ebreg: Implementation of the Empirical Bayes Method

Implements a Bayesian-like approach to the high-dimensional sparse linear regression problem based on an empirical or data-dependent prior distribution, which can be used for estimation/inference on the model parameters, variable selection, and prediction of a future response. The method was first presented in Martin, Ryan and Mess, Raymond and Walker, Stephen G (2017) <doi:10.3150/15-BEJ797>. More details focused on the prediction problem are given in Martin, Ryan and Tang, Yiqi (2019) <arXiv:1903.00961>.

Version: 0.1.3
Depends: lars, stats
Imports: Rdpack
Suggests: testthat, roxygen2
Published: 2021-05-26
Author: Yiqi Tang, Ryan Martin
Maintainer: Yiqi Tang <ytang22 at ncsu.edu>
License: GPL-3
NeedsCompilation: no
Materials: README
CRAN checks: ebreg results


Reference manual: ebreg.pdf


Package source: ebreg_0.1.3.tar.gz
Windows binaries: r-devel: ebreg_0.1.3.zip, r-release: ebreg_0.1.3.zip, r-oldrel: ebreg_0.1.3.zip
macOS binaries: r-release (arm64): ebreg_0.1.3.tgz, r-release (x86_64): ebreg_0.1.3.tgz, r-oldrel: ebreg_0.1.3.tgz
Old sources: ebreg archive


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