  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.
