We provide a wrapper function to generate from three data-generating models:

`sim_unif`

Five multivariate uniform distributions

`sim_normal`

Multivariate normal distributions with intraclass covariance matrices

`sim_student`

Multivariate Student's t distributions each with a common covariance matrix

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`family` |
the family of distributions from which to generate data |

`...` |
optional arguments that are passed to the data-generating function |

For each data-generating model, we generate *n_m*
observations *(m = 1, …, M)* from each of
*M* multivariate distributions so that the Euclidean
distance between each of the population centroids and the
origin is equal and scaled by *Δ ≥ 0*. For
each model, the argument `delta`

controls this
separation.

This wrapper function is useful for simulation studies, where the efficacy of supervised and unsupervised learning methods and algorithms are evaluated as a the population separation is increased.

named list containing:

- x:
A matrix whose rows are the observations generated and whose columns are the

`p`

features (variables)- y:
A vector denoting the population from which the observation in each row was generated.

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Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

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