High Performance Algorithms for Vine Copula Modeling
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 0.5.5). 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.
Vine copulas are a flexible class of dependence models consisting of bivariate building blocks (see e.g., Aas et al., 2009). You can find a comprehensive list of publications and other materials on vine-copula.org.
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. Advantages over VineCopula are
- a sleaker and more modern API,
- shorter runtimes, especially in high dimensions,
- nonparametric and multi-parameter families,
- ability to model discrete variables.
As VineCopula, the package is primarily made for the statistical analysis of vine copula models. The package includes tools for parameter estimation, model selection, simulation, and visualization. Tools for estimation, selection and exploratory data analysis of bivariate copula models are also provided. Please see the API documentation for a detailed description of all functions.
Table of contents
How to install
You can install:
the stable release on CRAN:
the latest development version:
Below, we list most functions and features you should know about. As usual in copula models, data are assumed to be serially independent and lie in the unit hypercube.
Bivariate copula modeling: bicop_dist and bicop
bicop_dist: Creates a bivariate copula by specifying the family, rotation and parameters. Returns an object of class
bicop_dist. The class has the following methods:
contour: surface/perspective and contour plots of the copula density. Possibly coupled with standard normal margins (default for
hbicop: Density, distribution function, random generation and H-functions (with their inverses) for bivariate copula distributions. Additionally to the evaluation points, you can provide either
parameter, or an object of class
bicop: Estimates parameters of a bivariate copula. Estimation can be done by maximum likelihood (
par_method = "mle") or inversion of the empirical Kendall's tau (
par_method = "itau", only available for one-parameter families) for parametric families, and using local-likelihood approximations of order zero/one/two for nonparametric models (
family_setis a vector of families, then the family is selected using
selcrit="bic". The function returns an object of classes
bicop_dist. The class
bicophas the following following methods:
fitted: predictions and fitted values for a bivariate copula model.
BIC: usual fit statistics.
Vine copula modeling: vinecop_dist and vinecop
vinecop_dist: Creates a vine copula by specifying a nested list of
bicop_distobjects and a quadratic structure matrix. Returns an object of class
vinecop_dist. The class has the following methods:
summary: a brief and more comprehensive overview of the vine copula.
plot: plots of the vine structure.
rvinecop: Density, distribution function, random generation for vine copula distributions.
vinecop: automated fitting for vine copula models. The function inherits the parameters of
bicop. Optionally, a quadratic
matrixcan be used as input to pre-specify the vine structure.
tree_critdescribes the criterion for tree selection, one of
"hoeffd"for Kendall's tau, Spearman's rho, and Hoeffding's D, respectively. Additionally,
thresholdallows to threshold the
trunc_lvlto truncate the vine copula, with
trunc_lvl_selto automatically select both parameters. The function returns an object of classes
vinecop_dist. The class has the
vinecophas the following following methods:
summary: a brief and more comprehensive overview of the vine copula with additional fit statistics information.
fitted: predictions and fitted values for a vine copula model.
BIC: usual fit statistics.
Bivariate copula families
In this package several bivariate copula families are included for bivariate and multivariate analysis using vine copulas. It provides functionality of elliptical (Gaussian and Student-t) as well as Archimedean (Clayton, Gumbel, Frank, Joe, BB1, BB6, BB7 and BB8) copulas to cover a large range of dependence patterns. For Archimedean copula families, rotated versions are included to cover negative dependence as well. Additionally, nonparametric families are also supported.
|type||name||name in R|
Note that several convenience vectors of families are included:
"all"contains all the families
"parametric"contains the parametric families (all except
"nonparametric"contains the nonparametric families (
"one_par"contains the parametric families with a single parameter (
"two_par"contains the parametric families with two parameters (
"elliptical"contains the elliptical families
"archimedean"contains the archimedean families
"BB"contains the BB families
"itau"families for which estimation by Kendall's tau inversion is available (
The following table shows the parameter ranges of bivariate copula families with one or two parameters:
Aas, K., C. Czado, A. Frigessi, and H. Bakken (2009). Pair-copula constructions of multiple dependence. Insurance: Mathematics and Economics 44 (2), 182-198.
Functions in rvinecopulib
|pairs_copula_data||Exploratory pairs plot for copula data|
|getters||Extracts components of bicop_dist and vinecop_dist objects|
|bicop||Fit and select bivariate copula models|
|bicop_dist||Bivariate copula models|
|par_to_ktau||Conversion between Kendall's tau and parameters|
|mBICV||Modified vine copula Bayesian information criterion (mBICv)|
|bicop_distributions||Bivariate copula distributions|
|plot.bicop_dist||Plotting tools for bicop_dist and bicop objects|
|as_rvine_structure||Coerce various kind of objects to R-vine structures and matrices|
|plot.rvine_structure||Plotting R-vine structures|
|vine_predict_and_fitted||Predictions and fitted values for a vine copula model|
|vinecop_predict_and_fitted||Predictions and fitted values for a vine copula model|
|rosenblatt||(Inverse) Rosenblatt transform|
|vine_distributions||Vine based distributions|
|vine||Vine copula models|
|rvinecopulib||High Performance Algorithms for Vine Copula Modeling|
|truncate_model||Truncate a vine copula model|
|vinecop_dist||Vine copula models|
|rvine_structure_sim||Simulate R-vine structures|
|plot.vinecop_dist||Plotting vinecop_dist and vinecop objects.|
|vinecop||Fitting vine copula models|
|bicop_predict_and_fitted||Predictions and fitted values for a bivariate copula model|
|vinecop_distributions||Vine copula distributions|
Last month downloads
|License||GPL-3 | file LICENSE|
|LinkingTo||BH, Rcpp, RcppEigen, RcppThread, wdm, kde1d|
|Packaged||2021-01-05 11:11:35 UTC; n5|
|Date/Publication||2021-01-06 16:00:12 UTC|
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