hBayesDM (hierarchical Bayesian modeling of Decision-Making tasks) is a user-friendly R package that offers hierarchical Bayesian analysis of various computational models on an array of decision-making tasks. hBayesDM uses Stan for Bayesian inference.

Getting Started


To install hBayesDM, RStan should be properly installed before you proceed. For detailed instructions, please go to this link: https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started


hBayesDM can be installed from CRAN by running the following command in R:

install.packages("hBayesDM")  # Install hBayesDM from CRAN

or you can also install via GitHub with:

Building at once

In default, you should build a Stan file into a binary for the first time to use the model, so it can be quite bothersome. In order to build all the models at once, you should set an environmental variable BUILD_ALL to true. We highly recommend you to use multiple cores for build, since it requires quite a long time to complete.

Sys.setenv(BUILD_ALL='true')  # Build all the models on installation
Sys.setenv(MAKEFLAGS='-j 4')  # Use 4 cores for compilation (or the number you want)

install.packages("hBayesDM")  # Install from CRAN
# or
devtools::install_github("CCS-Lab/hBayesDM")  # Install from GitHub


Before you load hBayesDM, you should load rstan to make sampling properly work.



If you used hBayesDM or some of its codes for your research, please cite this paper:

Ahn, W.-Y., Haines, N., & Zhang, L. (2017). Revealing neuro-computational mechanisms of reinforcement learning and decision-making with the hBayesDM package. Computational Psychiatry, 1, 24-57. https://doi.org/10.1162/CPSY_a_00002.

or for BibTeX: