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>.">

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
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 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
URL: https://github.com/giorgiazaccaria/PUGMM
NeedsCompilation: no
CRAN checks: PUGMM results

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