File R-SDGLM.spec of Package R-SDGLM

# Automatically generated by CRAN2OBS
# 
# Spec file for package SDGLM 
# 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) 2026 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  SDGLM 
%global rlibdir   %{_libdir}/R/library 
 
Name:           R-%{packname} 
Version:        0.4.0 
Release:        0 
Summary:        Scalable Bayesian Inference for Dynamic Generalized Linear Models 
Group:          Development/Libraries/Other 
License:        MIT + file LICENSE 
URL:            http://cran.r-project.org/web/packages/%{packname} 
Source:         SDGLM_0.4.0.tar.gz 
Requires:       R-base 
 
# %%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 
 
Suggests:	R-testthat
%description 
Implements scalable Markov chain Monte Carlo (Sca-MCMC) algorithms for 
Bayesian inference in dynamic generalized linear models (DGLMs). The 
package supports Pareto-type and Gamma-type DGLMs, which are suitable 
for modeling heavy-tailed phenomena such as wealth allocation and 
financial returns. It provides simulation tools for synthetic DGLM 
data, adaptive mutation-rate strategies (ScaI, ScaII, ScaIII), 
geometric temperature ladders for parallel tempering, and posterior 
predictive evaluation metrics (e.g., R2, RMSE). The methodology is 
based on the scalable MCMC framework described in Guo et al. (2025). 
 
%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
%{rlibdir}/%{packname}/R
%doc %{rlibdir}/%{packname}/help
%doc %{rlibdir}/%{packname}/html
 
%changelog