diproperm: An R Package for the DiProPerm Test
This provides a tool for researchers analyzing high-dimensional biomedical data, but it is incremental as it packages an existing test into an R package.
The paper introduces the diproperm R package for implementing the DiProPerm test, which addresses the problem of testing differences between two high-dimensional distributions in biomedical data using binary linear classifiers, and demonstrates its application on a real-world dataset.
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.