Most of the current methods explore spatial association using observations at sample locations, which are defined as the first dimension of spatial association (FDA). The proposed concept of the second dimension of spatial association (SDA), as described in Yongze Song (2022) <doi:10.1016/j.jag.2022.102834>, aims to extract in-depth information about the geographical environment from locations outside sample locations for exploring spatial association.
Version: | 3.2 |
Depends: | R (≥ 4.1.0) |
Imports: | stats, RcppArmadillo, methods, geosphere |
Suggests: | knitr, rmarkdown |
Published: | 2023-05-20 |
DOI: | 10.32614/CRAN.package.SecDim |
Author: | Yongze Song |
Maintainer: | Yongze Song <yongze.song at outlook.com> |
License: | GPL-2 |
NeedsCompilation: | no |
CRAN checks: | SecDim results [issues need fixing before 2024-09-30] |
Reference manual: | SecDim.pdf |
Vignettes: |
'SecDim' Package for The Second Dimension of Spatial Association |
Package source: | SecDim_3.2.tar.gz |
Windows binaries: | r-devel: SecDim_3.2.zip, r-release: SecDim_3.2.zip, r-oldrel: SecDim_3.2.zip |
macOS binaries: | r-release (arm64): SecDim_3.2.tgz, r-oldrel (arm64): SecDim_3.2.tgz, r-release (x86_64): SecDim_3.2.tgz, r-oldrel (x86_64): SecDim_3.2.tgz |
Old sources: | SecDim archive |
Reverse suggests: | geosimilarity |
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