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: 0.1.0
Depends: lars, stats
Imports: Rdpack
Suggests: testthat, roxygen2
Published: 2019-10-29
Author: Yiqi Tang, Ryan Martin
Maintainer: Yiqi Tang <ytang22 at ncsu.edu>
License: GPL-3
NeedsCompilation: no
Materials: README
CRAN checks: ebreg results

Downloads:

Reference manual: ebreg.pdf
Package source: ebreg_0.1.0.tar.gz
Windows binaries: r-devel: ebreg_0.1.0.zip, r-release: ebreg_0.1.0.zip, r-oldrel: ebreg_0.1.0.zip
OS X binaries: r-release: ebreg_0.1.0.tgz, r-oldrel: ebreg_0.1.0.tgz

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