[aut, cre],
Thomas Stewart [aut],
Jeffrey Blume [aut]Implementation of penalized regression with second-generation p-values for variable selection. The one-stage algorithm is extremely fast and the two-stage algorithm has lower parameter estimation bias when data are highly correlated. S3 methods print(), summary(), coef(), and predict() are available for both algorithms, and S3 method plot() is available for the two-stage algorithm. Technical details of the algorithms can be found at <arXiv:2012.07941>.
| Version: | 0.1.0 |
| Depends: | R (≥ 3.5.0), glmnet |
| Imports: | MASS |
| Published: | 2021-01-06 |
| Author: | Yi Zuo [aut, cre],
Thomas Stewart [aut],
Jeffrey Blume [aut] |
| Maintainer: | Yi Zuo <yi.zuo at vanderbilt.edu> |
| BugReports: | https://github.com/zuoyi93/ProSGPV/issues |
| License: | GPL-3 |
| URL: | https://github.com/zuoyi93/ProSGPV, https://arxiv.org/abs/2012.07941 |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | ProSGPV results |
| Reference manual: | ProSGPV.pdf |
| Package source: | ProSGPV_0.1.0.tar.gz |
| Windows binaries: | r-devel: ProSGPV_0.1.0.zip, r-release: ProSGPV_0.1.0.zip, r-oldrel: ProSGPV_0.1.0.zip |
| macOS binaries: | r-release: ProSGPV_0.1.0.tgz, r-oldrel: ProSGPV_0.1.0.tgz |
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