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CondCopulas: Estimation and Inference for Conditional Copula Models
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: |
0.1.3 |
| Imports: |
VineCopula, pbapply, glmnet, ordinalNet, tree, nnet, data.tree, statmod, wdm |
| Suggests: |
MASS, knitr, rmarkdown, ggplot2, mvtnorm |
| Published: |
2023-09-26 |
| Author: |
Alexis Derumigny 
[aut, cre],
Jean-David Fermanian
[ctb, ths],
Aleksey Min [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 |
Documentation:
Downloads:
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https://CRAN.R-project.org/package=CondCopulas
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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