MHMM: Finite Mixture of Hidden Markov Model

Estimation of the latent states and partition by maximum likelihood. Model can be used for analyzing accelerometer data. In such a case, the latent states corresponds to activity levels and the partition permits to consider heterogeneity within the population. Emission laws are zero-inflated gamma distributions. Their parameters depends on the latent states but not on the partition, to compare the time spent by activity levels between classes. Model description is available in Du Roy de Chaumaray, M. and Marbac, M. and Navarro, F. (2019) <arXiv:1906.01547>.

Version:	1.0.0
Depends:	R (≥ 3.4.4)
Imports:	Rcpp (≥ 0.11.1), methods, parallel, ggplot2, reshape2, gridExtra
LinkingTo:	Rcpp, RcppArmadillo, BH
Published:	2020-03-20
Author:	Marie Du-Roy-De-Chaumary, Matthieu Marbac and Fabien Navarro
Maintainer:	Matthieu Marbac <matthieu.marbac-lourdelle at ensai.fr>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	yes
Citation:	MHMM citation info
CRAN checks:	MHMM results

Documentation:

Reference manual:

MHMM.pdf

Downloads:

Package source:	MHMM_1.0.0.tar.gz
Windows binaries:	r-devel: MHMM_1.0.0.zip, r-release: MHMM_1.0.0.zip, r-oldrel: MHMM_1.0.0.zip
macOS binaries:	r-release (arm64): MHMM_1.0.0.tgz, r-oldrel (arm64): MHMM_1.0.0.tgz, r-release (x86_64): MHMM_1.0.0.tgz, r-oldrel (x86_64): MHMM_1.0.0.tgz

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