{
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  "Title": "Stability-enHanced Approaches using Resampling Procedures",
  "Version": "1.4.8",
  "Date": "2025-07-23",
  "Authors@R": "person(given = \"Barbara\",\nfamily = \"Bodinier\",\nrole = c(\"aut\", \"cre\"),\nemail = \"barbara.bodinier@gmail.com\")",
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  "Description": "In stability selection (N Meinshausen, P Bühlmann (2010)\n<doi:10.1111/j.1467-9868.2010.00740.x>) and consensus\nclustering (S Monti et al (2003)\n<doi:10.1023/A:1023949509487>), resampling techniques are used\nto enhance the reliability of the results. In this package (B\nBodinier et al (2025) <doi:10.18637/jss.v112.i05>),\nhyper-parameters are calibrated by maximising model stability,\nwhich is measured under the null hypothesis that all selection\n(or co-membership) probabilities are identical (B Bodinier et\nal (2023a) <doi:10.1093/jrsssc/qlad058> and B Bodinier et al\n(2023b) <doi:10.1093/bioinformatics/btad635>). Functions are\nreadily implemented for the use of LASSO regression, sparse\nPCA, sparse (group) PLS or graphical LASSO in stability\nselection, and hierarchical clustering, partitioning around\nmedoids, K means or Gaussian mixture models in consensus\nclustering.",
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    "VariableSelection",
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      "page": "sharp-package",
      "title": "sharp: Stability-enHanced Approaches using Resampling Procedures",
      "topics": [
        "sharp-package"
      ]
    },
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      "page": "AggregatedEffects",
      "title": "Summarised coefficients conditionally on selection",
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      ]
    },
    {
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      "title": "Calibrated hyper-parameter(s)",
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        "Argmax",
        "ArgmaxId"
      ]
    },
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      "page": "BiSelection",
      "title": "Stability selection of predictors and/or outcomes",
      "concept": [
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      ],
      "topics": [
        "BiSelection"
      ]
    },
    {
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      "title": "Multi-block grid",
      "concept": [
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    },
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      "page": "CalibrationPlot",
      "title": "Calibration plot",
      "topics": [
        "CalibrationPlot"
      ]
    },
    {
      "page": "CART",
      "title": "Classification And Regression Trees",
      "concept": [
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      ],
      "topics": [
        "CART"
      ]
    },
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      "title": "Consensus clustering",
      "concept": [
        "stability functions"
      ],
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        "Clustering"
      ]
    },
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      "title": "(Weighted) clustering algorithm",
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      ],
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      ]
    },
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      "title": "Clustering performance",
      "concept": [
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        "ClusteringPerformance"
      ]
    },
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      "title": "Merging stability selection outputs",
      "topics": [
        "Combine"
      ]
    },
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      "title": "Pairwise co-membership",
      "topics": [
        "CoMembership"
      ]
    },
    {
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      "title": "Consensus score",
      "concept": [
        "stability metric functions"
      ],
      "topics": [
        "ConsensusScore"
      ]
    },
    {
      "page": "DBSCANClustering",
      "title": "(Weighted) density-based clustering",
      "concept": [
        "clustering algorithms"
      ],
      "topics": [
        "DBSCANClustering"
      ]
    },
    {
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      "title": "Ensemble model",
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      ],
      "topics": [
        "Ensemble"
      ]
    },
    {
      "page": "EnsemblePredictions",
      "title": "Predictions from ensemble model",
      "concept": [
        "ensemble model functions"
      ],
      "topics": [
        "EnsemblePredictions"
      ]
    },
    {
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      "title": "Prediction performance in regression",
      "concept": [
        "prediction performance functions"
      ],
      "topics": [
        "ExplanatoryPerformance"
      ]
    },
    {
      "page": "FDP",
      "title": "False Discovery Proportion",
      "concept": [
        "stability metric functions"
      ],
      "topics": [
        "FDP"
      ]
    },
    {
      "page": "Folds",
      "title": "Splitting observations into folds",
      "topics": [
        "Folds"
      ]
    },
    {
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      "title": "Model-based clustering",
      "concept": [
        "clustering algorithms"
      ],
      "topics": [
        "GMMClustering"
      ]
    },
    {
      "page": "Graph",
      "title": "Graph visualisation",
      "topics": [
        "Graph"
      ]
    },
    {
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      "title": "Edge-wise comparison of two graphs",
      "topics": [
        "GraphComparison"
      ]
    },
    {
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      "title": "Graphical model algorithm",
      "concept": [
        "wrapping functions"
      ],
      "topics": [
        "GraphicalAlgo"
      ]
    },
    {
      "page": "GraphicalModel",
      "title": "Stability selection graphical model",
      "concept": [
        "stability functions"
      ],
      "topics": [
        "GraphicalModel"
      ]
    },
    {
      "page": "GroupPLS",
      "title": "Group Partial Least Squares",
      "concept": [
        "penalised dimensionality reduction functions"
      ],
      "topics": [
        "GroupPLS"
      ]
    },
    {
      "page": "HierarchicalClustering",
      "title": "(Weighted) hierarchical clustering",
      "concept": [
        "clustering algorithms"
      ],
      "topics": [
        "HierarchicalClustering"
      ]
    },
    {
      "page": "Incremental",
      "title": "Incremental prediction performance in regression",
      "concept": [
        "prediction performance functions"
      ],
      "topics": [
        "Incremental"
      ]
    },
    {
      "page": "KMeansClustering",
      "title": "(Sparse) K-means clustering",
      "concept": [
        "clustering algorithms"
      ],
      "topics": [
        "KMeansClustering"
      ]
    },
    {
      "page": "LambdaGridGraphical",
      "title": "Grid of penalty parameters (graphical model)",
      "concept": [
        "lambda grid functions"
      ],
      "topics": [
        "LambdaGridGraphical"
      ]
    },
    {
      "page": "LambdaGridRegression",
      "title": "Grid of penalty parameters (regression model)",
      "concept": [
        "lambda grid functions"
      ],
      "topics": [
        "LambdaGridRegression"
      ]
    },
    {
      "page": "LambdaSequence",
      "title": "Sequence of penalty parameters",
      "concept": [
        "lambda grid functions"
      ],
      "topics": [
        "LambdaSequence"
      ]
    },
    {
      "page": "LinearSystemMatrix",
      "title": "Matrix from linear system outputs",
      "topics": [
        "LinearSystemMatrix"
      ]
    },
    {
      "page": "OpenMxMatrix",
      "title": "Matrix from OpenMx outputs",
      "topics": [
        "OpenMxMatrix"
      ]
    },
    {
      "page": "OpenMxModel",
      "title": "Writing OpenMx model (matrix specification)",
      "topics": [
        "OpenMxModel"
      ]
    },
    {
      "page": "PAMClustering",
      "title": "(Weighted) Partitioning Around Medoids",
      "concept": [
        "clustering algorithms"
      ],
      "topics": [
        "PAMClustering"
      ]
    },
    {
      "page": "PenalisedGraphical",
      "title": "Graphical LASSO",
      "concept": [
        "underlying algorithm functions"
      ],
      "topics": [
        "PenalisedGraphical"
      ]
    },
    {
      "page": "PenalisedOpenMx",
      "title": "Penalised Structural Equation Model",
      "concept": [
        "underlying algorithm functions"
      ],
      "topics": [
        "PenalisedLinearSystem",
        "PenalisedOpenMx"
      ]
    },
    {
      "page": "PenalisedRegression",
      "title": "Penalised regression",
      "concept": [
        "underlying algorithm functions"
      ],
      "topics": [
        "PenalisedRegression"
      ]
    },
    {
      "page": "PFER",
      "title": "Per Family Error Rate",
      "concept": [
        "stability metric functions"
      ],
      "topics": [
        "PFER"
      ]
    },
    {
      "page": "plot.clustering",
      "title": "Consensus matrix heatmap",
      "topics": [
        "plot.clustering"
      ]
    },
    {
      "page": "plot.incremental",
      "title": "Plot of incremental performance",
      "topics": [
        "IncrementalPlot",
        "plot.incremental",
        "PlotIncremental"
      ]
    },
    {
      "page": "plot.roc_band",
      "title": "Receiver Operating Characteristic (ROC) band",
      "topics": [
        "plot.roc_band"
      ]
    },
    {
      "page": "plot.variable_selection",
      "title": "Plot of selection proportions",
      "topics": [
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      ]
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      "page": "PLS",
      "title": "Partial Least Squares 'a la carte'",
      "topics": [
        "PLS"
      ]
    },
    {
      "page": "predict.variable_selection",
      "title": "Predict method for stability selection",
      "topics": [
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      ]
    },
    {
      "page": "PredictPLS",
      "title": "Partial Least Squares predictions",
      "topics": [
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      ]
    },
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      "title": "Regression model refitting",
      "topics": [
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        "Refit"
      ]
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      "page": "Resample",
      "title": "Resampling observations",
      "topics": [
        "Resample"
      ]
    },
    {
      "page": "SelectionAlgo",
      "title": "Variable selection algorithm",
      "concept": [
        "wrapping functions"
      ],
      "topics": [
        "SelectionAlgo"
      ]
    },
    {
      "page": "SelectionPerformance",
      "title": "Selection performance",
      "concept": [
        "functions for model performance"
      ],
      "topics": [
        "SelectionPerformance"
      ]
    },
    {
      "page": "SelectionPerformanceGraph",
      "title": "Graph representation of selection performance",
      "concept": [
        "functions for model performance"
      ],
      "topics": [
        "SelectionPerformanceGraph"
      ]
    },
    {
      "page": "SelectionProportions",
      "title": "Selection/co-membership proportions",
      "topics": [
        "ConsensusMatrix",
        "SelectionProportions"
      ]
    },
    {
      "page": "SparseGroupPLS",
      "title": "Sparse group Partial Least Squares",
      "concept": [
        "penalised dimensionality reduction functions"
      ],
      "topics": [
        "SparseGroupPLS"
      ]
    },
    {
      "page": "SparsePCA",
      "title": "Sparse Principal Component Analysis",
      "concept": [
        "penalised dimensionality reduction functions"
      ],
      "topics": [
        "SparsePCA"
      ]
    },
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