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

Last updated: May 06,2024

1. Install "r-cran-robumeta" package

This is a short guide on how to install r-cran-robumeta on Ubuntu 21.10 (Impish Indri)


    
        
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                sudo apt update
            
                    
    
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            $
        

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

This guide let you learn how to uninstall r-cran-robumeta on Ubuntu 21.10 (Impish Indri):


    
        
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                sudo apt remove
            
                        
                r-cran-robumeta
            
                    
    
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            $
        

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

Package: r-cran-robumeta
Architecture: all
Version: 2.0-3build1
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: 575
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0
Recommends: r-cran-testthat
Suggests: r-cran-clubsandwich
Filename: pool/universe/r/r-cran-robumeta/r-cran-robumeta_2.0-3build1_all.deb
Size: 521236
MD5sum: e80273bf8898bf69a9adae26d124e5ed
SHA1: 4c1abebda212736527cc8662ebd20ee2770c650d
SHA256: 8bae018b8e791c00b8ba73d012831571ad0cceef853c354017fdcc7575270cce
SHA512: db1aeaa189c2c541ee2e785dab65ece4a6af8b78b6870bea2cc3e6da55d6785bd61508aa04a310fbd0569327a8982fcdcc6dacd14e4c7088d5a11bccf4583a29
Homepage: https://cran.r-project.org/package=robumeta
Description-en: GNU R robust variance meta-regression
Functions for conducting robust variance estimation (RVE) meta-
regression using both large and small sample RVE estimators under
various weighting schemes. These methods are distribution free and
provide valid point estimates, standard errors and hypothesis tests even
when the degree and structure of dependence between effect sizes is
unknown. Also included are functions for conducting sensitivity analyses
under correlated effects weighting and producing RVE-based forest plots.
Description-md5: 5a7b8ea38e1a6e6ef62502bbd36251bb