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>.
| Version: |
0.1.4 |
| Imports: |
Rcpp |
| LinkingTo: |
Rcpp, RcppArmadillo, RcppEigen, RcppNumerical, RcppClock |
| Suggests: |
posterior |
| Published: |
2026-01-29 |
| DOI: |
10.32614/CRAN.package.LassoHiDFastGibbs (may not be active yet) |
| Author: |
John Ormerod 
[aut],
Mohammad Javad Davoudabadi [aut, cre, cph],
Garth Tarr [aut],
Samuel Mueller
[aut],
Jonathon Tidswell [ctb] (Contributed code to src/lasso_distribution.cpp
(originally from BayesianLasso package)) |
| Maintainer: |
Mohammad Javad Davoudabadi <mohammad.davoudabadi at qut.edu.au> |
| BugReports: |
https://github.com/MJDavoudabadi/LassoHiDFastGibbs/issues |
| License: |
GPL-3 |
| Copyright: |
see file COPYRIGHTS |
| URL: |
https://github.com/MJDavoudabadi/LassoHiDFastGibbs |
| NeedsCompilation: |
yes |
| SystemRequirements: |
C++17 |
| Citation: |
LassoHiDFastGibbs citation info |
| Materials: |
README, NEWS |
| CRAN checks: |
LassoHiDFastGibbs results |