%global __brp_check_rpaths %{nil} %global __requires_exclude ^libmpi %global packname bayesforecast %global packver 1.0.5 %global rlibdir /usr/local/lib/R/library Name: R-CRAN-%{packname} Version: 1.0.5 Release: 1%{?dist}%{?buildtag} Summary: Bayesian Time Series Modeling with Stan License: GPL-2 URL: https://cran.r-project.org/package=%{packname} Source0: %{url}&version=%{packver}#/%{packname}_%{packver}.tar.gz BuildRequires: R-devel >= 4.0.0 Requires: R-core >= 4.0.0 BuildRequires: R-CRAN-RcppParallel >= 5.0.1 BuildRequires: R-CRAN-rstantools >= 2.4.0 BuildRequires: R-CRAN-rstan >= 2.32.0 BuildRequires: R-CRAN-StanHeaders >= 2.18.0 BuildRequires: R-CRAN-loo >= 2.1.0 BuildRequires: R-CRAN-BH >= 1.66.0 BuildRequires: R-CRAN-bayesplot >= 1.5.0 BuildRequires: R-CRAN-RcppEigen >= 0.3.3.3.0 BuildRequires: R-CRAN-bridgesampling >= 0.3.0 BuildRequires: R-CRAN-Rcpp >= 0.12.0 BuildRequires: R-CRAN-forecast BuildRequires: R-CRAN-ggplot2 BuildRequires: R-CRAN-gridExtra BuildRequires: R-CRAN-lubridate BuildRequires: R-CRAN-MASS BuildRequires: R-methods BuildRequires: R-CRAN-prophet BuildRequires: R-CRAN-zoo BuildRequires: R-CRAN-rstantools Requires: R-CRAN-RcppParallel >= 5.0.1 Requires: R-CRAN-rstantools >= 2.4.0 Requires: R-CRAN-rstan >= 2.32.0 Requires: R-CRAN-loo >= 2.1.0 Requires: R-CRAN-bayesplot >= 1.5.0 Requires: R-CRAN-bridgesampling >= 0.3.0 Requires: R-CRAN-Rcpp >= 0.12.0 Requires: R-CRAN-forecast Requires: R-CRAN-ggplot2 Requires: R-CRAN-gridExtra Requires: R-CRAN-lubridate Requires: R-CRAN-MASS Requires: R-methods Requires: R-CRAN-prophet Requires: R-CRAN-zoo Requires: R-CRAN-rstantools %description Fit Bayesian time series models using 'Stan' for full Bayesian inference. A wide range of distributions and models are supported, allowing users to fit Seasonal ARIMA, ARIMAX, Dynamic Harmonic Regression, GARCH, t-student innovation GARCH models, asymmetric GARCH, Random Walks, stochastic volatility models for univariate time series. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with typical visualization methods, information criteria such as loglik, AIC, BIC WAIC, Bayes factor and leave-one-out cross-validation methods. References: Hyndman (2017) ; Carpenter et al. (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}