KernelDensity.jl

Kernel density estimators for Julia.

Usage

Univariate

The main accessor function is kde:

U = kde(data)

will construct a UnivariateKDE object from the real vector data. The
optional keyword arguments are

• boundary: the lower and upper limits of the kde as a tuple. Due to the
  fourier transforms used internally, there should be sufficient spacing to
  prevent wrap-around at the boundaries.
• npoints: the number of interpolation points to use. The function uses
  fast Fourier transforms (FFTs) internally, so for optimal efficiency this
  should be a power of 2 (default = 2048).
• kernel: the distributional family from
  Distributions.jl to use as
  the kernel (default = Normal). To add your own kernel, extend the internal
  kernel_dist function.
• bandwidth: the bandwidth of the kernel. Default is to use Silverman’s
  rule.

The UnivariateKDE object U contains gridded coordinates (U.x) and the density
estimate (U.density). These are typically sufficient for plotting.
A related function

kde_lscv(data)

will construct a UnivariateKDE object, with the bandwidth selected by
least-squares cross validation. It accepts the above keyword arguments, except
bandwidth.

There are also some slightly more advanced interfaces:

kde(data, midpoints::R) where R<:AbstractRange

allows specifying the internal grid to use. Optional keyword arguments are
kernel and bandwidth.

kde(data, dist::Distribution)

allows specifying the exact distribution to use as the kernel. Optional
keyword arguments are boundary and npoints.

kde(data, midpoints::R, dist::Distribution) where R<:AbstractRange

allows specifying both the distribution and grid.

Bivariate

The usage mirrors that of the univariate case, except that data is now
either a tuple of vectors

B = kde((xdata, ydata))

or a matrix with two columns

B = kde(datamatrix)

Similarly, the optional arguments all now take tuple arguments:
e.g. boundary now takes a tuple of tuples ((xlo,xhi),(ylo,yhi)).

The BivariateKDE object B contains gridded coordinates (B.x and B.y) and the bivariate density
estimate (B.density).

Interpolation

The KDE objects are stored as gridded density values, with attached
coordinates. These are typically sufficient for plotting (see above), but
intermediate values can be interpolated using the
Interpolations.jl package via the pdf method
(extended from Distributions.jl).

pdf(k::UnivariateKDE, x)
pdf(k::BivariateKDE, x, y)

where x and y are real numbers or arrays.

If you are making multiple calls to pdf, it will be more efficient to
construct an intermediate InterpKDE to store the interpolation structure:

ik = InterpKDE(k)
pdf(ik, x)

InterpKDE will pass any extra arguments to interpolate.