How to Install and Uninstall r-cran-rocr Package on Ubuntu 21.10 (Impish Indri)

Last updated: May 16,2024

1. Install "r-cran-rocr" package

Please follow the guidance below to install r-cran-rocr on Ubuntu 21.10 (Impish Indri)


    
        
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                sudo apt update
            
                    
    
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            $
        

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

Please follow the guidelines below to uninstall r-cran-rocr on Ubuntu 21.10 (Impish Indri):


    
        
            $
        

        
            
                sudo apt remove
            
                        
                r-cran-rocr
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

3. Information about the r-cran-rocr package on Ubuntu 21.10 (Impish Indri)

Package: r-cran-rocr
Architecture: all
Version: 1.0-11-2build1
Priority: optional
Section: universe/math
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian R Packages Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 599
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-cran-gplots
Recommends: r-cran-testthat
Suggests: r-cran-knitr, r-cran-rmarkdown
Filename: pool/universe/r/r-cran-rocr/r-cran-rocr_1.0-11-2build1_all.deb
Size: 445740
MD5sum: 437912d4d2baa95391d55952af79af23
SHA1: 03f3b5f2f747efeb9cd249b279b734f9287ad496
SHA256: 445f0332a3f3237f724e1d21d4fd2aeee0dddc3e6af0fa624d5d0e2f3eedda1c
SHA512: 103e80a45699b76f0d171d2b9ec77e69d284fd2e8234f4e30177f4bd5dc707074c771c738a8a8eb636fe74a20a475ef7855b7b4a74ce3cd12c48a857e1e531f0
Homepage: https://cran.r-project.org/package=ROCR
Description-en: GNU R package to prepare and display ROC curves
ROC graphs, sensitivity/specificity curves, lift charts,
and precision/recall plots are popular examples of trade-off
visualizations for specific pairs of performance measures. ROCR is a
flexible tool for creating cutoff-parametrized 2D performance curves
by freely combining two from over 25 performance measures (new
performance measures can be added using a standard interface).
Curves from different cross-validation or bootstrapping runs can be
averaged by different methods, and standard deviations, standard
errors or box plots can be used to visualize the variability across
the runs. The parametrization can be visualized by printing cutoff
values at the corresponding curve positions, or by coloring the
curve according to cutoff. All components of a performance plot can
be quickly adjusted using a flexible parameter dispatching
mechanism. Despite its flexibility, ROCR is easy to use, with only
three commands and reasonable default values for all optional
parameters.
.
ROCR features: ROC curves, precision/recall plots, lift charts, cost
curves, custom curves by freely selecting one performance measure for the
x axis and one for the y axis, handling of data from cross-validation
or bootstrapping, curve averaging (vertically, horizontally, or by
threshold), standard error bars, box plots, curves that are color-coded
by cutoff, printing threshold values on the curve, tight integration
with Rs plotting facilities (making it easy to adjust plots or to combine
multiple plots), fully customizable, easy to use (only 3 commands).
.
Performance measures that ROCR knows: Accuracy, error rate, true
positive rate, false positive rate, true negative rate, false negative
rate, sensitivity, specificity, recall, positive predictive value,
negative predictive value, precision, fallout, miss, phi correlation
coefficient, Matthews correlation coefficient, mutual information, chi
square statistic, odds ratio, lift value, precision/recall F measure,
ROC convex hull, area under the ROC curve, precision/recall break-even
point, calibration error, mean cross-entropy, root mean squared error,
SAR measure, expected cost, explicit cost.
Description-md5: 67d77b1b5bfeb7e4e084ffd06446af6b