Source: r-cran-surveillance
Maintainer: Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
Uploaders: Andreas Tille <tille@debian.org>,
           Sebastian Carneiro <scarneiro@speedy.com.ar>
Section: gnu-r
Testsuite: autopkgtest-pkg-r
Priority: optional
Build-Depends: debhelper-compat (= 13),
               dh-r,
               r-base-dev,
               r-cran-sp,
               r-cran-xtable,
               r-cran-rcpp,
               r-cran-polycub (>= 0.8.0),
               r-cran-mass,
               r-cran-matrix,
               r-cran-nlme,
               r-cran-spatstat.geom
Standards-Version: 4.5.1
Vcs-Browser: https://salsa.debian.org/r-pkg-team/r-cran-surveillance
Vcs-Git: https://salsa.debian.org/r-pkg-team/r-cran-surveillance.git
Homepage: https://cran.r-project.org/package=surveillance
Rules-Requires-Root: no

Package: r-cran-surveillance
Architecture: any
Depends: ${R:Depends},
         ${shlibs:Depends},
         ${misc:Depends}
Recommends: ${R:Recommends}
Suggests: ${R:Suggests}
Description: GNU R package for the 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 Höhle 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 Höhle (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>.