%global __brp_check_rpaths %{nil}
%global packname  OTE
%global packver   1.0.1
%global rlibdir   /usr/local/lib/R/library

Name:             R-CRAN-%{packname}
Version:          1.0.1
Release:          3%{?dist}%{?buildtag}
Summary:          Optimal Trees Ensembles for Regression, Classification and ClassMembership Probability Estimation

License:          GPL (>= 3)
URL:              https://cran.r-project.org/package=%{packname}
Source0:          %{url}&version=%{packver}#/%{packname}_%{packver}.tar.gz


BuildRequires:    R-devel
Requires:         R-core
BuildArch:        noarch
BuildRequires:    R-CRAN-randomForest 
BuildRequires:    R-stats 
Requires:         R-CRAN-randomForest 
Requires:         R-stats 

%description
Functions for creating ensembles of optimal trees for regression,
classification (Khan, Z., Gul, A., Perperoglou, A., Miftahuddin, M.,
Mahmoud, O., Adler, W., & Lausen, B. (2019). (2019)
<doi:10.1007/s11634-019-00364-9>) and class membership probability
estimation (Khan, Z, Gul, A, Mahmoud, O, Miftahuddin, M, Perperoglou, A,
Adler, W & Lausen, B (2016) <doi:10.1007/978-3-319-25226-1_34>) are given.
A few trees are selected from an initial set of trees grown by random
forest for the ensemble on the basis of their individual and collective
performance. Three different methods of tree selection for the case of
classification are given. The prediction functions return estimates of the
test responses and their class membership probabilities. Unexplained
variations, error rates, confusion matrix, Brier scores, etc. are also
returned for the test data.

%prep
%setup -q -c -n %{packname}

find -type f -executable -exec grep -Iq . {} \; -exec sed -i -e '$a\' {} \;

%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

%files
%{rlibdir}/%{packname}