Package: SLOPE 0.5.1

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_0.5.1.tar.gz
SLOPE_0.5.1.tar.gz(r-4.5-noble)SLOPE_0.5.1.tar.gz(r-4.4-noble)
SLOPE_0.5.1.tgz(r-4.4-emscripten)SLOPE_0.5.1.tgz(r-4.3-emscripten)
SLOPE.pdf |SLOPE.html
SLOPE/json (API)
NEWS

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

Peer review:

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

Pkgdown:https://jolars.github.io

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

5.75 score 3 packages 63 scripts 398 downloads 16 mentions 7 exports 33 dependencies

Last updated 5 months agofrom:85dd17ca1c. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 07 2024
R-4.5-linux-x86_64OKNov 07 2024

Exports:caretSLOPEplotDiagnosticsregularizationWeightsscoreSLOPEsortedL1ProxtrainSLOPE

Dependencies:clicodetoolscolorspacefansifarverforeachggplot2gluegtableisobanditeratorslabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerRcppRcppArmadillorlangscalestibbleutf8vctrsviridisLitewithr

An introduction to SLOPE

Rendered fromintroduction.Rmdusingknitr::rmarkdownon Nov 07 2024.

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

Proximal Operator Algorithms

Rendered fromprox-algs.Rmdusingknitr::rmarkdownon Nov 07 2024.

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

Readme and manuals

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