Fast Spatially Discrete Approximation to Log-Gaussian Cox Processes for Aggregated Disease Count Data [R package SDALGCP2 version 0.1.0]

SDALGCP2: Fast Spatially Discrete Approximation to Log-Gaussian Cox Processes for Aggregated Disease Count Data

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'.

Version:	0.1.0
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, bench
Published:	2026-07-02
DOI:	10.32614/CRAN.package.SDALGCP2 (may not be active yet)
Author:	Olatunji Johnson [aut, cre], Emanuele Giorgi [aut], Peter Diggle [aut]
Maintainer:	Olatunji Johnson <olatunjijohnson21111 at gmail.com>
BugReports:	https://github.com/olatunjijohnson/SDALGCP2/issues
License:	GPL-2 \| GPL-3
URL:	https://github.com/olatunjijohnson/SDALGCP2, https://olatunjijohnson.github.io/SDALGCP2/
NeedsCompilation:	yes
Language:	en-GB
Materials:	README, NEWS
CRAN checks:	SDALGCP2 results

Documentation:

Reference manual:	SDALGCP2.html , SDALGCP2.pdf
Vignettes:	1. Spatial disease mapping with SDALGCP2 (source, R code) 6. Covariates measured on a different support (source) 2. Spatially continuous (raster) predictors (source) 4. Estimating the spatial scale: grid vs continuous (source) 5. Spatial confounding and restricted spatial regression (source) 3. Spatio-temporal disease mapping (source)

Downloads:

Package source:	SDALGCP2_0.1.0.tar.gz
Windows binaries:	r-devel: SDALGCP2_0.1.0.zip, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): SDALGCP2_0.1.0.tgz, r-oldrel (arm64): SDALGCP2_0.1.0.tgz, r-release (x86_64): SDALGCP2_0.1.0.tgz, r-oldrel (x86_64): SDALGCP2_0.1.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=SDALGCP2 to link to this page.