How to Install and Uninstall r-cran-rocr Package on Ubuntu 16.04 LTS (Xenial Xerus)

Last updated: May 17,2024

1. Install "r-cran-rocr" package

Please follow the step by step instructions below to install r-cran-rocr on Ubuntu 16.04 LTS (Xenial Xerus)


    
        
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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

Learn how to uninstall r-cran-rocr on Ubuntu 16.04 LTS (Xenial Xerus):


    
        
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                sudo apt remove
            
                        
                r-cran-rocr
            
                    
    
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            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the r-cran-rocr package on Ubuntu 16.04 LTS (Xenial Xerus)

Package: r-cran-rocr
Priority: optional
Section: universe/math
Installed-Size: 239
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Med Packaging Team
Architecture: all
Version: 1.0-7-1
Depends: r-base-core (>= 3.2.1-4), r-api-3, r-cran-gplots, r-cran-gtools, r-cran-gdata
Suggests: r-cran-runit
Filename: pool/universe/r/r-cran-rocr/r-cran-rocr_1.0-7-1_all.deb
Size: 161472
MD5sum: a1e88b8d8250e6f2f3b72e465fe69907
SHA1: a8007c1063a6d8c86d30c3a55b686f54c198fb2f
SHA256: b01d1398579ac10096c9dae4a12d030c5c2646956637185e1a8003350de436bf
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
Homepage: http://rocr.bioinf.mpi-sb.mpg.de
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Origin: Ubuntu