Boosting models for fitting generalized additive models for location, shape and scale (GAMLSS) to potentially high dimensional data. The current relase version can be found on CRAN (https://cran.r-project.org/package=gamboostLSS).
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README.md

gamboostLSS

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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).

Using gamboostLSS

  • 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).

Issues & Feature Requests

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

Installation

  • 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")