%global __brp_check_rpaths %{nil} %global packname forecastHybrid %global packver 5.0.19 %global rlibdir /usr/local/lib/R/library Name: R-CRAN-%{packname} Version: 5.0.19 Release: 1%{?dist}%{?buildtag} Summary: Convenient Functions for Ensemble Time Series Forecasts License: GPL-3 URL: https://cran.r-project.org/package=%{packname} Source0: %{url}&version=%{packver}#/%{packname}_%{packver}.tar.gz BuildRequires: R-devel >= 3.1.1 Requires: R-core >= 3.1.1 BuildArch: noarch BuildRequires: R-CRAN-forecast >= 8.12 BuildRequires: R-CRAN-ggplot2 >= 2.2.0 BuildRequires: R-CRAN-zoo >= 1.7 BuildRequires: R-CRAN-foreach >= 1.4.3 BuildRequires: R-CRAN-doParallel >= 1.0.10 BuildRequires: R-CRAN-purrr >= 0.2.5 BuildRequires: R-CRAN-thief Requires: R-CRAN-forecast >= 8.12 Requires: R-CRAN-ggplot2 >= 2.2.0 Requires: R-CRAN-zoo >= 1.7 Requires: R-CRAN-foreach >= 1.4.3 Requires: R-CRAN-doParallel >= 1.0.10 Requires: R-CRAN-purrr >= 0.2.5 Requires: R-CRAN-thief %description Convenient functions for ensemble forecasts in R combining approaches from the 'forecast' package. Forecasts generated from auto.arima(), ets(), thetaf(), nnetar(), stlm(), tbats(), and snaive() can be combined with equal weights, weights based on in-sample errors (introduced by Bates & Granger (1969) ), or cross-validated weights. Cross validation for time series data with user-supplied models and forecasting functions is also supported to evaluate model accuracy. %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}