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

Benjamin Hofner, Andreas Mayr and Matthias Schmid

Journal of Statistical Software, 2016, vol. 074, issue i01

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.

Date: 2016-10-20
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Persistent link: https://EconPapers.repec.org/RePEc:jss:jstsof:v:074:i01

DOI: 10.18637/jss.v074.i01

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