Automated Spatial Prediction using Ensemble Machine Learning

Functions and tools for spatial interpolation and/or prediction of environmental variables (points to grids) based on using Ensemble Machine Learning with geographical distances. Package also provides access to Global Environmental Layers (<>) produced by the foundation and collaborators. Some functions have been migrated and adopted from the Global Soil Information Facilities package.


Reference manual

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0.0.13 by Tomislav Hengl, 3 months ago

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Authors: Tomislav Hengl [aut, cre]

Documentation:   PDF Manual  

GPL-3 license

Imports methods, utils, parallel, matrixStats, mlr, parallelMap, sp, geoR, plyr, rgdal, gdalUtils, raster, ranger, rpart, forestError, nnet, xgboost, kernlab, glmnet, ParamHelpers, spdep, maptools

Suggests boot, nabor, meteo, mda, psych, fossil, rjson, spatstat, spatstat.core, maxlike, RCurl, deepnet, RSAGA, plotKML

System requirements: C++11, GDAL (>= 2.0.1), GEOS (>= 3.4.0), PROJ (>= 4.8.0)

Imported by plotKML.

See at CRAN