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

Last updated: May 18,2024

1. Install "r-cran-fpc" package

Please follow the instructions below to install r-cran-fpc on Ubuntu 20.10 (Groovy Gorilla)


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                r-cran-fpc
            
                    
    
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2. Uninstall "r-cran-fpc" package

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


    
        
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                sudo apt remove
            
                        
                r-cran-fpc
            
                    
    
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            $
        

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

Package: r-cran-fpc
Architecture: all
Version: 2.2-7-1
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: 914
Depends: r-base-core (>= 4.0.2-1build1), r-api-4.0, r-cran-mass, r-cran-cluster, r-cran-mclust, r-cran-flexmix, r-cran-prabclus, r-cran-class, r-cran-diptest, r-cran-robustbase, r-cran-kernlab
Recommends: r-cran-mvtnorm
Filename: pool/universe/r/r-cran-fpc/r-cran-fpc_2.2-7-1_all.deb
Size: 865964
MD5sum: ad7dee4da54f3d46af32f983342c8cfb
SHA1: 597db54ae4f140f9d1ebc2c8fc544edfda4345bb
SHA256: 53f7c0b990a18b2f3ef42805940931ad28594b5b412465767d7dbf8078aa2c78
SHA512: 8e6e05155f6867270d9f17d9b8be6bbac30b3a6e155438bc899fab3479a0513c28dd7571836791519d09bdfc50cce98c3ea8498c156a00333bee145ac41a02c8
Homepage: https://cran.r-project.org/package=fpc
Description-en: GNU R flexible procedures for clustering
Various methods for clustering and cluster validation. Fixed point
clustering. Linear regression clustering. Clustering by merging Gaussian
mixture components. Symmetric and asymmetric discriminant projections
for visualisation of the separation of groupings. Cluster validation
statistics for distance based clustering including corrected Rand index.
Cluster-wise cluster stability assessment. Methods for estimation of the
number of clusters: Calinski-Harabasz, Tibshirani and Walther's
prediction strength, Fang and Wang's bootstrap stability.
Gaussian/multinomial mixture fitting for mixed continuous/categorical
variables. Variable-wise statistics for cluster interpretation. DBSCAN
clustering. Interface functions for many clustering methods implemented
in R, including estimating the number of clusters with kmeans, pam and
clara. Modality diagnosis for Gaussian mixtures.
Description-md5: c6256855695264ff0fe58768fc1652d5