Package: geosimilarity
Type: Package
Title: Geographically Optimal Similarity
Date: 2022-11-08
Version: 2.2
Authors@R: person("Yongze Song", email = "yongze.song@outlook.com",
                  comment = c(ORCID = "0000-0003-3420-9622"),
                  role = c("aut", "cre"))
Maintainer: Yongze Song <yongze.song@outlook.com>
Description: 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. 
Imports: stats, SecDim, DescTools, ggplot2, dplyr, ggrepel
Depends: R (>= 4.1.0)
License: GPL-2
RoxygenNote: 7.2.1
LazyData: true
Encoding: UTF-8
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2022-11-08 14:10:13 UTC; 268222h
Author: Yongze Song [aut, cre] (<https://orcid.org/0000-0003-3420-9622>)
Repository: CRAN
Date/Publication: 2022-11-08 16:00:02 UTC
Built: R 4.2.0; ; 2022-11-09 13:47:08 UTC; unix
