%global __brp_check_rpaths %{nil}
%global __requires_exclude ^libmpi
%global packname  psrwe
%global packver   3.2-2
%global rlibdir   /usr/local/lib/R/library

Name:             R-CRAN-%{packname}
Version:          3.2.2
Release:          1%{?dist}%{?buildtag}
Summary:          PS-Integrated Methods for Incorporating Real-World Evidence in Clinical Studies

License:          GPL (>= 3)
URL:              https://cran.r-project.org/package=%{packname}
Source0:          %{url}&version=%{packver}#/%{packname}_%{packver}.tar.gz


BuildRequires:    R-devel >= 4.0
Requires:         R-core >= 4.0
BuildRequires:    R-CRAN-RcppParallel >= 5.0.2
BuildRequires:    R-CRAN-randomForest >= 4.6.14
BuildRequires:    R-CRAN-ggplot2 >= 3.3.2
BuildRequires:    R-parallel >= 3.2
BuildRequires:    R-CRAN-rstan >= 2.26.0
BuildRequires:    R-CRAN-StanHeaders >= 2.26.0
BuildRequires:    R-CRAN-rstantools >= 2.1.1
BuildRequires:    R-CRAN-BH >= 1.72.0.3
BuildRequires:    R-CRAN-Rcpp >= 1.0.5
BuildRequires:    R-CRAN-cowplot >= 1.0.0
BuildRequires:    R-CRAN-dplyr >= 0.8.5
BuildRequires:    R-CRAN-RcppEigen >= 0.3.3.7.0
BuildRequires:    R-methods 
BuildRequires:    R-CRAN-survival 
BuildRequires:    R-CRAN-rstantools
Requires:         R-CRAN-randomForest >= 4.6.14
Requires:         R-CRAN-ggplot2 >= 3.3.2
Requires:         R-parallel >= 3.2
Requires:         R-CRAN-rstan >= 2.26.0
Requires:         R-CRAN-rstantools >= 2.1.1
Requires:         R-CRAN-Rcpp >= 1.0.5
Requires:         R-CRAN-cowplot >= 1.0.0
Requires:         R-CRAN-dplyr >= 0.8.5
Requires:         R-methods 
Requires:         R-CRAN-survival 
Requires:         R-CRAN-rstantools

%description
High-quality real-world data can be transformed into scientific real-world
evidence for regulatory and healthcare decision-making using proven
analytical methods and techniques. For example, propensity score (PS)
methodology can be applied to select a subset of real-world data
containing patients that are similar to those in the current clinical
study in terms of baseline covariates, and to stratify the selected
patients together with those in the current study into more homogeneous
strata. Then, statistical methods such as the power prior approach or
composite likelihood approach can be applied in each stratum to draw
inference for the parameters of interest. This package provides functions
that implement the PS-integrated real-world evidence analysis methods such
as Wang et al. (2019) <doi:10.1080/10543406.2019.1657133>, Wang et al.
(2020) <doi:10.1080/10543406.2019.1684309>, and Chen et al. (2020)
<doi:10.1080/10543406.2020.1730877>.

%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}