py-irt: A Scalable Item Response Theory Library for Python
This provides a scalable tool for researchers and practitioners in psychometrics and social sciences, but it is incremental as it builds on existing frameworks like Pyro and PyTorch.
The authors developed py-irt, a Python library for fitting Bayesian Item Response Theory models, which estimates latent traits of subjects and items and scales to large datasets using GPU-accelerated training.
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).