File R-hdMTD.spec of Package R-hdMTD

# Automatically generated by CRAN2OBS
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# Spec file for package hdMTD 
# 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  hdMTD 
%global rlibdir   %{_libdir}/R/library 
 
Name:           R-%{packname} 
Version:        0.1.2 
Release:        0 
Summary:        Inference for High-Dimensional Mixture Transition Distribution Models 
Group:          Development/Libraries/Other 
License:        GPL-3 
URL:            http://cran.r-project.org/web/packages/%{packname} 
Source:         hdMTD_0.1.2.tar.gz 
Requires:       R-base 
Requires:	R-dplyr
Requires:	R-purrr
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-utf8
Requires:	R-pkgconfig
Requires:	R-withr
 
# %%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-dplyr
BuildRequires: 	R-purrr
BuildRequires: 	R-cli
BuildRequires: 	R-generics
BuildRequires: 	R-glue
BuildRequires: 	R-lifecycle
BuildRequires: 	R-magrittr
BuildRequires: 	R-pillar
BuildRequires: 	R-R6
BuildRequires: 	R-rlang
BuildRequires: 	R-tibble
BuildRequires: 	R-tidyselect
BuildRequires: 	R-vctrs
BuildRequires: 	R-utf8
BuildRequires: 	R-pkgconfig
BuildRequires: 	R-withr
 
Suggests:	R-testthat
%description 
Estimates parameters in Mixture Transition Distribution (MTD) models, a 
class of high-order Markov chains. The set of relevant pasts (lags) is 
selected using either the Bayesian Information Criterion or the Forward 
Stepwise and Cut algorithms. Other model parameters (e.g. transition 
probabilities and oscillations) can be estimated via maximum likelihood 
estimation or the Expectation-Maximization algorithm. Additionally, 
'hdMTD' includes a perfect sampling algorithm that generates samples of 
an MTD model from its invariant distribution. For theory, see Ost & 
Takahashi (2023) <http://jmlr.org/papers/v24/22-0266.html>. 
 
%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
%doc %{rlibdir}/%{packname}/NEWS.md
%{rlibdir}/%{packname}/R
%{rlibdir}/%{packname}/data
%doc %{rlibdir}/%{packname}/help
%doc %{rlibdir}/%{packname}/html
 
%changelog