[R-pkgs] copent: Estimating Copula Entropy
m@j|@n03 @end|ng |rom gm@||@com
Wed May 13 04:22:05 CEST 2020
I'm writing to you to introduce our new package, copent. This package estimates copula entropy, a new mathematical concept for multivariate statistical independence measure and testing . The estimating method is nonparametric and can be applied to any cases without making assumptions. The package has been used for
* association discovery , in which copula entropy is an association measure shown to be better than correlation coeffients,
* structure learning ,
* variable selection , and
* causal discovery  by estimating transfer entropy.
Hope it helpful for you. Any comments and suggestions are welcome.
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.
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