How to Install and Uninstall r-cran-surveillance Package on Ubuntu 20.10 (Groovy Gorilla)

Last updated: May 14,2024

1. Install "r-cran-surveillance" package

In this section, we are going to explain the necessary steps to install r-cran-surveillance on Ubuntu 20.10 (Groovy Gorilla)


    
        
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                sudo apt update
            
                    
    
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            $
        

        
            
                sudo apt install
            
                        
                r-cran-surveillance
            
                    
    
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2. Uninstall "r-cran-surveillance" package

This tutorial shows how to uninstall r-cran-surveillance on Ubuntu 20.10 (Groovy Gorilla):


    
        
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                sudo apt remove
            
                        
                r-cran-surveillance
            
                    
    
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            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

3. Information about the r-cran-surveillance package on Ubuntu 20.10 (Groovy Gorilla)

Package: r-cran-surveillance
Architecture: amd64
Version: 1.18.0-1build1
Priority: optional
Section: multiverse/science
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian R Packages Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 6667
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-cran-sp (>= 1.0-15), r-cran-xtable (>= 1.7-0), r-cran-rcpp (>= 0.11.1), r-cran-polycub (>= 0.6.0), r-cran-mass, r-cran-matrix, r-cran-nlme, r-cran-spatstat (>= 1.36-0), libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2)
Recommends: r-cran-testthat (>= 0.10.0), r-cran-spdep, r-cran-memoise, r-cran-maxlik
Suggests: r-cran-xts, r-cran-gridextra (>= 2.0.0), r-cran-lattice, r-cran-colorspace, r-cran-scales, r-cran-animation, r-cran-msm, r-cran-spc, r-cran-quadprog, r-cran-polyclip, r-cran-maptools, r-cran-intervals, r-cran-numderiv, r-cran-gsl, r-cran-testthat (>= 0.11.0), r-cran-coda, r-cran-ggplot2, r-cran-knitr
Filename: pool/multiverse/r/r-cran-surveillance/r-cran-surveillance_1.18.0-1build1_amd64.deb
Size: 5577064
MD5sum: 1b79541cf11169b686a2e32db13f479e
SHA1: 179e1a059042ae416080f3c4b9ec1acd74f86759
SHA256: 5b637f02dc272bc1d1d8ddcb2664c9f233f062274f4563d40a1ef0cca522475c
SHA512: 797d87998fe935acec3c43d46d576e5bb16d9aa48070374257074869b51aadaea9ed06be5f947eb4029b7f4ff32f0097245ed82f058704ba3622262613cc25b1
Homepage: https://cran.r-project.org/package=surveillance
Description-en: 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).
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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 .
Description-md5: 3206ab392e2d1549a03174efb7d99d18