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

Last updated: May 03,2024

1. Install "r-cran-bdgraph" package

This tutorial shows how to install r-cran-bdgraph on Ubuntu 21.10 (Impish Indri)


    
        
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                sudo apt update
            
                    
    
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            $
        

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

This tutorial shows how to uninstall r-cran-bdgraph on Ubuntu 21.10 (Impish Indri):


    
        
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                sudo apt remove
            
                        
                r-cran-bdgraph
            
                    
    
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            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the r-cran-bdgraph package on Ubuntu 21.10 (Impish Indri)

Package: r-cran-bdgraph
Architecture: amd64
Version: 2.63-1
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: 1575
Depends: r-base-core (>= 4.0.2-1build1), r-api-4.0, r-cran-igraph, libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2)
Filename: pool/universe/r/r-cran-bdgraph/r-cran-bdgraph_2.63-1_amd64.deb
Size: 1244580
MD5sum: 1320ffce018a0cf98689f88e70a81c8b
SHA1: 18b8bee585b83766bd70dc298f9bc5c2bf261447
SHA256: 2b972277df6981180ba5a172addddd43f79f4438e36bb0c5c55c63ca521464a8
SHA512: 9071bd1a4b7bfce220684e2ce235b349749e45ec1b6dc2bccdf35b4e4b05751743b4f841047e8402e42c70f85346a860337839bb68c32c097e067fadee754e4d
Homepage: https://cran.r-project.org/package=BDgraph
Description-en: GNU R bayesian structure learning in graphical models
Statistical tools for Bayesian structure learning in undirected
graphical models for continuous, discrete, and mixed data. The package
is implemented the recent improvements in the Bayesian graphical models
literature, including Mohammadi and Wit (2015) ,
Mohammadi and Wit (2019) .
Description-md5: e1b471b28a428f7222b8d3a86d9ed7c7