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

Last updated: May 15,2024

1. Install "r-cran-rsgcc" package

Please follow the instructions below to install r-cran-rsgcc on Ubuntu 20.10 (Groovy Gorilla)

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

2. Uninstall "r-cran-rsgcc" package

This guide let you learn how to uninstall r-cran-rsgcc on Ubuntu 20.10 (Groovy Gorilla):

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

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

Package: r-cran-rsgcc
Architecture: amd64
Version: 1.0.6-2build1
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: 246
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-cran-biwt, r-cran-cairodevice, r-cran-fbasics, r-cran-gplots, r-cran-gwidgets, r-cran-gwidgetsrgtk2, r-cran-minerva, r-cran-parmigene, r-cran-stringr, r-cran-snowfall, libc6 (>= 2.4), libgomp1 (>= 6)
Suggests: r-cran-bigmemory, r-bioc-ctc
Filename: pool/universe/r/r-cran-rsgcc/r-cran-rsgcc_1.0.6-2build1_amd64.deb
Size: 176772
MD5sum: 344f6307fed8b4de30ebbfb34bc76325
SHA1: 3f698fa3cb2e6e3f7cef91b14e7d0f6d8ca2158d
SHA256: 430c35daa94fbb28dc609cb89b0a1ddc82a333b65d381713b29220b14931a8a5
SHA512: 7ffb605f3aecc2d094298666064239777b2b913e034469ff6c93555d6f077aa749c141b05dbf0ea8b17870735da9e956ca9622d81b5d156c1185fb6407c66374
Homepage: https://cran.r-project.org/package=rsgcc
Description-en: Gini correlation and clustering of gene expression data
This package provides functions for calculating
associations between two genes with five correlation
methods(e.g., the Gini correlation coefficient [GCC], the
Pearson's product moment correlation coefficient [PCC], the
Kendall tau rank correlation coefficient [KCC], the Spearman's
rank correlation coefficient [SCC] and the Tukey's biweight
correlation coefficient [BiWt], and three non-correlation
methods (e.g., mutual information [MI] and the maximal
information-based nonparametric exploration [MINE], and the
euclidean distance [ED]). It can also been implemented to
perform the correlation and clustering analysis of
transcriptomic data profiled by microarray and RNA-Seq
technologies. Additionally, this package can be further applied
to construct gene co-expression networks (GCNs).
Description-md5: 8627b3333dc9a7799c39a4fd5e8cb711