choice_probs | Compute choice probabilities of an 'RprobitB_model'. |
classify | Classify deciders. |
compare | Compare fitted models. |
compute_log_likelihood | Compute log-likelihood of an 'RprobitB_model'. |
delta | Difference operator. |
dmvnrm_arma_mc | Multivariate normal density |
is_covariance_matrix | Check covariance matrix properties. |
mcmc | Perform Markov chain Monte Carlo simulation for fitting a (latent class) (mixed) (multinomial) probit model. |
overview_effects | Overview of effects. |
plot.RprobitB_model | Plot method for 'RprobitB_model'. |
predict | Predict choices. |
prepare | Prepare empirical choice data. |
print.RprobitB_data | Print method for 'RprobitB_data'. |
print.RprobitB_gibbs_samples_statistics | Print method for 'RprobitB_gibbs_samples_statistics'. |
print.RprobitB_latent_classes | Print method for 'RprobitB_latent_classes'. |
print.RprobitB_model | Print method for 'RprobitB_model'. |
print.RprobitB_normalization | Print method for 'RprobitB_normalization'. |
print.summary.RprobitB_data | Print method for the summary of 'RprobitB_data'. |
print.summary.RprobitB_model | Print method for the summary of 'RprobitB_model'. |
rdirichlet | Draw from Dirichlet |
RprobitB_data | Create object of class 'RprobitB_data'. |
RprobitB_gibbs_samples_statistics | Compute parameter statistics. |
RprobitB_latent_classes | Create object of class 'RprobitB_latent_classes'. |
RprobitB_model | Create object of class 'RprobitB_model'. |
RprobitB_normalization | Create object of class 'RprobitB_normalization'. |
RprobitB_parameter | Create object of class 'RprobitB_parameter'. |
rwishart | Draw from a Wishart |
R_hat | Compute Gelman-Rubin statistic. |
set_mfrow | Balancing visualization of multiple figures. |
simulate | Simulate choice data. |
summary.RprobitB_data | Summary method for 'RprobitB_data'. |
summary.RprobitB_model | Summary method for 'RprobitB_model'. |
transform | Change the length of the burn-in period, the thinning factor and the scale after Gibbs sampling. |
undiff_Sigma | Transform differenced to non-differenced error term covariance matrix. |