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  "Title": "Robust Marginal Bayesian Variable Selection for Gene-Environment\nInteractions",
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  "Authors@R": "c( person(\"Xi\", \"Lu\", role = c(\"aut\", \"cre\"),\nemail = \"xilu@ksu.edu\"),\nperson(\"Cen\", \"Wu\", role = \"aut\"))",
  "Description": "Recently, multiple marginal variable selection methods\nhave been developed and shown to be effective in\nGene-Environment interactions studies. We propose a novel\nmarginal Bayesian variable selection method for\nGene-Environment interactions studies. In particular, our\nmarginal Bayesian method is robust to data contamination and\noutliers in the outcome variables. With the incorporation of\nspike-and-slab priors, we have implemented the Gibbs sampler\nbased on Markov Chain Monte Carlo. The core algorithms of the\npackage have been developed in 'C++'.",
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