cpm: A python library for theory-driven modelling in computational psychiatry

Lenard Dome, Frank H Hezemans, Kenza Kadri, Ben J Wagner, Andrew Webb and Tobias U Hauser

PLOS Computational Biology, 2026, vol. 22, issue 7, 1-31

Abstract: The Computational Psychiatry Modelling (cpm) toolbox is a Python library for theory-driven modelling in computational psychiatry and cognitive (neuro-)science. cpm integrates a wide range of established approaches into a single framework. It is designed to be accessible to both expert and non-expert modellers in order to conduct cutting-edge computational modelling, while adhering to best practices. The toolbox provides a flexible, modular architecture that adjusts to different needs. It covers a wide range of problems (such as risky decision-making, reward/punishment learning, perceptual metacognition), models (including those based on associative, reinforcement learning, and signal detection theories), and methods (such as hierarchical parameter estimation using empirical and variational Bayesian techniques). Such a customisable toolbox aims to lower the barrier for beginners and to facilitate access to advanced modelling approaches in psychiatry and beyond.Author summary: Computational psychiatry is a field that makes extensive use of computational methods to understand mental health. In this field and related disciplines, there is a strong need to design, implement, and apply mathematical models of behaviour and cognition (such as reinforcement learning or Bayesian models). However, coding these models from scratch poses a significant challenge for researchers without extensive computational training, including clinicians and other stakeholders who are interested in understanding the underlying principles of mental disorders. Here, we describe the cpm toolbox, providing a step-by-step walkthrough of the current design, alongside links to related resources. We believe that building such an overarching, customisable, and user-friendly toolbox can facilitate access to computational modelling, and thus jump-start computational approaches in the field and beyond.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1014481

DOI: 10.1371/journal.pcbi.1014481

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