$\texttt{py-irt}$: A Scalable Item Response Theory Library for Python

• URL: http://arxiv.org/abs/2203.01282v1
• Date: Wed, 2 Mar 2022 18:09:46 GMT
• Title: $\texttt{py-irt}$: A Scalable Item Response Theory Library for Python
• Authors: John P. Lalor, Pedro Rodriguez
• Abstract summary: $textttpy-irt$ is a Python library for fitting Bayesian Item Response Theory (IRT) models. It estimates latent traits of subjects and items, making it appropriate for use in IRT tasks as well as ideal-point models.
• Score: 3.9828133571463935
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: $\texttt{py-irt}$ is a Python library for fitting Bayesian Item Response Theory (IRT) models. $\texttt{py-irt}$ estimates latent traits of subjects and items, making it appropriate for use in IRT tasks as well as ideal-point models. $\texttt{py-irt}$ 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. $\texttt{py-irt}$ can be installed from the GitHub page or the Python Package Index (PyPI).

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