Stability selection and consensus clustering in R: the R package sharp
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
The R package sharp (Stability-enHanced Approaches using Resampling Procedures) provides an integrated framework for stability-enhanced variable selection, graphical modeling and clustering. In stability selection, a feature selection algorithm is combined with a resampling technique to estimate feature selection probabilities. Features with selection proportions above a threshold are considered stably selected. Similarly, a clustering algorithm is applied on multiple subsamples of items to compute co-membership proportions in consensus clustering. The consensus clusters are obtained by clustering using comembership proportions as a measure of similarity. We calibrate the hyper-parameters of stability selection (or consensus clustering) jointly by maximizing a consensus score calculated under the null hypothesis of equiprobability of selection (or co-membership), which characterizes instability. The package offers flexibility in the modeling, includes diagnostic and visualization tools, and allows for parallelization.
Date Issued
2025-04-12
Date Acceptance
2025-04-01
Citation
Journal of Statistical Software, 2025, 112 (5), pp.1-27
ISSN
1548-7660
Publisher
Foundation for Open Access Statistics
Start Page
1
End Page
27
Journal / Book Title
Journal of Statistical Software
Volume
112
Issue
5
Copyright Statement
Creative Commons Attribution License (CC-BY)
License URL
Publication Status
Published
Date Publish Online
2025-04-12