jlaria/glasp: Group Linear Algorithm with Sparse Principal decomposition

Implements the the Group Linear Algorithm with Sparse Principal decomposition, an algorithm for supervised variable selection and clustering. Our approach extends the Sparse-Group Lasso regularization to calculate clusters as part of the model fit. Therefore, unlike Sparse-Group Lasso, our idea does not require prior specification of clusters between variables. To determine the clusters, we solve a particular case of sparse Singular Value Decomposition, with a regularization term that follows naturally from the Group Lasso penalty. Moreover, this paper proposes a unified implementation to deal with, but not limited to, linear regression, logistic regression, and proportional hazards models with right-censoring.

install.packages("remotes")
remotes::install_github("jlaria/glasp")