Spatial Data Analysis

Methods for spatial data analysis, especially raster data. Methods allow for low-level data manipulation as well as high-level global, local, zonal, and focal computation. The predict and interpolate methods facilitate the use of regression type (interpolation, machine learning) models for spatial prediction. Processing of very large files is supported. See the manual and tutorials on <> to get started. 'terra' is very similar to the 'raster' package; but 'terra' is simpler, better, and faster.


Reference manual

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1.0-10 by Robert J. Hijmans, a month ago

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Browse source code at

Authors: Robert J. Hijmans [cre, aut] , Roger Bivand [ctb] , Karl Forner [ctb] , Jeroen Ooms [ctb] , Edzer Pebesma [ctb]

Documentation:   PDF Manual  

GPL (>= 3) license

Imports methods, Rcpp, raster

Suggests parallel, tinytest, ncdf4

Linking to Rcpp

System requirements: C++11, GDAL (>= 3.0.4), GEOS (>= 3.8.0), PROJ (>= 6.3.1)

Imported by bomrang, fgdr, maptiles.

Suggested by Rsagacmd, disdat, dismo, nasapower, spatialEco, stars.

Enhanced by landscapemetrics.

See at CRAN