Package: UNPaC 1.1.1

Erika S. Helgeson

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>.

Authors:Erika S. Helgeson, David Vock, and Eric Bair

UNPaC_1.1.1.tar.gz
UNPaC_1.1.1.tar.gz(r-4.5-noble)UNPaC_1.1.1.tar.gz(r-4.4-noble)
UNPaC_1.1.1.tgz(r-4.4-emscripten)UNPaC_1.1.1.tgz(r-4.3-emscripten)
UNPaC.pdf |UNPaC.html
UNPaC/json (API)

# Install 'UNPaC' in R:
install.packages('UNPaC', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 142 downloads 2 exports 16 dependencies

Last updated 2 years agofrom:ddc440c54e. Checks:OK: 1 NOTE: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 31 2024
R-4.5-linuxNOTEOct 31 2024

Exports:UNPaC_CopulaUNPaC_num_clust

Dependencies:clicpp11gluehugeigraphlatticelifecyclemagrittrMASSMatrixPDSCEpkgconfigRcppRcppEigenrlangvctrs