CRAN: Package bst

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: 0.3-24

Imports: rpart, methods, foreach, doParallel, gbm

Suggests: hdi, pROC, R.rsp, knitr, gdata

Published: 2023年01月06日

DOI: 10.32614/CRAN.package.bst

Author: Zhu Wang ORCID iD [aut, cre], Torsten Hothorn [ctb]

Maintainer: Zhu Wang <zwang145 at uthsc.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.html , bst.pdf

Vignettes: Classification of Breast Cancer Clinical Stage with Gene Expression Data (with Results) (source)
Classification of Cancer Types Using Gene Expression Data (with Results) (source)
Classification of UCI Machine Learning Datasets (with Results) (source)
Classification of Breast Cancer Clinical Stage with Gene Expression Data (without Results) (source, R code)
Classification of UCI Machine Learning Datasets (without Results) (source, R code)
Classification of Cancer Types Using Gene Expression Data (without Results) (source, R code)
Cancer Classification Using Mass Spectrometry-based Proteomics Data (source, R code)

Downloads:

Package source: bst_0.3-24.tar.gz

Windows binaries: r-devel: bst_0.3-24.zip, r-release: bst_0.3-24.zip, r-oldrel: bst_0.3-24.zip

macOS binaries: r-release (arm64): bst_0.3-24.tgz, r-oldrel (arm64): bst_0.3-24.tgz, r-release (x86_64): bst_0.3-24.tgz, r-oldrel (x86_64): bst_0.3-24.tgz

Old sources: bst archive

Reverse dependencies:

Reverse imports: bujar, mpath

Reverse suggests: flowml, mlr

Linking:

Please use the canonical form https://CRAN.R-project.org/package=bst to link to this page.