# \[ANN\] EmpiricalCDFs.jl registered and documented

**URL:** <https://discourse.julialang.org/t/ann-empiricalcdfs-jl-registered-and-documented/11127>\
**Category:** Statistics\
**Tags:** package, announcement\
**Created:** [May 24, 2018, 12:36pm UTC](https://discourse.julialang.org/t/ann-empiricalcdfs-jl-registered-and-documented/11127 "2018-05-24T12:36:40Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![jlapeyre](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/jlapeyre/32/4514_2.png) [@jlapeyre](https://discourse.julialang.org/u/jlapeyre)\
**Post date:** [May 24, 2018, 12:36pm UTC](https://discourse.julialang.org/t/ann-empiricalcdfs-jl-registered-and-documented/11127/1 "2018-05-24T12:36:40Z")

</div>

I have just registered [EmpiricalCDFs.jl](https://jlapeyre.github.io/EmpiricalCDFs.jl/latest) and documented it with `Documenter.jl`.

I gave a brief description of the package a few weeks ago:

> [@ANN: EmpiricalCDFs.jl](https://discourse.julialang.org/t/ann-empiricalcdfs-jl/10411):
>
> [EmpiricalCDFs.jl](https://github.com/jlapeyre/EmpiricalCDFs.jl) EmpiricalCDFs implements empirical CDFs; building, evaluating, random sampling, evaluating the inverse, etc. It is useful especially for examining the tail of the CDF obtained from streaming a large number of data, more than can be stored in memory. For this purpose, you specify a lower cutoff; data points below this value will be silently rejected, but the resulting CDF will still be properly normalized. One of the main features of EmpiricalCDFs that is absent in StatsBase.ecd…

I have used it in conjunction with the following, as yet unregistered, package to estimate the exponent of a power law tail from a stream of terabytes of Monte Carlo samples.

> **[GitHub - jlapeyre/MaximumLikelihoodPower.jl: Maximum Likelihood Estimate for...](https://github.com/jlapeyre/MaximumLikelihoodPower.jl)**
>
> Maximum Likelihood Estimate for power laws. Contribute to jlapeyre/MaximumLikelihoodPower.jl development by creating an account on GitHub.