  GWR is a popular approach for investigating the spatial variation in
relationships between response and predictor variables, and critically for
investigating and understanding process spatial heterogeneity. The
geographically weighted (GW) framework is increasingly used to accommodate
different types of models and analyses reflecting a wider desire to explore
spatial variation in model parameters or components. However the growth in the
use of GWR and different GW models has only been partially supported by package
development in both R and Python, the major coding environments for spatial
analysis. The result is that refinements have been inconsistently included (if
at all) within GWR and GW functions in any given package. This paper outlines
the structure of a new `gwverse` package, that will over time replace
`GWmodel`, that takes advantage of recent developments in the composition of
complex, integrated packages. It conceptualises `gwverse` as having a modular
structure, that separates core GW functionality and applications such as GWR.
It adopts a function factory approach, in which bespoke functions are created
and returned to the user based on user-defined parameters. The paper introduces
two demonstrator modules that can be used to undertake GWR and identifies a
number of key considerations and next steps.
