Showing 8 of total 8 results (show query)
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 16 hours ago.
geospatialrasterspatialvectoronetbbprojgdalgeoscpp
560 stars 17.65 score 17k scripts 856 dependentsrspatial
raster:Geographic Data Analysis and Modeling
Reading, writing, manipulating, analyzing and modeling of spatial data. This package has been superseded by the "terra" package <https://CRAN.R-project.org/package=terra>.
Maintained by Robert J. Hijmans. Last updated 2 days ago.
163 stars 17.23 score 58k scripts 562 dependents
appsilon
shiny.semantic:Semantic UI Support for Shiny
Creating a great user interface for your Shiny apps can be a hassle, especially if you want to work purely in R and don't want to use, for instance HTML templates. This package adds support for a powerful UI library Fomantic UI - <https://fomantic-ui.com/> (before Semantic). It also supports universal UI input binding that works with various DOM elements.
Maintained by Jakub Nowicki. Last updated 12 months ago.
appsilonfomantic-uirhinoversesemanticsemantic-componentssemantic-uishiny
506 stars 13.00 score 586 scripts 3 dependents
radiant-rstats
radiant.data:Data Menu for Radiant: Business Analytics using R and Shiny
The Radiant Data menu includes interfaces for loading, saving, viewing, visualizing, summarizing, transforming, and combining data. It also contains functionality to generate reproducible reports of the analyses conducted in the application.
Maintained by Vincent Nijs. Last updated 5 months ago.
53 stars 8.25 score 146 scripts 6 dependents
datasketch
shinypanels:Shiny Layout with Collapsible Panels
Create 'Shiny Apps' with collapsible vertical panels. This package provides a new visual arrangement for elements on top of 'Shiny'. Use the expand and collapse capabilities to leverage web applications with many elements to focus the user attention on the panel of interest.
Maintained by Juan Pablo Marin Diaz. Last updated 10 months ago.
80 stars 6.01 score 43 scripts
globalgov
messydates:A Flexible Class for Messy Dates
Contains a set of tools for constructing and coercing into and from the "mdate" class. This date class implements ISO 8601-2:2019(E) and allows regular dates to be annotated to express unspecified date components, approximate or uncertain date components, date ranges, and sets of dates. This is useful for describing and analysing temporal information, whether historical or recent, where date precision may vary.
Maintained by James Hollway. Last updated 10 days ago.
15 stars 5.18 score
rinterface
shinyNextUI:'HeroUI' 'React' Template for 'shiny' Apps
A set of user interface components to create outstanding 'shiny' apps <https://shiny.posit.co/>, with the power of 'React' 'JavaScript' <https://react.dev/>. Seamlessly support dark and light themes, customize CSS with 'tailwind' <https://tailwindcss.com/>.
Maintained by David Granjon. Last updated 13 days ago.
26 stars 4.41 score
iiasa
ibis.iSDM:Modelling framework for integrated biodiversity distribution scenarios
Integrated framework of modelling the distribution of species and ecosystems in a suitability framing. This package allows the estimation of integrated species distribution models (iSDM) based on several sources of evidence and provided presence-only and presence-absence datasets. It makes heavy use of point-process models for estimating habitat suitability and allows to include spatial latent effects and priors in the estimation. To do so 'ibis.iSDM' supports a number of engines for Bayesian and more non-parametric machine learning estimation. Further, the 'ibis.iSDM' is specifically customized to support spatial-temporal projections of habitat suitability into the future.
Maintained by Martin Jung. Last updated 5 months ago.
bayesianbiodiversityintegrated-frameworkpoisson-processscenariossdmspatial-grainspatial-predictionsspecies-distribution-modelling
21 stars 4.36 score 12 scripts 1 dependents