Skip to content

EmanuelSommer/ShinyFOSR

Repository files navigation

{ShinyFOSR}

Lifecycle: stable

About

You are reading the doc about version: 0.0.1

{ShinyFOSR} is a Shiny app that allows users to interactively explore the functional regression models presented in the paper Predicting normative walking biomechanics across the lifespan using seven simple features by Liew et al. (2025).

A free online version is available for testing.

Note: The online version is limited in functionality and does not support Confidence Intervals (CI). Also, it is has limited monthly usage and may be unavailable at times. If you are interested in the full and fast version, please follow the installation instructions below.

Installation

Fully Fledged Version

For the full version that also supports Confidence Intervals (CI) you need to download the file containing the models with enhanced uncertainty quantification capabilities from this permanent link. Follow the instructions below to install the package and run the app.

  1. Clone the repository to your local machine and navigate to the directory in your terminal.
git clone
cd ShinyFOSR
  1. Place the downloaded file train_mod_sparse_sd.RDS into the inst/app/www/ directory.
  2. Install the dependencies using renv.
renv::restore()
  1. Run the app by running the whole dev/run_dev.R script in your R console.
source("dev/run_dev.R")

Package Version (Limited Functionality)

You can install the development version of {ShinyFOSR} like so:

devtools::install_github("EmanuelSommer/ShinyFOSR")

Then to launch the application by running the following code in your R console:

ShinyFOSR::run_app()

Any failure of the app is then likely connected to missing (potentially large) data files.


This README has been compiled on the

Sys.time()
#> [1] "2025-03-09 23:22:32 CET"

About

Predicting normative walking biomechanics across the lifespan using seven simple features

Topics

Resources

License

Unknown, MIT licenses found

Licenses found

Unknown
LICENSE
MIT
LICENSE.md

Stars

Watchers

Forks

Languages