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    "lmestCont",
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      "title": "Overview of the Package LMest",
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        "LMest"
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      "topics": [
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      "page": "decoding",
      "title": "Perform local and global decoding",
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      "title": "Draw simulated sample from a Generalized Latent Markov Model",
      "topics": [
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        "draw.LMbasic",
        "draw.LMbasiccont",
        "draw.LMlatent",
        "draw.LMlatentcont",
        "draw.LMmixed"
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    {
      "page": "draw_lm_basic",
      "title": "Draw samples from the basic LM model",
      "topics": [
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    },
    {
      "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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      "title": "Estimate basic LM model",
      "topics": [
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      "title": "Estimate basic LM model for continuous outcomes",
      "topics": [
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    {
      "page": "est_lm_cov_latent",
      "title": "Estimate LM model with covariates in the latent model",
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      "page": "est_lm_cov_latent_cont",
      "title": "Estimate LM model for continuous outcomes with covariates in the latent model",
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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"
      ]
    },
    {
      "page": "est_mc_basic",
      "title": "Estimate basic Markov chain (MC) model",
      "topics": [
        "est_mc_basic"
      ]
    },
    {
      "page": "est_mc_cov",
      "title": "Estimate Markov chain (MC) model with covariates",
      "topics": [
        "est_mc_cov"
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    },
    {
      "page": "LMbasic-class",
      "title": "Class ''LMbasic''",
      "topics": [
        "LMbasic-class"
      ]
    },
    {
      "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": [
        "lmestCont"
      ]
    },
    {
      "page": "lmestData",
      "title": "Data for 'LMest' functions",
      "topics": [
        "lmestData"
      ]
    },
    {
      "page": "lmestDecoding",
      "title": "Perform local and global decoding",
      "topics": [
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        "lmestDecoding.LMbasic",
        "lmestDecoding.LMbasiccont",
        "lmestDecoding.LMlatent",
        "lmestDecoding.LMmanifest",
        "lmestDecoding.LMmixed"
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      "title": "Formulas for 'LMest' functions",
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      ]
    },
    {
      "page": "lmestMc",
      "title": "Estimate Markov Chain models",
      "topics": [
        "lmestMc"
      ]
    },
    {
      "page": "lmestMixed",
      "title": "Estimate mixed Latent Markov models",
      "topics": [
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      ]
    },
    {
      "page": "lmestSearch",
      "title": "Search for the global maximum of the log-likelihood",
      "topics": [
        "lmestSearch"
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    },
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      "title": "Class ''LMlatent''",
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    },
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      "page": "LMlatentcont-class",
      "title": "Class ''LMlatentcont''",
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    {
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      "title": "Class ''LMmanifest''",
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    {
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      "title": "Class ''LMmanifestcont''",
      "topics": [
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      "title": "Class ''LMmixed''",
      "topics": [
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    {
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      "title": "From data in the long format to data in array format",
      "topics": [
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    },
    {
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      "topics": [
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    },
    {
      "page": "matrices2long",
      "title": "From data in array format to data in long format",
      "topics": [
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    },
    {
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      "title": "Class ''MCbasic''",
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    },
    {
      "page": "MCcov-class",
      "title": "Class ''MCcov''",
      "topics": [
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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.LMbasiccont",
        "plot.LMlatent",
        "plot.LMlatentcont",
        "plot.LMsearch"
      ]
    },
    {
      "page": "print",
      "title": "Print the output",
      "topics": [
        "print.LMbasic",
        "print.LMbasiccont",
        "print.LMlatent",
        "print.LMlatentcont",
        "print.LMmanifest",
        "print.LMmixed",
        "print.LMsearch",
        "print.MCbasic",
        "print.MCcov"
      ]
    },
    {
      "page": "PSIDlong",
      "title": "Dataset about income dynamics",
      "topics": [
        "PSIDlong"
      ]
    },
    {
      "page": "RLMSdat",
      "title": "Dataset about job satisfaction",
      "topics": [
        "RLMSdat"
      ]
    },
    {
      "page": "RLMSlong",
      "title": "Dataset about job satisfaction",
      "topics": [
        "RLMSlong"
      ]
    },
    {
      "page": "se",
      "title": "Standard errors",
      "topics": [
        "se",
        "se.LMbasic",
        "se.LMbasiccont",
        "se.LMlatent",
        "se.LMlatentcont"
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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.LMlatent",
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        "summary.LMmanifest",
        "summary.LMmixed",
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  "_vignettes": [
    {
      "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",
        "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"
      ],
      "created": "2020-09-12 08:00:03",
      "modified": "2024-08-31 02:49:53",
      "commits": 4
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