Simultaneous tests and confidence intervals are provided for one-way experimental designs with one or many normally distributed, primary response variables (endpoints). Differences (Hasler and Hothorn, 2011 <doi:10.2202/1557-4679.1258>) or ratios (Hasler and Hothorn, 2012 <doi:10.1080/19466315.2011.633868>) of means can be considered. Various contrasts can be chosen, unbalanced sample sizes are allowed as well as heterogeneous variances (Hasler and Hothorn, 2008 <doi:10.1002/bimj.200710466>) or covariance matrices (Hasler, 2014 <doi:10.1515/ijb-2012-0015>).

Provides a framework to generating random variates from
             arbitrary multivariate copulae, while concentrating on
             (bivariate) extreme value copulae.  Particularly useful if
             the multivariate copulae are not available in closed
             form.  Detailed discussion of the methodologies used can
             be found in Tajvidi and Turlach (2018)
             <doi:10.1111/anzs.12209>.

Simulates correlated multinomial responses conditional on a marginal model specification.

Simulate and plot general experimental crosses. The focus is on simulating genotypes with an aim towards flexibility rather than speed. Meiosis is simulated following the Stahl model, in which chiasma locations are the superposition of two processes: a proportion p coming from a process exhibiting no interference, and the remainder coming from a process following the chi-square model.

Algorithms for the implementation and evaluation of Monte Carlo tests, as well as for their use in multiple testing procedures.

Simulate complex data from a given directed acyclic graph and information about each individual node.
	Root nodes are simply sampled from the specified distribution. Child Nodes are simulated according to
	one of many implemented regressions, such as logistic regression, linear
	regression, poisson regression or any other function. Also includes a comprehensive framework for discrete-time
	simulation, and networks-based simulation which can generate even more complex longitudinal and dependent data.
	For more details, see Robin Denz, Nina Timmesfeld (2025) <doi:10.48550/arXiv.2506.01498>.