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.
Version: | 0.5.1 |
Depends: | R (≥ 3.5.0) |
Imports: | foreach, ggplot2, Matrix, methods, Rcpp |
LinkingTo: | Rcpp, RcppArmadillo (≥ 0.9.850.1.0) |
Suggests: | bench, caret, glmnet, covr, dplyr, knitr, rmarkdown, scales, spelling, stringr, testthat (≥ 2.1.0), tidyr, vdiffr |
Published: | 2024-07-09 |
DOI: | 10.32614/CRAN.package.SLOPE |
Author: | Johan Larsson |
Maintainer: | Johan Larsson <johanlarsson at outlook.com> |
BugReports: | https://github.com/jolars/SLOPE/issues |
License: | GPL-3 |
Copyright: | see file COPYRIGHTS |
URL: | https://jolars.github.io/SLOPE/, https://github.com/jolars/SLOPE |
NeedsCompilation: | yes |
Language: | en-US |
Citation: | SLOPE citation info |
Materials: | README NEWS |
CRAN checks: | SLOPE results |
Reference manual: | SLOPE.pdf |
Vignettes: |
An introduction to SLOPE Proximal Operator Algorithms |
Package source: | SLOPE_0.5.1.tar.gz |
Windows binaries: | r-devel: SLOPE_0.5.1.zip, r-release: SLOPE_0.5.1.zip, r-oldrel: SLOPE_0.5.1.zip |
macOS binaries: | r-release (arm64): SLOPE_0.5.1.tgz, r-oldrel (arm64): SLOPE_0.5.1.tgz, r-release (x86_64): SLOPE_0.5.1.tgz, r-oldrel (x86_64): SLOPE_0.5.1.tgz |
Old sources: | SLOPE archive |
Reverse depends: | geneSLOPE |
Reverse imports: | sgs |
Reverse suggests: | grpSLOPE |
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