High Performance Algorithms for Vine Copula Modeling
Coerce various kind of objects to R-vine structures and matrices
Convert list to bicop object
Bivariate copula models
Bivariate copula distributions
Fit and select bivariate copula models
Corrected Empirical CDF
Extracts components of bicop_dist and vinecop_dist objects
Modified vine copula Bayesian information criterion (mBICv)
Exploratory pairs plot for copula data
Conversion between Kendall's tau and parameters
Plotting tools for bicop_dist and bicop objects
Plotting R-vine structures
Plotting vinecop_dist and vinecop objects.
Predictions and fitted values for a bivariate copula model
Predictions and fitted values for a vine copula model
Predictions and fitted values for a vine copula model
Pseudo-Observations
(Inverse) Rosenblatt transform
Simulate R-vine structures
R-vine structure
High Performance Algorithms for Vine Copula Modeling
Truncate a vine copula model
Vine based distributions
Vine copula models
Vine copula models
Vine copula distributions
Fitting vine copula models
Provides an interface to 'vinecopulib', a C++ library for vine copula modeling. The 'rvinecopulib' package implements the core features of the popular 'VineCopula' package, in particular inference algorithms for both vine copula and bivariate copula models. Advantages over 'VineCopula' are a sleeker and more modern API, improved performances, especially in high dimensions, nonparametric and multi-parameter families, and the ability to model discrete variables. The 'rvinecopulib' package includes 'vinecopulib' as header-only C++ library (currently version 0.7.2). Thus users do not need to install 'vinecopulib' itself in order to use 'rvinecopulib'. Since their initial releases, 'vinecopulib' is licensed under the MIT License, and 'rvinecopulib' is licensed under the GNU GPL version 3.
Useful links