UPG
0.3.5Efficient Bayesian Algorithms for Binary and Categorical Data Regression Models
Overview
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".
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- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
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- Documented parameters
- 100%
- Return-value docs
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- References docs
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Code & Tests
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People & History
7 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- 0.3.5Latest
- RR 4.4.0 released · 2024-04-24
- 0.3.42023-11-04 · diff ↗
- 0.3.32023-08-07 · diff ↗
- 0.3.22023-04-28 · diff ↗
- RR 4.3.0 released · 2023-04-21
- 0.3.12022-08-05 · diff ↗
- 0.3.02022-06-21 · diff ↗
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- 0.2.22021-01-07
- RR 4.0.0 released · 2020-04-24
Package metadata
- First published
- 2021-01-07
- Total releases
- 7 / 5 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 16 KB / 3 files
- Download size
- 778 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet