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.">

geosimilarity: Geographically Optimal Similarity (original) (raw)

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 ORCID iD [aut, cph], Wenbo Lv ORCID iD [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

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