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luca-scrcran-robot
luca-scr
authored and
cran-robot
committedMay 20, 2022
version 5.4.10
1 parent cb07b2f commit 6d9fbc2

37 files changed

+222
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lines changed
 

‎DESCRIPTION

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Package: mclust
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Version: 5.4.9
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Date: 2021-12-17
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Version: 5.4.10
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Date: 2022-05-20
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Title: Gaussian Mixture Modelling for Model-Based Clustering,
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Classification, and Density Estimation
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Description: Gaussian finite mixture models fitted via EM algorithm for
@@ -29,11 +29,11 @@ ByteCompile: true
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NeedsCompilation: yes
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LazyData: yes
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Encoding: UTF-8
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Packaged: 2021-12-17 14:26:42 UTC; luca
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Packaged: 2022-05-20 07:46:14 UTC; luca
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Author: Chris Fraley [aut],
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Adrian E. Raftery [aut] (<https://orcid.org/0000-0002-6589-301X>),
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Luca Scrucca [aut, cre] (<https://orcid.org/0000-0003-3826-0484>),
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Thomas Brendan Murphy [ctb] (<https://orcid.org/0000-0002-5668-7046>),
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Michael Fop [ctb] (<https://orcid.org/0000-0003-3936-2757>)
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Maintainer: Luca Scrucca <luca.scrucca@unipg.it>
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Date/Publication: 2021-12-17 15:10:02 UTC
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Date/Publication: 2022-05-20 09:10:02 UTC

‎MD5

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3111dbac78536d0757a0b08390d591f2 *DESCRIPTION
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2bcb79cd210cf3986b854bf73cc1159b *DESCRIPTION
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f29893646b89738c493bd22446314bc4 *NAMESPACE
3-
2b1dd2a90efd9be7407bc170bd166548 *NEWS.md
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fe5b51db8754d5ca6d32248ca9d4db1b *R/bootstrap.R
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50dd1e793f6d30162776f5cd960293ad *NEWS.md
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6c7b363cb8374e4df827c1e6394faa5f *R/bootstrap.R
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eeb0e042ca8bcc1eb2d32da9ef594baf *R/clustCombi.R
6-
97c8d1f45c59c4da2ea7f6db09b68f36 *R/densityMclust.R
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dcad5ed0da37959e13faa123a2f6092d *R/densityMclust.R
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3cbe94a3f3c162d293154dc0a055c9bb *R/gmmhd.R
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2dc5411e8512b4da058597e17dd187fe *R/graphics.R
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87efc81c862d93c14860f710ad424765 *R/graphics.R
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ba84097ee321529784cd9c6db5143e77 *R/icl.R
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4b9155a000a45bb1f37fd1507c15f172 *R/impute.R
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d85f070c21b0742a056b87a9654773d2 *R/mbahc.R
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7b5f0026d92006d1a76e09714894f7d9 *R/mclust.R
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1c0ad0629bdef87f46a9811aa87011ea *R/mclustaddson.R
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3953487293520a00bc4e50a5483726f1 *R/mclustda.R
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37f2401aee7a35ce6d7bb0e2ca6839eb *R/mclustdr.R
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de7a05949adf6c735f8355bb495b6df5 *R/mclustssc.R
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b2216ff923e4a79da4d35b12a4015f6f *R/options.R
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13bf69f3940653e4a0de1b2ad1686250 *R/mclustda.R
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c5a0f7f8aa99fd7f8bb589ead06084a1 *R/mclustdr.R
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91a4d89022263a71f0e88f575a02b913 *R/mclustssc.R
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2c029b4f12b6c96982c034098d881bfe *R/options.R
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781ff8bea2efa10350791cd781eb4a2d *R/toremove.R
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ba0e509dd6446b85a79e6c24e45d59c8 *R/util.R
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ce4d61899bfc151f01017ae2a753baee *R/weights.R
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ea54da7ad04ea6cf47aad932539d5945 *R/zzz.R
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aa315fbfff90cd89ac8637b562a133bc *build/vignette.rds
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6fc941eda7a2bcddd64d711015c9d7db *data/Baudry_etal_2010_JCGS_examples.rda
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eae098659ecc267feb763f52c0de1a37 *data/EuroUnemployment.rda
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fa93433db4115af78edf00264634a412 *data/GvHD.rda
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2fbaa53e4f9b0aaa0d415e164883c364 *data/acidity.rda
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dce1aa8eed39b0a39cb74e3459ca5a87 *R/zzz.R
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243542f34ac9743086dbd93bbae49d91 *build/vignette.rds
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a31d931c438fbf3955ff74adfe38df1c *data/Baudry_etal_2010_JCGS_examples.rda
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de51cd86cc1795298d6a798209eb65b2 *data/EuroUnemployment.rda
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0ebe986448c4eb5974d226d55c6f89a0 *data/GvHD.rda
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586338f8f291cd917169b462689f35b6 *data/acidity.rda
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dcf0404be80a56cd040ed8cb8da1b428 *data/banknote.txt.gz
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6135e56ad3df989ce553eaf5924da9b6 *data/chevron.rda
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47be3bcb96ffb22598a9067ecd2701c8 *data/cross.rda
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cc529e91ffc45020a05da77f76e9f330 *data/diabetes.rda
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9e0bf66683dc263d24e018632136569c *data/thyroid.rda
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01367d78c80768963c8ac768ac65e656 *data/chevron.rda
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5be42c1e3959457b97735c2358f9c873 *data/cross.rda
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87c7d38efa0d818679b6807877c85248 *data/diabetes.rda
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9fad7b6a1368003198d262386a4bb43b *data/thyroid.rda
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b2a529e265721bcd2cb632d42ec6cc11 *data/wdbc.txt.gz
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2e2ce1f6b143173c17b6027c82cf7e7e *data/wreath.rda
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61037411e096ba5a4b1de00db01477ee *data/wreath.rda
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adc627c8c5c93d652e2b0109376135ac *inst/CITATION
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b2f3cb62dc61485b2a6fe6210ebd0d03 *inst/doc/mclust.R
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c7680542d4532804d5be21b3684986c7 *inst/doc/mclust.Rmd
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48dd50ef41d5d80914f3228dcead2180 *inst/doc/mclust.html
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8d39c22f943d32be50a07dcad31bb27c *man/Baudry_etal_2010_JCGS_examples.Rd
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f1f40bb4ceead4ea10ff6feb6589a526 *inst/doc/mclust.html
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7799d6093880e94a3bed07e6900f7249 *man/Baudry_etal_2010_JCGS_examples.Rd
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226b01ed5c258d406de2af5cfade2ba1 *man/BrierScore.Rd
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9260d85565aad1fa28e84cd2ca14d23d *man/EuroUnemployment.Rd
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0e1b6313cb53d6e937acd55cdded818e *man/GvHD.Rd
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1496c9e40a8326f4d922857a9beff127 *man/Mclust.Rd
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ed866eb3da5e973d336818b076d3685c *man/MclustBootstrap.Rd
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3f8136cfddb07b98461c1c505aea8fe8 *man/Mclust.Rd
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ea3ad560361c7274e200e317aca08df1 *man/MclustBootstrap.Rd
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6e1ae52a737008b85c9ed1c97a65479a *man/MclustDA.Rd
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a6a45328b7d5eaf35c375008599fe18f *man/MclustDR.Rd
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82520ec704397fa02b331969088e91be *man/MclustDRsubsel.Rd
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b2326163d0cd9784455a55a7c4044c8d *man/MclustDRsubsel.Rd
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b8d666ee6dceb9fbad3be67dea8e5c35 *man/MclustSSC.Rd
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38f7f5f909ac5e15e37b866ab979e842 *man/acidity.Rd
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6c924f5dc94fb10416016d5d027932e3 *man/adjustedRandIndex.Rd
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40bf7f2db2b9677c5b9bf7733da4aeac *man/banknote.Rd
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840501df1b562d5ba9df4d31c33672c6 *man/bic.Rd
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9458a1636740fbd8443308670f153d68 *man/cdens.Rd
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00ce09bbbe3b468350de0f9ee5bcbfae *man/cdensE.Rd
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3061f14dc3f95151d31f4183ff8f9aec *man/cdfMclust.Rd
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3844722391ea73731d1353ecbd726867 *man/cdfMclust.Rd
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b0cfe540c4eb91f99f49c2127562cd49 *man/chevron.Rd
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342f855aaedba7fe96aaf6a425a77519 *man/clPairs.Rd
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63d108fe436518c400eebb43430a5958 *man/classError.Rd
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9f86088d9d21126e7cc1ac28d4e5fa6a *man/decomp2sigma.Rd
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4175c1b284dbb6b7c5a5da13f32d037e *man/defaultPrior.Rd
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2676ec2d5d2b5b82dccc0cbd3c11bc13 *man/dens.Rd
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91ec0e3bb8bfa83dc7368653ebd203d6 *man/densityMclust.Rd
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266b5b8c466661154f0904ef3d615431 *man/densityMclust.diagnostic.Rd
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ec183d2e2f2cee6758cef7445c1f2d33 *man/diabetes.Rd
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2138f884fcb775643b142a66d3e5ed00 *man/densityMclust.Rd
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1ec97330567ea5ddec41ca6aff43bc90 *man/densityMclust.diagnostic.Rd
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ffa2e5d8d84e46efb64589a77dd134a2 *man/diabetes.Rd
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92ea0d6609afc260770a66d2ad957f04 *man/dmvnorm.Rd
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d8e808a964b5deb680007ae1ee1b5997 *man/dupPartition.Rd
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e0d38101ddddf5fd476ec651f2527af5 *man/em.Rd
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4e3901ea67e0c125d8e5ac4330df2e38 *man/mapClass.Rd
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f1edaf4e08180fa4edad91f6023b58c3 *man/mclust-deprecated.Rd
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c2686ac817e8bbb871363f12f143cc1d *man/mclust-internal.Rd
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0bc49dba3f3014ad2758c8ea1ff880d0 *man/mclust-package.Rd
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ecf47a755069d2b02eb63524defcaadf *man/mclust-package.Rd
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dbdb7e3336c3760b23504cb5361bb265 *man/mclust.options.Rd
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06f681ededb7169ee1e4dbe256233f4a *man/mclust1Dplot.Rd
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0d32e83fa5e8a19883b7189e597e2d08 *man/mclust2Dplot.Rd
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cfb781f6b9fe5bf1d2c8cdf49e1a8c04 *man/plot.MclustDR.Rd
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3fd0d79ca9549c14b822845673cad5c1 *man/plot.MclustSSC.Rd
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458f2b4c3940e714a015e03e553d364b *man/plot.clustCombi.Rd
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ba93777b3a9c268702cdad7a85bbc2f4 *man/plot.densityMclust.Rd
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78a23338f16271f0ae9bbb5b7a935c95 *man/plot.densityMclust.Rd
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339efd96c69d386b0e4a34094084a486 *man/plot.hc.Rd
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67acd4e69e1861bebf51d1f649e775a7 *man/plot.mclustBIC.Rd
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640264c2366087d920e4518af50281a7 *man/plot.mclustICL.Rd
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f640c08bd9098247a97a44f30c89a4cf *man/predict.Mclust.Rd
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79be9849129ebfbdf9c0a6cdfac3d328 *man/predict.MclustDA.Rd
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e84b696c5b8eff056814c3cba07bee9f *man/predict.MclustDR.Rd
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66b20d05c52e7dc62fcb4971f1b2287b *man/predict.MclustSSC.Rd
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bdb1679df5c649df2371270753107cca *man/predict.densityMclust.Rd
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691003fbb627468671f5a26fc0759d29 *man/predict.densityMclust.Rd
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8384a865dade63acabf451b136217287 *man/priorControl.Rd
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1a32c267ea6deb106ae19b6cec847115 *man/randProj.Rd
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d9477d3e3d801b783e42d10e37271e05 *man/randomOrthogonalMatrix.Rd
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e43a5fead9ef355e3d522ec94e224f66 *man/sigma2decomp.Rd
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1ed03f440760a36b9c66bf6c5259f1cb *man/sim.Rd
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77797cb68e57027ea316e1b068b9ed63 *man/simE.Rd
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63cfca85d2a01ff42db2c501780a8d8f *man/summary.Mclust.Rd
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2d9de49152e45c3362bdaf30a242d56c *man/summary.MclustBootstrap.Rd
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83eb12323099cbbf0f3e075d10f0be77 *man/summary.Mclust.Rd
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91268ae73944bd6d5845911c42142e58 *man/summary.MclustBootstrap.Rd
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4a8c675b46da86ca7075999c22e635f4 *man/summary.MclustDA.Rd
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103fff818063adef9f3262398342fb0a *man/summary.MclustDR.Rd
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fe2f8df16cc881677e427811d2d8a0d9 *man/summary.MclustSSC.Rd
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986ecadd060b62810d73fa37ec72dc19 *man/summary.mclustBIC.Rd
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35a02e5f739cbc7ced68ce7f232852cf *man/surfacePlot.Rd
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f4c0b79cc4812c5ebac9d75ea85239d1 *man/thyroid.Rd
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a584277d937587f96f984cbcf4d8a494 *man/thyroid.Rd
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b76a9e2d21683188dc8bce832e2ec9d1 *man/unmap.Rd
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be15964c22ee1ec25420a1352049519a *man/wdbc.Rd
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c48dce8f9817012850ce6d6ae81bd136 *man/wdbc.Rd
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c1b81d23059192faf2b0fdb40b0bc0d2 *man/wreath.Rd
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2a6f9e9e044a78154d3cfda5936d6f48 *src/Makevars
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d784799104d2c2350f9350b268761a2b *src/dmvnorm.f

‎NEWS.md

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# mclust 5.4.10
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- Updated banner on startup.
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- Updated info on man page for datasets `diabetes`, `wdbc`, and `thyroid`.
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- Std. error for cross-validation in `cvMclustDA()` uses formula for the weighted standard deviation with weights given by folds size.
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- Fix .Rd files.
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# mclust 5.4.9
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- Added `crimcoords()` to compute discriminant coordinates or crimcoords.

‎R/bootstrap.R

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max.nonfit = 10*nboot, verbose = interactive(), ...)
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{
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if(!any(class(object) %in% c("Mclust", "densityMclust")))
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stop("object must be of class 'Mclust' or 'densityMclust'")
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stopifnot("object must be of class 'Mclust' or 'densityMclust'" =
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inherits(object, c("Mclust", "densityMclust")))
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if(any(type %in% c("nonpara", "wlb")))
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{ type <- gsub("nonpara", "bs", type)

‎R/densityMclust.R

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# Loader C. (1999), Local Regression and Likelihood. New York, Springer,
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# pp. 87-90)
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if(!any(class(object) == "densityMclust"))
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{ stop("first argument must be an object of class 'densityMclust'") }
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stopifnot("first argument must be an object of class 'densityMclust'" =
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inherits(object, "densityMclust"))
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if(object$d > 1)
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{ warning("only available for one-dimensional data")
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return() }
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if(!any(class(object) == "densityMclust"))
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{ stop("first argument must be an object of class 'densityMclust'") }
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stopifnot("first argument must be an object of class 'densityMclust'" =
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inherits(object, "densityMclust"))
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if(missing(data))
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{ eval.points <- extendrange(object$data, f = 0.1)

‎R/graphics.R

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{ colors <- rep( "black", l)
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}
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if(is.null(cex)) cex <- rep(1, l)
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if(is.null(cex)) cex <- 1
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if(length(cex) == 1) cex <- rep(cex, l)
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cex <- cex[1:l]
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grid <- isTRUE(as.logical(grid))
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if(d > 2)

‎R/mclustda.R

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ce <- mean(class != pred$classification)
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tab <- try(table(class, pred$classification))
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if(class(tab) == "try-error")
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if(inherits(tab, "try-error"))
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setTxtProgressBar(pbar, i)
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}
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#
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cv <- apply(metric.cv, 2, function(m)
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sum(m*folds.size)/sum(folds.size))
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se <- apply(metric.cv, 2, function(m)
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sqrt(var(m)/nfold))
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cv <- sapply(1:2, function(m) sum(metric.cv[,m]*folds.size)/sum(folds.size))
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# se <- apply(metric.cv, 2, function(m) sqrt(var(m)/nfold))
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se <- sapply(1:2, function(m)
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sqrt( ( sum( (metric.cv[,m] - cv[m])^2 * folds.size) /
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(sum(folds.size)*(nfold-1)/nfold)) /
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nfold))
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#
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out <- list(classification = class.cv,
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‎R/mclustdr.R

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{
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# Dimension reduction for model-based clustering and classification
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call <- match.call()
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if(!any(class(object) %in% c("Mclust", "MclustDA")))
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stop("object must be of class 'Mclust' or 'MclustDA'")
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stopifnot("first argument must be an object of class 'Mclust' or 'MclustDA'" =
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inherits(object, c("Mclust", "MclustDA")))
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call <- match.call()
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x <- data.matrix(object$data)
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n <- nrow(x)
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p <- ncol(x)
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# of the search is printed; if 2 a detailed trace info is
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# is shown.
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757-
if(class(object) != "MclustDR")
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stop("Not a 'MclustDR' object")
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stopifnot("first argument must be an object of class 'MclustDR'" =
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inherits(object, "MclustDR"))
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hcUse <- mclust.options("hcUse")
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{
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if(class(object) != "MclustDR")
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stop("Not a 'MclustDR' object")
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stopifnot("first argument must be an object of class 'MclustDR'" =
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inherits(object, "MclustDR"))
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d <- object$numdir
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{
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# Single cycle of subset selection for GMMDRC directions based on bayes factors.
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if(class(object) != "MclustDR")
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stop("Not a 'MclustDR' object")
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stopifnot("first argument must be an object of class 'MclustDR'" =
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inherits(object, "MclustDR"))
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d <- object$numdir
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dir <- object$dir[,seq(d),drop=FALSE]
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# directions. This is useful if the directions are obtained from other
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# directions
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if(!any(class(object) == "MclustDR"))
1053-
stop("object must be of class 'mclustsir'")
1052+
stopifnot("first argument must be an object of class 'MclustDR'" =
1053+
inherits(object, "MclustDR"))
1054+
10541055
if(missing(data)) x <- object$x
10551056
else x <- as.matrix(data)
10561057
x <- scale(x, center=TRUE, scale=FALSE)

‎R/mclustssc.R

+2-2
Original file line numberDiff line numberDiff line change
@@ -53,8 +53,8 @@ MclustSSC <- function(data, class,
5353
c(args, list(modelName = modelNames[m]))),
5454
silent = TRUE)
5555
if(verbose)
56-
{ ipbar <- ipbar+1; setTxtProgressBar(pbar, ipbar) }
57-
if(class(mod) == "try-error") next()
56+
{ ipbar <- ipbar+1; setTxtProgressBar(pbar, ipbar) }
57+
if(inherits(mod, "try-error")) next()
5858
BIC[m] <- mod$bic
5959
if(!is.na(BIC[m]) && BIC[m] >= max(BIC, na.rm = TRUE))
6060
Model <- mod

‎R/options.R

+2-2
Original file line numberDiff line numberDiff line change
@@ -52,10 +52,10 @@ mclust.options <- function(...)
5252
if(length(args) == 0) return(current)
5353
n <- names(args)
5454
if (is.null(n)) stop("options must be given by name")
55-
# changed <- current[n]
55+
changed <- current[n]
5656
current[n] <- args
5757
assign(".mclust", current, envir = asNamespace("mclust"))
58-
# da provare
5958
# assignInNamespace(".mclust", current, ns = asNamespace("mclust"))
59+
# invisible(changed) # bettina suggestion...
6060
invisible(current)
6161
}

‎R/zzz.R

+14-5
Original file line numberDiff line numberDiff line change
@@ -7,12 +7,21 @@ mclustStartupMessage <- function()
77
{
88
# Startup message obtained as
99
# > figlet -f slant MCLUST
10+
# msg <- c(paste0(
11+
# " __ ___________ __ _____________
12+
# / |/ / ____/ / / / / / ___/_ __/
13+
# / /|_/ / / / / / / / /\\__ \\ / /
14+
# / / / / /___/ /___/ /_/ /___/ // /
15+
# /_/ /_/\\____/_____/\\____//____//_/ version ",
16+
#
17+
# Startup message obtained as
18+
# > figlet -f slant mclust
1019
msg <- c(paste0(
11-
" __ ___________ __ _____________
12-
/ |/ / ____/ / / / / / ___/_ __/
13-
/ /|_/ / / / / / / / /\\__ \\ / /
14-
/ / / / /___/ /___/ /_/ /___/ // /
15-
/_/ /_/\\____/_____/\\____//____//_/ version ",
20+
" __ __
21+
____ ___ _____/ /_ _______/ /_
22+
/ __ `__ \\/ ___/ / / / / ___/ __/
23+
/ / / / / / /__/ / /_/ (__ ) /_
24+
/_/ /_/ /_/\\___/_/\\__,_/____/\\__/ version ",
1625
packageVersion("mclust")),
1726
"\nType 'citation(\"mclust\")' for citing this R package in publications.")
1827
return(msg)

‎build/vignette.rds

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‎data/Baudry_etal_2010_JCGS_examples.rda

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‎data/EuroUnemployment.rda

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‎data/GvHD.rda

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‎data/acidity.rda

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‎data/chevron.rda

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‎data/cross.rda

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‎data/diabetes.rda

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‎data/thyroid.rda

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‎data/wreath.rda

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‎inst/doc/mclust.html

+89-52
Large diffs are not rendered by default.

‎man/Baudry_etal_2010_JCGS_examples.Rd

+1-1
Original file line numberDiff line numberDiff line change
@@ -43,7 +43,7 @@ ex4.4.2 has been simulated from a mixture of two uniform distributions in R^3.
4343

4444
}
4545
\references{
46-
J.-P. Baudry, A. E. Raftery, G. Celeux, K. Lo and R. Gottardo (2010). Combining mixture components for clustering. \emph{Journal of Computational and Graphical Statistics, 19(2):332-353.}
46+
J.-P. Baudry, A. E. Raftery, G. Celeux, K. Lo and R. Gottardo (2010). Combining mixture components for clustering. \emph{Journal of Computational and Graphical Statistics}, 19(2):332-353.
4747
}
4848
\examples{
4949
\donttest{

‎man/Mclust.Rd

+5-4
Original file line numberDiff line numberDiff line change
@@ -73,10 +73,11 @@ Mclust(data, G = NULL, modelNames = NULL,
7373
\item{\code{subset}}{
7474
A logical or numeric vector specifying a subset of the data
7575
to be used in the initial hierarchical clustering phase.
76-
By default no subset is used unless the number of observations exceeds
77-
the value specified by \code{mclust.options("subset")}. Note that to
78-
guarantee exact reproducibility of results a seed must be specified
79-
(see \code{\link{set.seed}}).}
76+
No subset is used unless the number of observations exceeds
77+
the value specified by \code{mclust.options("subset")}, which by
78+
default is set to 2000 (see \code{\link{mclust.options}}).
79+
Note that in this case to guarantee exact reproducibility of results
80+
a seed must be specified (see \code{\link{set.seed}}).}
8081
\item{\code{noise}}{
8182
A logical or numeric vector indicating an initial guess as to
8283
which observations are noise in the data. If numeric the entries

‎man/MclustBootstrap.Rd

+1-1
Original file line numberDiff line numberDiff line change
@@ -79,7 +79,7 @@ summary(bootClust, what = "se")
7979
summary(bootClust, what = "ci")
8080
8181
data(acidity)
82-
modDens <- densityMclust(acidity)
82+
modDens <- densityMclust(acidity, plot = FALSE)
8383
modDens <- MclustBootstrap(modDens)
8484
summary(modDens, what = "se")
8585
summary(modDens, what = "ci")

‎man/MclustDRsubsel.Rd

+9-9
Original file line numberDiff line numberDiff line change
@@ -29,19 +29,19 @@ MclustDRsubsel(object, G = 1:9,
2929
\item{\dots}{Further arguments passed through \code{\link{Mclust}} or \code{\link{MclustDA}}.}
3030
\item{bic.stop}{A criterion to terminate the search. If maximal BIC difference is less than \code{bic.stop} then the algorithm stops. \cr
3131
Two tipical values are:
32-
\describe{
33-
\item{}{\code{0}: algorithm stops when the BIC difference becomes negative (default)}
34-
\item{}{\code{-Inf}: algorithm continues until all directions have been selected}
35-
}}
32+
\tabular{ll}{
33+
\code{0}: \tab algorithm stops when the BIC difference becomes negative (default);\cr
34+
\code{-Inf}: \tab algorithm continues until all directions have been selected.
35+
}
36+
}
3637
\item{bic.cutoff}{A value specifying how to select simplest ``best'' model within \code{bic.cutoff} from the maximum value achieved. Setting this to \code{0} (default) simply select the model with the largest BIC difference.}
3738
\item{mindir}{An integer value specifying the minimum number of directions to be estimated.}
3839
\item{verbose}{A logical or integer value specifying if and how much detailed information should be reported during the iterations of the algorithm. \cr
3940
Possible values are:
40-
\describe{
41-
\item{}{\code{0} or \code{FALSE}: no trace info is shown;}
42-
\item{}{\code{1} or \code{TRUE}: a trace info is shown at each step of the search;}
43-
\item{}{\code{2}: a more detailed trace info is is shown.}
44-
}
41+
\tabular{ll}{
42+
\code{0} or \code{FALSE}: \tab no trace info is shown;\cr
43+
\code{1} or \code{TRUE}: \tab a trace info is shown at each step of the search;\cr
44+
\code{2}: \tab a more detailed trace info is is shown.}
4545
}
4646
}
4747
\details{

‎man/cdfMclust.Rd

+3-3
Original file line numberDiff line numberDiff line change
@@ -42,8 +42,9 @@ The quantiles are computed using bisection linear search algorithm.
4242
}
4343

4444
\examples{
45+
\donttest{
4546
x <- c(rnorm(100), rnorm(100, 3, 2))
46-
dens <- densityMclust(x)
47+
dens <- densityMclust(x, plot = FALSE)
4748
summary(dens, parameters = TRUE)
4849
cdf <- cdfMclust(dens)
4950
str(cdf)
@@ -54,15 +55,14 @@ points(q, cdfMclust(dens, q)$y, pch = 20, col = "red3")
5455

5556
par(mfrow = c(2,2))
5657
dens.waiting <- densityMclust(faithful$waiting)
57-
plot(dens.waiting)
5858
plot(cdfMclust(dens.waiting), type = "l",
5959
xlab = dens.waiting$varname, ylab = "CDF")
6060
dens.eruptions <- densityMclust(faithful$eruptions)
61-
plot(dens.eruptions)
6261
plot(cdfMclust(dens.eruptions), type = "l",
6362
xlab = dens.eruptions$varname, ylab = "CDF")
6463
par(mfrow = c(1,1))
6564
}
65+
}
6666

6767
\keyword{cluster}
6868
\keyword{dplot}

‎man/densityMclust.Rd

+1-1
Original file line numberDiff line numberDiff line change
@@ -67,7 +67,7 @@ summary(dens, parameters = TRUE)
6767
plot(dens, what = "BIC", legendArgs = list(x = "topright"))
6868
plot(dens, what = "density", data = faithful$waiting)
6969

70-
dens <- densityMclust(faithful, modelNames = "EEE", G = 3)
70+
dens <- densityMclust(faithful, modelNames = "EEE", G = 3, plot = FALSE)
7171
summary(dens)
7272
summary(dens, parameters = TRUE)
7373
plot(dens, what = "density", data = faithful,

‎man/densityMclust.diagnostic.Rd

+1-1
Original file line numberDiff line numberDiff line change
@@ -64,7 +64,7 @@ The two diagnostic plots for density estimation in the one-dimensional case are
6464
\examples{
6565
\donttest{
6666
x <- faithful$waiting
67-
dens <- densityMclust(x)
67+
dens <- densityMclust(x, plot = FALSE)
6868
plot(dens, x, what = "diagnostic")
6969
# or
7070
densityMclust.diagnostic(dens, type = "cdf")

‎man/diabetes.Rd

+2-2
Original file line numberDiff line numberDiff line change
@@ -2,7 +2,7 @@
22
\alias{diabetes}
33
\docType{data}
44

5-
\title{Diabetes data}
5+
\title{Diabetes Data (flawed)}
66

77
\description{The data set contains three measurements made on 145 non-obese adult patients classified into three groups.}
88

@@ -17,7 +17,7 @@
1717
}
1818
}
1919

20-
\details{This dataset is \emph{not correct} and it is provided here only for backward compatibility. Please refer to the correct version available in package \pkg{rrcov}.}
20+
\details{This dataset is \emph{flawed} (compare with the reference) and it is provided here only for backward compatibility. A 5-variable version of the Reaven and Miller data is available in package \pkg{rrcov}. The \emph{glucose} and \emph{sspg} columns in this datsset are identical to the \emph{fpg} and \emph{insulin} columns, respectively in the \pkg{rrcov} version. The \emph{insulin} column in this dataset differs from the \emph{glucose} column in the \pkg{rrcov} version in one entry: observation 104 has the value 45 in the \emph{insulin} column in this data, and 455 in the corresponding \emph{glucose} column of the \pkg{rrcov} version.}
2121

2222
\source{Reaven, G. M. and Miller, R. G. (1979). An attempt to define the nature of chemical diabetes using a multidimensional analysis. \emph{Diabetologia} 16:17-24.}
2323

‎man/mclust-package.Rd

+2-3
Original file line numberDiff line numberDiff line change
@@ -6,8 +6,6 @@
66
\title{Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation}
77

88
\description{
9-
\if{html}{\figure{logo.png}{options: align="right" alt="logo" width="120"}}
10-
119
Gaussian finite mixture models estimated via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization and dimension reduction.
1210
}
1311

@@ -38,6 +36,7 @@ Fraley C., Raftery A. E., Murphy T. B. and Scrucca L. (2012) mclust Version 4 fo
3836
}
3937

4038
\examples{
39+
\donttest{
4140
# Clustering
4241
mod1 <- Mclust(iris[,1:4])
4342
summary(mod1)
@@ -52,7 +51,7 @@ plot(mod2)
5251
# Density estimation
5352
mod3 <- densityMclust(faithful$waiting)
5453
summary(mod3)
55-
plot(mod3, faithful$waiting)
54+
}
5655
}
5756

5857
\keyword{package}

‎man/plot.densityMclust.Rd

+3-3
Original file line numberDiff line numberDiff line change
@@ -104,13 +104,13 @@ plotDensityMclustd(x, data = NULL, nlevels = 11, levels = NULL,
104104

105105
\examples{
106106
\donttest{
107-
dens <- densityMclust(faithful$waiting)
107+
dens <- densityMclust(faithful$waiting, plot = FALSE)
108108
summary(dens)
109109
summary(dens, parameters = TRUE)
110110
plot(dens, what = "BIC", legendArgs = list(x = "topright"))
111111
plot(dens, what = "density", data = faithful$waiting)
112112

113-
dens <- densityMclust(faithful)
113+
dens <- densityMclust(faithful, plot = FALSE)
114114
summary(dens)
115115
summary(dens, parameters = TRUE)
116116
plot(dens, what = "density", data = faithful,
@@ -120,7 +120,7 @@ plot(dens, what = "density", type = "hdr", prob = seq(0.1, 0.9, by = 0.1))
120120
plot(dens, what = "density", type = "hdr", data = faithful)
121121
plot(dens, what = "density", type = "persp")
122122

123-
dens <- densityMclust(iris[,1:4])
123+
dens <- densityMclust(iris[,1:4], plot = FALSE)
124124
summary(dens, parameters = TRUE)
125125
plot(dens, what = "density", data = iris[,1:4],
126126
col = "slategrey", drawlabels = FALSE, nlevels = 7)

‎man/predict.densityMclust.Rd

+2-2
Original file line numberDiff line numberDiff line change
@@ -37,10 +37,10 @@ Returns a vector or a matrix of densities evaluated at \code{newdata} depending
3737
\examples{
3838
\donttest{
3939
x <- faithful$waiting
40-
dens <- densityMclust(x)
40+
dens <- densityMclust(x, plot = FALSE)
4141
x0 <- seq(50, 100, by = 10)
4242
d0 <- predict(dens, x0)
43-
plot(dens)
43+
plot(dens, what = "density")
4444
points(x0, d0, pch = 20)
4545
}
4646
}

‎man/summary.Mclust.Rd

+3-1
Original file line numberDiff line numberDiff line change
@@ -36,13 +36,15 @@
3636
\seealso{\code{\link{Mclust}}, \code{\link{densityMclust}}.}
3737

3838
\examples{
39+
\donttest{
3940
mod1 = Mclust(iris[,1:4])
4041
summary(mod1)
4142
summary(mod1, parameters = TRUE, classification = FALSE)
4243

43-
mod2 = densityMclust(faithful)
44+
mod2 = densityMclust(faithful, plot = FALSE)
4445
summary(mod2)
4546
summary(mod2, parameters = TRUE)
4647
}
48+
}
4749

4850
\keyword{cluster}

‎man/summary.MclustBootstrap.Rd

+1-1
Original file line numberDiff line numberDiff line change
@@ -33,7 +33,7 @@ summary(bootClust, what = "se")
3333
summary(bootClust, what = "ci")
3434

3535
data(acidity)
36-
modDens = densityMclust(acidity)
36+
modDens = densityMclust(acidity, plot = FALSE)
3737
modDens = MclustBootstrap(modDens)
3838
summary(modDens, what = "se")
3939
summary(modDens, what = "ci")

‎man/thyroid.Rd

+2-2
Original file line numberDiff line numberDiff line change
@@ -2,7 +2,7 @@
22
\alias{thyroid}
33
\docType{data}
44

5-
\title{Thyroid gland data}
5+
\title{UCI Thyroid Gland Data}
66

77
\description{
88
Data on five laboratory tests administered to a sample of 215 patients. The tests are used to predict whether a patient's thyroid can be classified as euthyroidism (normal thyroid gland function), hypothyroidism (underactive thyroid not producing enough thyroid hormone) or hyperthyroidism (overactive thyroid producing and secreting excessive amounts of the free thyroid hormones T3 and/or thyroxine T4). Diagnosis of thyroid operation was based on a complete medical record, including anamnesis, scan, etc.}
@@ -22,7 +22,7 @@ Data on five laboratory tests administered to a sample of 215 patients. The test
2222
2323
}
2424
25-
\source{Thyroid Disease Data Set (\code{new-thyroid.data}, \code{new-thyroid.names}) is available at UCI Machine Learning Repository
25+
\source{One of several databases in the Thyroid Disease Data Set (\code{new-thyroid.data}, \code{new-thyroid.names}) of the UCI Machine Learning Repository
2626
\url{https://archive.ics.uci.edu/ml/datasets/thyroid+disease}. Please note the UCI conditions of use.}
2727
2828
\references{

‎man/wdbc.Rd

+2-2
Original file line numberDiff line numberDiff line change
@@ -2,7 +2,7 @@
22
\alias{wdbc}
33
\docType{data}
44

5-
\title{Wisconsin diagnostic breast cancer (WDBC) data}
5+
\title{UCI Wisconsin Diagnostic Breast Cancer Data}
66

77
\description{
88
The data set provides data for 569 patients on 30 features of the cell nuclei obtained from a digitized image of a fine needle aspirate (FNA) of a breast mass. For each patient the cancer was diagnosed as malignant or benign.}
@@ -63,7 +63,7 @@ The recorded features are:
6363
For each feature the recorded values are computed from each image as \code{<feature_name>_mean}, \code{<feature_name>_se}, and \code{<feature_name>_extreme}, for the mean, the standard error, and the mean of the three largest values.
6464
}
6565

66-
\source{Breast Cancer Wisconsin (Diagnostic) Data Set (\code{wdbc.data}, \code{wdbc.names}) is available at UCI Machine Learning Repository
66+
\source{The Breast Cancer Wisconsin (Diagnostic) Data Set (\code{wdbc.data}, \code{wdbc.names}) from the UCI Machine Learning Repository
6767
\url{https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)}. Please note the UCI conditions of use.}
6868

6969
\references{

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