The gwr.gauss function returns a vector of weights using
the Gaussian scheme:

$$w(g) = e^{{-(d/h)}^2}$$

where \(d\) are the distances between the observations
and \(h\) is the bandwidth.

The default (from release 0.5) gwr.Gauss function returns a vector of
weights using the Gaussian scheme:

$$w(g) = e^{-(1/2) {{(d/h)}^2}}$$

gwr.gauss(dist2, bandwidth)
gwr.Gauss(dist2, bandwidth)

## Arguments

dist2 |
vector of squared distances between observations and fit point |

bandwidth |
bandwidth |

## Value

vector of weights.

## References

Fotheringham, A.S., Brunsdon, C., and Charlton, M.E., 2000,
Quantitative Geography, London: Sage; C. Brunsdon, A.Stewart Fotheringham
and M.E. Charlton, 1996, "Geographically Weighted Regression: A Method for
Exploring Spatial Nonstationarity", Geographical Analysis, 28(4), 281-298;
http://gwr.nuim.ie/

## See also

## Examples