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: 0.3-13
Depends: gbm
Imports: rpart, methods, foreach, doParallel
Suggests: hdi, pROC, R.rsp, knitr
Published: 2016-02-28
Author: Zhu Wang [aut, cre], Torsten Hothorn [ctb]
Maintainer: Zhu Wang <zwang at connecticutchildrens.org>
BugReports: NA
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: NA
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 (Long)
Classification of UCI Machine Learning Datasets (Long)
Classification of UCI Machine Learning Datasets (Short)
Classification of Cancer Types Using Gene Expression Data (Short)
Cancer Classification Using Mass Spectrometry-based Proteomics Data
Package source: bst_0.3-13.tar.gz
Windows binaries: r-devel: bst_0.3-13.zip, r-release: bst_0.3-13.zip, r-oldrel: bst_0.3-13.zip
OS X Mavericks binaries: r-release: bst_0.3-13.tgz, r-oldrel: bst_0.3-13.tgz
Old sources: bst archive

Reverse dependencies:

Reverse imports: bujar
Reverse suggests: fscaret, mlr