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: | 2.4 |
| Depends: | mombf |
| Imports: | Rcpp (≥ 1.0.9),RcppArmadillo,fastglm,survival |
| LinkingTo: | Rcpp,RcppArmadillo |
| Suggests: | glmnet |
| Published: | 2025-10-29 |
| DOI: | 10.32614/CRAN.package.GWASinlps |
| Author: | Nilotpal Sanyal |
| 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.html ,GWASinlps.pdf |
| Package source: | GWASinlps_2.4.tar.gz |
| Windows binaries: | r-devel:GWASinlps_2.4.zip, r-release:GWASinlps_2.4.zip, r-oldrel:GWASinlps_2.4.zip |
| macOS binaries: | r-release (arm64):GWASinlps_2.4.tgz, r-oldrel (arm64):GWASinlps_2.4.tgz, r-release (x86_64):GWASinlps_2.4.tgz, r-oldrel (x86_64):GWASinlps_2.4.tgz |
| Old sources: | GWASinlps archive |
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