Nonlocal Prior Based Iterative SNP Selection Tool for
Genome-Wide Association Studies
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) [submitted].

c(person("Nilotpal", "Sanyal", role = c("aut", "cre"), email = "nilotpal.sanyal@gmail.com"))
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).

Non-Local Prior Based Iterative Variable Selection Tool for
Genome-Wide Association Studies
c(person("Nilotpal", "Sanyal", role = c("aut", "cre"), email = "nilotpal.sanyal@gmail.com", comment = c(ORCID = "0000-0003-4814-7602")))
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>.
Rcpp (>= 1.0.9), RcppArmadillo, horseshoe
Nilotpal Sanyal [aut, cre] (<https://orcid.org/0000-0003-4814-7602>)

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>).
Rcpp (>= 1.0.9), RcppArmadillo, fastglm, horseshoe, survival