Fast High-Dimensional Gibbs Samplers for Bayesian Lasso Regression R package LassoHiDFastGibbs version 0.1.5 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>. Software Imports: Rcpp LinkingTo: Rcpp, RcppArmadillo, RcppEigen, RcppNumerical Suggests: posterior Mohammad Javad Davoudabadi <mohammad.davoudabadi@qut.edu.au> Comprehensive R Archive Network (CRAN) John Ormerod [aut] (ORCID: <https://orcid.org/0000-0002-4650-7507>), Mohammad Javad Davoudabadi [aut, cre, cph], Garth Tarr [aut] (ORCID: <https://orcid.org/0000-0002-6605-7478>), Samuel Mueller [aut] (ORCID: <https://orcid.org/0000-0002-3087-8127>), Jonathon Tidswell [ctb] (Contributed code to src/lasso_distribution.cpp (originally from BayesianLasso package)) GPL-3 2026-02-22 application/tgz https://CRAN.R-project.org/package=LassoHiDFastGibbs doi:10.32614/CRAN.package.LassoHiDFastGibbs