bicop                   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
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
plot.bicop_dist         Plotting tools for 'bicop_dist' and 'bicop'
                        objects
plot.vinecop_dist       Plotting 'vinecop_dist' and 'vinecop' objects.
rvinecopulib            High Performance Algorithms for Vine Copula
                        Modeling
vinecop                 Vine copula models
vinecop_distributions   Vine copula distributions
vinecop_predict_and_fitted
                        Predictions and fitted values for a vine copula
                        model
