INTRODUCTION | IMPLEMENTING TWO-REGRESSION MODELS THAT ALREADY EXIST | Approach and Options | Help documentation | Assumptions and Data | Applying Crouter models (for activity count data) | Applying Hibbing models (for raw sensor data) | FITTING AND EXAMINING NEW MODELS | Background and Setup | Fitting the Model | Examining Model Performance | USING NEW MODELS | CONCLUSION
Package: TwoRegression 1.1.1
TwoRegression: Develop and Apply Two-Regression Algorithms
Facilitates development and application of two-regression algorithms for research-grade wearable devices. It provides an easy way for users to access previously-developed algorithms, and also to develop their own. Initial motivation came from Hibbing PR, LaMunion SR, Kaplan AS, & Crouter SE (2018) <doi:10.1249/MSS.0000000000001532>. However, other algorithms are now supported. Please see the associated references in the package documentation for full details of the algorithms that are supported.
Authors:
TwoRegression_1.1.1.tar.gz
TwoRegression_1.1.1.zip(r-4.7)TwoRegression_1.1.1.zip(r-4.6)TwoRegression_1.1.1.zip(r-4.5)
TwoRegression_1.1.1.tgz(r-4.6-any)TwoRegression_1.1.1.tgz(r-4.5-any)
TwoRegression_1.1.1.tar.gz(r-4.7-any)TwoRegression_1.1.1.tar.gz(r-4.6-any)
TwoRegression_1.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
TwoRegression/json (API)
| # Install 'TwoRegression' in R: |
| install.packages('TwoRegression', repos = c('https://paulhibbing.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/paulhibbing/tworegression/issues
Datasets:
- all_data - Two-regression-ready data frame
- count_data - Activity count data for demonstrating prior two-regression models
- imu_to_check - IMU data to check
- imu_to_collapse - IMU data to collapse
- raw_for_cv - Primary accelerometer data to calculate coefficient of variation per 10-s
- raw_to_collapse - Primary accelerometer data to collapse
Last updated from:1ecf30de33. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 168 | ||
| source / vignettes | OK | 233 | ||
| linux-release-x86_64 | OK | 159 | ||
| macos-release-arm64 | OK | 92 | ||
| macos-oldrel-arm64 | OK | 122 | ||
| windows-devel | OK | 95 | ||
| windows-release | OK | 94 | ||
| windows-oldrel | OK | 92 | ||
| wasm-release | OK | 130 |
Exports:cv_2rmfit_2rmis.TwoRegressionsmooth_2rmTwoRegression
Dependencies:bootclassclicpp11dplyre1071equivalencefarvergenericsggplot2gldgluegridExtragtableisobandlabelinglatticelazyevallifecyclelmomlubridatemagrittrMASSmvtnormPairedDataPAutilitiespillarpkgconfigplyrpROCproxypurrrR6RColorBrewerRcppRcppRollreshape2rlangS7scalesstringistringrtibbletidyrtidyselecttimechangeutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Two-regression-ready data frame | all_data |
| Activity count data for demonstrating prior two-regression models | count_data |
| Develop a two-regression algorithm | fit_2rm is.TwoRegression |
| IMU data to check | imu_to_check |
| IMU data to collapse | imu_to_collapse |
| Create summary plots for TwoRegression objects | plot.TwoRegression |
| Predict metabolic equivalents from a TwoRegression object | predict.TwoRegression |
| Primary accelerometer data to calculate coefficient of variation per 10-s | raw_for_cv |
| Primary accelerometer data to collapse | raw_to_collapse |
| Smooth two-regression estimates over specified periods | smooth_2rm |
| Develop and Apply Two-Regression Algorithms | TwoRegression-Package |
