UNPaC:Non-Parametric Cluster Significance Testing with Reference to a
Unimodal Null Distribution
Assess the significance of identified clusters and estimates the true number of clusters by comparing the
explained variation due to the clustering from the original
data to that produced by clustering a unimodal reference
distribution which preserves the covariance structure in the
data. The reference distribution is generated using kernel
density estimation and a Gaussian copula framework. A dimension
reduction strategy and sparse covariance estimation optimize
this method for the high-dimensional, low-sample size setting.
This method is described in Helgeson, Vock, and Bair (2021)
<doi:10.1111/biom.13376>.
