Robert Hijmans

Robert Hijmans

8 packages on CRAN

raster

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Reading, writing, manipulating, analyzing and modeling of gridded spatial data. The package implements basic and high-level functions. Processing of very large files is supported.

geosphere

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Spherical trigonometry for geographic applications. That is, compute distances and related measures for angular (longitude/latitude) locations.

dismo

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Functions for species distribution modeling, that is, predicting entire geographic distributions form occurrences at a number of sites and the environment at these sites.

sp

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Classes and methods for spatial data; the classes document where the spatial location information resides, for 2D or 3D data. Utility functions are provided, e.g. for plotting data as maps, spatial selection, as well as methods for retrieving coordinates, for subsetting, print, summary, etc.

rgdal

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Provides bindings to the 'Geospatial' Data Abstraction Library ('GDAL') (>= 1.11.4 and <= 2.5.0) and access to projection/transformation operations from the 'PROJ.4' library. The 'GDAL' and 'PROJ.4' libraries are external to the package, and, when installing the package from source, must be correctly installed first. From 'rgdal' 1.4.1, provision is made for 'PROJ6' accommodation, with 'PROJ6' functionality to follow; from 1.4.1 'rgdal' will build and function when 'PROJ' >= 6. Both 'GDAL' raster and 'OGR' vector map data can be imported into R, and 'GDAL' raster data and 'OGR' vector data exported. Use is made of classes defined in the 'sp' package. Windows and Mac Intel OS X binaries (including 'GDAL', 'PROJ.4' and 'Expat') are provided on 'CRAN'.

rasterVis

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Methods for enhanced visualization and interaction with raster data. It implements visualization methods for quantitative data and categorical data, both for univariate and multivariate rasters. It also provides methods to display spatiotemporal rasters, and vector fields. See the website for examples.

geostatsp

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Geostatistical modelling facilities using Raster and SpatialPoints objects are provided. Non-Gaussian models are fit using INLA, and Gaussian geostatistical models use Maximum Likelihood Estimation. For details see Brown (2015) <doi:10.18637/jss.v063.i12>.

RStoolbox

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Toolbox for remote sensing image processing and analysis such as calculating spectral indices, principal component transformation, unsupervised and supervised classification or fractional cover analyses.