%global __brp_check_rpaths %{nil} %global __requires_exclude ^libmpi %global packname calibmsm %global packver 1.1.3 %global rlibdir /usr/local/lib/R/library Name: R-CRAN-%{packname} Version: 1.1.3 Release: 1%{?dist}%{?buildtag} Summary: Calibration Plots for the Transition Probabilities from Multistate Models License: MIT + file LICENSE 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-boot BuildRequires: R-CRAN-dplyr BuildRequires: R-CRAN-ggplot2 BuildRequires: R-CRAN-ggpubr BuildRequires: R-CRAN-ggExtra BuildRequires: R-CRAN-gridExtra BuildRequires: R-CRAN-Hmisc BuildRequires: R-CRAN-mstate BuildRequires: R-CRAN-rms BuildRequires: R-stats BuildRequires: R-CRAN-survival BuildRequires: R-CRAN-tidyr BuildRequires: R-CRAN-VGAM Requires: R-CRAN-boot Requires: R-CRAN-dplyr Requires: R-CRAN-ggplot2 Requires: R-CRAN-ggpubr Requires: R-CRAN-ggExtra Requires: R-CRAN-gridExtra Requires: R-CRAN-Hmisc Requires: R-CRAN-mstate Requires: R-CRAN-rms Requires: R-stats Requires: R-CRAN-survival Requires: R-CRAN-tidyr Requires: R-CRAN-VGAM %description Assess the calibration of an existing (i.e. previously developed) multistate model through calibration plots. Calibration is assessed using one of three methods. 1) Calibration methods for binary logistic regression models applied at a fixed time point in conjunction with inverse probability of censoring weights. 2) Calibration methods for multinomial logistic regression models applied at a fixed time point in conjunction with inverse probability of censoring weights. 3) Pseudo-values estimated using the Aalen-Johansen estimator of observed risk. All methods are applied in conjunction with landmarking when required. These calibration plots evaluate the calibration (in a validation cohort of interest) of the transition probabilities estimated from an existing multistate model. While package development has focused on multistate models, calibration plots can be produced for any model which utilises information post baseline to update predictions (e.g. dynamic models); competing risks models; or standard single outcome survival models, where predictions can be made at any landmark time. Please see Pate et al. (2024) and Pate et al. (2024) . %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}