Statistical independence and conditional independence are the fundemental concepts in statistics and machine learning. Copula Entropy is a mathematical concept for multivariate statistical independence measuring and testing, and also closely related to conditional independence or transfer entropy. It has been applied to solve several statistical or machine learning problems, including association discovery, structure learning, variable selection, and causal discovery. Copula entropy was proposed to be estimated nonparametrically with rank statistic and the kNN method for estimating entropy. 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. Two illustration examples with simulated data and real-world data on causal discovery are also presented. The copent package is available on the Comprehensive R Archive Network (CRAN) and also on GitHub at https://github.com/majianthu/copent.

中文翻译：

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copent：估计 R 中的 Copula 熵

统计独立性和条件独立性是统计学和机器学习中的基本概念。Copula Entropy 是一个用于测量和检验多元统计独立性的数学概念，也与条件独立性或转移熵密切相关。它已被应用于解决多个统计或机器学习问题，包括关联发现、结构学习、变量选择和因果发现。Copula 熵被提议用秩统计和 kNN 估计熵的方法进行非参数估计。copent 是一个 R 包，它实现了这种用于估计 copula 熵的建议方法。本文介绍了该包的实现细节。还提供了两个关于因果发现的模拟数据和真实世界数据的示例。copent 包可在综合 R 档案网络 (CRAN) 和 GitHub 上获取，网址为 https://github.com/majianthu/copent。