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 |
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 El Capitan binaries: | r-release: bst_0.3-14.tgz |
OS X Mavericks binaries: | r-oldrel: bst_0.3-14.tgz |
Old sources: | bst archive |
Reverse imports: | bujar, mpath |
Reverse suggests: | fscaret, mlr |
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