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 126 articles , 1 standard article )

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  1. Deng, Yihao; Chaganty, N. R.: Pair-copula models for analyzing family data (2021)
  2. Fuchs, Sebastian; Di Lascio, F. Marta L.; Durante, Fabrizio: Dissimilarity functions for rank-invariant hierarchical clustering of continuous variables (2021)
  3. Górecki, Jan; Hofert, Marius; Okhrin, Ostap: Outer power transformations of hierarchical Archimedean copulas: construction, sampling and estimation (2021)
  4. Junker, Robert R.; Griessenberger, Florian; Trutschnig, Wolfgang: Estimating scale-invariant directed dependence of bivariate distributions (2021)
  5. Song, Zhi; Mukherjee, Amitava; Zhang, Jiujun: Some robust approaches based on copula for monitoring bivariate processes and component-wise assessment (2021)
  6. Yuan, Zhenfei; Hu, Taizhong: pyvine: the Python package for regular vine copula modeling, sampling and testing (2021)
  7. 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)
  8. Herwartz, Helmut; Maxand, Simone: Nonparametric tests for independence: a review and comparative simulation study with an application to malnutrition data in India (2020)
  9. 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)
  10. 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)
  11. Li, Huiqiong; Ma, Chenchen; Li, Ni; Sun, Jianguo: A vine copula approach for regression analysis of bivariate current status data with informative censoring (2020)
  12. Schomaker, Michael; Heumann, Christian: When and when not to use optimal model averaging (2020)
  13. 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)
  14. 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)
  15. Arbel, Julyan; Crispino, Marta; Girard, Stéphane: Dependence properties and Bayesian inference for asymmetric multivariate copulas (2019)
  16. Bücher, Axel; Fermanian, Jean-David; Kojadinovic, Ivan: Combining cumulative sum change-point detection tests for assessing the stationarity of univariate time series (2019)
  17. Côté, Marie-Pier; Genest, Christian; Omelka, Marek: Rank-based inference tools for copula regression, with property and casualty insurance applications (2019)
  18. Mhalla, Linda; Opitz, Thomas; Chavez-Demoulin, Valérie: Exceedance-based nonlinear regression of tail dependence (2019)
  19. Schwartzman, Armin; Schork, Andrew J.; Zablocki, Rong; Thompson, Wesley K.: A simple, consistent estimator of SNP heritability from genome-wide association studies (2019)
  20. Arbenz, Philipp; Cambou, Mathieu; Hofert, Marius; Lemieux, Christiane; Taniguchi, Yoshihiro: Importance sampling and stratification for copula models (2018)

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