The betaboost package-a software tool for modelling bounded outcome variables in potentially high-dimensional epidemiological data

• Published in: International journal of epidemiology Vol. 47; no. 5; pp. 1383 - 1388
• Main Authors: Mayr, Andreas, Weinhold, Leonie, Hofner, Benjamin, Titze, Stephanie, Gefeller, Olaf, Schmid, Matthias
• Format: Journal Article
• Language: English
• Published: England 01.10.2018
• ISSN: 0300-5771; 1464-3685
• DOI: 10.1093/ije/dyy093

To provide an integrated software environment for model fitting and variable selection in regression models with a bounded outcome variable. The proposed modelling framework is implemented in the add-on package betaboost of the statistical software environment R. The betaboost methodology is based on beta-regression, which is a state-of-the-art method for modelling bounded outcome variables. By combining traditional model fitting techniques with recent advances in statistical learning and distributional regression, betaboost allows users to carry out data-driven variable and/or confounder selection in potentially high-dimensional epidemiological data. The software package implements a flexible routine to incorporate linear and non-linear predictor effects in both the mean and the precision parameter (relating inversely to the variance) of a beta-regression model. The software is hosted publicly at [http://github.com/boost-R/betaboost] and has been published under General Public License (GPL) version 3 or newer.