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: | 0.1.2 |
| Depends: | lars, stats |
| Imports: | Rdpack |
| Suggests: | testthat, roxygen2 |
| Published: | 2020-05-26 |
| 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_0.1.2.tar.gz |
| Windows binaries: | r-devel: ebreg_0.1.2.zip, r-release: ebreg_0.1.2.zip, r-oldrel: ebreg_0.1.2.zip |
| macOS binaries: | r-release: ebreg_0.1.2.tgz, r-oldrel: ebreg_0.1.2.tgz |
| Old sources: | ebreg archive |
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