JIDT -- Java information dynamics toolkit
Open source project providing a stand-alone, open-source code Java implementation (usable in Matlab, Octave and Python) of information-theoretic measures of distributed computation in complex systems: i.e. information storage, transfer and modification. The distribution is available here or you can grab all the source code via github.
This toolkit includes Java implementation of all of the relevant information dynamics measures, including basic measures such as entropy, joint entropy, mutual information, conditional mutual information, as well as advanced measures such as transfer entropy, conditional transfer entropy, active information storage, excess entropy, separable information.
All measures are implemented using several different estimation techniques, including discrete-valued estimators, box-kernel estimation, Kraskov-Grassberger estimators, symbolic coding, and Gaussian approximation.
This standalone toolkit can be used as a Java plugin in Octave, Matlab and Python (see details on github site).
Please note that this toolkit replaces the previous Matlab transfer entropy code that was referred to in some of my earlier presentations.
A course of short lecture videos and activities accompanying JIDT is available.