Provides methods to deal with under sampling in ecological bipartite networks from
     Terry and Lewis (2020) Ecology <doi:10.1002/ecy.3047> Includes 
     tools to fit a variety of statistical network models and sample coverage estimators to highlight most likely 
     missing links. Also includes simple functions to resample from observed networks to generate confidence 
     intervals for common ecological network metrics.

Computes a range of scatterplot diagnostics (scagnostics) on pairs
  of numerical variables in a data set. A range of scagnostics, including graph
  and association-based scagnostics described by Leland Wilkinson and Graham
  Wills (2008) <doi:10.1198/106186008X320465> and association-based
  scagnostics described by Katrin Grimm (2016,ISBN:978-3-8439-3092-5) can be 
  computed. Summary and plotting functions are provided.

Supporting functionality to run 'caret' with spatial or spatial-temporal data. 'caret' is a frequently used package for model training and prediction using machine learning. CAST includes functions to improve spatial or spatial-temporal modelling tasks using 'caret'. It includes the newly suggested 'Nearest neighbor distance matching' cross-validation to estimate the performance of spatial prediction models and allows for spatial variable selection to selects suitable predictor variables in view to their contribution to the spatial model performance. CAST further includes functionality to estimate the (spatial) area of applicability of prediction models. Methods are described in Meyer et al. (2018) <doi:10.1016/j.envsoft.2017.12.001>; Meyer et al. (2019) <doi:10.1016/j.ecolmodel.2019.108815>; Meyer and Pebesma (2021) <doi:10.1111/2041-210X.13650>; Milà et al. (2022) <doi:10.1111/2041-210X.13851>; Meyer and Pebesma (2022) <doi:10.1038/s41467-022-29838-9>; Linnenbrink et al. (2023) <doi:10.5194/egusphere-2023-1308>; Schumacher et al. (2024) <doi:10.5194/egusphere-2024-2730>. The package is described in detail in Meyer et al. (2024) <doi:10.48550/arXiv.2404.06978>.