%global __brp_check_rpaths %{nil}
%global __requires_exclude ^libmpi
%global packname  mice
%global packver   3.16.0
%global rlibdir   /usr/local/lib/R/library

Name:             R-CRAN-%{packname}
Version:          3.16.0
Release:          1%{?dist}%{?buildtag}
Summary:          Multivariate Imputation by Chained Equations

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


BuildRequires:    R-devel >= 2.10.0
Requires:         R-core >= 2.10.0
BuildRequires:    R-CRAN-broom 
BuildRequires:    R-CRAN-dplyr 
BuildRequires:    R-CRAN-generics 
BuildRequires:    R-CRAN-glmnet 
BuildRequires:    R-graphics 
BuildRequires:    R-grDevices 
BuildRequires:    R-CRAN-lattice 
BuildRequires:    R-methods 
BuildRequires:    R-CRAN-mitml 
BuildRequires:    R-CRAN-nnet 
BuildRequires:    R-CRAN-Rcpp 
BuildRequires:    R-CRAN-rpart 
BuildRequires:    R-CRAN-rlang 
BuildRequires:    R-stats 
BuildRequires:    R-CRAN-tidyr 
BuildRequires:    R-utils 
BuildRequires:    R-CRAN-cpp11 
Requires:         R-CRAN-broom 
Requires:         R-CRAN-dplyr 
Requires:         R-CRAN-generics 
Requires:         R-CRAN-glmnet 
Requires:         R-graphics 
Requires:         R-grDevices 
Requires:         R-CRAN-lattice 
Requires:         R-methods 
Requires:         R-CRAN-mitml 
Requires:         R-CRAN-nnet 
Requires:         R-CRAN-Rcpp 
Requires:         R-CRAN-rpart 
Requires:         R-CRAN-rlang 
Requires:         R-stats 
Requires:         R-CRAN-tidyr 
Requires:         R-utils 

%description
Multiple imputation using Fully Conditional Specification (FCS)
implemented by the MICE algorithm as described in Van Buuren and
Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>. Each variable has
its own imputation model. Built-in imputation models are provided for
continuous data (predictive mean matching, normal), binary data (logistic
regression), unordered categorical data (polytomous logistic regression)
and ordered categorical data (proportional odds). MICE can also impute
continuous two-level data (normal model, pan, second-level variables).
Passive imputation can be used to maintain consistency between variables.
Various diagnostic plots are available to inspect the quality of the
imputations.

%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}