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marble  

Robust Marginal Bayesian Variable Selection for Gene-Environment Interactions
View on CRAN: Click here


Download and install marble package within the R console
Install from CRAN:
install.packages("marble")

Install from Github:
library("remotes")
install_github("cran/marble")

Install by package version:
library("remotes")
install_version("marble", "0.0.4")



Attach the package and use:
library("marble")
Maintained by
Xi Lu
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-05-02
Latest Update: 2024-04-04
Description:
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++'.
How to cite:
Xi Lu (2023). marble: Robust Marginal Bayesian Variable Selection for Gene-Environment Interactions. R package version 0.0.4, https://cran.r-project.org/web/packages/marble. Accessed 06 Oct. 2026.
Previous versions and publish date:
(2026-09-10 18:50), 0.0.1 (2023-05-02 10:20), 0.0.2 (2023-05-10 21:30), 0.0.3 (2024-04-05 00:13)
Other packages that cited marble R package
View marble citation profile
Other R packages that marble depends, imports, suggests or enhances
Complete documentation for marble
Functions, R codes and Examples using the marble R package
Some associated functions: GxESelection . dat . marble-package . marble . print.GxESelection . print.marble . 
Some associated R codes: BL_SS.R . BLasso.R . DataMatrix.R . GxESelection.NonSparse.R . GxESelection.R . GxESelection.Sparse.R . RcppExports.R . Robust.R . RobustBL.R . RobustBLSS.R . data.R . marble-package.R . marble.R . mpm.R . nonRobust.R . print.marble.R . quanfun.R .  Full marble package functions and examples
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