BGLR: An R Package for (Bayesian) High-Dimensional Regression
The BGLR Package (Perez & de los Campos, 2014) implements a variety of shrinkage and variable selection regression procedures. In this repository we maintain the latest version beta version. The latest stable release can be downloaded from CRAN.
From CRAN (stable release).
From GitHub (development version, added features).
install.packages(pkg='devtools',repos='https://cran.r-project.org/') #1# install devtools library(devtools) #2# load the library install_git('https://github.com/gdlc/BGLR-R') #3# install BGLR from GitHub
Note: when trying to install from github on a mac you may get the following error message
ld: library not found for -lgfortran
This can be fixed it by following the following advise.
1. Single-Trait Models
Metabolomics (milk-spectra): Ferragina et al., J.D.Sci, 2015
Markers or Pedigree and Environmental Covariates
-Wheat (SNPs and env. covariates): Jarquin et al. (TAG, 2014)
-Cotton (Pedigree and env. covariates): Perez-Rodriguez et al.(Crop. Sci, 2015)
-Maize (Image data): Aguate et al. (IBC, 2016)
2. Multi-trait models
The Multitrait function included in the BGLR package fits Bayesian multitrait models with arbitrary number of random effects using a Gibbs sampler. A functionality similar to this is implemented in the MTM package. In this implementation is possible to include regression on markers directly assigning Spike-slab or Gaussian priors for the regression coefficients and fixed effects can be different for all the traits. We also have improved the sampling routines to speed up computations. Next we include some examples.