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:
mboost_2.9-11.tar.gz
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mboost.pdf |mboost.html✨
mboost/json (API)
NEWS
| # 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
boosting-algorithmsgamglmmachine-learningmboostmodellingr-languagetutorialsvariable-selection
Last updated 30 days agofrom:de65755285. Checks:OK: 8 NOTE: 1. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Aug 19 2024 |
| R-4.5-win-x86_64 | OK | Aug 19 2024 |
| R-4.5-linux-x86_64 | NOTE | Aug 19 2024 |
| R-4.4-win-x86_64 | OK | Aug 19 2024 |
| R-4.4-mac-x86_64 | OK | Aug 19 2024 |
| R-4.4-mac-aarch64 | OK | Aug 19 2024 |
| R-4.3-win-x86_64 | OK | Aug 19 2024 |
| R-4.3-mac-x86_64 | OK | Aug 19 2024 |
| R-4.3-mac-aarch64 | OK | Aug 19 2024 |
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
Rendered frommboost.Rnwusingutils::Sweaveon Aug 19 2024.Last update: 2020-12-11
Started: 2016-04-22
mboost Illustrations
Rendered frommboost_illustrations.Rnwusingutils::Sweaveon Aug 19 2024.Last update: 2016-04-22
Started: 2016-04-22
mboost Tutorial
Rendered frommboost_tutorial.Rnwusingutils::Sweaveon Aug 19 2024.Last update: 2020-12-11
Started: 2016-04-22
Survival Ensembles
Rendered fromSurvivalEnsembles.Rnwusingutils::Sweaveon Aug 19 2024.Last update: 2020-12-11
Started: 2016-04-22
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| mboost: Model-Based Boosting | mboost-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 Trees | blackboost |
| Control Hyper-parameters for Boosting Algorithms | boost_control |
| Class "boost_family": Gradient Boosting Family | boost_family-class show,boost_family-method |
| Pointwise Bootstrap Confidence Intervals | confint.glmboost confint.mboost lines.mboost.ci plot.mboost.ci print.glmboost.ci |
| Cross-Validation | cv cvrisk cvrisk.mboost plot.cvrisk print.cvrisk |
| Gradient Boosting Families | AdaExp AUC Binomial Cindex CoxPH ExpectReg Family GammaReg GaussClass Gaussian GaussReg Gehan Huber Hurdle Laplace Loglog Lognormal Multinomial NBinomial Poisson PropOdds QuantReg RCG Weibull |
| Fractional Polynomials | FP |
| Gradient Boosting with Component-wise Linear Models | glmboost glmboost.default glmboost.formula glmboost.matrix |
| Inverse Probability of Censoring Weights | IPCweights |
| Gradient Boosting for Additive Models | gamboost mboost |
| Model-based Gradient Boosting | mboost_fit |
| Methods for Gradient Boosting Objects | AIC.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 models | lines.mboost plot plot.glmboost plot.mboost |
| Stability Selection | stabsel stabsel.mboost stabsel_parameters.mboost |
| Survival Curves for a Cox Proportional Hazards Model | plot.survFit survFit survFit.mboost |
| Variable Importance | as.data.frame.varimp plot.varimp varimp varimp.mboost |
