%global __brp_check_rpaths %{nil} %global packname lspartition %global packver 0.4 %global rlibdir /usr/local/lib/R/library Name: R-CRAN-%{packname} Version: 0.4 Release: 3%{?dist}%{?buildtag} Summary: Nonparametric Estimation and Inference Procedures usingPartitioning-Based Least Squares Regression License: GPL-2 URL: https://cran.r-project.org/package=%{packname} Source0: %{url}&version=%{packver}#/%{packname}_%{packver}.tar.gz BuildRequires: R-devel >= 3.1 Requires: R-core >= 3.1 BuildArch: noarch BuildRequires: R-CRAN-ggplot2 BuildRequires: R-CRAN-pracma BuildRequires: R-mgcv BuildRequires: R-CRAN-combinat BuildRequires: R-CRAN-matrixStats BuildRequires: R-MASS BuildRequires: R-CRAN-dplyr Requires: R-CRAN-ggplot2 Requires: R-CRAN-pracma Requires: R-mgcv Requires: R-CRAN-combinat Requires: R-CRAN-matrixStats Requires: R-MASS Requires: R-CRAN-dplyr %description Tools for statistical analysis using partitioning-based least squares regression as described in Cattaneo, Farrell and Feng (2019a, <arXiv:1804.04916>) and Cattaneo, Farrell and Feng (2019b, <arXiv:1906.00202>): lsprobust() for nonparametric point estimation of regression functions and their derivatives and for robust bias-corrected (pointwise and uniform) inference; lspkselect() for data-driven selection of the IMSE-optimal number of knots; lsprobust.plot() for regression plots with robust confidence intervals and confidence bands; lsplincom() for estimation and inference for linear combinations of regression functions from different groups. %prep %setup -q -c -n %{packname} %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}