Skip to main content

Python package for parameter estimation of random data

Project description

Welcome to ecdf_estimator

PyPI Version PyPI Downloads GitHub Repository Doxygen GitHub Issues

It contains a Python based framework for parameter estimation, which is currently under construction.

It may be installed using

$ pip install git+https://github.com/AndreasRupp/ecdf_estimator.git

for the latest version, which is located in the GitHub repository. Alternatively, you can use

$ python3 -m pip install ecdf_estimator

to obtain the latest stable version from PyPI.

Copyright, License, and Contribution Policy

This directory contains the ecdf_estimator library.

The ecdf_estimator library is copyrighted by the authors of ecdf_estimator. This term currently refers to Andreas Rupp.

This library is free software; you can use it, redistribute it, and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. The full text of the GNU Lesser General Public version 2.1 is quoted in License.txt.

Contributions

As a contributor to this project, you agree that all of your contributions be governed by the Developer Certificate of Origin version 1.1. This project does not require copyright assignments for contributions. This means that the copyright for code contributions in this project is held by its respective contributors who have each agreed to release their contributed code under a compatible open source license (LGPL v2.1 for library code). The full text of the Developer Certificate of Origin version 1.1 is quoted in DeveloperCertificateOfOrigin.txt.

Referencing the library

In addition to the terms imposed by the LGPL v2.1 or later, we ask for the following courtesy:

Every publication presenting numerical results obtained with the help of ecdf_estimator should state the name of the library and cite one or more of the following references

  • A. Kazarnikov, N. Ray, H. Haario, J. Lappalainen, and A. Rupp
    Parameter estimation for cellular automata
    arXiv preprint, doi: 10.48550/arXiv.2301.13320

This is the usual, fair way of giving credit to contributors to a scientific result. In addition, it helps us justify our effort in developing ecdf_estimator as an academic undertaking.

Contact

For further questions regarding licensing and commercial use please contact Andreas Rupp directly using Email.

Links

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

ecdf_estimator-0.1.4.tar.gz (9.9 kB view details)

Uploaded Source

Built Distribution

ecdf_estimator-0.1.4-py3-none-any.whl (9.4 kB view details)

Uploaded Python 3

File details

Details for the file ecdf_estimator-0.1.4.tar.gz.

File metadata

  • Download URL: ecdf_estimator-0.1.4.tar.gz
  • Upload date:
  • Size: 9.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.10.7

File hashes

Hashes for ecdf_estimator-0.1.4.tar.gz
Algorithm Hash digest
SHA256 9a5a0270ea00a76d3916e6a25b888f663a88052e4e47016a6a68a4ccd3ad5e8c
MD5 170788b523bb36657ffd23795f8445a7
BLAKE2b-256 00fd6a5b1d0e46161f1055d49c6641260614dac896f62ae739976ac8f40cddba

See more details on using hashes here.

File details

Details for the file ecdf_estimator-0.1.4-py3-none-any.whl.

File metadata

File hashes

Hashes for ecdf_estimator-0.1.4-py3-none-any.whl
Algorithm Hash digest
SHA256 e9568a3c4efd2b51025050ade43e08693dcba876bf3938c98f876850b856b304
MD5 a9b8f3fefdc6bd03023655453317f622
BLAKE2b-256 5dc52405a4bef703c8d634f471a0f9da4e0b48e5f29231f3248ffd7d0815120a

See more details on using hashes here.

Supported by

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