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mboost (version 2.4-1)

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.

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Version

Install

install.packages('mboost')

Monthly Downloads

3,661

Version

2.4-1

License

GPL-2

Maintainer

Torsten Hothorn

Last Published

December 15th, 2014

Functions in mboost (2.4-1)

FP

Fractional Polynomials
mboost-package

mboost: Model-Based Boosting
Family

Gradient Boosting Families
boost_family-class

Class "boost_family": Gradient Boosting Family
glmboost

Gradient Boosting with Component-wise Linear Models
methods

Methods for Gradient Boosting Objects
confint.mboost

Pointwise Bootstrap Confidence Intervals
mboost

Model-based Gradient Boosting
baselearners

Base-learners for Gradient Boosting
IPCweights

Inverse Probability of Censoring Weights
stabsel

Stability Selection
cvrisk

Cross-Validation
blackboost

Gradient Boosting with Regression Trees
gamboost

Gradient Boosting with Smooth Components
survFit

Survival Curves for a Cox Proportional Hazards Model
boost_control

Control Hyper-parameters for Boosting Algorithms