Package: MatrixHMM
Title: Parsimonious Families of Hidden Markov Models for Matrix-Variate
        Longitudinal Data
Version: 1.0.0
Authors@R: c(
    person(given = "Salvatore D.",
           family = "Tomarchio",
           role = c("aut","cre"),
           email = "daniele.tomarchio@unict.it"))
Description: 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.
License: GPL (>= 3)
Encoding: UTF-8
RoxygenNote: 7.3.2
Imports: data.table, doSNOW, foreach, LaplacesDemon, mclust, progress,
        snow, tensor, tidyr, withr
Depends: R (>= 2.10)
LazyData: true
NeedsCompilation: no
Packaged: 2024-08-22 16:58:17 UTC; Daniele
Author: Salvatore D. Tomarchio [aut, cre]
Maintainer: Salvatore D. Tomarchio <daniele.tomarchio@unict.it>
Repository: CRAN
Date/Publication: 2024-08-28 08:00:06 UTC
Built: R 4.6.0; ; 2026-04-26 19:35:16 UTC; unix
