Metadata-Version: 2.3
Name: HSSM
Version: 0.5.0
Summary: Bayesian inference for hierarchical sequential sampling models.
Keywords: HSSM,sequential sampling models,bayesian,bayes,mcmc
Author: Alexander Fengler, Paul Xu, Carlos Paniagua, Krishn Bera, Aisulu Omar, Michael Frank
Author-email: Alexander Fengler <alexander_fengler@brown.edu>, Paul Xu <yang_xu@brown.edu>, Carlos Paniagua <carlos_paniagua@brown.edu>, Krishn Bera <krishn_bera@brown.edu>, Aisulu Omar <aisulu_omar@brown.edu>, Michael Frank <michael_frank@brown.edu>
License: Copyright 2023, Brown University, Providence, RI.
         
                                 All Rights Reserved
         
         Permission to use, copy, modify, and distribute this software and
         its documentation for any purpose other than its incorporation into a
         commercial product or service is hereby granted without fee, provided
         that the above copyright notice appear in all copies and that both
         that copyright notice and this permission notice appear in supporting
         documentation, and that the name of Brown University not be used in
         advertising or publicity pertaining to distribution of the software
         without specific, written prior permission.
         
         BROWN UNIVERSITY DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE,
         INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR ANY
         PARTICULAR PURPOSE.  IN NO EVENT SHALL BROWN UNIVERSITY BE LIABLE FOR
         ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
         WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
         ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF
         OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Requires-Dist: absl-py>=2.5.0
Requires-Dist: bambi>=0.19.0
Requires-Dist: h5netcdf>=1.8.1
Requires-Dist: h5py>=3.16.0
Requires-Dist: hddm-wfpt>=0.1.6
Requires-Dist: huggingface-hub>=1.17.0
Requires-Dist: jaxonnxruntime>=0.3.0
Requires-Dist: numpy>=2.4.0,<2.5.0
Requires-Dist: numpyro>=0.20
Requires-Dist: onnx>=1.16.0
Requires-Dist: scipy>=1.10
Requires-Dist: ssm-simulators>=0.14.0
Requires-Dist: jax[cuda12]>=0.7.0 ; extra == 'cuda12'
Requires-Dist: jax[cuda13]>=0.7.0 ; extra == 'cuda13'
Requires-Python: >=3.12, <3.15
Project-URL: homepage, https://lnccbrown.github.io/HSSM/
Project-URL: documentation, https://lnccbrown.github.io/HSSM/
Project-URL: repository, https://github.com/lnccbrown/HSSM
Provides-Extra: cuda12
Provides-Extra: cuda13
Description-Content-Type: text/markdown

<img src="docs/images/mainlogo.png" alt="HSSM logo" width="175">

# HSSM — Hierarchical Sequential Sampling Modeling

[![Paper DOI](https://img.shields.io/badge/paper-10.64898%2F2026.06.05.730398-blue)](https://doi.org/10.64898/2026.06.05.730398)
[![PyPI](https://img.shields.io/pypi/v/hssm)](https://pypi.org/project/hssm/)
[![Run tests](https://github.com/lnccbrown/HSSM/actions/workflows/run_tests.yml/badge.svg)](https://github.com/lnccbrown/HSSM/actions/workflows/run_tests.yml)
[![codecov](https://codecov.io/gh/lnccbrown/HSSM/branch/main/graph/badge.svg)](https://codecov.io/gh/lnccbrown/HSSM)

HSSM is a Python toolbox for hierarchical Bayesian modeling of choice and
response-time data with sequential sampling models. It supports trial-wise and
hierarchical regression, reinforcement-learning models, posterior diagnostics,
model comparison, and custom likelihoods through a high-level PyMC and Bambi
interface. HSSM is a
[BRAINSTORM](https://ccbs.carney.brown.edu/brainstorm) project at Brown
University.

## Install

Use Python 3.12, 3.13, or 3.14 in a fresh environment:

```bash
pip install hssm
```

The [installation guide](https://lnccbrown.github.io/HSSM/getting_started/installation/)
covers uv, CUDA extras, Colab, development installs, and troubleshooting.

## Start with the documentation

The [HSSM documentation](https://lnccbrown.github.io/HSSM/) is the canonical
source for durable guidance. Begin with the
[quickstart](https://lnccbrown.github.io/HSSM/getting_started/getting_started/),
then follow the
[main tutorial](https://lnccbrown.github.io/HSSM/tutorials/main_tutorial/).
The [ecosystem map](https://lnccbrown.github.io/HSSM/ecosystem/) explains when
work belongs in HSSM or one of its sibling projects.

## Contributing and support

- Read the [contribution guide](docs/CONTRIBUTING.md) and
  [local development setup](docs/local_development.md).
- Ask modeling questions in
  [GitHub Discussions](https://github.com/lnccbrown/HSSM/discussions).
- Report bugs and request features through
  [GitHub Issues](https://github.com/lnccbrown/HSSM/issues).

## Citation

Please cite Fengler et al., *HSSM: A Widely Applicable Toolbox for Hierarchical
Bayesian Neurocognitive Modeling* ([paper DOI](https://doi.org/10.64898/2026.06.05.730398)).
For version-specific software citation, use the
[Zenodo archive](https://doi.org/10.5281/zenodo.17247695).

## License

HSSM carries the Brown University license in [LICENSE](LICENSE). Copyright
2023 Brown University. All Rights Reserved.
