diproperm: An R Package for the DiProPerm Test
Computation
2020-09-02 v1 Machine Learning
Machine Learning
Abstract
High-dimensional low sample size (HDLSS) data sets emerge frequently in many biomedical applications. A common task for analyzing HDLSS data is to assign data to the correct class using a classifier. Classifiers which use two labels and a linear combination of features are known as binary linear classifiers. The direction-projection-permutation (DiProPerm) test was developed for testing the difference of two high-dimensional distributions induced by a binary linear classifier. This paper discusses the key components of the DiProPerm test, introduces the diproperm R package, and demonstrates the package on a real-world data set.
Keywords
Cite
@article{arxiv.2009.00003,
title = {diproperm: An R Package for the DiProPerm Test},
author = {Andrew G. Allmon and J. S. Marron and Michael G. Hudgens},
journal= {arXiv preprint arXiv:2009.00003},
year = {2020}
}
Comments
Package located at https://github.com/allmondrew/diproperm