{ "_id": "6ad99e93c230b829876fd7d3", "Package": "choicer", "Title": "Discrete Choice Models for Economic Applications", "Version": "0.2.1.9000", "Authors@R": "person(\"Fernando\", \"Cordeiro\", , \"fernandolpcordeiro@gmail.com\", role = c(\"aut\", \"cre\", \"cph\"))", "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').", "License": "LGPL (>= 3)", "URL": "https://github.com/fpcordeiro/choicer,\nhttps://fpcordeiro.github.io/choicer/", "BugReports": "https://github.com/fpcordeiro/choicer/issues", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "VignetteBuilder": "knitr", "LazyData": "true", "Config/Needs/website": "pkgdown", "Config/testthat/edition": "3", "Config/roxygen2/version": "8.0.0", "Config/pak/sysreqs": "cmake", "Repository": "https://fpcordeiro.r-universe.dev", "Date/Publication": "2026-10-22 01:41:28 UTC", "RemoteUrl": "https://github.com/fpcordeiro/choicer", "RemoteRef": "HEAD", "RemoteSha": "893a24b0f2f7fef259571b74bffef73f239cc5b4", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-10-22 05:14:12 UTC", "User": "root" }, "Author": "Fernando Cordeiro [aut, cre, cph]", "Maintainer": "Fernando Cordeiro ", "_user": "fpcordeiro", "_type": "src", "_file": "choicer_0.2.1.9000.tar.gz", "_fileid": "https://r2.ropensci.org/bd8f8e0954e9b21c2330ecd63dc17f11f89c2393a6aec3a17141810a142a60ea", "_filesize": 1926069, "_sha256": "bd8f8e0954e9b21c2330ecd63dc17f11f89c2393a6aec3a17141810a142a60ea", "_expires": "2027-01-30T05:26:41.000Z", "_created": "2026-10-22T05:14:12.000Z", "_published": "2026-10-22T05:26:42.804Z", "_upstream": "https://github.com/fpcordeiro/choicer", "_status": "success", "_maintainer": { "name": "Fernando Cordeiro", "email": "fernandolpcordeiro@gmail.com", "login": "fpcordeiro", "description": "Economics PhD", "uuid": 9939519 }, "_jobs": [ { "job": 119653252754, "time": 313, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "12403518614" }, { "job": 119653252853, "time": 317, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "12403587560" }, { "job": 119653252812, "time": 313, "config": "linux-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "12404140497" }, { "job": 119653252708, "time": 354, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "12403941645" }, { "job": 119653252663, "time": 299, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "12403099338" }, { "job": 119653252681, "time": 566, "config": "macos-oldrel-x86_64", "r": "4.5.3", "check": "OK", "artifact": "12404200895" }, { "job": 119653252747, "time": 249, "config": "macos-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "12403394021" }, { "job": 119653252794, "time": 529, "config": "macos-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "12403174135" }, { "job": 119650990895, "time": 444, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "12403701087" }, { "job": 119653252732, "time": 161, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "12403247556" }, { "job": 119653252789, "time": 616, "config": "windows-devel-arm64", "r": "4.7.0", "check": "ERROR", "artifact": "12403424822" }, { "job": 119653252861, "time": 533, "config": "windows-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "12403782546" }, { "job": 119653252776, "time": 442, "config": "windows-oldrel-x86_64", "r": "4.5.3", "check": "OK", "artifact": "12403263164" }, { "job": 119653252770, "time": 560, "config": "windows-release-arm64", "r": "4.6.1", "check": "ERROR", "artifact": "12404360122" }, { "job": 119653252722, "time": 457, "config": "windows-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "12404155700" } ], "_buildurl": "https://github.com/r-universe/fpcordeiro/actions/runs/39726306455", "_commit": { "id": "893a24b0f2f7fef259571b74bffef73f239cc5b4", "author": "fpcordeiro ", "committer": "Fernando Cordeiro ", "message": "Consolidate the population-scale NEWS and link the vignettes\n\nNEWS's development section had grown a section per phase of the mixed\nlogit's scale audit, thirteen in all, with positional cross-references\nbetween them. They are regrouped and condensed:\n- \"Population-scale mixed logit\", with four subsections: defaults that move\n estimates; convergence with thousands of constants; standard errors with\n thousands of constants; memory and speed;\n- \"Estimation and inference in every model\", for the changes that reach the\n multinomial and nested logits too.\nThe panel likelihood's section and the Corrections stay, the latter with an\nintroduction. The figures and caveats are kept, including the exceptions to\nthe default changes: fits that stop at different points on degenerate\nlikelihoods, standard errors near a singular information, and a weakly\nidentified design's interior maximum. The provenance of the figures is\nstated, the sentences that later phases superseded are gone, and the claims\nof results unchanged bit for bit now say for which calls.\n\nThe MXL vignette gains a note on population scale after its first fit,\ncovering:\n- generate-mode draws, their scrambling and their cost;\n- the memory check against physical memory, and choicer.max_memory;\n- blp()'s keep_draws_bytes.\nIts \"Many alternatives\" section links to §5.6-§5.8 of the math companion\nand to the inference vignette, which links back to it, as does the package\noverview.\n\nrun_mxlogit()'s help documents theta_init under the profile likelihood,\nwhat concentration$failed counts, and that rows carrying outside_opt_label\nare kept as an inside alternative (prepare_mnl_data() drops them). It states\nthe polish's cost note as the study found it, and links to\nset_num_threads(), whose help now lists every routine its memory check\ncovers. The three wrappers' use_asc says that the warning on constants\nwithout a finite estimate counts rows. The study's time ratios are given\nboth as modeled from the kernels' calls and as measu", "time": 1792633288 }, "_distro": "resolute", "_host": "GitHub-Actions", "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.1.0", "role": "Depends" }, { "package": "Rcpp", "role": "LinkingTo" }, { "package": "RcppArmadillo", "role": "LinkingTo" }, { "package": "data.table", "role": "Imports" }, { "package": "graphics", "role": "Imports" }, { "package": "nloptr", "role": "Imports" }, { "package": "randtoolbox", "version": ">= 1.31.0", "role": "Imports" }, { "package": "Rcpp", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "utils", "role": "Imports" }, { "package": "testthat", "version": ">= 3.1.7", "role": "Suggests" }, { "package": "bit64", "role": "Suggests" }, { "package": "numDeriv", "role": "Suggests" }, { "package": "future.apply", "role": "Suggests" }, { "package": "goftest", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" 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9939519 }, { "user": "claude", "count": 24, "uuid": 81847 } ], "_userbio": { "uuid": 9939519, "type": "user", "name": "Fernando Cordeiro", "followers": 8, "description": "Economics PhD" }, "_downloads": { "count": 274, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/choicer" }, "_devurl": "https://github.com/fpcordeiro/choicer", "_pkgdown": "https://fpcordeiro.github.io/choicer/", "_searchresults": 21, "_topics": [ "openblas", "cpp", "openmp" ], "_rbuild": "4.6.1", "_assets": [ "extra/choicer.html", "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/manual.pdf", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md" ], "_homeurl": "https://github.com/fpcordeiro/choicer", "_realowner": "fpcordeiro", "_cranurl": true, "_releases": [ { "version": "0.1.0", "date": "2026-05-20" }, { "version": "0.2.0", "date": "2026-07-14" }, { "version": "0.2.1", "date": "2026-09-08" } ], "_exports": 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"page": "conditional_tastes", "title": "Conditional (individual-level) tastes from a mixed logit fit", "topics": [ "conditional_tastes", "conditional_tastes.choicer_mxl", "conditional_tastes.default" ] }, { "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_tastes", "title": "Print conditional tastes", "topics": [ "print.choicer_tastes" ] }, { "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" ] }, { "page": "simulate_hmnp_data", "title": "Simulate hierarchical multinomial probit data", "topics": [ "simulate_hmnp_data" ] }, { "page": "simulate_mnl_data", "title": "Simulate multinomial logit data", "topics": [ "simulate_mnl_data" ] }, { "page": "simulate_mnp_data", "title": "Simulate multinomial probit data", "topics": [ "simulate_mnp_data" ] }, { "page": "simulate_mxl_data", "title": "Simulate mixed logit data", "topics": [ "simulate_mxl_data" ] }, { "page": "simulate_nl_data", "title": "Simulate nested logit data", "topics": [ "simulate_nl_data" ] }, { "page": "summary.choicer_hb", "title": "Summarize a hierarchical Bayes fit", "topics": [ "summary.choicer_hb" ] }, { "page": "summary.choicer_mnl", "title": "Summary for multinomial logit model", "topics": [ "summary.choicer_mnl" ] }, { "page": "summary.choicer_mnp", "title": "Summary for Bayesian multinomial probit model", "topics": [ "summary.choicer_mnp" ] }, { "page": "summary.choicer_mxl", "title": "Summary for mixed logit model", "topics": [ "summary.choicer_mxl" ] }, { "page": "summary.choicer_nl", "title": "Summary for nested logit model", "topics": [ "summary.choicer_nl" ] }, { "page": "thread_info", "title": "Query choicer OpenMP thread settings", "topics": [ "thread_info" ] }, { "page": "traceplot", 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