hBayesDM

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!

Quick Links

• 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.

Citation

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},
}

Logo

We thank HuaFeng Lu who designed and donated the logo for the hBayesDM package.