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mboost (version 0.4-10)

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

3,522

Version

0.4-10

License

GPL

Maintainer

Torsten Hothorn

Last Published

August 22nd, 2024

Functions in mboost (0.4-10)

gamboost

Gradient Boosting with Componentwise Smoothing Splines
IPCweights

Inverse Probability of Censoring Weights
boost_family-class

Class "boost_family": Gradient Boosting Family
Family

Gradient Boosting Families
blackboost

Gradient Boosting with Regression Trees
glmboost

Gradient Boosting with Componentwise Linear Models
boost_dpp

Data Preprocessing for Gradient Boosting
wpbc

Wisconsin Prognostic Breast Cancer Data
cvrisk

Cross-Validation
westbc

Breast Cancer Gene Expression
boost_control

Control Hyper Parameters for Boosting Algorithms
methods

Methods for Gradient Boosting Objects
FP

Fractional Polynomials
bodyfat

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