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

Last updated: June 02,2024

1. Install "r-cran-ipred" package

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


    
        
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                sudo apt update
            
                    
    
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            $
        

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

This tutorial shows how to uninstall r-cran-ipred on Ubuntu 20.10 (Groovy Gorilla):


    
        
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                sudo apt remove
            
                        
                r-cran-ipred
            
                    
    
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            $
        

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

Package: r-cran-ipred
Architecture: amd64
Version: 0.9-9-1build1
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: 473
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-cran-rpart (>= 3.1-8), r-cran-mass, r-cran-survival, r-cran-nnet, r-cran-class, r-cran-prodlim
Recommends: r-cran-mlbench
Suggests: r-cran-mvtnorm, r-cran-th.data
Filename: pool/universe/r/r-cran-ipred/r-cran-ipred_0.9-9-1build1_amd64.deb
Size: 385824
MD5sum: ff639c9077f59acb993981e0edaf394f
SHA1: b45d786cb985ff8befe22380cd0bf757e0421a39
SHA256: 6bfa2dcc9fdee438b5ed5695baf24130dc34c657e33bffa714cf3ade4bf94bfb
SHA512: 492a625f8ef36edc33e537d7c4f2fc2a189ecbe9f14b4e671677788bd5ca235c02503a136648ae93c4a8fad191fa910c52ddaa74ba96ccac646e4b231ec8fad9
Homepage: https://cran.r-project.org/package=ipred
Description-en: GNU R improved predictors
Improved predictive models by indirect classification and
bagging for classification, regression and survival problems
as well as resampling based estimators of prediction error.
Description-md5: d7672fa42e70c4ef0c602fa35b380718