carl
carl: a likelihood-free inference toolbox - Published in JOSS (2016)
Science Score: 95.0%
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 9 DOI reference(s) in README and JOSS metadata -
✓Academic publication links
Links to: arxiv.org, joss.theoj.org, zenodo.org -
✓Committers with academic emails
1 of 6 committers (16.7%) from academic institutions -
○Institutional organization owner
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✓JOSS paper metadata
Published in Journal of Open Source Software
Scientific Fields
Repository
Likelihood-free inference toolbox.
Basic Info
Statistics
- Stars: 56
- Watchers: 13
- Forks: 22
- Open Issues: 17
- Releases: 2
Metadata Files
README.md
carl
carl is a toolbox for likelihood-free inference in Python.
The likelihood function is the central object that summarizes the information from an experiment needed for inference of model parameters. It is key to many areas of science that report the results of classical hypothesis tests or confidence intervals using the (generalized or profile) likelihood ratio as a test statistic. At the same time, with the advance of computing technology, it has become increasingly common that a simulator (or generative model) is used to describe complex processes that tie parameters of an underlying theory and measurement apparatus to high-dimensional observations. However, directly evaluating the likelihood function in these cases is often impossible or is computationally impractical.
In this context, the goal of this package is to provide tools for the likelihood-free setup, including likelihood (or density) ratio estimation algorithms, along with helpers to carry out inference on top of these. It currently supports:
- Composition and fitting of distributions;
- Likelihood-free inference from classifiers;
- Parameterized supervised learning;
- Calibration tools.
This project is still in its early stage of development. Join us if you feel like contributing!
Documentation
Extensive details regarding likelihood-free inference with calibrated classifiers can be found in the companion paper "Approximating Likelihood Ratios with Calibrated Discriminative Classifiers", Kyle Cranmer, Juan Pavez, Gilles Louppe. http://arxiv.org/abs/1506.02169
Installation
The following dependencies are required:
- Numpy >= 1.11
- Scipy >= 0.17
- Scikit-Learn >= 0.18-dev
- Theano >= 0.8
Once satisfied, carl can be installed from source using the following
commands:
git clone https://github.com/diana-hep/carl.git
cd carl
python setup.py install
See CONTRIBUTING.md for setup instructions to start
developing and contributing to carl.
Citation
@misc{carl,
author = {Gilles Louppe and Kyle Cranmer and Juan Pavez},
title = {carl: a likelihood-free inference toolbox},
month = mar,
year = 2016,
doi = {10.5281/zenodo.47798},
url = {http://dx.doi.org/10.5281/zenodo.47798}
}
Owner
- Name: diana-hep
- Login: diana-hep
- Kind: organization
- Repositories: 9
- Profile: https://github.com/diana-hep
JOSS Publication
carl: a likelihood-free inference toolbox
Authors
New York University
New York University
Federico Santa María University
Editor
Arfon Smith
Tags
likehood-free inference density ratio estimationGitHub Events
Total
Last Year
Committers
Last synced: 7 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Gilles Louppe | g****e@g****m | 176 |
| Kyle Cranmer | k****r@n****u | 13 |
| Juan Pavez | j****s@a****l | 5 |
| Juan Pavez | j****z@M****l | 4 |
| Tim Head | b****m@g****m | 2 |
| Arfon Smith | a****n | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 37
- Total pull requests: 25
- Average time to close issues: 28 days
- Average time to close pull requests: 6 days
- Total issue authors: 6
- Total pull request authors: 5
- Average comments per issue: 2.43
- Average comments per pull request: 1.12
- Merged pull requests: 21
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- glouppe (19)
- cranmer (8)
- ibab (7)
- kreczko (1)
- gnperdue (1)
- jgpavez (1)
Pull Request Authors
- glouppe (12)
- cranmer (8)
- jgpavez (3)
- arfon (1)
- betatim (1)
Top Labels
Issue Labels
Pull Request Labels
Dependencies
- astropy >=1.3
- numpy >=1.11
- scikit-learn >=0.18
- scipy >=0.17
- six *
- theano >=0.8