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SDALGCP2  

Fast Spatially Discrete Approximation to Log-Gaussian Cox Processes for Aggregated Disease Count Data
View on CRAN: Click here


Download and install SDALGCP2 package within the R console
Install from CRAN:
install.packages("SDALGCP2")

Install from Github:
library("remotes")
install_github("cran/SDALGCP2")

Install by package version:
library("remotes")
install_version("SDALGCP2", "0.1.0")



Attach the package and use:
library("SDALGCP2")
Maintained by
Olatunji Johnson
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-07-02
Latest Update: 2026-07-02
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'.
How to cite:
Olatunji Johnson (2026). SDALGCP2: Fast Spatially Discrete Approximation to Log-Gaussian Cox Processes for Aggregated Disease Count Data. R package version 0.1.0, https://cran.r-project.org/web/packages/SDALGCP2. Accessed 20 Sep. 2026.
Previous versions and publish date:
(2026-07-09 08:24), 0.1.0 (2026-07-02 20:40)
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Complete documentation for SDALGCP2
Functions, R codes and Examples using the SDALGCP2 R package
Full SDALGCP2 package functions and examples
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