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) . 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.


Reference manual

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install.packages("FourWayHMM")

1.0.0 by Salvatore D. Tomarchio, 5 years ago


Browse source code at https://github.com/cran/FourWayHMM


Authors: Salvatore D. Tomarchio [aut, cre] , Antonio Punzo [aut] , Antonello Maruotti [aut]


Documentation:   PDF Manual  


GPL (>= 3) license


Imports withr, snow, doSNOW, foreach, mclust, tensor, tidyr, data.table, LaplacesDemon


See at CRAN