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mboost

mboost implements boosting algorithms for fitting generalized linear, additive and interaction models to potentially high-dimensional data.

Using mboost

For installation instructions see below.

Instructions on how to use mboost can be found in various places:

Issues & Feature Requests

For issues, bugs, feature requests etc. please use the GitHub Issues.

Installation Instructions

  • Current version (from CRAN):

    install.packages("mboost")
  • Latest patch version (patched version of CRAN package; under development) from GitHub:

    library("devtools")
    install_github("boost-R/mboost")
    library("mboost")
  • Latest development version (version with new features; under development) from GitHub:

    library("devtools")
    install_github("boost-R/mboost", ref = "devel")
    library("mboost")

    To be able to use the install_github() command, one needs to install devtools first:

    install.packages("devtools")

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Version

Install

install.packages('mboost')

Monthly Downloads

3,661

Version

2.8-1

License

GPL-2

Issues

Pull Requests

Stars

Forks

Maintainer

Benjamin Hofner

Last Published

July 23rd, 2017

Functions in mboost (2.8-1)

cvrisk

Cross-Validation
mboost

Gradient Boosting for Additive Models
IPCweights

Inverse Probability of Censoring Weights
baselearners

Base-learners for Gradient Boosting
confint.mboost

Pointwise Bootstrap Confidence Intervals
boost_control

Control Hyper-parameters for Boosting Algorithms
FP

Fractional Polynomials
Family

Gradient Boosting Families
blackboost

Gradient Boosting with Regression Trees
boost_family-class

Class "boost\_family": Gradient Boosting Family
mboost_intern

Call internal functions.
stabsel

Stability Selection
methods

Methods for Gradient Boosting Objects
varimp

Variable Importance
survFit

Survival Curves for a Cox Proportional Hazards Model
mboost-package

mboost: Model-Based Boosting
mboost_fit

Model-based Gradient Boosting
plot

Plot effect estimates of boosting models
glmboost

Gradient Boosting with Component-wise Linear Models