How to Install and Uninstall golang-github-vividcortex-ewma-dev Package on Kali Linux
Last updated: December 24,2024
1. Install "golang-github-vividcortex-ewma-dev" package
Please follow the guidelines below to install golang-github-vividcortex-ewma-dev on Kali Linux
$
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 instructions below to uninstall golang-github-vividcortex-ewma-dev on Kali Linux:
$
sudo apt remove
golang-github-vividcortex-ewma-dev
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$
sudo apt autoclean && sudo apt autoremove
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3. Information about the golang-github-vividcortex-ewma-dev package on Kali Linux
Package: golang-github-vividcortex-ewma-dev
Source: golang-github-vividcortex-ewma
Version: 1.1.1-2
Installed-Size: 22
Maintainer: Debian Go Packaging Team
Architecture: all
Size: 5384
SHA256: 2a1ba60e63cd4ac7b6e12d58673ea21d1cadeac9f0450bdf72d21ac453dc3486
SHA1: 4524c4594f2f5594be0e324bb1a05934a3aef109
MD5sum: bfed422e8763fc9144b060f368615812
Description: 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:
Multi-Arch: foreign
Homepage: https://github.com/vividcortex/ewma
Section: golang
Priority: optional
Filename: pool/main/g/golang-github-vividcortex-ewma/golang-github-vividcortex-ewma-dev_1.1.1-2_all.deb
Source: golang-github-vividcortex-ewma
Version: 1.1.1-2
Installed-Size: 22
Maintainer: Debian Go Packaging Team
Architecture: all
Size: 5384
SHA256: 2a1ba60e63cd4ac7b6e12d58673ea21d1cadeac9f0450bdf72d21ac453dc3486
SHA1: 4524c4594f2f5594be0e324bb1a05934a3aef109
MD5sum: bfed422e8763fc9144b060f368615812
Description: 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:
Multi-Arch: foreign
Homepage: https://github.com/vividcortex/ewma
Section: golang
Priority: optional
Filename: pool/main/g/golang-github-vividcortex-ewma/golang-github-vividcortex-ewma-dev_1.1.1-2_all.deb