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R-ROCit
R-ROCit.spec
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File R-ROCit.spec of Package R-ROCit
# 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) 2024 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: 2.1.2 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_2.1.2.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} # the next line is needed, because we build without --clean in between two packages rm -rf ~/.R %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 %fdupes -s %{buildroot}%{rlibdir} #%%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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