boot_pb_vm: Parametric Bootstrap VM
In jeksterslabds/jeksterslabRds: Jekster's Lab Data Science and Statistics

Generates B number of parametric bootstrap resamples from the original sample data using gendat_vm.

1	boot_pb_vm(n, Sigma_dot, skew, kurt, rescale = TRUE, mu, sigma2, B = 2000)

`n`	Sample size.
`Sigma_dot`	p \times p correlation matrix (\dot{Σ}).
`skew`	Skewness vector. If a single value is given, the function assumes that the p elements in the vector will have the same skewness.
`kurt`	Kurtosis vector. If a single value is given, the function assumes that the p elements in the vector will have the same kurtosis.
`rescale`	Logical. Rescale the data using means `mu` (μ) and variances `sigma2` (σ^2) provided. Note that the actual means and variances that the final data set will have are drawn randomly from a normal distrbution using `mu` and `sigma2` as parameters.
`mu`	Vector of means corresponding to each element of `Sigma_dot` (\dot{Σ}) that will be used as population means when rescale is `TRUE`. If a single value is given, the function assumes that the p elements in the vector will have the same `mu`.
`sigma2`	Vector of variances corresponding to each element of `Sigma_dot` (\dot{Σ}) that will be used as population variances when rescale is `TRUE`. If a single value is given, the function assumes that the p elements in the vector will have the same `sigma2`.
`B`	Number of bootstrap resamples.