habCluster: Detecting Spatial Clustering Based on Connection Cost Between Grids

Based on landscape connectivity, spatial boundaries were identified using community detection algorithm at grid level. Methods using raster as input and the value of each cell of the raster is the "smoothness" to indicate how easy the cell connecting with neighbor cells. Details about the 'habCluster' package methods can be found in Zhang et al. <bioRxiv:2022.05.06.490926>.

Version: 1.0.5
Depends: R (≥ 4.0.0), igraph (≥ 1.3.0), stars (≥ 0.5-0), sf (≥ 1.0.0), methods
Imports: Rcpp, raster
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, testthat (≥ 3.1.0), spelling
Published: 2022-05-25
DOI: 10.32614/CRAN.package.habCluster
Author: Qiang Dai
Maintainer: Qiang Dai <daiqiang at cib.ac.cn>
License: GPL (≥ 3)
NeedsCompilation: yes
Language: en-US
Materials: README NEWS
CRAN checks: habCluster results


Reference manual: habCluster.pdf
Vignettes: introduction-to-habCluster


Package source: habCluster_1.0.5.tar.gz
Windows binaries: r-devel: habCluster_1.0.5.zip, r-release: habCluster_1.0.5.zip, r-oldrel: habCluster_1.0.5.zip
macOS binaries: r-release (arm64): habCluster_1.0.5.tgz, r-oldrel (arm64): habCluster_1.0.5.tgz, r-release (x86_64): habCluster_1.0.5.tgz, r-oldrel (x86_64): habCluster_1.0.5.tgz
Old sources: habCluster archive


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