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The seqHMM package is designed for fitting hidden
(latent) Markov models (HMMs) and their variations for social sequence
data and other categorical sequence data (e.g. categorical time series
and panel data). Restricted and extended variants include mixture HMMs,
Markov models and their mixtures, latent class models, non-homogeneous
hidden Markov models (NHMMs) and their mixtures, and feedback-augmented
hidden Markov models (FAN-HMMs).
The package supports models for one or multiple subjects with one or multiple parallel outcome sequences (channels). External covariates can be added to explain cluster membership in mixture models, and NHMMs and their variants support covariates in initial, transition and emission matrices as well.
Maximum likelihood estimation via EM algorithm and direct numerical
maximization with analytical gradients is supported. All main algorithms
are written in C++. Parallel computation is available via OpenMP
(pre-2.0.0 version models) and future (via
parallel multistart estimation with random prior_obss).
When using the package in publications, please cite:
Helske, Satu and Helske, Jouni (2019). Mixture hidden Markov models for sequence data: the seqHMM package in R. Journal of Statistical Software, 88(3). doi:10.18637/jss.v088.i03.
Helske, Jouni (2025). Feedback-augmented Non-homogeneous Hidden Markov Models for Longitudinal Causal Inference. arXiv preprint. doi:10.48550/arXiv.2503.16014.
If you find bugs, please add a new issue here in GitHub. You can also contact Satu Helske (firstname.lastname@utu.fi) or Jouni Helske (firstname.lastname@iki.fi). We would be happy to hear your feedback.
The package is available on CRAN. Install it via
install.packages("seqHMM")If you want to try the development version of the seqHMM
package, install it from the R-universe:
install.packages("seqHMM", repos = "https://helske.r-universe.dev")These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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