MATLAB Information Theory Toolbox for Entropy and Mutual Information Calculations

I have obtained an international MATLAB Information Theory Toolbox that serves as a highly practical instrument for calculating mutual information and entropy of random variables. The toolbox incorporates key algorithms for probability distribution computations (including PDF/CDF estimations), Markov chain modeling for stochastic processes, and implementations of signal processing techniques like entropy encoding and decoding. It features built-in functions such as mi() for mutual information and entropy() for Shannon entropy calculations using histogram- or kernel-based estimation methods. Additionally, the toolbox provides extensive example codes demonstrating applications in channel capacity simulation and feature selection algorithms, along with detailed documentation for rapid user onboarding. Notably, its applicability spans beyond communications and signal processing to diverse scientific engineering domains including bioinformatics (for gene expression analysis) and financial modeling (via time-series entropy measurements).