%global __brp_check_rpaths %{nil}
%global __requires_exclude ^libmpi
%global packname  AutoScore
%global packver   1.0.0
%global rlibdir   /usr/local/lib/R/library

Name:             R-CRAN-%{packname}
Version:          1.0.0
Release:          1%{?dist}%{?buildtag}
Summary:          An Interpretable Machine Learning-Based Automatic Clinical Score Generator

License:          GPL (>= 2)
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
BuildArch:        noarch
BuildRequires:    R-CRAN-tableone 
BuildRequires:    R-CRAN-pROC 
BuildRequires:    R-CRAN-randomForest 
BuildRequires:    R-CRAN-ggplot2 
BuildRequires:    R-CRAN-knitr 
BuildRequires:    R-CRAN-Hmisc 
BuildRequires:    R-CRAN-car 
BuildRequires:    R-CRAN-coxed 
BuildRequires:    R-CRAN-dplyr 
BuildRequires:    R-CRAN-ordinal 
BuildRequires:    R-CRAN-survival 
BuildRequires:    R-CRAN-tidyr 
BuildRequires:    R-CRAN-plotly 
BuildRequires:    R-CRAN-magrittr 
BuildRequires:    R-CRAN-randomForestSRC 
BuildRequires:    R-CRAN-rlang 
BuildRequires:    R-CRAN-survAUC 
BuildRequires:    R-CRAN-survminer 
Requires:         R-CRAN-tableone 
Requires:         R-CRAN-pROC 
Requires:         R-CRAN-randomForest 
Requires:         R-CRAN-ggplot2 
Requires:         R-CRAN-knitr 
Requires:         R-CRAN-Hmisc 
Requires:         R-CRAN-car 
Requires:         R-CRAN-coxed 
Requires:         R-CRAN-dplyr 
Requires:         R-CRAN-ordinal 
Requires:         R-CRAN-survival 
Requires:         R-CRAN-tidyr 
Requires:         R-CRAN-plotly 
Requires:         R-CRAN-magrittr 
Requires:         R-CRAN-randomForestSRC 
Requires:         R-CRAN-rlang 
Requires:         R-CRAN-survAUC 
Requires:         R-CRAN-survminer 

%description
A novel interpretable machine learning-based framework to automate the
development of a clinical scoring model for predefined outcomes. Our novel
framework consists of six modules: variable ranking with machine learning,
variable transformation, score derivation, model selection, domain
knowledge-based score fine-tuning, and performance evaluation.The The
original AutoScore structure is described in the research
paper<doi:10.2196/21798>. A full tutorial can be found
here<https://nliulab.github.io/AutoScore/>. Users or clinicians could
seamlessly generate parsimonious sparse-score risk models (i.e., risk
scores), which can be easily implemented and validated in clinical
practice. We hope to see its application in various medical case studies.

%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}