Package: SLOPE 2.1.0

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], Mathurin Massias [aut], Quentin Klopfenstein [aut], Michal Burdukiewicz [aut], Jerome Friedman [ctb], Trevor Hastie [ctb], Rob Tibshirani [ctb], Balasubramanian Narasimhan [ctb], Noah Simon [ctb], Junyang Qian [ctb]

SLOPE_2.1.0.tar.gz
SLOPE_2.1.0.tar.gz(r-4.7-arm64)SLOPE_2.1.0.tar.gz(r-4.7-x86_64)SLOPE_2.1.0.tar.gz(r-4.6-arm64)SLOPE_2.1.0.tar.gz(r-4.6-x86_64)
SLOPE_2.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
SLOPE/json (API)
NEWS

# Install 'SLOPE' in R:
install.packages('SLOPE', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jolars/slope/issues

Pkgdown/docs site:https://jolars.github.io

Uses libs:
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On CRAN:

Conda:

cppopenmp

6.57 score 2 packages 83 scripts 619 downloads 16 mentions 9 exports 8 dependencies

Last updated from:4ab6cf6d02. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK256
linux-devel-x86_64OK240
source / vignettesOK341
linux-release-arm64OK268
linux-release-x86_64OK205
wasm-releaseOK207

Exports:cvSLOPEplotClustersplotDiagnosticsrefitregularizationWeightsscoreSLOPEsortedL1ProxtrainSLOPE

Dependencies:BHbigmemorybigmemory.srilatticeMatrixRcppRcppEigenuuid

An introduction to SLOPE

Johan Larsson

Rendered fromintroduction.Rmdusingknitr::rmarkdownon May 27 2026.

Last update: 2026-01-28
Started: 2020-04-16

Models in SLOPE

Johan Larsson

Rendered frommodels.Rmdusingknitr::rmarkdownon May 27 2026.

Last update: 2026-01-28
Started: 2026-01-28

Solvers in SLOPE

Johan Larsson

Rendered fromsolvers.Rmdusingknitr::rmarkdownon May 27 2026.

Last update: 2025-06-30
Started: 2025-06-30

Readme and manuals

Help Manual

Help pageTopics
Abaloneabalone
Bodyfatbodyfat
Obtain Coefficientscoef.SLOPE
Tune SLOPE with Cross-ValidationcvSLOPE
Model Deviancedeviance.SLOPE
Glioma Metabolomicsglioma
Heart Diseaseheart
Plot Coefficientsplot.SLOPE
Plot Results from Cross-Validationplot.TrainedSLOPE
Plot Cluster StructureplotClusters
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
Print Summary of SLOPE Modelprint.summary_SLOPE
Print Summary of TrainedSLOPE Modelprint.summary_TrainedSLOPE
Refit SLOPE Model with Optimal Parametersrefit
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
Summarize SLOPE Modelsummary.SLOPE
Summarize TrainedSLOPE Modelsummary.TrainedSLOPE
Train a SLOPE ModeltrainSLOPE
Wine Cultivarswine