seqHMM: Hidden Markov Models for Life Sequences and Other Multivariate, Multichannel Categorical Time Series

Designed for fitting hidden (latent) Markov models and mixture hidden Markov models for social sequence data and other categorical time series. Also some more restricted versions of these type of models are available: Markov models, mixture Markov models, and latent class models. The package supports models for one or multiple subjects with one or multiple parallel sequences (channels). External covariates can be added to explain cluster membership in mixture models. The package provides functions for evaluating and comparing models, as well as functions for easy plotting of multichannel sequence data and hidden Markov models. Models are estimated using maximum likelihood via the EM algorithm and/or direct numerical maximization with analytical gradients. All main algorithms are written in C++ with support for parallel computation.

Version: 1.0.7
Depends: R (≥ 3.2.0)
Imports: gridBase, igraph, Matrix, nloptr, numDeriv, Rcpp (≥ 0.11.3), TraMineR (≥ 1.8-8), graphics, grDevices, grid, methods, stats, utils
LinkingTo: Rcpp, RcppArmadillo
Suggests: MASS, nnet, knitr
Published: 2017-04-04
Author: Jouni Helske, Satu Helske
Maintainer: Jouni Helske <jouni.helske at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Citation: seqHMM citation info
Materials: NEWS
CRAN checks: seqHMM results


Reference manual: seqHMM.pdf
Vignettes: Mixture Hidden Markov Models for Sequence Data: the seqHMM Package in R
The main algorithms used in the seqHMM package
Examples and tips for estimating Markovian models with seqHMM
Visualization tools in the seqHMM package
Package source: seqHMM_1.0.7.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: seqHMM_1.0.7.tgz
OS X Mavericks binaries: r-oldrel: seqHMM_1.0.7.tgz
Old sources: seqHMM archive


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