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:
SGPR_0.1.2.tar.gz
SGPR_0.1.2.zip(r-4.7)SGPR_0.1.2.zip(r-4.6)SGPR_0.1.2.zip(r-4.5)
SGPR_0.1.2.tgz(r-4.6-x86_64)SGPR_0.1.2.tgz(r-4.6-arm64)SGPR_0.1.2.tgz(r-4.5-x86_64)SGPR_0.1.2.tgz(r-4.5-arm64)
SGPR_0.1.2.tar.gz(r-4.7-arm64)SGPR_0.1.2.tar.gz(r-4.7-x86_64)SGPR_0.1.2.tar.gz(r-4.6-arm64)SGPR_0.1.2.tar.gz(r-4.6-x86_64)
SGPR_0.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
SGPR/json (API)
NEWS
| # Install 'SGPR' in R: |
| install.packages('SGPR', repos = c('https://gregorbuch.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:2bd3d649a6. Checks:13 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 123 | ||
| linux-devel-x86_64 | OK | 114 | ||
| source / vignettes | OK | 153 | ||
| linux-release-arm64 | OK | 107 | ||
| linux-release-x86_64 | OK | 111 | ||
| macos-release-arm64 | OK | 140 | ||
| macos-release-x86_64 | OK | 222 | ||
| macos-oldrel-arm64 | OK | 138 | ||
| macos-oldrel-x86_64 | OK | 276 | ||
| windows-devel | OK | 116 | ||
| windows-release | OK | 101 | ||
| windows-oldrel | OK | 117 | ||
| wasm-release | OK | 104 |
Dependencies:Rcpp
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Coefficients from an SGP model | coef.sgp |
| Coefficients from SGP models | coef.sgp.cv |
| A function that calculates the loss/cost | get.loss |
| Plots the coefficient path of an SGP object | plot.sgp |
| Plots the cross-validation curve from a SGP object | plot.sgp.cv |
| Predictions based on a SGP model | predict.sgp |
| Predictions based on a SGP models | predict.sgp.cv |
| Process groupings for a sparse group penalty | process.group |
| Set up a lambda sequence | process.lambda |
| Process the arguments about the sparse group penalty | process.penalty |
| Process X for a sparse group penalty | process.X |
| Process y for a sparse group penalty | process.y |
| Process Z for a sparse group penalty | process.Z |
| Fit a sparse group regularized regression path | sgp |
| Cross-validation for sparse group penalties | sgp.cv |
