This block shows how to produce the contour of a Violin viz using a Kernel Density Estimation.

- forked from mbostock‘s block: Kernel Density Estimation
- see also http://bl.ocks.org/jfirebaugh/900762
- and http://bl.ocks.org/z-m-k/5014368

==original README==

Kernel density estimation is a method of estimating the probability distribution of a random variable based on a random sample. In contrast to a histogram, kernel density estimation produces a smooth estimate. The smoothness can be tuned via the kernel’s *bandwidth* parameter. With the correct choice of bandwidth, important features of the distribution can be seen, while an incorrect choice results in undersmoothing or oversmoothing and obscured features.

This example shows a histogram and a kernel density estimation for times between eruptions of Old Faithful Geyser in Yellowstone National Park, taken from R’s `faithful`

dataset. The data follow a bimodal distribution; short eruptions are followed by a wait time averaging about 55 minutes, and long eruptions by a wait time averaging about 80 minutes. In recent years, wait times have been increasing, possibly due to the effects of earthquakes on the geyser’s geohydrology.

This example is based on a Protovis version by John Firebaugh.