%global __brp_check_rpaths %{nil}
%global __requires_exclude ^libmpi
%global packname  ODRF
%global packver   0.0.5
%global rlibdir   /usr/local/lib/R/library

Name:             R-CRAN-%{packname}
Version:          0.0.5
Release:          1%{?dist}%{?buildtag}
Summary:          Oblique Decision Random Forest for Classification and Regression

License:          GPL (>= 3)
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-rlang >= 0.4.11
BuildRequires:    R-CRAN-partykit 
BuildRequires:    R-CRAN-doParallel 
BuildRequires:    R-CRAN-foreach 
BuildRequires:    R-CRAN-glue 
BuildRequires:    R-graphics 
BuildRequires:    R-grid 
BuildRequires:    R-CRAN-lifecycle 
BuildRequires:    R-CRAN-magrittr 
BuildRequires:    R-CRAN-nnet 
BuildRequires:    R-parallel 
BuildRequires:    R-CRAN-Pursuit 
BuildRequires:    R-CRAN-Rcpp 
BuildRequires:    R-stats 
BuildRequires:    R-CRAN-rpart 
BuildRequires:    R-methods 
BuildRequires:    R-CRAN-glmnet 
BuildRequires:    R-CRAN-RcppArmadillo 
BuildRequires:    R-CRAN-RcppEigen 
Requires:         R-CRAN-rlang >= 0.4.11
Requires:         R-CRAN-partykit 
Requires:         R-CRAN-doParallel 
Requires:         R-CRAN-foreach 
Requires:         R-CRAN-glue 
Requires:         R-graphics 
Requires:         R-grid 
Requires:         R-CRAN-lifecycle 
Requires:         R-CRAN-magrittr 
Requires:         R-CRAN-nnet 
Requires:         R-parallel 
Requires:         R-CRAN-Pursuit 
Requires:         R-CRAN-Rcpp 
Requires:         R-stats 
Requires:         R-CRAN-rpart 
Requires:         R-methods 
Requires:         R-CRAN-glmnet 

%description
The oblique decision tree (ODT) uses linear combinations of predictors as
partitioning variables in a decision tree. Oblique Decision Random Forest
(ODRF) is an ensemble of multiple ODTs generated by feature bagging.
Oblique Decision Boosting Tree (ODBT) applies feature bagging during the
training process of ODT-based boosting trees to ensemble multiple boosting
trees. All three methods can be used for classification and regression,
and ODT and ODRF serve as supplements to the classical CART of Breiman
(1984) <DOI:10.1201/9781315139470> and Random Forest of Breiman (2001)
<DOI:10.1023/A:1010933404324> respectively.

%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}