direpack: A Python 3 package for state-of-the-art statistical dimension reduction methods
Abstract
The direpack package aims to establish a set of modern statistical dimension reduction techniques into the Python universe as a single, consistent package. The dimension reduction methods included resort into three categories: projection pursuit based dimension reduction, sufficient dimension reduction, and robust M estimators for dimension reduction. As a corollary, regularized regression estimators based on these reduced dimension spaces are provided as well, ranging from classical principal component regression up to sparse partial robust M regression. The package also contains a set of classical and robust pre-processing utilities, including generalized spatial signs, as well as dedicated plotting functionality and cross-validation utilities. Finally, direpack has been written consistent with the scikit-learn API, such that the estimators can flawlessly be included into (statistical and/or machine) learning pipelines in that framework.
Cite
@article{arxiv.2006.01635,
title = {direpack: A Python 3 package for state-of-the-art statistical dimension reduction methods},
author = {Emmanuel Jordy Menvouta and Sven Serneels and Tim Verdonck},
journal= {arXiv preprint arXiv:2006.01635},
year = {2020}
}