Packages/Libraries

R libraries

rvinecopulib: R interface to the vinecopulib C++ library

This library provides functions for statistical inference of vine copulas. It provides high-performance implementations of the core features of the popular VineCopula R library, in particular inference algorithms for both vine copula and bivariate copula models. Its advantages are shorter runtimes, especially in high dimensions; nonparametric and multi-parameter families; ability to model discrete variables and modern API.

  • Stable release on CRAN
  • Development version on github  (under construction)
  • Manual html

VineCopula: Statistical inference of vine copulas

(The library is no longer actively developed, but will continued to be maintained. Check out the rvinecopulib package for an alternative with several benefits.)

This library provides functions for statistical inference of vine copulas. It contains tools for bivariate exploratory data analysis, bivariate copula selection and (vine) tree construction. 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). For C- and D-vines links to the package CDVine are provided.

  • Stable release on CRAN
  • Development version on github
  • Manual pdf

CDVine: Statistical inference of C- and D-vine copulas

(Development of the package has been abandoned. Please consider using VineCopula.)

This library 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. 

  • Final release on CRAN
  • Package vignette pdf
  • Manual pdf

vinereg: An R package for D-vine quantile regression

This library provides functions for D-vine copula based mean and quantile regression.

  • Stable release on CRAN
  • Development version on github
  • Vignettes 1, 2

kdecopula: Kernel smoothing for bivariate copula densities

This library provides fast implementations of kernel estimators for the copula density. Due to its several plotting options it is particularly useful for the exploratory analysis of copula data. It can be further used for accurate estimation of unusually shaped copula densities and resampling.

  • Stable release on CRAN
  • Development version on github
  • Manual pdf

 

Python packages

pyvinecopulib: Python interface to the vinecopulib C++ library

This library provides functions for statistical inference of vine copulas.It provides high-performance implementations of the core features of the popular VineCopula R library, in particular inference algorithms for both vine copula and bivariate copula models.

  • Development version on github  (under construction)
  • Manual html

C++ libraries 

vinecopulib: A C++ library for vine copulas

vinecopulib provides high-performance implementations of the core features of the popular VineCopula R library, in particular inference algorithms for both vine copula and bivariate copula models. Advantages over VineCopula are a stand-alone C++ library with interfaces to both R and Python, a sleeker and more modern API, shorter runtimes and lower memory consumption, especially in high dimensions, nonparametric and multi-parameter families.

  • Sources for stand-alone C++ library on github