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

Mayr A, Weinhold L, Hofner B, Titze S, Gefeller O, Schmid M (2018)

Publication Type: Journal article

Publication year: 2018

Journal

International Journal of Epidemiology Oxford University Press

Book Volume: 47

Pages Range: 1383-1388

Journal Issue: 5

DOI: 10.1093/ije/dyy093

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.

Authors with CRIS profile

Benjamin Hofner
Olaf Gefeller Lehrstuhl für Biometrie und Epidemiologie

Involved external institutions

Universitätsklinikum Bonn DE Germany (DE) Paul Ehrlich Institute / Paul-Ehrlich-Institut – Bundesinstitut für Impfstoffe und biomedizinische Arzneimittel (PEI) DE Germany (DE)

How to cite

APA:

Mayr, A., Weinhold, L., Hofner, B., Titze, S., Gefeller, O., & Schmid, M. (2018). The betaboost package-a software tool for modelling bounded outcome variables in potentially high-dimensional epidemiological data. International Journal of Epidemiology, 47(5), 1383-1388. https://doi.org/10.1093/ije/dyy093

MLA:

Mayr, Andreas, et al. "The betaboost package-a software tool for modelling bounded outcome variables in potentially high-dimensional epidemiological data." International Journal of Epidemiology 47.5 (2018): 1383-1388.

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