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hubEnsembles

The goal of hubEnsembles is to provide standard implementations of commonly used methods for ensembling model outputs. The hubEnsembles package is part of the hubverse project and expects all input data to the key functions to be formatted as an object of a model_out_tbl class.

Installation

Latest

You can install the released version of hubEnsembles from CRAN with:

install.packages("hubEnsembles")

Development

If you want to test out new features that have not yet been released, you can install the development version of hubEnsembles from GitHub with:

remotes::install_github("hubverse-org/hubEnsembles")

Code of Conduct

Please note that the hubEnsembles package is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Contributing

Interested in contributing back to the open-source Hubverse project? Learn more about how to get involved in the Hubverse Community or how to contribute to the hubEnsembles package.

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Version

Install

install.packages('hubEnsembles')

Monthly Downloads

570

Version

0.1.9

License

MIT + file LICENSE

Issues

Pull Requests

Stars

Forks

Maintainer

Li Shandross

Last Published

October 2nd, 2024

Functions in hubEnsembles (0.1.9)

component_outputs

Example model output data for linear_pool()
simple_ensemble

Compute ensemble model outputs by summarizing component model outputs for each combination of model task, output type, and output type id. Supported output types include mean, median, quantile, cdf, and pmf.
linear_pool

Compute ensemble model outputs as a linear pool, otherwise known as a distributional mixture, of component model outputs for each combination of model task, output type, and output type id. Supported output types include mean, quantile, cdf, and pmf.
fweights

Example weights data for simple_ensemble()
model_outputs

Example model output data for simple_ensemble()
weights

Example weights data for linear_pool()