ArticlePLoS computational biology2026
cpm: A python library for theory-driven modelling in computational psychiatry.
Article in PLoS computational biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Drops in confidence precede symptoms of obsessive-compulsive disorder.Nature communications · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.