Published by the Foundation for Open Access Statistics
Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework | Hofner | Journal of Statistical Software
Authors: Benjamin Hofner, Andreas Mayr, Matthias Schmid
Title: gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework
Abstract: Generalized additive models for location, scale and shape are a flexible class of regression models that allow to model multiple parameters of a distribution function, such as the mean and the standard deviation, simultaneously. With the R package gamboostLSS, we provide a boosting method to fit these models. Variable selection and model choice are naturally available within this regularized regression framework. To introduce and illustrate the R package gamboostLSS and its infrastructure, we use a data set on stunted growth in India. In addition to the specification and application of the model itself, we present a variety of convenience functions, including methods for tuning parameter selection, prediction and visualization of results. The package gamboostLSS is available from the Comprehensive R Archive Network (CRAN) at https://CRAN.R-project.org/package=gamboostLSS.

Page views:: 1168. Submitted: 2014-07-04. Published: 2016-10-20.
Paper: gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework     Download PDF (Downloads: 662)
Supplements:
gamboostLSS_1.2-2.tar.gz: R source package Download (Downloads: 56; 1MB)
v74i01.R: R replication code Download (Downloads: 58; 9KB)

DOI: 10.18637/jss.v074.i01

by
This work is licensed under the licenses
Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.