| Name | copulax JSON |
| Version |
1.0.1
JSON |
| download |
| home_page | None |
| Summary | Copula, multivariate and univariate distribution fitting using JAX in python. |
| upload_time | 2025-07-30 16:37:28 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <3.13,>=3.10 |
| license | MIT License
Copyright (c) 2025 CopulAX Developers
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. |
| keywords |
sklar
copula
copulas
copulae
probability
distribution
univariate
bivariate
multivariate
joint
cdf
pdf
modeling
quantitative
fitting
scipy
statistics
mathematics
science
scientific
finance
risk
var
machine learning
variables
jax
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
jax
jaxopt
quadax
requests
tensorflow-probability
interpax
matplotlib
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# CopulAX
<p align="center">
<a href="https://www.python.org">
<img alt="PyPI - Python Version" src="https://img.shields.io/pypi/pyversions/copulax"></a>
<a href="https://pypi.org/project/copulax/">
<img alt="PyPI - Package Version" src="https://img.shields.io/pypi/v/copulax"></a>
<a href="https://github.com/tfm000/copulax/blob/main/LICENSE.txt">
<img src="https://img.shields.io/badge/license-MIT-brightgreen.svg"
alt="MIT license"></a>
<a href="https://github.com/tfm000/copulax/actions/workflows/tests.yml">
<img src="https://github.com/tfm000/copulax/actions/workflows/tests.yml/badge.svg?branch=main"
alt="build"></a>
<a href="https://codecov.io/github/tfm000/copulax">
<img src="https://codecov.io/gh/tfm000/copulax/graph/badge.svg?token=OM89GVW36L"
alt="coverage"></a>
<!-- <a href="https://sklarpy.readthedocs.io/en/latest/?badge=latest">
<img src="https://readthedocs.org/projects/sklarpy/badge/?version=latest"
alt="build"></a>
<a href="https://pepy.tech/project/sklarpy">
<img src="https://static.pepy.tech/personalized-badge/sklarpy?period=total&units=international_system&left_color=black&right_color=orange&left_text=Downloads"
alt="downloads"></a> -->
<!-- <a href="https://pypi.org/project/sklarpy/"> -->
<a href="https://github.com/tfm000/copulax/">
<img src="https://img.shields.io/badge/Maintained%3F-yes-green.svg"
alt="maintained"></a>
</p>
<p align="center">
<!-- <a href="https://pypi.org/project/sklarpy/"> -->
<a href="https://github.com/tfm000/copulax/">
<img src="https://img.shields.io/badge/mac%20os-000000?style=for-the-badge&logo=apple&logoColor=white"
alt="mac os"></a>
<!-- <a href="https://pypi.org/project/sklarpy/"> -->
<a href="https://github.com/tfm000/copulax/">
<img src="https://img.shields.io/badge/Windows-0078D6?style=for-the-badge&logo=windows&logoColor=white"
alt="windows"></a>
<!-- <a href="https://github.com/tfm000/copulax/">
<img src="https://img.shields.io/badge/Ubuntu-E95420?style=for-the-badge&logo=ubuntu&logoColor=white"
alt="windows"></a> -->
</p>
CopulAX is an open-source software for probability distribution fitting written in [JAX](https://github.com/jax-ml/jax/), with an emphasis on low-dimensional optimization. The spirital successor to the eariler [SklarPy](https://github.com/tfm000/sklarpy/) project, this JAX implementation provides improved standardization and optimization performance across distribution objects, in addition to inbuilt automatic differentiation capabilities and greater speed via JIT compilation for both CPUs and GPUs.
We foresee this library having many different possible use cases, ranging from machine learning to finance.
## Table of contents
- [Table of contents](#table_of_contents)
- [Installation](#installation)
- [Low-Dimensional Optimization](#low-dimensional-optimization)
- [Implemented Distributions](#implemented-distributions)
- [Examples](#examples)
## Installation
CopulAX is available on PyPI and can be installed by running:
```bash
pip install copulax
```
## Low-Dimensional Optimization
In many fields data remains limited, which can be one of the main motivators for using probabilistic software which can allow the generaton of additional data points with similar statistical properties. However, when dealing with multivariate data, even this can become challenging, due the shape / covariance / correlation matrix arguments of many multivariate and copula distributions resulting in the number of parameters required to be estimated to be O($d^2$). CopulAX aims to work around this constraint where possible, by using analytical relationships between the mean and covariance matrices and other parameters; Estimating the mean and covariance using techniques robust to low sample sizes, then allows for distribution fitting in such settings by removing a large number of the estimated parameters from the numerical optimization loop.
## Development Status
As CopulAX is still in its early stages, we have so far only released a limited number of continuous univariate and multivariate distributions and their copulas, however in the near future we aim to implement the following:
- Many more univariate distributions, including for discrete variables.
- Incorporating goodness of fit tests into univariate_fitter.
- More multivariate distributions. Namely, the special and limiting cases of the generalized hyperbolic.
- Copulas based on each of the aformentioned multivariate distributions.
- Cdf functions for multivariate and copula distributions. This will depend upon the progress of third party jax-based numerical integration libraries such as [quadax](https://github.com/f0uriest/quadax).
- Archimedean copulas.
- Empirical distributions, with different fitting methods (smoothing splines vs 'as is'/ non-smoothed).
CopulAX is currently under active development and so bugs are to be expected. However we have extensive tests for each distribution and function, so we are aiming to limit there number.
## Implemented Distributions
A list of all implemented distributions can be found here:
- <a href="https://github.com/tfm000/copulax/blob/main/copulax/univariate/README.md">Univariate implemented distributions</a>
- <a href="https://github.com/tfm000/copulax/blob/main/copulax/multivariate/README.md">Multivariate implemented distributions</a>
- <a href="https://github.com/tfm000/copulax/blob/main/copulax/copulas/README.md">Copula implemented distributions</a>
## Examples
We have provided <a href="https://github.com/tfm000/copulax/tree/main/examples">jupyter notebooks</a> containing example code for using univariate, multivariate and copula distribution objects.
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"description": "# CopulAX\r\n\r\n<p align=\"center\">\r\n <a href=\"https://www.python.org\">\r\n <img alt=\"PyPI - Python Version\" src=\"https://img.shields.io/pypi/pyversions/copulax\"></a> \r\n <a href=\"https://pypi.org/project/copulax/\">\r\n <img alt=\"PyPI - Package Version\" src=\"https://img.shields.io/pypi/v/copulax\"></a> \r\n <a href=\"https://github.com/tfm000/copulax/blob/main/LICENSE.txt\">\r\n <img src=\"https://img.shields.io/badge/license-MIT-brightgreen.svg\"\r\n alt=\"MIT license\"></a> \r\n <a href=\"https://github.com/tfm000/copulax/actions/workflows/tests.yml\">\r\n <img src=\"https://github.com/tfm000/copulax/actions/workflows/tests.yml/badge.svg?branch=main\"\r\n alt=\"build\"></a> \r\n <a href=\"https://codecov.io/github/tfm000/copulax\">\r\n <img src=\"https://codecov.io/gh/tfm000/copulax/graph/badge.svg?token=OM89GVW36L\"\r\n alt=\"coverage\"></a> \r\n <!-- <a href=\"https://sklarpy.readthedocs.io/en/latest/?badge=latest\">\r\n <img src=\"https://readthedocs.org/projects/sklarpy/badge/?version=latest\"\r\n alt=\"build\"></a> \r\n <a href=\"https://pepy.tech/project/sklarpy\">\r\n <img src=\"https://static.pepy.tech/personalized-badge/sklarpy?period=total&units=international_system&left_color=black&right_color=orange&left_text=Downloads\"\r\n alt=\"downloads\"></a> -->\r\n <!-- <a href=\"https://pypi.org/project/sklarpy/\"> -->\r\n <a href=\"https://github.com/tfm000/copulax/\">\r\n <img src=\"https://img.shields.io/badge/Maintained%3F-yes-green.svg\"\r\n alt=\"maintained\"></a>\r\n</p>\r\n\r\n<p align=\"center\">\r\n <!-- <a href=\"https://pypi.org/project/sklarpy/\"> -->\r\n <a href=\"https://github.com/tfm000/copulax/\">\r\n <img src=\"https://img.shields.io/badge/mac%20os-000000?style=for-the-badge&logo=apple&logoColor=white\"\r\n alt=\"mac os\"></a>\r\n <!-- <a href=\"https://pypi.org/project/sklarpy/\"> -->\r\n <a href=\"https://github.com/tfm000/copulax/\">\r\n <img src=\"https://img.shields.io/badge/Windows-0078D6?style=for-the-badge&logo=windows&logoColor=white\"\r\n alt=\"windows\"></a>\r\n <!-- <a href=\"https://github.com/tfm000/copulax/\">\r\n <img src=\"https://img.shields.io/badge/Ubuntu-E95420?style=for-the-badge&logo=ubuntu&logoColor=white\"\r\n alt=\"windows\"></a> -->\r\n</p>\r\n\r\nCopulAX is an open-source software for probability distribution fitting written in [JAX](https://github.com/jax-ml/jax/), with an emphasis on low-dimensional optimization. The spirital successor to the eariler [SklarPy](https://github.com/tfm000/sklarpy/) project, this JAX implementation provides improved standardization and optimization performance across distribution objects, in addition to inbuilt automatic differentiation capabilities and greater speed via JIT compilation for both CPUs and GPUs.\r\n\r\nWe foresee this library having many different possible use cases, ranging from machine learning to finance.\r\n\r\n## Table of contents\r\n\r\n- [Table of contents](#table_of_contents)\r\n- [Installation](#installation)\r\n- [Low-Dimensional Optimization](#low-dimensional-optimization)\r\n- [Implemented Distributions](#implemented-distributions)\r\n- [Examples](#examples)\r\n\r\n## Installation\r\n\r\nCopulAX is available on PyPI and can be installed by running:\r\n\r\n```bash\r\npip install copulax\r\n```\r\n\r\n## Low-Dimensional Optimization\r\n\r\nIn many fields data remains limited, which can be one of the main motivators for using probabilistic software which can allow the generaton of additional data points with similar statistical properties. However, when dealing with multivariate data, even this can become challenging, due the shape / covariance / correlation matrix arguments of many multivariate and copula distributions resulting in the number of parameters required to be estimated to be O($d^2$). CopulAX aims to work around this constraint where possible, by using analytical relationships between the mean and covariance matrices and other parameters; Estimating the mean and covariance using techniques robust to low sample sizes, then allows for distribution fitting in such settings by removing a large number of the estimated parameters from the numerical optimization loop.\r\n\r\n## Development Status\r\n\r\nAs CopulAX is still in its early stages, we have so far only released a limited number of continuous univariate and multivariate distributions and their copulas, however in the near future we aim to implement the following:\r\n\r\n- Many more univariate distributions, including for discrete variables.\r\n- Incorporating goodness of fit tests into univariate_fitter.\r\n- More multivariate distributions. 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