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
DOI: 10.32614/CRAN.package.MoEClust
Author: Keefe Murphy ORCID iD [aut, cre], Thomas Brendan Murphy ORCID iD [ctb]
Maintainer: Keefe Murphy <keefe.murphy at>
License: GPL (≥ 3)
NeedsCompilation: no
Citation: MoEClust citation info
Materials: README NEWS
In views: Cluster
CRAN checks: MoEClust results


Reference manual: MoEClust.pdf
Vignettes: MoEClust


Package source: MoEClust_1.5.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
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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