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:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=MatrixHMM
to link to this page.