%global __brp_check_rpaths %{nil} %global __requires_exclude ^libmpi %global packname RprobitB %global packver 1.1.4 %global rlibdir /usr/local/lib/R/library Name: R-CRAN-%{packname} Version: 1.1.4 Release: 1%{?dist}%{?buildtag} Summary: Bayesian Probit Choice Modeling 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 BuildRequires: R-CRAN-oeli >= 0.4.1 BuildRequires: R-CRAN-checkmate BuildRequires: R-CRAN-cli BuildRequires: R-CRAN-crayon BuildRequires: R-CRAN-doSNOW BuildRequires: R-CRAN-foreach BuildRequires: R-CRAN-ggplot2 BuildRequires: R-graphics BuildRequires: R-CRAN-gridExtra BuildRequires: R-CRAN-MASS BuildRequires: R-CRAN-mixtools BuildRequires: R-CRAN-mvtnorm BuildRequires: R-parallel BuildRequires: R-CRAN-plotROC BuildRequires: R-CRAN-progress BuildRequires: R-CRAN-Rcpp BuildRequires: R-CRAN-Rdpack BuildRequires: R-CRAN-rlang BuildRequires: R-stats BuildRequires: R-utils BuildRequires: R-CRAN-viridis BuildRequires: R-CRAN-RcppArmadillo Requires: R-CRAN-oeli >= 0.4.1 Requires: R-CRAN-checkmate Requires: R-CRAN-cli Requires: R-CRAN-crayon Requires: R-CRAN-doSNOW Requires: R-CRAN-foreach Requires: R-CRAN-ggplot2 Requires: R-graphics Requires: R-CRAN-gridExtra Requires: R-CRAN-MASS Requires: R-CRAN-mixtools Requires: R-CRAN-mvtnorm Requires: R-parallel Requires: R-CRAN-plotROC Requires: R-CRAN-progress Requires: R-CRAN-Rcpp Requires: R-CRAN-Rdpack Requires: R-CRAN-rlang Requires: R-stats Requires: R-utils Requires: R-CRAN-viridis %description Bayes estimation of probit choice models, both in the cross-sectional and panel setting. The package can analyze binary, multivariate, ordered, and ranked choices, as well as heterogeneity of choice behavior among deciders. The main functionality includes model fitting via Markov chain Monte Carlo m ethods, tools for convergence diagnostic, choice data simulation, in-sample and out-of-sample choice prediction, and model selection using information criteria and Bayes factors. The latent class model extension facilitates preference-based decider classification, where the number of latent classes can be inferred via the Dirichlet process or a weight-based updating heuristic. This allows for flexible modeling of choice behavior without the need to impose structural constraints. For a reference on the method see Oelschlaeger and Bauer (2021) . %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}