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

Author

Listed:
  • 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)

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.

Suggested Citation

  • John Patrick Lalor & Pedro Rodriguez, 2023. "py-irt : A Scalable Item Response Theory Library for Python," INFORMS Journal on Computing, INFORMS, vol. 35(1), pages 5-13, January.
    • Handle: RePEc:inm:orijoc:v:35:y:2023:i:1:p:5-13
    DOI: 10.1287/ijoc.2022.1250
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    References listed on IDEAS

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    6. Christopher J. Urban & Daniel J. Bauer, 2021. "A Deep Learning Algorithm for High-Dimensional Exploratory Item Factor Analysis," Psychometrika, Springer;The Psychometric Society, vol. 86(1), pages 1-29, March.
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