MoEClust: Gaussian Parsimonious Clustering Models with Covariates and a Noise 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:	1.5.2
Depends:	R (≥ 4.0.0)
Imports:	lattice (≥ 0.12), matrixStats (≥ 1.0.0), mclust (≥ 5.4), mvnfast, nnet (≥ 7.3-0), vcd
Suggests:	cluster (≥ 1.4.0), clustMD (≥ 1.2.1), geometry (≥ 0.4.0), knitr, rmarkdown, snow
Published:	2023-12-11
Author:	Keefe Murphy [aut, cre], Thomas Brendan Murphy [ctb]
Maintainer:	Keefe Murphy <keefe.murphy at mu.ie>
BugReports:	https://github.com/Keefe-Murphy/MoEClust
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.pdf
Vignettes:	MoEClust

Downloads:

Package source:	MoEClust_1.5.2.tar.gz
Windows binaries:	r-devel: MoEClust_1.5.2.zip, r-release: MoEClust_1.5.2.zip, r-oldrel: MoEClust_1.5.2.zip
macOS binaries:	r-release (arm64): MoEClust_1.5.2.tgz, r-oldrel (arm64): MoEClust_1.5.2.tgz, r-release (x86_64): MoEClust_1.5.2.tgz, r-oldrel (x86_64): MoEClust_1.5.2.tgz
Old sources:	MoEClust archive

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