| sharp-package | sharp: Stability-enHanced Approaches using Resampling Procedures | 
| Adjacency | Stable results | 
| AggregatedEffects | Summarised coefficients conditionally on selection | 
| Argmax | Calibrated hyper-parameter(s) | 
| ArgmaxId | Calibrated hyper-parameter(s) | 
| BiSelection | Stability selection of predictors and/or outcomes | 
| BlockLambdaGrid | Multi-block grid | 
| CalibrationPlot | Calibration plot | 
| CART | Classification And Regression Trees | 
| Clustering | Consensus clustering | 
| ClusteringAlgo | (Weighted) clustering algorithm | 
| ClusteringPerformance | Clustering performance | 
| Clusters | Stable results | 
| Combine | Merging stability selection outputs | 
| CoMembership | Pairwise co-membership | 
| ConsensusMatrix | Selection/co-membership proportions | 
| ConsensusScore | Consensus score | 
| DBSCANClustering | (Weighted) density-based clustering | 
| Ensemble | Ensemble model | 
| EnsemblePredictions | Predictions from ensemble model | 
| ExplanatoryPerformance | Prediction performance in regression | 
| FDP | False Discovery Proportion | 
| Folds | Splitting observations into folds | 
| GMMClustering | Model-based clustering | 
| Graph | Graph visualisation | 
| GraphComparison | Edge-wise comparison of two graphs | 
| GraphicalAlgo | Graphical model algorithm | 
| GraphicalModel | Stability selection graphical model | 
| GroupPLS | Group Partial Least Squares | 
| HierarchicalClustering | (Weighted) hierarchical clustering | 
| Incremental | Incremental prediction performance in regression | 
| IncrementalPlot | Plot of incremental performance | 
| KMeansClustering | (Sparse) K-means clustering | 
| LambdaGridGraphical | Grid of penalty parameters (graphical model) | 
| LambdaGridRegression | Grid of penalty parameters (regression model) | 
| LambdaSequence | Sequence of penalty parameters | 
| LinearSystemMatrix | Matrix from linear system outputs | 
| OpenMxMatrix | Matrix from OpenMx outputs | 
| OpenMxModel | Writing OpenMx model (matrix specification) | 
| PAMClustering | (Weighted) Partitioning Around Medoids | 
| PenalisedGraphical | Graphical LASSO | 
| PenalisedLinearSystem | Penalised Structural Equation Model | 
| PenalisedOpenMx | Penalised Structural Equation Model | 
| PenalisedRegression | Penalised regression | 
| PFER | Per Family Error Rate | 
| plot.clustering | Consensus matrix heatmap | 
| plot.incremental | Plot of incremental performance | 
| plot.roc_band | Receiver Operating Characteristic (ROC) band | 
| plot.variable_selection | Plot of selection proportions | 
| PlotIncremental | Plot of incremental performance | 
| PLS | Partial Least Squares 'a la carte' | 
| predict.variable_selection | Predict method for stability selection | 
| PredictPLS | Partial Least Squares predictions | 
| Recalibrate | Regression model refitting | 
| Refit | Regression model refitting | 
| Resample | Resampling observations | 
| SelectedVariables | Stable results | 
| SelectionAlgo | Variable selection algorithm | 
| SelectionPerformance | Selection performance | 
| SelectionPerformanceGraph | Graph representation of selection performance | 
| SelectionProportions | Selection/co-membership proportions | 
| SparseGroupPLS | Sparse group Partial Least Squares | 
| SparsePCA | Sparse Principal Component Analysis | 
| SparsePLS | Sparse Partial Least Squares | 
| Split | Splitting observations into non-overlapping sets | 
| Square | Adjacency from bipartite | 
| StabilityMetrics | Stability selection metrics | 
| StabilityScore | Stability score | 
| Stable | Stable results | 
| StructuralModel | Stability selection in Structural Equation Modelling | 
| VariableSelection | Stability selection in regression | 
| WeightBoxplot | Stable attribute weights |