Packages/Libraries
R libraries
Statistical inference of vine copulas
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.
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.
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.
CDVine: Statistical inference of C- and D-vine copulas
(Development of the package has been abandoned. Please consider using rvinecopulib.)
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.
Statistical learning with vine copulas
vineclust: Model-based clustering with vine copulas
This library provides functions for clustering with vine copulas. It fits vine copula based mixture model distributions to the continuous data and use its results for clustering. It is currently under development.
vinereg: An R package for D-vine quantile regression
This library provides functions for D-vine copula based mean and quantile regression.
Vine copulas for financial applications
portvine: Vine Based (Un)Conditional Portfolio Risk Measure Estimation
The library provides portfolio level unconditional as well as conditional risk measure estimation for backtesting and stress testing using Vine Copula and ARMA-GARCH models.
svines: Stationary vine copula models
This library provides functionality to fit and simulate from stationary vine copula models for time series.
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.
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