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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: Discrete Log-Gaussian Cox Processes for Aggregated Counts'
version: 0.1.1
doi: 10.32614/CRAN.package.SDALGCP2
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, 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).
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://olatunjijohnson.r-universe.dev
repository-code: https://github.com/olatunjijohnson/SDALGCP2
commit: 70d0bd499f613529a3d9c7280d9064f00a2532f1
url: https://olatunjijohnson.github.io/SDALGCP2/
date-released: '2026-07-02'
contact:
- family-names: Johnson
  given-names: Olatunji
  email: olatunjijohnson21111@gmail.com