%global __brp_check_rpaths %{nil}
%global __requires_exclude ^libmpi
%global packname  bayespm
%global packver   0.2.0
%global rlibdir   /usr/local/lib/R/library

Name:             R-CRAN-%{packname}
Version:          0.2.0
Release:          1%{?dist}%{?buildtag}
Summary:          Bayesian Statistical Process Monitoring

License:          GPL (>= 2)
URL:              https://cran.r-project.org/package=%{packname}
Source0:          %{url}&version=%{packver}#/%{packname}_%{packver}.tar.gz


BuildRequires:    R-devel >= 3.5.0
Requires:         R-core >= 3.5.0
BuildArch:        noarch
BuildRequires:    R-grDevices 
BuildRequires:    R-stats 
BuildRequires:    R-CRAN-ggplot2 
BuildRequires:    R-grid 
BuildRequires:    R-CRAN-gridExtra 
BuildRequires:    R-CRAN-extraDistr 
BuildRequires:    R-CRAN-rmutil 
BuildRequires:    R-CRAN-invgamma 
Requires:         R-grDevices 
Requires:         R-stats 
Requires:         R-CRAN-ggplot2 
Requires:         R-grid 
Requires:         R-CRAN-gridExtra 
Requires:         R-CRAN-extraDistr 
Requires:         R-CRAN-rmutil 
Requires:         R-CRAN-invgamma 

%description
The R-package bayespm implements Bayesian Statistical Process Control and
Monitoring (SPC/M) methodology. These methods utilize available prior
information and/or historical data, providing efficient online quality
monitoring of a process, in terms of identifying moderate/large transient
shifts (i.e., outliers) or persistent shifts of medium/small size in the
process. These self-starting, sequentially updated tools can also run
under complete absence of any prior information. The Predictive Control
Charts (PCC) are introduced for the quality monitoring of data from any
discrete or continuous distribution that is a member of the regular
exponential family. The Predictive Ratio CUSUMs (PRC) are introduced for
the Binomial, Poisson and Normal data (a later version of the library will
cover all the remaining distributions from the regular exponential
family). The PCC targets transient process shifts of typically large size
(a.k.a. outliers), while PRC is focused in detecting persistent
(structural) shifts that might be of medium or even small size. Apart from
monitoring, both PCC and PRC provide the sequentially updated posterior
inference for the monitored parameter. Bourazas K., Kiagias D. and
Tsiamyrtzis P. (2022) "Predictive Control Charts (PCC): A Bayesian
approach in online monitoring of short runs"
<doi:10.1080/00224065.2021.1916413>, Bourazas K., Sobas F. and
Tsiamyrtzis, P. 2023. "Predictive ratio CUSUM (PRC): A Bayesian approach
in online change point detection of short runs"
<doi:10.1080/00224065.2022.2161434>, Bourazas K., Sobas F. and
Tsiamyrtzis, P. 2023. "Design and properties of the predictive ratio cusum
(PRC) control charts" <doi:10.1080/00224065.2022.2161435>.

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

# fix end of executable files
find -type f -executable -exec grep -Iq . {} \; -exec sed -i -e '$a\' {} \;
# prevent binary stripping
[ -d %{packname}/src ] && find %{packname}/src -type f -exec \
  sed -i 's@/usr/bin/strip@/usr/bin/true@g' {} \; || true
[ -d %{packname}/src ] && find %{packname}/src/Make* -type f -exec \
  sed -i 's@-g0@@g' {} \; || true
# don't allow local prefix in executable scripts
find -type f -executable -exec sed -Ei 's@#!( )*/usr/local/bin@#!/usr/bin@g' {} \;

%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
# remove buildroot from installed files
find %{buildroot}%{rlibdir} -type f -exec sed -i "s@%{buildroot}@@g" {} \;

%files
%{rlibdir}/%{packname}