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.
| Version: |
1.5.2 |
| Depends: |
R (≥ 4.0.0) |
| 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 |
| Published: |
2023-12-11 |
| 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 |
| 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 |
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