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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++'.
| Version: | 0.0.3 |
| Depends: | R (≥ 3.5.0) |
| Imports: | Rcpp, stats |
| LinkingTo: | Rcpp, RcppArmadillo |
| Published: | 2024-04-04 |
| Author: | Xi Lu [aut, cre], Cen Wu [aut] |
| Maintainer: | Xi Lu <xilu at ksu.edu> |
| License: | GPL-2 |
| URL: | https://github.com/xilustat/marble |
| NeedsCompilation: | yes |
| CRAN checks: | marble results |
| Reference manual: | marble.pdf |
| Package source: | marble_0.0.3.tar.gz |
| Windows binaries: | r-devel: marble_0.0.3.zip, r-release: marble_0.0.3.zip, r-oldrel: marble_0.0.3.zip |
| macOS binaries: | r-release (arm64): marble_0.0.3.tgz, r-oldrel (arm64): marble_0.0.3.tgz, r-release (x86_64): marble_0.0.3.tgz, r-oldrel (x86_64): marble_0.0.3.tgz |
| Old sources: | marble archive |
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