%global __brp_check_rpaths %{nil} %global packname PLMIX %global packver 2.1.1 %global rlibdir /usr/local/lib/R/library Name: R-CRAN-%{packname} Version: 2.1.1 Release: 3%{?dist}%{?buildtag} Summary: Bayesian Analysis of Finite Mixtures of Plackett-Luce Models forPartial Rankings/Orderings License: GPL (>= 2) URL: https://cran.r-project.org/package=%{packname} Source0: %{url}&version=%{packver}#/%{packname}_%{packver}.tar.gz BuildRequires: R-devel Requires: R-core BuildRequires: R-CRAN-gtools >= 3.8.1 BuildRequires: R-CRAN-gridExtra >= 2.3 BuildRequires: R-CRAN-ggplot2 >= 2.2.1 BuildRequires: R-CRAN-label.switching >= 1.6 BuildRequires: R-CRAN-abind >= 1.4.5 BuildRequires: R-CRAN-foreach >= 1.4.4 BuildRequires: R-CRAN-reshape2 >= 1.4.3 BuildRequires: R-CRAN-MCMCpack >= 1.4.2 BuildRequires: R-CRAN-pmr >= 1.2.5 BuildRequires: R-CRAN-ggmcmc >= 1.2 BuildRequires: R-CRAN-rcdd >= 1.2 BuildRequires: R-CRAN-rankdist >= 1.1.3 BuildRequires: R-CRAN-Rcpp >= 1.0.0 BuildRequires: R-CRAN-prefmod >= 0.8.34 BuildRequires: R-CRAN-radarchart >= 0.3.1 BuildRequires: R-CRAN-PlackettLuce >= 0.2.3 BuildRequires: R-CRAN-coda >= 0.19.1 BuildRequires: R-CRAN-StatRank >= 0.0.6 BuildRequires: R-stats BuildRequires: R-utils Requires: R-CRAN-gtools >= 3.8.1 Requires: R-CRAN-gridExtra >= 2.3 Requires: R-CRAN-ggplot2 >= 2.2.1 Requires: R-CRAN-label.switching >= 1.6 Requires: R-CRAN-abind >= 1.4.5 Requires: R-CRAN-foreach >= 1.4.4 Requires: R-CRAN-reshape2 >= 1.4.3 Requires: R-CRAN-MCMCpack >= 1.4.2 Requires: R-CRAN-pmr >= 1.2.5 Requires: R-CRAN-ggmcmc >= 1.2 Requires: R-CRAN-rcdd >= 1.2 Requires: R-CRAN-rankdist >= 1.1.3 Requires: R-CRAN-Rcpp >= 1.0.0 Requires: R-CRAN-prefmod >= 0.8.34 Requires: R-CRAN-radarchart >= 0.3.1 Requires: R-CRAN-PlackettLuce >= 0.2.3 Requires: R-CRAN-coda >= 0.19.1 Requires: R-CRAN-StatRank >= 0.0.6 Requires: R-stats Requires: R-utils %description Fit finite mixtures of Plackett-Luce models for partial top rankings/orderings within the Bayesian framework. It provides MAP point estimates via EM algorithm and posterior MCMC simulations via Gibbs Sampling. It also fits MLE as a special case of the noninformative Bayesian analysis with vague priors. In addition to inferential techniques, the package assists other fundamental phases of a model-based analysis for partial rankings/orderings, by including functions for data manipulation, simulation, descriptive summary, model selection and goodness-of-fit evaluation. Main references on the methods are Mollica and Tardella (2017) and Mollica and Tardella (2014) . %prep %setup -q -c -n %{packname} %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 %files %{rlibdir}/%{packname}