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Title: Modeling and monitoring discrete response time series
Author: M. Höhle with contributions from T. Correa, M. Hofmann, C. Lang, M. Paul, A. Riebler, S. Steiner and V. Wimmer
Abstract: A package implementing statistical methods for the modeling and change-point detection
in time series of counts, proportions and categorical data. Focus is on outbreak detection in count
data time series originating from public health surveillance of infectious diseases, but
applications could just as well originate from environmetrics, reliability engineering,
econometrics or social sciences. 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, negative binomial CUSUM
methods and a detector based on generalized likelihood ratios. Furthermore, inference methods
for the retrospective infectious disease model in Held et al. (2005), Held et al. (2006) and Paul et
al. (2008) are provided. A novel CUSUM approach combining logistic and multinomial logistic
modelling is also included. 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.
Section: Science/Biology
Format: pdf
Files: /usr/share/doc/r-cran-surveillance/manual.pdf
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