%global __brp_check_rpaths %{nil} %global __requires_exclude ^libmpi %global packname surveil %global packver 0.3.0 %global rlibdir /usr/local/lib/R/library Name: R-CRAN-%{packname} Version: 0.3.0 Release: 1%{?dist}%{?buildtag} Summary: Time Series Models for Disease Surveillance License: GPL (>= 3) URL: https://cran.r-project.org/package=%{packname} Source0: %{url}&version=%{packver}#/%{packname}_%{packver}.tar.gz BuildRequires: R-devel >= 3.5.0 Requires: R-core >= 3.5.0 BuildRequires: R-CRAN-RcppParallel >= 5.0.1 BuildRequires: R-CRAN-tidybayes >= 3.0.0 BuildRequires: R-CRAN-ggplot2 >= 3.0.0 BuildRequires: R-CRAN-ggdist >= 3.0.0 BuildRequires: R-CRAN-rstan >= 2.26.0 BuildRequires: R-CRAN-StanHeaders >= 2.26.0 BuildRequires: R-CRAN-rstantools >= 2.1.1 BuildRequires: R-CRAN-gridExtra >= 2.0 BuildRequires: R-CRAN-BH >= 1.66.0 BuildRequires: R-CRAN-tidyr >= 1.1.0 BuildRequires: R-CRAN-dplyr >= 1.0.7 BuildRequires: R-CRAN-rlang >= 0.4.0 BuildRequires: R-CRAN-scales >= 0.4.0 BuildRequires: R-CRAN-RcppEigen >= 0.3.3.3.0 BuildRequires: R-CRAN-Rcpp >= 0.12.0 BuildRequires: R-methods BuildRequires: R-CRAN-rstantools Requires: R-CRAN-RcppParallel >= 5.0.1 Requires: R-CRAN-tidybayes >= 3.0.0 Requires: R-CRAN-ggplot2 >= 3.0.0 Requires: R-CRAN-ggdist >= 3.0.0 Requires: R-CRAN-rstan >= 2.26.0 Requires: R-CRAN-rstantools >= 2.1.1 Requires: R-CRAN-gridExtra >= 2.0 Requires: R-CRAN-tidyr >= 1.1.0 Requires: R-CRAN-dplyr >= 1.0.7 Requires: R-CRAN-rlang >= 0.4.0 Requires: R-CRAN-scales >= 0.4.0 Requires: R-CRAN-Rcpp >= 0.12.0 Requires: R-methods Requires: R-CRAN-rstantools %description Fits time trend models for routine disease surveillance tasks and returns probability distributions for a variety of quantities of interest, including age-standardized rates, period and cumulative percent change, and measures of health inequality. The models are appropriate for count data such as disease incidence and mortality data, employing a Poisson or binomial likelihood and the first-difference (random-walk) prior for unknown risk. Optionally add a covariance matrix for multiple, correlated time series models. Inference is completed using Markov chain Monte Carlo via the Stan modeling language. References: Donegan, Hughes, and Lee (2022) ; Stan Development Team (2021) ; Theil (1972, ISBN:0-444-10378-3). %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}