MatrixMixtures: Model-Based Clustering via Matrix-Variate Mixture Models

Implements finite mixtures of matrix-variate contaminated normal distributions via expectation conditional-maximization algorithm for model-based clustering, as described in Tomarchio et al.(2020) <arXiv:2005.03861>. One key advantage of this model is the ability to automatically detect potential outlying matrices by computing their a posteriori probability of being typical or atypical points. Finite mixtures of matrix-variate t and matrix-variate normal distributions are also implemented by using expectation-maximization algorithms.

Version: 1.0.0
Depends: R (≥ 2.10)
Imports: doSNOW, foreach, snow, withr
Published: 2021-06-11
Author: Salvatore D. Tomarchio [aut], Michael P.B. Gallaugher [aut, cre], Antonio Punzo [aut], Paul D. McNicholas [aut]
Maintainer: Michael P.B. Gallaugher <michael_gallaugher at baylor.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: MatrixMixtures results

Documentation:

Reference manual: MatrixMixtures.pdf

Downloads:

Package source: MatrixMixtures_1.0.0.tar.gz
Windows binaries: r-devel: MatrixMixtures_1.0.0.zip, r-release: MatrixMixtures_1.0.0.zip, r-oldrel: MatrixMixtures_1.0.0.zip
macOS binaries: r-release (arm64): MatrixMixtures_1.0.0.tgz, r-release (x86_64): MatrixMixtures_1.0.0.tgz, r-oldrel: MatrixMixtures_1.0.0.tgz

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