CDVine

R package CDVine: Statistical inference of C- and D-vine copulas. This package provides functions for statistical inference of canonical vine (C-vine) and D-vine copulas. It contains tools for bivariate exploratory data analysis and for bivariate as well as vine copula selection. Models can be estimated either sequentially or by joint maximum likelihood estimation. Sampling algorithms and plotting methods are also included. Data is assumed to lie in the unit hypercube (so-called copula data).


References in zbMATH (referenced in 33 articles , 1 standard article )

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  1. Bruneau, Catherine; Flageollet, Alexis; Peng, Zhun: Economic and financial risk factors, copula dependence and risk sensitivity of large multi-asset class portfolios (2020)
  2. Nasri, Bouchra R.: On non-central squared copulas (2020)
  3. Di Lascio, F. Marta L.; Giannerini, Simone: Clustering dependent observations with copula functions (2019)
  4. Müller, Dominik; Czado, Claudia: Selection of sparse vine copulas in high dimensions with the Lasso (2019)
  5. Müller, Dominik; Czado, Claudia: Dependence modelling in ultra high dimensions with vine copulas and the graphical lasso (2019)
  6. Poignard, Benjamin; Fermanian, Jean-David: Dynamic asset correlations based on vines (2019)
  7. Eling, Martin; Jung, Kwangmin: Copula approaches for modeling cross-sectional dependence of data breach losses (2018)
  8. Gruber, Lutz F.; Czado, Claudia: Bayesian model selection of regular vine copulas (2018)
  9. Stübinger, Johannes; Mangold, Benedikt; Krauss, Christopher: Statistical arbitrage with vine copulas (2018)
  10. Wyszynski, Karol; Marra, Giampiero: Sample selection models for count data in R (2018)
  11. Bollmann, Laslo; Scherer, Matthias: Modeling influenza-like illness activity in the United States (2017)
  12. Marra, Giampiero; Radice, Rosalba: Bivariate copula additive models for location, scale and shape (2017)
  13. Nagler, Thomas; Schellhase, Christian; Czado, Claudia: Nonparametric estimation of simplified vine copula models: comparison of methods (2017)
  14. Pircalabelu, Eugen; Claeskens, Gerda; Gijbels, Irène: Copula directed acyclic graphs (2017)
  15. Su, Jianxi; Hua, Lei: A general approach to full-range tail dependence copulas (2017)
  16. Tekumalla, Lavanya Sita; Rajan, Vaibhav; Bhattacharyya, Chiranjib: Vine copulas for mixed data: multi-view clustering for mixed data beyond meta-Gaussian dependencies (2017)
  17. Dalla Valle, Luciana; De Giuli, Maria Elena; Tarantola, Claudia; Manelli, Claudio: Default probability estimation via pair copula constructions (2016)
  18. Kosmidis, Ioannis; Karlis, Dimitris: Model-based clustering using copulas with applications (2016)
  19. Marra, Giampiero; Wyszynski, Karol: Semi-parametric copula sample selection models for count responses (2016)
  20. M. Wojtys; Giampiero Marra; Rosalba Radice: Copula Regression Spline Sample Selection Models: The R Package SemiParSampleSel (2016) not zbMATH

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