Stability Selection and Consensus Clustering in R: The R Package sharp
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 fe...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Foundation for Open Access Statistics
2025-04-01
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| Series: | Journal of Statistical Software |
| Online Access: | https://www.jstatsoft.org/index.php/jss/article/view/5063 |
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| Summary: | 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.
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| ISSN: | 1548-7660 |