The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

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.

Version: 0.1.0
Depends: R (≥ 4.0.0)
Imports: caret, Rdpack (≥ 0.7), stats
Suggests: doParallel, e1071, foreach, ggplot2, glmnet, grDevices, iterators, kernlab, knitr, lattice, methods, mlbench, ModelMetrics, nlme, parallel, plyr, pROC, randomForest, recipes, remotes, reshape2, stats4, rmarkdown, utils, withr
Published: 2020-11-10
DOI: 10.32614/CRAN.package.swag
Author: Samuel Orso [aut, cre], Gaetan Bakalli [aut], Cesare Miglioli [aut], Stephane Guerrier [ctb], Roberto Molinari [ctb]
Maintainer: Samuel Orso <Samuel.Orso at unige.ch>
BugReports: https://github.com/SMAC-Group/SWAG-R-Package/issues/
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/SMAC-Group/SWAG-R-Package/
NeedsCompilation: no
Materials: README NEWS
CRAN checks: swag results

Documentation:

Reference manual: swag.pdf
Vignettes: Introduction to swag

Downloads:

Package source: swag_0.1.0.tar.gz
Windows binaries: r-devel: swag_0.1.0.zip, r-release: swag_0.1.0.zip, r-oldrel: swag_0.1.0.zip
macOS binaries: r-release (arm64): swag_0.1.0.tgz, r-oldrel (arm64): swag_0.1.0.tgz, r-release (x86_64): swag_0.1.0.tgz, r-oldrel (x86_64): swag_0.1.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=swag to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.