How to Install and Uninstall libstatistics-pca-perl Package on Ubuntu 21.10 (Impish Indri)

Last updated: December 29,2024

1. Install "libstatistics-pca-perl" package

Please follow the instructions below to install libstatistics-pca-perl on Ubuntu 21.10 (Impish Indri)


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                libstatistics-pca-perl
            
                    
    
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2. Uninstall "libstatistics-pca-perl" package

Learn how to uninstall libstatistics-pca-perl on Ubuntu 21.10 (Impish Indri):


    
        
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                sudo apt remove
            
                        
                libstatistics-pca-perl
            
                    
    
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                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the libstatistics-pca-perl package on Ubuntu 21.10 (Impish Indri)

Package: libstatistics-pca-perl
Architecture: all
Version: 0.0.1-2
Priority: optional
Section: universe/perl
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Perl Group
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 46
Depends: perl:any, libcontextual-return-perl, libmath-cephes-perl, libmath-matrixreal-perl, libtext-simpletable-perl
Filename: pool/universe/libs/libstatistics-pca-perl/libstatistics-pca-perl_0.0.1-2_all.deb
Size: 14940
MD5sum: 95e22bce11448b80f61b28ded226d858
SHA1: f13c03dae3178269aa774750006f60f32722d5f3
SHA256: d73e56cc853aa87b156d0fb5eb4bbff8a4837635d39bfe843a84e2ddf6a3a92e
SHA512: ba7341af4e78ad4cdd86f38746853a377084c17c40076d3dcf6a16807d7e0c33b4d8d85cc4be3e04ce40a791d5b04cd1a14b94510a4b4e6e1ea00f081a8caee7
Homepage: https://metacpan.org/release/Statistics-PCA
Description-en: perl module for principal component analysis (PCA)
Statistics::PCA provides functions for principal component analysis (PCA).
PCA transforms higher-dimensional data consisting of a number of possibly
correlated variables into a smaller number of uncorrelated variables termed
principal components (PCs). The higher the ranking of the PCs the greater the
amount of variability that the PC accounts for.
.
This PCA procedure involves the calculation of the eigenvalue decomposition
from a data covariance matrix after mean centering the data.
.
See https://en.wikipedia.org/wiki/Principal_component_analysis
Description-md5: cfa3efc947a38e9af449df2289e6c0b3