R: Discrete Choice Models for Economic Applications

# Discrete Choice Models for Economic Applications

## Documentation for package ‘choicer’ version 0.1.0

- DESCRIPTION file
- Package NEWS
- Package README

## Help Pages

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_contraction | BLP95 contraction mapping to find delta given target shares
build_var_mat | Reconstruct variance matrix L from L_params
coef.choicer_fit | Extract coefficients from a choicer_fit object
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
elasticities | Compute aggregate elasticities
elasticities.choicer_mnl | Elasticities for multinomial logit model
elasticities.choicer_mxl | Elasticities for mixed logit model
get_halton_normals | Halton draws for mixed logit
jacobian_vech_Sigma | Utility to compute analytical Jacobian of random coefficient matrix transformed by vech (dVech(Sigma) / dTheta)
logLik.choicer_fit | Extract log-likelihood from a choicer_fit object
mc_asymptotics | Asymptotic diagnostics for a Monte Carlo study
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 fitted model
mnl_predict_shares | Prediction of market shares based on fitted model
monte_carlo | Monte Carlo parameter recovery
mxl_bhhh_parallel | BHHH (outer product of gradients) information matrix for Mixed Logit
mxl_blp_contraction | BLP contraction mapping for mixed logit
mxl_diversion_ratios_parallel | Diversion ratios for Mixed Logit (simulated, derivative-based)
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_predict | Per-observation simulated choice probabilities for Mixed Logit
mxl_predict_shares | Predicted aggregate market shares for Mixed Logit
new_choicer_sim | Construct a 'choicer_sim' object
nl_loglik_gradient_parallel | Log-likelihood and gradient for Nested Logit model
nl_loglik_numeric_hessian | Numerical Hessian of the log-likelihood via finite differences
nobs.choicer_fit | Extract number of observations from a choicer_fit object
predict.choicer_mnl | Predict from a multinomial logit model
predict.choicer_mxl | Predict from a mixed logit model
prepare_mnl_data | Prepare inputs for 'mnl_loglik_gradient_parallel()'
prepare_mxl_data | Prepare inputs for 'mxl_loglik_gradient_parallel()'
prepare_nl_data | Prepare inputs for nested logit estimation
print.choicer_fit | Print a choicer_fit object
print.summary.choicer_mnl | Print summary for multinomial logit 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
recovery_table.choicer_fit | Parameter recovery table
recovery_table.choicer_mc | Parameter recovery table
run_mnlogit | Runs multinomial logit estimation
run_mxlogit | Runs mixed logit estimation
run_nestlogit | Runs nested logit estimation
simulate_mnl_data | Simulate multinomial logit data
simulate_mxl_data | Simulate mixed logit data
simulate_nl_data | Simulate nested logit data
summary.choicer_mnl | Summary for multinomial logit model
summary.choicer_mxl | Summary for mixed logit model
summary.choicer_nl | Summary for nested logit model
vcov.choicer_fit | Extract variance-covariance matrix from a choicer_fit object