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

Last updated: May 20,2024

1. Install "r-cran-whatif" package

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


    
        
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                sudo apt update
            
                    
    
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            $
        

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

This guide covers the steps necessary to uninstall r-cran-whatif on Ubuntu 20.10 (Groovy Gorilla):


    
        
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                sudo apt remove
            
                        
                r-cran-whatif
            
                    
    
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            $
        

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

Package: r-cran-whatif
Architecture: all
Version: 1.5-9-3
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: 143
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-cran-lpsolve, r-cran-pbmcapply, r-cran-zelig (>= 5.0-17)
Recommends: r-cran-testthat
Filename: pool/universe/r/r-cran-whatif/r-cran-whatif_1.5-9-3_all.deb
Size: 79188
MD5sum: f2059eb8f4fa6afd9b026c2a74aed3fb
SHA1: f3c7773be0223205b0a1bde6f6933e962a66e402
SHA256: 833029b30cfb233da6bc03a368175147e0a44755dd2b75130be41b2bb95c3acd
SHA512: ca65a8b69b3820ea06ce1458b726f8a932ca45b0045194da12026eaf7ad22afea68329384d3c4a1fe0af912bc50ca12e148c492619542b3f4328d9a221e35d3e
Homepage: https://cran.r-project.org/package=WhatIf
Description-en: GNU R evaluate counterfactuals
Inferences about counterfactuals are essential for prediction,
answering what if questions, and estimating causal effects.
However, when the counterfactuals posed are too far from the data at
hand, conclusions drawn from well-specified statistical analyses
become based largely on speculation hidden in convenient modeling
assumptions that few would be willing to defend. Unfortunately,
standard statistical approaches assume the veracity of the model
rather than revealing the degree of model-dependence, which makes this
problem hard to detect. WhatIf offers easy-to-apply methods to
evaluate counterfactuals that do not require sensitivity testing over
specified classes of models. If an analysis fails the tests offered
here, then it is known that substantive inferences will be sensitive to
at least some modeling choices that are not based on empirical evidence,
no matter what method of inference one chooses to use. WhatIf
implements the methods for evaluating counterfactuals discussed in
Gary King and Langche Zeng, 2006, "The Dangers of Extreme
Counterfactuals," Political Analysis 14 (2) ;
and Gary King and Langche Zeng, 2007, "When Can History Be Our Guide? The
Pitfalls of Counterfactual Inference," International Studies
Quarterly 51 (March) .
Description-md5: b85ffa3a7c29f2b60fd2dc80b9ea1c55