Boosting Methods for 'GAMLSS'

Boosting models for fitting generalized additive models for location, shape and scale ('GAMLSS') to potentially high dimensional data.


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).

  • 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")
    

News

Reference manual

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install.packages("gamboostLSS")

2.0-1 by Benjamin Hofner, 2 years ago


For source code, development versions and issue tracker see https://github.com/boost-R/gamboostLSS


Browse source code at https://github.com/cran/gamboostLSS


Authors: Benjamin Hofner [aut, cre] , Andreas Mayr [aut] , Nora Fenske [aut] , Janek Thomas [aut] , Matthias Schmid [aut]


Documentation:   PDF Manual  


Task views: Machine Learning & Statistical Learning


GPL-2 license


Imports graphics, grDevices, stats, utils

Depends on mboost, stabs, parallel

Suggests gamlss, gamlss.dist, survival, BayesX, R2BayesX


Imported by FDboost.

Depended on by betaboost.

Suggested by stabs.


See at CRAN