Enables the creation of Chain Event Graphs over spatial areas, with an optional 'Shiny' user interface. Allows users to fully customise both the structure and underlying model of the Chain Event Graph, offering a high degree of flexibility for tailored analyses. For more details on Chain Event Graphs, see Freeman, G., & Smith, J. Q. (2011) <doi:10.1016/j.jmva.2011.03.008>, Collazo R. A., Görgen C. and Smith J. Q. (2018, ISBN:9781498729604) and Barclay, L. M., Hutton, J. L., & Smith, J. Q. (2014) <doi:10.1214/13-BA843>.

Spatio-temporal change of support (STCOS) methods are designed for statistical inference
	on geographic and time domains which differ from those on which the data were observed. In
	particular, a parsimonious class of STCOS models supporting Gaussian outcomes was introduced
	by Bradley, Wikle, and Holan <doi:10.1002/sta4.94>. The 'stcos' package contains tools which
	facilitate use of STCOS models.

Estimates a covariance matrix using Stein's isotonized covariance
    estimator, or a related estimator suggested by Haff.

Algorithms of nonparametric sequential test and online 
  change-point detection for streams of univariate (sub-)Gaussian, binary, 
  and bounded random variables, introduced in following publications -
  Shin et al. (2024) <doi:10.48550/arXiv.2203.03532>,
  Shin et al. (2021) <doi:10.48550/arXiv.2010.08082>.

Provides crop yield and meteorological data for Ontario, Canada. 
  Includes functions for fitting and predicting data using spatio-temporal models, as well as
  tools for visualizing the results. The package builds upon existing R packages, including
  'copula' (Hofert et al., 2025) <doi:10.32614/CRAN.package.copula>, and
  'bsts' (Scott, 2024) <doi:10.32614/CRAN.package.bsts>.

Implements the ST-DBSCAN (spatio-temporal density-based spatial
    clustering of applications with noise) clustering algorithm for detecting
    spatially and temporally dense regions in point data, with a fast C++ backend
    via 'Rcpp'. Birant and Kut (2007) <doi:10.1016/j.datak.2006.01.013>.