Package: geosimilarity 3.8

Wenbo Lv

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.

Authors:Yongze Song [aut, cph], Wenbo Lv [aut, cre]

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geosimilarity.pdf |geosimilarity.html✨
geosimilarity/json (API)
NEWS

# Install 'geosimilarity' in R:
install.packages('geosimilarity', repos = c('https://ausgis.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ausgis/geosimilarity/issues

Pkgdown site:https://ausgis.github.io

Datasets:
  • grid - Spatial grid data of explanatory variables.
  • zn - Spatial datasets of trace element Zn.

On CRAN:

geoinformaticsgeospatial-analyticsspatial-predictionsspatial-statistics

5.38 score 6 stars 5 scripts 238 downloads 4 exports 34 dependencies

Last updated 12 days agofrom:c7c6a92616. Checks:8 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 06 2025
R-4.5-winOKFeb 06 2025
R-4.5-macOKFeb 06 2025
R-4.5-linuxOKFeb 06 2025
R-4.4-winOKFeb 06 2025
R-4.4-macOKFeb 06 2025
R-4.3-winOKFeb 06 2025
R-4.3-macOKFeb 06 2025

Exports:%>%gosgos_bestkapparemoveoutlier

Dependencies:clicolorspacedplyrfansifarvergenericsggplot2ggrepelgluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigpurrrR6RColorBrewerRcpprlangscalestibbletidyselectutf8vctrsviridisLitewithr

Geographically Optimal Similarity (GOS) and the Third Law of Geography in R

Rendered fromgeosimilarity.Rmdusingknitr::rmarkdownon Feb 06 2025.

Last update: 2024-11-01
Started: 2024-08-20