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).
Version: | 1.2 |
Depends: | mombf, speedglm |
Imports: | horseshoe |
Suggests: | glmnet |
Published: | 2018-07-19 |
Author: | Nilotpal Sanyal [aut, cre] |
Maintainer: | Nilotpal Sanyal <nilotpal.sanyal at gmail.com> |
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_1.2.tar.gz |
Windows binaries: | r-devel: GWASinlps_1.2.zip, r-release: GWASinlps_1.2.zip, r-oldrel: GWASinlps_1.2.zip |
macOS binaries: | r-release (arm64): GWASinlps_1.2.tgz, r-oldrel (arm64): GWASinlps_1.2.tgz, r-release (x86_64): GWASinlps_1.2.tgz, r-oldrel (x86_64): GWASinlps_1.2.tgz |
Old sources: | GWASinlps archive |
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