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Implements parsimonious hidden Markov models for four-way data via expectation- conditional maximization algorithm, as described in Tomarchio et al. (2020) <doi:10.48550/arXiv.2107.04330>. The matrix-variate normal distribution is used as emission distribution. For each hidden state, parsimony is reached via the eigen-decomposition of the covariance matrices of the emission distribution. This produces a family of 98 parsimonious hidden Markov models.
| Version: | 1.0.0 |
| Depends: | R (≥ 2.10) |
| Imports: | withr, snow, doSNOW, foreach, mclust, tensor, tidyr, data.table, LaplacesDemon |
| Published: | 2021-11-30 |
| Author: | Salvatore D. Tomarchio [aut, cre], Antonio Punzo [aut], Antonello Maruotti [aut] |
| Maintainer: | Salvatore D. Tomarchio <daniele.tomarchio at unict.it> |
| License: | GPL (≥ 3) |
| NeedsCompilation: | no |
| CRAN checks: | FourWayHMM results |
| Reference manual: | FourWayHMM.pdf |
| Package source: | FourWayHMM_1.0.0.tar.gz |
| Windows binaries: | r-devel: FourWayHMM_1.0.0.zip, r-release: FourWayHMM_1.0.0.zip, r-oldrel: FourWayHMM_1.0.0.zip |
| macOS binaries: | r-release (arm64): FourWayHMM_1.0.0.tgz, r-oldrel (arm64): FourWayHMM_1.0.0.tgz, r-release (x86_64): FourWayHMM_1.0.0.tgz, r-oldrel (x86_64): FourWayHMM_1.0.0.tgz |
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