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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 + fileLICENSE
URL:https://github.com/jadonwagstaff/sboost
NeedsCompilation:yes
Materials:README,NEWS
CRAN checks:sboost results

Documentation:

Reference manual:sboost.html ,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:

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