copent - the package for estimating copula entropy

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copent - the package for estimating copula entropy

Ma Jian
Hi all,

I'm writing to you to introduce our new package, copent [6]. This package
estimates copula entropy, a new mathematical concept for multivariate
statistical independence measure and testing [1].  The estimating method is
nonparametric and can be applied to any cases without making assumptions.
The package has been used for  
    * association discovery [2], in which copula entropy is an association measure
shown to be better than correlation coeffients,
    * structure learning [3],
    * variable selection [4], and  
    * causal discovery [5] by estimating transfer entropy.


Hope it helpful for you. Any comments and suggestions are welcome.

Best Regards,
MA Jian
1. Ma Jian, Sun Zengqi. Mutual information is copula entropy. Tsinghua Science & Technology, 2011, 16(1): 51-54. See also arXiv preprint, arXiv:0808.0845, 2008.
2. Ma Jian. Discovering Association with Copula Entropy. arXiv preprint arXiv:1907.12268, 2019.
3. Ma Jian, Sun Zengqi. Dependence Structure Estimation via Copula. arXiv preprint arXiv:0804.4451v2, 2019.
4. Ma Jian. Variable Selection with Copula Entropy. arXiv preprint arXiv:1910.12389, 2019.
5. Ma Jian. Estimating Transfer Entropy via Copula Entropy. arXiv preprint arXiv:1910.04375, 2019.
6. Ma Jian. copent: Estimating Copula Entropy in R. arXiv preprint, arXiv:2005.14025, 2020.
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