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Generalized additive models for location, scale and shape (GAMLSS) are aflexible class of regression models that allow to model multiple parameters ofa distribution function, such as the mean and the standard deviation,simultaneously. With the R package gamboostLSS, we provide a boosting method tofit these models. Variable selection and model choice are naturally availablewithin this regularized regression framework. To introduce and illustrate the Rpackage gamboostLSS and its infrastructure, we use a data set on stunted growthin India. In addition to the specification and application of the model itself,we present a variety of convenience functions, including methods for tuningparameter selection, prediction and visualization of results. The packagegamboostLSS is available from CRAN(http://cran.r-project.org/package=gamboostLSS).

gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework