Package: seqHMM 1.2.6

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>).

Authors:Jouni Helske [aut, cre], Satu Helske [aut]

seqHMM_1.2.6.tar.gz
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seqHMM.pdf |seqHMM.html
seqHMM/json (API)
NEWS

# Install 'seqHMM' in R:
install.packages('seqHMM', repos = c('https://helske.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/helske/seqhmm/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On CRAN:

categorical-dataem-algorithmhidden-markov-modelshmmmixture-markov-modelstime-series

34 exports 93 stars 4.22 score 26 dependencies 1 dependents 3 mentions 93 scripts 481 downloads

Last updated 1 years agofrom:2a50b04b01. Checks:OK: 7 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 01 2024
R-4.5-win-x86_64NOTESep 01 2024
R-4.5-linux-x86_64NOTESep 01 2024
R-4.4-win-x86_64OKSep 01 2024
R-4.4-mac-x86_64OKSep 01 2024
R-4.4-mac-aarch64OKSep 01 2024
R-4.3-win-x86_64OKSep 01 2024
R-4.3-mac-x86_64OKSep 01 2024
R-4.3-mac-aarch64OKSep 01 2024

Exports:alphabetbuild_hmmbuild_lcmbuild_mhmmbuild_mmbuild_mmmcluster_namescluster_names<-estimate_coeffit_hmmfit_mhmmfit_modelforward_backwardgridplothidden_pathsmc_to_scmc_to_sc_datamssplotplot_colorsposterior_probsseparate_mhmmseqdefseqstatfsimulate_emission_probssimulate_hmmsimulate_initial_probssimulate_mhmmsimulate_transition_probssspssplotstate_namesstate_names<-trim_hmmtrim_model

Dependencies:bootcliclustercolorspacecpp11gluegridBaseigraphlatticelifecyclemagrittrMASSMatrixmgcvnlmenloptrnumDerivpermutepkgconfigRColorBrewerRcppRcppArmadillorlangTraMineRvctrsvegan

Examples and tips for estimating Markovian models with seqHMM

Rendered fromseqHMM_estimation.Rnwusingknitr::knitron Sep 01 2024.

Last update: 2018-05-03
Started: 2017-03-30

Mixture Hidden Markov Models for Sequence Data: the seqHMM Package in R

Rendered fromseqHMM.Rnwusingknitr::knitron Sep 01 2024.

Last update: 2023-03-31
Started: 2015-11-06

The main algorithms used in the seqHMM package

Rendered fromseqHMM_algorithms.Rnwusingknitr::knitron Sep 01 2024.

Last update: 2019-01-25
Started: 2017-03-30

Visualization tools in the seqHMM package

Rendered fromseqHMM_visualization.Rnwusingknitr::knitron Sep 01 2024.

Last update: 2023-03-31
Started: 2017-03-30

Readme and manuals

Help Manual

Help pageTopics
Three-channel biofam databiofam3c
Build a Hidden Markov Modelbuild_hmm
Build a Latent Class Modelbuild_lcm
Build a Mixture Hidden Markov Modelbuild_mhmm
Build a Markov Modelbuild_mm
Build a Mixture Markov Modelbuild_mmm
Get cluster names from mhmm objectcluster_names
Set cluster names for mhmm objectcluster_names<-
Color palettescolorpalette
Estimate Regression Coefficients of Mixture Hidden Markov Modelsestimate_coef
Estimate Parameters of (Mixture) Hidden Markov Models and Their Restricted Variantsfit_model
Forward and Backward Probabilities for Hidden Markov Modelforward_backward
Plot Multidimensional Sequence Plots in a Gridgridplot
Most Probable Paths of Hidden Stateshidden_paths
Hidden Markov model for the biofam datahmm_biofam
Hidden Markov model for the mvad datahmm_mvad
Log-likelihood of the Hidden Markov ModellogLik.hmm
Log-likelihood of the Mixture Hidden Markov ModellogLik.mhmm
Transform a Multichannel Hidden Markov Model into a Single Channel Representationmc_to_sc
Merge Multiple Sequence Objects into One (from Multichannel to Single Channel Data)mc_to_sc_data
Mixture hidden Markov model for the biofam datamhmm_biofam
Mixture hidden Markov model for the mvad datamhmm_mvad
Interactive Stacked Plots of Multichannel Sequences and/or Most Probable Paths for Mixture Hidden Markov Modelsmssplot
Plot Colorpalettesplot_colors
Plot hidden Markov modelsplot.hmm
Interactive Plotting for Mixed Hidden Markov Model (mhmm)plot.mhmm
Stack Multichannel Sequence Plots and/or Most Probable Paths Plots from Hidden Markov Modelsplot.ssp
Posterior Probabilities for (Mixture) Hidden Markov Modelsposterior_probs
Print Method for a Hidden Markov Modelprint.hmm print.mhmm print.summary.mhmm
Reorganize a mixture hidden Markov model to a list of separate hidden Markov models (covariates ignored)separate_mhmm
Imported Functions from 'TraMineR'alphabet seqdef seqstatf
The seqHMM packageseqHMM
Deprecated function(s) in the seqHMM packagefit_hmm fit_mhmm seqHMM-deprecated trim_hmm
Simulate hidden Markov modelssimulate_hmm
Simulate Parameters of Hidden Markov Modelssimulate_emission_probs simulate_initial_probs simulate_transition_probs
Simulate Mixture Hidden Markov Modelssimulate_mhmm
Define Arguments for Plotting Multichannel Sequences and/or Most Probable Paths from Hidden Markov Modelsssp
Stacked Plots of Multichannel Sequences and/or Most Probable Paths from Hidden Markov Modelsssplot
Get state names from hmm or mhmm objectstate_names
Set state names for hmm or mhmm objectstate_names<-
Summary method for mixture hidden Markov modelssummary.mhmm
Trim Small Probabilities of Hidden Markov Modeltrim_model
Variance-Covariance Matrix for Coefficients of Covariates of Mixture Hidden Markov Modelvcov.mhmm