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: 1.6.0 Depends: R (≥ 4.0.0) Imports: lattice (≥ 0.12), matrixStats (≥ 1.0.0), mclust (≥ 6.1), mvnfast, nnet (≥ 7.3-0), vcd Suggests: cluster (≥ 1.4.0), clustMD (≥ 1.2.1), geometry (≥ 0.4.0), knitr, rmarkdown, snow Published: 2025-03-05 DOI: 10.32614/CRAN.package.MoEClust Author: Keefe Murphy [aut, cre], Thomas Brendan Murphy [ctb] Maintainer: Keefe Murphy <keefe.murphy at mu.ie> BugReports: https://github.com/Keefe-Murphy/MoEClust/issues 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: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=MoEClust to link to this page.
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