calibr8 is a Python toolbox for likelihood-based calibration modeling and Bayesian inference. It supports uni- and multivariate problems, continuous & discrete distributions and includes ready to use models for many non-linear problems. It integrates with PyMC for advanced Bayesian inference.
calibr8
This package provides templates and functions for performing likelihood-based calibration modeling. To see implementation examples & excercises, you can go to notebooks/.
calibr8
is released on PyPI:
pip install calibr8
Read the package documentation here.
calibr8
is licensed under the GNU Affero General Public License v3.0.
When using calibr8
in your work, please cite the Helleckes & Osthege et al. (2022) paper and the corresponding software version.
Note that the paper is a shared first co-authorship, which can be indicated by 1 in the bibliography.
@article{calibr8Paper,
doi = {10.1371/journal.pcbi.1009223},
author = {Helleckes$^1$, Laura Marie and
Osthege$^1$, Michael and
Wiechert, Wolfgang and
von Lieres, Eric and
Oldiges, Marco},
journal = {PLOS Computational Biology},
publisher = {Public Library of Science},
title = {Bayesian and calibration, process modeling and uncertainty quantification in biotechnology},
year = {2022},
month = {03},
volume = {18},
url = {https://doi.org/10.1371/journal.pcbi.1009223},
pages = {1-46},
number = {3}
}
@software{calibr8version,
author = {Michael Osthege and
Laura Helleckes},
title = {JuBiotech/calibr8: v6.5.2},
month = mar,
year = 2022,
publisher = {Zenodo},
version = {v6.5.2},
doi = {10.5281/zenodo.4127012},
url = {https://doi.org/10.5281/zenodo.4127012}
}
Head over to Zenodo to generate a BibTeX citation for the latest release.