How to Install and Uninstall libt-digest-java Package on Kali Linux
Last updated: November 05,2024
1. Install "libt-digest-java" package
Learn how to install libt-digest-java on Kali Linux
$
sudo apt update
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$
sudo apt install
libt-digest-java
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2. Uninstall "libt-digest-java" package
This is a short guide on how to uninstall libt-digest-java on Kali Linux:
$
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 Kali Linux
Package: libt-digest-java
Source: t-digest
Version: 1:3.0-3
Installed-Size: 70
Maintainer: Debian Java Maintainers
Architecture: all
Suggests: libt-digest-java-doc
Size: 49288
SHA256: 4a8929a44e508d9180563dda6351a1efc963aea55249b77225056874c06ffa9f
SHA1: ee5b226cc2fe6f5d9781684215f1f6dae43b4ed3
MD5sum: 53dbdd1ff40d51d909a993e47f421c60
Description: 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:
Homepage: https://github.com/tdunning/t-digest
Section: java
Priority: optional
Filename: pool/main/t/t-digest/libt-digest-java_3.0-3_all.deb
Source: t-digest
Version: 1:3.0-3
Installed-Size: 70
Maintainer: Debian Java Maintainers
Architecture: all
Suggests: libt-digest-java-doc
Size: 49288
SHA256: 4a8929a44e508d9180563dda6351a1efc963aea55249b77225056874c06ffa9f
SHA1: ee5b226cc2fe6f5d9781684215f1f6dae43b4ed3
MD5sum: 53dbdd1ff40d51d909a993e47f421c60
Description: 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:
Homepage: https://github.com/tdunning/t-digest
Section: java
Priority: optional
Filename: pool/main/t/t-digest/libt-digest-java_3.0-3_all.deb