The Information Dynamics Toolkit xl (IDTxl) is a comprehensive software package for efficient inference of networks and their node dynamics from multivariate time series data using information theory.

Estimating Copula Entropy (Mutual Information), Transfer Entropy (Conditional Mutual Information), and the statistics for multivariate normality test and two-sample test, and change point detection in Python

Pythonic implementation of the Phase Transfer Entropy method using NumPy and SciPy

Python module for computing Symbolic Mutual Information and symbolic Transfer of Entropy

Python package for calculating various information measures, including entropy, mutual information, transfer entropy, and more, with support for both discrete and continuous variables.

My implementation of Symbolic Transfer Entropy (STE): a measure of asymmetric information flow between stochastic processes.

Transfer entropy analysis pipeline for studying causal information flow between financial news sentiment and STOXX 50 stock returns. Implements complete workflow from Eikon data collection through FinBERT sentiment analysis to transfer entropy computation using IDTxl.

TENET refined

Estimate Transfer Entropy from time series data using an Approximate Generative Model

Rewrite and extension of the research in my M.Sc. thesis for publication

SImulate time series data to validate transfer entropy estimators.