How to Install and Uninstall r-cran-mnp Package on Ubuntu 21.10 (Impish Indri)

Last updated: May 19,2024

1. Install "r-cran-mnp" package

Please follow the guidance below to install r-cran-mnp on Ubuntu 21.10 (Impish Indri)


    
        
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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

Please follow the guidance below to uninstall r-cran-mnp on Ubuntu 21.10 (Impish Indri):


    
        
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                sudo apt remove
            
                        
                r-cran-mnp
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

3. Information about the r-cran-mnp package on Ubuntu 21.10 (Impish Indri)

Package: r-cran-mnp
Architecture: amd64
Version: 3.1-1-1
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: 1079
Depends: r-base-core (>= 4.0.2-1build1), 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_3.1-1-1_amd64.deb
Size: 945424
MD5sum: f126415d4d1307756bd89ec2ccb793b9
SHA1: cbf16f7c93563ebd32a74e8f218207c0e661c2eb
SHA256: 3e7e62caeffe998f90db314f6fa5bfd79456e0b21e689bd04799d698ff31bacb
SHA512: f1bacb754d007d28a6e307f3f114a83ba3a23c88243cc51f450daf24073addaa7ff0488cea169117eae294f91983f26a037f70e4c7691b8806d1baae6ad8ca5a
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