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mboost (version 0.5-7)

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

0.5-7

License

GPL

Maintainer

Torsten Hothorn

Last Published

August 22nd, 2024

Functions in mboost (0.5-7)

boost_family-class

Class "boost_family": Gradient Boosting Family
methods

Methods for Gradient Boosting Objects
westbc

Breast Cancer Gene Expression
boost_control

Control Hyper Parameters for Boosting Algorithms
FP

Fractional Polynomials
gamboost

Gradient Boosting with Component-wise Smoothing Splines
boost_dpp

Data Preprocessing for Gradient Boosting
blackboost

Gradient Boosting with Regression Trees
IPCweights

Inverse Probability of Censoring Weights
bodyfat

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

Gradient Boosting Families
glmboost

Gradient Boosting with Component-wise Linear Models
cvrisk

Cross-Validation
wpbc

Wisconsin Prognostic Breast Cancer Data