  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).
