QMCPy: A Python Framework for (Quasi-)Monte Carlo Algorithms
QMCPy: A Python Framework for (Quasi-)Monte Carlo Algorithms - Published in JOSS (2026)
Science Score: 89.0%
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○CITATION.cff file
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○codemeta.json file
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○.zenodo.json file
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✓DOI references
Found 4 DOI reference(s) in README and JOSS metadata -
✓Academic publication links
Links to: zenodo.org -
✓Committers with academic emails
2 of 31 committers (6.5%) from academic institutions -
○Institutional organization owner
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✓JOSS paper metadata
Published in Journal of Open Source Software
Keywords
Keywords from Contributors
Repository
Quasi-Monte Carlo point generators, automatic transformations, and adaptive stopping criteria
Basic Info
- Host: GitHub
- Owner: QMCSoftware
- License: apache-2.0
- Language: Python
- Default Branch: master
- Homepage: https://qmcsoftware.github.io/QMCSoftware/
- Size: 764 MB
Statistics
- Stars: 77
- Watchers: 8
- Forks: 19
- Open Issues: 42
- Releases: 14
Topics
Metadata Files
README.md
QMCPy: Quasi-Monte Carlo Community Software in Python
Quasi-Monte Carlo (QMC) methods are used to approximate multivariate integrals. They have four main components: a discrete distribution, a true measure of randomness, an integrand, and a stopping criterion. Information about the integrand is obtained as a sequence of values of the function sampled at the data-sites of the discrete distribution. The stopping criterion tells the algorithm when the user-specified error tolerance has been satisfied. We are developing a framework that allows collaborators in the QMC community to develop plug-and-play modules in an effort to produce more efficient and portable QMC software. Each of the above four components is an abstract class. Abstract classes specify the common properties and methods of all subclasses. The ways in which the four kinds of classes interact with each other are also specified. Subclasses then flesh out different integrands, sampling schemes, and stopping criteria. Besides providing developers a way to link their new ideas with those implemented by the rest of the QMC community, we also aim to provide practitioners with state-of-the-art QMC software for their applications.
Resources
The QMCPy documentation contains a detailed package reference documenting functions and classes including thorough doctests. A number of example notebook demos are also rendered into the documentation from QMCSoftware/demos/. We recommend the following resources to start learning more about QMCPy
- mathematical description of QMCPy software and components.
- Aleksei Sorokin's 2023 PyData Chicago video tutorial and corresponding notebook
- Fred Hickernell's 2020 MCQMC video tutorial and corresponding notebook
- The QMCPy introduction notebook and quickstart notebook
Installation
bash
pip install qmcpy
To install from source, please see the contributing guidelines.
Citation
If you find QMCPy helpful in your work, please support us by citing the following work, which is also available as a QMCPy BibTex citation
~~~ Sou-Cheng T. Choi, Fred J. Hickernell, Michael McCourt, Jagadeeswaran Rathinavel, Aleksei G. Sorokin, QMCPy: A Quasi-Monte Carlo Python Library. 2026. https://qmcsoftware.github.io/QMCSoftware/ ~~~
We maintain a list of publications on the development and use of QMCPy as well as a list of select references upon which QMCPy was built.
Development
Want to contribute to QMCPy? Please see our guidelines for contributors which includes instructions on installation for developers, running tests, and compiling documentation.
This software would not be possible without the efforts of the QMCPy community including our steering council, collaborators, contributors, and sponsors.
QMCPy is distributed under an Apache 2.0 license from the Illinois Institute of Technology.
Owner
- Name: QMCSoftware
- Login: QMCSoftware
- Kind: organization
- Repositories: 2
- Profile: https://github.com/QMCSoftware
JOSS Publication
QMCPy: A Python Framework for (Quasi-)Monte Carlo Algorithms
Authors
Illinois Institute of Technology, USA, University of Chicago, USA
Illinois Institute of Technology, USA
Illinois Institute of Technology, USA, SouLab LLC, USA
Torc Robotics, USA
Sandia National Laboratories, USA
University of California San Diego, USA
Editor
Owen Lockwood
Tags
(quasi-)Monte Carlo numerical integration randomized low-discrepancy sequences automatic error estimation object oriented Python frameworkCommitters
Last synced: about 1 month ago
Top Committers
| Name | Commits | |
|---|---|---|
| alegresor | a****3@g****m | 1,243 |
| Sou-Cheng Choi | s****2@i****u | 383 |
| sou-cheng-choi | s****i@s****g | 112 |
| Fred J Hickernell | h****l@i****u | 102 |
| Sou-Cheng Choi | t****a@1****8 | 80 |
| aaditj1962161 | a****n@g****m | 53 |
| Jagadeeswaran Rathinavel | j****r@g****m | 52 |
| Joshua Herman | 3****g | 18 |
| thegman108 | 6****8 | 16 |
| dependabot[bot] | 4****] | 16 |
| Sealybla | l****8@g****m | 15 |
| Joey Coco | b****7@g****m | 14 |
| galois777 | g****7@g****m | 13 |
| jag.rathinavel | j****l@w****m | 12 |
| PieterjanRobbe | p****e@c****e | 11 |
| Ally Pascual Kwan | a****l@g****m | 11 |
| galois777 | 9****7 | 7 |
| Abdo Haji-Ali | a****i@g****m | 6 |
| Sou Cheng | s****i@k****m | 4 |
| Fred Hickernell | f****T@g****m | 4 |
| Pieterjan Robbe | p****e@k****e | 3 |
| Aleksei Sorokin | a****r@A****l | 2 |
| Jags Rathinavel | j****l@w****m | 2 |
| Lynn Matar | 5****r | 2 |
| Larysa Matiukha | 1****m | 2 |
| Fred J Hickernell | f****l@g****m | 2 |
| Copilot | 1****t | 2 |
| Nathan Kirk | n****k@l****m | 1 |
| Jag Rathinavel | j****l@w****m | 1 |
| Jungtaek Kim | j****m@p****r | 1 |
| and 1 more... | ||
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: about 1 month ago
All Time
- Total issues: 61
- Total pull requests: 154
- Average time to close issues: over 1 year
- Average time to close pull requests: 23 days
- Total issue authors: 16
- Total pull request authors: 17
- Average comments per issue: 1.26
- Average comments per pull request: 0.76
- Merged pull requests: 104
- Bot issues: 0
- Bot pull requests: 18
Past Year
- Issues: 9
- Pull requests: 40
- Average time to close issues: 1 day
- Average time to close pull requests: 3 days
- Issue authors: 7
- Pull request authors: 9
- Average comments per issue: 1.56
- Average comments per pull request: 1.25
- Merged pull requests: 21
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- alegresor (31)
- zitterbewegung (6)
- fjhickernell (4)
- sou-cheng-choi (4)
- mblaszkiewicz (3)
- lmatar (3)
- NielsRogge (1)
- ahadzic7 (1)
- dmetivie (1)
- keithbriggs (1)
- IanFla (1)
- aaditj1962161 (1)
- yding2 (1)
- jungtaekkim (1)
- ANaumann85 (1)
Pull Request Authors
- alegresor (53)
- sou-cheng-choi (30)
- dependabot[bot] (18)
- zitterbewegung (15)
- jagadeesr (8)
- CDHJ2000 (6)
- aaditj1962161 (6)
- fjhickernell (5)
- rvare (2)
- JimmyNguyenUCI (2)
- ggraoigr (2)
- nmkirk (2)
- thegman108 (1)
- Baronlegend27 (1)
- galois777 (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 823 last-month
- Total dependent packages: 3
- Total dependent repositories: 2
- Total versions: 34
- Total maintainers: 1
pypi.org: qmcpy
Quasi-Monte Carlo point generators, automatic transformations, and adaptive stopping criteria
- Documentation: https://qmcsoftware.github.io/QMCSoftware/
- License: other
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Latest release: 1.6.2
published 12 months ago
Rankings
Maintainers (1)
Dependencies
- actions/checkout v2 composite
- conda-incubator/setup-miniconda v2 composite
- python 3.7.0 build
- coverage ==6.4.1
- jupyter ==1.0.0
- matplotlib ==3.5.2
- numpy ==1.23.4
- pandas ==1.4.3
- pylint ==2.14.4
- pytest ==7.1.2
- recommonmark ==0.7.1
- scikit-learn *
- scipy ==1.9.3
- sphinx-markdown-tables ==0.0.17
- sphinx-math-dollar ==1.2.1
- sphinx-rtd-theme ==1.0.0
- umbridge ==1.2.1
- numpy *
- scipy *
- github-pages >= 0 development