Man pages for RprobitB
Bayesian Probit Choice Modeling

as_cov_namesRe-label alternative specific covariates
check_formCheck model formula
check_priorCheck prior parameters
choice_probabilitiesCompute choice probabilities
classificationPreference-based classification of deciders
coef.RprobitB_fitExtract model effects
compute_choice_probabilitiesCompute probit choice probabilities
compute_p_siCompute choice probabilities at posterior samples
cov_mixExtract estimated covariance matrix of mixing distribution
create_lagged_covCreate lagged choice covariates
draw_from_priorSample from prior distributions
d_to_gammaTransform increments to thresholds
filter_gibbs_samplesFilter Gibbs samples
fit_modelFit probit model to choice data
get_covExtract covariates of choice occasion
gibbs_samplerGibbs sampler for probit models
ll_orderedCompute ordered probit log-likelihood
missing_covariatesHandle missing covariates
mmlApproximate marginal model likelihood
mode_approxGibbs sample mode
model_selectionCompare fitted models
nparExtract number of model parameters
overview_effectsPrint effect overview
parameter_labelsCreate parameters labels
plot_acfAutocorrelation plot of Gibbs samples
plot_class_allocationPlot class allocation (for 'P_r = 2' only)
plot_class_seqVisualizing the number of classes during Gibbs sampling
plot_mixture_contourPlot bivariate contour of mixing distributions
plot_mixture_marginalPlot marginal mixing distributions
plot_rocPlot ROC curve
plot.RprobitB_fitVisualize fitted probit model
plot_traceVisualizing the trace of Gibbs samples.
point_estimatesCompute point estimates
posterior_parsParameter sets from posterior samples
pred_accCompute prediction accuracy
predict.RprobitB_fitPredict choices
preference_flipCheck for flip in preferences after change in model scale.
prepare_dataPrepare choice data for estimation
R_hatCompute Gelman-Rubin statistic
RprobitB_dataCreate object of class 'RprobitB_data'
RprobitB_fitCreate object of class 'RprobitB_fit'
RprobitB_gibbs_samples_statisticsCreate object of class 'RprobitB_gibbs_samples_statistics'
RprobitB_latent_classesCreate object of class 'RprobitB_latent_classes'
RprobitB_normalizationUtility normalization
RprobitB-packageRprobitB: Bayesian Probit Choice Modeling
RprobitB_parameterDefine probit model parameter
sample_allocationSample allocation
simulate_choicesSimulate choice data
sufficient_statisticsCompute sufficient statistics
train_choiceStated Preferences for Train Traveling
train_testSplit choice data into train and test subset
transformTransform fitted probit model
transform_gibbs_samplesTransformation of Gibbs samples
transform_parameterTransformation of parameter values
update_bUpdate class means
update_b_cUpdate mean of a single class
update_classes_dpDirichlet process class updates
update_classes_wbWeight-based class updates
update_coefficientUpdate coefficient vector
update_dUpdate utility threshold increments
update_mUpdate class sizes
update_OmegaUpdate class covariances
update_Omega_cUpdate covariance of a single class
update.RprobitB_fitUpdate and re-fit probit model
update_sUpdate class weight vector
update_SigmaUpdate error covariance matrix
update_UUpdate utility vector
update_U_rankedUpdate ranked utility vector
update_zUpdate class allocation vector
WAICCompute WAIC value
RprobitB documentation built on Aug. 26, 2025, 1:08 a.m.