2026-08-13 –, Room 5
Since 2022, Copulas.jl has provided native support for dependence modeling in Julia. Copulas are multivariate distribution functions on the unit hypercube that allow practitioners to model dependence structures separately from marginal behavior. By building on the Distributions.jl framework, Copulas.jl integrates seamlessly with Julia’s probabilistic and statistical ecosystem.
In this talk, we review the major design improvements and new features introduced since the first public releases of Copulas.jl. The package now offers a broad collection of classical parametric copula families, along with tools for evaluating distribution functions and densities, computing dependence measures such as Kendall’s tau and Spearman’s rho, estimating parameters via inversion of moments or maximum likelihood, and fitting models to data.
A key feature of the package is the Sklar type, inspired by Sklar’s Theorem, which enables users to construct full multivariate models by combining copulas with arbitrary marginal distributions. These composite models are fully compatible with the Distributions.jl API, making them directly usable in downstream tools such as Turing.jl for Bayesian inference.
We conclude with practical examples showcasing how the new features of Copulas.jl enable advanced dependence modeling workflows entirely in native Julia.
I am currently an associate professor in statistics in Marseille (France). Actuary by formation, I focus my researches on high dimensional statistics and dependence structures estimations, with a lot of applications in insurance, reinsurance, and more recently public health. I do have a taste for numerical code and open-source software, and most of my work is freely available on GitHub.