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

Last updated: April 29,2024

1. Install "r-cran-adegenet" package

This tutorial shows how to install r-cran-adegenet on Ubuntu 20.10 (Groovy Gorilla)

$ sudo apt update $ sudo apt install r-cran-adegenet

2. Uninstall "r-cran-adegenet" package

This guide covers the steps necessary to uninstall r-cran-adegenet on Ubuntu 20.10 (Groovy Gorilla):

$ sudo apt remove r-cran-adegenet $ sudo apt autoclean && sudo apt autoremove

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

Package: r-cran-adegenet
Architecture: amd64
Version: 2.1.3-1build1
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: 5004
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-cran-ade4, r-cran-mass, r-cran-igraph, r-cran-ape, r-cran-shiny, r-cran-ggplot2, r-cran-seqinr, r-cran-spdep, r-cran-boot, r-cran-reshape2, r-cran-dplyr (>= 0.4.1), r-cran-vegan, libc6 (>= 2.4)
Recommends: r-cran-testthat
Suggests: r-cran-maps
Filename: pool/universe/r/r-cran-adegenet/r-cran-adegenet_2.1.3-1build1_amd64.deb
Size: 2811108
MD5sum: e4ed8a27b814610142aae8415b917414
SHA1: 6a6e57db12d52c32d315fd3813ca5336aacd84be
SHA256: b670f96328c673ef39e6bb2bddf5b268769b91ba40c70157a0ce1cd38552656d
SHA512: 89070efb9c519bb4cacea184a1a9363a2e7e95a923169cda10162009dac4586c0687ddcfb7499f2db0e68a9b40f8cfb7e9bc1a072d9ab0a9b31828ef9e224c50
Homepage: https://cran.r-project.org/package=adegenet
Description-en: GNU R exploratory analysis of genetic and genomic data
Toolset for the exploration of genetic and genomic data. Adegenet
provides formal (S4) classes for storing and handling various genetic
data, including genetic markers with varying ploidy and hierarchical
population structure ('genind' class), alleles counts by populations
('genpop'), and genome-wide SNP data ('genlight'). It also implements
original multivariate methods (DAPC, sPCA), graphics, statistical tests,
simulation tools, distance and similarity measures, and several spatial
methods. A range of both empirical and simulated datasets is also
provided to illustrate various methods.
Description-md5: f76c4088266baabe642137115573a922