sboost: Machine Learning with AdaBoost on Decision Stumps

Creates classifier for binary outcomes using Adaptive Boosting (AdaBoost) algorithm on decision stumps with a fast C++ implementation. For a description of AdaBoost, see Freund and Schapire (1997) <doi:10.1006/jcss.1997.1504>. This type of classifier is nonlinear, but easy to interpret and visualize. Feature vectors may be a combination of continuous (numeric) and categorical (string, factor) elements. Methods for classifier assessment, predictions, and cross-validation also included.

Version:	0.1.2
Depends:	R (≥ 3.4.0)
Imports:	dplyr (≥ 0.7.6), rlang (≥ 0.2.1), Rcpp (≥ 0.12.17), stats (≥ 3.4)
LinkingTo:	Rcpp (≥ 0.12.17)
Suggests:	testthat
Published:	2022-05-26
DOI:	10.32614/CRAN.package.sboost
Author:	Jadon Wagstaff [aut, cre]
Maintainer:	Jadon Wagstaff <jadonw at gmail.com>
BugReports:	https://github.com/jadonwagstaff/sboost/issues
License:	MIT + file LICENSE
URL:	https://github.com/jadonwagstaff/sboost
NeedsCompilation:	yes
Materials:	README NEWS
CRAN checks:	sboost results

Documentation:

Reference manual:

sboost.pdf

Downloads:

Package source:	sboost_0.1.2.tar.gz
Windows binaries:	r-devel: sboost_0.1.2.zip, r-release: sboost_0.1.2.zip, r-oldrel: sboost_0.1.2.zip
macOS binaries:	r-release (arm64): sboost_0.1.2.tgz, r-oldrel (arm64): sboost_0.1.2.tgz, r-release (x86_64): sboost_0.1.2.tgz, r-oldrel (x86_64): sboost_0.1.2.tgz
Old sources:	sboost archive

Linking:

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