File R-BayesianMCPMod.spec of Package R-BayesianMCPMod

# Automatically generated by CRAN2OBS
# 
# Spec file for package BayesianMCPMod 
# This file is auto-generated using information in the package source, 
# esp. Description and Summary. Improvements in that area should be 
# discussed with upstream. 
# 
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%global packname  BayesianMCPMod 
%global rlibdir   %{_libdir}/R/library 
 
Name:           R-%{packname} 
Version:        1.2.0 
Release:        0 
Summary:        Simulate, Evaluate, and Analyze Dose Finding Trials with Bayesian MCPMod 
Group:          Development/Libraries/Other 
License:        Apache License (>= 2) 
URL:            http://cran.r-project.org/web/packages/%{packname} 
Source:         BayesianMCPMod_1.2.0.tar.gz 
Requires:       R-base 
Requires:	R-checkmate
Requires:	R-DoseFinding
Requires:	R-dplyr
Requires:	R-ggplot2
Requires:	R-nloptr
Requires:	R-RBesT
Requires:	R-tidyr
Requires:	R-backports
Requires:	R-mvtnorm
Requires:	R-cli
Requires:	R-generics
Requires:	R-glue
Requires:	R-lifecycle
Requires:	R-magrittr
Requires:	R-pillar
Requires:	R-R6
Requires:	R-rlang
Requires:	R-tibble
Requires:	R-tidyselect
Requires:	R-vctrs
Requires:	R-gtable
Requires:	R-isoband
Requires:	R-S7
Requires:	R-scales
Requires:	R-withr
Requires:	R-Rcpp
Requires:	R-RcppParallel
Requires:	R-rstan
Requires:	R-rstantools
Requires:	R-assertthat
Requires:	R-Formula
Requires:	R-bayesplot
Requires:	R-matrixStats
Requires:	R-abind
Requires:	R-jsonlite
Requires:	R-BH
Requires:	R-RcppEigen
Requires:	R-StanHeaders
Requires:	R-purrr
Requires:	R-stringr
Requires:	R-cpp11
Requires:	R-ggridges
Requires:	R-posterior
Requires:	R-reshape2
Requires:	R-utf8
Requires:	R-inline
Requires:	R-gridExtra
Requires:	R-loo
Requires:	R-pkgbuild
Requires:	R-QuickJSR
Requires:	R-desc
Requires:	R-farver
Requires:	R-labeling
Requires:	R-RColorBrewer
Requires:	R-viridisLite
Requires:	R-stringi
Requires:	R-pkgconfig
Requires:	R-callr
Requires:	R-processx
Requires:	R-tensorA
Requires:	R-distributional
Requires:	R-plyr
Requires:	R-numDeriv
Requires:	R-ps
 
# %%if 0%%{?sle_version} > 120400 || 0%%{?is_opensuse} 
# # Three others commonly needed 
# BuildRequires:  tex(ae.sty) 
# BuildRequires:  tex(fancyvrb.sty) 
# BuildRequires:  tex(inconsolata.sty) 
# BuildRequires:  tex(natbib.sty) 
# %else 
# BuildRequires:  texlive 
# %endif 
# BuildRequires:  texinfo 
BuildRequires:  fdupes 
BuildRequires:  R-base 
BuildRequires: 	R-checkmate
BuildRequires: 	R-DoseFinding
BuildRequires: 	R-dplyr
BuildRequires: 	R-ggplot2
BuildRequires: 	R-nloptr
BuildRequires: 	R-RBesT
BuildRequires: 	R-tidyr
BuildRequires: 	R-backports
BuildRequires: 	R-mvtnorm
BuildRequires: 	R-cli
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BuildRequires: 	R-tidyselect
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BuildRequires: 	R-isoband
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BuildRequires: 	R-scales
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BuildRequires: 	R-Rcpp-devel
BuildRequires: 	R-RcppParallel-devel
BuildRequires: 	R-rstan-devel
BuildRequires: 	R-rstantools
BuildRequires: 	R-assertthat
BuildRequires: 	R-Formula
BuildRequires: 	R-bayesplot
BuildRequires: 	R-matrixStats
BuildRequires: 	R-abind
BuildRequires: 	R-jsonlite
BuildRequires: 	R-BH-devel
BuildRequires: 	R-RcppEigen-devel
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BuildRequires: 	R-distributional
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BuildRequires: 	R-numDeriv
BuildRequires: 	R-ps
 
Suggests:	R-clinDR
Suggests:	R-doFuture
Suggests:	R-future.apply
Suggests:	R-kableExtra
Suggests:	R-knitr
Suggests:	R-MCPModPack
Suggests:	R-reactable
Suggests:	R-rmarkdown
Suggests:	R-spelling
Suggests:	R-testthat
Suggests:	R-tibble
%description 
Bayesian MCPMod (Fleischer et al. (2022) <doi:10.1002/pst.2193>) is an 
innovative method that improves the traditional MCPMod by 
systematically incorporating historical data, such as previous placebo 
group data. This R package offers functions for simulating, analyzing, 
and evaluating Bayesian MCPMod trials with normally distributed 
endpoints.  It enables the assessment of trial designs incorporating 
historical data across various true dose-response relationships and 
sample sizes. Robust mixture prior distributions, such as those derived 
with the Meta-Analytic-Predictive approach (Schmidli et al. (2014) 
<doi:10.1111/biom.12242>), can be specified for each dose group. 
Resulting mixture posterior distributions are used in the Bayesian 
Multiple Comparison Procedure and modeling steps. The modeling step 
also includes a weighted model averaging approach (Pinheiro et al. 
(2014) <doi:10.1002/sim.6052>). Estimated dose-response relationships 
can be bootstrapped and visualized. 
 
%prep 
%setup -q -c -n %{packname} 
# the next line is needed, because we build without --clean in between two packages 
rm -rf ~/.R  
 
 
%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 
%fdupes -s %{buildroot}%{rlibdir} 
 
#%%check 
#%%{_bindir}/R CMD check %%{packname} 
 
%files 
%dir %{rlibdir}/%{packname} 
%{rlibdir}/%{packname}/CITATION
%doc %{rlibdir}/%{packname}/DESCRIPTION
%{rlibdir}/%{packname}/INDEX
%{rlibdir}/%{packname}/Meta
%{rlibdir}/%{packname}/NAMESPACE
%doc %{rlibdir}/%{packname}/NEWS.md
%{rlibdir}/%{packname}/PSI_Poster_2024.pdf
%{rlibdir}/%{packname}/R
%{rlibdir}/%{packname}/WORDLIST
%{rlibdir}/%{packname}/doc
%doc %{rlibdir}/%{packname}/help
%doc %{rlibdir}/%{packname}/html
 
%changelog