File R-metafor.spec of Package R-metafor

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File R-metafor.spec of Package R-metafor

%global packname  metafor
%global rlibdir   %{_libdir}/R/library

Name:           R-%{packname}
Version:        1.9_4
Release:        1
Summary:        Meta-Analysis Package for R

Group:          Development/Libraries/Other
License:        GPL-2.0+
URL:            http://cran.r-project.org/web/packages/%{packname}/index.html
Source0:        metafor_1.9-4.tar.gz
BuildRoot:      %{_tmppath}/%{name}-%{version}-build
Requires:       R-base
Requires:         R-stats R-Formula 
# Package suggestions
#Requires:         R-Matrix R-lme4 R-numDeriv R-polycor R-BiasedUrn R-survival R-Epi R-minqa 
BuildRequires:  texlive
BuildRequires:  texinfo
BuildRequires:  fdupes
#
%if 0%{?suse_version} <= 1220 && 0%{?suse_version} != 1110
BuildRequires:  texlive-fonts-extra
%endif

BuildRequires:    R-base-devel R-stats R-Formula
# Package suggestions, not required to build
#BuildRequires: R-Matrix R-lme4 R-numDeriv R-polycor R-BiasedUrn R-survival R-Epi R-minqa

%description
The metafor package provides a comprehensive collection of functions for
conducting meta-analyses in R. The package includes functions to calculate
various effect sizes or outcome measures, fit fixed-, random-, and
mixed-effects models to such data, carry out moderator and meta-regression
analyses, and create various types of meta-analytical plots (e.g., forest,
funnel, radial, L'Abbe, Baujat plots). For meta-analyses of binomial and
person-time data, the package also provides functions that implement
specialized methods, including the Mantel-Haenszel method, Peto's method,
and a variety of suitable generalized linear (mixed-effects) models (i.e.,
mixed-effects (conditional) logistic and Poisson regression models).
Finally, the package provides functionality for fitting meta-analytic
multivariate/multilevel models that account for non-independent sampling
errors and/or true effects (e.g., due to the inclusion of multiple
treatment studies, multiple endpoints, or other forms of clustering).
Network meta-analyses and meta-analyses accounting for known correlation
structures (e.g., due to phylogenetic relatedness) can also be conducted.

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

%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

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

%changelog

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