Package: SLOPE 0.5.2
SLOPE: Sorted L1 Penalized Estimation
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.
Authors:
SLOPE_0.5.2.tar.gz
SLOPE_0.5.2.tar.gz(r-4.5-noble)SLOPE_0.5.2.tar.gz(r-4.4-noble)
SLOPE_0.5.2.tgz(r-4.4-emscripten)SLOPE_0.5.2.tgz(r-4.3-emscripten)
SLOPE.pdf |SLOPE.html✨
SLOPE/json (API)
NEWS
# Install 'SLOPE' in R: |
install.packages('SLOPE', repos = 'https://cloud.r-project.org') |
Bug tracker:https://github.com/jolars/slope/issues1 issues
Pkgdown site:https://jolars.github.io
Last updated 2 months agofrom:f4c4df79d9. Checks:3 OK. Indexed: no.
Target | Result | Latest binary |
---|---|---|
Doc / Vignettes | OK | Mar 07 2025 |
R-4.5-linux-x86_64 | OK | Mar 07 2025 |
R-4.4-linux-x86_64 | OK | Mar 07 2025 |
Exports:caretSLOPEplotDiagnosticsregularizationWeightsscoreSLOPEsortedL1ProxtrainSLOPE
Dependencies:clicodetoolscolorspacefansifarverforeachggplot2gluegtableisobanditeratorslabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerRcppRcppArmadillorlangscalestibbleutf8vctrsviridisLitewithr
Citation
To cite the SLOPE package in publications, please use:
Larsson J, Wallin J, Bogdan M, van den Berg E, Sabatti C, Candes E, Patterson E, Su W, Kała J, Grzesiak K, Burdukiewicz M (2025). SLOPE: Sorted L1 Penalized Estimation. R package version 0.5.2, https://CRAN.R-project.org/package=SLOPE.
To cite the SLOPE method in publications, please use:
Bogdan M, van den Berg E, Sabatti C, Su W, Candès E (2015). “SLOPE – Adaptive Variable Selection via Convex Optimization.” The annals of applied statistics, 9(3), 1103–1140. ISSN 1932-6157, doi:10.1214/15-AOAS842.
Corresponding BibTeX entries:
@Manual{, title = {{SLOPE}: Sorted L1 Penalized Estimation}, author = {Johan Larsson and Jonas Wallin and Malgorzata Bogdan and Ewout {van den Berg} and Chiara Sabatti and Emmanuel Candes and Evan Patterson and Weijie Su and Jakub Kała and Krystyna Grzesiak and Michal Burdukiewicz}, year = {2025}, note = {R package version 0.5.2}, url = {https://CRAN.R-project.org/package=SLOPE}, }
@Article{, title = {{{SLOPE}} -- Adaptive Variable Selection via Convex Optimization}, author = {Małgorzata Bogdan and Ewout {van den Berg} and Chiara Sabatti and Weijie Su and Emmanuel J. Candès}, journal = {The annals of applied statistics}, volume = {9}, number = {3}, pages = {1103--1140}, year = {2015}, doi = {10.1214/15-AOAS842}, issn = {1932-6157}, }
Readme and manuals
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.
Help Manual
Help page | Topics |
---|---|
Abalone | abalone |
Bodyfat | bodyfat |
Model objects for model tuning with caret (deprecated) | caretSLOPE |
Obtain coefficients | coef.SLOPE |
Model deviance | deviance.SLOPE |
Heart disease | heart |
Plot coefficients | plot.SLOPE |
Plot results from cross-validation | plot.TrainedSLOPE |
Plot results from diagnostics collected during model fitting | plotDiagnostics |
Generate predictions from SLOPE models | predict.BinomialSLOPE predict.GaussianSLOPE predict.MultinomialSLOPE predict.PoissonSLOPE predict.SLOPE |
Print results from SLOPE fit | print.SLOPE print.TrainedSLOPE |
Generate Regularization (Penalty) Weights for SLOPE | regularizationWeights |
Compute one of several loss metrics on a new data set | score score.BinomialSLOPE score.GaussianSLOPE score.MultinomialSLOPE score.PoissonSLOPE |
Sorted L-One Penalized Estimation | SLOPE |
Sorted L1 Proximal Operator | sortedL1Prox |
Student performance | student |
Train a SLOPE model | trainSLOPE |
Wine cultivars | wine |