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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 |
| Reference manual: | HDBRR.html , HDBRR.pdf |
| Vignettes: |
HDBRR-extdoc (source) |
| 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 |
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These binaries (installable software) and packages are in development.
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