diproperm: An R Package for the DiProPerm Test

TL;DR

The paper introduces the diproperm R package that implements the DiProPerm test, a statistical method for comparing two high-dimensional distributions in biomedical data analysis.

Contribution

It provides an implementation of the DiProPerm test in R and demonstrates its application on real-world high-dimensional data.

Findings

Effective in distinguishing high-dimensional distributions

Applicable to biomedical HDLSS datasets

Provides a practical R tool for statistical testing

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.