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)
- Version1.1.1
- R version≥ 3.6.0
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- Last release06/09/2022
Team
Erika S. Helgeson
Erika S. Helgeson, David Vock, and Eric Bair
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- Depends1 package
- Imports2 packages
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