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  "Title": "Time Series Models for Disease Surveillance",
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  "Authors@R": "person(given = \"Connor\",\nfamily = \"Donegan\",\nrole = c(\"aut\", \"cre\"),\nemail = \"connor.donegan@gmail.com\",\ncomment = c(ORCID = \"0000-0002-9698-5443\"))",
  "URL": "https://connordonegan.github.io/surveil/,\nhttps://github.com/ConnorDonegan/surveil/",
  "Description": "Fits time trend models for routine disease surveillance\ntasks and returns probability distributions for a variety of\nquantities of interest, including age-standardized rates,\nperiod and cumulative percent change, and measures of health\ninequality. The models are appropriate for count data such as\ndisease incidence and mortality data, employing a Poisson or\nbinomial likelihood and the first-difference (random-walk)\nprior for unknown risk. Optionally add a covariance matrix for\nmultiple, correlated time series models. Inference is completed\nusing Markov chain Monte Carlo via the Stan modeling language.\nReferences: Donegan, Hughes, and Lee (2022)\n<doi:10.2196/34589>; Stan Development Team (2021)\n<https://mc-stan.org>; Theil (1972, ISBN:0-444-10378-3).",
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  "Repository": "https://connordonegan.r-universe.dev",
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  "Author": "Connor Donegan [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-9698-5443>)",
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