%global __brp_check_rpaths %{nil}
%global __requires_exclude ^libmpi
%global packname  anticlust
%global packver   0.8.1
%global rlibdir   /usr/local/lib/R/library

Name:             R-CRAN-%{packname}
Version:          0.8.1
Release:          1%{?dist}%{?buildtag}
Summary:          Subset Partitioning via Anticlustering

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


BuildRequires:    glpk-devel
BuildRequires:    R-devel >= 3.6.0
Requires:         R-core >= 3.6.0
BuildRequires:    R-CRAN-RANN >= 2.6.0
BuildRequires:    R-CRAN-Matrix 
Requires:         R-CRAN-RANN >= 2.6.0
Requires:         R-CRAN-Matrix 

%description
The method of anticlustering partitions a pool of elements into groups
(i.e., anticlusters) with the goal of maximizing between-group similarity
or within-group heterogeneity.  The anticlustering approach thereby
reverses the logic of cluster analysis that strives for high within-group
homogeneity and clear separation between groups.  Computationally,
anticlustering is accomplished by maximizing instead of minimizing a
clustering objective function, such as the intra-cluster variance (used in
k-means clustering) or the sum of pairwise distances within clusters. The
main function anticlustering() gives access to exact and heuristic
anticlustering methods described in Papenberg and Klau (2021;
<doi:10.1037/met0000301>), Brusco et al. (2020; <doi:10.1111/bmsp.12186>),
and Papenberg (2023; <doi:10.1111/bmsp.12315>). The exact algorithms
require that an integer linear programming solver is installed, either the
GNU linear programming kit (<https://www.gnu.org/software/glpk/glpk.html>)
together with the interface package 'Rglpk'
(<https://cran.R-project.org/package=Rglpk>), or the SYMPHONY ILP solver
(<https://github.com/coin-or/SYMPHONY>) together with the interface
package 'Rsymphony' (<https://cran.r-project.org/package=Rsymphony>). Full
access to the bicriterion anticlustering method proposed by Brusco et al.
(2020) is given via the function bicriterion_anticlustering(), while
kplus_anticlustering() implements the full functionality of the k-plus
anticlustering approach proposed by Papenberg (2023). Some other functions
are available to solve classical clustering problems. The function
balanced_clustering() applies a cluster analysis under size constraints,
i.e., creates equal-sized clusters. The function matching() can be used
for (unrestricted, bipartite, or K-partite) matching. The function wce()
can be used optimally solve the (weighted) cluster editing problem, also
known as correlation clustering, clique partitioning problem or
transitivity clustering.

%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}