%global __brp_check_rpaths %{nil}
%global packname  SCOUTer
%global packver   1.0.0
%global rlibdir   /usr/local/lib/R/library

Name:             R-CRAN-%{packname}
Version:          1.0.0
Release:          2%{?dist}%{?buildtag}
Summary:          Simulate Controlled Outliers

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


BuildRequires:    R-devel >= 3.5.0
Requires:         R-core >= 3.5.0
BuildArch:        noarch
BuildRequires:    R-CRAN-ggplot2 
BuildRequires:    R-CRAN-ggpubr 
BuildRequires:    R-stats 
Requires:         R-CRAN-ggplot2 
Requires:         R-CRAN-ggpubr 
Requires:         R-stats 

%description
Using principal component analysis as a base model, 'SCOUTer' offers a new
approach to simulate outliers in a simple and precise way. The user can
generate new observations defining them by a pair of well-known
statistics: the Squared Prediction Error (SPE) and the Hotelling's T^2
(T^2) statistics. Just by introducing the target values of the SPE and
T^2, 'SCOUTer' returns a new set of observations with the desired target
properties. Authors: Alba González, Abel Folch-Fortuny, Francisco Arteaga
and Alberto Ferrer (2020).

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

find -type f -executable -exec grep -Iq . {} \; -exec sed -i -e '$a\' {} \;
[ -d %{packname}/src ] && find %{packname}/src -type f -exec \
  sed -i 's@/usr/bin/strip@/usr/bin/true@g' {} \; || true

%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
find %{buildroot}%{rlibdir} -type f -exec sed -i "s@%{buildroot}@@g" {} \;

%files
%{rlibdir}/%{packname}