Sparse-Group SLOPE: Adaptive Bi-Level Selection with FDR Control
Matrix Product in RcppArmadillo.
Matrix Product in RcppArmadillo.
Fits the adaptively scaled SGS model (AS-SGS).
Adaptive three operator splitting (ATOS).
Extracts coefficients for one of the following object types: "sgs", ...
Fit a gOSCAR model using k-fold cross-validation.
Fit a gOSCAR model.
Fit a gSLOPE model using k-fold cross-validation.
Fit a gSLOPE model.
Fit an SGO model using k-fold cross-validation.
Fit an SGO model.
Fit an SGS model using k-fold cross-validation.
Fit an SGS model.
Generate penalty sequences for SGS.
Generate toy data.
Plot models of the following object types: "sgs", "sgs_cv", `"gslo...
Predict using one of the following object types: "sgs", "sgs_cv", ...
Prints information for one of the following object types: "sgs", `"s...
Fits a scaled SGS model.
sgs: Sparse-Group SLOPE: Adaptive Bi-Level Selection with FDR Control
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.