Metadata-Version: 2.4
Name: jeam
Version: 0.0.2
Summary: Evidence accumulation models for judgment and continuous-response decisions
Author-email: Amir Hosein Hadian Rasanan <amir.h.hadian@gmail.com>
License-Expression: MIT
Project-URL: Homepage, https://github.com/AmirHoseinHadian/JEAM
Project-URL: Documentation, https://amirhoseinhadian.github.io/JEAM/
Project-URL: Repository, https://github.com/AmirHoseinHadian/JEAM
Project-URL: Issues, https://github.com/AmirHoseinHadian/JEAM/issues
Keywords: evidence accumulation,diffusion models,decision making,cognitive modeling,continuous response
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.24
Requires-Dist: scipy>=1.10
Requires-Dist: numba>=0.58
Requires-Dist: pandas>=2.0
Requires-Dist: mpmath>=1.3
Provides-Extra: test
Requires-Dist: pytest>=8; extra == "test"
Requires-Dist: pytest-cov; extra == "test"
Provides-Extra: docs
Requires-Dist: mkdocs; extra == "docs"
Requires-Dist: mkdocs-material; extra == "docs"
Dynamic: license-file

# JEAM:

JEAM is a Python package for evidence accumulation modeling of continuous judgment tasks. 

The package provides fast and numerically stable likelihood evaluation for multi-dimensional diffusion decision models using the integral equation method proposed by [Hadian Rasanan et al.,(2025)](https://doi.org/10.3758/s13428-025-02810-3). JEAM supports a wide range of continuous response scale, that can be employed in experimental studies including:
- Bounded one-dimensional scales (e.g., arcs or sliders),
- Circular scales (e.g., color wheels),
- Two-dimensional scales (e.g., 2D planes).

JEAM is designed for researchers in cognitive science, mathematical psychology, and neuroscience who work with diffusion models of continuous responses.

---

## Install
### Install via `pip`
The package can be installed via pip:
```bash
pip istall jeam
```
### Install from source
Alternatively, clone or download the source code and install locally:
```bash
python -m setup.py
```

---

## Dependencies
JEAM requires the following Python packages:

- `numpy`
- `scipy`
- `pandas`
- `numba`

All dependencies are installed automatically when using `pip`.

---

### Conda environment (suggested)
If you have Andaconda or miniconda installed and you would like to create a separate environment:

```bash
conda create --n jeam python=3 numpy scipy pandas numba
conda activate jeam
pip install jeam
```

## Documentation

The latest documentation can be found here: **[amirhoseinhadian.github.io/JEAM/](https://amirhoseinhadian.github.io/JEAM/)**

---

## Selected References

For background on diffusion models for continuous response tasks and the estimation methods implemented in JEAM, see:

- Hadian Rasanan, A. H., Evans, N. J., Amani Rad, J., & Rieskamp, J. (2025). Parameter estimation of hyper-spherical diffusion models with a time-dependent threshold: An integral equation method. Behavior research methods, 57(10), 283. https://doi.org/10.3758/s13428-025-02810-3

- Hadian Rasanan, A. H., Olschewski, S., & Rieskamp, J. (2026). The Projected Spherical Diffusion Model: An Evidence Accumulation Theory for Estimation. https://doi.org/10.31234/osf.io/mhj6v_v1

- Smith, P. L. (2016). Diffusion theory of decision making in continuous report. Psychological Review, 123 (4), 425–451, https://doi.org/10.1037/rev0000023

- Smith, P.L., & Corbett, E.A. (2019). Speeded multielement decision-making as diffusion in a hypersphere: Theory and application to double-target detection. Psychonomic Bulletin & Review, 26, https://doi.org/10.3758/s13423-018-1491-0
