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

Last updated: May 17,2024

1. Install "r-cran-mnp" package

This tutorial shows how to install r-cran-mnp on Ubuntu 20.10 (Groovy Gorilla)


    
        
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                sudo apt update
            
                    
    
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            $
        

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

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


    
        
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                sudo apt remove
            
                        
                r-cran-mnp
            
                    
    
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            $
        

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

Package: r-cran-mnp
Architecture: amd64
Version: 3.1-0-2build1
Priority: optional
Section: universe/math
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian R Packages Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 1090
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-cran-mass, libc6 (>= 2.29), liblapack3 | liblapack.so.3
Suggests: r-cran-testthat
Enhances: r-cran-mcmcpack
Filename: pool/universe/r/r-cran-mnp/r-cran-mnp_3.1-0-2build1_amd64.deb
Size: 956416
MD5sum: 13cbc3f0c452352f3d60a45c3a33b6f3
SHA1: b1f09da65016c32169d86ae9fb918a7cd34290c1
SHA256: 50d6a57f0444a32fdea155807be769bdd18ccdf9fec52d2a3499d7a316066d38
SHA512: 12a04626fff9c875213cf6f0c4e2c566e9b68cc0af41f7c3e2640ab963f765081ec5dcb2021766156d85095c59628edd5dd531c0870e94600de6f607498bbcb9
Homepage: https://cran.r-project.org/package=MNP
Description-en: GNU R package for fitting multinomial probit (MNP) models
MNP is an R package that fits Bayesian Multinomial Probit (MNP)
models via Markov chain Monte Carlo (MCMC). Along with the standard
multinomial probit model, it can also fit models with different
choice sets for each observation and complete or partial ordering of
all the available alternatives. The estimation is based on the
efficient marginal data augmentation algorithm that is developed by
Imai and van Dyk (2004).
Description-md5: a93002366341dd06c8c31e3607a7d2bc