# High Performance Algorithms for Vine Copula Modeling

## Documentation for package ‘rvinecopulib’ version 0.2.7.1.0

- DESCRIPTION file.

## Help Pages

rvinecopulib-package | High Performance Algorithms for Vine Copula Modeling
bicop | Bivariate copula models
bicop_dist | Bivariate copula models
bicop_distributions | Bivariate copula distributions
bicop_predict_and_fitted | Predictions and fitted values for a bivariate copula model
check_rvine_matrix | R-vine matrices
contour.bicop | Plotting tools for 'bicop_dist' and 'bicop' objects
contour.bicop_dist | Plotting tools for 'bicop_dist' and 'bicop' objects
contour.vinecop | Plotting 'vinecop_dist' and 'vinecop' objects.
contour.vinecop_dist | Plotting 'vinecop_dist' and 'vinecop' objects.
dbicop | Bivariate copula distributions
dbicop_dist | Bivariate copula distributions
dvinecop | Vine copula distributions
dvinecop_dist | Vine copula distributions
fitted.bicop | Predictions and fitted values for a bivariate copula model
fitted.vinecop | Predictions and fitted values for a vine copula model
hbicop | Bivariate copula distributions
hbicop_dist | Bivariate copula distributions
mBICV | calculates the vine copula Bayesian information criterion (vBIC), which is defined as \mathrm{BIC} = -2\, \mathrm{loglik} + nu \ln(n), - 2 * sum_{t=1}^(d - 1) \{q_t log(psi_0^t) - (d - t - q_t) log(1 - psi_0^t)\} where \mathrm{loglik} is the log-liklihood and nu is the (effective) number of parameters of the model, t is the tree level psi_0 is the prior probability of having a non-independence copula and q_t is the number of non-independence copulas in tree t. The vBIC is a consistent model selection criterion for parametric sparse vine copula models.
par_to_tau | Conversion between Kendall's tau and parameters
pbicop | Bivariate copula distributions
pbicop_dist | Bivariate copula distributions
plot.bicop | Plotting tools for 'bicop_dist' and 'bicop' objects
plot.bicop_dist | Plotting tools for 'bicop_dist' and 'bicop' objects
plot.vinecop | Plotting 'vinecop_dist' and 'vinecop' objects.
plot.vinecop_dist | Plotting 'vinecop_dist' and 'vinecop' objects.
predict.bicop | Predictions and fitted values for a bivariate copula model
predict.vinecop | Predictions and fitted values for a vine copula model
pvinecop | Vine copula distributions
pvinecop_dist | Vine copula distributions
rbicop | Bivariate copula distributions
rbicop_dist | Bivariate copula distributions
rvinecop | Vine copula distributions
rvinecopulib | High Performance Algorithms for Vine Copula Modeling
rvinecop_dist | Vine copula distributions
tau_to_par | Conversion between Kendall's tau and parameters
vinecop | Vine copula models
vinecop_dist | Vine copula models
vinecop_distributions | Vine copula distributions
vinecop_predict_and_fitted | Predictions and fitted values for a vine copula model