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

Last updated: November 26,2024

1. Install "r-cran-erm" package

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


    
        
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                sudo apt update
            
                    
    
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            $
        

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

This guide covers the steps necessary to uninstall r-cran-erm on Ubuntu 20.10 (Groovy Gorilla):


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

Package: r-cran-erm
Architecture: amd64
Version: 1.0-1-1build1
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: 1553
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-lattice, r-cran-colorspace, r-cran-psych, libc6 (>= 2.4), libgfortran5 (>= 8)
Filename: pool/universe/r/r-cran-erm/r-cran-erm_1.0-1-1build1_amd64.deb
Size: 1410832
MD5sum: 7dcdc9677f67dab51257b2e95b276dcc
SHA1: 0e75c8da41a689f39c2d151b8fd8b9797d1cd9eb
SHA256: cfc74ca705d515aaae835cca818aa6876d6492e6a2f50551b8e6cc1b13d64836
SHA512: 2d15976fcf2eb59b4039164c662cb97cfdd0a66ce43502ce5980447a43d93038202e848a81414cf5732b497d1da96b870d25d010611902b0629005c248988ea6
Homepage: https://cran.r-project.org/package=eRm
Description-en: GNU R package for 'extended Rasch modelling'
eRm fits Rasch models (RM), linear logistic test models (LLTM),
rating scale model (RSM), linear rating scale models (LRSM), partial
credit models (PCM), and linear partial credit models (LPCM). Missing
values are allowed in the data matrix. Additional features are the ML
estimation of the person parameters, Andersen's LR-test,
item-specific Wald test, itemfit and personfit statistics including
infit and outfit measures, various ICC and related plots, automated
stepwise item elimination, simulation module for various binary data
matrices. An eRm platform is provided at R-forge (see URL).
Description-md5: 50c3bc7255e7aec80c55d3e26b38834f