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

Last updated: May 17,2024

1. Install "r-cran-gnm" package

This is a short guide on how to install r-cran-gnm on Ubuntu 20.10 (Groovy Gorilla)


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                r-cran-gnm
            
                    
    
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2. Uninstall "r-cran-gnm" package

This is a short guide on how to uninstall r-cran-gnm on Ubuntu 20.10 (Groovy Gorilla):


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

Package: r-cran-gnm
Architecture: amd64
Version: 1.1-1-2build1
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: 1362
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-nnet, r-cran-qvcalc (>= 0.8-3), r-cran-relimp, libblas3 | libblas.so.3, libc6 (>= 2.4)
Recommends: r-cran-testthat
Suggests: r-cran-vcdextra
Filename: pool/universe/r/r-cran-gnm/r-cran-gnm_1.1-1-2build1_amd64.deb
Size: 1075456
MD5sum: 19339d716dcb3b512a5cf9bc8a22cd9b
SHA1: cb8e5d52a3a15c5b26b250360bed168c389180fa
SHA256: a6aa30e4e7b241ce27ef39dfe08b458ce7800bc2f0525d9e26bd1d579c080629
SHA512: bac47f36f9cc7787830044c63a60f865e9d2687a1296687d2f3be536f661881536480b83698e6b4f4533430389a5db7c27eab68a28be5a7aab5351fa381d06e7
Homepage: https://cran.r-project.org/package=gnm
Description-en: GNU R package for generalized nonlinear models
Functions to specify and fit generalized nonlinear models, including
models with multiplicative interaction terms such as the UNIDIFF model
from sociology and the AMMI model from crop science, and many others.
Over-parameterized representations of models are used throughout;
functions are provided for inference on estimable parameter
combinations, as well as standard methods for diagnostics etc.
Description-md5: c4832d92d9ca22e8e4964c7eacc1bf4e