Functional gradient descent algorithm (boosting) for optimizing general risk functions utilizing component-wise (penalised) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data. Models and algorithms are described in \doi{10.1214/07-STS242}, a hands-on tutorial is available from \doi{10.1007/s00180-012-0382-5}. The package allows user-specified loss functions and base-learners.
mboost
implements boosting algorithms for fitting generalized linear, additive and interaction models
to potentially high-dimensional data.
For installation instructions see below.
Instructions on how to use mboost
can be found in various places:
For issues, bugs, feature requests etc. please use the GitHub Issues.
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")