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Implementation of Sparse-group SLOPE: Adaptive bi-level with FDR-control (Feser et al. (2023) <doi:10.48550/arXiv.2305.09467>). 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) implementation is provided.
Version: | 0.1.1 |
Imports: | Matrix, MASS, caret, grDevices, graphics, methods, stats, faux, SLOPE, Rlab, Rcpp (≥ 1.0.10) |
LinkingTo: | Rcpp, RcppArmadillo |
Suggests: | SGL, gglasso, glmnet, testthat, knitr, rmarkdown |
Published: | 2023-08-22 |
Author: | Fabio Feser [aut, cre], Marina Evangelou [aut] |
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 |
Materials: | README |
CRAN checks: | sgs results |
Reference manual: | sgs.pdf |
Vignettes: |
SGS reproducible example |
Package source: | sgs_0.1.1.tar.gz |
Windows binaries: | r-devel: sgs_0.1.1.zip, r-release: sgs_0.1.1.zip, r-oldrel: sgs_0.1.1.zip |
macOS binaries: | r-release (arm64): sgs_0.1.1.tgz, r-oldrel (arm64): sgs_0.1.1.tgz, r-release (x86_64): sgs_0.1.1.tgz, r-oldrel (x86_64): sgs_0.1.1.tgz |
Old sources: | sgs archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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