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: | 3.8 |
| Depends: | R (≥ 4.1.0) |
| Imports: | stats, parallel,tibble,dplyr (≥ 1.1.0),purrr,ggplot2,magrittr,ggrepel |
| Suggests: | cowplot,viridis,car,DescTools,PerformanceAnalytics,testthat (≥ 3.0.0),sdsfun,rmarkdown,knitr |
| Published: | 2025-09-23 |
| 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 |
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