copula

Enjoy the Joy of Copulas: With a Package copula. Copulas have become a popular tool in multivariate modeling successfully applied in many fields. A good open-source implementation of copulas is much needed for more practitioners to enjoy the joy of copulas. This article presents the design, features, and some implementation details of the R package copula. The package provides a carefully designed and easily extensible platform for multivariate modeling with copulas in R. S4 classes for most frequently used elliptical copulas and Archimedean copulas are implemented, with methods for density/distribution evaluation, random number generation, and graphical display. Fitting copula-based models with maximum likelihood method is provided as template examples. With the classes and methods in the package, the package can be easily extended by user-defined copulas and margins to solve problems

This software is also peer reviewed by journal JSS.


References in zbMATH (referenced in 117 articles , 1 standard article )

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  1. Herwartz, Helmut; Maxand, Simone: Nonparametric tests for independence: a review and comparative simulation study with an application to malnutrition data in India (2020)
  2. Li, Huiqiong; Ma, Chenchen; Li, Ni; Sun, Jianguo: A vine copula approach for regression analysis of bivariate current status data with informative censoring (2020)
  3. Schomaker, Michael; Heumann, Christian: When and when not to use optimal model averaging (2020)
  4. van der Wurp, Hendrik; Groll, Andreas; Kneib, Thomas; Marra, Giampiero; Radice, Rosalba: Generalised joint regression for count data: a penalty extension for competitive settings (2020)
  5. Allevi, E.; Boffino, L.; De Giuli, M. E.; Oggioni, G.: Analysis of long-term natural gas contracts with vine copulas in optimization portfolio problems (2019)
  6. Arbel, Julyan; Crispino, Marta; Girard, Stéphane: Dependence properties and Bayesian inference for asymmetric multivariate copulas (2019)
  7. Bücher, Axel; Fermanian, Jean-David; Kojadinovic, Ivan: Combining cumulative sum change-point detection tests for assessing the stationarity of univariate time series (2019)
  8. Côté, Marie-Pier; Genest, Christian; Omelka, Marek: Rank-based inference tools for copula regression, with property and casualty insurance applications (2019)
  9. Mhalla, Linda; Opitz, Thomas; Chavez-Demoulin, Valérie: Exceedance-based nonlinear regression of tail dependence (2019)
  10. Schwartzman, Armin; Schork, Andrew J.; Zablocki, Rong; Thompson, Wesley K.: A simple, consistent estimator of SNP heritability from genome-wide association studies (2019)
  11. Arbenz, Philipp; Cambou, Mathieu; Hofert, Marius; Lemieux, Christiane; Taniguchi, Yoshihiro: Importance sampling and stratification for copula models (2018)
  12. Berghaus, Betina; Segers, Johan: Weak convergence of the weighted empirical beta copula process (2018)
  13. Eckert, Johanna; Gatzert, Nadine: Risk- and value-based management for non-life insurers under solvency constraints (2018)
  14. Einmahl, John H. J.; Kiriliouk, Anna; Segers, Johan: A continuous updating weighted least squares estimator of tail dependence in high dimensions (2018)
  15. Fasiolo, Matteo; Wood, Simon N.; Hartig, Florian; Bravington, Mark V.: An extended empirical saddlepoint approximation for intractable likelihoods (2018)
  16. Guillou, Armelle; Padoan, Simone A.; Rizzelli, Stefano: Inference for asymptotically independent samples of extremes (2018)
  17. Hofert, Marius; Huser, Raphaël; Prasad, Avinash: Hierarchical Archimax copulas (2018)
  18. Hofert, Marius; Kojadinovic, Ivan; Mächler, Martin; Yan, Jun: Elements of copula modeling with R (2018)
  19. Kiriliouk, Anna; Segers, Johan; Tafakori, Laleh: An estimator of the stable tail dependence function based on the empirical beta copula (2018)
  20. Marozzi, Marco: Tests for comparison of multiple endpoints with application to omics data (2018)

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