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

Last updated: May 20,2024

1. Install "r-cran-mice" package

This is a short guide on how to install r-cran-mice on Kali Linux


    
        
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                sudo apt update
            
                    
    
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            $
        

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

In this section, we are going to explain the necessary steps to uninstall r-cran-mice on Kali Linux:


    
        
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                sudo apt remove
            
                        
                r-cran-mice
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

3. Information about the r-cran-mice package on Kali Linux

Package: r-cran-mice
Version: 3.16.0-1
Installed-Size: 1821
Maintainer: Debian R Packages Maintainers
Architecture: amd64
Depends: r-base-core (>= 4.3.1-1), r-api-4.0, r-cran-broom, r-cran-dplyr, r-cran-generics, r-cran-glmnet, r-cran-lattice, r-cran-mitml, r-cran-nnet, r-cran-rcpp, r-cran-rpart, r-cran-rlang, r-cran-tidyr, r-cran-cpp11, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2)
Recommends: r-cran-broom.mixed, r-cran-decor, r-cran-future, r-cran-furrr, r-cran-haven, r-cran-knitr, r-cran-lme4, r-cran-lmtest, r-cran-mass, r-cran-metafor, r-cran-pan, r-cran-parallelly, r-cran-purrr, r-cran-randomforest, r-cran-ranger, r-cran-rmarkdown, r-cran-rstan, r-cran-survival, r-cran-testthat
Size: 1502712
SHA256: 9e61276b4b2461c002a3c1efa6c7ae9db36e9090c850831d1e23a90c235636b9
SHA1: a82e3003ffce9691617a6ae7a045e49cdf7b8fc2
MD5sum: f439419b574f8adcc2082e7ea98631e3
Description: GNU R multivariate imputation by chained equations
Multiple imputation using Fully Conditional Specification (FCS)
implemented by the MICE algorithm as described in Van Buuren and
Groothuis-Oudshoorn (2011) . Each variable has
its own imputation model. Built-in imputation models are provided for
continuous data (predictive mean matching, normal), binary data (logistic
regression), unordered categorical data (polytomous logistic regression)
and ordered categorical data (proportional odds). MICE can also impute
continuous two-level data (normal model, pan, second-level variables).
Passive imputation can be used to maintain consistency between variables.
Various diagnostic plots are available to inspect the quality of the
imputations.
Description-md5:
Homepage: https://cran.r-project.org/package=mice
Section: gnu-r
Priority: optional
Filename: pool/main/r/r-cran-mice/r-cran-mice_3.16.0-1_amd64.deb