|
|
Statistical Tools for Whole-Genome Prediction of Complex Traits and Diseases
Extension of BLR to deal with censored, binary traits and new shrinkage parametric and non parametric methods
The BLR (Bayesian Linear Regression, http://cran.r-project.org/web/packages/BLR/index.html)s package of R (http://cran.r-project.org) implements several types of Bayesian regression models, including fixed effects, Bayesian Lasso (BL, Park and Casella 2008) and Bayesian Ridge Regression. BLR can only handle continuous outcomes. We have produced a modified (beta) version of BLR (BGLR=Bayesian Generalized Linear Regression) that extends BLR by allowing regressions for binary and censored outcomes. Most of the inputs, processes and outputs are as in BLR.
We are currently working in an extension of BLR to include another parametric and non parametric models, for example
BayesA, BayesB, BayesCpi, Reproducing Kernel Hilbert Spaces, etc. A snapshoot of the current development can be found
in https://r-forge.r-project.org/R/?group_id=1525.
Tutorial/manual:
BGLR package manual Download
BGLR package tutorial Download
Future developments will be released first in the R-forge webpage https://r-forge.r-project.org/projects/bglr/ and subsequently as R-packages.