R-universe search: exports:warp

Showing 6 of total 6 results (show query)

asgr

imager:Image Processing Library Based on 'CImg'

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.

Maintained by Aaron Robotham. Last updated 6 days ago.

libx11 fftw3 tiff cpp openmp

17 stars 13.53 score 2.4k scripts 44 dependents

jonclayden

shades:Simple Colour Manipulation

Functions for easily manipulating colours, creating colour scales and calculating colour distances.

Maintained by Jon Clayden. Last updated 6 months ago.

color color-manipulation colour colour-manipulation colour-spaces

83 stars 9.58 score 178 scripts 37 dependents

usdaforestservice

gdalraster:Bindings to the 'Geospatial Data Abstraction Library' Raster API

Interface to the Raster API of the 'Geospatial Data Abstraction Library' ('GDAL', <https://gdal.org>). Bindings are implemented in an exposed C++ class encapsulating a 'GDALDataset' and its raster band objects, along with several stand-alone functions. These support manual creation of uninitialized datasets, creation from existing raster as template, read/set dataset parameters, low level I/O, color tables, raster attribute tables, virtual raster (VRT), and 'gdalwarp' wrapper for reprojection and mosaicing. Includes 'GDAL' algorithms ('dem_proc()', 'polygonize()', 'rasterize()', etc.), and functions for coordinate transformation and spatial reference systems. Calling signatures resemble the native C, C++ and Python APIs provided by the 'GDAL' project. Includes raster 'calc()' to evaluate a given R expression on a layer or stack of layers, with pixel x/y available as variables in the expression; and raster 'combine()' to identify and count unique pixel combinations across multiple input layers, with optional output of the pixel-level combination IDs. Provides raster display using base 'graphics'. Bindings to a subset of the 'OGR' API are also included for managing vector data sources. Bindings to a subset of the Virtual Systems Interface ('VSI') are also included to support operations on 'GDAL' virtual file systems. These are general utility functions that abstract file system operations on URLs, cloud storage services, 'Zip'/'GZip'/'7z'/'RAR' archives, and in-memory files. 'gdalraster' may be useful in applications that need scalable, low-level I/O, or prefer a direct 'GDAL' API.

Maintained by Chris Toney. Last updated 4 hours ago.

gdal geospatial raster vector cpp

41 stars 9.51 score 32 scripts 3 dependents

tonigi

dtw:Dynamic Time Warping Algorithms

A comprehensive implementation of dynamic time warping (DTW) algorithms in R. DTW computes the optimal (least cumulative distance) alignment between points of two time series. Common DTW variants covered include local (slope) and global (window) constraints, subsequence matches, arbitrary distance definitions, normalizations, minimum variance matching, and so on. Provides cumulative distances, alignments, specialized plot styles, etc., as described in Giorgino (2009) <doi:10.18637/jss.v031.i07>.

Maintained by Toni Giorgino. Last updated 3 years ago.

5 stars 8.48 score 582 scripts 49 dependents

astamm

roahd:Robust Analysis of High Dimensional Data

A collection of methods for the robust analysis of univariate and multivariate functional data, possibly in high-dimensional cases, and hence with attention to computational efficiency and simplicity of use. See the R Journal publication of Ieva et al. (2019) <doi:10.32614/RJ-2019-032> for an in-depth presentation of the 'roahd' package. See Aleman-Gomez et al. (2021) <arXiv:2103.08874> for details about the concept of depthgram.

Maintained by Aymeric Stamm. Last updated 3 years ago.

2 stars 6.29 score 164 scripts 2 dependents

ksurana21

revpref:Tools for Computational Revealed Preference Analysis

Tools to (i) check consistency of a finite set of consumer demand observations with a number of revealed preference axioms at a given efficiency level, (ii) compute goodness-of-fit indices when the data do not obey the axioms, and (iii) compute power against uniformly random behavior.

Maintained by Khushboo Surana. Last updated 4 years ago.

2.74 score 11 scripts