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[](https://codecov.io/gh/Serapieum-of-alex/statista)
Current release info
====================
| Name | Downloads | Version | Platforms |
| --- |--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| --- | --- |
| [](https://anaconda.org/conda-forge/statista) | [](https://anaconda.org/conda-forge/statista) [](https://pepy.tech/project/statista) [](https://pepy.tech/project/statista) [](https://pepy.tech/project/statista)  | [](https://anaconda.org/conda-forge/statista) [](https://badge.fury.io/py/statista) [](https://anaconda.org/conda-forge/statista) | [](https://anaconda.org/conda-forge/statista) [](https://gitter.im/Hapi-Nile/Hapi?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) |
statista - Statistics package
=====================================================================
**statista** is a statistics package
statista
Main Features
-------------
- Statistical Distributions
- GEV
- GUMBEL
- Normal
- Exponential
- Parameter estimation methods
- Lmoments
- ML
- MOM
- One-at-time (O-A-T) Sensitivity analysis.
- Sobol visualization
- Statistical descriptors
- Extreme value analysis
For the full documentation, please visit [statista documentation](https://statista.readthedocs.io/en/latest/?badge=latest)
Installing statista
===============
Installing `statista` from the `conda-forge` channel can be achieved by:
```
conda install -c conda-forge statista
```
It is possible to list all the versions of `statista` available on your platform with:
```
conda search statista --channel conda-forge
```
## Install from GitHub
to install the last development to time, you can install the library from GitHub
```
pip install git+https://github.com/MAfarrag/statista
```
## pip
to install the last release, you can use pip
```
pip install statista==0.6.0
```
Quick start
===========
```
>>> import statista
```
[other code samples](https://statista.readthedocs.io/en/latest/?badge=latest)
=======
History
=======
0.1.0 (2022-05-24)
------------------
* First release on PyPI.
0.1.7 (2022-12-26)
------------------
* lock numpy to version 1.23.5
0.1.8 (2023-01-31)
------------------
* bump up versions
0.2.0 (2023-02-08)
------------------
* add eva (Extreme value analysis) module
* fix bug in obtaining distribution parameters using optimization method
0.3.0 (2023-02-19)
------------------
* add documentations for both GEV and gumbel distributions.
* add lmoment parameter estimation method for all distributions.
* add exponential and normal distributions
* modify the pdf, cdf, and probability plot plots
* create separate plot and confidence_interval modules.
0.4.0 (2023-11-23)
------------------
* add Pearson 3 distribution
* Use setup.py instead of pyproject.toml.
* Correct pearson correlation coefficient and add documentation .
* replace the pdf and cdf by the methods from scipy package.
0.5.0 (2023-12-11)
------------------
* Unify the all the methods for the distributions.
* Use factory design pattern to create the distributions.
* add tests for the eva module.
* use snake_case for the methods and variables.
0.6.0 (2024-08-18)
------------------
dev
"""
* Add documentations for the `distributions`, and `eva` modules.
* Add autodoc for all modules.
* Test docstrings as part of CI and pre-commit hooks.
* Test notebooks as part of CI.
* Simplify test for the distributions module
distributions
"""""""""""""
* move the `cdf` and `parameters` for all the methods to be optional parameters.
* rename `theoretical_estimate` method to `inverse_cdf`.
* All distributions can be instantiated with the parameters and/or data.
* rename the `probability_plot` method to `plot`.
* move the `confidence_interval` plot from the `probability_plot/plot` to the method `confidence_interval` and can be
called by activating the `plot_figure=True`.
descriptors
"""""""""""
* rename the `metrics` module to `descriptors`.
Raw data
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Statistics package\n=====================================================================\n**statista** is a statistics package\n\nstatista\n\nMain Features\n-------------\n - Statistical Distributions\n - GEV\n - GUMBEL\n - Normal\n - Exponential\n - Parameter estimation methods\n - Lmoments\n - ML\n - MOM\n - One-at-time (O-A-T) Sensitivity analysis.\n - Sobol visualization\n - Statistical descriptors\n - Extreme value analysis\n\nFor the full documentation, please visit [statista documentation](https://statista.readthedocs.io/en/latest/?badge=latest)\n\nInstalling statista\n===============\n\nInstalling `statista` from the `conda-forge` channel can be achieved by:\n\n```\nconda install -c conda-forge statista\n```\n\nIt is possible to list all the versions of `statista` available on your platform with:\n\n```\nconda search statista --channel conda-forge\n```\n\n## Install from GitHub\nto install the last development to time, you can install the library from GitHub\n```\npip install git+https://github.com/MAfarrag/statista\n```\n\n## pip\nto install the last release, you can use pip\n```\npip install statista==0.6.0\n```\n\nQuick start\n===========\n\n```\n >>> import statista\n```\n\n[other code samples](https://statista.readthedocs.io/en/latest/?badge=latest)\n\n\n=======\nHistory\n=======\n\n0.1.0 (2022-05-24)\n------------------\n\n* First release on PyPI.\n\n0.1.7 (2022-12-26)\n------------------\n\n* lock numpy to version 1.23.5\n\n\n0.1.8 (2023-01-31)\n------------------\n\n* bump up versions\n\n\n0.2.0 (2023-02-08)\n------------------\n\n* add eva (Extreme value analysis) module\n* fix bug in obtaining distribution parameters using optimization method\n\n\n0.3.0 (2023-02-19)\n------------------\n\n* add documentations for both GEV and gumbel distributions.\n* add lmoment parameter estimation method for all distributions.\n* add exponential and normal distributions\n* modify the pdf, cdf, and probability plot plots\n* create separate plot and confidence_interval modules.\n\n0.4.0 (2023-11-23)\n------------------\n\n* add Pearson 3 distribution\n* Use setup.py instead of pyproject.toml.\n* Correct pearson correlation coefficient and add documentation .\n* replace the pdf and cdf by the methods from scipy package.\n\n0.5.0 (2023-12-11)\n------------------\n\n* Unify the all the methods for the distributions.\n* Use factory design pattern to create the distributions.\n* add tests for the eva module.\n* use snake_case for the methods and variables.\n\n0.6.0 (2024-08-18)\n------------------\n\ndev\n\"\"\"\n* Add documentations for the `distributions`, and `eva` modules.\n* Add autodoc for all modules.\n* Test docstrings as part of CI and pre-commit hooks.\n* Test notebooks as part of CI.\n* Simplify test for the distributions module\n\ndistributions\n\"\"\"\"\"\"\"\"\"\"\"\"\"\n* move the `cdf` and `parameters` for all the methods to be optional parameters.\n* rename `theoretical_estimate` method to `inverse_cdf`.\n* All distributions can be instantiated with the parameters and/or data.\n* rename the `probability_plot` method to `plot`.\n* move the `confidence_interval` plot from the `probability_plot/plot` to the method `confidence_interval` and can be\n called by activating the `plot_figure=True`.\n\ndescriptors\n\"\"\"\"\"\"\"\"\"\"\"\n* rename the `metrics` module to `descriptors`.\n",
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