UPG
Efficient Bayesian Algorithms for Binary and Categorical Data Regression Models
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> ".
- Version0.3.5
- R versionunknown
- LicenseGPL-3
- Needs compilation?No
- Languageen-US
- UPG citation info
- Last release11/10/2024
Documentation
Team
Gregor Zens
Sylvia Frühwirth-Schnatter
Show author detailsRolesAuthorHelga Wagner
Show author detailsRolesAuthor
Insights
Last 30 days
Last 365 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Imports8 packages