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.
Version: 1.0.0 Depends: R (≥ 2.10) Imports: data.table, doSNOW, foreach, LaplacesDemon, mclust, progress, snow, tensor, tidyr, withr Published: 2024-08-28 DOI: 10.32614/CRAN.package.MatrixHMM Author: Salvatore D. Tomarchio [aut, cre] Maintainer: Salvatore D. Tomarchio <daniele.tomarchio at unict.it> License: GPL (≥ 3) NeedsCompilation: no CRAN checks: MatrixHMM results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=MatrixHMM to link to this page.
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