Gaussian Parsimonious Clustering Models with Covariates and a Noise Component R package MoEClust version 1.6.0 CRAN Task View: Cluster (https://CRAN.R-project.org/view=Cluster) 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. Software 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 Keefe Murphy <keefe.murphy@mu.ie> Comprehensive R Archive Network (CRAN) Keefe Murphy [aut, cre] (ORCID: <https://orcid.org/0000-0002-7709-3159>), Thomas Brendan Murphy [ctb] (ORCID: <https://orcid.org/0000-0002-5668-7046>) GPL (>= 3) 2025-03-05 application/tgz https://CRAN.R-project.org/package=MoEClust doi:10.32614/CRAN.package.MoEClust