Evaluating copula density --> NaN and inaccuracies about rvinecopulib HOT 3 CLOSED

There's are two things at play here. The first is a numerical issue related to the bb7 family, when dependence is strong and the evaluation point near the boundary. That's what caused the NaN and should be fixed here. You can install a package version containing all recent fixes with

devtools::install_github("vinecopulib/rvinecopulib@new-vcl")

The second is a statistical issue not really related to the implementation of our library. If you look at your fitted vine, you will find many non-independence copulas. And if you look at pairwise scatter plots (or correlations) of the data, this shouldn't come at a surprise. With so little data, it's basically impossible to identify the independence copula as the correct model.

The problem is amplified if you have a vine with d > n, where all implemented model selection criteria will provably fail, no matter how large the data set. This issue is discussed in one of my papers and a more suitable criterion is proposed for moderately large d, but it still fails for d > n. So more research is needed...

from rvinecopulib.

New version is on CRAN, I'll close this.

from rvinecopulib.

Thank you very much.

from rvinecopulib.