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:	0.3-23
Imports:	rpart, methods, foreach, doParallel, gbm
Suggests:	hdi, pROC, R.rsp, knitr, gdata
Published:	2020-11-09
Author:	Zhu Wang [aut, cre], Torsten Hothorn [ctb]
Maintainer:	Zhu Wang <wangz1 at uthscsa.edu>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	no
Citation:	bst citation info
Materials:	NEWS
In views:	MachineLearning
CRAN checks:	bst results

Documentation:

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

Downloads:

Package source:	bst_0.3-23.tar.gz
Windows binaries:	r-devel: bst_0.3-23.zip, r-release: bst_0.3-23.zip, r-oldrel: bst_0.3-23.zip
macOS binaries:	r-release (arm64): bst_0.3-23.tgz, r-oldrel (arm64): bst_0.3-23.tgz, r-release (x86_64): bst_0.3-23.tgz, r-oldrel (x86_64): bst_0.3-23.tgz
Old sources:	bst archive

Reverse dependencies:

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

Linking:

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