Package: r-cran-surveillance
Version: 1.26.1-1
Architecture: amd64
Architecture-Variant: amd64v3
Maintainer: Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
Original-Maintainer: Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
Installed-Size: 6459
Depends: r-api-4.0, r-cran-sp (>= 2.1-4), r-cran-polycub (>= 0.8.0), r-cran-mass, r-cran-matrix, r-cran-nlme, r-cran-spatstat.geom, libc6 (>= 2.35), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-pkg-team-core-architecture
Recommends: r-cran-xtable, r-cran-gridextra, r-cran-lattice, r-cran-colorspace, r-cran-scales, r-cran-animation, r-cran-msm, r-cran-spc, r-cran-coda, r-cran-spdep, r-cran-numderiv, r-cran-maxlik, r-cran-gsl, r-cran-quadprog, r-cran-memoise, r-cran-polyclip, r-cran-intervals, r-cran-sf, r-cran-tinytest (>= 1.4.1), r-cran-knitr, r-cran-bibtex, r-cran-testthat
Section: gnu-r
Priority: optional
Homepage: https://cran.r-project.org/package=surveillance
Description: Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena
 Statistical methods for the modeling and monitoring of time series
 of counts, proportions and categorical data, as well as for the modeling
 of continuous-time point processes of epidemic phenomena. The monitoring
 methods focus on aberration detection in count data time series from public
 health surveillance of communicable diseases, but applications could
 just as well originate from environmetrics, reliability engineering,
 econometrics, or social sciences. The package implements many typical
 outbreak detection procedures such as the (improved) Farrington algorithm,
 or the negative binomial GLR-CUSUM method of Hoehle and Paul (2008)
 <doi:10.1016/j.csda.2008.02.015>. A novel CUSUM approach combining logistic
 and multinomial logistic modeling is also included. The package contains
 several real-world data sets, the ability to simulate outbreak data,
 and to visualize the results of the monitoring in a temporal, spatial or
 spatio-temporal fashion. A recent overview of the available monitoring
 procedures is given by Salmon et al. (2016) <doi:10.18637/jss.v070.i10>. For
 the retrospective analysis of epidemic spread, the package provides three
 endemic-epidemic modeling frameworks with tools for visualization, likelihood
 inference, and simulation. hhh4() estimates models for (multivariate)
 count time series following Paul and Held (2011) <doi:10.1002/sim.4177> and
 Meyer and Held (2014) <doi:10.1214/14-AOAS743>. twinSIR() models the
 susceptible-infectious-recovered (SIR) event history of a fixed population,
 e.g, epidemics across farms or networks, as a multivariate point process as
 proposed by Hoehle (2009) <doi:10.1002/bimj.200900050>. twinstim() estimates
 self-exciting point process models for a spatio-temporal point pattern
 of infective events, e.g., time-stamped geo-referenced surveillance data,
 as proposed by Meyer et al. (2012) <doi:10.1111/j.1541-0420.2011.01684.x>. A
 recent overview of the implemented space-time modeling frameworks for epidemic
 phenomena is given by Meyer et al. (2017) <doi:10.18637/jss.v077.i11>.
