Man pages for RprobitB
Bayesian Probit Choice Modeling

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_choice_probabilities	Compute probit choice probabilities
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
draw_from_prior	Sample from prior distributions
d_to_gamma	Transform increments to thresholds
filter_gibbs_samples	Filter Gibbs samples
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
missing_covariates	Handle missing covariates
mml	Approximate marginal model likelihood
mode_approx	Gibbs sample mode
model_selection	Compare fitted models
npar	Extract number of model parameters
overview_effects	Print effect overview
parameter_labels	Create parameters labels
plot_acf	Autocorrelation plot of Gibbs samples
plot_class_allocation	Plot class allocation (for 'P_r = 2' only)
plot_class_seq	Visualizing the number of classes during Gibbs sampling
plot_mixture_contour	Plot bivariate contour of mixing distributions
plot_mixture_marginal	Plot marginal mixing distributions
plot_roc	Plot ROC curve
plot.RprobitB_fit	Visualize fitted probit model
plot_trace	Visualizing the trace of Gibbs samples.
point_estimates	Compute point estimates
posterior_pars	Parameter sets from posterior samples
pred_acc	Compute prediction accuracy
predict.RprobitB_fit	Predict choices
preference_flip	Check for flip in preferences after change in model scale.
prepare_data	Prepare choice data for estimation
R_hat	Compute Gelman-Rubin statistic
RprobitB_data	Create object of class 'RprobitB_data'
RprobitB_fit	Create object of class 'RprobitB_fit'
RprobitB_gibbs_samples_statistics	Create object of class 'RprobitB_gibbs_samples_statistics'
RprobitB_latent_classes	Create object of class 'RprobitB_latent_classes'
RprobitB_normalization	Utility normalization
RprobitB-package	RprobitB: Bayesian Probit Choice Modeling
RprobitB_parameter	Define probit model parameter
sample_allocation	Sample allocation
simulate_choices	Simulate choice data
sufficient_statistics	Compute sufficient statistics
train_choice	Stated Preferences for Train Traveling
train_test	Split choice data into train and test subset
transform	Transform fitted probit model
transform_gibbs_samples	Transformation of Gibbs samples
transform_parameter	Transformation of parameter values
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.RprobitB_fit	Update and re-fit probit model
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
WAIC	Compute WAIC value