BGLR (A Statistical Package for Whole-Genome Regression)
|Use||Parametric and non-parametric regression with sets of high dimensional predictors. Whole-Genome Regression/Prediction.|
Use. The BGLR R-package implements a large collection Bayesian regression models, including various parametric regressions where regression coefficients are allowed to have different types of prior densities (flat, normal, scaled-t, double-exponential and various finite mixtures of the spike-slab family) and semi-parametric methods (Bayesian Reproducing kernel Hilbert spaces, RKHS). The software was originally developed as an extension of the BLR package (http://cran.r-project.org/web/packages/BLR/index.html ) and with a focus on genomic applications; however, the methods implemented are useful for many non-genomic applications as well. The response can be continuous (censored or not) or categorical (either binary, or ordinal).
Algorithms. The algorithm is based on a Gibbs Sampler with scalar updates and the implementation takes advantage of efficient compiled C and Fortran routines. The kernel of our software is written in R, but the computationally demanding steps are carried out using customized routines written in C and Fortran code. The implementation makes use of BLAS routines daxpy and ddot. The computational performance of the algorithm can be greatly improved if R is linked against a tuned BLAS implementation with multithread support, for example
|User’s Manual||PDF file|
|Download package||CRAN: http://cran.r-project.org/web/packages/BGLR/index.html
|Contact||Gustavo de los Campos ( email@example.com ) &
Paulino Pérez (firstname.lastname@example.org )
Go to Bayesian Linear Regression (BLR) web page.