How to Install and Uninstall r-cran-broom.mixed Package on Ubuntu 20.10 (Groovy Gorilla)

Last updated: May 17,2024

1. Install "r-cran-broom.mixed" package

This is a short guide on how to install r-cran-broom.mixed on Ubuntu 20.10 (Groovy Gorilla)

$ sudo apt update $ sudo apt install r-cran-broom.mixed

2. Uninstall "r-cran-broom.mixed" package

This is a short guide on how to uninstall r-cran-broom.mixed on Ubuntu 20.10 (Groovy Gorilla):

$ sudo apt remove r-cran-broom.mixed $ sudo apt autoclean && sudo apt autoremove

3. Information about the r-cran-broom.mixed package on Ubuntu 20.10 (Groovy Gorilla)

Package: r-cran-broom.mixed
Architecture: all
Version: 0.2.6-2
Priority: optional
Section: universe/gnu-r
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian R Packages Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 4475
Depends: r-base-core (>= 4.0.2-1build1), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-tidyr, r-cran-plyr, r-cran-purrr, r-cran-tibble, r-cran-reshape2, r-cran-nlme, r-cran-stringr, r-cran-coda, r-cran-tmb, r-cran-cubelyr
Recommends: r-cran-testthat, r-cran-lme4, r-cran-glmmtmb, r-cran-knitr, r-cran-ggplot2, r-cran-matrix, r-cran-brms, r-cran-mgcv, r-cran-lmertest, r-cran-pbkrtest, r-cran-rstan, r-cran-rstanarm
Filename: pool/universe/r/r-cran-broom.mixed/r-cran-broom.mixed_0.2.6-2_all.deb
Size: 4161160
MD5sum: 619df003a2a8ac926b1675407b014e1d
SHA1: bc518e7d26b48aa749ca4afd008f94a946f293ea
SHA256: 2174d53a62c66fdf9a558d1078f5fd24ac480d5cf9a8f891a52fc08ff7add0ca
SHA512: fd4a5a9c4706ba43c66df4b7c5c3148171da1156f3d99c235b34544c85f6eede54e2a913a5134ca2a07495a2884534cc1fe534554167fb7d810b84ba12af7ffe
Homepage: https://cran.r-project.org/package=broom.mixed
Description-en: GNU R tidying methods for mixed models
Convert fitted objects from various R mixed-model packages into tidy
data frames along the lines of the 'broom' package. The package provides
three S3 generics for each model: tidy(), which summarizes a model's
statistical findings such as coefficients of a regression; augment(),
which adds columns to the original data such as predictions, residuals
and cluster assignments; and glance(), which provides a one-row summary
of model-level statistics.
Description-md5: 60b01e3e62468a842a4c76160053d491