%global __brp_check_rpaths %{nil} %global __requires_exclude ^libmpi %global packname WaveletKNN %global packver 0.1.0 %global rlibdir /usr/local/lib/R/library Name: R-CRAN-%{packname} Version: 0.1.0 Release: 1%{?dist}%{?buildtag} Summary: Wavelet Based K-Nearest Neighbor Model 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-caret BuildRequires: R-CRAN-dplyr BuildRequires: R-CRAN-caretForecast BuildRequires: R-CRAN-Metrics BuildRequires: R-CRAN-tseries BuildRequires: R-stats BuildRequires: R-CRAN-wavelets Requires: R-CRAN-caret Requires: R-CRAN-dplyr Requires: R-CRAN-caretForecast Requires: R-CRAN-Metrics Requires: R-CRAN-tseries Requires: R-stats Requires: R-CRAN-wavelets %description The employment of the Wavelet decomposition technique proves to be highly advantageous in the modelling of noisy time series data. Wavelet decomposition technique using the "haar" algorithm has been incorporated to formulate a hybrid Wavelet KNN (K-Nearest Neighbour) model for time series forecasting, as proposed by Anjoy and Paul (2017) . %prep %setup -q -c -n %{packname} # fix end of executable files find -type f -executable -exec grep -Iq . {} \; -exec sed -i -e '$a\' {} \; # prevent binary stripping [ -d %{packname}/src ] && find %{packname}/src -type f -exec \ sed -i 's@/usr/bin/strip@/usr/bin/true@g' {} \; || true [ -d %{packname}/src ] && find %{packname}/src/Make* -type f -exec \ sed -i 's@-g0@@g' {} \; || true # don't allow local prefix in executable scripts find -type f -executable -exec sed -Ei 's@#!( )*/usr/local/bin@#!/usr/bin@g' {} \; %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 # remove buildroot from installed files find %{buildroot}%{rlibdir} -type f -exec sed -i "s@%{buildroot}@@g" {} \; %files %{rlibdir}/%{packname}