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

Last updated: November 24,2024

1. Install "r-cran-rsvd" package

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


    
        
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                sudo apt update
            
                    
    
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            $
        

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

Please follow the steps below to uninstall r-cran-rsvd on Ubuntu 20.10 (Groovy Gorilla):


    
        
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                sudo apt remove
            
                        
                r-cran-rsvd
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

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

Package: r-cran-rsvd
Architecture: all
Version: 1.0.3-3build1
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: 6174
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-cran-matrix
Recommends: r-cran-testthat, r-cran-ggplot2, r-cran-plyr, r-cran-scales
Filename: pool/universe/r/r-cran-rsvd/r-cran-rsvd_1.0.3-3build1_all.deb
Size: 6129192
MD5sum: 81e9e6410b1696520a3dc55e53f04561
SHA1: 3c123ca67703b31289ea60824b340d90028fb933
SHA256: b4a3047fbe29b99b5d4fdba7f5d91d489663d4587fd39a3ba3c185d3026e7ff5
SHA512: dec5c1b5bf882be14d84a990ecf667b068c0661ecdca7f4cc22adb9cfbe84b0b896b96a3d40cec12578535eccb09ebff0c82a9a67e5082eb32761e825e5c473c
Homepage: https://cran.r-project.org/package=rsvd
Description-en: Randomized Singular Value Decomposition
Low-rank matrix decompositions are fundamental tools and widely used for
data analysis, dimension reduction, and data compression. Classically,
highly accurate deterministic matrix algorithms are used for this task.
However, the emergence of large-scale data has severely challenged our
computational ability to analyze big data. The concept of randomness has
been demonstrated as an effective strategy to quickly produce
approximate answers to familiar problems such as the singular value
decomposition (SVD). The rsvd package provides several randomized matrix
algorithms such as the randomized singular value decomposition (rsvd),
randomized principal component analysis (rpca), randomized robust
principal component analysis (rrpca), randomized interpolative
decomposition (rid), and the randomized CUR decomposition (rcur). In
addition several plot functions are provided. The methods are discussed
in detail by Erichson et al. (2016) .
Description-md5: 5d63cddd80519f8618fc46b2cc71e66a