R: Bayesian Probit Choice Modeling

# Bayesian Probit Choice Modeling

## Documentation for package ‘RprobitB’ version 1.2.0

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

## 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 | Preference-based classification of deciders
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
d_to_gamma | Transform increments to thresholds
fit_model | Fit probit model to choice data
get_cov | Extract covariates of choice occasion
gibbs_sampler | Gibbs sampler for probit models
ll_ordered | Compute ordered probit log-likelihood
mml | Approximate marginal model likelihood
model_selection | Compare fitted models
mode_approx | Gibbs sample mode
npar | Extract number of model parameters
npar.RprobitB_fit | Extract number of model parameters
overview_effects | Print effect overview
plot.RprobitB_coef | Extract model effects
plot.RprobitB_fit | Visualize fitted probit model
plot.RprobitB_mml | Approximate marginal model likelihood
plot_class_allocation | Plot class allocation (for 'P_r = 2' only)
plot_mixture_contour | Plot bivariate contour of mixing distributions
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
print.RprobitB_coef | Extract model effects
print.RprobitB_mml | Approximate marginal model likelihood
print.RprobitB_model_selection | Compare fitted models
print.RprobitB_parameter | Define probit model parameter
RprobitB_parameter | Define probit model parameter
R_hat | Compute Gelman-Rubin statistic
sample_allocation | Sample allocation
simulate_choices | Simulate choice data
train_choice | Stated Preferences for Train Traveling
train_test | Split choice data into train and test subset
transform.RprobitB_fit | Transform fitted probit model
update.RprobitB_fit | Update and re-fit probit model
update_b | Update class means
update_b_c | Update mean of a single class
update_classes_dp | Dirichlet process class updates
update_classes_wb | Weight-based class updates
update_coefficient | Update coefficient vector
update_d | Update utility threshold increments
update_m | Update class sizes
update_Omega | Update class covariances
update_Omega_c | Update covariance of a single class
update_s | Update class weight vector
update_Sigma | Update error covariance matrix
update_U | Update utility vector
update_U_ranked | Update ranked utility vector
update_z | Update class allocation vector