PEtab Select: an extension to PEtab for model selection.
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Project description
The PEtab extension for model selection, including the additional file formats and package.
Install
The Python 3 library provides both the Python 3 and command-line (CLI)
interfaces, and can be installed from PyPI, with pip3 install petab-select.
Documentation
Further documentation is available at http://petab-select.readthedocs.io/.
Examples
There are example Jupyter notebooks covering visualization, custom non-SBML
models, and the CLI and Python API, in the doc/examples directory.
The notebooks can be viewed at https://petab-select.readthedocs.io/en/stable/examples.html.
Supported features
PEtab Select offers various methods and criteria for model selection, as well as a variety of visualization options.
Criteria
AIC: Akaike information criterionAICc: Corrected Akaike information criterionBIC: Bayesian information criterion
Methods
forward: Forward selection. Iteratively increase model complexity.backward: Backward selection. Iteratively decrease model complexity.brute_force. Calibrate all models.famos: Flexible and dynamic Algorithm for Model Selection (FAMoS)
Note that the directional methods (forward, backward) find models with the smallest step size (in terms of number of estimated parameters). For example, given the forward method and a predecessor model with 2 estimated parameters, if there are no models with 3 estimated parameters, but some models with 4 estimated parameters, then the search may return candidate models with 4 estimated parameters.
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deploy_release.yml on PEtab-dev/petab_select
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