How to Install and Uninstall R-qtl.x86_64 Package on CentOS Stream 9

Last updated: November 15,2024

1. Install "R-qtl.x86_64" package

Here is a brief guide to show you how to install R-qtl.x86_64 on CentOS Stream 9


    
        
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                sudo dnf update
            
                    
    
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            $
        

        
            
                sudo dnf install
            
                        
                R-qtl.x86_64
            
                    
    
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2. Uninstall "R-qtl.x86_64" package

Please follow the guidance below to uninstall R-qtl.x86_64 on CentOS Stream 9:


    
        
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                sudo dnf remove
            
                        
                R-qtl.x86_64
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo dnf autoremove
            
                    
    
    Copied

3. Information about the R-qtl.x86_64 package on CentOS Stream 9

Last metadata expiration check: 1:34:45 ago on Sat Mar 16 16:03:45 2024.
Available Packages
Name : R-qtl
Version : 1.66
Release : 1.el9
Architecture : x86_64
Size : 5.3 M
Source : R-qtl-1.66-1.el9.src.rpm
Repository : epel
Summary : Tools for analyzing QTL experiments
URL : https://rqtl.org/
License : GPL-3.0-only
Description : R-qtl is an extensible, interactive environment for mapping
: quantitative trait loci (QTLs) in experimental crosses. Our goal is to
: make complex QTL mapping methods widely accessible and allow users to
: focus on modeling rather than computing.
:
: A key component of computational methods for QTL mapping is the hidden
: Markov model (HMM) technology for dealing with missing genotype
: data. We have implemented the main HMM algorithms, with allowance for
: the presence of genotyping errors, for backcrosses, intercrosses, and
: phase-known four-way crosses.
:
: The current version of R-qtl includes facilities for estimating
: genetic maps, identifying genotyping errors, and performing single-QTL
: genome scans and two-QTL, two-dimensional genome scans, by interval
: mapping (with the EM algorithm), Haley-Knott regression, and multiple
: imputation. All of this may be done in the presence of covariates
: (such as sex, age or treatment). One may also fit higher-order QTL
: models by multiple imputation and Haley-Knott regression.