Artifacts using Raster (139)

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Set of tools for manipulating geographic data. It includes binary access to 'GSHHG' shoreline files. The package also provides interface wrappers for exchanging spatial objects with packages such as 'PBSmapping', 'spatstat', 'maps', 'RArcInfo', and others.

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

Create and customize interactive maps using the 'Leaflet' JavaScript library and the 'htmlwidgets' package. These maps can be used directly from the R console, from 'RStudio', in Shiny applications and R Markdown documents.

Fast image processing for images in up to 4 dimensions (two spatial dimensions, one time/depth dimension, one colour dimension). Provides most traditional image processing tools (filtering, morphology, transformations, etc.) as well as various functions for easily analysing image data using R. The package wraps 'CImg', <http://cimg.eu>, a simple, modern C++ library for image processing.

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.

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

Calculate distances and routes on geographic grids.

Diverse spatial datasets for demonstrating, benchmarking and teaching spatial data analysis. It includes R data of class sf (defined by the package 'sf'), Spatial ('sp'), and nb ('spdep'). Unlike other spatial data packages such as 'rnaturalearth' and 'maps', it also contains data stored in a range of file formats including GeoJSON, ESRI Shapefile and GeoPackage. Some of the datasets are designed to illustrate specific analysis techniques. cycle_hire() and cycle_hire_osm(), for example, is designed to ...

Functions to estimate the density and size of a spatially distributed animal population sampled with an array of passive detectors, such as traps, or by searching polygons or transects. Models incorporating distance-dependent detection are fitted by maximizing the likelihood. Tools are included for data manipulation and model selection.

Fits hierarchical models of animal abundance and occurrence to data collected using survey methods such as point counts, site occupancy sampling, distance sampling, removal sampling, and double observer sampling. Parameters governing the state and observation processes can be modeled as functions of covariates.