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 (2019) <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:	1.3.1
Depends:	R (≥ 4.0.0)
Imports:	lattice, matrixStats, mclust (≥ 5.1), mvnfast, nnet, vcd
Suggests:	cluster, clustMD, geometry, knitr, rmarkdown, snow
Published:	2020-05-12
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:

Linking:

Please use the canonical form https://CRAN.R-project.org/package=MoEClust to link to this page.