Understanding how features influence a specific response variable becomes crucial in classification problems, with applications ranging from medical diagnosis to customer behavior analysis. 
			 This packages provides tools to compute such an influence measure grounded on game theory concepts.
			 In particular, the influence measures presented in Davila-Pena, Saavedra-Nieves, and Casas-Méndez (2024) <doi:10.48550/arXiv.2408.02481> can be obtained.

Finite candidate-set approximate optimal designs for group   
  testing and related experiments, using convex optimization and equivalence
  checks. Implements the information matrix and cost structure for the
  prevalence / sensitivity / specificity model used in Huang and colleagues
    (2020), as in Chi-Kuang Yeh, Weng Kee Wong, and Julie Zhou (<doi:10.48550/arXiv.2508.08445>).

Computes the  probability density, survival function,
   the hazard rate functions and generates random samples from the 
   GTDL distribution given by Mackenzie, G. (1996) <doi:10.2307/2348408>. 
   The likelihood estimates, the randomized quantile (Louzada, F., et al. 
   (2020) <doi:10.1109/ACCESS.2020.3040525>)
   residuals and the normally transformed randomized survival 
   probability (Li,L., et al. (2021) <doi:10.1002/sim.8852>)
   residuals are obtained for the GTDL model.

Generalized Turnbull's estimator proposed by Dehghan and Duchesne
    (2011).

Implementation of the GTE (Group Technical Effects) model for single-cell data. GTE is a quantitative metric to assess batch effects for individual genes in single-cell data. For a single-cell dataset, the user can calculate the GTE value for individual features (such as genes), and then identify the highly batch-sensitive features. Removing these highly batch-sensitive features results in datasets with low batch effects.

Four graph-based tests are provided for testing whether two samples are from the same distribution.  It works for both continuous data and discrete data.

New multi-sample tests for testing whether multiple samples are from the same distribution. They work well particularly for high-dimensional data.
    Song, H. and Chen, H. (2022) 
    <arXiv:2205.13787>.