jeam 0.1.0

Evidence accumulation models of continuous judgments

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.

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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

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Dependencies

JEAM requires the following Python packages:

• numpy
• scipy
• pandas
• numba
• mpmath

All dependencies are installed automatically when using pip.

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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

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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