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: |
3.1 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
stats, parallel, tibble, dplyr (≥ 1.1.0), purrr, ggplot2, magrittr, ggrepel |
| Suggests: |
knitr, tidyverse, cowplot, viridis, car, SecDim, DescTools, PerformanceAnalytics, testthat (≥ 3.0.0) |
| Published: |
2024-08-29 |
| 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:
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