, -1, [1, 1, 1, NaN, 1, 1], 0), [y3(1:3), NaN, y3(5:6)])
459s ***** assert (gppdf (x, [-1, -1, -1, NaN, -1, -1], 1, 0), [y3(1:3), NaN, y3(5:6)])
459s ***** assert (gppdf ([x(1:3), NaN, x(5:6)], -1, 1, 0), [y3(1:3), NaN, y3(5:6)])
460s ***** assert (gppdf (single ([x, NaN]), 0, 1, 0), single ([y1, NaN]))
460s ***** assert (gppdf ([x, NaN], 0, 1, single (0)), single ([y1, NaN]))
460s ***** assert (gppdf ([x, NaN], 0, single (1), 0), single ([y1, NaN]))
460s ***** assert (gppdf ([x, NaN], single (0), 1, 0), single ([y1, NaN]))
460s ***** assert (gppdf (single ([x, NaN]), 1, 1, 0), single ([y2, NaN]))
460s ***** assert (gppdf ([x, NaN], 1, 1, single (0)), single ([y2, NaN]))
460s ***** assert (gppdf ([x, NaN], 1, single (1), 0), single ([y2, NaN]))
460s ***** assert (gppdf ([x, NaN], single (1), 1, 0), single ([y2, NaN]))
460s ***** assert (gppdf (single ([x, NaN]), -1, 1, 0), single ([y3, NaN]))
460s ***** assert (gppdf ([x, NaN], -1, 1, single (0)), single ([y3, NaN]))
460s ***** assert (gppdf ([x, NaN], -1, single (1), 0), single ([y3, NaN]))
460s ***** assert (gppdf ([x, NaN], single (-1), 1, 0), single ([y3, NaN]))
460s ***** error<gpcdf: function called with too few input arguments.> gpcdf ()
460s ***** error<gpcdf: function called with too few input arguments.> gpcdf (1)
460s ***** error<gpcdf: function called with too few input arguments.> gpcdf (1, 2)
460s ***** error<gpcdf: function called with too few input arguments.> gpcdf (1, 2, 3)
460s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
460s  gpcdf (ones (3), ones (2), ones(2), ones(2))
460s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
460s  gpcdf (ones (2), ones (3), ones(2), ones(2))
460s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
460s  gpcdf (ones (2), ones (2), ones(3), ones(2))
460s ***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
460s  gpcdf (ones (2), ones (2), ones(2), ones(3))
460s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (i, 2, 3, 4)
460s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, i, 3, 4)
460s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, 2, i, 4)
460s ***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, 2, 3, i)
460s 51 tests, 51 passed, 0 known failure, 0 skipped
460s [inst/dist_fun/gprnd.m]
460s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/gprnd.m
460s ***** assert (size (gprnd (0, 1, 0)), [1, 1])
460s ***** assert (size (gprnd (0, 1, zeros (2,1))), [2, 1])
460s ***** assert (size (gprnd (0, 1, zeros (2,2))), [2, 2])
460s ***** assert (size (gprnd (0, ones (2,1), 0)), [2, 1])
460s ***** assert (size (gprnd (0, ones (2,2), 0)), [2, 2])
460s ***** assert (size (gprnd (zeros (2,1), 1, 0)), [2, 1])
460s ***** assert (size (gprnd (zeros (2,2), 1, 0)), [2, 2])
460s ***** assert (size (gprnd (0, 1, 0, 3)), [3, 3])
460s ***** assert (size (gprnd (0, 1, 0, [4 1])), [4, 1])
460s ***** assert (size (gprnd (0, 1, 0, 4, 1)), [4, 1])
460s ***** assert (size (gprnd (1,1,0)), [1, 1])
460s ***** assert (size (gprnd (1, 1, zeros (2,1))), [2, 1])
460s ***** assert (size (gprnd (1, 1, zeros (2,2))), [2, 2])
460s ***** assert (size (gprnd (1, ones (2,1), 0)), [2, 1])
460s ***** assert (size (gprnd (1, ones (2,2), 0)), [2, 2])
460s ***** assert (size (gprnd (ones (2,1), 1, 0)), [2, 1])
460s ***** assert (size (gprnd (ones (2,2), 1, 0)), [2, 2])
460s ***** assert (size (gprnd (1, 1, 0, 3)), [3, 3])
460s ***** assert (size (gprnd (1, 1, 0, [4 1])), [4, 1])
460s ***** assert (size (gprnd (1, 1, 0, 4, 1)), [4, 1])
460s ***** assert (size (gprnd (-1, 1, 0)), [1, 1])
460s ***** assert (size (gprnd (-1, 1, zeros (2,1))), [2, 1])
460s ***** assert (size (gprnd (1, -1, zeros (2,2))), [2, 2])
460s ***** assert (size (gprnd (-1, ones (2,1), 0)), [2, 1])
460s ***** assert (size (gprnd (-1, ones (2,2), 0)), [2, 2])
460s ***** assert (size (gprnd (-ones (2,1), 1, 0)), [2, 1])
460s ***** assert (size (gprnd (-ones (2,2), 1, 0)), [2, 2])
460s ***** assert (size (gprnd (-1, 1, 0, 3)), [3, 3])
460s ***** assert (size (gprnd (-1, 1, 0, [4, 1])), [4, 1])
460s ***** assert (size (gprnd (-1, 1, 0, 4, 1)), [4, 1])
460s ***** assert (class (gprnd (0, 1, 0)), "double")
460s ***** assert (class (gprnd (0, 1, single (0))), "single")
460s ***** assert (class (gprnd (0, 1, single ([0, 0]))), "single")
460s ***** assert (class (gprnd (0, single (1),0)), "single")
460s ***** assert (class (gprnd (0, single ([1, 1]),0)), "single")
460s ***** assert (class (gprnd (single (0), 1, 0)), "single")
460s ***** assert (class (gprnd (single ([0, 0]), 1, 0)), "single")
460s ***** error<gprnd: function called with too few input arguments.> gprnd ()
460s ***** error<gprnd: function called with too few input arguments.> gprnd (1)
460s ***** error<gprnd: function called with too few input arguments.> gprnd (1, 2)
460s ***** error<gprnd: K, SIGMA, and THETA must be of common size or scalars.> ...
460s  gprnd (ones (3), ones (2), ones (2))
460s ***** error<gprnd: K, SIGMA, and THETA must be of common size or scalars.> ...
460s  gprnd (ones (2), ones (3), ones (2))
460s ***** error<gprnd: K, SIGMA, and THETA must be of common size or scalars.> ...
460s  gprnd (ones (2), ones (2), ones (3))
460s ***** error<gprnd: K, SIGMA, and THETA must not be complex.> gprnd (i, 2, 3)
460s ***** error<gprnd: K, SIGMA, and THETA must not be complex.> gprnd (1, i, 3)
460s ***** error<gprnd: K, SIGMA, and THETA must not be complex.> gprnd (1, 2, i)
460s ***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
460s  gprnd (1, 2, 3, -1)
460s ***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
460s  gprnd (1, 2, 3, 1.2)
460s ***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
460s  gprnd (1, 2, 3, ones (2))
460s ***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
460s  gprnd (1, 2, 3, [2 -1 2])
460s ***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
460s  gprnd (1, 2, 3, [2 0 2.5])
460s ***** error<gprnd: dimensions must be non-negative integers.> ...
460s  gprnd (1, 2, 3, 2, -1, 5)
460s ***** error<gprnd: dimensions must be non-negative integers.> ...
460s  gprnd (1, 2, 3, 2, 1.5, 5)
460s ***** error<gprnd: K, SIGMA, and THETA must be scalars or of size SZ.> ...
460s  gprnd (2, ones (2), 2, 3)
460s ***** error<gprnd: K, SIGMA, and THETA must be scalars or of size SZ.> ...
460s  gprnd (2, ones (2), 2, [3, 2])
460s ***** error<gprnd: K, SIGMA, and THETA must be scalars or of size SZ.> ...
460s  gprnd (2, ones (2), 2, 3, 2)
460s 56 tests, 56 passed, 0 known failure, 0 skipped
460s [inst/dist_fun/gumbelcdf.m]
460s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/gumbelcdf.m
460s ***** demo
460s  ## Plot various CDFs from the Gumbel distribution
460s  x = -5:0.01:20;
460s  p1 = gumbelcdf (x, 0.5, 2);
460s  p2 = gumbelcdf (x, 1.0, 2);
460s  p3 = gumbelcdf (x, 1.5, 3);
460s  p4 = gumbelcdf (x, 3.0, 4);
460s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
460s  grid on
460s  legend ({"μ = 0.5, β = 2", "μ = 1.0, β = 2", ...
460s           "μ = 1.5, β = 3", "μ = 3.0, β = 4"}, "location", "southeast")
460s  title ("Gumbel CDF")
460s  xlabel ("values in x")
460s  ylabel ("probability")
460s ***** shared x, y
460s  x = [-Inf, 1, 2, Inf];
460s  y = [0, 0.3679, 0.6922, 1];
460s ***** assert (gumbelcdf (x, ones (1,4), ones (1,4)), y, 1e-4)
460s ***** assert (gumbelcdf (x, 1, ones (1,4)), y, 1e-4)
460s ***** assert (gumbelcdf (x, ones (1,4), 1), y, 1e-4)
460s ***** assert (gumbelcdf (x, [0, -Inf, NaN, Inf], 1), [0, 1, NaN, NaN], 1e-4)
460s ***** assert (gumbelcdf (x, 1, [Inf, NaN, -1, 0]), [NaN, NaN, NaN, NaN], 1e-4)
460s ***** assert (gumbelcdf ([x(1:2), NaN, x(4)], 1, 1), [y(1:2), NaN, y(4)], 1e-4)
460s ***** assert (gumbelcdf (x, "upper"), [1, 0.3078, 0.1266, 0], 1e-4)
460s ***** assert (gumbelcdf ([x, NaN], 1, 1), [y, NaN], 1e-4)
460s ***** assert (gumbelcdf (single ([x, NaN]), 1, 1), single ([y, NaN]), 1e-4)
460s ***** assert (gumbelcdf ([x, NaN], single (1), 1), single ([y, NaN]), 1e-4)
460s ***** assert (gumbelcdf ([x, NaN], 1, single (1)), single ([y, NaN]), 1e-4)
460s ***** error<gumbelcdf: invalid number of input arguments.> gumbelcdf ()
460s ***** error<gumbelcdf: invalid number of input arguments.> gumbelcdf (1,2,3,4,5,6,7)
460s ***** error<gumbelcdf: invalid argument for upper tail.> gumbelcdf (1, 2, 3, 4, "uper")
460s ***** error<gumbelcdf: X, MU, and BETA must be of common size or scalars.> ...
460s  gumbelcdf (ones (3), ones (2), ones (2))
460s ***** error<gumbelcdf: invalid size of covariance matrix.> gumbelcdf (2, 3, 4, [1, 2])
460s ***** error<gumbelcdf: covariance matrix is required for confidence bounds.> ...
460s  [p, plo, pup] = gumbelcdf (1, 2, 3)
460s ***** error<gumbelcdf: invalid value for alpha.> [p, plo, pup] = ...
460s  gumbelcdf (1, 2, 3, [1, 0; 0, 1], 0)
460s ***** error<gumbelcdf: invalid value for alpha.> [p, plo, pup] = ...
460s  gumbelcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
460s ***** error<gumbelcdf: invalid value for alpha.> [p, plo, pup] = ...
460s  gumbelcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
460s ***** error<gumbelcdf: X, MU, and BETA must not be complex.> gumbelcdf (i, 2, 2)
460s ***** error<gumbelcdf: X, MU, and BETA must not be complex.> gumbelcdf (2, i, 2)
460s ***** error<gumbelcdf: X, MU, and BETA must not be complex.> gumbelcdf (2, 2, i)
460s ***** error<gumbelcdf: bad covariance matrix.> ...
460s  [p, plo, pup] = gumbelcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
460s 24 tests, 24 passed, 0 known failure, 0 skipped
460s [inst/dist_fun/gumbelinv.m]
460s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/gumbelinv.m
460s ***** demo
460s  ## Plot various iCDFs from the Gumbel distribution
460s  p = 0.001:0.001:0.999;
460s  x1 = gumbelinv (p, 0.5, 2);
460s  x2 = gumbelinv (p, 1.0, 2);
460s  x3 = gumbelinv (p, 1.5, 3);
460s  x4 = gumbelinv (p, 3.0, 4);
460s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
460s  grid on
460s  ylim ([-5, 20])
460s  legend ({"μ = 0.5, β = 2", "μ = 1.0, β = 2", ...
460s           "μ = 1.5, β = 3", "μ = 3.0, β = 4"}, "location", "northwest")
460s  title ("Gumbel iCDF")
460s  xlabel ("probability")
460s  ylabel ("values in x")
460s ***** shared p, x
460s  p = [0, 0.05, 0.5 0.95];
460s  x = [-Inf, -1.0972, 0.3665, 2.9702];
460s ***** assert (gumbelinv (p), x, 1e-4)
460s ***** assert (gumbelinv (p, zeros (1,4), ones (1,4)), x, 1e-4)
460s ***** assert (gumbelinv (p, 0, ones (1,4)), x, 1e-4)
460s ***** assert (gumbelinv (p, zeros (1,4), 1), x, 1e-4)
460s ***** assert (gumbelinv (p, [0, -Inf, NaN, Inf], 1), [-Inf, -Inf, NaN, Inf], 1e-4)
460s ***** assert (gumbelinv (p, 0, [Inf, NaN, -1, 0]), [-Inf, NaN, NaN, NaN], 1e-4)
460s ***** assert (gumbelinv ([p(1:2), NaN, p(4)], 0, 1), [x(1:2), NaN, x(4)], 1e-4)
460s ***** assert (gumbelinv ([p, NaN], 0, 1), [x, NaN], 1e-4)
460s ***** assert (gumbelinv (single ([p, NaN]), 0, 1), single ([x, NaN]), 1e-4)
460s ***** assert (gumbelinv ([p, NaN], single (0), 1), single ([x, NaN]), 1e-4)
460s ***** assert (gumbelinv ([p, NaN], 0, single (1)), single ([x, NaN]), 1e-4)
460s  p = [0.05, 0.5, 0.95];
460s  x = gumbelinv(p);
460s ***** assert (gumbelcdf(x), p, 1e-4)
460s ***** error<gumbelinv: invalid number of input arguments.> gumbelinv ()
460s ***** error gumbelinv (1,2,3,4,5,6)
460s ***** error<gumbelinv: P, MU, and BETA must be of common size or scalars.> ...
460s  gumbelinv (ones (3), ones (2), ones (2))
460s ***** error<gumbelinv: invalid size of covariance matrix.> ...
460s  [p, plo, pup] = gumbelinv (2, 3, 4, [1, 2])
460s ***** error<gumbelinv: covariance matrix is required for confidence bounds.> ...
460s  [p, plo, pup] = gumbelinv (1, 2, 3)
460s ***** error<gumbelinv: invalid value for alpha.> [p, plo, pup] = ...
460s  gumbelinv (1, 2, 3, [1, 0; 0, 1], 0)
460s ***** error<gumbelinv: invalid value for alpha.> [p, plo, pup] = ...
460s  gumbelinv (1, 2, 3, [1, 0; 0, 1], 1.22)
460s ***** error<gumbelinv: P, MU, and BETA must not be complex.> gumbelinv (i, 2, 2)
460s ***** error<gumbelinv: P, MU, and BETA must not be complex.> gumbelinv (2, i, 2)
460s ***** error<gumbelinv: P, MU, and BETA must not be complex.> gumbelinv (2, 2, i)
460s ***** error<gumbelinv: bad covariance matrix.> ...
460s  [p, plo, pup] = gumbelinv (1, 2, 3, [-1, 10; -Inf, -Inf], 0.04)
460s 23 tests, 23 passed, 0 known failure, 0 skipped
460s [inst/dist_fun/gumbelpdf.m]
460s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/gumbelpdf.m
460s ***** demo
460s  ## Plot various PDFs from the Extreme value distribution
460s  x = -5:0.001:20;
460s  y1 = gumbelpdf (x, 0.5, 2);
460s  y2 = gumbelpdf (x, 1.0, 2);
460s  y3 = gumbelpdf (x, 1.5, 3);
460s  y4 = gumbelpdf (x, 3.0, 4);
460s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
460s  grid on
460s  ylim ([0, 0.2])
460s  legend ({"μ = 0.5, β = 2", "μ = 1.0, β = 2", ...
460s           "μ = 1.5, β = 3", "μ = 3.0, β = 4"}, "location", "northeast")
460s  title ("Extreme value PDF")
460s  xlabel ("values in x")
460s  ylabel ("density")
460s ***** shared x, y0, y1
460s  x = [-5, 0, 1, 2, 3];
460s  y0 = [0, 0.3679, 0.2547, 0.1182, 0.0474];
460s  y1 = [0, 0.1794, 0.3679, 0.2547, 0.1182];
460s ***** assert (gumbelpdf (x), y0, 1e-4)
460s ***** assert (gumbelpdf (x, zeros (1,5), ones (1,5)), y0, 1e-4)
460s ***** assert (gumbelpdf (x, ones (1,5), ones (1,5)), y1, 1e-4)
460s ***** error<gumbelpdf: too few input arguments.> gumbelpdf ()
460s ***** error<gumbelpdf: X, MU, and BETA must be of common size or scalars.> ...
460s  gumbelpdf (ones (3), ones (2), ones (2))
460s ***** error<gumbelpdf: X, MU, and BETA must not be complex.> gumbelpdf (i, 2, 2)
460s ***** error<gumbelpdf: X, MU, and BETA must not be complex.> gumbelpdf (2, i, 2)
460s ***** error<gumbelpdf: X, MU, and BETA must not be complex.> gumbelpdf (2, 2, i)
460s 8 tests, 8 passed, 0 known failure, 0 skipped
460s [inst/dist_fun/gumbelrnd.m]
460s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/gumbelrnd.m
460s ***** assert (size (gumbelrnd (1, 1)), [1 1])
460s ***** assert (size (gumbelrnd (1, ones (2,1))), [2, 1])
460s ***** assert (size (gumbelrnd (1, ones (2,2))), [2, 2])
460s ***** assert (size (gumbelrnd (ones (2,1), 1)), [2, 1])
460s ***** assert (size (gumbelrnd (ones (2,2), 1)), [2, 2])
460s ***** assert (size (gumbelrnd (1, 1, 3)), [3, 3])
460s ***** assert (size (gumbelrnd (1, 1, [4, 1])), [4, 1])
460s ***** assert (size (gumbelrnd (1, 1, 4, 1)), [4, 1])
460s ***** assert (size (gumbelrnd (1, 1, 4, 1, 5)), [4, 1, 5])
460s ***** assert (size (gumbelrnd (1, 1, 0, 1)), [0, 1])
460s ***** assert (size (gumbelrnd (1, 1, 1, 0)), [1, 0])
460s ***** assert (size (gumbelrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
460s ***** assert (class (gumbelrnd (1, 1)), "double")
460s ***** assert (class (gumbelrnd (1, single (1))), "single")
460s ***** assert (class (gumbelrnd (1, single ([1, 1]))), "single")
460s ***** assert (class (gumbelrnd (single (1), 1)), "single")
460s ***** assert (class (gumbelrnd (single ([1, 1]), 1)), "single")
460s ***** error<gumbelrnd: function called with too few input arguments.> gumbelrnd ()
460s ***** error<gumbelrnd: function called with too few input arguments.> gumbelrnd (1)
460s ***** error<gumbelrnd: MU and BETA must be of common size or scalars.> ...
460s  gumbelrnd (ones (3), ones (2))
460s ***** error<gumbelrnd: MU and BETA must be of common size or scalars.> ...
460s  gumbelrnd (ones (2), ones (3))
460s ***** error<gumbelrnd: MU and BETA must not be complex.> gumbelrnd (i, 2, 3)
460s ***** error<gumbelrnd: MU and BETA must not be complex.> gumbelrnd (1, i, 3)
460s ***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
460s  gumbelrnd (1, 2, -1)
460s ***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
460s  gumbelrnd (1, 2, 1.2)
460s ***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
460s  gumbelrnd (1, 2, ones (2))
460s ***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
460s  gumbelrnd (1, 2, [2 -1 2])
460s ***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
460s  gumbelrnd (1, 2, [2 0 2.5])
460s ***** error<gumbelrnd: dimensions must be non-negative integers.> ...
460s  gumbelrnd (1, 2, 2, -1, 5)
460s ***** error<gumbelrnd: dimensions must be non-negative integers.> ...
460s  gumbelrnd (1, 2, 2, 1.5, 5)
460s ***** error<gumbelrnd: MU and BETA must be scalars or of size SZ.> ...
460s  gumbelrnd (2, ones (2), 3)
460s ***** error<gumbelrnd: MU and BETA must be scalars or of size SZ.> ...
460s  gumbelrnd (2, ones (2), [3, 2])
460s ***** error<gumbelrnd: MU and BETA must be scalars or of size SZ.> ...
460s  gumbelrnd (2, ones (2), 3, 2)
460s 33 tests, 33 passed, 0 known failure, 0 skipped
460s [inst/dist_fun/hncdf.m]
460s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/hncdf.m
460s ***** demo
460s  ## Plot various CDFs from the half-normal distribution
460s  x = 0:0.001:10;
460s  p1 = hncdf (x, 0, 1);
460s  p2 = hncdf (x, 0, 2);
460s  p3 = hncdf (x, 0, 3);
460s  p4 = hncdf (x, 0, 5);
460s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
460s  grid on
460s  xlim ([0, 10])
460s  legend ({"μ = 0, σ = 1", "μ = 0, σ = 2", ...
460s           "μ = 0, σ = 3", "μ = 0, σ = 5"}, "location", "southeast")
460s  title ("Half-normal CDF")
460s  xlabel ("values in x")
460s  ylabel ("probability")
460s ***** demo
460s  ## Plot half-normal against normal cumulative distribution function
460s  x = -5:0.001:5;
460s  p1 = hncdf (x, 0, 1);
460s  p2 = normcdf (x);
460s  plot (x, p1, "-b", x, p2, "-g")
460s  grid on
460s  xlim ([-5, 5])
460s  legend ({"half-normal with μ = 0, σ = 1", ...
460s           "standart normal (μ = 0, σ = 1)"}, "location", "southeast")
460s  title ("Half-normal against standard normal CDF")
460s  xlabel ("values in x")
460s  ylabel ("probability")
460s ***** shared x, p1, p1u, y2, y2u, y3, y3u
460s  x = [-Inf, -1, 0, 1/2, 1, Inf];
460s  p1 = [0, 0, 0, 0.3829, 0.6827, 1];
460s  p1u = [1, 1, 1, 0.6171, 0.3173, 0];
460s ***** assert (hncdf (x, zeros (1,6), ones (1,6)), p1, 1e-4)
460s ***** assert (hncdf (x, 0, 1), p1, 1e-4)
460s ***** assert (hncdf (x, 0, ones (1,6)), p1, 1e-4)
460s ***** assert (hncdf (x, zeros (1,6), 1), p1, 1e-4)
460s ***** assert (hncdf (x, 0, [1, 1, 1, NaN, 1, 1]), [p1(1:3), NaN, p1(5:6)], 1e-4)
460s ***** assert (hncdf (x, [0, 0, 0, NaN, 0, 0], 1), [p1(1:3), NaN, p1(5:6)], 1e-4)
460s ***** assert (hncdf ([x(1:3), NaN, x(5:6)], 0, 1), [p1(1:3), NaN, p1(5:6)], 1e-4)
460s ***** assert (hncdf (x, zeros (1,6), ones (1,6), "upper"), p1u, 1e-4)
460s ***** assert (hncdf (x, 0, 1, "upper"), p1u, 1e-4)
460s ***** assert (hncdf (x, 0, ones (1,6), "upper"), p1u, 1e-4)
460s ***** assert (hncdf (x, zeros (1,6), 1, "upper"), p1u, 1e-4)
460s ***** assert (class (hncdf (single ([x, NaN]), 0, 1)), "single")
460s ***** assert (class (hncdf ([x, NaN], 0, single (1))), "single")
460s ***** assert (class (hncdf ([x, NaN], single (0), 1)), "single")
460s ***** error<hncdf: function called with too few input arguments.> hncdf ()
460s ***** error<hncdf: function called with too few input arguments.> hncdf (1)
460s ***** error<hncdf: function called with too few input arguments.> hncdf (1, 2)
460s ***** error<hncdf: invalid argument for upper tail.> hncdf (1, 2, 3, "tail")
460s ***** error<hncdf: invalid argument for upper tail.> hncdf (1, 2, 3, 5)
460s ***** error<hncdf: X, MU, and SIGMA must be of common size or scalars.> ...
460s  hncdf (ones (3), ones (2), ones(2))
460s ***** error<hncdf: X, MU, and SIGMA must be of common size or scalars.> ...
460s  hncdf (ones (2), ones (3), ones(2))
460s ***** error<hncdf: X, MU, and SIGMA must be of common size or scalars.> ...
460s  hncdf (ones (2), ones (2), ones(3))
460s ***** error<hncdf: X, MU, and SIGMA must not be complex.> hncdf (i, 2, 3)
460s ***** error<hncdf: X, MU, and SIGMA must not be complex.> hncdf (1, i, 3)
460s ***** error<hncdf: X, MU, and SIGMA must not be complex.> hncdf (1, 2, i)
460s 25 tests, 25 passed, 0 known failure, 0 skipped
460s [inst/dist_fun/hninv.m]
460s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/hninv.m
460s ***** demo
460s  ## Plot various iCDFs from the half-normal distribution
460s  p = 0.001:0.001:0.999;
460s  x1 = hninv (p, 0, 1);
460s  x2 = hninv (p, 0, 2);
460s  x3 = hninv (p, 0, 3);
460s  x4 = hninv (p, 0, 5);
460s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
460s  grid on
460s  ylim ([0, 10])
460s  legend ({"μ = 0, σ = 1", "μ = 0, σ = 2", ...
460s           "μ = 0, σ = 3", "μ = 0, σ = 5"}, "location", "northwest")
460s  title ("Half-normal iCDF")
460s  xlabel ("probability")
460s  ylabel ("x")
460s ***** shared p, x
460s  p = [0, 0.3829, 0.6827, 1];
460s  x = [0, 1/2, 1, Inf];
460s ***** assert (hninv (p, 0, 1), x, 1e-4);
460s ***** assert (hninv (p, 5, 1), x + 5, 1e-4);
460s ***** assert (hninv (p, 0, ones (1,4)), x, 1e-4);
460s ***** assert (hninv (p, 0, [-1, 0, 1, 1]), [NaN, NaN, x(3:4)], 1e-4)
460s ***** assert (class (hninv (single ([p, NaN]), 0, 1)), "single")
460s ***** assert (class (hninv ([p, NaN], single (0), 1)), "single")
460s ***** assert (class (hninv ([p, NaN], 0, single (1))), "single")
460s ***** error<hninv: function called with too few input arguments.> hninv (1)
460s ***** error<hninv: function called with too few input arguments.> hninv (1, 2)
460s ***** error<hninv: P, MU, and SIGMA must be of common size or scalars.> ...
460s  hninv (1, ones (2), ones (3))
460s ***** error<hninv: P, MU, and SIGMA must be of common size or scalars.> ...
460s  hninv (ones (2), 1, ones (3))
460s ***** error<hninv: P, MU, and SIGMA must be of common size or scalars.> ...
460s  hninv (ones (2), ones (3), 1)
460s ***** error<hninv: P, MU, and SIGMA must not be complex.> hninv (i, 2, 3)
460s ***** error<hninv: P, MU, and SIGMA must not be complex.> hninv (1, i, 3)
460s ***** error<hninv: P, MU, and SIGMA must not be complex.> hninv (1, 2, i)
460s 15 tests, 15 passed, 0 known failure, 0 skipped
460s [inst/dist_fun/hnpdf.m]
460s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/hnpdf.m
460s ***** demo
460s  ## Plot various PDFs from the half-normal distribution
460s  x = 0:0.001:10;
460s  y1 = hnpdf (x, 0, 1);
460s  y2 = hnpdf (x, 0, 2);
460s  y3 = hnpdf (x, 0, 3);
460s  y4 = hnpdf (x, 0, 5);
460s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
460s  grid on
460s  xlim ([0, 10])
460s  ylim ([0, 0.9])
460s  legend ({"μ = 0, σ = 1", "μ = 0, σ = 2", ...
460s           "μ = 0, σ = 3", "μ = 0, σ = 5"}, "location", "northeast")
460s  title ("Half-normal PDF")
460s  xlabel ("values in x")
460s  ylabel ("density")
460s ***** demo
460s  ## Plot half-normal against normal probability density function
460s  x = -5:0.001:5;
460s  y1 = hnpdf (x, 0, 1);
460s  y2 = normpdf (x);
460s  plot (x, y1, "-b", x, y2, "-g")
460s  grid on
460s  xlim ([-5, 5])
460s  ylim ([0, 0.9])
460s  legend ({"half-normal with μ = 0, σ = 1", ...
460s           "standart normal (μ = 0, σ = 1)"}, "location", "northeast")
460s  title ("Half-normal against standard normal PDF")
460s  xlabel ("values in x")
460s  ylabel ("density")
460s ***** shared x, y
460s  x = [-Inf, -1, 0, 1/2, 1, Inf];
460s  y = [0, 0, 0.7979, 0.7041, 0.4839, 0];
460s ***** assert (hnpdf ([x, NaN], 0, 1), [y, NaN], 1e-4)
460s ***** assert (hnpdf (x, 0, [-2, -1, 0, 1, 1, 1]), [nan(1,3), y([4:6])], 1e-4)
460s ***** assert (class (hncdf (single ([x, NaN]), 0, 1)), "single")
460s ***** assert (class (hncdf ([x, NaN], 0, single (1))), "single")
460s ***** assert (class (hncdf ([x, NaN], single (0), 1)), "single")
460s ***** error<hnpdf: function called with too few input arguments.> hnpdf ()
460s ***** error<hnpdf: function called with too few input arguments.> hnpdf (1)
460s ***** error<hnpdf: function called with too few input arguments.> hnpdf (1, 2)
460s ***** error<hnpdf: X, MU, and SIGMA must be of common size or scalars.> ...
460s  hnpdf (1, ones (2), ones (3))
460s ***** error<hnpdf: X, MU, and SIGMA must be of common size or scalars.> ...
460s  hnpdf (ones (2), 1, ones (3))
460s ***** error<hnpdf: X, MU, and SIGMA must be of common size or scalars.> ...
460s  hnpdf (ones (2), ones (3), 1)
460s ***** error<hnpdf: X, MU, and SIGMA must not be complex.> hnpdf (i, 2, 3)
460s ***** error<hnpdf: X, MU, and SIGMA must not be complex.> hnpdf (1, i, 3)
460s ***** error<hnpdf: X, MU, and SIGMA must not be complex.> hnpdf (1, 2, i)
460s 14 tests, 14 passed, 0 known failure, 0 skipped
460s [inst/dist_fun/hnrnd.m]
460s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/hnrnd.m
460s ***** assert (size (hnrnd (1, 1, 1)), [1, 1])
460s ***** assert (size (hnrnd (1, 1, 2)), [2, 2])
460s ***** assert (size (hnrnd (1, 1, [2, 1])), [2, 1])
460s ***** assert (size (hnrnd (1, zeros (2, 2))), [2, 2])
460s ***** assert (size (hnrnd (1, ones (2, 1))), [2, 1])
460s ***** assert (size (hnrnd (1, ones (2, 2))), [2, 2])
460s ***** assert (size (hnrnd (ones (2, 1), 1)), [2, 1])
460s ***** assert (size (hnrnd (ones (2, 2), 1)), [2, 2])
460s ***** assert (size (hnrnd (1, 1, 3)), [3, 3])
460s ***** assert (size (hnrnd (1, 1, [4 1])), [4, 1])
460s ***** assert (size (hnrnd (1, 1, 4, 1)), [4, 1])
460s ***** test
460s  r =  hnrnd (1, [1, 0, -1]);
460s  assert (r([2:3]), [NaN, NaN])
460s ***** assert (class (hnrnd (1, 0)), "double")
460s ***** assert (class (hnrnd (1, single (0))), "single")
460s ***** assert (class (hnrnd (1, single ([0 0]))), "single")
460s ***** assert (class (hnrnd (1, single (1))), "single")
460s ***** assert (class (hnrnd (1, single ([1 1]))), "single")
460s ***** assert (class (hnrnd (single (1), 1)), "single")
461s ***** assert (class (hnrnd (single ([1 1]), 1)), "single")
461s ***** error<hnrnd: function called with too few input arguments.> hnrnd ()
461s ***** error<hnrnd: function called with too few input arguments.> hnrnd (1)
461s ***** error<hnrnd: MU and SIGMA must be of common size or scalars.> ...
461s  hnrnd (ones (3), ones (2))
461s ***** error<hnrnd: MU and SIGMA must be of common size or scalars.> ...
461s  hnrnd (ones (2), ones (3))
461s ***** error<hnrnd: MU and SIGMA must not be complex.> hnrnd (i, 2, 3)
461s ***** error<hnrnd: MU and SIGMA must not be complex.> hnrnd (1, i, 3)
461s ***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
461s  hnrnd (1, 2, -1)
461s ***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
461s  hnrnd (1, 2, 1.2)
461s ***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
461s  hnrnd (1, 2, ones (2))
461s ***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
461s  hnrnd (1, 2, [2 -1 2])
461s ***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
461s  hnrnd (1, 2, [2 0 2.5])
461s ***** error<hnrnd: dimensions must be non-negative integers.> ...
461s  hnrnd (1, 2, 2, -1, 5)
461s ***** error<hnrnd: dimensions must be non-negative integers.> ...
461s  hnrnd (1, 2, 2, 1.5, 5)
461s ***** error<hnrnd: MU and SIGMA must be scalars or of size SZ.> ...
461s  hnrnd (2, ones (2), 3)
461s ***** error<hnrnd: MU and SIGMA must be scalars or of size SZ.> ...
461s  hnrnd (2, ones (2), [3, 2])
461s ***** error<hnrnd: MU and SIGMA must be scalars or of size SZ.> ...
461s  hnrnd (2, ones (2), 3, 2)
461s 35 tests, 35 passed, 0 known failure, 0 skipped
461s [inst/dist_fun/hygecdf.m]
461s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/hygecdf.m
461s ***** demo
461s  ## Plot various CDFs from the hypergeometric distribution
461s  x = 0:60;
461s  p1 = hygecdf (x, 500, 50, 100);
461s  p2 = hygecdf (x, 500, 60, 200);
461s  p3 = hygecdf (x, 500, 70, 300);
461s  plot (x, p1, "*b", x, p2, "*g", x, p3, "*r")
461s  grid on
461s  xlim ([0, 60])
461s  legend ({"m = 500, k = 50, n = 100", "m = 500, k = 60, n = 200", ...
461s           "m = 500, k = 70, n = 300"}, "location", "southeast")
461s  title ("Hypergeometric CDF")
461s  xlabel ("values in x (number of successes)")
461s  ylabel ("probability")
461s ***** shared x, y
461s  x = [-1 0 1 2 3];
461s  y = [0 1/6 5/6 1 1];
461s ***** assert (hygecdf (x, 4*ones (1,5), 2, 2), y, 5*eps)
461s ***** assert (hygecdf (x, 4, 2*ones (1,5), 2), y, 5*eps)
461s ***** assert (hygecdf (x, 4, 2, 2*ones (1,5)), y, 5*eps)
461s ***** assert (hygecdf (x, 4*[1 -1 NaN 1.1 1], 2, 2), [y(1) NaN NaN NaN y(5)], 5*eps)
461s ***** assert (hygecdf (x, 4*[1 -1 NaN 1.1 1], 2, 2, "upper"), ...
461s  [y(5) NaN NaN NaN y(1)], 5*eps)
461s ***** assert (hygecdf (x, 4, 2*[1 -1 NaN 1.1 1], 2), [y(1) NaN NaN NaN y(5)], 5*eps)
461s ***** assert (hygecdf (x, 4, 2*[1 -1 NaN 1.1 1], 2, "upper"), ...
461s  [y(5) NaN NaN NaN y(1)], 5*eps)
461s ***** assert (hygecdf (x, 4, 5, 2), [NaN NaN NaN NaN NaN])
461s ***** assert (hygecdf (x, 4, 2, 2*[1 -1 NaN 1.1 1]), [y(1) NaN NaN NaN y(5)], 5*eps)
461s ***** assert (hygecdf (x, 4, 2, 2*[1 -1 NaN 1.1 1], "upper"), ...
461s  [y(5) NaN NaN NaN y(1)], 5*eps)
461s ***** assert (hygecdf (x, 4, 2, 5), [NaN NaN NaN NaN NaN])
461s ***** assert (hygecdf ([x(1:2) NaN x(4:5)], 4, 2, 2), [y(1:2) NaN y(4:5)], 5*eps)
461s ***** test
461s  p = hygecdf (x, 10, [1 2 3 4 5], 2, "upper");
461s  assert (p, [1, 34/90, 2/30, 0, 0], 10*eps);
461s ***** test
461s  p = hygecdf (2*x, 10, [1 2 3 4 5], 2, "upper");
461s  assert (p, [1, 34/90, 0, 0, 0], 10*eps);
461s ***** assert (hygecdf ([x, NaN], 4, 2, 2), [y, NaN], 5*eps)
461s ***** assert (hygecdf (single ([x, NaN]), 4, 2, 2), single ([y, NaN]), ...
461s  eps ("single"))
461s ***** assert (hygecdf ([x, NaN], single (4), 2, 2), single ([y, NaN]), ...
461s  eps ("single"))
461s ***** assert (hygecdf ([x, NaN], 4, single (2), 2), single ([y, NaN]), ...
461s  eps ("single"))
461s ***** assert (hygecdf ([x, NaN], 4, 2, single (2)), single ([y, NaN]), ...
461s  eps ("single"))
461s ***** error<hygecdf: function called with too few input arguments.> hygecdf ()
461s ***** error<hygecdf: function called with too few input arguments.> hygecdf (1)
461s ***** error<hygecdf: function called with too few input arguments.> hygecdf (1,2)
461s ***** error<hygecdf: function called with too few input arguments.> hygecdf (1,2,3)
461s ***** error<hygecdf: invalid argument for upper tail.> hygecdf (1,2,3,4,5)
461s ***** error<hygecdf: invalid argument for upper tail.> hygecdf (1,2,3,4,"uper")
461s ***** error<hygecdf: X, T, k, and N must be of common size or scalars.> ...
461s  hygecdf (ones (2), ones (3), 1, 1)
461s ***** error<hygecdf: X, T, k, and N must be of common size or scalars.> ...
461s  hygecdf (1, ones (2), ones (3), 1)
461s ***** error<hygecdf: X, T, k, and N must be of common size or scalars.> ...
461s  hygecdf (1, 1, ones (2), ones (3))
461s ***** error<hygecdf: X, T, k, and N must not be complex.> hygecdf (i, 2, 2, 2)
461s ***** error<hygecdf: X, T, k, and N must not be complex.> hygecdf (2, i, 2, 2)
461s ***** error<hygecdf: X, T, k, and N must not be complex.> hygecdf (2, 2, i, 2)
461s ***** error<hygecdf: X, T, k, and N must not be complex.> hygecdf (2, 2, 2, i)
461s 32 tests, 32 passed, 0 known failure, 0 skipped
461s [inst/dist_fun/hygeinv.m]
461s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/hygeinv.m
461s ***** demo
461s  ## Plot various iCDFs from the hypergeometric distribution
461s  p = 0.001:0.001:0.999;
461s  x1 = hygeinv (p, 500, 50, 100);
461s  x2 = hygeinv (p, 500, 60, 200);
461s  x3 = hygeinv (p, 500, 70, 300);
461s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
461s  grid on
461s  ylim ([0, 60])
461s  legend ({"m = 500, k = 50, n = 100", "m = 500, k = 60, n = 200", ...
461s           "m = 500, k = 70, n = 300"}, "location", "northwest")
461s  title ("Hypergeometric iCDF")
461s  xlabel ("probability")
461s  ylabel ("values in p (number of successes)")
461s ***** shared p
461s  p = [-1 0 0.5 1 2];
461s ***** assert (hygeinv (p, 4*ones (1,5), 2*ones (1,5), 2*ones (1,5)), [NaN 0 1 2 NaN])
461s ***** assert (hygeinv (p, 4*ones (1,5), 2, 2), [NaN 0 1 2 NaN])
461s ***** assert (hygeinv (p, 4, 2*ones (1,5), 2), [NaN 0 1 2 NaN])
461s ***** assert (hygeinv (p, 4, 2, 2*ones (1,5)), [NaN 0 1 2 NaN])
461s ***** assert (hygeinv (p, 4*[1 -1 NaN 1.1 1], 2, 2), [NaN NaN NaN NaN NaN])
461s ***** assert (hygeinv (p, 4, 2*[1 -1 NaN 1.1 1], 2), [NaN NaN NaN NaN NaN])
461s ***** assert (hygeinv (p, 4, 5, 2), [NaN NaN NaN NaN NaN])
461s ***** assert (hygeinv (p, 4, 2, 2*[1 -1 NaN 1.1 1]), [NaN NaN NaN NaN NaN])
461s ***** assert (hygeinv (p, 4, 2, 5), [NaN NaN NaN NaN NaN])
461s ***** assert (hygeinv ([p(1:2) NaN p(4:5)], 4, 2, 2), [NaN 0 NaN 2 NaN])
461s ***** assert (hygeinv ([p, NaN], 4, 2, 2), [NaN 0 1 2 NaN NaN])
461s ***** assert (hygeinv (single ([p, NaN]), 4, 2, 2), single ([NaN 0 1 2 NaN NaN]))
461s ***** assert (hygeinv ([p, NaN], single (4), 2, 2), single ([NaN 0 1 2 NaN NaN]))
461s ***** assert (hygeinv ([p, NaN], 4, single (2), 2), single ([NaN 0 1 2 NaN NaN]))
461s ***** assert (hygeinv ([p, NaN], 4, 2, single (2)), single ([NaN 0 1 2 NaN NaN]))
461s ***** error<hygeinv: function called with too few input arguments.> hygeinv ()
461s ***** error<hygeinv: function called with too few input arguments.> hygeinv (1)
461s ***** error<hygeinv: function called with too few input arguments.> hygeinv (1,2)
461s ***** error<hygeinv: function called with too few input arguments.> hygeinv (1,2,3)
461s ***** error<hygeinv: P, T, M, and N must be of common size or scalars.> ...
461s  hygeinv (ones (2), ones (3), 1, 1)
461s ***** error<hygeinv: P, T, M, and N must be of common size or scalars.> ...
461s  hygeinv (1, ones (2), ones (3), 1)
461s ***** error<hygeinv: P, T, M, and N must be of common size or scalars.> ...
461s  hygeinv (1, 1, ones (2), ones (3))
461s ***** error<hygeinv: P, T, M, and N must not be complex.> hygeinv (i, 2, 2, 2)
461s ***** error<hygeinv: P, T, M, and N must not be complex.> hygeinv (2, i, 2, 2)
461s ***** error<hygeinv: P, T, M, and N must not be complex.> hygeinv (2, 2, i, 2)
461s ***** error<hygeinv: P, T, M, and N must not be complex.> hygeinv (2, 2, 2, i)
461s 26 tests, 26 passed, 0 known failure, 0 skipped
461s [inst/dist_fun/hygepdf.m]
461s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/hygepdf.m
461s ***** demo
461s  ## Plot various PDFs from the hypergeometric distribution
461s  x = 0:60;
461s  y1 = hygepdf (x, 500, 50, 100);
461s  y2 = hygepdf (x, 500, 60, 200);
461s  y3 = hygepdf (x, 500, 70, 300);
461s  plot (x, y1, "*b", x, y2, "*g", x, y3, "*r")
461s  grid on
461s  xlim ([0, 60])
461s  ylim ([0, 0.18])
461s  legend ({"m = 500, k = 50, μ = 100", "m = 500, k = 60, μ = 200", ...
461s           "m = 500, k = 70, μ = 300"}, "location", "northeast")
461s  title ("Hypergeometric PDF")
461s  xlabel ("values in x (number of successes)")
461s  ylabel ("density")
461s ***** shared x, y
461s  x = [-1 0 1 2 3];
461s  y = [0 1/6 4/6 1/6 0];
461s ***** assert (hygepdf (x, 4 * ones (1, 5), 2, 2), y, 3 * eps)
461s ***** assert (hygepdf (x, 4, 2 * ones (1, 5), 2), y, 3 * eps)
461s ***** assert (hygepdf (x, 4, 2, 2 * ones (1, 5)), y, 3 * eps)
461s ***** assert (hygepdf (x, 4 * [1, -1, NaN, 1.1, 1], 2, 2), [0, NaN, NaN, NaN, 0])
461s ***** assert (hygepdf (x, 4, 2 * [1, -1, NaN, 1.1, 1], 2), [0, NaN, NaN, NaN, 0])
461s ***** assert (hygepdf (x, 4, 5, 2), [NaN, NaN, NaN, NaN, NaN], 3 * eps)
461s ***** assert (hygepdf (x, 4, 2, 2 * [1, -1, NaN, 1.1, 1]), [0, NaN, NaN, NaN, 0])
461s ***** assert (hygepdf (x, 4, 2, 5), [NaN, NaN, NaN, NaN, NaN], 3 * eps)
461s ***** assert (hygepdf ([x, NaN], 4, 2, 2), [y, NaN], 3 * eps)
461s ***** assert (hygepdf (single ([x, NaN]), 4, 2, 2), single ([y, NaN]), eps ("single"))
461s ***** assert (hygepdf ([x, NaN], single (4), 2, 2), single ([y, NaN]), eps ("single"))
461s ***** assert (hygepdf ([x, NaN], 4, single (2), 2), single ([y, NaN]), eps ("single"))
461s ***** assert (hygepdf ([x, NaN], 4, 2, single (2)), single ([y, NaN]), eps ("single"))
461s ***** test
461s  z = zeros(3,5);
461s  z([4,5,6,8,9,12]) = [1, 0.5, 1/6, 0.5, 2/3, 1/6];
461s  assert (hygepdf (x, 4, [0, 1, 2], 2, "vectorexpand"), z, 3 * eps);
461s  assert (hygepdf (x, 4, [0, 1, 2]', 2, "vectorexpand"), z, 3 * eps);
461s  assert (hygepdf (x', 4, [0, 1, 2], 2, "vectorexpand"), z, 3 * eps);
461s  assert (hygepdf (2, 4, [0 ,1, 2], 2, "vectorexpand"), z(:,4), 3 * eps);
461s  assert (hygepdf (x, 4, 1, 2, "vectorexpand"), z(2,:), 3 *eps);
461s  assert (hygepdf ([NaN, x], 4, [0 1 2]', 2, "vectorexpand"), [NaN(3, 1), z], 3 * eps);
461s ***** error<hygepdf: function called with too few input arguments.> hygepdf ()
461s ***** error<hygepdf: function called with too few input arguments.> hygepdf (1)
461s ***** error<hygepdf: function called with too few input arguments.> hygepdf (1,2)
461s ***** error<hygepdf: function called with too few input arguments.> hygepdf (1,2,3)
461s ***** error<hygepdf: X, T, M, and N must be of common size or scalars.> ...
461s  hygepdf (1, ones (3), ones (2), ones (2))
461s ***** error<hygepdf: X, T, M, and N must be of common size or scalars.> ...
461s  hygepdf (1, ones (2), ones (3), ones (2))
461s ***** error<hygepdf: X, T, M, and N must be of common size or scalars.> ...
461s  hygepdf (1, ones (2), ones (2), ones (3))
461s ***** error<hygepdf: X, T, M, and N must not be complex.> hygepdf (i, 2, 2, 2)
461s ***** error<hygepdf: X, T, M, and N must not be complex.> hygepdf (2, i, 2, 2)
461s ***** error<hygepdf: X, T, M, and N must not be complex.> hygepdf (2, 2, i, 2)
461s ***** error<hygepdf: X, T, M, and N must not be complex.> hygepdf (2, 2, 2, i)
461s 25 tests, 25 passed, 0 known failure, 0 skipped
461s [inst/dist_fun/hygernd.m]
461s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/hygernd.m
461s ***** assert (size (hygernd (4,2,2)), [1, 1])
461s ***** assert (size (hygernd (4*ones (2,1), 2,2)), [2, 1])
461s ***** assert (size (hygernd (4*ones (2,2), 2,2)), [2, 2])
461s ***** assert (size (hygernd (4, 2*ones (2,1), 2)), [2, 1])
461s ***** assert (size (hygernd (4, 2*ones (2,2), 2)), [2, 2])
461s ***** assert (size (hygernd (4, 2, 2*ones (2,1))), [2, 1])
461s ***** assert (size (hygernd (4, 2, 2*ones (2,2))), [2, 2])
461s ***** assert (size (hygernd (4, 2, 2, 3)), [3, 3])
461s ***** assert (size (hygernd (4, 2, 2, [4 1])), [4, 1])
461s ***** assert (size (hygernd (4, 2, 2, 4, 1)), [4, 1])
461s ***** assert (class (hygernd (4,2,2)), "double")
461s ***** assert (class (hygernd (single (4),2,2)), "single")
461s ***** assert (class (hygernd (single ([4 4]),2,2)), "single")
461s ***** assert (class (hygernd (4,single (2),2)), "single")
461s ***** assert (class (hygernd (4,single ([2 2]),2)), "single")
461s ***** assert (class (hygernd (4,2,single (2))), "single")
461s ***** assert (class (hygernd (4,2,single ([2 2]))), "single")
461s ***** error<hygernd: function called with too few input arguments.> hygernd ()
461s ***** error<hygernd: function called with too few input arguments.> hygernd (1)
461s ***** error<hygernd: function called with too few input arguments.> hygernd (1, 2)
461s ***** error<hygernd: T, M, and N must be of common size or scalars.> ...
461s  hygernd (ones (3), ones (2), ones (2))
461s ***** error<hygernd: T, M, and N must be of common size or scalars.> ...
461s  hygernd (ones (2), ones (3), ones (2))
461s ***** error<hygernd: T, M, and N must be of common size or scalars.> ...
461s  hygernd (ones (2), ones (2), ones (3))
461s ***** error<hygernd: T, M, and N must not be complex.> hygernd (i, 2, 3)
461s ***** error<hygernd: T, M, and N must not be complex.> hygernd (1, i, 3)
461s ***** error<hygernd: T, M, and N must not be complex.> hygernd (1, 2, i)
461s ***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
461s  hygernd (1, 2, 3, -1)
461s ***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
461s  hygernd (1, 2, 3, 1.2)
461s ***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
461s  hygernd (1, 2, 3, ones (2))
461s ***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
461s  hygernd (1, 2, 3, [2 -1 2])
461s ***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
461s  hygernd (1, 2, 3, [2 0 2.5])
461s ***** error<hygernd: dimensions must be non-negative integers.> ...
461s  hygernd (1, 2, 3, 2, -1, 5)
461s ***** error<hygernd: dimensions must be non-negative integers.> ...
461s  hygernd (1, 2, 3, 2, 1.5, 5)
461s ***** error<hygernd: T, M, and N must be scalars or of size SZ.> ...
461s  hygernd (2, ones (2), 2, 3)
461s ***** error<hygernd: T, M, and N must be scalars or of size SZ.> ...
461s  hygernd (2, ones (2), 2, [3, 2])
461s ***** error<hygernd: T, M, and N must be scalars or of size SZ.> ...
461s  hygernd (2, ones (2), 2, 3, 2)
461s 36 tests, 36 passed, 0 known failure, 0 skipped
461s [inst/dist_fun/invgcdf.m]
461s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/invgcdf.m
461s ***** demo
461s  ## Plot various CDFs from the inverse Gaussian distribution
461s  x = 0:0.001:3;
461s  p1 = invgcdf (x, 1, 0.2);
461s  p2 = invgcdf (x, 1, 1);
461s  p3 = invgcdf (x, 1, 3);
461s  p4 = invgcdf (x, 3, 0.2);
461s  p5 = invgcdf (x, 3, 1);
461s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-y")
461s  grid on
461s  xlim ([0, 3])
461s  legend ({"μ = 1, σ = 0.2", "μ = 1, σ = 1", "μ = 1, σ = 3", ...
461s           "μ = 3, σ = 0.2", "μ = 3, σ = 1"}, "location", "southeast")
461s  title ("Inverse Gaussian CDF")
461s  xlabel ("values in x")
461s  ylabel ("probability")
461s ***** shared x, p1, p1u, y2, y2u, y3, y3u
461s  x = [-Inf, -1, 0, 1/2, 1, Inf];
461s  p1 = [0, 0, 0, 0.3650, 0.6681, 1];
461s  p1u = [1, 1, 1, 0.6350, 0.3319, 0];
461s ***** assert (invgcdf (x, ones (1,6), ones (1,6)), p1, 1e-4)
461s ***** assert (invgcdf (x, 1, 1), p1, 1e-4)
461s ***** assert (invgcdf (x, 1, ones (1,6)), p1, 1e-4)
461s ***** assert (invgcdf (x, ones (1,6), 1), p1, 1e-4)
461s ***** assert (invgcdf (x, 1, [1, 1, 1, NaN, 1, 1]), [p1(1:3), NaN, p1(5:6)], 1e-4)
461s ***** assert (invgcdf (x, [1, 1, 1, NaN, 1, 1], 1), [p1(1:3), NaN, p1(5:6)], 1e-4)
461s ***** assert (invgcdf ([x(1:3), NaN, x(5:6)], 1, 1), [p1(1:3), NaN, p1(5:6)], 1e-4)
461s ***** assert (invgcdf (x, ones (1,6), ones (1,6), "upper"), p1u, 1e-4)
461s ***** assert (invgcdf (x, 1, 1, "upper"), p1u, 1e-4)
461s ***** assert (invgcdf (x, 1, ones (1,6), "upper"), p1u, 1e-4)
461s ***** assert (invgcdf (x, ones (1,6), 1, "upper"), p1u, 1e-4)
461s ***** assert (class (invgcdf (single ([x, NaN]), 1, 1)), "single")
461s ***** assert (class (invgcdf ([x, NaN], 1, single (1))), "single")
461s ***** assert (class (invgcdf ([x, NaN], single (1), 1)), "single")
461s ***** error<invgcdf: function called with too few input arguments.> invgcdf ()
461s ***** error<invgcdf: function called with too few input arguments.> invgcdf (1)
461s ***** error<invgcdf: function called with too few input arguments.> invgcdf (1, 2)
461s ***** error<invgcdf: invalid argument for upper tail.> invgcdf (1, 2, 3, "tail")
461s ***** error<invgcdf: invalid argument for upper tail.> invgcdf (1, 2, 3, 5)
461s ***** error<invgcdf: X, MU, and LAMBDA must be of common size or scalars.> ...
461s  invgcdf (ones (3), ones (2), ones(2))
461s ***** error<invgcdf: X, MU, and LAMBDA must be of common size or scalars.> ...
461s  invgcdf (ones (2), ones (3), ones(2))
461s ***** error<invgcdf: X, MU, and LAMBDA must be of common size or scalars.> ...
461s  invgcdf (ones (2), ones (2), ones(3))
461s ***** error<invgcdf: X, MU, and LAMBDA must not be complex.> invgcdf (i, 2, 3)
461s ***** error<invgcdf: X, MU, and LAMBDA must not be complex.> invgcdf (1, i, 3)
461s ***** error<invgcdf: X, MU, and LAMBDA must not be complex.> invgcdf (1, 2, i)
461s 25 tests, 25 passed, 0 known failure, 0 skipped
461s [inst/dist_fun/invginv.m]
461s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/invginv.m
461s ***** demo
461s  ## Plot various iCDFs from the inverse Gaussian distribution
461s  p = 0.001:0.001:0.999;
461s  x1 = invginv (p, 1, 0.2);
461s  x2 = invginv (p, 1, 1);
461s  x3 = invginv (p, 1, 3);
461s  x4 = invginv (p, 3, 0.2);
461s  x5 = invginv (p, 3, 1);
461s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-y")
461s  grid on
461s  ylim ([0, 3])
461s  legend ({"μ = 1, σ = 0.2", "μ = 1, σ = 1", "μ = 1, σ = 3", ...
461s           "μ = 3, σ = 0.2", "μ = 3, σ = 1"}, "location", "northwest")
461s  title ("Inverse Gaussian iCDF")
461s  xlabel ("probability")
461s  ylabel ("x")
461s ***** shared p, x
461s  p = [0, 0.3829, 0.6827, 1];
461s  x = [0, 0.5207, 1.0376, Inf];
461s ***** assert (invginv (p, 1, 1), x, 1e-4);
461s ***** assert (invginv (p, 1, ones (1,4)), x, 1e-4);
461s ***** assert (invginv (p, 1, [-1, 0, 1, 1]), [NaN, NaN, x(3:4)], 1e-4)
461s ***** assert (invginv (p, [-1, 0, 1, 1], 1), [NaN, NaN, x(3:4)], 1e-4)
461s ***** assert (class (invginv (single ([p, NaN]), 0, 1)), "single")
461s ***** assert (class (invginv ([p, NaN], single (0), 1)), "single")
461s ***** assert (class (invginv ([p, NaN], 0, single (1))), "single")
461s ***** error<invginv: function called with too few input arguments.> invginv (1)
461s ***** error<invginv: function called with too few input arguments.> invginv (1, 2)
461s ***** error<invginv: P, MU, and LAMBDA must be of common size or scalars.> ...
461s  invginv (1, ones (2), ones (3))
461s ***** error<invginv: P, MU, and LAMBDA must be of common size or scalars.> ...
461s  invginv (ones (2), 1, ones (3))
461s ***** error<invginv: P, MU, and LAMBDA must be of common size or scalars.> ...
461s  invginv (ones (2), ones (3), 1)
461s ***** error<invginv: P, MU, and LAMBDA must not be complex.> invginv (i, 2, 3)
461s ***** error<invginv: P, MU, and LAMBDA must not be complex.> invginv (1, i, 3)
461s ***** error<invginv: P, MU, and LAMBDA must not be complex.> invginv (1, 2, i)
461s 15 tests, 15 passed, 0 known failure, 0 skipped
461s [inst/dist_fun/invgpdf.m]
461s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/invgpdf.m
461s ***** demo
461s  ## Plot various PDFs from the inverse Gaussian distribution
461s  x = 0:0.001:3;
461s  y1 = invgpdf (x, 1, 0.2);
461s  y2 = invgpdf (x, 1, 1);
461s  y3 = invgpdf (x, 1, 3);
461s  y4 = invgpdf (x, 3, 0.2);
461s  y5 = invgpdf (x, 3, 1);
461s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-y")
461s  grid on
461s  xlim ([0, 3])
461s  ylim ([0, 3])
461s  legend ({"μ = 1, σ = 0.2", "μ = 1, σ = 1", "μ = 1, σ = 3", ...
461s           "μ = 3, σ = 0.2", "μ = 3, σ = 1"}, "location", "northeast")
461s  title ("Inverse Gaussian PDF")
461s  xlabel ("values in x")
461s  ylabel ("density")
461s ***** shared x, y
461s  x = [-Inf, -1, 0, 1/2, 1, Inf];
461s  y = [0, 0, 0, 0.8788, 0.3989, 0];
461s ***** assert (invgpdf ([x, NaN], 1, 1), [y, NaN], 1e-4)
461s ***** assert (invgpdf (x, 1, [-2, -1, 0, 1, 1, 1]), [nan(1,3), y([4:6])], 1e-4)
461s ***** assert (class (hncdf (single ([x, NaN]), 1, 1)), "single")
461s ***** assert (class (hncdf ([x, NaN], 1, single (1))), "single")
461s ***** assert (class (hncdf ([x, NaN], single (1), 1)), "single")
461s ***** error<invgpdf: function called with too few input arguments.> invgpdf ()
461s ***** error<invgpdf: function called with too few input arguments.> invgpdf (1)
461s ***** error<invgpdf: function called with too few input arguments.> invgpdf (1, 2)
461s ***** error<invgpdf: X, MU, and LAMBDA must be of common size or scalars.> ...
461s  invgpdf (1, ones (2), ones (3))
461s ***** error<invgpdf: X, MU, and LAMBDA must be of common size or scalars.> ...
461s  invgpdf (ones (2), 1, ones (3))
461s ***** error<invgpdf: X, MU, and LAMBDA must be of common size or scalars.> ...
461s  invgpdf (ones (2), ones (3), 1)
461s ***** error<invgpdf: X, MU, and LAMBDA must not be complex.> invgpdf (i, 2, 3)
461s ***** error<invgpdf: X, MU, and LAMBDA must not be complex.> invgpdf (1, i, 3)
461s ***** error<invgpdf: X, MU, and LAMBDA must not be complex.> invgpdf (1, 2, i)
461s 14 tests, 14 passed, 0 known failure, 0 skipped
461s [inst/dist_fun/invgrnd.m]
461s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/invgrnd.m
461s ***** assert (size (invgrnd (1, 1, 1)), [1, 1])
461s ***** assert (size (invgrnd (1, 1, 2)), [2, 2])
461s ***** assert (size (invgrnd (1, 1, [2, 1])), [2, 1])
461s ***** assert (size (invgrnd (1, zeros (2, 2))), [2, 2])
461s ***** assert (size (invgrnd (1, ones (2, 1))), [2, 1])
461s ***** assert (size (invgrnd (1, ones (2, 2))), [2, 2])
461s ***** assert (size (invgrnd (ones (2, 1), 1)), [2, 1])
461s ***** assert (size (invgrnd (ones (2, 2), 1)), [2, 2])
461s ***** assert (size (invgrnd (1, 1, 3)), [3, 3])
461s ***** assert (size (invgrnd (1, 1, [4 1])), [4, 1])
461s ***** assert (size (invgrnd (1, 1, 4, 1)), [4, 1])
461s ***** test
461s  r =  invgrnd (1, [1, 0, -1]);
461s  assert (r([2:3]), [NaN, NaN])
461s ***** assert (class (invgrnd (1, 0)), "double")
461s ***** assert (class (invgrnd (1, single (0))), "single")
461s ***** assert (class (invgrnd (1, single ([0 0]))), "single")
461s ***** assert (class (invgrnd (1, single (1))), "single")
461s ***** assert (class (invgrnd (1, single ([1 1]))), "single")
461s ***** assert (class (invgrnd (single (1), 1)), "single")
461s ***** assert (class (invgrnd (single ([1 1]), 1)), "single")
461s ***** error<invgrnd: function called with too few input arguments.> invgrnd ()
461s ***** error<invgrnd: function called with too few input arguments.> invgrnd (1)
462s ***** error<invgrnd: MU and LAMBDA must be of common size or scalars.> ...
462s  invgrnd (ones (3), ones (2))
462s ***** error<invgrnd: MU and LAMBDA must be of common size or scalars.> ...
462s  invgrnd (ones (2), ones (3))
462s ***** error<invgrnd: MU and LAMBDA must not be complex.> invgrnd (i, 2, 3)
462s ***** error<invgrnd: MU and LAMBDA must not be complex.> invgrnd (1, i, 3)
462s ***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
462s  invgrnd (1, 2, -1)
462s ***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
462s  invgrnd (1, 2, 1.2)
462s ***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
462s  invgrnd (1, 2, ones (2))
462s ***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
462s  invgrnd (1, 2, [2 -1 2])
462s ***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
462s  invgrnd (1, 2, [2 0 2.5])
462s ***** error<invgrnd: dimensions must be non-negative integers.> ...
462s  invgrnd (1, 2, 2, -1, 5)
462s ***** error<invgrnd: dimensions must be non-negative integers.> ...
462s  invgrnd (1, 2, 2, 1.5, 5)
462s ***** error<invgrnd: MU and LAMBDA must be scalars or of size SZ.> ...
462s  invgrnd (2, ones (2), 3)
462s ***** error<invgrnd: MU and LAMBDA must be scalars or of size SZ.> ...
462s  invgrnd (2, ones (2), [3, 2])
462s ***** error<invgrnd: MU and LAMBDA must be scalars or of size SZ.> ...
462s  invgrnd (2, ones (2), 3, 2)
462s 35 tests, 35 passed, 0 known failure, 0 skipped
462s [inst/dist_fun/iwishpdf.m]
462s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/iwishpdf.m
462s ***** assert(iwishpdf(4, 3, 3.1), 0.04226595, 1E-7);
462s ***** assert(iwishpdf([2 -0.3;-0.3 4], [1 0.3;0.3 1], 4), 1.60166e-05, 1E-10);
462s ***** assert(iwishpdf([6 2 5; 2 10 -5; 5 -5 25], ...
462s  [9 5 5; 5 10 -8; 5 -8 22], 5.1), 4.946831e-12, 1E-17);
462s ***** error iwishpdf ()
462s ***** error iwishpdf (1, 2)
462s ***** error iwishpdf (1, 2, 0)
462s 6 tests, 6 passed, 0 known failure, 0 skipped
462s [inst/dist_fun/iwishrnd.m]
462s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/iwishrnd.m
462s ***** assert(size (iwishrnd (1,2,1)), [1, 1]);
462s ***** assert(size (iwishrnd ([],2,1)), [1, 1]);
462s ***** assert(size (iwishrnd ([3 1; 1 3], 2.00001, [], 1)), [2, 2]);
462s ***** assert(size (iwishrnd (eye(2), 2, [], 3)), [2, 2, 3]);
462s ***** error iwishrnd ()
462s ***** error iwishrnd (1)
462s ***** error iwishrnd ([-3 1; 1 3],1)
462s ***** error iwishrnd ([1; 1],1)
462s 8 tests, 8 passed, 0 known failure, 0 skipped
462s [inst/dist_fun/jsucdf.m]
462s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/jsucdf.m
462s ***** error jsucdf ()
462s ***** error jsucdf (1, 2, 3, 4)
462s ***** error<jsucdf: X, ALPHA1, and ALPHA2 must be of common size or scalars.> ...
462s  jsucdf (1, ones (2), ones (3))
462s 3 tests, 3 passed, 0 known failure, 0 skipped
462s [inst/dist_fun/jsupdf.m]
462s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/jsupdf.m
462s ***** error jsupdf ()
462s ***** error jsupdf (1, 2, 3, 4)
462s ***** error<jsupdf: X, ALPHA1, and ALPHA2 must be of common size or scalars.> ...
462s  jsupdf (1, ones (2), ones (3))
462s 3 tests, 3 passed, 0 known failure, 0 skipped
462s [inst/dist_fun/laplacecdf.m]
462s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/laplacecdf.m
462s ***** demo
462s  ## Plot various CDFs from the Laplace distribution
462s  x = -10:0.01:10;
462s  p1 = laplacecdf (x, 0, 1);
462s  p2 = laplacecdf (x, 0, 2);
462s  p3 = laplacecdf (x, 0, 4);
462s  p4 = laplacecdf (x, -5, 4);
462s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
462s  grid on
462s  xlim ([-10, 10])
462s  legend ({"μ = 0, β = 1", "μ = 0, β = 2", ...
462s           "μ = 0, β = 4", "μ = -5, β = 4"}, "location", "southeast")
462s  title ("Laplace CDF")
462s  xlabel ("values in x")
462s  ylabel ("probability")
462s ***** shared x, y
462s  x = [-Inf, -log(2), 0, log(2), Inf];
462s  y = [0, 1/4, 1/2, 3/4, 1];
462s ***** assert (laplacecdf ([x, NaN], 0, 1), [y, NaN])
462s ***** assert (laplacecdf (x, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), 0.75, 1])
462s ***** assert (laplacecdf (single ([x, NaN]), 0, 1), single ([y, NaN]), eps ("single"))
462s ***** assert (laplacecdf ([x, NaN], single (0), 1), single ([y, NaN]), eps ("single"))
462s ***** assert (laplacecdf ([x, NaN], 0, single (1)), single ([y, NaN]), eps ("single"))
462s ***** error<laplacecdf: function called with too few input arguments.> laplacecdf ()
462s ***** error<laplacecdf: function called with too few input arguments.> laplacecdf (1)
462s ***** error<laplacecdf: function called with too few input arguments.> ...
462s  laplacecdf (1, 2)
462s ***** error<laplacecdf: function called with too many inputs> ...
462s  laplacecdf (1, 2, 3, 4, 5)
462s ***** error<laplacecdf: invalid argument for upper tail.> laplacecdf (1, 2, 3, "tail")
462s ***** error<laplacecdf: invalid argument for upper tail.> laplacecdf (1, 2, 3, 4)
462s ***** error<laplacecdf: X, MU, and BETA must be of common size or scalars.> ...
462s  laplacecdf (ones (3), ones (2), ones (2))
462s ***** error<laplacecdf: X, MU, and BETA must be of common size or scalars.> ...
462s  laplacecdf (ones (2), ones (3), ones (2))
462s ***** error<laplacecdf: X, MU, and BETA must be of common size or scalars.> ...
462s  laplacecdf (ones (2), ones (2), ones (3))
462s ***** error<laplacecdf: X, MU, and BETA must not be complex.> laplacecdf (i, 2, 2)
462s ***** error<laplacecdf: X, MU, and BETA must not be complex.> laplacecdf (2, i, 2)
462s ***** error<laplacecdf: X, MU, and BETA must not be complex.> laplacecdf (2, 2, i)
462s 17 tests, 17 passed, 0 known failure, 0 skipped
462s [inst/dist_fun/laplaceinv.m]
462s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/laplaceinv.m
462s ***** demo
462s  ## Plot various iCDFs from the Laplace distribution
462s  p = 0.001:0.001:0.999;
462s  x1 = cauchyinv (p, 0, 1);
462s  x2 = cauchyinv (p, 0, 2);
462s  x3 = cauchyinv (p, 0, 4);
462s  x4 = cauchyinv (p, -5, 4);
462s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
462s  grid on
462s  ylim ([-10, 10])
462s  legend ({"μ = 0, β = 1", "μ = 0, β = 2", ...
462s           "μ = 0, β = 4", "μ = -5, β = 4"}, "location", "northwest")
462s  title ("Laplace iCDF")
462s  xlabel ("probability")
462s  ylabel ("values in x")
462s ***** shared p, x
462s  p = [-1 0 0.5 1 2];
462s  x = [NaN, -Inf, 0, Inf, NaN];
462s ***** assert (laplaceinv (p, 0, 1), x)
462s ***** assert (laplaceinv (p, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), Inf, NaN])
462s ***** assert (laplaceinv ([p, NaN], 0, 1), [x, NaN])
462s ***** assert (laplaceinv (single ([p, NaN]), 0, 1), single ([x, NaN]))
462s ***** assert (laplaceinv ([p, NaN], single (0), 1), single ([x, NaN]))
462s ***** assert (laplaceinv ([p, NaN], 0, single (1)), single ([x, NaN]))
462s ***** error<laplaceinv: function called with too few input arguments.> laplaceinv ()
462s ***** error<laplaceinv: function called with too few input arguments.> laplaceinv (1)
462s ***** error<laplaceinv: function called with too few input arguments.> ...
462s  laplaceinv (1, 2)
462s ***** error<laplaceinv: function called with too many inputs> laplaceinv (1, 2, 3, 4)
462s ***** error<laplaceinv: P, MU, and BETA must be of common size or scalars.> ...
462s  laplaceinv (1, ones (2), ones (3))
462s ***** error<laplaceinv: P, MU, and BETA must be of common size or scalars.> ...
462s  laplaceinv (ones (2), 1, ones (3))
462s ***** error<laplaceinv: P, MU, and BETA must be of common size or scalars.> ...
462s  laplaceinv (ones (2), ones (3), 1)
462s ***** error<laplaceinv: P, MU, and BETA must not be complex.> laplaceinv (i, 2, 3)
462s ***** error<laplaceinv: P, MU, and BETA must not be complex.> laplaceinv (1, i, 3)
462s ***** error<laplaceinv: P, MU, and BETA must not be complex.> laplaceinv (1, 2, i)
462s 16 tests, 16 passed, 0 known failure, 0 skipped
462s [inst/dist_fun/laplacepdf.m]
462s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/laplacepdf.m
462s ***** demo
462s  ## Plot various PDFs from the Laplace distribution
462s  x = -10:0.01:10;
462s  y1 = laplacepdf (x, 0, 1);
462s  y2 = laplacepdf (x, 0, 2);
462s  y3 = laplacepdf (x, 0, 4);
462s  y4 = laplacepdf (x, -5, 4);
462s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
462s  grid on
462s  xlim ([-10, 10])
462s  ylim ([0, 0.6])
462s  legend ({"μ = 0, β = 1", "μ = 0, β = 2", ...
462s           "μ = 0, β = 4", "μ = -5, β = 4"}, "location", "northeast")
462s  title ("Laplace PDF")
462s  xlabel ("values in x")
462s  ylabel ("density")
462s ***** shared x, y
462s  x = [-Inf -log(2) 0 log(2) Inf];
462s  y = [0, 1/4, 1/2, 1/4, 0];
462s ***** assert (laplacepdf ([x, NaN], 0, 1), [y, NaN])
462s ***** assert (laplacepdf (x, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), 0.25, 0])
462s ***** assert (laplacepdf (single ([x, NaN]), 0, 1), single ([y, NaN]))
462s ***** assert (laplacepdf ([x, NaN], single (0), 1), single ([y, NaN]))
462s ***** assert (laplacepdf ([x, NaN], 0, single (1)), single ([y, NaN]))
462s ***** error<laplacepdf: function called with too few input arguments.> laplacepdf ()
462s ***** error<laplacepdf: function called with too few input arguments.> laplacepdf (1)
462s ***** error<laplacepdf: function called with too few input arguments.> ...
462s  laplacepdf (1, 2)
462s ***** error<laplacepdf: function called with too many inputs> laplacepdf (1, 2, 3, 4)
462s ***** error<laplacepdf: X, MU, and BETA must be of common size or scalars.> ...
462s  laplacepdf (1, ones (2), ones (3))
462s ***** error<laplacepdf: X, MU, and BETA must be of common size or scalars.> ...
462s  laplacepdf (ones (2), 1, ones (3))
462s ***** error<laplacepdf: X, MU, and BETA must be of common size or scalars.> ...
462s  laplacepdf (ones (2), ones (3), 1)
462s ***** error<laplacepdf: X, MU, and BETA must not be complex.> laplacepdf (i, 2, 3)
462s ***** error<laplacepdf: X, MU, and BETA must not be complex.> laplacepdf (1, i, 3)
462s ***** error<laplacepdf: X, MU, and BETA must not be complex.> laplacepdf (1, 2, i)
462s 15 tests, 15 passed, 0 known failure, 0 skipped
462s [inst/dist_fun/laplacernd.m]
462s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/laplacernd.m
462s ***** assert (size (laplacernd (1, 1)), [1 1])
462s ***** assert (size (laplacernd (1, ones (2,1))), [2, 1])
462s ***** assert (size (laplacernd (1, ones (2,2))), [2, 2])
462s ***** assert (size (laplacernd (ones (2,1), 1)), [2, 1])
462s ***** assert (size (laplacernd (ones (2,2), 1)), [2, 2])
462s ***** assert (size (laplacernd (1, 1, 3)), [3, 3])
462s ***** assert (size (laplacernd (1, 1, [4, 1])), [4, 1])
462s ***** assert (size (laplacernd (1, 1, 4, 1)), [4, 1])
462s ***** assert (size (laplacernd (1, 1, 4, 1, 5)), [4, 1, 5])
462s ***** assert (size (laplacernd (1, 1, 0, 1)), [0, 1])
462s ***** assert (size (laplacernd (1, 1, 1, 0)), [1, 0])
462s ***** assert (size (laplacernd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
462s ***** assert (class (laplacernd (1, 1)), "double")
462s ***** assert (class (laplacernd (1, single (1))), "single")
462s ***** assert (class (laplacernd (1, single ([1, 1]))), "single")
462s ***** assert (class (laplacernd (single (1), 1)), "single")
462s ***** assert (class (laplacernd (single ([1, 1]), 1)), "single")
462s ***** error<laplacernd: function called with too few input arguments.> laplacernd ()
462s ***** error<laplacernd: function called with too few input arguments.> laplacernd (1)
462s ***** error<laplacernd: MU and BETA must be of common size or scalars.> ...
462s  laplacernd (ones (3), ones (2))
462s ***** error<laplacernd: MU and BETA must be of common size or scalars.> ...
462s  laplacernd (ones (2), ones (3))
462s ***** error<laplacernd: MU and BETA must not be complex.> laplacernd (i, 2, 3)
462s ***** error<laplacernd: MU and BETA must not be complex.> laplacernd (1, i, 3)
462s ***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
462s  laplacernd (1, 2, -1)
462s ***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
462s  laplacernd (1, 2, 1.2)
462s ***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
462s  laplacernd (1, 2, ones (2))
462s ***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
462s  laplacernd (1, 2, [2 -1 2])
462s ***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
462s  laplacernd (1, 2, [2 0 2.5])
462s ***** error<laplacernd: dimensions must be non-negative integers.> ...
462s  laplacernd (1, 2, 2, -1, 5)
462s ***** error<laplacernd: dimensions must be non-negative integers.> ...
462s  laplacernd (1, 2, 2, 1.5, 5)
462s ***** error<laplacernd: MU and BETA must be scalars or of size SZ.> ...
462s  laplacernd (2, ones (2), 3)
462s ***** error<laplacernd: MU and BETA must be scalars or of size SZ.> ...
462s  laplacernd (2, ones (2), [3, 2])
462s ***** error<laplacernd: MU and BETA must be scalars or of size SZ.> ...
462s  laplacernd (2, ones (2), 3, 2)
462s 33 tests, 33 passed, 0 known failure, 0 skipped
462s [inst/dist_fun/logicdf.m]
462s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/logicdf.m
462s ***** demo
462s  ## Plot various CDFs from the logistic distribution
462s  x = -5:0.01:20;
462s  p1 = logicdf (x, 5, 2);
462s  p2 = logicdf (x, 9, 3);
462s  p3 = logicdf (x, 9, 4);
462s  p4 = logicdf (x, 6, 2);
462s  p5 = logicdf (x, 2, 1);
462s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
462s  grid on
462s  legend ({"μ = 5, σ = 2", "μ = 9, σ = 3", "μ = 9, σ = 4", ...
462s           "μ = 6, σ = 2", "μ = 2, σ = 1"}, "location", "southeast")
462s  title ("Logistic CDF")
462s  xlabel ("values in x")
462s  ylabel ("probability")
462s ***** shared x, y
462s  x = [-Inf -log(3) 0 log(3) Inf];
462s  y = [0, 1/4, 1/2, 3/4, 1];
462s ***** assert (logicdf ([x, NaN], 0, 1), [y, NaN], eps)
462s ***** assert (logicdf (x, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), 0.75, 1], eps)
462s ***** assert (logicdf (single ([x, NaN]), 0, 1), single ([y, NaN]), eps ("single"))
462s ***** assert (logicdf ([x, NaN], single (0), 1), single ([y, NaN]), eps ("single"))
462s ***** assert (logicdf ([x, NaN], 0, single (1)), single ([y, NaN]), eps ("single"))
462s ***** error<logicdf: function called with too few input arguments.> logicdf ()
462s ***** error<logicdf: function called with too few input arguments.> logicdf (1)
462s ***** error<logicdf: function called with too few input arguments.> ...
462s  logicdf (1, 2)
462s ***** error<logicdf: invalid argument for upper tail.> logicdf (1, 2, 3, "tail")
462s ***** error<logicdf: invalid argument for upper tail.> logicdf (1, 2, 3, 4)
462s ***** error<logicdf: X, MU, and SIGMA must be of common size or scalars.> ...
462s  logicdf (1, ones (2), ones (3))
462s ***** error<logicdf: X, MU, and SIGMA must be of common size or scalars.> ...
462s  logicdf (ones (2), 1, ones (3))
462s ***** error<logicdf: X, MU, and SIGMA must be of common size or scalars.> ...
462s  logicdf (ones (2), ones (3), 1)
462s ***** error<logicdf: X, MU, and SIGMA must not be complex.> logicdf (i, 2, 3)
462s ***** error<logicdf: X, MU, and SIGMA must not be complex.> logicdf (1, i, 3)
462s ***** error<logicdf: X, MU, and SIGMA must not be complex.> logicdf (1, 2, i)
462s 16 tests, 16 passed, 0 known failure, 0 skipped
462s [inst/dist_fun/logiinv.m]
462s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/logiinv.m
462s ***** demo
462s  ## Plot various iCDFs from the logistic distribution
462s  p = 0.001:0.001:0.999;
462s  x1 = logiinv (p, 5, 2);
462s  x2 = logiinv (p, 9, 3);
462s  x3 = logiinv (p, 9, 4);
462s  x4 = logiinv (p, 6, 2);
462s  x5 = logiinv (p, 2, 1);
462s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
462s  grid on
462s  legend ({"μ = 5, σ = 2", "μ = 9, σ = 3", "μ = 9, σ = 4", ...
462s           "μ = 6, σ = 2", "μ = 2, σ = 1"}, "location", "southeast")
462s  title ("Logistic iCDF")
462s  xlabel ("probability")
462s  ylabel ("x")
462s ***** test
462s  p = [0.01:0.01:0.99];
462s  assert (logiinv (p, 0, 1), log (p ./ (1-p)), 25*eps);
462s ***** shared p
462s  p = [-1 0 0.5 1 2];
462s ***** assert (logiinv (p, 0, 1), [NaN -Inf 0 Inf NaN])
462s ***** assert (logiinv (p, 0, [-1, 0, 1, 2, 3]), [NaN NaN 0 Inf NaN])
462s ***** assert (logiinv ([p, NaN], 0, 1), [NaN -Inf 0 Inf NaN NaN])
462s ***** assert (logiinv (single ([p, NaN]), 0, 1), single ([NaN -Inf 0 Inf NaN NaN]))
462s ***** assert (logiinv ([p, NaN], single (0), 1), single ([NaN -Inf 0 Inf NaN NaN]))
462s ***** assert (logiinv ([p, NaN], 0, single (1)), single ([NaN -Inf 0 Inf NaN NaN]))
462s ***** error<logiinv: function called with too few input arguments.> logiinv ()
462s ***** error<logiinv: function called with too few input arguments.> logiinv (1)
462s ***** error<logiinv: function called with too few input arguments.> ...
462s  logiinv (1, 2)
462s ***** error<logiinv: P, MU, and SIGMA must be of common size or scalars.> ...
462s  logiinv (1, ones (2), ones (3))
462s ***** error<logiinv: P, MU, and SIGMA must be of common size or scalars.> ...
462s  logiinv (ones (2), 1, ones (3))
462s ***** error<logiinv: P, MU, and SIGMA must be of common size or scalars.> ...
462s  logiinv (ones (2), ones (3), 1)
462s ***** error<logiinv: P, MU, and SIGMA must not be complex.> logiinv (i, 2, 3)
462s ***** error<logiinv: P, MU, and SIGMA must not be complex.> logiinv (1, i, 3)
462s ***** error<logiinv: P, MU, and SIGMA must not be complex.> logiinv (1, 2, i)
462s 16 tests, 16 passed, 0 known failure, 0 skipped
462s [inst/dist_fun/logipdf.m]
462s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/logipdf.m
462s ***** demo
462s  ## Plot various PDFs from the logistic distribution
462s  x = -5:0.01:20;
462s  y1 = logipdf (x, 5, 2);
462s  y2 = logipdf (x, 9, 3);
462s  y3 = logipdf (x, 9, 4);
462s  y4 = logipdf (x, 6, 2);
462s  y5 = logipdf (x, 2, 1);
462s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
462s  grid on
462s  ylim ([0, 0.3])
462s  legend ({"μ = 5, σ = 2", "μ = 9, σ = 3", "μ = 9, σ = 4", ...
462s           "μ = 6, σ = 2", "μ = 2, σ = 1"}, "location", "northeast")
462s  title ("Logistic PDF")
462s  xlabel ("values in x")
462s  ylabel ("density")
462s ***** shared x, y
462s  x = [-Inf -log(4) 0 log(4) Inf];
462s  y = [0, 0.16, 1/4, 0.16, 0];
462s ***** assert (logipdf ([x, NaN], 0, 1), [y, NaN], eps)
462s ***** assert (logipdf (x, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), y([4:5])], eps)
462s ***** assert (logipdf (single ([x, NaN]), 0, 1), single ([y, NaN]), eps ("single"))
462s ***** assert (logipdf ([x, NaN], single (0), 1), single ([y, NaN]), eps ("single"))
462s ***** assert (logipdf ([x, NaN], 0, single (1)), single ([y, NaN]), eps ("single"))
462s ***** error<logipdf: function called with too few input arguments.> logipdf ()
462s ***** error<logipdf: function called with too few input arguments.> logipdf (1)
462s ***** error<logipdf: function called with too few input arguments.> ...
462s  logipdf (1, 2)
462s ***** error<logipdf: X, MU, and SIGMA must be of common size or scalars.> ...
462s  logipdf (1, ones (2), ones (3))
462s ***** error<logipdf: X, MU, and SIGMA must be of common size or scalars.> ...
462s  logipdf (ones (2), 1, ones (3))
462s ***** error<logipdf: X, MU, and SIGMA must be of common size or scalars.> ...
462s  logipdf (ones (2), ones (3), 1)
462s ***** error<logipdf: X, MU, and SIGMA must not be complex.> logipdf (i, 2, 3)
462s ***** error<logipdf: X, MU, and SIGMA must not be complex.> logipdf (1, i, 3)
462s ***** error<logipdf: X, MU, and SIGMA must not be complex.> logipdf (1, 2, i)
462s 14 tests, 14 passed, 0 known failure, 0 skipped
462s [inst/dist_fun/logirnd.m]
462s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/logirnd.m
462s ***** assert (size (logirnd (1, 1)), [1 1])
462s ***** assert (size (logirnd (1, ones (2,1))), [2, 1])
462s ***** assert (size (logirnd (1, ones (2,2))), [2, 2])
462s ***** assert (size (logirnd (ones (2,1), 1)), [2, 1])
462s ***** assert (size (logirnd (ones (2,2), 1)), [2, 2])
462s ***** assert (size (logirnd (1, 1, 3)), [3, 3])
462s ***** assert (size (logirnd (1, 1, [4, 1])), [4, 1])
462s ***** assert (size (logirnd (1, 1, 4, 1)), [4, 1])
462s ***** assert (size (logirnd (1, 1, 4, 1, 5)), [4, 1, 5])
462s ***** assert (size (logirnd (1, 1, 0, 1)), [0, 1])
462s ***** assert (size (logirnd (1, 1, 1, 0)), [1, 0])
462s ***** assert (size (logirnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
462s ***** assert (class (logirnd (1, 1)), "double")
462s ***** assert (class (logirnd (1, single (1))), "single")
462s ***** assert (class (logirnd (1, single ([1, 1]))), "single")
462s ***** assert (class (logirnd (single (1), 1)), "single")
462s ***** assert (class (logirnd (single ([1, 1]), 1)), "single")
462s ***** error<logirnd: function called with too few input arguments.> logirnd ()
462s ***** error<logirnd: function called with too few input arguments.> logirnd (1)
462s ***** error<logirnd: MU and SIGMA must be of common size or scalars.> ...
462s  logirnd (ones (3), ones (2))
462s ***** error<logirnd: MU and SIGMA must be of common size or scalars.> ...
462s  logirnd (ones (2), ones (3))
462s ***** error<logirnd: MU and SIGMA must not be complex.> logirnd (i, 2, 3)
462s ***** error<logirnd: MU and SIGMA must not be complex.> logirnd (1, i, 3)
462s ***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
462s  logirnd (1, 2, -1)
462s ***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
462s  logirnd (1, 2, 1.2)
462s ***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
462s  logirnd (1, 2, ones (2))
462s ***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
462s  logirnd (1, 2, [2 -1 2])
462s ***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
462s  logirnd (1, 2, [2 0 2.5])
462s ***** error<logirnd: dimensions must be non-negative integers.> ...
462s  logirnd (1, 2, 2, -1, 5)
462s ***** error<logirnd: dimensions must be non-negative integers.> ...
462s  logirnd (1, 2, 2, 1.5, 5)
462s ***** error<logirnd: MU and SIGMA must be scalars or of size SZ.> ...
462s  logirnd (2, ones (2), 3)
462s ***** error<logirnd: MU and SIGMA must be scalars or of size SZ.> ...
462s  logirnd (2, ones (2), [3, 2])
462s ***** error<logirnd: MU and SIGMA must be scalars or of size SZ.> ...
462s  logirnd (2, ones (2), 3, 2)
462s 33 tests, 33 passed, 0 known failure, 0 skipped
462s [inst/dist_fun/loglcdf.m]
462s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/loglcdf.m
462s ***** demo
462s  ## Plot various CDFs from the log-logistic distribution
462s  x = 0:0.001:2;
462s  p1 = loglcdf (x, log (1), 1/0.5);
462s  p2 = loglcdf (x, log (1), 1);
462s  p3 = loglcdf (x, log (1), 1/2);
462s  p4 = loglcdf (x, log (1), 1/4);
462s  p5 = loglcdf (x, log (1), 1/8);
462s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
462s  legend ({"σ = 2 (β = 0.5)", "σ = 1 (β = 1)", "σ = 0.5 (β = 2)", ...
462s           "σ = 0.25 (β = 4)", "σ = 0.125 (β = 8)"}, "location", "northwest")
462s  grid on
462s  title ("Log-logistic CDF")
462s  xlabel ("values in x")
462s  ylabel ("probability")
462s  text (0.05, 0.64, "μ = 0 (α = 1), values of σ (β) as shown in legend")
462s ***** shared out1, out2
462s  out1 = [0, 0.5, 0.66666667, 0.75, 0.8, 0.83333333];
462s  out2 = [0, 0.4174, 0.4745, 0.5082, 0.5321, 0.5506];
462s ***** assert (loglcdf ([0:5], 0, 1), out1, 1e-8)
462s ***** assert (loglcdf ([0:5], 0, 1, "upper"), 1 - out1, 1e-8)
462s ***** assert (loglcdf ([0:5], 0, 1), out1, 1e-8)
462s ***** assert (loglcdf ([0:5], 0, 1, "upper"), 1 - out1, 1e-8)
462s ***** assert (loglcdf ([0:5], 1, 3), out2, 1e-4)
462s ***** assert (loglcdf ([0:5], 1, 3, "upper"), 1 - out2, 1e-4)
462s ***** assert (class (loglcdf (single (1), 2, 3)), "single")
462s ***** assert (class (loglcdf (1, single (2), 3)), "single")
462s ***** assert (class (loglcdf (1, 2, single (3))), "single")
462s ***** error<loglcdf: function called with too few input arguments.> loglcdf (1)
462s ***** error<loglcdf: function called with too few input arguments.> loglcdf (1, 2)
462s ***** error<loglcdf: invalid argument for upper tail.> ...
462s  loglcdf (1, 2, 3, 4)
462s ***** error<loglcdf: invalid argument for upper tail.> ...
462s  loglcdf (1, 2, 3, "uper")
462s ***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
462s  loglcdf (1, ones (2), ones (3))
462s ***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
462s  loglcdf (1, ones (2), ones (3), "upper")
462s ***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
462s  loglcdf (ones (2), 1, ones (3))
462s ***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
462s  loglcdf (ones (2), 1, ones (3), "upper")
462s ***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
462s  loglcdf (ones (2), ones (3), 1)
462s ***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
462s  loglcdf (ones (2), ones (3), 1, "upper")
462s ***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (i, 2, 3)
462s ***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (i, 2, 3, "upper")
462s ***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (1, i, 3)
462s ***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (1, i, 3, "upper")
462s ***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (1, 2, i)
462s ***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (1, 2, i, "upper")
462s 25 tests, 25 passed, 0 known failure, 0 skipped
462s [inst/dist_fun/loglinv.m]
462s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/loglinv.m
462s ***** demo
462s  ## Plot various iCDFs from the log-logistic distribution
462s  p = 0.001:0.001:0.999;
462s  x1 = loglinv (p, log (1), 1/0.5);
462s  x2 = loglinv (p, log (1), 1);
462s  x3 = loglinv (p, log (1), 1/2);
462s  x4 = loglinv (p, log (1), 1/4);
462s  x5 = loglinv (p, log (1), 1/8);
462s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
462s  ylim ([0, 20])
462s  grid on
462s  legend ({"σ = 2 (β = 0.5)", "σ = 1 (β = 1)", "σ = 0.5 (β = 2)", ...
462s           "σ = 0.25 (β = 4)", "σ = 0.125 (β = 8)"}, "location", "northwest")
462s  title ("Log-logistic iCDF")
462s  xlabel ("probability")
462s  ylabel ("x")
462s  text (0.03, 12.5, "μ = 0 (α = 1), values of σ (β) as shown in legend")
462s ***** shared p, out1, out2
462s  p = [-1, 0, 0.2, 0.5, 0.8, 0.95, 1, 2];
462s  out1 = [NaN, 0, 0.25, 1, 4, 19, Inf, NaN];
462s  out2 = [NaN, 0, 0.0424732, 2.718282, 173.970037, 18644.695061, Inf, NaN];
462s ***** assert (loglinv (p, 0, 1), out1, 1e-8)
462s ***** assert (loglinv (p, 0, 1), out1, 1e-8)
462s ***** assert (loglinv (p, 1, 3), out2, 1e-6)
462s ***** assert (class (loglinv (single (1), 2, 3)), "single")
462s ***** assert (class (loglinv (1, single (2), 3)), "single")
462s ***** assert (class (loglinv (1, 2, single (3))), "single")
462s ***** error<loglinv: function called with too few input arguments.> loglinv (1)
462s ***** error<loglinv: function called with too few input arguments.> loglinv (1, 2)
462s ***** error<loglinv: P, MU, and SIGMA must be of common size or scalars.> ...
462s  loglinv (1, ones (2), ones (3))
462s ***** error<loglinv: P, MU, and SIGMA must be of common size or scalars.> ...
462s  loglinv (ones (2), 1, ones (3))
463s ***** error<loglinv: P, MU, and SIGMA must be of common size or scalars.> ...
463s  loglinv (ones (2), ones (3), 1)
463s ***** error<loglinv: P, MU, and SIGMA must not be complex.> loglinv (i, 2, 3)
463s ***** error<loglinv: P, MU, and SIGMA must not be complex.> loglinv (1, i, 3)
463s ***** error<loglinv: P, MU, and SIGMA must not be complex.> loglinv (1, 2, i)
463s 14 tests, 14 passed, 0 known failure, 0 skipped
463s [inst/dist_fun/loglpdf.m]
463s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/loglpdf.m
463s ***** demo
463s  ## Plot various PDFs from the log-logistic distribution
463s  x = 0.001:0.001:2;
463s  y1 = loglpdf (x, log (1), 1/0.5);
463s  y2 = loglpdf (x, log (1), 1);
463s  y3 = loglpdf (x, log (1), 1/2);
463s  y4 = loglpdf (x, log (1), 1/4);
463s  y5 = loglpdf (x, log (1), 1/8);
463s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
463s  grid on
463s  ylim ([0,3])
463s  legend ({"σ = 2 (β = 0.5)", "σ = 1 (β = 1)", "σ = 0.5 (β = 2)", ...
463s           "σ = 0.25 (β = 4)", "σ = 0.125 (β = 8)"}, "location", "northeast")
463s  title ("Log-logistic PDF")
463s  xlabel ("values in x")
463s  ylabel ("density")
463s  text (0.1, 2.8, "μ = 0 (α = 1), values of σ (β) as shown in legend")
463s ***** shared out1, out2
463s  out1 = [0, 0, 1, 0.2500, 0.1111, 0.0625, 0.0400, 0.0278, 0];
463s  out2 = [0, 0, 0.0811, 0.0416, 0.0278, 0.0207, 0.0165, 0];
463s ***** assert (loglpdf ([-1,0,realmin,1:5,Inf], 0, 1), out1, 1e-4)
463s ***** assert (loglpdf ([-1,0,realmin,1:5,Inf], 0, 1), out1, 1e-4)
463s ***** assert (loglpdf ([-1:5,Inf], 1, 3), out2, 1e-4)
463s ***** assert (class (loglpdf (single (1), 2, 3)), "single")
463s ***** assert (class (loglpdf (1, single (2), 3)), "single")
463s ***** assert (class (loglpdf (1, 2, single (3))), "single")
463s ***** error<loglpdf: function called with too few input arguments.> loglpdf (1)
463s ***** error<loglpdf: function called with too few input arguments.> loglpdf (1, 2)
463s ***** error<loglpdf: X, MU, and SIGMA must be of common size or scalars.> ...
463s  loglpdf (1, ones (2), ones (3))
463s ***** error<loglpdf: X, MU, and SIGMA must be of common size or scalars.> ...
463s  loglpdf (ones (2), 1, ones (3))
463s ***** error<loglpdf: X, MU, and SIGMA must be of common size or scalars.> ...
463s  loglpdf (ones (2), ones (3), 1)
463s ***** error<loglpdf: X, MU, and SIGMA must not be complex.> loglpdf (i, 2, 3)
463s ***** error<loglpdf: X, MU, and SIGMA must not be complex.> loglpdf (1, i, 3)
463s ***** error<loglpdf: X, MU, and SIGMA must not be complex.> loglpdf (1, 2, i)
463s 14 tests, 14 passed, 0 known failure, 0 skipped
463s [inst/dist_fun/loglrnd.m]
463s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/loglrnd.m
463s ***** assert (size (loglrnd (1, 1)), [1 1])
463s ***** assert (size (loglrnd (1, ones (2,1))), [2, 1])
463s ***** assert (size (loglrnd (1, ones (2,2))), [2, 2])
463s ***** assert (size (loglrnd (ones (2,1), 1)), [2, 1])
463s ***** assert (size (loglrnd (ones (2,2), 1)), [2, 2])
463s ***** assert (size (loglrnd (1, 1, 3)), [3, 3])
463s ***** assert (size (loglrnd (1, 1, [4, 1])), [4, 1])
463s ***** assert (size (loglrnd (1, 1, 4, 1)), [4, 1])
463s ***** assert (size (loglrnd (1, 1, 4, 1, 5)), [4, 1, 5])
463s ***** assert (size (loglrnd (1, 1, 0, 1)), [0, 1])
463s ***** assert (size (loglrnd (1, 1, 1, 0)), [1, 0])
463s ***** assert (size (loglrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
463s ***** assert (class (loglrnd (1, 1)), "double")
463s ***** assert (class (loglrnd (1, single (1))), "single")
463s ***** assert (class (loglrnd (1, single ([1, 1]))), "single")
463s ***** assert (class (loglrnd (single (1), 1)), "single")
463s ***** assert (class (loglrnd (single ([1, 1]), 1)), "single")
463s ***** error<loglrnd: function called with too few input arguments.> loglrnd ()
463s ***** error<loglrnd: function called with too few input arguments.> loglrnd (1)
463s ***** error<loglrnd: MU and SIGMA must be of common size or scalars.> ...
463s  loglrnd (ones (3), ones (2))
463s ***** error<loglrnd: MU and SIGMA must be of common size or scalars.> ...
463s  loglrnd (ones (2), ones (3))
463s ***** error<loglrnd: MU and SIGMA must not be complex.> loglrnd (i, 2, 3)
463s ***** error<loglrnd: MU and SIGMA must not be complex.> loglrnd (1, i, 3)
463s ***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
463s  loglrnd (1, 2, -1)
463s ***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
463s  loglrnd (1, 2, 1.2)
463s ***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
463s  loglrnd (1, 2, ones (2))
463s ***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
463s  loglrnd (1, 2, [2 -1 2])
463s ***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
463s  loglrnd (1, 2, [2 0 2.5])
463s ***** error<loglrnd: dimensions must be non-negative integers.> ...
463s  loglrnd (1, 2, 2, -1, 5)
463s ***** error<loglrnd: dimensions must be non-negative integers.> ...
463s  loglrnd (1, 2, 2, 1.5, 5)
463s ***** error<loglrnd: MU and SIGMA must be scalars or of size SZ.> ...
463s  loglrnd (2, ones (2), 3)
463s ***** error<loglrnd: MU and SIGMA must be scalars or of size SZ.> ...
463s  loglrnd (2, ones (2), [3, 2])
463s ***** error<loglrnd: MU and SIGMA must be scalars or of size SZ.> ...
463s  loglrnd (2, ones (2), 3, 2)
463s 33 tests, 33 passed, 0 known failure, 0 skipped
463s [inst/dist_fun/logncdf.m]
463s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/logncdf.m
463s ***** demo
463s  ## Plot various CDFs from the log-normal distribution
463s  x = 0:0.01:3;
463s  p1 = logncdf (x, 0, 1);
463s  p2 = logncdf (x, 0, 0.5);
463s  p3 = logncdf (x, 0, 0.25);
463s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r")
463s  grid on
463s  legend ({"μ = 0, σ = 1", "μ = 0, σ = 0.5", "μ = 0, σ = 0.25"}, ...
463s          "location", "southeast")
463s  title ("Log-normal CDF")
463s  xlabel ("values in x")
463s  ylabel ("probability")
463s ***** shared x, y
463s  x = [-1, 0, 1, e, Inf];
463s  y = [0, 0, 0.5, 1/2+1/2*erf(1/2), 1];
463s ***** assert (logncdf (x, zeros (1,5), sqrt(2)*ones (1,5)), y, eps)
463s ***** assert (logncdf (x, zeros (1,5), sqrt(2)*ones (1,5), []), y, eps)
463s ***** assert (logncdf (x, 0, sqrt(2)*ones (1,5)), y, eps)
463s ***** assert (logncdf (x, zeros (1,5), sqrt(2)), y, eps)
463s ***** assert (logncdf (x, [0 1 NaN 0 1], sqrt(2)), [0 0 NaN y(4:5)], eps)
463s ***** assert (logncdf (x, 0, sqrt(2)*[0 NaN Inf 1 1]), [NaN NaN y(3:5)], eps)
463s ***** assert (logncdf ([x(1:3) NaN x(5)], 0, sqrt(2)), [y(1:3) NaN y(5)], eps)
463s ***** assert (logncdf ([x, NaN], 0, sqrt(2)), [y, NaN], eps)
463s ***** assert (logncdf (single ([x, NaN]), 0, sqrt(2)), single ([y, NaN]), eps ("single"))
463s ***** assert (logncdf ([x, NaN], single (0), sqrt(2)), single ([y, NaN]), eps ("single"))
463s ***** assert (logncdf ([x, NaN], 0, single (sqrt(2))), single ([y, NaN]), eps ("single"))
463s ***** error<logncdf: invalid number of input arguments.> logncdf ()
463s ***** error<logncdf: invalid number of input arguments.> logncdf (1,2,3,4,5,6,7)
463s ***** error<logncdf: invalid argument for upper tail.> logncdf (1, 2, 3, 4, "uper")
463s ***** error<logncdf: X, MU, and SIGMA must be of common size or scalars.> ...
463s  logncdf (ones (3), ones (2), ones (2))
463s ***** error<logncdf: invalid size of covariance matrix.> logncdf (2, 3, 4, [1, 2])
463s ***** error<logncdf: covariance matrix is required for confidence bounds.> ...
463s  [p, plo, pup] = logncdf (1, 2, 3)
463s ***** error<logncdf: invalid value for alpha.> [p, plo, pup] = ...
463s  logncdf (1, 2, 3, [1, 0; 0, 1], 0)
463s ***** error<logncdf: invalid value for alpha.> [p, plo, pup] = ...
463s  logncdf (1, 2, 3, [1, 0; 0, 1], 1.22)
463s ***** error<logncdf: invalid value for alpha.> [p, plo, pup] = ...
463s  logncdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
463s ***** error<logncdf: X, MU, and SIGMA must not be complex.> logncdf (i, 2, 2)
463s ***** error<logncdf: X, MU, and SIGMA must not be complex.> logncdf (2, i, 2)
463s ***** error<logncdf: X, MU, and SIGMA must not be complex.> logncdf (2, 2, i)
463s ***** error<logncdf: bad covariance matrix.> ...
463s  [p, plo, pup] =logncdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
463s 24 tests, 24 passed, 0 known failure, 0 skipped
463s [inst/dist_fun/logninv.m]
463s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/logninv.m
463s ***** demo
463s  ## Plot various iCDFs from the log-normal distribution
463s  p = 0.001:0.001:0.999;
463s  x1 = logninv (p, 0, 1);
463s  x2 = logninv (p, 0, 0.5);
463s  x3 = logninv (p, 0, 0.25);
463s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
463s  grid on
463s  ylim ([0, 3])
463s  legend ({"μ = 0, σ = 1", "μ = 0, σ = 0.5", "μ = 0, σ = 0.25"}, ...
463s          "location", "northwest")
463s  title ("Log-normal iCDF")
463s  xlabel ("probability")
463s  ylabel ("values in x")
463s ***** shared p
463s  p = [-1 0 0.5 1 2];
463s ***** assert (logninv (p, ones (1,5), ones (1,5)), [NaN 0 e Inf NaN], 2*eps)
463s ***** assert (logninv (p, 1, ones (1,5)), [NaN 0 e Inf NaN], 2*eps)
463s ***** assert (logninv (p, ones (1,5), 1), [NaN 0 e Inf NaN], 2*eps)
463s ***** assert (logninv (p, [1 1 NaN 0 1], 1), [NaN 0 NaN Inf NaN])
463s ***** assert (logninv (p, 1, [1 0 NaN Inf 1]), [NaN NaN NaN NaN NaN])
463s ***** assert (logninv ([p(1:2) NaN p(4:5)], 1, 2), [NaN 0 NaN Inf NaN])
463s ***** assert (logninv ([p, NaN], 1, 1), [NaN 0 e Inf NaN NaN], 2*eps)
463s ***** assert (logninv (single ([p, NaN]), 1, 1), single ([NaN 0 e Inf NaN NaN]))
463s ***** assert (logninv ([p, NaN], single (1), 1), single ([NaN 0 e Inf NaN NaN]))
463s ***** assert (logninv ([p, NaN], 1, single (1)), single ([NaN 0 e Inf NaN NaN]))
463s ***** error logninv ()
463s ***** error logninv (1,2,3,4)
463s ***** error logninv (ones (3), ones (2), ones (2))
463s ***** error logninv (ones (2), ones (3), ones (2))
463s ***** error logninv (ones (2), ones (2), ones (3))
463s ***** error logninv (i, 2, 2)
463s ***** error logninv (2, i, 2)
463s ***** error logninv (2, 2, i)
463s 18 tests, 18 passed, 0 known failure, 0 skipped
463s [inst/dist_fun/lognpdf.m]
463s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/lognpdf.m
463s ***** demo
463s  ## Plot various PDFs from the log-normal distribution
463s  x = 0:0.01:5;
463s  y1 = lognpdf (x, 0, 1);
463s  y2 = lognpdf (x, 0, 0.5);
463s  y3 = lognpdf (x, 0, 0.25);
463s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r")
463s  grid on
463s  ylim ([0, 2])
463s  legend ({"μ = 0, σ = 1", "μ = 0, σ = 0.5", "μ = 0, σ = 0.25"}, ...
463s           "location", "northeast")
463s  title ("Log-normal PDF")
463s  xlabel ("values in x")
463s  ylabel ("density")
463s ***** shared x, y
463s  x = [-1 0 e Inf];
463s  y = [0, 0, 1/(e*sqrt(2*pi)) * exp(-1/2), 0];
463s ***** assert (lognpdf (x, zeros (1,4), ones (1,4)), y, eps)
463s ***** assert (lognpdf (x, 0, ones (1,4)), y, eps)
463s ***** assert (lognpdf (x, zeros (1,4), 1), y, eps)
463s ***** assert (lognpdf (x, [0 1 NaN 0], 1), [0 0 NaN y(4)], eps)
463s ***** assert (lognpdf (x, 0, [0 NaN Inf 1]), [NaN NaN NaN y(4)], eps)
463s ***** assert (lognpdf ([x, NaN], 0, 1), [y, NaN], eps)
463s ***** assert (lognpdf (single ([x, NaN]), 0, 1), single ([y, NaN]), eps ("single"))
463s ***** assert (lognpdf ([x, NaN], single (0), 1), single ([y, NaN]), eps ("single"))
463s ***** assert (lognpdf ([x, NaN], 0, single (1)), single ([y, NaN]), eps ("single"))
463s ***** error lognpdf ()
463s ***** error lognpdf (1,2,3,4)
463s ***** error lognpdf (ones (3), ones (2), ones (2))
463s ***** error lognpdf (ones (2), ones (3), ones (2))
463s ***** error lognpdf (ones (2), ones (2), ones (3))
463s ***** error lognpdf (i, 2, 2)
463s ***** error lognpdf (2, i, 2)
463s ***** error lognpdf (2, 2, i)
463s 17 tests, 17 passed, 0 known failure, 0 skipped
463s [inst/dist_fun/lognrnd.m]
463s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/lognrnd.m
463s ***** assert (size (lognrnd (1, 1)), [1 1])
463s ***** assert (size (lognrnd (1, ones (2,1))), [2, 1])
463s ***** assert (size (lognrnd (1, ones (2,2))), [2, 2])
463s ***** assert (size (lognrnd (ones (2,1), 1)), [2, 1])
463s ***** assert (size (lognrnd (ones (2,2), 1)), [2, 2])
463s ***** assert (size (lognrnd (1, 1, 3)), [3, 3])
463s ***** assert (size (lognrnd (1, 1, [4, 1])), [4, 1])
463s ***** assert (size (lognrnd (1, 1, 4, 1)), [4, 1])
463s ***** assert (size (lognrnd (1, 1, 4, 1, 5)), [4, 1, 5])
463s ***** assert (size (lognrnd (1, 1, 0, 1)), [0, 1])
463s ***** assert (size (lognrnd (1, 1, 1, 0)), [1, 0])
463s ***** assert (size (lognrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
463s ***** assert (class (lognrnd (1, 1)), "double")
463s ***** assert (class (lognrnd (1, single (1))), "single")
463s ***** assert (class (lognrnd (1, single ([1, 1]))), "single")
463s ***** assert (class (lognrnd (single (1), 1)), "single")
463s ***** assert (class (lognrnd (single ([1, 1]), 1)), "single")
463s ***** error<lognrnd: function called with too few input arguments.> lognrnd ()
463s ***** error<lognrnd: function called with too few input arguments.> lognrnd (1)
463s ***** error<lognrnd: MU and SIGMA must be of common size or scalars.> ...
463s  lognrnd (ones (3), ones (2))
463s ***** error<lognrnd: MU and SIGMA must be of common size or scalars.> ...
463s  lognrnd (ones (2), ones (3))
463s ***** error<lognrnd: MU and SIGMA must not be complex.> lognrnd (i, 2, 3)
463s ***** error<lognrnd: MU and SIGMA must not be complex.> lognrnd (1, i, 3)
463s ***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
463s  lognrnd (1, 2, -1)
463s ***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
463s  lognrnd (1, 2, 1.2)
463s ***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
463s  lognrnd (1, 2, ones (2))
463s ***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
463s  lognrnd (1, 2, [2 -1 2])
463s ***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
463s  lognrnd (1, 2, [2 0 2.5])
463s ***** error<lognrnd: dimensions must be non-negative integers.> ...
463s  lognrnd (1, 2, 2, -1, 5)
463s ***** error<lognrnd: dimensions must be non-negative integers.> ...
463s  lognrnd (1, 2, 2, 1.5, 5)
463s ***** error<lognrnd: MU and SIGMA must be scalars or of size SZ.> ...
463s  lognrnd (2, ones (2), 3)
463s ***** error<lognrnd: MU and SIGMA must be scalars or of size SZ.> ...
463s  lognrnd (2, ones (2), [3, 2])
463s ***** error<lognrnd: MU and SIGMA must be scalars or of size SZ.> ...
463s  lognrnd (2, ones (2), 3, 2)
463s 33 tests, 33 passed, 0 known failure, 0 skipped
463s [inst/dist_fun/mnpdf.m]
463s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/mnpdf.m
463s ***** test
463s  x = [1, 4, 2];
463s  pk = [0.2, 0.5, 0.3];
463s  y = mnpdf (x, pk);
463s  assert (y, 0.11812, 0.001);
463s ***** test
463s  x = [1, 4, 2; 1, 0, 9];
463s  pk = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8];
463s  y = mnpdf (x, pk);
463s  assert (y, [0.11812; 0.13422], 0.001);
463s 2 tests, 2 passed, 0 known failure, 0 skipped
463s [inst/dist_fun/mnrnd.m]
463s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/mnrnd.m
463s ***** test
463s  n = 10;
463s  pk = [0.2, 0.5, 0.3];
463s  r = mnrnd (n, pk);
463s  assert (size (r), size (pk));
463s  assert (all (r >= 0));
463s  assert (all (round (r) == r));
463s  assert (sum (r) == n);
463s ***** test
463s  n = 10 * ones (3, 1);
463s  pk = [0.2, 0.5, 0.3];
463s  r = mnrnd (n, pk);
463s  assert (size (r), [length(n), length(pk)]);
463s  assert (all (r >= 0));
463s  assert (all (round (r) == r));
463s  assert (all (sum (r, 2) == n));
463s ***** test
463s  n = (1:2)';
463s  pk = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8];
463s  r = mnrnd (n, pk);
463s  assert (size (r), size (pk));
463s  assert (all (r >= 0));
463s  assert (all (round (r) == r));
463s  assert (all (sum (r, 2) == n));
463s 3 tests, 3 passed, 0 known failure, 0 skipped
463s [inst/dist_fun/mvncdf.m]
463s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/mvncdf.m
463s ***** demo
463s  mu = [1, -1];
463s  Sigma = [0.9, 0.4; 0.4, 0.3];
463s  [X1, X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
463s  X = [X1(:), X2(:)];
463s  p = mvncdf (X, mu, Sigma);
463s  Z = reshape (p, 25, 25);
463s  surf (X1, X2, Z);
463s  title ("Bivariate Normal Distribution");
463s  ylabel "X1"
463s  xlabel "X2"
463s ***** demo
463s  mu = [0, 0];
463s  Sigma = [0.25, 0.3; 0.3, 1];
463s  p = mvncdf ([0 0], [1 1], mu, Sigma);
463s  x1 = -3:.2:3;
463s  x2 = -3:.2:3;
463s  [X1, X2] = meshgrid (x1, x2);
463s  X = [X1(:), X2(:)];
463s  p = mvnpdf (X, mu, Sigma);
463s  p = reshape (p, length (x2), length (x1));
463s  contour (x1, x2, p, [0.0001, 0.001, 0.01, 0.05, 0.15, 0.25, 0.35]);
463s  xlabel ("x");
463s  ylabel ("p");
463s  title ("Probability over Rectangular Region");
463s  line ([0, 0, 1, 1, 0], [1, 0, 0, 1, 1], "Linestyle", "--", "Color", "k");
463s ***** test
463s  fD = (-2:2)';
463s  X = repmat (fD, 1, 4);
463s  p = mvncdf (X);
463s  assert (p, [0; 0.0006; 0.0625; 0.5011; 0.9121], ones (5, 1) * 1e-4);
463s ***** test
463s  mu = [1, -1];
463s  Sigma = [0.9, 0.4; 0.4, 0.3];
463s  [X1,X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
463s  X = [X1(:), X2(:)];
463s  p = mvncdf (X, mu, Sigma);
463s  p_out = [0.00011878988774500, 0.00034404112322371, ...
463s           0.00087682502191813, 0.00195221905058185, ...
463s           0.00378235566873474, 0.00638175749734415, ...
463s           0.00943764224329656, 0.01239164888125426, ...
463s           0.01472750274376648, 0.01623228313374828]';
463s  assert (p([1:10]), p_out, 1e-16);
463s ***** test
463s  mu = [1, -1];
463s  Sigma = [0.9, 0.4; 0.4, 0.3];
463s  [X1,X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
463s  X = [X1(:), X2(:)];
463s  p = mvncdf (X, mu, Sigma);
463s  p_out = [0.8180695783608276, 0.8854485749482751, ...
463s           0.9308108777385832, 0.9579855743025508, ...
463s           0.9722897881414742, 0.9788150170059926, ...
463s           0.9813597788804785, 0.9821977956568989, ...
463s           0.9824283794464095, 0.9824809345614861]';
463s  assert (p([616:625]), p_out, 3e-16);
463s ***** test
463s  mu = [0, 0];
463s  Sigma = [0.25, 0.3; 0.3, 1];
463s  [p, err] = mvncdf ([0, 0], [1, 1], mu, Sigma);
463s  assert (p, 0.2097424404755626, 1e-16);
463s  assert (err, 1e-08);
463s ***** test
463s  x = [1 2];
463s  mu = [0.5 1.5];
463s  sigma = [1.0, 0.5; 0.5, 1.0];
463s  p = mvncdf (x, mu, sigma);
463s  assert (p, 0.546244443857090, 1e-15);
463s ***** test
463s  x = [1 2];
463s  mu = [0.5 1.5];
463s  sigma = [1.0, 0.5; 0.5, 1.0];
463s  a = [-inf 0];
463s  p = mvncdf (a, x, mu, sigma);
463s  assert (p, 0.482672935215631, 1e-15);
463s ***** error p = mvncdf (randn (25,26), [], eye (26));
463s ***** error p = mvncdf (randn (25,8), [], eye (9));
463s ***** error p = mvncdf (randn (25,4), randn (25,5), [], eye (4));
463s ***** error p = mvncdf (randn (25,4), randn (25,4), [2, 3; 2, 3], eye (4));
463s ***** error p = mvncdf (randn (25,4), randn (25,4), ones (1, 5), eye (4));
463s ***** error p = mvncdf ([-inf, 0], [1, 2], [0.5, 1.5], [1.0, 0.5; 0.5, 1.0], option)
463s 12 tests, 12 passed, 0 known failure, 0 skipped
463s [inst/dist_fun/mvnpdf.m]
463s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/mvnpdf.m
463s ***** demo
463s  mu = [1, -1];
463s  sigma = [0.9, 0.4; 0.4, 0.3];
463s  [X1, X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
463s  x = [X1(:), X2(:)];
463s  p = mvnpdf (x, mu, sigma);
463s  surf (X1, X2, reshape (p, 25, 25));
463s ***** error<mvnpdf: too few input arguments.> y = mvnpdf ();
463s ***** error<mvnpdf: too few dimensions in X.> y = mvnpdf ([]);
463s ***** error<mvnpdf: wrong dimensions in X.> y = mvnpdf (ones (3,3,3));
463s ***** error<mvnpdf: columns in X and MU mismatch.> ...
463s  y = mvnpdf (ones (10, 2), [4, 2, 3]);
463s ***** error<mvnpdf: rows in X and MU mismatch.> ...
463s  y = mvnpdf (ones (10, 2), [4, 2; 3, 2]);
463s ***** error<mvnpdf: wrong size of MU.> ...
463s  y = mvnpdf (ones (10, 2), ones (3, 3, 3));
463s ***** shared x, mu, sigma
463s  x = [1, 2, 5, 4, 6];
463s  mu = [2, 0, -1, 1, 4];
463s  sigma = [2, 2, 2, 2, 2];
463s ***** assert (mvnpdf (x), 1.579343404440977e-20, 1e-30);
463s ***** assert (mvnpdf (x, mu), 1.899325144348102e-14, 1e-25);
463s ***** assert (mvnpdf (x, mu, sigma), 2.449062307156273e-09, 1e-20);
463s 9 tests, 9 passed, 0 known failure, 0 skipped
463s [inst/dist_fun/mvnrnd.m]
463s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/mvnrnd.m
463s ***** error<mvnrnd: too few input arguments.> mvnrnd ()
463s ***** error<mvnrnd: too few input arguments.> mvnrnd ([2, 3, 4])
463s ***** error<mvnrnd: wrong size of MU.> mvnrnd (ones (2, 2, 2), ones (1, 2, 3, 4))
463s ***** error<mvnrnd: wrong size of SIGMA.> mvnrnd (ones (1, 3), ones (1, 2, 3, 4))
463s ***** assert (size (mvnrnd ([2, 3, 4], [2, 2, 2])), [1, 3])
463s ***** assert (size (mvnrnd ([2, 3, 4], [2, 2, 2], 10)), [10, 3])
463s 6 tests, 6 passed, 0 known failure, 0 skipped
463s [inst/dist_fun/mvtcdf.m]
463s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/mvtcdf.m
463s ***** demo
463s  ## Compute the cdf of a multivariate Student's t distribution with
463s  ## correlation parameters rho = [1, 0.4; 0.4, 1] and 2 degrees of freedom.
463s 
463s  rho = [1, 0.4; 0.4, 1];
463s  df = 2;
463s  [X1, X2] = meshgrid (linspace (-2, 2, 25)', linspace (-2, 2, 25)');
463s  X = [X1(:), X2(:)];
463s  p = mvtcdf (X, rho, df);
463s  surf (X1, X2, reshape (p, 25, 25));
463s  title ("Bivariate Student's t cumulative distribution function");
463s ***** test
463s  x = [1, 2];
463s  rho = [1, 0.5; 0.5, 1];
463s  df = 4;
463s  a = [-1, 0];
463s  assert (mvtcdf(a, x, rho, df), 0.294196905339283, 1e-14);
463s ***** test
463s  x = [1, 2;2, 4;1, 5];
463s  rho = [1, 0.5; 0.5, 1];
463s  df = 4;
463s  p =[0.790285178602166; 0.938703291727784; 0.81222737321336];
463s  assert (mvtcdf(x, rho, df), p, 1e-14);
463s ***** test
463s  x = [1, 2, 2, 4, 1, 5];
463s  rho = eye (6);
463s  rho(rho == 0) = 0.5;
463s  df = 4;
463s  assert (mvtcdf(x, rho, df), 0.6874, 1e-4);
481s ***** error mvtcdf (1)
481s ***** error mvtcdf (1, 2)
481s ***** error<mvtcdf: correlation matrix RHO does not match dimensions in data.> ...
481s  mvtcdf (1, [2, 3; 3, 2], 1)
481s ***** error<mvtcdf: correlation matrix RHO does not match dimensions in data.> ...
481s  mvtcdf ([2, 3, 4], ones (2), 1)
481s ***** error<mvtcdf: X_LO and X_UP must be of the same size.> ...
481s  mvtcdf ([1, 2, 3], [2, 3], ones (2), 1)
481s ***** error<mvtcdf: correlation matrix RHO must be positive semi-definite.> ...
481s  mvtcdf ([2, 3], ones (2), [1, 2, 3])
481s ***** error<mvtcdf: DF must be a scalar or a vector with the same samples as in> ...
481s  mvtcdf ([2, 3], [1, 0.5; 0.5, 1], [1, 2, 3])
481s 10 tests, 10 passed, 0 known failure, 0 skipped
481s [inst/dist_fun/mvtcdfqmc.m]
481s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/mvtcdfqmc.m
481s ***** error mvtcdfqmc (1, 2, 3);
481s ***** error mvtcdfqmc (1, 2, 3, 4, 5, 6, 7, 8);
481s 2 tests, 2 passed, 0 known failure, 0 skipped
481s [inst/dist_fun/mvtpdf.m]
481s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/mvtpdf.m
481s ***** demo
481s  ## Compute the pdf of a multivariate t distribution with correlation
481s  ## parameters rho = [1 .4; .4 1] and 2 degrees of freedom.
481s 
481s  rho = [1, 0.4; 0.4, 1];
481s  df = 2;
481s  [X1, X2] = meshgrid (linspace (-2, 2, 25)', linspace (-2, 2, 25)');
481s  X = [X1(:), X2(:)];
481s  y = mvtpdf (X, rho, df);
481s  surf (X1, X2, reshape (y, 25, 25));
481s  title ("Bivariate Student's t probability density function");
481s ***** assert (mvtpdf ([0 0], eye(2), 1), 0.1591549, 1E-7)
481s ***** assert (mvtpdf ([1 0], [1 0.5; 0.5 1], 2), 0.06615947, 1E-7)
481s ***** assert (mvtpdf ([1 0.4 0; 1.2 0.5 0.5; 1.4 0.6 1], ...
481s  [1 0.5 0.3; 0.5 1 0.6; 0.3 0.6 1], [5 6 7]), ...
481s  [0.04713313 0.03722421 0.02069011]', 1E-7)
481s 3 tests, 3 passed, 0 known failure, 0 skipped
481s [inst/dist_fun/mvtrnd.m]
481s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/mvtrnd.m
481s ***** test
481s  rho = [1, 0.5; 0.5, 1];
481s  df = 3;
481s  n = 10;
481s  r = mvtrnd (rho, df, n);
481s  assert (size (r), [10, 2]);
481s ***** test
481s  rho = [1, 0.5; 0.5, 1];
481s  df = [2; 3];
481s  n = 2;
481s  r = mvtrnd (rho, df, 2);
481s  assert (size (r), [2, 2]);
481s 2 tests, 2 passed, 0 known failure, 0 skipped
481s [inst/dist_fun/nakacdf.m]
481s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/nakacdf.m
481s ***** demo
481s  ## Plot various CDFs from the Nakagami distribution
481s  x = 0:0.01:3;
481s  p1 = nakacdf (x, 0.5, 1);
481s  p2 = nakacdf (x, 1, 1);
481s  p3 = nakacdf (x, 1, 2);
481s  p4 = nakacdf (x, 1, 3);
481s  p5 = nakacdf (x, 2, 1);
481s  p6 = nakacdf (x, 2, 2);
481s  p7 = nakacdf (x, 5, 1);
481s  plot (x, p1, "-r", x, p2, "-g", x, p3, "-y", x, p4, "-m", ...
481s        x, p5, "-k", x, p6, "-b", x, p7, "-c")
481s  grid on
481s  xlim ([0, 3])
481s  legend ({"μ = 0.5, ω = 1", "μ = 1, ω = 1", "μ = 1, ω = 2", ...
481s           "μ = 1, ω = 3", "μ = 2, ω = 1", "μ = 2, ω = 2", ...
481s           "μ = 5, ω = 1"}, "location", "southeast")
481s  title ("Nakagami CDF")
481s  xlabel ("values in x")
481s  ylabel ("probability")
481s ***** shared x, y
481s  x = [-1, 0, 1, 2, Inf];
481s  y = [0, 0, 0.63212055882855778, 0.98168436111126578, 1];
481s ***** assert (nakacdf (x, ones (1,5), ones (1,5)), y, eps)
481s ***** assert (nakacdf (x, 1, 1), y, eps)
481s ***** assert (nakacdf (x, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)])
481s ***** assert (nakacdf (x, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)])
481s ***** assert (nakacdf ([x, NaN], 1, 1), [y, NaN], eps)
481s ***** assert (nakacdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps("single"))
481s ***** assert (nakacdf ([x, NaN], single (1), 1), single ([y, NaN]), eps("single"))
481s ***** assert (nakacdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps("single"))
481s ***** error<nakacdf: function called with too few input arguments.> nakacdf ()
481s ***** error<nakacdf: function called with too few input arguments.> nakacdf (1)
481s ***** error<nakacdf: function called with too few input arguments.> nakacdf (1, 2)
481s ***** error<nakacdf: invalid argument for upper tail.> nakacdf (1, 2, 3, "tail")
481s ***** error<nakacdf: invalid argument for upper tail.> nakacdf (1, 2, 3, 4)
481s ***** error<nakacdf: X, MU, and OMEGA must be of common size or scalars.> ...
481s  nakacdf (ones (3), ones (2), ones (2))
481s ***** error<nakacdf: X, MU, and OMEGA must be of common size or scalars.> ...
481s  nakacdf (ones (2), ones (3), ones (2))
481s ***** error<nakacdf: X, MU, and OMEGA must be of common size or scalars.> ...
481s  nakacdf (ones (2), ones (2), ones (3))
481s ***** error<nakacdf: X, MU, and OMEGA must not be complex.> nakacdf (i, 2, 2)
481s ***** error<nakacdf: X, MU, and OMEGA must not be complex.> nakacdf (2, i, 2)
481s ***** error<nakacdf: X, MU, and OMEGA must not be complex.> nakacdf (2, 2, i)
481s 19 tests, 19 passed, 0 known failure, 0 skipped
481s [inst/dist_fun/nakainv.m]
481s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/nakainv.m
481s ***** demo
481s  ## Plot various iCDFs from the Nakagami distribution
481s  p = 0.001:0.001:0.999;
481s  x1 = nakainv (p, 0.5, 1);
481s  x2 = nakainv (p, 1, 1);
481s  x3 = nakainv (p, 1, 2);
481s  x4 = nakainv (p, 1, 3);
481s  x5 = nakainv (p, 2, 1);
481s  x6 = nakainv (p, 2, 2);
481s  x7 = nakainv (p, 5, 1);
481s  plot (p, x1, "-r", p, x2, "-g", p, x3, "-y", p, x4, "-m", ...
481s        p, x5, "-k", p, x6, "-b", p, x7, "-c")
481s  grid on
481s  ylim ([0, 3])
481s  legend ({"μ = 0.5, ω = 1", "μ = 1, ω = 1", "μ = 1, ω = 2", ...
481s           "μ = 1, ω = 3", "μ = 2, ω = 1", "μ = 2, ω = 2", ...
481s           "μ = 5, ω = 1"}, "location", "northwest")
481s  title ("Nakagami iCDF")
481s  xlabel ("probability")
481s  ylabel ("values in x")
481s ***** shared p, y
481s  p = [-Inf, -1, 0, 1/2, 1, 2, Inf];
481s  y = [NaN, NaN, 0, 0.83255461115769769, Inf, NaN, NaN];
481s ***** assert (nakainv (p, ones (1,7), ones (1,7)), y, eps)
481s ***** assert (nakainv (p, 1, 1), y, eps)
481s ***** assert (nakainv (p, [1, 1, 1, NaN, 1, 1, 1], 1), [y(1:3), NaN, y(5:7)], eps)
481s ***** assert (nakainv (p, 1, [1, 1, 1, NaN, 1, 1, 1]), [y(1:3), NaN, y(5:7)], eps)
481s ***** assert (nakainv ([p, NaN], 1, 1), [y, NaN], eps)
481s ***** assert (nakainv (single ([p, NaN]), 1, 1), single ([y, NaN]))
481s ***** assert (nakainv ([p, NaN], single (1), 1), single ([y, NaN]))
481s ***** assert (nakainv ([p, NaN], 1, single (1)), single ([y, NaN]))
481s ***** error<nakainv: function called with too few input arguments.> nakainv ()
481s ***** error<nakainv: function called with too few input arguments.> nakainv (1)
481s ***** error<nakainv: function called with too few input arguments.> nakainv (1, 2)
481s ***** error<nakainv: P, MU, and OMEGA must be of common size or scalars.> ...
481s  nakainv (ones (3), ones (2), ones(2))
481s ***** error<nakainv: P, MU, and OMEGA must be of common size or scalars.> ...
481s  nakainv (ones (2), ones (3), ones(2))
481s ***** error<nakainv: P, MU, and OMEGA must be of common size or scalars.> ...
481s  nakainv (ones (2), ones (2), ones(3))
481s ***** error<nakainv: P, MU, and OMEGA must not be complex.> nakainv (i, 4, 3)
481s ***** error<nakainv: P, MU, and OMEGA must not be complex.> nakainv (1, i, 3)
481s ***** error<nakainv: P, MU, and OMEGA must not be complex.> nakainv (1, 4, i)
481s 17 tests, 17 passed, 0 known failure, 0 skipped
481s [inst/dist_fun/nakapdf.m]
481s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/nakapdf.m
481s ***** demo
481s  ## Plot various PDFs from the Nakagami distribution
481s  x = 0:0.01:3;
481s  y1 = nakapdf (x, 0.5, 1);
481s  y2 = nakapdf (x, 1, 1);
481s  y3 = nakapdf (x, 1, 2);
481s  y4 = nakapdf (x, 1, 3);
481s  y5 = nakapdf (x, 2, 1);
481s  y6 = nakapdf (x, 2, 2);
481s  y7 = nakapdf (x, 5, 1);
481s  plot (x, y1, "-r", x, y2, "-g", x, y3, "-y", x, y4, "-m", ...
481s        x, y5, "-k", x, y6, "-b", x, y7, "-c")
481s  grid on
481s  xlim ([0, 3])
481s  ylim ([0, 2])
481s  legend ({"μ = 0.5, ω = 1", "μ = 1, ω = 1", "μ = 1, ω = 2", ...
481s           "μ = 1, ω = 3", "μ = 2, ω = 1", "μ = 2, ω = 2", ...
481s           "μ = 5, ω = 1"}, "location", "northeast")
481s  title ("Nakagami PDF")
481s  xlabel ("values in x")
481s  ylabel ("density")
481s ***** shared x, y
481s  x = [-1, 0, 1, 2, Inf];
481s  y = [0, 0, 0.73575888234288467, 0.073262555554936715, 0];
481s ***** assert (nakapdf (x, ones (1,5), ones (1,5)), y, eps)
481s ***** assert (nakapdf (x, 1, 1), y, eps)
481s ***** assert (nakapdf (x, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)], eps)
481s ***** assert (nakapdf (x, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)], eps)
481s ***** assert (nakapdf ([x, NaN], 1, 1), [y, NaN], eps)
481s ***** assert (nakapdf (single ([x, NaN]), 1, 1), single ([y, NaN]))
481s ***** assert (nakapdf ([x, NaN], single (1), 1), single ([y, NaN]))
481s ***** assert (nakapdf ([x, NaN], 1, single (1)), single ([y, NaN]))
481s ***** error<nakapdf: function called with too few input arguments.> nakapdf ()
481s ***** error<nakapdf: function called with too few input arguments.> nakapdf (1)
481s ***** error<nakapdf: function called with too few input arguments.> nakapdf (1, 2)
481s ***** error<nakapdf: X, MU, and OMEGA must be of common size or scalars.> ...
481s  nakapdf (ones (3), ones (2), ones(2))
481s ***** error<nakapdf: X, MU, and OMEGA must be of common size or scalars.> ...
481s  nakapdf (ones (2), ones (3), ones(2))
481s ***** error<nakapdf: X, MU, and OMEGA must be of common size or scalars.> ...
481s  nakapdf (ones (2), ones (2), ones(3))
481s ***** error<nakapdf: X, MU, and OMEGA must not be complex.> nakapdf (i, 4, 3)
481s ***** error<nakapdf: X, MU, and OMEGA must not be complex.> nakapdf (1, i, 3)
481s ***** error<nakapdf: X, MU, and OMEGA must not be complex.> nakapdf (1, 4, i)
481s 17 tests, 17 passed, 0 known failure, 0 skipped
481s [inst/dist_fun/nakarnd.m]
481s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/nakarnd.m
481s ***** assert (size (nakarnd (1, 1)), [1 1])
481s ***** assert (size (nakarnd (1, ones (2,1))), [2, 1])
481s ***** assert (size (nakarnd (1, ones (2,2))), [2, 2])
481s ***** assert (size (nakarnd (ones (2,1), 1)), [2, 1])
481s ***** assert (size (nakarnd (ones (2,2), 1)), [2, 2])
481s ***** assert (size (nakarnd (1, 1, 3)), [3, 3])
481s ***** assert (size (nakarnd (1, 1, [4, 1])), [4, 1])
481s ***** assert (size (nakarnd (1, 1, 4, 1)), [4, 1])
481s ***** assert (size (nakarnd (1, 1, 4, 1, 5)), [4, 1, 5])
481s ***** assert (size (nakarnd (1, 1, 0, 1)), [0, 1])
481s ***** assert (size (nakarnd (1, 1, 1, 0)), [1, 0])
481s ***** assert (size (nakarnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
481s ***** assert (class (nakarnd (1, 1)), "double")
481s ***** assert (class (nakarnd (1, single (1))), "single")
481s ***** assert (class (nakarnd (1, single ([1, 1]))), "single")
481s ***** assert (class (nakarnd (single (1), 1)), "single")
481s ***** assert (class (nakarnd (single ([1, 1]), 1)), "single")
481s ***** error<nakarnd: function called with too few input arguments.> nakarnd ()
481s ***** error<nakarnd: function called with too few input arguments.> nakarnd (1)
481s ***** error<nakarnd: MU and OMEGA must be of common size or scalars.> ...
481s  nakarnd (ones (3), ones (2))
481s ***** error<nakarnd: MU and OMEGA must be of common size or scalars.> ...
481s  nakarnd (ones (2), ones (3))
481s ***** error<nakarnd: MU and OMEGA must not be complex.> nakarnd (i, 2, 3)
481s ***** error<nakarnd: MU and OMEGA must not be complex.> nakarnd (1, i, 3)
481s ***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
481s  nakarnd (1, 2, -1)
481s ***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
481s  nakarnd (1, 2, 1.2)
481s ***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
481s  nakarnd (1, 2, ones (2))
481s ***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
481s  nakarnd (1, 2, [2 -1 2])
481s ***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
481s  nakarnd (1, 2, [2 0 2.5])
481s ***** error<nakarnd: dimensions must be non-negative integers.> ...
481s  nakarnd (1, 2, 2, -1, 5)
481s ***** error<nakarnd: dimensions must be non-negative integers.> ...
481s  nakarnd (1, 2, 2, 1.5, 5)
481s ***** error<nakarnd: MU and OMEGA must be scalars or of size SZ.> ...
481s  nakarnd (2, ones (2), 3)
481s ***** error<nakarnd: MU and OMEGA must be scalars or of size SZ.> ...
481s  nakarnd (2, ones (2), [3, 2])
481s ***** error<nakarnd: MU and OMEGA must be scalars or of size SZ.> ...
481s  nakarnd (2, ones (2), 3, 2)
481s 33 tests, 33 passed, 0 known failure, 0 skipped
481s [inst/dist_fun/nbincdf.m]
481s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/nbincdf.m
481s ***** demo
481s  ## Plot various CDFs from the negative binomial distribution
481s  x = 0:50;
481s  p1 = nbincdf (x, 2, 0.15);
481s  p2 = nbincdf (x, 5, 0.2);
481s  p3 = nbincdf (x, 4, 0.4);
481s  p4 = nbincdf (x, 10, 0.3);
481s  plot (x, p1, "*r", x, p2, "*g", x, p3, "*k", x, p4, "*m")
481s  grid on
481s  xlim ([0, 40])
481s  legend ({"r = 2, ps = 0.15", "r = 5, ps = 0.2", "r = 4, p = 0.4", ...
481s           "r = 10, ps = 0.3"}, "location", "southeast")
481s  title ("Negative binomial CDF")
481s  xlabel ("values in x (number of failures)")
481s  ylabel ("probability")
481s ***** shared x, y
481s  x = [-1 0 1 2 Inf];
481s  y = [0 1/2 3/4 7/8 1];
481s ***** assert (nbincdf (x, ones (1,5), 0.5*ones (1,5)), y)
481s ***** assert (nbincdf (x, 1, 0.5*ones (1,5)), y)
481s ***** assert (nbincdf (x, ones (1,5), 0.5), y)
481s ***** assert (nbincdf (x, ones (1,5), 0.5, "upper"), 1 - y, eps)
481s ***** assert (nbincdf ([x(1:3) 0 x(5)], [0 1 NaN 1.5 Inf], 0.5), ...
481s  [NaN 1/2 NaN nbinpdf(0,1.5,0.5) NaN], eps)
481s ***** assert (nbincdf (x, 1, 0.5*[-1 NaN 4 1 1]), [NaN NaN NaN y(4:5)])
481s ***** assert (nbincdf ([x(1:2) NaN x(4:5)], 1, 0.5), [y(1:2) NaN y(4:5)])
481s ***** assert (nbincdf ([x, NaN], 1, 0.5), [y, NaN])
481s ***** assert (nbincdf (single ([x, NaN]), 1, 0.5), single ([y, NaN]))
482s ***** assert (nbincdf ([x, NaN], single (1), 0.5), single ([y, NaN]))
482s ***** assert (nbincdf ([x, NaN], 1, single (0.5)), single ([y, NaN]))
482s ***** error<nbincdf: function called with too few input arguments.> nbincdf ()
482s ***** error<nbincdf: function called with too few input arguments.> nbincdf (1)
482s ***** error<nbincdf: function called with too few input arguments.> nbincdf (1, 2)
482s ***** error<nbincdf: invalid argument for upper tail.> nbincdf (1, 2, 3, 4)
482s ***** error<nbincdf: invalid argument for upper tail.> nbincdf (1, 2, 3, "some")
482s ***** error<nbincdf: X, R, and PS must be of common size or scalars.> ...
482s  nbincdf (ones (3), ones (2), ones (2))
482s ***** error<nbincdf: X, R, and PS must be of common size or scalars.> ...
482s  nbincdf (ones (2), ones (3), ones (2))
482s ***** error<nbincdf: X, R, and PS must be of common size or scalars.> ...
482s  nbincdf (ones (2), ones (2), ones (3))
482s ***** error<nbincdf: X, R, and PS must not be complex.> nbincdf (i, 2, 2)
482s ***** error<nbincdf: X, R, and PS must not be complex.> nbincdf (2, i, 2)
482s ***** error<nbincdf: X, R, and PS must not be complex.> nbincdf (2, 2, i)
482s 22 tests, 22 passed, 0 known failure, 0 skipped
482s [inst/dist_fun/nbininv.m]
482s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/nbininv.m
482s ***** demo
482s  ## Plot various iCDFs from the negative binomial distribution
482s  p = 0.001:0.001:0.999;
482s  x1 = nbininv (p, 2, 0.15);
482s  x2 = nbininv (p, 5, 0.2);
482s  x3 = nbininv (p, 4, 0.4);
482s  x4 = nbininv (p, 10, 0.3);
482s  plot (p, x1, "-r", p, x2, "-g", p, x3, "-k", p, x4, "-m")
482s  grid on
482s  ylim ([0, 40])
482s  legend ({"r = 2, ps = 0.15", "r = 5, ps = 0.2", "r = 4, p = 0.4", ...
482s           "r = 10, ps = 0.3"}, "location", "northwest")
482s  title ("Negative binomial iCDF")
482s  xlabel ("probability")
482s  ylabel ("values in x (number of failures)")
482s ***** shared p
482s  p = [-1 0 3/4 1 2];
482s ***** assert (nbininv (p, ones (1,5), 0.5*ones (1,5)), [NaN 0 1 Inf NaN])
482s ***** assert (nbininv (p, 1, 0.5*ones (1,5)), [NaN 0 1 Inf NaN])
482s ***** assert (nbininv (p, ones (1,5), 0.5), [NaN 0 1 Inf NaN])
482s ***** assert (nbininv (p, [1 0 NaN Inf 1], 0.5), [NaN NaN NaN NaN NaN])
482s ***** assert (nbininv (p, [1 0 1.5 Inf 1], 0.5), [NaN NaN 2 NaN NaN])
482s ***** assert (nbininv (p, 1, 0.5*[1 -Inf NaN Inf 1]), [NaN NaN NaN NaN NaN])
482s ***** assert (nbininv ([p(1:2) NaN p(4:5)], 1, 0.5), [NaN 0 NaN Inf NaN])
482s ***** assert (nbininv ([p, NaN], 1, 0.5), [NaN 0 1 Inf NaN NaN])
482s ***** assert (nbininv (single ([p, NaN]), 1, 0.5), single ([NaN 0 1 Inf NaN NaN]))
482s ***** assert (nbininv ([p, NaN], single (1), 0.5), single ([NaN 0 1 Inf NaN NaN]))
482s ***** assert (nbininv ([p, NaN], 1, single (0.5)), single ([NaN 0 1 Inf NaN NaN]))
482s ***** shared y, tol
482s  y = magic (3) + 1;
482s  tol = 1;
482s ***** assert (nbininv (nbincdf (1:10, 3, 0.1), 3, 0.1), 1:10, tol)
482s ***** assert (nbininv (nbincdf (1:10, 3./(1:10), 0.1), 3./(1:10), 0.1), 1:10, tol)
482s ***** assert (nbininv (nbincdf (y, 3./y, 1./y), 3./y, 1./y), y, tol)
482s ***** error<nbininv: function called with too few input arguments.> nbininv ()
482s ***** error<nbininv: function called with too few input arguments.> nbininv (1)
482s ***** error<nbininv: function called with too few input arguments.> nbininv (1, 2)
482s ***** error<nbininv: P, R, and PS must be of common size or scalars.> ...
482s  nbininv (ones (3), ones (2), ones (2))
482s ***** error<nbininv: P, R, and PS must be of common size or scalars.> ...
482s  nbininv (ones (2), ones (3), ones (2))
482s ***** error<nbininv: P, R, and PS must be of common size or scalars.> ...
482s  nbininv (ones (2), ones (2), ones (3))
482s ***** error<nbininv: P, R, and PS must not be complex.> nbininv (i, 2, 2)
482s ***** error<nbininv: P, R, and PS must not be complex.> nbininv (2, i, 2)
482s ***** error<nbininv: P, R, and PS must not be complex.> nbininv (2, 2, i)
482s 23 tests, 23 passed, 0 known failure, 0 skipped
482s [inst/dist_fun/nbinpdf.m]
482s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/nbinpdf.m
482s ***** demo
482s  ## Plot various PDFs from the negative binomial distribution
482s  x = 0:40;
482s  y1 = nbinpdf (x, 2, 0.15);
482s  y2 = nbinpdf (x, 5, 0.2);
482s  y3 = nbinpdf (x, 4, 0.4);
482s  y4 = nbinpdf (x, 10, 0.3);
482s  plot (x, y1, "*r", x, y2, "*g", x, y3, "*k", x, y4, "*m")
482s  grid on
482s  xlim ([0, 40])
482s  ylim ([0, 0.12])
482s  legend ({"r = 2, ps = 0.15", "r = 5, ps = 0.2", "r = 4, p = 0.4", ...
482s           "r = 10, ps = 0.3"}, "location", "northeast")
482s  title ("Negative binomial PDF")
482s  xlabel ("values in x (number of failures)")
482s  ylabel ("density")
482s ***** shared x, y
482s  x = [-1 0 1 2 Inf];
482s  y = [0 1/2 1/4 1/8 NaN];
482s ***** assert (nbinpdf (x, ones (1,5), 0.5*ones (1,5)), y)
482s ***** assert (nbinpdf (x, 1, 0.5*ones (1,5)), y)
482s ***** assert (nbinpdf (x, ones (1,5), 0.5), y)
482s ***** assert (nbinpdf (x, [0 1 NaN 1.5 Inf], 0.5), [NaN 1/2 NaN 1.875*0.5^1.5/4 NaN], eps)
482s ***** assert (nbinpdf (x, 1, 0.5*[-1 NaN 4 1 1]), [NaN NaN NaN y(4:5)])
482s ***** assert (nbinpdf ([x, NaN], 1, 0.5), [y, NaN])
482s ***** assert (nbinpdf (single ([x, NaN]), 1, 0.5), single ([y, NaN]))
482s ***** assert (nbinpdf ([x, NaN], single (1), 0.5), single ([y, NaN]))
482s ***** assert (nbinpdf ([x, NaN], 1, single (0.5)), single ([y, NaN]))
482s ***** error<nbinpdf: function called with too few input arguments.> nbinpdf ()
482s ***** error<nbinpdf: function called with too few input arguments.> nbinpdf (1)
482s ***** error<nbinpdf: function called with too few input arguments.> nbinpdf (1, 2)
482s ***** error<nbinpdf: X, R, and PS must be of common size or scalars.> ...
482s  nbinpdf (ones (3), ones (2), ones (2))
482s ***** error<nbinpdf: X, R, and PS must be of common size or scalars.> ...
482s  nbinpdf (ones (2), ones (3), ones (2))
482s ***** error<nbinpdf: X, R, and PS must be of common size or scalars.> ...
482s  nbinpdf (ones (2), ones (2), ones (3))
482s ***** error<nbinpdf: X, R, and PS must not be complex.> nbinpdf (i, 2, 2)
482s ***** error<nbinpdf: X, R, and PS must not be complex.> nbinpdf (2, i, 2)
482s ***** error<nbinpdf: X, R, and PS must not be complex.> nbinpdf (2, 2, i)
482s 18 tests, 18 passed, 0 known failure, 0 skipped
482s [inst/dist_fun/nbinrnd.m]
482s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/nbinrnd.m
482s ***** assert (size (nbinrnd (1, 0.5)), [1 1])
482s ***** assert (size (nbinrnd (1, 0.5 * ones (2,1))), [2, 1])
482s ***** assert (size (nbinrnd (1, 0.5 * ones (2,2))), [2, 2])
482s ***** assert (size (nbinrnd (ones (2,1), 0.5)), [2, 1])
482s ***** assert (size (nbinrnd (ones (2,2), 0.5)), [2, 2])
482s ***** assert (size (nbinrnd (1, 0.5, 3)), [3, 3])
482s ***** assert (size (nbinrnd (1, 0.5, [4, 1])), [4, 1])
482s ***** assert (size (nbinrnd (1, 0.5, 4, 1)), [4, 1])
482s ***** assert (size (nbinrnd (1, 0.5, 4, 1, 5)), [4, 1, 5])
482s ***** assert (size (nbinrnd (1, 0.5, 0, 1)), [0, 1])
482s ***** assert (size (nbinrnd (1, 0.5, 1, 0)), [1, 0])
482s ***** assert (size (nbinrnd (1, 0.5, 1, 2, 0, 5)), [1, 2, 0, 5])
482s ***** assert (class (nbinrnd (1, 0.5)), "double")
482s ***** assert (class (nbinrnd (1, single (0.5))), "single")
482s ***** assert (class (nbinrnd (1, single ([0.5, 0.5]))), "single")
482s ***** assert (class (nbinrnd (single (1), 0.5)), "single")
482s ***** assert (class (nbinrnd (single ([1, 1]), 0.5)), "single")
482s ***** error<nbinrnd: function called with too few input arguments.> nbinrnd ()
482s ***** error<nbinrnd: function called with too few input arguments.> nbinrnd (1)
482s ***** error<nbinrnd: R and PS must be of common size or scalars.> ...
482s  nbinrnd (ones (3), ones (2))
482s ***** error<nbinrnd: R and PS must be of common size or scalars.> ...
482s  nbinrnd (ones (2), ones (3))
482s ***** error<nbinrnd: R and PS must not be complex.> nbinrnd (i, 2, 3)
482s ***** error<nbinrnd: R and PS must not be complex.> nbinrnd (1, i, 3)
482s ***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
482s  nbinrnd (1, 2, -1)
482s ***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
482s  nbinrnd (1, 2, 1.2)
482s ***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
482s  nbinrnd (1, 2, ones (2))
482s ***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
482s  nbinrnd (1, 2, [2 -1 2])
482s ***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
482s  nbinrnd (1, 2, [2 0 2.5])
482s ***** error<nbinrnd: dimensions must be non-negative integers.> ...
482s  nbinrnd (1, 2, 2, -1, 5)
482s ***** error<nbinrnd: dimensions must be non-negative integers.> ...
482s  nbinrnd (1, 2, 2, 1.5, 5)
482s ***** error<nbinrnd: R and PS must be scalars or of size SZ.> ...
482s  nbinrnd (2, ones (2), 3)
482s ***** error<nbinrnd: R and PS must be scalars or of size SZ.> ...
482s  nbinrnd (2, ones (2), [3, 2])
482s ***** error<nbinrnd: R and PS must be scalars or of size SZ.> ...
482s  nbinrnd (2, ones (2), 3, 2)
482s 33 tests, 33 passed, 0 known failure, 0 skipped
482s [inst/dist_fun/ncfcdf.m]
482s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/ncfcdf.m
482s ***** demo
482s  ## Plot various CDFs from the noncentral F distribution
482s  x = 0:0.01:5;
482s  p1 = ncfcdf (x, 2, 5, 1);
482s  p2 = ncfcdf (x, 2, 5, 2);
482s  p3 = ncfcdf (x, 5, 10, 1);
482s  p4 = ncfcdf (x, 10, 20, 10);
482s  plot (x, p1, "-r", x, p2, "-g", x, p3, "-k", x, p4, "-m")
482s  grid on
482s  xlim ([0, 5])
482s  legend ({"df1 = 2, df2 = 5, λ = 1", "df1 = 2, df2 = 5, λ = 2", ...
482s           "df1 = 5, df2 = 10, λ = 1", "df1 = 10, df2 = 20, λ = 10"}, ...
482s          "location", "southeast")
482s  title ("Noncentral F CDF")
482s  xlabel ("values in x")
482s  ylabel ("probability")
482s ***** demo
482s  ## Compare the noncentral F CDF with LAMBDA = 10 to the F CDF with the
482s  ## same number of numerator and denominator degrees of freedom (5, 20)
482s 
482s  x = 0.01:0.1:10.01;
482s  p1 = ncfcdf (x, 5, 20, 10);
482s  p2 = fcdf (x, 5, 20);
482s  plot (x, p1, "-", x, p2, "-");
482s  grid on
482s  xlim ([0, 10])
482s  legend ({"Noncentral F(5,20,10)", "F(5,20)"}, "location", "southeast")
482s  title ("Noncentral F vs F CDFs")
482s  xlabel ("values in x")
482s  ylabel ("probability")
482s ***** test
482s  x = -2:0.1:2;
482s  p = ncfcdf (x, 10, 1, 3);
482s  assert (p([1:21]), zeros (1, 21), 1e-76);
482s  assert (p(22), 0.004530737275319753, 1e-14);
482s  assert (p(30), 0.255842099135669, 1e-14);
482s  assert (p(41), 0.4379890998457305, 1e-14);
482s ***** test
482s  p = ncfcdf (12, 10, 3, 2);
482s  assert (p, 0.9582287900447416, 1e-14);
482s ***** test
482s  p = ncfcdf (2, 3, 2, 1);
482s  assert (p, 0.5731985522994989, 1e-14);
482s ***** test
482s  p = ncfcdf (2, 3, 2, 1, "upper");
482s  assert (p, 0.4268014477004823, 1e-14);
482s ***** test
482s  p = ncfcdf ([3, 6], 3, 2, 5, "upper");
482s  assert (p, [0.530248523596927, 0.3350482341323044], 1e-14);
482s ***** error<ncfcdf: function called with too few input arguments.> ncfcdf ()
482s ***** error<ncfcdf: function called with too few input arguments.> ncfcdf (1)
482s ***** error<ncfcdf: function called with too few input arguments.> ncfcdf (1, 2)
482s ***** error<ncfcdf: function called with too few input arguments.> ncfcdf (1, 2, 3)
482s ***** error<ncfcdf: invalid argument for upper tail.> ncfcdf (1, 2, 3, 4, "tail")
482s ***** error<ncfcdf: invalid argument for upper tail.> ncfcdf (1, 2, 3, 4, 5)
482s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
482s  ncfcdf (ones (3), ones (2), ones (2), ones (2))
482s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
482s  ncfcdf (ones (2), ones (3), ones (2), ones (2))
482s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
482s  ncfcdf (ones (2), ones (2), ones (3), ones (2))
482s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
482s  ncfcdf (ones (2), ones (2), ones (2), ones (3))
482s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfcdf (i, 2, 2, 2)
482s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfcdf (2, i, 2, 2)
482s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfcdf (2, 2, i, 2)
482s ***** error<ncfcdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfcdf (2, 2, 2, i)
482s 19 tests, 19 passed, 0 known failure, 0 skipped
482s [inst/dist_fun/ncfinv.m]
482s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/ncfinv.m
482s ***** demo
482s  ## Plot various iCDFs from the noncentral F distribution
482s  p = 0.001:0.001:0.999;
482s  x1 = ncfinv (p, 2, 5, 1);
482s  x2 = ncfinv (p, 2, 5, 2);
482s  x3 = ncfinv (p, 5, 10, 1);
482s  x4 = ncfinv (p, 10, 20, 10);
482s  plot (p, x1, "-r", p, x2, "-g", p, x3, "-k", p, x4, "-m")
482s  grid on
482s  ylim ([0, 5])
482s  legend ({"df1 = 2, df2 = 5, λ = 1", "df1 = 2, df2 = 5, λ = 2", ...
482s           "df1 = 5, df2 = 10, λ = 1", "df1 = 10, df2 = 20, λ = 10"}, ...
482s          "location", "northwest")
482s  title ("Noncentral F iCDF")
482s  xlabel ("probability")
482s  ylabel ("values in x")
482s ***** demo
482s  ## Compare the noncentral F iCDF with LAMBDA = 10 to the F iCDF with the
482s  ## same number of numerator and denominator degrees of freedom (5, 20)
482s 
482s  p = 0.001:0.001:0.999;
482s  x1 = ncfinv (p, 5, 20, 10);
482s  x2 = finv (p, 5, 20);
482s  plot (p, x1, "-", p, x2, "-");
482s  grid on
482s  ylim ([0, 10])
482s  legend ({"Noncentral F(5,20,10)", "F(5,20)"}, "location", "northwest")
482s  title ("Noncentral F vs F quantile functions")
482s  xlabel ("probability")
482s  ylabel ("values in x")
482s ***** test
482s  x = [0,0.1775,0.3864,0.6395,0.9564,1.3712,1.9471,2.8215,4.3679,8.1865,Inf];
482s  assert (ncfinv ([0:0.1:1], 2, 3, 1), x, 1e-4);
482s ***** test
482s  x = [0,0.7492,1.3539,2.0025,2.7658,3.7278,5.0324,6.9826,10.3955,18.7665,Inf];
482s  assert (ncfinv ([0:0.1:1], 2, 3, 5), x, 1e-4);
482s ***** test
482s  x = [0,0.2890,0.8632,1.5653,2.4088,3.4594,4.8442,6.8286,10.0983,17.3736,Inf];
482s  assert (ncfinv ([0:0.1:1], 1, 4, 3), x, 1e-4);
482s ***** test
482s  x = [0.078410, 0.212716, 0.288618, 0.335752, 0.367963, 0.391460];
482s  assert (ncfinv (0.05, [1, 2, 3, 4, 5, 6], 10, 3), x, 1e-6);
482s ***** test
482s  x = [0.2574, 0.2966, 0.3188, 0.3331, 0.3432, 0.3507];
482s  assert (ncfinv (0.05, 5, [1, 2, 3, 4, 5, 6], 3), x, 1e-4);
482s ***** test
482s  x = [1.6090, 1.8113, 1.9215, 1.9911, NaN, 2.0742];
482s  assert (ncfinv (0.05, 1, [1, 2, 3, 4, -1, 6], 10), x, 1e-4);
483s ***** test
483s  assert (ncfinv (0.996, 3, 5, 8), 58.0912074080671, 4e-12);
483s ***** error<ncfinv: function called with too few input arguments.> ncfinv ()
483s ***** error<ncfinv: function called with too few input arguments.> ncfinv (1)
483s ***** error<ncfinv: function called with too few input arguments.> ncfinv (1, 2)
483s ***** error<ncfinv: function called with too few input arguments.> ncfinv (1, 2, 3)
483s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
483s  ncfinv (ones (3), ones (2), ones (2), ones (2))
483s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
483s  ncfinv (ones (2), ones (3), ones (2), ones (2))
483s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
483s  ncfinv (ones (2), ones (2), ones (3), ones (2))
483s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
483s  ncfinv (ones (2), ones (2), ones (2), ones (3))
483s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must not be complex.> ncfinv (i, 2, 2, 2)
483s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must not be complex.> ncfinv (2, i, 2, 2)
483s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must not be complex.> ncfinv (2, 2, i, 2)
483s ***** error<ncfinv: P, DF1, DF2, and LAMBDA must not be complex.> ncfinv (2, 2, 2, i)
483s 19 tests, 19 passed, 0 known failure, 0 skipped
483s [inst/dist_fun/ncfpdf.m]
483s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/ncfpdf.m
483s ***** demo
483s  ## Plot various PDFs from the noncentral F distribution
483s  x = 0:0.01:5;
483s  y1 = ncfpdf (x, 2, 5, 1);
483s  y2 = ncfpdf (x, 2, 5, 2);
483s  y3 = ncfpdf (x, 5, 10, 1);
483s  y4 = ncfpdf (x, 10, 20, 10);
483s  plot (x, y1, "-r", x, y2, "-g", x, y3, "-k", x, y4, "-m")
483s  grid on
483s  xlim ([0, 5])
483s  ylim ([0, 0.8])
483s  legend ({"df1 = 2, df2 = 5, λ = 1", "df1 = 2, df2 = 5, λ = 2", ...
483s           "df1 = 5, df2 = 10, λ = 1", "df1 = 10, df2 = 20, λ = 10"}, ...
483s          "location", "northeast")
483s  title ("Noncentral F PDF")
483s  xlabel ("values in x")
483s  ylabel ("density")
483s ***** demo
483s  ## Compare the noncentral F PDF with LAMBDA = 10 to the F PDF with the
483s  ## same number of numerator and denominator degrees of freedom (5, 20)
483s 
483s  x = 0.01:0.1:10.01;
483s  y1 = ncfpdf (x, 5, 20, 10);
483s  y2 = fpdf (x, 5, 20);
483s  plot (x, y1, "-", x, y2, "-");
483s  grid on
483s  xlim ([0, 10])
483s  ylim ([0, 0.8])
483s  legend ({"Noncentral F(5,20,10)", "F(5,20)"}, "location", "northeast")
483s  title ("Noncentral F vs F PDFs")
483s  xlabel ("values in x")
483s  ylabel ("density")
483s ***** shared x1, df1, df2, lambda
483s  x1 = [-Inf, 2, NaN, 4, Inf];
483s  df1 = [2, 0, -1, 1, 4];
483s  df2 = [2, 4, 5, 6, 8];
483s  lambda = [1, NaN, 3, -1, 2];
483s ***** assert (ncfpdf (x1, df1, df2, lambda), [0, NaN, NaN, NaN, NaN]);
483s ***** assert (ncfpdf (x1, df1, df2, 1), [0, NaN, NaN, ...
483s                                    0.05607937264237208, NaN], 1e-14);
483s ***** assert (ncfpdf (x1, df1, df2, 3), [0, NaN, NaN, ...
483s                                    0.080125760971946518, NaN], 1e-14);
483s ***** assert (ncfpdf (x1, df1, df2, 2), [0, NaN, NaN, ...
483s                                    0.0715902008258656, NaN], 1e-14);
483s ***** assert (ncfpdf (x1, 3, 5, lambda), [0, NaN, NaN, NaN, NaN]);
483s ***** assert (ncfpdf (2, df1, df2, lambda), [0.1254046999837947, NaN, NaN, ...
483s                                       NaN, 0.2152571783045893], 1e-14);
483s ***** assert (ncfpdf (4, df1, df2, lambda), [0.05067089541001374, NaN, NaN, ...
483s                                       NaN, 0.05560846335398539], 1e-14);
483s ***** error<ncfpdf: function called with too few input arguments.> ncfpdf ()
483s ***** error<ncfpdf: function called with too few input arguments.> ncfpdf (1)
483s ***** error<ncfpdf: function called with too few input arguments.> ncfpdf (1, 2)
483s ***** error<ncfpdf: function called with too few input arguments.> ncfpdf (1, 2, 3)
483s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
483s  ncfpdf (ones (3), ones (2), ones (2), ones (2))
483s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
483s  ncfpdf (ones (2), ones (3), ones (2), ones (2))
483s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
483s  ncfpdf (ones (2), ones (2), ones (3), ones (2))
483s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
483s  ncfpdf (ones (2), ones (2), ones (2), ones (3))
483s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfpdf (i, 2, 2, 2)
483s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfpdf (2, i, 2, 2)
483s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfpdf (2, 2, i, 2)
483s ***** error<ncfpdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfpdf (2, 2, 2, i)
483s 19 tests, 19 passed, 0 known failure, 0 skipped
483s [inst/dist_fun/ncfrnd.m]
483s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/ncfrnd.m
483s ***** assert (size (ncfrnd (1, 1, 1)), [1 1])
483s ***** assert (size (ncfrnd (1, ones (2,1), 1)), [2, 1])
483s ***** assert (size (ncfrnd (1, ones (2,2), 1)), [2, 2])
483s ***** assert (size (ncfrnd (ones (2,1), 1, 1)), [2, 1])
483s ***** assert (size (ncfrnd (ones (2,2), 1, 1)), [2, 2])
483s ***** assert (size (ncfrnd (1, 1, 1, 3)), [3, 3])
483s ***** assert (size (ncfrnd (1, 1, 1, [4, 1])), [4, 1])
483s ***** assert (size (ncfrnd (1, 1, 1, 4, 1)), [4, 1])
483s ***** assert (size (ncfrnd (1, 1, 1, 4, 1, 5)), [4, 1, 5])
483s ***** assert (size (ncfrnd (1, 1, 1, 0, 1)), [0, 1])
483s ***** assert (size (ncfrnd (1, 1, 1, 1, 0)), [1, 0])
483s ***** assert (size (ncfrnd (1, 1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
483s ***** assert (class (ncfrnd (1, 1, 1)), "double")
483s ***** assert (class (ncfrnd (1, single (1), 1)), "single")
483s ***** assert (class (ncfrnd (1, 1, single (1))), "single")
483s ***** assert (class (ncfrnd (1, single ([1, 1]), 1)), "single")
483s ***** assert (class (ncfrnd (1, 1, single ([1, 1]))), "single")
483s ***** assert (class (ncfrnd (single (1), 1, 1)), "single")
483s ***** assert (class (ncfrnd (single ([1, 1]), 1, 1)), "single")
483s ***** error<ncfrnd: function called with too few input arguments.> ncfrnd ()
483s ***** error<ncfrnd: function called with too few input arguments.> ncfrnd (1)
483s ***** error<ncfrnd: function called with too few input arguments.> ncfrnd (1, 2)
483s ***** error<ncfrnd: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
483s  ncfrnd (ones (3), ones (2), ones (2))
483s ***** error<ncfrnd: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
483s  ncfrnd (ones (2), ones (3), ones (2))
483s ***** error<ncfrnd: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
483s  ncfrnd (ones (2), ones (2), ones (3))
483s ***** error<ncfrnd: DF1, DF2, and LAMBDA must not be complex.> ncfrnd (i, 2, 3)
483s ***** error<ncfrnd: DF1, DF2, and LAMBDA must not be complex.> ncfrnd (1, i, 3)
483s ***** error<ncfrnd: DF1, DF2, and LAMBDA must not be complex.> ncfrnd (1, 2, i)
483s ***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
483s  ncfrnd (1, 2, 3, -1)
483s ***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
483s  ncfrnd (1, 2, 3, 1.2)
483s ***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
483s  ncfrnd (1, 2, 3, ones (2))
483s ***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
483s  ncfrnd (1, 2, 3, [2 -1 2])
483s ***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
483s  ncfrnd (1, 2, 3, [2 0 2.5])
483s ***** error<ncfrnd: dimensions must be non-negative integers.> ...
483s  ncfrnd (1, 2, 3, 2, -1, 5)
483s ***** error<ncfrnd: dimensions must be non-negative integers.> ...
483s  ncfrnd (1, 2, 3, 2, 1.5, 5)
483s ***** error<ncfrnd: DF1, DF2, and LAMBDA must be scalars or of size SZ.> ...
483s  ncfrnd (2, ones (2), 2, 3)
483s ***** error<ncfrnd: DF1, DF2, and LAMBDA must be scalars or of size SZ.> ...
483s  ncfrnd (2, ones (2), 2, [3, 2])
483s ***** error<ncfrnd: DF1, DF2, and LAMBDA must be scalars or of size SZ.> ...
483s  ncfrnd (2, ones (2), 2, 3, 2)
483s 38 tests, 38 passed, 0 known failure, 0 skipped
483s [inst/dist_fun/nctcdf.m]
483s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/nctcdf.m
483s ***** demo
483s  ## Plot various CDFs from the noncentral Τ distribution
483s  x = -5:0.01:5;
483s  p1 = nctcdf (x, 1, 0);
483s  p2 = nctcdf (x, 4, 0);
483s  p3 = nctcdf (x, 1, 2);
483s  p4 = nctcdf (x, 4, 2);
483s  plot (x, p1, "-r", x, p2, "-g", x, p3, "-k", x, p4, "-m")
483s  grid on
483s  xlim ([-5, 5])
483s  legend ({"df = 1, μ = 0", "df = 4, μ = 0", ...
483s           "df = 1, μ = 2", "df = 4, μ = 2"}, "location", "southeast")
483s  title ("Noncentral Τ CDF")
483s  xlabel ("values in x")
483s  ylabel ("probability")
483s ***** demo
483s  ## Compare the noncentral T CDF with MU = 1 to the T CDF
483s  ## with the same number of degrees of freedom (10).
483s 
483s  x = -5:0.1:5;
483s  p1 = nctcdf (x, 10, 1);
483s  p2 = tcdf (x, 10);
483s  plot (x, p1, "-", x, p2, "-")
483s  grid on
483s  xlim ([-5, 5])
483s  legend ({"Noncentral T(10,1)", "T(10)"}, "location", "southeast")
483s  title ("Noncentral T vs T CDFs")
483s  xlabel ("values in x")
483s  ylabel ("probability")
483s ***** test
483s  x = -2:0.1:2;
483s  p = nctcdf (x, 10, 1);
483s  assert (p(1), 0.003302485766631558, 1e-14);
483s  assert (p(2), 0.004084668193532631, 1e-14);
483s  assert (p(3), 0.005052800319478737, 1e-14);
483s  assert (p(41), 0.8076115625303751, 1e-14);
483s ***** test
483s  p = nctcdf (12, 10, 3);
483s  assert (p, 0.9997719343243797, 1e-14);
483s ***** test
483s  p = nctcdf (2, 3, 2);
483s  assert (p, 0.4430757822176028, 1e-14);
483s ***** test
483s  p = nctcdf (2, 3, 2, "upper");
483s  assert (p, 0.5569242177823971, 1e-14);
483s ***** test
483s  p = nctcdf ([3, 6], 3, 2, "upper");
483s  assert (p, [0.3199728259444777, 0.07064855592441913], 1e-14);
483s ***** error<nctcdf: function called with too few input arguments.> nctcdf ()
483s ***** error<nctcdf: function called with too few input arguments.> nctcdf (1)
483s ***** error<nctcdf: function called with too few input arguments.> nctcdf (1, 2)
483s ***** error<nctcdf: invalid argument for upper tail.> nctcdf (1, 2, 3, "tail")
483s ***** error<nctcdf: invalid argument for upper tail.> nctcdf (1, 2, 3, 4)
483s ***** error<nctcdf: X, DF, and MU must be of common size or scalars.> ...
483s  nctcdf (ones (3), ones (2), ones (2))
483s ***** error<nctcdf: X, DF, and MU must be of common size or scalars.> ...
483s  nctcdf (ones (2), ones (3), ones (2))
483s ***** error<nctcdf: X, DF, and MU must be of common size or scalars.> ...
483s  nctcdf (ones (2), ones (2), ones (3))
483s ***** error<nctcdf: X, DF, and MU must not be complex.> nctcdf (i, 2, 2)
483s ***** error<nctcdf: X, DF, and MU must not be complex.> nctcdf (2, i, 2)
483s ***** error<nctcdf: X, DF, and MU must not be complex.> nctcdf (2, 2, i)
483s 16 tests, 16 passed, 0 known failure, 0 skipped
483s [inst/dist_fun/nctinv.m]
483s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/nctinv.m
483s ***** demo
483s  ## Plot various iCDFs from the noncentral T distribution
483s  p = 0.001:0.001:0.999;
483s  x1 = nctinv (p, 1, 0);
483s  x2 = nctinv (p, 4, 0);
483s  x3 = nctinv (p, 1, 2);
483s  x4 = nctinv (p, 4, 2);
483s  plot (p, x1, "-r", p, x2, "-g", p, x3, "-k", p, x4, "-m")
483s  grid on
483s  ylim ([-5, 5])
483s  legend ({"df = 1, μ = 0", "df = 4, μ = 0", ...
483s           "df = 1, μ = 2", "df = 4, μ = 2"}, "location", "northwest")
483s  title ("Noncentral T iCDF")
483s  xlabel ("probability")
483s  ylabel ("values in x")
483s ***** demo
483s  ## Compare the noncentral T iCDF with MU = 1 to the T iCDF
483s  ## with the same number of degrees of freedom (10).
483s 
483s  p = 0.001:0.001:0.999;
483s  x1 = nctinv (p, 10, 1);
483s  x2 = tinv (p, 10);
483s  plot (p, x1, "-", p, x2, "-");
483s  grid on
483s  ylim ([-5, 5])
483s  legend ({"Noncentral T(10,1)", "T(10)"}, "location", "northwest")
483s  title ("Noncentral T vs T quantile functions")
483s  xlabel ("probability")
483s  ylabel ("values in x")
483s ***** test
483s  x = [-Inf,-0.3347,0.1756,0.5209,0.8279,1.1424,1.5021,1.9633,2.6571,4.0845,Inf];
483s  assert (nctinv ([0:0.1:1], 2, 1), x, 1e-4);
484s ***** test
484s  x = [-Inf,1.5756,2.0827,2.5343,3.0043,3.5406,4.2050,5.1128,6.5510,9.6442,Inf];
484s  assert (nctinv ([0:0.1:1], 2, 3), x, 1e-4);
485s ***** test
485s  x = [-Inf,2.2167,2.9567,3.7276,4.6464,5.8455,7.5619,10.3327,15.7569,31.8159,Inf];
485s  assert (nctinv ([0:0.1:1], 1, 4), x, 1e-4);
486s ***** test
486s  x = [1.7791   1.9368   2.0239   2.0801   2.1195   2.1489];
486s  assert (nctinv (0.05, [1, 2, 3, 4, 5, 6], 4), x, 1e-4);
487s ***** test
487s  x = [-0.7755, 0.3670, 1.2554, 2.0239, 2.7348, 3.4154];
487s  assert (nctinv (0.05, 3, [1, 2, 3, 4, 5, 6]), x, 1e-4);
488s ***** test
488s  x = [-0.7183, 0.3624, 1.2878, 2.1195, -3.5413, 3.6430];
488s  assert (nctinv (0.05, 5, [1, 2, 3, 4, -1, 6]), x, 1e-4);
490s ***** test
490s  assert (nctinv (0.996, 5, 8), 30.02610554063658, 2e-11);
492s ***** error<nctinv: function called with too few input arguments.> nctinv ()
492s ***** error<nctinv: function called with too few input arguments.> nctinv (1)
492s ***** error<nctinv: function called with too few input arguments.> nctinv (1, 2)
492s ***** error<nctinv: P, DF, and MU must be of common size or scalars.> ...
492s  nctinv (ones (3), ones (2), ones (2))
492s ***** error<nctinv: P, DF, and MU must be of common size or scalars.> ...
492s  nctinv (ones (2), ones (3), ones (2))
492s ***** error<nctinv: P, DF, and MU must be of common size or scalars.> ...
492s  nctinv (ones (2), ones (2), ones (3))
492s ***** error<nctinv: P, DF, and MU must not be complex.> nctinv (i, 2, 2)
492s ***** error<nctinv: P, DF, and MU must not be complex.> nctinv (2, i, 2)
492s ***** error<nctinv: P, DF, and MU must not be complex.> nctinv (2, 2, i)
492s 16 tests, 16 passed, 0 known failure, 0 skipped
492s [inst/dist_fun/nctpdf.m]
492s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/nctpdf.m
492s ***** demo
492s  ## Plot various PDFs from the noncentral T distribution
492s  x = -5:0.01:10;
492s  y1 = nctpdf (x, 1, 0);
492s  y2 = nctpdf (x, 4, 0);
492s  y3 = nctpdf (x, 1, 2);
492s  y4 = nctpdf (x, 4, 2);
492s  plot (x, y1, "-r", x, y2, "-g", x, y3, "-k", x, y4, "-m")
492s  grid on
492s  xlim ([-5, 10])
492s  ylim ([0, 0.4])
492s  legend ({"df = 1, μ = 0", "df = 4, μ = 0", ...
492s           "df = 1, μ = 2", "df = 4, μ = 2"}, "location", "northeast")
492s  title ("Noncentral T PDF")
492s  xlabel ("values in x")
492s  ylabel ("density")
492s ***** demo
492s  ## Compare the noncentral T PDF with MU = 1 to the T PDF
492s  ## with the same number of degrees of freedom (10).
492s 
492s  x = -5:0.1:5;
492s  y1 = nctpdf (x, 10, 1);
492s  y2 = tpdf (x, 10);
492s  plot (x, y1, "-", x, y2, "-");
492s  grid on
492s  xlim ([-5, 5])
492s  ylim ([0, 0.4])
492s  legend ({"Noncentral χ^2(4,2)", "χ^2(4)"}, "location", "northwest")
492s  title ("Noncentral T vs T PDFs")
492s  xlabel ("values in x")
492s  ylabel ("density")
492s ***** shared x1, df, mu
492s  x1 = [-Inf, 2, NaN, 4, Inf];
492s  df = [2, 0, -1, 1, 4];
492s  mu = [1, NaN, 3, -1, 2];
492s ***** assert (nctpdf (x1, df, mu), [0, NaN, NaN, 0.00401787561306999, 0], 1e-14);
492s ***** assert (nctpdf (x1, df, 1), [0, NaN, NaN, 0.0482312135423008, 0], 1e-14);
492s ***** assert (nctpdf (x1, df, 3), [0, NaN, NaN, 0.1048493126401585, 0], 1e-14);
492s ***** assert (nctpdf (x1, df, 2), [0, NaN, NaN, 0.08137377919890307, 0], 1e-14);
492s ***** assert (nctpdf (x1, 3, mu), [0, NaN, NaN, 0.001185305171654381, 0], 1e-14);
492s ***** assert (nctpdf (2, df, mu), [0.1791097459405861, NaN, NaN, ...
492s                              0.0146500727180389, 0.3082302682110299], 1e-14);
492s ***** assert (nctpdf (4, df, mu), [0.04467929612254971, NaN, NaN, ...
492s                              0.00401787561306999, 0.0972086534042828], 1e-14);
492s ***** error<nctpdf: function called with too few input arguments.> nctpdf ()
492s ***** error<nctpdf: function called with too few input arguments.> nctpdf (1)
492s ***** error<nctpdf: function called with too few input arguments.> nctpdf (1, 2)
492s ***** error<nctpdf: X, DF, and MU must be of common size or scalars.> ...
492s  nctpdf (ones (3), ones (2), ones (2))
492s ***** error<nctpdf: X, DF, and MU must be of common size or scalars.> ...
492s  nctpdf (ones (2), ones (3), ones (2))
492s ***** error<nctpdf: X, DF, and MU must be of common size or scalars.> ...
492s  nctpdf (ones (2), ones (2), ones (3))
492s ***** error<nctpdf: X, DF, and MU must not be complex.> nctpdf (i, 2, 2)
492s ***** error<nctpdf: X, DF, and MU must not be complex.> nctpdf (2, i, 2)
492s ***** error<nctpdf: X, DF, and MU must not be complex.> nctpdf (2, 2, i)
492s 16 tests, 16 passed, 0 known failure, 0 skipped
492s [inst/dist_fun/nctrnd.m]
492s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/nctrnd.m
492s ***** assert (size (nctrnd (1, 1)), [1 1])
492s ***** assert (size (nctrnd (1, ones (2,1))), [2, 1])
492s ***** assert (size (nctrnd (1, ones (2,2))), [2, 2])
492s ***** assert (size (nctrnd (ones (2,1), 1)), [2, 1])
492s ***** assert (size (nctrnd (ones (2,2), 1)), [2, 2])
492s ***** assert (size (nctrnd (1, 1, 3)), [3, 3])
492s ***** assert (size (nctrnd (1, 1, [4, 1])), [4, 1])
492s ***** assert (size (nctrnd (1, 1, 4, 1)), [4, 1])
492s ***** assert (size (nctrnd (1, 1, 4, 1, 5)), [4, 1, 5])
492s ***** assert (size (nctrnd (1, 1, 0, 1)), [0, 1])
492s ***** assert (size (nctrnd (1, 1, 1, 0)), [1, 0])
492s ***** assert (size (nctrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
492s ***** assert (class (nctrnd (1, 1)), "double")
492s ***** assert (class (nctrnd (1, single (1))), "single")
492s ***** assert (class (nctrnd (1, single ([1, 1]))), "single")
492s ***** assert (class (nctrnd (single (1), 1)), "single")
492s ***** assert (class (nctrnd (single ([1, 1]), 1)), "single")
492s ***** error<nctrnd: function called with too few input arguments.> nctrnd ()
492s ***** error<nctrnd: function called with too few input arguments.> nctrnd (1)
492s ***** error<nctrnd: DF and MU must be of common size or scalars.> ...
492s  nctrnd (ones (3), ones (2))
492s ***** error<nctrnd: DF and MU must be of common size or scalars.> ...
492s  nctrnd (ones (2), ones (3))
492s ***** error<nctrnd: DF and MU must not be complex.> nctrnd (i, 2)
492s ***** error<nctrnd: DF and MU must not be complex.> nctrnd (1, i)
492s ***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
492s  nctrnd (1, 2, -1)
492s ***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
492s  nctrnd (1, 2, 1.2)
492s ***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
492s  nctrnd (1, 2, ones (2))
492s ***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
492s  nctrnd (1, 2, [2 -1 2])
492s ***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
492s  nctrnd (1, 2, [2 0 2.5])
492s ***** error<nctrnd: dimensions must be non-negative integers.> ...
492s  nctrnd (1, 2, 2, -1, 5)
492s ***** error<nctrnd: dimensions must be non-negative integers.> ...
492s  nctrnd (1, 2, 2, 1.5, 5)
492s ***** error<nctrnd: DF and MU must be scalars or of size SZ.> ...
492s  nctrnd (2, ones (2), 3)
492s ***** error<nctrnd: DF and MU must be scalars or of size SZ.> ...
492s  nctrnd (2, ones (2), [3, 2])
492s ***** error<nctrnd: DF and MU must be scalars or of size SZ.> ...
492s  nctrnd (2, ones (2), 3, 2)
492s 33 tests, 33 passed, 0 known failure, 0 skipped
492s [inst/dist_fun/ncx2cdf.m]
492s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/ncx2cdf.m
492s ***** demo
492s  ## Plot various CDFs from the noncentral chi-squared distribution
492s  x = 0:0.1:10;
492s  p1 = ncx2cdf (x, 2, 1);
492s  p2 = ncx2cdf (x, 2, 2);
492s  p3 = ncx2cdf (x, 2, 3);
492s  p4 = ncx2cdf (x, 4, 1);
492s  p5 = ncx2cdf (x, 4, 2);
492s  p6 = ncx2cdf (x, 4, 3);
492s  plot (x, p1, "-r", x, p2, "-g", x, p3, "-k", ...
492s        x, p4, "-m", x, p5, "-c", x, p6, "-y")
492s  grid on
492s  xlim ([0, 10])
492s  legend ({"df = 2, λ = 1", "df = 2, λ = 2", ...
492s           "df = 2, λ = 3", "df = 4, λ = 1", ...
492s           "df = 4, λ = 2", "df = 4, λ = 3"}, "location", "southeast")
492s  title ("Noncentral chi-squared CDF")
492s  xlabel ("values in x")
492s  ylabel ("probability")
492s ***** demo
492s  ## Compare the noncentral chi-squared CDF with LAMBDA = 2 to the
492s  ## chi-squared CDF with the same number of degrees of freedom (4).
492s 
492s  x = 0:0.1:10;
492s  p1 = ncx2cdf (x, 4, 2);
492s  p2 = chi2cdf (x, 4);
492s  plot (x, p1, "-", x, p2, "-")
492s  grid on
492s  xlim ([0, 10])
492s  legend ({"Noncentral χ^2(4,2)", "χ^2(4)"}, "location", "northwest")
492s  title ("Noncentral chi-squared vs chi-squared CDFs")
492s  xlabel ("values in x")
492s  ylabel ("probability")
492s ***** test
492s  x = -2:0.1:2;
492s  p = ncx2cdf (x, 10, 1);
492s  assert (p([1:21]), zeros (1, 21), 3e-84);
492s  assert (p(22), 1.521400636466575e-09, 1e-14);
492s  assert (p(30), 6.665480510026046e-05, 1e-14);
492s  assert (p(41), 0.002406447308399836, 1e-14);
492s ***** test
492s  p = ncx2cdf (12, 10, 3);
492s  assert (p, 0.4845555602398649, 1e-14);
493s ***** test
493s  p = ncx2cdf (2, 3, 2);
493s  assert (p, 0.2207330870741212, 1e-14);
493s ***** test
493s  p = ncx2cdf (2, 3, 2, "upper");
493s  assert (p, 0.7792669129258789, 1e-14);
493s ***** test
493s  p = ncx2cdf ([3, 6], 3, 2, "upper");
493s  assert (p, [0.6423318186400054, 0.3152299878943012], 1e-14);
493s ***** error<ncx2cdf: function called with too few input arguments.> ncx2cdf ()
493s ***** error<ncx2cdf: function called with too few input arguments.> ncx2cdf (1)
493s ***** error<ncx2cdf: function called with too few input arguments.> ncx2cdf (1, 2)
493s ***** error<ncx2cdf: invalid argument for upper tail.> ncx2cdf (1, 2, 3, "tail")
493s ***** error<ncx2cdf: invalid argument for upper tail.> ncx2cdf (1, 2, 3, 4)
493s ***** error<ncx2cdf: X, DF, and LAMBDA must be of common size or scalars.> ...
493s  ncx2cdf (ones (3), ones (2), ones (2))
493s ***** error<ncx2cdf: X, DF, and LAMBDA must be of common size or scalars.> ...
493s  ncx2cdf (ones (2), ones (3), ones (2))
493s ***** error<ncx2cdf: X, DF, and LAMBDA must be of common size or scalars.> ...
493s  ncx2cdf (ones (2), ones (2), ones (3))
493s ***** error<ncx2cdf: X, DF, and LAMBDA must not be complex.> ncx2cdf (i, 2, 2)
493s ***** error<ncx2cdf: X, DF, and LAMBDA must not be complex.> ncx2cdf (2, i, 2)
493s ***** error<ncx2cdf: X, DF, and LAMBDA must not be complex.> ncx2cdf (2, 2, i)
493s 16 tests, 16 passed, 0 known failure, 0 skipped
493s [inst/dist_fun/ncx2inv.m]
493s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/ncx2inv.m
493s ***** demo
493s  ## Plot various iCDFs from the noncentral chi-squared distribution
493s  p = 0.001:0.001:0.999;
493s  x1 = ncx2inv (p, 2, 1);
493s  x2 = ncx2inv (p, 2, 2);
493s  x3 = ncx2inv (p, 2, 3);
493s  x4 = ncx2inv (p, 4, 1);
493s  x5 = ncx2inv (p, 4, 2);
493s  x6 = ncx2inv (p, 4, 3);
493s  plot (p, x1, "-r", p, x2, "-g", p, x3, "-k", ...
493s        p, x4, "-m", p, x5, "-c", p, x6, "-y")
493s  grid on
493s  ylim ([0, 10])
493s  legend ({"df = 2, λ = 1", "df = 2, λ = 2", ...
493s           "df = 2, λ = 3", "df = 4, λ = 1", ...
493s           "df = 4, λ = 2", "df = 4, λ = 3"}, "location", "northwest")
493s  title ("Noncentral chi-squared iCDF")
493s  xlabel ("probability")
493s  ylabel ("values in x")
493s ***** demo
493s  ## Compare the noncentral chi-squared CDF with LAMBDA = 2 to the
493s  ## chi-squared CDF with the same number of degrees of freedom (4).
493s 
493s  p = 0.001:0.001:0.999;
493s  x1 = ncx2inv (p, 4, 2);
493s  x2 = chi2inv (p, 4);
493s  plot (p, x1, "-", p, x2, "-");
493s  grid on
493s  ylim ([0, 10])
493s  legend ({"Noncentral χ^2(4,2)", "χ^2(4)"}, "location", "northwest")
493s  title ("Noncentral chi-squared vs chi-squared quantile functions")
493s  xlabel ("probability")
493s  ylabel ("values in x")
493s ***** test
493s  x = [0,0.3443,0.7226,1.1440,1.6220,2.1770,2.8436,3.6854,4.8447,6.7701,Inf];
493s  assert (ncx2inv ([0:0.1:1], 2, 1), x, 1e-4);
493s ***** test
493s  x = [0,0.8295,1.6001,2.3708,3.1785,4.0598,5.0644,6.2765,7.8763,10.4199,Inf];
493s  assert (ncx2inv ([0:0.1:1], 2, 3), x, 1e-4);
493s ***** test
493s  x = [0,0.5417,1.3483,2.1796,3.0516,4.0003,5.0777,6.3726,8.0748,10.7686,Inf];
493s  assert (ncx2inv ([0:0.1:1], 1, 4), x, 1e-4);
493s ***** test
493s  x = [0.1808, 0.6456, 1.1842, 1.7650, 2.3760, 3.0105];
493s  assert (ncx2inv (0.05, [1, 2, 3, 4, 5, 6], 4), x, 1e-4);
493s ***** test
493s  x = [0.4887, 0.6699, 0.9012, 1.1842, 1.5164, 1.8927];
493s  assert (ncx2inv (0.05, 3, [1, 2, 3, 4, 5, 6]), x, 1e-4);
494s ***** test
494s  x = [1.3941, 1.6824, 2.0103, 2.3760, NaN, 3.2087];
494s  assert (ncx2inv (0.05, 5, [1, 2, 3, 4, -1, 6]), x, 1e-4);
494s ***** test
494s  assert (ncx2inv (0.996, 5, 8), 35.51298862765576, 3e-13);
494s ***** error<ncx2inv: function called with too few input arguments.> ncx2inv ()
494s ***** error<ncx2inv: function called with too few input arguments.> ncx2inv (1)
494s ***** error<ncx2inv: function called with too few input arguments.> ncx2inv (1, 2)
494s ***** error<ncx2inv: P, DF, and LAMBDA must be of common size or scalars.> ...
494s  ncx2inv (ones (3), ones (2), ones (2))
494s ***** error<ncx2inv: P, DF, and LAMBDA must be of common size or scalars.> ...
494s  ncx2inv (ones (2), ones (3), ones (2))
494s ***** error<ncx2inv: P, DF, and LAMBDA must be of common size or scalars.> ...
494s  ncx2inv (ones (2), ones (2), ones (3))
494s ***** error<ncx2inv: P, DF, and LAMBDA must not be complex.> ncx2inv (i, 2, 2)
494s ***** error<ncx2inv: P, DF, and LAMBDA must not be complex.> ncx2inv (2, i, 2)
494s ***** error<ncx2inv: P, DF, and LAMBDA must not be complex.> ncx2inv (2, 2, i)
494s 16 tests, 16 passed, 0 known failure, 0 skipped
494s [inst/dist_fun/ncx2pdf.m]
494s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/ncx2pdf.m
494s ***** demo
494s  ## Plot various PDFs from the noncentral chi-squared distribution
494s  x = 0:0.1:10;
494s  y1 = ncx2pdf (x, 2, 1);
494s  y2 = ncx2pdf (x, 2, 2);
494s  y3 = ncx2pdf (x, 2, 3);
494s  y4 = ncx2pdf (x, 4, 1);
494s  y5 = ncx2pdf (x, 4, 2);
494s  y6 = ncx2pdf (x, 4, 3);
494s  plot (x, y1, "-r", x, y2, "-g", x, y3, "-k", ...
494s        x, y4, "-m", x, y5, "-c", x, y6, "-y")
494s  grid on
494s  xlim ([0, 10])
494s  ylim ([0, 0.32])
494s  legend ({"df = 2, λ = 1", "df = 2, λ = 2", ...
494s           "df = 2, λ = 3", "df = 4, λ = 1", ...
494s           "df = 4, λ = 2", "df = 4, λ = 3"}, "location", "northeast")
494s  title ("Noncentral chi-squared PDF")
494s  xlabel ("values in x")
494s  ylabel ("density")
494s ***** demo
494s  ## Compare the noncentral chi-squared PDF with LAMBDA = 2 to the
494s  ## chi-squared PDF with the same number of degrees of freedom (4).
494s 
494s  x = 0:0.1:10;
494s  y1 = ncx2pdf (x, 4, 2);
494s  y2 = chi2pdf (x, 4);
494s  plot (x, y1, "-", x, y2, "-");
494s  grid on
494s  xlim ([0, 10])
494s  ylim ([0, 0.32])
494s  legend ({"Noncentral T(10,1)", "T(10)"}, "location", "northwest")
494s  title ("Noncentral chi-squared vs chi-squared PDFs")
494s  xlabel ("values in x")
494s  ylabel ("density")
494s ***** shared x1, df, d1
494s  x1 = [-Inf, 2, NaN, 4, Inf];
494s  df = [2, 0, -1, 1, 4];
494s  d1 = [1, NaN, 3, -1, 2];
494s ***** assert (ncx2pdf (x1, df, d1), [0, NaN, NaN, NaN, 0]);
494s ***** assert (ncx2pdf (x1, df, 1), [0, 0.07093996461786045, NaN, ...
494s                               0.06160064323277038, 0], 1e-14);
494s ***** assert (ncx2pdf (x1, df, 3), [0, 0.1208364909271113, NaN, ...
494s                               0.09631299762429098, 0], 1e-14);
494s ***** assert (ncx2pdf (x1, df, 2), [0, 0.1076346446244688, NaN, ...
494s                               0.08430464047296625, 0], 1e-14);
494s ***** assert (ncx2pdf (x1, 2, d1), [0, NaN, NaN, NaN, 0]);
494s ***** assert (ncx2pdf (2, df, d1), [0.1747201674611283, NaN, NaN, ...
494s                               NaN, 0.1076346446244688], 1e-14);
494s ***** assert (ncx2pdf (4, df, d1), [0.09355987820265799, NaN, NaN, ...
494s                               NaN, 0.1192317192431485], 1e-14);
494s ***** error<ncx2pdf: function called with too few input arguments.> ncx2pdf ()
494s ***** error<ncx2pdf: function called with too few input arguments.> ncx2pdf (1)
494s ***** error<ncx2pdf: function called with too few input arguments.> ncx2pdf (1, 2)
495s ***** error<ncx2pdf: X, DF, and LAMBDA must be of common size or scalars.> ...
495s  ncx2pdf (ones (3), ones (2), ones (2))
495s ***** error<ncx2pdf: X, DF, and LAMBDA must be of common size or scalars.> ...
495s  ncx2pdf (ones (2), ones (3), ones (2))
495s ***** error<ncx2pdf: X, DF, and LAMBDA must be of common size or scalars.> ...
495s  ncx2pdf (ones (2), ones (2), ones (3))
495s ***** error<ncx2pdf: X, DF, and LAMBDA must not be complex.> ncx2pdf (i, 2, 2)
495s ***** error<ncx2pdf: X, DF, and LAMBDA must not be complex.> ncx2pdf (2, i, 2)
495s ***** error<ncx2pdf: X, DF, and LAMBDA must not be complex.> ncx2pdf (2, 2, i)
495s 16 tests, 16 passed, 0 known failure, 0 skipped
495s [inst/dist_fun/ncx2rnd.m]
495s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/ncx2rnd.m
495s ***** assert (size (ncx2rnd (1, 1)), [1 1])
495s ***** assert (size (ncx2rnd (1, ones (2,1))), [2, 1])
495s ***** assert (size (ncx2rnd (1, ones (2,2))), [2, 2])
495s ***** assert (size (ncx2rnd (ones (2,1), 1)), [2, 1])
495s ***** assert (size (ncx2rnd (ones (2,2), 1)), [2, 2])
495s ***** assert (size (ncx2rnd (1, 1, 3)), [3, 3])
495s ***** assert (size (ncx2rnd (1, 1, [4, 1])), [4, 1])
495s ***** assert (size (ncx2rnd (1, 1, 4, 1)), [4, 1])
495s ***** assert (size (ncx2rnd (1, 1, 4, 1, 5)), [4, 1, 5])
495s ***** assert (size (ncx2rnd (1, 1, 0, 1)), [0, 1])
495s ***** assert (size (ncx2rnd (1, 1, 1, 0)), [1, 0])
495s ***** assert (size (ncx2rnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
495s ***** assert (class (ncx2rnd (1, 1)), "double")
495s ***** assert (class (ncx2rnd (1, single (1))), "single")
495s ***** assert (class (ncx2rnd (1, single ([1, 1]))), "single")
495s ***** assert (class (ncx2rnd (single (1), 1)), "single")
495s ***** assert (class (ncx2rnd (single ([1, 1]), 1)), "single")
495s ***** error<ncx2rnd: function called with too few input arguments.> ncx2rnd ()
495s ***** error<ncx2rnd: function called with too few input arguments.> ncx2rnd (1)
495s ***** error<ncx2rnd: DF and LAMBDA must be of common size or scalars.> ...
495s  ncx2rnd (ones (3), ones (2))
495s ***** error<ncx2rnd: DF and LAMBDA must be of common size or scalars.> ...
495s  ncx2rnd (ones (2), ones (3))
495s ***** error<ncx2rnd: DF and LAMBDA must not be complex.> ncx2rnd (i, 2)
495s ***** error<ncx2rnd: DF and LAMBDA must not be complex.> ncx2rnd (1, i)
495s ***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
495s  ncx2rnd (1, 2, -1)
495s ***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
495s  ncx2rnd (1, 2, 1.2)
495s ***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
495s  ncx2rnd (1, 2, ones (2))
495s ***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
495s  ncx2rnd (1, 2, [2 -1 2])
495s ***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
495s  ncx2rnd (1, 2, [2 0 2.5])
495s ***** error<ncx2rnd: dimensions must be non-negative integers.> ...
495s  ncx2rnd (1, 2, 2, -1, 5)
495s ***** error<ncx2rnd: dimensions must be non-negative integers.> ...
495s  ncx2rnd (1, 2, 2, 1.5, 5)
495s ***** error<ncx2rnd: DF and LAMBDA must be scalars or of size SZ.> ...
495s  ncx2rnd (2, ones (2), 3)
495s ***** error<ncx2rnd: DF and LAMBDA must be scalars or of size SZ.> ...
495s  ncx2rnd (2, ones (2), [3, 2])
495s ***** error<ncx2rnd: DF and LAMBDA must be scalars or of size SZ.> ...
495s  ncx2rnd (2, ones (2), 3, 2)
495s 33 tests, 33 passed, 0 known failure, 0 skipped
495s [inst/dist_fun/normcdf.m]
495s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/normcdf.m
495s ***** demo
495s  ## Plot various CDFs from the normal distribution
495s  x = -5:0.01:5;
495s  p1 = normcdf (x, 0, 0.5);
495s  p2 = normcdf (x, 0, 1);
495s  p3 = normcdf (x, 0, 2);
495s  p4 = normcdf (x, -2, 0.8);
495s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
495s  grid on
495s  xlim ([-5, 5])
495s  legend ({"μ = 0, σ = 0.5", "μ = 0, σ = 1", ...
495s           "μ = 0, σ = 2", "μ = -2, σ = 0.8"}, "location", "southeast")
495s  title ("Normal CDF")
495s  xlabel ("values in x")
495s  ylabel ("probability")
495s ***** shared x, y
495s  x = [-Inf 1 2 Inf];
495s  y = [0, 0.5, 1/2*(1+erf(1/sqrt(2))), 1];
495s ***** assert (normcdf (x, ones (1,4), ones (1,4)), y)
495s ***** assert (normcdf (x, 1, ones (1,4)), y)
495s ***** assert (normcdf (x, ones (1,4), 1), y)
495s ***** assert (normcdf (x, [0, -Inf, NaN, Inf], 1), [0, 1, NaN, NaN])
495s ***** assert (normcdf (x, 1, [Inf, NaN, -1, 0]), [NaN, NaN, NaN, 1])
495s ***** assert (normcdf ([x(1:2), NaN, x(4)], 1, 1), [y(1:2), NaN, y(4)])
495s ***** assert (normcdf (x, "upper"), [1, 0.1587, 0.0228, 0], 1e-4)
495s ***** assert (normcdf ([x, NaN], 1, 1), [y, NaN])
495s ***** assert (normcdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
495s ***** assert (normcdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
495s ***** assert (normcdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
495s ***** error<normcdf: invalid number of input arguments.> normcdf ()
495s ***** error<normcdf: invalid number of input arguments.> normcdf (1,2,3,4,5,6,7)
495s ***** error<normcdf: invalid argument for upper tail.> normcdf (1, 2, 3, 4, "uper")
495s ***** error<normcdf: X, MU, and SIGMA must be of common size or scalars.> ...
495s  normcdf (ones (3), ones (2), ones (2))
495s ***** error<normcdf: invalid size of covariance matrix.> normcdf (2, 3, 4, [1, 2])
495s ***** error<normcdf: covariance matrix is required for confidence bounds.> ...
495s  [p, plo, pup] = normcdf (1, 2, 3)
495s ***** error<normcdf: invalid value for alpha.> [p, plo, pup] = ...
495s  normcdf (1, 2, 3, [1, 0; 0, 1], 0)
495s ***** error<normcdf: invalid value for alpha.> [p, plo, pup] = ...
495s  normcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
495s ***** error<normcdf: invalid value for alpha.> [p, plo, pup] = ...
495s  normcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
495s ***** error<normcdf: X, MU, and SIGMA must not be complex.> normcdf (i, 2, 2)
495s ***** error<normcdf: X, MU, and SIGMA must not be complex.> normcdf (2, i, 2)
495s ***** error<normcdf: X, MU, and SIGMA must not be complex.> normcdf (2, 2, i)
495s ***** error<normcdf: bad covariance matrix.> ...
495s  [p, plo, pup] =normcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
495s 24 tests, 24 passed, 0 known failure, 0 skipped
495s [inst/dist_fun/norminv.m]
495s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/norminv.m
495s ***** demo
495s  ## Plot various iCDFs from the normal distribution
495s  p = 0.001:0.001:0.999;
495s  x1 = norminv (p, 0, 0.5);
495s  x2 = norminv (p, 0, 1);
495s  x3 = norminv (p, 0, 2);
495s  x4 = norminv (p, -2, 0.8);
495s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
495s  grid on
495s  ylim ([-5, 5])
495s  legend ({"μ = 0, σ = 0.5", "μ = 0, σ = 1", ...
495s           "μ = 0, σ = 2", "μ = -2, σ = 0.8"}, "location", "northwest")
495s  title ("Normal iCDF")
495s  xlabel ("probability")
495s  ylabel ("values in x")
495s ***** shared p
495s  p = [-1 0 0.5 1 2];
495s ***** assert (norminv (p, ones (1,5), ones (1,5)), [NaN -Inf 1 Inf NaN])
495s ***** assert (norminv (p, 1, ones (1,5)), [NaN -Inf 1 Inf NaN])
495s ***** assert (norminv (p, ones (1,5), 1), [NaN -Inf 1 Inf NaN])
495s ***** assert (norminv (p, [1 -Inf NaN Inf 1], 1), [NaN NaN NaN NaN NaN])
495s ***** assert (norminv (p, 1, [1 0 NaN Inf 1]), [NaN NaN NaN NaN NaN])
495s ***** assert (norminv ([p(1:2) NaN p(4:5)], 1, 1), [NaN -Inf NaN Inf NaN])
495s ***** assert (norminv (p), probit (p))
495s ***** assert (norminv (0.31254), probit (0.31254))
495s ***** assert (norminv ([p, NaN], 1, 1), [NaN -Inf 1 Inf NaN NaN])
495s ***** assert (norminv (single ([p, NaN]), 1, 1), single ([NaN -Inf 1 Inf NaN NaN]))
495s ***** assert (norminv ([p, NaN], single (1), 1), single ([NaN -Inf 1 Inf NaN NaN]))
495s ***** assert (norminv ([p, NaN], 1, single (1)), single ([NaN -Inf 1 Inf NaN NaN]))
495s ***** error<norminv: function called with too few input arguments.> norminv ()
495s ***** error<norminv: P, MU, and SIGMA must be of common size or scalars.> ...
495s  norminv (ones (3), ones (2), ones (2))
495s ***** error<norminv: P, MU, and SIGMA must be of common size or scalars.> ...
495s  norminv (ones (2), ones (3), ones (2))
495s ***** error<norminv: P, MU, and SIGMA must be of common size or scalars.> ...
495s  norminv (ones (2), ones (2), ones (3))
495s ***** error<norminv: P, MU, and SIGMA must not be complex.> norminv (i, 2, 2)
495s ***** error<norminv: P, MU, and SIGMA must not be complex.> norminv (2, i, 2)
495s ***** error<norminv: P, MU, and SIGMA must not be complex.> norminv (2, 2, i)
495s 19 tests, 19 passed, 0 known failure, 0 skipped
495s [inst/dist_fun/normpdf.m]
495s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/normpdf.m
495s ***** demo
495s  ## Plot various PDFs from the normal distribution
495s  x = -5:0.01:5;
495s  y1 = normpdf (x, 0, 0.5);
495s  y2 = normpdf (x, 0, 1);
495s  y3 = normpdf (x, 0, 2);
495s  y4 = normpdf (x, -2, 0.8);
495s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
495s  grid on
495s  xlim ([-5, 5])
495s  ylim ([0, 0.9])
495s  legend ({"μ = 0, σ = 0.5", "μ = 0, σ = 1", ...
495s           "μ = 0, σ = 2", "μ = -2, σ = 0.8"}, "location", "northeast")
495s  title ("Normal PDF")
495s  xlabel ("values in x")
495s  ylabel ("density")
495s ***** shared x, y
495s  x = [-Inf, 1, 2, Inf];
495s  y = 1 / sqrt (2 * pi) * exp (-(x - 1) .^ 2 / 2);
495s ***** assert (normpdf (x, ones (1,4), ones (1,4)), y, eps)
495s ***** assert (normpdf (x, 1, ones (1,4)), y, eps)
495s ***** assert (normpdf (x, ones (1,4), 1), y, eps)
495s ***** assert (normpdf (x, [0 -Inf NaN Inf], 1), [y(1) NaN NaN NaN], eps)
495s ***** assert (normpdf (x, 1, [Inf NaN -1 0]), [NaN NaN NaN NaN], eps)
495s ***** assert (normpdf ([x, NaN], 1, 1), [y, NaN], eps)
495s ***** assert (normpdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
495s ***** assert (normpdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
495s ***** assert (normpdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
495s ***** error<normpdf: function called with too few input arguments.> normpdf ()
495s ***** error<normpdf: X, MU, and SIGMA must be of common size or scalars.> ...
495s  normpdf (ones (3), ones (2), ones (2))
495s ***** error<normpdf: X, MU, and SIGMA must be of common size or scalars.> ...
495s  normpdf (ones (2), ones (3), ones (2))
495s ***** error<normpdf: X, MU, and SIGMA must be of common size or scalars.> ...
495s  normpdf (ones (2), ones (2), ones (3))
495s ***** error<normpdf: X, MU, and SIGMA must not be complex.> normpdf (i, 2, 2)
495s ***** error<normpdf: X, MU, and SIGMA must not be complex.> normpdf (2, i, 2)
495s ***** error<normpdf: X, MU, and SIGMA must not be complex.> normpdf (2, 2, i)
495s 16 tests, 16 passed, 0 known failure, 0 skipped
495s [inst/dist_fun/normrnd.m]
495s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/normrnd.m
495s ***** assert (size (normrnd (1, 1)), [1 1])
495s ***** assert (size (normrnd (1, ones (2,1))), [2, 1])
495s ***** assert (size (normrnd (1, ones (2,2))), [2, 2])
495s ***** assert (size (normrnd (ones (2,1), 1)), [2, 1])
495s ***** assert (size (normrnd (ones (2,2), 1)), [2, 2])
495s ***** assert (size (normrnd (1, 1, 3)), [3, 3])
495s ***** assert (size (normrnd (1, 1, [4, 1])), [4, 1])
495s ***** assert (size (normrnd (1, 1, 4, 1)), [4, 1])
495s ***** assert (size (normrnd (1, 1, 4, 1, 5)), [4, 1, 5])
495s ***** assert (size (normrnd (1, 1, 0, 1)), [0, 1])
495s ***** assert (size (normrnd (1, 1, 1, 0)), [1, 0])
495s ***** assert (size (normrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
495s ***** assert (class (normrnd (1, 1)), "double")
495s ***** assert (class (normrnd (1, single (1))), "single")
495s ***** assert (class (normrnd (1, single ([1, 1]))), "single")
495s ***** assert (class (normrnd (single (1), 1)), "single")
495s ***** assert (class (normrnd (single ([1, 1]), 1)), "single")
495s ***** error<normrnd: function called with too few input arguments.> normrnd ()
495s ***** error<normrnd: function called with too few input arguments.> normrnd (1)
495s ***** error<normrnd: MU and SIGMA must be of common size or scalars.> ...
495s  normrnd (ones (3), ones (2))
495s ***** error<normrnd: MU and SIGMA must be of common size or scalars.> ...
495s  normrnd (ones (2), ones (3))
495s ***** error<normrnd: MU and SIGMA must not be complex.> normrnd (i, 2, 3)
495s ***** error<normrnd: MU and SIGMA must not be complex.> normrnd (1, i, 3)
495s ***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
495s  normrnd (1, 2, -1)
495s ***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
495s  normrnd (1, 2, 1.2)
495s ***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
495s  normrnd (1, 2, ones (2))
495s ***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
495s  normrnd (1, 2, [2 -1 2])
495s ***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
495s  normrnd (1, 2, [2 0 2.5])
495s ***** error<normrnd: dimensions must be non-negative integers.> ...
495s  normrnd (1, 2, 2, -1, 5)
495s ***** error<normrnd: dimensions must be non-negative integers.> ...
495s  normrnd (1, 2, 2, 1.5, 5)
495s ***** error<normrnd: MU and SIGMA must be scalars or of size SZ.> ...
495s  normrnd (2, ones (2), 3)
495s ***** error<normrnd: MU and SIGMA must be scalars or of size SZ.> ...
495s  normrnd (2, ones (2), [3, 2])
495s ***** error<normrnd: MU and SIGMA must be scalars or of size SZ.> ...
495s  normrnd (2, ones (2), 3, 2)
495s 33 tests, 33 passed, 0 known failure, 0 skipped
495s [inst/dist_fun/plcdf.m]
495s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/plcdf.m
495s ***** demo
495s  ## Plot various CDFs from the Piecewise linear distribution
495s  data = 0:0.01:10;
495s  x1 = [0, 1, 3, 4, 7, 10];
495s  Fx1 = [0, 0.2, 0.5, 0.6, 0.7, 1];
495s  x2 = [0, 2, 5, 6, 7, 8];
495s  Fx2 = [0, 0.1, 0.3, 0.6, 0.9, 1];
495s  p1 = plcdf (data, x1, Fx1);
495s  p2 = plcdf (data, x2, Fx2);
495s  plot (data, p1, "-b", data, p2, "g")
495s  grid on
495s  ylim ([0, 1])
495s  xlim ([0, 10])
495s  legend ({"x1, Fx1", "x2, Fx2"}, "location", "southeast")
495s  title ("Piecewise linear CDF")
495s  xlabel ("values in data")
495s  ylabel ("probability")
495s ***** test
495s  data = 0:0.2:1;
495s  p = plcdf (data, [0, 1], [0, 1]);
495s  assert (p, data);
495s ***** test
495s  data = 0:0.2:1;
495s  p = plcdf (data, [0, 2], [0, 1]);
495s  assert (p, 0.5 * data);
495s ***** test
495s  data = 0:0.2:1;
495s  p = plcdf (data, [0, 1], [0, 0.5]);
495s  assert (p, 0.5 * data);
495s ***** test
495s  data = 0:0.2:1;
495s  p = plcdf (data, [0, 0.5], [0, 1]);
495s  assert (p, [0, 0.4, 0.8, 1, 1, 1]);
495s ***** test
495s  data = 0:0.2:1;
495s  p = plcdf (data, [0, 1], [0, 1], "upper");
495s  assert (p, 1 - data);
495s ***** error<plcdf: function called with too few input arguments.> plcdf ()
495s ***** error<plcdf: function called with too few input arguments.> plcdf (1)
495s ***** error<plcdf: function called with too few input arguments.> plcdf (1, 2)
495s ***** error<plcdf: invalid argument for upper tail.> plcdf (1, 2, 3, "uper")
495s ***** error<plcdf: invalid argument for upper tail.> plcdf (1, 2, 3, 4)
495s ***** error<plcdf: X and FX must be vectors of equal size.> ...
495s  plcdf (1, [0, 1, 2], [0, 1])
495s ***** error<plcdf: X and FX must be at least two-elements long.> ...
495s  plcdf (1, [0], [1])
495s ***** error<plcdf: FX must be bounded in the range> ...
495s  plcdf (1, [0, 1, 2], [0, 1, 1.5])
495s ***** error<plcdf: FX must be bounded in the range> ...
495s  plcdf (1, [0, 1, 2], [0, i, 1])
495s ***** error<plcdf: DATA, X, and FX must not be complex.> ...
495s  plcdf (i, [0, 1, 2], [0, 0.5, 1])
495s ***** error<plcdf: DATA, X, and FX must not be complex.> ...
495s  plcdf (1, [0, i, 2], [0, 0.5, 1])
495s ***** error<plcdf: DATA, X, and FX must not be complex.> ...
495s  plcdf (1, [0, 1, 2], [0, 0.5i, 1])
495s 17 tests, 17 passed, 0 known failure, 0 skipped
495s [inst/dist_fun/plinv.m]
495s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/plinv.m
495s ***** demo
495s  ## Plot various iCDFs from the Piecewise linear distribution
495s  p = 0.001:0.001:0.999;
495s  x1 = [0, 1, 3, 4, 7, 10];
495s  Fx1 = [0, 0.2, 0.5, 0.6, 0.7, 1];
495s  x2 = [0, 2, 5, 6, 7, 8];
495s  Fx2 = [0, 0.1, 0.3, 0.6, 0.9, 1];
495s  data1 = plinv (p, x1, Fx1);
495s  data2 = plinv (p, x2, Fx2);
495s  plot (p, data1, "-b", p, data2, "-g")
495s  grid on
495s  legend ({"x1, Fx1", "x2, Fx2"}, "location", "northwest")
495s  title ("Piecewise linear iCDF")
495s  xlabel ("probability")
495s  ylabel ("values in data")
495s ***** test
495s  p = 0:0.2:1;
495s  data = plinv (p, [0, 1], [0, 1]);
495s  assert (data, p);
495s ***** test
495s  p = 0:0.2:1;
495s  data = plinv (p, [0, 2], [0, 1]);
495s  assert (data, 2 * p);
495s ***** test
495s  p = 0:0.2:1;
495s  data_out = 1:6;
495s  data = plinv (p, [0, 1], [0, 0.5]);
495s  assert (data, [0, 0.4, 0.8, NA, NA, NA]);
495s ***** test
495s  p = 0:0.2:1;
495s  data_out = 1:6;
495s  data = plinv (p, [0, 0.5], [0, 1]);
495s  assert (data, [0:0.1:0.5]);
495s ***** error<plinv: function called with too few input arguments.> plinv ()
495s ***** error<plinv: function called with too few input arguments.> plinv (1)
495s ***** error<plinv: function called with too few input arguments.> plinv (1, 2)
495s ***** error<plinv: X and FX must be vectors of equal size.> ...
495s  plinv (1, [0, 1, 2], [0, 1])
495s ***** error<plinv: X and FX must be at least two-elements long.> ...
495s  plinv (1, [0], [1])
495s ***** error<plinv: FX must be bounded in the range> ...
495s  plinv (1, [0, 1, 2], [0, 1, 1.5])
495s ***** error<plinv: FX must be bounded in the range> ...
495s  plinv (1, [0, 1, 2], [0, i, 1])
495s ***** error<plinv: P, X, and FX must not be complex.> ...
495s  plinv (i, [0, 1, 2], [0, 0.5, 1])
495s ***** error<plinv: P, X, and FX must not be complex.> ...
495s  plinv (1, [0, i, 2], [0, 0.5, 1])
495s ***** error<plinv: P, X, and FX must not be complex.> ...
495s  plinv (1, [0, 1, 2], [0, 0.5i, 1])
495s 14 tests, 14 passed, 0 known failure, 0 skipped
495s [inst/dist_fun/plpdf.m]
495s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/plpdf.m
495s ***** demo
495s  ## Plot various PDFs from the Piecewise linear distribution
495s  data = 0:0.01:10;
495s  x1 = [0, 1, 3, 4, 7, 10];
495s  Fx1 = [0, 0.2, 0.5, 0.6, 0.7, 1];
495s  x2 = [0, 2, 5, 6, 7, 8];
495s  Fx2 = [0, 0.1, 0.3, 0.6, 0.9, 1];
495s  y1 = plpdf (data, x1, Fx1);
495s  y2 = plpdf (data, x2, Fx2);
495s  plot (data, y1, "-b", data, y2, "g")
495s  grid on
495s  ylim ([0, 0.6])
495s  xlim ([0, 10])
495s  legend ({"x1, Fx1", "x2, Fx2"}, "location", "northeast")
495s  title ("Piecewise linear CDF")
495s  xlabel ("values in data")
495s  ylabel ("density")
495s ***** shared x, Fx
495s  x = [0, 1, 3, 4, 7, 10];
495s  Fx = [0, 0.2, 0.5, 0.6, 0.7, 1];
495s ***** assert (plpdf (0.5, x, Fx), 0.2, eps);
495s ***** assert (plpdf (1.5, x, Fx), 0.15, eps);
495s ***** assert (plpdf (3.5, x, Fx), 0.1, eps);
495s ***** assert (plpdf (5, x, Fx), 0.1/3, eps);
495s ***** assert (plpdf (8, x, Fx), 0.1, eps);
495s ***** error<plpdf: function called with too few input arguments.> plpdf ()
495s ***** error<plpdf: function called with too few input arguments.> plpdf (1)
495s ***** error<plpdf: function called with too few input arguments.> plpdf (1, 2)
495s ***** error<plpdf: X and FX must be vectors of equal size.> ...
495s  plpdf (1, [0, 1, 2], [0, 1])
495s ***** error<plpdf: X and FX must be at least two-elements long.> ...
495s  plpdf (1, [0], [1])
495s ***** error<plpdf: FX must be bounded in the range> ...
495s  plpdf (1, [0, 1, 2], [0, 1, 1.5])
495s ***** error<plpdf: FX must be bounded in the range> ...
495s  plpdf (1, [0, 1, 2], [0, i, 1])
495s ***** error<plpdf: DATA, X, and FX must not be complex.> ...
495s  plpdf (i, [0, 1, 2], [0, 0.5, 1])
495s ***** error<plpdf: DATA, X, and FX must not be complex.> ...
495s  plpdf (1, [0, i, 2], [0, 0.5, 1])
495s ***** error<plpdf: DATA, X, and FX must not be complex.> ...
495s  plpdf (1, [0, 1, 2], [0, 0.5i, 1])
495s 15 tests, 15 passed, 0 known failure, 0 skipped
495s [inst/dist_fun/plrnd.m]
495s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/plrnd.m
495s ***** shared x, Fx
495s  x = [0, 1, 3, 4, 7, 10];
495s  Fx = [0, 0.2, 0.5, 0.6, 0.7, 1];
495s ***** assert (size (plrnd (x, Fx)), [1, 1])
495s ***** assert (size (plrnd (x, Fx, 3)), [3, 3])
495s ***** assert (size (plrnd (x, Fx, [4, 1])), [4, 1])
495s ***** assert (size (plrnd (x, Fx, 4, 1)), [4, 1])
495s ***** assert (size (plrnd (x, Fx, 4, 1, 5)), [4, 1, 5])
495s ***** assert (size (plrnd (x, Fx, 0, 1)), [0, 1])
495s ***** assert (size (plrnd (x, Fx, 1, 0)), [1, 0])
495s ***** assert (size (plrnd (x, Fx, 1, 2, 0, 5)), [1, 2, 0, 5])
495s ***** assert (class (plrnd (x, Fx)), "double")
495s ***** assert (class (plrnd (x, single (Fx))), "single")
495s ***** assert (class (plrnd (single (x), Fx)), "single")
495s ***** error<plrnd: function called with too few input arguments.> plrnd ()
495s ***** error<plrnd: function called with too few input arguments.> plrnd (1)
495s ***** error<plrnd: X and FX must be vectors of equal size.> ...
495s  plrnd ([0, 1, 2], [0, 1])
495s ***** error<plrnd: X and FX must be at least two-elements long.> ...
495s  plrnd ([0], [1])
495s ***** error<plrnd: FX must be bounded in the range> ...
495s  plrnd ([0, 1, 2], [0, 1, 1.5])
495s ***** error<plrnd: FX must be bounded in the range> ...
495s  plrnd ([0, 1, 2], [0, i, 1])
495s ***** error<plrnd: X and FX must not be complex.> ...
495s  plrnd ([0, i, 2], [0, 0.5, 1])
495s ***** error<plrnd: X and FX must not be complex.> ...
495s  plrnd ([0, i, 2], [0, 0.5i, 1])
495s ***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
495s  plrnd (x, Fx, -1)
495s ***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
495s  plrnd (x, Fx, 1.2)
495s ***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
495s  plrnd (x, Fx, ones (2))
495s ***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
495s  plrnd (x, Fx, [2 -1 2])
495s ***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
495s  plrnd (x, Fx, [2 0 2.5])
495s ***** error<plrnd: dimensions must be non-negative integers.> ...
495s  plrnd (x, Fx, 2, -1, 5)
495s ***** error<plrnd: dimensions must be non-negative integers.> ...
495s  plrnd (x, Fx, 2, 1.5, 5)
495s 26 tests, 26 passed, 0 known failure, 0 skipped
495s [inst/dist_fun/poisscdf.m]
495s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/poisscdf.m
495s ***** demo
495s  ## Plot various CDFs from the Poisson distribution
495s  x = 0:20;
495s  p1 = poisscdf (x, 1);
495s  p2 = poisscdf (x, 4);
495s  p3 = poisscdf (x, 10);
495s  plot (x, p1, "*b", x, p2, "*g", x, p3, "*r")
495s  grid on
495s  ylim ([0, 1])
495s  legend ({"λ = 1", "λ = 4", "λ = 10"}, "location", "southeast")
495s  title ("Poisson CDF")
495s  xlabel ("values in x (number of occurences)")
495s  ylabel ("probability")
495s ***** shared x, y
495s  x = [-1 0 1 2 Inf];
495s  y = [0, gammainc(1, (x(2:4) +1), "upper"), 1];
495s ***** assert (poisscdf (x, ones (1,5)), y)
495s ***** assert (poisscdf (x, 1), y)
495s ***** assert (poisscdf (x, [1 0 NaN 1 1]), [y(1) 1 NaN y(4:5)])
495s ***** assert (poisscdf ([x(1:2) NaN Inf x(5)], 1), [y(1:2) NaN 1 y(5)])
495s ***** assert (poisscdf ([x, NaN], 1), [y, NaN])
496s ***** assert (poisscdf (single ([x, NaN]), 1), single ([y, NaN]), eps ("single"))
496s ***** assert (poisscdf ([x, NaN], single (1)), single ([y, NaN]), eps ("single"))
496s ***** error<poisscdf: function called with too few input arguments.> poisscdf ()
496s ***** error<poisscdf: function called with too few input arguments.> poisscdf (1)
496s ***** error<poisscdf: invalid argument for upper tail.> poisscdf (1, 2, 3)
496s ***** error<poisscdf: invalid argument for upper tail.> poisscdf (1, 2, "tail")
496s ***** error<poisscdf: X and LAMBDA must be of common size or scalars.> ...
496s  poisscdf (ones (3), ones (2))
496s ***** error<poisscdf: X and LAMBDA must be of common size or scalars.> ...
496s  poisscdf (ones (2), ones (3))
496s ***** error<poisscdf: X and LAMBDA must not be complex.> poisscdf (i, 2)
496s ***** error<poisscdf: X and LAMBDA must not be complex.> poisscdf (2, i)
496s 15 tests, 15 passed, 0 known failure, 0 skipped
496s [inst/dist_fun/poissinv.m]
496s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/poissinv.m
496s ***** demo
496s  ## Plot various iCDFs from the Poisson distribution
496s  p = 0.001:0.001:0.999;
496s  x1 = poissinv (p, 13);
496s  x2 = poissinv (p, 4);
496s  x3 = poissinv (p, 10);
496s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
496s  grid on
496s  ylim ([0, 20])
496s  legend ({"λ = 1", "λ = 4", "λ = 10"}, "location", "northwest")
496s  title ("Poisson iCDF")
496s  xlabel ("probability")
496s  ylabel ("values in x (number of occurences)")
496s ***** shared p
496s  p = [-1 0 0.5 1 2];
496s ***** assert (poissinv (p, ones (1,5)), [NaN 0 1 Inf NaN])
496s ***** assert (poissinv (p, 1), [NaN 0 1 Inf NaN])
496s ***** assert (poissinv (p, [1 0 NaN 1 1]), [NaN NaN NaN Inf NaN])
496s ***** assert (poissinv ([p(1:2) NaN p(4:5)], 1), [NaN 0 NaN Inf NaN])
496s ***** assert (poissinv ([p, NaN], 1), [NaN 0 1 Inf NaN NaN])
496s ***** assert (poissinv (single ([p, NaN]), 1), single ([NaN 0 1 Inf NaN NaN]))
496s ***** assert (poissinv ([p, NaN], single (1)), single ([NaN 0 1 Inf NaN NaN]))
496s ***** error<poissinv: function called with too few input arguments.> poissinv ()
496s ***** error<poissinv: function called with too few input arguments.> poissinv (1)
496s ***** error<poissinv: P and LAMBDA must be of common size or scalars.> ...
496s  poissinv (ones (3), ones (2))
496s ***** error<poissinv: P and LAMBDA must be of common size or scalars.> ...
496s  poissinv (ones (2), ones (3))
496s ***** error<poissinv: P and LAMBDA must not be complex.> poissinv (i, 2)
496s ***** error<poissinv: P and LAMBDA must not be complex.> poissinv (2, i)
496s 13 tests, 13 passed, 0 known failure, 0 skipped
496s [inst/dist_fun/poisspdf.m]
496s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/poisspdf.m
496s ***** demo
496s  ## Plot various PDFs from the Poisson distribution
496s  x = 0:20;
496s  y1 = poisspdf (x, 1);
496s  y2 = poisspdf (x, 4);
496s  y3 = poisspdf (x, 10);
496s  plot (x, y1, "*b", x, y2, "*g", x, y3, "*r")
496s  grid on
496s  ylim ([0, 0.4])
496s  legend ({"λ = 1", "λ = 4", "λ = 10"}, "location", "northeast")
496s  title ("Poisson PDF")
496s  xlabel ("values in x (number of occurences)")
496s  ylabel ("density")
496s ***** shared x, y
496s  x = [-1 0 1 2 Inf];
496s  y = [0, exp(-1)*[1 1 0.5], 0];
496s ***** assert (poisspdf (x, ones (1,5)), y, eps)
496s ***** assert (poisspdf (x, 1), y, eps)
496s ***** assert (poisspdf (x, [1 0 NaN 1 1]), [y(1) NaN NaN y(4:5)], eps)
496s ***** assert (poisspdf ([x, NaN], 1), [y, NaN], eps)
496s ***** assert (poisspdf (single ([x, NaN]), 1), single ([y, NaN]), eps ("single"))
496s ***** assert (poisspdf ([x, NaN], single (1)), single ([y, NaN]), eps ("single"))
496s ***** error<poisspdf: function called with too few input arguments.> poisspdf ()
496s ***** error<poisspdf: function called with too few input arguments.> poisspdf (1)
496s ***** error<poisspdf: X and LAMBDA must be of common size or scalars.> ...
496s  poisspdf (ones (3), ones (2))
496s ***** error<poisspdf: X and LAMBDA must be of common size or scalars.> ...
496s  poisspdf (ones (2), ones (3))
496s ***** error<poisspdf: X and LAMBDA must not be complex.> poisspdf (i, 2)
496s ***** error<poisspdf: X and LAMBDA must not be complex.> poisspdf (2, i)
496s 12 tests, 12 passed, 0 known failure, 0 skipped
496s [inst/dist_fun/poissrnd.m]
496s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/poissrnd.m
496s ***** assert (size (poissrnd (2)), [1, 1])
496s ***** assert (size (poissrnd (ones (2,1))), [2, 1])
496s ***** assert (size (poissrnd (ones (2,2))), [2, 2])
496s ***** assert (size (poissrnd (1, 3)), [3, 3])
496s ***** assert (size (poissrnd (1, [4 1])), [4, 1])
496s ***** assert (size (poissrnd (1, 4, 1)), [4, 1])
496s ***** assert (size (poissrnd (1, 4, 1)), [4, 1])
496s ***** assert (size (poissrnd (1, 4, 1, 5)), [4, 1, 5])
496s ***** assert (size (poissrnd (1, 0, 1)), [0, 1])
496s ***** assert (size (poissrnd (1, 1, 0)), [1, 0])
496s ***** assert (size (poissrnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
496s ***** assert (poissrnd (0, 1, 1), 0)
496s ***** assert (poissrnd ([0, 0, 0], [1, 3]), [0 0 0])
496s ***** assert (class (poissrnd (2)), "double")
496s ***** assert (class (poissrnd (single (2))), "single")
496s ***** assert (class (poissrnd (single ([2 2]))), "single")
496s ***** error<poissrnd: function called with too few input arguments.> poissrnd ()
496s ***** error<poissrnd: LAMBDA must not be complex.> poissrnd (i)
496s ***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
496s  poissrnd (1, -1)
496s ***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
496s  poissrnd (1, 1.2)
496s ***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
496s  poissrnd (1, ones (2))
496s ***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
496s  poissrnd (1, [2 -1 2])
496s ***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
496s  poissrnd (1, [2 0 2.5])
496s ***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
496s  poissrnd (ones (2), ones (2))
496s ***** error<poissrnd: dimensions must be non-negative integers.> ...
496s  poissrnd (1, 2, -1, 5)
496s ***** error<poissrnd: dimensions must be non-negative integers.> ...
496s  poissrnd (1, 2, 1.5, 5)
496s ***** error<poissrnd: LAMBDA must be scalar or of size SZ.> poissrnd (ones (2,2), 3)
496s ***** error<poissrnd: LAMBDA must be scalar or of size SZ.> poissrnd (ones (2,2), [3, 2])
496s ***** error<poissrnd: LAMBDA must be scalar or of size SZ.> poissrnd (ones (2,2), 2, 3)
496s 29 tests, 29 passed, 0 known failure, 0 skipped
496s [inst/dist_fun/raylcdf.m]
496s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/raylcdf.m
496s ***** demo
496s  ## Plot various CDFs from the Rayleigh distribution
496s  x = 0:0.01:10;
496s  p1 = raylcdf (x, 0.5);
496s  p2 = raylcdf (x, 1);
496s  p3 = raylcdf (x, 2);
496s  p4 = raylcdf (x, 3);
496s  p5 = raylcdf (x, 4);
496s  plot (x, p1, "-b", x, p2, "g", x, p3, "-r", x, p4, "-m", x, p5, "-k")
496s  grid on
496s  ylim ([0, 1])
496s  legend ({"σ = 0.5", "σ = 1", "σ = 2", ...
496s           "σ = 3", "σ = 4"}, "location", "southeast")
496s  title ("Rayleigh CDF")
496s  xlabel ("values in x")
496s  ylabel ("probability")
496s ***** test
496s  x = 0:0.5:2.5;
496s  sigma = 1:6;
496s  p = raylcdf (x, sigma);
496s  expected_p = [0.0000, 0.0308, 0.0540, 0.0679, 0.0769, 0.0831];
496s  assert (p, expected_p, 0.001);
496s ***** test
496s  x = 0:0.5:2.5;
496s  p = raylcdf (x, 0.5);
496s  expected_p = [0.0000, 0.3935, 0.8647, 0.9889, 0.9997, 1.0000];
496s  assert (p, expected_p, 0.001);
496s ***** shared x, p
496s  x = [-1, 0, 1, 2, Inf];
496s  p = [0, 0, 0.39346934028737, 0.86466471676338, 1];
496s ***** assert (raylcdf (x, 1), p, 1e-14)
496s ***** assert (raylcdf (x, 1, "upper"), 1 - p, 1e-14)
496s ***** error<raylcdf: function called with too few input arguments.> raylcdf ()
496s ***** error<raylcdf: function called with too few input arguments.> raylcdf (1)
496s ***** error<raylcdf: invalid argument for upper tail.> raylcdf (1, 2, "uper")
496s ***** error<raylcdf: invalid argument for upper tail.> raylcdf (1, 2, 3)
496s ***** error<raylcdf: X and SIGMA must be of common size or scalars.> ...
496s  raylcdf (ones (3), ones (2))
496s ***** error<raylcdf: X and SIGMA must be of common size or scalars.> ...
496s  raylcdf (ones (2), ones (3))
496s ***** error<raylcdf: X and SIGMA must not be complex.> raylcdf (i, 2)
496s ***** error<raylcdf: X and SIGMA must not be complex.> raylcdf (2, i)
496s 12 tests, 12 passed, 0 known failure, 0 skipped
496s [inst/dist_fun/raylinv.m]
496s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/raylinv.m
496s ***** demo
496s  ## Plot various iCDFs from the Rayleigh distribution
496s  p = 0.001:0.001:0.999;
496s  x1 = raylinv (p, 0.5);
496s  x2 = raylinv (p, 1);
496s  x3 = raylinv (p, 2);
496s  x4 = raylinv (p, 3);
496s  x5 = raylinv (p, 4);
496s  plot (p, x1, "-b", p, x2, "g", p, x3, "-r", p, x4, "-m", p, x5, "-k")
496s  grid on
496s  ylim ([0, 10])
496s  legend ({"σ = 0,5", "σ = 1", "σ = 2", ...
496s           "σ = 3", "σ = 4"}, "location", "northwest")
496s  title ("Rayleigh iCDF")
496s  xlabel ("probability")
496s  ylabel ("values in x")
496s ***** test
496s  p = 0:0.1:0.5;
496s  sigma = 1:6;
496s  x = raylinv (p, sigma);
496s  expected_x = [0.0000, 0.9181, 2.0041, 3.3784, 5.0538, 7.0645];
496s  assert (x, expected_x, 0.001);
496s ***** test
496s  p = 0:0.1:0.5;
496s  x = raylinv (p, 0.5);
496s  expected_x = [0.0000, 0.2295, 0.3340, 0.4223, 0.5054, 0.5887];
496s  assert (x, expected_x, 0.001);
496s ***** error<raylinv: function called with too few input arguments.> raylinv ()
496s ***** error<raylinv: function called with too few input arguments.> raylinv (1)
496s ***** error<raylinv: P and SIGMA must be of common size or scalars.> ...
496s  raylinv (ones (3), ones (2))
496s ***** error<raylinv: P and SIGMA must be of common size or scalars.> ...
496s  raylinv (ones (2), ones (3))
496s ***** error<raylinv: P and SIGMA must not be complex.> raylinv (i, 2)
496s ***** error<raylinv: P and SIGMA must not be complex.> raylinv (2, i)
496s 8 tests, 8 passed, 0 known failure, 0 skipped
496s [inst/dist_fun/raylpdf.m]
496s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/raylpdf.m
496s ***** demo
496s  ## Plot various PDFs from the Rayleigh distribution
496s  x = 0:0.01:10;
496s  y1 = raylpdf (x, 0.5);
496s  y2 = raylpdf (x, 1);
496s  y3 = raylpdf (x, 2);
496s  y4 = raylpdf (x, 3);
496s  y5 = raylpdf (x, 4);
496s  plot (x, y1, "-b", x, y2, "g", x, y3, "-r", x, y4, "-m", x, y5, "-k")
496s  grid on
496s  ylim ([0, 1.25])
496s  legend ({"σ = 0,5", "σ = 1", "σ = 2", ...
496s           "σ = 3", "σ = 4"}, "location", "northeast")
496s  title ("Rayleigh PDF")
496s  xlabel ("values in x")
496s  ylabel ("density")
496s ***** test
496s  x = 0:0.5:2.5;
496s  sigma = 1:6;
496s  y = raylpdf (x, sigma);
496s  expected_y = [0.0000, 0.1212, 0.1051, 0.0874, 0.0738, 0.0637];
496s  assert (y, expected_y, 0.001);
496s ***** test
496s  x = 0:0.5:2.5;
496s  y = raylpdf (x, 0.5);
496s  expected_y = [0.0000, 1.2131, 0.5413, 0.0667, 0.0027, 0.0000];
496s  assert (y, expected_y, 0.001);
496s ***** error<raylpdf: function called with too few input arguments.> raylpdf ()
496s ***** error<raylpdf: function called with too few input arguments.> raylpdf (1)
496s ***** error<raylpdf: X and SIGMA must be of common size or scalars.> ...
496s  raylpdf (ones (3), ones (2))
496s ***** error<raylpdf: X and SIGMA must be of common size or scalars.> ...
496s  raylpdf (ones (2), ones (3))
496s ***** error<raylpdf: X and SIGMA must not be complex.> raylpdf (i, 2)
496s ***** error<raylpdf: X and SIGMA must not be complex.> raylpdf (2, i)
496s 8 tests, 8 passed, 0 known failure, 0 skipped
496s [inst/dist_fun/raylrnd.m]
496s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/raylrnd.m
496s ***** assert (size (raylrnd (2)), [1, 1])
496s ***** assert (size (raylrnd (ones (2,1))), [2, 1])
496s ***** assert (size (raylrnd (ones (2,2))), [2, 2])
496s ***** assert (size (raylrnd (1, 3)), [3, 3])
496s ***** assert (size (raylrnd (1, [4 1])), [4, 1])
496s ***** assert (size (raylrnd (1, 4, 1)), [4, 1])
496s ***** assert (size (raylrnd (1, 4, 1)), [4, 1])
496s ***** assert (size (raylrnd (1, 4, 1, 5)), [4, 1, 5])
496s ***** assert (size (raylrnd (1, 0, 1)), [0, 1])
496s ***** assert (size (raylrnd (1, 1, 0)), [1, 0])
496s ***** assert (size (raylrnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
496s ***** assert (raylrnd (0, 1, 1), NaN)
496s ***** assert (raylrnd ([0, 0, 0], [1, 3]), [NaN, NaN, NaN])
496s ***** assert (class (raylrnd (2)), "double")
496s ***** assert (class (raylrnd (single (2))), "single")
496s ***** assert (class (raylrnd (single ([2 2]))), "single")
496s ***** error<raylrnd: function called with too few input arguments.> raylrnd ()
496s ***** error<raylrnd: SIGMA must not be complex.> raylrnd (i)
496s ***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
496s  raylrnd (1, -1)
496s ***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
496s  raylrnd (1, 1.2)
496s ***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
496s  raylrnd (1, ones (2))
496s ***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
496s  raylrnd (1, [2 -1 2])
496s ***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
496s  raylrnd (1, [2 0 2.5])
496s ***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
496s  raylrnd (ones (2), ones (2))
496s ***** error<raylrnd: dimensions must be non-negative integers.> ...
496s  raylrnd (1, 2, -1, 5)
496s ***** error<raylrnd: dimensions must be non-negative integers.> ...
496s  raylrnd (1, 2, 1.5, 5)
496s ***** error<raylrnd: SIGMA must be scalar or of size SZ.> raylrnd (ones (2,2), 3)
496s ***** error<raylrnd: SIGMA must be scalar or of size SZ.> raylrnd (ones (2,2), [3, 2])
496s ***** error<raylrnd: SIGMA must be scalar or of size SZ.> raylrnd (ones (2,2), 2, 3)
496s 29 tests, 29 passed, 0 known failure, 0 skipped
496s [inst/dist_fun/ricecdf.m]
496s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/ricecdf.m
496s ***** demo
496s  ## Plot various CDFs from the Rician distribution
496s  x = 0:0.01:10;
496s  p1 = ricecdf (x, 0, 1);
496s  p2 = ricecdf (x, 0.5, 1);
496s  p3 = ricecdf (x, 1, 1);
496s  p4 = ricecdf (x, 2, 1);
496s  p5 = ricecdf (x, 4, 1);
496s  plot (x, p1, "-b", x, p2, "g", x, p3, "-r", x, p4, "-m", x, p5, "-k")
496s  grid on
496s  ylim ([0, 1])
496s  xlim ([0, 8])
496s  legend ({"s = 0, σ = 1", "s = 0.5, σ = 1", "s = 1, σ = 1", ...
496s           "s = 2, σ = 1", "s = 4, σ = 1"}, "location", "southeast")
496s  title ("Rician CDF")
496s  xlabel ("values in x")
496s  ylabel ("probability")
496s ***** demo
496s  ## Plot various CDFs from the Rician distribution
496s  x = 0:0.01:10;
496s  p1 = ricecdf (x, 0, 0.5);
496s  p2 = ricecdf (x, 0, 2);
496s  p3 = ricecdf (x, 0, 3);
496s  p4 = ricecdf (x, 2, 2);
496s  p5 = ricecdf (x, 4, 2);
496s  plot (x, p1, "-b", x, p2, "g", x, p3, "-r", x, p4, "-m", x, p5, "-k")
496s  grid on
496s  ylim ([0, 1])
496s  xlim ([0, 8])
496s  legend ({"ν = 0, σ = 0.5", "ν = 0, σ = 2", "ν = 0, σ = 3", ...
496s           "ν = 2, σ = 2", "ν = 4, σ = 2"}, "location", "southeast")
496s  title ("Rician CDF")
496s  xlabel ("values in x")
496s  ylabel ("probability")
496s ***** test
496s  x = 0:0.5:2.5;
496s  s = 1:6;
496s  p = ricecdf (x, s, 1);
496s  expected_p = [0.0000, 0.0179, 0.0108, 0.0034, 0.0008, 0.0001];
496s  assert (p, expected_p, 0.001);
496s ***** test
496s  x = 0:0.5:2.5;
496s  sigma = 1:6;
496s  p = ricecdf (x, 1, sigma);
496s  expected_p = [0.0000, 0.0272, 0.0512, 0.0659, 0.0754, 0.0820];
496s  assert (p, expected_p, 0.001);
496s ***** test
496s  x = 0:0.5:2.5;
496s  p = ricecdf (x, 0, 1);
496s  expected_p = [0.0000, 0.1175, 0.3935, 0.6753, 0.8647, 0.9561];
496s  assert (p, expected_p, 0.001);
496s ***** test
496s  x = 0:0.5:2.5;
496s  p = ricecdf (x, 1, 1);
496s  expected_p = [0.0000, 0.0735, 0.2671, 0.5120, 0.7310, 0.8791];
496s  assert (p, expected_p, 0.001);
496s ***** shared x, p
496s  x = [-1, 0, 1, 2, Inf];
496s  p = [0, 0, 0.26712019620318, 0.73098793996409, 1];
496s ***** assert (ricecdf (x, 1, 1), p, 1e-14)
496s ***** assert (ricecdf (x, 1, 1, "upper"), 1 - p, 1e-14)
496s ***** error<ricecdf: function called with too few input arguments.> ricecdf ()
496s ***** error<ricecdf: function called with too few input arguments.> ricecdf (1)
496s ***** error<ricecdf: function called with too few input arguments.> ricecdf (1, 2)
496s ***** error<ricecdf: invalid argument for upper tail.> ricecdf (1, 2, 3, "uper")
496s ***** error<ricecdf: invalid argument for upper tail.> ricecdf (1, 2, 3, 4)
496s ***** error<ricecdf: X, S, and SIGMA must be of common size or scalars.> ...
496s  ricecdf (ones (3), ones (2), ones (2))
496s ***** error<ricecdf: X, S, and SIGMA must be of common size or scalars.> ...
496s  ricecdf (ones (2), ones (3), ones (2))
496s ***** error<ricecdf: X, S, and SIGMA must be of common size or scalars.> ...
496s  ricecdf (ones (2), ones (2), ones (3))
496s ***** error<ricecdf: X, S, and SIGMA must not be complex.> ricecdf (i, 2, 3)
496s ***** error<ricecdf: X, S, and SIGMA must not be complex.> ricecdf (2, i, 3)
496s ***** error<ricecdf: X, S, and SIGMA must not be complex.> ricecdf (2, 2, i)
496s 17 tests, 17 passed, 0 known failure, 0 skipped
496s [inst/dist_fun/riceinv.m]
496s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/riceinv.m
496s ***** demo
496s  ## Plot various iCDFs from the Rician distribution
496s  p = 0.001:0.001:0.999;
496s  x1 = riceinv (p, 0, 1);
496s  x2 = riceinv (p, 0.5, 1);
496s  x3 = riceinv (p, 1, 1);
496s  x4 = riceinv (p, 2, 1);
496s  x5 = riceinv (p, 4, 1);
496s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-m", p, x5, "-k")
496s  grid on
496s  legend ({"s = 0, σ = 1", "s = 0.5, σ = 1", "s = 1, σ = 1", ...
496s           "s = 2, σ = 1", "s = 4, σ = 1"}, "location", "northwest")
496s  title ("Rician iCDF")
496s  xlabel ("probability")
496s  ylabel ("values in x")
496s ***** shared p
496s  p = [-1 0 0.75 1 2];
496s ***** assert (riceinv (p, ones (1,5), 2*ones (1,5)), [NaN 0 3.5354 Inf NaN], 1e-4)
496s ***** assert (riceinv (p, 1, 2*ones (1,5)), [NaN 0 3.5354 Inf NaN], 1e-4)
496s ***** assert (riceinv (p, ones (1,5), 2), [NaN 0 3.5354 Inf NaN], 1e-4)
496s ***** assert (riceinv (p, [1 0 NaN 1 1], 2), [NaN 0 NaN Inf NaN])
496s ***** assert (riceinv (p, 1, 2*[1 0 NaN 1 1]), [NaN NaN NaN Inf NaN])
496s ***** assert (riceinv ([p(1:2) NaN p(4:5)], 1, 2), [NaN 0 NaN Inf NaN])
496s ***** assert (riceinv ([p, NaN], 1, 2), [NaN 0 3.5354 Inf NaN NaN], 1e-4)
497s ***** assert (riceinv (single ([p, NaN]), 1, 2), ...
497s         single ([NaN 0 3.5354 Inf NaN NaN]), 1e-4)
497s ***** assert (riceinv ([p, NaN], single (1), 2), ...
497s         single ([NaN 0 3.5354 Inf NaN NaN]), 1e-4)
497s ***** assert (riceinv ([p, NaN], 1, single (2)), ...
497s         single ([NaN 0 3.5354 Inf NaN NaN]), 1e-4)
497s ***** error<riceinv: function called with too few input arguments.> riceinv ()
497s ***** error<riceinv: function called with too few input arguments.> riceinv (1)
497s ***** error<riceinv: function called with too few input arguments.> riceinv (1,2)
497s ***** error<riceinv: function called with too many inputs> riceinv (1,2,3,4)
497s ***** error<riceinv: P, S, and B must be of common size or scalars.> ...
497s  riceinv (ones (3), ones (2), ones (2))
497s ***** error<riceinv: P, S, and B must be of common size or scalars.> ...
497s  riceinv (ones (2), ones (3), ones (2))
497s ***** error<riceinv: P, S, and B must be of common size or scalars.> ...
497s  riceinv (ones (2), ones (2), ones (3))
497s ***** error<riceinv: P, S, and B must not be complex.> riceinv (i, 2, 2)
497s ***** error<riceinv: P, S, and B must not be complex.> riceinv (2, i, 2)
497s ***** error<riceinv: P, S, and B must not be complex.> riceinv (2, 2, i)
497s 20 tests, 20 passed, 0 known failure, 0 skipped
497s [inst/dist_fun/ricepdf.m]
497s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/ricepdf.m
497s ***** demo
497s  ## Plot various PDFs from the Rician distribution
497s  x = 0:0.01:8;
497s  y1 = ricepdf (x, 0, 1);
497s  y2 = ricepdf (x, 0.5, 1);
497s  y3 = ricepdf (x, 1, 1);
497s  y4 = ricepdf (x, 2, 1);
497s  y5 = ricepdf (x, 4, 1);
497s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-m", x, y5, "-k")
497s  grid on
497s  ylim ([0, 0.65])
497s  xlim ([0, 8])
497s  legend ({"s = 0, σ = 1", "s = 0.5, σ = 1", "s = 1, σ = 1", ...
497s           "s = 2, σ = 1", "s = 4, σ = 1"}, "location", "northeast")
497s  title ("Rician PDF")
497s  xlabel ("values in x")
497s  ylabel ("density")
497s ***** shared x, y
497s  x = [-1 0 0.5 1 2];
497s  y = [0 0 0.1073 0.1978 0.2846];
497s ***** assert (ricepdf (x, ones (1, 5), 2 * ones (1, 5)), y, 1e-4)
497s ***** assert (ricepdf (x, 1, 2 * ones (1, 5)), y, 1e-4)
497s ***** assert (ricepdf (x, ones (1, 5), 2), y, 1e-4)
497s ***** assert (ricepdf (x, [0 NaN 1 1 1], 2), [0 NaN y(3:5)], 1e-4)
497s ***** assert (ricepdf (x, 1, 2 * [0 NaN 1 1 1]), [0 NaN y(3:5)], 1e-4)
497s ***** assert (ricepdf ([x, NaN], 1, 2), [y, NaN], 1e-4)
497s ***** assert (ricepdf (single ([x, NaN]), 1, 2), single ([y, NaN]), 1e-4)
497s ***** assert (ricepdf ([x, NaN], single (1), 2), single ([y, NaN]), 1e-4)
497s ***** assert (ricepdf ([x, NaN], 1, single (2)), single ([y, NaN]), 1e-4)
497s ***** error<ricepdf: function called with too few input arguments.> ricepdf ()
497s ***** error<ricepdf: function called with too few input arguments.> ricepdf (1)
497s ***** error<ricepdf: function called with too few input arguments.> ricepdf (1,2)
497s ***** error<ricepdf: function called with too many inputs> ricepdf (1,2,3,4)
497s ***** error<ricepdf: X, S, and SIGMA must be of common size or scalars.> ...
497s  ricepdf (ones (3), ones (2), ones (2))
497s ***** error<ricepdf: X, S, and SIGMA must be of common size or scalars.> ...
497s  ricepdf (ones (2), ones (3), ones (2))
497s ***** error<ricepdf: X, S, and SIGMA must be of common size or scalars.> ...
497s  ricepdf (ones (2), ones (2), ones (3))
497s ***** error<ricepdf: X, S, and SIGMA must not be complex.> ricepdf (i, 2, 2)
497s ***** error<ricepdf: X, S, and SIGMA must not be complex.> ricepdf (2, i, 2)
497s ***** error<ricepdf: X, S, and SIGMA must not be complex.> ricepdf (2, 2, i)
497s 19 tests, 19 passed, 0 known failure, 0 skipped
497s [inst/dist_fun/ricernd.m]
497s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/ricernd.m
497s ***** assert (size (ricernd (2, 1/2)), [1, 1])
497s ***** assert (size (ricernd (2 * ones (2, 1), 1/2)), [2, 1])
497s ***** assert (size (ricernd (2 * ones (2, 2), 1/2)), [2, 2])
497s ***** assert (size (ricernd (2, 1/2 * ones (2, 1))), [2, 1])
497s ***** assert (size (ricernd (1, 1/2 * ones (2, 2))), [2, 2])
497s ***** assert (size (ricernd (ones (2, 1), 1)), [2, 1])
497s ***** assert (size (ricernd (ones (2, 2), 1)), [2, 2])
497s ***** assert (size (ricernd (2, 1/2, 3)), [3, 3])
497s ***** assert (size (ricernd (1, 1, [4, 1])), [4, 1])
497s ***** assert (size (ricernd (1, 1, 4, 1)), [4, 1])
497s ***** assert (size (ricernd (1, 1, 4, 1, 5)), [4, 1, 5])
497s ***** assert (size (ricernd (1, 1, 0, 1)), [0, 1])
497s ***** assert (size (ricernd (1, 1, 1, 0)), [1, 0])
497s ***** assert (size (ricernd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
497s ***** assert (class (ricernd (1, 1)), "double")
497s ***** assert (class (ricernd (1, single (0))), "single")
497s ***** assert (class (ricernd (1, single ([0, 0]))), "single")
497s ***** assert (class (ricernd (1, single (1), 2)), "single")
497s ***** assert (class (ricernd (1, single ([1, 1]), 1, 2)), "single")
497s ***** assert (class (ricernd (single (1), 1, 2)), "single")
497s ***** assert (class (ricernd (single ([1, 1]), 1, 1, 2)), "single")
497s ***** error<ricernd: function called with too few input arguments.> ricernd ()
497s ***** error<ricernd: function called with too few input arguments.> ricernd (1)
497s ***** error<ricernd: S and SIGMA must be of common size or scalars.> ...
497s  ricernd (ones (3), ones (2))
497s ***** error<ricernd: S and SIGMA must be of common size or scalars.> ...
497s  ricernd (ones (2), ones (3))
497s ***** error<ricernd: S and SIGMA must not be complex.> ricernd (i, 2)
497s ***** error<ricernd: S and SIGMA must not be complex.> ricernd (1, i)
497s ***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
497s  ricernd (1, 1/2, -1)
497s ***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
497s  ricernd (1, 1/2, 1.2)
497s ***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
497s  ricernd (1, 1/2, ones (2))
497s ***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
497s  ricernd (1, 1/2, [2 -1 2])
497s ***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
497s  ricernd (1, 1/2, [2 0 2.5])
497s ***** error<ricernd: dimensions must be non-negative integers.> ...
497s  ricernd (1, 1/2, 2, -1, 5)
497s ***** error<ricernd: dimensions must be non-negative integers.> ...
497s  ricernd (1, 1/2, 2, 1.5, 5)
497s ***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
497s  ricernd (2, 1/2 * ones (2), 3)
497s ***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
497s  ricernd (2, 1/2 * ones (2), [3, 2])
497s ***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
497s  ricernd (2, 1/2 * ones (2), 3, 2)
497s ***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
497s  ricernd (2 * ones (2), 1/2, 3)
497s ***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
497s  ricernd (2 * ones (2), 1/2, [3, 2])
497s ***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
497s  ricernd (2 * ones (2), 1/2, 3, 2)
497s 40 tests, 40 passed, 0 known failure, 0 skipped
497s [inst/dist_fun/tcdf.m]
497s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/tcdf.m
497s ***** demo
497s  ## Plot various CDFs from the Student's T distribution
497s  x = -5:0.01:5;
497s  p1 = tcdf (x, 1);
497s  p2 = tcdf (x, 2);
497s  p3 = tcdf (x, 5);
497s  p4 = tcdf (x, Inf);
497s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-m")
497s  grid on
497s  xlim ([-5, 5])
497s  ylim ([0, 1])
497s  legend ({"df = 1", "df = 2", ...
497s           "df = 5", 'df = \infty'}, "location", "southeast")
497s  title ("Student's T CDF")
497s  xlabel ("values in x")
497s  ylabel ("probability")
497s ***** shared x,y
497s  x = [-Inf 0 1 Inf];
497s  y = [0 1/2 3/4 1];
497s ***** assert (tcdf (x, ones (1,4)), y, eps)
497s ***** assert (tcdf (x, 1), y, eps)
497s ***** assert (tcdf (x, [0 1 NaN 1]), [NaN 1/2 NaN 1], eps)
497s ***** assert (tcdf ([x(1:2) NaN x(4)], 1), [y(1:2) NaN y(4)], eps)
497s ***** assert (tcdf (2, 3, "upper"), 0.0697, 1e-4)
497s ***** assert (tcdf (205, 5, "upper"), 2.6206e-11, 1e-14)
497s ***** assert (tcdf ([x, NaN], 1), [y, NaN], eps)
497s ***** assert (tcdf (single ([x, NaN]), 1), single ([y, NaN]), eps ("single"))
497s ***** assert (tcdf ([x, NaN], single (1)), single ([y, NaN]), eps ("single"))
497s ***** error<tcdf: function called with too few input arguments.> tcdf ()
497s ***** error<tcdf: function called with too few input arguments.> tcdf (1)
497s ***** error<tcdf: invalid argument for upper tail.> tcdf (1, 2, "uper")
497s ***** error<tcdf: invalid argument for upper tail.> tcdf (1, 2, 3)
497s ***** error<tcdf: X and DF must be of common size or scalars.> ...
497s  tcdf (ones (3), ones (2))
497s ***** error<tcdf: X and DF must be of common size or scalars.> ...
497s  tcdf (ones (3), ones (2))
497s ***** error<tcdf: X and DF must be of common size or scalars.> ...
497s  tcdf (ones (3), ones (2), "upper")
497s ***** error<tcdf: X and DF must not be complex.> tcdf (i, 2)
497s ***** error<tcdf: X and DF must not be complex.> tcdf (2, i)
497s ***** shared tol_rel
497s  tol_rel = 10 * eps;
497s ***** assert (tcdf (10^(-10), 2.5), 0.50000000003618087, -tol_rel)
497s ***** assert (tcdf (10^(-11), 2.5), 0.50000000000361809, -tol_rel)
497s ***** assert (tcdf (10^(-12), 2.5), 0.50000000000036181, -tol_rel)
497s ***** assert (tcdf (10^(-13), 2.5), 0.50000000000003618, -tol_rel)
497s ***** assert (tcdf (10^(-14), 2.5), 0.50000000000000362, -tol_rel)
497s ***** assert (tcdf (10^(-15), 2.5), 0.50000000000000036, -tol_rel)
497s ***** assert (tcdf (10^(-16), 2.5), 0.50000000000000004, -tol_rel)
497s ***** assert (tcdf (-10^1, 2.5), 2.2207478836537124e-03, -tol_rel)
497s ***** assert (tcdf (-10^2, 2.5), 7.1916492116661878e-06, -tol_rel)
497s ***** assert (tcdf (-10^3, 2.5), 2.2747463948307452e-08, -tol_rel)
497s ***** assert (tcdf (-10^4, 2.5), 7.1933970159922115e-11, -tol_rel)
497s ***** assert (tcdf (-10^5, 2.5), 2.2747519231756221e-13, -tol_rel)
497s 30 tests, 30 passed, 0 known failure, 0 skipped
497s [inst/dist_fun/tinv.m]
497s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/tinv.m
497s ***** demo
497s  ## Plot various iCDFs from the Student's T distribution
497s  p = 0.001:0.001:0.999;
497s  x1 = tinv (p, 1);
497s  x2 = tinv (p, 2);
497s  x3 = tinv (p, 5);
497s  x4 = tinv (p, Inf);
497s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-m")
497s  grid on
497s  xlim ([0, 1])
497s  ylim ([-5, 5])
497s  legend ({"df = 1", "df = 2", ...
497s           "df = 5", 'df = \infty'}, "location", "northwest")
497s  title ("Student's T iCDF")
497s  xlabel ("probability")
497s  ylabel ("values in x")
497s ***** shared p
497s  p = [-1 0 0.5 1 2];
497s ***** assert (tinv (p, ones (1,5)), [NaN -Inf 0 Inf NaN])
497s ***** assert (tinv (p, 1), [NaN -Inf 0 Inf NaN], eps)
497s ***** assert (tinv (p, [1 0 NaN 1 1]), [NaN NaN NaN Inf NaN], eps)
497s ***** assert (tinv ([p(1:2) NaN p(4:5)], 1), [NaN -Inf NaN Inf NaN])
497s ***** assert (tinv ([p, NaN], 1), [NaN -Inf 0 Inf NaN NaN], eps)
497s ***** assert (tinv (single ([p, NaN]), 1), single ([NaN -Inf 0 Inf NaN NaN]), eps ("single"))
497s ***** assert (tinv ([p, NaN], single (1)), single ([NaN -Inf 0 Inf NaN NaN]), eps ("single"))
497s ***** error<tinv: function called with too few input arguments.> tinv ()
497s ***** error<tinv: function called with too few input arguments.> tinv (1)
497s ***** error<tinv: P and DF must be of common size or scalars.> ...
497s  tinv (ones (3), ones (2))
497s ***** error<tinv: P and DF must be of common size or scalars.> ...
497s  tinv (ones (2), ones (3))
497s ***** error<tinv: P and DF must not be complex.> tinv (i, 2)
497s ***** error<tinv: P and DF must not be complex.> tinv (2, i)
497s 13 tests, 13 passed, 0 known failure, 0 skipped
497s [inst/dist_fun/tlscdf.m]
497s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/tlscdf.m
497s ***** demo
497s  ## Plot various CDFs from the location-scale Student's T distribution
497s  x = -8:0.01:8;
497s  p1 = tlscdf (x, 0, 1, 1);
497s  p2 = tlscdf (x, 0, 2, 2);
497s  p3 = tlscdf (x, 3, 2, 5);
497s  p4 = tlscdf (x, -1, 3, Inf);
497s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-m")
497s  grid on
497s  xlim ([-8, 8])
497s  ylim ([0, 1])
497s  legend ({"mu = 0, sigma = 1, nu = 1", "mu = 0, sigma = 2, nu = 2", ...
497s           "mu = 3, sigma = 2, nu = 5", 'mu = -1, sigma = 3, nu = \infty'}, ...
497s          "location", "northwest")
497s  title ("Location-scale Student's T CDF")
497s  xlabel ("values in x")
497s  ylabel ("probability")
497s ***** shared x,y
497s  x = [-Inf 0 1 Inf];
497s  y = [0 1/2 3/4 1];
497s ***** assert (tlscdf (x, 0, 1, ones (1,4)), y, eps)
497s ***** assert (tlscdf (x, 0, 1, 1), y, eps)
497s ***** assert (tlscdf (x, 0, 1, [0 1 NaN 1]), [NaN 1/2 NaN 1], eps)
497s ***** assert (tlscdf ([x(1:2) NaN x(4)], 0, 1, 1), [y(1:2) NaN y(4)], eps)
497s ***** assert (tlscdf (2, 0, 1, 3, "upper"), 0.0697, 1e-4)
497s ***** assert (tlscdf (205, 0, 1, 5, "upper"), 2.6206e-11, 1e-14)
497s ***** assert (tlscdf ([x, NaN], 0, 1, 1), [y, NaN], eps)
497s ***** assert (tlscdf (single ([x, NaN]), 0, 1, 1), single ([y, NaN]), eps ("single"))
497s ***** assert (tlscdf ([x, NaN], single (0), 1, 1), single ([y, NaN]), eps ("single"))
497s ***** assert (tlscdf ([x, NaN], 0, single (1), 1), single ([y, NaN]), eps ("single"))
497s ***** assert (tlscdf ([x, NaN], 0, 1, single (1)), single ([y, NaN]), eps ("single"))
497s ***** error<tlscdf: function called with too few input arguments.> tlscdf ()
497s ***** error<tlscdf: function called with too few input arguments.> tlscdf (1)
497s ***** error<tlscdf: function called with too few input arguments.> tlscdf (1, 2)
497s ***** error<tlscdf: function called with too few input arguments.> tlscdf (1, 2, 3)
497s ***** error<tlscdf: invalid argument for upper tail.> tlscdf (1, 2, 3, 4, "uper")
497s ***** error<tlscdf: invalid argument for upper tail.> tlscdf (1, 2, 3, 4, 5)
497s ***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
497s  tlscdf (ones (3), ones (2), 1, 1)
497s ***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
497s  tlscdf (ones (3), 1, ones (2), 1)
497s ***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
497s  tlscdf (ones (3), 1, 1, ones (2))
497s ***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
497s  tlscdf (ones (3), ones (2), 1, 1, "upper")
497s ***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
497s  tlscdf (ones (3), 1, ones (2), 1, "upper")
497s ***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
497s  tlscdf (ones (3), 1, 1, ones (2), "upper")
497s ***** error<tlscdf: X, MU, SIGMA, and NU must not be complex.> tlscdf (i, 2, 1, 1)
497s ***** error<tlscdf: X, MU, SIGMA, and NU must not be complex.> tlscdf (2, i, 1, 1)
497s ***** error<tlscdf: X, MU, SIGMA, and NU must not be complex.> tlscdf (2, 1, i, 1)
497s ***** error<tlscdf: X, MU, SIGMA, and NU must not be complex.> tlscdf (2, 1, 1, i)
497s 27 tests, 27 passed, 0 known failure, 0 skipped
497s [inst/dist_fun/tlsinv.m]
497s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/tlsinv.m
497s ***** demo
497s  ## Plot various iCDFs from the location-scale Student's T distribution
497s  p = 0.001:0.001:0.999;
497s  x1 = tlsinv (p, 0, 1, 1);
497s  x2 = tlsinv (p, 0, 2, 2);
497s  x3 = tlsinv (p, 3, 2, 5);
497s  x4 = tlsinv (p, -1, 3, Inf);
497s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-m")
497s  grid on
497s  xlim ([0, 1])
497s  ylim ([-8, 8])
497s  legend ({"mu = 0, sigma = 1, nu = 1", "mu = 0, sigma = 2, nu = 2", ...
497s           "mu = 3, sigma = 2, nu = 5", 'mu = -1, sigma = 3, nu = \infty'}, ...
497s          "location", "southeast")
497s  title ("Location-scale Student's T iCDF")
497s  xlabel ("probability")
497s  ylabel ("values in x")
497s ***** shared p
497s  p = [-1 0 0.5 1 2];
497s ***** assert (tlsinv (p, 0, 1, ones (1,5)), [NaN -Inf 0 Inf NaN])
497s ***** assert (tlsinv (p, 0, 1, 1), [NaN -Inf 0 Inf NaN], eps)
497s ***** assert (tlsinv (p, 0, 1, [1 0 NaN 1 1]), [NaN NaN NaN Inf NaN], eps)
497s ***** assert (tlsinv ([p(1:2) NaN p(4:5)], 0, 1, 1), [NaN -Inf NaN Inf NaN])
497s ***** assert (class (tlsinv ([p, NaN], 0, 1, 1)), "double")
497s ***** assert (class (tlsinv (single ([p, NaN]), 0, 1, 1)), "single")
497s ***** assert (class (tlsinv ([p, NaN], single (0), 1, 1)), "single")
497s ***** assert (class (tlsinv ([p, NaN], 0, single (1), 1)), "single")
497s ***** assert (class (tlsinv ([p, NaN], 0, 1, single (1))), "single")
497s ***** error<tlsinv: function called with too few input arguments.> tlsinv ()
497s ***** error<tlsinv: function called with too few input arguments.> tlsinv (1)
497s ***** error<tlsinv: function called with too few input arguments.> tlsinv (1, 2)
497s ***** error<tlsinv: function called with too few input arguments.> tlsinv (1, 2, 3)
497s ***** error<tlsinv: P, MU, SIGMA, and NU must be of common size or scalars.> ...
497s  tlsinv (ones (3), ones (2), 1, 1)
497s ***** error<tlsinv: P, MU, SIGMA, and NU must be of common size or scalars.> ...
497s  tlsinv (ones (2), 1, ones (3), 1)
497s ***** error<tlsinv: P, MU, SIGMA, and NU must be of common size or scalars.> ...
497s  tlsinv (ones (2), 1, 1, ones (3))
497s ***** error<tlsinv: P, MU, SIGMA, and NU must not be complex.> tlsinv (i, 2, 3, 4)
497s ***** error<tlsinv: P, MU, SIGMA, and NU must not be complex.> tlsinv (2, i, 3, 4)
497s ***** error<tlsinv: P, MU, SIGMA, and NU must not be complex.> tlsinv (2, 2, i, 4)
497s ***** error<tlsinv: P, MU, SIGMA, and NU must not be complex.> tlsinv (2, 2, 3, i)
497s 20 tests, 20 passed, 0 known failure, 0 skipped
497s [inst/dist_fun/tlspdf.m]
497s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/tlspdf.m
497s ***** demo
497s  ## Plot various PDFs from the Student's T distribution
497s  x = -8:0.01:8;
497s  y1 = tlspdf (x, 0, 1, 1);
497s  y2 = tlspdf (x, 0, 2, 2);
497s  y3 = tlspdf (x, 3, 2, 5);
497s  y4 = tlspdf (x, -1, 3, Inf);
497s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-m")
497s  grid on
497s  xlim ([-8, 8])
497s  ylim ([0, 0.41])
497s  legend ({"mu = 0, sigma = 1, nu = 1", "mu = 0, sigma = 2, nu = 2", ...
497s           "mu = 3, sigma = 2, nu = 5", 'mu = -1, sigma = 3, nu = \infty'}, ...
497s          "location", "northwest")
497s  title ("Location-scale Student's T PDF")
497s  xlabel ("values in x")
497s  ylabel ("density")
497s ***** test
497s  x = rand (10,1);
497s  y = 1./(pi * (1 + x.^2));
497s  assert (tlspdf (x, 0, 1, 1), y, 5*eps);
497s  assert (tlspdf (x+5, 5, 1, 1), y, 5*eps);
497s  assert (tlspdf (x.*2, 0, 2, 1), y./2, 5*eps);
497s ***** shared x, y
497s  x = [-Inf 0 0.5 1 Inf];
497s  y = 1./(pi * (1 + x.^2));
497s ***** assert (tlspdf (x, 0, 1, ones (1,5)), y, eps)
498s ***** assert (tlspdf (x, 0, 1, 1), y, eps)
498s ***** assert (tlspdf (x, 0, 1, [0 NaN 1 1 1]), [NaN NaN y(3:5)], eps)
498s ***** assert (tlspdf (x, 0, 1, Inf), normpdf (x))
498s ***** assert (class (tlspdf ([x, NaN], 1, 1, 1)), "double")
498s ***** assert (class (tlspdf (single ([x, NaN]), 1, 1, 1)), "single")
498s ***** assert (class (tlspdf ([x, NaN], single (1), 1, 1)), "single")
498s ***** assert (class (tlspdf ([x, NaN], 1, single (1), 1)), "single")
498s ***** assert (class (tlspdf ([x, NaN], 1, 1, single (1))), "single")
498s ***** error<tlspdf: function called with too few input arguments.> tlspdf ()
498s ***** error<tlspdf: function called with too few input arguments.> tlspdf (1)
498s ***** error<tlspdf: function called with too few input arguments.> tlspdf (1, 2)
498s ***** error<tlspdf: function called with too few input arguments.> tlspdf (1, 2, 3)
498s ***** error<tlspdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
498s  tlspdf (ones (3), ones (2), 1, 1)
498s ***** error<tlspdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
498s  tlspdf (ones (2), 1, ones (3), 1)
498s ***** error<tlspdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
498s  tlspdf (ones (2), 1, 1, ones (3))
498s ***** error<tlspdf: X, MU, SIGMA, and NU must not be complex.> tlspdf (i, 2, 1, 1)
498s ***** error<tlspdf: X, MU, SIGMA, and NU must not be complex.> tlspdf (2, i, 1, 1)
498s ***** error<tlspdf: X, MU, SIGMA, and NU must not be complex.> tlspdf (2, 1, i, 1)
498s ***** error<tlspdf: X, MU, SIGMA, and NU must not be complex.> tlspdf (2, 1, 1, i)
498s 21 tests, 21 passed, 0 known failure, 0 skipped
498s [inst/dist_fun/tlsrnd.m]
498s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/tlsrnd.m
498s ***** assert (size (tlsrnd (1, 2, 3)), [1, 1])
498s ***** assert (size (tlsrnd (ones (2,1), 2, 3)), [2, 1])
498s ***** assert (size (tlsrnd (ones (2,2), 2, 3)), [2, 2])
498s ***** assert (size (tlsrnd (1, 2, 3, 3)), [3, 3])
498s ***** assert (size (tlsrnd (1, 2, 3, [4 1])), [4, 1])
498s ***** assert (size (tlsrnd (1, 2, 3, 4, 1)), [4, 1])
498s ***** assert (size (tlsrnd (1, 2, 3, 4, 1)), [4, 1])
498s ***** assert (size (tlsrnd (1, 2, 3, 4, 1, 5)), [4, 1, 5])
498s ***** assert (size (tlsrnd (1, 2, 3, 0, 1)), [0, 1])
498s ***** assert (size (tlsrnd (1, 2, 3, 1, 0)), [1, 0])
498s ***** assert (size (tlsrnd (1, 2, 3, 1, 2, 0, 5)), [1, 2, 0, 5])
498s ***** assert (tlsrnd (1, 2, 0, 1, 1), NaN)
498s ***** assert (tlsrnd (1, 2, [0, 0, 0], [1, 3]), [NaN, NaN, NaN])
498s ***** assert (class (tlsrnd (1, 2, 3)), "double")
498s ***** assert (class (tlsrnd (single (1), 2, 3)), "single")
498s ***** assert (class (tlsrnd (single ([1, 1]), 2, 3)), "single")
498s ***** assert (class (tlsrnd (1, single (2), 3)), "single")
498s ***** assert (class (tlsrnd (1, single ([2, 2]), 3)), "single")
498s ***** assert (class (tlsrnd (1, 2, single (3))), "single")
498s ***** assert (class (tlsrnd (1, 2, single ([3, 3]))), "single")
498s ***** error<tlsrnd: function called with too few input arguments.> tlsrnd ()
498s ***** error<tlsrnd: function called with too few input arguments.> tlsrnd (1)
498s ***** error<tlsrnd: function called with too few input arguments.> tlsrnd (1, 2)
498s ***** error<tlsrnd: MU, SIGMA, and NU must be of common size or scalars.> ...
498s  tlsrnd (ones (3), ones (2), 1)
498s ***** error<tlsrnd: MU, SIGMA, and NU must be of common size or scalars.> ...
498s  tlsrnd (ones (2), 1, ones (3))
498s ***** error<tlsrnd: MU, SIGMA, and NU must be of common size or scalars.> ...
498s  tlsrnd (1, ones (2), ones (3))
498s ***** error<tlsrnd: MU, SIGMA, and NU must not be complex.> tlsrnd (i, 2, 3)
498s ***** error<tlsrnd: MU, SIGMA, and NU must not be complex.> tlsrnd (1, i, 3)
498s ***** error<tlsrnd: MU, SIGMA, and NU must not be complex.> tlsrnd (1, 2, i)
498s ***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
498s  tlsrnd (1, 2, 3, -1)
498s ***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
498s  tlsrnd (1, 2, 3, 1.2)
498s ***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
498s  tlsrnd (1, 2, 3, ones (2))
498s ***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
498s  tlsrnd (1, 2, 3, [2 -1 2])
498s ***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
498s  tlsrnd (1, 2, 3, [2 0 2.5])
498s ***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
498s  tlsrnd (ones (2), 2, 3, ones (2))
498s ***** error<tlsrnd: dimensions must be non-negative integers.> ...
498s  tlsrnd (1, 2, 3, 2, -1, 5)
498s ***** error<tlsrnd: dimensions must be non-negative integers.> ...
498s  tlsrnd (1, 2, 3, 2, 1.5, 5)
498s ***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
498s  tlsrnd (ones (2,2), 2, 3, 3)
498s ***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
498s  tlsrnd (1, ones (2,2), 3, 3)
498s ***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
498s  tlsrnd (1, 2, ones (2,2), 3)
498s ***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
498s  tlsrnd (1, 2, ones (2,2), [3, 3])
498s ***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
498s  tlsrnd (1, 2, ones (2,2), 2, 3)
498s 42 tests, 42 passed, 0 known failure, 0 skipped
498s [inst/dist_fun/tpdf.m]
498s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/tpdf.m
498s ***** demo
498s  ## Plot various PDFs from the Student's T distribution
498s  x = -5:0.01:5;
498s  y1 = tpdf (x, 1);
498s  y2 = tpdf (x, 2);
498s  y3 = tpdf (x, 5);
498s  y4 = tpdf (x, Inf);
498s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-m")
498s  grid on
498s  xlim ([-5, 5])
498s  ylim ([0, 0.41])
498s  legend ({"df = 1", "df = 2", ...
498s           "df = 5", 'df = \infty'}, "location", "northeast")
498s  title ("Student's T PDF")
498s  xlabel ("values in x")
498s  ylabel ("density")
498s ***** test
498s  x = rand (10,1);
498s  y = 1./(pi * (1 + x.^2));
498s  assert (tpdf (x, 1), y, 5*eps);
498s ***** shared x, y
498s  x = [-Inf 0 0.5 1 Inf];
498s  y = 1./(pi * (1 + x.^2));
498s ***** assert (tpdf (x, ones (1,5)), y, eps)
498s ***** assert (tpdf (x, 1), y, eps)
498s ***** assert (tpdf (x, [0 NaN 1 1 1]), [NaN NaN y(3:5)], eps)
498s ***** assert (tpdf (x, Inf), normpdf (x))
498s ***** assert (tpdf ([x, NaN], 1), [y, NaN], eps)
498s ***** assert (tpdf (single ([x, NaN]), 1), single ([y, NaN]), eps ("single"))
498s ***** assert (tpdf ([x, NaN], single (1)), single ([y, NaN]), eps ("single"))
498s ***** error<tpdf: function called with too few input arguments.> tpdf ()
498s ***** error<tpdf: function called with too few input arguments.> tpdf (1)
498s ***** error<tpdf: X and DF must be of common size or scalars.> ...
498s  tpdf (ones (3), ones (2))
498s ***** error<tpdf: X and DF must be of common size or scalars.> ...
498s  tpdf (ones (2), ones (3))
498s ***** error<tpdf: X and DF must not be complex.> tpdf (i, 2)
498s ***** error<tpdf: X and DF must not be complex.> tpdf (2, i)
498s 14 tests, 14 passed, 0 known failure, 0 skipped
498s [inst/dist_fun/tricdf.m]
498s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/tricdf.m
498s ***** demo
498s  ## Plot various CDFs from the triangular distribution
498s  x = 0.001:0.001:10;
498s  p1 = tricdf (x, 3, 4, 6);
498s  p2 = tricdf (x, 1, 2, 5);
498s  p3 = tricdf (x, 2, 3, 9);
498s  p4 = tricdf (x, 2, 5, 9);
498s  plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
498s  grid on
498s  xlim ([0, 10])
498s  legend ({"a = 3, b = 4, c = 6", "a = 1, b = 2, c = 5", ...
498s           "a = 2, b = 3, c = 9", "a = 2, b = 5, c = 9"}, ...
498s          "location", "southeast")
498s  title ("Triangular CDF")
498s  xlabel ("values in x")
498s  ylabel ("probability")
498s ***** shared x, y
498s  x = [-1, 0, 0.1, 0.5, 0.9, 1, 2] + 1;
498s  y = [0, 0, 0.02, 0.5, 0.98, 1 1];
498s ***** assert (tricdf (x, ones (1,7), 1.5 * ones (1, 7), 2 * ones (1, 7)), y, eps)
498s ***** assert (tricdf (x, 1 * ones (1, 7), 1.5, 2), y, eps)
498s ***** assert (tricdf (x, 1 * ones (1, 7), 1.5, 2, "upper"), 1 - y, eps)
498s ***** assert (tricdf (x, 1, 1.5, 2 * ones (1, 7)), y, eps)
498s ***** assert (tricdf (x, 1, 1.5 * ones (1, 7), 2), y, eps)
498s ***** assert (tricdf (x, 1, 1.5, 2), y, eps)
498s ***** assert (tricdf (x, [1, 1, NaN, 1, 1, 1, 1], 1.5, 2), ...
498s  [y(1:2), NaN, y(4:7)], eps)
498s ***** assert (tricdf (x, 1, 1.5, 2*[1, 1, NaN, 1, 1, 1, 1]), ...
498s  [y(1:2), NaN, y(4:7)], eps)
498s ***** assert (tricdf (x, 1, 1.5, 2*[1, 1, NaN, 1, 1, 1, 1]), ...
498s  [y(1:2), NaN, y(4:7)], eps)
498s ***** assert (tricdf ([x, NaN], 1, 1.5, 2), [y, NaN], eps)
498s ***** assert (tricdf (single ([x, NaN]), 1, 1.5, 2), ...
498s  single ([y, NaN]), eps("single"))
498s ***** assert (tricdf ([x, NaN], single (1), 1.5, 2), ...
498s  single ([y, NaN]), eps("single"))
498s ***** assert (tricdf ([x, NaN], 1, single (1.5), 2), ...
498s  single ([y, NaN]), eps("single"))
498s ***** assert (tricdf ([x, NaN], 1, 1.5, single (2)), ...
498s  single ([y, NaN]), eps("single"))
498s ***** error<tricdf: function called with too few input arguments.> tricdf ()
498s ***** error<tricdf: function called with too few input arguments.> tricdf (1)
498s ***** error<tricdf: function called with too few input arguments.> tricdf (1, 2)
498s ***** error<tricdf: function called with too few input arguments.> tricdf (1, 2, 3)
498s ***** error<tricdf: function called with too many inputs> ...
498s  tricdf (1, 2, 3, 4, 5, 6)
498s ***** error<tricdf: invalid argument for upper tail.> tricdf (1, 2, 3, 4, "tail")
498s ***** error<tricdf: invalid argument for upper tail.> tricdf (1, 2, 3, 4, 5)
498s ***** error<tricdf: X, A, B, and C must be of common size or scalars.> ...
498s  tricdf (ones (3), ones (2), ones(2), ones(2))
498s ***** error<tricdf: X, A, B, and C must be of common size or scalars.> ...
498s  tricdf (ones (2), ones (3), ones(2), ones(2))
498s ***** error<tricdf: X, A, B, and C must be of common size or scalars.> ...
498s  tricdf (ones (2), ones (2), ones(3), ones(2))
498s ***** error<tricdf: X, A, B, and C must be of common size or scalars.> ...
498s  tricdf (ones (2), ones (2), ones(2), ones(3))
498s ***** error<tricdf: X, A, B, and C must not be complex.> tricdf (i, 2, 3, 4)
498s ***** error<tricdf: X, A, B, and C must not be complex.> tricdf (1, i, 3, 4)
498s ***** error<tricdf: X, A, B, and C must not be complex.> tricdf (1, 2, i, 4)
498s ***** error<tricdf: X, A, B, and C must not be complex.> tricdf (1, 2, 3, i)
498s 29 tests, 29 passed, 0 known failure, 0 skipped
498s [inst/dist_fun/triinv.m]
498s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/triinv.m
498s ***** demo
498s  ## Plot various iCDFs from the triangular distribution
498s  p = 0.001:0.001:0.999;
498s  x1 = triinv (p, 3, 6, 4);
498s  x2 = triinv (p, 1, 5, 2);
498s  x3 = triinv (p, 2, 9, 3);
498s  x4 = triinv (p, 2, 9, 5);
498s  plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
498s  grid on
498s  ylim ([0, 10])
498s  legend ({"a = 3, b = 6, c = 4", "a = 1, b = 5, c = 2", ...
498s           "a = 2, b = 9, c = 3", "a = 2, b = 9, c = 5"}, ...
498s          "location", "northwest")
498s  title ("Triangular CDF")
498s  xlabel ("probability")
498s  ylabel ("values in x")
498s ***** shared p, y
498s  p = [-1, 0, 0.02, 0.5, 0.98, 1, 2];
498s  y = [NaN, 0, 0.1, 0.5, 0.9, 1, NaN] + 1;
498s ***** assert (triinv (p, ones (1, 7), 1.5 * ones (1, 7), 2 * ones (1, 7)), y, eps)
498s ***** assert (triinv (p, 1 * ones (1, 7), 1.5, 2), y, eps)
498s ***** assert (triinv (p, 1, 1.5, 2 * ones (1, 7)), y, eps)
498s ***** assert (triinv (p, 1, 1.5*ones (1,7), 2), y, eps)
498s ***** assert (triinv (p, 1, 1.5, 2), y, eps)
498s ***** assert (triinv (p, [1, 1, NaN, 1, 1, 1, 1], 1.5, 2), [y(1:2), NaN, y(4:7)], eps)
498s ***** assert (triinv (p, 1, 1.5 * [1, 1, NaN, 1, 1, 1, 1], 2), [y(1:2), NaN, y(4:7)], eps)
498s ***** assert (triinv (p, 1, 1.5, 2 * [1, 1, NaN, 1, 1, 1, 1]), [y(1:2), NaN, y(4:7)], eps)
498s ***** assert (triinv ([p, NaN], 1, 1.5, 2), [y, NaN], eps)
498s ***** assert (triinv (single ([p, NaN]), 1, 1.5, 2), single ([y, NaN]), eps('single'))
498s ***** assert (triinv ([p, NaN], single (1), 1.5, 2), single ([y, NaN]), eps('single'))
498s ***** assert (triinv ([p, NaN], 1, single (1.5), 2), single ([y, NaN]), eps('single'))
498s ***** assert (triinv ([p, NaN], 1, 1.5, single (2)), single ([y, NaN]), eps('single'))
498s ***** error<triinv: function called with too few input arguments.> triinv ()
498s ***** error<triinv: function called with too few input arguments.> triinv (1)
498s ***** error<triinv: function called with too few input arguments.> triinv (1, 2)
498s ***** error<triinv: function called with too few input arguments.> triinv (1, 2, 3)
498s ***** error<triinv: function called with too many inputs> ...
498s  triinv (1, 2, 3, 4, 5)
498s ***** error<triinv: P, A, B, and C must be of common size or scalars.> ...
498s  triinv (ones (3), ones (2), ones(2), ones(2))
498s ***** error<triinv: P, A, B, and C must be of common size or scalars.> ...
498s  triinv (ones (2), ones (3), ones(2), ones(2))
498s ***** error<triinv: P, A, B, and C must be of common size or scalars.> ...
498s  triinv (ones (2), ones (2), ones(3), ones(2))
498s ***** error<triinv: P, A, B, and C must be of common size or scalars.> ...
498s  triinv (ones (2), ones (2), ones(2), ones(3))
498s ***** error<triinv: P, A, B, and C must not be complex.> triinv (i, 2, 3, 4)
498s ***** error<triinv: P, A, B, and C must not be complex.> triinv (1, i, 3, 4)
498s ***** error<triinv: P, A, B, and C must not be complex.> triinv (1, 2, i, 4)
498s ***** error<triinv: P, A, B, and C must not be complex.> triinv (1, 2, 3, i)
498s 26 tests, 26 passed, 0 known failure, 0 skipped
498s [inst/dist_fun/tripdf.m]
498s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/tripdf.m
498s ***** demo
498s  ## Plot various CDFs from the triangular distribution
498s  x = 0.001:0.001:10;
498s  y1 = tripdf (x, 3, 4, 6);
498s  y2 = tripdf (x, 1, 2, 5);
498s  y3 = tripdf (x, 2, 3, 9);
498s  y4 = tripdf (x, 2, 5, 9);
498s  plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
498s  grid on
498s  xlim ([0, 10])
498s  legend ({"a = 3, b = 4, c = 6", "a = 1, b = 2, c = 5", ...
498s           "a = 2, b = 3, c = 9", "a = 2, b = 5, c = 9"}, ...
498s          "location", "northeast")
498s  title ("Triangular CDF")
498s  xlabel ("values in x")
498s  ylabel ("probability")
498s ***** shared x, y, deps
498s  x = [-1, 0, 0.1, 0.5, 0.9, 1, 2] + 1;
498s  y = [0, 0, 0.4, 2, 0.4, 0, 0];
498s  deps = 2*eps;
498s ***** assert (tripdf (x, ones (1,7), 1.5*ones (1,7), 2*ones (1,7)), y, deps)
498s ***** assert (tripdf (x, 1*ones (1,7), 1.5, 2), y, deps)
498s ***** assert (tripdf (x, 1, 1.5, 2*ones (1,7)), y, deps)
498s ***** assert (tripdf (x, 1, 1.5*ones (1,7), 2), y, deps)
498s ***** assert (tripdf (x, 1, 1.5, 2), y, deps)
498s ***** assert (tripdf (x, [1, 1, NaN, 1, 1, 1, 1], 1.5, 2), [y(1:2), NaN, y(4:7)], deps)
498s ***** assert (tripdf (x, 1, 1.5, 2*[1, 1, NaN, 1, 1, 1, 1]), [y(1:2), NaN, y(4:7)], deps)
498s ***** assert (tripdf (x, 1, 1.5*[1, 1, NaN, 1, 1, 1, 1], 2), [y(1:2), NaN, y(4:7)], deps)
498s ***** assert (tripdf ([x, NaN], 1, 1.5, 2), [y, NaN], deps)
498s ***** assert (tripdf (single ([x, NaN]), 1, 1.5, 2), single ([y, NaN]), eps("single"))
498s ***** assert (tripdf ([x, NaN], single (1), 1.5, 2), single ([y, NaN]), eps("single"))
498s ***** assert (tripdf ([x, NaN], 1, 1.5, single (2)), single ([y, NaN]), eps("single"))
498s ***** assert (tripdf ([x, NaN], 1, single (1.5), 2), single ([y, NaN]), eps("single"))
498s ***** error<tripdf: function called with too few input arguments.> tripdf ()
498s ***** error<tripdf: function called with too few input arguments.> tripdf (1)
498s ***** error<tripdf: function called with too few input arguments.> tripdf (1, 2)
498s ***** error<tripdf: function called with too few input arguments.> tripdf (1, 2, 3)
498s ***** error<tripdf: function called with too many inputs> ...
498s  tripdf (1, 2, 3, 4, 5)
498s ***** error<tripdf: X, A, B, and C must be of common size or scalars.> ...
498s  tripdf (ones (3), ones (2), ones(2), ones(2))
498s ***** error<tripdf: X, A, B, and C must be of common size or scalars.> ...
498s  tripdf (ones (2), ones (3), ones(2), ones(2))
498s ***** error<tripdf: X, A, B, and C must be of common size or scalars.> ...
498s  tripdf (ones (2), ones (2), ones(3), ones(2))
498s ***** error<tripdf: X, A, B, and C must be of common size or scalars.> ...
498s  tripdf (ones (2), ones (2), ones(2), ones(3))
498s ***** error<tripdf: X, A, B, and C must not be complex.> tripdf (i, 2, 3, 4)
498s ***** error<tripdf: X, A, B, and C must not be complex.> tripdf (1, i, 3, 4)
498s ***** error<tripdf: X, A, B, and C must not be complex.> tripdf (1, 2, i, 4)
498s ***** error<tripdf: X, A, B, and C must not be complex.> tripdf (1, 2, 3, i)
498s 26 tests, 26 passed, 0 known failure, 0 skipped
498s [inst/dist_fun/trirnd.m]
498s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/trirnd.m
498s ***** assert (size (trirnd (1, 1.5, 2)), [1, 1])
498s ***** assert (size (trirnd (1 * ones (2, 1), 1.5, 2)), [2, 1])
498s ***** assert (size (trirnd (1 * ones (2, 2), 1.5, 2)), [2, 2])
498s ***** assert (size (trirnd (1, 1.5 * ones (2, 1), 2)), [2, 1])
498s ***** assert (size (trirnd (1, 1.5 * ones (2, 2), 2)), [2, 2])
498s ***** assert (size (trirnd (1, 1.5, 2 * ones (2, 1))), [2, 1])
498s ***** assert (size (trirnd (1, 1.5, 2 * ones (2, 2))), [2, 2])
498s ***** assert (size (trirnd (1, 1.5, 2, 3)), [3, 3])
498s ***** assert (size (trirnd (1, 1.5, 2, [4, 1])), [4, 1])
498s ***** assert (size (trirnd (1, 1.5, 2, 4, 1)), [4, 1])
498s ***** assert (class (trirnd (1, 1.5, 2)), "double")
498s ***** assert (class (trirnd (single (1), 1.5, 2)), "single")
498s ***** assert (class (trirnd (single ([1, 1]), 1.5, 2)), "single")
498s ***** assert (class (trirnd (1, single (1.5), 2)), "single")
498s ***** assert (class (trirnd (1, single ([1.5, 1.5]), 2)), "single")
498s ***** assert (class (trirnd (1, 1.5, single (1.5))), "single")
498s ***** assert (class (trirnd (1, 1.5, single ([2, 2]))), "single")
498s ***** error<trirnd: function called with too few input arguments.> trirnd ()
498s ***** error<trirnd: function called with too few input arguments.> trirnd (1)
498s ***** error<trirnd: function called with too few input arguments.> trirnd (1, 2)
498s ***** error<trirnd: A, B, and C must be of common size or scalars.> ...
498s  trirnd (ones (3), 5 * ones (2), ones (2))
498s ***** error<trirnd: A, B, and C must be of common size or scalars.> ...
498s  trirnd (ones (2), 5 * ones (3), ones (2))
498s ***** error<trirnd: A, B, and C must be of common size or scalars.> ...
498s  trirnd (ones (2), 5 * ones (2), ones (3))
498s ***** error<trirnd: A, B, and C must not be complex.> trirnd (i, 5, 3)
498s ***** error<trirnd: A, B, and C must not be complex.> trirnd (1, 5+i, 3)
498s ***** error<trirnd: A, B, and C must not be complex.> trirnd (1, 5, i)
498s ***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
498s  trirnd (1, 5, 3, -1)
498s ***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
498s  trirnd (1, 5, 3, 1.2)
498s ***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
498s  trirnd (1, 5, 3, ones (2))
498s ***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
498s  trirnd (1, 5, 3, [2 -1 2])
498s ***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
498s  trirnd (1, 5, 3, [2 0 2.5])
498s ***** error<trirnd: dimensions must be non-negative integers.> ...
498s  trirnd (1, 5, 3, 2, -1, 5)
498s ***** error<trirnd: dimensions must be non-negative integers.> ...
498s  trirnd (1, 5, 3, 2, 1.5, 5)
498s ***** error<trirnd: A, B, and C must be scalar or of size SZ.> ...
498s  trirnd (2, 5 * ones (2), 2, 3)
498s ***** error<trirnd: A, B, and C must be scalar or of size SZ.> ...
498s  trirnd (2, 5 * ones (2), 2, [3, 2])
498s ***** error<trirnd: A, B, and C must be scalar or of size SZ.> ...
498s  trirnd (2, 5 * ones (2), 2, 3, 2)
498s 36 tests, 36 passed, 0 known failure, 0 skipped
498s [inst/dist_fun/trnd.m]
498s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/trnd.m
498s ***** assert (size (trnd (2)), [1, 1])
498s ***** assert (size (trnd (ones (2,1))), [2, 1])
498s ***** assert (size (trnd (ones (2,2))), [2, 2])
498s ***** assert (size (trnd (1, 3)), [3, 3])
498s ***** assert (size (trnd (1, [4 1])), [4, 1])
498s ***** assert (size (trnd (1, 4, 1)), [4, 1])
498s ***** assert (size (trnd (1, 4, 1)), [4, 1])
498s ***** assert (size (trnd (1, 4, 1, 5)), [4, 1, 5])
498s ***** assert (size (trnd (1, 0, 1)), [0, 1])
498s ***** assert (size (trnd (1, 1, 0)), [1, 0])
498s ***** assert (size (trnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
498s ***** assert (trnd (0, 1, 1), NaN)
498s ***** assert (trnd ([0, 0, 0], [1, 3]), [NaN, NaN, NaN])
498s ***** assert (class (trnd (2)), "double")
498s ***** assert (class (trnd (single (2))), "single")
498s ***** assert (class (trnd (single ([2 2]))), "single")
498s ***** error<trnd: function called with too few input arguments.> trnd ()
498s ***** error<trnd: DF must not be complex.> trnd (i)
498s ***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
498s  trnd (1, -1)
498s ***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
498s  trnd (1, 1.2)
498s ***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
498s  trnd (1, ones (2))
498s ***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
498s  trnd (1, [2 -1 2])
498s ***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
498s  trnd (1, [2 0 2.5])
498s ***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
498s  trnd (ones (2), ones (2))
498s ***** error<trnd: dimensions must be non-negative integers.> ...
498s  trnd (1, 2, -1, 5)
498s ***** error<trnd: dimensions must be non-negative integers.> ...
498s  trnd (1, 2, 1.5, 5)
498s ***** error<trnd: DF must be scalar or of size SZ.> trnd (ones (2,2), 3)
498s ***** error<trnd: DF must be scalar or of size SZ.> trnd (ones (2,2), [3, 2])
498s ***** error<trnd: DF must be scalar or of size SZ.> trnd (ones (2,2), 2, 3)
498s 29 tests, 29 passed, 0 known failure, 0 skipped
498s [inst/dist_fun/unidcdf.m]
498s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/unidcdf.m
498s ***** demo
498s  ## Plot various CDFs from the discrete uniform distribution
498s  x = 0:10;
498s  p1 = unidcdf (x, 5);
498s  p2 = unidcdf (x, 9);
498s  plot (x, p1, "*b", x, p2, "*g")
498s  grid on
498s  xlim ([0, 10])
498s  ylim ([0, 1])
498s  legend ({"N = 5", "N = 9"}, "location", "southeast")
498s  title ("Discrete uniform CDF")
498s  xlabel ("values in x")
498s  ylabel ("probability")
498s ***** shared x, y
498s  x = [0 1 2.5 10 11];
498s  y = [0, 0.1 0.2 1.0 1.0];
498s ***** assert (unidcdf (x, 10*ones (1,5)), y)
498s ***** assert (unidcdf (x, 10*ones (1,5), "upper"), 1 - y)
498s ***** assert (unidcdf (x, 10), y)
499s ***** assert (unidcdf (x, 10, "upper"), 1 - y)
499s ***** assert (unidcdf (x, 10*[0 1 NaN 1 1]), [NaN 0.1 NaN y(4:5)])
499s ***** assert (unidcdf ([x(1:2) NaN Inf x(5)], 10), [y(1:2) NaN 1 y(5)])
499s ***** assert (unidcdf ([x, NaN], 10), [y, NaN])
499s ***** assert (unidcdf (single ([x, NaN]), 10), single ([y, NaN]))
499s ***** assert (unidcdf ([x, NaN], single (10)), single ([y, NaN]))
499s ***** error<unidcdf: function called with too few input arguments.> unidcdf ()
499s ***** error<unidcdf: function called with too few input arguments.> unidcdf (1)
499s ***** error<unidcdf: invalid argument for upper tail.> unidcdf (1, 2, 3)
499s ***** error<unidcdf: invalid argument for upper tail.> unidcdf (1, 2, "tail")
499s ***** error<unidcdf: X and N must be of common size or scalars.> ...
499s  unidcdf (ones (3), ones (2))
499s ***** error<unidcdf: X and N must be of common size or scalars.> ...
499s  unidcdf (ones (2), ones (3))
499s ***** error<unidcdf: X and N must not be complex.> unidcdf (i, 2)
499s ***** error<unidcdf: X and N must not be complex.> unidcdf (2, i)
499s 17 tests, 17 passed, 0 known failure, 0 skipped
499s [inst/dist_fun/unidinv.m]
499s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/unidinv.m
499s ***** demo
499s  ## Plot various iCDFs from the discrete uniform distribution
499s  p = 0.001:0.001:0.999;
499s  x1 = unidinv (p, 5);
499s  x2 = unidinv (p, 9);
499s  plot (p, x1, "-b", p, x2, "-g")
499s  grid on
499s  xlim ([0, 1])
499s  ylim ([0, 10])
499s  legend ({"N = 5", "N = 9"}, "location", "northwest")
499s  title ("Discrete uniform iCDF")
499s  xlabel ("probability")
499s  ylabel ("values in x")
499s ***** shared p
499s  p = [-1 0 0.5 1 2];
499s ***** assert (unidinv (p, 10*ones (1,5)), [NaN NaN 5 10 NaN], eps)
499s ***** assert (unidinv (p, 10), [NaN NaN 5 10 NaN], eps)
499s ***** assert (unidinv (p, 10*[0 1 NaN 1 1]), [NaN NaN NaN 10 NaN], eps)
499s ***** assert (unidinv ([p(1:2) NaN p(4:5)], 10), [NaN NaN NaN 10 NaN], eps)
499s ***** assert (unidinv ([p, NaN], 10), [NaN NaN 5 10 NaN NaN], eps)
499s ***** assert (unidinv (single ([p, NaN]), 10), single ([NaN NaN 5 10 NaN NaN]), eps)
499s ***** assert (unidinv ([p, NaN], single (10)), single ([NaN NaN 5 10 NaN NaN]), eps)
499s ***** error<unidinv: function called with too few input arguments.> unidinv ()
499s ***** error<unidinv: function called with too few input arguments.> unidinv (1)
499s ***** error<unidinv: P and N must be of common size or scalars.> ...
499s  unidinv (ones (3), ones (2))
499s ***** error<unidinv: P and N must be of common size or scalars.> ...
499s  unidinv (ones (2), ones (3))
499s ***** error<unidinv: P and N must not be complex.> unidinv (i, 2)
499s ***** error<unidinv: P and N must not be complex.> unidinv (2, i)
499s 13 tests, 13 passed, 0 known failure, 0 skipped
499s [inst/dist_fun/unidpdf.m]
499s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/unidpdf.m
499s ***** demo
499s  ## Plot various PDFs from the discrete uniform distribution
499s  x = 0:10;
499s  y1 = unidpdf (x, 5);
499s  y2 = unidpdf (x, 9);
499s  plot (x, y1, "*b", x, y2, "*g")
499s  grid on
499s  xlim ([0, 10])
499s  ylim ([0, 0.25])
499s  legend ({"N = 5", "N = 9"}, "location", "northeast")
499s  title ("Discrete uniform PDF")
499s  xlabel ("values in x")
499s  ylabel ("density")
499s ***** shared x, y
499s  x = [-1 0 1 2 10 11];
499s  y = [0 0 0.1 0.1 0.1 0];
499s ***** assert (unidpdf (x, 10*ones (1,6)), y)
499s ***** assert (unidpdf (x, 10), y)
499s ***** assert (unidpdf (x, 10*[0 NaN 1 1 1 1]), [NaN NaN y(3:6)])
499s ***** assert (unidpdf ([x, NaN], 10), [y, NaN])
499s ***** assert (unidpdf (single ([x, NaN]), 10), single ([y, NaN]))
499s ***** assert (unidpdf ([x, NaN], single (10)), single ([y, NaN]))
499s ***** error<unidpdf: function called with too few input arguments.> unidpdf ()
499s ***** error<unidpdf: function called with too few input arguments.> unidpdf (1)
499s ***** error<unidpdf: X and N must be of common size or scalars.> ...
499s  unidpdf (ones (3), ones (2))
499s ***** error<unidpdf: X and N must be of common size or scalars.> ...
499s  unidpdf (ones (2), ones (3))
499s ***** error<unidpdf: X and N must not be complex.> unidpdf (i, 2)
499s ***** error<unidpdf: X and N must not be complex.> unidpdf (2, i)
499s 12 tests, 12 passed, 0 known failure, 0 skipped
499s [inst/dist_fun/unidrnd.m]
499s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/unidrnd.m
499s ***** assert (size (unidrnd (2)), [1, 1])
499s ***** assert (size (unidrnd (ones (2,1))), [2, 1])
499s ***** assert (size (unidrnd (ones (2,2))), [2, 2])
499s ***** assert (size (unidrnd (1, 3)), [3, 3])
499s ***** assert (size (unidrnd (1, [4 1])), [4, 1])
499s ***** assert (size (unidrnd (1, 4, 1)), [4, 1])
499s ***** assert (size (unidrnd (1, 4, 1)), [4, 1])
499s ***** assert (size (unidrnd (1, 4, 1, 5)), [4, 1, 5])
499s ***** assert (size (unidrnd (1, 0, 1)), [0, 1])
499s ***** assert (size (unidrnd (1, 1, 0)), [1, 0])
499s ***** assert (size (unidrnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
499s ***** assert (unidrnd (0, 1, 1), NaN)
499s ***** assert (unidrnd ([0, 0, 0], [1, 3]), [NaN, NaN, NaN])
499s ***** assert (class (unidrnd (2)), "double")
499s ***** assert (class (unidrnd (single (2))), "single")
499s ***** assert (class (unidrnd (single ([2 2]))), "single")
499s ***** error<unidrnd: function called with too few input arguments.> unidrnd ()
499s ***** error<unidrnd: N must not be complex.> unidrnd (i)
499s ***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
499s  unidrnd (1, -1)
499s ***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
499s  unidrnd (1, 1.2)
499s ***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
499s  unidrnd (1, ones (2))
499s ***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
499s  unidrnd (1, [2 -1 2])
499s ***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
499s  unidrnd (1, [2 0 2.5])
499s ***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
499s  unidrnd (ones (2), ones (2))
499s ***** error<unidrnd: dimensions must be non-negative integers.> ...
499s  unidrnd (1, 2, -1, 5)
499s ***** error<unidrnd: dimensions must be non-negative integers.> ...
499s  unidrnd (1, 2, 1.5, 5)
499s ***** error<unidrnd: N must be scalar or of size SZ.> unidrnd (ones (2,2), 3)
499s ***** error<unidrnd: N must be scalar or of size SZ.> unidrnd (ones (2,2), [3, 2])
499s ***** error<unidrnd: N must be scalar or of size SZ.> unidrnd (ones (2,2), 2, 3)
499s 29 tests, 29 passed, 0 known failure, 0 skipped
499s [inst/dist_fun/unifcdf.m]
499s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/unifcdf.m
499s ***** demo
499s  ## Plot various CDFs from the continuous uniform distribution
499s  x = 0:0.1:10;
499s  p1 = unifcdf (x, 2, 5);
499s  p2 = unifcdf (x, 3, 9);
499s  plot (x, p1, "-b", x, p2, "-g")
499s  grid on
499s  xlim ([0, 10])
499s  ylim ([0, 1])
499s  legend ({"a = 2, b = 5", "a = 3, b = 9"}, "location", "southeast")
499s  title ("Continuous uniform CDF")
499s  xlabel ("values in x")
499s  ylabel ("probability")
499s ***** shared x, y
499s  x = [-1 0 0.5 1 2] + 1;
499s  y = [0 0 0.5 1 1];
499s ***** assert (unifcdf (x, ones (1,5), 2*ones (1,5)), y)
499s ***** assert (unifcdf (x, ones (1,5), 2*ones (1,5), "upper"), 1 - y)
499s ***** assert (unifcdf (x, 1, 2*ones (1,5)), y)
499s ***** assert (unifcdf (x, 1, 2*ones (1,5), "upper"), 1 - y)
499s ***** assert (unifcdf (x, ones (1,5), 2), y)
499s ***** assert (unifcdf (x, ones (1,5), 2, "upper"), 1 - y)
499s ***** assert (unifcdf (x, [2 1 NaN 1 1], 2), [NaN 0 NaN 1 1])
499s ***** assert (unifcdf (x, [2 1 NaN 1 1], 2, "upper"), 1 - [NaN 0 NaN 1 1])
499s ***** assert (unifcdf (x, 1, 2*[0 1 NaN 1 1]), [NaN 0 NaN 1 1])
499s ***** assert (unifcdf (x, 1, 2*[0 1 NaN 1 1], "upper"), 1 - [NaN 0 NaN 1 1])
499s ***** assert (unifcdf ([x(1:2) NaN x(4:5)], 1, 2), [y(1:2) NaN y(4:5)])
499s ***** assert (unifcdf ([x(1:2) NaN x(4:5)], 1, 2, "upper"), 1 - [y(1:2) NaN y(4:5)])
499s ***** assert (unifcdf ([x, NaN], 1, 2), [y, NaN])
499s ***** assert (unifcdf (single ([x, NaN]), 1, 2), single ([y, NaN]))
499s ***** assert (unifcdf ([x, NaN], single (1), 2), single ([y, NaN]))
499s ***** assert (unifcdf ([x, NaN], 1, single (2)), single ([y, NaN]))
499s ***** error<unifcdf: function called with too few input arguments.> unifcdf ()
499s ***** error<unifcdf: function called with too few input arguments.> unifcdf (1)
499s ***** error<unifcdf: function called with too few input arguments.> unifcdf (1, 2)
499s ***** error<unifcdf: invalid argument for upper tail.> unifcdf (1, 2, 3, 4)
499s ***** error<unifcdf: invalid argument for upper tail.> unifcdf (1, 2, 3, "tail")
499s ***** error<unifcdf: X, A, and B must be of common size or scalars.> ...
499s  unifcdf (ones (3), ones (2), ones (2))
499s ***** error<unifcdf: X, A, and B must be of common size or scalars.> ...
499s  unifcdf (ones (2), ones (3), ones (2))
499s ***** error<unifcdf: X, A, and B must be of common size or scalars.> ...
499s  unifcdf (ones (2), ones (2), ones (3))
499s ***** error<unifcdf: X, A, and B must not be complex.> unifcdf (i, 2, 2)
499s ***** error<unifcdf: X, A, and B must not be complex.> unifcdf (2, i, 2)
499s ***** error<unifcdf: X, A, and B must not be complex.> unifcdf (2, 2, i)
499s 27 tests, 27 passed, 0 known failure, 0 skipped
499s [inst/dist_fun/unifinv.m]
499s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/unifinv.m
499s ***** demo
499s  ## Plot various iCDFs from the continuous uniform distribution
499s  p = 0.001:0.001:0.999;
499s  x1 = unifinv (p, 2, 5);
499s  x2 = unifinv (p, 3, 9);
499s  plot (p, x1, "-b", p, x2, "-g")
499s  grid on
499s  xlim ([0, 1])
499s  ylim ([0, 10])
499s  legend ({"a = 2, b = 5", "a = 3, b = 9"}, "location", "northwest")
499s  title ("Continuous uniform iCDF")
499s  xlabel ("probability")
499s  ylabel ("values in x")
499s ***** shared p
499s  p = [-1 0 0.5 1 2];
499s ***** assert (unifinv (p, ones (1,5), 2*ones (1,5)), [NaN 1 1.5 2 NaN])
499s ***** assert (unifinv (p, 0, 1), [NaN 1 1.5 2 NaN] - 1)
499s ***** assert (unifinv (p, 1, 2*ones (1,5)), [NaN 1 1.5 2 NaN])
499s ***** assert (unifinv (p, ones (1,5), 2), [NaN 1 1.5 2 NaN])
499s ***** assert (unifinv (p, [1 2 NaN 1 1], 2), [NaN NaN NaN 2 NaN])
499s ***** assert (unifinv (p, 1, 2*[1 0 NaN 1 1]), [NaN NaN NaN 2 NaN])
499s ***** assert (unifinv ([p(1:2) NaN p(4:5)], 1, 2), [NaN 1 NaN 2 NaN])
499s ***** assert (unifinv ([p, NaN], 1, 2), [NaN 1 1.5 2 NaN NaN])
499s ***** assert (unifinv (single ([p, NaN]), 1, 2), single ([NaN 1 1.5 2 NaN NaN]))
499s ***** assert (unifinv ([p, NaN], single (1), 2), single ([NaN 1 1.5 2 NaN NaN]))
499s ***** assert (unifinv ([p, NaN], 1, single (2)), single ([NaN 1 1.5 2 NaN NaN]))
499s ***** error<unifinv: function called with too few input arguments.> unifinv ()
499s ***** error<unifinv: function called with too few input arguments.> unifinv (1, 2)
499s ***** error<unifinv: P, A, and B must be of common size or scalars.> ...
499s  unifinv (ones (3), ones (2), ones (2))
499s ***** error<unifinv: P, A, and B must be of common size or scalars.> ...
499s  unifinv (ones (2), ones (3), ones (2))
499s ***** error<unifinv: P, A, and B must be of common size or scalars.> ...
499s  unifinv (ones (2), ones (2), ones (3))
499s ***** error<unifinv: P, A, and B must not be complex.> unifinv (i, 2, 2)
499s ***** error<unifinv: P, A, and B must not be complex.> unifinv (2, i, 2)
499s ***** error<unifinv: P, A, and B must not be complex.> unifinv (2, 2, i)
499s 19 tests, 19 passed, 0 known failure, 0 skipped
499s [inst/dist_fun/unifpdf.m]
499s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/unifpdf.m
499s ***** demo
499s  ## Plot various PDFs from the continuous uniform distribution
499s  x = 0:0.001:10;
499s  y1 = unifpdf (x, 2, 5);
499s  y2 = unifpdf (x, 3, 9);
499s  plot (x, y1, "-b", x, y2, "-g")
499s  grid on
499s  xlim ([0, 10])
499s  ylim ([0, 0.4])
499s  legend ({"a = 2, b = 5", "a = 3, b = 9"}, "location", "northeast")
499s  title ("Continuous uniform PDF")
499s  xlabel ("values in x")
499s  ylabel ("density")
499s ***** shared x, y
499s  x = [-1 0 0.5 1 2] + 1;
499s  y = [0 1 1 1 0];
499s ***** assert (unifpdf (x, ones (1,5), 2*ones (1,5)), y)
499s ***** assert (unifpdf (x, 1, 2*ones (1,5)), y)
499s ***** assert (unifpdf (x, ones (1,5), 2), y)
499s ***** assert (unifpdf (x, [2 NaN 1 1 1], 2), [NaN NaN y(3:5)])
499s ***** assert (unifpdf (x, 1, 2*[0 NaN 1 1 1]), [NaN NaN y(3:5)])
499s ***** assert (unifpdf ([x, NaN], 1, 2), [y, NaN])
499s ***** assert (unifpdf (x, 0, 1), [1 1 0 0 0])
499s ***** assert (unifpdf (single ([x, NaN]), 1, 2), single ([y, NaN]))
499s ***** assert (unifpdf (single ([x, NaN]), single (1), 2), single ([y, NaN]))
499s ***** assert (unifpdf ([x, NaN], 1, single (2)), single ([y, NaN]))
499s ***** error<unifpdf: function called with too few input arguments.> unifpdf ()
499s ***** error<unifpdf: function called with too few input arguments.> unifpdf (1)
499s ***** error<unifpdf: function called with too few input arguments.> unifpdf (1, 2)
499s ***** error<unifpdf: X, A, and B must be of common size or scalars.> ...
499s  unifpdf (ones (3), ones (2), ones (2))
499s ***** error<unifpdf: X, A, and B must be of common size or scalars.> ...
499s  unifpdf (ones (2), ones (3), ones (2))
499s ***** error<unifpdf: X, A, and B must be of common size or scalars.> ...
499s  unifpdf (ones (2), ones (2), ones (3))
499s ***** error<unifpdf: X, A, and B must not be complex.> unifpdf (i, 2, 2)
499s ***** error<unifpdf: X, A, and B must not be complex.> unifpdf (2, i, 2)
499s ***** error<unifpdf: X, A, and B must not be complex.> unifpdf (2, 2, i)
499s 19 tests, 19 passed, 0 known failure, 0 skipped
499s [inst/dist_fun/unifrnd.m]
499s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/unifrnd.m
499s ***** assert (size (unifrnd (1, 1)), [1 1])
499s ***** assert (size (unifrnd (1, ones (2,1))), [2, 1])
499s ***** assert (size (unifrnd (1, ones (2,2))), [2, 2])
499s ***** assert (size (unifrnd (ones (2,1), 1)), [2, 1])
499s ***** assert (size (unifrnd (ones (2,2), 1)), [2, 2])
499s ***** assert (size (unifrnd (1, 1, 3)), [3, 3])
499s ***** assert (size (unifrnd (1, 1, [4, 1])), [4, 1])
499s ***** assert (size (unifrnd (1, 1, 4, 1)), [4, 1])
499s ***** assert (size (unifrnd (1, 1, 4, 1, 5)), [4, 1, 5])
499s ***** assert (size (unifrnd (1, 1, 0, 1)), [0, 1])
499s ***** assert (size (unifrnd (1, 1, 1, 0)), [1, 0])
499s ***** assert (size (unifrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
499s ***** assert (class (unifrnd (1, 1)), "double")
499s ***** assert (class (unifrnd (1, single (1))), "single")
499s ***** assert (class (unifrnd (1, single ([1, 1]))), "single")
499s ***** assert (class (unifrnd (single (1), 1)), "single")
499s ***** assert (class (unifrnd (single ([1, 1]), 1)), "single")
499s ***** error<unifrnd: function called with too few input arguments.> unifrnd ()
499s ***** error<unifrnd: function called with too few input arguments.> unifrnd (1)
499s ***** error<unifrnd: A and B must be of common size or scalars.> ...
499s  unifrnd (ones (3), ones (2))
499s ***** error<unifrnd: A and B must be of common size or scalars.> ...
499s  unifrnd (ones (2), ones (3))
499s ***** error<unifrnd: A and B must not be complex.> unifrnd (i, 2, 3)
499s ***** error<unifrnd: A and B must not be complex.> unifrnd (1, i, 3)
499s ***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
499s  unifrnd (1, 2, -1)
499s ***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
499s  unifrnd (1, 2, 1.2)
499s ***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
499s  unifrnd (1, 2, ones (2))
499s ***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
499s  unifrnd (1, 2, [2 -1 2])
499s ***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
499s  unifrnd (1, 2, [2 0 2.5])
499s ***** error<unifrnd: dimensions must be non-negative integers.> ...
499s  unifrnd (1, 2, 2, -1, 5)
499s ***** error<unifrnd: dimensions must be non-negative integers.> ...
499s  unifrnd (1, 2, 2, 1.5, 5)
499s ***** error<unifrnd: A and B must be scalars or of size SZ.> ...
499s  unifrnd (2, ones (2), 3)
499s ***** error<unifrnd: A and B must be scalars or of size SZ.> ...
499s  unifrnd (2, ones (2), [3, 2])
499s ***** error<unifrnd: A and B must be scalars or of size SZ.> ...
499s  unifrnd (2, ones (2), 3, 2)
499s 33 tests, 33 passed, 0 known failure, 0 skipped
499s [inst/dist_fun/vmcdf.m]
499s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/vmcdf.m
499s ***** demo
499s  ## Plot various CDFs from the von Mises distribution
499s  x1 = [-pi:0.1:pi];
499s  p1 = vmcdf (x1, 0, 0.5);
499s  p2 = vmcdf (x1, 0, 1);
499s  p3 = vmcdf (x1, 0, 2);
499s  p4 = vmcdf (x1, 0, 4);
499s  plot (x1, p1, "-r", x1, p2, "-g", x1, p3, "-b", x1, p4, "-c")
499s  grid on
499s  xlim ([-pi, pi])
499s  legend ({"μ = 0, k = 0.5", "μ = 0, k = 1", ...
499s           "μ = 0, k = 2", "μ = 0, k = 4"}, "location", "northwest")
499s  title ("Von Mises CDF")
499s  xlabel ("values in x")
499s  ylabel ("probability")
499s ***** shared x, p0, p1
499s  x = [-pi:pi/2:pi];
499s  p0 = [0, 0.10975, 0.5, 0.89025, 1];
499s  p1 = [0, 0.03752, 0.5, 0.99622, 1];
499s ***** assert (vmcdf (x, 0, 1), p0, 1e-5)
500s ***** assert (vmcdf (x, 0, 1, "upper"), 1 - p0, 1e-5)
500s ***** assert (vmcdf (x, zeros (1,5), ones (1,5)), p0, 1e-5)
500s ***** assert (vmcdf (x, zeros (1,5), ones (1,5), "upper"), 1 - p0, 1e-5)
500s ***** assert (vmcdf (x, 0, [1 2 3 4 5]), p1, 1e-5)
501s ***** assert (vmcdf (x, 0, [1 2 3 4 5], "upper"), 1 - p1, 1e-5)
501s ***** assert (isa (vmcdf (single (pi), 0, 1), "single"), true)
501s ***** assert (isa (vmcdf (pi, single (0), 1), "single"), true)
501s ***** assert (isa (vmcdf (pi, 0, single (1)), "single"), true)
501s ***** error<vmcdf: function called with too few input arguments.> vmcdf ()
501s ***** error<vmcdf: function called with too few input arguments.> vmcdf (1)
501s ***** error<vmcdf: function called with too few input arguments.> vmcdf (1, 2)
501s ***** error<vmcdf: invalid argument for upper tail.> vmcdf (1, 2, 3, "tail")
501s ***** error<vmcdf: invalid argument for upper tail.> vmcdf (1, 2, 3, 4)
501s ***** error<vmcdf: X, MU, and K must be of common size or scalars.> ...
501s  vmcdf (ones (3), ones (2), ones (2))
501s ***** error<vmcdf: X, MU, and K must be of common size or scalars.> ...
501s  vmcdf (ones (2), ones (3), ones (2))
501s ***** error<vmcdf: X, MU, and K must be of common size or scalars.> ...
501s  vmcdf (ones (2), ones (2), ones (3))
501s ***** error<vmcdf: X, MU, and K must not be complex.> vmcdf (i, 2, 2)
501s ***** error<vmcdf: X, MU, and K must not be complex.> vmcdf (2, i, 2)
501s ***** error<vmcdf: X, MU, and K must not be complex.> vmcdf (2, 2, i)
501s 20 tests, 20 passed, 0 known failure, 0 skipped
501s [inst/dist_fun/vminv.m]
501s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/vminv.m
501s ***** demo
501s  ## Plot various iCDFs from the von Mises distribution
501s  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];
501s  x1 = vminv (p1, 0, 0.5);
501s  x2 = vminv (p1, 0, 1);
501s  x3 = vminv (p1, 0, 2);
501s  x4 = vminv (p1, 0, 4);
501s  plot (p1, x1, "-r", p1, x2, "-g", p1, x3, "-b", p1, x4, "-c")
501s  grid on
501s  ylim ([-pi, pi])
501s  legend ({"μ = 0, k = 0.5", "μ = 0, k = 1", ...
501s           "μ = 0, k = 2", "μ = 0, k = 4"}, "location", "northwest")
501s  title ("Von Mises iCDF")
501s  xlabel ("probability")
501s  ylabel ("values in x")
501s ***** shared x, p0, p1
501s  x = [-pi:pi/2:pi];
501s  p0 = [0, 0.10975, 0.5, 0.89025, 1];
501s  p1 = [0, 0.03752, 0.5, 0.99622, 1];
501s ***** assert (vminv (p0, 0, 1), x, 5e-5)
502s ***** assert (vminv (p0, zeros (1,5), ones (1,5)), x, 5e-5)
504s ***** assert (vminv (p1, 0, [1 2 3 4 5]), x, [5e-5, 5e-4, 5e-5, 5e-4, 5e-5])
506s ***** error<vminv: function called with too few input arguments.> vminv ()
506s ***** error<vminv: function called with too few input arguments.> vminv (1)
506s ***** error<vminv: function called with too few input arguments.> vminv (1, 2)
506s ***** error<vminv: P, MU, and K must be of common size or scalars.> ...
506s  vminv (ones (3), ones (2), ones (2))
506s ***** error<vminv: P, MU, and K must be of common size or scalars.> ...
506s  vminv (ones (2), ones (3), ones (2))
506s ***** error<vminv: P, MU, and K must be of common size or scalars.> ...
506s  vminv (ones (2), ones (2), ones (3))
506s ***** error<vminv: P, MU, and K must not be complex.> vminv (i, 2, 2)
506s ***** error<vminv: P, MU, and K must not be complex.> vminv (2, i, 2)
506s ***** error<vminv: P, MU, and K must not be complex.> vminv (2, 2, i)
506s 12 tests, 12 passed, 0 known failure, 0 skipped
506s [inst/dist_fun/vmpdf.m]
506s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/vmpdf.m
506s ***** demo
506s  ## Plot various PDFs from the von Mises distribution
506s  x1 = [-pi:0.1:pi];
506s  y1 = vmpdf (x1, 0, 0.5);
506s  y2 = vmpdf (x1, 0, 1);
506s  y3 = vmpdf (x1, 0, 2);
506s  y4 = vmpdf (x1, 0, 4);
506s  plot (x1, y1, "-r", x1, y2, "-g", x1, y3, "-b", x1, y4, "-c")
506s  grid on
506s  xlim ([-pi, pi])
506s  ylim ([0, 0.8])
506s  legend ({"μ = 0, k = 0.5", "μ = 0, k = 1", ...
506s           "μ = 0, k = 2", "μ = 0, k = 4"}, "location", "northwest")
506s  title ("Von Mises PDF")
506s  xlabel ("values in x")
506s  ylabel ("density")
506s ***** shared x, y0, y1
506s  x = [-pi:pi/2:pi];
506s  y0 = [0.046245, 0.125708, 0.341710, 0.125708, 0.046245];
506s  y1 = [0.046245, 0.069817, 0.654958, 0.014082, 0.000039];
506s ***** assert (vmpdf (x, 0, 1), y0, 1e-5)
506s ***** assert (vmpdf (x, zeros (1,5), ones (1,5)), y0, 1e-6)
506s ***** assert (vmpdf (x, 0, [1 2 3 4 5]), y1, 1e-6)
506s ***** assert (isa (vmpdf (single (pi), 0, 1), "single"), true)
506s ***** assert (isa (vmpdf (pi, single (0), 1), "single"), true)
506s ***** assert (isa (vmpdf (pi, 0, single (1)), "single"), true)
506s ***** error<vmpdf: function called with too few input arguments.> vmpdf ()
506s ***** error<vmpdf: function called with too few input arguments.> vmpdf (1)
506s ***** error<vmpdf: function called with too few input arguments.> vmpdf (1, 2)
506s ***** error<vmpdf: X, MU, and K must be of common size or scalars.> ...
506s  vmpdf (ones (3), ones (2), ones (2))
506s ***** error<vmpdf: X, MU, and K must be of common size or scalars.> ...
506s  vmpdf (ones (2), ones (3), ones (2))
506s ***** error<vmpdf: X, MU, and K must be of common size or scalars.> ...
506s  vmpdf (ones (2), ones (2), ones (3))
506s ***** error<vmpdf: X, MU, and K must not be complex.> vmpdf (i, 2, 2)
506s ***** error<vmpdf: X, MU, and K must not be complex.> vmpdf (2, i, 2)
506s ***** error<vmpdf: X, MU, and K must not be complex.> vmpdf (2, 2, i)
506s 15 tests, 15 passed, 0 known failure, 0 skipped
506s [inst/dist_fun/vmrnd.m]
506s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/vmrnd.m
506s ***** assert (size (vmrnd (1, 1)), [1 1])
506s ***** assert (size (vmrnd (1, ones (2,1))), [2, 1])
506s ***** assert (size (vmrnd (1, ones (2,2))), [2, 2])
506s ***** assert (size (vmrnd (ones (2,1), 1)), [2, 1])
506s ***** assert (size (vmrnd (ones (2,2), 1)), [2, 2])
506s ***** assert (size (vmrnd (1, 1, 3)), [3, 3])
506s ***** assert (size (vmrnd (1, 1, [4, 1])), [4, 1])
506s ***** assert (size (vmrnd (1, 1, 4, 1)), [4, 1])
506s ***** assert (size (vmrnd (1, 1, 4, 1, 5)), [4, 1, 5])
506s ***** assert (size (vmrnd (1, 1, 0, 1)), [0, 1])
506s ***** assert (size (vmrnd (1, 1, 1, 0)), [1, 0])
506s ***** assert (size (vmrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
506s ***** assert (class (vmrnd (1, 1)), "double")
506s ***** assert (class (vmrnd (1, single (1))), "single")
506s ***** assert (class (vmrnd (1, single ([1, 1]))), "single")
506s ***** assert (class (vmrnd (single (1), 1)), "single")
506s ***** assert (class (vmrnd (single ([1, 1]), 1)), "single")
506s ***** error<vmrnd: function called with too few input arguments.> vmrnd ()
506s ***** error<vmrnd: function called with too few input arguments.> vmrnd (1)
506s ***** error<vmrnd: MU and K must be of common size or scalars.> ...
506s  vmrnd (ones (3), ones (2))
506s ***** error<vmrnd: MU and K must be of common size or scalars.> ...
506s  vmrnd (ones (2), ones (3))
506s ***** error<vmrnd: MU and K must not be complex.> vmrnd (i, 2, 3)
506s ***** error<vmrnd: MU and K must not be complex.> vmrnd (1, i, 3)
506s ***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
506s  vmrnd (1, 2, -1)
506s ***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
506s  vmrnd (1, 2, 1.2)
506s ***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
506s  vmrnd (1, 2, ones (2))
506s ***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
506s  vmrnd (1, 2, [2 -1 2])
506s ***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
506s  vmrnd (1, 2, [2 0 2.5])
506s ***** error<vmrnd: dimensions must be non-negative integers.> ...
506s  vmrnd (1, 2, 2, -1, 5)
506s ***** error<vmrnd: dimensions must be non-negative integers.> ...
506s  vmrnd (1, 2, 2, 1.5, 5)
506s ***** error<vmrnd: MU and K must be scalars or of size SZ.> ...
506s  vmrnd (2, ones (2), 3)
506s ***** error<vmrnd: MU and K must be scalars or of size SZ.> ...
506s  vmrnd (2, ones (2), [3, 2])
506s ***** error<vmrnd: MU and K must be scalars or of size SZ.> ...
506s  vmrnd (2, ones (2), 3, 2)
506s 33 tests, 33 passed, 0 known failure, 0 skipped
506s [inst/dist_fun/wblcdf.m]
506s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/wblcdf.m
506s ***** demo
506s  ## Plot various CDFs from the Weibull distribution
506s  x = 0:0.001:2.5;
506s  p1 = wblcdf (x, 1, 0.5);
506s  p2 = wblcdf (x, 1, 1);
506s  p3 = wblcdf (x, 1, 1.5);
506s  p4 = wblcdf (x, 1, 5);
506s  plot (x, p1, "-b", x, p2, "-r", x, p3, "-m", x, p4, "-g")
506s  grid on
506s  legend ({"λ = 1, k = 0.5", "λ = 1, k = 1", ...
506s           "λ = 1, k = 1.5", "λ = 1, k = 5"}, "location", "southeast")
506s  title ("Weibull CDF")
506s  xlabel ("values in x")
506s  ylabel ("probability")
506s ***** shared x, y
506s  x = [-1 0 0.5 1 Inf];
506s  y = [0, 1-exp(-x(2:4)), 1];
506s ***** assert (wblcdf (x, ones (1,5), ones (1,5)), y, 1e-16)
506s ***** assert (wblcdf (x, ones (1,5), ones (1,5), "upper"), 1 - y)
506s ***** assert (wblcdf (x, "upper"), 1 - y)
506s ***** assert (wblcdf (x, 1, ones (1,5)), y, 1e-16)
506s ***** assert (wblcdf (x, ones (1,5), 1), y, 1e-16)
506s ***** assert (wblcdf (x, [0 1 NaN Inf 1], 1), [NaN 0 NaN 0 1])
506s ***** assert (wblcdf (x, [0 1 NaN Inf 1], 1, "upper"), 1 - [NaN 0 NaN 0 1])
506s ***** assert (wblcdf (x, 1, [0 1 NaN Inf 1]), [NaN 0 NaN y(4:5)])
506s ***** assert (wblcdf (x, 1, [0 1 NaN Inf 1], "upper"), 1 - [NaN 0 NaN y(4:5)])
506s ***** assert (wblcdf ([x(1:2) NaN x(4:5)], 1, 1), [y(1:2) NaN y(4:5)])
506s ***** assert (wblcdf ([x(1:2) NaN x(4:5)], 1, 1, "upper"), 1 - [y(1:2) NaN y(4:5)])
506s ***** assert (wblcdf ([x, NaN], 1, 1), [y, NaN], 1e-16)
506s ***** assert (wblcdf (single ([x, NaN]), 1, 1), single ([y, NaN]))
506s ***** assert (wblcdf ([x, NaN], single (1), 1), single ([y, NaN]))
506s ***** assert (wblcdf ([x, NaN], 1, single (1)), single ([y, NaN]))
506s ***** error<wblcdf: invalid number of input arguments.> wblcdf ()
506s ***** error<wblcdf: invalid number of input arguments.> wblcdf (1,2,3,4,5,6,7)
506s ***** error<wblcdf: invalid argument for upper tail.> wblcdf (1, 2, 3, 4, "uper")
506s ***** error<wblcdf: X, LAMBDA, and K must be of common size or scalars.> ...
506s  wblcdf (ones (3), ones (2), ones (2))
506s ***** error<wblcdf: invalid size of covariance matrix.> wblcdf (2, 3, 4, [1, 2])
506s ***** error<wblcdf: covariance matrix is required for confidence bounds.> ...
506s  [p, plo, pup] = wblcdf (1, 2, 3)
506s ***** error<wblcdf: invalid value for alpha.> [p, plo, pup] = ...
506s  wblcdf (1, 2, 3, [1, 0; 0, 1], 0)
506s ***** error<wblcdf: invalid value for alpha.> [p, plo, pup] = ...
506s  wblcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
506s ***** error<wblcdf: invalid value for alpha.> [p, plo, pup] = ...
506s  wblcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
506s ***** error<wblcdf: X, LAMBDA, and K must not be complex.> wblcdf (i, 2, 2)
506s ***** error<wblcdf: X, LAMBDA, and K must not be complex.> wblcdf (2, i, 2)
506s ***** error<wblcdf: X, LAMBDA, and K must not be complex.> wblcdf (2, 2, i)
506s ***** error<wblcdf: bad covariance matrix.> ...
506s  [p, plo, pup] =wblcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
506s 28 tests, 28 passed, 0 known failure, 0 skipped
506s [inst/dist_fun/wblinv.m]
506s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/wblinv.m
506s ***** demo
506s  ## Plot various iCDFs from the Weibull distribution
506s  p = 0.001:0.001:0.999;
506s  x1 = wblinv (p, 1, 0.5);
506s  x2 = wblinv (p, 1, 1);
506s  x3 = wblinv (p, 1, 1.5);
506s  x4 = wblinv (p, 1, 5);
506s  plot (p, x1, "-b", p, x2, "-r", p, x3, "-m", p, x4, "-g")
506s  ylim ([0, 2.5])
506s  grid on
506s  legend ({"λ = 1, k = 0.5", "λ = 1, k = 1",  ...
506s           "λ = 1, k = 1.5", "λ = 1, k = 5"}, "location", "northwest")
506s  title ("Weibull iCDF")
506s  xlabel ("probability")
506s  ylabel ("x")
506s ***** shared p
506s  p = [-1 0 0.63212055882855778 1 2];
506s ***** assert (wblinv (p, ones (1,5), ones (1,5)), [NaN 0 1 Inf NaN], eps)
506s ***** assert (wblinv (p, 1, ones (1,5)), [NaN 0 1 Inf NaN], eps)
506s ***** assert (wblinv (p, ones (1,5), 1), [NaN 0 1 Inf NaN], eps)
506s ***** assert (wblinv (p, [1 -1 NaN Inf 1], 1), [NaN NaN NaN NaN NaN])
506s ***** assert (wblinv (p, 1, [1 -1 NaN Inf 1]), [NaN NaN NaN NaN NaN])
506s ***** assert (wblinv ([p(1:2) NaN p(4:5)], 1, 1), [NaN 0 NaN Inf NaN])
506s ***** assert (wblinv ([p, NaN], 1, 1), [NaN 0 1 Inf NaN NaN], eps)
506s ***** assert (wblinv (single ([p, NaN]), 1, 1), single ([NaN 0 1 Inf NaN NaN]), eps ("single"))
506s ***** assert (wblinv ([p, NaN], single (1), 1), single ([NaN 0 1 Inf NaN NaN]), eps ("single"))
506s ***** assert (wblinv ([p, NaN], 1, single (1)), single ([NaN 0 1 Inf NaN NaN]), eps ("single"))
506s ***** error<wblinv: invalid number of input arguments.> wblinv ()
506s ***** error<wblinv: invalid number of input arguments.> wblinv (1,2,3,4)
506s ***** error<wblinv: P, LAMBDA, and K must be of common size or scalars.> ...
506s  wblinv (ones (3), ones (2), ones (2))
506s ***** error<wblinv: P, LAMBDA, and K must be of common size or scalars.> ...
506s  wblinv (ones (2), ones (3), ones (2))
506s ***** error<wblinv: P, LAMBDA, and K must be of common size or scalars.> ...
506s  wblinv (ones (2), ones (2), ones (3))
506s ***** error<wblinv: P, LAMBDA, and K must not be complex.> wblinv (i, 2, 2)
506s ***** error<wblinv: P, LAMBDA, and K must not be complex.> wblinv (2, i, 2)
506s ***** error<wblinv: P, LAMBDA, and K must not be complex.> wblinv (2, 2, i)
506s 18 tests, 18 passed, 0 known failure, 0 skipped
506s [inst/dist_fun/wblpdf.m]
506s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/wblpdf.m
506s ***** demo
506s  ## Plot various PDFs from the Weibul distribution
506s  x = 0:0.001:2.5;
506s  y1 = wblpdf (x, 1, 0.5);
506s  y2 = wblpdf (x, 1, 1);
506s  y3 = wblpdf (x, 1, 1.5);
506s  y4 = wblpdf (x, 1, 5);
506s  plot (x, y1, "-b", x, y2, "-r", x, y3, "-m", x, y4, "-g")
506s  grid on
506s  ylim ([0, 2.5])
506s  legend ({"λ = 5, k = 0.5", "λ = 9, k = 1", ...
506s           "λ = 6, k = 1.5", "λ = 2, k = 5"}, "location", "northeast")
506s  title ("Weibul PDF")
506s  xlabel ("values in x")
506s  ylabel ("density")
506s ***** shared x,y
506s  x = [-1 0 0.5 1 Inf];
506s  y = [0, exp(-x(2:4)), NaN];
506s ***** assert (wblpdf (x, ones (1,5), ones (1,5)), y)
506s ***** assert (wblpdf (x, 1, ones (1,5)), y)
506s ***** assert (wblpdf (x, ones (1,5), 1), y)
506s ***** assert (wblpdf (x, [0 NaN Inf 1 1], 1), [NaN NaN NaN y(4:5)])
506s ***** assert (wblpdf (x, 1, [0 NaN Inf 1 1]), [NaN NaN NaN y(4:5)])
506s ***** assert (wblpdf ([x, NaN], 1, 1), [y, NaN])
506s ***** assert (wblpdf (single ([x, NaN]), 1, 1), single ([y, NaN]))
506s ***** assert (wblpdf ([x, NaN], single (1), 1), single ([y, NaN]))
506s ***** assert (wblpdf ([x, NaN], 1, single (1)), single ([y, NaN]))
506s ***** error wblpdf ()
506s ***** error wblpdf (1,2,3,4)
506s ***** error wblpdf (ones (3), ones (2), ones (2))
506s ***** error wblpdf (ones (2), ones (3), ones (2))
506s ***** error wblpdf (ones (2), ones (2), ones (3))
506s ***** error wblpdf (i, 2, 2)
506s ***** error wblpdf (2, i, 2)
506s ***** error wblpdf (2, 2, i)
506s 17 tests, 17 passed, 0 known failure, 0 skipped
506s [inst/dist_fun/wblrnd.m]
506s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/wblrnd.m
506s ***** assert (size (wblrnd (1, 1)), [1 1])
506s ***** assert (size (wblrnd (1, ones (2,1))), [2, 1])
506s ***** assert (size (wblrnd (1, ones (2,2))), [2, 2])
506s ***** assert (size (wblrnd (ones (2,1), 1)), [2, 1])
506s ***** assert (size (wblrnd (ones (2,2), 1)), [2, 2])
506s ***** assert (size (wblrnd (1, 1, 3)), [3, 3])
506s ***** assert (size (wblrnd (1, 1, [4, 1])), [4, 1])
506s ***** assert (size (wblrnd (1, 1, 4, 1)), [4, 1])
507s ***** assert (size (wblrnd (1, 1, 4, 1, 5)), [4, 1, 5])
507s ***** assert (size (wblrnd (1, 1, 0, 1)), [0, 1])
507s ***** assert (size (wblrnd (1, 1, 1, 0)), [1, 0])
507s ***** assert (size (wblrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
507s ***** assert (class (wblrnd (1, 1)), "double")
507s ***** assert (class (wblrnd (1, single (1))), "single")
507s ***** assert (class (wblrnd (1, single ([1, 1]))), "single")
507s ***** assert (class (wblrnd (single (1), 1)), "single")
507s ***** assert (class (wblrnd (single ([1, 1]), 1)), "single")
507s ***** error<wblrnd: function called with too few input arguments.> wblrnd ()
507s ***** error<wblrnd: function called with too few input arguments.> wblrnd (1)
507s ***** error<wblrnd: LAMBDA and K must be of common size or scalars.> ...
507s  wblrnd (ones (3), ones (2))
507s ***** error<wblrnd: LAMBDA and K must be of common size or scalars.> ...
507s  wblrnd (ones (2), ones (3))
507s ***** error<wblrnd: LAMBDA and K must not be complex.> wblrnd (i, 2, 3)
507s ***** error<wblrnd: LAMBDA and K must not be complex.> wblrnd (1, i, 3)
507s ***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
507s  wblrnd (1, 2, -1)
507s ***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
507s  wblrnd (1, 2, 1.2)
507s ***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
507s  wblrnd (1, 2, ones (2))
507s ***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
507s  wblrnd (1, 2, [2 -1 2])
507s ***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
507s  wblrnd (1, 2, [2 0 2.5])
507s ***** error<wblrnd: dimensions must be non-negative integers.> ...
507s  wblrnd (1, 2, 2, -1, 5)
507s ***** error<wblrnd: dimensions must be non-negative integers.> ...
507s  wblrnd (1, 2, 2, 1.5, 5)
507s ***** error<wblrnd: LAMBDA and K must be scalar or of size SZ.> ...
507s  wblrnd (2, ones (2), 3)
507s ***** error<wblrnd: LAMBDA and K must be scalar or of size SZ.> ...
507s  wblrnd (2, ones (2), [3, 2])
507s ***** error<wblrnd: LAMBDA and K must be scalar or of size SZ.> ...
507s  wblrnd (2, ones (2), 3, 2)
507s 33 tests, 33 passed, 0 known failure, 0 skipped
507s [inst/dist_fun/wienrnd.m]
507s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/wienrnd.m
507s ***** error wienrnd (0)
507s ***** error wienrnd (1, 3, -50)
507s ***** error wienrnd (5, 0)
507s ***** error wienrnd (0.4, 3, 5)
507s ***** error wienrnd ([1 4], 3, 5)
507s 5 tests, 5 passed, 0 known failure, 0 skipped
507s [inst/dist_fun/wishpdf.m]
507s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/wishpdf.m
507s ***** assert(wishpdf(4, 3, 3.1), 0.07702496, 1E-7);
507s ***** assert(wishpdf([2 -0.3;-0.3 4], [1 0.3;0.3 1], 4), 0.004529741, 1E-7);
507s ***** 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);
507s ***** error wishpdf ()
507s ***** error wishpdf (1, 2)
507s ***** error wishpdf (1, 2, 0)
507s ***** error wishpdf (1, 2)
507s 7 tests, 7 passed, 0 known failure, 0 skipped
507s [inst/dist_fun/wishrnd.m]
507s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_fun/wishrnd.m
507s ***** assert(size (wishrnd (1,2)), [1, 1]);
507s ***** assert(size (wishrnd (1,2,[])), [1, 1]);
507s ***** assert(size (wishrnd (1,2,1)), [1, 1]);
507s ***** assert(size (wishrnd ([],2,1)), [1, 1]);
507s ***** assert(size (wishrnd ([3 1; 1 3], 2.00001, [], 1)), [2, 2]);
507s ***** assert(size (wishrnd (eye(2), 2, [], 3)), [2, 2, 3]);
507s ***** error wishrnd ()
507s ***** error wishrnd (1)
507s ***** error wishrnd ([1; 1], 2)
507s 9 tests, 9 passed, 0 known failure, 0 skipped
507s [inst/dist_obj/BetaDistribution.m]
507s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_obj/BetaDistribution.m
507s ***** demo
507s  ## Generate a data set of 5000 random samples from a Beta distribution with
507s  ## parameters a = 2 and b = 5.  Fit a Beta distribution to this data and plot
507s  ## a PDF of the fitted distribution superimposed on a histogram of the data.
507s 
507s  pd_fixed = makedist ("Beta", "a", 2, "b", 5)
507s  randg ("seed", 2);
507s  data = random (pd_fixed, 5000, 1);
507s  pd_fitted = fitdist (data, "Beta")
507s  plot (pd_fitted)
507s  msg = "Fitted Beta distribution with a = %0.2f and b = %0.2f";
507s  title (sprintf (msg, pd_fitted.a, pd_fitted.b))
507s ***** shared pd, t
507s  pd = BetaDistribution;
507s  t = truncate (pd, 0.2, 0.8);
507s ***** assert (cdf (pd, [0:0.2:1]), [0, 0.2, 0.4, 0.6, 0.8, 1], 1e-4);
507s ***** assert (cdf (t, [0:0.2:1]), [0, 0, 0.3333, 0.6667, 1, 1], 1e-4);
507s ***** assert (cdf (pd, [-1, 1, NaN]), [0, 1, NaN], 1e-4);
507s ***** assert (cdf (t, [-1, 1, NaN]), [0, 1, NaN], 1e-4);
507s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.2, 0.4, 0.6, 0.8, 1], 1e-4);
507s ***** assert (icdf (t, [0:0.2:1]), [0.2, 0.32, 0.44, 0.56, 0.68, 0.8], 1e-4);
507s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.4, 0.6, 0.8, 1, NaN], 1e-4);
507s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 0.44, 0.56, 0.68, 0.8, NaN], 1e-4);
507s ***** assert (iqr (pd), 0.5, 1e-4);
507s ***** assert (iqr (t), 0.3, 1e-4);
507s ***** assert (mean (pd), 0.5);
507s ***** assert (mean (t), 0.5, 1e-6);
507s ***** assert (median (pd), 0.5);
507s ***** assert (median (t), 0.5, 1e-6);
507s ***** assert (pdf (pd, [0:0.2:1]), [1, 1, 1, 1, 1, 1], 1e-4);
507s ***** assert (pdf (t, [0:0.2:1]), [0, 1.6667, 1.6667, 1.6667, 1.6667, 0], 1e-4);
507s ***** assert (pdf (pd, [-1, 1, NaN]), [0, 1, NaN], 1e-4);
507s ***** assert (pdf (t, [-1, 1, NaN]), [0, 0, NaN], 1e-4);
507s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
507s ***** assert (any (random (t, 1000, 1) < 0.2), false);
507s ***** assert (any (random (t, 1000, 1) > 0.8), false);
507s ***** assert (std (pd), 0.2887, 1e-4);
507s ***** assert (std (t), 0.1732, 1e-4);
507s ***** assert (var (pd), 0.0833, 1e-4);
507s ***** assert (var (t), 0.0300, 1e-4);
508s ***** error <BetaDistribution: A must be a positive real scalar.> ...
508s  BetaDistribution(0, 1)
508s ***** error <BetaDistribution: A must be a positive real scalar.> ...
508s  BetaDistribution(Inf, 1)
508s ***** error <BetaDistribution: A must be a positive real scalar.> ...
508s  BetaDistribution(i, 1)
508s ***** error <BetaDistribution: A must be a positive real scalar.> ...
508s  BetaDistribution("a", 1)
508s ***** error <BetaDistribution: A must be a positive real scalar.> ...
508s  BetaDistribution([1, 2], 1)
508s ***** error <BetaDistribution: A must be a positive real scalar.> ...
508s  BetaDistribution(NaN, 1)
508s ***** error <BetaDistribution: B must be a positive real scalar.> ...
508s  BetaDistribution(1, 0)
508s ***** error <BetaDistribution: B must be a positive real scalar.> ...
508s  BetaDistribution(1, -1)
508s ***** error <BetaDistribution: B must be a positive real scalar.> ...
508s  BetaDistribution(1, Inf)
508s ***** error <BetaDistribution: B must be a positive real scalar.> ...
508s  BetaDistribution(1, i)
508s ***** error <BetaDistribution: B must be a positive real scalar.> ...
508s  BetaDistribution(1, "a")
508s ***** error <BetaDistribution: B must be a positive real scalar.> ...
508s  BetaDistribution(1, [1, 2])
508s ***** error <BetaDistribution: B must be a positive real scalar.> ...
508s  BetaDistribution(1, NaN)
508s ***** error <cdf: invalid argument for upper tail.> ...
508s  cdf (BetaDistribution, 2, "uper")
508s ***** error <cdf: invalid argument for upper tail.> ...
508s  cdf (BetaDistribution, 2, 3)
508s ***** shared x
508s  randg ("seed", 1);
508s  x = betarnd (1, 1, [100, 1]);
508s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
508s  paramci (BetaDistribution.fit (x), "alpha")
508s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
508s  paramci (BetaDistribution.fit (x), "alpha", 0)
508s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
508s  paramci (BetaDistribution.fit (x), "alpha", 1)
508s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
508s  paramci (BetaDistribution.fit (x), "alpha", [0.5 2])
508s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
508s  paramci (BetaDistribution.fit (x), "alpha", "")
508s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
508s  paramci (BetaDistribution.fit (x), "alpha", {0.05})
508s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
508s  paramci (BetaDistribution.fit (x), "parameter", "a", "alpha", {0.05})
508s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
508s  paramci (BetaDistribution.fit (x), "parameter", {"a", "b", "param"})
508s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
508s  paramci (BetaDistribution.fit (x), "alpha", 0.01, ...
508s           "parameter", {"a", "b", "param"})
508s ***** error <paramci: unknown distribution parameter.> ...
508s  paramci (BetaDistribution.fit (x), "parameter", "param")
508s ***** error <paramci: unknown distribution parameter.> ...
508s  paramci (BetaDistribution.fit (x), "alpha", 0.01, "parameter", "param")
508s ***** error <paramci: invalid NAME for optional argument.> ...
508s  paramci (BetaDistribution.fit (x), "NAME", "value")
508s ***** error <paramci: invalid NAME for optional argument.> ...
508s  paramci (BetaDistribution.fit (x), "alpha", 0.01, "NAME", "value")
508s ***** error <paramci: invalid NAME for optional argument.> ...
508s  paramci (BetaDistribution.fit (x), "alpha", 0.01, "parameter", "a", ...
508s           "NAME", "value")
508s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
508s  plot (BetaDistribution, "Parent")
508s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
508s  plot (BetaDistribution, "PlotType", 12)
508s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
508s  plot (BetaDistribution, "PlotType", {"pdf", "cdf"})
508s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
508s  plot (BetaDistribution, "PlotType", "pdfcdf")
508s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
508s  plot (BetaDistribution, "Discrete", "pdfcdf")
508s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
508s  plot (BetaDistribution, "Discrete", [1, 0])
508s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
508s  plot (BetaDistribution, "Discrete", {true})
508s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
508s  plot (BetaDistribution, "Parent", 12)
508s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
508s  plot (BetaDistribution, "Parent", "hax")
508s ***** error <plot: invalid NAME for optional argument.> ...
508s  plot (BetaDistribution, "invalidNAME", "pdf")
508s ***** error <plot: no fitted DATA to plot a probability plot.> ...
508s  plot (BetaDistribution, "PlotType", "probability")
508s ***** error <proflik: no fitted data available.> ...
508s  proflik (BetaDistribution, 2)
508s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
508s  proflik (BetaDistribution.fit (x), 3)
508s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
508s  proflik (BetaDistribution.fit (x), [1, 2])
508s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
508s  proflik (BetaDistribution.fit (x), {1})
508s ***** error <proflik: SETPARAM must be a numeric vector.> ...
508s  proflik (BetaDistribution.fit (x), 1, ones (2))
508s ***** error <proflik: missing VALUE for 'Display' argument.> ...
508s  proflik (BetaDistribution.fit (x), 1, "Display")
508s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
508s  proflik (BetaDistribution.fit (x), 1, "Display", 1)
508s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
508s  proflik (BetaDistribution.fit (x), 1, "Display", {1})
508s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
508s  proflik (BetaDistribution.fit (x), 1, "Display", {"on"})
508s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
508s  proflik (BetaDistribution.fit (x), 1, "Display", ["on"; "on"])
508s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
508s  proflik (BetaDistribution.fit (x), 1, "Display", "onnn")
508s ***** error <proflik: invalid NAME for optional arguments.> ...
508s  proflik (BetaDistribution.fit (x), 1, "NAME", "on")
508s ***** error <proflik: invalid optional argument.> ...
508s  proflik (BetaDistribution.fit (x), 1, {"NAME"}, "on")
508s ***** error <proflik: invalid optional argument.> ...
508s  proflik (BetaDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
508s ***** error <truncate: missing input argument.> ...
508s  truncate (BetaDistribution)
508s ***** error <truncate: missing input argument.> ...
508s  truncate (BetaDistribution, 2)
508s ***** error <truncate: invalid lower upper limits.> ...
508s  truncate (BetaDistribution, 4, 2)
508s ***** shared pd
508s  pd = BetaDistribution(1, 1);
508s  pd(2) = BetaDistribution(1, 3);
508s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
508s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
508s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
508s ***** error <mean: requires a scalar probability distribution.> mean (pd)
508s ***** error <median: requires a scalar probability distribution.> median (pd)
508s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
508s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
508s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
508s ***** error <plot: requires a scalar probability distribution.> plot (pd)
508s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
508s ***** error <random: requires a scalar probability distribution.> random (pd)
508s ***** error <std: requires a scalar probability distribution.> std (pd)
508s ***** error <truncate: requires a scalar probability distribution.> ...
508s  truncate (pd, 2, 4)
508s ***** error <var: requires a scalar probability distribution.> var (pd)
508s 96 tests, 96 passed, 0 known failure, 0 skipped
508s [inst/dist_obj/BinomialDistribution.m]
508s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_obj/BinomialDistribution.m
508s ***** shared pd, t, t_inf
508s  pd = BinomialDistribution (5, 0.5);
508s  t = truncate (pd, 2, 4);
508s  t_inf = truncate (pd, 2, Inf);
508s ***** assert (cdf (pd, [0:5]), [0.0312, 0.1875, 0.5, 0.8125, 0.9688, 1], 1e-4);
508s ***** assert (cdf (t, [0:5]), [0, 0, 0.4, 0.8, 1, 1], 1e-4);
508s ***** assert (cdf (t_inf, [0:5]), [0, 0, 0.3846, 0.7692, 0.9615, 1], 1e-4);
508s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.1875, 0.5, 0.8125, 0.9688, NaN], 1e-4);
508s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0.4, 0.8, 1, NaN], 1e-4);
508s ***** assert (icdf (pd, [0:0.2:1]), [0, 2, 2, 3, 3, 5], 1e-4);
508s ***** assert (icdf (t, [0:0.2:1]), [2, 2, 2, 3, 3, 4], 1e-4);
508s ***** assert (icdf (t_inf, [0:0.2:1]), [2, 2, 3, 3, 4, 5], 1e-4);
508s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 2, 3, 3, 5, NaN], 1e-4);
508s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2, 3, 3, 4, NaN], 1e-4);
508s ***** assert (iqr (pd), 1);
508s ***** assert (iqr (t), 1);
508s ***** assert (mean (pd), 2.5, 1e-10);
508s ***** assert (mean (t), 2.8, 1e-10);
509s ***** assert (mean (t_inf), 2.8846, 1e-4);
509s ***** assert (median (pd), 2.5);
509s ***** assert (median (t), 3);
509s ***** assert (pdf (pd, [0:5]), [0.0312, 0.1562, 0.3125, 0.3125, 0.1562, 0.0312], 1e-4);
509s ***** assert (pdf (t, [0:5]), [0, 0, 0.4, 0.4, 0.2, 0], 1e-4);
509s ***** assert (pdf (t_inf, [0:5]), [0, 0, 0.3846, 0.3846, 0.1923, 0.0385], 1e-4);
509s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
509s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
509s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
509s ***** assert (any (random (t, 1000, 1) < 2), false);
509s ***** assert (any (random (t, 1000, 1) > 4), false);
509s ***** assert (std (pd), 1.1180, 1e-4);
509s ***** assert (std (t), 0.7483, 1e-4);
509s ***** assert (std (t_inf), 0.8470, 1e-4);
509s ***** assert (var (pd), 1.2500, 1e-4);
509s ***** assert (var (t), 0.5600, 1e-4);
509s ***** assert (var (t_inf), 0.7175, 1e-4);
509s ***** error <BinomialDistribution: N must be a positive integer scalar.> ...
509s  BinomialDistribution(Inf, 0.5)
509s ***** error <BinomialDistribution: N must be a positive integer scalar.> ...
509s  BinomialDistribution(i, 0.5)
509s ***** error <BinomialDistribution: N must be a positive integer scalar.> ...
509s  BinomialDistribution("a", 0.5)
509s ***** error <BinomialDistribution: N must be a positive integer scalar.> ...
509s  BinomialDistribution([1, 2], 0.5)
509s ***** error <BinomialDistribution: N must be a positive integer scalar.> ...
509s  BinomialDistribution(NaN, 0.5)
509s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
509s  BinomialDistribution(1, 1.01)
509s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
509s  BinomialDistribution(1, -0.01)
509s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
509s  BinomialDistribution(1, Inf)
509s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
509s  BinomialDistribution(1, i)
509s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
509s  BinomialDistribution(1, "a")
509s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
509s  BinomialDistribution(1, [1, 2])
509s ***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
509s  BinomialDistribution(1, NaN)
509s ***** error <cdf: invalid argument for upper tail.> ...
509s  cdf (BinomialDistribution, 2, "uper")
509s ***** error <cdf: invalid argument for upper tail.> ...
509s  cdf (BinomialDistribution, 2, 3)
509s ***** shared x
509s  rand ("seed", 2);
509s  x = binornd (5, 0.5, [1, 100]);
509s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
509s  paramci (BinomialDistribution.fit (x, 6), "alpha")
509s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
509s  paramci (BinomialDistribution.fit (x, 6), "alpha", 0)
509s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
509s  paramci (BinomialDistribution.fit (x, 6), "alpha", 1)
509s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
509s  paramci (BinomialDistribution.fit (x, 6), "alpha", [0.5 2])
509s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
509s  paramci (BinomialDistribution.fit (x, 6), "alpha", "")
509s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
509s  paramci (BinomialDistribution.fit (x, 6), "alpha", {0.05})
509s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
509s  paramci (BinomialDistribution.fit (x, 6), "parameter", "p", ...
509s           "alpha", {0.05})
509s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
509s  paramci (BinomialDistribution.fit (x, 6), ...
509s           "parameter", {"N", "p", "param"})
509s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
509s  paramci (BinomialDistribution.fit (x, 6), "alpha", 0.01, ...
509s           "parameter", {"N", "p", "param"})
509s ***** error <paramci: unknown distribution parameter.> ...
509s  paramci (BinomialDistribution.fit (x, 6), "parameter", "param")
509s ***** error <paramci: unknown distribution parameter.> ...
509s  paramci (BinomialDistribution.fit (x, 6), "parameter", "N")
509s ***** error <paramci: unknown distribution parameter.> ...
509s  paramci (BinomialDistribution.fit (x, 6), "alpha", 0.01, ...
509s           "parameter", "param")
509s ***** error <paramci: invalid NAME for optional argument.> ...
509s  paramci (BinomialDistribution.fit (x, 6), "NAME", "value")
509s ***** error <paramci: invalid NAME for optional argument.> ...
509s  paramci (BinomialDistribution.fit (x, 6), "alpha", 0.01, ...
509s           "NAME", "value")
509s ***** error <paramci: invalid NAME for optional argument.> ...
509s  paramci (BinomialDistribution.fit (x, 6), "alpha", 0.01, ...
509s           "parameter", "p", "NAME", "value")
509s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
509s  plot (BinomialDistribution, "Parent")
509s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
509s  plot (BinomialDistribution, "PlotType", 12)
509s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
509s  plot (BinomialDistribution, "PlotType", {"pdf", "cdf"})
509s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
509s  plot (BinomialDistribution, "PlotType", "pdfcdf")
509s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
509s  plot (BinomialDistribution, "Discrete", "pdfcdf")
509s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
509s  plot (BinomialDistribution, "Discrete", [1, 0])
509s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
509s  plot (BinomialDistribution, "Discrete", {true})
509s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
509s  plot (BinomialDistribution, "Parent", 12)
509s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
509s  plot (BinomialDistribution, "Parent", "hax")
509s ***** error <plot: invalid NAME for optional argument.> ...
509s  plot (BinomialDistribution, "invalidNAME", "pdf")
509s ***** error <plot: no fitted DATA to plot a probability plot.> ...
509s  plot (BinomialDistribution, "PlotType", "probability")
509s ***** error <proflik: no fitted data available.> ...
509s  proflik (BinomialDistribution, 2)
509s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
509s  proflik (BinomialDistribution.fit (x, 6), 3)
509s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
509s  proflik (BinomialDistribution.fit (x, 6), [1, 2])
509s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
509s  proflik (BinomialDistribution.fit (x, 6), {1})
509s ***** error <proflik: SETPARAM must be a numeric vector.> ...
509s  proflik (BinomialDistribution.fit (x, 6), 2, ones (2))
509s ***** error <proflik: missing VALUE for 'Display' argument.> ...
509s  proflik (BinomialDistribution.fit (x, 6), 2, "Display")
509s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
509s  proflik (BinomialDistribution.fit (x, 6), 2, "Display", 1)
509s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
509s  proflik (BinomialDistribution.fit (x, 6), 2, "Display", {1})
509s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
509s  proflik (BinomialDistribution.fit (x, 6), 2, "Display", {"on"})
509s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
509s  proflik (BinomialDistribution.fit (x, 6), 2, "Display", ["on"; "on"])
509s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
509s  proflik (BinomialDistribution.fit (x, 6), 2, "Display", "onnn")
509s ***** error <proflik: invalid NAME for optional arguments.> ...
509s  proflik (BinomialDistribution.fit (x, 6), 2, "NAME", "on")
509s ***** error <proflik: invalid optional argument.> ...
509s  proflik (BinomialDistribution.fit (x, 6), 2, {"NAME"}, "on")
509s ***** error <proflik: invalid optional argument.> ...
509s  proflik (BinomialDistribution.fit (x, 6), 2, {[1 2 3]}, "Display", "on")
509s ***** error <truncate: missing input argument.> ...
509s  truncate (BinomialDistribution)
509s ***** error <truncate: missing input argument.> ...
509s  truncate (BinomialDistribution, 2)
509s ***** error <truncate: invalid lower upper limits.> ...
509s  truncate (BinomialDistribution, 4, 2)
509s ***** shared pd
509s  pd = BinomialDistribution(1, 0.5);
509s  pd(2) = BinomialDistribution(1, 0.6);
509s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
509s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
509s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
509s ***** error <mean: requires a scalar probability distribution.> mean (pd)
509s ***** error <median: requires a scalar probability distribution.> median (pd)
509s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
509s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
509s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
509s ***** error <plot: requires a scalar probability distribution.> plot (pd)
509s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
509s ***** error <random: requires a scalar probability distribution.> random (pd)
509s ***** error <std: requires a scalar probability distribution.> std (pd)
509s ***** error <truncate: requires a scalar probability distribution.> ...
509s  truncate (pd, 2, 4)
509s ***** error <var: requires a scalar probability distribution.> var (pd)
509s 102 tests, 102 passed, 0 known failure, 0 skipped
509s [inst/dist_obj/BirnbaumSaundersDistribution.m]
510s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_obj/BirnbaumSaundersDistribution.m
510s ***** demo
510s  ## Generate a data set of 5000 random samples from a Birnbaum-Saunders
510s  ## distribution with parameters β = 1 and γ = 0.5.  Fit a Birnbaum-Saunders
510s  ## distribution to this data and plot a PDF of the fitted distribution
510s  ## superimposed on a histogram of the data.
510s 
510s  pd_fixed = makedist ("BirnbaumSaunders", "beta", 1, "gamma", 0.5)
510s  randg ("seed", 21);
510s  data = random (pd_fixed, 5000, 1);
510s  pd_fitted = fitdist (data, "BirnbaumSaunders")
510s  plot (pd_fitted)
510s  msg = "Fitted Birnbaum-Saunders distribution with beta = %0.2f and gamma = %0.2f";
510s  title (sprintf (msg, pd_fitted.beta, pd_fitted.gamma))
510s ***** shared pd, t
510s  pd = BirnbaumSaundersDistribution;
510s  t = truncate (pd, 2, 4);
510s ***** assert (cdf (pd, [0:5]), [0, 0.5, 0.7602, 0.8759, 0.9332, 0.9632], 1e-4);
510s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.6687, 1, 1], 1e-4);
510s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.6585, 0.7602, 0.8759, 0.9332, NaN], 1e-4);
510s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.6687, 1, NaN], 1e-4);
510s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.4411, 0.7767, 1.2875, 2.2673, Inf], 1e-4);
510s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2293, 2.5073, 2.8567, 3.3210, 4], 1e-4);
510s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.7767, 1.2875, 2.2673, Inf, NaN], 1e-4);
510s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5073, 2.8567, 3.3210, 4, NaN], 1e-4);
510s ***** assert (iqr (pd), 1.4236, 1e-4);
510s ***** assert (iqr (t), 0.8968, 1e-4);
510s ***** assert (mean (pd), 1.5, eps);
510s ***** assert (mean (t), 2.7723, 1e-4);
510s ***** assert (median (pd), 1, 1e-4);
510s ***** assert (median (t), 2.6711, 1e-4);
510s ***** assert (pdf (pd, [0:5]), [0, 0.3989, 0.1648, 0.0788, 0.0405, 0.0216], 1e-4);
510s ***** assert (pdf (t, [0:5]), [0, 0, 0.9528, 0.4559, 0.2340, 0], 1e-4);
510s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.2497, NaN], 1e-4);
510s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
510s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
510s ***** assert (any (random (t, 1000, 1) < 2), false);
510s ***** assert (any (random (t, 1000, 1) > 4), false);
510s ***** assert (std (pd), 1.5, eps);
510s ***** assert (std (t), 0.5528, 1e-4);
510s ***** assert (var (pd), 2.25, eps);
510s ***** assert (var (t), 0.3056, 1e-4);
510s ***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
510s  BirnbaumSaundersDistribution(0, 1)
510s ***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
510s  BirnbaumSaundersDistribution(Inf, 1)
510s ***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
510s  BirnbaumSaundersDistribution(i, 1)
510s ***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
510s  BirnbaumSaundersDistribution("beta", 1)
510s ***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
510s  BirnbaumSaundersDistribution([1, 2], 1)
510s ***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
510s  BirnbaumSaundersDistribution(NaN, 1)
510s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
510s  BirnbaumSaundersDistribution(1, 0)
510s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
510s  BirnbaumSaundersDistribution(1, -1)
510s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
510s  BirnbaumSaundersDistribution(1, Inf)
510s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
510s  BirnbaumSaundersDistribution(1, i)
510s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
510s  BirnbaumSaundersDistribution(1, "beta")
510s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
510s  BirnbaumSaundersDistribution(1, [1, 2])
510s ***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
510s  BirnbaumSaundersDistribution(1, NaN)
510s ***** error <cdf: invalid argument for upper tail.> ...
510s  cdf (BirnbaumSaundersDistribution, 2, "uper")
510s ***** error <cdf: invalid argument for upper tail.> ...
510s  cdf (BirnbaumSaundersDistribution, 2, 3)
510s ***** shared x
510s  rand ("seed", 5);
510s  x = bisarnd (1, 1, [100, 1]);
510s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
510s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha")
510s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
510s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0)
510s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
510s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 1)
510s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
510s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", [0.5 2])
510s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
510s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", "")
510s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
510s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", {0.05})
510s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
510s  paramci (BirnbaumSaundersDistribution.fit (x), "parameter", ...
510s           "beta", "alpha", {0.05})
510s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
510s  paramci (BirnbaumSaundersDistribution.fit (x), ...
510s           "parameter", {"beta", "gamma", "param"})
510s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
510s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0.01, ...
510s           "parameter", {"beta", "gamma", "param"})
510s ***** error <paramci: unknown distribution parameter.> ...
510s  paramci (BirnbaumSaundersDistribution.fit (x), "parameter", "param")
510s ***** error <paramci: unknown distribution parameter.> ...
510s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0.01, ...
510s           "parameter", "param")
510s ***** error <paramci: invalid NAME for optional argument.> ...
510s  paramci (BirnbaumSaundersDistribution.fit (x), "NAME", "value")
510s ***** error <paramci: invalid NAME for optional argument.> ...
510s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0.01, ...
510s           "NAME", "value")
511s ***** error <paramci: invalid NAME for optional argument.> ...
511s  paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0.01, ...
511s           "parameter", "beta", "NAME", "value")
511s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
511s  plot (BirnbaumSaundersDistribution, "Parent")
511s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
511s  plot (BirnbaumSaundersDistribution, "PlotType", 12)
511s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
511s  plot (BirnbaumSaundersDistribution, "PlotType", {"pdf", "cdf"})
511s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
511s  plot (BirnbaumSaundersDistribution, "PlotType", "pdfcdf")
511s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
511s  plot (BirnbaumSaundersDistribution, "Discrete", "pdfcdf")
511s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
511s  plot (BirnbaumSaundersDistribution, "Discrete", [1, 0])
511s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
511s  plot (BirnbaumSaundersDistribution, "Discrete", {true})
511s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
511s  plot (BirnbaumSaundersDistribution, "Parent", 12)
511s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
511s  plot (BirnbaumSaundersDistribution, "Parent", "hax")
511s ***** error <plot: invalid NAME for optional argument.> ...
511s  plot (BirnbaumSaundersDistribution, "invalidNAME", "pdf")
511s ***** error <plot: no fitted DATA to plot a probability plot.> ...
511s  plot (BirnbaumSaundersDistribution, "PlotType", "probability")
511s ***** error <proflik: no fitted data available.> ...
511s  proflik (BirnbaumSaundersDistribution, 2)
511s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
511s  proflik (BirnbaumSaundersDistribution.fit (x), 3)
511s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
511s  proflik (BirnbaumSaundersDistribution.fit (x), [1, 2])
511s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
511s  proflik (BirnbaumSaundersDistribution.fit (x), {1})
511s ***** error <proflik: SETPARAM must be a numeric vector.> ...
511s  proflik (BirnbaumSaundersDistribution.fit (x), 1, ones (2))
511s ***** error <proflik: missing VALUE for 'Display' argument.> ...
511s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display")
511s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
511s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", 1)
511s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
511s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", {1})
511s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
511s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", {"on"})
511s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
511s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", ["on"; "on"])
511s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
511s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", "onnn")
511s ***** error <proflik: invalid NAME for optional arguments.> ...
511s  proflik (BirnbaumSaundersDistribution.fit (x), 1, "NAME", "on")
511s ***** error <proflik: invalid optional argument.> ...
511s  proflik (BirnbaumSaundersDistribution.fit (x), 1, {"NAME"}, "on")
511s ***** error <proflik: invalid optional argument.> ...
511s  proflik (BirnbaumSaundersDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
511s ***** error <truncate: missing input argument.> ...
511s  truncate (BirnbaumSaundersDistribution)
511s ***** error <truncate: missing input argument.> ...
511s  truncate (BirnbaumSaundersDistribution, 2)
511s ***** error <truncate: invalid lower upper limits.> ...
511s  truncate (BirnbaumSaundersDistribution, 4, 2)
511s ***** shared pd
511s  pd = BirnbaumSaundersDistribution(1, 1);
511s  pd(2) = BirnbaumSaundersDistribution(1, 3);
511s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
511s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
511s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
511s ***** error <mean: requires a scalar probability distribution.> mean (pd)
511s ***** error <median: requires a scalar probability distribution.> median (pd)
511s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
511s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
511s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
511s ***** error <plot: requires a scalar probability distribution.> plot (pd)
511s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
511s ***** error <random: requires a scalar probability distribution.> random (pd)
511s ***** error <std: requires a scalar probability distribution.> std (pd)
511s ***** error <truncate: requires a scalar probability distribution.> ...
511s  truncate (pd, 2, 4)
511s ***** error <var: requires a scalar probability distribution.> var (pd)
511s 96 tests, 96 passed, 0 known failure, 0 skipped
511s [inst/dist_obj/BurrDistribution.m]
511s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_obj/BurrDistribution.m
511s ***** demo
511s  ## Generate a data set of 5000 random samples from a Burr type XII
511s  ## distribution with parameters alpha = 1, c = 2, and k = 1.  Fit a Burr type
511s  ## XII distribution to this data and plot a PDF of the fitted distribution
511s  ## superimposed on a histogram of the data
511s 
511s  pd = makedist ("Burr", "alpha", 1, "c", 2, "k", 1)
511s  rand ("seed", 21);
511s  data = random (pd, 5000, 1);
511s  pd = fitdist (data, "Burr")
511s  plot (pd)
511s  msg = strcat (["Fitted Burr type XII distribution with"], ...
511s                 [" alpha = %0.2f, c =  %0.2f, and k = %0.2f"]);
511s  title (sprintf (msg, pd.alpha, pd.c, pd.k))
511s ***** shared pd, t
511s  pd = BurrDistribution;
511s  t = truncate (pd, 2, 4);
511s ***** assert (cdf (pd, [0:5]), [0, 0.5, 0.6667, 0.75, 0.8, 0.8333], 1e-4);
511s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.625, 1, 1], 1e-4);
512s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.6, 0.6667, 0.75, 0.8], 1e-4);
512s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.625, 1], 1e-4);
512s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.25, 0.6667, 1.5, 4, Inf], 1e-4);
512s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2609, 2.5714, 2.9474, 3.4118, 4], 1e-4);
512s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.6667, 1.5, 4, Inf, NaN], 1e-4);
512s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5714, 2.9474, 3.4118, 4, NaN], 1e-4);
512s ***** assert (iqr (pd), 2.6667, 1e-4);
512s ***** assert (iqr (t), 0.9524, 1e-4);
512s ***** assert (mean (pd), Inf);
512s ***** assert (mean (t), 2.8312, 1e-4);
512s ***** assert (median (pd), 1, 1e-4);
512s ***** assert (median (t), 2.75, 1e-4);
512s ***** assert (pdf (pd, [0:5]), [1, 0.25, 0.1111, 0.0625, 0.04, 0.0278], 1e-4);
512s ***** assert (pdf (t, [0:5]), [0, 0, 0.8333, 0.4687, 0.3, 0], 1e-4);
512s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.25, 0.1111, 0.0625, 0.04, NaN], 1e-4);
512s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.8333, 0.4687, 0.3, NaN], 1e-4);
512s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
512s ***** assert (any (random (t, 1000, 1) < 2), false);
512s ***** assert (any (random (t, 1000, 1) > 4), false);
512s ***** assert (std (pd), Inf);
512s ***** assert (std (t), 0.5674, 1e-4);
512s ***** assert (var (pd), Inf);
512s ***** assert (var (t), 0.3220, 1e-4);
512s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
512s  BurrDistribution(0, 1, 1)
512s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
512s  BurrDistribution(-1, 1, 1)
512s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
512s  BurrDistribution(Inf, 1, 1)
512s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
512s  BurrDistribution(i, 1, 1)
512s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
512s  BurrDistribution("a", 1, 1)
512s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
512s  BurrDistribution([1, 2], 1, 1)
512s ***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
512s  BurrDistribution(NaN, 1, 1)
512s ***** error <BurrDistribution: C must be a positive real scalar.> ...
512s  BurrDistribution(1, 0, 1)
512s ***** error <BurrDistribution: C must be a positive real scalar.> ...
512s  BurrDistribution(1, -1, 1)
512s ***** error <BurrDistribution: C must be a positive real scalar.> ...
512s  BurrDistribution(1, Inf, 1)
512s ***** error <BurrDistribution: C must be a positive real scalar.> ...
512s  BurrDistribution(1, i, 1)
512s ***** error <BurrDistribution: C must be a positive real scalar.> ...
512s  BurrDistribution(1, "a", 1)
512s ***** error <BurrDistribution: C must be a positive real scalar.> ...
512s  BurrDistribution(1, [1, 2], 1)
512s ***** error <BurrDistribution: C must be a positive real scalar.> ...
512s  BurrDistribution(1, NaN, 1)
512s ***** error <BurrDistribution: K must be a positive real scalar.> ...
512s  BurrDistribution(1, 1, 0)
512s ***** error <BurrDistribution: K must be a positive real scalar.> ...
512s  BurrDistribution(1, 1, -1)
512s ***** error <BurrDistribution: K must be a positive real scalar.> ...
512s  BurrDistribution(1, 1, Inf)
512s ***** error <BurrDistribution: K must be a positive real scalar.> ...
512s  BurrDistribution(1, 1, i)
512s ***** error <BurrDistribution: K must be a positive real scalar.> ...
512s  BurrDistribution(1, 1, "a")
512s ***** error <BurrDistribution: K must be a positive real scalar.> ...
512s  BurrDistribution(1, 1, [1, 2])
512s ***** error <BurrDistribution: K must be a positive real scalar.> ...
512s  BurrDistribution(1, 1, NaN)
512s ***** error <cdf: invalid argument for upper tail.> ...
512s  cdf (BurrDistribution, 2, "uper")
512s ***** error <cdf: invalid argument for upper tail.> ...
512s  cdf (BurrDistribution, 2, 3)
512s ***** shared x
512s  rand ("seed", 4);
512s  x = burrrnd (1, 1, 1, [1, 100]);
512s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
512s  paramci (BurrDistribution.fit (x), "alpha")
512s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
512s  paramci (BurrDistribution.fit (x), "alpha", 0)
512s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
512s  paramci (BurrDistribution.fit (x), "alpha", 1)
512s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
512s  paramci (BurrDistribution.fit (x), "alpha", [0.5 2])
512s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
512s  paramci (BurrDistribution.fit (x), "alpha", "")
512s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
512s  paramci (BurrDistribution.fit (x), "alpha", {0.05})
512s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
512s  paramci (BurrDistribution.fit (x), "parameter", "c", "alpha", {0.05})
513s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
513s  paramci (BurrDistribution.fit (x), "parameter", {"alpha", "c", "k", "param"})
513s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
513s  paramci (BurrDistribution.fit (x), "alpha", 0.01, ...
513s           "parameter", {"alpha", "c", "k", "param"})
513s ***** error <paramci: unknown distribution parameter.> ...
513s  paramci (BurrDistribution.fit (x), "parameter", "param")
513s ***** error <paramci: unknown distribution parameter.> ...
513s  paramci (BurrDistribution.fit (x), "alpha", 0.01, "parameter", "param")
513s ***** error <paramci: invalid NAME for optional argument.> ...
513s  paramci (BurrDistribution.fit (x), "NAME", "value")
513s ***** error <paramci: invalid NAME for optional argument.> ...
513s  paramci (BurrDistribution.fit (x), "alpha", 0.01, "NAME", "value")
513s ***** error <paramci: invalid NAME for optional argument.> ...
513s  paramci (BurrDistribution.fit (x), "alpha", 0.01, "parameter", "c", ...
513s           "NAME", "value")
513s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
513s  plot (BurrDistribution, "Parent")
513s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
513s  plot (BurrDistribution, "PlotType", 12)
514s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
514s  plot (BurrDistribution, "PlotType", {"pdf", "cdf"})
514s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
514s  plot (BurrDistribution, "PlotType", "pdfcdf")
514s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
514s  plot (BurrDistribution, "Discrete", "pdfcdf")
514s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
514s  plot (BurrDistribution, "Discrete", [1, 0])
514s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
514s  plot (BurrDistribution, "Discrete", {true})
514s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
514s  plot (BurrDistribution, "Parent", 12)
514s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
514s  plot (BurrDistribution, "Parent", "hax")
514s ***** error <plot: invalid NAME for optional argument.> ...
514s  plot (BurrDistribution, "invalidNAME", "pdf")
514s ***** error <plot: no fitted DATA to plot a probability plot.> ...
514s  plot (BurrDistribution, "PlotType", "probability")
514s ***** error <proflik: no fitted data available.> ...
514s  proflik (BurrDistribution, 2)
514s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
514s  proflik (BurrDistribution.fit (x), 4)
514s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
514s  proflik (BurrDistribution.fit (x), [1, 2])
514s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
514s  proflik (BurrDistribution.fit (x), {1})
514s ***** error <proflik: SETPARAM must be a numeric vector.> ...
514s  proflik (BurrDistribution.fit (x), 1, ones (2))
514s ***** error <proflik: missing VALUE for 'Display' argument.> ...
514s  proflik (BurrDistribution.fit (x), 1, "Display")
514s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
514s  proflik (BurrDistribution.fit (x), 1, "Display", 1)
514s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
514s  proflik (BurrDistribution.fit (x), 1, "Display", {1})
514s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
514s  proflik (BurrDistribution.fit (x), 1, "Display", {"on"})
515s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
515s  proflik (BurrDistribution.fit (x), 1, "Display", ["on"; "on"])
515s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
515s  proflik (BurrDistribution.fit (x), 1, "Display", "onnn")
515s ***** error <proflik: invalid NAME for optional arguments.> ...
515s  proflik (BurrDistribution.fit (x), 1, "NAME", "on")
515s ***** error <proflik: invalid optional argument.> ...
515s  proflik (BurrDistribution.fit (x), 1, {"NAME"}, "on")
515s ***** error <proflik: invalid optional argument.> ...
515s  proflik (BurrDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
515s ***** error <truncate: missing input argument.> ...
515s  truncate (BurrDistribution)
515s ***** error <truncate: missing input argument.> ...
515s  truncate (BurrDistribution, 2)
515s ***** error <truncate: invalid lower upper limits.> ...
515s  truncate (BurrDistribution, 4, 2)
515s ***** shared pd
515s  pd = BurrDistribution(1, 1, 1);
515s  pd(2) = BurrDistribution(1, 3, 1);
515s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
515s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
515s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
515s ***** error <mean: requires a scalar probability distribution.> mean (pd)
515s ***** error <median: requires a scalar probability distribution.> median (pd)
515s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
515s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
515s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
515s ***** error <plot: requires a scalar probability distribution.> plot (pd)
515s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
515s ***** error <random: requires a scalar probability distribution.> random (pd)
515s ***** error <std: requires a scalar probability distribution.> std (pd)
515s ***** error <truncate: requires a scalar probability distribution.> ...
515s  truncate (pd, 2, 4)
515s ***** error <var: requires a scalar probability distribution.> var (pd)
515s 104 tests, 104 passed, 0 known failure, 0 skipped
515s [inst/dist_obj/ExponentialDistribution.m]
515s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_obj/ExponentialDistribution.m
515s ***** shared pd, t
515s  pd = ExponentialDistribution (1);
515s  t = truncate (pd, 2, 4);
515s ***** assert (cdf (pd, [0:5]), [0, 0.6321, 0.8647, 0.9502, 0.9817, 0.9933], 1e-4);
515s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7311, 1, 1], 1e-4);
515s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.7769, 0.8647, 0.9502, 0.9817], 1e-4);
515s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7311, 1], 1e-4);
515s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.2231, 0.5108, 0.9163, 1.6094, Inf], 1e-4);
515s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1899, 2.4244, 2.7315, 3.1768, 4], 1e-4);
515s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5108, 0.9163, 1.6094, Inf, NaN], 1e-4);
515s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4244, 2.7315, 3.1768, 4, NaN], 1e-4);
515s ***** assert (iqr (pd), 1.0986, 1e-4);
515s ***** assert (iqr (t), 0.8020, 1e-4);
515s ***** assert (mean (pd), 1);
515s ***** assert (mean (t), 2.6870, 1e-4);
515s ***** assert (median (pd), 0.6931, 1e-4);
515s ***** assert (median (t), 2.5662, 1e-4);
515s ***** assert (pdf (pd, [0:5]), [1, 0.3679, 0.1353, 0.0498, 0.0183, 0.0067], 1e-4);
515s ***** assert (pdf (t, [0:5]), [0, 0, 1.1565, 0.4255, 0.1565, 0], 1e-4);
515s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3679, 0.1353, 0.0498, 0.0183, NaN], 1e-4);
515s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.1565, 0.4255, 0.1565, NaN], 1e-4);
515s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
515s ***** assert (any (random (t, 1000, 1) < 2), false);
515s ***** assert (any (random (t, 1000, 1) > 4), false);
515s ***** assert (std (pd), 1);
515s ***** assert (std (t), 0.5253, 1e-4);
515s ***** assert (var (pd), 1);
515s ***** assert (var (t), 0.2759, 1e-4);
515s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
515s  ExponentialDistribution(0)
515s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
515s  ExponentialDistribution(-1)
515s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
515s  ExponentialDistribution(Inf)
515s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
515s  ExponentialDistribution(i)
515s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
515s  ExponentialDistribution("a")
515s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
515s  ExponentialDistribution([1, 2])
515s ***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
515s  ExponentialDistribution(NaN)
515s ***** error <cdf: invalid argument for upper tail.> ...
515s  cdf (ExponentialDistribution, 2, "uper")
515s ***** error <cdf: invalid argument for upper tail.> ...
515s  cdf (ExponentialDistribution, 2, 3)
515s ***** shared x
515s  x = exprnd (1, [100, 1]);
515s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
515s  paramci (ExponentialDistribution.fit (x), "alpha")
516s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
516s  paramci (ExponentialDistribution.fit (x), "alpha", 0)
516s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
516s  paramci (ExponentialDistribution.fit (x), "alpha", 1)
516s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
516s  paramci (ExponentialDistribution.fit (x), "alpha", [0.5 2])
516s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
516s  paramci (ExponentialDistribution.fit (x), "alpha", "")
517s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
517s  paramci (ExponentialDistribution.fit (x), "alpha", {0.05})
517s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
517s  paramci (ExponentialDistribution.fit (x), "parameter", "mu", ...
517s           "alpha", {0.05})
517s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
517s  paramci (ExponentialDistribution.fit (x), "parameter", {"mu", "param"})
517s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
517s  paramci (ExponentialDistribution.fit (x), "alpha", 0.01, ...
517s           "parameter", {"mu", "param"})
517s ***** error <paramci: unknown distribution parameter.> ...
517s  paramci (ExponentialDistribution.fit (x), "parameter", "param")
517s ***** error <paramci: unknown distribution parameter.> ...
517s  paramci (ExponentialDistribution.fit (x), "alpha", 0.01, "parameter", "parm")
517s ***** error <paramci: invalid NAME for optional argument.> ...
517s  paramci (ExponentialDistribution.fit (x), "NAME", "value")
517s ***** error <paramci: invalid NAME for optional argument.> ...
517s  paramci (ExponentialDistribution.fit (x), "alpha", 0.01, "NAME", "value")
517s ***** error <paramci: invalid NAME for optional argument.> ...
517s  paramci (ExponentialDistribution.fit (x), "alpha", 0.01, ...
517s           "parameter", "mu", "NAME", "value")
517s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
517s  plot (ExponentialDistribution, "Parent")
517s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
517s  plot (ExponentialDistribution, "PlotType", 12)
517s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
517s  plot (ExponentialDistribution, "PlotType", {"pdf", "cdf"})
517s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
517s  plot (ExponentialDistribution, "PlotType", "pdfcdf")
517s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
517s  plot (ExponentialDistribution, "Discrete", "pdfcdf")
517s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
517s  plot (ExponentialDistribution, "Discrete", [1, 0])
517s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
517s  plot (ExponentialDistribution, "Discrete", {true})
517s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
517s  plot (ExponentialDistribution, "Parent", 12)
517s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
517s  plot (ExponentialDistribution, "Parent", "hax")
517s ***** error <plot: invalid NAME for optional argument.> ...
517s  plot (ExponentialDistribution, "invalidNAME", "pdf")
517s ***** error <plot: no fitted DATA to plot a probability plot.> ...
517s  plot (ExponentialDistribution, "PlotType", "probability")
517s ***** error <proflik: no fitted data available.> ...
517s  proflik (ExponentialDistribution, 2)
517s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
517s  proflik (ExponentialDistribution.fit (x), 3)
517s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
517s  proflik (ExponentialDistribution.fit (x), [1, 2])
517s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
517s  proflik (ExponentialDistribution.fit (x), {1})
517s ***** error <proflik: SETPARAM must be a numeric vector.> ...
517s  proflik (ExponentialDistribution.fit (x), 1, ones (2))
517s ***** error <proflik: missing VALUE for 'Display' argument.> ...
517s  proflik (ExponentialDistribution.fit (x), 1, "Display")
517s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
517s  proflik (ExponentialDistribution.fit (x), 1, "Display", 1)
517s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
517s  proflik (ExponentialDistribution.fit (x), 1, "Display", {1})
517s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
517s  proflik (ExponentialDistribution.fit (x), 1, "Display", {"on"})
517s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
517s  proflik (ExponentialDistribution.fit (x), 1, "Display", ["on"; "on"])
517s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
517s  proflik (ExponentialDistribution.fit (x), 1, "Display", "onnn")
517s ***** error <proflik: invalid NAME for optional arguments.> ...
517s  proflik (ExponentialDistribution.fit (x), 1, "NAME", "on")
517s ***** error <proflik: invalid optional argument.> ...
517s  proflik (ExponentialDistribution.fit (x), 1, {"NAME"}, "on")
517s ***** error <proflik: invalid optional argument.> ...
517s  proflik (ExponentialDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
517s ***** error <truncate: missing input argument.> ...
517s  truncate (ExponentialDistribution)
517s ***** error <truncate: missing input argument.> ...
517s  truncate (ExponentialDistribution, 2)
517s ***** error <truncate: invalid lower upper limits.> ...
517s  truncate (ExponentialDistribution, 4, 2)
517s ***** shared pd
517s  pd = ExponentialDistribution(1);
517s  pd(2) = ExponentialDistribution(3);
517s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
517s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
517s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
517s ***** error <mean: requires a scalar probability distribution.> mean (pd)
517s ***** error <median: requires a scalar probability distribution.> median (pd)
517s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
517s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
517s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
517s ***** error <plot: requires a scalar probability distribution.> plot (pd)
517s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
517s ***** error <random: requires a scalar probability distribution.> random (pd)
517s ***** error <std: requires a scalar probability distribution.> std (pd)
517s ***** error <truncate: requires a scalar probability distribution.> ...
517s  truncate (pd, 2, 4)
517s ***** error <var: requires a scalar probability distribution.> var (pd)
517s 90 tests, 90 passed, 0 known failure, 0 skipped
517s [inst/dist_obj/ExtremeValueDistribution.m]
517s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_obj/ExtremeValueDistribution.m
517s ***** shared pd, t
517s  pd = ExtremeValueDistribution (0, 1);
517s  t = truncate (pd, 2, 4);
517s ***** assert (cdf (pd, [0:5]), [0.6321, 0.9340, 0.9994, 1, 1, 1], 1e-4);
517s ***** assert (cdf (t, [0:5]), [0, 0, 0, 1, 1, 1], 1e-4);
517s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.9887, 0.9994, 1, 1], 1e-4);
517s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 1, 1], 1e-4);
517s ***** assert (icdf (pd, [0:0.2:1]), [-Inf, -1.4999, -0.6717, -0.0874, 0.4759, Inf], 1e-4);
517s ***** assert (icdf (t, [0:0.2:1]), [2, 2.0298, 2.0668, 2.1169, 2.1971, 4], 1e-4);
517s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, -0.6717, -0.0874, 0.4759, Inf, NaN], 1e-4);
517s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.0668, 2.1169, 2.1971, 4, NaN], 1e-4);
517s ***** assert (iqr (pd), 1.5725, 1e-4);
517s ***** assert (iqr (t), 0.1338, 1e-4);
517s ***** assert (mean (pd), -0.5772, 1e-4);
517s ***** assert (mean (t), 2.1206, 1e-4);
517s ***** assert (median (pd), -0.3665, 1e-4);
517s ***** assert (median (t), 2.0897, 1e-4);
517s ***** assert (pdf (pd, [0:5]), [0.3679, 0.1794, 0.0046, 0, 0, 0], 1e-4);
517s ***** assert (pdf (t, [0:5]), [0, 0, 7.3891, 0.0001, 0, 0], 1e-4);
517s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0.2546, 0.1794, 0.0046, 0, 0, NaN], 1e-4);
517s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 7.3891, 0.0001, 0, NaN], 1e-4);
517s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
517s ***** assert (any (random (t, 1000, 1) < 2), false);
517s ***** assert (any (random (t, 1000, 1) > 4), false);
517s ***** assert (std (pd), 1.2825, 1e-4);
517s ***** assert (std (t), 0.1091, 1e-4);
517s ***** assert (var (pd), 1.6449, 1e-4);
517s ***** assert (var (t), 0.0119, 1e-4);
517s ***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
517s  ExtremeValueDistribution(Inf, 1)
517s ***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
517s  ExtremeValueDistribution(i, 1)
517s ***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
517s  ExtremeValueDistribution("a", 1)
517s ***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
517s  ExtremeValueDistribution([1, 2], 1)
517s ***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
517s  ExtremeValueDistribution(NaN, 1)
517s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
517s  ExtremeValueDistribution(1, 0)
517s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
517s  ExtremeValueDistribution(1, -1)
517s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
517s  ExtremeValueDistribution(1, Inf)
517s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
517s  ExtremeValueDistribution(1, i)
518s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
518s  ExtremeValueDistribution(1, "a")
518s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
518s  ExtremeValueDistribution(1, [1, 2])
518s ***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
518s  ExtremeValueDistribution(1, NaN)
518s ***** error <cdf: invalid argument for upper tail.> ...
518s  cdf (ExtremeValueDistribution, 2, "uper")
518s ***** error <cdf: invalid argument for upper tail.> ...
518s  cdf (ExtremeValueDistribution, 2, 3)
518s ***** shared x
518s  rand ("seed", 1);
518s  x = evrnd (1, 1, [1000, 1]);
518s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
518s  paramci (ExtremeValueDistribution.fit (x), "alpha")
518s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
518s  paramci (ExtremeValueDistribution.fit (x), "alpha", 0)
518s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
518s  paramci (ExtremeValueDistribution.fit (x), "alpha", 1)
518s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
518s  paramci (ExtremeValueDistribution.fit (x), "alpha", [0.5 2])
518s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
518s  paramci (ExtremeValueDistribution.fit (x), "alpha", "")
518s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
518s  paramci (ExtremeValueDistribution.fit (x), "alpha", {0.05})
518s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
518s  paramci (ExtremeValueDistribution.fit (x), ...
518s           "parameter", "mu", "alpha", {0.05})
518s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
518s  paramci (ExtremeValueDistribution.fit (x), ...
518s           "parameter", {"mu", "sigma", "param"})
518s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
518s  paramci (ExtremeValueDistribution.fit (x), "alpha", 0.01, ...
518s           "parameter", {"mu", "sigma", "param"})
518s ***** error <paramci: unknown distribution parameter.> ...
518s  paramci (ExtremeValueDistribution.fit (x), "parameter", "param")
518s ***** error <paramci: unknown distribution parameter.> ...
518s  paramci (ExtremeValueDistribution.fit (x), "alpha", 0.01, ...
518s           "parameter", "param")
518s ***** error <paramci: invalid NAME for optional argument.> ...
518s  paramci (ExtremeValueDistribution.fit (x), "NAME", "value")
518s ***** error <paramci: invalid NAME for optional argument.> ...
518s  paramci (ExtremeValueDistribution.fit (x), "alpha", 0.01, "NAME", "value")
518s ***** error <paramci: invalid NAME for optional argument.> ...
518s  paramci (ExtremeValueDistribution.fit (x), "alpha", 0.01, ...
518s           "parameter", "mu", "NAME", "value")
518s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
518s  plot (ExtremeValueDistribution, "Parent")
518s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
518s  plot (ExtremeValueDistribution, "PlotType", 12)
518s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
518s  plot (ExtremeValueDistribution, "PlotType", {"pdf", "cdf"})
518s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
518s  plot (ExtremeValueDistribution, "PlotType", "pdfcdf")
518s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
518s  plot (ExtremeValueDistribution, "Discrete", "pdfcdf")
518s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
518s  plot (ExtremeValueDistribution, "Discrete", [1, 0])
518s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
518s  plot (ExtremeValueDistribution, "Discrete", {true})
518s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
518s  plot (ExtremeValueDistribution, "Parent", 12)
518s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
518s  plot (ExtremeValueDistribution, "Parent", "hax")
518s ***** error <plot: invalid NAME for optional argument.> ...
518s  plot (ExtremeValueDistribution, "invalidNAME", "pdf")
518s ***** error <plot: no fitted DATA to plot a probability plot.> ...
518s  plot (ExtremeValueDistribution, "PlotType", "probability")
518s ***** error <proflik: no fitted data available.> ...
518s  proflik (ExtremeValueDistribution, 2)
518s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
518s  proflik (ExtremeValueDistribution.fit (x), 3)
518s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
518s  proflik (ExtremeValueDistribution.fit (x), [1, 2])
518s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
518s  proflik (ExtremeValueDistribution.fit (x), {1})
518s ***** error <proflik: SETPARAM must be a numeric vector.> ...
518s  proflik (ExtremeValueDistribution.fit (x), 1, ones (2))
518s ***** error <proflik: missing VALUE for 'Display' argument.> ...
518s  proflik (ExtremeValueDistribution.fit (x), 1, "Display")
518s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
518s  proflik (ExtremeValueDistribution.fit (x), 1, "Display", 1)
518s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
518s  proflik (ExtremeValueDistribution.fit (x), 1, "Display", {1})
518s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
518s  proflik (ExtremeValueDistribution.fit (x), 1, "Display", {"on"})
518s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
518s  proflik (ExtremeValueDistribution.fit (x), 1, "Display", ["on"; "on"])
518s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
518s  proflik (ExtremeValueDistribution.fit (x), 1, "Display", "onnn")
518s ***** error <proflik: invalid NAME for optional arguments.> ...
518s  proflik (ExtremeValueDistribution.fit (x), 1, "NAME", "on")
518s ***** error <proflik: invalid optional argument.> ...
518s  proflik (ExtremeValueDistribution.fit (x), 1, {"NAME"}, "on")
518s ***** error <proflik: invalid optional argument.> ...
518s  proflik (ExtremeValueDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
518s ***** error <truncate: missing input argument.> ...
518s  truncate (ExtremeValueDistribution)
518s ***** error <truncate: missing input argument.> ...
518s  truncate (ExtremeValueDistribution, 2)
518s ***** error <truncate: invalid lower upper limits.> ...
518s  truncate (ExtremeValueDistribution, 4, 2)
518s ***** shared pd
518s  pd = ExtremeValueDistribution(1, 1);
518s  pd(2) = ExtremeValueDistribution(1, 3);
518s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
518s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
518s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
518s ***** error <mean: requires a scalar probability distribution.> mean (pd)
518s ***** error <median: requires a scalar probability distribution.> median (pd)
518s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
518s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
518s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
518s ***** error <plot: requires a scalar probability distribution.> plot (pd)
518s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
518s ***** error <random: requires a scalar probability distribution.> random (pd)
518s ***** error <std: requires a scalar probability distribution.> std (pd)
518s ***** error <truncate: requires a scalar probability distribution.> ...
518s  truncate (pd, 2, 4)
518s ***** error <var: requires a scalar probability distribution.> var (pd)
518s 95 tests, 95 passed, 0 known failure, 0 skipped
518s [inst/dist_obj/GammaDistribution.m]
518s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_obj/GammaDistribution.m
518s ***** shared pd, t
518s  pd = GammaDistribution (1, 1);
518s  t = truncate (pd, 2, 4);
518s ***** assert (cdf (pd, [0:5]), [0, 0.6321, 0.8647, 0.9502, 0.9817, 0.9933], 1e-4);
518s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7311, 1, 1], 1e-4);
518s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.7769, 0.8647, 0.9502, 0.9817], 1e-4);
518s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7311, 1], 1e-4);
518s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.2231, 0.5108, 0.9163, 1.6094, Inf], 1e-4);
518s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1899, 2.4244, 2.7315, 3.1768, 4], 1e-4);
518s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5108, 0.9163, 1.6094, Inf, NaN], 1e-4);
518s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4244, 2.7315, 3.1768, 4, NaN], 1e-4);
518s ***** assert (iqr (pd), 1.0986, 1e-4);
518s ***** assert (iqr (t), 0.8020, 1e-4);
518s ***** assert (mean (pd), 1);
518s ***** assert (mean (t), 2.6870, 1e-4);
518s ***** assert (median (pd), 0.6931, 1e-4);
518s ***** assert (median (t), 2.5662, 1e-4);
518s ***** assert (pdf (pd, [0:5]), [1, 0.3679, 0.1353, 0.0498, 0.0183, 0.0067], 1e-4);
518s ***** assert (pdf (t, [0:5]), [0, 0, 1.1565, 0.4255, 0.1565, 0], 1e-4);
518s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3679, 0.1353, 0.0498, 0.0183, NaN], 1e-4);
518s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.1565, 0.4255, 0.1565, NaN], 1e-4);
518s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
518s ***** assert (any (random (t, 1000, 1) < 2), false);
518s ***** assert (any (random (t, 1000, 1) > 4), false);
518s ***** assert (std (pd), 1);
518s ***** assert (std (t), 0.5253, 1e-4);
518s ***** assert (var (pd), 1);
518s ***** assert (var (t), 0.2759, 1e-4);
518s ***** error <GammaDistribution: A must be a positive real scalar.> ...
518s  GammaDistribution(0, 1)
518s ***** error <GammaDistribution: A must be a positive real scalar.> ...
518s  GammaDistribution(Inf, 1)
518s ***** error <GammaDistribution: A must be a positive real scalar.> ...
518s  GammaDistribution(i, 1)
518s ***** error <GammaDistribution: A must be a positive real scalar.> ...
518s  GammaDistribution("a", 1)
518s ***** error <GammaDistribution: A must be a positive real scalar.> ...
518s  GammaDistribution([1, 2], 1)
518s ***** error <GammaDistribution: A must be a positive real scalar.> ...
518s  GammaDistribution(NaN, 1)
518s ***** error <GammaDistribution: B must be a positive real scalar.> ...
518s  GammaDistribution(1, 0)
518s ***** error <GammaDistribution: B must be a positive real scalar.> ...
518s  GammaDistribution(1, -1)
518s ***** error <GammaDistribution: B must be a positive real scalar.> ...
518s  GammaDistribution(1, Inf)
518s ***** error <GammaDistribution: B must be a positive real scalar.> ...
518s  GammaDistribution(1, i)
518s ***** error <GammaDistribution: B must be a positive real scalar.> ...
518s  GammaDistribution(1, "a")
518s ***** error <GammaDistribution: B must be a positive real scalar.> ...
518s  GammaDistribution(1, [1, 2])
518s ***** error <GammaDistribution: B must be a positive real scalar.> ...
518s  GammaDistribution(1, NaN)
518s ***** error <cdf: invalid argument for upper tail.> ...
518s  cdf (GammaDistribution, 2, "uper")
518s ***** error <cdf: invalid argument for upper tail.> ...
518s  cdf (GammaDistribution, 2, 3)
518s ***** shared x
518s  x = gamrnd (1, 1, [100, 1]);
518s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
518s  paramci (GammaDistribution.fit (x), "alpha")
518s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
518s  paramci (GammaDistribution.fit (x), "alpha", 0)
518s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
518s  paramci (GammaDistribution.fit (x), "alpha", 1)
518s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
518s  paramci (GammaDistribution.fit (x), "alpha", [0.5 2])
518s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
518s  paramci (GammaDistribution.fit (x), "alpha", "")
518s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
518s  paramci (GammaDistribution.fit (x), "alpha", {0.05})
518s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
518s  paramci (GammaDistribution.fit (x), "parameter", "a", "alpha", {0.05})
518s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
518s  paramci (GammaDistribution.fit (x), "parameter", {"a", "b", "param"})
518s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
518s  paramci (GammaDistribution.fit (x), "alpha", 0.01, ...
518s           "parameter", {"a", "b", "param"})
518s ***** error <paramci: unknown distribution parameter.> ...
518s  paramci (GammaDistribution.fit (x), "parameter", "param")
518s ***** error <paramci: unknown distribution parameter.> ...
518s  paramci (GammaDistribution.fit (x), "alpha", 0.01, "parameter", "param")
518s ***** error <paramci: invalid NAME for optional argument.> ...
518s  paramci (GammaDistribution.fit (x), "NAME", "value")
519s ***** error <paramci: invalid NAME for optional argument.> ...
519s  paramci (GammaDistribution.fit (x), "alpha", 0.01, "NAME", "value")
519s ***** error <paramci: invalid NAME for optional argument.> ...
519s  paramci (GammaDistribution.fit (x), "alpha", 0.01, "parameter", "a", ...
519s           "NAME", "value")
519s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
519s  plot (GammaDistribution, "Parent")
519s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
519s  plot (GammaDistribution, "PlotType", 12)
519s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
519s  plot (GammaDistribution, "PlotType", {"pdf", "cdf"})
519s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
519s  plot (GammaDistribution, "PlotType", "pdfcdf")
519s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
519s  plot (GammaDistribution, "Discrete", "pdfcdf")
519s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
519s  plot (GammaDistribution, "Discrete", [1, 0])
519s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
519s  plot (GammaDistribution, "Discrete", {true})
519s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
519s  plot (GammaDistribution, "Parent", 12)
519s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
519s  plot (GammaDistribution, "Parent", "hax")
519s ***** error <plot: invalid NAME for optional argument.> ...
519s  plot (GammaDistribution, "invalidNAME", "pdf")
519s ***** error <plot: no fitted DATA to plot a probability plot.> ...
519s  plot (GammaDistribution, "PlotType", "probability")
519s ***** error <proflik: no fitted data available.> ...
519s  proflik (GammaDistribution, 2)
519s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
519s  proflik (GammaDistribution.fit (x), 3)
519s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
519s  proflik (GammaDistribution.fit (x), [1, 2])
519s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
519s  proflik (GammaDistribution.fit (x), {1})
519s ***** error <proflik: SETPARAM must be a numeric vector.> ...
519s  proflik (GammaDistribution.fit (x), 1, ones (2))
519s ***** error <proflik: missing VALUE for 'Display' argument.> ...
519s  proflik (GammaDistribution.fit (x), 1, "Display")
519s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
519s  proflik (GammaDistribution.fit (x), 1, "Display", 1)
519s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
519s  proflik (GammaDistribution.fit (x), 1, "Display", {1})
519s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
519s  proflik (GammaDistribution.fit (x), 1, "Display", {"on"})
519s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
519s  proflik (GammaDistribution.fit (x), 1, "Display", ["on"; "on"])
519s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
519s  proflik (GammaDistribution.fit (x), 1, "Display", "onnn")
519s ***** error <proflik: invalid NAME for optional arguments.> ...
519s  proflik (GammaDistribution.fit (x), 1, "NAME", "on")
519s ***** error <proflik: invalid optional argument.> ...
519s  proflik (GammaDistribution.fit (x), 1, {"NAME"}, "on")
519s ***** error <proflik: invalid optional argument.> ...
519s  proflik (GammaDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
519s ***** error <truncate: missing input argument.> ...
519s  truncate (GammaDistribution)
519s ***** error <truncate: missing input argument.> ...
519s  truncate (GammaDistribution, 2)
519s ***** error <truncate: invalid lower upper limits.> ...
519s  truncate (GammaDistribution, 4, 2)
519s ***** shared pd
519s  pd = GammaDistribution(1, 1);
519s  pd(2) = GammaDistribution(1, 3);
519s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
519s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
519s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
519s ***** error <mean: requires a scalar probability distribution.> mean (pd)
519s ***** error <median: requires a scalar probability distribution.> median (pd)
519s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
519s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
519s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
519s ***** error <plot: requires a scalar probability distribution.> plot (pd)
519s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
519s ***** error <random: requires a scalar probability distribution.> random (pd)
519s ***** error <std: requires a scalar probability distribution.> std (pd)
519s ***** error <truncate: requires a scalar probability distribution.> ...
519s  truncate (pd, 2, 4)
519s ***** error <var: requires a scalar probability distribution.> var (pd)
519s 96 tests, 96 passed, 0 known failure, 0 skipped
519s [inst/dist_obj/GeneralizedExtremeValueDistribution.m]
519s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_obj/GeneralizedExtremeValueDistribution.m
519s ***** shared pd, t
519s  pd = GeneralizedExtremeValueDistribution;
519s  t = truncate (pd, 2, 4);
519s ***** assert (cdf (pd, [0:5]), [0.3679, 0.6922, 0.8734, 0.9514, 0.9819, 0.9933], 1e-4);
519s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7195, 1, 1], 1e-4);
519s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8, 0.8734, 0.9514, 0.9819], 1e-4);
519s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7195, 1], 1e-4);
519s ***** assert (icdf (pd, [0:0.2:1]), [-Inf, -0.4759, 0.0874, 0.6717, 1.4999, Inf], 1e-4);
519s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1999, 2.4433, 2.7568, 3.2028, 4], 1e-4);
519s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.0874, 0.6717, 1.4999, Inf, NaN], 1e-4);
519s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4433, 2.7568, 3.2028, 4, NaN], 1e-4);
519s ***** assert (iqr (pd), 1.5725, 1e-4);
519s ***** assert (iqr (t), 0.8164, 1e-4);
519s ***** assert (mean (pd), 0.5772, 1e-4);
519s ***** assert (mean (t), 2.7043, 1e-4);
519s ***** assert (median (pd), 0.3665, 1e-4);
519s ***** assert (median (t), 2.5887, 1e-4);
519s ***** assert (pdf (pd, [0:5]), [0.3679, 0.2546, 0.1182, 0.0474, 0.0180, 0.0067], 1e-4);
519s ***** assert (pdf (t, [0:5]), [0, 0, 1.0902, 0.4369, 0.1659, 0], 1e-4);
519s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0.1794, 0.2546, 0.1182, 0.0474, 0.0180, NaN], 1e-4);
519s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.0902, 0.4369, 0.1659, NaN], 1e-4);
519s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
519s ***** assert (any (random (t, 1000, 1) < 2), false);
519s ***** assert (any (random (t, 1000, 1) > 4), false);
519s ***** assert (std (pd), 1.2825, 1e-4);
519s ***** assert (std (t), 0.5289, 1e-4);
519s ***** assert (var (pd), 1.6449, 1e-4);
519s ***** assert (var (t), 0.2798, 1e-4);
519s ***** error <GeneralizedExtremeValueDistribution: K must be a real scalar.> ...
519s  GeneralizedExtremeValueDistribution(Inf, 1, 1)
519s ***** error <GeneralizedExtremeValueDistribution: K must be a real scalar.> ...
519s  GeneralizedExtremeValueDistribution(i, 1, 1)
519s ***** error <GeneralizedExtremeValueDistribution: K must be a real scalar.> ...
519s  GeneralizedExtremeValueDistribution("a", 1, 1)
519s ***** error <GeneralizedExtremeValueDistribution: K must be a real scalar.> ...
519s  GeneralizedExtremeValueDistribution([1, 2], 1, 1)
519s ***** error <GeneralizedExtremeValueDistribution: K must be a real scalar.> ...
519s  GeneralizedExtremeValueDistribution(NaN, 1, 1)
519s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
519s  GeneralizedExtremeValueDistribution(1, 0, 1)
519s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
519s  GeneralizedExtremeValueDistribution(1, -1, 1)
519s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
519s  GeneralizedExtremeValueDistribution(1, Inf, 1)
519s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
519s  GeneralizedExtremeValueDistribution(1, i, 1)
519s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
519s  GeneralizedExtremeValueDistribution(1, "a", 1)
519s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
519s  GeneralizedExtremeValueDistribution(1, [1, 2], 1)
519s ***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
519s  GeneralizedExtremeValueDistribution(1, NaN, 1)
519s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
519s  GeneralizedExtremeValueDistribution(1, 1, Inf)
519s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
519s  GeneralizedExtremeValueDistribution(1, 1, i)
519s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
519s  GeneralizedExtremeValueDistribution(1, 1, "a")
519s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
519s  GeneralizedExtremeValueDistribution(1, 1, [1, 2])
519s ***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
519s  GeneralizedExtremeValueDistribution(1, 1, NaN)
519s ***** error <cdf: invalid argument for upper tail.> ...
519s  cdf (GeneralizedExtremeValueDistribution, 2, "uper")
519s ***** error <cdf: invalid argument for upper tail.> ...
519s  cdf (GeneralizedExtremeValueDistribution, 2, 3)
519s ***** shared x
519s  x = gevrnd (1, 1, 1, [1, 100]);
519s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
519s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha")
519s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
519s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0)
520s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
520s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 1)
520s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
520s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", [0.5 2])
520s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
520s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", "")
520s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
520s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", {0.05})
520s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
520s  paramci (GeneralizedExtremeValueDistribution.fit (x), ...
520s           "parameter", "sigma", "alpha", {0.05})
521s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
521s  paramci (GeneralizedExtremeValueDistribution.fit (x), ...
521s           "parameter", {"k", "sigma", "mu", "param"})
521s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
521s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0.01, ...
521s           "parameter", {"k", "sigma", "mu", "param"})
521s ***** error <paramci: unknown distribution parameter.> ...
521s  paramci (GeneralizedExtremeValueDistribution.fit (x), "parameter", "param")
521s ***** error <paramci: unknown distribution parameter.> ...
521s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0.01, ...
521s           "parameter", "param")
521s ***** error <paramci: invalid NAME for optional argument.> ...
521s  paramci (GeneralizedExtremeValueDistribution.fit (x), "NAME", "value")
521s ***** error <paramci: invalid NAME for optional argument.> ...
521s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0.01, ...
521s           "NAME", "value")
522s ***** error <paramci: invalid NAME for optional argument.> ...
522s  paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0.01, ...
522s           "parameter", "sigma", "NAME", "value")
522s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
522s  plot (GeneralizedExtremeValueDistribution, "Parent")
522s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
522s  plot (GeneralizedExtremeValueDistribution, "PlotType", 12)
522s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
522s  plot (GeneralizedExtremeValueDistribution, "PlotType", {"pdf", "cdf"})
522s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
522s  plot (GeneralizedExtremeValueDistribution, "PlotType", "pdfcdf")
522s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
522s  plot (GeneralizedExtremeValueDistribution, "Discrete", "pdfcdf")
522s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
522s  plot (GeneralizedExtremeValueDistribution, "Discrete", [1, 0])
522s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
522s  plot (GeneralizedExtremeValueDistribution, "Discrete", {true})
522s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
522s  plot (GeneralizedExtremeValueDistribution, "Parent", 12)
522s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
522s  plot (GeneralizedExtremeValueDistribution, "Parent", "hax")
522s ***** error <plot: invalid NAME for optional argument.> ...
522s  plot (GeneralizedExtremeValueDistribution, "invalidNAME", "pdf")
522s ***** error <plot: no fitted DATA to plot a probability plot.> ...
522s  plot (GeneralizedExtremeValueDistribution, "PlotType", "probability")
522s ***** error <proflik: no fitted data available.> ...
522s  proflik (GeneralizedExtremeValueDistribution, 2)
522s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
522s  proflik (GeneralizedExtremeValueDistribution.fit (x), 4)
522s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
522s  proflik (GeneralizedExtremeValueDistribution.fit (x), [1, 2])
522s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
522s  proflik (GeneralizedExtremeValueDistribution.fit (x), {1})
522s ***** error <proflik: SETPARAM must be a numeric vector.> ...
522s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, ones (2))
522s ***** error <proflik: missing VALUE for 'Display' argument.> ...
522s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display")
523s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
523s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display", 1)
523s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
523s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display", {1})
523s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
523s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display", {"on"})
523s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
523s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, ...
523s           "Display", ["on"; "on"])
523s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
523s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display", "onnn")
524s ***** error <proflik: invalid NAME for optional arguments.> ...
524s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "NAME", "on")
524s ***** error <proflik: invalid optional argument.> ...
524s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, {"NAME"}, "on")
524s ***** error <proflik: invalid optional argument.> ...
524s  proflik (GeneralizedExtremeValueDistribution.fit (x), 1, {[1 2 3 4]}, ...
524s           "Display", "on")
524s ***** error <truncate: missing input argument.> ...
524s  truncate (GeneralizedExtremeValueDistribution)
524s ***** error <truncate: missing input argument.> ...
524s  truncate (GeneralizedExtremeValueDistribution, 2)
524s ***** error <truncate: invalid lower upper limits.> ...
524s  truncate (GeneralizedExtremeValueDistribution, 4, 2)
524s ***** shared pd
524s  pd = GeneralizedExtremeValueDistribution(1, 1, 1);
524s  pd(2) = GeneralizedExtremeValueDistribution(1, 3, 1);
524s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
524s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
524s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
524s ***** error <mean: requires a scalar probability distribution.> mean (pd)
524s ***** error <median: requires a scalar probability distribution.> median (pd)
524s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
524s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
524s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
524s ***** error <plot: requires a scalar probability distribution.> plot (pd)
524s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
524s ***** error <random: requires a scalar probability distribution.> random (pd)
524s ***** error <std: requires a scalar probability distribution.> std (pd)
524s ***** error <truncate: requires a scalar probability distribution.> ...
524s  truncate (pd, 2, 4)
524s ***** error <var: requires a scalar probability distribution.> var (pd)
524s 100 tests, 100 passed, 0 known failure, 0 skipped
524s [inst/dist_obj/GeneralizedParetoDistribution.m]
524s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_obj/GeneralizedParetoDistribution.m
524s ***** shared pd, t
524s  pd = GeneralizedParetoDistribution (1, 1, 1);
524s  t = truncate (pd, 2, 4);
524s ***** assert (cdf (pd, [0:5]), [0, 0, 0.5, 0.6667, 0.75, 0.8], 1e-4);
524s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.6667, 1, 1], 1e-4);
524s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.3333, 0.5, 0.6667, 0.75], 1e-4);
524s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.6667, 1], 1e-4);
524s ***** assert (icdf (pd, [0:0.2:1]), [1, 1.25, 1.6667, 2.5, 5, Inf], 1e-4);
524s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2222, 2.5, 2.8571, 3.3333, 4], 1e-4);
524s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1.6667, 2.5, 5, Inf, NaN], 1e-4);
524s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5, 2.8571, 3.3333, 4, NaN], 1e-4);
524s ***** assert (iqr (pd), 2.6667, 1e-4);
524s ***** assert (iqr (t), 0.9143, 1e-4);
524s ***** assert (mean (pd), Inf);
524s ***** assert (mean (t), 2.7726, 1e-4);
524s ***** assert (median (pd), 2);
524s ***** assert (median (t), 2.6667, 1e-4);
524s ***** assert (pdf (pd, [0:5]), [0, 1, 0.25, 0.1111, 0.0625, 0.04], 1e-4);
524s ***** assert (pdf (t, [0:5]), [0, 0, 1, 0.4444, 0.25, 0], 1e-4);
524s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 1, 0.25, 0.1111, 0.0625, NaN], 1e-4);
524s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1, 0.4444, 0.25, NaN], 1e-4);
524s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
524s ***** assert (any (random (t, 1000, 1) < 2), false);
524s ***** assert (any (random (t, 1000, 1) > 4), false);
524s ***** assert (std (pd), Inf);
524s ***** assert (std (t), 0.5592, 1e-4);
524s ***** assert (var (pd), Inf);
524s ***** assert (var (t), 0.3128, 1e-4);
524s ***** error <GeneralizedParetoDistribution: K must be a real scalar.> ...
524s  GeneralizedParetoDistribution(Inf, 1, 1)
524s ***** error <GeneralizedParetoDistribution: K must be a real scalar.> ...
524s  GeneralizedParetoDistribution(i, 1, 1)
524s ***** error <GeneralizedParetoDistribution: K must be a real scalar.> ...
524s  GeneralizedParetoDistribution("a", 1, 1)
524s ***** error <GeneralizedParetoDistribution: K must be a real scalar.> ...
524s  GeneralizedParetoDistribution([1, 2], 1, 1)
524s ***** error <GeneralizedParetoDistribution: K must be a real scalar.> ...
524s  GeneralizedParetoDistribution(NaN, 1, 1)
524s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
524s  GeneralizedParetoDistribution(1, 0, 1)
524s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
524s  GeneralizedParetoDistribution(1, -1, 1)
524s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
524s  GeneralizedParetoDistribution(1, Inf, 1)
524s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
524s  GeneralizedParetoDistribution(1, i, 1)
524s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
524s  GeneralizedParetoDistribution(1, "a", 1)
524s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
524s  GeneralizedParetoDistribution(1, [1, 2], 1)
524s ***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
524s  GeneralizedParetoDistribution(1, NaN, 1)
524s ***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
524s  GeneralizedParetoDistribution(1, 1, Inf)
524s ***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
524s  GeneralizedParetoDistribution(1, 1, i)
524s ***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
524s  GeneralizedParetoDistribution(1, 1, "a")
524s ***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
524s  GeneralizedParetoDistribution(1, 1, [1, 2])
524s ***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
524s  GeneralizedParetoDistribution(1, 1, NaN)
524s ***** error <cdf: invalid argument for upper tail.> ...
524s  cdf (GeneralizedParetoDistribution, 2, "uper")
524s ***** error <cdf: invalid argument for upper tail.> ...
524s  cdf (GeneralizedParetoDistribution, 2, 3)
524s ***** shared x
524s  x = gprnd (1, 1, 1, [1, 100]);
524s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
524s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha")
524s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
524s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0)
524s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
524s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 1)
524s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
524s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", [0.5 2])
524s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
524s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", "")
525s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
525s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", {0.05})
525s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
525s  paramci (GeneralizedParetoDistribution.fit (x, 1), ...
525s           "parameter", "sigma", "alpha", {0.05})
525s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
525s  paramci (GeneralizedParetoDistribution.fit (x, 1), ...
525s           "parameter", {"k", "sigma", "param"})
525s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
525s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0.01, ...
525s           "parameter", {"k", "sigma", "param"})
525s ***** error <paramci: unknown distribution parameter.> ...
525s  paramci (GeneralizedParetoDistribution.fit (x, 1), "parameter", "param")
525s ***** error <paramci: unknown distribution parameter.> ...
525s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0.01, ...
525s           "parameter", "param")
525s ***** error <paramci: invalid NAME for optional argument.> ...
525s  paramci (GeneralizedParetoDistribution.fit (x, 1), "NAME", "value")
525s ***** error <paramci: invalid NAME for optional argument.> ...
525s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0.01, ...
525s           "NAME", "value")
525s ***** error <paramci: invalid NAME for optional argument.> ...
525s  paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0.01, ...
525s           "parameter", "sigma", "NAME", "value")
525s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
525s  plot (GeneralizedParetoDistribution, "Parent")
525s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
525s  plot (GeneralizedParetoDistribution, "PlotType", 12)
525s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
525s  plot (GeneralizedParetoDistribution, "PlotType", {"pdf", "cdf"})
525s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
525s  plot (GeneralizedParetoDistribution, "PlotType", "pdfcdf")
525s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
525s  plot (GeneralizedParetoDistribution, "Discrete", "pdfcdf")
525s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
525s  plot (GeneralizedParetoDistribution, "Discrete", [1, 0])
525s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
525s  plot (GeneralizedParetoDistribution, "Discrete", {true})
525s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
525s  plot (GeneralizedParetoDistribution, "Parent", 12)
525s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
525s  plot (GeneralizedParetoDistribution, "Parent", "hax")
525s ***** error <plot: invalid NAME for optional argument.> ...
525s  plot (GeneralizedParetoDistribution, "invalidNAME", "pdf")
525s ***** error <plot: no fitted DATA to plot a probability plot.> ...
525s  plot (GeneralizedParetoDistribution, "PlotType", "probability")
525s ***** error <proflik: no fitted data available.> ...
525s  proflik (GeneralizedParetoDistribution, 2)
525s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
525s  proflik (GeneralizedParetoDistribution.fit (x, 1), 3)
525s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
525s  proflik (GeneralizedParetoDistribution.fit (x, 1), [1, 2])
525s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
525s  proflik (GeneralizedParetoDistribution.fit (x, 1), {1})
525s ***** error <proflik: SETPARAM must be a numeric vector.> ...
525s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, ones (2))
525s ***** error <proflik: missing VALUE for 'Display' argument.> ...
525s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display")
525s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
525s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display", 1)
525s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
525s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display", {1})
525s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
525s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display", {"on"})
525s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
525s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, ...
525s           "Display", ["on"; "on"])
525s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
525s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display", "onnn")
525s ***** error <proflik: invalid NAME for optional arguments.> ...
525s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "NAME", "on")
525s ***** error <proflik: invalid optional argument.> ...
525s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, {"NAME"}, "on")
525s ***** error <proflik: invalid optional argument.> ...
525s  proflik (GeneralizedParetoDistribution.fit (x, 1), 1, {[1 2 3 4]}, ...
525s           "Display", "on")
525s ***** error <truncate: missing input argument.> ...
525s  truncate (GeneralizedParetoDistribution)
525s ***** error <truncate: missing input argument.> ...
525s  truncate (GeneralizedParetoDistribution, 2)
525s ***** error <truncate: invalid lower upper limits.> ...
525s  truncate (GeneralizedParetoDistribution, 4, 2)
525s ***** shared pd
525s  pd = GeneralizedParetoDistribution(1, 1, 1);
525s  pd(2) = GeneralizedParetoDistribution(1, 3, 1);
525s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
525s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
525s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
525s ***** error <mean: requires a scalar probability distribution.> mean (pd)
525s ***** error <median: requires a scalar probability distribution.> median (pd)
525s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
525s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
525s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
525s ***** error <plot: requires a scalar probability distribution.> plot (pd)
525s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
525s ***** error <random: requires a scalar probability distribution.> random (pd)
525s ***** error <std: requires a scalar probability distribution.> std (pd)
525s ***** error <truncate: requires a scalar probability distribution.> ...
525s  truncate (pd, 2, 4)
525s ***** error <var: requires a scalar probability distribution.> var (pd)
525s 100 tests, 100 passed, 0 known failure, 0 skipped
525s [inst/dist_obj/HalfNormalDistribution.m]
525s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_obj/HalfNormalDistribution.m
525s ***** shared pd, t
525s  pd = HalfNormalDistribution (0, 1);
525s  t = truncate (pd, 2, 4);
525s ***** assert (cdf (pd, [0:5]), [0, 0.6827, 0.9545, 0.9973, 0.9999, 1], 1e-4);
525s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.9420, 1, 1], 1e-4);
525s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8664, 0.9545, 0.9973, 0.9999], 1e-4);
525s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.9420, 1], 1e-4);
525s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.2533, 0.5244, 0.8416, 1.2816, Inf], 1e-4);
525s ***** assert (icdf (t, [0:0.2:1]), [2, 2.0923, 2.2068, 2.3607, 2.6064, 4], 1e-4);
525s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5244, 0.8416, 1.2816, Inf, NaN], 1e-4);
525s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.2068, 2.3607, 2.6064, 4, NaN], 1e-4);
525s ***** assert (iqr (pd), 0.8317, 1e-4);
525s ***** assert (iqr (t), 0.4111, 1e-4);
525s ***** assert (mean (pd), 0.7979, 1e-4);
525s ***** assert (mean (t), 2.3706, 1e-4);
525s ***** assert (median (pd), 0.6745, 1e-4);
525s ***** assert (median (t), 2.2771, 1e-4);
525s ***** assert (pdf (pd, [0:5]), [0.7979, 0.4839, 0.1080, 0.0089, 0.0003, 0], 1e-4);
525s ***** assert (pdf (t, [0:5]), [0, 0, 2.3765, 0.1951, 0.0059, 0], 1e-4);
525s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.4839, 0.1080, 0.0089, 0.0003, NaN], 1e-4);
525s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 2.3765, 0.1951, 0.0059, NaN], 1e-4);
525s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
525s ***** assert (any (random (t, 1000, 1) < 2), false);
525s ***** assert (any (random (t, 1000, 1) > 4), false);
525s ***** assert (std (pd), 0.6028, 1e-4);
525s ***** assert (std (t), 0.3310, 1e-4);
525s ***** assert (var (pd), 0.3634, 1e-4);
525s ***** assert (var (t), 0.1096, 1e-4);
525s ***** error <HalfNormalDistribution: MU must be a real scalar.> ...
525s  HalfNormalDistribution(Inf, 1)
525s ***** error <HalfNormalDistribution: MU must be a real scalar.> ...
525s  HalfNormalDistribution(i, 1)
525s ***** error <HalfNormalDistribution: MU must be a real scalar.> ...
525s  HalfNormalDistribution("a", 1)
525s ***** error <HalfNormalDistribution: MU must be a real scalar.> ...
525s  HalfNormalDistribution([1, 2], 1)
525s ***** error <HalfNormalDistribution: MU must be a real scalar.> ...
525s  HalfNormalDistribution(NaN, 1)
525s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
525s  HalfNormalDistribution(1, 0)
525s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
525s  HalfNormalDistribution(1, -1)
525s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
525s  HalfNormalDistribution(1, Inf)
525s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
525s  HalfNormalDistribution(1, i)
525s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
525s  HalfNormalDistribution(1, "a")
525s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
525s  HalfNormalDistribution(1, [1, 2])
525s ***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
525s  HalfNormalDistribution(1, NaN)
525s ***** error <cdf: invalid argument for upper tail.> ...
525s  cdf (HalfNormalDistribution, 2, "uper")
525s ***** error <cdf: invalid argument for upper tail.> ...
525s  cdf (HalfNormalDistribution, 2, 3)
525s ***** shared x
525s  x = hnrnd (1, 1, [1, 100]);
525s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
525s  paramci (HalfNormalDistribution.fit (x, 1), "alpha")
525s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
525s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0)
526s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
526s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", 1)
526s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
526s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", [0.5 2])
526s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
526s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", "")
526s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
526s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", {0.05})
526s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
526s  paramci (HalfNormalDistribution.fit (x, 1), "parameter", "sigma", ...
526s           "alpha", {0.05})
526s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
526s  paramci (HalfNormalDistribution.fit (x, 1), ...
526s           "parameter", {"mu", "sigma", "param"})
526s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
526s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0.01, ...
526s           "parameter", {"mu", "sigma", "param"})
526s ***** error <paramci: unknown distribution parameter.> ...
526s  paramci (HalfNormalDistribution.fit (x, 1), "parameter", "param")
526s ***** error <paramci: unknown distribution parameter.> ...
526s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0.01, ...
526s           "parameter", "param")
526s ***** error <paramci: invalid NAME for optional argument.> ...
526s  paramci (HalfNormalDistribution.fit (x, 1),"NAME", "value")
526s ***** error <paramci: invalid NAME for optional argument.> ...
526s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0.01, ...
526s           "NAME", "value")
526s ***** error <paramci: invalid NAME for optional argument.> ...
526s  paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0.01, ...
526s           "parameter", "sigma", "NAME", "value")
526s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
526s  plot (HalfNormalDistribution, "Parent")
526s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
526s  plot (HalfNormalDistribution, "PlotType", 12)
526s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
526s  plot (HalfNormalDistribution, "PlotType", {"pdf", "cdf"})
526s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
526s  plot (HalfNormalDistribution, "PlotType", "pdfcdf")
526s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
526s  plot (HalfNormalDistribution, "Discrete", "pdfcdf")
526s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
526s  plot (HalfNormalDistribution, "Discrete", [1, 0])
526s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
526s  plot (HalfNormalDistribution, "Discrete", {true})
526s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
526s  plot (HalfNormalDistribution, "Parent", 12)
526s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
526s  plot (HalfNormalDistribution, "Parent", "hax")
526s ***** error <plot: invalid NAME for optional argument.> ...
526s  plot (HalfNormalDistribution, "invalidNAME", "pdf")
526s ***** error <plot: no fitted DATA to plot a probability plot.> ...
526s  plot (HalfNormalDistribution, "PlotType", "probability")
526s ***** error <proflik: no fitted data available.> ...
526s  proflik (HalfNormalDistribution, 2)
526s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
526s  proflik (HalfNormalDistribution.fit (x, 1), 3)
526s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
526s  proflik (HalfNormalDistribution.fit (x, 1), [1, 2])
526s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
526s  proflik (HalfNormalDistribution.fit (x, 1), {1})
526s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
526s  proflik (HalfNormalDistribution.fit (x, 1), 1)
526s ***** error <proflik: SETPARAM must be a numeric vector.> ...
526s  proflik (HalfNormalDistribution.fit (x, 1), 2, ones (2))
526s ***** error <proflik: missing VALUE for 'Display' argument.> ...
526s  proflik (HalfNormalDistribution.fit (x, 1), 2, "Display")
527s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
527s  proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", 1)
527s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
527s  proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", {1})
527s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
527s  proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", {"on"})
527s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
527s  proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", ["on"; "on"])
527s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
527s  proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", "onnn")
527s ***** error <proflik: invalid NAME for optional arguments.> ...
527s  proflik (HalfNormalDistribution.fit (x, 1), 2, "NAME", "on")
527s ***** error <proflik: invalid optional argument.> ...
527s  proflik (HalfNormalDistribution.fit (x, 1), 2, {"NAME"}, "on")
527s ***** error <proflik: invalid optional argument.> ...
527s  proflik (HalfNormalDistribution.fit (x, 1), 2, {[1 2 3 4]}, ...
527s           "Display", "on")
527s ***** error <truncate: missing input argument.> ...
527s  truncate (HalfNormalDistribution)
527s ***** error <truncate: missing input argument.> ...
527s  truncate (HalfNormalDistribution, 2)
527s ***** error <truncate: invalid lower upper limits.> ...
527s  truncate (HalfNormalDistribution, 4, 2)
527s ***** shared pd
527s  pd = HalfNormalDistribution(1, 1);
527s  pd(2) = HalfNormalDistribution(1, 3);
527s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
527s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
527s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
527s ***** error <mean: requires a scalar probability distribution.> mean (pd)
527s ***** error <median: requires a scalar probability distribution.> median (pd)
527s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
527s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
527s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
527s ***** error <plot: requires a scalar probability distribution.> plot (pd)
527s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
527s ***** error <random: requires a scalar probability distribution.> random (pd)
527s ***** error <std: requires a scalar probability distribution.> std (pd)
527s ***** error <truncate: requires a scalar probability distribution.> ...
527s  truncate (pd, 2, 4)
527s ***** error <var: requires a scalar probability distribution.> var (pd)
527s 96 tests, 96 passed, 0 known failure, 0 skipped
527s [inst/dist_obj/InverseGaussianDistribution.m]
527s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_obj/InverseGaussianDistribution.m
527s ***** shared pd, t
527s  pd = InverseGaussianDistribution (1, 1);
527s  t = truncate (pd, 2, 4);
527s ***** assert (cdf (pd, [0:5]), [0, 0.6681, 0.8855, 0.9532, 0.9791, 0.9901], 1e-4);
527s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7234, 1, 1], 1e-4);
527s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8108, 0.8855, 0.9532, 0.9791], 1e-4);
527s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7234, 1], 1e-4);
527s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.3320, 0.5411, 0.8483, 1.4479, Inf], 1e-4);
527s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1889, 2.4264, 2.7417, 3.1993, 4], 1e-4);
527s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5411, 0.8483, 1.4479, Inf, NaN], 1e-4);
527s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4264, 2.7417, 3.1993, 4, NaN], 1e-4);
527s ***** assert (iqr (pd), 0.8643, 1e-4);
527s ***** assert (iqr (t), 0.8222, 1e-4);
527s ***** assert (mean (pd), 1);
527s ***** assert (mean (t), 2.6953, 1e-4);
527s ***** assert (median (pd), 0.6758, 1e-4);
527s ***** assert (median (t), 2.5716, 1e-4);
527s ***** assert (pdf (pd, [0:5]), [0, 0.3989, 0.1098, 0.0394, 0.0162, 0.0072], 1e-4);
527s ***** assert (pdf (t, [0:5]), [0, 0, 1.1736, 0.4211, 0.1730, 0], 1e-4);
527s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3989, 0.1098, 0.0394, 0.0162, NaN], 1e-4);
527s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.1736, 0.4211, 0.1730, NaN], 1e-4);
527s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
527s ***** assert (any (random (t, 1000, 1) < 2), false);
527s ***** assert (any (random (t, 1000, 1) > 4), false);
527s ***** assert (std (pd), 1);
527s ***** assert (std (t), 0.5332, 1e-4);
527s ***** assert (var (pd), 1);
527s ***** assert (var (t), 0.2843, 1e-4);
527s ***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
527s  InverseGaussianDistribution(0, 1)
527s ***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
527s  InverseGaussianDistribution(Inf, 1)
527s ***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
527s  InverseGaussianDistribution(i, 1)
527s ***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
527s  InverseGaussianDistribution("a", 1)
527s ***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
527s  InverseGaussianDistribution([1, 2], 1)
527s ***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
527s  InverseGaussianDistribution(NaN, 1)
527s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
527s  InverseGaussianDistribution(1, 0)
527s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
527s  InverseGaussianDistribution(1, -1)
527s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
527s  InverseGaussianDistribution(1, Inf)
527s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
527s  InverseGaussianDistribution(1, i)
527s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
527s  InverseGaussianDistribution(1, "a")
527s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
527s  InverseGaussianDistribution(1, [1, 2])
527s ***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
527s  InverseGaussianDistribution(1, NaN)
527s ***** error <cdf: invalid argument for upper tail.> ...
527s  cdf (InverseGaussianDistribution, 2, "uper")
527s ***** error <cdf: invalid argument for upper tail.> ...
527s  cdf (InverseGaussianDistribution, 2, 3)
527s ***** shared x
527s  x = invgrnd (1, 1, [1, 100]);
527s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
527s  paramci (InverseGaussianDistribution.fit (x), "alpha")
527s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
527s  paramci (InverseGaussianDistribution.fit (x), "alpha", 0)
527s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
527s  paramci (InverseGaussianDistribution.fit (x), "alpha", 1)
527s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
527s  paramci (InverseGaussianDistribution.fit (x), "alpha", [0.5 2])
527s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
527s  paramci (InverseGaussianDistribution.fit (x), "alpha", "")
527s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
527s  paramci (InverseGaussianDistribution.fit (x), "alpha", {0.05})
527s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
527s  paramci (InverseGaussianDistribution.fit (x), "parameter", "mu", ...
527s           "alpha", {0.05})
527s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
527s  paramci (InverseGaussianDistribution.fit (x), ...
527s           "parameter", {"mu", "lambda", "param"})
527s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
527s  paramci (InverseGaussianDistribution.fit (x), "alpha", 0.01, ...
527s           "parameter", {"mu", "lambda", "param"})
527s ***** error <paramci: unknown distribution parameter.> ...
527s  paramci (InverseGaussianDistribution.fit (x), "parameter", "param")
528s ***** error <paramci: unknown distribution parameter.> ...
528s  paramci (InverseGaussianDistribution.fit (x), "alpha", 0.01, ...
528s           "parameter", "param")
528s ***** error <paramci: invalid NAME for optional argument.> ...
528s  paramci (InverseGaussianDistribution.fit (x), "NAME", "value")
528s ***** error <paramci: invalid NAME for optional argument.> ...
528s  paramci (InverseGaussianDistribution.fit (x), "alpha", 0.01, "NAME", "value")
528s ***** error <paramci: invalid NAME for optional argument.> ...
528s  paramci (InverseGaussianDistribution.fit (x), "alpha", 0.01, ...
528s           "parameter", "mu", "NAME", "value")
528s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
528s  plot (InverseGaussianDistribution, "Parent")
528s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
528s  plot (InverseGaussianDistribution, "PlotType", 12)
528s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
528s  plot (InverseGaussianDistribution, "PlotType", {"pdf", "cdf"})
528s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
528s  plot (InverseGaussianDistribution, "PlotType", "pdfcdf")
528s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
528s  plot (InverseGaussianDistribution, "Discrete", "pdfcdf")
528s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
528s  plot (InverseGaussianDistribution, "Discrete", [1, 0])
528s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
528s  plot (InverseGaussianDistribution, "Discrete", {true})
528s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
528s  plot (InverseGaussianDistribution, "Parent", 12)
528s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
528s  plot (InverseGaussianDistribution, "Parent", "hax")
528s ***** error <plot: invalid NAME for optional argument.> ...
528s  plot (InverseGaussianDistribution, "invalidNAME", "pdf")
528s ***** error <plot: no fitted DATA to plot a probability plot.> ...
528s  plot (InverseGaussianDistribution, "PlotType", "probability")
528s ***** error <proflik: no fitted data available.> ...
528s  proflik (InverseGaussianDistribution, 2)
528s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
528s  proflik (InverseGaussianDistribution.fit (x), 3)
528s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
528s  proflik (InverseGaussianDistribution.fit (x), [1, 2])
528s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
528s  proflik (InverseGaussianDistribution.fit (x), {1})
528s ***** error <proflik: SETPARAM must be a numeric vector.> ...
528s  proflik (InverseGaussianDistribution.fit (x), 1, ones (2))
528s ***** error <proflik: missing VALUE for 'Display' argument.> ...
528s  proflik (InverseGaussianDistribution.fit (x), 1, "Display")
528s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
528s  proflik (InverseGaussianDistribution.fit (x), 1, "Display", 1)
528s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
528s  proflik (InverseGaussianDistribution.fit (x), 1, "Display", {1})
528s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
528s  proflik (InverseGaussianDistribution.fit (x), 1, "Display", {"on"})
528s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
528s  proflik (InverseGaussianDistribution.fit (x), 1, "Display", ["on"; "on"])
528s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
528s  proflik (InverseGaussianDistribution.fit (x), 1, "Display", "onnn")
528s ***** error <proflik: invalid NAME for optional arguments.> ...
528s  proflik (InverseGaussianDistribution.fit (x), 1, "NAME", "on")
528s ***** error <proflik: invalid optional argument.> ...
528s  proflik (InverseGaussianDistribution.fit (x), 1, {"NAME"}, "on")
528s ***** error <proflik: invalid optional argument.> ...
528s  proflik (InverseGaussianDistribution.fit (x), 1, {[1 2 3]}, "Display", "on")
528s ***** error <truncate: missing input argument.> ...
528s  truncate (InverseGaussianDistribution)
528s ***** error <truncate: missing input argument.> ...
528s  truncate (InverseGaussianDistribution, 2)
528s ***** error <truncate: invalid lower upper limits.> ...
528s  truncate (InverseGaussianDistribution, 4, 2)
528s ***** shared pd
528s  pd = InverseGaussianDistribution(1, 1);
528s  pd(2) = InverseGaussianDistribution(1, 3);
528s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
528s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
528s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
528s ***** error <mean: requires a scalar probability distribution.> mean (pd)
528s ***** error <median: requires a scalar probability distribution.> median (pd)
528s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
528s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
528s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
528s ***** error <plot: requires a scalar probability distribution.> plot (pd)
528s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
528s ***** error <random: requires a scalar probability distribution.> random (pd)
528s ***** error <std: requires a scalar probability distribution.> std (pd)
528s ***** error <truncate: requires a scalar probability distribution.> ...
528s  truncate (pd, 2, 4)
528s ***** error <var: requires a scalar probability distribution.> var (pd)
528s 96 tests, 96 passed, 0 known failure, 0 skipped
528s [inst/dist_obj/LogisticDistribution.m]
528s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_obj/LogisticDistribution.m
528s ***** shared pd, t
528s  pd = LogisticDistribution (0, 1);
528s  t = truncate (pd, 2, 4);
528s ***** assert (cdf (pd, [0:5]), [0.5, 0.7311, 0.8808, 0.9526, 0.9820, 0.9933], 1e-4);
528s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7091, 1, 1], 1e-4);
528s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8176, 0.8808, 0.9526, 0.9820], 1e-4);
528s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7091, 1], 1e-4);
528s ***** assert (icdf (pd, [0:0.2:1]), [-Inf, -1.3863, -0.4055, 0.4055, 1.3863, Inf], 1e-4);
528s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2088, 2.4599, 2.7789, 3.2252, 4], 1e-4);
528s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, -0.4055, 0.4055, 1.3863, Inf, NaN], 1e-4);
528s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4599, 2.7789, 3.2252, 4, NaN], 1e-4);
528s ***** assert (iqr (pd), 2.1972, 1e-4);
528s ***** assert (iqr (t), 0.8286, 1e-4);
528s ***** assert (mean (pd), 0, 1e-4);
528s ***** assert (mean (t), 2.7193, 1e-4);
528s ***** assert (median (pd), 0);
528s ***** assert (median (t), 2.6085, 1e-4);
528s ***** assert (pdf (pd, [0:5]), [0.25, 0.1966, 0.1050, 0.0452, 0.0177, 0.0066], 1e-4);
528s ***** assert (pdf (t, [0:5]), [0, 0, 1.0373, 0.4463, 0.1745, 0], 1e-4);
528s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0.1966, 0.1966, 0.1050, 0.0452, 0.0177, NaN], 1e-4);
528s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.0373, 0.4463, 0.1745, NaN], 1e-4);
528s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
528s ***** assert (any (random (t, 1000, 1) < 2), false);
528s ***** assert (any (random (t, 1000, 1) > 4), false);
528s ***** assert (std (pd), 1.8138, 1e-4);
528s ***** assert (std (t), 0.5320, 1e-4);
528s ***** assert (var (pd), 3.2899, 1e-4);
528s ***** assert (var (t), 0.2830, 1e-4);
528s ***** error <LogisticDistribution: MU must be a finite real scalar.> ...
528s  LogisticDistribution(Inf, 1)
528s ***** error <LogisticDistribution: MU must be a finite real scalar.> ...
528s  LogisticDistribution(i, 1)
528s ***** error <LogisticDistribution: MU must be a finite real scalar.> ...
528s  LogisticDistribution("a", 1)
528s ***** error <LogisticDistribution: MU must be a finite real scalar.> ...
528s  LogisticDistribution([1, 2], 1)
528s ***** error <LogisticDistribution: MU must be a finite real scalar.> ...
528s  LogisticDistribution(NaN, 1)
528s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
528s  LogisticDistribution(1, 0)
528s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
528s  LogisticDistribution(1, -1)
528s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
528s  LogisticDistribution(1, Inf)
528s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
528s  LogisticDistribution(1, i)
528s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
528s  LogisticDistribution(1, "a")
528s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
528s  LogisticDistribution(1, [1, 2])
528s ***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
528s  LogisticDistribution(1, NaN)
528s ***** error <cdf: invalid argument for upper tail.> ...
528s  cdf (LogisticDistribution, 2, "uper")
528s ***** error <cdf: invalid argument for upper tail.> ...
528s  cdf (LogisticDistribution, 2, 3)
528s ***** shared x
528s  x = logirnd (1, 1, [1, 100]);
528s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
528s  paramci (LogisticDistribution.fit (x), "alpha")
528s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
528s  paramci (LogisticDistribution.fit (x), "alpha", 0)
528s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
528s  paramci (LogisticDistribution.fit (x), "alpha", 1)
528s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
528s  paramci (LogisticDistribution.fit (x), "alpha", [0.5 2])
528s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
528s  paramci (LogisticDistribution.fit (x), "alpha", "")
528s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
528s  paramci (LogisticDistribution.fit (x), "alpha", {0.05})
529s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
529s  paramci (LogisticDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
529s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
529s  paramci (LogisticDistribution.fit (x), "parameter", {"mu", "sigma", "param"})
529s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
529s  paramci (LogisticDistribution.fit (x), "alpha", 0.01, ...
529s           "parameter", {"mu", "sigma", "param"})
529s ***** error <paramci: unknown distribution parameter.> ...
529s  paramci (LogisticDistribution.fit (x), "parameter", "param")
529s ***** error <paramci: unknown distribution parameter.> ...
529s  paramci (LogisticDistribution.fit (x), "alpha", 0.01, "parameter", "param")
529s ***** error <paramci: invalid NAME for optional argument.> ...
529s  paramci (LogisticDistribution.fit (x), "NAME", "value")
529s ***** error <paramci: invalid NAME for optional argument.> ...
529s  paramci (LogisticDistribution.fit (x), "alpha", 0.01, "NAME", "value")
529s ***** error <paramci: invalid NAME for optional argument.> ...
529s  paramci (LogisticDistribution.fit (x), "alpha", 0.01, "parameter", "mu", ...
529s           "NAME", "value")
529s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
529s  plot (LogisticDistribution, "Parent")
529s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
529s  plot (LogisticDistribution, "PlotType", 12)
529s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
529s  plot (LogisticDistribution, "PlotType", {"pdf", "cdf"})
529s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
529s  plot (LogisticDistribution, "PlotType", "pdfcdf")
529s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
529s  plot (LogisticDistribution, "Discrete", "pdfcdf")
529s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
529s  plot (LogisticDistribution, "Discrete", [1, 0])
529s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
529s  plot (LogisticDistribution, "Discrete", {true})
529s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
529s  plot (LogisticDistribution, "Parent", 12)
529s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
529s  plot (LogisticDistribution, "Parent", "hax")
529s ***** error <plot: invalid NAME for optional argument.> ...
529s  plot (LogisticDistribution, "invalidNAME", "pdf")
529s ***** error <plot: no fitted DATA to plot a probability plot.> ...
529s  plot (LogisticDistribution, "PlotType", "probability")
529s ***** error <proflik: no fitted data available.> ...
529s  proflik (LogisticDistribution, 2)
529s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
529s  proflik (LogisticDistribution.fit (x), 3)
529s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
529s  proflik (LogisticDistribution.fit (x), [1, 2])
529s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
529s  proflik (LogisticDistribution.fit (x), {1})
529s ***** error <proflik: SETPARAM must be a numeric vector.> ...
529s  proflik (LogisticDistribution.fit (x), 1, ones (2))
529s ***** error <proflik: missing VALUE for 'Display' argument.> ...
529s  proflik (LogisticDistribution.fit (x), 1, "Display")
529s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
529s  proflik (LogisticDistribution.fit (x), 1, "Display", 1)
529s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
529s  proflik (LogisticDistribution.fit (x), 1, "Display", {1})
529s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
529s  proflik (LogisticDistribution.fit (x), 1, "Display", {"on"})
530s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
530s  proflik (LogisticDistribution.fit (x), 1, "Display", ["on"; "on"])
530s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
530s  proflik (LogisticDistribution.fit (x), 1, "Display", "onnn")
530s ***** error <proflik: invalid NAME for optional arguments.> ...
530s  proflik (LogisticDistribution.fit (x), 1, "NAME", "on")
530s ***** error <proflik: invalid optional argument.> ...
530s  proflik (LogisticDistribution.fit (x), 1, {"NAME"}, "on")
530s ***** error <proflik: invalid optional argument.> ...
530s  proflik (LogisticDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
530s ***** error <truncate: missing input argument.> ...
530s  truncate (LogisticDistribution)
530s ***** error <truncate: missing input argument.> ...
530s  truncate (LogisticDistribution, 2)
530s ***** error <truncate: invalid lower upper limits.> ...
530s  truncate (LogisticDistribution, 4, 2)
530s ***** shared pd
530s  pd = LogisticDistribution(1, 1);
530s  pd(2) = LogisticDistribution(1, 3);
530s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
530s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
530s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
530s ***** error <mean: requires a scalar probability distribution.> mean (pd)
530s ***** error <median: requires a scalar probability distribution.> median (pd)
530s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
530s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
530s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
530s ***** error <plot: requires a scalar probability distribution.> plot (pd)
530s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
530s ***** error <random: requires a scalar probability distribution.> random (pd)
530s ***** error <std: requires a scalar probability distribution.> std (pd)
530s ***** error <truncate: requires a scalar probability distribution.> ...
530s  truncate (pd, 2, 4)
530s ***** error <var: requires a scalar probability distribution.> var (pd)
530s 95 tests, 95 passed, 0 known failure, 0 skipped
530s [inst/dist_obj/LoglogisticDistribution.m]
530s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_obj/LoglogisticDistribution.m
530s ***** demo
530s  ## Generate a data set of 5000 random samples from a Log-logistic
530s  ## distribution with parameters mu = 0 and sigma = 1.  Fit a Log-logistic
530s  ## distribution to this data and plot a PDF of the fitted distribution
530s  ## superimposed on a histogram of the data.
530s 
530s  pd_fixed = makedist ("Loglogistic", "mu", 0, "sigma", 1)
530s  rand ("seed", 2);
530s  data = random (pd_fixed, 5000, 1);
530s  pd_fitted = fitdist (data, "Loglogistic")
530s  plot (pd_fitted)
530s  msg = "Fitted Log-logistic distribution with mu = %0.2f and sigma = %0.2f";
530s  title (sprintf (msg, pd_fitted.mu, pd_fitted.sigma))
530s ***** shared pd, t
530s  pd = LoglogisticDistribution;
530s  t = truncate (pd, 2, 4);
530s ***** assert (cdf (pd, [0:5]), [0, 0.5, 0.6667, 0.75, 0.8, 0.8333], 1e-4);
530s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.625, 1, 1], 1e-4);
530s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.6, 0.6667, 0.75, 0.8], 1e-4);
530s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.625, 1], 1e-4);
530s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.25, 0.6667, 1.5, 4, Inf], 1e-4);
530s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2609, 2.5714, 2.9474, 3.4118, 4], 1e-4);
530s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.6667, 1.5, 4, Inf, NaN], 1e-4);
530s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5714, 2.9474, 3.4118, 4, NaN], 1e-4);
530s ***** assert (iqr (pd), 2.6667, 1e-4);
530s ***** assert (iqr (t), 0.9524, 1e-4);
530s ***** assert (mean (pd), Inf);
530s ***** assert (mean (t), 2.8312, 1e-4);
530s ***** assert (median (pd), 1, 1e-4);
530s ***** assert (median (t), 2.75, 1e-4);
530s ***** assert (pdf (pd, [0:5]), [0, 0.25, 0.1111, 0.0625, 0.04, 0.0278], 1e-4);
530s ***** assert (pdf (t, [0:5]), [0, 0, 0.8333, 0.4687, 0.3, 0], 1e-4);
530s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.25, 0.1111, 0.0625, 0.04, NaN], 1e-4);
530s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.8333, 0.4687, 0.3, NaN], 1e-4);
530s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
530s ***** assert (any (random (t, 1000, 1) < 2), false);
530s ***** assert (any (random (t, 1000, 1) > 4), false);
530s ***** assert (std (pd), Inf);
530s ***** assert (std (t), 0.5674, 1e-4);
530s ***** assert (var (pd), Inf);
530s ***** assert (var (t), 0.3220, 1e-4);
530s ***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
530s  LoglogisticDistribution(Inf, 1)
530s ***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
530s  LoglogisticDistribution(i, 1)
530s ***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
530s  LoglogisticDistribution("a", 1)
530s ***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
530s  LoglogisticDistribution([1, 2], 1)
530s ***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
530s  LoglogisticDistribution(NaN, 1)
530s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
530s  LoglogisticDistribution(1, 0)
530s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
530s  LoglogisticDistribution(1, -1)
530s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
530s  LoglogisticDistribution(1, Inf)
530s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
530s  LoglogisticDistribution(1, i)
530s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
530s  LoglogisticDistribution(1, "a")
530s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
530s  LoglogisticDistribution(1, [1, 2])
530s ***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
530s  LoglogisticDistribution(1, NaN)
530s ***** error <cdf: invalid argument for upper tail.> ...
530s  cdf (LoglogisticDistribution, 2, "uper")
530s ***** error <cdf: invalid argument for upper tail.> ...
530s  cdf (LoglogisticDistribution, 2, 3)
530s ***** shared x
530s  x = loglrnd (1, 1, [1, 100]);
530s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
530s  paramci (LoglogisticDistribution.fit (x), "alpha")
530s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
530s  paramci (LoglogisticDistribution.fit (x), "alpha", 0)
530s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
530s  paramci (LoglogisticDistribution.fit (x), "alpha", 1)
530s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
530s  paramci (LoglogisticDistribution.fit (x), "alpha", [0.5 2])
530s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
530s  paramci (LoglogisticDistribution.fit (x), "alpha", "")
530s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
530s  paramci (LoglogisticDistribution.fit (x), "alpha", {0.05})
530s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
530s  paramci (LoglogisticDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
531s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
531s  paramci (LoglogisticDistribution.fit (x), "parameter", {"mu", "sigma", "pa"})
531s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
531s  paramci (LoglogisticDistribution.fit (x), "alpha", 0.01, ...
531s           "parameter", {"mu", "sigma", "param"})
531s ***** error <paramci: unknown distribution parameter.> ...
531s  paramci (LoglogisticDistribution.fit (x), "parameter", "param")
531s ***** error <paramci: unknown distribution parameter.> ...
531s  paramci (LoglogisticDistribution.fit (x), "alpha", 0.01, "parameter", "parm")
531s ***** error <paramci: invalid NAME for optional argument.> ...
531s  paramci (LoglogisticDistribution.fit (x), "NAME", "value")
531s ***** error <paramci: invalid NAME for optional argument.> ...
531s  paramci (LoglogisticDistribution.fit (x), "alpha", 0.01, "NAME", "value")
531s ***** error <paramci: invalid NAME for optional argument.> ...
531s  paramci (LoglogisticDistribution.fit (x), "alpha", 0.01, ...
531s           "parameter", "mu", "NAME", "value")
531s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
531s  plot (LoglogisticDistribution, "Parent")
531s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
531s  plot (LoglogisticDistribution, "PlotType", 12)
531s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
531s  plot (LoglogisticDistribution, "PlotType", {"pdf", "cdf"})
531s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
531s  plot (LoglogisticDistribution, "PlotType", "pdfcdf")
531s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
531s  plot (LoglogisticDistribution, "Discrete", "pdfcdf")
531s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
531s  plot (LoglogisticDistribution, "Discrete", [1, 0])
531s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
531s  plot (LoglogisticDistribution, "Discrete", {true})
531s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
531s  plot (LoglogisticDistribution, "Parent", 12)
531s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
531s  plot (LoglogisticDistribution, "Parent", "hax")
531s ***** error <plot: invalid NAME for optional argument.> ...
531s  plot (LoglogisticDistribution, "invalidNAME", "pdf")
531s ***** error <plot: no fitted DATA to plot a probability plot.> ...
531s  plot (LoglogisticDistribution, "PlotType", "probability")
531s ***** error <proflik: no fitted data available.> ...
531s  proflik (LoglogisticDistribution, 2)
531s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
531s  proflik (LoglogisticDistribution.fit (x), 3)
531s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
531s  proflik (LoglogisticDistribution.fit (x), [1, 2])
531s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
531s  proflik (LoglogisticDistribution.fit (x), {1})
531s ***** error <proflik: SETPARAM must be a numeric vector.> ...
531s  proflik (LoglogisticDistribution.fit (x), 1, ones (2))
531s ***** error <proflik: missing VALUE for 'Display' argument.> ...
531s  proflik (LoglogisticDistribution.fit (x), 1, "Display")
531s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
531s  proflik (LoglogisticDistribution.fit (x), 1, "Display", 1)
531s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
531s  proflik (LoglogisticDistribution.fit (x), 1, "Display", {1})
531s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
531s  proflik (LoglogisticDistribution.fit (x), 1, "Display", {"on"})
532s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
532s  proflik (LoglogisticDistribution.fit (x), 1, "Display", ["on"; "on"])
532s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
532s  proflik (LoglogisticDistribution.fit (x), 1, "Display", "onnn")
532s ***** error <proflik: invalid NAME for optional arguments.> ...
532s  proflik (LoglogisticDistribution.fit (x), 1, "NAME", "on")
532s ***** error <proflik: invalid optional argument.> ...
532s  proflik (LoglogisticDistribution.fit (x), 1, {"NAME"}, "on")
532s ***** error <proflik: invalid optional argument.> ...
532s  proflik (LoglogisticDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
532s ***** error <truncate: missing input argument.> ...
532s  truncate (LoglogisticDistribution)
532s ***** error <truncate: missing input argument.> ...
532s  truncate (LoglogisticDistribution, 2)
532s ***** error <truncate: invalid lower upper limits.> ...
532s  truncate (LoglogisticDistribution, 4, 2)
532s ***** shared pd
532s  pd = LoglogisticDistribution(1, 1);
532s  pd(2) = LoglogisticDistribution(1, 3);
532s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
532s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
532s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
532s ***** error <mean: requires a scalar probability distribution.> mean (pd)
532s ***** error <median: requires a scalar probability distribution.> median (pd)
532s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
532s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
532s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
532s ***** error <plot: requires a scalar probability distribution.> plot (pd)
532s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
532s ***** error <random: requires a scalar probability distribution.> random (pd)
532s ***** error <std: requires a scalar probability distribution.> std (pd)
532s ***** error <truncate: requires a scalar probability distribution.> ...
532s  truncate (pd, 2, 4)
532s ***** error <var: requires a scalar probability distribution.> var (pd)
532s 95 tests, 95 passed, 0 known failure, 0 skipped
532s [inst/dist_obj/LognormalDistribution.m]
532s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_obj/LognormalDistribution.m
532s ***** demo
532s  ## Generate a data set of 5000 random samples from a Lognormal distribution with
532s  ## parameters mu = 0 and sigma = 1.  Fit a Lognormal distribution to this data and plot
532s  ## a PDF of the fitted distribution superimposed on a histogram of the data.
532s 
532s  pd_fixed = makedist ("Lognormal", "mu", 0, "sigma", 1)
532s  randn ("seed", 2);
532s  data = random (pd_fixed, 5000, 1);
532s  pd_fitted = fitdist (data, "Lognormal")
532s  plot (pd_fitted)
532s  msg = "Fitted Lognormal distribution with mu = %0.2f and sigma = %0.2f";
532s  title (sprintf (msg, pd_fitted.mu, pd_fitted.sigma))
532s ***** shared pd, t
532s  pd = LognormalDistribution;
532s  t = truncate (pd, 2, 4);
532s ***** assert (cdf (pd, [0:5]), [0, 0.5, 0.7559, 0.8640, 0.9172, 0.9462], 1e-4);
532s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.6705, 1, 1], 1e-4);
532s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.6574, 0.7559, 0.8640, 0.9172], 1e-4);
532s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.6705, 1], 1e-4);
532s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.4310, 0.7762, 1.2883, 2.3201, Inf], 1e-4);
532s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2256, 2.5015, 2.8517, 3.3199, 4], 1e-4);
532s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.7762, 1.2883, 2.3201, Inf, NaN], 1e-4);
532s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5015, 2.8517, 3.3199, 4, NaN], 1e-4);
532s ***** assert (iqr (pd), 1.4536, 1e-4);
532s ***** assert (iqr (t), 0.8989, 1e-4);
532s ***** assert (mean (pd), 1.6487, 1e-4);
532s ***** assert (mean (t), 2.7692, 1e-4);
532s ***** assert (median (pd), 1, 1e-4);
532s ***** assert (median (t), 2.6653, 1e-4);
532s ***** assert (pdf (pd, [0:5]), [0, 0.3989, 0.1569, 0.0727, 0.0382, 0.0219], 1e-4);
532s ***** assert (pdf (t, [0:5]), [0, 0, 0.9727, 0.4509, 0.2366, 0], 1e-4);
532s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3989, 0.1569, 0.0727, 0.0382, NaN], 1e-4);
532s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.9727, 0.4509, 0.2366, NaN], 1e-4);
532s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
532s ***** assert (any (random (t, 1000, 1) < 2), false);
532s ***** assert (any (random (t, 1000, 1) > 4), false);
532s ***** assert (std (pd), 2.1612, 1e-4);
532s ***** assert (std (t), 0.5540, 1e-4);
532s ***** assert (var (pd), 4.6708, 1e-4);
532s ***** assert (var (t), 0.3069, 1e-4);
532s ***** error <LognormalDistribution: MU must be a real scalar.> ...
532s  LognormalDistribution(Inf, 1)
532s ***** error <LognormalDistribution: MU must be a real scalar.> ...
532s  LognormalDistribution(i, 1)
532s ***** error <LognormalDistribution: MU must be a real scalar.> ...
532s  LognormalDistribution("a", 1)
532s ***** error <LognormalDistribution: MU must be a real scalar.> ...
532s  LognormalDistribution([1, 2], 1)
532s ***** error <LognormalDistribution: MU must be a real scalar.> ...
532s  LognormalDistribution(NaN, 1)
532s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
532s  LognormalDistribution(1, 0)
532s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
532s  LognormalDistribution(1, -1)
532s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
532s  LognormalDistribution(1, Inf)
532s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
532s  LognormalDistribution(1, i)
532s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
532s  LognormalDistribution(1, "a")
532s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
532s  LognormalDistribution(1, [1, 2])
532s ***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
532s  LognormalDistribution(1, NaN)
532s ***** error <cdf: invalid argument for upper tail.> ...
532s  cdf (LognormalDistribution, 2, "uper")
532s ***** error <cdf: invalid argument for upper tail.> ...
532s  cdf (LognormalDistribution, 2, 3)
532s ***** shared x
532s  randn ("seed", 1);
532s  x = lognrnd (1, 1, [1, 100]);
532s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
532s  paramci (LognormalDistribution.fit (x), "alpha")
532s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
532s  paramci (LognormalDistribution.fit (x), "alpha", 0)
532s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
532s  paramci (LognormalDistribution.fit (x), "alpha", 1)
532s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
532s  paramci (LognormalDistribution.fit (x), "alpha", [0.5 2])
533s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
533s  paramci (LognormalDistribution.fit (x), "alpha", "")
533s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
533s  paramci (LognormalDistribution.fit (x), "alpha", {0.05})
533s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
533s  paramci (LognormalDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
533s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
533s  paramci (LognormalDistribution.fit (x), "parameter", {"mu", "sigma", "parm"})
533s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
533s  paramci (LognormalDistribution.fit (x), "alpha", 0.01, ...
533s           "parameter", {"mu", "sigma", "param"})
533s ***** error <paramci: unknown distribution parameter.> ...
533s  paramci (LognormalDistribution.fit (x), "parameter", "param")
533s ***** error <paramci: unknown distribution parameter.> ...
533s  paramci (LognormalDistribution.fit (x), "alpha", 0.01, "parameter", "param")
533s ***** error <paramci: invalid NAME for optional argument.> ...
533s  paramci (LognormalDistribution.fit (x), "NAME", "value")
533s ***** error <paramci: invalid NAME for optional argument.> ...
533s  paramci (LognormalDistribution.fit (x), "alpha", 0.01, "NAME", "value")
533s ***** error <paramci: invalid NAME for optional argument.> ...
533s  paramci (LognormalDistribution.fit (x), "alpha", 0.01, "parameter", "mu", ...
533s           "NAME", "value")
534s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
534s  plot (LognormalDistribution, "Parent")
534s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
534s  plot (LognormalDistribution, "PlotType", 12)
534s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
534s  plot (LognormalDistribution, "PlotType", {"pdf", "cdf"})
534s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
534s  plot (LognormalDistribution, "PlotType", "pdfcdf")
534s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
534s  plot (LognormalDistribution, "Discrete", "pdfcdf")
534s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
534s  plot (LognormalDistribution, "Discrete", [1, 0])
534s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
534s  plot (LognormalDistribution, "Discrete", {true})
534s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
534s  plot (LognormalDistribution, "Parent", 12)
534s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
534s  plot (LognormalDistribution, "Parent", "hax")
534s ***** error <plot: invalid NAME for optional argument.> ...
534s  plot (LognormalDistribution, "invalidNAME", "pdf")
534s ***** error <plot: no fitted DATA to plot a probability plot.> ...
534s  plot (LognormalDistribution, "PlotType", "probability")
534s ***** error <proflik: no fitted data available.> ...
534s  proflik (LognormalDistribution, 2)
534s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
534s  proflik (LognormalDistribution.fit (x), 3)
534s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
534s  proflik (LognormalDistribution.fit (x), [1, 2])
534s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
534s  proflik (LognormalDistribution.fit (x), {1})
534s ***** error <proflik: SETPARAM must be a numeric vector.> ...
534s  proflik (LognormalDistribution.fit (x), 1, ones (2))
534s ***** error <proflik: missing VALUE for 'Display' argument.> ...
534s  proflik (LognormalDistribution.fit (x), 1, "Display")
534s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
534s  proflik (LognormalDistribution.fit (x), 1, "Display", 1)
534s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
534s  proflik (LognormalDistribution.fit (x), 1, "Display", {1})
534s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
534s  proflik (LognormalDistribution.fit (x), 1, "Display", {"on"})
534s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
534s  proflik (LognormalDistribution.fit (x), 1, "Display", ["on"; "on"])
535s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
535s  proflik (LognormalDistribution.fit (x), 1, "Display", "onnn")
535s ***** error <proflik: invalid NAME for optional arguments.> ...
535s  proflik (LognormalDistribution.fit (x), 1, "NAME", "on")
535s ***** error <proflik: invalid optional argument.> ...
535s  proflik (LognormalDistribution.fit (x), 1, {"NAME"}, "on")
535s ***** error <proflik: invalid optional argument.> ...
535s  proflik (LognormalDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
535s ***** error <truncate: missing input argument.> ...
535s  truncate (LognormalDistribution)
535s ***** error <truncate: missing input argument.> ...
535s  truncate (LognormalDistribution, 2)
535s ***** error <truncate: invalid lower upper limits.> ...
535s  truncate (LognormalDistribution, 4, 2)
535s ***** shared pd
535s  pd = LognormalDistribution(1, 1);
535s  pd(2) = LognormalDistribution(1, 3);
535s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
535s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
535s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
535s ***** error <mean: requires a scalar probability distribution.> mean (pd)
535s ***** error <median: requires a scalar probability distribution.> median (pd)
535s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
535s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
535s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
535s ***** error <plot: requires a scalar probability distribution.> plot (pd)
535s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
535s ***** error <random: requires a scalar probability distribution.> random (pd)
535s ***** error <std: requires a scalar probability distribution.> std (pd)
535s ***** error <truncate: requires a scalar probability distribution.> ...
535s  truncate (pd, 2, 4)
535s ***** error <var: requires a scalar probability distribution.> var (pd)
535s 95 tests, 95 passed, 0 known failure, 0 skipped
535s [inst/dist_obj/LoguniformDistribution.m]
535s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_obj/LoguniformDistribution.m
535s ***** demo
535s  ## Generate a data set of 5000 random samples from a Log-uniform distribution with
535s  ## parameters Lower = 1 and Upper = 10.  Plot a PDF of the distribution superimposed
535s  ## on a histogram of the data.
535s 
535s  pd_fixed = makedist ("Loguniform", "Lower", 1, "Upper", 10);
535s  rand ("seed", 2);
535s  data = random (pd_fixed, 5000, 1);
535s  plot (pd_fixed)
535s  hold on
535s  hist (data, 50)
535s  hold off
535s  msg = "Log-uniform distribution with Lower = %0.2f and Upper = %0.2f";
535s  title (sprintf (msg, pd_fixed.Lower, pd_fixed.Upper))
535s ***** shared pd, t
535s  pd = LoguniformDistribution (1, 4);
535s  t = truncate (pd, 2, 4);
535s ***** assert (cdf (pd, [0, 1, 2, 3, 4, 5]), [0, 0, 0.5, 0.7925, 1, 1], 1e-4);
535s ***** assert (cdf (t, [0, 1, 2, 3, 4, 5]), [0, 0, 0, 0.5850, 1, 1], 1e-4);
535s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.2925, 0.5, 0.7925, 1], 1e-4);
535s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.5850, 1], 1e-4);
535s ***** assert (icdf (pd, [0:0.2:1]), [1, 1.3195, 1.7411, 2.2974, 3.0314, 4], 1e-4);
535s ***** assert (icdf (t, [0:0.2:1]), [2, 2.2974, 2.6390, 3.0314, 3.4822, 4], 1e-4);
535s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1.7411, 2.2974, 3.0314, 4, NaN], 1e-4);
535s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.6390, 3.0314, 3.4822, 4, NaN], 1e-4);
535s ***** assert (iqr (pd), 1.4142, 1e-4);
535s ***** assert (iqr (t), 0.9852, 1e-4);
535s ***** assert (mean (pd), 2.1640, 1e-4);
535s ***** assert (mean (t), 2.8854, 1e-4);
535s ***** assert (median (pd), 2);
535s ***** assert (median (t), 2.8284, 1e-4);
535s ***** assert (pdf (pd, [0, 1, 2, 3, 4, 5]), [0, 0.7213, 0.3607, 0.2404, 0.1803, 0], 1e-4);
535s ***** assert (pdf (t, [0, 1, 2, 3, 4, 5]), [0, 0, 0.7213, 0.4809, 0.3607, 0], 1e-4);
535s ***** assert (pdf (pd, [-1, 1, 2, 3, 4, NaN]), [0, 0.7213, 0.3607, 0.2404, 0.1803, NaN], 1e-4);
535s ***** assert (pdf (t, [-1, 1, 2, 3, 4, NaN]), [0, 0, 0.7213, 0.4809, 0.3607, NaN], 1e-4);
535s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
535s ***** assert (any (random (pd, 1000, 1) < 1), false);
535s ***** assert (any (random (pd, 1000, 1) > 4), false);
535s ***** assert (any (random (t, 1000, 1) < 2), false);
535s ***** assert (any (random (t, 1000, 1) > 4), false);
535s ***** assert (std (pd), 0.8527, 1e-4);
535s ***** assert (std (t), 0.5751, 1e-4);
535s ***** assert (var (pd), 0.7270, 1e-4);
535s ***** assert (var (t), 0.3307, 1e-4);
535s ***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
535s  LoguniformDistribution (i, 1)
535s ***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
535s  LoguniformDistribution (Inf, 1)
535s ***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
535s  LoguniformDistribution ([1, 2], 1)
535s ***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
535s  LoguniformDistribution ("a", 1)
535s ***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
535s  LoguniformDistribution (NaN, 1)
535s ***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
535s  LoguniformDistribution (1, i)
535s ***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
535s  LoguniformDistribution (1, Inf)
535s ***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
535s  LoguniformDistribution (1, [1, 2])
535s ***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
535s  LoguniformDistribution (1, "a")
535s ***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
535s  LoguniformDistribution (1, NaN)
535s ***** error <LoguniformDistribution: LOWER must be less than UPPER.> ...
535s  LoguniformDistribution (2, 1)
535s ***** error <cdf: invalid argument for upper tail.> ...
535s  cdf (LoguniformDistribution, 2, "uper")
535s ***** error <cdf: invalid argument for upper tail.> ...
535s  cdf (LoguniformDistribution, 2, 3)
535s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
535s  plot (LoguniformDistribution, "Parent")
535s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
535s  plot (LoguniformDistribution, "PlotType", 12)
535s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
535s  plot (LoguniformDistribution, "PlotType", {"pdf", "cdf"})
535s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
535s  plot (LoguniformDistribution, "PlotType", "pdfcdf")
535s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
535s  plot (LoguniformDistribution, "Discrete", "pdfcdf")
535s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
535s  plot (LoguniformDistribution, "Discrete", [1, 0])
535s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
535s  plot (LoguniformDistribution, "Discrete", {true})
535s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
535s  plot (LoguniformDistribution, "Parent", 12)
535s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
535s  plot (LoguniformDistribution, "Parent", "hax")
535s ***** error <plot: invalid NAME for optional argument.> ...
535s  plot (LoguniformDistribution, "invalidNAME", "pdf")
535s ***** error <plot: 'probability' PlotType is not supported for 'LoguniformDistribution'.> ...
535s  plot (LoguniformDistribution, "PlotType", "probability")
535s ***** error <truncate: missing input argument.> ...
535s  truncate (LoguniformDistribution)
535s ***** error <truncate: missing input argument.> ...
535s  truncate (LoguniformDistribution, 2)
535s ***** error <truncate: invalid lower upper limits.> ...
535s  truncate (LoguniformDistribution, 4, 2)
535s ***** shared pd
535s  pd = LoguniformDistribution(1, 4);
535s  pd(2) = LoguniformDistribution(2, 5);
535s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
535s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
535s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
535s ***** error <mean: requires a scalar probability distribution.> mean (pd)
535s ***** error <median: requires a scalar probability distribution.> median (pd)
535s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
535s ***** error <plot: requires a scalar probability distribution.> plot (pd)
535s ***** error <random: requires a scalar probability distribution.> random (pd)
535s ***** error <std: requires a scalar probability distribution.> std (pd)
535s ***** error <truncate: requires a scalar probability distribution.> ...
535s  truncate (pd, 2, 4)
535s ***** error <var: requires a scalar probability distribution.> var (pd)
535s 65 tests, 65 passed, 0 known failure, 0 skipped
535s [inst/dist_obj/MultinomialDistribution.m]
535s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_obj/MultinomialDistribution.m
535s ***** demo
535s  ## Generate a data set of 5000 random samples from a Multinomial distribution
535s  ## with parameters Probabilities = [0.1, 0.2, 0.3, 0.2, 0.1, 0.1].  Create
535s  ## the distribution and plot the PDF superimposed on a histogram of the data.
535s 
535s  probs = [0.1, 0.2, 0.3, 0.2, 0.1, 0.1];
535s  pd = makedist ("Multinomial", "Probabilities", probs);
535s  rand ("seed", 2);
535s  data = random (pd, 5000, 1);
535s  hist (data, length (probs));
535s  hold on
535s  x = 1:length (probs);
535s  y = pdf (pd, x) * 5000;
535s  stem (x, y, "r", "LineWidth", 2);
535s  hold off
535s  msg = "Multinomial distribution with Probabilities = [%s]";
535s  probs_str = num2str (probs, "%0.1f ");
535s  title (sprintf (msg, probs_str))
535s ***** shared pd, t
535s  pd = MultinomialDistribution ([0.1, 0.2, 0.3, 0.2, 0.1, 0.1]);
535s  t = truncate (pd, 2, 4);
535s ***** assert (cdf (pd, [2, 3, 4]), [0.3, 0.6, 0.8], eps);
535s ***** assert (cdf (t, [2, 3, 4]), [0.2857, 0.7143, 1], 1e-4);
535s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.1, 0.3, 0.6, 0.8], eps);
535s ***** assert (cdf (pd, [1.5, 2-eps, 3, 4]), [0.1, 0.1, 0.6, 0.8], eps);
535s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0.2857, 0.7143, 1], 1e-4);
535s ***** assert (cdf (t, [1.5, 2-eps, 3, 4]), [0, 0, 0.7143, 1], 1e-4);
535s ***** assert (cdf (pd, [1, 2.5, 4, 6]), [0.1, 0.3, 0.8, 1], eps);
535s ***** assert (icdf (pd, [0, 0.2857, 0.7143, 1]), [1, 2, 4, 6]);
535s ***** assert (icdf (t, [0, 0.2857, 0.7143, 1]), [2, 2, 4, 4]);
535s ***** assert (icdf (t, [0, 0.35, 0.7143, 1]), [2, 3, 4, 4]);
535s ***** assert (icdf (t, [0, 0.35, 0.7143, 1, NaN]), [2, 3, 4, 4, NaN]);
535s ***** assert (icdf (t, [-0.5, 0, 0.35, 0.7143, 1, NaN]), [NaN, 2, 3, 4, 4, NaN]);
535s ***** assert (icdf (pd, [-0.5, 0, 0.35, 0.7143, 1, NaN]), [NaN, 1, 3, 4, 6, NaN]);
535s ***** assert (iqr (pd), 2);
535s ***** assert (iqr (t), 2);
535s ***** assert (mean (pd), 3.3, 1e-14);
535s ***** assert (mean (t), 3, eps);
535s ***** assert (median (pd), 3);
535s ***** assert (median (t), 3);
535s ***** assert (pdf (pd, [-5, 1, 2.5, 4, 6, NaN, 9]), [0, 0.1, 0, 0.2, 0.1, NaN, 0]);
535s ***** assert (pdf (pd, [-5, 1, 2, 3, 4, 6, NaN, 9]), ...
535s  [0, 0.1, 0.2, 0.3, 0.2, 0.1, NaN, 0]);
536s ***** assert (pdf (t, [-5, 1, 2, 3, 4, 6, NaN, 0]), ...
536s  [0, 0, 0.2857, 0.4286, 0.2857, 0, NaN, 0], 1e-4);
536s ***** assert (pdf (t, [-5, 1, 2, 4, 6, NaN, 0]), ...
536s  [0, 0, 0.2857, 0.2857, 0, NaN, 0], 1e-4);
536s ***** assert (unique (random (pd, 1000, 5)), [1, 2, 3, 4, 5, 6]');
536s ***** assert (unique (random (t, 1000, 5)), [2, 3, 4]');
536s ***** assert (std (pd), 1.4177, 1e-4);
536s ***** assert (std (t), 0.7559, 1e-4);
536s ***** assert (var (pd), 2.0100, 1e-4);
536s ***** assert (var (t), 0.5714, 1e-4);
536s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
536s  MultinomialDistribution(0)
536s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
536s  MultinomialDistribution(-1)
536s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
536s  MultinomialDistribution(Inf)
536s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
536s  MultinomialDistribution(i)
536s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
536s  MultinomialDistribution("a")
536s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
536s  MultinomialDistribution([1, 2])
536s ***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
536s  MultinomialDistribution(NaN)
536s ***** error <cdf: invalid argument for upper tail.> ...
536s  cdf (MultinomialDistribution, 2, "uper")
536s ***** error <cdf: invalid argument for upper tail.> ...
536s  cdf (MultinomialDistribution, 2, 3)
536s ***** error <cdf: X must be real.> ...
536s  cdf (MultinomialDistribution, i)
536s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
536s  plot (MultinomialDistribution, "Parent")
536s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
536s  plot (MultinomialDistribution, "PlotType", 12)
536s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
536s  plot (MultinomialDistribution, "PlotType", {"pdf", "cdf"})
536s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
536s  plot (MultinomialDistribution, "PlotType", "pdfcdf")
536s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
536s  plot (MultinomialDistribution, "Discrete", "pdfcdf")
536s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
536s  plot (MultinomialDistribution, "Discrete", [1, 0])
536s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
536s  plot (MultinomialDistribution, "Discrete", {true})
536s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
536s  plot (MultinomialDistribution, "Parent", 12)
536s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
536s  plot (MultinomialDistribution, "Parent", "hax")
536s ***** error <plot: invalid NAME for optional argument.> ...
536s  plot (MultinomialDistribution, "invalidNAME", "pdf")
536s ***** error <plot: 'probability' PlotType is not supported for 'MultinomialDistribution'.> ...
536s  plot (MultinomialDistribution, "PlotType", "probability")
536s ***** error <truncate: is_nan input argument.> ...
536s  truncate (MultinomialDistribution)
536s ***** error <truncate: is_nan input argument.> ...
536s  truncate (MultinomialDistribution, 2)
536s ***** error <truncate: invalid lower upper limits.> ...
536s  truncate (MultinomialDistribution, 4, 2)
536s ***** shared pd
536s  pd = MultinomialDistribution([0.1, 0.2, 0.3, 0.4]);
536s  pd(2) = MultinomialDistribution([0.1, 0.2, 0.3, 0.4]);
536s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
536s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
536s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
536s ***** error <mean: requires a scalar probability distribution.> mean (pd)
536s ***** error <median: requires a scalar probability distribution.> median (pd)
536s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
536s ***** error <plot: requires a scalar probability distribution.> plot (pd)
536s ***** error <random: requires a scalar probability distribution.> random (pd)
536s ***** error <std: requires a scalar probability distribution.> std (pd)
536s ***** error <truncate: requires a scalar probability distribution.> ...
536s  truncate (pd, 2, 4)
536s ***** error <var: requires a scalar probability distribution.> var (pd)
536s 64 tests, 64 passed, 0 known failure, 0 skipped
536s [inst/dist_obj/NakagamiDistribution.m]
536s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_obj/NakagamiDistribution.m
536s ***** demo
536s  ## Generate a data set of 5000 random samples from a Nakagami distribution with
536s  ## parameters mu = 1 and omega = 1.  Fit a Nakagami distribution to this data and plot
536s  ## a PDF of the fitted distribution superimposed on a histogram of the data.
536s 
536s  pd_fixed = makedist ("Nakagami", "mu", 1, "omega", 1)
536s  rand ("seed", 2);
536s  data = random (pd_fixed, 5000, 1);
536s  pd_fitted = fitdist (data, "Nakagami")
536s  plot (pd_fitted)
536s  msg = "Fitted Nakagami distribution with mu = %0.2f and omega = %0.2f";
536s  title (sprintf (msg, pd_fitted.mu, pd_fitted.omega))
536s ***** shared pd, t
536s  pd = NakagamiDistribution;
536s  t = truncate (pd, 2, 4);
536s ***** assert (cdf (pd, [0:5]), [0, 0.6321, 0.9817, 0.9999, 1, 1], 1e-4);
536s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.9933, 1, 1], 1e-4);
536s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8946, 0.9817, 0.9999, 1], 1e-4);
536s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.9933, 1], 1e-4);
536s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.4724, 0.7147, 0.9572, 1.2686, Inf], 1e-4);
536s ***** assert (icdf (t, [0:0.2:1]), [2, 2.0550, 2.1239, 2.2173, 2.3684, 4], 1e-4);
536s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.7147, 0.9572, 1.2686, Inf, NaN], 1e-4);
536s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.1239, 2.2173, 2.3684, 4, NaN], 1e-4);
536s ***** assert (iqr (pd), 0.6411, 1e-4);
536s ***** assert (iqr (t), 0.2502, 1e-4);
536s ***** assert (mean (pd), 0.8862, 1e-4);
536s ***** assert (mean (t), 2.2263, 1e-4);
536s ***** assert (median (pd), 0.8326, 1e-4);
536s ***** assert (median (t), 2.1664, 1e-4);
536s ***** assert (pdf (pd, [0:5]), [0, 0.7358, 0.0733, 0.0007, 0, 0], 1e-4);
536s ***** assert (pdf (t, [0:5]), [0, 0, 4, 0.0404, 0, 0], 1e-4);
536s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.7358, 0.0733, 0.0007, 0, NaN], 1e-4);
536s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 4, 0.0404, 0, NaN], 1e-4);
536s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
536s ***** assert (any (random (t, 1000, 1) < 2), false);
536s ***** assert (any (random (t, 1000, 1) > 4), false);
536s ***** assert (std (pd), 0.4633, 1e-4);
536s ***** assert (std (t), 0.2083, 1e-4);
536s ***** assert (var (pd), 0.2146, 1e-4);
536s ***** assert (var (t), 0.0434, 1e-4);
536s ***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
536s  NakagamiDistribution(Inf, 1)
536s ***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
536s  NakagamiDistribution(i, 1)
536s ***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
536s  NakagamiDistribution("a", 1)
536s ***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
536s  NakagamiDistribution([1, 2], 1)
536s ***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
536s  NakagamiDistribution(NaN, 1)
536s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
536s  NakagamiDistribution(1, 0)
536s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
536s  NakagamiDistribution(1, -1)
536s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
536s  NakagamiDistribution(1, Inf)
536s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
536s  NakagamiDistribution(1, i)
536s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
536s  NakagamiDistribution(1, "a")
536s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
536s  NakagamiDistribution(1, [1, 2])
536s ***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
536s  NakagamiDistribution(1, NaN)
536s ***** error <cdf: invalid argument for upper tail.> ...
536s  cdf (NakagamiDistribution, 2, "uper")
536s ***** error <cdf: invalid argument for upper tail.> ...
536s  cdf (NakagamiDistribution, 2, 3)
536s ***** shared x
536s  x = nakarnd (1, 0.5, [1, 100]);
536s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
536s  paramci (NakagamiDistribution.fit (x), "alpha")
536s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
536s  paramci (NakagamiDistribution.fit (x), "alpha", 0)
536s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
536s  paramci (NakagamiDistribution.fit (x), "alpha", 1)
536s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
536s  paramci (NakagamiDistribution.fit (x), "alpha", [0.5 2])
536s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
536s  paramci (NakagamiDistribution.fit (x), "alpha", "")
536s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
536s  paramci (NakagamiDistribution.fit (x), "alpha", {0.05})
536s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
536s  paramci (NakagamiDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
536s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
536s  paramci (NakagamiDistribution.fit (x), "parameter", {"mu", "omega", "param"})
536s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
536s  paramci (NakagamiDistribution.fit (x), "alpha", 0.01, ...
536s           "parameter", {"mu", "omega", "param"})
536s ***** error <paramci: unknown distribution parameter.> ...
536s  paramci (NakagamiDistribution.fit (x), "parameter", "param")
536s ***** error <paramci: unknown distribution parameter.> ...
536s  paramci (NakagamiDistribution.fit (x), "alpha", 0.01, "parameter", "param")
536s ***** error <paramci: invalid NAME for optional argument.> ...
536s  paramci (NakagamiDistribution.fit (x), "NAME", "value")
536s ***** error <paramci: invalid NAME for optional argument.> ...
536s  paramci (NakagamiDistribution.fit (x), "alpha", 0.01, "NAME", "value")
536s ***** error <paramci: invalid NAME for optional argument.> ...
536s  paramci (NakagamiDistribution.fit (x), "alpha", 0.01, "parameter", "mu", ...
536s           "NAME", "value")
536s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
536s  plot (NakagamiDistribution, "Parent")
536s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
536s  plot (NakagamiDistribution, "PlotType", 12)
536s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
536s  plot (NakagamiDistribution, "PlotType", {"pdf", "cdf"})
536s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
536s  plot (NakagamiDistribution, "PlotType", "pdfcdf")
536s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
536s  plot (NakagamiDistribution, "Discrete", "pdfcdf")
536s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
536s  plot (NakagamiDistribution, "Discrete", [1, 0])
536s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
536s  plot (NakagamiDistribution, "Discrete", {true})
536s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
536s  plot (NakagamiDistribution, "Parent", 12)
536s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
536s  plot (NakagamiDistribution, "Parent", "hax")
536s ***** error <plot: invalid NAME for optional argument.> ...
536s  plot (NakagamiDistribution, "invalidNAME", "pdf")
536s ***** error <plot: no fitted DATA to plot a probability plot.> ...
536s  plot (NakagamiDistribution, "PlotType", "probability")
536s ***** error <proflik: no fitted data available.> ...
536s  proflik (NakagamiDistribution, 2)
536s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
536s  proflik (NakagamiDistribution.fit (x), 3)
536s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
536s  proflik (NakagamiDistribution.fit (x), [1, 2])
536s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
536s  proflik (NakagamiDistribution.fit (x), {1})
537s ***** error <proflik: SETPARAM must be a numeric vector.> ...
537s  proflik (NakagamiDistribution.fit (x), 1, ones (2))
537s ***** error <proflik: missing VALUE for 'Display' argument.> ...
537s  proflik (NakagamiDistribution.fit (x), 1, "Display")
537s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
537s  proflik (NakagamiDistribution.fit (x), 1, "Display", 1)
537s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
537s  proflik (NakagamiDistribution.fit (x), 1, "Display", {1})
537s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
537s  proflik (NakagamiDistribution.fit (x), 1, "Display", {"on"})
537s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
537s  proflik (NakagamiDistribution.fit (x), 1, "Display", ["on"; "on"])
537s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
537s  proflik (NakagamiDistribution.fit (x), 1, "Display", "onnn")
537s ***** error <proflik: invalid NAME for optional arguments.> ...
537s  proflik (NakagamiDistribution.fit (x), 1, "NAME", "on")
537s ***** error <proflik: invalid optional argument.> ...
537s  proflik (NakagamiDistribution.fit (x), 1, {"NAME"}, "on")
537s ***** error <proflik: invalid optional argument.> ...
537s  proflik (NakagamiDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
537s ***** error <truncate: missing input argument.> ...
537s  truncate (NakagamiDistribution)
537s ***** error <truncate: missing input argument.> ...
537s  truncate (NakagamiDistribution, 2)
537s ***** error <truncate: invalid lower upper limits.> ...
537s  truncate (NakagamiDistribution, 4, 2)
537s ***** shared pd
537s  pd = NakagamiDistribution(1, 0.5);
537s  pd(2) = NakagamiDistribution(1, 0.6);
537s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
537s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
537s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
537s ***** error <mean: requires a scalar probability distribution.> mean (pd)
537s ***** error <median: requires a scalar probability distribution.> median (pd)
537s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
537s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
537s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
537s ***** error <plot: requires a scalar probability distribution.> plot (pd)
537s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
537s ***** error <random: requires a scalar probability distribution.> random (pd)
537s ***** error <std: requires a scalar probability distribution.> std (pd)
537s ***** error <truncate: requires a scalar probability distribution.> ...
537s  truncate (pd, 2, 4)
537s ***** error <var: requires a scalar probability distribution.> var (pd)
537s 95 tests, 95 passed, 0 known failure, 0 skipped
537s [inst/dist_obj/NegativeBinomialDistribution.m]
537s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_obj/NegativeBinomialDistribution.m
537s ***** demo
537s  ## Generate a data set of 5000 random samples from a Negative Binomial
537s  ## distribution with parameters R = 5 and P = 0.5.  Fit a Negative Binomial
537s  ## distribution to this data and plot a PDF of the fitted distribution
537s  ## superimposed on a histogram of the data.
537s 
537s  pd_fixed = makedist ("NegativeBinomial", "R", 5, "P", 0.5)
537s  rand ("seed", 2);
537s  data = random (pd_fixed, 5000, 1);
537s  pd_fitted = fitdist (data, "NegativeBinomial")
537s  plot (pd_fitted)
537s  msg = "Fitted Negative Binomial distribution with R = %0.2f and P = %0.2f";
537s  title (sprintf (msg, pd_fitted.R, pd_fitted.P))
537s ***** shared pd, t, t_inf
537s  pd = NegativeBinomialDistribution (5, 0.5);
537s  t = truncate (pd, 2, 4);
537s  t_inf = truncate (pd, 2, Inf);
537s ***** assert (cdf (pd, [0:5]), [0.0312, 0.1094, 0.2266, 0.3633, 0.5, 0.6230], 1e-4);
537s ***** assert (cdf (t, [0:5]), [0, 0, 0.3, 0.65, 1, 1], 1e-4);
537s ***** assert (cdf (t_inf, [0:5]), [0, 0, 0.1316, 0.2851, 0.4386, 0.5768], 1e-4);
537s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.1094, 0.2266, 0.3633, 0.5000], 1e-4);
537s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0.3, 0.65, 1], 1e-4);
537s ***** assert (icdf (pd, [0:0.2:1]), [0, 2, 4, 5, 7, Inf], 1e-4);
537s ***** assert (icdf (t, [0:0.2:1]), [2, 2, 3, 3, 4, 4], 1e-4);
537s ***** assert (icdf (t_inf, [0:0.2:1]), [2, 3, 4, 6, 8, Inf], 1e-4);
537s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 4, 5, 7, Inf, NaN], 1e-4);
537s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 3, 3, 4, 4, NaN], 1e-4);
537s ***** assert (iqr (pd), 4);
537s ***** assert (iqr (t), 2);
537s ***** assert (mean (pd), 5);
537s ***** assert (mean (t), 3.0500, 1e-4);
537s ***** assert (mean (t_inf), 5.5263, 1e-4);
537s ***** assert (median (pd), 4);
537s ***** assert (median (t), 3);
537s ***** assert (pdf (pd, [0:5]), [0.0312, 0.0781, 0.1172, 0.1367, 0.1367, 0.1230], 1e-4);
537s ***** assert (pdf (t, [0:5]), [0, 0, 0.3, 0.35, 0.35, 0], 1e-4);
537s ***** assert (pdf (t_inf, [0:5]), [0, 0, 0.1316, 0.1535, 0.1535, 0.1382], 1e-4);
537s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.0781, 0.1172, 0.1367, 0.1367, NaN], 1e-4);
537s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.3, 0.35, 0.35, NaN], 1e-4);
537s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
537s ***** assert (any (random (t, 1000, 1) < 2), false);
537s ***** assert (any (random (t, 1000, 1) > 4), false);
537s ***** assert (std (pd), 3.1623, 1e-4);
537s ***** assert (std (t), 0.8047, 1e-4);
537s ***** assert (std (t_inf), 2.9445, 1e-4);
537s ***** assert (var (pd), 10);
537s ***** assert (var (t), 0.6475, 1e-4);
537s ***** assert (var (t_inf), 8.6704, 1e-4);
537s ***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
537s  NegativeBinomialDistribution(Inf, 1)
537s ***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
537s  NegativeBinomialDistribution(i, 1)
537s ***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
537s  NegativeBinomialDistribution("a", 1)
537s ***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
537s  NegativeBinomialDistribution([1, 2], 1)
537s ***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
537s  NegativeBinomialDistribution(NaN, 1)
537s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
537s  NegativeBinomialDistribution(1, 0)
537s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
537s  NegativeBinomialDistribution(1, -1)
537s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
537s  NegativeBinomialDistribution(1, Inf)
537s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
537s  NegativeBinomialDistribution(1, i)
537s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
537s  NegativeBinomialDistribution(1, "a")
537s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
537s  NegativeBinomialDistribution(1, [1, 2])
537s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
537s  NegativeBinomialDistribution(1, NaN)
537s ***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
537s  NegativeBinomialDistribution(1, 1.2)
537s ***** error <cdf: invalid argument for upper tail.> ...
537s  cdf (NegativeBinomialDistribution, 2, "uper")
537s ***** error <cdf: invalid argument for upper tail.> ...
537s  cdf (NegativeBinomialDistribution, 2, 3)
537s ***** shared x
537s  x = nbinrnd (1, 0.5, [1, 100]);
537s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
537s  paramci (NegativeBinomialDistribution.fit (x), "alpha")
537s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
537s  paramci (NegativeBinomialDistribution.fit (x), "alpha", 0)
537s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
537s  paramci (NegativeBinomialDistribution.fit (x), "alpha", 1)
537s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
537s  paramci (NegativeBinomialDistribution.fit (x), "alpha", [0.5 2])
537s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
537s  paramci (NegativeBinomialDistribution.fit (x), "alpha", "")
537s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
537s  paramci (NegativeBinomialDistribution.fit (x), "alpha", {0.05})
537s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
537s  paramci (NegativeBinomialDistribution.fit (x), "parameter", "R", ...
537s           "alpha", {0.05})
537s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
537s  paramci (NegativeBinomialDistribution.fit (x), ...
537s           "parameter", {"R", "P", "param"})
537s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
537s  paramci (NegativeBinomialDistribution.fit (x), "alpha", 0.01, ...
537s           "parameter", {"R", "P", "param"})
537s ***** error <paramci: unknown distribution parameter.> ...
537s  paramci (NegativeBinomialDistribution.fit (x), "parameter", "param")
537s ***** error <paramci: unknown distribution parameter.> ...
537s  paramci (NegativeBinomialDistribution.fit (x), "alpha", 0.01, ...
537s           "parameter", "param")
537s ***** error <paramci: invalid NAME for optional argument.> ...
537s  paramci (NegativeBinomialDistribution.fit (x), "NAME", "value")
537s ***** error <paramci: invalid NAME for optional argument.> ...
537s  paramci (NegativeBinomialDistribution.fit (x), "alpha", 0.01, ...
537s           "NAME", "value")
537s ***** error <paramci: invalid NAME for optional argument.> ...
537s  paramci (NegativeBinomialDistribution.fit (x), "alpha", 0.01, ...
537s           "parameter", "R", "NAME", "value")
537s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
537s  plot (NegativeBinomialDistribution, "Parent")
537s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
537s  plot (NegativeBinomialDistribution, "PlotType", 12)
537s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
537s  plot (NegativeBinomialDistribution, "PlotType", {"pdf", "cdf"})
537s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
537s  plot (NegativeBinomialDistribution, "PlotType", "pdfcdf")
537s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
537s  plot (NegativeBinomialDistribution, "Discrete", "pdfcdf")
537s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
537s  plot (NegativeBinomialDistribution, "Discrete", [1, 0])
537s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
537s  plot (NegativeBinomialDistribution, "Discrete", {true})
538s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
538s  plot (NegativeBinomialDistribution, "Parent", 12)
538s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
538s  plot (NegativeBinomialDistribution, "Parent", "hax")
538s ***** error <plot: invalid NAME for optional argument.> ...
538s  plot (NegativeBinomialDistribution, "invalidNAME", "pdf")
538s ***** error <plot: no fitted DATA to plot a probability plot.> ...
538s  plot (NegativeBinomialDistribution, "PlotType", "probability")
538s ***** error <proflik: no fitted data available.> ...
538s  proflik (NegativeBinomialDistribution, 2)
538s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
538s  proflik (NegativeBinomialDistribution.fit (x), 3)
538s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
538s  proflik (NegativeBinomialDistribution.fit (x), [1, 2])
538s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
538s  proflik (NegativeBinomialDistribution.fit (x), {1})
538s ***** error <proflik: SETPARAM must be a numeric vector.> ...
538s  proflik (NegativeBinomialDistribution.fit (x), 1, ones (2))
538s ***** error <proflik: missing VALUE for 'Display' argument.> ...
538s  proflik (NegativeBinomialDistribution.fit (x), 1, "Display")
538s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
538s  proflik (NegativeBinomialDistribution.fit (x), 1, "Display", 1)
538s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
538s  proflik (NegativeBinomialDistribution.fit (x), 1, "Display", {1})
538s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
538s  proflik (NegativeBinomialDistribution.fit (x), 1, "Display", {"on"})
538s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
538s  proflik (NegativeBinomialDistribution.fit (x), 1, "Display", ["on"; "on"])
538s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
538s  proflik (NegativeBinomialDistribution.fit (x), 1, "Display", "onnn")
538s ***** error <proflik: invalid NAME for optional arguments.> ...
538s  proflik (NegativeBinomialDistribution.fit (x), 1, "NAME", "on")
538s ***** error <proflik: invalid optional argument.> ...
538s  proflik (NegativeBinomialDistribution.fit (x), 1, {"NAME"}, "on")
538s ***** error <proflik: invalid optional argument.> ...
538s  proflik (NegativeBinomialDistribution.fit (x), 1, {[1 2 3]}, "Display", "on")
538s ***** error <truncate: missing input argument.> ...
538s  truncate (NegativeBinomialDistribution)
538s ***** error <truncate: missing input argument.> ...
538s  truncate (NegativeBinomialDistribution, 2)
538s ***** error <truncate: invalid lower upper limits.> ...
538s  truncate (NegativeBinomialDistribution, 4, 2)
538s ***** shared pd
538s  pd = NegativeBinomialDistribution(1, 0.5);
538s  pd(2) = NegativeBinomialDistribution(1, 0.6);
538s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
538s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
538s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
538s ***** error <mean: requires a scalar probability distribution.> mean (pd)
538s ***** error <median: requires a scalar probability distribution.> median (pd)
538s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
538s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
538s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
538s ***** error <plot: requires a scalar probability distribution.> plot (pd)
538s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
538s ***** error <random: requires a scalar probability distribution.> random (pd)
538s ***** error <std: requires a scalar probability distribution.> std (pd)
538s ***** error <truncate: requires a scalar probability distribution.> ...
538s  truncate (pd, 2, 4)
538s ***** error <var: requires a scalar probability distribution.> var (pd)
538s 102 tests, 102 passed, 0 known failure, 0 skipped
538s [inst/dist_obj/NormalDistribution.m]
538s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_obj/NormalDistribution.m
538s ***** demo
538s  ## Generate a data set of 5000 random samples from a Normal distribution with
538s  ## parameters mu = 0 and sigma = 1.  Fit a Normal distribution to this data and plot
538s  ## a PDF of the fitted distribution superimposed on a histogram of the data.
538s 
538s  pd_fixed = makedist ("Normal", "mu", 0, "sigma", 1)
538s  randn ("seed", 2);
538s  data = random (pd_fixed, 5000, 1);
538s  pd_fitted = fitdist (data, "Normal")
538s  plot (pd_fitted)
538s  msg = "Fitted Normal distribution with mu = %0.2f and sigma = %0.2f";
538s  title (sprintf (msg, pd_fitted.mu, pd_fitted.sigma))
538s ***** shared pd, t
538s  pd = NormalDistribution;
538s  t = truncate (pd, -2, 2);
538s ***** assert (cdf (pd, [0:5]), [0.5, 0.8413, 0.9772, 0.9987, 1, 1], 1e-4);
538s ***** assert (cdf (t, [0:5]), [0.5, 0.8576, 1, 1, 1, 1], 1e-4);
538s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.9332, 0.9772, 0.9987, 1], 1e-4);
538s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0.9538, 1, 1, 1], 1e-4);
538s ***** assert (icdf (pd, [0:0.2:1]), [-Inf, -0.8416, -0.2533, 0.2533, 0.8416, Inf], 1e-4);
538s ***** assert (icdf (t, [0:0.2:1]), [-2, -0.7938, -0.2416, 0.2416, 0.7938, 2], 1e-4);
538s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, -0.2533, 0.2533, 0.8416, Inf, NaN], 1e-4);
538s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, -0.2416, 0.2416, 0.7938, 2, NaN], 1e-4);
538s ***** assert (iqr (pd), 1.3490, 1e-4);
538s ***** assert (iqr (t), 1.2782, 1e-4);
538s ***** assert (mean (pd), 0);
538s ***** assert (mean (t), 0, 3e-16);
538s ***** assert (median (pd), 0);
538s ***** assert (median (t), 0, 3e-16);
538s ***** assert (pdf (pd, [0:5]), [0.3989, 0.2420, 0.0540, 0.0044, 0.0001, 0], 1e-4);
538s ***** assert (pdf (t, [0:5]), [0.4180, 0.2535, 0.0566, 0, 0, 0], 1e-4);
538s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0.2420, 0.2420, 0.0540, 0.0044, 0.0001, NaN], 1e-4);
538s ***** assert (pdf (t, [-1, 1:4, NaN]), [0.2535, 0.2535, 0.0566, 0, 0, NaN], 1e-4);
538s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
538s ***** assert (any (random (t, 1000, 1) < -2), false);
538s ***** assert (any (random (t, 1000, 1) > 2), false);
538s ***** assert (std (pd), 1);
538s ***** assert (std (t), 0.8796, 1e-4);
538s ***** assert (var (pd), 1);
538s ***** assert (var (t), 0.7737, 1e-4);
538s ***** error <NormalDistribution: MU must be a real scalar.> ...
538s  NormalDistribution(Inf, 1)
538s ***** error <NormalDistribution: MU must be a real scalar.> ...
538s  NormalDistribution(i, 1)
538s ***** error <NormalDistribution: MU must be a real scalar.> ...
538s  NormalDistribution("a", 1)
538s ***** error <NormalDistribution: MU must be a real scalar.> ...
538s  NormalDistribution([1, 2], 1)
538s ***** error <NormalDistribution: MU must be a real scalar.> ...
538s  NormalDistribution(NaN, 1)
538s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
538s  NormalDistribution(1, 0)
538s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
538s  NormalDistribution(1, -1)
538s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
538s  NormalDistribution(1, Inf)
538s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
538s  NormalDistribution(1, i)
538s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
538s  NormalDistribution(1, "a")
538s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
538s  NormalDistribution(1, [1, 2])
538s ***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
538s  NormalDistribution(1, NaN)
538s ***** error <cdf: invalid argument for upper tail.> ...
538s  cdf (NormalDistribution, 2, "uper")
538s ***** error <cdf: invalid argument for upper tail.> ...
538s  cdf (NormalDistribution, 2, 3)
538s ***** shared x
538s  x = normrnd (1, 1, [1, 100]);
538s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
538s  paramci (NormalDistribution.fit (x), "alpha")
538s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
538s  paramci (NormalDistribution.fit (x), "alpha", 0)
538s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
538s  paramci (NormalDistribution.fit (x), "alpha", 1)
538s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
538s  paramci (NormalDistribution.fit (x), "alpha", [0.5 2])
538s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
538s  paramci (NormalDistribution.fit (x), "alpha", "")
539s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
539s  paramci (NormalDistribution.fit (x), "alpha", {0.05})
539s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
539s  paramci (NormalDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
539s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
539s  paramci (NormalDistribution.fit (x), "parameter", {"mu", "sigma", "param"})
539s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
539s  paramci (NormalDistribution.fit (x), "alpha", 0.01, ...
539s           "parameter", {"mu", "sigma", "param"})
539s ***** error <paramci: unknown distribution parameter.> ...
539s  paramci (NormalDistribution.fit (x), "parameter", "param")
539s ***** error <paramci: unknown distribution parameter.> ...
539s  paramci (NormalDistribution.fit (x), "alpha", 0.01, "parameter", "param")
539s ***** error <paramci: invalid NAME for optional argument.> ...
539s  paramci (NormalDistribution.fit (x), "NAME", "value")
539s ***** error <paramci: invalid NAME for optional argument.> ...
539s  paramci (NormalDistribution.fit (x), "alpha", 0.01, "NAME", "value")
539s ***** error <paramci: invalid NAME for optional argument.> ...
539s  paramci (NormalDistribution.fit (x), "alpha", 0.01, "parameter", "mu", ...
539s           "NAME", "value")
539s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
539s  plot (NormalDistribution, "Parent")
539s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
539s  plot (NormalDistribution, "PlotType", 12)
539s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
539s  plot (NormalDistribution, "PlotType", {"pdf", "cdf"})
539s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
539s  plot (NormalDistribution, "PlotType", "pdfcdf")
539s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
539s  plot (NormalDistribution, "Discrete", "pdfcdf")
539s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
539s  plot (NormalDistribution, "Discrete", [1, 0])
539s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
539s  plot (NormalDistribution, "Discrete", {true})
539s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
539s  plot (NormalDistribution, "Parent", 12)
539s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
539s  plot (NormalDistribution, "Parent", "hax")
539s ***** error <plot: invalid NAME for optional argument.> ...
539s  plot (NormalDistribution, "invalidNAME", "pdf")
540s ***** error <plot: no fitted DATA to plot a probability plot.> ...
540s  plot (NormalDistribution, "PlotType", "probability")
540s ***** error <proflik: no fitted data available.> ...
540s  proflik (NormalDistribution, 2)
540s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
540s  proflik (NormalDistribution.fit (x), 3)
540s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
540s  proflik (NormalDistribution.fit (x), [1, 2])
540s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
540s  proflik (NormalDistribution.fit (x), {1})
540s ***** error <proflik: SETPARAM must be a numeric vector.> ...
540s  proflik (NormalDistribution.fit (x), 1, ones (2))
540s ***** error <proflik: missing VALUE for 'Display' argument.> ...
540s  proflik (NormalDistribution.fit (x), 1, "Display")
540s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
540s  proflik (NormalDistribution.fit (x), 1, "Display", 1)
540s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
540s  proflik (NormalDistribution.fit (x), 1, "Display", {1})
540s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
540s  proflik (NormalDistribution.fit (x), 1, "Display", {"on"})
540s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
540s  proflik (NormalDistribution.fit (x), 1, "Display", ["on"; "on"])
540s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
540s  proflik (NormalDistribution.fit (x), 1, "Display", "onnn")
541s ***** error <proflik: invalid NAME for optional arguments.> ...
541s  proflik (NormalDistribution.fit (x), 1, "NAME", "on")
541s ***** error <proflik: invalid optional argument.> ...
541s  proflik (NormalDistribution.fit (x), 1, {"NAME"}, "on")
541s ***** error <proflik: invalid optional argument.> ...
541s  proflik (NormalDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
541s ***** error <truncate: missing input argument.> ...
541s  truncate (NormalDistribution)
541s ***** error <truncate: missing input argument.> ...
541s  truncate (NormalDistribution, 2)
541s ***** error <truncate: invalid lower upper limits.> ...
541s  truncate (NormalDistribution, 4, 2)
541s ***** shared pd
541s  pd = NormalDistribution(1, 1);
541s  pd(2) = NormalDistribution(1, 3);
541s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
541s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
541s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
541s ***** error <mean: requires a scalar probability distribution.> mean (pd)
541s ***** error <median: requires a scalar probability distribution.> median (pd)
541s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
541s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
541s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
541s ***** error <plot: requires a scalar probability distribution.> plot (pd)
541s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
541s ***** error <random: requires a scalar probability distribution.> random (pd)
541s ***** error <std: requires a scalar probability distribution.> std (pd)
541s ***** error <truncate: requires a scalar probability distribution.> ...
541s  truncate (pd, 2, 4)
541s ***** error <var: requires a scalar probability distribution.> var (pd)
541s 95 tests, 95 passed, 0 known failure, 0 skipped
541s [inst/dist_obj/PiecewiseLinearDistribution.m]
541s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_obj/PiecewiseLinearDistribution.m
541s ***** demo
541s  ## Generate a data set of 5000 random samples from a Beta distribution with
541s  ## parameters a = 2 and b = 5 scaled to [0,10].
541s  ## Compute empirical CDF, subsample, create PiecewiseLinearDistribution,
541s  ## and plot the PDF superimposed on a histogram of the data.
541s 
541s  randg ("seed", 2);
541s  data = betarnd (2, 5, 5000, 1) * 10;
541s  [f, x] = ecdf (data);
541s  f = f(1:5:end);
541s  x = x(1:5:end);
541s  pd = PiecewiseLinearDistribution (x, f);
541s  [counts, centers] = hist (data, 50);
541s  bin_width = centers(2) - centers(1);
541s  bar (centers, counts / (sum (counts) * bin_width), 1);
541s  hold on
541s  vals = min (data):0.1:max (data);
541s  y = pdf (pd, vals);
541s  plot (vals, y, "-r", "LineWidth", 2)
541s  hold off
541s  title ("Piecewise Linear approximation to scaled Beta(2,5) data")
541s  legend ("Histogram", "Piecewise PDF")
541s ***** shared pd, t
541s  load patients
541s  [f, x] = ecdf (Weight);
541s  f = f(1:5:end);
541s  x = x(1:5:end);
541s  pd = PiecewiseLinearDistribution (x, f);
541s  t = truncate (pd, 130, 180);
541s ***** assert (cdf (pd, [120, 130, 140, 150, 200]), [0.0767, 0.25, 0.4629, 0.5190, 0.9908], 1e-4);
541s ***** assert (cdf (t, [120, 130, 140, 150, 200]), [0, 0, 0.4274, 0.5403, 1], 1e-4);
541s ***** assert (cdf (pd, [100, 250, NaN]), [0, 1, NaN], 1e-4);
541s ***** assert (cdf (t, [115, 290, NaN]), [0, 1, NaN], 1e-4);
541s ***** assert (icdf (pd, [0:0.2:1]), [111, 127.5, 136.62, 169.67, 182.17, 202], 1e-2);
541s ***** assert (icdf (t, [0:0.2:1]), [130, 134.15, 139.26, 162.5, 173.99, 180], 1e-2);
541s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NA, 136.62, 169.67, 182.17, 202, NA], 1e-2);
541s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NA, 139.26, 162.5, 173.99, 180, NA], 1e-2);
541s ***** assert (iqr (pd), 50.0833, 1e-4);
541s ***** assert (iqr (t), 36.8077, 1e-4);
541s ***** assert (mean (pd), 153.61, 1e-10);
541s ***** assert (mean (t), 152.311, 1e-3);
549s ***** assert (median (pd), 142, 1e-10);
549s ***** assert (median (t), 141.9462, 1e-4);
549s ***** assert (pdf (pd, [120, 130, 140, 150, 200]), [0.0133, 0.0240, 0.0186, 0.0024, 0.0004], 6e-3);
549s ***** assert (pdf (t, [120, 130, 140, 150, 200]), [0, 0.0482, 0.0373, 0.0048, 0], 1e-4);
549s ***** assert (pdf (pd, [100, 250, NaN]), [0, 0, NaN], 1e-4);
549s ***** assert (pdf (t, [100, 250, NaN]), [0, 0, NaN], 1e-4);
549s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
549s ***** assert (any (random (t, 1000, 1) < 130), false);
549s ***** assert (any (random (t, 1000, 1) > 180), false);
549s ***** assert (std (pd), 26.5196, 1e-4);
549s ***** assert (std (t), 18.2941, 1e-4);
564s ***** assert (var (pd), 703.2879, 1e-4);
564s ***** assert (var (t), 334.6757, 1e-4);
579s ***** error <PiecewiseLinearDistribution: X must be a real vector.> ...
579s  PiecewiseLinearDistribution ([0, i], [0, 1])
579s ***** error <PiecewiseLinearDistribution: X must be a real vector.> ...
579s  PiecewiseLinearDistribution ([0, Inf], [0, 1])
579s ***** error <PiecewiseLinearDistribution: X must be a real vector.> ...
579s  PiecewiseLinearDistribution (["a", "c"], [0, 1])
579s ***** error <PiecewiseLinearDistribution: X must be a real vector.> ...
579s  PiecewiseLinearDistribution ([NaN, 1], [0, 1])
579s ***** error <PiecewiseLinearDistribution: Fx must be a real vector.> ...
579s  PiecewiseLinearDistribution ([0, 1], [0, i])
579s ***** error <PiecewiseLinearDistribution: Fx must be a real vector.> ...
579s  PiecewiseLinearDistribution ([0, 1], [0, Inf])
579s ***** error <PiecewiseLinearDistribution: Fx must be a real vector.> ...
579s  PiecewiseLinearDistribution ([0, 1], ["a", "c"])
579s ***** error <PiecewiseLinearDistribution: Fx must be a real vector.> ...
579s  PiecewiseLinearDistribution ([0, 1], [NaN, 1])
579s ***** error <PiecewiseLinearDistribution: X and FX must be vectors of equal size.> ...
579s  PiecewiseLinearDistribution ([0, 1], [0, 0.5, 1])
579s ***** error <PiecewiseLinearDistribution: X and FX must be at least two-elements long.> ...
579s  PiecewiseLinearDistribution ([0], [1])
579s ***** error <PiecewiseLinearDistribution: FX must be bounded in the range> ...
579s  PiecewiseLinearDistribution ([0, 0.5, 1], [0, 1, 1.5])
579s ***** error <cdf: invalid argument for upper tail.> ...
579s  cdf (PiecewiseLinearDistribution, 2, "uper")
579s ***** error <cdf: invalid argument for upper tail.> ...
579s  cdf (PiecewiseLinearDistribution, 2, 3)
579s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
579s  plot (PiecewiseLinearDistribution, "Parent")
579s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
579s  plot (PiecewiseLinearDistribution, "PlotType", 12)
579s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
579s  plot (PiecewiseLinearDistribution, "PlotType", {"pdf", "cdf"})
579s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
579s  plot (PiecewiseLinearDistribution, "PlotType", "pdfcdf")
579s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
579s  plot (PiecewiseLinearDistribution, "Discrete", "pdfcdf")
579s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
579s  plot (PiecewiseLinearDistribution, "Discrete", [1, 0])
579s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
579s  plot (PiecewiseLinearDistribution, "Discrete", {true})
579s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
579s  plot (PiecewiseLinearDistribution, "Parent", 12)
579s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
579s  plot (PiecewiseLinearDistribution, "Parent", "hax")
579s ***** error <plot: invalid NAME for optional argument.> ...
579s  plot (PiecewiseLinearDistribution, "invalidNAME", "pdf")
579s ***** error <plot: 'probability' PlotType is not supported for 'PiecewiseLinearDistribution'.> ...
579s  plot (PiecewiseLinearDistribution, "PlotType", "probability")
579s ***** error <truncate: missing input argument.> ...
579s  truncate (PiecewiseLinearDistribution)
579s ***** error <truncate: missing input argument.> ...
579s  truncate (PiecewiseLinearDistribution, 2)
579s ***** error <truncate: invalid lower upper limits.> ...
579s  truncate (PiecewiseLinearDistribution, 4, 2)
579s ***** shared pd
579s  pd = PiecewiseLinearDistribution ();
579s  pd(2) = PiecewiseLinearDistribution ();
579s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
579s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
579s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
579s ***** error <mean: requires a scalar probability distribution.> mean (pd)
579s ***** error <median: requires a scalar probability distribution.> median (pd)
579s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
579s ***** error <plot: requires a scalar probability distribution.> plot (pd)
579s ***** error <random: requires a scalar probability distribution.> random (pd)
579s ***** error <std: requires a scalar probability distribution.> std (pd)
579s ***** error <truncate: requires a scalar probability distribution.> ...
579s  truncate (pd, 2, 4)
579s ***** error <var: requires a scalar probability distribution.> var (pd)
579s 63 tests, 63 passed, 0 known failure, 0 skipped
579s [inst/dist_obj/PoissonDistribution.m]
579s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_obj/PoissonDistribution.m
579s ***** demo
579s  ## Generate a data set of 5000 random samples from a Poisson distribution with
579s  ## parameter lambda = 5.  Fit a Poisson distribution to this data and plot
579s  ## a PDF of the fitted distribution superimposed on a histogram of the data.
579s 
579s  pd_fixed = makedist ("Poisson", "lambda", 5)
579s  rand ("seed", 2);
579s  data = random (pd_fixed, 5000, 1);
579s  pd_fitted = fitdist (data, "Poisson")
579s  plot (pd_fitted)
579s  msg = "Fitted Poisson distribution with lambda = %0.2f";
579s  title (sprintf (msg, pd_fitted.lambda))
579s ***** shared pd, t, t_inf
579s  pd = PoissonDistribution;
579s  t = truncate (pd, 2, 4);
579s  t_inf = truncate (pd, 2, Inf);
579s ***** assert (cdf (pd, [0:5]), [0.3679, 0.7358, 0.9197, 0.9810, 0.9963, 0.9994], 1e-4);
579s ***** assert (cdf (t, [0:5]), [0, 0, 0.7059, 0.9412, 1, 1], 1e-4);
579s ***** assert (cdf (t_inf, [0:5]), [0, 0, 0.6961, 0.9281, 0.9861, 0.9978], 1e-4);
579s ***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.7358, 0.9197, 0.9810, 0.9963], 1e-4);
579s ***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0.7059, 0.9412, 1], 1e-4);
579s ***** assert (icdf (pd, [0:0.2:1]), [0, 0, 1, 1, 2, Inf], 1e-4);
579s ***** assert (icdf (t, [0:0.2:1]), [2, 2, 2, 2, 3, 4], 1e-4);
579s ***** assert (icdf (t_inf, [0:0.2:1]), [2, 2, 2, 2, 3, Inf], 1e-4);
579s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1, 1, 2, Inf, NaN], 1e-4);
579s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2, 2, 3, 4, NaN], 1e-4);
579s ***** assert (iqr (pd), 2);
579s ***** assert (iqr (t), 1);
579s ***** assert (mean (pd), 1);
579s ***** assert (mean (t), 2.3529, 1e-4);
579s ***** assert (mean (t_inf), 2.3922, 1e-4);
579s ***** assert (median (pd), 1);
579s ***** assert (median (t), 2);
579s ***** assert (median (t_inf), 2);
579s ***** assert (pdf (pd, [0:5]), [0.3679, 0.3679, 0.1839, 0.0613, 0.0153, 0.0031], 1e-4);
579s ***** assert (pdf (t, [0:5]), [0, 0, 0.7059, 0.2353, 0.0588, 0], 1e-4);
579s ***** assert (pdf (t_inf, [0:5]), [0, 0, 0.6961, 0.2320, 0.0580, 0.0116], 1e-4);
579s ***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3679, 0.1839, 0.0613, 0.0153, NaN], 1e-4);
579s ***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.7059, 0.2353, 0.0588, NaN], 1e-4);
579s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
579s ***** assert (any (random (t, 1000, 1) < 2), false);
579s ***** assert (any (random (t, 1000, 1) > 4), false);
579s ***** assert (std (pd), 1);
579s ***** assert (std (t), 0.5882, 1e-4);
579s ***** assert (std (t_inf), 0.6738, 1e-4);
579s ***** assert (var (pd), 1);
579s ***** assert (var (t), 0.3460, 1e-4);
579s ***** assert (var (t_inf), 0.4540, 1e-4);
579s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
579s  PoissonDistribution(0)
579s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
579s  PoissonDistribution(-1)
579s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
579s  PoissonDistribution(Inf)
579s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
579s  PoissonDistribution(i)
579s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
579s  PoissonDistribution("a")
579s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
579s  PoissonDistribution([1, 2])
579s ***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
579s  PoissonDistribution(NaN)
579s ***** error <cdf: invalid argument for upper tail.> ...
579s  cdf (PoissonDistribution, 2, "uper")
579s ***** error <cdf: invalid argument for upper tail.> ...
579s  cdf (PoissonDistribution, 2, 3)
579s ***** shared x
579s  x = poissrnd (1, [1, 100]);
579s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
579s  paramci (PoissonDistribution.fit (x), "alpha")
579s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
579s  paramci (PoissonDistribution.fit (x), "alpha", 0)
579s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
579s  paramci (PoissonDistribution.fit (x), "alpha", 1)
579s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
579s  paramci (PoissonDistribution.fit (x), "alpha", [0.5 2])
579s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
579s  paramci (PoissonDistribution.fit (x), "alpha", "")
579s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
579s  paramci (PoissonDistribution.fit (x), "alpha", {0.05})
579s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
579s  paramci (PoissonDistribution.fit (x), "parameter", "lambda", "alpha", {0.05})
579s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
579s  paramci (PoissonDistribution.fit (x), "parameter", {"lambda", "param"})
579s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
579s  paramci (PoissonDistribution.fit (x), "alpha", 0.01, ...
579s           "parameter", {"lambda", "param"})
579s ***** error <paramci: unknown distribution parameter.> ...
579s  paramci (PoissonDistribution.fit (x), "parameter", "param")
579s ***** error <paramci: unknown distribution parameter.> ...
579s  paramci (PoissonDistribution.fit (x), "alpha", 0.01, "parameter", "param")
579s ***** error <paramci: invalid NAME for optional argument.> ...
579s  paramci (PoissonDistribution.fit (x), "NAME", "value")
579s ***** error <paramci: invalid NAME for optional argument.> ...
579s  paramci (PoissonDistribution.fit (x), "alpha", 0.01, "NAME", "value")
579s ***** error <paramci: invalid NAME for optional argument.> ...
579s  paramci (PoissonDistribution.fit (x), "alpha", 0.01, ...
579s           "parameter", "lambda", "NAME", "value")
579s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
579s  plot (PoissonDistribution, "Parent")
579s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
579s  plot (PoissonDistribution, "PlotType", 12)
579s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
579s  plot (PoissonDistribution, "PlotType", {"pdf", "cdf"})
579s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
579s  plot (PoissonDistribution, "PlotType", "pdfcdf")
579s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
579s  plot (PoissonDistribution, "Discrete", "pdfcdf")
579s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
579s  plot (PoissonDistribution, "Discrete", [1, 0])
579s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
579s  plot (PoissonDistribution, "Discrete", {true})
579s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
579s  plot (PoissonDistribution, "Parent", 12)
579s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
579s  plot (PoissonDistribution, "Parent", "hax")
579s ***** error <plot: invalid NAME for optional argument.> ...
579s  plot (PoissonDistribution, "invalidNAME", "pdf")
579s ***** error <plot: no fitted DATA to plot a probability plot.> ...
579s  plot (PoissonDistribution, "PlotType", "probability")
579s ***** error <proflik: no fitted data available.> ...
579s  proflik (PoissonDistribution, 2)
579s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
579s  proflik (PoissonDistribution.fit (x), 3)
579s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
579s  proflik (PoissonDistribution.fit (x), [1, 2])
579s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
579s  proflik (PoissonDistribution.fit (x), {1})
580s ***** error <proflik: SETPARAM must be a numeric vector.> ...
580s  proflik (PoissonDistribution.fit (x), 1, ones (2))
580s ***** error <proflik: missing VALUE for 'Display' argument.> ...
580s  proflik (PoissonDistribution.fit (x), 1, "Display")
580s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
580s  proflik (PoissonDistribution.fit (x), 1, "Display", 1)
580s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
580s  proflik (PoissonDistribution.fit (x), 1, "Display", {1})
580s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
580s  proflik (PoissonDistribution.fit (x), 1, "Display", {"on"})
580s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
580s  proflik (PoissonDistribution.fit (x), 1, "Display", ["on"; "on"])
580s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
580s  proflik (PoissonDistribution.fit (x), 1, "Display", "onnn")
580s ***** error <proflik: invalid NAME for optional arguments.> ...
580s  proflik (PoissonDistribution.fit (x), 1, "NAME", "on")
580s ***** error <proflik: invalid optional argument.> ...
580s  proflik (PoissonDistribution.fit (x), 1, {"NAME"}, "on")
580s ***** error <proflik: invalid optional argument.> ...
580s  proflik (PoissonDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
580s ***** error <truncate: missing input argument.> ...
580s  truncate (PoissonDistribution)
580s ***** error <truncate: missing input argument.> ...
580s  truncate (PoissonDistribution, 2)
580s ***** error <truncate: invalid lower upper limits.> ...
580s  truncate (PoissonDistribution, 4, 2)
580s ***** shared pd
580s  pd = PoissonDistribution(1);
580s  pd(2) = PoissonDistribution(3);
580s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
580s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
580s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
580s ***** error <mean: requires a scalar probability distribution.> mean (pd)
580s ***** error <median: requires a scalar probability distribution.> median (pd)
580s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
580s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
580s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
580s ***** error <plot: requires a scalar probability distribution.> plot (pd)
580s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
580s ***** error <random: requires a scalar probability distribution.> random (pd)
580s ***** error <std: requires a scalar probability distribution.> std (pd)
580s ***** error <truncate: requires a scalar probability distribution.> ...
580s  truncate (pd, 2, 4)
580s ***** error <var: requires a scalar probability distribution.> var (pd)
580s 97 tests, 97 passed, 0 known failure, 0 skipped
580s [inst/dist_obj/RayleighDistribution.m]
580s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_obj/RayleighDistribution.m
580s ***** demo
580s  ## Generate a data set of 5000 random samples from a Rayleigh distribution with
580s  ## parameter sigma = 2.  Fit a Rayleigh distribution to this data and plot
580s  ## a PDF of the fitted distribution superimposed on a histogram of the data.
580s 
580s  pd_fixed = makedist ("Rayleigh", "sigma", 2)
580s  rand ("seed", 2);
580s  data = random (pd_fixed, 5000, 1);
580s  pd_fitted = fitdist (data, "Rayleigh")
580s  plot (pd_fitted)
580s  msg = "Fitted Rayleigh distribution with sigma = %0.2f";
580s  title (sprintf (msg, pd_fitted.sigma))
580s ***** shared pd, t
580s  pd = RayleighDistribution;
580s  t = truncate (pd, 2, 4);
580s ***** assert (cdf (pd, [0:5]), [0, 0.3935, 0.8647, 0.9889, 0.9997, 1], 1e-4);
580s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.9202, 1, 1], 1e-4);
580s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.6753, 0.8647, 0.9889, 0.9997, NaN], 1e-4);
580s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.9202, 1, NaN], 1e-4);
580s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.6680, 1.0108, 1.3537, 1.7941, Inf], 1e-4);
580s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1083, 2.2402, 2.4135, 2.6831, 4], 1e-4);
580s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1.0108, 1.3537, 1.7941, Inf, NaN], 1e-4);
580s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.2402, 2.4135, 2.6831, 4, NaN], 1e-4);
580s ***** assert (iqr (pd), 0.9066, 1e-4);
580s ***** assert (iqr (t), 0.4609, 1e-4);
580s ***** assert (mean (pd), 1.2533, 1e-4);
580s ***** assert (mean (t), 2.4169, 1e-4);
580s ***** assert (median (pd), 1.1774, 1e-4);
580s ***** assert (median (t), 2.3198, 1e-4);
580s ***** assert (pdf (pd, [0:5]), [0, 0.6065, 0.2707, 0.0333, 0.0013, 0], 1e-4);
580s ***** assert (pdf (t, [0:5]), [0, 0, 2.0050, 0.2469, 0.0099, 0], 1e-4);
580s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.4870, NaN], 1e-4);
580s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
580s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
580s ***** assert (any (random (t, 1000, 1) < 2), false);
580s ***** assert (any (random (t, 1000, 1) > 4), false);
580s ***** assert (std (pd), 0.6551, 1e-4);
580s ***** assert (std (t), 0.3591, 1e-4);
580s ***** assert (var (pd), 0.4292, 1e-4);
580s ***** assert (var (t), 0.1290, 1e-4);
580s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
580s  RayleighDistribution(0)
580s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
580s  RayleighDistribution(-1)
580s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
580s  RayleighDistribution(Inf)
580s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
580s  RayleighDistribution(i)
580s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
580s  RayleighDistribution("a")
580s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
580s  RayleighDistribution([1, 2])
580s ***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
580s  RayleighDistribution(NaN)
580s ***** error <cdf: invalid argument for upper tail.> ...
580s  cdf (RayleighDistribution, 2, "uper")
580s ***** error <cdf: invalid argument for upper tail.> ...
580s  cdf (RayleighDistribution, 2, 3)
580s ***** shared x
580s  x = raylrnd (1, [1, 100]);
580s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
580s  paramci (RayleighDistribution.fit (x), "alpha")
580s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
580s  paramci (RayleighDistribution.fit (x), "alpha", 0)
580s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
580s  paramci (RayleighDistribution.fit (x), "alpha", 1)
580s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
580s  paramci (RayleighDistribution.fit (x), "alpha", [0.5 2])
580s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
580s  paramci (RayleighDistribution.fit (x), "alpha", "")
580s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
580s  paramci (RayleighDistribution.fit (x), "alpha", {0.05})
580s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
580s  paramci (RayleighDistribution.fit (x), "parameter", "sigma", "alpha", {0.05})
580s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
580s  paramci (RayleighDistribution.fit (x), "parameter", {"sigma", "param"})
580s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
580s  paramci (RayleighDistribution.fit (x), "alpha", 0.01, ...
580s           "parameter", {"sigma", "param"})
580s ***** error <paramci: unknown distribution parameter.> ...
580s  paramci (RayleighDistribution.fit (x), "parameter", "param")
580s ***** error <paramci: unknown distribution parameter.> ...
580s  paramci (RayleighDistribution.fit (x), "alpha", 0.01, "parameter", "param")
580s ***** error <paramci: invalid NAME for optional argument.> ...
580s  paramci (RayleighDistribution.fit (x), "NAME", "value")
580s ***** error <paramci: invalid NAME for optional argument.> ...
580s  paramci (RayleighDistribution.fit (x), "alpha", 0.01, "NAME", "value")
580s ***** error <paramci: invalid NAME for optional argument.> ...
580s  paramci (RayleighDistribution.fit (x), "alpha", 0.01, ...
580s           "parameter", "sigma", "NAME", "value")
580s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
580s  plot (RayleighDistribution, "Parent")
580s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
580s  plot (RayleighDistribution, "PlotType", 12)
580s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
580s  plot (RayleighDistribution, "PlotType", {"pdf", "cdf"})
580s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
580s  plot (RayleighDistribution, "PlotType", "pdfcdf")
580s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
580s  plot (RayleighDistribution, "Discrete", "pdfcdf")
580s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
580s  plot (RayleighDistribution, "Discrete", [1, 0])
580s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
580s  plot (RayleighDistribution, "Discrete", {true})
580s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
580s  plot (RayleighDistribution, "Parent", 12)
580s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
580s  plot (RayleighDistribution, "Parent", "hax")
580s ***** error <plot: invalid NAME for optional argument.> ...
580s  plot (RayleighDistribution, "invalidNAME", "pdf")
580s ***** error <plot: no fitted DATA to plot a probability plot.> ...
580s  plot (RayleighDistribution, "PlotType", "probability")
580s ***** error <proflik: no fitted data available.> ...
580s  proflik (RayleighDistribution, 2)
580s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
580s  proflik (RayleighDistribution.fit (x), 3)
580s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
580s  proflik (RayleighDistribution.fit (x), [1, 2])
580s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
580s  proflik (RayleighDistribution.fit (x), {1})
580s ***** error <proflik: SETPARAM must be a numeric vector.> ...
580s  proflik (RayleighDistribution.fit (x), 1, ones (2))
580s ***** error <proflik: missing VALUE for 'Display' argument.> ...
580s  proflik (RayleighDistribution.fit (x), 1, "Display")
580s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
580s  proflik (RayleighDistribution.fit (x), 1, "Display", 1)
580s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
580s  proflik (RayleighDistribution.fit (x), 1, "Display", {1})
580s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
580s  proflik (RayleighDistribution.fit (x), 1, "Display", {"on"})
581s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
581s  proflik (RayleighDistribution.fit (x), 1, "Display", ["on"; "on"])
581s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
581s  proflik (RayleighDistribution.fit (x), 1, "Display", "onnn")
581s ***** error <proflik: invalid NAME for optional arguments.> ...
581s  proflik (RayleighDistribution.fit (x), 1, "NAME", "on")
581s ***** error <proflik: invalid optional argument.> ...
581s  proflik (RayleighDistribution.fit (x), 1, {"NAME"}, "on")
581s ***** error <proflik: invalid optional argument.> ...
581s  proflik (RayleighDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
581s ***** error <truncate: missing input argument.> ...
581s  truncate (RayleighDistribution)
581s ***** error <truncate: missing input argument.> ...
581s  truncate (RayleighDistribution, 2)
581s ***** error <truncate: invalid lower upper limits.> ...
581s  truncate (RayleighDistribution, 4, 2)
581s ***** shared pd
581s  pd = RayleighDistribution(1);
581s  pd(2) = RayleighDistribution(3);
581s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
581s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
581s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
581s ***** error <mean: requires a scalar probability distribution.> mean (pd)
581s ***** error <median: requires a scalar probability distribution.> median (pd)
581s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
581s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
581s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
581s ***** error <plot: requires a scalar probability distribution.> plot (pd)
581s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
581s ***** error <random: requires a scalar probability distribution.> random (pd)
581s ***** error <std: requires a scalar probability distribution.> std (pd)
581s ***** error <truncate: requires a scalar probability distribution.> ...
581s  truncate (pd, 2, 4)
581s ***** error <var: requires a scalar probability distribution.> var (pd)
581s 90 tests, 90 passed, 0 known failure, 0 skipped
581s [inst/dist_obj/RicianDistribution.m]
581s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_obj/RicianDistribution.m
581s ***** demo
581s  ## Generate a data set of 5000 random samples from a Rician distribution with
581s  ## parameters s = 2 and sigma = 1. Fit a Rician distribution to this data and
581s  ## plot a PDF of the fitted distribution superimposed on a histogram of the data.
581s 
581s  pd_fixed = makedist ("Rician", "s", 2, "sigma", 1)
581s  rand ("seed", 2);
581s  data = random (pd_fixed, 5000, 1);
581s  pd_fitted = fitdist (data, "Rician")
581s  plot (pd_fitted)
581s  msg = "Fitted Rician distribution with s = %0.2f and sigma = %0.2f";
581s  title (sprintf (msg, pd_fitted.s, pd_fitted.sigma))
581s ***** shared pd, t
581s  pd = RicianDistribution;
581s  t = truncate (pd, 2, 4);
581s ***** assert (cdf (pd, [0:5]), [0, 0.2671, 0.7310, 0.9563, 0.9971, 0.9999], 1e-4);
581s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.8466, 1, 1], 1e-4);
581s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.5120, 0.7310, 0.9563, 0.9971, NaN], 1e-4);
581s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.8466, 1, NaN], 1e-4);
581s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.8501, 1.2736, 1.6863, 2.2011, Inf], 1e-4);
581s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1517, 2.3296, 2.5545, 2.8868, 4], 1e-4);
581s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1.2736, 1.6863, 2.2011, Inf, NaN], 1e-4);
581s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.3296, 2.5545, 2.8868, 4, NaN], 1e-4);
581s ***** assert (iqr (pd), 1.0890, 1e-4);
581s ***** assert (iqr (t), 0.5928, 1e-4);
581s ***** assert (mean (pd), 1.5486, 1e-4);
581s ***** assert (mean (t), 2.5380, 1e-4);
581s ***** assert (median (pd), 1.4755, 1e-4);
582s ***** assert (median (t), 2.4341, 1e-4);
582s ***** assert (pdf (pd, [0:5]), [0, 0.4658, 0.3742, 0.0987, 0.0092, 0.0003], 1e-4);
582s ***** assert (pdf (t, [0:5]), [0, 0, 1.4063, 0.3707, 0.0346, 0], 1e-4);
582s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.4864, NaN], 1e-4);
582s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
582s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
582s ***** assert (any (random (t, 1000, 1) < 2), false);
582s ***** assert (any (random (t, 1000, 1) > 4), false);
582s ***** assert (std (pd), 0.7758, 1e-4);
582s ***** assert (std (t), 0.4294, 1e-4);
582s ***** assert (var (pd), 0.6019, 1e-4);
582s ***** assert (var (t), 0.1844, 1e-4);
582s ***** error <RicianDistribution: S must be a non-negative real scalar.> ...
582s  RicianDistribution(-eps, 1)
582s ***** error <RicianDistribution: S must be a non-negative real scalar.> ...
582s  RicianDistribution(-1, 1)
582s ***** error <RicianDistribution: S must be a non-negative real scalar.> ...
582s  RicianDistribution(Inf, 1)
582s ***** error <RicianDistribution: S must be a non-negative real scalar.> ...
582s  RicianDistribution(i, 1)
582s ***** error <RicianDistribution: S must be a non-negative real scalar.> ...
582s  RicianDistribution("a", 1)
582s ***** error <RicianDistribution: S must be a non-negative real scalar.> ...
582s  RicianDistribution([1, 2], 1)
582s ***** error <RicianDistribution: S must be a non-negative real scalar.> ...
582s  RicianDistribution(NaN, 1)
582s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
582s  RicianDistribution(1, 0)
582s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
582s  RicianDistribution(1, -1)
582s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
582s  RicianDistribution(1, Inf)
582s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
582s  RicianDistribution(1, i)
582s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
582s  RicianDistribution(1, "a")
582s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
582s  RicianDistribution(1, [1, 2])
582s ***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
582s  RicianDistribution(1, NaN)
582s ***** error <cdf: invalid argument for upper tail.> ...
582s  cdf (RicianDistribution, 2, "uper")
582s ***** error <cdf: invalid argument for upper tail.> ...
582s  cdf (RicianDistribution, 2, 3)
582s ***** shared x
582s  x = gevrnd (1, 1, 1, [1, 100]);
582s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
582s  paramci (RicianDistribution.fit (x), "alpha")
582s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
582s  paramci (RicianDistribution.fit (x), "alpha", 0)
582s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
582s  paramci (RicianDistribution.fit (x), "alpha", 1)
582s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
582s  paramci (RicianDistribution.fit (x), "alpha", [0.5 2])
582s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
582s  paramci (RicianDistribution.fit (x), "alpha", "")
582s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
582s  paramci (RicianDistribution.fit (x), "alpha", {0.05})
582s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
582s  paramci (RicianDistribution.fit (x), "parameter", "s", "alpha", {0.05})
582s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
582s  paramci (RicianDistribution.fit (x), "parameter", {"s", "sigma", "param"})
582s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
582s  paramci (RicianDistribution.fit (x), "alpha", 0.01, ...
582s           "parameter", {"s", "sigma", "param"})
583s ***** error <paramci: unknown distribution parameter.> ...
583s  paramci (RicianDistribution.fit (x), "parameter", "param")
583s ***** error <paramci: unknown distribution parameter.> ...
583s  paramci (RicianDistribution.fit (x), "alpha", 0.01, "parameter", "param")
583s ***** error <paramci: invalid NAME for optional argument.> ...
583s  paramci (RicianDistribution.fit (x), "NAME", "value")
583s ***** error <paramci: invalid NAME for optional argument.> ...
583s  paramci (RicianDistribution.fit (x), "alpha", 0.01, "NAME", "value")
583s ***** error <paramci: invalid NAME for optional argument.> ...
583s  paramci (RicianDistribution.fit (x), "alpha", 0.01, "parameter", "s", ...
583s           "NAME", "value")
583s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
583s  plot (RicianDistribution, "Parent")
583s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
583s  plot (RicianDistribution, "PlotType", 12)
583s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
583s  plot (RicianDistribution, "PlotType", {"pdf", "cdf"})
583s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
583s  plot (RicianDistribution, "PlotType", "pdfcdf")
583s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
583s  plot (RicianDistribution, "Discrete", "pdfcdf")
583s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
583s  plot (RicianDistribution, "Discrete", [1, 0])
583s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
583s  plot (RicianDistribution, "Discrete", {true})
583s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
583s  plot (RicianDistribution, "Parent", 12)
583s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
583s  plot (RicianDistribution, "Parent", "hax")
583s ***** error <plot: invalid NAME for optional argument.> ...
583s  plot (RicianDistribution, "invalidNAME", "pdf")
583s ***** error <plot: no fitted DATA to plot a probability plot.> ...
583s  plot (RicianDistribution, "PlotType", "probability")
583s ***** error <proflik: no fitted data available.> ...
583s  proflik (RicianDistribution, 2)
583s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
583s  proflik (RicianDistribution.fit (x), 3)
583s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
583s  proflik (RicianDistribution.fit (x), [1, 2])
583s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
583s  proflik (RicianDistribution.fit (x), {1})
583s ***** error <proflik: SETPARAM must be a numeric vector.> ...
583s  proflik (RicianDistribution.fit (x), 1, ones (2))
583s ***** error <proflik: missing VALUE for 'Display' argument.> ...
583s  proflik (RicianDistribution.fit (x), 1, "Display")
583s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
583s  proflik (RicianDistribution.fit (x), 1, "Display", 1)
584s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
584s  proflik (RicianDistribution.fit (x), 1, "Display", {1})
584s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
584s  proflik (RicianDistribution.fit (x), 1, "Display", {"on"})
584s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
584s  proflik (RicianDistribution.fit (x), 1, "Display", ["on"; "on"])
584s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
584s  proflik (RicianDistribution.fit (x), 1, "Display", "onnn")
584s ***** error <proflik: invalid NAME for optional arguments.> ...
584s  proflik (RicianDistribution.fit (x), 1, "NAME", "on")
584s ***** error <proflik: invalid optional argument.> ...
584s  proflik (RicianDistribution.fit (x), 1, {"NAME"}, "on")
584s ***** error <proflik: invalid optional argument.> ...
584s  proflik (RicianDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
584s ***** error <truncate: missing input argument.> ...
584s  truncate (RicianDistribution)
584s ***** error <truncate: missing input argument.> ...
584s  truncate (RicianDistribution, 2)
584s ***** error <truncate: invalid lower upper limits.> ...
584s  truncate (RicianDistribution, 4, 2)
584s ***** shared pd
584s  pd = RicianDistribution(1, 1);
584s  pd(2) = RicianDistribution(1, 3);
584s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
584s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
584s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
584s ***** error <mean: requires a scalar probability distribution.> mean (pd)
584s ***** error <median: requires a scalar probability distribution.> median (pd)
584s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
584s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
584s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
584s ***** error <plot: requires a scalar probability distribution.> plot (pd)
584s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
584s ***** error <random: requires a scalar probability distribution.> random (pd)
584s ***** error <std: requires a scalar probability distribution.> std (pd)
584s ***** error <truncate: requires a scalar probability distribution.> ...
584s  truncate (pd, 2, 4)
584s ***** error <var: requires a scalar probability distribution.> var (pd)
584s 97 tests, 97 passed, 0 known failure, 0 skipped
584s [inst/dist_obj/TriangularDistribution.m]
584s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_obj/TriangularDistribution.m
584s ***** demo
584s  ## Generate a data set of 5000 random samples from a Triangular distribution
584s  ## with parameters A = 0, B = 1, C = 2. Fit a Triangular distribution to
584s  ## this data and plot a PDF of the fitted distribution superimposed on a
584s  ## histogram of the data.
584s 
584s  pd_fixed = makedist ("Triangular", "A", 0, "B", 1, "C", 2);
584s  rand ("seed", 2);
584s  data = random (pd_fixed, 5000, 1);
584s  A = min (data);
584s  C = mean (data);
584s  B = max (data);
584s 
584s  [counts, centers] = hist (data, 50);
584s  bin_width = centers(2) - centers(1);
584s  normalized_counts = counts / (sum (counts) * bin_width);
584s  bar (centers, normalized_counts, 1);
584s  hold on;
584s 
584s  x = linspace (A, B, 100);
584s  y = (2 * (x - A) / (C - A) .* (x <= C)) + (2 * (B - x) / (B - C) .* (x > C));
584s 
584s  plot (x, y, 'r-', 'LineWidth', 2);
584s 
584s  msg = sprintf ("Fitted Triangular distribution with A = %0.2f, C = %0.2f, B = %0.2f", A, C, B);
584s  title (msg);
584s 
584s  hold off;
584s ***** shared pd, t
584s  pd = TriangularDistribution (0, 3, 5);
584s  t = truncate (pd, 2, 4);
584s ***** assert (cdf (pd, [0:5]), [0, 0.0667, 0.2667, 0.6000, 0.9000, 1], 1e-4);
584s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.5263, 1, 1], 1e-4);
584s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.1500, 0.2667, 0.6, 0.9, NaN], 1e-4);
584s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.5263, 1, NaN], 1e-4);
584s ***** assert (icdf (pd, [0:0.2:1]), [0, 1.7321, 2.4495, 3, 3.5858, 5], 1e-4);
584s ***** assert (icdf (t, [0:0.2:1]), [2, 2.4290, 2.7928, 3.1203, 3.4945, 4], 1e-4);
584s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4495, 3, 3.5858, 5, NaN], 1e-4);
584s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.7928, 3.1203, 3.4945, 4, NaN], 1e-4);
584s ***** assert (iqr (pd), 1.4824, 1e-4);
584s ***** assert (iqr (t), 0.8678, 1e-4);
584s ***** assert (mean (pd), 2.6667, 1e-4);
584s ***** assert (mean (t), 2.9649, 1e-4);
584s ***** assert (median (pd), 2.7386, 1e-4);
584s ***** assert (median (t), 2.9580, 1e-4);
584s ***** assert (pdf (pd, [0:5]), [0, 0.1333, 0.2667, 0.4, 0.2, 0], 1e-4);
584s ***** assert (pdf (t, [0:5]), [0, 0, 0.4211, 0.6316, 0.3158, 0], 1e-4);
584s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.2, NaN], 1e-4);
584s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
584s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
584s ***** assert (any (random (t, 1000, 1) < 2), false);
584s ***** assert (any (random (t, 1000, 1) > 4), false);
584s ***** assert (std (pd), 1.0274, 1e-4);
584s ***** assert (std (t), 0.5369, 1e-4);
584s ***** assert (var (pd), 1.0556, 1e-4);
584s ***** assert (var (t), 0.2882, 1e-4);
584s ***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
584s  TriangularDistribution (i, 1, 2)
585s ***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
585s  TriangularDistribution (Inf, 1, 2)
585s ***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
585s  TriangularDistribution ([1, 2], 1, 2)
585s ***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
585s  TriangularDistribution ("a", 1, 2)
585s ***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
585s  TriangularDistribution (NaN, 1, 2)
585s ***** error <TriangularDistribution: mode B must be a real scalar.> ...
585s  TriangularDistribution (1, i, 2)
585s ***** error <TriangularDistribution: mode B must be a real scalar.> ...
585s  TriangularDistribution (1, Inf, 2)
585s ***** error <TriangularDistribution: mode B must be a real scalar.> ...
585s  TriangularDistribution (1, [1, 2], 2)
585s ***** error <TriangularDistribution: mode B must be a real scalar.> ...
585s  TriangularDistribution (1, "a", 2)
585s ***** error <TriangularDistribution: mode B must be a real scalar.> ...
585s  TriangularDistribution (1, NaN, 2)
585s ***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
585s  TriangularDistribution (1, 2, i)
585s ***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
585s  TriangularDistribution (1, 2, Inf)
585s ***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
585s  TriangularDistribution (1, 2, [1, 2])
585s ***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
585s  TriangularDistribution (1, 2, "a")
585s ***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
585s  TriangularDistribution (1, 2, NaN)
585s ***** error <TriangularDistribution: lower limit A must be less than upper limit C.> ...
585s  TriangularDistribution (1, 1, 1)
585s ***** error <TriangularDistribution: mode B must be within lower limit A and upper limit C.> ...
585s  TriangularDistribution (1, 0.5, 2)
585s ***** error <cdf: invalid argument for upper tail.> ...
585s  cdf (TriangularDistribution, 2, "uper")
585s ***** error <cdf: invalid argument for upper tail.> ...
585s  cdf (TriangularDistribution, 2, 3)
585s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
585s  plot (TriangularDistribution, "Parent")
585s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
585s  plot (TriangularDistribution, "PlotType", 12)
585s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
585s  plot (TriangularDistribution, "PlotType", {"pdf", "cdf"})
585s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
585s  plot (TriangularDistribution, "PlotType", "pdfcdf")
585s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
585s  plot (TriangularDistribution, "Discrete", "pdfcdf")
585s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
585s  plot (TriangularDistribution, "Discrete", [1, 0])
585s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
585s  plot (TriangularDistribution, "Discrete", {true})
585s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
585s  plot (TriangularDistribution, "Parent", 12)
585s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
585s  plot (TriangularDistribution, "Parent", "hax")
585s ***** error <plot: invalid NAME for optional argument.> ...
585s  plot (TriangularDistribution, "invalidNAME", "pdf")
585s ***** error <'probability' PlotType is not supported for 'TriangularDistribution'.> ...
585s  plot (TriangularDistribution, "PlotType", "probability")
585s ***** error <truncate: missing input argument.> ...
585s  truncate (TriangularDistribution)
585s ***** error <truncate: missing input argument.> ...
585s  truncate (TriangularDistribution, 2)
585s ***** error <truncate: invalid lower upper limits.> ...
585s  truncate (TriangularDistribution, 4, 2)
585s ***** shared pd
585s  pd = TriangularDistribution (0, 1, 2);
585s  pd(2) = TriangularDistribution (0, 1, 2);
585s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
585s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
585s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
585s ***** error <mean: requires a scalar probability distribution.> mean (pd)
585s ***** error <median: requires a scalar probability distribution.> median (pd)
585s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
585s ***** error <plot: requires a scalar probability distribution.> plot (pd)
585s ***** error <random: requires a scalar probability distribution.> random (pd)
585s ***** error <std: requires a scalar probability distribution.> std (pd)
585s ***** error <truncate: requires a scalar probability distribution.> ...
585s  truncate (pd, 2, 4)
585s ***** error <var: requires a scalar probability distribution.> var (pd)
585s 69 tests, 69 passed, 0 known failure, 0 skipped
585s [inst/dist_obj/UniformDistribution.m]
585s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_obj/UniformDistribution.m
585s ***** demo
585s  ## Generate a data set of 5000 random samples from a Uniform distribution with
585s  ## parameters Lower = 0 and Upper = 10. Create a Uniform distribution with these
585s  ## parameters and plot its PDF superimposed on a histogram of the data.
585s 
585s  pd = makedist ("Uniform", "Lower", 0, "Upper", 10);
585s  rand ("seed", 21);
585s  data = random (pd, 5000, 1);
585s  
585s  x = linspace (pd.Lower - 1, pd.Upper + 1, 500);
585s  y = pdf (pd, x);
585s  plot (x, y, 'r-', 'LineWidth', 2);
585s  hold on;
585s  
585s  [counts, centers] = hist (data, 50);
585s  bin_width = centers(2) - centers(1);
585s  normalized_counts = counts / (sum (counts) * bin_width);
585s  bar (centers, normalized_counts, 1);
585s  
585s  msg = "Uniform distribution with Lower = %0.2f and Upper = %0.2f";
585s  title (sprintf (msg, pd.Lower, pd.Upper));
585s  legend ("PDF", "Histogram", "location", "northeast");
585s  
585s  hold off;
585s ***** shared pd, t
585s  pd = UniformDistribution (0, 5);
585s  t = truncate (pd, 2, 4);
585s ***** assert (cdf (pd, [0:5]), [0, 0.2, 0.4, 0.6, 0.8, 1], 1e-4);
585s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.5, 1, 1], 1e-4);
585s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.3, 0.4, 0.6, 0.8, NaN], 1e-4);
585s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.5, 1, NaN], 1e-4);
585s ***** assert (icdf (pd, [0:0.2:1]), [0, 1, 2, 3, 4, 5], 1e-4);
585s ***** assert (icdf (t, [0:0.2:1]), [2, 2.4, 2.8, 3.2, 3.6, 4], 1e-4);
585s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 2, 3, 4, 5, NaN], 1e-4);
585s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.8, 3.2, 3.6, 4, NaN], 1e-4);
585s ***** assert (iqr (pd), 2.5, 1e-14);
585s ***** assert (iqr (t), 1, 1e-14);
585s ***** assert (mean (pd), 2.5, 1e-14);
585s ***** assert (mean (t), 3, 1e-14);
585s ***** assert (median (pd), 2.5, 1e-14);
585s ***** assert (median (t), 3, 1e-14);
585s ***** assert (pdf (pd, [0:5]), [0.2, 0.2, 0.2, 0.2, 0.2, 0.2], 1e-4);
585s ***** assert (pdf (t, [0:5]), [0, 0, 0.5, 0.5, 0.5, 0], 1e-4);
585s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.2, NaN], 1e-4);
585s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
585s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
585s ***** assert (any (random (t, 1000, 1) < 2), false);
585s ***** assert (any (random (t, 1000, 1) > 4), false);
585s ***** assert (std (pd), 1.4434, 1e-4);
585s ***** assert (std (t), 0.5774, 1e-4);
585s ***** assert (var (pd), 2.0833, 1e-4);
585s ***** assert (var (t), 0.3333, 1e-4);
585s ***** error <UniformDistribution: LOWER must be a real scalar.> ...
585s  UniformDistribution (i, 1)
585s ***** error <UniformDistribution: LOWER must be a real scalar.> ...
585s  UniformDistribution (Inf, 1)
585s ***** error <UniformDistribution: LOWER must be a real scalar.> ...
585s  UniformDistribution ([1, 2], 1)
585s ***** error <UniformDistribution: LOWER must be a real scalar.> ...
585s  UniformDistribution ("a", 1)
585s ***** error <UniformDistribution: LOWER must be a real scalar.> ...
585s  UniformDistribution (NaN, 1)
585s ***** error <UniformDistribution: UPPER must be a real scalar.> ...
585s  UniformDistribution (1, i)
585s ***** error <UniformDistribution: UPPER must be a real scalar.> ...
585s  UniformDistribution (1, Inf)
585s ***** error <UniformDistribution: UPPER must be a real scalar.> ...
585s  UniformDistribution (1, [1, 2])
585s ***** error <UniformDistribution: UPPER must be a real scalar.> ...
585s  UniformDistribution (1, "a")
585s ***** error <UniformDistribution: UPPER must be a real scalar.> ...
585s  UniformDistribution (1, NaN)
585s ***** error <UniformDistribution: LOWER must be less than UPPER.> ...
585s  UniformDistribution (2, 1)
585s ***** error <cdf: invalid argument for upper tail.> ...
585s  cdf (UniformDistribution, 2, "uper")
585s ***** error <cdf: invalid argument for upper tail.> ...
585s  cdf (UniformDistribution, 2, 3)
585s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
585s  plot (UniformDistribution, "Parent")
585s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
585s  plot (UniformDistribution, "PlotType", 12)
585s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
585s  plot (UniformDistribution, "PlotType", {"pdf", "cdf"})
585s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
585s  plot (UniformDistribution, "PlotType", "pdfcdf")
585s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
585s  plot (UniformDistribution, "Discrete", "pdfcdf")
585s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
585s  plot (UniformDistribution, "Discrete", [1, 0])
585s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
585s  plot (UniformDistribution, "Discrete", {true})
585s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
585s  plot (UniformDistribution, "Parent", 12)
585s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
585s  plot (UniformDistribution, "Parent", "hax")
585s ***** error <plot: invalid NAME for optional argument.> ...
585s  plot (UniformDistribution, "invalidNAME", "pdf")
585s ***** error <plot: 'probability' PlotType is not supported for 'UniformDistribution'.> ...
585s  plot (UniformDistribution, "PlotType", "probability")
585s ***** error <truncate: missing input argument.> ...
585s  truncate (UniformDistribution)
585s ***** error <truncate: missing input argument.> ...
585s  truncate (UniformDistribution, 2)
585s ***** error <truncate: invalid lower upper limits.> ...
585s  truncate (UniformDistribution, 4, 2)
585s ***** shared pd
585s  pd = UniformDistribution (0, 1);
585s  pd(2) = UniformDistribution (0, 2);
585s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
585s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
585s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
585s ***** error <mean: requires a scalar probability distribution.> mean (pd)
585s ***** error <median: requires a scalar probability distribution.> median (pd)
585s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
585s ***** error <plot: requires a scalar probability distribution.> plot (pd)
585s ***** error <random: requires a scalar probability distribution.> random (pd)
585s ***** error <std: requires a scalar probability distribution.> std (pd)
585s ***** error <truncate: requires a scalar probability distribution.> ...
585s  truncate (pd, 2, 4)
585s ***** error <var: requires a scalar probability distribution.> var (pd)
585s 63 tests, 63 passed, 0 known failure, 0 skipped
585s [inst/dist_obj/WeibullDistribution.m]
585s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_obj/WeibullDistribution.m
585s ***** demo
585s  ## Generate a data set of 5000 random samples from a Weibull distribution with
585s  ## parameters lambda = 1 and k = 2.  Fit a Weibull distribution to this data and plot
585s  ## a PDF of the fitted distribution superimposed on a histogram of a data.
585s 
585s  pd_fixed = makedist ("Weibull", "lambda", 1, "k", 2)
585s  rand ("seed", 2);
585s  data = random (pd_fixed, 5000, 1);
585s  pd_fitted = fitdist (data, "Weibull")
585s  plot (pd_fitted)
585s  msg = "Fitted Weibull distribution with lambda = %0.2f and k = %0.2f";
585s  title (sprintf (msg, pd_fitted.lambda, pd_fitted.k))
585s ***** shared pd, t
585s  pd = WeibullDistribution;
585s  t = truncate (pd, 2, 4);
585s ***** assert (cdf (pd, [0:5]), [0, 0.6321, 0.8647, 0.9502, 0.9817, 0.9933], 1e-4);
585s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7311, 1, 1], 1e-4);
585s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.7769, 0.8647, 0.9502, 0.9817, NaN], 1e-4);
585s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.7311, 1, NaN], 1e-4);
585s ***** assert (icdf (pd, [0:0.2:1]), [0, 0.2231, 0.5108, 0.9163, 1.6094, Inf], 1e-4);
585s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1899, 2.4244, 2.7315, 3.1768, 4], 1e-4);
585s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5108, 0.9163, 1.6094, Inf, NaN], 1e-4);
585s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4244, 2.7315, 3.1768, 4, NaN], 1e-4);
585s ***** assert (iqr (pd), 1.0986, 1e-4);
585s ***** assert (iqr (t), 0.8020, 1e-4);
585s ***** assert (mean (pd), 1, 1e-14);
585s ***** assert (mean (t), 2.6870, 1e-4);
585s ***** assert (median (pd), 0.6931, 1e-4);
585s ***** assert (median (t), 2.5662, 1e-4);
585s ***** assert (pdf (pd, [0:5]), [1, 0.3679, 0.1353, 0.0498, 0.0183, 0.0067], 1e-4);
585s ***** assert (pdf (t, [0:5]), [0, 0, 1.1565, 0.4255, 0.1565, 0], 1e-4);
585s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.2231, NaN], 1e-4);
585s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
585s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
585s ***** assert (any (random (t, 1000, 1) < 2), false);
585s ***** assert (any (random (t, 1000, 1) > 4), false);
585s ***** assert (std (pd), 1, 1e-14);
585s ***** assert (std (t), 0.5253, 1e-4);
585s ***** assert (var (pd), 1, 1e-14);
585s ***** assert (var (t), 0.2759, 1e-4);
585s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
585s  WeibullDistribution(0, 1)
585s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
585s  WeibullDistribution(-1, 1)
585s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
585s  WeibullDistribution(Inf, 1)
585s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
585s  WeibullDistribution(i, 1)
585s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
585s  WeibullDistribution("a", 1)
585s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
585s  WeibullDistribution([1, 2], 1)
585s ***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
585s  WeibullDistribution(NaN, 1)
585s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
585s  WeibullDistribution(1, 0)
585s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
585s  WeibullDistribution(1, -1)
585s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
585s  WeibullDistribution(1, Inf)
585s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
585s  WeibullDistribution(1, i)
585s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
585s  WeibullDistribution(1, "a")
585s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
585s  WeibullDistribution(1, [1, 2])
585s ***** error <WeibullDistribution: K must be a positive real scalar.> ...
585s  WeibullDistribution(1, NaN)
585s ***** error <cdf: invalid argument for upper tail.> ...
585s  cdf (WeibullDistribution, 2, "uper")
585s ***** error <cdf: invalid argument for upper tail.> ...
585s  cdf (WeibullDistribution, 2, 3)
585s ***** shared x
585s  x = wblrnd (1, 1, [1, 100]);
585s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
585s  paramci (WeibullDistribution.fit (x), "alpha")
585s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
585s  paramci (WeibullDistribution.fit (x), "alpha", 0)
585s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
585s  paramci (WeibullDistribution.fit (x), "alpha", 1)
585s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
585s  paramci (WeibullDistribution.fit (x), "alpha", [0.5 2])
585s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
585s  paramci (WeibullDistribution.fit (x), "alpha", "")
585s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
585s  paramci (WeibullDistribution.fit (x), "alpha", {0.05})
585s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
585s  paramci (WeibullDistribution.fit (x), "parameter", "k", "alpha", {0.05})
585s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
585s  paramci (WeibullDistribution.fit (x), "parameter", {"lambda", "k", "param"})
585s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
585s  paramci (WeibullDistribution.fit (x), "alpha", 0.01, ...
585s           "parameter", {"lambda", "k", "param"})
585s ***** error <paramci: unknown distribution parameter.> ...
585s  paramci (WeibullDistribution.fit (x), "parameter", "param")
585s ***** error <paramci: unknown distribution parameter.> ...
585s  paramci (WeibullDistribution.fit (x), "alpha", 0.01, "parameter", "param")
585s ***** error <paramci: invalid NAME for optional argument.> ...
585s  paramci (WeibullDistribution.fit (x), "NAME", "value")
585s ***** error <paramci: invalid NAME for optional argument.> ...
585s  paramci (WeibullDistribution.fit (x), "alpha", 0.01, "NAME", "value")
585s ***** error <paramci: invalid NAME for optional argument.> ...
585s  paramci (WeibullDistribution.fit (x), "alpha", 0.01, "parameter", "k", ...
585s           "NAME", "value")
585s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
585s  plot (WeibullDistribution, "Parent")
585s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
585s  plot (WeibullDistribution, "PlotType", 12)
585s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
585s  plot (WeibullDistribution, "PlotType", {"pdf", "cdf"})
585s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
585s  plot (WeibullDistribution, "PlotType", "pdfcdf")
585s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
585s  plot (WeibullDistribution, "Discrete", "pdfcdf")
585s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
585s  plot (WeibullDistribution, "Discrete", [1, 0])
585s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
585s  plot (WeibullDistribution, "Discrete", {true})
585s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
585s  plot (WeibullDistribution, "Parent", 12)
585s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
585s  plot (WeibullDistribution, "Parent", "hax")
585s ***** error <plot: invalid NAME for optional argument.> ...
585s  plot (WeibullDistribution, "invalidNAME", "pdf")
585s ***** error <plot: no fitted DATA to plot a probability plot.> ...
585s  plot (WeibullDistribution, "PlotType", "probability")
585s ***** error <proflik: no fitted data available.> ...
585s  proflik (WeibullDistribution, 2)
585s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
585s  proflik (WeibullDistribution.fit (x), 3)
585s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
585s  proflik (WeibullDistribution.fit (x), [1, 2])
585s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
585s  proflik (WeibullDistribution.fit (x), {1})
585s ***** error <proflik: SETPARAM must be a numeric vector.> ...
585s  proflik (WeibullDistribution.fit (x), 1, ones (2))
585s ***** error <proflik: missing VALUE for 'Display' argument.> ...
585s  proflik (WeibullDistribution.fit (x), 1, "Display")
585s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
585s  proflik (WeibullDistribution.fit (x), 1, "Display", 1)
585s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
585s  proflik (WeibullDistribution.fit (x), 1, "Display", {1})
585s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
585s  proflik (WeibullDistribution.fit (x), 1, "Display", {"on"})
585s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
585s  proflik (WeibullDistribution.fit (x), 1, "Display", ["on"; "on"])
586s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
586s  proflik (WeibullDistribution.fit (x), 1, "Display", "onnn")
586s ***** error <proflik: invalid NAME for optional arguments.> ...
586s  proflik (WeibullDistribution.fit (x), 1, "NAME", "on")
586s ***** error <proflik: invalid optional argument.> ...
586s  proflik (WeibullDistribution.fit (x), 1, {"NAME"}, "on")
586s ***** error <proflik: invalid optional argument.> ...
586s  proflik (WeibullDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
586s ***** error <truncate: missing input argument.> ...
586s  truncate (WeibullDistribution)
586s ***** error <truncate: missing input argument.> ...
586s  truncate (WeibullDistribution, 2)
586s ***** error <truncate: invalid lower upper limits.> ...
586s  truncate (WeibullDistribution, 4, 2)
586s ***** shared pd
586s  pd = WeibullDistribution(1, 1);
586s  pd(2) = WeibullDistribution(1, 3);
586s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
586s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
586s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
586s ***** error <mean: requires a scalar probability distribution.> mean (pd)
586s ***** error <median: requires a scalar probability distribution.> median (pd)
586s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
586s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
586s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
586s ***** error <plot: requires a scalar probability distribution.> plot (pd)
586s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
586s ***** error <random: requires a scalar probability distribution.> random (pd)
586s ***** error <std: requires a scalar probability distribution.> std (pd)
586s ***** error <truncate: requires a scalar probability distribution.> ...
586s  truncate (pd, 2, 4)
586s ***** error <var: requires a scalar probability distribution.> var (pd)
586s 97 tests, 97 passed, 0 known failure, 0 skipped
586s [inst/dist_obj/tLocationScaleDistribution.m]
586s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_obj/tLocationScaleDistribution.m
586s ***** demo
586s  ## Generate a data set of 5000 random samples from a t Location-Scale distribution
586s  ## with parameters mu = 0, sigma = 1, and nu = 5. Fit a t Location-Scale
586s  ## distribution to this data and plot a PDF of the fitted distribution
586s  ## superimposed on a histogram of the data.
586s 
586s  pd_fixed = makedist ("tLocationScale", "mu", 0, "sigma", 1, "nu", 5);
586s  rand ("seed", 2);
586s  data = random (pd_fixed, 5000, 1);
586s  pd_fitted = fitdist (data, "tLocationScale");
586s  plot (pd_fitted);
586s  msg = "Fitted t Location-Scale distribution with mu = %0.2f, sigma = %0.2f, nu = %0.2f";
586s  title (sprintf (msg, pd_fitted.mu, pd_fitted.sigma, pd_fitted.nu));
586s ***** shared pd, t
586s  pd = tLocationScaleDistribution;
586s  t = truncate (pd, 2, 4);
586s ***** assert (cdf (pd, [0:5]), [0.5, 0.8184, 0.9490, 0.9850, 0.9948, 0.9979], 1e-4);
586s ***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7841, 1, 1], 1e-4);
586s ***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.9030, 0.9490, 0.9850, 0.9948, NaN], 1e-4);
586s ***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.7841, 1, NaN], 1e-4);
586s ***** assert (icdf (pd, [0:0.2:1]), [-Inf, -0.9195, -0.2672, 0.2672, 0.9195, Inf], 1e-4);
586s ***** assert (icdf (t, [0:0.2:1]), [2, 2.1559, 2.3533, 2.6223, 3.0432, 4], 1e-4);
586s ***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, -0.2672, 0.2672, 0.9195, Inf, NaN], 1e-4);
586s ***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.3533, 2.6223, 3.0432, 4, NaN], 1e-4);
586s ***** assert (iqr (pd), 1.4534, 1e-4);
586s ***** assert (iqr (t), 0.7139, 1e-4);
586s ***** assert (mean (pd), 0, eps);
586s ***** assert (mean (t), 2.6099, 1e-4);
586s ***** assert (median (pd), 0, eps);
586s ***** assert (median (t), 2.4758, 1e-4);
586s ***** assert (pdf (pd, [0:5]), [0.3796, 0.2197, 0.0651, 0.0173, 0.0051, 0.0018], 1e-4);
586s ***** assert (pdf (t, [0:5]), [0, 0, 1.4209, 0.3775, 0.1119, 0], 1e-4);
586s ***** assert (pdf (pd, [-1, 1.5, NaN]), [0.2197, 0.1245, NaN], 1e-4);
586s ***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
586s ***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
586s ***** assert (any (random (t, 1000, 1) < 2), false);
586s ***** assert (any (random (t, 1000, 1) > 4), false);
586s ***** assert (std (pd), 1.2910, 1e-4);
586s ***** assert (std (t), 0.4989, 1e-4);
586s ***** assert (var (pd), 1.6667, 1e-4);
586s ***** assert (var (t), 0.2489, 1e-4);
586s ***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
586s  tLocationScaleDistribution(i, 1, 1)
586s ***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
586s  tLocationScaleDistribution(Inf, 1, 1)
586s ***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
586s  tLocationScaleDistribution([1, 2], 1, 1)
586s ***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
586s  tLocationScaleDistribution("a", 1, 1)
586s ***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
586s  tLocationScaleDistribution(NaN, 1, 1)
586s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
586s  tLocationScaleDistribution(0, 0, 1)
586s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
586s  tLocationScaleDistribution(0, -1, 1)
586s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
586s  tLocationScaleDistribution(0, Inf, 1)
586s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
586s  tLocationScaleDistribution(0, i, 1)
586s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
586s  tLocationScaleDistribution(0, "a", 1)
586s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
586s  tLocationScaleDistribution(0, [1, 2], 1)
586s ***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
586s  tLocationScaleDistribution(0, NaN, 1)
586s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
586s  tLocationScaleDistribution(0, 1, 0)
586s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
586s  tLocationScaleDistribution(0, 1, -1)
586s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
586s  tLocationScaleDistribution(0, 1, Inf)
586s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
586s  tLocationScaleDistribution(0, 1, i)
586s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
586s  tLocationScaleDistribution(0, 1, "a")
586s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
586s  tLocationScaleDistribution(0, 1, [1, 2])
586s ***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
586s  tLocationScaleDistribution(0, 1, NaN)
586s ***** error <cdf: invalid argument for upper tail.> ...
586s  cdf (tLocationScaleDistribution, 2, "uper")
586s ***** error <cdf: invalid argument for upper tail.> ...
586s  cdf (tLocationScaleDistribution, 2, 3)
586s ***** shared x
586s  x = tlsrnd (0, 1, 1, [1, 100]);
586s ***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
586s  paramci (tLocationScaleDistribution.fit (x), "alpha")
586s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
586s  paramci (tLocationScaleDistribution.fit (x), "alpha", 0)
586s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
586s  paramci (tLocationScaleDistribution.fit (x), "alpha", 1)
586s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
586s  paramci (tLocationScaleDistribution.fit (x), "alpha", [0.5 2])
586s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
586s  paramci (tLocationScaleDistribution.fit (x), "alpha", "")
587s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
587s  paramci (tLocationScaleDistribution.fit (x), "alpha", {0.05})
587s ***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
587s  paramci (tLocationScaleDistribution.fit (x), "parameter", "mu", ...
587s           "alpha", {0.05})
587s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
587s  paramci (tLocationScaleDistribution.fit (x), ...
587s           "parameter", {"mu", "sigma", "nu", "param"})
587s ***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
587s  paramci (tLocationScaleDistribution.fit (x), "alpha", 0.01, ...
587s           "parameter", {"mu", "sigma", "nu", "param"})
587s ***** error <paramci: unknown distribution parameter.> ...
587s  paramci (tLocationScaleDistribution.fit (x), "parameter", "param")
587s ***** error <paramci: unknown distribution parameter.> ...
587s  paramci (tLocationScaleDistribution.fit (x), "alpha", 0.01, ...
587s           "parameter", "param")
587s ***** error <paramci: invalid NAME for optional argument.> ...
587s  paramci (tLocationScaleDistribution.fit (x), "NAME", "value")
588s ***** error <paramci: invalid NAME for optional argument.> ...
588s  paramci (tLocationScaleDistribution.fit (x), "alpha", 0.01, "NAME", "value")
588s ***** error <paramci: invalid NAME for optional argument.> ...
588s  paramci (tLocationScaleDistribution.fit (x), "alpha", 0.01, ...
588s           "parameter", "mu", "NAME", "value")
588s ***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
588s  plot (tLocationScaleDistribution, "Parent")
588s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
588s  plot (tLocationScaleDistribution, "PlotType", 12)
588s ***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
588s  plot (tLocationScaleDistribution, "PlotType", {"pdf", "cdf"})
588s ***** error <plot: invalid VALUE for 'PlotType' argument.> ...
588s  plot (tLocationScaleDistribution, "PlotType", "pdfcdf")
588s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
588s  plot (tLocationScaleDistribution, "Discrete", "pdfcdf")
588s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
588s  plot (tLocationScaleDistribution, "Discrete", [1, 0])
588s ***** error <plot: invalid VALUE for 'Discrete' argument.> ...
588s  plot (tLocationScaleDistribution, "Discrete", {true})
588s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
588s  plot (tLocationScaleDistribution, "Parent", 12)
588s ***** error <plot: invalid VALUE for 'Parent' argument.> ...
588s  plot (tLocationScaleDistribution, "Parent", "hax")
588s ***** error <plot: invalid NAME for optional argument.> ...
588s  plot (tLocationScaleDistribution, "invalidNAME", "pdf")
588s ***** error <plot: no fitted DATA to plot a probability plot.> ...
588s  plot (tLocationScaleDistribution, "PlotType", "probability")
588s ***** error <proflik: no fitted data available.> ...
588s  proflik (tLocationScaleDistribution, 2)
588s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
588s  proflik (tLocationScaleDistribution.fit (x), 4)
588s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
588s  proflik (tLocationScaleDistribution.fit (x), [1, 2])
588s ***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
588s  proflik (tLocationScaleDistribution.fit (x), {1})
588s ***** error <proflik: SETPARAM must be a numeric vector.> ...
588s  proflik (tLocationScaleDistribution.fit (x), 1, ones (2))
588s ***** error <proflik: missing VALUE for 'Display' argument.> ...
588s  proflik (tLocationScaleDistribution.fit (x), 1, "Display")
588s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
588s  proflik (tLocationScaleDistribution.fit (x), 1, "Display", 1)
589s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
589s  proflik (tLocationScaleDistribution.fit (x), 1, "Display", {1})
589s ***** error <proflik: invalid VALUE type for 'Display' argument.> ...
589s  proflik (tLocationScaleDistribution.fit (x), 1, "Display", {"on"})
589s ***** error <proflik: invalid VALUE size for 'Display' argument.> ...
589s  proflik (tLocationScaleDistribution.fit (x), 1, "Display", ["on"; "on"])
589s ***** error <proflik: invalid VALUE for 'Display' argument.> ...
589s  proflik (tLocationScaleDistribution.fit (x), 1, "Display", "onnn")
589s ***** error <proflik: invalid NAME for optional arguments.> ...
589s  proflik (tLocationScaleDistribution.fit (x), 1, "NAME", "on")
589s ***** error <proflik: invalid optional argument.> ...
589s  proflik (tLocationScaleDistribution.fit (x), 1, {"NAME"}, "on")
589s ***** error <proflik: invalid optional argument.> ...
589s  proflik (tLocationScaleDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
590s ***** error <truncate: missing input argument.> ...
590s  truncate (tLocationScaleDistribution)
590s ***** error <truncate: missing input argument.> ...
590s  truncate (tLocationScaleDistribution, 2)
590s ***** error <truncate: invalid lower upper limits.> ...
590s  truncate (tLocationScaleDistribution, 4, 2)
590s ***** shared pd
590s  pd = tLocationScaleDistribution (0, 1, 1);
590s  pd(2) = tLocationScaleDistribution (0, 1, 3);
590s ***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
590s ***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
590s ***** error <iqr: requires a scalar probability distribution.> iqr (pd)
590s ***** error <mean: requires a scalar probability distribution.> mean (pd)
590s ***** error <median: requires a scalar probability distribution.> median (pd)
590s ***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
590s ***** error <paramci: requires a scalar probability distribution.> paramci (pd)
590s ***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
590s ***** error <plot: requires a scalar probability distribution.> plot (pd)
590s ***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
590s ***** error <random: requires a scalar probability distribution.> random (pd)
590s ***** error <std: requires a scalar probability distribution.> std (pd)
590s ***** error <truncate: requires a scalar probability distribution.> ...
590s  truncate (pd, 2, 4)
590s ***** error <var: requires a scalar probability distribution.> var (pd)
590s 102 tests, 102 passed, 0 known failure, 0 skipped
590s [inst/dist_stat/betastat.m]
590s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/betastat.m
590s ***** error<betastat: function called with too few input arguments.> betastat ()
590s ***** error<betastat: function called with too few input arguments.> betastat (1)
590s ***** error<betastat: A and B must be numeric.> betastat ({}, 2)
590s ***** error<betastat: A and B must be numeric.> betastat (1, "")
590s ***** error<betastat: A and B must not be complex.> betastat (i, 2)
590s ***** error<betastat: A and B must not be complex.> betastat (1, i)
590s ***** error<betastat: A and B must be of common size or scalars.> ...
590s  betastat (ones (3), ones (2))
590s ***** error<betastat: A and B must be of common size or scalars.> ...
590s  betastat (ones (2), ones (3))
590s ***** test
590s  a = -2:6;
590s  b = 0.4:0.2:2;
590s  [m, v] = betastat (a, b);
590s  expected_m = [NaN NaN NaN 1/2 2/3.2 3/4.4 4/5.6 5/6.8 6/8];
590s  expected_v = [NaN NaN NaN 0.0833, 0.0558, 0.0402, 0.0309, 0.0250, 0.0208];
590s  assert (m, expected_m, eps*100);
590s  assert (v, expected_v, 0.001);
590s ***** test
590s  a = -2:1:6;
590s  [m, v] = betastat (a, 1.5);
590s  expected_m = [NaN NaN NaN 1/2.5 2/3.5 3/4.5 4/5.5 5/6.5 6/7.5];
590s  expected_v = [NaN NaN NaN 0.0686, 0.0544, 0.0404, 0.0305, 0.0237, 0.0188];
590s  assert (m, expected_m);
590s  assert (v, expected_v, 0.001);
590s ***** test
590s  a = [14  Inf   10  NaN  10];
590s  b = [12    9  NaN  Inf  12];
590s  [m, v] = betastat (a, b);
590s  expected_m = [14/26 NaN NaN NaN 10/22];
590s  expected_v = [168/18252 NaN NaN NaN 120/11132];
590s  assert (m, expected_m);
590s  assert (v, expected_v);
590s ***** assert (nthargout (1:2, @betastat, 5, []), {[], []})
590s ***** assert (nthargout (1:2, @betastat, [], 5), {[], []})
590s ***** assert (size (betastat (rand (10, 5, 4), rand (10, 5, 4))), [10 5 4])
590s ***** assert (size (betastat (rand (10, 5, 4), 7)), [10 5 4])
590s 15 tests, 15 passed, 0 known failure, 0 skipped
590s [inst/dist_stat/binostat.m]
590s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/binostat.m
590s ***** error<binostat: function called with too few input arguments.> binostat ()
590s ***** error<binostat: function called with too few input arguments.> binostat (1)
590s ***** error<binostat: N and PS must be numeric.> binostat ({}, 2)
590s ***** error<binostat: N and PS must be numeric.> binostat (1, "")
590s ***** error<binostat: N and PS must not be complex.> binostat (i, 2)
590s ***** error<binostat: N and PS must not be complex.> binostat (1, i)
590s ***** error<binostat: N and PS must be of common size or scalars.> ...
590s  binostat (ones (3), ones (2))
590s ***** error<binostat: N and PS must be of common size or scalars.> ...
590s  binostat (ones (2), ones (3))
590s ***** test
590s  n = 1:6;
590s  ps = 0:0.2:1;
590s  [m, v] = binostat (n, ps);
590s  expected_m = [0.00, 0.40, 1.20, 2.40, 4.00, 6.00];
590s  expected_v = [0.00, 0.32, 0.72, 0.96, 0.80, 0.00];
590s  assert (m, expected_m, 0.001);
590s  assert (v, expected_v, 0.001);
590s ***** test
590s  n = 1:6;
590s  [m, v] = binostat (n, 0.5);
590s  expected_m = [0.50, 1.00, 1.50, 2.00, 2.50, 3.00];
590s  expected_v = [0.25, 0.50, 0.75, 1.00, 1.25, 1.50];
590s  assert (m, expected_m, 0.001);
590s  assert (v, expected_v, 0.001);
590s ***** test
590s  n = [-Inf -3 5 0.5 3 NaN 100, Inf];
590s  [m, v] = binostat (n, 0.5);
590s  assert (isnan (m), [true true false true false true false false])
590s  assert (isnan (v), [true true false true false true false false])
590s  assert (m(end), Inf);
590s  assert (v(end), Inf);
590s ***** assert (nthargout (1:2, @binostat, 5, []), {[], []})
590s ***** assert (nthargout (1:2, @binostat, [], 5), {[], []})
590s ***** assert (size (binostat (randi (100, 10, 5, 4), rand (10, 5, 4))), [10 5 4])
590s ***** assert (size (binostat (randi (100, 10, 5, 4), 7)), [10 5 4])
590s 15 tests, 15 passed, 0 known failure, 0 skipped
590s [inst/dist_stat/bisastat.m]
590s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/bisastat.m
590s ***** error<bisastat: function called with too few input arguments.> bisastat ()
590s ***** error<bisastat: function called with too few input arguments.> bisastat (1)
590s ***** error<bisastat: BETA and GAMMA must be numeric.> bisastat ({}, 2)
590s ***** error<bisastat: BETA and GAMMA must be numeric.> bisastat (1, "")
590s ***** error<bisastat: BETA and GAMMA must not be complex.> bisastat (i, 2)
590s ***** error<bisastat: BETA and GAMMA must not be complex.> bisastat (1, i)
590s ***** error<bisastat: BETA and GAMMA must be of common size or scalars.> ...
590s  bisastat (ones (3), ones (2))
590s ***** error<bisastat: BETA and GAMMA must be of common size or scalars.> ...
590s  bisastat (ones (2), ones (3))
590s ***** test
590s  beta = 1:6;
590s  gamma = 1:0.2:2;
590s  [m, v] = bisastat (beta, gamma);
590s  expected_m = [1.50, 3.44, 5.94,  9.12,  13.10, 18];
590s  expected_v = [2.25, 16.128, 60.858, 172.032, 409.050, 864];
590s  assert (m, expected_m, 1e-2);
590s  assert (v, expected_v, 1e-3);
590s ***** test
590s  beta = 1:6;
590s  [m, v] = bisastat (beta, 1.5);
590s  expected_m = [2.125, 4.25, 6.375, 8.5, 10.625, 12.75];
590s  expected_v = [8.5781, 34.3125, 77.2031, 137.2500, 214.4531, 308.8125];
590s  assert (m, expected_m, 1e-3);
590s  assert (v, expected_v, 1e-4);
590s 10 tests, 10 passed, 0 known failure, 0 skipped
590s [inst/dist_stat/burrstat.m]
590s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/burrstat.m
590s ***** error<burrstat: function called with too few input arguments.> burrstat ()
590s ***** error<burrstat: function called with too few input arguments.> burrstat (1)
590s ***** error<burrstat: function called with too few input arguments.> burrstat (1, 2)
590s ***** error<burrstat: LAMBDA, C, and K must be numeric.> burrstat ({}, 2, 3)
590s ***** error<burrstat: LAMBDA, C, and K must be numeric.> burrstat (1, "", 3)
590s ***** error<burrstat: LAMBDA, C, and K must be numeric.> burrstat (1, 2, "")
590s ***** error<burrstat: LAMBDA, C, and K must not be complex.> burrstat (i, 2, 3)
590s ***** error<burrstat: LAMBDA, C, and K must not be complex.> burrstat (1, i, 3)
590s ***** error<burrstat: LAMBDA, C, and K must not be complex.> burrstat (1, 2, i)
590s ***** error<burrstat: LAMBDA, C, and K must be of common size or scalars.> ...
590s  burrstat (ones (3), ones (2), 3)
590s ***** error<burrstat: LAMBDA, C, and K must be of common size or scalars.> ...
590s  burrstat (ones (2), 2, ones (3))
590s ***** error<burrstat: LAMBDA, C, and K must be of common size or scalars.> ...
590s  burrstat (1, ones (2), ones (3))
590s ***** test
590s  [m, v] = burrstat (1, 2, 5);
590s  assert (m, 0.4295, 1e-4);
590s  assert (v, 0.0655, 1e-4);
590s ***** test
590s  [m, v] = burrstat (1, 1, 1);
590s  assert (m, Inf);
590s  assert (v, Inf);
590s ***** test
590s  [m, v] = burrstat (2, 4, 1);
590s  assert (m, 2.2214, 1e-4);
590s  assert (v, 1.3484, 1e-4);
590s 15 tests, 15 passed, 0 known failure, 0 skipped
590s [inst/dist_stat/chi2stat.m]
590s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/chi2stat.m
590s ***** error<chi2stat: function called with too few input arguments.> chi2stat ()
590s ***** error<chi2stat: DF must be numeric.> chi2stat ({})
590s ***** error<chi2stat: DF must be numeric.> chi2stat ("")
590s ***** error<chi2stat: DF must not be complex.> chi2stat (i)
590s ***** test
590s  df = 1:6;
590s  [m, v] = chi2stat (df);
590s  assert (m, df);
590s  assert (v, [2, 4, 6, 8, 10, 12], 0.001);
590s 5 tests, 5 passed, 0 known failure, 0 skipped
590s [inst/dist_stat/evstat.m]
590s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/evstat.m
590s ***** error<evstat: function called with too few input arguments.> evstat ()
590s ***** error<evstat: function called with too few input arguments.> evstat (1)
590s ***** error<evstat: MU and SIGMA must be numeric.> evstat ({}, 2)
590s ***** error<evstat: MU and SIGMA must be numeric.> evstat (1, "")
590s ***** error<evstat: MU and SIGMA must not be complex.> evstat (i, 2)
590s ***** error<evstat: MU and SIGMA must not be complex.> evstat (1, i)
590s ***** error<evstat: MU and SIGMA must be of common size or scalars.> ...
590s  evstat (ones (3), ones (2))
590s ***** error<evstat: MU and SIGMA must be of common size or scalars.> ...
590s  evstat (ones (2), ones (3))
590s ***** shared x, y0, y1
590s  x = [-5, 0, 1, 2, 3];
590s  y0 = [NaN, NaN, 0.4228, 0.8456, 1.2684];
590s  y1 = [-5.5772, -3.4633, -3.0405, -2.6177, -2.1949];
590s ***** assert (evstat (x, x), y0, 1e-4)
590s ***** assert (evstat (x, x+6), y1, 1e-4)
590s ***** assert (evstat (x, x-6), NaN (1,5))
590s 11 tests, 11 passed, 0 known failure, 0 skipped
590s [inst/dist_stat/expstat.m]
590s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/expstat.m
590s ***** error<expstat: function called with too few input arguments.> expstat ()
590s ***** error<expstat: MU must be numeric.> expstat ({})
590s ***** error<expstat: MU must be numeric.> expstat ("")
590s ***** error<expstat: MU must not be complex.> expstat (i)
590s ***** test
590s  mu = 1:6;
590s  [m, v] = expstat (mu);
590s  assert (m, [1, 2, 3, 4, 5, 6], 0.001);
590s  assert (v, [1, 4, 9, 16, 25, 36], 0.001);
590s 5 tests, 5 passed, 0 known failure, 0 skipped
590s [inst/dist_stat/fstat.m]
590s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/fstat.m
590s ***** error<fstat: function called with too few input arguments.> fstat ()
590s ***** error<fstat: function called with too few input arguments.> fstat (1)
590s ***** error<fstat: DF1 and DF2 must be numeric.> fstat ({}, 2)
590s ***** error<fstat: DF1 and DF2 must be numeric.> fstat (1, "")
590s ***** error<fstat: DF1 and DF2 must not be complex.> fstat (i, 2)
590s ***** error<fstat: DF1 and DF2 must not be complex.> fstat (1, i)
590s ***** error<fstat: DF1 and DF2 must be of common size or scalars.> ...
590s  fstat (ones (3), ones (2))
590s ***** error<fstat: DF1 and DF2 must be of common size or scalars.> ...
590s  fstat (ones (2), ones (3))
590s ***** test
590s  df1 = 1:6;
590s  df2 = 5:10;
590s  [m, v] = fstat (df1, df2);
590s  expected_mn = [1.6667, 1.5000, 1.4000, 1.3333, 1.2857, 1.2500];
590s  expected_v = [22.2222, 6.7500, 3.4844, 2.2222, 1.5869, 1.2153];
590s  assert (m, expected_mn, 0.001);
590s  assert (v, expected_v, 0.001);
590s ***** test
590s  df1 = 1:6;
590s  [m, v] = fstat (df1, 5);
590s  expected_mn = [1.6667, 1.6667, 1.6667, 1.6667, 1.6667, 1.6667];
590s  expected_v = [22.2222, 13.8889, 11.1111, 9.7222, 8.8889, 8.3333];
590s  assert (m, expected_mn, 0.001);
590s  assert (v, expected_v, 0.001);
590s 10 tests, 10 passed, 0 known failure, 0 skipped
590s [inst/dist_stat/gamstat.m]
590s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/gamstat.m
590s ***** error<gamstat: function called with too few input arguments.> gamstat ()
590s ***** error<gamstat: function called with too few input arguments.> gamstat (1)
590s ***** error<gamstat: A and B must be numeric.> gamstat ({}, 2)
590s ***** error<gamstat: A and B must be numeric.> gamstat (1, "")
590s ***** error<gamstat: A and B must not be complex.> gamstat (i, 2)
590s ***** error<gamstat: A and B must not be complex.> gamstat (1, i)
590s ***** error<gamstat: A and B must be of common size or scalars.> ...
590s  gamstat (ones (3), ones (2))
590s ***** error<gamstat: A and B must be of common size or scalars.> ...
590s  gamstat (ones (2), ones (3))
590s ***** test
590s  a = 1:6;
590s  b = 1:0.2:2;
590s  [m, v] = gamstat (a, b);
590s  expected_m = [1.00, 2.40, 4.20,  6.40,  9.00, 12.00];
590s  expected_v = [1.00, 2.88, 5.88, 10.24, 16.20, 24.00];
590s  assert (m, expected_m, 0.001);
590s  assert (v, expected_v, 0.001);
590s ***** test
590s  a = 1:6;
590s  [m, v] = gamstat (a, 1.5);
590s  expected_m = [1.50, 3.00, 4.50, 6.00,  7.50,  9.00];
590s  expected_v = [2.25, 4.50, 6.75, 9.00, 11.25, 13.50];
590s  assert (m, expected_m, 0.001);
590s  assert (v, expected_v, 0.001);
590s 10 tests, 10 passed, 0 known failure, 0 skipped
590s [inst/dist_stat/geostat.m]
590s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/geostat.m
590s ***** error<geostat: function called with too few input arguments.> geostat ()
590s ***** error<geostat: PS must be numeric.> geostat ({})
590s ***** error<geostat: PS must be numeric.> geostat ("")
590s ***** error<geostat: PS must not be complex.> geostat (i)
590s ***** test
590s  ps = 1 ./ (1:6);
590s  [m, v] = geostat (ps);
590s  assert (m, [0, 1, 2, 3, 4, 5], 0.001);
590s  assert (v, [0, 2, 6, 12, 20, 30], 0.001);
590s 5 tests, 5 passed, 0 known failure, 0 skipped
590s [inst/dist_stat/gevstat.m]
590s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/gevstat.m
590s ***** error<gevstat: function called with too few input arguments.> gevstat ()
590s ***** error<gevstat: function called with too few input arguments.> gevstat (1)
590s ***** error<gevstat: function called with too few input arguments.> gevstat (1, 2)
590s ***** error<gevstat: K, SIGMA, and MU must be numeric.> gevstat ({}, 2, 3)
590s ***** error<gevstat: K, SIGMA, and MU must be numeric.> gevstat (1, "", 3)
590s ***** error<gevstat: K, SIGMA, and MU must be numeric.> gevstat (1, 2, "")
590s ***** error<gevstat: K, SIGMA, and MU must not be complex.> gevstat (i, 2, 3)
590s ***** error<gevstat: K, SIGMA, and MU must not be complex.> gevstat (1, i, 3)
590s ***** error<gevstat: K, SIGMA, and MU must not be complex.> gevstat (1, 2, i)
590s ***** error<gevstat: K, SIGMA, and MU must be of common size or scalars.> ...
590s  gevstat (ones (3), ones (2), 3)
590s ***** error<gevstat: K, SIGMA, and MU must be of common size or scalars.> ...
590s  gevstat (ones (2), 2, ones (3))
590s ***** error<gevstat: K, SIGMA, and MU must be of common size or scalars.> ...
590s  gevstat (1, ones (2), ones (3))
590s ***** test
590s  k = [-1, -0.5, 0, 0.2, 0.4, 0.5, 1];
590s  sigma = 2;
590s  mu = 1;
590s  [m, v] = gevstat (k, sigma, mu);
590s  expected_m = [1, 1.4551, 2.1544, 2.6423, 3.4460, 4.0898, Inf];
590s  expected_v = [4, 3.4336, 6.5797, 13.3761, 59.3288, Inf, Inf];
590s  assert (m, expected_m, -0.001);
590s  assert (v, expected_v, -0.001);
590s 13 tests, 13 passed, 0 known failure, 0 skipped
590s [inst/dist_stat/gpstat.m]
590s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/gpstat.m
590s ***** error<gpstat: function called with too few input arguments.> gpstat ()
590s ***** error<gpstat: function called with too few input arguments.> gpstat (1)
590s ***** error<gpstat: function called with too few input arguments.> gpstat (1, 2)
590s ***** error<gpstat: K, SIGMA, and MU must be numeric.> gpstat ({}, 2, 3)
590s ***** error<gpstat: K, SIGMA, and MU must be numeric.> gpstat (1, "", 3)
590s ***** error<gpstat: K, SIGMA, and MU must be numeric.> gpstat (1, 2, "")
590s ***** error<gpstat: K, SIGMA, and MU must not be complex.> gpstat (i, 2, 3)
590s ***** error<gpstat: K, SIGMA, and MU must not be complex.> gpstat (1, i, 3)
590s ***** error<gpstat: K, SIGMA, and MU must not be complex.> gpstat (1, 2, i)
590s ***** error<gpstat: K, SIGMA, and MU must be of common size or scalars.> ...
590s  gpstat (ones (3), ones (2), 3)
590s ***** error<gpstat: K, SIGMA, and MU must be of common size or scalars.> ...
590s  gpstat (ones (2), 2, ones (3))
590s ***** error<gpstat: K, SIGMA, and MU must be of common size or scalars.> ...
590s  gpstat (1, ones (2), ones (3))
590s ***** shared x, y
590s  x = [-Inf, -1, 0, 1/2, 1, Inf];
590s  y = [0, 0.5, 1, 2, Inf, Inf];
590s ***** assert (gpstat (x, ones (1,6), zeros (1,6)), y, eps)
590s ***** assert (gpstat (single (x), 1, 0), single (y), eps("single"))
590s ***** assert (gpstat (x, single (1), 0), single (y), eps("single"))
590s ***** assert (gpstat (x, 1, single (0)), single (y), eps("single"))
590s ***** assert (gpstat (single ([x, NaN]), 1, 0), single ([y, NaN]), eps("single"))
590s ***** assert (gpstat ([x, NaN], single (1), 0), single ([y, NaN]), eps("single"))
590s ***** assert (gpstat ([x, NaN], 1, single (0)), single ([y, NaN]), eps("single"))
590s 19 tests, 19 passed, 0 known failure, 0 skipped
590s [inst/dist_stat/hnstat.m]
590s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/hnstat.m
590s ***** error<hnstat: function called with too few input arguments.> hnstat ()
590s ***** error<hnstat: function called with too few input arguments.> hnstat (1)
590s ***** error<hnstat: MU and SIGMA must be numeric.> hnstat ({}, 2)
590s ***** error<hnstat: MU and SIGMA must be numeric.> hnstat (1, "")
590s ***** error<hnstat: MU and SIGMA must not be complex.> hnstat (i, 2)
590s ***** error<hnstat: MU and SIGMA must not be complex.> hnstat (1, i)
590s ***** error<hnstat: MU and SIGMA must be of common size or scalars.> ...
590s  hnstat (ones (3), ones (2))
590s ***** error<hnstat: MU and SIGMA must be of common size or scalars.> ...
590s  hnstat (ones (2), ones (3))
590s ***** test
590s  [m, v] = hnstat (0, 1);
590s  assert (m, 0.7979, 1e-4);
590s  assert (v, 0.3634, 1e-4);
590s ***** test
590s  [m, v] = hnstat (2, 1);
590s  assert (m, 2.7979, 1e-4);
590s  assert (v, 0.3634, 1e-4);
591s ***** test
591s  [m, v] = hnstat (2, 2);
591s  assert (m, 3.5958, 1e-4);
591s  assert (v, 1.4535, 1e-4);
591s ***** test
591s  [m, v] = hnstat (2, 2.5);
591s  assert (m, 3.9947, 1e-4);
591s  assert (v, 2.2711, 1e-4);
591s ***** test
591s  [m, v] = hnstat (1.5, 0.5);
591s  assert (m, 1.8989, 1e-4);
591s  assert (v, 0.0908, 1e-4);
591s ***** test
591s  [m, v] = hnstat (-1.5, 0.5);
591s  assert (m, -1.1011, 1e-4);
591s  assert (v, 0.0908, 1e-4);
591s 14 tests, 14 passed, 0 known failure, 0 skipped
591s [inst/dist_stat/hygestat.m]
591s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/hygestat.m
591s ***** error<hygestat: function called with too few input arguments.> hygestat ()
591s ***** error<hygestat: function called with too few input arguments.> hygestat (1)
591s ***** error<hygestat: function called with too few input arguments.> hygestat (1, 2)
591s ***** error<hygestat: M, K, and N must be numeric.> hygestat ({}, 2, 3)
591s ***** error<hygestat: M, K, and N must be numeric.> hygestat (1, "", 3)
591s ***** error<hygestat: M, K, and N must be numeric.> hygestat (1, 2, "")
591s ***** error<hygestat: M, K, and N must not be complex.> hygestat (i, 2, 3)
591s ***** error<hygestat: M, K, and N must not be complex.> hygestat (1, i, 3)
591s ***** error<hygestat: M, K, and N must not be complex.> hygestat (1, 2, i)
591s ***** error<hygestat: M, K, and N must be of common size or scalars.> ...
591s  hygestat (ones (3), ones (2), 3)
591s ***** error<hygestat: M, K, and N must be of common size or scalars.> ...
591s  hygestat (ones (2), 2, ones (3))
591s ***** error<hygestat: M, K, and N must be of common size or scalars.> ...
591s  hygestat (1, ones (2), ones (3))
591s ***** test
591s  m = 4:9;
591s  k = 0:5;
591s  n = 1:6;
591s  [mn, v] = hygestat (m, k, n);
591s  expected_mn = [0.0000, 0.4000, 1.0000, 1.7143, 2.5000, 3.3333];
591s  expected_v = [0.0000, 0.2400, 0.4000, 0.4898, 0.5357, 0.5556];
591s  assert (mn, expected_mn, 0.001);
591s  assert (v, expected_v, 0.001);
591s ***** test
591s  m = 4:9;
591s  k = 0:5;
591s  [mn, v] = hygestat (m, k, 2);
591s  expected_mn = [0.0000, 0.4000, 0.6667, 0.8571, 1.0000, 1.1111];
591s  expected_v = [0.0000, 0.2400, 0.3556, 0.4082, 0.4286, 0.4321];
591s  assert (mn, expected_mn, 0.001);
591s  assert (v, expected_v, 0.001);
591s 14 tests, 14 passed, 0 known failure, 0 skipped
591s [inst/dist_stat/invgstat.m]
591s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/invgstat.m
591s ***** error<invgstat: function called with too few input arguments.> invgstat ()
591s ***** error<invgstat: function called with too few input arguments.> invgstat (1)
591s ***** error<invgstat: MU and LAMBDA must be numeric.> invgstat ({}, 2)
591s ***** error<invgstat: MU and LAMBDA must be numeric.> invgstat (1, "")
591s ***** error<invgstat: MU and LAMBDA must not be complex.> invgstat (i, 2)
591s ***** error<invgstat: MU and LAMBDA must not be complex.> invgstat (1, i)
591s ***** error<invgstat: MU and LAMBDA must be of common size or scalars.> ...
591s  invgstat (ones (3), ones (2))
591s ***** error<invgstat: MU and LAMBDA must be of common size or scalars.> ...
591s  invgstat (ones (2), ones (3))
591s ***** test
591s  [m, v] = invgstat (1, 1);
591s  assert (m, 1);
591s  assert (v, 1);
591s ***** test
591s  [m, v] = invgstat (2, 1);
591s  assert (m, 2);
591s  assert (v, 8);
591s ***** test
591s  [m, v] = invgstat (2, 2);
591s  assert (m, 2);
591s  assert (v, 4);
591s ***** test
591s  [m, v] = invgstat (2, 2.5);
591s  assert (m, 2);
591s  assert (v, 3.2);
591s ***** test
591s  [m, v] = invgstat (1.5, 0.5);
591s  assert (m, 1.5);
591s  assert (v, 6.75);
591s 13 tests, 13 passed, 0 known failure, 0 skipped
591s [inst/dist_stat/logistat.m]
591s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/logistat.m
591s ***** error<logistat: function called with too few input arguments.> logistat ()
591s ***** error<logistat: function called with too few input arguments.> logistat (1)
591s ***** error<logistat: MU and SIGMA must be numeric.> logistat ({}, 2)
591s ***** error<logistat: MU and SIGMA must be numeric.> logistat (1, "")
591s ***** error<logistat: MU and SIGMA must not be complex.> logistat (i, 2)
591s ***** error<logistat: MU and SIGMA must not be complex.> logistat (1, i)
591s ***** error<logistat: MU and SIGMA must be of common size or scalars.> ...
591s  logistat (ones (3), ones (2))
591s ***** error<logistat: MU and SIGMA must be of common size or scalars.> ...
591s  logistat (ones (2), ones (3))
591s ***** test
591s  [m, v] = logistat (0, 1);
591s  assert (m, 0);
591s  assert (v, 3.2899, 0.001);
591s ***** test
591s  [m, v] = logistat (0, 0.8);
591s  assert (m, 0);
591s  assert (v, 2.1055, 0.001);
591s ***** test
591s  [m, v] = logistat (1, 0.6);
591s  assert (m, 1);
591s  assert (v, 1.1844, 0.001);
591s ***** test
591s  [m, v] = logistat (0, 0.4);
591s  assert (m, 0);
591s  assert (v, 0.5264, 0.001);
591s ***** test
591s  [m, v] = logistat (-1, 0.2);
591s  assert (m, -1);
591s  assert (v, 0.1316, 0.001);
591s 13 tests, 13 passed, 0 known failure, 0 skipped
591s [inst/dist_stat/loglstat.m]
591s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/loglstat.m
591s ***** error<loglstat: function called with too few input arguments.> loglstat ()
591s ***** error<loglstat: function called with too few input arguments.> loglstat (1)
591s ***** error<loglstat: MU and SIGMA must be numeric.> loglstat ({}, 2)
591s ***** error<loglstat: MU and SIGMA must be numeric.> loglstat (1, "")
591s ***** error<loglstat: MU and SIGMA must not be complex.> loglstat (i, 2)
591s ***** error<loglstat: MU and SIGMA must not be complex.> loglstat (1, i)
591s ***** error<loglstat: MU and SIGMA must be of common size or scalars.> ...
591s  loglstat (ones (3), ones (2))
591s ***** error<loglstat: MU and SIGMA must be of common size or scalars.> ...
591s  loglstat (ones (2), ones (3))
591s ***** test
591s  [m, v] = loglstat (0, 1);
591s  assert (m, Inf, 0.001);
591s  assert (v, Inf, 0.001);
591s ***** test
591s  [m, v] = loglstat (0, 0.8);
591s  assert (m, 4.2758, 0.001);
591s  assert (v, Inf, 0.001);
591s ***** test
591s  [m, v] = loglstat (0, 0.6);
591s  assert (m, 1.9820, 0.001);
591s  assert (v, Inf, 0.001);
591s ***** test
591s  [m, v] = loglstat (0, 0.4);
591s  assert (m, 1.3213, 0.001);
591s  assert (v, 2.5300, 0.001);
591s ***** test
591s  [m, v] = loglstat (0, 0.2);
591s  assert (m, 1.0690, 0.001);
591s  assert (v, 0.1786, 0.001);
591s 13 tests, 13 passed, 0 known failure, 0 skipped
591s [inst/dist_stat/lognstat.m]
591s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/lognstat.m
591s ***** error<lognstat: function called with too few input arguments.> lognstat ()
591s ***** error<lognstat: function called with too few input arguments.> lognstat (1)
591s ***** error<lognstat: MU and SIGMA must be numeric.> lognstat ({}, 2)
591s ***** error<lognstat: MU and SIGMA must be numeric.> lognstat (1, "")
591s ***** error<lognstat: MU and SIGMA must not be complex.> lognstat (i, 2)
591s ***** error<lognstat: MU and SIGMA must not be complex.> lognstat (1, i)
591s ***** error<lognstat: MU and SIGMA must be of common size or scalars.> ...
591s  lognstat (ones (3), ones (2))
591s ***** error<lognstat: MU and SIGMA must be of common size or scalars.> ...
591s  lognstat (ones (2), ones (3))
591s ***** test
591s  mu = 0:0.2:1;
591s  sigma = 0.2:0.2:1.2;
591s  [m, v] = lognstat (mu, sigma);
591s  expected_m = [1.0202, 1.3231, 1.7860, 2.5093,  3.6693,   5.5845];
591s  expected_v = [0.0425, 0.3038, 1.3823, 5.6447, 23.1345, 100.4437];
591s  assert (m, expected_m, 0.001);
591s  assert (v, expected_v, 0.001);
591s ***** test
591s  sigma = 0.2:0.2:1.2;
591s  [m, v] = lognstat (0, sigma);
591s  expected_m = [1.0202, 1.0833, 1.1972, 1.3771, 1.6487,  2.0544];
591s  expected_v = [0.0425, 0.2036, 0.6211, 1.7002, 4.6708, 13.5936];
591s  assert (m, expected_m, 0.001);
591s  assert (v, expected_v, 0.001);
591s 10 tests, 10 passed, 0 known failure, 0 skipped
591s [inst/dist_stat/nakastat.m]
591s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/nakastat.m
591s ***** error<nakastat: function called with too few input arguments.> nakastat ()
591s ***** error<nakastat: function called with too few input arguments.> nakastat (1)
591s ***** error<nakastat: MU and OMEGA must be numeric.> nakastat ({}, 2)
591s ***** error<nakastat: MU and OMEGA must be numeric.> nakastat (1, "")
591s ***** error<nakastat: MU and OMEGA must not be complex.> nakastat (i, 2)
591s ***** error<nakastat: MU and OMEGA must not be complex.> nakastat (1, i)
591s ***** error<nakastat: MU and OMEGA must be of common size or scalars.> ...
591s  nakastat (ones (3), ones (2))
591s ***** error<nakastat: MU and OMEGA must be of common size or scalars.> ...
591s  nakastat (ones (2), ones (3))
591s ***** test
591s  [m, v] = nakastat (1, 1);
591s  assert (m, 0.8862269254, 1e-10);
591s  assert (v, 0.2146018366, 1e-10);
591s ***** test
591s  [m, v] = nakastat (1, 2);
591s  assert (m, 1.25331413731, 1e-10);
591s  assert (v, 0.42920367321, 1e-10);
591s ***** test
591s  [m, v] = nakastat (2, 1);
591s  assert (m, 0.93998560299, 1e-10);
591s  assert (v, 0.11642706618, 1e-10);
591s 11 tests, 11 passed, 0 known failure, 0 skipped
591s [inst/dist_stat/nbinstat.m]
591s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/nbinstat.m
591s ***** error<nbinstat: function called with too few input arguments.> nbinstat ()
591s ***** error<nbinstat: function called with too few input arguments.> nbinstat (1)
591s ***** error<nbinstat: R and PS must be numeric.> nbinstat ({}, 2)
591s ***** error<nbinstat: R and PS must be numeric.> nbinstat (1, "")
591s ***** error<nbinstat: R and PS must not be complex.> nbinstat (i, 2)
591s ***** error<nbinstat: R and PS must not be complex.> nbinstat (1, i)
591s ***** error<nbinstat: R and PS must be of common size or scalars.> ...
591s  nbinstat (ones (3), ones (2))
591s ***** error<nbinstat: R and PS must be of common size or scalars.> ...
591s  nbinstat (ones (2), ones (3))
591s ***** test
591s  r = 1:4;
591s  ps = 0.2:0.2:0.8;
591s  [m, v] = nbinstat (r, ps);
591s  expected_m = [ 4.0000, 3.0000, 2.0000, 1.0000];
591s  expected_v = [20.0000, 7.5000, 3.3333, 1.2500];
591s  assert (m, expected_m, 0.001);
591s  assert (v, expected_v, 0.001);
591s ***** test
591s  r = 1:4;
591s  [m, v] = nbinstat (r, 0.5);
591s  expected_m = [1, 2, 3, 4];
591s  expected_v = [2, 4, 6, 8];
591s  assert (m, expected_m, 0.001);
591s  assert (v, expected_v, 0.001);
591s 10 tests, 10 passed, 0 known failure, 0 skipped
591s [inst/dist_stat/ncfstat.m]
591s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/ncfstat.m
591s ***** error<ncfstat: function called with too few input arguments.> ncfstat ()
591s ***** error<ncfstat: function called with too few input arguments.> ncfstat (1)
591s ***** error<ncfstat: function called with too few input arguments.> ncfstat (1, 2)
591s ***** error<ncfstat: DF1, DF2, and LAMBDA must be numeric.> ncfstat ({}, 2, 3)
591s ***** error<ncfstat: DF1, DF2, and LAMBDA must be numeric.> ncfstat (1, "", 3)
591s ***** error<ncfstat: DF1, DF2, and LAMBDA must be numeric.> ncfstat (1, 2, "")
591s ***** error<ncfstat: DF1, DF2, and LAMBDA must not be complex.> ncfstat (i, 2, 3)
591s ***** error<ncfstat: DF1, DF2, and LAMBDA must not be complex.> ncfstat (1, i, 3)
591s ***** error<ncfstat: DF1, DF2, and LAMBDA must not be complex.> ncfstat (1, 2, i)
591s ***** error<ncfstat: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
591s  ncfstat (ones (3), ones (2), 3)
591s ***** error<ncfstat: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
591s  ncfstat (ones (2), 2, ones (3))
591s ***** error<ncfstat: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
591s  ncfstat (1, ones (2), ones (3))
591s ***** shared df1, df2, lambda
591s  df1 = [2, 0, -1, 1, 4, 5];
591s  df2 = [2, 4, -1, 5, 6, 7];
591s  lambda = [1, NaN, 3, 0, 2, -1];
591s ***** assert (ncfstat (df1, df2, lambda), [NaN, NaN, NaN, 1.6667, 2.25, 1.12], 1e-4);
591s ***** assert (ncfstat (df1(4:6), df2(4:6), 1), [3.3333, 1.8750, 1.6800], 1e-4);
591s ***** assert (ncfstat (df1(4:6), df2(4:6), 2), [5.0000, 2.2500, 1.9600], 1e-4);
591s ***** assert (ncfstat (df1(4:6), df2(4:6), 3), [6.6667, 2.6250, 2.2400], 1e-4);
591s ***** assert (ncfstat (2, [df2(1), df2(4:6)], 5), [NaN,5.8333,5.2500,4.9000], 1e-4);
591s ***** assert (ncfstat (0, [df2(1), df2(4:6)], 5), [NaN, Inf, Inf, Inf]);
591s ***** assert (ncfstat (1, [df2(1), df2(4:6)], 5), [NaN, 10, 9, 8.4], 1e-14);
591s ***** assert (ncfstat (4, [df2(1), df2(4:6)], 5), [NaN, 3.75, 3.375, 3.15], 1e-14);
591s 20 tests, 20 passed, 0 known failure, 0 skipped
591s [inst/dist_stat/nctstat.m]
591s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/nctstat.m
591s ***** error<nctstat: function called with too few input arguments.> nctstat ()
591s ***** error<nctstat: function called with too few input arguments.> nctstat (1)
591s ***** error<nctstat: DF and MU must be numeric.> nctstat ({}, 2)
591s ***** error<nctstat: DF and MU must be numeric.> nctstat (1, "")
591s ***** error<nctstat: DF and MU must not be complex.> nctstat (i, 2)
591s ***** error<nctstat: DF and MU must not be complex.> nctstat (1, i)
591s ***** error<nctstat: DF and MU must be of common size or scalars.> ...
591s  nctstat (ones (3), ones (2))
591s ***** error<nctstat: DF and MU must be of common size or scalars.> ...
591s  nctstat (ones (2), ones (3))
591s ***** shared df, mu
591s  df = [2, 0, -1, 1, 4];
591s  mu = [1, NaN, 3, -1, 2];
591s ***** assert (nctstat (df, mu), [1.7725, NaN, NaN, NaN, 2.5066], 1e-4);
591s ***** assert (nctstat ([df(1:2), df(4:5)], 1), [1.7725, NaN, NaN, 1.2533], 1e-4);
591s ***** assert (nctstat ([df(1:2), df(4:5)], 3), [5.3174, NaN, NaN, 3.7599], 1e-4);
591s ***** assert (nctstat ([df(1:2), df(4:5)], 2), [3.5449, NaN, NaN, 2.5066], 1e-4);
591s ***** assert (nctstat (2, [mu(1), mu(3:5)]), [1.7725,5.3174,-1.7725,3.5449], 1e-4);
591s ***** assert (nctstat (0, [mu(1), mu(3:5)]), [NaN, NaN, NaN, NaN]);
591s ***** assert (nctstat (1, [mu(1), mu(3:5)]), [NaN, NaN, NaN, NaN]);
591s ***** assert (nctstat (4, [mu(1), mu(3:5)]), [1.2533,3.7599,-1.2533,2.5066], 1e-4);
591s 16 tests, 16 passed, 0 known failure, 0 skipped
591s [inst/dist_stat/ncx2stat.m]
591s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/ncx2stat.m
591s ***** error<ncx2stat: function called with too few input arguments.> ncx2stat ()
591s ***** error<ncx2stat: function called with too few input arguments.> ncx2stat (1)
591s ***** error<ncx2stat: DF and LAMBDA must be numeric.> ncx2stat ({}, 2)
591s ***** error<ncx2stat: DF and LAMBDA must be numeric.> ncx2stat (1, "")
591s ***** error<ncx2stat: DF and LAMBDA must not be complex.> ncx2stat (i, 2)
591s ***** error<ncx2stat: DF and LAMBDA must not be complex.> ncx2stat (1, i)
591s ***** error<ncx2stat: DF and LAMBDA must be of common size or scalars.> ...
591s  ncx2stat (ones (3), ones (2))
591s ***** error<ncx2stat: DF and LAMBDA must be of common size or scalars.> ...
591s  ncx2stat (ones (2), ones (3))
591s ***** shared df, d1
591s  df = [2, 0, -1, 1, 4];
591s  d1 = [1, NaN, 3, -1, 2];
591s ***** assert (ncx2stat (df, d1), [3, NaN, NaN, NaN, 6]);
591s ***** assert (ncx2stat ([df(1:2), df(4:5)], 1), [3, NaN, 2, 5]);
591s ***** assert (ncx2stat ([df(1:2), df(4:5)], 3), [5, NaN, 4, 7]);
591s ***** assert (ncx2stat ([df(1:2), df(4:5)], 2), [4, NaN, 3, 6]);
591s ***** assert (ncx2stat (2, [d1(1), d1(3:5)]), [3, 5, NaN, 4]);
591s ***** assert (ncx2stat (0, [d1(1), d1(3:5)]), [NaN, NaN, NaN, NaN]);
591s ***** assert (ncx2stat (1, [d1(1), d1(3:5)]), [2, 4, NaN, 3]);
591s ***** assert (ncx2stat (4, [d1(1), d1(3:5)]), [5, 7, NaN, 6]);
591s 16 tests, 16 passed, 0 known failure, 0 skipped
591s [inst/dist_stat/normstat.m]
591s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/normstat.m
591s ***** error<normstat: function called with too few input arguments.> normstat ()
591s ***** error<normstat: function called with too few input arguments.> normstat (1)
591s ***** error<normstat: MU and SIGMA must be numeric.> normstat ({}, 2)
591s ***** error<normstat: MU and SIGMA must be numeric.> normstat (1, "")
591s ***** error<normstat: MU and SIGMA must not be complex.> normstat (i, 2)
591s ***** error<normstat: MU and SIGMA must not be complex.> normstat (1, i)
591s ***** error<normstat: MU and SIGMA must be of common size or scalars.> ...
591s  normstat (ones (3), ones (2))
591s ***** error<normstat: MU and SIGMA must be of common size or scalars.> ...
591s  normstat (ones (2), ones (3))
591s ***** test
591s  mu = 1:6;
591s  sigma = 0.2:0.2:1.2;
591s  [m, v] = normstat (mu, sigma);
591s  expected_v = [0.0400, 0.1600, 0.3600, 0.6400, 1.0000, 1.4400];
591s  assert (m, mu);
591s  assert (v, expected_v, 0.001);
591s ***** test
591s  sigma = 0.2:0.2:1.2;
591s  [m, v] = normstat (0, sigma);
591s  expected_mn = [0, 0, 0, 0, 0, 0];
591s  expected_v = [0.0400, 0.1600, 0.3600, 0.6400, 1.0000, 1.4400];
591s  assert (m, expected_mn, 0.001);
591s  assert (v, expected_v, 0.001);
591s 10 tests, 10 passed, 0 known failure, 0 skipped
591s [inst/dist_stat/plstat.m]
591s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/plstat.m
591s ***** shared x, Fx
591s  x = [0, 1, 3, 4, 7, 10];
591s  Fx = [0, 0.2, 0.5, 0.6, 0.7, 1];
591s ***** assert (plstat (x, Fx), 4.15)
591s ***** test
591s  [m, v] = plstat (x, Fx);
591s  assert (v, 10.3775, 1e-14)
591s ***** error<plstat: function called with too few input arguments.> plstat ()
591s ***** error<plstat: function called with too few input arguments.> plstat (1)
591s ***** error<plstat: X and FX must be vectors of equal size.> ...
591s  plstat ([0, 1, 2], [0, 1])
591s ***** error<plstat: X and FX must be at least two-elements long.> ...
591s  plstat ([0], [1])
591s ***** error<plstat: FX must be bounded in the range> ...
591s  plstat ([0, 1, 2], [0, 1, 1.5])
591s ***** error<plstat: FX must be bounded in the range> ...
591s  plstat ([0, 1, 2], [0, i, 1])
591s ***** error<plstat: X and FX must not be complex.> ...
591s  plstat ([0, i, 2], [0, 0.5, 1])
591s ***** error<plstat: X and FX must not be complex.> ...
591s  plstat ([0, i, 2], [0, 0.5i, 1])
591s 10 tests, 10 passed, 0 known failure, 0 skipped
591s [inst/dist_stat/poisstat.m]
591s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/poisstat.m
591s ***** error<poisstat: function called with too few input arguments.> poisstat ()
591s ***** error<poisstat: SIGMA must be numeric.> poisstat ({})
591s ***** error<poisstat: SIGMA must be numeric.> poisstat ("")
591s ***** error<poisstat: SIGMA must not be complex.> poisstat (i)
591s ***** test
591s  lambda = 1 ./ (1:6);
591s  [m, v] = poisstat (lambda);
591s  assert (m, lambda);
591s  assert (v, lambda);
591s 5 tests, 5 passed, 0 known failure, 0 skipped
591s [inst/dist_stat/raylstat.m]
591s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/raylstat.m
591s ***** error<raylstat: function called with too few input arguments.> raylstat ()
591s ***** error<raylstat: SIGMA must be numeric.> raylstat ({})
591s ***** error<raylstat: SIGMA must be numeric.> raylstat ("")
591s ***** error<raylstat: SIGMA must not be complex.> raylstat (i)
591s ***** test
591s  sigma = 1:6;
591s  [m, v] = raylstat (sigma);
591s  expected_m = [1.2533, 2.5066, 3.7599, 5.0133, 6.2666, 7.5199];
591s  expected_v = [0.4292, 1.7168, 3.8628, 6.8673, 10.7301, 15.4513];
591s  assert (m, expected_m, 0.001);
591s  assert (v, expected_v, 0.001);
591s 5 tests, 5 passed, 0 known failure, 0 skipped
591s [inst/dist_stat/ricestat.m]
591s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/ricestat.m
591s ***** error<ricestat: function called with too few input arguments.> ricestat ()
591s ***** error<ricestat: function called with too few input arguments.> ricestat (1)
591s ***** error<ricestat: S and SIGMA must be numeric.> ricestat ({}, 2)
591s ***** error<ricestat: S and SIGMA must be numeric.> ricestat (1, "")
591s ***** error<ricestat: S and SIGMA must not be complex.> ricestat (i, 2)
591s ***** error<ricestat: S and SIGMA must not be complex.> ricestat (1, i)
591s ***** error<ricestat: S and SIGMA must be of common size or scalars.> ...
591s  ricestat (ones (3), ones (2))
591s ***** error<ricestat: S and SIGMA must be of common size or scalars.> ...
591s  ricestat (ones (2), ones (3))
591s ***** shared s, sigma
591s  s = [2, 0, -1, 1, 4];
591s  sigma = [1, NaN, 3, -1, 2];
591s ***** assert (ricestat (s, sigma), [2.2724, NaN, NaN, NaN, 4.5448], 1e-4);
591s ***** assert (ricestat ([s(1:2), s(4:5)], 1), [2.2724, 1.2533, 1.5486, 4.1272], 1e-4);
591s ***** assert (ricestat ([s(1:2), s(4:5)], 3), [4.1665, 3.7599, 3.8637, 5.2695], 1e-4);
591s ***** assert (ricestat ([s(1:2), s(4:5)], 2), [3.0971, 2.5066, 2.6609, 4.5448], 1e-4);
591s ***** assert (ricestat (2, [sigma(1), sigma(3:5)]), [2.2724, 4.1665, NaN, 3.0971], 1e-4);
591s ***** assert (ricestat (0, [sigma(1), sigma(3:5)]), [1.2533, 3.7599, NaN, 2.5066], 1e-4);
591s ***** assert (ricestat (1, [sigma(1), sigma(3:5)]), [1.5486, 3.8637, NaN, 2.6609], 1e-4);
591s ***** assert (ricestat (4, [sigma(1), sigma(3:5)]), [4.1272, 5.2695, NaN, 4.5448], 1e-4);
591s 16 tests, 16 passed, 0 known failure, 0 skipped
591s [inst/dist_stat/tlsstat.m]
591s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/tlsstat.m
591s ***** error<tlsstat: function called with too few input arguments.> tlsstat ()
591s ***** error<tlsstat: function called with too few input arguments.> tlsstat (1)
591s ***** error<tlsstat: function called with too few input arguments.> tlsstat (1, 2)
591s ***** error<tlsstat: MU, SIGMA, and NU must be numeric.> tlsstat ({}, 2, 3)
591s ***** error<tlsstat: MU, SIGMA, and NU must be numeric.> tlsstat (1, "", 3)
591s ***** error<tlsstat: MU, SIGMA, and NU must be numeric.> tlsstat (1, 2, ["d"])
591s ***** error<tlsstat: MU, SIGMA, and NU must not be complex.> tlsstat (i, 2, 3)
591s ***** error<tlsstat: MU, SIGMA, and NU must not be complex.> tlsstat (1, i, 3)
591s ***** error<tlsstat: MU, SIGMA, and NU must not be complex.> tlsstat (1, 2, i)
591s ***** error<tlsstat: MU, SIGMA, and NU must be of common size or scalars.> ...
591s  tlsstat (ones (3), ones (2), 1)
592s ***** error<tlsstat: MU, SIGMA, and NU must be of common size or scalars.> ...
592s  tlsstat (ones (2), 1, ones (3))
592s ***** error<tlsstat: MU, SIGMA, and NU must be of common size or scalars.> ...
592s  tlsstat (1, ones (2), ones (3))
592s ***** test
592s  [m, v] = tlsstat (0, 1, 0);
592s  assert (m, NaN);
592s  assert (v, NaN);
592s ***** test
592s  [m, v] = tlsstat (0, 1, 1);
592s  assert (m, NaN);
592s  assert (v, NaN);
592s ***** test
592s  [m, v] = tlsstat (2, 1, 1);
592s  assert (m, NaN);
592s  assert (v, NaN);
592s ***** test
592s  [m, v] = tlsstat (-2, 1, 1);
592s  assert (m, NaN);
592s  assert (v, NaN);
592s ***** test
592s  [m, v] = tlsstat (0, 1, 2);
592s  assert (m, 0);
592s  assert (v, NaN);
592s ***** test
592s  [m, v] = tlsstat (2, 1, 2);
592s  assert (m, 2);
592s  assert (v, NaN);
592s ***** test
592s  [m, v] = tlsstat (-2, 1, 2);
592s  assert (m, -2);
592s  assert (v, NaN);
592s ***** test
592s  [m, v] = tlsstat (0, 2, 2);
592s  assert (m, 0);
592s  assert (v, NaN);
592s ***** test
592s  [m, v] = tlsstat (2, 2, 2);
592s  assert (m, 2);
592s  assert (v, NaN);
592s ***** test
592s  [m, v] = tlsstat (-2, 2, 2);
592s  assert (m, -2);
592s  assert (v, NaN);
592s ***** test
592s  [m, v] = tlsstat (0, 1, 3);
592s  assert (m, 0);
592s  assert (v, 3);
592s ***** test
592s  [m, v] = tlsstat (0, 2, 3);
592s  assert (m, 0);
592s  assert (v, 6);
592s ***** test
592s  [m, v] = tlsstat (2, 1, 3);
592s  assert (m, 2);
592s  assert (v, 3);
592s ***** test
592s  [m, v] = tlsstat (2, 2, 3);
592s  assert (m, 2);
592s  assert (v, 6);
592s ***** test
592s  [m, v] = tlsstat (-2, 1, 3);
592s  assert (m, -2);
592s  assert (v, 3);
592s ***** test
592s  [m, v] = tlsstat (-2, 2, 3);
592s  assert (m, -2);
592s  assert (v, 6);
592s 28 tests, 28 passed, 0 known failure, 0 skipped
592s [inst/dist_stat/tristat.m]
592s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/tristat.m
592s ***** error<tristat: function called with too few input arguments.> tristat ()
592s ***** error<tristat: function called with too few input arguments.> tristat (1)
592s ***** error<tristat: function called with too few input arguments.> tristat (1, 2)
592s ***** error<tristat: A, B, and C must be numeric.> tristat ("i", 2, 1)
592s ***** error<tristat: A, B, and C must be numeric.> tristat (0, "d", 1)
592s ***** error<tristat: A, B, and C must be numeric.> tristat (0, 3, {})
592s ***** error<tristat: A, B, and C must be real.> tristat (i, 2, 1)
592s ***** error<tristat: A, B, and C must be real.> tristat (0, i, 1)
592s ***** error<tristat: A, B, and C must be real.> tristat (0, 3, i)
592s ***** test
592s  a = 1:5;
592s  b = 3:7;
592s  c = 5:9;
592s  [m, v] = tristat (a, b, c);
592s  expected_m = [3, 4, 5, 6, 7];
592s  assert (m, expected_m);
592s  assert (v, ones (1, 5) * (2/3));
592s 10 tests, 10 passed, 0 known failure, 0 skipped
592s [inst/dist_stat/tstat.m]
592s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/tstat.m
592s ***** error<tstat: function called with too few input arguments.> tstat ()
592s ***** error<tstat: DF must be numeric.> tstat ({})
592s ***** error<tstat: DF must be numeric.> tstat ("")
592s ***** error<tstat: DF must not be complex.> tstat (i)
592s ***** test
592s  df = 3:8;
592s  [m, v] = tstat (df);
592s  expected_m = [0, 0, 0, 0, 0, 0];
592s  expected_v = [3.0000, 2.0000, 1.6667, 1.5000, 1.4000, 1.3333];
592s  assert (m, expected_m);
592s  assert (v, expected_v, 0.001);
592s 5 tests, 5 passed, 0 known failure, 0 skipped
592s [inst/dist_stat/unidstat.m]
592s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/unidstat.m
592s ***** error<unidstat: function called with too few input arguments.> unidstat ()
592s ***** error<unidstat: N must be numeric.> unidstat ({})
592s ***** error<unidstat: N must be numeric.> unidstat ("")
592s ***** error<unidstat: N must not be complex.> unidstat (i)
592s ***** test
592s  N = 1:6;
592s  [m, v] = unidstat (N);
592s  expected_m = [1.0000, 1.5000, 2.0000, 2.5000, 3.0000, 3.5000];
592s  expected_v = [0.0000, 0.2500, 0.6667, 1.2500, 2.0000, 2.9167];
592s  assert (m, expected_m, 0.001);
592s  assert (v, expected_v, 0.001);
592s 5 tests, 5 passed, 0 known failure, 0 skipped
592s [inst/dist_stat/unifstat.m]
592s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/unifstat.m
592s ***** error<unifstat: function called with too few input arguments.> unifstat ()
592s ***** error<unifstat: function called with too few input arguments.> unifstat (1)
592s ***** error<unifstat: A and B must be numeric.> unifstat ({}, 2)
592s ***** error<unifstat: A and B must be numeric.> unifstat (1, "")
592s ***** error<unifstat: A and B must not be complex.> unifstat (i, 2)
592s ***** error<unifstat: A and B must not be complex.> unifstat (1, i)
592s ***** error<unifstat: A and B must be of common size or scalars.> ...
592s  unifstat (ones (3), ones (2))
592s ***** error<unifstat: A and B must be of common size or scalars.> ...
592s  unifstat (ones (2), ones (3))
592s ***** test
592s  a = 1:6;
592s  b = 2:2:12;
592s  [m, v] = unifstat (a, b);
592s  expected_m = [1.5000, 3.0000, 4.5000, 6.0000, 7.5000, 9.0000];
592s  expected_v = [0.0833, 0.3333, 0.7500, 1.3333, 2.0833, 3.0000];
592s  assert (m, expected_m, 0.001);
592s  assert (v, expected_v, 0.001);
592s ***** test
592s  a = 1:6;
592s  [m, v] = unifstat (a, 10);
592s  expected_m = [5.5000, 6.0000, 6.5000, 7.0000, 7.5000, 8.0000];
592s  expected_v = [6.7500, 5.3333, 4.0833, 3.0000, 2.0833, 1.3333];
592s  assert (m, expected_m, 0.001);
592s  assert (v, expected_v, 0.001);
592s 10 tests, 10 passed, 0 known failure, 0 skipped
592s [inst/dist_stat/wblstat.m]
592s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_stat/wblstat.m
592s ***** error<wblstat: function called with too few input arguments.> wblstat ()
592s ***** error<wblstat: function called with too few input arguments.> wblstat (1)
592s ***** error<wblstat: LAMBDA and K must be numeric.> wblstat ({}, 2)
592s ***** error<wblstat: LAMBDA and K must be numeric.> wblstat (1, "")
592s ***** error<wblstat: LAMBDA and K must not be complex.> wblstat (i, 2)
592s ***** error<wblstat: LAMBDA and K must not be complex.> wblstat (1, i)
592s ***** error<wblstat: LAMBDA and K must be of common size or scalars.> ...
592s  wblstat (ones (3), ones (2))
592s ***** error<wblstat: LAMBDA and K must be of common size or scalars.> ...
592s  wblstat (ones (2), ones (3))
592s ***** test
592s  lambda = 3:8;
592s  k = 1:6;
592s  [m, v] = wblstat (lambda, k);
592s  expected_m = [3.0000, 3.5449, 4.4649, 5.4384, 6.4272, 7.4218];
592s  expected_v = [9.0000, 3.4336, 2.6333, 2.3278, 2.1673, 2.0682];
592s  assert (m, expected_m, 0.001);
592s  assert (v, expected_v, 0.001);
592s ***** test
592s  k = 1:6;
592s  [m, v] = wblstat (6, k);
592s  expected_m = [ 6.0000, 5.3174, 5.3579, 5.4384, 5.5090, 5.5663];
592s  expected_v = [36.0000, 7.7257, 3.7920, 2.3278, 1.5923, 1.1634];
592s  assert (m, expected_m, 0.001);
592s  assert (v, expected_v, 0.001);
592s 10 tests, 10 passed, 0 known failure, 0 skipped
592s [inst/dist_wrap/cdf.m]
592s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_wrap/cdf.m
592s ***** shared x
592s  x = [1:5];
592s ***** assert (cdf ("Beta", x, 5, 2), betacdf (x, 5, 2))
592s ***** assert (cdf ("beta", x, 5, 2, "upper"), betacdf (x, 5, 2, "upper"))
592s ***** assert (cdf ("Binomial", x, 5, 2), binocdf (x, 5, 2))
592s ***** assert (cdf ("bino", x, 5, 2, "upper"), binocdf (x, 5, 2, "upper"))
592s ***** assert (cdf ("Birnbaum-Saunders", x, 5, 2), bisacdf (x, 5, 2))
592s ***** assert (cdf ("bisa", x, 5, 2, "upper"), bisacdf (x, 5, 2, "upper"))
592s ***** assert (cdf ("Burr", x, 5, 2, 2), burrcdf (x, 5, 2, 2))
592s ***** assert (cdf ("burr", x, 5, 2, 2, "upper"), burrcdf (x, 5, 2, 2, "upper"))
592s ***** assert (cdf ("Cauchy", x, 5, 2), cauchycdf (x, 5, 2))
592s ***** assert (cdf ("cauchy", x, 5, 2, "upper"), cauchycdf (x, 5, 2, "upper"))
592s ***** assert (cdf ("Chi-squared", x, 5), chi2cdf (x, 5))
592s ***** assert (cdf ("chi2", x, 5, "upper"), chi2cdf (x, 5, "upper"))
592s ***** assert (cdf ("Extreme Value", x, 5, 2), evcdf (x, 5, 2))
592s ***** assert (cdf ("ev", x, 5, 2, "upper"), evcdf (x, 5, 2, "upper"))
592s ***** assert (cdf ("Exponential", x, 5), expcdf (x, 5))
592s ***** assert (cdf ("exp", x, 5, "upper"), expcdf (x, 5, "upper"))
592s ***** assert (cdf ("F-Distribution", x, 5, 2), fcdf (x, 5, 2))
592s ***** assert (cdf ("f", x, 5, 2, "upper"), fcdf (x, 5, 2, "upper"))
592s ***** assert (cdf ("Gamma", x, 5, 2), gamcdf (x, 5, 2))
592s ***** assert (cdf ("gam", x, 5, 2, "upper"), gamcdf (x, 5, 2, "upper"))
592s ***** assert (cdf ("Geometric", x, 5), geocdf (x, 5))
592s ***** assert (cdf ("geo", x, 5, "upper"), geocdf (x, 5, "upper"))
592s ***** assert (cdf ("Generalized Extreme Value", x, 5, 2, 2), gevcdf (x, 5, 2, 2))
592s ***** assert (cdf ("gev", x, 5, 2, 2, "upper"), gevcdf (x, 5, 2, 2, "upper"))
592s ***** assert (cdf ("Generalized Pareto", x, 5, 2, 2), gpcdf (x, 5, 2, 2))
592s ***** assert (cdf ("gp", x, 5, 2, 2, "upper"), gpcdf (x, 5, 2, 2, "upper"))
592s ***** assert (cdf ("Gumbel", x, 5, 2), gumbelcdf (x, 5, 2))
592s ***** assert (cdf ("gumbel", x, 5, 2, "upper"), gumbelcdf (x, 5, 2, "upper"))
592s ***** assert (cdf ("Half-normal", x, 5, 2), hncdf (x, 5, 2))
592s ***** assert (cdf ("hn", x, 5, 2, "upper"), hncdf (x, 5, 2, "upper"))
592s ***** assert (cdf ("Hypergeometric", x, 5, 2, 2), hygecdf (x, 5, 2, 2))
592s ***** assert (cdf ("hyge", x, 5, 2, 2, "upper"), hygecdf (x, 5, 2, 2, "upper"))
592s ***** assert (cdf ("Inverse Gaussian", x, 5, 2), invgcdf (x, 5, 2))
592s ***** assert (cdf ("invg", x, 5, 2, "upper"), invgcdf (x, 5, 2, "upper"))
592s ***** assert (cdf ("Laplace", x, 5, 2), laplacecdf (x, 5, 2))
592s ***** assert (cdf ("laplace", x, 5, 2, "upper"), laplacecdf (x, 5, 2, "upper"))
592s ***** assert (cdf ("Logistic", x, 5, 2), logicdf (x, 5, 2))
592s ***** assert (cdf ("logi", x, 5, 2, "upper"), logicdf (x, 5, 2, "upper"))
592s ***** assert (cdf ("Log-Logistic", x, 5, 2), loglcdf (x, 5, 2))
592s ***** assert (cdf ("logl", x, 5, 2, "upper"), loglcdf (x, 5, 2, "upper"))
592s ***** assert (cdf ("Lognormal", x, 5, 2), logncdf (x, 5, 2))
592s ***** assert (cdf ("logn", x, 5, 2, "upper"), logncdf (x, 5, 2, "upper"))
592s ***** assert (cdf ("Nakagami", x, 5, 2), nakacdf (x, 5, 2))
592s ***** assert (cdf ("naka", x, 5, 2, "upper"), nakacdf (x, 5, 2, "upper"))
592s ***** assert (cdf ("Negative Binomial", x, 5, 2), nbincdf (x, 5, 2))
592s ***** assert (cdf ("nbin", x, 5, 2, "upper"), nbincdf (x, 5, 2, "upper"))
592s ***** assert (cdf ("Noncentral F-Distribution", x, 5, 2, 2), ncfcdf (x, 5, 2, 2))
592s ***** assert (cdf ("ncf", x, 5, 2, 2, "upper"), ncfcdf (x, 5, 2, 2, "upper"))
592s ***** assert (cdf ("Noncentral Student T", x, 5, 2), nctcdf (x, 5, 2))
592s ***** assert (cdf ("nct", x, 5, 2, "upper"), nctcdf (x, 5, 2, "upper"))
592s ***** assert (cdf ("Noncentral Chi-Squared", x, 5, 2), ncx2cdf (x, 5, 2))
592s ***** assert (cdf ("ncx2", x, 5, 2, "upper"), ncx2cdf (x, 5, 2, "upper"))
593s ***** assert (cdf ("Normal", x, 5, 2), normcdf (x, 5, 2))
593s ***** assert (cdf ("norm", x, 5, 2, "upper"), normcdf (x, 5, 2, "upper"))
593s ***** assert (cdf ("Poisson", x, 5), poisscdf (x, 5))
593s ***** assert (cdf ("poiss", x, 5, "upper"), poisscdf (x, 5, "upper"))
593s ***** assert (cdf ("Rayleigh", x, 5), raylcdf (x, 5))
593s ***** assert (cdf ("rayl", x, 5, "upper"), raylcdf (x, 5, "upper"))
593s ***** assert (cdf ("Rician", x, 5, 1), ricecdf (x, 5, 1))
593s ***** assert (cdf ("rice", x, 5, 1, "upper"), ricecdf (x, 5, 1, "upper"))
593s ***** assert (cdf ("Student T", x, 5), tcdf (x, 5))
593s ***** assert (cdf ("t", x, 5, "upper"), tcdf (x, 5, "upper"))
593s ***** assert (cdf ("location-scale T", x, 5, 1, 2), tlscdf (x, 5, 1, 2))
593s ***** assert (cdf ("tls", x, 5, 1, 2, "upper"), tlscdf (x, 5, 1, 2, "upper"))
593s ***** assert (cdf ("Triangular", x, 5, 2, 2), tricdf (x, 5, 2, 2))
593s ***** assert (cdf ("tri", x, 5, 2, 2, "upper"), tricdf (x, 5, 2, 2, "upper"))
593s ***** assert (cdf ("Discrete Uniform", x, 5), unidcdf (x, 5))
593s ***** assert (cdf ("unid", x, 5, "upper"), unidcdf (x, 5, "upper"))
593s ***** assert (cdf ("Uniform", x, 5, 2), unifcdf (x, 5, 2))
593s ***** assert (cdf ("unif", x, 5, 2, "upper"), unifcdf (x, 5, 2, "upper"))
593s ***** assert (cdf ("Von Mises", x, 5, 2), vmcdf (x, 5, 2))
593s ***** assert (cdf ("vm", x, 5, 2, "upper"), vmcdf (x, 5, 2, "upper"))
594s ***** assert (cdf ("Weibull", x, 5, 2), wblcdf (x, 5, 2))
594s ***** assert (cdf ("wbl", x, 5, 2, "upper"), wblcdf (x, 5, 2, "upper"))
594s ***** error<cdf: distribution NAME must be a char string.> cdf (1)
594s ***** error<cdf: distribution NAME must be a char string.> cdf ({"beta"})
594s ***** error<cdf: X must be numeric.> cdf ("beta", {[1 2 3 4 5]})
594s ***** error<cdf: X must be numeric.> cdf ("beta", "text")
594s ***** error<cdf: values in X must be real.> cdf ("beta", 1+i)
594s ***** error<cdf: distribution parameters must be numeric.> ...
594s  cdf ("Beta", x, "a", 2)
594s ***** error<cdf: distribution parameters must be numeric.> ...
594s  cdf ("Beta", x, 5, "")
594s ***** error<cdf: distribution parameters must be numeric.> ...
594s  cdf ("Beta", x, 5, {2})
594s ***** error<cdf: chi2 distribution requires 1 parameter.> cdf ("chi2", x)
594s ***** error<cdf: Beta distribution requires 2 parameters.> cdf ("Beta", x, 5)
594s ***** error<cdf: Burr distribution requires 3 parameters.> cdf ("Burr", x, 5)
594s ***** error<cdf: Burr distribution requires 3 parameters.> cdf ("Burr", x, 5, 2)
594s 86 tests, 86 passed, 0 known failure, 0 skipped
594s [inst/dist_wrap/fitdist.m]
594s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_wrap/fitdist.m
594s ***** test
594s  x = betarnd (1, 1, 100, 1);
594s  pd = fitdist (x, "Beta");
594s  [phat, pci] = betafit (x);
594s  assert ([pd.a, pd.b], phat);
594s  assert (paramci (pd), pci);
594s ***** test
594s  x1 = betarnd (1, 1, 100, 1);
594s  x2 = betarnd (5, 2, 100, 1);
594s  pd = fitdist ([x1; x2], "Beta", "By", [ones(100, 1); 2*ones(100, 1)]);
594s  [phat, pci] = betafit (x1);
594s  assert ([pd{1}.a, pd{1}.b], phat);
594s  assert (paramci (pd{1}), pci);
594s  [phat, pci] = betafit (x2);
594s  assert ([pd{2}.a, pd{2}.b], phat);
594s  assert (paramci (pd{2}), pci);
594s ***** warning <fitdist: no data in group '2' to fit a 'Beta' distribution.> ...
594s  fitdist ([betarnd(1, 1, 100, 1); nan(100, 1)], "Beta", ...
594s           "By", [ones(100, 1); 2*ones(100, 1)]);
594s ***** test
594s  N = 1;
594s  x = binornd (N, 0.5, 100, 1);
594s  pd = fitdist (x, "binomial");
594s  [phat, pci] = binofit (sum (x), numel (x));
594s  assert ([pd.N, pd.p], [N, phat]);
594s  assert (paramci (pd), pci);
594s ***** test
594s  N = 3;
594s  x = binornd (N, 0.4, 100, 1);
594s  pd = fitdist (x, "binomial", "ntrials", N);
594s  [phat, pci] = binofit (sum (x), numel (x) * N);
594s  assert ([pd.N, pd.p], [N, phat]);
594s  assert (paramci (pd), pci);
594s ***** test
594s  N = 1;
594s  x1 = binornd (N, 0.5, 100, 1);
594s  x2 = binornd (N, 0.7, 100, 1);
594s  pd = fitdist ([x1; x2], "binomial", "By", [ones(100, 1); 2*ones(100, 1)]);
594s  [phat, pci] = binofit (sum (x1), numel (x1));
594s  assert ([pd{1}.N, pd{1}.p], [N, phat]);
594s  assert (paramci (pd{1}), pci);
594s  [phat, pci] = binofit (sum (x2), numel (x2));
594s  assert ([pd{2}.N, pd{2}.p], [N, phat]);
594s  assert (paramci (pd{2}), pci);
594s ***** warning <fitdist: no data in group '2' to fit a 'binomial' distribution.> ...
594s  fitdist ([binornd(1, 0.5, 100, 1); nan(100, 1)], "binomial", ...
594s           "By", [ones(100, 1); 2*ones(100, 1)]);
594s ***** test
594s  N = 5;
594s  x1 = binornd (N, 0.5, 100, 1);
594s  x2 = binornd (N, 0.8, 100, 1);
594s  pd = fitdist ([x1; x2], "binomial", "ntrials", N, ...
594s                "By", [ones(100, 1); 2*ones(100, 1)]);
594s  [phat, pci] = binofit (sum (x1), numel (x1) * N);
594s  assert ([pd{1}.N, pd{1}.p], [N, phat]);
594s  assert (paramci (pd{1}), pci);
594s  [phat, pci] = binofit (sum (x2), numel (x2) * N);
594s  assert ([pd{2}.N, pd{2}.p], [N, phat]);
594s  assert (paramci (pd{2}), pci);
595s ***** warning <fitdist: no data in group '2' to fit a 'binomial' distribution.> ...
595s  fitdist ([binornd(5, 0.5, 100, 1); nan(100, 1)], "binomial", "ntrials", 5, ...
595s           "By", [ones(100, 1); 2*ones(100, 1)]);
595s ***** test
595s  x = bisarnd (1, 1, 100, 1);
595s  pd = fitdist (x, "BirnbaumSaunders");
595s  [phat, pci] = bisafit (x);
595s  assert ([pd.beta, pd.gamma], phat);
595s  assert (paramci (pd), pci);
595s ***** test
595s  x1 = bisarnd (1, 1, 100, 1);
595s  x2 = bisarnd (5, 2, 100, 1);
595s  pd = fitdist ([x1; x2], "bisa", "By", [ones(100,1); 2*ones(100,1)]);
595s  [phat, pci] = bisafit (x1);
595s  assert ([pd{1}.beta, pd{1}.gamma], phat);
595s  assert (paramci (pd{1}), pci);
595s  [phat, pci] = bisafit (x2);
595s  assert ([pd{2}.beta, pd{2}.gamma], phat);
595s  assert (paramci (pd{2}), pci);
595s ***** warning <fitdist: no data in group '2' to fit a 'bisa' distribution.> ...
595s  fitdist ([bisarnd(1, 1, 100, 1); nan(100, 1)], "bisa", ...
595s           "By", [ones(100, 1); 2*ones(100, 1)]);
595s ***** test
595s  x = burrrnd (1, 2, 1, 100, 1);
595s  pd = fitdist (x, "Burr");
595s  [phat, pci] = burrfit (x);
595s  assert ([pd.alpha, pd.c, pd.k], phat);
595s  assert (paramci (pd), pci);
596s ***** test
596s  rand ("seed", 4);   # for reproducibility
596s  x1 = burrrnd (1, 2, 1, 100, 1);
596s  rand ("seed", 3);   # for reproducibility
596s  x2 = burrrnd (1, 0.5, 2, 100, 1);
596s  pd = fitdist ([x1; x2], "burr", "By", [ones(100,1); 2*ones(100,1)]);
596s  [phat, pci] = burrfit (x1);
596s  assert ([pd{1}.alpha, pd{1}.c, pd{1}.k], phat);
596s  assert (paramci (pd{1}), pci);
596s  [phat, pci] = burrfit (x2);
596s  assert ([pd{2}.alpha, pd{2}.c, pd{2}.k], phat);
596s  assert (paramci (pd{2}), pci);
596s ***** warning <fitdist: no data in group '2' to fit a 'burr' distribution.> ...
596s  fitdist ([burrrnd(1, 2, 1, 100, 1); nan(100, 1)], "burr", ...
596s           "By", [ones(100, 1); 2*ones(100, 1)]);
596s ***** test
596s  x = exprnd (1, 100, 1);
596s  pd = fitdist (x, "exponential");
596s  [muhat, muci] = expfit (x);
596s  assert ([pd.mu], muhat);
596s  assert (paramci (pd), muci);
597s ***** test
597s  x1 = exprnd (1, 100, 1);
597s  x2 = exprnd (5, 100, 1);
597s  pd = fitdist ([x1; x2], "exponential", "By", [ones(100,1); 2*ones(100,1)]);
597s  [muhat, muci] = expfit (x1);
597s  assert ([pd{1}.mu], muhat);
597s  assert (paramci (pd{1}), muci);
597s  [muhat, muci] = expfit (x2);
597s  assert ([pd{2}.mu], muhat);
597s  assert (paramci (pd{2}), muci);
597s ***** warning <fitdist: no data in group '2' to fit a 'exponential' distribution.> ...
597s  fitdist ([exprnd(1, 100, 1); nan(100, 1)], "exponential", ...
597s           "By", [ones(100, 1); 2*ones(100, 1)]);
597s ***** test
597s  x = evrnd (1, 1, 100, 1);
597s  pd = fitdist (x, "ev");
597s  [phat, pci] = evfit (x);
597s  assert ([pd.mu, pd.sigma], phat);
597s  assert (paramci (pd), pci);
597s ***** test
597s  x1 = evrnd (1, 1, 100, 1);
597s  x2 = evrnd (5, 2, 100, 1);
597s  pd = fitdist ([x1; x2], "extremevalue", "By", [ones(100,1); 2*ones(100,1)]);
597s  [phat, pci] = evfit (x1);
597s  assert ([pd{1}.mu, pd{1}.sigma], phat);
597s  assert (paramci (pd{1}), pci);
597s  [phat, pci] = evfit (x2);
597s  assert ([pd{2}.mu, pd{2}.sigma], phat);
597s  assert (paramci (pd{2}), pci);
597s ***** warning <fitdist: no data in group '2' to fit a 'extremevalue' distribution.> ...
597s  fitdist ([evrnd(1, 1, 100, 1); nan(100, 1)], "extremevalue", ...
597s           "By", [ones(100, 1); 2*ones(100, 1)]);
597s ***** test
597s  x = gamrnd (1, 1, 100, 1);
597s  pd = fitdist (x, "Gamma");
597s  [phat, pci] = gamfit (x);
597s  assert ([pd.a, pd.b], phat);
597s  assert (paramci (pd), pci);
597s ***** test
597s  x1 = gamrnd (1, 1, 100, 1);
597s  x2 = gamrnd (5, 2, 100, 1);
597s  pd = fitdist ([x1; x2], "Gamma", "By", [ones(100,1); 2*ones(100,1)]);
597s  [phat, pci] = gamfit (x1);
597s  assert ([pd{1}.a, pd{1}.b], phat);
597s  assert (paramci (pd{1}), pci);
597s  [phat, pci] = gamfit (x2);
597s  assert ([pd{2}.a, pd{2}.b], phat);
597s  assert (paramci (pd{2}), pci);
597s ***** warning <fitdist: no data in group '2' to fit a 'Gamma' distribution.> ...
597s  fitdist ([gamrnd(1, 1, 100, 1); nan(100, 1)], "Gamma", ...
597s           "By", [ones(100, 1); 2*ones(100, 1)]);
597s ***** test
597s  rand ("seed", 4);   # for reproducibility
597s  x = gevrnd (-0.5, 1, 2, 1000, 1);
597s  pd = fitdist (x, "generalizedextremevalue");
597s  [phat, pci] = gevfit (x);
597s  assert ([pd.k, pd.sigma, pd.mu], phat);
597s  assert (paramci (pd), pci);
598s ***** test
598s  rand ("seed", 5);   # for reproducibility
598s  x1 = gevrnd (-0.5, 1, 2, 1000, 1);
598s  rand ("seed", 9);   # for reproducibility
598s  x2 = gevrnd (0, 1, -4, 1000, 1);
598s  pd = fitdist ([x1; x2], "gev", "By", [ones(1000,1); 2*ones(1000,1)]);
598s  [phat, pci] = gevfit (x1);
598s  assert ([pd{1}.k, pd{1}.sigma, pd{1}.mu], phat);
598s  assert (paramci (pd{1}), pci);
598s  [phat, pci] = gevfit (x2);
598s  assert ([pd{2}.k, pd{2}.sigma, pd{2}.mu], phat);
598s  assert (paramci (pd{2}), pci);
598s ***** warning <fitdist: no data in group '2' to fit a 'gev' distribution.> ...
598s  fitdist ([gevrnd(-0.5, 1, 2, 1000, 1); nan(1000, 1)], "gev", ...
598s           "By", [ones(1000, 1); 2*ones(1000, 1)]);
599s ***** test
599s  x = gprnd (1, 1, 1, 100, 1);
599s  pd = fitdist (x, "GeneralizedPareto");
599s  [phat, pci] = gpfit (x, 1);
599s  assert ([pd.k, pd.sigma, pd.theta], phat);
599s  assert (paramci (pd), pci);
599s ***** test
599s  x = gprnd (1, 1, 2, 100, 1);
599s  pd = fitdist (x, "GeneralizedPareto", "theta", 2);
599s  [phat, pci] = gpfit (x, 2);
599s  assert ([pd.k, pd.sigma, pd.theta], phat);
599s  assert (paramci (pd), pci);
599s ***** test
599s  x1 = gprnd (1, 1, 1, 100, 1);
599s  x2 = gprnd (0, 2, 1, 100, 1);
599s  pd = fitdist ([x1; x2], "gp", "By", [ones(100,1); 2*ones(100,1)]);
599s  [phat, pci] = gpfit (x1, 1);
599s  assert ([pd{1}.k, pd{1}.sigma, pd{1}.theta], phat);
599s  assert (paramci (pd{1}), pci);
599s  [phat, pci] = gpfit (x2, 1);
599s  assert ([pd{2}.k, pd{2}.sigma, pd{2}.theta], phat);
599s  assert (paramci (pd{2}), pci);
599s ***** warning <fitdist: no data in group '2' to fit a 'gp' distribution.> ...
599s  fitdist ([gprnd(1, 1, 1, 100, 1); nan(100, 1)], "gp", ...
599s           "By", [ones(100, 1); 2*ones(100, 1)]);
599s ***** test
599s  x1 = gprnd (3, 2, 2, 100, 1);
599s  x2 = gprnd (2, 3, 2, 100, 1);
599s  pd = fitdist ([x1; x2], "GeneralizedPareto", "theta", 2, ...
599s                "By", [ones(100,1); 2*ones(100,1)]);
599s  [phat, pci] = gpfit (x1, 2);
599s  assert ([pd{1}.k, pd{1}.sigma, pd{1}.theta], phat);
599s  assert (paramci (pd{1}), pci);
599s  [phat, pci] = gpfit (x2, 2);
599s  assert ([pd{2}.k, pd{2}.sigma, pd{2}.theta], phat);
599s  assert (paramci (pd{2}), pci);
599s ***** warning <fitdist: no data in group '2' to fit a 'gp' distribution.> ...
599s  fitdist ([gprnd(3, 2, 2, 100, 1); nan(100, 1)], "gp", "theta", 2, ...
599s           "By", [ones(100, 1); 2*ones(100, 1)]);
599s ***** test
599s  x = hnrnd (0, 1, 100, 1);
599s  pd = fitdist (x, "HalfNormal");
599s  [phat, pci] = hnfit (x, 0);
599s  assert ([pd.mu, pd.sigma], phat);
599s  assert (paramci (pd), pci);
599s ***** test
599s  x = hnrnd (1, 1, 100, 1);
599s  pd = fitdist (x, "HalfNormal", "mu", 1);
599s  [phat, pci] = hnfit (x, 1);
599s  assert ([pd.mu, pd.sigma], phat);
599s  assert (paramci (pd), pci);
599s ***** test
599s  x1 = hnrnd (0, 1, 100, 1);
599s  x2 = hnrnd (0, 2, 100, 1);
599s  pd = fitdist ([x1; x2], "HalfNormal", "By", [ones(100,1); 2*ones(100,1)]);
599s  [phat, pci] = hnfit (x1, 0);
599s  assert ([pd{1}.mu, pd{1}.sigma], phat);
599s  assert (paramci (pd{1}), pci);
599s  [phat, pci] = hnfit (x2, 0);
599s  assert ([pd{2}.mu, pd{2}.sigma], phat);
599s  assert (paramci (pd{2}), pci);
600s ***** warning <fitdist: no data in group '2' to fit a 'HalfNormal' distribution.> ...
600s  fitdist ([hnrnd(0, 1, 100, 1); nan(100, 1)], "HalfNormal", ...
600s           "By", [ones(100, 1); 2*ones(100, 1)]);
600s ***** test
600s  x1 = hnrnd (2, 1, 100, 1);
600s  x2 = hnrnd (2, 2, 100, 1);
600s  pd = fitdist ([x1; x2], "HalfNormal", "mu", 2, ...
600s                "By", [ones(100,1); 2*ones(100,1)]);
600s  [phat, pci] = hnfit (x1, 2);
600s  assert ([pd{1}.mu, pd{1}.sigma], phat);
600s  assert (paramci (pd{1}), pci);
600s  [phat, pci] = hnfit (x2, 2);
600s  assert ([pd{2}.mu, pd{2}.sigma], phat);
600s  assert (paramci (pd{2}), pci);
600s ***** warning <fitdist: no data in group '2' to fit a 'HalfNormal' distribution.> ...
600s  fitdist ([hnrnd(2, 1, 100, 1); nan(100, 1)], "HalfNormal", "mu", 2, ...
600s           "By", [ones(100, 1); 2*ones(100, 1)]);
600s ***** test
600s  x = invgrnd (1, 1, 100, 1);
600s  pd = fitdist (x, "InverseGaussian");
600s  [phat, pci] = invgfit (x);
600s  assert ([pd.mu, pd.lambda], phat);
600s  assert (paramci (pd), pci);
600s ***** test
600s  x1 = invgrnd (1, 1, 100, 1);
600s  x2 = invgrnd (5, 2, 100, 1);
600s  pd = fitdist ([x1; x2], "InverseGaussian", "By", [ones(100,1); 2*ones(100,1)]);
600s  [phat, pci] = invgfit (x1);
600s  assert ([pd{1}.mu, pd{1}.lambda], phat);
600s  assert (paramci (pd{1}), pci);
600s  [phat, pci] = invgfit (x2);
600s  assert ([pd{2}.mu, pd{2}.lambda], phat);
600s  assert (paramci (pd{2}), pci);
600s ***** warning <fitdist: no data in group '2' to fit a 'InverseGaussian' distribution.> ...
600s  fitdist ([invgrnd(1, 1, 100, 1); nan(100, 1)], "InverseGaussian", ...
600s           "By", [ones(100, 1); 2*ones(100, 1)]);
600s ***** test
600s  x = logirnd (1, 1, 100, 1);
600s  pd = fitdist (x, "logistic");
600s  [phat, pci] = logifit (x);
600s  assert ([pd.mu, pd.sigma], phat);
600s  assert (paramci (pd), pci);
600s ***** test
600s  x1 = logirnd (1, 1, 100, 1);
600s  x2 = logirnd (5, 2, 100, 1);
600s  pd = fitdist ([x1; x2], "logistic", "By", [ones(100,1); 2*ones(100,1)]);
600s  [phat, pci] = logifit (x1);
600s  assert ([pd{1}.mu, pd{1}.sigma], phat);
600s  assert (paramci (pd{1}), pci);
600s  [phat, pci] = logifit (x2);
600s  assert ([pd{2}.mu, pd{2}.sigma], phat);
600s  assert (paramci (pd{2}), pci);
601s ***** warning <fitdist: no data in group '2' to fit a 'logistic' distribution.> ...
601s  fitdist ([logirnd(1, 1, 100, 1); nan(100, 1)], "logistic", ...
601s           "By", [ones(100, 1); 2*ones(100, 1)]);
601s ***** test
601s  x = loglrnd (1, 1, 100, 1);
601s  pd = fitdist (x, "loglogistic");
601s  [phat, pci] = loglfit (x);
601s  assert ([pd.mu, pd.sigma], phat);
601s  assert (paramci (pd), pci);
601s ***** test
601s  x1 = loglrnd (1, 1, 100, 1);
601s  x2 = loglrnd (5, 2, 100, 1);
601s  pd = fitdist ([x1; x2], "loglogistic", "By", [ones(100,1); 2*ones(100,1)]);
601s  [phat, pci] = loglfit (x1);
601s  assert ([pd{1}.mu, pd{1}.sigma], phat);
601s  assert (paramci (pd{1}), pci);
601s  [phat, pci] = loglfit (x2);
601s  assert ([pd{2}.mu, pd{2}.sigma], phat);
601s  assert (paramci (pd{2}), pci);
601s ***** warning <fitdist: no data in group '2' to fit a 'loglogistic' distribution.> ...
601s  fitdist ([loglrnd(1, 1, 100, 1); nan(100, 1)], "loglogistic", ...
601s           "By", [ones(100, 1); 2*ones(100, 1)]);
601s ***** test
601s  x = lognrnd (1, 1, 100, 1);
601s  pd = fitdist (x, "lognormal");
601s  [phat, pci] = lognfit (x);
601s  assert ([pd.mu, pd.sigma], phat);
601s  assert (paramci (pd), pci);
602s ***** test
602s  x1 = lognrnd (1, 1, 100, 1);
602s  x2 = lognrnd (5, 2, 100, 1);
602s  pd = fitdist ([x1; x2], "lognormal", "By", [ones(100,1); 2*ones(100,1)]);
602s  [phat, pci] = lognfit (x1);
602s  assert ([pd{1}.mu, pd{1}.sigma], phat);
602s  assert (paramci (pd{1}), pci);
602s  [phat, pci] = lognfit (x2);
602s  assert ([pd{2}.mu, pd{2}.sigma], phat);
602s  assert (paramci (pd{2}), pci);
602s ***** warning <fitdist: no data in group '2' to fit a 'lognormal' distribution.> ...
602s  fitdist ([lognrnd(1, 1, 100, 1); nan(100, 1)], "lognormal", ...
602s           "By", [ones(100, 1); 2*ones(100, 1)]);
602s ***** test
602s  x = nakarnd (2, 0.5, 100, 1);
602s  pd = fitdist (x, "Nakagami");
602s  [phat, pci] = nakafit (x);
602s  assert ([pd.mu, pd.omega], phat);
602s  assert (paramci (pd), pci);
603s ***** test
603s  x1 = nakarnd (2, 0.5, 100, 1);
603s  x2 = nakarnd (5, 0.8, 100, 1);
603s  pd = fitdist ([x1; x2], "Nakagami", "By", [ones(100,1); 2*ones(100,1)]);
603s  [phat, pci] = nakafit (x1);
603s  assert ([pd{1}.mu, pd{1}.omega], phat);
603s  assert (paramci (pd{1}), pci);
603s  [phat, pci] = nakafit (x2);
603s  assert ([pd{2}.mu, pd{2}.omega], phat);
603s  assert (paramci (pd{2}), pci);
603s ***** warning <fitdist: no data in group '2' to fit a 'Nakagami' distribution.> ...
603s  fitdist ([nakarnd(2, 0.5, 100, 1); nan(100, 1)], "Nakagami", ...
603s           "By", [ones(100, 1); 2*ones(100, 1)]);
603s ***** test
603s  randp ("seed", 123);
603s  randg ("seed", 321);
603s  x = nbinrnd (2, 0.5, 100, 1);
603s  pd = fitdist (x, "negativebinomial");
603s  [phat, pci] = nbinfit (x);
603s  assert ([pd.R, pd.P], phat);
603s  assert (paramci (pd), pci);
603s ***** test
603s  randp ("seed", 345);
603s  randg ("seed", 543);
603s  x1 = nbinrnd (2, 0.5, 100, 1);
603s  randp ("seed", 432);
603s  randg ("seed", 234);
603s  x2 = nbinrnd (5, 0.8, 100, 1);
603s  pd = fitdist ([x1; x2], "nbin", "By", [ones(100,1); 2*ones(100,1)]);
603s  [phat, pci] = nbinfit (x1);
603s  assert ([pd{1}.R, pd{1}.P], phat);
603s  assert (paramci (pd{1}), pci);
603s  [phat, pci] = nbinfit (x2);
603s  assert ([pd{2}.R, pd{2}.P], phat);
603s  assert (paramci (pd{2}), pci);
603s ***** warning <fitdist: no data in group '2' to fit a 'nbin' distribution.> ...
603s  fitdist ([nbinrnd(2, 0.5, 100, 1); nan(100, 1)], "nbin", ...
603s           "By", [ones(100, 1); 2*ones(100, 1)]);
603s ***** test
603s  x = normrnd (1, 1, 100, 1);
603s  pd = fitdist (x, "normal");
603s  [muhat, sigmahat, muci, sigmaci] = normfit (x);
603s  assert ([pd.mu, pd.sigma], [muhat, sigmahat]);
603s  assert (paramci (pd), [muci, sigmaci]);
603s ***** test
603s  x1 = normrnd (1, 1, 100, 1);
603s  x2 = normrnd (5, 2, 100, 1);
603s  pd = fitdist ([x1; x2], "normal", "By", [ones(100,1); 2*ones(100,1)]);
603s  [muhat, sigmahat, muci, sigmaci] = normfit (x1);
603s  assert ([pd{1}.mu, pd{1}.sigma], [muhat, sigmahat]);
603s  assert (paramci (pd{1}), [muci, sigmaci]);
603s  [muhat, sigmahat, muci, sigmaci] = normfit (x2);
603s  assert ([pd{2}.mu, pd{2}.sigma], [muhat, sigmahat]);
603s  assert (paramci (pd{2}), [muci, sigmaci]);
604s ***** warning <fitdist: no data in group '2' to fit a 'normal' distribution.> ...
604s  fitdist ([normrnd(1, 1, 100, 1); nan(100, 1)], "normal", ...
604s           "By", [ones(100, 1); 2*ones(100, 1)]);
604s ***** test
604s  x = poissrnd (1, 100, 1);
604s  pd = fitdist (x, "poisson");
604s  [phat, pci] = poissfit (x);
604s  assert (pd.lambda, phat);
604s  assert (paramci (pd), pci);
604s ***** test
604s  x1 = poissrnd (1, 100, 1);
604s  x2 = poissrnd (5, 100, 1);
604s  pd = fitdist ([x1; x2], "poisson", "By", [ones(100,1); 2*ones(100,1)]);
604s  [phat, pci] = poissfit (x1);
604s  assert (pd{1}.lambda, phat);
604s  assert (paramci (pd{1}), pci);
604s  [phat, pci] = poissfit (x2);
604s  assert (pd{2}.lambda, phat);
604s  assert (paramci (pd{2}), pci);
604s ***** warning <fitdist: no data in group '2' to fit a 'poisson' distribution.> ...
604s  fitdist ([poissrnd(1, 100, 1); nan(100, 1)], "poisson", ...
604s           "By", [ones(100, 1); 2*ones(100, 1)]);
604s ***** test
604s  x = raylrnd (1, 100, 1);
604s  pd = fitdist (x, "rayleigh");
604s  [phat, pci] = raylfit (x);
604s  assert (pd.sigma, phat);
604s  assert (paramci (pd), pci);
604s ***** test
604s  x1 = raylrnd (1, 100, 1);
604s  x2 = raylrnd (5, 100, 1);
604s  pd = fitdist ([x1; x2], "rayleigh", "By", [ones(100,1); 2*ones(100,1)]);
604s  [phat, pci] = raylfit (x1);
604s  assert (pd{1}.sigma, phat);
604s  assert (paramci (pd{1}), pci);
604s  [phat, pci] = raylfit (x2);
604s  assert (pd{2}.sigma, phat);
604s  assert (paramci (pd{2}), pci);
605s ***** warning <fitdist: no data in group '2' to fit a 'rayleigh' distribution.> ...
605s  fitdist ([raylrnd(1, 100, 1); nan(100, 1)], "rayleigh", ...
605s           "By", [ones(100, 1); 2*ones(100, 1)]);
605s ***** test
605s  x = ricernd (1, 1, 100, 1);
605s  pd = fitdist (x, "rician");
605s  [phat, pci] = ricefit (x);
605s  assert ([pd.s, pd.sigma], phat);
605s  assert (paramci (pd), pci);
605s ***** test
605s  x1 = ricernd (1, 1, 100, 1);
605s  x2 = ricernd (5, 2, 100, 1);
605s  pd = fitdist ([x1; x2], "rician", "By", [ones(100,1); 2*ones(100,1)]);
605s  [phat, pci] = ricefit (x1);
605s  assert ([pd{1}.s, pd{1}.sigma], phat);
605s  assert (paramci (pd{1}), pci);
605s  [phat, pci] = ricefit (x2);
605s  assert ([pd{2}.s, pd{2}.sigma], phat);
605s  assert (paramci (pd{2}), pci);
605s ***** warning <fitdist: no data in group '2' to fit a 'rician' distribution.> ...
605s  fitdist ([ricernd(1, 1, 100, 1); nan(100, 1)], "rician", ...
605s           "By", [ones(100, 1); 2*ones(100, 1)]);
605s ***** warning <fitdist: 'Stable' distribution not supported yet.> ...
605s  fitdist ([1 2 3 4 5], "Stable");
605s ***** test
605s  x = tlsrnd (0, 1, 1, 100, 1);
605s  pd = fitdist (x, "tlocationscale");
605s  [phat, pci] = tlsfit (x);
605s  assert ([pd.mu, pd.sigma, pd.nu], phat);
605s  assert (paramci (pd), pci);
606s ***** test
606s  x1 = tlsrnd (0, 1, 1, 100, 1);
606s  x2 = tlsrnd (5, 2, 1, 100, 1);
606s  pd = fitdist ([x1; x2], "tlocationscale", "By", [ones(100,1); 2*ones(100,1)]);
606s  [phat, pci] = tlsfit (x1);
606s  assert ([pd{1}.mu, pd{1}.sigma, pd{1}.nu], phat);
606s  assert (paramci (pd{1}), pci);
606s  [phat, pci] = tlsfit (x2);
606s  assert ([pd{2}.mu, pd{2}.sigma, pd{2}.nu], phat);
606s  assert (paramci (pd{2}), pci);
606s ***** warning <fitdist: no data in group '2' to fit a 'tlocationscale' distribution.> ...
606s  fitdist ([tlsrnd(0, 1, 1, 100, 1); nan(100, 1)], "tlocationscale", ...
606s           "By", [ones(100, 1); 2*ones(100, 1)]);
606s ***** test
606s  x = [1 2 3 4 5];
606s  pd = fitdist (x, "weibull");
606s  [phat, pci] = wblfit (x);
606s  assert ([pd.lambda, pd.k], phat);
606s  assert (paramci (pd), pci);
606s ***** test
606s  x = [1 2 3 4 5 6 7 8 9 10];
606s  pd = fitdist (x, "weibull", "By", [1 1 1 1 1 2 2 2 2 2]);
606s  [phat, pci] = wblfit (x(1:5));
606s  assert ([pd{1}.lambda, pd{1}.k], phat);
606s  assert (paramci (pd{1}), pci);
606s  [phat, pci] = wblfit (x(6:10));
606s  assert ([pd{2}.lambda, pd{2}.k], phat);
606s  assert (paramci (pd{2}), pci);
606s ***** warning <fitdist: no data in group '2' to fit a 'weibull' distribution.> ...
606s  fitdist ([1 2 3 4 5 NaN NaN NaN NaN NaN], "weibull", "By", [1 1 1 1 1 2 2 2 2 2]);
607s ***** error <fitdist: DISTNAME is required.> fitdist (1)
607s ***** error <fitdist: DISTNAME must be a character vector.> fitdist (1, ["as";"sd"])
607s ***** error <fitdist: unrecognized distribution name.> fitdist (1, "some")
607s ***** error <fitdist: X must be a numeric vector of real values.> ...
607s  fitdist (ones (2), "normal")
607s ***** error <fitdist: X must be a numeric vector of real values.> ...
607s  fitdist ([i, 2, 3], "normal")
607s ***** error <fitdist: X must be a numeric vector of real values.> ...
607s  fitdist (["a", "s", "d"], "normal")
607s ***** error <fitdist: optional arguments must be in NAME-VALUE pairs.> ...
607s  fitdist ([1, 2, 3], "normal", "By")
607s ***** error <fitdist: GROUPVAR argument must have the same size as the input data in X.> ...
607s  fitdist ([1, 2, 3], "normal", "By", [1, 2])
607s ***** error <fitdist: 'censoring' argument must have the same size as the input data in X.> ...
607s  fitdist ([1, 2, 3], "normal", "Censoring", [1, 2])
607s ***** error <fitdist: 'frequency' argument must have the same size as the input data in X.> ...
607s  fitdist ([1, 2, 3], "normal", "frequency", [1, 2])
607s ***** error <fitdist: 'frequency' argument must contain non-negative integer values.> ...
607s  fitdist ([1, 2, 3], "negativebinomial", "frequency", [1, -2, 3])
607s ***** error <fitdist: invalid value for 'alpha' argument.> ...
607s  fitdist ([1, 2, 3], "normal", "alpha", [1, 2])
607s ***** error <fitdist: invalid value for 'alpha' argument.> ...
607s  fitdist ([1, 2, 3], "normal", "alpha", i)
607s ***** error <fitdist: invalid value for 'alpha' argument.> ...
607s  fitdist ([1, 2, 3], "normal", "alpha", -0.5)
607s ***** error <fitdist: invalid value for 'alpha' argument.> ...
607s  fitdist ([1, 2, 3], "normal", "alpha", 1.5)
607s ***** error <fitdist: 'ntrials' argument must be a positive integer scalar value.> ...
607s  fitdist ([1, 2, 3], "normal", "ntrials", [1, 2])
607s ***** error <fitdist: 'ntrials' argument must be a positive integer scalar value.> ...
607s  fitdist ([1, 2, 3], "normal", "ntrials", 0)
607s ***** error <fitdist: 'options' argument must be a structure compatible for 'fminsearch'.> ...
607s  fitdist ([1, 2, 3], "normal", "options", 0)
607s ***** error <fitdist: 'options' argument must be a structure compatible for 'fminsearch'.> ...
607s  fitdist ([1, 2, 3], "normal", "options", struct ("options", 1))
607s ***** warning fitdist ([1, 2, 3], "kernel", "kernel", "normal");
607s ***** warning fitdist ([1, 2, 3], "kernel", "support", "positive");
607s ***** warning fitdist ([1, 2, 3], "kernel", "width", 1);
607s ***** error <fitdist: unknown parameter name.> ...
607s  fitdist ([1, 2, 3], "normal", "param", struct ("options", 1))
607s ***** error <fitdist: no data in X to fit a 'normal' distribution.> ...
607s  fitdist (nan (100,1), "normal");
607s ***** error <fitdist: must define GROUPVAR for more than one output arguments.> ...
607s  [pdca, gn, gl] = fitdist ([1, 2, 3], "normal");
607s ***** error <fitdist: invalid THETA value for generalized Pareto distribution.> ...
607s  fitdist ([1, 2, 3], "generalizedpareto", "theta", 2);
607s ***** error <fitdist: invalid MU value for half-normal distribution.> ...
607s  fitdist ([1, 2, 3], "halfnormal", "mu", 2);
607s 103 tests, 103 passed, 0 known failure, 0 skipped
607s [inst/dist_wrap/icdf.m]
607s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_wrap/icdf.m
607s ***** shared p
607s  p = [0.05:0.05:0.5];
607s ***** assert (icdf ("Beta", p, 5, 2), betainv (p, 5, 2))
607s ***** assert (icdf ("beta", p, 5, 2), betainv (p, 5, 2))
607s ***** assert (icdf ("Binomial", p, 5, 2), binoinv (p, 5, 2))
607s ***** assert (icdf ("bino", p, 5, 2), binoinv (p, 5, 2))
607s ***** assert (icdf ("Birnbaum-Saunders", p, 5, 2), bisainv (p, 5, 2))
607s ***** assert (icdf ("bisa", p, 5, 2), bisainv (p, 5, 2))
607s ***** assert (icdf ("Burr", p, 5, 2, 2), burrinv (p, 5, 2, 2))
607s ***** assert (icdf ("burr", p, 5, 2, 2), burrinv (p, 5, 2, 2))
607s ***** assert (icdf ("Cauchy", p, 5, 2), cauchyinv (p, 5, 2))
607s ***** assert (icdf ("cauchy", p, 5, 2), cauchyinv (p, 5, 2))
607s ***** assert (icdf ("Chi-squared", p, 5), chi2inv (p, 5))
607s ***** assert (icdf ("chi2", p, 5), chi2inv (p, 5))
607s ***** assert (icdf ("Extreme Value", p, 5, 2), evinv (p, 5, 2))
607s ***** assert (icdf ("ev", p, 5, 2), evinv (p, 5, 2))
607s ***** assert (icdf ("Exponential", p, 5), expinv (p, 5))
607s ***** assert (icdf ("exp", p, 5), expinv (p, 5))
607s ***** assert (icdf ("F-Distribution", p, 5, 2), finv (p, 5, 2))
607s ***** assert (icdf ("f", p, 5, 2), finv (p, 5, 2))
607s ***** assert (icdf ("Gamma", p, 5, 2), gaminv (p, 5, 2))
607s ***** assert (icdf ("gam", p, 5, 2), gaminv (p, 5, 2))
607s ***** assert (icdf ("Geometric", p, 5), geoinv (p, 5))
607s ***** assert (icdf ("geo", p, 5), geoinv (p, 5))
607s ***** assert (icdf ("Generalized Extreme Value", p, 5, 2, 2), gevinv (p, 5, 2, 2))
607s ***** assert (icdf ("gev", p, 5, 2, 2), gevinv (p, 5, 2, 2))
607s ***** assert (icdf ("Generalized Pareto", p, 5, 2, 2), gpinv (p, 5, 2, 2))
607s ***** assert (icdf ("gp", p, 5, 2, 2), gpinv (p, 5, 2, 2))
607s ***** assert (icdf ("Gumbel", p, 5, 2), gumbelinv (p, 5, 2))
607s ***** assert (icdf ("gumbel", p, 5, 2), gumbelinv (p, 5, 2))
607s ***** assert (icdf ("Half-normal", p, 5, 2), hninv (p, 5, 2))
607s ***** assert (icdf ("hn", p, 5, 2), hninv (p, 5, 2))
607s ***** assert (icdf ("Hypergeometric", p, 5, 2, 2), hygeinv (p, 5, 2, 2))
607s ***** assert (icdf ("hyge", p, 5, 2, 2), hygeinv (p, 5, 2, 2))
607s ***** assert (icdf ("Inverse Gaussian", p, 5, 2), invginv (p, 5, 2))
607s ***** assert (icdf ("invg", p, 5, 2), invginv (p, 5, 2))
607s ***** assert (icdf ("Laplace", p, 5, 2), laplaceinv (p, 5, 2))
607s ***** assert (icdf ("laplace", p, 5, 2), laplaceinv (p, 5, 2))
607s ***** assert (icdf ("Logistic", p, 5, 2), logiinv (p, 5, 2))
607s ***** assert (icdf ("logi", p, 5, 2), logiinv (p, 5, 2))
607s ***** assert (icdf ("Log-Logistic", p, 5, 2), loglinv (p, 5, 2))
607s ***** assert (icdf ("logl", p, 5, 2), loglinv (p, 5, 2))
607s ***** assert (icdf ("Lognormal", p, 5, 2), logninv (p, 5, 2))
607s ***** assert (icdf ("logn", p, 5, 2), logninv (p, 5, 2))
607s ***** assert (icdf ("Nakagami", p, 5, 2), nakainv (p, 5, 2))
607s ***** assert (icdf ("naka", p, 5, 2), nakainv (p, 5, 2))
607s ***** assert (icdf ("Negative Binomial", p, 5, 2), nbininv (p, 5, 2))
607s ***** assert (icdf ("nbin", p, 5, 2), nbininv (p, 5, 2))
607s ***** assert (icdf ("Noncentral F-Distribution", p, 5, 2, 2), ncfinv (p, 5, 2, 2))
607s ***** assert (icdf ("ncf", p, 5, 2, 2), ncfinv (p, 5, 2, 2))
607s ***** assert (icdf ("Noncentral Student T", p, 5, 2), nctinv (p, 5, 2))
608s ***** assert (icdf ("nct", p, 5, 2), nctinv (p, 5, 2))
609s ***** assert (icdf ("Noncentral Chi-Squared", p, 5, 2), ncx2inv (p, 5, 2))
609s ***** assert (icdf ("ncx2", p, 5, 2), ncx2inv (p, 5, 2))
609s ***** assert (icdf ("Normal", p, 5, 2), norminv (p, 5, 2))
609s ***** assert (icdf ("norm", p, 5, 2), norminv (p, 5, 2))
609s ***** assert (icdf ("Poisson", p, 5), poissinv (p, 5))
610s ***** assert (icdf ("poiss", p, 5), poissinv (p, 5))
610s ***** assert (icdf ("Rayleigh", p, 5), raylinv (p, 5))
610s ***** assert (icdf ("rayl", p, 5), raylinv (p, 5))
610s ***** assert (icdf ("Rician", p, 5, 1), riceinv (p, 5, 1))
610s ***** assert (icdf ("rice", p, 5, 1), riceinv (p, 5, 1))
611s ***** assert (icdf ("Student T", p, 5), tinv (p, 5))
611s ***** assert (icdf ("t", p, 5), tinv (p, 5))
612s ***** assert (icdf ("location-scale T", p, 5, 1, 2), tlsinv (p, 5, 1, 2))
612s ***** assert (icdf ("tls", p, 5, 1, 2), tlsinv (p, 5, 1, 2))
612s ***** assert (icdf ("Triangular", p, 5, 2, 2), triinv (p, 5, 2, 2))
612s ***** assert (icdf ("tri", p, 5, 2, 2), triinv (p, 5, 2, 2))
612s ***** assert (icdf ("Discrete Uniform", p, 5), unidinv (p, 5))
612s ***** assert (icdf ("unid", p, 5), unidinv (p, 5))
612s ***** assert (icdf ("Uniform", p, 5, 2), unifinv (p, 5, 2))
612s ***** assert (icdf ("unif", p, 5, 2), unifinv (p, 5, 2))
612s ***** assert (icdf ("Von Mises", p, 5, 2), vminv (p, 5, 2))
627s ***** assert (icdf ("vm", p, 5, 2), vminv (p, 5, 2))
642s ***** assert (icdf ("Weibull", p, 5, 2), wblinv (p, 5, 2))
642s ***** assert (icdf ("wbl", p, 5, 2), wblinv (p, 5, 2))
642s ***** error<icdf: distribution NAME must be a char string.> icdf (1)
642s ***** error<icdf: distribution NAME must be a char string.> icdf ({"beta"})
642s ***** error<icdf: P must be numeric.> icdf ("beta", {[1 2 3 4 5]})
642s ***** error<icdf: P must be numeric.> icdf ("beta", "text")
642s ***** error<icdf: values in P must be real.> icdf ("beta", 1+i)
642s ***** error<icdf: distribution parameters must be numeric.> ...
642s  icdf ("Beta", p, "a", 2)
642s ***** error<icdf: distribution parameters must be numeric.> ...
642s  icdf ("Beta", p, 5, "")
642s ***** error<icdf: distribution parameters must be numeric.> ...
642s  icdf ("Beta", p, 5, {2})
642s ***** error<icdf: chi2 distribution requires 1 parameter.> icdf ("chi2", p)
642s ***** error<icdf: Beta distribution requires 2 parameters.> icdf ("Beta", p, 5)
642s ***** error<icdf: Burr distribution requires 3 parameters.> icdf ("Burr", p, 5)
642s ***** error<icdf: Burr distribution requires 3 parameters.> icdf ("Burr", p, 5, 2)
642s 86 tests, 86 passed, 0 known failure, 0 skipped
642s [inst/dist_wrap/makedist.m]
642s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_wrap/makedist.m
642s ***** test
642s  pd = makedist ("beta");
642s  assert (class (pd), "BetaDistribution");
642s  assert (pd.a, 1);
642s  assert (pd.b, 1);
642s ***** test
642s  pd = makedist ("beta", "a", 5);
642s  assert (pd.a, 5);
642s  assert (pd.b, 1);
642s ***** test
642s  pd = makedist ("beta", "b", 5);
642s  assert (pd.a, 1);
642s  assert (pd.b, 5);
642s ***** test
642s  pd = makedist ("beta", "a", 3, "b", 5);
642s  assert (pd.a, 3);
642s  assert (pd.b, 5);
642s ***** test
642s  pd = makedist ("binomial");
642s  assert (class (pd), "BinomialDistribution");
642s  assert (pd.N, 1);
642s  assert (pd.p, 0.5);
642s ***** test
642s  pd = makedist ("binomial", "N", 5);
642s  assert (pd.N, 5);
642s  assert (pd.p, 0.5);
642s ***** test
642s  pd = makedist ("binomial", "p", 0.2);
642s  assert (pd.N, 1);
642s  assert (pd.p, 0.2);
642s ***** test
642s  pd = makedist ("binomial", "N", 3, "p", 0.3);
642s  assert (pd.N, 3);
642s  assert (pd.p, 0.3);
642s ***** test
642s  pd = makedist ("birnbaumsaunders");
642s  assert (class (pd), "BirnbaumSaundersDistribution");
642s  assert (pd.beta, 1);
642s  assert (pd.gamma, 1);
642s ***** test
642s  pd = makedist ("birnbaumsaunders", "beta", 5);
642s  assert (pd.beta, 5);
642s  assert (pd.gamma, 1);
642s ***** test
642s  pd = makedist ("birnbaumsaunders", "gamma", 5);
642s  assert (pd.beta, 1);
642s  assert (pd.gamma, 5);
642s ***** test
642s  pd = makedist ("birnbaumsaunders", "beta", 3, "gamma", 5);
642s  assert (pd.beta, 3);
642s  assert (pd.gamma, 5);
642s ***** test
642s  pd = makedist ("burr");
642s  assert (class (pd), "BurrDistribution");
642s  assert (pd.alpha, 1);
642s  assert (pd.c, 1);
642s  assert (pd.k, 1);
642s ***** test
642s  pd = makedist ("burr", "k", 5);
642s  assert (pd.alpha, 1);
642s  assert (pd.c, 1);
642s  assert (pd.k, 5);
642s ***** test
642s  pd = makedist ("burr", "c", 5);
642s  assert (pd.alpha, 1);
642s  assert (pd.c, 5);
642s  assert (pd.k, 1);
642s ***** test
642s  pd = makedist ("burr", "alpha", 3, "c", 5);
642s  assert (pd.alpha, 3);
642s  assert (pd.c, 5);
642s  assert (pd.k, 1);
642s ***** test
642s  pd = makedist ("burr", "k", 3, "c", 5);
642s  assert (pd.alpha, 1);
642s  assert (pd.c, 5);
642s  assert (pd.k, 3);
642s ***** test
642s  pd = makedist ("exponential");
642s  assert (class (pd), "ExponentialDistribution");
642s  assert (pd.mu, 1);
642s ***** test
642s  pd = makedist ("exponential", "mu", 5);
642s  assert (pd.mu, 5);
642s ***** test
642s  pd = makedist ("extremevalue");
642s  assert (class (pd), "ExtremeValueDistribution");
642s  assert (pd.mu, 0);
642s  assert (pd.sigma, 1);
642s ***** test
642s  pd = makedist ("extremevalue", "mu", 5);
642s  assert (class (pd), "ExtremeValueDistribution");
642s  assert (pd.mu, 5);
642s  assert (pd.sigma, 1);
642s ***** test
642s  pd = makedist ("ev", "sigma", 5);
642s  assert (class (pd), "ExtremeValueDistribution");
642s  assert (pd.mu, 0);
642s  assert (pd.sigma, 5);
642s ***** test
642s  pd = makedist ("ev", "mu", -3, "sigma", 5);
642s  assert (class (pd), "ExtremeValueDistribution");
642s  assert (pd.mu, -3);
642s  assert (pd.sigma, 5);
642s ***** test
642s  pd = makedist ("gamma");
642s  assert (class (pd), "GammaDistribution");
642s  assert (pd.a, 1);
642s  assert (pd.b, 1);
642s ***** test
642s  pd = makedist ("gamma", "a", 5);
642s  assert (pd.a, 5);
642s  assert (pd.b, 1);
642s ***** test
642s  pd = makedist ("gamma", "b", 5);
642s  assert (pd.a, 1);
642s  assert (pd.b, 5);
642s ***** test
642s  pd = makedist ("gamma", "a", 3, "b", 5);
642s  assert (pd.a, 3);
642s  assert (pd.b, 5);
642s ***** test
642s  pd = makedist ("GeneralizedExtremeValue");
642s  assert (class (pd), "GeneralizedExtremeValueDistribution");
642s  assert (pd.k, 0);
642s  assert (pd.sigma, 1);
642s  assert (pd.mu, 0);
642s ***** test
642s  pd = makedist ("GeneralizedExtremeValue", "k", 5);
642s  assert (pd.k, 5);
642s  assert (pd.sigma, 1);
642s  assert (pd.mu, 0);
642s ***** test
642s  pd = makedist ("GeneralizedExtremeValue", "sigma", 5);
642s  assert (pd.k, 0);
642s  assert (pd.sigma, 5);
642s  assert (pd.mu, 0);
642s ***** test
642s  pd = makedist ("GeneralizedExtremeValue", "k", 3, "sigma", 5);
642s  assert (pd.k, 3);
642s  assert (pd.sigma, 5);
642s  assert (pd.mu, 0);
642s ***** test
642s  pd = makedist ("GeneralizedExtremeValue", "mu", 3, "sigma", 5);
642s  assert (pd.k, 0);
642s  assert (pd.sigma, 5);
642s  assert (pd.mu, 3);
642s ***** test
642s  pd = makedist ("GeneralizedPareto");
642s  assert (class (pd), "GeneralizedParetoDistribution");
642s  assert (pd.k, 1);
642s  assert (pd.sigma, 1);
642s  assert (pd.theta, 1);
642s ***** test
642s  pd = makedist ("GeneralizedPareto", "k", 5);
642s  assert (pd.k, 5);
642s  assert (pd.sigma, 1);
642s  assert (pd.theta, 1);
642s ***** test
642s  pd = makedist ("GeneralizedPareto", "sigma", 5);
642s  assert (pd.k, 1);
642s  assert (pd.sigma, 5);
642s  assert (pd.theta, 1);
642s ***** test
642s  pd = makedist ("GeneralizedPareto", "k", 3, "sigma", 5);
642s  assert (pd.k, 3);
642s  assert (pd.sigma, 5);
642s  assert (pd.theta, 1);
642s ***** test
642s  pd = makedist ("GeneralizedPareto", "theta", 3, "sigma", 5);
642s  assert (pd.k, 1);
642s  assert (pd.sigma, 5);
642s  assert (pd.theta, 3);
642s ***** test
642s  pd = makedist ("HalfNormal");
642s  assert (class (pd), "HalfNormalDistribution");
642s  assert (pd.mu, 0);
642s  assert (pd.sigma, 1);
642s ***** test
642s  pd = makedist ("HalfNormal", "mu", 5);
642s  assert (pd.mu, 5);
642s  assert (pd.sigma, 1);
642s ***** test
642s  pd = makedist ("HalfNormal", "sigma", 5);
642s  assert (pd.mu, 0);
642s  assert (pd.sigma, 5);
642s ***** test
642s  pd = makedist ("HalfNormal", "mu", 3, "sigma", 5);
642s  assert (pd.mu, 3);
642s  assert (pd.sigma, 5);
642s ***** test
642s  pd = makedist ("InverseGaussian");
642s  assert (class (pd), "InverseGaussianDistribution");
642s  assert (pd.mu, 1);
642s  assert (pd.lambda, 1);
642s ***** test
642s  pd = makedist ("InverseGaussian", "mu", 5);
642s  assert (pd.mu, 5);
642s  assert (pd.lambda, 1);
642s ***** test
642s  pd = makedist ("InverseGaussian", "lambda", 5);
642s  assert (pd.mu, 1);
642s  assert (pd.lambda, 5);
642s ***** test
642s  pd = makedist ("InverseGaussian", "mu", 3, "lambda", 5);
642s  assert (pd.mu, 3);
642s  assert (pd.lambda, 5);
642s ***** test
642s  pd = makedist ("logistic");
642s  assert (class (pd), "LogisticDistribution");
642s  assert (pd.mu, 0);
642s  assert (pd.sigma, 1);
642s ***** test
642s  pd = makedist ("logistic", "mu", 5);
642s  assert (pd.mu, 5);
642s  assert (pd.sigma, 1);
642s ***** test
642s  pd = makedist ("logistic", "sigma", 5);
642s  assert (pd.mu, 0);
642s  assert (pd.sigma, 5);
642s ***** test
642s  pd = makedist ("logistic", "mu", 3, "sigma", 5);
642s  assert (pd.mu, 3);
642s  assert (pd.sigma, 5);
642s ***** test
642s  pd = makedist ("loglogistic");
642s  assert (class (pd), "LoglogisticDistribution");
642s  assert (pd.mu, 0);
642s  assert (pd.sigma, 1);
642s ***** test
642s  pd = makedist ("loglogistic", "mu", 5);
642s  assert (pd.mu, 5);
642s  assert (pd.sigma, 1);
642s ***** test
642s  pd = makedist ("loglogistic", "sigma", 5);
642s  assert (pd.mu, 0);
642s  assert (pd.sigma, 5);
642s ***** test
642s  pd = makedist ("loglogistic", "mu", 3, "sigma", 5);
642s  assert (pd.mu, 3);
642s  assert (pd.sigma, 5);
642s ***** test
642s  pd = makedist ("Lognormal");
642s  assert (class (pd), "LognormalDistribution");
642s  assert (pd.mu, 0);
642s  assert (pd.sigma, 1);
642s ***** test
642s  pd = makedist ("Lognormal", "mu", 5);
642s  assert (pd.mu, 5);
642s  assert (pd.sigma, 1);
642s ***** test
642s  pd = makedist ("Lognormal", "sigma", 5);
642s  assert (pd.mu, 0);
642s  assert (pd.sigma, 5);
642s ***** test
642s  pd = makedist ("Lognormal", "mu", -3, "sigma", 5);
642s  assert (pd.mu, -3);
642s  assert (pd.sigma, 5);
642s ***** test
642s  pd = makedist ("Loguniform");
642s  assert (class (pd), "LoguniformDistribution");
642s  assert (pd.Lower, 1);
642s  assert (pd.Upper, 4);
642s ***** test
642s  pd = makedist ("Loguniform", "Lower", 2);
642s  assert (pd.Lower, 2);
642s  assert (pd.Upper, 4);
642s ***** test
642s  pd = makedist ("Loguniform", "Lower", 1, "Upper", 3);
642s  assert (pd.Lower, 1);
642s  assert (pd.Upper, 3);
642s ***** test
642s  pd = makedist ("Multinomial");
642s  assert (class (pd), "MultinomialDistribution");
642s  assert (pd.Probabilities, [0.5, 0.5]);
642s ***** test
642s  pd = makedist ("Multinomial", "Probabilities", [0.2, 0.3, 0.1, 0.4]);
642s  assert (class (pd), "MultinomialDistribution");
642s  assert (pd.Probabilities, [0.2, 0.3, 0.1, 0.4]);
642s ***** test
642s  pd = makedist ("Nakagami");
642s  assert (class (pd), "NakagamiDistribution");
642s  assert (pd.mu, 1);
642s  assert (pd.omega, 1);
642s ***** test
642s  pd = makedist ("Nakagami", "mu", 5);
642s  assert (class (pd), "NakagamiDistribution");
642s  assert (pd.mu, 5);
642s  assert (pd.omega, 1);
642s ***** test
642s  pd = makedist ("Nakagami", "omega", 0.3);
642s  assert (class (pd), "NakagamiDistribution");
642s  assert (pd.mu, 1);
642s  assert (pd.omega, 0.3);
642s ***** test
642s  pd = makedist ("NegativeBinomial");
642s  assert (class (pd), "NegativeBinomialDistribution");
642s  assert (pd.R, 1);
642s  assert (pd.P, 0.5);
642s ***** test
642s  pd = makedist ("NegativeBinomial", "R", 5);
642s  assert (class (pd), "NegativeBinomialDistribution");
642s  assert (pd.R, 5);
642s  assert (pd.P, 0.5);
642s ***** test
642s  pd = makedist ("NegativeBinomial", "p", 0.3);
642s  assert (class (pd), "NegativeBinomialDistribution");
642s  assert (pd.R, 1);
642s  assert (pd.P, 0.3);
642s ***** test
642s  pd = makedist ("Normal");
642s  assert (class (pd), "NormalDistribution");
642s  assert (pd.mu, 0);
642s  assert (pd.sigma, 1);
642s ***** test
642s  pd = makedist ("Normal", "mu", 5);
642s  assert (class (pd), "NormalDistribution");
642s  assert (pd.mu, 5);
642s  assert (pd.sigma, 1);
642s ***** test
642s  pd = makedist ("Normal", "sigma", 5);
642s  assert (class (pd), "NormalDistribution");
642s  assert (pd.mu, 0);
642s  assert (pd.sigma, 5);
642s ***** test
642s  pd = makedist ("Normal", "mu", -3, "sigma", 5);
642s  assert (class (pd), "NormalDistribution");
642s  assert (pd.mu, -3);
642s  assert (pd.sigma, 5);
642s ***** test
642s  pd = makedist ("PiecewiseLinear");
642s  assert (class (pd), "PiecewiseLinearDistribution");
642s  assert (pd.x, [0; 1]);
642s  assert (pd.Fx, [0; 1]);
642s ***** test
642s  pd = makedist ("PiecewiseLinear", "x", [0, 1, 2], "Fx", [0, 0.5, 1]);
642s  assert (pd.x, [0; 1; 2]);
642s  assert (pd.Fx, [0; 0.5; 1]);
642s ***** test
642s  pd = makedist ("Poisson");
642s  assert (class (pd), "PoissonDistribution");
642s  assert (pd.lambda, 1);
642s ***** test
642s  pd = makedist ("Poisson", "lambda", 5);
642s  assert (pd.lambda, 5);
642s ***** test
642s  pd = makedist ("Rayleigh");
642s  assert (class (pd), "RayleighDistribution");
642s  assert (pd.sigma, 1);
642s ***** test
642s  pd = makedist ("Rayleigh", "sigma", 5);
642s  assert (pd.sigma, 5);
642s ***** test
642s  pd = makedist ("Rician");
642s  assert (class (pd), "RicianDistribution");
642s  assert (pd.s, 1);
642s  assert (pd.sigma, 1);
642s ***** test
642s  pd = makedist ("Rician", "s", 3);
642s  assert (pd.s, 3);
642s  assert (pd.sigma, 1);
642s ***** test
642s  pd = makedist ("Rician", "sigma", 3);
642s  assert (pd.s, 1);
642s  assert (pd.sigma, 3);
642s ***** test
642s  pd = makedist ("Rician", "s", 2, "sigma", 3);
642s  assert (pd.s, 2);
642s  assert (pd.sigma, 3);
642s ***** warning
642s  pd = makedist ("stable");
642s  assert (class (pd), "double");
642s  assert (isempty (pd), true);
642s ***** test
642s  pd = makedist ("tlocationscale");
642s  assert (class (pd), "tLocationScaleDistribution");
642s  assert (pd.mu, 0);
642s  assert (pd.sigma, 1);
642s  assert (pd.nu, 5);
642s ***** test
642s  pd = makedist ("tlocationscale", "mu", 5);
642s  assert (pd.mu, 5);
642s  assert (pd.sigma, 1);
642s  assert (pd.nu, 5);
642s ***** test
642s  pd = makedist ("tlocationscale", "sigma", 2);
642s  assert (pd.mu, 0);
642s  assert (pd.sigma, 2);
642s  assert (pd.nu, 5);
642s ***** test
642s  pd = makedist ("tlocationscale", "mu", 5, "sigma", 2);
642s  assert (pd.mu, 5);
642s  assert (pd.sigma, 2);
642s  assert (pd.nu, 5);
643s ***** test
643s  pd = makedist ("tlocationscale", "nu", 1, "sigma", 2);
643s  assert (pd.mu, 0);
643s  assert (pd.sigma, 2);
643s  assert (pd.nu, 1);
643s ***** test
643s  pd = makedist ("tlocationscale", "mu", -2, "sigma", 3, "nu", 1);
643s  assert (pd.mu, -2);
643s  assert (pd.sigma, 3);
643s  assert (pd.nu, 1);
643s ***** test
643s  pd = makedist ("Triangular");
643s  assert (class (pd), "TriangularDistribution");
643s  assert (pd.A, 0);
643s  assert (pd.B, 0.5);
643s  assert (pd.C, 1);
643s ***** test
643s  pd = makedist ("Triangular", "A", -2);
643s  assert (pd.A, -2);
643s  assert (pd.B, 0.5);
643s  assert (pd.C, 1);
643s ***** test
643s  pd = makedist ("Triangular", "A", 0.5, "B", 0.9);
643s  assert (pd.A, 0.5);
643s  assert (pd.B, 0.9);
643s  assert (pd.C, 1);
643s ***** test
643s  pd = makedist ("Triangular", "A", 1, "B", 2, "C", 5);
643s  assert (pd.A, 1);
643s  assert (pd.B, 2);
643s  assert (pd.C, 5);
643s ***** test
643s  pd = makedist ("Uniform");
643s  assert (class (pd), "UniformDistribution");
643s  assert (pd.Lower, 0);
643s  assert (pd.Upper, 1);
643s ***** test
643s  pd = makedist ("Uniform", "Lower", -2);
643s  assert (pd.Lower, -2);
643s  assert (pd.Upper, 1);
643s ***** test
643s  pd = makedist ("Uniform", "Lower", 1, "Upper", 3);
643s  assert (pd.Lower, 1);
643s  assert (pd.Upper, 3);
643s ***** test
643s  pd = makedist ("Weibull");
643s  assert (class (pd), "WeibullDistribution");
643s  assert (pd.lambda, 1);
643s  assert (pd.k, 1);
643s ***** test
643s  pd = makedist ("Weibull", "lambda", 3);
643s  assert (pd.lambda, 3);
643s  assert (pd.k, 1);
643s ***** test
643s  pd = makedist ("Weibull", "lambda", 3, "k", 2);
643s  assert (pd.lambda, 3);
643s  assert (pd.k, 2);
643s ***** error <makedist: DISTNAME must be a character vector.> makedist (1)
643s ***** error <makedist: DISTNAME must be a character vector.> makedist (["as";"sd"])
643s ***** error <makedist: unrecognized distribution name.> makedist ("some")
643s ***** error <makedist: optional arguments must be in NAME-VALUE pairs.> ...
643s  makedist ("Beta", "a")
643s ***** error <makedist: unknown parameter for 'Beta' distribution.> ...
643s  makedist ("Beta", "a", 1, "Q", 23)
643s ***** error <makedist: unknown parameter for 'Binomial' distribution.> ...
643s  makedist ("Binomial", "N", 1, "Q", 23)
643s ***** error <makedist: unknown parameter for 'BirnbaumSaunders' distribution.> ...
643s  makedist ("BirnbaumSaunders", "N", 1)
643s ***** error <makedist: unknown parameter for 'Burr' distribution.> ...
643s  makedist ("Burr", "lambda", 1, "sdfs", 34)
643s ***** error <makedist: unknown parameter for 'ExtremeValue' distribution.> ...
643s  makedist ("extremevalue", "mu", 1, "sdfs", 34)
643s ***** error <makedist: unknown parameter for 'Exponential' distribution.> ...
643s  makedist ("exponential", "mu", 1, "sdfs", 34)
643s ***** error <makedist: unknown parameter for 'Gamma' distribution.> ...
643s  makedist ("Gamma", "k", 1, "sdfs", 34)
643s ***** error <makedist: unknown parameter for 'GeneralizedExtremeValue' distribution.> ...
643s  makedist ("GeneralizedExtremeValue", "k", 1, "sdfs", 34)
643s ***** error <makedist: unknown parameter for 'GeneralizedPareto' distribution.> ...
643s  makedist ("GeneralizedPareto", "k", 1, "sdfs", 34)
643s ***** error <makedist: unknown parameter for 'HalfNormal' distribution.> ...
643s  makedist ("HalfNormal", "k", 1, "sdfs", 34)
643s ***** error <makedist: unknown parameter for 'InverseGaussian' distribution.> ...
643s  makedist ("InverseGaussian", "k", 1, "sdfs", 34)
643s ***** error <makedist: unknown parameter for 'Logistic' distribution.> ...
643s  makedist ("Logistic", "k", 1, "sdfs", 34)
643s ***** error <makedist: unknown parameter for 'Loglogistic' distribution.> ...
643s  makedist ("Loglogistic", "k", 1, "sdfs", 34)
643s ***** error <makedist: unknown parameter for 'Lognormal' distribution.> ...
643s  makedist ("Lognormal", "k", 1, "sdfs", 34)
643s ***** error <makedist: unknown parameter for 'Loguniform' distribution.> ...
643s  makedist ("Loguniform", "k", 1, "sdfs", 34)
643s ***** error <makedist: unknown parameter for 'Multinomial' distribution.> ...
643s  makedist ("Multinomial", "k", 1, "sdfs", 34)
643s ***** error <makedist: unknown parameter for 'Nakagami' distribution.> ...
643s  makedist ("Nakagami", "mu", 1, "sdfs", 34)
643s ***** error <makedist: unknown parameter for 'NegativeBinomial' distribution.> ...
643s  makedist ("NegativeBinomial", "mu", 1, "sdfs", 34)
643s ***** error <makedist: unknown parameter for 'Normal' distribution.> ...
643s  makedist ("Normal", "mu", 1, "sdfs", 34)
643s ***** error <makedist: unknown parameter for 'PiecewiseLinear' distribution.> ...
643s  makedist ("PiecewiseLinear", "mu", 1, "sdfs", 34)
643s ***** error <makedist: unknown parameter for 'Poisson' distribution.> ...
643s  makedist ("Poisson", "mu", 1, "sdfs", 34)
643s ***** error <makedist: unknown parameter for 'Rayleigh' distribution.> ...
643s  makedist ("Rayleigh", "mu", 1, "sdfs", 34)
643s ***** error <makedist: unknown parameter for 'Rician' distribution.> ...
643s  makedist ("Rician", "mu", 1, "sdfs", 34)
643s ***** error <makedist: unknown parameter for 'Stable' distribution.> ...
643s  makedist ("Stable", "mu", 1, "sdfs", 34)
643s ***** error <makedist: unknown parameter for 'tLocationScale' distribution.> ...
643s  makedist ("tLocationScale", "mu", 1, "sdfs", 34)
643s ***** error <makedist: unknown parameter for 'Triangular' distribution.> ...
643s  makedist ("Triangular", "mu", 1, "sdfs", 34)
643s ***** error <makedist: unknown parameter for 'Uniform' distribution.> ...
643s  makedist ("Uniform", "mu", 1, "sdfs", 34)
643s ***** error <makedist: unknown parameter for 'Weibull' distribution.> ...
643s  makedist ("Weibull", "mu", 1, "sdfs", 34)
643s 131 tests, 131 passed, 0 known failure, 0 skipped
643s [inst/dist_wrap/mle.m]
643s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_wrap/mle.m
643s ***** error <mle: X must be a numeric vector of real values.> mle (ones (2))
643s ***** error <mle: X must be a numeric vector of real values.> mle ("text")
643s ***** error <mle: X must be a numeric vector of real values.> mle ([1, 2, 3, i, 5])
643s ***** error <mle: optional arguments must be in NAME-VALUE pairs.> ...
643s  mle ([1:50], "distribution")
643s ***** error <mle: 'censoring' argument must have the same size as the input data in X.> ...
643s  mle ([1:50], "censoring", logical ([1,0,1,0]))
643s ***** error <mle: 'frequency' argument must have the same size as the input data in X.> ...
643s  mle ([1:50], "frequency", [1,0,1,0])
643s ***** error <mle: 'frequency' argument must contain non-negative integer values.> ...
643s  mle ([1 0 1 0], "frequency", [-1 1 0 0])
643s ***** error <mle: 'frequency' argument must contain non-negative integer values.> ...
643s  mle ([1 0 1 0], "distribution", "nbin", "frequency", [-1 1 0 0])
643s ***** error <mle: invalid value for 'alpha' argument.> mle ([1:50], "alpha", [0.05, 0.01])
643s ***** error <mle: invalid value for 'alpha' argument.> mle ([1:50], "alpha", 1)
643s ***** error <mle: invalid value for 'alpha' argument.> mle ([1:50], "alpha", -1)
643s ***** error <mle: invalid value for 'alpha' argument.> mle ([1:50], "alpha", i)
643s ***** error <mle: 'ntrials' argument must be a positive integer scalar value.> ...
643s  mle ([1:50], "ntrials", -1)
643s ***** error <mle: 'ntrials' argument must be a positive integer scalar value.> ...
643s  mle ([1:50], "ntrials", [20, 50])
643s ***** error <mle: 'ntrials' argument must be a positive integer scalar value.> ...
643s  mle ([1:50], "ntrials", [20.3])
643s ***** error <mle: 'ntrials' argument must be a positive integer scalar value.> ...
643s  mle ([1:50], "ntrials", 3i)
643s ***** error <mle: 'options' argument must be a structure compatible for 'fminsearch'.> ...
643s  mle ([1:50], "options", 4)
643s ***** error <mle: 'options' argument must be a structure compatible for 'fminsearch'.> ...
643s  mle ([1:50], "options", struct ("x", 3))
643s ***** error <mle: unknown parameter name.> mle ([1:50], "NAME", "value")
643s ***** error <mle: censoring is not supported for the Bernoulli distribution.> ...
643s  mle ([1 0 1 0], "distribution", "bernoulli", "censoring", [1 1 0 0])
643s ***** error <mle: invalid data for the Bernoulli distribution.> ...
643s  mle ([1 2 1 0], "distribution", "bernoulli")
643s ***** error <mle: censoring is not supported for the Beta distribution.> ...
643s  mle ([1 0 1 0], "distribution", "beta", "censoring", [1 1 0 0])
643s ***** error <mle: censoring is not supported for the Binomial distribution.> ...
643s  mle ([1 0 1 0], "distribution", "bino", "censoring", [1 1 0 0])
643s ***** error <mle: 'Ntrials' parameter is required for the Binomial distribution.> ...
643s  mle ([1 0 1 0], "distribution", "bino")
643s ***** error <mle: censoring is not supported for the Geometric distribution.> ...
643s  mle ([1 0 1 0], "distribution", "geo", "censoring", [1 1 0 0])
643s ***** error <mle: censoring is not supported for the Generalized Extreme Value distribution.> ...
643s  mle ([1 0 1 0], "distribution", "gev", "censoring", [1 1 0 0])
643s ***** error <mle: censoring is not supported for the Generalized Pareto distribution.> ...
643s  mle ([1 0 1 0], "distribution", "gp", "censoring", [1 1 0 0])
643s ***** error <mle: invalid 'theta' location parameter for the Generalized Pareto distribution.> ...
643s  mle ([1 0 -1 0], "distribution", "gp")
643s ***** error <mle: censoring is not supported for the Half Normal distribution.> ...
643s  mle ([1 0 1 0], "distribution", "hn", "censoring", [1 1 0 0])
643s ***** error <mle: invalid 'mu' location parameter for the Half Normal distribution.> ...
643s  mle ([1 0 -1 0], "distribution", "hn")
643s ***** error <mle: censoring is not supported for the Negative Binomial distribution.> ...
643s  mle ([1 0 1 0], "distribution", "nbin", "censoring", [1 1 0 0])
643s ***** error <mle: censoring is not supported for the Poisson distribution.> ...
643s  mle ([1 0 1 0], "distribution", "poisson", "censoring", [1 1 0 0])
643s ***** error <mle: censoring is not supported for the Discrete Uniform distribution.> ...
643s  mle ([1 0 1 0], "distribution", "unid", "censoring", [1 1 0 0])
643s ***** error <mle: censoring is not supported for the Continuous Uniform distribution.> ...
643s  mle ([1 0 1 0], "distribution", "unif", "censoring", [1 1 0 0])
643s ***** error <mle: unrecognized distribution name.> mle ([1:50], "distribution", "value")
643s ***** error <mle: censoring is not supported for the Continuous Uniform distribution.> ...
643s  mle ([1 0 1 0], "distribution", "unif", "censoring", [1 1 0 0])
643s 36 tests, 36 passed, 0 known failure, 0 skipped
643s [inst/dist_wrap/pdf.m]
643s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_wrap/pdf.m
643s ***** shared x
643s  x = [1:5];
643s ***** assert (pdf ("Beta", x, 5, 2), betapdf (x, 5, 2))
643s ***** assert (pdf ("beta", x, 5, 2), betapdf (x, 5, 2))
643s ***** assert (pdf ("Binomial", x, 5, 2), binopdf (x, 5, 2))
643s ***** assert (pdf ("bino", x, 5, 2), binopdf (x, 5, 2))
643s ***** assert (pdf ("Birnbaum-Saunders", x, 5, 2), bisapdf (x, 5, 2))
643s ***** assert (pdf ("bisa", x, 5, 2), bisapdf (x, 5, 2))
643s ***** assert (pdf ("Burr", x, 5, 2, 2), burrpdf (x, 5, 2, 2))
643s ***** assert (pdf ("burr", x, 5, 2, 2), burrpdf (x, 5, 2, 2))
643s ***** assert (pdf ("Cauchy", x, 5, 2), cauchypdf (x, 5, 2))
643s ***** assert (pdf ("cauchy", x, 5, 2), cauchypdf (x, 5, 2))
643s ***** assert (pdf ("Chi-squared", x, 5), chi2pdf (x, 5))
643s ***** assert (pdf ("chi2", x, 5), chi2pdf (x, 5))
643s ***** assert (pdf ("Extreme Value", x, 5, 2), evpdf (x, 5, 2))
643s ***** assert (pdf ("ev", x, 5, 2), evpdf (x, 5, 2))
643s ***** assert (pdf ("Exponential", x, 5), exppdf (x, 5))
643s ***** assert (pdf ("exp", x, 5), exppdf (x, 5))
643s ***** assert (pdf ("F-Distribution", x, 5, 2), fpdf (x, 5, 2))
643s ***** assert (pdf ("f", x, 5, 2), fpdf (x, 5, 2))
643s ***** assert (pdf ("Gamma", x, 5, 2), gampdf (x, 5, 2))
643s ***** assert (pdf ("gam", x, 5, 2), gampdf (x, 5, 2))
643s ***** assert (pdf ("Geometric", x, 5), geopdf (x, 5))
643s ***** assert (pdf ("geo", x, 5), geopdf (x, 5))
643s ***** assert (pdf ("Generalized Extreme Value", x, 5, 2, 2), gevpdf (x, 5, 2, 2))
643s ***** assert (pdf ("gev", x, 5, 2, 2), gevpdf (x, 5, 2, 2))
643s ***** assert (pdf ("Generalized Pareto", x, 5, 2, 2), gppdf (x, 5, 2, 2))
643s ***** assert (pdf ("gp", x, 5, 2, 2), gppdf (x, 5, 2, 2))
643s ***** assert (pdf ("Gumbel", x, 5, 2), gumbelpdf (x, 5, 2))
643s ***** assert (pdf ("gumbel", x, 5, 2), gumbelpdf (x, 5, 2))
643s ***** assert (pdf ("Half-normal", x, 5, 2), hnpdf (x, 5, 2))
643s ***** assert (pdf ("hn", x, 5, 2), hnpdf (x, 5, 2))
643s ***** assert (pdf ("Hypergeometric", x, 5, 2, 2), hygepdf (x, 5, 2, 2))
643s ***** assert (pdf ("hyge", x, 5, 2, 2), hygepdf (x, 5, 2, 2))
643s ***** assert (pdf ("Inverse Gaussian", x, 5, 2), invgpdf (x, 5, 2))
643s ***** assert (pdf ("invg", x, 5, 2), invgpdf (x, 5, 2))
643s ***** assert (pdf ("Laplace", x, 5, 2), laplacepdf (x, 5, 2))
643s ***** assert (pdf ("laplace", x, 5, 2), laplacepdf (x, 5, 2))
643s ***** assert (pdf ("Logistic", x, 5, 2), logipdf (x, 5, 2))
643s ***** assert (pdf ("logi", x, 5, 2), logipdf (x, 5, 2))
643s ***** assert (pdf ("Log-Logistic", x, 5, 2), loglpdf (x, 5, 2))
643s ***** assert (pdf ("logl", x, 5, 2), loglpdf (x, 5, 2))
643s ***** assert (pdf ("Lognormal", x, 5, 2), lognpdf (x, 5, 2))
643s ***** assert (pdf ("logn", x, 5, 2), lognpdf (x, 5, 2))
643s ***** assert (pdf ("Nakagami", x, 5, 2), nakapdf (x, 5, 2))
643s ***** assert (pdf ("naka", x, 5, 2), nakapdf (x, 5, 2))
643s ***** assert (pdf ("Negative Binomial", x, 5, 2), nbinpdf (x, 5, 2))
643s ***** assert (pdf ("nbin", x, 5, 2), nbinpdf (x, 5, 2))
643s ***** assert (pdf ("Noncentral F-Distribution", x, 5, 2, 2), ncfpdf (x, 5, 2, 2))
643s ***** assert (pdf ("ncf", x, 5, 2, 2), ncfpdf (x, 5, 2, 2))
643s ***** assert (pdf ("Noncentral Student T", x, 5, 2), nctpdf (x, 5, 2))
643s ***** assert (pdf ("nct", x, 5, 2), nctpdf (x, 5, 2))
644s ***** assert (pdf ("Noncentral Chi-Squared", x, 5, 2), ncx2pdf (x, 5, 2))
644s ***** assert (pdf ("ncx2", x, 5, 2), ncx2pdf (x, 5, 2))
644s ***** assert (pdf ("Normal", x, 5, 2), normpdf (x, 5, 2))
644s ***** assert (pdf ("norm", x, 5, 2), normpdf (x, 5, 2))
644s ***** assert (pdf ("Poisson", x, 5), poisspdf (x, 5))
644s ***** assert (pdf ("poiss", x, 5), poisspdf (x, 5))
644s ***** assert (pdf ("Rayleigh", x, 5), raylpdf (x, 5))
644s ***** assert (pdf ("rayl", x, 5), raylpdf (x, 5))
644s ***** assert (pdf ("Rician", x, 5, 1), ricepdf (x, 5, 1))
644s ***** assert (pdf ("rice", x, 5, 1), ricepdf (x, 5, 1))
644s ***** assert (pdf ("Student T", x, 5), tpdf (x, 5))
644s ***** assert (pdf ("t", x, 5), tpdf (x, 5))
644s ***** assert (pdf ("location-scale T", x, 5, 1, 2), tlspdf (x, 5, 1, 2))
644s ***** assert (pdf ("tls", x, 5, 1, 2), tlspdf (x, 5, 1, 2))
644s ***** assert (pdf ("Triangular", x, 5, 2, 2), tripdf (x, 5, 2, 2))
644s ***** assert (pdf ("tri", x, 5, 2, 2), tripdf (x, 5, 2, 2))
644s ***** assert (pdf ("Discrete Uniform", x, 5), unidpdf (x, 5))
644s ***** assert (pdf ("unid", x, 5), unidpdf (x, 5))
644s ***** assert (pdf ("Uniform", x, 5, 2), unifpdf (x, 5, 2))
644s ***** assert (pdf ("unif", x, 5, 2), unifpdf (x, 5, 2))
644s ***** assert (pdf ("Von Mises", x, 5, 2), vmpdf (x, 5, 2))
644s ***** assert (pdf ("vm", x, 5, 2), vmpdf (x, 5, 2))
644s ***** assert (pdf ("Weibull", x, 5, 2), wblpdf (x, 5, 2))
644s ***** assert (pdf ("wbl", x, 5, 2), wblpdf (x, 5, 2))
644s ***** error<pdf: distribution NAME must a char string.> pdf (1)
644s ***** error<pdf: distribution NAME must a char string.> pdf ({"beta"})
644s ***** error<pdf: X must be numeric.> pdf ("beta", {[1 2 3 4 5]})
644s ***** error<pdf: X must be numeric.> pdf ("beta", "text")
644s ***** error<pdf: values in X must be real.> pdf ("beta", 1+i)
644s ***** error<pdf: distribution parameters must be numeric.> ...
644s  pdf ("Beta", x, "a", 2)
644s ***** error<pdf: distribution parameters must be numeric.> ...
644s  pdf ("Beta", x, 5, "")
644s ***** error<pdf: distribution parameters must be numeric.> ...
644s  pdf ("Beta", x, 5, {2})
644s ***** error<pdf: chi2 distribution requires 1 parameter.> pdf ("chi2", x)
644s ***** error<pdf: Beta distribution requires 2 parameters.> pdf ("Beta", x, 5)
644s ***** error<pdf: Burr distribution requires 3 parameters.> pdf ("Burr", x, 5)
644s ***** error<pdf: Burr distribution requires 3 parameters.> pdf ("Burr", x, 5, 2)
644s 86 tests, 86 passed, 0 known failure, 0 skipped
644s [inst/dist_wrap/random.m]
644s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/dist_wrap/random.m
644s ***** assert (size (random ("Beta", 5, 2, 2, 10)), size (betarnd (5, 2, 2, 10)))
644s ***** assert (size (random ("beta", 5, 2, 2, 10)), size (betarnd (5, 2, 2, 10)))
644s ***** assert (size (random ("Binomial", 5, 2, [10, 20])), size (binornd (5, 2, 10, 20)))
644s ***** assert (size (random ("bino", 5, 2, [10, 20])), size (binornd (5, 2, 10, 20)))
644s ***** assert (size (random ("Birnbaum-Saunders", 5, 2, [10, 20])), size (bisarnd (5, 2, 10, 20)))
644s ***** assert (size (random ("bisa", 5, 2, [10, 20])), size (bisarnd (5, 2, 10, 20)))
644s ***** assert (size (random ("Burr", 5, 2, 2, [10, 20])), size (burrrnd (5, 2, 2, 10, 20)))
644s ***** assert (size (random ("burr", 5, 2, 2, [10, 20])), size (burrrnd (5, 2, 2, 10, 20)))
644s ***** assert (size (random ("Cauchy", 5, 2, [10, 20])), size (cauchyrnd (5, 2, 10, 20)))
644s ***** assert (size (random ("cauchy", 5, 2, [10, 20])), size (cauchyrnd (5, 2, 10, 20)))
644s ***** assert (size (random ("Chi-squared", 5, [10, 20])), size (chi2rnd (5, 10, 20)))
644s ***** assert (size (random ("chi2", 5, [10, 20])), size (chi2rnd (5, 10, 20)))
644s ***** assert (size (random ("Extreme Value", 5, 2, [10, 20])), size (evrnd (5, 2, 10, 20)))
644s ***** assert (size (random ("ev", 5, 2, [10, 20])), size (evrnd (5, 2, 10, 20)))
644s ***** assert (size (random ("Exponential", 5, [10, 20])), size (exprnd (5, 10, 20)))
644s ***** assert (size (random ("exp", 5, [10, 20])), size (exprnd (5, 10, 20)))
644s ***** assert (size (random ("F-Distribution", 5, 2, [10, 20])), size (frnd (5, 2, 10, 20)))
644s ***** assert (size (random ("f", 5, 2, [10, 20])), size (frnd (5, 2, 10, 20)))
644s ***** assert (size (random ("Gamma", 5, 2, [10, 20])), size (gamrnd (5, 2, 10, 20)))
644s ***** assert (size (random ("gam", 5, 2, [10, 20])), size (gamrnd (5, 2, 10, 20)))
644s ***** assert (size (random ("Geometric", 5, [10, 20])), size (geornd (5, 10, 20)))
644s ***** assert (size (random ("geo", 5, [10, 20])), size (geornd (5, 10, 20)))
644s ***** assert (size (random ("Generalized Extreme Value", 5, 2, 2, [10, 20])), size (gevrnd (5, 2, 2, 10, 20)))
644s ***** assert (size (random ("gev", 5, 2, 2, [10, 20])), size (gevrnd (5, 2, 2, 10, 20)))
644s ***** assert (size (random ("Generalized Pareto", 5, 2, 2, [10, 20])), size (gprnd (5, 2, 2, 10, 20)))
644s ***** assert (size (random ("gp", 5, 2, 2, [10, 20])), size (gprnd (5, 2, 2, 10, 20)))
644s ***** assert (size (random ("Gumbel", 5, 2, [10, 20])), size (gumbelrnd (5, 2, 10, 20)))
644s ***** assert (size (random ("gumbel", 5, 2, [10, 20])), size (gumbelrnd (5, 2, 10, 20)))
644s ***** assert (size (random ("Half-normal", 5, 2, [10, 20])), size (hnrnd (5, 2, 10, 20)))
644s ***** assert (size (random ("hn", 5, 2, [10, 20])), size (hnrnd (5, 2, 10, 20)))
644s ***** assert (size (random ("Hypergeometric", 5, 2, 2, [10, 20])), size (hygernd (5, 2, 2, 10, 20)))
644s ***** assert (size (random ("hyge", 5, 2, 2, [10, 20])), size (hygernd (5, 2, 2, 10, 20)))
644s ***** assert (size (random ("Inverse Gaussian", 5, 2, [10, 20])), size (invgrnd (5, 2, 10, 20)))
644s ***** assert (size (random ("invg", 5, 2, [10, 20])), size (invgrnd (5, 2, 10, 20)))
644s ***** assert (size (random ("Laplace", 5, 2, [10, 20])), size (laplacernd (5, 2, 10, 20)))
644s ***** assert (size (random ("laplace", 5, 2, [10, 20])), size (laplacernd (5, 2, 10, 20)))
644s ***** assert (size (random ("Logistic", 5, 2, [10, 20])), size (logirnd (5, 2, 10, 20)))
644s ***** assert (size (random ("logi", 5, 2, [10, 20])), size (logirnd (5, 2, 10, 20)))
644s ***** assert (size (random ("Log-Logistic", 5, 2, [10, 20])), size (loglrnd (5, 2, 10, 20)))
644s ***** assert (size (random ("logl", 5, 2, [10, 20])), size (loglrnd (5, 2, 10, 20)))
644s ***** assert (size (random ("Lognormal", 5, 2, [10, 20])), size (lognrnd (5, 2, 10, 20)))
644s ***** assert (size (random ("logn", 5, 2, [10, 20])), size (lognrnd (5, 2, 10, 20)))
644s ***** assert (size (random ("Nakagami", 5, 2, [10, 20])), size (nakarnd (5, 2, 10, 20)))
644s ***** assert (size (random ("naka", 5, 2, [10, 20])), size (nakarnd (5, 2, 10, 20)))
644s ***** assert (size (random ("Negative Binomial", 5, 2, [10, 20])), size (nbinrnd (5, 2, 10, 20)))
644s ***** assert (size (random ("nbin", 5, 2, [10, 20])), size (nbinrnd (5, 2, 10, 20)))
644s ***** assert (size (random ("Noncentral F-Distribution", 5, 2, 2, [10, 20])), size (ncfrnd (5, 2, 2, 10, 20)))
644s ***** assert (size (random ("ncf", 5, 2, 2, [10, 20])), size (ncfrnd (5, 2, 2, 10, 20)))
644s ***** assert (size (random ("Noncentral Student T", 5, 2, [10, 20])), size (nctrnd (5, 2, 10, 20)))
644s ***** assert (size (random ("nct", 5, 2, [10, 20])), size (nctrnd (5, 2, 10, 20)))
644s ***** assert (size (random ("Noncentral Chi-Squared", 5, 2, [10, 20])), size (ncx2rnd (5, 2, 10, 20)))
644s ***** assert (size (random ("ncx2", 5, 2, [10, 20])), size (ncx2rnd (5, 2, 10, 20)))
644s ***** assert (size (random ("Normal", 5, 2, [10, 20])), size (normrnd (5, 2, 10, 20)))
644s ***** assert (size (random ("norm", 5, 2, [10, 20])), size (normrnd (5, 2, 10, 20)))
644s ***** assert (size (random ("Poisson", 5, [10, 20])), size (poissrnd (5, 10, 20)))
644s ***** assert (size (random ("poiss", 5, [10, 20])), size (poissrnd (5, 10, 20)))
644s ***** assert (size (random ("Rayleigh", 5, [10, 20])), size (raylrnd (5, 10, 20)))
644s ***** assert (size (random ("rayl", 5, [10, 20])), size (raylrnd (5, 10, 20)))
644s ***** assert (size (random ("Rician", 5, 1, [10, 20])), size (ricernd (5, 1, 10, 20)))
644s ***** assert (size (random ("rice", 5, 1, [10, 20])), size (ricernd (5, 1, 10, 20)))
644s ***** assert (size (random ("Student T", 5, [10, 20])), size (trnd (5, 10, 20)))
644s ***** assert (size (random ("t", 5, [10, 20])), size (trnd (5, 10, 20)))
644s ***** assert (size (random ("location-scale T", 5, 1, 2, [10, 20])), size (tlsrnd (5, 1, 2, 10, 20)))
644s ***** assert (size (random ("tls", 5, 1, 2, [10, 20])), size (tlsrnd (5, 1, 2, 10, 20)))
644s ***** assert (size (random ("Triangular", 5, 2, 2, [10, 20])), size (trirnd (5, 2, 2, 10, 20)))
644s ***** assert (size (random ("tri", 5, 2, 2, [10, 20])), size (trirnd (5, 2, 2, 10, 20)))
644s ***** assert (size (random ("Discrete Uniform", 5, [10, 20])), size (unidrnd (5, 10, 20)))
644s ***** assert (size (random ("unid", 5, [10, 20])), size (unidrnd (5, 10, 20)))
644s ***** assert (size (random ("Uniform", 5, 2, [10, 20])), size (unifrnd (5, 2, 10, 20)))
644s ***** assert (size (random ("unif", 5, 2, [10, 20])), size (unifrnd (5, 2, 10, 20)))
644s ***** assert (size (random ("Von Mises", 5, 2, [10, 20])), size (vmrnd (5, 2, 10, 20)))
644s ***** assert (size (random ("vm", 5, 2, [10, 20])), size (vmrnd (5, 2, 10, 20)))
644s ***** assert (size (random ("Weibull", 5, 2, [10, 20])), size (wblrnd (5, 2, 10, 20)))
644s ***** assert (size (random ("wbl", 5, 2, [10, 20])), size (wblrnd (5, 2, 10, 20)))
644s ***** error<random: distribution NAME must be a char string.> random (1)
644s ***** error<random: distribution NAME must be a char string.> random ({"beta"})
644s ***** error<random: distribution parameters must be numeric.> ...
644s  random ("Beta", "a", 2)
644s ***** error<random: distribution parameters must be numeric.> ...
644s  random ("Beta", 5, "")
644s ***** error<random: distribution parameters must be numeric.> ...
644s  random ("Beta", 5, {2})
644s ***** error<random: distribution parameters must be numeric.> ...
644s  random ("Beta", "a", 2, 2, 10)
644s ***** error<random: distribution parameters must be numeric.> ...
644s  random ("Beta", 5, "", 2, 10)
644s ***** error<random: distribution parameters must be numeric.> ...
644s  random ("Beta", 5, {2}, 2, 10)
644s ***** error<random: distribution parameters must be numeric.> ...
644s  random ("Beta", 5, "", 2, 10)
644s ***** error<random: chi2 distribution requires 1 parameter.> random ("chi2")
644s ***** error<random: Beta distribution requires 2 parameters.> random ("Beta", 5)
644s ***** error<random: Burr distribution requires 3 parameters.> random ("Burr", 5)
644s ***** error<random: Burr distribution requires 3 parameters.> random ("Burr", 5, 2)
644s 87 tests, 87 passed, 0 known failure, 0 skipped
644s [inst/ecdf.m]
644s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/ecdf.m
644s ***** demo
644s  y = exprnd (10, 50, 1);    ## random failure times are exponential(10)
644s  d = exprnd (20, 50, 1);    ## drop-out times are exponential(20)
644s  t = min (y, d);            ## we observe the minimum of these times
644s  censored = (y > d);        ## we also observe whether the subject failed
644s 
644s  ## Calculate and plot the empirical cdf and confidence bounds
644s  [f, x, flo, fup] = ecdf (t, "censoring", censored);
644s  stairs (x, f);
644s  hold on;
644s  stairs (x, flo, "r:"); stairs (x, fup, "r:");
644s 
644s  ## Superimpose a plot of the known true cdf
644s  xx = 0:.1:max (t); yy = 1 - exp (-xx / 10); plot (xx, yy, "g-");
644s  hold off;
644s ***** demo
644s  R = wblrnd (100, 2, 100, 1);
644s  ecdf (R, "Function", "survivor", "Alpha", 0.01, "Bounds", "on");
644s  hold on
644s  x = 1:1:250;
644s  wblsurv = 1 - cdf ("weibull", x, 100, 2);
644s  plot (x, wblsurv, "g-", "LineWidth", 2)
644s  legend ("Empirical survivor function", "Lower confidence bound", ...
644s          "Upper confidence bound", "Weibull survivor function", ...
644s          "Location", "northeast");
644s  hold off
644s ***** error ecdf ();
644s ***** error ecdf (randi (15,2));
644s ***** error ecdf ([3,2,4,3+2i,5]);
644s ***** error kstest ([2,3,4,5,6],"tail");
644s ***** error kstest ([2,3,4,5,6],"tail", "whatever");
644s ***** error kstest ([2,3,4,5,6],"function", "");
644s ***** error kstest ([2,3,4,5,6],"badoption", 0.51);
644s ***** error kstest ([2,3,4,5,6],"tail", 0);
644s ***** error kstest ([2,3,4,5,6],"alpha", 0);
644s ***** error kstest ([2,3,4,5,6],"alpha", NaN);
644s ***** error kstest ([NaN,NaN,NaN,NaN,NaN],"tail", "unequal");
644s ***** error kstest ([2,3,4,5,6],"alpha", 0.05, "CDF", [2,3,4;1,3,4;1,2,1]);
644s ***** test
644s  hf = figure ("visible", "off");
644s  unwind_protect
644s    x = [2, 3, 4, 3, 5, 4, 6, 5, 8, 3, 7, 8, 9, 0];
644s    [F, x, Flo, Fup] = ecdf (x);
644s    F_out = [0; 0.0714; 0.1429; 0.3571; 0.5; 0.6429; 0.7143; 0.7857; 0.9286; 1];
644s    assert (F, F_out, ones (10,1) * 1e-4);
644s    x_out = [0 0 2 3 4 5 6 7 8 9]';
644s    assert (x, x_out);
644s    Flo_out = [NaN, 0, 0, 0.1061, 0.2381, 0.3919, 0.4776, 0.5708, 0.7937, NaN]';
644s    assert (Flo, Flo_out, ones (10,1) * 1e-4);
644s    Fup_out = [NaN, 0.2063, 0.3262, 0.6081, 0.7619, 0.8939, 0.9509, 1, 1, NaN]';
644s    assert (Fup, Fup_out, ones (10,1) * 1e-4);
644s  unwind_protect_cleanup
644s    close (hf);
644s  end_unwind_protect
644s ***** test
644s  hf = figure ("visible", "off");
644s  unwind_protect
644s    x = [2, 3, 4, 3, 5, 4, 6, 5, 8, 3, 7, 8, 9, 0];
644s    ecdf (x);
644s  unwind_protect_cleanup
644s    close (hf);
644s  end_unwind_protect
645s 14 tests, 14 passed, 0 known failure, 0 skipped
645s [inst/einstein.m]
645s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/einstein.m
645s ***** demo
645s  einstein (0.4, 0.6)
645s ***** demo
645s  einstein (0.2, 0.5)
645s ***** demo
645s  einstein (0.6, 0.1)
645s ***** test
645s  hf = figure ("visible", "off");
645s  unwind_protect
645s    tiles = einstein (0.4, 0.6);
645s    assert (isstruct (tiles), true);
645s  unwind_protect_cleanup
645s    close (hf);
645s  end_unwind_protect
645s ***** error<Invalid call to einstein.  Correct usage is> einstein
645s ***** error<Invalid call to einstein.  Correct usage is> einstein (0.5)
645s ***** error<einstein: A and B must be within the open interval> einstein (0, 0.9)
645s ***** error<einstein: A and B must be within the open interval> einstein (0.4, 1)
645s ***** error<einstein: A and B must be within the open interval> einstein (-0.4, 1)
645s 6 tests, 6 passed, 0 known failure, 0 skipped
645s [inst/evalclusters.m]
645s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/evalclusters.m
645s ***** demo
645s  load fisheriris;
645s  eva = evalclusters (meas, "kmeans", "calinskiharabasz", "KList", [1:6])
645s  plot (eva)
645s ***** error evalclusters ()
645s ***** error evalclusters ([1 1;0 1])
645s ***** error evalclusters ([1 1;0 1], "kmeans")
645s ***** error <'x' must be a numeric*> evalclusters ("abc", "kmeans", "gap")
645s ***** error <unknown clustering*> evalclusters ([1 1;0 1], "xxx", "gap")
645s ***** error <invalid matrix*> evalclusters ([1 1;0 1], [1 2], "gap")
645s ***** error <invalid argument*> evalclusters ([1 1;0 1], 1.2, "gap")
645s ***** error <invalid criterion*> evalclusters ([1 1;0 1], [1; 2], 123)
645s ***** error <unknown criterion*> evalclusters ([1 1;0 1], [1; 2], "xxx")
645s ***** error <'KList' can be empty*> evalclusters ([1 1;0 1], "kmeans", "gap")
645s ***** error <invalid parameter*> evalclusters ([1 1;0 1], [1; 2], "gap", 1)
645s ***** error <invalid property*> evalclusters ([1 1;0 1], [1; 2], "gap", 1, 1)
645s ***** error <unknown property*> evalclusters ([1 1;0 1], [1; 2], "gap", "xxx", 1)
645s ***** error <'KList'*> evalclusters ([1 1;0 1], [1; 2], "gap", "KList", [-1 0])
645s ***** error <'KList'*> evalclusters ([1 1;0 1], [1; 2], "gap", "KList", [1 .5])
645s ***** error <'KList'*> evalclusters ([1 1;0 1], [1; 2], "gap", "KList", [1 1; 1 1])
645s ***** error <unknown distance*> evalclusters ([1 1;0 1], [1; 2], "gap", ...
645s                                         "distance", "a")
645s ***** error <distance metric*> evalclusters ([1 1;0 1], [1; 2], "daviesbouldin", ...
645s                                        "distance", "a")
645s ***** error <cluster prior*> evalclusters ([1 1;0 1], [1; 2], "gap", ...
645s                                      "clusterpriors", "equal")
645s ***** error <invalid cluster prior*> evalclusters ([1 1;0 1], [1; 2], ...
645s                                          "silhouette", "clusterpriors", "xxx")
645s ***** error <'clust' must be a clustering*> evalclusters ([1 1;0 1], [1; 2], "gap")
645s ***** test
645s  load fisheriris;
645s  eva = evalclusters (meas, "kmeans", "calinskiharabasz", "KList", [1:6]);
645s  assert (isa (eva, "CalinskiHarabaszEvaluation"));
645s  assert (eva.NumObservations, 150);
645s  assert (eva.OptimalK, 3);
645s  assert (eva.InspectedK, [1 2 3 4 5 6]);
645s 22 tests, 22 passed, 0 known failure, 0 skipped
645s [inst/factoran.m]
645s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/factoran.m
645s ***** demo
645s  x = [ 7    26     6    60;
645s        1    29    15    52;
645s       11    56     8    20;
645s       11    31     8    47;
645s        7    52     6    33;
645s       11    55     9    22;
645s        3    71    17     6;
645s        1    31    22    44;
645s        2    54    18    22;
645s       21    47     4    26;
645s        1    40    23    34;
645s       11    66     9    12;
645s       10    68     8    12
645s      ];
645s  [loadings, specvar, fscores] = factoran (x, 2);
645s ***** test
645s  x = [1, 2; 2, 1; 3, 3];
645s  [loadings, specvar, fscores] = factoran (x, 1);
645s  l_out = [0.7071; 0.7071];
645s  s_out = [0.5000; 0.5000];
645s  f_out = [-0.7071; -0.7071; 1.4142];
645s  assert (loadings, l_out, 1.3e-4);
645s  assert (specvar, s_out, 1.3e-4);
645s  assert (fscores, f_out, 1.3e-4);
645s ***** error factoran ()
645s ***** error factoran (ones (5,3), 0)
645s ***** error factoran (ones (5,3), 3)
645s ***** error<factoran: X must be a numeric matrix.> factoran ({1,2}, 1)
645s ***** error<factoran: X must be a numeric matrix.> factoran (ones (2,2,2), 1)
645s ***** error<EIG: matrix contains Inf or NaN values> x=ones (3,2); x(:,2)=0; factoran (x,1)
645s 7 tests, 7 passed, 0 known failure, 0 skipped
645s [inst/ff2n.m]
645s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/ff2n.m
645s ***** error ff2n ();
645s ***** error ff2n (2, 5);
645s ***** error ff2n (2.5);
645s ***** error ff2n (0);
645s ***** error ff2n (-3);
645s ***** error ff2n (3+2i);
645s ***** error ff2n (Inf);
645s ***** error ff2n (NaN);
645s ***** test
645s  A = ff2n (3);
645s  assert (A, [0, 0, 0; 0, 0, 1; 0, 1, 0; 0, 1, 1; ...
645s              1, 0, 0; 1, 0, 1; 1, 1, 0; 1, 1, 1]);
645s ***** test
645s  A = ff2n (2);
645s  assert (A, [0, 0; 0, 1; 1, 0; 1, 1]);
645s 10 tests, 10 passed, 0 known failure, 0 skipped
645s [inst/fillmissing.m]
645s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/fillmissing.m
645s ***** assert (fillmissing ([1, 2, 3], "constant", 99), [1, 2, 3])
645s ***** assert (fillmissing ([1, 2, NaN], "constant", 99), [1, 2, 99])
645s ***** assert (fillmissing ([NaN, 2, NaN], "constant", 99), [99, 2, 99])
645s ***** assert (fillmissing ([1, 2, 3]', "constant", 99), [1, 2, 3]')
645s ***** assert (fillmissing ([1, 2, NaN]', "constant", 99), [1, 2, 99]')
645s ***** assert (fillmissing ([1, 2, 3; 4, 5, 6], "constant", 99), [1, 2, 3; 4, 5, 6])
645s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "constant", 99), [1, 2, 99; 4, 99, 6])
645s ***** assert (fillmissing ([NaN, 2, NaN; 4, NaN, 6], "constant", [97, 98, 99]), [97, 2, 99; 4, 98, 6])
645s ***** test
645s  x = cat (3, [1, 2, NaN; 4, NaN, 6], [NaN, 2, 3; 4, 5, NaN]);
645s  y = cat (3, [1, 2, 99; 4, 99, 6], [99, 2, 3; 4, 5, 99]);
645s  assert (fillmissing (x, "constant", 99), y);
645s  y = cat (3, [1, 2, 96; 4, 95, 6], [97, 2, 3; 4, 5, 99]);
645s  assert (fillmissing (x, "constant", [94:99]), y);
645s  assert (fillmissing (x, "constant", [94:99]'), y);
645s  assert (fillmissing (x, "constant", permute ([94:99], [1 3 2])), y);
645s  assert (fillmissing (x, "constant", [94, 96, 98; 95, 97, 99]), y);
645s  assert (fillmissing (x, "constant", [94:99], 1), y);
645s  y = cat (3, [1, 2, 96; 4, 97, 6], [98, 2, 3; 4, 5, 99]);
645s  assert (fillmissing (x, "constant", [96:99], 2), y);
645s  y = cat (3, [1, 2, 98; 4, 97, 6], [94, 2, 3; 4, 5, 99]);
645s  assert (fillmissing (x, "constant", [94:99], 3), y);
645s  y = cat (3, [1, 2, 92; 4, 91, 6], [94, 2, 3; 4, 5, 99]);
645s  assert (fillmissing (x, "constant", [88:99], 99), y);
645s ***** test
645s  x = reshape ([1:24], 4, 3, 2);
645s  x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
645s  y = x;
645s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [94, 95, 95, 96, 96, 97, 97, 98, 99, 99];
645s  assert (fillmissing (x, "constant", [94:99], 1), y);
645s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [92, 93, 94, 92, 95, 97, 99, 98, 97, 98];
645s  assert (fillmissing (x, "constant", [92:99], 2), y);
645s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [88, 93, 94, 96, 99, 89, 91, 94, 97, 98];
645s  assert (fillmissing (x, "constant", [88:99], 3), y);
645s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [76, 81, 82, 84, 87, 89, 91, 94, 97, 98];
645s  assert (fillmissing (x, "constant", [76:99], 99), y);
645s ***** assert (fillmissing ([1, 2, 3], "constant", 99, "endvalues", 88), [1, 2, 3])
645s ***** assert (fillmissing ([1, NaN, 3], "constant", 99, "endvalues", 88), [1, 99, 3])
645s ***** assert (fillmissing ([1, 2, NaN], "constant", 99, "endvalues", 88), [1, 2, 88])
645s ***** assert (fillmissing ([NaN, 2, 3], "constant", 99, "endvalues", 88), [88, 2, 3])
645s ***** assert (fillmissing ([NaN, NaN, 3], "constant", 99, "endvalues", 88), [88, 88, 3])
645s ***** assert (fillmissing ([1, NaN, NaN], "constant", 99, "endvalues", 88), [1, 88, 88])
645s ***** assert (fillmissing ([NaN, 2, NaN], "constant", 99, "endvalues", 88), [88, 2, 88])
645s ***** assert (fillmissing ([NaN, 2, NaN]', "constant", 99, "endvalues", 88), [88, 2, 88]')
645s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", 99, "endvalues", 88), [1, 99, 3, 99, 5])
645s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "constant", 99, "endvalues", 88), [1, 99, 99, 99, 5])
645s ***** assert (fillmissing ([NaN, NaN, NaN, NaN, 5], "constant", 99, "endvalues", 88), [88, 88, 88, 88, 5])
645s ***** assert (fillmissing ([1, NaN, 3, 4, NaN], "constant", 99, "endvalues", 88), [1, 99, 3, 4, 88])
645s ***** assert (fillmissing ([1, NaN, 3, 4, NaN], "constant", 99, 1, "endvalues", 88), [1, 88, 3, 4, 88])
645s ***** assert (fillmissing ([1, NaN, 3, 4, NaN], "constant", 99, 1, "endvalues", "extrap"), [1, 99, 3, 4, 99])
645s ***** test
645s  x = reshape ([1:24], 3, 4, 2);
645s  y = x;
645s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
645s  y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 88;
645s  y([8]) = 99;
645s  assert (fillmissing (x, "constant", 99, "endvalues", 88), y);
645s  assert (fillmissing (x, "constant", 99, 1, "endvalues", 88), y);
645s  y = x;
645s  y([1, 2, 5, 8, 10, 13, 16, 19, 22]) = 88;
645s  y([6, 18, 20, 21]) = 99;
645s  assert (fillmissing (x, "constant", 99, 2, "endvalues", 88), y);
645s  y(y == 99) = 88;
645s  assert (fillmissing (x, "constant", 99, 3, "endvalues", 88), y);
645s  assert (fillmissing (x, "constant", 99, 4, "endvalues", 88), y);
645s  assert (fillmissing (x, "constant", 99, 99, "endvalues", 88), y);
645s  y([8]) = 94;
645s  assert (fillmissing (x, "constant", [92:99], 1, "endvalues", 88), y);
645s  y([6, 8, 18, 20, 21]) = [96, 88, 99, 98, 99];
645s  assert (fillmissing (x, "constant", [94:99], 2, "endvalues", 88), y);
645s  y = x;
645s  y(isnan (y)) = 88;
645s  assert (fillmissing (x, "constant", [88:99], 3, "endvalues", 88), y);
645s  y = x;
645s  y(isnan (y)) = [82, 82, 83, 83, 94, 85, 86, 87, 87, 88, 88, 88, 89];
645s  assert (fillmissing (x, "constant", [92:99], 1, "endvalues", [82:89]), y);
645s  y = x;
645s  y(isnan (y)) = [84, 85, 85, 96, 85, 84, 87, 87, 99, 87, 98, 99, 87];
645s  assert (fillmissing (x, "constant", [94:99], 2, "endvalues", [84:89]), y);
645s  y = x;
645s  y(isnan (y)) = [68, 69, 72, 73, 75, 77, 68, 71, 73, 74, 75, 76, 77];
645s  assert (fillmissing (x, "constant", [88:99], 3, "endvalues", [68:79]), y);
645s  assert (fillmissing (x, "constant", [88:93; 94:99]', 3, "endvalues", [68:73; 74:79]'), y)
645s ***** test
645s  x = reshape ([1:24],4,3,2);
645s  x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
645s  y = x;
645s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [94, 95, 95, 96, 96, 97, 97, 98, 99, 99];
645s  assert (fillmissing (x, "constant", [94:99], 1), y);
645s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [92, 93, 94, 92, 95, 97, 99, 98, 97, 98];
645s  assert (fillmissing (x, "constant", [92:99], 2), y);
645s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [88, 93, 94, 96, 99, 89, 91, 94, 97, 98];
645s  assert (fillmissing (x, "constant", [88:99], 3), y);
645s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [76, 81, 82, 84, 87, 89, 91, 94, 97, 98];
645s  assert (fillmissing (x, "constant", [76:99], 99), y);
645s ***** assert (fillmissing ([1, 2, 3], "previous"), [1, 2, 3])
645s ***** assert (fillmissing ([1, 2, 3], "next"), [1, 2, 3])
645s ***** assert (fillmissing ([1, 2, 3]', "previous"), [1, 2, 3]')
645s ***** assert (fillmissing ([1, 2, 3]', "next"), [1, 2, 3]')
645s ***** assert (fillmissing ([1, 2, NaN], "previous"), [1, 2, 2])
645s ***** assert (fillmissing ([1, 2, NaN], "next"), [1, 2, NaN])
645s ***** assert (fillmissing ([NaN, 2, NaN], "previous"), [NaN, 2, 2])
646s ***** assert (fillmissing ([NaN, 2, NaN], "next"), [2, 2, NaN])
646s ***** assert (fillmissing ([1, NaN, 3], "previous"), [1, 1, 3])
646s ***** assert (fillmissing ([1, NaN, 3], "next"), [1, 3, 3])
646s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "previous", 1), [1, 2, NaN; 4, 2, 6])
646s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "previous", 2), [1, 2, 2; 4, 4, 6])
646s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "previous", 3), [1, 2, NaN; 4, NaN, 6])
646s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "next", 1), [1, 2, 6; 4, NaN, 6])
646s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "next", 2), [1, 2, NaN; 4, 6, 6])
646s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "next", 3), [1, 2, NaN; 4, NaN, 6])
646s ***** test
646s  x = reshape ([1:24], 4, 3, 2);
646s  x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
646s  y = x;
646s  y([1, 6, 7, 9, 14, 19, 22, 23]) = [2, 8, 8, 10, 15, 20, 24, 24];
646s  assert (fillmissing (x, "next", 1), y);
646s  y = x;
646s  y([1, 6, 7, 14, 16]) = [5, 10, 11, 18, 20];
646s  assert (fillmissing (x, "next", 2), y);
646s  y = x;
646s  y([1, 6, 9, 12]) = [13, 18, 21, 24];
646s  assert (fillmissing (x, "next", 3), y);
646s  assert (fillmissing (x, "next", 99), x);
646s  y = x;
646s  y([6, 7, 12, 14, 16, 19, 22, 23]) = [5, 5, 11, 13, 15, 18, 21, 21];
646s  assert (fillmissing (x, "previous", 1), y);
646s  y = x;
646s  y([6, 7, 9, 12, 19, 22, 23]) = [2, 3, 5, 8, 15, 18, 15];
646s  assert (fillmissing (x, "previous", 2), y);
646s  y = x;
646s  y([14, 16, 22, 23]) = [2, 4, 10, 11];
646s  assert (fillmissing (x, "previous", 3), y);
646s  assert (fillmissing (x, "previous", 99), x);
646s ***** assert (fillmissing ([1, 2, 3], "constant", 0, "endvalues", "previous"), [1, 2, 3])
646s ***** assert (fillmissing ([1, 2, 3], "constant", 0, "endvalues", "next"), [1, 2, 3])
646s ***** assert (fillmissing ([1, NaN, 3], "constant", 0, "endvalues", "previous"), [1, 0, 3])
646s ***** assert (fillmissing ([1, NaN, 3], "constant", 0, "endvalues", "next"), [1, 0, 3])
646s ***** assert (fillmissing ([1, 2, NaN], "constant", 0, "endvalues", "previous"), [1, 2, 2])
646s ***** assert (fillmissing ([1, 2, NaN], "constant", 0, "endvalues", "next"), [1, 2, NaN])
646s ***** assert (fillmissing ([1, NaN, NaN], "constant", 0, "endvalues", "previous"), [1, 1, 1])
646s ***** assert (fillmissing ([1, NaN, NaN], "constant", 0, "endvalues", "next"), [1, NaN, NaN])
646s ***** assert (fillmissing ([NaN, 2, 3], "constant", 0, "endvalues", "previous"), [NaN, 2, 3])
646s ***** assert (fillmissing ([NaN, 2, 3], "constant", 0, "endvalues", "next"), [2, 2, 3])
646s ***** assert (fillmissing ([NaN, NaN, 3], "constant", 0, "endvalues", "previous"), [NaN, NaN, 3])
646s ***** assert (fillmissing ([NaN, NaN, 3], "constant", 0, "endvalues", "next"), [3, 3, 3])
646s ***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "endvalues", "previous"), [NaN, NaN, NaN])
646s ***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "endvalues", "next"), [NaN, NaN, NaN])
646s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, "endvalues", "previous"), [NaN, 2, 0, 4, 4])
646s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, "endvalues", "next"), [2, 2, 0, 4, NaN])
646s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 1, "endvalues", "previous"), [NaN, 2, NaN, 4, NaN])
646s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 1, "endvalues", "next"), [NaN, 2, NaN, 4, NaN])
646s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 2, "endvalues", "previous"), [NaN, 2, 0, 4, 4])
646s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 2, "endvalues", "next"), [2, 2, 0, 4, NaN])
646s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 3, "endvalues", "previous"), [NaN, 2, NaN, 4, NaN])
646s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 3, "endvalues", "next"), [NaN, 2, NaN, 4, NaN])
646s ***** test
646s  x = reshape ([1:24], 3, 4, 2);
646s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
646s  y = x;
646s  y([5, 6, 8, 18]) = [4, 4, 0, 17];
646s  assert (fillmissing (x, "constant", 0, "endvalues", "previous"), y);
646s  assert (fillmissing (x, "constant", 0, 1, "endvalues", "previous"), y);
646s  y = x;
646s  y([6, 10, 18, 20, 21]) = [0, 7, 0, 0, 0];
646s  assert (fillmissing (x, "constant", 0, 2, "endvalues", "previous"), y);
646s  y = x;
646s  y([16, 19, 21]) = [4, 7, 9];
646s  assert (fillmissing (x, "constant", 0, 3, "endvalues", "previous"), y);
646s  assert (fillmissing (x, "constant", 0, 4, "endvalues", "previous"), x);
646s  assert (fillmissing (x, "constant", 0, 99, "endvalues", "previous"), x);
646s  y = x;
646s  y([1, 2, 8, 10, 13, 16, 22]) = [3, 3, 0, 11, 14, 17, 23];
646s  assert (fillmissing (x, "constant", 0, "endvalues", "next"), y);
646s  assert (fillmissing (x, "constant", 0, 1, "endvalues", "next"), y);
646s  y = x;
646s  y([1, 2, 5, 6, 8, 18, 20, 21]) = [4, 11, 11, 0, 11, 0, 0, 0];
646s  assert (fillmissing (x, "constant", 0, 2, "endvalues", "next"), y);
646s  y = x;
646s  y([2, 5]) = [14, 17];
646s  assert (fillmissing (x, "constant", 0, 3, "endvalues", "next"), y);
646s  assert (fillmissing (x, "constant", 0, 4, "endvalues", "next"), x);
646s  assert (fillmissing (x, "constant", 0, 99, "endvalues", "next"), x);
646s ***** assert (fillmissing ([1, 2, 3], "nearest"), [1, 2, 3])
646s ***** assert (fillmissing ([1, 2, 3]', "nearest"), [1, 2, 3]')
646s ***** assert (fillmissing ([1, 2, NaN], "nearest"), [1, 2, 2])
646s ***** assert (fillmissing ([NaN, 2, NaN], "nearest"), [2, 2, 2])
646s ***** assert (fillmissing ([1, NaN, 3], "nearest"), [1, 3, 3])
646s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "nearest", 1), [1, 2, 6; 4, 2, 6])
646s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "nearest", 2), [1, 2, 2; 4, 6, 6])
646s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "nearest", 3), [1, 2, NaN; 4, NaN, 6])
646s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "nearest"), [1, 3, 3, 5, 5])
646s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "nearest", "samplepoints", [0, 1, 2, 3, 4]), [1, 3, 3, 5, 5])
646s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "nearest", "samplepoints", [0.5, 1, 2, 3, 5]), [1, 1, 3, 3, 5])
646s ***** test
646s  x = reshape ([1:24], 4, 3, 2);
646s  x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
646s  y = x;
646s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [2, 5, 8, 10, 11, 15, 15, 20, 21, 24];
646s  assert (fillmissing (x, "nearest", 1), y);
646s  y = x;
646s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [5, 10, 11, 5, 8, 18, 20, 15, 18, 15];
646s  assert (fillmissing (x, "nearest", 2), y);
646s  y = x;
646s  y([1, 6, 9, 12, 14, 16, 22, 23]) = [13, 18, 21, 24, 2, 4, 10, 11];
646s  assert (fillmissing (x, "nearest", 3), y);
646s  assert (fillmissing (x, "nearest", 99), x);
646s ***** assert (fillmissing ([1, 2, 3], "constant", 0, "endvalues", "nearest"), [1, 2, 3])
646s ***** assert (fillmissing ([1, NaN, 3], "constant", 0, "endvalues", "nearest"), [1 0 3])
646s ***** assert (fillmissing ([1, 2, NaN], "constant", 0, "endvalues", "nearest"), [1, 2, 2])
646s ***** assert (fillmissing ([1, NaN, NaN], "constant", 0, "endvalues", "nearest"), [1, 1, 1])
646s ***** assert (fillmissing ([NaN, 2, 3], "constant", 0, "endvalues", "nearest"), [2, 2, 3])
646s ***** assert (fillmissing ([NaN, NaN, 3], "constant", 0, "endvalues", "nearest"), [3, 3, 3])
646s ***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "endvalues", "nearest"), [NaN, NaN, NaN])
646s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, "endvalues", "nearest"), [2, 2, 0, 4, 4])
646s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 1, "endvalues", "nearest"), [NaN, 2, NaN, 4, NaN])
646s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 2, "endvalues", "nearest"), [2, 2, 0, 4, 4])
646s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 3, "endvalues", "nearest"), [NaN, 2, NaN, 4, NaN])
646s ***** test
646s  x = reshape ([1:24], 3, 4, 2);
646s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
646s  y = x;
646s  y([1, 2, 5, 6, 8, 10, 13, 16, 18, 22]) = [3, 3, 4, 4, 0, 11, 14, 17, 17, 23];
646s  assert (fillmissing (x, "constant", 0, "endvalues", "nearest"), y);
646s  assert (fillmissing (x, "constant", 0, 1, "endvalues", "nearest"), y);
646s  y = x;
646s  y([1, 2, 5, 6, 8, 10, 18, 20, 21]) = [4, 11, 11, 0, 11, 7, 0, 0, 0];
646s  assert (fillmissing (x, "constant", 0, 2, "endvalues", "nearest"), y);
646s  y = x;
646s  y([2, 5, 16, 19, 21]) = [14, 17, 4, 7, 9];
646s  assert (fillmissing (x, "constant", 0, 3, "endvalues", "nearest"), y);
646s  assert (fillmissing (x, "constant", 0, 99, "endvalues", "nearest"), x);
646s ***** assert (fillmissing ([1, 2, 3], "linear"), [1, 2, 3])
646s ***** assert (fillmissing ([1, 2, 3]', "linear"), [1, 2, 3]')
646s ***** assert (fillmissing ([1, 2, NaN], "linear"), [1, 2, 3])
646s ***** assert (fillmissing ([NaN, 2, NaN], "linear"), [NaN, 2, NaN])
646s ***** assert (fillmissing ([1, NaN, 3], "linear"), [1, 2, 3])
646s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "linear", 1), [1, 2, NaN; 4, NaN, 6])
646s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "linear", 2), [1, 2, 3; 4, 5, 6])
646s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "linear", 3), [1, 2, NaN; 4, NaN, 6])
646s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "linear"), [1, 2, 3, 4, 5])
646s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "linear", "samplepoints", [0, 1, 2, 3, 4]), [1, 2, 3, 4, 5])
646s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "linear", "samplepoints", [0, 1.5, 2, 5, 14]), [1, 2.5, 3, 3.5, 5], eps)
646s ***** test
646s  x = reshape ([1:24], 4, 3, 2);
646s  x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
646s  assert (fillmissing (x, "linear", 1), reshape ([1:24], 4, 3, 2));
646s  y = reshape ([1:24], 4, 3, 2);
646s  y([1, 9, 14, 19, 22, 23]) = NaN;
646s  assert (fillmissing (x, "linear", 2), y);
646s  y = reshape ([1:24], 4, 3, 2);
646s  y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
646s  assert (fillmissing (x, "linear", 3), y);
646s  assert (fillmissing (x, "linear", 99), x);
646s ***** assert (fillmissing ([1, 2, 3], "linear", "endvalues", 0), [1, 2, 3])
646s ***** assert (fillmissing ([1, NaN, 3], "linear", "endvalues", 0), [1, 2, 3])
646s ***** assert (fillmissing ([1, 2, NaN], "linear", "endvalues", 0), [1, 2, 0])
646s ***** assert (fillmissing ([1, NaN, NaN], "linear", "endvalues", 0), [1, 0, 0])
646s ***** assert (fillmissing ([NaN, 2, 3], "linear", "endvalues", 0), [0, 2, 3])
646s ***** assert (fillmissing ([NaN, NaN, 3], "linear", "endvalues", 0), [0, 0, 3])
646s ***** assert (fillmissing ([NaN, NaN, NaN], "linear", "endvalues", 0), [0, 0, 0])
646s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "linear", "endvalues", 0), [0, 2, 3, 4, 0])
646s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "linear", 1, "endvalues", 0), [0, 2, 0, 4, 0])
646s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "linear", 2, "endvalues", 0), [0, 2, 3, 4, 0])
646s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "linear", 3, "endvalues", 0), [0, 2, 0, 4, 0])
646s ***** test
646s  x = reshape ([1:24], 3, 4, 2);
646s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
646s  y = x;
646s  y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 0;
646s  y(8) = 8;
646s  assert (fillmissing (x, "linear", "endvalues", 0), y);
646s  assert (fillmissing (x, "linear", 1, "endvalues", 0), y);
646s  y = x;
646s  y([1, 2, 5, 8, 10, 13, 16, 19, 22]) = 0;
646s  y([6, 18, 20, 21]) = [6, 18, 20, 21];
646s  assert (fillmissing (x, "linear", 2, "endvalues", 0), y);
646s  y = x;
646s  y(isnan(y)) = 0;
646s  assert (fillmissing (x, "linear", 3, "endvalues", 0), y);
646s  assert (fillmissing (x, "linear", 99, "endvalues", 0), y);
646s ***** assert (fillmissing ([1, 2, 3], "constant", 99, "endvalues", "linear"), [1, 2, 3])
646s ***** assert (fillmissing ([1, NaN, 3], "constant", 99, "endvalues", "linear"), [1, 99, 3])
646s ***** assert (fillmissing ([1, NaN, 3, NaN], "constant", 99, "endvalues", "linear"), [1, 99, 3, 4])
646s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "linear"), [1, 2, 99, 4, 5])
646s ***** assert (fillmissing ([NaN, 2, NaN, NaN], "constant", 99, "endvalues", "linear"), [NaN, 2, NaN, NaN])
646s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "linear", "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 99, 4, 5])
646s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "linear", "samplepoints", [0, 2, 3, 4, 10]), [0, 2, 99, 4, 10])
646s ***** test
646s  x = reshape ([1:24], 3, 4, 2);
646s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
646s  y = x;
646s  y([1, 6, 10, 18, 20, 21]) = [2.5, 5, 8.5, 17.25, 21, 21.75];
646s  assert (fillmissing (x, "linear", 2, "samplepoints", [2 4 8 10]), y, eps);
646s  y([1, 6, 10, 18, 20, 21]) = [2.5, 4.5, 8.5, 17.25, 21.5, 21.75];
646s  assert (fillmissing (x, "spline", 2, "samplepoints", [2, 4, 8, 10]), y, eps);
646s  y([1, 6, 10, 18, 20, 21]) = [2.5, 4.559386973180077, 8.5, 17.25, 21.440613026819925, 21.75];
646s  assert (fillmissing (x, "pchip", 2, "samplepoints", [2, 4, 8, 10]), y, 10*eps);
646s ***** test <60965>
646s  x = reshape ([1:24], 3, 4, 2);
646s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
646s  y = x;
646s  y([1, 6, 10, 18, 20, 21]) = [2.5, 4.609523809523809, 8.5, 17.25, 21.390476190476186, 21.75];
646s  assert (fillmissing (x, "makima", 2, "samplepoints", [2, 4, 8, 10]), y, 10*eps);
646s !!!!! known bug: https://octave.org/testfailure/?60965
646s interp1: invalid METHOD 'makima'
646s ***** assert (fillmissing ([1, 2, 3], "constant", 99, "endvalues", "spline"), [1, 2, 3])
646s ***** assert (fillmissing ([1, NaN, 3], "constant", 99, "endvalues", "spline"), [1, 99, 3])
646s ***** assert (fillmissing ([1, NaN, 3, NaN], "constant", 99, "endvalues", "spline"), [1, 99, 3, 4])
646s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "spline"), [1, 2, 99, 4, 5])
646s ***** assert (fillmissing ([NaN, 2, NaN, NaN], "constant", 99, "endvalues", "spline"), [NaN, 2, NaN, NaN])
646s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "spline", "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 99, 4, 5])
646s ***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "spline", "samplepoints", [0, 2, 3, 4, 10]), [0, 2, 99, 4, 10])
646s ***** assert (fillmissing ([1, 2, 3], "movmean", 1), [1, 2, 3])
646s ***** assert (fillmissing ([1, 2, NaN], "movmean", 1), [1, 2, NaN])
646s ***** assert (fillmissing ([1, 2, 3], "movmean", 2), [1, 2, 3])
646s ***** assert (fillmissing ([1, 2, 3], "movmean", [1, 0]), [1, 2, 3])
646s ***** assert (fillmissing ([1, 2, 3]', "movmean", 2), [1, 2, 3]')
646s ***** assert (fillmissing ([1, 2, NaN], "movmean", 2), [1, 2, 2])
646s ***** assert (fillmissing ([1, 2, NaN], "movmean", [1, 0]), [1, 2, 2])
646s ***** assert (fillmissing ([1, 2, NaN], "movmean", [1, 0]'), [1, 2, 2])
646s ***** assert (fillmissing ([NaN, 2, NaN], "movmean", 2), [NaN, 2, 2])
646s ***** assert (fillmissing ([NaN, 2, NaN], "movmean", [1, 0]), [NaN, 2, 2])
646s ***** assert (fillmissing ([NaN, 2, NaN], "movmean", [0, 1]), [2, 2, NaN])
646s ***** assert (fillmissing ([NaN, 2, NaN], "movmean", [0, 1.1]), [2, 2, NaN])
646s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmean", [3, 0]), [1, 1, 3, 2, 5])
646s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmean", 3, 1), [1, 2, 6; 4, 2, 6])
646s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmean", 3, 2), [1, 2, 2; 4, 5, 6])
646s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmean", 3, 3), [1, 2, NaN; 4, NaN, 6])
646s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmean", 99), [1, 3, 3, 3, 5])
646s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmean", 99, 1), [1, NaN, 3, NaN, 5])
646s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "movmean", 99, 1), [1, 3, 3, 3, 5]')
646s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmean", 99, 2), [1, 3, 3, 3, 5])
646s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "movmean", 99, 2), [1, NaN, 3, NaN, 5]')
646s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 3, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
646s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1, 1], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
646s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1.5, 1.5], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
646s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 4, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 1, 5, 5])
646s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [2, 2], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 3, 5, 5])
646s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 4.0001, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 3, 5, 5])
646s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 3, "samplepoints", [1.5, 2, 3, 4, 5]), [1, 1, 1, 5, 5])
646s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 3, "samplepoints", [1 2, 3, 4, 4.5]), [1, 1, NaN, 5, 5])
646s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 3, "samplepoints", [1.5, 2, 3, 4, 4.5]), [1, 1, 1, 5, 5])
646s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1.5, 1.5], "samplepoints", [1.5, 2, 3, 4, 5]), [1, 1, 1, 5, 5])
646s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1.5, 1.5], "samplepoints", [1, 2, 3, 4, 4.5]), [1, 1, 5, 5, 5])
646s ***** 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])
647s ***** test
647s  x = reshape ([1:24], 3, 4, 2);
647s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
647s  y = x;
647s  y([2, 5, 8, 10, 13, 16, 18, 22]) = [3, 4, 8, 11, 14, 17, 17, 23];
647s  assert (fillmissing (x, "movmean", 3), y);
647s  assert (fillmissing (x, "movmean", [1, 1]), y);
647s  assert (fillmissing (x, "movmean", 3, "endvalues", "extrap"), y);
647s  assert (fillmissing (x, "movmean", 3, "samplepoints", [1, 2, 3]), y);
647s  y = x;
647s  y([1, 6, 8, 10, 18, 20, 21]) = [4, 6, 11, 7, 15, 20, 24];
647s  assert (fillmissing (x, "movmean", 3, 2), y);
647s  assert (fillmissing (x, "movmean", [1, 1], 2), y);
647s  assert (fillmissing (x, "movmean", 3, 2, "endvalues", "extrap"), y);
647s  assert (fillmissing (x, "movmean", 3, 2, "samplepoints", [1, 2, 3, 4]), y);
647s  y([1, 18]) = NaN;
647s  y(6) = 9;
647s  assert (fillmissing (x, "movmean", 3, 2, "samplepoints", [0, 2, 3, 4]), y);
647s  y = x;
647s  y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 99;
647s  y(8) = 8;
647s  assert (fillmissing (x, "movmean", 3, "endvalues", 99), y);
647s  y = x;
647s  y([1, 2, 5, 8, 10, 13, 16, 19, 22]) = 99;
647s  y([6, 18, 20, 21]) = [6, 15, 20, 24];
647s  assert (fillmissing (x, "movmean", 3, 2, "endvalues", 99), y);
647s ***** assert (fillmissing ([1, 2, 3], "movmedian", 1), [1, 2, 3])
647s ***** assert (fillmissing ([1, 2, NaN], "movmedian", 1), [1, 2, NaN])
647s ***** assert (fillmissing ([1, 2, 3], "movmedian", 2), [1, 2, 3])
647s ***** assert (fillmissing ([1, 2, 3], "movmedian", [1, 0]), [1, 2, 3])
647s ***** assert (fillmissing ([1, 2, 3]', "movmedian", 2), [1, 2, 3]')
647s ***** assert (fillmissing ([1, 2, NaN], "movmedian", 2), [1, 2, 2])
647s ***** assert (fillmissing ([1, 2, NaN], "movmedian", [1, 0]), [1, 2, 2])
647s ***** assert (fillmissing ([1, 2, NaN], "movmedian", [1, 0]'), [1, 2, 2])
647s ***** assert (fillmissing ([NaN, 2, NaN], "movmedian", 2), [NaN, 2, 2])
647s ***** assert (fillmissing ([NaN, 2, NaN], "movmedian", [1, 0]), [NaN, 2, 2])
647s ***** assert (fillmissing ([NaN, 2, NaN], "movmedian", [0, 1]), [2, 2, NaN])
647s ***** assert (fillmissing ([NaN, 2, NaN], "movmedian", [0, 1.1]), [2, 2, NaN])
647s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmedian", [3, 0]), [1, 1, 3, 2, 5])
647s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmedian", 3, 1), [1, 2, 6; 4, 2, 6])
647s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmedian", 3, 2), [1, 2, 2; 4, 5, 6])
647s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmedian", 3, 3), [1, 2, NaN; 4, NaN, 6])
647s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmedian", 99), [1, 3, 3, 3, 5])
647s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmedian", 99, 1), [1, NaN, 3, NaN, 5])
647s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "movmedian", 99, 1), [1, 3, 3, 3, 5]')
647s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmedian", 99, 2), [1, 3, 3, 3, 5])
647s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "movmedian", 99, 2), [1, NaN, 3, NaN, 5]')
647s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 3, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
647s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1, 1], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
647s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1.5, 1.5], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
647s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 4, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 1, 5, 5])
647s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [2, 2], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 3, 5, 5])
647s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 4.0001, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 3, 5, 5])
647s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 3, "samplepoints", [1.5 2 3 4 5]), [1, 1, 1, 5, 5])
647s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 3, "samplepoints", [1 2 3 4 4.5]), [1, 1, NaN, 5, 5])
647s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 3, "samplepoints", [1.5 2 3 4 4.5]), [1, 1, 1, 5, 5])
647s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1.5, 1.5], "samplepoints", [1.5 2 3 4 5]), [1, 1, 1, 5, 5])
647s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1.5, 1.5], "samplepoints", [1 2 3 4 4.5]), [1, 1, 5, 5, 5])
647s ***** 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])
647s ***** test
647s  x = reshape ([1:24], 3, 4, 2);
647s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
647s  y = x;
647s  y([2, 5, 8, 10, 13, 16, 18, 22]) = [3, 4, 8, 11, 14, 17, 17, 23];
647s  assert (fillmissing (x, "movmedian", 3), y);
647s  assert (fillmissing (x, "movmedian", [1, 1]), y);
647s  assert (fillmissing (x, "movmedian", 3, "endvalues", "extrap"), y);
647s  assert (fillmissing (x, "movmedian", 3, "samplepoints", [1, 2, 3]), y);
647s  y = x;
647s  y([1, 6, 8, 10, 18, 20, 21]) = [4, 6, 11, 7, 15, 20, 24];
647s  assert (fillmissing (x, "movmedian", 3, 2), y);
647s  assert (fillmissing (x, "movmedian", [1, 1], 2), y);
647s  assert (fillmissing (x, "movmedian", 3, 2, "endvalues", "extrap"), y);
647s  assert (fillmissing (x, "movmedian", 3, 2, "samplepoints", [1, 2, 3, 4]), y);
647s  y([1,18]) = NaN;
647s  y(6) = 9;
647s  assert (fillmissing (x, "movmedian", 3, 2, "samplepoints", [0, 2, 3, 4]), y);
647s  y = x;
647s  y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 99;
647s  y(8) = 8;
647s  assert (fillmissing (x, "movmedian", 3, "endvalues", 99), y);
647s  y = x;
647s  y([1, 2, 5, 8, 10, 13, 16, 19, 22]) = 99;
647s  y([6, 18, 20, 21]) = [6, 15, 20, 24];
647s  assert (fillmissing (x, "movmedian", 3, 2, "endvalues", 99), y);
647s ***** assert (fillmissing ([1, 2, 3], @(x,y,z) x+y+z, 2), [1, 2, 3])
647s ***** assert (fillmissing ([1, 2, NaN], @(x,y,z) x+y+z, 1), [1, 2, NaN])
647s ***** assert (fillmissing ([1, 2, 3], @(x,y,z) x+y+z, 2), [1, 2, 3])
647s ***** assert (fillmissing ([1, 2, 3], @(x,y,z) x+y+z, [1, 0]), [1, 2, 3])
647s ***** assert (fillmissing ([1, 2, 3]', @(x,y,z) x+y+z, 2), [1, 2, 3]')
647s ***** assert (fillmissing ([1, 2, NaN], @(x,y,z) x+y+z, 2), [1, 2, 7])
647s ***** assert (fillmissing ([1, 2, NaN], @(x,y,z) x+y+z, [1, 0]), [1, 2, 7])
647s ***** assert (fillmissing ([1, 2, NaN], @(x,y,z) x+y+z, [1, 0]'), [1, 2, 7])
647s ***** assert (fillmissing ([NaN, 2, NaN], @(x,y,z) x+y+z, 2), [5, 2, 7])
647s ***** assert (fillmissing ([NaN, 2, NaN], @(x,y,z) x+y+z, [1, 0]), [NaN, 2, 7])
647s ***** assert (fillmissing ([NaN, 2, NaN], @(x,y,z) x+y+z, [0, 1]), [5, 2, NaN])
647s ***** assert (fillmissing ([NaN, 2, NaN], @(x,y,z) x+y+z, [0, 1.1]), [5, 2, NaN])
647s ***** assert (fillmissing ([1, 2, NaN, NaN, 3, 4], @(x,y,z) x+y+z, 2), [1, 2, 7, 12, 3, 4])
647s ***** assert (fillmissing ([1, 2, NaN, NaN, 3, 4], @(x,y,z) x+y+z, 0.5), [1, 2, NaN, NaN, 3, 4])
647s ***** function A = testfcn (x, y, z)
647s   if (isempty (y))
647s     A = z;
647s   elseif (numel (y) == 1)
647s     A = repelem (x(1), numel(z));
647s   else
647s     A = interp1 (y, x, z, "linear", "extrap");
647s   endif
647s ***** endfunction
647s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, [3, 0]), [1, 1, 3, NaN, 5])
647s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], @testfcn, 3, 1), [1, 2, 6; 4, 2, 6])
647s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], @testfcn, 3, 2), [1, 2, 2; 4, 5, 6])
647s ***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], @testfcn, 3, 3), [1, 2, NaN; 4, NaN, 6])
647s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, 99), [1, 2, 3, 4, 5])
647s ***** 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]
647s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', @testfcn, 99, 1), [1, 2, 3, 4, 5]')
647s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, 99, 2), [1, 2, 3, 4, 5])
647s ***** 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]'
647s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, 99, 3), [1, NaN, 3, NaN, 5])
647s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', @testfcn, 99, 3), [1, NaN, 3, NaN, 5]')
647s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, 3, "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
647s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, [1, 1], "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
647s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, [1.5, 1.5], "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
647s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, 4, "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
647s ***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, [2, 2], "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
647s ***** 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)
647s ***** 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]
647s ***** test
647s ***** function A = testfcn (x, y, z)
647s   if (isempty (y))
647s     A = z;
647s   elseif (numel (y) == 1)
647s     A = repelem (x(1), numel(z));
647s   else
647s     A = interp1 (y, x, z, "linear", "extrap");
647s   endif
647s ***** endfunction
647s  x = reshape ([1:24], 3, 4, 2);
647s  x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
647s  y = x;
647s  y([1, 2, 5, 6, 8, 10, 13, 16, 18, 22]) = [3, 3, 4, 4, 8, 11, 14, 17, 17, 23];
647s  assert (fillmissing (x, @testfcn, 3), y);
647s  assert (fillmissing (x, @testfcn, [1, 1]), y);
647s  assert (fillmissing (x, @testfcn, 3, "endvalues", "extrap"), y);
647s  assert (fillmissing (x, @testfcn, 3, "samplepoints", [1, 2, 3]), y);
647s  y= x;
647s  y(isnan (x)) = 99;
647s  y(8) = 8;
647s  assert (fillmissing (x, @testfcn, 3, "endvalues", 99), y)
647s  y = x;
647s  y([1, 2, 5, 6, 8, 10, 18, 20, 21]) = [4, 11, 11, 6, 11, 7, 18, 20, 21];
647s  assert (fillmissing (x, @testfcn, 3, 2), y);
647s  assert (fillmissing (x, @testfcn, [1, 1], 2), y);
647s  assert (fillmissing (x, @testfcn, 3, 2, "endvalues", "extrap"), y);
647s  assert (fillmissing (x, @testfcn, 3, 2, "samplepoints", [1, 2, 3, 4]), y);
647s  y(1) = NaN;
647s  y([6, 18, 21]) = [9, 24, 24];
647s  assert (fillmissing (x, @testfcn, 3, 2, "samplepoints", [0, 2, 3, 4]), y);
647s  y = x;
647s  y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 99;
647s  y(8) = 8;
647s  assert (fillmissing (x, @testfcn, 3, "endvalues", 99), y);
647s  y([6, 18, 20, 21]) = [6, 18, 20, 21];
647s  y(8) = 99;
647s  assert (fillmissing (x, @testfcn, 3, 2, "endvalues", 99), y);
647s  y([6, 18, 20, 21]) = 99;
647s  assert (fillmissing (x, @testfcn, 3, 3, "endvalues", 99), y);
647s ***** assert (fillmissing ([1, 2, 3], "constant", 0, "maxgap", 1), [1, 2, 3])
647s ***** assert (fillmissing ([1, 2, 3], "constant", 0, "maxgap", 99), [1, 2, 3])
647s ***** assert (fillmissing ([1, NaN, 3], "constant", 0, "maxgap", 1), [1, NaN, 3])
647s ***** assert (fillmissing ([1, NaN, 3], "constant", 0, "maxgap", 1.999), [1, NaN, 3])
647s ***** assert (fillmissing ([1, NaN, 3], "constant", 0, "maxgap", 2), [1, 0, 3])
647s ***** assert (fillmissing ([1, NaN, NaN, 4], "constant", 0, "maxgap", 2), [1, NaN, NaN, 4])
647s ***** assert (fillmissing ([1, NaN, NaN, 4], "constant", 0, "maxgap", 3), [1, 0, 0, 4])
647s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", 0, "maxgap", 2), [1, 0, 3, 0, 5])
647s ***** assert (fillmissing ([NaN, 2, NaN], "constant", 0, "maxgap", 0.999), [NaN, 2, NaN])
647s ***** assert (fillmissing ([NaN, 2, NaN], "constant", 0, "maxgap", 1), [0, 2, 0])
647s ***** assert (fillmissing ([NaN, 2, NaN, NaN], "constant", 0, "maxgap", 1), [0, 2, NaN, NaN])
647s ***** assert (fillmissing ([NaN, 2, NaN, NaN], "constant", 0, "maxgap", 2), [0, 2, 0, 0])
647s ***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "maxgap", 1), [NaN, NaN, NaN])
647s ***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "maxgap", 3), [NaN, NaN, NaN])
647s ***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "maxgap", 999), [NaN, NaN, NaN])
647s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", 0, "maxgap", 2, "samplepoints", [0, 1, 2, 3, 5]), [1, 0, 3, NaN, 5])
647s ***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "constant", 0, "maxgap", 2, "samplepoints", [0, 1, 2, 3, 5]), [1, 0, 3, NaN, 5]')
647s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", 0, "maxgap", 2, "samplepoints", [0, 2, 3, 4, 5]), [1, NaN, 3, 0, 5])
647s ***** 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])
647s ***** test
647s  x = cat (3, [1, 2, NaN; 4, NaN, NaN], [NaN, 2, 3; 4, 5, NaN]);
647s  assert (fillmissing (x, "constant", 0, "maxgap", 0.1), x);
647s  y = x;
647s  y([4, 7, 12]) = 0;
647s  assert (fillmissing (x, "constant", 0, "maxgap", 1), y);
647s  assert (fillmissing (x, "constant", 0, 1, "maxgap", 1), y);
647s  y = x;
647s  y([5, 7, 12]) = 0;
647s  assert (fillmissing (x, "constant", 0, 2, "maxgap", 1), y);
647s  y = x;
647s  y([4, 5, 7]) = 0;
647s  assert (fillmissing (x, "constant", 0, 3, "maxgap", 1), y);
647s ***** test
647s  x = cat (3, [1, 2, NaN; 4, NaN, NaN], [NaN, 2, 3; 4, 5, NaN]);
647s  [~, idx] = fillmissing (x, "constant", 0, "maxgap", 1);
647s  assert (idx, logical (cat (3, [0, 0, 0; 0, 1, 0], [1, 0, 0; 0, 0, 1])));
647s  [~, idx] = fillmissing (x, "constant", 0, 1, "maxgap", 1);
647s  assert (idx, logical (cat (3, [0, 0, 0; 0, 1, 0], [1, 0, 0; 0, 0, 1])));
647s  [~, idx] = fillmissing (x, "constant", 0, 2, "maxgap", 1);
647s  assert (idx, logical (cat (3, [0, 0, 1; 0, 0, 0], [1, 0, 0; 0, 0, 1])));
647s  [~, idx] = fillmissing (x, "constant", 0, 3, "maxgap", 1);
647s  assert (idx, logical (cat (3, [0, 0, 1; 0, 1, 0], [1, 0, 0; 0, 0, 0])));
647s ***** test
647s  x = [NaN, 2, 3];
647s  [~, idx] = fillmissing (x, "previous");
647s  assert (idx, logical ([0, 0, 0]));
647s  [~, idx] = fillmissing (x, "movmean", 1);
647s  assert (idx, logical ([0, 0, 0]));
647s  x = [1:3; 4:6; 7:9];
647s  x([2, 4, 7, 9]) = NaN;
647s  [~, idx] = fillmissing (x, "linear");
647s  assert (idx, logical ([0, 1, 0; 1, 0, 0; 0, 0, 0]));
647s  [~, idx] = fillmissing (x, "movmean", 2);
647s  assert (idx, logical ([0, 0, 0; 1, 0, 0; 0, 0, 1]));
647s  [A, idx] = fillmissing ([1, 2, 3, NaN, NaN], "movmean",2);
647s  assert (A, [1, 2, 3, 3, NaN]);
647s  assert (idx, logical ([0, 0, 0, 1, 0]));
647s  [A, idx] = fillmissing ([1, 2, 3, NaN, NaN], "movmean",3);
647s  assert (A, [1, 2, 3, 3, NaN]);
647s  assert (idx, logical ([0, 0, 0, 1, 0]));
647s  [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmedian", 2);
647s  assert (A, [1, 2, 2, NaN, NaN]);
647s  assert (idx, logical ([0, 0, 1, 0, 0]));
647s  [A, idx] = fillmissing ([1, 2, 3, NaN, NaN], "movmedian", 3);
647s  assert (A, [1, 2, 3, 3, NaN]);
647s  assert (idx, logical ([0, 0, 0, 1, 0]));
647s  [A, idx] = fillmissing ([1, NaN, 1, NaN, 1],  @(x,y,z) z, 3);
647s  assert (A, [1, 2, 1, 4, 1]);
647s  assert (idx, logical ([0, 1, 0, 1, 0]));
647s  [A, idx] = fillmissing ([1, NaN, 1, NaN, 1],  @(x,y,z) NaN (size (z)), 3);
647s  assert (A, [1, NaN, 1, NaN, 1]);
647s  assert (idx, logical ([0, 0, 0, 0, 0]));
647s ***** assert (fillmissing ([1, 2, 3], "constant", 99, "missinglocations", logical ([0, 0, 0])), [1, 2, 3])
647s ***** assert (fillmissing ([1, 2, 3], "constant", 99, "missinglocations", logical ([1, 1, 1])), [99, 99, 99])
647s ***** assert (fillmissing ([1, NaN, 2, 3, NaN], "constant", 99, "missinglocations", logical ([1, 0, 1, 0, 1])), [99, NaN, 99, 3, 99])
647s ***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", NaN, "missinglocations", logical ([0, 1, 1, 1, 0])), [1, NaN, NaN, NaN, 5])
647s ***** assert (fillmissing (["foo "; " bar"], "constant", "X", "missinglocations", logical ([0, 0, 0, 0; 0, 0, 0, 0])), ["foo "; " bar"])
647s ***** assert (fillmissing (["foo "; " bar"], "constant", "X", "missinglocations", logical ([1, 0, 1, 0; 0, 1, 1, 0])), ["XoX "; " XXr"])
647s ***** assert (fillmissing ({"foo", "", "bar"}, "constant", "X", "missinglocations", logical ([0, 0, 0])), {"foo", "", "bar"})
647s ***** assert (fillmissing ({"foo", "", "bar"}, "constant", "X", "missinglocations", logical ([1, 1, 0])), {"X", "X", "bar"})
647s ***** test
647s  [~, idx] = fillmissing ([1, NaN, 3, NaN, 5], "constant", NaN);
647s  assert (idx, logical ([0, 0, 0, 0, 0]));
647s  [~, idx] = fillmissing ([1 NaN 3 NaN 5], "constant", NaN, "missinglocations", logical ([0, 1, 1, 1, 0]));
647s  assert (idx, logical ([0, 1, 1, 1, 0]));
647s  [A, idx] = fillmissing ([1, 2, NaN, 1, NaN], "movmean", 3.1, "missinglocations", logical ([0, 0, 1, 1, 0]));
647s  assert (A, [1, 2, 2, NaN, NaN]);
647s  assert (idx, logical ([0, 0, 1, 0, 0]));
647s  [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmean", 2, "missinglocations", logical ([0, 0, 1, 1, 0]));
647s  assert (A, [1, 2, 2, NaN, NaN]);
647s  assert (idx, logical ([0, 0, 1, 0, 0]));
647s  [A, idx] = fillmissing ([1, 2, NaN, 1, NaN], "movmean", 3, "missinglocations", logical ([0, 0, 1, 1, 0]));
647s  assert (A, [1, 2, 2, NaN, NaN]);
647s  assert (idx, logical ([0, 0, 1, 0, 0]));
647s  [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmean", 3, "missinglocations", logical ([0, 0, 1, 1, 0]));
647s  assert (A, [1, 2, 2, NaN, NaN]);
647s  assert (idx, logical ([0, 0, 1, 0, 0]));
647s  [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmedian", 2, "missinglocations", logical ([0, 0, 1, 1, 0]));
647s  assert (A, [1, 2, 2, NaN, NaN]);
647s  assert (idx, logical ([0, 0, 1, 0, 0]));
647s  [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmedian", 3, "missinglocations", logical ([0, 0, 1, 1, 0]));
647s  assert (A, [1, 2, 2, NaN, NaN]);
647s  assert (idx, logical ([0, 0, 1, 0, 0]));
647s  [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmedian", 3.1, "missinglocations", logical ([0, 0, 1, 1, 0]));
647s  assert (A, [1, 2, 2, NaN, NaN]);
647s  assert (idx, logical ([0, 0, 1, 0, 0]));
647s  [A, idx] = fillmissing ([1, NaN, 1, NaN, 1],  @(x,y,z) ones (size (z)), 3, "missinglocations", logical ([0, 1, 0, 1, 1]));
647s  assert (A, [1, 1, 1, 1, 1]);
647s  assert (idx, logical ([0, 1, 0, 1, 1]));
647s  [A, idx] = fillmissing ([1, NaN, 1, NaN, 1],  @(x,y,z) NaN (size (z)), 3, "missinglocations", logical ([0, 1, 0, 1, 1]));
647s  assert (A, [1, NaN, 1, NaN, NaN]);
647s  assert (idx, logical ([0, 0, 0, 0, 0]));
647s ***** test
647s  [A, idx] = fillmissing ([1, 2, 5], "movmedian", 3, "missinglocations", logical ([0, 1, 0]));
647s  assert (A, [1, 3, 5]);
647s  assert (idx, logical ([0, 1, 0]));
647s ***** assert (fillmissing (" foo bar ", "constant", "X"), "XfooXbarX")
647s ***** assert (fillmissing ([" foo"; "bar "], "constant", "X"), ["Xfoo"; "barX"])
647s ***** assert (fillmissing ([" foo"; "bar "], "next"), ["bfoo"; "bar "])
647s ***** assert (fillmissing ([" foo"; "bar "], "next", 1), ["bfoo"; "bar "])
647s ***** assert (fillmissing ([" foo"; "bar "], "previous"), [" foo"; "baro"])
647s ***** assert (fillmissing ([" foo"; "bar "], "previous", 1), [" foo"; "baro"])
647s ***** assert (fillmissing ([" foo"; "bar "], "nearest"), ["bfoo"; "baro"])
647s ***** assert (fillmissing ([" foo"; "bar "], "nearest", 1), ["bfoo"; "baro"])
647s ***** assert (fillmissing ([" foo"; "bar "], "next", 2), ["ffoo"; "bar "])
647s ***** assert (fillmissing ([" foo"; "bar "], "previous", 2), [" foo"; "barr"])
647s ***** assert (fillmissing ([" foo"; "bar "], "nearest", 2), ["ffoo"; "barr"])
647s ***** assert (fillmissing ([" foo"; "bar "], "next", 3), [" foo"; "bar "])
647s ***** assert (fillmissing ([" foo"; "bar "], "previous", 3), [" foo"; "bar "])
647s ***** assert (fillmissing ([" foo"; "bar "], "nearest", 3), [" foo"; "bar "])
647s ***** assert (fillmissing ({"foo", "bar"}, "constant", "a"), {"foo", "bar"})
647s ***** assert (fillmissing ({"foo", "bar"}, "constant", {"a"}), {"foo", "bar"})
647s ***** assert (fillmissing ({"foo", "", "bar"}, "constant", "a"), {"foo", "a", "bar"})
647s ***** assert (fillmissing ({"foo", "", "bar"}, "constant", {"a"}), {"foo", "a", "bar"})
647s ***** assert (fillmissing ({"foo", "", "bar"}, "previous"), {"foo", "foo", "bar"})
647s ***** assert (fillmissing ({"foo", "", "bar"}, "next"), {"foo", "bar", "bar"})
647s ***** assert (fillmissing ({"foo", "", "bar"}, "nearest"), {"foo", "bar", "bar"})
647s ***** assert (fillmissing ({"foo", "", "bar"}, "previous", 2), {"foo", "foo", "bar"})
647s ***** assert (fillmissing ({"foo", "", "bar"}, "next", 2), {"foo", "bar", "bar"})
647s ***** assert (fillmissing ({"foo", "", "bar"}, "nearest", 2), {"foo", "bar", "bar"})
647s ***** assert (fillmissing ({"foo", "", "bar"}, "previous", 1), {"foo", "", "bar"})
647s ***** assert (fillmissing ({"foo", "", "bar"}, "previous", 1), {"foo", "", "bar"})
647s ***** assert (fillmissing ({"foo", "", "bar"}, "next", 1), {"foo", "", "bar"})
647s ***** assert (fillmissing ({"foo", "", "bar"}, "nearest", 1), {"foo", "", "bar"})
647s ***** assert (fillmissing ("abc ", @(x,y,z) x+y+z, 2), "abcj")
647s ***** assert (fillmissing ({"foo", "", "bar"}, @(x,y,z) x(1), 3), {"foo", "foo", "bar"})
647s ***** test
647s  [A, idx] = fillmissing (" a b c", "constant", " ");
647s  assert (A, " a b c");
647s  assert (idx, logical ([0, 0, 0, 0, 0, 0]));
647s  [A, idx] = fillmissing ({"foo", "", "bar", ""}, "constant", "");
647s  assert (A, {"foo", "", "bar", ""});
647s  assert (idx, logical ([0, 0, 0, 0]));
647s  [A, idx] = fillmissing ({"foo", "", "bar", ""}, "constant", {""});
647s  assert (A, {"foo", "", "bar", ""});
647s  assert (idx, logical ([0, 0, 0, 0]));
647s  [A,idx] = fillmissing (" f o o ", @(x,y,z) repelem ("a", numel (z)), 3);
647s  assert (A, "afaoaoa");
647s  assert (idx, logical ([1, 0, 1, 0, 1, 0, 1]));
647s  [A,idx] = fillmissing (" f o o ", @(x,y,z) repelem (" ", numel (z)), 3);
647s  assert (A, " f o o ");
647s  assert (idx, logical ([0, 0, 0, 0, 0, 0, 0]));
647s  [A,idx] = fillmissing ({"", "foo", ""}, @(x,y,z) repelem ({"a"}, numel (z)), 3);
647s  assert (A, {"a", "foo", "a"});
647s  assert (idx, logical ([1, 0, 1]));
647s  [A,idx] = fillmissing ({"", "foo", ""}, @(x,y,z) repelem ({""}, numel (z)), 3);
647s  assert (A, {"", "foo", ""});
647s  assert (idx, logical ([0, 0, 0]));
648s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "constant", true), logical ([1, 0, 1, 0, 1]))
648s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "constant", false, "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([0, 0, 0, 0, 0]))
648s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "previous",  "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([1, 0, 0, 0, 0]))
648s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "next",  "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([0, 0, 0, 0, 1]))
648s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "nearest", "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([0, 0, 0, 0, 0]))
648s ***** assert (fillmissing (logical ([1, 0, 1, 0, 1]),  @(x,y,z) false(size(z)), 3), logical ([1, 0, 1, 0, 1]))
648s ***** 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]))
648s ***** 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]))
648s ***** test
648s  x = logical ([1, 0, 1, 0, 1]);
648s  [~, idx] = fillmissing (x, "constant", true);
648s  assert (idx, logical ([0, 0, 0, 0, 0]));
648s  [~, idx] = fillmissing (x, "constant", false, "missinglocations", logical ([1, 0, 1, 0, 1]));
648s  assert (idx, logical ([1, 0, 1, 0, 1]));
648s  [~, idx] = fillmissing (x, "constant", true, "missinglocations", logical ([1, 0, 1, 0, 1]));
648s  assert (idx, logical ([1, 0, 1, 0, 1]));
648s  [~, idx] = fillmissing (x, "previous", "missinglocations", logical ([1, 0, 1, 0, 1]));
648s  assert (idx, logical ([0, 0, 1, 0, 1]));
648s  [~, idx] = fillmissing (x, "next",  "missinglocations", logical ([1, 0, 1, 0, 1]));
648s  assert (idx, logical ([1, 0, 1, 0, 0]));
648s  [~, idx] = fillmissing (x, "nearest", "missinglocations", logical ([1, 0, 1, 0, 1]));
648s  assert (idx, logical ([1, 0, 1, 0, 1]));
648s  [~, idx] = fillmissing (x, @(x,y,z) false(size(z)), 3);
648s  assert (idx, logical ([0, 0, 0, 0, 0]))
648s  [~, idx] = fillmissing (x, @(x,y,z) false(size(z)), 3, "missinglocations", logical ([1, 0, 1, 0, 1]));
648s  assert (idx, logical ([1, 0, 1, 0, 1]))
648s  [~, idx] = fillmissing (x, @(x,y,z) false(size(z)), [2 0], "missinglocations", logical ([1, 0, 1, 0, 1]));
648s  assert (idx, logical ([0, 0, 1, 0, 1]))
648s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "constant", 0), int32 ([1, 2, 3, 4, 5]))
648s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "constant", 0, "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([0, 2, 0, 4, 0]))
648s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "previous", "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([1, 2, 2, 4, 4]))
648s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "next", "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([2, 2, 4, 4, 5]))
648s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "nearest", "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([2, 2, 4, 4, 4]))
648s ***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), @(x,y,z) z+10, 3), int32 ([1, 2, 3, 4, 5]))
648s ***** 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]))
648s ***** 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]))
648s ***** test
648s  x = int32 ([1, 2, 3, 4, 5]);
648s  [~, idx] = fillmissing (x, "constant", 0);
648s  assert (idx, logical ([0, 0, 0, 0, 0]));
648s  [~, idx] = fillmissing (x, "constant", 0, "missinglocations", logical ([1, 0, 1, 0, 1]));
648s  assert (idx, logical ([1, 0, 1, 0, 1]));
648s  [~, idx] = fillmissing (x, "constant", 3, "missinglocations", logical ([0, 0, 1, 0, 0]));
648s  assert (idx, logical ([0, 0, 1, 0, 0]));
648s  [~, idx] = fillmissing (x, "previous", "missinglocations", logical ([1, 0, 1, 0, 1]));
648s  assert (idx, logical ([0, 0, 1, 0, 1]));
648s  [~, idx] = fillmissing (x, "next", "missinglocations", logical ([1, 0, 1, 0, 1]));
648s  assert (idx, logical ([1, 0, 1, 0, 0]));
648s  [~, idx] = fillmissing (x, "nearest", "missinglocations", logical ([1, 0, 1, 0, 1]));
648s  assert (idx, logical ([1, 0, 1, 0, 1]));
648s  [~, idx] = fillmissing (x, @(x,y,z) z+10, 3);
648s  assert (idx, logical ([0, 0, 0, 0, 0]));
648s  [~, idx] = fillmissing (x, @(x,y,z) z+10, 3, "missinglocations", logical ([1, 0, 1, 0, 1]));
648s  assert (idx, logical ([1, 0, 1, 0, 1]));
648s  [~, idx] = fillmissing (x, @(x,y,z) z+10, [2 0], "missinglocations", logical ([1, 0, 1, 0, 1]));
648s  assert (idx, logical ([0, 0, 1, 0, 1]));
648s ***** test
648s  [A, idx] = fillmissing ([struct, struct], "constant", 1);
648s  assert (A, [struct, struct])
648s  assert (idx, [false, false])
648s ***** error <Invalid call> fillmissing ()
648s ***** error <Invalid call> fillmissing (1)
648s ***** error <Invalid call> fillmissing (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
648s ***** error <second input must be a> fillmissing (1, 2)
648s ***** error <unknown fill method 'foo'> fillmissing (1, "foo")
648s ***** error <fill function must accept at least> fillmissing (1, @(x) x, 1)
648s ***** error <fill function must accept at least> fillmissing (1, @(x,y) x+y, 1)
648s ***** error <interpolation methods only valid for numeric> fillmissing ("a b c", "linear")
648s ***** error <interpolation methods only valid for numeric> fillmissing ({"a", "b"}, "linear")
648s ***** error <'movmean' and 'movmedian' methods only valid for numeric> fillmissing ("a b c", "movmean", 2)
648s ***** error <'movmean' and 'movmedian' methods only valid for numeric> fillmissing ({"a", "b"}, "movmean", 2)
648s ***** error <'constant' method must be followed by> fillmissing (1, "constant")
648s ***** error <a numeric fill value cannot be emtpy> fillmissing (1, "constant", [])
648s ***** error <fill value must be the same data type> fillmissing (1, "constant", "a")
648s ***** error <fill value must be the same data type> fillmissing ("a", "constant", 1)
648s ***** error <fill value must be the same data type> fillmissing ("a", "constant", {"foo"})
648s ***** error <fill value must be the same data type> fillmissing ({"foo"}, "constant", 1)
648s ***** error <moving window method must be followed by> fillmissing (1, "movmean")
648s ***** error <moving window method must be followed by> fillmissing (1, "movmedian")
648s ***** error <DIM must be a positive scalar> fillmissing (1, "constant", 1, 0)
648s ***** error <DIM must be a positive scalar> fillmissing (1, "constant", 1, -1)
648s ***** error <DIM must be a positive scalar> fillmissing (1, "constant", 1, [1, 2])
648s ***** error <properties must be given as> fillmissing (1, "constant", 1, "samplepoints")
648s ***** error <properties must be given as> fillmissing (1, "constant", 1, "foo")
648s ***** error <properties must be given as> fillmissing (1, "constant", 1, 1, "foo")
648s ***** error <invalid parameter name specified> fillmissing (1, "constant", 1, 2, {1}, 4)
648s ***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", [1, 2])
648s ***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", [3, 1, 2])
648s ***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", [1, 1, 2])
648s ***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", "abc")
648s ***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", logical ([1, 1, 1]))
648s ***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 1, "samplepoints", [1, 2, 3])
648s ***** error <EndValues method 'constant' only valid> fillmissing ("foo", "next", "endvalues", 1)
648s ***** error <invalid EndValues method 'foo'> fillmissing (1, "constant", 1, 1, "endvalues", "foo")
648s ***** error <EndValues must be a scalar or a 1 element array> fillmissing ([1, 2, 3], "constant", 1, 2, "endvalues", [1, 2, 3])
648s ***** error <EndValues must be a scalar or a 3 element array> fillmissing ([1, 2, 3], "constant", 1, 1, "endvalues", [1, 2])
648s ***** error <EndValues must be a scalar or a 12 element array> fillmissing (randi(5,4,3,2), "constant", 1, 3, "endvalues", [1, 2])
648s ***** error <EndValues must be numeric or a> fillmissing (1, "constant", 1, 1, "endvalues", {1})
648s ***** error <invalid parameter name 'foo'> fillmissing (1, "constant", 1, 2, "foo", 4)
648s ***** error <MissingLocations option is not compatible with> fillmissing (struct, "constant", 1, "missinglocations", false)
648s ***** error <MissingLocations and MaxGap options> fillmissing (1, "constant", 1, 2, "maxgap", 1, "missinglocations", false)
648s ***** error <MissingLocations and MaxGap options> fillmissing (1, "constant", 1, 2, "missinglocations", false, "maxgap", 1)
648s ***** error <the 'replacevalues' option has not> fillmissing (1, "constant", 1, "replacevalues", true)
648s ***** error <the 'datavariables' option has not> fillmissing (1, "constant", 1, "datavariables", "Varname")
648s ***** error <MissingLocations must be a> fillmissing (1, "constant", 1, 2, "missinglocations", 1)
648s ***** error <MissingLocations must be a> fillmissing (1, "constant", 1, 2, "missinglocations", "a")
648s ***** error <MissingLocations must be a> fillmissing (1, "constant", 1, 2, "missinglocations", [true, false])
648s ***** error <MissingLocations cannot be used with method> fillmissing (true, "linear", "missinglocations", true)
648s ***** error <MissingLocations cannot be used with method> fillmissing (int8 (1), "linear", "missinglocations", true)
648s ***** error <MissingLocations cannot be used with EndValues method> fillmissing (true, "next", "missinglocations", true, "EndValues", "linear")
648s ***** error <MissingLocations cannot be used with EndValues method> fillmissing (true, "next", "EndValues", "linear", "missinglocations", true)
648s ***** error <MissingLocations cannot be used with EndValues method> fillmissing (int8 (1), "next", "missinglocations", true, "EndValues", "linear")
648s ***** error <MissingLocations cannot be used with EndValues method> fillmissing (int8 (1), "next", "EndValues", "linear", "missinglocations", true)
648s ***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", true)
648s ***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", "a")
648s ***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", [1, 2])
648s ***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", 0)
648s ***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", -1)
648s ***** error <fill value 'V' must be a scalar or a 1> fillmissing ([1, 2, 3], "constant", [1, 2, 3])
648s ***** error <fill value 'V' must be a scalar or a 1> fillmissing ([1, 2, 3]', "constant", [1, 2, 3])
648s ***** error <fill value 'V' must be a scalar or a 1> fillmissing ([1, 2, 3]', "constant", [1, 2, 3], 1)
648s ***** error <fill value 'V' must be a scalar or a 1> fillmissing ([1, 2, 3], "constant", [1, 2, 3], 2)
648s ***** error <fill value 'V' must be a scalar or a 6> fillmissing (randi (5, 4, 3, 2), "constant", [1, 2], 1)
648s ***** error <fill value 'V' must be a scalar or a 8> fillmissing (randi (5, 4, 3, 2), "constant", [1, 2], 2)
648s ***** error <fill value 'V' must be a scalar or a 12> fillmissing (randi (5, 4, 3, 2), "constant", [1, 2], 3)
648s ***** error <fill function handle must be followed by> fillmissing (1, @(x,y,z) x+y+z)
648s ***** error <fill function return values must be the same size> fillmissing ([1, NaN, 2], @(x,y,z) [1, 2], 2)
648s 380 tests, 379 passed, 0 known failure, 1 skipped
648s [inst/fishertest.m]
648s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/fishertest.m
648s ***** demo
648s  ## A Fisher's exact test example
648s 
648s  x = [3, 1; 1, 3]
648s  [h, p, stats] = fishertest(x)
648s ***** assert (fishertest ([3, 4; 5, 7]), false);
648s ***** assert (isa (fishertest ([3, 4; 5, 7]), "logical"), true);
648s ***** test
648s  [h, pval, stats] = fishertest ([3, 4; 5, 7]);
648s  assert (pval, 1, 1e-14);
648s  assert (stats.OddsRatio, 1.05);
648s  CI = [0.159222057151289, 6.92429189601808];
648s  assert (stats.ConfidenceInterval, CI, 1e-14)
648s ***** test
648s  [h, pval, stats] = fishertest ([3, 4; 5, 0]);
648s  assert (pval, 0.08080808080808080, 1e-14);
648s  assert (stats.OddsRatio, 0);
648s  assert (stats.ConfidenceInterval, [-Inf, Inf])
648s ***** error fishertest ();
648s ***** error fishertest (1, 2, 3, 4, 5, 6);
648s ***** error<fishertest: X must be a 2-dimensional matrix.> ...
648s  fishertest (ones (2, 2, 2));
648s ***** error<fishertest: X must contain only non-negative real integers.> ...
648s  fishertest ([1, 2; -3, 4]);
648s ***** error<fishertest: X must contain only non-negative real integers.> ...
648s  fishertest ([1, 2; 3, 4+i]);
648s ***** error<fishertest: X must contain only non-negative real integers.> ...
648s  fishertest ([1, 2; 3, 4.2]);
648s ***** error<fishertest: X must contain only non-negative real integers.> ...
648s  fishertest ([NaN, 2; 3, 4]);
648s ***** error<fishertest: X must contain only non-negative real integers.> ...
648s  fishertest ([1, Inf; 3, 4]);
648s ***** error<fishertest: cannot handle large entries.> ...
648s  fishertest (ones (2) * 1e8);
648s ***** error<fishertest: invalid value for alpha.> ...
648s  fishertest ([1, 2; 3, 4], "alpha", 0);
648s ***** error<fishertest: invalid value for alpha.> ...
648s  fishertest ([1, 2; 3, 4], "alpha", 1.2);
648s ***** error<fishertest: invalid value for alpha.> ...
648s  fishertest ([1, 2; 3, 4], "alpha", "val");
648s ***** error<fishertest: invalid value for tail.>  ...
648s  fishertest ([1, 2; 3, 4], "tail", "val");
648s ***** error<fishertest: invalid value for tail.>  ...
648s  fishertest ([1, 2; 3, 4], "alpha", 0.01, "tail", "val");
648s ***** error<fishertest: invalid name for optional arguments.> ...
648s  fishertest ([1, 2; 3, 4], "alpha", 0.01, "badoption", 3);
648s 19 tests, 19 passed, 0 known failure, 0 skipped
648s [inst/fitcdiscr.m]
648s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/fitcdiscr.m
648s ***** demo
648s  ## Train a linear discriminant classifier for Gamma = 0.5
648s  ## and plot the decision boundaries.
648s 
648s  load fisheriris
648s  idx = ! strcmp (species, "setosa");
648s  X = meas(idx,3:4);
648s  Y = cast (strcmpi (species(idx), "virginica"), "double");
648s  obj = fitcdiscr (X, Y, "Gamma", 0.5)
648s  x1 = [min(X(:,1)):0.03:max(X(:,1))];
648s  x2 = [min(X(:,2)):0.02:max(X(:,2))];
648s  [x1G, x2G] = meshgrid (x1, x2);
648s  XGrid = [x1G(:), x2G(:)];
648s  pred = predict (obj, XGrid);
648s  gidx = logical (pred);
648s 
648s  figure
648s  scatter (XGrid(gidx,1), XGrid(gidx,2), "markerfacecolor", "magenta");
648s  hold on
648s  scatter (XGrid(!gidx,1), XGrid(!gidx,2), "markerfacecolor", "red");
648s  plot (X(Y == 0, 1), X(Y == 0, 2), "ko", X(Y == 1, 1), X(Y == 1, 2), "kx");
648s  xlabel ("Petal length (cm)");
648s  ylabel ("Petal width (cm)");
648s  title ("Linear Discriminant Analysis Decision Boundary");
648s  legend ({"Versicolor Region", "Virginica Region", ...
648s          "Sampled Versicolor", "Sampled Virginica"}, ...
648s          "location", "northwest")
648s  axis tight
648s  hold off
648s ***** test
648s  load fisheriris
648s  Mdl = fitcdiscr (meas, species, "Gamma", 0.5);
648s  [label, score, cost] = predict (Mdl, [2, 2, 2, 2]);
648s  assert (label, {'versicolor'})
648s  assert (score, [0, 0.9999, 0.0001], 1e-4)
648s  assert (cost, [1, 0.0001, 0.9999], 1e-4)
648s  [label, score, cost] = predict (Mdl, [2.5, 2.5, 2.5, 2.5]);
648s  assert (label, {'versicolor'})
648s  assert (score, [0, 0.6368, 0.3632], 1e-4)
648s  assert (cost, [1, 0.3632, 0.6368], 1e-4)
648s  assert (class (Mdl), "ClassificationDiscriminant");
648s  assert ({Mdl.X, Mdl.Y, Mdl.NumObservations}, {meas, species, 150})
648s  assert ({Mdl.DiscrimType, Mdl.ResponseName}, {"linear", "Y"})
648s  assert ({Mdl.Gamma, Mdl.MinGamma}, {0.5, 0})
648s  assert (Mdl.ClassNames, unique (species))
648s  sigma = [0.265008, 0.046361, 0.083757, 0.019201; ...
648s           0.046361, 0.115388, 0.027622, 0.016355; ...
648s           0.083757, 0.027622, 0.185188, 0.021333; ...
648s           0.019201, 0.016355, 0.021333, 0.041882];
648s  assert (Mdl.Sigma, sigma, 1e-6)
648s  mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
648s        5.9360, 2.7700, 4.2600, 1.3260; ...
648s        6.5880, 2.9740, 5.5520, 2.0260];
648s  assert (Mdl.Mu, mu, 1e-14)
648s  assert (Mdl.LogDetSigma, -8.6884, 1e-4)
648s ***** error<fitcdiscr: too few arguments.> fitcdiscr ()
648s ***** error<fitcdiscr: too few arguments.> fitcdiscr (ones (4,1))
648s ***** error<fitcdiscr: name-value arguments must be in pairs.>
648s  fitcdiscr (ones (4,2), ones (4, 1), "K")
648s ***** error<fitcdiscr: number of rows in X and Y must be equal.>
648s  fitcdiscr (ones (4,2), ones (3, 1))
648s ***** error<fitcdiscr: number of rows in X and Y must be equal.>
648s  fitcdiscr (ones (4,2), ones (3, 1), "K", 2)
648s 6 tests, 6 passed, 0 known failure, 0 skipped
648s [inst/fitcgam.m]
648s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/fitcgam.m
648s ***** demo
648s  ## Train a GAM classifier for binary classification
648s  ## using specific data and plot the decision boundaries.
648s 
648s  ## Define specific data
648s  X = [1, 2; 2, 3; 3, 3; 4, 5; 5, 5; ...
648s      6, 7; 7, 8; 8, 8; 9, 9; 10, 10];
648s  Y = [0; 0; 0; 0; 0; ...
648s      1; 1; 1; 1; 1];
648s 
648s  ## Train the GAM model
648s  obj = fitcgam (X, Y, "Interactions", "all");
648s 
648s  ## Create a grid of values for prediction
648s  x1 = [min(X(:,1)):0.1:max(X(:,1))];
648s  x2 = [min(X(:,2)):0.1:max(X(:,2))];
648s  [x1G, x2G] = meshgrid (x1, x2);
648s  XGrid = [x1G(:), x2G(:)];
648s  pred = predict (obj, XGrid);
648s 
648s  ## Plot decision boundaries and data points
648s  predNumeric = str2double (pred);
648s  gidx = predNumeric > 0.5;
648s 
648s  figure
648s  scatter(XGrid(gidx,1), XGrid(gidx,2), "markerfacecolor", "magenta");
648s  hold on
648s  scatter(XGrid(!gidx,1), XGrid(!gidx,2), "markerfacecolor", "red");
648s  plot(X(Y == 0, 1), X(Y == 0, 2), "ko", X(Y == 1, 1), X(Y == 1, 2), "kx");
648s  xlabel("Feature 1");
648s  ylabel("Feature 2");
648s  title("Generalized Additive Model (GAM) Decision Boundary");
648s  legend({"Class 1 Region", "Class 0 Region", ...
648s        "Class 1 Samples", "Class 0 Samples"}, ...
648s        "location", "northwest")
648s  axis tight
648s  hold off
648s ***** test
648s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
648s  y = [0; 0; 1; 1];
648s  PredictorNames = {'Feature1', 'Feature2', 'Feature3'};
648s  a = fitcgam (x, y, "PredictorNames", PredictorNames);
648s  assert (class (a), "ClassificationGAM");
648s  assert ({a.X, a.Y, a.NumObservations}, {x, y, 4})
648s  assert ({a.NumPredictors, a.ResponseName}, {3, "Y"})
648s  assert (a.ClassNames, {'0'; '1'})
648s  assert (a.PredictorNames, PredictorNames)
648s  assert (a.BaseModel.Intercept, 0)
650s ***** test
650s  x = [1, 2; 3, 4; 5, 6; 7, 8; 9, 10];
650s  y = [1; 0; 1; 0; 1];
650s  a = fitcgam (x, y, "interactions", "all");
650s  assert (class (a), "ClassificationGAM");
650s  assert ({a.X, a.Y, a.NumObservations}, {x, y, 5})
650s  assert ({a.NumPredictors, a.ResponseName}, {2, "Y"})
650s  assert (a.ClassNames, {'1'; '0'})
650s  assert (a.PredictorNames, {'x1', 'x2'})
650s  assert (a.ModelwInt.Intercept, 0.4055, 1e-1)
654s ***** test
654s  load fisheriris
654s  inds = strcmp (species,'versicolor') | strcmp (species,'virginica');
654s  X = meas(inds, :);
654s  Y = species(inds, :)';
654s  Y = strcmp (Y, 'virginica')';
654s  a = fitcgam (X, Y, 'Formula', 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3');
654s  assert (class (a), "ClassificationGAM");
654s  assert ({a.X, a.Y, a.NumObservations}, {X, Y, 100})
654s  assert ({a.NumPredictors, a.ResponseName}, {4, "Y"})
654s  assert (a.ClassNames, {'0'; '1'})
654s  assert (a.Formula, 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3')
654s  assert (a.PredictorNames, {'x1', 'x2', 'x3', 'x4'})
654s  assert (a.ModelwInt.Intercept, 0)
661s ***** error<fitcgam: too few arguments.> fitcgam ()
661s ***** error<fitcgam: too few arguments.> fitcgam (ones (4,1))
661s ***** error<fitcgam: name-value arguments must be in pairs.>
661s  fitcgam (ones (4,2), ones (4, 1), "K")
661s ***** error<fitcgam: number of rows in X and Y must be equal.>
661s  fitcgam (ones (4,2), ones (3, 1))
661s ***** error<fitcgam: number of rows in X and Y must be equal.>
661s  fitcgam (ones (4,2), ones (3, 1), "K", 2)
661s 8 tests, 8 passed, 0 known failure, 0 skipped
661s [inst/fitcnet.m]
661s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/fitcnet.m
661s ***** demo
661s  ## Train a Neural Network on the Fisher's Iris data set and display
661s  ## a confusion chart with the classification results.
661s 
661s  load fisheriris
661s  Mdl = fitcnet (meas, species);
661s  pred_species = resubPredict (Mdl);
661s  confusionchart (species, pred_species, "Title", ...
661s  "Fully Connected Neural Network classification on Fisher's Iris dataset");
661s ***** test
661s  load fisheriris
661s  x = meas;
661s  y = grp2idx (species);
661s  Mdl = fitcnet (x, y, "IterationLimit", 50);
661s  assert (class (Mdl), "ClassificationNeuralNetwork");
661s  assert (numel (Mdl.ModelParameters.LayerWeights), 2);
661s  assert (size (Mdl.ModelParameters.LayerWeights{1}), [10, 5]);
661s  assert (size (Mdl.ModelParameters.LayerWeights{2}), [3, 11]);
661s ***** error<fitcnet: too few arguments.> fitcnet ()
661s ***** error<fitcnet: too few arguments.> fitcnet (ones (4,1))
661s ***** error<fitcnet: Name-Value arguments must be in pairs.>
661s  fitcnet (ones (4,2), ones (4, 1), 'LayerSizes')
661s ***** error<fitcnet: number of rows in X and Y must be equal.>
661s  fitcnet (ones (4,2), ones (3, 1))
661s ***** error<fitcnet: number of rows in X and Y must be equal.>
661s  fitcnet (ones (4,2), ones (3, 1), 'LayerSizes', 2)
661s 6 tests, 6 passed, 0 known failure, 0 skipped
661s [inst/fitcsvm.m]
661s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/fitcsvm.m
661s ***** demo
661s  ## Use a subset of Fisher's iris data set
661s 
661s  load fisheriris
661s  inds = ! strcmp (species, 'setosa');
661s  X = meas(inds, [3,4]);
661s  Y = species(inds);
661s 
661s  ## Train a linear SVM classifier
661s  SVMModel = fitcsvm (X, Y)
661s 
661s  ## Plot a scatter diagram of the data and circle the support vectors.
661s  sv = SVMModel.SupportVectors;
661s  figure
661s  gscatter (X(:,1), X(:,2), Y)
661s  hold on
661s  plot (sv(:,1), sv(:,2), 'ko', 'MarkerSize', 10)
661s  legend ('versicolor', 'virginica', 'Support Vector')
661s  hold off
661s ***** test
661s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
661s  y = {"a"; "a"; "b"; "b"};
661s  a = fitcsvm (x, y);
661s  assert (class (a), "ClassificationSVM");
661s  assert ({a.X, a.Y}, {x, y})
661s  assert (a.NumObservations, 4)
661s  assert ({a.ResponseName, a.PredictorNames}, {"Y", {"x1", "x2", "x3"}})
661s  assert (a.ModelParameters.SVMtype, "c_svc")
661s  assert (a.ClassNames, {"a"; "b"})
661s ***** test
661s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
661s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
661s  a = fitcsvm (x, y);
661s  assert (class (a), "ClassificationSVM");
661s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "linear"})
661s  assert (a.ModelParameters.BoxConstraint, 1)
661s  assert (a.ModelParameters.KernelOffset, 0)
661s  assert (a.ClassNames, [1; -1])
661s ***** test
661s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
661s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
661s  a = fitcsvm (x, y, "KernelFunction", "rbf", "BoxConstraint", 2, ...
661s  "KernelOffset", 2);
661s  assert (class (a), "ClassificationSVM");
661s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "rbf"})
661s  assert (a.ModelParameters.BoxConstraint, 2)
661s  assert (a.ModelParameters.KernelOffset, 2)
661s  assert (isempty (a.Alpha), true)
661s  assert (isempty (a.Beta), false)
661s ***** test
661s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
661s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
661s  a = fitcsvm (x, y, "KernelFunction", "polynomial", "PolynomialOrder", 3);
661s  assert (class (a), "ClassificationSVM");
661s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "polynomial"})
661s  assert (a.ModelParameters.PolynomialOrder, 3)
661s  assert (isempty (a.Alpha), true)
661s  assert (isempty (a.Beta), false)
661s ***** test
661s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
661s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
661s  a = fitcsvm (x, y, "KernelFunction", "linear", "PolynomialOrder", 3);
661s  assert (class (a), "ClassificationSVM");
661s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "linear"})
661s  assert (a.ModelParameters.PolynomialOrder, 3)
661s  assert (isempty (a.Alpha), false)
661s  assert (isempty (a.Beta), true)
661s ***** test
661s  x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
661s  y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
661s  a = fitcsvm (x, y, "KernelFunction", "linear", "CrossVal", 'on');
661s  assert (class (a), "ClassificationPartitionedModel");
661s  assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "linear"})
661s  assert (a.ModelParameters.PolynomialOrder, 3)
661s  assert (isempty (a.Trained{1}.Alpha), false)
661s  assert (isempty (a.Trained{1}.Beta), true)
661s warning: One or more of the unique class values in the stratification variable is not present in one or more folds.
661s warning: called from
661s     cvpartition at line 764 column 19
661s     crossval at line 1300 column 9
661s     fitcsvm at line 284 column 7
661s     __test__ at line 5 column 2
661s     test at line 685 column 11
661s     /tmp/tmp.scCa8Po4At at line 2838 column 2
661s 
661s ***** error<fitcsvm: too few arguments.> fitcsvm ()
661s ***** error<fitcsvm: too few arguments.> fitcsvm (ones (4,1))
661s ***** error<fitcsvm: Name-Value arguments must be in pairs.>
661s  fitcsvm (ones (4,2), ones (4, 1), 'KFold')
661s ***** error<fitcsvm: number of rows in X and Y must be equal.>
661s  fitcsvm (ones (4,2), ones (3, 1))
661s ***** error<fitcsvm: number of rows in X and Y must be equal.>
661s  fitcsvm (ones (4,2), ones (3, 1), 'KFold', 2)
661s ***** error <fitcsvm: 'CrossVal' must be either 'off' or 'on'.>
661s  fitcsvm (ones (4,2), ones (4, 1), "CrossVal", 2)
661s ***** error <fitcsvm: 'CrossVal' must be either 'off' or 'on'.>
661s  fitcsvm (ones (4,2), ones (4, 1), "CrossVal", 'a')
661s ***** error <fitcsvm: You can use only one cross-validation name-value pair argument> ...
661s  fitcsvm (ones (4,2), ones (4, 1), "KFold", 10, "Holdout", 0.3)
661s 14 tests, 14 passed, 0 known failure, 0 skipped
661s [inst/fitgmdist.m]
661s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/fitgmdist.m
661s ***** demo
661s  ## Generate a two-cluster problem
661s  C1 = randn (100, 2) + 2;
661s  C2 = randn (100, 2) - 2;
661s  data = [C1; C2];
661s 
661s  ## Perform clustering
661s  GMModel = fitgmdist (data, 2);
661s 
661s  ## Plot the result
661s  figure
661s  [heights, bins] = hist3([C1; C2]);
661s  [xx, yy] = meshgrid(bins{1}, bins{2});
661s  bbins = [xx(:), yy(:)];
661s  contour (reshape (GMModel.pdf (bbins), size (heights)));
661s ***** demo
661s  Angle_Theta = [ 30 + 10 * randn(1, 10),  60 + 10 * randn(1, 10) ]';
661s  nbOrientations = 2;
661s  initial_orientations = [38.0; 18.0];
661s  initial_weights = ones (1, nbOrientations) / nbOrientations;
661s  initial_Sigma = 10 * ones (1, 1, nbOrientations);
661s  start = struct ("mu", initial_orientations, "Sigma", initial_Sigma, ...
661s                  "ComponentProportion", initial_weights);
661s  GMModel_Theta = fitgmdist (Angle_Theta, nbOrientations, "Start", start , ...
661s                             "RegularizationValue", 0.0001)
661s ***** test
661s  load fisheriris
661s  classes = unique (species);
661s  [~, score] = pca (meas, "NumComponents", 2);
661s  options.MaxIter = 1000;
661s  options.TolFun = 1e-6;
661s  options.Display = "off";
661s  GMModel = fitgmdist (score, 2, "Options", options);
661s  assert (isa (GMModel, "gmdistribution"), true);
661s  assert (GMModel.mu, [1.3212, -0.0954; -2.6424, 0.1909], 1e-4);
661s 1 test, 1 passed, 0 known failure, 0 skipped
661s [inst/fitlm.m]
661s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/fitlm.m
661s ***** demo
661s  y =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
661s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
661s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
661s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
661s         25.694 ]';
661s  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]';
661s 
661s  [TAB,STATS] = fitlm (X,y,"linear","CategoricalVars",1,"display","on");
661s ***** demo
661s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
661s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
661s  brands = {'Gourmet', 'National', 'Generic'; ...
661s            'Gourmet', 'National', 'Generic'; ...
661s            'Gourmet', 'National', 'Generic'; ...
661s            'Gourmet', 'National', 'Generic'; ...
661s            'Gourmet', 'National', 'Generic'; ...
661s            'Gourmet', 'National', 'Generic'};
661s  popper = {'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; ...
661s            'air', 'air', 'air'; 'air', 'air', 'air'; 'air', 'air', 'air'};
661s 
661s  [TAB, STATS] = fitlm ({brands(:),popper(:)},popcorn(:),"interactions",...
661s                           "CategoricalVars",[1,2],"display","on");
661s ***** test
661s  y =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
661s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
661s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
661s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
661s         25.694 ]';
661s  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]';
661s  [TAB,STATS] = fitlm (X,y,"continuous",[],"display","off");
661s  [TAB,STATS] = fitlm (X,y,"CategoricalVars",1,"display","off");
661s  [TAB,STATS] = fitlm (X,y,"constant","categorical",1,"display","off");
661s  [TAB,STATS] = fitlm (X,y,"linear","categorical",1,"display","off");
661s  [TAB,STATS] = fitlm (X,y,[0,0;1,0],"categorical",1,"display","off");
661s  assert (TAB{2,2}, 10, 1e-04);
661s  assert (TAB{3,2}, 7.99999999999999, 1e-09);
661s  assert (TAB{4,2}, 8.99999999999999, 1e-09);
661s  assert (TAB{5,2}, 11.0001428571429, 1e-09);
661s  assert (TAB{6,2}, 19.0001111111111, 1e-09);
661s  assert (TAB{2,3}, 1.01775379540949, 1e-09);
661s  assert (TAB{3,3}, 1.64107868458008, 1e-09);
661s  assert (TAB{4,3}, 1.43932122062479, 1e-09);
661s  assert (TAB{5,3}, 1.48983900477565, 1e-09);
661s  assert (TAB{6,3}, 1.3987687997822, 1e-09);
661s  assert (TAB{2,6}, 9.82555903510687, 1e-09);
661s  assert (TAB{3,6}, 4.87484242844031, 1e-09);
661s  assert (TAB{4,6}, 6.25294748040552, 1e-09);
661s  assert (TAB{5,6}, 7.38344399756088, 1e-09);
661s  assert (TAB{6,6}, 13.5834536158296, 1e-09);
661s  assert (TAB{3,7}, 2.85812420217862e-05, 1e-12);
661s  assert (TAB{4,7}, 5.22936741204002e-07, 1e-06);
661s  assert (TAB{5,7}, 2.12794763209106e-08, 1e-07);
661s  assert (TAB{6,7}, 7.82091664406755e-15, 1e-08);
661s ***** test
661s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
661s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
661s  brands = bsxfun (@times, ones(6,1), [1,2,3]);
661s  popper = bsxfun (@times, [1;1;1;2;2;2], ones(1,3));
661s 
661s  [TAB, STATS] = fitlm ({brands(:),popper(:)},popcorn(:),"interactions",...
661s                           "categoricalvars",[1,2],"display","off");
661s  assert (TAB{2,2}, 5.66666666666667, 1e-09);
661s  assert (TAB{3,2}, -1.33333333333333, 1e-09);
661s  assert (TAB{4,2}, -2.16666666666667, 1e-09);
661s  assert (TAB{5,2}, 1.16666666666667, 1e-09);
661s  assert (TAB{6,2}, -0.333333333333334, 1e-09);
661s  assert (TAB{7,2}, -0.166666666666667, 1e-09);
661s  assert (TAB{2,3}, 0.215165741455965, 1e-09);
661s  assert (TAB{3,3}, 0.304290309725089, 1e-09);
661s  assert (TAB{4,3}, 0.304290309725089, 1e-09);
661s  assert (TAB{5,3}, 0.304290309725089, 1e-09);
661s  assert (TAB{6,3}, 0.43033148291193, 1e-09);
661s  assert (TAB{7,3}, 0.43033148291193, 1e-09);
661s  assert (TAB{2,6}, 26.3362867542108, 1e-09);
661s  assert (TAB{3,6}, -4.38178046004138, 1e-09);
661s  assert (TAB{4,6}, -7.12039324756724, 1e-09);
661s  assert (TAB{5,6}, 3.83405790253621, 1e-09);
661s  assert (TAB{6,6}, -0.774596669241495, 1e-09);
661s  assert (TAB{7,6}, -0.387298334620748, 1e-09);
661s  assert (TAB{2,7}, 5.49841502258254e-12, 1e-09);
661s  assert (TAB{3,7}, 0.000893505495903642, 1e-09);
661s  assert (TAB{4,7}, 1.21291454302428e-05, 1e-09);
661s  assert (TAB{5,7}, 0.00237798044119407, 1e-09);
661s  assert (TAB{6,7}, 0.453570536021938, 1e-09);
661s  assert (TAB{7,7}, 0.705316781644046, 1e-09);
661s  ## Test with string ids for categorical variables
661s  brands = {'Gourmet', 'National', 'Generic'; ...
661s            'Gourmet', 'National', 'Generic'; ...
661s            'Gourmet', 'National', 'Generic'; ...
661s            'Gourmet', 'National', 'Generic'; ...
661s            'Gourmet', 'National', 'Generic'; ...
661s            'Gourmet', 'National', 'Generic'};
661s  popper = {'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; ...
661s            'air', 'air', 'air'; 'air', 'air', 'air'; 'air', 'air', 'air'};
661s  [TAB, STATS] = fitlm ({brands(:),popper(:)},popcorn(:),"interactions",...
661s                           "categoricalvars",[1,2],"display","off");
662s ***** test
662s  load carsmall
662s  X = [Weight,Horsepower,Acceleration];
662s  [TAB, STATS] = fitlm (X, MPG,"constant","display","off");
662s  [TAB, STATS] = fitlm (X, MPG,"linear","display","off");
662s  assert (TAB{2,2}, 47.9767628118615, 1e-09);
662s  assert (TAB{3,2}, -0.00654155878851796, 1e-09);
662s  assert (TAB{4,2}, -0.0429433065881864, 1e-09);
662s  assert (TAB{5,2}, -0.0115826516894871, 1e-09);
662s  assert (TAB{2,3}, 3.87851641748551, 1e-09);
662s  assert (TAB{3,3}, 0.00112741016370336, 1e-09);
662s  assert (TAB{4,3}, 0.0243130608813806, 1e-09);
662s  assert (TAB{5,3}, 0.193325043113178, 1e-09);
662s  assert (TAB{2,6}, 12.369874881944, 1e-09);
662s  assert (TAB{3,6}, -5.80228828790225, 1e-09);
662s  assert (TAB{4,6}, -1.76626492228599, 1e-09);
662s  assert (TAB{5,6}, -0.0599128364487485, 1e-09);
662s  assert (TAB{2,7}, 4.89570341688996e-21, 1e-09);
662s  assert (TAB{3,7}, 9.87424814144e-08, 1e-09);
662s  assert (TAB{4,7}, 0.0807803098213114, 1e-09);
662s  assert (TAB{5,7}, 0.952359384151778, 1e-09);
662s 3 tests, 3 passed, 0 known failure, 0 skipped
662s [inst/fitrgam.m]
662s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/fitrgam.m
662s ***** demo
662s  # Train a RegressionGAM Model for synthetic values
662s 
662s  f1 = @(x) cos (3 *x);
662s  f2 = @(x) x .^ 3;
662s 
662s  # generate x1 and x2 for f1 and f2
662s  x1 = 2 * rand (50, 1) - 1;
662s  x2 = 2 * rand (50, 1) - 1;
662s 
662s  # calculate y
662s  y = f1(x1) + f2(x2);
662s 
662s  # add noise
662s  y = y + y .* 0.2 .* rand (50,1);
662s  X = [x1, x2];
662s 
662s  # create an object
662s  a = fitrgam (X, y, "tol", 1e-3)
662s ***** test
662s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
662s  y = [1; 2; 3; 4];
662s  a = fitrgam (x, y);
662s  assert ({a.X, a.Y}, {x, y})
662s  assert ({a.BaseModel.Intercept}, {2.5000})
662s  assert ({a.Knots, a.Order, a.DoF}, {[5, 5, 5], [3, 3, 3], [8, 8, 8]})
662s  assert ({a.NumObservations, a.NumPredictors}, {4, 3})
662s  assert ({a.ResponseName, a.PredictorNames}, {"Y", {"x1", "x2", "x3"}})
662s  assert ({a.Formula}, {[]})
662s ***** test
662s  x = [1, 2, 3, 4; 4, 5, 6, 7; 7, 8, 9, 1; 3, 2, 1, 2];
662s  y = [1; 2; 3; 4];
662s  pnames = {"A", "B", "C", "D"};
662s  formula = "Y ~ A + B + C + D + A:C";
662s  intMat = logical ([1,0,0,0;0,1,0,0;0,0,1,0;0,0,0,1;1,0,1,0]);
662s  a = fitrgam (x, y, "predictors", pnames, "formula", formula);
662s  assert ({a.IntMatrix}, {intMat})
662s  assert ({a.ResponseName, a.PredictorNames}, {"Y", pnames})
662s  assert ({a.Formula}, {formula})
662s ***** error<fitrgam: too few arguments.> fitrgam ()
662s ***** error<fitrgam: too few arguments.> fitrgam (ones(10,2))
662s ***** error<fitrgam: Name-Value arguments must be in pairs.>
662s  fitrgam (ones (4,2), ones (4, 1), "K")
662s ***** error<fitrgam: number of rows in X and Y must be equal.>
662s  fitrgam (ones (4,2), ones (3, 1))
662s ***** error<fitrgam: number of rows in X and Y must be equal.>
662s  fitrgam (ones (4,2), ones (3, 1), "K", 2)
662s 7 tests, 7 passed, 0 known failure, 0 skipped
662s [inst/friedman.m]
662s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/friedman.m
662s ***** demo
662s  load popcorn;
662s  friedman (popcorn, 3);
662s ***** demo
662s  load popcorn;
662s  [p, atab] = friedman (popcorn, 3, "off");
662s  disp (p);
662s ***** test
662s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
662s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
662s  [p, atab] = friedman (popcorn, 3, "off");
662s  assert (p, 0.001028853354594794, 1e-14);
662s  assert (atab{2,2}, 99.75, 1e-14);
662s  assert (atab{2,3}, 2, 0);
662s  assert (atab{2,4}, 49.875, 1e-14);
662s  assert (atab{2,5}, 13.75862068965517, 1e-14);
662s  assert (atab{2,6}, 0.001028853354594794, 1e-14);
662s  assert (atab{3,2}, 0.08333333333333215, 1e-14);
662s  assert (atab{3,4}, 0.04166666666666607, 1e-14);
662s  assert (atab{4,3}, 12, 0);
662s ***** test
662s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
662s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
662s  [p, atab, stats] = friedman (popcorn, 3, "off");
662s  assert (atab{5,2}, 116, 0);
662s  assert (atab{5,3}, 17, 0);
662s  assert (stats.source, "friedman");
662s  assert (stats.n, 2);
662s  assert (stats.meanranks, [8, 4.75, 2.25], 0);
662s  assert (stats.sigma, 2.692582403567252, 1e-14);
662s 2 tests, 2 passed, 0 known failure, 0 skipped
662s [inst/fullfact.m]
662s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/fullfact.m
662s ***** demo
662s  ## Full factorial design with 3 ordinal variables
662s  fullfact ([2, 3, 4])
662s ***** error<fullfact: one input argument is required.> fullfact ();
662s ***** error<fullfact: input argument must be a finite real numeric vector.> ...
662s  fullfact (Inf);
662s ***** error<fullfact: input argument must be a finite real numeric vector.> ...
662s  fullfact (NaN);
662s ***** error<fullfact: input argument must be a finite real numeric vector.> ...
662s  fullfact (ones (2));
662s ***** error<fullfact: input argument must be a finite real numeric vector.> ...
662s  fullfact ([1, 2, NaN]);
662s ***** error<fullfact: input argument must be a finite real numeric vector.> ...
662s  fullfact ([1, 2, Inf]);
662s ***** error<fullfact: factor levels must be real positive integers.> fullfact (2.5);
662s ***** error<fullfact: factor levels must be real positive integers.> fullfact (0);
662s ***** error<fullfact: factor levels must be real positive integers.> fullfact (-3);
662s ***** error<fullfact: factor levels must be real positive integers.> fullfact (3+2i);
662s ***** error<fullfact: factor levels must be real positive integers.> fullfact ([1, 2, -3]);
662s ***** error<fullfact: factor levels must be real positive integers.> fullfact ([0, 1, 2]);
662s ***** test
662s  A = fullfact (1);
662s  assert (A, 1);
662s ***** test
662s  A = fullfact (2);
662s  assert (A, [1; 2]);
662s ***** test
662s ***** test
662s  A = fullfact (3);
662s  assert (A, [1; 2; 3]);
662s ***** test
662s  A = fullfact ([1, 2, 4]);
662s  A_out = [1, 1, 1; 1, 2, 1; 1, 1, 2; 1, 2, 2; ...
662s           1, 1, 3; 1, 2, 3; 1, 1, 4; 1, 2, 4];
662s  assert (A, A_out);
662s ***** test
662s  A = fullfact ([2, 2]);
662s  assert (A, [1, 1; 2, 1; 1, 2; 2, 2]);
662s ***** test
662s  A = fullfact ([2, 2, 4]);
662s  A_out = [1, 1, 1; 2, 1, 1; 1, 2, 1; 2, 2, 1; ...
662s           1, 1, 2; 2, 1, 2; 1, 2, 2; 2, 2, 2; ...
662s           1, 1, 3; 2, 1, 3; 1, 2, 3; 2, 2, 3; ...
662s           1, 1, 4; 2, 1, 4; 1, 2, 4; 2, 2, 4];
662s  assert (A, A_out);
662s ***** test
662s  A = fullfact ([3, 2, 4]);
662s  A_out = [1, 1, 1; 2, 1, 1; 3, 1, 1; 1, 2, 1; 2, 2, 1; 3, 2, 1; ...
662s           1, 1, 2; 2, 1, 2; 3, 1, 2; 1, 2, 2; 2, 2, 2; 3, 2, 2; ...
662s           1, 1, 3; 2, 1, 3; 3, 1, 3; 1, 2, 3; 2, 2, 3; 3, 2, 3; ...
662s           1, 1, 4; 2, 1, 4; 3, 1, 4; 1, 2, 4; 2, 2, 4; 3, 2, 4];
662s  assert (A, A_out);
662s ***** test
662s  A = fullfact ([4, 2]);
662s  assert (A, [1, 1; 2, 1; 3, 1; 4, 1; 1, 2; 2, 2; 3, 2; 4, 2]);
662s 21 tests, 21 passed, 0 known failure, 0 skipped
662s [inst/geomean.m]
662s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/geomean.m
662s ***** test
662s  x = [0:10];
662s  y = [x;x+5;x+10];
662s  assert (geomean (x), 0);
662s  m = [0 9.462942809849169 14.65658770861967];
662s  assert (geomean (y, 2), m', 4e-14);
662s  assert (geomean (y, "all"), 0);
662s  y(2,4) = NaN;
662s  m(2) = 9.623207231679554;
662s  assert (geomean (y, 2), [0 NaN m(3)]', 4e-14);
662s  assert (geomean (y', "omitnan"), m, 4e-14);
662s  z = y + 20;
662s  assert (geomean (z, "all"), NaN);
662s  assert (geomean (z, "all", "includenan"), NaN);
662s  assert (geomean (z, "all", "omitnan"), 29.59298474535024, 4e-14);
662s  m = [24.79790781765634 NaN 34.85638839503932];
662s  assert (geomean (z'), m, 4e-14);
662s  assert (geomean (z', "includenan"), m, 4e-14);
662s  m(2) = 30.02181156156319;
662s  assert (geomean (z', "omitnan"), m, 4e-14);
662s  assert (geomean (z, 2, "omitnan"), m', 4e-14);
662s ***** test
662s  x = repmat ([1:20;6:25], [5 2 6 3]);
662s  assert (size (geomean (x, [3 2])), [10 1 1 3]);
662s  assert (size (geomean (x, [1 2])), [1 1 6 3]);
662s  assert (size (geomean (x, [1 2 4])), [1 1 6]);
662s  assert (size (geomean (x, [1 4 3])), [1 40]);
662s  assert (size (geomean (x, [1 2 3 4])), [1 1]);
662s ***** test
662s  x = repmat ([1:20;6:25], [5 2 6 3]);
662s  m = repmat ([8.304361203739333;14.3078118884256], [5 1 1 3]);
662s  assert (geomean (x, [3 2]), m, 4e-13);
662s  x(2,5,6,3) = NaN;
662s  m(2,3) = NaN;
662s  assert (geomean (x, [3 2]), m, 4e-13);
662s  m(2,3) = 14.3292729579901;
662s  assert (geomean (x, [3 2], "omitnan"), m, 4e-13);
662s ***** error <geomean: X must contain real nonnegative values.> geomean ("char")
662s ***** error <geomean: X must contain real nonnegative values.> geomean ([1 -1 3])
662s ***** error <geomean: DIM must be a positive integer scalar or vector.> ...
662s  geomean (repmat ([1:20;6:25], [5 2 6 3 5]), -1)
662s ***** error <geomean: DIM must be a positive integer scalar or vector.> ...
662s  geomean (repmat ([1:20;6:25], [5 2 6 3 5]), 0)
662s ***** error <geomean: VECDIM must contain non-repeating positive integers.> ...
662s  geomean (repmat ([1:20;6:25], [5 2 6 3 5]), [1 1])
662s 8 tests, 8 passed, 0 known failure, 0 skipped
662s [inst/glmfit.m]
662s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/glmfit.m
662s ***** demo
662s  x = [210, 230, 250, 270, 290, 310, 330, 350, 370, 390, 410, 430]';
662s  n = [48, 42, 31, 34, 31, 21, 23, 23, 21, 16, 17, 21]';
662s  y = [1, 2, 0, 3, 8, 8, 14, 17, 19, 15, 17, 21]';
662s  b = glmfit (x, [y n], "binomial", "Link", "probit");
662s  yfit = glmval (b, x, "probit", "Size", n);
662s  plot (x, y./n, 'o', x, yfit ./ n, '-')
662s ***** demo
662s  load fisheriris
662s  X = meas (51:end, :);
662s  y = strcmp ("versicolor", species(51:end));
662s  b = glmfit (X, y, "binomial", "link", "logit")
662s ***** test
662s  load fisheriris;
662s  X = meas(51:end,:);
662s  y = strcmp ("versicolor", species(51:end));
662s  b = glmfit (X, y, "binomial", "link", "logit");
662s  assert (b, [42.6379; 2.4652; 6.6809; -9.4294; -18.2861], 1e-4);
662s ***** test
662s  X = [1.2, 2.3, 3.4, 4.5, 5.6, 6.7, 7.8, 8.9, 9.0, 10.1]';
662s  y = [0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4]';
662s  [Bnew, dev] = glmfit (X, y, "gamma", "link", "log");
662s  b_matlab = [-0.7631; 0.1113];
662s  dev_matlab = 0.0111;
662s  assert (Bnew, b_matlab, 0.001);
662s  assert (dev, dev_matlab, 0.001);
662s ***** test
662s  X = [1.2, 2.3, 3.4, 4.5, 5.6, 6.7, 7.8, 8.9, 9.0, 10.1]';
662s  y = [0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4]';
662s  p_input = 1;
662s  [Bnew, dev] = glmfit (X, y, "inverse gaussian", "link", p_input);
662s  b_matlab = [0.3813; 0.0950];
662s  dev_matlab = 0.0051;
662s  assert (Bnew, b_matlab, 0.001);
662s  assert (dev, dev_matlab, 0.001);
662s ***** error <glmfit: too few input arguments.> glmfit ()
662s ***** error <glmfit: too few input arguments.> glmfit (1)
662s ***** error <glmfit: too few input arguments.> glmfit (1, 2)
662s ***** error <glmfit: Name-Value arguments must be in pairs.> ...
662s  glmfit (rand (6, 1), rand (6, 1), 'poisson', 'link')
662s ***** error <glmfit: X must be a numeric matrix.> ...
662s  glmfit ('abc', rand (6, 1), 'poisson')
662s ***** error <glmfit: X must be a numeric matrix.> ...
662s  glmfit ([], rand (6, 1), 'poisson')
662s ***** error <glmfit: Y must be either a numeric matrix or a logical vector.> ...
662s  glmfit (rand (5, 2), 'abc', 'poisson')
662s ***** error <glmfit: Y must be either a numeric matrix or a logical vector.> ...
662s  glmfit (rand (5, 2), [], 'poisson')
662s ***** error <glmfit: X and Y must have the same number of observations.> ...
662s  glmfit (rand (5, 2), rand (6, 1), 'poisson')
662s ***** error <glmfit: DISTRIBUTION must be a character vector.> ...
662s  glmfit (rand (6, 2), rand (6, 1), 3)
662s ***** error <glmfit: DISTRIBUTION must be a character vector.> ...
662s  glmfit (rand (6, 2), rand (6, 1), {'poisson'})
662s ***** error <glmfit: for a 'binomial' distribution, Y must be an n-by-1 or n-by-2 matrix.> ...
662s  glmfit (rand (5, 2), rand (5, 3), 'binomial')
662s ***** error <glmfit: n-by-2 matrix Y for 'binomial' distribution must be numeric.> ...
662s  glmfit (rand (2, 2), [true, true; false, false], 'binomial')
662s ***** error <glmfit: for distributions other than 'binomial', Y must be an n-by-1 column vector> ...
662s  glmfit (rand (5, 2), rand (5, 2), 'normal')
662s ***** error <glmfit: unsupported distribution.> ...
662s  glmfit (rand (5, 2), rand (5, 1), 'chebychev')
662s ***** error <glmfit: 'B0' must be a numeric vector of the same size as Y.> ...
662s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'B0', [1; 2; 3; 4])
662s ***** error <glmfit: 'Constant' should be either 'on' or 'off'.> ...
662s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'constant', 1)
662s ***** error <glmfit: 'Constant' should be either 'on' or 'off'.> ...
662s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'constant', 'o')
662s ***** error <glmfit: 'Constant' should be either 'on' or 'off'.> ...
662s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'constant', true)
662s ***** error <glmfit: 'EstDisp' should be either 'on' or 'off'.> ...
662s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'estdisp', 1)
662s ***** error <glmfit: 'EstDisp' should be either 'on' or 'off'.> ...
662s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'estdisp', 'o')
662s ***** error <glmfit: 'EstDisp' should be either 'on' or 'off'.> ...
662s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'estdisp', true)
662s ***** error <glmfit: structure with custom link functions must be a scalar.> ...
662s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", {1, 2}))
662s ***** error <glmfit: structure with custom link functions requires the fields 'Link', 'Derivative', and 'Inverse'.> ...
662s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", "norminv"))
662s ***** error <glmfit: bad 'Link' function in custom link function structure.> ...
662s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", "some", "Derivative", @(x)x, "Inverse", "normcdf"))
663s ***** error <glmfit: bad 'Link' function in custom link function structure.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", 1, "Derivative", @(x)x, "Inverse", "normcdf"))
663s ***** error <glmfit: custom 'Link' function must return an output of the same size as input.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x) [x, x], "Derivative", @(x)x, "Inverse", "normcdf"))
663s ***** error <glmfit: invalid custom 'Link' function.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", "what", "Derivative", @(x)x, "Inverse", "normcdf"))
663s ***** error <glmfit: bad 'Derivative' function in custom link function structure.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "some", "Inverse", "normcdf"))
663s ***** error <glmfit: bad 'Derivative' function in custom link function structure.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", 1, "Inverse", "normcdf"))
663s ***** error <glmfit: custom 'Derivative' function must return an output of the same size as input.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", @(x) [x, x], "Inverse", "normcdf"))
663s ***** error <glmfit: invalid custom 'Derivative' function.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "what", "Inverse", "normcdf"))
663s ***** error <glmfit: bad 'Inverse' function in custom link function structure.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", "some"))
663s ***** error <glmfit: bad 'Inverse' function in custom link function structure.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", 1))
663s ***** error <glmfit: custom 'Inverse' function must return an output of the same size as input.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", @(x) [x, x]))
663s ***** error <glmfit: invalid custom 'Inverse' function.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", "what"))
663s ***** error <glmfit: cell array with custom link functions must have three elements.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {'log'})
663s ***** error <glmfit: cell array with custom link functions must have three elements.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {'log', 'hijy'})
663s ***** error <glmfit: cell array with custom link functions must have three elements.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {1, 2, 3, 4})
663s ***** error <glmfit: bad 'Link' function in custom link function cell array.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {"log", "dfv", "dfgvd"})
663s ***** error <glmfit: custom 'Link' function must return an output of the same size as input.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) [x, x], "dfv", "dfgvd"})
663s ***** error <glmfit: invalid custom 'Link' function.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) what (x), "dfv", "dfgvd"})
663s ***** error <glmfit: bad 'Derivative' function in custom link function cell array.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, "dfv", "dfgvd"})
663s ***** error <glmfit: custom 'Derivative' function must return an output of the same size as input.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, @(x) [x, x], "dfgvd"})
663s ***** error <glmfit: invalid custom 'Derivative' function.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, @(x) what (x), "dfgvd"})
663s ***** error <glmfit: bad 'Inverse' function in custom link function cell array.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, @(x) x, "dfgvd"})
663s ***** error <glmfit: custom 'Inverse' function must return an output of the same size as input.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, @(x) x, @(x) [x, x]})
663s ***** error <glmfit: invalid custom 'Inverse' function.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, @(x) x, @(x) what (x)})
663s ***** error <glmfit: numeric input for custom link function must be a finite real scalar value.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', NaN)
663s ***** error <glmfit: numeric input for custom link function must be a finite real scalar value.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', [1, 2])
663s ***** error <glmfit: numeric input for custom link function must be a finite real scalar value.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', [1i])
663s ***** error <glmfit: canonical link function name must be a character vector.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', ["log"; "log1"])
663s ***** error <glmfit: canonical link function 'somelinkfunction' is not supported.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', 'somelinkfunction')
663s ***** error <glmfit: invalid value for custom link function.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'link', true)
663s ***** error <glmfit: 'Options' must be a structure containing the fields 'MaxIter', and 'TolX'.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', true)
663s ***** error <glmfit: 'Options' must be a structure containing the fields 'MaxIter', and 'TolX'.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 100))
663s ***** error <glmfit: 'MaxIter' in 'Options' structure must be a positive integer.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 4.5, "TolX", 1e-6))
663s ***** error <glmfit: 'MaxIter' in 'Options' structure must be a positive integer.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 0, "TolX", 1e-6))
663s ***** error <glmfit: 'MaxIter' in 'Options' structure must be a positive integer.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", -100, "TolX", 1e-6))
663s ***** error <glmfit: 'MaxIter' in 'Options' structure must be a positive integer.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", [50 ,50], "TolX", 1e-6))
663s ***** error <glmfit: 'TolX' in 'Options' structure must be a positive scalar.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 100, "TolX", 0))
663s ***** error <glmfit: 'TolX' in 'Options' structure must be a positive scalar.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 100, "TolX", -1e-6))
663s ***** error <glmfit: 'TolX' in 'Options' structure must be a positive scalar.> ...
663s  glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 100, "TolX", [1e-6, 1e-6]))
663s ***** error <glmfit: 'Offset' must be a numeric vector of the same size as Y.> ...
663s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'offset', [1; 2; 3; 4])
663s ***** error <glmfit: 'Offset' must be a numeric vector of the same size as Y.> ...
663s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'offset', 'asdfg')
663s ***** error <glmfit: 'Weights' must be a numeric vector of the same size as Y.> ...
663s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'weights', [1; 2; 3; 4])
663s ***** error <glmfit: 'Weights' must be a numeric vector of the same size as Y.> ...
663s  glmfit (rand (5, 2), rand (5, 1), 'normal', 'weights', 'asdfg')
663s 70 tests, 70 passed, 0 known failure, 0 skipped
663s [inst/glmval.m]
663s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/glmval.m
663s ***** demo
663s  x = [210, 230, 250, 270, 290, 310, 330, 350, 370, 390, 410, 430]';
663s  n = [48, 42, 31, 34, 31, 21, 23, 23, 21, 16, 17, 21]';
663s  y = [1, 2, 0, 3, 8, 8, 14, 17, 19, 15, 17, 21]';
663s  b = glmfit (x, [y n], "binomial", "Link", "probit");
663s  yfit = glmval (b, x, "probit", "Size", n);
663s  plot (x, y./n, 'o', x, yfit ./ n, '-')
663s ***** error <glmval: too few input arguments.> glmval ()
663s ***** error <glmval: too few input arguments.> glmval (1)
663s ***** error <glmval: too few input arguments.> glmval (1, 2)
663s ***** error <glmval: B must be a numeric vector of coefficient estimates.> ...
663s  glmval ("asd", [1; 1; 1], 'probit')
663s ***** error <glmval: B must be a numeric vector of coefficient estimates.> ...
663s  glmval ([], [1; 1; 1], 'probit')
663s ***** error <glmval: X must be a numeric matrix.> ...
663s  glmval ([0.1; 0.3; 0.4], [], 'probit')
663s ***** error <glmval: X must be a numeric matrix.> ...
663s  glmval ([0.1; 0.3; 0.4], "asd", 'probit')
663s ***** error <glmval: structure with custom link functions must be a scalar.> ...
663s  glmval (rand (3,1), rand (5,2), struct ("Link", {1, 2}))
663s ***** error <glmval: structure with custom link functions requires the fields 'Link', 'Derivative', and 'Inverse'.> ...
663s  glmval (rand (3,1), rand (5,2), struct ("Link", "norminv"))
663s ***** error <glmval: bad 'Link' function in custom link function structure.> ...
663s  glmval (rand (3,1), rand (5,2), struct ("Link", "some", "Derivative", @(x)x, "Inverse", "normcdf"))
663s ***** error <glmval: bad 'Link' function in custom link function structure.> ...
663s  glmval (rand (3,1), rand (5,2), struct ("Link", 1, "Derivative", @(x)x, "Inverse", "normcdf"))
663s ***** error <glmval: custom 'Link' function must return an output of the same size as input.> ...
663s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x) [x, x], "Derivative", @(x)x, "Inverse", "normcdf"))
663s ***** error <glmval: invalid custom 'Link' function.> ...
663s  glmval (rand (3,1), rand (5,2), struct ("Link", "what", "Derivative", @(x)x, "Inverse", "normcdf"))
663s ***** error <glmval: bad 'Derivative' function in custom link function structure.> ...
663s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "some", "Inverse", "normcdf"))
663s ***** error <glmval: bad 'Derivative' function in custom link function structure.> ...
663s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", 1, "Inverse", "normcdf"))
663s ***** error <glmval: custom 'Derivative' function must return an output of the same size as input.> ...
663s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", @(x) [x, x], "Inverse", "normcdf"))
663s ***** error <glmval: invalid custom 'Derivative' function.> ...
663s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "what", "Inverse", "normcdf"))
663s ***** error <glmval: bad 'Inverse' function in custom link function structure.> ...
663s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", "some"))
663s ***** error <glmval: bad 'Inverse' function in custom link function structure.> ...
663s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", 1))
663s ***** error <glmval: custom 'Inverse' function must return an output of the same size as input.> ...
663s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", @(x) [x, x]))
663s ***** error <glmval: invalid custom 'Inverse' function.> ...
663s  glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", "what"))
663s ***** error <glmval: cell array with custom link functions must have three elements.> ...
663s  glmval (rand (3,1), rand (5,2), {'log'})
663s ***** error <glmval: cell array with custom link functions must have three elements.> ...
663s  glmval (rand (3,1), rand (5,2), {'log', 'hijy'})
663s ***** error <glmval: cell array with custom link functions must have three elements.> ...
663s  glmval (rand (3,1), rand (5,2), {1, 2, 3, 4})
663s ***** error <glmval: bad 'Link' function in custom link function cell array.> ...
663s  glmval (rand (3,1), rand (5,2), {"log", "dfv", "dfgvd"})
663s ***** error <glmval: custom 'Link' function must return an output of the same size as input.> ...
663s  glmval (rand (3,1), rand (5,2), {@(x) [x, x], "dfv", "dfgvd"})
663s ***** error <glmval: invalid custom 'Link' function.> ...
663s  glmval (rand (3,1), rand (5,2), {@(x) what (x), "dfv", "dfgvd"})
663s ***** error <glmval: bad 'Derivative' function in custom link function cell array.> ...
663s  glmval (rand (3,1), rand (5,2), {@(x) x, "dfv", "dfgvd"})
663s ***** error <glmval: custom 'Derivative' function must return an output of the same size as input.> ...
663s  glmval (rand (3,1), rand (5,2), {@(x) x, @(x) [x, x], "dfgvd"})
663s ***** error <glmval: invalid custom 'Derivative' function.> ...
663s  glmval (rand (3,1), rand (5,2), {@(x) x, @(x) what (x), "dfgvd"})
663s ***** error <glmval: bad 'Inverse' function in custom link function cell array.> ...
663s  glmval (rand (3,1), rand (5,2), {@(x) x, @(x) x, "dfgvd"})
663s ***** error <glmval: custom 'Inverse' function must return an output of the same size as input.> ...
663s  glmval (rand (3,1), rand (5,2), {@(x) x, @(x) x, @(x) [x, x]})
663s ***** error <glmval: invalid custom 'Inverse' function.> ...
663s  glmval (rand (3,1), rand (5,2), {@(x) x, @(x) x, @(x) what (x)})
663s ***** error <glmval: numeric input for custom link function must be a finite real scalar value.> ...
663s  glmval (rand (3,1), rand (5,2), NaN)
663s ***** error <glmval: numeric input for custom link function must be a finite real scalar value.> ...
663s  glmval (rand (3,1), rand (5,2), [1, 2])
663s ***** error <glmval: numeric input for custom link function must be a finite real scalar value.> ...
663s  glmval (rand (3,1), rand (5,2), [1i])
663s ***** error <glmval: canonical link function name must be a character vector.> ...
663s  glmval (rand (3,1), rand (5,2), ["log"; "log1"])
663s ***** error <glmval: canonical link function 'somelinkfunction' is not supported.> ...
663s  glmval (rand (3,1), rand (5,2), 'somelinkfunction')
663s ***** error <glmval: invalid value for custom link function.> ...
663s  glmval (rand (3,1), rand (5,2), true)
663s ***** error <glmval: invalid 'stats' structure.> ...
663s  glmval (rand (3,1), rand (5,2), 'probit', struct ("s", 1))
663s ***** error <glmval: Name-Value arguments must be in pairs.> ...
663s  glmval (rand (3,1), rand (5,2), 'probit', 'confidence')
663s ***** error <glmval: 'Confidence' must be a scalar between 0 and 1.> ...
663s  glmval (rand (3,1), rand (5,2), 'probit', 'confidence', 0)
663s ***** error <glmval: 'Confidence' must be a scalar between 0 and 1.> ...
663s  glmval (rand (3,1), rand (5,2), 'probit', 'confidence', 1.2)
663s ***** error <glmval: 'Confidence' must be a scalar between 0 and 1.> ...
663s  glmval (rand (3,1), rand (5,2), 'probit', 'confidence', [0.9, 0.95])
663s ***** error <glmval: 'Constant' should be either 'on' or 'off'.> ...
663s  glmval (rand (3, 1), rand (5, 2), 'probit', 'constant', 1)
663s ***** error <glmval: 'Constant' should be either 'on' or 'off'.> ...
663s  glmval (rand (3, 1), rand (5, 2), 'probit', 'constant', 'o')
663s ***** error <glmval: 'Constant' should be either 'on' or 'off'.> ...
663s  glmval (rand (3, 1), rand (5, 2), 'probit', 'constant', true)
663s ***** error <glmval: 'Offset' must be a numeric vector of the same length as the rows in X.> ...
663s  glmval (rand (3, 1), rand (5, 2), 'probit', 'offset', [1; 2; 3; 4])
663s ***** error <glmval: 'Offset' must be a numeric vector of the same length as the rows in X.> ...
663s  glmval (rand (3, 1), rand (5, 2), 'probit', 'offset', 'asdfg')
663s ***** error <glmval: 'simultaneous' must be a boolean scalar.> ...
663s  glmval (rand (3, 1), rand (5, 2), 'probit', 'simultaneous', 'asdfg')
663s ***** error <glmval: 'simultaneous' must be a boolean scalar.> ...
663s  glmval (rand (3, 1), rand (5, 2), 'probit', 'simultaneous', [true, false])
663s ***** error <glmval: 'size' must be a scalar or a vector with one value for each row of X.> ...
663s  glmval (rand (3, 1), rand (5, 2), 'probit', 'size', "asd")
663s ***** error <glmval: 'size' must be a scalar or a vector with one value for each row of X.> ...
663s  glmval (rand (3, 1), rand (5, 2), 'probit', 'size', [2, 3, 4])
663s ***** error <glmval: 'size' must be a scalar or a vector with one value for each row of X.> ...
663s  glmval (rand (3, 1), rand (5, 2), 'probit', 'size', [2; 3; 4])
663s ***** error <glmval: 'size' must be a scalar or a vector with one value for each row of X.> ...
663s  glmval (rand (3, 1), rand (5, 2), 'probit', 'size', ones (3))
663s ***** error <glmval: unknown parameter name.> ...
663s  glmval (rand (3, 1), rand (5, 2), 'probit', 'someparam', 4)
663s ***** error <glmval: cannot compute confidence intervals without STATS structure.> ...
663s  [y,lo,hi] = glmval (rand (3, 1), rand (5, 2), 'probit')
663s 57 tests, 57 passed, 0 known failure, 0 skipped
663s [inst/gmdistribution.m]
663s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/gmdistribution.m
663s ***** test
663s  mu = eye(2);
663s  Sigma = eye(2);
663s  GM = gmdistribution (mu, Sigma);
663s  density = GM.pdf ([0 0; 1 1]);
663s  assert (density(1) - density(2), 0, 1e-6);
663s 
663s  [idx, nlogl, P, logpdf,M] = cluster (GM, eye(2));
663s  assert (idx, [1; 2]);
663s  [idx2,nlogl2,P2,logpdf2] = GM.cluster (eye(2));
663s  assert (nlogl - nlogl2, 0, 1e-6);
663s  [idx3,nlogl3,P3] = cluster (GM, eye(2));
663s  assert (P - P3, zeros (2), 1e-6);
663s  [idx4,nlogl4] = cluster (GM, eye(2));
663s  assert (size (nlogl4), [1 1]);
663s  idx5 = cluster (GM, eye(2));
663s  assert (idx - idx5, zeros (2,1));
663s 
663s  D = GM.mahal ([1;0]);
663s  assert (D - M(1,:), zeros (1,2), 1e-6);
663s 
663s  P = GM.posterior ([0 1]);
663s  assert (P - P2(2,:), zeros (1,2), 1e-6);
663s 
663s  R = GM.random(20);
663s  assert (size(R), [20, 2]);
663s 
663s  R = GM.random();
663s  assert (size(R), [1, 2]);
663s 1 test, 1 passed, 0 known failure, 0 skipped
663s [inst/grp2idx.m]
663s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/grp2idx.m
663s ***** test
663s  in = [true false false true];
663s  out = {[1; 2; 2; 1] {"1"; "0"} [true; false]};
663s  assert (nthargout (1:3, @grp2idx, in), out)
663s  assert (nthargout (1:3, @grp2idx, in), nthargout (1:3, @grp2idx, in'))
663s ***** test
663s  assert (nthargout (1:3, @grp2idx, [false, true]),
663s          {[1; 2] {"0"; "1"} [false; true]});
663s  assert (nthargout (1:3, @grp2idx, [true, false]),
663s          {[1; 2] {"1"; "0"} [true; false]});
663s ***** assert (nthargout (1:3, @grp2idx, ["oct"; "sci"; "oct"; "oct"; "sci"]),
663s         {[1; 2; 1; 1; 2] {"oct"; "sci"} ["oct"; "sci"]});
663s ***** assert (nthargout (1:3, @grp2idx, {"oct"; "sci"; "oct"; "oct"; "sci"}),
663s         {[1; 2; 1; 1; 2] {"oct"; "sci"} {"oct"; "sci"}});
663s ***** assert (nthargout (1:3, @grp2idx, [ 1 -3 -2 -3 -3  2  1 -1  3 -3]),
663s         {[1; 2; 3; 2; 2; 4; 1; 5; 6; 2], {"1"; "-3"; "-2"; "2"; "-1"; "3"}, ...
663s          [1; -3; -2; 2; -1; 3]});
663s ***** assert (nthargout (1:3, @grp2idx, [2 2 3 NaN 2 3]),
663s         {[1; 1; 2; NaN; 1; 2] {"2"; "3"} [2; 3]})
663s ***** assert (nthargout (1:3, @grp2idx, {"et" "sa" "sa" "" "et"}),
663s         {[1; 2; 2; NaN; 1] {"et"; "sa"} {"et"; "sa"}})
663s ***** test assert (nthargout (1:3, @grp2idx, ["sci"; "oct"; "sci"; "oct"; "oct"]),
663s         {[1; 2; 1; 2; 2] {"sci"; "oct"} ["sci"; "oct"]});
663s ***** test assert (nthargout (1:3, @grp2idx, {"sci"; "oct"; "sci"; "oct"; "oct"}),
663s         {[1; 2; 1; 2; 2] {"sci"; "oct"} {"sci"; "oct"}});
663s ***** test assert (nthargout (1:3, @grp2idx, {"sa" "et" "et" "" "sa"}),
663s         {[1; 2; 2; NaN; 1] {"sa"; "et"} {"sa"; "et"}})
663s 10 tests, 10 passed, 0 known failure, 0 skipped
663s [inst/grpstats.m]
663s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/grpstats.m
663s ***** demo
663s  load carsmall;
663s  [m,p,g] = grpstats (Weight, Model_Year, {"mean", "predci", "gname"})
663s  n = length(m);
663s  errorbar((1:n)',m,p(:,2)-m);
663s  set (gca, "xtick", 1:n, "xticklabel", g);
663s  title ("95% prediction intervals for mean weight by year");
663s ***** demo
663s  load carsmall;
663s  [m,p,g] = grpstats ([Acceleration,Weight/1000],Cylinders, ...
663s                      {"mean", "meanci", "gname"}, 0.05)
663s  [c,r] = size (m);
663s  errorbar((1:c)'.*ones(c,r),m,p(:,[(1:r)])-m);
663s  set (gca, "xtick", 1:c, "xticklabel", g);
663s  title ("95% prediction intervals for mean weight by year");
663s ***** test
663s  load carsmall
663s  means = grpstats (Acceleration, Origin);
663s  assert (means, [14.4377; 18.0500; 15.8867; 16.3778; 16.6000; 15.5000], 0.001);
663s ***** test
663s  load carsmall
663s  [grpMin,grpMax,grp] = grpstats (Acceleration, Origin, {"min","max","gname"});
663s  assert (grpMin, [8.0; 15.3; 13.9; 12.2; 15.7; 15.5]);
663s  assert (grpMax, [22.2; 21.9; 18.2; 24.6; 17.5; 15.5]);
664s ***** test
664s  load carsmall
664s  [grpMin,grpMax,grp] = grpstats (Acceleration, Origin, {"min","max","gname"});
664s  assert (grp', {"USA", "France", "Japan", "Germany", "Sweden", "Italy"});
664s ***** test
664s  load carsmall
664s  [m,p,g] = grpstats ([Acceleration,Weight/1000], Cylinders, ...
664s                      {"mean", "meanci", "gname"}, 0.05);
664s  assert (p(:,1), [11.17621760075134, 16.13845847655224, 16.16222663683362]', ...
664s                  [1e-14, 2e-14, 1e-14]');
664s ***** test
664s  [mC, g] = grpstats ([], []);
664s  assert (isempty (mC), true);
664s  assert (isempty (g), true);
664s ***** error<grpstats: X must be a vector or 2d matrix.> ...
664s  grpstats (ones (3, 3, 3));
664s ***** error<grpstats: samples in X and GROUPS mismatch.> ...
664s  grpstats ([], {'A'; 'B'; 'A'; 'B'})
664s ***** error<grpstats: missing VALUE for optional 'alpha' parameter.> ...
664s  grpstats ([1:4]', {'A'; 'B'; 'A'; 'B'}, "predci", "alpha");
664s ***** error<grpstats: invalid NAME for optional 'alpha' parameter.> ...
664s  grpstats ([1:4]', {'A'; 'B'; 'A'; 'B'}, "predci", "somename", -0.1);
664s ***** error<grpstats: invalid fourth input argument.> ...
664s  grpstats ([1:4]', {'A'; 'B'; 'A'; 'B'}, "predci", {2, 3}, -0.1);
664s ***** error<grpstats: 'alpha' must be a real scalar in the range \(0,1\).> ...
664s  grpstats ([1:4]', {'A'; 'B'; 'A'; 'B'}, "predci", "alpha", -0.1);
664s ***** error<grpstats: inconsistent number of output arguments.> ...
664s  grpstats ([1:4]', {'A'; 'B'; 'A'; 'B'}, {'mean', 'sum'});
664s ***** error<grpstats: inconsistent number of output arguments.> ...
664s  [q, w] = grpstats ([1:4]', {'A'; 'B'; 'A'; 'B'});
664s ***** error<grpstats: wrong whichstats option.> ...
664s  grpstats ([1:4]', {'A'; 'B'; 'A'; 'B'}, "whatever");
664s 14 tests, 14 passed, 0 known failure, 0 skipped
664s [inst/gscatter.m]
664s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/gscatter.m
664s ***** demo
664s  load fisheriris;
664s  X = meas(:,3:4);
664s  cidcs = kmeans (X, 3, "Replicates", 5);
664s  gscatter (X(:,1), X(:,2), cidcs, [.75 .75 0; 0 .75 .75; .75 0 .75], "os^");
664s  title ("Fisher's iris data");
664s ***** shared visibility_setting
664s  visibility_setting = get (0, "DefaultFigureVisible");
664s ***** test
664s  hf = figure ("visible", "off");
664s  unwind_protect
664s    load fisheriris;
664s    X = meas(:,3:4);
664s    cidcs = kmeans (X, 3, "Replicates", 5);
664s    gscatter (X(:,1), X(:,2), cidcs, [.75 .75 0; 0 .75 .75; .75 0 .75], "os^");
664s    title ("Fisher's iris data");
664s  unwind_protect_cleanup
664s    close (hf);
664s  end_unwind_protect
664s warning: legend: 'best' not yet implemented for location specifier, using 'northeast' instead
664s ***** error gscatter ();
664s ***** error gscatter ([1]);
664s ***** error gscatter ([1], [2]);
664s ***** error <x must be a numeric vector> gscatter ('abc', [1 2 3], [1]);
664s ***** error <x and y must have the same size> gscatter ([1 2 3], [1 2], [1]);
664s ***** error <y must be a numeric vector> gscatter ([1 2 3], 'abc', [1]);
664s ***** error <g must have the same size as x and y> gscatter ([1 2], [1 2], [1]);
664s ***** error <invalid dolegend> gscatter ([1 2], [1 2], [1 2], 'rb', 'so', 12, 'xxx');
664s 9 tests, 9 passed, 0 known failure, 0 skipped
664s [inst/harmmean.m]
664s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/harmmean.m
664s ***** test
664s  x = [0:10];
664s  y = [x;x+5;x+10];
664s  assert (harmmean (x), 0);
664s  m = [0 8.907635160795225 14.30854471766802];
664s  assert (harmmean (y, 2), m', 4e-14);
664s  assert (harmmean (y, "all"), 0);
664s  y(2,4) = NaN;
664s  m(2) = 9.009855936313949;
664s  assert (harmmean (y, 2), [0 NaN m(3)]', 4e-14);
664s  assert (harmmean (y', "omitnan"), m, 4e-14);
664s  z = y + 20;
664s  assert (harmmean (z, "all"), NaN);
664s  assert (harmmean (z, "all", "includenan"), NaN);
664s  assert (harmmean (z, "all", "omitnan"), 29.1108719858295, 4e-14);
664s  m = [24.59488458841874 NaN 34.71244385944397];
664s  assert (harmmean (z'), m, 4e-14);
664s  assert (harmmean (z', "includenan"), m, 4e-14);
664s  m(2) = 29.84104075528277;
664s  assert (harmmean (z', "omitnan"), m, 4e-14);
664s  assert (harmmean (z, 2, "omitnan"), m', 4e-14);
664s ***** test
664s  x = repmat ([1:20;6:25], [5 2 6 3]);
664s  assert (size (harmmean (x, [3 2])), [10 1 1 3]);
664s  assert (size (harmmean (x, [1 2])), [1 1 6 3]);
664s  assert (size (harmmean (x, [1 2 4])), [1 1 6]);
664s  assert (size (harmmean (x, [1 4 3])), [1 40]);
664s  assert (size (harmmean (x, [1 2 3 4])), [1 1]);
664s ***** test
664s  x = repmat ([1:20;6:25], [5 2 6 3]);
664s  m = repmat ([5.559045930488016;13.04950789021461], [5 1 1 3]);
664s  assert (harmmean (x, [3 2]), m, 4e-14);
664s  x(2,5,6,3) = NaN;
664s  m(2,3) = NaN;
664s  assert (harmmean (x, [3 2]), m, 4e-14);
664s  m(2,3) = 13.06617961315406;
664s  assert (harmmean (x, [3 2], "omitnan"), m, 4e-14);
664s ***** error <harmmean: X must contain real nonnegative values.> harmmean ("char")
664s ***** error <harmmean: X must contain real nonnegative values.> harmmean ([1 -1 3])
664s ***** error <harmmean: DIM must be a positive integer scalar or vector.> ...
664s  harmmean (repmat ([1:20;6:25], [5 2 6 3 5]), -1)
664s ***** error <harmmean: DIM must be a positive integer scalar or vector.> ...
664s  harmmean (repmat ([1:20;6:25], [5 2 6 3 5]), 0)
664s ***** error <harmmean: VECDIM must contain non-repeating positive integers.> ...
664s  harmmean (repmat ([1:20;6:25], [5 2 6 3 5]), [1 1])
664s 8 tests, 8 passed, 0 known failure, 0 skipped
664s [inst/hist3.m]
664s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/hist3.m
664s ***** demo
664s  X = [
664s     1    1
664s     1    1
664s     1   10
664s     1   10
664s     5    5
664s     5    5
664s     5    5
664s     5    5
664s     5    5
664s     7    3
664s     7    3
664s     7    3
664s    10   10
664s    10   10];
664s  hist3 (X)
664s ***** test
664s  N_exp = [ 0  0  0  5 20
664s            0  0 10 15  0
664s            0 15 10  0  0
664s           20  5  0  0  0];
664s 
664s  n = 100;
664s  x = [1:n]';
664s  y = [n:-1:1]';
664s  D = [x y];
664s  N = hist3 (D, [4 5]);
664s  assert (N, N_exp);
664s ***** test
664s  N_exp = [0  0  0  0  1
664s           0  0  0  0  1
664s           0  0  0  0  1
664s           1  1  1  1 93];
664s 
664s  n = 100;
664s  x = [1:n]';
664s  y = [n:-1:1]';
664s  D = [x y];
664s  C{1} = [1 1.7 3 4];
664s  C{2} = [1:5];
664s  N = hist3 (D, C);
664s  assert (N, N_exp);
664s ***** test
664s  D = [1 1; 3 1; 3 3; 3 1];
664s  [c, nn] = hist3 (D, {0:4, 0:4});
664s  exp_c = zeros (5);
664s  exp_c([7 9 19]) = [1 2 1];
664s  assert (c, exp_c);
664s  assert (nn, {0:4, 0:4});
664s ***** test
664s  for i = 10
664s    assert (size (hist3 (rand (9, 2), "Edges", {[0:.2:1]; [0:.2:1]})), [6 6])
664s  endfor
664s ***** test
664s  edge_1 = linspace (0, 10, 10);
664s  edge_2 = linspace (0, 50, 10);
664s  [c, nn] = hist3 ([1:10; 1:5:50]', "Edges", {edge_1, edge_2});
664s  exp_c = zeros (10, 10);
664s  exp_c([1 12 13 24 35 46 57 68 79 90]) = 1;
664s  assert (c, exp_c);
664s 
664s  assert (nn{1}, edge_1 + edge_1(2)/2, eps*10^4)
664s  assert (nn{2}, edge_2 + edge_2(2)/2, eps*10^4)
664s ***** shared X
664s  X = [
664s   5  2
664s   5  3
664s   1  4
664s   5  3
664s   4  4
664s   1  2
664s   2  3
664s   3  3
664s   5  4
664s   5  3];
664s ***** test
664s  N = zeros (10);
664s  N([1 10 53 56 60 91 98 100]) = [1 1 1 1 3 1 1 1];
664s  C = {(1.2:0.4:4.8), (2.1:0.2:3.9)};
664s  assert (nthargout ([1 2], @hist3, X), {N C}, eps*10^3)
664s ***** test
664s  N = zeros (5, 7);
664s  N([1 5 17 18 20 31 34 35]) = [1 1 1 1 3 1 1 1];
664s  C = {(1.4:0.8:4.6), ((2+(1/7)):(2/7):(4-(1/7)))};
664s  assert (nthargout ([1 2], @hist3, X, [5 7]), {N C}, eps*10^3)
664s  assert (nthargout ([1 2], @hist3, X, "Nbins", [5 7]), {N C}, eps*10^3)
664s ***** test
664s  N = [0 1 0; 0 1 0; 0 0 1; 0 0 0];
664s  C = {(2:5), (2.5:1:4.5)};
664s  assert (nthargout ([1 2], @hist3, X, "Edges", {(1.5:4.5), (2:4)}), {N C})
664s ***** test
664s  N = [0 0 1 0 1 0; 0 0 0 1 0 0; 0 0 1 4 2 0];
664s  C = {(1.2:3.2), (0:5)};
664s  assert (nthargout ([1 2], @hist3, X, "Ctrs", C), {N C})
664s  assert (nthargout ([1 2], @hist3, X, C), {N C})
664s ***** test
664s  [~, C] = hist3 (rand (10, 2), "Edges", {[0 .05 .15 .35 .55 .95],
664s                                          [-1 .05 .07 .2 .3 .5 .89 1.2]});
664s  C_exp = {[ 0.025  0.1   0.25   0.45  0.75  1.15], ...
664s           [-0.475  0.06  0.135  0.25  0.4   0.695  1.045  1.355]};
664s  assert (C, C_exp, eps*10^2)
664s ***** test
664s  Xv = repmat ([1:10]', [1 2]);
664s 
664s  ## Test Centers
664s  assert (hist3 (Xv, "Ctrs", {1:10, 1:10}), eye (10))
664s 
664s  N_exp = eye (6);
664s  N_exp([1 end]) = 3;
664s  assert (hist3 (Xv, "Ctrs", {3:8, 3:8}), N_exp)
664s 
664s  N_exp = zeros (8, 6);
664s  N_exp([1 2 11 20 29 38 47 48]) = [2 1 1 1 1 1 1 2];
664s  assert (hist3 (Xv, "Ctrs", {2:9, 3:8}), N_exp)
664s 
664s  ## Test Edges
664s  assert (hist3 (Xv, "Edges", {1:10, 1:10}), eye (10))
664s  assert (hist3 (Xv, "Edges", {3:8, 3:8}), eye (6))
664s  assert (hist3 (Xv, "Edges", {2:9, 3:8}), [zeros(1, 6); eye(6); zeros(1, 6)])
664s 
664s  N_exp = zeros (14);
664s  N_exp(3:12, 3:12) = eye (10);
664s  assert (hist3 (Xv, "Edges", {-1:12, -1:12}), N_exp)
664s 
664s  ## Test for Nbins
664s  assert (hist3 (Xv), eye (10))
664s  assert (hist3 (Xv, [10 10]), eye (10))
664s  assert (hist3 (Xv, "nbins", [10 10]), eye (10))
664s  assert (hist3 (Xv, [5 5]), eye (5) * 2)
664s 
664s  N_exp = zeros (7, 5);
664s  N_exp([1 9 10 18 26 27 35]) = [2 1 1 2 1 1 2];
664s  assert (hist3 (Xv, [7 5]), N_exp)
664s ***** test # bug #51059
664s  D = [1 1; NaN 2; 3 1; 3 3; 1 NaN; 3 1];
664s  [c, nn] = hist3 (D, {0:4, 0:4});
664s  exp_c = zeros (5);
664s  exp_c([7 9 19]) = [1 2 1];
664s  assert (c, exp_c)
664s  assert (nn, {0:4, 0:4})
664s ***** test
664s  [c, nn] = hist3 ([1 8]);
664s  exp_c = zeros (10, 10);
664s  exp_c(6, 6) = 1;
664s  exp_nn = {-4:5, 3:12};
664s  assert (c, exp_c)
664s  assert (nn, exp_nn, eps)
664s 
664s  [c, nn] = hist3 ([1 8], [10 11]);
664s  exp_c = zeros (10, 11);
664s  exp_c(6, 6) = 1;
664s  exp_nn = {-4:5, 3:13};
664s  assert (c, exp_c)
664s  assert (nn, exp_nn, eps)
664s ***** test
664s  [c, nn] = hist3 ([1 NaN; 2 3; 6 9; 8 NaN]);
664s  exp_c = zeros (10, 10);
664s  exp_c(2, 1) = 1;
664s  exp_c(8, 10) = 1;
664s  exp_nn = {linspace(1.35, 7.65, 10) linspace(3.3, 8.7, 10)};
664s  assert (c, exp_c)
664s  assert (nn, exp_nn, eps*100)
664s ***** test
664s  [c, nn] = hist3 ([1 NaN; 2 NaN; 6 NaN; 8 NaN]);
664s  exp_c = zeros (10, 10);
664s  exp_nn = {linspace(1.35, 7.65, 10) NaN(1, 10)};
664s  assert (c, exp_c)
664s  assert (nn, exp_nn, eps*100)
664s ***** test
664s  [c, nn] = hist3 ([1 NaN; NaN 3; NaN 9; 8 NaN]);
664s  exp_c = zeros (10, 10);
664s  exp_nn = {linspace(1.35, 7.65, 10) linspace(3.3, 8.7, 10)};
664s  assert (c, exp_c)
664s  assert (nn, exp_nn, eps*100)
664s 16 tests, 16 passed, 0 known failure, 0 skipped
664s [inst/histfit.m]
664s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/histfit.m
664s ***** demo
664s  histfit (randn (100, 1))
664s ***** demo
664s  histfit (poissrnd (2, 1000, 1), 10, "Poisson")
664s ***** demo
664s  histfit (betarnd (3, 10, 1000, 1), 10, "beta")
664s ***** test
664s  hf = figure ("visible", "off");
664s  unwind_protect
664s    x = [2, 4, 3, 2, 4, 3, 2, 5, 6, 4, 7, 5, 9, 8, 10, 4, 11];
664s    histfit (x);
664s  unwind_protect_cleanup
664s    close (hf);
664s  end_unwind_protect
665s ***** test
665s  hf = figure ("visible", "off");
665s  unwind_protect
665s    x = [2, 4, 3, 2, NaN, 3, 2, 5, 6, 4, 7, 5, 9, 8, 10, 4, 11];
665s    histfit (x);
665s  unwind_protect_cleanup
665s    close (hf);
665s  end_unwind_protect
665s ***** test
665s  hf = figure ("visible", "off");
665s  unwind_protect
665s    x = [2, 4, 3, 2, NaN, 3, 2, 5, 6, 4, 7, 5, 9, 8, 10, 4, 11];
665s    histfit (x, 3);
665s  unwind_protect_cleanup
665s    close (hf);
665s  end_unwind_protect
665s ***** test
665s  hf = figure ("visible", "off");
665s  unwind_protect
665s    histfit (randn (100, 1));
665s  unwind_protect_cleanup
665s    close (hf);
665s  end_unwind_protect
665s ***** test
665s  hf = figure ("visible", "off");
665s  unwind_protect
665s    histfit (poissrnd (2, 1000, 1), 10, "Poisson");
665s  unwind_protect_cleanup
665s    close (hf);
665s  end_unwind_protect
665s ***** test
665s  hf = figure ("visible", "off");
665s  unwind_protect
665s    histfit (betarnd (3, 10, 1000, 1), 10, "beta");
665s  unwind_protect_cleanup
665s    close (hf);
665s  end_unwind_protect
665s ***** test
665s  hf = figure ("visible", "off");
665s  unwind_protect
665s    ax = gca ();
665s    histfit (ax, randn (100, 1));
665s  unwind_protect_cleanup
665s    close (hf);
665s  end_unwind_protect
666s ***** test
666s  hf = figure ("visible", "off");
666s  unwind_protect
666s    ax = gca ();
666s    histfit (ax, poissrnd (2, 1000, 1), 10, "Poisson");
666s  unwind_protect_cleanup
666s    close (hf);
666s  end_unwind_protect
666s ***** test
666s  hf = figure ("visible", "off");
666s  unwind_protect
666s    ax = gca ();
666s    histfit (ax, betarnd (3, 10, 1000, 1), 10, "beta");
666s  unwind_protect_cleanup
666s    close (hf);
666s  end_unwind_protect
666s ***** test
666s  hf = figure ("visible", "off");
666s  unwind_protect
666s    ax = axes ("parent", hf);
666s    fail ("histfit (ax)", "histfit: too few input arguments.");
666s  unwind_protect_cleanup
666s    close (hf);
666s  end_unwind_protect
666s ***** error<histfit: X must be a numeric vector of real numbers.> ...
666s  histfit ('wer')
666s ***** error<histfit: no data in X.> histfit ([NaN, NaN, NaN]);
666s ***** error<histfit: NBINS must be a real scalar integer value.> ...
666s  histfit (randn (100, 1), 5.6)
666s ***** error<histfit: DISTNAME must be a character vector.> ...
666s  histfit (randn (100, 1), 8, 5)
666s ***** error<histfit: DISTNAME must be a character vector.> ...
666s  histfit (randn (100, 1), 8, {'normal'})
666s ***** error<histfit: 'Kernel' distribution is not supported yet.> ...
666s  histfit (randn (100, 1), 8, 'Kernel')
666s ***** error<histfit: unrecognized distribution name.> ...
666s  histfit (randn (100, 1), 8, 'ASDASDASD')
666s 17 tests, 17 passed, 0 known failure, 0 skipped
666s [inst/hmmestimate.m]
666s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/hmmestimate.m
666s ***** test
666s  sequence = [1, 2, 1, 1, 1, 2, 2, 1, 2, 3, 3, ...
666s              3, 3, 2, 3, 1, 1, 1, 1, 3, 3, 2, 3, 1, 3];
666s  states =   [1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, ...
666s              1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1];
666s  [transprobest, outprobest] = hmmestimate (sequence, states);
666s  expectedtransprob = [0.88889, 0.11111; 0.28571, 0.71429];
666s  expectedoutprob = [0.16667, 0.33333, 0.50000; 1.00000, 0.00000, 0.00000];
666s  assert (transprobest, expectedtransprob, 0.001);
666s  assert (outprobest, expectedoutprob, 0.001);
666s ***** test
666s  sequence = {"A", "B", "A", "A", "A", "B", "B", "A", "B", "C", "C", "C", ...
666s              "C", "B", "C", "A", "A", "A", "A", "C", "C", "B", "C", "A", "C"};
666s  states = {"One", "One", "Two", "Two", "Two", "One", "One", "One", "One", ...
666s            "One", "One", "One", "One", "One", "One", "Two", "Two", "Two", ...
666s            "Two", "One", "One", "One", "One", "One", "One"};
666s  symbols = {"A", "B", "C"};
666s  statenames = {"One", "Two"};
666s  [transprobest, outprobest] = hmmestimate (sequence, states, "symbols", ...
666s                                            symbols, "statenames", statenames);
666s  expectedtransprob = [0.88889, 0.11111; 0.28571, 0.71429];
666s  expectedoutprob = [0.16667, 0.33333, 0.50000; 1.00000, 0.00000, 0.00000];
666s  assert (transprobest, expectedtransprob, 0.001);
666s  assert (outprobest, expectedoutprob, 0.001);
666s ***** test
666s  sequence = [1, 2, 1, 1, 1, 2, 2, 1, 2, 3, 3, 3, ...
666s              3, 2, 3, 1, 1, 1, 1, 3, 3, 2, 3, 1, 3];
666s  states =   [1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, ...
666s              1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1];
666s  pseudotransitions = [8, 2; 4, 6];
666s  pseudoemissions = [2, 4, 4; 7, 2, 1];
666s  [transprobest, outprobest] = hmmestimate (sequence, states, ...
666s   "pseudotransitions", pseudotransitions, "pseudoemissions", pseudoemissions);
666s  expectedtransprob = [0.85714, 0.14286; 0.35294, 0.64706];
666s  expectedoutprob = [0.178571, 0.357143, 0.464286; ...
666s                     0.823529, 0.117647, 0.058824];
666s  assert (transprobest, expectedtransprob, 0.001);
666s  assert (outprobest, expectedoutprob, 0.001);
666s 3 tests, 3 passed, 0 known failure, 0 skipped
666s [inst/hmmgenerate.m]
666s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/hmmgenerate.m
666s ***** test
666s  len = 25;
666s  transprob = [0.8, 0.2; 0.4, 0.6];
666s  outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
666s  [sequence, states] = hmmgenerate (len, transprob, outprob);
666s  assert (length (sequence), len);
666s  assert (length (states), len);
666s  assert (min (sequence) >= 1);
666s  assert (max (sequence) <= columns (outprob));
666s  assert (min (states) >= 1);
666s  assert (max (states) <= rows (transprob));
666s ***** test
666s  len = 25;
666s  transprob = [0.8, 0.2; 0.4, 0.6];
666s  outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
666s  symbols = {"A", "B", "C"};
666s  statenames = {"One", "Two"};
666s  [sequence, states] = hmmgenerate (len, transprob, outprob, ...
666s                       "symbols", symbols, "statenames", statenames);
666s  assert (length (sequence), len);
666s  assert (length (states), len);
666s  assert (strcmp (sequence, "A") + strcmp (sequence, "B") + ...
666s                                   strcmp (sequence, "C") == ones (1, len));
666s  assert (strcmp (states, "One") + strcmp (states, "Two") == ones (1, len));
666s 2 tests, 2 passed, 0 known failure, 0 skipped
666s [inst/hmmviterbi.m]
666s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/hmmviterbi.m
666s ***** test
666s  sequence = [1, 2, 1, 1, 1, 2, 2, 1, 2, 3, 3, 3, ...
666s              3, 2, 3, 1, 1, 1, 1, 3, 3, 2, 3, 1, 3];
666s  transprob = [0.8, 0.2; 0.4, 0.6];
666s  outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
666s  vpath = hmmviterbi (sequence, transprob, outprob);
666s  expected = [1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, ...
666s              1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1];
666s  assert (vpath, expected);
666s ***** test
666s  sequence = {"A", "B", "A", "A", "A", "B", "B", "A", "B", "C", "C", "C", ...
666s              "C", "B", "C", "A", "A", "A", "A", "C", "C", "B", "C", "A", "C"};
666s  transprob = [0.8, 0.2; 0.4, 0.6];
666s  outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
666s  symbols = {"A", "B", "C"};
666s  statenames = {"One", "Two"};
666s  vpath = hmmviterbi (sequence, transprob, outprob, "symbols", symbols, ...
666s                                                    "statenames", statenames);
666s  expected = {"One", "One", "Two", "Two", "Two", "One", "One", "One", ...
666s              "One", "One", "One", "One", "One", "One", "One", "Two", ...
666s              "Two", "Two", "Two", "One", "One", "One", "One", "One", "One"};
666s  assert (vpath, expected);
666s 2 tests, 2 passed, 0 known failure, 0 skipped
666s [inst/hotelling_t2test.m]
666s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/hotelling_t2test.m
666s ***** error<Invalid call to hotelling_t2test.  Correct usage> hotelling_t2test ();
666s ***** error<hotelling_t2test: X must be a vector or a 2D matrix.> ...
666s  hotelling_t2test (1);
666s ***** error<hotelling_t2test: X must be a vector or a 2D matrix.> ...
666s  hotelling_t2test (ones(2,2,2));
666s ***** error<hotelling_t2test: invalid value for alpha.> ...
666s  hotelling_t2test (ones(20,2), [0, 0], "alpha", 1);
666s ***** error<hotelling_t2test: invalid value for alpha.> ...
666s  hotelling_t2test (ones(20,2), [0, 0], "alpha", -0.2);
666s ***** error<hotelling_t2test: invalid value for alpha.> ...
666s  hotelling_t2test (ones(20,2), [0, 0], "alpha", "a");
666s ***** error<hotelling_t2test: invalid value for alpha.> ...
666s  hotelling_t2test (ones(20,2), [0, 0], "alpha", [0.01, 0.05]);
666s ***** error<hotelling_t2test: invalid Name argument.> ...
666s  hotelling_t2test (ones(20,2), [0, 0], "name", 0.01);
666s ***** error<hotelling_t2test: if X is a vector, M must be a scalar.> ...
666s  hotelling_t2test (ones(20,1), [0, 0]);
666s ***** error<hotelling_t2test: X must have more rows than columns.> ...
666s  hotelling_t2test (ones(4,5), [0, 0, 0, 0, 0]);
666s ***** error<hotelling_t2test: if X is a matrix, M must be a vector of length> ...
666s  hotelling_t2test (ones(20,5), [0, 0, 0, 0]);
666s ***** test
666s  randn ("seed", 1);
666s  x = randn (50000, 5);
666s  [h, pval, stats] = hotelling_t2test (x);
666s  assert (h, 0);
666s  assert (stats.df1, 5);
666s  assert (stats.df2, 49995);
666s ***** test
666s  randn ("seed", 1);
666s  x = randn (50000, 5);
666s  [h, pval, stats] = hotelling_t2test (x, ones (1, 5) * 10);
666s  assert (h, 1);
666s  assert (stats.df1, 5);
666s  assert (stats.df2, 49995);
666s 13 tests, 13 passed, 0 known failure, 0 skipped
666s [inst/hotelling_t2test2.m]
666s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/hotelling_t2test2.m
666s ***** error<Invalid call to hotelling_t2test2.  Correct usage> hotelling_t2test2 ();
666s ***** error<Invalid call to hotelling_t2test2.  Correct usage> ...
666s  hotelling_t2test2 ([2, 3, 4, 5, 6]);
666s ***** error<hotelling_t2test2: X must be a vector or a 2D matrix.> ...
666s  hotelling_t2test2 (1, [2, 3, 4, 5, 6]);
666s ***** error<hotelling_t2test2: X must be a vector or a 2D matrix.> ...
666s  hotelling_t2test2 (ones (2,2,2), [2, 3, 4, 5, 6]);
666s ***** error<hotelling_t2test2: Y must be a vector or a 2D matrix.> ...
666s  hotelling_t2test2 ([2, 3, 4, 5, 6], 2);
666s ***** error<hotelling_t2test2: Y must be a vector or a 2D matrix.> ...
666s  hotelling_t2test2 ([2, 3, 4, 5, 6], ones (2,2,2));
666s ***** error<hotelling_t2test2: invalid value for alpha.> ...
666s  hotelling_t2test2 (ones (20,2), ones (20,2), "alpha", 1);
666s ***** error<hotelling_t2test2: invalid value for alpha.> ...
666s  hotelling_t2test2 (ones (20,2), ones (20,2), "alpha", -0.2);
666s ***** error<hotelling_t2test2: invalid value for alpha.> ...
666s  hotelling_t2test2 (ones (20,2), ones (20,2), "alpha", "a");
666s ***** error<hotelling_t2test2: invalid value for alpha.> ...
666s  hotelling_t2test2 (ones (20,2), ones (20,2), "alpha", [0.01, 0.05]);
666s ***** error<hotelling_t2test2: invalid Name argument.> ...
666s  hotelling_t2test2 (ones (20,2), ones (20,2), "name", 0.01);
666s ***** error<hotelling_t2test2: if X is a vector, Y must also be a vector.> ...
666s  hotelling_t2test2 (ones (20,1), ones (20,2));
666s ***** error<hotelling_t2test2: X and Y must have the same number of columns.> ...
666s  hotelling_t2test2 (ones (20,2), ones (25,3));
666s ***** test
666s  randn ("seed", 1);
666s  x1 = randn (60000, 5);
666s  randn ("seed", 5);
666s  x2 = randn (30000, 5);
666s  [h, pval, stats] = hotelling_t2test2 (x1, x2);
666s  assert (h, 0);
666s  assert (stats.df1, 5);
666s  assert (stats.df2, 89994);
666s 14 tests, 14 passed, 0 known failure, 0 skipped
666s [inst/inconsistent.m]
666s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/inconsistent.m
666s ***** error inconsistent ()
666s ***** error inconsistent ([1 2 1], 2, 3)
666s ***** error <Z must be .* generated by the linkage .*> inconsistent (ones (2, 2))
666s ***** error <d must be a positive integer scalar> inconsistent ([1 2 1], -1)
666s ***** error <d must be a positive integer scalar> inconsistent ([1 2 1], 1.3)
666s ***** error <d must be a positive integer scalar> inconsistent ([1 2 1], [1 1])
666s ***** error <Z must be .* generated by the linkage .*> inconsistent (ones (2, 3))
666s ***** test
666s  load fisheriris;
666s  Z = linkage(meas, 'average', 'chebychev');
666s  assert (cond (inconsistent (Z)), 39.9, 1e-3);
667s 8 tests, 8 passed, 0 known failure, 0 skipped
667s [inst/ismissing.m]
667s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/ismissing.m
667s ***** assert (ismissing ([1,NaN,3]), [false,true,false])
667s ***** assert (ismissing ('abcd f'), [false,false,false,false,true,false])
667s ***** assert (ismissing ({'xxx','','xyz'}), [false,true,false])
667s ***** assert (ismissing ({'x','','y'}), [false,true,false])
667s ***** assert (ismissing ({'x','','y';'z','a',''}), logical([0,1,0;0,0,1]))
667s ***** assert (ismissing ([1,2;NaN,2]), [false,false;true,false])
667s ***** assert (ismissing ([1,2;NaN,2], 2), [false,true;false,true])
667s ***** assert (ismissing ([1,2;NaN,2], [1 2]), [true,true;false,true])
667s ***** assert (ismissing ([1,2;NaN,2], NaN), [false,false;true,false])
667s ***** assert (ismissing (cat(3,magic(2),magic(2))), logical (zeros (2,2,2)))
667s ***** assert (ismissing (cat(3,magic(2),[1 2;3 NaN])), logical (cat(3,[0,0;0,0],[0,0;0,1])))
667s ***** assert (ismissing ([1 2; 3 4], [5 1; 2 0]), logical([1 1; 0 0]))
667s ***** assert (ismissing (cat(3,'f oo','ba r')), logical(cat(3,[0 1 0 0],[0 0 1 0])))
667s ***** assert (ismissing (cat(3,{'foo'},{''},{'bar'})), logical(cat(3,0,1,0)))
667s ***** assert (ismissing (double (NaN)), true)
667s ***** assert (ismissing (single (NaN)), true)
667s ***** assert (ismissing (' '), true)
667s ***** assert (ismissing ({''}), true)
667s ***** assert (ismissing ({' '}), false)
667s ***** assert (ismissing (double (eye(3)), single (1)), logical(eye(3)))
667s ***** assert (ismissing (double (eye(3)), true), logical(eye(3)))
667s ***** assert (ismissing (double (eye(3)), int32 (1)), logical(eye(3)))
667s ***** assert (ismissing (single (eye(3)), true), logical(eye(3)))
667s ***** assert (ismissing (single (eye(3)), double (1)), logical(eye(3)))
667s ***** assert (ismissing (single(eye(3)), int32 (1)), logical(eye(3)))
667s ***** assert (ismissing ({'123', '', 123}), [false false false])
667s ***** assert (ismissing (logical ([1 0 1])), [false false false])
667s ***** assert (ismissing (int32 ([1 2 3])), [false false false])
667s ***** assert (ismissing (uint32 ([1 2 3])), [false false false])
667s ***** assert (ismissing ({1, 2, 3}), [false false false])
667s ***** assert (ismissing ([struct struct struct]), [false false false])
667s ***** assert (ismissing (logical (eye(3)), true), logical(eye(3)))
667s ***** assert (ismissing (logical (eye(3)), double (1)), logical(eye(3)))
667s ***** assert (ismissing (logical (eye(3)), single (1)), logical(eye(3)))
667s ***** assert (ismissing (logical (eye(3)), int32 (1)), logical(eye(3)))
667s ***** assert (ismissing (int32 (eye(3)), int32 (1)), logical(eye(3)))
667s ***** assert (ismissing (int32 (eye(3)), true), logical(eye(3)))
667s ***** assert (ismissing (int32 (eye(3)), double (1)), logical(eye(3)))
667s ***** assert (ismissing (int32 (eye(3)), single (1)), logical(eye(3)))
667s ***** assert (ismissing ([]), logical([]))
667s ***** assert (ismissing (''), logical([]))
667s ***** assert (ismissing (ones (0,1)), logical(ones(0,1)))
667s ***** assert (ismissing (ones (1,0)), logical(ones(1,0)))
667s ***** assert (ismissing (ones (1,2,0)), logical(ones(1,2,0)))
667s ***** error ismissing ()
667s ***** error <'indicator' and 'A' must have the same> ismissing ([1 2; 3 4], "abc")
667s ***** error <'indicator' and 'A' must have the same> ismissing ({"", "", ""}, 1)
667s ***** error <'indicator' and 'A' must have the same> ismissing (1, struct)
667s ***** error <indicators not supported for data type> ismissing (struct, 1)
667s 49 tests, 49 passed, 0 known failure, 0 skipped
667s [inst/isoutlier.m]
667s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/isoutlier.m
667s ***** demo
667s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
667s  TF = isoutlier (A, "mean")
667s ***** demo
667s  ## Use a moving detection method to detect local outliers in a sine wave
667s 
667s  x = -2*pi:0.1:2*pi;
667s  A = sin(x);
667s  A(47) = 0;
667s  time = datenum (2023,1,1,0,0,0) + (1/24)*[0:length(x)-1] - 730485;
667s  TF = isoutlier (A, "movmedian", 5*(1/24), "SamplePoints", time);
667s  plot (time, A)
667s  hold on
667s  plot (time(TF), A(TF), "x")
667s  datetick ('x', 20, 'keepticks')
667s  legend ("Original Data", "Outlier Data")
667s ***** demo
667s  ## Locate an outlier in a vector of data and visualize the outlier
667s 
667s  x = 1:10;
667s  A = [60 59 49 49 58 100 61 57 48 58];
667s  [TF, L, U, C] = isoutlier (A);
667s  plot (x, A);
667s  hold on
667s  plot (x(TF), A(TF), "x");
667s  xlim ([1,10]);
667s  line ([1,10], [L, L], "Linestyle", ":");
667s  text (1.1, L-2, "Lower Threshold");
667s  line ([1,10], [U, U], "Linestyle", ":");
667s  text (1.1, U-2, "Upper Threshold");
667s  line ([1,10], [C, C], "Linestyle", ":");
667s  text (1.1, C-3, "Center Value");
667s  legend ("Original Data", "Outlier Data");
667s ***** test
667s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
667s  assert (isoutlier (A, "mean"), logical([zeros(1,8) 1 zeros(1,6)]))
667s  assert (isoutlier (A, "median"), ...
667s  logical([zeros(1,3) 1 zeros(1,4) 1 zeros(1,6)]))
667s ***** test
667s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
667s  [TF, L, U, C] = isoutlier (A, "mean");
667s  assert (L, -109.2459044922864, 1e-12)
667s  assert (U, 264.9792378256198, 1e-12)
667s  assert (C, 77.8666666666666, 1e-12)
667s ***** test
667s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
667s  [TF, L, U, C] = isoutlier (A, "median");
667s  assert (L, 50.104386688966386, 1e-12)
667s  assert (U, 67.895613311033610, 1e-12)
667s  assert (C, 59)
667s ***** test
667s  A = magic(5) + diag(200*ones(1,5));
667s  T = logical (eye (5));
667s  assert (isoutlier (A, 2), T)
667s ***** test
667s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
667s  [TF, L, U, C] = isoutlier (A, "movmedian", 5);
667s  l = [54.5522, 52.8283, 54.5522, 54.5522, 54.5522, 53.5522, 53.5522, ...
667s       53.5522, 47.6566, 56.5522, 57.5522, 56.5522, 51.1044, 52.3283, 53.5522];
667s  u = [63.4478, 66.1717, 63.4478, 63.4478, 63.4478, 62.4478, 62.4478, ...
667s       62.4478, 74.3434, 65.4478, 66.4478, 65.4478, 68.8956, 65.6717, 62.4478];
667s  c = [59, 59.5, 59, 59, 59, 58, 58, 58, 61, 61, 62, 61, 60, 59, 58];
667s  assert (L, l, 1e-4)
667s  assert (U, u, 1e-4)
667s  assert (C, c)
667s ***** test
667s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
667s  [TF, L, U, C] = isoutlier (A, "movmedian", 5, "SamplePoints", [1:15]);
667s  l = [54.5522, 52.8283, 54.5522, 54.5522, 54.5522, 53.5522, 53.5522, ...
667s       53.5522, 47.6566, 56.5522, 57.5522, 56.5522, 51.1044, 52.3283, 53.5522];
667s  u = [63.4478, 66.1717, 63.4478, 63.4478, 63.4478, 62.4478, 62.4478, ...
667s       62.4478, 74.3434, 65.4478, 66.4478, 65.4478, 68.8956, 65.6717, 62.4478];
667s  c = [59, 59.5, 59, 59, 59, 58, 58, 58, 61, 61, 62, 61, 60, 59, 58];
667s  assert (L, l, 1e-4)
667s  assert (U, u, 1e-4)
667s  assert (C, c)
667s ***** test
667s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
667s  [TF, L, U, C] = isoutlier (A, "movmean", 5);
667s  l = [54.0841,  6.8872, 11.5608, 12.1518, 11.0210, 10.0112, -218.2840, ...
667s       -217.2375, -215.1239, -213.4890, -211.3264, 55.5800, 52.9589, ...
667s       52.5979, 51.0627];
667s  u = [63.2492, 131.1128, 122.4392, 122.2482, 122.5790, 122.7888, 431.0840, ...
667s       430.8375, 430.3239, 429.8890, 429.3264, 65.6200, 66.6411, 65.9021, ...
667s       66.9373];
667s  c = [58.6667, 69, 67, 67.2, 66.8, 66.4, 106.4, 106.8, 107.6, 108.2, 109, ...
667s       60.6, 59.8, 59.25, 59];
667s  assert (L, l, 1e-4)
667s  assert (U, u, 1e-4)
667s  assert (C, c, 1e-4)
667s ***** test
667s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
667s  [TF, L, U, C] = isoutlier (A, "movmean", 5, "SamplePoints", [1:15]);
667s  l = [54.0841,  6.8872, 11.5608, 12.1518, 11.0210, 10.0112, -218.2840, ...
667s       -217.2375, -215.1239, -213.4890, -211.3264, 55.5800, 52.9589, ...
667s       52.5979, 51.0627];
667s  u = [63.2492, 131.1128, 122.4392, 122.2482, 122.5790, 122.7888, 431.0840, ...
667s       430.8375, 430.3239, 429.8890, 429.3264, 65.6200, 66.6411, 65.9021, ...
667s       66.9373];
667s  c = [58.6667, 69, 67, 67.2, 66.8, 66.4, 106.4, 106.8, 107.6, 108.2, 109, ...
667s       60.6, 59.8, 59.25, 59];
667s  assert (L, l, 1e-4)
667s  assert (U, u, 1e-4)
667s  assert (C, c, 1e-4)
667s ***** test
667s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
667s  [TF, L, U, C] = isoutlier (A, "gesd");
667s  assert (TF, logical ([0 0 0 1 0 0 0 0 1 0 0 0 0 0 0]))
667s  assert (L, 34.235977035439944, 1e-12)
667s  assert (U, 89.764022964560060, 1e-12)
667s  assert (C, 62)
667s ***** test
667s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
667s  [TF, L, U, C] = isoutlier (A, "gesd", "ThresholdFactor", 0.01);
667s  assert (TF, logical ([0 0 0 1 0 0 0 0 1 0 0 0 0 0 0]))
667s  assert (L, 31.489256770616173, 1e-12)
667s  assert (U, 92.510743229383820, 1e-12)
667s  assert (C, 62)
667s ***** test
667s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
667s  [TF, L, U, C] = isoutlier (A, "gesd", "ThresholdFactor", 5e-10);
667s  assert (TF, logical ([0 0 0 0 0 0 0 0 1 0 0 0 0 0 0]))
667s  assert (L, 23.976664158788935, 1e-12)
667s  assert (U, 100.02333584121110, 1e-12)
667s  assert (C, 62)
667s ***** test
667s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
667s  [TF, L, U, C] = isoutlier (A, "grubbs");
667s  assert (TF, logical ([0 0 0 1 0 0 0 0 1 0 0 0 0 0 0]))
667s  assert (L, 54.642809574646606, 1e-12)
667s  assert (U, 63.511036579199555, 1e-12)
667s  assert (C, 59.076923076923080, 1e-12)
667s ***** test
667s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
667s  [TF, L, U, C] = isoutlier (A, "grubbs", "ThresholdFactor", 0.01);
667s  assert (TF, logical ([0 0 0 1 0 0 0 0 1 0 0 0 0 0 0]))
667s  assert (L, 54.216083184201850, 1e-12)
667s  assert (U, 63.937762969644310, 1e-12)
667s  assert (C, 59.076923076923080, 1e-12)
667s ***** test
667s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
667s  [TF, L, U, C] = isoutlier (A,  "percentiles", [10 90]);
667s  assert (TF, logical ([0 0 0 0 0 0 0 0 1 0 0 0 0 0 0]))
667s  assert (L, 57)
667s  assert (U, 100)
667s  assert (C, 78.5)
667s ***** test
667s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
667s  [TF, L, U, C] = isoutlier (A,  "percentiles", [20 80]);
667s  assert (TF, logical ([1 0 0 1 0 0 1 0 1 0 0 0 0 0 1]))
667s  assert (L, 57.5)
667s  assert (U, 62)
667s  assert (C, 59.75)
667s ***** shared A
667s  A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
667s ***** error<isoutlier: WINDOW must be a positive scalar> ...
667s  isoutlier (A, "movmedian", 0);
667s ***** error<isoutlier: WINDOW must be a positive scalar> ...
667s  isoutlier (A, "movmedian", []);
667s ***** error<isoutlier: WINDOW must be a positive scalar> ...
667s  isoutlier (A, "movmedian", [2 3 4]);
667s ***** error<isoutlier: WINDOW must be a positive integer> ...
667s  isoutlier (A, "movmedian", 1.4);
667s ***** error<isoutlier: WINDOW must be a positive scalar> ...
667s  isoutlier (A, "movmedian", [0 1]);
667s ***** error<isoutlier: WINDOW must be a positive scalar> ...
667s  isoutlier (A, "movmedian", [2 -1]);
667s ***** error<isoutlier: WINDOW must be a positive scalar> ...
667s  isoutlier (A, "movmedian", {2 3});
667s ***** error<isoutlier: WINDOW must be a positive scalar> ...
667s  isoutlier (A, "movmedian", "char");
667s 
667s ***** error<isoutlier: WINDOW must be a positive scalar> ...
667s  isoutlier (A, "movmean", 0);
667s ***** error<isoutlier: WINDOW must be a positive scalar> ...
667s  isoutlier (A, "movmean", []);
667s ***** error<isoutlier: WINDOW must be a positive scalar> ...
667s  isoutlier (A, "movmean", [2 3 4]);
667s ***** error<isoutlier: WINDOW must be a positive integer> ...
667s  isoutlier (A, "movmean", 1.4);
667s ***** error<isoutlier: WINDOW must be a positive scalar> ...
667s  isoutlier (A, "movmean", [0 1]);
667s ***** error<isoutlier: WINDOW must be a positive scalar> ...
667s  isoutlier (A, "movmean", [2 -1]);
667s ***** error<isoutlier: WINDOW must be a positive scalar> ...
667s  isoutlier (A, "movmean", {2 3});
667s ***** error<isoutlier: WINDOW must be a positive scalar> ...
667s  isoutlier (A, "movmean", "char");
667s 
667s ***** error<isoutlier: THRESHOLD must be a two-element> ...
667s  isoutlier (A, "percentiles", [-1 90]);
667s ***** error<isoutlier: THRESHOLD must be a two-element> ...
667s  isoutlier (A, "percentiles", [10 -90]);
667s ***** error<isoutlier: THRESHOLD must be a two-element> ...
667s  isoutlier (A, "percentiles", [90]);
667s ***** error<isoutlier: THRESHOLD must be a two-element> ...
667s  isoutlier (A, "percentiles", [90 20]);
667s ***** error<isoutlier: THRESHOLD must be a two-element> ...
667s  isoutlier (A, "percentiles", [90 20]);
667s ***** error<isoutlier: THRESHOLD must be a two-element> ...
667s  isoutlier (A, "percentiles", [10 20 90]);
667s ***** error<isoutlier: THRESHOLD must be a two-element> ...
667s  isoutlier (A, "percentiles", {10 90});
667s ***** error<isoutlier: THRESHOLD must be a two-element> ...
667s  isoutlier (A, "percentiles", "char");
667s 
667s ***** error<isoutlier: sample points must be a vector.> ...
667s  isoutlier (A, "movmean", 5, "SamplePoints", ones(3,15));
667s ***** error<isoutlier: sample points must be a vector.> ...
667s  isoutlier (A, "movmean", 5, "SamplePoints", 15);
667s ***** error<isoutlier: sample points must be unique.> ...
667s  isoutlier (A, "movmean", 5, "SamplePoints", [1,1:14]);
667s ***** error<isoutlier: sample points must be sorted.> ...
667s  isoutlier (A, "movmean", 5, "SamplePoints", [2,1,3:15]);
667s ***** error<isoutlier: sample points must have the same size> ...
667s  isoutlier (A, "movmean", 5, "SamplePoints", [1:14]);
667s 
667s ***** error<isoutlier: threshold factor must be a nonnegative scalar.> ...
667s  isoutlier (A, "movmean", 5, "ThresholdFactor", [1:14]);
667s ***** error<isoutlier: threshold factor must be a nonnegative scalar.> ...
667s  isoutlier (A, "movmean", 5, "ThresholdFactor", -1);
667s ***** error<isoutlier: threshold factor must must be in> ...
667s  isoutlier (A, "gesd", "ThresholdFactor", 3);
667s ***** error<isoutlier: threshold factor must must be in> ...
667s  isoutlier (A, "grubbs", "ThresholdFactor", 3);
667s 
667s ***** error<isoutlier: maximum outlier count must be a positive integer scalar.> ...
667s  isoutlier (A, "movmean", 5, "MaxNumOutliers", [1:14]);
667s ***** error<isoutlier: maximum outlier count must be a positive integer scalar.> ...
667s  isoutlier (A, "movmean", 5, "MaxNumOutliers", -1);
667s ***** error<isoutlier: maximum outlier count must be a positive integer scalar.> ...
667s  isoutlier (A, "movmean", 5, "MaxNumOutliers", 0);
667s ***** error<isoutlier: maximum outlier count must be a positive integer scalar.> ...
667s  isoutlier (A, "movmean", 5, "MaxNumOutliers", 1.5);
667s 
667s ***** error<isoutlier: invalid input argument.> ...
667s  isoutlier (A, {"movmean"}, 5, "SamplePoints", [1:15]);
667s ***** error<isoutlier: invalid input argument.> isoutlier (A, {1});
667s ***** error<isoutlier: invalid input argument.> isoutlier (A, true);
667s ***** error<isoutlier: invalid input argument.> isoutlier (A, false);
667s ***** error<isoutlier: DIM must be a positive integer scalar.> isoutlier (A, 0);
667s ***** error<isoutlier: DIM must be a positive integer scalar.> isoutlier (A, [1 2]);
667s ***** error<isoutlier: DIM must be a positive integer scalar.> isoutlier (A, -2);
667s 59 tests, 59 passed, 0 known failure, 0 skipped
667s [inst/jackknife.m]
667s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/jackknife.m
667s ***** demo
667s  for k = 1:1000
667s    rand ("seed", k);  # for reproducibility
667s    x = rand (10, 1);
667s    s(k) = std (x);
667s    jackstat = jackknife (@std, x);
667s    j(k) = 10 * std (x) - 9 * mean (jackstat);
667s  endfor
667s  figure();
667s  hist ([s', j'], 0:sqrt(1/12)/10:2*sqrt(1/12))
667s ***** demo
667s  for k = 1:1000
667s    randn ("seed", k); # for reproducibility
667s    x = randn (1, 50);
667s    rand ("seed", k);  # for reproducibility
667s    y = rand (1, 50);
667s    jackstat = jackknife (@(x) std(x{1})/std(x{2}), y, x);
667s    j(k) = 50 * std (y) / std (x) - 49 * mean (jackstat);
667s    v(k) = sumsq ((50 * std (y) / std (x) - 49 * jackstat) - j(k)) / (50 * 49);
667s  endfor
667s  t = (j - sqrt (1 / 12)) ./ sqrt (v);
667s  figure();
667s  plot (sort (tcdf (t, 49)), ...
667s        "-;Almost linear mapping indicates good fit with t-distribution.;")
667s ***** test
667s  ##Example from Quenouille, Table 1
667s  d=[0.18 4.00 1.04 0.85 2.14 1.01 3.01 2.33 1.57 2.19];
667s  jackstat = jackknife ( @(x) 1/mean(x), d );
667s  assert ( 10 / mean(d) - 9 * mean(jackstat), 0.5240, 1e-5 );
667s 1 test, 1 passed, 0 known failure, 0 skipped
667s [inst/kmeans.m]
668s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/kmeans.m
668s ***** demo
668s  ## Generate a two-cluster problem
668s  randn ("seed", 31)  # for reproducibility
668s  C1 = randn (100, 2) + 1;
668s  randn ("seed", 32)  # for reproducibility
668s  C2 = randn (100, 2) - 1;
668s  data = [C1; C2];
668s 
668s  ## Perform clustering
668s  rand ("seed", 1)  # for reproducibility
668s  [idx, centers] = kmeans (data, 2);
668s 
668s  ## Plot the result
668s  figure;
668s  plot (data (idx==1, 1), data (idx==1, 2), "ro");
668s  hold on;
668s  plot (data (idx==2, 1), data (idx==2, 2), "bs");
668s  plot (centers (:, 1), centers (:, 2), "kv", "markersize", 10);
668s  title ("A simple two-clusters example");
668s  hold off;
668s ***** demo
668s  ## Cluster data using k-means clustering, then plot the cluster regions
668s  ## Load Fisher's iris data set and use the petal lengths and widths as
668s  ## predictors
668s 
668s  load fisheriris
668s  X = meas(:,3:4);
668s 
668s  plot (X(:,1), X(:,2), "k*", "MarkerSize", 5);
668s  title ("Fisher's Iris Data");
668s  xlabel ("Petal Lengths (cm)");
668s  ylabel ("Petal Widths (cm)");
668s 
668s  ## Cluster the data. Specify k = 3 clusters
668s  rand ("seed", 1)  # for reproducibility
668s  [idx, C] = kmeans (X, 3);
668s  x1 = min (X(:,1)):0.01:max (X(:,1));
668s  x2 = min (X(:,2)):0.01:max (X(:,2));
668s  [x1G, x2G] = meshgrid (x1, x2);
668s  XGrid = [x1G(:), x2G(:)];
668s 
668s  idx2Region = kmeans (XGrid, 3, "MaxIter", 10, "Start", C);
668s  figure;
668s  gscatter (XGrid(:,1), XGrid(:,2), idx2Region, ...
668s            [0, 0.75, 0.75; 0.75, 0, 0.75; 0.75, 0.75, 0], "..");
668s  hold on;
668s  plot (X(:,1), X(:,2), "k*", "MarkerSize", 5);
668s  title ("Fisher's Iris Data");
668s  xlabel ("Petal Lengths (cm)");
668s  ylabel ("Petal Widths (cm)");
668s  legend ("Region 1", "Region 2", "Region 3", "Data", "Location", "SouthEast");
668s  hold off
668s ***** demo
668s  ## Partition Data into Two Clusters
668s 
668s  randn ("seed", 1)  # for reproducibility
668s  r1 = randn (100, 2) * 0.75 + ones (100, 2);
668s  randn ("seed", 2)  # for reproducibility
668s  r2 = randn (100, 2) * 0.5 - ones (100, 2);
668s  X = [r1; r2];
668s 
668s  plot (X(:,1), X(:,2), ".");
668s  title ("Randomly Generated Data");
668s  rand ("seed", 1)  # for reproducibility
668s  [idx, C] = kmeans (X, 2, "Distance", "cityblock", ...
668s                           "Replicates", 5, "Display", "final");
668s  figure;
668s  plot (X(idx==1,1), X(idx==1,2), "r.", "MarkerSize", 12);
668s  hold on
668s  plot(X(idx==2,1), X(idx==2,2), "b.", "MarkerSize", 12);
668s  plot (C(:,1), C(:,2), "kx", "MarkerSize", 15, "LineWidth", 3);
668s  legend ("Cluster 1", "Cluster 2", "Centroids", "Location", "NorthWest");
668s  title ("Cluster Assignments and Centroids");
668s  hold off
668s ***** demo
668s  ## Assign New Data to Existing Clusters
668s 
668s  ## Generate a training data set using three distributions
668s  randn ("seed", 5)  # for reproducibility
668s  r1 = randn (100, 2) * 0.75 + ones (100, 2);
668s  randn ("seed", 7)  # for reproducibility
668s  r2 = randn (100, 2) * 0.5 - ones (100, 2);
668s  randn ("seed", 9)  # for reproducibility
668s  r3 = randn (100, 2) * 0.75;
668s  X = [r1; r2; r3];
668s 
668s  ## Partition the training data into three clusters by using kmeans
668s 
668s  rand ("seed", 1)  # for reproducibility
668s  [idx, C] = kmeans (X, 3);
668s 
668s  ## Plot the clusters and the cluster centroids
668s 
668s  gscatter (X(:,1), X(:,2), idx, "bgm", "***");
668s  hold on
668s  plot (C(:,1), C(:,2), "kx");
668s  legend ("Cluster 1", "Cluster 2", "Cluster 3", "Cluster Centroid")
668s 
668s  ## Generate a test data set
668s  randn ("seed", 25)  # for reproducibility
668s  r1 = randn (100, 2) * 0.75 + ones (100, 2);
668s  randn ("seed", 27)  # for reproducibility
668s  r2 = randn (100, 2) * 0.5 - ones (100, 2);
668s  randn ("seed", 29)  # for reproducibility
668s  r3 = randn (100, 2) * 0.75;
668s  Xtest = [r1; r2; r3];
668s 
668s  ## Classify the test data set using the existing clusters
668s  ## Find the nearest centroid from each test data point by using pdist2
668s 
668s  D = pdist2 (C, Xtest, "euclidean");
668s  [group, ~] = find (D == min (D));
668s 
668s  ## Plot the test data and label the test data using idx_test with gscatter
668s 
668s  gscatter (Xtest(:,1), Xtest(:,2), group, "bgm", "ooo");
668s  box on;
668s  legend ("Cluster 1", "Cluster 2", "Cluster 3", "Cluster Centroid", ...
668s          "Data classified to Cluster 1", "Data classified to Cluster 2", ...
668s          "Data classified to Cluster 3", "Location", "NorthWest");
668s  title ("Assign New Data to Existing Clusters");
668s ***** test
668s  samples = 4;
668s  dims = 3;
668s  k = 2;
668s  [cls, c, d, z] = kmeans (rand (samples,dims), k, "start", rand (k,dims, 5),
668s                           "emptyAction", "singleton");
668s  assert (size (cls), [samples, 1]);
668s  assert (size (c), [k, dims]);
668s  assert (size (d), [k, 1]);
668s  assert (size (z), [samples, k]);
668s ***** test
668s  samples = 4;
668s  dims = 3;
668s  k = 2;
668s  [cls, c, d, z] = kmeans (rand (samples,dims), [], "start", rand (k,dims, 5),
668s                           "emptyAction", "singleton");
668s  assert (size (cls), [samples, 1]);
668s  assert (size (c), [k, dims]);
668s  assert (size (d), [k, 1]);
668s  assert (size (z), [samples, k]);
668s ***** test
668s  [cls, c] = kmeans ([1 0; 2 0], 2, "start", [8,0;0,8], "emptyaction", "drop");
668s  assert (cls, [1; 1]);
668s  assert (c, [1.5, 0; NA, NA]);
668s ***** test
668s  kmeans (rand (4,3), 2, "start", rand (2,3, 5), "replicates", 5,
668s          "emptyAction", "singleton");
668s ***** test
668s  kmeans (rand (3,4), 2, "start", "sample", "emptyAction", "singleton");
668s ***** test
668s  kmeans (rand (3,4), 2, "start", "plus", "emptyAction", "singleton");
668s ***** test
668s  kmeans (rand (3,4), 2, "start", "cluster", "emptyAction", "singleton");
668s ***** test
668s  kmeans (rand (3,4), 2, "start", "uniform", "emptyAction", "singleton");
668s ***** test
668s  kmeans (rand (4,3), 2, "distance", "sqeuclidean", "emptyAction", "singleton");
668s ***** test
668s  kmeans (rand (4,3), 2, "distance", "cityblock", "emptyAction", "singleton");
668s ***** test
668s  kmeans (rand (4,3), 2, "distance", "cosine", "emptyAction", "singleton");
668s ***** test
668s  kmeans (rand (4,3), 2, "distance", "correlation", "emptyAction", "singleton");
668s ***** test
668s  kmeans (rand (4,3), 2, "distance", "hamming", "emptyAction", "singleton");
668s ***** test
668s  kmeans ([1 0; 1.1 0], 2, "start", eye(2), "emptyaction", "singleton");
668s ***** error kmeans (rand (3,2), 4);
668s ***** error kmeans ([1 0; 1.1 0], 2, "start", eye(2), "emptyaction", "panic");
668s ***** error kmeans (rand (4,3), 2, "start", rand (2,3, 5), "replicates", 1);
668s ***** error kmeans (rand (4,3), 2, "start", rand (2,2));
668s ***** error kmeans (rand (4,3), 2, "distance", "manhattan");
668s ***** error kmeans (rand (3,4), 2, "start", "normal");
668s ***** error kmeans (rand (4,3), 2, "replicates", i);
668s ***** error kmeans (rand (4,3), 2, "replicates", -1);
668s ***** error kmeans (rand (4,3), 2, "replicates", []);
668s ***** error kmeans (rand (4,3), 2, "replicates", [1 2]);
668s ***** error kmeans (rand (4,3), 2, "replicates", "one");
668s ***** error kmeans (rand (4,3), 2, "MAXITER", i);
668s ***** error kmeans (rand (4,3), 2, "MaxIter", -1);
668s ***** error kmeans (rand (4,3), 2, "maxiter", []);
668s ***** error kmeans (rand (4,3), 2, "maxiter", [1 2]);
668s ***** error kmeans (rand (4,3), 2, "maxiter", "one");
668s ***** error <empty cluster created> kmeans ([1 0; 1.1 0], 2, "start", eye(2), "emptyaction", "error");
668s 31 tests, 31 passed, 0 known failure, 0 skipped
668s [inst/knnsearch.m]
668s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/knnsearch.m
668s ***** demo
668s  ## find 10 nearest neighbour of a point using different distance metrics
668s  ## and compare the results by plotting
668s  load fisheriris
668s  X = meas(:,3:4);
668s  Y = species;
668s  point = [5, 1.45];
668s 
668s  ## calculate 10 nearest-neighbours by minkowski distance
668s  [id, d] = knnsearch (X, point, "K", 10);
668s 
668s  ## calculate 10 nearest-neighbours by minkowski distance
668s  [idm, dm] = knnsearch (X, point, "K", 10, "distance", "minkowski", "p", 5);
668s 
668s  ## calculate 10 nearest-neighbours by chebychev distance
668s  [idc, dc] = knnsearch (X, point, "K", 10, "distance", "chebychev");
668s 
668s  ## plotting the results
668s  gscatter (X(:,1), X(:,2), species, [.75 .75 0; 0 .75 .75; .75 0 .75], ".", 20);
668s  title ("Fisher's Iris Data - Nearest Neighbors with different types of distance metrics");
668s  xlabel("Petal length (cm)");
668s  ylabel("Petal width (cm)");
668s 
668s  line (point(1), point(2), "marker", "X", "color", "k", ...
668s        "linewidth", 2, "displayname", "query point")
668s  line (X(id,1), X(id,2), "color", [0.5 0.5 0.5], "marker", "o", ...
668s        "linestyle", "none", "markersize", 10, "displayname", "euclidean")
668s  line (X(idm,1), X(idm,2), "color", [0.5 0.5 0.5], "marker", "d", ...
668s        "linestyle", "none", "markersize", 10, "displayname", "Minkowski")
668s  line (X(idc,1), X(idc,2), "color", [0.5 0.5 0.5], "marker", "p", ...
668s        "linestyle", "none", "markersize", 10, "displayname", "chebychev")
668s  xlim ([4.5 5.5]);
668s  ylim ([1 2]);
668s  axis square;
668s ***** demo
668s  ## knnsearch on iris dataset using kdtree method
668s  load fisheriris
668s  X = meas(:,3:4);
668s  gscatter (X(:,1), X(:,2), species, [.75 .75 0; 0 .75 .75; .75 0 .75], ".", 20);
668s  title ("Fisher's iris dataset : Nearest Neighbors with kdtree search");
668s 
668s  ## new point to be predicted
668s  point = [5 1.45];
668s 
668s  line (point(1), point(2), "marker", "X", "color", "k", ...
668s        "linewidth", 2, "displayname", "query point")
668s 
668s  ## knnsearch using kdtree method
668s  [idx, d] = knnsearch (X, point, "K", 10, "NSMethod", "kdtree");
668s 
668s  ## plotting predicted neighbours
668s  line (X(idx,1), X(idx,2), "color", [0.5 0.5 0.5], "marker", "o", ...
668s        "linestyle", "none", "markersize", 10, ...
668s        "displayname", "nearest neighbour")
668s  xlim ([4 6])
668s  ylim ([1 3])
668s  axis square
668s  ## details of predicted labels
668s  tabulate (species(idx))
668s 
668s  ctr = point - d(end);
668s  diameter = 2 * d(end);
668s  ##  Draw a circle around the 10 nearest neighbors.
668s  h = rectangle ("position", [ctr, diameter, diameter], "curvature", [1 1]);
668s 
668s  ## here only 8 neighbours are plotted instead of 10 since the dataset
668s  ## contains duplicate values
668s ***** shared X, Y
668s  X = [1, 2, 3, 4; 2, 3, 4, 5; 3, 4, 5, 6];
668s  Y = [1, 2, 2, 3; 2, 3, 3, 4];
668s ***** test
668s  [idx, D] = knnsearch (X, Y, "Distance", "euclidean");
668s  assert (idx, [1; 1]);
668s  assert (D, ones (2, 1) * sqrt (2));
668s ***** test
668s  eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
668s  [idx, D] = knnsearch (X, Y, "Distance", eucldist);
668s  assert (idx, [1; 1]);
668s  assert (D, ones (2, 1) * sqrt (2));
668s ***** test
668s  [idx, D] = knnsearch (X, Y, "Distance", "euclidean", "includeties", true);
668s  assert (iscell (idx), true);
668s  assert (iscell (D), true)
668s  assert (idx {1}, [1]);
668s  assert (idx {2}', [1, 2]);
668s  assert (D{1}, ones (1, 1) * sqrt (2));
668s  assert (D{2}', ones (1, 2) * sqrt (2));
668s ***** test
668s  [idx, D] = knnsearch (X, Y, "Distance", "euclidean", "k", 2);
668s  assert (idx, [1, 2; 1, 2]);
668s  assert (D, [sqrt(2), 3.162277660168380; sqrt(2), sqrt(2)], 1e-14);
668s ***** test
668s  [idx, D] = knnsearch (X, Y, "Distance", "seuclidean");
668s  assert (idx, [1; 1]);
668s  assert (D, ones (2, 1) * sqrt (2));
668s ***** test
668s  [idx, D] = knnsearch (X, Y, "Distance", "seuclidean", "k", 2);
668s  assert (idx, [1, 2; 1, 2]);
668s  assert (D, [sqrt(2), 3.162277660168380; sqrt(2), sqrt(2)], 1e-14);
668s ***** test
668s  xx = [1, 2; 1, 3; 2, 4; 3, 6];
668s  yy = [2, 4; 2, 6];
668s  [idx, D] = knnsearch (xx, yy, "Distance", "mahalanobis");
668s  assert (idx, [3; 2]);
668s  assert (D, [0; 3.162277660168377], 1e-14);
668s ***** test
668s  [idx, D] = knnsearch (X, Y, "Distance", "minkowski");
668s  assert (idx, [1; 1]);
668s  assert (D, ones (2, 1) * sqrt (2));
668s ***** test
668s  [idx, D] = knnsearch (X, Y, "Distance", "minkowski", "p", 3);
668s  assert (idx, [1; 1]);
668s  assert (D, ones (2, 1) * 1.259921049894873, 1e-14);
668s ***** test
668s  [idx, D] = knnsearch (X, Y, "Distance", "cityblock");
668s  assert (idx, [1; 1]);
668s  assert (D, [2; 2]);
668s ***** test
668s  [idx, D] = knnsearch (X, Y, "Distance", "chebychev");
668s  assert (idx, [1; 1]);
668s  assert (D, [1; 1]);
668s ***** test
668s  [idx, D] = knnsearch (X, Y, "Distance", "cosine");
668s  assert (idx, [2; 3]);
668s  assert (D, [0.005674536395645; 0.002911214328620], 1e-14);
668s ***** test
668s  [idx, D] = knnsearch (X, Y, "Distance", "correlation");
668s  assert (idx, [1; 1]);
668s  assert (D, ones (2, 1) * 0.051316701949486, 1e-14);
668s ***** test
668s  [idx, D] = knnsearch (X, Y, "Distance", "spearman");
668s  assert (idx, [1; 1]);
668s  assert (D, ones (2, 1) * 0.051316701949486, 1e-14);
668s ***** test
668s  [idx, D] = knnsearch (X, Y, "Distance", "hamming");
668s  assert (idx, [1; 1]);
668s  assert (D, [0.5; 0.5]);
668s ***** test
668s  [idx, D] = knnsearch (X, Y, "Distance", "jaccard");
668s  assert (idx, [1; 1]);
668s  assert (D, [0.5; 0.5]);
668s ***** test
668s  [idx, D] = knnsearch (X, Y, "Distance", "jaccard", "k", 2);
668s  assert (idx, [1, 2; 1, 2]);
668s  assert (D, [0.5, 1; 0.5, 0.5]);
668s ***** test
668s  a = [1, 5; 1, 2; 2, 2; 1.5, 1.5; 5, 1; 2 -1.34; 1, -3; 4, -4; -3, 1; 8, 9];
668s  b = [1, 1];
668s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree", "includeties", true);
668s  assert (iscell (idx), true);
668s  assert (iscell (D), true)
668s  assert (cell2mat (idx)', [4, 2, 3, 6, 1, 5, 7, 9]);
668s  assert (cell2mat (D)', [0.7071, 1.0000, 1.4142, 2.5447, 4.0000, 4.0000, 4.0000, 4.0000], 1e-4);
668s ***** test
668s  a = [1, 5; 1, 2; 2, 2; 1.5, 1.5; 5, 1; 2 -1.34; 1, -3; 4, -4; -3, 1; 8, 9];
668s  b = [1, 1];
668s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "exhaustive", "includeties", true);
668s  assert (iscell (idx), true);
668s  assert (iscell (D), true)
668s  assert (cell2mat (idx), [4, 2, 3, 6, 1, 5, 7, 9]);
668s  assert (cell2mat (D), [0.7071, 1.0000, 1.4142, 2.5447, 4.0000, 4.0000, 4.0000, 4.0000], 1e-4);
668s ***** test
668s  a = [1, 5; 1, 2; 2, 2; 1.5, 1.5; 5, 1; 2 -1.34; 1, -3; 4, -4; -3, 1; 8, 9];
668s  b = [1, 1];
668s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree", "includeties", false);
668s  assert (iscell (idx), false);
668s  assert (iscell (D), false)
668s  assert (idx, [4, 2, 3, 6, 1]);
668s  assert (D, [0.7071, 1.0000, 1.4142, 2.5447, 4.0000], 1e-4);
668s ***** test
668s  a = [1, 5; 1, 2; 2, 2; 1.5, 1.5; 5, 1; 2 -1.34; 1, -3; 4, -4; -3, 1; 8, 9];
668s  b = [1, 1];
668s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "exhaustive", "includeties", false);
668s  assert (iscell (idx), false);
668s  assert (iscell (D), false)
668s  assert (idx, [4, 2, 3, 6, 1]);
668s  assert (D, [0.7071, 1.0000, 1.4142, 2.5447, 4.0000], 1e-4);
668s ***** test
668s  load fisheriris
668s  a = meas;
668s  b = min(meas);
668s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree");
668s  assert (idx, [42, 9, 14, 39, 13]);
668s  assert (D, [0.5099, 0.9950, 1.0050, 1.0536, 1.1874], 1e-4);
668s ***** test
668s  load fisheriris
668s  a = meas;
668s  b = mean(meas);
668s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree");
668s  assert (idx, [65, 83, 89, 72, 100]);
668s  assert (D, [0.3451, 0.3869, 0.4354, 0.4481, 0.4625], 1e-4);
668s ***** test
668s  load fisheriris
668s  a = meas;
668s  b = max(meas);
668s  [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree");
668s  assert (idx, [118, 132, 110, 106, 136]);
668s  assert (D, [0.7280, 0.9274, 1.3304, 1.5166, 1.6371], 1e-4);
668s 
668s ***** test
668s  load fisheriris
668s  a = meas;
668s  b = max(meas);
668s  [idx, D] = knnsearch (a, b, "K", 5, "includeties", true);
668s  assert (iscell (idx), true);
668s  assert (iscell (D), true);
668s  assert (cell2mat (idx)', [118, 132, 110, 106, 136]);
668s  assert (cell2mat (D)', [0.7280, 0.9274, 1.3304, 1.5166, 1.6371], 1e-4);
668s ***** error<knnsearch: too few input arguments.> knnsearch (1)
668s ***** error<knnsearch: number of columns in X and Y must match.> ...
668s  knnsearch (ones (4, 5), ones (4))
668s ***** error<knnsearch: invalid NAME in optional pairs of arguments.> ...
668s  knnsearch (ones (4, 2), ones (3, 2), "Distance", "euclidean", "some", "some")
668s ***** error<knnsearch: only a single distance parameter can be defined.> ...
668s  knnsearch (ones (4, 5), ones (1, 5), "scale", ones (1, 5), "P", 3)
668s ***** error<knnsearch: invalid value of K.> ...
668s  knnsearch (ones (4, 5), ones (1, 5), "K", 0)
668s ***** error<knnsearch: invalid value of Minkowski Exponent.> ...
668s  knnsearch (ones (4, 5), ones (1, 5), "P", -2)
668s ***** error<knnsearch: invalid value in Scale or the size of Scale.> ...
668s  knnsearch (ones (4, 5), ones (1, 5), "scale", ones(4,5), "distance", "euclidean")
668s ***** error<knnsearch: invalid value in Cov, Cov can only be given for mahalanobis distance.> ...
668s  knnsearch (ones (4, 5), ones (1, 5), "cov", ["some" "some"])
668s ***** error<knnsearch: invalid value in Cov, Cov can only be given for mahalanobis distance.> ...
668s  knnsearch (ones (4, 5), ones (1, 5), "cov", ones(4,5), "distance", "euclidean")
668s ***** error<knnsearch: invalid value of bucketsize.> ...
668s  knnsearch (ones (4, 5), ones (1, 5), "bucketsize", -1)
668s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
668s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "cosine")
668s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
668s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "mahalanobis")
668s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
668s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "correlation")
668s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
668s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "seuclidean")
668s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
668s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "spearman")
668s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
668s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "hamming")
668s ***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
668s  knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "jaccard")
668s 42 tests, 42 passed, 0 known failure, 0 skipped
668s [inst/kruskalwallis.m]
668s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/kruskalwallis.m
668s ***** demo
668s  x = meshgrid (1:6);
668s  x = x + normrnd (0, 1, 6, 6);
668s  kruskalwallis (x, [], 'off');
668s ***** demo
668s  x = meshgrid (1:6);
668s  x = x + normrnd (0, 1, 6, 6);
668s  [p, atab] = kruskalwallis(x);
668s ***** demo
668s  x = ones (30, 4) .* [-2, 0, 1, 5];
668s  x = x + normrnd (0, 2, 30, 4);
668s  group = {"A", "B", "C", "D"};
668s  kruskalwallis (x, group);
668s ***** test
668s  data = [1.006, 0.996, 0.998, 1.000, 0.992, 0.993, 1.002, 0.999, 0.994, 1.000, ...
668s          0.998, 1.006, 1.000, 1.002, 0.997, 0.998, 0.996, 1.000, 1.006, 0.988, ...
668s          0.991, 0.987, 0.997, 0.999, 0.995, 0.994, 1.000, 0.999, 0.996, 0.996, ...
668s          1.005, 1.002, 0.994, 1.000, 0.995, 0.994, 0.998, 0.996, 1.002, 0.996, ...
668s          0.998, 0.998, 0.982, 0.990, 1.002, 0.984, 0.996, 0.993, 0.980, 0.996, ...
668s          1.009, 1.013, 1.009, 0.997, 0.988, 1.002, 0.995, 0.998, 0.981, 0.996, ...
668s          0.990, 1.004, 0.996, 1.001, 0.998, 1.000, 1.018, 1.010, 0.996, 1.002, ...
668s          0.998, 1.000, 1.006, 1.000, 1.002, 0.996, 0.998, 0.996, 1.002, 1.006, ...
668s          1.002, 0.998, 0.996, 0.995, 0.996, 1.004, 1.004, 0.998, 0.999, 0.991, ...
668s          0.991, 0.995, 0.984, 0.994, 0.997, 0.997, 0.991, 0.998, 1.004, 0.997];
668s  group = [1:10] .* ones (10,10);
668s  group = group(:);
668s  [p, tbl] = kruskalwallis (data, group, "off");
668s  assert (p, 0.048229, 1e-6);
668s  assert (tbl{2,5}, 17.03124, 1e-5);
668s  assert (tbl{2,3}, 9, 0);
668s  assert (tbl{4,2}, 82655.5, 1e-16);
668s  data = reshape (data, 10, 10);
668s  [p, tbl, stats] = kruskalwallis (data, [], "off");
668s  assert (p, 0.048229, 1e-6);
668s  assert (tbl{2,5}, 17.03124, 1e-5);
668s  assert (tbl{2,3}, 9, 0);
668s  assert (tbl{4,2}, 82655.5, 1e-16);
668s  means = [51.85, 60.45, 37.6, 51.1, 29.5, 54.25, 64.55, 66.7, 53.65, 35.35];
668s  N = 10 * ones (1, 10);
668s  assert (stats.meanranks, means, 1e-6);
668s  assert (length (stats.gnames), 10, 0);
668s  assert (stats.n, N, 0);
668s 1 test, 1 passed, 0 known failure, 0 skipped
668s [inst/kstest.m]
668s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/kstest.m
668s ***** demo
668s  ## Use the stock return data set to test the null hypothesis that the data
668s  ## come from a standard normal distribution against the alternative
668s  ## hypothesis that the population CDF of the data is larger that the
668s  ## standard normal CDF.
668s 
668s  load stockreturns;
668s  x = stocks(:,2);
668s  [h, p, k, c] = kstest (x, "Tail", "larger")
668s 
668s  ## Compute the empirical CDF and plot against the standard normal CDF
668s  [f, x_values] = ecdf (x);
668s  h1 = plot (x_values, f);
668s  hold on;
668s  h2 = plot (x_values, normcdf (x_values), 'r--');
668s  set (h1, "LineWidth", 2);
668s  set (h2, "LineWidth", 2);
668s  legend ([h1, h2], "Empirical CDF", "Standard Normal CDF", ...
668s          "Location", "southeast");
668s  title ("Empirical CDF of stock return data against standard normal CDF")
668s ***** error<kstest: too few inputs.> kstest ()
668s ***** error<kstest: X must be a vector of real numbers.> kstest (ones (2, 4))
668s ***** error<kstest: X must be a vector of real numbers.> kstest ([2, 3, 5, 3+3i])
668s ***** error<kstest: unknown option 'opt'.> kstest ([2, 3, 4, 5, 6], "opt", 0.51)
668s ***** error<kstest: optional parameters must be in name/value pairs.> ...
668s  kstest ([2, 3, 4, 5, 6], "tail")
668s ***** error<kstest: alpha must be a numeric scalar in the range> ...
668s  kstest ([2,3,4,5,6],"alpha", [0.05, 0.05])
668s ***** error<kstest: alpha must be a numeric scalar in the range> ...
668s  kstest ([2, 3, 4, 5, 6], "alpha", NaN)
668s ***** error<kstest: tail argument must be a string.> ...
668s  kstest ([2, 3, 4, 5, 6], "tail", 0)
668s ***** error<kstest: tail value must be either 'both', right' or 'left'.> ...
668s  kstest ([2,3,4,5,6], "tail", "whatever")
668s ***** error<kstest: invalid function handle.> ...
668s  kstest ([1, 2, 3, 4, 5], "CDF", @(x) repmat (x, 2, 3))
668s ***** error<kstest: 'somedist' is not a supported distribution.> ...
668s  kstest ([1, 2, 3, 4, 5], "CDF", "somedist")
668s ***** error<kstest: 'CDF' must be a probability distribution object.> ...
668s  kstest ([1, 2, 3, 4, 5], "CDF", cvpartition (5, 'resubstitution'))
668s ***** error<kstest: numerical CDF should have only 2 columns.> ...
668s  kstest ([2, 3, 4, 5, 6], "alpha", 0.05, "CDF", [2, 3, 4; 1, 3, 4; 1, 2, 1])
668s ***** error<kstest: numerical CDF should have at least one row.> ...
668s  kstest ([2, 3, 4, 5, 6], "alpha", 0.05, "CDF", nan (5, 2))
668s ***** error<kstest: non-incrementing numerical CDF.> ...
668s  kstest ([2, 3, 4, 5, 6], "CDF", [2, 3; 1, 4; 3, 2])
668s ***** error<kstest: wrong duplicates in numerical CDF.> ...
668s  kstest ([2, 3, 4, 5, 6], "CDF", [2, 3; 2, 4; 3, 5])
668s ***** error<kstest: invalid value parsed as CDF optional argument.> ...
668s  kstest ([2, 3, 4, 5, 6], "CDF", {1, 2, 3, 4, 5})
668s ***** test
668s  load examgrades
668s  [h, p] = kstest (grades(:,1));
668s  assert (h, true);
668s  assert (p, 7.58603305206105e-107, 1e-14);
668s ***** test
668s  load examgrades
668s  [h, p] = kstest (grades(:,1), "CDF", @(x) normcdf(x, 75, 10));
668s  assert (h, false);
668s  assert (p, 0.5612, 1e-4);
668s ***** test
668s  load examgrades
668s  x = grades(:,1);
668s  test_cdf = makedist ("tlocationscale", "mu", 75, "sigma", 10, "nu", 1);
668s  [h, p] = kstest (x, "alpha", 0.01, "CDF", test_cdf);
668s  assert (h, true);
668s  assert (p, 0.0021, 1e-4);
668s ***** test
668s  load stockreturns
668s  x = stocks(:,3);
668s  [h,p,k,c] = kstest (x, "Tail", "larger");
668s  assert (h, true);
668s  assert (p, 5.085438806199252e-05, 1e-14);
668s  assert (k, 0.2197, 1e-4);
668s  assert (c, 0.1207, 1e-4);
668s 21 tests, 21 passed, 0 known failure, 0 skipped
668s [inst/kstest2.m]
668s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/kstest2.m
668s ***** error kstest2 ([1,2,3,4,5,5])
668s ***** error kstest2 (ones(2,4), [1,2,3,4,5,5])
668s ***** error kstest2 ([2,3,5,7,3+3i], [1,2,3,4,5,5])
668s ***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"tail")
668s ***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"tail", "whatever")
668s ***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"badoption", 0.51)
668s ***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"tail", 0)
668s ***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"alpha", 0)
668s ***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"alpha", NaN)
668s ***** error kstest2 ([NaN,NaN,NaN,NaN,NaN],[3;5;7;8;7;6;5],"tail", "unequal")
668s ***** test
668s  load examgrades
668s  [h, p] = kstest2 (grades(:,1), grades(:,2));
668s  assert (h, false);
668s  assert (p, 0.1222791870137312, 1e-14);
668s ***** test
668s  load examgrades
668s  [h, p] = kstest2 (grades(:,1), grades(:,2), "tail", "larger");
668s  assert (h, false);
668s  assert (p, 0.1844421391011258, 1e-14);
668s ***** test
668s  load examgrades
668s  [h, p] = kstest2 (grades(:,1), grades(:,2), "tail", "smaller");
668s  assert (h, false);
668s  assert (p, 0.06115357930171663, 1e-14);
668s ***** test
668s  load examgrades
668s  [h, p] = kstest2 (grades(:,1), grades(:,2), "tail", "smaller", "alpha", 0.1);
668s  assert (h, true);
668s  assert (p, 0.06115357930171663, 1e-14);
668s 14 tests, 14 passed, 0 known failure, 0 skipped
668s [inst/levene_test.m]
668s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/levene_test.m
668s ***** error<levene_test: invalid number of input arguments.> levene_test ()
668s ***** error<levene_test: invalid number of input arguments.> ...
668s  levene_test (1, 2, 3, 4, 5);
668s ***** error<levene_test: wrong value for alpha.> levene_test (randn (50, 2), 0);
668s ***** error<levene_test: GROUP and X mismatch.> ...
668s  levene_test (randn (50, 2), [1, 2, 3]);
668s ***** error<levene_test: GROUP and X mismatch.> ...
668s  levene_test (randn (50, 1), ones (55, 1));
668s ***** error<levene_test: invalid second input argument.> ...
668s  levene_test (randn (50, 1), ones (50, 2));
668s ***** error<levene_test: wrong value for alpha.> ...
668s  levene_test (randn (50, 2), [], 1.2);
668s ***** error<levene_test: GROUP and X mismatch.> ...
668s  levene_test (randn (50, 2), "some_string");
668s ***** error<levene_test: invalid third input argument.> ...
668s  levene_test (randn (50, 2), [], "alpha");
668s ***** error<levene_test: wrong value for alpha.> ...
668s  levene_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], 1.2);
668s ***** error<levene_test: invalid third input argument.> ...
668s  levene_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], "err");
668s ***** error<levene_test: invalid option for TESTTYPE as 4th argument.> ...
668s  levene_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], 0.05, "type");
668s ***** warning<levene_test: GROUP> ...
668s  levene_test (randn (50, 1), [ones(24, 1); 2*ones(25, 1); 3]);
668s ***** test
668s  load examgrades
668s  [h, pval, W, df] = levene_test (grades);
668s  assert (h, 1);
668s  assert (pval, 9.523239714592791e-07, 1e-14);
668s  assert (W, 8.59529, 1e-5);
668s  assert (df, [4, 595]);
668s ***** test
668s  load examgrades
668s  [h, pval, W, df] = levene_test (grades, [], "quadratic");
668s  assert (h, 1);
668s  assert (pval, 9.523239714592791e-07, 1e-14);
668s  assert (W, 8.59529, 1e-5);
668s  assert (df, [4, 595]);
669s ***** test
669s  load examgrades
669s  [h, pval, W, df] = levene_test (grades, [], "median");
669s  assert (h, 1);
669s  assert (pval, 1.312093241723211e-06, 1e-14);
669s  assert (W, 8.415969, 1e-6);
669s  assert (df, [4, 595]);
669s ***** test
669s  load examgrades
669s  [h, pval, W, df] = levene_test (grades(:,[1:3]));
669s  assert (h, 1);
669s  assert (pval, 0.004349390980463497, 1e-14);
669s  assert (W, 5.52139, 1e-5);
669s  assert (df, [2, 357]);
669s ***** test
669s  load examgrades
669s  [h, pval, W, df] = levene_test (grades(:,[1:3]), "median");
669s  assert (h, 1);
669s  assert (pval, 0.004355216763951453, 1e-14);
669s  assert (W, 5.52001, 1e-5);
669s  assert (df, [2, 357]);
669s ***** test
669s  load examgrades
669s  [h, pval, W, df] = levene_test (grades(:,[3,4]), "quadratic");
669s  assert (h, 0);
669s  assert (pval, 0.1807494957440653, 2e-14);
669s  assert (W, 1.80200, 1e-5);
669s  assert (df, [1, 238]);
669s ***** test
669s  load examgrades
669s  [h, pval, W, df] = levene_test (grades(:,[3,4]), "median");
669s  assert (h, 0);
669s  assert (pval, 0.1978225622063785, 2e-14);
669s  assert (W, 1.66768, 1e-5);
669s  assert (df, [1, 238]);
669s 20 tests, 20 passed, 0 known failure, 0 skipped
669s [inst/linkage.m]
669s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/linkage.m
669s ***** shared x, t
669s  x = reshape (mod (magic (6),5), [], 3);
669s  t = 1e-6;
669s ***** assert (cond (linkage (pdist (x))),                   34.119045, t);
669s ***** assert (cond (linkage (pdist (x), "complete")),       21.793345, t);
669s ***** assert (cond (linkage (pdist (x), "average")),        27.045012, t);
669s ***** assert (cond (linkage (pdist (x), "weighted")),       27.412889, t);
669s  lastwarn(); # Clear last warning before the test
669s ***** warning <cluster distances> linkage (pdist (x), "centroid");
669s ***** test
669s  warning off Octave:clustering
669s  assert (cond (linkage (pdist (x), "centroid")),      27.457477, t);
669s  warning on Octave:clustering
669s ***** warning <cluster distances> linkage (pdist (x), "median");
669s ***** test
669s  warning off Octave:clustering
669s  assert (cond (linkage (pdist (x), "median")),        27.683325, t);
669s  warning on Octave:clustering
669s ***** assert (cond (linkage (pdist (x), "ward")),           17.195198, t);
669s ***** assert (cond (linkage (x, "ward", "euclidean")),      17.195198, t);
669s ***** assert (cond (linkage (x, "ward", {"euclidean"})),    17.195198, t);
669s ***** assert (cond (linkage (x, "ward", {"minkowski", 2})), 17.195198, t);
669s 12 tests, 12 passed, 0 known failure, 0 skipped
669s [inst/loadmodel.m]
669s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/loadmodel.m
669s ***** error<loadmodel: too few arguments.> loadmodel ()
669s ***** error<loadmodel: 'fisheriris.mat' does not contain a Classification or Regression object.> ...
669s  loadmodel ("fisheriris.mat")
669s ***** error<loadmodel: 'ClassificationModel' is not supported.> ...
669s  loadmodel ("fail_loadmodel.mdl")
669s ***** error<ClassificationKNN.load_model: invalid model in 'fail_load_model.mdl'.> ...
669s  loadmodel ("fail_load_model.mdl")
669s 4 tests, 4 passed, 0 known failure, 0 skipped
669s [inst/logistic_regression.m]
669s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/logistic_regression.m
669s ***** test
669s  # Output compared to following MATLAB commands
669s  # [B, DEV, STATS] = mnrfit(X,Y+1,'model','ordinal');
669s  # P = mnrval(B,X)
669s  X = [1.489381332449196, 1.1534152241851305; ...
669s       1.8110085304863965, 0.9449666896938425; ...
669s       -0.04453299665130296, 0.34278203449678646; ...
669s       -0.36616019468850347, 1.130254275908322; ...
669s        0.15339143291005095, -0.7921044310668951; ...
669s       -1.6031878794469698, -1.8343471035233376; ...
669s       -0.14349521143198166, -0.6762996896828459; ...
669s       -0.4403818557740143, -0.7921044310668951; ...
669s       -0.7372685001160434, -0.027793137932169563; ...
669s       -0.11875465773681024, 0.5512305689880763];
669s  Y = [1,1,1,1,1,0,0,0,0,0]';
669s  [INTERCEPT, SLOPE, DEV, DL, D2L, P] = logistic_regression (Y, X, false);
669s ***** test
669s  # Output compared to following MATLAB commands
669s  # [B, DEV, STATS] = mnrfit(X,Y+1,'model','ordinal');
669s  load carbig
669s  X = [Acceleration Displacement Horsepower Weight];
669s  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, ...
669s           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, ...
669s           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, ...
669s           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, ...
669s           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, ...
669s           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, ...
669s           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, ...
669s           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, ...
669s           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, ...
669s           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, ...
669s           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, ...
669s           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, ...
669s           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]';
669s  [INTERCEPT, SLOPE, DEV, DL, D2L, P] = logistic_regression (miles, X, false);
669s  assert (DEV, 433.197174495549, 1e-05);
669s  assert (INTERCEPT(1), -16.6895155618903, 1e-05);
669s  assert (INTERCEPT(2), -11.7207818178493, 1e-05);
669s  assert (INTERCEPT(3), -8.0605768506075, 1e-05);
669s  assert (SLOPE(1), 0.104762463756714, 1e-05);
669s  assert (SLOPE(2), 0.0103357623191891, 1e-05);
669s  assert (SLOPE(3), 0.0645199313242276, 1e-05);
669s  assert (SLOPE(4), 0.00166377028388103, 1e-05);
669s 2 tests, 2 passed, 0 known failure, 0 skipped
669s [inst/logit.m]
669s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/logit.m
669s ***** test
669s  p = [0.01:0.01:0.99];
669s  assert (logit (p), log (p ./ (1-p)), 25*eps);
669s ***** assert (logit ([-1, 0, 0.5, 1, 2]), [NaN, -Inf, 0, +Inf, NaN])
669s ***** error logit ()
669s ***** error logit (1, 2)
669s 4 tests, 4 passed, 0 known failure, 0 skipped
669s [inst/mahal.m]
669s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/mahal.m
669s ***** error mahal ()
669s ***** error mahal (1, 2, 3)
669s ***** error mahal ("A", "B")
669s ***** error <must be numeric> mahal ([1, 2], ["A", "B"])
669s ***** error mahal (ones (2, 2, 2))
669s ***** error <must be 2-D matrices> mahal (ones (2, 2), ones (2, 2, 2))
669s ***** error <same number of columns> mahal (ones (2, 2), ones (2, 3))
669s ***** test
669s  X = [1 0; 0 1; 1 1; 0 0];
669s  assert (mahal (X, X), [1.5; 1.5; 1.5; 1.5], 10*eps)
669s  assert (mahal (X, X+1), [7.5; 7.5; 1.5; 13.5], 10*eps)
669s ***** assert (mahal ([true; true], [false; true]), [0.5; 0.5], eps)
669s 9 tests, 9 passed, 0 known failure, 0 skipped
669s [inst/manova1.m]
669s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/manova1.m
669s ***** demo
669s  load carbig
669s  [d,p] = manova1([MPG, Acceleration, Weight, Displacement], Origin)
669s ***** test
669s  load carbig
669s  [d,p] = manova1([MPG, Acceleration, Weight, Displacement], Origin);
669s  assert (d, 3);
669s  assert (p, [0, 3.140583347827075e-07, 0.007510999577743149, ...
669s              0.1934100745898493]', [1e-12, 1e-12, 1e-12, 1e-12]');
669s ***** test
669s  load carbig
669s  [d,p] = manova1([MPG, Acceleration, Weight], Origin);
669s  assert (d, 2);
669s  assert (p, [0, 0.00516082975137544, 0.1206528056514453]', ...
669s             [1e-12, 1e-12, 1e-12]');
669s 2 tests, 2 passed, 0 known failure, 0 skipped
669s [inst/manovacluster.m]
669s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/manovacluster.m
669s ***** demo
669s  load carbig
669s  X = [MPG Acceleration Weight Displacement];
669s  [d, p, stats] = manova1 (X, Origin);
669s  manovacluster (stats)
669s ***** test
669s  hf = figure ("visible", "off");
669s  unwind_protect
669s    load carbig
669s    X = [MPG Acceleration Weight Displacement];
669s    [d, p, stats] = manova1 (X, Origin);
669s    manovacluster (stats);
669s  unwind_protect_cleanup
669s    close (hf);
669s  end_unwind_protect
669s ***** error manovacluster (stats, "some");
669s 2 tests, 2 passed, 0 known failure, 0 skipped
669s [inst/mcnemar_test.m]
669s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/mcnemar_test.m
669s ***** test
669s  [h, pval, chisq] = mcnemar_test ([101,121;59,33]);
669s  assert (h, 1);
669s  assert (pval, 3.8151e-06, 1e-10);
669s  assert (chisq, 21.356, 1e-3);
670s ***** test
670s  [h, pval, chisq] = mcnemar_test ([59,6;16,80]);
670s  assert (h, 1);
670s  assert (pval, 0.034690, 1e-6);
670s  assert (isempty (chisq), true);
670s ***** test
670s  [h, pval, chisq] = mcnemar_test ([59,6;16,80], 0.01);
670s  assert (h, 0);
670s  assert (pval, 0.034690, 1e-6);
670s  assert (isempty (chisq), true);
670s ***** test
670s  [h, pval, chisq] = mcnemar_test ([59,6;16,80], "mid-p");
670s  assert (h, 1);
670s  assert (pval, 0.034690, 1e-6);
670s  assert (isempty (chisq), true);
670s ***** test
670s  [h, pval, chisq] = mcnemar_test ([59,6;16,80], "asymptotic");
670s  assert (h, 1);
670s  assert (pval, 0.033006, 1e-6);
670s  assert (chisq, 4.5455, 1e-4);
670s ***** test
670s  [h, pval, chisq] = mcnemar_test ([59,6;16,80], "exact");
670s  assert (h, 0);
670s  assert (pval, 0.052479, 1e-6);
670s  assert (isempty (chisq), true);
670s ***** test
670s  [h, pval, chisq] = mcnemar_test ([59,6;16,80], "corrected");
670s  assert (h, 0);
670s  assert (pval, 0.055009, 1e-6);
670s  assert (chisq, 3.6818, 1e-4);
670s ***** test
670s  [h, pval, chisq] = mcnemar_test ([59,6;16,80], 0.1, "corrected");
670s  assert (h, 1);
670s  assert (pval, 0.055009, 1e-6);
670s  assert (chisq, 3.6818, 1e-4);
670s ***** error<mcnemar_test: too many input arguments.> mcnemar_test (59, 6, 16, 80)
670s ***** error<mcnemar_test: X must be a 2x2 matrix.> mcnemar_test (ones (3, 3))
670s ***** error<mcnemar_test: all entries of X must be non-negative integers.> ...
670s  mcnemar_test ([59,6;16,-80])
670s ***** error<mcnemar_test: all entries of X must be non-negative integers.> ...
670s  mcnemar_test ([59,6;16,4.5])
670s ***** error<mcnemar_test: invalid 2nd input argument.> ...
670s  mcnemar_test ([59,6;16,80], {""})
670s ***** error<mcnemar_test: invalid value for ALPHA.> ...
670s  mcnemar_test ([59,6;16,80], -0.2)
670s ***** error<mcnemar_test: invalid value for ALPHA.> ...
670s  mcnemar_test ([59,6;16,80], [0.05, 0.1])
670s ***** error<mcnemar_test: invalid value for ALPHA.> ...
670s  mcnemar_test ([59,6;16,80], 1)
670s ***** error<mcnemar_test: invalid value for TESTTYPE.> ...
670s  mcnemar_test ([59,6;16,80], "")
670s 17 tests, 17 passed, 0 known failure, 0 skipped
670s [inst/mhsample.m]
670s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/mhsample.m
670s ***** demo
670s  ## Define function to sample
670s  d = 2;
670s  mu = [-1; 2];
670s  rand ("seed", 5)  # for reproducibility
670s  Sigma = rand (d);
670s  Sigma = (Sigma + Sigma');
670s  Sigma += eye (d) * abs (eigs (Sigma, 1, "sa")) * 1.1;
670s  pdf = @(x)(2*pi)^(-d/2)*det(Sigma)^-.5*exp(-.5*sum((x.'-mu).*(Sigma\(x.'-mu)),1));
670s  ## Inputs
670s  start = ones (1, 2);
670s  nsamples = 500;
670s  sym = true;
670s  K = 500;
670s  m = 10;
670s  rand ("seed", 8)  # for reproducibility
670s  proprnd = @(x) (rand (size (x)) - .5) * 3 + x;
670s  [smpl, accept] = mhsample (start, nsamples, "pdf", pdf, "proprnd", proprnd, ...
670s                             "symmetric", sym, "burnin", K, "thin", m);
670s  figure;
670s  hold on;
670s  plot (smpl(:, 1), smpl(:, 2), 'x');
670s  [x, y] = meshgrid (linspace (-6, 4), linspace(-3, 7));
670s  z = reshape (pdf ([x(:), y(:)]), size(x));
670s  mesh (x, y, z, "facecolor", "None");
670s  ## Using sample points to find the volume of half a sphere with radius of .5
670s  f = @(x) ((.25-(x(:,1)+1).^2-(x(:,2)-2).^2).^.5.*(((x(:,1)+1).^2+(x(:,2)-2).^2)<.25)).';
670s  int = mean (f (smpl) ./ pdf (smpl));
670s  errest = std (f (smpl) ./ pdf (smpl)) / nsamples ^ .5;
670s  trueerr = abs (2 / 3 * pi * .25 ^ (3 / 2) - int);
670s  printf ("Monte Carlo integral estimate int f(x) dx = %f\n", int);
670s  printf ("Monte Carlo integral error estimate %f\n", errest);
670s  printf ("The actual error %f\n", trueerr);
670s  mesh (x, y, reshape (f([x(:), y(:)]), size(x)), "facecolor", "None");
670s ***** demo
670s  ## Integrate truncated normal distribution to find normalization constant
670s  pdf = @(x) exp (-.5*x.^2)/(pi^.5*2^.5);
670s  nsamples = 1e3;
670s  rand ("seed", 5)  # for reproducibility
670s  proprnd = @(x) (rand (size (x)) - .5) * 3 + x;
670s  [smpl, accept] = mhsample (1, nsamples, "pdf", pdf, "proprnd", proprnd, ...
670s                             "symmetric", true, "thin", 4);
670s  f = @(x) exp(-.5 * x .^ 2) .* (x >= -2 & x <= 2);
670s  x = linspace (-3, 3, 1000);
670s  area(x, f(x));
670s  xlabel ('x');
670s  ylabel ('f(x)');
670s  int = mean (f (smpl) ./ pdf (smpl));
670s  errest = std (f (smpl) ./ pdf (smpl)) / nsamples^ .5;
670s  trueerr = abs (erf (2 ^ .5) * 2 ^ .5 * pi ^ .5 - int);
670s  printf ("Monte Carlo integral estimate int f(x) dx = %f\n", int);
670s  printf ("Monte Carlo integral error estimate %f\n", errest);
670s  printf ("The actual error %f\n", trueerr);
670s ***** test
670s  nchain = 1e4;
670s  start = rand (nchain, 1);
670s  nsamples = 1e3;
670s  pdf = @(x) exp (-.5*(x-1).^2)/(2*pi)^.5;
670s  proppdf = @(x, y) 1/3;
670s  proprnd = @(x) 3 * (rand (size (x)) - .5) + x;
670s  [smpl, accept] = mhsample (start, nsamples, "pdf", pdf, "proppdf", proppdf, ...
670s                             "proprnd", proprnd, "thin", 2, "nchain", nchain, ...
670s                             "burnin", 0);
670s  assert (mean (mean (smpl, 1), 3), 1, .01);
670s  assert (mean (var (smpl, 1), 3), 1, .01)
676s ***** error mhsample ();
676s ***** error mhsample (1);
676s ***** error mhsample (1, 1);
676s ***** error mhsample (1, 1, "pdf", @(x)x);
676s ***** error mhsample (1, 1, "pdf", @(x)x, "proprnd", @(x)x+rand(size(x)));
676s 6 tests, 6 passed, 0 known failure, 0 skipped
676s [inst/mnrfit.m]
676s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/mnrfit.m
676s ***** error<mnrfit: too few input arguments.> mnrfit (ones (50,1))
676s ***** error<mnrfit: Predictors must be numeric.> ...
676s  mnrfit ({1 ;2 ;3 ;4 ;5}, ones (5,1))
676s ***** error<mnrfit: Predictors must be a vector or a 2D matrix.> ...
676s  mnrfit (ones (50, 4, 2), ones (50, 1))
676s ***** error<mnrfit: Response must be a vector or a 2D matrix.> ...
676s  mnrfit (ones (50, 4), ones (50, 1, 3))
676s ***** error<mnrfit: Y must have the same number of rows as X.> ...
676s  mnrfit (ones (50, 4), ones (45,1))
676s ***** error<mnrfit: Response labels must be a character array, a cell vector> ...
676s  mnrfit (ones (5, 4), {1 ;2 ;3 ;4 ;5})
676s ***** error<mnrfit: optional arguments must be in pairs.> ...
676s  mnrfit (ones (5, 4), ones (5, 1), "model")
676s ***** error<mnrfit: Y must be a column vector when given as cellstr.> ...
676s  mnrfit (ones (5, 4), {"q","q";"w","w";"q","q";"w","w";"q","q"})
676s ***** error<mnrfit: Y must contain only 1 and 0 when given as a 2D matrix.> ...
676s  mnrfit (ones (5, 4), [1, 2; 1, 2; 1, 2; 1, 2; 1, 2])
676s ***** error<mnrfit: Y must contain positive integer category numbers.> ...
676s  mnrfit (ones (5, 4), [1; -1; 1; 2; 1])
676s ***** error<mnrfit: fitting more than 2 nominal responses not supported.> ...
676s  mnrfit (ones (5, 4), [1; 2; 3; 2; 1], "model", "nominal")
676s ***** error<mnrfit: fitting hierarchical responses not supported.> ...
676s  mnrfit (ones (5, 4), [1; 2; 3; 2; 1], "model", "hierarchical")
676s ***** error<mnrfit: model type not recognised.> ...
676s  mnrfit (ones (5, 4), [1; 2; 3; 2; 1], "model", "whatever")
676s 13 tests, 13 passed, 0 known failure, 0 skipped
676s [inst/monotone_smooth.m]
676s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/monotone_smooth.m
676s ***** error<Invalid call to monotone_smooth.  Correct usage is> ...
676s  monotone_smooth (1)
676s ***** error<monotone_smooth: X must be a numeric vector.> ...
676s  monotone_smooth ("char", 1)
676s ***** error<monotone_smooth: X must be a numeric vector.> ...
676s  monotone_smooth ({1,2,3}, 1)
676s ***** error<monotone_smooth: X must be a numeric vector.> ...
676s  monotone_smooth (ones(20,3), 1)
676s ***** error<monotone_smooth: Y must be a numeric vector.> ...
676s  monotone_smooth (1, "char")
676s ***** error<monotone_smooth: Y must be a numeric vector.> ...
676s  monotone_smooth (1, {1,2,3})
676s ***** error<monotone_smooth: Y must be a numeric vector.> ...
676s  monotone_smooth (1, ones(20,3))
676s ***** error<monotone_smooth: H>  monotone_smooth (ones (10,1), ones(10,1), [1, 2])
676s ***** error<monotone_smooth: H>  monotone_smooth (ones (10,1), ones(10,1), {2})
676s ***** error<monotone_smooth: H>  monotone_smooth (ones (10,1), ones(10,1), "char")
676s 10 tests, 10 passed, 0 known failure, 0 skipped
676s [inst/multcompare.m]
676s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/multcompare.m
676s ***** demo
676s 
676s  ## Demonstration using balanced one-way ANOVA from anova1
676s 
676s  x = ones (50, 4) .* [-2, 0, 1, 5];
676s  randn ("seed", 1);    # for reproducibility
676s  x = x + normrnd (0, 2, 50, 4);
676s  groups = {"A", "B", "C", "D"};
676s  [p, tbl, stats] = anova1 (x, groups, "off");
676s  multcompare (stats);
676s ***** demo
676s 
676s  ## Demonstration using unbalanced one-way ANOVA example from anovan
676s 
676s  dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
676s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
676s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
676s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
676s         25.694 ]';
676s  g = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 ...
676s       4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
676s 
676s  [P,ATAB, STATS] = anovan (dv, g, "varnames", "score", "display", "off");
676s 
676s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "holm", ...
676s                                   "ControlGroup", 1, "display", "on")
676s 
676s ***** demo
676s 
676s  ## Demonstration using factorial ANCOVA example from anovan
676s 
676s  score = [95.6 82.2 97.2 96.4 81.4 83.6 89.4 83.8 83.3 85.7 ...
676s  97.2 78.2 78.9 91.8 86.9 84.1 88.6 89.8 87.3 85.4 ...
676s  81.8 65.8 68.1 70.0 69.9 75.1 72.3 70.9 71.5 72.5 ...
676s  84.9 96.1 94.6 82.5 90.7 87.0 86.8 93.3 87.6 92.4 ...
676s  100. 80.5 92.9 84.0 88.4 91.1 85.7 91.3 92.3 87.9 ...
676s  91.7 88.6 75.8 75.7 75.3 82.4 80.1 86.0 81.8 82.5]';
676s  treatment = {"yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
676s               "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
676s               "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
676s               "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
676s               "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
676s               "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"}';
676s  exercise = {"lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
676s              "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
676s              "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  ...
676s              "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
676s              "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
676s              "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"}';
676s  age = [59 65 70 66 61 65 57 61 58 55 62 61 60 59 55 57 60 63 62 57 ...
676s  58 56 57 59 59 60 55 53 55 58 68 62 61 54 59 63 60 67 60 67 ...
676s  75 54 57 62 65 60 58 61 65 57 56 58 58 58 52 53 60 62 61 61]';
676s 
676s  [P, ATAB, STATS] = anovan (score, {treatment, exercise, age}, "model", ...
676s                             [1 0 0; 0 1 0; 0 0 1; 1 1 0], "continuous", 3, ...
676s                             "sstype", "h", "display", "off", "contrasts", ...
676s                             {"simple","poly",""});
676s 
676s  [C, M, H, GNAMES] = multcompare (STATS, "dim", [1 2], "ctype", "holm", ...
676s                                   "display", "on")
676s 
676s ***** demo
676s 
676s  ## Demonstration using one-way ANOVA from anovan, with fit by weighted least
676s  ## squares to account for heteroskedasticity.
676s 
676s  g = [1, 1, 1, 1, 1, 1, 1, 1, ...
676s       2, 2, 2, 2, 2, 2, 2, 2, ...
676s       3, 3, 3, 3, 3, 3, 3, 3]';
676s 
676s  y = [13, 16, 16,  7, 11,  5,  1,  9, ...
676s       10, 25, 66, 43, 47, 56,  6, 39, ...
676s       11, 39, 26, 35, 25, 14, 24, 17]';
676s 
676s  [P,ATAB,STATS] = anovan(y, g, "display", "off");
676s  fitted = STATS.X * STATS.coeffs(:,1); # fitted values
676s  b = polyfit (fitted, abs (STATS.resid), 1);
676s  v = polyval (b, fitted);  # Variance as a function of the fitted values
676s  [P,ATAB,STATS] = anovan (y, g, "weights", v.^-1, "display", "off");
676s  [C, M] =  multcompare (STATS, "display", "on", "ctype", "mvt")
676s ***** demo
676s 
676s  ## Demonstration of p-value adjustments to control the false discovery rate
676s  ## Data from Westfall (1997) JASA. 92(437):299-306
676s 
676s  p = [.005708; .023544; .024193; .044895; ...
676s        .048805; .221227; .395867; .693051; .775755];
676s 
676s  padj = multcompare(p,'ctype','fdr')
676s ***** test
676s 
676s  ## Tests using unbalanced one-way ANOVA example from anovan and anova1
676s 
676s  ## Test for anovan - compare pairwise comparisons with matlab for CTYPE "lsd"
676s 
676s  dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
676s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
676s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
676s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
676s         25.694 ]';
676s  g = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 ...
676s       4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
676s 
676s  [P, ATAB, STATS] = anovan (dv, g, "varnames", "score", "display", "off");
676s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "lsd", ...
676s                                   "display", "off");
676s  assert (C(1,6), 2.85812420217898e-05, 1e-09);
676s  assert (C(2,6), 5.22936741204085e-07, 1e-09);
676s  assert (C(3,6), 2.12794763209146e-08, 1e-09);
676s  assert (C(4,6), 7.82091664406946e-15, 1e-09);
676s  assert (C(5,6), 0.546591417210693, 1e-09);
676s  assert (C(6,6), 0.0845897945254446, 1e-09);
676s  assert (C(7,6), 9.47436557975328e-08, 1e-09);
676s  assert (C(8,6), 0.188873478781067, 1e-09);
676s  assert (C(9,6), 4.08974010364197e-08, 1e-09);
676s  assert (C(10,6), 4.44427348175241e-06, 1e-09);
676s  assert (M(1,1), 10, 1e-09);
676s  assert (M(2,1), 18, 1e-09);
676s  assert (M(3,1), 19, 1e-09);
676s  assert (M(4,1), 21.0001428571429, 1e-09);
676s  assert (M(5,1), 29.0001111111111, 1e-09);
676s  assert (M(1,2), 1.0177537954095, 1e-09);
676s  assert (M(2,2), 1.28736803631001, 1e-09);
676s  assert (M(3,2), 1.0177537954095, 1e-09);
676s  assert (M(4,2), 1.0880245732889, 1e-09);
676s  assert (M(5,2), 0.959547480416536, 1e-09);
676s 
676s  ## Compare "fdr" adjusted p-values to those obtained using p.adjust in R
676s 
676s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "fdr", ...
676s                                   "display", "off");
676s  assert (C(1,6), 4.08303457454140e-05, 1e-09);
676s  assert (C(2,6), 1.04587348240817e-06, 1e-09);
676s  assert (C(3,6), 1.06397381604573e-07, 1e-09);
676s  assert (C(4,6), 7.82091664406946e-14, 1e-09);
676s  assert (C(5,6), 5.46591417210693e-01, 1e-09);
676s  assert (C(6,6), 1.05737243156806e-01, 1e-09);
676s  assert (C(7,6), 2.36859139493832e-07, 1e-09);
676s  assert (C(8,6), 2.09859420867852e-01, 1e-09);
676s  assert (C(9,6), 1.36324670121399e-07, 1e-09);
676s  assert (C(10,6), 7.40712246958735e-06, 1e-09);
676s 
676s  ## Compare "hochberg" adjusted p-values to those obtained using p.adjust in R
676s 
676s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "hochberg", ...
676s                                   "display", "off");
676s  assert (C(1,6), 1.14324968087159e-04, 1e-09);
676s  assert (C(2,6), 3.13762044722451e-06, 1e-09);
676s  assert (C(3,6), 1.91515286888231e-07, 1e-09);
676s  assert (C(4,6), 7.82091664406946e-14, 1e-09);
676s  assert (C(5,6), 5.46591417210693e-01, 1e-09);
676s  assert (C(6,6), 2.53769383576334e-01, 1e-09);
676s  assert (C(7,6), 6.63205590582730e-07, 1e-09);
676s  assert (C(8,6), 3.77746957562134e-01, 1e-09);
676s  assert (C(9,6), 3.27179208291358e-07, 1e-09);
676s  assert (C(10,6), 2.22213674087620e-05, 1e-09);
676s 
676s  ## Compare "holm" adjusted p-values to those obtained using p.adjust in R
676s 
676s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "holm", ...
676s                                   "display", "off");
676s  assert (C(1,6), 1.14324968087159e-04, 1e-09);
676s  assert (C(2,6), 3.13762044722451e-06, 1e-09);
676s  assert (C(3,6), 1.91515286888231e-07, 1e-09);
676s  assert (C(4,6), 7.82091664406946e-14, 1e-09);
676s  assert (C(5,6), 5.46591417210693e-01, 1e-09);
676s  assert (C(6,6), 2.53769383576334e-01, 1e-09);
676s  assert (C(7,6), 6.63205590582730e-07, 1e-09);
676s  assert (C(8,6), 3.77746957562134e-01, 1e-09);
676s  assert (C(9,6), 3.27179208291358e-07, 1e-09);
676s  assert (C(10,6), 2.22213674087620e-05, 1e-09);
676s 
676s  ## Compare "scheffe" adjusted p-values to those obtained using 'scheffe' in Matlab
676s 
676s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "scheffe", ...
676s                                   "display", "off");
676s  assert (C(1,6), 0.00108105386141085, 1e-09);
676s  assert (C(2,6), 2.7779386789517e-05, 1e-09);
676s  assert (C(3,6), 1.3599854038198e-06, 1e-09);
676s  assert (C(4,6), 7.58830197867751e-13, 1e-09);
676s  assert (C(5,6), 0.984039948220281, 1e-09);
676s  assert (C(6,6), 0.539077018557706, 1e-09);
676s  assert (C(7,6), 5.59475764460574e-06, 1e-09);
676s  assert (C(8,6), 0.771173490574105, 1e-09);
676s  assert (C(9,6), 2.52838425729905e-06, 1e-09);
676s  assert (C(10,6), 0.000200719143889168, 1e-09);
676s 
676s  ## Compare "bonferroni" adjusted p-values to those obtained using p.adjust in R
676s 
676s  [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "bonferroni", ...
676s                                   "display", "off");
676s  assert (C(1,6), 2.85812420217898e-04, 1e-09);
676s  assert (C(2,6), 5.22936741204085e-06, 1e-09);
676s  assert (C(3,6), 2.12794763209146e-07, 1e-09);
676s  assert (C(4,6), 7.82091664406946e-14, 1e-09);
676s  assert (C(5,6), 1.00000000000000e+00, 1e-09);
676s  assert (C(6,6), 8.45897945254446e-01, 1e-09);
676s  assert (C(7,6), 9.47436557975328e-07, 1e-09);
676s  assert (C(8,6), 1.00000000000000e+00, 1e-09);
676s  assert (C(9,6), 4.08974010364197e-07, 1e-09);
676s  assert (C(10,6), 4.44427348175241e-05, 1e-09);
676s 
676s  ## Test for anova1 ("equal")- comparison of results from Matlab
676s 
676s  [P, ATAB, STATS] = anova1 (dv, g, "off", "equal");
676s  [C, M, H, GNAMES] = multcompare (STATS, "ctype", "lsd", "display", "off");
676s  assert (C(1,6), 2.85812420217898e-05, 1e-09);
676s  assert (C(2,6), 5.22936741204085e-07, 1e-09);
676s  assert (C(3,6), 2.12794763209146e-08, 1e-09);
676s  assert (C(4,6), 7.82091664406946e-15, 1e-09);
676s  assert (C(5,6), 0.546591417210693, 1e-09);
676s  assert (C(6,6), 0.0845897945254446, 1e-09);
676s  assert (C(7,6), 9.47436557975328e-08, 1e-09);
676s  assert (C(8,6), 0.188873478781067, 1e-09);
676s  assert (C(9,6), 4.08974010364197e-08, 1e-09);
676s  assert (C(10,6), 4.44427348175241e-06, 1e-09);
676s  assert (M(1,1), 10, 1e-09);
676s  assert (M(2,1), 18, 1e-09);
676s  assert (M(3,1), 19, 1e-09);
676s  assert (M(4,1), 21.0001428571429, 1e-09);
676s  assert (M(5,1), 29.0001111111111, 1e-09);
676s  assert (M(1,2), 1.0177537954095, 1e-09);
676s  assert (M(2,2), 1.28736803631001, 1e-09);
676s  assert (M(3,2), 1.0177537954095, 1e-09);
676s  assert (M(4,2), 1.0880245732889, 1e-09);
676s  assert (M(5,2), 0.959547480416536, 1e-09);
676s 
676s  ## Test for anova1 ("unequal") - comparison with results from GraphPad Prism 8
676s  [P, ATAB, STATS] = anova1 (dv, g, "off", "unequal");
676s  [C, M, H, GNAMES] = multcompare (STATS, "ctype", "lsd", "display", "off");
676s  assert (C(1,6), 0.001247025266382, 1e-09);
676s  assert (C(2,6), 0.000018037115146, 1e-09);
676s  assert (C(3,6), 0.000002974595187, 1e-09);
676s  assert (C(4,6), 0.000000000786046, 1e-09);
676s  assert (C(5,6), 0.5693192886650109, 1e-09);
676s  assert (C(6,6), 0.110501699029776, 1e-09);
676s  assert (C(7,6), 0.000131226488700, 1e-09);
676s  assert (C(8,6), 0.1912101409715992, 1e-09);
676s  assert (C(9,6), 0.000005385256394, 1e-09);
676s  assert (C(10,6), 0.000074089106171, 1e-09);
677s ***** test
677s 
677s  ## Test for anova2 ("interaction") - comparison with results from Matlab for column effect
677s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
677s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
677s  [P, ATAB, STATS] = anova2 (popcorn, 3, "off");
677s  [C, M, H, GNAMES] = multcompare (STATS, "estimate", "column",...
677s                                   "ctype", "lsd", "display", "off");
677s  assert (C(1,6), 1.49311100811177e-05, 1e-09);
677s  assert (C(2,6), 2.20506904243535e-07, 1e-09);
677s  assert (C(3,6), 0.00449897860490058, 1e-09);
677s  assert (M(1,1), 6.25, 1e-09);
677s  assert (M(2,1), 4.75, 1e-09);
677s  assert (M(3,1), 4, 1e-09);
677s  assert (M(1,2), 0.152145154862547, 1e-09);
677s  assert (M(2,2), 0.152145154862547, 1e-09);
677s  assert (M(3,2), 0.152145154862547, 1e-09);
677s ***** test
677s 
677s  ## Test for anova2 ("linear") - comparison with results from GraphPad Prism 8
677s  words = [10 13 13; 6 8 8; 11 14 14; 22 23 25; 16 18 20; ...
677s           15 17 17; 1 1 4; 12 15 17;  9 12 12;  8 9 12];
677s  [P, ATAB, STATS] = anova2 (words, 1, "off", "linear");
677s  [C, M, H, GNAMES] = multcompare (STATS, "estimate", "column",...
677s                                   "ctype", "lsd", "display", "off");
677s  assert (C(1,6), 0.000020799832702, 1e-09);
677s  assert (C(2,6), 0.000000035812410, 1e-09);
677s  assert (C(3,6), 0.003038942449215, 1e-09);
677s ***** test
677s 
677s  ## Test for anova2 ("nested") - comparison with results from GraphPad Prism 8
677s  data = [4.5924 7.3809 21.322; -0.5488 9.2085 25.0426; ...
677s          6.1605 13.1147 22.66; 2.3374 15.2654 24.1283; ...
677s          5.1873 12.4188 16.5927; 3.3579 14.3951 10.2129; ...
677s          6.3092 8.5986 9.8934; 3.2831 3.4945 10.0203];
677s  [P, ATAB, STATS] = anova2 (data, 4, "off", "nested");
677s  [C, M, H, GNAMES] = multcompare (STATS, "estimate", "column",...
677s                                   "ctype", "lsd", "display", "off");
677s  assert (C(1,6), 0.261031111511073, 1e-09);
677s  assert (C(2,6), 0.065879755907745, 1e-09);
677s  assert (C(3,6), 0.241874613529270, 1e-09);
677s ***** shared visibility_setting
677s  visibility_setting = get (0, "DefaultFigureVisible");
677s ***** test
677s  set (0, "DefaultFigureVisible", "off");
677s 
677s  ## Test for kruskalwallis - comparison with results from MATLAB
677s  data = [3,2,4; 5,4,4; 4,2,4; 4,2,4; 4,1,5; ...
677s          4,2,3; 4,3,5; 4,2,4; 5,2,4; 5,3,3];
677s  group = [1:3] .* ones (10,3);
677s  [P, ATAB, STATS] = kruskalwallis (data(:), group(:), "off");
677s  C = multcompare (STATS, "ctype", "lsd", "display", "off");
677s  assert (C(1,6), 0.000163089828959986, 1e-09);
677s  assert (C(2,6), 0.630298044801257, 1e-09);
677s  assert (C(3,6), 0.00100567660695682, 1e-09);
677s  C = multcompare (STATS, "ctype", "bonferroni", "display", "off");
677s  assert (C(1,6), 0.000489269486879958, 1e-09);
677s  assert (C(2,6), 1, 1e-09);
677s  assert (C(3,6), 0.00301702982087047, 1e-09);
677s  C = multcompare(STATS, "ctype", "scheffe", "display", "off");
677s  assert (C(1,6), 0.000819054880289573, 1e-09);
677s  assert (C(2,6), 0.890628039849261, 1e-09);
677s  assert (C(3,6), 0.00447816059021654, 1e-09);
677s  set (0, "DefaultFigureVisible", visibility_setting);
677s ***** test
677s  set (0, "DefaultFigureVisible", "off");
677s  ## Test for friedman - comparison with results from MATLAB
677s  popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
677s             6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
677s  [P, ATAB, STATS] = friedman (popcorn, 3, "off");
677s  C = multcompare(STATS, "ctype", "lsd", "display", "off");
677s  assert (C(1,6), 0.227424558028569, 1e-09);
677s  assert (C(2,6), 0.0327204848315735, 1e-09);
677s  assert (C(3,6), 0.353160353315988, 1e-09);
677s  C = multcompare(STATS, "ctype", "bonferroni", "display", "off");
677s  assert (C(1,6), 0.682273674085708, 1e-09);
677s  assert (C(2,6), 0.0981614544947206, 1e-09);
677s  assert (C(3,6), 1, 1e-09);
677s  C = multcompare(STATS, "ctype", "scheffe", "display", "off");
677s  assert (C(1,6), 0.482657360384373, 1e-09);
677s  assert (C(2,6), 0.102266573027672, 1e-09);
677s  assert (C(3,6), 0.649836502233148, 1e-09);
677s  set (0, "DefaultFigureVisible", visibility_setting);
677s ***** test
677s  set (0, "DefaultFigureVisible", "off");
677s  ## Test for fitlm - same comparisons as for first anovan example
677s  y =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
677s         22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
677s         22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
677s         22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
677s         25.694 ]';
677s  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]';
677s  [TAB,STATS] = fitlm (X,y,"linear","categorical",1,"display","off",...
677s                       "contrasts","simple");
677s  [C, M] = multcompare(STATS, "ctype", "lsd", "display", "off");
677s  assert (C(1,6), 2.85812420217898e-05, 1e-09);
677s  assert (C(2,6), 5.22936741204085e-07, 1e-09);
677s  assert (C(3,6), 2.12794763209146e-08, 1e-09);
677s  assert (C(4,6), 7.82091664406946e-15, 1e-09);
677s  assert (C(5,6), 0.546591417210693, 1e-09);
677s  assert (C(6,6), 0.0845897945254446, 1e-09);
677s  assert (C(7,6), 9.47436557975328e-08, 1e-09);
677s  assert (C(8,6), 0.188873478781067, 1e-09);
677s  assert (C(9,6), 4.08974010364197e-08, 1e-09);
677s  assert (C(10,6), 4.44427348175241e-06, 1e-09);
677s  assert (M(1,1), 10, 1e-09);
677s  assert (M(2,1), 18, 1e-09);
677s  assert (M(3,1), 19, 1e-09);
677s  assert (M(4,1), 21.0001428571429, 1e-09);
677s  assert (M(5,1), 29.0001111111111, 1e-09);
677s  assert (M(1,2), 1.0177537954095, 1e-09);
677s  assert (M(2,2), 1.28736803631001, 1e-09);
677s  assert (M(3,2), 1.0177537954095, 1e-09);
677s  assert (M(4,2), 1.0880245732889, 1e-09);
677s  assert (M(5,2), 0.959547480416536, 1e-09);
677s  set (0, "DefaultFigureVisible", visibility_setting);
677s ***** test
677s  ## Test p-value adjustments compared to R stats package function p.adjust
677s  ## Data from Westfall (1997) JASA. 92(437):299-306
677s  p = [.005708; .023544; .024193; .044895; ...
677s        .048805; .221227; .395867; .693051; .775755];
677s  padj = multcompare (p);
677s  assert (padj(1), 0.051372, 1e-06);
677s  assert (padj(2), 0.188352, 1e-06);
677s  assert (padj(3), 0.188352, 1e-06);
677s  assert (padj(4), 0.269370, 1e-06);
677s  assert (padj(5), 0.269370, 1e-06);
677s  assert (padj(6), 0.884908, 1e-06);
677s  assert (padj(7), 1.000000, 1e-06);
677s  assert (padj(8), 1.000000, 1e-06);
677s  assert (padj(9), 1.000000, 1e-06);
677s  padj = multcompare(p,'ctype','holm');
677s  assert (padj(1), 0.051372, 1e-06);
677s  assert (padj(2), 0.188352, 1e-06);
677s  assert (padj(3), 0.188352, 1e-06);
677s  assert (padj(4), 0.269370, 1e-06);
677s  assert (padj(5), 0.269370, 1e-06);
677s  assert (padj(6), 0.884908, 1e-06);
677s  assert (padj(7), 1.000000, 1e-06);
677s  assert (padj(8), 1.000000, 1e-06);
677s  assert (padj(9), 1.000000, 1e-06);
677s  padj = multcompare(p,'ctype','hochberg');
677s  assert (padj(1), 0.051372, 1e-06);
677s  assert (padj(2), 0.169351, 1e-06);
677s  assert (padj(3), 0.169351, 1e-06);
677s  assert (padj(4), 0.244025, 1e-06);
677s  assert (padj(5), 0.244025, 1e-06);
677s  assert (padj(6), 0.775755, 1e-06);
677s  assert (padj(7), 0.775755, 1e-06);
677s  assert (padj(8), 0.775755, 1e-06);
677s  assert (padj(9), 0.775755, 1e-06);
677s  padj = multcompare(p,'ctype','fdr');
677s  assert (padj(1), 0.0513720, 1e-07);
677s  assert (padj(2), 0.0725790, 1e-07);
677s  assert (padj(3), 0.0725790, 1e-07);
677s  assert (padj(4), 0.0878490, 1e-07);
677s  assert (padj(5), 0.0878490, 1e-07);
677s  assert (padj(6), 0.3318405, 1e-07);
677s  assert (padj(7), 0.5089719, 1e-07);
677s  assert (padj(8), 0.7757550, 1e-07);
677s  assert (padj(9), 0.7757550, 1e-07);
677s 8 tests, 8 passed, 0 known failure, 0 skipped
677s [inst/multiway.m]
677s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/multiway.m
677s ***** test
677s  numbers = [4, 5, 6, 7, 8];
677s  num_parts = 2;
677s  [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "completeKK");
677s  assert (sort (cellfun (@sum, partition)), sort ([15, 15]));
677s ***** test
677s  numbers = [1, 2, 3, 4, 5, 6];
677s  num_parts = 3;
677s  [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "completeKK");
677s  assert (sort (cellfun (@sum, partition)), sort ([7, 7, 7]));
677s ***** test
677s  numbers = [24, 21, 18, 17, 12, 11, 8, 2];
677s  num_parts = 3;
677s  [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "completeKK");
677s  assert (sort (cellfun (@sum, partition)), sort ([38, 38, 37]));
677s ***** test
677s  numbers = [10, 10, 10];
677s  num_parts = 3;
677s  [~, partition] = multiway (numbers, num_parts, "completeKK");
677s  assert (sort (cellfun (@sum, partition)), [10, 10, 10]);
677s ***** test
677s  numbers = 1:10;
677s  num_parts = 2;
677s  [~, partition] = multiway (numbers, num_parts, "completeKK");
677s  assert (sort (cellfun (@sum, partition)), [27, 28]);
677s ***** test
677s  numbers = [4, 5, 6, 7, 8];
677s  num_parts = 2;
677s  [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "greedy");
677s  assert (sort (cellfun (@sum, partition)), sort ([13, 17]));
677s ***** test
677s  numbers = [1, 2, 3, 4, 5, 6];
677s  num_parts = 3;
677s  [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "greedy");
677s  assert (sort (cellfun (@sum, partition)), sort ([7, 7, 7]));
677s ***** test
677s  numbers = [10, 7, 5, 5, 6, 4, 10, 11, 12, 9, 10, 4, 3, 4, 5];
677s  num_parts = 4;
677s  [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "greedy");
677s  assert (sort (cellfun (@sum, partition)), sort ([27, 27, 27, 24]));
677s ***** test
677s  numbers = [24, 21, 18, 17, 12, 11, 8, 2];
677s  num_parts = 3;
677s  [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "greedy");
677s  assert (sort (cellfun (@sum, partition)), sort ([35, 37, 41]));
677s ***** test
677s  numbers = [10, 10, 10];
677s  num_parts = 3;
677s  [~, partition] = multiway (numbers, num_parts, "greedy");
677s  assert (sort (cellfun (@sum, partition)), [10, 10, 10]);
677s ***** test
677s  numbers = 1:10;
677s  num_parts = 2;
677s  [~, partition] = multiway (numbers, num_parts, "greedy");
677s  assert (sort (cellfun (@sum, partition)), [27, 28]);
677s ***** test
677s  grpidx_ckk = multiway ([3 2 4 3 9 3 64], 3);
677s  grpidx_greedy = multiway ([3 2 4 3 9 3 64], 3, 'greedy');
677s  assert (isequal (grpidx_ckk, grpidx_greedy), false);
678s ***** test
678s  numbers = [4; 5; 6; 7; 8];
678s  num_parts = 2;
678s  [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "completeKK");
678s  assert (iscolumn (groupindex), true)
678s  assert (iscolumn (groupsizes), true);
678s  assert (sort (cellfun (@sum, partition)), sort ([15, 15]));
678s  numbers = [4; 5; 6; 7; 8];
678s  num_parts = 2;
678s  [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "greedy");
678s  assert (iscolumn (groupindex), true)
678s  assert (iscolumn (groupsizes), true);
678s  assert (sort (cellfun (@sum, partition)), sort ([13, 17]));
678s ***** test
678s  numbers = [4, 5, 6, 7, 8];
678s  num_parts = 2;
678s  [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "completeKK");
678s  assert (isrow (groupindex), true)
678s  assert (isrow (groupsizes), true);
678s  assert (sort (cellfun (@sum, partition)), sort ([15, 15]));
678s ***** test
678s  numbers = [4, 5, 6, 7, 8];
678s  num_parts = 2;
678s  [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "greedy");
678s  assert (isrow (groupindex), true)
678s  assert (isrow (groupsizes), true);
678s  assert (sort (cellfun (@sum, partition)), sort ([13, 17]));
678s ***** error<multiway: too few input arguments.> multiway ()
678s ***** error<multiway: too few input arguments.> multiway ([1, 2])
678s ***** error<multiway: METHOD value must be a character vector.> ...
678s  multiway ([1, 2, 3], 2, 1)
678s ***** error<multiway: NUMBERS must be a non-empty vector.> multiway ([], 2)
678s ***** error<multiway: NUMBERS must be a non-empty vector.> multiway (ones (2, 2), 2)
678s ***** error<multiway: NUMBERS must be numeric and cannot contain NaN values.> ...
678s  multiway ({1, 2, 3}, 2)
678s ***** error<multiway: NUMBERS must be non-negative.> multiway ([1, -2, 3], 2)
678s ***** error<multiway: NUMBERS must be numeric and cannot contain NaN values.> ...
678s  multiway ([1, 2, NaN], 2)
678s ***** error<multiway: NUM_PARTS must be a scalar.> multiway ([1,2,3], [1,2])
678s ***** error<multiway: NUM_PARTS must be a real numeric value.> ...
678s  multiway ([1, 2, 3], "2")
678s ***** error<multiway: NUM_PARTS must be a positive integer.> multiway ([1, 2, 3], 0)
678s ***** error<multiway: NUM_PARTS must be a positive integer.> multiway ([1, 2, 3], 1.5)
678s ***** error<multiway: NUM_PARTS must be a positive integer.> multiway ([1, 2, 3], -1)
678s ***** error<multiway: NUM_PARTS cannot be greater than number of elements in NUMBERS.> ...
678s  multiway ([1, 2], 3)
678s ***** error <multiway: unsupported method 'greedyalgo'.> ...
678s  multiway ([1,2,3], 2, "greedyalgo")
678s 30 tests, 30 passed, 0 known failure, 0 skipped
678s [inst/nanmax.m]
678s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/nanmax.m
678s ***** demo
678s  ## Find the column maximum values and their indices
678s  ## for matrix data with missing values.
678s 
678s  x = magic (3);
678s  x([1, 6:9]) = NaN
678s  [y, ind] = nanmax (x)
678s ***** demo
678s  ## Find the maximum of all the values in an array, ignoring missing values.
678s  ## Create a 2-by-5-by-3 array x with some missing values.
678s 
678s  x = reshape (1:30, [2, 5, 3]);
678s  x([10:12, 25]) = NaN
678s 
678s  ## Find the maximum of the elements of x.
678s 
678s  y = nanmax (x, [], 'all')
678s ***** assert (nanmax ([2, 4, NaN, 7]), 7)
678s ***** assert (nanmax ([2, 4, NaN, Inf]), Inf)
678s ***** assert (nanmax ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN]), [7, 8, 6])
678s ***** assert (nanmax ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN]'), [3, 6, 8])
678s ***** assert (nanmax (single ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN])), single ([7, 8, 6]))
678s ***** shared x, y
678s  x(:,:,1) = [1.77, -0.005, NaN, -2.95; NaN, 0.34, NaN, 0.19];
678s  x(:,:,2) = [1.77, -0.005, NaN, -2.95; NaN, 0.34, NaN, 0.19] + 5;
678s  y = x;
678s  y(2,3,1) = 0.51;
678s ***** assert (nanmax (x, [], [1, 2])(:), [1.77;6.77])
678s ***** assert (nanmax (x, [], [1, 3])(:), [6.77;5.34;NaN;5.19])
678s ***** assert (nanmax (x, [], [2, 3])(:), [6.77;5.34])
678s ***** assert (nanmax (x, [], [1, 2, 3]), 6.77)
678s ***** assert (nanmax (x, [], 'all'), 6.77)
678s ***** assert (nanmax (y, [], [1, 3])(:), [6.77;5.34;0.51;5.19])
678s ***** assert (nanmax (x(1,:,1), x(2,:,1)), [1.77, 0.34, NaN, 0.19])
678s ***** assert (nanmax (x(1,:,2), x(2,:,2)), [6.77, 5.34, NaN, 5.19])
678s ***** assert (nanmax (y(1,:,1), y(2,:,1)), [1.77, 0.34, 0.51, 0.19])
678s ***** assert (nanmax (y(1,:,2), y(2,:,2)), [6.77, 5.34, NaN, 5.19])
678s ***** test
678s  xx = repmat ([1:20;6:25], [5 2 6 3]);
678s  assert (size (nanmax (xx, [], [3, 2])), [10, 1, 1, 3]);
678s  assert (size (nanmax (xx, [], [1, 2])), [1, 1, 6, 3]);
678s  assert (size (nanmax (xx, [], [1, 2, 4])), [1, 1, 6]);
678s  assert (size (nanmax (xx, [], [1, 4, 3])), [1, 40]);
678s  assert (size (nanmax (xx, [], [1, 2, 3, 4])), [1, 1]);
678s ***** assert (nanmax (ones (2), [], 3), ones (2, 2))
678s ***** assert (nanmax (ones (2, 2, 2), [], 99), ones (2, 2, 2))
678s ***** assert (nanmax (magic (3), [], 3), magic (3))
678s ***** assert (nanmax (magic (3), [], [1, 3]), [8, 9, 7])
678s ***** assert (nanmax (magic (3), [], [1, 99]), [8, 9, 7])
678s ***** assert (nanmax (ones (2), 3), 3 * ones (2,2))
678s ***** error <nanmax: VECDIM must contain non-repeating positive integers.> ...
678s  nanmax (y, [], [1, 1, 2])
678s ***** error <nanmax: a second output is not supported with this syntax.> ...
678s  [v, idx] = nanmax(x, y, [1 2])
678s 24 tests, 24 passed, 0 known failure, 0 skipped
678s [inst/nanmean.m]
678s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/nanmean.m
678s ***** demo
678s  ## Find the column means for a matrix with missing values.,
678s 
678s  x = magic (3);
678s  x([1, 4, 7:9]) = NaN
678s  y = nanmean (x)
678s ***** demo
678s  ## Find the row means for a matrix with missing values.,
678s 
678s  x = magic (3);
678s  x([1, 4, 7:9]) = NaN
678s  y = nanmean (x, 2)
678s ***** demo
678s  ## Find the mean of all the values in a multidimensional array
678s  ## with missing values.
678s 
678s  x = reshape (1:30, [2, 5, 3]);
678s  x([10:12, 25]) = NaN
678s  y = nanmean (x, "all")
678s ***** demo
678s  ## Find the mean of a multidimensional array with missing values over
678s  ## multiple dimensions.
678s 
678s  x = reshape (1:30, [2, 5, 3]);
678s  x([10:12, 25]) = NaN
678s  y = nanmean (x, [2, 3])
678s ***** assert (nanmean ([]), NaN)
678s ***** assert (nanmean (NaN), NaN)
678s ***** assert (nanmean (NaN(3)), [NaN, NaN, NaN])
678s ***** assert (nanmean ([3 2 NaN 7]), 4)
678s ***** assert (nanmean ([2 4 NaN Inf]), Inf)
678s ***** assert (nanmean ([1 NaN 3; NaN 4 6; 7 8 NaN]), [4 6 4.5])
678s ***** assert (nanmean ([1 NaN 3; NaN 5 6; 7 8 NaN], 2), [2; 5.5; 7.5])
678s ***** assert (nanmean (uint8 ([2 4 1 7])), 3.5)
678s ***** test
678s  x = magic(3);
678s  x([1 6:9]) = NaN;
678s  assert (nanmean (x), [3.5, 3, NaN])
678s  assert (nanmean (x, 2), [1; 4; 4])
678s ***** test
678s  x = reshape(1:24, [2, 4, 3]);
678s  x([5:6, 20]) = NaN;
678s  assert (nanmean (x, "all"), 269/21)
678s ***** test
678s  x = reshape(1:24,[2, 4, 3]);
678s  x([5:6, 20]) = NaN;
678s  assert (squeeze (nanmean (x, [1, 2])), [25/6; 100/8; 144/7])
678s  assert (nanmean (x, [2, 3]), [139/11; 13])
678s 11 tests, 11 passed, 0 known failure, 0 skipped
678s [inst/nanmin.m]
678s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/nanmin.m
678s ***** demo
678s  ## Find the column minimum values and their indices
678s  ## for matrix data with missing values.
678s 
678s  x = magic (3);
678s  x([1, 6:9]) = NaN
678s  [y, ind] = nanmin (x)
678s ***** demo
678s  ## Find the minimum of all the values in an array, ignoring missing values.
678s  ## Create a 2-by-5-by-3 array x with some missing values.
678s 
678s  x = reshape (1:30, [2, 5, 3]);
678s  x([10:12, 25]) = NaN
678s 
678s  ## Find the minimum of the elements of x.
678s 
678s  y = nanmin (x, [], 'all')
678s ***** assert (nanmin ([2, 4, NaN, 7]), 2)
678s ***** assert (nanmin ([2, 4, NaN, -Inf]), -Inf)
678s ***** assert (nanmin ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN]), [1, 5, 3])
678s ***** assert (nanmin ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN]'), [1, 5, 7])
678s ***** assert (nanmin (single ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN])), single ([1, 5, 3]))
678s ***** shared x, y
678s  x(:,:,1) = [1.77, -0.005, NaN, -2.95; NaN, 0.34, NaN, 0.19];
678s  x(:,:,2) = [1.77, -0.005, NaN, -2.95; NaN, 0.34, NaN, 0.19] + 5;
678s  y = x;
678s  y(2,3,1) = 0.51;
678s ***** assert (nanmin (x, [], [1, 2])(:), [-2.95; 2.05])
678s ***** assert (nanmin (x, [], [1, 3])(:), [1.77; -0.005; NaN; -2.95])
678s ***** assert (nanmin (x, [], [2, 3])(:), [-2.95; 0.19])
678s ***** assert (nanmin (x, [], [1, 2, 3]), -2.95)
678s ***** assert (nanmin (x, [], 'all'), -2.95)
678s ***** assert (nanmin (y, [], [1, 3])(:), [1.77; -0.005; 0.51; -2.95])
678s ***** assert (nanmin (x(1,:,1), x(2,:,1)), [1.77, -0.005, NaN, -2.95])
678s ***** assert (nanmin (x(1,:,2), x(2,:,2)), [6.77, 4.995, NaN, 2.05])
678s ***** assert (nanmin (y(1,:,1), y(2,:,1)), [1.77, -0.005, 0.51, -2.95])
678s ***** assert (nanmin (y(1,:,2), y(2,:,2)), [6.77, 4.995, NaN, 2.05])
678s ***** test
678s  xx = repmat ([1:20;6:25], [5 2 6 3]);
678s  assert (size (nanmin (xx, [], [3, 2])), [10, 1, 1, 3]);
678s  assert (size (nanmin (xx, [], [1, 2])), [1, 1, 6, 3]);
678s  assert (size (nanmin (xx, [], [1, 2, 4])), [1, 1, 6]);
678s  assert (size (nanmin (xx, [], [1, 4, 3])), [1, 40]);
678s  assert (size (nanmin (xx, [], [1, 2, 3, 4])), [1, 1]);
678s ***** assert (nanmin (ones (2), [], 3), ones (2, 2))
678s ***** assert (nanmin (ones (2, 2, 2), [], 99), ones (2, 2, 2))
678s ***** assert (nanmin (magic (3), [], 3), magic (3))
678s ***** assert (nanmin (magic (3), [], [1, 3]), [3, 1, 2])
678s ***** assert (nanmin (magic (3), [], [1, 99]), [3, 1, 2])
678s ***** assert (nanmin (ones (2), 3), ones (2,2))
678s ***** error <nanmin: VECDIM must contain non-repeating positive integers.> ...
678s  nanmin (y, [], [1, 1, 2])
678s ***** error <nanmin: a second output is not supported with this syntax.> ...
678s  [v, idx] = nanmin(x, y, [1 2])
678s 24 tests, 24 passed, 0 known failure, 0 skipped
678s [inst/nansum.m]
678s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/nansum.m
678s ***** demo
678s  ## Find the column sums for a matrix with missing values.,
678s 
678s  x = magic (3);
678s  x([1, 4, 7:9]) = NaN
678s  s = nansum (x)
678s ***** demo
678s  ## Find the row sums for a matrix with missing values.,
678s 
678s  x = magic (3);
678s  x([1, 4, 7:9]) = NaN
678s  s = nansum (x, 2)
678s ***** demo
678s  ## Find the sum of all the values in a multidimensional array
678s  ## with missing values.
678s 
678s  x = reshape (1:30, [2, 5, 3]);
678s  x([10:12, 25]) = NaN
678s  s = nansum (x, "all")
678s ***** demo
678s  ## Find the sum of a multidimensional array with missing values over
678s  ## multiple dimensions.
678s 
678s  x = reshape (1:30, [2, 5, 3]);
678s  x([10:12, 25]) = NaN
678s  s = nansum (x, [2, 3])
678s ***** assert (nansum ([]), 0)
678s ***** assert (nansum (NaN), 0)
678s ***** assert (nansum (NaN(3)), [0, 0, 0])
678s ***** assert (nansum ([2 4 NaN 7]), 13)
678s ***** assert (nansum ([2 4 NaN Inf]), Inf)
678s ***** assert (nansum ([1 NaN 3; NaN 5 6; 7 8 NaN]), [8 13 9])
678s ***** assert (nansum ([1 NaN 3; NaN 5 6; 7 8 NaN], 2), [4; 11; 15])
678s ***** assert (nansum (uint8 ([2 4 1 7])), 14)
678s ***** test
678s  x = magic(3);
678s  x([1 6:9]) = NaN;
678s  assert (nansum (x), [7, 6, 0])
678s  assert (nansum (x, 2), [1; 8; 4])
678s ***** test
678s  x = reshape(1:24, [2, 4, 3]);
678s  x([5:6, 20]) = NaN;
678s  assert (nansum (x, "all"), 269)
678s ***** test
678s  x = reshape(1:24,[2, 4, 3]);
678s  x([5:6, 20]) = NaN;
678s  assert (squeeze (nansum (x, [1, 2])), [25; 100; 144])
678s  assert (nansum (x, [2, 3]), [139; 130])
678s ***** error <nansum: X must be numeric.> nansum ({3})
678s ***** error <nansum: DIM must be a positive integer.> nansum (ones (3), 0)
679s ***** error <nansum: DIM must be a positive integer.> nansum (ones (3), 1.5)
679s ***** error <nansum: DIM must be a positive integer.> nansum (ones (3), 1.5)
679s ***** error <nansum: VECDIM must be a vector of positive integer.> ...
679s  nansum (ones (3, 3, 3), [2, 2.5])
679s ***** error <nansum: VECDIM must be a vector of positive integer.> ...
679s  nansum (ones (3, 3, 3), [-1, 2])
679s ***** error <nansum: VECDIM must contain non-repeating positive integers.> ...
679s  nansum (ones (3, 3, 3), [2, 2, 3])
679s 18 tests, 18 passed, 0 known failure, 0 skipped
679s [inst/normalise_distribution.m]
679s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/normalise_distribution.m
679s ***** test
679s  v = normalise_distribution ([1 2 3], [], 1);
679s  assert (v, [0 0 0])
679s ***** test
679s  v = normalise_distribution ([1 2 3], [], 2);
679s  assert (v, norminv ([1 3 5] / 6), 3 * eps)
679s ***** test
679s  v = normalise_distribution ([1 2 3]', [], 2);
679s  assert (v, [0 0 0]')
679s ***** test
679s  v = normalise_distribution ([1 2 3]', [], 1);
679s  assert (v, norminv ([1 3 5]' / 6), 3 * eps)
679s ***** test
679s  v = normalise_distribution ([1 1 2 2 3 3], [], 2);
679s  assert (v, norminv ([3 3 7 7 11 11] / 12), 3 * eps)
679s ***** test
679s  v = normalise_distribution ([1 1 2 2 3 3]', [], 1);
679s  assert (v, norminv ([3 3 7 7 11 11]' / 12), 3 * eps)
679s ***** test
679s  A = randn ( 10 );
679s  N = normalise_distribution (A, @normcdf);
679s  assert (A, N, 10000 * eps)
679s ***** test
679s  A = exprnd (1, 100);
679s  N = normalise_distribution (A, @(x)(expcdf (x, 1)));
679s  assert (mean (vec (N)), 0, 0.1)
679s  assert (std (vec (N)), 1, 0.1)
679s ***** test
679s  A = rand (1000,1);
679s  N = normalise_distribution (A, {@(x)(unifcdf (x, 0, 1))});
679s  assert (mean (vec (N)), 0, 0.2)
679s  assert (std (vec (N)), 1, 0.1)
679s ***** test
679s  A = [rand(1000,1), randn(1000, 1)];
679s  N = normalise_distribution (A, {@(x)(unifcdf (x, 0, 1)), @normcdf});
679s  assert (mean (N), [0, 0], 0.2)
679s  assert (std (N), [1, 1], 0.1)
679s ***** test
679s  A = [rand(1000,1), randn(1000, 1), exprnd(1, 1000, 1)]';
679s  N = normalise_distribution  (A, {@(x)(unifcdf (x, 0, 1)); @normcdf; @(x)(expcdf (x, 1))}, 2);
679s  assert (mean (N, 2), [0, 0, 0]', 0.2);
679s  assert (std (N, [], 2), [1, 1, 1]', 0.1);
679s ***** xtest
679s  A = exprnd (1, 1000, 9); A (300:500, 4:6) = 17;
679s  N = normalise_distribution (A);
679s  assert (mean (N), [0 0 0 0.38 0.38 0.38 0 0 0], 0.1);
679s  assert (var (N), [1 1 1 2.59 2.59 2.59 1 1 1], 0.1);
679s ***** test
679s ***** error normalise_distribution (zeros (3, 4), ...
679s  {@(x)(unifcdf (x, 0, 1)); @normcdf; @(x)(expcdf (x,1))});
679s 14 tests, 14 passed, 0 known failure, 0 skipped
679s [inst/normplot.m]
679s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/normplot.m
679s ***** demo
679s  h = normplot([1:20]);
679s ***** demo
679s  h = normplot([1:20;5:2:44]');
679s ***** demo
679s  ax = newplot();
679s  h = normplot(ax, [1:20]);
679s  ax = gca;
679s  h = normplot(ax, [-10:10]);
679s  set (ax, "xlim", [-11, 21]);
679s ***** error normplot ();
679s ***** error normplot (23);
679s ***** error normplot (23, [1:20]);
679s ***** error normplot (ones(3,4,5));
679s ***** test
679s  hf = figure ("visible", "off");
679s  unwind_protect
679s    ax = newplot (hf);
679s    h = normplot (ax, [1:20]);
679s    ax = gca;
679s    h = normplot(ax, [-10:10]);
679s    set (ax, "xlim", [-11, 21]);
679s  unwind_protect_cleanup
679s    close (hf);
679s  end_unwind_protect
679s ***** test
679s  hf = figure ("visible", "off");
679s  unwind_protect
679s    h = normplot([1:20;5:2:44]');
679s  unwind_protect_cleanup
679s    close (hf);
679s  end_unwind_protect
679s 6 tests, 6 passed, 0 known failure, 0 skipped
679s [inst/optimalleaforder.m]
679s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/optimalleaforder.m
679s ***** demo
679s  randn ("seed", 5)  # for reproducibility
679s  X = randn (10, 2);
679s  D = pdist (X);
679s  tree = linkage(D, 'average');
679s  optimalleaforder (tree, D, 'Transformation', 'linear')
679s ***** error optimalleaforder ()
679s ***** error optimalleaforder (1)
679s ***** error <tree must be .*> optimalleaforder (ones (2, 2), 1)
679s ***** error <character inputs expected> optimalleaforder ([1 2 3], [1 2; 3 4], "criteria", 5)
679s ***** error <D must be .*> optimalleaforder ([1 2 1], [1 2 3])
679s ***** error <unknown property .*> optimalleaforder ([1 2 1], 1, "xxx", "xxx")
679s ***** error optimalleaforder ([1 2 1], 1, "Transformation", "xxx")
679s 7 tests, 7 passed, 0 known failure, 0 skipped
679s [inst/pca.m]
679s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/pca.m
679s ***** shared COEFF,SCORE,latent,tsquare,m,x,R,V,lambda,i,S,F
679s ***** test
679s  x=[7 4 3
679s     4 1 8
679s     6 3 5
679s     8 6 1
679s     8 5 7
679s     7 2 9
679s     5 3 3
679s     9 5 8
679s     7 4 5
679s     8 2 2];
679s  R = corrcoef (x);
679s  [V, lambda] = eig (R);
679s  [~, i] = sort(diag(lambda), "descend"); #arrange largest PC first
679s  S = V(:, i) * diag(sqrt(diag(lambda)(i)));
679s ***** 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
679s  B = V(:, i) * diag( 1./ sqrt(diag(lambda)(i)));
679s  F = zscore(x)*B;
679s  [COEFF,SCORE,latent,tsquare] = pca(zscore(x, 1));
679s ***** assert(tsquare,sumsq(F, 2),1E4*eps);
679s ***** test
679s  x=[1,2,3;2,1,3]';
679s  [COEFF,SCORE,latent,tsquare] = pca(x, "Economy", false);
679s  m=[sqrt(2),sqrt(2);sqrt(2),-sqrt(2);-2*sqrt(2),0]/2;
679s  m(:,1) = m(:,1)*sign(COEFF(1,1));
679s  m(:,2) = m(:,2)*sign(COEFF(1,2));
679s ***** assert(COEFF,m(1:2,:),10*eps);
679s ***** assert(SCORE,-m,10*eps);
679s ***** assert(latent,[1.5;.5],10*eps);
679s ***** assert(tsquare,[4;4;4]/3,10*eps);
679s  [COEFF,SCORE,latent,tsquare] = pca(x, "Economy", false, "weights", [1 2 1], "variableweights", "variance");
679s ***** assert(COEFF, [0.632455532033676 -0.632455532033676; 0.741619848709566 0.741619848709566], 10*eps);
679s ***** assert(SCORE, [-0.622019449426284 0.959119380657905; -0.505649896847432 -0.505649896847431; 1.633319243121148 0.052180413036957], 10*eps);
679s ***** assert(latent, [1.783001790889027; 0.716998209110974], 10*eps);
679s ***** xtest assert(tsquare, [1.5; 0.5; 1.5], 10*eps);  #currently, [4; 2; 4]/3 is actually returned; see comments above
679s !!!!! known failure
679s ASSERT errors for:  assert (tsquare,([1.5; 0.5; 1.5]),10 * eps)
679s 
679s   Location  |  Observed  |  Expected  |  Reason
679s     (1)         1.3333        1.5        Abs err 0.16667 exceeds tol 2.2204e-15 by 0.2
679s     (2)        0.66667        0.5        Abs err 0.16667 exceeds tol 2.2204e-15 by 0.2
679s     (3)         1.3333        1.5        Abs err 0.16667 exceeds tol 2.2204e-15 by 0.2
679s ***** test
679s  x=x';
679s  [COEFF,SCORE,latent,tsquare] = pca(x, "Economy", false);
679s  m=[sqrt(2),sqrt(2),0;-sqrt(2),sqrt(2),0;0,0,2]/2;
679s  m(:,1) = m(:,1)*sign(COEFF(1,1));
679s  m(:,2) = m(:,2)*sign(COEFF(1,2));
679s  m(:,3) = m(:,3)*sign(COEFF(3,3));
679s ***** assert(COEFF,m,10*eps);
679s ***** assert(SCORE(:,1),-m(1:2,1),10*eps);
679s ***** assert(SCORE(:,2:3),zeros(2),10*eps);
679s ***** assert(latent,[1;0;0],10*eps);
679s ***** assert(tsquare,[0.5;0.5],10*eps)
679s ***** test
679s  [COEFF,SCORE,latent,tsquare] = pca(x);
679s ***** assert(COEFF,m(:, 1),10*eps);
679s ***** assert(SCORE,-m(1:2,1),10*eps);
679s ***** assert(latent,[1],10*eps);
679s ***** assert(tsquare,[0.5;0.5],10*eps)
679s ***** error <invalid algorithm> pca([1 2; 3 4], "Algorithm", "xxx")
679s ***** error <'centered' requires a boolean value> pca([1 2; 3 4], "Centered", "xxx")
679s ***** error <must be a positive integer> pca([1 2; 3 4], "NumComponents", -4)
679s ***** error <invalid value for rows> pca([1 2; 3 4], "Rows", 1)
679s ***** error <weights must be> pca([1 2; 3 4], "Weights", [1 2 3])
679s ***** error <weights must be> pca([1 2; 3 4], "Weights", [-1 2])
679s ***** error <variable weights must be> pca([1 2; 3 4], "VariableWeights", [-1 2])
679s ***** error <variable weights must be> pca([1 2; 3 4], "VariableWeights", "xxx")
679s ***** error <unknown property> pca([1 2; 3 4], "XXX", 1)
679s 32 tests, 31 passed, 1 known failure, 0 skipped
679s [inst/pcacov.m]
679s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/pcacov.m
679s ***** demo
679s  x = [ 7    26     6    60;
679s        1    29    15    52;
679s       11    56     8    20;
679s       11    31     8    47;
679s        7    52     6    33;
679s       11    55     9    22;
679s        3    71    17     6;
679s        1    31    22    44;
679s        2    54    18    22;
679s       21    47     4    26;
679s        1    40    23    34;
679s       11    66     9    12;
679s       10    68     8    12
679s      ];
679s  Kxx = cov (x);
679s  [coeff, latent, explained] = pcacov (Kxx)
679s ***** test
679s  load hald
679s  Kxx = cov (ingredients);
679s  [coeff,latent,explained] = pcacov(Kxx);
679s  c_out = [-0.0678, -0.6460,  0.5673, 0.5062; ...
679s           -0.6785, -0.0200, -0.5440, 0.4933; ...
679s            0.0290,  0.7553,  0.4036, 0.5156; ...
679s            0.7309, -0.1085, -0.4684, 0.4844];
679s  l_out = [517.7969; 67.4964; 12.4054; 0.2372];
679s  e_out = [ 86.5974; 11.2882;  2.0747; 0.0397];
679s  assert (coeff, c_out, 1e-4);
679s  assert (latent, l_out, 1e-4);
679s  assert (explained, e_out, 1e-4);
679s ***** error<pcacov: K must be a square matrix.> pcacov (ones (2,3))
679s ***** error<pcacov: K must be a square matrix.> pcacov (ones (3,3,3))
679s 3 tests, 3 passed, 0 known failure, 0 skipped
679s [inst/pcares.m]
679s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/pcares.m
679s ***** demo
679s  x = [ 7    26     6    60;
679s        1    29    15    52;
679s       11    56     8    20;
679s       11    31     8    47;
679s        7    52     6    33;
679s       11    55     9    22;
679s        3    71    17     6;
679s        1    31    22    44;
679s        2    54    18    22;
679s       21    47     4    26;
679s        1    40    23    34;
679s       11    66     9    12;
679s       10    68     8    12];
679s 
679s  ## As we increase the number of principal components, the norm
679s  ## of the residuals matrix will decrease
679s  r1 = pcares (x,1);
679s  n1 = norm (r1)
679s  r2 = pcares (x,2);
679s  n2 = norm (r2)
679s  r3 = pcares (x,3);
679s  n3 = norm (r3)
679s  r4 = pcares (x,4);
679s  n4 = norm (r4)
679s ***** test
679s  load hald
679s  r1 = pcares (ingredients,1);
679s  r2 = pcares (ingredients,2);
679s  r3 = pcares (ingredients,3);
679s  assert (r1(1,:), [2.0350,  2.8304, -6.8378, 3.0879], 1e-4);
679s  assert (r2(1,:), [-2.4037, 2.6930, -1.6482, 2.3425], 1e-4);
679s  assert (r3(1,:), [ 0.2008, 0.1957,  0.2045, 0.1921], 1e-4);
679s ***** error<pcares: too few input arguments.> pcares (ones (20, 3))
679s ***** error<pcares: NDIM must be less than or equal to the column of X.> ...
679s  pcares (ones (30, 2), 3)
679s 3 tests, 3 passed, 0 known failure, 0 skipped
679s [inst/pdist.m]
679s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/pdist.m
679s ***** shared xy, t, eucl, x
679s  xy = [0 1; 0 2; 7 6; 5 6];
679s  t = 1e-3;
679s  eucl = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
679s  x = [1 2 3; 4 5 6; 7 8 9; 3 2 1];
679s ***** assert (pdist (xy),                 [1.000 8.602 7.071 8.062 6.403 2.000], t);
679s ***** assert (pdist (xy, eucl),           [1.000 8.602 7.071 8.062 6.403 2.000], t);
679s ***** assert (pdist (xy, "euclidean"),    [1.000 8.602 7.071 8.062 6.403 2.000], t);
679s ***** assert (pdist (xy, "seuclidean"),   [0.380 2.735 2.363 2.486 2.070 0.561], t);
679s ***** assert (pdist (xy, "mahalanobis"),  [1.384 1.967 2.446 2.384 1.535 2.045], t);
679s ***** assert (pdist (xy, "cityblock"),    [1.000 12.00 10.00 11.00 9.000 2.000], t);
679s ***** assert (pdist (xy, "minkowski"),    [1.000 8.602 7.071 8.062 6.403 2.000], t);
679s ***** assert (pdist (xy, "minkowski", 3), [1.000 7.763 6.299 7.410 5.738 2.000], t);
679s ***** assert (pdist (xy, "cosine"),       [0.000 0.349 0.231 0.349 0.231 0.013], t);
679s ***** assert (pdist (xy, "correlation"),  [0.000 2.000 0.000 2.000 0.000 2.000], t);
679s ***** assert (pdist (xy, "spearman"),     [0.000 2.000 0.000 2.000 0.000 2.000], t);
679s ***** assert (pdist (xy, "hamming"),      [0.500 1.000 1.000 1.000 1.000 0.500], t);
679s ***** assert (pdist (xy, "jaccard"),      [1.000 1.000 1.000 1.000 1.000 0.500], t);
679s ***** assert (pdist (xy, "chebychev"),    [1.000 7.000 5.000 7.000 5.000 2.000], t);
679s ***** assert (pdist (x), [5.1962, 10.3923, 2.8284, 5.1962, 5.9161, 10.7703], 1e-4);
679s ***** assert (pdist (x, "euclidean"), ...
679s         [5.1962, 10.3923, 2.8284, 5.1962, 5.9161, 10.7703], 1e-4);
679s ***** assert (pdist (x, eucl), ...
679s         [5.1962, 10.3923, 2.8284, 5.1962, 5.9161, 10.7703], 1e-4);
679s ***** assert (pdist (x, "squaredeuclidean"), [27, 108, 8, 27, 35, 116]);
679s ***** assert (pdist (x, "seuclidean"), ...
679s         [1.8071, 3.6142, 0.9831, 1.8071, 1.8143, 3.4854], 1e-4);
679s ***** warning<pdist: matrix is close to singular> ...
679s  pdist (x, "mahalanobis");
679s ***** assert (pdist (x, "cityblock"), [9, 18, 4, 9, 9, 18]);
679s ***** assert (pdist (x, "minkowski"), ...
679s         [5.1962, 10.3923, 2.8284, 5.1962, 5.9161, 10.7703], 1e-4);
679s ***** assert (pdist (x, "minkowski", 3), ...
679s         [4.3267, 8.6535, 2.5198, 4.3267, 5.3485, 9.2521], 1e-4);
679s ***** assert (pdist (x, "cosine"), ...
679s         [0.0254, 0.0406, 0.2857, 0.0018, 0.1472, 0.1173], 1e-4);
679s ***** assert (pdist (x, "correlation"), [0, 0, 2, 0, 2, 2], 1e-14);
680s ***** assert (pdist (x, "spearman"), [0, 0, 2, 0, 2, 2], 1e-14);
680s ***** assert (pdist (x, "hamming"), [1, 1, 2/3, 1, 1, 1]);
680s ***** assert (pdist (x, "jaccard"), [1, 1, 2/3, 1, 1, 1]);
680s ***** assert (pdist (x, "chebychev"), [3, 6, 2, 3, 5, 8]);
680s 29 tests, 29 passed, 0 known failure, 0 skipped
680s [inst/pdist2.m]
680s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/pdist2.m
680s ***** shared x, y, xx
680s  x = [1, 1, 1; 2, 2, 2; 3, 3, 3];
680s  y = [0, 0, 0; 1, 2, 3; 0, 2, 4; 4, 7, 1];
680s  xx = [1 2 3; 4 5 6; 7 8 9; 3 2 1];
680s ***** test
680s  d = sqrt([3, 5, 11, 45; 12, 2, 8, 30; 27, 5, 11, 21]);
680s  assert (pdist2 (x, y), d);
680s ***** test
680s  d = [5.1962, 2.2361, 3.3166, 6.7082; ...
680s       3.4641, 2.2361, 3.3166, 5.4772];
680s  i = [3, 1, 1, 1; 2, 3, 3, 2];
680s  [D, I] = pdist2 (x, y, "euclidean", "largest", 2);
680s  assert ({D, I}, {d, i}, 1e-4);
680s ***** test
680s  d = [1.7321, 1.4142, 2.8284, 4.5826; ...
680s       3.4641, 2.2361, 3.3166, 5.4772];
680s  i = [1, 2, 2, 3;2, 1, 1, 2];
680s  [D, I] = pdist2 (x, y, "euclidean", "smallest", 2);
680s  assert ({D, I}, {d, i}, 1e-4);
680s ***** test
680s  yy = [1 2 3;5 6 7;9 5 1];
680s  d = [0, 6.1644, 5.3852; 1.4142, 6.9282, 8.7750; ...
680s       3.7417, 7.0711, 9.9499; 6.1644, 10.4881, 10.3441];
680s  i = [2, 4, 4; 3, 2, 2; 1, 3, 3; 4, 1, 1];
680s  [D, I] = pdist2 (y, yy, "euclidean", "smallest", 4);
680s  assert ({D, I}, {d, i}, 1e-4);
680s ***** test
680s  yy = [1 2 3;5 6 7;9 5 1];
680s  d = [0, 38, 29; 2, 48, 77; 14, 50, 99; 38, 110, 107];
680s  i = [2, 4, 4; 3, 2, 2; 1, 3, 3; 4, 1, 1];
680s  [D, I] = pdist2 (y, yy, "squaredeuclidean", "smallest", 4);
680s  assert ({D, I}, {d, i}, 1e-4);
680s ***** test
680s  yy = [1 2 3;5 6 7;9 5 1];
680s  d = [0, 3.3256, 2.7249; 0.7610, 3.3453, 4.4799; ...
680s       1.8514, 3.3869, 5.0703; 2.5525, 5.0709, 5.1297];
680s  i = [2, 2, 4; 3, 4, 2; 1, 3, 1; 4, 1, 3];
680s  [D, I] = pdist2 (y, yy, "seuclidean", "smallest", 4);
680s  assert ({D, I}, {d, i}, 1e-4);
680s ***** test
680s  d = [2.1213, 4.2426, 6.3640; 1.2247, 2.4495, 4.4159; ...
680s       3.2404, 4.8990, 6.8191; 2.7386, 4.2426, 6.1237];
680s  assert (pdist2 (y, x, "mahalanobis"), d, 1e-4);
680s ***** test
680s  xx = [1, 3, 4; 3, 5, 4; 8, 7, 6];
680s  d = [1.3053, 1.8257, 15.0499; 1.3053, 3.3665, 16.5680];
680s  i = [2, 2, 2; 3, 4, 4];
680s  [D, I] = pdist2 (y, xx, "mahalanobis", "smallest", 2);
680s  assert ({D, I}, {d, i}, 1e-4);
680s ***** test
680s  d = [2.5240, 4.1633, 17.3638; 2.0905, 3.9158, 17.0147];
680s  i = [1, 1, 3; 4, 3, 1];
680s  [D, I] = pdist2 (y, xx, "mahalanobis", "largest", 2);
680s  assert ({D, I}, {d, i}, 1e-4);
680s ***** test
680s  d = [3, 3, 5, 9; 6, 2, 4, 8; 9, 3, 5, 7];
680s  assert (pdist2 (x, y, "cityblock"), d);
680s ***** test
680s  d = [1, 2, 3, 6; 2, 1, 2, 5; 3, 2, 3, 4];
680s  assert (pdist2 (x, y, "chebychev"), d);
680s ***** test
680s  d = repmat ([NaN, 0.0742, 0.2254, 0.1472], [3, 1]);
680s  assert (pdist2 (x, y, "cosine"), d, 1e-4);
680s ***** test
680s  yy = [1 2 3;5 6 7;9 5 1];
680s  d = [0, 0, 0.5; 0, 0, 2; 1.5, 1.5, 2; NaN, NaN, NaN];
680s  i = [2, 2, 4; 3, 3, 2; 4, 4, 3; 1, 1, 1];
680s  [D, I] = pdist2 (y, yy, "correlation", "smallest", 4);
680s  assert ({D, I}, {d, i}, eps);
680s  [D, I] = pdist2 (y, yy, "spearman", "smallest", 4);
680s  assert ({D, I}, {d, i}, eps);
680s ***** test
680s  d = [1, 2/3, 1, 1; 1, 2/3, 1, 1; 1, 2/3, 2/3, 2/3];
680s  i = [1, 1, 1, 2; 2, 2, 3, 3; 3, 3, 2, 1];
680s  [D, I] = pdist2 (x, y, "hamming", "largest", 4);
680s  assert ({D, I}, {d, i}, eps);
680s  [D, I] = pdist2 (x, y, "jaccard", "largest", 4);
680s  assert ({D, I}, {d, i}, eps);
680s ***** test
680s  xx = [1, 2, 3, 4; 2, 3, 4, 5; 3, 4, 5, 6];
680s  yy = [1, 2, 2, 3; 2, 3, 3, 4];
680s  [D, I] = pdist2 (x, y, "euclidean", "Smallest", 4);
680s  eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
680s  [d, i] = pdist2 (x, y, eucldist, "Smallest", 4);
680s  assert ({D, I}, {d, i});
680s ***** warning<pdist2: matrix is close to singular> ...
680s  pdist2 (xx, xx, "mahalanobis");
680s ***** error<pdist2: too few input arguments.> pdist2 (1)
680s ***** error<pdist2: X and Y must have equal number of columns.> ...
680s  pdist2 (ones (4, 5), ones (4))
680s ***** error<pdist2: X and Y must be 2 dimensional matrices.> ...
680s  pdist2 (ones (4, 2, 3), ones (3, 2))
680s ***** error<pdist2: missing value in optional name/value paired arguments.> ...
680s  pdist2 (ones (3), ones (3), "euclidean", "Largest")
680s ***** error<pdist2: missing value in optional name/value paired arguments.> ...
680s  pdist2 (ones (3), ones (3), "minkowski", 3, "Largest")
680s ***** error<pdist2: invalid NAME in optional pairs of arguments.> ...
680s  pdist2 (ones (3), ones (3), "minkowski", 3, "large", 4)
680s ***** error<pdist2: you can only use either Smallest or Largest.> ...
680s  pdist2 (ones (3), ones (3), "minkowski", 3, "Largest", 4, "smallest", 5)
680s ***** error<pdist2: Smallest or Largest must be specified to compute second output.> ...
680s  [d, i] = pdist2(ones (3), ones (3), "minkowski", 3)
680s ***** error<pdist2: DistParameter for standardized euclidean must be a vector of> ...
680s  pdist2 (ones (3), ones (3), "seuclidean", 3)
680s ***** error<pdist2: DistParameter for standardized euclidean must be a nonnegative> ...
680s  pdist2 (ones (3), ones (3), "seuclidean", [1, -1, 3])
680s ***** error<pdist2: DistParameter for mahalanobis distance must be a covariance> ...
680s  pdist2 (ones (3), eye (3), "mahalanobis", eye(2))
680s ***** error<pdist2: covariance matrix for mahalanobis distance must be symmetric> ...
680s  pdist2 (ones (3), eye (3), "mahalanobis", ones(3))
680s ***** error<pdist2: DistParameter for minkowski distance must be a positive scalar.> ...
680s  pdist2 (ones (3), eye (3), "minkowski", 0)
680s ***** error<pdist2: DistParameter for minkowski distance must be a positive scalar.> ...
680s  pdist2 (ones (3), eye (3), "minkowski", -5)
680s ***** error<pdist2: DistParameter for minkowski distance must be a positive scalar.> ...
680s  pdist2 (ones (3), eye (3), "minkowski", [1, 2])
680s ***** error<pdist2: invalid function handle for distance metric.> ...
680s  pdist2 (ones (3), ones (3), @(v,m) sqrt(repmat(v,rows(m),1)-m,2))
680s ***** error<pdist2: custom distance function produces wrong output size.> ...
680s  pdist2 (ones (3), ones (3), @(v,m) sqrt(sum(sumsq(repmat(v,rows(m),1)-m,2))))
680s 33 tests, 33 passed, 0 known failure, 0 skipped
680s [inst/plsregress.m]
680s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/plsregress.m
680s ***** demo
680s  ## Perform Partial Least-Squares Regression
680s 
680s  ## Load the spectra data set and use the near infrared (NIR) spectral
680s  ## intensities (NIR) as the predictor and the corresponding octave
680s  ## ratings (octave) as the response.
680s  load spectra
680s 
680s  ## Perform PLS regression with 10 components
680s  [xload, yload, xscore, yscore, coef, ptcVar] = plsregress (NIR, octane, 10);
680s 
680s  ## Plot the percentage of explained variance in the response variable
680s  ## (PCTVAR) as a function of the number of components.
680s  plot (1:10, cumsum (100 * ptcVar(2,:)), "-ro");
680s  xlim ([1, 10]);
680s  xlabel ("Number of PLS components");
680s  ylabel ("Percentage of Explained Variance in octane");
680s  title ("Explained Variance per PLS components");
680s 
680s  ## Compute the fitted response and display the residuals.
680s  octane_fitted = [ones(size(NIR,1),1), NIR] * coef;
680s  residuals = octane - octane_fitted;
680s  figure
680s  stem (residuals, "color", "r", "markersize", 4, "markeredgecolor", "r")
680s  xlabel ("Observations");
680s  ylabel ("Residuals");
680s  title ("Residuals in octane's fitted response");
680s ***** demo
680s  ## Calculate Variable Importance in Projection (VIP) for PLS Regression
680s 
680s  ## Load the spectra data set and use the near infrared (NIR) spectral
680s  ## intensities (NIR) as the predictor and the corresponding octave
680s  ## ratings (octave) as the response.  Variables with a VIP score greater than
680s  ## 1 are considered important for the projection of the PLS regression model.
680s  load spectra
680s 
680s  ## Perform PLS regression with 10 components
680s  [xload, yload, xscore, yscore, coef, pctVar, mse, stats] = ...
680s                                                  plsregress (NIR, octane, 10);
680s 
680s  ## Calculate the normalized PLS weights
680s  W0 = stats.W ./ sqrt(sum(stats.W.^2,1));
680s 
680s  ## Calculate the VIP scores for 10 components
680s  nobs = size (xload, 1);
680s  SS = sum (xscore .^ 2, 1) .* sum (yload .^ 2, 1);
680s  VIPscore = sqrt (nobs * sum (SS .* (W0 .^ 2), 2) ./ sum (SS, 2));
680s 
680s  ## Find variables with a VIP score greater than or equal to 1
680s  VIPidx = find (VIPscore >= 1);
680s 
680s  ## Plot the VIP scores
680s  scatter (1:length (VIPscore), VIPscore, "xb");
680s  hold on
680s  scatter (VIPidx, VIPscore (VIPidx), "xr");
680s  plot ([1, length(VIPscore)], [1, 1], "--k");
680s  hold off
680s  axis ("tight");
680s  xlabel ("Predictor Variables");
680s  ylabel ("VIP scores");
680s  title ("VIP scores for each predictor variable with 10 components");
680s ***** test
680s  load spectra
680s  [xload, yload, xscore, yscore, coef, pctVar] = plsregress (NIR, octane, 10);
680s  xload1_out = [-0.0170, 0.0039, 0.0095,  0.0258, 0.0025, ...
680s                -0.0075, 0.0000, 0.0018, -0.0027, 0.0020];
680s  yload_out = [6.6384, 9.3106, 2.0505, 0.6471, 0.9625, ...
680s               0.5905, 0.4244, 0.2437, 0.3516, 0.2548];
680s  xscore1_out = [-0.0401, -0.1764, -0.0340, 0.1669,  0.1041, ...
680s                 -0.2067,  0.0457,  0.1565, 0.0706, -0.1471];
680s  yscore1_out = [-12.4635, -15.0003,  0.0638,  0.0652, -0.0070, ...
680s                  -0.0634,   0.0062, -0.0012, -0.0151, -0.0173];
680s  assert (xload(1,:), xload1_out, 1e-4);
680s  assert (yload, yload_out, 1e-4);
680s  assert (xscore(1,:), xscore1_out, 1e-4);
680s  assert (yscore(1,:), yscore1_out, 1e-4);
680s ***** test
680s  load spectra
680s  [xload, yload, xscore, yscore, coef, pctVar] = plsregress (NIR, octane, 5);
680s  xload1_out = [-0.0170, 0.0039, 0.0095, 0.0258, 0.0025];
680s  yload_out = [6.6384, 9.3106, 2.0505, 0.6471, 0.9625];
680s  xscore1_out = [-0.0401, -0.1764, -0.0340, 0.1669, 0.1041];
680s  yscore1_out = [-12.4635, -15.0003, 0.0638, 0.0652, -0.0070];
680s  assert (xload(1,:), xload1_out, 1e-4);
680s  assert (yload, yload_out, 1e-4);
680s  assert (xscore(1,:), xscore1_out, 1e-4);
680s  assert (yscore(1,:), yscore1_out, 1e-4);
680s ***** error<plsregress: function called with too few input arguments.>
680s  plsregress (1)
680s ***** error<plsregress: X and Y must be real matrices.> plsregress (1, "asd")
680s ***** error<plsregress: X and Y must be real matrices.> plsregress (1, {1,2,3})
680s ***** error<plsregress: X and Y must be real matrices.> plsregress ("asd", 1)
680s ***** error<plsregress: X and Y must be real matrices.> plsregress ({1,2,3}, 1)
680s ***** error<plsregress: X and Y observations mismatch.> ...
680s  plsregress (ones (20,3), ones (15,1))
680s ***** error<plsregress: invalid value for NCOMP.> ...
680s  plsregress (ones (20,3), ones (20,1), 0)
680s ***** error<plsregress: invalid value for NCOMP.> ...
680s  plsregress (ones (20,3), ones (20,1), -5)
680s ***** error<plsregress: invalid value for NCOMP.> ...
680s  plsregress (ones (20,3), ones (20,1), 3.2)
680s ***** error<plsregress: invalid value for NCOMP.> ...
680s  plsregress (ones (20,3), ones (20,1), [2, 3])
680s ***** error<plsregress: NCOMP exceeds maximum components for X.> ...
680s  plsregress (ones (20,3), ones (20,1), 4)
680s ***** error<plsregress: invalid VALUE for 'cv' optional argument.> ...
680s  plsregress (ones (20,3), ones (20,1), 3, "cv", 4.5)
680s ***** error<plsregress: invalid VALUE for 'cv' optional argument.> ...
680s  plsregress (ones (20,3), ones (20,1), 3, "cv", -1)
680s ***** error<plsregress: invalid VALUE for 'cv' optional argument.> ...
680s  plsregress (ones (20,3), ones (20,1), 3, "cv", "somestring")
680s ***** error<plsregress: invalid VALUE for 'mcreps' optional argument.> ...
680s  plsregress (ones (20,3), ones (20,1), 3, "cv", 3, "mcreps", 2.2)
680s ***** error<plsregress: invalid VALUE for 'mcreps' optional argument.> ...
680s  plsregress (ones (20,3), ones (20,1), 3, "cv", 3, "mcreps", -2)
680s ***** error<plsregress: invalid VALUE for 'mcreps' optional argument.> ...
680s  plsregress (ones (20,3), ones (20,1), 3, "cv", 3, "mcreps", [1, 2])
680s ***** error<plsregress: invalid NAME argument.> ...
680s  plsregress (ones (20,3), ones (20,1), 3, "Name", 3, "mcreps", 1)
680s ***** error<plsregress: invalid NAME argument.> ...
680s  plsregress (ones (20,3), ones (20,1), 3, "cv", 3, "Name", 1)
680s ***** error<plsregress: 'mcreps' must be 1 when 'resubstitution' is specified> ...
680s  plsregress (ones (20,3), ones (20,1), 3, "mcreps", 2)
680s ***** error<plsregress: 'mcreps' must be 1 when 'resubstitution' is specified> ...
680s  plsregress (ones (20,3), ones (20,1), 3, "cv", "resubstitution", "mcreps", 2)
680s ***** error<Invalid call to plsregress.  Correct usage is:> plsregress (1, 2)
680s 24 tests, 24 passed, 0 known failure, 0 skipped
680s [inst/ppplot.m]
680s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/ppplot.m
680s ***** test
680s  hf = figure ("visible", "off");
680s  unwind_protect
680s    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]);
680s  unwind_protect_cleanup
680s    close (hf);
680s  end_unwind_protect
680s ***** error ppplot ()
680s ***** error <ppplot: X must be a numeric vector> ppplot (ones (2,2))
680s ***** error <ppplot: X must be a numeric vector> ppplot (1, 2)
680s ***** error <ppplot: DIST must be a string> ppplot ([1 2 3 4], 2)
680s 5 tests, 5 passed, 0 known failure, 0 skipped
680s [inst/princomp.m]
680s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/princomp.m
680s ***** shared COEFF,SCORE,latent,tsquare,m,x,R,V,lambda,i,S,F
680s ***** test
680s  x=[7 4 3
680s     4 1 8
680s     6 3 5
680s     8 6 1
680s     8 5 7
680s     7 2 9
680s     5 3 3
680s     9 5 8
680s     7 4 5
680s     8 2 2];
680s  R = corrcoef (x);
680s  [V, lambda] = eig (R);
680s  [~, i] = sort(diag(lambda), "descend"); #arrange largest PC first
680s  S = V(:, i) * diag(sqrt(diag(lambda)(i)));
680s  ## contribution of first 2 PCs to each original variable
680s ***** assert(diag(S(:, 1:2)*S(:, 1:2)'), [0.8662; 0.8420; 0.9876], 1E-4);
680s  B = V(:, i) * diag( 1./ sqrt(diag(lambda)(i)));
680s  F = zscore(x)*B;
680s  [COEFF,SCORE,latent,tsquare] = princomp(zscore(x, 1));
680s ***** assert(tsquare,sumsq(F, 2),1E4*eps);
680s ***** test
680s  x=[1,2,3;2,1,3]';
680s  [COEFF,SCORE,latent,tsquare] = princomp(x);
680s  m=[sqrt(2),sqrt(2);sqrt(2),-sqrt(2);-2*sqrt(2),0]/2;
680s  m(:,1) = m(:,1)*sign(COEFF(1,1));
680s  m(:,2) = m(:,2)*sign(COEFF(1,2));
680s ***** assert(COEFF,m(1:2,:),10*eps);
680s ***** assert(SCORE,-m,10*eps);
680s ***** assert(latent,[1.5;.5],10*eps);
680s ***** assert(tsquare,[4;4;4]/3,10*eps);
680s ***** test
680s  x=x';
680s  [COEFF,SCORE,latent,tsquare] = princomp(x);
680s  m=[sqrt(2),sqrt(2),0;-sqrt(2),sqrt(2),0;0,0,2]/2;
680s  m(:,1) = m(:,1)*sign(COEFF(1,1));
680s  m(:,2) = m(:,2)*sign(COEFF(1,2));
680s  m(:,3) = m(:,3)*sign(COEFF(3,3));
680s ***** assert(COEFF,m,10*eps);
680s ***** assert(SCORE(:,1),-m(1:2,1),10*eps);
680s ***** assert(SCORE(:,2:3),zeros(2),10*eps);
680s ***** assert(latent,[1;0;0],10*eps);
680s ***** assert(tsquare,[0.5;0.5],10*eps)
680s ***** test
680s  [COEFF,SCORE,latent,tsquare] = princomp(x, "econ");
680s ***** assert(COEFF,m(:, 1),10*eps);
680s ***** assert(SCORE,-m(1:2,1),10*eps);
680s ***** assert(latent,[1],10*eps);
680s ***** assert(tsquare,[0.5;0.5],10*eps)
680s 19 tests, 19 passed, 0 known failure, 0 skipped
680s [inst/probit.m]
680s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/probit.m
680s ***** assert (probit ([-1, 0, 0.5, 1, 2]), [NaN, -Inf, 0, Inf, NaN])
680s ***** assert (probit ([0.2, 0.99]), norminv ([0.2, 0.99]))
680s ***** error probit ()
680s ***** error probit (1, 2)
680s 4 tests, 4 passed, 0 known failure, 0 skipped
680s [inst/procrustes.m]
680s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/procrustes.m
680s ***** demo
680s  ## Create some random points in two dimensions
680s  n = 10;
680s  randn ("seed", 1);
680s  X = normrnd (0, 1, [n, 2]);
680s 
680s  ## Those same points, rotated, scaled, translated, plus some noise
680s  S = [0.5, -sqrt(3)/2; sqrt(3)/2, 0.5]; # rotate 60 degrees
680s  Y = normrnd (0.5*X*S + 2, 0.05, n, 2);
680s 
680s  ## Conform Y to X, plot original X and Y, and transformed Y
680s  [d, Z] = procrustes (X, Y);
680s  plot (X(:,1), X(:,2), "rx", Y(:,1), Y(:,2), "b.", Z(:,1), Z(:,2), "bx");
680s ***** demo
680s  ## Find Procrustes distance and plot superimposed shape
680s 
680s  X = [40 88; 51 88; 35 78; 36 75; 39 72; 44 71; 48 71; 52 74; 55 77];
680s  Y = [36 43; 48 42; 31 26; 33 28; 37 30; 40 31; 45 30; 48 28; 51 24];
680s  plot (X(:,1),X(:,2),"x");
680s  hold on
680s  plot (Y(:,1),Y(:,2),"o");
680s  xlim ([0 100]);
680s  ylim ([0 100]);
680s  legend ("Target shape (X)", "Source shape (Y)");
680s  [d, Z] = procrustes (X, Y)
680s  plot (Z(:,1), Z(:,2), "s");
680s  legend ("Target shape (X)", "Source shape (Y)", "Transformed shape (Z)");
680s  hold off
680s ***** demo
680s  ## Apply Procrustes transformation to larger set of points
680s 
680s  ## Create matrices with landmark points for two triangles
680s  X = [5, 0; 5, 5; 8, 5];   # target
680s  Y = [0, 0; 1, 0; 1, 1];   # source
680s 
680s  ## Create a matrix with more points on the source triangle
680s  Y_mp = [linspace(Y(1,1),Y(2,1),10)', linspace(Y(1,2),Y(2,2),10)'; ...
680s          linspace(Y(2,1),Y(3,1),10)', linspace(Y(2,2),Y(3,2),10)'; ...
680s          linspace(Y(3,1),Y(1,1),10)', linspace(Y(3,2),Y(1,2),10)'];
680s 
680s  ## Plot both shapes, including the larger set of points for the source shape
680s  plot ([X(:,1); X(1,1)], [X(:,2); X(1,2)], "bx-");
680s  hold on
680s  plot ([Y(:,1); Y(1,1)], [Y(:,2); Y(1,2)], "ro-", "MarkerFaceColor", "r");
680s  plot (Y_mp(:,1), Y_mp(:,2), "ro");
680s  xlim ([-1 10]);
680s  ylim ([-1 6]);
680s  legend ("Target shape (X)", "Source shape (Y)", ...
680s          "More points on Y", "Location", "northwest");
680s  hold off
680s 
680s  ## Obtain the Procrustes transformation
680s  [d, Z, transform] = procrustes (X, Y)
680s 
680s  ## Use the Procrustes transformation to superimpose the more points (Y_mp)
680s  ## on the source shape onto the target shape, and then visualize the results.
680s  Z_mp = transform.b * Y_mp * transform.T + transform.c(1,:);
680s  figure
680s  plot ([X(:,1); X(1,1)], [X(:,2); X(1,2)], "bx-");
680s  hold on
680s  plot ([Y(:,1); Y(1,1)], [Y(:,2); Y(1,2)], "ro-", "MarkerFaceColor", "r");
680s  plot (Y_mp(:,1), Y_mp(:,2), "ro");
680s  xlim ([-1 10]);
680s  ylim ([-1 6]);
680s  plot ([Z(:,1); Z(1,1)],[Z(:,2); Z(1,2)],"ks-","MarkerFaceColor","k");
680s  plot (Z_mp(:,1),Z_mp(:,2),"ks");
680s  legend ("Target shape (X)", "Source shape (Y)", ...
680s          "More points on Y", "Transformed source shape (Z)", ...
680s          "Transformed additional points", "Location", "northwest");
680s  hold off
680s ***** demo
680s  ## Compare shapes without reflection
680s 
680s  T = [33, 93; 33, 87; 33, 80; 31, 72; 32, 65; 32, 58; 30, 72; ...
680s       28, 72; 25, 69; 22, 64; 23, 59; 26, 57; 30, 57];
680s  S = [48, 83; 48, 77; 48, 70; 48, 65; 49, 59; 49, 56; 50, 66; ...
680s       52, 66; 56, 65; 58, 61; 57, 57; 54, 56; 51, 55];
680s  plot (T(:,1), T(:,2), "x-");
680s  hold on
680s  plot (S(:,1), S(:,2), "o-");
680s  legend ("Target shape (d)", "Source shape (b)");
680s  hold off
680s  d_false = procrustes (T, S, "reflection", false);
680s  printf ("Procrustes distance without reflection: %f\n", d_false);
680s  d_true = procrustes (T, S, "reflection", true);
680s  printf ("Procrustes distance with reflection: %f\n", d_true);
680s  d_best = procrustes (T, S, "reflection", "best");
680s  printf ("Procrustes distance with best fit: %f\n", d_true);
680s ***** error procrustes ();
680s ***** error procrustes (1, 2, 3, 4, 5, 6);
680s ***** error<procrustes: X and Y must be 2-dimensional matrices.> ...
680s  procrustes (ones (2, 2, 2), ones (2, 2, 2));
680s ***** error<procrustes: values in X and Y must be real.> ...
680s  procrustes ([1, 2; -3, 4; 2, 3], [1, 2; -3, 4; 2, 3+i]);
680s ***** error<procrustes: values in X and Y must be real.> ...
680s  procrustes ([1, 2; -3, 4; 2, 3], [1, 2; -3, 4; 2, NaN]);
680s ***** error<procrustes: values in X and Y must be real.> ...
680s  procrustes ([1, 2; -3, 4; 2, 3], [1, 2; -3, 4; 2, Inf]);
680s ***** error<procrustes: X and Y must have equal number of rows.> ...
680s  procrustes (ones (10 ,3), ones (11, 3));
680s ***** error<procrustes: X must have at least as many columns as Y.> ...
680s  procrustes (ones (10 ,3), ones (10, 4));
680s ***** error<procrustes: optional arguments must be in Name-Value pairs.> ...
680s  procrustes (ones (10 ,3), ones (10, 3), "reflection");
680s ***** error<procrustes: optional arguments must be in Name-Value pairs.> ...
680s  procrustes (ones (10 ,3), ones (10, 3), true);
680s ***** error<procrustes: invalid value for scaling.> ...
680s  procrustes (ones (10 ,3), ones (10, 3), "scaling", 0);
680s ***** error<procrustes: invalid value for scaling.> ...
680s  procrustes (ones (10 ,3), ones (10, 3), "scaling", [true true]);
680s ***** error<procrustes: invalid value for reflection.> ...
680s  procrustes (ones (10 ,3), ones (10, 3), "reflection", 1);
680s ***** error<procrustes: invalid value for reflection.> ...
680s  procrustes (ones (10 ,3), ones (10, 3), "reflection", "some");
680s ***** error<procrustes: invalid name for optional arguments.> ...
680s  procrustes (ones (10 ,3), ones (10, 3), "param1", "some");
680s 15 tests, 15 passed, 0 known failure, 0 skipped
680s [inst/qqplot.m]
680s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/qqplot.m
680s ***** test
680s  hf = figure ("visible", "off");
680s  unwind_protect
680s    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]);
680s  unwind_protect_cleanup
680s    close (hf);
680s  end_unwind_protect
680s ***** error qqplot ()
680s ***** error <qqplot: X must be a numeric vector> qqplot ({1})
680s ***** error <qqplot: X must be a numeric vector> qqplot (ones (2,2))
680s ***** error <qqplot: X must be a numeric vector> qqplot (1, "foobar")
680s ***** error <qqplot: no inverse CDF found> qqplot ([1 2 3], "foobar")
680s 6 tests, 6 passed, 0 known failure, 0 skipped
680s [inst/qrandn.m]
680s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/qrandn.m
680s ***** demo
680s  z = qrandn (-5, 5e6);
680s  [c x] = hist (z,linspace(-1.5,1.5,200),1);
680s  figure(1)
680s  plot(x,c,"r."); axis tight; axis([-1.5,1.5]);
680s 
680s  z = qrandn (-0.14286, 5e6);
680s  [c x] = hist (z,linspace(-2,2,200),1);
680s  figure(2)
680s  plot(x,c,"r."); axis tight; axis([-2,2]);
680s 
680s  z = qrandn (2.75, 5e6);
680s  [c x] = hist (z,linspace(-1e3,1e3,1e3),1);
680s  figure(3)
680s  semilogy(x,c,"r."); axis tight; axis([-100,100]);
680s 
680s  # ---------
680s  # Figures from the reference paper.
680s ***** error<qrandn: the parameter q must be a scalar.> qrandn ([1 2], 1)
680s ***** error<qrandn: the parameter q must be lower than 3.> qrandn (4, 1)
680s ***** error<qrandn: the parameter q must be lower than 3.> qrandn (3, 1)
680s ***** error qrandn (2.5, 1, 2, 3)
680s ***** error qrandn (2.5)
680s ***** test
680s  q = 1.5;
680s  s = [2, 3];
680s  z = qrandn (q, s);
680s  assert (isnumeric (z) && isequal (size (z), s));
680s 6 tests, 6 passed, 0 known failure, 0 skipped
680s [inst/randsample.m]
680s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/randsample.m
680s ***** test
680s  n = 20;
680s  k = 5;
680s  x = randsample(n, k);
680s  assert (size(x), [1 k]);
680s  x = randsample(n, k, true);
680s  assert (size(x), [1 k]);
680s  x = randsample(n, k, false);
680s  assert (size(x), [1 k]);
680s  x = randsample(n, k, true, ones(n, 1));
680s  assert (size(x), [1 k]);
680s  x = randsample(1:n, k);
680s  assert (size(x), [1 k]);
680s  x = randsample(1:n, k, true);
680s  assert (size(x), [1 k]);
680s  x = randsample(1:n, k, false);
680s  assert (size(x), [1 k]);
680s  x = randsample(1:n, k, true, ones(n, 1));
680s  assert (size(x), [1 k]);
680s  x = randsample((1:n)', k);
680s  assert (size(x), [k 1]);
680s  x = randsample((1:n)', k, true);
680s  assert (size(x), [k 1]);
680s  x = randsample((1:n)', k, false);
680s  assert (size(x), [k 1]);
680s  x = randsample((1:n)', k, true, ones(n, 1));
680s  assert (size(x), [k 1]);
680s  n = 10;
680s  k = 100;
680s  x = randsample(n, k, true, 1:n);
680s  assert (size(x), [1 k]);
680s  x = randsample((1:n)', k, true);
680s  assert (size(x), [k 1]);
680s  x = randsample(k, k, false, 1:k);
680s  assert (size(x), [1 k]);
680s 1 test, 1 passed, 0 known failure, 0 skipped
680s [inst/rangesearch.m]
680s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/rangesearch.m
680s ***** demo
680s  ## Generate 1000 random 2D points from each of five distinct multivariate
680s  ## normal distributions that form five separate classes
680s  N = 1000;
680s  d = 10;
680s  randn ("seed", 5);
680s  X1 = mvnrnd (d * [0, 0], eye (2), 1000);
680s  randn ("seed", 6);
680s  X2 = mvnrnd (d * [1, 1], eye (2), 1000);
680s  randn ("seed", 7);
680s  X3 = mvnrnd (d * [-1, -1], eye (2), 1000);
680s  randn ("seed", 8);
680s  X4 = mvnrnd (d * [1, -1], eye (2), 1000);
680s  randn ("seed", 8);
680s  X5 = mvnrnd (d * [-1, 1], eye (2), 1000);
680s  X = [X1; X2; X3; X4; X5];
680s 
680s  ## For each point in X, find the points in X that are within a radius d
680s  ## away from the points in X.
680s  Idx = rangesearch (X, X, d, "NSMethod", "exhaustive");
680s 
680s  ## Select the first point in X (corresponding to the first class) and find
680s  ## its nearest neighbors within the radius d.  Display these points in
680s  ## one color and the remaining points in a different color.
680s  x = X(1,:);
680s  nearestPoints = X (Idx{1},:);
680s  nonNearestIdx = true (size (X, 1), 1);
680s  nonNearestIdx(Idx{1}) = false;
680s 
680s  scatter (X(nonNearestIdx,1), X(nonNearestIdx,2))
680s  hold on
680s  scatter (nearestPoints(:,1),nearestPoints(:,2))
680s  scatter (x(1), x(2), "black", "filled")
680s  hold off
680s 
680s  ## Select the last point in X (corresponding to the fifth class) and find
680s  ## its nearest neighbors within the radius d.  Display these points in
680s  ## one color and the remaining points in a different color.
680s  x = X(end,:);
680s  nearestPoints = X (Idx{1},:);
680s  nonNearestIdx = true (size (X, 1), 1);
680s  nonNearestIdx(Idx{1}) = false;
680s 
680s  figure
680s  scatter (X(nonNearestIdx,1), X(nonNearestIdx,2))
680s  hold on
680s  scatter (nearestPoints(:,1),nearestPoints(:,2))
680s  scatter (x(1), x(2), "black", "filled")
680s  hold off
680s ***** shared x, y, X, Y
680s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
680s  y = [2, 3, 4; 1, 4, 3];
680s  X = [1, 2, 3, 4; 2, 3, 4, 5; 3, 4, 5, 6];
680s  Y = [1, 2, 2, 3; 2, 3, 3, 4];
680s ***** test
680s  [idx, D] = rangesearch (x, y, 4);
680s  assert (idx, {[1; 4; 2]; [1; 4]});
680s  assert (D, {[1.7321; 3.3166; 3.4641]; [2; 3.4641]}, 1e-4);
680s ***** test
680s  [idx, D] = rangesearch (x, y, 4, "NSMethod", "exhaustive");
680s  assert (idx, {[1, 4, 2]; [1, 4]});
680s  assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
680s ***** test
680s  [idx, D] = rangesearch (x, y, 4, "NSMethod", "kdtree");
680s  assert (idx, {[1; 4; 2]; [1; 4]});
680s  assert (D, {[1.7321; 3.3166; 3.4641]; [2; 3.4641]}, 1e-4);
680s ***** test
680s  [idx, D] = rangesearch (x, y, 4, "SortIndices", true);
680s  assert (idx, {[1; 4; 2]; [1; 4]});
680s  assert (D, {[1.7321; 3.3166; 3.4641]; [2; 3.4641]}, 1e-4);
681s ***** test
681s  [idx, D] = rangesearch (x, y, 4, "SortIndices", false);
681s  assert (idx, {[1; 2; 4]; [1; 4]});
681s  assert (D, {[1.7321; 3.4641; 3.3166]; [2; 3.4641]}, 1e-4);
681s ***** test
681s  [idx, D] = rangesearch (x, y, 4, "NSMethod", "exhaustive", ...
681s                          "SortIndices", false);
681s  assert (idx, {[1, 2, 4]; [1, 4]});
681s  assert (D, {[1.7321, 3.4641, 3.3166]; [2, 3.4641]}, 1e-4);
681s ***** test
681s  eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
681s  [idx, D] = rangesearch (x, y, 4, "Distance", eucldist);
681s  assert (idx, {[1, 4, 2]; [1, 4]});
681s  assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
681s ***** test
681s  eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
681s  [idx, D] = rangesearch (x, y, 4, "Distance", eucldist, ...
681s                          "NSMethod", "exhaustive");
681s  assert (idx, {[1, 4, 2]; [1, 4]});
681s  assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
681s ***** test
681s  [idx, D] = rangesearch (x, y, 1.5, "Distance", "seuclidean", ...
681s                          "NSMethod", "exhaustive");
681s  assert (idx, {[1, 4, 2]; [1, 4]});
681s  assert (D, {[0.6024, 1.0079, 1.2047]; [0.6963, 1.2047]}, 1e-4);
681s ***** test
681s  [idx, D] = rangesearch (x, y, 1.5, "Distance", "seuclidean", ...
681s                          "NSMethod", "exhaustive", "SortIndices", false);
681s  assert (idx, {[1, 2, 4]; [1, 4]});
681s  assert (D, {[0.6024, 1.2047, 1.0079]; [0.6963, 1.2047]}, 1e-4);
681s ***** test
681s  [idx, D] = rangesearch (X, Y, 4);
681s  assert (idx, {[1; 2]; [1; 2; 3]});
681s  assert (D, {[1.4142; 3.1623]; [1.4142; 1.4142; 3.1623]}, 1e-4);
681s ***** test
681s  [idx, D] = rangesearch (X, Y, 2);
681s  assert (idx, {[1]; [1; 2]});
681s  assert (D, {[1.4142]; [1.4142; 1.4142]}, 1e-4);
681s ***** test
681s  eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
681s  [idx, D] = rangesearch (X, Y, 4, "Distance", eucldist);
681s  assert (idx, {[1, 2]; [1, 2, 3]});
681s  assert (D, {[1.4142, 3.1623]; [1.4142, 1.4142, 3.1623]}, 1e-4);
681s ***** test
681s  [idx, D] = rangesearch (X, Y, 4, "SortIndices", false);
681s  assert (idx, {[1; 2]; [1; 2; 3]});
681s  assert (D, {[1.4142; 3.1623]; [1.4142; 1.4142; 3.1623]}, 1e-4);
681s ***** test
681s  [idx, D] = rangesearch (X, Y, 4, "Distance", "seuclidean", ...
681s                          "NSMethod", "exhaustive");
681s  assert (idx, {[1, 2]; [1, 2, 3]});
681s  assert (D, {[1.4142, 3.1623]; [1.4142, 1.4142, 3.1623]}, 1e-4);
681s ***** error<rangesearch: too few input arguments.> rangesearch (1)
681s ***** error<rangesearch: too few input arguments.> rangesearch (ones (4, 5))
681s ***** error<rangesearch: too few input arguments.> ...
681s  rangesearch (ones (4, 5), ones (4))
681s ***** error<rangesearch: number of columns in X and Y must match.> ...
681s  rangesearch (ones (4, 5), ones (4), 1)
681s ***** error<rangesearch: invalid NAME in optional pairs of arguments.> ...
681s  rangesearch (ones (4, 2), ones (3, 2), 1, "Distance", "euclidean", "some", "some")
681s ***** error<rangesearch: only a single distance parameter can be defined.> ...
681s  rangesearch (ones (4, 5), ones (1, 5), 1, "scale", ones (1, 5), "P", 3)
681s ***** error<rangesearch: invalid value of Minkowski Exponent.> ...
681s  rangesearch (ones (4, 5), ones (1, 5), 1, "P", -2)
681s ***** error<rangesearch: invalid value in Scale or the size of Scale.> ...
681s  rangesearch (ones (4, 5), ones (1, 5), 1, "scale", ones(4,5), "distance", "euclidean")
681s ***** error<rangesearch: invalid value in Cov, Cov can only be given for mahalanobis distance.> ...
681s  rangesearch (ones (4, 5), ones (1, 5), 1, "cov", ["some" "some"])
681s ***** error<rangesearch: invalid value in Cov, Cov can only be given for mahalanobis distance.> ...
681s  rangesearch (ones (4, 5), ones (1, 5), 1, "cov", ones(4,5), "distance", "euclidean")
681s ***** error<rangesearch: invalid value of bucketsize.> ...
681s  rangesearch (ones (4, 5), ones (1, 5), 1, "bucketsize", -1)
681s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
681s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "cosine")
681s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
681s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "mahalanobis")
681s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
681s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "correlation")
681s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
681s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "seuclidean")
681s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
681s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "spearman")
681s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
681s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "hamming")
681s ***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
681s  rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "jaccard")
681s 33 tests, 33 passed, 0 known failure, 0 skipped
681s [inst/ranksum.m]
681s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/ranksum.m
681s ***** test
681s  mileage = [33.3, 34.5, 37.4; 33.4, 34.8, 36.8; ...
681s             32.9, 33.8, 37.6; 32.6, 33.4, 36.6; ...
681s             32.5, 33.7, 37.0; 33.0, 33.9, 36.7];
681s  [p,h,stats] = ranksum(mileage(:,1),mileage(:,2));
681s  assert (p, 0.004329004329004329, 1e-14);
681s  assert (h, true);
681s  assert (stats.ranksum, 21.5);
681s ***** test
681s  year1 = [51 52 62 62 52 52 51 53 59 63 59 56 63 74 68 86 82 70 69 75 73 ...
681s           49 47 50 60 59 60 62 61 71]';
681s  year2 = [54 53 64 66 57 53 54 54 62 66 59 59 67 76 75 86 82 67 74 80 75 ...
681s           54 50 53 62 62 62 72 60 67]';
681s  [p,h,stats] = ranksum(year1, year2, "alpha", 0.01, "tail", "left");
681s  assert (p, 0.1270832752950605, 1e-14);
681s  assert (h, false);
681s  assert (stats.ranksum, 837.5);
681s  assert (stats.zval, -1.140287483634606, 1e-14);
681s  [p,h,stats] = ranksum(year1, year2, "alpha", 0.01, "tail", "left", ...
681s                        "method", "exact");
681s  assert (p, 0.127343916432862, 1e-14);
681s  assert (h, false);
681s  assert (stats.ranksum, 837.5);
694s 2 tests, 2 passed, 0 known failure, 0 skipped
694s [inst/regress.m]
694s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/regress.m
694s ***** test
694s  % Longley data from the NIST Statistical Reference Dataset
694s  Z = [  60323    83.0   234289   2356     1590    107608  1947
694s         61122    88.5   259426   2325     1456    108632  1948
694s         60171    88.2   258054   3682     1616    109773  1949
694s         61187    89.5   284599   3351     1650    110929  1950
694s         63221    96.2   328975   2099     3099    112075  1951
694s         63639    98.1   346999   1932     3594    113270  1952
694s         64989    99.0   365385   1870     3547    115094  1953
694s         63761   100.0   363112   3578     3350    116219  1954
694s         66019   101.2   397469   2904     3048    117388  1955
694s         67857   104.6   419180   2822     2857    118734  1956
694s         68169   108.4   442769   2936     2798    120445  1957
694s         66513   110.8   444546   4681     2637    121950  1958
694s         68655   112.6   482704   3813     2552    123366  1959
694s         69564   114.2   502601   3931     2514    125368  1960
694s         69331   115.7   518173   4806     2572    127852  1961
694s         70551   116.9   554894   4007     2827    130081  1962 ];
694s  % Results certified by NIST using 500 digit arithmetic
694s  % b and standard error in b
694s  V = [  -3482258.63459582         890420.383607373
694s          15.0618722713733         84.9149257747669
694s         -0.358191792925910E-01    0.334910077722432E-01
694s         -2.02022980381683         0.488399681651699
694s         -1.03322686717359         0.214274163161675
694s         -0.511041056535807E-01    0.226073200069370
694s          1829.15146461355         455.478499142212 ];
694s  Rsq = 0.995479004577296;
694s  F = 330.285339234588;
694s  y = Z(:,1); X = [ones(rows(Z),1), Z(:,2:end)];
694s  alpha = 0.05;
694s  [b, bint, r, rint, stats] = regress (y, X, alpha);
694s  assert(b,V(:,1),4e-6);
694s  assert(stats(1),Rsq,1e-12);
694s  assert(stats(2),F,3e-8);
694s  assert(((bint(:,1)-bint(:,2))/2)/tinv(alpha/2,9),V(:,2),-1.e-5);
694s warning: matrix singular to machine precision, rcond = 3.50566e-20
694s warning: called from
694s     regress at line 131 column 5
694s     __test__ at line 33 column 3
694s     test at line 685 column 11
694s     /tmp/tmp.scCa8Po4At at line 3366 column 2
694s 
694s 1 test, 1 passed, 0 known failure, 0 skipped
694s [inst/regress_gp.m]
694s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/regress_gp.m
694s ***** demo
694s  ## Linear fitting of 1D Data
694s  rand ("seed", 125);
694s  X = 2 * rand (5, 1) - 1;
694s  randn ("seed", 25);
694s  Y = 2 * X - 1 + 0.3 * randn (5, 1);
694s 
694s  ## Points for interpolation/extrapolation
694s  Xfit = linspace (-2, 2, 10)';
694s 
694s  ## Fit regression model
694s  [Yfit, Yint, m] = regress_gp (X, Y, Xfit);
694s 
694s  ## Plot fitted data
694s  plot (X, Y, "xk", Xfit, Yfit, "r-", Xfit, Yint, "b-");
694s  title ("Gaussian process regression with linear kernel");
694s ***** demo
694s  ## Linear fitting of 2D Data
694s  rand ("seed", 135);
694s  X = 2 * rand (4, 2) - 1;
694s  randn ("seed", 35);
694s  Y = 2 * X(:,1) - 3 * X(:,2) - 1 + 1 * randn (4, 1);
694s 
694s  ## Mesh for interpolation/extrapolation
694s  [x1, x2] = meshgrid (linspace (-1, 1, 10));
694s  Xfit = [x1(:), x2(:)];
694s 
694s  ## Fit regression model
694s  [Ypred, Yint, Ysd] = regress_gp (X, Y, Xfit);
694s  Ypred = reshape (Ypred, 10, 10);
694s  YintU = reshape (Yint(:,1), 10, 10);
694s  YintL = reshape (Yint(:,2), 10, 10);
694s 
694s  ## Plot fitted data
694s  plot3 (X(:,1), X(:,2), Y, ".k", "markersize", 16);
694s  hold on;
694s  h = mesh (x1, x2, Ypred, zeros (10, 10));
694s  set (h, "facecolor", "none", "edgecolor", "yellow");
694s  h = mesh (x1, x2, YintU, ones (10, 10));
694s  set (h, "facecolor", "none", "edgecolor", "cyan");
694s  h = mesh (x1, x2, YintL, ones (10, 10));
694s  set (h, "facecolor", "none", "edgecolor", "cyan");
694s  hold off
694s  axis tight
694s  view (75, 25)
694s  title ("Gaussian process regression with linear kernel");
694s ***** demo
694s  ## Projection over basis function with linear kernel
694s  pp = [2, 2, 0.3, 1];
694s  n = 10;
694s  rand ("seed", 145);
694s  X = 2 * rand (n, 1) - 1;
694s  randn ("seed", 45);
694s  Y = polyval (pp, X) + 0.3 * randn (n, 1);
694s 
694s  ## Powers
694s  px = [sqrt(abs(X)), X, X.^2, X.^3];
694s 
694s  ## Points for interpolation/extrapolation
694s  Xfit = linspace (-1, 1, 100)';
694s  pxi = [sqrt(abs(Xfit)), Xfit, Xfit.^2, Xfit.^3];
694s 
694s  ## Define a prior covariance assuming that the sqrt component is not present
694s  Sp = 100 * eye (size (px, 2) + 1);
694s  Sp(2,2) = 1; # We don't believe the sqrt(abs(X)) is present
694s 
694s  ## Fit regression model
694s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, Sp);
694s 
694s  ## Plot fitted data
694s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
694s                          Xfit, polyval (pp, Xfit), "g-;True;");
694s  axis tight
694s  axis manual
694s  hold on
694s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
694s  hold off
694s  title ("Linear kernel over basis function with prior covariance");
694s ***** demo
694s  ## Projection over basis function with linear kernel
694s  pp = [2, 2, 0.3, 1];
694s  n = 10;
694s  rand ("seed", 145);
694s  X = 2 * rand (n, 1) - 1;
694s  randn ("seed", 45);
694s  Y = polyval (pp, X) + 0.3 * randn (n, 1);
694s 
694s  ## Powers
694s  px = [sqrt(abs(X)), X, X.^2, X.^3];
694s 
694s  ## Points for interpolation/extrapolation
694s  Xfit = linspace (-1, 1, 100)';
694s  pxi = [sqrt(abs(Xfit)), Xfit, Xfit.^2, Xfit.^3];
694s 
694s  ## Fit regression model without any assumption on prior covariance
694s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi);
694s 
694s  ## Plot fitted data
694s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
694s                          Xfit, polyval (pp, Xfit), "g-;True;");
694s  axis tight
694s  axis manual
694s  hold on
694s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
694s  hold off
694s  title ("Linear kernel over basis function without prior covariance");
694s ***** demo
694s  ## Projection over basis function with rbf kernel
694s  pp = [2, 2, 0.3, 1];
694s  n = 10;
694s  rand ("seed", 145);
694s  X = 2 * rand (n, 1) - 1;
694s  randn ("seed", 45);
694s  Y = polyval (pp, X) + 0.3 * randn (n, 1);
694s 
694s  ## Powers
694s  px = [sqrt(abs(X)), X, X.^2, X.^3];
694s 
694s  ## Points for interpolation/extrapolation
694s  Xfit = linspace (-1, 1, 100)';
694s  pxi = [sqrt(abs(Xfit)), Xfit, Xfit.^2, Xfit.^3];
694s 
694s  ## Fit regression model with RBF kernel (standard parameters)
694s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf");
694s 
694s  ## Plot fitted data
694s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
694s                          Xfit, polyval (pp, Xfit), "g-;True;");
694s  axis tight
694s  axis manual
694s  hold on
694s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
694s  hold off
694s  title ("RBF kernel over basis function with standard parameters");
694s  text (-0.5, 4, "theta = 5\n g = 0.01");
694s ***** demo
694s  ## Projection over basis function with rbf kernel
694s  pp = [2, 2, 0.3, 1];
694s  n = 10;
694s  rand ("seed", 145);
694s  X = 2 * rand (n, 1) - 1;
694s  randn ("seed", 45);
694s  Y = polyval (pp, X) + 0.3 * randn (n, 1);
694s 
694s  ## Powers
694s  px = [sqrt(abs(X)), X, X.^2, X.^3];
694s 
694s  ## Points for interpolation/extrapolation
694s  Xfit = linspace (-1, 1, 100)';
694s  pxi = [sqrt(abs(Xfit)), Xfit, Xfit.^2, Xfit.^3];
694s 
694s  ## Fit regression model with RBF kernel with different parameters
694s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf", 10, 0.01);
694s 
694s  ## Plot fitted data
694s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
694s                          Xfit, polyval (pp, Xfit), "g-;True;");
694s  axis tight
694s  axis manual
694s  hold on
694s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
694s  hold off
694s  title ("GP regression with RBF kernel and non default parameters");
694s  text (-0.5, 4, "theta = 10\n g = 0.01");
694s 
694s  ## Fit regression model with RBF kernel with different parameters
694s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf", 50, 0.01);
694s 
694s  ## Plot fitted data
694s  figure
694s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
694s                          Xfit, polyval (pp, Xfit), "g-;True;");
694s  axis tight
694s  axis manual
694s  hold on
694s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
694s  hold off
694s  title ("GP regression with RBF kernel and non default parameters");
694s  text (-0.5, 4, "theta = 50\n g = 0.01");
694s 
694s  ## Fit regression model with RBF kernel with different parameters
694s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf", 50, 0.001);
694s 
694s  ## Plot fitted data
694s  figure
694s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
694s                          Xfit, polyval (pp, Xfit), "g-;True;");
694s  axis tight
694s  axis manual
694s  hold on
694s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
694s  hold off
694s  title ("GP regression with RBF kernel and non default parameters");
694s  text (-0.5, 4, "theta = 50\n g = 0.001");
694s 
694s  ## Fit regression model with RBF kernel with different parameters
694s  [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf", 50, 0.05);
694s 
694s  ## Plot fitted data
694s  figure
694s  plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
694s                          Xfit, polyval (pp, Xfit), "g-;True;");
694s  axis tight
694s  axis manual
694s  hold on
694s  plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
694s  hold off
694s  title ("GP regression with RBF kernel and non default parameters");
694s  text (-0.5, 4, "theta = 50\n g = 0.05");
694s ***** demo
694s  ## RBF fitting on noiseless 1D Data
694s  x = [0:2*pi/7:2*pi]';
694s  y = 5 * sin (x);
694s 
694s  ## Predictive grid of 500 equally spaced locations
694s  xi = [-0.5:(2*pi+1)/499:2*pi+0.5]';
694s 
694s  ## Fit regression model with RBF kernel
694s  [Yfit, Yint, Ysd] = regress_gp (x, y, xi, "rbf");
694s 
694s  ## Plot fitted data
694s  r = mvnrnd (Yfit, diag (Ysd)', 50);
694s  plot (xi, r', "c-");
694s  hold on
694s  plot (xi, Yfit, "r-;Estimation;", xi, Yint, "b-;Confidence interval;");
694s  plot (x, y, ".k;Predictor points;", "markersize", 20)
694s  plot (xi, 5 * sin (xi), "-y;True Function;");
694s  xlim ([-0.5,2*pi+0.5]);
694s  ylim ([-10,10]);
694s  hold off
694s  title ("GP regression with RBF kernel on noiseless 1D data");
694s  text (0, -7, "theta = 5\n g = 0.01");
694s ***** demo
694s  ## RBF fitting on noisy 1D Data
694s  x = [0:2*pi/7:2*pi]';
694s  x = [x; x];
694s  y = 5 * sin (x) + randn (size (x));
694s 
694s  ## Predictive grid of 500 equally spaced locations
694s  xi = [-0.5:(2*pi+1)/499:2*pi+0.5]';
694s 
694s  ## Fit regression model with RBF kernel
694s  [Yfit, Yint, Ysd] = regress_gp (x, y, xi, "rbf");
694s 
694s  ## Plot fitted data
694s  r = mvnrnd (Yfit, diag (Ysd)', 50);
694s  plot (xi, r', "c-");
694s  hold on
694s  plot (xi, Yfit, "r-;Estimation;", xi, Yint, "b-;Confidence interval;");
694s  plot (x, y, ".k;Predictor points;", "markersize", 20)
694s  plot (xi, 5 * sin (xi), "-y;True Function;");
694s  xlim ([-0.5,2*pi+0.5]);
694s  ylim ([-10,10]);
694s  hold off
694s  title ("GP regression with RBF kernel on noisy 1D data");
694s  text (0, -7, "theta = 5\n g = 0.01");
694s ***** error<Invalid call to regress_gp.> regress_gp (ones (20, 2))
694s ***** error<Invalid call to regress_gp.> regress_gp (ones (20, 2), ones (20, 1))
694s ***** error<regress_gp: X must be a 2-D matrix.> ...
694s  regress_gp (ones (20, 2, 3), ones (20, 1), ones (20, 2))
694s ***** error<regress_gp: Y must be a column vector.> ...
694s  regress_gp (ones (20, 2), ones (20, 2), ones (20, 2))
694s ***** error<regress_gp: rows in X must equal the length of Y.> ...
694s  regress_gp (ones (20, 2), ones (15, 1), ones (20, 2))
694s ***** error<regress_gp: X and XI must have the same number of columns.> ...
694s  regress_gp (ones (20, 2), ones (20, 1), ones (20, 3))
694s ***** error<regress_gp: invalid 4th argument.> ...
694s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), {[3]})
694s ***** error<regress_gp: invalid 4th argument.> ...
694s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "kernel")
694s ***** error<regress_gp: theta must be a scalar when using RBF kernel.> ...
694s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", ones (4))
694s ***** error<regress_gp: wrong size for prior covariance matrix Sp.> ...
694s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "linear", 1)
694s ***** error<regress_gp: invalid 5th argument.> ...
694s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", "value")
694s ***** error<regress_gp: invalid 5th argument.> ...
694s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", {5})
694s ***** error<regress_gp: invalid 5th argument.> ...
694s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), ones (3), 5)
694s ***** error<regress_gp: wrong size for prior covariance matrix Sp.> ...
694s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "linear", 5)
694s ***** error<regress_gp: invalid 6th argument.> ...
694s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", 5, {5})
694s ***** error<regress_gp: invalid 6th argument.> ...
694s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", 5, ones (2))
694s ***** error<regress_gp: invalid 6th argument.> ...
694s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), 5, 0.01, [1, 1])
694s ***** error<regress_gp: invalid 6th argument.> ...
694s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), 5, 0.01, "f")
694s ***** error<regress_gp: invalid 6th argument.> ...
694s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), 5, 0.01, "f")
694s ***** error<regress_gp: invalid 7th argument.> ...
694s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", 5, 0.01, "f")
694s ***** error<regress_gp: invalid 7th argument.> ...
694s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", 5, 0.01, [1, 1])
694s ***** error<regress_gp: wrong size for prior covariance matrix Sp.> ...
694s  regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "linear", 1)
694s 22 tests, 22 passed, 0 known failure, 0 skipped
694s [inst/regression_ftest.m]
694s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/regression_ftest.m
694s ***** error<Invalid call to regression_ftest.  Correct usage> regression_ftest ();
694s ***** error<Invalid call to regression_ftest.  Correct usage> ...
694s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]');
694s ***** error<regression_ftest: Y must contain finite real numbers.> ...
694s  regression_ftest ([1 2 NaN]', [2 3 4; 3 4 5]', [1 0.5]);
694s ***** error<regression_ftest: Y must contain finite real numbers.> ...
694s  regression_ftest ([1 2 Inf]', [2 3 4; 3 4 5]', [1 0.5]);
694s ***** error<regression_ftest: Y must contain finite real numbers.> ...
694s  regression_ftest ([1 2 3+i]', [2 3 4; 3 4 5]', [1 0.5]);
694s ***** error<regression_ftest: X must contain finite real numbers.> ...
694s  regression_ftest ([1 2 3]', [2 3 NaN; 3 4 5]', [1 0.5]);
694s ***** error<regression_ftest: X must contain finite real numbers.> ...
694s  regression_ftest ([1 2 3]', [2 3 Inf; 3 4 5]', [1 0.5]);
694s ***** error<regression_ftest: X must contain finite real numbers.> ...
694s  regression_ftest ([1 2 3]', [2 3 4; 3 4 3+i]', [1 0.5]);
694s ***** error<regression_ftest: invalid value for alpha.> ...
694s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "alpha", 0);
694s ***** error<regression_ftest: invalid value for alpha.> ...
694s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "alpha", 1.2);
694s ***** error<regression_ftest: invalid value for alpha.> ...
694s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "alpha", [.02 .1]);
694s ***** error<regression_ftest: invalid value for alpha.> ...
694s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "alpha", "a");
694s ***** error<regression_ftest: invalid Name argument.> ...
694s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "some", 0.05);
694s ***** error<regression_ftest: Y must be a vector of length> ...
694s  regression_ftest ([1 2 3]', [2 3; 3 4]', [1 0.5]);
694s ***** error<regression_ftest: Y must be a vector of length> ...
694s  regression_ftest ([1 2; 3 4]', [2 3; 3 4]', [1 0.5]);
694s ***** error<regression_ftest: reduced model, RM, must be a numeric vector or> ...
694s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], ones (2));
694s ***** error<regression_ftest: reduced model, RM, must be a numeric vector or> ...
694s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], "alpha");
694s ***** error<regression_ftest: reduced model, RM, must have smaller length than> ...
694s  regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [1 2]);
694s 18 tests, 18 passed, 0 known failure, 0 skipped
694s [inst/regression_ttest.m]
694s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/regression_ttest.m
694s ***** error<Invalid call to regression_ttest.  Correct usage> regression_ttest ();
694s ***** error<Invalid call to regression_ttest.  Correct usage> regression_ttest (1);
694s ***** error<regression_ttest: Y must contain finite real numbers.> ...
694s  regression_ttest ([1 2 NaN]', [2 3 4]');
694s ***** error<regression_ttest: Y must contain finite real numbers.> ...
694s  regression_ttest ([1 2 Inf]', [2 3 4]');
694s ***** error<regression_ttest: Y must contain finite real numbers.> ...
694s  regression_ttest ([1 2 3+i]', [2 3 4]');
694s ***** error<regression_ttest: X must contain finite real numbers.> ...
694s  regression_ttest ([1 2 3]', [2 3 NaN]');
694s ***** error<regression_ttest: X must contain finite real numbers.> ...
694s  regression_ttest ([1 2 3]', [2 3 Inf]');
694s ***** error<regression_ttest: X must contain finite real numbers.> ...
694s  regression_ttest ([1 2 3]', [3 4 3+i]');
694s ***** error<regression_ttest: Y and X must be vectors of equal length.> ...
694s  regression_ttest ([1 2 3]', [3 4 4 5]');
694s ***** error<regression_ttest: invalid value for alpha.> ...
694s  regression_ttest ([1 2 3]', [2 3 4]', "alpha", 0);
694s ***** error<regression_ttest: invalid value for alpha.> ...
694s  regression_ttest ([1 2 3]', [2 3 4]', "alpha", 1.2);
694s ***** error<regression_ttest: invalid value for alpha.> ...
694s  regression_ttest ([1 2 3]', [2 3 4]', "alpha", [.02 .1]);
694s ***** error<regression_ttest: invalid value for alpha.> ...
694s  regression_ttest ([1 2 3]', [2 3 4]', "alpha", "a");
694s ***** error<regression_ttest: invalid Name argument.> ...
694s  regression_ttest ([1 2 3]', [2 3 4]', "some", 0.05);
694s ***** error<regression_ttest: invalid value for tail.>  ...
694s  regression_ttest ([1 2 3]', [2 3 4]', "tail", "val");
694s ***** error<regression_ttest: invalid value for tail.>  ...
694s  regression_ttest ([1 2 3]', [2 3 4]', "alpha", 0.01, "tail", "val");
694s 16 tests, 16 passed, 0 known failure, 0 skipped
694s [inst/ridge.m]
694s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/ridge.m
694s ***** demo
694s  ## Perform ridge regression for a range of ridge parameters and observe
694s  ## how the coefficient estimates change based on the acetylene dataset.
694s 
694s  load acetylene
694s 
694s  X = [x1, x2, x3];
694s 
694s  x1x2 = x1 .* x2;
694s  x1x3 = x1 .* x3;
694s  x2x3 = x2 .* x3;
694s 
694s  D = [x1, x2, x3, x1x2, x1x3, x2x3];
694s 
694s  k = 0:1e-5:5e-3;
694s 
694s  b = ridge (y, D, k);
694s 
694s  figure
694s  plot (k, b, "LineWidth", 2)
694s  ylim ([-100, 100])
694s  grid on
694s  xlabel ("Ridge Parameter")
694s  ylabel ("Standardized Coefficient")
694s  title ("Ridge Trace")
694s  legend ("x1", "x2", "x3", "x1x2", "x1x3", "x2x3")
694s 
694s ***** demo
694s 
694s  load carbig
694s  X = [Acceleration Weight Displacement Horsepower];
694s  y = MPG;
694s 
694s  n = length(y);
694s 
694s  rand("seed",1); % For reproducibility
694s 
694s  c = cvpartition(n,'HoldOut',0.3);
694s  idxTrain = training(c,1);
694s  idxTest = ~idxTrain;
694s 
694s  idxTrain = training(c,1);
694s  idxTest = ~idxTrain;
694s 
694s  k = 5;
694s  b = ridge(y(idxTrain),X(idxTrain,:),k,0);
694s 
694s  % Predict MPG values for the test data using the model.
694s  yhat = b(1) + X(idxTest,:)*b(2:end);
694s  scatter(y(idxTest),yhat)
694s 
694s  hold on
694s  plot(y(idxTest),y(idxTest),"r")
694s  xlabel('Actual MPG')
694s  ylabel('Predicted MPG')
694s  hold off
694s 
694s ***** test
694s  b = ridge ([1 2 3 4]', [1 2 3 4; 2 3 4 5]', 1);
694s  assert (b, [0.5533; 0.5533], 1e-4);
694s ***** test
694s  b = ridge ([1 2 3 4]', [1 2 3 4; 2 3 4 5]', 2);
694s  assert (b, [0.4841; 0.4841], 1e-4);
694s ***** test
694s  load acetylene
694s  x = [x1, x2, x3];
694s  b = ridge (y, x, 0);
694s  assert (b,[10.2273;1.97128;-0.601818],1e-4);
694s ***** test
694s  load acetylene
694s  x = [x1, x2, x3];
694s  b = ridge (y, x, 0.0005);
694s  assert (b,[10.2233;1.9712;-0.6056],1e-4);
694s ***** test
694s  load acetylene
694s  x = [x1, x2, x3];
694s  b = ridge (y, x, 0.001);
694s  assert (b,[10.2194;1.9711;-0.6094],1e-4);
694s ***** test
694s  load acetylene
694s  x = [x1, x2, x3];
694s  b = ridge (y, x, 0.002);
694s  assert (b,[10.2116;1.9709;-0.6169],1e-4);
694s ***** test
694s  load acetylene
694s  x = [x1, x2, x3];
694s  b = ridge (y, x, 0.005);
694s  assert (b,[10.1882;1.9704;-0.6393],1e-4);
694s ***** test
694s  load acetylene
694s  x = [x1, x2, x3];
694s  b = ridge (y, x, 0.01);
694s  assert (b,[10.1497;1.9695;-0.6761],1e-4);
694s ***** error<ridge: function called with too few input arguments.> ridge (1)
694s ***** error<ridge: function called with too few input arguments.> ridge (1, 2)
694s ***** error<ridge: Y must be a numeric column vector.> ridge (ones (3), ones (3), 2)
694s ***** error<ridge: Y must be a numeric column vector.> ridge ([1, 2], ones (2), 2)
694s ***** error<ridge: Y must be a numeric column vector.> ridge ([], ones (3), 2)
694s ***** error<ridge: X must be a numeric matrix.> ridge (ones (5,1), [], 2)
694s ***** error<ridge: Y and X must contain the same number of rows.> ...
694s  ridge ([1; 2; 3; 4; 5], ones (3), 3)
694s ***** error<ridge: wrong value for SCALED argument.> ...
694s  ridge ([1; 2; 3], ones (3), 3, 2)
694s ***** error<ridge: wrong value for SCALED argument.> ...
694s  ridge ([1; 2; 3], ones (3), 3, "some")
694s 17 tests, 17 passed, 0 known failure, 0 skipped
694s [inst/rmmissing.m]
694s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/rmmissing.m
694s ***** assert (rmmissing ([1,NaN,3]), [1,3])
694s ***** assert (rmmissing ('abcd f'), 'abcdf')
694s ***** assert (rmmissing ({'xxx','','xyz'}), {'xxx','xyz'})
694s ***** assert (rmmissing ({'xxx','';'xyz','yyy'}), {'xyz','yyy'})
694s ***** assert (rmmissing ({'xxx','';'xyz','yyy'}, 2), {'xxx';'xyz'})
694s ***** assert (rmmissing ([1,2;NaN,2]), [1,2])
694s ***** assert (rmmissing ([1,2;NaN,2], 2), [2,2]')
694s ***** assert (rmmissing ([1,2;NaN,4;NaN,NaN],"MinNumMissing", 2), [1,2;NaN,4])
694s ***** test
694s  x = [1:6];
694s  x([2,4]) = NaN;
694s  [~, idx] = rmmissing (x);
694s  assert (idx, logical ([0, 1, 0, 1, 0, 0]));
694s  assert (class(idx), 'logical');
694s  x = reshape (x, [2, 3]);
694s  [~, idx] = rmmissing (x);
694s  assert (idx, logical ([0; 1]));
694s  assert (class(idx), 'logical');
694s  [~, idx] = rmmissing (x, 2);
694s  assert (idx, logical ([1, 1, 0]));
694s  assert (class(idx), 'logical');
694s  [~, idx] = rmmissing (x, 1, "MinNumMissing", 2);
694s  assert (idx, logical ([0; 1]));
694s  assert (class(idx), 'logical');
694s  [~, idx] = rmmissing (x, 2, "MinNumMissing", 2);
694s  assert (idx, logical ([0, 0, 0]));
694s  assert (class(idx), 'logical');
694s ***** assert (rmmissing (single ([1 2 NaN; 3 4 5])), single ([3 4 5]))
694s ***** assert (rmmissing (logical (ones (3))), logical (ones (3)))
694s ***** assert (rmmissing (int32 (ones (3))), int32 (ones (3)))
694s ***** assert (rmmissing (uint32 (ones (3))), uint32 (ones (3)))
694s ***** assert (rmmissing ({1, 2, 3}), {1, 2, 3})
694s ***** assert (rmmissing ([struct, struct, struct]), [struct, struct, struct])
694s ***** assert (rmmissing ([]), [])
694s ***** assert (rmmissing (ones (1,0)), ones (1,0))
694s ***** assert (rmmissing (ones (1,0), 1), ones (1,0))
694s ***** assert (rmmissing (ones (1,0), 2), ones (1,0))
694s ***** assert (rmmissing (ones (0,1)), ones (0,1))
694s ***** assert (rmmissing (ones (0,1), 1), ones (0,1))
694s ***** assert (rmmissing (ones (0,1), 2), ones (0,1))
694s ***** error <input dimension> rmmissing (ones (0,1,2))
694s ***** error rmmissing ()
694s ***** error <input dimension> rmmissing (ones(2,2,2))
694s ***** error <must be either 1 or 2> rmmissing ([1 2; 3 4], 5)
694s ***** error <unknown parameter name> rmmissing ([1 2; 3 4], "XXX", 1)
694s ***** error <'MinNumMissing'> rmmissing ([1 2; 3 4], 2, "MinNumMissing", -2)
694s ***** error <'MinNumMissing'> rmmissing ([1 2; 3 4], "MinNumMissing", 3.8)
694s ***** error <'MinNumMissing'> rmmissing ([1 2; 3 4], "MinNumMissing", [1 2 3])
694s ***** error <'MinNumMissing'> rmmissing ([1 2; 3 4], "MinNumMissing", 'xxx')
694s 31 tests, 31 passed, 0 known failure, 0 skipped
694s [inst/runstest.m]
694s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/runstest.m
694s ***** test
694s  ## NIST beam deflection data
694s  ## http://www.itl.nist.gov/div898/handbook/eda/section4/eda425.htm
694s  data = [-213, -564, -35, -15, 141, 115, -420, -360, 203, -338, -431, ...
694s           194, -220, -513, 154, -125, -559, 92, -21, -579, -52, 99, -543, ...
694s          -175, 162, -457, -346, 204, -300, -474, 164, -107, -572, -8, 83, ...
694s          -541, -224, 180, -420, -374, 201, -236, -531, 83, 27, -564, -112, ...
694s           131, -507, -254, 199, -311, -495, 143, -46, -579, -90, 136, ...
694s          -472, -338, 202, -287, -477, 169, -124, -568, 17, 48, -568, -135, ...
694s           162, -430, -422, 172, -74, -577, -13, 92, -534, -243, 194, -355, ...
694s          -465, 156, -81, -578, -64, 139, -449, -384, 193, -198, -538, 110, ...
694s           -44, -577, -6, 66, -552, -164, 161, -460, -344, 205, -281, -504, ...
694s           134, -28, -576, -118, 156, -437, -381, 200, -220, -540, 83, 11, ...
694s          -568, -160, 172, -414, -408, 188, -125, -572, -32, 139, -492, ...
694s          -321, 205, -262, -504, 142, -83, -574, 0, 48, -571, -106, 137, ...
694s          -501, -266, 190, -391, -406, 194, -186, -553, 83, -13, -577, -49, ...
694s           103, -515, -280, 201, 300, -506, 131, -45, -578, -80, 138, -462, ...
694s          -361, 201, -211, -554, 32, 74, -533, -235, 187, -372, -442, 182, ...
694s          -147, -566, 25, 68, -535, -244, 194, -351, -463, 174, -125, -570, ...
694s            15, 72, -550, -190, 172, -424, -385, 198, -218, -536, 96];
694s  [h, p, stats] = runstest (data, median (data));
694s  expected_h = 1;
694s  expected_p = 0.008562;
694s  expected_z = 2.6229;
694s  assert (h, expected_h);
694s  assert (p, expected_p, 1E-6);
694s  assert (stats.z, expected_z, 1E-4);
694s ***** shared x
694s  x = [45, -60, 1.225, 55.4, -9 27];
694s ***** test
694s  [h, p, stats] = runstest (x);
694s  assert (h, 0);
694s  assert (p, 0.6, 1e-14);
694s  assert (stats.nruns, 5);
694s  assert (stats.n1, 3);
694s  assert (stats.n0, 3);
694s  assert (stats.z, 0.456435464587638, 1e-14);
694s ***** test
694s  [h, p, stats] = runstest (x, [], "method", "approximate");
694s  assert (h, 0);
694s  assert (p, 0.6481, 1e-4);
694s  assert (stats.z, 0.456435464587638, 1e-14);
694s ***** test
694s  [h, p, stats] = runstest (x, [], "tail", "left");
694s  assert (h, 0);
694s  assert (p, 0.9, 1e-14);
694s  assert (stats.z, 1.369306393762915, 1e-14);
694s ***** error<runstest: X must be a vector a scalar values.> runstest (ones (2,20))
694s ***** error<runstest: X must be a vector a scalar values.> runstest (["asdasda"])
694s ***** error<runstest: V must be either a scalar number or> ...
694s  runstest ([2 3 4 3 2 3 4], "updown")
694s ***** error<runstest: invalid value for alpha.> ...
694s  runstest ([2 3 4 3 2 3 4], [], "alpha", 0)
694s ***** error<runstest: invalid value for alpha.> ...
694s  runstest ([2 3 4 3 2 3 4], [], "alpha", [0.02 0.2])
694s ***** error<runstest: invalid value for alpha.> ...
694s  runstest ([2 3 4 3 2 3 4], [], "alpha", 1.2)
694s ***** error<runstest: invalid value for alpha.> ...
694s  runstest ([2 3 4 3 2 3 4], [], "alpha", -0.05)
694s ***** error<runstest: invalid value for method.> ...
694s  runstest ([2 3 4 3 2 3 4], [], "method", "some")
694s ***** error<runstest: invalid value for tail.> ...
694s  runstest ([2 3 4 3 2 3 4], [], "tail", "some")
694s ***** error<runstest: invalid optional argument.> ...
694s  runstest ([2 3 4 3 2 3 4], [], "option", "some")
694s 14 tests, 14 passed, 0 known failure, 0 skipped
694s [inst/sampsizepwr.m]
694s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/sampsizepwr.m
694s ***** demo
694s  ## Compute the mean closest to 100 that can be determined to be
694s  ## significantly different from 100 using a t-test with a sample size
694s  ## of 60 and a power of 0.8.
694s  mu1 = sampsizepwr ("t", [100, 10], [], 0.8, 60);
694s  disp (mu1);
694s ***** demo
694s  ## Compute the sample sizes required to distinguish mu0 = 100 from
694s  ## mu1 = 110 by a two-sample t-test with a ratio of the larger and the
694s  ## smaller sample sizes of 1.5 and a power of 0.6.
694s  [N1,N2] = sampsizepwr ("t2", [100, 10], 110, 0.6, [], "ratio", 1.5)
694s ***** demo
694s  ## Compute the sample size N required to distinguish p=.26 from p=.2
694s  ## with a binomial test.  The result is approximate, so make a plot to
694s  ## see if any smaller N values also have the required power of 0.6.
694s  Napprox = sampsizepwr ("p", 0.2, 0.26, 0.6);
694s  nn = 1:250;
694s  pwr = sampsizepwr ("p", 0.2, 0.26, [], nn);
694s  Nexact = min (nn(pwr >= 0.6));
694s  plot(nn,pwr,'b-', [Napprox Nexact],pwr([Napprox Nexact]),'ro');
694s  grid on
694s ***** demo
694s  ## The company must test 52 bottles to detect the difference between a mean
694s  ## volume of 100 mL and 102 mL with a power of 0.80.  Generate a power curve
694s  ## to visualize how the sample size affects the power of the test.
694s 
694s  nout = sampsizepwr('t',[100 5],102,0.80);
694s  nn = 1:100;
694s  pwrout = sampsizepwr('t',[100 5],102,[],nn);
694s 
694s  figure;
694s  plot (nn, pwrout, "b-", nout, 0.8, "ro")
694s  title ("Power versus Sample Size")
694s  xlabel ("Sample Size")
694s  ylabel ("Power")
694s ***** error<sampsizepwr: test type must be a non-empty string.> ...
694s  out = sampsizepwr ([], [100, 10], [], 0.8, 60);
694s ***** error<sampsizepwr: test type must be a non-empty string.> ...
694s  out = sampsizepwr (3, [100, 10], [], 0.8, 60);
694s ***** error<sampsizepwr: test type must be a non-empty string.> ...
694s  out = sampsizepwr ({"t", "t2"}, [100, 10], [], 0.8, 60);
694s ***** error<sampsizepwr: invalid test type.> ...
694s  out = sampsizepwr ("reg", [100, 10], [], 0.8, 60);
694s ***** error<sampsizepwr: parameters must be numeric.> ...
694s  out = sampsizepwr ("t", ["a", "e"], [], 0.8, 60);
694s ***** error<sampsizepwr: invalid size of parameters for this test type.> ...
694s  out = sampsizepwr ("z", 100, [], 0.8, 60);
694s ***** error<sampsizepwr: invalid size of parameters for this test type.> ...
694s  out = sampsizepwr ("t", 100, [], 0.8, 60);
694s ***** error<sampsizepwr: invalid size of parameters for this test type.> ...
694s  out = sampsizepwr ("t2", 60, [], 0.8, 60);
694s ***** error<sampsizepwr: invalid size of parameters for this test type.> ...
694s  out = sampsizepwr ("var", [100, 10], [], 0.8, 60);
694s ***** error<sampsizepwr: invalid size of parameters for this test type.> ...
694s  out = sampsizepwr ("p", [100, 10], [], 0.8, 60);
694s ***** error<sampsizepwr: invalid size of parameters for this test type.> ...
694s  out = sampsizepwr ("r", [100, 10], [], 0.8, 60);
694s ***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
694s  [out, N1] = sampsizepwr ("z", [100, 10], [], 0.8, 60);
694s ***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
694s  [out, N1] = sampsizepwr ("t", [100, 10], [], 0.8, 60);
694s ***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
694s  [out, N1] = sampsizepwr ("var", 2, [], 0.8, 60);
694s ***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
694s  [out, N1] = sampsizepwr ("p", 0.1, [], 0.8, 60);
694s ***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
694s  [out, N1] = sampsizepwr ("r", 0.5, [], 0.8, 60);
694s ***** error<sampsizepwr: negative or zero variance.> ...
694s  out = sampsizepwr ("z", [100, 0], [], 0.8, 60);
694s ***** error<sampsizepwr: negative or zero variance.> ...
694s  out = sampsizepwr ("z", [100, -5], [], 0.8, 60);
694s ***** error<sampsizepwr: negative or zero variance.> ...
694s  out = sampsizepwr ("t", [100, 0], [], 0.8, 60);
694s ***** error<sampsizepwr: negative or zero variance.> ...
694s  out = sampsizepwr ("t", [100, -5], [], 0.8, 60);
694s ***** error<sampsizepwr: negative or zero variance.> ...
694s  [out, N1] = sampsizepwr ("t2", [100, 0], [], 0.8, 60);
694s ***** error<sampsizepwr: negative or zero variance.> ...
694s  [out, N1] = sampsizepwr ("t2", [100, -5], [], 0.8, 60);
694s ***** error<sampsizepwr: negative or zero variance.> ...
694s  out = sampsizepwr ("var", 0, [], 0.8, 60);
694s ***** error<sampsizepwr: negative or zero variance.> ...
694s  out = sampsizepwr ("var", -5, [], 0.8, 60);
694s ***** error<sampsizepwr: out of range probability.> ...
694s  out = sampsizepwr ("p", 0, [], 0.8, 60);
694s ***** error<sampsizepwr: out of range probability.> ...
694s  out = sampsizepwr ("p", 1.2, [], 0.8, 60);
694s ***** error<sampsizepwr: out of range regression coefficient.> ...
694s  out = sampsizepwr ("r", -1.5, [], 0.8, 60);
694s ***** error<sampsizepwr: out of range regression coefficient.> ...
694s  out = sampsizepwr ("r", -1, [], 0.8, 60);
694s ***** error<sampsizepwr: out of range regression coefficient.> ...
694s  out = sampsizepwr ("r", 1.2, [], 0.8, 60);
695s ***** error<sampsizepwr: regression coefficient must not be 0.> ...
695s  out = sampsizepwr ("r", 0, [], 0.8, 60);
695s ***** error<sampsizepwr: invalid value for 'alpha' parameter.> ...
695s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "alpha", -0.2);
695s ***** error<sampsizepwr: invalid value for 'alpha' parameter.> ...
695s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "alpha", 0);
695s ***** error<sampsizepwr: invalid value for 'alpha' parameter.> ...
695s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "alpha", 1.5);
695s ***** error<sampsizepwr: invalid value for 'alpha' parameter.> ...
695s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "alpha", "zero");
695s ***** error<sampsizepwr: 'tail' parameter must be a non-empty string.> ...
695s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "tail", 1.5);
695s ***** error<sampsizepwr: 'tail' parameter must be a non-empty string.> ...
695s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "tail", {"both", "left"});
695s ***** error<sampsizepwr: invalid value for 'tail' parameter.> ...
695s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "tail", "other");
695s ***** error<sampsizepwr: invalid value for 'ratio' parameter.> ...
695s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "ratio", "some");
695s ***** error<sampsizepwr: invalid value for 'ratio' parameter.> ...
695s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "ratio", 0.5);
695s ***** error<sampsizepwr: invalid value for 'ratio' parameter.> ...
695s  out = sampsizepwr ("r", 0.2, [], 0.8, 60, "ratio", [2, 1.3, 0.3]);
695s ***** error<sampsizepwr: only one of either p1, power, or n must be missing.> ...
695s  out = sampsizepwr ("z", [100, 5], [], [], 60);
695s ***** error<sampsizepwr: only one of either p1, power, or n must be missing.> ...
695s  out = sampsizepwr ("z", [100, 5], 110, [], []);
695s ***** error<sampsizepwr: only one of either p1, power, or n must be missing.> ...
695s  out = sampsizepwr ("z", [100, 5], [], 0.8, []);
695s ***** error<sampsizepwr: only one of either p1, power, or n must be missing.> ...
695s  out = sampsizepwr ("z", [100, 5], 110, 0.8, 60);
695s ***** error<sampsizepwr: alternative hypothesis parameter must be numeric.> ...
695s  out = sampsizepwr ("z", [100, 5], "mu", [], 60);
695s ***** error<sampsizepwr: alternative hypothesis parameter out of range.> ...
695s  out = sampsizepwr ("var", 5, -1, [], 60);
695s ***** error<sampsizepwr: alternative hypothesis parameter out of range.> ...
695s  out = sampsizepwr ("p", 0.8, 1.2, [], 60, "tail", "right");
695s ***** error<sampsizepwr: alternative hypothesis parameter out of range.> ...
695s  out = sampsizepwr ("r", 0.8, 1.2, [], 60);
695s ***** error<sampsizepwr: alternative hypothesis parameter out of range.> ...
695s  out = sampsizepwr ("r", 0.8, -1.2, [], 60);
695s ***** error<sampsizepwr: invalid value for POWER.> ...
695s  out = sampsizepwr ("z", [100, 5], 110, 1.2);
695s ***** error<sampsizepwr: invalid value for POWER.> ...
695s  out = sampsizepwr ("z", [100, 5], 110, 0);
695s ***** error<sampsizepwr: Cannot compute N or P1 unless POWER> ...
695s  out = sampsizepwr ("z", [100, 5], 110, 0.05, [], "alpha", 0.1);
695s ***** error<sampsizepwr: input arguments size mismatch.> ...
695s  out = sampsizepwr ("z", [100, 5], [], [0.8, 0.7], [60, 80, 100]);
695s ***** error<sampsizepwr: Same value for null and alternative hypothesis.> ...
695s  out = sampsizepwr ("t", [100, 5], 100, 0.8, []);
695s ***** error<sampsizepwr: Invalid P1 for testing left tail.> ...
695s  out = sampsizepwr ("t", [100, 5], 110, 0.8, [], "tail", "left");
695s ***** error<sampsizepwr: Invalid P1 for testing right tail.> ...
695s  out = sampsizepwr ("t", [100, 5], 90, 0.8, [], "tail", "right");
695s ***** warning<sampsizepwr: approximate N.> ...
695s  Napprox = sampsizepwr ("p", 0.2, 0.26, 0.6);
695s ***** warning<sampsizepwr: approximate N.> ...
695s  Napprox = sampsizepwr ("p", 0.30, 0.36, 0.8);
695s ***** test
695s  mu1 = sampsizepwr ("t", [100, 10], [], 0.8, 60);
695s  assert (mu1, 103.67704316, 1e-8);
696s ***** test
696s  [N1,N2] = sampsizepwr ("t2", [100, 10], 110, 0.6, [], "ratio", 1.5);
696s  assert (N1, 9);
696s  assert (N2, 14);
697s ***** test
697s  nn = 1:250;
697s  pwr = sampsizepwr ("p", 0.2, 0.26, [], nn);
697s  pwr_out = [0, 0.0676, 0.0176, 0.0566, 0.0181, 0.0431, 0.0802, 0.0322];
697s  assert (pwr([1:8]), pwr_out, 1e-4 * ones (1,8));
697s  pwr_out = [0.59275, 0.6073, 0.62166, 0.6358, 0.6497, 0.6087, 0.6229, 0.6369];
697s  assert (pwr([243:end]), pwr_out, 1e-4 * ones (1,8));
697s ***** test
697s  nout = sampsizepwr ("t", [100, 5], 102, 0.80);
697s  assert (nout, 52);
697s ***** test
697s  power = sampsizepwr ("t", [20, 5], 25, [], 5, "Tail", "right");
697s  assert (power, 0.5797373588621888, 1e-14);
697s ***** test
697s  nout = sampsizepwr ("t", [20, 5], 25, 0.99, [], "Tail", "right");
697s  assert (nout, 18);
697s ***** test
697s  p1out = sampsizepwr ("t", [20, 5], [], 0.95, 10, "Tail", "right");
697s  assert (p1out, 25.65317979360237, 2e-14);
698s ***** test
698s  pwr = sampsizepwr ("t2", [1.4, 0.2], 1.7, [], 5, "Ratio", 2);
698s  assert (pwr, 0.716504004686586, 1e-14);
698s ***** test
698s  n = sampsizepwr ("t2", [1.4, 0.2], 1.7, 0.9, []);
698s  assert (n, 11);
698s ***** test
698s  [n1, n2] = sampsizepwr ("t2", [1.4, 0.2], 1.7, 0.9, [], "Ratio", 2);
698s  assert ([n1, n2], [8, 16]);
699s 68 tests, 68 passed, 0 known failure, 0 skipped
699s [inst/sigma_pts.m]
699s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/sigma_pts.m
699s ***** demo
699s  K      = [1 0.5; 0.5 1]; # covariance matrix
699s  # calculate and build associated ellipse
699s  [R,S,~] = svd (K);
699s  theta   = atan2 (R(2,1), R(1,1));
699s  v       = sqrt (diag (S));
699s  v       = v .* [cos(theta) sin(theta); -sin(theta) cos(theta)];
699s  t       = linspace (0, 2*pi, 100).';
699s  xe      = v(1,1) * cos (t) + v(2,1) * sin (t);
699s  ye      = v(1,2) * cos (t) + v(2,2) * sin (t);
699s 
699s  figure(1); clf; hold on
699s  # Plot ellipse and axes
699s  line ([0 0; v(:,1).'],[0 0; v(:,2).'])
699s  plot (xe,ye,'-r');
699s 
699s  col = 'rgb';
699s  l     = [-1.8 -1 1.5];
699s  for li = 1:3
699s   p     = sigma_pts (2, [], K, l(li));
699s   tmp   = plot (p(2:end,1), p(2:end,2), ['x' col(li)], ...
699s                p(1,1), p(1,2), ['o' col(li)]);
699s   h(li) = tmp(1);
699s  endfor
699s  hold off
699s  axis image
699s  legend (h, arrayfun (@(x) sprintf ("l:%.2g", x), l, "unif", 0));
699s ***** test
699s  p = sigma_pts (5);
699s  assert (mean (p), zeros(1,5), sqrt(eps));
699s  assert (cov (p), eye(5), sqrt(eps));
699s ***** test
699s  m = randn(1, 5);
699s  p = sigma_pts (5, m);
699s  assert (mean (p), m, sqrt(eps));
699s  assert (cov (p), eye(5), sqrt(eps));
699s ***** test
699s  x = linspace (0,1,5);
699s  K = exp (- (x.' - x).^2/ 0.5);
699s  p = sigma_pts (5, [], K);
699s  assert (mean (p), zeros(1,5), sqrt(eps));
699s  assert (cov (p), K, sqrt(eps));
699s ***** error sigma_pts(2,1);
699s ***** error sigma_pts(2,[],1);
699s ***** error sigma_pts(2,1,1);
699s ***** error sigma_pts(2,[0.5 0.5],[-1 0; 0 0]);
699s 7 tests, 7 passed, 0 known failure, 0 skipped
699s [inst/signrank.m]
699s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/signrank.m
699s ***** test
699s  load gradespaired.mat
699s  [p, h, stats] = signrank (gradespaired(:,1), ...
699s                            gradespaired(:,2), 'tail', 'left');
699s  assert (p, 0.0047, 1e-4);
699s  assert (h, true);
699s  assert (stats.zval, -2.5982, 1e-4);
699s  assert (stats.signedrank, 2017.5);
699s ***** test
699s  load ('gradespaired.mat');
699s  [p, h, stats] = signrank (gradespaired(:,1), gradespaired(:,2), ...
699s                            'tail', 'left', 'method', 'exact');
699s  assert (p, 0.0045, 1e-4);
699s  assert (h, true);
699s  assert (stats.zval, NaN);
699s  assert (stats.signedrank, 2017.5);
699s ***** test
699s  load mileage
699s  [p, h, stats] = signrank (mileage(:,2), 33);
699s  assert (p, 0.0312, 1e-4);
699s  assert (h, true);
699s  assert (stats.zval, NaN);
699s  assert (stats.signedrank, 21);
699s ***** test
699s  load mileage
699s  [p, h, stats] = signrank (mileage(:,2), 33, 'tail', 'right');
699s  assert (p, 0.0156, 1e-4);
699s  assert (h, true);
699s  assert (stats.zval, NaN);
699s  assert (stats.signedrank, 21);
699s ***** test
699s  load mileage
699s  [p, h, stats] = signrank (mileage(:,2), 33, 'tail', 'right', ...
699s                            'alpha', 0.01, 'method', 'approximate');
699s  assert (p, 0.0180, 1e-4);
699s  assert (h, false);
699s  assert (stats.zval, 2.0966, 1e-4);
699s  assert (stats.signedrank, 21);
699s ***** error <signrank: X must be a vector.> signrank (ones (2))
699s ***** error <signrank: Y must be either a scalar of a vector.> ...
699s  signrank ([1, 2, 3, 4], ones (2))
699s ***** error <signrank: X and Y vectors have different lengths.> ...
699s  signrank ([1, 2, 3, 4], [1, 2, 3])
699s ***** error <signrank: optional arguments must be in pairs.> ...
699s  signrank ([1, 2, 3, 4], [], 'tail')
699s ***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
699s  signrank ([1, 2, 3, 4], [], 'alpha', 1.2)
699s ***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
699s  signrank ([1, 2, 3, 4], [], 'alpha', 0)
699s ***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
699s  signrank ([1, 2, 3, 4], [], 'alpha', -0.05)
699s ***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
699s  signrank ([1, 2, 3, 4], [], 'alpha', "a")
699s ***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
699s  signrank ([1, 2, 3, 4], [], 'alpha', [0.01, 0.05])
699s ***** error <signrank: 'tail' argument must be a character vector.> ...
699s  signrank ([1, 2, 3, 4], [], 'tail', 0.01)
699s ***** error <signrank: 'tail' argument must be a character vector.> ...
699s  signrank ([1, 2, 3, 4], [], 'tail', {"both"})
699s ***** error <signrank: 'tail' value must be either 'both', right' or 'left'.> ...
699s  signrank ([1, 2, 3, 4], [], 'tail', "some")
699s ***** error <signrank: 'tail' value must be either 'both', right' or 'left'.> ...
699s  signrank ([1, 2, 3, 4], [], 'method', 'exact', 'tail', "some")
699s ***** error <signrank: 'method' argument must be a character vector.> ...
699s  signrank ([1, 2, 3, 4], [], 'method', 0.01)
699s ***** error <signrank: 'method' argument must be a character vector.> ...
699s  signrank ([1, 2, 3, 4], [], 'method', {"exact"})
699s ***** error <signrank: 'method' value must be either 'exact' or 'approximate'.> ...
699s  signrank ([1, 2, 3, 4], [], 'method', "some")
699s ***** error <signrank: 'method' value must be either 'exact' or 'approximate'.> ...
699s  signrank ([1, 2, 3, 4], [], 'tail', "both", 'method', "some")
699s 22 tests, 22 passed, 0 known failure, 0 skipped
699s [inst/signtest.m]
699s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/signtest.m
699s ***** test
699s  [pval, h, stats] = signtest ([-ones(1, 1000) 1], 0, "tail", "left");
699s  assert (pval, 1.091701889420221e-218, 1e-14);
699s  assert (h, 1);
699s  assert (stats.zval, -31.5437631079266, 1e-14);
699s ***** test
699s  [pval, h, stats] = signtest ([-2 -1 0 2 1 3 1], 0);
699s  assert (pval, 0.6875000000000006, 1e-14);
699s  assert (h, 0);
699s  assert (stats.zval, NaN);
699s  assert (stats.sign, 4);
699s ***** test
699s  [pval, h, stats] = signtest ([-2 -1 0 2 1 3 1], 0, "method", "approximate");
699s  assert (pval, 0.6830913983096086, 1e-14);
699s  assert (h, 0);
699s  assert (stats.zval, 0.4082482904638631, 1e-14);
699s  assert (stats.sign, 4);
699s ***** error <signtest: X must be a vector.> signtest (ones (2))
699s ***** error <signtest: Y must be either a scalar of a vector.> ...
699s  signtest ([1, 2, 3, 4], ones (2))
699s ***** error <signtest: X and Y vectors have different lengths.> ...
699s  signtest ([1, 2, 3, 4], [1, 2, 3])
699s ***** error <signtest: optional arguments must be in pairs.> ...
699s  signtest ([1, 2, 3, 4], [], 'tail')
699s ***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
699s  signtest ([1, 2, 3, 4], [], 'alpha', 1.2)
699s ***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
699s  signtest ([1, 2, 3, 4], [], 'alpha', 0)
699s ***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
699s  signtest ([1, 2, 3, 4], [], 'alpha', -0.05)
699s ***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
699s  signtest ([1, 2, 3, 4], [], 'alpha', "a")
699s ***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
699s  signtest ([1, 2, 3, 4], [], 'alpha', [0.01, 0.05])
699s ***** error <signtest: 'tail' argument must be a character vector.> ...
699s  signtest ([1, 2, 3, 4], [], 'tail', 0.01)
699s ***** error <signtest: 'tail' argument must be a character vector.> ...
699s  signtest ([1, 2, 3, 4], [], 'tail', {"both"})
699s ***** error <signtest: 'tail' value must be either 'both', right' or 'left'.> ...
699s  signtest ([1, 2, 3, 4], [], 'tail', "some")
699s ***** error <signtest: 'tail' value must be either 'both', right' or 'left'.> ...
699s  signtest ([1, 2, 3, 4], [], 'method', 'exact', 'tail', "some")
699s ***** error <signtest: 'method' argument must be a character vector.> ...
699s  signtest ([1, 2, 3, 4], [], 'method', 0.01)
699s ***** error <signtest: 'method' argument must be a character vector.> ...
699s  signtest ([1, 2, 3, 4], [], 'method', {"exact"})
699s ***** error <signtest: 'method' value must be either 'exact' or 'approximate'.> ...
699s  signtest ([1, 2, 3, 4], [], 'method', "some")
699s ***** error <signtest: 'method' value must be either 'exact' or 'approximate'.> ...
699s  signtest ([1, 2, 3, 4], [], 'tail', "both", 'method', "some")
699s 20 tests, 20 passed, 0 known failure, 0 skipped
699s [inst/silhouette.m]
699s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/silhouette.m
699s ***** demo
699s  load fisheriris;
699s  X = meas(:,3:4);
699s  cidcs = kmeans (X, 3, "Replicates", 5);
699s  silhouette (X, cidcs);
699s  y_labels(cidcs([1 51 101])) = unique (species);
699s  set (gca, "yticklabel", y_labels);
699s  title ("Fisher's iris data");
699s ***** error silhouette ();
699s ***** error silhouette ([1 2; 1 1]);
699s ***** error <X .* doesn't match .* clust> silhouette ([1 2; 1 1], [1 2 3]');
699s ***** error <invalid metric> silhouette ([1 2; 1 1], [1 2]', "xxx");
700s 4 tests, 4 passed, 0 known failure, 0 skipped
700s [inst/slicesample.m]
700s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/slicesample.m
700s ***** demo
700s  ## Define function to sample
700s  d = 2;
700s  mu = [-1; 2];
700s  rand ("seed", 5)  # for reproducibility
700s  Sigma = rand (d);
700s  Sigma = (Sigma + Sigma');
700s  Sigma += eye (d)*abs (eigs (Sigma, 1, "sa")) * 1.1;
700s  pdf = @(x)(2*pi)^(-d/2)*det(Sigma)^-.5*exp(-.5*sum((x.'-mu).*(Sigma\(x.'-mu)),1));
700s 
700s  ## Inputs
700s  start = ones (1,2);
700s  nsamples = 500;
700s  K = 500;
700s  m = 10;
700s  rande ("seed", 4);  rand ("seed", 5)  # for reproducibility
700s  [smpl, accept] = slicesample (start, nsamples, "pdf", pdf, "burnin", K, "thin", m, "width", [20, 30]);
700s  figure;
700s  hold on;
700s  plot (smpl(:,1), smpl(:,2), 'x');
700s  [x, y] = meshgrid (linspace (-6,4), linspace(-3,7));
700s  z = reshape (pdf ([x(:), y(:)]), size(x));
700s  mesh (x, y, z, "facecolor", "None");
700s 
700s  ## Using sample points to find the volume of half a sphere with radius of .5
700s  f = @(x) ((.25-(x(:,1)+1).^2-(x(:,2)-2).^2).^.5.*(((x(:,1)+1).^2+(x(:,2)-2).^2)<.25)).';
700s  int = mean (f (smpl) ./ pdf (smpl));
700s  errest = std (f (smpl) ./ pdf (smpl)) / nsamples^.5;
700s  trueerr = abs (2/3*pi*.25^(3/2)-int);
700s  fprintf ("Monte Carlo integral estimate int f(x) dx = %f\n", int);
700s  fprintf ("Monte Carlo integral error estimate %f\n", errest);
700s  fprintf ("The actual error %f\n", trueerr);
700s  mesh (x,y,reshape (f([x(:), y(:)]), size(x)), "facecolor", "None");
700s ***** demo
700s  ## Integrate truncated normal distribution to find normalization constant
700s  pdf = @(x) exp (-.5*x.^2)/(pi^.5*2^.5);
700s  nsamples = 1e3;
700s  rande ("seed", 4);  rand ("seed", 5)  # for reproducibility
700s  [smpl, accept] = slicesample (1, nsamples, "pdf", pdf, "thin", 4);
700s  f = @(x) exp (-.5 * x .^ 2) .* (x >= -2 & x <= 2);
700s  x = linspace (-3, 3, 1000);
700s  area (x, f(x));
700s  xlabel ("x");
700s  ylabel ("f(x)");
700s  int = mean (f (smpl) ./ pdf (smpl));
700s  errest = std (f (smpl) ./ pdf (smpl)) / nsamples ^ 0.5;
700s  trueerr = abs (erf (2 ^ 0.5) * 2 ^ 0.5 * pi ^ 0.5 - int);
700s  fprintf("Monte Carlo integral estimate int f(x) dx = %f\n", int);
700s  fprintf("Monte Carlo integral error estimate %f\n", errest);
700s  fprintf("The actual error %f\n", trueerr);
700s ***** test
700s  start = 0.5;
700s  nsamples = 1e3;
700s  pdf = @(x) exp (-.5*(x-1).^2)/(2*pi)^.5;
700s  [smpl, accept] = slicesample (start, nsamples, "pdf", pdf, "thin", 2, "burnin", 0, "width", 5);
700s  assert (mean (smpl, 1), 1, .15);
700s  assert (var (smpl, 1), 1, .25);
700s ***** error slicesample ();
700s ***** error slicesample (1);
700s ***** error slicesample (1, 1);
700s 4 tests, 4 passed, 0 known failure, 0 skipped
700s [inst/squareform.m]
700s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/squareform.m
700s ***** shared v, m
700s  v = 1:6;
700s  m = [0 1 2 3;1 0 4 5;2 4 0 6;3 5 6 0];
700s ***** assert (squareform (v), m)
700s ***** assert (squareform (squareform (v)), v)
700s ***** assert (squareform (m), v)
700s ***** assert (squareform (v'), m)
700s ***** assert (squareform (1), [0 1;1 0])
700s ***** assert (squareform (1, "tomatrix"), [0 1; 1 0])
700s ***** assert (squareform (0, "tovector"), zeros (1, 0))
700s ***** warning <not a symmetric matrix> squareform ([0 1 2; 3 0 4; 5 6 0]);
700s ***** test
700s  for c = {@single, @double, @uint8, @uint32, @uint64}
700s    f = c{1};
700s    assert (squareform (f (v)), f (m))
700s    assert (squareform (f (m)), f (v))
700s  endfor
700s 9 tests, 9 passed, 0 known failure, 0 skipped
700s [inst/standardizeMissing.m]
700s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/standardizeMissing.m
700s ***** assert (standardizeMissing (1, 1), NaN)
700s ***** assert (standardizeMissing (1, 0), 1)
701s ***** assert (standardizeMissing (eye(2), 1), [NaN 0;0 NaN])
701s ***** assert (standardizeMissing ([1:3;4:6], [2 3; 4 5]), [1, NaN, NaN; NaN, NaN, 6])
701s ***** assert (standardizeMissing (cat (3,1,2,3,4), 3), cat (3,1,2,NaN,4))
701s ***** assert (standardizeMissing ('foo', 'a'), 'foo')
701s ***** assert (standardizeMissing ('foo', 'f'), ' oo')
701s ***** assert (standardizeMissing ('foo', 'o'), 'f  ')
701s ***** assert (standardizeMissing ('foo', 'oo'), 'f  ')
701s ***** assert (standardizeMissing ({'foo'}, 'f'), {'foo'})
701s ***** assert (standardizeMissing ({'foo'}, {'f'}), {'foo'})
701s ***** assert (standardizeMissing ({'foo'}, 'test'), {'foo'})
701s ***** assert (standardizeMissing ({'foo'}, {'test'}), {'foo'})
701s ***** assert (standardizeMissing ({'foo'}, 'foo'), {''})
701s ***** assert (standardizeMissing ({'foo'}, {'foo'}), {''})
701s ***** assert (standardizeMissing (['foo';'bar'], 'oar'), ['f  ';'b  '])
701s ***** assert (standardizeMissing (['foo';'bar'], ['o';'a';'r']), ['f  ';'b  '])
701s ***** assert (standardizeMissing (['foo';'bar'], ['o ';'ar']), ['f  ';'b  '])
701s ***** assert (standardizeMissing ({'foo','bar'}, 'foo'), {'','bar'})
701s ***** assert (standardizeMissing ({'foo','bar'}, 'f'), {'foo','bar'})
701s ***** assert (standardizeMissing ({'foo','bar'}, {'foo', 'a'}), {'','bar'})
701s ***** assert (standardizeMissing ({'foo'}, {'f', 'oo'}), {'foo'})
701s ***** assert (standardizeMissing ({'foo','bar'}, {'foo'}), {'','bar'})
701s ***** assert (standardizeMissing ({'foo','bar'}, {'foo', 'a'}), {'','bar'})
701s ***** assert (standardizeMissing (double (1), single (1)), double (NaN))
701s ***** assert (standardizeMissing (single (1), single (1)), single (NaN))
701s ***** assert (standardizeMissing (single (1), double (1)), single (NaN))
701s ***** assert (standardizeMissing (single (1), true), single (NaN))
701s ***** assert (standardizeMissing (double (1), int32(1)), double (NaN))
701s ***** assert (standardizeMissing (true, true), true)
701s ***** assert (standardizeMissing (true, 1), true)
701s ***** assert (standardizeMissing (int32 (1), int32 (1)), int32 (1))
701s ***** assert (standardizeMissing (int32 (1), 1), int32 (1))
701s ***** assert (standardizeMissing (uint32 (1), uint32 (1)), uint32 (1))
701s ***** assert (standardizeMissing (uint32 (1), 1), uint32 (1))
701s ***** error standardizeMissing ();
701s ***** error standardizeMissing (1);
701s ***** error standardizeMissing (1,2,3);
701s ***** error <only cells of strings> standardizeMissing ({'abc', 1}, 1);
701s ***** error <unsupported data type> standardizeMissing (struct ('a','b'), 1);
701s ***** error <'indicator' and 'A' must have > standardizeMissing ([1 2 3], {1});
701s ***** error <'indicator' and 'A' must have > standardizeMissing ([1 2 3], 'a');
701s ***** error <'indicator' and 'A' must have > standardizeMissing ([1 2 3], struct ('a', 1));
701s ***** error <'indicator' and 'A' must have > standardizeMissing ('foo', 1);
701s ***** error <'indicator' and 'A' must have > standardizeMissing ('foo', {1});
701s ***** error <'indicator' and 'A' must have > standardizeMissing ('foo', {'f'});
701s ***** error <'indicator' and 'A' must have > standardizeMissing ('foo', struct ('a', 1));
701s ***** error <'indicator' and 'A' must have > standardizeMissing ({'foo'}, 1);
701s ***** error <'indicator' and 'A' must have > standardizeMissing ({'foo'}, 1);
701s 49 tests, 49 passed, 0 known failure, 0 skipped
701s [inst/stepwisefit.m]
701s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/stepwisefit.m
701s ***** test
701s  % Sample data from Draper and Smith (n = 13, k = 4)
701s  X = [7 1 11 11 7 11 3 1 2 21 1 11 10; ...
701s      26 29 56 31 52 55 71 31 54 47 40 66 68; ...
701s      6 15 8 8 6 9 17 22 18 4 23 9 8; ...
701s      60 52 20 47 33 22 6 44 22 26 34 12 12]';
701s  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]';
701s  [X_use, b, bint, r, rint, stats] = stepwisefit(y, X);
701s  assert(X_use, [4 1])
701s  assert(b, regress(y, [ones(size(y)) X(:, X_use)], 0.05))
701s  [X_use, b, bint, r, rint, stats] = stepwisefit(y, X, 0.05, 0.1, "corr");
701s  assert(X_use, [4 1])
701s  assert(b, regress(y, [ones(size(y)) X(:, X_use)], 0.05))
701s  [X_use, b, bint, r, rint, stats] = stepwisefit(y, X, [], [], "p");
701s  assert(X_use, [4 1])
701s  assert(b, regress(y, [ones(size(y)) X(:, X_use)], 0.05))
701s 1 test, 1 passed, 0 known failure, 0 skipped
701s [inst/tabulate.m]
701s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/tabulate.m
701s ***** demo
701s  ## Generate a frequency table for a vector of data in a cell array
701s  load patients
701s 
701s  ## Display the first seven entries of the Gender variable
701s  gender = Gender(1:7)
701s 
701s  ## Compute the frequency table that shows the number and
701s  ## percentage of Male and Female patients
701s  tabulate (Gender)
701s ***** demo
701s  ## Create a frequency table for a vector of positive integers
701s  load patients
701s 
701s  ## Display the first seven entries of the Gender variable
701s  height = Height(1:7)
701s 
701s  ## Create a frequency table that shows, in its second and third columns,
701s  ## the number and percentage of patients with a particular height.
701s  table = tabulate (Height);
701s 
701s  ## Display the first and last seven entries of the frequency table
701s  first = table(1:7,:)
701s 
701s  last = table(end-6:end,:)
701s ***** demo
701s  ## Create a frequency table from a character array
701s  load carsmall
701s 
701s  ## Tabulate the data in the Origin variable, which shows the
701s  ## country of origin of each car in the data set
701s  tabulate (Origin)
701s ***** demo
701s  ## Create a frequency table from a numeric vector with NaN values
701s  load carsmall
701s 
701s  ## The carsmall dataset contains measurements of 100 cars
701s  total_cars = length (MPG)
701s  ## For six cars, the MPG value is missing
701s  missingMPG = length (MPG(isnan (MPG)))
701s 
701s  ## Create a frequency table using MPG
701s  tabulate (MPG)
701s  table = tabulate (MPG);
701s 
701s  ## Only 94 cars were used
701s  valid_cars = sum (table(:,2))
701s ***** test
701s  load patients
701s  table = tabulate (Gender);
701s  assert (table{1,1}, "Male");
701s  assert (table{2,1}, "Female");
701s  assert (table{1,2}, 47);
701s  assert (table{2,2}, 53);
701s ***** test
701s  load patients
701s  table = tabulate (Height);
701s  assert (table(end-4,:), [68, 15, 15]);
701s  assert (table(end-3,:), [69, 8, 8]);
701s  assert (table(end-2,:), [70, 11, 11]);
701s  assert (table(end-1,:), [71, 10, 10]);
701s  assert (table(end,:), [72, 4, 4]);
701s ***** error<tabulate: X must be either a numeric vector> tabulate (ones (3))
701s ***** error<tabulate: X must be either a numeric vector> tabulate ({1, 2, 3, 4})
701s ***** error<tabulate: X must be either a numeric vector> ...
701s  tabulate ({"a", "b"; "a", "c"})
701s 5 tests, 5 passed, 0 known failure, 0 skipped
701s [inst/tiedrank.m]
701s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/tiedrank.m
701s ***** test
701s  [r,tieadj] = tiedrank ([10, 20, 30, 40, 20]);
701s  assert (r, [1, 2.5, 4, 5, 2.5]);
701s  assert (tieadj, 3);
701s ***** test
701s  [r,tieadj] = tiedrank ([10; 20; 30; 40; 20]);
701s  assert (r, [1; 2.5; 4; 5; 2.5]);
701s  assert (tieadj, 3);
701s ***** test
701s  [r,tieadj] = tiedrank ([10, 20, 30, 40, 20], 1);
701s  assert (r, [1, 2.5, 4, 5, 2.5]);
701s  assert (tieadj, [1; 0; 18]);
701s ***** test
701s  [r,tieadj] = tiedrank ([10, 20, 30, 40, 20], 0, 1);
701s  assert (r, [1, 2.5, 2, 1, 2.5]);
701s  assert (tieadj, 3);
701s ***** test
701s  [r,tieadj] = tiedrank ([10, 20, 30, 40, 20], 1, 1);
701s  assert (r, [1, 2.5, 2, 1, 2.5]);
701s  assert (tieadj, [1; 0; 18]);
701s ***** error <tiedrank: X must be a vector.> tiedrank (ones (2))
701s ***** error <tiedrank: TIEFLAG must be a numeric or boolean scalar.> ...
701s  tiedrank ([1, 2, 3, 4, 5], [1, 1])
701s ***** error <tiedrank: TIEFLAG must be a numeric or boolean scalar.> ...
701s  tiedrank ([1, 2, 3, 4, 5], "A")
701s ***** error <tiedrank: TIEFLAG must be a numeric or boolean scalar.> ...
701s  tiedrank ([1, 2, 3, 4, 5], [true, true])
701s ***** error <tiedrank: BIDIR must be a numeric or boolean scalar.> ...
701s  tiedrank ([1, 2, 3, 4, 5], 0, [1, 1])
701s ***** error <tiedrank: BIDIR must be a numeric or boolean scalar.> ...
701s  tiedrank ([1, 2, 3, 4, 5], 0, "A")
701s ***** error <tiedrank: BIDIR must be a numeric or boolean scalar.> ...
701s  tiedrank ([1, 2, 3, 4, 5], 0, [true, true])
701s 12 tests, 12 passed, 0 known failure, 0 skipped
701s [inst/trimmean.m]
701s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/trimmean.m
701s ***** test
701s  x = reshape (1:40, [5, 4, 2]);
701s  x([3, 37]) = -100;
701s  assert (trimmean (x, 10, "all"), 19.4722, 1e-4);
701s ***** test
701s  x = reshape (1:40, [5, 4, 2]);
701s  x([3, 37]) = -100;
701s  out = trimmean (x, 10, [1, 2]);
701s  assert (out(1,1,1), 10.3889, 1e-4);
701s  assert (out(1,1,2), 29.6111, 1e-4);
701s ***** test
701s  x = reshape (1:40, [5, 4, 2]);
701s  x([3, 37]) = -100;
701s  x([4, 38]) = NaN;
701s  assert (trimmean (x, 10, "all"), 19.3824, 1e-4);
701s ***** test
701s  x = reshape (1:40, [5, 4, 2]);
701s  x([3, 37]) = -100;
701s  out = trimmean (x, 10, 1);
701s  assert (out(:,:,1), [-17.6, 8, 13, 18]);
701s  assert (out(:,:,2), [23, 28, 33, 10.6]);
701s ***** test
701s  x = reshape (1:40, [5, 4, 2]);
701s  x([3, 37]) = -100;
701s  x([4, 38]) = NaN;
701s  out = trimmean (x, 10, 1);
701s  assert (out(:,:,1), [-23, 8, 13, 18]);
701s  assert (out(:,:,2), [23, 28, 33, 3.75]);
701s ***** test
701s  x = reshape (1:40, [5, 4, 2]);
701s  x([3, 37]) = -100;
701s  out = trimmean (x, 10, 2);
701s  assert (out(:,:,1), [8.5; 9.5; -15.25; 11.5; 12.5]);
701s  assert (out(:,:,2), [28.5; -4.75; 30.5; 31.5; 32.5]);
701s ***** test
701s  x = reshape (1:40, [5, 4, 2]);
701s  x([3, 37]) = -100;
701s  x([4, 38]) = NaN;
701s  out = trimmean (x, 10, 2);
701s  assert (out(:,:,1), [8.5; 9.5; -15.25; 14; 12.5]);
701s  assert (out(:,:,2), [28.5; -4.75; 28; 31.5; 32.5]);
701s ***** test
701s  x = reshape (1:40, [5, 4, 2]);
701s  x([3, 37]) = -100;
701s  out = trimmean (x, 10, [1, 2, 3]);
701s  assert (out, trimmean (x, 10, "all"));
701s ***** test
701s  x = reshape (1:40, [5, 4, 2]);
701s  x([3, 37]) = -100;
701s  x([4, 38]) = NaN;
701s  out = trimmean (x, 10, [1, 2]);
701s  assert (out(1,1,1), 10.7647, 1e-4);
701s  assert (out(1,1,2), 29.1176, 1e-4);
701s ***** test
701s  x = reshape (1:40, [5, 4, 2]);
701s  x([3, 37]) = -100;
701s  x([4, 38]) = NaN;
701s  out = trimmean (x, 10, [1, 3]);
701s  assert (out, [2.5556, 18, 23, 11.6667], 1e-4);
701s ***** test
701s  x = reshape (1:40, [5, 4, 2]);
701s  x([3, 37]) = -100;
701s  x([4, 38]) = NaN;
701s  out = trimmean (x, 10, [2, 3]);
701s  assert (out, [18.5; 2.3750; 3.2857; 24; 22.5], 1e-4);
701s ***** test
701s  x = reshape (1:40, [5, 4, 2]);
701s  x([3, 37]) = -100;
701s  x([4, 38]) = NaN;
701s  out = trimmean (x, 10, [1, 2, 3]);
701s  assert (out, trimmean (x, 10, "all"));
701s ***** test
701s  x = reshape (1:40, [5, 4, 2]);
701s  x([3, 37]) = -100;
701s  x([4, 38]) = NaN;
701s  out = trimmean (x, 10, [2, 3, 5]);
701s  assert (out, [18.5; 2.3750; 3.2857; 24; 22.5], 1e-4);
701s ***** assert (trimmean (reshape (1:40, [5, 4, 2]), 10, 4), reshape(1:40, [5, 4, 2]))
701s ***** assert (trimmean ([], 10), NaN)
701s ***** assert (trimmean ([1;2;3;4;5], 10, 2), [1;2;3;4;5])
701s ***** error<Invalid call to trimmean.  Correct usage is:> trimmean (1)
701s ***** error<Invalid call to trimmean.  Correct usage is:> trimmean (1,2,3,4,5)
701s ***** error<trimmean: invalid percent.> trimmean ([1 2 3 4], -10)
701s ***** error<trimmean: invalid percent.> trimmean ([1 2 3 4], 100)
701s ***** error<trimmean: invalid FLAG argument.> trimmean ([1 2 3 4], 10, "flag")
701s ***** error<trimmean: invalid FLAG argument.> trimmean ([1 2 3 4], 10, "flag", 1)
701s ***** error<trimmean: DIM must be a positive integer scalar or vector.> ...
701s  trimmean ([1 2 3 4], 10, -1)
701s ***** error<trimmean: DIM must be a positive integer scalar or vector.> ...
701s  trimmean ([1 2 3 4], 10, "floor", -1)
701s ***** error<trimmean: DIM must be a positive integer scalar or vector.> ...
701s  trimmean (reshape (1:40, [5, 4, 2]), 10, [-1, 2])
701s ***** error<trimmean: VECDIM must contain non-repeating positive integers.> ...
701s  trimmean (reshape (1:40, [5, 4, 2]), 10, [1, 2, 2])
701s 26 tests, 26 passed, 0 known failure, 0 skipped
701s [inst/ttest.m]
701s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/ttest.m
701s ***** test
701s  x = 8:0.1:12;
701s  [h, pval, ci] = ttest (x, 10);
701s  assert (h, 0)
701s  assert (pval, 1, 10*eps)
701s  assert (ci, [9.6219 10.3781], 1E-5)
701s  [h, pval, ci0] = ttest (x, 0);
701s  assert (h, 1)
701s  assert (pval, 0)
701s  assert (ci0, ci, 2e-15)
701s  [h, pval, ci] = ttest (x, 10, "tail", "right", "dim", 2, "alpha", 0.05);
701s  assert (h, 0)
701s  assert (pval, 0.5, 10*eps)
701s  assert (ci, [9.68498 Inf], 1E-5)
702s ***** error ttest ([8:0.1:12], 10, "tail", "invalid");
702s ***** error ttest ([8:0.1:12], 10, "tail", 25);
702s 3 tests, 3 passed, 0 known failure, 0 skipped
702s [inst/ttest2.m]
702s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/ttest2.m
702s ***** test
702s  a = 1:5;
702s  b = 6:10;
702s  b(5) = NaN;
702s  [h,p,ci,stats] = ttest2 (a,b);
702s  assert (h, 1);
702s  assert (p, 0.002535996080258229, 1e-14);
702s  assert (ci, [-6.822014919225481, -2.17798508077452], 1e-14);
702s  assert (stats.tstat, -4.582575694955839, 1e-14);
702s  assert (stats.df, 7);
702s  assert (stats.sd, 1.4638501094228, 1e-13);
702s ***** error ttest2 ([8:0.1:12], [8:0.1:12], "tail", "invalid");
702s ***** error ttest2 ([8:0.1:12], [8:0.1:12], "tail", 25);
702s 3 tests, 3 passed, 0 known failure, 0 skipped
702s [inst/vartest.m]
702s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/vartest.m
702s ***** error<vartest: too few input arguments.> vartest ();
702s ***** error<vartest: invalid value for variance.> vartest ([1, 2, 3, 4], -0.5);
702s ***** error<vartest: invalid value for alpha.> ...
702s  vartest ([1, 2, 3, 4], 1, "alpha", 0);
702s ***** error<vartest: invalid value for alpha.> ...
702s  vartest ([1, 2, 3, 4], 1, "alpha", 1.2);
702s ***** error<vartest: invalid value for alpha.> ...
702s  vartest ([1, 2, 3, 4], 1, "alpha", "val");
702s ***** error<vartest: invalid value for tail.>  ...
702s  vartest ([1, 2, 3, 4], 1, "tail", "val");
702s ***** error<vartest: invalid value for tail.>  ...
702s  vartest ([1, 2, 3, 4], 1, "alpha", 0.01, "tail", "val");
702s ***** error<vartest: invalid value for operating dimension.> ...
702s  vartest ([1, 2, 3, 4], 1, "dim", 3);
702s ***** error<vartest: invalid value for operating dimension.> ...
702s  vartest ([1, 2, 3, 4], 1, "alpha", 0.01, "tail", "both", "dim", 3);
702s ***** error<vartest: invalid name for optional arguments.> ...
702s  vartest ([1, 2, 3, 4], 1, "alpha", 0.01, "tail", "both", "badoption", 3);
702s ***** error<vartest: optional arguments must be in name/value pairs.> ...
702s  vartest ([1, 2, 3, 4], 1, "alpha", 0.01, "tail");
702s ***** test
702s  load carsmall
702s  [h, pval, ci] = vartest (MPG, 7^2);
702s  assert (h, 1);
702s  assert (pval, 0.04335086742174443, 1e-14);
702s  assert (ci, [49.397; 88.039], 1e-3);
702s ***** test
702s  load carsmall
702s  [h, pval, ci] = vartest (MPG, 7^2, "tail", "left");
702s  assert (h, 0);
702s  assert (pval, 0.978324566289128, 1e-14);
702s  assert (ci, [0; 83.685], 1e-3);
702s ***** test
702s  load carsmall
702s  [h, pval, ci] = vartest (MPG, 7^2, "tail", "right");
702s  assert (h, 1);
702s  assert (pval, 0.021675433710872, 1e-14);
702s  assert (ci, [51.543; Inf], 1e-3);
702s 14 tests, 14 passed, 0 known failure, 0 skipped
702s [inst/vartest2.m]
702s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/vartest2.m
702s ***** error<vartest2: too few input arguments.> vartest2 ();
702s ***** error<vartest2: too few input arguments.> vartest2 (ones (20,1));
702s ***** error<vartest2: X and Y must be vectors or matrices or N-D arrays.> ...
702s  vartest2 (rand (20,1), 5);
702s ***** error<vartest2: invalid value for alpha.> ...
702s  vartest2 (rand (20,1), rand (25,1)*2, "alpha", 0);
702s ***** error<vartest2: invalid value for alpha.> ...
702s  vartest2 (rand (20,1), rand (25,1)*2, "alpha", 1.2);
702s ***** error<vartest2: invalid value for alpha.> ...
702s  vartest2 (rand (20,1), rand (25,1)*2, "alpha", "some");
702s ***** error<vartest2: invalid value for alpha.> ...
702s  vartest2 (rand (20,1), rand (25,1)*2, "alpha", [0.05, 0.001]);
702s ***** error<vartest2: invalid value for tail.> ...
702s  vartest2 (rand (20,1), rand (25,1)*2, "tail", [0.05, 0.001]);
702s ***** error<vartest2: invalid value for tail.> ...
702s  vartest2 (rand (20,1), rand (25,1)*2, "tail", "some");
702s ***** error<vartest2: invalid value for operating dimension.> ...
702s  vartest2 (rand (20,1), rand (25,1)*2, "dim", 3);
702s ***** error<vartest2: invalid value for operating dimension.> ...
702s  vartest2 (rand (20,1), rand (25,1)*2, "alpha", 0.001, "dim", 3);
702s ***** error<vartest2: invalid name for optional arguments.> ...
702s  vartest2 (rand (20,1), rand (25,1)*2, "some", 3);
702s ***** error<vartest2: optional arguments must be in name/value pairs.> ...
702s  vartest2 (rand (20,1), rand (25,1)*2, "some");
702s ***** test
702s  load carsmall
702s  [h, pval, ci, stat] = vartest2 (MPG(Model_Year==82), MPG(Model_Year==76));
702s  assert (h, 0);
702s  assert (pval, 0.6288022362718455, 1e-13);
702s  assert (ci, [0.4139; 1.7193], 1e-4);
702s  assert (stat.fstat, 0.8384, 1e-4);
702s  assert (stat.df1, 30);
702s  assert (stat.df2, 33);
702s ***** test
702s  load carsmall
702s  [h, pval, ci, stat] = vartest2 (MPG(Model_Year==82), MPG(Model_Year==76), ...
702s                                  "tail", "left");
702s  assert (h, 0);
702s  assert (pval, 0.314401118135922, 1e-13);
702s  assert (ci, [0; 1.5287], 1e-4);
702s  assert (stat.fstat, 0.8384, 1e-4);
702s  assert (stat.df1, 30);
702s  assert (stat.df2, 33);
702s ***** test
702s  load carsmall
702s  [h, pval, ci, stat] = vartest2 (MPG(Model_Year==82), MPG(Model_Year==76), ...
702s                                  "tail", "right");
702s  assert (h, 0);
702s  assert (pval, 0.685598881864077, 1e-13);
702s  assert (ci, [0.4643; Inf], 1e-4);
702s  assert (stat.fstat, 0.8384, 1e-4);
702s  assert (stat.df1, 30);
702s  assert (stat.df2, 33);
702s 16 tests, 16 passed, 0 known failure, 0 skipped
702s [inst/vartestn.m]
702s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/vartestn.m
702s ***** demo
702s  ## Test the null hypothesis that the variances are equal across the five
702s  ## columns of data in the students’ exam grades matrix, grades.
702s 
702s  load examgrades
702s  vartestn (grades)
702s ***** demo
702s  ## Test the null hypothesis that the variances in miles per gallon (MPG) are
702s  ## equal across different model years.
702s 
702s  load carsmall
702s  vartestn (MPG, Model_Year)
702s ***** demo
702s  ## Use Levene’s test to test the null hypothesis that the variances in miles
702s  ## per gallon (MPG) are equal across different model years.
702s 
702s  load carsmall
702s  p = vartestn (MPG, Model_Year, "TestType", "LeveneAbsolute")
702s ***** demo
702s  ## Test the null hypothesis that the variances are equal across the five
702s  ## columns of data in the students’ exam grades matrix, grades, using the
702s  ## Brown-Forsythe test.  Suppress the display of the summary table of
702s  ## statistics and the box plot.
702s 
702s  load examgrades
702s  [p, stats] = vartestn (grades, "TestType", "BrownForsythe", "Display", "off")
702s ***** error<vartestn: too few input arguments.> vartestn ();
702s ***** error<vartestn: X must be a vector or a matrix.> vartestn (1);
702s ***** error<vartestn: if X is a vector then a group vector is required.> ...
702s  vartestn ([1, 2, 3, 4, 5, 6, 7]);
702s ***** error<vartestn: if X is a vector then a group vector is required.> ...
702s  vartestn ([1, 2, 3, 4, 5, 6, 7], []);
702s ***** error<vartestn: if X is a vector then a group vector is required.> ...
702s  vartestn ([1, 2, 3, 4, 5, 6, 7], "TestType", "LeveneAbsolute");
702s ***** error<vartestn: if X is a vector then a group vector is required.> ...
702s  vartestn ([1, 2, 3, 4, 5, 6, 7], [], "TestType", "LeveneAbsolute");
702s ***** error<vartestn: invalid value for display.> ...
702s  vartestn ([1, 2, 3, 4, 5, 6, 7], [1, 1, 1, 2, 2, 2, 2], "Display", "some");
702s ***** error<vartestn: invalid value for display.> ...
702s  vartestn (ones (50,3), "Display", "some");
702s ***** error<vartestn: invalid value for testtype.> ...
702s  vartestn (ones (50,3), "Display", "off", "testtype", "some");
702s ***** error<vartestn: optional arguments must be in name/value pairs.> ...
702s  vartestn (ones (50,3), [], "som");
702s ***** error<vartestn: invalid name for optional arguments.> ...
702s  vartestn (ones (50,3), [], "some", "some");
702s ***** error<vartestn: columns in X and GROUP length do not match.> ...
702s  vartestn (ones (50,3), [1, 2], "Display", "off");
702s ***** test
702s  load examgrades
702s  [p, stat] = vartestn (grades, "Display", "off");
702s  assert (p, 7.908647337018238e-08, 1e-14);
702s  assert (stat.chisqstat, 38.7332, 1e-4);
702s  assert (stat.df, 4);
702s ***** test
702s  load examgrades
702s  [p, stat] = vartestn (grades, "Display", "off", "TestType", "LeveneAbsolute");
702s  assert (p, 9.523239714592791e-07, 1e-14);
702s  assert (stat.fstat, 8.5953, 1e-4);
702s  assert (stat.df, [4, 595]);
702s ***** test
702s  load examgrades
702s  [p, stat] = vartestn (grades, "Display", "off", "TestType", "LeveneQuadratic");
702s  assert (p, 7.219514351897161e-07, 1e-14);
702s  assert (stat.fstat, 8.7503, 1e-4);
702s  assert (stat.df, [4, 595]);
702s ***** test
702s  load examgrades
702s  [p, stat] = vartestn (grades, "Display", "off", "TestType", "BrownForsythe");
702s  assert (p, 1.312093241723211e-06, 1e-14);
702s  assert (stat.fstat, 8.4160, 1e-4);
702s  assert (stat.df, [4, 595]);
702s ***** test
702s  load examgrades
702s  [p, stat] = vartestn (grades, "Display", "off", "TestType", "OBrien");
702s  assert (p, 8.235660885480556e-07, 1e-14);
702s  assert (stat.fstat, 8.6766, 1e-4);
702s  assert (stat.df, [4, 595]);
702s 17 tests, 17 passed, 0 known failure, 0 skipped
702s [inst/violin.m]
702s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/violin.m
702s ***** demo
702s  clf
702s  x = zeros (9e2, 10);
702s  for i=1:10
702s    x(:,i) = (0.1 * randn (3e2, 3) * (randn (3,1) + 1) + 2 * randn (1,3))(:);
702s  endfor
702s  h = violin (x, "color", "c");
702s  axis tight
702s  set (h.violin, "linewidth", 2);
702s  set (gca, "xgrid", "on");
702s  xlabel ("Variables")
702s  ylabel ("Values")
702s ***** demo
702s  clf
702s  data = {randn(100,1)*5+140, randn(130,1)*8+135};
702s  subplot (1,2,1)
702s  title ("Grade 3 heights - vertical");
702s  set (gca, "xtick", 1:2, "xticklabel", {"girls"; "boys"});
702s  violin (data, "Nbins", 10);
702s  axis tight
702s 
702s  subplot(1,2,2)
702s  title ("Grade 3 heights - horizontal");
702s  set (gca, "ytick", 1:2, "yticklabel", {"girls"; "boys"});
702s  violin (data, "horizontal", "Nbins", 10);
702s  axis tight
702s ***** demo
702s  clf
702s  data = exprnd (0.1, 500,4);
702s  violin (data, "nbins", {5,10,50,100});
702s  axis ([0 5 0 max(data(:))])
702s ***** demo
702s  clf
702s  data = exprnd (0.1, 500,4);
702s  violin (data, "color", jet(4));
702s  axis ([0 5 0 max(data(:))])
702s ***** demo
702s  clf
702s  data = repmat(exprnd (0.1, 500,1), 1, 4);
702s  violin (data, "width", linspace (0.1,0.5,4));
702s  axis ([0 5 0 max(data(:))])
702s ***** demo
702s  clf
702s  data = repmat(exprnd (0.1, 500,1), 1, 4);
702s  violin (data, "nbins", [5,10,50,100], "smoothfactor", [4 4 8 10]);
702s  axis ([0 5 0 max(data(:))])
702s ***** test
702s  hf = figure ("visible", "off");
702s  unwind_protect
702s    data = exprnd (0.1, 500,4);
702s    violin (data, "color", jet(4));
702s    axis ([0 5 0 max(data(:))])
702s  unwind_protect_cleanup
702s    close (hf);
702s  end_unwind_protect
703s ***** test
703s  hf = figure ("visible", "off");
703s  unwind_protect
703s    data = {randn(100,1)*5+140, randn(130,1)*8+135};
703s    subplot (1,2,1)
703s    title ("Grade 3 heights - vertical");
703s    set (gca, "xtick", 1:2, "xticklabel", {"girls"; "boys"});
703s    violin (data, "Nbins", 10);
703s    axis tight
703s  unwind_protect_cleanup
703s    close (hf);
703s  end_unwind_protect
703s ***** test
703s  hf = figure ("visible", "off");
703s  unwind_protect
703s    data = {randn(100,1)*5+140, randn(130,1)*8+135};
703s    subplot (1,2,1)
703s    title ("Grade 3 heights - vertical");
703s    set (gca, "xtick", 1:2, "xticklabel", {"girls"; "boys"});
703s    violin (data, "Nbins", 10);
703s    axis tight
703s    subplot(1,2,2)
703s    title ("Grade 3 heights - horizontal");
703s    set (gca, "ytick", 1:2, "yticklabel", {"girls"; "boys"});
703s    violin (data, "horizontal", "Nbins", 10);
703s    axis tight
703s  unwind_protect_cleanup
703s    close (hf);
703s  end_unwind_protect
704s ***** test
704s  hf = figure ("visible", "off");
704s  unwind_protect
704s    data = repmat(exprnd (0.1, 500,1), 1, 4);
704s    violin (data, "nbins", [5,10,50,100], "smoothfactor", [4 4 8 10]);
704s    axis ([0 5 0 max(data(:))])
704s  unwind_protect_cleanup
704s    close (hf);
704s  end_unwind_protect
705s ***** test
705s  hf = figure ("visible", "off");
705s  unwind_protect
705s    data = repmat(exprnd (0.1, 500,1), 1, 4);
705s    violin (data, "width", linspace (0.1,0.5,4));
705s    axis ([0 5 0 max(data(:))])
705s  unwind_protect_cleanup
705s    close (hf);
705s  end_unwind_protect
706s 5 tests, 5 passed, 0 known failure, 0 skipped
706s [inst/wblplot.m]
706s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/wblplot.m
706s ***** demo
706s  x = [16 34 53 75 93 120];
706s  wblplot (x);
706s ***** demo
706s  x = [2 3 5 7 11 13 17 19 23 29 31 37 41 43 47 53 59 61 67]';
706s  c = [0 1 0 1 0 1 1 1 0 0 1 0 1 0 1 1 0 1 1]';
706s  [h, p] = wblplot (x, c);
706s  p
706s ***** demo
706s  x = [16, 34, 53, 75, 93, 120, 150, 191, 240 ,339];
706s  [h, p] = wblplot (x, [], [], 0.05);
706s  p
706s  ## Benchmark Reliasoft eta = 146.2545 beta 1.1973 rho = 0.9999
706s ***** demo
706s  x = [46 64 83 105 123 150 150];
706s  c = [0 0 0 0 0 0 1];
706s  f = [1 1 1 1 1 1 4];
706s  wblplot (x, c, f, 0.05);
706s ***** demo
706s  x = [46 64 83 105 123 150 150];
706s  c = [0 0 0 0 0 0 1];
706s  f = [1 1 1 1 1 1 4];
706s  ## Subtract 30.92 from x to simulate a 3 parameter wbl with gamma = 30.92
706s  wblplot (x - 30.92, c, f, 0.05);
706s ***** test
706s  hf = figure ("visible", "off");
706s  unwind_protect
706s    x = [16, 34, 53, 75, 93, 120, 150, 191, 240 ,339];
706s    [h, p] = wblplot (x, [], [], 0.05);
706s    assert (numel (h), 4)
706s    assert (p(1), 146.2545, 1E-4)
706s    assert (p(2), 1.1973, 1E-4)
706s    assert (p(3), 0.9999, 5E-5)
706s  unwind_protect_cleanup
706s    close (hf);
706s  end_unwind_protect
707s 1 test, 1 passed, 0 known failure, 0 skipped
707s [inst/x2fx.m]
707s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/x2fx.m
707s ***** test
707s  X = [1, 10; 2, 20; 3, 10; 4, 20; 5, 15; 6, 15];
707s  D = x2fx(X,'quadratic');
707s  assert (D(1,:), [1, 1, 10, 10, 1, 100]);
707s  assert (D(2,:), [1, 2, 20, 40, 4, 400]);
707s ***** test
707s  X = [1, 10; 2, 20; 3, 10; 4, 20; 5, 15; 6, 15];
707s  model = [0, 0; 1, 0; 0, 1; 1, 1; 2, 0];
707s  D = x2fx(X,model);
707s  assert (D(1,:), [1, 1, 10, 10, 1]);
707s  assert (D(2,:), [1, 2, 20, 40, 4]);
707s  assert (D(4,:), [1, 4, 20, 80, 16]);
707s ***** test
707s  x = [1, 2, 3; 2, 3, 4; 3, 4, 5];
707s  D = x2fx (x, 'linear');
707s  assert (D, [1, 1, 2, 3; 1, 2, 3, 4;, 1, 3, 4, 5]);
707s  D = x2fx (x, 'interaction');
707s  assert (D(1,:), [1, 1, 2, 3, 2, 3, 6]);
707s  assert (D(2,:), [1, 2, 3, 4, 6, 8, 12]);
707s  assert (D(3,:), [1, 3, 4, 5, 12, 15, 20]);
707s  D = x2fx (x, 'quadratic');
707s  assert (D(1,:), [1, 1, 2, 3, 2, 3, 6, 1, 4, 9]);
707s  assert (D(2,:), [1, 2, 3, 4, 6, 8, 12, 4, 9, 16]);
707s  assert (D(3,:), [1, 3, 4, 5, 12, 15, 20, 9, 16, 25]);
707s  D = x2fx (x, 'purequadratic');
707s  assert (D(1,:), [1, 1, 2, 3, 1, 4, 9]);
707s  assert (D(2,:), [1, 2, 3, 4, 4, 9, 16]);
707s  assert (D(3,:), [1, 3, 4, 5, 9, 16, 25]);
707s ***** test
707s  x = [1, 2, 3; 2, 3, 4; 3, 4, 5];
707s  D = x2fx (x, [0, 0, 1; 1, 0, 2]);
707s  assert (D, [3, 9; 4, 32; 5, 75]);
707s ***** test
707s  x = [1, 2, 3; 2, 3, 4; 3, 4, 5];
707s  D = x2fx (x, 'linear', [1, 3]);
707s  assert (D, [1, 1, 0, 2, 1, 0; 1, 0, 1, 3, 0, 1; 1, 0, 0, 4, 0, 0]);
707s ***** test
707s  x = [1, 2, 3; 2, 3, 4; 3, 4, 5];
707s  D = x2fx (x, 'quadratic', [1, 3]);
707s  assert (D(1,:), [1, 1, 0, 2, 1, 0, 2, 0, 1, 0, 0, 0, 2, 0, 4]);
707s  assert (D(2,:), [1, 0, 1, 3, 0, 1, 0, 3, 0, 0, 0, 1, 0, 3, 9]);
707s  assert (D(3,:), [1, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 16]);
707s ***** test
707s  x = [1, 2, 3; 2, 3, 4; 3, 4, 5];
707s  D = x2fx (x, 'cos');
707s  assert (D(1,:), [0.5403, -0.4161, -0.9900], 1e-4);
707s  assert (D(2,:), [-0.4161, -0.9900, -0.6536], 1e-4);
707s  assert (D(3,:), [-0.9900, -0.6536, 0.2837], 1e-4);
707s ***** error <x2fx: category index exceeds number of columns in X.> ...
707s  x2fx ([1, 2, 3; 2, 3, 4], 'quadratic', [1, 4])
707s ***** error <x2fx: unrecognized function 'cosine'.> ...
707s  D = x2fx ([1, 2, 3; 2, 3, 4; 3, 4, 5], 'cosine')
707s ***** error <x2fx: wrong number of columns between X and MODEL.> ...
707s  x2fx ([1, 10; 2, 20; 3, 10], [0; 1]);
707s ***** error <x2fx: wrong number of columns between X and MODEL.> ...
707s  x2fx ([1, 10, 15; 2, 20, 40; 3, 10, 25], [0, 0; 1, 0; 0, 1; 1, 1; 2, 0]);
707s 11 tests, 11 passed, 0 known failure, 0 skipped
707s [inst/ztest.m]
707s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/ztest.m
707s ***** error<ztest: too few input arguments.> ztest ();
707s ***** error<ztest: invalid value for standard deviation.> ...
707s  ztest ([1, 2, 3, 4], 2, -0.5);
707s ***** error<ztest: invalid VALUE for alpha.> ...
707s  ztest ([1, 2, 3, 4], 1, 2, "alpha", 0);
707s ***** error<ztest: invalid VALUE for alpha.> ...
707s  ztest ([1, 2, 3, 4], 1, 2, "alpha", 1.2);
707s ***** error<ztest: invalid VALUE for alpha.> ...
707s  ztest ([1, 2, 3, 4], 1, 2, "alpha", "val");
707s ***** error<ztest: invalid VALUE for tail.>  ...
707s  ztest ([1, 2, 3, 4], 1, 2, "tail", "val");
707s ***** error<ztest: invalid VALUE for tail.>  ...
707s  ztest ([1, 2, 3, 4], 1, 2, "alpha", 0.01, "tail", "val");
707s ***** error<ztest: invalid VALUE for operating dimension.> ...
707s  ztest ([1, 2, 3, 4], 1, 2, "dim", 3);
707s ***** error<ztest: invalid VALUE for operating dimension.> ...
707s  ztest ([1, 2, 3, 4], 1, 2, "alpha", 0.01, "tail", "both", "dim", 3);
707s ***** error<ztest: invalid NAME for optional arguments.> ...
707s  ztest ([1, 2, 3, 4], 1, 2, "alpha", 0.01, "tail", "both", "badoption", 3);
707s ***** test
707s  load carsmall
707s  [h, pval, ci] = ztest (MPG, mean (MPG, "omitnan"), std (MPG, "omitnan"));
707s  assert (h, 0);
707s  assert (pval, 1, 1e-14);
707s  assert (ci, [22.094; 25.343], 1e-3);
707s ***** test
707s  load carsmall
707s  [h, pval, ci] = ztest (MPG, 26, 8);
707s  assert (h, 1);
707s  assert (pval, 0.00568359158544743, 1e-14);
707s  assert (ci, [22.101; 25.335], 1e-3);
707s ***** test
707s  load carsmall
707s  [h, pval, ci] = ztest (MPG, 26, 4);
707s  assert (h, 1);
707s  assert (pval, 3.184168011941316e-08, 1e-14);
707s  assert (ci, [22.909; 24.527], 1e-3);
707s ***** test
707s  x = normrnd (10, 2, 100, 1);
707s  [h, pval, ci] = ztest (x, 10, 2, "tail", "right");
707s  assert (isnan (pval), false);
707s  assert (pval >= 0 && pval <= 1, true);
707s ***** test
707s  x = normrnd (10, 2, 100, 1);
707s  [h, pval, ci] = ztest (x, 10, 2, "tail", "left");
707s  assert (isnan (pval), false);
707s  assert (pval >= 0 && pval <= 1, true);
707s ***** test
707s  load fisheriris;
707s  x = meas(:,1);
707s  m = 5.8;
707s  sigma = 0.8;
707s  [h, pval, ci] = ztest (x, m, sigma, "tail", "right");
707s  assert (h, 0)
707s  assert (pval, 0.2535, 1e-4)
707s  assert (ci, [5.7359; Inf], 1e-5)
707s ***** test
707s  load fisheriris;
707s  x = meas(:,1);
707s  m = 5.8;
707s  sigma = 0.8;
707s  [h, pval, ci] = ztest (x, m, sigma, "tail", "left");
707s  assert (h, 0)
707s  assert (pval, 0.7465, 1e-4)
707s  assert (ci, [-Inf; 5.9508], 1e-4)    
707s 17 tests, 17 passed, 0 known failure, 0 skipped
707s [inst/ztest2.m]
707s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/ztest2.m
707s ***** error ztest2 ();
707s ***** error ztest2 (1);
707s ***** error ztest2 (1, 2);
707s ***** error ztest2 (1, 2, 3);
707s ***** error ztest2 (1, 2, 3, 2);
707s ***** error<ztest2: optional arguments must be in NAME-VALUE pairs.> ...
707s  ztest2 (1, 2, 3, 4, "alpha")
707s ***** error<ztest2: invalid VALUE for alpha.> ...
707s  ztest2 (1, 2, 3, 4, "alpha", 0);
707s ***** error<ztest2: invalid VALUE for alpha.> ...
707s  ztest2 (1, 2, 3, 4, "alpha", 1.2);
707s ***** error<ztest2: invalid VALUE for alpha.> ...
707s  ztest2 (1, 2, 3, 4, "alpha", "val");
707s ***** error<ztest2: invalid VALUE for tail.>  ...
707s  ztest2 (1, 2, 3, 4, "tail", "val");
707s ***** error<ztest2: invalid VALUE for tail.>  ...
707s  ztest2 (1, 2, 3, 4, "alpha", 0.01, "tail", "val");
707s ***** error<ztest2: invalid NAME for optional arguments.> ...
707s  ztest2 (1, 2, 3, 4, "alpha", 0.01, "tail", "both", "badoption", 3);
707s 12 tests, 12 passed, 0 known failure, 0 skipped
707s [inst/fitcknn.m]
707s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/inst/fitcknn.m
707s ***** demo
707s  ## Train a k-nearest neighbor classifier for k = 10
707s  ## and plot the decision boundaries.
707s 
707s  load fisheriris
707s  idx = ! strcmp (species, "setosa");
707s  X = meas(idx,3:4);
707s  Y = cast (strcmpi (species(idx), "virginica"), "double");
707s  obj = fitcknn (X, Y, "Standardize", 1, "NumNeighbors", 10, "NSMethod", "exhaustive")
707s  x1 = [min(X(:,1)):0.03:max(X(:,1))];
707s  x2 = [min(X(:,2)):0.02:max(X(:,2))];
707s  [x1G, x2G] = meshgrid (x1, x2);
707s  XGrid = [x1G(:), x2G(:)];
707s  pred = predict (obj, XGrid);
707s  gidx = logical (pred);
707s 
707s  figure
707s  scatter (XGrid(gidx,1), XGrid(gidx,2), "markerfacecolor", "magenta");
707s  hold on
707s  scatter (XGrid(!gidx,1), XGrid(!gidx,2), "markerfacecolor", "red");
707s  plot (X(Y == 0, 1), X(Y == 0, 2), "ko", X(Y == 1, 1), X(Y == 1, 2), "kx");
707s  xlabel ("Petal length (cm)");
707s  ylabel ("Petal width (cm)");
707s  title ("5-Nearest Neighbor Classifier Decision Boundary");
707s  legend ({"Versicolor Region", "Virginica Region", ...
707s          "Sampled Versicolor", "Sampled Virginica"}, ...
707s          "location", "northwest")
707s  axis tight
707s  hold off
707s ***** test
707s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
707s  y = ["a"; "a"; "b"; "b"];
707s  a = fitcknn (x, y);
707s  assert (class (a), "ClassificationKNN");
707s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
707s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
707s  assert ({a.BucketSize}, {50})
707s ***** test
707s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
707s  y = ["a"; "a"; "b"; "b"];
707s  a = fitcknn (x, y, "NSMethod", "exhaustive");
707s  assert (class (a), "ClassificationKNN");
707s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
707s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
707s  assert ({a.BucketSize}, {50})
707s ***** test
707s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
707s  y = ["a"; "a"; "b"; "b"];
707s  k = 10;
707s  a = fitcknn (x, y, "NumNeighbors" ,k);
707s  assert (class (a), "ClassificationKNN");
707s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
707s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
707s  assert ({a.BucketSize}, {50})
707s ***** test
707s  x = ones (4, 11);
707s  y = ["a"; "a"; "b"; "b"];
707s  k = 10;
707s  a = fitcknn (x, y, "NumNeighbors" ,k);
707s  assert (class (a), "ClassificationKNN");
707s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
707s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
707s  assert ({a.BucketSize}, {50})
707s ***** test
707s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
707s  y = ["a"; "a"; "b"; "b"];
707s  k = 10;
707s  a = fitcknn (x, y, "NumNeighbors" ,k, "NSMethod", "exhaustive");
707s  assert (class (a), "ClassificationKNN");
707s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
707s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
707s  assert ({a.BucketSize}, {50})
707s ***** test
707s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
707s  y = ["a"; "a"; "b"; "b"];
707s  k = 10;
707s  a = fitcknn (x, y, "NumNeighbors" ,k, "Distance", "hamming");
707s  assert (class (a), "ClassificationKNN");
707s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
707s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "hamming"})
707s  assert ({a.BucketSize}, {50})
707s ***** test
707s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
707s  y = ["a"; "a"; "b"; "b"];
707s  weights = ones (4,1);
707s  a = fitcknn (x, y, "Standardize", 1);
707s  assert (class (a), "ClassificationKNN");
707s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
707s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
707s  assert ({a.Standardize}, {true})
707s  assert ({a.Sigma}, {std(x, [], 1)})
707s  assert ({a.Mu}, {[3.75, 4.25, 4.75]})
707s ***** test
707s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
707s  y = ["a"; "a"; "b"; "b"];
707s  weights = ones (4,1);
707s  a = fitcknn (x, y, "Standardize", false);
707s  assert (class (a), "ClassificationKNN");
707s  assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
707s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
707s  assert ({a.Standardize}, {false})
707s  assert ({a.Sigma}, {[]})
707s  assert ({a.Mu}, {[]})
707s ***** test
707s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
707s  y = ["a"; "a"; "b"; "b"];
707s  s = ones (1, 3);
707s  a = fitcknn (x, y, "Scale" , s, "Distance", "seuclidean");
707s  assert (class (a), "ClassificationKNN");
707s  assert ({a.DistParameter}, {s})
707s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "seuclidean"})
707s  assert ({a.BucketSize}, {50})
707s ***** test
707s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
707s  y = ["a"; "a"; "b"; "b"];
707s  a = fitcknn (x, y, "Exponent" , 5, "Distance", "minkowski");
707s  assert (class (a), "ClassificationKNN");
707s  assert (a.DistParameter, 5)
707s  assert ({a.NSMethod, a.Distance}, {"kdtree", "minkowski"})
707s ***** test
707s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
707s  y = ["a"; "a"; "b"; "b"];
707s  a = fitcknn (x, y, "Exponent" , 5, "Distance", "minkowski", ...
707s                     "NSMethod", "exhaustive");
707s  assert (class (a), "ClassificationKNN");
707s  assert (a.DistParameter, 5)
707s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "minkowski"})
707s ***** test
707s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
707s  y = ["a"; "a"; "b"; "b"];
707s  a = fitcknn (x, y, "BucketSize" , 20, "distance", "mahalanobis");
707s  assert (class (a), "ClassificationKNN");
707s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "mahalanobis"})
707s  assert ({a.BucketSize}, {20})
707s ***** test
707s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
707s  y = ["a"; "a"; "b"; "b"];
707s  a = fitcknn (x, y, "IncludeTies", true);
707s  assert (class (a), "ClassificationKNN");
707s  assert (a.IncludeTies, true);
707s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
707s ***** test
707s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
707s  y = ["a"; "a"; "b"; "b"];
707s  a = fitcknn (x, y);
707s  assert (class (a), "ClassificationKNN");
707s  assert (a.IncludeTies, false);
707s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
707s ***** test
707s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
707s  y = ["a"; "a"; "b"; "b"];
707s  a = fitcknn (x, y);
707s  assert (class (a), "ClassificationKNN")
707s  assert (a.Prior, [0.5; 0.5])
707s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
707s  assert ({a.BucketSize}, {50})
708s ***** test
708s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
708s  y = ["a"; "a"; "b"; "b"];
708s  prior = [0.5; 0.5];
708s  a = fitcknn (x, y, "Prior", "empirical");
708s  assert (class (a), "ClassificationKNN")
708s  assert (a.Prior, prior)
708s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
708s  assert ({a.BucketSize}, {50})
708s ***** test
708s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
708s  y = ["a"; "a"; "a"; "b"];
708s  prior = [0.75; 0.25];
708s  a = fitcknn (x, y, "Prior", "empirical");
708s  assert (class (a), "ClassificationKNN")
708s  assert (a.Prior, prior)
708s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
708s  assert ({a.BucketSize}, {50})
708s ***** test
708s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
708s  y = ["a"; "a"; "a"; "b"];
708s  prior = [0.5; 0.5];
708s  a = fitcknn (x, y, "Prior", "uniform");
708s  assert (class (a), "ClassificationKNN")
708s  assert (a.Prior, prior)
708s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
708s  assert ({a.BucketSize}, {50})
708s ***** test
708s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
708s  y = ["a"; "a"; "b"; "b"];
708s  cost = eye (2);
708s  a = fitcknn (x, y, "Cost", cost);
708s  assert (class (a), "ClassificationKNN")
708s  assert (a.Cost, [1, 0; 0, 1])
708s  assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
708s  assert ({a.BucketSize}, {50})
708s ***** test
708s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
708s  y = ["a"; "a"; "b"; "b"];
708s  cost = eye (2);
708s  a = fitcknn (x, y, "Cost", cost, "Distance", "hamming" );
708s  assert (class (a), "ClassificationKNN")
708s  assert (a.Cost, [1, 0; 0, 1])
708s  assert ({a.NSMethod, a.Distance}, {"exhaustive", "hamming"})
708s  assert ({a.BucketSize}, {50})
708s ***** test
708s  x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
708s  y = ["a"; "a"; "b"; "b"];
708s  rand ("seed", 23);
708s  a = fitcknn (x, y, "NSMethod", "exhaustive", "CrossVal", "on");
708s  assert (class (a), "ClassificationPartitionedModel");
708s  assert ({a.X, a.Y, a.Trained{1}.NumNeighbors}, {x, y, 1})
708s  assert (a.ModelParameters.NSMethod, "exhaustive")
708s  assert (a.ModelParameters.Distance, "euclidean")
708s  assert ({a.Trained{1}.BucketSize}, {50})
708s warning: One or more of the unique class values in the stratification variable is not present in one or more folds.
708s warning: called from
708s     cvpartition at line 764 column 19
708s     crossval at line 1701 column 9
708s     fitcknn at line 354 column 7
708s     __test__ at line 6 column 2
708s     test at line 685 column 11
708s     /tmp/tmp.scCa8Po4At at line 3598 column 2
708s 
708s ***** error<fitcknn: too few arguments.> fitcknn ()
708s ***** error<fitcknn: too few arguments.> fitcknn (ones (4,1))
708s ***** error<fitcknn: Name-Value arguments must be in pairs.>
708s  fitcknn (ones (4,2), ones (4, 1), "K")
708s ***** error<fitcknn: number of rows in X and Y must be equal.>
708s  fitcknn (ones (4,2), ones (3, 1))
708s ***** error<fitcknn: number of rows in X and Y must be equal.>
708s  fitcknn (ones (4,2), ones (3, 1), "K", 2)
708s ***** error <fitcknn: 'CrossVal' must be either 'off' or 'on'.>
708s  fitcknn (ones (4,2), ones (4, 1), "CrossVal", 2)
708s ***** error <fitcknn: 'CrossVal' must be either 'off' or 'on'.>
708s  fitcknn (ones (4,2), ones (4, 1), "CrossVal", 'a')
708s ***** error <fitcknn: You can use only one cross-validation name-value pair argument> ...
708s  fitcknn (ones (4,2), ones (4, 1), "KFold", 10, "Holdout", 0.3)
708s 29 tests, 29 passed, 0 known failure, 0 skipped
708s Checking C++ files ...
708s [src/editDistance.cc]
708s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/src/editDistance.cc
708s ***** error <editDistance: too many input arguments.> d = editDistance (1, 2, 3, 4);
708s ***** error <editDistance: too many output arguments.> ...
708s  [C, IA, IC, I] = editDistance ({"AS","SD","AD"}, 1);
708s ***** error <editDistance: too many output arguments.> ...
708s  [C, IA] = editDistance ({"AS","SD","AD"});
708s ***** error <editDistance: minDist must be a scalar value.> ...
708s  d = editDistance ({"AS","SD","AD"}, [1, 2]);
708s ***** error <editDistance: minDist must be a nonnegative integer.> ...
708s  d = editDistance ({"AS","SD","AD"}, -2);
708s ***** error <editDistance: minDist must be a nonnegative integer.> ...
708s  d = editDistance ({"AS","SD","AD"}, 1.25);
708s ***** error <editDistance: minDist must be a scalar value.> ...
708s  d = editDistance ({"AS","SD","AD"}, {"AS","SD","AD"}, [1, 2]);
708s ***** error <editDistance: minDist must be a nonnegative integer.> ...
708s  d = editDistance ({"AS","SD","AD"}, {"AS","SD","AD"}, -2);
708s ***** error <editDistance: minDist must be a nonnegative integer.> ...
708s  d = editDistance ({"AS","SD","AD"}, {"AS","SD","AD"}, 1.25);
708s ***** error <editDistance: minDist must be a scalar value.> ...
708s  d = editDistance ("string1", "string2", [1, 2]);
708s ***** error <editDistance: minDist must be a nonnegative integer.> ...
708s  d = editDistance ("string1", "string2", -2);
708s ***** error <editDistance: minDist must be a nonnegative integer.> ...
708s  d = editDistance ("string1", "string2", 1.25);
708s ***** error <editDistance: tokenizedDocument must contain cellstr arrays.> ...
708s  d = editDistance ({{"string1", "string2"}, 2});
708s ***** error <editDistance: tokenizedDocument must contain cellstr arrays.> ...
708s  d = editDistance ({{"string1", "string2"}, 2}, 2);
708s ***** error <editDistance: STR1 must be a cellstr.> ...
708s  d = editDistance ([1, 2, 3]);
708s ***** error <editDistance: STR1 must be a cellstr.> ...
708s  d = editDistance (["AS","SD","AD","AS"]);
708s ***** error <editDistance: STR1 must be a cellstr.> ...
708s  d = editDistance (["AS","SD","AD"], 2);
708s ***** error <editDistance: STR1 and STR2 must be either strings or cellstr.> ...
708s  d = editDistance (logical ([1,2,3]), {"AS","AS","AD"});
708s ***** error <editDistance: STR1 and STR2 must be either strings or cellstr.> ...
708s  d = editDistance ({"AS","SD","AD"}, logical ([1,2,3]));
708s ***** error <editDistance: STR1 and STR2 must be either strings or cellstr.> ...
708s  d = editDistance ([1,2,3], {"AS","AS","AD"});
708s ***** error <editDistance: first tokenizedDocument does not contain cellstr arrays.> ...
708s  d = editDistance ({1,2,3}, {"AS","SD","AD"});
708s ***** error <editDistance: second tokenizedDocument does not contain cellstr arrays.> ...
708s  d = editDistance ({"AS","SD","AD"}, {1,2,3});
708s ***** error <editDistance: cellstr input arguments size mismatch.> ...
708s  d = editDistance ({"AS","SD","AD"}, {"AS", "AS"});
708s ***** test
708s  d = editDistance ({"AS","SD","AD"});
708s  assert (d, [2; 1; 1]);
708s  assert (class (d), "double");
708s ***** test
708s  C = editDistance ({"AS","SD","AD"}, 1);
708s  assert (iscellstr (C), true);
708s  assert (C, {"AS";"SD"});
708s ***** test
708s  [C, IA] = editDistance ({"AS","SD","AD"}, 1);
708s  assert (class (IA), "double");
708s  assert (IA, [1;2]);
708s ***** test
708s  A = {"ASS"; "SDS"; "FDE"; "EDS"; "OPA"};
708s  [C, IA] = editDistance (A, 2, "OutputAllIndices", false);
708s  assert (class (IA), "double");
708s  assert (A(IA), C);
708s ***** test
708s  A = {"ASS"; "SDS"; "FDE"; "EDS"; "OPA"};
708s  [C, IA] = editDistance (A, 2, "OutputAllIndices", true);
708s  assert (class (IA), "cell");
708s  assert (C, {"ASS"; "FDE"; "OPA"});
708s  assert (A(IA{1}), {"ASS"; "SDS"; "EDS"});
708s  assert (A(IA{2}), {"FDE"; "EDS"});
708s  assert (A(IA{3}), {"OPA"});
708s ***** test
708s  A = {"ASS"; "SDS"; "FDE"; "EDS"; "OPA"};
708s  [C, IA, IC] = editDistance (A, 2);
708s  assert (class (IA), "double");
708s  assert (A(IA), C);
708s  assert (IC, [1; 1; 3; 1; 5]);
708s ***** test
708s  d = editDistance ({"AS","SD","AD"}, {"AS", "AD", "SE"});
708s  assert (d, [0; 1; 2]);
708s  assert (class (d), "double");
708s ***** test
708s  d = editDistance ({"AS","SD","AD"}, {"AS"});
708s  assert (d, [0; 2; 1]);
708s  assert (class (d), "double");
708s ***** test
708s  d = editDistance ({"AS"}, {"AS","SD","AD"});
708s  assert (d, [0; 2; 1]);
708s  assert (class (d), "double");
708s ***** test
708s  b = editDistance ("Octave", "octave");
708s  assert (b, 1);
708s  assert (class (b), "double");
708s 33 tests, 33 passed, 0 known failure, 0 skipped
708s [src/fcnnpredict.cc]
708s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/src/fcnnpredict.cc
708s ***** shared X, Y, MODEL
708s  load fisheriris
708s  X = meas;
708s  Y = grp2idx (species);
708s  MODEL = fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 100, false);
708s ***** test
708s  [Y_pred, Y_scores] = fcnnpredict (MODEL, X);
708s  assert (numel (Y_pred), numel (Y));
708s  assert (isequal (size (Y_pred), size (Y)), true);
708s  assert (columns (Y_scores), numel (unique (Y)));
708s  assert (rows (Y_scores), numel (Y));
708s ***** error <fcnnpredict: too few input arguments.> ...
708s  fcnnpredict (MODEL);
708s ***** error <fcnnpredict: too many output arguments.> ...
708s  [Q, W, E] = fcnnpredict (MODEL, X);
708s ***** error <fcnnpredict: first argument must be a scalar structure.> ...
708s  fcnnpredict (1, X);
708s ***** error <fcnnpredict: first argument must be a scalar structure.> ...
708s  fcnnpredict (struct ("L", {1, 2, 3}), X);
708s ***** error <fcnnpredict: model does not have a 'LayerWeights' field.> ...
708s  fcnnpredict (struct ("L", 1), X);
708s ***** error <fcnnpredict: 'LayerWeights' must be a cell row vector.> ...
708s  fcnnpredict (struct ("LayerWeights", 1), X);
708s ***** error <fcnnpredict: 'LayerWeights' must be a cell row vector.> ...
708s  fcnnpredict (struct ("LayerWeights", {1}), X);
708s ***** error <fcnnpredict: 'LayerWeights' must be a cell row vector.> ...
708s  fcnnpredict (struct ("LayerWeights", {{1; 2; 3}}), X);
708s ***** error <fcnnpredict: model does not have an 'Activations' field.> ...
708s  fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, "R", 2), X);
708s ***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
708s  fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
708s                       "Activations", [2]), X);
708s ***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
708s  fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
708s                       "Activations", [2; 2]), X);
708s ***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
708s  fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
708s                       "Activations", {{2, 2}}), X);
708s ***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
708s  fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
708s                       "Activations", {{"sigmoid", "softmax"}}), X);
708s ***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
708s  fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
708s                       "Activations", "sigmoid"), X);
708s ***** error <fcnnpredict: XC must be a real numeric matrix.> ...
708s  fcnnpredict (MODEL, complex (X));
708s ***** error <fcnnpredict: XC must be a real numeric matrix.> ...
708s  fcnnpredict (MODEL, {1, 2, 3, 4});
708s ***** error <fcnnpredict: XC must be a real numeric matrix.> ...
708s  fcnnpredict (MODEL, "asd");
708s ***** error <fcnnpredict: XC cannot be empty.> ...
708s  fcnnpredict (MODEL, []);
708s ***** error <fcnnpredict: the features in XC do not match the trained model.> ...
708s  fcnnpredict (MODEL, X(:,[1:3]));
708s 20 tests, 20 passed, 0 known failure, 0 skipped
708s [src/fcnntrain.cc]
708s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/src/fcnntrain.cc
708s ***** shared X, Y, MODEL
708s  load fisheriris
708s  X = meas;
708s  Y = grp2idx (species);
708s ***** error <fcnntrain: too few input arguments.> ...
708s  model = fcnntrain (X, Y);
708s ***** error <fcnntrain: too many output arguments.> ...
708s  [Q, W] = fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 50, false);
708s ***** error <fcnntrain: X must be a real numeric matrix.> ...
708s  fcnntrain (complex (X), Y, 10, [1, 1], 1, 0.01, 0.025, 50, false);
708s ***** error <fcnntrain: X must be a real numeric matrix.> ...
708s  fcnntrain ({X}, Y, 10, [1, 1], 1, 0.01, 0.025, 50, false);
708s ***** error <fcnntrain: X cannot be empty.> ...
708s  fcnntrain ([], Y, 10, [1, 1], 1, 0.01, 0.025, 50, false);
708s ***** error <fcnntrain: Y must be a real numeric matrix.> ...
708s  fcnntrain (X, complex (Y), 10, 1, 0.01, [1, 1], 0.025, 50, false);
708s ***** error <fcnntrain: Y must be a real numeric matrix.> ...
708s  fcnntrain (X, {Y}, 10, [1, 1], 1, 0.01, 0.025, 50, false);
708s ***** error <fcnntrain: Y cannot be empty.> ...
708s  fcnntrain (X, [], 10, [1, 1], 1, 0.01, 0.025, 50, false);
708s ***** error <fcnntrain: X and Y must have the same number of rows.> ...
708s  fcnntrain (X, Y([1:50]), 10, [1, 1], 1, 0.01, 0.025, 50, false);
708s ***** error <fcnntrain: labels in Y must be positive integers.> ...
708s  fcnntrain (X, Y - 1, 10, [1, 1], 1, 0.01, 0.025, 50, false);
708s ***** error <fcnntrain: 'LayerSizes' must be a row vector of integer values.> ...
708s  fcnntrain (X, Y, [10; 5], [1, 1, 1], 1, 0.01, 0.025, 50, false);
708s ***** error <fcnntrain: 'LayerSizes' must be a row vector of integer values.> ...
708s  fcnntrain (X, Y, "10", [1, 1], 1, 0.01, 0.025, 50, false);
708s ***** error <fcnntrain: 'LayerSizes' must be a row vector of integer values.> ...
708s  fcnntrain (X, Y, {10}, [1, 1], 1, 0.01, 0.025, 50, false);
708s ***** error <fcnntrain: 'LayerSizes' must be a row vector of integer values.> ...
708s  fcnntrain (X, Y, complex (10), [1, 1], 1, 0.01, 0.025, 50, false);
708s ***** error <fcnntrain: 'Activations' must be a row vector of integer values.> ...
708s  fcnntrain (X, Y, 10, [1; 1], 1, 0.01, 0.025, 50, false);
708s ***** error <fcnntrain: 'Activations' must be a row vector of integer values.> ...
708s  fcnntrain (X, Y, 10, {1, 1}, 1, 0.01, 0.025, 50, false);
708s ***** error <fcnntrain: 'Activations' must be a row vector of integer values.> ...
708s  fcnntrain (X, Y, 10, "1", 1, 0.01, 0.025, 50, false);
708s ***** error <fcnntrain: 'Activations' must be a row vector of integer values.> ...
708s  fcnntrain (X, Y, 10, complex ([1, 1]), 1, 0.01, 0.025, 50, false);
708s ***** error <fcnntrain: 'Activations' do not match LayerSizes.> ...
708s  fcnntrain (X, Y, 10, [1, 1, 1], 1, 0.01, 0.025, 50, false);
708s ***** error <fcnntrain: cannot have a layer of zero size.> ...
708s  fcnntrain (X, Y, [10, 0, 5], [1, 1, 1, 1], 1, 0.01, 0.025, 50, false);
708s ***** error <fcnntrain: invalid 'Activations' code.> ...
708s  fcnntrain (X, Y, 10, [-1, 1], 1, 0.01, 0.025, 50, false);
708s ***** error <fcnntrain: invalid 'Activations' code.> ...
708s  fcnntrain (X, Y, 10, [8, 1], 1, 0.01, 0.025, 50, false);
708s ***** error <fcnntrain: 'NumThreads' must be a positive integer scalar value.> ...
708s  fcnntrain (X, Y, 10, [1, 1], 0, 0.01, 0.025, 50, false);
708s ***** error <fcnntrain: 'Alpha' must be a positive scalar value.> ...
708s  fcnntrain (X, Y, 10, [1, 1], 1, -0.01, 0.025, 50, false);
708s ***** error <fcnntrain: 'LearningRate' must be a positive scalar value.> ...
708s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, -0.025, 50, false);
708s ***** error <fcnntrain: 'LearningRate' must be a positive scalar value.> ...
708s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0, 50, false);
708s ***** error <fcnntrain: 'LearningRate' must be a positive scalar value.> ...
708s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, [0.025, 0.001], 50, false);
708s ***** error <fcnntrain: 'LearningRate' must be a positive scalar value.> ...
708s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, {0.025}, 50, false);
708s ***** error <fcnntrain: 'Epochs' must be a positive scalar value.> ...
708s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 0, false);
708s ***** error <fcnntrain: 'Epochs' must be a positive scalar value.> ...
708s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, [50, 25], false);
708s ***** error <fcnntrain: 'DisplayInfo' must be a boolean scalar.> ...
708s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 50, 0);
708s ***** error <fcnntrain: 'DisplayInfo' must be a boolean scalar.> ...
708s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 50, 1);
708s ***** error <fcnntrain: 'DisplayInfo' must be a boolean scalar.> ...
708s  fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 50, [false, false]);
708s 33 tests, 33 passed, 0 known failure, 0 skipped
708s [src/libsvmread.cc]
708s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/src/libsvmread.cc
708s ***** error <libsvmread: filename must be a string.> [L, D] = libsvmread (24);
708s ***** error <libsvmread: wrong number of input or output arguments.> ...
708s  D = libsvmread ("filename");
708s ***** test
708s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
708s  assert (size (L), [270, 1]);
708s  assert (size (D), [270, 13]);
708s ***** test
708s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
708s  assert (issparse (L), false);
708s  assert (issparse (D), true);
708s 4 tests, 4 passed, 0 known failure, 0 skipped
708s [src/libsvmwrite.cc]
708s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/src/libsvmwrite.cc
708s ***** shared L, D
708s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
708s ***** error <libsvmwrite: error opening file for write.> libsvmwrite ("", L, D);
708s ***** error <libsvmwrite: length of label vector does not match instances.> ...
708s  libsvmwrite (tempname (), [L;L], D);
708s ***** error <libsvmwrite: wrong number of output arguments.> ...
708s  OUT = libsvmwrite (tempname (), L, D);
708s ***** error <libsvmwrite: label vector and instance matrix must be double.> ...
708s  libsvmwrite (tempname (), single (L), D);
708s ***** error <libsvmwrite: filename must be a string.> libsvmwrite (13412, L, D);
708s ***** error <libsvmwrite: instance_matrix must be sparse.> ...
708s  libsvmwrite (tempname (), L, full (D));
708s ***** error <libsvmwrite: wrong number of input arguments.> ...
708s  libsvmwrite (tempname (), L, D, D);
708s 7 tests, 7 passed, 0 known failure, 0 skipped
708s [src/svmpredict.cc]
708s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/src/svmpredict.cc
708s ***** test
708s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
708s  model = svmtrain (L, D, '-c 1 -g 0.07');
708s  [predict_label, accuracy, dec_values] = svmpredict (L, D, model);
708s  assert (size (predict_label), size (dec_values));
708s  assert (accuracy, [86.666, 0.533, 0.533]', [1e-3, 1e-3, 1e-3]');
708s  assert (dec_values(1), 1.225836001973273, 1e-14);
708s  assert (dec_values(2), -0.3212992933043805, 1e-14);
708s  assert (predict_label(1), 1);
708s ***** shared L, D, model
708s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
708s  model = svmtrain (L, D, '-c 1 -g 0.07');
708s ***** error <svmpredict: wrong number of output arguments.> ...
708s  [p, a] = svmpredict (L, D, model);
708s ***** error <svmpredict: wrong number of input arguments.> p = svmpredict (L, D);
708s ***** error <svmpredict: label vector and instance matrix must be double.> ...
708s  p = svmpredict (single (L), D, model);
708s ***** error <svmpredict: model should be a struct array.> p = svmpredict (L, D, 123);
708s 5 tests, 5 passed, 0 known failure, 0 skipped
708s [src/svmtrain.cc]
708s >>>>> /tmp/autopkgtest.RVoS4M/build.3Au/src/src/svmtrain.cc
708s ***** test
708s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
708s  model = svmtrain(L, D, '-c 1 -g 0.07');
708s  [predict_label, accuracy, dec_values] = svmpredict(L, D, model);
708s  assert (isstruct (model), true);
708s  assert (isfield (model, "Parameters"), true);
708s  assert (model.totalSV, 130);
708s  assert (model.nr_class, 2);
708s  assert (size (model.Label), [2, 1]);
708s ***** shared L, D
708s  [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
708s ***** error <svmtrain: wrong number of output arguments.> [L, D] = svmtrain (L, D);
708s ***** error <svmtrain: label vector and instance matrix must be double.> ...
708s  model = svmtrain (single (L), D);
708s ***** error <svmtrain: wrong number of input arguments.> ...
708s  model = svmtrain (L, D, "", "");
708s 4 tests, 4 passed, 0 known failure, 0 skipped
709s Done running the unit tests.
709s Summary: 11023 tests, 11021 passed, 1 known failures, 1 skipped
710s autopkgtest [07:24:44]: test command1: -----------------------]
715s command1             PASS
715s autopkgtest [07:24:49]: test command1:  - - - - - - - - - - results - - - - - - - - - -
719s autopkgtest [07:24:53]: @@@@@@@@@@@@@@@@@@@@ summary
719s command1             PASS