Description: Provision of the S4 SpatialGraph class built on top of objects provided by 'igraph' and 'sp' packages, and associated utilities. See the documentation of the SpatialGraph-class within this package for further description. An example of how from a few points one can arrive to a SpatialGraph is provided in the function sl2sg().

Title: Kernel Density Estimation for Spatial Data
Description: Calculate Kernel Density Estimation (KDE) for spatial data.
The algorithm is inspired by the tool 'Heatmap' from 'QGIS'. The method is described by:
Chainey, S., Tompson, L., Uhlig, S.(2008) <doi:10.1057/palgrave.sj.8350066>.
Imports: Rcpp, sf, dplyr, glue, magrittr, rlang, methods, raster
Suggests: tmap, sp, knitr, testthat (>= 2.1.0)
Author: Jan Caha [aut, cre] (<https://orcid.org/0000-0003-0165-0606>)

Suggests: tmap, sp, knitr, rgdal, testthat (>= 2.1.0)

Imports: Rcpp, sf, dplyr, glue, magrittr, rlang, vctrs, methods, raster
Suggests: tmap, sp, knitr, rgdal, rmarkdown, testthat (>= 2.99.0)

Imports: sf, dplyr, glue, magrittr, rlang, vctrs, methods, raster
Suggests: tmap, sp, knitr, rgdal, rmarkdown, testthat (>= 2.99.0), xml2

Title: Spatial KWD for Large Spatial Maps
Authors@R: c(person("Stefano", "Gualandi", role = c("aut", "cre"),
Description: 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 three main helper functions: compareOneToOne() (to compare two spatial maps), compareOneToMany() (to compare a reference maps with a list of other maps), and compareAll() (to compute a matrix of distances between a list of maps). In case of non-convex maps, the helper functions first build the convex-hull of the input bins and pads the weights with zeros.

Description: 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 three main helper functions: "compareOneToOne" (to compare two spatial maps), "compareOneToMany" (to compare a reference maps with a list of other maps), and "compareAll" (to compute a matrix of distances between a list of maps). In case of non-convex maps, the helper functions first build the convex-hull of the input bins and pads the weights with zeros.