Package: sharp 1.4.6

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.

Authors:Barbara Bodinier [aut, cre]

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NEWS

# Install 'sharp' in R:
install.packages('sharp', repos = c('https://barbarabodinier.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/barbarabodinier/sharp/issues

On CRAN:

66 exports 13 stars 2.11 score 38 dependencies 103 scripts 313 downloads

Last updated 8 months agofrom:d2cc3d9bc6. Checks:OK: 5 NOTE: 2. Indexed: yes.

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Exports:AdjacencyAggregatedEffectsArgmaxArgmaxIdBiSelectionBlockLambdaGridCalibrationPlotCARTClusteringClusteringAlgoClusteringPerformanceClustersCombineCoMembershipConsensusMatrixConsensusScoreDBSCANClusteringEnsembleEnsemblePredictionsExplanatoryPerformanceFDPFoldsGMMClusteringGraphGraphComparisonGraphicalAlgoGraphicalModelGroupPLSHierarchicalClusteringIncrementalIncrementalPlotKMeansClusteringLambdaGridGraphicalLambdaGridRegressionLambdaSequenceLinearSystemMatrixOpenMxMatrixOpenMxModelPAMClusteringPenalisedGraphicalPenalisedLinearSystemPenalisedOpenMxPenalisedRegressionPFERPlotIncrementalPLSPredictPLSRecalibrateRefitResampleSelectedVariablesSelectionAlgoSelectionPerformanceSelectionPerformanceGraphSelectionProportionsSparseGroupPLSSparsePCASparsePLSSplitSquareStabilityMetricsStabilityScoreStableStructuralModelVariableSelectionWeightBoxplot

Dependencies:abindaudiobeeprclicodetoolscpp11digestfakeforeachfuturefuture.applyglassoFastglmnetglobalsgluehugeigraphiteratorslatticelifecyclelistenvmagrittrMASSMatrixmclustnloptrparallellypkgconfigplotrixrbibutilsRcppRcppEigenRdpackrlangshapesurvivalvctrswithr

Sharp-JSS-2024

Rendered fromoverview.Rnwusingutils::Sweaveon Aug 31 2024.

Last update: 2024-02-03
Started: 2023-05-30

Readme and manuals

Help Manual

Help pageTopics
sharp: Stability-enHanced Approaches using Resampling Proceduressharp-package
Summarised coefficients conditionally on selectionAggregatedEffects
Calibrated hyper-parameter(s)Argmax ArgmaxId
Stability selection of predictors and/or outcomesBiSelection
Multi-block gridBlockLambdaGrid
Calibration plotCalibrationPlot
Classification And Regression TreesCART
Consensus clusteringClustering
(Weighted) clustering algorithmClusteringAlgo
Clustering performanceClusteringPerformance
Merging stability selection outputsCombine
Pairwise co-membershipCoMembership
Consensus scoreConsensusScore
(Weighted) density-based clusteringDBSCANClustering
Ensemble modelEnsemble
Predictions from ensemble modelEnsemblePredictions
Prediction performance in regressionExplanatoryPerformance
False Discovery ProportionFDP
Splitting observations into foldsFolds
Model-based clusteringGMMClustering
Graph visualisationGraph
Edge-wise comparison of two graphsGraphComparison
Graphical model algorithmGraphicalAlgo
Stability selection graphical modelGraphicalModel
Group Partial Least SquaresGroupPLS
(Weighted) hierarchical clusteringHierarchicalClustering
Incremental prediction performance in regressionIncremental
(Sparse) K-means clusteringKMeansClustering
Grid of penalty parameters (graphical model)LambdaGridGraphical
Grid of penalty parameters (regression model)LambdaGridRegression
Sequence of penalty parametersLambdaSequence
Matrix from linear system outputsLinearSystemMatrix
Matrix from OpenMx outputsOpenMxMatrix
Writing OpenMx model (matrix specification)OpenMxModel
(Weighted) Partitioning Around MedoidsPAMClustering
Graphical LASSOPenalisedGraphical
Penalised Structural Equation ModelPenalisedLinearSystem PenalisedOpenMx
Penalised regressionPenalisedRegression
Per Family Error RatePFER
Consensus matrix heatmapplot.clustering
Plot of incremental performanceIncrementalPlot plot.incremental PlotIncremental
Receiver Operating Characteristic (ROC) bandplot.roc_band
Plot of selection proportionsplot.variable_selection
Partial Least Squares 'a la carte'PLS
Predict method for stability selectionpredict.variable_selection
Partial Least Squares predictionsPredictPLS
Regression model refittingRecalibrate Refit
Resampling observationsResample
Variable selection algorithmSelectionAlgo
Selection performanceSelectionPerformance
Graph representation of selection performanceSelectionPerformanceGraph
Selection/co-membership proportionsConsensusMatrix SelectionProportions
Sparse group Partial Least SquaresSparseGroupPLS
Sparse Principal Component AnalysisSparsePCA
Sparse Partial Least SquaresSparsePLS
Splitting observations into non-overlapping setsSplit
Adjacency from bipartiteSquare
Stability selection metricsStabilityMetrics
Stability scoreStabilityScore
Stable resultsAdjacency Clusters SelectedVariables Stable
Stability selection in Structural Equation ModellingStructuralModel
Stability selection in regressionVariableSelection
Stable attribute weightsWeightBoxplot