Predictor variables can have any marginal distribution as long as a function is provided to sample from the distribution. Multivariate distributions are also supported: if the random generation function returns multiple columns, multiple random variables will be created, successively numbered.

## Arguments

- dist
Name (as character vector) of the function to generate draws from this predictor's distribution.

- ...
Additional arguments to pass to

`dist`

when generating draws.

## Value

A `predictor_dist`

object, to be used in `population()`

to specify a
population distribution

## Details

The random generation function must take an argument named `n`

specifying the
number of draws. For univariate distributions, it should return a vector of
length `n`

; for multivariate distributions, it should return an array or
matrix with `n`

rows and a column per variable.

Multivariate predictors are successively numbered. For instance, if predictor
`X`

is specified with

```
library(mvtnorm)
predictor(dist = "rmvnorm", mean = c(0, 1),
sigma = matrix(c(1, 0.5, 0.5, 1), nrow = 2))
```

then the population predictors will be named `X1`

and `X2`

, and will have
covariance 0.5.