%global __brp_check_rpaths %{nil} %global __requires_exclude ^libmpi %global packname decisionSupport %global packver 1.114 %global rlibdir /usr/local/lib/R/library Name: R-CRAN-%{packname} Version: 1.114 Release: 1%{?dist}%{?buildtag} Summary: Quantitative Support of Decision Making under Uncertainty License: GPL-3 URL: https://cran.r-project.org/package=%{packname} Source0: %{url}&version=%{packver}#/%{packname}_%{packver}.tar.gz BuildRequires: R-devel >= 3.1.3 Requires: R-core >= 3.1.3 BuildArch: noarch BuildRequires: R-CRAN-ggplot2 >= 3.2.0 BuildRequires: R-stats >= 3.1.3 BuildRequires: R-CRAN-nleqslv >= 2.6 BuildRequires: R-CRAN-rriskDistributions >= 2.0 BuildRequires: R-CRAN-msm >= 1.5 BuildRequires: R-CRAN-mvtnorm >= 1.0.2 BuildRequires: R-CRAN-chillR >= 0.62 BuildRequires: R-CRAN-fANCOVA >= 0.5 BuildRequires: R-CRAN-assertthat BuildRequires: R-CRAN-class BuildRequires: R-CRAN-dplyr BuildRequires: R-grDevices BuildRequires: R-CRAN-magrittr BuildRequires: R-CRAN-patchwork BuildRequires: R-CRAN-stringr BuildRequires: R-CRAN-tidyr BuildRequires: R-CRAN-tidyselect Requires: R-CRAN-ggplot2 >= 3.2.0 Requires: R-stats >= 3.1.3 Requires: R-CRAN-nleqslv >= 2.6 Requires: R-CRAN-rriskDistributions >= 2.0 Requires: R-CRAN-msm >= 1.5 Requires: R-CRAN-mvtnorm >= 1.0.2 Requires: R-CRAN-chillR >= 0.62 Requires: R-CRAN-fANCOVA >= 0.5 Requires: R-CRAN-assertthat Requires: R-CRAN-class Requires: R-CRAN-dplyr Requires: R-grDevices Requires: R-CRAN-magrittr Requires: R-CRAN-patchwork Requires: R-CRAN-stringr Requires: R-CRAN-tidyr Requires: R-CRAN-tidyselect %description Supporting the quantitative analysis of binary welfare based decision making processes using Monte Carlo simulations. Decision support is given on two levels: (i) The actual decision level is to choose between two alternatives under probabilistic uncertainty. This package calculates the optimal decision based on maximizing expected welfare. (ii) The meta decision level is to allocate resources to reduce the uncertainty in the underlying decision problem, i.e to increase the current information to improve the actual decision making process. This problem is dealt with using the Value of Information Analysis. The Expected Value of Information for arbitrary prospective estimates can be calculated as well as Individual Expected Value of Perfect Information. The probabilistic calculations are done via Monte Carlo simulations. This Monte Carlo functionality can be used on its own. %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}