File R-rema.spec of Package R-rema

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# Spec file for package rema 
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%global packname  rema 
%global rlibdir   %{_libdir}/R/library 
 
Name:           R-%{packname} 
Version:        0.0.1 
Release:        0 
Summary:        Rare Event Meta Analysis 
Group:          Development/Libraries/Other 
License:        GPL (>= 3) | file LICENSE 
URL:            http://cran.r-project.org/web/packages/%{packname} 
Source:         rema_0.0.1.tar.gz 
Requires:       R-base 
Requires:	R-Rcpp
Requires:	R-Rdpack
Requires:	R-progress
Requires:	R-crayon
Requires:	R-hms
Requires:	R-prettyunits
Requires:	R-R6
Requires:	R-rbibutils
Requires:	R-cli
Requires:	R-lifecycle
Requires:	R-pkgconfig
Requires:	R-rlang
Requires:	R-vctrs
Requires:	R-glue
 
# %%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 
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# BuildRequires:  texinfo 
BuildRequires:  fdupes 
BuildRequires:  R-base 
BuildRequires: 	R-Rcpp-devel
BuildRequires: 	R-Rdpack
BuildRequires: 	R-progress
BuildRequires: 	R-crayon
BuildRequires: 	R-hms
BuildRequires: 	R-prettyunits
BuildRequires: 	R-R6
BuildRequires: 	R-rbibutils
BuildRequires: 	R-cli
BuildRequires: 	R-lifecycle
BuildRequires: 	R-pkgconfig
BuildRequires: 	R-rlang
BuildRequires: 	R-vctrs
BuildRequires: 	R-glue
BuildRequires:  gcc gcc-c++ gcc-fortran
 
Suggests:	R-testthat
Suggests:	R-knitr
Suggests:	R-rmarkdown
%description 
The rema package implements a permutation-based approach for binary 
meta-analyses of 2x2 tables, founded on conditional logistic 
regression, that provides more reliable statistical tests when 
heterogeneity is observed in rare event data (Zabriskie et al. 2021 
<doi:10.1002/sim.9142>). To adjust for the effect of heterogeneity, 
this method conditions on the sufficient statistic of a proxy for the 
heterogeneity effect as opposed to estimating the heterogeneity 
variance. While this results in the model not strictly falling under 
the random-effects framework, it is akin to a random-effects approach 
in that it assumes differences in variability due to treatment. 
Further, this method does not rely on large-sample approximations or 
continuity corrections for rare event data. This method uses the 
permutational distribution of the test statistic instead of asymptotic 
approximations for inference. The number of observed events drives the 
computation complexity for creating this permutational distribution. 
Accordingly, for this method to be computationally feasible, it should 
only be applied to meta-analyses with a relatively low number of 
observed events. To create this permutational distribution, a network 
algorithm, based on the work of Mehta et al. (1992) 
<doi:10.2307/1390598> and Corcoran et al. (2001) 
<doi:10.1111/j.0006-341x.2001.00941.x>, is employed using C++ and 
integrated into the package. 
 
%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
%license %{rlibdir}/%{packname}/LICENSE
%{rlibdir}/%{packname}/Meta
%{rlibdir}/%{packname}/NAMESPACE
%doc %{rlibdir}/%{packname}/NEWS.md
%{rlibdir}/%{packname}/R
%{rlibdir}/%{packname}/REFERENCES.bib
%doc %{rlibdir}/%{packname}/doc
%doc %{rlibdir}/%{packname}/help
%doc %{rlibdir}/%{packname}/html
%{rlibdir}/%{packname}/libs
 
%changelog 
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