DESCRIPTION

Package: MoEClust
Type: Package
Date: 2023-12-10
Title: Gaussian Parsimonious Clustering Models with Covariates and a Noise Component
Version: 1.5.2
Authors@R: c(person("Keefe", "Murphy", email = "keefe.murphy@mu.ie", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-7709-3159")),
           person("Thomas Brendan", "Murphy", email = "brendan.murphy@ucd.ie", role = "ctb", comment = c(ORCID = "0000-0002-5668-7046")))
Description: 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.
Depends: R (>= 4.0.0)
License: GPL (>= 3)
Encoding: UTF-8
URL: https://cran.r-project.org/package=MoEClust
BugReports: https://github.com/Keefe-Murphy/MoEClust
LazyData: true
Imports:
    lattice (>= 0.12),
    matrixStats (>= 1.0.0),
    mclust (>= 5.4),
    mvnfast,
    nnet (>= 7.3-0),
    vcd
Suggests:
    cluster (>= 1.4.0),
    clustMD (>= 1.2.1),
    geometry (>= 0.4.0),
    knitr,
    rmarkdown,
    snow
RoxygenNote: 7.2.3
VignetteBuilder: knitr