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  "Title": "Bayesian Probit Choice Modeling",
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  "Authors@R": "c(\nperson(\"Lennart\", \"Oelschl\\u00e4ger\",\nemail = \"oelschlaeger.lennart@gmail.com\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0001-5421-9313\")),\nperson(\"Dietmar\", \"Bauer\",\nemail = \"dietmar.bauer@uni-bielefeld.de\",\nrole = \"ctb\",\ncomment = c(ORCID = \"0000-0003-2920-7032\")))",
  "Description": "Bayes estimation of probit choice models in\ncross-sectional and panel settings. The package can analyze\nbinary, multivariate, ordered, and ranked choices, as well as\nheterogeneity of choice behavior among deciders. The main\nfunctionality includes model fitting via Gibbs sampling, tools\nfor convergence diagnostic, choice data simulation, in-sample\nand out-of-sample choice prediction, and model selection using\ninformation criteria and Bayes factors. The latent class model\nextension facilitates preference-based decider classification,\nwhere the number of latent classes can be inferred via the\nDirichlet process or a weight-based updating heuristic. This\nallows for flexible modeling of choice behavior without the\nneed to impose structural constraints. For a reference on the\nmethod, see Oelschlaeger and Bauer (2021)\n<https://trid.trb.org/view/1759753>.",
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  "Date/Publication": "2025-08-26 08:42:57 UTC",
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  "Author": "Lennart Oelschläger [aut, cre] (ORCID:\n<https://orcid.org/0000-0001-5421-9313>),\nDietmar Bauer [ctb] (ORCID: <https://orcid.org/0000-0003-2920-7032>)",
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    "d_to_gamma",
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    "mml",
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    "overview_effects",
    "plot_class_allocation",
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    "point_estimates",
    "pred_acc",
    "prepare_data",
    "R_hat",
    "RprobitB_parameter",
    "sample_allocation",
    "simulate_choices",
    "train_test",
    "update_b",
    "update_b_c",
    "update_classes_dp",
    "update_classes_wb",
    "update_coefficient",
    "update_d",
    "update_m",
    "update_Omega",
    "update_Omega_c",
    "update_s",
    "update_Sigma",
    "update_U",
    "update_U_ranked",
    "update_z",
    "WAIC"
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      "name": "train_choice",
      "title": "Stated Preferences for Train Traveling",
      "object": "train_choice",
      "class": [
        "data.frame"
      ],
      "fields": [
        "deciderID",
        "occasionID",
        "choice",
        "price_A",
        "time_A",
        "change_A",
        "comfort_A",
        "price_B",
        "time_B",
        "change_B",
        "comfort_B"
      ],
      "rows": 2929,
      "table": true,
      "tojson": true
    }
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    {
      "page": "as_cov_names",
      "title": "Re-label alternative specific covariates",
      "topics": [
        "as_cov_names"
      ]
    },
    {
      "page": "check_form",
      "title": "Check model formula",
      "topics": [
        "check_form"
      ]
    },
    {
      "page": "check_prior",
      "title": "Check prior parameters",
      "topics": [
        "check_prior"
      ]
    },
    {
      "page": "choice_probabilities",
      "title": "Compute choice probabilities",
      "topics": [
        "choice_probabilities"
      ]
    },
    {
      "page": "classification",
      "title": "Preference-based classification of deciders",
      "topics": [
        "classification"
      ]
    },
    {
      "page": "coef.RprobitB_fit",
      "title": "Extract model effects",
      "topics": [
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        "plot.RprobitB_coef",
        "print.RprobitB_coef"
      ]
    },
    {
      "page": "compute_p_si",
      "title": "Compute choice probabilities at posterior samples",
      "topics": [
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      ]
    },
    {
      "page": "cov_mix",
      "title": "Extract estimated covariance matrix of mixing distribution",
      "topics": [
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    },
    {
      "page": "create_lagged_cov",
      "title": "Create lagged choice covariates",
      "topics": [
        "create_lagged_cov"
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    },
    {
      "page": "d_to_gamma",
      "title": "Transform increments to thresholds",
      "topics": [
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    },
    {
      "page": "fit_model",
      "title": "Fit probit model to choice data",
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    },
    {
      "page": "get_cov",
      "title": "Extract covariates of choice occasion",
      "topics": [
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    },
    {
      "page": "gibbs_sampler",
      "title": "Gibbs sampler for probit models",
      "topics": [
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    {
      "page": "ll_ordered",
      "title": "Compute ordered probit log-likelihood",
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      ]
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        "plot.RprobitB_mml",
        "print.RprobitB_mml"
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      "topics": [
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        "print.RprobitB_model_selection"
      ]
    },
    {
      "page": "npar",
      "title": "Extract number of model parameters",
      "topics": [
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        "npar.RprobitB_fit"
      ]
    },
    {
      "page": "overview_effects",
      "title": "Print effect overview",
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      "page": "plot_class_allocation",
      "title": "Plot class allocation (for 'P_r = 2' only)",
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    },
    {
      "page": "plot_mixture_contour",
      "title": "Plot bivariate contour of mixing distributions",
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      "page": "plot_roc",
      "title": "Plot ROC curve",
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      "page": "plot.RprobitB_fit",
      "title": "Visualize fitted probit model",
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      "page": "point_estimates",
      "title": "Compute point estimates",
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      "title": "Compute prediction accuracy",
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      "title": "Predict choices",
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