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

John Patrick Lalor () and Pedro Rodriguez ()
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John Patrick Lalor: IT, Analytics, and Operations, University of Notre Dame, Notre Dame, Indiana 46556
Pedro Rodriguez: Computer Science, University of Maryland, College Park, Maryland 20742

INFORMS Journal on Computing, 2023, vol. 35, issue 1, 5-13

Abstract: py-irt is a Python library for fitting Bayesian item response theory (IRT) models. At present, there is no Python package for fitting large-scale IRT models. py-irt estimates latent traits of subjects and items, making it appropriate for use in IRT tasks as well as in 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. It is the first Python package for large-scale IRT model fitting. py-irt is easy to use for practitioners and also allows for researchers to build and fit custom IRT models. py-irt is available as open-source software and can be installed from GitHub or the Python Package Index.

Keywords: item response theory; approximate Bayesian inference; open-source software; deep learning (search for similar items in EconPapers)
Date: 2023
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