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

Last updated: December 23,2024

1. Install "r-cran-brglm" package

This is a short guide on how to install r-cran-brglm on Ubuntu 20.10 (Groovy Gorilla)


    
        
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                sudo apt update
            
                    
    
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            $
        

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

This is a short guide on how to uninstall r-cran-brglm on Ubuntu 20.10 (Groovy Gorilla):


    
        
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                sudo apt remove
            
                        
                r-cran-brglm
            
                    
    
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            $
        

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

Package: r-cran-brglm
Architecture: amd64
Version: 0.6.2-1build1
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: 169
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-cran-profilemodel
Suggests: r-cran-mass
Filename: pool/universe/r/r-cran-brglm/r-cran-brglm_0.6.2-1build1_amd64.deb
Size: 121052
MD5sum: 93268611cb2a6aa6479abda19a638403
SHA1: 442a831bccb759ef1a64de04e7fcccf35af29c0c
SHA256: ed96c7ed6375ce3f03f2f22ba4f7645c84bd6707f9a6b32c68ea306187e94e02
SHA512: 6ded0406ef45fd19fb7f822b883f20718067e495a47bc7491208803da3d3b3211dbbb86833669f1d1a0c7233f6ccb8878d6f0d62cd66efba06997b416b7529d8
Homepage: https://cran.r-project.org/package=brglm
Description-en: GNU R package for bias reduction in binomial-response GLMs
Fit generalized linear models with binomial responses using either an
adjusted-score approach to bias reduction or maximum penalized
likelihood where penalization is by Jeffreys invariant prior. These
procedures return estimates with improved frequentist properties
(bias, mean squared error) that are always finite even in cases where
the maximum likelihood estimates are infinite (data separation).
Fitting takes place by fitting generalized linear models on
iteratively updated pseudo-data. The interface is essentially the same
as 'glm'. More flexibility is provided by the fact that custom
pseudo-data representations can be specified and used for model
fitting. Functions are provided for the construction of confidence
intervals for the reduced-bias estimates.
Description-md5: 340c38cf9522c3d017fd5ca41007e4c6