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 49 articles , 1 standard article )

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  1. Deng, Yihao; Chaganty, N. R.: Pair-copula models for analyzing family data (2021)
  2. Lin, Huihui; Chaganty, N. Rao: Multivariate distributions of correlated binary variables generated by pair-copulas (2021)
  3. Maximilian Coblenz: MATVines: A vine copula package for MATLAB (2021) not zbMATH
  4. Yuan, Zhenfei; Hu, Taizhong: pyvine: the Python package for regular vine copula modeling, sampling and testing (2021)
  5. Zhu, Kailun; Kurowicka, Dorota; Nane, Gabriela F.: Simplified R-vine based forward regression (2021)
  6. Bruneau, Catherine; Flageollet, Alexis; Peng, Zhun: Economic and financial risk factors, copula dependence and risk sensitivity of large multi-asset class portfolios (2020)
  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. 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)
  9. Kwofie, Charles; Akoto, Isaac; Opoku-Ameyaw, Kwaku: Modelling the dependency between inflation and exchange rate using copula (2020)
  10. Marra, Giampiero; Radice, Rosalba: Copula link-based additive models for right-censored event time data (2020)
  11. Nasri, Bouchra R.: On non-central squared copulas (2020)
  12. Senarathne, S. G. J.; Drovandi, C. C.; McGree, J. M.: Bayesian sequential design for copula models (2020)
  13. Zhang, Jiaxin; Shields, Michael: On the quantification and efficient propagation of imprecise probabilities with copula dependence (2020)
  14. Di Lascio, F. Marta L.; Giannerini, Simone: Clustering dependent observations with copula functions (2019)
  15. Müller, Dominik; Czado, Claudia: Selection of sparse vine copulas in high dimensions with the Lasso (2019)
  16. Müller, Dominik; Czado, Claudia: Dependence modelling in ultra high dimensions with vine copulas and the graphical lasso (2019)
  17. Poignard, Benjamin; Fermanian, Jean-David: Dynamic asset correlations based on vines (2019)
  18. Tursunalieva, Ainura; Hudson, Irene; Chase, Geoff: Copula modelling of nurses’ agitation-sedation rating of ICU patients (2019)
  19. Eling, Martin; Jung, Kwangmin: Copula approaches for modeling cross-sectional dependence of data breach losses (2018)
  20. Gruber, Lutz F.; Czado, Claudia: Bayesian model selection of regular vine copulas (2018)

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