Package: sgs

Title: Sparse-Group SLOPE: Adaptive Bi-Level Selection with FDR Control

Version: 0.3.7

Date: 2025-05-06

Authors@R: person("Fabio", "Feser", role = c("aut", "cre"), email = "ff120@ic.ac.uk",comment = c(ORCID = "0009-0007-3088-9727"))

Maintainer: Fabio Feser <ff120@ic.ac.uk>

Description: 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.

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

Roxygen: list(markdown = TRUE)

RoxygenNote: 7.3.1

License: GPL (>= 3)

Encoding: UTF-8

URL: https://github.com/ff1201/sgs

BugReports: https://github.com/ff1201/sgs/issues

VignetteBuilder: knitr