Bayesian Group Sparse Multi-Task Regression

Fits a Bayesian group-sparse multi-task regression model using Gibbs sampling. The hierarchical prior encourages shrinkage of the estimated regression coefficients at both the gene and SNP level. The model has been extended to a spatial model that allows for two type correlation in neuroimaging genetics data and been applied successfully to imaging phenotypes of dimension up to 100; it can be used more generally for multivariate (non-imaging) phenotypes.


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0.5 by Yin Song, 10 months ago

Browse source code at

Authors: Yin Song , Shufei Ge , Liangliang Wang , Farouk S. Nathoo , Keelin Greenlaw , Mary Lesperance

Documentation:   PDF Manual  

GPL-2 license

Imports coda, EDISON, statmod, methods, sparseMVN, inline, LaplacesDemon, CholWishart, mnormt, Rcpp

Depends on Matrix, mvtnorm, matrixcalc, miscTools

See at CRAN