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

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 initial values).

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")

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Version

Install

install.packages('seqHMM')

Monthly Downloads

2,601

Version

2.1.0

License

GPL (>= 2)

Maintainer

Jouni Helske

Last Published

September 25th, 2025

Functions in seqHMM (2.1.0)

Estimate Parameters of (Mixture) Hidden Markov Models and Their Restricted Variants

get_emission_probs

Extract the Emission Probabilities of Hidden Markov Model

Compute the Marginal Probabilities from NHMMs

Estimate a Non-homogeneous Hidden Markov Model

Estimate a Mixture Non-homogeneous Hidden Markov Model

Merge Multiple Sequence Objects into One (from Multichannel to Single Channel Data)

Plot Multidimensional Sequence Plots in a Grid

Hidden Markov model for the biofam data

Transform a Multichannel Hidden Markov Model into a Single Channel Representation

get_initial_probs

Extract the Initial State Probabilities of Hidden Markov Model

Log-likelihood of a Non-homogeneous Hidden Markov Model

Log-likelihood of a Hidden Markov Model

Most Probable Paths of Hidden States

Hidden Markov model for the mvad data

Synthetic data on fathers' parental leaves in Finland

Stack Multichannel Sequence Plots and/or Most Probable Paths Plots from Hidden Markov Models

Mixture hidden Markov model for the biofam data

Interactive Stacked Plots of Multichannel Sequences and/or Most Probable Paths for Mixture Hidden Markov Models

Plot Colorpalettes

most_probable_cluster

Extract Most Probable Cluster for Each Sequence

Permute the states of NHMM using Hungarian algorithm

seqHMM-deprecated

Deprecated function(s) in the seqHMM package

Interactive Plotting for Mixed Hidden Markov Model (mhmm)

Print Method for a Hidden Markov Model

Reorganize a mixture hidden Markov model to a list of separate hidden Markov models (covariates ignored)

Plot hidden Markov models

Predictions from Non-homogeneous Hidden Markov Models

Number of Observations in Hidden Markov Model

posterior_cluster_probabilities

Extract Posterior Cluster Probabilities

The seqHMM package

Mixture hidden Markov model for the mvad data

Convert return code from estimate_nhmm and estimate_mnhmm to text

posterior_probs

Posterior Probabilities for Hidden Markov Models

Objects exported from other packages

Simulate Non-homogeneous Hidden Markov Models

Get State Names of Hidden Markov Model

simulate_initial_probs

Simulate Parameters of Hidden Markov Models

Summary method for mixture hidden Markov models

Sort sequences in a sequence object

Simulate Mixture Hidden Markov Models

Simulate Mixture Non-homogeneous Hidden Markov Models

Simulate hidden Markov models

Stacked Plots of Multichannel Sequences and/or Most Probable Paths from Hidden Markov Models

Define Arguments for Plotting Multichannel Sequences and/or Most Probable Paths from Hidden Markov Models

get_transition_probs

Extract the State Transition Probabilities of Hidden Markov Model

Summary method for mixture non-homogenous hidden Markov models

stacked_sequence_plot

Stacked Sequence Plots of Multichannel Sequences and/or Most Probable Paths from Hidden Markov Models

Variance-Covariance Matrix for Coefficients of Covariates of Mixture Hidden Markov Model

Update Covariate Values of NHMM

Trim Small Probabilities of Hidden Markov Model

Get Cluster Names from Mixture HMMs

Build a Mixture Hidden Markov Model

Build a Hidden Markov Model

bootstrap_coefs

Bootstrap Sampling of NHMM Coefficients

get_cluster_probs

Extract the Prior Cluster Probabilities of MHMM or MNHMM

Build a Mixture Markov Model

Build a Latent Class Model

cluster_names<-

Set Cluster Names for Mixture Models

Build a Markov Model

Three-channel biofam data

Get the Estimated Regression Coefficients of Non-Homogeneous Hidden Markov Models

forward_backward

Forward and Backward Probabilities for Hidden Markov Model

data_to_stslist

Transform TraMineR's state sequence object to data.table and vice versa

A feedback-augmented non-homogeneous hidden Markov Model for leaves data