# Bayesian Probit Choice Modeling

## Documentation for package ‘RprobitB’ version 1.1.4

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

## Help Pages

as_cov_names | Re-label alternative specific covariates
check_form | Check model formula
check_prior | Check prior parameters
choice_probabilities | Compute choice probabilities
classification | Classify deciders preference-based
coef.RprobitB_fit | Extract model effects
compute_p_si | Compute choice probabilities at posterior samples
cov_mix | Extract estimated covariance matrix of mixing distribution
create_lagged_cov | Create lagged choice covariates
fit_model | Fit probit model to choice data
get_cov | Extract covariates of choice occasion
mml | Approximate marginal model likelihood
model_selection | Compare fitted models
npar | Extract number of model parameters
npar.RprobitB_fit | Extract number of model parameters
overview_effects | Print effect overview
plot.RprobitB_data | Visualize choice data
plot.RprobitB_fit | Visualize fitted probit model
plot_roc | Plot ROC curve
point_estimates | Compute point estimates
predict.RprobitB_fit | Predict choices
pred_acc | Compute prediction accuracy
prepare_data | Prepare choice data for estimation
RprobitB_parameter | Define probit model parameter
R_hat | Compute Gelman-Rubin statistic
simulate_choices | Simulate choice data
train_choice | Stated Preferences for Train Traveling
train_test | Split choice data in train and test subset
transform.RprobitB_fit | Transform fitted probit model
update.RprobitB_fit | Update and re-fit probit model