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HDBRR: High Dimensional Bayesian Ridge Regression without MCMC

Implements Bayesian ridge regression for high-dimensional data without using Markov chain Monte Carlo (MCMC). Posterior computations are performed using singular value decomposition (SVD) or QR decomposition. The package also provides variable selection and prediction methods.

Version: 1.1.5
Depends: R (≥ 3.0.0)
Imports: numDeriv, parallel, bigparallelr, bigstatsr, graphics, stats
Published: 2026-09-23
DOI: 10.32614/CRAN.package.HDBRR
Author: Blanca Monroy-Castillo [aut, cre], Paulino Perez-Rodriguez [ctb], Jose Crossa [ctb], Sergio Perez-Elizalde [aut]
Maintainer: Blanca Monroy-Castillo <blancamonroy.96 at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: HDBRR results

Documentation:

Reference manual: HDBRR.html , HDBRR.pdf
Vignettes: HDBRR-extdoc (source)

Downloads:

Package source: HDBRR_1.1.5.tar.gz
Windows binaries: r-devel: HDBRR_1.1.5.zip, r-release: not available, r-oldrel: not available
macOS binaries: r-release (arm64): HDBRR_1.1.5.tgz, r-oldrel (arm64): HDBRR_1.1.5.tgz, r-release (x86_64): HDBRR_1.1.5.tgz, r-oldrel (x86_64): HDBRR_1.1.5.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.
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