%global __brp_check_rpaths %{nil} %global __requires_exclude ^libmpi %global packname CohortMethod %global packver 6.0.3 %global rlibdir /usr/local/lib/R/library Name: R-CRAN-%{packname} Version: 6.0.3 Release: 1%{?dist}%{?buildtag} Summary: Comparative Cohort Method with Large Scale Propensity and Outcome Models License: Apache License 2.0 URL: https://cran.r-project.org/package=%{packname} Source0: %{url}&version=%{packver}#/%{packname}_%{packver}.tar.gz BuildRequires: R-devel >= 4.1.0 Requires: R-core >= 4.1.0 BuildRequires: R-CRAN-DatabaseConnector >= 6.0.0 BuildRequires: R-CRAN-Cyclops >= 3.6.0 BuildRequires: R-CRAN-ParallelLogger >= 3.4.2 BuildRequires: R-CRAN-FeatureExtraction >= 3.0.0 BuildRequires: R-CRAN-SqlRender >= 1.18.0 BuildRequires: R-CRAN-Andromeda >= 0.6.3 BuildRequires: R-CRAN-Rcpp >= 0.11.2 BuildRequires: R-methods BuildRequires: R-utils BuildRequires: R-CRAN-ggplot2 BuildRequires: R-CRAN-gridExtra BuildRequires: R-grid BuildRequires: R-CRAN-readr BuildRequires: R-CRAN-plyr BuildRequires: R-CRAN-dplyr BuildRequires: R-CRAN-rlang BuildRequires: R-CRAN-survival BuildRequires: R-CRAN-checkmate BuildRequires: R-CRAN-EmpiricalCalibration BuildRequires: R-CRAN-jsonlite BuildRequires: R-CRAN-R6 BuildRequires: R-CRAN-digest Requires: R-CRAN-DatabaseConnector >= 6.0.0 Requires: R-CRAN-Cyclops >= 3.6.0 Requires: R-CRAN-ParallelLogger >= 3.4.2 Requires: R-CRAN-FeatureExtraction >= 3.0.0 Requires: R-CRAN-SqlRender >= 1.18.0 Requires: R-CRAN-Andromeda >= 0.6.3 Requires: R-CRAN-Rcpp >= 0.11.2 Requires: R-methods Requires: R-utils Requires: R-CRAN-ggplot2 Requires: R-CRAN-gridExtra Requires: R-grid Requires: R-CRAN-readr Requires: R-CRAN-plyr Requires: R-CRAN-dplyr Requires: R-CRAN-rlang Requires: R-CRAN-survival Requires: R-CRAN-checkmate Requires: R-CRAN-EmpiricalCalibration Requires: R-CRAN-jsonlite Requires: R-CRAN-R6 Requires: R-CRAN-digest %description Functions for performing comparative cohort studies in an observational database in the Observational Medical Outcomes Partnership (OMOP) Common Data Model. Can extract all necessary data from a database. This implements large-scale propensity scores (LSPS) as described in Tian et al. (2018) , using a large set of covariates, including for example all drugs, diagnoses, procedures, as well as age, comorbidity indexes, etc. Large scale regularized regression is used to fit the propensity and outcome models as described in Suchard et al. (2013) . Functions are included for trimming, stratifying, (variable and fixed ratio) matching and weighting by propensity scores, as well as diagnostic functions, such as propensity score distribution plots and plots showing covariate balance before and after matching and/or trimming. Supported outcome models are (conditional) logistic regression, (conditional) Poisson regression, and (stratified) Cox regression. Also included are Kaplan-Meier plots that can adjust for the stratification or matching. %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}