JOSS Review: State of the field

[wcjochem]

State of the field: Do the authors describe how this software compares to other commonly-used packages?

The manuscript currently does not provide a sufficient description, comparison, and references to the understand the state of the field. While the manuscript does mention INLA and inlabru (which this package works with), there is not a clear presentation of the features and other common packages for Bayesian spatial statistical modelling. Expanding the state of the field should also help with the framing to identify the clear statement of need and the gap that this project is filling.

openjournals/joss-reviews#7047

[mnky9800n]

Hi, please see below (will be updated):

State of the field

There are number of packages within the R ecosystem that perform various spatio-temporal functions (e.g., spTimer MCMC modeling for space-time data, spBayes MCMC modeling of random effects for space-time data, and bmstdr a model comparison tool that uses MCMC, INLA, and other methods to fit to data and then compare between models). A critical expectation of each of these packages is a deep background in both bayesian modeling and understanding bayesian data. Users are expected to build their own fit equations, have a deep understanding of random effects, and are expected to be able to interpret predictions and uncertainties. However, none of these packages offer much if any scaffolding to do so. 4D-Modeller, attempts to bridge this gap with it's shiny apps.