SpatialML: Spatial Machine Learning

Implements a spatial extension of the random forest algorithm (Georganos et al. (2019) <doi:10.1080/10106049.2019.1595177>). Allows for a geographically weighted random forest regression including a function to find the optical bandwidth. (Georganos and Kalogirou (2022) <https://www.mdpi.com/2220-9964/11/9/471>).

Version: 0.1.7
Depends: R (≥ 4.3.0), ranger (≥ 0.15.1), caret (≥ 6.0), randomForest (≥ 4.7)
Published: 2024-04-02
DOI: NA
Author: Stamatis Kalogirou [aut, cre], Stefanos Georganos [aut, ctb]
Maintainer: Stamatis Kalogirou <stamatis.science at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://stamatisgeoai.eu/
NeedsCompilation: no
CRAN checks: SpatialML results

Documentation:

Reference manual: SpatialML.pdf

Downloads:

Package source: SpatialML_0.1.7.tar.gz
Windows binaries: r-devel: SpatialML_0.1.7.zip, r-release: SpatialML_0.1.7.zip, r-oldrel: SpatialML_0.1.7.zip
macOS binaries: r-release (arm64): SpatialML_0.1.7.tgz, r-oldrel (arm64): SpatialML_0.1.7.tgz, r-release (x86_64): SpatialML_0.1.7.tgz, r-oldrel (x86_64): SpatialML_0.1.7.tgz
Old sources: SpatialML archive

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