Time Series Models for Disease Surveillance R package surveil version 0.3.0 Fits time trend models for routine disease surveillance tasks and returns probability distributions for a variety of quantities of interest, including age-standardized rates, period and cumulative percent change, and measures of health inequality. The models are appropriate for count data such as disease incidence and mortality data, employing a Poisson or binomial likelihood and the first-difference (random-walk) prior for unknown risk. Optionally add a covariance matrix for multiple, correlated time series models. Inference is completed using Markov chain Monte Carlo via the Stan modeling language. References: Donegan, Hughes, and Lee (2022) <doi:10.2196/34589>; Stan Development Team (2021) <https://mc-stan.org>; Theil (1972, ISBN:0-444-10378-3). Software Depends: R (>= 3.5.0) Imports: rstantools (>= 2.1.1), methods, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), rstan (>= 2.26.0), tidybayes (>= 3.0.0), dplyr (>= 1.0.7), rlang (>= 0.4.0), tidyr (>= 1.1.0), ggplot2 (>= 3.0.0), gridExtra (>= 2.0), scales (>= 0.4.0), ggdist (>= 3.0.0) LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.26.0), StanHeaders (>= 2.26.0) Suggests: rmarkdown, knitr, testthat Connor Donegan <connor.donegan@gmail.com> Comprehensive R Archive Network (CRAN) Connor Donegan [aut, cre] (ORCID: <https://orcid.org/0000-0002-9698-5443>) GPL (>= 3) 2024-07-08 application/tgz https://CRAN.R-project.org/package=surveil doi:10.32614/CRAN.package.surveil