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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "rts2" in publications use:'
type: software
license: CC-BY-SA-4.0
title: 'rts2: Real-Time Disease Surveillance'
version: 1.0.2
doi: 10.32614/CRAN.package.rts2
abstract: Supports modelling real-time case data to facilitate the real-time surveillance
  of infectious diseases and other point phenomena. 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. Both Bayesian and maximum likelihood methods are
  provided. Log-Gaussian Cox Processes are described by Diggle et al. (2013) <https://doi.org/10.1214/13-STS441>
  and we provide both the low-rank approximation for Gaussian processes described
  by Solin and Särkkä (2020) <https://doi.org/10.1007/s11222-019-09886-w> and Riutort-Mayol
  et al (2023) <https://doi.org/10.1007/s11222-022-10167-2> and the nearest neighbour
  Gaussian process described by Datta et al (2016) <https://doi.org/10.1080/01621459.2015.1044091>.
authors:
- family-names: Watson
  given-names: Sam
  email: s.i.watson@bham.ac.uk
  orcid: https://orcid.org/0000-0002-8972-769X
repository: https://cran.r-universe.dev
commit: 71f560d224226a8f315f535ec177bb47b9b00840
date-released: '2026-04-25'
contact:
- family-names: Watson
  given-names: Sam
  email: s.i.watson@bham.ac.uk
  orcid: https://orcid.org/0000-0002-8972-769X