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>.)

Play casino games in the R console,
  including poker, blackjack, and a slot machine.
  Try to build your fortune before you succumb to the gambler's ruin!

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.

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.