Ubuntu
r-cran-surveillance package

r-cran-surveillance 1.10-0-1 source package in Ubuntu

Changelog

r-cran-surveillance (1.10-0-1) unstable; urgency=medium

  * New upstream version
  * Recommends: r-cran-spdep (uploaded to new)

 -- Andreas Tille <email address hidden>  Tue, 10 Nov 2015 13:42:43 +0100

Upload details

Uploaded by:
Debian Med
Uploaded to:
Sid
Original maintainer:
Debian Med
Architectures:
any
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section

Downloads

File Size SHA-256 Checksum
r-cran-surveillance_1.10-0-1.dsc 2.2 KiB 22a850d6c69aa865223f337b9c5c6be1e0804accd054b677c1b5200a2b0dc268
r-cran-surveillance_1.10-0.orig.tar.gz 2.3 MiB 3d8638344a7c7ba2aecbcddfbe07aaab02945d272c839b2174825d23108f6ca5
r-cran-surveillance_1.10-0-1.debian.tar.xz 4.8 KiB e72a865e7e170dfb3d74c3f0eaebd60582757ff50014701ca98cbf50067bdf7e

Available diffs

No changes file available.

Binary packages built by this source

r-cran-surveillance: GNU R package for the Modeling and Monitoring of Epidemic Phenomena

 Implementation of statistical methods for the modeling and change-point
 detection in time series of counts, proportions and categorical data, as
 well as for the modeling of continuous-time epidemic phenomena, e.g.,
 discrete-space setups such as the spatially enriched
 Susceptible-Exposed-Infectious-Recovered (SEIR) models, or
 continuous-space point process data such as the occurrence of infectious
 diseases. Main focus is on outbreak detection in count data time series
 originating from public health surveillance of communicable diseases,
 but applications could just as well originate from environmetrics,
 reliability engineering, econometrics or social sciences.
 .
 Currently, the package contains implementations of many typical
 outbreak detection procedures such as Farrington et al (1996), Noufaily
 et al (2012) or the negative binomial LR-CUSUM method described in Höhle
 and Paul (2008). A novel CUSUM approach combining logistic and
 multinomial logistic modelling is also included. Furthermore, inference
 methods for the retrospective infectious disease models in Held et al
 (2005), Held et al (2006), Paul et al (2008), Paul and Held (2011), Held
 and Paul (2012), and Meyer and Held (2014) are provided.
 .
 Continuous self-exciting spatio-temporal point processes are modeled
 through additive-multiplicative conditional intensities as described in
 Höhle (2009) ('twinSIR', discrete space) and Meyer et al (2012)
 ('twinstim', continuous space).
 .
 The package contains several real-world data sets, the ability to
 simulate outbreak data, visualize the results of the monitoring in
 temporal, spatial or spatio-temporal fashion.
 .
 Note: Using the 'boda' algorithm requires the 'INLA' package, which
 is available from <http://www.r-inla.org/download>.

r-cran-surveillance-dbgsym: debug symbols for package r-cran-surveillance

 Implementation of statistical methods for the modeling and change-point
 detection in time series of counts, proportions and categorical data, as
 well as for the modeling of continuous-time epidemic phenomena, e.g.,
 discrete-space setups such as the spatially enriched
 Susceptible-Exposed-Infectious-Recovered (SEIR) models, or
 continuous-space point process data such as the occurrence of infectious
 diseases. Main focus is on outbreak detection in count data time series
 originating from public health surveillance of communicable diseases,
 but applications could just as well originate from environmetrics,
 reliability engineering, econometrics or social sciences.
 .
 Currently, the package contains implementations of many typical
 outbreak detection procedures such as Farrington et al (1996), Noufaily
 et al (2012) or the negative binomial LR-CUSUM method described in Höhle
 and Paul (2008). A novel CUSUM approach combining logistic and
 multinomial logistic modelling is also included. Furthermore, inference
 methods for the retrospective infectious disease models in Held et al
 (2005), Held et al (2006), Paul et al (2008), Paul and Held (2011), Held
 and Paul (2012), and Meyer and Held (2014) are provided.
 .
 Continuous self-exciting spatio-temporal point processes are modeled
 through additive-multiplicative conditional intensities as described in
 Höhle (2009) ('twinSIR', discrete space) and Meyer et al (2012)
 ('twinstim', continuous space).
 .
 The package contains several real-world data sets, the ability to
 simulate outbreak data, visualize the results of the monitoring in
 temporal, spatial or spatio-temporal fashion.
 .
 Note: Using the 'boda' algorithm requires the 'INLA' package, which
 is available from <http://www.r-inla.org/download>.