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

Last updated: May 14,2024

1. Install "r-cran-party" package

In this section, we are going to explain the necessary steps to install r-cran-party on Ubuntu 20.10 (Groovy Gorilla)

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

2. Uninstall "r-cran-party" package

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

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

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

Package: r-cran-party
Architecture: amd64
Version: 1.3-4-1build1
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: 1396
Depends: r-base-core (>= 4.0.0.20200528-1), 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_1.3-4-1build1_amd64.deb
Size: 1090600
MD5sum: 443076acc2cbef47ae7b7c9457429a82
SHA1: d7b3abc3901b379d497a7285d2fee26ee7257d28
SHA256: 9cc612a3e9770ae079c30fc6b1bf2fc032736c2802400b40dbbe6865f0955585
SHA512: 2ba23e3f34ca2ae31c1ac663cdd3fb8a2bd900c10b1a126630b3c764b64d21a0f2d2253cc8eeb343556f3ee169ddf0dee14ce77155f6450e0b8f19d23065f312
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