hBayesDM

This is the Python version of hBayesDM (hierarchical Bayesian modeling of Decision-Making tasks), a user-friendly package that offers hierarchical Bayesian analysis of various computational models on an array of decision-making tasks. hBayesDM in Python uses PyStan (Python interface for Stan) for Bayesian inference.

.. _PyStan: https://github.com/stan-dev/pystan .. _Stan: http://mc-stan.org/

It supports Python 3.5 or higher versions and requires several packages including: NumPy, SciPy, Pandas, PyStan, Matplotlib, and ArviZ.

.. _NumPy: https://www.numpy.org/ .. _SciPy: https://www.scipy.org/ .. _Pandas: https://pandas.pydata.org/ .. _Matplotlib: https://matplotlib.org/ .. _ArviZ: https://arviz-devs.github.io/arviz/

• Documentation: http://hbayesdm.readthedocs.io/

Installation

You can install hBayesDM from PyPI with the following line:

.. code:: bash

pip install hbayesdm # Install using pip

If you want to install from source (by cloning from GitHub):

.. code:: bash

git clone https://github.com/CCS-Lab/hBayesDM.git cd hBayesDM cd Python

python setup.py install # Install from source

Citation

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

.. _this paper: https://www.mitpressjournals.org/doi/full/10.1162/CPSY_a_00002

.. code:: bibtex

@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}, }