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

Last updated: May 16,2024

1. Install "r-cran-conquer" package

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


    
        
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                sudo apt update
            
                    
    
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            $
        

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

This guide let you learn how to uninstall r-cran-conquer on Ubuntu 20.10 (Groovy Gorilla):


    
        
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                sudo apt remove
            
                        
                r-cran-conquer
            
                    
    
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            $
        

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

Package: r-cran-conquer
Architecture: amd64
Version: 1.0.1-1
Priority: optional
Section: universe/gnu-r
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Dirk Eddelbuettel
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 356
Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 9), r-base-core (>= 4.0.2-1), r-api-4.0, r-cran-rcpp (>= 1.0.3), r-cran-matrix, r-cran-matrixstats, r-cran-rcpparmadillo
Filename: pool/universe/r/r-cran-conquer/r-cran-conquer_1.0.1-1_amd64.deb
Size: 118264
MD5sum: b7a95501d16c9310f20cd045df448770
SHA1: 0f7272580ff4cc43796e05877706b7aa0e582322
SHA256: 4c411a7cb3c342383e11af67f2b67a3e7e5dd279b4809757cb2b1979731ee9a3
SHA512: 898f48294612cc3d7a2c82934f756a6f678bb858a5690997b42103345742b45390bc6336ad801a5792f8c41860d7db2c7e40f5eaf60f548ecdf0d455711086bb
Homepage: https://cran.r-project.org/package=conquer
Description-en: GNU R package for convolution-type smoothed quantile regression
The package offers fast and accurate convolution-type smoothed
quantile regression. It is implemented using Barzilai-Borwein gradient
descent with a Huber regression warm start. It constructs confidence
intervals for regression coefficients using multiplier bootstrap.
Description-md5: c772aabccbc90420fcb6aba252008345