| Name | copul JSON |
| Version |
0.3.8
JSON |
| download |
| home_page | None |
| Summary | A Python package for copula analysis and computation. |
| upload_time | 2025-10-21 03:18:07 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | None |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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# copul
**copul** is a package designed for mathematical computation with and visualization of bivariate copula families.
# Install
Install the copul library using pip.
```bash
pip install copul
```
# Documentation
A guide and documentation is available at [https://copul.readthedocs.io/](https://copul.readthedocs.io/).
# Copula families and copulas
The `copul` package covers implementations of the following copula families:
- **Archimedean copula families**: The 22 Archimedean copula families from the book "Nelsen - An Introduction to Copulas" including
* Clayton
* Gumbel-Hougaard
* Frank
* Joe
* Ali-Mikhail-Haq
* etc.
- **Extreme-value copulas families**:
* BB5
* Cuadras-Augé
* Galambos
* Gumbel
* Husler-Reiss
* Joe
* Marshall-Olkin
* tEV
* Tawn
- **Elliptical copula families**:
* Gaussian
* Student's t
* Laplace.
- **Other copula families**:
* Farlie-Gumbel-Morgenstern
* Fréchet
* Mardia
* Plackett
* Raftery
Furthermore, the package provides the following copulas:
- Independence copula
- Lower and upper Fréchet bounds
- Checkerboard copulas
<!--A list of all implemented copulas can be found in `copul.Families`.
> **Note**: The following examples are also available as a Jupyter notebook in the `notes/examples` folder.-->
# Copula properties
The following properties are available for the above copula families and copulas if they exist and are known:
- `cdf`: Cumulative distribution function
- `pdf`: Probability density function
- `cond_distr_1`, `cond_distr_2`: Conditional distribution functions
- `lambda_L`, `lambda_U`: Lower and upper tail dependence coefficients
- `rho`, `tau`, `xi`: Spearman's rho, Kendall's tau, and Chatterjee's xi
- `generator`, `inv_generator`: Generator and inverse generator for Archimedean copula families
- `pickands`: Pickands dependence functions for extreme-value copula families
# Copula methods
The following methods are available for the above copula families and copulas:
- `rvs`: Generate random samples from the copula
- `scatter_plot`: Generate a scatter plot of the copula
- `plot_cdf`: Visualize the cumulative distribution function
- `plot_pdf`: Visualize the probability density function
- `plot_rank_correlations`: Visualize Spearman's rho, Kendall's tau, and Chatterjee's xi
- `plot_generator`: Visualize the generator function
- `plot_pickands`: Visualize the Pickands dependence function
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"description": "# copul\n\n**copul** is a package designed for mathematical computation with and visualization of bivariate copula families.\n\n# Install\n\nInstall the copul library using pip.\n\n```bash\npip install copul\n```\n\n# Documentation\n\nA guide and documentation is available at [https://copul.readthedocs.io/](https://copul.readthedocs.io/).\n\n# Copula families and copulas\n\nThe `copul` package covers implementations of the following copula families:\n\n- **Archimedean copula families**: The 22 Archimedean copula families from the book \"Nelsen - An Introduction to Copulas\" including \n * Clayton\n * Gumbel-Hougaard\n * Frank\n * Joe\n * Ali-Mikhail-Haq\n * etc.\n- **Extreme-value copulas families**:\n * BB5\n * Cuadras-Aug\u00e9\n * Galambos\n * Gumbel\n * Husler-Reiss\n * Joe\n * Marshall-Olkin\n * tEV\n * Tawn\n- **Elliptical copula families**:\n * Gaussian\n * Student's t\n * Laplace.\n- **Other copula families**:\n * Farlie-Gumbel-Morgenstern\n * Fr\u00e9chet\n * Mardia\n * Plackett\n * Raftery\n\n\nFurthermore, the package provides the following copulas:\n\n- Independence copula\n- Lower and upper Fr\u00e9chet bounds\n- Checkerboard copulas\n\n<!--A list of all implemented copulas can be found in `copul.Families`.\n\n> **Note**: The following examples are also available as a Jupyter notebook in the `notes/examples` folder.-->\n\n# Copula properties\n\nThe following properties are available for the above copula families and copulas if they exist and are known:\n\n- `cdf`: Cumulative distribution function\n- `pdf`: Probability density function\n- `cond_distr_1`, `cond_distr_2`: Conditional distribution functions\n- `lambda_L`, `lambda_U`: Lower and upper tail dependence coefficients\n- `rho`, `tau`, `xi`: Spearman's rho, Kendall's tau, and Chatterjee's xi\n- `generator`, `inv_generator`: Generator and inverse generator for Archimedean copula families\n- `pickands`: Pickands dependence functions for extreme-value copula families\n\n# Copula methods\n\nThe following methods are available for the above copula families and copulas:\n\n- `rvs`: Generate random samples from the copula\n- `scatter_plot`: Generate a scatter plot of the copula\n- `plot_cdf`: Visualize the cumulative distribution function\n- `plot_pdf`: Visualize the probability density function\n- `plot_rank_correlations`: Visualize Spearman's rho, Kendall's tau, and Chatterjee's xi\n- `plot_generator`: Visualize the generator function\n- `plot_pickands`: Visualize the Pickands dependence function\n",
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