Package: LassoHiDFastGibbs 0.1.5

Mohammad Javad Davoudabadi

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>.

Authors:John Ormerod [aut], Mohammad Javad Davoudabadi [aut, cre, cph], Garth Tarr [aut], Samuel Mueller [aut], Jonathon Tidswell [ctb]

LassoHiDFastGibbs_0.1.5.tar.gz
LassoHiDFastGibbs_0.1.5.tar.gz(r-4.7-arm64)LassoHiDFastGibbs_0.1.5.tar.gz(r-4.7-x86_64)LassoHiDFastGibbs_0.1.5.tar.gz(r-4.6-arm64)LassoHiDFastGibbs_0.1.5.tar.gz(r-4.6-x86_64)
LassoHiDFastGibbs_0.1.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
LassoHiDFastGibbs/json (API)
NEWS

# Install 'LassoHiDFastGibbs' in R:
install.packages('LassoHiDFastGibbs', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mjdavoudabadi/lassohidfastgibbs/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

openblascpp

2.00 score 1 scripts 207 downloads 10 exports 4 dependencies

Last updated from:cefea8a5b6. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK228
linux-devel-x86_64OK202
source / vignettesOK291
linux-release-arm64OK220
linux-release-x86_64OK229
wasm-releaseOK196

Exports:blasso_gibbs_2block_blblasso_gibbs_2block_bsblasso_pcg_lambda2_vablasso_pcg_sigma2_vanormalizepenalized_nested_Gibbspenalized_pcg_beta_sigma2penalized_pcg_lambda2_sigma2penalized_pcg_sigma2_betapenalized_pcg_sigma2_lambda2

Dependencies:RcppRcppArmadilloRcppEigenRcppNumerical