Estimation and Inference for Conditional Copula Models

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Documentation for package ‘CondCopulas’ version 0.1.3

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

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