# psignifit
Python toolbox for Bayesian psychometric function estimation
[](https://github.com/wichmann-lab/python-psignifit/actions/workflows/ci-tests.yml)
[](https://psignifit.readthedocs.io/en/latest/?badge=latest)
[](https://pypi.python.org/pypi/psignifit)
[](https://doi.org/10.5281/zenodo.14750140)
## Getting started
Install *psignifit* with `pip`:
```
pip install psignifit
```
See [the documentation](https://psignifit.readthedocs.io/en/latest/) to get started.
## How to cite
If you use this package, please cite both *this implementation*:
**Zito, T., Künstle, D., Aguilar, G., Berkes, P., & Schwetlick, L. psignifit 4.3 (Version 4.3) [Computer software]. https://doi.org/10.5281/zenodo.14750140**
as well as the *original paper*:
**Schütt, H. H., Harmeling, S., Macke, J. H., & Wichmann, F. A. (2016). Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data. Vision Research, 122, 105–123. [doi:10.1016/j.visres.2016.02.002](https://doi.org/10.1016/j.visres.2016.02.002)**
## Contributors
See the [CONTRIBUTORS](https://github.com/wichmann-lab/python-psignifit/blob/master/CONTRIBUTORS) file
## License and COPYRIGHT
See the [COPYRIGHT](https://github.com/wichmann-lab/python-psignifit/blob/master/COPYRIGHT) file
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"description": "# psignifit\n\nPython toolbox for Bayesian psychometric function estimation\n\n[](https://github.com/wichmann-lab/python-psignifit/actions/workflows/ci-tests.yml)\n[](https://psignifit.readthedocs.io/en/latest/?badge=latest)\n[](https://pypi.python.org/pypi/psignifit)\n[](https://doi.org/10.5281/zenodo.14750140)\n\n## Getting started\n\nInstall *psignifit* with `pip`:\n```\npip install psignifit\n```\n\nSee [the documentation](https://psignifit.readthedocs.io/en/latest/) to get started.\n\n## How to cite\n\nIf you use this package, please cite both *this implementation*:\n\n**Zito, T., K\u00fcnstle, D., Aguilar, G., Berkes, P., & Schwetlick, L. psignifit 4.3 (Version 4.3) [Computer software]. https://doi.org/10.5281/zenodo.14750140**\n\nas well as the *original paper*:\n\n\n**Sch\u00fctt, H. H., Harmeling, S., Macke, J. H., & Wichmann, F. A. (2016). Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data. Vision Research, 122, 105\u2013123. [doi:10.1016/j.visres.2016.02.002](https://doi.org/10.1016/j.visres.2016.02.002)**\n\n\n## Contributors\n\nSee the [CONTRIBUTORS](https://github.com/wichmann-lab/python-psignifit/blob/master/CONTRIBUTORS) file\n\n## License and COPYRIGHT\n\nSee the [COPYRIGHT](https://github.com/wichmann-lab/python-psignifit/blob/master/COPYRIGHT) file\n",
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