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UPG  

Efficient Bayesian Algorithms for Binary and Categorical Data Regression Models
View on CRAN: Click here


Download and install UPG package within the R console
Install from CRAN:
install.packages("UPG")

Install from Github:
library("remotes")
install_github("cran/UPG")

Install by package version:
library("remotes")
install_version("UPG", "0.3.5")



Attach the package and use:
library("UPG")
Maintained by
Gregor Zens
[Scholar Profile | Author Map]
First Published: 2021-01-07
Latest Update: 2023-11-04
Description:
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>".
How to cite:
Gregor Zens (2021). UPG: Efficient Bayesian Algorithms for Binary and Categorical Data Regression Models. R package version 0.3.5, https://cran.r-project.org/web/packages/UPG. Accessed 12 May. 2025.
Previous versions and publish date:
0.2.2 (2021-01-07 10:00), 0.3.0 (2022-06-21 14:00), 0.3.1 (2022-08-05 12:50), 0.3.2 (2023-04-28 13:30), 0.3.3 (2023-08-07 21:00), 0.3.4 (2023-11-04 15:30)
Other packages that cited UPG R package
View UPG citation profile
Other R packages that UPG depends, imports, suggests or enhances
Complete documentation for UPG
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