Parsimonious Families of Hidden Markov Models for Matrix-Variate Longitudinal Data R package MatrixHMM version 1.0.0 Implements three families of parsimonious hidden Markov models (HMMs) for matrix-variate longitudinal data using the Expectation-Conditional Maximization (ECM) algorithm. The package supports matrix-variate normal, t, and contaminated normal distributions as emission distributions. For each hidden state, parsimony is achieved through the eigen-decomposition of the covariance matrices associated with the emission distribution. This approach results in a comprehensive set of 98 parsimonious HMMs for each type of emission distribution. Atypical matrix detection is also supported, utilizing the fitted (heavy-tailed) models. Software Depends: R (>= 2.10) Imports: data.table, doSNOW, foreach, LaplacesDemon, mclust, progress, snow, tensor, tidyr, withr Salvatore D. Tomarchio <daniele.tomarchio@unict.it> Comprehensive R Archive Network (CRAN) Salvatore D. Tomarchio [aut, cre] GPL (>= 3) 2024-08-28 application/tgz https://CRAN.R-project.org/package=MatrixHMM doi:10.32614/CRAN.package.MatrixHMM