🔥 This is in progress Python implementation. For stable software see the Matlab delay-discounting-analysis toolbox 🔥
This (will be) a Python based toolbox for the analysis of experimental data from Delayed and Risky Choice (DARC) experiments.
Experimental data could be obtained by any means, but the approach I take in my lab is to run adaptive experiments. And of course, we have a toolbox for that. This is currently implemented in Matlab (see the darc-experiments-matlab repo), which accompanies the paper by Vincent & Rainforth (preprint).
- Implement some/all of the ideas discussed in Vincent (2016). This was originally implemented in Matlab (see the delay-discounting-analysis repo) using JAGS. This implementation will be in Python, using PyMC3 to do the hard work.
- Extend beyond the original scope of the paper (delay discounting tasks) to the more general case of Delayed and Risky Choice (DARC) tasks.
Vincent, B. T. (2016) Hierarchical Bayesian estimation and hypothesis testing for delay discounting tasks, Behavior Research Methods. 48(4), 1608-1620. doi:10.3758/s13428-015-0672-2
Vincent, B. T., & Rainforth, T. (2017, October 20). The DARC Toolbox: automated, flexible, and efficient delayed and risky choice experiments using Bayesian adaptive design. Retrieved from psyarxiv.com/yehjb