Implementation of Sparse-group SLOPE (SGS) (Feser and Evangelou (2023) <doi:10.48550/arXiv.2305.09467>) models. Linear and logistic regression models are supported, both of which can be fit using k-fold cross-validation. Dense and sparse input matrices are supported. In addition, a general Adaptive Three Operator Splitting (ATOS) (Pedregosa and Gidel (2018) <doi:10.48550/arXiv.1804.02339>) implementation is provided. Group SLOPE (gSLOPE) (Brzyski et al. (2019) <doi:10.1080/01621459.2017.1411269>) and group-based OSCAR models (Feser and Evangelou (2024) <doi:10.48550/arXiv.2405.15357>) are also implemented. All models are available with strong screening rules (Feser and Evangelou (2024) <doi:10.48550/arXiv.2405.15357>) for computational speed-up.
Version: 0.3.8 Imports: Matrix, MASS, caret, grDevices, graphics, methods, stats, SLOPE, Rlab, Rcpp (≥ 1.0.10) LinkingTo: Rcpp, RcppArmadillo Suggests: SGL, gglasso, glmnet, testthat, knitr, grpSLOPE, rmarkdown Published: 2025-06-12 DOI: 10.32614/CRAN.package.sgs Author: Fabio Feser [aut, cre] Maintainer: Fabio Feser <ff120 at ic.ac.uk> BugReports: https://github.com/ff1201/sgs/issues License: GPL (≥ 3) URL: https://github.com/ff1201/sgs NeedsCompilation: yes Citation: sgs citation info Materials: README CRAN checks: sgs results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=sgs to link to this page.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4