%global packname  Routliers
%global packver   0.0.0.3
%global rlibdir   /usr/local/lib/R/library

Name:             R-CRAN-%{packname}
Version:          0.0.0.3
Release:          3%{?dist}
Summary:          Robust Outliers Detection

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


BuildRequires:    R-devel >= 2.10
Requires:         R-core >= 2.10
BuildArch:        noarch
BuildRequires:    R-MASS 
BuildRequires:    R-stats 
BuildRequires:    R-graphics 
BuildRequires:    R-CRAN-ggplot2 
Requires:         R-MASS 
Requires:         R-stats 
Requires:         R-graphics 
Requires:         R-CRAN-ggplot2 

%description
Detecting outliers using robust methods, i.e. the Median Absolute
Deviation (MAD) for univariate outliers; Leys, Ley, Klein, Bernard, &
Licata (2013) <doi:10.1016/j.jesp.2013.03.013> and the Mahalanobis-Minimum
Covariance Determinant (MMCD) for multivariate outliers; Leys, C., Klein,
O., Dominicy, Y. & Ley, C. (2018) <doi:10.1016/j.jesp.2017.09.011>. There
is also the more known but less robust Mahalanobis distance method, only
for comparison purposes.

%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
%dir %{rlibdir}/%{packname}
%doc %{rlibdir}/%{packname}/html
%{rlibdir}/%{packname}/Meta
%{rlibdir}/%{packname}/help
%{rlibdir}/%{packname}/data
%{rlibdir}/%{packname}/DESCRIPTION
%license %{rlibdir}/%{packname}/LICENSE
%{rlibdir}/%{packname}/NAMESPACE
%doc %{rlibdir}/%{packname}/NEWS.md
%{rlibdir}/%{packname}/R
%{rlibdir}/%{packname}/INDEX