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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summary(<SLOPE>) - Summarize SLOPE Model
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print(<summary_SLOPE>) - Print Summary of SLOPE Model
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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
Model Tuning
Use cvSLOPE() to perform cross-validation for selecting the optimal regularization parameters, plot.TrainedSLOPE() to visualize the results, and refit() to fit the final model to your data set.
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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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refit() - Refit SLOPE Model with Optimal Parameters
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summary(<TrainedSLOPE>) - Summarize TrainedSLOPE Model
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print(<summary_TrainedSLOPE>) - Print Summary of TrainedSLOPE Model
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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