Package: SLOPE 0.5.1
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_0.5.1.tar.gz
SLOPE_0.5.1.zip(r-4.5)SLOPE_0.5.1.zip(r-4.4)SLOPE_0.5.1.zip(r-4.3)
SLOPE_0.5.1.tgz(r-4.4-x86_64)SLOPE_0.5.1.tgz(r-4.4-arm64)SLOPE_0.5.1.tgz(r-4.3-x86_64)SLOPE_0.5.1.tgz(r-4.3-arm64)
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://jolars.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/jolars/slope/issues
generalized-linear-modelsslopesparse-regression
Last updated 4 days agofrom:0b39e479fa. Checks:OK: 9. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 01 2024 |
| R-4.5-win-x86_64 | OK | Oct 01 2024 |
| R-4.5-linux-x86_64 | OK | Oct 01 2024 |
| R-4.4-win-x86_64 | OK | Oct 01 2024 |
| R-4.4-mac-x86_64 | OK | Oct 01 2024 |
| R-4.4-mac-aarch64 | OK | Oct 01 2024 |
| R-4.3-win-x86_64 | OK | Oct 01 2024 |
| R-4.3-mac-x86_64 | OK | Oct 01 2024 |
| R-4.3-mac-aarch64 | OK | Oct 01 2024 |
Exports:caretSLOPEplotDiagnosticsregularizationWeightsscoreSLOPEsortedL1ProxtrainSLOPE
Dependencies:clicodetoolscolorspacefansifarverforeachggplot2gluegtableisobanditeratorslabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerRcppRcppArmadillorlangscalestibbleutf8vctrsviridisLitewithr
Readme and manuals
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 |
