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

Last updated: May 20,2024

1. Install "r-cran-statmod" package

Here is a brief guide to show you how to install r-cran-statmod on Ubuntu 20.10 (Groovy Gorilla)


    
        
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                sudo apt update
            
                    
    
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            $
        

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

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


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

Package: r-cran-statmod
Architecture: amd64
Version: 1.4.34-1build1
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: 330
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0
Suggests: r-cran-mass
Filename: pool/universe/r/r-cran-statmod/r-cran-statmod_1.4.34-1build1_amd64.deb
Size: 255408
MD5sum: a582056be099523f8675fd0cd71f05ff
SHA1: d8499b40c98e34822f7acbe2044bb687668a3444
SHA256: 238424a84796f1a0a99d443fc7ec52f521df2e4b35fe005859338ed8007b4f91
SHA512: 107e0f88ab3bb779c76584aba041c4ffe608a7b7201a0eb944fbbb4f7b704d0b43ee9acf66173b1e1d1cef4f2d585ff84dfeaf9dd7f6387b2b94b358eb8d647d
Homepage: https://cran.r-project.org/package=statmod
Description-en: GNU R package providing algorithms and functions for statistical modeling
This R package provides a collection of algorithms and functions to aid
statistical modeling. Includes limiting dilution analysis (aka ELDA), growth
curve comparisons, mixed linear models, heteroscedastic regression,
inverse-Gaussian probability calculations, Gauss quadrature and a secure
convergence algorithm for nonlinear models. Also includes a number of advanced
generalized linear model functions including new Tweedie and Digamma glm
families and a secure convergence algorithm.
Description-md5: ec23192a68b9b7eac0b88184e35e22fd