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: 3.7 Depends: R (≥ 4.1.0) Imports: stats, parallel, tibble, dplyr (≥ 1.1.0), purrr, ggplot2, magrittr, ggrepel Suggests: knitr, cowplot, viridis, car, DescTools, PerformanceAnalytics, testthat (≥ 3.0.0), rmarkdown Published: 2024-10-17 DOI: 10.32614/CRAN.package.geosimilarity Author: Yongze Song [aut, cph], Wenbo Lv [aut, cre] Maintainer: Wenbo Lv <lyu.geosocial at gmail.com> BugReports: https://github.com/ausgis/geosimilarity/issues License: GPL-3 URL: https://github.com/ausgis/geosimilarity, https://ausgis.github.io/geosimilarity/ NeedsCompilation: no Citation: geosimilarity citation info Materials: README NEWS CRAN checks: geosimilarity results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=geosimilarity to link to this page.
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