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.
- Version1.1.1
- R versionunknown
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- Last release06/09/2022
Team
Erika S. Helgeson
Eric Bair
Show author detailsRolesAuthorDavid Vock
Show author detailsRolesAuthor
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Last 30 days
This package has been downloaded 157 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 6 times.
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Last 365 days
This package has been downloaded 2,243 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Jan 24, 2024 with 117 downloads.
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- Imports2 packages
- Suggests1 package