BayesReg Ver 1.5

Version 1.5 of the BayesReg package has now been released. There are two main changes to this version:

  1. We have written a new version of the logistic regression sampling code in C++ that makes use of multiple cores. This can result in significant speed-ups when sampling.
  2. An efficient MATLAB implementation of logistic regression sampling has been added to the code, so that it now runs even without MEX files (though it will not be as fast).

Precompiled MEX files for Windows, Linux and MacOSX can be obtained here. To use these, all you need to do is download this file and unzip the contents into the “bayesreg” folder.

BayesReg Ver 1.4: High-Dimensional Bayesian Regularised Regression, now with grouping of variables

Version 1.4 of the BayesReg package has been released. This has a large additional feature — users can now assign predictors to logical groupings (potentially overlapping, so predictors can be part of multiple groups). This can be used to exploit a priori knowledge regarding predictors and how they may be related to each other (for example, in grouping genetic data into genes and collections of genes such as pathways). The features added are:

  1. Added option ‘groups’ which allows grouping of variables into potentially overlapping groups
  2. Grouping works with HS, HS+ and lasso priors
  3. Fixed a regression bug with g priors and logistic models
  4. Updated examples to demonstrate grouping

You can obtain the latest version of the BayesReg software from here.