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causalprog

A Python package for causal modelling and inference with stochastic causal programming

https://github.com/ucl/causalprog

Science Score: 52.0%

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

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Academic email domains
  • ✓
    Institutional organization owner
    Organization ucl has institutional domain (www.ucl.ac.uk)
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (13.6%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

A Python package for causal modelling and inference with stochastic causal programming

Basic Info
Statistics
  • Stars: 2
  • Watchers: 1
  • Forks: 0
  • Open Issues: 24
  • Releases: 0
Created about 1 year ago · Last pushed 6 months ago
Metadata Files
Readme License Citation

README.md

causalprog

pre-commit Tests status Linting status Documentation status License

A Python package for causal modelling and inference with stochastic causal programming

This project is developed in collaboration with the Centre for Advanced Research Computing, University College London.

About

Project team

Research software engineering contact

Centre for Advanced Research Computing, University College London (arc.collaborations@ucl.ac.uk)

Getting Started

Prerequisites

causalprog requires Python 3.11–3.13.

Installation

We recommend installing in a project specific virtual environment. To install the latest development version of causalprog using pip in the currently active environment run

sh pip install git+https://github.com/UCL/causalprog.git

Alternatively create a local clone of the repository with

sh git clone https://github.com/UCL/causalprog.git

and then install in editable mode by running

sh pip install -e .

Running tests

Tests can be run across all compatible Python versions in isolated environments using tox by running

sh tox

To run tests manually in a Python environment with pytest installed run

sh pytest tests

again from the root of the repository.

For more information about the testing suite, please see the documentation page.

Building documentation

The MkDocs HTML documentation can be built locally by running

sh tox -e docs

from the root of the repository. The built documentation will be written to site.

Alternatively to build and preview the documentation locally, in a Python environment with the optional docs dependencies installed, run

sh mkdocs serve

Acknowledgements

This work was funded by Engineering and Physical Sciences Research Council (EPSRC).

Owner

  • Name: University College London
  • Login: UCL
  • Kind: organization
  • Email: rc-softdev@ucl.ac.uk

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
type: software
authors:
  - family-names: "Graham"
    given-names: "Matthew M."
    affiliation: "University College London"
    orcid: "https://orcid.org/0000-0001-9104-7960"
  - family-names: "Graham"
    given-names: "William"
    affiliation: "University College London"
    orcid: "https://orcid.org/0000-0003-0058-263X"
  - family-names: "Scroggs"
    given-names: "Matthew W."
    affiliation: "University College London"
    orcid: "https://orcid.org/0000-0002-4658-2443"
  - family-names: "Silva"
    given-names: "Ricardo"
    affiliation: "University College London"
    orcid: "https://orcid.org/0000-0002-6502-9563"
  - family-names: "Yu"
    given-names: "Jialin"
    affiliation: "University College London"
    orcid: "https://orcid.org/0000-0003-1381-2203"
repository-code: "https://github.com/UCL/causalprog"
title: "causalprog: A Python package for causal modelling and inference with stochastic causal programming"
license: "MIT"

GitHub Events

Total
  • Create event: 33
  • Issues event: 52
  • Watch event: 1
  • Delete event: 32
  • Issue comment event: 34
  • Member event: 3
  • Push event: 222
  • Pull request review comment event: 102
  • Pull request review event: 93
  • Pull request event: 69
Last Year
  • Create event: 33
  • Issues event: 52
  • Watch event: 1
  • Delete event: 32
  • Issue comment event: 34
  • Member event: 3
  • Push event: 222
  • Pull request review comment event: 102
  • Pull request review event: 93
  • Pull request event: 69

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 41
  • Total pull requests: 55
  • Average time to close issues: 28 days
  • Average time to close pull requests: 3 days
  • Total issue authors: 2
  • Total pull request authors: 3
  • Average comments per issue: 0.12
  • Average comments per pull request: 0.2
  • Merged pull requests: 42
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 41
  • Pull requests: 55
  • Average time to close issues: 28 days
  • Average time to close pull requests: 3 days
  • Issue authors: 2
  • Pull request authors: 3
  • Average comments per issue: 0.12
  • Average comments per pull request: 0.2
  • Merged pull requests: 42
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • willGraham01 (23)
  • mscroggs (18)
Pull Request Authors
  • willGraham01 (37)
  • mscroggs (16)
  • matt-graham (2)
Top Labels
Issue Labels
enhancement (11) question (3) documentation (1) bug (1)
Pull Request Labels
documentation (2)