This package implements a vine copula based kernel density estimator. The estimator does not suffer from the curse of dimensionality and is therefore well suited for high-dimensional applications (see, Nagler and Czado, 2016).
The multivariate kernel density estimators is implemented by the
kdevine function. It combines a kernel density estimator for
the margins (kde1d) and a kernel estimator of the vine copula
density (kdevinecop). The package is built on top of the copula
density estimators in the kdecopula::kdecopula-package and let's you
choose from all its implemented methods. Optionally, the vine copula can be
estimated parameterically (only the margins are nonparametric).
Nagler, T., Czado, C. (2016) Evading the curse of dimensionality in nonparametric density estimation with simplified vine copulas. Journal of Multivariate Analysis 151, 69-89 (doi:10.1016/j.jmva.2016.07.003)
Nagler, T., Schellhase, C. and Czado, C. (2017) Nonparametric estimation of simplified vine copula models: comparison of methods arXiv:1701.00845
Nagler, T. (2017) A generic approach to nonparametric function estimation with mixed data. arXiv:1704.07457