%global __brp_check_rpaths %{nil} %global packname envoutliers %global packver 1.1.0 %global rlibdir /usr/local/lib/R/library Name: R-CRAN-%{packname} Version: 1.1.0 Release: 3%{?dist}%{?buildtag} Summary: Methods for Identification of Outliers in Environmental Data License: GPL-2 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-MASS BuildRequires: R-CRAN-car BuildRequires: R-CRAN-changepoint BuildRequires: R-CRAN-ecp BuildRequires: R-graphics BuildRequires: R-CRAN-ismev BuildRequires: R-CRAN-lokern BuildRequires: R-CRAN-robustbase BuildRequires: R-stats Requires: R-MASS Requires: R-CRAN-car Requires: R-CRAN-changepoint Requires: R-CRAN-ecp Requires: R-graphics Requires: R-CRAN-ismev Requires: R-CRAN-lokern Requires: R-CRAN-robustbase Requires: R-stats %description Three semi-parametric methods for detection of outliers in environmental data based on kernel regression and subsequent analysis of smoothing residuals. The first method (Campulova, Michalek, Mikuska and Bokal (2018) ) analyzes the residuals using changepoint analysis, the second method is based on control charts (Campulova, Veselik and Michalek (2017) ) and the third method (Holesovsky, Campulova and Michalek (2018) ) analyzes the residuals using extreme value theory (Holesovsky, Campulova and Michalek (2018) ). %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}