# rmvss: Multivariate Subgaussian Stable Random Variates In mvpd: Multivariate Product Distributions for Elliptically Contoured Distributions

 rmvss R Documentation

## Multivariate Subgaussian Stable Random Variates

### Description

Computes random vectors of the multivariate subgaussian stable distribution for arbitrary alpha, shape matrices, and location vectors. See Nolan (2013).

### Usage

``````rmvss(
n,
alpha = 1,
Q = NULL,
delta = rep(0, d),
which.stable = c("libstable4u", "stabledist")
)
``````

### Arguments

 `n` number of observations `alpha` default to 1 (Cauchy). Must be 0<`alpha`<2 `Q` Shape matrix. See Nolan (2013). `delta` location vector. `which.stable` defaults to `"libstable4u"`, other option is `"stabledist"`. Indicates which package should provide the univariate stable distribution in this production distribution form of a univariate stable and multivariate normal.

### Value

Returns the `n` by `d` matrix containing multivariate subgaussian stable random variates where `d=nrow(Q)`.

### References

Nolan JP (2013), Multivariate elliptically contoured stable distributions: theory and estimation. Comput Stat (2013) 28:2067–2089 DOI 10.1007/s00180-013-0396-7

### Examples

``````## generate 10 random variates of a bivariate mvss
rmvss(n=10, alpha=1.71, Q=matrix(c(10,7.5,7.5,10),2))

## generate 10 random variates of a trivariate mvss
Q <- matrix(c(10,7.5,7.5,7.5,10,7.5,7.5,7.5,10),3)
rmvss(n=10, alpha=1.71, Q=Q)

``````

mvpd documentation built on Sept. 3, 2023, 5:07 p.m.