The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

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:

Please use the canonical form https://CRAN.R-project.org/package=HDBRR to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.