Stability-enHanced Approaches using Resampling Procedures R package sharp version 1.4.8 In stability selection (N Meinshausen, P Bühlmann (2010) <doi:10.1111/j.1467-9868.2010.00740.x>) and consensus clustering (S Monti et al (2003) <doi:10.1023/A:1023949509487>), resampling techniques are used to enhance the reliability of the results. In this package (B Bodinier et al (2025) <doi:10.18637/jss.v112.i05>), hyper-parameters are calibrated by maximising model stability, which is measured under the null hypothesis that all selection (or co-membership) probabilities are identical (B Bodinier et al (2023a) <doi:10.1093/jrsssc/qlad058> and B Bodinier et al (2023b) <doi:10.1093/bioinformatics/btad635>). Functions are readily implemented for the use of LASSO regression, sparse PCA, sparse (group) PLS or graphical LASSO in stability selection, and hierarchical clustering, partitioning around medoids, K means or Gaussian mixture models in consensus clustering. Software Depends: fake (>= 1.4.0), R (>= 3.5) Imports: abind, beepr, future, future.apply, glassoFast (>= 1.0.0), glmnet, grDevices, igraph, mclust, nloptr, plotrix, Rdpack, withr (>= 2.4.0) Suggests: cluster, corpcor, dbscan, elasticnet, gglasso, mixOmics, nnet, OpenMx, RCy3, randomcoloR, rCOSA, rmarkdown, rpart, sgPLS, sparcl, survival (>= 3.2.13), testthat (>= 3.0.0), visNetwork Barbara Bodinier <barbara.bodinier@gmail.com> Comprehensive R Archive Network (CRAN) Barbara Bodinier [aut, cre] GPL (>= 3) 2025-07-23 application/tgz https://CRAN.R-project.org/package=sharp doi:10.32614/CRAN.package.sharp en-GB