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:
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