  Non-Gaussian spatial and spatio-temporal data are becoming increasingly
prevalent, and their analysis is needed in a variety of disciplines. FRK is an
R package for spatial/spatio-temporal modelling and prediction with very large
data sets that, to date, has only supported linear process models and Gaussian
data models. In this paper, we describe a major upgrade to FRK that allows for
non-Gaussian data to be analysed in a generalised linear mixed model framework.
These vastly more general spatial and spatio-temporal models are fitted using
the Laplace approximation via the software TMB. The existing functionality of
FRK is retained with this advance into non-Gaussian models; in particular, it
allows for automatic basis-function construction, it can handle both
point-referenced and areal data simultaneously, and it can predict process
values at any spatial support from these data. This new version of FRK also
allows for the use of a large number of basis functions when modelling the
spatial process, and is thus often able to achieve more accurate predictions
than previous versions of the package in a Gaussian setting. We demonstrate
innovative features in this new version of FRK, highlight its ease of use, and
compare it to alternative packages using both simulated and real data sets.
