%global __brp_check_rpaths %{nil}
%global __requires_exclude ^libmpi
%global packname  wrMisc
%global packver   1.15.0.3
%global rlibdir   /usr/local/lib/R/library

Name:             R-CRAN-%{packname}
Version:          1.15.0.3
Release:          1%{?dist}%{?buildtag}
Summary:          Analyze Experimental High-Throughput (Omics) Data

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


BuildRequires:    R-devel >= 3.1.0
Requires:         R-core >= 3.1.0
BuildArch:        noarch
BuildRequires:    R-grDevices 
BuildRequires:    R-graphics 
BuildRequires:    R-CRAN-MASS 
BuildRequires:    R-stats 
BuildRequires:    R-utils 
Requires:         R-grDevices 
Requires:         R-graphics 
Requires:         R-CRAN-MASS 
Requires:         R-stats 
Requires:         R-utils 

%description
The efficient treatment and convenient analysis of experimental
high-throughput (omics) data gets facilitated through this collection of
diverse functions. Several functions address advanced object-conversions,
like manipulating lists of lists or lists of arrays, reorganizing lists to
arrays or into separate vectors, merging of multiple entries, etc. Another
set of functions provides speed-optimized calculation of standard
deviation (sd), coefficient of variance (CV) or standard error of the mean
(SEM) for data in matrixes or means per line with respect to additional
grouping (eg n groups of replicates). A group of functions facilitate
dealing with non-redundant information, by indexing unique, adding
counters to redundant or eliminating lines with respect redundancy in a
given reference-column, etc. Help is provided to identify very closely
matching numeric values to generate (partial) distance matrixes for very
big data in a memory efficient manner or to reduce the complexity of large
data-sets by combining very close values. Other functions help aligning a
matrix or data.frame to a reference using partial matching or to mine an
experimental setup to extract patterns of replicate samples. Many times
large experimental datasets need some additional filtering, adequate
functions are provided. Convenient data normalization is supported in
various different modes, parameter estimation via permutations or
boot-strap as well as flexible testing of multiple pair-wise combinations
using the framework of 'limma' is provided, too. Batch reading (or
writing) of sets of files and combining data to arrays is supported, too.

%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}