%global __brp_check_rpaths %{nil}
%global packname  SAutomata
%global packver   0.1.0
%global rlibdir   /usr/local/lib/R/library

Name:             R-CRAN-%{packname}
Version:          0.1.0
Release:          3%{?dist}%{?buildtag}
Summary:          Inference and Learning in Stochastic Automata

License:          GPL (>= 3)
URL:              https://cran.r-project.org/package=%{packname}
Source0:          %{url}&version=%{packver}#/%{packname}_%{packver}.tar.gz


BuildRequires:    R-devel >= 2.0.0
Requires:         R-core >= 2.0.0
BuildArch:        noarch

%description
Machine learning provides algorithms that can learn from data and make
inferences or predictions. Stochastic automata is a class of input/output
devices which can model components. This work provides implementation an
inference algorithm for stochastic automata which is similar to the
Viterbi algorithm. Moreover, we specify a learning algorithm using the
expectation-maximization technique and provide a more efficient
implementation of the Baum-Welch algorithm for stochastic automata. This
work is based on Inference and learning in stochastic automata was by
Karl-Heinz Zimmermann(2017) <doi:10.12732/ijpam.v115i3.15>.

%prep
%setup -q -c -n %{packname}


%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

%files
%{rlibdir}/%{packname}