MatrixHMM: Parsimonious Families of Hidden Markov Models for Matrix-Variate Longitudinal Data

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:

Reference manual: MatrixHMM.pdf

Downloads:

Package source: MatrixHMM_1.0.0.tar.gz
Windows binaries: r-devel: MatrixHMM_1.0.0.zip, r-release: MatrixHMM_1.0.0.zip, r-oldrel: MatrixHMM_1.0.0.zip
macOS binaries: r-release (arm64): MatrixHMM_1.0.0.tgz, r-oldrel (arm64): MatrixHMM_1.0.0.tgz, r-release (x86_64): MatrixHMM_1.0.0.tgz, r-oldrel (x86_64): MatrixHMM_1.0.0.tgz

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