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
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NEWS

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

Peer review:

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

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

On CRAN:

geoinformaticsgeospatial-analyticsspatial-predictionsspatial-statistics

5.24 score 5 stars 5 scripts 399 downloads 4 exports 34 dependencies

Last updated 5 days agofrom:fd1ca0ce57. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 01 2024
R-4.5-winOKNov 01 2024
R-4.5-linuxOKNov 01 2024
R-4.4-winOKNov 01 2024
R-4.4-macOKNov 01 2024
R-4.3-winOKNov 01 2024
R-4.3-macOKNov 01 2024

Exports:%>%gosgos_bestkapparemoveoutlier

Dependencies:clicolorspacedplyrfansifarvergenericsggplot2ggrepelgluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigpurrrR6RColorBrewerRcpprlangscalestibbletidyselectutf8vctrsviridisLitewithr

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

Rendered fromgeosimilarity.Rmdusingknitr::rmarkdownon Nov 01 2024.

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