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>.

Implementation of a model-based bootstrap approach for testing whether two formulations are similar. The package provides a function for fitting a pharmacokinetic model to time-concentration data and comparing the results for all five candidate models regarding the Residual Sum of Squares (RSS). The candidate set contains a First order, Hixson-Crowell, Higuchi, Weibull and a logistic model. The assessment of similarity implemented in this package is performed regarding the maximum deviation of the profiles. See Moellenhoff et al. (2018) <doi:10.1002/sim.7689> for details.