CKT.KendallReg.LambdaCV
                        Kendall's regression: choice of the
                        penalization parameter by K-folds
                        cross-validation
CKT.estimate            Estimation of conditional Kendall's tau between
                        two variables X1 and X2 given Z = z
CKT.fit.GLM             Estimation of conditional Kendall's taus by
                        penalized GLM
CKT.fit.nNets           Estimation of conditional Kendall's taus by
                        model averaging of neural networks
CKT.fit.randomForest    Fit a Random Forest that can be used for the
                        estimation of conditional Kendall's tau.
CKT.fit.tree            Estimation of conditional Kendall's taus using
                        a classification tree
CKT.hCV.l1out           Choose the bandwidth for kernel estimation of
                        conditional Kendall's tau using
                        cross-validation
CKT.kendallReg.fit      Fit Kendall's regression, a GLM-type model for
                        conditional Kendall's tau
CKT.kernel              Estimation of conditional Kendall's tau using
                        kernel smoothing
CKT.predict.kNN         Prediction of conditional Kendall's tau using
                        nearest neighbors
CKT.predict.nNets       Predict the values of conditional Kendall's tau
                        using Model Averaging of Neural Networks
CKTmatrix.kernel        Estimate the conditional Kendall's tau matrix
                        at different conditioning points
bCond.estParamCopula    Estimation of the conditional parameters of a
                        parametric conditional copula with discrete
                        conditioning events.
bCond.pobs              Computing the pseudo-observations in case of
                        discrete conditioning events
bCond.simpA.CKT         Function for testing the simplifying assumption
                        with data-driven box-type conditioning events
bCond.simpA.param       Test of the assumption that a conditional
                        copulas does not vary through a list of
                        discrete conditioning events
bCond.treeCKT           Construct a binary tree for the modeling the
                        conditional Kendall's tau
computeKernelMatrix     Computing the kernel matrix
computeMatrixSignPairs
                        Compute the matrix of signs of pairs
conv_treeCKT            Converting to matrix of indicators / matrix of
                        conditional Kendall's tau
datasetPairs            Construct a dataset of pairs of observations
                        for the estimation of conditional Kendall's tau
estimateCondCDF_matrix
                        Compute kernel-based conditional marginal
                        (univariate) cdfs
estimateCondCDF_vec     Compute kernel-based conditional marginal
                        (univariate) cdfs
estimateCondQuantiles   Compute kernel-based conditional quantiles
estimateNPCondCopula    Compute a kernel-based estimator of the
                        conditional copula
estimateParCondCopula   Estimation of parametric conditional copulas
measures_nonsimplifyingness_NP
                        Compute a measure of non-simplifyingness based
                        on non-parametric estimation of the conditional
                        copula
se                      Standard Error of Model Coefficients
se.estimated_CKT_kernel
                        Methods for class 'estimated_CKT_kernel'
simpA.NP                Nonparametric testing of the simplifying
                        assumption
simpA.kendallReg        Test of the simplifying assumption using the
                        constancy of conditional Kendall's tau
simpA.param             Semiparametric testing of the simplifying
                        assumption
