Package: SGPR 0.1.2

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>.

Authors:Gregor Buch [aut, cre, cph], Andreas Schulz [ths], Irene Schmidtmann [ths], Konstantin Strauch [ths], Philipp Wild [ths]

SGPR_0.1.2.tar.gz
SGPR_0.1.2.zip(r-4.5)SGPR_0.1.2.zip(r-4.4)SGPR_0.1.2.zip(r-4.3)
SGPR_0.1.2.tgz(r-4.4-x86_64)SGPR_0.1.2.tgz(r-4.4-arm64)SGPR_0.1.2.tgz(r-4.3-x86_64)SGPR_0.1.2.tgz(r-4.3-arm64)
SGPR_0.1.2.tar.gz(r-4.5-noble)SGPR_0.1.2.tar.gz(r-4.4-noble)
SGPR_0.1.2.tgz(r-4.4-emscripten)SGPR_0.1.2.tgz(r-4.3-emscripten)
SGPR.pdf |SGPR.html✨
SGPR/json (API)
NEWS

Peer review:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2 exports 0.09 score 1 dependencies 130 downloads

Last updated 4 months agofrom:2bd3d649a6. Checks:OK: 9. Indexed: yes.

Exports:sgpsgp.cv

Dependencies:Rcpp