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) Li, J., Heap, A. D., Potter, A., Huang, Z. and Daniell, J. (2011) Li, J., Heap, A. D., Potter, A. and Daniell, J. (2011) Li, J., Potter, A., Huang, Z. and Heap, A. (2012) .

Documentation

Manual: spm.pdf
Vignette: A Brief Introduction to the spm Package

Maintainer: Jin Li <jin.li at ga.gov.au>

Author(s): Jin Li*

Install package and any missing dependencies by running this line in your R console:

install.packages("spm")

Depends R (>= 2.10)
Imports gstat, sp, randomForest, psy, gbm, stats
Suggests knitr, rmarkdown
Enhances
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depends
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imports
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Package spm
Materials
URL
Task Views Spatial
Version 1.0.0
Published 2017-08-25
License GPL (>= 2)
BugReports
SystemRequirements
NeedsCompilation no
Citation
CRAN checks spm check results
Package source spm_1.0.0.tar.gz