Package: sfgpu 0.1.0

sfgpu: Explicit CPU, CUDA, and Metal Distances for Projected Points

Computes dense Euclidean distance matrices and sparse within-radius point matches for Cartesian two-column coordinates and projected 'sf' POINT geometries. A portable double-precision CPU backend is always available; optional NVIDIA CUDA and Apple Metal backends must be requested explicitly at build time and at each call.

Authors:Egor Kotov [aut, cre, cph]

sfgpu_0.1.0.tar.gz
sfgpu_0.1.0.zip(r-4.7-x86_64)sfgpu_0.1.0.zip(r-4.7-arm64)sfgpu_0.1.0.zip(r-4.6-x86_64)sfgpu_0.1.0.zip(r-4.6-arm64)sfgpu_0.1.0.zip(r-4.5-x86_64)
sfgpu_0.1.0.tgz(r-4.6-x86_64)sfgpu_0.1.0.tgz(r-4.6-arm64)sfgpu_0.1.0.tgz(r-4.5-x86_64)sfgpu_0.1.0.tgz(r-4.5-arm64)
sfgpu_0.1.0.tar.gz(r-4.7-arm64)sfgpu_0.1.0.tar.gz(r-4.7-x86_64)sfgpu_0.1.0.tar.gz(r-4.6-arm64)sfgpu_0.1.0.tar.gz(r-4.6-x86_64)
sfgpu_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
sfgpu/json (API)

# Install 'sfgpu' in R:
install.packages('sfgpu', repos = c('https://e-kotov.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/e-kotov/sfgpu/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

cpp

2.30 score 2 stars 5 exports 0 dependencies

Last updated from:c40de03841 (on dev). Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK289
linux-devel-x86_64OK236
source / vignettesOK404
linux-release-arm64OK314
linux-release-x86_64OK224
macos-release-arm64OK128
macos-release-x86_64OK232
macos-oldrel-arm64OK118
macos-oldrel-x86_64OK235
windows-devel-arm64OK185
windows-devel-x86_64OK131
windows-release-arm64OK197
windows-release-x86_64OK84
windows-oldrel-x86_64OK71
wasm-releaseOK193

Exports:install_cudasfgpu_backendssfgpu_distancesfgpu_install_cudasfgpu_within_distance

Dependencies: