sboost: Machine Learning with AdaBoost on Decision Stumps

Creates classifier for binary outcomes using Freund and Schapire's Adaptive Boosting (AdaBoost) algorithm on decision stumps with a fast C++ implementation. 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: 0.1.0
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: 2018-10-28
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

Downloads:

Reference manual: sboost.pdf
Package source: sboost_0.1.0.tar.gz
Windows binaries: r-devel: sboost_0.1.0.zip, r-release: sboost_0.1.0.zip, r-oldrel: sboost_0.1.0.zip
OS X binaries: r-release: sboost_0.1.0.tgz, r-oldrel: sboost_0.1.0.tgz

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