Provides an R API to the Open Source Geometry Engine
  ('GEOS') library (<https://libgeos.org/>) and a vector format 
  with which to efficiently store 'GEOS' geometries. High-performance functions 
  to extract information from, calculate relationships between, and
  transform geometries are provided. Finally, facilities to import 
  and export geometry vectors to other spatial formats are provided.

This function is an extension of the Small Area Estimation (SAE) model. Geoadditive Small Area Model is a combination of the geoadditive model with the Small Area Estimation (SAE) model, by adding geospatial information to the SAE model. This package refers to J.N.K Rao and Isabel Molina (2015, ISBN: 978-1-118-73578-7), Bocci, C., & Petrucci, A. (2016)<doi:10.1002/9781118814963.ch13>, and Ardiansyah, M., Djuraidah, A., & Kurnia, A. (2018)<doi:10.21082/jpptp.v2n2.2018.p101-110>.

Provides an R interface to the GeoServer REST API, allowing to upload 
 and publish data in a GeoServer web-application and expose data to OGC Web-Services. 
 The package currently supports all CRUD (Create,Read,Update,Delete) operations
 on GeoServer workspaces, namespaces, datastores (stores of vector data), featuretypes,
 layers, styles, as well as vector data upload operations. For more information about 
 the GeoServer REST API, see <https://docs.geoserver.org/stable/en/user/rest/>.

Functionality for adding the geological timescale to bivariate plots.

Find the smallest circle that contains all longitude and latitude input points. From the generated center and radius, variable side polygons can be created, navigation based on bearing and distance can be applied, and more. Based on a modified version of Welzl's algorithm for smallest circle. Distance calculations are based on the haversine formula. Calculations for distance, midpoint, bearing and more are derived from <https://www.movable-type.co.uk>.

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.