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

Last updated: May 17,2024

1. Install "r-cran-metamix" package

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


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

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


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

Package: r-cran-metamix
Architecture: amd64
Version: 0.3-2build1
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: 1758
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-cran-data.table (>= 1.9.2), r-cran-matrix, r-cran-gtools, r-cran-rmpi, r-cran-ggplot2, libc6 (>= 2.2.5), libstdc++6 (>= 4.9), libopenmpi-dev
Suggests: r-cran-knitr
Filename: pool/universe/r/r-cran-metamix/r-cran-metamix_0.3-2build1_amd64.deb
Size: 743868
MD5sum: 52b8b15e73f8a19d61c6f3ba0a19b48f
SHA1: 2e8f3daf2df235f67599325c666e8c10c56df145
SHA256: 6115bdfe556aa713ada0d86dcb17ea9b88ac24e22605c94f4e84f6bc028d5d55
SHA512: c483b9f922849ca301f2b6d4a3fc04947d03703d3b70ab438b2db4283299f316196054a9146a0f87830106e0dc2d14c7b1ec81a2af79355a3b328e4187c4cfaf
Homepage: https://cran.r-project.org/package=metaMix
Description-en: GNU R bayesian mixture analysis for metagenomic community profiling
Resolves complex metagenomic mixtures by analysing deep sequencing
data, using a mixture model based approach. The use of parallel Monte
Carlo Markov chains for the exploration of the species space enables
the identification of the set of species more likely to contribute to
the mixture.
Description-md5: 9f51ca1b979fb77ee86077f0f70ab8bf