Title: Estimating Copula Entropy and Transfer Entropy
Description: 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.

Description: The nonparametric methods for estimating copula entropy, transfer entropy, and the statistic for testing multivariate normality are implemented. The methods for estimating transfer entropy and the statistic for testing multivariate normality are based on the method for estimating copula entropy. Please refer to Ma and Sun (2011) <doi:10.1016/S1007-0214(11)70008-6>, Ma (2019) <arXiv:1910.04375>, and Ma (2022) <arXiv:2206.05956> for more information.

Title: COPPE-Cosenza Fuzzy Hierarchy Model
Authors@R: c(person("Pier", "Taranti", email = "ptaranti@gmail.com",
Description: The program implements the COPPE-Cosenza Fuzzy Hierarchy Model.
The model was based on the evaluation of local alternatives, representing
regional potentialities, so as to fulfill demands of economic projects.
After defining demand profiles in terms of their technological coefficients,
the degree of importance of factors is defined so as to represent
the productive activity. The method can detect a surplus of supply without
the restriction of the distance of classical algebra, defining a hierarchy
of location alternatives. In COPPE-Cosenza Model, the distance between
factors is measured in terms of the difference between grades of memberships
of the same factors belonging to two or more  sets under comparison. The
required factors are classified under the following linguistic variables:
Critical (CR); Conditioning (C); Little Conditioning (LC); and Irrelevant
(I). And the alternatives can assume the following linguistic variables:
Excellent (Ex), Good (G), Regular (R), Weak (W), Empty (Em), Zero (Z) and
Inexistent (In). The model also provides flexibility, allowing different
aggregation rules to be performed and defined by the Decision Maker. Such
feature is considered in this package, allowing the user to define other
aggregation matrices, since it considers the same linguistic variables