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>.
| Version: |
0.1.2 |
| Imports: |
Rcpp |
| LinkingTo: |
Rcpp |
| Published: |
2024-05-16 |
| Author: |
Gregor Buch [aut, cre, cph],
Andreas Schulz [ths],
Irene Schmidtmann [ths],
Konstantin Strauch [ths],
Philipp Wild [ths] |
| Maintainer: |
Gregor Buch <buchgregor at gmail.com> |
| License: |
GPL (≥ 3) |
| NeedsCompilation: |
yes |
| Materials: |
NEWS |
| CRAN checks: |
SGPR results |
Documentation:
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