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PUGMM: Parsimonious Ultrametric Gaussian Mixture Models

Parsimonious Ultrametric Gaussian Mixture Models via grouped coordinate ascent (equivalent to EM) algorithm characterized by the inspection of hierarchical relationships among variables via parsimonious extended ultrametric covariance structures. The methodologies are described in Cavicchia, Vichi, Zaccaria (2024) <doi:10.1007/s11222-024-10405-9>, (2022) <doi:10.1007/s11634-021-00488-x> and (2020) <doi:10.1007/s11634-020-00400-z>.

Version: 0.1.1
Depends: R (≥ 4.0)
Imports: ClusterR, doParallel, foreach, igraph, MASS, Matrix, mclust, mcompanion, ppclust
Published: 2025-06-23
DOI: 10.32614/CRAN.package.PUGMM
Author: Giorgia Zaccaria ORCID iD [aut, cre], Carlo Cavicchia ORCID iD [aut], Lorenzo Balzotti ORCID iD [aut]
Maintainer: Giorgia Zaccaria <giorgia.zaccaria at unimib.it>
BugReports: https://github.com/giorgiazaccaria/PUGMM/issues
License: MIT + file LICENSE
URL: https://github.com/giorgiazaccaria/PUGMM
NeedsCompilation: no
Materials: NEWS
CRAN checks: PUGMM results

Documentation:

Reference manual: PUGMM.pdf

Downloads:

Package source: PUGMM_0.1.1.tar.gz
Windows binaries: r-devel: PUGMM_0.1.1.zip, r-release: PUGMM_0.1.1.zip, r-oldrel: PUGMM_0.1.1.zip
macOS binaries: r-release (arm64): PUGMM_0.1.1.tgz, r-oldrel (arm64): PUGMM_0.1.1.tgz, r-release (x86_64): PUGMM_0.1.1.tgz, r-oldrel (x86_64): PUGMM_0.1.1.tgz
Old sources: PUGMM archive

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