%global __brp_check_rpaths %{nil}
%global __requires_exclude ^libmpi
%global packname  sts
%global packver   1.6
%global rlibdir   /usr/local/lib/R/library

Name:             R-CRAN-%{packname}
Version:          1.6
Release:          1%{?dist}%{?buildtag}
Summary:          Estimation of the Structural Topic and Sentiment-Discourse Model for Text Analysis

License:          MIT + file LICENSE
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-Rcpp 
BuildRequires:    R-CRAN-glmnet 
BuildRequires:    R-CRAN-matrixStats 
BuildRequires:    R-CRAN-slam 
BuildRequires:    R-CRAN-foreach 
BuildRequires:    R-CRAN-doParallel 
BuildRequires:    R-parallel 
BuildRequires:    R-CRAN-Matrix 
BuildRequires:    R-CRAN-mvtnorm 
BuildRequires:    R-CRAN-ggplot2 
BuildRequires:    R-CRAN-tm 
BuildRequires:    R-CRAN-SnowballC 
BuildRequires:    R-graphics 
BuildRequires:    R-stats 
BuildRequires:    R-CRAN-RcppArmadillo 
Requires:         R-CRAN-Rcpp 
Requires:         R-CRAN-glmnet 
Requires:         R-CRAN-matrixStats 
Requires:         R-CRAN-slam 
Requires:         R-CRAN-foreach 
Requires:         R-CRAN-doParallel 
Requires:         R-parallel 
Requires:         R-CRAN-Matrix 
Requires:         R-CRAN-mvtnorm 
Requires:         R-CRAN-ggplot2 
Requires:         R-CRAN-tm 
Requires:         R-CRAN-SnowballC 
Requires:         R-graphics 
Requires:         R-stats 

%description
The Structural Topic and Sentiment-Discourse (STS) model allows
researchers to estimate topic models with document-level metadata that
determines both topic prevalence and sentiment-discourse. The
sentiment-discourse is modeled as a document-level latent variable for
each topic that modulates the word frequency within a topic. These latent
topic sentiment-discourse variables are controlled by the document-level
metadata. The STS model can be useful for regression analysis with text
data in addition to topic modeling’s traditional use of descriptive
analysis. The method was developed in Chen and Mankad (2025)
<doi:10.1287/mnsc.2022.00261>.

%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}