sharp

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.