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: 2026-09-25 11:09:45 UTC; root
Author: Salvatore D. Tomarchio [aut, cre]
Maintainer: Salvatore D. Tomarchio <daniele.tomarchio@unict.it>
Repository: https://cran.r-universe.dev
Date/Publication: 2024-08-29 02:51:20 UTC
RemoteUrl: https://github.com/cran/MatrixHMM
RemoteRef: HEAD
RemoteSha: 2625f5a46ec1bfc6db4ccccd70c2d25feef1e467