Man pages for choicer
Discrete Choice Models for Economic Applications
| blp | BLP contraction mapping |
| blp.choicer_mnl | BLP contraction mapping for multinomial logit model |
| blp.choicer_mxl | BLP contraction mapping for mixed logit model |
| blp.choicer_nl | BLP contraction mapping for nested logit model |
| blp_contraction | BLP95 contraction mapping to find delta given target shares |
| build_var_mat | Reconstruct variance matrix L from L_params |
| choicer-package | choicer: Discrete Choice Models for Economic Applications |
| coef.choicer_fit | Extract coefficients from a choicer_fit object |
| coef.choicer_hb | Extract posterior means from a hierarchical Bayes fit |
| coef.choicer_mnp | Extract coefficients from a choicer_mnp object |
| consumer_surplus | Expected consumer surplus |
| diversion_ratios | Compute aggregate diversion ratios |
| diversion_ratios.choicer_mnl | Diversion ratios for multinomial logit model |
| diversion_ratios.choicer_mxl | Diversion ratios for mixed logit model |
| diversion_ratios.choicer_nl | Diversion ratios for nested logit model |
| elasticities | Compute aggregate elasticities |
| elasticities.choicer_mnl | Elasticities for multinomial logit model |
| elasticities.choicer_mxl | Elasticities for mixed logit model |
| elasticities.choicer_nl | Elasticities for nested logit model |
| ess | Rank-normalized effective sample size (bulk and tail) |
| get_halton_normals | Halton draws for mixed logit |
| gof | Goodness of fit for a fitted choice model |
| hmnl_gibbs | Gibbs sampler for the hierarchical Bayesian multinomial logit... |
| hmnp_gibbs | Gibbs sampler for the hierarchical Bayesian multinomial... |
| jacobian_vech_Sigma | Utility to compute analytical Jacobian of random coefficient... |
| logLik.choicer_fit | Extract log-likelihood from a choicer_fit object |
| logsum | Expected logsum (inclusive value) per choice situation |
| mc_asymptotics | Asymptotic diagnostics for a Monte Carlo study |
| mcse | Monte Carlo standard error of posterior summaries |
| mnl_bhhh_parallel | BHHH/OPG information matrix for multinomial logit model |
| mnl_diversion_ratios_parallel | Compute MNL diversion ratios (parallelized over individuals) |
| mnl_elasticities_parallel | Compute aggregate elasticities for MNL model |
| mnl_loglik_gradient_parallel | Log-likelihood and gradient for multinomial logit model |
| mnl_loglik_hessian_parallel | Hessian matrix for multinomial logit model |
| mnl_predict | Prediction of choice probabilities and utilities based on... |
| mnl_predict_shares | Prediction of market shares based on fitted model |
| mnp_gibbs | Gibbs sampler for the Bayesian multinomial probit model |
| mode_choice | Intercity travel mode choice |
| monte_carlo | Monte Carlo parameter recovery |
| mxl_bhhh_parallel | BHHH (outer product of gradients) information matrix for... |
| mxl_blp_contraction | BLP contraction mapping for mixed logit |
| mxl_diversion_ratios_parallel | Diversion ratios for Mixed Logit (simulated,... |
| mxl_elasticities_parallel | Compute aggregate elasticities for mixed logit model |
| mxl_hessian_parallel | Analytical Hessian of the log-likelihood v2 |
| mxl_loglik_gradient_parallel | Log-likelihood and gradient for Mixed Logit |
| mxl_logsum | Simulated expected logsum (inclusive value) for Mixed Logit |
| mxl_predict | Per-observation simulated choice probabilities for Mixed... |
| mxl_predict_shares | Predicted aggregate market shares for Mixed Logit |
| new_choicer_sim | Construct a 'choicer_sim' object |
| nl_bhhh_parallel | BHHH/OPG information matrix for the Nested Logit model |
| nl_blp_contraction | BLP95 contraction mapping for the Nested Logit model |
| nl_diversion_ratios_parallel | Compute Nested Logit diversion ratios (parallelized over... |
| nl_elasticities_parallel | Compute aggregate elasticities for the Nested Logit model |
| nl_loglik_gradient_parallel | Log-likelihood and gradient for Nested Logit model |
| nl_loglik_hessian_parallel | Analytical Hessian of the negated log-likelihood for the... |
| nl_loglik_numeric_hessian | Numerical Hessian of the log-likelihood via finite... |
| nl_predict | Prediction of choice probabilities and utilities for the... |
| nl_predict_shares | Prediction of market shares for the Nested Logit model |
| nobs.choicer_fit | Extract number of observations from a choicer_fit object |
| nobs.choicer_hb | Number of choice situations behind a hierarchical Bayes fit |
| nobs.choicer_mnp | Extract number of observations from a choicer_mnp object |
| ppc_shares | Posterior-predictive share check for hierarchical Bayes fits |
| predict.choicer_hb | Posterior choice probabilities and shares for hierarchical... |
| predict.choicer_mnl | Predict from a multinomial logit model |
| predict.choicer_mxl | Predict from a mixed logit model |
| predict.choicer_nl | Predict from a nested logit model |
| prepare_hmnl_data | Prepare inputs for hierarchical multinomial logit estimation |
| prepare_hmnp_data | Prepare inputs for hierarchical multinomial probit estimation |
| prepare_mnl_data | Prepare inputs for multinomial logit estimation |
| prepare_mnp_data | Prepare inputs for Bayesian multinomial probit estimation |
| prepare_mxl_data | Prepare inputs for mixed logit estimation |
| prepare_nl_data | Prepare inputs for nested logit estimation |
| print.choicer_cs | Print a consumer surplus summary |
| print.choicer_fit | Print a choicer_fit object |
| print.choicer_gof | Print goodness-of-fit measures |
| print.choicer_hb | Print a hierarchical Bayes fit |
| print.choicer_mnp | Print a choicer_mnp object |
| print.choicer_wtp | Print a WTP table |
| print.summary.choicer_hb | Print the summary of a hierarchical Bayes fit |
| print.summary.choicer_mnl | Print summary for multinomial logit model |
| print.summary.choicer_mnp | Print summary for Bayesian multinomial probit model |
| print.summary.choicer_mxl | Print summary for mixed logit model |
| print.summary.choicer_nl | Print summary for nested logit model |
| recovery_table | Parameter recovery table |
| rhat | Split-\widehat{R} convergence diagnostic |
| run_hmnlogit | Fit a hierarchical Bayesian multinomial logit (HMNL) |
| run_hmnprobit | Fit a hierarchical Bayesian multinomial probit (HMNP) |
| run_mnlogit | Runs multinomial logit estimation |
| run_mnprobit | Runs Bayesian multinomial probit estimation |
| run_mxlogit | Runs mixed logit estimation |
| run_nestlogit | Runs nested logit estimation |
| sample_by_choice | Draw a choice-based sample stratified by the chosen... |
| set_num_threads | Set the number of OpenMP threads used by choicer |
| simulate_hmnl_data | Simulate hierarchical multinomial logit data |
| simulate_hmnp_data | Simulate hierarchical multinomial probit data |
| simulate_mnl_data | Simulate multinomial logit data |
| simulate_mnp_data | Simulate multinomial probit data |
| simulate_mxl_data | Simulate mixed logit data |
| simulate_nl_data | Simulate nested logit data |
| summary.choicer_hb | Summarize a hierarchical Bayes fit |
| summary.choicer_mnl | Summary for multinomial logit model |
| summary.choicer_mnp | Summary for Bayesian multinomial probit model |
| summary.choicer_mxl | Summary for mixed logit model |
| summary.choicer_nl | Summary for nested logit model |
| thread_info | Query choicer OpenMP thread settings |
| traceplot | Traceplot for a hierarchical Bayes fit |
| traceplot.choicer_hb | Traceplot method for hierarchical Bayes fits |
| vcov.choicer_fit | Extract variance-covariance matrix from a choicer_fit object |
| vcov.choicer_hb | Posterior covariance of the population coefficients |
| vcov.choicer_mnp | Extract variance-covariance matrix from a choicer_mnp object |
| wesml_vcov | Robust (sandwich) variance for a weighted / choice-based... |
| wesml_weights | WESML weights for choice-based (endogenous stratified)... |
| wtp | Compute willingness to pay |
