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, discrete-event 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>.

Generate simulated datasets from an initial underlying
    distribution and apply transformations to obtain realistic data. Implements
    the 'NORTA' (Normal-to-anything) approach from Cario and Nelson (1997)
    and other data generating mechanisms. Simple network visualization tools 
    are provided to facilitate communicating the simulation setup.

Simulation methods for the Fisher Bingham distribution on the unit sphere, the matrix Bingham distribution on a Grassmann manifold, the matrix Fisher distribution on SO(3), and the bivariate von Mises sine model on the torus.
 The methods use an acceptance/rejection simulation algorithm for the Bingham distribution and are described fully by Kent, Ganeiber and Mardia (2018) <doi:10.1080/10618600.2017.1390468>.
 These methods supersede earlier MCMC simulation methods and are more general than earlier simulation methods.
 The methods can be slower in specific situations where there are existing non-MCMC simulation methods (see Section 8 of Kent, Ganeiber and Mardia (2018) <doi:10.1080/10618600.2017.1390468> for further details).