c(person("Max", "Sommerfeld",
email = "max.sommerfeld@mathematik.uni-goettingen.de",
role = c("aut", "cre")),
person("Joshua", "French",
role = c("aut")),
person("Armin", "Schwartzman",
role = c("aut")))
Provides functions to  compute and plot Coverage
Probability Excursion (CoPE) sets
for real valued functions on a 2-dimensional domain. CoPE sets are obtained
from repeated noisy observations of the function on the entire domain.
They are designed to bound the excursion
set of the target function at a given level from above and below with
a predefined probability. The target
function can be a parameter in spatially-indexed linear regression.
Support by NIH grant R01 CA157528 is gratefully acknowledged.
Max Sommerfeld [aut, cre],
Joshua French [aut],
Armin Schwartzman [aut]

c(person("MA Jian", role = c("aut", "cre"),
email = "majian03@gmail.com"))
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.

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.