Please check the CCS Lab GitHub that contains codes for hierarchical Bayesian and machine learning analyses.

hBayesDM package

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

With the hBayesDM package, any researcher with minimal knowledge in R or Python should be able to do hierarchical Bayesian parameter estimation of computational models with a single line of coding for various decision-making tasks. Check out its tutorial in R, tutorial in Python, and GitHub repository.

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

ADOpy package

ADOpy is a Python implementation of Adaptive Design Optimization (ADO), which is a general-purpose method for conducting adaptive experiments on the fly. ADO can lead to rapid accumulation of information about the phenomenon of interest with the fewest number of trials. The nontrivial technical skills required to use ADO have been a barrier to its wider adoption. To increase its accessibility to experimentalists at large, we introduce an open-source Python package, ADOpy. The development of ADOpy was led by Jaeyeong Yang in collaboration with Profs. Jay Myung and Mark Pitt at Ohio State who are leading experts in ADO. Check out its website and GitHub repository: https://github.com/adopy.

Reference

Yang, J., Pitt, M. A., Ahn, W.-Y., & Myung, J. I. (in press) ADOpy: A Python Package for Adaptive Design Optimization. Behavior Research Methods. https://doi.org/10.31234/osf.io/mdu23.

Machine learning package

We are building a package (both in R and Python) for easily building and evaluating machine learning models including penalized regression, random forest, support vector machine, and neural network models in a single line of coding in R and Python. Check out its GitHub repository.

Reference

Ahn, W.-Y., Hendricks, P. & Haines, N. (2017) Easyml: Easily Build And Evaluate Machine Learning Models. bioRxiv. https://doi.org/10.1101/137240.