File R-dapper.spec of Package R-dapper

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# Spec file for package dapper 
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%global packname  dapper 
%global rlibdir   %{_libdir}/R/library 
Name:           R-%{packname} 
Version:        1.0.0 
Release:        0 
Summary:        Data Augmentation for Private Posterior Estimation 
Group:          Development/Libraries/Other 
License:        MIT + file LICENSE 
URL:  {packname} 
Source:         dapper_1.0.0.tar.gz 
Requires:       R-base 
Requires:	R-bayesplot
Requires:	R-checkmate
Requires:	R-furrr
Requires:	R-memoise
Requires:	R-posterior
Requires:	R-progressr
Requires:	R-dplyr
Requires:	R-ggplot2
Requires:	R-ggridges
Requires:	R-glue
Requires:	R-reshape2
Requires:	R-rlang
Requires:	R-tibble
Requires:	R-tidyselect
Requires:	R-backports
Requires:	R-future
Requires:	R-globals
Requires:	R-lifecycle
Requires:	R-purrr
Requires:	R-vctrs
Requires:	R-cachem
Requires:	R-abind
Requires:	R-tensorA
Requires:	R-pillar
Requires:	R-distributional
Requires:	R-matrixStats
Requires:	R-digest
Requires:	R-fastmap
Requires:	R-generics
Requires:	R-numDeriv
Requires:	R-cli
Requires:	R-magrittr
Requires:	R-R6
Requires:	R-listenv
Requires:	R-parallelly
Requires:	R-gtable
Requires:	R-isoband
Requires:	R-scales
Requires:	R-withr
Requires:	R-fansi
Requires:	R-utf8
Requires:	R-plyr
Requires:	R-Rcpp
Requires:	R-stringr
Requires:	R-pkgconfig
Requires:	R-farver
Requires:	R-labeling
Requires:	R-munsell
Requires:	R-RColorBrewer
Requires:	R-viridisLite
Requires:	R-stringi
Requires:	R-colorspace
# %%if 0%%{?sle_version} > 120400 || 0%%{?is_opensuse} 
# # Three others commonly needed 
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# BuildRequires:  tex(natbib.sty) 
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# BuildRequires:  texinfo 
BuildRequires:  fdupes 
BuildRequires:  R-base 
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Suggests:	R-testthat
A data augmentation based sampler for conducting privacy-aware Bayesian 
inference. The dapper_sample() function takes an existing sampler as 
input and automatically constructs a privacy-aware sampler. The process 
of constructing a sampler is simplified through the specification of 
four independent modules, allowing for easy comparison between 
different privacy mechanisms by only swapping out the relevant modules. 
Probability mass functions for the discrete Gaussian and discrete 
Laplacian are provided to facilitate analyses dealing with privatized 
count data. The output of dapper_sample() can be analyzed using many of 
the same tools from the `rstan` ecosystem. For methodological details 
on the sampler see Ju et al. (2022) <doi:10.48550/arXiv.2206.00710>, 
and for details on the discrete Gaussian and discrete Laplacian 
distributions see Canonne et al. (2020) 
%setup -q -c -n %{packname} 
# the next line is needed, because we build without --clean in between two packages 
rm -rf ~/.R  
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} 
#%%{_bindir}/R CMD check %%{packname} 
%dir %{rlibdir}/%{packname} 
%doc %{rlibdir}/%{packname}/DESCRIPTION
%license %{rlibdir}/%{packname}/LICENSE
%doc %{rlibdir}/%{packname}/
%doc %{rlibdir}/%{packname}/help
%doc %{rlibdir}/%{packname}/html
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