论文标题
Py-pirt：Python的可扩展项目响应理论库
py-irt: A Scalable Item Response Theory Library for Python
论文作者
论文摘要
Py-sirt是适合贝叶斯项目响应理论（IRT）模型的Python库。 Py-sirt估计受试者和项目的潜在特征，使其适合于IRT任务以及理想点模型。 Py-sirt建在Pyro和Pytorch框架之上，并使用GPU加速训练来扩展到大型数据集。可以在https://github.com/nd-ball/py-irt上找到代码，文档和示例。可以从GitHub页面或Python软件包索引（PYPI）安装Py-tirt。
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).
