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gamboostLSS implements boosting algorithms for fitting
generalized linear, additive and interaction models for to potentially
high-dimensional data. Instead of modeling only the mean,
gamboostLSS enables the user to model various distribution
parameters such as location, scale and shape at the same time (hence the
name GAMLSS, generalized additive models for location, scale and
shape).
For installation instructions see below.
Instructions on how to use gamboostLSS can be found
in the gamboostLSS
tutorial.
Details on the noncyclical fitting method can be found in
Thomas, J., Mayr, A., Bischl, B., Schmid, M., Smith, A., and Hofner, B. (2018), Gradient boosting for distributional regression - faster tuning and improved variable selection via noncyclical updates. Statistics and Computing. 28: 673-687. DOI 10.1007/s11222-017-9754-6. (Preliminary version: ArXiv 1611.10171).
For issues, bugs, feature requests etc. please use the GitHub Issues.
Current version (from CRAN):
install.packages("gamboostLSS")Latest patch version (patched version of CRAN package; under development) from GitHub:
library("devtools")
install_github("boost-R/gamboostLSS")
library("gamboostLSS")Latest development version (version with new features; under development) from GitHub:
library("devtools")
install_github("boost-R/gamboostLSS", ref = "devel")
library("gamboostLSS")To be able to use the install_github() command, one
needs to install devtools first:
install.packages("devtools")These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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