File R-SeBR.spec of Package R-SeBR

# Automatically generated by CRAN2OBS
# 
# Spec file for package SeBR 
# This file is auto-generated using information in the package source, 
# esp. Description and Summary. Improvements in that area should be 
# discussed with upstream. 
# 
# Copyright (c) 2025 SUSE LINUX GmbH, Nuernberg, Germany. 
# 
# All modifications and additions to the file contributed by third parties 
# remain the property of their copyright owners, unless otherwise agreed 
# upon. The license for this file, and modifications and additions to the 
# file, is the same license as for the pristine package itself (unless the 
# license for the pristine package is not an Open Source License, in which 
# case the license is the MIT License). An "Open Source License" is a 
# license that conforms to the Open Source Definition (Version 1.9) 
# published by the Open Source Initiative. 
#  
# Please submit bugfixes or comments via http://bugs.opensuse.org/ 
# 
 
%global packname  SeBR 
%global rlibdir   %{_libdir}/R/library 
 
Name:           R-%{packname} 
Version:        1.0.0 
Release:        0 
Summary:        Semiparametric Bayesian Regression Analysis 
Group:          Development/Libraries/Other 
License:        MIT + file LICENSE 
URL:            http://cran.r-project.org/web/packages/%{packname} 
Source:         SeBR_1.0.0.tar.gz 
Requires:       R-base 
Requires:	R-fields
Requires:	R-GpGp
Requires:	R-quantreg
Requires:	R-spikeSlabGAM
Requires:	R-statmod
Requires:	R-spam
Requires:	R-viridisLite
Requires:	R-maps
Requires:	R-Rcpp
Requires:	R-FNN
Requires:	R-RcppArmadillo
Requires:	R-BH
Requires:	R-SparseM
Requires:	R-MatrixModels
Requires:	R-coda
Requires:	R-ggplot2
Requires:	R-gridExtra
Requires:	R-interp
Requires:	R-MCMCpack
Requires:	R-mvtnorm
Requires:	R-R2WinBUGS
Requires:	R-reshape
Requires:	R-scales
Requires:	R-cli
Requires:	R-glue
Requires:	R-gtable
Requires:	R-isoband
Requires:	R-lifecycle
Requires:	R-rlang
Requires:	R-tibble
Requires:	R-vctrs
Requires:	R-withr
Requires:	R-deldir
Requires:	R-RcppEigen
Requires:	R-mcmc
Requires:	R-plyr
Requires:	R-farver
Requires:	R-labeling
Requires:	R-R6
Requires:	R-RColorBrewer
Requires:	R-dotCall64
Requires:	R-magrittr
Requires:	R-pillar
Requires:	R-pkgconfig
Requires:	R-utf8
 
# %%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-fields
BuildRequires: 	R-GpGp
BuildRequires: 	R-quantreg
BuildRequires: 	R-spikeSlabGAM
BuildRequires: 	R-statmod
BuildRequires: 	R-spam
BuildRequires: 	R-viridisLite
BuildRequires: 	R-maps
BuildRequires: 	R-Rcpp-devel
BuildRequires: 	R-FNN
BuildRequires: 	R-RcppArmadillo-devel
BuildRequires: 	R-BH-devel
BuildRequires: 	R-SparseM
BuildRequires: 	R-MatrixModels
BuildRequires: 	R-coda
BuildRequires: 	R-ggplot2
BuildRequires: 	R-gridExtra
BuildRequires: 	R-interp
BuildRequires: 	R-MCMCpack
BuildRequires: 	R-mvtnorm
BuildRequires: 	R-R2WinBUGS
BuildRequires: 	R-reshape
BuildRequires: 	R-scales
BuildRequires: 	R-cli
BuildRequires: 	R-glue
BuildRequires: 	R-gtable
BuildRequires: 	R-isoband
BuildRequires: 	R-lifecycle
BuildRequires: 	R-rlang
BuildRequires: 	R-tibble
BuildRequires: 	R-vctrs
BuildRequires: 	R-withr
BuildRequires: 	R-deldir
BuildRequires: 	R-RcppEigen-devel
BuildRequires: 	R-mcmc
BuildRequires: 	R-plyr
BuildRequires: 	R-farver
BuildRequires: 	R-labeling
BuildRequires: 	R-R6
BuildRequires: 	R-RColorBrewer
BuildRequires: 	R-dotCall64
BuildRequires: 	R-magrittr
BuildRequires: 	R-pillar
BuildRequires: 	R-pkgconfig
BuildRequires: 	R-utf8
 
Suggests:	R-knitr
Suggests:	R-rmarkdown
%description 
Monte Carlo and MCMC sampling algorithms for semiparametric Bayesian 
regression analysis. These models feature a nonparametric (unknown) 
transformation of the data paired with widely-used regression models 
including linear regression, spline regression, quantile regression, 
and Gaussian processes. The transformation enables broader 
applicability of these key models, including for real-valued, positive, 
and compactly-supported data with challenging distributional features. 
The samplers prioritize computational scalability and, for most cases, 
Monte Carlo (not MCMC) sampling for greater efficiency. Details of the 
methods and algorithms are provided in Kowal and Wu (2023) 
<arXiv:2306.05498>. 
 
%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} 
%doc %{rlibdir}/%{packname}/DESCRIPTION
%{rlibdir}/%{packname}/INDEX
%license %{rlibdir}/%{packname}/LICENSE
%{rlibdir}/%{packname}/Meta
%{rlibdir}/%{packname}/NAMESPACE
%doc %{rlibdir}/%{packname}/NEWS.md
%{rlibdir}/%{packname}/R
%{rlibdir}/%{packname}/doc
%doc %{rlibdir}/%{packname}/help
%doc %{rlibdir}/%{packname}/html
 
%changelog