%global __brp_check_rpaths %{nil}
%global __requires_exclude ^libmpi
%global packname  spBFA
%global packver   1.3
%global rlibdir   /usr/local/lib/R/library

Name:             R-CRAN-%{packname}
Version:          1.3
Release:          1%{?dist}%{?buildtag}
Summary:          Spatial Bayesian Factor Analysis

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


BuildRequires:    R-devel >= 3.0.2
Requires:         R-core >= 3.0.2
BuildRequires:    R-CRAN-msm >= 1.0
BuildRequires:    R-CRAN-mvtnorm >= 1.0.0
BuildRequires:    R-CRAN-pgdraw >= 1.0
BuildRequires:    R-CRAN-RcppArmadillo >= 0.7.500.0.0
BuildRequires:    R-CRAN-Rcpp >= 0.12.9
BuildRequires:    R-graphics 
BuildRequires:    R-grDevices 
BuildRequires:    R-stats 
BuildRequires:    R-utils 
Requires:         R-CRAN-msm >= 1.0
Requires:         R-CRAN-mvtnorm >= 1.0.0
Requires:         R-CRAN-pgdraw >= 1.0
Requires:         R-CRAN-Rcpp >= 0.12.9
Requires:         R-graphics 
Requires:         R-grDevices 
Requires:         R-stats 
Requires:         R-utils 

%description
Implements a spatial Bayesian non-parametric factor analysis model with
inference in a Bayesian setting using Markov chain Monte Carlo (MCMC).
Spatial correlation is introduced in the columns of the factor loadings
matrix using a Bayesian non-parametric prior, the probit stick-breaking
process. Areal spatial data is modeled using a conditional autoregressive
(CAR) prior and point-referenced spatial data is treated using a Gaussian
process. The response variable can be modeled as Gaussian, probit, Tobit,
or Binomial (using Polya-Gamma augmentation). Temporal correlation is
introduced for the latent factors through a hierarchical structure and can
be specified as exponential or first-order autoregressive. Full details of
the package can be found in the accompanying vignette. Furthermore, the
details of the package can be found in "Bayesian Non-Parametric Factor
Analysis for Longitudinal Spatial Surfaces", by Berchuck et al (2019),
<arXiv:1911.04337>. The paper is in press at the journal Bayesian
Analysis.

%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}