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
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.choicer_hmnl
                        Expected consumer surplus
diversion_ratios.choicer_hb
                        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.choicer_hb
                        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
logLik.choicer_fit      Extract log-likelihood from a choicer_fit
                        object
logsum.choicer_hmnl     Expected logsum (inclusive value) per choice
                        situation
mc_asymptotics          Asymptotic diagnostics for a Monte Carlo study
mcse                    Monte Carlo standard error of posterior
                        summaries
mode_choice             Intercity travel mode choice
monte_carlo             Monte Carlo parameter recovery
mxl_blp_contraction     BLP contraction mapping for mixed logit
new_choicer_sim         Construct a 'choicer_sim' object
nl_blp_contraction      BLP95 contraction mapping 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 Bayes fits
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 alternative
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 logit fit
wesml_weights           WESML weights for choice-based (endogenous
                        stratified) samples
wtp.choicer_hb          Compute willingness to pay
