MoEClust (1.3.1)

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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) . 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.

Maintainer: Keefe Murphy
Author(s): Keefe Murphy [aut, cre] (<>), Thomas Brendan Murphy [ctb] (<>)

License: GPL (>= 2)

Uses: lattice, matrixStats, mclust, mvnfast, nnet, vcd, cluster, geometry, snow, knitr, clustMD, rmarkdown

Released 18 days ago.

8 previous versions



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