%global __brp_check_rpaths %{nil} %global packname miWQS %global packver 0.4.4 %global rlibdir /usr/local/lib/R/library Name: R-CRAN-%{packname} Version: 0.4.4 Release: 1%{?dist}%{?buildtag} Summary: Multiple Imputation Using Weighted Quantile Sum Regression License: GPL-3 URL: https://cran.r-project.org/package=%{packname} Source0: %{url}&version=%{packver}#/%{packname}_%{packver}.tar.gz BuildRequires: R-devel >= 3.5.0 Requires: R-core >= 3.5.0 BuildArch: noarch BuildRequires: R-CRAN-MASS >= 7.3.49 BuildRequires: R-CRAN-Hmisc >= 4.1.1 BuildRequires: R-CRAN-survival >= 3.1.12 BuildRequires: R-CRAN-ggplot2 >= 3.1.0 BuildRequires: R-CRAN-condMVNorm >= 2015.2 BuildRequires: R-CRAN-MCMCpack >= 1.4.4 BuildRequires: R-CRAN-tmvtnorm >= 1.4.10 BuildRequires: R-CRAN-glm2 >= 1.2.1 BuildRequires: R-CRAN-Rsolnp >= 1.16 BuildRequires: R-CRAN-invgamma >= 1.1 BuildRequires: R-CRAN-truncnorm >= 1.0.8 BuildRequires: R-CRAN-tmvmixnorm >= 1.0.2 BuildRequires: R-CRAN-mvtnorm >= 1.0.10 BuildRequires: R-CRAN-tidyr >= 1.0.0 BuildRequires: R-CRAN-rlist >= 0.4.6.1 BuildRequires: R-CRAN-purrr >= 0.3.2 BuildRequires: R-CRAN-coda >= 0.19.2 BuildRequires: R-CRAN-matrixNormal >= 0.0.0 BuildRequires: R-methods BuildRequires: R-parallel BuildRequires: R-stats BuildRequires: R-utils Requires: R-CRAN-MASS >= 7.3.49 Requires: R-CRAN-Hmisc >= 4.1.1 Requires: R-CRAN-survival >= 3.1.12 Requires: R-CRAN-ggplot2 >= 3.1.0 Requires: R-CRAN-condMVNorm >= 2015.2 Requires: R-CRAN-MCMCpack >= 1.4.4 Requires: R-CRAN-tmvtnorm >= 1.4.10 Requires: R-CRAN-glm2 >= 1.2.1 Requires: R-CRAN-Rsolnp >= 1.16 Requires: R-CRAN-invgamma >= 1.1 Requires: R-CRAN-truncnorm >= 1.0.8 Requires: R-CRAN-tmvmixnorm >= 1.0.2 Requires: R-CRAN-mvtnorm >= 1.0.10 Requires: R-CRAN-tidyr >= 1.0.0 Requires: R-CRAN-rlist >= 0.4.6.1 Requires: R-CRAN-purrr >= 0.3.2 Requires: R-CRAN-coda >= 0.19.2 Requires: R-CRAN-matrixNormal >= 0.0.0 Requires: R-methods Requires: R-parallel Requires: R-stats Requires: R-utils %description The miWQS package handles the uncertainty due to below the detection limit in a correlated component mixture problem. Researchers want to determine if a set/mixture of continuous and correlated components/chemicals is associated with an outcome and if so, which components are important in that mixture. These components share a common outcome but are interval-censored between zero and low thresholds, or detection limits, that may be different across the components. This package applies the multiple imputation (MI) procedure to the weighted quantile sum regression (WQS) methodology for continuous, binary, or count outcomes (Hargarten & Wheeler (2020) ). The imputation models are: bootstrapping imputation (Lubin et.al (2004) ), univariate Bayesian imputation (Hargarten & Wheeler (2020) ), and multivariate Bayesian regression imputation. %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 # 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}