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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>).
Getting started
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Package details |
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| Author | Nilotpal Sanyal [aut, cre] (<https://orcid.org/0000-0003-4814-7602>) |
| Maintainer | Nilotpal Sanyal <nilotpal.sanyal@gmail.com> |
| License | GPL (>= 2) |
| Version | 2.2 |
| URL | https://nilotpalsanyal.github.io/GWASinlps/ |
| Package repository | View on CRAN |
| Installation |
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