The nonparametric method for estimating copula entropy is implemented. The method composes of two simple steps: estimating empirical copula by rank statistic and estimating copula entropy with k-Nearest-Neighbour method. Copula Entropy is a mathematical concept for multivariate statistical independence measuring and testing, and proved to be equivalent to mutual information. Estimating copula entropy can be applied to many cases, including but not limited to variable selection and causal discovery (by estimating transfer entropy). Please refer to Ma and Sun (2011) <doi:10.1016/S1007-0214(11)70008-6> for more information.
| Version: | 0.1 |
| Depends: | R (≥ 2.7.0) |
| Imports: | stats |
| Suggests: | mnormt |
| Published: | 2020-04-16 |
| Author: | MA Jian [aut, cre] |
| Maintainer: | MA Jian <majian03 at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/majianthu/copent |
| NeedsCompilation: | no |
| CRAN checks: | copent results |
| Reference manual: | copent.pdf |
| Package source: | copent_0.1.tar.gz |
| Windows binaries: | r-devel: copent_0.1.zip, r-release: copent_0.1.zip, r-oldrel: copent_0.1.zip |
| macOS binaries: | r-release: copent_0.1.tgz, r-oldrel: copent_0.1.tgz |
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