  An overview of existing nonparametric tests of extreme-value dependence is
presented. Given an i.i.d.\ sample of random vectors from a continuous
distribution, such tests aim at assessing whether the underlying unknown copula
is of the {\em extreme-value} type or not. The existing approaches available in
the literature are summarized according to how departure from extreme-value
dependence is assessed. Related statistical procedures useful when modeling
data with this type of dependence are briefly described next. Two illustrations
on real data sets are then carried out using some of the statistical procedures
under consideration implemented in the \textsf{R} package {\tt copula}.
Finally, the related problem of testing the {\em maximum domain of attraction}
condition is discussed.
