Discrete Log-Gaussian Cox Processes for Aggregated Disease Counts // Fits a spatially discrete approximation to a log-Gaussian Cox process model for spatially aggregated disease count data, estimated by Monte Carlo Maximum Likelihood as in Christensen (2004) doi:10.1198/106186004X2525 and Johnson, Diggle and Giorgi (2019) doi:10.1002/sim.8339. Performance-critical steps (aggregated correlation assembly, Metropolis-adjusted Langevin algorithm (MALA) sampling, the Monte Carlo likelihood, and the Kronecker-structured space-time likelihood) are implemented in C++ via 'RcppArmadillo'. Provides a one-line, 'glm'-like interface and statistical extensions including a nugget term, general 'Matern' smoothness, raster and misaligned covariates, restricted spatial regression, importance-sampling diagnostics and re-anchored Monte Carlo maximum likelihood (MCML).