Performs variable selection with data from Genome-wide association studies (GWAS), or other high-dimensional data with continuous, binary or survival outcomes, combining in an iterative framework the computational efficiency of the structured screen-and-select variable selection strategy based on some association learning and the parsimonious uncertainty quantification provided by the use of non-local priors (see Sanyal et al., 2019 <doi:10.1093/bioinformatics/bty472>).
Version: | 2.2 |
Depends: | mombf |
Imports: | Rcpp (≥ 1.0.9), RcppArmadillo, fastglm, horseshoe, survival |
LinkingTo: | Rcpp, RcppArmadillo |
Suggests: | glmnet |
Published: | 2022-11-23 |
DOI: | 10.32614/CRAN.package.GWASinlps |
Author: | Nilotpal Sanyal [aut, cre] |
Maintainer: | Nilotpal Sanyal <nilotpal.sanyal at gmail.com> |
BugReports: | https://github.com/nilotpalsanyal/GWASinlps/issues |
License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
URL: | https://nilotpalsanyal.github.io/GWASinlps/ |
NeedsCompilation: | yes |
Materials: | README NEWS |
CRAN checks: | GWASinlps results |
Reference manual: | GWASinlps.pdf |
Package source: | GWASinlps_2.2.tar.gz |
Windows binaries: | r-devel: GWASinlps_2.2.zip, r-release: GWASinlps_2.2.zip, r-oldrel: GWASinlps_2.2.zip |
macOS binaries: | r-release (arm64): GWASinlps_2.2.tgz, r-oldrel (arm64): GWASinlps_2.2.tgz, r-release (x86_64): GWASinlps_2.2.tgz, r-oldrel (x86_64): GWASinlps_2.2.tgz |
Old sources: | GWASinlps archive |
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