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

Abstract

Motivation: To provide an integrated software environment for model fitting and variable selection in regression models with a bounded outcome variable. Implementation: The proposed modelling framework is implemented in the add-on package betaboost of the statistical software environment R. General features: 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. Availability: 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.