{ "_id": "6a1bff091d7bb097a0a28d75", "Package": "surveillance", "Title": "Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic\nPhenomena", "Version": "1.25.0.9000", "Date": "2025-06-25", "Authors@R": "c(\nMH = person(\"Michael\", \"Hoehle\",\nrole = c(\"aut\", \"ths\"),\ncomment = c(ORCID = \"0000-0002-0423-6702\")),\nSM = person(\"Sebastian\", \"Meyer\",\nemail = \"seb.meyer@fau.de\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-1791-9449\")),\nMP = person(\"Michaela\", \"Paul\",\nrole = \"aut\"),\nLH = person(\"Leonhard\", \"Held\",\nrole = c(\"ctb\", \"ths\"),\ncomment = c(ORCID = \"0000-0002-8686-5325\")),\nperson(\"Howard\", \"Burkom\", role = \"ctb\"),\nperson(\"Thais\", \"Correa\", role = \"ctb\"),\nperson(\"Mathias\", \"Hofmann\", role = \"ctb\"),\nperson(\"Christian\", \"Lang\", role = \"ctb\"),\nperson(\"Juliane\", \"Manitz\", role = \"ctb\"),\nperson(\"Sophie\", \"Reichert\", role = \"ctb\"),\nperson(\"Andrea\", \"Riebler\", role = \"ctb\"),\nperson(\"Daniel\", \"Sabanes Bove\", role = \"ctb\"),\nMS = person(\"Maelle\", \"Salmon\", role = \"ctb\"),\nDS = person(\"Dirk\", \"Schumacher\", role = \"ctb\"),\nperson(\"Stefan\", \"Steiner\", role = \"ctb\"),\nperson(\"Mikko\", \"Virtanen\", role = \"ctb\"),\nperson(\"Wei\", \"Wei\", role = \"ctb\"),\nperson(\"Valentin\", \"Wimmer\", role = \"ctb\"),\nperson(\"R Core Team\", role = \"ctb\",\ncomment = c(ROR = \"02zz1nj61\",\n\"src/ks.c and a few code fragments of standard S3 methods\"))\n)", "Description": "Statistical methods for the modeling and monitoring of\ntime series of counts, proportions and categorical data, as\nwell as for the modeling of continuous-time point processes of\nepidemic phenomena. The monitoring methods focus on aberration\ndetection in count data time series from public health\nsurveillance of communicable diseases, but applications could\njust as well originate from environmetrics, reliability\nengineering, econometrics, or social sciences. The package\nimplements many typical outbreak detection procedures such as\nthe (improved) Farrington algorithm, or the negative binomial\nGLR-CUSUM method of Hoehle and Paul (2008)\n. A novel CUSUM approach\ncombining logistic and multinomial logistic modeling is also\nincluded. The package contains several real-world data sets,\nthe ability to simulate outbreak data, and to visualize the\nresults of the monitoring in a temporal, spatial or\nspatio-temporal fashion. A recent overview of the available\nmonitoring procedures is given by Salmon et al. (2016)\n. For the retrospective analysis of\nepidemic spread, the package provides three endemic-epidemic\nmodeling frameworks with tools for visualization, likelihood\ninference, and simulation. hhh4() estimates models for\n(multivariate) count time series following Paul and Held (2011)\n and Meyer and Held (2014)\n. twinSIR() models the\nsusceptible-infectious-recovered (SIR) event history of a fixed\npopulation, e.g, epidemics across farms or networks, as a\nmultivariate point process as proposed by Hoehle (2009)\n. twinstim() estimates\nself-exciting point process models for a spatio-temporal point\npattern of infective events, e.g., time-stamped geo-referenced\nsurveillance data, as proposed by Meyer et al. (2012)\n. A recent overview of\nthe implemented space-time modeling frameworks for epidemic\nphenomena is given by Meyer et al. (2017)\n.", "License": "GPL-2", "URL": "https://surveillance.R-Forge.R-project.org/", "Additional_repositories": "https://inla.r-inla-download.org/R/stable/", "VignetteBuilder": "utils, knitr", "Repository": "https://r-forge.r-universe.dev", "Date/Publication": "2026-04-30 16:02:26 UTC", "RemoteUrl": "https://github.com/r-forge/surveillance", "RemoteRef": "HEAD", "RemoteSha": "8bb0bc2200a23ea6c5a4808a35ce10c7d485a953", "RemoteSubdir": "pkg", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-31 09:15:18 UTC", "User": "root" }, "Author": "Michael Hoehle [aut, ths] (ORCID:\n),\nSebastian Meyer [aut, cre] (ORCID:\n),\nMichaela Paul [aut],\nLeonhard Held [ctb, ths] (ORCID:\n),\nHoward Burkom [ctb],\nThais Correa [ctb],\nMathias Hofmann [ctb],\nChristian Lang [ctb],\nJuliane Manitz [ctb],\nSophie Reichert [ctb],\nAndrea Riebler [ctb],\nDaniel Sabanes Bove [ctb],\nMaelle Salmon [ctb],\nDirk Schumacher [ctb],\nStefan Steiner [ctb],\nMikko Virtanen [ctb],\nWei Wei [ctb],\nValentin Wimmer [ctb],\nR Core Team [ctb] (ROR: , src/ks.c and a few\ncode fragments of standard S3 methods)", "Maintainer": "Sebastian Meyer ", "MD5sum": "9002f110c3e00558b8d6c55ef6aef2ff", "_user": "r-forge", "_type": "src", "_file": "surveillance_1.25.0.9000.tar.gz", "_fileid": "30932f27fe14d40826e1a1a444f3edaaba4c3d251afb2c4ac571762b5d2fec64", "_filesize": 7576111, "_sha256": "30932f27fe14d40826e1a1a444f3edaaba4c3d251afb2c4ac571762b5d2fec64", "_created": "2026-05-31T09:15:18.000Z", "_published": "2026-05-31T09:27:37.526Z", "_distro": "noble", "_jobs": [ { "job": 78714723100, "time": 251, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "7315552452" }, { "job": 78714723108, "time": 299, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7315557737" }, { "job": 78714723104, "time": 237, "config": "linux-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7315550902" }, { "job": 78714723103, "time": 264, "config": "linux-release-x86_64", "r": 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"title": "Print Quality Value Object", "topics": [ "print.algoQV" ] }, { "page": "R0", "title": "Computes reproduction numbers from fitted models", "topics": [ "R0", "R0.simEpidataCS", "R0.twinstim", "simpleR0" ] }, { "page": "ranef", "title": "Import from package 'nlme'", "topics": [ "fixef", "ranef" ] }, { "page": "refvalIdxByDate", "title": "Compute indices of reference value using Date class", "topics": [ "refvalIdxByDate" ] }, { "page": "residualsCT", "title": "Extract Cox-Snell-like Residuals of a Fitted Point Process", "topics": [ "residuals.simEpidataCS", "residuals.twinSIR", "residuals.twinstim" ] }, { "page": "rotaBB", "title": "Rotavirus cases in Brandenburg, Germany, during 2002-2013 stratified by 5 age categories", "topics": [ "rotaBB" ] }, { "page": "salmAllOnset", "title": "Salmonella cases in Germany 2001-2014 by data of symptoms onset", "topics": [ "salmAllOnset" ] }, { "page": "salmHospitalized", "title": "Hospitalized Salmonella cases in Germany 2004-2014", "topics": [ "salmHospitalized" ] }, { "page": "salmNewport", "title": "Salmonella Newport cases in Germany 2004-2013", "topics": [ "salmNewport" ] }, { "page": "salmonella.agona", "title": "Salmonella Agona cases in the UK 1990-1995", "topics": [ "salmonella.agona" ] }, { "page": "scores", "title": "Proper Scoring Rules for Poisson or Negative Binomial Predictions", "topics": [ "dss", "logs", "rps", "scores", "scores.default", "ses" ] }, { "page": "shadar", "title": "Salmonella Hadar cases in Germany 2001-2006", "topics": [ "shadar" ] }, { "page": "sim.pointSource", "title": "Simulate Point-Source Epidemics", "topics": [ "sim.pointSource" ] }, { "page": "sim.seasonalNoise", "title": "Generation of Background Noise for Simulated Timeseries", "topics": [ "sim.seasonalNoise" ] }, { "page": "stcd", "title": "Spatio-temporal cluster detection", "topics": [ "stcd" ] }, { "page": "stK", "title": "Diggle et al (1995) K-function test for space-time clustering", "topics": [ "plot.stKtest", "stKtest" ] }, { "page": "sts_animate", "title": "Animated Maps and Time Series of Disease Counts or Incidence", "topics": [ "animate.sts" ] }, { "page": "sts_creation", "title": "Simulate Count Time Series with Outbreaks", "topics": [ "sts_creation" ] }, { "page": "sts_ggplot", "title": "Time-Series Plots for '\"sts\"' Objects Using 'ggplot2'", "topics": [ "autoplot.sts" ] }, { "page": "sts_observation", "title": "Create an 'sts' object with a given observation date", "topics": [ "sts_observation" ] }, { "page": "sts-class", "title": "Class '\"sts\"' - surveillance time series", "topics": [ "alarms,sts-method", "alarms<-,sts-method", "as.data.frame,sts-method", "as.data.frame.sts", "as.ts.sts", "as.xts.sts", "coerce,sts,ts-method", "coerce,ts,sts-method", "control,sts-method", "control<-,sts-method", "dim,sts-method", "dimnames,sts-method", "epoch,sts-method", "epoch<-,sts-method", "epochInYear", "epochInYear,sts-method", "frequency,sts-method", "multinomialTS,sts-method", 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"Animate a Sequence of Nowcasts", "topics": [ "animate_nowcasts", "stsNClist_animate" ] }, { "page": "stsNewport", "title": "Salmonella Newport cases in Germany 2001-2015", "topics": [ "stsNewport" ] }, { "page": "stsplot", "title": "Plot Methods for Surveillance Time-Series Objects", "topics": [ "plot,sts,missing-method", "plot,stsNC,missing-method", "plot.sts", "stsplot" ] }, { "page": "stsplot_space", "title": "Map of Disease Counts/Incidence accumulated over a Given Period", "topics": [ "stsplot_space" ] }, { "page": "stsplot_time", "title": "Time-Series Plots for '\"sts\"' Objects", "topics": [ "stsplot_alarm", "stsplot_time", "stsplot_time1" ] }, { "page": "stsSlots", "title": "Generic Functions to Access '\"sts\"' Slots", "topics": [ "alarms", "alarms<-", "control", "control<-", "epoch", "epoch<-", "multinomialTS", "multinomialTS<-", "neighbourhood", "neighbourhood<-", "observed", "observed<-", "population", "population<-", "upperbound", "upperbound<-" ] }, { "page": "stsXtrct", "title": "Subsetting '\"sts\"' Objects", "topics": [ "[,sts,ANY,ANY,ANY-method", "[,sts-method" ] }, { "page": "surveillance.options", "title": "Options of the 'surveillance' Package", "topics": [ "reset.surveillance.options", "surveillance.options" ] }, { "page": "sts_tidy", "title": "Convert an '\"sts\"' Object to a Data Frame in Long (Tidy) Format", "topics": [ "tidy.sts" ] }, { "page": "toLatex.sts", "title": "'toLatex'-Method for '\"sts\"' Objects", "topics": [ "toLatex,sts-method", "toLatex.sts" ] }, { "page": "twinSIR", "title": "Fit an Additive-Multiplicative Intensity Model for SIR Data", "topics": [ "twinSIR" ] }, { "page": "twinSIR_intensityplot", "title": "Plotting Paths of Infection Intensities for 'twinSIR' Models", "topics": [ "intensityplot.simEpidata", "intensityplot.twinSIR", "plot.twinSIR" ] }, { "page": "twinSIR_methods", "title": "Print, Summary and Extraction Methods for '\"twinSIR\"' Objects", "topics": [ "AIC.twinSIR", "extractAIC.twinSIR", "logLik.twinSIR", 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Temporal Interaction Function of a 'twimstim'", "topics": [ "iafplot" ] }, { "page": "twinstim_intensity", "title": "Plotting Intensities of Infection over Time or Space", "topics": [ "intensity.twinstim", "intensityplot.simEpidataCS", "intensityplot.twinstim" ] }, { "page": "twinstim_methods", "title": "Print, Summary and Extraction Methods for '\"twinstim\"' Objects", "topics": [ "coeflist.twinstim", "logLik.twinstim", "nobs.twinstim", "print.summary.twinstim", "print.twinstim", "summary.twinstim", "toLatex.summary.twinstim", "vcov.twinstim", "xtable.summary.twinstim", "xtable.twinstim" ] }, { "page": "twinstim_plot", "title": "Plots for a Fitted 'twinstim'", "topics": [ "plot.twinstim" ] }, { "page": "twinstim_profile", "title": "Profile Likelihood Computation and Confidence Intervals for 'twinstim' objects", "topics": [ "profile.twinstim" ] }, { "page": "twinstim_siaf", "title": "Spatial Interaction Function Objects", "topics": [ "siaf" ] }, { "page": "twinstim_simEndemicEvents", 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