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:
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