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

Last updated: May 04,2024

1. Install "r-cran-party" package

This guide covers the steps necessary to install r-cran-party on Ubuntu 21.10 (Impish Indri)

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

2. Uninstall "r-cran-party" package

Please follow the step by step instructions below to uninstall r-cran-party on Ubuntu 21.10 (Impish Indri):

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

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

Package: r-cran-party
Architecture: amd64
Version: 1.3-5-1
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: 1465
Depends: r-base-core (>= 4.0.2-1build1), r-api-4.0, r-cran-mvtnorm (>= 1.0-2), r-cran-modeltools (>= 0.2-21), r-cran-strucchange, r-cran-survival (>= 2.37-7), r-cran-coin (>= 1.1-0), r-cran-zoo, r-cran-sandwich (>= 1.1-1), libblas3 | libblas.so.3, libc6 (>= 2.14), liblapack3 | liblapack.so.3
Recommends: r-cran-mlbench
Suggests: r-cran-th.data (>= 1.0-3), r-cran-colorspace, r-cran-mass, r-cran-vcd, r-cran-ipred
Filename: pool/universe/r/r-cran-party/r-cran-party_1.3-5-1_amd64.deb
Size: 1164388
MD5sum: 310e3a70002aa3eb3566670f770a22ea
SHA1: 67b67433ae3d36bfb873b3bd119298b0945c026a
SHA256: bf55adfe45e22b33c69bd6bda043ca654deddde852cf848e6882abdfcc2e8182
SHA512: 720f3d178c3c04ee3fd55008729c7189841bb38c8252e22d80ba2855f9bb95f5eeecb24f37741eb16e2f72ddbb787967e00469886fce50c28daa963a4c3c1e6a
Homepage: https://cran.r-project.org/package=party
Description-en: GNU R laboratory for recursive partytioning
A computational toolbox for recursive partitioning.
The core of the package is ctree(), an implementation of
conditional inference trees which embed tree-structured
regression models into a well defined theory of conditional
inference procedures. This non-parametric class of regression
trees is applicable to all kinds of regression problems, including
nominal, ordinal, numeric, censored as well as multivariate response
variables and arbitrary measurement scales of the covariates.
Based on conditional inference trees, cforest() provides an
implementation of Breiman's random forests. The function mob()
implements an algorithm for recursive partitioning based on
parametric models (e.g. linear models, GLMs or survival
regression) employing parameter instability tests for split
selection. Extensible functionality for visualizing tree-structured
regression models is available. The methods are described in
Hothorn et al. (2006) ,
Zeileis et al. (2008) and
Strobl et al. (2007) .
Description-md5: 9e691608bc4b8a9206a2a54193d5d545