  Meta-elliptical copulas are often proposed to model dependence between the
components of a random vector. They are specified by a correlation matrix and a
map $g$, called density generator. While the latter correlation matrix can
easily be estimated from pseudo-samples of observations, the density generator
is harder to estimate, especially when it does not belong to a parametric
family. We give sufficient conditions to non-parametrically identify this
generator. Several nonparametric estimators of $g$ are then proposed, by
M-estimation, simulation-based inference, or by an iterative procedure
available in the R package ElliptCopulas. Some simulations illustrate the
relevance of the latter method.
