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mcf

Official repository for the mcf package.

https://github.com/mcfpy/mcf

Science Score: 49.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 2 DOI reference(s) in README
  • ○
    Academic publication links
  • ✓
    Committers with academic emails
    2 of 17 committers (11.8%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (3.9%) to scientific vocabulary
Last synced: 7 months ago · JSON representation

Repository

Official repository for the mcf package.

Basic Info
Statistics
  • Stars: 21
  • Watchers: 2
  • Forks: 9
  • Open Issues: 0
  • Releases: 0
Created almost 5 years ago · Last pushed 7 months ago
Metadata Files
Readme License

.github/README.md

mcf - Modified Causal Forests

The Python package mcf implements the Modified Causal Forest introduced by Lechner (2018). This package allows you to estimate heterogeneous treatment effects for binary and multiple treatments from experimental or observational data. Additionally, mcf offers the capability to learn optimal policy allocations.

  • Website and documentation: https://mcfpy.github.io/mcf/#/
  • Source code: https://github.com/MCFpy/mcf
  • Bug reports: https://github.com/MCFpy/mcf/issues

Owner

  • Name: MCFpy
  • Login: MCFpy
  • Kind: organization

GitHub Events

Total
  • Issues event: 13
  • Watch event: 5
  • Issue comment event: 5
  • Push event: 412
  • Pull request event: 1
  • Fork event: 1
Last Year
  • Issues event: 13
  • Watch event: 5
  • Issue comment event: 5
  • Push event: 412
  • Pull request event: 1
  • Fork event: 1

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 350
  • Total Committers: 17
  • Avg Commits per committer: 20.588
  • Development Distribution Score (DDS): 0.246
Past Year
  • Commits: 146
  • Committers: 8
  • Avg Commits per committer: 18.25
  • Development Distribution Score (DDS): 0.274
Top Committers
Name Email Commits
hannahbus 3****s 264
Joel Frischknecht j****i@h****m 25
Hannah Busshoff h****h@h****h 22
hbodory h****y@u****h 8
Joel Frischknecht 4****t 5
Hannah Busshoff h****h@e****h 5
Hannah Busshoff h****h@H****l 4
Michael m****r@u****h 3
Hannah Busshoff h****h@v****h 3
Hannah Busshoff h****h@v****h 3
Hannah Busshoff h****h@v****h 2
Hannah Busshoff h****h@v****h 1
Hannah Busshoff h****h@v****h 1
Hannah Busshoff h****h@e****h 1
Hannah Busshoff h****h@v****h 1
Hannah Busshoff h****h@v****h 1
Jonathan Chassot J****C 1
Committer Domains (Top 20 + Academic)
unisg.ch: 2 vpn-intern-130-82-66-167.unisg.ch: 1 vpn-intern-130-82-66-41.unisg.ch: 1 eduroamverw-130-82-28-45.unisg.ch: 1 vpn-intern-130-82-66-15.unisg.ch: 1 vpn-intern-130-82-66-5.unisg.ch: 1 vpn-intern-130-82-66-254.unisg.ch: 1 vpn-intern-130-82-67-159.unisg.ch: 1 vpn-intern-130-82-67-210.unisg.ch: 1 eduroamverw-130-82-26-3.unisg.ch: 1 hannahs-mbp.unisg.ch: 1

Issues and Pull Requests

Last synced: 7 months ago

All Time
  • Total issues: 15
  • Total pull requests: 4
  • Average time to close issues: 22 days
  • Average time to close pull requests: 3 months
  • Total issue authors: 6
  • Total pull request authors: 3
  • Average comments per issue: 1.13
  • Average comments per pull request: 0.5
  • Merged pull requests: 2
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 6
  • Pull requests: 0
  • Average time to close issues: 15 days
  • Average time to close pull requests: N/A
  • Issue authors: 1
  • Pull request authors: 0
  • Average comments per issue: 1.17
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • verbeemen (7)
  • paluigi (4)
  • federicamas (2)
  • bwrowland (1)
  • marja-w (1)
  • joostbollens (1)
  • aparmendariz (1)
Pull Request Authors
  • JLDC (3)
  • aparmendariz (2)
  • paluigi (1)
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 617 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 29
  • Total maintainers: 5
pypi.org: mcf

The Python package mcf implements the Modified Causal Forest introduced by Lechner (2018). This package allows you to estimate heterogeneous treatment effects for binary and multiple treatments from experimental or observational data. Additionally, mcf offers the capability to learn optimal policy allocations.

  • Versions: 29
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 617 Last month
Rankings
Dependent packages count: 10.1%
Forks count: 14.2%
Stargazers count: 16.1%
Average: 16.4%
Downloads: 19.8%
Dependent repos count: 21.5%
Maintainers (5)
hannahbussi hbodory mlechner ana_armendariz fodermatt
Last synced: 7 months ago

Dependencies

setup.py pypi
  • importlib >=1.0.4
  • matplotlib >=3.4.2
  • numba >=0.53.1
  • numpy >=1.20.0
  • pandas >=1.3.0
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  • psutil >=5.8.0
  • ray >=1.4.0
  • scikit-learn >=0.24.2
  • scipy >=1.7.0
  • sympy >=1.8
environment.yml conda
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