# Copyright 2013-2021 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)

from spack import *


class RFields(RPackage):
    """fields: Tools for Spatial Data"""

    homepage = "https://github.com/NCAR/Fields"
    url      = "https://cloud.r-project.org/src/contrib/fields_9.9.tar.gz"
    list_url = "https://cloud.r-project.org/src/contrib/Archive/fields"

    version('9.9', sha256='262f03c630773b580c7162ab2a031c894ca489fd83989fd8a2f67573306e78e1')

    depends_on('r@3.0:', type=('build', 'run'))
    depends_on('r-maps', type=('build', 'run'))
    depends_on('r-spam', type=('build', 'run'))

# Copyright 2013-2021 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)

from spack import *


class RFields(RPackage):
    """Tools for Spatial Data

    For curve, surface and function fitting with an emphasis; on splines,
    spatial data, geostatistics,  and spatial statistics. The major methods;
    include cubic, and thin plate splines, Kriging, and compactly supported;
    covariance functions for large data sets. The splines and Kriging methods
    are; supported by functions that can determine the smoothing parameter;
    (nugget and sill variance) and other covariance function parameters by
    cross; validation and also by restricted maximum likelihood. For Kriging;
    there is an easy to use function that also estimates the correlation; scale
    (range parameter).  A major feature is that any covariance function;
    implemented in R and following a simple format can be used for; spatial
    prediction. There are also many useful functions for plotting; and working
    with spatial data as images. This package also contains; an implementation
    of sparse matrix methods for large spatial data; sets and currently
    requires the sparse matrix (spam) package. Use; help(fields) to get started
    and for an overview.  The fields source; code is deliberately commented and
    provides useful explanations of; numerical details as a companion to the
    manual pages. The commented; source code can be viewed by expanding the
    source code version; and looking in the R subdirectory. The reference for
    fields can be generated; by the citation function in R and has DOI
    <doi:10.5065/D6W957CT>. Development; of this package was supported in part
    by the National Science Foundation  Grant; 1417857,  the National Center
    for Atmospheric Research, and Colorado School of Mines.; See the Fields
    URL; for a vignette on using this package and some background on spatial
    statistics."""

    homepage = "https://github.com/NCAR/Fields"
    url      = "https://cloud.r-project.org/src/contrib/fields_9.9.tar.gz"
    list_url = "https://cloud.r-project.org/src/contrib/Archive/fields"

    version('11.6', sha256='8600d1d992c40668cc2ab01b3c17d0e1bd44a001ec7ba9f468bc0e9ef87c59db')
    version('9.9', sha256='262f03c630773b580c7162ab2a031c894ca489fd83989fd8a2f67573306e78e1')

    depends_on('r@3.0:', type=('build', 'run'))
    depends_on('r-spam', type=('build', 'run'))
    depends_on('r-maps', type=('build', 'run'))

add v11.6 (#20921)