hBayesDM (hierarchical Bayesian modeling of Decision-Making tasks) is a user-friendly package that offers hierarchical Bayesian analysis of various computational models on an array of decision-making tasks. hBayesDM uses Stan for Bayesian inference.
Now, hBayesDM supports both R and Python!
- Tutorial: https://ccs-lab.github.io/hBayesDM/articles/getting_started.html (R) & https://hbayesdm.readthedocs.io (Python)
- Mailing list: https://groups.google.com/forum/#!forum/hbayesdm-users
- Bug reports: https://github.com/CCS-Lab/hBayesDM/issues
- Contributing: See the Wiki of this repository.
If you used hBayesDM or some of its codes for your research, please cite this paper:
@article{hBayesDM,
title = {Revealing Neurocomputational Mechanisms of Reinforcement Learning and Decision-Making With the {hBayesDM} Package},
author = {Ahn, Woo-Young and Haines, Nathaniel and Zhang, Lei},
journal = {Computational Psychiatry},
year = {2017},
volume = {1},
pages = {24--57},
publisher = {MIT Press},
url = {doi:10.1162/CPSY_a_00002},
}We thank HuaFeng Lu who designed and donated the logo for the hBayesDM package.
The research was supported by the Institute for Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2019-0-01367, BabyMind), the Basic Science Research Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Science, ICT, and Future Planning (NRF-2018R1C1B3007313 and NRF-2018R1A4A1025891), and the Creative-Pioneering Researchers Program through Seoul National University to Woo-Young Ahn.