Package: cort
Title: Some Empiric and Nonparametric Copula Models
Version: 0.3.2
Authors@R: 
    person(given = "Oskar",
           family = "Laverny",
           role = c("aut", "cre"),
           email = "oskar.laverny@gmail.com",
           comment = c(ORCID = "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.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Depends: R (>= 2.10)
Imports: Rdpack, methods, purrr, nloptr, osqp, Rcpp, furrr (>= 0.2.0)
URL: https://github.com/lrnv/cort
BugReports: https://github.com/lrnv/cort/issues
Suggests: covr, testthat (>= 2.1.0), spelling, knitr, rmarkdown
Language: en-US
Collate: 'utils.R' 'generics.R' 'ConvexCombCopula.R'
        'empiricalCopula.R' 'Cort.R' 'CortForest.R' 'RcppExports.R'
        'cbCopula.R' 'cbkmCopula.R' 'cort-package.R' 'data.R'
VignetteBuilder: knitr
RdMacros: Rdpack
LinkingTo: Rcpp
NeedsCompilation: yes
Packaged: 2020-11-26 18:37:20 UTC; U009192
Author: Oskar Laverny [aut, cre] (<https://orcid.org/0000-0002-7508-999X>)
Maintainer: Oskar Laverny <oskar.laverny@gmail.com>
Repository: CRAN
Date/Publication: 2020-12-01 00:30:20 UTC
