Copyright: 2016 Merck Sharp & Dohme Corp. a subsidiary of Merck & Co.,

Title: Some Empiric and Nonparametric Copula Models
Description: Provides S4 classes and methods to fit several copula models: The classic empirical checkerboard copula and the empirical checkerboard copula with known margins, see Cuberos, Masiello and Maume-Deschamps (2019) <doi:10.1080/03610926.2019.1586936> are proposed. These two models allow to fit copulas in high dimension with a small number of observations, and they are always proper copulas. Some flexibility is added via a possibility to differentiate the checkerboard parameter by dimension. The last model consist of the implementation of the Copula Recursive Tree algorithm, including the localised dimension reduction, which fits a copula by recursive splitting of the copula domain. We also provide an efficient way of mixing copulas, allowing to bag the algorithm into a forest, and a generic way of measuring d-dimensional boxes with a copula.
Imports: Rdpack, magrittr, methods, purrr, nloptr, osqp, Rcpp, furrr
Suggests: covr, testthat (>= 2.1.0), spelling, knitr, rmarkdown
Collate: 'utils-pipe.R' 'utils.R' 'generics.R' 'Box.R'
Author: Oskar Laverny [aut, cre] (<https://orcid.org/0000-0002-7508-999X>)

Description: Provides S4 classes and methods to fit several copula models: The classic empirical checkerboard copula and the empirical checkerboard copula with known margins, see Cuberos, Masiello and Maume-Deschamps (2019) <doi:10.1080/03610926.2019.1586936> are proposed. These two models allow to fit copulas in high dimension with a small number of observations, and they are always proper copulas. Some flexibility is added via a possibility to differentiate the checkerboard parameter by dimension. The last model consist of the implementation of the Copula Recursive Tree algorithm proposed by Laverny, Maume-Deschamps, Masiello and Rullière (2020) <arXiv:2005.02912>, including the localised dimension reduction, which fits a copula by recursive splitting of the copula domain. We also provide an efficient way of mixing copulas, allowing to bag the algorithm into a forest, and a generic way of measuring d-dimensional boxes with a copula.
Imports: Rdpack, methods, purrr, nloptr, osqp, Rcpp, furrr