Package: swag 0.1.1
Samuel Orso
swag: Sparse Wrapper Algorithm
An algorithm that trains a meta-learning procedure that combines screening and wrapper methods to find a set of extremely low-dimensional attribute combinations. This package works on top of the 'caret' package and proceeds in a forward-step manner. More specifically, it builds and tests learners starting from very few attributes until it includes a maximal number of attributes by increasing the number of attributes at each step. Hence, for each fixed number of attributes, the algorithm tests various (randomly selected) learners and picks those with the best performance in terms of training error. Throughout, the algorithm uses the information coming from the best learners at the previous step to build and test learners in the following step. In the end, it outputs a set of strong low-dimensional learners.
Authors:
swag_0.1.1.tar.gz
swag_0.1.1.zip(r-4.5)swag_0.1.1.zip(r-4.4)swag_0.1.1.zip(r-4.3)
swag_0.1.1.tgz(r-4.4-any)swag_0.1.1.tgz(r-4.3-any)
swag_0.1.1.tar.gz(r-4.5-noble)swag_0.1.1.tar.gz(r-4.4-noble)
swag_0.1.1.tgz(r-4.4-emscripten)swag_0.1.1.tgz(r-4.3-emscripten)
swag.pdf |swag.html✨
swag/json (API)
NEWS
| # Install 'swag' in R: |
| install.packages('swag', repos = c('https://smac-group.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/smac-group/swag-r-package/issues
Last updated 1 years agofrom:9dcac38f77. Checks:OK: 1 NOTE: 6. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 12 2024 |
| R-4.5-win | NOTE | Sep 12 2024 |
| R-4.5-linux | NOTE | Sep 12 2024 |
| R-4.4-win | NOTE | Sep 12 2024 |
| R-4.4-mac | NOTE | Sep 12 2024 |
| R-4.3-win | NOTE | Sep 12 2024 |
| R-4.3-mac | NOTE | Sep 12 2024 |
Exports:return_glm_beta_selected_modelsreturn_lm_beta_selected_modelsswagswagControl
Dependencies:caretclasscliclockcodetoolscolorspacecpp11data.tablediagramdigestdplyre1071fansifarverforeachfuturefuture.applygenericsggplot2globalsgluegowergtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmgcvModelMetricsmunsellnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6rbibutilsRColorBrewerRcppRdpackrecipesreshape2rlangrpartscalesshapeSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Predict method for SWAG | predict.swag |
| Returned estimated logistic regression coefficients for each selected model in a summary.swag object | return_glm_beta_selected_models |
| Returned estimated linear regression coefficients for each selected model in a summary.swag object | return_lm_beta_selected_models |
| Summary method for SWAG | summary.swag |
| Spare Wrapper AlGorithm (swag) | swag |
| Control for swag function | swagControl |
