Bayesian Causal Inference Python Toolbox
BCI Toolbox is a statistical and analytical tool in python, developed by Haocheng Zhu, Ulrik Beierholm and Ladan Shams, which can help researchers perform quantitative modeling and analysis for behavioral data based on Bayesian causal inference model. The present BCI toolbox offers a robust platform for BCI model implementation, facilitating its widespread use and enabling researchers to delve into the data to uncover underlying cognitive mechanisms.
Key functions:
- One-click simulation for human multisensory perception
- Simply model fitting for multiple types of behavioral data
- Easily generate RDM analysis
More details, please check our documentation and paper.
Please cite:
Zhu, H., Beierholm, U., & Shams, L. (2024). BCI Toolbox: An open-source python package for the Bayesian causal inference model. PLoS Computational Biology, 20(7), e1011791.
If you have any inquiries or feedback, please don’t hesitate to contact Haocheng Zhu, Ulrik Beierholm and Ladan Shams. We will get back to you as soon as possible.