%global __brp_check_rpaths %{nil}
%global __requires_exclude ^libmpi
%global packname  beastt
%global packver   0.0.3
%global rlibdir   /usr/local/lib/R/library

Name:             R-CRAN-%{packname}
Version:          0.0.3
Release:          1%{?dist}%{?buildtag}
Summary:          Bayesian Evaluation, Analysis, and Simulation Software Tools for Trials

License:          GPL (>= 3)
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-RcppParallel >= 5.0.1
BuildRequires:    R-CRAN-rstantools >= 2.4.0
BuildRequires:    R-CRAN-rstan >= 2.18.1
BuildRequires:    R-CRAN-StanHeaders >= 2.18.0
BuildRequires:    R-CRAN-BH >= 1.66.0
BuildRequires:    R-CRAN-RcppEigen >= 0.3.3.3.0
BuildRequires:    R-CRAN-Rcpp >= 0.12.0
BuildRequires:    R-CRAN-cli 
BuildRequires:    R-CRAN-cobalt 
BuildRequires:    R-CRAN-distributional 
BuildRequires:    R-CRAN-dplyr 
BuildRequires:    R-CRAN-generics 
BuildRequires:    R-CRAN-ggdist 
BuildRequires:    R-CRAN-ggplot2 
BuildRequires:    R-methods 
BuildRequires:    R-CRAN-mixtools 
BuildRequires:    R-CRAN-purrr 
BuildRequires:    R-CRAN-rlang 
BuildRequires:    R-CRAN-stringr 
BuildRequires:    R-CRAN-tidyr 
BuildRequires:    R-CRAN-rstantools
Requires:         R-CRAN-RcppParallel >= 5.0.1
Requires:         R-CRAN-rstantools >= 2.4.0
Requires:         R-CRAN-rstan >= 2.18.1
Requires:         R-CRAN-Rcpp >= 0.12.0
Requires:         R-CRAN-cli 
Requires:         R-CRAN-cobalt 
Requires:         R-CRAN-distributional 
Requires:         R-CRAN-dplyr 
Requires:         R-CRAN-generics 
Requires:         R-CRAN-ggdist 
Requires:         R-CRAN-ggplot2 
Requires:         R-methods 
Requires:         R-CRAN-mixtools 
Requires:         R-CRAN-purrr 
Requires:         R-CRAN-rlang 
Requires:         R-CRAN-stringr 
Requires:         R-CRAN-tidyr 
Requires:         R-CRAN-rstantools

%description
Bayesian dynamic borrowing with covariate adjustment via inverse
probability weighting for simulations and data analyses in clinical
trials. This makes it easy to use propensity score methods to balance
covariate distributions between external and internal data. This
methodology based on Psioda et al (2025)
<doi:10.1080/10543406.2025.2489285>.

%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}