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mboost (version 2.9-11)

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 , a hands-on tutorial is available from . The package allows user-specified loss functions and base-learners.

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Install

install.packages('mboost')

Monthly Downloads

5,064

Version

2.9-11

License

GPL-2

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Maintainer

Last Published

August 22nd, 2024

Functions in mboost (2.9-11)

IPCweights

Inverse Probability of Censoring Weights
confint.mboost

Pointwise Bootstrap Confidence Intervals
boost_control

Control Hyper-parameters for Boosting Algorithms
cvrisk

Cross-Validation
mboost

Gradient Boosting for Additive Models
FP

Fractional Polynomials
blackboost

Gradient Boosting with Regression Trees
baselearners

Base-learners for Gradient Boosting
methods

Methods for Gradient Boosting Objects
mboost_intern

Call internal functions.
glmboost

Gradient Boosting with Component-wise Linear Models
mboost-package

mboost: Model-Based Boosting
stabsel

Stability Selection
boost_family-class

Class "boost_family": Gradient Boosting Family
Family

Gradient Boosting Families
plot

Plot effect estimates of boosting models
survFit

Survival Curves for a Cox Proportional Hazards Model
mboost_fit

Model-based Gradient Boosting
varimp

Variable Importance