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FourWayHMM: Parsimonious Hidden Markov Models for Four-Way Data
Implements parsimonious hidden Markov models for four-way data via expectation- conditional maximization algorithm, as described in Tomarchio et al. (2020) <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.
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Package details |
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| Author | Salvatore D. Tomarchio [aut, cre], Antonio Punzo [aut], Antonello Maruotti [aut] |
| 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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