%global __brp_check_rpaths %{nil} %global __requires_exclude ^libmpi %global packname baldur %global packver 0.0.3 %global rlibdir /usr/local/lib/R/library Name: R-CRAN-%{packname} Version: 0.0.3 Release: 1%{?dist}%{?buildtag} Summary: Bayesian Hierarchical Modeling for Label-Free Proteomics License: MIT + file LICENSE URL: https://cran.r-project.org/package=%{packname} Source0: %{url}&version=%{packver}#/%{packname}_%{packver}.tar.gz BuildRequires: R-devel >= 4.2.0 Requires: R-core >= 4.2.0 BuildRequires: R-CRAN-RcppParallel >= 5.0.1 BuildRequires: R-CRAN-ggplot2 >= 3.3.6 BuildRequires: R-CRAN-tibble >= 3.1.7 BuildRequires: R-CRAN-Rdpack >= 2.4 BuildRequires: R-CRAN-rstan >= 2.26.0 BuildRequires: R-CRAN-StanHeaders >= 2.26.0 BuildRequires: R-CRAN-rstantools >= 2.2.0 BuildRequires: R-CRAN-magrittr >= 2.0.3 BuildRequires: R-CRAN-BH >= 1.66.0 BuildRequires: R-CRAN-tidyr >= 1.2.0 BuildRequires: R-CRAN-dplyr >= 1.0.9 BuildRequires: R-CRAN-stringr >= 1.0.4 BuildRequires: R-CRAN-rlang >= 1.0.2 BuildRequires: R-CRAN-viridisLite >= 0.4.1 BuildRequires: R-CRAN-purrr >= 0.3.4 BuildRequires: R-CRAN-RcppEigen >= 0.3.3.3.0 BuildRequires: R-CRAN-Rcpp >= 0.12.0 BuildRequires: R-CRAN-multidplyr >= 0.1.1 BuildRequires: R-methods BuildRequires: R-stats BuildRequires: R-CRAN-lifecycle BuildRequires: R-CRAN-rstantools Requires: R-CRAN-RcppParallel >= 5.0.1 Requires: R-CRAN-ggplot2 >= 3.3.6 Requires: R-CRAN-tibble >= 3.1.7 Requires: R-CRAN-Rdpack >= 2.4 Requires: R-CRAN-rstan >= 2.26.0 Requires: R-CRAN-rstantools >= 2.2.0 Requires: R-CRAN-magrittr >= 2.0.3 Requires: R-CRAN-tidyr >= 1.2.0 Requires: R-CRAN-dplyr >= 1.0.9 Requires: R-CRAN-stringr >= 1.0.4 Requires: R-CRAN-rlang >= 1.0.2 Requires: R-CRAN-viridisLite >= 0.4.1 Requires: R-CRAN-purrr >= 0.3.4 Requires: R-CRAN-Rcpp >= 0.12.0 Requires: R-CRAN-multidplyr >= 0.1.1 Requires: R-methods Requires: R-stats Requires: R-CRAN-lifecycle Requires: R-CRAN-rstantools %description Statistical decision in proteomics data using a hierarchical Bayesian model. There are two regression models for describing the mean-variance trend, a gamma regression or a latent gamma mixture regression. The regression model is then used as an Empirical Bayes estimator for the prior on the variance in a peptide. Further, it assumes that each measurement has an uncertainty (increased variance) associated with it that is also inferred. Finally, it tries to estimate the posterior distribution (by Hamiltonian Monte Carlo) for the differences in means for each peptide in the data. Once the posterior is inferred, it integrates the tails to estimate the probability of error from which a statistical decision can be made. See Berg and Popescu for details (). %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}