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  "Title": "Hierarchical Dirichlet Process Generalized Linear Models",
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  "Description": "Implementation of MCMC algorithms to estimate the\nHierarchical Dirichlet Process Generalized Linear Model\n(hdpGLM) presented in the paper Ferrari (2020) Modeling\nContext-Dependent Latent Heterogeneity, Political Analysis\n<DOI:10.1017/pan.2019.13> and <doi:10.18637/jss.v107.i10>.",
  "License": "MIT + file LICENSE",
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  "Date/Publication": "2025-04-18 23:51:28 UTC",
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        "inequality",
        "income",
        "ideology"
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      "table": true,
      "tojson": true
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    {
      "name": "welfare2",
      "title": "Fake data set with 2000 observations",
      "object": "welfare2",
      "class": [
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        "tbl",
        "data.frame"
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      "page": "classify",
      "title": "Classify data points",
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      "title": "Extract dpGLM fitted coefficients",
      "topics": [
        "coef.dpGLM"
      ]
    },
    {
      "page": "coef.hdpGLM",
      "title": "Extract hdpGLM fitted coefficients",
      "topics": [
        "coef.hdpGLM"
      ]
    },
    {
      "page": "hdpGLM",
      "title": "Fit Hierarchical Dirichlet Process GLM",
      "topics": [
        "hdpGLM"
      ]
    },
    {
      "page": "hdpGLM_classify",
      "title": "Deprecated",
      "topics": [
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      ]
    },
    {
      "page": "hdpGLM_package",
      "title": "hdpGLM: A package for computating Hierarchical Dirichlet Process Generalized Linear Models",
      "topics": [
        "hdpGLM_package"
      ]
    },
    {
      "page": "hdpGLM_simParameters",
      "title": "Simulate the parameters of the model",
      "topics": [
        "hdpGLM_simParameters"
      ]
    },
    {
      "page": "hdpGLM_simulateData",
      "title": "Simulate a Data Set from hdpGLM",
      "topics": [
        "hdpGLM_simulateData"
      ]
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    {
      "page": "mcmc_info.dpGLM",
      "title": "mcmc",
      "topics": [
        "mcmc_info.dpGLM"
      ]
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    {
      "page": "mcmc_info.hdpGLM",
      "title": "mcmc",
      "topics": [
        "mcmc_info.hdpGLM"
      ]
    },
    {
      "page": "nclusters",
      "title": "nclusters",
      "topics": [
        "nclusters"
      ]
    },
    {
      "page": "plot_beta",
      "title": "Plot beta posterior distribution",
      "topics": [
        "plot_beta"
      ]
    },
    {
      "page": "plot_beta_sim",
      "title": "Plot simulated data",
      "topics": [
        "plot_beta_sim"
      ]
    },
    {
      "page": "plot_hdpglm",
      "title": "Plot posterior distributions",
      "topics": [
        "plot_hdpglm"
      ]
    },
    {
      "page": "plot_pexp_beta",
      "title": "Plot beta posterior expectation",
      "topics": [
        "plot_pexp_beta"
      ]
    },
    {
      "page": "plot_tau",
      "title": "Plot tau",
      "topics": [
        "plot_tau"
      ]
    },
    {
      "page": "plot.dpGLM",
      "title": "Default plot for class dpGLM",
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      "page": "plot.hdpGLM",
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      "page": "predict.dpGLM",
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    },
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      "page": "predict.hdpGLM",
      "title": "hdpGLM Predicted values",
      "topics": [
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      ]
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      "page": "print.dpGLM",
      "title": "Print",
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        "print.dpGLM"
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    {
      "page": "print.dpGLM_data",
      "title": "Print",
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        "print.dpGLM_data"
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    {
      "page": "print.hdpGLM",
      "title": "Print",
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        "print.hdpGLM"
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      "page": "print.hdpGLM_data",
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