adopy 0.4.1

Description:

adopy 0.4.1

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 3.6 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)

Installation
# Install from PyPI
pip install adopy

# Install from Github (developmental version)
pip install git+https://github.com/adopy/adopy.git@develop

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. (2020).
ADOpy: A Python Package for Adaptive Design Optimization.
Behavior Research Methods, 1-24.
https://doi.org/10.3758/s13428-020-01386-4

Acknowledgement
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.
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.