copula

R package copula: Multivariate Dependence with Copulas. Classes (S4) of commonly used elliptical, Archimedean, extreme value and some more copula families. Methods for density, distribution, random number generation, bivariate dependence measures, perspective and contour plots. Fitting copula models including variance estimates. Independence and serial (univariate and multivariate) independence tests, and other copula related tests. Empirical copula and multivariate CDF. Goodness-of-fit tests for copulas based on multipliers, the parametric bootstrap with several transformation options. Merged former package ’nacopula’ for nested Archimedean copulas: Efficient sampling algorithms, various estimators, goodness-of-fit tests and related tools and special functions.


References in zbMATH (referenced in 144 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. Jordi Tur, David Moriña, Pedro Puig, Alejandra Cabaña, Argimiro Arratia, Amanda Fernández-Fontelo: Good distribution modelling with the R package good (2021) arXiv
  5. Junker, Robert R.; Griessenberger, Florian; Trutschnig, Wolfgang: Estimating scale-invariant directed dependence of bivariate distributions (2021)
  6. Song, Zhi; Mukherjee, Amitava; Zhang, Jiujun: Some robust approaches based on copula for monitoring bivariate processes and component-wise assessment (2021)
  7. Yuan, Zhenfei; Hu, Taizhong: pyvine: the Python package for regular vine copula modeling, sampling and testing (2021)
  8. Alexander Lange, Bernhard Dalheimer, Helmut Herwartz, Simone Maxand: svars: An R Package for Data-Driven Identification in Multivariate Time Series Analysis (2020) not zbMATH
  9. Amini-Seresht, Ebrahim; Milošević, Bojana: New non-parametric tests for independence (2020)
  10. Böttcher, Björn: Copula versions of distance multivariance and dHSIC via the distributional transform -- a general approach to construct invariant dependence measures (2020)
  11. 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)
  12. Gaigall, Daniel: Testing marginal homogeneity of a continuous bivariate distribution with possibly incomplete paired data (2020)
  13. Herwartz, Helmut; Maxand, Simone: Nonparametric tests for independence: a review and comparative simulation study with an application to malnutrition data in India (2020)
  14. 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)
  15. Jan Górecki, Marius Hofert, Martin Holeňa: Hierarchical Archimedean Copulas for MATLAB and Octave: The HACopula Toolbox (2020) not zbMATH
  16. 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)
  17. Li, Huiqiong; Ma, Chenchen; Li, Ni; Sun, Jianguo: A vine copula approach for regression analysis of bivariate current status data with informative censoring (2020)
  18. Oskar Laverny: Empirical and non-parametric copula models with the cort R package (2020) not zbMATH
  19. Schomaker, Michael; Heumann, Christian: When and when not to use optimal model averaging (2020)
  20. 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)

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