%global __brp_check_rpaths %{nil} %global packname CIMTx %global packver 1.2.0 %global rlibdir /usr/local/lib/R/library Name: R-CRAN-%{packname} Version: 1.2.0 Release: 1%{?dist}%{?buildtag} Summary: Causal Inference for Multiple Treatments with a Binary Outcome License: MIT + file LICENSE 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-nnet >= 7.3.16 BuildRequires: R-CRAN-Matching >= 4.9.11 BuildRequires: R-CRAN-ggplot2 >= 3.3.5 BuildRequires: R-CRAN-BART >= 2.9 BuildRequires: R-CRAN-twang >= 2.5 BuildRequires: R-CRAN-SuperLearner >= 2.0.28 BuildRequires: R-CRAN-magrittr >= 2.0.1 BuildRequires: R-CRAN-mgcv >= 1.8.38 BuildRequires: R-CRAN-foreach >= 1.5.1 BuildRequires: R-CRAN-tmle >= 1.5.0.2 BuildRequires: R-CRAN-stringr >= 1.4.0 BuildRequires: R-CRAN-arm >= 1.2.12 BuildRequires: R-CRAN-tidyr >= 1.1.4 BuildRequires: R-CRAN-cowplot >= 1.1.1 BuildRequires: R-CRAN-dplyr >= 1.0.7 BuildRequires: R-CRAN-doParallel >= 1.0.16 BuildRequires: R-CRAN-WeightIt >= 0.12.0 BuildRequires: R-CRAN-metR >= 0.11.0 BuildRequires: R-stats Requires: R-CRAN-nnet >= 7.3.16 Requires: R-CRAN-Matching >= 4.9.11 Requires: R-CRAN-ggplot2 >= 3.3.5 Requires: R-CRAN-BART >= 2.9 Requires: R-CRAN-twang >= 2.5 Requires: R-CRAN-SuperLearner >= 2.0.28 Requires: R-CRAN-magrittr >= 2.0.1 Requires: R-CRAN-mgcv >= 1.8.38 Requires: R-CRAN-foreach >= 1.5.1 Requires: R-CRAN-tmle >= 1.5.0.2 Requires: R-CRAN-stringr >= 1.4.0 Requires: R-CRAN-arm >= 1.2.12 Requires: R-CRAN-tidyr >= 1.1.4 Requires: R-CRAN-cowplot >= 1.1.1 Requires: R-CRAN-dplyr >= 1.0.7 Requires: R-CRAN-doParallel >= 1.0.16 Requires: R-CRAN-WeightIt >= 0.12.0 Requires: R-CRAN-metR >= 0.11.0 Requires: R-stats %description Different methods to conduct causal inference for multiple treatments with a binary outcome, including regression adjustment, vector matching, Bayesian additive regression trees, targeted maximum likelihood and inverse probability of treatment weighting using different generalized propensity score models such as multinomial logistic regression, generalized boosted models and super learner. For more details, see the paper by Hu et al. . %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}