  Copulas are functions that describe dependence structures of random vectors,
without describing their univariate marginals. In statistics, the separation is
sometimes useful, the quality and/or quantity of available information on these
two objects might differ. This separation can be formally stated through
Sklar's theorem.
  Copulas are standard tools in probability and statistics, with a wide range
of applications from biostatistics, finance or medicine, to fuzzy logic, global
sensitivity and broader analysis.
  The Julia package \texttt{Copulas.jl} brings most standard copula-related
features into native Julia: random number generation, density and distribution
function evaluations, fitting, construction of multivariate models through
Sklar's theorem, and many more related functionalities. Copulas being
fundamentally distributions of random vectors, we fully comply with the
\texttt{Distributions.jl} API, the Julian standard for implementation of random
variables and random vectors. This compliance allows interoperability with
other packages based on this API such as, e.g., \texttt{Turing.jl} and several
others.
