Package: SDALGCP2 Title: Fast Spatially Discrete Approximation to Log-Gaussian Cox Processes for Aggregated Disease Count Data Version: 0.1.0 Authors@R: c( person("Olatunji", "Johnson", email = "olatunjijohnson21111@gmail.com", role = c("aut", "cre")), person("Emanuele", "Giorgi", role = "aut"), person("Peter", "Diggle", role = "aut")) Description: 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) and Johnson, Diggle and Giorgi (2019) . Performance-critical steps (aggregated correlation assembly, '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 'MCML'. Depends: R (>= 4.2.0) License: GPL-2 | GPL-3 Encoding: UTF-8 Language: en-GB LazyData: true LinkingTo: Rcpp, RcppArmadillo Imports: Rcpp, sf, terra, spatstat.geom, spatstat.random, ggplot2, progress, stats, utils Suggests: knitr, rmarkdown, testthat (>= 3.0.0), numDeriv, bench VignetteBuilder: knitr Config/testthat/edition: 3 RoxygenNote: 7.3.1 URL: https://github.com/olatunjijohnson/SDALGCP2, https://olatunjijohnson.github.io/SDALGCP2/ BugReports: https://github.com/olatunjijohnson/SDALGCP2/issues NeedsCompilation: yes Packaged: 2026-07-02 21:19:57 UTC; root Author: Olatunji Johnson [aut, cre], Emanuele Giorgi [aut], Peter Diggle [aut] Maintainer: Olatunji Johnson Repository: https://cran.r-universe.dev Date/Publication: 2026-07-02 18:40:25 UTC RemoteUrl: https://github.com/cran/SDALGCP2 RemoteRef: HEAD RemoteSha: cc2e16b6ed9c3351d616d42ce0045f2f9bcca979