PUGMM: Parsimonious Ultrametric Gaussian Mixture Models (original) (raw)
Finite Gaussian mixture models with parsimonious extended ultrametric covariance structures estimated via a grouped coordinate ascent algorithm, which is equivalent to the Expectation-Maximization algorithm. The thirteen ultrametric covariance structures implemented allow for the inspection of different hierarchical relationships among variables. The estimation of an ultrametric correlation matrix is included as a function. The methodologies are described in Cavicchia, Vichi, Zaccaria (2024) <doi:10.1007/s11222-024-10405-9>, Cavicchia, Vichi, Zaccaria (2022) <doi:10.1007/s11634-021-00488-x> and Cavicchia, Vichi, Zaccaria (2020) <doi:10.1007/s11634-020-00400-z>.
Version: | 0.1.0 |
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Depends: | R (≥ 4.0) |
Imports: | ClusterR, doParallel, foreach, igraph, MASS, Matrix, mclust, mcompanion, ppclust |
Published: | 2024-05-10 |
DOI: | 10.32614/CRAN.package.PUGMM |
Author: | Giorgia Zaccaria [aut, cre], Carlo Cavicchia [aut], Lorenzo Balzotti [aut] |
Maintainer: | Giorgia Zaccaria <giorgia.zaccaria at unimib.it> |
BugReports: | https://github.com/giorgiazaccaria/PUGMM/issues |
License: | MIT + file |
URL: | https://github.com/giorgiazaccaria/PUGMM |
NeedsCompilation: | no |
CRAN checks: | PUGMM results |
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