How to Install and Uninstall libt-digest-java Package on Ubuntu 21.10 (Impish Indri)

Last updated: November 05,2024

1. Install "libt-digest-java" package

In this section, we are going to explain the necessary steps to install libt-digest-java on Ubuntu 21.10 (Impish Indri)


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                libt-digest-java
            
                    
    
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2. Uninstall "libt-digest-java" package

Here is a brief guide to show you how to uninstall libt-digest-java on Ubuntu 21.10 (Impish Indri):


    
        
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                sudo apt remove
            
                        
                libt-digest-java
            
                    
    
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                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the libt-digest-java package on Ubuntu 21.10 (Impish Indri)

Package: libt-digest-java
Architecture: all
Version: 1:3.0-3
Priority: optional
Section: universe/java
Source: t-digest
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Java Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 70
Suggests: libt-digest-java-doc
Filename: pool/universe/t/t-digest/libt-digest-java_3.0-3_all.deb
Size: 49340
MD5sum: d7c230383cbbb53ebb611779d1259bd9
SHA1: d16bb85c8c25fe8762741e11b7a6d8dfbff668af
SHA256: 4d9e47856d26edb76b07e40869cc8d03ea7353d8d7599b39ecdeb9e975035bea
SHA512: a95509a9125b121e744994d47f58ae4bd96d28e7995fc38b1cf6bf4e70c2413a32b9c3941e6f8b6218a8109cc3565dff529fb530f9bc58bc6adfb30f598c5146
Homepage: https://github.com/tdunning/t-digest
Description-en: Data structure for quantiles and related rank statistics
The t-digest construction algorithm uses a variant of 1-dimensional
k-means clustering to product a data structure that is related to the
Q-digest. This t-digest data structure can be used to estimate
quantiles or compute other rank statistics. The advantage of the
t-digest over the Q-digest is that the t-digest can handle floating
point values while the Q-digest is limited to integers. With small
changes, the t-digest can handle any values from any ordered set that
has something akin to a mean. The accuracy of quantile estimates
produced by t-digests can be orders of magnitude more accurate than
those produced by Q-digests in spite of the fact that t-digests are
more compact when stored on disk.
Description-md5: 66f122eeb099f2b7c4da5fb6e8d311d8