Some Empiric and Nonparametric Copula Models

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) 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) , 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.


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("cort")

0.3.1 by Oskar Laverny, 5 months ago


https://github.com/lrnv/cort


Report a bug at https://github.com/lrnv/cort/issues


Browse source code at https://github.com/cran/cort


Authors: Oskar Laverny [aut, cre]


Documentation:   PDF Manual  


Task views: Probability Distributions


MIT + file LICENSE license


Imports Rdpack, methods, purrr, nloptr, osqp, Rcpp, furrr

Suggests covr, testthat, spelling, knitr, rmarkdown

Linking to Rcpp


See at CRAN