File R-TDSTNN.spec of Package R-TDSTNN

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File R-TDSTNN.spec of Package R-TDSTNN

# Automatically generated by CRAN2OBS
# 
# Spec file for package TDSTNN 
# 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) 2024 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  TDSTNN 
%global rlibdir   %{_libdir}/R/library 
 
Name:           R-%{packname} 
Version:        0.1.0 
Release:        0 
Summary:        Time Delay Spatio Temporal Neural Network 
Group:          Development/Libraries/Other 
License:        GPL-3 
URL:            http://cran.r-project.org/web/packages/%{packname} 
Source:         TDSTNN_0.1.0.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 
 
%description 
STARMA (Space-Time Autoregressive Moving Average) models are commonly 
utilized in modeling and forecasting spatiotemporal time series data. 
However, the intricate nonlinear dynamics observed in many space-time 
rainfall patterns often exceed the capabilities of conventional STARMA 
models. This R package enables the fitting of Time Delay 
Spatio-Temporal Neural Networks, which are adept at handling such 
complex nonlinear dynamics efficiently. For detailed methodology, 
please refer to Saha et al. (2020) <doi:10.1007/s00704-020-03374-2>. 
 
%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
%{rlibdir}/%{packname}/Meta
%{rlibdir}/%{packname}/NAMESPACE
%{rlibdir}/%{packname}/R
%doc %{rlibdir}/%{packname}/help
%doc %{rlibdir}/%{packname}/html
 
%changelog

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