Discrete Log-Gaussian Cox Processes for Aggregated Disease Counts R package SDALGCP2 version 0.1.1 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). Software Depends: R (>= 4.2.0) Imports: Rcpp, sf, terra, spatstat.geom, spatstat.random, ggplot2, progress, stats, utils LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, testthat (>= 3.0.0), numDeriv Olatunji Johnson <olatunjijohnson21111@gmail.com> Comprehensive R Archive Network (CRAN) Olatunji Johnson [aut, cre], Emanuele Giorgi [aut], Peter Diggle [aut] GPL-2 GPL-3 2026-07-07 application/tgz https://CRAN.R-project.org/package=SDALGCP2 doi:10.32614/CRAN.package.SDALGCP2 en-GB