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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.
Getting started
Browse package contents
Package details |
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| Author | Salvatore D. Tomarchio [aut, cre] |
| Maintainer | Salvatore D. Tomarchio <daniele.tomarchio@unict.it> |
| License | GPL (>= 3) |
| Version | 1.0.0 |
| Package repository | View on CRAN |
| Installation |
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