direpack: A Python 3 package for state-of-the-art statistical dimension reduction methods

Emmanuel Jordy Menvouta; Sven Serneels; Tim Verdonck

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arXiv · 2006.01635

direpack: A Python 3 package for state-of-the-art statistical dimension reduction methods

Emmanuel Jordy Menvouta·Sven Serneels·Tim Verdonck

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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.

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BibTeX RIS

Emmanuel Jordy Menvouta, Sven Serneels, Tim Verdonck. 2020-05-30. direpack: A Python 3 package for state-of-the-art statistical dimension reduction methods. https://arxiv.org/abs/2006.01635

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