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 |
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