Skip to main content

A package for forgotten effects theory computation using TensorFlow, NumPy, and Pandas.

Project description

PyPI License

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

Installation

Install directly from PyPI using:

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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

forgeffects-0.1.7.tar.gz (16.7 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

forgeffects-0.1.7-py3-none-any.whl (18.8 kB view details)

Uploaded Python 3

File details

Details for the file forgeffects-0.1.7.tar.gz.

File metadata

  • Download URL: forgeffects-0.1.7.tar.gz
  • Upload date:
  • Size: 16.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.3

File hashes

Hashes for forgeffects-0.1.7.tar.gz
Algorithm Hash digest
SHA256 704c1aa1e0e50c78be2647809c2875be3cfabd31bda623370c1609a5a435d703
MD5 d28a602e7b149aa4758c8883dcc39309
BLAKE2b-256 0b33df21dd824bc880f4232e0d3e6ac45cb133317ed315e8df57efdf15199a8c

See more details on using hashes here.

File details

Details for the file forgeffects-0.1.7-py3-none-any.whl.

File metadata

  • Download URL: forgeffects-0.1.7-py3-none-any.whl
  • Upload date:
  • Size: 18.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.3

File hashes

Hashes for forgeffects-0.1.7-py3-none-any.whl
Algorithm Hash digest
SHA256 5b0d4f500407c39b615be2b100417d498720c3e02688b7eb0c93b420afa11291
MD5 77169b558cdfacfe69766bb23f8815ec
BLAKE2b-256 695b4c58c627df3eb42d60c7f5e5789ec72d77bcea07fc62c9e08793b5453755

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page