Package: SLOPE 0.5.2

Johan Larsson

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:Johan Larsson [aut, cre], Jonas Wallin [aut], Malgorzata Bogdan [aut], Ewout van den Berg [aut], Chiara Sabatti [aut], Emmanuel Candes [aut], Evan Patterson [aut], Weijie Su [aut], Jakub Kała [aut], Krystyna Grzesiak [aut], Michal Burdukiewicz [aut], Jerome Friedman [ctb], Trevor Hastie [ctb], Rob Tibshirani [ctb], Balasubramanian Narasimhan [ctb], Noah Simon [ctb], Junyang Qian [ctb], Akarsh Goyal [ctb]

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

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

openblascpp

5.26 score 3 packages 566 downloads 16 mentions 7 exports 33 dependencies

Last updated 2 months agofrom:f4c4df79d9. Checks:3 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 07 2025
R-4.5-linux-x86_64OKMar 07 2025
R-4.4-linux-x86_64OKMar 07 2025

Exports:caretSLOPEplotDiagnosticsregularizationWeightsscoreSLOPEsortedL1ProxtrainSLOPE

Dependencies:clicodetoolscolorspacefansifarverforeachggplot2gluegtableisobanditeratorslabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerRcppRcppArmadillorlangscalestibbleutf8vctrsviridisLitewithr

An introduction to SLOPE

Rendered fromintroduction.Rmdusingknitr::rmarkdownon Mar 07 2025.

Last update: 2024-07-10
Started: 2020-04-16

Proximal Operator Algorithms

Rendered fromprox-algs.Rmdusingknitr::rmarkdownon Mar 07 2025.

Last update: 2021-12-10
Started: 2021-12-10

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 pageTopics
Abaloneabalone
Bodyfatbodyfat
Model objects for model tuning with caret (deprecated)caretSLOPE
Obtain coefficientscoef.SLOPE
Model deviancedeviance.SLOPE
Heart diseaseheart
Plot coefficientsplot.SLOPE
Plot results from cross-validationplot.TrainedSLOPE
Plot results from diagnostics collected during model fittingplotDiagnostics
Generate predictions from SLOPE modelspredict.BinomialSLOPE predict.GaussianSLOPE predict.MultinomialSLOPE predict.PoissonSLOPE predict.SLOPE
Print results from SLOPE fitprint.SLOPE print.TrainedSLOPE
Generate Regularization (Penalty) Weights for SLOPEregularizationWeights
Compute one of several loss metrics on a new data setscore score.BinomialSLOPE score.GaussianSLOPE score.MultinomialSLOPE score.PoissonSLOPE
Sorted L-One Penalized EstimationSLOPE
Sorted L1 Proximal OperatorsortedL1Prox
Student performancestudent
Train a SLOPE modeltrainSLOPE
Wine cultivarswine