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MoEClust: Gaussian Parsimonious Clustering Models with Covariates and aNoise Component

Clustering via parsimonious Gaussian Mixtures of Experts using the MoEClust models introduced by Murphy and Murphy (2020) <doi:10.1007/s11634-019-00373-8>. This package fits finite Gaussian mixture models with a formula interface for supplying gating and/or expert network covariates using a range of parsimonious covariance parameterisations from the GPCM family via the EM/CEM algorithm. Visualisation of the results of such models using generalised pairs plots and the inclusion of an additional noise component is also facilitated. A greedy forward stepwise search algorithm is provided for identifying the optimal model in terms of the number of components, the GPCM covariance parameterisation, and the subsets of gating/expert network covariates.

Version:1.6.0
Depends:R (≥ 4.0.0)
Imports:lattice (≥ 0.12),matrixStats (≥ 1.0.0),mclust (≥ 6.1),mvnfast,nnet (≥ 7.3-0),vcd
Suggests:cluster (≥ 1.4.0),clustMD (≥ 1.2.1),geometry (≥ 0.4.0),knitr,rmarkdown,snow
Published:2025-03-05
DOI:10.32614/CRAN.package.MoEClust
Author:Keefe MurphyORCID iD [aut, cre], Thomas Brendan MurphyORCID iD [ctb]
Maintainer:Keefe Murphy <keefe.murphy at mu.ie>
BugReports:https://github.com/Keefe-Murphy/MoEClust/issues
License:GPL (≥ 3)
URL:https://cran.r-project.org/package=MoEClust
NeedsCompilation:no
Citation:MoEClust citation info
Materials:README,NEWS
In views:Cluster
CRAN checks:MoEClust results

Documentation:

Reference manual:MoEClust.html ,MoEClust.pdf
Vignettes:MoEClust (source,R code)

Downloads:

Package source: MoEClust_1.6.0.tar.gz
Windows binaries: r-devel:MoEClust_1.6.0.zip, r-release:MoEClust_1.6.0.zip, r-oldrel:MoEClust_1.6.0.zip
macOS binaries: r-release (arm64):MoEClust_1.6.0.tgz, r-oldrel (arm64):MoEClust_1.6.0.tgz, r-release (x86_64):MoEClust_1.6.0.tgz, r-oldrel (x86_64):MoEClust_1.6.0.tgz
Old sources: MoEClust archive

Reverse dependencies:

Reverse imports:clustTMB

Linking:

Please use the canonical formhttps://CRAN.R-project.org/package=MoEClustto link to this page.


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