GWASinlps: Non-Local Prior Based Iterative Variable Selection Tool for Genome-Wide Association Studies

Performs variable selection with data from Genome-wide association studies (GWAS), or other high-dimensional data, combining in an iterative framework, the computational efficiency of the screen-and-select variable selection approach based on some association learning and the parsimonious uncertainty quantification provided by the use of non-local priors as described in Sanyal et al. (2019) <doi:10.1093/bioinformatics/bty472>.

Version: 2.1
Depends: mombf, fastglm
Imports: Rcpp (≥ 1.0.9), RcppArmadillo, horseshoe
LinkingTo: Rcpp, RcppArmadillo
Suggests: glmnet
Published: 2022-09-17
Author: Nilotpal Sanyal ORCID iD [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

Documentation:

Reference manual: GWASinlps.pdf

Downloads:

Package source: GWASinlps_2.1.tar.gz
Windows binaries: r-devel: GWASinlps_2.1.zip, r-release: GWASinlps_2.1.zip, r-oldrel: GWASinlps_2.1.zip
macOS binaries: r-release (arm64): GWASinlps_2.1.tgz, r-oldrel (arm64): GWASinlps_2.1.tgz, r-release (x86_64): GWASinlps_2.1.tgz, r-oldrel (x86_64): GWASinlps_2.1.tgz
Old sources: GWASinlps archive

Linking:

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