gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework
Abstract
Generalized additive models for location, scale and shape (GAMLSS) 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 CRAN (http://cran.r-project.org/package=gamboostLSS).
Cite
@article{arxiv.1407.1774,
title = {gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework},
author = {Benjamin Hofner and Andreas Mayr and Matthias Schmid},
journal= {arXiv preprint arXiv:1407.1774},
year = {2014}
}