Package: SpatialKWD 0.4.1

Stefano Gualandi

SpatialKWD: Spatial KWD for Large Spatial Maps

Contains efficient implementations of Discrete Optimal Transport algorithms for the computation of Kantorovich-Wasserstein distances between pairs of large spatial maps (Bassetti, Gualandi, Veneroni (2020), <doi:10.1137/19M1261195>). All the algorithms are based on an ad-hoc implementation of the Network Simplex algorithm. The package has four main helper functions: compareOneToOne() (to compare two spatial maps), compareOneToMany() (to compare a reference map with a list of other maps), compareAll() (to compute a matrix of distances between a list of maps), and focusArea() (to compute the KWD distance within a focus area). In non-convex maps, the helper functions first build the convex-hull of the input bins and pad the weights with zeros.

Authors:Stefano Gualandi [aut, cre]

SpatialKWD_0.4.1.tar.gz
SpatialKWD_0.4.1.tar.gz(r-4.5-noble)SpatialKWD_0.4.1.tar.gz(r-4.4-noble)
SpatialKWD_0.4.1.tgz(r-4.4-emscripten)SpatialKWD_0.4.1.tgz(r-4.3-emscripten)
SpatialKWD.pdf |SpatialKWD.html✨
SpatialKWD/json (API)

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

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cppopenmp

1.28 score 19 scripts 185 downloads 6 exports 1 dependencies

Last updated 2 years agofrom:7d79e02686. Checks:OK: 1 WARNING: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 19 2024
R-4.5-linux-x86_64WARNINGDec 19 2024

Exports:compareAllcompareOneToManycompareOneToOnefocusAreaHistogram2DSolver

Dependencies:Rcpp