Implements Bayesian spatial and spatiotemporal models that
optionally allow for extreme spatial deviations through time. 'glmmfields'
uses a predictive process approach with random fields implemented through
a multivariate-t distribution instead of the usual multivariate normal.
Sampling is conducted with 'Stan'. References: Anderson and Ward (2019)
Add support for random walk year effects with covariates. There are a few specific cases where covariates or the random year effect are not estimable. Examples are:
Import S3 methods from rstantools instead of rstanarm (#5)
Adjust calculation of year index values to better allow for missing time slices