# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "rts2" in publications use:' type: software license: CC-BY-SA-4.0 title: 'rts2: Log-Gaussian Cox Process Models with Approximations' version: 1.0.3 doi: 10.32614/CRAN.package.rts2 abstract: 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) 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) . authors: - family-names: Watson given-names: Sam email: s.i.watson@bham.ac.uk orcid: https://orcid.org/0000-0002-8972-769X repository: https://samueliwatson.r-universe.dev commit: 6899cb62b2c40a789f8eb188fff5c656fd475328 date-released: '2026-06-06' contact: - family-names: Watson given-names: Sam email: s.i.watson@bham.ac.uk orcid: https://orcid.org/0000-0002-8972-769X