doi:10.1515/demo-2019-0016>, <doi:10.1016/j.csda.2019.01.013>, <doi:10.1016/j.jmva.2020.104610>), and test procedures for the simplifying assumption (proposed in Derumigny and Fermanian (2017) <doi:10.1515/demo-2017-0011> and Derumigny, Fermanian and Min (2022) <doi:10.1002/cjs.11742>).">

CondCopulas: Estimation and Inference for Conditional Copula Models (original) (raw)

Provides functions for the estimation of conditional copulas models, various estimators of conditional Kendall's tau (proposed in Derumigny and Fermanian (2019a, 2019b, 2020) <doi:10.1515/demo-2019-0016>, <doi:10.1016/j.csda.2019.01.013>, <doi:10.1016/j.jmva.2020.104610>), and test procedures for the simplifying assumption (proposed in Derumigny and Fermanian (2017) <doi:10.1515/demo-2017-0011> and Derumigny, Fermanian and Min (2022) <doi:10.1002/cjs.11742>).

Version: 0.1.4.1
Imports: VineCopula, pbapply, glmnet, ordinalNet, tree, nnet, data.tree, statmod, wdm
Suggests: MASS, knitr, rmarkdown, DiagrammeR, ggplot2, mvtnorm, testthat (≥ 3.0.0)
Published: 2024-09-03
DOI: 10.32614/CRAN.package.CondCopulas
Author: Alexis Derumigny ORCID iD [aut, cre], Jean-David FermanianORCID iD [ctb, ths], Aleksey Min ORCID iD [ctb], Rutger van der Spek [ctb]
Maintainer: Alexis Derumigny <a.f.f.derumigny at tudelft.nl>
BugReports: https://github.com/AlexisDerumigny/CondCopulas/issues
License: GPL-3
URL: https://github.com/AlexisDerumigny/CondCopulas
NeedsCompilation: no
Materials: README, NEWS
CRAN checks: CondCopulas results

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