spm: Spatial Predictive Modeling

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:www.ga.gov.au/metadata-gateway/metadata/record/gcat_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:www.ga.gov.au/metadata-gateway/metadata/record/74030>.

Version: 1.0.0
Depends: R (≥ 2.10)
Imports: gstat, sp, randomForest, psy, gbm, stats
Suggests: knitr, rmarkdown
Published: 2017-08-25
Author: Jin Li [aut, cre]
Maintainer: Jin Li <jin.li at ga.gov.au>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
In views: Spatial
CRAN checks: spm results


Reference manual: spm.pdf
Vignettes: A Brief Introduction to the spm Package
Package source: spm_1.0.0.tar.gz
Windows binaries: r-devel: spm_1.0.0.zip, r-release: spm_1.0.0.zip, r-oldrel: spm_1.0.0.zip
OS X El Capitan binaries: r-release: spm_1.0.0.tgz
OS X Mavericks binaries: r-oldrel: spm_1.0.0.tgz


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