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JOSS: https://github.com/dit/dit

information-theory python

Score: 18.350243773112833

Last synced: about 7 hours ago
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Repository metadata:

Python package for information theory.


Owner metadata:


GitHub Events

Total
Last Year

Committers metadata

Last synced: 8 days ago

Total Commits: 1,908
Total Committers: 24
Avg Commits per committer: 79.5
Development Distribution Score (DDS): 0.381

Commits in past year: 108
Committers in past year: 4
Avg Commits per committer in past year: 27.0
Development Distribution Score (DDS) in past year: 0.278

Name Email Commits
Ryan James r****s@g****m 1182
chebee7i c****i@g****m 478
Ryan James r****s@r****m 177
Marc Harper m****r@g****m 29
dependabot[bot] 4****] 9
Artemy Kolchinsky a****k@g****m 5
Scott Sievert s****t 5
Ryan Gregory James r****s@g****m 4
Ryan G James r****s@d****u 2
Elias Jaffe 3****e 2
Robin Ince g****b@r****t 2
Aaron Griffith a****i@g****m 1
David A. Ehrlich 6****h 1
Diego Volpatto v****o@l****r 1
Freya Behrens f****s 1
Harald Schilly h****y@g****m 1
Kunal Marwaha m****a@b****u 1
Making GitHub Delicious i****n@w****o 1
Pattarawat Chormai p****i@g****m 1
Thomas Kluyver t****l@g****m 1
jemenheiser j****r@u****u 1
kokokostation k****n@g****m 1
tobmag t****s@g****e 1
赵丰 (Zhao Feng) 6****8@q****m 1

Issue and Pull Request metadata

Last synced: about 2 months ago

Total issues: 62
Total pull requests: 55
Average time to close issues: about 2 years
Average time to close pull requests: 25 days
Total issue authors: 25
Total pull request authors: 21
Average comments per issue: 1.1
Average comments per pull request: 0.98
Merged pull request: 47
Bot issues: 0
Bot pull requests: 8

Past year issues: 1
Past year pull requests: 9
Past year average time to close issues: N/A
Past year average time to close pull requests: 20 days
Past year issue authors: 1
Past year pull request authors: 2
Past year average comments per issue: 1.0
Past year average comments per pull request: 0.22
Past year merged pull request: 5
Past year bot issues: 0
Past year bot pull requests: 8

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/dit/dit

Top Issue Authors

  • Autoplectic (30)
  • stsievert (4)
  • chebee7i (3)
  • whyihaveyou (2)
  • juancarlosfarah (2)
  • vigneswaran-chandrasekaran (2)
  • Benjamin-Lee (1)
  • ivan-marroquin (1)
  • d-jeon (1)
  • ZBC043 (1)
  • fabioanza (1)
  • alimasn (1)
  • wuhaochen (1)
  • htcml (1)
  • ilongshan (1)

Top Pull Request Authors

  • Autoplectic (16)
  • dependabot[bot] (8)
  • artemyk (6)
  • stsievert (5)
  • Ejjaffe (2)
  • robince (2)
  • takluyver (2)
  • volpatto (1)
  • zhaofeng-shu33 (1)
  • haraldschilly (1)
  • kokokostation (1)
  • feeds (1)
  • marwahaha (1)
  • tobmag (1)
  • j1c (1)

Top Issue Labels

  • enhancement (16)
  • measure (10)
  • channels (4)
  • PID (3)
  • distribution (1)

Top Pull Request Labels

  • python:uv (8)
  • dependencies (8)
  • enhancement (1)

Package metadata

proxy.golang.org: github.com/dit/dit

  • Homepage:
  • Documentation: https://pkg.go.dev/github.com/dit/dit#section-documentation
  • Licenses: bsd-3-clause
  • Latest release: v1.2.3 (published over 7 years ago)
  • Last Synced: 2026-06-06T20:02:04.805Z (16 days ago)
  • Versions: 7
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Stargazers count: 3.164%
    • Forks count: 3.184%
    • Average: 4.286%
    • Dependent packages count: 5.223%
    • Dependent repos count: 5.574%
pypi.org: dit

Python package for information theory.

  • Homepage: http://dit.io
  • Documentation: https://dit.readthedocs.io/
  • Licenses: BSD
  • Latest release: 1.2.3 (published over 7 years ago)
  • Last Synced: 2026-06-09T02:02:23.128Z (14 days ago)
  • Versions: 36
  • Dependent Packages: 0
  • Dependent Repositories: 18
  • Downloads: 6,574 Last month
  • Rankings:
    • Dependent repos count: 3.423%
    • Average: 6.953%
    • Downloads: 7.434%
    • Dependent packages count: 10.002%
  • Maintainers (1)
conda-forge.org: dit

Information theory is a powerful extension to probability and statistics, quantifying dependencies among arbitrary random variables in a way that is consistent and comparable across systems and scales. Information theory was originally developed to quantify how quickly and reliably information could be transmitted across an arbitrary channel. The demands of modern, data-driven science have been coopting and extending these quantities and methods into unknown, multivariate settings where the interpretation and best practices are not known. For example, there are at least four reasonable multivariate generalizations of the mutual information, none of which inherit all the interpretations of the standard bivariate case. Which is best to use is context-dependent. dit implements a vast range of multivariate information measures in an effort to allow information practitioners to study how these various measures behave and interact in a variety of contexts. We hope that having all these measures and techniques implemented in one place will allow the development of robust techniques for the automated quantification of dependencies within a system and concrete interpretation of what those dependencies mean.

  • Homepage: http://dit.io
  • Licenses: BSD-3-Clause
  • Latest release: 1.2.3 (published about 7 years ago)
  • Last Synced: 2026-04-01T01:58:48.824Z (3 months ago)
  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Stargazers count: 17.504%
    • Forks count: 19.206%
    • Average: 30.478%
    • Dependent repos count: 34.025%
    • Dependent packages count: 51.175%