How to Install and Uninstall golang-github-vividcortex-ewma-dev Package on Ubuntu 21.10 (Impish Indri)

Last updated: May 21,2024

1. Install "golang-github-vividcortex-ewma-dev" package

Please follow the guidelines below to install golang-github-vividcortex-ewma-dev on Ubuntu 21.10 (Impish Indri)


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                golang-github-vividcortex-ewma-dev
            
                    
    
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2. Uninstall "golang-github-vividcortex-ewma-dev" package

Please follow the guidance below to uninstall golang-github-vividcortex-ewma-dev on Ubuntu 21.10 (Impish Indri):


    
        
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                sudo apt remove
            
                        
                golang-github-vividcortex-ewma-dev
            
                    
    
    Copied


    
        
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                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

3. Information about the golang-github-vividcortex-ewma-dev package on Ubuntu 21.10 (Impish Indri)

Package: golang-github-vividcortex-ewma-dev
Architecture: all
Version: 1.1.1-2
Multi-Arch: foreign
Priority: extra
Section: universe/devel
Source: golang-github-vividcortex-ewma
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Go Packaging Team
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 22
Filename: pool/universe/g/golang-github-vividcortex-ewma/golang-github-vividcortex-ewma-dev_1.1.1-2_all.deb
Size: 5420
MD5sum: 59c02007e1701a9739b1e2e00d67789b
SHA1: 7381c3804e07eba434863ea37390748944cdedd7
SHA256: 204d8f3bf10e073eff7f23c59f59b332af0922cf4b67fc4cebe8e384046093aa
SHA512: f5843af7af755a4ddfa752a19da596d1cbc802d392147539afbb8901adc535de8102ea76ce505ddbea7f7504059995a127a561c12e07dd413638adc76d2c64e6
Homepage: https://github.com/vividcortex/ewma
Description-en: Exponentially Weighted Moving Average algorithms for Go
An exponentially weighted moving average is a way to continuously
compute a type of average for a series of numbers, as the numbers
arrive. After a value in the series is added to the average, its
weight in the average decreases exponentially over time. This biases
the average towards more recent data. EWMAs are useful for several
reasons, chiefly their inexpensive computational and memory cost, as
well as the fact that they represent the recent central tendency of
the series of values.
Description-md5: 73a2f7e0eeb3ebdd3634eb8bb6792b1c