# The R package psvmSDR: A Unified Algorithm for Sufficient Dimension Reduction via Principal Machines

arXiv: https://arxiv.org/abs/2409.01547 · HTML: https://mlsanity.com/paper/2409.01547/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 3 Sep 2024.

## Builds on (most cited first, 2 of 2)

- [Object-Oriented Programming, Functional Programming and R](https://mlsanity.com/paper/1409.3531/) (2014)
- [On sufficient dimension reduction via principal asymmetric least squares](https://mlsanity.com/paper/2002.05264/) (2020)

## Abstract (arXiv)

Sufficient dimension reduction (SDR), which seeks a lower-dimensional subspace of the predictors containing regression or classification information has been popular in a machine learning community. In this work, we present a new R software package psvmSDR that implements a new class of SDR estimators, which we call the principal machine (PM) generalized from the principal support vector machine (PSVM). The package covers both linear and nonlinear SDR and provides a function applicable to realtime update scenarios. The package implements the descent algorithm for the PMs to efficiently compute the SDR estimators in various situations. This easy-to-use package will be an attractive alternative to the dr R package that implements classical SDR methods.