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

Last updated: November 07,2024

1. Install "r-cran-rms" package

Here is a brief guide to show you how to install r-cran-rms on Kali Linux


    
        
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                sudo apt update
            
                    
    
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            $
        

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

Please follow the instructions below to uninstall r-cran-rms on Kali Linux:


    
        
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                sudo apt remove
            
                        
                r-cran-rms
            
                    
    
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            $
        

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

Package: r-cran-rms
Version: 6.7-1-1
Installed-Size: 2528
Maintainer: Dirk Eddelbuettel
Architecture: amd64
Replaces: r-noncran-design
Depends: libc6 (>= 2.29), r-api-4.0, r-cran-hmisc (>= 5.1-0), r-cran-survival (>= 2.40), r-cran-quantreg, r-cran-ggplot2, r-cran-sparsem, r-cran-rpart, r-cran-nlme (>= 3.1-123), r-cran-polspline, r-cran-multcomp, r-cran-htmltable (>= 1.11.0), r-cran-htmltools, r-cran-mass, r-cran-cluster, r-cran-digest, r-cran-colorspace, r-cran-knitr, r-cran-foreign, r-cran-nnet
Conflicts: r-noncran-design
Size: 2198188
SHA256: 7659ba5ab670ced48c85ffdc66a0bcc4cbbf5957cc092d72e645d3c78c25844a
SHA1: 061b4d45fd70d9b4f6ac6709f2e35c6518521675
MD5sum: e9080bf26a55df61a6f8eecc6457a20b
Description: GNU R regression modeling strategies by Frank Harrell
Regression modeling, testing, estimation, validation, graphics,
prediction, and typesetting by storing enhanced model design
attributes in the fit. rms is a collection of 229 functions that
assist with and streamline modeling. It also contains functions for
binary and ordinal logistic regression models and the Buckley-James
multiple regression model for right-censored responses, and implements
penalized maximum likelihood estimation for logistic and ordinary
linear models. rms works with almost any regression model, but it
was especially written to work with binary or ordinal logistic
regression, Cox regression, accelerated failure time models,
ordinary linear models, the Buckley-James model, generalized least
squares for serially or spatially correlated observations, generalized
linear models, and quantile regression.
.
See Frank Harrell (2001), Regression Modeling Strategies, Springer
Series in Statistics, as well as http://biostat.mc.vanderbilt.edu/Rrms.
Description-md5:
Homepage: https://cran.r-project.org/package=rms
Section: gnu-r
Priority: optional
Filename: pool/main/r/r-cran-rms/r-cran-rms_6.7-1-1_amd64.deb