Bayesian IRT models in Python

This repository includes code for fitting Item Response Theory (IRT) models using variational inference.

At present, the one parameter logistic (1PL) model, aka Rasch model, and two parameter logistic model (2PL) are implemented. The user can specify whether vague or hierarchical priors are used. The three-parameter logistic model is in the pipeline and will be added when available.

py-irt is licensed under the MIT license.

py-irt is now available on PyPi!

1. Install PyTorch.
2. Install Pyro
3. Install py-irt:

pip install py-irt 

If you use this code, please consider citing the following paper:

@inproceedings{lalor2019emnlp,
  author    = {Lalor, John P and Wu, Hao and Yu, Hong},
  title     = {Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds},
  year      = {2019},
  booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing},
}

Implementation is based on the following paper:

@article{natesan2016bayesian,
  title={Bayesian prior choice in IRT estimation using MCMC and variational Bayes},
  author={Natesan, Prathiba and Nandakumar, Ratna and Minka, Tom and Rubright, Jonathan D},
  journal={Frontiers in psychology},
  volume={7},
  pages={1422},
  year={2016},
  publisher={Frontiers}
}

This is research code. Pull requests and issues are welcome!

Let me know if you have any requests, bugs, etc.

Email: [email protected]