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SLOPE

Efficient implementations for Sorted L-One Penalized Estimation (SLOPE): generalized linear models regularized with the sorted L1-norm. There is support for ordinary least-squares regression, binomial regression, multinomial regression, and poisson regression, as well as both dense and sparse predictor matrices. In addition, the package features predictor screening rules that enable efficient solutions to high-dimensional problems.

Installation

You can install the current stable release from CRAN with

install.packages("SLOPE")

or the development version from GitHub with

# install.packages("remotes")
remotes::install_github("jolars/SLOPE")

Versioning

SLOPE uses semantic versioning.

Code of conduct

Please note that the ‘SLOPE’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

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Version

Install

install.packages('SLOPE')

Monthly Downloads

399

Version

0.3.2

License

GPL-3

Issues

Pull Requests

Stars

Forks

Maintainer

Johan Larsson

Last Published

July 10th, 2020

Functions in SLOPE (0.3.2)

coef.SLOPE

Obtain coefficients
predict.SLOPE

Generate predictions from SLOPE models
plotDiagnostics

Plot results from diagnostics collected during model fitting
SLOPE

Sorted L-One Penalized Estimation
SLOPE-package

SLOPE: Sorted L1 Penalized Estimation
interpolatePenalty

Interpolate penalty values
trainSLOPE

Train a SLOPE model
wine

Wine cultivars
heart

Heart disease
setupDiagnostics

Setup a data.frame of diagnostics
plot.SLOPE

Plot coefficients
student

Student performance
print.SLOPE

Print results from SLOPE fit
plot.TrainedSLOPE

Plot results from cross-validation
score

Compute one of several loss metrics on a new data set
caretSLOPE

Model objects for model tuning with caret
deviance.SLOPE

Model deviance
abalone

Abalone
interpolateCoefficients

Interpolate coefficients
bodyfat

Bodyfat