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

Last updated: November 07,2024

1. Install "r-cran-fpc" package

Please follow the guidelines below to install r-cran-fpc on Ubuntu 21.10 (Impish Indri)


    
        
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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

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


    
        
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                sudo apt remove
            
                        
                r-cran-fpc
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

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

Package: r-cran-fpc
Architecture: all
Version: 2.2-9-1
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: 916
Depends: r-base-core (>= 4.0.3-1), 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_2.2-9-1_all.deb
Size: 869900
MD5sum: f23156a85c66aaa17aa250824f521821
SHA1: 2e0c09da74eaf5a921a3591093cf47c32cc0fe9c
SHA256: b5cccb532bf1fa6121363ef0cd32b985865246e87ff857bab41d3c6a2ef3359d
SHA512: 920957351cd34a615a0ce6716299bfb47f91411726406826700132e6506e485f0ebbe062533c418f053f2a5261f468b51be6a8090e17688188e7a474ba8373e2
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