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 similar to them method described in Helgeson and Bair (2016) <arXiv:1610.01424> except a Gaussian copula approach is used to account for feature correlation.

Version: 1.1.0
Depends: R (≥ 3.6.0)
Imports: huge, PDSCE
Published: 2020-04-13
Author: Erika S. Helgeson, David Vock, and Eric Bair
Maintainer: Erika S. Helgeson <helge at umn.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: UNPaC results


Reference manual: UNPaC.pdf
Package source: UNPaC_1.1.0.tar.gz
Windows binaries: r-devel: UNPaC_1.1.0.zip, r-release: UNPaC_1.1.0.zip, r-oldrel: UNPaC_1.1.0.zip
macOS binaries: r-release: UNPaC_1.1.0.tgz, r-oldrel: UNPaC_1.1.0.tgz
Old sources: UNPaC archive


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