mboost v2.9-4

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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.

Functions in mboost

Name Description
FP Fractional Polynomials
boost_family-class Class "boost\_family": Gradient Boosting Family
confint.mboost Pointwise Bootstrap Confidence Intervals
cvrisk Cross-Validation
blackboost Gradient Boosting with Regression Trees
IPCweights Inverse Probability of Censoring Weights
Family Gradient Boosting Families
baselearners Base-learners for Gradient Boosting
mboost Gradient Boosting for Additive Models
boost_control Control Hyper-parameters for Boosting Algorithms
stabsel Stability Selection
survFit Survival Curves for a Cox Proportional Hazards Model
plot Plot effect estimates of boosting models
methods Methods for Gradient Boosting Objects
glmboost Gradient Boosting with Component-wise Linear Models
mboost_fit Model-based Gradient Boosting
mboost-package mboost: Model-Based Boosting
mboost_intern Call internal functions.
varimp Variable Importance
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Vignettes of mboost

Name
SurvivalEnsembles.Rnw
SurvivalEnsembles.Rout.save
boost.bib
jmlr2e.sty
mboost.Rnw
mboost.Rout.save
mboost_illustrations.Rnw
mboost_illustrations.Rout.save
mboost_tutorial.Rnw
mboost_tutorial.Rout.save
mboost_tutorial.bib
setup.R
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