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>.

Performs treatment wetland modeling consistent with the original 
             'Excel' spreadsheet and VBA code version of DMSTA2 developed by
             Dr. Bill Walker <http://wwwalker.net/dmsta/>.

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.

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.

The function takes a DNA sequence, a start point, an end point in the sequence, dot size and dot color and draws a fractal image of the sequence. The fractal starts in the center of the canvas. The image is drawn by moving base by base along the sequence and dropping a midpoint between the actual point and the corner designated by the actual base. For more details see Jeffrey (1990) <doi:10.1093/nar/18.8.2163>, Hill, Schisler, and Singh (1992) <doi:10.1007/BF00178602>, and Löchel and Heider (2021) <doi:10.1016/j.csbj.2021.11.008>.

Performs Diffusion Non-Additive (DNA) model proposed by Heo, Boutelet, and Sung (2025+) <doi:10.48550/arXiv.2506.08328> for multi-fidelity computer experiments with tuning parameters. The DNA model captures nonlinear dependencies across fidelity levels using Gaussian process priors and is particularly effective when simulations at different fidelity levels are nonlinearly correlated. The DNA model targets not only interpolation across given fidelity levels but also extrapolation to smaller tuning parameters including the exact solution corresponding to a zero-valued tuning parameter, leveraging a nonseparable covariance kernel structure that models interactions between the tuning parameter and input variables. Closed-form expressions for the predictive mean and variance enable efficient inference and uncertainty quantification. Hyperparameters in the model are estimated via maximum likelihood estimation.