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

Last updated: November 26,2024

1. Install "r-cran-gbm" package

This guide covers the steps necessary to install r-cran-gbm on Ubuntu 20.10 (Groovy Gorilla)


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

Learn how to uninstall r-cran-gbm on Ubuntu 20.10 (Groovy Gorilla):


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

Package: r-cran-gbm
Architecture: amd64
Version: 2.1.8-1
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: 754
Depends: r-base-core (>= 4.0.2-1build1), r-api-4.0, r-cran-lattice, r-cran-survival, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 4.9)
Suggests: r-cran-covr, r-cran-gridextra, r-cran-knitr, r-cran-runit, r-cran-tinytest, r-cran-viridis
Filename: pool/universe/r/r-cran-gbm/r-cran-gbm_2.1.8-1_amd64.deb
Size: 559420
MD5sum: 528f4c0869b1af85e93251b115507256
SHA1: c35958d13b01f648956c35716dbfd251c8964e9a
SHA256: f5036e8c659dbbb9892e63b8e28ba49e7d1725d8aa65e2ef860f8cd8bb711c10
SHA512: a77f39695c0b05a28e761eec5cdd27db32ff8613578d853c7eda612e539cff2786f86266139961beb552fcad0e2c474883b2e5222c924126eff18890e090b760
Homepage: https://cran.r-project.org/package=gbm
Description-en: GNU R package providing Generalized Boosted Regression Models
This package implements extensions to Freund and Schapire's AdaBoost algorithm
and Friedman's gradient boosting machine. Includes regression methods for least
squares, absolute loss, t-distribution loss, quantile regression, logistic,
multinomial logistic, Poisson, Cox proportional hazards partial likelihood,
AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures
(LambdaMart).
Description-md5: bb38159ea69aa73da1340c69243cf5cd