File R-LatticeKrig.spec of Package R-LatticeKrig
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# Spec file for package LatticeKrig
# This file is auto-generated using information in the package source,
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%global packname LatticeKrig
%global rlibdir %{_libdir}/R/library
Name: R-%{packname}
Version: 9.3.0
Release: 0
Summary: Multi-Resolution Kriging Based on Markov Random Fields
Group: Development/Libraries/Other
License: GPL (>= 2)
URL: http://cran.r-project.org/web/packages/%{packname}
Source: LatticeKrig_9.3.0.tar.gz
Requires: R-base
Requires: R-spam
Requires: R-spam64
Requires: R-fftwtools
Requires: R-fields
Requires: R-viridisLite
Requires: R-RColorBrewer
Requires: R-maps
Requires: R-dotCall64
Requires: R-Rcpp
# %%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-spam
BuildRequires: R-spam64
BuildRequires: R-fftwtools
BuildRequires: R-fields
BuildRequires: R-viridisLite
BuildRequires: R-RColorBrewer
BuildRequires: R-maps
BuildRequires: R-dotCall64
BuildRequires: R-Rcpp-devel
BuildRequires: gcc gcc-c++ gcc-fortran
%description
Methods for the interpolation of large spatial datasets. This package
uses a basis function approach that provides a surface fitting method
that can approximate standard spatial data models. Using a large number
of basis functions allows for estimates that can come close to
interpolating the observations (a spatial model with a small nugget
variance.) Moreover, the covariance model for this method can
approximate the Matern covariance family but also allows for a
multi-resolution model and supports efficient computation of the
profile likelihood for estimating covariance parameters. This is
accomplished through compactly supported basis functions and a Markov
random field model for the basis coefficients. These features lead to
sparse matrices for the computations and this package makes of the R
spam package for sparse linear algebra. An extension of this version
over previous ones ( < 5.4 ) is the support for different geometries
besides a rectangular domain. The Markov random field approach combined
with a basis function representation makes the implementation of
different geometries simple where only a few specific R functions need
to be added with most of the computation and evaluation done by generic
routines that have been tuned to be efficient. One benefit of this
package's model/approach is the facility to do unconditional and
conditional simulation of the field for large numbers of arbitrary
points. There is also the flexibility for estimating non-stationary
covariances and also the case when the observations are a linear
combination (e.g. an integral) of the spatial process. Included are
generic methods for prediction, standard errors for prediction,
plotting of the estimated surface and conditional and unconditional
simulation. See the 'LatticeKrigRPackage' GitHub repository for a
vignette of this package. Development of this package was supported in
part by the National Science Foundation Grant 1417857 and the National
Center for Atmospheric Research.
%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}
%{rlibdir}/%{packname}/CITATION
%doc %{rlibdir}/%{packname}/DESCRIPTION
%{rlibdir}/%{packname}/INDEX
%{rlibdir}/%{packname}/Meta
%{rlibdir}/%{packname}/NAMESPACE
%{rlibdir}/%{packname}/R
%{rlibdir}/%{packname}/data
%doc %{rlibdir}/%{packname}/help
%doc %{rlibdir}/%{packname}/html
%{rlibdir}/%{packname}/libs
%changelog
