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:1711.05632v2>. 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: 1.2.3
Depends: R (≥ 3.3.0)
Imports: lattice, matrixStats, mclust (≥ 5.1), mvnfast, nnet, vcd
Suggests: cluster, clustMD, geometry, knitr, parallel, rmarkdown
Published: 2019-07-31
Author: Keefe Murphy [aut, cre], Thomas Brendan Murphy [ctb]
Maintainer: Keefe Murphy <keefe.murphy at ucd.ie>
BugReports: https://github.com/Keefe-Murphy/MoEClust
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://cran.r-project.org/package=MoEClust
NeedsCompilation: no
Citation: MoEClust citation info
Materials: README NEWS
In views: Cluster
CRAN checks: MoEClust results

Downloads:

Reference manual: MoEClust.pdf
Vignettes: MoEClust
Package source: MoEClust_1.2.3.tar.gz
Windows binaries: r-devel: MoEClust_1.2.3.zip, r-release: MoEClust_1.2.3.zip, r-oldrel: MoEClust_1.2.3.zip
OS X binaries: r-release: MoEClust_1.2.3.tgz, r-oldrel: MoEClust_1.2.3.tgz
Old sources: MoEClust archive

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