Package: mboost 2.9-11

Torsten Hothorn

mboost: 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. Models and algorithms are described in <doi:10.1214/07-STS242>, a hands-on tutorial is available from <doi:10.1007/s00180-012-0382-5>. The package allows user-specified loss functions and base-learners.

Authors:Torsten Hothorn [cre, aut], Peter Buehlmann [aut], Thomas Kneib [aut], Matthias Schmid [aut], Benjamin Hofner [aut], Fabian Otto-Sobotka [ctb], Fabian Scheipl [ctb], Andreas Mayr [ctb]

mboost_2.9-11.tar.gz
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mboost_2.9-11.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
mboost/json (API)

# Install 'mboost' in R:
install.packages('mboost', repos = c('https://boost-r.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/boost-r/mboost/issues

Uses libs:
  • openblas– Optimized BLAS

On CRAN:

Conda:

boosting-algorithmsgamglmmachine-learningmboostmodellingr-languagetutorialsvariable-selectionopenblas

12.57 score 77 stars 25 packages 770 scripts 7.0k downloads 32 mentions 77 exports 12 dependencies

Last updated from:af37f26c00. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK202
linux-devel-x86_64OK196
source / vignettesOK287
linux-release-arm64OK180
linux-release-x86_64OK185
macos-release-arm64OK131
macos-release-x86_64OK216
macos-oldrel-arm64OK166
macos-oldrel-x86_64OK237
windows-develOK191
windows-releaseOK167
windows-oldrelOK151
wasm-releaseOK163

Exports:%+%%O%%X%AdaExpas.data.frame.varimpAUCbbsBinomialbkernelblackboostbmonobmrfbnsbolsboost_controlbradbrandombspatialbssbtreebuserCindexconfint.glmboostconfint.mboostCoxPHcvcvriskdownstream.testExpectRegextractFamilyfitted.mboostFPgamboostGammaRegGaussClassGaussianGaussRegGehanglmboostHuberHurdleIPCweightsLaplacelines.mboostlines.mboost.ciLoglogLognormalmboostmboost_fitmboost_internmstopmstop<-MultinomialNBinomialnuisanceplot.cvriskplot.glmboostplot.mboostplot.mboost.ciplot.varimpPoissonpredict.glmboostpredict.mboostprint.glmboost.ciPropOddsQuantRegRCGriskselectedselected.mboostshowstabsel_parameters.mbooststabsel.mboostsurvFitvarimpWeibull

Dependencies:FormulainumlatticelibcoinMatrixmvtnormnnlspartykitquadprogrpartstabssurvival

mboost
Overview | Design and Implementation | User Interface by Example | Overview on 2.0 Series Features
Rendered frommboost.Rnwusingutils::Sweave

Last update: 2020-12-11
Started: 2016-04-22

mboost Tutorial
Introduction | A Brief Theoretical Overview of Component-Wise Gradient Boosting | The Package mboost | Summary
Rendered frommboost_tutorial.Rnwusingutils::Sweave

Last update: 2020-12-11
Started: 2016-04-22

Survival Ensembles
Illustrations and Applications
Rendered fromSurvivalEnsembles.Rnwusingutils::Sweave

Last update: 2020-12-11
Started: 2016-04-22

mboost Illustrations
Illustrations
Rendered frommboost_illustrations.Rnwusingutils::Sweave

Last update: 2016-04-22
Started: 2016-04-22

Readme and manuals

Help Manual

Help pageTopics
mboost: Model-Based Boostingmboost-package mboost_package package-mboost package_mboost
Base-learners for Gradient Boosting%+% %O% %X% base-learner baselearner baselearners bbs bkernel bmono bmrf bns bols brad brandom bspatial bss btree buser
Gradient Boosting with Regression Treesblackboost
Control Hyper-parameters for Boosting Algorithmsboost_control
Class "boost_family": Gradient Boosting Familyboost_family-class show,boost_family-method
Pointwise Bootstrap Confidence Intervalsconfint.glmboost confint.mboost lines.mboost.ci plot.mboost.ci print.glmboost.ci
Cross-Validationcv cvrisk cvrisk.mboost plot.cvrisk print.cvrisk
Gradient Boosting FamiliesAdaExp AUC Binomial Cindex CoxPH ExpectReg Family GammaReg GaussClass Gaussian GaussReg Gehan Huber Hurdle Laplace Loglog Lognormal Multinomial NBinomial Poisson PropOdds QuantReg RCG Weibull
Fractional PolynomialsFP
Gradient Boosting with Component-wise Linear Modelsglmboost glmboost.default glmboost.formula glmboost.matrix
Inverse Probability of Censoring WeightsIPCweights
Gradient Boosting for Additive Modelsgamboost mboost
Model-based Gradient Boostingmboost_fit
Methods for Gradient Boosting ObjectsAIC.mboost coef.glmboost coef.mboost downstream.test extract extract.blackboost extract.blg extract.bl_lin extract.bl_tree extract.gamboost extract.glmboost extract.mboost fitted.mboost hatvalues.gamboost hatvalues.glmboost logLik.mboost mboost_methods mstop mstop.cvrisk mstop.gbAIC mstop.mboost mstop<- nuisance nuisance.mboost predict.blackboost predict.gamboost predict.glmboost predict.mboost print.glmboost print.mboost resid.mboost residuals.mboost risk risk.mboost selected selected.mboost summary.mboost variable.names.glmboost variable.names.mboost [.mboost
Plot effect estimates of boosting modelslines.mboost plot plot.glmboost plot.mboost
Stability Selectionstabsel stabsel.mboost stabsel_parameters.mboost
Survival Curves for a Cox Proportional Hazards Modelplot.survFit survFit survFit.mboost
Variable Importanceas.data.frame.varimp plot.varimp varimp varimp.mboost