Random number generation from the multivariate normal (Gaussian) distribution.

1 |

`n` |
the number of samples requested |

`mean` |
a vector giving the means of each variable |

`Sigma` |
a positive-definite covariance matrix |

The function `rmnorm`

is an interface to C routines, which make calls to
subroutines from LAPACK. The matrix decomposition is internally done using
the Cholesky decomposition. If `Sigma`

is not non-negative definite then
there will be a warning message.

If `n = 1`

a vector of the same length as `mean`

, otherwise a
matrix of `n`

rows of random vectors.

Devroye, L. (1986).
*Non-Uniform Random Variate Generation*.
Springer-Verlag, New York.

`rnorm`

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
# covariance parameters
Sigma <- matrix(c(10,3,3,2), ncol = 2)
Sigma
# generate the sample
y <- rmnorm(n = 1000, Sigma = Sigma)
var(y)
# scatterplot of a random bivariate normal sample with mean
# vector zero and covariance matrix 'Sigma'
par(pty = "s")
plot(y, xlab = "", ylab = "")
title("bivariate normal sample", font.main = 1)
``` |

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