IRTorch: An item response theory package for python.
Project description
IRTorch
is a Python package for item response theory (IRT). It utilizes PyTorch for model optimization and GPU support.
Check out our documentation page for usage examples and API reference.
Installation
Install from Python Package Index (PyPI)
pip install irtorch
Install from GitHub
pip install git+https://github.com/joakimwallmark/irtorch.git
Citation
Citations hold great value to us since they allow us to explore the various ways the software is being used. Additionally, citations serve as proof of usage, which can assist in securing grant funding.
To cite IRTorch in publications use:
Wallmark, J. (2024). IRTorch: Item response theory with Python (Version X.X) https://github.com/joakimwallmark/irtorch
Or use the following BibTeX entry:
@manual{irtorch,
title = {{IRTorch}: Item response theory with Python},
author = {Wallmark, Joakim},
year = {2024},
note = {Version X.X},
url = {https://github.com/joakimwallmark/irtorch}
}
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file irtorch-0.4.0.tar.gz
.
File metadata
- Download URL: irtorch-0.4.0.tar.gz
- Upload date:
- Size: 443.8 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.0.0 CPython/3.11.5
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 703b06476f055659f48cf48acef6c366c50572aefd886e166f1aa6cbe77118ca |
|
MD5 | eb176fb1795477aa0bd05ddbf676d94e |
|
BLAKE2b-256 | c70fdd5c10dd4e3d3a2361540f5aebd907216f8c2e98fc591e49e00a44465665 |
File details
Details for the file irtorch-0.4.0-py3-none-any.whl
.
File metadata
- Download URL: irtorch-0.4.0-py3-none-any.whl
- Upload date:
- Size: 1.1 MB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.0.0 CPython/3.11.5
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 19d5d8009a55e5537ad0f02485573090ebfdae96c8e34cf905ac14d6a8c7f17f |
|
MD5 | 14bf4220cf96d3e999d88b65037547cb |
|
BLAKE2b-256 | 81ced0ff20b4c8f3a619a452e119563f3ff7050d75560781e0a2b3c350507a64 |