Function reference • SLOPE

Main Functionality Fit the model with `SLOPE()`, visualize the results with `plot.SLOPE()`, and produce predictions for new data with `predict.SLOPE()`.
`SLOPE()`	Sorted L-One Penalized Estimation
`plot(<SLOPE>)`	Plot coefficients
`print(<SLOPE>)` `print(<TrainedSLOPE>)`	Print results from SLOPE fit
`predict(<SLOPE>)` `predict(<GaussianSLOPE>)` `predict(<BinomialSLOPE>)` `predict(<PoissonSLOPE>)` `predict(<MultinomialSLOPE>)`	Generate predictions from SLOPE models
`coef(<SLOPE>)`	Obtain coefficients
`deviance(<SLOPE>)`	Model deviance
`score()`	Compute one of several loss metrics on a new data set
Model Tuning
`trainSLOPE()`	Train a SLOPE model
`caretSLOPE()`	Model objects for model tuning with caret (deprecated)
`plot(<TrainedSLOPE>)`	Plot results from cross-validation
`print(<SLOPE>)` `print(<TrainedSLOPE>)`	Print results from SLOPE fit
Utilities
`plotDiagnostics()`	Plot results from diagnostics collected during model fitting
`regularizationWeights()`	Generate Regularization (Penalty) Weights for SLOPE
`sortedL1Prox()`	Sorted L1 Proximal Operator
Datasets
`abalone`	Abalone
`bodyfat`	Bodyfat
`heart`	Heart disease
`student`	Student performance
`wine`	Wine cultivars

Reference