Metadata-Version: 2.1
Name: adopy
Version: 0.4.0rc1
Summary: Adaptive Design Optimization on Experimental Tasks
Home-page: https://adopy.org
License: GPL-3.0+
Author: Jaeyeong Yang
Author-email: jaeyeong.yang1125@gmail.com
Requires-Python: >=3.5
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Provides-Extra: docs
Provides-Extra: test
Requires-Dist: codecov; extra == "test"
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: pytest-cov; extra == "test"
Requires-Dist: pytest; extra == "test"
Requires-Dist: recommonmark; extra == "docs"
Requires-Dist: scipy (>=1.0.0)
Requires-Dist: sphinx-autobuild; extra == "docs"
Requires-Dist: sphinx; extra == "docs"
Requires-Dist: sphinx_rtd_theme; extra == "docs"
Requires-Dist: travis-sphinx; extra == "docs"
Project-URL: Documentation, https://adopy.org
Project-URL: Repository, https://github.com/adopy/adopy
Description-Content-Type: text/markdown

# ADOpy <img src="https://adopy.github.io/logo/adopy-logo.svg" align="right" width="300px">

[![PyPI](https://img.shields.io/pypi/v/adopy.svg?color=green)](https://pypi.org/project/adopy/)
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[![Travis CI](https://travis-ci.org/adopy/adopy.svg?branch=master)](https://travis-ci.org/adopy/adopy)
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**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][example-code])

[example-code]: https://github.com/adopy/adopy/tree/master/examples

### Resources

- [**Getting started**](https://adopy.org/getting-started.html)
- [**Documentation**](https://adopy.org)
- [**Bug reports**](https://github.com/adopy/adopy/issues)

## 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.


