%global __brp_check_rpaths %{nil}
%global packname  tea
%global packver   1.1
%global rlibdir   /usr/local/lib/R/library

Name:             R-CRAN-%{packname}
Version:          1.1
Release:          3%{?dist}%{?buildtag}
Summary:          Threshold Estimation Approaches

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-Matrix 
BuildRequires:    R-stats 
BuildRequires:    R-graphics 
Requires:         R-Matrix 
Requires:         R-stats 
Requires:         R-graphics 

%description
Different approaches for selecting the threshold in generalized Pareto
distributions. Most of them are based on minimizing the AMSE-criterion or
at least by reducing the bias of the assumed GPD-model. Others are
heuristically motivated by searching for stable sample paths, i.e. a
nearly constant region of the tail index estimator with respect to k,
which is the number of data in the tail. The third class is motivated by
graphical inspection. In addition, a sequential testing procedure for
GPD-GoF-tests is also implemented here.

%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}