Model-Based Clustering via Matrix-Variate Mixture Models R package MatrixMixtures version 1.0.0 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. Software Depends: R (>= 2.10) Imports: doSNOW, foreach, snow, withr Michael P.B. Gallaugher <michael_gallaugher@baylor.edu> Comprehensive R Archive Network (CRAN) Salvatore D. Tomarchio [aut], Michael P.B. Gallaugher [aut, cre], Antonio Punzo [aut], Paul D. McNicholas [aut] GPL (>= 2) 2021-06-11 application/tgz https://CRAN.R-project.org/package=MatrixMixtures doi:10.32614/CRAN.package.MatrixMixtures