The Information Dynamics Toolkit xl (IDTxl) in Python. IDTxl is a comprehensive software package for the efficient analysis of information dynamics of large data sets.
IDTxl provides estimators for the following information theoretic measures:
- mutual information (MI)
- bivariate transfer entropy (bTE)
- multivariate transfer entropy (mTE)
- Granger causality (GC)
- active information storage (AIS)
- partial information decomposition (PID)
IDTxl uses GPU-accelerated estimators as well as parallel processing and is designed for the application on high-performance-computing clusters.
To get started have a look at the wiki and the documentation.
- Patricia Wollstadt, Brain Imaging Center, MEG Unit, Goethe-University, Frankfurt, Germany
- Michael Wibral, Brain Imaging Center, MEG Unit, Goethe-University, Frankfurt, Germany
- Joseph T. Lizier, Complex Systems Research Group, The University of Sydney, Sydney, Australia
- Raul Vicente, Computational Neuroscience Lab, Institute of Computer Science, University of Tartu, Tartu, Estonia
- Connor Finn, Complex Systems Research Group, The University of Sydney, Sydney, Australia
- Mario Martínez Zarzuela, Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Valladolid, Spain
- Michael Lindner, Center for Integrative Neuroscience and Neurodynamics, University of Reading, Reading, UK
- Multivariate transfer entropy: Lizier & Rubinov, 2012, Preprint, Technical Report 25/2012, Max Planck Institute for Mathematics in the Sciences. Available from: http://www.mis.mpg.de/preprints/2012/preprint2012_25.pdf
- Kraskov estimator: Kraskov et al., 2004, Phys Rev E 69, 066138
- Nonuniform embedding: Faes et al., 2011, Phys Rev E 83, 051112
- Faes' compensated transfer entropy: Faes et al., 2013, Entropy 15, 198-219
- Uniform embedding: Takens, 1981, Detecting strange attractors in turbulence (pp. 366-381). Springer Berlin Heidelberg
- Ragwitz' criterion: Ragwitz & Kantz, 2002, Phys Rev E 65, 056201