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Releases: embodied-computation-group/Hierarchical-Interoception

Pre-publication Release v0.9.0-beta

05 Sep 06:42
@micahgallen micahgallen
v0.9.0-beta
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Release Notes

v0.9.0-beta (2025-01-XX)

Pre-publication Release

⚠️ Important: This is pre-peer-review research software. The methods and results are under review. Use with appropriate caution for research applications.

What's New

This is the initial release of the Hierarchical-Interoception toolkit, providing comprehensive tools for hierarchical Bayesian modeling of interoceptive psychophysics data.

Features

  • Hierarchical Psychometric Models: Complete Stan implementations for HRDT and RRST data
  • Parameter Recovery Validation: Extensive validation of model parameter recovery
  • Power Analysis Tools: Interactive Shiny app for power analysis exploration
  • Educational Resources: Complete BRMS demo with step-by-step workflow
  • Population Fitting: Tools for deriving normative priors from large datasets
  • Model Comparison: LOO-based model comparison between different psychometric functions

Components

  • Stan Models: Population fitting, power analysis, and parameter recovery models
  • Analysis Scripts: Complete analysis pipeline from data preparation to visualization
  • Shiny App: Interactive power analysis explorer with three main panels
  • BRMS Demo: Educational R Markdown with complete workflow example
  • Raw Data: HRDT and RRST datasets for analysis and validation

Getting Started

  1. Clone the repository
  2. Run source("setup.R") to install dependencies
  3. Follow the BRMS demo for basic usage
  4. Use the Shiny app for power analysis exploration

Dependencies

  • R (>= 4.0.0)
  • Stan/CmdStan via cmdstanr
  • brms, tidyverse, posterior, bayesplot, tidybayes
  • shiny, flextable, here, loo, pracma, furrr

Known Issues

  • Some large RData files may cause slow repository cloning
  • Stan compilation required on first use
  • Path resolution may vary across operating systems

Citation

If you use this software in your research, please cite:

Courtin, A.S., Fischer Ehmsen, J., Banellis, L., Fardo, F., & Allen, M. (2025). 
Hierarchical Bayesian Modelling of Interoceptive Psychophysics. 
bioRxiv. https://doi.org/10.1101/2025.08.27.672360

Roadmap

  • v1.0.0: Planned after peer review acceptance
  • v0.9.1+: Bug fixes and minor improvements before v1.0.0

Support

For questions or issues, please:

  1. Check the README.md for usage instructions
  2. Open an issue on the GitHub repository
  3. Contact the authors directly

This software is released under the MIT License. See LICENSE file for details.

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