How to Install and Uninstall libghc-statistics-prof Package on Kali Linux
Last updated: December 18,2024
1. Install "libghc-statistics-prof" package
Please follow the guidance below to install libghc-statistics-prof on Kali Linux
$
sudo apt update
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$
sudo apt install
libghc-statistics-prof
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2. Uninstall "libghc-statistics-prof" package
This is a short guide on how to uninstall libghc-statistics-prof on Kali Linux:
$
sudo apt remove
libghc-statistics-prof
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$
sudo apt autoclean && sudo apt autoremove
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3. Information about the libghc-statistics-prof package on Kali Linux
Package: libghc-statistics-prof
Source: haskell-statistics (0.16.2.1-1)
Version: 0.16.2.1-1+b1
Installed-Size: 19022
Maintainer: Debian Haskell Group
Architecture: amd64
Provides: libghc-statistics-prof-0.16.2.1-95aa9
Depends: libghc-aeson-prof-2.1.2.1-a5136, libghc-async-prof-2.2.4-2b734, libghc-base-prof-4.17.2.0-68dfe, libghc-binary-prof-0.8.9.1-6a9d2, libghc-data-default-class-prof-0.1.2.0-50c88, libghc-deepseq-prof-1.4.8.0-ddf16, libghc-dense-linear-algebra-prof-0.1.0.0-360b2, libghc-math-functions-prof-0.3.4.2-08752, libghc-mwc-random-prof-0.15.0.2-4e96f, libghc-parallel-prof-3.2.2.0-afc9a, libghc-primitive-prof-0.8.0.0-a075c, libghc-random-prof-1.2.1.1-68746, libghc-statistics-dev (= 0.16.2.1-1+b1), libghc-vector-algorithms-prof-0.9.0.1-9226e, libghc-vector-binary-instances-prof-0.2.5.2-e0127, libghc-vector-prof-0.13.1.0-1ffee, libghc-vector-th-unbox-prof-0.2.2-782ae
Size: 1691808
SHA256: fafd2b56337415994af4d2f9dcf5e4fda2d717e28fd8d7b5cba882f42f49ef6f
SHA1: 1cf4d8507f42b2ea5680212560c1a3187436a90d
MD5sum: e228644548dc16836d687949498f9f96
Description: A library of statistical types, data, and functions; profiling libraries
This library provides a number of common functions and types useful
in statistics. Our focus is on high performance, numerical
robustness, and use of good algorithms. Where possible, we provide
references to the statistical literature.
.
The library's facilities can be divided into three broad categories:
.
Working with widely used discrete and continuous probability
distributions. (There are dozens of exotic distributions in use; we
focus on the most common.)
.
Computing with sample data: quantile estimation, kernel density
estimation, bootstrap methods, and autocorrelation analysis.
.
Random variate generation under several different distributions.
.
This package provides a library for the Haskell programming language, compiled
for profiling. See http://www.haskell.org/ for more information on Haskell.
Description-md5:
Homepage: https://github.com/haskell/statistics
Section: haskell
Priority: optional
Filename: pool/main/h/haskell-statistics/libghc-statistics-prof_0.16.2.1-1+b1_amd64.deb
Source: haskell-statistics (0.16.2.1-1)
Version: 0.16.2.1-1+b1
Installed-Size: 19022
Maintainer: Debian Haskell Group
Architecture: amd64
Provides: libghc-statistics-prof-0.16.2.1-95aa9
Depends: libghc-aeson-prof-2.1.2.1-a5136, libghc-async-prof-2.2.4-2b734, libghc-base-prof-4.17.2.0-68dfe, libghc-binary-prof-0.8.9.1-6a9d2, libghc-data-default-class-prof-0.1.2.0-50c88, libghc-deepseq-prof-1.4.8.0-ddf16, libghc-dense-linear-algebra-prof-0.1.0.0-360b2, libghc-math-functions-prof-0.3.4.2-08752, libghc-mwc-random-prof-0.15.0.2-4e96f, libghc-parallel-prof-3.2.2.0-afc9a, libghc-primitive-prof-0.8.0.0-a075c, libghc-random-prof-1.2.1.1-68746, libghc-statistics-dev (= 0.16.2.1-1+b1), libghc-vector-algorithms-prof-0.9.0.1-9226e, libghc-vector-binary-instances-prof-0.2.5.2-e0127, libghc-vector-prof-0.13.1.0-1ffee, libghc-vector-th-unbox-prof-0.2.2-782ae
Size: 1691808
SHA256: fafd2b56337415994af4d2f9dcf5e4fda2d717e28fd8d7b5cba882f42f49ef6f
SHA1: 1cf4d8507f42b2ea5680212560c1a3187436a90d
MD5sum: e228644548dc16836d687949498f9f96
Description: A library of statistical types, data, and functions; profiling libraries
This library provides a number of common functions and types useful
in statistics. Our focus is on high performance, numerical
robustness, and use of good algorithms. Where possible, we provide
references to the statistical literature.
.
The library's facilities can be divided into three broad categories:
.
Working with widely used discrete and continuous probability
distributions. (There are dozens of exotic distributions in use; we
focus on the most common.)
.
Computing with sample data: quantile estimation, kernel density
estimation, bootstrap methods, and autocorrelation analysis.
.
Random variate generation under several different distributions.
.
This package provides a library for the Haskell programming language, compiled
for profiling. See http://www.haskell.org/ for more information on Haskell.
Description-md5:
Homepage: https://github.com/haskell/statistics
Section: haskell
Priority: optional
Filename: pool/main/h/haskell-statistics/libghc-statistics-prof_0.16.2.1-1+b1_amd64.deb