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# CITATION.cff file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "SDALGCP2" in publications use:'
type: software
license:
- GPL-2.0-only
- GPL-3.0-only
title: 'SDALGCP2: Fast Spatially Discrete Approximation to Log-Gaussian Cox Processes
  for Aggregated Disease Count Data'
version: 0.1.0
abstract: 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) <https://doi.org/10.1198/106186004X2525> and Johnson, Diggle
  and Giorgi (2019) <https://doi.org/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'.
authors:
- family-names: Johnson
  given-names: Olatunji
  email: olatunjijohnson21111@gmail.com
- family-names: Giorgi
  given-names: Emanuele
- family-names: Diggle
  given-names: Peter
repository: https://cran.r-universe.dev
repository-code: https://github.com/olatunjijohnson/SDALGCP2
commit: cc2e16b6ed9c3351d616d42ce0045f2f9bcca979
url: https://olatunjijohnson.github.io/SDALGCP2/
date-released: '2026-07-02'
contact:
- family-names: Johnson
  given-names: Olatunji
  email: olatunjijohnson21111@gmail.com