Reference Guide

On these pages you can find documentation for infomeasure. The package implements these information-theoretic measures—such as Entropy (H), Mutual Information (MI), Conditional MI, Transfer Entropy (TE), Conditional TE, and composite measures like Jensen–Shannon Divergence (JSD)—for both discrete and continuous-valued data. A variety of estimation techniques are employed to calculate these measures, including methods such as the Kernel method, Kraskov-Stögbauer-Grassberger algorithm, among others. For detailed information on how to use these measures programmatically or access their underlying implementations, please refer to the API Reference.

• Introduction
• Estimation
  • Types of Estimation techniques available
• Estimator Usage
  • 1. Utility functions
  • 2. Estimator classes
  • Package configuration
• Entropy (H)
  • Information Measures
  • Entropy Estimation
• Conditional H
  • Local Conditional H
• Mutual Information (MI)
  • Local Mutual Information
  • Time-lagged Mutual Information
  • MI estimation
  • List of MI Estimation Techniques Implemented:
• Conditional MI
  • Local Conditional MI
  • CMI Estimation
• Transfer Entropy (TE)
  • Local Transfer Entropy
  • Effective Transfer Entropy (eTE)
  • List of Estimation Techniques Implemented:
• Conditional TE
  • Local Conditional TE
  • CTE Estimation
• Kullback–Leibler Divergence (KLD)
• Jensen–Shannon Divergence (JSD)