{ "_id": "6a1f38dbb401979e73428e3f", "Package": "surveil", "Title": "Time Series Models for Disease Surveillance", "Version": "0.3.0", "Date": "2024-07-07", "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; Stan Development Team (2021)\n; Theil (1972, ISBN:0-444-10378-3).", "License": "GPL (>= 3)", "Encoding": "UTF-8", "LazyData": "true", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.1", "Biarch": "true", "SystemRequirements": "GNU make", "VignetteBuilder": "knitr", "Config/pak/sysreqs": "make libicu-dev", "Repository": "https://connordonegan.r-universe.dev", "Date/Publication": "2024-07-08 16:10:42 UTC", "RemoteUrl": "https://github.com/connordonegan/surveil", "RemoteRef": "HEAD", "RemoteSha": "847d19fc67e7b10c67fa28cd717282b65bd4bfbe", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-11 08:48:24 UTC", "User": "root" }, "Author": "Connor Donegan [aut, cre] (ORCID:\n)", "Maintainer": "Connor Donegan ", "MD5sum": "4df7c017120535c0a6a13a2abd357c09", "_user": "connordonegan", "_type": "src", "_file": "surveil_0.3.0.tar.gz", "_fileid": 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