%global __brp_check_rpaths %{nil}
%global packname  influenceAUC
%global packver   0.1.2
%global rlibdir   /usr/local/lib/R/library

Name:             R-CRAN-%{packname}
Version:          0.1.2
Release:          3%{?dist}%{?buildtag}
Summary:          Identify Influential Observations in Binary Classification

License:          GPL-3
URL:              https://cran.r-project.org/package=%{packname}
Source0:          %{url}&version=%{packver}#/%{packname}_%{packver}.tar.gz


BuildRequires:    R-devel
Requires:         R-core
BuildArch:        noarch
BuildRequires:    R-CRAN-dplyr 
BuildRequires:    R-CRAN-geigen 
BuildRequires:    R-CRAN-ggplot2 
BuildRequires:    R-CRAN-ggrepel 
BuildRequires:    R-methods 
BuildRequires:    R-CRAN-ROCR 
Requires:         R-CRAN-dplyr 
Requires:         R-CRAN-geigen 
Requires:         R-CRAN-ggplot2 
Requires:         R-CRAN-ggrepel 
Requires:         R-methods 
Requires:         R-CRAN-ROCR 

%description
Ke, B. S., Chiang, A. J., & Chang, Y. C. I. (2018)
<doi:10.1080/10543406.2017.1377728> provide two theoretical methods
(influence function and local influence) based on the area under the
receiver operating characteristic curve (AUC) to quantify the numerical
impact of each observation to the overall AUC. Alternative graphical
tools, cumulative lift charts, are proposed to reveal the existences and
approximate locations of those influential observations through data
visualization.

%prep
%setup -q -c -n %{packname}

find -type f -executable -exec grep -Iq . {} \; -exec sed -i -e '$a\' {} \;

%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

%files
%{rlibdir}/%{packname}