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PUGMM: Parsimonious Ultrametric Gaussian Mixture Models
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 |
| 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 LICENSE |
| URL: |
https://github.com/giorgiazaccaria/PUGMM |
| NeedsCompilation: |
no |
| CRAN checks: |
PUGMM results |
Documentation:
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These binaries (installable software) and packages are in development.
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