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 (2018) <arXiv:1711.05632>. 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.

Version: 1.2.2
Depends: R (≥ 3.3.0)
Imports: lattice, matrixStats, mclust (≥ 5.1), mvnfast, nnet, vcd
Suggests: cluster, clustMD, geometry, knitr, rmarkdown, snow
Published: 2019-05-15
Author: Keefe Murphy [aut, cre], Thomas Brendan Murphy [ctb]
Maintainer: Keefe Murphy <keefe.murphy at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
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_1.2.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: MoEClust_1.2.2.tgz, r-oldrel: MoEClust_1.2.2.tgz
Old sources: MoEClust archive


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