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File R-ROCit.spec of Package R-ROCit (Revision 787f6f12626a7851a83f24e4281bc7d4)

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# Automatically generated by CRAN2OBS
# 
# spec file for package ROCit 
# This file is auto-generated using information in the package source, 
# esp. Description and Summary. Improvements in that area should be 
# discussed with upstream. 
# 
# Copyright (c) 2021 SUSE LINUX GmbH, Nuernberg, Germany. 
# 
# All modifications and additions to the file contributed by third parties 
# remain the property of their copyright owners, unless otherwise agreed 
# upon. The license for this file, and modifications and additions to the 
# file, is the same license as for the pristine package itself (unless the 
# license for the pristine package is not an Open Source License, in which 
# case the license is the MIT License). An "Open Source License" is a 
# license that conforms to the Open Source Definition (Version 1.9) 
# published by the Open Source Initiative. 
#  
# Please submit bugfixes or comments via http://bugs.opensuse.org/ 
# 
 
%global packname  ROCit 
%global rlibdir   %{_libdir}/R/library 
 
Name:           R-%{packname} 
Version:        2.1.1 
Release:        0 
Summary:        Performance Assessment of Binary Classifier with Visualization 
Group:          Development/Libraries/Other 
License:        GPL-3 
URL:            http://cran.r-project.org/web/packages/%{packname} 
Source:         ROCit_2.1.1.tar.gz 
Requires:       R-base 
 
%if 0%{?sle_version} > 120400 || 0%{?is_opensuse} 
# Three others commonly needed 
BuildRequires:  tex(ae.sty) 
BuildRequires:  tex(fancyvrb.sty) 
BuildRequires:  tex(inconsolata.sty) 
BuildRequires:  tex(natbib.sty) 
%else 
BuildRequires:  texlive 
%endif 
BuildRequires:  texinfo 
BuildRequires:  fdupes 
BuildRequires:  R-base 
 
Suggests:	R-testthat
Suggests:	R-knitr
Suggests:	R-rmarkdown
%description 
Sensitivity (or recall or true positive rate), false positive rate, 
specificity, precision (or positive predictive value), negative 
predictive value, misclassification rate, accuracy, F-score- these are 
popular metrics for assessing performance of binary classifier for 
certain threshold. These metrics are calculated at certain threshold 
values. Receiver operating characteristic (ROC) curve is a common tool 
for assessing overall diagnostic ability of the binary classifier. 
Unlike depending on a certain threshold, area under ROC curve (also 
known as AUC), is a summary statistic about how well a binary 
classifier performs overall for the classification task. ROCit package 
provides flexibility to easily evaluate threshold-bound metrics. Also, 
ROC curve, along with AUC, can be obtained using different methods, 
such as empirical, binormal and non-parametric. ROCit encompasses a 
wide variety of methods for constructing confidence interval of ROC 
curve and AUC. ROCit also features the option of constructing empirical 
gains table, which is a handy tool for direct marketing. The package 
offers options for commonly used visualization, such as, ROC curve, KS 
plot, lift plot. Along with in-built default graphics setting, there 
are rooms for manual tweak by providing the necessary values as 
function arguments. ROCit is a powerful tool offering a range of 
things, yet it is very easy to use. 
 
%prep 
%setup -q -c -n %{packname} 
 
%build 
 
%install 
mkdir -p %{buildroot}%{rlibdir} 
%{_bindir}/R CMD INSTALL -l %{buildroot}%{rlibdir} %{packname} 
test -d %{packname}/src && (cd %{packname}/src; rm -f *.o *.so) 
rm -f %{buildroot}%{rlibdir}/R.css 
 
#%%check 
#%%{_bindir}/R CMD check %%{packname} 
 
%files 
%dir %{rlibdir}/%{packname} 
%doc %{rlibdir}/%{packname}/DESCRIPTION
%{rlibdir}/%{packname}/INDEX
%{rlibdir}/%{packname}/Meta
%{rlibdir}/%{packname}/NAMESPACE
%doc %{rlibdir}/%{packname}/NEWS.md
%{rlibdir}/%{packname}/R
%{rlibdir}/%{packname}/data
%doc %{rlibdir}/%{packname}/doc
%doc %{rlibdir}/%{packname}/help
%doc %{rlibdir}/%{packname}/html
 
%changelog

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File R-ROCit.spec of Package R-ROCit (Revision 787f6f12626a7851a83f24e4281bc7d4)

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