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marble: Robust Marginal Bayesian Variable Selection for Gene-Environment Interactions
Recently, multiple marginal variable selection methods have been developed and shown to be effective in Gene-Environment interactions studies. We propose a novel marginal Bayesian variable selection method for Gene-Environment interactions studies. In particular, our marginal Bayesian method is robust to data contamination and outliers in the outcome variables. With the incorporation of spike-and-slab priors, we have implemented the Gibbs sampler based on Markov Chain Monte Carlo. The core algorithms of the package have been developed in 'C++'.
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Package details |
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| Author | Xi Lu [aut, cre], Cen Wu [aut] |
| Maintainer | Xi Lu <xilu@ksu.edu> |
| License | GPL-2 |
| Version | 0.0.3 |
| URL | https://github.com/xilustat/marble |
| Package repository | View on CRAN |
| Installation |
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