Parsimonious Hidden Markov Models for Four-Way Data R package FourWayHMM version 1.0.0 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. Software Depends: R (>= 2.10) Imports: withr, snow, doSNOW, foreach, mclust, tensor, tidyr, data.table, LaplacesDemon Salvatore D. Tomarchio <daniele.tomarchio@unict.it> Comprehensive R Archive Network (CRAN) Salvatore D. Tomarchio [aut, cre], Antonio Punzo [aut], Antonello Maruotti [aut] GPL (>= 3) 2021-11-30 application/tgz https://CRAN.R-project.org/package=FourWayHMM doi:10.32614/CRAN.package.FourWayHMM