The first paper from Elliot Dovers's PhD thesis is now out in the Journal of Computational and Graphical Statistics, on fast methods of fitting log-Gaussian Cox process models (LGCPs) using an R package he wrote called scampr, available on GitHub. LGCPs are a good way to analyse presence-only data in ecology, they are a type of point process model that can account for clustering in data (which usually happens in practice). If this is not accounted for your results can have false confidence, very much like what happens if you have overdispersed count data and you don't account for the overdispersion.
The problem has always been that LGCPs are hard to fit - the fastest tool at the moment is INLA, which can be a bit fiddly to use and can take a long time for the typical dataset - and Elliot's work addresses this by using a basis function approximation (a lot like what is done to fit a smoother in a GAM) and coding it using TMB, which uses C++ to do the heavy lifting which is much faster computationally than R.
Look out for a tutorial on how to use scampr coming here soon...
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