Package: sparsestep 1.0.1
sparsestep: SparseStep Regression
Implements the SparseStep model for solving regression problems with a sparsity constraint on the parameters. The SparseStep regression model was proposed in Van den Burg, Groenen, and Alfons (2017) <arxiv:1701.06967>. In the model, a regularization term is added to the regression problem which approximates the counting norm of the parameters. By iteratively improving the approximation a sparse solution to the regression problem can be obtained. In this package both the standard SparseStep algorithm is implemented as well as a path algorithm which uses golden section search to determine solutions with different values for the regularization parameter.
Authors:
sparsestep_1.0.1.tar.gz
sparsestep_1.0.1.zip(r-4.7-any)sparsestep_1.0.1.zip(r-4.6-any)sparsestep_1.0.1.zip(r-4.5-any)
sparsestep_1.0.1.tgz(r-4.6-any)sparsestep_1.0.1.tgz(r-4.5-any)
sparsestep_1.0.1.tar.gz(r-4.7-any)sparsestep_1.0.1.tar.gz(r-4.6-any)
sparsestep_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
sparsestep/json (API)
| # Install 'sparsestep' in R: |
| install.packages('sparsestep', repos = c('https://gjjvdburg.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/gjjvdburg/sparsestep/issues
feature-selectionlasso-variantsregularized-linear-regressionsparse-regressionsparse-regularization
Last updated from:4c49fabae8. Checks:7 NOTE, 2 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | NOTE | 95 | ||
| source / vignettes | OK | 203 | ||
| linux-release | NOTE | 111 | ||
| macos-release | NOTE | 87 | ||
| macos-oldrel | NOTE | 100 | ||
| windows-devel | NOTE | 71 | ||
| windows-release | NOTE | 108 | ||
| windows-oldrel | NOTE | 69 | ||
| wasm-release | OK | 71 |
Exports:path.sparsestepsparsestep
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| SparseStep: Approximating the Counting Norm for Sparse Regularization | sparsestep-package |
| Get the coefficients of a fitted SparseStep model | coef coef.sparsestep |
| Approximate path algorithm for the SparseStep model | path.sparsestep |
| Plot the SparseStep path | plot plot.sparsestep |
| Make predictions from a SparseStep model | predict predict.sparsestep |
| Print the fitted SparseStep model | print.sparsestep |
| Fit the SparseStep model | sparsestep |