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mboost (version 1.0-5)

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

1.0-5

License

GPL-2

Maintainer

Torsten Hothorn

Last Published

August 22nd, 2024

Functions in mboost (1.0-5)

gamboost

Gradient Boosting with Smooth Components
IPCweights

Inverse Probability of Censoring Weights
Family

Gradient Boosting Families
methods

Methods for Gradient Boosting Objects
boost_dpp

Data Preprocessing for Gradient Boosting
boost_family-class

Class "boost_family": Gradient Boosting Family
bodyfat

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

Base learners for Gradient Boosting with Smooth Components
boost_control

Control Hyper-parameters for Boosting Algorithms
Westbc

Breast Cancer Gene Expression
glmboost

Gradient Boosting with Component-wise Linear Models
survFit

Survival Curves for a Cox Proportional Hazards Model
wpbc

Wisconsin Prognostic Breast Cancer Data
blackboost

Gradient Boosting with Regression Trees
birds

Habitat Suitability for Breeding Bird Communities
FP

Fractional Polynomials
cvrisk

Cross-Validation