%global __brp_check_rpaths %{nil} %global packname MPTmultiverse %global packver 0.4-2 %global rlibdir /usr/local/lib/R/library Name: R-CRAN-%{packname} Version: 0.4.2 Release: 2%{?dist}%{?buildtag} Summary: Multiverse Analysis of Multinomial Processing Tree Models License: GPL-2 URL: https://cran.r-project.org/package=%{packname} Source0: %{url}&version=%{packver}#/%{packname}_%{packver}.tar.gz BuildRequires: R-devel >= 2.11.1 Requires: R-core >= 2.11.1 BuildArch: noarch BuildRequires: R-CRAN-TreeBUGS >= 1.4.4 BuildRequires: R-parallel BuildRequires: R-CRAN-magrittr BuildRequires: R-CRAN-tidyr BuildRequires: R-CRAN-dplyr BuildRequires: R-CRAN-tibble BuildRequires: R-CRAN-rlang BuildRequires: R-CRAN-reshape2 BuildRequires: R-CRAN-ggplot2 BuildRequires: R-CRAN-MPTinR BuildRequires: R-CRAN-runjags BuildRequires: R-CRAN-coda BuildRequires: R-CRAN-purrr BuildRequires: R-CRAN-readr BuildRequires: R-CRAN-limSolve BuildRequires: R-utils Requires: R-CRAN-TreeBUGS >= 1.4.4 Requires: R-parallel Requires: R-CRAN-magrittr Requires: R-CRAN-tidyr Requires: R-CRAN-dplyr Requires: R-CRAN-tibble Requires: R-CRAN-rlang Requires: R-CRAN-reshape2 Requires: R-CRAN-ggplot2 Requires: R-CRAN-MPTinR Requires: R-CRAN-runjags Requires: R-CRAN-coda Requires: R-CRAN-purrr Requires: R-CRAN-readr Requires: R-CRAN-limSolve Requires: R-utils %description Statistical or cognitive modeling usually requires a number of more or less arbitrary choices creating one specific path through a 'garden of forking paths'. The multiverse approach (Steegen, Tuerlinckx, Gelman, & Vanpaemel, 2016, ) offers a principled alternative in which results for all possible combinations of reasonable modeling choices are reported. MPTmultiverse performs a multiverse analysis for multinomial processing tree (MPT, Riefer & Batchelder, 1988, ) models combining maximum-likelihood/frequentist and Bayesian estimation approaches with different levels of pooling (i.e., data aggregation). For the frequentist approaches, no pooling (with and without parametric or nonparametric bootstrap) and complete pooling are implemented using MPTinR . For the Bayesian approaches, no pooling, complete pooling, and three different variants of partial pooling are implemented using TreeBUGS . The main function is fit_mpt() who performs the multiverse analysis in one call. %prep %setup -q -c -n %{packname} find -type f -executable -exec grep -Iq . {} \; -exec sed -i -e '$a\' {} \; [ -d %{packname}/src ] && find %{packname}/src -type f -exec \ sed -i 's@/usr/bin/strip@/usr/bin/true@g' {} \; || true %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}