Measuring Copula-Based Dependence Between Random Vectors

Provides functions for estimation (parametric, semi-parametric and non-parametric) of copula-based dependence coefficients between a finite collection of random vectors, including phi-dependence measures and Bures-Wasserstein dependence measures. An algorithm for agglomerative hierarchical variable clustering is also implemented. Following the articles De Keyser & Gijbels (2024) , De Keyser & Gijbels (2024) , and De Keyser & Gijbels (2024) .

VecDep

This R package gathers together several functions that can be used for copula-based measuring of dependence between a finite amount of random vectors.

In particular, several estimation procedures are implemented for the class of phi-dependence measures, including Gaussian copula and hierarchical Archimedean copula methods, as studied in

• De Keyser, S. & Gijbels, I. (2024). Parametric dependence between random vectors via copula-based divergence measures. Journal of Multivariate Analysis 203:105336. doi: https://doi.org/10.1016/j.jmva.2024.105336,

and a semi-parametric meta-elliptical method and fully non-parametric methods, as investigated in

• De Keyser, S. & Gijbels, I. (2024). Hierarchical variable clustering via copula-based divergence measures between random vectors. International Journal of Approximate Reasoning 165:109090. doi: https://doi.org/10.1016/j.ijar.2023.109090.

The latter reference also discusses an algorithm for hierarchical variable clustering based on multivariate similarities between random vectors, which is implemented in this R package as well. Next to this, functions for Bures-Wasserstein dependence coefficients and Gaussian copula correlation matrix penalization techniques, as discussed in

• De Keyser, S. & Gijbels, I. (2024). High-dimensional copula-based Wasserstein dependence. doi: https://doi.org/10.48550/arXiv.2404.07141,

are implemented as well.