trending aims to provides a coherent interface to several modelling tools. Whilst it is useful in an interactive context, it’s main focus is to provide an intuitive interface on which other packages can be developed (e.g. trendbreaker).

You can install the stable version from CRAN with:

install.packages("trending")

The development version can be installed from GitHub with:

if (!require(remotes)) {
  install.packages("remotes")
}
remotes::install_github("reconverse/trending", build_vignettes = TRUE)

• Model specification: Interfaces to common models through intuitive functions; lm_model(), glm_model(), glm_nb_model and brms_model*.

• Model fitting and prediction: Once specified, models can be fit to data and generate confidence and prediction intervals for future data using fit() and predict().

• Error and warning catching: The provided methods for fit and predict catch all warnings and errors, returning the output and these captured values in a list.

*   Requires brms

An overview of trending is provided in the included vignette: * vignette("Introduction", package = "trending")

Bug reports and feature requests should be posted on github using the issue system. All other questions should be posted on the RECON slack channel see https://www.repidemicsconsortium.org/forum/ for details on how to join.

• Gavin Simpson; Our method to calculate prediction intervals follows one that he described in two posts on his blog; see part 1 and part 2.

• John Haman and Matthew Avery; Our implementation of prediction intervals was guided by their bootstrapped approach within the ciTools package.