bst: Gradient Boosting

Functional gradient descent algorithm for a variety of convex and nonconvex loss functions, for both classical and robust regression and classification problems. HingeBoost is implemented for binary and multi-class classification, with unequal misclassification costs for binary case. The algorithm can fit linear and nonlinear classifiers.

Version: 0.3-11
Depends: gbm
Imports: rpart, methods, foreach, doParallel
Suggests: hdi, pROC
Published: 2015-12-19
Author: Zhu Wang [aut, cre], Torsten Hothorn [ctb]
Maintainer: Zhu Wang <zwang at connecticutchildrens.org>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: NEWS
In views: MachineLearning
CRAN checks: bst results

Downloads:

Reference manual: bst.pdf
Vignettes: Classification of Cancer Types Using Gene Expression Data
Cancer Classification Using Mass Spectrometry-based Proteomics Data
Package source: bst_0.3-11.tar.gz
Windows binaries: r-devel: bst_0.3-11.zip, r-release: bst_0.3-11.zip, r-oldrel: bst_0.3-11.zip
OS X Snow Leopard binaries: r-release: bst_0.3-11.tgz, r-oldrel: bst_0.3-4.tgz
OS X Mavericks binaries: r-release: bst_0.3-11.tgz
Old sources: bst archive

Reverse dependencies:

Reverse imports: bujar
Reverse suggests: fscaret, mlr