mboost (2.2-3)

Model-Based Boosting.

http://r-forge.r-project.org/projects/mboost/
http://cran.r-project.org/web/packages/mboost

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.

Maintainer: Torsten Hothorn
Author(s): Torsten Hothorn [aut, cre], Peter Buehlmann [aut], Thomas Kneib [aut], Matthias Schmid [aut], Benjamin Hofner [aut], Fabian Sobotka [ctb], Fabian Scheipl [ctb]

License: GPL-2

Uses: lattice, Matrix, survival, RColorBrewer, fields, gbm, mlbench, party, rpart, BayesX, MASS, TH.data
Reverse depends: betaboost, bujar, expectreg, FDboost, gamboostLSS, globalboosttest, InvariantCausalPrediction, parboost, stratasphere, tbm
Reverse suggests: caret, CompareCausalNetworks, compboost, Daim, fscaret, HSAUR2, HSAUR3, imputeR, MachineShop, mlr, multcomp, OpenML, pre, RBPcurve, spikeSlabGAM, sqlscore, stabs
Reverse enhances: stabs

Released about 6 years ago.