Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

https://github.com/JuliaStats/KernelDensity.jl

Kernel density estimators for Julia
https://github.com/JuliaStats/KernelDensity.jl

julia kernel-density-estimation statistics

Last synced: 12 days ago
JSON representation

Kernel density estimators for Julia

Lists

README 

        
# KernelDensity.jl



[![CI](https://github.com/JuliaStats/KernelDensity.jl/actions/workflows/CI.yml/badge.svg)](https://github.com/JuliaStats/KernelDensity.jl/actions/workflows/CI.yml)

[![Coverage Status](https://coveralls.io/repos/github/JuliaStats/KernelDensity.jl/badge.svg)](https://coveralls.io/github/JuliaStats/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](https://github.com/JuliaStats/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](https://github.com/tlycken/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`.