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. See Wang (2011) <doi:10.2202/1557-4679.1304>, Wang (2012) <doi:10.3414/ME11-02-0020>, Wang (2018) <doi:10.1080/10618600.2018.1424635>, Wang (2018) <doi:10.1214/18-EJS1404>.

Version: 0.3-15
Depends: gbm
Imports: rpart, methods, foreach, doParallel
Suggests: hdi, pROC, R.rsp, knitr, gdata
Published: 2018-07-23
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: Classification of Breast Cancer Clinical Stage with Gene Expression Data (with Results)
Classification of Cancer Types Using Gene Expression Data (with Results)
Classification of UCI Machine Learning Datasets (with Results)
Classification of Breast Cancer Clinical Stage with Gene Expression Data (without Results)
Classification of UCI Machine Learning Datasets (without Results)
Classification of Cancer Types Using Gene Expression Data (without Results)
Cancer Classification Using Mass Spectrometry-based Proteomics Data
Package source: bst_0.3-15.tar.gz
Windows binaries: r-devel: bst_0.3-15.zip, r-release: bst_0.3-15.zip, r-oldrel: bst_0.3-15.zip
OS X binaries: r-release: bst_0.3-15.tgz, r-oldrel: bst_0.3-15.tgz
Old sources: bst archive

Reverse dependencies:

Reverse imports: bujar, mpath
Reverse suggests: fscaret, mlr

Linking:

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