%global __brp_check_rpaths %{nil} %global __requires_exclude ^libmpi %global packname crmPack %global packver 2.0.0 %global rlibdir /usr/local/lib/R/library Name: R-CRAN-%{packname} Version: 2.0.0 Release: 1%{?dist}%{?buildtag} Summary: Object-Oriented Implementation of Dose Escalation Designs License: GPL (>= 2) URL: https://cran.r-project.org/package=%{packname} Source0: %{url}&version=%{packver}#/%{packname}_%{packver}.tar.gz BuildRequires: R-devel >= 4.1.0 Requires: R-core >= 4.1.0 BuildArch: noarch BuildRequires: R-CRAN-ggplot2 >= 3.0.0 BuildRequires: R-CRAN-checkmate >= 2.2.0 BuildRequires: R-CRAN-tidyselect >= 1.2.0 BuildRequires: R-graphics BuildRequires: R-CRAN-dplyr BuildRequires: R-CRAN-futile.logger BuildRequires: R-CRAN-GenSA BuildRequires: R-CRAN-gridExtra BuildRequires: R-CRAN-kableExtra BuildRequires: R-CRAN-knitr BuildRequires: R-CRAN-lifecycle BuildRequires: R-CRAN-magrittr BuildRequires: R-methods BuildRequires: R-CRAN-mvtnorm BuildRequires: R-parallel BuildRequires: R-CRAN-parallelly BuildRequires: R-CRAN-Rdpack BuildRequires: R-CRAN-rjags BuildRequires: R-CRAN-rlang BuildRequires: R-CRAN-survival BuildRequires: R-CRAN-tibble BuildRequires: R-tools BuildRequires: R-utils Requires: R-CRAN-ggplot2 >= 3.0.0 Requires: R-CRAN-checkmate >= 2.2.0 Requires: R-CRAN-tidyselect >= 1.2.0 Requires: R-graphics Requires: R-CRAN-dplyr Requires: R-CRAN-futile.logger Requires: R-CRAN-GenSA Requires: R-CRAN-gridExtra Requires: R-CRAN-kableExtra Requires: R-CRAN-knitr Requires: R-CRAN-lifecycle Requires: R-CRAN-magrittr Requires: R-methods Requires: R-CRAN-mvtnorm Requires: R-parallel Requires: R-CRAN-parallelly Requires: R-CRAN-Rdpack Requires: R-CRAN-rjags Requires: R-CRAN-rlang Requires: R-CRAN-survival Requires: R-CRAN-tibble Requires: R-tools Requires: R-utils %description Implements a wide range of dose escalation designs. The focus is on model-based designs, ranging from classical and modern continual reassessment methods (CRMs) based on dose-limiting toxicity endpoints to dual-endpoint designs taking into account a biomarker/efficacy outcome. Bayesian inference is performed via MCMC sampling in JAGS, and it is easy to setup a new design with custom JAGS code. However, it is also possible to implement 3+3 designs for comparison or models with non-Bayesian estimation. The whole package is written in a modular form in the S4 class system, making it very flexible for adaptation to new models, escalation or stopping rules. Further details are presented in Sabanes Bove et al. (2019) . %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}