The release version on CRAN:

From GitHub, using the `devtools`

package:

`simpA.NP`

: in a purely nonparametric framework`simpA.param`

: assuming that the conditional copula belongs to a parametric family of copulas for all values of the conditioning variable`simpA.kendallReg`

: test of the simplifying assumption based on the constancy of the conditional Kendall’s tau assuming that it satisfies a regression-like equation

`estimateNPCondCopula`

: nonparametric estimation of conditional copulas`estimateParCondCopula`

: parametric estimation of conditional copulas`estimateParCondCopula_ZIJ`

: parametric estimation of conditional copulas using (already computed) conditional pseudo-observations

A general wrapper function:

`CKT.estimate`

: that can be used for any method of estimating conditional Kendall’s tau. Each of these methods is detailed below and has its own function.

`CKT.kernel`

: for any number of variable and with possible choice of the bandwidth

`CKT.kendallReg.fit`

: fit Kendall’s regression, a regression-like method for the estimation of conditional Kendall’s tau`CKT.kendallReg.predict`

: for prediction of the new conditional Kendall’s tau (given new covariates)

- using tree:
`CKT.fit.tree`

: for fitting a tree-based model for the conditional Kendall’s tau`CKT.predict.tree`

: for prediction of new conditional Kendall’s taus

- using random forests:
`CKT.fit.randomForest`

: for fitting a random forest-based model for the conditional Kendall’s tau`CKT.predict.randomForest`

: for prediction of new conditional Kendall’s taus

- using nearest neighbors:
`CKT.predict.kNN`

: for several numbers of nearest neighbors

- using neural networks:
`CKT.fit.nNets`

: for fitting a neural networks-based model for the conditional Kendall’s tau`CKT.predict.nNets`

: for prediction of new conditional Kendall’s taus

- using GLM:
`CKT.fit.GLM`

: for fitting a GLM-like model for the conditional Kendall’s tau`CKT.predict.GLM`

: for prediction of new conditional Kendall’s taus

`CKT.hCV.Kfolds`

: for K-fold cross-validation choice of the bandwidth for kernel smoothing`CKT.hCV.l1out`

: for leave-one-out cross-validation choice of the bandwidth for kernel smoothing`CKT.KendallReg.LambdaCV`

: cross-validated choice of the penalization parameter lambda`CKT.adaptkNN`

: for a (local) aggregation of the number of nearest neighbors based on Lepski’s method

`bCond.simpA.param`

: assuming that the copula belongs to a parametric family

`bCond.pobs`

: computation of the conditional pseudo-observations \(F_{1|A(i)}(X_{i,1} | A(i))\) and \(F_{2|A(i)}(X_{i,2} | A(i))\) for every \(i=1, \dots, n\).`bCond.estParamCopula`

: estimation of a conditional parametric copula, i.e. for every set \(A\), a conditional parameter \(\theta(A)\) is estimated.

Derumigny, A., & Fermanian, J. D. (2017). About tests of the “simplifying” assumption for conditional copulas. *Dependence Modeling*, 5(1), 154-197.

Derumigny, A., & Fermanian, J. D. (2019). A classification point-of-view about conditional Kendall’s tau. *Computational Statistics & Data Analysis*, 135, 70-94.

Derumigny, A., & Fermanian, J. D. (2019). On kernel-based estimation of conditional Kendall’s tau: finite-distance bounds and asymptotic behavior. *Dependence Modeling*, 7(1), 292-321.

Derumigny, A., & Fermanian, J. D. (2020). On Kendall’s regression. *Journal of Multivariate Analysis*, 178, 104610.