Package: accelEE 0.3.1
Paul R. Hibbing
accelEE: Predict Energy Expenditure from Accelerometer Data
Simplifies the application of various energy expenditure models. The package is intended as a hub that brings together methods from a variety of other, themed packages such as 'Sojourn' and 'TwoRegression'. Several methods are supported locally as well, including the linear methods of Hildebrand et al. (2014) <doi:10.1249/MSS.0000000000000289> and the non-linear adaptation by Ellingson et al. (2017) <doi:10.1088/1361-6579/aa6d00>. The package can combine output from different methods and produce standardized output in a range of units.
Authors:
accelEE_0.3.1.tar.gz
accelEE_0.3.1.tar.gz(r-4.7-any)accelEE_0.3.1.tar.gz(r-4.6-any)
accelEE_0.3.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
accelEE/json (API)
NEWS
| # Install 'accelEE' in R: |
| install.packages('accelEE', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/paulhibbing/accelee/issues
Last updated from:aac8229a8d. Checks:4 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 126 | ||
| source / vignettes | OK | 178 | ||
| linux-release-x86_64 | OK | 126 | ||
| wasm-release | OK | 108 |
Exports:accelEEee_fileee_summaryee_summary_hibbing23generic_featuresmontoye_featuresstaudenmayer_features
Dependencies:bootclassclicpp11digestdplyre1071equivalencefarvergenericsggplot2gldgluegridExtragtableisobandlabelinglatticelazyevallifecyclelmomlubridatemagrittrMASSmvtnormnnetPairedDataPAutilitiespillarpkgconfigplyrpROCproxypurrrR6randomForestRColorBrewerRcppRcppRollreshape2rlangrstudioapiS7scalesSojournstringistringrsvDialogssvGUItibbletidyrtidyselecttimechangetreeTwoRegressionutf8vctrsviridisLitewithrzoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Predict energy expenditure for accelerometry data | accelEE accelEE-function crouter15 hildebrand_linear hildebrand_nonlinear montoye sojourn staudenmayer wrap_2RM |
| Run a pre-specified processing scheme | ee_file |
| Run a pre-specified summary scheme | ee_summary |
| Calculate generic features for model application | generic_features |
| Run the Hibbing 2023 summary scheme | ee_summary_hibbing23 hibbing23-summary |
| Calculate features for Montoye's neural networks | montoye_features |
| Calculate features for Staudenmayer models | staudenmayer_features |
