Enables simultaneous statistical inference for the accuracy of multiple classifiers in multiple subgroups (strata). For instance, allows to perform multiple comparisons in diagnostic accuracy studies with co-primary endpoints sensitivity and specificity. (Westphal, Max, and Antonia Zapf. (2021). "Statistical Inference for Diagnostic Test Accuracy Studies with Multiple Comparisons." <arXiv:2105.13469>.)

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A significant pattern mining-based toolbox for region-based genome-wide association studies and higher-order epistasis analyses, implementing the methods described in Llinares-López et al. (2017) <doi:10.1093/bioinformatics/btx071>.

Contains Coverage Adjusted Standardized Mutual Information ('CASMI')-based functions. 'CASMI' is a fundamental concept of a series of methods. For more information about 'CASMI' and 'CASMI'-related methods, please refer to the corresponding publications (e.g., a feature selection method, Shi, J., Zhang, J., & Ge, Y. (2019) <doi:10.3390/e21121179>, and a dataset quality measurement method, Shi, J., Zhang, J., & Ge, Y. (2019) <doi:10.1109/ICHI.2019.8904553>) or contact the package author for the latest updates.

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>. The package is described in detail in Meyer et al. (2024) <doi:10.48550/arXiv.2404.06978>.