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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.
Getting started
Browse package contents
Package details |
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| Author | Blanca Monroy-Castillo [aut, cre], Paulino Perez-Rodriguez [ctb], Jose Crossa [ctb], Sergio Perez-Elizalde [aut] |
| Maintainer | Blanca Monroy-Castillo <blancamonroy.96@gmail.com> |
| License | GPL (>= 2) |
| Version | 1.1.5 |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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