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

Last updated: May 09,2024

1. Install "r-cran-susier" package

In this section, we are going to explain the necessary steps to install r-cran-susier on Kali Linux


    
        
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                sudo apt update
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt install
            
                        
                r-cran-susier
            
                    
    
    Copied

2. Uninstall "r-cran-susier" package

This guide let you learn how to uninstall r-cran-susier on Kali Linux:


    
        
            $
        

        
            
                sudo apt remove
            
                        
                r-cran-susier
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

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

Package: r-cran-susier
Version: 0.12.35+dfsg-1
Installed-Size: 1944
Maintainer: Debian R Packages Maintainers
Architecture: all
Depends: r-base-core (>= 4.2.2.20221110-2), r-api-4.0, r-cran-matrix, r-cran-matrixstats, r-cran-mixsqp, r-cran-reshape, r-cran-crayon, r-cran-ggplot2, python3
Recommends: r-cran-testthat
Suggests: r-cran-curl, r-cran-knitr, r-cran-rmarkdown, r-cran-cowplot, caviar, finemap
Size: 1795072
SHA256: 6ea0d0cedd7ffa42510a206c7435aa34273f0863ad91bbbb3aa0c3868e68ac07
SHA1: 9677b39765302bc2564dc388db82384acd4c3c6d
MD5sum: bbbd4745d2a06b8a7c4614e736ab2f0a
Description: GNU R sum of single effects linear regression
Implements methods for variable selection in linear
regression based on the "Sum of Single Effects" (SuSiE) model, as
described in Wang et al (2020) . These methods
provide simple summaries, called "Credible Sets", for accurately
quantifying uncertainty in which variables should be selected.
The methods are motivated by genetic fine-mapping applications,
and are particularly well-suited to settings where variables are
highly correlated and detectable effects are sparse. The fitting
algorithm, a Bayesian analogue of stepwise selection methods
called "Iterative Bayesian Stepwise Selection" (IBSS), is simple
and fast, allowing the SuSiE model be fit to large data sets
(thousands of samples and hundreds of thousands of variables).
Description-md5:
Homepage: https://cran.r-project.org/package=susieR
Section: gnu-r
Priority: optional
Filename: pool/main/r/r-cran-susier/r-cran-susier_0.12.35+dfsg-1_all.deb