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) doi:10.48550/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.
- Version1.0.0
- R version≥ 2.10
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- Last release06/11/2021
Team
Michael P.B. Gallaugher
Salvatore D. Tomarchio
Show author detailsRolesAuthorPaul D. McNicholas
Show author detailsRolesAuthorAntonio Punzo
Show author detailsRolesAuthor
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- Imports4 packages