%global __brp_check_rpaths %{nil} %global __requires_exclude ^libmpi %global packname FlexReg %global packver 1.3.0 %global rlibdir /usr/local/lib/R/library Name: R-CRAN-%{packname} Version: 1.3.0 Release: 1%{?dist}%{?buildtag} Summary: Regression Models for Bounded Continuous and Discrete Responses License: GPL (>= 2) 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-rstan >= 2.26.0 BuildRequires: R-CRAN-StanHeaders >= 2.26.0 BuildRequires: R-CRAN-rstantools >= 2.0.0 BuildRequires: R-CRAN-BH >= 1.66.0 BuildRequires: R-CRAN-RcppEigen >= 0.3.3.3.0 BuildRequires: R-CRAN-Rcpp >= 0.12.0 BuildRequires: R-methods BuildRequires: R-CRAN-loo BuildRequires: R-CRAN-bayesplot BuildRequires: R-CRAN-ggplot2 BuildRequires: R-CRAN-Formula BuildRequires: R-utils BuildRequires: R-grDevices BuildRequires: R-CRAN-RcppParallel BuildRequires: R-CRAN-rstantools Requires: R-CRAN-rstan >= 2.26.0 Requires: R-CRAN-rstantools >= 2.0.0 Requires: R-CRAN-Rcpp >= 0.12.0 Requires: R-methods Requires: R-CRAN-loo Requires: R-CRAN-bayesplot Requires: R-CRAN-ggplot2 Requires: R-CRAN-Formula Requires: R-utils Requires: R-grDevices Requires: R-CRAN-RcppParallel Requires: R-CRAN-rstantools %description Functions to fit regression models for bounded continuous and discrete responses. In case of bounded continuous responses (e.g., proportions and rates), available models are the flexible beta (Migliorati, S., Di Brisco, A. M., Ongaro, A. (2018) ), the variance-inflated beta (Di Brisco, A. M., Migliorati, S., Ongaro, A. (2020) ), the beta (Ferrari, S.L.P., Cribari-Neto, F. (2004) ), and their augmented versions to handle the presence of zero/one values (Di Brisco, A. M., Migliorati, S. (2020) ) are implemented. In case of bounded discrete responses (e.g., bounded counts, such as the number of successes in n trials), available models are the flexible beta-binomial (Ascari, R., Migliorati, S. (2021) ), the beta-binomial, and the binomial are implemented. Inference is dealt with a Bayesian approach based on the Hamiltonian Monte Carlo (HMC) algorithm (Gelman, A., Carlin, J. B., Stern, H. S., Rubin, D. B. (2014) ). Besides, functions to compute residuals, posterior predictives, goodness of fit measures, convergence diagnostics, and graphical representations are provided. %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}