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mboost (version 2.9-4)

Model-Based Boosting

Description

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.

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Install

install.packages('mboost')

Monthly Downloads

3,661

Version

2.9-4

License

GPL-2

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Maintainer

Torsten Hothorn

Last Published

December 10th, 2020

Functions in mboost (2.9-4)

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