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 |

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.Kfolds | Choose the bandwidth for kernel estimation of conditional Kendall's tau using cross-validation |

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.KendallReg.LambdaCV | Kendall's regression: choice of the penalization parameter by K-folds cross-validation |

CKT.kendallReg.predict | 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.GLM | Estimation of conditional Kendall's taus by penalized GLM |

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 |

CKT.predict.randomForest | Fit a Random Forest that can be used for the estimation of conditional Kendall's tau. |

CKT.predict.tree | Estimation of conditional Kendall's taus using a classification tree |

CKTmatrix.kernel | Estimate the conditional Kendall's tau matrix at different conditioning points |

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 |

estimateParCondCopula_ZIJ | Estimation of parametric conditional copulas |

matrixInd2matrixCKT | Converting to matrix of indicators / matrix of conditional Kendall's tau |

simpA.kendallReg | Test of the simplifying assumption using the constancy of conditional Kendall's tau |

simpA.NP | Nonparametric testing of the simplifying assumption |

simpA.param | Semiparametric testing of the simplifying assumption |

treeCKT2matrixCKT | Converting to matrix of indicators / matrix of conditional Kendall's tau |

treeCKT2matrixInd | Converting to matrix of indicators / matrix of conditional Kendall's tau |