How to Install and Uninstall r-cran-mi Package on Ubuntu 21.10 (Impish Indri)

Last updated: December 25,2024

1. Install "r-cran-mi" package

Here is a brief guide to show you how to install r-cran-mi on Ubuntu 21.10 (Impish Indri)


    
        
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                sudo apt update
            
                    
    
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            $
        

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

This guide covers the steps necessary to uninstall r-cran-mi on Ubuntu 21.10 (Impish Indri):


    
        
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                sudo apt remove
            
                        
                r-cran-mi
            
                    
    
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            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the r-cran-mi package on Ubuntu 21.10 (Impish Indri)

Package: r-cran-mi
Architecture: all
Version: 1.0-8build1
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: 2244
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-cran-matrix, r-cran-arm (>= 1.4-11)
Recommends: r-cran-truncnorm, r-cran-survival
Suggests: r-cran-lattice, r-cran-knitr, r-cran-mass, r-cran-nnet, r-cran-sn, r-cran-foreign
Filename: pool/universe/r/r-cran-mi/r-cran-mi_1.0-8build1_all.deb
Size: 1847436
MD5sum: 9067a754e962b7adbeafd72665c2d048
SHA1: 5f07e911780c9ee1d00291574bf35a3a8c71e0d8
SHA256: bc17d23d0e730340b65feced943033b7c3469dc10fe772cb9b8f2d6b11a7ed1b
SHA512: 87a015b683a853b589e6f14c3f0b6e9cd01db03899c34cf5e4ba173b43551a94bdfc2c6588f3a8444d6539f2f96d847b43eb7967cca219fb6af6cae20afc2ba2
Homepage: https://cran.r-project.org/package=mi
Description-en: GNU R package for Missing Data Imputation and Model Checking -- mi
The mi R package provides functions for data manipulation, imputing missing
values in an approximate Bayesian framework, diagnostics of the models used to
generate the imputations, confidence-building mechanisms to validate some of
the assumptions of the imputation algorithm, and functions to analyze multiply
imputed data sets with the appropriate degree of sampling uncertainty.
Description-md5: 448b680bae0ec9b057fa5850aaa00c28