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

Last updated: May 20,2024

1. Install "r-cran-brglm" package

This guide let you learn how to install r-cran-brglm on Ubuntu 21.10 (Impish Indri)


    
        
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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 tutorial shows how to uninstall r-cran-brglm on Ubuntu 21.10 (Impish Indri):


    
        
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                sudo apt remove
            
                        
                r-cran-brglm
            
                    
    
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            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

3. Information about the r-cran-brglm package on Ubuntu 21.10 (Impish Indri)

Package: r-cran-brglm
Architecture: amd64
Version: 0.7.1-1
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: 174
Depends: r-base-core (>= 4.0.2-1build1), r-api-4.0, r-cran-profilemodel
Suggests: r-cran-mass
Filename: pool/universe/r/r-cran-brglm/r-cran-brglm_0.7.1-1_amd64.deb
Size: 123488
MD5sum: 792657865455e093b02010d94b815036
SHA1: f08202d50d64c38f638ac27fe86bca6ebf0162fc
SHA256: 61f84cc27726258f3ca5d0a267831cd10c788e0fbdef4fd543b2c0488cb8bebe
SHA512: ba7bbc9d44dbeb8f86bdd5ddba547cc34792781c4b4415338f2fab8496294b4b4ac3f7e63f9e1c3391c94066e2f50a47118f0bb7abef768960052c021a4491e9
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