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  "Title": "Discrete Choice Models for Economic Applications",
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  "Description": "Fast estimation of discrete-choice models for applied\neconomics. Frequentist likelihoods, analytical gradients, and\nHessians are implemented in C++ with 'OpenMP' parallelism,\nscaling efficiently to specifications with many\nalternative-specific constants. Compiled Gibbs samplers provide\nBayesian multinomial probit and hierarchical models.\nPost-estimation routines cover predicted shares, own- and\ncross-price elasticities, diversion ratios, willingness to pay,\nand welfare counterfactuals. Supports multinomial logit\n('MNL'), mixed logit ('MXL'), nested logit ('NL'), Bayesian\nmultinomial probit ('MNP'), and hierarchical Bayesian\nmultinomial logit and probit ('HMNL', 'HMNP').",
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    "blp",
    "blp_contraction",
    "consumer_surplus",
    "diversion_ratios",
    "elasticities",
    "ess",
    "get_halton_normals",
    "gof",
    "logsum",
    "mc_asymptotics",
    "mcse",
    "monte_carlo",
    "mxl_blp_contraction",
    "new_choicer_sim",
    "nl_blp_contraction",
    "ppc_shares",
    "prepare_hmnl_data",
    "prepare_hmnp_data",
    "prepare_mnl_data",
    "prepare_mnp_data",
    "prepare_mxl_data",
    "prepare_nl_data",
    "recovery_table",
    "rhat",
    "run_hmnlogit",
    "run_hmnprobit",
    "run_mnlogit",
    "run_mnprobit",
    "run_mxlogit",
    "run_nestlogit",
    "sample_by_choice",
    "set_num_threads",
    "simulate_hmnl_data",
    "simulate_hmnp_data",
    "simulate_mnl_data",
    "simulate_mnp_data",
    "simulate_mxl_data",
    "simulate_nl_data",
    "thread_info",
    "traceplot",
    "wesml_vcov",
    "wesml_weights",
    "wtp"
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      "title": "Intercity travel mode choice",
      "object": "mode_choice",
      "class": [
        "data.frame"
      ],
      "fields": [
        "id",
        "mode",
        "choice",
        "wait",
        "travel",
        "vcost",
        "gcost",
        "income",
        "size"
      ],
      "rows": 840,
      "table": true,
      "tojson": true
    }
  ],
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    {
      "page": "blp",
      "title": "BLP contraction mapping",
      "topics": [
        "blp"
      ]
    },
    {
      "page": "blp_contraction",
      "title": "BLP95 contraction mapping to find delta given target shares",
      "topics": [
        "blp_contraction"
      ]
    },
    {
      "page": "blp.choicer_mnl",
      "title": "BLP contraction mapping for multinomial logit model",
      "topics": [
        "blp.choicer_mnl"
      ]
    },
    {
      "page": "blp.choicer_mxl",
      "title": "BLP contraction mapping for mixed logit model",
      "topics": [
        "blp.choicer_mxl"
      ]
    },
    {
      "page": "blp.choicer_nl",
      "title": "BLP contraction mapping for nested logit model",
      "topics": [
        "blp.choicer_nl"
      ]
    },
    {
      "page": "coef.choicer_fit",
      "title": "Extract coefficients from a choicer_fit object",
      "topics": [
        "coef.choicer_fit"
      ]
    },
    {
      "page": "coef.choicer_hb",
      "title": "Extract posterior means from a hierarchical Bayes fit",
      "topics": [
        "coef.choicer_hb"
      ]
    },
    {
      "page": "coef.choicer_mnp",
      "title": "Extract coefficients from a choicer_mnp object",
      "topics": [
        "coef.choicer_mnp"
      ]
    },
    {
      "page": "consumer_surplus",
      "title": "Expected consumer surplus",
      "topics": [
        "consumer_surplus",
        "consumer_surplus.choicer_hmnl",
        "consumer_surplus.choicer_hmnp",
        "consumer_surplus.choicer_mnl",
        "consumer_surplus.choicer_mxl",
        "consumer_surplus.choicer_nl"
      ]
    },
    {
      "page": "diversion_ratios",
      "title": "Compute aggregate diversion ratios",
      "topics": [
        "diversion_ratios",
        "diversion_ratios.choicer_hb"
      ]
    },
    {
      "page": "diversion_ratios.choicer_mnl",
      "title": "Diversion ratios for multinomial logit model",
      "topics": [
        "diversion_ratios.choicer_mnl"
      ]
    },
    {
      "page": "diversion_ratios.choicer_mxl",
      "title": "Diversion ratios for mixed logit model",
      "topics": [
        "diversion_ratios.choicer_mxl"
      ]
    },
    {
      "page": "diversion_ratios.choicer_nl",
      "title": "Diversion ratios for nested logit model",
      "topics": [
        "diversion_ratios.choicer_nl"
      ]
    },
    {
      "page": "elasticities",
      "title": "Compute aggregate elasticities",
      "topics": [
        "elasticities",
        "elasticities.choicer_hb"
      ]
    },
    {
      "page": "elasticities.choicer_mnl",
      "title": "Elasticities for multinomial logit model",
      "topics": [
        "elasticities.choicer_mnl"
      ]
    },
    {
      "page": "elasticities.choicer_mxl",
      "title": "Elasticities for mixed logit model",
      "topics": [
        "elasticities.choicer_mxl"
      ]
    },
    {
      "page": "elasticities.choicer_nl",
      "title": "Elasticities for nested logit model",
      "topics": [
        "elasticities.choicer_nl"
      ]
    },
    {
      "page": "ess",
      "title": "Rank-normalized effective sample size (bulk and tail)",
      "topics": [
        "ess"
      ]
    },
    {
      "page": "get_halton_normals",
      "title": "Halton draws for mixed logit",
      "topics": [
        "get_halton_normals"
      ]
    },
    {
      "page": "gof",
      "title": "Goodness of fit for a fitted choice model",
      "topics": [
        "gof",
        "gof.choicer_fit"
      ]
    },
    {
      "page": "logLik.choicer_fit",
      "title": "Extract log-likelihood from a choicer_fit object",
      "topics": [
        "logLik.choicer_fit"
      ]
    },
    {
      "page": "logsum",
      "title": "Expected logsum (inclusive value) per choice situation",
      "topics": [
        "logsum",
        "logsum.choicer_hmnl",
        "logsum.choicer_hmnp",
        "logsum.choicer_mnl",
        "logsum.choicer_mxl",
        "logsum.choicer_nl"
      ]
    },
    {
      "page": "mc_asymptotics",
      "title": "Asymptotic diagnostics for a Monte Carlo study",
      "topics": [
        "mc_asymptotics"
      ]
    },
    {
      "page": "mcse",
      "title": "Monte Carlo standard error of posterior summaries",
      "topics": [
        "mcse"
      ]
    },
    {
      "page": "mode_choice",
      "title": "Intercity travel mode choice",
      "topics": [
        "mode_choice"
      ]
    },
    {
      "page": "monte_carlo",
      "title": "Monte Carlo parameter recovery",
      "topics": [
        "monte_carlo"
      ]
    },
    {
      "page": "mxl_blp_contraction",
      "title": "BLP contraction mapping for mixed logit",
      "topics": [
        "mxl_blp_contraction"
      ]
    },
    {
      "page": "new_choicer_sim",
      "title": "Construct a 'choicer_sim' object",
      "topics": [
        "new_choicer_sim"
      ]
    },
    {
      "page": "nl_blp_contraction",
      "title": "BLP95 contraction mapping for the Nested Logit model",
      "topics": [
        "nl_blp_contraction"
      ]
    },
    {
      "page": "nobs.choicer_fit",
      "title": "Extract number of observations from a choicer_fit object",
      "topics": [
        "nobs.choicer_fit"
      ]
    },
    {
      "page": "nobs.choicer_hb",
      "title": "Number of choice situations behind a hierarchical Bayes fit",
      "topics": [
        "nobs.choicer_hb"
      ]
    },
    {
      "page": "nobs.choicer_mnp",
      "title": "Extract number of observations from a choicer_mnp object",
      "topics": [
        "nobs.choicer_mnp"
      ]
    },
    {
      "page": "ppc_shares",
      "title": "Posterior-predictive share check for hierarchical Bayes fits",
      "topics": [
        "ppc_shares"
      ]
    },
    {
      "page": "predict.choicer_hb",
      "title": "Posterior choice probabilities and shares for hierarchical Bayes fits",
      "topics": [
        "predict.choicer_hb"
      ]
    },
    {
      "page": "predict.choicer_mnl",
      "title": "Predict from a multinomial logit model",
      "topics": [
        "predict.choicer_mnl"
      ]
    },
    {
      "page": "predict.choicer_mxl",
      "title": "Predict from a mixed logit model",
      "topics": [
        "predict.choicer_mxl"
      ]
    },
    {
      "page": "predict.choicer_nl",
      "title": "Predict from a nested logit model",
      "topics": [
        "predict.choicer_nl"
      ]
    },
    {
      "page": "prepare_hmnl_data",
      "title": "Prepare inputs for hierarchical multinomial logit estimation",
      "topics": [
        "prepare_hmnl_data"
      ]
    },
    {
      "page": "prepare_hmnp_data",
      "title": "Prepare inputs for hierarchical multinomial probit estimation",
      "topics": [
        "prepare_hmnp_data"
      ]
    },
    {
      "page": "prepare_mnl_data",
      "title": "Prepare inputs for multinomial logit estimation",
      "topics": [
        "prepare_mnl_data"
      ]
    },
    {
      "page": "prepare_mnp_data",
      "title": "Prepare inputs for Bayesian multinomial probit estimation",
      "topics": [
        "prepare_mnp_data"
      ]
    },
    {
      "page": "prepare_mxl_data",
      "title": "Prepare inputs for mixed logit estimation",
      "topics": [
        "prepare_mxl_data"
      ]
    },
    {
      "page": "prepare_nl_data",
      "title": "Prepare inputs for nested logit estimation",
      "topics": [
        "prepare_nl_data"
      ]
    },
    {
      "page": "print.choicer_cs",
      "title": "Print a consumer surplus summary",
      "topics": [
        "print.choicer_cs"
      ]
    },
    {
      "page": "print.choicer_fit",
      "title": "Print a choicer_fit object",
      "topics": [
        "print.choicer_fit"
      ]
    },
    {
      "page": "print.choicer_gof",
      "title": "Print goodness-of-fit measures",
      "topics": [
        "print.choicer_gof"
      ]
    },
    {
      "page": "print.choicer_hb",
      "title": "Print a hierarchical Bayes fit",
      "topics": [
        "print.choicer_hb"
      ]
    },
    {
      "page": "print.choicer_mnp",
      "title": "Print a choicer_mnp object",
      "topics": [
        "print.choicer_mnp"
      ]
    },
    {
      "page": "print.choicer_wtp",
      "title": "Print a WTP table",
      "topics": [
        "print.choicer_wtp"
      ]
    },
    {
      "page": "print.summary.choicer_hb",
      "title": "Print the summary of a hierarchical Bayes fit",
      "topics": [
        "print.summary.choicer_hb"
      ]
    },
    {
      "page": "print.summary.choicer_mnl",
      "title": "Print summary for multinomial logit model",
      "topics": [
        "print.summary.choicer_mnl"
      ]
    },
    {
      "page": "print.summary.choicer_mnp",
      "title": "Print summary for Bayesian multinomial probit model",
      "topics": [
        "print.summary.choicer_mnp"
      ]
    },
    {
      "page": "print.summary.choicer_mxl",
      "title": "Print summary for mixed logit model",
      "topics": [
        "print.summary.choicer_mxl"
      ]
    },
    {
      "page": "print.summary.choicer_nl",
      "title": "Print summary for nested logit model",
      "topics": [
        "print.summary.choicer_nl"
      ]
    },
    {
      "page": "recovery_table",
      "title": "Parameter recovery table",
      "topics": [
        "recovery_table",
        "recovery_table.choicer_fit",
        "recovery_table.choicer_hb",
        "recovery_table.choicer_mc",
        "recovery_table.choicer_mnp"
      ]
    },
    {
      "page": "rhat",
      "title": "Split-\\widehat{R} convergence diagnostic",
      "topics": [
        "rhat"
      ]
    },
    {
      "page": "run_hmnlogit",
      "title": "Fit a hierarchical Bayesian multinomial logit (HMNL)",
      "topics": [
        "run_hmnlogit"
      ]
    },
    {
      "page": "run_hmnprobit",
      "title": "Fit a hierarchical Bayesian multinomial probit (HMNP)",
      "topics": [
        "run_hmnprobit"
      ]
    },
    {
      "page": "run_mnlogit",
      "title": "Runs multinomial logit estimation",
      "topics": [
        "run_mnlogit"
      ]
    },
    {
      "page": "run_mnprobit",
      "title": "Runs Bayesian multinomial probit estimation",
      "topics": [
        "run_mnprobit"
      ]
    },
    {
      "page": "run_mxlogit",
      "title": "Runs mixed logit estimation",
      "topics": [
        "run_mxlogit"
      ]
    },
    {
      "page": "run_nestlogit",
      "title": "Runs nested logit estimation",
      "topics": [
        "run_nestlogit"
      ]
    },
    {
      "page": "sample_by_choice",
      "title": "Draw a choice-based sample stratified by the chosen alternative",
      "topics": [
        "sample_by_choice"
      ]
    },
    {
      "page": "set_num_threads",
      "title": "Set the number of OpenMP threads used by choicer",
      "topics": [
        "set_num_threads"
      ]
    },
    {
      "page": "simulate_hmnl_data",
      "title": "Simulate hierarchical multinomial logit data",
      "topics": [
        "simulate_hmnl_data"
      ]
    },
    {
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