SGPR
Sparse Group Penalized Regression for Bi-Level Variable Selection
Fits the regularization path of regression models (linear and logistic) with additively combined penalty terms. All possible combinations with Least Absolute Shrinkage and Selection Operator (LASSO), Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Penalty (MCP) and Exponential Penalty (EP) are supported. This includes Sparse Group LASSO (SGL), Sparse Group SCAD (SGS), Sparse Group MCP (SGM) and Sparse Group EP (SGE). For more information, see Buch, G., Schulz, A., Schmidtmann, I., Strauch, K., & Wild, P. S. (2024) doi:10.1002/bimj.202200334.
- Version0.1.2
- R versionunknown
- LicenseGPL (≥ 3)
- Needs compilation?Yes
- Last release05/16/2024
Documentation
Team
Gregor Buch
Andreas Schulz
Show author detailsRolesThesis advisorIrene Schmidtmann
Show author detailsRolesThesis advisorKonstantin Strauch
Show author detailsRolesThesis advisorPhilipp Wild
Show author detailsRolesThesis advisor
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