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

Last updated: November 26,2024

1. Install "r-cran-powerlaw" package

Please follow the instructions below to install r-cran-powerlaw on Ubuntu 20.10 (Groovy Gorilla)


    
        
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                sudo apt update
            
                    
    
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            $
        

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

Please follow the guidance below to uninstall r-cran-powerlaw on Ubuntu 20.10 (Groovy Gorilla):


    
        
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                sudo apt remove
            
                        
                r-cran-powerlaw
            
                    
    
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            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the r-cran-powerlaw package on Ubuntu 20.10 (Groovy Gorilla)

Package: r-cran-powerlaw
Architecture: all
Version: 0.70.6-2build1
Priority: optional
Section: universe/gnu-r
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian R Packages Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 3711
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-cran-pracma
Recommends: r-cran-testthat
Suggests: r-cran-covr, r-cran-knitr
Filename: pool/universe/r/r-cran-powerlaw/r-cran-powerlaw_0.70.6-2build1_all.deb
Size: 3368224
MD5sum: 45af123e5f0e6b073fcc61d83b5fcc8c
SHA1: ab38114c0d641708d55f0b54d1aa1a8ef831ae89
SHA256: c0a796f686260f985f7332869925bd4d8bfc1da9e88a250271cc3dcb39b5a463
SHA512: f97edc9c48f33546e559c25c1c2b863907a151a1f3e89845bd1d8bea241c82c2daa501630014de7bb81a2b1af6411c0dac2a56df9ac1d6f4710027c1e2fb063e
Homepage: https://cran.r-project.org/package=poweRlaw
Description-en: GNU R analysis of heavy tailed distributions
GNU R implementation of maximum likelihood estimators for a variety
of heavy tailed distributions, including both the discrete and continuous
power law distributions. Additionally, a goodness-of-fit based approach is
used to estimate the lower cut-off for the scaling region.
Description-md5: 878052bf6819167ab4504e3132bef78b