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.

The nonparametric methods for estimating copula entropy, transfer entropy, and the statistics for multivariate normality test and two-sample test are implemented. The methods for estimating transfer entropy and the statistics for multivariate normality test and two-sample test 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>, Ma (2022) <arXiv:2206.05956>, and Ma (2023) <arXiv:2307.07247> for more information.

Copernicus Digital Elevation Model datasets (DEM) of 90 and 30 meters resolution using the 'awscli' command line tool. The Copernicus (DEM) is included in the Registry of Open Data on 'AWS (Amazon Web Services)' and represents the surface of the Earth including buildings, infrastructure and vegetation.

Subset and download data from EU Copernicus Marine
    Service Information: <https://data.marine.copernicus.eu>.
    Import data on the oceans physical and biogeochemical state
    from Copernicus into R without the need of external software.

A Bayesian method for Phenome-wide association studies (PheWAS) that identifies causal associations between genetic variants and traits, while simultaneously addressing confounding due to linkage disequilibrium. For details see   
     Manipur et al (2023) <doi:10.1101/2023.06.29.546856>.

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 
    mentioned.