Groupyr is a Python library for penalized regression of grouped covariates. This is the groupyr development site. You can view the source code, file new issues, and contribute to groupyr's development. If you just want to learn how to install and use groupyr, please look at the groupyr documentation.

We love contributions! Groupyr is open source, built on open source, and we'd love to have you hang out in our community.

We have developed some guidelines for contributing to groupyr.

If you use groupyr in a scientific publication, please see cite us:

Richie-Halford et al., (2021). Groupyr: Sparse Group Lasso in Python. Journal of Open Source Software, 6(58), 3024, https://doi.org/10.21105/joss.03024

@article{richie-halford-groupyr,
    doi = {10.21105/joss.03024},
    url = {https://doi.org/10.21105/joss.03024},
    year = {2021},
    publisher = {The Open Journal},
    volume = {6},
    number = {58},
    pages = {3024},
    author = {Adam {R}ichie-{H}alford and Manjari Narayan and Noah Simon and Jason Yeatman and Ariel Rokem},
    title = {{G}roupyr: {S}parse {G}roup {L}asso in {P}ython},
    journal = {Journal of Open Source Software}
}

Groupyr development is supported through a grant from the Gordon and Betty Moore Foundation and from the Alfred P. Sloan Foundation to the University of Washington eScience Institute, as well as NIMH BRAIN Initiative grant 1RF1MH121868-01 to Ariel Rokem (University of Washington).

The API design of groupyr was facilitated by the scikit-learn project template and it therefore borrows heavily from scikit-learn. Groupyr relies on the copt optimization library for its solver. The groupyr logo is a flipped silhouette of an image from J. E. Randall and is licensed CC BY-SA.