File R-rnmamod.spec of Package R-rnmamod
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# Spec file for package rnmamod
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%global packname rnmamod
%global rlibdir %{_libdir}/R/library
Name: R-%{packname}
Version: 0.5.0
Release: 0
Summary: Bayesian Network Meta-Analysis with Missing Participants
Group: Development/Libraries/Other
License: GPL (>= 3)
URL: http://cran.r-project.org/web/packages/%{packname}
Source: rnmamod_0.5.0.tar.gz
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%description
A comprehensive suite of functions to perform and visualise pairwise
and network meta-analysis with aggregate binary or continuous missing
participant outcome data. The package covers core Bayesian one-stage
models implemented in a systematic review with multiple interventions,
including fixed-effect and random-effects network meta-analysis,
meta-regression, evaluation of the consistency assumption via the
node-splitting approach and the unrelated mean effects model (original
and revised model proposed by Spineli, (2022)
<doi:10.1177/0272989X211068005>), and sensitivity analysis (see Spineli
et al., (2021) <doi:10.1186/s12916-021-02195-y>). Missing participant
outcome data are addressed in all models of the package (see Spineli,
(2019) <doi:10.1186/s12874-019-0731-y>, Spineli et al., (2019)
<doi:10.1002/sim.8207>, Spineli, (2019)
<doi:10.1016/j.jclinepi.2018.09.002>, and Spineli et al., (2021)
<doi:10.1002/jrsm.1478>). The robustness to primary analysis results
can also be investigated using a novel intuitive index (see Spineli et
al., (2021) <doi:10.1177/0962280220983544>). Methods to evaluate the
transitivity assumption using trial dissimilarities and hierarchical
clustering are provided (see Spineli, (2024)
<doi:10.1186/s12874-024-02436-7>, and Spineli et al., (2025)
<doi:10.1002/sim.70068>). A novel index to facilitate interpretation of
local inconsistency is also available (see Spineli, (2024)
<doi:10.1186/s13643-024-02680-4>) The package also offers a rich,
user-friendly visualisation toolkit that aids in appraising and
interpreting the results thoroughly and preparing the manuscript for
journal submission. The visualisation tools comprise the network plot,
forest plots, panel of diagnostic plots, heatmaps on the extent of
missing participant outcome data in the network, league heatmaps on
estimation and prediction, rankograms, Bland-Altman plot, leverage
plot, deviance scatterplot, heatmap of robustness, barplot of
Kullback-Leibler divergence, heatmap of comparison dissimilarities and
dendrogram of comparison clustering. The package also allows the user
to export the results to an Excel file at the working directory.
%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
%{rlibdir}/%{packname}/data
%doc %{rlibdir}/%{packname}/doc
%{rlibdir}/%{packname}/extdata
%doc %{rlibdir}/%{packname}/help
%doc %{rlibdir}/%{packname}/html
%changelog
