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  [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, r-oldrel (x86_64): geosimilarity_2.2.tgz |
| Old sources: | geosimilarity archive |
Linking:
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