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>
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:, r-release:, r-oldrel:
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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