mboost

Model-Based Boosting

Functional gradient descent algorithm (boosting) for optimizing general risk functions utilizing component-wise (penalised) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data.

Total

238,174

Last month

4,648

Last week

1,027

Average per day

155

Daily downloads

Total downloads

Description file content

Package
mboost
Title
Model-Based Boosting
Version
2.9-1
Date
2018-08-21
Description
Functional gradient descent algorithm (boosting) for optimizing general risk functions utilizing component-wise (penalised) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data.
Depends
R (>= 3.2.0), methods, stats, parallel, stabs (>= 0.5-0)
Imports
Matrix, survival, splines, lattice, nnls, quadprog, utils, graphics, grDevices, partykit (>= 1.2-1)
Suggests
TH.data, MASS, fields, BayesX, gbm, mlbench, RColorBrewer, rpart (>= 4.0-3), randomForest, nnet, testthat (>= 0.10.0), kangar00
LazyData
yes
License
GPL-2
BugReports
https://github.com/boost-R/mboost/issues
URL
NeedsCompilation
yes
Packaged
2018-08-21 09:12:58 UTC; hofbe
Author
Torsten Hothorn [aut] (), Peter Buehlmann [aut], Thomas Kneib [aut], Matthias Schmid [aut], Benjamin Hofner [aut, cre] (), Fabian Sobotka [ctb], Fabian Scheipl [ctb], Andreas Mayr [ctb]
Maintainer
Benjamin Hofner
Repository
CRAN
Date/Publication
2018-08-22 05:10:02 UTC

install.packages('mboost')

2.9-1

9 months ago

https://github.com/boost-R/mboost

Benjamin Hofner

GPL-2

Depends on

R (>= 3.2.0), methods, stats, parallel, stabs (>= 0.5-0)

Imports

Matrix, survival, splines, lattice, nnls, quadprog, utils, graphics, grDevices, partykit (>= 1.2-1)

Suggests

TH.data, MASS, fields, BayesX, gbm, mlbench, RColorBrewer, rpart (>= 4.0-3), randomForest, nnet, testthat (>= 0.10.0), kangar00

Discussions