File R-GeoModels.spec of Package R-GeoModels
# Automatically generated by CRAN2OBS
#
# Spec file for package GeoModels
# This file is auto-generated using information in the package source,
# esp. Description and Summary. Improvements in that area should be
# discussed with upstream.
#
# Copyright (c) 2026 SUSE LINUX GmbH, Nuernberg, Germany.
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%global packname GeoModels
%global rlibdir %{_libdir}/R/library
Name: R-%{packname}
Version: 2.2.3
Release: 0
Summary: Procedures for Gaussian and Non Gaussian Geostatistical (Large) Data Analysis
Group: Development/Libraries/Other
License: GPL (>= 3)
URL: http://cran.r-project.org/web/packages/%{packname}
Source: GeoModels_2.2.3.tar.gz
Requires: R-base
Requires: R-fields
Requires: R-mapproj
Requires: R-shape
Requires: R-progressr
Requires: R-future.apply
Requires: R-spam
Requires: R-scatterplot3d
Requires: R-dotCall64
Requires: R-FastGP
Requires: R-plotrix
Requires: R-pracma
Requires: R-pbivnorm
Requires: R-sn
Requires: R-sp
Requires: R-nabor
Requires: R-hypergeo
Requires: R-VGAM
Requires: R-foreach
Requires: R-future
Requires: R-doFuture
Requires: R-minqa
Requires: R-withr
Requires: R-globals
Requires: R-iterators
Requires: R-Rcpp
Requires: R-mvtnorm
Requires: R-rbenchmark
Requires: R-RcppEigen
Requires: R-viridisLite
Requires: R-RColorBrewer
Requires: R-maps
Requires: R-digest
Requires: R-listenv
Requires: R-parallelly
Requires: R-elliptic
Requires: R-contfrac
Requires: R-deSolve
Requires: R-BH
Requires: R-mnormt
Requires: R-numDeriv
Requires: R-quantreg
Requires: R-SparseM
Requires: R-MatrixModels
# %%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-fields
BuildRequires: R-mapproj
BuildRequires: R-shape
BuildRequires: R-progressr
BuildRequires: R-future.apply
BuildRequires: R-spam
BuildRequires: R-scatterplot3d
BuildRequires: R-dotCall64
BuildRequires: R-FastGP
BuildRequires: R-plotrix
BuildRequires: R-pracma
BuildRequires: R-pbivnorm
BuildRequires: R-sn
BuildRequires: R-sp
BuildRequires: R-nabor
BuildRequires: R-hypergeo
BuildRequires: R-VGAM
BuildRequires: R-foreach
BuildRequires: R-future
BuildRequires: R-doFuture
BuildRequires: R-minqa
BuildRequires: R-withr
BuildRequires: R-globals
BuildRequires: R-iterators
BuildRequires: R-Rcpp-devel
BuildRequires: R-mvtnorm
BuildRequires: R-rbenchmark
BuildRequires: R-RcppEigen-devel
BuildRequires: R-viridisLite
BuildRequires: R-RColorBrewer
BuildRequires: R-maps
BuildRequires: R-digest
BuildRequires: R-listenv
BuildRequires: R-parallelly
BuildRequires: R-elliptic
BuildRequires: R-contfrac
BuildRequires: R-deSolve-devel
BuildRequires: R-BH-devel
BuildRequires: R-mnormt
BuildRequires: R-numDeriv
BuildRequires: R-quantreg
BuildRequires: R-SparseM
BuildRequires: R-MatrixModels
BuildRequires: gcc gcc-c++ gcc-fortran
Suggests: R-numDeriv
Suggests: R-memuse
%description
Functions for Gaussian and Non Gaussian (bivariate) spatial and
spatio-temporal data analysis are provided for a) (fast) simulation of
random fields, b) inference for random fields using standard likelihood
and a likelihood approximation method called weighted composite
likelihood based on pairs and b) prediction using (local) best linear
unbiased prediction. Weighted composite likelihood can be very
efficient for estimating massive datasets. Both regression and spatial
(temporal) dependence analysis can be jointly performed. Flexible
covariance models for spatial and spatial-temporal data on Euclidean
domains and spheres are provided. There are also many useful functions
for plotting and performing diagnostic analysis. Different non Gaussian
random fields can be considered in the analysis. Among them, random
fields with marginal distributions such as Skew-Gaussian, Student-t,
Tukey-h, Sin-Arcsin, Two-piece, Weibull, Gamma, Log-Gaussian, Binomial,
Negative Binomial and Poisson. See the URL for the papers associated
with this package, as for instance, Bevilacqua and Gaetan (2015)
<doi:10.1007/s11222-014-9460-6>, Bevilacqua et al. (2016)
<doi:10.1007/s13253-016-0256-3>, Vallejos et al. (2020)
<doi:10.1007/978-3-030-56681-4>, Bevilacqua et. al (2020)
<doi:10.1002/env.2632>, Bevilacqua et. al (2021)
<doi:10.1111/sjos.12447>, Bevilacqua et al. (2022)
<doi:10.1016/j.jmva.2022.104949>, Morales-Navarrete et al. (2023)
<doi:10.1080/01621459.2022.2140053>, and a large class of examples and
tutorials.
%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}
%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
