Combining predictions from multiple models into an ensemble is a widely used practice across many fields with demonstrated performance benefits. The R package hubEnsembles provides a flexible framework for ensembling various types of predictions, including point estimates and probabilistic predictions. A range of common methods for generating ensembles are supported, including weighted averages, quantile averages, and linear pools. The hubEnsembles package fits within a broader framework of open-source software and data tools called the “hubverse”, which facilitates the development and management of collaborative modelling exercises.

The authors have declared no competing interest.

L. Shandross, A. Krystalli, N. G. Reich, and E. L. Ray were supported by the National Institutes of General Medical Sciences (R35GM119582) and the US Centers for Disease Control and Prevention (U01IP001122 and NU38FT000008). E. Howerton was supported by the Eberly College of Science Barbara McClintock Science Achievement Graduate Scholarship in Biology at the Pennsylvania State University. L. Contamin and H. Hochheiser were supported by NIGMS grant U24GM132013. The content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS, the National Institutes of Health, or CDC.

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