geosimilarity: Geographically Optimal Similarity

Understanding spatial association is essential for spatial statistical inference, including factor exploration and spatial prediction. Geographically optimal similarity (GOS) model is an effective method for spatial prediction, as described in Yongze Song (2022) <doi:10.1007/s11004-022-10036-8>. GOS was developed based on the geographical similarity principle, as described in Axing Zhu (2018) <doi:10.1080/19475683.2018.1534890>. GOS has advantages in more accurate spatial prediction using fewer samples and critically reduced prediction uncertainty.

Version: 2.2
Depends: R (≥ 4.1.0)
Imports: stats, SecDim, DescTools, ggplot2, dplyr, ggrepel
Suggests: knitr, rmarkdown
Published: 2022-11-08
DOI: 10.32614/CRAN.package.geosimilarity
Author: Yongze Song ORCID iD [aut, cre]
Maintainer: Yongze Song < at>
License: GPL-2
NeedsCompilation: no
Citation: geosimilarity citation info
CRAN checks: geosimilarity results


Reference manual: geosimilarity.pdf
Vignettes: Optimal Parameters-based Geographical Detectors (OPGD) Model for Spatial Heterogeneity Analysis and Factor Exploration


Package source: geosimilarity_2.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): geosimilarity_2.2.tgz, r-oldrel (arm64): geosimilarity_2.2.tgz, r-release (x86_64): geosimilarity_2.2.tgz, r-oldrel (x86_64): geosimilarity_2.2.tgz
Old sources: geosimilarity archive


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