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

Last updated: May 20,2024

1. Install "r-cran-mcmc" package

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


    
        
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                sudo apt update
            
                    
    
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            $
        

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

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


    
        
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                sudo apt remove
            
                        
                r-cran-mcmc
            
                    
    
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            $
        

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

Package: r-cran-mcmc
Architecture: amd64
Version: 0.9-7-1build1
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: 1634
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, libc6 (>= 2.4)
Recommends: r-cran-iso
Suggests: r-cran-xtable
Enhances: r-cran-mcmcpack
Filename: pool/universe/r/r-cran-mcmc/r-cran-mcmc_0.9-7-1build1_amd64.deb
Size: 1375912
MD5sum: d7bd8b527b62b5ba92c358ae390b9176
SHA1: 1e20f552dc82042e70558f6fb8ece7b5740ae582
SHA256: f9116f538c3699c8ef183a6c219c07973ed45d949edb8bef633c34b509bbb5b5
SHA512: 4acf4c772fa06c89cd327be4c08872536dd5291835ae76b8fe25c3e30a2d02e5b7123ff0740d0b45548585ac0c201a19aa91b5e67f8f40d0732b27d51f510713
Homepage: https://cran.r-project.org/package=mcmc
Description-en: GNU R package for Markov Chain Monte Carlo simulations
Simulates continuous distributions of random vectors using Markov
chain Monte Carlo (MCMC). Users specify the distribution by an R
function that evaluates the log unnormalized density. Algorithms are
random walk Metropolis algorithm (function metrop), simulated
tempering (function temper), and morphometric random walk Metropolis
(Johnson and Geyer, Annals of Statistics, 2012, function
morph.metrop), which achieves geometric ergodicity by change of
variable.
Description-md5: 1e9119e3559202f27646ae4fb171aa9f