%global __brp_check_rpaths %{nil} %global packname crmReg %global packver 1.0.2 %global rlibdir /usr/local/lib/R/library Name: R-CRAN-%{packname} Version: 1.0.2 Release: 1%{?dist}%{?buildtag} Summary: Cellwise Robust M-Regression and SPADIMO 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 BuildArch: noarch BuildRequires: R-CRAN-FNN BuildRequires: R-CRAN-ggplot2 BuildRequires: R-CRAN-gplots BuildRequires: R-CRAN-pcaPP BuildRequires: R-CRAN-plyr BuildRequires: R-CRAN-robustbase BuildRequires: R-CRAN-rrcov Requires: R-CRAN-FNN Requires: R-CRAN-ggplot2 Requires: R-CRAN-gplots Requires: R-CRAN-pcaPP Requires: R-CRAN-plyr Requires: R-CRAN-robustbase Requires: R-CRAN-rrcov %description Method for fitting a cellwise robust linear M-regression model (CRM, Filzmoser et al. (2020) ) that yields both a map of cellwise outliers consistent with the linear model, and a vector of regression coefficients that is robust against vertical outliers and leverage points. As a by-product, the method yields an imputed data set that contains estimates of what the values in cellwise outliers would need to amount to if they had fit the model. The package also provides diagnostic tools for analyzing casewise and cellwise outliers using sparse directions of maximal outlyingness (SPADIMO, Debruyne et al. (2019) ). %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 find %{buildroot}%{rlibdir} -type f -exec sed -i "s@%{buildroot}@@g" {} \; %files %{rlibdir}/%{packname}