### Description

`visKernels`

is supposed to visualize a series of neighborhood
kernels, each of which is a non-increasing functions of: i) the
distance `d_{wi}`

between the hexagon/rectangle `i`

and the
winner `w`

, and ii) the radius `\delta_t`

at time `t`

.

### Usage

visKernels(newpage = TRUE)

### Arguments

- newpage
- logical to indicate whether to open a new page. By
default, it sets to true for opening a new page

### Value

invisible

### Note

There are five kernels that are currently supported:

- For "gaussian" kernel,
`h_{wi}(t)=e^{-d_{wi}^2/(2*\delta_t^2)}`

- For "cutguassian" kernel,
`h_{wi}(t)=e^{-d_{wi}^2/(2*\delta_t^2)}*(d_{wi} \le \delta_t)`

- For "bubble" kernel,
`h_{wi}(t)=(d_{wi} \le \delta_t)`

- For "ep" kernel,
```
h_{wi}(t)=(1-d_{wi}^2/\delta_t^2)*(d_{wi}
\le \delta_t)
```

- For "gamma" kernel,
`h_{wi}(t)=1/\Gamma(d_{wi}^2/(4*\delta_t^2)+2)`

These kernels above are displayed within a plot for each fixed radius.
Three different radii (i.e., 1 and 2) are illustrated.

### Examples

# visualise currently supported five kernels
visKernels()