bst: Gradient Boosting

Functional gradient descent algorithm for a variety of convex and non-convex loss functions, for both classical and robust regression and classification problems.

Version: 0.3-14
Depends: gbm
Imports: rpart, methods, foreach, doParallel
Suggests: hdi, pROC, R.rsp, knitr, gdata
Published: 2016-09-21
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
Citation: bst citation info
Materials: NEWS
In views: MachineLearning
CRAN checks: bst results

Downloads:

Reference manual: bst.pdf
Vignettes: Analysis of Cancer Data with Boosting Algorithm for Nonconvex Loss (Long)
Classification of Cancer Types Using Gene Expression Data (Long)
Classification of UCI Machine Learning Datasets (Long)
Analysis of Real Data with Nonconvex Loss Boosting
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_0.3-14.tar.gz
Windows binaries: r-devel: bst_0.3-14.zip, r-release: bst_0.3-14.zip, r-oldrel: bst_0.3-14.zip
OS X Mavericks binaries: r-release: bst_0.3-14.tgz, r-oldrel: bst_0.3-14.tgz
Old sources: bst archive

Reverse dependencies:

Reverse imports: bujar
Reverse suggests: fscaret, mlr

Linking:

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