Package: HDBRR
Type: Package
Title: High Dimensional Bayesian Ridge Regression without MCMC
Version: 1.1.5
Authors@R: c(
    person("Blanca", "Monroy-Castillo", role = c("aut", "cre"),
           email = "blancamonroy.96@gmail.com"),
    person("Paulino", "Perez-Rodriguez", role = "ctb"),
    person("Jose", "Crossa", role = "ctb"),
    person("Sergio", "Perez-Elizalde", role = "aut")
    )
Description: 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.
License: GPL (>= 2)
Depends: R (>= 3.0.0)
Encoding: UTF-8
Imports: numDeriv, parallel, bigparallelr, bigstatsr, graphics, stats
NeedsCompilation: no
LazyData: true
Config/roxygen2/version: 8.1.0
Packaged: 2026-09-22 17:00:20 UTC; estel
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>
Repository: CRAN
Date/Publication: 2026-09-23 09:30:20 UTC
Built: R 4.7.0; ; 2026-09-23 23:52:47 UTC; windows
