Non-local prior based iterative variable selection tool for genome-wide association study data, or other high-dimensional data

The GWASinlps package performs variable selection for 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 the References).

Details

Package:	GWASinlps
Type:	Package
Version:	2.2
Date:	2022-11-22
License:	GPL (>= 2)

The main function:
GWASinlps

The main function calls the following functions:
nlpsLM
nlpsGLM
nlpsAFTM

Author

Nilotpal Sanyal <nilotpal.sanyal@gmail.com>

Maintainer: Nilotpal Sanyal <nilotpal.sanyal@gmail.com>

References

Sanyal et al. (2019), "GWASinlps: Non-local prior based iterative SNP selection tool for genome-wide association studies". Bioinformatics, 35(1), 1-11.

Sanyal, N. (2022). "Iterative variable selection for high-dimensional data with binary outcomes". arXiv preprint arXiv:2211.03190.