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.

Quick Links

• 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.
• Python interface for hBayesDM: PyPI, documentation

Getting Started

Prerequisites

hBayesDM 2.0 requires R ≥ 4.4 and uses CmdStan (via the cmdstanr R package) as its Stan backend, replacing rstan in 1.x. CmdStan ships as a system dependency, so models compile on first use rather than at package install time.

# 1. Install cmdstanr (not on CRAN — use the Stan r-universe)
install.packages(
  "cmdstanr",
  repos = c("https://stan-dev.r-universe.dev", getOption("repos"))
)

# 2. Install CmdStan itself (one-time, ~5 min)
cmdstanr::install_cmdstan()

Installation

The latest stable version of hBayesDM can be installed from CRAN:

install.packages("hBayesDM")

or from GitHub:

if (!require(remotes)) install.packages("remotes")
remotes::install_github("CCS-Lab/hBayesDM", subdir = "R")

For the latest development version:

remotes::install_github("CCS-Lab/hBayesDM", ref = "develop", subdir = "R")

First-fit compile cost

Each Stan model compiles on first use (~30 s) and cmdstanr caches the binary for subsequent fits. This replaces the install-time BUILD_ALL precompile that earlier versions used.

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},
}