Package: MoEClust 1.5.2

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 (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.

Authors:Keefe Murphy [aut, cre], Thomas Brendan Murphy [ctb]

MoEClust_1.5.2.tar.gz
MoEClust_1.5.2.zip(r-4.5)MoEClust_1.5.2.zip(r-4.4)MoEClust_1.5.2.zip(r-4.3)
MoEClust_1.5.2.tgz(r-4.4-any)MoEClust_1.5.2.tgz(r-4.3-any)
MoEClust_1.5.2.tar.gz(r-4.5-noble)MoEClust_1.5.2.tar.gz(r-4.4-noble)
MoEClust_1.5.2.tgz(r-4.4-emscripten)MoEClust_1.5.2.tgz(r-4.3-emscripten)
MoEClust.pdf |MoEClust.html✨
MoEClust/json (API)
NEWS

Peer review:

Bug tracker:https://github.com/keefe-murphy/moeclust/issues

On CRAN:

gaussian-mixture-modelsmixture-of-expertsmodel-based-clustering

26 exports 7 stars 1.35 score 13 dependencies 44 scripts 462 downloads

Last updated 9 months agofrom:901d7a2b83. Checks:OK: 7. Indexed: yes.

Exports:aitkendrop_constantsdrop_levelsexpert_covarFARIforce_posiDiagMoE_AvePPMoE_clustMoE_compareMoE_controlMoE_critMoE_cstepMoE_densMoE_entropyMoE_estepMoE_gpairsMoE_mahalaMoE_newsMoE_plotCritMoE_plotGateMoE_plotLogLikMoE_SimilarityMoE_stepwiseMoE_Uncertaintynoise_volquant_clust

Dependencies:BHcolorspacelatticelmtestMASSmatrixStatsmclustmvnfastnnetRcppRcppArmadillovcdzoo