Generation of survival data with one (binary)
  time-dependent covariate.  Generation of survival data arising
  from a progressive illness-death model.

The GenSVM classifier is a generalized multiclass support vector
	machine (SVM). This classifier aims to find decision boundaries that
	separate the classes with as wide a margin as possible. In GenSVM, the
	loss function is very flexible in the way that misclassifications are
	penalized.  This allows the user to tune the classifier to the dataset
	at hand and potentially obtain higher classification accuracy than
	alternative multiclass SVMs.  Moreover, this flexibility means that
	GenSVM has a number of other multiclass SVMs as special cases. One of
	the other advantages of GenSVM is that it is trained in the primal
	space, allowing the use of warm starts during optimization.  This
	means that for common tasks such as cross validation or repeated model
	fitting, GenSVM can be trained very quickly. Based on: G.J.J. van den
	Burg and P.J.F. Groenen (2018) <https://www.jmlr.org/papers/v17/14-526.html>.

Implement a coherent and flexible protocol for animal color tagging. 'GenTag' provides a simple computational routine with low CPU usage to create color sequences for animal tag. First, a single-color tag sequence is created from an algorithm selected by the user, followed by verification of the combination uniqueness. Three methods to produce color tag sequences are provided. Users can modify the main function core to allow a wide range of applications.

Provide estimation and data generation tools for a generalization of the 
	transmuted distributions discussed in Shaw and Buckley (2007).
	See <doi:10.48550/arXiv.0901.0434> for more information.

Make it easy to create simplified trial summary (TS) domain
  based on FDA FDA guide
  <https://github.com/TuCai/phuse/blob/master/inst/examples/07_genTS/www/Simplified_TS_Creation_Guide_v2.pdf>.

Two arms clinical trials required sample size is calculated in the comprehensive parametric context. The calculation is based on the type of endpoints(continuous/binary/time-to-event/ordinal), design (parallel/crossover), hypothesis tests (equality/noninferiority/superiority/equivalence), trial arms noncompliance rates and expected loss of follow-up. Methods are described in: Chow SC, Shao J, Wang H, Lokhnygina Y (2017) <doi:10.1201/9781315183084>, Wittes, J (2002) <doi:10.1093/epirev/24.1.39>, Sato, T (2000) <doi:10.1002/1097-0258(20001015)19:19%3C2689::aid-sim555%3E3.0.co;2-0>, Lachin J M, Foulkes, M A (1986) <doi:10.2307/2531201>, Whitehead J(1993) <doi:10.1002/sim.4780122404>, Julious SA (2023) <doi:10.1201/9780429503658>.