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
CondCopulas_0.2.0.zip(r-4.7)CondCopulas_0.2.0.zip(r-4.6)CondCopulas_0.2.0.zip(r-4.5)
CondCopulas_0.2.0.tgz(r-4.6-any)CondCopulas_0.2.0.tgz(r-4.5-any)
CondCopulas_0.2.0.tar.gz(r-4.7-any)CondCopulas_0.2.0.tar.gz(r-4.6-any)
CondCopulas_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
CondCopulas/json (API)
NEWS

# 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:

Conda:

conditional-copulasconditional-kendalls-taucopulasr-pkgsimplifying-assumption

4.90 score 2 stars 7 scripts 244 downloads 39 exports 23 dependencies

Last updated from:92dc08e847. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK279
source / vignettesOK200
linux-release-x86_64OK272
macos-release-arm64OK319
macos-oldrel-arm64OK265
windows-develOK213
windows-releaseOK230
windows-oldrelOK212
wasm-releaseOK123

Exports:bCond.estParamCopulabCond.pobsbCond.simpA.CKTbCond.simpA.parambCond.treeCKTCKT.estimateCKT.fit.GLMCKT.fit.nNetsCKT.fit.randomForestCKT.fit.treeCKT.hCV.KfoldsCKT.hCV.l1outCKT.kendallReg.fitCKT.KendallReg.LambdaCVCKT.kendallReg.predictCKT.kernelCKT.predict.GLMCKT.predict.kNNCKT.predict.nNetsCKT.predict.randomForestCKT.predict.treeCKTmatrix.kernelcomputeKernelMatrixcomputeMatrixSignPairsdatasetPairsestimateCondCDF_matrixestimateCondCDF_vecestimateCondQuantilesestimateNPCondCopulaestimateParCondCopulaestimateParCondCopula_ZIJmatrixInd2matrixCKTmeasures_nonsimplifyingness_NPsesimpA.kendallRegsimpA.NPsimpA.paramtreeCKT2matrixCKTtreeCKT2matrixInd

Dependencies:ADGofTestcodetoolsdata.treeforeachglmnetiteratorslatticeMASSMatrixmvtnormnnetordinalNetpbapplyR6RcppRcppEigenshapestatmodstringisurvivaltreeVineCopulawdm

Simulation and estimation from conditional copula models

Alexis Derumigny

Rendered fromsimulatedData.Rmdusingknitr::rmarkdownon May 16 2026.

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

Readme and manuals

Help Manual

Help pageTopics
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 eventsbCond.pobs
Function for testing the simplifying assumption with data-driven box-type conditioning eventsbCond.simpA.CKT
Test of the assumption that a conditional copulas does not vary through a list of discrete conditioning eventsbCond.simpA.param
Construct a binary tree for the modeling the conditional Kendall's taubCond.treeCKT
Estimation of conditional Kendall's tau between two variables X1 and X2 given Z = zCKT.estimate
Estimation of conditional Kendall's taus by penalized GLMCKT.fit.GLM CKT.predict.GLM
Estimation of conditional Kendall's taus by model averaging of neural networksCKT.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 treeCKT.fit.tree CKT.predict.tree
Choose the bandwidth for kernel estimation of conditional Kendall's tau using cross-validationCKT.hCV.Kfolds CKT.hCV.l1out
Fit Kendall's regression, a GLM-type model for conditional Kendall's tauCKT.kendallReg.fit CKT.kendallReg.predict
Kendall's regression: choice of the penalization parameter by K-folds cross-validationCKT.KendallReg.LambdaCV
Estimation of conditional Kendall's tau using kernel smoothingCKT.kernel
Prediction of conditional Kendall's tau using nearest neighborsCKT.predict.kNN
Predict the values of conditional Kendall's tau using Model Averaging of Neural NetworksCKT.predict.nNets
Estimate the conditional Kendall's tau matrix at different conditioning pointsCKTmatrix.kernel
Computing the kernel matrixcomputeKernelMatrix
Compute the matrix of signs of pairscomputeMatrixSignPairs
Converting to matrix of indicators / matrix of conditional Kendall's tauconv_treeCKT matrixInd2matrixCKT treeCKT2matrixCKT treeCKT2matrixInd
Construct a dataset of pairs of observations for the estimation of conditional Kendall's taudatasetPairs
Compute kernel-based conditional marginal (univariate) cdfsestimateCondCDF_matrix
Compute kernel-based conditional marginal (univariate) cdfsestimateCondCDF_vec
Compute kernel-based conditional quantilesestimateCondQuantiles
Compute a kernel-based estimator of the conditional copulaestimateNPCondCopula
Estimation of parametric conditional copulasestimateParCondCopula estimateParCondCopula_ZIJ
Compute a measure of non-simplifyingness based on non-parametric estimation of the conditional copulameasures_nonsimplifyingness_NP
Standard Error of Model Coefficientsse
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 taucoef.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 assumptionsimpA.NP
Semiparametric testing of the simplifying assumptionsimpA.param