Stability Selection and Consensus Clustering in R: The R Package sharp

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 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.

Barbara Bodinier, Sabrina Rodrigues, Maryam Karimi, Sarah Filippi, Julien Chiquet, Marc Chadeau-Hyam

https://www.jstatsoft.org/article/view/v112i05

OJS/PKP

12 APRIL 2025

FAJAR BAGUS W

PDF

ENGLISH

TEXT