seqHMM: Mixture Hidden Markov Models for Social Sequence Data 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 visualizing 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. Documentation is available via several vignettes in this page, and the paper by Helske and Helske (2019, <doi:10.18637/jss.v088.i03>).

Version:	1.2.6
Depends:	R (≥ 3.5.0)
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, testthat (≥ 3.0.0), covr
Published:	2023-07-06
DOI:	10.32614/CRAN.package.seqHMM
Author:	Jouni Helske [aut, cre], Satu Helske [aut]
Maintainer:	Jouni Helske <jouni.helske at iki.fi>
BugReports:	https://github.com/helske/seqHMM/issues
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	yes
Citation:	seqHMM citation info
Materials:	README NEWS
CRAN checks:	seqHMM results

Documentation:

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

Downloads:

Package source:	seqHMM_1.2.6.tar.gz
Windows binaries:	r-devel: seqHMM_1.2.6.zip, r-release: seqHMM_1.2.6.zip, r-oldrel: seqHMM_1.2.6.zip
macOS binaries:	r-release (arm64): seqHMM_1.2.6.tgz, r-oldrel (arm64): seqHMM_1.2.6.tgz, r-release (x86_64): seqHMM_1.2.6.tgz, r-oldrel (x86_64): seqHMM_1.2.6.tgz
Old sources:	seqHMM archive

Reverse dependencies:

Reverse imports:	DBHC
Reverse suggests:	clickb, tna

Linking:

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