Efficient Bayesian Algorithms for Binary and Categorical Data Regression Models
Extract coefficients from UPG.Binomial objects
Extract coefficients from UPG.Logit objects
Extract coefficients from UPG.MNL objects
Extract coefficients from UPG.Probit objects
Compute log-likelihoods from UPG.Binomial objects
Compute log-likelihoods from UPG.Logit objects
Compute log-likelihoods from UPG.MNL objects
Compute log-likelihoods from UPG.Probit objects
Coefficient plots for UPG.Binomial objects
Coefficient plots for UPG.Logit objects
Coefficient plots for UPG.MNL objects
Coefficient plots for UPG.Probit objects
Predicted probabilities from UPG.Binomial objects
Predicted probabilities from UPG.Logit objects
Predicted probabilities from UPG.MNL objects
Predicted probabilities from UPG.Probit objects
Print information for UPG.Binomial objects
Print information for UPG.Logit objects
Print information for UPG.MNL objects
Print information for UPG.Probit objects
Estimation results and tables for UPG.Binomial objects
Estimation results and tables for UPG.Logit objects
Estimation results and tables for UPG.MNL objects
Estimation result summary and tables for UPG.Probit objects
MCMC Diagnostics for UPG.Binomial objects
MCMC Diagnostics for UPG.Logit objects
MCMC Diagnostics for UPG.MNL objects
MCMC Diagnostics for UPG.Probit objects
MCMC Diagnostics for UPG.Probit, UPG.Logit, UPG.MNL and `UPG.Bin...
Efficient MCMC Samplers for Bayesian probit regression and various log...
Efficient Bayesian implementations of probit, logit, multinomial logit and binomial logit models. Functions for plotting and tabulating the estimation output are available as well. Estimation is based on Gibbs sampling where the Markov chain Monte Carlo algorithms are based on the latent variable representations and marginal data augmentation algorithms described in "Gregor Zens, Sylvia Frühwirth-Schnatter & Helga Wagner (2023). Ultimate Pólya Gamma Samplers – Efficient MCMC for possibly imbalanced binary and categorical data, Journal of the American Statistical Association <doi:10.1080/01621459.2023.2259030>".