File R-gslnls.spec of Package R-gslnls

# Automatically generated by CRAN2OBS
# 
# Spec file for package gslnls 
# 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) 2025 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. 
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# Please submit bugfixes or comments via http://bugs.opensuse.org/ 
# 
 
%global packname  gslnls 
%global rlibdir   %{_libdir}/R/library 
 
Name:           R-%{packname} 
Version:        1.4.2 
Release:        0 
Summary:        GSL Multi-Start Nonlinear Least-Squares Fitting 
Group:          Development/Libraries/Other 
License:        LGPL-3 
URL:            http://cran.r-project.org/web/packages/%{packname} 
Source:         gslnls_1.4.2.tar.gz 
Requires:       R-base 
Requires:	gsl
 
# %%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: 	gsl-devel
BuildRequires:  gcc gcc-c++ gcc-fortran
 
%description 
An R interface to weighted nonlinear least-squares optimization with 
the GNU Scientific Library (GSL), see M. Galassi et al. (2009, 
ISBN:0954612078). The available trust region methods include the 
Levenberg-Marquardt algorithm with and without geodesic acceleration, 
the Steihaug-Toint conjugate gradient algorithm for large systems and 
several variants of Powell's dogleg algorithm. Multi-start optimization 
based on quasi-random samples is implemented using a modified version 
of the algorithm in Hickernell and Yuan (1997, OR Transactions). Robust 
nonlinear regression can be performed using various robust loss 
functions, in which case the optimization problem is solved by 
iterative reweighted least squares (IRLS). Bindings are provided to 
tune a number of parameters affecting the low-level aspects of the 
trust region algorithms. The interface mimics R's nls() function and 
returns model objects inheriting from the same class. 
 
%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} 
%doc %{rlibdir}/%{packname}/DESCRIPTION
%{rlibdir}/%{packname}/INDEX
%{rlibdir}/%{packname}/Meta
%{rlibdir}/%{packname}/NAMESPACE
%doc %{rlibdir}/%{packname}/NEWS.md
%{rlibdir}/%{packname}/R
%doc %{rlibdir}/%{packname}/help
%doc %{rlibdir}/%{packname}/html
%{rlibdir}/%{packname}/libs
%{rlibdir}/%{packname}/unit_tests
 
%changelog