Metadata-Version: 2.4
Name: twoblock
Version: 0.2.0
Summary: A Scikit-Learn Compatible Library for Simultaneous Two-Block Sufficient Dimension Reduction Methods
Home-page: https://github.com/SvenSerneels/twoblock
Author: Sven Serneels
Author-email: svenserneels@gmail.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.22.0
Requires-Dist: scipy>=1.8.0
Requires-Dist: scikit-learn>=1.3.0
Requires-Dist: pandas>=1.4.0
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license-file
Dynamic: requires-dist
Dynamic: summary

# twoblock
Two-block dense and sparse simultaneous dimension reduction 

The dense version is a `scikit-learn` compatible implementation of simultaneous two-block dimension reduction, as proposed in [1]. 

The sparse version is is a `scikit-learn` compatible implementation of sparse twoblock dimension reduction, recently published by the author [2]. 
 

References
----------
[1] Cook, R. Dennis, Liliana Forzani, and Lan Liu.
    ["Partial least squares for simultaneous reduction of response and predictor
    vectors in regression."](https://doi.org/10.1016/j.jmva.2023.105163) Journal 
    of Multivariate Analysis 196 (2023): 105163.
    
[2] S. Serneels. ["Sparse Twoblock Dimension Reduction: A Versatile Alternative 
    to Sparse PLS2 and CCA."](https://doi.org/10.1002/cem.70051) Journal of 
    Chemometrics, 39 (2025): e70051.
