%global __brp_check_rpaths %{nil} %global packname stray %global packver 0.1.1 %global rlibdir /usr/local/lib/R/library Name: R-CRAN-%{packname} Version: 0.1.1 Release: 2%{?dist}%{?buildtag} Summary: Anomaly Detection in High Dimensional and Temporal Data License: GPL-2 URL: https://cran.r-project.org/package=%{packname} Source0: %{url}&version=%{packver}#/%{packname}_%{packver}.tar.gz BuildRequires: R-devel >= 3.4.0 Requires: R-core >= 3.4.0 BuildArch: noarch BuildRequires: R-CRAN-FNN BuildRequires: R-CRAN-ggplot2 BuildRequires: R-CRAN-colorspace BuildRequires: R-CRAN-pcaPP BuildRequires: R-stats BuildRequires: R-CRAN-ks Requires: R-CRAN-FNN Requires: R-CRAN-ggplot2 Requires: R-CRAN-colorspace Requires: R-CRAN-pcaPP Requires: R-stats Requires: R-CRAN-ks %description This is a modification of 'HDoutliers' package. The 'HDoutliers' algorithm is a powerful unsupervised algorithm for detecting anomalies in high-dimensional data, with a strong theoretical foundation. However, it suffers from some limitations that significantly hinder its performance level, under certain circumstances. This package implements the algorithm proposed in Talagala, Hyndman and Smith-Miles (2019) for detecting anomalies in high-dimensional data that addresses these limitations of 'HDoutliers' algorithm. We define an anomaly as an observation that deviates markedly from the majority with a large distance gap. An approach based on extreme value theory is used for the anomalous threshold calculation. %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}