Package index
Main Functionality
Fit the model with SLOPE(), visualize the results with plot.SLOPE(), and produce predictions for new data with predict.SLOPE().
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SLOPE() - Sorted L-One Penalized Estimation
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plot(<SLOPE>) - Plot coefficients
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print(<SLOPE>)print(<TrainedSLOPE>) - Print results from SLOPE fit
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predict(<SLOPE>)predict(<GaussianSLOPE>)predict(<BinomialSLOPE>)predict(<PoissonSLOPE>)predict(<MultinomialSLOPE>) - Generate predictions from SLOPE models
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coef(<SLOPE>) - Obtain coefficients
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deviance(<SLOPE>) - Model deviance
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score() - Compute one of several loss metrics on a new data set
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plotClusters() - Plot cluster structure
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cvSLOPE() - Tune SLOPE with cross-validation
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trainSLOPE() - Train a SLOPE model
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plot(<TrainedSLOPE>) - Plot results from cross-validation
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print(<SLOPE>)print(<TrainedSLOPE>) - Print results from SLOPE fit
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plotDiagnostics() - Plot results from diagnostics collected during model fitting
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regularizationWeights() - Generate Regularization (Penalty) Weights for SLOPE
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sortedL1Prox() - Sorted L1 Proximal Operator