The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
Implementation of Sparse-group SLOPE (SGS), a sparse-group penalisation regression approach. SGS performs adaptive bi-level selection, controlling the FDR under orthogonal designs. Linear (Gaussian) and logistic (Binomial) regression are supported, both with dense and sparse matrix implementations. Cross-validation functionality is also supported. SGS is implemented using adaptive three operator splitting (ATOS) and the package also contains a general implementation of ATOS.
A detailed description of SGS can be found in F. Feser, M. Evangelou (2023) “Sparse-group SLOPE: adaptive bi-level selection with FDR-control”.
You can install the current stable release from CRAN with
install.packages("sgs")Your R configuration must allow for a working Rcpp. To install a develop the development version from GitHub run
library(devtools)
install_github("ff1201/sgs")The code for fitting a basic SGS model is:
library(sgs)
model = fit_sgs(X = X, y = y, groups = groups, vFDR=0.1, gFDR=0.1)where X is the input matrix, y the response
vector, groups a vector containing indices for the groups
of the predictors, and vFDR and gFDR are the
the target variable/group false discovery rates.
A more extensive example can be found here.
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.
Health stats visible at Monitor.