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UID:pretalx-juliacon-2026-ZBQKTY@pretalx.com
DTSTART;TZID=CET:20260813T170000
DTEND;TZID=CET:20260813T171500
DESCRIPTION:Since 2022\, Copulas.jl has provided native support for depende
 nce 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 fr
 amework\, Copulas.jl integrates seamlessly with Julia’s probabilistic an
 d statistical ecosystem.\n\nIn this talk\, we review the major design impr
 ovements and new features introduced since the first public releases of Co
 pulas.jl. The package now offers a broad collection of classical parametri
 c 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 maxi
 mum likelihood\, and fitting models to data.\n\nA key feature of the packa
 ge 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.\n\nWe conclude with practical e
 xamples showcasing how the new features of Copulas.jl enable advanced depe
 ndence modeling workflows entirely in native Julia.
DTSTAMP:20260502T104020Z
LOCATION:Room 5
SUMMARY:What's new in Copulas.jl - Oskar Laverny
URL:https://pretalx.com/juliacon-2026/talk/ZBQKTY/
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