GamboostLSS: An R package for model building and variable selection in the GAMLSS framework

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. © 2016,american statistical association. all rights reserved.