JIDT

JIDT: Java Information Dynamics Toolkit for studying information-theoretic measures of computation in complex systems. JIDT provides a stand-alone, open-source code Java implementation (also usable in Matlab, Octave, Python, R, Julia and Clojure) of information-theoretic measures of distributed computation in complex systems: i.e. information storage, transfer and modification. JIDT includes implementations: principally for the measures transfer entropy, mutual information, and their conditional variants, as well as active information storage, entropy, etc; for both discrete and continuous-valued data; using various types of estimators (e.g. Kraskov-Stögbauer-Grassberger estimators, box-kernel estimation, linear-Gaussian). JIDT ships with a large number of demos, and includes a GUI app for automatic push-button analysis as well as code template generation to get you started.


References in zbMATH (referenced in 9 articles )

Showing results 1 to 9 of 9.
Sorted by year (citations)

  1. Roy, Subhradeep: Quantifying interactions among car drivers using information theory (2020)
  2. Sipahi, Rifat; Porfiri, Maurizio: Improving on transfer entropy-based network reconstruction using time-delays: approach and validation (2020)
  3. Stefan McCabe, Leo Torres, Timothy LaRock, Syed Arefinul Haque, Chia-Hung Yang, Harrison Hartle, Brennan Klein: netrd: A library for network reconstruction and graph distances (2020) arXiv
  4. Broniatowski, Michel; Stummer, Wolfgang: Some universal insights on divergences for statistics, machine learning and artificial intelligence (2019)
  5. SimonBehrendt; ThomasDimpfl; Franziska J.Peter; David J.Zimmermann: RTransferEntropy - Quantifying information flow between different time series using effective transfer entropy (2019) not zbMATH
  6. Romano, Simone; Vinh, Nguyen Xuan; Verspoor, Karin; Bailey, James: The randomized information coefficient: assessing dependencies in noisy data (2018)
  7. Warchoł, Piotr: Buses of Cuernavaca -- an agent-based model for universal random matrix behavior minimizing mutual information (2018)
  8. Wollstadt; Patricia; Lizier; Joseph T.; Vicente; Raul; Finn; Conor; Martínez-Zarzuela; Mario; Mediano; Pedro; Novelli; Leonardo; Wibral; Michael: IDTxl: The Information Dynamics Toolkit xl: a Python package for the efficient analysis of multivariate information dynamics in networks (2018) arXiv
  9. Joseph T. Lizier: JIDT: An information-theoretic toolkit for studying the dynamics of complex systems (2014) arXiv