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 145 articles , 1 standard article )

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  1. 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)
  2. Grønneberg, Steffen; Moss, Jonas; Foldnes, Njål: Partial identification of latent correlations with binary data (2020)
  3. Herwartz, Helmut; Maxand, Simone: Nonparametric tests for independence: a review and comparative simulation study with an application to malnutrition data in India (2020)
  4. 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)
  5. 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)
  6. Li, Huiqiong; Ma, Chenchen; Li, Ni; Sun, Jianguo: A vine copula approach for regression analysis of bivariate current status data with informative censoring (2020)
  7. Marozzi, Marco; Mukherjee, Amitava; Kalina, Jan: Interpoint distance tests for high-dimensional comparison studies (2020)
  8. Schomaker, Michael; Heumann, Christian: When and when not to use optimal model averaging (2020)
  9. 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)
  10. 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)
  11. Arbel, Julyan; Crispino, Marta; Girard, Stéphane: Dependence properties and Bayesian inference for asymmetric multivariate copulas (2019)
  12. Bücher, Axel; Fermanian, Jean-David; Kojadinovic, Ivan: Combining cumulative sum change-point detection tests for assessing the stationarity of univariate time series (2019)
  13. Côté, Marie-Pier; Genest, Christian; Omelka, Marek: Rank-based inference tools for copula regression, with property and casualty insurance applications (2019)
  14. Mhalla, Linda; Opitz, Thomas; Chavez-Demoulin, Valérie: Exceedance-based nonlinear regression of tail dependence (2019)
  15. Schepsmeier, Ulf: A goodness-of-fit test for regular vine copula models (2019)
  16. Schwartzman, Armin; Schork, Andrew J.; Zablocki, Rong; Thompson, Wesley K.: A simple, consistent estimator of SNP heritability from genome-wide association studies (2019)
  17. Arbenz, Philipp; Cambou, Mathieu; Hofert, Marius; Lemieux, Christiane; Taniguchi, Yoshihiro: Importance sampling and stratification for copula models (2018)
  18. Berghaus, Betina; Segers, Johan: Weak convergence of the weighted empirical beta copula process (2018)
  19. Eckert, Johanna; Gatzert, Nadine: Risk- and value-based management for non-life insurers under solvency constraints (2018)
  20. Einmahl, John H. J.; Kiriliouk, Anna; Segers, Johan: A continuous updating weighted least squares estimator of tail dependence in high dimensions (2018)

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