How to Install and Uninstall perl-Statistics-Descriptive.noarch Package on Rocky Linux 8
Last updated: November 30,2024
1. Install "perl-Statistics-Descriptive.noarch" package
Here is a brief guide to show you how to install perl-Statistics-Descriptive.noarch on Rocky Linux 8
$
sudo dnf update
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$
sudo dnf install
perl-Statistics-Descriptive.noarch
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2. Uninstall "perl-Statistics-Descriptive.noarch" package
This tutorial shows how to uninstall perl-Statistics-Descriptive.noarch on Rocky Linux 8:
$
sudo dnf remove
perl-Statistics-Descriptive.noarch
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$
sudo dnf autoremove
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3. Information about the perl-Statistics-Descriptive.noarch package on Rocky Linux 8
Last metadata expiration check: 1:02:42 ago on Mon Sep 12 10:27:18 2022.
Available Packages
Name : perl-Statistics-Descriptive
Version : 3.0702
Release : 6.el8
Architecture : noarch
Size : 82 k
Source : perl-Statistics-Descriptive-3.0702-6.el8.src.rpm
Repository : epel
Summary : Perl module of basic descriptive statistical functions
URL : https://metacpan.org/release/Statistics-Descriptive
License : (GPL+ or Artistic) and MIT
Description : This module provides basic functions used in descriptive statistics. It has
: an object oriented design and supports two different types of data storage
: and calculation objects: sparse and full. With the sparse method, none of
: the data is stored and only a few statistical measures are available. Using
: the full method, the entire data set is retained and additional functions
: are available.
Available Packages
Name : perl-Statistics-Descriptive
Version : 3.0702
Release : 6.el8
Architecture : noarch
Size : 82 k
Source : perl-Statistics-Descriptive-3.0702-6.el8.src.rpm
Repository : epel
Summary : Perl module of basic descriptive statistical functions
URL : https://metacpan.org/release/Statistics-Descriptive
License : (GPL+ or Artistic) and MIT
Description : This module provides basic functions used in descriptive statistics. It has
: an object oriented design and supports two different types of data storage
: and calculation objects: sparse and full. With the sparse method, none of
: the data is stored and only a few statistical measures are available. Using
: the full method, the entire data set is retained and additional functions
: are available.