This package is not intended for broad public use. Its main purpose is to:
provide transparent documentation of the data analyses forming the backbone of the empirical part of Mana et al. (2026), and
supply the set of functions used to generate those results, which can be reused or adapted for projects with similar objectives.
Installation
To install a local instance of the package, run:
# If you do not have the devtools package,
# install it by uncommenting the following line:
#install.packages(devtools)
devtools::install_deps()
devtools::install_github("josefmana/demcrit")How to Use
There are two main ways to work with the package (each described in its own vignette):
With raw data available – Place the raw data files in the data-raw folder and run the included targets pipeline. See the vignette “Targets Pipeline” for details.
Without raw data – You can still use many of the package functions with your own, similarly structured dataset. See the vignette “Out of Pipeline” for guidance.
Future Directions
Currently, demcrit is designed around the dataset used in our original study. As such, it primarily supports direct replications rather than general applications.
If you find the approach useful and would like to adapt it for your data, feel free to reach out — we are happy to discuss possible extensions. We also welcome reports of bugs or mistakes you may encounter.
At the moment, explanation and proper integration of the newest analysis (feature selection exploraton of screening indexes for comprehensive battery dementia classification) is on a to do. It was fully implemented into the targets pipeline and added to the manuscript, however, integrating it into the package documentation seems to be a bit cumbersome.
If I ever get to it and have a good reason to do so, I may trasnform demcrit to a “proper” R package {demcritr}. Most likely not though.