spm2: Spatial Predictive Modeling

An updated and extended version of 'spm' package, by introducing some further novel functions for modern statistical methods (i.e., generalised linear models, glmnet, generalised least squares), support vector machine, kriging methods (i.e., simple kriging, universal kriging, block kriging, kriging with an external drift), and novel hybrid methods (228 hybrids plus numerous variants) of modern statistical methods or machine learning methods with mathematical and/or univariate geostatistical methods for spatial predictive modelling. For each method, two functions are provided, with one function for assessing the predictive errors and accuracy of the method based on cross-validation, and the other for generating spatial predictions. It also contains a couple of functions for data preparation and predictive accuracy assessment.

Version: 1.1.0
Depends: R (≥ 2.10)
Imports: spm, gstat, sp, randomForest, psy, gbm, stats, ranger, MASS, nlme, glmnet, e1071
Suggests: knitr, rmarkdown
Published: 2021-10-08
Author: Jin Li [aut, cre]
Maintainer: Jin Li <jinli68 at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: spm2 results


Reference manual: spm2.pdf


Package source: spm2_1.1.0.tar.gz
Windows binaries: r-devel: spm2_1.1.0.zip, r-release: spm2_1.1.0.zip, r-oldrel: spm2_1.1.0.zip
macOS binaries: r-release (arm64): spm2_1.1.0.tgz, r-release (x86_64): spm2_1.1.0.tgz, r-oldrel: spm2_1.1.0.tgz

Reverse dependencies:

Reverse imports: steprf


Please use the canonical form https://CRAN.R-project.org/package=spm2 to link to this page.