rts2: Log-Gaussian Cox Process Models with Approximations

Supports modelling case data to facilitate. The package provides automated computational grid generation over an area of interest with methods to map covariates between geographies, model fitting including spatially aggregated case counts, and predictions and visualisation. Monte Carlo maximum likelihood is the main fitting method with a low-rank approximation for Gaussian processes described by Solin and Särkkä (2020) <doi:10.1007/s11222-019-09886-w> and a stochastic partial differential equation approximation. Bayesian methods are also provided for some methods. Log-Gaussian Cox Processes are described by Diggle et al. (2013) <doi:10.1214/13-STS441>.

Version:	1.0.3
Depends:	R (≥ 3.5.0), sf (≥ 1.0-14)
Imports:	methods, R6, Rcpp (≥ 0.12.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.30.0), rstantools (≥ 2.1.1), lubridate (≥ 1.9.0), stars (≥ 0.6-1), raster (≥ 3.6-1), glmmrBase (≥ 1.3.0), spdep, fmesher, FNN, quadprog
LinkingTo:	BH (≥ 1.66.0), Rcpp (≥ 0.12.0), RcppEigen (≥ 0.3.3.3.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.30.0), StanHeaders (≥ 2.32.0), glmmrBase (≥ 1.3.0)
Published:	2026-06-07
DOI:	10.32614/CRAN.package.rts2
Author:	Sam Watson [aut, cre]
Maintainer:	Sam Watson <s.i.watson at bham.ac.uk>
License:	CC BY-SA 4.0
NeedsCompilation:	yes
SystemRequirements:	GNU make
Materials:	README
CRAN checks:	rts2 results

Documentation:

Reference manual:

rts2.html , rts2.pdf

Downloads:

Package source:	rts2_1.0.3.tar.gz
Windows binaries:	r-devel: rts2_1.0.3.zip, r-release: not available, r-oldrel: rts2_1.0.3.zip
macOS binaries:	r-release (arm64): rts2_1.0.3.tgz, r-oldrel (arm64): rts2_1.0.3.tgz, r-release (x86_64): rts2_1.0.3.tgz, r-oldrel (x86_64): rts2_1.0.3.tgz
Old sources:	rts2 archive

Linking:

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