Discrete Choice Models for Economic Applications

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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