How to Install and Uninstall r-cran-party Package on Kali Linux

Last updated: May 03,2024

1. Install "r-cran-party" package

Please follow the steps below to install r-cran-party on Kali Linux


    
        
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                sudo apt update
            
                    
    
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            $
        

        
            
                sudo apt install
            
                        
                r-cran-party
            
                    
    
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2. Uninstall "r-cran-party" package

Learn how to uninstall r-cran-party on Kali Linux:


    
        
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                sudo apt remove
            
                        
                r-cran-party
            
                    
    
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            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the r-cran-party package on Kali Linux

Package: r-cran-party
Version: 1.3-14-1
Installed-Size: 1454
Maintainer: Debian R Packages Maintainers
Architecture: amd64
Depends: 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-th.data (>= 1.0-3), r-cran-mlbench, r-cran-colorspace, r-cran-mass, r-cran-vcd, r-cran-ipred, r-cran-randomforest
Size: 1139960
SHA256: ba2756388035534627a76bdbe0ad81f6f3e95ca7301b3ac7cbd9e23adde42f65
SHA1: 644bae6725d532a9fffb45f3af40298a6b5493cf
MD5sum: 71e2691b69ba41761776d899e8c891c4
Description: 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:
Homepage: https://cran.r-project.org/package=party
Section: gnu-r
Priority: optional
Filename: pool/main/r/r-cran-party/r-cran-party_1.3-14-1_amd64.deb