CRAN/E | geosimilarity

geosimilarity

Geographically Optimal Similarity

Installation

About

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.

Citation geosimilarity citation info

Key Metrics

Version 2.2
R ≥ 4.1.0
Published 2022-11-08 507 days ago
Needs compilation? no
License GPL-2
CRAN checks geosimilarity results

Downloads

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Maintainer

Maintainer

Yongze Song

yongze.song@outlook.com

Authors

Yongze Song

aut / cre

Material

Reference manual
Package source

Vignettes

Optimal Parameters-based Geographical Detectors (OPGD) Model for Spatial Heterogeneity Analysis and Factor Exploration

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

geosimilarity archive

Depends

R ≥ 4.1.0

Imports

stats
SecDim
DescTools
ggplot2
dplyr
ggrepel

Suggests

knitr
rmarkdown