adopy 0.4.0rc2

Adaptive Design Optimization on Experimental Tasks

ADOpy

ADOpy is a Python implementation of Adaptive Design Optimization (ADO; Myung, Cavagnaro, & Pitt, 2013), which computes optimal designs dynamically in an experiment. Its modular structure permit easy integration into existing experimentation code.

ADOpy supports Python 3.5 or above and relies on NumPy, SciPy, and Pandas.

Features

• Grid-based computation of optimal designs using only three classes: adopy.Task, adopy.Model, and adopy.Engine.
• Easily customizable for your own tasks and models
• Pre-implemented Task and Model classes including:
  • Psychometric function estimation for 2AFC tasks (adopy.tasks.psi)
  • Delay discounting task (adopy.tasks.ddt)
  • Choice under risk and ambiguity task (adopy.tasks.cra)
• Example code for experiments using PsychoPy (link)

Resources

• Getting started
• Documentation
• Bug reports

Citation

If you use ADOpy, please cite this package along with the specific version. It greatly encourages contributors to continue supporting ADOpy.

Yang, J., Pitt, M. A., Ahn, W., & Myung, J. I. (2019). ADOpy: A Python Package for Adaptive Design Optimization. https://doi.org/10.31234/osf.io/mdu23

References

• Myung, J. I., Cavagnaro, D. R., and Pitt, M. A. (2013). A tutorial on adaptive design optimization. Journal of Mathematical Psychology, 57, 53–67.