Provides statistical tools for 
	analyzing net and relative survival, with a key feature 
	of relaxing the assumption of independent censoring and 
	incorporating the effect of dependent competing risks. 
	It employs a copula-based methodology, specifically the 
	Archimedean copula, to simulate data, conduct survival 
	analysis, and offer comparisons with other methods. 
	This approach is detailed in the work of 
	Adatorwovor et al. (2022) <doi:10.1515/ijb-2021-0016>.

Several tests for differential methylation in methylation array data, including one-sided differential mean and variance test. Methods used in the package refer to Dai, J, Wang, X, Chen, H and others (2021) "Incorporating increased variability in discovering cancer methylation markers", Biostatistics, submitted.

Implementation of a transfer learning framework employing distribution mapping based domain transfer. Uses the renowned concept of histogram matching (see Gonzalez and Fittes (1977) <doi:10.1016/0094-114X(77)90062-3>, Gonzalez and Woods (2008) <isbn:9780131687288>) and extends it to include distribution measures like kernel density estimates (KDE; see Wand and Jones (1995) <isbn:978-0-412-55270-0>, Jones et al. (1996) <doi:10.2307/2291420). In the typical application scenario, one can use the underlying sample distributions (histogram or KDE) to generate a map between two distinct but related domains to transfer the target data to the source domain and utilize the available source data for better predictive modeling design. Suitable for the case where a one-to-one sample matching is not possible, thus one needs to transform the underlying data distribution to utilize the more available data for modeling.

For checking the dataset from EDC(Electronic Data Capture) in clinical trials.
             'dmtools' reshape your dataset in a tidy view and check events.
             You can reshape the dataset and choose your target to check, for example, the laboratory reference range.

Work within the 'dplyr' workflow to add random variates to your data frame. 
  Variates can be added at any level of an existing column.  Also, bounds can be specified 
  for simulated variates.