Copulas.jl: A fully Distributions.jl-compliant copula package
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913576920285184 |
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| author | Laverny, Oskar Jimenez, Santiago |
| author_facet | Laverny, Oskar Jimenez, Santiago |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_01784 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Copulas.jl: A fully Distributions.jl-compliant copula package Laverny, Oskar Jimenez, Santiago Computation 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. |
| title | Copulas.jl: A fully Distributions.jl-compliant copula package |
| topic | Computation |
| url | https://arxiv.org/abs/2401.01784 |