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rspatial
terra:Spatial Data Analysis
Methods for spatial data analysis with vector (points, lines, polygons) and raster (grid) data. Methods for vector data include geometric operations such as intersect and buffer. Raster methods include local, focal, global, zonal and geometric operations. The predict and interpolate methods facilitate the use of regression type (interpolation, machine learning) models for spatial prediction, including with satellite remote sensing data. Processing of very large files is supported. See the manual and tutorials on <https://rspatial.org/> to get started. 'terra' replaces the 'raster' package ('terra' can do more, and it is faster and easier to use).
Maintained by Robert J. Hijmans. Last updated 15 hours ago.
geospatialrasterspatialvectoronetbbprojgdalgeoscpp
560 stars 17.65 score 17k scripts 856 dependents
r-lidar
lidR:Airborne LiDAR Data Manipulation and Visualization for Forestry Applications
Airborne LiDAR (Light Detection and Ranging) interface for data manipulation and visualization. Read/write 'las' and 'laz' files, computation of metrics in area based approach, point filtering, artificial point reduction, classification from geographic data, normalization, individual tree segmentation and other manipulations.
Maintained by Jean-Romain Roussel. Last updated 2 months ago.
alsforestrylaslazlidarpoint-cloudremote-sensingopenblascppopenmp
623 stars 14.47 score 844 scripts 8 dependents
bioc
SpatialFeatureExperiment:Integrating SpatialExperiment with Simple Features in sf
A new S4 class integrating Simple Features with the R package sf to bring geospatial data analysis methods based on vector data to spatial transcriptomics. Also implements management of spatial neighborhood graphs and geometric operations. This pakage builds upon SpatialExperiment and SingleCellExperiment, hence methods for these parent classes can still be used.
Maintained by Lambda Moses. Last updated 2 months ago.
datarepresentationtranscriptomicsspatial
49 stars 9.40 score 322 scripts 1 dependents
arcadeantics
this.path:Get Executing Script's Path
Determine the path of the executing script. Compatible with several popular GUIs: 'Rgui', 'RStudio', 'Positron', 'VSCode', 'Jupyter', 'Emacs', and 'Rscript' (shell). Compatible with several functions and packages: 'source()', 'sys.source()', 'debugSource()' in 'RStudio', 'compiler::loadcmp()', 'utils::Sweave()', 'box::use()', 'knitr::knit()', 'plumber::plumb()', 'shiny::runApp()', 'package:targets', and 'testthat::source_file()'.
Maintained by Iris Simmons. Last updated 6 days ago.
48 stars 8.73 score 388 scripts 3 dependents
adamlilith
fasterRaster:Faster Raster and Spatial Vector Processing Using 'GRASS GIS'
Processing of large-in-memory/large-on disk rasters and spatial vectors using 'GRASS GIS' <https://grass.osgeo.org/>. Most functions in the 'terra' package are recreated. Processing of medium-sized and smaller spatial objects will nearly always be faster using 'terra' or 'sf', but for large-in-memory/large-on-disk objects, 'fasterRaster' may be faster. To use most of the functions, you must have the stand-alone version (not the 'OSGeoW4' installer version) of 'GRASS GIS' 8.0 or higher.
Maintained by Adam B. Smith. Last updated 3 days ago.
aspectdistancefragmentationfragmentation-indicesgisgrassgrass-gisrasterraster-projectionrasterizeslopetopographyvectorization
57 stars 7.68 score 8 scripts
courtiol
IsoriX:Isoscape Computation and Inference of Spatial Origins using Mixed Models
Building isoscapes using mixed models and inferring the geographic origin of samples based on their isotopic ratios. This package is essentially a simplified interface to several other packages which implements a new statistical framework based on mixed models. It uses 'spaMM' for fitting and predicting isoscapes, and assigning an organism's origin depending on its isotopic ratio. 'IsoriX' also relies heavily on the package 'rasterVis' for plotting the maps produced with 'terra' using 'lattice'.
Maintained by Alexandre Courtiol. Last updated 7 months ago.
14 stars 5.59 score 56 scripts
r-forge
Rwave:Time-Frequency analysis of 1-D signals
Rwave is a library of R functions which provide an environment for the Time-Frequency analysis of 1-D signals (and especially for the wavelet and Gabor transforms of noisy signals). It was originally written for Splus by Rene Carmona, Bruno Torresani, and Wen L. Hwang, first at the University of California at Irvine and then at Princeton University. Credit should also be given to Andrea Wang whose functions on the dyadic wavelet transform are included. Rwave is based on the book: "Practical Time-Frequency Analysis: Gabor and Wavelet Transforms with an Implementation in S", by Rene Carmona, Wen L. Hwang and Bruno Torresani, Academic Press, 1998. This package is no longer actively maintained. A C++ rewrite of core functionality is in progress. If you'd like to participate, please contact Christian Gunning.
Maintained by Brandon Whitcher. Last updated 13 years ago.
4.82 score 88 scripts 5 dependents
adamkocsis
via:Virtual Arrays
The base class 'VirtualArray' is defined, which acts as a wrapper around lists allowing users to fold arbitrary sequential data into n-dimensional, R-style virtual arrays. The derived 'XArray' class is defined to be used for homogeneous lists that contain a single class of objects. The 'RasterArray' and 'SfArray' classes enable the use of stacked spatial data instead of lists.
Maintained by Adam T. Kocsis. Last updated 2 years ago.
3 stars 3.18 score 8 scripts