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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.
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Package details |
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| Author | Keefe Murphy [aut, cre] (<https://orcid.org/0000-0002-7709-3159>), Thomas Brendan Murphy [ctb] (<https://orcid.org/0000-0002-5668-7046>) |
| Maintainer | Keefe Murphy <keefe.murphy@mu.ie> |
| License | GPL (>= 3) |
| Version | 1.5.2 |
| URL | https://cran.r-project.org/package=MoEClust |
| Package repository | View on CRAN |
| Installation |
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