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mclust: Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation

Gaussian finite mixture models fitted via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization, dimension reduction for visualisation, and resampling-based inference.

Version: 6.1.1
Depends: R (≥ 3.0)
Imports: stats, utils, graphics, grDevices
Suggests: knitr (≥ 1.4), rmarkdown (≥ 2.10), mix (≥ 1.0), geometry (≥ 0.4), MASS
Published: 2024-04-29
DOI: 10.32614/CRAN.package.mclust
Author: Chris Fraley [aut], Adrian E. Raftery ORCID iD [aut], Luca Scrucca ORCID iD [aut, cre], Thomas Brendan Murphy ORCID iD [ctb], Michael Fop ORCID iD [ctb]
Maintainer: Luca Scrucca <luca.scrucca at unipg.it>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://mclust-org.github.io/mclust/
NeedsCompilation: yes
Citation: mclust citation info
Materials: NEWS
In views: Cluster, Distributions, Environmetrics
CRAN checks: mclust results

Documentation:

Reference manual: mclust.pdf
Vignettes: A quick tour of mclust

Downloads:

Package source: mclust_6.1.1.tar.gz
Windows binaries: r-devel: mclust_6.1.1.zip, r-release: mclust_6.1.1.zip, r-oldrel: mclust_6.1.1.zip
macOS binaries: r-release (arm64): mclust_6.1.1.tgz, r-oldrel (arm64): mclust_6.1.1.tgz, r-release (x86_64): mclust_6.1.1.tgz, r-oldrel (x86_64): mclust_6.1.1.tgz
Old sources: mclust archive

Reverse dependencies:

Reverse depends: baggingbwsel, clustvarsel, CNprep, cqn, EBcoexpress, FEAST, FitUltD, GenProSeq, HDoutliers, HyperG, IntNMF, iPath, LifeTables, LPKsample, manet, MBCbook, mclustAddons, MetabolAnalyze, MineICA, msos, netresponse, prabclus, probout, puma, robustDA, SC.MEB, SpeCond, SQN, VAExprs
Reverse imports: airpart, AneuFinder, AnimalSequences, autocogs, Banksy, bayesCureRateModel, BayesCVI, BayesSpace, BCClong, Bchron, beadplexr, BimodalIndex, BiSEp, bootcluster, bpgmm, cemco, chemometrics, CHMM, CICA, clap, ClassDiscovery, clickb, clustAnalytics, Cluster.OBeu, clusterHD, clusterMI, clustMD, clustTMB, cogena, cola, ContaminatedMixt, CrossClustering, cytometree, dCUR, deepgmm, diceR, DIscBIO, do3PCA, doseR, doubletrouble, DR.SC, drcte, dsb, DuoClustering2018, em, EMMIXgene, evaluomeR, evclust, evprof, expSBM, fabMix, FCPS, fdm2id, FishResp, flexCWM, FourWayHMM, fpc, geocausal, GloScope, GridOnClusters, hdbayes, HDCD, HGC, HMMmlselect, iClusterVB, ICSClust, IMIFA, IMIX, immunaut, InTAD, integIRTy, JANE, ks, KScorrect, linkspotter, Linnorm, LMest, lnmCluster, LogConcDEAD, LOMAR, LUCIDus, Luminescence, MAINT.Data, maSigPro, MatrixHMM, Melissa, mem, MesKit, metasnf, methylKit, minfi, miRSM, MixtureMissing, MoEClust, mombf, MSclassifR, MSclust, msir, mtlgmm, multiClust, nethet, norMmix, npde, oclust, odseq, oncomix, opGMMassessment, otrimle, PINSPlus, pivmet, PoDCall, PPbigdata, ppgmmga, PRECAST, PredPsych, ProFAST, pRoloc, PUGMM, PureCN, rCGH, RclusTool, RGMM, RJcluster, robCompositions, rties, saemix, SAGMM, sasfunclust, sBIC, scDD, scDDboost, scDesign3, SCORPIUS, scPipe, ScRNAIMM, scutr, SenTinMixt, sharp, shinyWGD, SIBERG, sigQC, smartid, sovereign, spaceNet, splinetree, theftdlc, tidyLPA, TipDatingBeast, TSCAN, tsrobprep, UniversalCVI, UpDown, VBLPCM, vimpclust, vscc, WACS, wavClusteR, wevid, XINA
Reverse suggests: AdaptGauss, andrews, arrayQuality, bayestestR, broom, ccImpute, CerioliOutlierDetection, ChemoSpec, CiteFuse, clusternomics, clValid, coca, condvis2, dcanr, Evacluster, factoextra, HDCytoData, HSAUR, HSAUR2, HSAUR3, insight, klic, KSD, latrend, maftools, MineICA, mixedLSR, mlr3cluster, motifcluster, mulgar, MultIS, MVA, NewWave, optimCheck, OTclust, parameters, performance, QuadratiK, qVarSel, RankAggreg, RCSL, RCTS, REdaS, rexposome, RnBeads, robustfa, SC3, scDHA, scISR, scone, scReClassify, see, shipunov, SillyPutty, simplifyEnrichment, slingshot, starvz, StatDA, tclust, telescope, tidySEM, Umpire, varclust
Reverse enhances: clue, MixSim

Linking:

Please use the canonical form https://CRAN.R-project.org/package=mclust to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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