sharp: Stability-enHanced Approaches using Resampling Procedures

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, 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.

Version: 1.4.6
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
Published: 2024-02-03
DOI: 10.32614/
Author: Barbara Bodinier [aut, cre]
Maintainer: Barbara Bodinier <barbara.bodinier at>
License: GPL (≥ 3)
NeedsCompilation: no
Language: en-GB
Materials: README NEWS
CRAN checks: sharp results


Reference manual: sharp.pdf
Vignettes: Sharp-JSS-2024


Package source: sharp_1.4.6.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): sharp_1.4.6.tgz, r-oldrel (arm64): sharp_1.4.6.tgz, r-release (x86_64): sharp_1.4.6.tgz, r-oldrel (x86_64): sharp_1.4.6.tgz
Old sources: sharp archive


Please use the canonical form to link to this page.