How to Install and Uninstall r-cran-glmnet Package on Kali Linux

Last updated: November 26,2024

1. Install "r-cran-glmnet" package

Please follow the guidance below to install r-cran-glmnet on Kali Linux


    
        
            $
        

        
            
                sudo apt update
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt install
            
                        
                r-cran-glmnet
            
                    
    
    Copied

2. Uninstall "r-cran-glmnet" package

This guide covers the steps necessary to uninstall r-cran-glmnet on Kali Linux:


    
        
            $
        

        
            
                sudo apt remove
            
                        
                r-cran-glmnet
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

3. Information about the r-cran-glmnet package on Kali Linux

Package: r-cran-glmnet
Version: 4.1-8-1
Installed-Size: 2484
Maintainer: Debian R Packages Maintainers
Architecture: amd64
Depends: r-api-4.0, r-cran-matrix (>= 1.0-6), r-cran-foreach, r-cran-shape, r-cran-survival, r-cran-rcpp, r-cran-rcppeigen, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgfortran5 (>= 8), libstdc++6 (>= 13.1), libjs-mathjax
Suggests: r-cran-knitr, r-cran-testthat, r-cran-xfun, r-cran-rmarkdown
Size: 1925756
SHA256: c6219cc9c0e7bef58906c96bfcec85a4965c6e93e1c6e01591b14546b461d427
SHA1: 5424746d7f836d9820190542b9bc536b0e37f633
MD5sum: 52088b7f2409594a9c2e40d85453947f
Description: Lasso and Elastic-Net Regularized Generalized Linear Models
Extremely efficient procedures for fitting the entire lasso or elastic-net
regularization path for linear regression, logistic and multinomial
regression models, Poisson regression and the Cox model. Two recent
additions are the multiple-response Gaussian, and the grouped multinomial.
The algorithm uses cyclical coordinate descent in a path-wise fashion, as
described in the paper Introduction to Glmnet.
Description-md5:
Homepage: https://cran.r-project.org/package=glmnet
Section: gnu-r
Priority: optional
Filename: pool/main/r/r-cran-glmnet/r-cran-glmnet_4.1-8-1_amd64.deb