# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "geosimilarity" in publications use:' type: software license: GPL-3.0-only title: 'geosimilarity: Geographically Optimal Similarity' version: '3.8' doi: 10.1007/s11004-022-10036-8 identifiers: - type: doi value: 10.32614/CRAN.package.geosimilarity abstract: 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) . GOS was developed based on the geographical similarity principle, as described in Axing Zhu (2018) . GOS has advantages in more accurate spatial prediction using fewer samples and critically reduced prediction uncertainty. authors: - family-names: Song given-names: Yongze email: yongze.song@outlook.com orcid: https://orcid.org/0000-0003-3420-9622 - family-names: Lv given-names: Wenbo email: lyu.geosocial@gmail.com orcid: https://orcid.org/0009-0002-6003-3800 preferred-citation: type: article title: Geographically Optimal Similarity authors: - family-names: Yongze given-names: Song year: '2022' month: '11' volume: '55' issue: '3' journal: Mathematical Geosciences issn: 1874-8953 publisher: name: Springer Science and Business Media LLC doi: 10.1007/s11004-022-10036-8 url: https://doi.org/10.1007/s11004-022-10036-8 start: 295–320 repository: https://ausgis.r-universe.dev repository-code: https://github.com/ausgis/geosimilarity commit: fd1ca0ce573c1d163087a29f8d6b6771a4a4daea url: https://ausgis.github.io/geosimilarity/ contact: - family-names: Lv given-names: Wenbo email: lyu.geosocial@gmail.com orcid: https://orcid.org/0009-0002-6003-3800