About: ITE (Information Theoretical Estimators) is capable of estimating many different variants of entropy, mutual information, divergence, association measures and cross quantities. Thanks to its highly modular design, ITE supports additionally (i) the combinations of the estimation techniques, (ii) the easy construction and embedding of novel information theoretical estimators, and (iii) their immediate application in information theoretical optimization problems.

• Symmetric Bregman distance estimation based on nonsymmetric Bregman distance: added,

• Symmetric Bregman distance estimation using the k-nearest neighbor method: added.

About: A mutual information library for C and Mex bindings for MATLAB. Aimed at feature selection, and provides simple methods to calculate mutual information, conditional mutual information, entropy, conditional entropy, and Renyi entropy/mutual information. Works with discrete distributions, and expects column vectors of features.

Updated documentation & link to JMLR publication.

About: FEAST provides implementations of common mutual information based filter feature selection algorithms (mim, mifs, mrmr, cmim, icap, jmi, disr, fcbf, etc), and an implementation of RELIEF.

Initial Announcement on mloss.org.