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).

Provides tools to safely and efficiently organize and execute 
    Monte Carlo simulation experiments in R.
    The package controls the structure and back-end of Monte Carlo simulation experiments
    by utilizing a generate-analyse-summarise workflow. The workflow safeguards against 
    common simulation coding issues, such as automatically re-simulating non-convergent results, 
    prevents inadvertently overwriting simulation files, catches error and warning messages
    during execution, implicitly supports parallel processing with high-quality random number 
    generation, and provides tools for managing high-performance computing (HPC) array jobs
    submitted to schedulers such as SLURM. For a pedagogical introduction to the package see
    Sigal and Chalmers (2016) <doi:10.1080/10691898.2016.1246953>. For a more in-depth overview of 
    the package and its design philosophy see Chalmers and Adkins (2020) <doi:10.20982/tqmp.16.4.p248>.