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 vinecopula.org.
This package is the R API to the C++ library vinecopulib, a headeronly C++ library for vine copula models based on Boost and Eigen.
It provides highperformance 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 multiparameter 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.
You can install:
the stable release on CRAN:
r
install.packages("rvinecopulib")
the latest development version:
r
remotes::install_github("vinecopulib/rvinecopulib")
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.
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:
print
: a brief overview of the bivariate copula.
plot
, contour
: surface/perspective and contour plots of the copula
density. Possibly coupled with standard normal margins (default for
contour
).
dbicop
, pbicop
, rbicop
, hbicop
: Density, distribution function, random
generation and Hfunctions (with their inverses) for bivariate copula
distributions. Additionally to the evaluation points, you can provide
either family
, rotation
and parameter
, or an object of class
bicop_dist
.
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 oneparameter
families) for parametric families, and using locallikelihood
approximations of order zero/one/two for nonparametric models
(nonpar_method="constant"
/nonpar_method="linear"
/nonpar_method="quadratic"
).
If family_set
is a vector of families, then the family is selected using
selcrit="loglik"
, selcrit="aic"
or selcrit="bic"
. The function
returns an object of classes bicop
and bicop_dist
.
The class bicop
has the following following methods:
print
: a more comprehensive overview of the bivariate copula model
with fit statistics.
predict
, fitted
: predictions and fitted values for a bivariate
copula model.
nobs
, logLik
, AIC
, BIC
: usual fit statistics.
vinecop_dist
: Creates a vine copula by specifying a nested list of
bicop_dist
objects and a quadratic structure matrix.
Returns an object of class vinecop_dist
. The class has the
following methods:
print
, summary
: a brief and more comprehensive overview of the vine
copula.
plot
: plots of the vine structure.
dvinecop
, pvinecop
, 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 matrix
can be used as
input to prespecify the vine structure. tree_crit
describes the
criterion for tree selection, one of "tau"
, "rho"
, "hoeffd"
for
Kendall's tau, Spearman's rho, and Hoeffding's D, respectively.
Additionally, threshold
allows to threshold the tree_crit
and
trunc_lvl
to truncate the vine copula, with threshold_sel
and
trunc_lvl_sel
to automatically select both parameters. The function
returns an object of classes vinecop
and vinecop_dist
.
The class has the vinecop
has the following following methods:
print
, summary
: a brief and more comprehensive overview of the vine
copula with additional fit statistics information.
predict
, fitted
: predictions and fitted values for a vine
copula model.
nobs
, logLik
, AIC
, BIC
: usual fit statistics.
In this package several bivariate copula families are included for bivariate and multivariate analysis using vine copulas. It provides functionality of elliptical (Gaussian and Studentt) 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            Independence  "indep"   Elliptical  Gaussian  "gaussian"   "  Student t  "t"   Archimedean  Clayton  "clayton"   "  Gumbel  "gumbel"   "  Frank  "frank"   "  Joe  "joe"   "  ClaytonGumbel (BB1)  "bb1"   "  JoeGumbel (BB6)  "bb6"   "  JoeClayton (BB7)  "bb7"   "  JoeFrank (BB8)  "bb8"   Nonparametric  Transformation kernel  "tll" 
Note that several convenience vectors of families are included:
"all"
contains all the families
"parametric"
contains the parametric families (all except "tll"
)
"nonparametric"
contains the nonparametric families ("indep"
and "tll"
)
"one_par"
contains the parametric families with a single parameter
("gaussian"
, "clayton"
, "gumbel"
, "frank"
, and "joe"
)
"two_par"
contains the parametric families with two parameters
("t"
, "bb1"
, "bb6"
, "bb7"
, and "bb8"
)
"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
("indep"
,"gaussian"
, "student"
,"clayton"
, "gumbel"
, "frank"
, "joe"
)
The following table shows the parameter ranges of bivariate copula families with one or two parameters:
 Copula family  par[1]
 par[2]

 :  :  : 
 Gaussian  (1, 1)
  
 Student t  (1, 1)
 (2,Inf)

 Clayton  (0, Inf)
  
 Gumbel  [1, Inf)
  
 Frank  R \ {0}
  
 Joe  (1, Inf)
  
 ClaytonGumbel (BB1)  (0, Inf)
 [1, Inf)

 JoeGumbel (BB6)  [1 ,Inf)
 [1, Inf)

 JoeClayton (BB7)  [1, Inf)
 (0, Inf)

 JoeFrank (BB8)  [1, Inf)
 (0, 1]

Aas, K., C. Czado, A. Frigessi, and H. Bakken (2009). Paircopula constructions of multiple dependence. Insurance: Mathematics and Economics 44 (2), 182198.
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