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) <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 |
| DOI: |
10.32614/CRAN.package.FourWayHMM |
| 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 |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=FourWayHMM
to link to this page.