Sorted L1 Penalized Estimation
Create cross-validation folds
Obtain coefficients
Setup a data.frame of diagnostics
Tune SLOPE with cross-validation
Model deviance
Interpolate coefficients
Interpolate penalty values
Plot coefficients
Plot results from cross-validation
Plot cluster structure
Plot results from diagnostics collected during model fitting
Generate predictions from SLOPE models
Print results from SLOPE fit
Generate Regularization (Penalty) Weights for SLOPE
Compute one of several loss metrics on a new data set
SLOPE: Sorted L1 Penalized Estimation
Sorted L-One Penalized Estimation
Sorted L1 Proximal Operator
Train a SLOPE model
Efficient implementations for Sorted L-One Penalized Estimation (SLOPE): generalized linear models regularized with the sorted L1-norm (Bogdan et al. 2015). Supported models include ordinary least-squares regression, binomial regression, multinomial regression, and Poisson regression. Both dense and sparse predictor matrices are supported. In addition, the package features predictor screening rules that enable fast and efficient solutions to high-dimensional problems.
Useful links