File R-flexclust.spec of Package R-flexclust

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File R-flexclust.spec of Package R-flexclust

# Automatically generated by CRAN2OBS
# 
# Spec file for package flexclust 
# This file is auto-generated using information in the package source, 
# esp. Description and Summary. Improvements in that area should be 
# discussed with upstream. 
# 
# Copyright (c) 2024 SUSE LINUX GmbH, Nuernberg, Germany. 
# 
# All modifications and additions to the file contributed by third parties 
# remain the property of their copyright owners, unless otherwise agreed 
# upon. The license for this file, and modifications and additions to the 
# file, is the same license as for the pristine package itself (unless the 
# license for the pristine package is not an Open Source License, in which 
# case the license is the MIT License). An "Open Source License" is a 
# license that conforms to the Open Source Definition (Version 1.9) 
# published by the Open Source Initiative. 
#  
# Please submit bugfixes or comments via http://bugs.opensuse.org/ 
# 
 
%global packname  flexclust 
%global rlibdir   %{_libdir}/R/library 
 
Name:           R-%{packname} 
Version:        1.4.2 
Release:        0 
Summary:        Flexible Cluster Algorithms 
Group:          Development/Libraries/Other 
License:        GPL-2 
URL:            http://cran.r-project.org/web/packages/%{packname} 
Source:         flexclust_1.4-2.tar.gz 
Requires:       R-base 
Requires:	R-modeltools
 
# %%if 0%%{?sle_version} > 120400 || 0%%{?is_opensuse} 
# # Three others commonly needed 
# BuildRequires:  tex(ae.sty) 
# BuildRequires:  tex(fancyvrb.sty) 
# BuildRequires:  tex(inconsolata.sty) 
# BuildRequires:  tex(natbib.sty) 
# %else 
# BuildRequires:  texlive 
# %endif 
# BuildRequires:  texinfo 
BuildRequires:  fdupes 
BuildRequires:  R-base 
BuildRequires: 	R-modeltools
BuildRequires:  gcc gcc-c++ gcc-fortran
 
Suggests:	R-ellipse
Suggests:	R-clue
Suggests:	R-seriation
Suggests:	R-skmeans
%description 
The main function kcca implements a general framework for k-centroids 
cluster analysis supporting arbitrary distance measures and centroid 
computation. Further cluster methods include hard competitive learning, 
neural gas, and QT clustering. There are numerous visualization methods 
for cluster results (neighborhood graphs, convex cluster hulls, 
barcharts of centroids, ...), and bootstrap methods for the analysis of 
cluster stability. 
 
%prep 
%setup -q -c -n %{packname} 
# the next line is needed, because we build without --clean in between two packages 
rm -rf ~/.R  
 
 
%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 
%fdupes -s %{buildroot}%{rlibdir} 
 
#%%check 
#%%{_bindir}/R CMD check %%{packname} 
 
%files 
%dir %{rlibdir}/%{packname} 
%{rlibdir}/%{packname}/CITATION
%doc %{rlibdir}/%{packname}/DESCRIPTION
%{rlibdir}/%{packname}/INDEX
%{rlibdir}/%{packname}/Meta
%{rlibdir}/%{packname}/NAMESPACE
%doc %{rlibdir}/%{packname}/NEWS
%{rlibdir}/%{packname}/R
%{rlibdir}/%{packname}/data
%doc %{rlibdir}/%{packname}/help
%doc %{rlibdir}/%{packname}/html
%{rlibdir}/%{packname}/libs
%{rlibdir}/%{packname}/
 
%changelog

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File R-flexclust.spec of Package R-flexclust

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