Robert Hijmans

Robert Hijmans

12 packages on CRAN

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.

geosphere

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

meteor

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A set of functions for weather and climate data manipulation, and other helper functions, to support dynamic ecological modelling, particularly crop and crop disease modeling.

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. There is a also support for vector data operations such as intersections. See the manual and tutorials on <https://rspatial.org/> to get started.

Rquefts

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An implementation of the QUEFTS (Quantitative Evaluation of the Native Fertility of Tropical Soils) model. The model (1) estimates native nutrient (N, P, K) supply of soils from a few soil chemical properties; and (2) computes crop yield given that supply, fertilizer application and crop parameters. See Janssen et al. (1990) <doi:10.1016/0016-7061(90)90021-Z> for the technical details and Sattari et al. (2014) <doi:10.1016/j.fcr.2013.12.005> for a recent evaluation and improvements.

Rwofost

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An implementation of the WOFOST ("World Food Studies") crop growth model. WOFOST is a dynamic simulation model that uses daily weather data, and crop, soil and management parameters to simulate crop growth and development. See De Wit et al. (2019) <doi:10.1016/j.agsy.2018.06.018> for a recent review of the history and use of the model.

terra

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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 <https://rspatial.org/terra/> to get started. The package is similar to the 'raster' package; but it is simpler and faster.

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>.

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.

rgdal

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Provides bindings to the 'Geospatial' Data Abstraction Library ('GDAL') (>= 1.11.4) 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. From 'rgdal' 1.5-1, attempts are made to adapt the handling of coordinate reference systems to 'GDAL' 3 and 'PROJ' 6, using 'WKT2_2019' strings. 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'.

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.

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.