%global __brp_check_rpaths %{nil} %global __requires_exclude ^libmpi %global packname sphereML %global packver 0.1.1 %global rlibdir /usr/local/lib/R/library Name: R-CRAN-%{packname} Version: 0.1.1 Release: 1%{?dist}%{?buildtag} Summary: Analyzing Students' Performance Dataset in Physics Education Research (SPHERE) using Machine Learning (ML) License: MIT + file LICENSE URL: https://cran.r-project.org/package=%{packname} Source0: %{url}&version=%{packver}#/%{packname}_%{packver}.tar.gz BuildRequires: R-devel >= 3.50 Requires: R-core >= 3.50 BuildArch: noarch BuildRequires: R-CRAN-shiny BuildRequires: R-CRAN-shinydashboard BuildRequires: R-CRAN-spheredata BuildRequires: R-CRAN-lavaan BuildRequires: R-CRAN-semPlot BuildRequires: R-CRAN-CTT BuildRequires: R-CRAN-mirt BuildRequires: R-CRAN-shinycssloaders BuildRequires: R-CRAN-FSelectorRcpp BuildRequires: R-CRAN-randomForest BuildRequires: R-CRAN-caret BuildRequires: R-CRAN-caTools BuildRequires: R-CRAN-pROC BuildRequires: R-CRAN-GA BuildRequires: R-CRAN-readxl Requires: R-CRAN-shiny Requires: R-CRAN-shinydashboard Requires: R-CRAN-spheredata Requires: R-CRAN-lavaan Requires: R-CRAN-semPlot Requires: R-CRAN-CTT Requires: R-CRAN-mirt Requires: R-CRAN-shinycssloaders Requires: R-CRAN-FSelectorRcpp Requires: R-CRAN-randomForest Requires: R-CRAN-caret Requires: R-CRAN-caTools Requires: R-CRAN-pROC Requires: R-CRAN-GA Requires: R-CRAN-readxl %description A simple package facilitating ML based analysis for physics education research (PER) purposes. The implemented machine learning technique is random forest optimized by item response theory (IRT) for feature selection and genetic algorithm (GA) for hyperparameter tuning. The data analyzed here has been made available in the CRAN repository through the 'spheredata' package. The SPHERE stands for Students' Performance in Physics Education Research (PER). The students are the eleventh graders learning physics at the high school curriculum. We follow the stream of multidimensional students' assessment as probed by some research based assessments in PER. The goal is to predict the students' performance at the end of the learning process. Three learning domains are measured including conceptual understanding, scientific ability, and scientific attitude. Furthermore, demographic backgrounds and potential variables predicting students' performance on physics are also demonstrated. %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}