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

Last updated: May 17,2024

1. Install "r-cran-factominer" package

This guide let you learn how to install r-cran-factominer on Kali Linux


    
        
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                sudo apt update
            
                    
    
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            $
        

        
            
                sudo apt install
            
                        
                r-cran-factominer
            
                    
    
    Copied

2. Uninstall "r-cran-factominer" package

Please follow the guidance below to uninstall r-cran-factominer on Kali Linux:


    
        
            $
        

        
            
                sudo apt remove
            
                        
                r-cran-factominer
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

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

Package: r-cran-factominer
Version: 2.9-1
Installed-Size: 3788
Maintainer: Debian R Packages Maintainers
Architecture: amd64
Depends: r-api-4.0, r-cran-car, r-cran-cluster, r-cran-dt, r-cran-ellipse, r-cran-emmeans, r-cran-flashclust, r-cran-lattice, r-cran-leaps, r-cran-mass, r-cran-multcompview, r-cran-scatterplot3d, r-cran-ggplot2, r-cran-ggrepel, libc6 (>= 2.2.5)
Recommends: r-cran-knitr, r-cran-markdown
Size: 3788904
SHA256: 58bd9a3088899f1de75155e9c8622a012c07410822dd7ffb733a33ba506a9b0d
SHA1: 51c8fe82c70a38b3123eea8284c18e1bfd7db5ec
MD5sum: 00b8b2b56f95c407fe8ff6d66a3303c6
Description: Multivariate Exploratory Data Analysis and Data Mining
Exploratory data analysis methods to summarize, visualize and describe
datasets. The main principal component methods are available, those with
the largest potential in terms of applications: principal component
analysis (PCA) when variables are quantitative, correspondence analysis
(CA) and multiple correspondence analysis (MCA) when variables are
categorical, Multiple Factor Analysis when variables are structured in
groups, etc. and hierarchical cluster analysis.
Description-md5:
Homepage: https://cran.r-project.org/package=FactoMineR
Section: gnu-r
Priority: optional
Filename: pool/main/r/r-cran-factominer/r-cran-factominer_2.9-1_amd64.deb