%global __brp_check_rpaths %{nil}
%global __requires_exclude ^libmpi
%global packname  diversityForest
%global packver   0.6.0
%global rlibdir   /usr/local/lib/R/library

Name:             R-CRAN-%{packname}
Version:          0.6.0
Release:          1%{?dist}%{?buildtag}
Summary:          Innovative Complex Split Procedures in Random Forests Through Candidate Split Sampling

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


BuildRequires:    R-devel >= 3.5
Requires:         R-core >= 3.5
BuildRequires:    R-CRAN-Rcpp >= 0.11.2
BuildRequires:    R-CRAN-Matrix 
BuildRequires:    R-CRAN-ggplot2 
BuildRequires:    R-CRAN-ggpubr 
BuildRequires:    R-CRAN-scales 
BuildRequires:    R-CRAN-nnet 
BuildRequires:    R-CRAN-sgeostat 
BuildRequires:    R-CRAN-rms 
BuildRequires:    R-CRAN-MapGAM 
BuildRequires:    R-CRAN-gam 
BuildRequires:    R-CRAN-rlang 
BuildRequires:    R-grDevices 
BuildRequires:    R-CRAN-RColorBrewer 
BuildRequires:    R-CRAN-RcppEigen 
BuildRequires:    R-CRAN-survival 
BuildRequires:    R-CRAN-patchwork 
Requires:         R-CRAN-Rcpp >= 0.11.2
Requires:         R-CRAN-Matrix 
Requires:         R-CRAN-ggplot2 
Requires:         R-CRAN-ggpubr 
Requires:         R-CRAN-scales 
Requires:         R-CRAN-nnet 
Requires:         R-CRAN-sgeostat 
Requires:         R-CRAN-rms 
Requires:         R-CRAN-MapGAM 
Requires:         R-CRAN-gam 
Requires:         R-CRAN-rlang 
Requires:         R-grDevices 
Requires:         R-CRAN-RColorBrewer 
Requires:         R-CRAN-RcppEigen 
Requires:         R-CRAN-survival 
Requires:         R-CRAN-patchwork 

%description
Implementation of three methods based on the diversity forest (DF)
algorithm (Hornung, 2022, <doi:10.1007/s42979-021-00920-1>), a
split-finding approach that enables complex split procedures in random
forests. The package includes: 1. Interaction forests (IFs) (Hornung &
Boulesteix, 2022, <doi:10.1016/j.csda.2022.107460>): Model quantitative
and qualitative interaction effects using bivariable splitting. Come with
the Effect Importance Measure (EIM), which can be used to identify
variable pairs that have well-interpretable quantitative and qualitative
interaction effects with high predictive relevance. 2. Two random
forest-based variable importance measures (VIMs) for multi-class outcomes:
the class-focused VIM, which ranks covariates by their ability to
distinguish individual outcome classes from the others, and the
discriminatory VIM, which measures overall covariate influence
irrespective of class-specific relevance. 3. The basic form of diversity
forests that uses conventional univariable, binary splitting (Hornung,
2022). Except for the multi-class VIMs, all methods support categorical,
metric, and survival outcomes. The package includes visualization tools
for interpreting the identified covariate effects. Built as a fork of the
'ranger' R package (main author: Marvin N. Wright), which implements
random forests using an efficient C++ implementation.

%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}