How to Install and Uninstall r-bioc-ihw Package on Ubuntu 20.10 (Groovy Gorilla)

Last updated: December 23,2024

1. Install "r-bioc-ihw" package

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


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                r-bioc-ihw
            
                    
    
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2. Uninstall "r-bioc-ihw" package

Here is a brief guide to show you how to uninstall r-bioc-ihw on Ubuntu 20.10 (Groovy Gorilla):


    
        
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                sudo apt remove
            
                        
                r-bioc-ihw
            
                    
    
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                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the r-bioc-ihw package on Ubuntu 20.10 (Groovy Gorilla)

Package: r-bioc-ihw
Architecture: all
Version: 1.16.0+dfsg-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: 346
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-api-bioc-3.11, r-cran-slam, r-bioc-lpsymphony, r-cran-fdrtool, r-bioc-biocgenerics
Recommends: r-cran-testthat, r-cran-ggplot2, r-cran-scales
Suggests: r-cran-dplyr, r-cran-gridextra, r-bioc-deseq2, r-cran-matrix, r-cran-knitr, r-cran-rmarkdown, r-cran-devtools
Filename: pool/universe/r/r-bioc-ihw/r-bioc-ihw_1.16.0+dfsg-1build1_all.deb
Size: 245024
MD5sum: 849ccc8756a1a5242a3717976d743bba
SHA1: cdbbed1fa226ca9608ce0dda605f3dbbb7cd77db
SHA256: d877cfc319504d990bacbb2ad349e19f2e2e12181693e3705b0928cb1611c2de
SHA512: ec6c571afe455adc59e3c3e9f3dbd9884b51feec39d2667bd12e7596fb5cf72731313e620ea609b667b3e637f45ff50daa0f34335573cd5e196e18d0ac52a8a0
Homepage: https://bioconductor.org/packages/IHW/
Description-en: GNU R independent hypothesis weighting
Independent hypothesis weighting (IHW) is a multiple testing procedure
that increases power compared to the method of Benjamini and Hochberg by
assigning data-driven weights to each hypothesis. The input to IHW is a
two-column table of p-values and covariates. The covariate can be any
continuous-valued or categorical variable that is thought to be
informative on the statistical properties of each hypothesis test, while
it is independent of the p-value under the null hypothesis.
Description-md5: e4005f10a90f6b670f0dd79018f4c723