%global __brp_check_rpaths %{nil}
%global __requires_exclude ^libmpi
%global packname  CGMissingDataR
%global packver   0.0.2
%global rlibdir   /usr/local/lib/R/library

Name:             R-CRAN-%{packname}
Version:          0.0.2
Release:          1%{?dist}%{?buildtag}
Summary:          Impute Missing Glucose Values in CGM Data

License:          GPL (>= 2)
URL:              https://cran.r-project.org/package=%{packname}
Source0:          %{url}&version=%{packver}#/%{packname}_%{packver}.tar.gz


BuildRequires:    R-devel >= 4.3
Requires:         R-core >= 4.3
BuildArch:        noarch
BuildRequires:    R-CRAN-mice 
BuildRequires:    R-CRAN-FNN 
BuildRequires:    R-CRAN-ranger 
BuildRequires:    R-CRAN-data.table 
BuildRequires:    R-CRAN-xgboost 
BuildRequires:    R-CRAN-lightgbm 
BuildRequires:    R-CRAN-forecast 
BuildRequires:    R-CRAN-CGManalyzer 
BuildRequires:    R-CRAN-lifecycle 
BuildRequires:    R-CRAN-reticulate 
BuildRequires:    R-CRAN-shiny 
Requires:         R-CRAN-mice 
Requires:         R-CRAN-FNN 
Requires:         R-CRAN-ranger 
Requires:         R-CRAN-data.table 
Requires:         R-CRAN-xgboost 
Requires:         R-CRAN-lightgbm 
Requires:         R-CRAN-forecast 
Requires:         R-CRAN-CGManalyzer 
Requires:         R-CRAN-lifecycle 
Requires:         R-CRAN-reticulate 
Requires:         R-CRAN-shiny 

%description
Imputes missing glucose values in repeated-measures continuous glucose
monitoring (CGM) data. Workflows create time-series features from raw
timestamps, support model selection, and return the user's original
columns plus an imputed glucose column. Methods include multiple
imputation by chained equations (MICE; Azur et al. (2011)
<doi:10.1002/mpr.329>), Random Forest regression (Breiman (2001)
<doi:10.1023/A:1010933404324>), k-nearest-neighbor regression (Zhang
(2016) <doi:10.21037/atm.2016.03.37>), XGBoost (Chen and Guestrin (2016)
<doi:10.1145/2939672.2939785>), LightGBM (Ke et al. (2017)
<https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision>),
and ARIMA forecasting with the forecast framework (Hyndman and Khandakar
(2008) <doi:10.18637/jss.v027.i03>). A Python-compatible backend uses
'reticulate' to call 'pandas', 'scikit-learn', 'statsmodels', Python
'xgboost', and optional Python 'lightgbm'.

%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}