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    "bootstrap_lm_cov_latent_cont",
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    "draw_lm_basic_cont",
    "draw_lm_cov_latent",
    "draw_lm_cov_latent_cont",
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    "est_lm_basic_cont",
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    "est_lm_cov_latent",
    "est_lm_cov_latent_cont",
    "est_lm_cov_manifest",
    "est_lm_mixed",
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    "est_mc_cov",
    "lmest",
    "lmestCont",
    "lmestData",
    "lmestDecoding",
    "lmestFormula",
    "lmestMc",
    "lmestMixed",
    "lmestSearch",
    "long2matrices",
    "long2wide",
    "matrices2long",
    "mc_multinom",
    "se",
    "search.model.LM"
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      "file": "data_criminal_sim.rda",
      "class": [
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      "fields": [
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    {
      "page": "LMest-package",
      "title": "Overview of the Package LMest",
      "topics": [
        "LMest-package",
        "LMest"
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    },
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      "title": "Parametric bootstrap",
      "topics": [
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        "bootstrap.LMbasic",
        "bootstrap.LMbasiccont",
        "bootstrap.LMlatent",
        "bootstrap.LMlatentcont"
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    },
    {
      "page": "bootstrap_lm_basic",
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      "topics": [
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    {
      "page": "bootstrap_lm_basic_cont",
      "title": "Parametric bootstrap for the basic LM model for continuous outcomes",
      "topics": [
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      "page": "bootstrap_lm_cov_latent",
      "title": "Parametric bootstrap for LM models with individual covariates in the latent model",
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    {
      "page": "bootstrap_lm_cov_latent_cont",
      "title": "Parametric bootstrap for LM models for continuous outcomes with individual covariates in the latent model",
      "topics": [
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    {
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      "topics": [
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    {
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      "title": "Employment dataset",
      "topics": [
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    {
      "page": "data_heart_sim",
      "title": "Health dataset",
      "topics": [
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    {
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      "topics": [
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    },
    {
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      "title": "Marketing dataset",
      "topics": [
        "data_market_sim"
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    },
    {
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      "title": "Self-reported health status dataset",
      "topics": [
        "data_SRHS_long"
      ]
    },
    {
      "page": "decoding",
      "title": "Perform local and global decoding",
      "topics": [
        "decoding"
      ]
    },
    {
      "page": "design_matrices",
      "title": "Build design matrices for 'mc_multinom'",
      "topics": [
        "design_matrices"
      ]
    },
    {
      "page": "draw",
      "title": "Draw simulated sample from a Generalized Latent Markov Model",
      "topics": [
        "draw",
        "draw.LMbasic",
        "draw.LMbasiccont",
        "draw.LMlatent",
        "draw.LMlatentcont",
        "draw.LMmixed"
      ]
    },
    {
      "page": "draw_lm_basic",
      "title": "Draw samples from the basic LM model",
      "topics": [
        "draw_lm_basic"
      ]
    },
    {
      "page": "draw_lm_basic_cont",
      "title": "Draw samples from the basic LM model for continuous outcomes",
      "topics": [
        "draw_lm_basic_cont"
      ]
    },
    {
      "page": "draw_lm_cov_latent",
      "title": "Draw samples from LM model with covariaates in the latent model",
      "topics": [
        "draw_lm_cov_latent"
      ]
    },
    {
      "page": "draw_lm_cov_latent_cont",
      "title": "Draw samples from LM model for continuous outcomes with covariaates in the latent model",
      "topics": [
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    },
    {
      "page": "draw_lm_mixed",
      "title": "Draws samples from the mixed LM model",
      "topics": [
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      "page": "est_lm_basic",
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      "page": "est_lm_basic_cont",
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      "topics": [
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    {
      "page": "est_LM_cat_multinom",
      "title": "Estimation of Constrained Hidden Markov Models",
      "topics": [
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    {
      "page": "est_lm_cov_latent",
      "title": "Estimate LM model with covariates in the latent model",
      "topics": [
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    {
      "page": "est_lm_cov_latent_cont",
      "title": "Estimate LM model for continuous outcomes with covariates in the latent model",
      "topics": [
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    },
    {
      "page": "est_lm_cov_manifest",
      "title": "Estimate LM model with covariates in the measurement model",
      "topics": [
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    {
      "page": "est_lm_mixed",
      "title": "Estimate mixed LM model",
      "topics": [
        "est_lm_mixed"
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    {
      "page": "est_mc_basic",
      "title": "Estimate basic Markov chain (MC) model",
      "topics": [
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    },
    {
      "page": "est_mc_cov",
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      "topics": [
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    {
      "page": "LMbasic-class",
      "title": "Class ''LMbasic''",
      "topics": [
        "LMbasic-class"
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    },
    {
      "page": "LMbasiccont-class",
      "title": "Class ''LMbasiccont''",
      "topics": [
        "LMbasiccont-class"
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    },
    {
      "page": "lmest",
      "title": "Estimate Latent Markov models for categorical responses",
      "topics": [
        "lmest"
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    },
    {
      "page": "lmestCont",
      "title": "Estimate Latent Markov models for continuous responses",
      "topics": [
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    {
      "page": "lmestData",
      "title": "Data for 'LMest' functions",
      "topics": [
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    },
    {
      "page": "lmestDecoding",
      "title": "Perform local and global decoding",
      "topics": [
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        "lmestDecoding.LMbasiccont",
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        "lmestDecoding.LMmanifest",
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    },
    {
      "page": "lmestMc",
      "title": "Estimate Markov Chain models",
      "topics": [
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    {
      "page": "lmestMixed",
      "title": "Estimate mixed Latent Markov models",
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      "page": "lmestSearch",
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      "topics": [
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      "title": "Class ''LMmanifestcont''",
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    {
      "page": "LMmixed-class",
      "title": "Class ''LMmixed''",
      "topics": [
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    {
      "page": "long2matrices",
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      "topics": [
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    },
    {
      "page": "long2wide",
      "title": "From data in the long format to data in the wide format",
      "topics": [
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    {
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      "topics": [
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    },
    {
      "page": "mc_multinom",
      "title": "Estimate MC model with constraints",
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      "title": "Class ''MCbasic''",
      "topics": [
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    },
    {
      "page": "MCcov-class",
      "title": "Class ''MCcov''",
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    },
    {
      "page": "NLSYlong",
      "title": "National Longitudinal Survey of Youth data",
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    },
    {
      "page": "plot.LMest",
      "title": "Plots for Generalized Latent Markov Models",
      "topics": [
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        "plot.LMlatentcont",
        "plot.LMsearch"
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    },
    {
      "page": "print",
      "title": "Print the output",
      "topics": [
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        "print.LMbasiccont",
        "print.LMlatent",
        "print.LMlatentcont",
        "print.LMmanifest",
        "print.LMmixed",
        "print.LMsearch",
        "print.LM_cat_multinom",
        "print.MCbasic",
        "print.MCcov",
        "print.mc_multinom"
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    },
    {
      "page": "PSIDlong",
      "title": "Dataset about income dynamics",
      "topics": [
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    },
    {
      "page": "RLMSdat",
      "title": "Dataset about job satisfaction",
      "topics": [
        "RLMSdat"
      ]
    },
    {
      "page": "RLMSlong",
      "title": "Dataset about job satisfaction",
      "topics": [
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    },
    {
      "page": "se",
      "title": "Standard errors",
      "topics": [
        "se",
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        "se.LMbasiccont",
        "se.LMlatent",
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    },
    {
      "page": "search.model.LM",
      "title": "Search for the global maximum of the log-likelihood",
      "topics": [
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    },
    {
      "page": "summary",
      "title": "Summary of LM fits",
      "topics": [
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        "summary.LMbasiccont",
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        "summary.LMlatentcont",
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      "source": "vignetteLMest.Rmd",
      "filename": "vignetteLMest.html",
      "title": "Introduction to LMest",
      "author": "Bartolucci, F., Pandolfi, S., Pennoni, F., Serafini, A.",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Data: RLMSlong",
        "Data: PSIDlong",
        "Data: data_criminal_sim",
        "Data: NLSYlong",
        "Prepare and explore data",
        "Building a formula",
        "Latent Markov models for categorical responses",
        "Markov chain models with constrained transition probabilities",
        "Latent Markov model for continuous outcomes",
        "Mixed Latent Markov model",
        "Search for the global maximum of the log-likelihood",
        "Local and global decoding",
        "Bootstrapping",
        "Draw samples"
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      "created": "2020-09-12 08:00:03",
      "modified": "2026-10-08 16:10:02",
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