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This is a data-only package containing model objects that predict human energy expenditure from wearable sensor data. Supported methods include the neural networks of Montoye et al. (2017) <doi:10.1080/1091367X.2017.1337638> and the models of Staudenmayer et al. (2015) <doi:10.1152/japplphysiol.00026.2015>, one a linear model and the other a random forest. The package is intended as a spoke for the hub-package 'accelEE', which brings together the above methods and others from packages such as 'Sojourn' and 'TwoRegression.'
| Version: | 0.1.1 |
| Depends: | R (≥ 2.10) |
| Suggests: | nnet, randomForest |
| Published: | 2026-04-01 |
| DOI: | 10.32614/CRAN.package.EE.Data |
| Author: | Paul R. Hibbing [aut, cre], Alexander H.K. Montoye [ctb], John Staudenmayer [ctb], Children's Mercy Kansas City [cph] |
| Maintainer: | Paul R. Hibbing <paulhibbing at gmail.com> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | EE.Data results |
| Reference manual: | EE.Data.html , EE.Data.pdf |
| Package source: | EE.Data_0.1.1.tar.gz |
| Windows binaries: | r-devel: EE.Data_0.1.1.zip, r-release: not available, r-oldrel: not available |
| macOS binaries: | r-release (arm64): EE.Data_0.1.1.tgz, r-oldrel (arm64): not available, r-release (x86_64): EE.Data_0.1.1.tgz, r-oldrel (x86_64): EE.Data_0.1.1.tgz |
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These binaries (installable software) and packages are in development.
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