Data

Tools

Indexes

Applications

Experiments

Open Source Science

seqHMM

Multivariate and Multichannel Discrete Hidden Markov Models for Categorical Sequences

https://github.com/helske/seqhmm

Science Score: 39.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 7 DOI reference(s) in README
  • ○
    Academic publication links
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (12.6%) to scientific vocabulary

Keywords

categorical-data em-algorithm hidden-markov-models hmm mixture-markov-models r time-series

Keywords from Contributors

bayesian-inference markov-chain-monte-carlo particle-filter state-space
Last synced: 6 months ago · JSON representation

Repository

Multivariate and Multichannel Discrete Hidden Markov Models for Categorical Sequences

Basic Info
  • Host: GitHub
  • Owner: helske
  • Language: R
  • Default Branch: main
  • Size: 17.3 MB
Statistics
  • Stars: 100
  • Watchers: 8
  • Forks: 30
  • Open Issues: 0
  • Releases: 4
Topics
categorical-data em-algorithm hidden-markov-models hmm mixture-markov-models r time-series
Created almost 12 years ago · Last pushed 8 months ago
Metadata Files
Readme Changelog

README.md

R-CMD-check Codecov test coverage cran version downloads

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 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

R install.packages("seqHMM") If you want to try the development version of the seqHMM package, install it from the R-universe:

R install.packages("seqHMM", repos = "https://helske.r-universe.dev")

Owner

  • Name: Jouni Helske
  • Login: helske
  • Kind: user
  • Location: Finland
  • Company: University of Jyväskylä

Bayesian statistics, time series, causal inference, state space models, hidden Markov models, visualization.

GitHub Events

Total
  • Issues event: 3
  • Watch event: 4
  • Delete event: 1
  • Issue comment event: 3
  • Push event: 105
  • Pull request review event: 1
  • Pull request event: 3
  • Fork event: 1
  • Create event: 1
Last Year
  • Issues event: 3
  • Watch event: 4
  • Delete event: 1
  • Issue comment event: 3
  • Push event: 105
  • Pull request review event: 1
  • Pull request event: 3
  • Fork event: 1
  • Create event: 1

Committers

Last synced: 9 months ago

All Time
  • Total Commits: 1,058
  • Total Committers: 12
  • Avg Commits per committer: 88.167
  • Development Distribution Score (DDS): 0.324
Past Year
  • Commits: 264
  • Committers: 3
  • Avg Commits per committer: 88.0
  • Development Distribution Score (DDS): 0.182
Top Committers
Name Email Commits
Jouni Helske j****e@j****i 715
satuhelske s****e@j****i 248
Jouni Helske j****s@u****i 47
Satu Helske s****e 28
Satu Helske s****e@l****e 5
Satu Helske S****e 5
Jouni Helske j****1@l****e 4
Satu Helske s****7@l****e 2
clayton-aldern 4****n 1
unknown j****e@M****i 1
Perhe Helske P****e 1
Helske j****e@j****i 1
Committer Domains (Top 20 + Academic)
liu.se: 3 jyu.fi: 3 mad-320-03.ad.jyu.fi: 1 utu.fi: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 66
  • Total pull requests: 3
  • Average time to close issues: 10 months
  • Average time to close pull requests: 1 day
  • Total issue authors: 39
  • Total pull request authors: 2
  • Average comments per issue: 2.2
  • Average comments per pull request: 1.0
  • Merged pull requests: 3
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 2
  • Average time to close issues: N/A
  • Average time to close pull requests: 13 minutes
  • Issue authors: 0
  • Pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 2
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • helske (7)
  • satuhelske (7)
  • vrodriguezf (6)
  • loxavia (3)
  • sapphire75710 (2)
  • kyrantgs (2)
  • tbeason (2)
  • liatmarc (2)
  • Sandy4321 (2)
  • mark-jy (2)
  • faezehMovahedi (2)
  • mackancurtaincheeks (1)
  • mmervecerit (1)
  • NazaninEs (1)
  • grahamammal (1)
Pull Request Authors
  • helske (4)
  • clayton-aldern (1)
Top Labels
Issue Labels
enhancement (1)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • cran 17,875 last-month
  • Total dependent packages: 2
  • Total dependent repositories: 3
  • Total versions: 25
  • Total maintainers: 1
cran.r-project.org: seqHMM

Mixture Hidden Markov Models for Social Sequence Data and Other Multivariate, Multichannel Categorical Time Series

  • Versions: 25
  • Dependent Packages: 2
  • Dependent Repositories: 3
  • Downloads: 17,875 Last month
Rankings
Forks count: 2.7%
Stargazers count: 4.2%
Average: 12.3%
Dependent packages count: 13.7%
Dependent repos count: 16.5%
Downloads: 24.6%
Maintainers (1)
jouni.helske@iki.fi
Last synced: 6 months ago

Dependencies

DESCRIPTION cran
  • R >= 3.5.0 depends
  • Matrix * imports
  • Rcpp >= 0.11.3 imports
  • TraMineR >= 1.8 imports
  • grDevices * imports
  • graphics * imports
  • grid * imports
  • gridBase * imports
  • igraph * imports
  • methods * imports
  • nloptr * imports
  • numDeriv * imports
  • stats * imports
  • utils * imports
  • MASS * suggests
  • knitr * suggests
  • nnet * suggests
.github/workflows/R-CMD-check.yaml actions
  • actions/cache v2 composite
  • actions/checkout v2 composite
  • actions/upload-artifact main composite
  • r-lib/actions/setup-pandoc v1 composite
  • r-lib/actions/setup-r v1 composite
  • r-lib/actions/setup-tinytex master composite