LassoHiDFastGibbs
0.1.5Fast High-Dimensional Gibbs Samplers for Bayesian Lasso Regression
Overview
Provides fast and scalable Gibbs sampling algorithms for Bayesian Lasso regression model in high-dimensional settings. The package implements efficient partially collapsed and nested Gibbs samplers for Bayesian Lasso, with a focus on computational efficiency when the number of predictors is large relative to the sample size. Methods are described at Davoudabadi and Ormerod (2026) https://github.com/MJDavoudabadi/LassoHiDFastGibbs.
Install
Health
- NOTE r-devel-linux-x86_64-debian-gcc
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-04-2512 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-04-2212 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-04-1811 OK · 2 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- NOTE2026-03-1012 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 55%
- Return-value docs
- 100%
- References docs
- 0%
Downloads
Repository
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Checks run against github.com/mjdavoudabadi/lassohidfastgibbs on 2026-08-03.
Dependencies
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Code & Tests
People & History
2 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 0.1.5Latest
- 0.1.42026-01-29
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2026-01-29
- Total releases
- 2 / 1 yrs
- License
- GPL-3 OSI
- Download size
- 336 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet