Metadata-Version: 2.1
Name: forgeffects
Version: 0.1.6
Summary: A package for forgotten effects theory computation using TensorFlow, NumPy, and Pandas.
Home-page: https://github.com/claudio-araya/forgeffects
Author: Claudio Esteban Araya Toro
Author-email: claudioesteban.at@gmail.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8, <=3.11
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: tensorflow==2.13
Requires-Dist: tensorflow_probability==0.20.0
Requires-Dist: numpy>=1.18
Requires-Dist: pandas>=1.0

![PyPI](https://img.shields.io/pypi/v/forgeffects) ![License](https://img.shields.io/pypi/l/forgeffects)

**Forgeffects** is a package for analyzing and computing the forgotten effects theory.

## Installation

Install directly from PyPI using:

```bash
pip install forgeffects
```

## References

[1] Kaufmann, A. and Gil Aluja, J. *Models for the Research of Forgotten Effects*. Milladoiro, Santiago de Compostela, Spain, 1988.

[2] Mardones-Arias, E.; Rojas-Mora, J. *foRgotten*. R package version 1.1.0, 2022.

[3] Chávez-Bustamante, F.; Mardones-Arias, E.; Rojas-Mora, J.; Tijmes-Ihl, J.  
*A Forgotten Effects Approach to the Analysis of Complex Economic Systems: Identifying Indirect Effects on Trade Networks*. Mathematics, 11(3), Article 531, 2023.
