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 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 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 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.


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.