The function generates n realizations of a Gaussian process, either with squared exponential covariance $$Cov(x(t), x(t')) = \exp(-(t'-t)^2)/s) + n \delta_{t}(t')$$ or Wiener process covariance $$Cov(x(t), x(t')) = \min(t',t)/s + n \delta_{t}(t')$$ with scale parameter s and nugget effect n.

tf_rgp(
n,
arg = 51L,
scale = diff(range(arg))/10,
cor = c("squareexp", "wiener"),
nugget = scale/200
)

## Arguments

n how many realizations to draw vector of evaluation points (arg of the return object). Defaults to (0, 0.02, 0.04, ..., 1). If a single integer (don't forget the L...), creates a grid of the given length over (0,1). scale parameter (see Description). Defaults to the width of the domain divided by 10. type of correlation structure to use. Currently available: "squareexp" or "wiener", see Description. nugget effect for additional white noise / unstructured variability. Defaults to scale/200 (so: very little noise).

## Value

an tfd-vector of length n