Last data update: 2014.03.03
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bst
Package: bst
Type: Package
Title: Gradient Boosting
Version: 0.3-13
Date: 2016-02-17
Authors@R: c(person("Zhu", "Wang", role = c("aut", "cre"),
email = "zwang@connecticutchildrens.org"),
person("Torsten", "Hothorn", role = "ctb"))
Author: Zhu Wang [aut, cre],
Torsten Hothorn [ctb]
Maintainer: Zhu Wang <zwang@connecticutchildrens.org>
Description: Functional gradient descent algorithm for a variety of convex and nonconvex loss functions, for both classical and robust regression and classification problems. HingeBoost is implemented for binary and multi-class classification, with unequal misclassification costs for binary case. The algorithm can fit linear and nonlinear classifiers.
Imports: rpart, methods, foreach, doParallel
Depends: gbm
Suggests: hdi, pROC, R.rsp, knitr
VignetteBuilder: R.rsp, knitr
License: GPL (>= 2)
LazyLoad: yes
Packaged: 2016-02-17 21:03:41 UTC; zhu
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2016-02-28 09:59:24
● Cran Task View: MachineLearning
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0 images,
21 functions,
0 datasets
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Reverse Depends: 0
Install log
* installing to library '/home/ddbj/local/lib64/R/library'
* installing *source* package 'bst' ...
** package 'bst' successfully unpacked and MD5 sums checked
** R
** inst
** preparing package for lazy loading
** help
*** installing help indices
converting help for package 'bst'
finding HTML links ... done
bst-internal html
bst-package html
bst html
bst.sel html
bst_control html
cv.bst html
cv.mada html
cv.mbst html
cv.mhingebst html
cv.mhingeova html
cv.rbst html
cv.rmbst html
ex1data html
mada html
mbst html
mhingebst html
mhingeova html
nsel.mhingebst html
rbst html
rbstpath html
rmbst html
** building package indices
** installing vignettes
** testing if installed package can be loaded
* DONE (bst)
Making 'packages.html' ... done
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