Skip to main content

IRTorch: An item response theory package for python.

Project description

tests codecov Documentation Status

IRTorch

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) [Software]. GitHub. https://github.com/joakimwallmark/irtorch

Or use the following BibTeX entry:

@misc{irtorch2024,
  author = {Wallmark, Joakim},
  title = {IRTorch: Item response theory with Python},
  year = {2024},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/joakimwallmark/irtorch}},
  version = {X.X}
}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

irtorch-0.0.1.tar.gz (601.1 kB view details)

Uploaded Source

Built Distribution

irtorch-0.0.1-py3-none-any.whl (591.4 kB view details)

Uploaded Python 3

File details

Details for the file irtorch-0.0.1.tar.gz.

File metadata

  • Download URL: irtorch-0.0.1.tar.gz
  • Upload date:
  • Size: 601.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.11.5

File hashes

Hashes for irtorch-0.0.1.tar.gz
Algorithm Hash digest
SHA256 2282a5eef150904f83db3f7cba3be039688a18a481d9bf99d2b72b6c64e6c2ca
MD5 1f200f3fa60794e25aee0dc392aaf2c0
BLAKE2b-256 879f57f14246bf29187aa592dfcdf8090c4a90ab7cd8c644f0918cd8bab8dd5d

See more details on using hashes here.

File details

Details for the file irtorch-0.0.1-py3-none-any.whl.

File metadata

  • Download URL: irtorch-0.0.1-py3-none-any.whl
  • Upload date:
  • Size: 591.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.11.5

File hashes

Hashes for irtorch-0.0.1-py3-none-any.whl
Algorithm Hash digest
SHA256 fef029867c65ac6118737f0c04a7c6ac6e70affa2ab3c1c7e3430ee2a4447a31
MD5 a66664e5ff586f785cdd1221abd5bcf2
BLAKE2b-256 d8942963dc70d9c43bde5acb91ddb2a8bc9b8ad2873c612479b06aec389f31ac

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page