mboost (2.8-1)

Model-Based Boosting.

https://github.com/boost-R/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: Benjamin Hofner
Author(s): 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]

License: GPL-2

Uses: lattice, Matrix, nnls, party, quadprog, stabs, survival, RColorBrewer, fields, gbm, mlbench, randomForest, rpart, BayesX, MASS, nnet, testthat, TH.data, kangar00
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 over 2 years ago.