Package: spinBayes
Type: Package
Title: Semi-Parametric Gene-Environment Interaction via Bayesian
        Variable Selection
Version: 0.1.0
Author: Jie Ren, Fei Zhou, Xiaoxi Li, Cen Wu, Yu Jiang
Maintainer: Jie Ren <jieren@ksu.edu>
Description: Many complex diseases are known to be affected by the interactions between 
   genetic variants and environmental exposures beyond the main genetic and environmental 
   effects. Existing Bayesian methods for gene-environment (G×E) interaction studies are 
   challenged by the high-dimensional nature of the study and the complexity of environmental 
   influences. We have developed a novel and powerful semi-parametric Bayesian variable 
   selection method that can accommodate linear and nonlinear G×E interactions simultaneously 
   (Ren et al. (2019) <arXiv:1906.01057>). Furthermore, the proposed method can conduct 
   structural identification by distinguishing nonlinear interactions from main effects only 
   case within Bayesian framework. Spike-and-slab priors are incorporated on both individual 
   and group level to shrink coefficients corresponding to irrelevant main and interaction 
   effects to zero exactly. The Markov chain Monte Carlo algorithms of the proposed and 
   alternative methods are  efficiently implemented in C++. 
Depends: R (>= 3.5.0)
License: GPL-2
Encoding: UTF-8
LazyData: true
LinkingTo: Rcpp, RcppArmadillo
Imports: Rcpp, splines, MASS, glmnet, utils
URL: https://github.com/jrhub/spinBayes
BugReports: https://github.com/jrhub/spinBayes/issues
RoxygenNote: 6.1.1
NeedsCompilation: yes
Packaged: 2019-06-05 19:52:21 UTC; JieRen
Repository: CRAN
Date/Publication: 2019-06-06 14:40:03 UTC
Built: R 3.6.3; x86_64-w64-mingw32; 2020-08-05 05:18:39 UTC; windows
Archs: i386, x64
