Markov Chain Models: Sequenzo and seqHMM Mapping
This page maps Sequenzo sequenzo.seqhmm functions to their closest seqHMM (R) counterparts.
What This Section Covers
This section focuses on building, fitting, predicting, comparing, and simulating hidden Markov models for sequence data. For R users, the workflow mirrors seqHMM's build_* → fit_* → predict / posterior_probs pattern, with SequenceData replacing TraMineR stslist objects.
Mapping Table
| Sequenzo function | seqHMM counterpart | Notes |
|---|---|---|
build_hmm() | build_hmm() | Creates an unfitted HMM; supports single- and multichannel input. |
fit_model() | fit_model() | EM fitting for basic HMM. Sequenzo's default wrapper uses EM only; use fit_model_advanced() for global/local steps. |
fit_model_advanced() | fit_model(..., global_step, local_step) | Optional EM + global (MLSL-style) + local (L-BFGS) optimization with restarts. |
predict() | hidden_paths() / predict.hmm | Viterbi decoding of the most likely hidden-state path. |
posterior_probs() | posterior_probs() | Forward–backward state probabilities at each time point. |
plot_hmm() | plot.hmm() | Transition/emission matrices and network graph. |
build_mhmm() | build_mhmm() | Mixture HMM structure. Sequenzo: single-channel only for now. |
fit_mhmm() | fit_model.mhmm | EM for mixture HMM. |
predict_mhmm() | most_probable_cluster() | Hard cluster assignment per sequence. |
posterior_probs_mhmm() | posterior_cluster_probabilities() | Soft cluster membership probabilities. |
plot_mhmm() | plot.mhmm() | Cluster weights and per-cluster parameters. |
build_nhmm() | build_nhmm() | Covariate-dependent NHMM; formula or X matrix. |
fit_nhmm() | fit_nhmm() | Numerical optimization of NHMM coefficients. |
aic() / bic() | stats::AIC(logLik()) / stats::BIC(logLik()) | Information criteria after fitting. |
compare_models() | Manual comparison of fitted models | Ranks models by AIC or BIC. |
simulate_hmm() | simulate_hmm() | Generate synthetic sequences from HMM parameters. |
simulate_mhmm() | simulate_mhmm() | Generate synthetic MHMM sequences; supports formula-based mixture weights. |
simulate_nhmm() | simulate_nhmm() | Generate synthetic NHMM sequences from formulas. |
bootstrap_model() | bootstrap_coefs() | Nonparametric bootstrap confidence intervals (partial parity). |
Not Yet Mapped in Sequenzo
The R package also exports utilities that do not yet have a direct Sequenzo equivalent:
| seqHMM (R) | Status in Sequenzo |
|---|---|
build_mnhmm(), estimate_mnhmm() | Not implemented |
hidden_paths() as standalone export | Use predict() instead |
get_initial_probs(), get_transition_probs(), get_emission_probs() | Read attributes on fitted model objects |
stacked_sequence_plot(), ssplot(), gridplot() | Not implemented |
trim_model(), permute_states(), separate_mhmm() | Not implemented |
data_to_stslist() / stslist_to_data() | Use SequenceData directly |
Input Type Mapping
| Concept | seqHMM (R) | Sequenzo |
|---|---|---|
| Sequence input | TraMineR stslist (seqdef) | SequenceData |
| Multichannel input | List of stslist | List[SequenceData] in build_hmm() only |
| Covariates | Formula + data.frame | Formula strings + pandas.DataFrame, or NumPy X tensor |
| Fitted model class | hmm, mhmm, nhmm, mnhmm | HMM, MHMM, NHMM |
Authors
Code: Yuqi Liang and Yapeng Wei
Documentation: Yuqi Liang
References
Helske, S., & Helske, J. (2019). Mixture hidden Markov models for sequence data: The seqHMM package in R. Journal of Statistical Software, 88(3), 1–32. https://doi.org/10.18637/jss.v088.i03