{
  "_id": "6a2723c824555f66ed5296f8",
  "Package": "choicer",
  "Title": "Discrete Choice Models for Economic Applications",
  "Version": "0.1.0.9000",
  "Authors@R": "person(\"Fernando\", \"Cordeiro\", , \"fernandolpcordeiro@gmail.com\", role = c(\"aut\", \"cre\", \"cph\"))",
  "Description": "Fast estimation of discrete-choice models for applied\neconomics. Likelihoods, analytical gradients and Hessians are\nimplemented in C++ with 'OpenMP' parallelism, scaling\nefficiently to specifications with many alternative-specific\nconstants. Post-estimation routines return predicted shares,\nown- and cross-price elasticities, and diversion ratios.\nSupports multinomial logit ('MNL'), mixed logit ('MXL'), and\nnested logit ('NL').",
  "License": "LGPL (>= 3)",
  "URL": "https://github.com/fpcordeiro/choicer",
  "BugReports": "https://github.com/fpcordeiro/choicer/issues",
  "Encoding": "UTF-8",
  "Roxygen": "list(markdown = TRUE)",
  "Config/testthat/edition": "3",
  "Config/roxygen2/version": "8.0.0",
  "Config/pak/sysreqs": "cmake",
  "Repository": "https://fpcordeiro.r-universe.dev",
  "Date/Publication": "2026-06-08 19:01:16 UTC",
  "RemoteUrl": "https://github.com/fpcordeiro/choicer",
  "RemoteRef": "HEAD",
  "RemoteSha": "c077424e89154e3ce6275df76ee77f7871b33031",
  "NeedsCompilation": "yes",
  "Packaged": {
    "Date": "2026-06-08 20:11:26 UTC",
    "User": "root"
  },
  "Author": "Fernando Cordeiro [aut, cre, cph]",
  "Maintainer": "Fernando Cordeiro <fernandolpcordeiro@gmail.com>",
  "MD5sum": "6e02e061e3543b0f7be2aa4750ef47e5",
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  "_file": "choicer_0.1.0.9000.tar.gz",
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  "_created": "2026-06-08T20:11:26.000Z",
  "_published": "2026-06-08T20:19:20.528Z",
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  "_buildurl": "https://github.com/r-universe/fpcordeiro/actions/runs/27163906842",
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  "_host": "GitHub-Actions",
  "_upstream": "https://github.com/fpcordeiro/choicer",
  "_commit": {
    "id": "c077424e89154e3ce6275df76ee77f7871b33031",
    "author": "Claude <noreply@anthropic.com>",
    "committer": "Fernando Cordeiro <fernandolpcordeiro@gmail.com>",
    "message": "fix(blp): correct MNL BLP contraction NaN with outside option\n\nThe MNL blp_contraction kernel built a length-(J+1) delta vector (index 0 =\noutside option, 1..J = inside ASCs) but passed the FULL vector to\nmnl_predict_shares_internal, which indexes delta by inside-alt index {0..J-1}.\nIt therefore read [outside, inside_1, ..., inside_{J-1}] as the inside ASCs,\nusing the outside slot as the first inside ASC and dropping the last. The\noutside delta was also never pinned at 0, so shares were corrupted each\niteration and the contraction diverged to NaN whenever\ninclude_outside_option = TRUE.\n\nMirror the fix already applied to nl_blp_contraction:\n- feed the internal the length-J inside slice (delta.subvec(1, num_alts-1));\n- pin delta[0] = 0 at init and every iteration;\n- guard against non-positive target_shares (log undefined).\n\nThe MXL mxl_blp_contraction kernel was already correct (it operates on a\nlength-J_inside inside-delta and works with inside shares), verified by a new\noutside-option round-trip test.\n\nTests:\n- strengthen the MNL outside-option test to assert finite output and a\n  round-trip back to the fitted inside ASCs (previously only checked shape and\n  documented the NaN divergence);\n- add a target_shares positivity guard test;\n- add an MXL outside-option round-trip test.\n",
    "time": 1780945276
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    "extra/citation.cff",
    "extra/citation.html",
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    "extra/citation.txt",
    "extra/contents.json",
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    "blp_contraction",
    "build_var_mat",
    "diversion_ratios",
    "elasticities",
    "get_halton_normals",
    "jacobian_vech_Sigma",
    "mc_asymptotics",
    "mnl_diversion_ratios_parallel",
    "mnl_elasticities_parallel",
    "mnl_loglik_gradient_parallel",
    "mnl_loglik_hessian_parallel",
    "mnl_predict",
    "mnl_predict_shares",
    "monte_carlo",
    "mxl_bhhh_parallel",
    "mxl_blp_contraction",
    "mxl_diversion_ratios_parallel",
    "mxl_elasticities_parallel",
    "mxl_hessian_parallel",
    "mxl_loglik_gradient_parallel",
    "mxl_predict",
    "mxl_predict_shares",
    "new_choicer_sim",
    "nl_blp_contraction",
    "nl_diversion_ratios_parallel",
    "nl_elasticities_parallel",
    "nl_loglik_gradient_parallel",
    "nl_loglik_numeric_hessian",
    "nl_predict",
    "nl_predict_shares",
    "prepare_mnl_data",
    "prepare_mxl_data",
    "prepare_nl_data",
    "recovery_table",
    "run_mnlogit",
    "run_mxlogit",
    "run_nestlogit",
    "sample_by_choice",
    "simulate_mnl_data",
    "simulate_mxl_data",
    "simulate_nl_data",
    "wesml_vcov",
    "wesml_weights"
  ],
  "_help": [
    {
      "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": "build_var_mat",
      "title": "Reconstruct variance matrix L from L_params",
      "topics": [
        "build_var_mat"
      ]
    },
    {
      "page": "coef.choicer_fit",
      "title": "Extract coefficients from a choicer_fit object",
      "topics": [
        "coef.choicer_fit"
      ]
    },
    {
      "page": "diversion_ratios",
      "title": "Compute aggregate diversion ratios",
      "topics": [
        "diversion_ratios"
      ]
    },
    {
      "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"
      ]
    },
    {
      "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": "get_halton_normals",
      "title": "Halton draws for mixed logit",
      "topics": [
        "get_halton_normals"
      ]
    },
    {
      "page": "jacobian_vech_Sigma",
      "title": "Utility to compute analytical Jacobian of random coefficient matrix transformed by vech (dVech(Sigma) / dTheta)",
      "topics": [
        "jacobian_vech_Sigma"
      ]
    },
    {
      "page": "logLik.choicer_fit",
      "title": "Extract log-likelihood from a choicer_fit object",
      "topics": [
        "logLik.choicer_fit"
      ]
    },
    {
      "page": "mc_asymptotics",
      "title": "Asymptotic diagnostics for a Monte Carlo study",
      "topics": [
        "mc_asymptotics"
      ]
    },
    {
      "page": "mnl_diversion_ratios_parallel",
      "title": "Compute MNL diversion ratios (parallelized over individuals)",
      "topics": [
        "mnl_diversion_ratios_parallel"
      ]
    },
    {
      "page": "mnl_elasticities_parallel",
      "title": "Compute aggregate elasticities for MNL model",
      "topics": [
        "mnl_elasticities_parallel"
      ]
    },
    {
      "page": "mnl_loglik_gradient_parallel",
      "title": "Log-likelihood and gradient for multinomial logit model",
      "topics": [
        "mnl_loglik_gradient_parallel"
      ]
    },
    {
      "page": "mnl_loglik_hessian_parallel",
      "title": "Hessian matrix for multinomial logit model",
      "topics": [
        "mnl_loglik_hessian_parallel"
      ]
    },
    {
      "page": "mnl_predict",
      "title": "Prediction of choice probabilities and utilities based on fitted model",
      "topics": [
        "mnl_predict"
      ]
    },
    {
      "page": "mnl_predict_shares",
      "title": "Prediction of market shares based on fitted model",
      "topics": [
        "mnl_predict_shares"
      ]
    },
    {
      "page": "monte_carlo",
      "title": "Monte Carlo parameter recovery",
      "topics": [
        "monte_carlo"
      ]
    },
    {
      "page": "mxl_bhhh_parallel",
      "title": "BHHH (outer product of gradients) information matrix for Mixed Logit",
      "topics": [
        "mxl_bhhh_parallel"
      ]
    },
    {
      "page": "mxl_blp_contraction",
      "title": "BLP contraction mapping for mixed logit",
      "topics": [
        "mxl_blp_contraction"
      ]
    },
    {
      "page": "mxl_diversion_ratios_parallel",
      "title": "Diversion ratios for Mixed Logit (simulated, derivative-based)",
      "topics": [
        "mxl_diversion_ratios_parallel"
      ]
    },
    {
      "page": "mxl_elasticities_parallel",
      "title": "Compute aggregate elasticities for mixed logit model",
      "topics": [
        "mxl_elasticities_parallel"
      ]
    },
    {
      "page": "mxl_hessian_parallel",
      "title": "Analytical Hessian of the log-likelihood v2",
      "topics": [
        "mxl_hessian_parallel"
      ]
    },
    {
      "page": "mxl_loglik_gradient_parallel",
      "title": "Log-likelihood and gradient for Mixed Logit",
      "topics": [
        "mxl_loglik_gradient_parallel"
      ]
    },
    {
      "page": "mxl_predict",
      "title": "Per-observation simulated choice probabilities for Mixed Logit",
      "topics": [
        "mxl_predict"
      ]
    },
    {
      "page": "mxl_predict_shares",
      "title": "Predicted aggregate market shares for Mixed Logit",
      "topics": [
        "mxl_predict_shares"
      ]
    },
    {
      "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": "nl_diversion_ratios_parallel",
      "title": "Compute Nested Logit diversion ratios (parallelized over individuals)",
      "topics": [
        "nl_diversion_ratios_parallel"
      ]
    },
    {
      "page": "nl_elasticities_parallel",
      "title": "Compute aggregate elasticities for the Nested Logit model",
      "topics": [
        "nl_elasticities_parallel"
      ]
    },
    {
      "page": "nl_loglik_gradient_parallel",
      "title": "Log-likelihood and gradient for Nested Logit model",
      "topics": [
        "nl_loglik_gradient_parallel"
      ]
    },
    {
      "page": "nl_loglik_numeric_hessian",
      "title": "Numerical Hessian of the log-likelihood via finite differences",
      "topics": [
        "nl_loglik_numeric_hessian"
      ]
    },
    {
      "page": "nl_predict",
      "title": "Prediction of choice probabilities and utilities for the Nested Logit model",
      "topics": [
        "nl_predict"
      ]
    },
    {
      "page": "nl_predict_shares",
      "title": "Prediction of market shares for the Nested Logit model",
      "topics": [
        "nl_predict_shares"
      ]
    },
    {
      "page": "nobs.choicer_fit",
      "title": "Extract number of observations from a choicer_fit object",
      "topics": [
        "nobs.choicer_fit"
      ]
    },
    {
      "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_mnl_data",
      "title": "Prepare inputs for 'mnl_loglik_gradient_parallel()'",
      "topics": [
        "prepare_mnl_data"
      ]
    },
    {
      "page": "prepare_mxl_data",
      "title": "Prepare inputs for 'mxl_loglik_gradient_parallel()'",
      "topics": [
        "prepare_mxl_data"
      ]
    },
    {
      "page": "prepare_nl_data",
      "title": "Prepare inputs for nested logit estimation",
      "topics": [
        "prepare_nl_data"
      ]
    },
    {
      "page": "print.choicer_fit",
      "title": "Print a choicer_fit object",
      "topics": [
        "print.choicer_fit"
      ]
    },
    {
      "page": "print.summary.choicer_mnl",
      "title": "Print summary for multinomial logit model",
      "topics": [
        "print.summary.choicer_mnl"
      ]
    },
    {
      "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_mc"
      ]
    },
    {
      "page": "run_mnlogit",
      "title": "Runs multinomial logit estimation",
      "topics": [
        "run_mnlogit"
      ]
    },
    {
      "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": "simulate_mnl_data",
      "title": "Simulate multinomial logit data",
      "topics": [
        "simulate_mnl_data"
      ]
    },
    {
      "page": "simulate_mxl_data",
      "title": "Simulate mixed logit data",
      "topics": [
        "simulate_mxl_data"
      ]
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