Bayesian Logistic Regression with Heavy-Tailed Priors
Overview
Efficient Bayesian multinomial logistic regression based on heavy-tailed (hyper-LASSO, non-convex) priors. The posterior of coefficients and hyper-parameters is sampled with restricted Gibbs sampling for leveraging the high-dimensionality and Hamiltonian Monte Carlo for handling the high-correlation among coefficients. A detailed description of the method: Li and Yao (2018), Journal of Statistical Computation and Simulation, 88:14, 2827-2851, doi:10.48550/arXiv.1405.3319.
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Health
- OK13 OK2026-10-15
- WARNING12 OK, 1 WARNING2026-10-01
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK12 OK2026-04-25
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- NOTE12 OK, 2 NOTE2026-04-22
- ERROR11 OK, 2 NOTE, 1 ERROR2026-04-18
- NOTE12 OK, 2 NOTE2026-03-10
Documentation
- Help pages
- 21 (8 internal)
- Examples that run
- 100%10 run in a plain check
- Exported functions with examples
- 50%
- Documented parameters
- 89%
- Return-value docs
- 100%
- Help pages citing references
- 33%
Downloads
Repository
- PRs merged (all time)
- 100%
- Median days to close, 50 most recently updated closed issues
- 49
- Median days to close, 50 most recently updated closed PRs
- 4
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Checks run against github.com/longhaisk/htlr on 2026-10-18.
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Dependencies
Code & Tests
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People & History
Previously maintained by Longhai Li (0.4 to 0.4-4).
Pick two releases to compare their code metrics.
- RR 4.6.0 released · 2026-04-24
- 1.0Latest
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- 0.4-42022-10-22 · diff ↗
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- 0.4-3
- RR 4.0.0 released · 2020-04-24
- 0.4-2
- 0.4-12019-10-09 · diff ↗
- 0.42019-10-06
- RR 3.6.0 released · 2019-04-26
Package metadata
- First published
- 2019-10-06
- Total releases
- 6 / 7 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 3.6.2
- Bundled data
- 952 KB / 2 files
- Download size
- 1.5 MB
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