mboost (2.9-1)

2 users

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: Benjamin Hofner
Author(s): Torsten Hothorn [aut] (<https://orcid.org/0000-0001-8301-0471>), Peter Buehlmann [aut], Thomas Kneib [aut], Matthias Schmid [aut], Benjamin Hofner [aut, cre] (<https://orcid.org/0000-0003-2810-3186>), Fabian Sobotka [ctb], Fabian Scheipl [ctb], Andreas Mayr [ctb]

License: GPL-2

Uses: lattice, Matrix, nnls, partykit, 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 1 year ago.

32 previous versions



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