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.
||R (≥ 2.10)
||doSNOW, foreach, snow, withr
||Salvatore D. Tomarchio [aut],
Michael P.B. Gallaugher [aut, cre],
Antonio Punzo [aut],
Paul D. McNicholas [aut]
||Michael P.B. Gallaugher <michael_gallaugher at baylor.edu>
||GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
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