********
About us
********

PyIBS is open-source Python software for estimating the log-likelihood of
models from simulated responses using inverse binomial sampling.

Work on the PyIBS package is supported by the Research Council of Finland
(grants 356498 and 358980 to Luigi Acerbi) and its Flagship programme:
`Finnish Center for Artificial Intelligence FCAI <https://fcai.fi/>`__.

The method
**********

Bas van Opheusden, Luigi Acerbi and Wei Ji Ma introduced inverse binomial
sampling for estimating the log-likelihood of models with a simulator
(`van Opheusden, Acerbi and Ma, 2020 <https://doi.org/10.1371/journal.pcbi.1008483>`__),
building on the mid-20th-century theory of inverse sampling. Luigi Acerbi
and Bas van Opheusden wrote ``ibslike.m`` in
:labrepos:`MATLAB IBS <ibs>`, the reference implementation that PyIBS
follows.

Development team
****************

PyIBS is developed by members (past and current) of the
`Machine and Human Intelligence Group <https://www.helsinki.fi/en/researchgroups/machine-and-human-intelligence>`__
at the University of Helsinki and
`ELLIS Institute Finland <https://www.ellisinstitute.fi/>`__, led by
`Luigi Acerbi <https://lacerbi.github.io/>`__. We thank Julia Maria
Perathoner for her work on PyIBS 0.1.0, an earlier Python port of IBS.
Development of PyIBS 1.5 was assisted by
coding agents, including Anthropic's `Claude <https://www.anthropic.com/claude>`__.

Join the team
-------------

We welcome pull requests, bug reports, and feature requests. For code
contributions, follow the
:doc:`instructions for developers and contributors <development>`. For
other opportunities to work with us, visit the
`Machine and Human Intelligence Group <https://www.helsinki.fi/en/researchgroups/machine-and-human-intelligence>`__,
or email the lab's principal investigator at luigi.acerbi@helsinki.fi.