copent: Estimating Copula Entropy in R

Statistical independence and conditional independence are two fundamental concepts in statistics and machine learning. Copula Entropy is a mathematical concept defined by Ma and Sun for multivariate statistical independence measuring and testing, and also proved to be closely related to conditional independence or transfer entropy. As the unified framework for measuring both independence and causality, it has been applied to solve several related statistical or machine learning problems, including association discovery, structure learning, variable selection, and causal discovery. The method for estimating copula entropy nonparametrically with rank statistic and the kNN method was also proposed. copent is a R package which implements this proposed method for estimating copula entropy. The implementation detail of the package is presented in this paper. Three examples with simulated data and real-world data on variable selection and causal discovery are also presented to demonstrate the usage of the package. The copent package is available on the Comprehensive R Archive Network (CRAN) and also on GitHub at https://github.com/majianthu/copent.