CRAN/E | UNPaC

UNPaC

Non-Parametric Cluster Significance Testing with Reference to a Unimodal Null Distribution

Installation

About

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.

Key Metrics

Version 1.1.1
R ≥ 3.6.0
Published 2022-06-09 291 days ago
Needs compilation? no
License GPL-2
License GPL-3
CRAN checks UNPaC results

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Maintainer

Maintainer

Erika S. Helgeson

helge@umn.edu

Authors

Erika S. Helgeson
David Vock
Eric Bair

Material

Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

UNPaC archive

Depends

R ≥ 3.6.0

Imports

huge
PDSCE

Suggests

testthat ≥ 3.0.0