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
Author: Yongze Song ORCID iD [aut, cre]
Maintainer: Yongze Song <yongze.song at outlook.com>
License: GPL-2
NeedsCompilation: no
Citation: geosimilarity citation info
CRAN checks: geosimilarity results

Documentation:

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

Downloads:

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

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