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 |
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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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| author | Barbara Bodinier Sabrina Rodrigues Maryam Karimi Sarah Filippi Julien Chiquet Marc Chadeau-Hyam |
| author_facet | Barbara Bodinier Sabrina Rodrigues Maryam Karimi Sarah Filippi Julien Chiquet Marc Chadeau-Hyam |
| author_sort | Barbara Bodinier |
| collection | DOAJ |
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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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| format | Article |
| id | doaj-art-dcf6c477756b4fc6a10f10ef2cf66e08 |
| institution | Kabale University |
| issn | 1548-7660 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj-art-dcf6c477756b4fc6a10f10ef2cf66e082025-08-20T03:32:16ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602025-04-01112110.18637/jss.v112.i05Stability Selection and Consensus Clustering in R: The R Package sharpBarbara Bodinier0https://orcid.org/0000-0002-0781-3624Sabrina Rodrigues1https://orcid.org/0000-0003-0116-7710Maryam Karimi2https://orcid.org/0000-0003-3301-8795Sarah Filippi3https://orcid.org/0000-0001-8652-358XJulien Chiquet4https://orcid.org/0000-0002-3629-3429Marc Chadeau-Hyam5https://orcid.org/0000-0001-8341-5436Imperial College LondonImperial College LondonINSERMImperial College LondonUniversity Paris-Saclay, AgroParisTech, INRAEImperial College London 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. https://www.jstatsoft.org/index.php/jss/article/view/5063 |
| spellingShingle | Barbara Bodinier Sabrina Rodrigues Maryam Karimi Sarah Filippi Julien Chiquet Marc Chadeau-Hyam Stability Selection and Consensus Clustering in R: The R Package sharp Journal of Statistical Software |
| title | Stability Selection and Consensus Clustering in R: The R Package sharp |
| title_full | Stability Selection and Consensus Clustering in R: The R Package sharp |
| title_fullStr | Stability Selection and Consensus Clustering in R: The R Package sharp |
| title_full_unstemmed | Stability Selection and Consensus Clustering in R: The R Package sharp |
| title_short | Stability Selection and Consensus Clustering in R: The R Package sharp |
| title_sort | stability selection and consensus clustering in r the r package sharp |
| url | https://www.jstatsoft.org/index.php/jss/article/view/5063 |
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