Generates `n`

realizations of a zero-mean Gaussian process. The function also
accepts user-defined covariance functions (without "nugget" effect, see
`cov`

), The implemented defaults with `scale`

parameter \(\phi\), `order`

\(o\) and `nugget`

effect variance \(\sigma^2\) are:

*squared exponential*covariance \(Cov(x(t), x(t')) = \exp(-(t-t')^2)/\phi) + \sigma^2 \delta_{t}(t')\).*Wiener*process covariance \(Cov(x(t), x(t')) = \min(t',t)/\phi + \sigma^2 \delta_{t}(t')\),*Matèrn*process covariance \(Cov(x(t), x(t')) = \tfrac{2^{1-o}}{\Gamma(o)} (\tfrac{\sqrt{2o}|t-t'|}{\phi})^o \text{Bessel}_o(\tfrac{\sqrt{2o}|t-t'|}{s}) + \sigma^2 \delta_{t}(t')\)

## Arguments

- n
how many realizations to draw

- arg
vector of evaluation points (

`arg`

of the return object). Defaults to (0, 0.02, 0.04, ..., 1). If given as a single**integer**(don't forget the...), creates a regular grid of that length over (0,1).`L`

- cov
type of covariance function to use. Implemented defaults are

`"squareexp"`

,`"wiener"`

,`"matern"`

, see Description. Can also be any vectorized function returning \(Cov(x(t), x(t'))\)*without nugget effect*for pairs of inputs t and t'.- scale
scale parameter (see Description). Defaults to the width of the domain divided by 10.

- nugget
nugget effect for additional white noise / unstructured variability. Defaults to

`scale/200`

(so: very little white noise).- order
order of the Matèrn covariance (if used, must be >0), defaults to 1.5. The higher, the smoother the process. Evaluation of the covariance function becomes numerically unstable for large (>20)

`order`

, use "squareexp".

## See also

Other tidyfun RNG functions:
`tf_jiggle()`