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 23 articles )

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  1. Vettori, Sabrina; Huser, Raphaël; Genton, Marc G.: A comparison of dependence function estimators in multivariate extremes (2018)
  2. Bücher, Axel; Kinsvater, Paul; Kojadinovic, Ivan: Detecting breaks in the dependence of multivariate extreme-value distributions (2017)
  3. Erdely, Arturo: A subcopula based dependence measure. (2017)
  4. Guido Masarotto and Cristiano Varin: Gaussian Copula Regression in R (2017)
  5. Janssen, Paul; Swanepoel, Jan; Veraverbeke, Noël: Smooth copula-based estimation of the conditional density function with a single covariate (2017)
  6. Komorník, Jozef; Komorníková, Magda; Kalická, Jana: Dependence measures for perturbations of copulas (2017)
  7. Pircalabelu, Eugen; Claeskens, Gerda; Gijbels, Irène: Copula directed acyclic graphs (2017)
  8. Segers, Johan; Sibuya, Masaaki; Tsukahara, Hideatsu: The empirical beta copula (2017)
  9. Huser, Raphaël; Davison, Anthony C.; Genton, Marc G.: Likelihood estimators for multivariate extremes (2016)
  10. Kojadinovic, Ivan; Quessy, Jean-François; Rohmer, Tom: Testing the constancy of Spearman’s rho in multivariate time series (2016)
  11. Kosmidis, Ioannis; Karlis, Dimitris: Model-based clustering using copulas with applications (2016)
  12. Rohmer, Tom: Some results on change-point detection in cross-sectional dependence of multivariate data with changes in marginal distributions (2016)
  13. Wang, Wan-Lun; Lin, Tsung-I: Maximum likelihood inference for the multivariate $t$ mixture model (2016)
  14. Bacigál, Tomáš; Najjari, Vadoud; Mesiar, Radko; Bal, Hasan: Additive generators of copulas (2015)
  15. Foldnes, Njål; Grønneberg, Steffen: How general is the Vale-Maurelli simulation approach? (2015)
  16. Hlubinka, Daniel; Šiman, Miroslav: On generalized elliptical quantiles in the nonlinear quantile regression setup (2015)
  17. Mazo, Gildas; Girard, Stéphane; Forbes, Florence: A class of multivariate copulas based on products of bivariate copulas (2015)
  18. Targino, Rodrigo S.; Peters, Gareth W.; Shevchenko, Pavel V.: Sequential Monte Carlo samplers for capital allocation under copula-dependent risk models (2015)
  19. Wadsworth, Jennifer L.: On the occurrence times of componentwise maxima and bias in likelihood inference for multivariate max-stable distributions (2015)
  20. Bücher, Axel; Kojadinovic, Ivan; Rohmer, Tom; Segers, Johan: Detecting changes in cross-sectional dependence in multivariate time series (2014)

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