  The R package rts2 provides data manipulation and model fitting tools for Log
Gaussian Cox Process (LGCP) models. LGCP models are a key method for disease
and other types of surveillance, and provide a means of predicting risk across
an area of interest based on spatially-referenced and time-stamped case data.
However, these models can be difficult to specify and computationally demanding
to estimate. For many surveillance scenarios we require results in near
real-time using routinely available data to guide and direct policy responses,
or due to limited availability of computational resources. There are limited
software implementations available for this real-time context with reliable
predictions and quantification of uncertainty. The rts2 package provides a
range of modern Gaussian process approximations and model fitting methods to
fit the LGCP, including estimation of covariance parameters, using both
Bayesian and stochastic Maximum Likelihood methods. The package provides a
suite of data manipulation tools. We also provide a novel implementation to
estimate the LGCP when case data are aggregated to an irregular grid such as
census tract areas.
