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 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
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

Documentation:

Reference manual:

GWASinlps.pdf

Downloads:

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

Linking:

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