Package: CondCopulas 0.2.0

CondCopulas: Estimation and Inference for Conditional Copula Models

Provides functions for the estimation of conditional copulas models, various estimators of conditional Kendall's tau (proposed in Derumigny and Fermanian (2019a, 2019b, 2020) <doi:10.1515/demo-2019-0016>, <doi:10.1016/j.csda.2019.01.013>, <doi:10.1016/j.jmva.2020.104610>), test procedures for the simplifying assumption (proposed in Derumigny and Fermanian (2017) <doi:10.1515/demo-2017-0011> and Derumigny, Fermanian and Min (2022) <doi:10.1002/cjs.11742>), and measures of non-simplifyingness (proposed in Derumigny (2025) <doi:10.48550/arXiv.2504.07704>).

Authors:Alexis Derumigny [aut, cre], Jean-David Fermanian [ctb, ths], Aleksey Min [ctb], Rutger van der Spek [ctb]

CondCopulas_0.2.0.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
CondCopulas/json (API)

# Install 'CondCopulas' in R:

install.packages('CondCopulas', repos = c('https://alexisderumigny.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/alexisderumigny/condcopulas/issues

On CRAN:

conditional-copulas conditional-kendalls-tau copulas r-pkg simplifying-assumption

5.38 score 3 stars 8 scripts 324 downloads 39 exports 23 dependencies

Last updated from:70346ab717. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	298
source / vignettes	OK	239
linux-release	OK	328
macos-release	OK	357
macos-oldrel	OK	252
windows-devel	OK	282
windows-release	OK	297
windows-oldrel	OK	342
wasm-release	OK	129

Exports:bCond.estParamCopula bCond.pobs bCond.simpA.CKT bCond.simpA.param bCond.treeCKT CKT.estimate CKT.fit.GLM CKT.fit.nNets CKT.fit.randomForest CKT.fit.tree CKT.hCV.Kfolds CKT.hCV.l1out CKT.kendallReg.fit CKT.KendallReg.LambdaCV CKT.kendallReg.predict CKT.kernel CKT.predict.GLM CKT.predict.kNN CKT.predict.nNets CKT.predict.randomForest CKT.predict.tree CKTmatrix.kernel computeKernelMatrix computeMatrixSignPairs datasetPairs estimateCondCDF_matrix estimateCondCDF_vec estimateCondQuantiles estimateNPCondCopula estimateParCondCopula estimateParCondCopula_ZIJ matrixInd2matrixCKT measures_nonsimplifyingness_NP se simpA.kendallReg simpA.NP simpA.param treeCKT2matrixCKT treeCKT2matrixInd

Dependencies:ADGofTest codetools data.tree foreach glmnet iterators lattice MASS Matrix mvtnorm nnet ordinalNet pbapply R6 Rcpp RcppEigen shape statmod stringi survival tree VineCopula wdm

Simulation and estimation from conditional copula models

Simulation of the data | Estimation of the conditional margins | Visualization of the conditional dependence | Parametric estimation of the conditional copula

Rendered fromsimulatedData.Rmdusingknitr::rmarkdown

Last update: 2023-09-26
Started: 2021-08-16

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Estimation of the conditional parameters of a parametric conditional copula with discrete conditioning events.	bCond.estParamCopula
Computing the pseudo-observations in case of discrete conditioning events	bCond.pobs
Function for testing the simplifying assumption with data-driven box-type conditioning events	bCond.simpA.CKT
Test of the assumption that a conditional copulas does not vary through a list of discrete conditioning events	bCond.simpA.param
Construct a binary tree for the modeling the conditional Kendall's tau	bCond.treeCKT
Estimation of conditional Kendall's tau between two variables X1 and X2 given Z = z	CKT.estimate
Estimation of conditional Kendall's taus by penalized GLM	CKT.fit.GLM CKT.predict.GLM
Estimation of conditional Kendall's taus by model averaging of neural networks	CKT.fit.nNets
Fit a Random Forest that can be used for the estimation of conditional Kendall's tau.	CKT.fit.randomForest CKT.predict.randomForest
Estimation of conditional Kendall's taus using a classification tree	CKT.fit.tree CKT.predict.tree
Choose the bandwidth for kernel estimation of conditional Kendall's tau using cross-validation	CKT.hCV.Kfolds CKT.hCV.l1out
Fit Kendall's regression, a GLM-type model for conditional Kendall's tau	CKT.kendallReg.fit CKT.kendallReg.predict
Kendall's regression: choice of the penalization parameter by K-folds cross-validation	CKT.KendallReg.LambdaCV
Estimation of conditional Kendall's tau using kernel smoothing	CKT.kernel
Prediction of conditional Kendall's tau using nearest neighbors	CKT.predict.kNN
Predict the values of conditional Kendall's tau using Model Averaging of Neural Networks	CKT.predict.nNets
Estimate the conditional Kendall's tau matrix at different conditioning points	CKTmatrix.kernel
Computing the kernel matrix	computeKernelMatrix
Compute the matrix of signs of pairs	computeMatrixSignPairs
Converting to matrix of indicators / matrix of conditional Kendall's tau	conv_treeCKT matrixInd2matrixCKT treeCKT2matrixCKT treeCKT2matrixInd
Construct a dataset of pairs of observations for the estimation of conditional Kendall's tau	datasetPairs
Compute kernel-based conditional marginal (univariate) cdfs	estimateCondCDF_matrix
Compute kernel-based conditional marginal (univariate) cdfs	estimateCondCDF_vec
Compute kernel-based conditional quantiles	estimateCondQuantiles
Compute a kernel-based estimator of the conditional copula	estimateNPCondCopula
Estimation of parametric conditional copulas	estimateParCondCopula estimateParCondCopula_ZIJ
Compute a measure of non-simplifyingness based on non-parametric estimation of the conditional copula	measures_nonsimplifyingness_NP
Standard Error of Model Coefficients	se
Methods for class `estimated_CKT_kernel`	confint.estimated_CKT_kernel plot.estimated_CKT_kernel se.estimated_CKT_kernel
Test of the simplifying assumption using the constancy of conditional Kendall's tau	coef.simpA_kendallReg_test plot.simpA_kendallReg_test print.simpA_kendallReg_test se.simpA_kendallReg_test simpA.kendallReg vcov.simpA_kendallReg_test
Nonparametric testing of the simplifying assumption	simpA.NP
Semiparametric testing of the simplifying assumption	simpA.param