The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

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
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

Linking:

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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.