Performs variable selection with data from Genome-wide association studies (GWAS) combining, in an iterative variable selection framework, the computational efficiency of the screen-and-select approach based on some association learning and the parsimonious uncertainty quantification provided by the use of nonlocal priors, as described in Sanyal et al. (2018) [submitted].
| Version: | 1.1 |
| Depends: | mombf |
| Imports: | horseshoe |
| Suggests: | glmnet |
| Published: | 2018-02-02 |
| Author: | Nilotpal Sanyal [aut, cre] |
| Maintainer: | Nilotpal Sanyal <nisanyal at ucsd.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://www.r-project.org |
| NeedsCompilation: | no |
| Materials: | ChangeLog |
| CRAN checks: | GWASinlps results |
| Reference manual: | GWASinlps.pdf |
| Package source: | GWASinlps_1.1.tar.gz |
| Windows binaries: | r-devel: GWASinlps_1.1.zip, r-release: GWASinlps_1.1.zip, r-oldrel: GWASinlps_1.1.zip |
| OS X El Capitan binaries: | r-release: GWASinlps_1.1.tgz |
| OS X Mavericks binaries: | r-oldrel: GWASinlps_1.0.tgz |
| Old sources: | GWASinlps archive |
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