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Simple Features for R

Bringing financial analysis to the tidyverse

Time series analysis in the `tidyverse`

R package for spatial data handling https://rspatial.github.io/terra/reference/terra-package.html

Sustainable transport planning with R

Thematic Cartography for R

R package for downloading OpenStreetMap data

R interface to many NOAA data APIs

An R package to hold and facilitate interaction with natural earth map data :earth_africa:

Interface between R and the OpenStreetMap-based routing service OSRM

Thematic cartography with R

R package interfacing the Bloomberg API from https://www.bloomberglabs.com/api/

Time-aware tibbles

Data visualisation using OpenStreetMap objects

R interface to the QuantLib library

Deep learning for medical images in R: semantic and instance segmentation, object detection, 2D and 3D, DICOM and NIfTI, one specification.

Data Access Client for Global Surface Summary of the Day (GSOD) Weather Written in R

Targets extensions for geospatial data

R package to print maps, draw on them, and scan them back in

Tanaka Maps with R

Download and import geographical data from Eurostat GISCO into R.

nlrx NetLogo R

The goal of supercells is to utilize the concept of superpixels to a variety of spatial data.

Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.

📦 R package to simulate neutral landscape models 🏔

R Bindings to GDAL (Geospatial Data Abstraction Library)

Simplify the acquisition of remote sensing data

An ecological niche model workflow based on dismo

An R-package to calculate Land Productivity Dynamics using variables derived from Earth Observation imagery.