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

Last updated: November 05,2024

1. Install "r-cran-huge" package

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


    
        
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                sudo apt update
            
                    
    
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            $
        

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

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


    
        
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                sudo apt remove
            
                        
                r-cran-huge
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

3. Information about the r-cran-huge package on Ubuntu 20.10 (Groovy Gorilla)

Package: r-cran-huge
Architecture: amd64
Version: 1.3.4.1-1build1
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: 1601
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-cran-matrix, r-cran-igraph, r-cran-mass, r-cran-rcpp, r-cran-rcppeigen, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 5.2)
Filename: pool/universe/r/r-cran-huge/r-cran-huge_1.3.4.1-1build1_amd64.deb
Size: 1405560
MD5sum: 68e66f187320a2c6d2766d2e50c4f983
SHA1: f6dad5743580512189c219f76d16d4875ba93b60
SHA256: d7917f51f8e7cbb07466224ac27ae6e0558d166dad70965cadef319b902aa939
SHA512: fabd8292c4dd636c34d2329951fc68df90e95b7665409d174f2ee59f5f7b8712c8e676e1a4ad058eab34646c07069971db9fd2b1c8157e493db898954fdc5979
Homepage: https://cran.r-project.org/package=huge
Description-en: GNU R high-dimensional undirected graph estimation
Provides a general framework for high-dimensional undirected graph
estimation. It integrates data preprocessing, neighborhood screening,
graph estimation, and model selection techniques into a pipeline. In
preprocessing stage, the nonparanormal(npn) transformation is applied to
help relax the normality assumption. In the graph estimation stage, the
graph structure is estimated by Meinshausen-Buhlmann graph estimation or
the graphical lasso, and both methods can be further accelerated by the
lossy screening rule preselecting the neighborhood of each variable by
correlation thresholding.
Description-md5: b782b8db13f52fd691a0d3231a8ef6b8