mboost (2.0-10)

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.

Maintainer: Torsten Hothorn
Author(s): Torsten Hothorn, Peter Buehlmann, Thomas Kneib, Matthias Schmid and Benjamin Hofner

License: GPL-2

Uses: lattice, Matrix, survival, gbm, ipred, party, multicore, MASS
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 8 years ago.