论文标题
肾小管：估计R中的副熵和转移熵
copent: Estimating Copula Entropy and Transfer Entropy in R
论文作者
论文摘要
统计独立性和有条件独立性是统计和机器学习中的两个基本概念。 Copula熵是由MA和Sun定义的数学概念，用于多元统计独立性测量和测试，也被证明与条件独立性（或转移熵）密切相关。作为统一衡量独立性和因果关系的统一框架，CE已应用于解决几个相关的统计或机器学习问题，包括关联发现，结构学习，可变选择和因果发现。先前还提出了用于估计副熵和转移熵的非参数方法。本文介绍了副本，即R包，该副本实现了这些提出的方法，用于估计副熵和转移熵。引入了软件包的实现细节。还提供了有关可变选择和因果发现的三个示例和现实世界数据，以证明此软件包的使用情况。与相关软件包相比，可变选择和因果发现的示例表明，与相关软件包相比，欧洲在测试（条件）独立性方面的能力很强。可在综合R档案网络（CRAN）以及https://github.com/majianthu/copent上的GitHub上找到副本。
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, CE has been applied to solve several related statistical or machine learning problems, including association discovery, structure learning, variable selection, and causal discovery. The nonparametric methods for estimating copula entropy and transfer entropy were also proposed previously. This paper introduces copent, the R package which implements these proposed methods for estimating copula entropy and transfer entropy. The implementation detail of the package is introduced. Three examples with simulated data and real-world data on variable selection and causal discovery are also presented to demonstrate the usage of this package. The examples on variable selection and causal discovery show the strong ability of copent on testing (conditional) independence compared with the related packages. The copent package is available on the Comprehensive R Archive Network (CRAN) and also on GitHub at https://github.com/majianthu/copent.
