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.

翻译：Copula是描述随机向量依赖结构的函数，但不涉及单变量边际分布。在统计学中，这种分离有时是有用的——因为这两类对象可获得信息的质量和/或数量可能存在差异。这种分离可通过Sklar定理进行形式化表述。Copula是概率论与统计学中的标准工具，其应用范围涵盖生物统计学、金融学、医学、模糊逻辑、全局敏感性分析及更广泛的分析领域。Julia软件包\texttt{Copulas.jl}将大多数标准copula相关功能引入原生Julia环境：随机数生成、密度函数与分布函数计算、参数拟合、基于Sklar定理的多元模型构建，以及诸多相关功能。由于Copula本质上是随机向量的分布，我们完全遵循\texttt{Distributions.jl}应用程序接口——该API是Julia语言中随机变量与随机向量实现的行业标准。这种兼容性使得本软件包能与基于该API的其他软件包（例如\texttt{Turing.jl}等）实现互操作。