Produces one or more samples from the specified multivariate normal distribution.

`mvrnorm(n = 1, mu, Sigma, tol = 1e-6, empirical = FALSE, EISPACK = FALSE)`

n

the number of samples required.

mu

a vector giving the means of the variables.

Sigma

a positive-definite symmetric matrix specifying the covariance matrix of the variables.

tol

tolerance (relative to largest variance) for numerical lack
of positive-definiteness in `Sigma`

.

empirical

logical. If true, mu and Sigma specify the empirical not population mean and covariance matrix.

EISPACK

logical: values other than `FALSE`

are an error.

If `n = 1`

a vector of the same length as `mu`

, otherwise an
`n`

by `length(mu)`

matrix with one sample in each row.

Causes creation of the dataset `.Random.seed`

if it does
not already exist, otherwise its value is updated.

The matrix decomposition is done via `eigen`

; although a Choleski
decomposition might be faster, the eigendecomposition is
stabler.

B. D. Ripley (1987) *Stochastic Simulation.* Wiley. Page 98.

# NOT RUN { Sigma <- matrix(c(10,3,3,2),2,2) Sigma var(mvrnorm(n = 1000, rep(0, 2), Sigma)) var(mvrnorm(n = 1000, rep(0, 2), Sigma, empirical = TRUE)) # }

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