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mboost (version 2.1-3)

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

3,608

Version

2.1-3

License

GPL-2

Maintainer

Torsten Hothorn

Last Published

September 27th, 2012

Functions in mboost (2.1-3)

IPCweights

Inverse Probability of Censoring Weights
blackboost

Gradient Boosting with Regression Trees
FP

Fractional Polynomials
cvrisk

Cross-Validation
bodyfat

Prediction of Body Fat by Skinfold Thickness, Circumferences, and Bone Breadths
birds

Habitat Suitability for Breeding Bird Communities
mboost

Model-based Gradient Boosting
mboost-package

mboost: Model-Based Boosting
survFit

Survival Curves for a Cox Proportional Hazards Model
boost_control

Control Hyper-parameters for Boosting Algorithms
boost_family-class

Class "boost_family": Gradient Boosting Family
wpbc

Wisconsin Prognostic Breast Cancer Data
glmboost

Gradient Boosting with Component-wise Linear Models
stabsel

Stability Selection
baselearners

Base-learners for Gradient Boosting
Westbc

Breast Cancer Gene Expression
methods

Methods for Gradient Boosting Objects
Family

Gradient Boosting Families
gamboost

Gradient Boosting with Smooth Components