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mboost (version 2.0-1)

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

2.0-1

License

GPL-2

Maintainer

Torsten Hothorn

Last Published

March 1st, 2010

Functions in mboost (2.0-1)

birds

Habitat Suitability for Breeding Bird Communities
baselearners

Base-learners for Gradient Boosting
FP

Fractional Polynomials
boost_family-class

Class "boost_family": Gradient Boosting Family
boost_control

Control Hyper-parameters for Boosting Algorithms
IPCweights

Inverse Probability of Censoring Weights
blackboost

Gradient Boosting with Regression Trees
Westbc

Breast Cancer Gene Expression
methods

Methods for Gradient Boosting Objects
glmboost

Gradient Boosting with Component-wise Linear Models
cvrisk

Cross-Validation
survFit

Survival Curves for a Cox Proportional Hazards Model
gamboost

Gradient Boosting with Smooth Components
bodyfat

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

Gradient Boosting Families
mboost-package

mboost: Model-Based Boosting
mboost

Model-based Gradient Boosting
wpbc

Wisconsin Prognostic Breast Cancer Data
stabsel

Stability Selection