mboost (1.1-3)

Model-Based Boosting.


Functional gradient descent algorithms (boosting) for optimizing general loss functions utilizing componentwise least squares, either of parametric linear form or smoothing splines, 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, Peter Buhlmann, Thomas Kneib, Matthias Schmid and Benjamin Hofner

License: GPL-2

Uses: modeltools, party, ipred, mlbench, multicore
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 10 years ago.