bayesreg: Bayesian Regression Models with Global-Local Shrinkage Priors

Fits linear or generalized linear regression models using Bayesian global-local shrinkage prior hierarchies as described in Polson and Scott (2010) <doi:10.1093/acprof:oso/9780199694587.003.0017>. Provides an efficient implementation of ridge, lasso, horseshoe and horseshoe+ regression with logistic, Gaussian, Laplace, Student-t, Poisson or geometric distributed targets using the algorithms summarized in Makalic and Schmidt (2016) <arXiv:1611.06649>.

Version: 1.2
Imports: stats (≥ 3.0), pgdraw (≥ 1.0)
Published: 2021-03-29
Author: Daniel F. Schmidt ORCID iD [aut, cph, cre], Enes Makalic ORCID iD [aut, cph]
Maintainer: Daniel F. Schmidt <daniel.schmidt at monash.edu>
License: GPL (≥ 3)
NeedsCompilation: no
Citation: bayesreg citation info
CRAN checks: bayesreg results

Documentation:

Reference manual: bayesreg.pdf

Downloads:

Package source: bayesreg_1.2.tar.gz
Windows binaries: r-devel: bayesreg_1.2.zip, r-release: bayesreg_1.2.zip, r-oldrel: bayesreg_1.2.zip
macOS binaries: r-release (arm64): bayesreg_1.2.tgz, r-release (x86_64): bayesreg_1.2.tgz, r-oldrel: bayesreg_1.2.tgz
Old sources: bayesreg archive

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