How to Install and Uninstall R-broom.noarch Package on Fedora 36
Last updated: October 05,2024
1. Install "R-broom.noarch" package
In this section, we are going to explain the necessary steps to install R-broom.noarch on Fedora 36
$
sudo dnf update
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$
sudo dnf install
R-broom.noarch
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2. Uninstall "R-broom.noarch" package
This guide covers the steps necessary to uninstall R-broom.noarch on Fedora 36:
$
sudo dnf remove
R-broom.noarch
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$
sudo dnf autoremove
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3. Information about the R-broom.noarch package on Fedora 36
Last metadata expiration check: 4:31:51 ago on Thu Sep 8 02:05:26 2022.
Available Packages
Name : R-broom
Version : 0.7.7
Release : 3.fc36
Architecture : noarch
Size : 1.8 M
Source : R-broom-0.7.7-3.fc36.src.rpm
Repository : fedora
Summary : Convert Statistical Objects into Tidy Tibbles
URL : https://CRAN.R-project.org/package=broom
License : MIT
Description : Summarizes key information about statistical objects in tidy tibbles. This
: makes it easy to report results, create plots and consistently work with
: large numbers of models at once. Broom provides three verbs that each
: provide different types of information about a model. tidy() summarizes
: information about model components such as coefficients of a regression.
: glance() reports information about an entire model, such as goodness of fit
: measures like AIC and BIC. augment() adds information about individual
: observations to a dataset, such as fitted values or influence measures.
Available Packages
Name : R-broom
Version : 0.7.7
Release : 3.fc36
Architecture : noarch
Size : 1.8 M
Source : R-broom-0.7.7-3.fc36.src.rpm
Repository : fedora
Summary : Convert Statistical Objects into Tidy Tibbles
URL : https://CRAN.R-project.org/package=broom
License : MIT
Description : Summarizes key information about statistical objects in tidy tibbles. This
: makes it easy to report results, create plots and consistently work with
: large numbers of models at once. Broom provides three verbs that each
: provide different types of information about a model. tidy() summarizes
: information about model components such as coefficients of a regression.
: glance() reports information about an entire model, such as goodness of fit
: measures like AIC and BIC. augment() adds information about individual
: observations to a dataset, such as fitted values or influence measures.