icomo

v1.0.1

This toolbox aims to simplify the construction of compartmental models and the inference of their parameters For more information about how to use this package see README

Latest version published 27 days ago

License: MIT

PyPI

GitHub

Package Health Score

63 / 100

security
No known security issues
popularity
Limited
maintenance
Sustainable
community
Limited

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Popularity

Limited

GitHub Stars: 7
Forks: 1
Contributors: 1

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package icomo, we found that it has been starred 7 times.

Security

No known security issues

Security and license risk for latest version

Release Date: Nov 27, 2024
Direct Vulnerabilities: 0
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Indirect Vulnerabilities: 0
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License Risk: 0
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All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

MIT

Security Policy

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Maintenance

Sustainable

Commit Frequency

Open Issues: 0
Open PR: 0
Last Release: 27 days ago
Last Commit: 27 days ago

Further analysis of the maintenance status of icomo based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Sustainable.

We found that icomo demonstrates a positive version release cadence with at least one new version released in the past 3 months.

In the past month we didn't find any pull request activity or change in issues status has been detected for the GitHub repository.

Community

Limited

Readme: Yes
Contributing.md: Yes
Code of Conduct: No
Contributors: 1
Funding: No

This project has seen only 10 or less contributors.

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Package

Python Versions Compatibility: <3.13,>=3.10

Age: 1 year
Latest Release: 27 days ago
Dependencies: 6 Direct
Versions: 9
Maintainers: 0
Wheels: OS Independent

Readme

Inference of Compartmental Models toolbox

Leverage the power of JAX libraries for PyMC models

This toolbox aims to simplify the construction of compartmental models and the inference of their parameters.

The aim isn't to provide a complete package that will build models from A to Z, but rather provide different helper functions examples and guidelines to help leverage modern python packages like JAX, Diffrax and PyMC to build, automatically differentiate and fit compartmental models.

A central part of the toolbox is the possibility to wrap JAX functions to be used in PyMC models (see [here](https://icomo.readthedocs. io/en/stable/api/jax2pytensor.html)), which is used tro wrap the Diffrax ODE solvers, but might be also useful for other projects.

Documentation: https://icomo.readthedocs.io.

Features

Facilitate the construction of compartmental models by only defining flow between compartments, and automatically generating the corresponding ODEs.
Plot the graph of the compartmental model to verify the correctness of the model.
Integrate the ODEs using diffrax, automatically generating the Jacobian of the parameters of the ODE
Fit the parameters using minimization algorithms or build a Bayesian model using PyMC.

Credits

Logo by Fabian Mikulasch

icomo dependencies

diffrax equinox graphviz jax numpy pytensor

FAQs

What is icomo?

This toolbox aims to simplify the construction of compartmental models and the inference of their parameters. Visit Snyk Advisor to see a full health score report for icomo, including popularity, security, maintenance & community analysis.

Is icomo popular?

The python package icomo receives a total of 97 weekly downloads. As such, icomo popularity was classified as limited. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is icomo well maintained?

We found indications that icomo maintenance is sustainable demonstrating some project activity. We saw a total of 1 open source contributors collaborating on the project. See the full package health analysis to learn more about the package maintenance status.

Is icomo safe to use?

The python package icomo was scanned for known vulnerabilities and missing license, and no issues were found. Thus the package was deemed as safe to use. See the full health analysis review.

Last updated on 24 December-2024, at 16:37 (UTC).

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