Package: spm
Title: Spatial Predictive Modeling
Version: 1.2.3
Date: 2025-09-05
Authors@R: person("Jin", "Li", email = "jinli68@gmail.com", role = c("aut", "cre"))
Description: Introduction to some novel accurate hybrid methods of
        geostatistical and machine learning methods for spatial
        predictive modelling. It contains two commonly used
        geostatistical methods, two machine learning methods, four
        hybrid methods and two averaging methods. For each method, two
        functions are provided. One function is for assessing the
        predictive errors and accuracy of the method based on
        cross-validation. The other one is for generating spatial
        predictions using the method. For details please see: Li, J.,
        Potter, A., Huang, Z., Daniell, J. J. and Heap, A. (2010)
        <https://ecat.ga.gov.au/geonetwork/srv/eng/catalog.search#/metadata/71407>
        Li, J., Heap, A. D., Potter, A., Huang, Z. and Daniell, J.
        (2011) <doi:10.1016/j.csr.2011.05.015> Li, J., Heap, A. D.,
        Potter, A. and Daniell, J. (2011)
        <doi:10.1016/j.envsoft.2011.07.004> Li, J., Potter, A., Huang,
        Z. and Heap, A. (2012)
        <https://ecat.ga.gov.au/geonetwork/srv/eng/catalog.search#/metadata/74030>.
Depends: R (>= 2.10)
Imports: gstat, sp, randomForest, psy, gbm, stats, ranger
License: GPL (>= 2)
LazyData: true
RoxygenNote: 7.3.3
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2026-06-17 08:41:45 UTC; root
Author: Jin Li [aut, cre]
Maintainer: Jin Li <jinli68@gmail.com>
Config/pak/sysreqs: libabsl-dev cmake libgdal-dev gdal-bin libgeos-dev
        libssl-dev libproj-dev libsqlite3-dev libudunits2-dev
Repository: https://jinli22.r-universe.dev
Date/Publication: 2025-09-06 05:10:28 UTC
RemoteUrl: https://github.com/cran/spm
RemoteRef: HEAD
RemoteSha: 30450953bbd130426ad26282749be9737a063e69