I would like to invite comments on a new module, named Statistics::KernelEstimation.

This modules calculates Kernel Density Estimates and related quantities for a collection of random points.

A Kernel Density Estimate (KDE) is similar to a histogram, but improves on two known problems of histograms: it is smooth (whereas a histogram is ragged) and does not suffer from ambiguity in regards to the placement of bins.

In a KDE, a smooth, strongly peaked function is placed at the location of each point in the collection, and the contributions from all points is summed. The resulting function is a smooth approximation to the probability density from which the set of points was drawn.

This module calculates KDEs as well as Cumulative Density Functions (CDF). Three different kernels are available (Gaussian, Box, Epanechnikov).

The module also includes limited support for bandwidth optimization.

Finally, the module can generate "classical" histograms and distribution functions.

The full POD is available here:

Documentation for Statistics::KernelEstimation

Let me know what you think!

In reply to RFC: Statistics::KernelEstimation - Kernel Density Estimates and Histograms by janert

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