Skip to main content

Adaptive Design Optimization on Experimental Tasks

Project description


PyPI Project Status: Active – The project has reached a stable, usable state and is being actively developed. Travis CI CodeCov

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 3.6 or above and relies on NumPy, SciPy, and Pandas.


  • 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)


# Install from PyPI
pip install adopy

# Install from Github (developmental version)
pip install git+



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. (2020). ADOpy: A Python Package for Adaptive Design Optimization. Behavior Research Methods, 1-24.


The research was supported by National Institute of Health Grant R01-MH093838 to Mark A. Pitt and Jay I. Myung, the Basic Science Research Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Science, ICT, & Future Planning (NRF-2018R1C1B3007313 and NRF-2018R1A4A1025891), the Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2019-0-01367, BabyMind), and the Creative-Pioneering Researchers Program through Seoul National University to Woo-Young Ahn.


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

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

adopy-0.4.1.tar.gz (30.8 kB view hashes)

Uploaded source

Built Distribution

adopy-0.4.1-py3-none-any.whl (33.9 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page