%global __brp_check_rpaths %{nil}
%global __requires_exclude ^libmpi
%global packname  rts2
%global packver   1.0.3
%global rlibdir   /usr/local/lib/R/library

Name:             R-CRAN-%{packname}
Version:          1.0.3
Release:          1%{?dist}%{?buildtag}
Summary:          Log-Gaussian Cox Process Models with Approximations

License:          CC BY-SA 4.0
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
BuildRequires:    R-CRAN-RcppParallel >= 5.0.1
BuildRequires:    R-CRAN-raster >= 3.6.1
BuildRequires:    R-CRAN-StanHeaders >= 2.32.0
BuildRequires:    R-CRAN-rstan >= 2.30.0
BuildRequires:    R-CRAN-rstantools >= 2.1.1
BuildRequires:    R-CRAN-lubridate >= 1.9.0
BuildRequires:    R-CRAN-BH >= 1.66.0
BuildRequires:    R-CRAN-glmmrBase >= 1.3.0
BuildRequires:    R-CRAN-sf >= 1.0.14
BuildRequires:    R-CRAN-stars >= 0.6.1
BuildRequires:    R-CRAN-RcppEigen >= 0.3.3.3.0
BuildRequires:    R-CRAN-Rcpp >= 0.12.0
BuildRequires:    R-methods 
BuildRequires:    R-CRAN-R6 
BuildRequires:    R-CRAN-spdep 
BuildRequires:    R-CRAN-fmesher 
BuildRequires:    R-CRAN-FNN 
BuildRequires:    R-CRAN-quadprog 
BuildRequires:    R-CRAN-rstantools
Requires:         R-CRAN-RcppParallel >= 5.0.1
Requires:         R-CRAN-raster >= 3.6.1
Requires:         R-CRAN-rstan >= 2.30.0
Requires:         R-CRAN-rstantools >= 2.1.1
Requires:         R-CRAN-lubridate >= 1.9.0
Requires:         R-CRAN-glmmrBase >= 1.3.0
Requires:         R-CRAN-sf >= 1.0.14
Requires:         R-CRAN-stars >= 0.6.1
Requires:         R-CRAN-Rcpp >= 0.12.0
Requires:         R-methods 
Requires:         R-CRAN-R6 
Requires:         R-CRAN-spdep 
Requires:         R-CRAN-fmesher 
Requires:         R-CRAN-FNN 
Requires:         R-CRAN-quadprog 
Requires:         R-CRAN-rstantools

%description
Supports modelling case data to facilitate. The package provides automated
computational grid generation over an area of interest with methods to map
covariates between geographies, model fitting including spatially
aggregated case counts, and predictions and visualisation. Monte Carlo
maximum likelihood is the main fitting method with a low-rank
approximation for Gaussian processes described by Solin and Särkkä (2020)
<doi:10.1007/s11222-019-09886-w> and a stochastic partial differential
equation approximation. Bayesian methods are also provided for some
methods. Log-Gaussian Cox Processes are described by Diggle et al. (2013)
<doi:10.1214/13-STS441>.

%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}