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 136 articles , 1 standard article )

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  1. Ahn, Jae Youn; Fuchs, Sebastian; Oh, Rosy: A copula transformation in multivariate mixed discrete-continuous models (2021)
  2. Alanazi, Fadhah Amer: A mixture of regular vines for multiple dependencies (2021)
  3. Deng, Yihao; Chaganty, N. R.: Pair-copula models for analyzing family data (2021)
  4. Fuchs, Sebastian; Di Lascio, F. Marta L.; Durante, Fabrizio: Dissimilarity functions for rank-invariant hierarchical clustering of continuous variables (2021)
  5. Górecki, Jan; Hofert, Marius; Okhrin, Ostap: Outer power transformations of hierarchical Archimedean copulas: construction, sampling and estimation (2021)
  6. Hallin, Marc; Mordant, Gilles; Segers, Johan: Multivariate goodness-of-fit tests based on Wasserstein distance (2021)
  7. Junker, Robert R.; Griessenberger, Florian; Trutschnig, Wolfgang: Estimating scale-invariant directed dependence of bivariate distributions (2021)
  8. Kasper, Thimo M.; Fuchs, Sebastian; Trutschnig, Wolfgang: On weak conditional convergence of bivariate Archimedean and extreme value copulas, and consequences to nonparametric estimation (2021)
  9. Kojadinovic, Ivan; Verdier, Ghislain: Nonparametric sequential change-point detection for multivariate time series based on empirical distribution functions (2021)
  10. Neumann, Matthias; Machado Charry, Eduardo; Zojer, Karin; Schmidt, Volker: On variability and interdependence of local porosity and local tortuosity in porous materials: a case study for sack paper (2021)
  11. Nowak, Claus P.; Konietschke, Frank: Simultaneous inference for Kendall’s tau (2021)
  12. Song, Zhi; Mukherjee, Amitava; Zhang, Jiujun: Some robust approaches based on copula for monitoring bivariate processes and component-wise assessment (2021)
  13. Yuan, Zhenfei; Hu, Taizhong: pyvine: the Python package for regular vine copula modeling, sampling and testing (2021)
  14. Akdur, Hatice Tul Kubra; Ozonur, Deniz; Bayrak, Hulya: An adaptation of pseudo-score confidence interval method for linear mixed models (2020)
  15. Dang, Phuc Ho; Vo Thi, Truc Giang: Gaussian copula of stable random vectors and application (2020)
  16. Di Lascio, F. Marta L.; Menapace, Andrea; Righetti, Maurizio: Joint and conditional dependence modelling of peak district heating demand and outdoor temperature: a copula-based approach (2020)
  17. Herwartz, Helmut; Maxand, Simone: Nonparametric tests for independence: a review and comparative simulation study with an application to malnutrition data in India (2020)
  18. Islam, Shofiqul; Anand, Sonia; Hamid, Jemila; Thabane, Lehana; Beyene, Joseph: A copula-based method of classifying individuals into binary disease categories using dependent biomarkers (2020)
  19. Li, Dongdong; Hu, X. Joan; McBride, Mary L.; Spinelli, John J.: Multiple event times in the presence of informative censoring: modeling and analysis by copulas (2020)
  20. Li, Huiqiong; Ma, Chenchen; Li, Ni; Sun, Jianguo: A vine copula approach for regression analysis of bivariate current status data with informative censoring (2020)

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