jeam 0.1.0

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). 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:

pip install jeam

Install from source

Alternatively, the latest version of the package can be installed directly from Gihub repository:

pip install git+https://github.com/AmirHoseinHadian/JEAM.git

Dependencies

JEAM requires the following Python packages:

• numpy
• scipy
• pandas
• numba
• mpmath

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:

conda create --name jeam_env python=3
conda activate jeam_env
pip install jeam

Documentation

The latest documentation can be found here: amirhoseinhadian.github.io/JEAM/

Credits

This package was developed by me, Amir Hosein Hadian Rasanan, with support from Dr. Nathan J Evans and Prof. Dr. Jörg Rieskamp.

When using this package or part of the code for your own research, we ask you to cite us:

Hadian Rasanan, A. H., Evans, N. J., and Rieskamp, J. (2026). JEAM: A Framework and Tutorial for Applying Evidence Accumulation Models to Continuous Judgments

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