How to Install and Uninstall r-cran-genie Package on Ubuntu 21.10 (Impish Indri)

Last updated: November 07,2024

1. Install "r-cran-genie" package

Here is a brief guide to show you how to install r-cran-genie on Ubuntu 21.10 (Impish Indri)

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

2. Uninstall "r-cran-genie" package

Please follow the steps below to uninstall r-cran-genie on Ubuntu 21.10 (Impish Indri):

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

3. Information about the r-cran-genie package on Ubuntu 21.10 (Impish Indri)

Package: r-cran-genie
Architecture: amd64
Version: 1.0.5-2
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: 294
Depends: r-base-core (>= 4.0.3-1), r-api-4.0, r-cran-genieclust, r-cran-rcpp (>= 1.0.0), libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 5.2)
Recommends: r-cran-testthat
Suggests: r-cran-stringi
Filename: pool/universe/r/r-cran-genie/r-cran-genie_1.0.5-2_amd64.deb
Size: 93172
MD5sum: f18c31919c3bf1e0ea720308b3b907f7
SHA1: 7dd20d68003f450b364bd5e03cd6e2b36c68ef07
SHA256: c4401f43e152ad09bc8a27f7b4ef6a50efac99bd0421b9647ec509120f37c27c
SHA512: 417d3431aef3e37f5a8d3c0e7ab678e866f7dd51d276fd376989e36c08fe43ba134778f2923eb9be4d166d32fdc42f7ea173419a1b30a241bc534d310db18148
Homepage: https://cran.r-project.org/package=genie
Description-en: GNU R fast, robust, and outlier resistant hierarchical clustering
Includes the reference implementation of Genie - a hierarchical
clustering algorithm that links two point groups in such a way that
an inequity measure (namely, the Gini index) of the cluster sizes
does not significantly increase above a given threshold.
This method most often outperforms many other data segmentation approaches
in terms of clustering quality as tested on a wide range of benchmark
datasets. At the same time, Genie retains the high speed of the single
linkage approach, therefore it is also suitable for analysing larger data sets.
For more details see (Gagolewski et al. 2016 ).
For an even faster and more feature-rich implementation, including,
amongst others, noise point detection, see the 'genieclust' package.
Description-md5: 614d1b3d15ec311e4a34d3026471006b