%global __brp_check_rpaths %{nil} %global packname HCTR %global packver 0.1.1 %global rlibdir /usr/local/lib/R/library Name: R-CRAN-%{packname} Version: 0.1.1 Release: 3%{?dist}%{?buildtag} Summary: Higher Criticism Tuned Regression 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-ncvreg >= 3.11.1 BuildRequires: R-CRAN-harmonicmeanp >= 3.0 BuildRequires: R-CRAN-glmnet >= 2.0.18 BuildRequires: R-CRAN-Rdpack >= 0.11.0 BuildRequires: R-MASS BuildRequires: R-stats Requires: R-CRAN-ncvreg >= 3.11.1 Requires: R-CRAN-harmonicmeanp >= 3.0 Requires: R-CRAN-glmnet >= 2.0.18 Requires: R-CRAN-Rdpack >= 0.11.0 Requires: R-MASS Requires: R-stats %description A novel searching scheme for tuning parameter in high-dimensional penalized regression. We propose a new estimate of the regularization parameter based on an estimated lower bound of the proportion of false null hypotheses (Meinshausen and Rice (2006) <doi:10.1214/009053605000000741>). The bound is estimated by applying the empirical null distribution of the higher criticism statistic, a second-level significance testing, which is constructed by dependent p-values from a multi-split regression and aggregation method (Jeng, Zhang and Tzeng (2019) <doi:10.1080/01621459.2018.1518236>). An estimate of tuning parameter in penalized regression is decided corresponding to the lower bound of the proportion of false null hypotheses. Different penalized regression methods are provided in the multi-split algorithm. %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}