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mboost (version 1.1-2)

Model-Based Boosting

Description

Functional gradient descent algorithms (boosting) for optimizing general loss functions utilizing componentwise least squares, either of parametric linear form or smoothing splines, 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

1.1-2

License

GPL-2

Maintainer

Torsten Hothorn

Last Published

July 21st, 2009

Functions in mboost (1.1-2)

wpbc

Wisconsin Prognostic Breast Cancer Data
boost_control

Control Hyper-parameters for Boosting Algorithms
IPCweights

Inverse Probability of Censoring Weights
FP

Fractional Polynomials
glmboost

Gradient Boosting with Component-wise Linear Models
blackboost

Gradient Boosting with Regression Trees
cvrisk

Cross-Validation
boost_dpp

Data Preprocessing for Gradient Boosting
birds

Habitat Suitability for Breeding Bird Communities
survFit

Survival Curves for a Cox Proportional Hazards Model
baselearners

Base learners for Gradient Boosting with Smooth Components
gamboost

Gradient Boosting with Smooth Components
bodyfat

Prediction of Body Fat by Skinfold Thickness, Circumferences, and Bone Breadths
boost_family-class

Class "boost_family": Gradient Boosting Family
Westbc

Breast Cancer Gene Expression
Family

Gradient Boosting Families
methods

Methods for Gradient Boosting Objects