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mboost (version 2.1-0)

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

3,971

Version

2.1-0

License

GPL-2

Maintainer

Torsten Hothorn

Last Published

November 15th, 2011

Functions in mboost (2.1-0)

FP

Fractional Polynomials
Westbc

Breast Cancer Gene Expression
blackboost

Gradient Boosting with Regression Trees
cvrisk

Cross-Validation
bodyfat

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

Habitat Suitability for Breeding Bird Communities
methods

Methods for Gradient Boosting Objects
mboost

Model-based Gradient Boosting
IPCweights

Inverse Probability of Censoring Weights
glmboost

Gradient Boosting with Component-wise Linear Models
mboost-package

mboost: Model-Based Boosting
wpbc

Wisconsin Prognostic Breast Cancer Data
survFit

Survival Curves for a Cox Proportional Hazards Model
Family

Gradient Boosting Families
stabsel

Stability Selection
baselearners

Base-learners for Gradient Boosting
boost_family-class

Class "boost_family": Gradient Boosting Family
boost_control

Control Hyper-parameters for Boosting Algorithms
gamboost

Gradient Boosting with Smooth Components