py-irt: A Scalable Item Response Theory Library for Python

py-irt is a Python library for fitting Bayesian Item Response Theory (IRT) models. py-irt estimates latent traits of subjects and items, making it appropriate for use in IRT tasks as well as ideal-point models. py-irt is built on top of the Pyro and PyTorch frameworks and uses GPU-accelerated traini...

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Bibliographic Details
Published in:	arXiv.org
Main Authors:	Lalor, John P, Rodriguez, Pedro
Format:	Paper
Language:	English
Published:	Ithaca Cornell University Library, arXiv.org 13.03.2022
Subjects:	Libraries Python
ISSN:	2331-8422
Online Access:	Get full text
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Description
Summary:	py-irt is a Python library for fitting Bayesian Item Response Theory (IRT) models. py-irt estimates latent traits of subjects and items, making it appropriate for use in IRT tasks as well as ideal-point models. py-irt is built on top of the Pyro and PyTorch frameworks and uses GPU-accelerated training to scale to large data sets. Code, documentation, and examples can be found at https://github.com/nd-ball/py-irt. py-irt can be installed from the GitHub page or the Python Package Index (PyPI).
Bibliography:	SourceType-Working Papers-1 ObjectType-Working Paper/Pre-Print-1 content type line 50
ISSN:	2331-8422
DOI:	10.48550/arxiv.2203.01282