r-cran-surveillance 1.9-1-1 source package in Ubuntu
Changelog
r-cran-surveillance (1.9-1-1) unstable; urgency=medium * New upstream version * autopkgtest needs versioned dependency freom r-cran-testthat -- Andreas Tille <email address hidden> Wed, 08 Jul 2015 06:08:33 +0200
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 | Published | Component | Section |
|---|
Downloads
| File | Size | SHA-256 Checksum |
|---|---|---|
| r-cran-surveillance_1.9-1-1.dsc | 2.2 KiB | a773ce8725fa6443be4a6072d6b4ab3f3f82000951a10c31d2000e963c428320 |
| r-cran-surveillance_1.9-1.orig.tar.gz | 2.3 MiB | c58bf1c7b8f7e25d02d89234f2c19a37d1ad466d6e307368a451637b898d254e |
| r-cran-surveillance_1.9-1-1.debian.tar.xz | 5.0 KiB | 138c80138b8bb39e16c48771b50849441a6a9cff3378899b36a7395807d794c4 |
Available diffs
- diff from 1.8-0-1 to 1.9-1-1 (257.9 KiB)
- diff from 1.8-3-1 to 1.9-1-1 (140.6 KiB)
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>.
