py-irt: A Scalable Item Response Theory Library for Python
Computation and Language
2022-11-16 v2
Abstract
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).
Keywords
Cite
@article{arxiv.2203.01282,
title = {py-irt: A Scalable Item Response Theory Library for Python},
author = {John P. Lalor and Pedro Rodriguez},
journal= {arXiv preprint arXiv:2203.01282},
year = {2022}
}