# Discrete Choice Models for Economic Applications

## Documentation for package ‘choicer’ version 0.2.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

B C D E G L M N P R S T V W

## -- B --

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

## -- C --

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
consumer_surplus.choicer_hmnl | Expected consumer surplus
consumer_surplus.choicer_hmnp | Expected consumer surplus
consumer_surplus.choicer_mnl | Expected consumer surplus
consumer_surplus.choicer_mxl | Expected consumer surplus
consumer_surplus.choicer_nl | Expected consumer surplus

## -- D --

diversion_ratios | Compute aggregate diversion ratios
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

## -- E --

elasticities | Compute aggregate elasticities
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)

## -- G --

get_halton_normals | Halton draws for mixed logit
gof | Goodness of fit for a fitted choice model
gof.choicer_fit | Goodness of fit for a fitted choice model

## -- L --

logLik.choicer_fit | Extract log-likelihood from a choicer_fit object
logsum | Expected logsum (inclusive value) per choice situation
logsum.choicer_hmnl | Expected logsum (inclusive value) per choice situation
logsum.choicer_hmnp | Expected logsum (inclusive value) per choice situation
logsum.choicer_mnl | Expected logsum (inclusive value) per choice situation
logsum.choicer_mxl | Expected logsum (inclusive value) per choice situation
logsum.choicer_nl | Expected logsum (inclusive value) per choice situation

## -- M --

mcse | Monte Carlo standard error of posterior summaries
mc_asymptotics | Asymptotic diagnostics for a Monte Carlo study
mode_choice | Intercity travel mode choice
monte_carlo | Monte Carlo parameter recovery
mxl_blp_contraction | BLP contraction mapping for mixed logit

## -- N --

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

## -- P --

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

## -- R --

recovery_table | Parameter recovery table
recovery_table.choicer_fit | Parameter recovery table
recovery_table.choicer_hb | Parameter recovery table
recovery_table.choicer_mc | Parameter recovery table
recovery_table.choicer_mnp | 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

## -- S --

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

## -- T --

thread_info | Query choicer OpenMP thread settings
traceplot | Traceplot for a hierarchical Bayes fit
traceplot.choicer_hb | Traceplot method for hierarchical Bayes fits

## -- V --

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

## -- W --

wesml_vcov | Robust (sandwich) variance for a weighted / choice-based logit fit
wesml_vcov.choicer_mnl | Robust (sandwich) variance for a weighted / choice-based logit fit
wesml_vcov.choicer_mxl | Robust (sandwich) variance for a weighted / choice-based logit fit
wesml_vcov.choicer_nl | Robust (sandwich) variance for a weighted / choice-based logit fit
wesml_weights | WESML weights for choice-based (endogenous stratified) samples
wtp | Compute willingness to pay
wtp.choicer_fit | Compute willingness to pay
wtp.choicer_hb | Compute willingness to pay
wtp.choicer_mxl | Compute willingness to pay