Log-Gaussian Cox Process Models with Approximations R package rts2 version 1.0.4 Supports modelling case data to facilitate. The package provides automated computational grid generation over an area of interest with methods to map covariates between geographies, model fitting including spatially aggregated case counts, and predictions and visualisation. Monte Carlo maximum likelihood is the main fitting method with a low-rank approximation for Gaussian processes described by Solin and Särkkä (2020) <doi:10.1007/s11222-019-09886-w> and a stochastic partial differential equation approximation. Bayesian methods are also provided for some methods. Log-Gaussian Cox Processes are described by Diggle et al. (2013) <doi:10.1214/13-STS441>. Software Depends: R (>= 3.5.0), sf (>= 1.0-14) Imports: methods, R6, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), rstan (>= 2.30.0), rstantools (>= 2.1.1), lubridate (>= 1.9.0), stars (>= 0.6-1), raster (>= 3.6-1), glmmrBase (>= 1.3.0), spdep, fmesher, FNN, quadprog LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.30.0), StanHeaders (>= 2.32.0), glmmrBase (>= 1.3.0) Sam Watson <s.i.watson@bham.ac.uk> Comprehensive R Archive Network (CRAN) Sam Watson [aut, cre] (ORCID: <https://orcid.org/0000-0002-8972-769X>) CC BY-SA 4.0 2026-07-19 application/tgz https://CRAN.R-project.org/package=rts2 doi:10.32614/CRAN.package.rts2