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

Last updated: December 24,2024

1. Install "r-cran-learnbayes" package

In this section, we are going to explain the necessary steps to install r-cran-learnbayes on Ubuntu 20.10 (Groovy Gorilla)


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

Please follow the step by step instructions below to uninstall r-cran-learnbayes on Ubuntu 20.10 (Groovy Gorilla):


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

Package: r-cran-learnbayes
Architecture: all
Version: 2.15.1-4build1
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: 1182
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0
Filename: pool/universe/r/r-cran-learnbayes/r-cran-learnbayes_2.15.1-4build1_all.deb
Size: 963412
MD5sum: 7dfa82e9c1710749d01c710871eeb517
SHA1: 84e9a64ce5d3106ff9fdda591b86c0b49f7ee54b
SHA256: 59fbed9a215cf320e85ab730ba9b929d826e0e6ecb015b9fffbb937b3dacf419
SHA512: 60d20a485a89347013a18bc5f9a1ee71e176ddfaccfffcbcfebc7ad3af9a2652363959833a26449291e0f7befe789dc472664da2c9e8fe5fcf3077ef7e6526fe
Homepage: https://cran.r-project.org/package=LearnBayes
Description-en: GNU R functions for learning bayesian inference
LearnBayes contains a collection of functions helpful in learning the
basic tenets of Bayesian statistical inference. It contains functions
for summarizing basic one and two parameter posterior distributions and
predictive distributions. It contains MCMC algorithms for summarizing
posterior distributions defined by the user. It also contains functions
for regression models, hierarchical models, Bayesian tests, and
illustrations of Gibbs sampling.
Description-md5: 3f847eda16e3c7ccdf8cee73c04611fd