diproperm: An R Package for the DiProPerm Test

Advancements in modern technology and computer software have dramatically increased the demand and feasibility to collect high-dimensional data sets. Such data possess challenges which require the creation of new and adaptation of existing statistical methods. One such challenge is that we may observe many more predictors, p, than the number of observations, n, especially in small sample size studies. These data structures are known as high-dimensional, low sample size (HDLSS) data sets, or "small n, big p ". HDLSS data emerge frequently in many health-related fields. For example, in genomic studies, a single microarray experiment might produce tens of thousands of gene expressions compared to the few samples studied, often being less than a hundred (Alag, 2019).