File R-rsvd.spec of Package R-rsvd

#
# spec file for package rsvd
# This file is (mostly) auto-generated using information
# in the package source, esp. Description and Summary.
# Improvements in that area should be discussed with upstream.
#
# Copyright (c) 2018 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  rsvd
%global rlibdir   %{_libdir}/R/library

Name:           R-%{packname}
Version:        1.0.2
Release:        0
Summary:        Randomized Singular Value Decomposition
License:        GPL (>= 3)
Group:		Development/Libraries/Other
URL:            http://cran.r-project.org/web/packages/%{packname}
Source:         http://cran.r-project.org/src/contrib/%{packname}_%{version}.tar.gz
BuildRoot:      %{_tmppath}/%{name}-%{version}-build
Requires:       R-base

Requires:       R-Matrix 

# Package suggestions
#Recommends:    R-ggplot2, R-testthat 
BuildRequires:  texinfo
%if 0%{?sle_version} > 120400 || 0%{?is_opensuse}
BuildRequires:  tex(inconsolata.sty)
# Three others commonly needed
BuildRequires:  tex(ae.sty)
BuildRequires:  tex(fancyvrb.sty)
BuildRequires:  tex(natbib.sty)
%else
BuildRequires:  texlive
%endif
BuildRequires:  fdupes
BuildRequires:  R-base-devel

BuildRequires:  R-Matrix-devel 


# Package suggestions, not required to build, but for packaging checks
#BuildRequires:  R-ggplot2, R-testthat 
%description
Low-rank matrix decompositions are fundamental tools and widely used for
data analysis, dimension reduction, and data compression. Classically,
highly accurate deterministic matrix algorithms are used for this task.
However, the emergence of large-scale data has severely challenged our
computational ability to analyze big data. The concept of randomness has
been demonstrated as an effective strategy to quickly produce approximate
answers to familiar problems such as the singular value decomposition
(SVD). The rsvd package provides several randomized matrix algorithms such
as the randomized singular value decomposition (rsvd), randomized
principal component analysis (rpca), randomized robust principal component
analysis (rrpca), randomized interpolative decomposition (rid), and the
randomized CUR decomposition (rcur). In addition several plot functions
are provided. The methods are discussed in detail by Erichson et al.
(2016) <arXiv:1608.02148>.

%prep
%setup -q -c -n %{packname}


%build

%install
mkdir -p %{buildroot}%{rlibdir}
%{_bindir}/R CMD INSTALL -l %{buildroot}%{rlibdir} %{packname}

rm -f %{buildroot}%{rlibdir}/R.css
%fdupes %{buildroot}%{rlibdir}/%{packname}


%check
#export LANG=en_US.UTF-8
#export _R_CHECK_FORCE_SUGGESTS_=false
#%{_bindir}/R CMD check %{packname}


%files
%defattr(-, root, root, -)
%dir %{rlibdir}/%{packname}
%doc %{rlibdir}/%{packname}/html
%doc %{rlibdir}/%{packname}/CITATION
%doc %{rlibdir}/%{packname}/DESCRIPTION
%{rlibdir}/%{packname}/INDEX
%{rlibdir}/%{packname}/NAMESPACE
%{rlibdir}/%{packname}/Meta
%{rlibdir}/%{packname}/R
%{rlibdir}/%{packname}/data
%{rlibdir}/%{packname}/help

%changelog