R/marble-package.R

In marble: Robust Marginal Bayesian Variable Selection for Gene-Environment Interactions

#' @useDynLib marble, .registration = TRUE
#' @importFrom Rcpp sourceCpp
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#' _PACKAGE
#' @keywords overview
#' @name marble-package
#' @title  Robust Marginal Bayesian Variable Selection for Gene-Environment Interactions
#' @aliases marble-package
#' @description In this package, we provide a set of robust marginal Bayesian variable selection methods for gene-environment interaction analysis. A Bayesian formulation of the quantile regression has been adopted to accommodate data contamination and heavy-tailed distributions in the response. The proposed method conducts a robust marginal variable selection by accounting for structural sparsity. In particular, the spike-and-slab priors are imposed to identify important main and interaction effects.
#' In addition to the default method, users can also choose different structures (robust or non-robust), methods without spike-and-slab priors.
#'
#' @details The user friendly, integrated interface \strong{marble()} allows users to flexibly choose the fitting methods they prefer. There are two arguments in marble() that control the fitting method:
#' robust: whether to use robust methods; sparse: whether to use the spike-and-slab priors to create sparsity.
#' The function marble() returns a marble object that contains the posterior estimates of each coefficients. Moreover, it also provides a rank list of the genetic factors and gene-environment interactions.
#' Functions GxESelection() and print.marble() are implemented for marble objects.
#' GxESelection() takes a marble object and returns the variable selection results.
#'
#' @references
#' Lu, X., Fan, K., Ren, J., and Wu, C. (2021). Identifying Gene–Environment Interactions With Robust Marginal Bayesian Variable Selection.
#' {\emph{Frontiers in Genetics}, 12:667074} \doi{10.3389/fgene.2021.667074}
#' 
#' Ren, J., Zhou, F., Li, X., Ma, S., Jiang, Y. and Wu, C. (2020). Robust Bayesian variable selection for gene-environment interactions.
#' \doi{10.1111/biom.13670}
#'
#' Zhou, F., Ren, J., Lu, X., Ma, S. and Wu, C. (2020). Gene–Environment Interaction: a Variable Selection Perspective. Epistasis. Methods in Molecular Biology.
#' {\emph{Humana Press} (Accepted)} \url{https://arxiv.org/abs/2003.02930}
#'
#' Wu, C., Cui, Y., and Ma, S. (2014). Integrative analysis of gene–environment interactions under a multi–response partially linear varying coefficient model.
#' {\emph{Statistics in Medicine}, 33(28), 4988–4998} \doi{10.1002/sim.6287}
#' 
#' Shi, X., Liu, J., Huang, J., Zhou, Y., Xie, Y. and Ma, S. (2014). A penalized robust method for identifying gene–environment interactions. 
#' {\emph{Genetic epidemiology}, 38(3), 220-230} \doi{10.1002/gepi.21795}
#' 
#' Chai, H., Zhang, Q., Jiang, Y., Wang, G., Zhang, S., Ahmed, S. E. and Ma, S. (2017). Identifying gene-environment interactions for prognosis using a robust approach. 
#' {\emph{Econometrics and statistics}, 4, 105-120} \doi{10.1016/j.ecosta.2016.10.004}
#' @seealso \code{\link{marble}}
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