copent: Estimating Copula Entropy and Transfer Entropy

The nonparametric methods for estimating copula entropy and transfer entropy are implemented. The method for estimating copula entropy composes of two simple steps: estimating empirical copula by rank statistic and estimating copula entropy with k-Nearest-Neighbour method. The method for estimating transfer entropy composes of two steps: estimating three copula entropy terms and then calculate transfer entropy from the estimated copula entropy terms. 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> and Ma (2019) <arXiv:1910.04375> for more information.

Version:	0.2
Depends:	R (≥ 2.7.0)
Imports:	stats
Suggests:	mnormt
Published:	2021-03-21
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

Documentation:

Reference manual:

copent.pdf

Downloads:

Package source:	copent_0.2.tar.gz
Windows binaries:	r-devel: copent_0.2.zip, r-release: copent_0.2.zip, r-oldrel: copent_0.2.zip
macOS binaries:	r-release (arm64): copent_0.2.tgz, r-oldrel (arm64): copent_0.2.tgz, r-release (x86_64): copent_0.2.tgz, r-oldrel (x86_64): copent_0.2.tgz
Old sources:	copent archive

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