LassoHiDFastGibbs: Fast High-Dimensional Gibbs Samplers for Bayesian Lasso Regression

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
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

Documentation:

Reference manual:

LassoHiDFastGibbs.html , LassoHiDFastGibbs.pdf

Downloads:

Package source:	LassoHiDFastGibbs_0.1.4.tar.gz
Windows binaries:	r-devel: LassoHiDFastGibbs_0.1.4.zip, r-release: LassoHiDFastGibbs_0.1.4.zip, r-oldrel: LassoHiDFastGibbs_0.1.4.zip
macOS binaries:	r-release (arm64): LassoHiDFastGibbs_0.1.4.tgz, r-oldrel (arm64): LassoHiDFastGibbs_0.1.4.tgz, r-release (x86_64): LassoHiDFastGibbs_0.1.4.tgz, r-oldrel (x86_64): LassoHiDFastGibbs_0.1.4.tgz

Linking:

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