Metadata-Version: 2.1
Name: pyibl
Version: 5.1.3
Summary: A Python implementation of a subset of Instance Based Learning Theory
Home-page: http://pyibl.ddmlab.com/
Author: Dynamic Decision Making Laboratory of Carnegie Mellon University
Author-email: dfm2@cmu.edu
License: Free for research purposes
Platform: any
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE.txt

PyIBL is a Python implementation of a subset of Instance Based Learning Theory
(IBLT) (Cleotilde Gonzalez, Javier F. Lerch and Christian Lebiere (2003),
Instance-based learning in dynamic decision making, Cognitive Science, 27,
591-635. DOI: 10.1016/S0364-0213(03)00031-4). It is made and distributed by
the Dynamic Decision Making Laboratory of Carnegie Mellon University for
making computational cognitive models supporting research in how people make
decisions in dynamic environments.

PyIBL requires Python version 3.8 or later. PyIBL also works in recent
versions of PyPy.

The latest released version of PyIBL may be installed from PyPi with pip:

    pip install pyibl


For further information, including the documentation see the
[online documentation](http://pyibl.ddmlab.com).

PyIBL is copyright 2014-2024 by Carnegie Mellon University. It may be
freely used, and modified, but only for research purposes.


