{
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  "Package": "seqHMM",
  "Title": "Mixture Hidden Markov Models for Social Sequence Data and Other\nMultivariate, Multichannel Categorical Time Series",
  "Version": "2.2.0",
  "Authors@R": "c(person(given = \"Jouni\",\nfamily = \"Helske\",\nrole = c(\"aut\", \"cre\"),\nemail = \"jouni.helske@iki.fi\",\ncomment = c(ORCID = \"0000-0001-7130-793X\")),\nperson(given = \"Satu\",\nfamily = \"Helske\",\nrole = \"aut\",\ncomment = c(ORCID = \"0000-0003-0532-0153\")))",
  "Description": "Designed for estimating variants of hidden (latent) Markov\nmodels (HMMs), mixture HMMs, and non-homogeneous HMMs (NHMMs)\nfor social sequence data and other categorical time series.\nSpecial cases include feedback-augmented NHMMs, Markov models\nwithout latent layer, mixture Markov models, and latent class\nmodels. The package supports models for one or multiple\nsubjects with one or multiple parallel sequences (channels).\nExternal covariates can be added to explain cluster membership\nin mixture models as well as initial, transition and emission\nprobabilities in NHMMs. The package provides functions for\nevaluating and comparing models, as well as functions for\nvisualizing of multichannel sequence data and HMMs. For NHMMs,\nmethods for computing average causal effects and marginal state\nand emission probabilities are available. Models are estimated\nusing maximum likelihood via the EM algorithm or direct\nnumerical maximization with analytical gradients. Documentation\nis available via several vignettes, and Helske and Helske\n(2019, <doi:10.18637/jss.v088.i03>). For methodology behind the\nNHMMs, see Helske (2025, <doi:10.48550/arXiv.2503.16014>).",
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  "Repository": "https://helske.r-universe.dev",
  "Date/Publication": "2026-03-15 14:14:16 UTC",
  "RemoteUrl": "https://github.com/helske/seqHMM",
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  "Packaged": {
    "Date": "2026-05-14 09:23:23 UTC",
    "User": "root"
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  "Author": "Jouni Helske [aut, cre] (ORCID:\n<https://orcid.org/0000-0001-7130-793X>),\nSatu Helske [aut] (ORCID: <https://orcid.org/0000-0003-0532-0153>)",
  "Maintainer": "Jouni Helske <jouni.helske@iki.fi>",
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  "_created": "2026-05-14T09:23:23.000Z",
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  "_exports": [
    "alphabet",
    "bootstrap_coefs",
    "build_hmm",
    "build_lcm",
    "build_mhmm",
    "build_mm",
    "build_mmm",
    "cluster_names",
    "cluster_names<-",
    "data_to_stslist",
    "estimate_mnhmm",
    "estimate_nhmm",
    "fit_model",
    "forward_backward",
    "get_cluster_probs",
    "get_emission_probs",
    "get_initial_probs",
    "get_marginals",
    "get_transition_probs",
    "gridplot",
    "hidden_paths",
    "mc_to_sc",
    "mc_to_sc_data",
    "most_probable_cluster",
    "mssplot",
    "permute_states",
    "plot_colors",
    "posterior_cluster_probabilities",
    "posterior_probs",
    "separate_mhmm",
    "seqdef",
    "seqstatf",
    "simulate_emission_probs",
    "simulate_hmm",
    "simulate_initial_probs",
    "simulate_mhmm",
    "simulate_mnhmm",
    "simulate_nhmm",
    "simulate_transition_probs",
    "sort_sequences",
    "ssp",
    "ssplot",
    "stacked_sequence_plot",
    "state_names",
    "state_names<-",
    "stslist_to_data",
    "trim_model"
  ],
  "_datasets": [
    {
      "name": "biofam3c",
      "title": "Three-channel biofam data",
      "object": "biofam3c",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "colorpalette",
      "title": "Color palettes",
      "object": "colorpalette",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "fanhmm_leaves",
      "title": "A feedback-augmented non-homogeneous hidden Markov Model for leaves data",
      "object": "fanhmm_leaves",
      "class": [
        "fanhmm",
        "nhmm"
      ],
      "fields": [],
      "table": false,
      "tojson": false
    },
    {
      "name": "hmm_biofam",
      "title": "Hidden Markov model for the biofam data",
      "object": "hmm_biofam",
      "class": [
        "hmm"
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      "fields": [],
      "table": false,
      "tojson": false
    },
    {
      "name": "hmm_mvad",
      "title": "Hidden Markov model for the mvad data",
      "object": "hmm_mvad",
      "class": [
        "hmm"
      ],
      "fields": [],
      "table": false,
      "tojson": false
    },
    {
      "name": "leaves",
      "title": "Synthetic data on fathers' parental leaves in Finland",
      "object": "leaves",
      "class": [
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      ],
      "fields": [
        "workplace",
        "father",
        "year",
        "leave",
        "occupation",
        "reform2013",
        "same_occupation",
        "lag_reform2013",
        "lag_occupation"
      ],
      "rows": 9281,
      "table": true,
      "tojson": true
    },
    {
      "name": "mhmm_biofam",
      "title": "Mixture hidden Markov model for the biofam data",
      "object": "mhmm_biofam",
      "class": [
        "mhmm"
      ],
      "fields": [],
      "table": false,
      "tojson": false
    },
    {
      "name": "mhmm_mvad",
      "title": "Mixture hidden Markov model for the mvad data",
      "object": "mhmm_mvad",
      "class": [
        "mhmm"
      ],
      "fields": [],
      "table": false,
      "tojson": false
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  "_help": [
    {
      "page": "seqHMM-package",
      "title": "The seqHMM package",
      "topics": [
        "seqHMM-package",
        "seqHMM"
      ]
    },
    {
      "page": "biofam3c",
      "title": "Three-channel biofam data",
      "topics": [
        "biofam3c"
      ]
    },
    {
      "page": "bootstrap",
      "title": "Bootstrap Sampling of NHMM Coefficients",
      "topics": [
        "bootstrap_coefs",
        "bootstrap_coefs.mnhmm",
        "bootstrap_coefs.nhmm"
      ]
    },
    {
      "page": "build_hmm",
      "title": "Build a Hidden Markov Model",
      "topics": [
        "build_hmm"
      ]
    },
    {
      "page": "build_lcm",
      "title": "Build a Latent Class Model",
      "topics": [
        "build_lcm"
      ]
    },
    {
      "page": "build_mhmm",
      "title": "Build a Mixture Hidden Markov Model",
      "topics": [
        "build_mhmm"
      ]
    },
    {
      "page": "build_mm",
      "title": "Build a Markov Model",
      "topics": [
        "build_mm"
      ]
    },
    {
      "page": "build_mmm",
      "title": "Build a Mixture Markov Model",
      "topics": [
        "build_mmm"
      ]
    },
    {
      "page": "cluster_names",
      "title": "Get Cluster Names from Mixture HMMs",
      "topics": [
        "cluster_names"
      ]
    },
    {
      "page": "cluster_names-set",
      "title": "Set Cluster Names for Mixture Models",
      "topics": [
        "cluster_names<-"
      ]
    },
    {
      "page": "coef",
      "title": "Get the Estimated Regression Coefficients of Non-Homogeneous Hidden Markov Models",
      "topics": [
        "coef.mnhmm",
        "coef.nhmm"
      ]
    },
    {
      "page": "colorpalette",
      "title": "Color palettes",
      "topics": [
        "colorpalette"
      ]
    },
    {
      "page": "data_to_stslist",
      "title": "Transform TraMineR's state sequence object to data.table and vice versa",
      "topics": [
        "data_to_stslist",
        "stslist_to_data"
      ]
    },
    {
      "page": "estimate_mnhmm",
      "title": "Estimate a Mixture Non-homogeneous Hidden Markov Model",
      "topics": [
        "estimate_mnhmm"
      ]
    },
    {
      "page": "estimate_nhmm",
      "title": "Estimate a Non-homogeneous Hidden Markov Model",
      "topics": [
        "estimate_nhmm"
      ]
    },
    {
      "page": "fanhmm_leaves",
      "title": "A feedback-augmented non-homogeneous hidden Markov Model for leaves data",
      "topics": [
        "fanhmm_leaves"
      ]
    },
    {
      "page": "fit_model",
      "title": "Estimate Parameters of (Mixture) Hidden Markov Models and Their Restricted Variants",
      "topics": [
        "fit_model"
      ]
    },
    {
      "page": "forward_backward",
      "title": "Forward and Backward Probabilities for Hidden Markov Model",
      "topics": [
        "forward_backward",
        "forward_backward.hmm",
        "forward_backward.mhmm",
        "forward_backward.mnhmm",
        "forward_backward.nhmm"
      ]
    },
    {
      "page": "cluster_probs",
      "title": "Extract the Prior Cluster Probabilities of MHMM or MNHMM",
      "topics": [
        "get_cluster_probs",
        "get_cluster_probs.mhmm",
        "get_cluster_probs.mnhmm"
      ]
    },
    {
      "page": "emission_probs",
      "title": "Extract the Emission Probabilities of Hidden Markov Model",
      "topics": [
        "get_emission_probs",
        "get_emission_probs.hmm",
        "get_emission_probs.mhmm",
        "get_emission_probs.mnhmm",
        "get_emission_probs.nhmm"
      ]
    },
    {
      "page": "initial_probs",
      "title": "Extract the Initial State Probabilities of Hidden Markov Model",
      "topics": [
        "get_initial_probs",
        "get_initial_probs.hmm",
        "get_initial_probs.mhmm",
        "get_initial_probs.mnhmm",
        "get_initial_probs.nhmm"
      ]
    },
    {
      "page": "get_marginals",
      "title": "Compute the Marginal Probabilities from NHMMs",
      "topics": [
        "get_marginals"
      ]
    },
    {
      "page": "transition_probs",
      "title": "Extract the State Transition Probabilities of Hidden Markov Model",
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