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UNPaC  

Non-Parametric Cluster Significance Testing with Reference to a Unimodal Null Distribution
View on CRAN: Click here


Download and install UNPaC package within the R console
Install from CRAN:
install.packages("UNPaC")

Install from Github:
library("remotes")
install_github("cran/UNPaC")

Install by package version:
library("remotes")
install_version("UNPaC", "1.2.0")



Attach the package and use:
library("UNPaC")
Maintained by
Erika S. Helgeson
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-07-02
Latest Update: 2022-06-09
Description:
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>.
How to cite:
Erika S. Helgeson (2019). UNPaC: Non-Parametric Cluster Significance Testing with Reference to a Unimodal Null Distribution. R package version 1.2.0, https://cran.r-project.org/web/packages/UNPaC. Accessed 07 Oct. 2026.
Previous versions and publish date:
(2026-07-09 08:28), 1.0.0 (2019-07-02 18:30), 1.1.0 (2020-04-13 16:30), 1.1.1 (2022-06-10 01:00)
Other packages that cited UNPaC R package
View UNPaC citation profile
Other R packages that UNPaC depends, imports, suggests or enhances
Complete documentation for UNPaC
Functions, R codes and Examples using the UNPaC R package
Some associated functions: UNPaC_Copula . UNPaC_num_clust . 
Some associated R codes: UNPaC_Copula.R . UNPaC_null.R . UNPaC_num_clust.R . find_h1.R . var_selection.R .  Full UNPaC package functions and examples
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