Package: EE.Data 0.1.1
EE.Data: Objects for Predicting Energy Expenditure
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.'
Authors:
EE.Data_0.1.1.tar.gz
EE.Data_0.1.1.zip(r-4.7)EE.Data_0.1.1.zip(r-4.6)EE.Data_0.1.1.zip(r-4.5)
EE.Data_0.1.1.tgz(r-4.6-any)EE.Data_0.1.1.tgz(r-4.5-any)
EE.Data_0.1.1.tar.gz(r-4.7-any)EE.Data_0.1.1.tar.gz(r-4.6-any)
EE.Data_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
EE.Data/json (API)
NEWS
| # Install 'EE.Data' in R: |
| install.packages('EE.Data', repos = c('https://paulhibbing.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/paulhibbing/ee.data/issues
- montoye_lw - Neural networks for energy expenditure prediction
- montoye_rw - Neural networks for energy expenditure prediction
- staudenmayer_lm - Linear model and random forest for energy expenditure prediction
- staudenmayer_rf - Linear model and random forest for energy expenditure prediction
Last updated from:b8a3e800b0. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 139 | ||
| source / vignettes | OK | 248 | ||
| linux-release-x86_64 | OK | 115 | ||
| macos-release-arm64 | OK | 92 | ||
| macos-oldrel-arm64 | OK | 84 | ||
| windows-devel | OK | 73 | ||
| windows-release | OK | 82 | ||
| windows-oldrel | OK | 74 | ||
| wasm-release | OK | 101 |
Exports:
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Neural networks for energy expenditure prediction | montoye montoye_lw montoye_rw |
| Linear model and random forest for energy expenditure prediction | staudenmayer staudenmayer_lm staudenmayer_rf |
