HDBRR: High Dimensional Bayesian Ridge Regression without MCMC

Ridge regression provide biased estimators of the regression parameters with lower variance. The HDBRR ("High Dimensional Bayesian Ridge Regression") function fits Bayesian Ridge regression without MCMC, this one uses the SVD or QR decomposition for the posterior computation.

Version:	1.1.4
Depends:	R (≥ 3.0.0)
Imports:	numDeriv, parallel, bigstatsr, MASS, graphics
Published:	2022-10-05
DOI:	10.32614/CRAN.package.HDBRR
Author:	Sergio Perez-Elizalde Developer [aut], Blanca Monroy-Castillo Developer [aut, cre], Paulino Perez-Rodriguez User [ctb], Jose Crossa User [ctb]
Maintainer:	Blanca Monroy-Castillo Developer <blancamonroy.96 at gmail.com>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	no
CRAN checks:	HDBRR results

Documentation:

Reference manual:	HDBRR.pdf
Vignettes:	HDBRR-extdoc

Downloads:

Package source:	HDBRR_1.1.4.tar.gz
Windows binaries:	r-devel: HDBRR_1.1.4.zip, r-release: HDBRR_1.1.4.zip, r-oldrel: HDBRR_1.1.4.zip
macOS binaries:	r-release (arm64): HDBRR_1.1.4.tgz, r-oldrel (arm64): HDBRR_1.1.4.tgz, r-release (x86_64): HDBRR_1.1.4.tgz, r-oldrel (x86_64): HDBRR_1.1.4.tgz
Old sources:	HDBRR archive

Linking:

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