Develop and Apply Two-Regression Algorithms
Plot behaviors for visualizing a walk/run cut-point
Calculate direction changes per five seconds in sliding windows
Perform leave-one-participant-out-cross-validation on a two-regression...
Develop a two-regression algorithm
Convert an object of class TwoRegression to a textual representation o...
Develop a cut-point as part of the process for developing a two-regres...
Coefficient of variation for two-regression models
Convert a linear model to a text representation of the prediction equa...
Apply a Hibbing 2018 two-regression algorithm
Internal functions for the 2RM wrapper
Plot behaviors for visualizing a sedentary cut-point
Create summary plots for TwoRegression objects
Add a cut-point to a plot
Predict metabolic equivalents from a TwoRegression object
Smooth two-regression estimates over specified periods
Summary method for TwoRegression objects
Apply an existing two-regression model
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.
Useful links