This package provides a set of tools for analysing and estimating extreme value distributions.
It defines two types, `BlockMaxima`

and `PeakOverThreshold`

, which can be used to filter a
collection of values into a collection of maxima.

Given a collection of maxima produced by either model above, one can start estimating heavy-tail distributions and plotting classical extreme value statistics.

Get the latest stable release with Julia's package manager:

`] add ExtremeStats`

Given a collection of values `xs`

(e.g. time series), one can retrieve its maxima:

```
using ExtremeStats
# find maxima with blocks of size 50
bm = BlockMaxima(xs, 50)
# get values above a threshold of 100.
pm = PeakOverThreshold(xs, 100.)
```

For the block maxima model, the values `xs`

need to represent a measurement over time,
whereas the peak over threshold model does not assume any ordering in the data. Both
models are lazy, and the maxima are only returned via a `collect`

call.

A few plot recipes are defined for maxima as well as for the original values `xs`

:

```
using Plots
# mean excess plot
excessplot(xs)
# Pareto quantile plot
paretoplot(xs)
# return level plot
returnplot(xs)
```

Generalized extreme value (GEV) and generalized Pareto (GP) distributions from the `Distributions.jl`

package can be fit
to maxima via constrained optimization (maximum likelihood + extreme value index constraints):

```
using Distributions
# fit GEV to block maxima
fit(GeneralizedExtremeValue, bm)
# fit GP to peak over threshold
fit(GeneralizedPareto, pm)
```

A few statistics are defined:

```
# return statistics
returnlevels(xs)
# mean excess with previous k values
meanexcess(xs, k)
```

The book An Introduction to Statistical Modeling of Extreme Values by Stuart Coles gives a practical introduction to the theory. Most other books I've encountered are too theoretical or expose topics that are somewhat disconnected.