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) <doi:10.1198/106186004X2525> and
    Johnson, Diggle and Giorgi (2019) <doi:10.1002/sim.8339>. 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: 2026-06-26 16:46:31 UTC; olatunji-johnson
Author: Olatunji Johnson [aut, cre],
  Emanuele Giorgi [aut],
  Peter Diggle [aut]
Maintainer: Olatunji Johnson <olatunjijohnson21111@gmail.com>
Repository: CRAN
Date/Publication: 2026-07-02 18:40:25 UTC
Built: R 4.6.1; x86_64-w64-mingw32; 2026-07-03 23:51:14 UTC; windows
Archs: x64
