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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 |
| 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 |
| Reference manual: | HDBRR.pdf |
| Vignettes: |
HDBRR-extdoc |
| 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 |
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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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