swaglm: Fast Sparse Wrapper Algorithm for Generalized Linear Models and Testing Procedures for Network of Highly Predictive Variables

Provides a fast implementation of the SWAG algorithm for Generalized Linear Models which allows to perform a meta-learning procedure that combines screening and wrapper methods to find a set of extremely low-dimensional attribute combinations. The package then performs test on the network of selected models to identify the variables that are highly predictive by using entropy-based network measures.

Version:	0.0.1
Imports:	Rcpp, fastglm, stats, igraph, gdata, plyr, progress, DescTools, scales, fields
LinkingTo:	Rcpp, RcppArmadillo
Suggests:	knitr, MASS, rmarkdown
Published:	2025-09-18
Author:	Lionel Voirol [aut, cre], Yagmur Ozdemir [aut]
Maintainer:	Lionel Voirol <lionelvoirol at hotmail.com>
License:	AGPL-3
NeedsCompilation:	yes
Materials:	README
CRAN checks:	swaglm results

Documentation:

Reference manual:	swaglm.html , swaglm.pdf
Vignettes:	Run the SWAG algorithm for generalized linear models (source, R code)

Downloads:

Package source:	swaglm_0.0.1.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available

Linking:

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