surveillance-package: [stage=build]{(meta <- packageDescription("surveillance", encoding="latin1"))$Title}[stage=build]{meta$Description}
ll{
Package: [stage=build]{meta$Package}
Version: [stage=build]{meta$Version}
License: [stage=build]{meta$License}
URL: http://surveillance.r-forge.r-project.org/
surveillance is an Rpackage implementing statistical methods
for the retrospective modeling and prospective change-point detection
in time series of counts, proportions and categorical data. The main
application is in the detection of aberrations in routine collected
public health data seen as univariate and multivariate time series of
counts or point-processes. However, applications could just as well
originate from environmetrics, econometrics or social sciences. As
many methods rely on statistical process control methodology, the
package is thus also relevant to quality control and reliability
engineering.
The fundamental data structure of the package is an S4 class
sts wrapping observations, monitoring results and date handling
for multivariate time series. Currently the package contains
implementations typical outbreak detection procedures such as Stroup et
al. (1989), Farrington et al., (1996), Rossi et al. (1999), Rogerson
and Yamada (2001), a Bayesian approach (H�{oe}hle, 2007),
negative binomial CUSUM methods (H�{oe}hle and Mazick, 2009), and a
detector based on generalized likelihood ratios (H�{oe}hle
and Paul, 2008). However, also CUSUMs for the prospective change-point
detection in binomial, beta-binomial and multinomial time series is
covered based on generalized linear modelling. This includes
e.g. paired binary CUSUM described by Steiner et al. (1999) or paired
comparison Bradley-Terry modelling described in H�{oe}hle
(2010). The package contains several real-world datasets, the ability
to simulate outbreak data, visualize the results of the monitoring in
temporal, spatial or spatio-temporal fashion.
Furthermore, the package contains inference methods for the
retrospective infectious disease model in Held et al. (2005), Paul et
al. (2008) ("algo.hhh") and Paul and Held (2011) ("hhh4") handling
multivariate time series of counts. Furthermore, the fully Bayesian
approach for univariate time series of counts from Held et al. (2006)
("twins") is also implemented. Self-exciting spatio-temporal point
processes are modeled through additive-multiplicative conditional
intensities as described in H�{oe}hle (2009) ("twinSIR") and
Meyer et al (2012) ("twinstim").
Altogether, the package allows the modelling and monitoring of
epidemic phenomena in temporal and spatio-temporal contexts.[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]