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Update Readme.md #82

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13 changes: 9 additions & 4 deletions README.md
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Expand Up @@ -11,7 +11,7 @@ Kernel density estimators for Julia.
The main accessor function is `kde`:

```
kde(data)
U = kde(data)
```

will construct a `UnivariateKDE` object from the real vector `data`. The
Expand All @@ -29,6 +29,8 @@ optional keyword arguments are
* `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) ```
Expand Down Expand Up @@ -61,19 +63,22 @@ allows specifying both the distribution and grid.
The usage mirrors that of the univariate case, except that `data` is now
either a tuple of vectors
```
kde((xdata, ydata))
B = kde((xdata, ydata))
```
or a matrix with two columns
```
kde(datamatrix)
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 below), but
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).
Expand Down