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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 |
| Reference manual: | MatrixHMM.pdf |
| 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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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.