online turns a statistic (a summary or fold of data) into an online algorithm.

Imagine a data stream, like an ordered indexed container or a time-series of measurements. An exponential moving average can be calculated as a repeated iteration over a stream of xs:

\[ ema_t = ema_{t-1} * 0.9 + x_t * 0.1\]

The 0.1 is akin to the learning rate in machine learning, or 0.9 can be thought of as a decaying or a rate of forgetting. An exponential moving average learns about what the value of x has been lately, where lately is, on average, about 1/0.1 = 10 x's ago. All very neat.

The first bit of neat is speed. There's 2 times and a plus. The next is space: an ema is representing the recent xs in a size as big as a single x. Compare that with a simple moving average where you have to keep the history of the last n xs around to keep up (just try it).

It's so neat, it's probably a viable monoidal category all by itself.

Haskell allows us to abstract the compound ideas in an ema and create polymorphic routines over a wide variety of statistics, so that they all retain these properties of speed, space and rigour.

```
av xs = L.fold (online identity (.* 0.9)) xs
-- av [0..10] == 6.030559401413827
-- av [0..100] == 91.00241448887785
```

`online identity (.* 0.9)`

is how you express an ema with a decay rate of 0.9.

Here's an average of recent values for the grey line, for r=0.9 and r=0.99.

online works for any statistic. Here's the construction of standard deviation using applicative style:

```
std :: Double -> L.Fold Double Double
std r = (\s ss -> sqrt (ss - s**2)) <$> ma r <*> sqma r
where
ma r = online identity (.*r)
sqma r = online (**2) (.*r)
```

And the results over our fake data:

`stack build --copy-bins --exec "online-examples" --exec "pandoc -f markdown -t html -i examples/examples.md -o index.html --mathjax --filter pandoc-include"`